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ids) after running '${t}'`)}runKernelFunc(t){let e,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=ww(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(ww(t)){let{kernelName:f,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=tc(f,this.backendName);$(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=t,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=t,m=ww(t)?null:t.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),e=d.outputs)}),n&&this.addTapeNode(u,c,e,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:e.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?e:e[0]}saveTensorsForBackwardMode(t){return 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className(){return"Adadelta"}constructor(t,e,o=null){super(),this.learningRate=t,this.rho=e,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(t)?t[n].tensor:t[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(er(i),1-this.rho)),l=se(je(Dr(Ce(u,this.epsilon)),Dr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(er(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Mt(this.accumulatedGrads.map(t=>t.variable)),Mt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,o=!1;this.accumulatedGrads=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var sp=class extends Nr{static get className(){return"Adagrad"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>Ea(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(t)?t[n].tensor:t[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,er(a));i.assign(p);let u=Ce(se(je(a,Dr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Mt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(o=>({originalName:o.name,variable:o.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var ap=class extends Nr{static get className(){return"Adam"}constructor(t,e,o,n=null){super(),this.learningRate=t,this.beta1=e,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(e).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(o=>o.name):Object.keys(t);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);e.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Ht(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Ht(i).variable(p))});let u=Array.isArray(t)?t[a].tensor:t[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(er(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Dr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Mt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Mt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),De(()=>{this.accBeta1.assign(ui(this.beta1,this.iterations_+1)),this.accBeta2.assign(ui(this.beta2,this.iterations_+1))});let e=t.length/2,o=!1;this.accumulatedFirstMoment=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var ip=class extends Nr{static get className(){return"Adamax"}constructor(t,e,o,n=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(e).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(o=>o.name):Object.keys(t);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));e.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ht(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ht(i).variable(p)});let u=Array.isArray(t)?t[a].tensor:t[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Jt(u),h=Fd(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Mt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Mt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var mi=class extends Nr{static get className(){return"SGD"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=Array.isArray(t)?t[n].tensor:t[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Rr(ke(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}};var up=class extends mi{static get className(){return"Momentum"}constructor(t,e,o=!1){super(t),this.learningRate=t,this.momentum=e,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(t)?t[n].tensor:t[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Mt(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(o=>({originalName:o.name,variable:o.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var pp=class extends Nr{static get className(){return"RMSProp"}constructor(t,e=.9,o=0,n=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Ht(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Ht(s).variable(a))});let i=Array.isArray(t)?t[n].tensor:t[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(er(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Dr(Te(c,Ce(er(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(er(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Dr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Mt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Mt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Mt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,o=!1;this.accumulatedMeanSquares=t.slice(0,e).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=t.slice(e,e*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};var Jj=[np,sp,ap,ip,up,pp,mi];function UN(){for(let r of Jj)tS(r)}var 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performance."),console.log(p);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let o=await t.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=fo(e),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I("keyDType",r,t,e),a=I("valueDType",r,t,e),i=new vf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let n=I("tableHandle",r,t,e,o),s=I("keys",r,t,e),a=I("values",r,t,e);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let 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OT=(r,t,e,o=Je)=>{switch(r.op){case"Equal":return[o.equal(I("a",r,t,e),I("b",r,t,e))];case"NotEqual":return[o.notEqual(I("a",r,t,e),I("b",r,t,e))];case"Greater":return[o.greater(I("a",r,t,e),I("b",r,t,e))];case"GreaterEqual":return[o.greaterEqual(I("a",r,t,e),I("b",r,t,e))];case"Less":return[o.less(I("a",r,t,e),I("b",r,t,e))];case"LessEqual":return[o.lessEqual(I("a",r,t,e),I("b",r,t,e))];case"LogicalAnd":return[o.logicalAnd(I("a",r,t,e),I("b",r,t,e))];case"LogicalNot":return[o.logicalNot(I("a",r,t,e))];case"LogicalOr":return[o.logicalOr(I("a",r,t,e),I("b",r,t,e))];case"Select":case"SelectV2":return[o.where(I("condition",r,t,e),I("a",r,t,e),I("b",r,t,e))];case"BitwiseAnd":return[o.bitwiseAnd(I("a",r,t,e),I("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,t,e,o=Je)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[o.matMul(I("a",r,t,e),I("b",r,t,e),I("transposeA",r,t,e),I("transposeB",r,t,e))];case"Einsum":return[o.einsum(I("equation",r,t,e),...I("tensors",r,t,e))];case"Transpose":return[o.transpose(I("x",r,t,e),I("perm",r,t,e))];case"_FusedMatMul":let[n,s]=I("fusedOps",r,t,e),a=n==="biasadd",i=s==="prelu",p=I("numArgs",r,t,e),u=I("leakyreluAlpha",r,t,e);if(a){if(i&&p!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&p!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,l]=I("args",r,t,e);return[o.fused.matMul({a:I("a",r,t,e),b:I("b",r,t,e),transposeA:I("transposeA",r,t,e),transposeB:I("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case"MatrixBandPart":return[o.linalg.bandPart(I("a",r,t,e),I("numLower",r,t,e),I("numUpper",r,t,e))];default:throw TypeError(`Node type 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WT=(r,t,e,o=Je)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I("indices",r,t,e),I("values",r,t,e),I("denseShape",r,t,e),I("defaultValue",r,t,e));return[n,s,a,i]}case"SparseReshape":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I("inputIndices",r,t,e),I("inputShape",r,t,e),I("newShape",r,t,e));return[n,s]}case"SparseSegmentMean":return[o.sparse.sparseSegmentMean(I("data",r,t,e),I("indices",r,t,e),I("segmentIds",r,t,e))];case"SparseSegmentSum":return[o.sparse.sparseSegmentSum(I("data",r,t,e),I("indices",r,t,e),I("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(r,t,e,o=Je)=>{switch(r.op){case"FFT":return[o.fft(I("x",r,t,e))];case"IFFT":return[o.ifft(I("x",r,t,e))];case"RFFT":return[o.rfft(I("x",r,t,e))];case"IRFFT":return[o.irfft(I("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GT=(r,t,e,o=Je)=>{switch(r.op){case"StaticRegexReplace":return[o.string.staticRegexReplace(I("input",r,t,e),I("pattern",r,t,e),I("rewrite",r,t,e),I("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I("data",r,t,e),I("dataSplits",r,t,e),I("separator",r,t,e),I("nGramWidths",r,t,e),I("leftPad",r,t,e),I("rightPad",r,t,e),I("padWidth",r,t,e),I("preserveShortSequences",r,t,e));return[n,s]}case"StringSplit":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I("input",r,t,e),I("delimiter",r,t,e),I("skipEmpty",r,t,e));return[n,s,a]}case"StringToHashBucketFast":return[o.string.stringToHashBucketFast(I("input",r,t,e),I("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HT=(r,t,e,o=Je)=>{switch(r.op){case"Cast":return[o.cast(I("x",r,t,e),I("dtype",r,t,e))];case"ExpandDims":{let n=I("axis",r,t,e);return[o.expandDims(I("x",r,t,e),n)]}case"Squeeze":{let n=I("axis",r,t,e);return[o.squeeze(I("x",r,t,e),n)]}case"Reshape":return[o.reshape(I("x",r,t,e),I("shape",r,t,e))];case"EnsureShape":return[o.ensureShape(I("x",r,t,e),I("shape",r,t,e))];case"MirrorPad":return[o.mirrorPad(I("x",r,t,e),I("padding",r,t,e),I("mode",r,t,e))];case"PadV2":case"Pad":return[o.pad(I("x",r,t,e),I("padding",r,t,e),I("constantValue",r,t,e))];case"SpaceToBatchND":{let n=I("blockShape",r,t,e),s=I("paddings",r,t,e);return[o.spaceToBatchND(I("x",r,t,e),n,s)]}case"BatchToSpaceND":{let n=I("blockShape",r,t,e),s=I("crops",r,t,e);return[o.batchToSpaceND(I("x",r,t,e),n,s)]}case"DepthToSpace":{let n=I("blockSize",r,t,e),s=I("dataFormat",r,t,e).toUpperCase();return[o.depthToSpace(I("x",r,t,e),n,s)]}case"BroadcastTo":return[o.broadcastTo(I("x",r,t,e),I("shape",r,t,e))];case"BroadcastArgs":return[o.broadcastArgs(I("s0",r,t,e),I("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function OS(r,t,e,o,n=De){let s=((a,i,p)=>{switch(a.category){case"arithmetic":return n(()=>CT(a,i,p));case"basic_math":return n(()=>wT(a,i,p));case"control":return TT(a,i,p);case"convolution":return n(()=>$T(a,i,p));case"creation":return n(()=>ET(a,i,p));case"dynamic":return RT(a,i,p);case"evaluation":return n(()=>DT(a,i,p));case"image":return n(()=>PT(a,i,p));case"graph":return n(()=>AT(a,i,p));case"logical":return n(()=>OT(a,i,p));case"matrices":return n(()=>MT(a,i,p));case"normalization":return n(()=>LT(a,i,p));case"ragged":return n(()=>BT(a,i,p));case"reduction":return n(()=>zT(a,i,p));case"slice_join":return n(()=>VT(a,i,p));case"sparse":return n(()=>WT(a,i,p));case"spectral":return n(()=>UT(a,i,p));case"string":return n(()=>GT(a,i,p));case"transformation":return n(()=>HT(a,i,p));case"hash_table":return FT(a,i,p,o);case"custom":let u=pf(a.op);if(u&&u.customExecutor)return u.customExecutor(new wf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Ll=class{constructor(t={},e={},o={},n={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function MS(r,t,e,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Tr(m)[0]));o=o||[];let c=new Set(o.map(m=>Tr(m.name)[0])),l=[...t];for(;l.length>0;){let m=l.pop();if((gu(m)||k8(m)||N8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),e[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:t,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function KT(r,t){let{usedNodes:e,inputs:o}=t,n=Object.keys(o).map(g=>Tr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>e.has(typeof g=="string"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=C8(f,p);return w8(h,p),h}function C8(r,t){let e=new Map(r.map(a=>[a.name,a])),o=t.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=e.get(a);for(let p of i.children)!e.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var Cc=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function w8(r,t){let e=new Map(r.map((i,p)=>[i.name,p])),o=new Set(t.map(i=>i.name)),n=i=>o.has(typeof i=="string"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i=="string"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!e.has(p.name))throw new Cc(`Child ${p.name} of node ${i.name} is unreachable.`);if(e.get(i.name)>e.get(p.name))throw new Cc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!e.has(p.name))throw new Cc(`Input ${p.name} of node ${i.name} is unreachable.`);if(e.get(p.name)>e.get(i.name))throw new Cc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function qT(r){let t=new Map(r.map((i,p)=>[i.name,p])),e=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>gu(i)?e:p),n=i=>{let p=o[t.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;it[o].map(n=>n.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(o=>{this._functionExecutorMap[o]=new lp(t.functions[o],this)})}getCompilationKey(t,e){let o=t.map(s=>s.name).sort(),n=e.map(s=>s.name).sort();return o.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(t,e){let o=MS(t,e,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=e.map(l=>l.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=KT(this.graph,o),p=qT(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return Rr(e),e}cloneTensorList(t){return t?t.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,o])=>[e,this.cloneTensorList(o)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let o=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let n=o.map(m=>this.graph.nodes[Tr(m)[0]]),s=e.map(m=>Tr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(t,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Ll(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,C]=Tr(x,m),S=[];S[C]=t[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=OS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),e.map(x=>zt(x,d,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(o=>t[o]).map(o=>o.map(n=>n.id)));return new Set(e)}checkTensorForDisposal(t,e,o,n,s,a,i){if(!(gu(e)||a.has(t))){for(let p of o[t])p!=null&&(i[p.id]=(i[p.id]||0)+e.children.length);for(let p of e.inputs){if(gu(p))continue;let u=fS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,o,n,s,a){function i(p){return gu(p)||s.has(p.name)}if(!(gu(t)||a==null))for(let p of a){if(i(p))continue;let u=fS(p.name,e,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Ll(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(t,a,e,o),p=e.map(m=>zt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(t,e,o){let n=t.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,e,o)}async executeWithControlFlow(t,e,o,n){let s=Object.keys(t),a=s.map(S=>this.graph.nodes[Tr(S)[0]]),i=o.map(S=>Tr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=MS(t,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(S=>{let[k,_]=Tr(S),E=[];E[_]=t[S],h[k]=E});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,e,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let C=u.filter(S=>!gu(S)&&!zt(S.name,h,e)).map(S=>S.name);if(C.length>0){let S="";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(t,e,o,n,s,a,i,p,u){let c=[];for(;e.length>0;){let l=e.pop();o.currentContext=l.contexts;let m="";if(l.node.op==="Enter"&&I("isConstant",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=OS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u))}else this.processChildNodes(l.node,e,o,n,s,u)}return c}processChildNodes(t,e,o,n,s,a){t.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!zt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!zt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let o=t[e],[n]=Tr(e),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(t){var e,o;let n={};for(let s in t){let a=(o=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=t[s]:n[s]=t[s]}return n}checkInputs(t){let e=Object.keys(t).filter(o=>{let[n]=Tr(o);return this.graph.nodes[n]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[o]=Tr(e);if(!this.graph.nodes[o])throw new Error(`The output '${e}' is not found in the graph`)})}};var kf=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var T8="?tfjs-format=file",_8="model.json",Bl=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},o=fi){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=o,e==null&&(this.loadOptions={}),this.resourceManager=new kf}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new lp(Ml.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Ml.Instance.transformGraph(t.modelInitializer);this.initializer=new lp(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let o=this.io.getSaveHandlers(t);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${t}'`);t=o[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof ut?[t]:t,o={};return e.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return t}predict(t,e){let o=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(t,e){let o=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(t){var e;if(!(t instanceof ut)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(t[a]=this.resourceIdToCapturedInput[i.resourceId])}return t}t=Array.isArray(t)?t:[t];let o=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${t.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let 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c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=c.strideDepth,m=c.strideHeight,d=c.strideWidth,f=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,C=c.dilationWidth,S=c.effectiveFilterDepth,k=c.effectiveFilterHeight,_=c.effectiveFilterWidth,E=S-1-c.padInfo.front,R=_-1-c.padInfo.left,D=k-1-c.padInfo.top,P=me(s.shape,"float32"),O=1/(f*h*g),M=e.bufferSync(n);for(let L=0;L=c.outDepth||Math.floor(ee)!==ee))for(let oe=0;oe=c.outHeight||Math.floor(ie)!==ie))for(let le=0;le<_;le+=C){let be=(J+le)/d;if(be<0||be>=c.outWidth||Math.floor(be)!==be)continue;let _e=M.get(L,ee,ie,be,B);re+=_e}}}P.set(re*O,L,z,U,j,B)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var r$={kernelName:Ai,backendName:"cpu",kernelFunc:_Y};function $Y(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;Q([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,C=b-1-c.padInfo.left,S=x-1-c.padInfo.top,k=me(a.shape,"float32"),_=1/(d*f),E=e.data.get(n.dataId).values,R=me(n.shape,"float32",E);for(let D=0;D=c.outHeight||Math.floor(j)!==j))for(let q=0;q=c.outWidth||Math.floor(Y)!==Y)continue;let J=R.get(D,j,Y,P);z+=J}}k.set(z*_,D,O,M,P)}return e.makeTensorInfo(k.shape,k.dtype,k.values)}var o$={kernelName:Di,backendName:"cpu",kernelFunc:$Y};function EY(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=t;y.assert(i.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient 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e.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var i$={kernelName:ea,backendName:"cpu",kernelFunc:AY};var FY=Ie(Co,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,o=new Float32Array(y.sizeFromShape(t.shape)),n=e.data.get(t.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=e.data.get(s.dataId).values,p=e.data.get(a.dataId).values;for(let u=0;uh.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return mr({inputs:{x:p[0]},backend:e});if(p[0].dtype==="complex64"){let h=p.map(S=>Ro({inputs:{input:S},backend:e})),g=p.map(S=>Ma({inputs:{input:S},backend:e})),x=yu({inputs:h,backend:e,attrs:{axis:s}}),b=yu({inputs:g,backend:e,attrs:{axis:s}}),C=Kt({inputs:{real:x,imag:b},backend:e});return h.forEach(S=>e.disposeIntermediateTensorInfo(S)),g.forEach(S=>e.disposeIntermediateTensorInfo(S)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Ve({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=u.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));i=w.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=mp(c,i,t[0].dtype,l),d=w.computeOutShape(p.map(h=>h.shape),s),f=e.makeTensorInfo(d,t[0].dtype,m);return u.forEach(h=>e.disposeIntermediateTensorInfo(h)),f}var l$={kernelName:ta,backendName:"cpu",kernelFunc:yu};function yI(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;Q([n,s],"conv2d");let l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat==="channelsLast",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),E=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=e.data.get(n.dataId).values,U=e.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let lt=le+Pe*_[1],Ge=be+st*D,mt=lt;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],lt=le+Pe*u.inChannels,Ge=st;for(let mt=0;mtMath.cos(r)),y$={kernelName:an,backendName:"cpu",kernelFunc:VY};var WY=Ie(un,r=>Math.cosh(r)),b$={kernelName:un,backendName:"cpu",kernelFunc:WY};function UY(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],"float32"),b=e.data.get(s.dataId).values,C=e.data.get(a.dataId).values,S=e.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let E=0;E=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=e.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,S=b.top,k=d.outChannels/d.inChannels,_=new tt(d.outShape,n.dtype),E=e.data.get(n.dataId).values,R=e.data.get(s.dataId).values,D=_.values;for(let P=0;P=d.inHeight)continue;let q=U*l[0],Y=O+j*c[1];for(let J=0;J=d.inWidth)continue;let ie=q+ee*l[1],le=Y+oe*d.inChannels,be=re,_e=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=e,p=t,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:S,strideWidth:k,filterHeight:_,filterWidth:E,dilationHeight:R,dilationWidth:D,outShape:P}=w.computeDilation2DInfo(o.shape,n.shape,s,a,"NHWC",i),O=y.sizeFromShape(P),M=P.length,L=y.getArrayFromDType(o.dtype,O);for(let z=0;z=0&&oe=0&&lere&&(re=ve)}}}let ne=y.locToIndex([z,U,q,J],M,y.computeStrides(P));L[ne]=re}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),P,o.dtype),shape:P,dtype:o.dtype}}};var E$={kernelName:zi,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=e,u=t,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${zi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ne,re=oe)}}}O[J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var R$={kernelName:Bi,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=e,u=t,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:E,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${Bi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ee,re=ie)}}}O[L][J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function QY(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{canvas:s,options:a}=o,{contextOptions:i,imageOptions:p}=a||{},u=(p==null?void 0:p.alpha)||1,c=(i==null?void 0:i.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);let l=s.getContext(c,(i==null?void 0:i.contextAttributes)||{});if(l==null)throw new Error(`Could not get the context with ${c} type.`);let[m,d]=n.shape.slice(0,2),f=n.shape.length===2?1:n.shape[2],h=e.data.get(n.dataId).values,g=n.dtype==="float32"?255:1,x=new Uint8ClampedArray(d*m*4);for(let C=0;C1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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e=this.gl;ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),rv(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ce(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&Yl(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,o=!0){return this.throwIfDisposed(),o?MI(this.gl,t,e):LI(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,o){this.throwIfDisposed(),this.throwIfNoProgram(),BI(this.gl,t,e,o)}setOutputMatrixTexture(t,e,o){this.setOutputMatrixTextureDriver(t,o,e)}setOutputPackedMatrixTexture(t,e,o){this.throwIfDisposed();let[n,s]=La(e,o);this.setOutputMatrixTextureDriver(t,n,s)}setOutputMatrixWriteRegion(t,e,o,n){this.setOutputMatrixWriteRegionDriver(o,t,n,e)}setOutputPackedMatrixWriteRegion(t,e,o,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Yl(this.gl,this.program),Rc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and 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this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(t,e,o){this.throwIfDisposed();let n=this.gl;Ql(n,t,this.framebuffer),this.debug&&Rc(n),this.outputTexture=t,ce(n,()=>n.viewport(0,0,e,o)),ce(n,()=>n.scissor(0,0,e,o))}setOutputMatrixWriteRegionDriver(t,e,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(t,e,o,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function HZ(r){let t=0;for(;t`${r}.${e}`)}function Dt(r,t){return t===1?[r]:lv(r,t)}function SD(r,t){if(r===1)return"rc";let e="";for(let o=0;o ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let e="";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o}; bool rEdge = rp1 >= ${n}; `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= 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n===rr.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):n===rr.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):n===rr.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):n===rr.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):n===rr.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(t,e,o,n){if(this.freeTextures==null)return;let s=vD(o,n),a=kD(e,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=ID(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(t),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function qZ(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function ID(r,t,e,o,n){let s=jZ(t,o),a;if(n){let[p,u]=La(r[0],r[1]);a=p*u}else{let[p,u]=Sp(r[0],r[1]);a=p*u}let i=qZ(e,s);return a*i}function jZ(r,t){switch(r){case rr.PACKED_2X2_FLOAT32:return ih(t);case rr.PACKED_2X2_FLOAT16:return uh(t);case rr.UNPACKED_FLOAT32:return nh(t);case rr.UNPACKED_FLOAT16:return sh(t);case rr.PACKED_4X1_UNSIGNED_BYTE:return ah(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function XZ(r){return A().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?rr.PACKED_2X2_FLOAT32:rr.UNPACKED_FLOAT32:r?rr.PACKED_2X2_FLOAT16:rr.UNPACKED_FLOAT16}function vD(r,t){if(r===dr.UPLOAD)return rr.PACKED_2X2_FLOAT32;if(r===dr.RENDER||r==null)return XZ(t);if(r===dr.DOWNLOAD||r===dr.PIXELS)return rr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function kD(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var or=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${e} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},Ut="if (isnan(x)) return x;",ND="return x;",mv="return abs(x);";var TD="return (x >= 0.0) ? x : (exp(x) - 1.0);",_D=Ut+` return (x < 0.0) ? 0.0 : x; `,$D=Ut+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Ba="return x;",ED="return 1.0 / (1.0 + exp(-1.0 * x));";var DD="return x;",AD=` vec4 result; result.r = (x.r >= 0.0) ? 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WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof kp)e=t;else{let o=Kr(A().getNumber("WEBGL_VERSION"),t);e=new kp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Kr(A().getNumber("WEBGL_VERSION"));e=new kp(o),this.binaryCache=e9(A().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new dh(this.gpgpu),this.numMBBeforeWarning=o9(),this.texData=new zo(this,pr())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,o,n,s,a){let i=this.makeTensorInfo(e,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:t,texShape:[n,s]},p.texShape=[n,s];let u=Dc(e),c=new Zl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=e,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(t,e,o){if((A().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||A().getBool("DEBUG"))&&this.checkNumericalProblems(t),o==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:e,dtype:o,values:t,usage:dr.UPLOAD,refCount:1}),n}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,o,n,s){if(A().getBool("DEBUG")&&this.checkNumericalProblems(e),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:o,dtype:n,values:e,usage:dr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=e;if(a!=null){let m;p?m=new Fr(i,Ba):m=new or(i,Ba);let d=this.runWebGLProgram(m,[{dataId:t,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(t);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n==="complex64"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,l)}async read(t){if(this.pendingRead.has(t)){let f=this.pendingRead.get(t);return new Promise(h=>f.push(h))}let e=this.texData.get(t),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=e;if(s!=null){let f;p?f=new Fr(n,Ba):f=new or(n,Ba);let h=this.runWebGLProgram(f,[{dataId:t,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(t);if(A().getBool("DEBUG")&&!A().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&A().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&A().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...jl(n))}this.pendingRead.set(t,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(t);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(t,l),d=this.pendingRead.get(t);return this.pendingRead.delete(t),d.forEach(f=>f(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&pr().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let o=this.texData.get(t),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;p?d=new Fr(s,Ba):d=new or(s,Ba);let f=this.runWebGLProgram(d,[{dataId:t,shape:s,dtype:i}],i),h=this.readToGPU(f,e);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),l=pr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let o=e.map(n=>y.decodeString(n));return me(t.shape,t.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return me(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,t();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=e,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:o}=this.texData.get(t);return o!=null&&(this.disposeData(o.real.dataId,e),this.disposeData(o.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(t),p=i&&i.origDataId||t,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),e!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(e,n,s,a)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=t9){return A().getBool("WEBGL_CPU_FORWARD")&&t.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,t,e)}else n=this.write(o,t,e);return this.texData.get(n).usage=null,{dataId:n,shape:t,dtype:e}}makeOutput(t,e,o){return pr().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,o),this)}unpackTensor(t){let e=new fh(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new mh(t.shape),o=!0;return this.runWebGLProgram(e,[t],t.dtype,null,o)}packedReshape(t,e){let o=[yi(t.shape),...bi(t.shape)],n={dtype:t.dtype,shape:o,dataId:t.dataId},s=[yi(e),...bi(e)],a=new Vc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],t.dtype,p,i);return{dataId:u.dataId,shape:e,dtype:u.dtype}}decode(t,e){let o=this.texData.get(t),{isPacked:n,shape:s,dtype:a}=o;if(e!=null){let m=y.sizeFromShape(s),d=e[0]*e[1]*4;y.assert(m<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Dc(s),p;n?p=new eh(i):p=new Jf(i);let u=!0,c=[e!=null?e:jl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:t}],a,c,u,e);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(t,e,o,n,s=!1,a){let i=this.makeTensorInfo(t.outputShape,o),p=this.texData.get(i.dataId);if(t.packedOutput&&(p.isPacked=!0),t.outPackingScheme===bu.DENSE){let x=a!=null?a:jl(t.outputShape);p.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(p.usage=t.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Cu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=ER(t,c,l),d=this.getAndSaveBinary(m,()=>_R(this.gpgpu,t,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get("ENGINE_COMPILE_ONLY")||$R(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=A().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool("WEBGL_LAZILY_UNPACK")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(t,e,o,n,s=!1){return o=o||e[0].dtype,this.runWebGLProgram(t,e,o,n,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=A().getBool("DEBUG");A().set("DEBUG",!1);let e=this.abs(ke(1e-8)).dataSync()[0];if(A().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?ZZ:JZ}uploadToGPU(t){let e=this.texData.get(t),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=e;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=e.texShape;if(l==null&&(l=zI(o,p),e.texShape=l),s!=null){let m=Dc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=La(l[0],l[1])),p?d=new oh(m,g):d=new Zl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=dr.PIXELS:C.usage=dr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,S,k),E=this.texData.get(_.dataId);e.texShape=E.texShape,e.isPacked=E.isPacked,e.usage=E.usage,A().get("ENGINE_COMPILE_ONLY")?this.disposeData(_.dataId):(e.texture=E.texture,e.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);e.texture=m}}convertAndCacheOnCPU(t,e){let o=this.texData.get(t),{dtype:n}=o;return e!=null&&(o.values=n9(e,n)),o.values}acquireTexture(t,e,o,n){if(this.numBytesInGPU+=this.computeBytes(t,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,n)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(e),n(!0)}catch(s){throw s}});t.push(o)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await pS(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(qf(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=qI(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=o,t.infLoc=n,t.nanLoc=s,t.outShapeLocation=a,t.outShapeStridesLocation=i,t.outTexShapeLocation=p}}createTensorFromGPUData(t,e,o){t.channels=t.channels||"RGBA";let{texture:n,height:s,width:a,channels:i}=t,p=pr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=p.writeTexture(n,e,o,s,a,i);return pr().makeTensorFromDataId(u,e,o,p)}};wu.nextDataId=0;function n9(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew wu,2);var Cat={forceHalfFloat:MD};var Wc=` if (isnan(a)) return a; if (isnan(b)) return b; `;var Pr=class{constructor(t,e,o){this.variableNames=["A","B"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.enableShapeUniforms=pt(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${t} } void main() { float a = getAAtOutCoords(); float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } `}};var Xr=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; `;var jr=class{constructor(t,e,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o);let s=this.outputShape.length;this.enableShapeUniforms=pt(s);let a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(a=` ${Re(s)} coords = getOutputCoords(); `,s===1)this.enableShapeUniforms?a+=` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; result.w = 0.; `:a+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let p=Dt("coords",s);this.enableShapeUniforms?a+=` bool nextRowOutOfBounds = (${p[s-2]} + 1) >= outShape[${s} - 2]; bool nextColOutOfBounds = (${p[s-1]} + 1) >= outShape[${s} - 1]; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; `:a+=` bool nextRowOutOfBounds = (${p[s-2]} + 1) >= ${this.outputShape[s-2]}; bool nextColOutOfBounds = (${p[s-1]} + 1) >= ${this.outputShape[s-1]}; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; `}this.userCode=` vec4 binaryOperation(vec4 a, vec4 b) { ${t} } void main() { vec4 a = getAAtOutCoords(); vec4 b = getBAtOutCoords(); vec4 result = binaryOperation(a, b); ${a} setOutput(result); } `}};function At(r){let{inputs:t,backend:e}=r,{x:o}=t;return e.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var LD={kernelName:wo,backendName:"webgl",kernelFunc:At};function Or(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,"complex64"),a=e.texData.get(s.dataId),i=At({inputs:{x:o},backend:e}),p=At({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var BD={kernelName:Fi,backendName:"webgl",kernelFunc:Or};var dv="return (a < 0.) ? b * a : a;",fv=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function a9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{alpha:s}=o,a=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(fv,n.shape,a.shape):new Pr(dv,n.shape,a.shape),p=e.runWebGLProgram(i,[n,a],"float32");return e.disposeIntermediateTensorInfo(a),p}var zD={kernelName:Rn,backendName:"webgl",kernelFunc:a9};var hv="return (a < 0.) ? b * a : a;",gv=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function i9(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(gv,o.shape,n.shape):new Pr(hv,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],"float32")}var VD={kernelName:os,backendName:"webgl",kernelFunc:i9};var Po="if (isnan(x)) return x;";function xe({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&e!=null){let l=i.texData.get(a.dataId),m=e(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Fr(a.shape,t):c=new or(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype==="complex64"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},E={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Pr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,E],dt(S.dtype,k.dtype))}),b=Or({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype==="string"?w.fromUint8ToStringArray(f):f,x=p.dtype==="string"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,d;return m?d=new jr(t,p.shape,u.shape,e):d=new Pr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Ci(r,t=!1){if(r==="linear")return t?DD:ND;if(r==="relu")return t?FD:_D;if(r==="elu")return t?AD:TD;if(r==="relu6")return t?PD:$D;if(r==="prelu")return t?gv:hv;if(r==="leakyrelu")return t?fv:dv;if(r==="sigmoid")return t?OD:ED;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Uc=class{constructor(t,e,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=pt(this.outputShape.length);let c=n?t[1]:t[2],l=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",f=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(p?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:u?g=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${i} }`:g=`vec4 activation(vec4 x) { ${i} }`,x="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let C="rc.x",S="rc.x";t[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. 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${u} } int inIdx = inOffset + ${i}; if (${p===1}) { vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0); ${u} } else if (${p===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), 0.0, 0.0); ${u} } else if (${p===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), 0.0); ${u} } setOutput(sumValue); } `}};var gh=class{constructor(t,e){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=t;this.outputShape=[n,a];let i="0.0",p="";e==="prod"?i="1.0":e==="min"?(i="1.0 / 1e-20",p="min"):e==="max"&&(i="-1.0 / 1e-20",p="max");let u=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?u="sumValue":e==="prod"?u="prodValue":e==="all"?u="allValue":e==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,l=o%4,m=` if (${e==="sum"}) { sumValue += dot(values, ones); } else if (${e==="prod"}) { vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]); prodValue *= tmp[0] * tmp[1]; } else { minMaxValue = ${p}(values, minMaxValue); if (${e==="min"} || ${e==="max"}) { minMaxValue = ${p}(values, minMaxValue); bvec4 isNaN = isnan(values); if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) { minMaxValue = vec4(NAN); } } } `,d="vec4";e==="all"?(i="1.0",m=` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); `,d="bvec4"):e==="any"&&(i="0.0",m=` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); `,d="bvec4");let f="";s%o>0&&(f=` if (inIdx < 0 || inIdx >= ${s}) { return initializationValue; } `),this.userCode=` const float initializationValue = ${i}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float getValue(int batch, int inIdx) { ${f} return getX(batch, inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${o}; vec4 minMaxValue = vec4(${i}); float prodValue = 1.0; float sumValue = 0.0; float allValue = 1.0; float anyValue = 0.0; for (int i = 0; i < ${c}; i += 4) { int inIdx = inOffset + i; ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); ${m} } int inIdx = inOffset + ${c}; if (${l===1}) { ${d} values = ${d}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); ${m} } else if (${l===2}) { ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); ${m} } else if (${l===3}) { ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); ${m} } setOutput(${u}); } `}};function p9(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],o=w.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:o,outSize:Math.ceil(e/o)})}return t}function Yr(r,t,e,o){let n=p9(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(t);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=lv("rc",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${o} must match.`);let k=e?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=te({inputs:{x:r},backend:n,attrs:{shape:k}}),R=te({inputs:{x:t},backend:n,attrs:{shape:_}}),D=[E,R],P=Math.max(x,b),O=e?E.shape[1]:E.shape[2],M=s!=null,L=a!=null,B=p==="leakyrelu",z=p!=null?Ci(p,!0):null,U=M||L||B||z!=null,j;if((d===1||f===1)&&O>yv&&U===!1){let Y=E,J=R;e&&(Y=Ct({inputs:{x:E},backend:n,attrs:{perm:[0,2,1]}}),D.push(Y)),o&&(J=Ct({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),D.push(J));let re=f!==1,ne=f===1,ee=Y;re&&(ee=te({inputs:{x:Y},backend:n,attrs:{shape:[P,O,1]}}),D.push(ee));let oe=f===1?2:1,ie=J;ne&&(ie=te({inputs:{x:J},backend:n,attrs:{shape:[P,1,O]}}),D.push(ie));let le=tm({inputs:{a:ee,b:ie},backend:n});j=Tp({inputs:{x:le},backend:n,attrs:{axis:oe,keepDims:!0}}),D.push(le)}else{let Y=dt(r.dtype,t.dtype),J=new Uc(k,_,[P,d,f],e,o,M,z,L,B),re=[E,R];if(s!=null&&re.push(s),L&&re.push(a),B){let ne=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function l9(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return _p({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var XD={kernelName:Io,backendName:"webgl",kernelFunc:l9};var YD="return abs(x);";function m9(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=e.texData.get(o.dataId),a=ch(s.values);return e.