mirror of https://github.com/vladmandic/human
8290 lines
1.4 MiB
8290 lines
1.4 MiB
/*
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Human
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homepage: <https://github.com/vladmandic/human>
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author: <https://github.com/vladmandic>'
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*/
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n=++this.pendingBackendInitId,r=a.then(s=>n<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,Dr(`Initialization of backend ${e} failed`),Dr(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=a,{success:!0,asyncInit:!1}}catch(a){return Dr(`Initialization of backend ${e} failed`),Dr(a.stack||a.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let a=e[t],{success:n,asyncInit:r}=this.initializeBackend(a);if(r||n)return{name:a,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let a=this.state.tensorInfo.get(t),n=a.backend,r=this.readSync(t),s=n.refCount(t);n.disposeData(t,!0),a.backend=e,e.move(t,r,a.shape,a.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let a=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");a=e}let n;return this.scopedRun(()=>this.startScope(a),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,a){e();try{let n=a();return t(),n}catch(n){throw t(),n}}nextTensorId(){return od.nextTensorId++}nextVariableId(){return od.nextVariableId++}clone(e){let t=L.runKernel(wi,{x:e}),a={x:e},n=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return L.runKernel(ti,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,a,[t],n,r,{}),t}runKernel(e,t,a){if(this.backendName==null&&this.backend,Nc(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:a})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,a){let n=this.backend.numDataIds(),r=0;a.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,a=[],n=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Rm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Rm(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Nc(h,this.backendName);F(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let x=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,x);let A=x.map(b=>b.rank!=null?b:this.makeTensorFromTensorInfo(b));if(n){let b=this.getTensorsForGradient(h,f,A);a=this.saveTensorsForBackwardMode(b)}return A}}else{let{forwardFunc:h}=e,f=m=>{n&&(a=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:p}=e,c=Rm(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(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(l,u,t,c,a,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,a){let n=Wm(e);if(n!=null){let r=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=a.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,a,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");a=a||"float32",n=n||this.backend;let r=e;a==="string"&&zr(e[0])&&(r=e.map(o=>qd(o)));let s=n.write(r,t,a),i=new ct(t,a,s,this.nextTensorId());if(this.trackTensor(i,n),a==="string"){let o=this.state.tensorInfo.get(s),l=lx(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,a,n){a=a||"float32";let r={dataId:e,shape:t,dtype:a};return this.makeTensorFromTensorInfo(r,n)}makeTensorFromTensorInfo(e,t){let{dataId:a,shape:n,dtype:r}=e,s=new ct(n,r,a,this.nextTensorId());return this.trackTensor(s,t),s}makeVariable(e,t=!0,a,n){a=a||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let r=new id(e,t,a,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let a=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(a=e.size*Tc(e.dtype)),this.state.numBytes+=a,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:a})),e instanceof id||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let 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 a=e.size*Tc(e.dtype);this.state.numBytes-=a}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 t=this.state.numBytes,a=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-a;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,a,n,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:a,saved:r},o=Wm(e);o!=null&&(n=o.gradFunc),n!=null&&(i.gradient=l=>(l=l.map((u,p)=>{if(u==null){let c=a[p],d=jc(c.size,c.dtype);return this.makeTensor(d,c.shape,c.dtype)}return u}),n(l.length>1?l:l[0],r,s))),this.state.activeTape.push(i)}keep(e){return 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=X2(e),a=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!a.has(s.id)&&s.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===n.id&&this.track(r)})}gradients(e,t,a,n=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),a!=null&&a.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${a.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(r instanceof ct,()=>"The result y returned by f() must be a tensor.");let s=qS(this.state.activeTape,t,r);if(!n&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[r.id]=a==null?sT(r.shape):a,XS(i,s,l=>this.tidy(l),iT);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:r,grads:o}})}customGrad(e){return F(Gr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(t.every(i=>i instanceof ct),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let a,n={};t.forEach((i,o)=>{n[o]=i});let r=(i,o)=>(a=e(...t,o),F(a.value instanceof ct,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(Gr(a.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),a.value),s=(i,o)=>{let l=a.gradFunc(i,o),u=Array.isArray(l)?l:[l];F(u.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),F(u.every(c=>c instanceof ct),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let p={};return u.forEach((c,d)=>{p[d]=()=>c}),p};return this.runKernelFunc({forwardFunc:r,backwardsFunc:s,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=sd(),a=await this.backend.time(e);return a.wallMs=sd()-t,a}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new z5;for(let e in 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n!=null&&(p=E(n,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),p!=null&&F(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${p.rank}.`),tp(i,o,l,p,u,s)}var Zx=z({batchNorm2d_:PC});function FC(e,t,a,n,r,s){let i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(a,"variance","batchNorm"),u;r!=null&&(u=E(r,"scale","batchNorm"));let p;return n!=null&&(p=E(n,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),p!=null&&F(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${p.rank}.`),tp(i,o,l,p,u,s)}var Yx=z({batchNorm3d_:FC});function OC(e,t,a,n,r,s){let i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(a,"variance","batchNorm"),u;r!=null&&(u=E(r,"scale","batchNorm"));let p;return n!=null&&(p=E(n,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),p!=null&&F(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),tp(i,o,l,p,u,s)}var Jx=z({batchNorm4d_:OC});function DC(e,t,a){let n=E(e,"x","bincount"),r=E(t,"weights","bincount");F(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),F(a>=0,()=>`size must be non-negative, but got ${a}.`),F(r.size===n.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${r.shape}.`);let s={x:n,weights:r},i={size:a};return L.runKernel(Sd,s,i)}var i1=z({bincount_:DC});function zC(e,t){let a=E(e,"s0","broadcastArgs","int32"),n=E(t,"s1","broadcastArgs","int32");if(a.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${a.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let r={s0:a,s1:n};return L.runKernel(Zc,r)}var Qx=z({broadcastArgs_:zC});function LC(e,t){let a=E(e,"broadcastTo","x"),n=a.shape;if(tn(t),t.length<a.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${a.rank}.`);if(t.length>a.rank){let l=a.shape.slice();for(;l.length<t.length;)l.unshift(1);a=Q(a,l)}let r=a.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(r[l]===t[l])s[l]=1;else if(a.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return Ia(a);let i={x:a},o={reps:s};return L.runKernel(ns,i,o)}var rl=z({broadcastTo_:LC});function BC(e){let t={x:E(e,"x","ceil","float32")};return L.runKernel(ai,t)}var eA=z({ceil_:BC});function nr(e,t,a){tn(e);let n={shape:e,value:t,dtype:a};return L.runKernel(Pl,{},n)}function WC(e,t,a){let n=E(e,"x","clipByValue");if(F(t<=a,()=>`Error in clip: min (${t}) must be less than or equal to max (${a}).`),t===a)return nr(n.shape,t,n.dtype);let r={x:n},s={clipValueMin:t,clipValueMax:a};return L.runKernel(as,r,s)}var tA=z({clipByValue_:WC});function VC(e){return st(e,0)}var aA=z({concat1d_:VC});function UC(e,t){return st(e,t)}var ru=z({concat2d_:UC});function GC(e,t){return st(e,t)}var nA=z({concat3d_:GC});function HC(e,t){return st(e,t)}var rA=z({concat4d_:HC});function jC(e,t,a,n,r="NHWC",s=[1,1],i){let o=E(e,"x","conv2d","float32"),l=E(t,"filter","conv2d","float32"),u=o,p=!1;o.rank===3&&(p=!0,u=Q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),Cn("conv2d",n,i);let c=r==="NHWC"?u.shape[3]:u.shape[1];F(c===l.shape[2],()=>`Error in conv2d: depth of input (${c}) must match input depth for filter ${l.shape[2]}.`),F(wr(a,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${a} and dilations '${s}'`),F(Bs(s),()=>"Error in conv2D: Dilated rates should be larger than 0."),F(Bs(a),()=>"Error in conv2D: Strides should be larger than 0.");let d={x:u,filter:l},h={strides:a,pad:n,dataFormat:r,dilations:s,dimRoundingMode:i},f=L.runKernel(ni,d,h);return p?Q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ap=z({conv2d_:jC});function qC(e,t,a,n,r="NWC",s=1,i){let o=E(e,"x","conv1d"),l=E(t,"filter","conv1d"),u=o,p=!1;o.rank===2&&(p=!0,u=Q(o,[1,o.shape[0],o.shape[1]])),F(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),Cn("conv1d",n,i),F(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F(wr(a,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${a} and dilation '${s}'`),F(Bs(s),()=>"Error in conv1D: Dilated rates should be larger than 0."),F(Bs(a),()=>"Error in conv1D: Stride should be larger than 0."),F(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let c=Q(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=Q(u,[u.shape[0],1,u.shape[1],u.shape[2]]),h=ap(d,c,[1,a],n,"NHWC",[1,s],i);return p?Q(h,[h.shape[2],h.shape[3]]):Q(h,[h.shape[0],h.shape[2],h.shape[3]])}var sA=z({conv1d_:qC});function XC(e,t,a,n,r,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=Q(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),F(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),F(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),F(a.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${a.rank}`);let p=s==="NHWC"?o[3]:o[1],c=s==="NHWC"?l.shape[3]:l.shape[1];F(p===a.shape[2],()=>`Error in conv2dDerInput: depth of input (${p}) must match input depth for filter ${a.shape[2]}.`),F(c===a.shape[3],()=>`Error in conv2dDerInput: depth of output (${c}) must match output depth for filter ${a.shape[3]}.`),Cn("conv2dDerInput",r,i);let d={dy:l,filter:a},h={strides:n,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},f=L.runKernel(ri,d,h);return u?Q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var iA=z({conv2DBackpropInput_:XC});function KC(e,t,a,n,r,s){let i=E(e,"x","conv2dTranspose"),o=E(t,"filter","conv2dTranspose");return iA(a,i,o,n,r,"NHWC",s)}var oA=z({conv2dTranspose_:KC});function ZC(e,t,a,n,r="NDHWC",s=[1,1,1]){let i=E(e,"x","conv3d"),o=E(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=Q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),F(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),F(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),F(wr(a,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${a} and dilations '${s}'`),F(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`),F(Bs(s),()=>"Error in conv3D: Dilated rates should be larger than 0."),F(Bs(a),()=>"Error in conv3D: Strides should be larger than 0.");let p={x:l,filter:o},c={strides:a,pad:n,dataFormat:r,dilations:s},d=L.runKernel(Jc,p,c);return u?Q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var lA=z({conv3d_:ZC});function YC(e,t,a,n,r){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=Q(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],u=i.shape[4];F(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),F(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),F(a.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${a.rank}`),F(l===a.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${a.shape[3]}.`),F(u===a.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${a.shape[4]}.`);let p={dy:i,filter:a},c={pad:r,strides:n,inputShape:s},d=L.runKernel(Qc,p,c);return o?Q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var JC=z({conv3DBackpropInput_:YC});function QC(e,t,a,n,r){let s=E(e,"x","conv3dTranspose"),i=E(t,"filter","conv3dTranspose");return JC(a,s,i,n,r)}var uA=z({conv3dTranspose_:QC});function eN(e){let t={x:E(e,"x","cos","float32")};return L.runKernel(si,t)}var dA=z({cos_:eN});function tN(e){let t={x:E(e,"x","cosh","float32")};return L.runKernel(ii,t)}var pA=z({cosh_:tN});function aN(e,t=0,a=!1,n=!1){let r={x:E(e,"x","cumprod")},s={axis:t,exclusive:a,reverse:n};return L.runKernel(oi,r,s)}var cA=z({cumprod_:aN});function nN(e,t=0,a=!1,n=!1){let r={x:E(e,"x","cumsum")},s={axis:t,exclusive:a,reverse:n};return L.runKernel(li,r,s)}var hA=z({cumsum_:nN});function rN(e,t,a,n=!1){let r=E(e,"x","denseBincount"),s=E(t,"weights","denseBincount");F(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),F(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),F(a>=0,()=>`size must be non-negative, but got ${a}.`),F(s.size===r.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${s.shape}.`);let i={x:r,weights:s},o={size:a,binaryOutput:n};return L.runKernel(Nd,i,o)}var fA=z({denseBincount_:rN});function sN(e,t,a="NHWC"){let n=E(e,"x","depthToSpace","float32"),r=a==="NHWC"?n.shape[1]:n.shape[2],s=a==="NHWC"?n.shape[2]:n.shape[3],i=a==="NHWC"?n.shape[3]:n.shape[1];F(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),F(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${r} and ${t} for depthToSpace with input shape
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${n.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${s} and ${t} for depthToSpace with input shape
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Actual: ${r}.
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Actual: ${r}.
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r=E(e,"sparseIndices","sparseToDense","int32"),s=E(t,"sparseValues","sparseToDense","string_or_numeric"),i=E(n,"defaultValue","sparseToDense",s.dtype);GR(r,s,a,i);let o={sparseIndices:r,sparseValues:s,defaultValue:i},l={outputShape:a};return L.runKernel(Vd,o,l)}var zb=z({sparseToDense_:HR});function jR(e,t){let a=E(t,"indices","gatherND","int32"),n={params:E(e,"x","gatherND","string_or_numeric"),indices:a};return L.runKernel(bi,n)}var Lb=z({gatherND_:jR});function qR(e,t){if(t==null)return e.shape.slice();if(es(e.shape,t))return t;if(e.shape.length===t.length){let a=[];for(let n=0;n<e.shape.length;n++)t[n]==null&&e.shape[n]!=null?a.push(e.shape[n]):a.push(t[n]);return a}return t}function XR(e,t,a,n){let r=E(e,"x","dropout");if(F(r.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`),F(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof ct?r.clone():r;let 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s=E(e,"labels","logLoss"),i=E(t,"predictions","logLoss"),o=null;a!=null&&(o=E(a,"weights","logLoss")),Ca(s.shape,i.shape,"Error in logLoss: ");let l=ze(1),u=ze(n),p=Xn(te(s,ul(be(i,u)))),c=te(me(l,s),ul(be(me(l,i),u))),d=me(p,c);return Ir(d,o,r)}var p$=z({logLoss_:d$});function c$(e,t,a,n=va.SUM_BY_NONZERO_WEIGHTS){let r=E(e,"labels","meanSquaredError"),s=E(t,"predictions","meanSquaredError"),i=null;a!=null&&(i=E(a,"weights","meanSquaredError")),Ca(r.shape,s.shape,"Error in meanSquaredError: ");let o=O1(r,s);return Ir(o,i,n)}var h$=z({meanSquaredError_:c$});function f$(e,t){let a=E(e,"labels","sigmoidCrossEntropyWithLogits"),n=E(t,"logits","sigmoidCrossEntropyWithLogits");Ca(a.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=op(n),s=te(n,a),i=g1(Zr(Xn(Za(n))));return be(me(r,s),i)}function m$(e,t,a,n=0,r=va.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"multiClassLabels","sigmoidCrossEntropy"),i=E(t,"logits","sigmoidCrossEntropy"),o=null;if(a!=null&&(o=E(a,"weights","sigmoidCrossEntropy")),Ca(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),n>0){let u=ze(n),p=ze(1),c=ze(.5);s=be(te(s,me(p,u)),te(c,u))}let l=f$(s,i);return Ir(l,o,r)}var g$=z({sigmoidCrossEntropy_:m$});function y$(e,t,a=-1){if(a===-1&&(a=t.rank-1),a!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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${r.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(i.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(o.rank!==0)throw new Error(`Default value should be a scalar but received shape ${o.shape}`);let l={indices:r,values:s,denseShape:i,defaultValue:o},u=L.runKernel(Ld,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var v$=z({sparseFillEmptyRows_:b$});function k$(e,t,a){let n=E(e,"inputIndices","sparseReshape","int32"),r=E(t,"inputShape","sparseReshape","int32"),s=E(a,"newShape","sparseReshape","int32");if(n.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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${n.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:n,inputShape:r,newShape:s},o=L.runKernel(eu,i);return{outputIndices:o[0],outputShape:o[1]}}var w$=z({sparseReshape_:k$});function I$(e,t,a){let n=E(e,"data","sparseSegmentMean"),r=E(t,"indices","sparseSegmentMean","int32"),s=E(a,"segmentIds","sparseSegmentMean","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${r.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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${s.shape}`);let i={data:n,indices:r,segmentIds:s};return L.runKernel(Bd,i)}var S$=z({sparseSegmentMean_:I$});function T$(e,t,a){let n=E(e,"data","sparseSegmentSum"),r=E(t,"indices","sparseSegmentSum","int32"),s=E(a,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${r.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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${s.shape}`);let i={data:n,indices:r,segmentIds:s};return L.runKernel(Wd,i)}var C$=z({sparseSegmentSum_:T$});function N$(e,t,a,n,r,s,i,o){let l=E(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=E(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let p={separator:a,nGramWidths:n,leftPad:r,rightPad:s,padWidth:i,preserveShortSequences:o},c={data:l,dataSplits:u},d=L.runKernel(tu,c,p);return{nGrams:d[0],nGramsSplits:d[1]}}var E$=z({stringNGrams_:N$});function R$(e,t,a=!0){let n=E(e,"input","stringSplit","string"),r=E(t,"delimiter","stringSplit","string");if(n.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${n.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let s={skipEmpty:a},i={input:n,delimiter:r},o=L.runKernel(Gd,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var M$=z({stringSplit_:R$});function $$(e,t){let a=E(e,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:a};return L.runKernel(Hd,r,n)}var _$=z({stringToHashBucketFast_:$$}),qb={fft:xh,ifft:fd,rfft:Ah,irfft:F1},Xb={hammingWindow:uM,hannWindow:Vb,frame:Ub,stft:hM},ce={flipLeftRight:yM,grayscaleToRGB:AM,resizeNearestNeighbor:UM,resizeBilinear:WM,rotateWithOffset:vM,cropAndResize:mM,nonMaxSuppression:wM,nonMaxSuppressionAsync:MM,nonMaxSuppressionWithScore:_M,nonMaxSuppressionWithScoreAsync:FM,nonMaxSuppressionPadded:DM,nonMaxSuppressionPaddedAsync:LM,threshold:jM,transform:XM},Kb={bandPart:ZM,gramSchmidt:JM,qr:e$},Zb={absoluteDifference:n$,computeWeightedLoss:Ir,cosineDistance:s$,hingeLoss:o$,huberLoss:u$,logLoss:p$,meanSquaredError:h$,sigmoidCrossEntropy:g$,softmaxCrossEntropy:A$},Yb={sparseFillEmptyRows:v$,sparseReshape:w$,sparseSegmentMean:S$,sparseSegmentSum:C$},Jb={stringNGrams:E$,stringSplit:M$,stringToHashBucketFast:_$},Qb={};Ze(Qb,{Serializable:()=>e4,SerializationMap:()=>Ps,registerClass:()=>t4});var e4=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new 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_A(e,t)}dispose(){this.iterations_!=null&&J(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ze(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(is,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var j1=class extends is{constructor(e,t,a=null){super(),this.learningRate=e,this.rho=t,this.epsilon=a,this.accumulatedGrads=[],this.accumulatedUpdates=[],a==null&&(this.epsilon=L.backend.epsilon())}static get className(){return"Adadelta"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t],r=!1;this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accum_grad`,variable:Oe(()=>Ja(n).variable(r))}),this.accumulatedUpdates[a]==null&&(this.accumulatedUpdates[a]={originalName:`${t}/accum_var`,variable:Oe(()=>Ja(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[a].variable,o=this.accumulatedUpdates[a].variable;Oe(()=>{let l=be(te(i,this.rho),te(Tn(s),1-this.rho)),u=te(xe(Jn(be(o,this.epsilon)),Jn(be(i,this.epsilon))),s),p=be(te(o,this.rho),te(Tn(u),1-this.rho));i.assign(l),o.assign(p);let c=be(te(u,-this.learningRate),n);n.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(J(this.accumulatedGrads.map(e=>e.variable)),J(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,a=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},q1=class extends is{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}static get className(){return"Adagrad"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accumulator`,variable:Oe(()=>nr(n.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[a].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[a].variable;Oe(()=>{let i=be(s,Tn(r));s.assign(i);let o=be(te(xe(r,Jn(be(i,L.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&J(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(a=>({originalName:a.name,variable:a.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},X1=class extends is{constructor(e,t,a,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Oe(()=>{this.accBeta1=ze(t).variable(),this.accBeta2=ze(a).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}static get className(){return"Adam"}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);Oe(()=>{let a=me(1,this.accBeta1),n=me(1,this.accBeta2);t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Oe(()=>Ja(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:Oe(()=>Ja(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,c=be(te(u,this.beta1),te(l,1-this.beta1)),d=be(te(p,this.beta2),te(Tn(l),1-this.beta2)),h=xe(c,a),f=xe(d,n);u.assign(c),p.assign(d);let m=be(te(xe(h,be(Jn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(te(this.accBeta1,this.beta1)),this.accBeta2.assign(te(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&J(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),Oe(()=>{this.accBeta1.assign(ll(this.beta1,this.iterations_+1)),this.accBeta2.assign(ll(this.beta2,this.iterations_+1))});let t=e.length/2,a=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}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)}},K1=class extends is{constructor(e,t,a,n=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Oe(()=>{this.iteration=ze(0).variable(),this.accBeta1=ze(t).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}static get className(){return"Adamax"}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);Oe(()=>{let a=me(1,this.accBeta1),n=xe(-this.learningRate,be(te(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ja(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ja(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,c=be(te(u,this.beta1),te(l,1-this.beta1)),d=te(p,this.beta2),h=Za(l),f=k1(d,h);u.assign(c),p.assign(f);let m=be(te(xe(n,a),xe(c,be(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(te(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&J(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)}},Sh=class extends is{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}static get className(){return"SGD"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=L.registeredVariables[t];Oe(()=>{let s=be(te(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=On(ze(-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 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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(a=>({originalName:a.name,variable:a.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)}},Y1=class extends is{constructor(e,t=.9,a=0,n=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=a,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,n==null&&(this.epsilon=L.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}static get className(){return"RMSProp"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t],r=!1;this.accumulatedMeanSquares[a]==null&&(this.accumulatedMeanSquares[a]={originalName:`${t}/rms`,variable:Oe(()=>Ja(n).variable(r))}),this.accumulatedMoments[a]==null&&(this.accumulatedMoments[a]={originalName:`${t}/momentum`,variable:Oe(()=>Ja(n).variable(r))}),this.accumulatedMeanGrads[a]==null&&this.centered&&(this.accumulatedMeanGrads[a]={originalName:`${t}/mg`,variable:Oe(()=>Ja(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[a].variable,o=this.accumulatedMoments[a].variable;Oe(()=>{let l=be(te(i,this.decay),te(Tn(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[a].variable,p=be(te(u,this.decay),te(s,1-this.decay)),c=xe(te(s,this.learningRate),Jn(me(l,be(Tn(p),this.epsilon)))),d=be(te(o,this.momentum),c);i.assign(l),u.assign(p),o.assign(d);let h=me(n,d);n.assign(h)}else{let u=be(te(i,this.decay),te(Tn(s),1-this.decay)),p=be(te(o,this.momentum),xe(te(s,this.learningRate),Jn(be(u,this.epsilon))));i.assign(u),o.assign(p);let c=me(n,p);n.assign(c)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&J(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&J(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&J(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,a=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}},P$=[j1,q1,X1,K1,Z1,Y1,Sh];function F$(){for(let e of P$)t4(e)}var 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indices.shape[0] = ${e}`}function nP(e,t){return`indices(${e}, 0) is invalid: ${t} < 0`}function rP(e,t,a){return`indices(${e}, 0) is invalid: ${t} >= ${a}`}function sP(e,t){return`only one output dimension may be -1, not both ${e} and ${t}`}function iP(e,t){return`size ${e} must be non-negative, not ${t}`}function oP(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function lP(e,t){let a=xt(e),n=xt(t);return`Input to reshape is a SparseTensor with ${a}
