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r{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new fd}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){ad(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof mo)&&typeof o.then=="function"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of 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t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Pw(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Pw(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=tl(f,this.backendName);$(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 w=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,w);let S=w.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}=e,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:l,attrs:c}=e,m=Pw(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,l,t,m,o,c),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(l).map(f=>l[f]!=null?l[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Cw(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?($(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let p=o.filter((u,l)=>a[l]);return i.concat(p)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");o=o||"float32",n=n||this.backend;let s=e;o==="string"&&dn(e[0])&&(s=e.map(p=>iu(p)));let a=n.write(s,t,o),i=new dt(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o==="string"){let p=this.state.tensorInfo.get(a),u=gw(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||"float32";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new dt(n,s,o,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new 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t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let o=e.size*jp(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let 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e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Tc(e),o=new Set(t.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if($(t.length>0,()=>"gradients() received an empty list of xs."),o!=null&&o.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));$(s instanceof dt,()=>"The result y returned by f() must be a tensor.");let a=Dk(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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l=t?p.shape[p.rank-2]:p.shape[p.rank-1],c=o?u.shape[u.rank-1]:u.shape[u.rank-2],m=t?p.shape[p.rank-1]:p.shape[p.rank-2],d=o?u.shape[u.rank-2]:u.shape[u.rank-1],f=p.shape.slice(0,-2),h=u.shape.slice(0,-2),g=ze(f),x=ze(h);$(l===c,()=>`Error in fused matMul: inner shapes (${l}) and (${c}) of Tensors with shapes ${p.shape} and ${u.shape} and transposeA=${t} and transposeB=${o} must match.`);let w=rt(p.shape.slice(0,-2),u.shape.slice(0,-2)).concat([m,d]),S=t?W(p,[g,l,m]):W(p,[g,m,l]),k=o?W(u,[x,d,c]):W(u,[x,c,d]),T;n!=null&&(T=v(n,"bias","fused matMul"),[T]=Oe(T,p),rt(w,T.shape));let E;a!=null&&(E=v(a,"prelu weights","fused matMul"));let R=(O,M)=>{let[L,B,z,U]=M,j=tp(W(O,z.shape),z,s),q,Y;if(!t&&!o?(q=Je(j,B,!1,!0),Y=Je(L,j,!0,!1)):!t&&o?(q=Je(j,B,!1,!1),Y=Je(j,L,!0,!1)):t&&!o?(q=Je(B,j,!1,!0),Y=Je(L,j,!1,!1)):(q=Je(B,j,!0,!0),Y=Je(j,L,!0,!0)),n!=null){let J=rp(U,j);return[q,Y,J]}else 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W(Tr(zo(W(o,[-1,s,a])).map(f=>Lo(m,f,d))),n)}var dT=N({bandPart_:XX});function YX(r){let e;if(Array.isArray(r)){e=!1,$(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let n=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`)}else e=!0,r=Ci(r,r.shape[0],0).map(n=>gl(n,[0]));$(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let t=[],o=r;for(let n=0;n{let s=o[n];if(n>0)for(let a=0;a=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return hT(r,e);{let t=r.shape.slice(0,r.shape.length-2).reduce((p,u)=>p*u),o=zo(W(r,[t,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),n=[],s=[];o.forEach(p=>{let[u,l]=hT(p,e);n.push(u),s.push(l)});let a=W(Tr(n,0),r.shape),i=W(Tr(s,0),r.shape);return[a,i]}}function hT(r,e=!1){return _.tidy(()=>{$(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let t=r.shape[0],o=r.shape[1],n=$d(t),s=Xr(r),a=bu([[1]],[1,1]),i=Xr(a),p=t>=o?o:t;for(let u=0;u{let d=Ye(s,[u,u],[t-u,1]),f=qu(d),h=Ye(s,[u,u],[1,1]),g=Lo(ju(h,0),bu([[-1]]),bu([[1]])),x=Te(h,se(g,f)),b=Xe(d,x);b.shape[0]===1?i=Xr(a):i=bt([a,Ye(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let w=mr(Xe(Je(g,x),f)),S=Ye(s,[u,0],[t-u,o]),k=se(w,i),T=yl(i);if(u===0)s=Te(S,Je(k,Je(T,S)));else{let D=Te(S,Je(k,Je(T,S)));s=bt([Ye(s,[0,0],[u,o]),D],0)}let E=yl(k),R=Ye(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=Te(R,Je(Je(R,i),E));else{let D=Te(R,Je(Je(R,i),E));n=bt([Ye(n,[0,0],[t,u]),D],1)}return[i,s,n]}),Lt([l,c,m])}return!e&&t>o&&(n=Ye(n,[0,0],[t,o]),s=Ye(s,[0,0],[o,o])),[n,s]})}var gT=N({qr_:QX});var Dt;(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"})(Dt||(Dt={}));function ZX(r,e,t=Dt.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"losses","computeWeightedLoss"),n=null;e!=null&&(n=v(e,"weights","computeWeightedLoss"));let s=n==null?o:se(o,n);if(t===Dt.NONE)return s;if(t===Dt.SUM)return ot(s);if(t===Dt.MEAN){if(n==null)return Yu(s);{let a=o.size/n.size,i=Xe(ot(s),ot(n));return a>1?Xe(i,ke(a)):i}}if(t===Dt.SUM_BY_NONZERO_WEIGHTS){if(n==null)return Xe(ot(s),ke(o.size));{let a=se(n,Ba(o.shape)),i=Ue(ot(Gd(a,ke(0))),"float32");return Xe(ot(s),i)}}throw Error(`Unknown reduction: ${t}`)}var dr=N({computeWeightedLoss_:ZX});function JX(r,e,t,o=Dt.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","absoluteDifference"),s=v(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=v(t,"weights","absoluteDifference")),yt(n.shape,s.shape,"Error in absoluteDifference: ");let i=er(Te(n,s));return dr(i,a,o)}var xT=N({absoluteDifference_:JX});function e5(r,e,t,o,n=Dt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","cosineDistance"),a=v(e,"predictions","cosineDistance"),i=null;o!=null&&(i=v(o,"weights","cosineDistance")),yt(s.shape,a.shape,"Error in cosineDistance: ");let p=ke(1),u=Te(p,ot(se(s,a),t,!0));return dr(u,i,n)}var yT=N({cosineDistance_:e5});function t5(r,e,t,o=Dt.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","hingeLoss"),s=v(e,"predictions","hingeLoss"),a=null;t!=null&&(a=v(t,"weights","hingeLoss")),yt(n.shape,s.shape,"Error in hingeLoss: ");let i=ke(1);n=Te(se(ke(2),n),i);let p=yu(Te(i,se(n,s)));return dr(p,a,o)}var bT=N({hingeLoss_:t5});function r5(r,e,t,o=1,n=Dt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","huberLoss"),a=v(e,"predictions","huberLoss"),i=null;t!=null&&(i=v(t,"weights","huberLoss")),yt(s.shape,a.shape,"Error in huberLoss: ");let p=ke(o),u=er(Te(a,s)),l=Qu(u,p),c=Te(u,l),m=Ce(se(ke(.5),tr(l)),se(p,c));return dr(m,i,n)}var CT=N({huberLoss_:r5});function o5(r,e,t,o=1e-7,n=Dt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","logLoss"),a=v(e,"predictions","logLoss"),i=null;t!=null&&(i=v(t,"weights","logLoss")),yt(s.shape,a.shape,"Error in logLoss: ");let p=ke(1),u=ke(o),l=mr(se(s,yi(Ce(a,u)))),c=se(Te(p,s),yi(Ce(Te(p,a),u))),m=Te(l,c);return dr(m,i,n)}var wT=N({logLoss_:o5});function n5(r,e,t,o=Dt.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","meanSquaredError"),s=v(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=v(t,"weights","meanSquaredError")),yt(n.shape,s.shape,"Error in meanSquaredError: ");let i=rf(n,s);return dr(i,a,o)}var ST=N({meanSquaredError_:n5});function s5(r,e){let t=v(r,"labels","sigmoidCrossEntropyWithLogits"),o=v(e,"logits","sigmoidCrossEntropyWithLogits");yt(t.shape,o.shape,"Error in sigmoidCrossEntropyWithLogits: ");let n=yu(o),s=se(o,t),a=Pd(Jo(mr(er(o))));return Ce(Te(n,s),a)}function a5(r,e,t,o=0,n=Dt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),a=v(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","sigmoidCrossEntropy")),yt(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),o>0){let u=ke(o),l=ke(1),c=ke(.5);s=Ce(se(s,Te(l,u)),se(c,u))}let p=s5(s,a);return dr(p,i,n)}var IT=N({sigmoidCrossEntropy_:a5});function i5(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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a={inputIndices:o,inputShape:n,newShape:s},i=_.runKernel(ui,a);return{outputIndices:i[0],outputShape:i[1]}}var NT=N({sparseReshape_:l5});function c5(r,e,t){let o=v(r,"data","sparseSegmentMean"),n=v(e,"indices","sparseSegmentMean","int32"),s=v(t,"segmentIds","sparseSegmentMean","int32");if(o.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return _.runKernel(va,a)}var TT=N({sparseSegmentMean_:c5});function m5(r,e,t){let o=v(r,"data","sparseSegmentSum"),n=v(e,"indices","sparseSegmentSum","int32"),s=v(t,"segmentIds","sparseSegmentSum","int32");if(o.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape 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className(){return"Adadelta"}constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=_.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=_.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Kt(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Kt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let l=Ce(se(p,this.rho),se(tr(i),1-this.rho)),c=se(Xe(Pr(Ce(u,this.epsilon)),Pr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(tr(c),1-this.rho));p.assign(l),u.assign(m);let d=Ce(se(c,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Lt(this.accumulatedGrads.map(e=>e.variable)),Lt(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var ap=class extends _r{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=_.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>Ma(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,tr(a));i.assign(p);let u=Ce(se(Xe(a,Pr(Ce(p,_.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Lt(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var ip=class extends _r{static get className(){return"Adam"}constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=_.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);t.forEach((s,a)=>{let i=_.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Kt(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Kt(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,m=Ce(se(l,this.beta1),se(u,1-this.beta1)),d=Ce(se(c,this.beta2),se(tr(u),1-this.beta2)),f=Xe(m,o),h=Xe(d,n);l.assign(m),c.assign(d);let g=Ce(se(Xe(f,Ce(Pr(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&&Lt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Lt(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(xi(this.beta1,this.iterations_+1)),this.accBeta2.assign(xi(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*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(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var up=class extends _r{static get className(){return"Adamax"}constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),n==null&&(this.epsilon=_.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Xe(-this.learningRate,Ce(se(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=_.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Kt(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Kt(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(l,this.beta1),se(u,1-this.beta1)),d=se(c,this.beta2),f=er(u),h=Ud(d,f);l.assign(m),c.assign(h);let g=Ce(se(Xe(n,o),Xe(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&&Lt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Lt(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){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(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var wi=class extends _r{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=_.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Fr(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var pp=class extends wi{static get className(){return"Momentum"}constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=_.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Kt(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Lt(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var lp=class extends _r{static get className(){return"RMSProp"}constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=_.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=_.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Kt(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Kt(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Kt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let l=Ce(se(p,this.decay),se(tr(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,m=Ce(se(c,this.decay),se(i,1-this.decay)),d=Xe(se(i,this.learningRate),Pr(Te(l,Ce(tr(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(l),c.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let 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TypeError(`Node type ${r.op} is not implemented`)}};function C_(r,e,t){let[o,n]=I("fusedOps",r,e,t),s=o==="biasadd",a=!s,i=n==="prelu",p=o==="fusedbatchnorm",u=I("numArgs",r,e,t);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 l=I("strides",r,e,t),c=Wc(r,e,t),m=I("dataFormat",r,e,t).toUpperCase(),d=I("dilations",r,e,t),[f,h]=I("args",r,e,t);a&&(h=f,f=void 0);let g=I("leakyreluAlpha",r,e,t);return{stride:l,pad:c,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var w_=(r,e,t,o=et)=>{switch(r.op){case"Conv1D":{let 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performance."),console.log(p);for(let l=0;le.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=zo(t),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,e,t),a=I("valueDType",r,e,t),i=new Ff(s,a);return o.addHashTable(r.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let n=I("tableHandle",r,e,t,o),s=I("keys",r,e,t),a=I("values",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let 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n=I("images",r,e,t),s=I("transforms",r,e,t),a=I("outputShape",r,e,t),i=I("fillValue",r,e,t),p=I("interpolation",r,e,t),u=I("fillMode",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var __=(r,e,t,o=et)=>{switch(r.op){case"Equal":return[o.equal(I("a",r,e,t),I("b",r,e,t))];case"NotEqual":return[o.notEqual(I("a",r,e,t),I("b",r,e,t))];case"Greater":return[o.greater(I("a",r,e,t),I("b",r,e,t))];case"GreaterEqual":return[o.greaterEqual(I("a",r,e,t),I("b",r,e,t))];case"Less":return[o.less(I("a",r,e,t),I("b",r,e,t))];case"LessEqual":return[o.lessEqual(I("a",r,e,t),I("b",r,e,t))];case"LogicalAnd":return[o.logicalAnd(I("a",r,e,t),I("b",r,e,t))];case"LogicalNot":return[o.logicalNot(I("a",r,e,t))];case"LogicalOr":return[o.logicalOr(I("a",r,e,t),I("b",r,e,t))];case"Select":case"SelectV2":return[o.where(I("condition",r,e,t),I("a",r,e,t),I("b",r,e,t))];case"BitwiseAnd":return[o.bitwiseAnd(I("a",r,e,t),I("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var E_=(r,e,t,o=et)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[o.matMul(I("a",r,e,t),I("b",r,e,t),I("transposeA",r,e,t),I("transposeB",r,e,t))];case"Einsum":return[o.einsum(I("equation",r,e,t),...I("tensors",r,e,t))];case"Transpose":return[o.transpose(I("x",r,e,t),I("perm",r,e,t))];case"_FusedMatMul":let[n,s]=I("fusedOps",r,e,t),a=n==="biasadd",i=s==="prelu",p=I("numArgs",r,e,t),u=I("leakyreluAlpha",r,e,t);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[l,c]=I("args",r,e,t);return[o.fused.matMul({a:I("a",r,e,t),b:I("b",r,e,t),transposeA:I("transposeA",r,e,t),transposeB:I("transposeB",r,e,t),bias:l,activation:s,preluActivationWeights:c,leakyreluAlpha:u})];case"MatrixBandPart":return[o.linalg.bandPart(I("a",r,e,t),I("numLower",r,e,t),I("numUpper",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $_=(r,e,t,o=et)=>{switch(r.op){case"EuclideanNorm":return[o.euclideanNorm(I("x",r,e,t),I("axis",r,e,t),I("keepDims",r,e,t))];case"FusedBatchNorm":case"FusedBatchNormV2":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"FusedBatchNormV3":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"LRN":return[o.localResponseNormalization(I("x",r,e,t),I("radius",r,e,t),I("bias",r,e,t),I("alpha",r,e,t),I("beta",r,e,t))];case"Softmax":return[o.softmax(I("x",r,e,t))];case"LogSoftmax":return[o.logSoftmax(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var R_=(r,e,t,o=et)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I("paramsNestedSplits",r,e,t),I("paramsDenseValues",r,e,t),I("indices",r,e,t),I("outputRaggedRank",r,e,t));return 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i=I("axis",r,e,t);return[o.argMax(I("x",r,e,t),i)]}case"ArgMin":{let i=I("axis",r,e,t);return[o.argMin(I("x",r,e,t),i)]}case"Prod":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.prod(I("x",r,e,t),i,p)]}case"Cumprod":{let i=I("axis",r,e,t),p=I("exclusive",r,e,t),u=I("reverse",r,e,t);return[o.cumprod(I("x",r,e,t),i,p,u)]}case"Cumsum":{let i=I("axis",r,e,t),p=I("exclusive",r,e,t),u=I("reverse",r,e,t);return[o.cumsum(I("x",r,e,t),i,p,u)]}case"Bincount":let n=I("x",r,e,t),s=I("weights",r,e,t),a=I("size",r,e,t);return[o.bincount(n,s,a)];case"DenseBincount":{let i=I("x",r,e,t),p=I("weights",r,e,t),u=I("size",r,e,t),l=I("binaryOutput",r,e,t);return[o.denseBincount(i,p,u,l)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var A_=(r,e,t,o=et)=>{switch(r.op){case"ConcatV2":case"Concat":{let n=I("n",r,e,t),s=I("axis",r,e,t),a=I("tensors",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case"Gather":{let n=I("x",r,e,t),s=I("indices",r,e,t);return[o.gather(n,o.cast(s,"int32"),0)]}case"GatherV2":{let n=I("axis",r,e,t),s=I("batchDims",r,e,t),a=I("x",r,e,t),i=I("indices",r,e,t);return[o.gather(a,o.cast(i,"int32"),n,s)]}case"Reverse":{let n=I("dims",r,e,t),s=[];for(let i=0;i{let n=I("axis",r,e,t),s=I("tensors",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let l=y.arraysEqual(u.shape,a);if(!l&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error("the input tensors shape does not match");return l?u:o.reshape(u,a)});return[o.stack(p,n)]});case"Unpack":{let n=I("axis",r,e,t),s=I("tensor",r,e,t);return o.unstack(s,n)}case"Tile":{let n=I("reps",r,e,t);return[o.tile(I("x",r,e,t),n)]}case"Split":case"SplitV":{let n=I("axis",r,e,t),s=I("numOrSizeSplits",r,e,t),a=I("x",r,e,t);return o.split(a,s,n)}case"ScatterNd":{let n=I("indices",r,e,t),s=I("values",r,e,t),a=I("shape",r,e,t);return[o.scatterND(n,s,a)]}case"GatherNd":{let n=I("x",r,e,t),s=I("indices",r,e,t);return[o.gatherND(n,s)]}case"SparseToDense":{let n=I("sparseIndices",r,e,t),s=I("outputShape",r,e,t),a=I("sparseValues",r,e,t),i=I("defaultValue",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case"TensorScatterUpdate":{let n=I("indices",r,e,t),s=I("values",r,e,t),a=I("tensor",r,e,t);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var F_=(r,e,t,o=et)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I("indices",r,e,t),I("values",r,e,t),I("denseShape",r,e,t),I("defaultValue",r,e,t));return[n,s,a,i]}case"SparseReshape":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I("inputIndices",r,e,t),I("inputShape",r,e,t),I("newShape",r,e,t));return[n,s]}case"SparseSegmentMean":return[o.sparse.sparseSegmentMean(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];case"SparseSegmentSum":return[o.sparse.sparseSegmentSum(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var P_=(r,e,t,o=et)=>{switch(r.op){case"FFT":return[o.fft(I("x",r,e,t))];case"IFFT":return[o.ifft(I("x",r,e,t))];case"RFFT":return[o.rfft(I("x",r,e,t))];case"IRFFT":return[o.irfft(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var O_=(r,e,t,o=et)=>{switch(r.op){case"StaticRegexReplace":return[o.string.staticRegexReplace(I("input",r,e,t),I("pattern",r,e,t),I("rewrite",r,e,t),I("replaceGlobal",r,e,t))];case"StringNGrams":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I("data",r,e,t),I("dataSplits",r,e,t),I("separator",r,e,t),I("nGramWidths",r,e,t),I("leftPad",r,e,t),I("rightPad",r,e,t),I("padWidth",r,e,t),I("preserveShortSequences",r,e,t));return[n,s]}case"StringSplit":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I("input",r,e,t),I("delimiter",r,e,t),I("skipEmpty",r,e,t));return[n,s,a]}case"StringToHashBucketFast":return[o.string.stringToHashBucketFast(I("input",r,e,t),I("numBuckets",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var M_=(r,e,t,o=et)=>{switch(r.op){case"Cast":return[o.cast(I("x",r,e,t),I("dtype",r,e,t))];case"ExpandDims":{let n=I("axis",r,e,t);return[o.expandDims(I("x",r,e,t),n)]}case"Squeeze":{let n=I("axis",r,e,t);return[o.squeeze(I("x",r,e,t),n)]}case"Reshape":return[o.reshape(I("x",r,e,t),I("shape",r,e,t))];case"EnsureShape":return[o.ensureShape(I("x",r,e,t),I("shape",r,e,t))];case"MirrorPad":return[o.mirrorPad(I("x",r,e,t),I("padding",r,e,t),I("mode",r,e,t))];case"PadV2":case"Pad":return[o.pad(I("x",r,e,t),I("padding",r,e,t),I("constantValue",r,e,t))];case"SpaceToBatchND":{let n=I("blockShape",r,e,t),s=I("paddings",r,e,t);return[o.spaceToBatchND(I("x",r,e,t),n,s)]}case"BatchToSpaceND":{let n=I("blockShape",r,e,t),s=I("crops",r,e,t);return[o.batchToSpaceND(I("x",r,e,t),n,s)]}case"DepthToSpace":{let n=I("blockSize",r,e,t),s=I("dataFormat",r,e,t).toUpperCase();return[o.depthToSpace(I("x",r,e,t),n,s)]}case"BroadcastTo":return[o.broadcastTo(I("x",r,e,t),I("shape",r,e,t))];case"BroadcastArgs":return[o.broadcastArgs(I("s0",r,e,t),I("s1",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function YS(r,e,t,o,n=De){let s=((a,i,p)=>{switch(a.category){case"arithmetic":return n(()=>m_(a,i,p));case"basic_math":return n(()=>d_(a,i,p));case"control":return b_(a,i,p);case"convolution":return n(()=>w_(a,i,p));case"creation":return n(()=>S_(a,i,p));case"dynamic":return I_(a,i,p);case"evaluation":return n(()=>v_(a,i,p));case"image":return n(()=>T_(a,i,p));case"graph":return n(()=>k_(a,i,p));case"logical":return n(()=>__(a,i,p));case"matrices":return n(()=>E_(a,i,p));case"normalization":return n(()=>$_(a,i,p));case"ragged":return n(()=>R_(a,i,p));case"reduction":return n(()=>D_(a,i,p));case"slice_join":return n(()=>A_(a,i,p));case"sparse":return n(()=>F_(a,i,p));case"spectral":return n(()=>P_(a,i,p));case"string":return n(()=>O_(a,i,p));case"transformation":return n(()=>M_(a,i,p));case"hash_table":return N_(a,i,p,o);case"custom":let u=bf(a.op);if(u&&u.customExecutor)return u.customExecutor(new Rf(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,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Gc=class{constructor(e={},t={},o={},n={},s){this.weightMap=e,this.tensorArrayMap=t,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(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),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 e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function QS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Er(m)[0]));o=o||[];let l=new Set(o.map(m=>Er(m.name)[0])),c=[...e];for(;c.length>0;){let m=c.pop();if((wu(m)||ZY(m)||JY(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&!u.has(m.name)&&!l.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),c.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function L_(r,e){let{usedNodes:t,inputs:o}=e,n=Object.keys(o).map(g=>Er(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>t.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),l=new Map(u.map(g=>[g.name,g])),c={};for(let g of u){c[g.name]=c[g.name]||0;for(let x of g.children)a(x)||(c[x.name]=Number.POSITIVE_INFINITY),c[x.name]=(c[x.name]||0)+1}let m=Object.entries(c).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=l.get(g);for(let b of x.children.filter(a))--c[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>l.get(g)),h=qY(f,p);return jY(h,p),h}function qY(r,e){let t=new Map(r.map(a=>[a.name,a])),o=e.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=t.get(a);for(let p of i.children)!t.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var Sl=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function jY(r,e){let t=new Map(r.map((i,p)=>[i.name,p])),o=new Set(e.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(!t.has(p.name))throw new Sl(`Child ${p.name} of node ${i.name} is unreachable.`);if(t.get(i.name)>t.get(p.name))throw new Sl(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!t.has(p.name))throw new Sl(`Input ${p.name} of node ${i.name} is unreachable.`);if(t.get(p.name)>t.get(i.name))throw new Sl(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function B_(r){let e=new Map(r.map((i,p)=>[i.name,p])),t=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>wu(i)?t:p),n=i=>{let p=o[e.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,l)=>Math.max(u,l),o[p])),a=new Map;for(let i=0;ie[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new r(e.functions[o],this)})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(e,t){let o=QS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${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=t.map(c=>c.name),l=Object.keys(e);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${l}]. Missing the following inputs: [${n}]`)}let i=L_(this.graph,o),p=B_(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Fr(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(m=>this.graph.nodes[Er(m)[0]]),s=t.map(m=>Er(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(e,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let l={},c={};return De(()=>{let m=new Gc(this.weightMap,l,c,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(x=>{let[b,w]=Er(x,m),S=[];S[w]=e[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=YS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${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),t.map(x=>Vt(x,d,m))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){if(!(wu(t)||a.has(e))){for(let p of o[e])p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length);for(let p of t.inputs){if(wu(p))continue;let u=_S(p.name,o,n);if(u!=null)for(let l of u){if(!l||l.kept||s.has(l.id))continue;let c=i[l.id];c===1?(l.dispose(),delete i[l.id]):c!=null&&i[l.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,o,n,s,a){function i(p){return wu(p)||s.has(p.name)}if(!(wu(e)||a==null))for(let p of a){if(i(p))continue;let u=_S(p.name,t,o);for(let l of u)!l||l.kept||n.has(l.id)||l.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Gc(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Vt(m,i,a)),u=p.map(m=>m.id),l=Object.keys(e).map(m=>e[m].id),c=new Set([...u,...l,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!c.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(c),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(S=>this.graph.nodes[Er(S)[0]]),i=o.map(S=>Er(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:m,syncInputs:d}=QS(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(S=>{let[k,T]=Er(S),E=[];E[T]=e[S],h[k]=E});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,t,h,b,x,p,g,l);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 w=u.filter(S=>!wu(S)&&!Vt(S.name,h,t)).map(S=>S.name);if(w.length>0){let S="";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${S}`)}return h}processStack(e,t,o,n,s,a,i,p,u){let l=[];for(;t.length>0;){let c=t.pop();o.currentContext=c.contexts;let m="";if(c.node.op==="Enter"&&I("isConstant",c.node,n,o)&&([m]=qs(c.node.name,o)),n[c.node.name]==null){let d=YS(c.node,n,o,this._resourceManager);m||([m]=qs(c.node.name,o));let f=o.currentContext;y.isPromise(d)?l.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,c.node,n,o,a,i,p),this.processChildNodes(c.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,c.node,n,o,a,i,p),this.processChildNodes(c.node,t,o,n,s,u))}else this.processChildNodes(c.node,t,o,n,s,u)}return l}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=qs(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!Vt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Vt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Er(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${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 ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Er(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Er(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var Pf=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var e7="?tfjs-format=file",t7="model.json",Kc=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},o=Si){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new Pf}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return y.isPromise(e)?e.then(t=>t.getWeightStream==null?this.loadSync(t):this.loadStreaming(t)):this.loadSync(e)}loadSync(e){let t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(e.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let t=await gd(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let o=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=n,this.version=`${o.versions.producer}.${o.versions.minConsumer}`,this.executor=new Hc(Uc.Instance.transformGraph(o,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Uc.Instance.transformGraph(e.modelInitializer);this.initializer=new Hc(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new 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J7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Q(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(C.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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l=C.computePool3DInfo(s.shape,a,i,1,p,u),c=l.strideDepth,m=l.strideHeight,d=l.strideWidth,f=l.filterDepth,h=l.filterHeight,g=l.filterWidth,x=l.dilationDepth,b=l.dilationHeight,w=l.dilationWidth,S=l.effectiveFilterDepth,k=l.effectiveFilterHeight,T=l.effectiveFilterWidth,E=S-1-l.padInfo.front,R=T-1-l.padInfo.left,D=k-1-l.padInfo.top,F=ie(s.shape,"float32"),O=1/(f*h*g),M=t.bufferSync(n);for(let L=0;L=l.outDepth||Math.floor(ee)!==ee))for(let oe=0;oe=l.outHeight||Math.floor(ue)!==ue))for(let me=0;me=l.outWidth||Math.floor(be)!==be)continue;let _e=M.get(L,ee,ue,be,B);re+=_e}}}F.set(re*O,L,z,U,j,B)}return t.makeTensorInfo(F.shape,F.dtype,F.values)}var jE={kernelName:Vi,backendName:"cpu",kernelFunc:tQ};function rQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Q([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,l=C.computePool2DInfo(a.shape,i,p,1,u),c=l.strideHeight,m=l.strideWidth,d=l.filterHeight,f=l.filterWidth,h=l.dilationHeight,g=l.dilationWidth,x=l.effectiveFilterHeight,b=l.effectiveFilterWidth,w=b-1-l.padInfo.left,S=x-1-l.padInfo.top,k=ie(a.shape,"float32"),T=1/(d*f),E=t.data.get(n.dataId).values,R=ie(n.shape,"float32",E);for(let D=0;D=l.outHeight||Math.floor(j)!==j))for(let q=0;q=l.outWidth||Math.floor(Y)!==Y)continue;let J=R.get(D,j,Y,F);z+=J}}k.set(z*T,D,O,M,F)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var XE={kernelName:zi,backendName:"cpu",kernelFunc:rQ};function oQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=e;y.assert(i.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||i.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Q([n,i,p,s,a],"batchNorm");let{varianceEpsilon:u}=o;u==null&&(u=.001);let l=t.data.get(n.dataId).values,c=t.data.get(i.dataId).values,m=t.data.get(p.dataId).values,d=s?t.data.get(s.dataId).values:new Float32Array([1]),f=a?t.data.get(a.dataId).values:new Float32Array([0]),h=new Float32Array(l.length),g=f.length,x=d.length,b=m.length,w=c.length,S=0,k=0,T=0,E=0;for(let R=0;R=g&&(S=0),k>=w&&(k=0),T>=x&&(T=0),E>=b&&(E=0);return t.makeTensorInfo(n.shape,n.dtype,h)}var YE={kernelName:Hn,backendName:"cpu",kernelFunc:oQ};function nQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;Q([n],"batchToSpaceND");let i=s.reduce((x,b)=>x*b),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),l=C.getReshapedPermuted(n.shape,s,i),c=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(l,a,s.length),d=We({inputs:{x:n},backend:t,attrs:{shape:p}}),f=vt({inputs:{x:d},backend:t,attrs:{perm:u}}),h=We({inputs:{x:f},backend:t,attrs:{shape:l}}),g=nn({inputs:{x:h},backend:t,attrs:{begin:c,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var QE={kernelName:ia,backendName:"cpu",kernelFunc:nQ};function sQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=Nl(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var ZE={kernelName:Tn,backendName:"cpu",kernelFunc:sQ};function aQ(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=C.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var JE={kernelName:ua,backendName:"cpu",kernelFunc:aQ};var iQ=Ie(Go,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);C.assertParamsConsistent(a,s);let i=C.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return fr({inputs:{x:p[0]},backend:t});if(p[0].dtype==="complex64"){let h=p.map(S=>tn({inputs:{input:S},backend:t})),g=p.map(S=>Ua({inputs:{input:S},backend:t})),x=Su({inputs:h,backend:t,attrs:{axis:s}}),b=Su({inputs:g,backend:t,attrs:{axis:s}}),w=qt({inputs:{real:x,imag:b},backend:t});return h.forEach(S=>t.disposeIntermediateTensorInfo(S)),g.forEach(S=>t.disposeIntermediateTensorInfo(S)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),w}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return We({inputs:{x:h},backend:t,attrs:{shape:x}})}),l=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=C.computeOutShape(u.map(h=>h.shape),1);let c=u[0].shape[0]===1,m=mp(l,i,e[0].dtype,c),d=C.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var o$={kernelName:pa,backendName:"cpu",kernelFunc:Su};function AI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:l}=o;Q([n,s],"conv2d");let c=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,s.shape,a,u,i,l,!1,c),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",S=new Ge(m.outShape,n.dtype),k=y.computeStrides(n.shape),T=y.computeStrides(s.shape),E=k[0],R=w?k[1]:k[2],D=w?k[2]:1,F=w?1:k[1],O=S.strides[0],M=w?S.strides[1]:S.strides[2],L=w?S.strides[2]:1,B=w?1:S.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let me=oe*T[0],be=Y+ue*R;for(let _e=0;_e=m.inWidth)continue;let ct=me+Pe*T[1],Ke=be+at*D,mt=ct;for(let ut=0;ut=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ue=q+ee*D[1],me=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let at=ue+Fe*D[2],ct=me+Pe*u.inChannels,Ke=at;for(let mt=0;mtMath.cos(r)),l$={kernelName:An,backendName:"cpu",kernelFunc:fQ};var hQ=Ie(Fn,r=>Math.cosh(r)),c$={kernelName:Fn,backendName:"cpu",kernelFunc:hQ};function gQ(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[l,c,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=ie([f,h,g,d],"float32"),b=t.data.get(s.dataId).values,w=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),T=y.computeStrides(x.shape);for(let E=0;E=l)continue;let B=h>1?(O-D)*(c-1)/(h-1):0,z=g>1?