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r{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new rd}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){jm(t).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let o=e.factory();if(o&&!(o instanceof so)&&typeof o.then=="function"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(o),()=>this.endScope(n),()=>(n=e(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(t,e,o){t();try{let n=o();return e(),n}catch(n){throw e(),n}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(t){let e=T.runKernel(Co,{x:t}),o={x:t},n=a=>({x:()=>{let i="float32",p={x:a},u={dtype:i};return T.runKernel(yo,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[e],n,s,{}),e}runKernel(t,e,o){if(this.backendName==null&&this.backend,!(qp(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-e-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${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=qp(f,this.backendName);$(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=t,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=t,m=ww(t)?null:t.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),e=d.outputs)}),n&&this.addTapeNode(u,c,e,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:e.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(t,e,o){let n=aw(t);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?($(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(e).map(u=>e[u])):i=s.map(u=>e[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(t,e,o,n){if(t==null)throw new Error("Values passed to engine.makeTensor() are null");o=o||"float32",n=n||this.backend;let s=t;o==="string"&&zo(t[0])&&(s=t.map(p=>Zi(p)));let a=n.write(s,e,o),i=new mt(e,o,a,this.nextTensorId());if(this.trackTensor(i,n),o==="string"){let p=this.state.tensorInfo.get(a),u=rw(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(t,e,o,n){o=o||"float32";let s={dataId:t,shape:e,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(t,e){let{dataId:o,shape:n,dtype:s}=t,a=new mt(n,s,o,this.nextTensorId());return this.trackTensor(a,e),a}makeVariable(t,e=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==t.dtype&&(t=t.cast(n));let s=new 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e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!=="complex64"&&t.dtype!=="string"){let o=t.size*zp(t.dtype);this.state.numBytes-=o}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:o,saved:s},p=aw(t);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=Wp(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=yl(t),o=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(t,e,o,n=!1){if($(e.length>0,()=>"gradients() received an empty list of xs."),o!=null&&o.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));$(s instanceof mt,()=>"The result y returned by f() must be a tensor.");let a=K0(this.state.activeTape,e,s);if(!n&&a.length===0&&e.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[s.id]=o==null?l4(s.shape):o,q0(i,a,u=>this.tidy(u),m4);let p=e.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(t){return $(qs(t),()=>"The f passed in customGrad(f) must be a function."),(...e)=>{$(e.every(i=>i instanceof mt),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let o,n={};e.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=t(...e,p),$(o.value instanceof mt,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),$(qs(o.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];$(c.length===e.length,()=>"The function f passed in customGrad(f) must 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T.tidy(()=>{$(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let e=r.shape[0],o=r.shape[1],n=bd(e),s=Wr(r),a=cu([[1]],[1,1]),i=Wr(a),p=e>=o?o:e;for(let u=0;u{let d=Xe(s,[u,u],[e-u,1]),f=Bu(d),h=Xe(s,[u,u],[1,1]),g=co(zu(h,0),cu([[-1]]),cu([[1]])),x=Te(h,se(g,f)),b=je(d,x);b.shape[0]===1?i=Wr(a):i=yt([a,Xe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=pr(je(Ze(g,x),f)),S=Xe(s,[u,0],[e-u,o]),k=se(C,i),_=cc(i);if(u===0)s=Te(S,Ze(k,Ze(_,S)));else{let D=Te(S,Ze(k,Ze(_,S)));s=yt([Xe(s,[0,0],[u,o]),D],0)}let E=cc(k),R=Xe(n,[0,u],[e,n.shape[1]-u]);if(u===0)n=Te(R,Ze(Ze(R,i),E));else{let D=Te(R,Ze(Ze(R,i),E));n=yt([Xe(n,[0,0],[e,u]),D],1)}return[i,s,n]}),Ot([c,l,m])}return!t&&e>o&&(n=Xe(n,[0,0],[e,o]),s=Xe(s,[0,0],[o,o])),[n,s]})}var NN=N({qr_:vj});var Et;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Et||(Et={}));function kj(r,t,e=Et.SUM_BY_NONZERO_WEIGHTS){let 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d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(t=>t.variable)),Ot(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 Zu=class extends kr{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,Zt(a));i.assign(p);let u=Ce(se(je(a,Rr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(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 Ju=class extends kr{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:`${s}/m`,variable:De(()=>Gt(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Gt(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(Zt(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(Rr(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&&Ot(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Ot(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(ii(this.beta1,this.iterations_+1)),this.accBeta2.assign(ii(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 ep=class extends kr{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:`${s}/m`,variable:Gt(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Gt(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=Qt(u),h=Dd(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&&Ot(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(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 li=class extends kr{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=Er(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 tp=class extends li{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:`${o}/momentum`,variable:De(()=>Gt(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(t)?t[n].tensor:t[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(o=>({originalName:o.name,variable:o.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var rp=class extends kr{static get className(){return"RMSProp"}constructor(t,e=.9,o=0,n=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){(Array.isArray(t)?t.map(o=>o.name):Object.keys(t)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Gt(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(Zt(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),Rr(Te(c,Ce(Zt(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(Zt(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Rr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Ot(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=[Qu,Zu,Ju,ep,tp,rp,li];function GN(){for(let r of Jj)tS(r)}var 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TypeError(`Node type ${r.op} is not implemented`)}};function $T(r,t,e){let[o,n]=I("fusedOps",r,t,e),s=o==="biasadd",a=!s,i=n==="prelu",p=o==="fusedbatchnorm",u=I("numArgs",r,t,e);if(s){if(i&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(p)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",r,t,e),l=Al(r,t,e),m=I("dataFormat",r,t,e).toUpperCase(),d=I("dilations",r,t,e),[f,h]=I("args",r,t,e);a&&(h=f,f=void 0);let g=I("leakyreluAlpha",r,t,e);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var ET=(r,t,e,o=Je)=>{switch(r.op){case"Conv1D":{let n=I("stride",r,t,e),s=I("pad",r,t,e),a=I("dataFormat",r,t,e).toUpperCase(),i=I("dilation",r,t,e);return[o.conv1d(I("x",r,t,e),I("filter",r,t,e),n,s,a,i)]}case"Conv2D":{let n=I("strides",r,t,e),s=Al(r,t,e),a=I("dataFormat",r,t,e).toUpperCase(),i=I("dilations",r,t,e);return[o.conv2d(I("x",r,t,e),I("filter",r,t,e),[n[1],n[2]],s,a,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=$T(r,t,e);return[o.fused.conv2d({x:I("x",r,t,e),filter:I("filter",r,t,e),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=$T(r,t,e);return[o.fused.depthwiseConv2d({x:I("x",r,t,e),filter:I("filter",r,t,e),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("kernelSize",r,t,e),i=I("includeBatchInIndex",r,t,e),{result:p,indexes:u}=o.maxPoolWithArgmax(I("x",r,t,e),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case"AvgPool3D":{let n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("kernelSize",r,t,e);return[o.avgPool3d(I("x",r,t,e),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"MaxPool3D":{let n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("kernelSize",r,t,e);return[o.maxPool3d(I("x",r,t,e),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"Dilation2D":{let n=I("strides",r,t,e),s=I("pad",r,t,e),a=I("dilations",r,t,e),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I("x",r,t,e),I("filter",r,t,e),[i,p],s,[u,c],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RT=(r,t,e,o=Je)=>{switch(r.op){case"Fill":{let n=I("shape",r,t,e),s=I("dtype",r,t,e),a=I("value",r,t,e);return[o.fill(n,a,s)]}case"LinSpace":{let n=I("start",r,t,e),s=I("stop",r,t,e),a=I("num",r,t,e);return[o.linspace(n,s,a)]}case"Multinomial":{let 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n=I("x",r,t,e),s=I("axis",r,t,e),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FT=(r,t,e,o=Je)=>{switch(r.op){case"Const":return t[r.name];case"PlaceholderWithDefault":let n=I("default",r,t,e);return[Bt(r.name,t,e)||n];case"Placeholder":return[Bt(r.name,t,e)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",r,t,e);return[Bs(c)]}case"IdentityN":return I("x",r,t,e).map(c=>Bs(c));case"Snapshot":let s=I("x",r,t,e);return[Bs(s)];case"Shape":return[o.tensor1d(I("x",r,t,e).shape,"int32")];case"ShapeN":return I("x",r,t,e).map(c=>o.tensor1d(c.shape));case"Size":return[o.scalar(I("x",r,t,e).size,"int32")];case"Rank":return[o.scalar(I("x",r,t,e).rank,"int32")];case"NoOp":return[o.scalar(1)];case"Print":let a=I("x",r,t,e),i=I("data",r,t,e),p=I("message",r,t,e),u=I("summarize",r,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down 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=mo(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 If(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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n=I("images",r,t,e),s=I("transforms",r,t,e),a=I("outputShape",r,t,e),i=I("fillValue",r,t,e),p=I("interpolation",r,t,e),u=I("fillMode",r,t,e);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(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 LT=(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 ${r.op} is not implemented`)}};var BT=(r,t,e,o=Je)=>{switch(r.op){case"EuclideanNorm":return[o.euclideanNorm(I("x",r,t,e),I("axis",r,t,e),I("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[o.batchNorm(I("x",r,t,e),I("mean",r,t,e),I("variance",r,t,e),I("offset",r,t,e),I("scale",r,t,e),I("epsilon",r,t,e))];case"FusedBatchNormV3":return[o.batchNorm(I("x",r,t,e),I("mean",r,t,e),I("variance",r,t,e),I("offset",r,t,e),I("scale",r,t,e),I("epsilon",r,t,e))];case"LRN":return[o.localResponseNormalization(I("x",r,t,e),I("radius",r,t,e),I("bias",r,t,e),I("alpha",r,t,e),I("beta",r,t,e))];case"Softmax":return[o.softmax(I("x",r,t,e))];case"LogSoftmax":return[o.logSoftmax(I("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zT=(r,t,e,o=Je)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I("paramsNestedSplits",r,t,e),I("paramsDenseValues",r,t,e),I("indices",r,t,e),I("outputRaggedRank",r,t,e));return n.concat(s)}case"RaggedRange":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I("starts",r,t,e),I("limits",r,t,e),I("splits",r,t,e));return[n,s]}case"RaggedTensorToTensor":return[o.raggedTensorToTensor(I("shape",r,t,e),I("values",r,t,e),I("defaultValue",r,t,e),I("rowPartitionTensors",r,t,e),I("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var VT=(r,t,e,o=Je)=>{switch(r.op){case"Max":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.max(I("x",r,t,e),i,p)]}case"Mean":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.mean(I("x",r,t,e),i,p)]}case"Min":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.min(I("x",r,t,e),i,p)]}case"Sum":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.sum(I("x",r,t,e),i,p)]}case"All":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.all(I("x",r,t,e),i,p)]}case"Any":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.any(I("x",r,t,e),i,p)]}case"ArgMax":{let i=I("axis",r,t,e);return[o.argMax(I("x",r,t,e),i)]}case"ArgMin":{let i=I("axis",r,t,e);return[o.argMin(I("x",r,t,e),i)]}case"Prod":{let i=I("axis",r,t,e),p=I("keepDims",r,t,e);return[o.prod(I("x",r,t,e),i,p)]}case"Cumprod":{let i=I("axis",r,t,e),p=I("exclusive",r,t,e),u=I("reverse",r,t,e);return[o.cumprod(I("x",r,t,e),i,p,u)]}case"Cumsum":{let i=I("axis",r,t,e),p=I("exclusive",r,t,e),u=I("reverse",r,t,e);return[o.cumsum(I("x",r,t,e),i,p,u)]}case"Bincount":let n=I("x",r,t,e),s=I("weights",r,t,e),a=I("size",r,t,e);return[o.bincount(n,s,a)];case"DenseBincount":{let i=I("x",r,t,e),p=I("weights",r,t,e),u=I("size",r,t,e),c=I("binaryOutput",r,t,e);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var WT=(r,t,e,o=Je)=>{switch(r.op){case"ConcatV2":case"Concat":{let n=I("n",r,t,e),s=I("axis",r,t,e),a=I("tensors",r,t,e);return a=a.slice(0,n),[o.concat(a,s)]}case"Gather":{let n=I("x",r,t,e),s=I("indices",r,t,e);return[o.gather(n,o.cast(s,"int32"),0)]}case"GatherV2":{let n=I("axis",r,t,e),s=I("batchDims",r,t,e),a=I("x",r,t,e),i=I("indices",r,t,e);return[o.gather(a,o.cast(i,"int32"),n,s)]}case"Reverse":{let n=I("dims",r,t,e),s=[];for(let i=0;i{let n=I("axis",r,t,e),s=I("tensors",r,t,e),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error("the input tensors shape does not match");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case"Unpack":{let n=I("axis",r,t,e),s=I("tensor",r,t,e);return o.unstack(s,n)}case"Tile":{let n=I("reps",r,t,e);return[o.tile(I("x",r,t,e),n)]}case"Split":case"SplitV":{let n=I("axis",r,t,e),s=I("numOrSizeSplits",r,t,e),a=I("x",r,t,e);return o.split(a,s,n)}case"ScatterNd":{let n=I("indices",r,t,e),s=I("values",r,t,e),a=I("shape",r,t,e);return[o.scatterND(n,s,a)]}case"GatherNd":{let n=I("x",r,t,e),s=I("indices",r,t,e);return[o.gatherND(n,s)]}case"SparseToDense":{let n=I("sparseIndices",r,t,e),s=I("outputShape",r,t,e),a=I("sparseValues",r,t,e),i=I("defaultValue",r,t,e);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case"TensorScatterUpdate":{let n=I("indices",r,t,e),s=I("values",r,t,e),a=I("tensor",r,t,e);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(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 GT=(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 HT=(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 KT=(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(()=>wT(a,i,p));case"basic_math":return n(()=>ST(a,i,p));case"control":return _T(a,i,p);case"convolution":return n(()=>ET(a,i,p));case"creation":return n(()=>RT(a,i,p));case"dynamic":return DT(a,i,p);case"evaluation":return n(()=>AT(a,i,p));case"image":return n(()=>OT(a,i,p));case"graph":return n(()=>FT(a,i,p));case"logical":return n(()=>MT(a,i,p));case"matrices":return n(()=>LT(a,i,p));case"normalization":return n(()=>BT(a,i,p));case"ragged":return n(()=>zT(a,i,p));case"reduction":return n(()=>VT(a,i,p));case"slice_join":return n(()=>WT(a,i,p));case"sparse":return n(()=>UT(a,i,p));case"spectral":return n(()=>GT(a,i,p));case"string":return n(()=>HT(a,i,p));case"transformation":return n(()=>KT(a,i,p));case"hash_table":return PT(a,i,p,o);case"custom":let u=uf(a.op);if(u&&u.customExecutor)return u.customExecutor(new Cf(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 Pl=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=>Nr(m)[0]));o=o||[];let c=new Set(o.map(m=>Nr(m.name)[0])),l=[...t];for(;l.length>0;){let m=l.pop();if((mu(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 qT(r,t){let{usedNodes:e,inputs:o}=t,n=Object.keys(o).map(g=>Nr(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 fc=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 fc(`Child ${p.name} of node ${i.name} is unreachable.`);if(e.get(i.name)>e.get(p.name))throw new fc(`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 fc(`Input ${p.name} of node ${i.name} is unreachable.`);if(e.get(p.name)>e.get(i.name))throw new fc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function jT(r){let t=new Map(r.map((i,p)=>[i.name,p])),e=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>mu(i)?e:p),n=i=>{let p=o[t.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;it[o].map(n=>n.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(o=>{this._functionExecutorMap[o]=new r(t.functions[o],this)})}getCompilationKey(t,e){let o=t.map(s=>s.name).sort(),n=e.map(s=>s.name).sort();return o.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(t,e){let o=MS(t,e,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${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: [${n}]`)}let i=qT(this.graph,o),p=jT(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return Er(e),e}cloneTensorList(t){return t?t.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,o])=>[e,this.cloneTensorList(o)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let o=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let n=o.map(m=>this.graph.nodes[Nr(m)[0]]),s=e.map(m=>Nr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(t,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Pl(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,C]=Nr(x,m),S=[];S[C]=t[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=OS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${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=>Bt(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(!(mu(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(mu(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 mu(p)||s.has(p.name)}if(!(mu(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 Pl(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=>Bt(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[Nr(S)[0]]),i=o.map(S=>Nr(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,_]=Nr(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=>!mu(S)&&!Bt(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}]. ${S}`)}return h}processStack(t,e,o,n,s,a,i,p,u){let c=[];for(;e.length>0;){let l=e.pop();o.currentContext=l.contexts;let m="";if(l.node.op==="Enter"&&I("isConstant",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=OS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,e,o,n,s,u))}else this.processChildNodes(l.node,e,o,n,s,u)}return c}processChildNodes(t,e,o,n,s,a){t.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,e.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let o=t[e],[n]=Nr(e),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${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 ${o.dtype}`)})}mapInputs(t){var e,o;let n={};for(let s in t){let a=(o=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=t[s]:n[s]=t[s]}return n}checkInputs(t){let e=Object.keys(t).filter(o=>{let[n]=Nr(o);return this.graph.nodes[n]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Nr(e);if(!this.graph.nodes[o])throw new Error(`The output '${e}' is not found in the graph`)})}};var vf=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var T8="?tfjs-format=file",_8="model.json",Ml=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},o=mi){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=o,e==null&&(this.loadOptions={}),this.resourceManager=new vf}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${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}.