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dense values, but the requested shape requires a multiple of ${n}. inputShape=${e} outputShape= ${t}`}function uP(e,t){let a=xt(e),n=xt(t);return`Input to reshape is a tensor with ${a} dense values, but the requested shape has ${n}. inputShape=${e} outputShape=${t}`}function dP(){return"segment ids must be >= 0"}function pP(){return"segment ids are not increasing"}function cP(e,t){return`Segment id ${e} out of range [0, ${t}), possibly because segmentIds input is not sorted.`}function hP(e,t,a){return`Bad: indices[${e}] == ${t} out of range [0, ${a})`}var b4={};Ze(b4,{collectGatherOpShapeInfo:()=>gP,computeOutShape:()=>mP,segOpComputeOptimalWindowSize:()=>fP});function fP(e,t){let a=!1,n;for(e<=a3?(n=e,a=!0):n=Cc(e,Math.floor(Math.sqrt(e)));!a;)n>t||n===e?a=!0:n=Cc(e,n+1);return n}function mP(e,t,a){let n=[],r=e.length;for(let s=0;s<r;s++)s!==t?n.push(e[s]):n.push(a);return n}function gP(e,t,a,n){let r=t.shape.length,s=e.shape.length;if(n!==0&&(n<-r||n>r))throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${n}`);if(n<0&&(n+=r),n>s)throw new Error(`batchDims (${n}) must be less than rank(x) (
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jP=class{constructor(e,t,a,n,r,s,i){this.name=e,this.dtype=t,this.maxSize=a,this.elementShape=n,this.identicalElementShapes=r,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ze(0),On(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let a=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
|
|
because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),Sn(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),a.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(a.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);a.tensor=t,On(t),a.written=!0,this.tensors[e]=a}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((a,n)=>this.write(a,t[n]))}gather(e,t){if(t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let n=0;n<this.size();n++)e.push(n)}if(e.length===0)return Ue([],[0].concat(this.elementShape));let a=this.readMany(e);return Sn(this.elementShape,a[0].shape,"TensorArray shape mismatch: "),ua(a,0)}concat(e){if(e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return Ue([],[0].concat(this.elementShape));let t=[];for(let n=0;n<this.size();n++)t.push(n);let a=this.readMany(t);return Sn(this.elementShape,a[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${a[0].shape})`),st(a,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let a=Math.max(...e);if(!this.dynamicSize&&a>=this.maxSize)throw new Error(`Max index must be < array size (${a} vs. ${this.maxSize})`);this.writeMany(e,Na(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let a=0,n=e.map(o=>(a+=o,a));if(a!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${a}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=a===0?0:t.size/a,s=[];Oe(()=>{t=Q(t,[1,a,r]);for(let o=0;o<e.length;++o){let l=[0,o===0?0:n[o-1],0],u=[1,e[o],r];s[o]=Q(Fe(t,l,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},hl=class{constructor(e,t,a,n=-1){this.tensors=e,this.elementShape=t,this.elementDtype=a,e!=null&&e.forEach(r=>{if(a!==r.dtype)throw new Error(`Invalid data types; op elements ${a}, but list elements ${r.dtype}`);Sn(t,r.shape,"TensorList shape mismatch: "),On(r)}),this.idTensor=ze(0),this.maxNumElements=n,On(this.idTensor)}get id(){return this.idTensor.id}copy(){return new hl([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,a=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(a!==-1&&this.tensors.length!==a)throw new Error(`Operation expected a list with ${a} elements but got a list with ${this.tensors.length} elements.`);Sn(e,this.elementShape,"TensorList shape mismatch: ");let n=Wu(this.elementShape,this.tensors,e);return Oe(()=>{let r=this.tensors.map(s=>Q(s,n));return ua(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let a=Wu(this.elementShape,this.tensors,e),n=this.tensors.pop();return n.kept=!1,Sn(n.shape,e,"TensorList shape mismatch: "),Q(n,a)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Sn(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");On(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. 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|
|
tensor.shape[0], but sum of lengths is
|
|
${n}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=h2(s,a),o=n===0?0:e.size/n,l=Oe(()=>{let p=[];e=Q(e,[1,n,o]);for(let c=0;c<t.length;++c){let d=[0,c===0?0:r[c-1],0],h=[1,t[c],o];p[c]=Q(Fe(e,d,h),i)}return e.dispose(),p}),u=new hl([],a,e.dtype,t.length);for(let p=0;p<l.length;p++)u.setItem(p,l[p]);return u}var YP=async(e,t,a)=>{switch(e.op){case"If":case"StatelessIf":{let n=w("thenBranch",e,t,a),r=w("elseBranch",e,t,a),s=w("cond",e,t,a),i=w("args",e,t,a);return(await s.data())[0]?a.functionMap[n].executeFunctionAsync(i,a.tensorArrayMap,a.tensorListMap):a.functionMap[r].executeFunctionAsync(i,a.tensorArrayMap,a.tensorListMap)}case"While":case"StatelessWhile":{let n=w("body",e,t,a),r=w("cond",e,t,a),s=w("args",e,t,a),i=await a.functionMap[r].executeFunctionAsync(s,a.tensorArrayMap,a.tensorListMap),o=s.map(p=>p.id),l=await i[0].data();i.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&p.dispose()});let u=s;for(;l[0];){let p=u;u=await a.functionMap[n].executeFunctionAsync(u,a.tensorArrayMap,a.tensorListMap);let c=u.map(h=>h.id);p.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&c.indexOf(h.id)===-1&&h.dispose()});let d=await a.functionMap[r].executeFunctionAsync(u,a.tensorArrayMap,a.tensorListMap);l=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&c.indexOf(h.id)===-1&&h.dispose()})}return u}case"LoopCond":{let n=w("pred",e,t,a);return[yr(n)]}case"Switch":{let n=w("pred",e,t,a),r=w("data",e,t,a);return r.kept||(r=yr(r)),(await n.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let n=e.inputNames.find(r=>ka(r,t,a)!==void 0);if(n){let r=ka(n,t,a);return[yr(r)]}return}case"Enter":{let n=w("frameName",e,t,a),r=w("tensor",e,t,a);return a.enterFrame(n),[yr(r)]}case"Exit":{let n=w("tensor",e,t,a);return a.exitFrame(),[yr(n)]}case"NextIteration":{let n=w("tensor",e,t,a);return a.nextIteration(),[yr(n)]}case"TensorArrayV3":{let n=w("size",e,t,a),r=w("dtype",e,t,a),s=w("elementShape",e,t,a),i=w("dynamicSize",e,t,a),o=w("clearAfterRead",e,t,a),l=w("identicalElementShapes",e,t,a),u=w("name",e,t,a),p=new jP(u,r,n,s,l,i,o);return a.addTensorArray(p),[p.idTensor,ze(1)]}case"TensorArrayWriteV3":{let n=w("tensorArrayId",e,t,a),r=w("index",e,t,a),s=w("tensor",e,t,a),i=a.getTensorArray(n.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let n=w("tensorArrayId",e,t,a),r=w("index",e,t,a);return[a.getTensorArray(n.id).read(r)]}case"TensorArrayGatherV3":{let n=w("tensorArrayId",e,t,a),r=w("indices",e,t,a),s=w("dtype",e,t,a);return[a.getTensorArray(n.id).gather(r,s)]}case"TensorArrayScatterV3":{let n=w("tensorArrayId",e,t,a),r=w("indices",e,t,a),s=w("tensor",e,t,a),i=a.getTensorArray(n.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let n=w("tensorArrayId",e,t,a),r=a.getTensorArray(n.id),s=w("dtype",e,t,a);return[r.concat(s)]}case"TensorArraySplitV3":{let 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n=w("elementShape",e,t,a),r=w("elementDType",e,t,a),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=w(s,e,t,a),o=e.op==="TensorListReserve"?-1:i,l=XP(n,r,i,o);return a.addTensorList(l),[l.idTensor]}case"TensorListGather":{let n=w("tensorListId",e,t,a),r=w("indices",e,t,a),s=w("elementShape",e,t,a),i=w("elementDType",e,t,a);return[a.getTensorList(n.id).gather(r,i,s)]}case"TensorListStack":{let n=w("tensorListId",e,t,a),r=w("elementShape",e,t,a),s=w("elementDType",e,t,a),i=w("numElements",e,t,a);return[a.getTensorList(n.id).stack(r,s,i)]}case"TensorListFromTensor":{let n=w("tensor",e,t,a),r=w("elementShape",e,t,a),s=w("elementDType",e,t,a),i=qP(n,r,s);return a.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=w("tensorListId",e,t,a),r=a.getTensorList(n.id),s=w("dtype",e,t,a),i=w("elementShape",e,t,a);return[r.concat(s,i)]}case"TensorListPushBack":{let n=w("tensorListId",e,t,a),r=w("tensor",e,t,a),s=a.getTensorList(n.id);return 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implemented`)}},uF=(e,t,a,n=Zt)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=n.raggedGather(w("paramsNestedSplits",e,t,a),w("paramsDenseValues",e,t,a),w("indices",e,t,a),w("outputRaggedRank",e,t,a));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=n.raggedRange(w("starts",e,t,a),w("limits",e,t,a),w("splits",e,t,a));return[r,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(w("shape",e,t,a),w("values",e,t,a),w("defaultValue",e,t,a),w("rowPartitionTensors",e,t,a),w("rowPartitionTypes",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},dF=(e,t,a,n=Zt)=>{switch(e.op){case"Max":{let o=w("axis",e,t,a),l=w("keepDims",e,t,a);return[n.max(w("x",e,t,a),o,l)]}case"Mean":{let o=w("axis",e,t,a),l=w("keepDims",e,t,a);return[n.mean(w("x",e,t,a),o,l)]}case"Min":{let o=w("axis",e,t,a),l=w("keepDims",e,t,a);return[n.min(w("x",e,t,a),o,l)]}case"Sum":{let o=w("axis",e,t,a),l=w("keepDims",e,t,a);return[n.sum(w("x",e,t,a),o,l)]}case"All":{let o=w("axis",e,t,a),l=w("keepDims",e,t,a);return[n.all(w("x",e,t,a),o,l)]}case"Any":{let o=w("axis",e,t,a),l=w("keepDims",e,t,a);return[n.any(w("x",e,t,a),o,l)]}case"ArgMax":{let o=w("axis",e,t,a);return[n.argMax(w("x",e,t,a),o)]}case"ArgMin":{let o=w("axis",e,t,a);return[n.argMin(w("x",e,t,a),o)]}case"Prod":{let o=w("axis",e,t,a),l=w("keepDims",e,t,a);return[n.prod(w("x",e,t,a),o,l)]}case"Cumprod":{let o=w("axis",e,t,a),l=w("exclusive",e,t,a),u=w("reverse",e,t,a);return[n.cumprod(w("x",e,t,a),o,l,u)]}case"Cumsum":{let o=w("axis",e,t,a),l=w("exclusive",e,t,a),u=w("reverse",e,t,a);return[n.cumsum(w("x",e,t,a),o,l,u)]}case"Bincount":let r=w("x",e,t,a),s=w("weights",e,t,a),i=w("size",e,t,a);return[n.bincount(r,s,i)];case"DenseBincount":{let o=w("x",e,t,a),l=w("weights",e,t,a),u=w("size",e,t,a),p=w("binaryOutput",e,t,a);return[n.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},pF=(e,t,a,n=Zt)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=w("n",e,t,a),s=w("axis",e,t,a),i=w("tensors",e,t,a);return i=i.slice(0,r),[n.concat(i,s)]}case"Gather":{let r=w("x",e,t,a),s=w("indices",e,t,a);return[n.gather(r,n.cast(s,"int32"),0)]}case"GatherV2":{let r=w("axis",e,t,a),s=w("batchDims",e,t,a),i=w("x",e,t,a),o=w("indices",e,t,a);return[n.gather(i,n.cast(o,"int32"),r,s)]}case"Reverse":{let r=w("dims",e,t,a),s=[];for(let o=0;o<r.length;o++)r[o]&&s.push(o);let i=w("x",e,t,a);return[n.reverse(i,s)]}case"ReverseV2":{let r=w("axis",e,t,a),s=w("x",e,t,a);return[n.reverse(s,r)]}case"Slice":{let r=w("begin",e,t,a),s=w("size",e,t,a);return[n.slice(w("x",e,t,a),r,s)]}case"StridedSlice":{let r=w("begin",e,t,a),s=w("end",e,t,a),i=w("strides",e,t,a),o=w("beginMask",e,t,a),l=w("endMask",e,t,a),u=w("ellipsisMask",e,t,a),p=w("newAxisMask",e,t,a),c=w("shrinkAxisMask",e,t,a),d=w("x",e,t,a);return[n.stridedSlice(d,r,s,i,o,l,u,p,c)]}case"Pack":return Oe(()=>{let r=w("axis",e,t,a),s=w("tensors",e,t,a),i=s[0].shape,o=n.squeeze(s[0]).shape,l=s.map(u=>{let p=v.arraysEqual(u.shape,i);if(!p&&!v.arraysEqual(n.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:n.reshape(u,i)});return[n.stack(l,r)]});case"Unpack":{let r=w("axis",e,t,a),s=w("tensor",e,t,a);return n.unstack(s,r)}case"Tile":{let r=w("reps",e,t,a);return[n.tile(w("x",e,t,a),r)]}case"Split":case"SplitV":{let r=w("axis",e,t,a),s=w("numOrSizeSplits",e,t,a),i=w("x",e,t,a);return n.split(i,s,r)}case"ScatterNd":{let r=w("indices",e,t,a),s=w("values",e,t,a),i=w("shape",e,t,a);return[n.scatterND(r,s,i)]}case"GatherNd":{let r=w("x",e,t,a),s=w("indices",e,t,a);return[n.gatherND(r,s)]}case"SparseToDense":{let r=w("sparseIndices",e,t,a),s=w("outputShape",e,t,a),i=w("sparseValues",e,t,a),o=w("defaultValue",e,t,a);return[n.sparseToDense(r,i,s,i.dtype===o.dtype?o:n.cast(o,i.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},cF=(e,t,a,n=Zt)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=n.sparse.sparseFillEmptyRows(w("indices",e,t,a),w("values",e,t,a),w("denseShape",e,t,a),w("defaultValue",e,t,a));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=n.sparse.sparseReshape(w("inputIndices",e,t,a),w("inputShape",e,t,a),w("newShape",e,t,a));return[r,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(w("data",e,t,a),w("indices",e,t,a),w("segmentIds",e,t,a))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(w("data",e,t,a),w("indices",e,t,a),w("segmentIds",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},hF=(e,t,a,n=Zt)=>{switch(e.op){case"FFT":return[n.fft(w("x",e,t,a))];case"IFFT":return[n.ifft(w("x",e,t,a))];case"RFFT":return[n.rfft(w("x",e,t,a))];case"IRFFT":return[n.irfft(w("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},fF=(e,t,a,n=Zt)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=n.string.stringNGrams(w("data",e,t,a),w("dataSplits",e,t,a),w("separator",e,t,a),w("nGramWidths",e,t,a),w("leftPad",e,t,a),w("rightPad",e,t,a),w("padWidth",e,t,a),w("preserveShortSequences",e,t,a));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=n.string.stringSplit(w("input",e,t,a),w("delimiter",e,t,a),w("skipEmpty",e,t,a));return[r,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(w("input",e,t,a),w("numBuckets",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mF=(e,t,a,n=Zt)=>{switch(e.op){case"Cast":return[n.cast(w("x",e,t,a),w("dtype",e,t,a))];case"ExpandDims":{let r=w("axis",e,t,a);return[n.expandDims(w("x",e,t,a),r)]}case"Squeeze":{let r=w("axis",e,t,a);return[n.squeeze(w("x",e,t,a),r)]}case"Reshape":return[n.reshape(w("x",e,t,a),w("shape",e,t,a))];case"MirrorPad":return[n.mirrorPad(w("x",e,t,a),w("padding",e,t,a),w("mode",e,t,a))];case"PadV2":case"Pad":return[n.pad(w("x",e,t,a),w("padding",e,t,a),w("constantValue",e,t,a))];case"SpaceToBatchND":{let r=w("blockShape",e,t,a),s=w("paddings",e,t,a);return[n.spaceToBatchND(w("x",e,t,a),r,s)]}case"BatchToSpaceND":{let r=w("blockShape",e,t,a),s=w("crops",e,t,a);return[n.batchToSpaceND(w("x",e,t,a),r,s)]}case"DepthToSpace":{let r=w("blockSize",e,t,a),s=w("dataFormat",e,t,a).toUpperCase();return[n.depthToSpace(w("x",e,t,a),r,s)]}case"BroadcastTo":return[n.broadcastTo(w("x",e,t,a),w("shape",e,t,a))];case"BroadcastArgs":return[n.broadcastArgs(w("s0",e,t,a),w("s1",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ey(e,t,a,n,r=Oe){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>GP(i,o,l));case"basic_math":return r(()=>HP(i,o,l));case"control":return YP(i,o,l);case"convolution":return r(()=>JP(i,o,l));case"creation":return r(()=>QP(i,o,l));case"dynamic":return eF(i,o,l);case"evaluation":return r(()=>tF(i,o,l));case"image":return r(()=>sF(i,o,l));case"graph":return r(()=>aF(i,o,l));case"logical":return r(()=>iF(i,o,l));case"matrices":return r(()=>oF(i,o,l));case"normalization":return r(()=>lF(i,o,l));case"ragged":return r(()=>uF(i,o,l));case"reduction":return r(()=>dF(i,o,l));case"slice_join":return r(()=>pF(i,o,l));case"sparse":return r(()=>cF(i,o,l));case"spectral":return r(()=>hF(i,o,l));case"string":return r(()=>fF(i,o,l));case"transformation":return r(()=>mF(i,o,l));case"hash_table":return rF(i,o,l,n);case"custom":let u=v4(i.op);if(u&&u.customExecutor)return u.customExecutor(new UP(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,a);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var ty=class{constructor(e={},t={},a={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=a,this.functionMap=n,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;t<this.contexts.length-1;t++){let a=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(a))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.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 ay(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>Ka(d)[0]),p=[];n!=null&&(p=n.map(d=>Ka(d.name)[0]));let c=[...t];for(;c.length>0;){let d=c.pop();if((U4(d)||bF(d)||vF(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&u.indexOf(d.name)===-1&&p.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function gF(e,t,a){let{usedNodes:n,inputs:r}=a,s=[],i=Object.keys(r).map(p=>Ka(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{n.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{n.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{n.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(c=>{!l.has(c.name)&&n.has(c.name)&&c.inputs.every(d=>l.has(d.name))&&s.push(c)})}return u}var yF=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],xF=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],AF=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function U4(e){return yF.indexOf(e.op)>=0}function bF(e){return xF.indexOf(e.op)>=0}function vF(e){return AF.indexOf(e.op)>=0}var f2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",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(a=>{this._functionExecutorMap[a]=new f2(e.functions[a],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(a=>e[a].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),{})}getCompilationKey(e,t){let a=e.map(r=>r.name).sort(),n=t.map(r=>r.name).sort();return a.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let a=ay(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:r,syncInputs:s}=a;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${n}]`)}return gF(this.graph,this.weightMap,a)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return On(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,a])=>[t,this.cloneTensorList(a)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let a=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=a.map(p=>this.graph.nodes[Ka(p)[0]]),r=t.map(p=>Ka(p)[0]),s=r.map(p=>this.graph.nodes[p]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(n,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));try{this.keepIntermediateTensors=V().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},u={};return Oe(()=>{let p=new ty(this.weightMap,l,u,this.functionExecutorMap),c=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(f=>{let[m,g]=Ka(f),y=[];y[g]=e[f],c[m]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(y))});let d=this.getFrozenTensorIds(c),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!c[m.name]){let g=ey(m,c,p,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);c[m.name]=g,this.keepIntermediateTensors&&(this.clonedTensorsMap[m.name]=this.cloneTensorList(g)),this.checkTensorForDisposal(m.name,m,c,p,d,r,h)}}return this.parent==null&&p.dispose(d),t.map(f=>ka(f,c,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(a=>e[a]).map(a=>a.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,a,n,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(a[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=kP(o.name,a,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}})}}))}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,a=!1,n={},r={}){this.disposeIntermediateTensors(),a||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=V().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let s=new ty(this.weightMap,n,r,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,a),o=t.map(c=>ka(c,i,s)),l=o.map(c=>c.id),u=Object.keys(e).map(c=>e[c].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(c=>{c.forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,a){let n=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(n,this.outputNodes,!0,t,a)}async executeWithControlFlow(e,t,a,n){let r=Object.keys(e),s=r.map(x=>this.graph.nodes[Ka(x)[0]]),i=a.map(x=>Ka(x)[0]),o=i.map(x=>this.graph.nodes[x]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:c}=ay(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(x=>{let[A,b]=Ka(x),k=[];k[b]=e[x],h[A]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let x=this.processStack(s,d,t,h,g,m,i,f,l);await Promise.all(x)}p==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 y=o.filter(x=>!U4(x)&&!ka(x.name,h,t)).map(x=>x.name);if(y.length>0){let x="";throw p!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${c}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${x}`)}return h}processStack(e,t,a,n,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();a.currentContext=p.contexts;let c="";if(p.node.op==="Enter"&&w("isConstant",p.node,n,a)&&([c]=gr(p.node.name,a)),n[p.node.name]==null){let d=ey(p.node,n,a,this._resourceManager);c||([c]=gr(p.node.name,a));let h=a.currentContext;v.isPromise(d)?u.push(d.then(f=>(n[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=h,this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l),f))):(n[c]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(d)),this.checkTensorForDisposal(c,p.node,n,a,s,i,o),this.processChildNodes(p.node,t,a,n,r,l))}else this.processChildNodes(p.node,t,a,n,r,l)}return u}processChildNodes(e,t,a,n,r,s){e.children.forEach(i=>{let[o]=gr(i.name,a);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!ka(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})):i.inputNames.every(l=>!!ka(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let a=e[t],[n]=Ka(t),r=this.graph.nodes[n];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===a.shape.length&&a.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${a.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(a.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(e){var t,a;let n={};for(let r in e){let s=(a=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||a===void 0?void 0:a[r];s!=null?n[s.name]=e[r]:n[r]=e[r]}return n}checkInputs(e){let t=Object.keys(e).filter(a=>{let[n]=Ka(a);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var a,n;let r=(n=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||n===void 0?void 0:n[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[a]=Ka(t);if(!this.graph.nodes[a])throw new Error(`The output '${t}' is not found in the graph`)})}},kF=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},wF="?tfjs-format=file",IF="model.json",up=class{constructor(e,t={},a=jn){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=a,t==null&&(this.loadOptions={}),this.resourceManager=new kF}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=a,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new f2(Z5.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Z5.Instance.transformGraph(e.modelInitializer);this.initializer=new f2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof ct?[e]:e,a={};return t.forEach((n,r)=>a[this.structuredOutputKeys[r]]=n),a}return e}predict(e,t){let a=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(a)}async predictAsync(e,t){let a=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(a)}normalizeInputs(e){var t;if(!(e instanceof ct)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let a=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+a!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-a} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[n++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,a=Object.keys(t);for(let n=0;n<a.length;n++){let r=a[n],s=t[r];this.resourceIdToCapturedInput[s.resourceId]=e[n]}}}execute(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let a=this.executor.execute(e,t);return a.length>1?a:a[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let a=await this.executor.executeAsync(e,t);return a.length>1?a:a[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,a)=>(t[a]=[e[a]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&J(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function s3(e,t={},a=jn){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=TF(e));let n=new up(e,t,a);return await n.load(),n}function SF(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[n,r]=e;if(!n)throw new Error("modelJSON must be the first element of the array");if(!r||!(r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in n))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in n))throw new Error("Model JSON is missing 'weightsManifest'");let s=jn.getWeightSpecs(n.weightsManifest),i=jn.getModelArtifactsForJSONSync(n,s,r);t=jn.fromMemorySync(i)}else if("load"in e)t=e;else if("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e)t=jn.fromMemorySync(e);else throw new Error("Unknown model format");let a=new up(t);return a.load(),a}function TF(e){return e.endsWith("/")||(e=e+"/"),`${e}${IF}${wF}`}var CF="4.2.0";function Ae(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var NF=Nn.whereImpl,Th=class extends Al{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new kd(this,vt())}nextDataId(){return Th.nextDataId++}write(e,t,a){this.firstUse&&(this.firstUse=!1,V().get("IS_NODE")&&T.warn(`
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============================
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Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details.