(M-F)*(m-1)/(g-1):0;for(let U=0;U1?D*(c-1)+U*B:.5*(D+O)*(c-1);if(j<0||j>c-1){for(let q=0;q1?F*(m-1)+re*z:.5*(F+M)*(m-1);if(ne<0||ne>m-1){for(let me=0;me1?F*(m-1)+q*z:.5*(F+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],l=n.shape[3],c=p*s,m=u*s,d=l/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*c*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=C.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,w=b.left,S=b.top,k=d.outChannels/d.inChannels,T=new Ge(d.outShape,n.dtype),E=t.data.get(n.dataId).values,R=t.data.get(s.dataId).values,D=T.values;for(let F=0;F=d.inHeight)continue;let q=U*c[0],Y=O+j*l[1];for(let J=0;J=d.inWidth)continue;let ue=q+ee*c[1],me=Y+oe*d.inChannels,be=re,_e=ue;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,l=o.shape.length,c=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:S,strideWidth:k,filterHeight:T,filterWidth:E,dilationHeight:R,dilationWidth:D,outShape:F}=C.computeDilation2DInfo(o.shape,n.shape,s,a,"NHWC",i),O=y.sizeFromShape(F),M=F.length,L=y.getArrayFromDType(o.dtype,O);for(let z=0;z=0&&oe=0&&mere&&(re=ve)}}}let ne=y.locToIndex([z,U,q,J],M,y.computeStrides(F));L[ne]=re}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),F,o.dtype),shape:F,dtype:o.dtype}}};var S$={kernelName:qi,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,l=y.toNestedArray(o.shape,u.data.get(o.dataId).values),c=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:w,strideWidth:S,filterHeight:k,filterWidth:T,dilationHeight:E,dilationWidth:R,outShape:D}=C.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${qi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let F=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ueY&&(Y=me,J=ne,re=oe)}}}O[J][re][q]+=F[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var I$={kernelName:Ki,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,l=y.toNestedArray(o.shape,u.data.get(o.dataId).values),c=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:w,strideWidth:S,filterHeight:k,filterWidth:T,dilationHeight:E,dilationWidth:R,outShape:D}=C.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${Ki}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let F=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&ee=0&&ueY&&(Y=me,J=ee,re=ue)}}}O[L][J][re][q]+=F[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function vQ(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,{contextOptions:i,imageOptions:p}=a||{},u=(p==null?void 0:p.alpha)||1,l=(i==null?void 0:i.contextType)||"2d";if(l!=="2d")throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);let c=s.getContext(l,(i==null?void 0:i.contextAttributes)||{});if(c==null)throw new Error(`Could not get the context with ${l} type.`);let[m,d]=n.shape.slice(0,2),f=n.shape.length===2?1:n.shape[2],h=t.data.get(n.dataId).values,g=n.dtype==="float32"?255:1,x=new Uint8ClampedArray(d*m*4);for(let w=0;w1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${E}.`)}else if(n.dtype==="int32"&&(E<0||E>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${E}.`);f===1?(S[0]=E*g,S[1]=E*g,S[2]=E*g):S[T]=E*g}let k=w*4;x[k+0]=Math.round(S[0]),x[k+1]=Math.round(S[1]),x[k+2]=Math.round(S[2]),x[k+3]=Math.round(S[3])}s.width=d,s.height=m;let b=new ImageData(x,d,m);return c.putImageData(b,0,0),n}var v$={kernelName:Mu,backendName:"cpu",kernelFunc:vQ};function Ii(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;Q(n,"sum");let i;n.dtype==="bool"?i=rn({inputs:{x:n},backend:t,attrs:{dtype:"int32"}}):i=fr({inputs:{x:n},backend:t});let p=i.shape.length,u=y.parseAxisParam(s,i.shape),l=C.getAxesPermutation(u,p),c=u,m=i;l!=null&&(m=vt({inputs:{x:i},backend:t,attrs:{perm:l}}),c=C.getInnerMostAxes(c.length,p)),C.assertAxesAreInnerMostDims("sum",c,m.shape.length);let[d,f]=C.computeOutAndReduceShapes(m.shape,c),h=C.upcastType(m.dtype,"int32"),g=vl(t,d,h),x=y.sizeFromShape(f),b=t.data.get(g.dataId).values,w=t.data.get(m.dataId).values;for(let S=0;S=0&&(m=Ii({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var N$={kernelName:ji,backendName:"cpu",kernelFunc:kQ};function NQ(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e;Q([o,n],"eluGrad");let s=new Float32Array(y.sizeFromShape(n.shape)),a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values;for(let p=0;p=0?s[p]=i[p]:s[p]=i[p]*(u+1)}return t.makeTensorInfo(n.shape,"float32",s)}var T$={kernelName:ri,backendName:"cpu",kernelFunc:NQ};var TQ=C.ERF_P,_Q=C.ERF_A1,EQ=C.ERF_A2,$Q=C.ERF_A3,RQ=C.ERF_A4,DQ=C.ERF_A5,AQ=Ie(Un,r=>{let e=Math.sign(r),t=Math.abs(r),o=1/(1+TQ*t);return e*(1-((((DQ*o+RQ)*o+$Q)*o+EQ)*o+_Q)*o*Math.exp(-t*t))}),_$={kernelName:Un,backendName:"cpu",kernelFunc:AQ};function $l(r){let{inputs:e,backend:t,attrs:o}=r,{input:n}=e,{dim:s}=o,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),We({inputs:{x:n},backend:t,attrs:{shape:i}})}var E$={kernelName:ma,backendName:"cpu",kernelFunc:$l};var FQ=Ve((r,e)=>r/e),Yc=Qe(Vn,FQ),Qc={kernelName:Vn,backendName:"cpu",kernelFunc:Yc};function Zf(r,e,t){let o=r.shape,n=o[0],s=o[1],a=t.data.get(r.dataId),i=a.complexTensorInfos.real,p=a.complexTensorInfos.imag,u=[n,s],l=y.sizeFromShape(u),c=y.getTypedArrayFromDType("float32",l),m=y.getTypedArrayFromDType("float32",l);for(let g=0;g{let{image:o}=r,n=t,s=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[a,i,p,u]=o.shape,l=n.data.get(o.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${k} is not in [0, ${l-1}]`)}let c=i;i==null&&(c=0);let m=y.sizeFromShape(s.shape),d=C.segment_util.collectGatherOpShapeInfo(n,s,p,c),f=We({inputs:{x:n},backend:t,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=We({inputs:{x:s},backend:t,attrs:{shape:[d.batchSize,m/d.batchSize]}}),g=[d.batchSize,d.outerSize,m/d.batchSize,d.sliceSize],x=t.bufferSync(h),b=t.bufferSync(f),w=Lf(b,x,g);return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.makeTensorInfo(d.outputShape,w.dtype,w.values)}var O$={kernelName:fa,backendName:"cpu",kernelFunc:UQ};function GQ(r){let{inputs:e,backend:t}=r,{input:o}=e,n=y.sizeFromShape(o.shape),s=o.shape[o.shape.length-1],a=n/s,i=We({inputs:{x:o},backend:t,attrs:{shape:[a,s]}}),p=Zf(i,!0,t),u=We({inputs:{x:p},backend:t,attrs:{shape:o.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(p),u}var M$={kernelName:Yi,backendName:"cpu",kernelFunc:GQ};var HQ=Ie(qn,r=>Number.isFinite(r)?1:0,"bool"),L$={kernelName:qn,backendName:"cpu",kernelFunc:HQ};var KQ=Ie(jn,r=>Math.abs(r)===1/0?1:0,"bool"),B$={kernelName:jn,backendName:"cpu",kernelFunc:KQ};var qQ=Ie(Xn,r=>Number.isNaN(r)?1:0,"bool"),z$={kernelName:Xn,backendName:"cpu",kernelFunc:qQ};function jQ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=Bf(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var V$={kernelName:Qn,backendName:"cpu",kernelFunc:jQ};var XQ=Ie(Zn,r=>Math.log1p(r)),W$={kernelName:Zn,backendName:"cpu",kernelFunc:XQ};var YQ=Ve((r,e)=>r&&e),QQ=Qe(Jn,YQ,null,"bool"),U$={kernelName:Jn,backendName:"cpu",kernelFunc:QQ};var ZQ=Ie(es,r=>r?0:1,"bool"),G$={kernelName:es,backendName:"cpu",kernelFunc:ZQ};var JQ=Ve((r,e)=>r||e),eZ=Qe(ts,JQ,null,"bool"),H$={kernelName:ts,backendName:"cpu",kernelFunc:eZ};function tZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;Q(n,"LRN");let u=n.shape[3],l=u-1,c=t.data.get(n.dataId).values,m=y.sizeFromShape(n.shape),d=new Float32Array(m);function f(h){let g=h%u,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,l),w=0;for(;x<=b;x++){let S=c[x];w+=S*S}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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am(r,n,s,gh(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function xh(r){return r.downloadTextureFormat}function f0(r,e,t,o){let[n,s]=Sp(e,t);return am(r,n,s,xh(o),r.RGBA,r.UNSIGNED_BYTE)}function yh(r){return r.internalFormatPackedFloat}function h0(r,e,t,o){let[n,s]=Ga(e,t);return am(r,n,s,yh(o),r.RGBA,r.FLOAT)}function bh(r){return r.internalFormatPackedHalfFloat}function g0(r,e,t,o){let[n,s]=Ga(e,t);return am(r,n,s,bh(o),r.RGBA,o.textureTypeHalfFloat)}function x0(r,e,t){return ce(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),sh(r,e,"clipSpacePos",t,3,20,0)&&sh(r,e,"uv",t,2,20,12)}function y0(r,e,t,o,n,s){ce(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,p;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,p=r.RGBA):(a=new 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t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),x0(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&om(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?ZI(this.gl,e,t):JI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),e0(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Ga(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&om(this.gl,this.program),Dl(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and 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this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;nm(n,e,this.framebuffer),this.debug&&Dl(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function yJ(r){let e=0;for(;e`${r}.${t}`)}function At(r,e){return e===1?[r]:N0(r,e)}function fA(r,e){if(r===1)return"rc";let t="";for(let o=0;o ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o}; bool rEdge = rp1 >= ${n}; `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= 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n===or.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===or.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===or.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===or.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===or.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=gA(o,n),a=xA(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=hA(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u&&u.indexOf(e);if(l==null||l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u[l]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}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 e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function CJ(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function hA(r,e,t,o,n){let s=wJ(e,o),a;if(n){let[p,u]=Ga(r[0],r[1]);a=p*u}else{let[p,u]=Sp(r[0],r[1]);a=p*u}let i=CJ(t,s);return a*i}function wJ(r,e){switch(r){case or.PACKED_2X2_FLOAT32:return yh(e);case or.PACKED_2X2_FLOAT16:return bh(e);case or.UNPACKED_FLOAT32:return hh(e);case or.UNPACKED_FLOAT16:return gh(e);case or.PACKED_4X1_UNSIGNED_BYTE:return xh(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function SJ(r){return A().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?or.PACKED_2X2_FLOAT32:or.UNPACKED_FLOAT32:r?or.PACKED_2X2_FLOAT16:or.UNPACKED_FLOAT16}function gA(r,e){if(r===hr.UPLOAD)return or.PACKED_2X2_FLOAT32;if(r===hr.RENDER||r==null)return SJ(e);if(r===hr.DOWNLOAD||r===hr.PIXELS)return or.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function xA(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var nr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},Gt="if (isnan(x)) return x;",yA="return x;",T0="return abs(x);";var bA="return (x >= 0.0) ? x : (exp(x) - 1.0);",CA=Gt+` return (x < 0.0) ? 0.0 : x; `,wA=Gt+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Ha="return x;",SA="return 1.0 / (1.0 + exp(-1.0 * x));";var vA="return x;",kA=` 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; `,NA=` 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; `,TA=` 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; `,_A="return 1.0 / (1.0 + exp(-1.0 * x));",Lr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } void main() { vec4 x = getAAtOutCoords(); vec4 y = unaryOperation(x); setOutput(y); } `}};var kh=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=lt(this.outputShape.length);let t=e.length,o=At("rc",t),n=Re(t),s=fA(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` void main() { ${n} rc = getOutputCoords(); vec4 packedInput = getA(${s}); setOutput(getChannel(packedInput, ${i})); } `}};var vJ=Ut.whereImpl,kJ=1e-7,NJ=1e-4,Nh={};function TJ(r){return r in Nh||(Nh[r]={}),Nh[r]}var _J=A().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),EJ=600;function $J(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*EJ/1024/1024}var Ul=class r extends mo{nextDataId(){return r.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof kp)t=e;else{let o=Zr(A().getNumber("WEBGL_VERSION"),e);t=new kp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Zr(A().getNumber("WEBGL_VERSION"));t=new kp(o),this.binaryCache=TJ(A().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new vh(this.gpgpu),this.numMBBeforeWarning=$J(),this.texData=new mn(this,cr())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=Al(t),l=new sm(u,!1,a),c=this.runWebGLProgram(l,[i],o,[[n,s]]);return c.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),c.dataId}write(e,t,o){if((A().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||A().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=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:t,dtype:o,values:e,usage:hr.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(A().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:hr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Lr(i,Ha):m=new nr(i,Ha);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,l;u&&(l=y.now());let c;if(n==="complex64"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);c=C.mergeRealAndImagArrays(m,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Lr(n,Ha):f=new nr(n,Ha);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);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,l;if(a!=="complex64"&&A().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let f=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...tm(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];c=C.mergeRealAndImagArrays(h,g)}else if(u==null)c=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&cr().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{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 Lr(s,Ha):d=new nr(s,Ha);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);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 l=this.decode(e,t.customTexShape),c=cr().makeTensorFromTensorInfo(l),m=this.texData.get(l.dataId);return Object.assign({tensorRef:c},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let o=t.map(n=>y.decodeString(n));return ie(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return ie(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();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=t,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,l)=>({name:a[l],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(e){return A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(A().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=_J){return A().getBool("WEBGL_CPU_FORWARD")&&e.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,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return cr().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new kh(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Ih(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){let o=[ki(e.shape),...Ni(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[ki(t),...Ni(t)],a=new Wl(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[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=Al(s),p;n?p=new ch(i):p=new lh(i);let u=!0,l=[t!=null?t:tm(i)],c=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:s,dataId:c.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===Iu.DENSE){let x=a!=null?a:tm(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.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(!e.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!vu(b.shape,x.shape)){let w=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),w.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:p,isUniform:!1},m=SD(e,l,c),d=this.getAndSaveBinary(m,()=>CD(this.gpgpu,e,l,c)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get("ENGINE_COMPILE_ONLY")||wD(this.gpgpu,d,l,c,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.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(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),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 e=A().getBool("DEBUG");A().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(A().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?kJ:NJ}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=y.now());let c=t.texShape;if(c==null&&(c=t0(o,p),t.texShape=c),s!=null){let m=Al(o),d,f=c[1],h=c[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ga(c[0],c[1])),p?d=new fh(m,g):d=new sm(m,g);let x=g?[h,f]:c,b=this.makeTensorInfo(x,n),w=this.texData.get(b.dataId);g?w.usage=hr.PIXELS:w.usage=hr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],T=this.runWebGLProgram(d,[b],n,S,!0),E=this.texData.get(T.dataId);t.texShape=E.texShape,t.isPacked=E.isPacked,t.usage=E.usage,A().get("ENGINE_COMPILE_ONLY")?this.disposeData(T.dataId):(t.texture=E.texture,t.values=null,this.texData.delete(T.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-l)}else{let m=this.acquireTexture(c,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=RJ(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,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(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return 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NAN : result.a; `;var eo=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=lt(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) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let p=At("coords",s);this.enableShapeUniforms?a+=` bool nextRowOutOfBounds = (${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) >= ${this.outputShape[s-1]}; result.y = nextColOutOfBounds ? 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vec4 minMaxValue = vec4(${i}); float prodValue = 1.0; float sumValue = 0.0; float allValue = 1.0; float anyValue = 0.0; for (int i = 0; i < ${l}; 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 + ${l}; if (${c===1}) { ${d} values = ${d}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); ${m} } else if (${c===2}) { ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); ${m} } else if (${c===3}) { ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); ${m} } setOutput(${u}); } `}};function OJ(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=C.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function ro(r,e,t,o){let n=OJ(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=N0("rc",this.rank),a=new Array(this.rank);for(let l=0;l`Error in matMul: inner shapes (${c}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,c,d]:[x,d,c],T=o?[b,f,m]:[b,m,f],E=te({inputs:{x:r},backend:n,attrs:{shape:k}}),R=te({inputs:{x:e},backend:n,attrs:{shape:T}}),D=[E,R],F=Math.max(x,b),O=t?E.shape[1]:E.shape[2],M=s!=null,L=a!=null,B=p==="leakyrelu",z=p!=null?Ti(p,!0):null,U=M||L||B||z!=null,j;if((d===1||f===1)&&O>A0&&U===!1){let Y=E,J=R;t&&(Y=Ct({inputs:{x:E},backend:n,attrs:{perm:[0,2,1]}}),D.push(Y)),o&&(J=Ct({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),D.push(J));let re=f!==1,ne=f===1,ee=Y;re&&(ee=te({inputs:{x:Y},backend:n,attrs:{shape:[F,O,1]}}),D.push(ee));let oe=f===1?2:1,ue=J;ne&&(ue=te({inputs:{x:J},backend:n,attrs:{shape:[F,1,O]}}),D.push(ue));let me=um({inputs:{a:ee,b:ue},backend:n});j=Tp({inputs:{x:me},backend:n,attrs:{axis:oe,keepDims:!0}}),D.push(me)}else{let Y=pt(r.dtype,e.dtype),J=new Hl(k,T,[F,d,f],t,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 LJ(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:l,leakyreluAlpha:c}=o;return _p({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:c,activation:l})}var VA={kernelName:qo,backendName:"webgl",kernelFunc:LJ};var WA="return abs(x);";function BJ(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=wh(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Lr(o.shape,WA):n=new nr(o.shape,WA),t.runWebGLProgram(n,[o],o.dtype)}var UA={kernelName:fn,backendName:"webgl",kernelFunc:BJ};var zJ=Gt+` if (abs(x) > 1.) { return NAN; 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setOutput(result); } `}};function Dh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Ft({inputs:{x:o[0]},backend:t});if(o.length>A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=Dh({inputs:o.slice(0,p),backend:t}),l=Dh({inputs:o.slice(p),backend:t});return Dh({inputs:[u,l],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>pt(p,u)),s=o.map(p=>p.shape),i=A().getBool("WEBGL_PACK")?new Rh(o[0].shape,s):new $h(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var jA={kernelName:xn,backendName:"webgl",kernelFunc:Dh};function HJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,l=C.getAxesPermutation(u,i),c=n;l!=null&&(c=Ct({inputs:{x:n},backend:t,attrs:{perm:l}}),u=C.getInnerMostAxes(u.length,i)),C.assertAxesAreInnerMostDims("all",u,i);let[m,d]=C.computeOutAndReduceShapes(c.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:c},backend:t,attrs:{shape:[-1,f]}}),g=ro(h,h.dtype,"all",t),x;if(a){let b=C.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),l!=null&&t.disposeIntermediateTensorInfo(c),x}var XA={kernelName:yn,backendName:"webgl",kernelFunc:HJ};function KJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,l=C.getAxesPermutation(u,i),c=n;l!=null&&(c=Ct({inputs:{x:n},backend:t,attrs:{perm:l}}),u=C.getInnerMostAxes(u.length,i)),C.assertAxesAreInnerMostDims("any",u,i);let[m,d]=C.computeOutAndReduceShapes(c.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:c},backend:t,attrs:{shape:[-1,f]}}),g=ro(h,h.dtype,"any",t),x;if(a){let b=C.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),l!=null&&t.disposeIntermediateTensorInfo(c),x}var YA={kernelName:bn,backendName:"webgl",kernelFunc:KJ};var Ah=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="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 ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}};var Fh=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,p=i.length,u=Re(p),l=At("coords",p),c,m;if(a===1){m=p+1;let R=Re(m);c=` ${R} sourceLocR = ${R}(${l.join()}, 0); ++${l[p-1]}; ${R} sourceLocG = ${R}(${l.join()}, 0); ++${l[p-2]}; ${R} sourceLocA = ${R}(${l.join()}, 0); --${l[p-1]}; ${R} sourceLocB = ${R}(${l.join()}, 0); --${l[p-2]};`}else m=p,c=` ${u} sourceLocR = coords; ++${l[p-1]}; ${u} sourceLocG = coords; ++${l[p-2]}; ${u} sourceLocA = coords; --${l[p-1]}; ${u} sourceLocB = coords; --${l[p-2]};`;let d=["x","y","z","w","u","v"].slice(0,m),f="."+d[m-1],h=d.map(R=>"int "+R),g=At("sourceLocR",m-1).concat("inIdx.r"),x=At("sourceLocG",m-1).concat("inIdx.g"),b=At("sourceLocB",m-1).concat("inIdx.b"),w=At("sourceLocA",m-1).concat("inIdx.a"),S=o==="max"?"greaterThan":"lessThan",k=n?"":` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), getBestIndicesAChannel(${w.join()})));`,T=`vec4( getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., hasNextRow && hasNextCol ? getAChannel(${w.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 = ${l[p-1]} < ${i[p-1]-1}; bool hasNextRow = ${l[p-2]} < ${i[p-2]-1}; ${c} ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f}, sourceLocB${f}, sourceLocA${f}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${T}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; ${k} vec4 candidate = ${T}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${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 QA(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=C.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new Ah(i,t,o==null),u=[e];o!=null&&u.push(o);let l=r.runWebGLProgram(p,u,"int32");if(l.shape[1]===1)return l;let c=QA(r,e,t,l);return r.disposeIntermediateTensorInfo(l),c}function ZA(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=C.computeOptimalWindowSize(s),i=new Fh(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,"int32");if(u.shape.length===e.shape.length){let l=ZA(r,e,t,u);return r.disposeIntermediateTensorInfo(u),l}return u}function Ph(r,e,t,o){let n=[t];if(C.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!A().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,l]=C.computeOutAndReduceShapes(p.shape,n),c=y.sizeFromShape(l),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,c]}});s.push(m);let d=QA(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 ZA(r,e,o)}function qJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let l=Ph(t,p,a[0],"max");return u.forEach(c=>t.disposeIntermediateTensorInfo(c)),l}var JA={kernelName:na,backendName:"webgl",kernelFunc:qJ};function jJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let l=Ph(t,p,a[0],"min");return u.forEach(c=>t.disposeIntermediateTensorInfo(c)),l}var eF={kernelName:sa,backendName:"webgl",kernelFunc:jJ};var XJ=Gt+` if (abs(x) > 1.) { return NAN; } return asin(x); `,YJ=xe({opSnippet:XJ}),tF={kernelName:Cn,backendName:"webgl",kernelFunc:YJ};var QJ=Gt+"return log(x + sqrt(x * x + 1.0));",ZJ=xe({opSnippet:QJ}),rF={kernelName:wn,backendName:"webgl",kernelFunc:ZJ};var JJ=Gt+` return atan(x); `,eee=xe({opSnippet:JJ}),oF={kernelName:Sn,backendName:"webgl",kernelFunc:eee};var tee=Gl+` return atan(a, b); `,ree=` 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); `+to+` return result; `,oee=st({opSnippet:tee,packedOpSnippet:ree}),nF={kernelName:vn,backendName:"webgl",kernelFunc:oee};var nee=Gt+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,see=xe({opSnippet:nee}),sF={kernelName:In,backendName:"webgl",kernelFunc:see};var Zs=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=` const ivec2 strides = ivec2(${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 < ${c}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${m}; wC += ${l}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.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 * ${m} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let w="max",S=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(S="avgValue / max(count, 1.0)");let k=Math.floor(a/4)*4,T=a%4,E=` if (${h}) { avgValue += dot(values, ones); } else { minMaxValue = ${w}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${b}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${c}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${k}; wC += 4) { int xC = xCCorner + wC * ${l}; vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${l}, d), getValue(batch, xR, xC + 2 * ${l}, d), getValue(batch, xR, xC + 3 * ${l}, d) ); ${E} } int xC = xCCorner + ${k}; if (${T===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${E} } else if (${T===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${l}, d), initializationValue, initializationValue ); ${E} } else if (${T===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${l}, d), getValue(batch, xR, xC + 2 * ${l}, d), initializationValue ); ${E} } } setOutput(${S}); } `}},Nu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let w=t==="avg",S="0.0";if(w||(S="-1.0 / 1e-20"),o){let F=">=";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 += ${l}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${f}; wR += ${c}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${h}; wC += ${m}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.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 ${F} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} + wR * ${h} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let k="max",T=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(T="avgValue / max(count, 1.0)");let E=Math.floor(a/4)*4,R=a%4,D=` if (${w}) { 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${S}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${d}; wD += ${l}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${f}; wR += ${c}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.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 ); ${D} } } } setOutput(${T}); } `}};function aee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Ys(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(C.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let l=C.computePool2DInfo(n.shape,s,a,u,i,p);if(l.filterWidth===1&&l.filterHeight===1&&y.arraysEqual(l.inShape,l.outShape))return Ft({inputs:{x:n},backend:t});let c=new Zs(l,"avg",!1);return t.runWebGLProgram(c,[n],"float32")}var aF={kernelName:kn,backendName:"webgl",kernelFunc:aee};function iee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,l=[1,1,1],c=C.computePool3DInfo(n.shape,s,a,l,i,p,u),m=new Nu(c,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var iF={kernelName:aa,backendName:"webgl",kernelFunc:iee};var Oh=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=p-1-e.padInfo.top,c=u-1-e.padInfo.left,m=1/(t*o);this.userCode=` const ivec2 pads = ivec2(${l}, ${c}); const float avgMultiplier = float(${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 >= ${e.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 >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},Mh=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=c-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=` const ivec3 pads = ivec3(${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 < ${c}; wD += ${p}) { float dyD = float(dyDCorner + wD) / ${s}.0; if (dyD < 0.0 || dyD >= ${e.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 >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${d}; wC += ${l}) { float dyC = float(dyCCorner + wC) / ${i}.0; if (dyC < 0.0 || dyC >= ${e.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 uee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:l}=o,c=[1,1,1],m=C.computePool3DInfo(a.shape,i,p,c,u,l),d=new Mh(m);return t.runWebGLProgram(d,[n],a.dtype)}var uF={kernelName:Vi,backendName:"webgl",kernelFunc:uee};function pee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Ys([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,l=C.computePool2DInfo(a.shape,i,p,1,u),c=new Oh(l);return t.runWebGLProgram(c,[n],a.dtype)}var pF={kernelName:zi,backendName:"webgl",kernelFunc:pee};function lee(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return _p({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var lF={kernelName:Nn,backendName:"webgl",kernelFunc:lee};var Lh=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="1.0";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=e,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 Bh=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=e,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 cee=({inputs:r,backend:e,attrs:t})=>{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}=t;p==null&&(p=.001);let u=[o,n,s],l=null;a!=null&&(l=a.shape,u.push(a));let c=null;i!=null&&(c=i.shape,u.push(i));let m=A().getBool("WEBGL_PACK_NORMALIZATION")?new Bh(o.shape,n.shape,s.shape,l,c,p):new Lh(o.shape,n.shape,s.shape,l,c,p);return e.runWebGLProgram(m,u,u[0].dtype)},cF={kernelName:Hn,backendName:"webgl",kernelFunc:cee};var zh=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Re(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let o=mee(this.rank),n,s=e.map((a,i)=>`sourceLoc.${F0[i]} = start[${i}] + coords.${F0[i]};`);n=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` `)} `,this.userCode=` void main() { ${n} setOutput(getSource(${o})); } `}},F0=["x","y","z","w","u","v"];function mee(r){if(r===1)return"sourceLoc";if(r<=6)return F0.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Vh=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=Re(this.rank),o=At("coords",this.rank),n=At("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=` result.x = ${a}; if (++${o[this.rank-1]} < ${e[this.rank-1]}) { ++${n[this.rank-1]}; result.y = ${a}; --${n[this.rank-1]}; } `,p=this.rank===1?"":` --${o[this.rank-1]}; if (++${o[this.rank-2]} < ${e[this.rank-2]}) { ++${n[this.rank-2]}; result.z = ${a}; if (++${o[this.rank-1]} < ${e[this.rank-1]}) { ++${n[this.rank-1]}; result.w = ${a}; } } `,u=this.rank<=4?`sourceLoc = coords + ${t}(${e.map((l,c)=>`start[${c}]`).join()});`:e.map((l,c)=>`${n[c]} = ${o[c]} + start[${c}];`).join(` `);this.userCode=` void main() { ${t} coords = getOutputCoords(); ${t} sourceLoc; ${u} vec4 result = vec4(0.); ${i} ${p} setOutput(result); } `}};function dee(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=nt.computeFlatOffset(e,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 Js(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=nt.parseSliceParams(n,s,a);if(nt.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let c=t.texData.get(n.dataId),m=tA(c.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),l=nt.isSliceContinous(n.shape,i,p);if(u||!l){let c=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Vh(p):new zh(p),m=[i];return t.runWebGLProgram(c,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),dee(n,i,p,t)}var mF={kernelName:_s,backendName:"webgl",kernelFunc:Js};var fee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{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,w)=>b*w),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),l=C.getReshapedPermuted(n.shape,s,i),c=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(l,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Ct({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:l}}),x=Js({inputs:{x:g},backend:t,attrs:{begin:c,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},dF={kernelName:ia,backendName:"webgl",kernelFunc:fee};function hee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=Ch(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var fF={kernelName:Tn,backendName:"webgl",kernelFunc:hee};var gee=` int r = int(a.r) & int(b.r); int g = int(a.g) & int(b.g); int rb = int(a.b) & int(b.b); int ra = int(a.a) & int(b.a); return vec4(r, g, rb, ra); `,xee=` return float(int(a.r) & int(b.r)); `;function yee(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS"),a=A().getNumber("WEBGL_VERSION");if(t.shouldExecuteOnCPU([o,n])||a===1){let p=t.texData.get(o.dataId).values,u=t.texData.get(n.dataId).values,[l,c]=kD(o.shape,n.shape,p,u,o.dtype),m=t.makeTensorInfo(c,o.dtype),d=t.texData.get(m.dataId);return d.values=l,m}let i;return s?i=new eo(gee,o.shape,n.shape,!1):i=new Br(xee,o.shape,n.shape),t.runWebGLProgram(i,[o,n],o.dtype)}var hF={kernelName:_n,backendName:"webgl",kernelFunc:yee};function bee(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=C.