${e.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Ol(Fl.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Fl.Instance.transformGraph(t.modelInitializer);this.initializer=new Ol(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let o=this.io.getSaveHandlers(t);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${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 mt?[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 mt)&&!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 c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=t[n++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,o=Object.keys(e);for(let n=0;n1?o:o[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await 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Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof r=="string"&&(r=R8(r));let o=new Ml(r,t,e);return await o.load(),o}function E8(r){if(r==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(r instanceof Array){let[o,n]=r;if(!o)throw new Error("modelJSON must be the first element of the array");if(!n||!(n instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in o))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in o))throw new Error("Model JSON is missing 'weightsManifest'");let s=mi.getWeightSpecs(o.weightsManifest),a=mi.getModelArtifactsForJSONSync(o,s,n);t=mi.fromMemorySync(a)}else if("load"in r)t=r;else if("modelTopology"in r&&"weightSpecs"in r&&"weightData"in r)t=mi.fromMemorySync(r);else throw new Error("Unknown model format");let e=new Ml(t);return e.load(),e}function R8(r){return r.endsWith("/")||(r=r+"/"),`${r}${_8}${T8}`}var D8="4.14.0";function Q(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 CPU backend.`)})}var A8=Vt.whereImpl,hc=class r extends so{nextDataId(){return r.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Bo(this,ur())}write(t,e,o){this.firstUse&&(this.firstUse=!1,A().get("IS_NODE")&&w.warn(` ============================ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. ============================`));let n={id:this.nextDataId()};return this.data.set(n,{values:t,dtype:o,refCount:1}),n}makeTensorInfo(t,e,o){let n;if(e==="string"&&o!=null&&o.length>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{dataId:n,shape:t,dtype:e}}refCount(t){return this.data.has(t)?this.data.get(t).refCount:0}incRef(t){let e=this.data.get(t);e.refCount++}decRef(t){if(this.data.has(t)){let e=this.data.get(t);e.refCount--}}move(t,e,o,n,s){this.data.set(t,{values:e,dtype:n,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(t){return this.readSync(t)}readSync(t){let{dtype:e,complexTensorInfos:o}=this.data.get(t);if(e==="complex64"){let n=this.readSync(o.real.dataId),s=this.readSync(o.imag.dataId);return w.mergeRealAndImagArrays(n,s)}return y.convertBackendValuesAndArrayBuffer(this.data.get(t).values,e)}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)}makeOutput(t,e,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e,o,t),this)}disposeData(t,e=!1){if(this.data.has(t)){if(this.data.get(t).refCount--,!e&&this.data.get(t).refCount>0)return!1;let{complexTensorInfos:o}=this.data.get(t);o!=null&&(this.disposeData(o.real.dataId,!0),this.disposeData(o.imag.dataId,!0)),this.data.delete(t)}return!0}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}async time(t){let e=y.now();return t(),{kernelMs:y.now()-e}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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i=s.reduce((x,b)=>x*b),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=Ve({inputs:{x:n},backend:e,attrs:{shape:p}}),f=St({inputs:{x:d},backend:e,attrs:{perm:u}}),h=Ve({inputs:{x:f},backend:e,attrs:{shape:c}}),g=Ao({inputs:{x:h},backend:e,attrs:{begin:l,size:m}});return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var a$={kernelName:Js,backendName:"cpu",kernelFunc:RY};function DY(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=e.data.get(n.dataId).values,p=e.data.get(s.dataId).values,u=yc(i,p,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,u)}var i$={kernelName:Jo,backendName:"cpu",kernelFunc:DY};function AY(r){let{inputs:t,backend:e}=r,{s0:o,s1:n}=t,s=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values,i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return e.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var u$={kernelName:ea,backendName:"cpu",kernelFunc:AY};var FY=Ie(bo,(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 lr({inputs:{x:p[0]},backend:e});if(p[0].dtype==="complex64"){let h=p.map(S=>Eo({inputs:{input:S},backend:e})),g=p.map(S=>Oa({inputs:{input:S},backend:e})),x=du({inputs:h,backend:e,attrs:{axis:s}}),b=du({inputs:g,backend:e,attrs:{axis:s}}),C=Ht({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=np(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 m$={kernelName:ta,backendName:"cpu",kernelFunc:du};function bI(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;Q([n,s],"conv2d");let l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat==="channelsLast",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),E=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=e.data.get(n.dataId).values,U=e.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let ct=le+Pe*_[1],Ge=be+st*D,lt=ct;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],ct=le+Pe*u.inChannels,Ge=st;for(let lt=0;ltMath.cos(r)),b$={kernelName:sn,backendName:"cpu",kernelFunc:VY};var WY=Ie(an,r=>Math.cosh(r)),C$={kernelName:an,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 R$={kernelName:Mi,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 ${Mi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ne,re=oe)}}}O[J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var D$={kernelName:Oi,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 ${Oi}, 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)),ov(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&&jl(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,o=!0){return this.throwIfDisposed(),o?LI(this.gl,t,e):BI(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return 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n===er.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):n===er.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):n===er.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):n===er.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):n===er.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=kD(o,n),a=ND(e,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=vD(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 vD(r,t,e,o,n){let s=jZ(t,o),a;if(n){let[p,u]=Ma(r[0],r[1]);a=p*u}else{let[p,u]=fp(r[0],r[1]);a=p*u}let i=qZ(e,s);return a*i}function jZ(r,t){switch(r){case er.PACKED_2X2_FLOAT32:return ah(t);case er.PACKED_2X2_FLOAT16:return ih(t);case er.UNPACKED_FLOAT32:return oh(t);case er.UNPACKED_FLOAT16:return nh(t);case er.PACKED_4X1_UNSIGNED_BYTE:return sh(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function XZ(r){return A().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?er.PACKED_2X2_FLOAT32:er.UNPACKED_FLOAT32:r?er.PACKED_2X2_FLOAT16:er.UNPACKED_FLOAT16}function kD(r,t){if(r===mr.UPLOAD)return er.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return XZ(t);if(r===mr.DOWNLOAD||r===mr.PIXELS)return er.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function ND(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var tr=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${e} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},Wt="if (isnan(x)) return x;",TD="return x;",dv="return abs(x);";var _D="return (x >= 0.0) ? x : (exp(x) - 1.0);",$D=Wt+` return (x < 0.0) ? 0.0 : x; `,ED=Wt+` return (x < 0.0) ? 0.0 : min(6.0, x); `,La="return x;",RD="return 1.0 / (1.0 + exp(-1.0 * x));";var AD="return x;",FD=` 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; `,PD=` 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; `,OD=` 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; `,MD="return 1.0 / (1.0 + exp(-1.0 * x));",Ar=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${e} } void main() { vec4 x = getAAtOutCoords(); vec4 y = unaryOperation(x); setOutput(y); } `}};var dh=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=ut(this.outputShape.length);let e=t.length,o=Rt("rc",e),n=Re(e),s=ID(e,o),a=o.slice(-2),i=e<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` void main() { ${n} rc = getOutputCoords(); vec4 packedInput = getA(${s}); setOutput(getChannel(packedInput, ${i})); } `}};var QZ=Vt.whereImpl,ZZ=1e-7,JZ=1e-4,fh={};function e9(r){return r in fh||(fh[r]={}),fh[r]}var t9=A().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),r9=600;function o9(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*r9/1024/1024}var Oc=class r extends so{nextDataId(){return r.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new 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 xp)e=t;else{let o=Hr(A().getNumber("WEBGL_VERSION"),t);e=new xp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Hr(A().getNumber("WEBGL_VERSION"));e=new xp(o),this.binaryCache=e9(A().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new mh(this.gpgpu),this.numMBBeforeWarning=o9(),this.texData=new Bo(this,ur())}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=Nc(e),c=new Yl(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:mr.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:mr.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 Ar(i,La):m=new tr(i,La);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 Ar(n,La):f=new tr(n,La);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,...Kl(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)&&ur().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 Ar(s,La):d=new tr(s,La);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=ur().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 ur().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,o),this)}unpackTensor(t){let e=new dh(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new lh(t.shape),o=!0;return this.runWebGLProgram(e,[t],t.dtype,null,o)}packedReshape(t,e){let o=[hi(t.shape),...gi(t.shape)],n={dtype:t.dtype,shape:o,dataId:t.dataId},s=[hi(e),...gi(e)],a=new Pc(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=Nc(s),p;n?p=new Jf(i):p=new Zf(i);let u=!0,c=[e!=null?e:Kl(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===fu.DENSE){let x=a!=null?a:Kl(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&&!hu(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=RR(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")||ER(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=VI(o,p),e.texShape=l),s!=null){let m=Nc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ma(l[0],l[1])),p?d=new rh(m,g):d=new Yl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=mr.PIXELS:C.usage=mr.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?(Kf(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}=jI(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=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. 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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 qr=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=ut(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) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let p=Rt("coords",s);this.enableShapeUniforms?a+=` bool nextRowOutOfBounds = (${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 Dt(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 BD={kernelName:Co,backendName:"webgl",kernelFunc:Dt};function Pr(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=Dt({inputs:{x:o},backend:e}),p=Dt({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var zD={kernelName:Ri,backendName:"webgl",kernelFunc:Pr};var fv="return (a < 0.) ? b * a : a;",hv=` 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 qr(hv,n.shape,a.shape):new Fr(fv,n.shape,a.shape),p=e.runWebGLProgram(i,[n,a],"float32");return e.disposeIntermediateTensorInfo(a),p}var VD={kernelName:En,backendName:"webgl",kernelFunc:a9};var gv="return (a < 0.) ? b * a : a;",xv=` 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 qr(xv,o.shape,n.shape):new Fr(gv,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],"float32")}var WD={kernelName:rs,backendName:"webgl",kernelFunc:i9};var Fo="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 Ar(a.shape,t):c=new tr(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 Fr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,E],dt(S.dtype,k.dtype))}),b=Pr({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 qr(t,p.shape,u.shape,e):d=new Fr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function xi(r,t=!1){if(r==="linear")return t?AD:TD;if(r==="relu")return t?PD:$D;if(r==="elu")return t?FD:_D;if(r==="relu6")return t?OD:ED;if(r==="prelu")return t?xv:gv;if(r==="leakyrelu")return t?hv:fv;if(r==="sigmoid")return t?MD:RD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Lc=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=ut(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); ${u} } setOutput(sumValue); } `}};var hh=class{constructor(t,e){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=t;this.outputShape=[n,a];let i="0.0",p="";e==="prod"?i="1.0":e==="min"?(i="1.0 / 1e-20",p="min"):e==="max"&&(i="-1.0 / 1e-20",p="max");let u=`${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 Xr(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 ${this.rank} is not yet supported.`);let n=Re(this.rank),s=mv("rc",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${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?xi(p,!0):null,U=M||L||B||z!=null,j;if((d===1||f===1)&&O>bv&&U===!1){let Y=E,J=R;e&&(Y=bt({inputs:{x:E},backend:n,attrs:{perm:[0,2,1]}}),D.push(Y)),o&&(J=bt({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=Jl({inputs:{a:ee,b:ie},backend:n});j=bp({inputs:{x:le},backend:n,attrs:{axis:oe,keepDims:!0}}),D.push(le)}else{let Y=dt(r.dtype,t.dtype),J=new Lc(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 Cp({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var YD={kernelName:So,backendName:"webgl",kernelFunc:l9};var QD="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=ph(s.values);return e.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Ar(o.shape,QD):n=new tr(o.shape,QD),e.runWebGLProgram(n,[o],o.dtype)}var ZD={kernelName:Xs,backendName:"webgl",kernelFunc:m9};var d9=Wt+` if (abs(x) > 1.) { return NAN; } return acos(x); `,f9=xe({opSnippet:d9}),JD={kernelName:Vo,backendName:"webgl",kernelFunc:f9};var h9=Wt+` if (x < 1.0) return NAN; return log(x + sqrt(x * x - 1.0));`,g9=xe({opSnippet:h9}),eA={kernelName:Wo,backendName:"webgl",kernelFunc:g9};var tA="return a + b;",x9=nt({opSnippet:tA,packedOpSnippet:tA,supportsComplex:!0,cpuKernelImpl:DR}),rA={kernelName:io,backendName:"webgl",kernelFunc:x9};var yh=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 = ${n}; setOutput(result); } `}};var bh=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,a)=>`T${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 Ch(r){let{inputs:t,backend:e}=r,o=t;if(o.length===1)return Dt({inputs:{x:o[0]},backend:e});if(o.length>A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=Ch({inputs:o.slice(0,p),backend:e}),c=Ch({inputs:o.slice(p),backend:e});return Ch({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 bh(o[0].shape,s):new yh(o[0].shape,s);return e.runWebGLProgram(i,o,n)}var oA={kernelName:Uo,backendName:"webgl",kernelFunc:Ch};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=bt({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=Xr(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 nA={kernelName:Go,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=bt({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=Xr(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 sA={kernelName:Ho,backendName:"webgl",kernelFunc:b9};var wh=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 ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}};var Sh=class{constructor(t,e,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${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=Rt("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=Rt("sourceLocR",m-1).concat("inIdx.r"),x=Rt("sourceLocG",m-1).concat("inIdx.g"),b=Rt("sourceLocB",m-1).concat("inIdx.b"),C=Rt("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 aA(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 wh(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=aA(r,t,e,c);return r.disposeIntermediateTensorInfo(c),l}function iA(r,t,e,o=null){let n=o!=null?o.shape:t.shape,s=n[n.length-1],a=w.computeOptimalWindowSize(s),i=new Sh(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=iA(r,t,e,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Ih(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=aA(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 iA(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=bt({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=Ih(e,p,a[0],"max");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var uA={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=bt({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=Ih(e,p,a[0],"min");return u.forEach(l=>e.disposeIntermediateTensorInfo(l)),c}var pA={kernelName:Qs,backendName:"webgl",kernelFunc:w9};var S9=Wt+` if (abs(x) > 1.) { return NAN; } return asin(x); `,I9=xe({opSnippet:S9}),cA={kernelName:Ko,backendName:"webgl",kernelFunc:I9};var v9=Wt+"return log(x + sqrt(x * x + 1.0));",k9=xe({opSnippet:v9}),lA={kernelName:qo,backendName:"webgl",kernelFunc:k9};var N9=Wt+` return atan(x); `,T9=xe({opSnippet:N9}),mA={kernelName:jo,backendName:"webgl",kernelFunc:T9};var _9=Mc+` 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); `+jr+` return result; `,E9=nt({opSnippet:_9,packedOpSnippet:$9}),dA={kernelName:Yo,backendName:"webgl",kernelFunc:E9};var R9=Wt+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,D9=xe({opSnippet:R9}),fA={kernelName:Xo,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}); } `}},xu=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 Dt({inputs:{x:n},backend:e});let l=new Us(c,"avg",!1);return e.runWebGLProgram(l,[n],"float32")}var hA={kernelName:Qo,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 xu(l,"avg",!1);return e.runWebGLProgram(m,[n],"float32")}var gA={kernelName:Zs,backendName:"webgl",kernelFunc:F9};var vh=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 >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},kh=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,m=t.effectiveFilterHeight,d=t.effectiveFilterWidth,f=l-1-t.padInfo.front,h=m-1-t.padInfo.top,g=d-1-t.padInfo.left,x=1/(e*o*n);this.userCode=` const ivec3 pads = ivec3(${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 >= ${t.