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============================`));let n={id:this.nextDataId()};return this.data.set(n,{values:e,dtype:a,refCount:1}),n}makeTensorInfo(e,t,a){let n;if(t==="string"&&a!=null&&a.length>0&&v.isString(a[0])){let r=a.map(s=>v.encodeString(s));n=this.write(r,e,t)}else n=this.write(a,e,t);return{dataId:n,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,a,n,r){this.data.set(e,{values:t,dtype:n,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:a}=this.data.get(e);if(t==="complex64"){let n=this.readSync(a.real.dataId),r=this.readSync(a.imag.dataId);return T.mergeRealAndImagArrays(n,r)}return v.convertBackendValuesAndArrayBuffer(this.data.get(e).values,t)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return _e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,t)}makeOutput(e,t,a){return vt().makeTensorFromTensorInfo(this.makeTensorInfo(t,a,e),this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:a}=this.data.get(e);a!=null&&(this.disposeData(a.real.dataId,!0),this.disposeData(a.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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o=T.assertAndGetBroadcastShape(t,a),l=v.sizeFromShape(o),u=o.length,p=v.computeStrides(o),c=v.getTypedArrayFromDType("float32",l),d=v.getTypedArrayFromDType("float32",l),h=T.getBroadcastDims(t,o),f=T.getBroadcastDims(a,o),m=T.mergeRealAndImagArrays(n,r),g=T.mergeRealAndImagArrays(s,i),y=t.length,x=v.computeStrides(t),A=a.length,b=v.computeStrides(a);if(h.length+f.length===0)for(let k=0;k<c.length;k++){let S=k%m.length,C=k%g.length,N=e(m[S*2],m[S*2+1],g[C*2],g[C*2+1]);c[k]=N.real,d[k]=N.imag}else for(let k=0;k<c.length;k++){let S=v.indexToLoc(k,u,p),C=S.slice(-y);h.forEach(I=>C[I]=0);let N=v.locToIndex(C,y,x),$=S.slice(-A);f.forEach(I=>$[I]=0);let M=v.locToIndex($,A,b),R=e(m[N*2],m[N*2+1],g[M*2],g[M*2+1]);c[k]=R.real,d[k]=R.imag}return[c,d,o]}}var j4=Lt((e,t)=>e+t),FF=i3((e,t,a,n)=>({real:e+a,imag:t+n})),fl=Yt(ts,j4,FF),OF={kernelName:ts,backendName:"cpu",kernelFunc:fl};function o3(e,t,a,n,r){let s=v.sizeFromShape(n),i=v.makeZerosTypedArray(r,a);for(let o=0;o<e.length;o++){let 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if(b=Math.ceil(Math.abs((x-y)/A)),b>ry)throw new Error(`Requires ((limit - start) / delta) <= ${ry}`);d[g+1]=d[g]+b}let h=d[c],f=v.getArrayFromDType(a,h),m=0;for(let g=0;g<c;++g){let y=d[g+1]-d[g],x=o?e[0]:e[g],A=u?s[0]:s[g];for(let b=0;b<y;++b)f[m++]=x,x+=A}return[d,f]}var wn=T.RowPartitionType,m2=class{constructor(e,t,a,n,r,s,i,o,l,u){this.shape=e,this.shapeShape=t,this.values=a,this.valuesShape=n,this.valuesDType=r,this.defaultValue=s,this.defaultValueShape=i,this.rowPartitionValues=o,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=T.getRowPartitionTypesHelper(u),this.raggedRank=T.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===wn.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===wn.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){let t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case wn.VALUE_ROWIDS:return m2.getMaxWidthValueRowID(t);case wn.ROW_SPLITS:return m2.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${wn[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){let t=e.length;if(t===0||t===1)return 0;let a=0;for(let n=0;n<t-1;++n){let r=e[n+1]-e[n];r>a&&(a=r)}return a}static getMaxWidthValueRowID(e){let t=e.length;if(t===0)return 0;let a=0,n=e[0],r=0;for(let s=1;s<t;++s){let i=e[s];i!==n&&(n=i,r=Math.max(s-a,r),a=s)}return Math.max(t-a,r)}tensorShapeFromTensor(e,t,a=!0){if(t.length===0){if(e[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return iy(e,a)}calculateOutputSize(e){let t=this.valuesShape,a=this.defaultValueShape;T.validateDefaultValueShape(a,t);let n=this.tensorShapeFromTensor(this.shape,this.shapeShape),r=T.combineRaggedTensorToTensorShapes(this.raggedRank,n,t);r[0]<0&&(r[0]=e);for(let s=1;s<=this.raggedRank;++s)r[s]<0&&(r[s]=this.getMaxWidth(s));return r}calculateFirstParentOutputIndex(e,t,a){let n=Math.min(e,a),r=[],s=0;for(let i=0;i<n;++i,s+=t)r.push(s);for(let i=n;i<e;++i)r.push(-1);return v.assert(r.length===e,()=>"Final length of result must be equal to firstDimension."),r}calculateOutputIndexRowSplit(e,t,a,n){let r=e.length,s=[];for(let i=0;i<r-1;++i){let o=e[i+1]-e[i],l=Math.min(n,o),u=t[i];u===-1&&(l=0);for(let p=0;p<l;++p)s.push(u),u+=a;for(let p=0;p<o-l;++p)s.push(-1)}if(r>0&&s.length!==e[r-1])throw new Error("Invalid row split size.");return s}calculateOutputIndexValueRowID(e,t,a,n){let r=e.length,s=[];if(r===0)return[];let i=0,o=e[0];if(o>=t.length)throw new Error(`Got currentValueRowId=${o}, which is not less than ${t.length}`);let l=t[o];s.push(l);for(let u=1;u<r;++u){let p=e[u];if(p===o)l>=0&&(++i,i<n?l+=a:l=-1);else{if(i=0,o=p,p>=t.length)throw new Error(`Got nextValueRowId=${p} which is not less than ${t.length}`);l=t[p]}s.push(l)}if(s.length!==e.length)throw new Error("Invalid row ids.");return s}calculateOutputIndex(e,t,a,n){let r=this.getRowPartitionTensor(e),s=this.getRowPartitionTypeByDimension(e);switch(s){case wn.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(r,t,a,n);case wn.ROW_SPLITS:if(r.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${r.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(r,t,a,n);default:throw new Error(`Unsupported partition type: ${wn[s]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let t=this.rowPartitionTypes[0];switch(t){case wn.FIRST_DIM_SIZE:return e[0];case wn.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case wn.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${wn[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");let e=this.getFirstDimensionSize(),t=this.calculateOutputSize(e),a=new Array(this.raggedRank+1);a[a.length-1]=1;for(let s=a.length-2;s>=0;--s)a[s]=a[s+1]*t[s+1];let n=iy(t,!1),r=v.getArrayFromDType(this.valuesDType,v.sizeFromShape(n));if(a[0]*t[0]>0){let s=this.calculateFirstParentOutputIndex(e,a[0],t[0]);for(let i=1;i<=this.raggedRank;++i)s=this.calculateOutputIndex(i-1,s,a[i],t[i]);this.setOutput(this.raggedRank,s,r,n)}return[n,r]}setOutput(e,t,a,n){if(a.length===0)return;let r=this.values,s=a,i=n.slice();i=i.slice(e+1);let o=v.sizeFromShape(i),l=t.length,u=this.defaultValue;if(u.length!==o&&u.length!==1){let h=this.defaultValueShape;Oe(()=>{let f=Q(u,h);u=rl(f,i).dataSync()})}let p=0,c=0,d=0;for(let h=0;h<=l;++h){let f=h<l?t[h]:-1;if(f===d){++d;continue}if(c<d){let m=r.subarray(p*o),g=s.subarray(c*o),y=(d-c)*o;sy(g,m,y)}if(h>=l){let 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a.makeTensorInfo(r.shape,r.dtype,m)}var TD={kernelName:Ai,backendName:"cpu",kernelFunc:SD};function CD(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;Ae([r],"batchToSpaceND");let o=s.reduce((y,x)=>y*x),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=mt({inputs:{x:r},backend:a,attrs:{shape:l}}),f=Va({inputs:{x:h},backend:a,attrs:{perm:u}}),m=mt({inputs:{x:f},backend:a,attrs:{shape:p}}),g=Gs({inputs:{x:m},backend:a,attrs:{begin:c,size:d}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(m),g}var ND={kernelName:El,backendName:"cpu",kernelFunc:CD};function ED(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=o3(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var 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ml(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.data.get(n.dataId).complexTensorInfos.imag,s=a.data.get(r.dataId).values;return a.makeTensorInfo(r.shape,r.dtype,s)}var DD={kernelName:Pd,backendName:"cpu",kernelFunc:ml};function gl(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(m=>m.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(m=>v.sizeFromShape(m.shape)>0);if(l.length===1)return er({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let m=l.map(b=>Us({inputs:{input:b},backend:a})),g=l.map(b=>ml({inputs:{input:b},backend:a})),y=gl({inputs:m,backend:a,attrs:{axis:s}}),x=gl({inputs:g,backend:a,attrs:{axis:s}}),A=Ya({inputs:{real:y,imag:x},backend:a});return 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BD(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n;Ae([r,s],"conv2dBackpropFilter");let c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,y=d.dataFormat==="channelsLast",x=new jt(d.filterShape,"float32"),A=d.padInfo.left,b=d.padInfo.top,k=a.data.get(r.dataId).values,S=a.data.get(s.dataId).values,C=new jt(r.shape,r.dtype,k),N=new jt(s.shape,s.dtype,S);for(let $=0;$<m;++$){let M=Math.max(0,Math.ceil((b-$)/h)),R=Math.min(d.outHeight,(d.inHeight+b-$)/h);for(let I=0;I<g;++I){let _=Math.max(0,Math.ceil((A-I)/f)),D=Math.min(d.outWidth,(d.inWidth+A-I)/f);for(let W=0;W<d.inChannels;++W)for(let P=0;P<d.outChannels;++P){let U=0;for(let G=0;G<d.batchSize;++G)for(let q=M;q<R;++q){let H=$+q*h-b;for(let B=_;B<D;++B){let Z=I+B*f-A;y?U+=C.get(G,H,Z,W)*N.get(G,q,B,P):U+=C.get(G,W,H,Z)*N.get(G,P,q,B)}}x.set(U,$,I,W,P)}}}return a.makeTensorInfo(x.shape,x.dtype,x.values)}var WD={kernelName:Cd,backendName:"cpu",kernelFunc:BD};function VD(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n;Ae([r,s],"conv2dBackpropInput");let c=v.computeStrides(s.shape),d=v.computeStrides(r.shape),h=T.convertConv2DDataFormat(u),f=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,h),m=new jt(f.inShape,"float32"),g=m.values,y=a.data.get(r.dataId).values,x=a.data.get(s.dataId).values,[A,b,k]=c,{batchSize:S,filterHeight:C,filterWidth:N,inChannels:$,inHeight:M,inWidth:R,outChannels:I,outHeight:_,outWidth:D,strideHeight:W,strideWidth:P}=f;h=f.dataFormat;let U=C-1-f.padInfo.top,G=N-1-f.padInfo.left,q=h==="channelsLast",H=m.strides[0],B=q?m.strides[1]:m.strides[2],Z=q?m.strides[2]:1,X=q?1:m.strides[1],re=d[0],ee=q?d[1]:d[2],he=q?d[2]:1,ie=q?1:d[1];for(let ye=0;ye<S;++ye)for(let Se=0;Se<$;++Se)for(let Ne=0;Ne<M;++Ne){let 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u=v.computeStrides(r.shape),p=v.computeStrides(s.shape),c=T.computeConv3DInfo(r.shape,l,i,1,o),d=c.strideDepth,h=c.strideHeight,f=c.strideWidth,m=c.filterDepth,g=c.filterHeight,y=c.filterWidth,x=new jt(c.filterShape,"float32"),A=x.values,[b,k,S,C]=x.strides,N=a.data.get(s.dataId).values,[$,M,R,I]=p,_=a.data.get(r.dataId).values,[D,W,P,U]=u,G=c.padInfo.front,q=c.padInfo.left,H=c.padInfo.top;for(let B=0;B<m;++B){let Z=Math.max(0,Math.ceil((G-B)/d)),X=Math.min(c.outDepth,(c.inDepth+G-B)/d),re=B*b;for(let ee=0;ee<g;++ee){let he=Math.max(0,Math.ceil((H-ee)/h)),ie=Math.min(c.outHeight,(c.inHeight+H-ee)/h),ye=ee*k+re;for(let Se=0;Se<y;++Se){let Ne=Math.max(0,Math.ceil((q-Se)/f)),Be=Math.min(c.outWidth,(c.inWidth+q-Se)/f),qe=Se*S+ye;for(let pt=0;pt<c.inChannels;++pt){let ot=pt*C+qe;for(let at=0;at<c.outChannels;++at){let nt=0;for(let Ge=0;Ge<c.batchSize;++Ge){let ht=Ge*D,Ha=Ge*$;for(let Ot=Z;Ot<X;++Ot){let ln=(B+Ot*d-G)*W+ht,sa=Ot*M+Ha;for(let Fa=he;Fa<ie;++Fa){let 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cz={kernelName:eh,backendName:"cpu",kernelFunc:pz};function hz(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n;Ae([r,s],"depthwiseConv2DNativeBackpropInput");let c=v.computeStrides(r.shape),d=v.computeStrides(s.shape),h=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),f=new jt(h.inShape,"float32"),m=f.values,[g,y,x]=f.strides,A=a.data.get(r.dataId).values,[b,k,S]=c,C=a.data.get(s.dataId).values,[N,$,M]=d,{batchSize:R,filterHeight:I,filterWidth:_,inChannels:D,inHeight:W,inWidth:P,outChannels:U,outHeight:G,outWidth:q,strideHeight:H,strideWidth:B}=h,Z=I-1-h.padInfo.top,X=_-1-h.padInfo.left,re=U/D;for(let ee=0;ee<R;++ee)for(let he=0;he<D;++he)for(let ie=0;ie<W;++ie){let ye=ie-Z,Se=Math.max(0,Math.ceil(ye/H)),Ne=Math.min(G,(I+ye)/H);for(let Be=0;Be<P;++Be){let qe=Be-X,pt=Math.max(0,Math.ceil(qe/B)),ot=Math.min(q,(_+qe)/B),at=0;for(let nt=Se;nt<Ne;++nt){let Ge=nt*H-ye;for(let ht=pt;ht<ot;++ht){let 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gz={kernelName:Ed,backendName:"cpu",kernelFunc:mz},yz={kernelName:Rd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,p=n.shape.length,c=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:x,padInfo:A,strideHeight:b,strideWidth:k,filterHeight:S,filterWidth:C,dilationHeight:N,dilationWidth:$,outShape:M}=T.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),R=v.sizeFromShape(M),I=M.length,_=v.getArrayFromDType(n.dtype,R);for(let D=0;D<h;++D)for(let W=0;W<y;++W){let P=W*b-A.top;for(let U=0;U<x;++U){let G=U*k-A.left;for(let q=0;q<g;++q){let H=Number.MIN_SAFE_INTEGER;for(let Z=0;Z<S;++Z){let X=P+Z*N;if(X>=0&&X<f)for(let re=0;re<C;++re){let ee=G+re*$;if(ee>=0&&ee<m){let he=v.locToIndex([D,X,ee,q],p,v.computeStrides(n.shape)),ie=v.locToIndex([Z,re,q],d,v.computeStrides(r.shape)),ye=u[he]+c[ie];ye>H&&(H=ye)}}}let 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WB={kernelName:zd,backendName:"cpu",kernelFunc:BB};function VB(e){let{inputs:t,backend:a}=e,{condition:n,t:r,e:s}=t;Ae([n,r,s],"select");let i=n.shape.length,o=a.data.get(n.dataId).values,l=a.data.get(r.dataId).values,u=a.data.get(s.dataId).values,p=ma(r.dtype,s.dtype),c=v.makeZerosTypedArray(v.sizeFromShape(r.shape),p),d=0,h=i===0||i>1||r.shape.length===1?1:v.sizeFromShape(r.shape.slice(1));for(let f=0;f<o.length;f++)for(let m=0;m<h;m++)o[f]===1?c[d++]=l[f]:c[d++]=u[f];return a.makeTensorInfo(r.shape,p,c)}var UB={kernelName:jl,backendName:"cpu",kernelFunc:VB},GB=T.SELU_SCALEALPHA,HB=T.SELU_SCALE,jB=ut(ql,e=>e>=0?HB*e:GB*(Math.exp(e)-1)),qB={kernelName:ql,backendName:"cpu",kernelFunc:jB},XB=ut(Zl,e=>e<0?-1:e>0?1:0),KB={kernelName:Zl,backendName:"cpu",kernelFunc:XB},ZB=ut(no,e=>Math.sin(e)),YB={kernelName:no,backendName:"cpu",kernelFunc:ZB},JB=ut(Kl,e=>Math.sinh(e)),QB={kernelName:Kl,backendName:"cpu",kernelFunc:JB},eW=11920928955078125e-23,oy=Math.log(eW)+2,tW=ut(Yl,e=>{let t=e>-oy,a=e<oy,n=Math.exp(e),r;return a?r=n:t?r=e:r=Math.log(1+n),r}),aW={kernelName:Yl,backendName:"cpu",kernelFunc:tW};function nW(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;Ae([r],"spaceToBatchND");let o=v.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<r.shape.length;++g)l.push([0,0]);let u=G7.kernelFunc({inputs:{x:r},backend:a,attrs:{paddings:l,constantValue:0}}),p=T.getReshaped(u.shape,s,o,!1),c=T.getPermuted(p.length,s.length,!1),d=T.getReshapedPermuted(u.shape,s,o,!1),h=mt({inputs:{x:u},backend:a,attrs:{shape:p}}),f=Va({inputs:{x:h},backend:a,attrs:{perm:c}}),m=mt({inputs:{x:f},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),m}var rW={kernelName:Jl,backendName:"cpu",kernelFunc:nW};function sW(e){let{inputs:t,backend:a}=e,{indices:n,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${n.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
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|
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
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|
${i.shape}`);let o=a.data.get(n.dataId).values,l=a.data.get(r.dataId).values,u=a.data.get(s.dataId).values,p=a.data.get(i.dataId).values[0],[c,d,h,f,m]=b7(o,n.shape,n.dtype,l,r.dtype,u,p);return[a.makeTensorInfo(d,n.dtype,c),a.makeTensorInfo([d[0]],r.dtype,h),a.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),a.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var iW={kernelName:Ld,backendName:"cpu",kernelFunc:sW};function oW(e){let{inputs:t,backend:a}=e,{inputIndices:n,inputShape:r,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
|
|
${n.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
|
|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(a.data.get(r.dataId).values),o=a.data.get(n.dataId).values,l=Array.from(a.data.get(s.dataId).values),[u,p,c]=v7(o,n.shape,n.dtype,i,l);return[a.makeTensorInfo(p,n.dtype,u),a.makeTensorInfo([c.length],s.dtype,new Int32Array(c))]}var lW={kernelName:eu,backendName:"cpu",kernelFunc:oW};function uW(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);if(r.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=a.data.get(n.dataId).values,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,[u,p]=c3(i,n.shape,n.dtype,o,l,!0);return a.makeTensorInfo(p,n.dtype,u)}var dW={kernelName:Bd,backendName:"cpu",kernelFunc:uW};function pW(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
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${s.shape}`);if(r.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=a.data.get(n.dataId).values,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,[u,p]=c3(i,n.shape,n.dtype,o,l);return a.makeTensorInfo(p,n.dtype,u)}var cW={kernelName:Wd,backendName:"cpu",kernelFunc:pW};function hW(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=T.calculateShapes(s,r,o),h=!1,f=a.bufferSync(r),m;switch(s.dtype){case"bool":{let g=a.bufferSync(s),y=!!a.data.get(i.dataId).values[0];m=el(f,g,o,d,p,u,l,c,y,h);break}case"float32":{let g=a.bufferSync(s),y=a.data.get(i.dataId).values[0];m=el(f,g,o,d,p,u,l,c,y,h);break}case"int32":{let g=a.bufferSync(s),y=a.data.get(i.dataId).values[0];m=el(f,g,o,d,p,u,l,c,y,h);break}case"string":{let 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Tr(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function t6(e,t){let a=V().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let n=`[${e}x${t}]`;throw new Error("Requested texture size "+n+" is invalid.")}if(e>a||t>a){let n=`[${e}x${t}]`,r=`[${a}x${a}]`;throw new Error("Requested texture size "+n+" greater than WebGL maximum on this browser / GPU "+r+".")}}function a6(e){return Tr(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function x2(e,t,a,n,r,s,i){let o=e.getAttribLocation(t,a);return o===-1?!1:(ue(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),ue(e,()=>e.vertexAttribPointer(o,r,e.FLOAT,!1,s,i)),ue(e,()=>e.enableVertexAttribArray(o)),!0)}function n6(e,t,a){l6(e,a),ue(e,()=>e.activeTexture(e.TEXTURE0+a)),ue(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function iV(e,t){l6(e,t),ue(e,()=>e.activeTexture(e.TEXTURE0+t)),ue(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function r6(e,t,a){return Tr(e,()=>e.getUniformLocation(t,a),'uniform "'+a+'" not present in program.')}function s6(e,t,a){return e.getUniformLocation(t,a)}function i6(e,t,a,n){ue(e,()=>n6(e,t,n)),ue(e,()=>e.uniform1i(a,n))}function oV(e){ue(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ue(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),ue(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function bc(e,t,a){ue(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,a)),ue(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function A2(e,t){ue(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),ue(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function qu(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+o6(e,t))}function o6(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function Tr(e,t,a){let n=ue(e,()=>t());if(n==null)throw new Error(a);return n}function l6(e,t){let a=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,n=t+e.TEXTURE0;if(n<e.TEXTURE0||n>a){let r=`[gl.TEXTURE0, gl.TEXTURE${a}]`;throw new Error(`textureUnit must be in ${r}.`)}}function Hs(e,t=2){return v.sizeFromShape(e.slice(0,e.length-t))}function js(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function Xu(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[Hs(e),...js(e)]),t}function u6(e,t=!1){let a=V().getNumber("WEBGL_MAX_TEXTURE_SIZE"),n=V().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");n===1/0&&V().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE")&&(n=a/2),t&&(a=a*2,n=n*2,e=e.map((o,l)=>l>=e.length-2?v.nearestLargerEven(e[l]):e[l]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=v.squeezeShape(e).newShape);let r=v.sizeFromShape(e),s=null;e.length<=1&&r<=a?s=[1,r]:e.length===2&&e[0]<=a&&e[1]<=a?s=e:e.length===3&&e[0]*e[1]<=a&&e[2]<=a?s=[e[0]*e[1],e[2]]:e.length===3&&e[0]<=a&&e[1]*e[2]<=a?s=[e[0],e[1]*e[2]]:e.length===4&&e[0]*e[1]*e[2]<=a&&e[3]<=a?s=[e[0]*e[1]*e[2],e[3]]:e.length===4&&e[0]<=a&&e[1]*e[2]*e[3]<=a&&(s=[e[0],e[1]*e[2]*e[3]]);let i=s!=null&&Math.max(...s)>n&&Math.min(...s)<=(t?2:1)&&Math.min(...s)>0;if(s==null||i)if(t){let o=Hs(e),l=2,u=2;e.length&&([l,u]=js(e)),r=o*(l/2)*(u/2),s=v.sizeToSquarishShape(r).map(p=>p*2)}else s=v.sizeToSquarishShape(r);return s}function hc(e){return e%2===0}function gd(e,t){if(e=e.slice(-2),t=t.slice(-2),v.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let a=e.slice(-1)[0],n=t.slice(-1)[0];if(a===n||hc(a)&&hc(n)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&hc(e[0])&&hc(t[0])}var vc,kc;function d6(e){if(vc==null){let t=Dn(e);vc=t.getParameter(t.MAX_TEXTURE_SIZE)}return vc}function lV(){vc=null}function uV(){kc=null}function p6(e){if(kc==null){let t=Dn(e);kc=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,kc)}function c6(e){if(e===0)return 0;let t,a=Dn(e);return fn(a,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:fn(a,"EXT_disjoint_timer_query")?t=1:t=0,t}function fn(e,t){return e.getExtension(t)!=null}function b2(e){try{if(Dn(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function h6(e){if(e===0)return!1;let t=Dn(e);if(e===1){if(!fn(t,"OES_texture_float"))return!1}else if(!fn(t,"EXT_color_buffer_float"))return!1;return v2(t)}function f6(e){if(e===0)return!1;let t=Dn(e);if(e===1){if(!fn(t,"OES_texture_float")||!fn(t,"WEBGL_color_buffer_float"))return!1}else{if(fn(t,"EXT_color_buffer_float"))return v2(t);let a="EXT_color_buffer_half_float";if(fn(t,a)){let n=t.getExtension(a);return dV(t,n)}return!1}return v2(t)}function v2(e){let t=b3(e),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let n=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,n,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(s),i}function dV(e,t){let a=b3(e,t),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,a.internalFormatHalfFloat,r,s,0,a.textureFormatFloat,a.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(i),o}function m6(e){return e!==2?!1:Dn(e).fenceSync!=null}function uu(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var ve=V();ve.registerFlag("HAS_WEBGL",()=>ve.getNumber("WEBGL_VERSION")>0);ve.registerFlag("WEBGL_VERSION",()=>b2(2)?2:b2(1)?1:0);ve.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ve.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ve.get("WEBGL_VERSION")===2);ve.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ve.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ve.registerFlag("WEBGL_PACK",()=>ve.getBool("HAS_WEBGL"));ve.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_CLIP",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_PACK_REDUCE",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_LAZILY_UNPACK",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_CONV_IM2COL",()=>ve.getBool("WEBGL_PACK"));ve.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>d6(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>p6(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=ve.getNumber("WEBGL_VERSION");return e===0?0:c6(e)});ve.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ve.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Kd.isMobile());ve.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>h6(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ve.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ve.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ve.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>f6(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_FENCE_API_ENABLED",()=>m6(ve.getNumber("WEBGL_VERSION")));ve.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>ve.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);ve.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});ve.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Kd.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});ve.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);ve.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);ve.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);ve.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);ve.registerFlag("WEBGL_EXP_CONV",()=>!1);ve.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>ve.getBool("IS_TEST"));ve.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);ve.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);ve.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);ve.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function Ea(){let e,t,a,n,r,s,i,o,l,u;return V().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",a="out",n="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=V().getBool("WEBGL2_ISNAN_CUSTOM")?`
|
|
bool isnan_custom(float val) {
|
|
uint floatToUint = floatBitsToUint(val);
|
|
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
|
|
}
|
|
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan_custom(val.x),
|
|
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
|
|
}
|
|
|
|
#define isnan(value) isnan_custom(value)
|
|
`:"",l="",u=`
|
|
#define round(value) newRound(value)
|
|
int newRound(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 newRound(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`):(e="",t="attribute",a="varying",n="varying",r="texture2D",s="gl_FragColor",i="",o=`
|
|
#define isnan(value) isnan_custom(value)
|
|
bool isnan_custom(float val) {
|
|
return (val > 0. || val < 1. || val == 0.) ? false : true;
|
|
}
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
|
|
}
|
|
`,l=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,u=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:e,attribute:t,varyingVs:a,varyingFs:n,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Ao(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / ${r}`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Rh(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function pV(e,t){let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}function cV(e,t,a="index"){let n=e.map((s,i)=>i),r=pV(n,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${a} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${a} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function k3(e){let t=v.computeStrides(e).map(a=>a.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}function w3(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var g6=`
|
|
const float FLOAT_MAX = 1.70141184e38;
|
|
const float FLOAT_MIN = 1.17549435e-38;
|
|
|
|
lowp vec4 encode_float(highp float v) {
|
|
if (isnan(v)) {
|
|
return vec4(255, 255, 255, 255);
|
|
}
|
|
|
|
highp float av = abs(v);
|
|
|
|
if(av < FLOAT_MIN) {
|
|
return vec4(0.0, 0.0, 0.0, 0.0);
|
|
} else if(v > FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
|
|
} else if(v < -FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
|
|
}
|
|
|
|
highp vec4 c = vec4(0,0,0,0);
|
|
|
|
highp float e = floor(log2(av));
|
|
highp float m = exp2(fract(log2(av))) - 1.0;
|
|
|
|
c[2] = floor(128.0 * m);
|
|
m -= c[2] / 128.0;
|
|
c[1] = floor(32768.0 * m);
|
|
m -= c[1] / 32768.0;
|
|
c[0] = floor(8388608.0 * m);
|
|
|
|
highp float ebias = e + 127.0;
|
|
c[3] = floor(ebias / 2.0);
|
|
ebias -= c[3] * 2.0;
|
|
c[2] += floor(ebias) * 128.0;
|
|
|
|
c[3] += 128.0 * step(0.0, -v);
|
|
|
|
return c / 255.0;
|
|
}
|
|
`,{getBroadcastDims:y6}=T;function hV(e,t,a){let n=[];if(e.forEach(d=>{let h=v.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`)),a.enableShapeUniforms){let{uniformShape:f}=I3(a.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${d.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${d.name}TexShape;`)}}),a.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}a.customUniforms&&a.customUniforms.forEach(d=>{n.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let r=n.join(`
|
|
`),s=e.map(d=>fV(d,t,a.packedInputs,a.enableShapeUniforms)).join(`
|
|
`),i=t.texShape,o=Ea(),l=yV(o),u,p,c=bV(o);return t.isPacked?(u=mV(t.logicalShape,i,a.enableShapeUniforms),p=AV(o)):(u=gV(t.logicalShape,i,a.enableShapeUniforms),p=xV(o)),a.packedInputs&&(c+=IV),[c,l,p,r,u,s,a.userCode].join(`
|
|
`)}function du(e,t=!1){let a=e.shapeInfo.logicalShape;switch(a.length){case 0:return OV(e,t);case 1:return zV(e,t);case 2:return BV(e,t);case 3:return VV(e,t);case 4:return GV(e,t);case 5:return HV(e);case 6:return jV(e);default:throw new Error(`${a.length}-D input sampling is not yet supported`)}}function x6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return FV(e);case 1:return DV(e,t);case 2:return LV(e,t);case 3:return WV(e,t);default:return UV(e,t)}}function fV(e,t,a=!1,n){let r="";a?r+=x6(e,n):r+=du(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(a?r+=qV(e,t):r+=XV(e,t)),r}function mV(e,t,a){switch(e.length){case 0:return A6();case 1:return SV(e,t,a);case 2:return _V(e,t,a);case 3:return CV(e,t,a);default:return EV(e,t,a)}}function gV(e,t,a){switch(e.length){case 0:return A6();case 1:return TV(e,t,a);case 2:return PV(e,t,a);case 3:return NV(e,t,a);case 4:return RV(e,t,a);case 5:return MV(e,t);case 6:return $V(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function yV(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function xV(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function AV(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function bV(e){return`${e.version}
|
|
precision highp float;
|
|
precision highp int;
|
|
precision highp sampler2D;
|
|
${e.varyingFs} vec2 resultUV;
|
|
${e.defineOutput}
|
|
const vec2 halfCR = vec2(0.5, 0.5);
|
|
|
|
struct ivec5
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
};
|
|
|
|
struct ivec6
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
int v;
|
|
};
|
|
|
|
uniform float NAN;
|
|
${e.defineSpecialNaN}
|
|
${e.defineSpecialInf}
|
|
${e.defineRound}
|
|
|
|
int imod(int x, int y) {
|
|
return x - y * (x / y);
|
|
}
|
|
|
|
int idiv(int a, int b, float sign) {
|
|
int res = a / b;
|
|
int mod = imod(a, b);
|
|
if (sign < 0. && mod != 0) {
|
|
res -= 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
//Based on the work of Dave Hoskins
|
|
//https://www.shadertoy.com/view/4djSRW
|
|
#define HASHSCALE1 443.8975
|
|
float random(float seed){
|
|
vec2 p = resultUV * seed;
|
|
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
|
|
p3 += dot(p3, p3.yzx + 19.19);
|
|
return fract((p3.x + p3.y) * p3.z);
|
|
}
|
|
|
|
${vV}
|
|
${kV}
|
|
${wV}
|
|
`}var vV=`
|
|
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
|
|
int texR = index / texNumC;
|
|
int texC = index - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
|
|
int texelIndex = index / 2;
|
|
int texR = texelIndex / texNumC;
|
|
int texC = texelIndex - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,kV=`
|
|
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
|
|
int texNumC, int row, int col) {
|
|
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
|
|
int texR = texelIndex / texNumC;
|
|
int texC = texelIndex - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,wV=`
|
|
vec2 packedUVfrom3D(int texNumR, int texNumC,
|
|
int texelsInBatch, int texelsInLogicalRow, int b,
|
|
int row, int col) {
|
|
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
|
|
int texR = index / texNumC;
|
|
int texC = index - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,IV=`
|
|
float getChannel(vec4 frag, vec2 innerDims) {
|
|
vec2 modCoord = mod(innerDims, 2.);
|
|
return modCoord.x == 0. ?