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Fr(o.shape,YD):n=new or(o.shape,YD),e.runWebGLProgram(n,[o],o.dtype)}var QD={kernelName:Xs,backendName:"webgl",kernelFunc:m9};var d9=Ut+` if (abs(x) > 1.) { return NAN; } return acos(x); `,f9=xe({opSnippet:d9}),ZD={kernelName:Wo,backendName:"webgl",kernelFunc:f9};var h9=Ut+` if (x < 1.0) return NAN; return log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),JD={kernelName:Uo,backendName:"webgl",kernelFunc:g9};var eA="return a + b;",x9=nt({opSnippet:eA,packedOpSnippet:eA,supportsComplex:!0,cpuKernelImpl:RR}),tA={kernelName:uo,backendName:"webgl",kernelFunc:x9};var bh=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${o.join(` `)} float result = ${n}; setOutput(result); } `}};var Ch=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${o.join(` `)} vec4 result = ${n}; setOutput(result); } `}};function wh(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return At({inputs:{x:o[0]},backend:e});if(o.length>A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=wh({inputs:o.slice(0,p),backend:e}),c=wh({inputs:o.slice(p),backend:e});return wh({inputs:[u,c],backend:e})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool("WEBGL_PACK")?new Ch(o[0].shape,s):new bh(o[0].shape,s);return e.runWebGLProgram(i,o,n)}var rA={kernelName:Go,backendName:"webgl",kernelFunc:wh};function y9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:e,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("all",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:e,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,"all",e),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(l),x}var oA={kernelName:Ho,backendName:"webgl",kernelFunc:y9};function b9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:e,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("any",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:e,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,"any",e),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(l),x}var nA={kernelName:Ko,backendName:"webgl",kernelFunc:b9};var Sh=class{constructor(t,e,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=t;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=e==="max"?">":"<",p=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${n}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); for (int i = 0; i < ${n}; i++) { int inIdx = ${p}; float candidate = getA(batch, inIdx); if (candidate ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}};var Ih=class{constructor(t,e,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],a=Math.ceil(s/e);this.outputShape=t.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,p=i.length,u=Re(p),c=Dt("coords",p),l,m;if(a===1){m=p+1;let R=Re(m);l=` ${R} sourceLocR = ${R}(${c.join()}, 0); ++${c[p-1]}; ${R} sourceLocG = ${R}(${c.join()}, 0); ++${c[p-2]}; ${R} sourceLocA = ${R}(${c.join()}, 0); --${c[p-1]}; ${R} sourceLocB = ${R}(${c.join()}, 0); --${c[p-2]};`}else m=p,l=` ${u} sourceLocR = coords; ++${c[p-1]}; ${u} sourceLocG = coords; ++${c[p-2]}; ${u} sourceLocA = coords; --${c[p-1]}; ${u} sourceLocB = coords; --${c[p-2]};`;let d=["x","y","z","w","u","v"].slice(0,m),f="."+d[m-1],h=d.map(R=>"int "+R),g=Dt("sourceLocR",m-1).concat("inIdx.r"),x=Dt("sourceLocG",m-1).concat("inIdx.g"),b=Dt("sourceLocB",m-1).concat("inIdx.b"),C=Dt("sourceLocA",m-1).concat("inIdx.a"),S=o==="max"?"greaterThan":"lessThan",k=n?"":` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), getBestIndicesAChannel(${C.join()})));`,_=`vec4( getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,E=n?"":` float getBestIndicesAChannel(${h.join()}) { return getChannel(getBestIndicesA(${d.join()}), vec2(${d.slice(-2).join()})); }`;this.userCode=` float getAChannel(${h.join()}) { return getChannel(getA(${d.join()}), vec2(${d.slice(-2).join()})); } ${E} void main() { ${u} coords = getOutputCoords(); bool hasNextCol = ${c[p-1]} < ${i[p-1]-1}; bool hasNextRow = ${c[p-2]} < ${i[p-2]-1}; ${l} ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f}, sourceLocB${f}, sourceLocA${f}) * ${e}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${_}; for (int i = 0; i < ${e}; i++) { inIdx = srcIdx; ${k} vec4 candidate = ${_}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${S}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); bestValue = vec4(replace.x ? candidate.x : bestValue.x, replace.y ? candidate.y : bestValue.y, replace.z ? candidate.z : bestValue.z, replace.w ? candidate.w : bestValue.w); bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace)); srcIdx++; } setOutput(bestIndex); } `}};function sA(r,t,e,o=null){let n=t.shape[0],s=t.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=w.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new Sh(i,e,o==null),u=[t];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,"int32");if(c.shape[1]===1)return c;let l=sA(r,t,e,c);return r.disposeIntermediateTensorInfo(c),l}function aA(r,t,e,o=null){let n=o!=null?o.shape:t.shape,s=n[n.length-1],a=w.computeOptimalWindowSize(s),i=new Ih(n,a,e,o==null),p=o==null?[t]:[t,o],u=r.runWebGLProgram(i,p,"int32");if(u.shape.length===t.shape.length){let c=aA(r,t,e,u);return r.disposeIntermediateTensorInfo(u),c}return u}function vh(r,t,e,o){let n=[e];if(w.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,t.shape.length),!A().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],a=r.texData.get(t.dataId),i=a!==null&&a.isPacked,p=t;i&&(p=r.unpackTensor(t),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=sA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return aA(r,t,o)}function C9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=vh(e,p,a[0],"max");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var iA={kernelName:Ys,backendName:"webgl",kernelFunc:C9};function w9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=vh(e,p,a[0],"min");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var uA={kernelName:Qs,backendName:"webgl",kernelFunc:w9};var S9=Ut+` if (abs(x) > 1.) { return NAN; } return asin(x); `,I9=xe({opSnippet:S9}),pA={kernelName:qo,backendName:"webgl",kernelFunc:I9};var v9=Ut+"return log(x + sqrt(x * x + 1.0));",k9=xe({opSnippet:v9}),cA={kernelName:jo,backendName:"webgl",kernelFunc:k9};var N9=Ut+` return atan(x); `,T9=xe({opSnippet:N9}),lA={kernelName:Xo,backendName:"webgl",kernelFunc:T9};var _9=Wc+` return atan(a, b); `,$9=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); `+Xr+` return result; `,E9=nt({opSnippet:_9,packedOpSnippet:$9}),mA={kernelName:Qo,backendName:"webgl",kernelFunc:E9};var R9=Ut+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),dA={kernelName:Yo,backendName:"webgl",kernelFunc:D9};var Us=class{constructor(t,e,o,n=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=t.filterWidth,i=t.strideHeight,p=t.strideWidth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterHeight,m=t.effectiveFilterWidth,d=t.padInfo.top,f=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${p}); const ivec2 pads = ivec2(${d}, ${f}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; float avgValue = 0.0; for (int wR = 0; wR < ${l}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${m}; wC += ${c}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${t.inWidth}) { continue; } float value = getX(batch, xR, xC, d); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${R} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let C="max",S=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(S="avgValue / max(count, 1.0)");let k=Math.floor(a/4)*4,_=a%4,E=` if (${h}) { avgValue += dot(values, ones); } else { minMaxValue = ${C}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${p}); const ivec2 pads = ivec2(${d}, ${f}); const float initializationValue = ${b}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xR, int xC, int d) { if (xC < 0 || xC >= ${t.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${b}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${l}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${k}; wC += 4) { int xC = xCCorner + wC * ${c}; vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), getValue(batch, xR, xC + 3 * ${c}, d) ); ${E} } int xC = xCCorner + ${k}; if (${_===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${E} } else if (${_===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), initializationValue, initializationValue ); ${E} } else if (${_===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), initializationValue ); ${E} } } setOutput(${S}); } `}},Iu=class{constructor(t,e,o,n=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=t.filterWidth,i=t.strideDepth,p=t.strideHeight,u=t.strideWidth,c=t.dilationDepth,l=t.dilationHeight,m=t.dilationWidth,d=t.effectiveFilterDepth,f=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let C=e==="avg",S="0.0";if(C||(S="-1.0 / 1e-20"),o){let P=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${p}, ${u}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; for (int wD = 0; wD < ${d}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${t.inDepth}) { continue; } for (int wR = 0; wR < ${f}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${h}; wC += ${m}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${t.inWidth}) { continue; } float value = getX(batch, xD, xR, xC, ch); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${P} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${f} * ${h} + wR * ${h} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let k="max",_=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(_="avgValue / max(count, 1.0)");let E=Math.floor(a/4)*4,R=a%4,D=` if (${C}) { avgValue += dot(values, ones); } else { minMaxValue = ${k}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${p}, ${u}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); const float initializationValue = ${S}; const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0); float count = 0.0; float getValue(int batch, int xD, int xR, int xC, int ch) { if (xC < 0 || xC >= ${t.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${S}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${d}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${t.inDepth}) { continue; } for (int wR = 0; wR < ${f}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${E}; wC += 4) { int xC = xCCorner + wC * ${m}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), getValue(batch, xD, xR, xC + 2 * ${m}, ch), getValue(batch, xD, xR, xC + 3 * ${m}, ch) ); ${D} } int xC = xCCorner + ${E}; if (${R===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${D} } else if (${R===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), initializationValue, initializationValue ); ${D} } else if (${R===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), getValue(batch, xD, xR, xC + 2 * ${m}, ch), initializationValue ); ${D} } } } setOutput(${_}); } `}};function A9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;Vs(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:e});let l=new Us(c,"avg",!1);return e.runWebGLProgram(l,[n],"float32")}var fA={kernelName:Zo,backendName:"webgl",kernelFunc:A9};function F9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new Iu(l,"avg",!1);return e.runWebGLProgram(m,[n],"float32")}var hA={kernelName:Zs,backendName:"webgl",kernelFunc:F9};var kh=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,p=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=p-1-t.padInfo.top,l=u-1-t.padInfo.left,m=1/(e*o);this.userCode=` const ivec2 pads = ivec2(${c}, ${l}); const float avgMultiplier = float(${m}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${p}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${u}; wC+= ${i}) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},Nh=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,m=t.effectiveFilterHeight,d=t.effectiveFilterWidth,f=l-1-t.padInfo.front,h=m-1-t.padInfo.top,g=d-1-t.padInfo.left,x=1/(e*o*n);this.userCode=` const ivec3 pads = ivec3(${f}, ${h}, ${g}); const float avgMultiplier = float(${x}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${l}; wD += ${p}) { float dyD = float(dyDCorner + wD) / ${s}.0; if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${m}; wR += ${u}) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${d}; wC += ${c}) { float dyC = float(dyCCorner + wC) / ${i}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); dotProd += dyValue * avgMultiplier; } } } setOutput(dotProd); } `}};function P9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Nh(m);return e.runWebGLProgram(d,[n],a.dtype)}var gA={kernelName:Ai,backendName:"webgl",kernelFunc:P9};function O9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;Vs([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new kh(c);return e.runWebGLProgram(l,[n],a.dtype)}var xA={kernelName:Di,backendName:"webgl",kernelFunc:O9};function M9(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s}=t,{transposeA:a,transposeB:i}=o;return _p({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var yA={kernelName:Jo,backendName:"webgl",kernelFunc:M9};var Th=class{constructor(t,e,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],w.assertAndGetBroadcastShape(t,e),w.assertAndGetBroadcastShape(t,o);let i="0.0";n!=null&&(w.assertAndGetBroadcastShape(t,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="1.0";s!=null&&(w.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${i}; float scale = ${p}; float inv = scale * inversesqrt(variance + float(${a})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}};var _h=class{constructor(t,e,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],w.assertAndGetBroadcastShape(t,e),w.assertAndGetBroadcastShape(t,o);let i="vec4(0.0)";n!=null&&(w.assertAndGetBroadcastShape(t,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(w.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${p}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${a})); setOutput((x - mean) * inv + offset); } `}};var L9=({inputs:r,backend:t,attrs:e})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=e;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool("WEBGL_PACK_NORMALIZATION")?new _h(o.shape,n.shape,s.shape,c,l,p):new Th(o.shape,n.shape,s.shape,c,l,p);return t.runWebGLProgram(m,u,u[0].dtype)},bA={kernelName:vn,backendName:"webgl",kernelFunc:L9};var $h=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Re(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let o=B9(this.rank),n,s=t.map((a,i)=>`sourceLoc.${bv[i]} = start[${i}] + coords.${bv[i]};`);n=` ${e} sourceLoc; ${e} coords = getOutputCoords(); ${s.join(` `)} `,this.userCode=` void main() { ${n} setOutput(getSource(${o})); } `}},bv=["x","y","z","w","u","v"];function B9(r){if(r===1)return"sourceLoc";if(r<=6)return bv.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Eh=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=Re(this.rank),o=Dt("coords",this.rank),n=Dt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=` result.x = ${a}; if (++${o[this.rank-1]} < ${t[this.rank-1]}) { ++${n[this.rank-1]}; result.y = ${a}; --${n[this.rank-1]}; } `,p=this.rank===1?"":` --${o[this.rank-1]}; if (++${o[this.rank-2]} < ${t[this.rank-2]}) { ++${n[this.rank-2]}; result.z = ${a}; if (++${o[this.rank-1]} < ${t[this.rank-1]}) { ++${n[this.rank-1]}; result.w = ${a}; } } `,u=this.rank<=4?`sourceLoc = coords + ${e}(${t.map((c,l)=>`start[${l}]`).join()});`:t.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(` `);this.userCode=` void main() { ${e} coords = getOutputCoords(); ${e} sourceLoc; ${u} vec4 result = vec4(0.); ${i} ${p} setOutput(result); } `}};function z9(r,t,e,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(e,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=e,a.dtype=r.dtype;let i=ct.computeFlatOffset(t,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Gs(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return e.makeTensorInfo(p,n.dtype,[]);if(e.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=e.texData.get(n.dataId),m=pD(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=e.texData.get(n.dataId),c=ct.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Eh(p):new $h(p),m=[i];return e.runWebGLProgram(l,[n],n.dtype,m)}return e.uploadToGPU(n.dataId),z9(n,i,p,e)}var CA={kernelName:ha,backendName:"webgl",kernelFunc:Gs};var V9=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:e,attrs:{shape:p}}),h=Ct({inputs:{x:f},backend:e,attrs:{perm:u}}),g=te({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},wA={kernelName:Js,backendName:"webgl",kernelFunc:V9};function W9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=e.readSync(n.dataId),p=e.readSync(s.dataId),u=ph(i,p,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,u)}var SA={kernelName:en,backendName:"webgl",kernelFunc:W9};var U9=` int r = int(a.r) & int(b.r); 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${o} }`:s?S=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${o} }`:S=` float activation(float x) { ${o} } `,k="result = activation(result);");let _=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${S} const ivec2 strides = ivec2(${p}, ${u}); const ivec2 pads = ivec2(${a}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${C}]; ivec2 xRCCorner = ivec2(coords[${x}], coords[${b}]) * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${m}; wR++) { int xR = xRCorner + wR * ${c}; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${d}; wC++) { int xC = xCCorner + wC * ${l}; if (xC < 0 || xC >= ${t.inWidth}) { continue; } for (int d1 = 0; d1 < ${f}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${g}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), getX(batch, xR, xC, d1 + 2), getX(batch, xR, xC, d1 + 3) ); dotProd += dot(xValues, wValues); } else { vec4 xValues = vec4( getX(batch, d1, xR, xC), getX(batch, d1 + 1, xR, xC), getX(batch, d1 + 2, xR, xC), getX(batch, d1 + 3, xR, xC) ); dotProd += dot(xValues, wValues); } } if (${h===1}) { if (${g}) { dotProd += getX(batch, xR, xC, ${f}) * getW(wR, wC, ${f}, d2); } else { dotProd += getX(batch, ${f}, xR, xC) * getW(wR, wC, ${f}, d2); } } else if (${h===2}) { vec2 wValues = vec2( getW(wR, wC, ${f}, d2), getW(wR, wC, ${f} + 1, d2) ); if (${g}) { vec2 xValues = vec2( getX(batch, xR, xC, ${f}), getX(batch, xR, xC, ${f} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${f}, xR, xC), getX(batch, ${f} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${h===3}) { vec3 wValues = vec3( getW(wR, wC, ${f}, d2), getW(wR, wC, ${f} + 1, d2), getW(wR, wC, ${f} + 2, d2) ); if (${g}) { vec3 xValues = vec3( getX(batch, xR, xC, ${f}), getX(batch, xR, xC, ${f} + 1), getX(batch, xR, xC, ${f} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( getX(batch, ${f}, xR, xC), getX(batch, ${f} + 1, xR, xC), getX(batch, ${f} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${_} ${k} setOutput(result); } `}},Mh=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,o=t.padInfo.top,n=t.padInfo.left,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,m=t.filterHeight,d=t.filterWidth,f=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${s}, ${a}, ${i}); const ivec3 pads = ivec3(${e}, ${o}, ${n}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d2 = coords.u; ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xFCorner = xFRCCorner.x; int xRCorner = xFRCCorner.y; int xCCorner = xFRCCorner.z; // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; for (int wF = 0; wF < ${l}; wF++) { int xF = xFCorner + wF * ${p}; if (xF < 0 || xF >= ${t.inDepth}) { continue; } for (int wR = 0; wR < ${m}; wR++) { int xR = xRCorner + wR * ${u}; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int wC = 0; wC < ${d}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${t.inWidth}) { continue; } for (int d1 = 0; d1 < ${f}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${h===1}) { dotProd += getX(batch, xF, xR, xC, ${f}) * getW(wF, wR, wC, ${f}, d2); } else if (${h===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${f}), getX(batch, xF, xR, xC, ${f} + 1) ); vec2 wValues = vec2( getW(wF, wR, wC, ${f}, d2), getW(wF, wR, wC, ${f} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${h===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${f}), getX(batch, xF, xR, xC, ${f} + 1), getX(batch, xF, xR, xC, ${f} + 2) ); vec3 wValues = vec3( getW(wF, wR, wC, ${f}, d2), getW(wF, wR, wC, ${f} + 1, d2), getW(wF, wR, wC, ${f} + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutput(dotProd); } `}};var Kc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=t.padInfo.left,i=t.strideWidth,p=t.dilationWidth,u=t.filterHeight,c=t.filterWidth,l=c,m=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=` xC = xCCorner + ${x*p}; `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } `,p===1&&x>0?m+=` xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy); `:m+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { previous.zw = vec2(0.0); } xC${x} = vec4(previous.zw, xTexelC${x}.xy); } else { xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy); } `):m+=` if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } xC${x} = xTexelC${x}; `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.0); } xTexelC${x+1}Ready = 1; } `,p>1?m+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy); } else { xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy); } `:m+=` xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy); `):b===1?m+=` xC${x+1} = xTexelC${x}; `:m+=` xCOffset = xC + ${b}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.0); } xTexelC${x+1}Ready = 1; } xC${x+1} = xTexelC${x+1}; `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.0); } xTexelC${x+1}Ready = 1; } xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw); `,x+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy); `)):(m+=` if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.); } xTexelC${x+1}Ready = 1; } xC${x} = vec4( xTexelC${x}.xy, xTexelC${x+1}.xy); `,x+1= 0) { // Use custom imod instead mod. On Intel GPU, mod may generate // unexpected value. // https://github.com/tensorflow/tfjs/issues/5447 offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1]; d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / inChannels); if(d1 < inputShape[${i}] && d1 >= 0) { ch = imod(pos, inChannels); if (${s}) { innerDims = vec2(d1, ch); result[${c*2+l}] = getChannel( getA(rc.x, d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${c*2+l}] = getChannel( getA(rc.x, ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${u} ${n.output} = result; } `}};function Bh(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function zh({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=e.inChannels,l=p[0]*p[1]*p[2],m=e.outChannels,d=e.dataFormat==="channelsLast",f=!1,h=!1,g,x=[];if(s!=null){let S=Bh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Bh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>yv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,e.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(Cu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=te({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(E);let R=_p({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=_,D.shape=e.outShape,g=At({inputs:{x:R},backend:o}),g.shape=e.outShape,x.push(R)}else{let S=e.outHeight*e.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[e.batchSize,S,e.inChannels]:[e.batchSize,e.inChannels,S]}}),_=te({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}}),E=_p({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:E},backend:o,attrs:{shape:e.outShape}}),x.push(k),x.push(_),x.push(E)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Vh({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=e,f=d==="channelsLast",h=p*u*c,g=m*l,x=[e.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Bh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Bh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:t},backend:o,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});S.push(k);let _=new Lh(x,e),E=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],R=o.runWebGLProgram(_,[r],"float32",E),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i==="leakyrelu",L=i?Ci(i,!0):null,B=new Uc(f?D.shape:k.shape,f?k.shape:D.shape,f?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,"float32"),j=te({inputs:{x:U},backend:o,attrs:{shape:e.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function J9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))d=zh({x:n,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&l==="channelsLast"&&A().getBool("WEBGL_EXP_CONV")){let h=new Kc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=e.runWebGLProgram(h,[n,s],"float32",g)}else if(A().getBool("WEBGL_CONV_IM2COL"))d=Vh({x:n,filter:s,convInfo:m,backend:e});else{let h=new Hc(m);d=e.runWebGLProgram(h,[n,s],"float32")}let f=te({inputs:{x:d},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(d),f}var OA={kernelName:rn,backendName:"webgl",kernelFunc:J9};var Wh=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,o=t.strideWidth,n=t.padInfo.top,s=t.padInfo.left,a=t.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int d2 = coords.w; // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int b = 0; b < ${t.batchSize}; b++) { for (int yR = 0; yR < ${t.outHeight}; yR++) { int xR = wR + yR * ${e} - ${n}; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int yC = 0; yC < ${t.outWidth}; yC++) { int xC = wC + yC * ${o} - ${s}; if (xC < 0 || xC >= ${t.inWidth}) { continue; } ${a?`float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue);`} } } } setOutput(dotProd); } `}},Uh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=t.dataFormat==="channelsLast",i=e-1-t.padInfo.top,p=o-1-t.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${p}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${l}]; ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${e}; wR++) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${e} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${o} - 1 - wC; for (int d2 = 0; d2 < ${t.outChannels}; d2++) { if (${a}) { float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } else { float xValue = getDy(batch, d2, idyR, idyC); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}},Gh=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.padInfo.front,a=t.padInfo.top,i=t.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; int wR = coords.y; int wC = coords.z; int d1 = coords.w; int d2 = coords.u; float dotProd = 0.0; for (int b = 0; b < ${t.batchSize}; b++) { for (int yF = 0; yF < ${t.outDepth}; yF++) { int xF = wF + yF * ${e} - ${s}; if (xF < 0 || xF >= ${t.inDepth}) { continue; } for (int yR = 0; yR < ${t.outHeight}; yR++) { int xR = wR + yR * ${o} - ${a}; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int yC = 0; yC < ${t.outWidth}; yC++) { int xC = wC + yC * ${n} - ${i}; if (xC < 0 || xC >= ${t.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},Hh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=e-1-t.padInfo.front,u=o-1-t.padInfo.top,c=n-1-t.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${p}, ${u}, ${c}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyFCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; float dotProd = 0.0; for (int wF = 0; wF < ${e}; wF++) { float dyF = float(dyFCorner + wF) / ${s}.0; if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); int wFPerm = ${e} - 1 - wF; for (int wR = 0; wR < ${o}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${o} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${i}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; for (int d2 = 0; d2 < ${t.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function eJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Wh(m);return e.runWebGLProgram(d,[n,s],"float32")}var MA={kernelName:Oi,backendName:"webgl",kernelFunc:eJ};var Kh=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=pt(this.outputShape.length);let e=t.filterHeight,o=t.filterWidth,n=e-1-t.padInfo.top,s=o-1-t.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${n}, ${s}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[3]; ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; vec4 result = vec4(0.); for (int wR = 0; wR < ${e}; wR++) { float dyR = float(dyRCorner + wR) / strides[0]; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${e} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { int wCPerm = ${o} - 1 - wC; float dyC = float(dyCCorner + wC) / strides[1]; bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0) && (fract(dyC) == 0.0); int idyC = int(dyC); float dyC2 = float(dyCCorner + wC + 1) / strides[1]; bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0) && (fract(dyC2) == 0.0); int idyC2 = int(dyC2); if (idyCVal && idyCVal2) { for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC, d2); vec4 dySample2 = (idyC / 2 == idyC2 / 2) ? dySample : getDy(batch, idyR, idyC2, d2); vec2 dyValue = mod(float(idyC), 2.) == 0. ? dySample.xy : dySample.zw; result.xy += vec2(dot(dyValue, wValue.xy), dot(dyValue, wValue.zw)); dyValue = mod(float(idyC2), 2.) == 0. ? dySample2.xy : dySample2.zw; result.zw += vec2(dot(dyValue, wValue.xy), dot(dyValue, wValue.zw)); } } else if (idyCVal) { for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC, d2); vec2 dyValue = mod(float(idyC), 2.) == 0. ? dySample.xy : dySample.zw; result.xy += vec2(dot(dyValue, wValue.xy), dot(dyValue, wValue.zw)); } } else if (idyCVal2) { for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) { vec4 wValue = getW(wRPerm, wCPerm, d1, d2); vec4 dySample = getDy(batch, idyR, idyC2, d2); vec2 dyValue = mod(float(idyC2), 2.) == 0. ? dySample.xy : dySample.zw; result.zw += vec2(dot(dyValue, wValue.xy), dot(dyValue, wValue.zw)); } } } } setOutput(result); } `}};function tJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&l==="channelsLast"){let d=[[m.strideHeight,m.strideWidth]],f=new Kh(m);return e.runWebGLProgram(f,[n,s],"float32",d)}else{let d=new Uh(m);return e.runWebGLProgram(d,[n,s],"float32")}}var LA={kernelName:on,backendName:"webgl",kernelFunc:tJ};function rJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Mh(u);return e.runWebGLProgram(c,[n,s],"float32")}var BA={kernelName:nn,backendName:"webgl",kernelFunc:rJ};function oJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Gh(u);return e.runWebGLProgram(c,[n,s],"float32")}var zA={kernelName:Xa,backendName:"webgl",kernelFunc:oJ};function nJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Hh(u);return e.runWebGLProgram(c,[n,s],"float32")}var VA={kernelName:sn,backendName:"webgl",kernelFunc:nJ};var sJ=Po+` return cos(x); `,aJ=` vec4 result = cos(x); bvec4 isNaN = isnan(x); ${Xr} return result; `,iJ=xe({opSnippet:sJ,packedOpSnippet:aJ}),WA={kernelName:an,backendName:"webgl",kernelFunc:iJ};var uJ=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,pJ=xe({opSnippet:uJ}),UA={kernelName:un,backendName:"webgl",kernelFunc:pJ};var qh=class{constructor(t,e,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=t,[c]=e,[l,m]=o;this.outputShape=[c,l,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${f} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` const float height_ratio = float(${g}); const float width_ratio = float(${C}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int y = coords[1]; int x = coords[2]; int d = coords[3]; // get box vals float y1 = getBoxes(b,0); float x1 = getBoxes(b,1); float y2 = getBoxes(b,2); float x2 = getBoxes(b,3); // get image in batch index int bInd = round(getBoxInd(b)); if(bInd < 0 || bInd >= ${a}) { return; } float height_scale = ${x}; float width_scale = ${S}; float in_y = ${b}; if( in_y < 0.0 || in_y > ${f} ) { setOutput(float(${s})); return; } float in_x = ${k}; if( in_x < 0.0 || in_x > ${h} ) { setOutput(float(${s})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${d} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}};var cJ=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new qh(n.shape,s.shape,i,p,u);return e.runWebGLProgram(c,[n,s,a],"float32")},GA={kernelName:ln,backendName:"webgl",kernelFunc:cJ};var Ep;(function(r){r.Prod="*",r.Sum="+"})(Ep||(Ep={}));var om=class{constructor(t,e,o,n){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===Ep.Prod?"1.0":"0.0",i=o?a:`getX(${HA(s,"coords",this.op)})`,p=this.outputShape[this.outputShape.length-1],u="",c="";o?(u=n?`end != ${p-1}`:"end != 0",c=n?"end + 1":"end - 1"):(u=n?`end + pow2 < ${p}`:"end >= pow2",c=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${Re(s)} coords = getOutputCoords(); int end = ${KA(s,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${u}) { int idx = ${c}; ${KA(s,"coords",this.op)} = idx; val ${this.op}= getX(${HA(s,"coords",this.op)}); } setOutput(val); } `}};function HA(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function KA(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function jh(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=Ct({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new om(r,p.shape,!1,s),f=[[m]],h=l;l=e.runWebGLProgram(d,[l],l.dtype,f),e.disposeIntermediateTensorInfo(h)}if(n){let m=new om(r,p.shape,n,s),d=l;l=e.runWebGLProgram(m,[l],l.dtype),e.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=Ct({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(p),d}return l}function lJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return jh(Ep.Prod,n,e,s,a,i)}var qA={kernelName:pn,backendName:"webgl",kernelFunc:lJ};function mJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return jh(Ep.Sum,n,e,s,a,i)}var jA={kernelName:cn,backendName:"webgl",kernelFunc:mJ};function dJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=ph(p,u,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=e.bufferSync(n),u=e.bufferSync(s),c=DR(p,u,a,i);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var XA={kernelName:ra,backendName:"webgl",kernelFunc:dJ};var Xh=class{constructor(t,e,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=o,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int h = ${this.getHeightCoordString()}; int w = ${this.getWidthCoordString()}; int d = ${this.getDepthCoordString()}; int in_h = h / ${e}; int offset_h = imod(h, ${e}); int in_w = w / ${e}; int offset_w = imod(w, ${e}); int offset_d = (offset_h * ${e} + offset_w) * ${this.getOutputDepthSize()}; int in_d = d + offset_d; float result = ${this.getInputSamplingString()}; setOutput(result); } `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function fJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=new Xh(f,s,a);return e.runWebGLProgram(h,[n],n.dtype)}var YA={kernelName:mn,backendName:"webgl",kernelFunc:fJ};var qc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=t.filterHeight,i=t.filterWidth,p=t.outChannels/t.inChannels,u="",c="";o&&(n?u=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} }`:s?u=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${o} }`:u=` float activation(float x) { ${o} } `,c="result = activation(result);");let l=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${u} void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${p}; int q = d2 - d1 * ${p}; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations. for (int wR = 0; wR < ${a}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } for (int wC = 0; wC < ${i}; wC++) { int xC = xCCorner + wC * dilations[1]; if (xC < 0 || xC >= inDims[1]) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${l} ${c} setOutput(result); } `}};var jc=class{constructor(t,e=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=pt(this.outputShape.length);let a=t.outChannels/t.inChannels,i=t.padInfo.left,p=t.strideWidth,u=t.dilationWidth,c=t.filterHeight,l=t.filterWidth,m=l,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) { `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=` xC = xCCorner + ${b*u}; `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } `,u===1&&b>0?d+=` xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy); `:d+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { previous.zw = vec2(0.0); } xC${b} = vec4(previous.zw, xTexelC${b}.xy); } else { xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy); } `):d+=` if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } xC${b} = xTexelC${b}; `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } `,u>1?d+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy); } else { xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy); } `:d+=` xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy); `):C===1?d+=` xC${b+1} = xTexelC${b}; `:d+=` xCOffset = xC + ${C}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } xC${b+1} = xTexelC${b+1}; `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw); `,b+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy); `)):(d+=` if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.); } xTexelC${b+1}Ready = 1; } xC${b} = vec4( xTexelC${b}.xy, xTexelC${b+1}.xy); `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool("WEBGL_PACK_DEPTHWISECONV")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new jc(l):m=new qc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return e.runWebGLProgram(m,[n,s],"float32",d)}var QA={kernelName:dn,backendName:"webgl",kernelFunc:hJ};var Yh=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,o=t.strideWidth,n=t.padInfo.top,s=t.padInfo.left,a=t.outChannels/t.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int dm = coords.w; int d2 = d1 * ${a} + dm; float dotProd = 0.0; // TO DO: Vec4 over the batch size for (int b = 0; b < ${t.batchSize}; b++) { for (int yR = 0; yR < ${t.outHeight}; yR++) { int xR = wR + yR * ${e} - ${n}; if (xR < 0 || xR >= ${t.inHeight}) { continue; } for (int yC = 0; yC < ${t.outWidth}; yC++) { int xC = wC + yC * ${o} - ${s}; if (xC < 0 || xC >= ${t.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},Qh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=e-1-t.padInfo.top,i=o-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=` const ivec2 pads = ivec2(${a}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[3]; ivec2 dyCorner = coords.yz - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; float dotProd = 0.0; for (int wR = 0; wR < ${e}; wR++) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${e} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${o} - 1 - wC; // TO DO: Vec4 over the channelMul for (int dm = 0; dm < ${p}; dm++) { int d2 = d1 * ${p} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function gJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Yh(l);return e.runWebGLProgram(m,[n,s],"float32")}var ZA={kernelName:Mi,backendName:"webgl",kernelFunc:gJ};function xJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Qh(l);return e.runWebGLProgram(m,[n,s],"float32")}var JA={kernelName:Li,backendName:"webgl",kernelFunc:xJ};var Zh=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function yJ(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Zh(s),p=e.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(p),u}var eF={kernelName:oa,backendName:"webgl",kernelFunc:yJ};var Jh=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=t,{top:l,left:m}=n;this.userCode=` const ivec2 strides = ivec2(${s}, ${a}); const ivec2 pads = ivec2(${l}, ${m}); const float neg_infinity = -3.4e38; void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.w; ivec2 outTopLeftCorner = coords.yz * strides - pads; int hBeg = outTopLeftCorner.x; int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; for (int h = 0; h < ${i}; h++) { int hIn = hBeg + h * ${u}; if (hIn >= 0 && hIn < ${e}) { for (int w = 0; w < ${p}; w++) { int wIn = wBeg + w * ${c}; if (wIn >= 0 && wIn < ${o}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); float val = xVal + wVal; if (val > curVal) { curVal = val; } } } } } float result = curVal; setOutput(result); } `}};function bJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c,l=new Jh(u);c=e.runWebGLProgram(l,[n,s],"float32");let m=te({inputs:{x:c},backend:e,attrs:{shape:u.outShape}});return e.disposeIntermediateTensorInfo(c),m}var tF={kernelName:fn,backendName:"webgl",kernelFunc:bJ};function CJ(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Tp({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var rF={kernelName:Vi,backendName:"webgl",kernelFunc:CJ};var wJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",SJ=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0); result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0); result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; `,IJ=xe({opSnippet:wJ,packedOpSnippet:SJ}),oF={kernelName:gn,backendName:"webgl",kernelFunc:IJ};var vJ="return (b >= 0.0) ? a : a * (b + 1.0);",kJ=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,NJ=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(kJ,o.shape,n.shape):new Pr(vJ,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],o.dtype)},nF={kernelName:Ya,backendName:"webgl",kernelFunc:NJ};var TJ=` return vec4(equal(a, b)); `,_J="return float(a == b);",$J=nt({opSnippet:_J,packedOpSnippet:TJ,dtype:"bool",cpuKernelImpl:MR}),sF={kernelName:yn,backendName:"webgl",kernelFunc:$J};var EJ=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${w.ERF_P}; float a1 = ${w.ERF_A1}; float a2 = ${w.ERF_A2}; float a3 = ${w.ERF_A3}; float a4 = ${w.ERF_A4}; float a5 = ${w.