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var gF={kernelName:ua,backendName:"webgl",kernelFunc:bee};var Cee="return float(a != b);",P0=st({opSnippet:Cee,cpuKernelImpl:KD,dtype:"bool"}),xF={kernelName:Ro,backendName:"webgl",kernelFunc:P0};function _i(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Ft({inputs:{x:n.complexTensorInfos.real},backend:t})}var yF={kernelName:si,backendName:"webgl",kernelFunc:_i};var wee="return float(int(x));";function bF(r,e){let t=new nr(r.shape,wee),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function O0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Ft({inputs:{x:n},backend:t});let a=Yr(n.shape),i=O0({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),p=zr({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),p}if(n.dtype==="complex64"){let a=_i({inputs:{input:n},backend:t}),i=O0({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Ft({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=ND(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s==="int32")return bF(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=P0({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var CF={kernelName:ho,backendName:"webgl",kernelFunc:O0};var wF="return ceil(x);",See=xe({opSnippet:wF,packedOpSnippet:wF,cpuKernelImpl:TD}),SF={kernelName:go,backendName:"webgl",kernelFunc:See};var Wh=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); if (isnan(value)) { setOutput(value); return; } setOutput(clamp(value, minVal, maxVal)); } `}};var Uh=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { vec4 value = getAAtOutCoords(); if (any(isnan(value))) { setOutput(value); return; } setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } `}};function Iee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool("WEBGL_PACK_CLIP")?i=new Uh(n.shape):i=new Wh(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var IF={kernelName:Go,backendName:"webgl",kernelFunc:Iee};var Gh=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); float mx = max(re, im); // sadly the length function in glsl is not underflow-safe // (at least not on Intel GPUs). So the safe solution is // to ensure underflow-safety in all cases. setOutput( mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } `}};function vF(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function vee(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new Gh(o.shape),a=[vF(o,n.complexTensorInfos.real),vF(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var kF={kernelName:Wi,backendName:"webgl",kernelFunc:vee};var Hh=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h= ${p[h-1]}) { return getChannel( getT${h}(${Kh(i,u,g)}), vec2(${Kh(l,u,g)})); }`}let d=p.length,f=p[p.length-1];m+=` return getChannel( getT${d}(${Kh(i,u,f)}), vec2(${Kh(l,u,f)}));`,this.userCode=` float getValue(${i.map(h=>"int "+h)}) { ${m} } void main() { ${s} coords = getOutputCoords(); vec4 result = vec4(getValue(${a}), 0., 0., 0.); ${a[n-1]} = ${a[n-1]} + 1; if (${a[n-1]} < ${o[n-1]}) { result.g = getValue(${a}); } ${a[n-2]} = ${a[n-2]} + 1; if (${a[n-2]} < ${o[n-2]}) { result.a = getValue(${a}); } ${a[n-1]} = ${a[n-1]} - 1; if (${a[n-2]} < ${o[n-2]} && ${a[n-1]} < ${o[n-1]}) { result.b = getValue(${a}); } setOutput(result); } `}};function Kh(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Ep(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Ft({inputs:{x:n.complexTensorInfos.imag},backend:t})}var NF={kernelName:Qi,backendName:"webgl",kernelFunc:Ep};function Kl(r,e,t){let o=r[0].dtype;if(o==="complex64"){let d=r.map(b=>_i({inputs:{input:b},backend:t})),f=r.map(b=>Ep({inputs:{input:b},backend:t})),h=Kl(d,e,t),g=Kl(f,e,t),x=zr({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let d=r.map(S=>{let T=[-1,y.sizeFromShape(S.shape.slice(e))];return te({inputs:{x:S},backend:t,attrs:{shape:T}})}),f=d.map(S=>({vals:t.readSync(S.dataId),shape:S.shape})),h=C.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=_D(f,h,o,g),b=C.computeOutShape(r.map(S=>S.shape),e),w=t.makeTensorInfo(b,o,x);return d.forEach(S=>t.disposeIntermediateTensorInfo(S)),w}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let d=a?new nr(r[0].shape,Ha):new Lr(r[0].shape,Ha);return t.runWebGLProgram(d,r,o)}let i=A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=kee(s,e,t),l=new Hh(p.map(d=>d.shape)),c=t.runWebGLProgram(l,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:c},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(c),m}function kee(r,e,t){let o=C.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function M0(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);C.assertParamsConsistent(a,s);let i=C.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Ft({inputs:{x:p[0]},backend:t}):Kl(p,s,t)}var TF={kernelName:pa,backendName:"webgl",kernelFunc:M0};var ql=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,S="",k="";o&&(n?S=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} }`:s?S=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${o} }`:S=` float activation(float x) { ${o} } `,k="result = activation(result);");let T=t?"result += getBiasAtOutCoords();":"";t&&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}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${w}]; 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 * ${l}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${d}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${e.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; ${T} ${k} setOutput(result); } `}},jh=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${s}, ${a}, ${i}); const ivec3 pads = ivec3(${t}, ${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 < ${c}; wF++) { int xF = xFCorner + wF * ${p}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${m}; wR++) { int xR = xRCorner + wR * ${u}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${d}; wC++) { int xC = xCCorner + wC * ${l}; if (xC < 0 || xC >= ${e.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 jl=class{constructor(e,t=!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=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,l=e.filterWidth,c=l,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<(c+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 (${s}) { innerDims = vec2(d1, ch); result[${l*2+c}] = getChannel( getA(rc.x, d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${l*2+c}] = 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 Yh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Qh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),l=t.inChannels,c=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat==="channelsLast",f=!1,h=!1,g,x=[];if(s!=null){let S=Yh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Yh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((c===1||m===1)&&l>A0)&&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,t.inChannels],dtype:r.dtype},T=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(vu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let E=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(E);let R=_p({a:k,b:E,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=T,D.shape=t.outShape,g=Ft({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}else{let S=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,S,t.inChannels]:[t.batchSize,t.inChannels,S]}}),T=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),E=_p({a:d?k:T,b:d?T:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:E},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(T),x.push(E)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Zh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:l,outWidth:c,outHeight:m,dataFormat:d}=t,f=d==="channelsLast",h=p*u*l,g=m*c,x=[t.batchSize,h,g],b=!0,w=!1,S=[];if(s!=null){let q=Yh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Yh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});S.push(k);let T=new Xh(x,t),E=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],R=o.runWebGLProgram(T,[r],"float32",E),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let F=n!=null,O=s!=null,M=i==="leakyrelu",L=i?Ti(i,!0):null,B=new Hl(f?D.shape:k.shape,f?k.shape:D.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,w,F,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:t.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function Nee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:l}=o,c=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,s.shape,a,u,i,l,!1,c),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=Qh({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&c==="channelsLast"&&A().getBool("WEBGL_EXP_CONV")){let h=new jl(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],"float32",g)}else if(A().getBool("WEBGL_CONV_IM2COL"))d=Zh({x:n,filter:s,convInfo:m,backend:t});else{let h=new ql(m);d=t.runWebGLProgram(h,[n,s],"float32")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var _F={kernelName:En,backendName:"webgl",kernelFunc:Nee};var Jh=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.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 < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${t} - ${n}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${o} - ${s}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } ${a?`float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue);`} } } } setOutput(dotProd); } `}},eg=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${p}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${c}]; ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - 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 < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${o} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { if (${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); } `}},tg=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.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 < ${e.batchSize}; b++) { for (int yF = 0; yF < ${e.outDepth}; yF++) { int xF = wF + yF * ${t} - ${s}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${o} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${i}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},rg=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,l=n-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${p}, ${u}, ${l}); 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 < ${t}; wF++) { float dyF = float(dyFCorner + wF) / ${s}.0; if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); int wFPerm = ${t} - 1 - wF; for (int wR = 0; wR < ${o}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.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 >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function Tee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:l}=o,c=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,l,a,1,i,u,!1,c),d=new Jh(m);return t.runWebGLProgram(d,[n,s],"float32")}var EF={kernelName:Ui,backendName:"webgl",kernelFunc:Tee};var og=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=lt(this.outputShape.length);let t=e.filterHeight,o=e.filterWidth,n=t-1-e.padInfo.top,s=o-1-e.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${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 < ${t}; wR++) { float dyR = float(dyRCorner + wR) / strides[0]; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 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 < ${e.outWidth}.0) && (fract(dyC) == 0.0); int idyC = int(dyC); float dyC2 = float(dyCCorner + wC + 1) / strides[1]; bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0) && (fract(dyC2) == 0.0); int idyC2 = int(dyC2); if (idyCVal && idyCVal2) { for (int d2 = 0; d2 < ${e.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 < ${e.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 < ${e.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 _ee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:l}=o,c=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(a,s.shape,i,1,p,l,!1,c);if(A().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&c==="channelsLast"){let d=[[m.strideHeight,m.strideWidth]],f=new og(m);return t.runWebGLProgram(f,[n,s],"float32",d)}else{let d=new eg(m);return t.runWebGLProgram(d,[n,s],"float32")}}var $F={kernelName:$n,backendName:"webgl",kernelFunc:_ee};function Eee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeConv3DInfo(n.shape,s.shape,a,p,i),l=new jh(u);return t.runWebGLProgram(l,[n,s],"float32")}var RF={kernelName:Rn,backendName:"webgl",kernelFunc:Eee};function $ee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=C.computeConv3DInfo(n.shape,p,a,1,i),l=new tg(u);return t.runWebGLProgram(l,[n,s],"float32")}var DF={kernelName:ti,backendName:"webgl",kernelFunc:$ee};function Ree(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=C.computeConv3DInfo(p,s.shape,i,1,a),l=new rg(u);return t.runWebGLProgram(l,[n,s],"float32")}var AF={kernelName:Dn,backendName:"webgl",kernelFunc:Ree};var Dee=sn+` return cos(x); `,Aee=` vec4 result = cos(x); bvec4 isNaN = isnan(x); ${to} return result; `,Fee=xe({opSnippet:Dee,packedOpSnippet:Aee}),FF={kernelName:An,backendName:"webgl",kernelFunc:Fee};var Pee=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,Oee=xe({opSnippet:Pee}),PF={kernelName:Fn,backendName:"webgl",kernelFunc:Oee};var ng=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=e,[l]=t,[c,m]=o;this.outputShape=[l,c,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${f} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${f}`],[w,S,k]=m>1?[`${(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(${w}); 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 Mee=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,l=new ng(n.shape,s.shape,i,p,u);return t.runWebGLProgram(l,[n,s,a],"float32")},OF={kernelName:Mn,backendName:"webgl",kernelFunc:Mee};var $p;(function(r){r.Prod="*",r.Sum="+"})($p||($p={}));var lm=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===$p.Prod?"1.0":"0.0",i=o?a:`getX(${MF(s,"coords",this.op)})`,p=this.outputShape[this.outputShape.length-1],u="",l="";o?(u=n?`end != ${p-1}`:"end != 0",l=n?"end + 1":"end - 1"):(u=n?`end + pow2 < ${p}`:"end >= pow2",l=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${Re(s)} coords = getOutputCoords(); int end = ${LF(s,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${u}) { int idx = ${l}; ${LF(s,"coords",this.op)} = idx; val ${this.op}= getX(${MF(s,"coords",this.op)}); } setOutput(val); } `}};function MF(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function LF(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function sg(r,e,t,o,n,s){let a=e.shape.length,i=C.getAxesPermutation([o],a),p=e;i!=null&&(p=Ct({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=C.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let l=p.shape[u],c=Ft({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(l))-1;m++){let d=new lm(r,p.shape,!1,s),f=[[m]],h=c;c=t.runWebGLProgram(d,[c],c.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new lm(r,p.shape,n,s),d=c;c=t.runWebGLProgram(m,[c],c.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=C.getUndoAxesPermutation(i),d=Ct({inputs:{x:c},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),d}return c}function Lee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return sg($p.Prod,n,t,s,a,i)}var BF={kernelName:Pn,backendName:"webgl",kernelFunc:Lee};function Bee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return sg($p.Sum,n,t,s,a,i)}var zF={kernelName:On,backendName:"webgl",kernelFunc:Bee};function zee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),l=Ch(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,l)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),l=vD(p,u,a,i);return t.makeTensorInfo(l.shape,s.dtype,l.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var VF={kernelName:la,backendName:"webgl",kernelFunc:zee};var ag=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,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 / ${t}; int offset_h = imod(h, ${t}); int in_w = w / ${t}; int offset_w = imod(w, ${t}); int offset_d = (offset_h * ${t} + 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 Vee(r){let{inputs:e,backend:t,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],l=a==="NHWC"?n.shape[3]:n.shape[1],c=p*s,m=u*s,d=l/(s*s),f=a==="NHWC"?[i,c,m,d]:[i,d,c,m],h=new ag(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var WF={kernelName:Ln,backendName:"webgl",kernelFunc:Vee};var Xl=class{constructor(e,t=!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=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u="",l="";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} } `,l="result = activation(result);");let c=t?"result += getBiasAtOutCoords();":"";t&&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; ${c} ${l} setOutput(result); } `}};var Yl=class{constructor(e,t=!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=e.outShape,this.enableShapeUniforms=lt(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,m=c,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${b+1}.xy); `):w===1?d+=` xC${b+1} = xTexelC${b}; `:d+=` xCOffset = xC + ${w}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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 ${a} and dilations '${l}'`);let c=C.computeConv2DInfo(n.shape,s.shape,a,l,i,u,!0),m;A().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?m=new Yl(c):m=new Xl(c);let d=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return t.runWebGLProgram(m,[n,s],"float32",d)}var UF={kernelName:Bn,backendName:"webgl",kernelFunc:Wee};var ig=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.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 < ${e.batchSize}; b++) { for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${t} - ${n}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${o} - ${s}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},ug=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${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 < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.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 * ${p} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function Uee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:l}=o,c=C.computeConv2DInfo(n.shape,l,a,i,p,u,!0),m=new ig(c);return t.runWebGLProgram(m,[n,s],"float32")}var GF={kernelName:Gi,backendName:"webgl",kernelFunc:Uee};function Gee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:l}=o,c=C.computeConv2DInfo(l,s.shape,a,i,p,u,!0),m=new ug(c);return t.runWebGLProgram(m,[n,s],"float32")}var HF={kernelName:Hi,backendName:"webgl",kernelFunc:Gee};var pg=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function Hee(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new pg(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var KF={kernelName:ca,backendName:"webgl",kernelFunc:Hee};var lg=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:l}=e,{top:c,left:m}=n;this.userCode=` const ivec2 strides = ivec2(${s}, ${a}); const ivec2 pads = ivec2(${c}, ${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 < ${t}) { for (int w = 0; w < ${p}; w++) { int wIn = wBeg + w * ${l}; 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 Kee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),l,c=new lg(u);l=t.runWebGLProgram(c,[n,s],"float32");let m=te({inputs:{x:l},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(l),m}var qF={kernelName:zn,backendName:"webgl",kernelFunc:Kee};function qee(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=C.decodeEinsumEquation(n,s.length);C.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:l}=C.getEinsumComputePath(i,p),c=l.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Tp({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var jF={kernelName:ji,backendName:"webgl",kernelFunc:qee};var jee="return (x >= 0.0) ? x : (exp(x) - 1.0);",Xee=` 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; `,Yee=xe({opSnippet:jee,packedOpSnippet:Xee}),XF={kernelName:Wn,backendName:"webgl",kernelFunc:Yee};var Qee="return (b >= 0.0) ? a : a * (b + 1.0);",Zee=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,Jee=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new eo(Zee,o.shape,n.shape):new Br(Qee,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},YF={kernelName:ri,backendName:"webgl",kernelFunc:Jee};var ete=` return vec4(equal(a, b)); `,tte="return float(a == b);",rte=st({opSnippet:tte,packedOpSnippet:ete,dtype:"bool",cpuKernelImpl:ED}),QF={kernelName:xo,backendName:"webgl",kernelFunc:rte};var ote=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${C.ERF_P}; float a1 = ${C.ERF_A1}; float a2 = ${C.ERF_A2}; float a3 = ${C.ERF_A3}; float a4 = ${C.ERF_A4}; float a5 = ${C.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,nte=xe({opSnippet:ote}),ZF={kernelName:Un,backendName:"webgl",kernelFunc:nte};var ste=sn+` return exp(x); `,ate=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,L0=xe({opSnippet:ste,packedOpSnippet:ate,cpuKernelImpl:$D,dtype:"float32"}),JF={kernelName:yo,backendName:"webgl",kernelFunc:L0};function cg(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,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 e3={kernelName:ma,backendName:"webgl",kernelFunc:cg};var t3="return exp(x) - 1.0;",ite=xe({opSnippet:t3,packedOpSnippet:t3,cpuKernelImpl:RD}),r3={kernelName:bo,backendName:"webgl",kernelFunc:ite};var cm=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);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 mg(r,e,t){let o=t.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:t,attrs:{shape:[a,s]}}),p=i.shape,u=new cm("real",p,e),l=new cm("imag",p,e),c=[{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=t.runWebGLProgram(u,c,"float32"),d=t.runWebGLProgram(l,c,"float32"),f=zr({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function ute(r){let{inputs:e,backend:t}=r,{input:o}=e;return mg(o,!1,t)}var o3={kernelName:Xi,backendName:"webgl",kernelFunc:ute};var dg=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } `}};function Ei(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new dg(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var n3={kernelName:da,backendName:"webgl",kernelFunc:Ei};var fg=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int coordX = ${t} - x - 1; float outputValue; if(coordX >= 0 && coordX < ${t}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}};var s3={kernelName:Gn,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new fg(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var a3="return floor(x);",pte=xe({opSnippet:a3,packedOpSnippet:a3,cpuKernelImpl:DD}),i3={kernelName:Co,backendName:"webgl",kernelFunc:pte};var lte=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); if (ib != 0) { // Windows (D3D) wants guaranteed non-zero int division at compile-time. return float(idiv(ia, ib, s)); } else { return NAN; } `,cte=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); ivec4 result = ivec4(0); vec4 s = sign(a) * sign(b); // Windows (D3D) wants guaranteed non-zero int division at compile-time. if (cond[0]) { result[0] = idiv(ia[0], ib[0], s[0]); } if (cond[1]) { result[1] = idiv(ia[1], ib[1], s[1]); } if (cond[2]) { result[2] = idiv(ia[2], ib[2], s[2]); } if (cond[3]) { result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); `,mte=st({opSnippet:lte,packedOpSnippet:cte,dtype:"int32"}),u3={kernelName:wo,backendName:"webgl",kernelFunc:mte};var hg=class{constructor(e){this.variableNames=["A"];let t=kt(),[o,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}};var gg=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=kt(),[o,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0); vec4 values = ${t.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); } } ${t.output} = result; } `}};var p3={kernelName:Lu,backendName:"webgl",kernelFunc:dte},Ql,B0=A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function dte(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{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],l=[u,p],c=[u,p,s];if(i||a){let h=A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ql==null||h!==B0)&&(B0=h,Ql=document.createElement("canvas").getContext("2d",{willReadFrequently:B0})),Ql.canvas.width=p,Ql.canvas.height=u,Ql.drawImage(n,0,0,p,u),n=Ql.canvas}let m=t.makeTensorInfo(l,"int32");t.texData.get(m.dataId).usage=hr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=A().getBool("WEBGL_PACK")?new gg(c):new hg(c),f=t.runWebGLProgram(d,[m],"int32");return t.disposeData(m.dataId),f}function fte(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:l,dilations:c,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=C.convertConv2DDataFormat(l),g=C.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!1,h),x,b=[],w=a!=null,S=i!=null,k=d==="leakyrelu",T=()=>{let R=[n,s],D=(F,O)=>{if(O==="NCHW"&&F.shape.length===1&&F.shape[0]!==1){let M=te({inputs:{x:F},backend:t,attrs:{shape:[F.shape[0],1,1]}});return b.push(M),M}return F};if(w&&R.push(D(a,l)),S&&R.push(D(i,l)),k){let F=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));R.push(F),b.push(F)}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=Qh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h==="channelsLast"&&A().getBool("WEBGL_EXP_CONV")){let R=d?Ti(d,!0):null,D=new jl(g,w,R,S,k),F=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],O=T();x=t.runWebGLProgram(D,O,"float32",F)}else if(A().getBool("WEBGL_CONV_IM2COL"))x=Zh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?Ti(d,!1):null,D=new ql(g,w,R,S,k),F=T();x=t.runWebGLProgram(D,F,"float32")}let E=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(R=>t.disposeIntermediateTensorInfo(R)),E}var l3={kernelName:jo,backendName:"webgl",kernelFunc:fte};function hte(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:l,dimRoundingMode:c,activation:m,leakyreluAlpha:d}=o,f=[],h=l;h==null&&(h=[1,1]),y.assert(C.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`);let g=C.computeConv2DInfo(n.shape,s.shape,p,h,u,c,!0),x=A().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Ti(m,x):null,w=[n,s],S=a!=null,k=i!=null,T=m==="leakyrelu";if(S&&w.push(a),k&&w.push(i),T){let F=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));w.push(F),f.push(F)}let E;x?E=new Yl(g,S,b,k,T):E=new Xl(g,S,b,k,T);let R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],D=t.runWebGLProgram(E,w,"float32",R);return f.forEach(F=>t.disposeIntermediateTensorInfo(F)),D}var c3={kernelName:Xo,backendName:"webgl",kernelFunc:hte};var xg=class{constructor(e,t,o,n){this.sliceDim=e,this.strides=t,this.paramsShape=n,this.variableNames=["x","indices"],this.outputShape=o;let s=Re(o.length),a=` int index;`;for(let i=0;i= ${this.paramsShape[i]}; flattenIndex += index * ${this.strides[i]};`;this.userCode=` void main() { ${s} coords = getOutputCoords(); int flattenIndex = 0; bool out_of_bounds = false; ${a} setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1])); } `}};function gte(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,l,c]=C.prepareAndValidate(o,n),m=te({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=te({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/l,l]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let x=t.readSync(n.dataId),b=t.bufferSync(o),w=AD(x,b,o.dtype,u,a,l,c,o.shape,i);return t.makeTensorInfo(p,o.dtype,w.values)}let f=new xg(a,c,[u,l],o.shape),h=t.runWebGLProgram(f,[d,m],d.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:p}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var m3={kernelName:Kn,backendName:"webgl",kernelFunc:gte};var yg=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=Re(this.rank),n=xte(e,2);this.userCode=` void main() { ${o} resRC = getOutputCoords(); int index = int(getIndices(resRC.x, resRC.z)); float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0; setOutput(inBounds * getA(${n})); } `}};function xte(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${w-1}]`)}}let u=C.segment_util.collectGatherOpShapeInfo(n,s,p,i),l=y.sizeFromShape(s.shape),c=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,l/u.batchSize]}});c.push(m),c.push(d);let f=[u.batchSize,u.outerSize,l/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype==="string"){let b=t.bufferSync(d),w=t.bufferSync(m),S=FD(w,b,f);return c.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,S.dtype,S.values)}let h=new yg(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);c.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return c.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var d3={kernelName:fa,backendName:"webgl",kernelFunc:z0};var yte="return float(a > b);",bte=` return vec4(greaterThan(a, b)); `,Cte=st({opSnippet:yte,packedOpSnippet:bte,cpuKernelImpl:PD,dtype:"bool"}),f3={kernelName:So,backendName:"webgl",kernelFunc:Cte};var wte="return float(a >= b);",Ste=` return vec4(greaterThanEqual(a, b)); `,Ite=st({opSnippet:wte,packedOpSnippet:Ste,dtype:"bool",cpuKernelImpl:OD}),h3={kernelName:Io,backendName:"webgl",kernelFunc:Ite};function vte(r){let{inputs:e,backend:t}=r,{input:o}=e;return mg(o,!0,t)}var g3={kernelName:Yi,backendName:"webgl",kernelFunc:vte};var kte="return float(!isnan(x) && !isinf(x));",Nte=xe({opSnippet:kte,dtype:"bool"}),x3={kernelName:qn,backendName:"webgl",kernelFunc:Nte};var Tte="return float(isinf(x));",_te=xe({opSnippet:Tte,dtype:"bool"}),y3={kernelName:jn,backendName:"webgl",kernelFunc:_te};var Ete="return float(isnan(x));",$te=xe({opSnippet:Ete,dtype:"bool"}),b3={kernelName:Xn,backendName:"webgl",kernelFunc:$te};var Rte="return float(a < b);",Dte=` return vec4(lessThan(a, b)); `,Ate=st({opSnippet:Rte,packedOpSnippet:Dte,cpuKernelImpl:MD,dtype:"bool"}),C3={kernelName:ko,backendName:"webgl",kernelFunc:Ate};var Fte="return float(a <= b);",Pte=` return vec4(lessThanEqual(a, b)); `,Ote=st({opSnippet:Fte,packedOpSnippet:Pte,cpuKernelImpl:LD,dtype:"bool"}),w3={kernelName:No,backendName:"webgl",kernelFunc:Ote};function Mte(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=BD(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var S3={kernelName:Qn,backendName:"webgl",kernelFunc:Mte};var Lte=sn+` return x < 0.0 ? 0./0. : log(x); `,Bte=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g); result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; `,zte=xe({opSnippet:Lte,packedOpSnippet:Bte,cpuKernelImpl:zD}),I3={kernelName:To,backendName:"webgl",kernelFunc:zte};var Vte=sn+` return log(1.0 + x); `,Wte=xe({opSnippet:Vte}),v3={kernelName:Zn,backendName:"webgl",kernelFunc:Wte};var Ute="return float(a >= 1.0 && b >= 1.0);",Gte=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,Hte=st({opSnippet:Ute,packedOpSnippet:Gte,dtype:"bool"}),k3={kernelName:Jn,backendName:"webgl",kernelFunc:Hte};var Kte="return float(!(x >= 1.0));",qte=xe({opSnippet:Kte}),N3={kernelName:es,backendName:"webgl",kernelFunc:qte};var jte="return float(a >= 1.0 || b >= 1.0);",Xte=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,Yte=st({opSnippet:jte,packedOpSnippet:Xte,dtype:"bool"}),T3={kernelName:ts,backendName:"webgl",kernelFunc:Yte};var bg=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${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 * ${p}; setOutput(val); } `}};var Cg=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${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 Qte=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool("WEBGL_PACK_NORMALIZATION")?new Cg(n.shape,s,a,i,p):new bg(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},_3={kernelName:rs,backendName:"webgl",kernelFunc:Qte};var wg=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,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 - ${t}))); int depthEnd = int(min(float(${this.depth}), float(d + ${t} + 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 Zte=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:l}=o,c=new wg(n.shape,i,p,u,l);return t.runWebGLProgram(c,[n,s,a],n.dtype)},E3={kernelName:oi,backendName:"webgl",kernelFunc:Zte};function $3(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=ro(i,r.dtype,"max",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function V0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,l=C.getAxesPermutation(u,i),c=l!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(c){if(m){let w=t.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 l=C.computePool2DInfo(n.shape,s,a,u,i,p);if(l.filterWidth===1&&l.filterHeight===1&&y.arraysEqual(l.inShape,l.outShape))return Ft({inputs:{x:n},backend:t});let c=new Zs(l,"max",!1);return t.runWebGLProgram(c,[n],n.dtype)}var A3={kernelName:ns,backendName:"webgl",kernelFunc:rre};function ore(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,l=[1,1,1],c=C.computePool3DInfo(n.shape,s,a,l,i,u,p),m=new Nu(c,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var F3={kernelName:ha,backendName:"webgl",kernelFunc:ore};var Sg=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.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) / ${t}.0; if (dyR < 0.0 || dyR >= ${e.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 >= ${e.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 * ${a} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},Ig=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=l-1-e.padInfo.left,f=p*u*l-1;this.userCode=` const ivec3 pads = ivec3(${c}, ${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) / ${t}.0; if (dyD < 0.0 || dyD >= ${e.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 >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${l}; wC += ${i}) { float dyC = float(dyCCorner + wC) / ${n}.0; if (dyC < 0.0 || dyC >= ${e.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} * ${l} + wR * ${l} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function nre(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:l}=o,c=[1,1,1],m=C.computePool3DInfo(a.shape,i,p,c,u,l),d=new Nu(m,"max",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new Ig(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var P3={kernelName:Ji,backendName:"webgl",kernelFunc:nre};function sre(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Ys([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:l,dimRoundingMode:c}=o,m=C.computePool2DInfo(i.shape,p,u,1,l,c),d=!0,f=new Zs(m,"max",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new Sg(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var O3={kernelName:Zi,backendName:"webgl",kernelFunc:sre};function M3(r,e,t,o){let n=new Zs(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Zs(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var L3={kernelName:ga,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;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(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let l=C.computePool2DInfo(o.shape,n,s,u,a),[c,m]=M3(o,i,l,p);return[c,m]}};function B3(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=ro(i,"float32","mean",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var z3={kernelName:ss,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,p=y.parseAxisParam(s,o.shape),u=p,l=C.getAxesPermutation(u,i),c=l!