|
|
(modCoord.y == 0. ? frag.r : frag.g) :
|
|
(modCoord.y == 0. ? frag.b : frag.a);
|
|
}
|
|
float getChannel(vec4 frag, int dim) {
|
|
float modCoord = mod(float(dim), 2.);
|
|
return modCoord == 0. ? frag.r : frag.g;
|
|
}
|
|
`;function A6(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function SV(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?a?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${n[1]}.0);
|
|
}
|
|
`:n[1]===1?a?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${n[0]}.0);
|
|
}
|
|
`:a?`
|
|
int getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
|
|
}
|
|
`}function TV(e,t,a){return t[0]===1?a?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * float(outTexShape[1]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?a?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * float(outTexShape[0]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:a?`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
return resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return resTexRC.x * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function CV(e,t,a){if(a)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),s=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
int b = index / ${s};
|
|
index -= b * ${s};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function NV(e,t,a){if(a)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${Rh(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let n=Ao(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function EV(e,t,a){if(a)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),s=r*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u<e.length-1;u++)i*=e[e.length-u-1],o=`
|
|
int b${u} = index / ${i};
|
|
index -= b${u} * ${i};
|
|
`+o,l=`b${u}, `+l;return`
|
|
ivec${e.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
${o}
|
|
|
|
int b = index / ${s};
|
|
index -= b * ${s};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec${e.length}(${l});
|
|
}
|
|
`}function RV(e,t,a){if(a)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${Rh(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let n=Ao(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function MV(e,t){let a=Ao(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${a}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function $V(e,t){let a=Ao(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${a}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function _V(e,t,a){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return a?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
|
|
}
|
|
`;let r=Math.ceil(e[1]/2);return a?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function PV(e,t,a){return v.arraysEqual(e,t)?a?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?a?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?a?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:a?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
int r = index / outShape[1];
|
|
int c = index - r * outShape[1];
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function bo(e){return`offset${e}`}function FV(e){let t=e.name,a="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ea();return`
|
|
vec4 ${a}() {
|
|
return ${n.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function OV(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${a};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return`
|
|
float ${n}() {
|
|
return sampleTexture(${a}, halfCR);
|
|
}
|
|
`;let i=bo(a);if(t)return`
|
|
float ${n}() {
|
|
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], ${i});
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`;let[o,l]=e.shapeInfo.texShape;return`
|
|
float ${n}() {
|
|
vec2 uv = uvFromFlat(${o}, ${l}, ${i});
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`}function DV(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),r=e.shapeInfo.texShape,s=Ea();if(t)return`
|
|
vec4 ${n}(int index) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom1D(
|
|
packedTexShape[0], packedTexShape[1], index);
|
|
return ${s.texture2D}(${a}, uv);
|
|
}
|
|
`;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
|
|
vec4 ${n}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${i[0]}, ${i[1]}, index);
|
|
return ${s.texture2D}(${a}, uv);
|
|
}
|
|
`}function zV(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${n}(int index) {
|
|
${pu(e)}
|
|
}
|
|
`;let r=e.shapeInfo.texShape,s=r[0],i=r[1];if(i===1&&s===1)return`
|
|
float ${n}(int index) {
|
|
return sampleTexture(${a}, halfCR);
|
|
}
|
|
`;let o=bo(a);return i===1?t?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${a}TexShape[0]));
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:s===1?t?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${a}TexShape[1]), 0.5);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:t?`
|
|
float ${n}(int index) {
|
|
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index + ${o});
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${n}(int index) {
|
|
vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`}function LV(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Ea();if(s!=null&&v.arraysEqual(a,s))return t?`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
|
|
return ${l.texture2D}(${n}, uv);
|
|
}
|
|
`:`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);
|
|
|
|
return ${l.texture2D}(${n}, uv);
|
|
}
|
|
`;if(t)return`
|
|
vec4 ${r}(int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${n}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
|
|
return ${l.texture2D}(${n}, uv);
|
|
}
|
|
`;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],p=Math.ceil(a[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col);
|
|
return ${l.texture2D}(${n}, uv);
|
|
}
|
|
`}function BV(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(a,s)){if(t)return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let d=s[0],h=s[1];return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}let{newShape:i,keptDims:o}=v.squeezeShape(a),l=i;if(l.length<a.length){let d=cu(e,l),h=["row","col"];return`
|
|
${du(d,t)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${hu(h,o)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${a[1]}, 1)));
|
|
${pu(e)}
|
|
}
|
|
`;let u=s[0],p=s[1],c=bo(n);return p===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${c}), vec3(${n}Shape[1], 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${c}), vec3(${a[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:u===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${c}), vec3(${n}Shape[1], 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / float(${n}TexShape[1]), 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${c}), vec3(${a[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${p}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:t?`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${n}Shape[1] + col + ${c};
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a[1]} + col + ${c};
|
|
vec2 uv = uvFromFlat(${u}, ${p}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function WV(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(a[0]===1){let d=a.slice(1),h=[1,2],f=cu(e,d),m=["b","row","col"];return`
|
|
${x6(f,t)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${hu(m,h)});
|
|
}
|
|
`}let o=Ea();if(t)return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom3D(
|
|
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`;let l=i[0],u=i[1],p=Math.ceil(a[2]/2),c=p*Math.ceil(a[1]/2);return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${l}, ${u}, ${c}, ${p}, b, row, col);
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`}function VV(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=a[1]*a[2],i=a[2],{newShape:o,keptDims:l}=v.squeezeShape(a),u=o;if(u.length<a.length){let m=cu(e,u),g=["row","col","depth"];return`
|
|
${du(m,t)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${hu(g,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${s}, ${i}, 1)));
|
|
${pu(e)}
|
|
}
|
|
`;let p=e.shapeInfo.texShape,c=p[0],d=p[1],h=e.shapeInfo.flatOffset;if(d===s&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
int stride1 = ${n}Shape[2];
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(stride1, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${i}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(d===i&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${n}Shape[1], 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${a[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=bo(n);return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int stride0 = ${n}Shape[1] * ${n}Shape[2];
|
|
int stride1 = ${n}Shape[2];
|
|
int index = row * stride0 + col * stride1 + depth + ${f};
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${s} + col * ${i} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${c}, ${d}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function UV(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),r=Ea();if(t)return`
|
|
vec4 ${n}(int b2, int b, int row, int col) {
|
|
int valuesPerRow = int(ceil(float(${a}Shape[3]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${a}Shape[2]) / 2.0));
|
|
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
|
|
texelsInBatch *= ${a}Shape[1];
|
|
index = b2 * texelsInBatch + index;
|
|
ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
|
|
int texR = index / packedTexShape[1];
|
|
int texC = index - texR * packedTexShape[1];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${a}, uv);
|
|
}
|
|
`;let s=e.shapeInfo.logicalShape,i=s.length,o=e.shapeInfo.texShape,l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=l[0],p=l[1],c=Math.ceil(s[i-1]/2),d=c*Math.ceil(s[i-2]/2),h="int b, int row, int col",f=`b * ${d} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,d*=s[i-m-1],f=`b${m} * ${d} + `+f;return`
|
|
vec4 ${n}(${h}) {
|
|
int index = ${f};
|
|
int texR = index / ${p};
|
|
int texC = index - texR * ${p};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}, ${u});
|
|
return ${r.texture2D}(${a}, uv);
|
|
}
|
|
`}function GV(e,t){let a=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=a[3],i=a[2]*s,o=a[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(a);if(l.length<a.length){let x=cu(e,l),A=["row","col","depth","depth2"];return`
|
|
${du(x,t)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${hu(A,u)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${o}, ${i}, ${s}, 1)));
|
|
${pu(e)}
|
|
}
|
|
`;let p=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,d=c[0],h=c[1],f=`int stride2 = ${n}Shape[3];`,m=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(h===o&&p==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
${f}
|
|
${m}
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(stride1, stride2, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${i}, ${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===s&&p==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${n}Shape[1] * ${n}Shape[2], ${n}Shape[2], 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${a[1]*a[2]}, ${a[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let y=bo(n);return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
${f}
|
|
${m}
|
|
${g}
|
|
int index = row * stride0 + col * stride1 +
|
|
depth * stride2 + depth2;
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${y});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${o} + col * ${i} +
|
|
depth * ${s} + depth2;
|
|
vec2 uv = uvFromFlat(${d}, ${h}, index + ${y});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function HV(e){let t=e.shapeInfo.logicalShape,a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=cu(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${du(m)}
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${n}(${hu(g,u)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${o}, ${i}, ${s}, ${r})) +
|
|
depth3;
|
|
${pu(e)}
|
|
}
|
|
`;let p=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,d=c[0],h=c[1];if(h===o&&p==null)return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${i}, ${s}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${d}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`;if(h===r&&p==null)return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${d}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`;let f=bo(a);return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${o} + col * ${i} + depth * ${s} +
|
|
depth2 * ${r} + depth3 + ${f};
|
|
vec2 uv = uvFromFlat(${d}, ${h}, index);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`}function jV(e){let t=e.shapeInfo.logicalShape,a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1),{newShape:r,keptDims:s}=v.squeezeShape(t);if(r.length<t.length){let g=cu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${du(g)}
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${n}(${hu(y,s)});
|
|
}
|
|
`}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,p=t[1]*u;if(e.shapeInfo.isUniform)return`
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${p}, ${u}, ${l}, ${o})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${i}, 1)));
|
|
${pu(e)}
|
|
}
|
|
`;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===p&&c==null)return`
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${u}, ${l}, ${o}, ${i})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`;if(f===i&&c==null)return`
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`;let m=bo(a);return`
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${p} + col * ${u} + depth * ${l} +
|
|
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${h}, ${f}, index);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`}function pu(e){let t=e.name,a=v.sizeFromShape(e.shapeInfo.logicalShape);return a<2?`return ${t};`:`
|
|
for (int i = 0; i < ${a}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function qV(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=y6(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,p,c=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(`
|
|
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${c[y+u]}`).join(", ");let h="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)h=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(f&&!m)i===1?h=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:h=`
|
|
return vec4(outputValue.x);
|
|
`;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${p}
|
|
vec4 outputValue = get${n}(${d});
|
|
${h}
|
|
}
|
|
`}function XV(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
|
|
float ${r}() {
|
|
return sampleTexture(${a}, resultUV);
|
|
}
|
|
`;let u=gt(l),p=y6(e.shapeInfo.logicalShape,t.logicalShape),c=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&p.length>=1?d="coords = 0;":d=p.map(m=>`coords.${h[m+c]} = 0;`).join(`
|
|
`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+c]}`).join(", "),`
|
|
float ${r}() {
|
|
${u} coords = getOutputCoords();
|
|
${d}
|
|
return get${n}(${f});
|
|
}
|
|
`}function gt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function I3(e,t,a){let{newShape:n,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,a)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:r}}function cu(e,t){let a=JSON.parse(JSON.stringify(e));return a.shapeInfo.logicalShape=t,a}function hu(e,t){return t.map(a=>e[a]).join(", ")}function KV(e,t,a,n){let r=a.map((p,c)=>{let d={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(d.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[c],shapeInfo:d}}),s=r.map(p=>p.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=hV(r,i,t),l=K7(e.gl,o),u=e.createProgram(l);return V().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},b6(e,t,u))}function b6(e,t,a){let n={},r={},s={},i=[],o,l,u,p=null,c=null;c=e.getUniformLocation(a,"NAN",!1),V().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(a,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];n[f]=e.getUniformLocation(a,f,d),n[`offset${f}`]=e.getUniformLocation(a,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(a,`${f}Shape`,d),s[`${f}TexShape`]=e.getUniformLocation(a,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(a,"outShape",d),u=e.getUniformLocation(a,"outShapeStrides",d),l=e.getUniformLocation(a,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(a,h.name,d)}),{uniformLocations:n,customUniformLocations:i,infLoc:p,nanLoc:c,inShapesLocations:r,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function uy(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((a,n)=>{let r=a.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(a.isUniform&&s.isUniform)return;let o=a.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function ZV(e,t,a,n,r){t.program.enableShapeUniforms||(uy(t.inShapeInfos,a),uy([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),V().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),a.forEach((l,u)=>{let p=t.program.variableNames[u],c=t.uniformLocations[p],d=t.uniformLocations[`offset${p}`],h=t.inShapesLocations[`${p}Shape`],f=t.inTexShapesLocations[`${p}TexShape`];if(h){let{uniformShape:m}=I3(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),c!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(c,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(c,m)}return}l.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,c,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(n.shape);switch(n.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let p=t.customUniformLocations[u],c=r[u];if(l.type==="float")e.gl.uniform1fv(p,c);else if(l.type==="vec2")e.gl.uniform2fv(p,c);else if(l.type==="vec3")e.gl.uniform3fv(p,c);else if(l.type==="vec4")e.gl.uniform4fv(p,c);else if(l.type==="int")e.gl.uniform1iv(p,c);else if(l.type==="ivec2")e.gl.uniform2iv(p,c);else if(l.type==="ivec3")e.gl.uniform3iv(p,c);else if(l.type==="ivec4")e.gl.uniform4iv(p,c);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function YV(e,t,a){let n="";t.concat(a).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:c}=I3(e.packedInputs,i.shape,l),d="",h="",f="";if(p.length===1&&e.packedInputs){let k=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let k=v.computeStrides(p);f=`${k[0]===l[1]}_${k[k.length-1]===l[1]}`}let m=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,x=T.getBroadcastDims(i.shape,a.shape),A=!e.packedInputs&&m===a.shape.length&&v.arraysEqual(l,a.texData.texShape),b=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${A}_${u?c:""}_${p.length}_${y}_${x}_${g}_${d}_${h}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+r+`${V().getNumber("WEBGL_VERSION")}`,s}function Ra(e){return V().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var JV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=md.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ea();this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Rh(["r","c","d"],e):Ao(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getA(rc.x, rc.y, rc.z);
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},QV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=md.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ea();this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Rh(["r","c","d"],e):Ao(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},eU=class{constructor(e){this.variableNames=["A"],this.outTexUsage=hn.DOWNLOAD;let t=Ea();this.outputShape=e,this.userCode=`
|
|
${g6}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},tU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=hn.DOWNLOAD;let t=Ea();this.outputShape=e,this.userCode=`
|
|
${g6}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},aU={R:0,G:1,B:2,A:3},dy=class{constructor(e,t=!1,a="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Ea();this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;i<a.length;i++){let o=a[i];s+=`
|
|
if(offset == ${i}) {
|
|
result = values[${aU[o]}];
|
|
}`}this.userCode=`
|
|
${this.enableShapeUniforms?w3():k3(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int flatIndex = getFlatIndex(coords);
|
|
float result = 0.;
|
|
int offset = imod(flatIndex, ${a.length});
|
|
|
|
flatIndex = idiv(flatIndex, ${a.length}, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
if (r < texShape[0]) {
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
vec4 values = ${n.texture2D}(A, uv);
|
|
${s}
|
|
}
|
|
${n.output} = vec4(${r}, 0., 0., 0.);
|
|
}
|
|
`}},nU=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let a=Ea();this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length);let n="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;n+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
|
|
localCoords[2] += ${i};
|
|
if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
|
|
localCoords[1] += ${s};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
values = ${a.texture2D}(A, uv);
|
|
|
|
if (offset == 0) {
|
|
result[${o}] = values[0];
|
|
} else if (offset == 1) {
|
|
result[${o}] = values[1];
|
|
} else if (offset == 2) {
|
|
result[${o}] = values[2];
|
|
} else {
|
|
result[${o}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${this.enableShapeUniforms?w3():k3(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${n}
|
|
|
|
${a.output} = ${r};
|
|
}
|
|
`}},v6={};Ze(v6,{bindVertexProgramAttributeStreams:()=>R6,createBufferFromOutputTexture:()=>_6,createFloat16MatrixTexture:()=>T6,createFloat16PackedMatrixTexture:()=>E6,createFloat32MatrixTexture:()=>S6,createIndexBuffer:()=>I6,createPackedMatrixTexture:()=>N6,createUnsignedBytesMatrixTexture:()=>C6,createVertexBuffer:()=>w6,createVertexShader:()=>k6,downloadByteEncodedFloatMatrixFromOutputTexture:()=>F6,downloadFloat32MatrixFromBuffer:()=>P6,downloadMatrixFromPackedOutputTexture:()=>D6,downloadPackedMatrixFromBuffer:()=>O6,getInternalFormatForFloat16MatrixTexture:()=>T3,getInternalFormatForFloat16PackedMatrixTexture:()=>E3,getInternalFormatForFloat32MatrixTexture:()=>S3,getInternalFormatForPackedMatrixTexture:()=>N3,getInternalFormatForUnsignedBytesMatrixTexture:()=>C3,uploadDenseMatrixToTexture:()=>M6,uploadPixelDataToTexture:()=>$6});function k6(e){let t=Ea(),a=`${t.version}
|
|
precision highp float;
|
|
${t.attribute} vec3 clipSpacePos;
|
|
${t.attribute} vec2 uv;
|
|