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); dotProd += dyValue * avgMultiplier; } } } setOutput(dotProd); } `}};function P9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new kh(m);return e.runWebGLProgram(d,[n],a.dtype)}var xA={kernelName:Ei,backendName:"webgl",kernelFunc:P9};function O9(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;Vs([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new vh(c);return e.runWebGLProgram(l,[n],a.dtype)}var yA={kernelName:$i,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 Cp({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var bA={kernelName:Zo,backendName:"webgl",kernelFunc:M9};var Nh=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 Th=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 Th(o.shape,n.shape,s.shape,c,l,p):new Nh(o.shape,n.shape,s.shape,c,l,p);return t.runWebGLProgram(m,u,u[0].dtype)},CA={kernelName:In,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.${Cv[i]} = start[${i}] + coords.${Cv[i]};`);n=` ${e} sourceLoc; ${e} coords = getOutputCoords(); ${s.join(` `)} `,this.userCode=` void main() { ${n} setOutput(getSource(${o})); } `}},Cv=["x","y","z","w","u","v"];function B9(r){if(r===1)return"sourceLoc";if(r<=6)return Cv.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var $h=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=Re(this.rank),o=Rt("coords",this.rank),n=Rt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${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=pt.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]=pt.parseSliceParams(n,s,a);if(pt.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=cD(l.values,i,p,n.shape,n.dtype);return e.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=e.texData.get(n.dataId),c=pt.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $h(p):new _h(p),m=[i];return e.runWebGLProgram(l,[n],n.dtype,m)}return e.uploadToGPU(n.dataId),z9(n,i,p,e)}var wA={kernelName:ha,backendName:"webgl",kernelFunc:Gs};var V9=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:e,attrs:{shape:p}}),h=bt({inputs:{x:f},backend:e,attrs:{perm:u}}),g=te({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:e,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},SA={kernelName:Js,backendName:"webgl",kernelFunc:V9};function W9(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=e.readSync(n.dataId),p=e.readSync(s.dataId),u=uh(i,p,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,u)}var IA={kernelName:Jo,backendName:"webgl",kernelFunc:W9};var U9=` int r = int(a.r) & int(b.r); 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${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; ${_} ${k} setOutput(result); } `}},Oh=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,o=t.padInfo.top,n=t.padInfo.left,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,m=t.filterHeight,d=t.filterWidth,f=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${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 Vc=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=ut(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 Lh(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 Bh({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=Lh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Lh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>bv)&&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(hu(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=Cp({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=Dt({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=Cp({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 zh({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=Lh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Lh(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 Mh(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?xi(i,!0):null,B=new Lc(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=Bh({x:n,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&l==="channelsLast"&&A().getBool("WEBGL_EXP_CONV")){let h=new Vc(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=zh({x:n,filter:s,convInfo:m,backend:e});else{let h=new zc(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 MA={kernelName:tn,backendName:"webgl",kernelFunc:J9};var Vh=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; } ${a?`float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue);`} } } } setOutput(dotProd); } `}},Wh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=t.dataFormat==="channelsLast",i=e-1-t.padInfo.top,p=o-1-t.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=` const ivec2 pads = ivec2(${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); } `}},Uh=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 >= ${t.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},Gh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,o=t.filterHeight,n=t.filterWidth,s=t.strideDepth,a=t.strideHeight,i=t.strideWidth,p=e-1-t.padInfo.front,u=o-1-t.padInfo.top,c=n-1-t.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${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 < ${t.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function eJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Vh(m);return e.runWebGLProgram(d,[n,s],"float32")}var LA={kernelName:Ai,backendName:"webgl",kernelFunc:eJ};var Hh=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=ut(this.outputShape.length);let e=t.filterHeight,o=t.filterWidth,n=e-1-t.padInfo.top,s=o-1-t.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${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 Hh(m);return e.runWebGLProgram(f,[n,s],"float32",d)}else{let d=new Wh(m);return e.runWebGLProgram(d,[n,s],"float32")}}var BA={kernelName:rn,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 Oh(u);return e.runWebGLProgram(c,[n,s],"float32")}var zA={kernelName:on,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 Uh(u);return e.runWebGLProgram(c,[n,s],"float32")}var VA={kernelName:ja,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 Gh(u);return e.runWebGLProgram(c,[n,s],"float32")}var WA={kernelName:nn,backendName:"webgl",kernelFunc:nJ};var sJ=Fo+` return cos(x); `,aJ=` vec4 result = cos(x); bvec4 isNaN = isnan(x); ${jr} return result; `,iJ=xe({opSnippet:sJ,packedOpSnippet:aJ}),UA={kernelName:sn,backendName:"webgl",kernelFunc:iJ};var uJ=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,pJ=xe({opSnippet:uJ}),GA={kernelName:an,backendName:"webgl",kernelFunc:pJ};var Kh=class{constructor(t,e,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=t,[c]=e,[l,m]=o;this.outputShape=[c,l,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${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(${d} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}};var cJ=r=>{let{inputs:t,backend:e,attrs:o}=r,{image:n,boxes:s,boxInd:a}=t,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Kh(n.shape,s.shape,i,p,u);return e.runWebGLProgram(c,[n,s,a],"float32")},HA={kernelName:cn,backendName:"webgl",kernelFunc:cJ};var Sp;(function(r){r.Prod="*",r.Sum="+"})(Sp||(Sp={}));var tm=class{constructor(t,e,o,n){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===Sp.Prod?"1.0":"0.0",i=o?a:`getX(${KA(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 = ${qA(s,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${u}) { int idx = ${c}; ${qA(s,"coords",this.op)} = idx; val ${this.op}= getX(${KA(s,"coords",this.op)}); } setOutput(val); } `}};function KA(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 ${r} is not yet supported`)}function qA(r,t,e){if(r===1)return`${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 ${r} is not yet supported`)}function qh(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=bt({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new tm(r,p.shape,!1,s),f=[[m]],h=l;l=e.runWebGLProgram(d,[l],l.dtype,f),e.disposeIntermediateTensorInfo(h)}if(n){let m=new tm(r,p.shape,n,s),d=l;l=e.runWebGLProgram(m,[l],l.dtype),e.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=bt({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(p),d}return l}function lJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return qh(Sp.Prod,n,e,s,a,i)}var jA={kernelName:un,backendName:"webgl",kernelFunc:lJ};function mJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return qh(Sp.Sum,n,e,s,a,i)}var XA={kernelName:pn,backendName:"webgl",kernelFunc:mJ};function dJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=e.readSync(n.dataId),u=e.readSync(s.dataId),c=uh(p,u,s.dtype,s.shape,a);return e.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=e.bufferSync(n),u=e.bufferSync(s),c=AR(p,u,a,i);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var YA={kernelName:ra,backendName:"webgl",kernelFunc:dJ};var jh=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 jh(f,s,a);return e.runWebGLProgram(h,[n],n.dtype)}var QA={kernelName:ln,backendName:"webgl",kernelFunc:fJ};var Wc=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=ut(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 Uc=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=ut(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 Uc(l):m=new Wc(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 ZA={kernelName:mn,backendName:"webgl",kernelFunc:hJ};var Xh=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 >= ${t.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},Yh=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,o=t.filterWidth,n=t.strideHeight,s=t.strideWidth,a=e-1-t.padInfo.top,i=o-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=` const ivec2 pads = ivec2(${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 * ${p} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function gJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,dy:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Xh(l);return e.runWebGLProgram(m,[n,s],"float32")}var JA={kernelName:Fi,backendName:"webgl",kernelFunc:gJ};function xJ(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,filter:s}=t,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Yh(l);return e.runWebGLProgram(m,[n,s],"float32")}var eF={kernelName:Pi,backendName:"webgl",kernelFunc:xJ};var Qh=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function yJ(r){let{inputs:t,backend:e}=r,{x:o}=t,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:e,attrs:{shape:[s]}}),i=new Qh(s),p=e.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:e,attrs:{shape:n}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(p),u}var tF={kernelName:oa,backendName:"webgl",kernelFunc:yJ};var Zh=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=t,{top:l,left:m}=n;this.userCode=` const ivec2 strides = ivec2(${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 < ${o}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); float val = xVal + wVal; if (val > curVal) { curVal = val; } } } } } float result = curVal; setOutput(result); } `}};function bJ(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s}=t,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c,l=new Zh(u);c=e.runWebGLProgram(l,[n,s],"float32");let m=te({inputs:{x:c},backend:e,attrs:{shape:u.outShape}});return e.disposeIntermediateTensorInfo(c),m}var rF={kernelName:dn,backendName:"webgl",kernelFunc:bJ};function CJ(r){let{inputs:t,backend:e,attrs:o}=r,{equation:n}=o,s=t,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=bp({inputs:{x:m},backend:e,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var oF={kernelName:Li,backendName:"webgl",kernelFunc:CJ};var wJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",SJ=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0); result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0); result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; `,IJ=xe({opSnippet:wJ,packedOpSnippet:SJ}),nF={kernelName:hn,backendName:"webgl",kernelFunc:IJ};var vJ="return (b >= 0.0) ? a : a * (b + 1.0);",kJ=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,NJ=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(kJ,o.shape,n.shape):new Fr(vJ,o.shape,n.shape);return e.runWebGLProgram(s,[o,n],o.dtype)},sF={kernelName:Xa,backendName:"webgl",kernelFunc:NJ};var TJ=` return vec4(equal(a, b)); `,_J="return float(a == b);",$J=nt({opSnippet:_J,packedOpSnippet:TJ,dtype:"bool",cpuKernelImpl:LR}),aF={kernelName:xn,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 = ${w.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,RJ=xe({opSnippet:EJ}),iF={kernelName:gn,backendName:"webgl",kernelFunc:RJ};var DJ=Fo+` return exp(x); `,AJ=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,vv=xe({opSnippet:DJ,packedOpSnippet:AJ,cpuKernelImpl:BR,dtype:"float32"}),uF={kernelName:yn,backendName:"webgl",kernelFunc:vv};function Jh(r){let{inputs:t,attrs:e,backend:o}=r,{dim:n}=e,{input:s}=t,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var pF={kernelName:na,backendName:"webgl",kernelFunc:Jh};var cF="return exp(x) - 1.0;",FJ=xe({opSnippet:cF,packedOpSnippet:cF,cpuKernelImpl:zR}),lF={kernelName:bn,backendName:"webgl",kernelFunc:FJ};var rm=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 eg(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 rm("real",p,t),c=new rm("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=Pr({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 eg(o,!1,e)}var mF={kernelName:Bi,backendName:"webgl",kernelFunc:PJ};var tg=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 bi(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 tg(o,n),i=[[n]];return t.runWebGLProgram(a,[],s,i)}}var dF={kernelName:sa,backendName:"webgl",kernelFunc:bi};var rg=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 fF={kernelName:Cn,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new rg(e.shape);return o.runWebGLProgram(n,[e],e.dtype)}};var hF="return floor(x);",OJ=xe({opSnippet:hF,packedOpSnippet:hF,cpuKernelImpl:VR}),gF={kernelName:wn,backendName:"webgl",kernelFunc:OJ};var MJ=` float s = sign(a) * sign(b); 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vec4 values = ${e.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}};var ng=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=It(),[o,n]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${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 yF={kernelName:Eu,backendName:"webgl",kernelFunc:zJ},Gc,kv=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");(Gc==null||h!==kv)&&(kv=h,Gc=document.createElement("canvas").getContext("2d",{willReadFrequently:kv})),Gc.canvas.width=p,Gc.canvas.height=u,Gc.drawImage(n,0,0,p,u),n=Gc.canvas}let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=mr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),n);let d=A().getBool("WEBGL_PACK")?new ng(l):new og(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=Bh({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?xi(d,!0):null,D=new Vc(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=zh({x:n,filter:s,convInfo:g,backend:e,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?xi(d,!1):null,D=new zc(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 bF={kernelName:Io,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:XR}),DF={kernelName:Fn,backendName:"webgl",kernelFunc:dee};var fee=Fo+` return log(1.0 + x); `,hee=xe({opSnippet:fee}),AF={kernelName:Pn,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"}),FF={kernelName:On,backendName:"webgl",kernelFunc:yee};var bee="return float(!(x >= 1.0));",Cee=xe({opSnippet:bee}),PF={kernelName:Mn,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"}),OF={kernelName:Ln,backendName:"webgl",kernelFunc:Iee};var ig=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 * ${p}; setOutput(val); } `}};var ug=class{constructor(t,e,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=e,i=t[3]-1;this.outputShape=t;let p,u=`float(${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 ug(n.shape,s,a,i,p):new ig(n.shape,s,a,i,p);return e.runWebGLProgram(u,[n],n.dtype)},MF={kernelName:Bn,backendName:"webgl",kernelFunc:vee};var pg=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 pg(n.shape,i,p,u,c);return e.runWebGLProgram(l,[n,s,a],n.dtype)},LF={kernelName:Ya,backendName:"webgl",kernelFunc:kee};function BF(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=Xr(i,r.dtype,"max",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Tv(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 Dt({inputs:{x:n},backend:e});let l=new Us(c,"max",!1);return e.runWebGLProgram(l,[n],n.dtype)}var WF={kernelName:Wn,backendName:"webgl",kernelFunc:$ee};function Eee(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new xu(l,"max",!1);return e.runWebGLProgram(m,[n],n.dtype)}var UF={kernelName:ia,backendName:"webgl",kernelFunc:Eee};var cg=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,o=t.strideWidth,n=t.dilationHeight,s=t.effectiveFilterHeight,a=t.effectiveFilterWidth,i=s-1-t.padInfo.top,p=a-1-t.padInfo.left,u=s*a-1;this.userCode=` const ivec2 pads = ivec2(${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 * ${a} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},lg=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,o=t.strideHeight,n=t.strideWidth,s=t.dilationDepth,a=t.dilationHeight,i=t.dilationWidth,p=t.effectiveFilterDepth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=p-1-t.padInfo.front,m=u-1-t.padInfo.top,d=c-1-t.padInfo.left,f=p*u*c-1;this.userCode=` const ivec3 pads = ivec3(${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 xu(m,"max",!0),f=e.runWebGLProgram(d,[a],a.dtype),h=new lg(m),g=e.runWebGLProgram(h,[n,f],a.dtype);return e.disposeIntermediateTensorInfo(f),g}var GF={kernelName:Ui,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 cg(m),x=e.runWebGLProgram(g,[n,h],i.dtype);return e.disposeIntermediateTensorInfo(h),x}var HF={kernelName:Wi,backendName:"webgl",kernelFunc:Dee};function KF(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 qF={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]=KF(o,i,c,p);return[l,m]}};function jF(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=Xr(i,"float32","mean",o),u=te({inputs:{x:p},attrs:{shape:e},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var XF={kernelName:Un,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 dg=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=Rt("rc",n),u=Rt("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 dg(o.shape,n,s):new mg(o.shape,n,s);return t.runWebGLProgram(a,[o],o.dtype)},ZF={kernelName:Kn,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)); `+jr+` return result; `,zee=nt({opSnippet:Lee,packedOpSnippet:Bee}),JF={kernelName:qn,backendName:"webgl",kernelFunc:zee};var fg=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; `,_v=nt({opSnippet:Vee,packedOpSnippet:Wee,checkOutOfBounds:!0}),e3={kernelName:fn,backendName:"webgl",kernelFunc:_v};var t3="return a - b;",$v=nt({opSnippet:t3,packedOpSnippet:t3,supportsComplex:!0,cpuKernelImpl:bD}),r3={kernelName:Ts,backendName:"webgl",kernelFunc:$v};function Ev(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Tv({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=vv({inputs:{x:c},backend:e}),m=bp({inputs:{x:l},backend:e,attrs:{axis:a,keepDims:!1}}),d=te({inputs:{x:m},backend:e,attrs:{shape:p}}),f=_v({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 o3={kernelName:Is,backendName:"webgl",kernelFunc:Ev};function Uee(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o,p=i?n:Ev({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new fg(u,c,s),m=[[a]],d=e.runWebGLProgram(l,[p],"int32",m);return i||e.disposeIntermediateTensorInfo(p),d}var n3={kernelName:jn,backendName:"webgl",kernelFunc:Uee};var Gee=Wt+` 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]=eD(s.values,o.shape,o.dtype);return e.makeTensorInfo(i,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Ar(o.shape,Hee):n=new tr(o.shape,Gee),e.runWebGLProgram(n,[o],o.dtype)}var s3={kernelName:pa,backendName:"webgl",kernelFunc:Kee};var qee=Vt.