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,RJ=xe({opSnippet:EJ}),aF={kernelName:xn,backendName:"webgl",kernelFunc:RJ};var DJ=Po+` return exp(x); `,AJ=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,Iv=xe({opSnippet:DJ,packedOpSnippet:AJ,cpuKernelImpl:LR,dtype:"float32"}),iF={kernelName:bn,backendName:"webgl",kernelFunc:Iv};function eg(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var uF={kernelName:na,backendName:"webgl",kernelFunc:eg};var pF="return exp(x) - 1.0;",FJ=xe({opSnippet:pF,packedOpSnippet:pF,cpuKernelImpl:BR}),cF={kernelName:Cn,backendName:"webgl",kernelFunc:FJ};var nm=class{constructor(t,e,o){this.variableNames=["real","imag"];let n=e[1];this.outputShape=e;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(t==="real")i="return real * expR - imag * expI;";else if(t==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=` const float exponentMultiplier = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { ${i} } float mulMatDFT(int batch, int index) { float indexRatio = float(index) / float(${n}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; for (int i = 0; i < ${n}; i++) { // x = (-2|2 * PI / N) * index * i; float x = exponentMultiplierTimesIndexRatio * float(i); float expR = cos(x); float expI = sin(x); float real = getReal(batch, i); float imag = getImag(batch, i); result += unaryOpComplex(real, expR, imag, expI) / ${a}; } return result; } void main() { ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } `}};function tg(r,t,e){let o=e.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:e,attrs:{shape:[a,s]}}),p=i.shape,u=new nm("real",p,t),c=new nm("imag",p,t),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=e.runWebGLProgram(u,l,"float32"),d=e.runWebGLProgram(c,l,"float32"),f=Or({inputs:{real:m,imag:d},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(f),h}function PJ(r){let{inputs:t,backend:e}=r,{input:o}=t;return tg(o,!1,e)}var lF={kernelName:Wi,backendName:"webgl",kernelFunc:PJ};var rg=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } `}};function Si(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new rg(o,n),i=[[n]];return t.runWebGLProgram(a,[],s,i)}}var mF={kernelName:sa,backendName:"webgl",kernelFunc:Si};var og=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int coordX = ${e} - x - 1; float outputValue; if(coordX >= 0 && coordX < ${e}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}};var dF={kernelName:wn,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new og(e.shape);return o.runWebGLProgram(n,[e],e.dtype)}};var fF="return floor(x);",OJ=xe({opSnippet:fF,packedOpSnippet:fF,cpuKernelImpl:zR}),hF={kernelName:Sn,backendName:"webgl",kernelFunc:OJ};var MJ=` float s = sign(a) * sign(b); 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vec4 values = ${e.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}};var sg=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=vt(),[o,n]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0); vec4 values = ${e.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } result[row * 2 + col] = floor(value * 255.0 + 0.5); } } ${e.output} = result; } `}};var xF={kernelName:Mu,backendName:"webgl",kernelFunc:zJ},Xc,vv=A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function zJ(r){let{inputs:t,backend:e,attrs:o}=r,{pixels:n}=t,{numChannels:s}=o,a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Xc==null||h!==vv)&&(vv=h,Xc=document.createElement("canvas").getContext("2d",{willReadFrequently:vv})),Xc.canvas.width=p,Xc.canvas.height=u,Xc.drawImage(n,0,0,p,u),n=Xc.canvas}let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=dr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),n);let d=A().getBool("WEBGL_PACK")?new sg(l):new ng(l),f=e.runWebGLProgram(d,[m],"int32");return e.disposeData(m.dataId),f}function VJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,S=i!=null,k=d==="leakyrelu",_=()=>{let R=[n,s],D=(P,O)=>{if(O==="NCHW"&&P.shape.length===1&&P.shape[0]!==1){let M=te({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(M),M}return P};if(C&&R.push(D(a,c)),S&&R.push(D(i,c)),k){let P=e.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));R.push(P),b.push(P)}return R};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=zh({x:n,filter:s,convInfo:g,backend:e,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h==="channelsLast"&&A().getBool("WEBGL_EXP_CONV")){let R=d?Ci(d,!0):null,D=new Kc(g,C,R,S,k),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],O=_();x=e.runWebGLProgram(D,O,"float32",P)}else if(A().getBool("WEBGL_CONV_IM2COL"))x=Vh({x:n,filter:s,convInfo:g,backend:e,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?Ci(d,!1):null,D=new Hc(g,C,R,S,k),P=_();x=e.runWebGLProgram(D,P,"float32")}let E=te({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(R=>e.disposeIntermediateTensorInfo(R)),E}var yF={kernelName:vo,backendName:"webgl",kernelFunc:VJ};function WJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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0./0. : log(x); `,mee=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g); result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; `,dee=xe({opSnippet:lee,packedOpSnippet:mee,cpuKernelImpl:jR}),RF={kernelName:Pn,backendName:"webgl",kernelFunc:dee};var fee=Po+` return log(1.0 + x); `,hee=xe({opSnippet:fee}),DF={kernelName:On,backendName:"webgl",kernelFunc:hee};var gee="return float(a >= 1.0 && b >= 1.0);",xee=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,yee=nt({opSnippet:gee,packedOpSnippet:xee,dtype:"bool"}),AF={kernelName:Mn,backendName:"webgl",kernelFunc:yee};var bee="return float(!(x >= 1.0));",Cee=xe({opSnippet:bee}),FF={kernelName:Ln,backendName:"webgl",kernelFunc:Cee};var wee="return float(a >= 1.0 || b >= 1.0);",See=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,Iee=nt({opSnippet:wee,packedOpSnippet:See,dtype:"bool"}),PF={kernelName:Bn,backendName:"webgl",kernelFunc:Iee};var ug=class{constructor(t,e,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; for (int j = -${a}; j <= ${a}; j++) { int idx = d + j; if (idx >= 0 && idx <= ${i}) { float z = getX(b, r, c, idx); sum += z * z; } } float val = x * ${p}; setOutput(val); } `}};var pg=class{constructor(t,e,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; int r = coords.y; int c = coords.z; int d = coords.w; bool hasNextCol = d < ${this.outputShape[3]}; bool hasNextRow = c < ${this.outputShape[2]}; vec4 sum = vec4(0.); vec4 xFragAtOutputCoords = getX(b, r, c, d); vec4 xAtOutputCoords = vec4( getChannel(xFragAtOutputCoords, vec2(c, d)), hasNextCol ? getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0, hasNextRow ? getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0, (hasNextRow && hasNextCol) ? getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); int firstChannel = d - ${a}; vec2 cache = vec2(0.); if(firstChannel >= 0){ vec4 firstChannelFrag = getX(b, r, c, firstChannel); cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel)); if(hasNextRow){ cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel)); } } ivec2 depth = ivec2(d, d + 1); for (int j = - ${a}; j <= ${a}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i})); bool depthInRange = aboveLowerBound.x && belowUpperBound.x; bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; if(depthInRange || depthPlusOneInRange){ vec4 z = vec4(0.); vec4 xFragAtCurrentDepth; z.xz = cache.xy; if(depthPlusOneInRange && hasNextCol){ xFragAtCurrentDepth = idx.y != d ? getX(b, r, c, idx.y) : xFragAtOutputCoords; z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y)); if(hasNextRow){ z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y)); } } cache.xy = z.yw; sum += z * z; } } vec4 result = xAtOutputCoords * ${p}; setOutput(result); } `}};var vee=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool("WEBGL_PACK_NORMALIZATION")?new pg(n.shape,s,a,i,p):new ug(n.shape,s,a,i,p);return e.runWebGLProgram(u,[n],n.dtype)},OF={kernelName:zn,backendName:"webgl",kernelFunc:vee};var cg=class{constructor(t,e,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=o,this.alpha=n,this.beta=s,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { int depthBegin = int(max(0.0, float(d - ${e}))); int depthEnd = int(min(float(${this.depth}), float(d + ${e} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${n}) * norm + float(${o}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${n}) * float(${s}) * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${s}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}};var kee=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new cg(n.shape,i,p,u,c);return e.runWebGLProgram(l,[n,s,a],n.dtype)},MF={kernelName:Qa,backendName:"webgl",kernelFunc:kee};function LF(r,t,e,o){let n=y.sizeFromShape(t),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,r.dtype,"max",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Nv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=e.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=e.texData.get(d.dataId).values,S=new Array(i);for(let E=0;E`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:e});let l=new Us(c,"max",!1);return e.runWebGLProgram(l,[n],n.dtype)}var VF={kernelName:Un,backendName:"webgl",kernelFunc:$ee};function Eee(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,"max",!1);return e.runWebGLProgram(m,[n],n.dtype)}var WF={kernelName:ia,backendName:"webgl",kernelFunc:Eee};var lg=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,o=t.strideWidth,n=t.dilationHeight,s=t.effectiveFilterHeight,a=t.effectiveFilterWidth,i=s-1-t.padInfo.top,p=a-1-t.padInfo.left,u=s*a-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${p}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wR = 0; wR < ${s}; wR += ${n}) { float dyR = float(dyRCorner + wR) / ${e}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${a}; wC++) { float dyC = float(dyCCorner + wC) / ${o}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wR * ${a} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},mg=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.dilationDepth,a=t.dilationHeight,i=t.dilationWidth,p=t.effectiveFilterDepth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=p-1-t.padInfo.front,m=u-1-t.padInfo.top,d=c-1-t.padInfo.left,f=p*u*c-1;this.userCode=` const ivec3 pads = ivec3(${l}, ${m}, ${d}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${p}; wD += ${s}) { float dyD = float(dyDCorner + wD) / ${e}.0; if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${u}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${o}.0; if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${c}; wC += ${i}) { float dyC = float(dyCCorner + wC) / ${n}.0; if (dyC < 0.0 || dyC >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); int maxPosValue = ${f} - int(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wD * ${u} * ${c} + wR * ${c} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function Ree(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Iu(m,"max",!0),f=e.runWebGLProgram(d,[a],a.dtype),h=new mg(m),g=e.runWebGLProgram(h,[n,f],a.dtype);return e.disposeIntermediateTensorInfo(f),g}var UF={kernelName:Ki,backendName:"webgl",kernelFunc:Ree};function Dee(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s,output:a}=t,i=s;Vs([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Us(m,"max",d),h=e.runWebGLProgram(f,[i],i.dtype),g=new lg(m),x=e.runWebGLProgram(g,[n,h],i.dtype);return e.disposeIntermediateTensorInfo(h),x}var GF={kernelName:Hi,backendName:"webgl",kernelFunc:Dee};function HF(r,t,e,o){let n=new Us(e,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Us(e,"max",!0,!0,t);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var KF={kernelName:ua,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=t,p=e;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(o.shape,n,s,u,a),[l,m]=HF(o,i,c,p);return[l,m]}};function qF(r,t,e,o){let n=y.sizeFromShape(t),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,"float32","mean",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var jF={kernelName:Gn,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{keepDims:n,axis:s}=t,a=e,i=o.shape.length,p=y.parseAxisParam(s,o.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=a.shouldExecuteOnCPU([o]),d=[],f=o;if(l){if(m){let S=a.texData.get(f.dataId).values,k=new Array(i);for(let R=0;Rc[0]+t[l]+c[1]);let n=t.length,s=Re(n),a=e.map(c=>c[0]).join(","),i=e.map((c,l)=>c[0]+t[l]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=` int start = ${a}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start) { outC = start * 2 - outC - ${u}; } else if(outC >= end) { outC = (end - 1) * 2 - outC + ${u}; } setOutput(getX(outC - start)); } `;return}this.userCode=` ${s} start = ${s}(${a}); ${s} end = ${s}(${i}); void main() { ${s} outC = getOutputCoords(); for (int i = 0; i < ${n}; i++) { if (outC[i] < start[i]) { outC[i] = start[i] * 2 - outC[i] - ${u}; } else if(outC[i] >= end[i]) { outC[i] = (end[i] - 1) * 2 - outC[i] + ${u}; } } ${s} coords = outC - start; setOutput(getX(${p})); } `}};var fg=class{constructor(t,e,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((f,h)=>f[0]+t[h]+f[1]);let n=t.length,s=Re(n),a=e.map(f=>f[0]).join(","),i=e.map((f,h)=>f[0]+t[h]).join(","),p=Dt("rc",n),u=Dt("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,d="";if(n===1){let f=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; } else if (source >= end) { source = (end - 1) * 2 - source + ${m}; } source -= start; `;d=` ${s} rc = outputLoc; ${f} result[0] = getChannel(getX(${u.join()}), ${l}); ${p[n-1]} += 1; if(${c}) { ${f} result[1] = getChannel(getX(${u.join()}), ${l}); } `}else{let f=` ${s} source = rc; ${s} lt = ${s}(lessThan(source, start)); ${s} gte = ${s}(greaterThanEqual(source, end)); ${s} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${m}) + gte * ((end - 1) * 2 - source + ${m}); source -= start; `;d=` ${s} rc = outputLoc; ${f} result[0] = getChannel(getX(${u.join()}), ${l}); ${p[n-1]} += 1; if(${c}) { ${f} result[1] = getChannel(getX(${u.join()}), ${l}); } rc = outputLoc; ${p[n-2]} += 1; if(${p[n-2]} < ${this.outputShape[n-2]}) { ${f} result[2] = getChannel(getX(${u.join()}), ${l}); ${p[n-1]} += 1; if(${c}) { ${f} result[3] = getChannel(getX(${u.join()}), ${l}); } } `}this.userCode=` const ${s} start = ${s}(${a}); const ${s} end = ${s}(${i}); void main() { ${s} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${d} setOutput(result); } `}};var Mee=({inputs:r,backend:t,attrs:e})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fg(o.shape,n,s):new dg(o.shape,n,s);return t.runWebGLProgram(a,[o],o.dtype)},QF={kernelName:qn,backendName:"webgl",kernelFunc:Mee};var Lee=`if (b == 0.0) return NAN; return mod(a, b);`,Bee=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); `+Xr+` return result; `,zee=nt({opSnippet:Lee,packedOpSnippet:Bee}),ZF={kernelName:jn,backendName:"webgl",kernelFunc:zee};var hg=class{constructor(t,e,o){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,o],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; float r = random(seed); float cdf = 0.0; for (int i = 0; i < ${e-1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { setOutput(float(i)); return; } } // If no other event happened, last event happened. setOutput(float(${e-1})); } `}};var Vee=` if (a == b) { return 1.0; }; return a / b;`,Wee=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; if(a.x == b.x) { result.x = 1.; } if(a.y == b.y) { result.y = 1.; } if(a.z == b.z) { result.z = 1.; } if(a.w == b.w) { result.w = 1.; } return result; `,Tv=nt({opSnippet:Vee,packedOpSnippet:Wee,checkOutOfBounds:!0}),JF={kernelName:hn,backendName:"webgl",kernelFunc:Tv};var e3="return a - b;",_v=nt({opSnippet:e3,packedOpSnippet:e3,supportsComplex:!0,cpuKernelImpl:yD}),t3={kernelName:_s,backendName:"webgl",kernelFunc:_v};function $v(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Nv({inputs:{x:n},backend:e,attrs:{reductionIndices:a,keepDims:!1}}),p=w.expandShapeToKeepDim(i.shape,a),u=te({inputs:{x:i},backend:e,attrs:{shape:p}}),c=_v({inputs:{a:n,b:u},backend:e}),l=Iv({inputs:{x:c},backend:e}),m=Tp({inputs:{x:l},backend:e,attrs:{axis:a,keepDims:!1}}),d=te({inputs:{x:m},backend:e,attrs:{shape:p}}),f=Tv({inputs:{a:l,b:d},backend:e});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(d),f}var r3={kernelName:vs,backendName:"webgl",kernelFunc:$v};function Uee(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o,p=i?n:$v({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new hg(u,c,s),m=[[a]],d=e.runWebGLProgram(l,[p],"int32",m);return i||e.disposeIntermediateTensorInfo(p),d}var o3={kernelName:Xn,backendName:"webgl",kernelFunc:Uee};var Gee=Ut+` return -x; `,Hee=` vec4 result = -x; bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `;function Kee(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])){let s=e.texData.get(o.dataId),[a,i]=JR(s.values,o.shape,o.dtype);return e.makeTensorInfo(i,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Fr(o.shape,Hee):n=new or(o.shape,Gee),e.runWebGLProgram(n,[o],o.dtype)}var n3={kernelName:pa,backendName:"webgl",kernelFunc:Kee};var qee=Wt.nonMaxSuppressionV3Impl;function jee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),{selectedIndices:l}=qee(u,c,a,i,p);return e.makeTensorInfo([l.length],"int32",new Int32Array(l))}var s3={kernelName:Zn,backendName:"webgl",kernelFunc:jee};var Xee=Wt.nonMaxSuppressionV4Impl;function Yee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,padToMaxOutputSize:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),{selectedIndices:m,validOutputs:d}=Xee(c,l,a,i,p,u);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([d]))]}var a3={kernelName:Za,backendName:"webgl",kernelFunc:Yee};var Qee=Wt.nonMaxSuppressionV5Impl;function Zee(r){w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Qee(c,l,m,d,f,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var i3={kernelName:Jn,backendName:"webgl",kernelFunc:Zee};var gg=class{constructor(t,e,o,n){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${n}), float(${o}), float(index == coords.y))); } `}};var Jee=r=>{let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new gg(u,a,i,p),l=te({inputs:{x:n},backend:e,attrs:{shape:[u]}}),m=e.runWebGLProgram(c,[l],s);e.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=te({inputs:{x:m},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(m),f},u3={kernelName:es,backendName:"webgl",kernelFunc:Jee};function sm(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="complex64"){let n=wi({inputs:{input:o},backend:e}),s=sm({inputs:{x:n},backend:e}),a=$p({inputs:{input:o},backend:e}),i=sm({inputs:{x:a},backend:e}),p=Or({inputs:{real:s,imag:i},backend:e});return e.disposeIntermediateTensorInfo(n),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(i),p}else return Si({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:e})}var p3={kernelName:Sa,backendName:"webgl",kernelFunc:sm};function c3(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=wi({inputs:{input:o},backend:e}),s=c3({inputs:{x:n},backend:e}),a=$p({inputs:{input:o},backend:e}),i=sm({inputs:{x:a},backend:e}),p=Or({inputs:{real:s,imag:i},backend:e});return e.disposeIntermediateTensorInfo(n),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(i),p}else return Si({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:e})}var l3={kernelName:ca,backendName:"webgl",kernelFunc:c3};function ete(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return eg({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=t.map(c=>{let l=eg({inputs:{input:c},backend:e,attrs:{dim:n}});return i.push(l),l}),u=Sv({inputs:p,backend:e,attrs:{axis:n}});return i.forEach(c=>e.disposeIntermediateTensorInfo(c)),u}var m3={kernelName:la,backendName:"webgl",kernelFunc:ete};var xg=class{constructor(t,e,o){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((u,c)=>u[0]+t[c]+u[1]);let n=t.length,s=Re(n),a=e.map(u=>u[0]).join(","),i=e.map((u,c)=>u[0]+t[c]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` int start = ${a}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(value); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${s} start = ${s}(${a}); ${s} end = ${s}(${i}); void main() { ${s} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { ${s} coords = outC - start; setOutput(getX(${p})); } } `}};var yg=class{constructor(t,e,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let n=t.length,s=Re(n),a=e.map(h=>h[0]).join(","),i=e.map((h,g)=>h[0]+t[g]).join(","),p=Dt("rc",n),u=Dt("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1; if(${c}) { `,n===1?"":`} rc = outputLoc; ${p[n-2]} += 1; if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${p[n-1]} += 1; if(${c}) {`],d=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",f="";for(let h=0,g=n===1?2:4;h{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Si({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yg(n.shape,s,a):new xg(n.shape,s,a),p=[[a]];return e.runWebGLProgram(i,[n],n.dtype,p)},d3={kernelName:ts,backendName:"webgl",kernelFunc:Ev};var tte=` if(a < 0.0 && floor(b) < b){ return NAN; } if (b == 0.0) { return 1.0; } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); `,rte=` // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise. vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1))); vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); vec4 result = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS bvec4 isExpZero = equal(b, vec4(0.0)); result.r = isExpZero.r ? 1.0 : result.r; result.g = isExpZero.g ? 1.0 : result.g; result.b = isExpZero.b ? 1.0 : result.b; result.a = isExpZero.a ? 1.0 : result.a; bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); `+Xr+` return result; `,ote=nt({opSnippet:tte,packedOpSnippet:rte}),f3={kernelName:rs,backendName:"webgl",kernelFunc:ote};function nte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=w.getAxesPermutation(c,i),m=n;l!=null&&(m=Ct({inputs:{x:n},backend:e,attrs:{perm:l}}),c=w.getInnerMostAxes(c.length,i),p.push(m)),w.assertAxesAreInnerMostDims("prod",c,i);let d;if(e.shouldExecuteOnCPU([m])){let f=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=tD(m.shape,m.dtype,f,c);d=e.makeTensorInfo(g,x,h)}else{let[f,h]=w.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=ni(n.dtype),C=Yr(x,b,"prod",e);d=te({inputs:{x:C},backend:e,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:e,attrs:{shape:f}})}return p.forEach(f=>e.disposeIntermediateTensorInfo(f)),d}var h3={kernelName:ns,backendName:"webgl",kernelFunc:nte};function ste(r){let{inputs:t,backend:e,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=t,{outputRaggedRank:i}=o,p=n.map(x=>e.readSync(x.dataId)),u=n.map(x=>x.shape),c=e.readSync(s.dataId),l=e.readSync(a.dataId),[m,d,f]=rD(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var g3={kernelName:Qp,backendName:"webgl",kernelFunc:ste};function ate(r){let{inputs:t,backend:e}=r,{starts:o,limits:n,deltas:s}=t,a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=oD(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=e.makeTensorInfo([u.length],"int32",u),m=e.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var x3={kernelName:Zp,backendName:"webgl",kernelFunc:ate};function ite(r){let{inputs:t,backend:e,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=t,{rowPartitionTypes:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),l=e.readSync(a.dataId),m=i.map(g=>e.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=nD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return e.makeTensorInfo(f,s.dtype,h)}var y3={kernelName:Jp,backendName:"webgl",kernelFunc:ite};var Rv=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=sD(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},b3={kernelName:ma,backendName:"webgl",kernelFunc:Rv};var ute="return 1.0 / x;",pte=xe({opSnippet:ute}),C3={kernelName:ss,backendName:"webgl",kernelFunc:pte};var cte=Ut+` return (x < 0.0) ? 0.0 : x; `,lte=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,mte=xe({opSnippet:cte,packedOpSnippet:lte}),w3={kernelName:as,backendName:"webgl",kernelFunc:mte};var dte=Ut+` return (x < 0.0) ? 0.0 : min(6.0, x); `,fte=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,hte=xe({opSnippet:dte,packedOpSnippet:fte}),S3={kernelName:ps,backendName:"webgl",kernelFunc:hte};var bg=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/l[0]}, ${c[1]/l[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${p}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${m}; // Compute the four integer indices. ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0))); ivec2 sourceCeilRC = ivec2( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d); float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d); float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d); float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d); vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC); float top = topLeft + (topRight - topLeft) * fracRC.y; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; float newValue = top + (bottom - top) * fracRC.x; setOutput(newValue); } `}};var Cg=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/l[0]}, ${c[1]/l[1]}, ${c[1]/l[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${p}.0, ${p}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${m}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); ivec3 sourceCeilRC = ivec3( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${u-1}; bool hasNextRow = coords.z < ${o-1}; // In parallel, construct four corners for all four components in // packed 2x2 cell. vec4 topLeft = vec4( getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 bottomLeft = vec4( getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 topRight = vec4( getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0); vec4 bottomRight = vec4( getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0); vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC); vec4 top = mix(topLeft, topRight, fracRC.yyzz); vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz); vec4 newValue = mix(top, bottom, fracRC.x); setOutput(newValue); } `}};function gte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Cg(n.shape,p,u,s,a):new bg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],"float32")}var I3={kernelName:us,backendName:"webgl",kernelFunc:gte};var wg=class{constructor(t,e,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${l}); const float invHeightScale = float(${m}); const float invWidthScale = float(${d}); const int winHeight = int(${f}); const int winWidth = int(${h}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(startRLerp - float(winHeight / 2)); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(startCLerp - float(winWidth / 2)); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${a}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${i}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0)); float dxRLerp = dxR - float(topDxRIndex); float inverseDxRLerp = 1.0 - dxRLerp; float dxC = float(dyC) * widthScale; int leftDxCIndex = int(floor(dxC)); int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; if (r == topDxRIndex && c == leftDxCIndex) { // topLeft accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; } if (r == topDxRIndex && c == rightDxCIndex) { // topRight accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; } if (r == bottomDxRIndex && c == leftDxCIndex) { // bottomLeft accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; } if (r == bottomDxRIndex && c == rightDxCIndex) { // bottomRight accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; } } } // End loop over dy setOutput(accumulator); } `}};function xte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new wg(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var v3={kernelName:ei,backendName:"webgl",kernelFunc:xte};var Sg=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/l[0]}, ${c[1]/l[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${p}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${d}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}};var Ig=class{constructor(t,e,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=t;this.outputShape=[a,e,o,u];let c=[n&&e>1?i-1:i,n&&o>1?p-1:p],l=[n&&e>1?e-1:e,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/l[0]}, ${c[1]/l[1]}, ${c[1]/l[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${p}.0, ${p}.0); float getAValue(int b, int r, int c, int d) { return getChannel(getA(b, r, c, d), vec2(c, d)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${d}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m}))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${u-1}; bool hasNextRow = coords.z < ${o-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function yte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ig(n.shape,p,u,s,a):new Sg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],n.dtype)}var k3={kernelName:is,backendName:"webgl",kernelFunc:yte};var vg=class{constructor(t,e,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${l}); const float invHeightScale = float(${m}); const float invWidthScale = float(${d}); const int winHeight = int(${f}); const int winWidth = int(${h}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(floor(startRLerp - float(winHeight / 2))); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(floor(startCLerp - float(winWidth / 2))); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${a}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${i}) { continue; } float sourceFracRow = float(${p[0]}) * (float(dyR) / float(${u[0]})); float sourceFracCol = float(${p[1]}) * (float(dyC) / float(${u[1]})); int sourceNearestRow = int(min( float(int(${n}) - 1), ${o} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${s}) - 1), ${o} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function bte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new vg(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var N3={kernelName:Ja,backendName:"webgl",kernelFunc:bte};var kg=class{constructor(t,e){this.variableNames=["x"];let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=t,o===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${t[0]} - coord - 1)); } `;return}let n=i=>e.indexOf(i)!==-1&&t[i]!==1?`${t[i]} - coords[${i}] - 1`:`coords[${i}]`,s=t.map((i,p)=>n(p)).join(","),a=Re(o);this.userCode=` void main() { ${a} coords = getOutputCoords(); setOutput(getX(${s})); } `}};var Ng=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=t;let n=Dt("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${t[0]} - rc - 1), ${t[0]} - rc - 1); if(${s}){ result.g = getChannel(getX(${t[0]} - (rc + 1) - 1), ${t[0]} - (rc + 1) - 1); } setOutput(result); } `:this.userCode=` void main() { ${i} rc = getOutputCoords(); vec4 result = vec4(0.); result.r = ${p(n.slice())}; if(${s}){ result.g = ${u(n.slice())}; } if(${a}) { result.b = ${c(n.slice())}; if(${s}) { result.a = ${l(n.slice())}; } } setOutput(result); } `;function p(f){return m(f)}function u(f){return f[o-1]="("+f[o-1]+" + 1)",m(f)}function c(f){return f[o-2]="("+f[o-2]+" + 1)",m(f)}function l(f){return f[o-1]="("+f[o-1]+" + 1)",f[o-2]="("+f[o-2]+" + 1)",m(f)}function m(f){let h=t.map((b,C)=>d(C,f)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return e.indexOf(f)!==-1&&t[f]!==1?`${t[f]} - ${h[f]} - 1`:`${h[f]}`}}};function Cte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:e});let p=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ng(n.shape,i):new kg(n.shape,i);return e.runWebGLProgram(p,[n],n.dtype)}var T3={kernelName:cs,backendName:"webgl",kernelFunc:Cte};var Tg=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let o=t[1],n=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=` vec3 fill = vec3(${e.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int y = coords[1]; float coordXFloat = (float(x) - params[0]) * params[3] - (float(y) - params[1]) * params[2]; float coordYFloat = (float(x) - params[0]) * params[2] + (float(y) - params[1]) * params[3]; int coordX = int(round(coordXFloat + params[0])); int coordY = int(round(coordYFloat + params[1])); ${s} if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}};var _3={kernelName:As,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new Tg(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var wte=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,Ste=xe({opSnippet:wte}),$3={kernelName:ls,backendName:"webgl",kernelFunc:Ste};var Ite="return inversesqrt(x);",vte=xe({opSnippet:Ite,cpuKernelImpl:aD}),E3={kernelName:ms,backendName:"webgl",kernelFunc:vte};var vu=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${l})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides";this.userCode=` ${u} strides = ${u}(${s}); void main() { ${c} coords = getOutputCoords(); float sum = 0.0; bool found = false; for (int i = 0; i < ${t}; i++) { int flattenedIndex = 0; for (int j = 0; j < ${e}; j++) { int index = round(${m}); flattenedIndex += index * ${x}; } if (flattenedIndex == coords[0]) { sum += ${f}; found = true; } } setOutput(mix(${g}, sum, float(found))); } `}};var _g=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${l})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides",b=e>1?"strides[j + 1]":"strides";this.userCode=` ${u} strides = ${u}(${s}); void main() { ${c} coords = getOutputCoords(); vec4 sum = vec4(0.); vec4 found = vec4(0.); for (int i = 0; i < ${t}; i+=2) { ivec2 flattenedIndex = ivec2(0); for (int j = 0; j < ${e}; j+=2) { ivec4 index = round(${m}); flattenedIndex += index.xz * ${x}; if (j + 1 < ${e}) { flattenedIndex += index.yw * ${b}; } } if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] || flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) { vec4 updVals = ${f}; if (flattenedIndex[0] == coords[0]) { sum.xy += updVals.xy; found.xy = vec2(1.); } else if (flattenedIndex[0] == coords[0] + 1) { sum.zw += updVals.xy; found.zw = vec2(1.); } if (flattenedIndex[1] == coords[0]) { sum.xy += updVals.zw; found.xy = vec2(1.); } else if (flattenedIndex[1] == coords[0] + 1) { sum.zw += updVals.zw; found.zw = vec2(1.); } } } setOutput(mix(${g}, sum, found)); } `}};function kte(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g;A().getBool("WEBGL_PACK")?g=new _g(p,i,d.shape.length,f.shape.length,c,m):g=new vu(p,i,d.shape.length,f.shape.length,c,m);let x=e.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var R3={kernelName:ds,backendName:"webgl",kernelFunc:kte};var $g=class{constructor(t,e,o,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,o];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,i=A().getNumber("WEBGL_VERSION")===2?s:a,p=n==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; int mid; ${i} mid = (left + right) / 2; if (getSortedSequence(batch, mid) ${p} value) { left = mid + 1; } else { right = mid; } } return right; } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int valueIndex = coords[1]; float value = getValues(batch, valueIndex); setOutput(float(findBound(batch, value))); } `}};function Nte(r){let{inputs:t,backend:e,attrs:o}=r,{sortedSequence:n,values:s}=t,{side:a}=o,i=new $g(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return e.runWebGLProgram(i,[n,s],"int32",p)}var D3={kernelName:hs,backendName:"webgl",kernelFunc:Nte};var Eg=class{constructor(t,e,o){this.variableNames=["c","a","b"],this.outputShape=e;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],u=[];for(let c=0;c= 1.0) { setOutput(getA(${s})); } else { setOutput(getB(${s})); } } `}};function Tte(r){let{inputs:t,backend:e}=r,{condition:o,t:n,e:s}=t,a=new Eg(o.shape.length,n.shape,n.shape.length);return e.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var A3={kernelName:fa,backendName:"webgl",kernelFunc:Tte};var _te=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${w.SELU_SCALEALPHA}; float scale = ${w.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,$te=xe({opSnippet:_te}),F3={kernelName:gs,backendName:"webgl",kernelFunc:$te};var Ete=Po+` return 1.0 / (1.0 + exp(-1.0 * x)); `,Rte=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,Dte=xe({opSnippet:Ete,packedOpSnippet:Rte,cpuKernelImpl:uD}),P3={kernelName:Cs,backendName:"webgl",kernelFunc:Dte};var Ate=` if (isnan(x)) { return 0.0; } return sign(x); `,Fte=xe({opSnippet:Ate}),O3={kernelName:bs,backendName:"webgl",kernelFunc:Fte};var Pte=Po+` return sin(x); `,Ote=` vec4 result = sin(x); bvec4 isNaN = isnan(x); ${Xr} return result; `,Mte=xe({opSnippet:Pte,packedOpSnippet:Ote}),M3={kernelName:xs,backendName:"webgl",kernelFunc:Mte};var Lte=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,Bte=xe({opSnippet:Lte}),L3={kernelName:ys,backendName:"webgl",kernelFunc:Bte};var zte=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; bool too_large = x > -threshold; bool too_small = x < threshold; float result; float exp_x = exp(x); if (too_large){ result = x; } else if (too_small){ result = exp_x; } else{ result = log(exp_x + 1.0); } return result; `,Vte=xe({opSnippet:zte}),B3={kernelName:ws,backendName:"webgl",kernelFunc:Vte};var Wte=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},z3={kernelName:ga,backendName:"webgl",kernelFunc:Wte};function Ute(r){let{inputs:t,backend:e}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw: ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw: ${a.shape}`);let i=e.readSync(o.dataId),p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=e.readSync(a.dataId)[0],[l,m,d,f,h]=cD(i,o.shape,o.dtype,p,n.dtype,u,c);return[e.makeTensorInfo(m,o.dtype,l),e.makeTensorInfo([m[0]],n.dtype,d),e.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),e.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var V3={kernelName:ji,backendName:"webgl",kernelFunc:Ute};function Gte(r){let{inputs:t,backend:e}=r,{inputIndices:o,inputShape:n,newShape:s}=t;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(e.readSync(n.dataId)),i=e.readSync(o.dataId),p=Array.from(e.readSync(s.dataId)),[u,c,l]=lD(i,o.shape,o.dtype,a,p);return[e.makeTensorInfo(c,o.dtype,u),e.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var W3={kernelName:ti,backendName:"webgl",kernelFunc:Gte};function Hte(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p,!0);return e.makeTensorInfo(c,o.dtype,u)}var U3={kernelName:ya,backendName:"webgl",kernelFunc:Hte};function Kte(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p);return e.makeTensorInfo(c,o.dtype,u)}var G3={kernelName:ba,backendName:"webgl",kernelFunc:Kte};function qte(r){let{inputs:t,backend:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let x=e.bufferSync(n),b=e.bufferSync(s),C=y.decodeString(e.readSync(a.dataId)[0]),S=iD(x,b,i,m,c,u,p,l,C,d);return e.makeTensorInfo(i,S.dtype,S.values)}let f=new vu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=e.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(h),g}var H3={kernelName:ks,backendName:"webgl",kernelFunc:qte};function jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:e,attrs:{begin:c,size:d}});return c[i]+=m,f})}var K3={kernelName:xa,backendName:"webgl",kernelFunc:jte};var q3="return sqrt(x);",Xte=xe({opSnippet:q3,packedOpSnippet:q3,cpuKernelImpl:mD}),j3={kernelName:Ss,backendName:"webgl",kernelFunc:Xte};var Yte="return x * x;",Qte=xe({opSnippet:Yte}),X3={kernelName:Xi,backendName:"webgl",kernelFunc:Qte};var Y3="return (a - b) * (a - b);",Zte=nt({opSnippet:Y3,packedOpSnippet:Y3}),Q3={kernelName:Ns,backendName:"webgl",kernelFunc:Zte};function Jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;if(n.dtype!=="string")throw new Error("Input must be of datatype string");let s=e.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=dD(a,"string",o);return e.makeTensorInfo(n.shape,"string",i)}var Z3={kernelName:Ou,backendName:"webgl",kernelFunc:Jte};function ere({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=Ut+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new or(o.shape,n);return e.runWebGLProgram(s,[o],o.dtype)}var J3={kernelName:So,backendName:"webgl",kernelFunc:ere};var Rg=class{constructor(t,e,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i="";if(n===1)i="coords * strides + begin";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` ${s} begin = ${s}(${t}); ${s} strides = ${s}(${e}); void main() { ${a} coords = getOutputCoords(); setOutput(getX(${i})); } `}};function tre(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=ct.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:e,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let E=ct.computeOutShape(b,C,S),R=Gs({inputs:{x:n},backend:e,attrs:{begin:b,size:E}});k=te({inputs:{x:R},backend:e,attrs:{shape:f}}),e.disposeIntermediateTensorInfo(R)}else if(e.shouldExecuteOnCPU([n])){let R=e.readSync(n.dataId),D=me(n.shape,n.dtype,R),P=fD(d,D,S,b);k=e.makeTensorInfo(f,n.dtype,P.values)}else{let R=new Rg(b,S,d);k=e.runWebGLProgram(R,[n],n.dtype)}let _=te({inputs:{x:k},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(k),_}var eP={kernelName:Ts,backendName:"webgl",kernelFunc:tre};function rre(r){let{inputs:t,backend:e,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=t,m=e.readSync(c.dataId),d=e.readSync(l.dataId),[f,h]=hD(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],"string",f),e.makeTensorInfo(l.shape,"int32",h)]}var tP={kernelName:Ca,backendName:"webgl",kernelFunc:rre};function ore(r){let{inputs:t,backend:e,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=e.readSync(s.dataId),p=e.readSync(a.dataId)[0],[u,c,l]=gD(i,p,n),m=c.length;return[e.makeTensorInfo([m,2],"int32",u),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(l))]}var rP={kernelName:Yi,backendName:"webgl",kernelFunc:ore};function nre(r){let{inputs:t,backend:e,attrs:o}=r,{numBuckets:n}=o,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(n<=0)throw new Error("Number of buckets must be at least 1");let a=e.readSync(s.dataId),i=xD(a,n);return e.makeTensorInfo(s.shape,"int32",i)}var oP={kernelName:Qi,backendName:"webgl",kernelFunc:nre};var sre="return tan(x);",are=xe({opSnippet:sre}),nP={kernelName:$s,backendName:"webgl",kernelFunc:are};var ire=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,ure=xe({opSnippet:ire}),sP={kernelName:Es,backendName:"webgl",kernelFunc:ure};function pre(r){let{inputs:t,backend:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=t,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return e.makeTensorInfo(n.shape,s.dtype);let d=te({inputs:{x:s},backend:e,attrs:{shape:[p,i]}}),f=te({inputs:{x:a},backend:e,attrs:{shape:[p,u]}}),h=te({inputs:{x:n},backend:e,attrs:{shape:m}}),g=new vu(p,i,d.shape.length,f.