=null,m=a.shouldExecuteOnCPU([o]),d=[],f=o;if(c){if(m){let S=a.texData.get(f.dataId).values,k=new Array(i);for(let R=0;Rl[0]+e[c]+l[1]);let n=e.length,s=Re(n),a=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).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 kg=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,h)=>f[0]+e[h]+f[1]);let n=e.length,s=Re(n),a=t.map(f=>f[0]).join(","),i=t.map((f,h)=>f[0]+e[h]).join(","),p=At("rc",n),u=At("source",n),l=`${p[n-1]} < ${this.outputShape[n-1]}`,c=n===1?"source":`vec2(${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()}), ${c}); ${p[n-1]} += 1; if(${l}) { ${f} result[1] = getChannel(getX(${u.join()}), ${c}); } `}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()}), ${c}); ${p[n-1]} += 1; if(${l}) { ${f} result[1] = getChannel(getX(${u.join()}), ${c}); } rc = outputLoc; ${p[n-2]} += 1; if(${p[n-2]} < ${this.outputShape[n-2]}) { ${f} result[2] = getChannel(getX(${u.join()}), ${c}); ${p[n-1]} += 1; if(${l}) { ${f} result[3] = getChannel(getX(${u.join()}), ${c}); } } `}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 lre=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new kg(o.shape,n,s):new vg(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},U3={kernelName:is,backendName:"webgl",kernelFunc:lre};var cre=`if (b == 0.0) return NAN; return mod(a, b);`,mre=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); `+to+` return result; `,dre=st({opSnippet:cre,packedOpSnippet:mre}),G3={kernelName:us,backendName:"webgl",kernelFunc:dre};var Ng=class{constructor(e,t,o){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,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 < ${t-1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { setOutput(float(i)); return; } } // If no other event happened, last event happened. setOutput(float(${t-1})); } `}};var fre=` if (a == b) { return 1.0; }; return a / b;`,hre=` // 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; `,W0=st({opSnippet:fre,packedOpSnippet:hre,checkOutOfBounds:!0}),H3={kernelName:Vn,backendName:"webgl",kernelFunc:W0};var K3="return a - b;",U0=st({opSnippet:K3,packedOpSnippet:K3,supportsComplex:!0,cpuKernelImpl:lA}),q3={kernelName:Oo,backendName:"webgl",kernelFunc:U0};function G0(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=V0({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=C.expandShapeToKeepDim(i.shape,a),u=te({inputs:{x:i},backend:t,attrs:{shape:p}}),l=U0({inputs:{a:n,b:u},backend:t}),c=L0({inputs:{x:l},backend:t}),m=Tp({inputs:{x:c},backend:t,attrs:{axis:a,keepDims:!1}}),d=te({inputs:{x:m},backend:t,attrs:{shape:p}}),f=W0({inputs:{a:c,b:d},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),f}var j3={kernelName:Fs,backendName:"webgl",kernelFunc:G0};function gre(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:G0({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],l=p.shape[1],c=new Ng(u,l,s),m=[[a]],d=t.runWebGLProgram(c,[p],"int32",m);return i||t.disposeIntermediateTensorInfo(p),d}var X3={kernelName:ps,backendName:"webgl",kernelFunc:gre};var xre=Gt+` return -x; `,yre=` 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 bre(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=HD(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return A().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Lr(o.shape,yre):n=new nr(o.shape,xre),t.runWebGLProgram(n,[o],o.dtype)}var Y3={kernelName:ls,backendName:"webgl",kernelFunc:bre};var Cre=Ut.nonMaxSuppressionV3Impl;function wre(r){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),l=t.readSync(s.dataId),{selectedIndices:c}=Cre(u,l,a,i,p);return t.makeTensorInfo([c.length],"int32",new Int32Array(c))}var Q3={kernelName:cs,backendName:"webgl",kernelFunc:wre};var Sre=Ut.nonMaxSuppressionV4Impl;function Ire(r){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,padToMaxOutputSize:u}=o,l=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:m,validOutputs:d}=Sre(l,c,a,i,p,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([d]))]}var Z3={kernelName:ni,backendName:"webgl",kernelFunc:Ire};var vre=Ut.nonMaxSuppressionV5Impl;function kre(r){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,l=t.readSync(n.dataId),c=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=vre(l,c,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var J3={kernelName:ms,backendName:"webgl",kernelFunc:kre};var Tg=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${n}), float(${o}), float(index == coords.y))); } `}};var Nre=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),l=new Tg(u,a,i,p),c=te({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=t.runWebGLProgram(l,[c],s);t.disposeIntermediateTensorInfo(c);let d=[...n.shape,a],f=te({inputs:{x:m},backend:t,attrs:{shape:d}});return t.disposeIntermediateTensorInfo(m),f},eP={kernelName:ds,backendName:"webgl",kernelFunc:Nre};function mm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=_i({inputs:{input:o},backend:t}),s=mm({inputs:{x:n},backend:t}),a=Ep({inputs:{input:o},backend:t}),i=mm({inputs:{x:a},backend:t}),p=zr({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return Ei({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var tP={kernelName:_a,backendName:"webgl",kernelFunc:mm};function rP(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=_i({inputs:{input:o},backend:t}),s=rP({inputs:{x:n},backend:t}),a=Ep({inputs:{input:o},backend:t}),i=mm({inputs:{x:a},backend:t}),p=zr({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return Ei({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var oP={kernelName:xa,backendName:"webgl",kernelFunc:rP};function Tre(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return cg({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(l=>{y.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(l=>{let c=cg({inputs:{input:l},backend:t,attrs:{dim:n}});return i.push(c),c}),u=M0({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(l=>t.disposeIntermediateTensorInfo(l)),u}var nP={kernelName:ya,backendName:"webgl",kernelFunc:Tre};var _g=class{constructor(e,t,o){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let n=e.length,s=Re(n),a=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).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 Eg=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Re(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),p=At("rc",n),u=At("source",n),l=`${p[n-1]} < ${this.outputShape[n-1]}`,c=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1; if(${l}) { `,n===1?"":`} rc = outputLoc; ${p[n-2]} += 1; if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${p[n-1]} += 1; if(${l}) {`],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:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((l,c)=>l[0]+n.shape[c]+l[1]);return Ei({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Eg(n.shape,s,a):new _g(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},sP={kernelName:fs,backendName:"webgl",kernelFunc:H0};var _re=` 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); `,Ere=` // 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); `+to+` return result; `,$re=st({opSnippet:_re,packedOpSnippet:Ere}),aP={kernelName:hs,backendName:"webgl",kernelFunc:$re};function Rre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),l=u,c=C.getAxesPermutation(l,i),m=n;c!=null&&(m=Ct({inputs:{x:n},backend:t,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,i),p.push(m)),C.assertAxesAreInnerMostDims("prod",l,i);let d;if(t.shouldExecuteOnCPU([m])){let f=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=qD(m.shape,m.dtype,f,l);d=t.makeTensorInfo(g,x,h)}else{let[f,h]=C.computeOutAndReduceShapes(m.shape,l),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=mi(n.dtype),w=ro(x,b,"prod",t);d=te({inputs:{x:w},backend:t,attrs:{shape:f}}),p.push(x),p.push(w)}if(a){p.push(d);let f=C.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var iP={kernelName:Ho,backendName:"webgl",kernelFunc:Rre};function Dre(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),l=t.readSync(s.dataId),c=t.readSync(a.dataId),[m,d,f]=jD(p,u,l,s.shape,s.dtype,c,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],"int32",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var uP={kernelName:Qp,backendName:"webgl",kernelFunc:Dre};function Are(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,l]=XD(a,o.shape,o.dtype,i,n.shape,p,s.shape),c=t.makeTensorInfo([u.length],"int32",u),m=t.makeTensorInfo([l.length],o.dtype,l);return[c,m]}var pP={kernelName:Zp,backendName:"webgl",kernelFunc:Are};function Fre(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),l=t.readSync(s.dataId),c=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=YD(u,n.shape,l,s.shape,s.dtype,c,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var lP={kernelName:Jp,backendName:"webgl",kernelFunc:Fre};var K0=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=QD(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},cP={kernelName:ba,backendName:"webgl",kernelFunc:K0};var Pre="return 1.0 / x;",Ore=xe({opSnippet:Pre}),mP={kernelName:xs,backendName:"webgl",kernelFunc:Ore};var Mre=Gt+` return (x < 0.0) ? 0.0 : x; `,Lre=` 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; `,Bre=xe({opSnippet:Mre,packedOpSnippet:Lre}),dP={kernelName:ys,backendName:"webgl",kernelFunc:Bre};var zre=Gt+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Vre=` 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; `,Wre=xe({opSnippet:zre,packedOpSnippet:Vre}),fP={kernelName:ws,backendName:"webgl",kernelFunc:Wre};var $g=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let l=[n&&t>1?i-1:i,n&&o>1?p-1:p],c=[n&&t>1?t-1:t,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( ${l[0]/c[0]}, ${l[1]/c[1]}); const vec2 inputShapeRC = vec2(${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 Rg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let l=[n&&t>1?i-1:i,n&&o>1?p-1:p],c=[n&&t>1?t-1:t,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( ${l[0]/c[0]}, ${l[1]/c[1]}, ${l[1]/c[1]}); const vec3 inputShapeRC = vec3(${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 Ure(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,l=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Rg(n.shape,p,u,s,a):new $g(n.shape,p,u,s,a);return t.runWebGLProgram(l,[n],"float32")}var hP={kernelName:Cs,backendName:"webgl",kernelFunc:Ure};var Dg=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],l=p[0]/u[0],c=p[1]/u[1],m=1/l,d=1/c,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${l}); const float widthScale = float(${c}); const float invHeightScale = float(${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 Gre(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new Dg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var gP={kernelName:ii,backendName:"webgl",kernelFunc:Gre};var Ag=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let l=[n&&t>1?i-1:i,n&&o>1?p-1:p],c=[n&&t>1?t-1:t,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( ${l[0]/c[0]}, ${l[1]/c[1]}); const vec2 inputShapeRC = vec2(${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 Fg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let l=[n&&t>1?i-1:i,n&&o>1?p-1:p],c=[n&&t>1?t-1:t,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( ${l[0]/c[0]}, ${l[1]/c[1]}, ${l[1]/c[1]}); const vec3 inputShapeRC = vec3(${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 < ${o-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function Hre(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,l=A().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Fg(n.shape,p,u,s,a):new Ag(n.shape,p,u,s,a);return t.runWebGLProgram(l,[n],n.dtype)}var xP={kernelName:bs,backendName:"webgl",kernelFunc:Hre};var Pg=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],l=p[0]/u[0],c=p[1]/u[1],m=1/l,d=1/c,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${l}); const float widthScale = float(${c}); const float invHeightScale = float(${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), ${o} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function Kre(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new Pg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var yP={kernelName:ai,backendName:"webgl",kernelFunc:Kre};var Og=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(","),a=Re(o);this.userCode=` void main() { ${a} coords = getOutputCoords(); setOutput(getX(${s})); } `}};var Mg=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=At("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${e[0]} - rc - 1), ${e[0]} - rc - 1); if(${s}){ result.g = getChannel(getX(${e[0]} - (rc + 1) - 1), ${e[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 = ${l(n.slice())}; if(${s}) { result.a = ${c(n.slice())}; } } setOutput(result); } `;function p(f){return m(f)}function u(f){return f[o-1]="("+f[o-1]+" + 1)",m(f)}function l(f){return f[o-2]="("+f[o-2]+" + 1)",m(f)}function c(f){return f[o-1]="("+f[o-1]+" + 1)",f[o-2]="("+f[o-2]+" + 1)",m(f)}function m(f){let h=e.map((b,w)=>d(w,f)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function qre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Ft({inputs:{x:n},backend:t});let p=A().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Mg(n.shape,i):new Og(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var bP={kernelName:Ss,backendName:"webgl",kernelFunc:qre};var Lg=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let o=e[1],n=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=` vec3 fill = vec3(${t.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 < ${o}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}};var CP={kernelName:Vs,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new Lg(o.shape,s),[u,l]=C.getImageCenter(a,o.shape[1],o.shape[2]),c=[[u,l,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,c)}};var jre=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,Xre=xe({opSnippet:jre}),wP={kernelName:Is,backendName:"webgl",kernelFunc:Xre};var Yre="return inversesqrt(x);",Qre=xe({opSnippet:Yre,cpuKernelImpl:ZD}),SP={kernelName:Do,backendName:"webgl",kernelFunc:Qre};var Tu=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let u=Re(s.length),l=Re(a.length),c="";o===1?c="i":o===2&&(c="i, j");let m=`getIndices(${c})`,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=t>1?"strides[j]":"strides";this.userCode=` ${u} strides = ${u}(${s}); void main() { ${l} coords = getOutputCoords(); float sum = 0.0; bool found = false; for (int i = 0; i < ${e}; i++) { int flattenedIndex = 0; for (int j = 0; j < ${t}; j++) { int index = round(${m}); flattenedIndex += index * ${x}; } if (flattenedIndex == coords[0]) { sum += ${f}; found = true; } } setOutput(mix(${g}, sum, float(found))); } `}};var Bg=class{constructor(e,t,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),l=Re(a.length),c="";o===1?c="i":o===2&&(c="i, j");let m=`getIndices(${c})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let f=`getUpdates(${d})`,h="";p&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=t>1?"strides[j]":"strides",b=t>1?"strides[j + 1]":"strides";this.userCode=` ${u} strides = ${u}(${s}); void main() { ${l} coords = getOutputCoords(); vec4 sum = vec4(0.); vec4 found = vec4(0.); for (int i = 0; i < ${e}; i+=2) { ivec2 flattenedIndex = ivec2(0); for (int j = 0; j < ${t}; j+=2) { ivec4 index = round(${m}); flattenedIndex += index.xz * ${x}; if (j + 1 < ${t}) { 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 Zre(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:l,outputSize:c}=C.calculateShapes(s,n,a),m=[c/u,u];if(c===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g;A().getBool("WEBGL_PACK")?g=new Bg(p,i,d.shape.length,f.shape.length,l,m):g=new Tu(p,i,d.shape.length,f.shape.length,l,m);let x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var IP={kernelName:vs,backendName:"webgl",kernelFunc:Zre};var zg=class{constructor(e,t,o,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,o];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+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 Jre(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new zg(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return t.runWebGLProgram(i,[n,s],"int32",p)}var vP={kernelName:Ns,backendName:"webgl",kernelFunc:Jre};var Vg=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],u=[];for(let l=0;l= 1.0) { setOutput(getA(${s})); } else { setOutput(getB(${s})); } } `}};function eoe(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new Vg(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],pt(n.dtype,s.dtype))}var kP={kernelName:wa,backendName:"webgl",kernelFunc:eoe};var toe=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${C.SELU_SCALEALPHA}; float scale = ${C.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,roe=xe({opSnippet:toe}),NP={kernelName:Ts,backendName:"webgl",kernelFunc:roe};var ooe=sn+` return 1.0 / (1.0 + exp(-1.0 * x)); `,noe=` 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; `,soe=xe({opSnippet:ooe,packedOpSnippet:noe,cpuKernelImpl:eA}),TP={kernelName:Ao,backendName:"webgl",kernelFunc:soe};var aoe=` if (isnan(x)) { return 0.0; } return sign(x); `,ioe=xe({opSnippet:aoe}),_P={kernelName:Rs,backendName:"webgl",kernelFunc:ioe};var uoe=sn+` return sin(x); `,poe=` vec4 result = sin(x); bvec4 isNaN = isnan(x); ${to} return result; `,loe=xe({opSnippet:uoe,packedOpSnippet:poe}),EP={kernelName:Es,backendName:"webgl",kernelFunc:loe};var coe=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,moe=xe({opSnippet:coe}),$P={kernelName:$s,backendName:"webgl",kernelFunc:moe};var doe=` 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; `,foe=xe({opSnippet:doe}),RP={kernelName:Ds,backendName:"webgl",kernelFunc:foe};var hoe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{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;xt.disposeIntermediateTensorInfo(x)),g},DP={kernelName:Sa,backendName:"webgl",kernelFunc:hoe};function goe(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;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=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),l=t.readSync(a.dataId)[0],[c,m,d,f,h]=rA(i,o.shape,o.dtype,p,n.dtype,u,l);return[t.makeTensorInfo(m,o.dtype,c),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var AP={kernelName:eu,backendName:"webgl",kernelFunc:goe};function xoe(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;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(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,l,c]=oA(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(l,o.dtype,u),t.makeTensorInfo([c.length],s.dtype,new Int32Array(c))]}var FP={kernelName:ui,backendName:"webgl",kernelFunc:xoe};function yoe(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;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=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,l]=Sh(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(l,o.dtype,u)}var PP={kernelName:va,backendName:"webgl",kernelFunc:yoe};function boe(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;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 ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,l]=Sh(a,o.shape,o.dtype,i,p);return t.makeTensorInfo(l,o.dtype,u)}var OP={kernelName:ka,backendName:"webgl",kernelFunc:boe};function Coe(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:l,strides:c,outputSize:m}=C.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let x=t.bufferSync(n),b=t.bufferSync(s),w=y.decodeString(t.readSync(a.dataId)[0]),S=JD(x,b,i,m,l,u,p,c,w,d);return t.makeTensorInfo(i,S.dtype,S.values)}let f=new Tu(u,p,n.shape.length,s.shape.length,c,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var MP={kernelName:Ps,backendName:"webgl",kernelFunc:Coe};function woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=C.prepareSplitSize(n,s,i),u=n.shape.length,l=new Array(u).fill(0),c=n.shape.slice();return p.map(m=>{let d=[...c];d[i]=m;let f=Js({inputs:{x:n},backend:t,attrs:{begin:l,size:d}});return l[i]+=m,f})}var LP={kernelName:Ia,backendName:"webgl",kernelFunc:woe};var BP="return sqrt(x);",Soe=xe({opSnippet:BP,packedOpSnippet:BP,cpuKernelImpl:nA}),zP={kernelName:Fo,backendName:"webgl",kernelFunc:Soe};var Ioe="return x * x;",voe=xe({opSnippet:Ioe}),VP={kernelName:tu,backendName:"webgl",kernelFunc:voe};var WP="return (a - b) * (a - b);",koe=st({opSnippet:WP,packedOpSnippet:WP}),UP={kernelName:Po,backendName:"webgl",kernelFunc:koe};function Noe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;if(n.dtype!=="string")throw new Error("Input must be of datatype string");let s=t.readSync(n.dataId),a=C.fromUint8ToStringArray(s),i=sA(a,"string",o);return t.makeTensorInfo(n.shape,"string",i)}var GP={kernelName:pi,backendName:"webgl",kernelFunc:Noe};function Toe({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=Gt+` return x > 0.0 ? 1.0 : float(${e.alpha}); `,s=new nr(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var HP={kernelName:Ko,backendName:"webgl",kernelFunc:Toe};var Wg=class{constructor(e,t,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,l)=>(p++,o.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${p-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=` ${s} begin = ${s}(${e}); ${s} strides = ${s}(${t}); void main() { ${a} coords = getOutputCoords(); setOutput(getX(${i})); } `}};function _oe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:l,newAxisMask:c,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:S}=nt.sliceInfo(n.shape,s,a,i,p,u,l,c,m),k;if(h)k=te({inputs:{x:n},backend:t,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=nt.computeOutShape(b,w,S),R=Js({inputs:{x:n},backend:t,attrs:{begin:b,size:E}});k=te({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo(R)}else if(t.shouldExecuteOnCPU([n])){let R=t.readSync(n.dataId),D=ie(n.shape,n.dtype,R),F=aA(d,D,S,b);k=t.makeTensorInfo(f,n.dtype,F.values)}else{let R=new Wg(b,S,d);k=t.runWebGLProgram(R,[n],n.dtype)}let T=te({inputs:{x:k},backend:t,attrs:{shape:f}});return t.disposeIntermediateTensorInfo(k),T}var KP={kernelName:Os,backendName:"webgl",kernelFunc:_oe};function Eoe(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:l,dataSplits:c}=e,m=t.readSync(l.dataId),d=t.readSync(c.dataId),[f,h]=iA(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],"string",f),t.makeTensorInfo(c.shape,"int32",h)]}var qP={kernelName:Na,backendName:"webgl",kernelFunc:Eoe};function $oe(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;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: ${a.shape}`);let i=t.readSync(s.dataId),p=t.readSync(a.dataId)[0],[u,l,c]=uA(i,p,n),m=l.length;return[t.makeTensorInfo([m,2],"int32",u),t.makeTensorInfo([m],"string",l),t.makeTensorInfo([2],"int32",new Int32Array(c))]}var jP={kernelName:ru,backendName:"webgl",kernelFunc:$oe};function Roe(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;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=t.readSync(s.dataId),i=pA(a,n);return t.makeTensorInfo(s.shape,"int32",i)}var XP={kernelName:ou,backendName:"webgl",kernelFunc:Roe};var Doe="return tan(x);",Aoe=xe({opSnippet:Doe}),YP={kernelName:Ms,backendName:"webgl",kernelFunc:Aoe};var Foe=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,Poe=xe({opSnippet:Foe}),QP={kernelName:Ls,backendName:"webgl",kernelFunc:Poe};function Ooe(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:l,outputSize:c}=C.calculateShapes(a,s,n.shape),m=[c/u,u];if(c===0)return t.makeTensorInfo(n.shape,s.dtype);let d=te({inputs:{x:s},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:a},backend:t,attrs:{shape:[p,u]}}),h=te({inputs:{x:n},backend:t,attrs:{shape:m}}),g=new Tu(p,i,d.shape.length,f.shape.length,l,m,!1,!0),x=t.runWebGLProgram(g,[f,d,h],h.dtype),b=te({inputs:{x},backend:t,attrs:{shape:n.shape}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(x),b}var ZP={kernelName:ks,backendName:"webgl",kernelFunc:Ooe};var Ug=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],o=[];for(let n=0;n5){let p=t.readSync(n.dataId),u=n.dtype==="string"?p.map(m=>y.decodeString(m)):p,l=ie(n.shape,n.dtype,u),c=cA(l,s);return t.makeTensorInfo(c.shape,c.dtype,c.values)}let a=new Ug(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var JP={kernelName:Mo,backendName:"webgl",kernelFunc:q0};var Gg=class{constructor(e){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=e,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)); } } `}},Hg=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // The output size is half of the previous size. // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4), // we only need to output the indices at positions |, the indices at // positions _ can be thrown away, see Figure5(b) After Phase 2 // (Merge phase) in the Bitonic Top K paper referenced above. // For example, the paper shows we only need to output the orange bars. // The output sequence should look like this | | | | | | | |. // Because the sequence is halved, to map the output index back // to the previous sequence to find the corresponding value, // we need to double the index. When we double the index, // we basically interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position // of each 2k positions by - elemIdx % k. E.g. for output at // index 4,5,6,7, we want to get the corresponding element at // original index 8,9,10,11, for output at index 8,9,10,11, // we want to get the corresponding element at original index // 16,17,18,19, so on and so forth. int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k)); int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k)); float x0 = getX(batch, i0); float x1 = i1 < n ? getX(batch, i1) : x0; setOutput(x0 >= x1 ? float(i0) : float(i1)); } `}};function Rp(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function eO(r){let e=1;for(;ep){let F=t.readSync(n.dataId),[O,M]=mA(F,u,n.dtype,s,a);return[t.makeTensorInfo(O.shape,O.dtype,O.values),t.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,n.dtype,[]),t.makeTensorInfo(u,"int32",[])];if(l===1)return[n,Ei({attrs:{shape:u,dtype:"int32",value:0},backend:t})];let c=t.texData.get(n.dataId),m=c!==null&&c.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/l,g=te({inputs:{x:d},attrs:{shape:[h,l]},backend:t});m&&Rp(t,d);let x=eO(s),b=eO(l),w=null,S=()=>w===null?[g,g]:[g,w],k=(F,O,M)=>{let L=S(),B=new Gg(M),U=[[l],[w===null?1:0],[Number.NEGATIVE_INFINITY],[F],[O]],j=w;w=t.runWebGLProgram(B,L,"int32",U),Rp(t,j)};for(let F=1;F=1;M/=2)k(O,M,[h,b])}for(let F=b;F>x;F/=2){let O=S(),M=new Hg([h,F/2]),B=[[l],[w===null?1:0],[x]],z=w;w=t.runWebGLProgram(M,O,"int32",B),Rp(t,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,w.shape)}let T=w;w=Js({inputs:{x:w},backend:t,attrs:{begin:0,size:[h,s]}}),Rp(t,T);let E=z0({inputs:{x:g,indices:w},backend:t,attrs:{axis:1,batchDims:1}});Rp(t,g);let R=u.slice(0,-1);R.push(s),T=w,w=te({inputs:{x:w},attrs:{shape:R},backend:t}),Rp(t,T);let D=E;return E=te({inputs:{x:E},attrs:{shape:R},backend:t}),Rp(t,D),[E,w]}var tO={kernelName:Bs,backendName:"webgl",kernelFunc:Loe};var Kg=class{constructor(e,t,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 < ${e} && 0 <= coordX && coordX < ${t}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${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(${s}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${t})); float mapY = mapCoord(inY, float(${e})); if (${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 Boe(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[l,c,m,d]=n.shape,[f,h]=u!=null?u:[c,m],g=[l,f,h,d],x=new Kg(c,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],"float32")}var rO={kernelName:zs,backendName:"webgl",kernelFunc:Boe};function zoe(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;Ys(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}=dA(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var oO={kernelName:nu,backendName:"webgl",kernelFunc:zoe};function Voe(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{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),l=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var nO={kernelName:Ta,backendName:"webgl",kernelFunc:Voe};var qg=class{constructor(e,t){this.variableNames=["x","segmentIds"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p="0.0",u="sumValue",l=Math.floor(o/4)*4,c=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 < ${l}; 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 + ${l}; if (${c===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 ); ${m} } else if (${c===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 ); ${m} } else if (${c===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 Woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,l=C.getAxesPermutation([u],i),c=n;l!=null&&(c=Ct({inputs:{x:n},backend:t,attrs:{perm:l}}),p.push(c),u=C.getInnerMostAxes(1,i)[0]);let m=C.segment_util.computeOutShape(c.shape,u,a),d=y.sizeFromShape([c.shape[u]]),f=te({inputs:{x:c},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=mi(n.dtype),g=(S,k,T,E,R)=>{let D=S.shape[0],F=S.shape[1],O=C.segment_util.segOpComputeOptimalWindowSize(F,R),M={windowSize:O,inSize:F,batchSize:D,numSegments:R},L=new qg(M,k),B=t.compileAndRun(L,[S,T],E);if(p.push(B),B.shape[1]===R)return B;let z=K0({backend:t,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),U=q0({inputs:{x:z},backend:t,attrs:{reps:[F/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:t,attrs:{shape:m}}),w=b;if(l!=null){p.push(b);let S=C.getUndoAxesPermutation(l);w=Ct({inputs:{x:w},backend:t,attrs:{perm:S}})}return p.forEach(S=>t.disposeIntermediateTensorInfo(S)),w}var sO={kernelName:su,backendName:"webgl",kernelFunc:Woe};var Uoe=[VA,UA,GA,HA,qA,jA,XA,YA,JA,eF,tF,rF,oF,nF,sF,aF,iF,uF,pF,lF,cF,dF,fF,hF,gF,CF,SF,IF,RA,kF,TF,_F,EF,$F,RF,DF,AF,FF,PF,OF,BF,zF,VF,WF,UF,GF,HF,KF,qF,jF,XF,YF,QF,ZF,JF,e3,r3,o3,n3,s3,i3,u3,p3,l3,c3,m3,d3,f3,h3,$A,g3,NF,x3,y3,b3,DA,C3,w3,S3,I3,v3,k3,N3,T3,_3,E3,R3,D3,A3,F3,P3,O3,L3,z3,V3,W3,U3,G3,X3,PA,Y3,Q3,Z3,J3,xF,eP,oP,nP,sP,aP,AA,iP,uP,pP,lP,cP,yF,H3,mP,dP,fP,MA,hP,gP,xP,yP,bP,CP,wP,SP,IP,vP,kP,NP,TP,_P,EP,$P,mF,j3,RP,DP,AP,FP,PP,OP,MP,LP,zP,VP,UP,GP,HP,KP,qP,jP,XP,q3,BA,YP,QP,ZP,JP,tO,rO,zA,oO,nO,sO,tP];for(let r of Uoe)li(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 _u;(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"})(_u||(_u={}));var aO;function Goe(r){aO=r.wasm.cwrap(qo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Hoe(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;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:l,leakyreluAlpha:c}=o,m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=t.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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lO=he(gn);function He(r,e,t){let o;function n(a){o=a.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(a){let{backend:i,inputs:p}=a,{a:u,b:l}=p,c=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(l.dataId).id,d=t!=null?t:u.dtype,f=C.assertAndGetBroadcastShape(u.shape,l.shape),h=i.makeOutput(f,d);if(y.sizeFromShape(f)===0)return h;let g=new Uint8Array(new Int32Array(u.shape).buffer),x=new Uint8Array(new Int32Array(l.shape).buffer),b=i.dataIdMap.get(h.dataId).id;return o(c,g,u.shape.length,m,x,l.shape.length,we[u.dtype],b),h}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:s}}var Koe=!0,cO=He(Rr,Koe);var mO;function qoe(r){mO=r.wasm.cwrap(xn,null,["array","number","number","number"])}function joe(r){let{inputs:e,backend:t}=r,o=t.makeOutput(e[0].shape,e[0].dtype);if(y.sizeFromShape(o.shape)===0)return o;let n=e.map(i=>t.dataIdMap.get(i.dataId).id),s=new Uint8Array(new Int32Array(n).buffer),a=t.dataIdMap.get(o.dataId).id;return mO(s,n.length,we[o.dtype],a),o}var dO={kernelName:xn,backendName:"wasm",setupFunc:qoe,kernelFunc:joe};function Dp(r){let{inputs:{x:e},backend:t}=r;if(e.dtype==="string")return pr(t.readSync(e.dataId),e.shape,e.dtype);let o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var fO={kernelName:vo,backendName:"wasm",kernelFunc:Dp};var hO;function Xoe(r){hO=r.wasm.cwrap(Kr,null,["number","array","number","number","number","array","number"])}function Vo(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=Qoe(e.x.shape,o.perm),a=!0;for(let f=0;f=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var gO={kernelName:Kr,backendName:"wasm",kernelFunc:Vo,setupFunc:Xoe};function $r(r,e,t){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(e,o),a=s,i=C.getAxesPermutation(a,n),p=null,u=!1;if(i!=null){let l=new Array(n);for(let d=0;d`new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var OO={kernelName:Ca,backendName:"wasm",kernelFunc:Wt};var MO;function lne(r){MO=r.wasm.cwrap(Nn,null,["number","array","number","number","array","number","number","number","number"])}function cne(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let p=n.shape.length,u=s.shape.length,l=a?n.shape[p-2]:n.shape[p-1],c=i?s.shape[u-1]:s.shape[u-2],m=a?n.shape[p-1]:n.shape[p-2],d=i?s.shape[u-2]:s.shape[u-1],f=n.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(f),x=y.sizeFromShape(h),w=kr.assertAndGetBroadcastShape(n.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,d]);y.assert(l===c,()=>`Error in matMul: inner shapes (${l}) and (${c}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`);let S=a?[g,l,m]:[g,m,l],k=i?