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 a3={kernelName:Qn,backendName:"webgl",kernelFunc:jee};var Xee=Vt.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 i3={kernelName:Qa,backendName:"webgl",kernelFunc:Yee};var Qee=Vt.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 u3={kernelName:Zn,backendName:"webgl",kernelFunc:Zee};var hg=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 hg(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},p3={kernelName:Jn,backendName:"webgl",kernelFunc:Jee};function om(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="complex64"){let n=yi({inputs:{input:o},backend:e}),s=om({inputs:{x:n},backend:e}),a=wp({inputs:{input:o},backend:e}),i=om({inputs:{x:a},backend:e}),p=Pr({inputs:{real:s,imag:i},backend:e});return e.disposeIntermediateTensorInfo(n),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(i),p}else return bi({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:e})}var c3={kernelName:Sa,backendName:"webgl",kernelFunc:om};function l3(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=yi({inputs:{input:o},backend:e}),s=l3({inputs:{x:n},backend:e}),a=wp({inputs:{input:o},backend:e}),i=om({inputs:{x:a},backend:e}),p=Pr({inputs:{real:s,imag:i},backend:e});return e.disposeIntermediateTensorInfo(n),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(i),p}else return bi({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:e})}var m3={kernelName:ca,backendName:"webgl",kernelFunc:l3};function ete(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Jh({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=Jh({inputs:{input:c},backend:e,attrs:{dim:n}});return i.push(l),l}),u=Iv({inputs:p,backend:e,attrs:{axis:n}});return i.forEach(c=>e.disposeIntermediateTensorInfo(c)),u}var d3={kernelName:la,backendName:"webgl",kernelFunc:ete};var gg=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 xg=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=Rt("rc",n),u=Rt("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 bi({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xg(n.shape,s,a):new gg(n.shape,s,a),p=[[a]];return e.runWebGLProgram(i,[n],n.dtype,p)},f3={kernelName:es,backendName:"webgl",kernelFunc:Rv};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); `+jr+` return result; `,ote=nt({opSnippet:tte,packedOpSnippet:rte}),h3={kernelName:ts,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=bt({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}=rD(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=oi(n.dtype),C=Xr(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 g3={kernelName:os,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]=oD(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 x3={kernelName:Up,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]=nD(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 y3={kernelName:Gp,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]=sD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return e.makeTensorInfo(f,s.dtype,h)}var b3={kernelName:Hp,backendName:"webgl",kernelFunc:ite};var Dv=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=aD(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},C3={kernelName:ma,backendName:"webgl",kernelFunc:Dv};var ute="return 1.0 / x;",pte=xe({opSnippet:ute}),w3={kernelName:ns,backendName:"webgl",kernelFunc:pte};var cte=Wt+` 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}),S3={kernelName:ss,backendName:"webgl",kernelFunc:mte};var dte=Wt+` 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}),I3={kernelName:us,backendName:"webgl",kernelFunc:hte};var yg=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 bg=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 bg(n.shape,p,u,s,a):new yg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],"float32")}var v3={kernelName:is,backendName:"webgl",kernelFunc:gte};var Cg=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 Cg(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var k3={kernelName:Ja,backendName:"webgl",kernelFunc:xte};var wg=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 Sg=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 < ${o-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function yte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Sg(n.shape,p,u,s,a):new wg(n.shape,p,u,s,a);return e.runWebGLProgram(c,[n],n.dtype)}var N3={kernelName:as,backendName:"webgl",kernelFunc:yte};var Ig=class{constructor(t,e,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,n,s]=e,[,a,i]=t,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${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), ${o} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function bte(r){let{inputs:t,backend:e,attrs:o}=r,{images:n,dy:s}=t,{alignCorners:a}=o,i=new Ig(s.shape,n.shape,a);return e.runWebGLProgram(i,[s],s.dtype)}var T3={kernelName:Za,backendName:"webgl",kernelFunc:bte};var vg=class{constructor(t,e){this.variableNames=["x"];let o=t.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${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 kg=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-${o} tensor is not yet supported`);this.outputShape=t;let n=Rt("rc",o),s=`${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 Dt({inputs:{x:n},backend:e});let p=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new kg(n.shape,i):new vg(n.shape,i);return e.runWebGLProgram(p,[n],n.dtype)}var _3={kernelName:ps,backendName:"webgl",kernelFunc:Cte};var Ng=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 $3={kernelName:Ds,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new Ng(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var wte=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,Ste=xe({opSnippet:wte}),E3={kernelName:cs,backendName:"webgl",kernelFunc:Ste};var Ite="return inversesqrt(x);",vte=xe({opSnippet:Ite,cpuKernelImpl:iD}),R3={kernelName:ls,backendName:"webgl",kernelFunc:vte};var yu=class{constructor(t,e,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l="";o===1?l="i":o===2&&(l="i, j");let m=`getIndices(${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 Tg=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 Tg(p,i,d.shape.length,f.shape.length,c,m):g=new yu(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 D3={kernelName:ms,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 A3={kernelName:fs,backendName:"webgl",kernelFunc:Nte};var $g=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 $g(o.shape.length,n.shape,n.shape.length);return e.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var F3={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}),P3={kernelName:hs,backendName:"webgl",kernelFunc:$te};var Ete=Fo+` 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:pD}),O3={kernelName:bs,backendName:"webgl",kernelFunc:Dte};var Ate=` if (isnan(x)) { return 0.0; } return sign(x); `,Fte=xe({opSnippet:Ate}),M3={kernelName:ys,backendName:"webgl",kernelFunc:Fte};var Pte=Fo+` return sin(x); `,Ote=` vec4 result = sin(x); bvec4 isNaN = isnan(x); ${jr} return result; `,Mte=xe({opSnippet:Pte,packedOpSnippet:Ote}),L3={kernelName:gs,backendName:"webgl",kernelFunc:Mte};var Lte=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,Bte=xe({opSnippet:Lte}),B3={kernelName:xs,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}),z3={kernelName:Cs,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},V3={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]=lD(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 W3={kernelName:Hi,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]=mD(i,o.shape,o.dtype,a,p);return[e.makeTensorInfo(c,o.dtype,u),e.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var U3={kernelName:ei,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]=ch(a,o.shape,o.dtype,i,p,!0);return e.makeTensorInfo(c,o.dtype,u)}var G3={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 ${s.shape}`);let a=e.readSync(o.dataId),i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c]=ch(a,o.shape,o.dtype,i,p);return e.makeTensorInfo(c,o.dtype,u)}var H3={kernelName:ba,backendName:"webgl",kernelFunc:Kte};function qte(r){let{inputs:t,backend:e,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=t,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let x=e.bufferSync(n),b=e.bufferSync(s),C=y.decodeString(e.readSync(a.dataId)[0]),S=uD(x,b,i,m,c,u,p,l,C,d);return e.makeTensorInfo(i,S.dtype,S.values)}let f=new yu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=e.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(h),g}var K3={kernelName:vs,backendName:"webgl",kernelFunc:qte};function jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:e,attrs:{begin:c,size:d}});return c[i]+=m,f})}var q3={kernelName:xa,backendName:"webgl",kernelFunc:jte};var j3="return sqrt(x);",Xte=xe({opSnippet:j3,packedOpSnippet:j3,cpuKernelImpl:dD}),X3={kernelName:ws,backendName:"webgl",kernelFunc:Xte};var Yte="return x * x;",Qte=xe({opSnippet:Yte}),Y3={kernelName:Ki,backendName:"webgl",kernelFunc:Qte};var Q3="return (a - b) * (a - b);",Zte=nt({opSnippet:Q3,packedOpSnippet:Q3}),Z3={kernelName:ks,backendName:"webgl",kernelFunc:Zte};function Jte(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t;if(n.dtype!=="string")throw new Error("Input must be of datatype string");let s=e.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=fD(a,"string",o);return e.makeTensorInfo(n.shape,"string",i)}var J3={kernelName:$u,backendName:"webgl",kernelFunc:Jte};function ere({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=Wt+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new tr(o.shape,n);return e.runWebGLProgram(s,[o],o.dtype)}var eP={kernelName:wo,backendName:"webgl",kernelFunc:ere};var Eg=class{constructor(t,e,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i="";if(n===1)i="coords * strides + begin";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${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}=pt.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=pt.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=hD(d,D,S,b);k=e.makeTensorInfo(f,n.dtype,P.values)}else{let R=new Eg(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 tP={kernelName:Ns,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]=gD(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],"string",f),e.makeTensorInfo(l.shape,"int32",h)]}var rP={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: ${a.shape}`);let i=e.readSync(s.dataId),p=e.readSync(a.dataId)[0],[u,c,l]=xD(i,p,n),m=c.length;return[e.makeTensorInfo([m,2],"int32",u),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(l))]}var oP={kernelName:qi,backendName:"webgl",kernelFunc:ore};function nre(r){let{inputs:t,backend:e,attrs:o}=r,{numBuckets:n}=o,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(n<=0)throw new Error("Number of buckets must be at least 1");let a=e.readSync(s.dataId),i=yD(a,n);return e.makeTensorInfo(s.shape,"int32",i)}var nP={kernelName:ji,backendName:"webgl",kernelFunc:nre};var sre="return tan(x);",are=xe({opSnippet:sre}),sP={kernelName:_s,backendName:"webgl",kernelFunc:are};var ire=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,ure=xe({opSnippet:ire}),aP={kernelName:$s,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 yu(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 iP={kernelName:ds,backendName:"webgl",kernelFunc:pre};var Rg=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=CD(c,s);return e.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Rg(n.shape,s);return e.runWebGLProgram(a,[n],n.dtype)}var uP={kernelName:uo,backendName:"webgl",kernelFunc:Av};var Dg=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)); } } `}},Ag=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 Ip(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function pP(r){let t=1;for(;tp){let P=e.readSync(n.dataId),[O,M]=wD(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,bi({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&&Ip(e,d);let x=pP(s),b=pP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Dg(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=e.runWebGLProgram(B,L,"int32",U),Ip(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 Ag([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=e.runWebGLProgram(M,O,"int32",B),Ip(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]}}),Ip(e,_);let E=Nv({inputs:{x:g,indices:C},backend:e,attrs:{axis:1,batchDims:1}});Ip(e,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:e}),Ip(e,_);let D=E;return E=te({inputs:{x:E},attrs:{shape:R},backend:e}),Ip(e,D),[E,C]}var cP={kernelName:Es,backendName:"webgl",kernelFunc:lre};var Fg=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 Fg(l,m,a,i,p,g);return e.runWebGLProgram(x,[n,s],"float32")}var lP={kernelName:Rs,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}=SD(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var mP={kernelName:Xi,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 dP={kernelName:wa,backendName:"webgl",kernelFunc:fre};var Pg=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=bt({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=oi(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 Pg(M,k),B=e.compileAndRun(L,[S,_],E);if(p.push(B),B.shape[1]===R)return B;let z=Dv({backend:e,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),U=Av({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=bt({inputs:{x:C},backend:e,attrs:{perm:S}})}return p.forEach(S=>e.disposeIntermediateTensorInfo(S)),C}var fP={kernelName:Yi,backendName:"webgl",kernelFunc:hre};var gre=[YD,ZD,JD,eA,rA,oA,nA,sA,uA,pA,cA,lA,mA,dA,fA,hA,gA,xA,yA,bA,CA,SA,IA,vA,kA,$A,RA,DA,zD,FA,OA,MA,LA,BA,zA,VA,WA,UA,GA,HA,jA,XA,YA,QA,ZA,JA,eF,tF,rF,oF,nF,sF,aF,iF,uF,pF,lF,mF,dF,fF,gF,xF,yF,bF,CF,wF,SF,IF,vF,BD,kF,PA,NF,TF,_F,VD,$F,EF,RF,DF,AF,FF,PF,OF,MF,LF,zF,VF,WF,UF,GF,HF,qF,XF,YF,QF,ZF,JF,n3,GD,s3,a3,i3,u3,NA,p3,m3,d3,f3,h3,WD,g3,x3,y3,b3,C3,TA,e3,w3,S3,I3,KD,v3,k3,N3,T3,_3,$3,E3,R3,D3,A3,F3,P3,O3,M3,L3,B3,wA,o3,z3,V3,W3,U3,G3,H3,K3,q3,X3,Y3,Z3,J3,eP,tP,rP,oP,nP,r3,jD,sP,aP,iP,uP,cP,lP,XD,mP,dP,fP,c3];for(let r of gre)ti(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 bu;(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"})(bu||(bu={}));var hP;function xre(r){hP=r.wasm.cwrap(So,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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CO={kernelName:un,backendName:"wasm",setupFunc:ioe,kernelFunc:uoe};var wO;function poe(r){wO=r.wasm.cwrap(pn,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=ho({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;wO(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let x=w.getUndoAxesPermutation(u);g=ho({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var SO={kernelName:pn,backendName:"wasm",setupFunc:poe,kernelFunc:coe};var IO;function loe(r){IO=r.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function moe(r){let{backend:t,inputs:e,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=s.shape.reduce((m,d)=>m*d,1)!==0,u=n.shape.length===1?[a]:[n.shape[0],a],c=t.makeOutput(u,s.dtype);function l(m){return t.dataIdMap.get(m.dataId).id}return IO(l(n),new Uint8Array(new Int32Array(n.shape).buffer),n.shape.length,a,p,l(s),we[s.dtype],i,l(c)),c}var vO={kernelName:ra,backendName:"wasm",setupFunc:loe,kernelFunc:moe};var kO;function doe(r){kO=r.wasm.cwrap(ln,null,["number","number","number","array","number","array","array","number","number"])}function 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 kO(x,s,a==="NHWC"?1:0,b,n.shape.length-1,C,S,f.length,k),h}var NO={kernelName:ln,backendName:"wasm",setupFunc:doe,kernelFunc:foe};var TO;function hoe(r){TO=r.wasm.cwrap(mn,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 RO(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 DO={kernelName:dn,backendName:"wasm",setupFunc:boe,kernelFunc:Coe};var AO;function woe(r){AO=r.wasm.cwrap(Mi,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 AO(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 FO={kernelName:Mi,backendName:"wasm",setupFunc:woe,kernelFunc:Soe};var PO;function Ioe(r){PO=r.wasm.cwrap(Oi,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 ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=e.makeOutput(n.shape,n.dtype);return PO(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,e.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var OO={kernelName:Oi,backendName:"wasm",setupFunc:Ioe,kernelFunc:voe};var MO=he(hn);var LO;function koe(r){LO=r.wasm.cwrap(Xa,null,["number","number","number"])}function Noe(r){let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=e.makeOutput(n.shape,"float32"),a=i=>e.dataIdMap.get(i.dataId).id;return LO(a(n),a(o),a(s)),s}var BO={kernelName:Xa,backendName:"wasm",setupFunc:koe,kernelFunc:Noe};var Toe=!1,zO=Ue(xn,Toe,"bool");var VO=he(gn);var WO=he(yn,"float32");function Mg(r){let{inputs:t,attrs:e,backend:o}=r,{input:n}=t,{dim:s}=e,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var UO={kernelName:na,backendName:"wasm",kernelFunc:Mg};var GO=he(bn,"float32");function Ov(r){let{attrs:{shape:t,value:e},backend:o}=r,{attrs:{dtype:n}}=r;n=n||y.inferDtype(e);let s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var HO={kernelName:sa,backendName:"wasm",kernelFunc:Ov};var KO;function _oe(r){KO=r.wasm.cwrap(Cn,null,["number","number","number","number","number","number"])}function $oe(r){let{inputs:t,backend:e}=r,{image:o}=t,n=e.makeOutput(o.shape,o.dtype),s=e.dataIdMap.get(o.dataId).id,a=e.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return KO(s,i,p,u,c,a),n}var qO={kernelName:Cn,backendName:"wasm",kernelFunc:$oe,setupFunc:_oe};var jO=he(wn);var Eoe=!1,XO=Ue(Sn,Eoe);var YO;function Roe(r){YO=r.wasm.cwrap(In,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 YO(c,l,m,d,f,n,g),h}var QO={kernelName:In,backendName:"wasm",setupFunc:Roe,kernelFunc:Doe};var ZO;function Aoe(r){ZO=r.wasm.cwrap(Io,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=bu[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 ZO(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 JO={kernelName:Io,backendName:"wasm",setupFunc:Aoe,kernelFunc:Foe};var eM;function Poe(r){eM=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 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=bu[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 eM(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 tM={kernelName:vo,backendName:"wasm",setupFunc:Poe,kernelFunc:Ooe};var rM;function Moe(r){rM=r.wasm.cwrap(vn,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]=sf.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 rM(d,we[o.dtype],h,a,l,i,g,x),u}var oM={kernelName:vn,backendName:"wasm",setupFunc:Moe,kernelFunc:Loe};var nM;function Boe(r){nM=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, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:t}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:t}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=t.