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[f,d,h],h.dtype),b=te({inputs:{x},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var aP={kernelName:fs,backendName:"webgl",kernelFunc:pre};var Dg=class{constructor(t,e){this.variableNames=["A"];let o=new Array(t.length);for(let a=0;a5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],o=[];for(let n=0;n5){let p=e.readSync(n.dataId),u=n.dtype==="string"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=bD(c,s);return e.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Dg(n.shape,s);return e.runWebGLProgram(a,[n],n.dtype)}var iP={kernelName:po,backendName:"webgl",kernelFunc:Dv};var Ag=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=t,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // We compare elements pair-wise within a group of size 2 * inc. // The comparing rule for each group alternates between ascending // and descending. Within each group, we compare each pair at // positions i and i+inc. To decide whether an element at position i // is x0 or x1, we mod it by 2 * inc, if the result is smaller than // inc, it is in the first half of the group, we denote it as x0, // otherwise we denote it as x1. // For example, as shown in the Bitonic top K paper referenced above, // Figure5(a) shows that element[1] is in the // second half of the group when group size is 2, but it is in the // first half of the group when group size is 4. bool isFirstInPair = imod(elemIdx, 2 * inc) < inc; int i = isFirstInPair ? elemIdx : elemIdx - inc; int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc)); float x0 = i0 < n ? getX(batch, i0) : negativeInf; float x1 = i1 < n ? getX(batch, i1) : negativeInf; // Denotes which direction indices are in (ascending or descending). bool reverse = imod(elemIdx, 2 * dir) >= dir; bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction int iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutput(float(i0)); } else { setOutput(float(i1)); } } `}},Fg=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=t,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // The output size is half of the previous size. // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4), // we only need to output the indices at positions |, the indices at // positions _ can be thrown away, see Figure5(b) After Phase 2 // (Merge phase) in the Bitonic Top K paper referenced above. // For example, the paper shows we only need to output the orange bars. // The output sequence should look like this | | | | | | | |. // Because the sequence is halved, to map the output index back // to the previous sequence to find the corresponding value, // we need to double the index. When we double the index, // we basically interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position // of each 2k positions by - elemIdx % k. E.g. for output at // index 4,5,6,7, we want to get the corresponding element at // original index 8,9,10,11, for output at index 8,9,10,11, // we want to get the corresponding element at original index // 16,17,18,19, so on and so forth. int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k)); int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k)); float x0 = getX(batch, i0); float x1 = i1 < n ? getX(batch, i1) : x0; setOutput(x0 >= x1 ? float(i0) : float(i1)); } `}};function Rp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function uP(r){let t=1;for(;tp){let P=e.readSync(n.dataId),[O,M]=CD(P,u,n.dtype,s,a);return[e.makeTensorInfo(O.shape,O.dtype,O.values),e.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[e.makeTensorInfo(u,n.dtype,[]),e.makeTensorInfo(u,"int32",[])];if(c===1)return[n,Si({attrs:{shape:u,dtype:"int32",value:0},backend:e})];let l=e.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?e.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:e});m&&Rp(e,d);let x=uP(s),b=uP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Ag(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=e.runWebGLProgram(B,L,"int32",U),Rp(e,j)};for(let P=1;P=1;M/=2)k(O,M,[h,b])}for(let P=b;P>x;P/=2){let O=S(),M=new Fg([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=e.runWebGLProgram(M,O,"int32",B),Rp(e,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,C.shape)}let _=C;C=Gs({inputs:{x:C},backend:e,attrs:{begin:0,size:[h,s]}}),Rp(e,_);let E=kv({inputs:{x:g,indices:C},backend:e,attrs:{axis:1,batchDims:1}});Rp(e,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:e}),Rp(e,_);let D=E;return E=te({inputs:{x:E},attrs:{shape:R},backend:e}),Rp(e,D),[E,C]}var pP={kernelName:Rs,backendName:"webgl",kernelFunc:lre};var Pg=class{constructor(t,e,o,n,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=o==="nearest"?1:2,p;switch(n){case"constant":p=1;break;case"reflect":p=2;break;case"wrap":p=3;break;case"nearest":p=4;break;default:p=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${p} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * float(int(float(-inCoord / sz2))) + inCoord; } inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0; } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; inCoord -= sz2 * float(int(float(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } else if (${p} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord += len * (float(int(float(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord -= len * float(int(float(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (${p} == 4) { return clamp(outCoord, 0.0, len - 1.0); } else { return outCoord; } } float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${s}); } return outputValue; } void main() { ivec4 coords = getOutputCoords(); float outputValue; int batch = coords[0]; int x = coords[2]; int y = coords[1]; int channel = coords[3]; float xf = float(x); float yf = float(y); float a1 = getTransforms(batch, 0); float a2 = getTransforms(batch, 1); float a3 = getTransforms(batch, 2); float b1 = getTransforms(batch, 3); float b2 = getTransforms(batch, 4); float b3 = getTransforms(batch, 5); float c1 = getTransforms(batch, 6); float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = float(${s}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${e})); float mapY = mapCoord(inY, float(${t})); if (${i} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { float yFloor = floor(mapY); float xFloor = floor(mapX); float yCeil = yFloor + 1.0; float xCeil = xFloor + 1.0; float valueYFloor = (xCeil - mapX) * readWithFillValue(batch, int(yFloor), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yFloor), int(xCeil), channel); float valueYCeil = (xCeil - mapX) * readWithFillValue(batch, int(yCeil), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yCeil), int(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutput(outputValue); } `}};function mre(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,transforms:s}=t,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Pg(l,m,a,i,p,g);return e.runWebGLProgram(x,[n,s],"float32")}var cP={kernelName:Ds,backendName:"webgl",kernelFunc:mre};function dre(r){let{inputs:t,attrs:e,backend:o}=r,{axis:n}=e,{x:s}=t;Vs(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=wD(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var lP={kernelName:Zi,backendName:"webgl",kernelFunc:dre};function fre(r){let{inputs:t,backend:e,attrs:o}=r,{value:n}=t,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),f}var mP={kernelName:wa,backendName:"webgl",kernelFunc:fre};var Og=class{constructor(t,e){this.variableNames=["x","segmentIds"];let o=t.windowSize,n=t.batchSize,s=t.inSize,a=t.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p="0.0",u="sumValue",c=Math.floor(o/4)*4,l=o%4,m=` sumValue += dot(values, segFilter); `,d="";s%o>0&&(d=` if (inIdx < 0 || inIdx >= ${s}) { return initializationValue; } `);let f="";s%o>0&&(f=` if (inIdx < 0 || inIdx >= ${s}) { return -1.0; } `),this.userCode=` const float initializationValue = ${p}; float getValue(int batch, int inIdx) { ${d} return getX(batch, inIdx); } float getSegmentIdAtIndex(int inIdx) { ${f} return getSegmentIds(inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = int(floor(float(outIdx) / float( ${a})) * float(${o})); int currentSeg = int(mod(float(outIdx), float(${a}))); float sumValue = 0.0; for (int i = 0; i < ${c}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 ); ${m} } int inIdx = inOffset + ${c}; if (${l===1}) { vec4 values = vec4( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); int inIdxSeg = int(getSegmentIdAtIndex(inIdx)); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, 0, 0, 0 ); ${m} } else if (${l===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, 0, 0 ); ${m} } else if (${l===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, 0 ); ${m} } setOutput(${u}); } `}};function hre(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,segmentIds:s}=t,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=Ct({inputs:{x:n},backend:e,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=te({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});p.push(f);let h=ni(n.dtype),g=(S,k,_,E,R)=>{let D=S.shape[0],P=S.shape[1],O=w.segment_util.segOpComputeOptimalWindowSize(P,R),M={windowSize:O,inSize:P,batchSize:D,numSegments:R},L=new Og(M,k),B=e.compileAndRun(L,[S,_],E);if(p.push(B),B.shape[1]===R)return B;let z=Rv({backend:e,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),U=Dv({inputs:{x:z},backend:e,attrs:{reps:[P/O]}});return p.push(z),p.push(U),g(B,k,U,E,R)},x=g(f,"unsortedSegmentSum",s,h,a),b=te({inputs:{x},backend:e,attrs:{shape:m}}),C=b;if(c!=null){p.push(b);let S=w.getUndoAxesPermutation(c);C=Ct({inputs:{x:C},backend:e,attrs:{perm:S}})}return p.forEach(S=>e.disposeIntermediateTensorInfo(S)),C}var dP={kernelName:Ji,backendName:"webgl",kernelFunc:hre};var gre=[XD,QD,ZD,JD,tA,rA,oA,nA,iA,uA,pA,cA,lA,mA,dA,fA,hA,gA,xA,yA,bA,wA,SA,IA,vA,_A,EA,RA,BD,AA,PA,OA,MA,LA,BA,zA,VA,WA,UA,GA,qA,jA,XA,YA,QA,ZA,JA,eF,tF,rF,oF,nF,sF,aF,iF,uF,cF,lF,mF,dF,hF,gF,xF,yF,bF,CF,wF,SF,IF,LD,vF,FA,kF,NF,TF,zD,_F,$F,EF,RF,DF,AF,FF,PF,OF,MF,BF,zF,VF,WF,UF,GF,KF,jF,XF,YF,QF,ZF,o3,UD,n3,s3,a3,i3,kA,u3,l3,m3,d3,f3,VD,h3,g3,x3,y3,b3,NA,JF,C3,w3,S3,HD,I3,v3,k3,N3,T3,_3,$3,E3,R3,D3,A3,F3,P3,O3,M3,L3,CA,r3,B3,z3,V3,W3,U3,G3,H3,K3,j3,X3,Q3,Z3,J3,eP,tP,rP,oP,t3,qD,nP,sP,aP,iP,pP,cP,jD,lP,mP,dP,p3];for(let r of gre)ri(r);var we;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(we||(we={}));var ku;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu",r[r.sigmoid=5]="sigmoid",r[r.elu=6]="elu"})(ku||(ku={}));var fP;function xre(r){fP=r.wasm.cwrap(Io,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function yre(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=e.dataIdMap.get(n.dataId).id,d=e.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=e.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank 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yP=he(Uo);function Ue(r,t,e){let o;function n(a){o=a.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(a){let{backend:i,inputs:p}=a,{a:u,b:c}=p,l=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(c.dataId).id,d=e!=null?e:u.dtype,f=w.assertAndGetBroadcastShape(u.shape,c.shape),h=i.makeOutput(f,d);if(y.sizeFromShape(f)===0)return h;let g=new Uint8Array(new Int32Array(u.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=i.dataIdMap.get(h.dataId).id;return(()=>o(l,g,u.shape.length,m,x,c.shape.length,we[u.dtype],b))(),h}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:s}}var bre=!0,bP=Ue(uo,bre);var CP;function Cre(r){CP=r.wasm.cwrap(Go,null,["array","number","number","number"])}function wre(r){let{inputs:t,backend:e}=r,o=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(o.shape)===0)return o;let n=t.map(i=>e.dataIdMap.get(i.dataId).id),s=new Uint8Array(new 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bO={kernelName:pn,backendName:"wasm",setupFunc:ioe,kernelFunc:uoe};var CO;function poe(r){CO=r.wasm.cwrap(cn,null,["number","number","number","number","number","number"])}function coe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o,p=n.shape.length;y.assert(n.dtype==="float32"||n.dtype==="int32",()=>`cumsum does not support ${n.dtype} tensors in the WASM backend`);let u=w.getAxesPermutation([s],p),c=n;u!==null&&(c=go({inputs:{x:n},attrs:{perm:u},backend:e}));let l=w.getInnerMostAxes(1,p)[0];w.assertAxesAreInnerMostDims("cumsum",[l],p);let m=e.makeOutput(c.shape,c.dtype),d=c.shape[l],f=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;CO(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let x=w.getUndoAxesPermutation(u);g=go({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var wO={kernelName:cn,backendName:"wasm",setupFunc:poe,kernelFunc:coe};var SO;function 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foe(r){let{backend:t,inputs:e,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=t.makeOutput(f,"float32"),x=t.dataIdMap.get(n.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),C=new Uint8Array(new Int32Array(f).buffer),S=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer),k=t.dataIdMap.get(h.dataId).id;return vO(x,s,a==="NHWC"?1:0,b,n.shape.length-1,C,S,f.length,k),h}var kO={kernelName:mn,backendName:"wasm",setupFunc:doe,kernelFunc:foe};var NO;function hoe(r){NO=r.wasm.cwrap(dn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function goe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s}=t,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=e,m=u==null?[1,1]:u,d=w.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,E=d.strideWidth,R=d.inChannels,D=d.outChannels,P=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. 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Got ${n.dtype} and ${s.dtype}`);let u=w.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c=e.makeOutput(u.outShape,n.dtype);return EO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,we[n.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),c}var RO={kernelName:fn,backendName:"wasm",setupFunc:boe,kernelFunc:Coe};var DO;function woe(r){DO=r.wasm.cwrap(zi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Soe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=e.makeOutput(s.shape,s.dtype);return DO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var AO={kernelName:zi,backendName:"wasm",setupFunc:woe,kernelFunc:Soe};var FO;function Ioe(r){FO=r.wasm.cwrap(Bi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function voe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=e.makeOutput(n.shape,n.dtype);return FO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var PO={kernelName:Bi,backendName:"wasm",setupFunc:Ioe,kernelFunc:voe};var OO=he(gn);var MO;function koe(r){MO=r.wasm.cwrap(Ya,null,["number","number","number"])}function Noe(r){let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=e.makeOutput(n.shape,"float32"),a=i=>e.dataIdMap.get(i.dataId).id;return MO(a(n),a(o),a(s)),s}var LO={kernelName:Ya,backendName:"wasm",setupFunc:koe,kernelFunc:Noe};var Toe=!1,BO=Ue(yn,Toe,"bool");var zO=he(xn);var VO=he(bn,"float32");function Lg(r){let{inputs:t,attrs:e,backend:o}=r,{input:n}=t,{dim:s}=e,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Vt({inputs:{x:n},backend:o,attrs:{shape:i}})}var WO={kernelName:na,backendName:"wasm",kernelFunc:Lg};var UO=he(Cn,"float32");function Pv(r){let{attrs:{shape:t,value:e,dtype:o},backend:n}=r,s=n.makeOutput(t,o);return n.typedArrayFromHeap(s).fill(e),s}var GO={kernelName:sa,backendName:"wasm",kernelFunc:Pv};var HO;function _oe(r){HO=r.wasm.cwrap(wn,null,["number","number","number","number","number","number"])}function $oe(r){let{inputs:t,backend:e}=r,{image:o}=t,n=e.makeOutput(o.shape,o.dtype),s=e.dataIdMap.get(o.dataId).id,a=e.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return HO(s,i,p,u,c,a),n}var KO={kernelName:wn,backendName:"wasm",kernelFunc:$oe,setupFunc:_oe};var qO=he(Sn);var Eoe=!1,jO=Ue(In,Eoe);var XO;function Roe(r){XO=r.wasm.cwrap(vn,null,["number","number","number","number","number","number","number"])}function Doe(r){let{backend:t,inputs:e,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=e,c=t.dataIdMap.get(s.dataId).id,l=t.dataIdMap.get(a.dataId).id,m=t.dataIdMap.get(i.dataId).id,d=p!=null?t.dataIdMap.get(p.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return XO(c,l,m,d,f,n,g),h}var YO={kernelName:vn,backendName:"wasm",setupFunc:Roe,kernelFunc:Doe};var QO;function Aoe(r){QO=r.wasm.cwrap(vo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Foe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=e,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=ku[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type==="SAME"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,"float32"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return QO(x,j,q,Y,b,k,_,S,E,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var ZO={kernelName:vo,backendName:"wasm",setupFunc:Aoe,kernelFunc:Foe};var JO;function Poe(r){JO=r.wasm.cwrap(ko,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ooe(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=e,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!0),g=ku[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type==="SAME"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,"float32"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return JO(x,j,q,Y,b,k,_,S,E,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var eM={kernelName:ko,backendName:"wasm",setupFunc:Poe,kernelFunc:Ooe};var tM;function Moe(r){tM=r.wasm.cwrap(kn,null,["number","number","number","number","number","number","array","number"])}function Loe(r){let{backend:t,inputs:e}=r,{params:o,indices:n}=e,[s,a,i,p]=af.prepareAndValidate(o,n),u=t.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=t.dataIdMap.get(o.dataId).id,h=t.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=t.dataIdMap.get(u.dataId).id;return tM(d,we[o.dtype],h,a,l,i,g,x),u}var rM={kernelName:kn,backendName:"wasm",setupFunc:Moe,kernelFunc:Loe};var oM;function Boe(r){oM=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function zoe(r){let{backend:t,inputs:e,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=t.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=Vt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:t}),d=y.sizeFromShape(s.shape),f=Vt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:t}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=t.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=t.dataIdMap.get(m.dataId).id,k=t.dataIdMap.get(f.dataId).id,_=t.dataIdMap.get(g.dataId).id,E=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return oM(C,we[n.dtype],E,x,k,l.batchSize,R,_),t.disposeData(m.dataId),t.disposeData(f.dataId),g.shape=l.outputShape,g}var nM={kernelName:aa,backendName:"wasm",setupFunc:Boe,kernelFunc:zoe};var Voe=!1,sM=Ue(Nn,Voe,"bool");var Woe=!1,aM=Ue(Tn,Woe,"bool");var iM=he(_n,"bool");var uM=he($n,"bool");var pM=he(En,"bool");var cM;function Uoe(r){cM=r.wasm.cwrap(Rn,null,["number","number","number","number"])}function Goe(r){let{inputs:{x:t},attrs:{alpha:e},backend:o}=r,n=o.dataIdMap.get(t.dataId).id,s=o.makeOutput(t.shape,"float32");if(y.sizeFromShape(t.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;cM(n,we[t.dtype],e,a)}return s}var lM={kernelName:Rn,backendName:"wasm",setupFunc:Uoe,kernelFunc:Goe};var Hoe=!1,mM=Ue(Dn,Hoe,"bool");var Koe=!1,dM=Ue(An,Koe,"bool");var fM;function qoe(r){fM=r.wasm.cwrap(Fn,null,["number","number","number","number"])}function joe(r){let{attrs:t,backend:e}=r,{start:o,stop:n,num:s}=t,a=Math.floor(s),i=e.makeOutput([a],"float32");return fM(e.dataIdMap.get(i.dataId).id,o,n,a),i}var hM={kernelName:Fn,backendName:"wasm",setupFunc:qoe,kernelFunc:joe};var gM=he(Pn);var xM=he(On);var Xoe=!1,yM=Ue(Mn,Xoe,"bool");var bM=he(Ln);var Yoe=!1,CM=Ue(Bn,Yoe,"bool");var Qoe=!1,wM=Ue($0,Qoe,"bool");var SM;function Zoe(r){SM=r.wasm.cwrap(zn,null,["number","number","number","number","number","number","number"])}function Joe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=e.makeOutput(n.shape,n.dtype);return SM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var IM={kernelName:zn,backendName:"wasm",setupFunc:Zoe,kernelFunc:Joe};var vM;function ene(r){vM=r.wasm.cwrap(Qa,null,["number","number","number","number","number","number","number","number","number"])}function tne(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!=="float32"||s.dtype!=="float32"||a.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let l=e.makeOutput(n.shape,n.dtype);return vM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var kM={kernelName:Qa,backendName:"wasm",setupFunc:ene,kernelFunc:tne};var NM;function rne(r){NM=r.wasm.cwrap(Vn,null,["number","number","number","number"])}function one(r){let{backend:t,inputs:e,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=e,p=t.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t);if(d){let C=t.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims("max",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=t.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=t.dataIdMap.get(b.dataId).id;NM(p,we[a.dtype],x,C)}if(d&&t.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var TM={kernelName:Vn,backendName:"wasm",setupFunc:rne,kernelFunc:one};var nne=!1,_M=Ue(Wn,nne);var $M;function sne(r){$M=r.wasm.cwrap(Un,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ane(r){let{inputs:t,attrs:e,backend:o}=r,n=t.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=e,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let E=o.makeOutput(c.outShape,"float32"),R=o.dataIdMap.get(E.dataId).id;return $M(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),E}var EM={kernelName:Un,backendName:"wasm",setupFunc:sne,kernelFunc:ane};var RM;function ine(r){RM=r.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function une(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=e.makeOutput(c.outShape,n.dtype);return RM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var DM={kernelName:ia,backendName:"wasm",setupFunc:ine,kernelFunc:une};var AM;function pne(r){AM=r.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cne(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=e.makeOutput(s.shape,s.dtype);return AM(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var FM={kernelName:Ki,backendName:"wasm",setupFunc:pne,kernelFunc:cne};var PM;function lne(r){PM=r.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mne(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=e.makeOutput(s.shape,s.dtype);return PM(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var OM={kernelName:Hi,backendName:"wasm",setupFunc:lne,kernelFunc:mne};var MM;function dne(r){MM=r.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fne(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=e.makeOutput(c.outShape,n.dtype),m=e.makeOutput(c.outShape,"int32");return MM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(l.dataId).id,e.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var LM={kernelName:ua,backendName:"wasm",setupFunc:dne,kernelFunc:fne};var BM;function hne(r){BM=r.wasm.cwrap(Gn,null,["number, number, number"])}function gne(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t),f=l;if(d){let S=t.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Mr({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),p=t.dataIdMap.get(b.dataId).id);let C=t.makeOutput(h,"float32");if(y.sizeFromShape(u.shape)!==0){let S=t.dataIdMap.get(C.dataId).id;BM(p,x,S)}if(d&&t.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!=="float32"&&t.disposeData(b.dataId),C}var zM={kernelName:Gn,backendName:"wasm",setupFunc:hne,kernelFunc:gne};var VM;function xne(r){VM=r.wasm.cwrap(Hn,null,["number","number","number","number"])}function yne(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t);if(d){let C=t.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C)}let 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x=t.makeOutput([f],"int32",d),b=t.makeOutput([f],"float32",h);return[x,b]}var nL={kernelName:Jn,backendName:"wasm",setupFunc:Ene,kernelFunc:Rne};var Dne=!1,sL=Ue(Qn,Dne,"bool");var aL;function Ane(r){aL=r.wasm.cwrap(es,null,["number","number","number","number","number"])}function Fne(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=e.makeOutput([...n.shape,a],s),c=e.dataIdMap.get(u.dataId).id,m=e.dataIdMap.get(n.dataId).id;return aL(m,a,i,p,c),u}var iL={kernelName:es,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne};function Pne(r){let{inputs:{x:t},backend:e}=r,o=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(o).fill(1),o}var uL={kernelName:ca,backendName:"wasm",kernelFunc:Pne};function One(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Lg({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching 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Bg={kernelName:ts,backendName:"wasm",kernelFunc:Lne,setupFunc:Mne};var Bne=!1,lL=Ue(rs,Bne);var mL;function zne(r){mL=r.wasm.cwrap(os,null,["number","number","number"])}function Vne(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=e.dataIdMap.get(o.dataId).id,a=e.dataIdMap.get(n.dataId).id,i=s,p=o,u=p;p.dtype!=="float32"&&(u=Mr({backend:e,inputs:{x:o},attrs:{dtype:"float32"}}),i=e.dataIdMap.get(u.dataId).id);let c=e.makeOutput(o.shape,"float32"),l=e.dataIdMap.get(c.dataId).id;return mL(i,a,l),p.dtype!=="float32"&&e.disposeData(u.dataId),c}var dL={kernelName:os,backendName:"wasm",setupFunc:zne,kernelFunc:Vne};var fL;function Wne(r){fL=r.wasm.cwrap(ns,null,["number","number","number","number"])}function Une(r){let{backend:t,inputs:e,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=e,i=t.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=_r(a,n,t),f=l;if(d){let 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qne(r){let{backend:t,inputs:e,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=t.dataIdMap.get(n.dataId),g;h.dtype!=="float32"&&(g=Mr({backend:t,inputs:{x:n},attrs:{dtype:"float32"}}),h=t.dataIdMap.get(g.dataId));let x=h.id,b=t.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return b;let C=t.dataIdMap.get(b.dataId).id;return wL(x,c,l,m,d,p,u,s?1:0,a?1:0,C),g!=null&&t.disposeData(g.dataId),b}var SL={kernelName:us,backendName:"wasm",setupFunc:Kne,kernelFunc:qne};var IL;function jne(r){IL=r.wasm.cwrap(ei,null,["number","number","number","array","array","boolean"])}function Xne(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=e.makeOutput(n.shape,"float32"),p=e.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Mr({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(u.dataId)),IL(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,new 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Jne(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=e.makeOutput(n.shape,"float32"),p=e.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Mr({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(u.dataId)),TL(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&e.disposeData(u.dataId),i}var _L={kernelName:Ja,backendName:"wasm",setupFunc:Zne,kernelFunc:Jne};var $L;function ese(r){$L=r.wasm.cwrap(cs,null,["number","array","number","array","number","number"])}function tse(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return Dp({inputs:{x:n},backend:e});let i=e.makeOutput(n.shape,n.dtype),p=e.dataIdMap.get(n.dataId).id,u=e.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);$L(p,c,a.length,l,n.shape.length,u);let m=Vt({inputs:{x:i},attrs:{shape:n.shape},backend:e});return e.disposeData(i.dataId),m}var EL={kernelName:cs,backendName:"wasm",kernelFunc:tse,setupFunc:ese};var RL;function rse(r){RL=r.wasm.cwrap(As,null,["number","number","number","number","number","number","number","number","array","number","number"])}function ose(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{radians:s,fillValue:a,center:i}=o,p=e.makeOutput(n.shape,n.dtype),u=e.dataIdMap.get(n.dataId).id,c=e.dataIdMap.get(p.dataId).id,[l,m,d,f]=n.shape,[h,g]=w.getImageCenter(i,m,d),x=a===0,b=255,C=typeof a=="number"?[a,a,a,x?0:b]:[...a,b],S=new Uint8Array(new Int32Array(C).buffer);return RL(u,l,m,d,f,s,h,g,S,C.length,c),p}var DL={kernelName:As,backendName:"wasm",kernelFunc:ose,setupFunc:rse};var AL=he(ls);var FL=he(ms);var PL;function nse(r){PL=r.wasm.cwrap(ds,null,["number","number","number","number","number","number","array","number","number"])}function sse(r){let{backend:t,inputs:e,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,i=t.makeOutput(a,s.dtype);if(y.sizeFromShape(a)===0)return i;let{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=hu.calculateShapes(s,n,a),f=t.dataIdMap.get(n.dataId).id,g=t.dataIdMap.get(s.dataId).id,x=new Uint8Array(new Int32Array(l).buffer),b=t.dataIdMap.get(i.dataId).id;return PL(f,g,we[s.dtype],p,u,c,x,m,b),i}var OL={kernelName:ds,backendName:"wasm",setupFunc:nse,kernelFunc:sse};var ML;function ase(r){ML=r.wasm.cwrap(hs,null,["number","number","number","number","number","number","bool","number"])}function ise(r){let{inputs:t,backend:e,attrs:o}=r,{sortedSequence:n,values:s}=t,{side:a}=o;if(n.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${n.dtype} and ${s.dtype}`);let i=e.makeOutput(s.shape,"int32");function p(u){return e.dataIdMap.get(u.dataId).id}return ML(p(n),p(s),n.shape[0],n.shape[1],s.shape[1],we[n.dtype],a==="left",p(i)),i}var LL={kernelName:hs,backendName:"wasm",setupFunc:ase,kernelFunc:ise};var BL;function use(r){BL=r.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function pse(r){let{inputs:t,backend:e}=r,{condition:o,t:n,e:s}=t,a=e.dataIdMap.get(o.dataId).id,i=e.dataIdMap.get(n.dataId).id,p=e.dataIdMap.get(s.dataId).id,u=e.makeOutput(n.shape,n.dtype),c=e.dataIdMap.get(u.dataId).id,l=o.shape.length,m=n.shape.length,d=l===0||l>1||m===1?1:y.sizeFromShape(n.shape.slice(1));return BL(a,i,p,d,c),u}var zL={kernelName:fa,backendName:"wasm",kernelFunc:pse,setupFunc:use};var VL=he(gs);var WL;function cse(r){WL=r.wasm.cwrap(Cs,null,["number","number"])}function lse(r){let{backend:t,inputs:{x:e}}=r,o=t.dataIdMap.get(e.dataId).id,n=t.makeOutput(e.shape,e.dtype),s=t.dataIdMap.get(n.dataId).id;return y.sizeFromShape(n.shape)===0||WL(o,s),n}var UL={kernelName:"Sigmoid",backendName:"wasm",setupFunc:cse,kernelFunc:lse};var GL=he(bs);var HL=he(xs);var KL=he(ys);var qL=he(ws);function mse(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o,i=y.sizeFromShape(s),p=[[0,0]];p.push(...a);for(let _=1+s.length;_0?p+1:0;if(c<0)throw new Error(w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let l=n.shape.slice();l[0]=c;let m=e.dataIdMap.get(n.dataId).id,d=e.dataIdMap.get(s.dataId).id,f=e.dataIdMap.get(a.dataId).id,h=e.makeOutput(l,n.dtype),g=e.dataIdMap.get(h.dataId).id,x=e.makeOutput([4],"int32"),b=e.dataIdMap.get(x.dataId).id;JL(m,we[n.dtype],n.shape[0],d,f,g,b,t,0);let C=e.readSync(x.dataId),S;switch(C[0]){case 0:{S=w.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{S=w.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:S=w.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(C[1],C[2]);break;case 3:S=w.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(C[1],C[2],C[3]);break;default:S=""}if(e.disposeData(x.dataId),S)throw e.disposeData(h.dataId),new Error(S);return h}function xse(r){return Vg(r,!0)}var eB={kernelName:ya,backendName:"wasm",setupFunc:zg,kernelFunc:xse};function yse(r){return Vg(r,!1)}var tB={kernelName:ba,backendName:"wasm",setupFunc:zg,kernelFunc:yse};var rB;function bse(r){rB=r.wasm.cwrap(ks,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Cse(r){let{backend:t,inputs:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,p=t.makeOutput(i,a.dtype);if(y.sizeFromShape(i)===0)return 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Number(this.architecture.match(/\d+/));if(this.architecture.startsWith("xe"))return 12}return 0}isIntel(){return this.vendor==="intel"}};var Kg=class{constructor(t){this.device=t,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(t,e,o=!1,n=!0){let s,a=UB(t,e);return n?(this.freeBuffers.has(a)||this.freeBuffers.set(a,[]),this.freeBuffers.get(a).length>0?(s=this.freeBuffers.get(a).pop(),this.numFreeBuffers--):(s=this.device.createBuffer({size:t,usage:e,mappedAtCreation:o}),this.numBytesAllocated+=t)):(s=this.device.createBuffer({size:t,usage:e,mappedAtCreation:o}),this.numBytesAllocated+=t),this.usedBuffers.has(a)||this.usedBuffers.set(a,[]),this.usedBuffers.get(a).push(s),this.numUsedBuffers++,this.numBytesUsed+=t,s}releaseBuffer(t,e=!0){if(this.freeBuffers.size===0)return;let o=t.size,n=t.usage,s=UB(o,n),a=this.usedBuffers.get(s),i=a.indexOf(t);if(i<0)throw new Error("Cannot find the 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s=HB(o),a=t*e*s,i=GB(t,e,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[t,e],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(t){if(this.freeTextures.size===0)return;let e=t.width,o=t.height,n=t.format,s=t.usage,a=GB(e,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(t),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(t);if(p<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(p,1);let u=HB(n),c=e*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return 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o=[],n=e.workgroupSize[0]*e.workgroupSize[1]*e.workgroupSize[2];if(e.outputComponent=e.outputComponent?e.outputComponent:1,o.push(` var localId: vec3; var localIndex: u32; var globalId: vec3; var numWorkgroups: vec3; var workgroupId: vec3; // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { ${ZB(e)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u + localIndex); `} } `),e.pixelsOpType!=null){let f=e.pixelsOpType===Ii.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Nu(t.dtype,e.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Nu(r[0].dtype,e.outputComponent)}>;`,h=t.shape.length===3?"vec2":"i32";o.push(` struct Uniform { outShapeStrides : ${h}, size : i32, numChannels : i32, alpha : f32, }; ${f} @group(0) @binding(2) var uniforms: Uniform; `);let g=XB(e);return[jB,o.join(` `),cm(t.shape),e.getUserCode(),qB(g,e)].join(` `)}let s,a,i="struct Uniforms { NAN : f32, INFINITY : f32, ";e.variableNames.forEach((f,h)=>{let g=ht(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ht(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ht(t.shape.length);i+=`outShape : ${p}, `,s=t.shape.length-1,a=ht(s),i+=` outShapeStrides: ${a}, `,e.size&&(i+="size : i32, "),e.uniforms&&(i+=e.uniforms),i+="};",i=cae(i),o.push(i),e.atomic?o.push(` @group(0) @binding(0) var result: array>; `):o.push(` @group(0) @binding(0) var result: array<${Nu(t.dtype,e.outputComponent)}>; `),e.variableNames.forEach((f,h)=>{o.push(` @group(0) @binding(${1+h}) var ${f}: array<${e.variableComponents?Nu(r[h].dtype,e.variableComponents[h]):Nu(r[h].dtype,e.outputComponent)}>; `)}),i!==""&&o.push(` @group(0) @binding(${1+e.variableNames.length}) var uniforms: Uniforms; `);let u=iae(t.shape,e.dispatchLayout),c=[jB,o.join(` 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shape.w * shape.u, shape.u, 1); return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u; } fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 { let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1); return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v; } // NaN defination in IEEE 754-1985 is : // - sign = either 0 or 1. // - biased exponent = all 1 bits. // - fraction = anything except all 0 bits (since all 0 bits represents infinity). // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers fn isnan(val: f32) -> bool { let floatToUint: u32 = bitcast(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; } fn isnanVec4(val : vec4) -> vec4 { let 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e=r.name,o=r.shape.length,n=ht(o),s="get"+e.charAt(0).toUpperCase()+e.slice(1),a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=a.map(c=>`${c} : i32`).join(", ");if(o<1)return` fn ${s}() -> ${Ae(t)} { return ${Ae(t)}(${e}[0]); } `;let p=`uniforms.${e.charAt(0).toLowerCase()+e.slice(1)}Shape`,u=`${o}D`;return o===0&&(u="1D"),` fn ${s}(${i}) -> ${Ae(t)} { return ${Ae(t)}(${e}[getIndexFromCoords${u}(${n}(${a.join(",")}), ${p})${t===1?"":` / ${t}`}]); } `}function sae(r,t,e,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a="get"+s+"ByOutput",i=r.shape.length,p=t.length,u=ht(p);if(y.arraysEqual(r.shape,t)&&o)return` fn ${a}Index(globalIndex : i32) -> ${Ae(e)} { return ${Ae(e)}(${n}[globalIndex]); } fn ${a}Coords(coords : ${u}) -> ${Ae(e)} { return ${Ae(e)}(${n}[${p>1?"getOutputIndexFromCoords(coords)":"coords"}${e===1?"":` / ${e}`}]); } `;let c=w.getBroadcastDims(r.shape,t),l=p-i,m="";if(i===0)return` fn ${a}Index(globalIndex : i32) -> ${Ae(e)}{ return get${s}(); } fn ${a}Coords(coords 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fm(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Lo;(function(r){r[r.MatMulReduceProgram=0]="MatMulReduceProgram",r[r.MatMulSplitKProgram=1]="MatMulSplitKProgram",r[r.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",r[r.MatMulPackedProgram=3]="MatMulPackedProgram",r[r.MatMulMax=4]="MatMulMax"})(Lo||(Lo={}));var dae=A().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),fae=(r,t)=>{let e=r.limits.maxComputeWorkgroupsPerDimension,o=t.dispatchLayout,n=t.dispatch;if(n.every(a=>a<=e))return n;y.assert(n[0]>e&&o.y===void 0&&o.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(n[0]));return s>e?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=e,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Tu=class extends ao{nextDataId(){return Tu.nextDataId++}constructor(t,e){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!dm())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=t,this.queue=t.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Hg(e),this.supportTimestampQuery=this.device.features.has("timestamp-query"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Kg(this.device),this.textureManager=new qg(this.device),this.tensorMap=new zo(this,pr()),A().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:t,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(t,e=!1){if(!this.tensorMap.has(t))return!0;let o=this.tensorMap.get(t);return e?o.refCount=0:o.refCount--,o.refCount>0?!1:(o.complexTensorInfos!=null&&(this.disposeData(o.complexTensorInfos.real.dataId),this.disposeData(o.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(t)?(this.tensorDataPendingDisposal.push(t),!0):(this.releaseResource(t),this.tensorMap.delete(t),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(t){let e=this.tensorMap.get(t);if(!(!e||!e.resource)){if(e.external){e.resource=null;return}e.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(e.resource):e.resource instanceof GPUTexture&&this.textureManager.releaseTexture(e.resource),e.resource=null}}refCount(t){return this.tensorMap.has(t)?this.tensorMap.get(t).refCount:0}incRef(t){let e=this.tensorMap.get(t);e.refCount++}decRef(t){if(this.tensorMap.has(t)){let e=this.tensorMap.get(t);e.refCount--}}write(t,e,o){if(o==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:e,values:t,refCount:1}),n}move(t,e,o,n,s){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(t,{dtype:n,shape:o,values:e,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(t=>{this.releaseResource(t),this.tensorMap.delete(t)}),this.uniformPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t)),this.stagingPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let t;try{t=await Promise.all(Object.values(this.pipelineCache))}catch(e){throw new Error(e.message)}Object.keys(this.pipelineCache).map((e,o)=>{this.pipelineCache[e]=t[o]})}async getBufferData(t){if(A().getBool("WEBGPU_ENGINE_COMPILE_ONLY"))return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"),null;let e=t.size,o=this.bufferManager.acquireBuffer(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,o,0,e),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool("WEBGPU_USE_PROFILE_TOOL")&&(y.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(t,e){let o=this.tensorMap.get(t);return o.values=e,o.values}readSync(t){let e=this.tensorMap.get(t),{values:o,complexTensorInfos:n}=e;if(o!=null||e.dtype==="string")return o;if(e.dtype==="complex64"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,"float32");return this.convertAndCacheOnCPU(t,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead."));let s=["opaque","premultiplied"],a=e.resource,i=a.size;y.assert(i%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext("webgpu");return x.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext("2d",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=E%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,e.dtype);return this.convertAndCacheOnCPU(t,f),f}async read(t){if(!this.tensorMap.has(t))throw new Error(`Tensor ${t} was not registered!