[x,d,c]:[x,c,d],T=Wt({inputs:{x:n},backend:t,attrs:{shape:S}}),E=Wt({inputs:{x:s},backend:t,attrs:{shape:k}}),R=t.dataIdMap.get(T.dataId).id,D=t.dataIdMap.get(E.dataId).id,F=a?T.shape[2]:T.shape[1],O=i?E.shape[1]:E.shape[2],M=Math.max(g,x),L=t.makeOutput([M,F,O],T.dtype),B=t.dataIdMap.get(L.dataId).id,z=new Uint8Array(new Int32Array(T.shape).buffer),U=new Uint8Array(new Int32Array(E.shape).buffer);return MO(R,z,T.shape.length,D,U,E.shape.length,a,i,B),t.disposeData(T.dataId),t.disposeData(E.dataId),L.shape=w,L}var LO={kernelName:Nn,backendName:"wasm",setupFunc:lne,kernelFunc:cne};function an(r){let{inputs:{x:e},attrs:{begin:t,size:o},backend:n}=r,[s,a]=nt.parseSliceParams(e,t,o),i=nt.isSliceContinous(e.shape,s,a),p=n.readSync(e.dataId),u=n.makeOutput(a,e.dtype),l=y.computeStrides(e.shape),c=n.dataIdMap.get(u.dataId);if(i){let f=nt.computeFlatOffset(s,l);return e.dtype==="string"?c.stringBytes=p.slice(f,f+y.sizeFromShape(a)):n.typedArrayFromHeap(u).set(p.subarray(f,f+y.sizeFromShape(a))),u}if(e.dtype==="string"){let f=hp(p,s,a,e.shape,e.dtype);return c.stringBytes=f,u}let m=n.typedArrayFromHeap(u),d=e.shape.length;if(d===2)mne(p,l[0],m,s,a);else if(d===3)dne(p,l[0],l[1],m,s,a);else if(d===4)fne(p,l[0],l[1],l[2],m,s,a);else{let f=hp(p,s,a,e.shape,e.dtype);m.set(f)}return u}function mne(r,e,t,o,n){let s=0,a=o[0],i=o[1],p=a+n[0];for(let u=a;ux*b),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),l=C.getReshapedPermuted(n.shape,s,i),c=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(l,a,s.length),d=Wt({inputs:{x:n},backend:t,attrs:{shape:p}}),f=Vo({inputs:{x:d},backend:t,attrs:{perm:u}}),h=Wt({inputs:{x:f},backend:t,attrs:{shape:l}}),g=an({inputs:{x:h},backend:t,attrs:{begin:c,size:m}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(h.dataId),g}var zO={kernelName:ia,backendName:"wasm",kernelFunc:hne};var VO;function gne(r){VO=r.wasm.cwrap(Tn,null,["number","number","boolean","number","number","number"])}function xne(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,weights:s}=t,{size:a}=o,i=s.shape.reduce((c,m)=>c*m,1)!==0,p=n.shape.length===1?[a]:[n.shape[0],a],u=e.makeOutput(p,s.dtype);function l(c){return e.dataIdMap.get(c.dataId).id}return VO(l(n),a,i,l(s),we[s.dtype],l(u)),u}var WO={kernelName:Tn,backendName:"wasm",setupFunc:gne,kernelFunc:xne};var yne=!0,UO=He(_n,yne);function bne(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.typedArrayFromHeap(o),a=t.typedArrayFromHeap(n),i=C.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeOutput([i.length],"int32",void 0,new Int32Array(i))}var GO={kernelName:ua,backendName:"wasm",kernelFunc:bne};function Vr(r){let{inputs:{x:e},attrs:{dtype:t},backend:o}=r,n=o.makeOutput(e.shape,t),s=o.typedArrayFromHeap(e);return o.typedArrayFromHeap(n).set(s),n}var HO={kernelName:ho,backendName:"wasm",kernelFunc:Vr};var KO=he(go);var qO;function Cne(r){qO=r.wasm.cwrap(Go,null,["number","number","number","number"])}function wne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i=t.dataIdMap.get(n.dataId).id,p=t.makeOutput(n.shape,n.dtype),u=t.dataIdMap.get(p.dataId).id;return qO(i,s,a,u),p}var jO={kernelName:Go,backendName:"wasm",setupFunc:Cne,kernelFunc:wne};function j0(r){let{inputs:e,backend:t}=r,o=y.parseAxisParam(r.attrs.axis,e[0].shape)[0],n=e.map(d=>d.shape);C.assertParamsConsistent(n,o);let s=C.computeOutShape(e.map(d=>d.shape),o),a=e.filter(d=>y.sizeFromShape(d.shape)>0);if(a.length===1)return Dp({inputs:{x:a[0]},backend:t});let i=t.makeOutput(s,e[0].dtype);if(y.sizeFromShape(s)===0)return i;if(a[0].dtype==="string"){let d=a.map(w=>{let k=[-1,y.sizeFromShape(w.shape.slice(o))];return Wt({inputs:{x:w},backend:t,attrs:{shape:k}})}),f=d.map(w=>({vals:t.readSync(w.dataId),shape:w.shape}));s=C.computeOutShape(d.map(w=>w.shape),1);let h=d[0].shape[0]===1,g=mp(f,s,e[0].dtype,h),x=C.computeOutShape(a.map(w=>w.shape),o);i.shape=x;let b=t.dataIdMap.get(i.dataId);return b.stringBytes=C.fromStringArrayToUint8(g),d.forEach(w=>t.disposeData(w.dataId)),i}let p=y.sizeFromShape(a[0].shape.slice(0,o)),u=0,l=a.map(d=>{let f=y.sizeFromShape(d.shape.slice(o));return u+=f,f}),c=a.map(d=>t.typedArrayFromHeap(d)),m=t.typedArrayFromHeap(i);for(let d=0;d`cumprod does not support ${n.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],p),l=n;u!==null&&(l=Vo({inputs:{x:n},attrs:{perm:u},backend:t}));let c=C.getInnerMostAxes(1,p)[0];C.assertAxesAreInnerMostDims("cumprod",[c],p);let m=t.makeOutput(l.shape,l.dtype),d=l.shape[c],f=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(m.dataId).id;lM(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let x=C.getUndoAxesPermutation(u);g=Vo({inputs:{x:m},attrs:{perm:x},backend:t}),t.disposeData(l.dataId),t.disposeData(m.dataId)}return g}var cM={kernelName:Pn,backendName:"wasm",setupFunc:Fne,kernelFunc:Pne};var mM;function One(r){mM=r.wasm.cwrap(On,null,["number","number","number","number","number","number"])}function Mne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{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=C.getAxesPermutation([s],p),l=n;u!==null&&(l=Vo({inputs:{x:n},attrs:{perm:u},backend:t}));let c=C.getInnerMostAxes(1,p)[0];C.assertAxesAreInnerMostDims("cumsum",[c],p);let m=t.makeOutput(l.shape,l.dtype),d=l.shape[c],f=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(m.dataId).id;mM(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let x=C.getUndoAxesPermutation(u);g=Vo({inputs:{x:m},attrs:{perm:x},backend:t}),t.disposeData(l.dataId),t.disposeData(m.dataId)}return g}var dM={kernelName:On,backendName:"wasm",setupFunc:One,kernelFunc:Mne};var fM;function Lne(r){fM=r.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function Bne(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,weights:s}=t,{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],l=e.makeOutput(u,s.dtype);function c(m){return e.dataIdMap.get(m.dataId).id}return fM(c(n),new Uint8Array(new Int32Array(n.shape).buffer),n.shape.length,a,p,c(s),we[s.dtype],i,c(l)),l}var hM={kernelName:la,backendName:"wasm",setupFunc:Lne,kernelFunc:Bne};var gM;function zne(r){gM=r.wasm.cwrap(Ln,null,["number","number","number","array","number","array","array","number","number"])}function Vne(r){let{backend:e,inputs:t,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],l=a==="NHWC"?n.shape[3]:n.shape[1],c=p*s,m=u*s,d=l/(s*s),f=a==="NHWC"?[i,c,m,d]:[i,d,c,m],h=e.makeOutput(f,"float32"),x=e.dataIdMap.get(n.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),w=new Uint8Array(new Int32Array(f).buffer),S=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer),k=e.dataIdMap.get(h.dataId).id;return gM(x,s,a==="NHWC"?1:0,b,n.shape.length-1,w,S,f.length,k),h}var xM={kernelName:Ln,backendName:"wasm",setupFunc:zne,kernelFunc:Vne};var yM;function Wne(r){yM=r.wasm.cwrap(Bn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Une(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:l,dimRoundingMode:c}=t,m=u==null?[1,1]:u,d=C.computeConv2DInfo(n.shape,s.shape,p,m,l,c,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,w=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,T=d.strideHeight,E=d.strideWidth,R=d.inChannels,D=d.outChannels,F=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}, ${s.dtype}, and ${a.dtype}`);let l=C.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),c=t.makeOutput(s.shape,s.dtype);return vM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(c.dataId).id,we[n.dtype],l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.filterHeight,l.filterWidth,l.padInfo.top,l.padInfo.left),c}var kM={kernelName:qi,backendName:"wasm",setupFunc:jne,kernelFunc:Xne};var NM;function Yne(r){NM=r.wasm.cwrap(Ki,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{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 l=C.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),c=t.makeOutput(n.shape,n.dtype);return NM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(c.dataId).id,we[n.dtype],l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.filterHeight,l.filterWidth,l.padInfo.top,l.padInfo.left),c}var TM={kernelName:Ki,backendName:"wasm",setupFunc:Yne,kernelFunc:Qne};var _M=he(Wn);var EM;function Zne(r){EM=r.wasm.cwrap(ri,null,["number","number","number"])}function Jne(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,"float32"),a=i=>t.dataIdMap.get(i.dataId).id;return EM(a(n),a(o),a(s)),s}var $M={kernelName:ri,backendName:"wasm",setupFunc:Zne,kernelFunc:Jne};var ese=!1,RM=He(xo,ese,"bool");var DM=he(Un);var AM=he(yo,"float32");function Xg(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Wt({inputs:{x:n},backend:o,attrs:{shape:i}})}var FM={kernelName:ma,backendName:"wasm",kernelFunc:Xg};var PM=he(bo,"float32");function Y0(r){let{attrs:{shape:e,value:t},backend:o}=r,{attrs:{dtype:n}}=r;n=n||y.inferDtype(t);let s=o.makeOutput(e,n);return o.typedArrayFromHeap(s).fill(t),s}var OM={kernelName:da,backendName:"wasm",kernelFunc:Y0};var MM;function tse(r){MM=r.wasm.cwrap(Gn,null,["number","number","number","number","number","number"])}function rse(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,l]=o.shape;return MM(s,i,p,u,l,a),n}var LM={kernelName:Gn,backendName:"wasm",kernelFunc:rse,setupFunc:tse};var BM=he(Co);var ose=!1,zM=He(wo,ose);var VM;function nse(r){VM=r.wasm.cwrap(Hn,null,["number","number","number","number","number","number","number"])}function sse(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,l=e.dataIdMap.get(s.dataId).id,c=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return VM(l,c,m,d,f,n,g),h}var WM={kernelName:Hn,backendName:"wasm",setupFunc:nse,kernelFunc:sse};var UM;function ase(r){UM=r.wasm.cwrap(jo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ise(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:l,dataFormat:c,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=C.computeConv2DInfo(n.shape,s.shape,p,l,u,m),g=_u[d];if(g==null)throw new Error(`${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,w=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 ${ee.shape.length}.`);if(ee.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${w})`);S=ee.id}let k=h.filterHeight,T=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,F=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type==="SAME"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${c}'. 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 UM(x,j,q,Y,b,k,T,S,E,R,D,F,U,O,M,L,B,z,w,g,ne,f||0,re),J}var GM={kernelName:jo,backendName:"wasm",setupFunc:ase,kernelFunc:ise};var HM;function use(r){HM=r.wasm.cwrap(Xo,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 pse(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:l,dataFormat:c,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=C.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!0),g=_u[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,w=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 ${ee.shape.length}.`);if(ee.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${w})`);S=ee.id}let k=h.filterHeight,T=h.filterWidth,E=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,F=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type==="SAME"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${c}'. 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 HM(x,j,q,Y,b,k,T,S,E,R,D,F,U,O,M,L,B,z,w,g,ne,f||0,re),J}var KM={kernelName:Xo,backendName:"wasm",setupFunc:use,kernelFunc:pse};var qM;function lse(r){qM=r.wasm.cwrap(Kn,null,["number","number","number","number","number","number","array","number"])}function cse(r){let{backend:e,inputs:t}=r,{params:o,indices:n}=t,[s,a,i,p]=xf.prepareAndValidate(o,n),u=e.makeOutput(s,o.dtype);if(a===0)return u;let l=n.shape,c=l[l.length-1],d=e.dataIdMap.get(o.dataId).id,h=e.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=e.dataIdMap.get(u.dataId).id;return qM(d,we[o.dtype],h,a,c,i,g,x),u}var jM={kernelName:Kn,backendName:"wasm",setupFunc:lse,kernelFunc:cse};var XM;function mse(r){XM=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function dse(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),l=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${F} is not in [0, ${l-1}]`)}let c=C.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=Wt({inputs:{x:n},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=Wt({inputs:{x:s},attrs:{shape:[c.batchSize,d/c.batchSize]},backend:e}),h=[c.batchSize,c.outerSize,d/c.batchSize,c.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,w=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,T=e.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 XM(w,we[n.dtype],E,x,k,c.batchSize,R,T),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=c.outputShape,g}var YM={kernelName:fa,backendName:"wasm",setupFunc:mse,kernelFunc:dse};var fse=!1,QM=He(So,fse,"bool");var hse=!1,ZM=He(Io,hse,"bool");var JM=he(qn,"bool");var eL=he(jn,"bool");var tL=he(Xn,"bool");var rL;function gse(r){rL=r.wasm.cwrap(Yn,null,["number","number","number","number"])}function xse(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,"float32");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;rL(n,we[e.dtype],t,a)}return s}var oL={kernelName:Yn,backendName:"wasm",setupFunc:gse,kernelFunc:xse};var yse=!1,nL=He(ko,yse,"bool");var bse=!1,sL=He(No,bse,"bool");var aL;function Cse(r){aL=r.wasm.cwrap(Qn,null,["number","number","number","number"])}function wse(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],"float32");return aL(t.dataIdMap.get(i.dataId).id,o,n,a),i}var iL={kernelName:Qn,backendName:"wasm",setupFunc:Cse,kernelFunc:wse};var uL=he(To);var pL=he(Zn);var Sse=!1,lL=He(Jn,Sse,"bool");var cL=he(es);var Ise=!1,mL=He(ts,Ise,"bool");var vse=!1,dL=He(gk,vse,"bool");var fL;function kse(r){fL=r.wasm.cwrap(rs,null,["number","number","number","number","number","number","number"])}function Nse(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{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=t.makeOutput(n.shape,n.dtype);return fL(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var hL={kernelName:rs,backendName:"wasm",setupFunc:kse,kernelFunc:Nse};var gL;function Tse(r){gL=r.wasm.cwrap(oi,null,["number","number","number","number","number","number","number","number","number"])}function _se(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:l}=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 c=t.makeOutput(n.shape,n.dtype);return gL(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(c.dataId).id,a.shape[3],i,p,u,l),c}var xL={kernelName:oi,backendName:"wasm",setupFunc:Tse,kernelFunc:_se};var yL;function Ese(r){yL=r.wasm.cwrap(os,null,["number","number","number","number"])}function $se(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:l,axes:c,originalAxes:m,inputWasTransposed:d}=$r(a,n,e);if(d){let w=e.dataIdMap.get(l.dataId).id;u=l,p=w}let f=u.shape.length;C.assertAxesAreInnerMostDims("max",c,f);let[h,g]=C.computeOutAndReduceShapes(u.shape,c),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(b.dataId).id;yL(p,we[a.dtype],x,w)}if(d&&e.disposeData(l.dataId),s){let w=C.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var bL={kernelName:os,backendName:"wasm",setupFunc:Ese,kernelFunc:$se};var Rse=!1,CL=He(_o,Rse);var wL;function Dse(r){wL=r.wasm.cwrap(ns,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ase(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,l=C.computePool2DInfo(n.shape,a,i,1,p,u),c=l.filterHeight,m=l.filterWidth,d=l.padInfo.top,f=l.padInfo.right,h=l.padInfo.bottom,g=l.padInfo.left,x=l.dilationHeight,b=l.dilationWidth,w=l.strideHeight,S=l.strideWidth,k=l.inChannels,T=l.outChannels;if(l.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${l.dataFormat}'. Please use 'channelsLast'.`);let E=o.makeOutput(l.outShape,"float32"),R=o.dataIdMap.get(E.dataId).id;return wL(s,n.shape[0],n.shape[1],n.shape[2],c,m,d,f,h,g,x,b,w,S,k,T,R),E}var SL={kernelName:ns,backendName:"wasm",setupFunc:Dse,kernelFunc:Ase};var IL;function Fse(r){IL=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 Pse(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,l=C.computePool3DInfo(n.shape,s,a,1,i,p,u),c=t.makeOutput(l.outShape,n.dtype);return IL(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(c.dataId).id,l.batchSize,l.inChannels,l.inDepth,l.inHeight,l.inWidth,l.outDepth,l.outHeight,l.outWidth,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var vL={kernelName:ha,backendName:"wasm",setupFunc:Fse,kernelFunc:Pse};var kL;function Ose(r){kL=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 Mse(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,l=C.computePool3DInfo(s.shape,a,i,1,p,u),c=t.makeOutput(s.shape,s.dtype);return kL(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(c.dataId).id,l.batchSize,l.inChannels,l.inDepth,l.inHeight,l.inWidth,l.outDepth,l.outHeight,l.outWidth,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var NL={kernelName:Ji,backendName:"wasm",setupFunc:Ose,kernelFunc:Mse};var TL;function Lse(r){TL=r.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bse(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,l=C.computePool2DInfo(s.shape,a,i,1,p,u),c=t.makeOutput(s.shape,s.dtype);return TL(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(c.dataId).id,l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.effectiveFilterHeight,l.effectiveFilterWidth,l.padInfo.top,l.padInfo.left),c}var _L={kernelName:Zi,backendName:"wasm",setupFunc:Lse,kernelFunc:Bse};var EL;function zse(r){EL=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 Vse(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{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(C.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let l=C.computePool2DInfo(n.shape,s,a,[1,1],i),c=t.makeOutput(l.outShape,n.dtype),m=t.makeOutput(l.outShape,"int32");return EL(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(c.dataId).id,t.dataIdMap.get(m.dataId).id,we[n.dtype],p,l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.effectiveFilterHeight,l.effectiveFilterWidth,l.padInfo.top,l.padInfo.left),[c,m]}var $L={kernelName:ga,backendName:"wasm",setupFunc:zse,kernelFunc:Vse};var RL;function Wse(r){RL=r.wasm.cwrap(ss,null,["number, number, number"])}function Use(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:l,axes:c,originalAxes:m,inputWasTransposed:d}=$r(a,n,e),f=c;if(d){let S=e.dataIdMap.get(l.dataId).id;S!==i&&(u=l,p=S,f=C.getInnerMostAxes(f.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[h,g]=C.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Vr({backend:e,inputs:{x:u},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(b.dataId).id);let w=e.makeOutput(h,"float32");if(y.sizeFromShape(u.shape)!==0){let S=e.dataIdMap.get(w.dataId).id;RL(p,x,S)}if(d&&e.disposeData(l.dataId),s){let S=C.expandShapeToKeepDim(w.shape,m);w.shape=S}return u.dtype!=="float32"&&e.disposeData(b.dataId),w}var DL={kernelName:ss,backendName:"wasm",setupFunc:Wse,kernelFunc:Use};var AL;function Gse(r){AL=r.wasm.cwrap(as,null,["number","number","number","number"])}function Hse(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:l,axes:c,originalAxes:m,inputWasTransposed:d}=$r(a,n,e);if(d){let w=e.dataIdMap.get(l.dataId).id;w!==i&&(u=l,p=w)}let f=u.shape.length;C.assertAxesAreInnerMostDims("min",c,f);let[h,g]=C.computeOutAndReduceShapes(u.shape,c),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(b.dataId).id;AL(p,we[a.dtype],x,w)}if(d&&e.disposeData(l.dataId),s){let w=C.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var FL={kernelName:as,backendName:"wasm",setupFunc:Gse,kernelFunc:Hse};var Kse=!1,PL=He(Eo,Kse);var Q0;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(Q0||(Q0={}));var OL;function qse(r){OL=r.wasm.cwrap(is,null,["number","array","number","number","array","array","number","number"])}function jse(r){let{inputs:{x:e},backend:t,attrs:{paddings:o,mode:n}}=r,s=o.map((f,h)=>f[0]+e.shape[h]+f[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),p=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),l=o.map(f=>f[0]),c=o.map(f=>f[1]),m=new Uint8Array(new Int32Array(l).buffer),d=new Uint8Array(new Int32Array(c).buffer);return OL(a,u,e.shape.length,we[e.dtype],m,d,Q0[n],p),i}var ML={kernelName:is,backendName:"wasm",kernelFunc:jse,setupFunc:qse};var LL;function Xse(r){LL=r.wasm.cwrap(Fs,null,["number","number","number","number"])}function Z0(r){let{backend:e,inputs:{logits:t},attrs:{dim:o}}=r,n=e.dataIdMap.get(t.dataId).id,s=e.makeOutput(t.shape,t.dtype),a=e.dataIdMap.get(s.dataId).id,i=t.shape[o],p=y.sizeFromShape(t.shape)/i;return y.sizeFromShape(s.shape)===0||LL(n,a,i,p),s}var BL={kernelName:Fs,backendName:"wasm",setupFunc:Xse,kernelFunc:Z0};var zL;function Yse(r){zL=r.wasm.cwrap(ps,null,["number","number","number","number","number","number"])}function Qse(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o;if(n.dtype!=="float32")throw new Error(`Tensor logits must have dtype float32, got ${n.dtype}`);let p=i?n:Z0({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),[u,l]=p.shape,c=t.makeOutput([u,s],"int32");return zL(t.dataIdMap.get(p.dataId).id,u,l,s,a,t.dataIdMap.get(c.dataId).id),i||t.disposeData(p.dataId),c}var VL={kernelName:ps,backendName:"wasm",setupFunc:Yse,kernelFunc:Qse};var WL=He(us,!0);var Zse=!0,UL=He($o,Zse);var GL=he(ls);function Zl(r,e){let t=new Int32Array(r.wasm.HEAPU8.buffer,e,4),o=t[0],n=t[1],s=t[2],a=t[3];return r.wasm._free(e),{pSelectedIndices:o,selectedSize:n,pSelectedScores:s,pValidOutputs:a}}var HL;function Jse(r){HL=r.wasm.cwrap(cs,"number",["number","number","number","number","number"])}function eae(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a}=o,{boxes:i,scores:p}=t,u=e.dataIdMap.get(i.dataId).id,l=e.dataIdMap.get(p.dataId).id,c=HL(u,l,s,n,a),{pSelectedIndices:m,selectedSize:d,pSelectedScores:f,pValidOutputs:h}=Zl(e,c);return e.wasm._free(f),e.wasm._free(h),e.makeOutput([d],"int32",m)}var KL={kernelName:cs,backendName:"wasm",setupFunc:Jse,kernelFunc:eae};var qL;function tae(r){qL=r.wasm.cwrap(ni,"number",["number","number","number","number","number","bool"])}function rae(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,padToMaxOutputSize:i}=o,{boxes:p,scores:u}=t,l=e.dataIdMap.get(p.dataId).id,c=e.dataIdMap.get(u.dataId).id,m=qL(l,c,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Zl(e,m);e.wasm._free(h);let x=e.makeOutput([f],"int32",d),b=e.makeOutput([],"int32",g);return[x,b]}var jL={kernelName:ni,backendName:"wasm",setupFunc:tae,kernelFunc:rae};var XL;function oae(r){XL=r.wasm.cwrap(ms,"number",["number","number","number","number","number","number"])}function nae(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,softNmsSigma:i}=o,{boxes:p,scores:u}=t,l=e.dataIdMap.get(p.dataId).id,c=e.dataIdMap.get(u.dataId).id,m=XL(l,c,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Zl(e,m);e.wasm._free(g);let x=e.makeOutput([f],"int32",d),b=e.makeOutput([f],"float32",h);return[x,b]}var YL={kernelName:ms,backendName:"wasm",setupFunc:oae,kernelFunc:nae};var sae=!1,QL=He(Ro,sae,"bool");var ZL;function aae(r){ZL=r.wasm.cwrap(ds,null,["number","number","number","number","number"])}function iae(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=t.makeOutput([...n.shape,a],s),l=t.dataIdMap.get(u.dataId).id,m=t.dataIdMap.get(n.dataId).id;return ZL(m,a,i,p,l),u}var JL={kernelName:ds,backendName:"wasm",setupFunc:aae,kernelFunc:iae};function uae(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(1),o}var eB={kernelName:xa,backendName:"wasm",kernelFunc:uae};function pae(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Xg({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(l=>{y.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching 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Yg={kernelName:fs,backendName:"wasm",kernelFunc:cae,setupFunc:lae};var mae=!1,oB=He(hs,mae);var nB;function dae(r){nB=r.wasm.cwrap(gs,null,["number","number","number"])}function fae(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,i=s,p=o,u=p;p.dtype!=="float32"&&(u=Vr({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),i=t.dataIdMap.get(u.dataId).id);let l=t.makeOutput(o.shape,"float32"),c=t.dataIdMap.get(l.dataId).id;return nB(i,a,c),p.dtype!=="float32"&&t.disposeData(u.dataId),l}var sB={kernelName:gs,backendName:"wasm",setupFunc:dae,kernelFunc:fae};var aB;function hae(r){aB=r.wasm.cwrap(Ho,null,["number","number","number","number"])}function gae(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:l,axes:c,originalAxes:m,inputWasTransposed:d}=$r(a,n,e),f=c;if(d){let 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: ${u}) -> ${Ae(t)}{ return get${s}(); } `;p<2&&l.length>=1?m="coords = 0;":m=l.map(g=>`coords.${un(g+c)} = 0;`).join(` `);let d="";if(p<2&&i>0)d="coords";else if(p>1){let g=ft(i),x=r.shape.map((b,w)=>`coords.${un(w+c)}`).join(", ");d=`${g}(${x})`}else d="coords";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return` fn ${a}Index(globalIndex : i32) -> ${Ae(t)} { var coords = getCoordsFromIndex(globalIndex); ${m} return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?"":` / ${t}`}]); } fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t)} { var coords = coordsIn; ${m} return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?"":` / ${t}`}]); } `}function Aie(r,e,t,o){let n=Rie(r,t);return r.shape.length<=e.length&&(n+=Die(r,e,t,o)),n}function Fie(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return"";if(t.length===s)return`fn getOutputCoords() -> ${ft(s)}{ let globalIndex = getGlobalIndex(); return 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+ coords.u; } `;break;case 6:e+=` 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 e}function Gz(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function Eu(r,e=1){if(r==="float32")return Ae(e,"f32");if(r==="int32"||r==="bool")return Ae(e,"i32");throw new Error(`type ${r} is not supported.`)}function Oie(r,e,t){let o=r.length,n=Eu(e,t),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t)}) { result[flatIndex] = ${n}(value); } fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t,"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 : ${Ae(t)}) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndex(flatIndex${t===1?"":` / ${t}`}, value); } fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(", ")}, value : ${Ae(t,"i32")}) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndexI32(flatIndex${t===1?"":` / ${t}`}, value); } `}return s}function Mie(r){let e=/(\w+)\s*:\s*vec(5|6)/g;r=r.replace(e,o=>"@align(16) "+o);let t=/vec(5|6)\s*,\s*(\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function Vz(r){return!(r.dispatchLayout.hasOwnProperty("y")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty("z")&&r.dispatchLayout.z.length!==0)}var cv={};qe(cv,{GPUBytesPerElement:()=>sx,MatMulProgramType:()=>pn,assertNotComplex:()=>wm,computeDispatch:()=>H,computeWorkPerThreadForConv2d:()=>bm,computeWorkgroupInfoForMatMul:()=>lv,computeWorkgroupSizeForConv2d:()=>ym,flatDispatchLayout:()=>X,isWebGPUSupported:()=>Cm,tilesFitEvenlyIntoShape:()=>Bie});var Ap=r=>{let e=1;for(let t=0;tt%r[o]===0)}function H(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Ap(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Ap(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Ap(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function lv(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function ym(r,e,t=!1){if(t)return[8,8,1];let o=Ap(r.x.map(s=>e[s])),n=Ap(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function bm(r,e,t=!1){if(t)return[4,4,1];let o=Ap(r.x.map(s=>e[s])),n=Ap(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((e,t)=>t)}}function sx(r){if(r==="float32"||r==="int32"||r==="bool"||r==="string")return 4;if(r==="complex64")return 8;throw new Error(`Unknown dtype ${r}`)}function Cm(){return!!(globalThis&&globalThis.navigator&&globalThis.navigator.gpu)}function wm(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var pn;(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"})(pn||(pn={}));var zie=A().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Vie=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&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>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Jl=class r extends mo{nextDataId(){return r.nextDataId++}constructor(e,t){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,!Cm())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new rx(t),this.supportTimestampQuery=this.device.features.has("timestamp-query"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new ox(this.device),this.textureManager=new nx(this.device),this.tensorMap=new mn(this,cr()),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:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(e,t=!1){if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);return t?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(e)?(this.tensorDataPendingDisposal.push(e),!0):(this.releaseResource(e),this.tensorMap.delete(e),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resource)){if(t.external){t.resource=null;return}t.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(t.resource):t.resource instanceof GPUTexture&&this.textureManager.releaseTexture(t.resource),t.resource=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o==="complex64"&&e!=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:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e,!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 e;try{e=await Promise.all(Object.values(this.pipelineCache))}catch(t){throw new Error(t.message)}Object.keys(this.pipelineCache).map((t,o)=>{this.pipelineCache[t]=e[o]})}async getBufferData(e){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 t=e.size,o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,o,0,t),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(e,t){let o=this.tensorMap.get(e);return o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o,complexTensorInfos:n}=t;if(o!=null||t.dtype==="string")return o;if(t.dtype==="complex64"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(C.mergeRealAndImagArrays(h,g).buffer,"float32");return this.convertAndCacheOnCPU(e,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=t.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),l=256,c=256,m=s.map(h=>new OffscreenCanvas(l,c)),d=new OffscreenCanvas(l,c);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=l*4,b=(R,D,F)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:F},{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,F,R*D*4);for(let z=0;z0&&(b(S,k,T),T+=k*(l*4)),S=E%l,S>0&&b(S,1,T)});let f=y.convertBackendValuesAndArrayBuffer(u,t.dtype);return this.convertAndCacheOnCPU(e,f),f}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return o;let n;if(t.dtype==="complex64"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=C.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(t.resource);n=y.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e){let t=e.size,o=e.usage,n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.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:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=sx(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return ie(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return ie(e.shape,e.dtype,t)}async time(e){!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 t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();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=t,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,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t==="string"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let o=this.tensorMap.get(e.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resource!=null)return;let o=sx(t.dtype)*y.sizeFromShape(t.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(t.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();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(i).set(t.values):new Float32Array(i).set(t.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(o,s);t.resource=n}makeUniforms(e){let t=0,o=0,n=[],s=1;e.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),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((p,u)=>{let l=n[u];p.type==="int32"?new Int32Array(a,l,p.data.length).set(p.data):p.type==="uint32"?new Uint32Array(a,l,p.data.length).set(p.data):new Float32Array(a,l,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,t),this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=Vie(this.device,e);let a=t.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:e.variableNames[u]}});e.shaderKey=Uz(e,a,s);let i=A().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return e.shaderKey in this.pipelineCache||(this.pipelineCache[e.shaderKey]=Wz(this.device,e,a,s,i)),e.pipeline=this.pipelineCache[e.shaderKey],i||this.recordAndSubmit(e,s,t,n),s}recordAndSubmit(e,t,o,n){if(e.pipeline instanceof Promise)throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");let s=[],a=[],i="int32";if(e.pixelsOpType==null){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),a=o.concat(t).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(t.shape);s.push({type:i,data:m})}if(e.size){let m=y.sizeFromShape(e.outputShape);s.push({type:i,data:[e.outputComponent?m/e.outputComponent:m]})}n&&(s=[...s,...n]);let p=[this.tensorToBinding(t),...o.map(m=>this.tensorToBinding(m)),this.makeUniforms(s)];o.forEach(m=>{this.commandQueueOwnedIds.add(m.dataId)}),this.commandQueueOwnedIds.add(t.dataId);let u=this.device.createBindGroup({layout:e.pipeline.getBindGroupLayout(0),entries:p.map((m,d)=>({binding:d,resource:m}))}),l=this.activeTimers!=null;this.ensureCommandEncoderReady();let c={};l&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.querySetCount})),c.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},this.computePassEncoder=this.commandEncoder.beginComputePass(c)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(c)),this.computePassEncoder.setPipeline(e.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),this.dispatchCountInPass++,(l||A().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchCountInPass||e.pixelsOpType===$i.DRAW)&&(this.endComputePassEncoder(),l?this.activeTimers.push({name:e.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 e=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.querySetCount*8),this.submitQueue(),await e.mapAsync(GPUMapMode.READ);let t=new BigUint64Array(e.getMappedRange()),o=Number(t[1]-t[0])/1e6;return e.unmap(),this.bufferManager.releaseBuffer(e),o}shouldExecuteOnCPU(e,t=zie){return A().getBool("WEBGPU_CPU_FORWARD")&&e.