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=t.dataIdMap.get(m.dataId).id,k=t.dataIdMap.get(f.dataId).id,_=t.dataIdMap.get(g.dataId).id,E=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return nM(C,we[n.dtype],E,x,k,l.batchSize,R,_),t.disposeData(m.dataId),t.disposeData(f.dataId),g.shape=l.outputShape,g}var sM={kernelName:aa,backendName:"wasm",setupFunc:Boe,kernelFunc:zoe};var Voe=!1,aM=Ue(kn,Voe,"bool");var Woe=!1,iM=Ue(Nn,Woe,"bool");var uM=he(Tn,"bool");var pM=he(_n,"bool");var cM=he($n,"bool");var lM;function Uoe(r){lM=r.wasm.cwrap(En,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;lM(n,we[t.dtype],e,a)}return s}var mM={kernelName:En,backendName:"wasm",setupFunc:Uoe,kernelFunc:Goe};var Hoe=!1,dM=Ue(Rn,Hoe,"bool");var Koe=!1,fM=Ue(Dn,Koe,"bool");var hM;function qoe(r){hM=r.wasm.cwrap(An,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 hM(e.dataIdMap.get(i.dataId).id,o,n,a),i}var gM={kernelName:An,backendName:"wasm",setupFunc:qoe,kernelFunc:joe};var xM=he(Fn);var yM=he(Pn);var Xoe=!1,bM=Ue(On,Xoe,"bool");var CM=he(Mn);var Yoe=!1,wM=Ue(Ln,Yoe,"bool");var Qoe=!1,SM=Ue(E0,Qoe,"bool");var IM;function Zoe(r){IM=r.wasm.cwrap(Bn,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 IM(e.dataIdMap.get(n.dataId).id,e.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var vM={kernelName:Bn,backendName:"wasm",setupFunc:Zoe,kernelFunc:Joe};var kM;function ene(r){kM=r.wasm.cwrap(Ya,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 kM(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 NM={kernelName:Ya,backendName:"wasm",setupFunc:ene,kernelFunc:tne};var TM;function rne(r){TM=r.wasm.cwrap(zn,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}=Tr(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;TM(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 _M={kernelName:zn,backendName:"wasm",setupFunc:rne,kernelFunc:one};var nne=!1,$M=Ue(Vn,nne);var EM;function sne(r){EM=r.wasm.cwrap(Wn,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 EM(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),E}var RM={kernelName:Wn,backendName:"wasm",setupFunc:sne,kernelFunc:ane};var DM;function ine(r){DM=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 DM(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 AM={kernelName:ia,backendName:"wasm",setupFunc:ine,kernelFunc:une};var FM;function pne(r){FM=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 FM(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 PM={kernelName:Ui,backendName:"wasm",setupFunc:pne,kernelFunc:cne};var OM;function lne(r){OM=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 OM(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 MM={kernelName:Wi,backendName:"wasm",setupFunc:lne,kernelFunc:mne};var LM;function dne(r){LM=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 LM(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 BM={kernelName:ua,backendName:"wasm",setupFunc:dne,kernelFunc:fne};var zM;function hne(r){zM=r.wasm.cwrap(Un,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}=Tr(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=Or({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;zM(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 VM={kernelName:Un,backendName:"wasm",setupFunc:hne,kernelFunc:gne};var WM;function xne(r){WM=r.wasm.cwrap(Gn,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}=Tr(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 sL={kernelName:Zn,backendName:"wasm",setupFunc:Ene,kernelFunc:Rne};var Dne=!1,aL=Ue(Yn,Dne,"bool");var iL;function Ane(r){iL=r.wasm.cwrap(Jn,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 iL(m,a,i,p,c),u}var uL={kernelName:Jn,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 pL={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 Mg({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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Lg={kernelName:es,backendName:"wasm",kernelFunc:Lne,setupFunc:Mne};var Bne=!1,mL=Ue(ts,Bne);var dL;function zne(r){dL=r.wasm.cwrap(rs,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=Or({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 dL(i,a,l),p.dtype!=="float32"&&e.disposeData(u.dataId),c}var fL={kernelName:rs,backendName:"wasm",setupFunc:zne,kernelFunc:Vne};var hL;function Wne(r){hL=r.wasm.cwrap(os,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}=Tr(a,n,t),f=l;if(d){let 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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=Or({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(u.dataId)),_L(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:Za,backendName:"wasm",setupFunc:Zne,kernelFunc:Jne};var EL;function ese(r){EL=r.wasm.cwrap(ps,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 vp({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);EL(p,c,a.length,l,n.shape.length,u);let m=zt({inputs:{x:i},attrs:{shape:n.shape},backend:e});return e.disposeData(i.dataId),m}var RL={kernelName:ps,backendName:"wasm",kernelFunc:tse,setupFunc:ese};var DL;function rse(r){DL=r.wasm.cwrap(Ds,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 DL(u,l,m,d,f,s,h,g,S,C.length,c),p}var AL={kernelName:Ds,backendName:"wasm",kernelFunc:ose,setupFunc:rse};var FL=he(cs);var PL=he(ls);var OL;function nse(r){OL=r.wasm.cwrap(ms,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}=lu.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 OL(f,g,we[s.dtype],p,u,c,x,m,b),i}var ML={kernelName:ms,backendName:"wasm",setupFunc:nse,kernelFunc:sse};var LL;function ase(r){LL=r.wasm.cwrap(fs,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. 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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||UL(o,s),n}var GL={kernelName:"Sigmoid",backendName:"wasm",setupFunc:cse,kernelFunc:lse};var HL=he(ys);var KL=he(gs);var qL=he(xs);var jL=he(Cs);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;eB(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 zg(r,!0)}var tB={kernelName:ya,backendName:"wasm",setupFunc:Bg,kernelFunc:xse};function yse(r){return zg(r,!1)}var rB={kernelName:ba,backendName:"wasm",setupFunc:Bg,kernelFunc:yse};var oB;function bse(r){oB=r.wasm.cwrap(vs,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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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Ug=r,Xv=t}function Yse(r,t=!1){if(am)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof r=="string")nm=r;else{sm=r;let e=jse.filter(o=>sm[o]==null);if(e.length>0)throw new Error(`There were no entries found for the following binaries: ${e.join(",")}. 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Number(this.architecture.match(/\d+/));if(this.architecture.startsWith("xe"))return 12}return 0}isIntel(){return this.vendor==="intel"}};var Hg=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=GB(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=GB(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=KB(o),a=t*e*s,i=HB(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=HB(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=KB(n),c=e*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((t,e)=>{t.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function HB(r,t,e,o){return`${r}_${t}_${e}_${o}`}function KB(r){if(r==="rgba8unorm")return 16;throw new Error(`${r} is not supported!`)}function qB(r,t){if(Math.max(...r)>5)throw new Error("Cannot symbolically compute strides for rank > 6 tensor.");let e=r.length,o="xyzwuv",n=r.map(a=>`${t}.${o[a]}`),s=new Array(e-1);s[e-2]=n[e-1];for(let a=e-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Yr=(r,t,e)=>e==="int32"?`atomicAdd(${r}, bitcast(${t}));`:` { var oldValue = 0; loop { let newValueF32 = bitcast(oldValue) + (${t}); let newValue = bitcast(newValueF32); let res = atomicCompareExchangeWeak(${r}, oldValue, newValue); if res.exchanged { break; } oldValue = res.old_value; } }`;var Ci;(function(r){r[r.FROM_PIXELS=0]="FROM_PIXELS",r[r.DRAW=1]="DRAW"})(Ci||(Ci={}));var QB=(r,t,e,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=rae(e,s,t),i=r.createShaderModule({code:a,label:t.constructor.name}),p=A().get("WEBGPU_PRINT_SHADER");if(p!==""){p=p.toLowerCase();let u=p.split(",");(p==="all"||u.some(c=>t.shaderKey.toLowerCase().includes(c)))&&(console.group(t.shaderKey),console.debug(a),console.groupEnd())}return n?r.createComputePipelineAsync({compute:{module:i,entryPoint:"_start"},label:t.constructor.name,layout:"auto"}):r.createComputePipeline({compute:{module:i,entryPoint:"_start"},label:t.constructor.name,layout:"auto"})},Ae=(r,t="f32")=>{switch(r){case 1:return`${t}`;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${r}-component ${t} is not supported.`)}};function ft(r){if(r<=1)return"i32";if(r===2)return"vec2";if(r===3)return"vec3";if(r===4)return"vec4";if(r===5)return"vec5";if(r===6)return"vec6";throw Error(`GPU 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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 { ${JB(e)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u + localIndex); `} } `),e.pixelsOpType!=null){let f=e.pixelsOpType===Ci.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Cu(t.dtype,e.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Cu(r[0].dtype,e.outputComponent)}>;`,h=t.shape.length===3?"vec2":"i32";o.push(` struct Uniform { outShapeStrides : ${h}, size : i32, numChannels : i32, alpha : f32, }; ${f} @group(0) @binding(2) var uniforms: Uniform; `);let g=YB(e);return[XB,o.join(` `),um(t.shape),e.getUserCode(),jB(g,e)].join(` `)}let s,a,i="struct Uniforms { NAN : f32, INFINITY : f32, ";e.variableNames.forEach((f,h)=>{let g=ft(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ft(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ft(t.shape.length);i+=`outShape : ${p}, `,s=t.shape.length-1,a=ft(s),i+=` outShapeStrides: ${a}, `,e.size&&(i+="size : i32, "),e.uniforms&&(i+=e.uniforms),i+="};",i=cae(i),o.push(i),e.atomic?o.push(` @group(0) @binding(0) var result: array>; `):o.push(` @group(0) @binding(0) var result: array<${Cu(t.dtype,e.outputComponent)}>; `),e.variableNames.forEach((f,h)=>{o.push(` @group(0) @binding(${1+h}) var ${f}: array<${e.variableComponents?Cu(r[h].dtype,e.variableComponents[h]):Cu(r[h].dtype,e.outputComponent)}>; `)}),i!==""&&o.push(` @group(0) @binding(${1+e.variableNames.length}) var uniforms: Uniforms; `);let u=iae(t.shape,e.dispatchLayout),c=[XB,o.join(` `)+oae,um(t.shape),u,uae(t.shape.length)];e.atomic||c.push(pae(t.shape,t.dtype,e.outputComponent)),e.variableNames.forEach((f,h)=>{c.push(`${um(r[h].shape,f)}`)});let l=r.map((f,h)=>aae(f,t.shape,e.variableComponents?e.variableComponents[h]:e.outputComponent,e.dispatchLayout.x.length===t.shape.length)).join(` `);c.push(l),c.push(e.getUserCode());let m=YB(e);return c.push(jB(m,e)),c.join(` `)}function ZB(r,t,e){let o=r.shaderKey;if(r.pixelsOpType!=null)return o;let n=[],s=[];t.forEach(c=>{n.push(c.shape),s.push(c.dtype)}),n.push(e.shape),s.push(e.dtype);let a=t.map(c=>w.getBroadcastDims(c.shape,e.shape)),i=t.map(c=>y.arraysEqual(c.shape,e.shape)).join("_"),p=a.map(c=>c.join("_")).join(";"),u=JB(r)?"flatDispatch":"";return o+="_"+(r.workgroupSize?r.workgroupSize.join(","):"")+n.map(c=>c.length).join(",")+s.join(",")+r.variableNames.join(",")+p+i+u,o}var XB=` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; // Checks whether coordinates lie within the bounds of the shape. fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool { return all(coord >= vec2(0)) && all(coord < shape); } fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool { return all(coord >= vec3(0)) && all(coord < shape); } fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool { return all(coord >= vec4(0)) && all(coord < shape); } fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 { return coord; } fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 { return dot(coords, vec2(shape.y, 1)); } fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 { return dot(coords, vec3(shape.y * shape.z, shape.z, 1)); } fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { return dot(coords, vec4( shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); } fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 { let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, 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 floatToUint: vec4 = bitcast>(val); return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u); } `,oae=` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } `;function um(r,t=""){let e=r.length,o=t!==""?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",n=t!==""?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:"outShapeStrides";if(e<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ft(e),i=[];for(let u=0;u vec2 { let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n}; return vec2(d0, d1); }`;let p;return p="var index2 = index;"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`;return`${l}; ${m};`}).join(""),` fn ${o}(index : i32) -> ${a} { ${p} return ${a}(${i.join(",")}); } `}function nae(r,t){let e=r.name,o=r.shape.length,n=ft(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=ft(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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+ coords.u; } `;break;case 6:t+=` fn getOutputIndexFromCoords(coords : vec6) -> i32 { return coords.x * uniforms.outShapeStrides.x + coords.y * uniforms.outShapeStrides.y + coords.z * uniforms.outShapeStrides.z + coords.w * uniforms.outShapeStrides.w + coords.u * uniforms.outShapeStrides.u + coords.v; } `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return t}function JB(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function Cu(r,t=1){if(r==="float32")return Ae(t,"f32");if(r==="int32"||r==="bool")return Ae(t,"i32");throw new Error(`type ${r} is not supported.`)}function pae(r,t,e){let o=r.length,n=Cu(t,e),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(e)}) { result[flatIndex] = ${n}(value); } fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(e,"i32")}) { result[flatIndex] = ${n}(value); } `;if(o>=2){let a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=ft(o);s+=` fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(", ")}, value : ${Ae(e)}) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndex(flatIndex${e===1?"":` / ${e}`}, value); } fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(", ")}, value : ${Ae(e,"i32")}) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndexI32(flatIndex${e===1?"":` / ${e}`}, value); } `}return s}function cae(r){let t=/(\w+)\s*:\s*vec(5|6)/g;r=r.replace(t,o=>"@align(16) "+o);let e=/vec(5|6)\s*,\s*(\w+)/g;return r=r.replace(e,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function YB(r){return!(r.dispatchLayout.hasOwnProperty("y")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty("z")&&r.dispatchLayout.z.length!==0)}var Qv={};qe(Qv,{GPUBytesPerElement:()=>qg,MatMulProgramType:()=>Mo,assertNotComplex:()=>mm,computeDispatch:()=>H,computeWorkPerThreadForConv2d:()=>cm,computeWorkgroupInfoForMatMul:()=>Yv,computeWorkgroupSizeForConv2d:()=>pm,flatDispatchLayout:()=>X,isWebGPUSupported:()=>lm,tilesFitEvenlyIntoShape:()=>mae});var kp=r=>{let t=1;for(let e=0;ee%r[o]===0)}function H(r,t,e=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(kp(r.x.map(i=>t[i]))/(e[0]*o[0])),r.y?Math.ceil(kp(r.y.map(i=>t[i]))/(e[1]*o[1])):1,r.z?Math.ceil(kp(r.z.map(i=>t[i]))/(e[2]*o[2])):1];return[n,s,a]}function Yv(r,t,e,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),t<=16&&e<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function pm(r,t,e=!1){if(e)return[8,8,1];let o=kp(r.x.map(s=>t[s])),n=kp(r.y.map(s=>t[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function cm(r,t,e=!1){if(e)return[4,4,1];let o=kp(r.x.map(s=>t[s])),n=kp(r.y.map(s=>t[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((t,e)=>e)}}function qg(r){if(r==="float32"||r==="int32"||r==="bool"||r==="string")return 4;if(r==="complex64")return 8;throw new Error(`Unknown dtype ${r}`)}function lm(){return!!(globalThis&&globalThis.navigator&&globalThis.navigator.gpu)}function mm(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Mo;(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"})(Mo||(Mo={}));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]},Kc=class r extends so{nextDataId(){return r.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,!lm())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 Gg(e),this.supportTimestampQuery=this.device.features.has("timestamp-query"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Hg(this.device),this.textureManager=new Kg(this.device),this.tensorMap=new Bo(this,ur()),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=qg(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 --enable-dawn-features=allow_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=qg(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=ZB(t,a,s);let i=A().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return t.shaderKey in this.pipelineCache||(this.pipelineCache[t.shaderKey]=QB(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,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},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===Ci.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 Kc(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 wi(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 dr(r,t=!1,e=!1,o=3){if(r===null)return"";let n="";if(r==="linear")n=wi(Z.LINEAR);else if(r==="relu")n=wi(Z.RELU,e);else if(r==="elu")n=wi(Z.ELU,e);else if(r==="relu6")n=wi(Z.RELU6,e);else if(r==="prelu")n=qc(fe.PRELU,e);else if(r==="sigmoid")n=wi(Z.SIGMOID,e);else if(r==="leakyrelu")n=wi(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 Qr(r,t){return` ${r?"value = value + getBiasByOutputCoords(coords);":""} ${t?"value = activation(value, coords);":""} `}function Zv(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 dm(r,t,e,o,n=!1,s=!1,a=!1,i=1){return` ${Zv(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); ${Qr(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 Np(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 ez=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 Tp(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]}) { ${ez(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; ${ez(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 * ${e} + k * 4; let BCached = vec4(mm_readB(batchB, rowB, globalCol), mm_readB(batchB, rowB + 1, globalCol), mm_readB(batchB, rowB + 2, globalCol), mm_readB(batchB, rowB + 3, globalCol)); let ACached = mm_Asub[k]; acc = acc + dot(ACached, BCached); } workgroupBarrier(); } mm_write(batch, globalRow, globalCol, acc); } `}var jg=class{constructor(t,e,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?t[1]:t[2];if(this.isVec4=(u%4===0&&!o||e[1]%4===0&&o)&&e[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=e[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Yv(e[1],u,e[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(e[1],e[2],u),this.shaderKey=`matMulPacked_${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` ${dr(this.activation,this.hasPreluActivationWeights,this.isVec4)} ${dm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} ${this.isVec4?Np(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?Bie(this.workgroupSize,this.transposeA):Tp(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 = ${r/2}u; currentSize > 1u; currentSize = currentSize / 2u) { if (localId.x < currentSize) { sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize]; } workgroupBarrier(); } if (localId.x == 0u) { sum = sumValues[0] + sumValues[1]; mm_write(batch, row, col, sum); } } `}var Xg=class{constructor(t,e=!1,o=!1,n=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.transposeA=e,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${e}_${o}`}getUserCode(){return` ${dr(this.activation,this.hasPreluActivationWeights)} ${dm(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 < ${o}; k = k + 1) { acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol]; } workgroupBarrier(); } mm_write(batch, globalRow, globalCol, acc); } `}var Yg=class{constructor(t,e,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push("bias");let c=p!