${t.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return X7(e,a)}function w6(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return J7(e,t)}function I6(e){let t=new Uint16Array([0,1,2,2,1,3]);return Q7(e,t)}function cp(e,t,a,n,r,s){t6(t,a);let i=e6(e),o=e.TEXTURE_2D;return ue(e,()=>e.bindTexture(o,i)),ue(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ue(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ue(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ue(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),V().getNumber("WEBGL_VERSION")===1?ue(e,()=>e.texImage2D(o,0,n,t,a,0,r,s,null)):ue(e,()=>e.texStorage2D(o,1,n,t,a)),ue(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[a,t]}}function S3(e){return e.internalFormatFloat}function S6(e,t,a,n){let[r,s]=pp(t,a);return cp(e,r,s,S3(n),n.textureFormatFloat,e.FLOAT)}function T3(e){return e.internalFormatHalfFloat}function T6(e,t,a,n){let[r,s]=pp(t,a);return cp(e,r,s,T3(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function C3(e){return e.downloadTextureFormat}function C6(e,t,a,n){let[r,s]=pp(t,a);return cp(e,r,s,C3(n),e.RGBA,e.UNSIGNED_BYTE)}function N3(e){return e.internalFormatPackedFloat}function N6(e,t,a,n){let[r,s]=lu(t,a);return cp(e,r,s,N3(n),e.RGBA,e.FLOAT)}function E3(e){return e.internalFormatPackedHalfFloat}function E6(e,t,a,n){let[r,s]=lu(t,a);return cp(e,r,s,E3(n),e.RGBA,n.textureTypeHalfFloat)}function R6(e,t,a){return ue(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),x2(e,t,"clipSpacePos",a,3,20,0)&&x2(e,t,"uv",a,2,20,12)}function M6(e,t,a,n,r,s){ue(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(a*n*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(a*n*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),V().getNumber("WEBGL_VERSION")===2?ue(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,a,n,e.RGBA,o,i)):ue(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,a,n,0,e.RGBA,o,i)),ue(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function $6(e,t,a){ue(e,()=>e.bindTexture(e.TEXTURE_2D,t)),a.data instanceof Uint8Array?V().getNumber("WEBGL_VERSION")===2?ue(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,a.width,a.height,e.RGBA,e.UNSIGNED_BYTE,a.data)):ue(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,a.width,a.height,0,e.RGBA,e.UNSIGNED_BYTE,a.data)):V().getNumber("WEBGL_VERSION")===2?ue(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,a)):ue(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,a)),ue(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function _6(e,t,a,n){let r=e.createBuffer();ue(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*a;return ue(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ue(e,()=>e.readPixels(0,0,a,t,e.RGBA,e.FLOAT,0)),ue(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function P6(e,t,a){let n=e,r=new Float32Array(a);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,r),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),r}function F6(e,t,a,n){let[r,s]=pp(t,a),i=4,o=new Uint8Array(QW(t*a,i));return ue(e,()=>e.readPixels(0,0,r,s,n.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function O6(e,t,a,n,r,s,i,o){let l=e,u=new Float32Array(eV(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function D6(e,t,a){let n=new Float32Array(t*a*4);return ue(e,()=>e.readPixels(0,0,a,t,e.RGBA,e.FLOAT,n)),n}var sl=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=V().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,Eh(t,e)):this.gl=Dn(t),e=this.gl,V().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>ue(r,()=>r.createVertexArray()),this.bindVertexArray=s=>ue(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>ue(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>ue(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ue(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>ue(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>ue(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>ue(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let a="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),V().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=ju(this.gl,r),fn(this.gl,s))this.textureHalfFloatExtension=ju(this.gl,s);else if(V().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(a),fn(this.gl,n))this.colorBufferHalfFloatExtension=ju(this.gl,n);else if(V().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(a="EXT_color_buffer_float",fn(this.gl,a))this.colorBufferFloatExtension=this.gl.getExtension(a);else if(fn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=w6(this.gl),this.indexBuffer=I6(this.gl),this.framebuffer=a6(this.gl),this.textureConfig=b3(this.gl,this.textureHalfFloatExtension)}get debug(){return V().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ue(e,()=>e.finish()),ue(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ue(e,()=>e.deleteFramebuffer(this.framebuffer)),ue(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ue(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ue(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),S6(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),T6(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),C6(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),$6(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,a,n){this.throwIfDisposed(),M6(this.gl,e,t,a,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),E6(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),N6(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(A2(this.gl,this.framebuffer),this.outputTexture=null),ue(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,a){return this.downloadMatrixDriver(e,()=>F6(this.gl,t,a,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,a,n,r,s){return O6(this.gl,e,t,a,n,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return P6(this.gl,e,t)}createBufferFromTexture(e,t,a){this.bindTextureToFrameBuffer(e);let n=_6(this.gl,t,a,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,a;if(V().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,r=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),a=()=>{let s=n.clientWaitSync(r,0,0);return s===n.ALREADY_SIGNALED||s===n.CONDITION_SATISFIED},t=r}else V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),a=()=>this.isQueryAvailable(t,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):a=()=>!0;return{query:t,isFencePassed:a}}downloadMatrixFromPackedTexture(e,t,a){return this.downloadMatrixDriver(e,()=>D6(this.gl,t,a))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=k6(t));let a=Z7(t);ue(t,()=>t.attachShader(a,this.vertexShader)),ue(t,()=>t.attachShader(a,e)),Y7(t,a);let n;return n=Object.assign(a,{vao:this.createVertexArray()}),this.bindVertexArray(n.vao),ue(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(R6(t,n,this.vertexBuffer),"gpgpu_util.bindVertexProgramAttributeStreams not fully successful."),this.debug&&Ac(t,n),this.setProgram(n),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ue(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&Ac(this.gl,this.program)),ue(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,a=!0){return this.throwIfDisposed(),a?r6(this.gl,e,t):s6(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ue(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,a){this.throwIfDisposed(),this.throwIfNoProgram(),i6(this.gl,e,t,a)}setOutputMatrixTexture(e,t,a){this.setOutputMatrixTextureDriver(e,a,t)}setOutputPackedMatrixTexture(e,t,a){this.throwIfDisposed();let[n,r]=lu(t,a);this.setOutputMatrixTextureDriver(e,n,r)}setOutputMatrixWriteRegion(e,t,a,n){this.setOutputMatrixWriteRegionDriver(a,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,a,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Ac(this.gl,this.program),qu(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 executeProgram!"),this.debugValidate()}ue(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ue(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ju(this.gl,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.createQuery();return a.beginQuery(n.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,a=this.getQueryTimerExtensionWebGL2();t.endQuery(a.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let a=this.gl;return a.getQueryParameter(e,a.QUERY_RESULT)/1e6}else{let a=this.getQueryTimerExtensionWebGL1();return a.getQueryObjectEXT(e,a.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.getQueryParameter(e,a.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let a=this.getQueryTimerExtensionWebGL1(),n=a.getQueryObjectEXT(e,a.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=rU(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:a}=this.itemsToPoll[t];a()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let a;"setTimeoutCustom"in V().platform&&(a=V().platform.setTimeoutCustom.bind(V().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,a)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),bc(this.gl,e,this.framebuffer),this.debug&&qu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bc(this.gl,this.outputTexture,this.framebuffer),this.debug&&qu(this.gl)):A2(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let a=t();return this.unbindTextureToFrameBuffer(),a}setOutputMatrixTextureDriver(e,t,a){this.throwIfDisposed();let n=this.gl;bc(n,e,this.framebuffer),this.debug&&qu(n),this.outputTexture=e,ue(n,()=>n.viewport(0,0,t,a)),ue(n,()=>n.scissor(0,0,t,a))}setOutputMatrixWriteRegionDriver(e,t,a,n){this.throwIfDisposed(),ue(this.gl,()=>this.gl.scissor(e,t,a,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 rU(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:sU,bincountImpl:z6,bincountReduceImpl:iU,castImpl:oU,ceilImpl:lU,concatImpl:uU,equalImpl:dU,expImpl:pU,expm1Impl:cU,floorImpl:hU,gatherNdImpl:fU,gatherV2Impl:mU,greaterImpl:gU,greaterEqualImpl:yU,lessImpl:xU,lessEqualImpl:AU,linSpaceImpl:bU,logImpl:vU,maxImpl:kU,maximumImpl:wU,minimumImpl:IU,multiplyImpl:SU,negImpl:TU,notEqualImpl:CU,prodImpl:NU,raggedGatherImpl:EU,raggedRangeImpl:RU,raggedTensorToTensorImpl:MU,rangeImpl:$U,rsqrtImpl:_U,scatterImpl:PU,sigmoidImpl:FU,simpleAbsImpl:L6,sliceImpl:OU,sparseFillEmptyRowsImpl:DU,sparseReshapeImpl:zU,sparseSegmentReductionImpl:B6,sqrtImpl:LU,stridedSliceImpl:BU,stringNGramsImpl:WU,stringSplitImpl:VU,stringToHashBucketFastImpl:UU,subImpl:GU,tileImpl:HU,topKImpl:jU,transposeImpl:R3,uniqueImpl:qU}=Ch;function W6(e,t){return["x","y","z","w","u","v"].slice(0,t).map(a=>`${e}.${a}`)}function wa(e,t){return t===1?[e]:W6(e,t)}function XU(e,t){if(e===1)return"rc";let a="";for(let n=0;n<e;n++)a+=t[n],n<e-1&&(a+=",");return a}var KU=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Ra(this.outputShape.length),this.rank===0)this.userCode=`
|
|
void main() {
|
|
setOutput(vec4(getA(), 0., 0., 0.));
|
|
}
|
|
`;else{let t=wa("rc",this.rank),a=gt(this.rank),n=this.getOutOfBoundsCondition(t),r=this.getSetup(t),s=this.getOutput(t);this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
|
|
if(${n}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${r}
|
|
|
|
setOutput(vec4(${s}));
|
|
}
|
|
}
|
|
`}}getSourceCoordsArr(e){let t=[];for(let a=0;a<=1;a++)for(let n=0;n<=1;n++){let r=`${a===0?"r":"rp1"}, ${n===0?"c":"cp1"}`;for(let s=2;s<this.rank;s++)r=`${e[e.length-1-s]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let a=this.rank-2;a<this.rank;a++)t+=`${e[a]} >= ${this.enableShapeUniforms?`outShape[${a}]`:this.outputShape[a]}`,a<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),a=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],n=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
|
|
int r = ${t[0]};
|
|
int c = ${t[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${a};
|
|
bool rEdge = rp1 >= ${n};
|
|
`}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
|
|
cEdge ? 0. : getA(${t[1]}),
|
|
rEdge ? 0. : getA(${t[2]}),
|
|
rEdge || cEdge ? 0. : getA(${t[3]})`}},V6=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=`
|
|
${r}
|
|
${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${n}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${n>0?"}":""}
|
|
`}this.userCode=`
|
|
${ZU(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?w3():k3(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
|
|
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
|
|
|
|
${a}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function ZU(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?cV(["r","c","d"],"inputShape"):Ao(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var YU=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,a){let n=cy(t,a),r=hy(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=py(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return n===oa.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===oa.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===oa.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===oa.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===oa.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=cy(a,n),s=hy(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=py(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=V().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 JU(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function py(e,t,a,n,r){let s=QU(t,n),i;if(r){let[l,u]=lu(e[0],e[1]);i=l*u}else{let[l,u]=pp(e[0],e[1]);i=l*u}let o=JU(a,s);return i*o}function QU(e,t){switch(e){case oa.PACKED_2X2_FLOAT32:return N3(t);case oa.PACKED_2X2_FLOAT16:return E3(t);case oa.UNPACKED_FLOAT32:return S3(t);case oa.UNPACKED_FLOAT16:return T3(t);case oa.PACKED_4X1_UNSIGNED_BYTE:return C3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function eG(e){return V().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?oa.PACKED_2X2_FLOAT32:oa.UNPACKED_FLOAT32:e?oa.PACKED_2X2_FLOAT16:oa.UNPACKED_FLOAT16}function cy(e,t){if(e===hn.UPLOAD)return oa.PACKED_2X2_FLOAT32;if(e===hn.RENDER||e==null)return eG(t);if(e===hn.DOWNLOAD||e===hn.PIXELS)return oa.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function hy(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var qn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length),this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},En="if (isnan(x)) return x;",tG="return x;",fy="return abs(x);",aG="return (x >= 0.0) ? x : (exp(x) - 1.0);",nG=En+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,rG=En+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Or="return x;",sG="return 1.0 / (1.0 + exp(-1.0 * x));",iG="return x;",oG=`
|
|
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;
|
|
`,lG=`
|
|
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;
|
|
`,uG=`
|
|
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;
|
|
`,dG="return 1.0 / (1.0 + exp(-1.0 * x));",Wr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length),this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},pG=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length);let t=e.length,a=wa("rc",t),n=gt(t),r=XU(t,a),s=a.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${n} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}},cG=Nn.whereImpl,hG=1e-7,fG=1e-4,Fm={};function mG(e){return e in Fm||(Fm[e]={}),Fm[e]}var gG=V().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),yG=600;function xG(){return V().global.screen==null?1024:V().global.screen.height*V().global.screen.width*window.devicePixelRatio*yG/1024/1024}var fu=class extends Al{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,!V().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof sl)t=e;else{let a=Dn(V().getNumber("WEBGL_VERSION"),e);t=new sl(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=Dn(V().getNumber("WEBGL_VERSION"));t=new sl(a),this.binaryCache=mG(V().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new YU(this.gpgpu),this.numMBBeforeWarning=xG(),this.texData=new kd(this,vt())}nextDataId(){return fu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,a,n,r,s){let i=this.makeTensorInfo(t,a),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[n,r]},o.texShape=[n,r];let l=Xu(t),u=new dy(l,!1,s),p=this.runWebGLProgram(u,[i],a,[[n,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,a){if((V().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||V().getBool("DEBUG"))&&this.checkNumericalProblems(e),a==="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:a,values:e,usage:hn.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,a,n,r){if(V().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:a,dtype:n,values:t,usage:hn.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:a,dtype:n,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let c;o?c=new Wr(i,Or):c=new qn(i,Or);let d=this.runWebGLProgram(c,[{dataId:e,shape:i,dtype:n}],n),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(a!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return a;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(n==="complex64"){let c=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);p=T.mergeRealAndImagArrays(c,d)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:a,shape:n,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Wr(n,Or):h=new qn(n,Or);let f=this.runWebGLProgram(h,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(a!=null)return this.convertAndCacheOnCPU(e);if(V().getBool("DEBUG")&&!V().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&V().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&V().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...cc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];p=T.mergeRealAndImagArrays(f,m)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ue(h,()=>h.deleteBuffer(l))}let c=this.convertAndCacheOnCPU(e,p),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(c)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&vt().removeDataId(e,this),this.pendingDeletes--),c}readToGPU(e,t={}){let a=this.texData.get(e),{values:n,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=a;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let d;o?d=new Wr(r,Or):d=new qn(r,Or);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(l==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 u=this.decode(e,t.customTexShape),p=vt().makeTensorFromTensorInfo(u),c=this.texData.get(u.dataId);return Object.assign({tensorRef:p},c.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return _e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let a=e[t];if(!j7(a))throw V().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${a} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${a} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:a,isPacked:n}=this.texData.get(e),r=v.sizeFromShape(t);if(V().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let c=this.decode(e),d=this.texData.get(c.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...cc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(c),h}let s=V().getBool("WEBGL_PACK")&&n===!0,i=s?Xu(t):t,o=s?new tU(i):new eU(i),l=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],"float32"),u=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.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 V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(V().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:a}=this.texData.get(e);return a!=null&&(this.disposeData(a.real.dataId,t),this.disposeData(a.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:a,texShape:n,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,a),this.textureManager.releaseTexture(t,n,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=gG){return V().getBool("WEBGL_CPU_FORWARD")&&e.every(a=>this.texData.get(a.dataId).texture==null&&v.sizeFromShape(a.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){T.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return cG(e.shape,t)}packedUnaryOp(e,t,a){let n=new Wr(e.shape,t),r=this.compileAndRun(n,[e],a);return vt().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=L6(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(V().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,fy,e.dtype);let t=new qn(e.shape,fy),a=this.compileAndRun(t,[e]);return vt().makeTensorFromTensorInfo(a)}makeTensorInfo(e,t,a){let n;if(t==="string"&&a!=null&&a.length>0&&v.isString(a[0])){let r=a.map(s=>v.encodeString(s));n=this.write(r,e,t)}else n=this.write(a,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,a){return vt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,a),this)}unpackTensor(e){let t=new pG(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new KU(e.shape),a=!0;return this.runWebGLProgram(t,[e],e.dtype,null,a)}packedReshape(e,t){let a=[Hs(e.shape),...js(e.shape)],n={dtype:e.dtype,shape:a,dataId:e.dataId},r=[Hs(t),...js(t)],s=new V6(r,a),i=!0,o=[a],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let a=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=a;if(t!=null){let c=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(c<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Xu(r),o;n?o=new QV(i):o=new JV(i);let l=!0,u=[t!=null?t:cc(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,a,n,r=!1,s){let i=this.makeTensorInfo(e.outputShape,a),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===md.DENSE){let g=s!=null?s:cc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=V().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!gd(y.shape,g.shape)){let x=g,A=g.shape;g.shape=y.shape,g=this.packedReshape(g,A),l.push(g),y=this.texData.get(g.dataId),x.shape=A}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},c=YV(e,u,p),d=this.getAndSaveBinary(c,()=>KV(this.gpgpu,e,u,p)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),V().get("ENGINE_COMPILE_ONLY")||ZV(this.gpgpu,d,u,p,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=V().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!V().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,a,n,r=!1){return a=a||t[0].dtype,this.runWebGLProgram(e,t,a,n,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(V().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Oe(()=>{if(!V().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=V().getBool("DEBUG");V().set("DEBUG",!1);let t=this.abs(ze(1e-8)).dataSync()[0];if(V().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?hG:fG}uploadToGPU(e){let t=this.texData.get(e),{shape:a,dtype:n,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=u6(a,o),t.texShape=p),r!=null){let c=Xu(a),d,h=p[1],f=p[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=lu(p[0],p[1])),o?d=new nU(c,m):d=new dy(c,m);let g=m?[f,h]:p,y=this.makeTensorInfo(g,n),x=this.texData.get(y.dataId);m?x.usage=hn.PIXELS:x.usage=hn.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,r);let A=[[f,h]],b=!0,k=this.runWebGLProgram(d,[y],n,A,b),S=this.texData.get(k.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,V().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let c=this.acquireTexture(p,i,n,o);t.texture=c}}convertAndCacheOnCPU(e,t){let a=this.texData.get(e),{dtype:n}=a;return t!=null&&(a.values=AG(t,n)),a.values}acquireTexture(e,t,a,n){if(this.numBytesInGPU+=this.computeBytes(e,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*v.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 a=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(r){throw r}});e.push(a)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await A4(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(v3(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:a,infLoc:n,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=b6(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=a,e.infLoc=n,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromGPUData(e,t,a){e.channels=e.channels||"RGBA";let{texture:n,height:r,width:s,channels:i}=e,o=vt().backend;if(!o.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(n,t,a,r,s,i);return vt().makeTensorFromDataId(l,t,a,o)}};fu.nextDataId=0;function AG(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<a.length;++n)a[n]=Math.round(e[n]);return a}else throw new Error(`Unknown dtype ${t}`)}var bG="4.2.0";function U6(){V().set("WEBGL_FORCE_F16_TEXTURES",!0)}Kd.isBrowser()&&yo("webgl",()=>new fu,2);var vG={forceHalfFloat:U6},M3=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,yl=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=Ra(this.outputShape.length),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},hp=`
|
|
result.r = isNaN.r ? NAN : result.r;
|
|
result.g = isNaN.g ? NAN : result.g;
|
|
result.b = isNaN.b ? NAN : result.b;
|
|
result.a = isNaN.a ? NAN : result.a;
|
|
`,fp=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=Ra(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(s=`
|
|
${gt(r)} coords = getOutputCoords();
|
|
`,r===1)this.enableShapeUniforms?s+=`
|
|
result.y = (coords + 1) >= outShape ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`:s+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=wa("coords",r);this.enableShapeUniforms?s+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= outShape[${r} - 2];
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= outShape[${r} - 1];
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`:s+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`}this.userCode=`
|
|
vec4 binaryOperation(vec4 a, vec4 b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 a = getAAtOutCoords();
|
|
vec4 b = getBAtOutCoords();
|
|
|
|
vec4 result = binaryOperation(a, b);
|
|
${s}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function Qa(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var kG={kernelName:wi,backendName:"webgl",kernelFunc:Qa};function ls(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=Qa({inputs:{x:n},backend:a}),l=Qa({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var wG={kernelName:Td,backendName:"webgl",kernelFunc:ls},G6="return (a < 0.) ? b * a : a;",H6=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function IG(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new fp(H6,r.shape,i.shape):new yl(G6,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var SG={kernelName:Si,backendName:"webgl",kernelFunc:IG},j6="return (a < 0.) ? b * a : a;",q6=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function TG(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new fp(q6,n.shape,r.shape):new yl(j6,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var CG={kernelName:ji,backendName:"webgl",kernelFunc:TG},mu="if (isnan(x)) return x;";function Qe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=V().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Wr(i.shape,t):p=new qn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function pa({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let f=p.texData.get(l.dataId),m=p.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,k]=A,S={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C={dataId:k.dataId,dtype:k.dtype,shape:u.shape},N=new yl(e,l.shape,u.shape);return p.runWebGLProgram(N,[S,C],ma(b.dtype,k.dtype))}),x=ls({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),x}let c=s||ma(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let f=p.texData.get(l.dataId).values,m=p.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(f):f,y=l.dtype==="string"?T.fromUint8ToStringArray(m):m,[x,A]=r(l.shape,u.shape,g,y,c),b=p.makeTensorInfo(A,c),k=p.texData.get(b.dataId);return k.values=x,b}let d=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new fp(t,l.shape,u.shape,a):h=new yl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function yd(e,t=!1){if(e==="linear")return t?iG:tG;if(e==="relu")return t?lG:nG;if(e==="elu")return t?oG:aG;if(e==="relu6")return t?uG:rG;if(e==="prelu")return t?q6:j6;if(e==="leakyrelu")return t?H6:G6;if(e==="sigmoid")return t?dG:sG;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var X6=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=Ra(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${i}
|
|
}`:l?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${i}
|
|
}`:m=`vec4 activation(vec4 x) {
|
|
${i}
|
|
}`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let x="rc.x",A="rc.x";e[0]<t[0]?x=`imod(rc.x, ${e[0]})`:t[0]<e[0]&&(A=`imod(rc.x, ${t[0]})`),this.userCode=`
|
|
${m}
|
|
// Don't use uniform for sharedDimensionPacked for performance.