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"},e=await navigator.gpu.requestAdapter(r),t={},o=[];e.features.has("timestamp-query")&&o.push("timestamp-query"),e.features.has("bgra8unorm-storage")&&o.push(["bgra8unorm-storage"]),t.requiredFeatures=o;let n=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let s=await e.requestDevice(t),a=await e.requestAdapterInfo();return new Jl(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 Wie="let resultTemp = a + b;",Uie="let resultTemp = atan2(a, b);",Gie="let resultTemp = areal * breal - aimag * bimag;",Hie="let resultTemp = areal * bimag + aimag * breal;",Kie="let resultTemp = a / b;",qie="let resultTemp = select(a * (b + 1.0), a, b >= b - b);",jie=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a == b); `,Xie=` 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))); `,Yie=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a > b); `,Qie=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a >= b); `,Zie=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a < b); `,Jie=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a <= b); `,eue="return f32(a >= 1.0 && b >= 1.0);",tue=`return (vec4(a >= vec4(1.0)) * vec4(b >= vec4(1.0)));`,rue="return f32(a >= 1.0 || b >= 1.0);",oue=`return min(vec4(a >= vec4(1.0)) + vec4(b >= vec4(1.0)), vec4(1.0));`,nue="let resultTemp = max(a, b);",sue="let resultTemp = min(a, b);",aue=` let isNaN = b == 0.; var resultTemp = a % b; resultTemp = select((resultTemp + b) % b, resultTemp, (a < 0. && b < 0.) || (a >= 0. && b > 0.)); `,iue=` 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]; } `,uue="let resultTemp = a * b;",pue=` var resultTemp = f32(a != b); let valueForNaN = 1.0; `,lue=` var resultTemp = vec4(a != b); let valueForNaN = 1.0; `,cue=` 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); `,mue=` 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); `,due="if (a < 0.0) { return b * a; } return a;",fue=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `,hue="let resultTemp = (a - b) * (a - b);",gue="let resultTemp = a - b;";function ec(r,e){let t;do{switch(r){case fe.ATAN2:t=Uie;break;case fe.MAX:t=nue;break;case fe.MIN:t=sue;break;case fe.MOD:t=e?iue:aue;break;case fe.NOT_EQUAL:t=e?lue:pue;break;case fe.POW:t=e?mue:cue;break;default:continue}let o,n,s;return e?(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; ${t} return select( resultTemp, ${n}(valueForNaN), ${s}(isNaN) | aIsNaN | bIsNaN); } `}while(!1);switch(r){case fe.ADD:t=Wie;break;case fe.COMPLEX_MULTIPLY_IMAG:t=Hie;break;case fe.COMPLEX_MULTIPLY_REAL:t=Gie;break;case fe.DIV:t=Kie;break;case fe.ELU_DER:t=qie;break;case fe.EQUAL:t=jie;break;case fe.FLOOR_DIV:t=Xie;break;case fe.GREATER:t=Yie;break;case fe.GREATER_EQUAL:t=Qie;break;case fe.LESS:t=Zie;break;case fe.LESS_EQUAL:t=Jie;break;case fe.LOGICAL_AND:return e?tue:eue;case fe.LOGICAL_OR:return e?oue:rue;case fe.MUL:t=uue;break;case fe.PRELU:return e?fue:due;case fe.SQUARED_DIFFERENCE:t=hue;break;case fe.SUB:t=gue;break;default:}return` ${t} return resultTemp; `}var Z;(function(r){r[r.ABS=0]="ABS",r[r.ACOS=1]="ACOS",r[r.ACOSH=2]="ACOSH",r[r.ASIN=3]="ASIN",r[r.ASINH=4]="ASINH",r[r.ATAN=5]="ATAN",r[r.ATANH=6]="ATANH",r[r.CEIL=7]="CEIL",r[r.COS=8]="COS",r[r.COSH=9]="COSH",r[r.ELU=10]="ELU",r[r.ERF=11]="ERF",r[r.EXP=12]="EXP",r[r.EXPM1=13]="EXPM1",r[r.FLOOR=14]="FLOOR",r[r.IS_FINITE=15]="IS_FINITE",r[r.IS_INF=16]="IS_INF",r[r.IS_NAN=17]="IS_NAN",r[r.LINEAR=18]="LINEAR",r[r.LOG=19]="LOG",r[r.LOG1P=20]="LOG1P",r[r.LOGICAL_NOT=21]="LOGICAL_NOT",r[r.NEG=22]="NEG",r[r.RELU=23]="RELU",r[r.RELU6=24]="RELU6",r[r.LEAKYRELU=25]="LEAKYRELU",r[r.RECIPROCAL=26]="RECIPROCAL",r[r.ROUND=27]="ROUND",r[r.RSQRT=28]="RSQRT",r[r.SELU=29]="SELU",r[r.SIGMOID=30]="SIGMOID",r[r.SIGN=31]="SIGN",r[r.SIN=32]="SIN",r[r.SINH=33]="SINH",r[r.SOFTPLUS=34]="SOFTPLUS",r[r.SQRT=35]="SQRT",r[r.SQUARE=36]="SQUARE",r[r.STEP=37]="STEP",r[r.TAN=38]="TAN",r[r.TANH=39]="TANH",r[r.TO_INT=40]="TO_INT"})(Z||(Z={}));var xue="return abs(a);",yue=` if (abs(a) > 1.) { return uniforms.NAN; } return acos(a); `,bue=` if (a < 1.) { return uniforms.NAN; } return acosh(a); `,Cue=` if (abs(a) > 1.) { return uniforms.NAN; } return asin(a); `,wue="return asinh(a);",Sue=` if (isnan(a)) { return uniforms.NAN; } return atan(a); `,Iue=` if (abs(a) > 1.) { return uniforms.NAN; } if (a == 1.) { return uniforms.INFINITY; } if (a == -1.) { return -uniforms.INFINITY; } return atanh(a); `,vue="return ceil(a);",kue="return cos(a);",Nue=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; `,Tue="return exp(a) - 1.0;",_ue="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Eue=` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; } if (a.g >= 0.0) { resFloat.g = a.g; } if (a.b >= 0.0) { resFloat.b = a.b; } if (a.a >= 0.0) { resFloat.a = a.a; } return resFloat; `,$ue=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. let p = ${C.ERF_P}; let a1 = ${C.ERF_A1}; let a2 = ${C.ERF_A2}; let a3 = ${C.ERF_A3}; let a4 = ${C.ERF_A4}; let a5 = ${C.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)); `,Rue="return exp(a);",Due="return floor(a);",Aue="return f32(!isnan(a) && !isinf(a));",Fue="return f32(isinf(a));",Pue="return f32(isnan(a));",Oue="return a;",Mue=`if (a < 0.0) { return uniforms.NAN; } return log(a);`,Lue=` if (isnan(a)) { return a; } return log(1.0 + a); `,Bue="return f32(!(a >= 1.0));",zue="return -a;",Vue="if (a < 0.0) { return uniforms.alpha * a; } return a;",Wue=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); `,Uue="return 1.0 / a;",Gue="return select(a, 0.0, a < 0.0);",Hue="return clamp(a, 0.0, 6.0);",Kue="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",que=` return select(a, vec4(0.0), a < vec4(0.0)); `,jue="return round(a);",Xue="return inverseSqrt(a);",Yue=` if (a >= 0.0) { return ${C.SELU_SCALE} * a; } else { return ${C.SELU_SCALEALPHA} * (exp(a) - 1.0); } `,Que="return 1.0 / (1.0 + exp(-1.0 * a));",Zue="return sign(a);",Jue="return sin(a);",epe=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; `,tpe=` 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); } `,rpe="return sqrt(a);",ope="return a * a;",npe=` if (isnan(a)) { return a; } return select(uniforms.stepAlpha, 1.0, a > 0.0); `,spe="return tan(a);",ape=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); `,ipe="return f32(i32((a)));";function Ri(r,e){switch(r){case Z.ABS:return xue;case Z.ACOS:return yue;case Z.ACOSH:return bue;case Z.ASIN:return Cue;case Z.ASINH:return wue;case Z.ATAN:return Sue;case Z.ATANH:return Iue;case Z.COS:return kue;case Z.COSH:return Nue;case Z.CEIL:return vue;case Z.ELU:return e?Eue:_ue;case Z.ERF:return $ue;case Z.EXP:return Rue;case Z.EXPM1:return Tue;case Z.FLOOR:return Due;case Z.IS_FINITE:return Aue;case Z.IS_INF:return Fue;case Z.IS_NAN:return Pue;case Z.LINEAR:return Oue;case Z.LOG:return Mue;case Z.LOG1P:return Lue;case Z.LOGICAL_NOT:return Bue;case Z.NEG:return zue;case Z.LEAKYRELU:return e?Wue:Vue;case Z.RECIPROCAL:return Uue;case Z.RELU:return e?que:Gue;case Z.RELU6:return e?Kue:Hue;case Z.ROUND:return jue;case Z.RSQRT:return Xue;case Z.SELU:return Yue;case Z.SIGMOID:return Que;case Z.SIGN:return Zue;case Z.SIN:return Jue;case Z.SINH:return epe;case Z.SOFTPLUS:return tpe;case Z.SQRT:return rpe;case Z.SQUARE:return ope;case Z.STEP:return npe;case Z.TAN:return spe;case Z.TANH:return ape;case Z.TO_INT:return ipe;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function gr(r,e=!1,t=!1,o=3){if(r===null)return"";let n="";if(r==="linear")n=Ri(Z.LINEAR);else if(r==="relu")n=Ri(Z.RELU,t);else if(r==="elu")n=Ri(Z.ELU,t);else if(r==="relu6")n=Ri(Z.RELU6,t);else if(r==="prelu")n=ec(fe.PRELU,t);else if(r==="sigmoid")n=Ri(Z.SIGMOID,t);else if(r==="leakyrelu")n=Ri(Z.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(t?4:1),i="";return e?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 no(r,e){return` ${r?"value = value + getBiasByOutputCoords(coords);":""} ${e?"value = activation(value, coords);":""} `}function mv(r,e,t=!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=e?"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); ${t&&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 Sm(r,e,t,o,n=!1,s=!1,a=!1,i=1){return` ${mv(t,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); ${no(r,e)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } `}var upe=(r,e)=>r?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol * ${e}); `:` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRow + innerRow, kStart + inputCol * ${e}); `,ppe=(r,e,t,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 < ${t}; i++) { acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]); } }`;{let n="",s="";for(let a=0;a(ACached[${a}]), acc[i]);`;return` for (var k = 0; k < ${o/e}; k++) { ${n} for (var i = 0; i < ${t}; i++) { let ACached = mm_Asub[tileRow + i][k]; ${s} } }`}};function Fp(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,l=t?o:i,c=u/e[0],m=o/e[1],d=r[1],f=r[0];return y.assert((t&&c===4&&r[1]===4||!t&&(c===3||c===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${c} and workPerThread[1] ${r[1]} must be 4. Otherwise, innerElementSize ${c} must be 3 or 4. tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),` var mm_Asub : array, ${u/c}>, ${l}>; 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; ${upe(t,c)} } // 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. ${ppe(t,c,d,o)} workgroupBarrier(); } for (var innerRow = 0; innerRow < ${d}; innerRow++) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } }`}var Hz=r=>r?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); `:` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRowStart + inputRow, kStart + inputCol); `,lpe=r=>r?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Pp(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],l=t?p:o,c=t?o:p;y.assert(c%e[1]===0&&l%e[0]===0&&o%e[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${l} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=c/e[1],d=l/e[0],f=o/e[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 < ${c}; inputRow = inputRow + ${e[1]}) { for (var inputCol = localCol; inputCol < ${l}; inputCol = inputCol + ${e[0]}) { ${Hz(t)} } } // Load one tile of B into local memory. for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) { for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[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 * ${e[0]}]; } for (var innerRow = 0; innerRow < ${h}; innerRow++) { let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[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 * ${e[1]}; for (var innerCol = 0; innerCol < ${g}; innerCol++) { let gCol = globalColStart + localCol + innerCol * ${e[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; ${Hz(t)} } } // 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++) { ${lpe(t)} 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, ${c}>; 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 cpe=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 mpe(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=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) / ${t} + 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 * ${t} + tileCol * 4; mm_Asub[tileCol] = vec4(${cpe(e)}); workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < ${t/4}; k++) { let rowB = t * ${t} + k * 4; let BCached = vec4(mm_readB(batchB, rowB, globalCol), mm_readB(batchB, rowB + 1, globalCol), mm_readB(batchB, rowB + 2, globalCol), mm_readB(batchB, rowB + 3, globalCol)); let ACached = mm_Asub[k]; acc = acc + dot(ACached, BCached); } workgroupBarrier(); } mm_write(batch, globalRow, globalCol, acc); } `}var ax=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=lv(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let l=s!=null,c=i!=null;l&&this.variableNames.push("bias"),c&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=l,this.activation=a,this.hasPreluActivationWeights=c,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,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=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return` ${gr(this.activation,this.hasPreluActivationWeights,this.isVec4)} ${Sm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} ${this.isVec4?Fp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?mpe(this.workgroupSize,this.transposeA):Pp(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} `}};function dpe(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 = ${r/2}u; currentSize > 1u; currentSize = currentSize / 2u) { if (localId.x < currentSize) { sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize]; } workgroupBarrier(); } if (localId.x == 0u) { sum = sumValues[0] + sumValues[1]; mm_write(batch, row, col, sum); } } `}var ix=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return` ${gr(this.activation,this.hasPreluActivationWeights)} ${Sm(this.addBias,this.activation,this.transposeA,this.transposeB)} ${dpe(this.workgroupSize[0])} `}};function fpe(r){let e=r[1],t=r[0],o=e>t?e:t;return` var mm_Asub : array, ${e}>; 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 < ${o}; k = k + 1) { acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol]; } workgroupBarrier(); } mm_write(batch, globalRow, globalCol, acc); } `}var ux=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push("bias");let l=p!=null;l&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=l,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return` ${gr(this.activation,this.hasPreluActivationWeights)} ${Sm(this.addBias,this.activation,this.transposeA,this.transposeB)} ${fpe(this.workgroupSize)} `}};var px=class{constructor(e,t,o=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return` ${mv(!1,this.transposeB,!1,!1,!1,e)} fn mm_write(batch: i32, row : i32, col : i32, value : ${Ae(e)}) { 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 < ${e}; i = i + 1) { ${oo("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} } } } ${e===4?Fp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Pp(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} `}},lx=class{constructor(e,t=null,o=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return` ${gr(this.activation,this.hasPreluActivationWeights)} ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); ${no(this.addBias,this.activation)} setOutputAtIndex(index, value); } } `}};var cx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : 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.shaderKey="fill"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } `}};function Nt(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new cx(o),i=[{type:"float32",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var Kz={kernelName:da,backendName:"webgpu",kernelFunc:Nt};function le(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var qz={kernelName:Ca,backendName:"webgpu",kernelFunc:le};function Op({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,l=e.shape.length,c=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[l-1]:e.shape[l-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[l-2]:e.shape[l-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=kr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(c===m,()=>`Error in matMul: inner shapes (${c}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,c,d]:[x,d,c],T=o?[b,f,m]:[b,m,f],E=le({inputs:{x:r},backend:n,attrs:{shape:k}}),R=le({inputs:{x:e},backend:n,attrs:{shape:T}}),D=[E,R],F=Math.max(x,b),O=[E,R],M=[{type:"int32",data:[d]},{type:"int32",data:[f]},{type:"int32",data:[c]}],L,B,z=[F,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=F*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?F*d*f<=128?U=pn.MatMulReduceProgram:F===1&&m>=2e3?U=pn.MatMulSplitKProgram:U=pn.MatMulSmallOutputSizeProgram:U=pn.MatMulPackedProgram}switch(U){case pn.MatMulReduceProgram:L=new ix(z,t,o,s,p,a);break;case pn.MatMulSplitKProgram:{if(B=Nt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new px(z,m,t,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new lx(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=le({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case pn.MatMulSmallOutputSizeProgram:L=new ux(k,T,z,t,o,s,p,a);break;case pn.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new ax(k,z,t,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p==="leakyrelu"&&(M.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=le({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function hpe(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:l,leakyreluAlpha:c}=o;return Op({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:c,activation:l})}var jz={kernelName:qo,backendName:"webgpu",kernelFunc:hpe};var Im=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { ${ec(this.op,!1)} } ${G("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); let breal = getBRealByOutputIndex(index); let bimag = getBImagByOutputIndex(index); setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } `}};var Di=class{constructor(e,t,o){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?"vec4":"f32",o=` fn binaryOperation(a : ${t}, b : ${t}) -> ${t} { ${ec(this.op,this.outputComponent===4)} }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; let b = getBByOutputIndex(index);`;e=` ${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 e=` ${o} ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index * ${this.outputComponent}); let a = ${t}(getAByOutputCoords(coords)); let b = ${t}(getBByOutputCoords(coords)); setOutputAtIndex(index, binaryOperation(a, b)); } } `;return e}};function Pt(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var Xz={kernelName:vo,backendName:"webgpu",kernelFunc:Pt};function Uo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.tensorMap.get(s.dataId),i=Pt({inputs:{x:o},backend:t}),p=Pt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var Yz={kernelName:ei,backendName:"webgpu",kernelFunc:Uo};var so=class{constructor(e,t,o=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==""&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { ${Ri(this.op,!1)} } ${G("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); setOutputAtIndex(index, unaryOperation(a)); } } `}};function ye({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),l=e(u.values,i);return a.makeTensorInfo(s.shape,i,l)}let p=new so(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function tt({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype==="complex64"){let c=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[c.complexTensorInfos.real,m.complexTensorInfos.real],[c.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,w={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Di(r,a.shape,i.shape);return p.runWebGPUProgram(k,[w,S],pt(x.dtype,b.dtype))});else{let g=new Im(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new Im(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:a.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.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=Uo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||pt(a.dtype,i.dtype);if((a.dtype==="string"||i.dtype==="string"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let c=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype==="string"?C.fromUint8ToStringArray(c):c,f=a.dtype==="string"?C.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let l=new Di(r,a.shape,i.shape);return p.runWebGPUProgram(l,[a,i],u)}}var Lv={};qe(Lv,{addImpl:()=>hv,bincountImpl:()=>Jz,bincountReduceImpl:()=>eV,bitwiseAndImpl:()=>gv,castImpl:()=>fv,ceilImpl:()=>xv,concatImpl:()=>tV,equalImpl:()=>yv,expImpl:()=>bv,expm1Impl:()=>Cv,floorDivImpl:()=>Sv,floorImpl:()=>wv,gatherNdImpl:()=>rV,gatherV2Impl:()=>oV,greaterEqualImpl:()=>vv,greaterImpl:()=>Iv,lessEqualImpl:()=>Nv,lessImpl:()=>kv,linSpaceImpl:()=>nV,logImpl:()=>Tv,maxImpl:()=>sV,maximumImpl:()=>_v,minimumImpl:()=>Ev,multiplyImpl:()=>km,negImpl:()=>aV,notEqualImpl:()=>$v,prodImpl:()=>iV,raggedGatherImpl:()=>pV,raggedRangeImpl:()=>cV,raggedTensorToTensorImpl:()=>fV,rangeImpl:()=>hV,rsqrtImpl:()=>Av,scatterImpl:()=>gV,sigmoidImpl:()=>xV,simpleAbsImpl:()=>Qz,sliceImpl:()=>yV,sparseFillEmptyRowsImpl:()=>bV,sparseReshapeImpl:()=>CV,sparseSegmentReductionImpl:()=>wV,sqrtImpl:()=>SV,squaredDifferenceImpl:()=>Fv,staticRegexReplaceImpl:()=>Pv,stridedSliceImpl:()=>IV,stringNGramsImpl:()=>vV,stringSplitImpl:()=>kV,stringToHashBucketFastImpl:()=>NV,subImpl:()=>Mv,tileImpl:()=>TV,topKImpl:()=>EV,transposeImpl:()=>Rv,uniqueImpl:()=>$V});function Ai(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}function Qz(r){let e=new Float32Array(r.length);for(let t=0;t{let a=C.assertAndGetBroadcastShape(e,t),i=a.length,p=y.computeStrides(a),u=y.sizeFromShape(a),l=y.getTypedArrayFromDType(s,u),c=e.length,m=t.length,d=y.computeStrides(e),f=y.computeStrides(t),h=C.getBroadcastDims(e,a),g=C.getBroadcastDims(t,a);if(h.length+g.length===0)for(let x=0;xw[E]=0);let S=y.locToIndex(w,c,d),k=b.slice(-m);g.forEach(E=>k[E]=0);let T=y.locToIndex(k,m,f);l[x]=r(o[S],n[T])}return[l,a]}}function tc(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=t.makeTensorInfo(o.shape,"complex64"),p=t.data.get(i.dataId);return p.complexTensorInfos={real:t.makeTensorInfo(o.shape,"float32",s),imag:t.makeTensorInfo(n.shape,"float32",a)},i}function mx(r,e,t="float32"){if(t==="complex64"){let n=mx(r,e,"float32"),s=mx(r,e,"float32");return tc({inputs:{real:n,imag:s},backend:r})}let o=y.makeZerosTypedArray(y.sizeFromShape(e),t);return r.makeTensorInfo(e,t,o)}function dv(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Zz(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.data.get(o.dataId).complexTensorInfos.real,s=t.data.get(n.dataId).values;return t.makeTensorInfo(n.shape,n.dtype,s)}function fv(r,e,t,o){if(o==="int32"){let n=Int32Array.from(r);return[e,"int32",n]}if(o==="bool"){let n=y.toTypedArray([0],t),[s,a]=ht((i,p)=>i!==p?1:0)(e,[],r,n,"bool");return[a,"bool",s]}throw new Error(`Error in Cast: failed to cast ${t} to ${o}`)}function vm(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return dv({inputs:{x:n},backend:t});let l=mx(t,n.shape,n.dtype),c=vm({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),m=tc({inputs:{real:c,imag:l},backend:t});return 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i=o-t+1,p=e-t+1,u=Math.log(i),l=.5*Math.exp(2*u/3),c=.5*Math.sqrt(u*l*(i-l)/i)*Math.sign(p-i/2),m=Math.max(t,Math.floor(e-p*l/i+c)),d=Math.min(o,Math.floor(e+(i-p)*l/i+c));_V(r,e,m,d)}let n=r[e],s=t,a=o;for(y.swap(r,t,e),Nm(r[o],n)>0&&y.swap(r,t,o);s0;)a=a-1}Nm(r[t],n)===0?y.swap(r,t,a):(a=a+1,y.swap(r,a,o)),a<=e&&(t=a+1),e<=a&&(o=a-1)}}function EV(r,e,t,o,n){let s=e[e.length-1],[a,i]=[r.length/s,s],p=y.getTypedArrayFromDType(t,a*o),u=y.getTypedArrayFromDType("int32",a*o);for(let c=0;cf[w]={value:b,index:w}),o{for(let g=0;g`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=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 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i32(workgroupId.y) * ${e} + i32(localId.x); y = i32(workgroupId.x) * ${e} + i32(localId.y); if (x < height && y < width) { setOutputAtIndex((y * height + x), tile[localId.x] [localId.y]); } } `}};var gx=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0",o=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(e=` if (isnan(candidate)) { bestValue = uniforms.NAN; } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue) { bestValue = candidate; }`,t="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"?(e=" bestValue = bestValue * candidate; ",t="1.0"):this.reduceType==="all"?(e=" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ",t="1.0"):this.reduceType==="any"&&(e=" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ",t="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 = ${t}; 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]); ${e} } 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]; ${e} xBestValues[localId.x] = bestValue; } reduceSize = interval; workgroupBarrier(); } if (localId.x == 0u && outputIndex < uniforms.size) { ${n} } } `}};var Rpe={mean:"float32",all:"bool",any:"bool"};function ao(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=C.getAxesPermutation(p,s),l=r;u!=null&&(l=Cr({inputs:{x:r},attrs:{perm:u},backend:n}),p=C.getInnerMostAxes(p.length,s),a.push(l)),C.assertAxesAreInnerMostDims(o,p,s);let[c,m]=C.computeOutAndReduceShapes(l.shape,p),d=c;t&&(d=C.expandShapeToKeepDim(c,i));let f;if((o==="max"||o==="prod")&&n.shouldExecuteOnCPU([l])){let h=n.tensorMap.get(l.dataId).values;switch(o){case"max":let g=qV(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case"prod":let{outVals:x,outShape:b,outDtype:w}=JV(l.shape,l.dtype,h,p);f=n.makeTensorInfo(b,w,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(l.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},w=Rpe[o]||mi(r.dtype),S=[{type:"int32",data:[h]}],k=new xx(b,o,n.device.limits.maxComputeWorkgroupSizeX),T=n.runWebGPUProgram(k,[l],w,S);a.push(T),f=le({inputs:{x:T},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function Dpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return ao(n,a,s,"all",t)}var xW={kernelName:yn,backendName:"webgpu",kernelFunc:Dpe};function Ape(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return ao(n,a,s,"any",t)}var yW={kernelName:bn,backendName:"webgpu",kernelFunc:Ape};var oc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=o==="min"?"<":">";let[s,a]=C.computeOutAndReduceShapes(e,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=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${un(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 / ${e}; let reduceLength = ${t()}; 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 + ${e}) { let candidate = getX(${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), ${e}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 = ${t()}; for (var i = 1; i < reduceLength; i++) { let candidate = getX(${o()} i); if (candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = i; } } setOutputAtIndexI32(index, bestIndex); } } `}};function Fpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Cr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let l=new oc(p.shape,a[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(l,[p],"int32",c);return u.forEach(d=>t.disposeData(d.dataId)),m}var bW={kernelName:na,backendName:"webgpu",kernelFunc:Fpe};function Ppe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=C.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Cr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=C.getInnerMostAxes(a.length,p.shape.length)),C.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let l=new oc(p.shape,a[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(l,[p],"int32",c);return u.forEach(d=>t.disposeData(d.dataId)),m}var CW={kernelName:sa,backendName:"webgpu",kernelFunc:Ppe};var Ope=ye({opType:Z.ASIN}),wW={kernelName:Cn,backendName:"webgpu",kernelFunc:Ope};var Mpe=ye({opType:Z.ASINH}),SW={kernelName:wn,backendName:"webgpu",kernelFunc:Mpe};var Lpe=ye({opType:Z.ATAN}),IW={kernelName:Sn,backendName:"webgpu",kernelFunc:Lpe};var Bpe=tt({opType:fe.ATAN2}),vW={kernelName:vn,backendName:"webgpu",kernelFunc:Bpe};var zpe=ye({opType:Z.ATANH}),kW={kernelName:In,backendName:"webgpu",kernelFunc:zpe};var yx=class{constructor(e){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d = coords[3]; let xRCCorner = coords.yz * uniforms.strides; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; let value = getX(batch, xRCorner, xCCorner, d); setOutputAtIndex(index, value); } } `}};var Ka=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?e=`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"}; }`:e="resultValue = max(value, resultValue);";let t="resultValue";return this.poolType==="avg"&&(t="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); ${e} } } ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } `}},$u=class{constructor(e,t,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,t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue += value; count += 1.0;":this.computePositions?e=`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"}; }`:e="resultValue = max(value, resultValue);";let t="resultValue";return this.poolType==="avg"&&(t="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); ${e} } } } ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } `}};function zv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return ao(n,s,a,"max",t)}var NW={kernelName:os,backendName:"webgpu",kernelFunc:zv};function Vv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return ao(n,a,s,"mean",t)}var TW={kernelName:ss,backendName:"webgpu",kernelFunc:Vv};function bx(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return Pt({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type==="VALID"){let a=r.shape.length,i=le({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t==="avg"?p=Vv({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t==="max",()=>`Invalid pool type ${t}`),p=zv({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=le({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new yx(e):(t==="avg"?n=new Ka(e,"avg"):(y.assert(t==="max",()=>`Invalid pool type ${t}`),n=new Ka(e,"max")),s.push({type:"int32",data:[e.padInfo.top,e.padInfo.left]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]},{type:"int32",data:[e.inHeight,e.inWidth]},{type:"int32",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function Vpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,l=C.computePool2DInfo(n.shape,s,a,1,i,p);return bx(n,l,"avg",t)}var _W={kernelName:kn,backendName:"webgpu",kernelFunc:Vpe};function Wpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,l=[1,1,1],c=C.computePool3DInfo(n.shape,s,a,l,i,u,p),m=new $u(c,"avg"),d=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var EW={kernelName:aa,backendName:"webgpu",kernelFunc:Wpe};var Cx=class{constructor(e){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=e.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); } } `}},wx=class{constructor(e){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=e.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 Upe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:l}=o,c=C.computePool3DInfo(a.shape,i,p,1,u,l),m=new wx(c),d=1/(c.filterDepth*c.filterHeight*c.filterWidth),f=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterDepth-1-c.padInfo.front,c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outDepth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[d]}];return t.runWebGPUProgram(m,[n],a.dtype,f)}var $W={kernelName:Vi,backendName:"webgpu",kernelFunc:Upe};function Gpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;wm([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,l=C.computePool2DInfo(a.shape,i,p,1,u),c=new Cx(l),m=1/(l.filterHeight*l.filterWidth),d=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:"int32",data:[l.dilationHeight,l.dilationWidth]},{type:"int32",data:[l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:"int32",data:[l.outHeight]},{type:"int32",data:[l.outWidth]},{type:"float32",data:[m]}];return t.runWebGPUProgram(c,[n],a.dtype,d)}var RW={kernelName:zi,backendName:"webgpu",kernelFunc:Gpe};function Hpe(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Op({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var DW={kernelName:Nn,backendName:"webgpu",kernelFunc:Hpe};var Sx=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ft(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=ft(this.rank),t=Kpe(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):o=this.outputShape.map((s,a)=>`sourceLoc.${Wv[a]} = uniforms.start.${un(a)} + coords.${Wv[a]};`),` ${G("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getCoordsFromIndex(index); ${o.join(` `)} setOutputAtIndex(index, getSource(${t})); } } `}},Wv=["x","y","z","w","u","v"];function Kpe(r){if(r===1)return"sourceLoc";if(r<=6)return Wv.slice(0,r).map(e=>`sourceLoc.${e}`).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}function ea(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=nt.parseSliceParams(n,s,a);if(nt.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype==="string"){let c=t.tensorMap.get(n.dataId),m=nW(c.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new Sx(i,p),l=[{type:"int32",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,l)}var AW={kernelName:_s,backendName:"webgpu",kernelFunc:ea};var qpe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{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,w)=>b*w),p=C.getReshaped(n.shape,s,i),u=C.getPermuted(p.length,s.length),l=C.getReshapedPermuted(n.shape,s,i),c=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(l,a,s.length),d=[],f=le({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Cr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=le({inputs:{x:h},backend:t,attrs:{shape:l}}),x=ea({inputs:{x:g},backend:t,attrs:{begin:c,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},FW={kernelName:ia,backendName:"webgpu",kernelFunc:qpe};var jpe=` fn bincount_write(index: i32, value: f32) { ${oo("&result[index]","value","float32")} } `,Xpe=` fn bincount_write(index: i32, value: f32) { atomicStore(&result[index], bitcast(value)); } `,nc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` ${this.binaryOutput?Xpe:jpe} ${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 Ype(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,l=[a],c=s.dtype,m=Nt({backend:t,attrs:{shape:l,value:0,dtype:c}}),d=new nc([i],u),f=[{type:"int32",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,c,f,m)}var PW={kernelName:Tn,backendName:"webgpu",kernelFunc:Ype};var Ix=class{constructor(e){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",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="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 Qpe(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e;if(t.shouldExecuteOnCPU([o,n])){let l=t.tensorMap.get(o.dataId),c=t.tensorMap.get(n.dataId),m=l.values,d=c.values,f=C.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return t.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 Ix(i),u=[{type:"int32",data:[s]},{type:"int32",data:[a]}];return t.runWebGPUProgram(p,[o,n],"int32",u)}var OW={kernelName:ua,backendName:"webgpu",kernelFunc:Qpe};var Uv=tt({opType:fe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:ZV}),MW={kernelName:Ro,backendName:"webgpu",kernelFunc:Uv};function Fi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Pt({inputs:{x:n.complexTensorInfos.real},backend:t})}var LW={kernelName:si,backendName:"webgpu",kernelFunc:Fi};function BW(r,e){let t=new so(r.shape,Z.TO_INT),o=e.runWebGPUProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Gv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Pt({inputs:{x:n},backend:t});let a=Yr(n.shape),i=Gv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),p=Uo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype==="complex64"){let a=Fi({inputs:{input:n},backend:t}),i=Gv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Pt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=DV(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s==="int32")return BW(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=Uv({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var zW={kernelName:ho,backendName:"webgpu",kernelFunc:Gv};var Zpe=ye({opType:Z.CEIL,cpuKernelImpl:AV}),VW={kernelName:go,backendName:"webgpu",kernelFunc:Zpe};var vx=class{constructor(e){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=e,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 kx=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : 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.