=null;c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return` ${dr(this.activation,this.hasPreluActivationWeights)} ${dm(this.addBias,this.activation,this.transposeA,this.transposeB)} ${Vie(this.workgroupSize)} `}};var Qg=class{constructor(t,e,o=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(t[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&e%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],e],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let t=this.outputComponent;return` ${Zv(!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) { ${Yr("&result[flatIndex + i]",`${t>1?"value[i]":"value"}`,"float32")} } } } ${t===4?Np(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Tp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} `}},Zg=class{constructor(t,e=null,o=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return` ${dr(this.activation,this.hasPreluActivationWeights)} ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); ${Qr(this.addBias,this.activation)} setOutputAtIndex(index, value); } } `}};var Jg=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` ${G("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } `}};function vt(r){let{backend:t,attrs:e}=r,{shape:o,value:n}=e,{dtype:s}=e;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),t.makeTensorInfo(o,s,a)}else{let a=new Jg(o),i=[{type:"float32",data:[n]}];return t.runWebGPUProgram(a,[],s,i)}}var tz={kernelName:sa,backendName:"webgpu",kernelFunc:vt};function pe(r){let{inputs:t,attrs:e}=r,{x:o}=t,{shape:n}=e,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var rz={kernelName:da,backendName:"webgpu",kernelFunc:pe};function _p({a:r,b:t,transposeA:e,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=t.shape.length,l=e?r.shape[u-2]:r.shape[u-1],m=o?t.shape[c-1]:t.shape[c-2],d=e?r.shape[u-1]:r.shape[u-2],f=o?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Sr.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${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=Mo.MatMulReduceProgram:P===1&&m>=2e3?U=Mo.MatMulSplitKProgram:U=Mo.MatMulSmallOutputSizeProgram:U=Mo.MatMulPackedProgram}switch(U){case Mo.MatMulReduceProgram:L=new Xg(z,e,o,s,p,a);break;case Mo.MatMulSplitKProgram:{if(B=vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Qg(z,m,e,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Zg(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 Mo.MatMulSmallOutputSizeProgram:L=new Yg(k,_,z,e,o,s,p,a);break;case Mo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new jg(k,z,e,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p==="leakyrelu"&&(M.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Wie(r){let{inputs:t,backend:e,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=t,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return _p({a:n,b:s,transposeA:p,transposeB:u,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var oz={kernelName:So,backendName:"webgpu",kernelFunc:Wie};var fm=class{constructor(t,e,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${t}`,this.op=t}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { ${qc(this.op,!1)} } ${G("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); let breal = getBRealByOutputIndex(index); let bimag = getBImagByOutputIndex(index); setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } `}};var Si=class{constructor(t,e,o){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=w.assertAndGetBroadcastShape(e,o),this.dispatchLayout=X(this.outputShape),this.op=t,this.useSharedMemoryWithA=e.length<=1&&o.length>1&&e[0]<128,this.useSharedMemoryWithB=o.length<=1&&e.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:e[0],this.shaderKey=`binary_${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 = ${e}(getBByOutputCoords(coords)); setOutputAtIndex(index, binaryOperation(a, b)); } } `;return t}};function At(r){let{inputs:t}=r,{x:e}=t;return r.backend.incRef(e.dataId),{dataId:e.dataId,shape:e.shape,dtype:e.dtype}}var nz={kernelName:Co,backendName:"webgpu",kernelFunc:At};function xo(r){let{inputs:t,backend:e}=r,{real:o,imag:n}=t,s=e.makeTensorInfo(o.shape,"complex64"),a=e.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:e}),p=At({inputs:{x:n},backend:e});return a.complexTensorInfos={real:i,imag:p},s}var sz={kernelName:Ri,backendName:"webgpu",kernelFunc:xo};var Zr=class{constructor(t,e,o=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=e,o!==""&&(this.uniforms=o),this.shaderKey=`unary_${e}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { ${wi(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 Zr(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 Si(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new fm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new fm(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=xo({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 Si(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:az,castImpl:iz,ceilImpl:uz,concatImpl:pz,equalImpl:cz,expImpl:lz,expm1Impl:mz,floorImpl:dz,floorDivImpl:fz,gatherNdImpl:hz,gatherV2Impl:gz,greaterEqualImpl:xz,greaterImpl:yz,lessEqualImpl:bz,lessImpl:Cz,logImpl:wz,maxImpl:Sz,maximumImpl:Iz,minimumImpl:vz,multiplyImpl:kz,negImpl:Nz,notEqualImpl:Tz,prodImpl:_z,rangeImpl:$z,rsqrtImpl:Ez,scatterImpl:Rz,simpleAbsImpl:Dz,sliceImpl:Az,stridedSliceImpl:Fz,stringNGramsImpl:Pz,subImpl:Oz,tileImpl:Mz,topKImpl:Lz,transposeImpl:Bz,uniqueImpl:KPt}=wc;var Uie=ye({opType:Z.ABS,cpuKernelImpl:Dz}),zz={kernelName:Xs,backendName:"webgpu",kernelFunc:Uie};var Gie=ye({opType:Z.ACOS}),Vz={kernelName:Vo,backendName:"webgpu",kernelFunc:Gie};var Hie=ye({opType:Z.ACOSH}),Wz={kernelName:Wo,backendName:"webgpu",kernelFunc:Hie};var Kie=et({opType:fe.ADD,cpuKernelImpl:az,supportsComplex:!0}),Uz={kernelName:io,backendName:"webgpu",kernelFunc:Kie};var ex=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 At({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 ex(s);return e.runWebGPUProgram(a,o,n)}var Gz={kernelName:Uo,backendName:"webgpu",kernelFunc:qie};var tx=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 rx=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) { ${n} } } `}};var jie={mean:"float32",all:"bool",any:"bool"};function Jr(r,t,e,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(t,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=xr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;e&&(d=w.expandShapeToKeepDim(l,i));let f;if((o==="max"||o==="prod")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case"max":let g=Sz(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case"prod":let{outVals:x,outShape:b,outDtype:C}=_z(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=jie[o]||oi(r.dtype),S=[{type:"int32",data:[h]}],k=new ox(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],C,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function Xie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return Jr(n,a,s,"all",e)}var Kz={kernelName:Go,backendName:"webgpu",kernelFunc:Xie};function Yie(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return Jr(n,a,s,"any",e)}var qz={kernelName:Ho,backendName:"webgpu",kernelFunc:Yie};var jc=class{constructor(t,e,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let 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.${Oo(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=xr({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 jc(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 jz={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=xr({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 jc(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 Xz={kernelName:Qs,backendName:"webgpu",kernelFunc:Zie};var Jie=ye({opType:Z.ASIN}),Yz={kernelName:Ko,backendName:"webgpu",kernelFunc:Jie};var eue=ye({opType:Z.ASINH}),Qz={kernelName:qo,backendName:"webgpu",kernelFunc:eue};var tue=ye({opType:Z.ATAN}),Zz={kernelName:jo,backendName:"webgpu",kernelFunc:tue};var rue=et({opType:fe.ATAN2}),Jz={kernelName:Yo,backendName:"webgpu",kernelFunc:rue};var oue=ye({opType:Z.ATANH}),eV={kernelName:Xo,backendName:"webgpu",kernelFunc:oue};var nx=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` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d = coords[3]; let xRCCorner = coords.yz * uniforms.strides; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; let value = getX(batch, xRCorner, xCCorner, d); setOutputAtIndex(index, value); } } `}};var Ba=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${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, ${e});`} } } `}},wu=class{constructor(t,e,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,e==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=e,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${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 e0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{reductionIndices:s,keepDims:a}=o;return Jr(n,s,a,"max",e)}var tV={kernelName:zn,backendName:"webgpu",kernelFunc:e0};function t0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{keepDims:s,axis:a}=o;return Jr(n,a,s,"mean",e)}var rV={kernelName:Un,backendName:"webgpu",kernelFunc:t0};function sx(r,t,e,o){if(t.filterWidth===1&&t.filterHeight===1&&y.arraysEqual(t.inShape,t.outShape))return At({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=t0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(e==="max",()=>`Invalid pool type ${e}`),p=e0({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 nx(t):(e==="avg"?n=new Ba(t,"avg"):(y.assert(e==="max",()=>`Invalid pool type ${e}`),n=new Ba(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 sx(n,c,"avg",e)}var oV={kernelName:Qo,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 wu(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 nV={kernelName:Zs,backendName:"webgpu",kernelFunc:sue};var ax=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); } } `}},ix=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 ix(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 sV={kernelName:Ei,backendName:"webgpu",kernelFunc:aue};function iue(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s}=t,a=s;mm([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new ax(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 aV={kernelName:$i,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 _p({a:n,b:s,transposeA:a,transposeB:i,backend:e})}var iV={kernelName:Zo,backendName:"webgpu",kernelFunc:uue};var ux=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 : ${ft(t.length)}, `,this.shaderKey="slice"}getUserCode(){let t=ft(this.rank),e=pue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):o=this.outputShape.map((s,a)=>`sourceLoc.${r0[a]} = uniforms.start.${Oo(a)} + coords.${r0[a]};`),` ${G("index")} { if (index < uniforms.size) { var sourceLoc : ${t}; let coords = getCoordsFromIndex(index); ${o.join(` `)} setOutputAtIndex(index, getSource(${e})); } } `}},r0=["x","y","z","w","u","v"];function pue(r){if(r===1)return"sourceLoc";if(r<=6)return r0.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]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),e.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=e.tensorMap.get(n.dataId),m=Az(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 ux(i,p),c=[{type:"int32",data:i}];return e.runWebGPUProgram(u,[n],n.dtype,c)}var uV={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=xr({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},pV={kernelName:Js,backendName:"webgpu",kernelFunc:cue};var lue=` fn bincount_write(index: i32, value: f32) { ${Yr("&result[index]","value","float32")} } `,mue=` fn bincount_write(index: i32, value: f32) { atomicStore(&result[index], bitcast(value)); } `,Xc=class{constructor(t,e,o=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=e,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${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=vt({backend:e,attrs:{shape:c,value:0,dtype:l}}),d=new Xc([i],u),f=[{type:"int32",data:[a]}],h=u?[n,s]:[n];return e.runWebGPUProgram(d,h,l,f,m)}var cV={kernelName:Jo,backendName:"webgpu",kernelFunc:due};var px=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 px(i),u=[{type:"int32",data:[s]},{type:"int32",data:[a]}];return e.runWebGPUProgram(p,[o,n],"int32",u)}var lV={kernelName:ea,backendName:"webgpu",kernelFunc:fue};var o0=et({opType:fe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Tz}),mV={kernelName:Yn,backendName:"webgpu",kernelFunc:o0};function Ii(r){let{inputs:t,backend:e}=r,{input:o}=t,n=e.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:e})}var dV={kernelName:Gi,backendName:"webgpu",kernelFunc:Ii};function fV(r,t){let e=new Zr(r.shape,Z.TO_INT),o=t.runWebGPUProgram(e,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function n0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return At({inputs:{x:n},backend:e});let a=Ur(n.shape),i=n0({inputs:{x:n},backend:e,attrs:{dtype:"float32"}}),p=xo({inputs:{real:i,imag:a},backend:e});return a.dispose(),e.disposeData(i.dataId),p}if(n.dtype==="complex64"){let a=Ii({inputs:{input:n},backend:e}),i=n0({inputs:{x:a},backend:e,attrs:{dtype:s}});return e.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({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]=iz(a,n.shape,n.dtype,s);return e.makeTensorInfo(i,p,u)}if(s==="int32")return fV(n,e);if(s==="bool"){let a=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=o0({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 hV={kernelName:yo,backendName:"webgpu",kernelFunc:n0};var hue=ye({opType:Z.CEIL,cpuKernelImpl:uz}),gV={kernelName:en,backendName:"webgpu",kernelFunc:hue};var cx=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 lx=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 cx(n.shape):i=new lx(n.shape),e.runWebGPUProgram(i,[n],n.dtype,p)}var xV={kernelName:bo,backendName:"webgpu",kernelFunc:gue};var mx=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` ${G("index")} { if (index < uniforms.size) { let re = abs(getRealByOutputIndex(index)); let im = abs(getImagByOutputIndex(index)); let mx = max(re, im); // The length function in wgsl may be not underflow-safe on some GPUs. // So the safe solution is to ensure underflow-safety in all cases. setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0)); } } `}};function yV(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function xue(r){let{inputs:t,backend:e}=r,{x:o}=t,n=e.tensorMap.get(o.dataId),s=new mx(o.shape),a=[yV(o,n.complexTensorInfos.real),yV(o,n.complexTensorInfos.imag)];return e.runWebGPUProgram(s,a,a[0].dtype)}var bV={kernelName:Di,backendName:"webgpu",kernelFunc:xue};var dx=class{constructor(t){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=w.computeOutShape(t,1),this.variableNames=t.map((e,o)=>`T${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;sIi({inputs:{input:C},backend:e})),h=r.map(C=>$p({inputs:{input:C},backend:e})),g=Yc(f,t,e),x=Yc(h,t,e),b=xo({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=pz(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 dx(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 s0(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?At({inputs:{x:p[0]},backend:e}):Yc(p,s,e)}var wV={kernelName:ta,backendName:"webgpu",kernelFunc:s0};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` ${dr(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} ${Qr(n,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } }`}var fx=class{constructor(t,e,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.isVec4=((t.inChannels%4===0||t.inChannels%3===0)&&this.isChannelsLast||t.outWidth%4===0&&!this.isChannelsLast)&&t.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=pm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=cm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&t.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=e%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let t=this.isVec4?Np(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Tp(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])} ${t} `}};var hx=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=t.outShape,this.isChannelsLast=t.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=e,this.activation=o,this.hasPreluActivationWeights=n,e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` ${dr(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; ${Qr(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) { ${this.isChannelsLast?"let v = readInp(batch, xRow, xCol, xChannel);":"let v = readInp(batch, xChannel, xRow, xCol);"} let f = readFilt(row, col, xChannel, outChannel); acc = acc + v * f; } } } writeResult(batch, outRow, outCol, outChannel, acc); } `}};var gx=class{constructor(t,e){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e,this.shaderKey=`im2col_${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 xx(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=xx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=xx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=_p({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 gx(_,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=xx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=xx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=_p({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 yx({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 hx(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 fx(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 yx({x:n,filter:s,convInfo:m,backend:o})}var SV={kernelName:tn,backendName:"webgpu",kernelFunc:Sue};var bx=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 = ${this.isChannelsLast?"getDy(batch, idyR, idyC, d2)":"getDy(batch, d2, idyR, idyC)"}; let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } } } setOutputAtIndex(index, dotProd); } } `}},Cx=class{constructor(t){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${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); } } `}},wx=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); } } `}},Sx=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 Cx(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 IV={kernelName:Ai,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 Ix=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=pm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=cm(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?Np(this.elementsPerThread,this.workgroupSize):Tp(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 bx(m);else{f=new Ix(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 vV={kernelName:rn,backendName:"webgpu",kernelFunc:kue};var vx=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 vx(u),d=dt(n.dtype,s.dtype);return e.runWebGPUProgram(m,[n,s],d,l)}var kV={kernelName:on,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 wx(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 NV={kernelName:ja,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 Sx(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 TV={kernelName:nn,backendName:"webgpu",kernelFunc:_ue};var $ue=ye({opType:Z.COS}),_V={kernelName:sn,backendName:"webgpu",kernelFunc:$ue};var Eue=ye({opType:Z.COSH}),$V={kernelName:an,backendName:"webgpu",kernelFunc:Eue};var kx=class{constructor(t,e,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=e;this.outputShape=[s,o[0],o[1],t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${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 kx(n.shape[3],s.shape,i,p),l=[{type:"float32",data:[u]}];return e.runWebGPUProgram(c,[n,s,a],"float32",l)},EV={kernelName:cn,backendName:"webgpu",kernelFunc:Rue};var Ep;(function(r){r.Prod="*",r.Sum="+"})(Ep||(Ep={}));var hm=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}_${this.reverse}`}getUserCode(){let t=this.outputShape.length,e=this.op===Ep.Prod?"1.0":"0.0",o=this.exclusive?e:`getX(${RV(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 = ${DV(t,"coords",this.op)}; var val = ${o}; let pow2 = i32(pow(2.0, uniforms.index)); if (${s}) { let idx = ${a}; ${DV(t,"coords",this.op)} = idx; val ${this.op}= getX(${RV(t,"coords",this.op)}); } setOutputAtIndex(index, val); } } `}};function RV(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 ${r} is not yet supported`)}function DV(r,t,e){if(r===1)return`${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 ${r} is not yet supported`)}function Nx(r,t,e,o,n,s){let a=t.shape.length,i=w.getAxesPermutation([o],a),p=t;i!