`);let e=this.tensorMap.get(t),{values:o}=e;if(o!=null)return o;let n;if(e.dtype==="complex64"){let s=await Promise.all([this.read(e.complexTensorInfos.real.dataId),this.read(e.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(e.resource);n=y.convertBackendValuesAndArrayBuffer(s,e.dtype)}return this.convertAndCacheOnCPU(t,n),n}copyBuffer(t){let e=t.size,o=t.usage,n=this.bufferManager.acquireBuffer(e,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,n,0,e),this.submitQueue(),n}createTensorFromGPUData(t,e,o){let n=t.buffer;if(o==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:e,values:null,refCount:1,external:t.zeroCopy});let a=this.tensorMap.get(s),i=jg(a.dtype)*y.sizeFromShape(a.shape);if(t.buffer.sizey.decodeString(n));return me(t.shape,t.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return me(t.shape,t.dtype,e)}async time(t){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."),this.hasTimestampQueryWarned=!0);let e=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(t,e,o){return e==="string"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,t,e),shape:t,dtype:e}}tensorToBinding(t){if(!t)return null;let o=this.tensorMap.get(t.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(t){let e=this.tensorMap.get(t);if(e.resource!=null)return;let o=jg(e.dtype)*y.sizeFromShape(e.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(e.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState==="unmapped"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();e.dtype==="int32"||e.dtype==="bool"?new Int32Array(i).set(e.values):new Float32Array(i).set(e.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();e.dtype==="int32"||e.dtype==="bool"?new Int32Array(a).set(e.values):new Float32Array(a).set(e.values),n.unmap()}e.values=null}else n=this.bufferManager.acquireBuffer(o,s);e.resource=n}makeUniforms(t){let e=0,o=0,n=[],s=1;t.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),e=Math.ceil(e/u)*u,o=p.data.length,n.push(e),e+=p.data.length*4}),e=Math.ceil(e/s)*s;let a=new ArrayBuffer(e);t.forEach((p,u)=>{let c=n[u];p.type==="int32"?new Int32Array(a,c,p.data.length).set(p.data):p.type==="uint32"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,e),this.uniformPendingDisposal.push(i),{offset:0,size:e,buffer:i}}runWebGPUProgram(t,e,o,n,s){if(s||(s=this.makeTensorInfo(t.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),t.dispatch=fae(this.device,t);let a=e.map((p,u)=>{if(p.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:t.variableNames[u]}});t.shaderKey=QB(t,a,s);let i=A().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return t.shaderKey in this.pipelineCache||(this.pipelineCache[t.shaderKey]=YB(this.device,t,a,s,i)),t.pipeline=this.pipelineCache[t.shaderKey],i||this.recordAndSubmit(t,s,e,n),s}recordAndSubmit(t,e,o,n){if(t.pipeline instanceof Promise)throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");let s=[],a=[],i="int32";if(t.pixelsOpType==null){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),a=o.concat(e).map(d=>d.shape);let m="int32";a.map(d=>{s.push({type:m,data:d});let f=y.computeStrides(d);s.push({type:m,data:f})})}else{let m=y.computeStrides(e.shape);s.push({type:i,data:m})}if(t.size){let m=y.sizeFromShape(t.outputShape);s.push({type:i,data:[t.outputComponent?m/t.outputComponent:m]})}n&&(s=[...s,...n]);let p=[this.tensorToBinding(e),...o.map(m=>this.tensorToBinding(m)),this.makeUniforms(s)];o.forEach(m=>{this.commandQueueOwnedIds.add(m.dataId)}),this.commandQueueOwnedIds.add(e.dataId);let u=this.device.createBindGroup({layout:t.pipeline.getBindGroupLayout(0),entries:p.map((m,d)=>({binding:d,resource:m}))}),c=this.activeTimers!=null;this.ensureCommandEncoderReady();let l={};c&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.querySetCount})),l.timestampWrites=[{querySet:this.querySet,queryIndex:0,location:"beginning"},{querySet:this.querySet,queryIndex:1,location:"end"}],this.computePassEncoder=this.commandEncoder.beginComputePass(l)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(l)),this.computePassEncoder.setPipeline(t.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(t.dispatch[0],t.dispatch[1],t.dispatch[2]),this.dispatchCountInPass++,(c||A().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchCountInPass||t.pixelsOpType===Ii.DRAW)&&(this.endComputePassEncoder(),c?this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let t=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.querySetCount*8),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let e=new BigUint64Array(t.getMappedRange()),o=Number(e[1]-e[0])/1e6;return t.unmap(),this.bufferManager.releaseBuffer(t),o}shouldExecuteOnCPU(t,e=dae){return A().getBool("WEBGPU_CPU_FORWARD")&&t.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(r),e={},o=[];t.features.has("timestamp-query")&&o.push("timestamp-query"),t.features.has("bgra8unorm-storage")&&o.push(["bgra8unorm-storage"]),e.requiredFeatures=o;let n=t.limits;e.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let s=await t.requestDevice(e),a=await t.requestAdapterInfo();return new Tu(s,a)},3);var fe;(function(r){r[r.ADD=0]="ADD",r[r.ATAN2=1]="ATAN2",r[r.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",r[r.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",r[r.DIV=4]="DIV",r[r.ELU_DER=5]="ELU_DER",r[r.EQUAL=6]="EQUAL",r[r.FLOOR_DIV=7]="FLOOR_DIV",r[r.GREATER=8]="GREATER",r[r.GREATER_EQUAL=9]="GREATER_EQUAL",r[r.LESS=10]="LESS",r[r.LESS_EQUAL=11]="LESS_EQUAL",r[r.LOGICAL_AND=12]="LOGICAL_AND",r[r.LOGICAL_OR=13]="LOGICAL_OR",r[r.MAX=14]="MAX",r[r.MIN=15]="MIN",r[r.MOD=16]="MOD",r[r.MUL=17]="MUL",r[r.NOT_EQUAL=18]="NOT_EQUAL",r[r.POW=19]="POW",r[r.PRELU=20]="PRELU",r[r.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",r[r.SUB=22]="SUB"})(fe||(fe={}));var hae="let resultTemp = a + b;",gae="let resultTemp = atan2(a, b);",xae="let resultTemp = areal * breal - aimag * bimag;",yae="let resultTemp = areal * bimag + aimag * breal;",bae="let resultTemp = a / b;",Cae="let resultTemp = select(a * (b + 1.0), a, b >= b - b);",wae=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a == b); `,Sae=` let remainder = select(a % b, round(a % b), (round(a) == a) & (round(b) == b)); let quotient = (a - remainder) / b; let resultTemp = round(select(quotient, quotient - 1, sign(remainder) == -sign(b))); `,Iae=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a > b); `,vae=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a >= b); `,kae=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a < b); `,Nae=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a <= b); `,Tae="return f32(a >= 1.0 && b >= 1.0);",_ae=`return (vec4(a >= vec4(1.0)) * vec4(b >= vec4(1.0)));`,$ae="return f32(a >= 1.0 || b >= 1.0);",Eae=`return min(vec4(a >= vec4(1.0)) + vec4(b >= vec4(1.0)), vec4(1.0));`,Rae="let resultTemp = max(a, b);",Dae="let resultTemp = min(a, b);",Aae=` let isNaN = b == 0.; var resultTemp = a % b; resultTemp = select((resultTemp + b) % b, resultTemp, (a < 0. && b < 0.) || (a >= 0. && b > 0.)); `,Fae=` let isNaN = !vec4(b); var resultTemp = vec4(a % b); if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) { resultTemp[0] = (resultTemp[0] + b[0]) % b[0]; } if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) { resultTemp[1] = (resultTemp[1] + b[1]) % b[1]; } if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) { resultTemp[2] = (resultTemp[2] + b[2]) % b[2]; } if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) { resultTemp[3] = (resultTemp[3] + b[3]) % b[3]; } `,Pae="let resultTemp = a * b;",Oae=` var resultTemp = f32(a != b); let valueForNaN = 1.0; `,Mae=` var resultTemp = vec4(a != b); let valueForNaN = 1.0; `,Lae=` let isNaN = a < 0.0 && floor(b) < b; if (b == 0.0) { return 1.0; } var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b), round(abs(b) % 2.0) != 1.0); `,Bae=` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); var resultTemp = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS let isExpZero = b == vec4(0.0); if (isExpZero.r) { resultTemp.r = 1.0; } if (isExpZero.g) { resultTemp.g = 1.0; } if (isExpZero.b) { resultTemp.b = 1.0; } if (isExpZero.a) { resultTemp.a = 1.0; } let isNaN = (a < vec4(0.0)) & (floor(b) < b); `,zae="if (a < 0.0) { return b * a; } return a;",Vae=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `,Wae="let resultTemp = (a - b) * (a - b);",Uae="let resultTemp = a - b;";function Qc(r,t){let e;do{switch(r){case fe.ATAN2:e=gae;break;case fe.MAX:e=Rae;break;case fe.MIN:e=Dae;break;case fe.MOD:e=t?Fae:Aae;break;case fe.NOT_EQUAL:e=t?Mae:Oae;break;case fe.POW:e=t?Bae:Lae;break;default:continue}let o,n,s;return t?(o="isnanVec4",n="vec4",s="vec4"):(o="isnan",n="f32",s="bool"),` let aIsNaN = ${o}(a); let aPostLegalization = select(a, ${n}(42), aIsNaN); let bIsNaN = ${o}(b); let bPostLegalization = select(b, ${n}(42), bIsNaN); let isNaN = false; let valueForNaN = uniforms.NAN; { let a = aPostLegalization; let b = bPostLegalization; ${e} return select( resultTemp, ${n}(valueForNaN), ${s}(isNaN) | aIsNaN | bIsNaN); } `}while(!1);switch(r){case fe.ADD:e=hae;break;case fe.COMPLEX_MULTIPLY_IMAG:e=yae;break;case fe.COMPLEX_MULTIPLY_REAL:e=xae;break;case fe.DIV:e=bae;break;case fe.ELU_DER:e=Cae;break;case fe.EQUAL:e=wae;break;case fe.FLOOR_DIV:e=Sae;break;case fe.GREATER:e=Iae;break;case fe.GREATER_EQUAL:e=vae;break;case fe.LESS:e=kae;break;case fe.LESS_EQUAL:e=Nae;break;case fe.LOGICAL_AND:return t?_ae:Tae;case fe.LOGICAL_OR:return t?Eae:$ae;case fe.MUL:e=Pae;break;case fe.PRELU:return t?Vae:zae;case fe.SQUARED_DIFFERENCE:e=Wae;break;case fe.SUB:e=Uae;break;default:}return` ${e} return resultTemp; `}var Z;(function(r){r[r.ABS=0]="ABS",r[r.ACOS=1]="ACOS",r[r.ACOSH=2]="ACOSH",r[r.ASIN=3]="ASIN",r[r.ASINH=4]="ASINH",r[r.ATAN=5]="ATAN",r[r.ATANH=6]="ATANH",r[r.CEIL=7]="CEIL",r[r.COS=8]="COS",r[r.COSH=9]="COSH",r[r.ELU=10]="ELU",r[r.ERF=11]="ERF",r[r.EXP=12]="EXP",r[r.EXPM1=13]="EXPM1",r[r.FLOOR=14]="FLOOR",r[r.IS_FINITE=15]="IS_FINITE",r[r.IS_INF=16]="IS_INF",r[r.IS_NAN=17]="IS_NAN",r[r.LINEAR=18]="LINEAR",r[r.LOG=19]="LOG",r[r.LOG1P=20]="LOG1P",r[r.LOGICAL_NOT=21]="LOGICAL_NOT",r[r.NEG=22]="NEG",r[r.RELU=23]="RELU",r[r.RELU6=24]="RELU6",r[r.LEAKYRELU=25]="LEAKYRELU",r[r.RECIPROCAL=26]="RECIPROCAL",r[r.ROUND=27]="ROUND",r[r.RSQRT=28]="RSQRT",r[r.SELU=29]="SELU",r[r.SIGMOID=30]="SIGMOID",r[r.SIGN=31]="SIGN",r[r.SIN=32]="SIN",r[r.SINH=33]="SINH",r[r.SOFTPLUS=34]="SOFTPLUS",r[r.SQRT=35]="SQRT",r[r.SQUARE=36]="SQUARE",r[r.STEP=37]="STEP",r[r.TAN=38]="TAN",r[r.TANH=39]="TANH",r[r.TO_INT=40]="TO_INT"})(Z||(Z={}));var Gae="return abs(a);",Hae=` if (abs(a) > 1.) { return uniforms.NAN; } return acos(a); `,Kae=` if (a < 1.) { return uniforms.NAN; } return acosh(a); `,qae=` if (abs(a) > 1.) { return uniforms.NAN; } return asin(a); `,jae="return asinh(a);",Xae=` if (isnan(a)) { return uniforms.NAN; } return atan(a); `,Yae=` if (abs(a) > 1.) { return uniforms.NAN; } if (a == 1.) { return uniforms.INFINITY; } if (a == -1.) { return -uniforms.INFINITY; } return atanh(a); `,Qae="return ceil(a);",Zae="return cos(a);",Jae=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; `,eie="return exp(a) - 1.0;",tie="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",rie=` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; } if (a.g >= 0.0) { resFloat.g = a.g; } if (a.b >= 0.0) { resFloat.b = a.b; } if (a.a >= 0.0) { resFloat.a = a.a; } return resFloat; `,oie=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. let p = ${w.ERF_P}; let a1 = ${w.ERF_A1}; let a2 = ${w.ERF_A2}; let a3 = ${w.ERF_A3}; let a4 = ${w.ERF_A4}; let a5 = ${w.ERF_A5}; let sign = sign(a); let absA = abs(a); let t = 1.0 / (1.0 + p * absA); return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA)); `,nie="return exp(a);",sie="return floor(a);",aie="return f32(!isnan(a) && !isinf(a));",iie="return f32(isinf(a));",uie="return f32(isnan(a));",pie="return a;",cie=`if (a < 0.0) { return uniforms.NAN; } return log(a);`,lie=` if (isnan(a)) { return a; } return log(1.0 + a); `,mie="return f32(!(a >= 1.0));",die="return -a;",fie="if (a < 0.0) { return uniforms.alpha * a; } return a;",hie=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); `,gie="return 1.0 / a;",xie="return select(a, 0.0, a < 0.0);",yie="return clamp(a, 0.0, 6.0);",bie="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",Cie=` return select(a, vec4(0.0), a < vec4(0.0)); `,wie="return round(a);",Sie="return inverseSqrt(a);",Iie=` if (a >= 0.0) { return ${w.SELU_SCALE} * a; } else { return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0); } `,vie="return 1.0 / (1.0 + exp(-1.0 * a));",kie="return sign(a);",Nie="return sin(a);",Tie=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; `,_ie=` let epsilon = 1.1920928955078125e-7; let threshold = log(epsilon) + 2.0; let too_large = a > -threshold; let too_small = a < threshold; let exp_a = exp(a); if (too_large) { return a; } else if (too_small) { return exp_a; } else { return log(exp_a + 1.0); } `,$ie="return sqrt(a);",Eie="return a * a;",Rie=` if (isnan(a)) { return a; } return select(uniforms.stepAlpha, 1.0, a > 0.0); `,Die="return tan(a);",Aie=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); `,Fie="return f32(i32((a)));";function vi(r,t){switch(r){case Z.ABS:return Gae;case Z.ACOS:return Hae;case Z.ACOSH:return Kae;case Z.ASIN:return qae;case Z.ASINH:return jae;case Z.ATAN:return Xae;case Z.ATANH:return Yae;case Z.COS:return Zae;case Z.COSH:return Jae;case Z.CEIL:return Qae;case Z.ELU:return t?rie:tie;case Z.ERF:return oie;case Z.EXP:return nie;case Z.EXPM1:return eie;case Z.FLOOR:return sie;case Z.IS_FINITE:return aie;case Z.IS_INF:return iie;case Z.IS_NAN:return uie;case Z.LINEAR:return pie;case Z.LOG:return cie;case Z.LOG1P:return lie;case Z.LOGICAL_NOT:return mie;case Z.NEG:return die;case Z.LEAKYRELU:return t?hie:fie;case Z.RECIPROCAL:return gie;case Z.RELU:return t?Cie:xie;case Z.RELU6:return t?bie:yie;case Z.ROUND:return wie;case Z.RSQRT:return Sie;case Z.SELU:return Iie;case Z.SIGMOID:return vie;case Z.SIGN:return kie;case Z.SIN:return Nie;case Z.SINH:return Tie;case Z.SOFTPLUS:return _ie;case Z.SQRT:return $ie;case Z.SQUARE:return Eie;case Z.STEP:return Rie;case Z.TAN:return Die;case Z.TANH:return Aie;case Z.TO_INT:return Fie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function fr(r,t=!1,e=!1,o=3){if(r===null)return"";let n="";if(r==="linear")n=vi(Z.LINEAR);else if(r==="relu")n=vi(Z.RELU,e);else if(r==="elu")n=vi(Z.ELU,e);else if(r==="relu6")n=vi(Z.RELU6,e);else if(r==="prelu")n=Qc(fe.PRELU,e);else if(r==="sigmoid")n=vi(Z.SIGMOID,e);else if(r==="leakyrelu")n=vi(Z.LEAKYRELU,e);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(e?4:1),i="";return t?i=` fn activation(a : ${a}, coords : vec${o}) -> ${a} { let b = getPreluActivationWeightsByOutputCoords(coords); ${n} }`:i=` fn activation(a : ${a}, coords : vec${o}) -> ${a} { ${n} }`,i}function Zr(r,t){return` ${r?"value = value + getBiasByOutputCoords(coords);":""} ${t?"value = activation(value, coords);":""} `}function Qv(r,t,e=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=` ${r?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} `,i=t?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` fn mm_readA(batch: i32, row: i32, col: i32) -> ${Ae(s)} { var value = ${Ae(s)}(0.0); ${e&&n?a:` ${r?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} { ${a} } `} return value; } fn mm_readB(batch: i32, row: i32, col: i32) -> ${Ae(s)} { var value = ${Ae(s)}(0.0); ${i} return value; } `}function hm(r,t,e,o,n=!1,s=!1,a=!1,i=1){return` ${Qv(e,o,n,s,a,i)} fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Ae(i)}) { ${n&&s?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); ${Zr(r,t)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } `}var Pie=(r,t)=>r?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol * ${t}); `:` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRow + innerRow, kStart + inputCol * ${t}); `,Oie=(r,t,e,o)=>{if(r)return` for (var k = 0; k < ${o}; k++) { let BCached0 = mm_Bsub[k][tileCol]; let ACached0 = mm_Asub[k][localRow]; for (var i = 0; i < ${e}; i++) { acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]); } }`;{let n="",s="";for(let a=0;a(ACached[${a}]), acc[i]);`;return` for (var k = 0; k < ${o/t}; k++) { ${n} for (var i = 0; i < ${e}; i++) { let ACached = mm_Asub[tileRow + i][k]; ${s} } }`}};function Fp(r,t,e=!1,o=32,n=!1,s=32,a=!1){let i=t[1]*r[1],p=t[0]*r[0],u=e?i:o,c=e?o:i,l=u/t[0],m=o/t[1],d=r[1],f=r[0];return y.assert((e&&l===4&&r[1]===4||!e&&(l===3||l===4))&&u%t[0]===0&&o%t[1]===0&&r[0]===4,()=>`If transposeA ${e} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. Otherwise, innerElementSize ${l} must be 3 or 4. tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${r[0]} must be 4.`),` var mm_Asub : array, ${u/l}>, ${c}>; var mm_Bsub : array, ${p/r[0]}>, ${o}>; ${G()} { let localRow = i32(localId.y); let tileRow = localRow * ${d}; let tileCol = i32(localId.x); let globalRow = i32(globalId.y) * ${d}; let globalCol = i32(globalId.x) * ${f}; let batch = ${n?"0":"i32(globalId.z)"}; let batchA = ${n||!a?"batch":"batch % uniforms.aShape[0]"}; let batchB = ${n||!a?"batch":"batch % uniforms.bShape[0]"}; let globalRowStart = i32(workgroupId.y) * ${i}; let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`}; var kStart = ${n?`i32(globalId.z) * ${s}`:"0"}; var acc: array, ${d}>; // Loop over shared dimension. let tileRowB = localRow * ${m}; for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. for (var innerRow = 0; innerRow < ${d}; innerRow++) { let inputRow = tileRow + innerRow; let inputCol = tileCol; ${Pie(e,l)} } // Load one tile of B into local memory. for (var innerRow = 0; innerRow < ${m}; innerRow++) { let inputRow = tileRowB + innerRow; let inputCol = tileCol; mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol); } kStart = kStart + ${o}; workgroupBarrier(); // Compute acc values for a single thread. ${Oie(e,l,d,o)} workgroupBarrier(); } for (var innerRow = 0; innerRow < ${d}; innerRow++) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } }`}var JB=r=>r?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); `:` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRowStart + inputRow, kStart + inputCol); `,Mie=r=>r?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Pp(r,t,e=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*t[1],u=r[0]*t[0],c=e?p:o,l=e?o:p;y.assert(l%t[1]===0&&c%t[0]===0&&o%t[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${t[0]}, tileInner ${o} must be divisible by workgroupSize[1]${t[1]}`);let m=l/t[1],d=c/t[0],f=o/t[1],h=r[1],g=r[0],x=a?` let localRow = i32(localId.y); let localCol = i32(localId.x); let globalRowStart = i32(workgroupId.y) * ${p}; let globalColStart = i32(workgroupId.x) * ${u}; // Loop over shared dimension. for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${t[1]}) { for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${t[0]}) { ${JB(e)} } } // Load one tile of B into local memory. for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${t[1]}) { for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) { mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalColStart + inputCol); } } kStart = kStart + ${o}; workgroupBarrier(); // Compute acc values for a single thread. var BCached : array; for (var k = 0; k < ${o}; k++) { for (var inner = 0; inner < ${g}; inner++) { BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}]; } for (var innerRow = 0; innerRow < ${h}; innerRow++) { let ACached = ${e?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`} for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = fma(ACached, BCached[innerCol], acc[innerRow][innerCol]); } } } workgroupBarrier(); } for (var innerRow = 0; innerRow < ${h}; innerRow++) { let gRow = globalRowStart + localRow + innerRow * ${t[1]}; for (var innerCol = 0; innerCol < ${g}; innerCol++) { let gCol = globalColStart + localCol + innerCol * ${t[0]}; mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); } } `:` let tileRow = i32(localId.y) * ${h}; let tileCol = i32(localId.x) * ${g}; let globalRow = i32(globalId.y) * ${h}; let globalCol = i32(globalId.x) * ${g}; let globalRowStart = i32(workgroupId.y) * ${p}; let tileRowA = i32(localId.y) * ${m}; let tileColA = i32(localId.x) * ${d}; let tileRowB = i32(localId.y) * ${f}; // Loop over shared dimension. for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. for (var innerRow = 0; innerRow < ${m}; innerRow++) { for (var innerCol = 0; innerCol < ${d}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; ${JB(e)} } } // Load one tile of B into local memory. for (var innerRow = 0; innerRow < ${f}; innerRow++) { for (var innerCol = 0; innerCol < ${g}; innerCol++) { let inputRow = tileRowB + innerRow; let inputCol = tileCol + innerCol; mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol + innerCol); } } kStart = kStart + ${o}; workgroupBarrier(); // Compute acc values for a single thread. var BCached : array; for (var k = 0; k < ${o}; k++) { for (var inner = 0; inner < ${g}; inner++) { BCached[inner] = mm_Bsub[k][tileCol + inner]; } for (var innerRow = 0; innerRow < ${h}; innerRow++) { ${Mie(e)} for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = fma(ACached, BCached[innerCol], acc[innerRow][innerCol]); } } } workgroupBarrier(); } for (var innerRow = 0; innerRow < ${h}; innerRow++) { for (var innerCol = 0; innerCol < ${g}; innerCol++) { mm_write(batch, globalRow + innerRow, globalCol + innerCol, acc[innerRow][innerCol]); } } `;return` var mm_Asub : array, ${l}>; var mm_Bsub : array, ${o}>; ${G()} { let batch = ${n?"0":"i32(globalId.z)"}; let batchA = ${n||!i?"batch":"batch % uniforms.aShape[0]"}; let batchB = ${n||!i?"batch":"batch % uniforms.bShape[0]"}; let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`}; var kStart = ${n?`i32(globalId.z) * ${s}`:"0"}; var acc : array, ${h}>; // Without this initialization strange values show up in acc. for (var innerRow = 0; innerRow < ${h}; innerRow++) { for (var innerCol = 0; innerCol < ${g}; innerCol++) { acc[innerRow][innerCol] = 0.0; } } ${x} } `}var Lie=r=>r?` mm_readA(batchA, colA, globalRow), mm_readA(batchA, colA + 1, globalRow), mm_readA(batchA, colA + 2, globalRow), mm_readA(batchA, colA + 3, globalRow) `:` mm_readA(batchA, globalRow, colA), mm_readA(batchA, globalRow, colA + 1), mm_readA(batchA, globalRow, colA + 2), mm_readA(batchA, globalRow, colA + 3) `;function Bie(r,t=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let e=r[0]*4;return` var mm_Asub : array, ${r[0]}>; ${G()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); let numTiles = (uniforms.dimInner - 1) / ${e} + 1; let batch = i32(globalId.z); let batchA = batch % uniforms.aShape[0]; let batchB = batch % uniforms.bShape[0]; // Without this initialization strange values show up in acc. var acc = 0.0; // Loop over shared dimension. for (var t = 0; t < numTiles; t++) { // Load one tile of A into local memory. let colA = t * ${e} + tileCol * 4; mm_Asub[tileCol] = vec4(${Lie(t)}); workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < ${e/4}; k++) { let rowB = t * ${e} + k * 4; let BCached = vec4(mm_readB(batchB, rowB, globalCol), mm_readB(batchB, rowB + 1, globalCol), mm_readB(batchB, rowB + 2, globalCol), mm_readB(batchB, rowB + 3, globalCol)); let ACached = mm_Asub[k]; acc = acc + dot(ACached, BCached); } workgroupBarrier(); } mm_write(batch, globalRow, globalCol, acc); } `}var Xg=class{constructor(t,e,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?t[1]:t[2];if(this.isVec4=(u%4===0&&!o||e[1]%4===0&&o)&&e[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=e[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Xv(e[1],u,e[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(e[1],e[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(t,e,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=t%n===0,i=e%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return` ${fr(this.activation,this.hasPreluActivationWeights,this.isVec4)} ${hm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} ${this.isVec4?Fp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Bie(this.workgroupSize,this.transposeA):Pp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} `}};function zie(r){return` var sumValues : array; ${G()} { let coords = getOutputCoords(); let batch = coords[0]; let batchA = batch % uniforms.aShape[0]; let batchB = batch % uniforms.bShape[0]; let row = coords[1]; let col = coords[2]; var sum = 0.0; let Length = uniforms.dimInner; for (var k = i32(localId.x); k < Length; k = k + ${r}) { let dataA = mm_readA(batchA, row, k); let dataB = mm_readB(batchB, k, col); sum = sum + dataA * dataB; } sumValues[localId.x] = sum; workgroupBarrier(); for(var currentSize = ${r/2}u; currentSize > 1u; currentSize = currentSize / 2u) { if (localId.x < currentSize) { sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize]; } workgroupBarrier(); } if (localId.x == 0u) { sum = sumValues[0] + sumValues[1]; mm_write(batch, row, col, sum); } } `}var Yg=class{constructor(t,e=!1,o=!1,n=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.transposeA=e,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${e}_${o}`}getUserCode(){return` ${fr(this.activation,this.hasPreluActivationWeights)} ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)} ${zie(this.workgroupSize[0])} `}};function Vie(r){let t=r[1],e=r[0],o=t>e?t:e;return` var mm_Asub : array, ${t}>; var mm_Bsub : array, ${o}>; // If the output size is small for matrix multiplication, avoid to use vec4 // and handle some elements per thread to optimally utilize the ALU. // Read data from global memory to registers firstly, then store them into // shared memory, so it is instruction-Level parallelism for arithmetic // operations and others handle IO operations between barrier api, makes ALU // and load/store units work simultaneously, could improves the performance. ${G()} { let tileRow = i32(localId.y); let tileCol = i32(localId.x); let globalRow = i32(globalId.y); let globalCol = i32(globalId.x); let batch = i32(globalId.z); let batchA = batch % uniforms.aShape[0]; let batchB = batch % uniforms.bShape[0]; // uniforms.dimInner should be greater than 0. let numTiles = (uniforms.dimInner - 1) / ${o} + 1; var acc = 0.0; var globalColA = tileCol; var globalRowB = 0; var regA = mm_readA(batchA, globalRow, globalColA); var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol); var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${o}; globalRowB = globalRowB + ${o}; for (var t = 0; t < numTiles; t = t + 1) { mm_Asub[tileRow][tileCol] = regA; mm_Bsub[2 * tileRow][tileCol] = regB0; mm_Bsub[2 * tileRow + 1][tileCol] = regB1; workgroupBarrier(); regA = mm_readA(batchA, globalRow, globalColA); regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol); regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${o}; globalRowB = globalRowB + ${o}; for (var k = 0; k < ${o}; k = k + 1) { acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol]; } workgroupBarrier(); } mm_write(batch, globalRow, globalCol, acc); } `}var Qg=class{constructor(t,e,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push("bias");let c=p!=null;c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return` ${fr(this.activation,this.hasPreluActivationWeights)} ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)} ${Vie(this.workgroupSize)} `}};var Zg=class{constructor(t,e,o=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(t[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&e%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],e],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let t=this.outputComponent;return` ${Qv(!1,this.transposeB,!1,!1,!1,t)} fn mm_write(batch: i32, row : i32, col : i32, value : ${Ae(t)}) { if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); let flatIndex = getOutputIndexFromCoords(coords); // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. for (var i = 0; i < ${t}; i = i + 1) { ${Qr("&result[flatIndex + i]",`${t>1?"value[i]":"value"}`,"float32")} } } } ${t===4?Fp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Pp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} `}},Jg=class{constructor(t,e=null,o=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return` ${fr(this.activation,this.hasPreluActivationWeights)} ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); ${Zr(this.addBias,this.activation)} setOutputAtIndex(index, value); } } `}};var ex=class{constructor(t){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } `}};function kt(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new ex(o),i=[{type:"float32",data:[n]}];return t.runWebGPUProgram(a,[],s,i)}}var ez={kernelName:sa,backendName:"webgpu",kernelFunc:kt};function pe(r){let{inputs:t,attrs:e}=r,{x:o}=t,{shape:n}=e,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var tz={kernelName:da,backendName:"webgpu",kernelFunc:pe};function Op({a:r,b:t,transposeA:e,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=t.shape.length,l=e?r.shape[u-2]:r.shape[u-1],m=o?t.shape[c-1]:t.shape[c-2],d=e?r.shape[u-1]:r.shape[u-2],f=o?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Ir.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${o} must match.`);let k=e?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],E=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:t},backend:n,attrs:{shape:_}}),D=[E,R],P=Math.max(x,b),O=[E,R],M=[{type:"int32",data:[d]},{type:"int32",data:[f]},{type:"int32",data:[l]}],L,B,z=[P,d,f],U=A().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(U<0){let q=A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Lo.MatMulReduceProgram:P===1&&m>=2e3?U=Lo.MatMulSplitKProgram:U=Lo.MatMulSmallOutputSizeProgram:U=Lo.MatMulPackedProgram}switch(U){case Lo.MatMulReduceProgram:L=new Yg(z,e,o,s,p,a);break;case Lo.MatMulSplitKProgram:{if(B=kt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Zg(z,m,e,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Jg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p==="leakyrelu"&&(J=[{type:"float32",data:[i]}],Y.uniforms+=" alpha : f32,");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Lo.MatMulSmallOutputSizeProgram:L=new Qg(k,_,z,e,o,s,p,a);break;case Lo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new Xg(k,z,e,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p==="leakyrelu"&&(M.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Wie(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Op({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var rz={kernelName:Io,backendName:"webgpu",kernelFunc:Wie};var gm=class{constructor(t,e,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${t}`,this.op=t}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { ${Qc(this.op,!1)} } ${G("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); let breal = getBRealByOutputIndex(index); let bimag = getBImagByOutputIndex(index); setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } `}};var ki=class{constructor(t,e,o){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.op=t,this.useSharedMemoryWithA=e.length<=1&&o.length>1&&e[0]<128,this.useSharedMemoryWithB=o.length<=1&&e.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:e[0],this.shaderKey=`binary_${t}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=e.length>0&&e[e.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(e)||e[e.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${t}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let t,e=this.outputComponent===4?"vec4":"f32",o=` fn binaryOperation(a : ${e}, b : ${e}) -> ${e} { ${Qc(this.op,this.outputComponent===4)} }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; let b = getBByOutputIndex(index);`;t=` ${o} var sharedBuf : array; ${G("index")} { // Fill in the shared memory buffer. let localIndex = i32(localId.x); if(localIndex < ${this.lastDimensionSize}) { sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}[localIndex]); } workgroupBarrier(); if(index < uniforms.size) { let coords = getCoordsFromIndex(index); ${s} setOutputAtIndex(index, binaryOperation(a, b)); } } `}else t=` ${o} ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index * ${this.outputComponent}); let a = ${e}(getAByOutputCoords(coords)); let b = ${e}(getBByOutputCoords(coords)); setOutputAtIndex(index, binaryOperation(a, b)); } } `;return t}};function Ft(r){let{inputs:t}=r,{x:e}=t;return r.backend.incRef(e.dataId),{dataId:e.dataId,shape:e.shape,dtype:e.dtype}}var oz={kernelName:wo,backendName:"webgpu",kernelFunc:Ft};function yo(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,"complex64"),a=e.tensorMap.get(s.dataId),i=Ft({inputs:{x:o},backend:e}),p=Ft({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var nz={kernelName:Fi,backendName:"webgpu",kernelFunc:yo};var Jr=class{constructor(t,e,o=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=e,o!==""&&(this.uniforms=o),this.shaderKey=`unary_${e}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { ${vi(this.op,!1)} } ${G("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); setOutputAtIndex(index, unaryOperation(a)); } } `}};function ye({opType:r,cpuKernelImpl:t,dtype:e}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=e||s.dtype;if(a.shouldExecuteOnCPU([s])&&t!=null){let u=a.tensorMap.get(s.dataId),c=t(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Jr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:t,supportsComplex:e=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(e&&a.dtype==="complex64"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new ki(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new gm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new gm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,"float32"),f=p.runWebGPUProgram(x,b,"float32")}let h=yo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype==="string"||i.dtype==="string"||p.shouldExecuteOnCPU([a,i]))&&t!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype==="string"?w.fromUint8ToStringArray(l):l,f=a.dtype==="string"?w.fromUint8ToStringArray(m):m,[h,g]=t(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new ki(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:sz,castImpl:az,ceilImpl:iz,concatImpl:uz,equalImpl:pz,expImpl:cz,expm1Impl:lz,floorImpl:mz,floorDivImpl:dz,gatherNdImpl:fz,gatherV2Impl:hz,greaterEqualImpl:gz,greaterImpl:xz,lessEqualImpl:yz,lessImpl:bz,logImpl:Cz,maxImpl:wz,maximumImpl:Sz,minimumImpl:Iz,multiplyImpl:vz,negImpl:kz,notEqualImpl:Nz,prodImpl:Tz,rangeImpl:_z,rsqrtImpl:$z,scatterImpl:Ez,simpleAbsImpl:Rz,sliceImpl:Dz,stridedSliceImpl:Az,stringNGramsImpl:Fz,subImpl:Pz,tileImpl:Oz,topKImpl:Mz,transposeImpl:Lz,uniqueImpl:HPt}=Tc;var Uie=ye({opType:Z.ABS,cpuKernelImpl:Rz}),Bz={kernelName:Xs,backendName:"webgpu",kernelFunc:Uie};var Gie=ye({opType:Z.ACOS}),zz={kernelName:Wo,backendName:"webgpu",kernelFunc:Gie};var Hie=ye({opType:Z.ACOSH}),Vz={kernelName:Uo,backendName:"webgpu",kernelFunc:Hie};var Kie=et({opType:fe.ADD,cpuKernelImpl:sz,supportsComplex:!0}),Wz={kernelName:uo,backendName:"webgpu",kernelFunc:Kie};var tx=class{constructor(t){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t[0],this.variableNames=t.map((e,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let t=[];this.variableNames.forEach(n=>{t.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let e=this.variableNames.map(n=>`v${n}`).join(" + ");return` ${G("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); ${t.join(` `)} setOutputAtIndex(flatIndex, ${e}); } } } `}};function qie(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return Ft({inputs:{x:o[0]},backend:e});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new tx(s);return e.runWebGPUProgram(a,o,n)}var Uz={kernelName:Go,backendName:"webgpu",kernelFunc:qie};var rx=class{constructor(t,e){this.variableNames=["A"],this.workgroupSize=[16,16,1];let o=new Array(t.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let t=this.workgroupSize[0];return` var tile : array, ${this.workgroupSize[0]}>; ${G()} { var x = i32(workgroupId.x) * ${t} + i32(localId.x); var y = i32(workgroupId.y) * ${t} + i32(localId.y); let width = uniforms.outShape[0]; let height = uniforms.outShape[1]; if (x < width && y < height) { tile[localId.y][localId.x] = f32(A[y * width + x]); } workgroupBarrier(); x = i32(workgroupId.y) * ${t} + i32(localId.x); y = i32(workgroupId.x) * ${t} + i32(localId.y); if (x < height && y < width) { setOutputAtIndex((y * height + x), tile[localId.x] [localId.y]); } } `}};var ox=class{constructor(t,e){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(t.length);for(let n=0;n6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=new Array(t);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:t.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=e,this.shaderKey=`reduce_${e}`}getUserCode(){let t="",e="0.0",o=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(t=` if (isnan(candidate)) { bestValue = uniforms.NAN; } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue) { bestValue = candidate; }`,e="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?t=" bestValue = bestValue + candidate; ":this.reduceType==="prod"?(t=" bestValue = bestValue * candidate; ",e="1.0"):this.reduceType==="all"?(t=" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ",e="1.0"):this.reduceType==="any"&&(t=" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ",e="0.0");let n=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return` fn DIV_CEIL(a : u32, b : u32) -> u32 { return ((a - 1u) / b + 1u); } ${` var xBestValues : array; `} fn getOffset(outputIndex : i32) -> i32 { let outputCoords = getCoordsFromIndex(outputIndex); let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize; return offset; } ${G("index")} { let outputIndex = index / ${o}; let offset = getOffset(outputIndex); var bestValue = ${e}; let Length = uniforms.reduceSize; let WorkPerThread = DIV_CEIL(u32(Length), ${o}u); for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size; k = k + ${o}) { let candidate = f32(x[offset + k]); ${t} } xBestValues[localId.x] = bestValue; workgroupBarrier(); var reduceSize = min(u32(Length), ${o}u); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); if (localId.x < currentSize) { let candidate = xBestValues[localId.x + interval]; ${t} xBestValues[localId.x] = bestValue; } reduceSize = interval; workgroupBarrier(); } if (localId.x == 0u && outputIndex < uniforms.size) { ${n} } } `}};var jie={mean:"float32",all:"bool",any:"bool"};function eo(r,t,e,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(t,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=yr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;e&&(d=w.expandShapeToKeepDim(l,i));let f;if((o==="max"||o==="prod")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case"max":let g=wz(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case"prod":let{outVals:x,outShape:b,outDtype:C}=Tz(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU 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n=[e];this.op=o==="min"?"<":">";let[s,a]=w.computeOutAndReduceShapes(t,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type="plain",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=t,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let t=this.workgroupSize[0],e=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Mo(this.inputShape.length-1)}`,o=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let s=0;s u32 { return ((a - 1u) / b + 1u); } ${` var xBestIndices : array; var xBestValues : array; `} ${G("index")} { let outputIndex = index / ${t}; let reduceLength = ${e()}; var bestIndex = i32(localId.x); var bestValue = uniforms.infinityValue; let outputCoords = getCoordsFromIndex(outputIndex); for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size; k = k + ${t}) { let candidate = getX(${o()} k); if (!isnan(candidate) && candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = k; } } xBestValues[localId.x] = bestValue; xBestIndices[localId.x] = bestIndex; workgroupBarrier(); var reduceSize = min(u32(reduceLength), ${t}u); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); if (localId.x < currentSize) { let candidate = xBestValues[localId.x + interval]; if (candidate ${this.op} bestValue) { bestValue = candidate; xBestValues[localId.x] = bestValue; xBestIndices[localId.x] = xBestIndices[localId.x + interval]; } } reduceSize = interval; workgroupBarrier(); } if (localId.x == 0u && outputIndex < uniforms.size) { setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]); } } `:` ${G("index")} { if (index < uniforms.size) { let outputCoords = getCoordsFromIndex(index); var bestIndex = 0; var bestValue = getX(${o()} 0); let reduceLength = ${e()}; for (var i = 1; i < reduceLength; i++) { let candidate = getX(${o()} i); if (candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = i; } } setOutputAtIndexI32(index, bestIndex); } } `}};function Qie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yr({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=new Zc(p.shape,a[0],"max"),l=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],m=e.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>e.disposeData(d.dataId)),m}var qz={kernelName:Ys,backendName:"webgpu",kernelFunc:Qie};function Zie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=yr({inputs:{x:n},backend:e,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=new Zc(p.shape,a[0],"min"),l=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],m=e.