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 Jpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:"float32",data:[s]},{type:"float32",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new vx(n.shape):i=new kx(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var WW={kernelName:Go,backendName:"webgpu",kernelFunc:Jpe};var Nx=class{constructor(e){this.outputShape=[],this.variableNames=["real","imag"],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="complexAbs"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let re = abs(getRealByOutputIndex(index)); let im = abs(getImagByOutputIndex(index)); let mx = max(re, im); // The length function in wgsl may be not underflow-safe on some GPUs. // So the safe solution is to ensure underflow-safety in all cases. setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0)); } } `}};function UW(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function ele(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.tensorMap.get(o.dataId),s=new Nx(o.shape),a=[UW(o,n.complexTensorInfos.real),UW(o,n.complexTensorInfos.imag)];return t.runWebGPUProgram(s,a,a[0].dtype)}var GW={kernelName:Wi,backendName:"webgpu",kernelFunc:ele};var Tx=class{constructor(e){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;sFi({inputs:{input:w},backend:t})),h=r.map(w=>Mp({inputs:{input:w},backend:t})),g=sc(f,e,t),x=sc(h,e,t),b=Uo({inputs:{real:g,imag:x},backend:t});return f.forEach(w=>t.disposeData(w.dataId)),h.forEach(w=>t.disposeData(w.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let f=r.map(k=>{let E=[-1,y.sizeFromShape(k.shape.slice(e))];return le({inputs:{x:k},backend:t,attrs:{shape:E}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=C.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=FV(h,g,o,x),w=C.computeOutShape(r.map(k=>k.shape),e),S=t.makeTensorInfo(w,o,b);return f.forEach(k=>t.disposeData(k.dataId)),S}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new Tx(p),l=[],c=new Array(p.length-1);if(c.length>0){c[0]=p[0][1],l.push({type:"int32",data:[c[0]]});for(let f=1;ft.disposeData(f.dataId));let d=le({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function tle(r,e,t){let o=C.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>le({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function Hv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);C.assertParamsConsistent(a,s);let i=C.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Pt({inputs:{x:p[0]},backend:t}):sc(p,s,t)}var KW={kernelName:pa,backendName:"webgpu",kernelFunc:Hv};function rle(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let l=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.`)}},c=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); ${l(i)} } return resData;`,w=r?e&&o?` ${b}`:` if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${b} } return ${Ae(i)}(0.0);`:o&&t?` ${b}`:` if (row < uniforms.dimInner && col < uniforms.dimBOuter) { ${b} } return ${Ae(i)}(0.0);`,S=`${c(p)}`,k=Ae(u),T=r?Ae(i):Ae(p),E=r?Ae(p):Ae(i);return` ${gr(s,a,u===4,4)} fn mm_readA(batch: i32, row : i32, col : i32) -> ${T} { ${r?w:S} } fn mm_readB(batch: i32, row : i32, col : i32) -> ${E} { ${r?S:w} } 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} ${no(n,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } }`}var _x=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=ym(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=bm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Fp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Pp(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` ${rle(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} ${e} `}};var Ex=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` ${gr(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; ${no(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 $x=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32, inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=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 = ${n}; let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0]; let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow); var value = 0.0; if(xRow < uniforms.xShape[${e}] && 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[${t}] && xCol >= 0) { value = ${s}; } } setOutputAtIndex(index, value); } } `}};function Rx(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function ole({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat==="channelsLast",u=!p,l=!1,c=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=[],d,f;if(c){let x=t.inHeight*t.inWidth*t.inChannels;d=le({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=le({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=le({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=le({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=Rx(s.shape,p);x!=null&&(s=le({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=Rx(n.shape,p);x!=null&&(n=le({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Op({a:p?d:f,b:p?f:d,transposeA:u,transposeB:l,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=le({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function nle({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:l,strideWidth:c,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,w=b==="channelsLast",S=p*u*l,k=h*f,T=w?[t.batchSize,k,S]:[t.batchSize,S,k],E=new $x(T,w),R=[{type:"int32",data:[d.top,d.left]},{type:"int32",data:[m,c]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[l*p]},{type:"int32",data:[l]}],D=o.runWebGPUProgram(E,[r],r.dtype,R),F=[];F.push(D);let O=le({inputs:{x:e},backend:o,attrs:{shape:[1,S,-1]}});if(F.push(O),s!=null){let U=Rx(s.shape,w);U!=null&&(s=le({inputs:{x:s},backend:o,attrs:{shape:U}}),F.push(s))}if(n!=null){let U=Rx(n.shape,w);U!=null&&(n=le({inputs:{x:n},backend:o,attrs:{shape:U}}),F.push(n))}let B=Op({a:w?D:O,b:w?O:D,transposeA:!w,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=le({inputs:{x:B},backend:o,attrs:{shape:t.outShape}});F.push(B);for(let U of F)o.disposeData(U.dataId);return z}function Dx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,l=t.dataFormat==="channelsLast",c=l&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=A().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!m&&(c||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type==="SAME"||t.padInfo.type==="VALID")))return ole({x:r,filter:e,convInfo:t,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=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(A().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||h<=f)return nle({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:"int32",data:[t.filterHeight,t.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[t.strideHeight,t.strideWidth]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new Ex(t,p,i,u);else{let T=l?t.outHeight*t.outWidth:t.outChannels,E=l?t.outChannels:t.outHeight*t.outWidth,R=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:"int32",data:[T]},{type:"int32",data:[E]},{type:"int32",data:[R]});let D=o.adapterInfo.isIntel();g=new _x(t,T,E,R,p,i,u,D)}let w=[],S=[r,e];p&&(!l&&n.shape.length===1&&(n=le({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),w.push(n)),S.push(n)),u&&(!l&&s.shape.length===1&&(s=le({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),w.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 T of w)o.disposeData(T.dataId);return k}function sle(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:l}=t,c=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,s.shape,a,u,i,l,!1,c);return Dx({x:n,filter:s,convInfo:m,backend:o})}var qW={kernelName:En,backendName:"webgpu",kernelFunc:sle};var Ax=class{constructor(e){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=e.inShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.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 e=this.isChannelsLast?1:2,t=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[${e}], coords[${t}]) - 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 = ${this.isChannelsLast?"getDy(batch, idyR, idyC, d2)":"getDy(batch, d2, idyR, idyC)"}; let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } } } setOutputAtIndex(index, dotProd); } } `}},Fx=class{constructor(e){this.variableNames=["x","dy"],this.uniforms="pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${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); } } `}},Px=class{constructor(e){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=e.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); } } `}},Ox=class{constructor(e){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=e.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 ale(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:l}=o,c=C.convertConv2DDataFormat(p),m=C.computeConv2DInfo(n.shape,l,a,1,i,u,!1,c),d=new Fx(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 t.runWebGPUProgram(d,[n,s],n.dtype,f)}var jW={kernelName:Ui,backendName:"webgpu",kernelFunc:ale};function ile(r=4){let e=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); ${e(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 Mx=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=ym(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=bm(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 e=this.isVec4?Fp(this.elementsPerThread,this.workgroupSize):Pp(this.elementsPerThread,this.workgroupSize);return` ${ile(this.isVec4?4:1)} ${e} `}};function ule(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:l}=o,c=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(a,s.shape,i,1,p,l,!1,c),d=[{type:"int32",data:[m.filterHeight,m.filterWidth]},{type:"int32",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||m.dataFormat!=="channelsLast")f=new Ax(m);else{f=new Mx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:"uint32",data:[h]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return t.runWebGPUProgram(f,[n,s],"float32",d)}var XW={kernelName:$n,backendName:"webgpu",kernelFunc:ule};var Lx=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.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 ple(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeConv3DInfo(n.shape,s.shape,a,p,i),l=[u.padInfo.front,u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...l]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new Lx(u),d=pt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,c)}var YW={kernelName:Rn,backendName:"webgpu",kernelFunc:ple};function lle(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=C.computeConv3DInfo(n.shape,p,a,1,i),l=new Px(u),c=[{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 t.runWebGPUProgram(l,[n,s],s.dtype,c)}var QW={kernelName:ti,backendName:"webgpu",kernelFunc:lle};function cle(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=C.computeConv3DInfo(p,s.shape,a,1,i),l=new Ox(u),c=[{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 t.runWebGPUProgram(l,[n,s],n.dtype,c)}var ZW={kernelName:Dn,backendName:"webgpu",kernelFunc:cle};var mle=ye({opType:Z.COS}),JW={kernelName:An,backendName:"webgpu",kernelFunc:mle};var dle=ye({opType:Z.COSH}),eU={kernelName:Fn,backendName:"webgpu",kernelFunc:dle};var Bx=class{constructor(e,t,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],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[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];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 > ${e} ) { setOutputAtIndex(index, uniforms.extrapolationValue); return; } let in_x = ${p}; if( in_x < 0.0 || in_x > ${t} ) { setOutputAtIndex(index, uniforms.extrapolationValue); return; } let sourceFracIndexCR = vec2(in_x,in_y); if(${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 fle=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,l=new Bx(n.shape[3],s.shape,i,p),c=[{type:"float32",data:[u]}];return t.runWebGPUProgram(l,[n,s,a],"float32",c)},tU={kernelName:Mn,backendName:"webgpu",kernelFunc:fle};var Lp;(function(r){r.Prod="*",r.Sum="+"})(Lp||(Lp={}));var Tm=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Lp.Prod?"1.0":"0.0",o=this.exclusive?t:`getX(${rU(e,"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 = ${oU(e,"coords",this.op)}; var val = ${o}; let pow2 = i32(pow(2.0, uniforms.index)); if (${s}) { let idx = ${a}; ${oU(e,"coords",this.op)} = idx; val ${this.op}= getX(${rU(e,"coords",this.op)}); } setOutputAtIndex(index, val); } } `}};function rU(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function oU(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function zx(r,e,t,o,n,s){let a=e.shape.length,i=C.getAxesPermutation([o],a),p=e;i!=null&&(p=Cr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=C.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let l=p.shape[u],c=Pt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(l))-1;m++){let d=new Tm(r,p.shape,!1,s),f=c,h=[{type:"float32",data:[m]}];c=t.runWebGPUProgram(d,[c],c.dtype,h),t.disposeData(f.dataId)}if(n){let m=new Tm(r,p.shape,n,s),d=c,f=[{type:"float32",data:[0]}];c=t.runWebGPUProgram(m,[c],c.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=C.getUndoAxesPermutation(i),d=Cr({inputs:{x:c},backend:t,attrs:{perm:m}});return t.disposeData(c.dataId),t.disposeData(p.dataId),d}return c}function hle(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return zx(Lp.Prod,n,t,s,a,i)}var nU={kernelName:Pn,backendName:"webgpu",kernelFunc:hle};function gle(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return zx(Lp.Sum,n,t,s,a,i)}var sU={kernelName:On,backendName:"webgpu",kernelFunc:gle};function xle(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,l=y.sizeFromShape(s.shape)>0,c=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=Nt({backend:t,attrs:{shape:d,value:0,dtype:c}}),h=new nc(m,l,i),g=[{type:"int32",data:[a]}],x=l?[n,s]:[n];return t.runWebGPUProgram(h,x,c,g,f)}var aU={kernelName:la,backendName:"webgpu",kernelFunc:xle};var Vx=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}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 = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function yle(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],l=a==="NHWC"?n.shape[3]:n.shape[1],c=p*s,m=u*s,d=l/(s*s),f=a==="NHWC"?[i,c,m,d]:[i,d,c,m],h=[{type:"int32",data:[s]}],g=new Vx(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var iU={kernelName:Ln,backendName:"webgpu",kernelFunc:yle};var Wx=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` ${gr(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); ${e, inDims : vec2, virtualWidth : i32,",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=H(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return` ${gr(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) * ${this.workPerThread}; index1 = index1 / width1; let r = index1 % uniforms.outShape[1]; let batch = index1 / uniforms.outShape[1]; let xRCCorner = vec2(r, c) * vec2(${t}, ${o}) - uniforms.pads; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; var xVals : array, ${e}>; 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 < ${e}; 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]; ${no(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } `}};var ic=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` ${gr(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 = ${e}; 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 = ${e}; let wVal = getW(wR, wC, d1, q); value = value + xVal * wVal; } } } ${no(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } `}};function ble(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:l}=o,c=C.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=C.computeConv2DInfo(n.shape,s.shape,a,m,i,l,!0,c),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 Wx(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 ac(d),f.push({type:"int32",data:[g.virtualWidth]})):(g=new ic(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]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var uU={kernelName:Bn,backendName:"webgpu",kernelFunc:ble};var Ux=class{constructor(e){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=e.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); } } `}},Gx=class{constructor(e){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=e.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 Cle(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:l}=o,c=C.computeConv2DInfo(n.shape,l,a,i,p,u,!0),m=new Ux(c),d=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.inHeight]},{type:"int32",data:[c.inWidth]},{type:"int32",data:[c.batchSize]},{type:"int32",data:[c.outChannels/c.inChannels]}];return t.runWebGPUProgram(m,[n,s],"float32",d)}var pU={kernelName:Gi,backendName:"webgpu",kernelFunc:Cle};function wle(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:l}=o,c=C.computeConv2DInfo(l,s.shape,a,i,p,u,!0),m=new Gx(c),d=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.filterHeight-1-c.padInfo.top,c.filterWidth-1-c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.outChannels/c.inChannels]}];return t.runWebGPUProgram(m,[n,s],n.dtype,d)}var lU={kernelName:Hi,backendName:"webgpu",kernelFunc:wle};var Hx=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],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 Sle(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=le({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Hx(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=le({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var cU={kernelName:ca,backendName:"webgpu",kernelFunc:Sle};var Kx=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.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 Ile(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=C.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),l=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...l]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],m=new Kx(u);return t.runWebGPUProgram(m,[n,s],n.dtype,c)}var mU={kernelName:zn,backendName:"webgpu",kernelFunc:Ile};var qx=class{constructor(e,t){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=e.inShape,this.dispatchLayout=X(e.outShape),this.dispatch=H(this.dispatchLayout,e.outShape,this.workgroupSize),t!=="float32"&&t!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 types, does not support ${t} type.`);this.type=t,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); ${oo("&result[flatIndexIn]","value",this.type)} } } `}},jx=class{constructor(e,t,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=e.filterShape,this.dispatchLayout=X(e.outShape),this.dispatch=H(this.dispatchLayout,e.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); ${oo("&result[flatIndexIn]","value",this.type)} } } `}};function vle(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,l=C.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),c=s.dtype,m=new jx(l,s.shape,c),d=[{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.dilationHeight,l.dilationWidth]},{type:"int32",data:[y.sizeFromShape(l.outShape)]}],f=Nt({backend:t,attrs:{shape:s.shape,value:0,dtype:c}});return t.runWebGPUProgram(m,[n,s,a],c,d,f)}var dU={kernelName:qi,backendName:"webgpu",kernelFunc:vle};function kle(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,l=C.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),c=n.dtype,m=new qx(l,c),d=[{type:"int32",data:[l.filterHeight,l.filterWidth]},{type:"int32",data:[l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.dilationHeight,l.dilationWidth]},{type:"int32",data:[y.sizeFromShape(l.outShape)]}],f=Nt({backend:t,attrs:{shape:l.inShape,value:0,dtype:c}});return t.runWebGPUProgram(m,[n,s,a],c,d,f)}var fU={kernelName:Ki,backendName:"webgpu",kernelFunc:kle};var Xx=class{constructor(e,t,o){this.variableNames=["Image"],this.uniforms="alpha: f32,",this.workgroupSize=[64,1,1],this.pixelsOpType=$i.DRAW,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=t,this.textureFormat=o,this.shaderKey=`draw_${t}_${o}`}getUserCode(){let e,t=this.type==="float32"?"value":"value / 255.0";return e=` if (uniforms.numChannels == 1) { rgba[0] = ${t}; rgba[1] = ${t}; rgba[2] = ${t}; } else { rgba[d] = ${t}; }`,` @group(0) @binding(0) var outImage : texture_storage_2d<${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]); ${e} } rgba.x = rgba.x * rgba.w; rgba.y = rgba.y * rgba.w; rgba.z = rgba.z * rgba.w; let coords = getCoordsFromIndex(index); textureStore(outImage, vec2(coords.yx), rgba); } } `}};function Nle(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},l=(u==null?void 0:u.alpha)||1,c=t.device.features.has("bgra8unorm-storage")?"bgra8unorm":"rgba8unorm",m=[i,p],d=new Xx(m,n.dtype,c);s.width=p,s.height=i;let f="webgpu",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:t.device,format:c,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:"premultiplied"});let b="int32",w=t.makeTensorInfo(m,b),S=t.tensorMap.get(w.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:"uint32",data:[x]},{type:"float32",data:[l]}];if(t.runWebGPUProgram(d,[n],b,k,w),g){let T=s.getContext("2d");if(!T)throw new Error("Please make sure this canvas has only been used for 2d or webgpu context!");T.drawImage(g,0,0)}return t.disposeData(w.dataId),n}var hU={kernelName:Mu,backendName:"webgpu",kernelFunc:Nle};var Kv=tt({opType:fe.MUL,cpuKernelImpl:YV,supportsComplex:!0}),gU={kernelName:$o,backendName:"webgpu",kernelFunc:Kv};function qv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return ao(n,s,a,"sum",t)}var xU={kernelName:As,backendName:"webgpu",kernelFunc:qv};function Tle(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=C.decodeEinsumEquation(n,s.length);C.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:l}=C.getEinsumComputePath(i,p),c=l.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=qv({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var yU={kernelName:ji,backendName:"webgpu",kernelFunc:Tle};var _le=ye({opType:Z.ELU}),bU={kernelName:Wn,backendName:"webgpu",kernelFunc:_le};var Ele=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new Di(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},CU={kernelName:ri,backendName:"webgpu",kernelFunc:Ele};var $le=tt({opType:fe.EQUAL,dtype:"bool",cpuKernelImpl:PV}),wU={kernelName:xo,backendName:"webgpu",kernelFunc:$le};var Rle=ye({opType:Z.ERF}),SU={kernelName:Un,backendName:"webgpu",kernelFunc:Rle};var Dle=ye({opType:Z.EXP,cpuKernelImpl:OV,dtype:"float32"}),IU={kernelName:yo,backendName:"webgpu",kernelFunc:Dle};function Yx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,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)}, ${a}]`),p=a+n+1),i.splice(p,0,1),le({inputs:{x:s},backend:o,attrs:{shape:i}})}var vU={kernelName:ma,backendName:"webgpu",kernelFunc:Yx};var Ale=ye({opType:Z.EXPM1,cpuKernelImpl:MV}),kU={kernelName:bo,backendName:"webgpu",kernelFunc:Ale};var _m=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}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 Qx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=le({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,l=new _m("real",u),c=new _m("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=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=t.runWebGPUProgram(l,m,"float32",h);i.push(g);let x=t.runWebGPUProgram(c,m,"float32",h);i.push(x);let b=Uo({inputs:{real:g,imag:x},backend:t});i.push(b);let w=le({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),w}function Fle(r){let{inputs:e,backend:t}=r,{input:o}=e;return Qx(o,!1,t)}var NU={kernelName:Xi,backendName:"webgpu",kernelFunc:Fle};var Zx=class{constructor(e){this.outputShape=[],this.variableNames=["x"],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="flipLeftRight"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; let outputValue = getX(coords[0], coords[1], coordX, coords[3]); setOutputAtIndex(index, outputValue); } } `}};var TU={kernelName:Gn,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Zx(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var Ple=ye({opType:Z.FLOOR,cpuKernelImpl:LV}),_U={kernelName:Co,backendName:"webgpu",kernelFunc:Ple};var Ole=tt({opType:fe.FLOOR_DIV,cpuKernelImpl:BV,dtype:"int32"}),EU={kernelName:wo,backendName:"webgpu",kernelFunc:Ole};var Jx=class{constructor(e,t,o=!1){this.pixelsOpType=$i.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=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 = ${e}; for (var i = 0; i < uniforms.numChannels; i = i + 1) { result[flatIndex + i] = i32(floor(255.0 * values[i])); } } } `}};var $U={kernelName:Lu,backendName:"webgpu",kernelFunc:Mle},uc,jv=A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function Mle(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{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,[l,c]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[c,l,s],d=A().getBool("WEBGPU_IMPORT_EXTERNAL_TEXTURE")&&a,f=a||i;if(u||p||f){let b;if(d)b=t.device.importExternalTexture({source:n});else{if(f){let L=A().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(uc==null||L!==jv)&&(jv=L,uc=document.createElement("canvas").getContext("2d",{willReadFrequently:jv})),uc.canvas.width=l,uc.canvas.height=c,uc.drawImage(n,0,0,l,c),n=uc.canvas}let F=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,M=t.textureManager.acquireTexture(m[1],m[0],"rgba8unorm",F);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b=M}let w=y.sizeFromShape(m),S=y.computeStrides(m),k=new Jx(m,s,d),T=[{type:"uint32",data:[w]},{type:"uint32",data:[s]},{type:"uint32",data:[...S]}],E=t.makeTensorInfo([c,l],"int32"),R=t.tensorMap.get(E.dataId);R.resource=b;let D=t.runWebGPUProgram(k,[E],"int32",T);return t.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,w=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } `}};var RU={kernelName:Hn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,l=[o,a,i],c=null;s!=null&&(c=s.shape,l.push(s));let m=null;n!=null&&(m=n.shape,l.push(n));let d=new ey(o.shape,a.shape,i.shape,c,m),f=[{type:"float32",data:[p]}];return u.runWebGPUProgram(d,l,o.dtype,f)}};function Lle(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:l,dilations:c,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=C.convertConv2DDataFormat(l),g=C.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!1,h);return Dx({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var DU={kernelName:jo,backendName:"webgpu",kernelFunc:Lle};function Ble(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:l,dimRoundingMode:c,activation:m,leakyreluAlpha:d}=o,f=l;f==null&&(f=[1,1]),y.assert(C.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=C.computeConv2DInfo(n.shape,s.shape,p,f,u,c,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let w=[{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 ac(h,x,m,b),w.push({type:"int32",data:[S.virtualWidth]})):(S=new ic(h,x,m,b),w.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"&&(w.push({type:"float32",data:[d]}),S.uniforms+=" alpha : f32,"),t.runWebGPUProgram(S,g,"float32",w)}var AU={kernelName:Xo,backendName:"webgpu",kernelFunc:Ble};var ty=class{constructor(e,t){this.variableNames=["A","indices"],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=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${ft(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="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 = ${e}; flattenIndex = flattenIndex + indexTemp * strideNum; } setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } `}};function zle(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,l,c]=C.prepareAndValidate(o,n),m=le({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=le({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/l,l]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let b=t.readSync(n.dataId),w=t.bufferSync(o),S=zV(b,w,o.dtype,u,a,l,c,o.shape,i);return t.makeTensorInfo(p,o.dtype,S.values)}let f=new ty(a,[u,l]),h=[{type:"int32",data:[a]},{type:"int32",data:c}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=le({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var FU={kernelName:Kn,backendName:"webgpu",kernelFunc:zle};var ry=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=Vle(this.aShape);return` ${G("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let indexZ = i32(getIndices(resRC.x, resRC.z)); let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]); setOutputAtIndex(index, inBounds * getA(${e})); } } `}};function Vle(r){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],t=[];for(let o=0;ot.disposeData(D.dataId)),t.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new ry(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);c.push(g);let x=le({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return c.forEach(b=>t.disposeData(b.dataId)),x}var PU={kernelName:fa,backendName:"webgpu",kernelFunc:Xv};var Wle=tt({opType:fe.GREATER,cpuKernelImpl:UV,dtype:"bool"}),OU={kernelName:So,backendName:"webgpu",kernelFunc:Wle};var Ule=tt({opType:fe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:WV}),MU={kernelName:Io,backendName:"webgpu",kernelFunc:Ule};function Gle(r){let{inputs:e,backend:t}=r,{input:o}=e;return Qx(o,!0,t)}var LU={kernelName:Yi,backendName:"webgpu",kernelFunc:Gle};var Hle=ye({opType:Z.IS_FINITE,dtype:"bool"}),BU={kernelName:qn,backendName:"webgpu",kernelFunc:Hle};var Kle=ye({opType:Z.IS_INF,dtype:"bool"}),zU={kernelName:jn,backendName:"webgpu",kernelFunc:Kle};var qle=ye({opType:Z.IS_NAN,dtype:"bool"}),VU={kernelName:Xn,backendName:"webgpu",kernelFunc:qle};function jle(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:"float32",data:[s]}],i=new so(n.shape,Z.LEAKYRELU,"alpha : f32,");return t.runWebGPUProgram(i,[n],"float32",a)}var WU={kernelName:Yn,backendName:"webgpu",kernelFunc:jle};var Xle=tt({opType:fe.LESS,dtype:"bool",cpuKernelImpl:HV}),UU={kernelName:ko,backendName:"webgpu",kernelFunc:Xle};var Yle=tt({opType:fe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:GV}),GU={kernelName:No,backendName:"webgpu",kernelFunc:Yle};var oy=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : 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.shaderKey="linSpace"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); } } `}};function Qle(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new oy(s),p=[{type:"float32",data:[o]},{type:"float32",data:[a]}];return e.runWebGPUProgram(i,[],"float32",p)}var HU={kernelName:Qn,backendName:"webgpu",kernelFunc:Qle};var Zle=ye({opType:Z.LOG,cpuKernelImpl:KV}),KU={kernelName:To,backendName:"webgpu",kernelFunc:Zle};var Jle=ye({opType:Z.LOG1P}),qU={kernelName:Zn,backendName:"webgpu",kernelFunc:Jle};var ece=tt({opType:fe.LOGICAL_AND,dtype:"bool"}),jU={kernelName:Jn,backendName:"webgpu",kernelFunc:ece};var tce=ye({opType:Z.LOGICAL_NOT}),XU={kernelName:es,backendName:"webgpu",kernelFunc:tce};var rce=tt({opType:fe.LOGICAL_OR}),YU={kernelName:ts,backendName:"webgpu",kernelFunc:rce};var QU=` 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)); } `,ny=class{constructor(e){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=e,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; } } ${QU} setOutputAtIndex(index, x * powValue); } } `}},sy=class{constructor(e,t){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(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,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; } ${QU} setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); } } `}};function oce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new ny(n.shape):u=new sy(n.shape,s);let l=[{type:"int32",data:[s]},{type:"float32",data:[a]},{type:"float32",data:[i]},{type:"float32",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,l)}var ZU={kernelName:rs,backendName:"webgpu",kernelFunc:oce};var ay=class{constructor(e){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=e,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 nce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:l}=o,c=new ay(n.shape),m=[{type:"int32",data:[i]},{type:"float32",data:[p]},{type:"float32",data:[u]},{type:"float32",data:[l]}];return t.runWebGPUProgram(c,[n,s,a],n.dtype,m)}var JU={kernelName:oi,backendName:"webgpu",kernelFunc:nce};var sce=tt({opType:fe.MAX,cpuKernelImpl:jV}),eG={kernelName:_o,backendName:"webgpu",kernelFunc:sce};function ace(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,l=C.computePool2DInfo(n.shape,s,a,1,i,p);return bx(n,l,"max",t)}var tG={kernelName:ns,backendName:"webgpu",kernelFunc:ace};function ice(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,l=[1,1,1],c=C.computePool3DInfo(n.shape,s,a,l,i,u,p),m=new $u(c,"max"),d=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var rG={kernelName:ha,backendName:"webgpu",kernelFunc:ice};var iy=class{constructor(e){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=e.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); } } `}},uy=class{constructor(e){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=e.