|
|
const float sharedDimension = ${p}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
int batchA = ${x};
|
|
int batchB = ${A};
|
|
for (int i = 0; i < ${p}; i++) {
|
|
vec4 a = getMatrixA(batchA, ${c});
|
|
vec4 b = getMatrixB(batchB, ${d});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${h[0]} * ${f[0]});
|
|
result += (${h[1]} * ${f[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${y}
|
|
|
|
${g}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},my={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},gy=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},yy="return a * b;";function $3(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=T.upcastType(n.dtype,r.dtype);if(n.dtype==="complex64"){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),u=new gy(my.REAL,n.shape,r.shape),p=new gy(my.IMAG,n.shape,r.shape),c=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),f=ls({inputs:{real:d,imag:h},backend:a});return a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),f}if(a.shouldExecuteOnCPU([n,r])){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),[u,p]=SU(n.shape,r.shape,o.values,l.values,s),c=a.makeTensorInfo(p,s),d=a.texData.get(c.dataId);return d.values=u,c}let i;return V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new fp(yy,n.shape,r.shape):i=new yl(yy,n.shape,r.shape),a.runWebGLProgram(i,[n,r],s)}var NG={kernelName:Li,backendName:"webgl",kernelFunc:$3};function EG(e,t,a){let n=[Hs(e.shape),...js(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Hs(t),...js(t)],i=new V6(s,n),o=!0,l=[n],u=a.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function pe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{shape:s}=n,i=a,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!gd(r.shape,l)&&!(p.texture!==null&&gd(p.shape,l))?EG(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var RG={kernelName:Hl,backendName:"webgl",kernelFunc:pe},xy=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(a/4)*4,o=a%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%a>0&&(u=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${u}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${a};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${i}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${l}
|
|
}
|
|
|
|
int inIdx = inOffset + ${i};
|
|
if (${o===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},MG=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(a/4)*4,p=a%4,c=`
|
|
if (${t==="sum"}) {
|
|
sumValue += dot(values, ones);
|
|
} else if (${t==="prod"}) {
|
|
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
|
|
prodValue *= tmp[0] * tmp[1];
|
|
} else {
|
|
minMaxValue = ${o}(values, minMaxValue);
|
|
if (${t==="min"} || ${t==="max"}) {
|
|
minMaxValue = ${o}(values, minMaxValue);
|
|
bvec4 isNaN = isnan(values);
|
|
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
|
|
minMaxValue = vec4(NAN);
|
|
}
|
|
}
|
|
}
|
|
`,d="vec4";t==="all"?(i="1.0",c=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,d="bvec4"):t==="any"&&(i="0.0",c=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,d="bvec4");let h="";r%a>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${h}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${a};
|
|
|
|
vec4 minMaxValue = vec4(${i});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${u}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${c}
|
|
}
|
|
|
|
int inIdx = inOffset + ${u};
|
|
if (${p===1}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${c}
|
|
} else if (${p===2}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${c}
|
|
} else if (${p===3}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${c}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function $G(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let a=t.length?t[t.length-1].outSize:e[1],n=T.computeOptimalWindowSize(a);t.push({inSize:a,windowSize:n,outSize:Math.ceil(a/n)})}return t}function vo(e,t,a,n){let r=$G(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:u}=r[i],p,c;a==="mean"?p=i===0?new xy({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new xy({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):p=new MG({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},a),c=s,s=n.runWebGLProgram(p,[s],t),c.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(c)}return s}var _G=class{constructor(e,t){this.variableNames=["A"];let a=new Array(e.length);for(let s=0;s<a.length;s++)a[s]=e[t[s]];this.outputShape=a,this.rank=a.length;let n=gt(this.rank),r=PG(t);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function PG(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r<e.length;r++)n[e[r]]=a[r];return n.join()}var FG=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let a=new Array(e.length);for(let u=0;u<a.length;u++)a[u]=e[t[u]];if(this.outputShape=a,this.rank=a.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=gt(this.rank),r=W6("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=r[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${a[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${n} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${o}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${r[this.rank-1]};
|
|
if(++${r[this.rank-2]} < ${a[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${o}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Mh(e,t,a){let n=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new FG(e.shape,t):new _G(e.shape,t);return a.runWebGLProgram(n,[e],e.dtype)}function OG(e,t,a,n){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=T.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Mh(e,l,n),o=T.getInnerMostAxes(o.length,s)),T.assertAxesAreInnerMostDims("sum",o,s);let[c,d]=T.computeOutAndReduceShapes(p.shape,o),h=c;a&&(h=T.expandShapeToKeepDim(c,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,g=pe({inputs:{x:p},attrs:{shape:[m,f]},backend:n}),y=Xd(e.dtype),x=vo(g,y,"sum",n),A=pe({inputs:{x},attrs:{shape:h},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x),u&&n.disposeIntermediateTensorInfo(p),A}function $h(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return OG(r,s,i,a)}var DG={kernelName:io,backendName:"webgl",kernelFunc:$h};function Ta(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{perm:s}=n,i=a,o=r.shape.length,l=new Array(o);for(let p=0;p<l.length;p++)l[p]=r.shape[s[p]];let u;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,c=R3(p,r.shape,r.dtype,s,l);u=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(u.dataId);d.values=c}else u=Mh(r,s,i);return u}var zG={kernelName:Ar,backendName:"webgl",kernelFunc:Ta},K6=1e3;function Lc({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],f=n?t.shape[p-2]:t.shape[p-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),A=xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],k=n?[x,f,d]:[x,d,f],S=pe({inputs:{x:e},backend:r,attrs:{shape:b}}),C=pe({inputs:{x:t},backend:r,attrs:{shape:k}}),N=[S,C],$=Math.max(y,x),M=a?S.shape[1]:S.shape[2],R=s!=null,I=i!=null,_=l==="leakyrelu",D=l!=null?yd(l,!0):null,W=R||I||_||D!=null,P;if((h===1||f===1)&&M>K6&&W===!1){let G=S,q=C;a&&(G=Ta({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),N.push(G)),n&&(q=Ta({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),N.push(q));let H=f!==1,B=f===1,Z=G;H&&(Z=pe({inputs:{x:G},backend:r,attrs:{shape:[$,M,1]}}),N.push(Z));let X=f===1?2:1,re=q;B&&(re=pe({inputs:{x:q},backend:r,attrs:{shape:[$,1,M]}}),N.push(re));let ee=$3({inputs:{a:Z,b:re},backend:r});P=$h({inputs:{x:ee},backend:r,attrs:{axis:X,keepDims:!0}}),N.push(ee)}else{let G=ma(e.dtype,t.dtype),q=new X6(b,k,[$,h,f],a,n,R,D,I,_),H=[S,C];if(s!=null&&H.push(s),I&&H.push(i),_){let B=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(B),N.push(B)}P=r.runWebGLProgram(q,H,G)}let U=pe({inputs:{x:P},backend:r,attrs:{shape:A}});N.push(P);for(let G of N)r.disposeIntermediateTensorInfo(G);return U}function LG(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Lc({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var BG={kernelName:Hr,backendName:"webgl",kernelFunc:LG},Ay="return abs(x);";function WG(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=L6(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return V().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Wr(n.shape,Ay):r=new qn(n.shape,Ay),a.runWebGLProgram(r,[n],n.dtype)}var VG={kernelName:vl,backendName:"webgl",kernelFunc:WG},UG=En+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,GG=Qe({opSnippet:UG}),HG={kernelName:kl,backendName:"webgl",kernelFunc:GG},jG=En+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,qG=Qe({opSnippet:jG}),XG={kernelName:wl,backendName:"webgl",kernelFunc:qG},by="return a + b;",KG=pa({opSnippet:by,packedOpSnippet:by,supportsComplex:!0,cpuKernelImpl:sU}),ZG={kernelName:ts,backendName:"webgl",kernelFunc:KG},YG=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`float v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${a.join(`
|
|
`)}
|
|
|
|
float result = ${n};
|
|
setOutput(result);
|
|
}
|
|
`}},JG=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`vec4 v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${a.join(`
|
|
`)}
|
|
|
|
vec4 result = ${n};
|
|
setOutput(result);
|
|
}
|
|
`}};function wc(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return Qa({inputs:{x:n[0]},backend:a});if(n.length>V().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=wc({inputs:n.slice(0,o),backend:a}),u=wc({inputs:n.slice(o),backend:a});return wc({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>ma(o,l)),s=n.map(o=>o.shape),i=V().getBool("WEBGL_PACK")?new JG(n[0].shape,s):new YG(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var QG={kernelName:Ks,backendName:"webgl",kernelFunc:wc};function eH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Ta({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("all",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"all",a),y;if(i){let x=T.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var tH={kernelName:Zs,backendName:"webgl",kernelFunc:eH};function aH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Ta({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,o)),T.assertAxesAreInnerMostDims("any",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"any",a),y;if(i){let x=T.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var nH={kernelName:Ys,backendName:"webgl",kernelFunc:aH},rH=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"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 = ${o};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},sH=class{constructor(e,t,a,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${a.charAt(0).toUpperCase()+a.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=gt(o),u=wa("coords",o),p,c;if(s===1){c=o+1;let C=gt(c);p=`
|
|
${C} sourceLocR = ${C}(${u.join()}, 0);
|
|
++${u[o-1]};
|
|
${C} sourceLocG = ${C}(${u.join()}, 0);
|
|
++${u[o-2]};
|
|
${C} sourceLocA = ${C}(${u.join()}, 0);
|
|
--${u[o-1]};
|
|
${C} sourceLocB = ${C}(${u.join()}, 0);
|
|
--${u[o-2]};`}else c=o,p=`
|
|
${l} sourceLocR = coords;
|
|
++${u[o-1]};
|
|
${l} sourceLocG = coords;
|
|
++${u[o-2]};
|
|
${l} sourceLocA = coords;
|
|
--${u[o-1]};
|
|
${l} sourceLocB = coords;
|
|
--${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,c),h="."+d[c-1],f=d.map(C=>"int "+C),m=wa("sourceLocR",c-1).concat("inIdx.r"),g=wa("sourceLocG",c-1).concat("inIdx.g"),y=wa("sourceLocB",c-1).concat("inIdx.b"),x=wa("sourceLocA",c-1).concat("inIdx.a"),A=a==="max"?"greaterThan":"lessThan",b=n?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${x.join()})));`,k=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${g.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${y.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,S=n?"":`
|
|
float getBestIndicesAChannel(${f.join()}) {
|
|
return getChannel(getBestIndicesA(${d.join()}),
|
|
vec2(${d.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${f.join()}) {
|
|
return getChannel(getA(${d.join()}),
|
|
vec2(${d.slice(-2).join()}));
|
|
}
|
|
${S}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
|
|
bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
|
|
${p}
|
|
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
|
|
sourceLocB${h}, sourceLocA${h}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${k};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${b}
|
|
vec4 candidate = ${k};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${A}(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 Z6(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=T.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new rH(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=Z6(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function Y6(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=T.computeOptimalWindowSize(s),o=new sH(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=Y6(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function J6(e,t,a,n){let r=[a];if(T.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!V().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=T.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(p),d=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(d);let h=Z6(e,d,n);s.push(h);let f=pe({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return Y6(e,t,n)}function iH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ta({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=J6(a,l,i[0],"max");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var oH={kernelName:Js,backendName:"webgl",kernelFunc:iH};function lH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ta({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=J6(a,l,i[0],"min");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var uH={kernelName:Id,backendName:"webgl",kernelFunc:lH},dH=En+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,pH=Qe({opSnippet:dH}),cH={kernelName:Il,backendName:"webgl",kernelFunc:pH},hH=En+"return log(x + sqrt(x * x + 1.0));",fH=Qe({opSnippet:hH}),mH={kernelName:Sl,backendName:"webgl",kernelFunc:fH},gH=En+`
|
|
return atan(x);
|
|
`,yH=Qe({opSnippet:gH}),xH={kernelName:Tl,backendName:"webgl",kernelFunc:yH},AH=M3+`
|
|
return atan(a, b);
|
|
`,bH=`
|
|
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);
|
|
`+hp+`
|
|
return result;
|
|
`,vH=pa({opSnippet:AH,packedOpSnippet:bH}),kH={kernelName:Nl,backendName:"webgl",kernelFunc:vH},wH=En+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IH=Qe({opSnippet:wH}),SH={kernelName:Cl,backendName:"webgl",kernelFunc:IH},xd=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),a){let C=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${d}, ${h});
|
|
|
|
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 < ${p};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${c};
|
|
wC += ${u}) {
|
|
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 ${C} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${n?r?m:g:`wR * ${c} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let x="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / max(count, 1.0)");let b=Math.floor(s/4)*4,k=s%4,S=`
|
|
if (${f}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${x}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${d}, ${h});
|
|
const float initializationValue = ${y};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${y});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${b}; wC += 4) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
getValue(batch, xR, xC + 3 * ${u}, d)
|
|
);
|
|
|
|
${S}
|
|
}
|
|
|
|
int xC = xCCorner + ${b};
|
|
if (${k===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${S}
|
|
} else if (${k===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${S}
|
|
} else if (${k===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${S}
|
|
}
|
|
}
|
|
setOutput(${A});
|
|
}
|
|
`}},_3=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,c=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),a){let $=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${y});
|
|
|
|
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 += ${u}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${p}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f};
|
|
wC += ${c}) {
|
|
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 ${$} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${n?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
|
|
wR * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let b="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / max(count, 1.0)");let S=Math.floor(s/4)*4,C=s%4,N=`
|
|
if (${x}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${b}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${y});
|
|
const float initializationValue = ${A};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${A});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${d};
|
|
wD += ${u}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${p}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${S}; wC += 4) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${c}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${c}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${c}, ch)
|
|
);
|
|
|
|
${N}
|
|
}
|
|
|
|
int xC = xCCorner + ${S};
|
|
if (${C===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
} else if (${C===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${c}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
} else if (${C===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${c}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${c}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
}
|
|
}
|
|
}
|
|
setOutput(${k});
|
|
}
|
|
`}};function TH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;uu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Qa({inputs:{x:r},backend:a});let c=new xd(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var CH={kernelName:Qs,backendName:"webgl",kernelFunc:TH};function NH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new _3(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var EH={kernelName:Kc,backendName:"webgl",kernelFunc:NH},RH=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=`
|
|
const ivec2 pads = ivec2(${u}, ${p});
|
|
const float avgMultiplier = float(${c});
|
|
|
|
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 < ${o};
|
|
wR += ${s}) {
|
|
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 < ${l};
|
|
wC+= ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},MH=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=p-1-e.padInfo.front,f=c-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*a*n);this.userCode=`
|
|
const ivec3 pads = ivec3(${h}, ${f}, ${m});
|
|
const float avgMultiplier = float(${g});
|
|
|
|
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 < ${p};
|
|
wD += ${o}) {
|
|
float dyD = float(dyDCorner + wD) / ${r}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${c};
|
|
wR += ${l}) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.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 += ${u}) {
|
|
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 $H(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=T.computePool3DInfo(i.shape,o,l,c,u,p),h=new MH(d);return a.runWebGLProgram(h,[r],i.dtype)}var _H={kernelName:L2,backendName:"webgl",kernelFunc:$H};function PH(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;uu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=T.computePool2DInfo(i.shape,o,l,1,u),c=new RH(p);return a.runWebGLProgram(c,[r],i.dtype)}var FH={kernelName:Xc,backendName:"webgl",kernelFunc:PH};function OH(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Lc({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var DH={kernelName:ei,backendName:"webgl",kernelFunc:OH},zH=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${i};
|
|
float scale = ${o};
|
|
float inv = scale * inversesqrt(variance + float(${s}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}},LH=class{constructor(e,t,a,n,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a);let i="vec4(0.0)";n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${i};
|
|
vec4 scale = ${o};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}},BH=({inputs:e,backend:t,attrs:a})=>{let{x:n,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=a;l==null&&(l=.001);let u=[n,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let c=null;o!=null&&(c=o.shape,u.push(o));let d=V().getBool("WEBGL_PACK_NORMALIZATION")?new LH(n.shape,r.shape,s.shape,p,c,l):new zH(n.shape,r.shape,s.shape,p,c,l);return t.runWebGLProgram(d,u,u[0].dtype)},WH={kernelName:Ai,backendName:"webgl",kernelFunc:BH},VH=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let a=UH(this.rank),n,r=e.map((s,i)=>`sourceLoc.${k2[i]} = start[${i}] + coords.${k2[i]};`);n=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${n}
|
|
setOutput(getSource(${a}));
|
|
}
|
|
`}},k2=["x","y","z","w","u","v"];function UH(e){if(e===1)return"sourceLoc";if(e<=6)return k2.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var GH=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),a=wa("coords",this.rank),n=wa("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${r})`,i=`
|
|
result.x = ${s};
|
|
if (++${a[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${n[this.rank-1]};
|
|
result.y = ${s};
|
|
--${n[this.rank-1]};
|
|
}
|
|
`,o=this.rank===1?"":`
|
|
--${a[this.rank-1]};
|
|
if (++${a[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${n[this.rank-2]};
|
|
result.z = ${s};
|
|
if (++${a[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${n[this.rank-1]};
|
|
result.w = ${s};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((u,p)=>`start[${p}]`).join()});`:e.map((u,p)=>`${n[p]} = ${a[p]} + start[${p}];`).join(`
|
|
`);this.userCode=`
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${i}
|
|
${o}
|
|
setOutput(result);
|
|
}
|
|
`}};function HH(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=St.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function gu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=St.parseSliceParams(r,s,i);if(St.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=OU(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=St.isSliceContinous(r.shape,o,l);if(u||!p){let c=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new GH(l):new VH(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),HH(r,o,l,a)}var jH={kernelName:Xl,backendName:"webgl",kernelFunc:gu},qH=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=[],f=pe({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Ta({inputs:{x:f},backend:a,attrs:{perm:u}}),g=pe({inputs:{x:m},backend:a,attrs:{shape:p}}),y=gu({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>a.disposeIntermediateTensorInfo(x)),y},XH={kernelName:El,backendName:"webgl",kernelFunc:qH};function KH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.readSync(r.dataId),l=a.readSync(s.dataId),u=z6(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var ZH={kernelName:Sd,backendName:"webgl",kernelFunc:KH};function YH(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var JH={kernelName:Zc,backendName:"webgl",kernelFunc:YH},QH="return float(a != b);",Q6=pa({opSnippet:QH,cpuKernelImpl:CU,dtype:"bool"}),ej={kernelName:Bi,backendName:"webgl",kernelFunc:Q6};function mp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Qa({inputs:{x:r.complexTensorInfos.real},backend:a})}var tj={kernelName:Dd,backendName:"webgl",kernelFunc:mp},aj="return float(int(x));";function nj(e,t){let a=new qn(e.shape,aj),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function w2(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return Qa({inputs:{x:r},backend:a});let i=gn(r.shape),o=w2({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=ls({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=mp({inputs:{input:r},backend:a}),o=w2({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=Qa({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=oU(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return nj(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=Q6({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var rj={kernelName:ti,backendName:"webgl",kernelFunc:w2},vy="return ceil(x);",sj=Qe({opSnippet:vy,packedOpSnippet:vy,cpuKernelImpl:lU}),ij={kernelName:ai,backendName:"webgl",kernelFunc:sj},oj=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));
|
|
}
|
|
`}},lj=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 uj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o;V().getBool("WEBGL_PACK_CLIP")?o=new lj(r.shape):o=new oj(r.shape);let l=[[s],[i]];return a.runWebGLProgram(o,[r],r.dtype,l)}var dj={kernelName:as,backendName:"webgl",kernelFunc:uj},pj=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 ky(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function cj(e){let{inputs:t,backend:a}=e,{x:n}=t,r=a.texData.get(n.dataId),s=new pj(n.shape),i=[ky(n,r.complexTensorInfos.real),ky(n,r.complexTensorInfos.imag)];return a.runWebGLProgram(s,i,i[0].dtype)}var hj={kernelName:Yc,backendName:"webgl",kernelFunc:cj},fj=class{constructor(e){this.outputShape=[],this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let a=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];a.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,r=t[t.length-1];a.push(`else setOutput(getT${n}(yR, yC-${r}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${a.join(`
|
|
`)}
|
|
}
|
|
`}},mj=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=T.computeOutShape(e,t);let a=this.outputShape,n=a.length,r=gt(n),s=wa("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],u=i.slice(-2),p=i.join(),c=`if (${l} < ${o[0]}) {
|
|
return getChannel(
|
|
getT0(${p}), vec2(${u.join()}));
|
|
}`;for(let f=1;f<o.length;f++){let m=o[f-1];c+=`
|
|
if (${l} < ${o[f]} && ${l} >= ${o[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${fc(i,l,m)}),
|
|
vec2(${fc(u,l,m)}));
|
|
}`}let d=o.length,h=o[o.length-1];c+=`
|
|
return getChannel(
|
|
getT${d}(${fc(i,l,h)}),
|
|
vec2(${fc(u,l,h)}));`,this.userCode=`
|
|
float getValue(${i.map(f=>"int "+f)}) {
|
|
${c}
|
|
}
|
|
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
|
|
|
|
${s[n-1]} = ${s[n-1]} + 1;
|
|
if (${s[n-1]} < ${a[n-1]}) {
|
|
result.g = getValue(${s});
|
|
}
|
|
|
|
${s[n-2]} = ${s[n-2]} + 1;
|
|
if (${s[n-2]} < ${a[n-2]}) {
|
|
result.a = getValue(${s});
|
|
}
|
|
|
|
${s[n-1]} = ${s[n-1]} - 1;
|
|
if (${s[n-2]} < ${a[n-2]} &&
|
|
${s[n-1]} < ${a[n-1]}) {
|
|
result.b = getValue(${s});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function fc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - ${a}`:r).join()}function _h(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return Qa({inputs:{x:r.complexTensorInfos.imag},backend:a})}var gj={kernelName:Pd,backendName:"webgl",kernelFunc:_h};function Ku(e,t,a){let n=e[0].dtype;if(n==="complex64"){let h=e.map(x=>mp({inputs:{input:x},backend:a})),f=e.map(x=>_h({inputs:{input:x},backend:a})),m=Ku(h,t,a),g=Ku(f,t,a),y=ls({inputs:{real:m,imag:g},backend:a});return h.forEach(x=>a.disposeIntermediateTensorInfo(x)),f.forEach(x=>a.disposeIntermediateTensorInfo(x)),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),y}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let h=e.map(b=>{let k=[-1,v.sizeFromShape(b.shape.slice(t))];return pe({inputs:{x:b},backend:a,attrs:{shape:k}})}),f=h.map(b=>({vals:a.readSync(b.dataId),shape:b.shape})),m=T.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=uU(f,m,n,g),x=T.computeOutShape(e.map(b=>b.shape),t),A=a.makeTensorInfo(x,n,y);return h.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new qn(e[0].shape,Or):new Wr(e[0].shape,Or);return a.runWebGLProgram(h,e,n)}let o=V().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let m=0;m<s.length;m+=o){let g=s.slice(m,m+o);h.push(Ku(g,t,a))}let f=Ku(h,t,a);for(let m of h)a.disposeIntermediateTensorInfo(m);return f}if(i){let h=new mj(s.map(f=>f.shape),t);return a.runWebGLProgram(h,s,n)}let{tensors2D:l,outShape:u}=yj(s,t,a),p=new fj(l.map(h=>h.shape)),c=a.runWebGLProgram(p,l,n);l.forEach(h=>a.disposeIntermediateTensorInfo(h));let d=pe({inputs:{x:c},attrs:{shape:u},backend:a});return a.disposeIntermediateTensorInfo(c),d}function yj(e,t,a){let n=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>pe({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:a})),outShape:n}}function ev(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?Qa({inputs:{x:l[0]},backend:a}):Ku(l,s,a)}var xj={kernelName:Rl,backendName:"webgl",kernelFunc:ev},tv=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,x=m?3:1,A="",b="";a&&(n?A=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${a}
|
|
}`:r?A=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${a}
|
|
}`:A=`
|
|
float activation(float x) {
|
|
${a}
|
|
}
|
|
`,b="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${A}
|
|
|
|
const ivec2 strides = ivec2(${o}, ${l});
|
|
const ivec2 pads = ivec2(${s}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${x}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${g}], coords[${y}]) * 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 < ${c}; wR++) {
|
|
int xR = xRCorner + wR * ${u};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d}; wC++) {
|
|
int xC = xCCorner + wC * ${p};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
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 (${f===1}) {
|
|
|
|
if (${m}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${h}) *
|
|
getW(wR, wC, ${h}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${h}, xR, xC) *
|
|
getW(wR, wC, ${h}, d2);
|
|
}
|
|
|
|
} else if (${f===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${f===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2),
|
|
getW(wR, wC, ${h} + 2, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1),
|
|
getX(batch, xR, xC, ${h} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC),
|
|
getX(batch, ${h} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${k}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},Aj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,a=e.padInfo.top,n=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,p=e.filterDepth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${r}, ${s}, ${i});
|
|
const ivec3 pads = ivec3(${t}, ${a}, ${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 < ${p}; wF++) {
|
|
int xF = xFCorner + wF * ${o};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${c}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d}; wC++) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${f===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${h}) *
|
|
getW(wF, wR, wC, ${h}, d2);
|
|
} else if (${f===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${f===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1),
|
|
getX(batch, xF, xR, xC, ${h} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2),
|
|
getW(wF, wR, wC, ${h} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},av=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ra(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let m=0;m<u;m++)c+=`
|
|
vec4 xTexelC${m*2};
|
|
int xTexelC${m*2}Ready;
|