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>e.disposeData(d.dataId)),m}var jz={kernelName:Qs,backendName:"webgpu",kernelFunc:Zie};var Jie=ye({opType:Z.ASIN}),Xz={kernelName:qo,backendName:"webgpu",kernelFunc:Jie};var eue=ye({opType:Z.ASINH}),Yz={kernelName:jo,backendName:"webgpu",kernelFunc:eue};var tue=ye({opType:Z.ATAN}),Qz={kernelName:Xo,backendName:"webgpu",kernelFunc:tue};var rue=et({opType:fe.ATAN2}),Zz={kernelName:Qo,backendName:"webgpu",kernelFunc:rue};var oue=ye({opType:Z.ATANH}),Jz={kernelName:Yo,backendName:"webgpu",kernelFunc:oue};var sx=class{constructor(t){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d = coords[3]; let xRCCorner = coords.yz * uniforms.strides; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; let value = getX(batch, xRCorner, xCCorner, d); setOutputAtIndex(index, value); } } `}};var za=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${e}_${o}_${n}_${s}`}getUserCode(){let t;this.poolType==="avg"?t="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?t=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; maxPosition = ${this.flattenPositions?this.includeBatchIndex?"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d":"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d":"wR * uniforms.filterDims.y + wC"}; }`:t="resultValue = max(value, resultValue);";let e="resultValue";return this.poolType==="avg"&&(e="resultValue / max(count, 1.0)"),` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d = coords[3]; let xRCCorner = vec2(coords.yz) * uniforms.strides - uniforms.pads; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; ${this.computePositions?`var maxValue = 0.0; var maxValueFound = 0.0; var maxPosition = 0;`:`var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};`} var count = 0.0; for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) { let xR = xRCorner + wR; if (xR < 0 || xR >= uniforms.convDims.x) { continue; } for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) { let xC = xCCorner + wC; if (xC < 0 || xC >= uniforms.convDims.y) { continue; } let value = getX(batch, xR, xC, d); ${t} } } ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${e});`} } } `}},_u=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${e}_${o}_${n}_${s}`}getUserCode(){let t;this.poolType==="avg"?t="resultValue += value; count += 1.0;":this.computePositions?t=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; maxPosition = ${this.flattenPositions?this.includeBatchIndex?"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch":"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch":"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC"}; }`:t="resultValue = max(value, resultValue);";let e="resultValue";return this.poolType==="avg"&&(e="resultValue / max(count, 1.0)"),` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; let ch = coords.u; let xCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads; let xDCorner = xCorner.x; let xRCorner = xCorner.y; let xCCorner = xCorner.z; ${this.computePositions?`var maxValue = 0.0; var maxValueFound = 0.0; var maxPosition = 0;`:`var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};`} var count = 0.0; for (var wD = 0; wD < uniforms.filterDims.x; wD++) { let xD = xDCorner + wD; if (xD < 0 || xD >= uniforms.convDims.x) { continue; } for (var wR = 0; wR < uniforms.filterDims.y; wR++) { let xR = xRCorner + wR; if (xR < 0 || xR >= uniforms.convDims.y) { continue; } for (var wC = 0; wC < uniforms.filterDims.z; wC++) { let xC = xCCorner + wC; if (xC < 0 || xC >= uniforms.convDims.z) { continue; } let value = getX(batch, xD, xR, xC, ch); ${t} } } } ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${e});`} } } `}};function Jv(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o;return eo(n,s,a,"max",e)}var eV={kernelName:Vn,backendName:"webgpu",kernelFunc:Jv};function e0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return eo(n,a,s,"mean",e)}var tV={kernelName:Gn,backendName:"webgpu",kernelFunc:e0};function ax(r,t,e,o){if(t.filterWidth===1&&t.filterHeight===1&&y.arraysEqual(t.inShape,t.outShape))return Ft({inputs:{x:r},backend:o});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;e==="avg"?p=e0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(e==="max",()=>`Invalid pool type ${e}`),p=Jv({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:t.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?n=new sx(t):(e==="avg"?n=new za(t,"avg"):(y.assert(e==="max",()=>`Invalid pool type ${e}`),n=new za(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function nue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return ax(n,c,"avg",e)}var rV={kernelName:Zo,backendName:"webgpu",kernelFunc:nue};function sue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new _u(l,"avg"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return e.runWebGPUProgram(m,[n],n.dtype,d)}var oV={kernelName:Zs,backendName:"webgpu",kernelFunc:sue};var ix=class{constructor(t){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool2DBackprop"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d = coords[3]; let dyRCCorner = vec2(coords.yz) - uniforms.pads; let dyRCorner = dyRCCorner.x; let dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. var dotProd = 0.0; for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) { let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]); if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) { continue; } let idyR = i32(dyR); for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) { let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]); if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) { continue; } let idyC = i32(dyC); let dyValue = getDy(batch, idyR, idyC, d); dotProd = dotProd + dyValue * uniforms.avgMultiplier; } } setOutputAtIndex(index, dotProd); } } `}},ux=class{constructor(t){this.variableNames=["dy"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool3DBackprop"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; let ch = coords.u; let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads; let dyDCorner = dyCorner.x; let dyRCorner = dyCorner.y; let dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. var dotProd = 0.0; for (var wD = 0; wD < uniforms.filterDims[0]; wD++) { let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]); if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) { continue; } let idyD = i32(dyD); for (var wR = 0; wR < uniforms.filterDims[1]; wR++) { let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]); if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) { continue; } let idyR = i32(dyR); for (var wC = 0; wC < uniforms.filterDims[2]; wC++) { let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]); if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) { continue; } let idyC = i32(dyC); let dyValue = getDy(batch, idyD, idyR, idyC, ch); dotProd += dyValue * uniforms.avgMultiplier; } } } setOutputAtIndex(index, dotProd); } } `}};function aue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ux(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:"int32",data:[l.outDepth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"float32",data:[d]}];return e.runWebGPUProgram(m,[n],a.dtype,f)}var nV={kernelName:Ai,backendName:"webgpu",kernelFunc:aue};function iue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;fm([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new ix(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[m]}];return e.runWebGPUProgram(l,[n],a.dtype,d)}var sV={kernelName:Di,backendName:"webgpu",kernelFunc:iue};function uue(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s}=t,{transposeA:a,transposeB:i}=o;return Op({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var aV={kernelName:Jo,backendName:"webgpu",kernelFunc:uue};var px=class{constructor(t,e){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=t,this.uniforms=`start : ${ht(t.length)}, `,this.shaderKey="slice"}getUserCode(){let t=ht(this.rank),e=pue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):o=this.outputShape.map((s,a)=>`sourceLoc.${t0[a]} = uniforms.start.${Mo(a)} + coords.${t0[a]};`),` ${G("index")} { if (index < uniforms.size) { var sourceLoc : ${t}; let coords = getCoordsFromIndex(index); ${o.join(` `)} setOutputAtIndex(index, getSource(${e})); } } `}},t0=["x","y","z","w","u","v"];function pue(r){if(r===1)return"sourceLoc";if(r<=6)return t0.slice(0,r).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}function Hs(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,size:a}=o,[i,p]=ct.parseSliceParams(n,s,a);if(ct.assertParamsValid(n,i,p),e.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=e.tensorMap.get(n.dataId),m=Dz(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return e.makeTensorInfo(p,n.dtype,[]);let u=new px(i,p),c=[{type:"int32",data:i}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var iV={kernelName:ha,backendName:"webgpu",kernelFunc:Hs};var cue=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:e,attrs:{shape:p}}),h=yr({inputs:{x:f},backend:e,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Hs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeData(b.dataId)),x},uV={kernelName:Js,backendName:"webgpu",kernelFunc:cue};var lue=` fn bincount_write(index: i32, value: f32) { ${Qr("&result[index]","value","float32")} } `,mue=` fn bincount_write(index: i32, value: f32) { atomicStore(&result[index], bitcast(value)); } `,Jc=class{constructor(t,e,o=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=e,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` ${this.binaryOutput?mue:lue} ${G("index")} { ${this.rank===1?`if (index < uniforms.xShape) { let indexVal = i32(getX(index)); if (indexVal < uniforms.binCountSize) { let value = ${this.binaryOutput?1:this.hasWeights?"getW(index)":"1."}; bincount_write(indexVal, value); } }`:`let coord = getCoordsFromIndex(index); if (coordsInBounds2D(coord, uniforms.xShape)) { let indexVal = i32(getX(coord[0], coord[1])); if (indexVal < uniforms.binCountSize) { let value = ${this.binaryOutput?1:this.hasWeights?"getW(coord[0], coord[1])":"1."}; bincount_write(coord.x * uniforms.binCountSize + indexVal, value); } }`} } `}};function due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=kt({backend:e,attrs:{shape:c,value:0,dtype:l}}),d=new Jc([i],u),f=[{type:"int32",data:[a]}],h=u?[n,s]:[n];return e.runWebGPUProgram(d,h,l,f,m)}var pV={kernelName:en,backendName:"webgpu",kernelFunc:due};var cx=class{constructor(t){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="broadcastArgs"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { var s0 = 1.0; var s1 = 1.0; let indexS0 = index - uniforms.size + uniforms.s0Size; let indexS1 = index - uniforms.size + uniforms.s1Size; if (indexS0 >= 0) { s0 = getS0(indexS0); } if (indexS1 >= 0) { s1 = getS1(indexS1); } if (s0 == 1.0) { setOutputAtIndex(index, s1); } else if (s1 == 1.0) { setOutputAtIndex(index, s0); } else if (s0 != s1) { setOutputAtIndex(index, uniforms.NAN); } else { setOutputAtIndex(index, s0); } } } `}};function fue(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t;if(e.shouldExecuteOnCPU([o,n])){let c=e.tensorMap.get(o.dataId),l=e.tensorMap.get(n.dataId),m=c.values,d=l.values,f=w.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return e.makeTensorInfo([f.length],"int32",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new cx(i),u=[{type:"int32",data:[s]},{type:"int32",data:[a]}];return e.runWebGPUProgram(p,[o,n],"int32",u)}var cV={kernelName:ea,backendName:"webgpu",kernelFunc:fue};var r0=et({opType:fe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Nz}),lV={kernelName:Qn,backendName:"webgpu",kernelFunc:r0};function Ni(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.tensorMap.get(o.dataId);return Ft({inputs:{x:n.complexTensorInfos.real},backend:e})}var mV={kernelName:qi,backendName:"webgpu",kernelFunc:Ni};function dV(r,t){let e=new Jr(r.shape,Z.TO_INT),o=t.runWebGPUProgram(e,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function o0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Ft({inputs:{x:n},backend:e});let a=Gr(n.shape),i=o0({inputs:{x:n},backend:e,attrs:{dtype:"float32"}}),p=yo({inputs:{real:i,imag:a},backend:e});return a.dispose(),e.disposeData(i.dataId),p}if(n.dtype==="complex64"){let a=Ni({inputs:{input:n},backend:e}),i=o0({inputs:{x:a},backend:e,attrs:{dtype:s}});return e.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Ft({inputs:{x:n},backend:e});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(e.shouldExecuteOnCPU([n])){let a=e.tensorMap.get(n.dataId).values,[i,p,u]=az(a,n.shape,n.dtype,s);return e.makeTensorInfo(i,p,u)}if(s==="int32")return dV(n,e);if(s==="bool"){let a=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=r0({inputs:{a:n,b:a},backend:e});return e.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var fV={kernelName:bo,backendName:"webgpu",kernelFunc:o0};var hue=ye({opType:Z.CEIL,cpuKernelImpl:iz}),hV={kernelName:tn,backendName:"webgpu",kernelFunc:hue};var lx=class{constructor(t){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); var clampedValue = clamp( value, vec4(uniforms.minVal), vec4(uniforms.maxVal)); clampedValue = select(clampedValue, value, isnanVec4(value)); setOutputAtIndex(index, clampedValue); } } `}};var mx=class{constructor(t){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); if (isnan(value)) { setOutputAtIndex(index, value); return; } setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } `}};function gue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:"float32",data:[s]},{type:"float32",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new lx(n.shape):i=new mx(n.shape),e.runWebGPUProgram(i,[n],n.dtype,p)}var gV={kernelName:Co,backendName:"webgpu",kernelFunc:gue};var dx=class{constructor(t){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let re = abs(getRealByOutputIndex(index)); let im = abs(getImagByOutputIndex(index)); let mx = max(re, im); // The length function in wgsl may be not underflow-safe on some GPUs. // So the safe solution is to ensure underflow-safety in all cases. setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0)); } } `}};function xV(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function xue(r){let{inputs:t,backend:e}=r,{x:o}=t,n=e.tensorMap.get(o.dataId),s=new dx(o.shape),a=[xV(o,n.complexTensorInfos.real),xV(o,n.complexTensorInfos.imag)];return e.runWebGPUProgram(s,a,a[0].dtype)}var yV={kernelName:Pi,backendName:"webgpu",kernelFunc:xue};var fx=class{constructor(t){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=w.computeOutShape(t,1),this.variableNames=t.map((e,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=t.length-1;for(let e=0;e0){t.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;sNi({inputs:{input:C},backend:e})),h=r.map(C=>Mp({inputs:{input:C},backend:e})),g=el(f,t,e),x=el(h,t,e),b=yo({inputs:{real:g,imag:x},backend:e});return f.forEach(C=>e.disposeData(C.dataId)),h.forEach(C=>e.disposeData(C.dataId)),e.disposeData(g.dataId),e.disposeData(x.dataId),b}let n=e.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(t))];return pe({inputs:{x:k},backend:e,attrs:{shape:E}})}),h=f.map(k=>({vals:e.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=uz(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),t),S=e.makeTensorInfo(C,o,b);return f.forEach(k=>e.disposeData(k.dataId)),S}let s=e.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new fx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:"int32",data:[l[0]]});for(let f=1;fe.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:e,attrs:{shape:i}});return e.disposeData(m.dataId),d}function yue(r,t,e){let o=w.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:e,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,t)),y.sizeFromShape(s.shape.slice(t))]}})),outShape:o}}function n0(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,t[0].shape)[0],a=t.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(t.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let p=t.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Ft({inputs:{x:p[0]},backend:e}):el(p,s,e)}var CV={kernelName:ta,backendName:"webgpu",kernelFunc:n0};function bue(r,t,e,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return"resData = f32(x[xIndex]);";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = vec4(x[xIndex / 4]);";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return"return f32(W[row * uniforms.wShape[3] + col]);";case 4:return"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?` let coord = vec4(batch, xRow, xCol, xCh); `:` let coord = vec4(batch, xCh, xRow, xCol); `,d=r?` let coords = vec4( batch, row / outWidth, row % outWidth, col); `:` let coords = vec4( batch, row, col / outWidth, col % outWidth); `,f=r?"uniforms.xShape[1]":"uniforms.xShape[2]",h=r?"uniforms.xShape[2]":"uniforms.xShape[3]",g=r?"row":"col",x=r?"col":"row",b=` let inChannels = uniforms.wShape[2]; let outWidth = ${r?"uniforms.outShape[2]":"uniforms.outShape[3]"}; let outRow = ${g} / outWidth; let outCol = ${g} % outWidth; let WRow = ${x} / (uniforms.filterDims[1] * inChannels); let WCol = ${x} / inChannels % uniforms.filterDims[1]; let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0]; let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1]; let xCh = ${x} % inChannels; var resData = ${Ae(i)}(0.0); // The bounds checking is always needed since we use it to pad zero for // the 'same' padding type. if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) { ${m} let xIndex = getIndexFromCoords4D(coord, uniforms.xShape); ${c(i)} } return resData;`,C=r?t&&o?` ${b}`:` if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${b} } return ${Ae(i)}(0.0);`:o&&e?` ${b}`:` if (row < uniforms.dimInner && col < uniforms.dimBOuter) { ${b} } return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),E=r?Ae(p):Ae(i);return` ${fr(s,a,u===4,4)} fn mm_readA(batch: i32, row : i32, col : i32) -> ${_} { ${r?C:S} } fn mm_readB(batch: i32, row : i32, col : i32) -> ${E} { ${r?S:C} } fn mm_write(batch: i32, row : i32, col : i32, valueIn : ${k}) { if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { var value = valueIn; let outWidth = ${r?"uniforms.outShape[2]":"uniforms.outShape[3]"}; ${d} ${Zr(n,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } }`}var hx=class{constructor(t,e,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.isVec4=((t.inChannels%4===0||t.inChannels%3===0)&&this.isChannelsLast||t.outWidth%4===0&&!this.isChannelsLast)&&t.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&t.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=e%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let t=this.isVec4?Fp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Pp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),e=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` ${bue(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,e[0],e[1],e[2])} ${t} `}};var gx=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e,this.activation=o,this.hasPreluActivationWeights=n,e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` ${fr(this.activation,this.hasPreluActivationWeights,!1,4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); if (coordsInBounds4D(coords, uniforms.xShape)) { return getX(batch, row, col, chan); } else { return 0.0; } } fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{ let coords = vec4(row, col, xChannel, outChannel); if(coordsInBounds4D(coords, uniforms.wShape)) { return getW(row, col, xChannel, outChannel); } else { return 0.0; } } fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) { let coords = ${this.isChannelsLast?"vec4(batch, row, col, chan);":"vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; ${Zr(this.addBias,this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } ${G("index")} { let coords = getOutputCoords(); let batch = coords[0]; let outChannel = ${this.isChannelsLast?"coords[3];":"coords[1];"} let outRow = ${this.isChannelsLast?"coords[1];":"coords[2];"} let outCol = ${this.isChannelsLast?"coords[2];":"coords[3];"} var acc : f32 = 0.0; for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) { for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) { let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0]; let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1]; for (var xChannel = 0; xChannel < ${this.isChannelsLast?"uniforms.xShape[3];":"uniforms.xShape[1];"} xChannel = xChannel + 1) { ${this.isChannelsLast?"let v = readInp(batch, xRow, xCol, xChannel);":"let v = readInp(batch, xChannel, xRow, xCol);"} let f = readFilt(row, col, xChannel, outChannel); acc = acc + v * f; } } } writeResult(batch, outRow, outCol, outChannel, acc); } `}};var xx=class{constructor(t,e){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let t=this.isChannelsLast?1:2,e=this.isChannelsLast?2:3,o=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",s=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` ${G("index")} { let coords = getCoordsFromIndex(index); if(index < uniforms.size) { let batch = coords[0]; let row = ${o}; let col = ${n}; let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0]; let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow); var value = 0.0; if(xRow < uniforms.xShape[${t}] && xRow >= 0) { let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] - uniforms.pads[1]; let xCol = offsetX + uniforms.dilations[1] * ((col % uniforms.itemsPerBlockRow) / uniforms.inChannels); let ch = col % uniforms.inChannels; if(xCol < uniforms.xShape[${e}] && xCol >= 0) { value = ${s}; } } setOutputAtIndex(index, value); } } `}};function yx(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function Cue({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=e.dataFormat==="channelsLast",u=!p,c=!1,l=p&&e.filterHeight===e.inHeight&&e.filterWidth===e.inWidth&&e.padInfo.type==="VALID",m=[],d,f;if(l){let x=e.inHeight*e.inWidth*e.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,e.batchSize,x]}}),f=pe({inputs:{x:t},backend:o,attrs:{shape:[1,x,e.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[e.batchSize,e.inHeight*e.inWidth,e.inChannels]:[e.batchSize,e.inChannels,e.inHeight*e.inWidth]}}),f=pe({inputs:{x:t},backend:o,attrs:{shape:[1,e.inChannels,e.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=yx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=yx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Op({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:e.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function wue({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=e,C=b==="channelsLast",S=p*u*c,k=h*f,_=C?[e.batchSize,k,S]:[e.batchSize,S,k],E=new xx(_,C),R=[{type:"int32",data:[d.top,d.left]},{type:"int32",data:[m,l]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[c*p]},{type:"int32",data:[c]}],D=o.runWebGPUProgram(E,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:t},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=yx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=yx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=Op({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:e.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function bx({x:r,filter:t,convInfo:e,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=e.dataFormat==="channelsLast",l=c&&e.filterHeight===e.inHeight&&e.filterWidth===e.inWidth&&e.padInfo.type==="VALID",m=A().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!m&&(l||e.filterHeight===1&&e.filterWidth===1&&e.dilationHeight===1&&e.dilationWidth===1&&e.strideHeight===1&&e.strideWidth===1&&(e.padInfo.type==="SAME"||e.padInfo.type==="VALID")))return Cue({x:r,filter:t,convInfo:e,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=d>-1?d:o.thresholdToIncreaseWorkgroups,h=e.batchSize*Math.ceil(e.outHeight*e.outWidth/32)*Math.ceil(e.outChannels/32);if(A().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||h<=f)return wue({x:r,filter:t,convInfo:e,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[e.padInfo.top,e.padInfo.left],b=[{type:"int32",data:[e.filterHeight,e.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[e.strideHeight,e.strideWidth]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]}];if(m)g=new gx(e,p,i,u);else{let _=c?e.outHeight*e.outWidth:e.outChannels,E=c?e.outChannels:e.outHeight*e.outWidth,R=e.filterHeight*e.filterWidth*e.inChannels;b.push({type:"int32",data:[_]},{type:"int32",data:[E]},{type:"int32",data:[R]});let D=o.adapterInfo.isIntel();g=new hx(e,_,E,R,p,i,u,D)}let C=[],S=[r,t];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i==="leakyrelu"&&(b.push({type:"float32",data:[a]}),g.uniforms+=" alpha : f32,");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function Sue(r){let{inputs:t,attrs:e,backend:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=e,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return bx({x:n,filter:s,convInfo:m,backend:o})}var wV={kernelName:rn,backendName:"webgpu",kernelFunc:Sue};var Cx=class{constructor(t){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=t.inShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&t.outChannels%4===0&&t.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let t=this.isChannelsLast?1:2,e=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=` ${G()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; let c = i32(globalId.y) * ${this.workPerThread}; let d1 = i32(globalId.x) * 4; let dyCorner = vec2(r, c) - uniforms.pads; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. var dotProd: array, ${this.workPerThread}>; for (var i = 0; i < ${this.workPerThread}; i++) { dotProd[i] = vec4(0.0); } for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) { let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x); let wRPerm = uniforms.filterDims.x - 1 - wR; if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0) { continue; } let idyR = i32(dyR); for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) { let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y); let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y); let wCPerm = uniforms.filterDims.y - 1 - wC; var bDyCVal = true; var bDyCVal2 = true; if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) || fract(dyC) > 0.0) { bDyCVal = false; } if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) || fract(dyC2) > 0.0) { bDyCVal2 = false; } let idyC = i32(dyC); let idyC2 = i32(dyC2); if (bDyCVal && bDyCVal2) { let d2Length = uniforms.outBackprop[3]; for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) { let wValue0 = getW(wRPerm, wCPerm, d1, d2); let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2); let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2); let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2); var xValue = getDy(batch, idyR, idyC, d2); let tmpval = vec4(dot(xValue, wValue0), dot(xValue, wValue1), dot(xValue, wValue2), dot(xValue, wValue3)); dotProd[0] = dotProd[0] + tmpval; xValue = getDy(batch, idyR, idyC2, d2); dotProd[1] = dotProd[1] + vec4(dot(xValue, wValue0), dot(xValue, wValue1), dot(xValue, wValue2), dot(xValue, wValue3)); } } else if (bDyCVal) { let d2Length = uniforms.outBackprop[3]; for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) { let wValue0 = getW(wRPerm, wCPerm, d1, d2); let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2); let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2); let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2); var xValue = getDy(batch, idyR, idyC, d2); let tmpval = vec4(dot(xValue, wValue0), dot(xValue, wValue1), dot(xValue, wValue2), dot(xValue, wValue3)); dotProd[0] = dotProd[0] + tmpval; } } else if (bDyCVal2) { let d2Length = uniforms.outBackprop[3]; for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) { let wValue0 = getW(wRPerm, wCPerm, d1, d2); let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2); let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2); let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2); var xValue = getDy(batch, idyR, idyC2, d2); let tmpval = vec4(dot(xValue, wValue0), dot(xValue, wValue1), dot(xValue, wValue2), dot(xValue, wValue3)); dotProd[1] = dotProd[1] + tmpval; } } } } for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]); } } } `;return this.isVec4?` ${n} `:` ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d1 = coords[${o}]; let dyCorner = vec2(coords[${t}], coords[${e}]) - uniforms.pads; let dyRCorner = dyCorner.x; let dyCCorner = dyCorner.y; // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). // ? = to be determined. : = across all values in that axis. var dotProd = 0.0; for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) { let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x); let wRPerm = uniforms.filterDims.x - 1 - wR; if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 || wRPerm < 0) { continue; } let idyR = i32(dyR); for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) { let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y); let wCPerm = uniforms.filterDims.y - 1 - wC; if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) || fract(dyC) > 0.0 || wCPerm < 0) { continue; } let idyC = i32(dyC); for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) { let xValue = ${this.isChannelsLast?"getDy(batch, idyR, idyC, d2)":"getDy(batch, d2, idyR, idyC)"}; let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } } } setOutputAtIndex(index, dotProd); } } `}},wx=class{constructor(t){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let wR = coords[0]; let wC = coords[1]; let d1 = coords[2]; let d2 = coords[3]; // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). // ? = to be determined. : = across all values in that axis. var dotProd = 0.0; for (var b = 0; b < uniforms.batchSize; b = b + 1) { for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) { let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0]; if (xR < 0 || xR >= uniforms.inHeight) { continue; } for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) { let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1]; if (xC < 0 || xC >= uniforms.inWidth) { continue; } if (${this.isChannelsLast}) { let dyValue = getDy(b, yR, yC, d2); let xValue = getX(b, xR, xC, d1); dotProd = dotProd + xValue * dyValue; } else { let dyValue = getDy(b, d2, yR, yC); let xValue = getX(b, d1, xR, xC); dotProd = dotProd + xValue * dyValue; } } } } setOutputAtIndex(index, dotProd); } } `}},Sx=class{constructor(t){this.variableNames=["x","dy"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32, outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerFilter"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let wF = coords.x; let wR = coords.y; let wC = coords.z; let d1 = coords.w; let d2 = coords.u; var dotProd = 0.0; for (var b = 0; b < uniforms.batchSize; b++) { for (var yF = 0; yF < uniforms.outDepth; yF++) { let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0]; if (xF < 0 || xF >= uniforms.inDepth) { continue; } for (var yR = 0; yR < uniforms.outHeight; yR++) { let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1]; if (xR < 0 || xR >= uniforms.inHeight) { continue; } for (var yC = 0; yC < uniforms.outWidth; yC++) { let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2]; if (xC < 0 || xC >= uniforms.inWidth) { continue; } let dyValue = getDy(b, yF, yR, yC, d2); let xValue = getX(b, xF, xR, xC, d1); dotProd += xValue * dyValue; } } } } setOutputAtIndex(index, dotProd); } } `}},Ix=class{constructor(t){this.variableNames=["dy","W"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3, outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3DDerInput"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; let d1 = coords.u; let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads; let dyFCorner = dyCorner.x; let dyRCorner = dyCorner.y; let dyCCorner = dyCorner.z; var dotProd = 0.0; for (var wF = 0; wF < uniforms.filterDims[0]; wF++) { let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]); if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) { continue; } let idyF = i32(dyF); let wFPerm = uniforms.filterDims[0] - 1 - wF; for (var wR = 0; wR < uniforms.filterDims[1]; wR++) { let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]); if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) { continue; } let idyR = i32(dyR); let wRPerm = uniforms.filterDims[1] - 1 - wR; for (var wC = 0; wC < uniforms.filterDims[2]; wC++) { let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]); if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) { continue; } let idyC = i32(dyC); let wCPerm = uniforms.filterDims[2] - 1 - wC; for (var d2 = 0; d2 < uniforms.outChannels; d2++) { let xValue = getDy(batch, idyF, idyR, idyC, d2); let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutputAtIndex(index, dotProd); } } `}};function Iue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new wx(m),f=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]},{type:"int32",data:[m.inHeight]},{type:"int32",data:[m.inWidth]}];return e.runWebGPUProgram(d,[n,s],n.dtype,f)}var SV={kernelName:Oi,backendName:"webgpu",kernelFunc:Iue};function vue(r=4){let t=s=>{switch(s){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` let coord1 = vec4(coordX, coordY, col + 1, rowInner); let coord2 = vec4(coordX, coordY, col + 2, rowInner); let coord3 = vec4(coordX, coordY, col + 3, rowInner); let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)]; let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)]; let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)]; let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)]; return vec4(v0, v1, v2, v3); `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${` let outRow = row / uniforms.outShape[2]; let outCol = row % uniforms.outShape[2]; let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]); let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1]; let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]); let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]); if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) { return ${Ae(r)}(0.0); } if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) { return ${Ae(r)}(0.0); } let coord = vec4( batch, i32(xR), i32(xC), col % uniforms.outBackprop[3]); return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`} } return ${Ae(r)}(0.0);`;return` fn mm_readA(batch: i32, row : i32, col : i32) -> ${Ae(r)} { ${o} } fn mm_readB(batch: i32, row : i32, col : i32) -> ${Ae(r)} { let coordX = uniforms.filterDims.x - 1 - row / (uniforms.filterDims[1] * uniforms.outBackprop[3]); let coordY = uniforms.filterDims.y - 1 - (row / uniforms.outBackprop[3]) % uniforms.filterDims[1]; if (row < uniforms.dimInner && col < uniforms.dimBOuter && coordX >= 0 && coordY >= 0) { let rowInner = row % uniforms.outBackprop[3]; let coord = vec4(coordX, coordY, col, rowInner); ${t(r)} } return ${Ae(r)}(0.0); } fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Ae(r)}) { if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { var value = valueInput; let outCoord = vec4( batch, row / uniforms.outShape[2], row % uniforms.outShape[2], col); result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value; } }`}var vx=class{constructor(t){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t.inShape,y.assert(t.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=t.inChannels%4===0&&t.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let t=this.isVec4?Fp(this.elementsPerThread,this.workgroupSize):Pp(this.elementsPerThread,this.workgroupSize);return` ${vue(this.isVec4?4:1)} ${t} `}};function kue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:"int32",data:[m.filterHeight,m.filterWidth]},{type:"int32",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||m.dataFormat!=="channelsLast")f=new Cx(m);else{f=new vx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:"uint32",data:[h]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return e.runWebGPUProgram(f,[n,s],"float32",d)}var IV={kernelName:on,backendName:"webgpu",kernelFunc:kue};var kx=class{constructor(t){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords.x; let d2 = coords.u; let xFRCCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads; let xFCorner = xFRCCorner.x; let xRCorner = xFRCCorner.y; let xCCorner = xFRCCorner.z; let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4; let inputDepthVec4Remainder = uniforms.xShape.u % 4; var dotProd = 0.0; for (var wF = 0; wF < uniforms.filterDims[0]; wF++) { let xF = xFCorner + wF * uniforms.dilations[0]; if (xF < 0 || xF >= uniforms.xShape.y) { continue; } for (var wR = 0; wR < uniforms.filterDims[1]; wR++) { let xR = xRCorner + wR * uniforms.dilations[1]; if (xR < 0 || xR >= uniforms.xShape.z) { continue; } for (var wC = 0; wC < uniforms.filterDims[2]; wC++) { let xC = xCCorner + wC * uniforms.dilations[2]; if (xC < 0 || xC >= uniforms.xShape.w) { continue; } for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) { let xValues = vec4( 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 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (inputDepthVec4Remainder == 1) { dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) * getW(wF, wR, wC, inputDepthNearestVec4, d2); } else if (inputDepthVec4Remainder == 2) { let xValues = vec2( getX(batch, xF, xR, xC, inputDepthNearestVec4), getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1) ); let wValues = vec2( getW(wF, wR, wC, inputDepthNearestVec4, d2), getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2) ); dotProd += dot(xValues, wValues); } else if (inputDepthVec4Remainder == 3) { let xValues = vec3( getX(batch, xF, xR, xC, inputDepthNearestVec4), getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1), getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2) ); let wValues = vec3( getW(wF, wR, wC, inputDepthNearestVec4, d2), getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2), getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutputAtIndex(index, dotProd); } }`}};function Nue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new kx(u),d=dt(n.dtype,s.dtype);return e.runWebGPUProgram(m,[n,s],d,l)}var vV={kernelName:nn,backendName:"webgpu",kernelFunc:Nue};function Tue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Sx(u),l=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return e.runWebGPUProgram(c,[n,s],s.dtype,l)}var kV={kernelName:Xa,backendName:"webgpu",kernelFunc:Tue};function _ue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Ix(u),l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return e.runWebGPUProgram(c,[n,s],n.dtype,l)}var NV={kernelName:sn,backendName:"webgpu",kernelFunc:_ue};var $ue=ye({opType:Z.COS}),TV={kernelName:an,backendName:"webgpu",kernelFunc:$ue};var Eue=ye({opType:Z.COSH}),_V={kernelName:un,backendName:"webgpu",kernelFunc:Eue};var Nx=class{constructor(t,e,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=e;this.outputShape=[s,o[0],o[1],t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[t,e]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${t} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${t} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${t}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${e} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${e} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${e}`];return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let height_ratio = f32(${o}); let width_ratio = f32(${a}); let b = coords[0]; let y = coords[1]; let x = coords[2]; let d = coords[3]; // get box vals let y1 = getBoxes(b, 0); let x1 = getBoxes(b, 1); let y2 = getBoxes(b, 2); let x2 = getBoxes(b, 3); // get image in batch index let bInd = i32(round(getBoxInd(b))); if(bInd < 0 || bInd >= uniforms.outShape[0]) { return; } let height_scale = ${n}; let width_scale = ${i}; let in_y = ${s}; if( in_y < 0.0 || in_y > ${t} ) { setOutputAtIndex(index, uniforms.extrapolationValue); return; } let in_x = ${p}; if( in_x < 0.0 || in_x > ${e} ) { setOutputAtIndex(index, uniforms.extrapolationValue); return; } let sourceFracIndexCR = vec2(in_x,in_y); if(${this.methodId} == 1) { // Compute the four integer indices. let sourceFloorCR = vec2(sourceFracIndexCR); let sourceCeilCR = vec2(ceil(sourceFracIndexCR)); let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d); let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d); let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d); let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d); let fracCR = sourceFracIndexCR - vec2(sourceFloorCR); let top = topLeft + (topRight - topLeft) * fracCR.x; let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; let newValue = top + (bottom - top) * fracCR.y; setOutputAtIndex(index, newValue); } else { // Compute the coordinators of nearest neighbor point. let sourceNearestCR = vec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); let newValue = getImage( bInd, sourceNearestCR.y, sourceNearestCR.x, d); setOutputAtIndex(index, newValue); } } } `}};var Rue=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Nx(n.shape[3],s.shape,i,p),l=[{type:"float32",data:[u]}];return e.runWebGPUProgram(c,[n,s,a],"float32",l)},$V={kernelName:ln,backendName:"webgpu",kernelFunc:Rue};var Lp;(function(r){r.Prod="*",r.Sum="+"})(Lp||(Lp={}));var xm=class{constructor(t,e,o,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=t,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let t=this.outputShape.length,e=this.op===Lp.Prod?"1.0":"0.0",o=this.exclusive?e:`getX(${EV(t,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],s="",a="";return this.exclusive?