=null&&(p=xr({inputs:{x:t},backend:e,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new hm(r,p.shape,!1,s),f=l,h=[{type:"float32",data:[m]}];l=e.runWebGPUProgram(d,[l],l.dtype,h),e.disposeData(f.dataId)}if(n){let m=new hm(r,p.shape,n,s),d=l,f=[{type:"float32",data:[0]}];l=e.runWebGPUProgram(m,[l],l.dtype,f),e.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=xr({inputs:{x:l},backend:e,attrs:{perm:m}});return e.disposeData(l.dataId),e.disposeData(p.dataId),d}return l}function Due(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Nx(Ep.Prod,n,e,s,a,i)}var AV={kernelName:un,backendName:"webgpu",kernelFunc:Due};function Aue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,exclusive:a,reverse:i}=o;return Nx(Ep.Sum,n,e,s,a,i)}var FV={kernelName:pn,backendName:"webgpu",kernelFunc:Aue};function Fue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,weights:s}=t,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=vt({backend:e,attrs:{shape:d,value:0,dtype:l}}),h=new Xc(m,c,i),g=[{type:"int32",data:[a]}],x=c?[n,s]:[n];return e.runWebGPUProgram(h,x,l,g,f)}var PV={kernelName:ra,backendName:"webgpu",kernelFunc:Fue};var Tx=class{constructor(t,e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${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 = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Pue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=[{type:"int32",data:[s]}],g=new Tx(f,a);return e.runWebGPUProgram(g,[n],n.dtype,h)}var OV={kernelName:ln,backendName:"webgpu",kernelFunc:Pue};var _x=class{constructor(t,e,o,n=!1,s=null,a=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=e,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${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` ${dr(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` ${dr(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]; ${Qr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } `}};var Zc=class{constructor(t,e=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=t.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t.dataFormat==="channelsLast",e&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=t,this.addBias=e,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let t=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` ${dr(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; } } } ${Qr(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 Qc(d),f.push({type:"int32",data:[g.virtualWidth]})):(g=new Zc(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 MV={kernelName:mn,backendName:"webgpu",kernelFunc:Oue};var $x=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); } } `}},Ex=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 $x(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 LV={kernelName:Fi,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 Ex(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 BV={kernelName:Pi,backendName:"webgpu",kernelFunc:Lue};var Rx=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 Rx(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 zV={kernelName:oa,backendName:"webgpu",kernelFunc:Bue};var Dx=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 Dx(u);return e.runWebGPUProgram(m,[n,s],n.dtype,l)}var VV={kernelName:dn,backendName:"webgpu",kernelFunc:zue};var Ax=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); ${Yr("&result[flatIndexIn]","value",this.type)} } } `}},Fx=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); ${Yr("&result[flatIndexIn]","value",this.type)} } } `}};function Vue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=s.dtype,m=new Fx(c,s.shape,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:e,attrs:{shape:s.shape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var WV={kernelName:Mi,backendName:"webgpu",kernelFunc:Vue};function Wue(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,filter:s,dy:a}=t,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=n.dtype,m=new Ax(c,l),d=[{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:e,attrs:{shape:c.inShape,value:0,dtype:l}});return e.runWebGPUProgram(m,[n,s,a],l,d,f)}var UV={kernelName:Oi,backendName:"webgpu",kernelFunc:Wue};var Px=class{constructor(t,e,o){this.variableNames=["Image"],this.uniforms="alpha: f32,",this.workgroupSize=[64,1,1],this.pixelsOpType=Ci.DRAW,this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=e,this.textureFormat=o,this.shaderKey=`draw_${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 Px(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 GV={kernelName:_u,backendName:"webgpu",kernelFunc:Uue};var a0=et({opType:fe.MUL,cpuKernelImpl:kz,supportsComplex:!0}),HV={kernelName:Xn,backendName:"webgpu",kernelFunc:a0};function i0(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return Jr(n,s,a,"sum",e)}var KV={kernelName:Ss,backendName:"webgpu",kernelFunc:i0};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=i0({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 qV={kernelName:Li,backendName:"webgpu",kernelFunc:Gue};var Hue=ye({opType:Z.ELU}),jV={kernelName:hn,backendName:"webgpu",kernelFunc:Hue};var Kue=r=>{let{inputs:t,backend:e}=r,{dy:o,y:n}=t,s=new Si(fe.ELU_DER,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],o.dtype)},XV={kernelName:Xa,backendName:"webgpu",kernelFunc:Kue};var que=et({opType:fe.EQUAL,dtype:"bool",cpuKernelImpl:cz}),YV={kernelName:xn,backendName:"webgpu",kernelFunc:que};var jue=ye({opType:Z.ERF}),QV={kernelName:gn,backendName:"webgpu",kernelFunc:jue};var Xue=ye({opType:Z.EXP,cpuKernelImpl:lz,dtype:"float32"}),ZV={kernelName:yn,backendName:"webgpu",kernelFunc:Xue};function Ox(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)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var JV={kernelName:na,backendName:"webgpu",kernelFunc:Ox};var Yue=ye({opType:Z.EXPM1,cpuKernelImpl:mz}),eW={kernelName:bn,backendName:"webgpu",kernelFunc:Yue};var gm=class{constructor(t,e){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=t,this.shaderKey=`fft_${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 Mx(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 gm("real",u),l=new gm("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=xo({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 Mx(o,!1,e)}var tW={kernelName:Bi,backendName:"webgpu",kernelFunc:Que};var Lx=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` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; let outputValue = getX(coords[0], coords[1], coordX, coords[3]); setOutputAtIndex(index, outputValue); } } `}};var rW={kernelName:Cn,backendName:"webgpu",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,o=t,n=new Lx(e.shape);return o.runWebGPUProgram(n,[e],e.dtype)}};var Zue=ye({opType:Z.FLOOR,cpuKernelImpl:dz}),oW={kernelName:wn,backendName:"webgpu",kernelFunc:Zue};var Jue=et({opType:fe.FLOOR_DIV,cpuKernelImpl:fz,dtype:"int32"}),nW={kernelName:Sn,backendName:"webgpu",kernelFunc:Jue};var Bx=class{constructor(t,e,o=!1){this.pixelsOpType=Ci.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[e,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${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 sW={kernelName:Eu,backendName:"webgpu",kernelFunc:epe},Jc,u0=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=A().getBool("WEBGPU_IMPORT_EXTERNAL_TEXTURE")&&a,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");(Jc==null||L!==u0)&&(u0=L,Jc=document.createElement("canvas").getContext("2d",{willReadFrequently:u0})),Jc.canvas.width=c,Jc.canvas.height=l,Jc.drawImage(n,0,0,c,l),n=Jc.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 Bx(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 aW={kernelName:In,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 zx(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 yx({x:n,filter:s,convInfo:g,backend:e,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var iW={kernelName:Io,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 Qc(h,x,m,b),C.push({type:"int32",data:[S.virtualWidth]})):(S=new Zc(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 uW={kernelName:vo,backendName:"webgpu",kernelFunc:rpe};var Vx=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 : ${ft(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=hz(b,C,o.dtype,u,a,c,l,o.shape,i);return e.makeTensorInfo(p,o.dtype,S.values)}let f=new Vx(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 pW={kernelName:vn,backendName:"webgpu",kernelFunc:ope};var Wx=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 Wx(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 cW={kernelName:aa,backendName:"webgpu",kernelFunc:p0};var spe=et({opType:fe.GREATER,cpuKernelImpl:yz,dtype:"bool"}),lW={kernelName:kn,backendName:"webgpu",kernelFunc:spe};var ape=et({opType:fe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:xz}),mW={kernelName:Nn,backendName:"webgpu",kernelFunc:ape};function ipe(r){let{inputs:t,backend:e}=r,{input:o}=t;return Mx(o,!0,e)}var dW={kernelName:zi,backendName:"webgpu",kernelFunc:ipe};var upe=ye({opType:Z.IS_FINITE,dtype:"bool"}),fW={kernelName:Tn,backendName:"webgpu",kernelFunc:upe};var ppe=ye({opType:Z.IS_INF,dtype:"bool"}),hW={kernelName:_n,backendName:"webgpu",kernelFunc:ppe};var cpe=ye({opType:Z.IS_NAN,dtype:"bool"}),gW={kernelName:$n,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 Zr(n.shape,Z.LEAKYRELU,"alpha : f32,");return e.runWebGPUProgram(i,[n],"float32",a)}var xW={kernelName:En,backendName:"webgpu",kernelFunc:lpe};var mpe=et({opType:fe.LESS,dtype:"bool",cpuKernelImpl:Cz}),yW={kernelName:Rn,backendName:"webgpu",kernelFunc:mpe};var dpe=et({opType:fe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:bz}),bW={kernelName:Dn,backendName:"webgpu",kernelFunc:dpe};var Ux=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 Ux(s),p=[{type:"float32",data:[o]},{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],"float32",p)}var CW={kernelName:An,backendName:"webgpu",kernelFunc:fpe};var hpe=ye({opType:Z.LOG,cpuKernelImpl:wz}),wW={kernelName:Fn,backendName:"webgpu",kernelFunc:hpe};var gpe=ye({opType:Z.LOG1P}),SW={kernelName:Pn,backendName:"webgpu",kernelFunc:gpe};var xpe=et({opType:fe.LOGICAL_AND,dtype:"bool"}),IW={kernelName:On,backendName:"webgpu",kernelFunc:xpe};var ype=ye({opType:Z.LOGICAL_NOT}),vW={kernelName:Mn,backendName:"webgpu",kernelFunc:ype};var bpe=et({opType:fe.LOGICAL_OR}),kW={kernelName:Ln,backendName:"webgpu",kernelFunc:bpe};var NW=` 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)); } `,Gx=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; } } ${NW} setOutputAtIndex(index, x * powValue); } } `}},Hx=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; } ${NW} 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 Gx(n.shape):u=new Hx(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 TW={kernelName:Bn,backendName:"webgpu",kernelFunc:Cpe};var Kx=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` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let b = coords[0]; let r = coords[1]; let c = coords[2]; let MIN_DEPTH_BEGIN = 0; let MAX_DEPTH_END = uniforms.outShape[3]; var result = 0.0; for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) { let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius); let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1); var norm = 0.0; for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) { if (k < depthBegin) { continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = uniforms.alpha * norm + uniforms.bias; for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) { if (k < depthBegin) { continue; } else if (k >= depthBegin && k < depthEnd) { var dyi = -2.0 * uniforms.alpha * uniforms.beta * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * uniforms.beta); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutputAtIndex(index, result); } } `}};function wpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,y:s,dy:a}=t,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new Kx(n.shape),m=[{type:"int32",data:[i]},{type:"float32",data:[p]},{type:"float32",data:[u]},{type:"float32",data:[c]}];return e.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var _W={kernelName:Ya,backendName:"webgpu",kernelFunc:wpe};var Spe=et({opType:fe.MAX,cpuKernelImpl:Iz}),$W={kernelName:Vn,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 sx(n,c,"max",e)}var EW={kernelName:Wn,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 wu(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 RW={kernelName:ia,backendName:"webgpu",kernelFunc:vpe};var qx=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); } } `}},jx=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 wu(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 jx(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 DW={kernelName:Ui,backendName:"webgpu",kernelFunc:kpe};function Npe(r){let{inputs:t,backend:e,attrs:o}=r,{dy:n,input:s,output:a}=t,i=s;mm([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 Ba(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 qx(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 AW={kernelName:Wi,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 Ba(c,"max",!1),d=e.runWebGPUProgram(m,[p],p.dtype,l);m=new Ba(c,"max",!0,!0,i);let f=e.runWebGPUProgram(m,[p],"int32",l);return[d,f]}var FW={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 Jr(n,s,a,"min",e)}var PW={kernelName:Gn,backendName:"webgpu",kernelFunc:_pe};var $pe=et({opType:fe.MIN,cpuKernelImpl:vz}),OW={kernelName:Hn,backendName:"webgpu",kernelFunc:$pe};var Xx=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=ft(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 MW={kernelName:Kn,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 Xx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Epe=et({opType:fe.MOD}),LW={kernelName:qn,backendName:"webgpu",kernelFunc:Epe};var Yx=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 Qx=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 c0(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 Qx(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 BW={kernelName:Is,backendName:"webgpu",kernelFunc:c0};function Rpe(r){let{inputs:t,backend:e,attrs:o}=r,{logits:n}=t,{numSamples:s,seed:a,normalized:i}=o,p=i?n:c0({inputs:{logits:n},backend:e,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Yx(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 zW={kernelName:jn,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]=Nz(s.values,o.shape,o.dtype);return e.makeTensorInfo(i,o.dtype,a)}let n=new Zr(o.shape,Z.NEG);return e.runWebGPUProgram(n,[o],o.dtype)}var VW={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}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return e.makeTensorInfo([l.length],"int32",new Int32Array(l))}var WW={kernelName:Qn,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}=Vt.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 UW={kernelName:Zn,backendName:"webgpu",kernelFunc:Fpe};var Zx=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` ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue, f32(i32(round(getX(coords.x))) == coords.y))); } } `}};function Ppe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n}=t,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Zx(u,a),l=pe({inputs:{x:n},backend:e,attrs:{shape:[u]}}),m=[{type:"float32",data:[i]},{type:"float32",data:[p]}],d=e.runWebGPUProgram(c,[l],s,m);e.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:e,attrs:{shape:f}});return e.disposeData(d.dataId),h}var GW={kernelName:Jn,backendName:"webgpu",kernelFunc:Ppe};function xm(r){let{inputs:t,backend:e}=r,{x:o}=t;if(o.dtype==="complex64"){let n=Ii({inputs:{input:o},backend:e}),s=xm({inputs:{x:n},backend:e}),a=$p({inputs:{input:o},backend:e}),i=xm({inputs:{x:a},backend:e}),p=xo({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 vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:e})}var HW={kernelName:Sa,backendName:"webgpu",kernelFunc:xm};function KW(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=Ii({inputs:{input:o},backend:e}),s=KW({inputs:{x:n},backend:e}),a=$p({inputs:{input:o},backend:e}),i=xm({inputs:{x:a},backend:e}),p=xo({inputs:{real:s,imag:i},backend:e});return e.disposeData(n.dataId),e.disposeData(s.dataId),e.disposeData(a.dataId),e.