|
vec4 xTexelC${m*2+1};
|
|
int xTexelC${m*2+1}Ready;
|
|
vec4 xC${m};`;c+=`
|
|
for (int r = 0; r < ${l}; r++) {
|
|
for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {
|
|
`;for(let m=0;m<u;m++)c+=`
|
|
xTexelC${m*2} = vec4(0.0);
|
|
xTexelC${m*2}Ready = 0;
|
|
xTexelC${m*2+1} = vec4(0.0);
|
|
xTexelC${m*2+1}Ready = 0;
|
|
xC${m} = vec4(0.0);`;c+=`
|
|
xR = xRCorner + r * dilations[0];
|
|
if (xR >=0 && xR < inDims[0]) {
|
|
`;for(let m=0;m<(p+1)/2;m++){let g=m*2;if(c+=`
|
|
xC = xCCorner + ${g*o};
|
|
`,i===1){if(g<u&&(s%2===1?(c+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) {
|
|
xTexelC${g} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${g}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${g}Ready = 1;
|
|
}
|
|
`,o===1&&g>0?c+=`
|
|
xC${g} = vec4(xTexelC${g-2}.zw, xTexelC${g}.xy);
|
|
`:c+=`
|
|
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${g} = vec4(previous.zw, xTexelC${g}.xy);
|
|
} else {
|
|
xC${g} = vec4(0.0, 0.0, xTexelC${g}.xy);
|
|
}
|
|
`):c+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) {
|
|
xTexelC${g} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${g}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${g}Ready = 1;
|
|
}
|
|
|
|
xC${g} = xTexelC${g};
|
|
`,g+1<u)){let y=s%2===0?v.nearestLargerEven(o):o;o%2===0&&s%2===1||o%2!==0&&s%2!==1?(c+=`
|
|
xCOffset = xC + imod(pads[1], 2) + ${y};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) {
|
|
xTexelC${g+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${g+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${g+1}Ready = 1;
|
|
}
|
|
`,o>1?c+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
xC${g+1} = vec4(previous.zw, xTexelC${g+1}.xy);
|
|
} else {
|
|
xC${g+1} = vec4(0.0, 0.0, xTexelC${g+1}.xy);
|
|
}
|
|
`:c+=`
|
|
xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.xy);
|
|
`):y===1?c+=`
|
|
xC${g+1} = xTexelC${g};
|
|
`:c+=`
|
|
xCOffset = xC + ${y};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) {
|
|
xTexelC${g+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${g+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${g+1}Ready = 1;
|
|
}
|
|
|
|
xC${g+1} = xTexelC${g+1};
|
|
`}}else g<u&&(s%2===1?(c+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) {
|
|
xTexelC${g} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${g}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${g}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${g+1}Ready == 0) {
|
|
xTexelC${g+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${g+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${g+1}Ready = 1;
|
|
}
|
|
|
|
xC${g} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw);
|
|
`,g+1<u&&(c+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${g+1} = vec4(xTexelC${g+1}.xy, final.xy);
|
|
`)):(c+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) {
|
|
xTexelC${g} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${g}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${g}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) {
|
|
xTexelC${g+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${g+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${g+1}Ready = 1;
|
|
}
|
|
|
|
xC${g} = vec4(
|
|
xTexelC${g}.xy, xTexelC${g+1}.xy);
|
|
`,g+1<u&&(c+=`
|
|
xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw);
|
|
`)));g<u&&(c+=`
|
|
wTexel = getW(r, ${g}, d1, d2);
|
|
dotProd += xC${g}.xxzz * vec4(wTexel.xy, wTexel.xy);
|
|
if(d1 + 1 < ${e.inChannels}) {
|
|
dotProd += xC${g}.yyww * vec4(wTexel.zw, wTexel.zw);
|
|
}
|
|
`,g+1<u&&(c+=`
|
|
wTexel = getW(r, ${g+1}, d1, d2);
|
|
dotProd += xC${g+1}.xxzz * vec4(wTexel.xy, wTexel.xy);
|
|
if(d1 + 1 < ${e.inChannels}) {
|
|
dotProd += xC${g+1}.yyww * vec4(wTexel.zw, wTexel.zw);
|
|
}
|
|
`))}c+=`
|
|
}
|
|
`,c+=`
|
|
}
|
|
`,c+=`
|
|
}
|
|
`;let d="",h="";a&&(n?d=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${a}
|
|
}`:r?d=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${a}
|
|
}`:d=`vec4 activation(vec4 x) {
|
|
${a}
|
|
}`,h="result = activation(result);");let f=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${d}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
|
vec4 dotProd = vec4(0.000000000000001);
|
|
|
|
${c}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${f}
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}},bj=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length);let{dataFormat:a}=t,n=Ea(),r=a==="channelsLast",s=r?1:2,i=r?2:3,o=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,l="";for(let u=0;u<=1;u++)for(let p=0;p<=1;p++)l+=`
|
|
blockIndex = rc.z + ${p};
|
|
pos = rc.y + ${u};
|
|
|
|
${o}
|
|
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
|
|
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
|
|
|
|
if(d0 < inputShape[${s}] && d0 >= 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[${i}] && d1 >= 0) {
|
|
|
|
ch = imod(pos, inChannels);
|
|
|
|
if (${r}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${u*2+p}] = getChannel(
|
|
getA(rc.x, d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${u*2+p}] = 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;
|
|
|
|
${l}
|
|
|
|
${n.output} = result;
|
|
}
|
|
`}};function Bc(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function nv({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(s!=null){let x=Bc(s.shape,h);x!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:x}}),y.push(s))}if(r!=null){let x=Bc(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:x}}),y.push(r))}if(!((c===1||d===1)&&p>K6)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let x=l[0]*l[1]*(l[2]+1),A={dataId:e.dataId,shape:[1,x,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(gd(u.shape,A.shape),()=>`packed reshape ${u.shape} to ${A.shape} isn't free`);let k=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});y.push(k);let S=Lc({a:A,b:k,backend:n,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=n.texData.get(S.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,C.shape=a.outShape,g=Qa({inputs:{x:S},backend:n}),g.shape=a.outShape,y.push(S)}else{let x=a.outHeight*a.outWidth,A=pe({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,x,a.inChannels]:[a.batchSize,a.inChannels,x]}}),b=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),k=Lc({a:h?A:b,b:h?b:A,transposeA:!h,transposeB:m,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=pe({inputs:{x:k},backend:n,attrs:{shape:a.outShape}}),y.push(A),y.push(b),y.push(k)}for(let x of y)n.disposeIntermediateTensorInfo(x);return g}function rv({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,f=h==="channelsLast",m=l*u*p,g=d*c,y=[a.batchSize,m,g],x=!0,A=!1,b=[];if(s!=null){let G=Bc(s.shape,f);G!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:G}}),b.push(s))}if(r!=null){let G=Bc(r.shape,f);G!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:G}}),b.push(r))}let k=pe({inputs:{x:t},backend:n,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(k);let S=new bj(y,a),C=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],N=n.runWebGLProgram(S,[e],"float32",C),$=pe({inputs:{x:N},backend:n,attrs:{shape:y}});b.push(N),b.push($);let M=r!=null,R=s!=null,I=o==="leakyrelu",_=o?yd(o,!0):null,D=new X6(f?$.shape:k.shape,f?k.shape:$.shape,f?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],x,A,M,_,R,I),W=f?[$,k]:[k,$];if(r&&W.push(r),R&&W.push(s),I){let G=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));W.push(G),b.push(G)}let P=n.runWebGLProgram(D,W,"float32"),U=pe({inputs:{x:P},backend:n,attrs:{shape:a.outShape}});b.push(P);for(let G of b)n.disposeIntermediateTensorInfo(G);return U}function vj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=nv({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&V().getBool("WEBGL_EXP_CONV")){let m=new av(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(m,[r,s],"float32",g)}else if(V().getBool("WEBGL_CONV_IM2COL"))h=rv({x:r,filter:s,convInfo:d,backend:a});else{let m=new tv(d);h=a.runWebGLProgram(m,[r,s],"float32")}let f=pe({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),f}var kj={kernelName:ni,backendName:"webgl",kernelFunc:vj},wj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=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 * ${a} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
if (${s}) {
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
} else {
|
|
float dyValue = getDy(b, d2, yR, yC);
|
|
float xValue = getX(b, d1, xR, xC);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Ij=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=a-1-e.padInfo.left,l=s?1:2,u=s?2:3,p=s?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${p}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - 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 < ${a}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${a} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
|
|
if (${s}) {
|
|
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);
|
|
}
|
|
`}},Sj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.padInfo.front,s=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} - ${r};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${a} - ${s};
|
|
|
|
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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Tj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=a-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${o}, ${l}, ${u});
|
|
|
|
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) / ${r}.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 < ${a}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${a} - 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 Cj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new wj(d);return a.runWebGLProgram(h,[r,s],"float32")}var Nj={kernelName:Cd,backendName:"webgl",kernelFunc:Cj};function Ej(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=new Ij(d);return a.runWebGLProgram(h,[r,s],"float32")}var Rj={kernelName:ri,backendName:"webgl",kernelFunc:Ej};function Mj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new Aj(u);return a.runWebGLProgram(p,[r,s],"float32")}var $j={kernelName:Jc,backendName:"webgl",kernelFunc:Mj};function _j(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=T.computeConv3DInfo(r.shape,l,i,1,o),p=new Sj(u);return a.runWebGLProgram(p,[r,s],"float32")}var Pj={kernelName:B2,backendName:"webgl",kernelFunc:_j};function Fj(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=T.computeConv3DInfo(l,s.shape,o,1,i),p=new Tj(u);return a.runWebGLProgram(p,[r,s],"float32")}var Oj={kernelName:Qc,backendName:"webgl",kernelFunc:Fj},Dj=mu+`
|
|
return cos(x);
|
|
`,zj=Qe({opSnippet:Dj}),Lj={kernelName:si,backendName:"webgl",kernelFunc:zj},Bj=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,Wj=Qe({opSnippet:Bj}),Vj={kernelName:ii,backendName:"webgl",kernelFunc:Wj},Uj=class{constructor(e,t,a,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,c]=a;this.outputShape=[u,p,c,l];let d=n==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=c>1?[`${(o-1)/(c-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
|
|
const float height_ratio = float(${m});
|
|
const float width_ratio = float(${x});
|
|
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 >= ${s}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${g};
|
|
float width_scale = ${A};
|
|
|
|
float in_y = ${y};
|
|
if( in_y < 0.0 || in_y > ${h} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
float in_x = ${b};
|
|
if( in_x < 0.0 || in_x > ${f} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${d} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},Gj=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Uj(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},Hj={kernelName:ui,backendName:"webgl",kernelFunc:Gj},Ad;(function(e){e.Prod="*",e.Sum="+"})(Ad||(Ad={}));var wy=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===Ad.Prod?"1.0":"0.0",i=a?s:`getX(${Iy(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
void main() {
|
|
${gt(r)} coords = getOutputCoords();
|
|
int end = ${Sy(r,"coords",this.op)};
|
|
float val = ${i};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${l}) {
|
|
int idx = ${u};
|
|
${Sy(r,"coords",this.op)} = idx;
|
|
val ${this.op}= getX(${Iy(r,"coords",this.op)});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function Iy(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Sy(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function sv(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=Ta({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=Qa({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new wy(e,l.shape,!1,s),f=[[d]],m=c;c=a.runWebGLProgram(h,[c],c.dtype,f),a.disposeIntermediateTensorInfo(m)}if(r){let d=new wy(e,l.shape,r,s),h=c;c=a.runWebGLProgram(d,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let d=T.getUndoAxesPermutation(o),h=Ta({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function jj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return sv(Ad.Prod,r,a,s,i,o)}var qj={kernelName:oi,backendName:"webgl",kernelFunc:jj};function Xj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return sv(Ad.Sum,r,a,s,i,o)}var Kj={kernelName:li,backendName:"webgl",kernelFunc:Xj};function Zj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=z6(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=iU(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Yj={kernelName:Nd,backendName:"webgl",kernelFunc:Zj},Jj=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,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 Qj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=new Jj(f,s,i);return a.runWebGLProgram(m,[r],r.dtype)}var eq={kernelName:di,backendName:"webgl",kernelFunc:Qj},iv=class{constructor(e,t=!1,a=null,n=!1,r=!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=Ra(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${a}
|
|
}`:r?l=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${a}
|
|
}`:l=`
|
|
float activation(float x) {
|
|
${a}
|
|
}
|
|
`,u="result = activation(result);");let p=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${l}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${o};
|
|
int q = d2 - d1 * ${o};
|
|
|
|
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 < ${s}; 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;
|
|
${p}
|
|
${u}
|
|
setOutput(result);
|
|
}
|
|
`}},ov=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ra(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<p;g++)d+=`
|
|
vec4 xTexelC${g*2};
|
|
int xTexelC${g*2}Ready;
|
|
vec4 xTexelC${g*2+1};
|
|
int xTexelC${g*2+1}Ready;
|
|
vec4 xC${g};`;d+=`
|
|
for (int r = 0; r < ${u}; r++) {
|
|
`;for(let g=0;g<p;g++)d+=`
|
|
xTexelC${g*2} = vec4(0.0);
|
|
xTexelC${g*2}Ready = 0;
|
|
xTexelC${g*2+1} = vec4(0.0);
|
|
xTexelC${g*2+1}Ready = 0;
|
|
xC${g} = vec4(0.0);`;d+=`
|
|
xR = xRCorner + r * dilations[0];
|
|
if (xR >=0 && xR < inDims[0]) {
|
|
`;for(let g=0;g<(c+1)/2;g++){let y=g*2;if(d+=`
|
|
xC = xCCorner + ${y*l};
|
|
`,o===1){if(y<p&&(i%2===1?(d+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`,l===1&&y>0?d+=`
|
|
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.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${y} = vec4(previous.zw, xTexelC${y}.xy);
|
|
} else {
|
|
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
|
|
}
|
|
`):d+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xC${y} = xTexelC${y};
|
|
`,y+1<p)){let x=i%2===0?v.nearestLargerEven(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(d+=`
|
|
xCOffset = xC + imod(pads[1], 2) + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
`,l>1?d+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
xC${y+1} = vec4(previous.zw, xTexelC${y+1}.xy);
|
|
} else {
|
|
xC${y+1} = vec4(0.0, 0.0, xTexelC${y+1}.xy);
|
|
}
|
|
`:d+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
|
|
`):x===1?d+=`
|
|
xC${y+1} = xTexelC${y};
|
|
`:d+=`
|
|
xCOffset = xC + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y+1} = xTexelC${y+1};
|
|
`}}else y<p&&(i%2===1?(d+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`,y+1<p&&(d+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
|
|
`)):(d+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(
|
|
xTexelC${y}.xy, xTexelC${y+1}.xy);
|
|
`,y+1<p&&(d+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`)));y<p&&(d+=`
|
|
wTexel = getW(r, ${y}, d1, q);
|
|
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
|
|
`,y+1<p&&(d+=`
|
|
wTexel = getW(r, ${y+1}, d1, q);
|
|
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}d+=`
|
|
}
|
|
`,d+=`
|
|
}
|
|
`;let h="",f="";a&&(n?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${a}
|
|
}`:r?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${a}
|
|
}`:h=`vec4 activation(vec4 x) {
|
|
${a}
|
|
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${h}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${s};
|
|
int q = d2 - d1 * ${s};
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
|
vec4 dotProd = vec4(0.000000000000001);
|
|
|
|
${d}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${m}
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};function tq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,p=l;p==null&&(p=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;V().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new ov(c):d=new iv(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var aq={kernelName:pi,backendName:"webgl",kernelFunc:tq},nq=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=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 * ${s} + 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 * ${a} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},rq=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=a-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${s}, ${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 < ${a}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${a} - 1 - wC;
|
|
|
|
// TO DO: Vec4 over the channelMul
|
|
for (int dm = 0; dm < ${o}; dm++) {
|
|
int d2 = d1 * ${o} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function sq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=T.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new nq(c);return a.runWebGLProgram(d,[r,s],"float32")}var iq={kernelName:eh,backendName:"webgl",kernelFunc:sq};function oq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=T.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new rq(c);return a.runWebGLProgram(d,[r,s],"float32")}var lq={kernelName:th,backendName:"webgl",kernelFunc:oq},uq=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function dq(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=pe({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new uq(s),l=a.runWebGLProgram(o,[i],i.dtype),u=pe({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var pq={kernelName:Ed,backendName:"webgl",kernelFunc:dq},cq=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=`
|
|
const ivec2 strides = ivec2(${r}, ${s});
|
|
const ivec2 pads = ivec2(${p}, ${c});
|
|
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 * ${l};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${o}; w++) {
|
|
int wIn = wBeg + w * ${u};
|
|
|
|
if (wIn >= 0 && wIn < ${a}) {
|
|
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 hq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new cq(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=pe({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var fq={kernelName:Rd,backendName:"webgl",kernelFunc:hq};function mq(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m<c;++m){for(let g of p[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=s[g]:(A=Ta({inputs:{x:s[g]},backend:a,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let k=0;k<x.length;++k)b.splice(x[k],0,1);v.arraysEqual(A.shape,b)||(A=pe({inputs:{x:A},backend:a,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=$3({inputs:{a:A,b:d},backend:a}),f.push(d))}m<c-1&&(u[m]>=0&&(d=$h({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeIntermediateTensorInfo(m);return d}var gq={kernelName:Md,backendName:"webgl",kernelFunc:mq},yq="return (x >= 0.0) ? x : (exp(x) - 1.0);",xq=`
|
|
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;
|
|
`,Aq=Qe({opSnippet:yq,packedOpSnippet:xq}),bq={kernelName:hi,backendName:"webgl",kernelFunc:Aq},vq="return (b >= 1.0) ? a : a * (b + 1.0);",kq=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,wq=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new fp(kq,n.shape,r.shape):new yl(vq,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},Iq={kernelName:W2,backendName:"webgl",kernelFunc:wq},Sq=`
|
|
return vec4(equal(a, b));
|
|
`,Tq="return float(a == b);",Cq=pa({opSnippet:Tq,packedOpSnippet:Sq,dtype:"bool",cpuKernelImpl:dU}),Nq={kernelName:fi,backendName:"webgl",kernelFunc:Cq},Eq=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${T.ERF_P};
|
|
float a1 = ${T.ERF_A1};
|
|
float a2 = ${T.ERF_A2};
|
|
float a3 = ${T.ERF_A3};
|
|
float a4 = ${T.ERF_A4};
|
|
float a5 = ${T.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,Rq=Qe({opSnippet:Eq}),Mq={kernelName:Ml,backendName:"webgl",kernelFunc:Rq},$q=mu+`
|
|
return exp(x);
|
|
`,_q=`
|
|
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;
|
|
`,lv=Qe({opSnippet:$q,packedOpSnippet:_q,cpuKernelImpl:pU,dtype:"float32"}),Pq={kernelName:mi,backendName:"webgl",kernelFunc:lv};function I2(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),pe({inputs:{x:s},backend:n,attrs:{shape:o}})}var Fq={kernelName:$l,backendName:"webgl",kernelFunc:I2},Ty="return exp(x) - 1.0;",Oq=Qe({opSnippet:Ty,packedOpSnippet:Ty,cpuKernelImpl:cU}),Dq={kernelName:_l,backendName:"webgl",kernelFunc:Oq},Cy=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${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 = ${r};
|
|
|
|
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) / ${s};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function uv(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=pe({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new Cy("real",l,t),p=new Cy("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),f=ls({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let m=pe({inputs:{x:f},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(f),m}function zq(e){let{inputs:t,backend:a}=e,{input:n}=t;return uv(n,!1,a)}var Lq={kernelName:$d,backendName:"webgl",kernelFunc:zq},Bq=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 gp(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new Bq(n,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Wq={kernelName:Pl,backendName:"webgl",kernelFunc:gp},Vq=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},Uq={kernelName:gi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Vq(a.shape);return n.runWebGLProgram(r,[a],a.dtype)}},Ny="return floor(x);",Gq=Qe({opSnippet:Ny,packedOpSnippet:Ny,cpuKernelImpl:hU}),Hq={kernelName:yi,backendName:"webgl",kernelFunc:Gq},jq=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,qq=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,Xq=pa({opSnippet:jq,packedOpSnippet:qq,dtype:"int32"}),Kq={kernelName:xi,backendName:"webgl",kernelFunc:Xq},Zq=class{constructor(e){this.variableNames=["A"];let t=Ea(),[a,n]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${a}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},Yq=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Ea(),[a,n]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${n}.0, ${a}.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);
|
|
}
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},Jq={kernelName:nd,backendName:"webgl",kernelFunc:Qq},Ko,Om=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function Qq(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,l],c=[u,l,s];if(o||i){let m=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ko==null||m!==Om)&&(Om=m,Ko=document.createElement("canvas").getContext("2d",{willReadFrequently:Om})),Ko.canvas.width=l,Ko.canvas.height=u,Ko.drawImage(r,0,0,l,u),r=Ko.canvas}let d=a.makeTensorInfo(p,"int32");a.texData.get(d.dataId).usage=hn.PIXELS,a.gpgpu.uploadPixelDataToTexture(a.getTexture(d.dataId),r);let h=V().getBool("WEBGL_PACK")?new Yq(c):new Zq(c),f=a.runWebGLProgram(h,[d],"int32");return a.disposeData(d.dataId),f}function eX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=T.convertConv2DDataFormat(p),g=T.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,m),y,x=[],A=i!=null,b=o!=null,k=h==="leakyrelu",S=()=>{let N=[r,s],$=(M,R)=>{if(R==="NCHW"&&M.shape.length===1&&M.shape[0]!==1){let I=pe({inputs:{x:M},backend:a,attrs:{shape:[M.shape[0],1,1]}});return x.push(I),I}return M};if(A&&N.push($(i,p)),b&&N.push($(o,p)),k){let M=a.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));N.push(M),x.push(M)}return N};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"))y=nv({x:r,filter:s,convInfo:g,backend:a,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(g.strideWidth<=2&&m==="channelsLast"&&V().getBool("WEBGL_EXP_CONV")){let N=h?yd(h,!0):null,$=new av(g,A,N,b,k),M=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=S();y=a.runWebGLProgram($,R,"float32",M)}else if(V().getBool("WEBGL_CONV_IM2COL"))y=rv({x:r,filter:s,convInfo:g,backend:a,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let N=h?yd(h,!1):null,$=new tv(g,A,N,b,k),M=S();y=a.runWebGLProgram($,M,"float32")}let C=pe({inputs:{x:y},backend:a,attrs:{shape:g.outShape}});return x.push(y),x.forEach(N=>a.disposeIntermediateTensorInfo(N)),C}var tX={kernelName:jr,backendName:"webgl",kernelFunc:eX};function aX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,f=[],m=p;m==null&&(m=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=T.computeConv2DInfo(r.shape,s.shape,l,m,u,c,!0),y=V().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,x=d?yd(d,y):null,A=[r,s],b=i!=null,k=o!=null,S=d==="leakyrelu";if(b&&A.push(i),k&&A.push(o),S){let M=a.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));A.push(M),f.push(M)}let C;y?C=new ov(g,b,x,k,S):C=new iv(g,b,x,k,S);let N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=a.runWebGLProgram(C,A,"float32",N);return f.forEach(M=>a.disposeIntermediateTensorInfo(M)),$}var nX={kernelName:qr,backendName:"webgl",kernelFunc:aX},rX=class{constructor(e,t,a,n){this.sliceDim=e,this.strides=t,this.paramsShape=n,this.variableNames=["x","indices"],this.outputShape=a;let r=gt(a.length),s=`
|
|
int index;`;for(let i=0;i<this.sliceDim;i++)s+=`
|
|
index = round(getIndices(coords[0], ${i}));
|
|
out_of_bounds = out_of_bounds || index < 0;
|
|
out_of_bounds = out_of_bounds || index >= ${this.paramsShape[i]};
|
|
flattenIndex += index * ${this.strides[i]};`;this.userCode=`
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
int flattenIndex = 0;
|
|
bool out_of_bounds = false;
|
|
|
|
${s}
|
|
|
|
setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function sX(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,p,c]=T.prepareAndValidate(n,r),d=pe({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=pe({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/p,p]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let y=a.readSync(r.dataId),x=a.bufferSync(n),A=fU(y,x,n.dtype,u,i,p,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,A.values)}let f=new rX(i,c,[u,p],n.shape),m=a.runWebGLProgram(f,[h,d],h.dtype),g=pe({inputs:{x:m},backend:a,attrs:{shape:l}});return a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),g}var iX={kernelName:bi,backendName:"webgl",kernelFunc:sX},oX=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let a=gt(this.rank),n=lX(e,2);this.userCode=`
|
|
void main() {
|
|
${a} 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 lX(e,t){let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r<e.length;r++)r===2?n.push("index"):n.push(`${a[r]}`);return n.join()}function dv(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0];if(V().get("DEBUG")){let x=a.readSync(s.dataId),A=r.shape[l];for(let b=0;b<x.length;++b){let k=x[b];v.assert(k<=A-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${A-1}]`)}}let u=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=v.sizeFromShape(s.shape),c=[],d=pe({inputs:{x:r},backend:a,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=pe({inputs:{x:s},backend:a,attrs:{shape:[u.batchSize,p/u.batchSize]}});c.push(d),c.push(h);let f=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(a.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let x=a.bufferSync(h),A=a.bufferSync(d),b=mU(A,x,f);return c.forEach(k=>a.disposeIntermediateTensorInfo(k)),a.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new oX(d.shape,f),g=a.runWebGLProgram(m,[d,h],d.dtype);c.push(g);let y=pe({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeIntermediateTensorInfo(x)),y}var uX={kernelName:Fl,backendName:"webgl",kernelFunc:dv},dX="return float(a > b);",pX=`
|
|
return vec4(greaterThan(a, b));
|
|
`,cX=pa({opSnippet:dX,packedOpSnippet:pX,cpuKernelImpl:gU,dtype:"bool"}),hX={kernelName:vi,backendName:"webgl",kernelFunc:cX},fX="return float(a >= b);",mX=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,gX=pa({opSnippet:fX,packedOpSnippet:mX,dtype:"bool",cpuKernelImpl:yU}),yX={kernelName:ki,backendName:"webgl",kernelFunc:gX};function xX(e){let{inputs:t,backend:a}=e,{input:n}=t;return uv(n,!0,a)}var AX={kernelName:_d,backendName:"webgl",kernelFunc:xX},bX="return float(!isnan(x) && !isinf(x));",vX=Qe({opSnippet:bX,dtype:"bool"}),kX={kernelName:Ol,backendName:"webgl",kernelFunc:vX},wX="return float(isinf(x));",IX=Qe({opSnippet:wX,dtype:"bool"}),SX={kernelName:Dl,backendName:"webgl",kernelFunc:IX},TX="return float(isnan(x));",CX=Qe({opSnippet:TX,dtype:"bool"}),NX={kernelName:Ii,backendName:"webgl",kernelFunc:CX},EX="return float(a < b);",RX=`
|
|
return vec4(lessThan(a, b));
|
|
`,MX=pa({opSnippet:EX,packedOpSnippet:RX,cpuKernelImpl:xU,dtype:"bool"}),$X={kernelName:Ti,backendName:"webgl",kernelFunc:MX},_X="return float(a <= b);",PX=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,FX=pa({opSnippet:_X,packedOpSnippet:PX,cpuKernelImpl:AU,dtype:"bool"}),OX={kernelName:Ci,backendName:"webgl",kernelFunc:FX};function DX(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=bU(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var zX={kernelName:Fd,backendName:"webgl",kernelFunc:DX},LX=mu+`
|
|
return x < 0.0 ? 0./0. : log(x);
|
|
`,BX=`
|
|
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;
|
|
`,WX=Qe({opSnippet:LX,packedOpSnippet:BX,cpuKernelImpl:vU}),VX={kernelName:Ni,backendName:"webgl",kernelFunc:WX},UX=mu+`
|
|
return log(1.0 + x);
|
|
`,GX=Qe({opSnippet:UX}),HX={kernelName:zl,backendName:"webgl",kernelFunc:GX},jX="return float(a >= 1.0 && b >= 1.0);",qX=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,XX=pa({opSnippet:jX,packedOpSnippet:qX,dtype:"bool"}),KX={kernelName:Ei,backendName:"webgl",kernelFunc:XX},ZX="return float(!(x >= 1.0));",YX=Qe({opSnippet:ZX}),JX={kernelName:Ri,backendName:"webgl",kernelFunc:YX},QX="return float(a >= 1.0 || b >= 1.0);",eK=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,tK=pa({opSnippet:QX,packedOpSnippet:eK,dtype:"bool"}),aK={kernelName:Mi,backendName:"webgl",kernelFunc:tK},nK=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,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 = -${s}; j <= ${s}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${i}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${o};