(s=this.reverse?`end != ${n-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(s=this.reverse?`end + pow2 < ${n}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),` ${G("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); let end = ${RV(t,"coords",this.op)}; var val = ${o}; let pow2 = i32(pow(2.0, uniforms.index)); if (${s}) { let idx = ${a}; ${RV(t,"coords",this.op)} = idx; val ${this.op}= getX(${EV(t,"coords",this.op)}); } setOutputAtIndex(index, val); } } `}};function EV(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function RV(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function Tx(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=yr({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Ft({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new xm(r,p.shape,!1,s),f=l,h=[{type:"float32",data:[m]}];l=e.runWebGPUProgram(d,[l],l.dtype,h),e.disposeData(f.dataId)}if(n){let m=new xm(r,p.shape,n,s),d=l,f=[{type:"float32",data:[0]}];l=e.runWebGPUProgram(m,[l],l.dtype,f),e.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=yr({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeData(l.dataId),e.disposeData(p.dataId),d}return l}function Due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Tx(Lp.Prod,n,e,s,a,i)}var DV={kernelName:pn,backendName:"webgpu",kernelFunc:Due};function Aue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Tx(Lp.Sum,n,e,s,a,i)}var AV={kernelName:cn,backendName:"webgpu",kernelFunc:Aue};function Fue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=kt({backend:e,attrs:{shape:d,value:0,dtype:l}}),h=new Jc(m,c,i),g=[{type:"int32",data:[a]}],x=c?[n,s]:[n];return e.runWebGPUProgram(h,x,l,g,f)}var FV={kernelName:ra,backendName:"webgpu",kernelFunc:Fue};var _x=class{constructor(t,e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${e}`,this.dataFormat=e}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; let h = ${this.getHeightCoordString()}; let w = ${this.getWidthCoordString()}; let d = ${this.getDepthCoordString()}; let in_h = h / uniforms.blockSize; let offset_h = h % uniforms.blockSize; let in_w = w / uniforms.blockSize; let offset_w = w % uniforms.blockSize; let offset_d = (offset_h * uniforms.blockSize + offset_w) * ${this.getOutputDepthSize()}; let in_d = d + offset_d; let rlt = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Pue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=[{type:"int32",data:[s]}],g=new _x(f,a);return e.runWebGPUProgram(g,[n],n.dtype,h)}var PV={kernelName:mn,backendName:"webgpu",kernelFunc:Pue};var $x=class{constructor(t,e,o,n=!1,s=null,a=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=e,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let t=this.filterWidth*this.filterHeight,e=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` ${fr(this.activation,this.hasPreluActivation,!1,4)} var mm_Asub : array, ${o}>; var mm_Bsub : array, ${this.filterHeight}>; fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 { var value = 0.0; if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1]) { value = getX(batch, channel, row, col); } return value; } ${G()} { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.zw) - uniforms.pads; let channelMul = uniforms.wShape[3]; let d1 = coords[1] / channelMul; let q = coords[1] % channelMul; let inputRowStart = xRCCorner.x; let inputColStart = xRCCorner.y; let localRow = i32(localId.y); let localCol = i32(localId.x); // Load one tile of X into local memory. for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) { for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) { let rowOffset = inputRow - localRow; let colOffset = inputCol - localCol; mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset); } } // Load one tile of W into local memory. var wIndex = i32(localIndex); ${t, inDims : vec2, virtualWidth : i32,",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=t.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=H(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(t.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=t,this.addBias=e,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let t=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,e=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return` ${fr(this.activation,this.hasPreluActivation,!0,4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); if (col >=0 && col < uniforms.inDims[1]) { value = getX(batch, row, col, channel); } return value; } ${G("index")} { let width0 = uniforms.outShape[3] / ${this.outputComponent}; let d1 = (index % width0) * ${this.outputComponent}; var index1 = index / width0; let width1 = uniforms.virtualWidth / ${this.workPerThread}; let c = (index1 % width1) * ${this.workPerThread}; index1 = index1 / width1; let r = index1 % uniforms.outShape[1]; let batch = index1 / uniforms.outShape[1]; let xRCCorner = vec2(r, c) * vec2(${e}, ${o}) - uniforms.pads; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; var xVals : array, ${t}>; var dotProd : array, ${this.workPerThread}>; for (var i = 0; i < ${this.workPerThread}; i++) { dotProd[i] = vec4(0.0); } // Use constant instead of uniform can give better performance. for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) { let xR = xRCorner + wR; if (xR >=0 && xR < uniforms.inDims[0]) { for (var i = 0; i < ${t}; i++) { xVals[i] = readX(batch, xR, xCCorner + i, d1); } for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) { let wValue = getW(wR, wC, d1, 0); for (var i = 0; i < ${this.workPerThread}; i++) { dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]); } } } } for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; ${Zr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } `}};var rl=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat==="channelsLast",e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=t,this.addBias=e,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let t=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` ${fr(this.activation,this.hasPreluActivation,!1,4)} ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.${this.isChannelsLast?"yz":"zw"}) * uniforms.strides - uniforms.pads; let d2 = coords[${this.isChannelsLast?3:1}]; let channelMul = uniforms.wShape[3]; let d1 = d2 / channelMul; let q = d2 % channelMul; let inputRowStart = xRCCorner.x; let inputColStart = xRCCorner.y; let inputRowEnd = inputRowStart + uniforms.filterHeight * uniforms.dilations[0]; let inputColEnd = inputColStart + uniforms.filterWidth * uniforms.dilations[1]; // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC. // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW. var value = 0.0; // Extract if checking out of for loop for performance. if (inputRowStart >= 0 && inputColStart >= 0 && inputRowEnd < uniforms.inDims[0] && inputColEnd < uniforms.inDims[1]) { for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) { let xR = inputRowStart + wR * uniforms.dilations[0]; for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) { let xC = inputColStart + wC * uniforms.dilations[1]; let xVal = ${t}; let wVal = getW(wR, wC, d1, q); value = value + xVal * wVal; } } } else { for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) { let xR = inputRowStart + wR * uniforms.dilations[0]; if (xR < 0 || xR >= uniforms.inDims[0]) { continue; } for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) { let xC = inputColStart + wC * uniforms.dilations[1]; if (xC < 0 || xC >= uniforms.inDims[1]) { continue; } let xVal = ${t}; let wVal = getW(wR, wC, d1, q); value = value + xVal * wVal; } } } ${Zr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } `}};function Oue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inHeight,d.inWidth]}],h=d.dataFormat==="channelsLast",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new $x(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new tl(d),f.push({type:"int32",data:[g.virtualWidth]})):(g=new rl(d),f.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]})),e.runWebGPUProgram(g,[n,s],n.dtype,f)}var OV={kernelName:dn,backendName:"webgpu",kernelFunc:Oue};var Ex=class{constructor(t){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_filter"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let wR = coords[0]; let wC = coords[1]; let d1 = coords[2]; let dm = coords[3]; let d2 = d1 * uniforms.channelMul + dm; var dotProd = 0.0; for (var b = 0; b < uniforms.batchSize; b++) { for (var yR = 0; yR < uniforms.outHeight; yR++) { let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0]; if (xR < 0 || xR >= uniforms.inHeight) { continue; } for (var yC = 0; yC < uniforms.outWidth; yC++) { let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1]; if (xC < 0 || xC >= uniforms.inWidth) { continue; } let dyValue = getDy(b, yR, yC, d2); let xValue = getX(b, xR, xC, d1); dotProd += xValue * dyValue; } } } setOutputAtIndex(index, dotProd); } } `}},Rx=class{constructor(t){this.variableNames=["dy","W"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_input"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d1 = coords[3]; let dyCorner = coords.yz - uniforms.pads; let dyRCorner = dyCorner.x; let dyCCorner = dyCorner.y; var dotProd = 0.0; for (var wR = 0; wR < uniforms.filterDims[0]; wR++) { let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]); if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) { continue; } let idyR = i32(dyR); let wRPerm = uniforms.filterDims[0] - 1 - wR; for (var wC = 0; wC < uniforms.filterDims[1]; wC++) { let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]); if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) { continue; } let idyC = i32(dyC); let wCPerm = uniforms.filterDims[1] - 1 - wC; for (var dm = 0; dm < uniforms.channelMul; dm++) { let d2 = d1 * uniforms.channelMul + dm; let xValue = getDy(batch, idyR, idyC, d2); let wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutputAtIndex(index, dotProd); } } `}};function Mue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Ex(l),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"int32",data:[l.inHeight]},{type:"int32",data:[l.inWidth]},{type:"int32",data:[l.batchSize]},{type:"int32",data:[l.outChannels/l.inChannels]}];return e.runWebGPUProgram(m,[n,s],"float32",d)}var MV={kernelName:Mi,backendName:"webgpu",kernelFunc:Mue};function Lue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Rx(l),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"int32",data:[l.outChannels/l.inChannels]}];return e.runWebGPUProgram(m,[n,s],n.dtype,d)}var LV={kernelName:Li,backendName:"webgpu",kernelFunc:Lue};var Dx=class{constructor(t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let value = select(0.0, getX(coords[0]), coords[0] == coords[1]); setOutputAtIndex(index, value); } } `}};function Bue(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Dx(s),p=e.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeData(a.dataId),e.disposeData(p.dataId),u}var BV={kernelName:oa,backendName:"webgpu",kernelFunc:Bue};var Ax=class{constructor(t){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let neg_infinity = -3.4e38; let coords = getOutputCoords(); let batch = coords.x; let d1 = coords.w; let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads; let hBeg = outTopLeftCorner.x; let wBeg = outTopLeftCorner.y; var curVal = neg_infinity; for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) { let hIn = hBeg + h * uniforms.dilations[0]; if (hIn >= 0 && hIn < uniforms.xShape[1]) { for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) { let wIn = wBeg + w * uniforms.dilations[1]; if (wIn >= 0 && wIn < uniforms.xShape[2]) { let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1); if (val > curVal) { curVal = val; } } } } } setOutputAtIndex(index, curVal); } } `}};function zue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],m=new Ax(u);return e.runWebGPUProgram(m,[n,s],n.dtype,l)}var zV={kernelName:fn,backendName:"webgpu",kernelFunc:zue};var Fx=class{constructor(t,e){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t.inShape,this.dispatchLayout=X(t.outShape),this.dispatch=H(this.dispatchLayout,t.outShape,this.workgroupSize),e!=="float32"&&e!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 types, does not support ${e} type.`);this.type=e,this.shaderKey="dilation2DBackpropInput"}getUserCode(){return` ${G("index")} { if (index < uniforms.dySize) { let coords = getDyCoordsFromIndex(index); let b = coords[0]; let r = coords[1]; let c = coords[2]; let d = coords[3]; let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads; var curVal = -3.4e38; // neg_infinity var xRMax = 0; var xCMax = 0; // In the case of multiple argmax branches, we only back-propagate // along the last branch, i.e., the one with largest value of // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling // backward routines. for (var wR = 0; wR < uniforms.filterDims[0]; wR++) { let xR = dyCorner.x + wR * uniforms.dilations[0]; if (xR >= 0 && xR < uniforms.xShape[1]) { for (var wC = 0; wC < uniforms.filterDims[1]; wC++) { let xC = dyCorner.y + wC * uniforms.dilations[1]; if (xC >= 0 && xC < uniforms.xShape[2]) { let val = getX(b, xR, xC, d) + getW(wR, wC, d); if (val > curVal) { curVal = val; xRMax = xR; xCMax = xC; } } } } } let flatIndexIn = d + uniforms.xShape[3] * (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b)); let value = getDy(b, r, c, d); ${Qr("&result[flatIndexIn]","value",this.type)} } } `}},Px=class{constructor(t,e,o){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(t.outShape),this.dispatch=H(this.dispatchLayout,t.outShape,this.workgroupSize),o!=="float32"&&o!=="int32")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 types, does not support ${o} type.`);this.type=o,this.shaderKey="dilation2DBackpropFilter"}getUserCode(){return` ${G("index")} { if (index < uniforms.dySize) { let coords = getDyCoordsFromIndex(index); let b = coords[0]; let r = coords[1]; let c = coords[2]; let d = coords[3]; let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads; var curVal = -3.4e38; // neg_infinity var wRMax = 0; var wCMax = 0; // In the case of multiple argmax branches, we only back-propagate // along the last branch, i.e., the one with largest value of // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling // backward routines. for (var wR = 0; wR < uniforms.filterDims[0]; wR++) { let xR = dyCorner.x + wR * uniforms.dilations[0]; if (xR >= 0 && xR < uniforms.xShape[1]) { for (var wC = 0; wC < uniforms.filterDims[1]; wC++) { let xC = dyCorner.y + wC * uniforms.dilations[1]; if (xC >= 0 && xC < uniforms.xShape[2]) { let val = getX(b, xR, xC, d) + getW(wR, wC, d); if (val > curVal) { curVal = val; wRMax = wR; wCMax = wC; } } } } } let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]); let value = getDy(b, r, c, d); ${Qr("&result[flatIndexIn]","value",this.type)} } } `}};function Vue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=s.dtype,m=new Px(c,s.shape,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=kt({backend:e,attrs:{shape:s.shape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var VV={kernelName:zi,backendName:"webgpu",kernelFunc:Vue};function Wue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=n.dtype,m=new Fx(c,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=kt({backend:e,attrs:{shape:c.inShape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var WV={kernelName:Bi,backendName:"webgpu",kernelFunc:Wue};var Ox=class{constructor(t,e,o){this.variableNames=["Image"],this.uniforms="alpha: f32,",this.workgroupSize=[64,1,1],this.pixelsOpType=Ii.DRAW,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=e,this.textureFormat=o,this.shaderKey=`draw_${e}_${o}`}getUserCode(){let t,e=this.type==="float32"?"value":"value / 255.0";return t=` if (uniforms.numChannels == 1) { rgba[0] = ${e}; rgba[1] = ${e}; rgba[2] = ${e}; } else { rgba[d] = ${e}; }`,` @group(0) @binding(0) var outImage : texture_storage_2d<${this.textureFormat}, write>; ${G("index")} { if (index < uniforms.size) { var rgba = vec4(0.0, 0.0, 0.0, uniforms.alpha); for (var d = 0; d < uniforms.numChannels; d = d + 1) { let value = f32(inBuf[index * uniforms.numChannels + d]); ${t} } rgba.x = rgba.x * rgba.w; rgba.y = rgba.y * rgba.w; rgba.z = rgba.z * rgba.w; let coords = getCoordsFromIndex(index); textureStore(outImage, vec2(coords.yx), rgba); } } `}};function Uue(r){let{inputs:t,backend:e,attrs:o}=r,{image:n}=t,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=e.device.features.has("bgra8unorm-storage")?"bgra8unorm":"rgba8unorm",m=[i,p],d=new Ox(m,n.dtype,l);s.width=p,s.height=i;let f="webgpu",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:e.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:"premultiplied"});let b="int32",C=e.makeTensorInfo(m,b),S=e.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:"uint32",data:[x]},{type:"float32",data:[c]}];if(e.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext("2d");if(!_)throw new Error("Please make sure this canvas has only been used for 2d or webgpu context!");_.drawImage(g,0,0)}return e.disposeData(C.dataId),n}var UV={kernelName:Pu,backendName:"webgpu",kernelFunc:Uue};var s0=et({opType:fe.MUL,cpuKernelImpl:vz,supportsComplex:!0}),GV={kernelName:Yn,backendName:"webgpu",kernelFunc:s0};function a0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return eo(n,s,a,"sum",e)}var HV={kernelName:Is,backendName:"webgpu",kernelFunc:a0};function Gue(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=a0({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeData(h.dataId);return m}var KV={kernelName:Vi,backendName:"webgpu",kernelFunc:Gue};var Hue=ye({opType:Z.ELU}),qV={kernelName:gn,backendName:"webgpu",kernelFunc:Hue};var Kue=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=new ki(fe.ELU_DER,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],o.dtype)},jV={kernelName:Ya,backendName:"webgpu",kernelFunc:Kue};var que=et({opType:fe.EQUAL,dtype:"bool",cpuKernelImpl:pz}),XV={kernelName:yn,backendName:"webgpu",kernelFunc:que};var jue=ye({opType:Z.ERF}),YV={kernelName:xn,backendName:"webgpu",kernelFunc:jue};var Xue=ye({opType:Z.EXP,cpuKernelImpl:cz,dtype:"float32"}),QV={kernelName:bn,backendName:"webgpu",kernelFunc:Xue};function Mx(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var ZV={kernelName:na,backendName:"webgpu",kernelFunc:Mx};var Yue=ye({opType:Z.EXPM1,cpuKernelImpl:lz}),JV={kernelName:Cn,backendName:"webgpu",kernelFunc:Yue};var ym=class{constructor(t,e){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=t,this.shaderKey=`fft_${t}`}getUserCode(){return` fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 { ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"} } fn mulMatDFT(batch: i32, index: i32) -> f32 { let indexRatio = f32(index) / f32(uniforms.realShape[1]); let exponentMultiplierTimesIndexRatio = uniforms.exponentMultiplier * indexRatio; var result = 0.0; for (var i = 0; i < uniforms.realShape[1]; i = i + 1) { // x = (-2|2 * PI / N) * index * i; let x = exponentMultiplierTimesIndexRatio * f32(i); let expR = cos(x); let expI = sin(x); let real = getReal(batch, i); let imag = getImag(batch, i); result = result + unaryOpComplex(real, expR, imag, expI) / uniforms.denominator; } return result; } ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); } } `}};function Lx(r,t,e){let o=e.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:e,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ym("real",u),l=new ym("imag",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=e.runWebGPUProgram(c,m,"float32",h);i.push(g);let x=e.runWebGPUProgram(l,m,"float32",h);i.push(x);let b=yo({inputs:{real:g,imag:x},backend:e});i.push(b);let C=pe({inputs:{x:b},backend:e,attrs:{shape:r.shape}});return i.forEach(S=>e.disposeData(S.dataId)),C}function Que(r){let{inputs:t,backend:e}=r,{input:o}=t;return Lx(o,!1,e)}var eW={kernelName:Wi,backendName:"webgpu",kernelFunc:Que};var Bx=class{constructor(t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; let outputValue = getX(coords[0], coords[1], coordX, coords[3]); setOutputAtIndex(index, outputValue); } } `}};var tW={kernelName:wn,backendName:"webgpu",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new Bx(e.shape);return o.runWebGPUProgram(n,[e],e.dtype)}};var Zue=ye({opType:Z.FLOOR,cpuKernelImpl:mz}),rW={kernelName:Sn,backendName:"webgpu",kernelFunc:Zue};var Jue=et({opType:fe.FLOOR_DIV,cpuKernelImpl:dz,dtype:"int32"}),oW={kernelName:In,backendName:"webgpu",kernelFunc:Jue};var zx=class{constructor(t,e,o=!1){this.pixelsOpType=Ii.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[e,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let t=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d"}; ${G("index")} { let flatIndex = index * uniforms.numChannels; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); let values = ${t}; for (var i = 0; i < uniforms.numChannels; i = i + 1) { result[flatIndex + i] = i32(floor(255.0 * values[i])); } } } `}};var nW={kernelName:Mu,backendName:"webgpu",kernelFunc:epe},ol,i0=A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function epe(r){let{inputs:t,backend:e,attrs:o}=r,{pixels:n}=t,{numChannels:s}=o;if(n==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!="undefined"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d)b=e.device.importExternalTexture({source:n});else{if(f){let L=A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(ol==null||L!==i0)&&(i0=L,ol=document.createElement("canvas").getContext("2d",{willReadFrequently:i0})),ol.canvas.width=c,ol.canvas.height=l,ol.drawImage(n,0,0,c,l),n=ol.canvas}let P=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,O="rgba8unorm",M=e.textureManager.acquireTexture(m[1],m[0],O,P);e.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b=M}let C=y.sizeFromShape(m),S=y.computeStrides(m),k=new zx(m,s,d),_=[{type:"uint32",data:[C]},{type:"uint32",data:[s]},{type:"uint32",data:[...S]}],E=e.makeTensorInfo([l,c],"int32"),R=e.tensorMap.get(E.dataId);R.resource=b;let D=e.runWebGPUProgram(k,[E],"int32",_);return e.disposeData(E.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } `}};var sW={kernelName:vn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=t,u=e,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Vx(o.shape,a.shape,i.shape,l,m),f=[{type:"float32",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function tpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return bx({x:n,filter:s,convInfo:g,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var aW={kernelName:vo,backendName:"webgpu",kernelFunc:tpe};function rpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=t,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new tl(h,x,m,b),C.push({type:"int32",data:[S.virtualWidth]})):(S=new rl(h,x,m,b),C.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),m==="leakyrelu"&&(C.push({type:"float32",data:[d]}),S.uniforms+=" alpha : f32,"),e.runWebGPUProgram(S,g,"float32",C)}var iW={kernelName:ko,backendName:"webgpu",kernelFunc:rpe};var Wx=class{constructor(t,e){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${t}`,this.sliceDim=t,this.uniforms=`sliceDim : i32, strides : ${ht(t)},`}getUserCode(){let t;return this.sliceDim>1?t="uniforms.strides[j]":t="uniforms.strides",` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var flattenIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexTemp = i32(round(getIndices(coords[0], j))); let strideNum = ${t}; flattenIndex = flattenIndex + indexTemp * strideNum; } setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } `}};function ope(r){let{inputs:t,backend:e}=r,{params:o,indices:n}=t,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:e,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:e,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(e.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let b=e.readSync(n.dataId),C=e.bufferSync(o),S=fz(b,C,o.dtype,u,a,c,l,o.shape,i);return e.makeTensorInfo(p,o.dtype,S.values)}let f=new Wx(a,[u,c]),h=[{type:"int32",data:[a]},{type:"int32",data:l}],g=e.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:e,attrs:{shape:p}});return e.disposeData(m.dataId),e.disposeData(d.dataId),e.disposeData(g.dataId),x}var uW={kernelName:kn,backendName:"webgpu",kernelFunc:ope};var Ux=class{constructor(t,e){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.slice(),this.aShape=t,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let t=npe(this.aShape);return` ${G("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let indexZ = i32(getIndices(resRC.x, resRC.z)); let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]); setOutputAtIndex(index, inBounds * getA(${t})); } } `}};function npe(r){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],e=[];for(let o=0;oe.disposeData(D.dataId)),e.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Ux(m.shape,f),g=e.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:e,attrs:{shape:u.outputShape}});return l.forEach(b=>e.disposeData(b.dataId)),x}var pW={kernelName:aa,backendName:"webgpu",kernelFunc:u0};var spe=et({opType:fe.GREATER,cpuKernelImpl:xz,dtype:"bool"}),cW={kernelName:Nn,backendName:"webgpu",kernelFunc:spe};var ape=et({opType:fe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:gz}),lW={kernelName:Tn,backendName:"webgpu",kernelFunc:ape};function ipe(r){let{inputs:t,backend:e}=r,{input:o}=t;return Lx(o,!0,e)}var mW={kernelName:Ui,backendName:"webgpu",kernelFunc:ipe};var upe=ye({opType:Z.IS_FINITE,dtype:"bool"}),dW={kernelName:_n,backendName:"webgpu",kernelFunc:upe};var ppe=ye({opType:Z.IS_INF,dtype:"bool"}),fW={kernelName:$n,backendName:"webgpu",kernelFunc:ppe};var cpe=ye({opType:Z.IS_NAN,dtype:"bool"}),hW={kernelName:En,backendName:"webgpu",kernelFunc:cpe};function lpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{alpha:s}=o,a=[{type:"float32",data:[s]}],i=new Jr(n.shape,Z.LEAKYRELU,"alpha : f32,");return e.runWebGPUProgram(i,[n],"float32",a)}var gW={kernelName:Rn,backendName:"webgpu",kernelFunc:lpe};var mpe=et({opType:fe.LESS,dtype:"bool",cpuKernelImpl:bz}),xW={kernelName:Dn,backendName:"webgpu",kernelFunc:mpe};var dpe=et({opType:fe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:yz}),yW={kernelName:An,backendName:"webgpu",kernelFunc:dpe};var Gx=class{constructor(t){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); } } `}};function fpe(r){let{backend:t,attrs:e}=r,{start:o,stop:n,num:s}=e,a=(n-o)/(s-1),i=new Gx(s),p=[{type:"float32",data:[o]},{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],"float32",p)}var bW={kernelName:Fn,backendName:"webgpu",kernelFunc:fpe};var hpe=ye({opType:Z.LOG,cpuKernelImpl:Cz}),CW={kernelName:Pn,backendName:"webgpu",kernelFunc:hpe};var gpe=ye({opType:Z.LOG1P}),wW={kernelName:On,backendName:"webgpu",kernelFunc:gpe};var xpe=et({opType:fe.LOGICAL_AND,dtype:"bool"}),SW={kernelName:Mn,backendName:"webgpu",kernelFunc:xpe};var ype=ye({opType:Z.LOGICAL_NOT}),IW={kernelName:Ln,backendName:"webgpu",kernelFunc:ype};var bpe=et({opType:fe.LOGICAL_OR}),vW={kernelName:Bn,backendName:"webgpu",kernelFunc:bpe};var kW=` var powValue = 0.0; let basis = uniforms.bias + uniforms.alpha * sum; if (uniforms.beta == 0.5) { powValue = inverseSqrt(basis); } else if (uniforms.beta == 1.0) { powValue = 1.0 / basis; } else { powValue = exp(log(basis) * (-uniforms.beta)); } `,Hx=class{constructor(t){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; let r = coords[1]; let c = coords[2]; let d = coords[3]; let x = getX(b, r, c, d); var sum = 0.0; for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) { let idx = d + i; if (idx >= 0 && idx < uniforms.xShape[3]) { let z = getX(b, r, c, idx); sum = sum + z * z; } } ${kW} setOutputAtIndex(index, x * powValue); } } `}},Kx=class{constructor(t,e){this.outputShape=[],this.variableNames=["x"],this.uniforms="radius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(e<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${e}`),this.outputShape=t,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return` var lrnSub: array; const elementsPerWorkgroup = ${this.elementsPerWorkgroup}; const maxAllowRadius = ${this.maxAllowRadius}; ${G()} { let localDepth = i32(localId.x); let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup; let xDepth = workgroupDepth + localDepth - maxAllowRadius; let b = i32(globalId.z) / uniforms.xShape[1]; let r = i32(globalId.z) - b * uniforms.xShape[1]; let c = i32(globalId.y); let d = workgroupDepth + localDepth; var x = 0.0; if (xDepth >= 0 && xDepth < uniforms.xShape[3]) { x = getX(b, r, c, xDepth); } lrnSub[localDepth] = x; workgroupBarrier(); if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) { var sum = 0.0; let index = localDepth + maxAllowRadius; for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) { let z = lrnSub[index + i]; sum = sum + z * z; } ${kW} setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); } } `}};function Cpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Hx(n.shape):u=new Kx(n.shape,s);let c=[{type:"int32",data:[s]},{type:"float32",data:[a]},{type:"float32",data:[i]},{type:"float32",data:[p]}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var NW={kernelName:zn,backendName:"webgpu",kernelFunc:Cpe};var qx=class{constructor(t){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; let r = coords[1]; let c = coords[2]; let MIN_DEPTH_BEGIN = 0; let MAX_DEPTH_END = uniforms.outShape[3]; var result = 0.0; for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) { let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius); let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1); var norm = 0.0; for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) { if (k < depthBegin) { continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = uniforms.alpha * norm + uniforms.bias; for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) { if (k < depthBegin) { continue; } else if (k >= depthBegin && k < depthEnd) { var dyi = -2.0 * uniforms.alpha * uniforms.beta * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * uniforms.beta); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutputAtIndex(index, result); } } `}};function wpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qx(n.shape),m=[{type:"int32",data:[i]},{type:"float32",data:[p]},{type:"float32",data:[u]},{type:"float32",data:[c]}];return e.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var TW={kernelName:Qa,backendName:"webgpu",kernelFunc:wpe};var Spe=et({opType:fe.MAX,cpuKernelImpl:Sz}),_W={kernelName:Wn,backendName:"webgpu",kernelFunc:Spe};function Ipe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=w.computePool2DInfo(n.shape,s,a,u,i,p);return ax(n,c,"max",e)}var $W={kernelName:Un,backendName:"webgpu",kernelFunc:Ipe};function vpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new _u(l,"max"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return e.runWebGPUProgram(m,[n],n.dtype,d)}var EW={kernelName:ia,backendName:"webgpu",kernelFunc:vpe};var jx=class{constructor(t){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool2DBackprop"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d = coords[3]; let dyRCCorner = vec2(coords.yz) - uniforms.pads; let dyRCorner = dyRCCorner.x; let dyCCorner = dyRCCorner.y; // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d). // ? = to be determined. : = across all values in that axis. var dotProd = 0.0; let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1; for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) { let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]); if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) { continue; } let idyR = i32(dyR); for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) { let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]); if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) { continue; } let idyC = i32(dyC); let dyValue = getDy(batch, idyR, idyC, d); let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. let curPosValue = wR * uniforms.filterDims[1] + wC; let mask = select(0.0, 1.0, maxPosValue == curPosValue); dotProd += dyValue * mask; } } setOutputAtIndex(index, dotProd); } } `}},Xx=class{constructor(t){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3, outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool3DBackprop"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords.x; let ch = coords.u; let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads; let dyDCorner = dyCorner.x; let dyRCorner = dyCorner.y; let dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. var dotProd = 0.0; let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1; for (var wD = 0; wD < uniforms.filterDims[0]; wD++) { let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]); if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) { continue; } let idyD = i32(dyD); for (var wR = 0; wR < uniforms.filterDims[1]; wR++) { let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]); if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) { continue; } let idyR = i32(dyR); for (var wC = 0; wC < uniforms.filterDims[2]; wC++) { let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]); if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) { continue; } let idyC = i32(dyC); let dyValue = getDy(batch, idyD, idyR, idyC, ch); let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch)); // Get the current value, check it against the value from the // position matrix. let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC; let mask = select(0.0, 1.0, maxPosValue == curPosValue); dotProd += dyValue * mask; } } } setOutputAtIndex(index, dotProd); } } `}};function kpe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new _u(m,"max",!0),f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inDepth,m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=e.runWebGPUProgram(d,[a],"int32",f),g=new Xx(m);f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outDepth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=e.runWebGPUProgram(g,[n,h],a.dtype,f);return e.disposeData(h.dataId),x}var RW={kernelName:Ki,backendName:"webgpu",kernelFunc:kpe};function Npe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s,output:a}=t,i=s;fm([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new za(m,"max",!0),f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=e.runWebGPUProgram(d,[i],"int32",f),g=new jx(m);f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=e.runWebGPUProgram(g,[n,h],i.dtype,f);return e.disposeData(h.dataId),x}var DW={kernelName:Hi,backendName:"webgpu",kernelFunc:Npe};function Tpe(r){let{inputs:t,backend:e,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=t;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new za(c,"max",!1),d=e.runWebGPUProgram(m,[p],p.dtype,l);m=new za(c,"max",!0,!0,i);let f=e.runWebGPUProgram(m,[p],"int32",l);return[d,f]}var AW={kernelName:ua,backendName:"webgpu",kernelFunc:Tpe};function _pe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return eo(n,s,a,"min",e)}var FW={kernelName:Hn,backendName:"webgpu",kernelFunc:_pe};var $pe=et({opType:fe.MIN,cpuKernelImpl:Iz}),PW={kernelName:Kn,backendName:"webgpu",kernelFunc:$pe};var Yx=class{constructor(t,e,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.map((n,s)=>n[0]+t[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=t,e.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let t=this.xShape.length,e=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${t>1?`[${c}]`:""}`).join(","),n=t===1?"start":"start[i]",s=t===1?"end":"end[i]",a=t===1?"outC":"outC[i]",i=ht(t),p=t>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,t):"coords";return` ${G("index")} { if (index < uniforms.size) { let start = ${i}(${e}); let end = ${i}(${o}); var outC = getCoordsFromIndex(index); for (var i = 0; i < ${t}; i = i + 1) { if (${a} < ${n}) { ${a} = ${n} * 2 - ${a} - ${this.offset}; } else if(${a} >= ${s}) { ${a} = (${s} - 1) * 2 - ${a} + ${this.offset}; } } let coords = outC - start; setOutputAtIndex(index, getX(${p})); } } `}};var OW={kernelName:qn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=e,i=n.map(c=>({type:"int32",data:[c[0],c[1]]})),p=new Yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Epe=et({opType:fe.MOD}),MW={kernelName:jn,backendName:"webgpu",kernelFunc:Epe};var Qx=class{constructor(t,e){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` //Based on the work of Dave Hoskins //https://www.shadertoy.com/view/4djSRW fn random (seed : f32, resultUV : vec2) -> f32 { let HASHSCALE1 = 443.8975; let p = resultUV * seed; var p3 = fract(vec3(p.xyx) * HASHSCALE1); p3 = p3 + dot(p3, p3.yzx + 19.19); return fract((p3.x + p3.y) * p3.z); } ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; let resUV = vec2(f32(coords[1]) / f32(uniforms.outShape[1]), f32(coords[0]) / f32(uniforms.outShape[0])); let r = random(uniforms.seed, resUV); var cdf = 0.0; for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) { cdf = cdf + getProbs(batch, i); if (r < cdf) { setOutputAtIndexI32(index, i); return; } } // If no other event happened, last event happened. setOutputAtIndexI32(index, uniforms.numOutcomes - 1); } } `}};var Zx=class{constructor(t){this.variableNames=["logits"],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey="softmax"}getUserCode(){return` var buf : array; var rowMaxShared : f32; var rowSumShared : f32; const blockSize = ${this.workgroupSize[0]}; ${G("index")} { let row = index / blockSize; let tid = i32(localId.x); let cols = uniforms.outShape[1]; var threadMax = -3.402823e+38f; for (var col = tid; col < cols; col += blockSize) { let value = getLogits(row, col); threadMax = max(threadMax, value); } if (tid < cols) { buf[tid] = threadMax; } workgroupBarrier(); var reduceSize = min(cols, blockSize); for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) { reduceSize = currSize + (reduceSize & 1); if (tid < currSize) { buf[tid] = max(buf[tid], buf[tid + reduceSize]); } workgroupBarrier(); } if (tid == 0) { rowMaxShared = buf[0]; } workgroupBarrier(); var threadSum = 0.0; for (var col = tid; col < cols; col += blockSize) { let subExp = exp(getLogits(row, col) - rowMaxShared); threadSum += subExp; } buf[tid] = threadSum; workgroupBarrier(); for (var currSize = blockSize >> 1; currSize > 0; currSize = currSize >> 1) { if (tid < currSize) { buf[tid] = buf[tid] + buf[tid + currSize]; } workgroupBarrier(); } if (tid == 0) { rowSumShared = buf[0]; } workgroupBarrier(); for (var col = tid; col < cols; col += blockSize) { let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared; setOutputAtCoords(row, col, value); } } `}};function p0(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{dim:s}=o,a=pe({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Zx(a.shape),p=e.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:e,attrs:{shape:n.shape}});return e.disposeData(a.dataId),e.disposeData(p.dataId),u}var LW={kernelName:vs,backendName:"webgpu",kernelFunc:p0};function Rpe(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o,p=i?n:p0({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qx(u,s),m=[{type:"float32",data:[a]},{type:"int32",data:[c]}],d=e.runWebGPUProgram(l,[p],"int32",m);return i||e.disposeData(p.dataId),d}var BW={kernelName:Xn,backendName:"webgpu",kernelFunc:Rpe};function Dpe(r){let{inputs:t,backend:e}=r,{x:o}=t;if(e.shouldExecuteOnCPU([o])){let s=e.tensorMap.get(o.dataId),[a,i]=kz(s.values,o.shape,o.dtype);return e.makeTensorInfo(i,o.dtype,a)}let n=new Jr(o.shape,Z.NEG);return e.runWebGPUProgram(n,[o],o.dtype)}var zW={kernelName:pa,backendName:"webgpu",kernelFunc:Dpe};function Ape(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=e.readSync(n.dataId),c=e.readSync(s.dataId),{selectedIndices:l}=Wt.nonMaxSuppressionV3Impl(u,c,a,i,p);return e.makeTensorInfo([l.length],"int32",new Int32Array(l))}var VW={kernelName:Zn,backendName:"webgpu",kernelFunc:Ape};function Fpe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:o}=r,{boxes:n,scores:s}=t,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=e.readSync(n.dataId),l=e.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Wt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var WW={kernelName:Jn,backendName:"webgpu",kernelFunc:Fpe};var Jx=class{constructor(t,e){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue, f32(i32(round(getX(coords.x))) == coords.y))); } } `}};function Ppe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Jx(u,a),l=pe({inputs:{x:n},backend:e,attrs:{shape:[u]}}),m=[{type:"float32",data:[i]},{type:"float32",data:[p]}],d=e.runWebGPUProgram(c,[l],s,m);e.