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:e})}var qW={kernelName:ca,backendName:"webgpu",kernelFunc:KW};function Ope(r){let{inputs:t,backend:e,attrs:o}=r,{axis:n}=o;if(t.length===1)return Ox({inputs:{input:t[0]},backend:e,attrs:{dim:n}});let s=t[0].shape,a=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=t.map(c=>{let l=Ox({inputs:{input:c},backend:e,attrs:{dim:n}});return i.push(l),l}),u=s0({inputs:p,backend:e,attrs:{axis:n}});return i.forEach(c=>e.disposeData(c.dataId)),u}var jW={kernelName:la,backendName:"webgpu",kernelFunc:Ope};function l0(r,t=!1){let e=r.length,o=ft(e),n=r.map((l,m)=>`uniforms.pad${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 Jx=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); ${l0(this.xShape)} } } `}};var Mpe=r=>{let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:e});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return vt({backend:e,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new Jx(n.shape,s);return e.runWebGPUProgram(p,[n],n.dtype,i)},XW={kernelName:es,backendName:"webgpu",kernelFunc:Mpe};var Lpe=et({opType:fe.POW}),YW={kernelName:ts,backendName:"webgpu",kernelFunc:Lpe};function Bpe(r){let{inputs:t,backend:e}=r,{x:o,alpha:n}=t,s=new Si(fe.PRELU,o.shape,n.shape);return e.runWebGPUProgram(s,[o,n],"float32")}var QW={kernelName:rs,backendName:"webgpu",kernelFunc:Bpe};function zpe(r){let{inputs:t,backend:e,attrs:o}=r,{x:n}=t,{axis:s,keepDims:a}=o;return Jr(n,s,a,"prod",e)}var ZW={kernelName:os,backendName:"webgpu",kernelFunc:zpe};var Vpe=r=>{let{backend:t,attrs:e}=r,{start:o,stop:n,step:s,dtype:a}=e,i=$z(o,n,s,a);return t.makeTensorInfo([i.length],a,i)},JW={kernelName:ma,backendName:"webgpu",kernelFunc:Vpe};var Wpe=et({opType:fe.DIV}),eU={kernelName:fn,backendName:"webgpu",kernelFunc:Wpe};var Upe=ye({opType:Z.RECIPROCAL}),tU={kernelName:ns,backendName:"webgpu",kernelFunc:Upe};var Gpe=ye({opType:Z.RELU}),rU={kernelName:ss,backendName:"webgpu",kernelFunc:Gpe};var Hpe=ye({opType:Z.RELU6}),oU={kernelName:us,backendName:"webgpu",kernelFunc:Hpe};var ey=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 ey(n.shape,p,u);return e.runWebGPUProgram(f,[n],"float32",d)}var nU={kernelName:is,backendName:"webgpu",kernelFunc:Kpe};var ty=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 ty(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 sU={kernelName:Ja,backendName:"webgpu",kernelFunc:qpe};var ry=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 = ${t}; // Compute the coordinators of nearest neighbor point. let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z)); let sourceNearestRC = vec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase))); let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutputAtIndex(index, newValue); } } `}};function jpe(r){let{inputs:t,backend:e,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[s?.5:0]}],f=new ry(n.shape,p,u,a);return e.runWebGPUProgram(f,[n],n.dtype,d)}var aU={kernelName:as,backendName:"webgpu",kernelFunc:jpe};var oy=class{constructor(t,e){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=e,this.shaderKey=`resizeNearestNeigborBackprop_${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 oy(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 iU={kernelName:Za,backendName:"webgpu",kernelFunc:Xpe};var ny=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 At({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 ny(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 uU={kernelName:ps,backendName:"webgpu",kernelFunc:Ype};var sy=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)); ${this.fillSnippet} if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 && coordY < uniforms.xShape[1]) { outputValue = getX(coords[0], coordY, coordX, coords[3]); } setOutputAtIndex(index, outputValue); } } `}};var pU={kernelName:Ds,backendName:"webgpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=t,i=e,p=new sy(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?l.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:"float32",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Qpe=ye({opType:Z.ROUND}),cU={kernelName:cs,backendName:"webgpu",kernelFunc:Qpe};var Zpe=ye({opType:Z.RSQRT,cpuKernelImpl:Ez}),lU={kernelName:ls,backendName:"webgpu",kernelFunc:Zpe};var za=class{constructor(t,e,o,n,s,a,i,p=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.sliceDimGreaterThanOne=e>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}_${s.length}`;let u=ft(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 = ${Cu(this.type)}(${i}); let flatIndex = getOutputIndexFromCoords(${n}); ${this.sumDupeIndices?Yr("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} } }`}};function Jpe(r){let{inputs:t,backend:e,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return e.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:e,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:e,attrs:{shape:[p,u]}}),h=f.dtype,g=vt({backend:e,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[x]}],C=new za(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=e.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:e,attrs:{shape:a}});return e.disposeData(d.dataId),e.disposeData(f.dataId),e.disposeData(S.dataId),k}var mU={kernelName:ms,backendName:"webgpu",kernelFunc:Jpe};var ay=class{constructor(t,e){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=e,this.shaderKey=`search_sorted_${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 ay([s.shape[0],s.shape[1]],a),p=[{type:"int32",data:[n.shape[1]]}];return e.runWebGPUProgram(i,[n,s],"int32",p)}var dU={kernelName:fs,backendName:"webgpu",kernelFunc:ece};var iy=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(${e})); } } } `}};function tce(r){let{inputs:t,backend:e}=r,{condition:o,t:n,e:s}=t,a=new iy(o.shape.length,n.shape,n.shape.length);return e.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var fU={kernelName:fa,backendName:"webgpu",kernelFunc:tce};var rce=ye({opType:Z.SELU}),hU={kernelName:hs,backendName:"webgpu",kernelFunc:rce};var oce=ye({opType:Z.SIGMOID}),gU={kernelName:bs,backendName:"webgpu",kernelFunc:oce};var nce=ye({opType:Z.SIGN}),xU={kernelName:ys,backendName:"webgpu",kernelFunc:nce};var sce=ye({opType:Z.SIN}),yU={kernelName:gs,backendName:"webgpu",kernelFunc:sce};var ace=ye({opType:Z.SINH}),bU={kernelName:xs,backendName:"webgpu",kernelFunc:ace};var ice=ye({opType:Z.SOFTPLUS}),CU={kernelName:Cs,backendName:"webgpu",kernelFunc:ice};var uy=class{constructor(t,e,o,n,s,a){this.variableNames=["x"],this.outputShape=[],this.uniforms="",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let t=ft(this.outputShape.length),e=Jv(this.newDim);return` ${um(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); ${l0(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 uy(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},wU={kernelName:ga,backendName:"webgpu",kernelFunc:uce};var py=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; ${Yr("&result[outIndex]","value",this.type)} } } `}},cy=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]; ${Yr("&result[segmentId]","1","int32")} } } `}},ly=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 my(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=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new py(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=vt({backend:n,attrs:{shape:[l],value:0,dtype:"int32"}});m=new cy(l,e.shape);let C=n.runWebGPUProgram(m,[e],"int32",null,b),S=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new ly(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 my(o,n,s,!1,e)}var SU={kernelName:ya,backendName:"webgpu",kernelFunc:pce};function cce(r){let{inputs:t,backend:e}=r,{data:o,indices:n,segmentIds:s}=t;return my(o,n,s,!0,e)}var IU={kernelName:ba,backendName:"webgpu",kernelFunc:cce};var dy=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=Mz(c,s);return e.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new dy(n.shape,s);return e.runWebGPUProgram(a,[n],n.dtype)}var vU={kernelName:uo,backendName:"webgpu",kernelFunc:ym};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=Rz(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]}}):At({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=ym({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 za([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 za([u,c],p,h.shape.length,b.shape.length,l,f,x,d);e.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new za([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 kU={kernelName:vs,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 NU={kernelName:xa,backendName:"webgpu",kernelFunc:dce};var fce=ye({opType:Z.SQRT}),TU={kernelName:ws,backendName:"webgpu",kernelFunc:fce};var _U={kernelName:Ki,backendName:"webgpu",kernelFunc:({inputs:r,backend:t})=>{let{x:e}=r,o=t,n=new Zr(e.shape,Z.SQUARE);return o.runWebGPUProgram(n,[e],e.dtype)}};var hce=et({opType:fe.SQUARED_DIFFERENCE}),$U={kernelName:ks,backendName:"webgpu",kernelFunc:hce};function gce({inputs:r,attrs:t,backend:e}){let{x:o}=r,n=new Zr(o.shape,Z.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return e.runWebGPUProgram(n,[o],o.dtype,s)}var EU={kernelName:wo,backendName:"webgpu",kernelFunc:gce};var fy=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=ft(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}=pt.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: ${n.shape.length}`);let _=pt.computeOutShape(b,C,S),E=Hs({inputs:{x:n},backend:e,attrs:{begin:b,size:_}});k=pe({inputs:{x:E},backend:e,attrs:{shape:f}}),e.disposeData(E.dataId)}else if(e.shouldExecuteOnCPU([n])){let E=e.readSync(n.dataId),R=me(n.shape,n.dtype,E),D=Fz(d,R,S,b);k=e.makeTensorInfo(f,n.dtype,D.values)}else{let E=new fy(d),R=[{type:"int32",data:b},{type:"int32",data:S}],D=e.runWebGPUProgram(E,[n],n.dtype,R);k=pe({inputs:{x:D},backend:e,attrs:{shape:f}}),e.disposeData(D.dataId)}return k}var RU={kernelName:Ns,backendName:"webgpu",kernelFunc:xce};function yce(r){let{inputs:t,backend:e,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=t,m=e.readSync(c.dataId),d=e.readSync(l.dataId),[f,h]=Pz(m,d,n,s,a,i,p,u);return[e.makeTensorInfo([f.length],"string",f),e.makeTensorInfo(l.shape,"int32",h)]}var DU={kernelName:Ca,backendName:"webgpu",kernelFunc:yce};var bce=et({opType:fe.SUB,cpuKernelImpl:Oz,supportsComplex:!0}),AU={kernelName:Ts,backendName:"webgpu",kernelFunc:bce};var Cce=ye({opType:Z.TAN}),FU={kernelName:_s,backendName:"webgpu",kernelFunc:Cce};var wce=ye({opType:Z.TANH}),PU={kernelName:$s,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=ym({inputs:{x:g},backend:e,attrs:{reps:Array(m.length).fill(1)}}),b=new za([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 OU={kernelName:ds,backendName:"webgpu",kernelFunc:Sce};var hy=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)); } } } `}},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, 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 el(r,t){t!==null&&r.disposeData(t.dataId)}function MU(r){let t=1;for(;tf===null?[l,l]:[l,f],g=(k,_,E)=>{let R=h(),D=new hy(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),el(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 gy([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),el(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]}}),el(e,x);let b=p0({inputs:{x:l,indices:f},backend:e,attrs:{axis:1,batchDims:1}});el(e,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:e}),el(e,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:e}),el(e,S),[b,f]}var LU={kernelName:Es,backendName:"webgpu",kernelFunc:Ice};var xy=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 xy(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 BU={kernelName:Rs,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 zU={kernelName:wa,backendName:"webgpu",kernelFunc:kce};var yy=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); ${Yr("&result[flatIndex]","value",this.type)} } } } `}};function Nce(r){let{inputs:t,backend:e,attrs:o}=r,{x:n,segmentIds:s}=t,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=xr({inputs:{x:n},backend:e,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=vt({backend:e,attrs:{shape:g,value:0,dtype:h}}),b=new yy(f.shape,g,h),C=[{type:"int32",data:[a]},{type:"int32",data:[y.sizeFromShape(f.shape)]}],S=e.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:e,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let E=w.getUndoAxesPermutation(c);_=xr({inputs:{x:_},backend:e,attrs:{perm:E}})}return p.forEach(E=>e.disposeData(E.dataId)),_}var VU={kernelName:Yi,backendName:"webgpu",kernelFunc:Nce};var Tce=[oz,zz,Vz,Wz,Uz,Gz,Kz,qz,jz,Xz,Yz,Qz,Zz,Jz,eV,oV,nV,sV,aV,iV,pV,cV,lV,hV,gV,xV,sz,bV,wV,SV,IV,vV,kV,NV,TV,_V,$V,EV,AV,FV,PV,OV,LV,BV,MV,zV,VV,WV,UV,GV,qV,jV,XV,YV,QV,ZV,JV,eW,tW,tz,rW,sW,oW,nW,aW,iW,uW,pW,cW,lW,mW,nz,dW,CV,fW,hW,gW,xW,yW,bW,CW,SW,wW,IW,vW,kW,TW,_W,tV,$W,EW,AW,RW,DW,FW,rV,PW,OW,MW,LW,zW,HV,VW,WW,UW,mV,GW,qW,jW,XW,YW,QW,ZW,JW,dV,eU,tU,rU,oU,rz,nU,sU,aU,iU,uU,pU,cU,lU,mU,dU,fU,hU,gU,xU,yU,bU,uV,EU,RU,DU,BW,CU,wU,SU,IU,kU,NU,TU,_U,$U,AU,KV,FU,PU,OU,vU,LU,BU,Hz,zU,VU,HW];for(let r of Tce)ti(r);var WU="4.14.0",_ce="4.14.0",$ce="4.14.0",Ece="4.14.0",Rce="4.14.0",Dce="4.14.0",Ace={tfjs:WU,"tfjs-core":WU,"tfjs-converter":_ce,"tfjs-backend-cpu":$ce,"tfjs-backend-webgl":Ece,"tfjs-backend-wasm":Rce,"tfjs-backend-webgpu":Dce};var bQt=void 0;export{Xs as Abs,Vo as Acos,Wo as Acosh,Qu as AdadeltaOptimizer,Zu as AdagradOptimizer,Ju as AdamOptimizer,ep as AdamaxOptimizer,io as Add,Uo as AddN,Go as All,Ho as Any,Ys as ArgMax,Qs as ArgMin,Ko as Asin,qo as Asinh,jo as Atan,Yo as Atan2,Xo as Atanh,Qo as AvgPool,Zs as AvgPool3D,Ei as AvgPool3DGrad,$i as AvgPoolGrad,im as BackendWasm,Zo as BatchMatMul,Js as BatchToSpaceND,Jo as Bincount,qa as BitwiseAnd,ea as BroadcastArgs,Bce as BroadcastTo,yo as Cast,en as Ceil,bo as ClipByValue,Ri as Complex,Di as ComplexAbs,ta as Concat,tn as Conv2D,Ai as Conv2DBackpropFilter,rn as Conv2DBackpropInput,on as Conv3D,ja as Conv3DBackpropFilterV2,nn as Conv3DBackpropInputV2,sn as Cos,an as Cosh,cn as CropAndResize,un as Cumprod,pn as Cumsum,Bo as DataStorage,ra as DenseBincount,ln as DepthToSpace,mn as DepthwiseConv2dNative,Fi as DepthwiseConv2dNativeBackpropFilter,Pi as DepthwiseConv2dNativeBackpropInput,oa as Diag,dn as Dilation2D,Mi as Dilation2DBackpropFilter,Oi as Dilation2DBackpropInput,_u as Draw,ow as ENV,Li as Einsum,hn as Elu,Xa as EluGrad,ll as Environment,xn as Equal,gn as Erf,yn as Exp,na as ExpandDims,bn as Expm1,Bi as FFT,sa as Fill,Cn as FlipLeftRight,wn as Floor,Sn as FloorDiv,Eu as FromPixels,In as FusedBatchNorm,Io as FusedConv2D,vo as FusedDepthwiseConv2D,xp as GPGPUContext,vn as GatherNd,aa as GatherV2,Ml as GraphModel,kn as Greater,Nn as GreaterEqual,zi as IFFT,Co as Identity,Vi as Imag,Tn as IsFinite,_n as IsInf,$n as IsNan,so as KernelBackend,Bn as LRN,Ya as LRNGrad,En as LeakyRelu,Rn as Less,Dn as LessEqual,An as LinSpace,Fn as Log,Pn as Log1p,zce as LogSoftmax,On as LogicalAnd,Mn as LogicalNot,Ln as LogicalOr,E0 as LogicalXor,Vce as LowerBound,hc as MathBackendCPU,Oc as MathBackendWebGL,Wce as MatrixBandPart,zn as Max,Wn as MaxPool,ia as MaxPool3D,Ui as MaxPool3DGrad,Wi as MaxPoolGrad,ua as MaxPoolWithArgmax,Vn as Maximum,Un as Mean,Gn as Min,Hn as Minimum,Kn as MirrorPad,qn as Mod,tp as MomentumOptimizer,jn as Multinomial,Xn as Multiply,pa as Neg,Qn as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,Zn as NonMaxSuppressionV5,Yn as NotEqual,kw as OP_SCOPE_SUFFIX,Jn as OneHot,ca as OnesLike,kr as Optimizer,Dl as OptimizerConstructors,la as Pack,es as PadV2,Uce as Pool,ts as Pow,rs as Prelu,os as Prod,rp as RMSPropOptimizer,Up as RaggedGather,Gp as RaggedRange,Hp as RaggedTensorToTensor,ma as Range,hw as Rank,Gi as Real,fn as RealDiv,ns as Reciprocal,Et as Reduction,ss as Relu,us as Relu6,da as Reshape,is as ResizeBilinear,Ja as ResizeBilinearGrad,as as ResizeNearestNeighbor,Za as ResizeNearestNeighborGrad,ps as Reverse,Ds as RotateWithOffset,cs as Round,ls as Rsqrt,li as SGDOptimizer,ms as ScatterNd,fs as SearchSorted,fa as Select,hs as Selu,bs as Sigmoid,ys as Sign,gs as Sin,xs as Sinh,ha as Slice,Is as Softmax,Cs as Softplus,ga as SpaceToBatchND,Hi as SparseFillEmptyRows,ei as SparseReshape,ya as SparseSegmentMean,ba as SparseSegmentSum,vs as SparseToDense,xa as SplitV,ws as Sqrt,Ki as Square,ks as SquaredDifference,$u as StaticRegexReplace,wo as Step,Ns as StridedSlice,Ca as StringNGrams,qi as StringSplit,ji as StringToHashBucketFast,Ts as Sub,Ss as Sum,_s as Tan,$s as Tanh,mt as Tensor,tt as TensorBuffer,ds as TensorScatterUpdate,uo as Tile,Es as TopK,Rs as Transform,po as Transpose,Xi as Unique,wa as Unpack,Yi as UnsortedSegmentSum,Gce as UpperBound,ri as Variable,Kc as WebGPUBackend,Sa as ZerosLike,So as _FusedMatMul,Qt as abs,Nk as acos,Tk as acosh,Ce as add,_k as addN,$k as all,Ek as any,Rk as argMax,Dk as argMin,Ak as asin,Fk as asinh,Pk as atan,Ok as atan2,Mk as atanh,md as avgPool,zk as avgPool3d,vde as backend,w as backend_util,Vk as basicLSTMCell,ru as batchNorm,Uk as batchNorm2d,Gk as batchNorm3d,Hk as batchNorm4d,dd as batchToSpaceND,fd as bincount,Kk as bitwiseAnd,E6 as booleanMaskAsync,qk as broadcastArgs,ou as broadcastTo,Sr as broadcast_util,nT as browser,me as buffer,We as cast,jk as ceil,Xk as clipByValue,Wr as clone,$r as complex,yt as concat,Yk as concat1d,Qk as concat2d,Zk as concat3d,Jk as concat4d,e2 as conv1d,nu as conv2d,t2 as conv2dTranspose,r2 as conv3d,n2 as conv3dTranspose,Zce as copyRegisteredKernels,s2 as cos,a2 as cosh,_l as cosineWindow,i2 as cumprod,u2 as cumsum,Ir as customGrad,p2 as denseBincount,Pw as deprecationWarn,c2 as depthToSpace,oc as depthwiseConv2d,A5 as deregisterOp,Ji as device_util,l2 as diag,m2 as dilation2d,dde as disableDeprecationWarnings,Ot as dispose,fde as disposeVariables,je as div,f2 as divNoNan,h2 as dot,W6 as dropout,su as einsum,yd as elu,mde as enableDebugMode,lde as enableProdMode,Qw as enclosingPowerOfTwo,ur as engine,g2 as ensureShape,A as env,xd as equal,x2 as erf,C2 as euclideanNorm,_o as exp,Ms as expandDims,w2 as expm1,bd as eye,ac as fft,Ea as fill,Sde as findBackend,Ide as findBackendFactory,Cd as floor,ld as floorDiv,LD as forceHalfFloat,Zw as fused,wd as gather,z6 as gatherND,sf as gather_util,Cde as getBackend,aw as getGradient,qp as getKernel,jm as getKernelsForBackend,Zse as getThreadsCount,lv as gpgpu_util,AK as grad,FK as grads,zu as greater,Sd as greaterEqual,Ku as ifft,iu as imag,Kj as image,G6 as inTopKAsync,mi as io,Gd as irfft,S2 as isFinite,I2 as isInf,v2 as isNaN,Er as keep,Vt as kernel_impls,Id as leakyRelu,kl as less,nc as lessEqual,qj as linalg,k2 as linspace,$8 as loadGraphModel,E8 as loadGraphModelSync,N2 as localResponseNormalization,ui as log,vd as log1p,T2 as logSigmoid,_2 as logSoftmax,Td as logSumExp,Vu as logicalAnd,_d as logicalNot,$d as logicalOr,$2 as logicalXor,jj as losses,E2 as lowerBound,Ze as matMul,eT as math,Ra as max,Rd as maxPool,R2 as maxPool3d,D2 as maxPoolWithArgmax,Dd as maximum,Wu as mean,hde as memory,A2 as meshgrid,vl as min,Uu as minimum,F2 as mirrorPad,P2 as mod,O2 as moments,A6 as movingAverage,se as mul,M2 as multiRNNCell,L2 as multinomial,pr as neg,pS as nextFrame,bQt as node,Bu as norm,Ad as notEqual,Tl as oneHot,Da as ones,B2 as onesLike,N as op,z2 as outerProduct,Aa as pad,V2 as pad1d,W2 as pad2d,U2 as pad3d,G2 as pad4d,H2 as pool,ii as pow,Pd as prelu,cd as print,K2 as prod,gde as profile,q2 as raggedGather,j2 as raggedRange,X2 as raggedTensorToTensor,Y2 as rand,x1 as randomGamma,Vd as randomNormal,y1 as randomStandardNormal,sc as randomUniform,b1 as randomUniformInt,uu as range,bde as ready,pi as real,C1 as reciprocal,tu as registerBackend,Xce as registerGradient,ti as registerKernel,D5 as registerOp,pu as relu,Wd as relu6,wde as removeBackend,W as reshape,lo as reverse,w1 as reverse1d,S1 as reverse2d,I1 as reverse3d,v1 as reverse4d,ic as rfft,Ud as round,k1 as rsqrt,ke as scalar,P6 as scatterND,lu as scatter_util,Nl as searchSorted,N1 as selu,T1 as separableConv2d,UN as serialization,yde as setBackend,kde as setPlatform,Qse as setThreadsCount,Xse as setWasmPath,Yse as setWasmPaths,kI as setWebGLContext,_1 as setdiff1dAsync,wc as shared,$a as sigmoid,$1 as sign,Hj as signal,E1 as sin,R1 as sinh,Xe as slice,D1 as slice1d,A1 as slice2d,F1 as slice3d,P1 as slice4d,pt as slice_util,O1 as softmax,Nd as softplus,Fd as spaceToBatchND,Xj as sparse,L6 as sparseToDense,Gj as spectral,ci as split,Rr as sqrt,Zt as square,Hd as squaredDifference,uc as squeeze,vr as stack,Kd as step,M1 as stridedSlice,Yj as string,Te as sub,ot as sum,oi as sumOutType,L1 as tan,Il as tanh,ar as tensor,Jt as tensor1d,cu as tensor2d,qd as tensor3d,B1 as tensor4d,z1 as tensor5d,V1 as tensor6d,U1 as tensorScatterUpdate,tk as tensor_util,g1 as test_util,De as tidy,au as tile,xde as time,G1 as topk,TGe as train,cc as transpose,H1 as truncatedNormal,K1 as unique,Qce as unregisterGradient,Yce as unregisterKernel,q1 as unsortedSegmentSum,mo as unstack,dt as upcastType,j1 as upperBound,y as util,PK as valueAndGrad,OK as valueAndGrads,X1 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,Tc as webgl_util,Qv as webgpu_util,co as where,Xd as whereAsync,Ur as zeros,Gt as zerosLike};