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 uce(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:l}=o,c=[1,1,1],m=C.computePool3DInfo(a.shape,i,p,c,u,l),d=new $u(m,"max",!0),f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inDepth,m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],"int32",f),g=new uy(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=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var oG={kernelName:Ji,backendName:"webgpu",kernelFunc:uce};function pce(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;wm([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:l,dimRoundingMode:c}=o,m=C.computePool2DInfo(i.shape,p,u,1,l,c),d=new Ka(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=t.runWebGPUProgram(d,[i],"int32",f),g=new iy(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=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var nG={kernelName:Zi,backendName:"webgpu",kernelFunc:pce};function lce(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;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(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let l=C.computePool2DInfo(p.shape,n,s,u,a),c=[{type:"int32",data:[l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.dilationHeight,l.dilationWidth]},{type:"int32",data:[l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterHeight,l.effectiveFilterWidth]}],m=new Ka(l,"max",!1),d=t.runWebGPUProgram(m,[p],p.dtype,c);m=new Ka(l,"max",!0,!0,i);let f=t.runWebGPUProgram(m,[p],"int32",c);return[d,f]}var sG={kernelName:ga,backendName:"webgpu",kernelFunc:lce};function cce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return ao(n,s,a,"min",t)}var aG={kernelName:as,backendName:"webgpu",kernelFunc:cce};var mce=tt({opType:fe.MIN,cpuKernelImpl:XV}),iG={kernelName:Eo,backendName:"webgpu",kernelFunc:mce};var py=class{constructor(e,t,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),o=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),n=e===1?"start":"start[i]",s=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=ft(e),p=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${G("index")} { if (index < uniforms.size) { let start = ${i}(${t}); let end = ${i}(${o}); var outC = getCoordsFromIndex(index); for (var i = 0; i < ${e}; i = i + 1) { if (${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 uG={kernelName:is,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(l=>({type:"int32",data:[l[0],l[1]]})),p=new py(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var dce=tt({opType:fe.MOD}),pG={kernelName:us,backendName:"webgpu",kernelFunc:dce};var ly=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],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 cy=class{constructor(e){this.variableNames=["logits"],this.outputShape=e,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 Yv(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=le({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new cy(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=le({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var lG={kernelName:Fs,backendName:"webgpu",kernelFunc:Yv};function fce(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:Yv({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],l=p.shape[1],c=new ly(u,s),m=[{type:"float32",data:[a]},{type:"int32",data:[l]}],d=t.runWebGPUProgram(c,[p],"int32",m);return i||t.disposeData(p.dataId),d}var cG={kernelName:ps,backendName:"webgpu",kernelFunc:fce};function hce(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=QV(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new so(o.shape,Z.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var mG={kernelName:ls,backendName:"webgpu",kernelFunc:hce};function gce(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),l=t.readSync(s.dataId),{selectedIndices:c}=Ut.nonMaxSuppressionV3Impl(u,l,a,i,p);return t.makeTensorInfo([c.length],"int32",new Int32Array(c))}var dG={kernelName:cs,backendName:"webgpu",kernelFunc:gce};function xce(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,l=t.readSync(n.dataId),c=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Ut.nonMaxSuppressionV5Impl(l,c,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var fG={kernelName:ms,backendName:"webgpu",kernelFunc:xce};var my=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],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 yce(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),l=new my(u,a),c=le({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:"float32",data:[i]},{type:"float32",data:[p]}],d=t.runWebGPUProgram(l,[c],s,m);t.disposeData(c.dataId);let f=[...n.shape,a],h=le({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var hG={kernelName:ds,backendName:"webgpu",kernelFunc:yce};function Em(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Fi({inputs:{input:o},backend:t}),s=Em({inputs:{x:n},backend:t}),a=Mp({inputs:{input:o},backend:t}),i=Em({inputs:{x:a},backend:t}),p=Uo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return Nt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var gG={kernelName:_a,backendName:"webgpu",kernelFunc:Em};function xG(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Fi({inputs:{input:o},backend:t}),s=xG({inputs:{x:n},backend:t}),a=Mp({inputs:{input:o},backend:t}),i=Em({inputs:{x:a},backend:t}),p=Uo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return Nt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var yG={kernelName:xa,backendName:"webgpu",kernelFunc:xG};function bce(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Yx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(l=>{y.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(l=>{let c=Yx({inputs:{input:l},backend:t,attrs:{dim:n}});return i.push(c),c}),u=Hv({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(l=>t.disposeData(l.dataId)),u}var bG={kernelName:ya,backendName:"webgpu",kernelFunc:bce};function Qv(r,e=!1){let t=r.length,o=ft(t),n=r.map((c,m)=>`uniforms.pad${m}[0]`).join(","),s=r.map((c,m)=>`uniforms.pad${m}[0] + uniforms.xShape${t>1?`[${m}]`:""}`).join(","),a=t>1?`${o}(${n})`:`${n}`,i=t>1?`${o}(${s})`:`${s}`,p=t>1?"any(paddedCoords < start)":"paddedCoords < start",u=t>1?"any(paddedCoords >= end)":"paddedCoords >= end",l=t>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,t):"coords";return` let start = ${a}; let end = ${i}; if (${p} || ${u}) { setOutputAtIndex(index, ${e?0:"uniforms.constantValue"}); } else { let coords = paddedCoords - start; setOutputAtIndex(index, getX(${l})); } `}var dy=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let paddedCoords = getCoordsFromIndex(index); ${Qv(this.xShape)} } } `}};var Cce=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Pt({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((l,c)=>l[0]+n.shape[c]+l[1]);return Nt({backend:t,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 dy(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},CG={kernelName:fs,backendName:"webgpu",kernelFunc:Cce};var wce=tt({opType:fe.POW}),wG={kernelName:hs,backendName:"webgpu",kernelFunc:wce};function Sce(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Di(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],"float32")}var SG={kernelName:gs,backendName:"webgpu",kernelFunc:Sce};function Ice(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return ao(n,s,a,"prod",t)}var IG={kernelName:Ho,backendName:"webgpu",kernelFunc:Ice};var vce=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=eW(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},vG={kernelName:ba,backendName:"webgpu",kernelFunc:vce};var kce=tt({opType:fe.DIV}),kG={kernelName:Vn,backendName:"webgpu",kernelFunc:kce};var Nce=ye({opType:Z.RECIPROCAL}),NG={kernelName:xs,backendName:"webgpu",kernelFunc:Nce};var Tce=ye({opType:Z.RELU}),TG={kernelName:ys,backendName:"webgpu",kernelFunc:Tce};var _ce=ye({opType:Z.RELU6}),_G={kernelName:ws,backendName:"webgpu",kernelFunc:_ce};var fy=class{constructor(e,t,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[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 Ece(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,l=s&&p>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[l,c]},{type:"float32",data:[i?.5:0]}],f=new fy(n.shape,p,u);return t.runWebGPUProgram(f,[n],"float32",d)}var EG={kernelName:Cs,backendName:"webgpu",kernelFunc:Ece};var hy=class{constructor(e,t){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=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}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 $ce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,l]=s.shape,c=[a&&u>1?i-1:i,a&&l>1?p-1:p],m=[a&&u>1?u-1:u,a&&l>1?l-1:l],d=c[0]/m[0],f=c[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,w=new hy(n.shape,a),S=[{type:"int32",data:c},{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 t.runWebGPUProgram(w,[s],s.dtype,S)}var $G={kernelName:ii,backendName:"webgpu",kernelFunc:$ce};var gy=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="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 = ${e}; // Compute the coordinators of nearest neighbor point. let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z)); let sourceNearestRC = vec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase))); let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutputAtIndex(index, newValue); } } `}};function Rce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,l=s&&p>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[l,c]},{type:"float32",data:[s?.5:0]}],f=new gy(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var RG={kernelName:bs,backendName:"webgpu",kernelFunc:Rce};var xy=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}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 Dce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,l]=s.shape,c=[a&&u>1?i-1:i,a&&l>1?p-1:p],m=[a&&u>1?u-1:u,a&&l>1?l-1:l],d=c[0]/m[0],f=c[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,w=new xy(n.shape,a),S=[{type:"int32",data:c},{type:"int32",data:m},{type:"float32",data:[h]},{type:"float32",data:[g]},{type:"int32",data:[x]},{type:"int32",data:[b]}];return t.runWebGPUProgram(w,[s],s.dtype,S)}var DG={kernelName:ai,backendName:"webgpu",kernelFunc:Dce};var yy=class{constructor(e){this.variableNames=["x"],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.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 Ace(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return Pt({inputs:{x:n},backend:t});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),l=[0,0,0,0];u.forEach(g=>{let x=g+4-a;l[x]=1});let c=[{type:"int32",data:l}],m=le({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new yy(p),f=t.runWebGPUProgram(d,[m],m.dtype,c);t.disposeData(m.dataId);let h=le({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var AG={kernelName:Ss,backendName:"webgpu",kernelFunc:Ace};var by=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],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.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="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)); ${this.fillSnippet} if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 && coordY < uniforms.xShape[1]) { outputValue = getX(coords[0], coordY, coordX, coords[3]); } setOutputAtIndex(index, outputValue); } } `}};var FG={kernelName:Vs,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new by(o.shape,s),[u,l]=C.getImageCenter(a,o.shape[1],o.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[l]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),i.runWebGPUProgram(p,[o],o.dtype,c)}};var Fce=ye({opType:Z.ROUND}),PG={kernelName:Is,backendName:"webgpu",kernelFunc:Fce};var Pce=ye({opType:Z.RSQRT,cpuKernelImpl:tW}),OG={kernelName:Do,backendName:"webgpu",kernelFunc:Pce};var qa=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}_${s.length}`;let u=ft(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,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(${Array.from({length:this.updatesRank},(u,l)=>`coords[${l}]`).join(", ")})`;return` ${s} ${G("index")} { if (index < uniforms.updatesSize) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexInside = i32(round(${t})); flattenedIndex = flattenedIndex + indexInside * ${o}; } let updateValue = ${Eu(this.type)}(${i}); let flatIndex = getOutputIndexFromCoords(${n}); ${this.sumDupeIndices?oo("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} } }`}};function Oce(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:l,outputSize:c}=C.calculateShapes(s,n,a),m=[c/u,u];if(c===0)return t.makeTensorInfo(a,n.dtype);let d=le({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=le({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=Nt({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:"int32",data:[i]},{type:"int32",data:l},{type:"int32",data:[x]}],w=new qa(f.shape,i,d.shape.length,f.shape.length,l,m,h),S=t.runWebGPUProgram(w,[f,d],h,b,g),k=le({inputs:{x:S},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(S.dataId),k}var MG={kernelName:vs,backendName:"webgpu",kernelFunc:Oce};var Cy=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}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 Mce(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new Cy([s.shape[0],s.shape[1]],a),p=[{type:"int32",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],"int32",p)}var LG={kernelName:Ns,backendName:"webgpu",kernelFunc:Mce};var wy=class{constructor(e,t,o){this.variableNames=["c","a","b"],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.cRank=e,this.rank=o,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let i=0;i= 1.0) { setOutputAtIndex(index, getA(${t})); } else { setOutputAtIndex(index, getB(${t})); } } } `}};function Lce(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new wy(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],pt(n.dtype,s.dtype))}var BG={kernelName:wa,backendName:"webgpu",kernelFunc:Lce};var Bce=ye({opType:Z.SELU}),zG={kernelName:Ts,backendName:"webgpu",kernelFunc:Bce};var zce=ye({opType:Z.SIGMOID}),VG={kernelName:Ao,backendName:"webgpu",kernelFunc:zce};var Vce=ye({opType:Z.SIGN}),WG={kernelName:Rs,backendName:"webgpu",kernelFunc:Vce};var Wce=ye({opType:Z.SIN}),UG={kernelName:Es,backendName:"webgpu",kernelFunc:Wce};var Uce=ye({opType:Z.SINH}),GG={kernelName:$s,backendName:"webgpu",kernelFunc:Uce};var Gce=ye({opType:Z.SOFTPLUS}),HG={kernelName:Ds,backendName:"webgpu",kernelFunc:Gce};var Sy=class{constructor(e,t,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 e=ft(this.outputShape.length),t=Bv(this.newDim);return` ${xm(this.paddedXShape,"PaddedX")} ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t}), uniforms.reshapedPaddedXShape); let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex); ${Qv(this.xShape,!0)} } } `}};var Hce=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{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,w)=>b*w),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[w]+b[1]),l=C.getReshaped(u,s,i,!1),c=C.getPermuted(l.length,s.length,!1),m=C.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new Sy(n.shape,u,p,l,c,d.length),h=[{type:"int32",data:l},{type:"int32",data:d}];p.map(b=>h.push({type:"int32",data:[b[0],b[1]]}));let g=t.runWebGPUProgram(f,[n],n.dtype,h),x=le({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeData(g.dataId),x},KG={kernelName:Sa,backendName:"webgpu",kernelFunc:Hce};var Iy=class{constructor(e,t,o){this.variableNames=["input","indices","segmentIds"],this.outputShape=[],this.uniforms="segmentSize : i32, sparseSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.type=o,this.dispatchLayout=X([t]),this.dispatch=H(this.dispatchLayout,[t],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; ${oo("&result[outIndex]","value",this.type)} } } `}},vy=class{constructor(e,t){this.variableNames=["segmentIds"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[e],this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.shaderKey="sparseSegmentIdCountProgram"}getUserCode(){return` ${G("index")} { if (index < uniforms.segmentIdsShape) { let segmentId = segmentIds[index]; ${oo("&result[segmentId]","1","int32")} } } `}},ky=class{constructor(e,t){this.variableNames=["segmentSum","sameSegmentIdCount"],this.outputShape=[],this.uniforms="segmentSize : i32",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.type=t,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,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 Ny(r,e,t,o=!1,n){let a=y.sizeFromShape(r.shape)/r.shape[0],i=r.dtype,p=y.sizeFromShape(e.shape),u=n.readSync(t.dataId),c=p>0?u[p-1]+1:0,m,d=r.shape.slice();d[0]=c;let f=p*a,h=Nt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new Iy(d,f,i);let g=[{type:"int32",data:[a]},{type:"int32",data:[f]}],x=n.runWebGPUProgram(m,[r,e,t],i,g,h);if(o)return x;let b=Nt({backend:n,attrs:{shape:[c],value:0,dtype:"int32"}});m=new vy(c,t.shape);let w=n.runWebGPUProgram(m,[t],"int32",null,b),S=Nt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new ky(d,i),g=[{type:"int32",data:[a]}];let k=n.runWebGPUProgram(m,[x,w],i,g,S);return n.disposeData(x.dataId),n.disposeData(w.dataId),k}function Kce(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;return Ny(o,n,s,!1,t)}var qG={kernelName:va,backendName:"webgpu",kernelFunc:Kce};function qce(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;return Ny(o,n,s,!0,t)}var jG={kernelName:ka,backendName:"webgpu",kernelFunc:qce};var Ty=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype==="string"?p.map(m=>y.decodeString(m)):p,l=ie(n.shape,n.dtype,u),c=uW(l,s);return t.makeTensorInfo(c.shape,c.dtype,c.values)}let a=new Ty(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var XG={kernelName:Mo,backendName:"webgpu",kernelFunc:$m};function Xce(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:l,strides:c,outputSize:m}=C.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let R=t.bufferSync(n),D=t.bufferSync(s),F=y.decodeString(t.readSync(a.dataId)[0]),O=rW(R,D,i,m,l,u,p,c,F,d);return t.makeTensorInfo(i,O.dtype,O.values)}let f=[m/l,l],h=le({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?le({inputs:{x:s},backend:t,attrs:{shape:[u,l]}}):Pt({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),w=le({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),S=$m({inputs:{x:w},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,l]),T=[{type:"int32",data:[p]},{type:"int32",data:c},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let R=new qa([u,l],p,h.shape.length,g.shape.length,c,f,x,d);t.runWebGPUProgram(R,[g,h],x,T,S)}break;default:{let R=new qa([u,l],p,h.shape.length,b.shape.length,c,f,x,d);t.runWebGPUProgram(R,[b,h],x,T,S)}{let R=new qa([u,l],p,h.shape.length,g.shape.length,c,f,x);t.runWebGPUProgram(R,[g,h],x,T,S)}}let E=le({inputs:{x:S},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(w.dataId),t.disposeData(b.dataId),t.disposeData(S.dataId),E}var YG={kernelName:Ps,backendName:"webgpu",kernelFunc:Xce};function Yce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=C.prepareSplitSize(n,s,i),u=n.shape.length,l=new Array(u).fill(0),c=n.shape.slice();return p.map(m=>{let d=[...c];d[i]=m;let f=ea({inputs:{x:n},backend:t,attrs:{begin:l,size:d}});return l[i]+=m,f})}var QG={kernelName:Ia,backendName:"webgpu",kernelFunc:Yce};var Qce=ye({opType:Z.SQRT}),ZG={kernelName:Fo,backendName:"webgpu",kernelFunc:Qce};var JG={kernelName:tu,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new so(t.shape,Z.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Zce=tt({opType:fe.SQUARED_DIFFERENCE}),e4={kernelName:Po,backendName:"webgpu",kernelFunc:Zce};function Jce({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new so(o.shape,Z.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var t4={kernelName:Ko,backendName:"webgpu",kernelFunc:Jce};var _y=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,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.workPerThread,1,1]);let t=ft(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${t})); } } `}};function eme(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:l,newAxisMask:c,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:S}=nt.sliceInfo(n.shape,s,a,i,p,u,l,c,m),k;if(h)k=le({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let T=nt.computeOutShape(b,w,S),E=ea({inputs:{x:n},backend:t,attrs:{begin:b,size:T}});k=le({inputs:{x:E},backend:t,attrs:{shape:f}}),t.disposeData(E.dataId)}else if(t.shouldExecuteOnCPU([n])){let E=t.readSync(n.dataId),R=ie(n.shape,n.dtype,E),D=sW(d,R,S,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let E=new _y(d),R=[{type:"int32",data:b},{type:"int32",data:S}],D=t.runWebGPUProgram(E,[n],n.dtype,R);k=le({inputs:{x:D},backend:t,attrs:{shape:f}}),t.disposeData(D.dataId)}return k}var r4={kernelName:Os,backendName:"webgpu",kernelFunc:eme};function tme(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:l,dataSplits:c}=e,m=t.readSync(l.dataId),d=t.readSync(c.dataId),[f,h]=aW(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],"string",f),t.makeTensorInfo(c.shape,"int32",h)]}var o4={kernelName:Na,backendName:"webgpu",kernelFunc:tme};var rme=tt({opType:fe.SUB,cpuKernelImpl:iW,supportsComplex:!0}),n4={kernelName:Oo,backendName:"webgpu",kernelFunc:rme};var ome=ye({opType:Z.TAN}),s4={kernelName:Ms,backendName:"webgpu",kernelFunc:ome};var nme=ye({opType:Z.TANH}),a4={kernelName:Ls,backendName:"webgpu",kernelFunc:nme};function sme(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:l,outputSize:c}=C.calculateShapes(a,s,n.shape),m=[c/u,u];if(c===0)return t.makeTensorInfo(n.shape,s.dtype);let d=[],f=le({inputs:{x:s},backend:t,attrs:{shape:[p,i]}});d.push(f);let h=le({inputs:{x:a},backend:t,attrs:{shape:[p,u]}});d.push(h);let g=le({inputs:{x:n},backend:t,attrs:{shape:m}});d.push(g);let x=$m({inputs:{x:g},backend:t,attrs:{reps:Array(m.length).fill(1)}}),b=new qa([p,u],i,f.shape.length,h.shape.length,l,m,n.dtype,!1),w=y.sizeFromShape([p,u]),S=[{type:"int32",data:[i]},{type:"int32",data:l},{type:"int32",data:[w]}],k=t.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let T=le({inputs:{x:k},backend:t,attrs:{shape:n.shape}});return d.forEach(E=>t.disposeData(E.dataId)),T}var i4={kernelName:ks,backendName:"webgpu",kernelFunc:sme};var Ey=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,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` ${G("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; let elemIdx = outC[1]; // We compare elements pair-wise within a group of size 2 * inc. // The comparing rule for each group alternates between ascending // and descending. Within each group, we compare each pair at // positions i and i+inc. To decide whether an element at position i // is x0 or x1, we mod it by 2 * inc, if the result is smaller than // inc, it is in the first half of the group, we denote it as x0, // otherwise we denote it as x1. // For example, as shown in the Bitonic top K paper referenced // above, Figure5(a) shows that element[1] is in the second half of // the group when group size is 2, but it is in the first half of // the group when group size is 4. let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc; var i = 0; if (isFirstInPair) { i = elemIdx; } else { i = elemIdx - uniforms.inc; } var i0 = 0; if (uniforms.firstPass == 1) { i0 = i; } else { i0 = i32(getIndices(batch, i)); } var i1 = 0; if (uniforms.firstPass == 1) { i1 = i + uniforms.inc; } else { i1 = i32(getIndices(batch, i + uniforms.inc)); } var x0 = f32(0.0); var x1 = f32(0.0); if (i0 < uniforms.inputSize) { x0 = getX(batch, i0); } else { x0 = uniforms.negativeInf; } if (i1 < uniforms.inputSize) { x1 = getX(batch, i1); } else { x1 = uniforms.negativeInf; } let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir; let isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction let iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutputAtIndex(index, f32(i0)); } else { setOutputAtIndex(index, f32(i1)); } } } `}},$y=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,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 pc(r,e){e!==null&&r.disposeData(e.dataId)}function u4(r){let e=1;for(;ef===null?[c,c]:[c,f],g=(k,T,E)=>{let R=h(),D=new Ey(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:[T]}],M=f;f=t.runWebGPUProgram(D,R,"int32",O),pc(t,M)};for(let k=1;k=1;E/=2)g(T,E,[l,d])}for(let k=d;k>m;k/=2){let T=h(),E=new $y([l,k/2]),D=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[m]}],F=f;f=t.runWebGPUProgram(E,T,"int32",D),pc(t,F);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=ea({inputs:{x:f},backend:t,attrs:{begin:0,size:[l,s]}}),pc(t,x);let b=Xv({inputs:{x:c,indices:f},backend:t,attrs:{axis:1,batchDims:1}});pc(t,c);let w=i.slice(0,-1);w.push(s),x=f,f=le({inputs:{x:f},attrs:{shape:w},backend:t}),pc(t,x);let S=b;return b=le({inputs:{x:b},attrs:{shape:w},backend:t}),pc(t,S),[b,f]}var p4={kernelName:Bs,backendName:"webgpu",kernelFunc:ame};var Ry=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : 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.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 ime(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[l,c,m,d]=n.shape,[f,h]=u!=null?u:[c,m],g=[l,f,h,d],x=new Ry(g),b=a==="nearest"?1:2,w;switch(i){case"constant":w=1;break;case"reflect":w=2;break;case"wrap":w=3;break;case"nearest":w=4;break;default:w=1;break}let S=[{type:"int32",data:[b]},{type:"int32",data:[w]},{type:"float32",data:[p]}];return t.runWebGPUProgram(x,[n,s],"float32",S)}var l4={kernelName:zs,backendName:"webgpu",kernelFunc:ime};function ume(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{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),l=0;for(let h=0;ht.disposeData(h.dataId)),f}var c4={kernelName:Ta,backendName:"webgpu",kernelFunc:ume};var Dy=class{constructor(e,t,o){if(this.outputShape=[],this.variableNames=["x","segmentIds"],this.uniforms="numSegments : i32, xSize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,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); ${oo("&result[flatIndex]","value",this.type)} } } } `}};function pme(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,l=C.getAxesPermutation([u],i),c=n;l!=null&&(c=Cr({inputs:{x:n},backend:t,attrs:{perm:l}}),p.push(c),u=C.getInnerMostAxes(1,i)[0]);let m=C.segment_util.computeOutShape(c.shape,u,a),d=y.sizeFromShape([c.shape[u]]),f=le({inputs:{x:c},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=Nt({backend:t,attrs:{shape:g,value:0,dtype:h}}),b=new Dy(f.shape,g,h),w=[{type:"int32",data:[a]},{type:"int32",data:[y.sizeFromShape(f.shape)]}],S=t.runWebGPUProgram(b,[f,s],h,w,x),k=le({inputs:{x:S},backend:t,attrs:{shape:m}});p.push(S);let T=k;if(l!=null){p.push(k);let E=C.getUndoAxesPermutation(l);T=Cr({inputs:{x:T},backend:t,attrs:{perm:E}})}return p.forEach(E=>t.disposeData(E.dataId)),T}var m4={kernelName:su,backendName:"webgpu",kernelFunc:pme};var lme=[jz,cW,mW,dW,fW,hW,xW,yW,bW,CW,wW,SW,IW,vW,kW,_W,EW,$W,RW,DW,FW,PW,OW,zW,VW,WW,Yz,GW,KW,qW,jW,XW,YW,QW,ZW,JW,eU,tU,nU,sU,aU,iU,pU,lU,uU,cU,mU,dU,fU,hU,yU,bU,CU,wU,SU,IU,vU,kU,NU,Kz,TU,$U,_U,EU,RU,DU,AU,FU,PU,OU,MU,Xz,LU,HW,BU,zU,VU,WU,UU,GU,HU,qU,KU,jU,XU,YU,ZU,JU,NW,eG,tG,nG,rG,oG,sG,TW,aG,iG,uG,pG,cG,gU,mG,dG,fG,MW,hG,yG,bG,CG,wG,SG,IG,vG,LW,kG,NG,TG,_G,qz,EG,$G,RG,DG,AG,FG,PG,OG,MG,LG,BG,zG,VG,WG,UG,GG,AW,t4,r4,o4,lG,HG,KG,qG,jG,YG,QG,ZG,JG,e4,n4,xU,s4,a4,i4,XG,p4,l4,gW,c4,m4,gG];for(let r of lme)li(r);var d4="4.17.0",cme="4.17.0",mme="4.17.0",dme="4.17.0",fme="4.17.0",hme="4.14.0",gme={tfjs:d4,"tfjs-core":d4,"tfjs-converter":cme,"tfjs-backend-cpu":mme,"tfjs-backend-webgl":dme,"tfjs-backend-wasm":fme,"tfjs-backend-webgpu":hme};var qtr=void 0;export{fn as Abs,hn as Acos,gn as Acosh,sp as AdadeltaOptimizer,ap as AdagradOptimizer,ip as AdamOptimizer,up as AdamaxOptimizer,Rr as Add,xn as AddN,yn as All,bn as Any,na as ArgMax,sa as ArgMin,Cn as Asin,wn as Asinh,Sn as Atan,vn as Atan2,In as Atanh,kn as AvgPool,aa as AvgPool3D,Vi as AvgPool3DGrad,zi as AvgPoolGrad,gm as BackendWasm,Nn as BatchMatMul,ia as BatchToSpaceND,Tn as Bincount,_n as BitwiseAnd,ua as BroadcastArgs,Sme as BroadcastTo,ho as Cast,go as Ceil,Go as ClipByValue,ei as Complex,Wi as ComplexAbs,pa as Concat,En as Conv2D,Ui as Conv2DBackpropFilter,$n as Conv2DBackpropInput,Rn as Conv3D,ti as Conv3DBackpropFilterV2,Dn as Conv3DBackpropInputV2,An as Cos,Fn as Cosh,Mn as CropAndResize,Pn as Cumprod,On as Cumsum,mn as DataStorage,la as DenseBincount,Ln as DepthToSpace,Bn as DepthwiseConv2dNative,Gi as DepthwiseConv2dNativeBackpropFilter,Hi as DepthwiseConv2dNativeBackpropInput,ca as Diag,zn as Dilation2D,qi as Dilation2DBackpropFilter,Ki as Dilation2DBackpropInput,Mu as Draw,xw as ENV,ji as Einsum,Wn as Elu,ri as EluGrad,Cc as Environment,xo as Equal,Un as Erf,yo as Exp,ma as ExpandDims,bo as Expm1,Xi as FFT,da as Fill,Gn as FlipLeftRight,Co as Floor,wo as FloorDiv,Lu as FromPixels,Hn as FusedBatchNorm,jo as FusedConv2D,Xo as FusedDepthwiseConv2D,kp as GPGPUContext,Kn as GatherNd,fa as GatherV2,Kc as GraphModel,So as Greater,Io as GreaterEqual,Yi as IFFT,vo as Identity,Qi as Imag,qn as IsFinite,jn as IsInf,Xn as IsNan,mo as KernelBackend,rs as LRN,oi as LRNGrad,Yn as LeakyRelu,ko as Less,No as LessEqual,Qn as LinSpace,To as Log,Zn as Log1p,Ime as LogSoftmax,Jn as LogicalAnd,es as LogicalNot,ts as LogicalOr,gk as LogicalXor,vme as LowerBound,Il as MathBackendCPU,Ul as MathBackendWebGL,kme as MatrixBandPart,os as Max,ns as MaxPool,ha as MaxPool3D,Ji as MaxPool3DGrad,Zi as MaxPoolGrad,ga as MaxPoolWithArgmax,_o as Maximum,ss as Mean,as as Min,Eo as Minimum,is as MirrorPad,us as Mod,pp as MomentumOptimizer,ps as Multinomial,$o as Multiply,ls as Neg,cs as NonMaxSuppressionV3,ni as NonMaxSuppressionV4,ms as NonMaxSuppressionV5,Ro as NotEqual,Bw as OP_SCOPE_SUFFIX,ds as OneHot,xa as OnesLike,_r as Optimizer,Vc as OptimizerConstructors,ya as Pack,fs as PadV2,Nme as Pool,hs as Pow,gs as Prelu,Ho as Prod,lp as RMSPropOptimizer,Qp as RaggedGather,Zp as RaggedRange,Jp as RaggedTensorToTensor,ba as Range,Ew as Rank,si as Real,Vn as RealDiv,xs as Reciprocal,Dt as Reduction,ys as Relu,ws as Relu6,Ca as Reshape,Cs as ResizeBilinear,ii as ResizeBilinearGrad,bs as ResizeNearestNeighbor,ai as ResizeNearestNeighborGrad,Ss as Reverse,Vs as RotateWithOffset,Is as Round,Do as Rsqrt,wi as SGDOptimizer,vs as ScatterNd,Ns as SearchSorted,wa as Select,Ts as Selu,Ao as Sigmoid,Rs as Sign,Es as Sin,$s as Sinh,_s as Slice,Fs as Softmax,Ds as Softplus,Sa as SpaceToBatchND,eu as SparseFillEmptyRows,ui as SparseReshape,va as SparseSegmentMean,ka as SparseSegmentSum,Ps as SparseToDense,Ia as SplitV,Fo as Sqrt,tu as Square,Po as SquaredDifference,pi as StaticRegexReplace,Ko as Step,Os as StridedSlice,Na as StringNGrams,ru as StringSplit,ou as StringToHashBucketFast,Oo as Sub,As as Sum,Ms as Tan,Ls as Tanh,dt as Tensor,Ge as TensorBuffer,ks as TensorScatterUpdate,Mo as Tile,Bs as TopK,zs as Transform,Kr as Transpose,nu as Unique,Ta as Unpack,su as UnsortedSegmentSum,Tme as UpperBound,ci as Variable,Jl as WebGPUBackend,_a as ZerosLike,qo as _FusedMatMul,er as abs,g1 as acos,x1 as acosh,Ce as add,y1 as addN,b1 as all,C1 as any,w1 as argMax,S1 as argMin,I1 as asin,v1 as asinh,k1 as atan,N1 as atan2,T1 as atanh,Id as avgPool,$1 as avgPool3d,Hk as backend,C as backend_util,R1 as basicLSTMCell,mu as batchNorm,A1 as batchNorm2d,F1 as batchNorm3d,P1 as batchNorm4d,vd as batchToSpaceND,kd as bincount,O1 as bitwiseAnd,oX as booleanMaskAsync,M1 as broadcastArgs,Oa as broadcastTo,kr as broadcast_util,XT as browser,ie as buffer,Ue as cast,L1 as ceil,B1 as clipByValue,Xr as clone,Ar as complex,bt as concat,z1 as concat1d,V1 as concat2d,W1 as concat3d,U1 as concat4d,G1 as conv1d,du as conv2d,H1 as conv2dTranspose,K1 as conv3d,j1 as conv3dTranspose,Pme as copyRegisteredKernels,X1 as cos,Y1 as cosh,Mc as cosineWindow,Q1 as cumprod,Z1 as cumsum,Nr as customGrad,J1 as denseBincount,zw as deprecationWarn,e2 as depthToSpace,cl as depthwiseConv2d,aY as deregisterOp,uu as device_util,t2 as diag,r2 as dilation2d,Kde as disableDeprecationWarnings,Lt as dispose,qde as disposeVariables,Xe as div,n2 as divNoNan,s2 as dot,hX as dropout,fu as einsum,Ed as elu,Hde as enableDebugMode,Gde as enableProdMode,cS as enclosingPowerOfTwo,cr as engine,a2 as ensureShape,A as env,_d as equal,i2 as erf,l2 as euclideanNorm,Jo as exp,Ks as expandDims,c2 as expm1,$d as eye,fl as fft,Ma as fill,efe as findBackend,tfe as findBackendFactory,Rd as floor,Sd as floorDiv,EA as forceHalfFloat,mS as fused,Dd as gather,dX as gatherND,xf as gather_util,Gk as getBackend,Cw as getGradient,tl as getKernel,ad as getKernelsForBackend,kie as getThreadsCount,k0 as gpgpu_util,a6 as grad,i6 as grads,ju as greater,Ad as greaterEqual,ep as ifft,gu as imag,b5 as image,xX as inTopKAsync,Si as io,tf as irfft,m2 as isFinite,d2 as isInf,f2 as isNaN,Fr as keep,Ut as kernel_impls,Fd as leakyRelu,Fc as less,ml as lessEqual,C5 as linalg,h2 as linspace,r7 as loadGraphModel,o7 as loadGraphModelSync,g2 as localResponseNormalization,yi as log,Pd as log1p,x2 as logSigmoid,y2 as logSoftmax,Ld as logSumExp,Xu as logicalAnd,Bd as logicalNot,zd as logicalOr,b2 as logicalXor,w5 as losses,C2 as lowerBound,Je as matMul,HT as math,La as max,Wd as maxPool,w2 as maxPool3d,S2 as maxPoolWithArgmax,Ud as maximum,Yu as mean,jde as memory,I2 as meshgrid,Ac as min,Qu as minimum,v2 as mirrorPad,k2 as mod,N2 as moments,aX as movingAverage,se as mul,T2 as multiRNNCell,_2 as multinomial,mr as neg,IS as nextFrame,qtr as node,qu as norm,Gd as notEqual,Oc as oneHot,Ba as ones,E2 as onesLike,N as op,$2 as outerProduct,za as pad,R2 as pad1d,D2 as pad2d,A2 as pad3d,F2 as pad4d,P2 as pool,xi as pow,Kd as prelu,wd as print,O2 as prod,Xde as profile,M2 as raggedGather,L2 as raggedRange,B2 as raggedTensorToTensor,z2 as rand,iN as randomGamma,Zd as randomNormal,uN as randomStandardNormal,dl as randomUniform,pN as randomUniformInt,xu as range,Zde as ready,bi as real,lN as reciprocal,pu as registerBackend,Dme as registerGradient,li as registerKernel,sY as registerOp,yu as relu,Jd as relu6,Jde as removeBackend,W as reshape,Bo as reverse,cN as reverse1d,mN as reverse2d,dN as reverse3d,fN as reverse4d,hl as rfft,ef as round,hN as rsqrt,ke as scalar,uX as scatterND,Cu as scatter_util,Pc as searchSorted,gN as selu,xN as separableConv2d,AT as serialization,Qde as setBackend,rfe as setPlatform,vie as setThreadsCount,Sie as setWasmPath,Iie as setWasmPaths,BI as setWebGLContext,yN as setdiff1dAsync,Xf as shared,Pa as sigmoid,bN as sign,y5 as signal,CN as sin,wN as sinh,Ye as slice,SN as slice1d,IN as slice2d,vN as slice3d,kN as slice4d,nt as slice_util,NN as softmax,Md as softplus,Hd as spaceToBatchND,S5 as sparse,cX as sparseToDense,x5 as spectral,Ci as split,Pr as sqrt,tr as square,rf as squaredDifference,gl as squeeze,Tr as stack,of as step,TN as stridedSlice,I5 as string,Te as sub,ot as sum,mi as sumOutType,_N as tan,Dc as tanh,pr as tensor,rr as tensor1d,bu as tensor2d,nf as tensor3d,EN as tensor4d,$N as tensor5d,RN as tensor6d,AN as tensorScatterUpdate,Vk as tensor_util,aN as test_util,De as tidy,hu as tile,Yde as time,FN as topk,cHe as train,yl as transpose,PN as truncatedNormal,ON as unique,Fme as unregisterGradient,Ame as unregisterKernel,MN as unsortedSegmentSum,zo as unstack,pt as upcastType,LN as upperBound,y as util,u6 as valueAndGrad,p6 as valueAndGrads,BN as variable,eS as variableGrads,gme as version,s7 as version_converter,t8 as version_core,M7 as version_cpu,Nie as version_wasm,DJ as version_webgl,sut as webgl,Fl as webgl_util,cv as webgpu_util,Lo as where,af as whereAsync,Yr as zeros,Kt as zerosLike};