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:e,attrs:{shape:f}});return e.disposeData(d.dataId),h}var UW={kernelName:es,backendName:"webgpu",kernelFunc:Ppe};function bm(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="complex64"){let n=Ni({inputs:{input:o},backend:e}),s=bm({inputs:{x:n},backend:e}),a=Mp({inputs:{input:o},backend:e}),i=bm({inputs:{x:a},backend:e}),p=yo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return kt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:e})}var GW={kernelName:Sa,backendName:"webgpu",kernelFunc:bm};function HW(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Ni({inputs:{input:o},backend:e}),s=HW({inputs:{x:n},backend:e}),a=Mp({inputs:{input:o},backend:e}),i=bm({inputs:{x:a},backend:e}),p=yo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return kt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:e})}var KW={kernelName:ca,backendName:"webgpu",kernelFunc:HW};function Ope(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Mx({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=t.map(c=>{let l=Mx({inputs:{input:c},backend:e,attrs:{dim:n}});return i.push(l),l}),u=n0({inputs:p,backend:e,attrs:{axis:n}});return i.forEach(c=>e.disposeData(c.dataId)),u}var qW={kernelName:la,backendName:"webgpu",kernelFunc:Ope};function c0(r,t=!1){let e=r.length,o=ht(e),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(","),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:""}`).join(","),a=e>1?`${o}(${n})`:`${n}`,i=e>1?`${o}(${s})`:`${s}`,p=e>1?"any(paddedCoords < start)":"paddedCoords < start",u=e>1?"any(paddedCoords >= end)":"paddedCoords >= end",c=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` let start = ${a}; let end = ${i}; if (${p} || ${u}) { setOutputAtIndex(index, ${t?0:"uniforms.constantValue"}); } else { let coords = paddedCoords - start; setOutputAtIndex(index, getX(${c})); } `}var ey=class{constructor(t,e){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.map((o,n)=>o[0]+t[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),e.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=t,this.shaderKey="pad"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let paddedCoords = getCoordsFromIndex(index); ${c0(this.xShape)} } } `}};var Mpe=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Ft({inputs:{x:n},backend:e});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return kt({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new ey(n.shape,s);return e.runWebGPUProgram(p,[n],n.dtype,i)},jW={kernelName:ts,backendName:"webgpu",kernelFunc:Mpe};var Lpe=et({opType:fe.POW}),XW={kernelName:rs,backendName:"webgpu",kernelFunc:Lpe};function Bpe(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=new ki(fe.PRELU,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],"float32")}var YW={kernelName:os,backendName:"webgpu",kernelFunc:Bpe};function zpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return eo(n,s,a,"prod",e)}var QW={kernelName:ns,backendName:"webgpu",kernelFunc:zpe};var Vpe=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=_z(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},ZW={kernelName:ma,backendName:"webgpu",kernelFunc:Vpe};var Wpe=et({opType:fe.DIV}),JW={kernelName:hn,backendName:"webgpu",kernelFunc:Wpe};var Upe=ye({opType:Z.RECIPROCAL}),eU={kernelName:ss,backendName:"webgpu",kernelFunc:Upe};var Gpe=ye({opType:Z.RELU}),tU={kernelName:as,backendName:"webgpu",kernelFunc:Gpe};var Hpe=ye({opType:Z.RELU6}),rU={kernelName:ps,backendName:"webgpu",kernelFunc:Hpe};var ty=class{constructor(t,e,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t[0],e,o,t[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; let d = coords[3]; let rc = coords.yz; let effectiveInSize = vec2( f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]); let effectiveOutSize = vec2( f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]); let effectiveInputOverOutputRatioRC = effectiveInSize / effectiveOutSize; // Fractional source index let sourceFracIndexRC = (vec2(rc) + vec2(uniforms.halfPixelCenters)) * effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters); // Compute the four integer indices. let sourceFloorRC = vec2(sourceFracIndexRC); let sourceCeilRC = vec2( min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC))); let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d); let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d); let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d); let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d); let fracRC = sourceFracIndexRC - vec2(sourceFloorRC); let top = topLeft + (topRight - topLeft) * fracRC.y; let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; let newValue = top + (bottom - top) * fracRC.x; setOutputAtIndex(index, newValue); } } `}};function Kpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[i?.5:0]}],f=new ty(n.shape,p,u);return e.runWebGPUProgram(f,[n],"float32",d)}var oU={kernelName:us,backendName:"webgpu",kernelFunc:Kpe};var ry=class{constructor(t,e){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=e,this.shaderKey=`resizeBilinearBackprop_${e}`}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; let d = coords[3]; let r = coords[1]; let c = coords[2]; var accumulator = 0.0; // Compute bounds for where in dy we will look let startRLerp = floor(f32(r) * uniforms.invHeightScale); let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2)); let startCLerp = floor(f32(c) * uniforms.invWidthScale); let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2)); // Loop over dy for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) { let dyR = startDyR + dyROffset; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= uniforms.dyShape[1]) { continue; } for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) { let dyC = startDyC + dyCOffset; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= uniforms.dyShape[2]) { continue; } let dxR = f32(dyR) * uniforms.heightScale; let topDxRIndex = i32(floor(dxR)); let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1))); let dxRLerp = dxR - f32(topDxRIndex); let inverseDxRLerp = 1.0 - dxRLerp; let dxC = f32(dyC) * uniforms.widthScale; let leftDxCIndex = i32(floor(dxC)); let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1))); let dxCLerp = dxC - f32(leftDxCIndex); let inverseDxCLerp = 1.0 - dxCLerp; if (r == topDxRIndex && c == leftDxCIndex) { // topLeft accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; } if (r == topDxRIndex && c == rightDxCIndex) { // topRight accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; } if (r == bottomDxRIndex && c == leftDxCIndex) { // bottomLeft accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; } if (r == bottomDxRIndex && c == rightDxCIndex) { // bottomRight accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; } } } // End loop over dy setOutputAtIndex(index, accumulator); } } `}};function qpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ry(n.shape,a),S=[{type:"int32",data:l},{type:"int32",data:m},{type:"float32",data:[d]},{type:"float32",data:[f]},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return e.runWebGPUProgram(C,[s],s.dtype,S)}var nU={kernelName:ei,backendName:"webgpu",kernelFunc:qpe};var oy=class{constructor(t,e,o,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[t[0],e,o,t[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let t;return this.halfPixelCenters?t="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":t="vec2(rc) * effectiveInputOverOutputRatioRC",` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; let d = coords[3]; let rc = coords.yz; let effectiveInSize = vec2( f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]); let effectiveOutSize = vec2( f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]); let effectiveInputOverOutputRatioRC = effectiveInSize / effectiveOutSize; // Fractional source index let sourceFracIndexRC = ${t}; // Compute the coordinators of nearest neighbor point. let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z)); let sourceNearestRC = vec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase))); let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutputAtIndex(index, newValue); } } `}};function jpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[s?.5:0]}],f=new oy(n.shape,p,u,a);return e.runWebGPUProgram(f,[n],n.dtype,d)}var sU={kernelName:is,backendName:"webgpu",kernelFunc:jpe};var ny=class{constructor(t,e){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=e,this.shaderKey=`resizeNearestNeigborBackprop_${e}`}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; let d = coords[3]; let r = coords[1]; let c = coords[2]; var accumulator = 0.0; // Compute bounds for where in dy we will look let startRLerp = floor(f32(r) * uniforms.invHeightScale); let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2))); let startCLerp = floor(f32(c) * uniforms.invWidthScale); let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2))); // Loop over dy for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) { let dyR = startDyR + dyROffset; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= uniforms.dyShape[1]) { continue; } for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) { let dyC = startDyC + dyCOffset; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= uniforms.dyShape[2]) { continue; } let sourceFracRow = f32(uniforms.effectiveXSize[0]) * (f32(dyR) / f32(uniforms.effectiveYSize[0])); let sourceFracCol = f32(uniforms.effectiveXSize[1]) * (f32(dyC) / f32(uniforms.effectiveYSize[1])); let sourceNearestRow = i32(min(f32(uniforms.outShape[1] - 1), ${this.alignCorners?"floor(sourceFracRow + 0.5)":"floor(sourceFracRow)"})); let sourceNearestCol = i32(min(f32(uniforms.outShape[2] - 1), ${this.alignCorners?"floor(sourceFracCol + 0.5)":"floor(sourceFracCol)"})); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutputAtIndex(index, accumulator); } } `}};function Xpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ny(n.shape,a),S=[{type:"int32",data:l},{type:"int32",data:m},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return e.runWebGPUProgram(C,[s],s.dtype,S)}var aU={kernelName:Ja,backendName:"webgpu",kernelFunc:Xpe};var sy=class{constructor(t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` // Using uniform variables as judging conditions, so the function has // coherent execution within all threads. fn getReverseCoords(coords : vec4) -> vec4 { var reverseCoords = coords; if (uniforms.axis[0] == 1) { reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1; } if (uniforms.axis[1] == 1) { reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1; } if (uniforms.axis[2] == 1) { reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1; } if (uniforms.axis[3] == 1) { reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1; } return reverseCoords; } ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let reverseCoords = getReverseCoords(coords); setOutputAtIndex(index, getX(reverseCoords[0], reverseCoords[1], reverseCoords[2], reverseCoords[3])); } } `}};function Ype(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dims:s}=o,a=n.shape.length;if(a===0)return Ft({inputs:{x:n},backend:e});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:"int32",data:c}],m=pe({inputs:{x:n},backend:e,attrs:{shape:p}}),d=new sy(p),f=e.runWebGPUProgram(d,[m],m.dtype,l);e.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:e,attrs:{shape:i}});return e.disposeData(f.dataId),h}var iU={kernelName:cs,backendName:"webgpu",kernelFunc:Ype};var ay=class{constructor(t,e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=t,typeof e=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordXFloat = (f32(coords[2]) - uniforms.centerX) * uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) * uniforms.sinRadians; let coordYFloat = (f32(coords[2]) - uniforms.centerX) * uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) * uniforms.cosRadians; let coordX = i32(round(coordXFloat + uniforms.centerX)); let coordY = i32(round(coordYFloat + uniforms.centerY)); ${this.fillSnippet} if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 && coordY < uniforms.xShape[1]) { outputValue = getX(coords[0], coordY, coordX, coords[3]); } setOutputAtIndex(index, outputValue); } } `}};var uU={kernelName:As,backendName:"webgpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new ay(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?l.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:"float32",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Qpe=ye({opType:Z.ROUND}),pU={kernelName:ls,backendName:"webgpu",kernelFunc:Qpe};var Zpe=ye({opType:Z.RSQRT,cpuKernelImpl:$z}),cU={kernelName:ms,backendName:"webgpu",kernelFunc:Zpe};var Va=class{constructor(t,e,o,n,s,a,i,p=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.sliceDimGreaterThanOne=e>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=ht(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let t="";this.indicesRank===1?t="coords[0]":this.indicesRank===2&&(t="coords[0], j");let e=`getIndices(${t})`,o=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",s="";this.dispatchLayout.x.length===1?(n="flattenedIndex",s=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } `):this.dispatchLayout.x.length===2&&(n="vec2(flattenedIndex, coords[1])",s=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 { // N.B. |updates| could be a scalar tensor, conceptually representing a // 2D tensor with all values equal to that. By design, its size must be // the same as |outShape[1]| in one dimension, and |indicesShape[0]| // gives the other. let sliceSize = uniforms.outShape[1]; let d0 = index / sliceSize; let d1 = index - d0 * sliceSize; return vec2(d0, d1); } `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(", ")})`;return` ${s} ${G("index")} { if (index < uniforms.updatesSize) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexInside = i32(round(${e})); flattenedIndex = flattenedIndex + indexInside * ${o}; } let updateValue = ${Nu(this.type)}(${i}); let flatIndex = getOutputIndexFromCoords(${n}); ${this.sumDupeIndices?Qr("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} } }`}};function Jpe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=f.dtype,g=kt({backend:e,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[x]}],C=new Va(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=e.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:e,attrs:{shape:a}});return e.disposeData(d.dataId),e.disposeData(f.dataId),e.disposeData(S.dataId),k}var lU={kernelName:ds,backendName:"webgpu",kernelFunc:Jpe};var iy=class{constructor(t,e){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=e,this.shaderKey=`search_sorted_${e}`}getUserCode(){return` fn findBound(batch: i32, value: f32) -> i32 { var left = i32(0); var right = uniforms.numInputs; while (left < right) { var mid = (left + right) / 2; if (getSortedSequence(batch, mid) ${this.side==="left"?"<":"<="} value) { left = mid + 1; } else { right = mid; } } return right; } ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let value = getValuesByOutputIndex(index); setOutputAtIndexI32(index, findBound(coords[0], value)); } } `}};function ece(r){let{inputs:t,backend:e,attrs:o}=r,{sortedSequence:n,values:s}=t,{side:a}=o,i=new iy([s.shape[0],s.shape[1]],a),p=[{type:"int32",data:[n.shape[1]]}];return e.runWebGPUProgram(i,[n,s],"int32",p)}var mU={kernelName:hs,backendName:"webgpu",kernelFunc:ece};var uy=class{constructor(t,e,o){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=t,this.rank=o,this.shaderKey="select"}getUserCode(){let t,e;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)e="resRC",t="resRC";else{let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let i=0;i= 1.0) { setOutputAtIndex(index, getA(${e})); } else { setOutputAtIndex(index, getB(${e})); } } } `}};function tce(r){let{inputs:t,backend:e}=r,{condition:o,t:n,e:s}=t,a=new uy(o.shape.length,n.shape,n.shape.length);return e.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var dU={kernelName:fa,backendName:"webgpu",kernelFunc:tce};var rce=ye({opType:Z.SELU}),fU={kernelName:gs,backendName:"webgpu",kernelFunc:rce};var oce=ye({opType:Z.SIGMOID}),hU={kernelName:Cs,backendName:"webgpu",kernelFunc:oce};var nce=ye({opType:Z.SIGN}),gU={kernelName:bs,backendName:"webgpu",kernelFunc:nce};var sce=ye({opType:Z.SIN}),xU={kernelName:xs,backendName:"webgpu",kernelFunc:sce};var ace=ye({opType:Z.SINH}),yU={kernelName:ys,backendName:"webgpu",kernelFunc:ace};var ice=ye({opType:Z.SOFTPLUS}),bU={kernelName:ws,backendName:"webgpu",kernelFunc:ice};var py=class{constructor(t,e,o,n,s,a){this.variableNames=["x"],this.outputShape=[],this.uniforms="",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let t=ht(this.outputShape.length),e=Zv(this.newDim);return` ${cm(this.paddedXShape,"PaddedX")} ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${t}(${e}), uniforms.reshapedPaddedXShape); let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex); ${c0(this.xShape,!0)} } } `}};var uce=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new py(n.shape,u,p,c,l,d.length),h=[{type:"int32",data:c},{type:"int32",data:d}];p.map(b=>h.push({type:"int32",data:[b[0],b[1]]}));let g=e.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeData(g.dataId),x},CU={kernelName:ga,backendName:"webgpu",kernelFunc:uce};var cy=class{constructor(t,e,o){this.variableNames=["input","indices","segmentIds"],this.outputShape=[],this.uniforms="segmentSize : i32, sparseSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.type=o,this.dispatchLayout=X([e]),this.dispatch=H(this.dispatchLayout,[e],this.workgroupSize),this.shaderKey="sparseSegmentSum"}getUserCode(){return` ${G("index")} { if (index < uniforms.sparseSize) { let indexInSegmentIds = index / uniforms.segmentSize; let indexInSegment = index % uniforms.segmentSize; let indexInInput = indices[indexInSegmentIds]; let segmentId = segmentIds[indexInSegmentIds]; let value = input[indexInInput * uniforms.segmentSize + indexInSegment]; let outIndex = segmentId * uniforms.segmentSize + indexInSegment; ${Qr("&result[outIndex]","value",this.type)} } } `}},ly=class{constructor(t,e){this.variableNames=["segmentIds"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[t],this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.shaderKey="sparseSegmentIdCountProgram"}getUserCode(){return` ${G("index")} { if (index < uniforms.segmentIdsShape) { let segmentId = segmentIds[index]; ${Qr("&result[segmentId]","1","int32")} } } `}},my=class{constructor(t,e){this.variableNames=["segmentSum","sameSegmentIdCount"],this.outputShape=[],this.uniforms="segmentSize : i32",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.type=e,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.shaderKey="sparseSegmentMean"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let segmentId = index / uniforms.segmentSize; let count = sameSegmentIdCount[segmentId]; if (count != 0) { ${this.type==="float32"?"setOutputAtIndex(index, segmentSum[index] / f32(count));":"setOutputAtIndexI32(index, segmentSum[index] / count);"} } } } `}};function dy(r,t,e,o=!1,n){let a=y.sizeFromShape(r.shape)/r.shape[0],i=r.dtype,p=y.sizeFromShape(t.shape),u=n.readSync(e.dataId),l=p>0?u[p-1]+1:0,m,d=r.shape.slice();d[0]=l;let f=p*a,h=kt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new cy(d,f,i);let g=[{type:"int32",data:[a]},{type:"int32",data:[f]}],x=n.runWebGPUProgram(m,[r,t,e],i,g,h);if(o)return x;let b=kt({backend:n,attrs:{shape:[l],value:0,dtype:"int32"}});m=new ly(l,e.shape);let C=n.runWebGPUProgram(m,[e],"int32",null,b),S=kt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new my(d,i),g=[{type:"int32",data:[a]}];let k=n.runWebGPUProgram(m,[x,C],i,g,S);return n.disposeData(x.dataId),n.disposeData(C.dataId),k}function pce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return dy(o,n,s,!1,e)}var wU={kernelName:ya,backendName:"webgpu",kernelFunc:pce};function cce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return dy(o,n,s,!0,e)}var SU={kernelName:ba,backendName:"webgpu",kernelFunc:cce};var fy=class{constructor(t,e){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(t.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${t}aShape)`;let e=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n=5){let p=e.readSync(n.dataId),u=n.dtype==="string"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=Oz(c,s);return e.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new fy(n.shape,s);return e.runWebGPUProgram(a,[n],n.dtype)}var IU={kernelName:po,backendName:"webgpu",kernelFunc:Cm};function mce(r){let{inputs:t,backend:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let R=e.bufferSync(n),D=e.bufferSync(s),P=y.decodeString(e.readSync(a.dataId)[0]),O=Ez(R,D,i,m,c,u,p,l,P,d);return e.makeTensorInfo(i,O.dtype,O.values)}let f=[m/c,c],h=pe({inputs:{x:n},backend:e,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:e,attrs:{shape:[u,c]}}):Ft({inputs:{x:s},backend:e}),x=g.dtype,b=e.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:e,attrs:{shape:Array(f.length).fill(1)}}),S=Cm({inputs:{x:C},backend:e,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:"int32",data:[p]},{type:"int32",data:l},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let R=new Va([u,c],p,h.shape.length,g.shape.length,l,f,x,d);e.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new Va([u,c],p,h.shape.length,b.shape.length,l,f,x,d);e.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new Va([u,c],p,h.shape.length,g.shape.length,l,f,x);e.runWebGPUProgram(R,[g,h],x,_,S)}}let E=pe({inputs:{x:S},backend:e,attrs:{shape:i}});return e.disposeData(h.dataId),e.disposeData(g.dataId),e.disposeData(C.dataId),e.disposeData(b.dataId),e.disposeData(S.dataId),E}var vU={kernelName:ks,backendName:"webgpu",kernelFunc:mce};function dce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Hs({inputs:{x:n},backend:e,attrs:{begin:c,size:d}});return c[i]+=m,f})}var kU={kernelName:xa,backendName:"webgpu",kernelFunc:dce};var fce=ye({opType:Z.SQRT}),NU={kernelName:Ss,backendName:"webgpu",kernelFunc:fce};var TU={kernelName:Xi,backendName:"webgpu",kernelFunc:({inputs:r,backend:t})=>{let{x:e}=r,o=t,n=new Jr(e.shape,Z.SQUARE);return o.runWebGPUProgram(n,[e],e.dtype)}};var hce=et({opType:fe.SQUARED_DIFFERENCE}),_U={kernelName:Ns,backendName:"webgpu",kernelFunc:hce};function gce({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=new Jr(o.shape,Z.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return e.runWebGPUProgram(n,[o],o.dtype,s)}var $U={kernelName:So,backendName:"webgpu",kernelFunc:gce};var hy=class{constructor(t){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let e=ht(this.outputShape.length);this.uniforms=`begin : ${e}, strides : ${e}, `,this.shaderKey="stridedSlice"}getUserCode(){let t=this.outputShape.length,e="";if(t===1)e="coords * uniforms.strides + uniforms.begin";else{let n=0;e=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${e})); } } `}};function xce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=ct.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:e,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ct.computeOutShape(b,C,S),E=Hs({inputs:{x:n},backend:e,attrs:{begin:b,size:_}});k=pe({inputs:{x:E},backend:e,attrs:{shape:f}}),e.disposeData(E.dataId)}else if(e.shouldExecuteOnCPU([n])){let E=e.readSync(n.dataId),R=me(n.shape,n.dtype,E),D=Az(d,R,S,b);k=e.makeTensorInfo(f,n.dtype,D.values)}else{let E=new hy(d),R=[{type:"int32",data:b},{type:"int32",data:S}],D=e.runWebGPUProgram(E,[n],n.dtype,R);k=pe({inputs:{x:D},backend:e,attrs:{shape:f}}),e.disposeData(D.dataId)}return k}var EU={kernelName:Ts,backendName:"webgpu",kernelFunc:xce};function yce(r){let{inputs:t,backend:e,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=t,m=e.readSync(c.dataId),d=e.readSync(l.dataId),[f,h]=Fz(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],"string",f),e.makeTensorInfo(l.shape,"int32",h)]}var RU={kernelName:Ca,backendName:"webgpu",kernelFunc:yce};var bce=et({opType:fe.SUB,cpuKernelImpl:Pz,supportsComplex:!0}),DU={kernelName:_s,backendName:"webgpu",kernelFunc:bce};var Cce=ye({opType:Z.TAN}),AU={kernelName:$s,backendName:"webgpu",kernelFunc:Cce};var wce=ye({opType:Z.TANH}),FU={kernelName:Es,backendName:"webgpu",kernelFunc:wce};function Sce(r){let{inputs:t,backend:e,attrs:o}=r,{tensor:n,indices:s,updates:a}=t,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return e.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:e,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:e,attrs:{shape:m}});d.push(g);let x=Cm({inputs:{x:g},backend:e,attrs:{reps:Array(m.length).fill(1)}}),b=new Va([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[C]}],k=e.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:e,attrs:{shape:n.shape}});return d.forEach(E=>e.disposeData(E.dataId)),_}var PU={kernelName:fs,backendName:"webgpu",kernelFunc:Sce};var gy=class{constructor(t){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; let elemIdx = outC[1]; // We compare elements pair-wise within a group of size 2 * inc. // The comparing rule for each group alternates between ascending // and descending. Within each group, we compare each pair at // positions i and i+inc. To decide whether an element at position i // is x0 or x1, we mod it by 2 * inc, if the result is smaller than // inc, it is in the first half of the group, we denote it as x0, // otherwise we denote it as x1. // For example, as shown in the Bitonic top K paper referenced // above, Figure5(a) shows that element[1] is in the second half of // the group when group size is 2, but it is in the first half of // the group when group size is 4. let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc; var i = 0; if (isFirstInPair) { i = elemIdx; } else { i = elemIdx - uniforms.inc; } var i0 = 0; if (uniforms.firstPass == 1) { i0 = i; } else { i0 = i32(getIndices(batch, i)); } var i1 = 0; if (uniforms.firstPass == 1) { i1 = i + uniforms.inc; } else { i1 = i32(getIndices(batch, i + uniforms.inc)); } var x0 = f32(0.0); var x1 = f32(0.0); if (i0 < uniforms.inputSize) { x0 = getX(batch, i0); } else { x0 = uniforms.negativeInf; } if (i1 < uniforms.inputSize) { x1 = getX(batch, i1); } else { x1 = uniforms.negativeInf; } let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir; let isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction let iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutputAtIndex(index, f32(i0)); } else { setOutputAtIndex(index, f32(i1)); } } } `}},xy=class{constructor(t){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; let elemIdx = outC[1]; // The output size is half of the previous size. // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ // (k=4), we only need to output the indices at positions |, the // indices at positions _ can be thrown away, see Figure5(b) After // Phase 2 (Merge phase) in the Bitonic Top K paper referenced // above. // For example, the paper shows we only need to output the orange // bars. The output sequence should look like this | | | | | | | |. // Because the sequence is halved, to map the output index back to // the previous sequence to find the corresponding value, we need // to double the index. When we double the index, we basically // interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k // position of each 2k positions by - elemIdx % k. E.g. for output // at index 4,5,6,7, we want to get the corresponding element at // original index 8,9,10,11, for output at index 8,9,10,11, // we want to get the corresponding element at original index // 16,17,18,19, so on and so forth. var i = 0; if (elemIdx < uniforms.k) { i = elemIdx; } else { i = elemIdx * 2 - elemIdx % uniforms.k; } var i0 = 0; if (uniforms.firstPass == 1) { i0 = i; } else { i0 = i32(getIndices(batch, i)); } var i1 = 0; if (uniforms.firstPass == 1) { i1 = i + uniforms.k; } else { i1 = i32(getIndices(batch, i + uniforms.k)); } let x0 = getX(batch, i0); var x1 = f32(0.0); if (i1 < uniforms.inputSize) { x1 = getX(batch, i1); } else { x1 = x0; } if (x0 >= x1) { setOutputAtIndex(index, f32(i0)); } else { setOutputAtIndex(index, f32(i1)); } } } `}};function nl(r,t){t!==null&&r.disposeData(t.dataId)}function OU(r){let t=1;for(;tf===null?[l,l]:[l,f],g=(k,_,E)=>{let R=h(),D=new gy(E),O=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[_]}],M=f;f=e.runWebGPUProgram(D,R,"int32",O),nl(e,M)};for(let k=1;k=1;E/=2)g(_,E,[c,d])}for(let k=d;k>m;k/=2){let _=h(),E=new xy([c,k/2]),D=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[m]}],P=f;f=e.runWebGPUProgram(E,_,"int32",D),nl(e,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Hs({inputs:{x:f},backend:e,attrs:{begin:0,size:[c,s]}}),nl(e,x);let b=u0({inputs:{x:l,indices:f},backend:e,attrs:{axis:1,batchDims:1}});nl(e,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:e}),nl(e,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:e}),nl(e,S),[b,f]}var MU={kernelName:Rs,backendName:"webgpu",kernelFunc:Ice};var yy=class{constructor(t){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` fn mapCoord(outCoord : f32, len : f32) -> f32{ var inCoord = outCoord; if(uniforms.fillModeId == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) + inCoord; } if (inCoord < -len) { inCoord = inCoord + sz2; } else { inCoord = -inCoord - 1.0; } } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz2 = 2.0 * len; inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } else if (uniforms.fillModeId == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz = len - 1.0; inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz = len - 1.0; inCoord = inCoord - len * f32(i32(f32(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (uniforms.fillModeId == 4) { return clamp(outCoord, 0.0, len - 1.0); } return outCoord; } fn readWithFillValue(batch : i32, coordY : i32, coordX : i32, channel : i32) -> f32 { var outputValue : f32; if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = uniforms.fillValue; } return outputValue; } ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var outputValue : f32; let batch = coords[0]; let x = coords[2]; let y = coords[1]; let channel = coords[3]; let xf = f32(x); let yf = f32(y); let a1 = getTransforms(batch, 0); let a2 = getTransforms(batch, 1); let a3 = getTransforms(batch, 2); let b1 = getTransforms(batch, 3); let b2 = getTransforms(batch, 4); let b3 = getTransforms(batch, 5); let c1 = getTransforms(batch, 6); let c2 = getTransforms(batch, 7); let projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = uniforms.fillValue; } else { let inX = (a1 * xf + a2 * yf + a3) / projection; let inY = (b1 * xf + b2 * yf + b3) / projection; let mapX = mapCoord(inX, f32(uniforms.imageShape[2])); let mapY = mapCoord(inY, f32(uniforms.imageShape[1])); if (uniforms.interpolationModeId == 1) { let coordY = i32(round(mapY)); let coordX = i32(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { let yFloor = floor(mapY); let xFloor = floor(mapX); let yCeil = yFloor + 1.0; let xCeil = xFloor + 1.0; let valueYFloor = (xCeil - mapX) * readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, i32(yFloor), i32(xCeil), channel); let valueYCeil = (xCeil - mapX) * readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, i32(yCeil), i32(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutputAtIndex(index, outputValue); } } `}};function vce(r){let{inputs:t,backend:e,attrs:o}=r,{image:n,transforms:s}=t,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new yy(g),b=a==="nearest"?1:2,C;switch(i){case"constant":C=1;break;case"reflect":C=2;break;case"wrap":C=3;break;case"nearest":C=4;break;default:C=1;break}let S=[{type:"int32",data:[b]},{type:"int32",data:[C]},{type:"float32",data:[p]}];return e.runWebGPUProgram(x,[n,s],"float32",S)}var LU={kernelName:Ds,backendName:"webgpu",kernelFunc:vce};function kce(r){let{inputs:t,backend:e,attrs:o}=r,{value:n}=t,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;he.disposeData(h.dataId)),f}var BU={kernelName:wa,backendName:"webgpu",kernelFunc:kce};var by=class{constructor(t,e,o){if(this.outputShape=[],this.variableNames=["x","segmentIds"],this.uniforms="numSegments : i32, xSize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),o!=="float32"&&o!=="int32")throw new Error(`UnsortedSegmentSum only supports float32 and int32 types, does not support ${o} type.`);this.type=o,this.shaderKey="unsortedSegmentSum"}getUserCode(){return` ${G("index")} { if (index < uniforms.xSize) { let coords = getXCoordsFromIndex(index); let b = coords[0]; let inCol = coords[1]; let segmentId = i32(getSegmentIds(inCol)); if (segmentId >= 0) { let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments; let value = getX(b, inCol); ${Qr("&result[flatIndex]","value",this.type)} } } } `}};function Nce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,segmentIds:s}=t,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=yr({inputs:{x:n},backend:e,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=kt({backend:e,attrs:{shape:g,value:0,dtype:h}}),b=new by(f.shape,g,h),C=[{type:"int32",data:[a]},{type:"int32",data:[y.sizeFromShape(f.shape)]}],S=e.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:e,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let E=w.getUndoAxesPermutation(c);_=yr({inputs:{x:_},backend:e,attrs:{perm:E}})}return p.forEach(E=>e.disposeData(E.dataId)),_}var zU={kernelName:Ji,backendName:"webgpu",kernelFunc:Nce};var Tce=[rz,Bz,zz,Vz,Wz,Uz,Hz,Kz,qz,jz,Xz,Yz,Qz,Zz,Jz,rV,oV,nV,sV,aV,uV,pV,cV,fV,hV,gV,nz,yV,CV,wV,SV,IV,vV,kV,NV,TV,_V,$V,DV,AV,FV,PV,MV,LV,OV,BV,zV,VV,WV,UV,KV,qV,jV,XV,YV,QV,ZV,JV,eW,ez,tW,nW,rW,oW,sW,aW,iW,uW,pW,cW,lW,oz,mW,bV,dW,fW,hW,gW,xW,yW,bW,wW,CW,SW,IW,vW,NW,TW,eV,_W,$W,DW,EW,RW,AW,tV,FW,PW,OW,MW,BW,GV,zW,VW,WW,lV,UW,KW,qW,jW,XW,YW,QW,ZW,mV,JW,eU,tU,rU,tz,oU,nU,sU,aU,iU,uU,pU,cU,lU,mU,dU,fU,hU,gU,xU,yU,iV,$U,EU,RU,LW,bU,CU,wU,SU,vU,kU,NU,TU,_U,DU,HV,AU,FU,PU,IU,MU,LU,Gz,BU,zU,GW];for(let r of Tce)ri(r);var VU="4.11.0",_ce="4.11.0",$ce="4.11.0",Ece="4.11.0",Rce="4.11.0",Dce="4.11.0",Ace={tfjs:VU,"tfjs-core":VU,"tfjs-converter":_ce,"tfjs-backend-cpu":$ce,"tfjs-backend-webgl":Ece,"tfjs-backend-wasm":Rce,"tfjs-backend-webgpu":Dce};export{Xs as Abs,Wo as Acos,Uo as Acosh,np as AdadeltaOptimizer,sp as AdagradOptimizer,ap as AdamOptimizer,ip as AdamaxOptimizer,uo as Add,Go as AddN,Ho as All,Ko as Any,Ys as ArgMax,Qs as ArgMin,qo as Asin,jo as Asinh,Xo as Atan,Qo as Atan2,Yo as Atanh,Zo as AvgPool,Zs as AvgPool3D,Ai as AvgPool3DGrad,Di as AvgPoolGrad,pm as BackendWasm,Jo as BatchMatMul,Js as BatchToSpaceND,en as Bincount,ja as BitwiseAnd,ea as BroadcastArgs,Bce as BroadcastTo,bo as Cast,tn as Ceil,Co as ClipByValue,Fi as Complex,Pi as ComplexAbs,ta as Concat,rn as Conv2D,Oi as Conv2DBackpropFilter,on as Conv2DBackpropInput,nn as Conv3D,Xa as Conv3DBackpropFilterV2,sn as Conv3DBackpropInputV2,an as Cos,un as Cosh,ln as CropAndResize,pn as Cumprod,cn as Cumsum,zo as DataStorage,ra as DenseBincount,mn as DepthToSpace,dn as DepthwiseConv2dNative,Mi as DepthwiseConv2dNativeBackpropFilter,Li as DepthwiseConv2dNativeBackpropInput,oa as Diag,fn as Dilation2D,zi as Dilation2DBackpropFilter,Bi as Dilation2DBackpropInput,Pu as Draw,nw as ENV,Vi as Einsum,gn as Elu,Ya as EluGrad,hl as Environment,yn as Equal,xn as Erf,bn as Exp,na as ExpandDims,Cn as Expm1,Wi as FFT,sa as Fill,wn as FlipLeftRight,Sn as Floor,In as FloorDiv,Mu as FromPixels,vn as FusedBatchNorm,vo as FusedConv2D,ko as FusedDepthwiseConv2D,kp as GPGPUContext,kn as GatherNd,aa as GatherV2,Bl as GraphModel,Nn as Greater,Tn as GreaterEqual,Ui as IFFT,wo as Identity,Gi as Imag,_n as IsFinite,$n as IsInf,En as IsNan,ao as KernelBackend,zn as LRN,Qa as LRNGrad,Rn as LeakyRelu,Dn as Less,An as LessEqual,Fn as LinSpace,Pn as Log,On as Log1p,zce as LogSoftmax,Mn as LogicalAnd,Ln as LogicalNot,Bn as LogicalOr,$0 as LogicalXor,Vce as LowerBound,xu as MathBackendCPU,wu as MathBackendWebGL,Wce as MatrixBandPart,Vn as Max,Un as MaxPool,ia as MaxPool3D,Ki as MaxPool3DGrad,Hi as MaxPoolGrad,ua as MaxPoolWithArgmax,Wn as Maximum,Gn as Mean,Hn as Min,Kn as Minimum,qn as MirrorPad,jn as Mod,up as MomentumOptimizer,Xn as Multinomial,Yn as Multiply,pa as Neg,Zn as NonMaxSuppressionV3,Za as NonMaxSuppressionV4,Jn as NonMaxSuppressionV5,Qn as NotEqual,kw as OP_SCOPE_SUFFIX,es as OneHot,ca as OnesLike,Nr as Optimizer,Pl as OptimizerConstructors,la as Pack,ts as PadV2,Uce as Pool,rs as Pow,os as Prelu,ns as Prod,pp as RMSPropOptimizer,Qp as RaggedGather,Zp as RaggedRange,Jp as RaggedTensorToTensor,ma as Range,hw as Rank,qi as Real,hn as RealDiv,ss as Reciprocal,Rt as Reduction,as as Relu,ps as Relu6,da as Reshape,us as ResizeBilinear,ei as ResizeBilinearGrad,is as ResizeNearestNeighbor,Ja as ResizeNearestNeighborGrad,cs as Reverse,As as RotateWithOffset,ls as Round,ms as Rsqrt,mi as SGDOptimizer,ds as ScatterNd,hs as SearchSorted,fa as Select,gs as Selu,Cs as Sigmoid,bs as Sign,xs as Sin,ys as Sinh,ha as Slice,vs as Softmax,ws as Softplus,ga as SpaceToBatchND,ji as SparseFillEmptyRows,ti as SparseReshape,ya as SparseSegmentMean,ba as SparseSegmentSum,ks as SparseToDense,xa as SplitV,Ss as Sqrt,Xi as Square,Ns as SquaredDifference,Ou as StaticRegexReplace,So as Step,Ts as StridedSlice,Ca as StringNGrams,Yi as StringSplit,Qi as StringToHashBucketFast,_s as Sub,Is as Sum,$s as Tan,Es as Tanh,ut as Tensor,tt as TensorBuffer,fs as TensorScatterUpdate,po as Tile,Rs as TopK,Ds as Transform,co as Transpose,Zi as Unique,wa as Unpack,Ji as UnsortedSegmentSum,Gce as UpperBound,oi as Variable,Tu as WebGPUBackend,Sa as ZerosLike,Io as _FusedMatMul,Jt as abs,kk as acos,Nk as acosh,Ce as add,Tk as addN,_k as all,$k as any,Ek as argMax,Rk as argMin,Dk as asin,Ak as asinh,Fk as atan,Pk as atan2,Ok as atanh,fd as avgPool,Bk as avgPool3d,vde as backend,w as backend_util,zk as basicLSTMCell,au as batchNorm,Wk as batchNorm2d,Uk as batchNorm3d,Gk as batchNorm4d,hd as batchToSpaceND,gd as bincount,Hk as bitwiseAnd,E6 as booleanMaskAsync,Kk as broadcastArgs,iu as broadcastTo,Ir as broadcast_util,oT as browser,me as buffer,We as cast,qk as ceil,jk as clipByValue,Ur as clone,Er as complex,bt as concat,Xk as concat1d,Yk as concat2d,Qk as concat3d,Zk as concat4d,Jk as conv1d,uu as conv2d,e2 as conv2dTranspose,t2 as conv3d,o2 as conv3dTranspose,Zce as copyRegisteredKernels,n2 as cos,s2 as cosh,Rl as cosineWindow,a2 as cumprod,i2 as cumsum,vr as customGrad,u2 as denseBincount,Pw as deprecationWarn,p2 as depthToSpace,lc as depthwiseConv2d,A5 as deregisterOp,ou as device_util,c2 as diag,l2 as dilation2d,dde as disableDeprecationWarnings,Mt as dispose,fde as disposeVariables,je as div,d2 as divNoNan,f2 as dot,W6 as dropout,pu as einsum,Cd as elu,mde as enableDebugMode,lde as enableProdMode,Qw as enclosingPowerOfTwo,pr as engine,h2 as ensureShape,A as env,bd as equal,g2 as erf,b2 as euclideanNorm,$o as exp,Ms as expandDims,C2 as expm1,wd as eye,fc as fft,Ea as fill,Sde as findBackend,Ide as findBackendFactory,Sd as floor,dd as floorDiv,MD as forceHalfFloat,Zw as fused,Id as gather,z6 as gatherND,af as gather_util,Cde as getBackend,iw as getGradient,tc as getKernel,Ym as getKernelsForBackend,Zse as getThreadsCount,cv as gpgpu_util,AK as grad,FK as grads,qu as greater,vd as greaterEqual,Ju as ifft,lu as imag,Kj as image,G6 as inTopKAsync,fi as io,Kd as irfft,w2 as isFinite,S2 as isInf,I2 as isNaN,Rr as keep,Wt as kernel_impls,kd as leakyRelu,_l as less,mc as lessEqual,qj as linalg,v2 as linspace,$8 as loadGraphModel,E8 as loadGraphModelSync,k2 as localResponseNormalization,pi as log,Nd as log1p,N2 as logSigmoid,T2 as logSoftmax,$d as logSumExp,ju as logicalAnd,Ed as logicalNot,Rd as logicalOr,_2 as logicalXor,jj as losses,$2 as lowerBound,Ze as matMul,JN as math,Ra as max,Ad as maxPool,E2 as maxPool3d,R2 as maxPoolWithArgmax,Fd as maximum,Xu as mean,hde as memory,D2 as meshgrid,Tl as min,Yu as minimum,A2 as mirrorPad,F2 as mod,P2 as moments,A6 as movingAverage,se as mul,O2 as multiRNNCell,M2 as multinomial,cr as neg,pS as nextFrame,Ku as norm,Pd as notEqual,El as oneHot,Da as ones,L2 as onesLike,N as op,B2 as outerProduct,Aa as pad,z2 as pad1d,V2 as pad2d,W2 as pad3d,U2 as pad4d,G2 as pool,ui as pow,Md as prelu,md as print,H2 as prod,gde as profile,K2 as raggedGather,q2 as raggedRange,j2 as raggedTensorToTensor,X2 as rand,g1 as randomGamma,Ud as randomNormal,x1 as randomStandardNormal,dc as randomUniform,y1 as randomUniformInt,mu as range,bde as ready,ci as real,b1 as reciprocal,su as registerBackend,Xce as registerGradient,ri as registerKernel,D5 as registerOp,du as relu,Gd as relu6,wde as removeBackend,W as reshape,mo as reverse,C1 as reverse1d,w1 as reverse2d,S1 as reverse3d,I1 as reverse4d,hc as rfft,Hd as round,v1 as rsqrt,ke as scalar,P6 as scatterND,hu as scatter_util,$l as searchSorted,k1 as selu,N1 as separableConv2d,WN as serialization,yde as setBackend,kde as setPlatform,Qse as setThreadsCount,Xse as setWasmPath,Yse as setWasmPaths,vI as setWebGLContext,T1 as setdiff1dAsync,Tc as shared,$a as sigmoid,_1 as sign,Hj as signal,$1 as sin,E1 as sinh,Xe as slice,R1 as slice1d,D1 as slice2d,A1 as slice3d,F1 as slice4d,ct as slice_util,P1 as softmax,_d as softplus,Od as spaceToBatchND,Xj as sparse,L6 as sparseToDense,Gj as spectral,li as split,Dr as sqrt,er as square,qd as squaredDifference,gc as squeeze,kr as stack,jd as step,O1 as stridedSlice,Yj as string,Te as sub,ot as sum,ni as sumOutType,M1 as tan,Nl as tanh,ur as tensor,tr as tensor1d,fu as tensor2d,Xd as tensor3d,L1 as tensor4d,B1 as tensor5d,z1 as tensor6d,W1 as tensorScatterUpdate,ek as tensor_util,h1 as test_util,De as tidy,cu as tile,xde as time,U1 as topk,TGe as train,yc as transpose,G1 as truncatedNormal,H1 as unique,Qce as unregisterGradient,Yce as unregisterKernel,K1 as unsortedSegmentSum,fo as unstack,dt as upcastType,q1 as upperBound,y as util,PK as valueAndGrad,OK as valueAndGrads,j1 as variable,zw as variableGrads,Ace as version,D8 as version_converter,_X as version_core,cY as version_cpu,Jse as version_wasm,s9 as version_webgl,Cat as webgl,Ac as webgl_util,Yv as webgpu_util,lo as where,Qd as whereAsync,Gr as zeros,Ht as zerosLike};