|
|
setOutput(val);
|
|
}
|
|
`}},rK=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,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 - ${s};
|
|
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 = - ${s}; j <= ${s}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${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 * ${o};
|
|
setOutput(result);
|
|
}
|
|
`}},sK=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=V().getBool("WEBGL_PACK_NORMALIZATION")?new rK(r.shape,s,i,o,l):new nK(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},iK={kernelName:Od,backendName:"webgl",kernelFunc:sK},oK=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,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(${a});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${n})
|
|
* float(${r})
|
|
* getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${r});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},lK=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new oK(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},uK={kernelName:V2,backendName:"webgl",kernelFunc:lK};function dK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=vo(i,e.dtype,"max",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function pv(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let x=a.texData.get(h.dataId).values,A=new Array(o);for(let S=0;S<A.length;S++)A[S]=r.shape[p[S]];let b=R3(x,r.shape,r.dtype,p,A);h=a.makeTensorInfo(A,r.dtype);let k=a.texData.get(h.dataId);k.values=b}else h=Mh(r,p,a);u=T.getInnerMostAxes(u.length,o)}T.assertAxesAreInnerMostDims("max",u,o);let[f,m]=T.computeOutAndReduceShapes(h.shape,u),g=f;i&&(g=T.expandShapeToKeepDim(f,l));let y;if(d){let x=a.texData.get(h.dataId).values,A=kU(x,v.sizeFromShape(m),g,r.dtype);y=a.makeTensorInfo(g,r.dtype);let b=a.texData.get(y.dataId);b.values=A}else y=dK(h,m,g,a);return c&&a.disposeIntermediateTensorInfo(h),y}var pK={kernelName:$i,backendName:"webgl",kernelFunc:pv},cK=M3+`
|
|
return max(a, b);
|
|
`,hK=`
|
|
vec4 result = vec4(max(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);
|
|
`+hp+`
|
|
return result;
|
|
`,fK=pa({opSnippet:cK,packedOpSnippet:hK,cpuKernelImpl:wU}),mK={kernelName:_i,backendName:"webgl",kernelFunc:fK};function gK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;uu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return Qa({inputs:{x:r},backend:a});let c=new xd(p,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var yK={kernelName:Pi,backendName:"webgl",kernelFunc:gK};function xK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new _3(c,"max",!1);return a.runWebGLProgram(d,[r],r.dtype)}var AK={kernelName:ah,backendName:"webgl",kernelFunc:xK},bK=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
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 < ${r};
|
|
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 < ${s}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${a}.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 = ${l} - int(getMaxPos(b, idyR, idyC, d));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue = wR * ${s} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},vK=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,p=o-1-e.padInfo.front,c=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${p}, ${c}, ${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 < ${o};
|
|
wD += ${r}) {
|
|
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 < ${l};
|
|
wR += ${s}) {
|
|
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 < ${u};
|
|
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 = ${h} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${l} * ${u} +
|
|
wR * ${u} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function kK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=T.computePool3DInfo(i.shape,o,l,c,u,p),h=new _3(d,"max",!0),f=a.runWebGLProgram(h,[i],i.dtype),m=new vK(d),g=a.runWebGLProgram(m,[r,f],i.dtype);return a.disposeIntermediateTensorInfo(f),g}var wK={kernelName:G2,backendName:"webgl",kernelFunc:kK};function IK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;uu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=T.computePool2DInfo(o.shape,l,u,1,p,c),h=!0,f=new xd(d,"max",h),m=a.runWebGLProgram(f,[o],o.dtype),g=new bK(d),y=a.runWebGLProgram(g,[r,m],o.dtype);return a.disposeIntermediateTensorInfo(m),y}var SK={kernelName:U2,backendName:"webgl",kernelFunc:IK};function TK(e,t,a,n){let r=new xd(a,"max",!1),s=n.runWebGLProgram(r,[e],"float32");r=new xd(a,"max",!0,!0,t);let i=n.runWebGLProgram(r,[e],"float32");return[s,i]}var CK={kernelName:nh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=T.computePool2DInfo(n.shape,r,s,u,i),[c,d]=TK(n,o,p,l);return[c,d]}};function NK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=vo(i,"float32","mean",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var EK={kernelName:Fi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{keepDims:r,axis:s}=t,i=a,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,p=T.getAxesPermutation(u,o),c=p!=null,d=i.shouldExecuteOnCPU([n]),h=[],f=n;if(c){if(d){let A=i.texData.get(f.dataId).values,b=new Array(o);for(let C=0;C<b.length;C++)b[C]=n.shape[p[C]];let k=R3(A,n.shape,n.dtype,p,b);f=i.makeTensorInfo(b,n.dtype);let S=i.texData.get(f.dataId);S.values=k}else f=Mh(n,p,i);h.push(f),u=T.getInnerMostAxes(u.length,o)}T.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=T.computeOutAndReduceShapes(f.shape,u),y=m;r&&(y=T.expandShapeToKeepDim(m,l));let x=NK(f,g,y,i);for(let A of h)i.disposeIntermediateTensorInfo(A);return x}};function RK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=T.getAxesPermutation(u,o),c=r;p!=null&&(c=Ta({inputs:{x:r},backend:a,attrs:{perm:p}}),u=T.getInnerMostAxes(u.length,r.shape.length)),T.assertAxesAreInnerMostDims("min",u,o);let[d,h]=T.computeOutAndReduceShapes(c.shape,u),f=v.sizeFromShape(h),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,f]}}),g=vo(m,m.dtype,"min",a),y;if(i){let x=T.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var MK={kernelName:Oi,backendName:"webgl",kernelFunc:RK},$K=M3+`
|
|
return min(a, b);
|
|
`,_K=`
|
|
vec4 result = vec4(min(a, b));
|
|
bvec4 isNaNA = isnan(a);
|
|
bvec4 isNaNB = isnan(b);
|
|
bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
|
|
`+hp+`
|
|
return result;
|
|
`,PK=pa({opSnippet:$K,packedOpSnippet:_K,cpuKernelImpl:IU}),FK={kernelName:Di,backendName:"webgl",kernelFunc:PK},OK=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let n=e.length,r=gt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=a==="reflect"?0:1;if(n===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${l};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${l};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${s});
|
|
${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${l};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
|
|
}
|
|
}
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}},DK=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let n=e.length,r=gt(n),s=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=wa("rc",n),l=wa("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,c=a==="reflect"?0:1,d="";if(n===1){let h=`
|
|
${r} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${c};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${c};
|
|
}
|
|
source -= start;
|
|
`;d=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${p});
|
|
${o[n-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${p});
|
|
}
|
|
`}else{let h=`
|
|
${r} source = rc;
|
|
${r} lt = ${r}(lessThan(source, start));
|
|
${r} gte = ${r}(greaterThanEqual(source, end));
|
|
${r} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${c}) +
|
|
gte * ((end - 1) * 2 - source + ${c});
|
|
source -= start;
|
|
`;d=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${p});
|
|
${o[n-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${p});
|
|
}
|
|
rc = outputLoc;
|
|
${o[n-2]} += 1;
|
|
if(${o[n-2]} < ${this.outputShape[n-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${l.join()}), ${p});
|
|
${o[n-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${l.join()}), ${p});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${s});
|
|
const ${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${d}
|
|
setOutput(result);
|
|
}
|
|
`}},zK=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{paddings:r,mode:s}=a,i=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new DK(n.shape,r,s):new OK(n.shape,r,s);return t.runWebGLProgram(i,[n],n.dtype)},LK={kernelName:zi,backendName:"webgl",kernelFunc:zK},BK=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,WK=`
|
|
vec4 result = mod(a, b);
|
|
bvec4 isNaN = equal(b, vec4(0.0));
|
|
`+hp+`
|
|
return result;
|
|
`,VK=pa({opSnippet:BK,packedOpSnippet:WK}),UK={kernelName:Ll,backendName:"webgl",kernelFunc:VK},GK=class{constructor(e,t,a){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,a],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}));
|
|
}
|
|
`}},HK=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,jK=`
|
|
// 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;
|
|
`,cv=pa({opSnippet:HK,packedOpSnippet:jK,checkOutOfBounds:!0}),qK={kernelName:ci,backendName:"webgl",kernelFunc:cv},Ey="return a - b;",hv=pa({opSnippet:Ey,packedOpSnippet:Ey,supportsComplex:!0,cpuKernelImpl:GU}),XK={kernelName:po,backendName:"webgl",kernelFunc:hv};function fv(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=pv({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=pe({inputs:{x:o},backend:a,attrs:{shape:l}}),p=hv({inputs:{a:r,b:u},backend:a}),c=lv({inputs:{x:p},backend:a}),d=$h({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=pe({inputs:{x:d},backend:a,attrs:{shape:l}}),f=cv({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),f}var KK={kernelName:oo,backendName:"webgl",kernelFunc:fv};function ZK(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:fv({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new GK(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var YK={kernelName:rh,backendName:"webgl",kernelFunc:ZK},JK=En+`
|
|
return -x;
|
|
`,QK=`
|
|
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 eZ(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.texData.get(n.dataId),[i,o]=TU(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r;return V().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Wr(n.shape,QK):r=new qn(n.shape,JK),a.runWebGLProgram(r,[n],n.dtype)}var tZ={kernelName:Bl,backendName:"webgl",kernelFunc:eZ},aZ=Nn.nonMaxSuppressionV3Impl;function nZ(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=aZ(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var rZ={kernelName:Wi,backendName:"webgl",kernelFunc:nZ},sZ=Nn.nonMaxSuppressionV4Impl;function iZ(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=sZ(p,c,i,o,l,u);return[a.makeTensorInfo([d.length],"int32",new Int32Array(d)),a.makeTensorInfo([],"int32",new Int32Array([h]))]}var oZ={kernelName:Wl,backendName:"webgl",kernelFunc:iZ},lZ=Nn.nonMaxSuppressionV5Impl;function uZ(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=lZ(p,c,d,h,f,m);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var dZ={kernelName:Vi,backendName:"webgl",kernelFunc:uZ},pZ=class{constructor(e,t,a,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(${a}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},cZ=e=>{let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new pZ(u,i,o,l),c=pe({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=a.runWebGLProgram(p,[c],s);a.disposeIntermediateTensorInfo(c);let h=[...r.shape,i],f=pe({inputs:{x:d},backend:a,attrs:{shape:h}});return a.disposeIntermediateTensorInfo(d),f},hZ={kernelName:Ui,backendName:"webgl",kernelFunc:cZ};function Wc(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=mp({inputs:{input:n},backend:a}),s=Wc({inputs:{x:r},backend:a}),i=_h({inputs:{input:n},backend:a}),o=Wc({inputs:{x:i},backend:a}),l=ls({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return gp({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var fZ={kernelName:nu,backendName:"webgl",kernelFunc:Wc};function mv(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=mp({inputs:{input:n},backend:a}),s=mv({inputs:{x:r},backend:a}),i=_h({inputs:{input:n},backend:a}),o=Wc({inputs:{x:i},backend:a}),l=ls({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return gp({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var mZ={kernelName:Vl,backendName:"webgl",kernelFunc:mv};function gZ(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return I2({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=I2({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=ev({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var yZ={kernelName:Ul,backendName:"webgl",kernelFunc:gZ},xZ=class{constructor(e,t,a){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,r=gt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${s});
|
|
${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
}
|
|
`}},AZ=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let n=e.length,r=gt(n),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=wa("rc",n),l=wa("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${l.slice(-2).join()})`,c=[`${r} rc = outputLoc;`,`${o[n-1]} += 1;
|
|
if(${u}) {
|
|
`,n===1?"":`}
|
|
rc = outputLoc;
|
|
${o[n-2]} += 1;
|
|
if(${o[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${o[n-1]} += 1;
|
|
if(${u}) {`],d=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=n===1?2:4;f<m;f++)h+=`
|
|
${c[f]}
|
|
if (${d}) {
|
|
result[${f}] = float(value);
|
|
} else {
|
|
${r} source = rc - start;
|
|
result[${f}] = getChannel(getX(${l.join()}), ${p});
|
|
}
|
|
`;h+=n===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${s});
|
|
const ${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}},gv=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return gp({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AZ(r.shape,s,i):new xZ(r.shape,s,i),l=[[i]];return a.runWebGLProgram(o,[r],r.dtype,l)},bZ={kernelName:Gi,backendName:"webgl",kernelFunc:gv},vZ=`
|
|
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);
|
|
`,kZ=`
|
|
// 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);
|
|
`+hp+`
|
|
return result;
|
|
`,wZ=pa({opSnippet:vZ,packedOpSnippet:kZ}),IZ={kernelName:Hi,backendName:"webgl",kernelFunc:wZ};function SZ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,c=T.getAxesPermutation(p,o),d=r;c!=null&&(d=Ta({inputs:{x:r},backend:a,attrs:{perm:c}}),p=T.getInnerMostAxes(p.length,o),l.push(d)),T.assertAxesAreInnerMostDims("prod",p,o);let h;if(a.shouldExecuteOnCPU([d])){let f=a.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:y}=NU(d.shape,d.dtype,f,p);h=a.makeTensorInfo(g,y,m)}else{let[f,m]=T.computeOutAndReduceShapes(d.shape,p),g=v.sizeFromShape(m),y=pe({inputs:{x:d},backend:a,attrs:{shape:[-1,g]}}),x=Xd(r.dtype),A=vo(y,x,"prod",a);h=pe({inputs:{x:A},backend:a,attrs:{shape:f}}),l.push(y),l.push(A)}if(i){l.push(h);let f=T.expandShapeToKeepDim(h.shape,u);h=pe({inputs:{x:h},backend:a,attrs:{shape:f}})}return l.forEach(f=>a.disposeIntermediateTensorInfo(f)),h}var TZ={kernelName:qi,backendName:"webgl",kernelFunc:SZ};function CZ(e){let{inputs:t,backend:a,attrs:n}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=n,l=r.map(y=>a.readSync(y.dataId)),u=r.map(y=>y.shape),p=a.readSync(s.dataId),c=a.readSync(i.dataId),[d,h,f]=EU(l,u,p,s.shape,s.dtype,c,i.shape,o),m=d.map(y=>a.makeTensorInfo([y.length],"int32",y)),g=a.makeTensorInfo(f,s.dtype,h);return m.concat([g])}var NZ={kernelName:sh,backendName:"webgl",kernelFunc:CZ};function EZ(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=RU(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var RZ={kernelName:ih,backendName:"webgl",kernelFunc:EZ};function MZ(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),c=a.readSync(i.dataId),d=o.map(g=>a.readSync(g.dataId)),h=o.map(g=>g.shape),[f,m]=MU(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(f,s.dtype,m)}var $Z={kernelName:oh,backendName:"webgl",kernelFunc:MZ},yv=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=$U(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},_Z={kernelName:Gl,backendName:"webgl",kernelFunc:yv},PZ="return 1.0 / x;",FZ=Qe({opSnippet:PZ}),OZ={kernelName:Xi,backendName:"webgl",kernelFunc:FZ},DZ=En+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,zZ=`
|
|
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;
|
|
`,LZ=Qe({opSnippet:DZ,packedOpSnippet:zZ}),BZ={kernelName:Ki,backendName:"webgl",kernelFunc:LZ},WZ=En+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,VZ=`
|
|
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;
|
|
`,UZ=Qe({opSnippet:WZ,packedOpSnippet:VZ}),GZ={kernelName:Ji,backendName:"webgl",kernelFunc:UZ},HZ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${u[0]/p[0]},
|
|
${u[1]/p[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${c};
|
|
|
|
// 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);
|
|
}
|
|
`}},jZ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${u[0]/p[0]},
|
|
${u[1]/p[1]},
|
|
${u[1]/p[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.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 = ${c};
|
|
|
|
// 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 < ${l-1};
|
|
bool hasNextRow = coords.z < ${a-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 qZ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=V().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new jZ(r.shape,l,u,s,i):new HZ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],"float32")}var XZ={kernelName:Yi,backendName:"webgl",kernelFunc:qZ},KZ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,f=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(${u});
|
|
const float widthScale = float(${p});
|
|
|
|
const float invHeightScale = float(${c});
|
|
const float invWidthScale = float(${d});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${f});
|
|
|
|
// 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 >= ${s}) {
|
|
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), ${r-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 ZZ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new KZ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var YZ={kernelName:j2,backendName:"webgl",kernelFunc:ZZ},JZ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${u[0]/p[0]},
|
|
${u[1]/p[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.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 + ${c})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},QZ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${u[0]/p[0]},
|
|
${u[1]/p[1]},
|
|
${u[1]/p[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.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 + ${c})));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${a-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function eY(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=V().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new QZ(r.shape,l,u,s,i):new JZ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],r.dtype)}var tY={kernelName:Zi,backendName:"webgl",kernelFunc:eY},aY=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${u});
|
|
const float widthScale = float(${p});
|
|
|
|
const float invHeightScale = float(${c});
|
|
const float invWidthScale = float(${d});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${f});
|
|
|
|
// 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 >= ${s}) {
|
|
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 >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${o[0]}) *
|
|
(float(dyR) / float(${l[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${o[1]}) *
|
|
(float(dyC) / float(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${n}) - 1),
|
|
${a} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${r}) - 1),
|
|
${a} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function nY(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new aY(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var rY={kernelName:H2,backendName:"webgl",kernelFunc:nY},sY=class{constructor(e,t){this.variableNames=["x"];let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);if(this.outputShape=e,a===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}]`,r=e.map((i,o)=>n(o)).join(","),s=gt(a);this.userCode=`
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},iY=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);this.outputShape=e;let n=wa("rc",a),r=`${n[a-1]} + 1 < ${this.outputShape[a-1]}`,s=`${n[a-2]} + 1 < ${this.outputShape[a-2]}`,i=gt(a);a===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${r}){
|
|
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 = ${o(n.slice())};
|
|
if(${r}){
|
|
result.g = ${l(n.slice())};
|
|
}
|
|
if(${s}) {
|
|
result.b = ${u(n.slice())};
|
|
if(${r}) {
|
|
result.a = ${p(n.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function o(h){return c(h)}function l(h){return h[a-1]="("+h[a-1]+" + 1)",c(h)}function u(h){return h[a-2]="("+h[a-2]+" + 1)",c(h)}function p(h){return h[a-1]="("+h[a-1]+" + 1)",h[a-2]="("+h[a-2]+" + 1)",c(h)}function c(h){let f=e.map((y,x)=>d(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function oY(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return Qa({inputs:{x:r},backend:a});let l=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iY(r.shape,o):new sY(r.shape,o);return a.runWebGLProgram(l,[r],r.dtype)}var lY={kernelName:Qi,backendName:"webgl",kernelFunc:oY},uY=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let a=e[1],n=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
|
|
vec3 fill = vec3(${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]));
|
|
${r}
|
|
if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${a}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},dY={kernelName:go,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new uY(n.shape,s),[u,p]=T.getImageCenter(i,n.shape[1],n.shape[2]),c=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[n],n.dtype,c)}},pY=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,cY=Qe({opSnippet:pY}),hY={kernelName:eo,backendName:"webgl",kernelFunc:cY},fY="return inversesqrt(x);",mY=Qe({opSnippet:fY,cpuKernelImpl:_U}),gY={kernelName:to,backendName:"webgl",kernelFunc:mY},xv=class{constructor(e,t,a,n,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),u="";a===1?u="i":a===2&&(u="i, j");let p=`getIndices(${u})`,c="";n===1?c="i":n===2&&(c="i, coords[1]");let d=`getUpdates(${c})`,h=t>1?"strides[j]":"strides";this.userCode=`
|
|
${o} strides = ${o}(${r});
|
|
|
|
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 < ${t}; j++) {
|
|
int index = round(${p});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${d};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function yY(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=T.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=pe({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=pe({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=a.makeTensorInfo([],"float32",new Float32Array([0])),g=new xv(l,o,h.shape.length,f.shape.length,p,d),y=a.runWebGLProgram(g,[f,h,m],f.dtype),x=pe({inputs:{x:y},backend:a,attrs:{shape:i}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(m),x}var xY={kernelName:ao,backendName:"webgl",kernelFunc:yY},AY=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=V().getNumber("WEBGL_VERSION")===2?r:s,o=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) ${o} 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 bY(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new AY(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return a.runWebGLProgram(o,[r,s],"int32",l)}var vY={kernelName:zd,backendName:"webgl",kernelFunc:bY},kY=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.outputShape=t;let n,r;if(a>4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),r=l.join()}let s=gt(a);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
float cVal = getC(${n});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function wY(e){let{inputs:t,backend:a}=e,{condition:n,t:r,e:s}=t,i=new kY(n.shape.length,r.shape,r.shape.length);return a.runWebGLProgram(i,[n,r,s],ma(r.dtype,s.dtype))}var IY={kernelName:jl,backendName:"webgl",kernelFunc:wY},SY=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${T.SELU_SCALEALPHA};
|
|
float scale = ${T.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,TY=Qe({opSnippet:SY}),CY={kernelName:ql,backendName:"webgl",kernelFunc:TY},NY=mu+`
|
|
return 1.0 / (1.0 + exp(-1.0 * x));
|
|
`,EY=`
|
|
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,RY=Qe({opSnippet:NY,packedOpSnippet:EY,cpuKernelImpl:FU}),MY={kernelName:ro,backendName:"webgl",kernelFunc:RY},$Y=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,_Y=Qe({opSnippet:$Y}),PY={kernelName:Zl,backendName:"webgl",kernelFunc:_Y},FY=mu+`
|
|
return sin(x);
|
|
`,OY=Qe({opSnippet:FY}),DY={kernelName:no,backendName:"webgl",kernelFunc:OY},zY=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,LY=Qe({opSnippet:zY}),BY={kernelName:Kl,backendName:"webgl",kernelFunc:LY},WY=`
|
|
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;
|
|
`,VY=Qe({opSnippet:WY}),UY={kernelName:Yl,backendName:"webgl",kernelFunc:VY},GY=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let u=[],p=gv({inputs:{x:r},backend:a,attrs:{paddings:l,constantValue:0}}),c=T.getReshaped(p.shape,s,o,!1),d=T.getPermuted(c.length,s.length,!1),h=T.getReshapedPermuted(p.shape,s,o,!1),f=pe({inputs:{x:p},backend:a,attrs:{shape:c}}),m=Ta({inputs:{x:f},backend:a,attrs:{perm:d}}),g=pe({inputs:{x:m},backend:a,attrs:{shape:h}});return u.push(p),u.push(f),u.push(m),u.forEach(y=>a.disposeIntermediateTensorInfo(y)),g},HY={kernelName:Jl,backendName:"webgl",kernelFunc:GY};function jY(e){let{inputs:t,backend:a}=e,{indices:n,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${n.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${i.shape}`);let o=a.readSync(n.dataId),l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=a.readSync(i.dataId)[0],[c,d,h,f,m]=DU(o,n.shape,n.dtype,l,r.dtype,u,p);return[a.makeTensorInfo(d,n.dtype,c),a.makeTensorInfo([d[0]],r.dtype,h),a.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),a.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var qY={kernelName:Ld,backendName:"webgl",kernelFunc:jY};function XY(e){let{inputs:t,backend:a}=e,{inputIndices:n,inputShape:r,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(a.readSync(r.dataId)),o=a.readSync(n.dataId),l=Array.from(a.readSync(s.dataId)),[u,p,c]=zU(o,n.shape,n.dtype,i,l);return[a.makeTensorInfo(p,n.dtype,u),a.makeTensorInfo([c.length],s.dtype,new Int32Array(c))]}var KY={kernelName:eu,backendName:"webgl",kernelFunc:XY};function ZY(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=B6(i,n.shape,n.dtype,o,l,!0);return a.makeTensorInfo(p,n.dtype,u)}var YY={kernelName:Bd,backendName:"webgl",kernelFunc:ZY};function JY(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=B6(i,n.shape,n.dtype,o,l);return a.makeTensorInfo(p,n.dtype,u)}var QY={kernelName:Wd,backendName:"webgl",kernelFunc:JY};function eJ(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let y=a.bufferSync(r),x=a.bufferSync(s),A=v.decodeString(a.readSync(i.dataId)[0]),b=PU(y,x,o,d,p,u,l,c,A,h);return a.makeTensorInfo(o,b.dtype,b.values)}let f=new xv(u,l,r.shape.length,s.shape.length,c,[d,1],h),m=a.runWebGLProgram(f,[s,r,i],s.dtype),g=pe({inputs:{x:m},backend:a,attrs:{shape:o}});return a.disposeIntermediateTensorInfo(m),g}var tJ={kernelName:Vd,backendName:"webgl",kernelFunc:eJ};function aJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let f=gu({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,f})}var nJ={kernelName:Ql,backendName:"webgl",kernelFunc:aJ},Ry="return sqrt(x);",rJ=Qe({opSnippet:Ry,packedOpSnippet:Ry,cpuKernelImpl:LU}),sJ={kernelName:so,backendName:"webgl",kernelFunc:rJ},iJ="return x * x;",oJ=Qe({opSnippet:iJ}),lJ={kernelName:Ud,backendName:"webgl",kernelFunc:oJ},My="return (a - b) * (a - b);",uJ=pa({opSnippet:My,packedOpSnippet:My}),dJ={kernelName:lo,backendName:"webgl",kernelFunc:uJ};function pJ({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=En+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new qn(n.shape,r);return a.runWebGLProgram(s,[n],n.dtype)}var cJ={kernelName:rs,backendName:"webgl",kernelFunc:pJ},hJ=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=a;let n=a.length,r=gt(a.length),s=gt(a.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=a.map((l,u)=>(o++,a.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function fJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=St.sliceInfo(r.shape,s,i,o,l,u,p,c,d),k;if(m)k=pe({inputs:{x:r},backend:a,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=St.computeOutShape(x,A,b),N=gu({inputs:{x:r},backend:a,attrs:{begin:x,size:C}});k=pe({inputs:{x:N},backend:a,attrs:{shape:f}}),a.disposeIntermediateTensorInfo(N)}else if(a.shouldExecuteOnCPU([r])){let C=a.readSync(r.dataId),N=_e(r.shape,r.dtype,C),$=BU(h,N,b,x);k=a.makeTensorInfo(f,r.dtype,$.values)}else{let C=new hJ(x,b,h);k=a.runWebGLProgram(C,[r],r.dtype)}let S=pe({inputs:{x:k},backend:a,attrs:{shape:f}});return a.disposeIntermediateTensorInfo(k),S}var mJ={kernelName:uo,backendName:"webgl",kernelFunc:fJ};function gJ(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[f,m]=WU(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([f.length],"string",f),a.makeTensorInfo(c.shape,"int32",m)]}var yJ={kernelName:tu,backendName:"webgl",kernelFunc:gJ};function xJ(e){let{inputs:t,backend:a,attrs:n}=e,{skipEmpty:r}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=a.readSync(s.dataId),l=a.readSync(i.dataId)[0],[u,p,c]=VU(o,l,r),d=p.length;return[a.makeTensorInfo([d,2],"int32",u),a.makeTensorInfo([d],"string",p),a.makeTensorInfo([2],"int32",new Int32Array(c))]}var AJ={kernelName:Gd,backendName:"webgl",kernelFunc:xJ};function bJ(e){let{inputs:t,backend:a,attrs:n}=e,{numBuckets:r}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=a.readSync(s.dataId),o=UU(i,r);return a.makeTensorInfo(s.shape,"int32",o)}var vJ={kernelName:Hd,backendName:"webgl",kernelFunc:bJ},kJ="return tan(x);",wJ=Qe({opSnippet:kJ}),IJ={kernelName:co,backendName:"webgl",kernelFunc:wJ},SJ=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,TJ=Qe({opSnippet:SJ}),CJ={kernelName:ho,backendName:"webgl",kernelFunc:TJ},NJ=class{constructor(e,t){this.variableNames=["A"];let a=new Array(e.length);for(let s=0;s<a.length;s++)a[s]=e[s]*t[s];this.outputShape=a,this.rank=a.length;let n=gt(this.rank),r=EJ(e);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function EJ(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let r=0;r<e.length;r++)n.push(`imod(${a[r]}, ${e[r]})`);return n.join()}function Av(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reps:s}=n;if(r.dtype==="string"||r.shape.length>5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=HU(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new NJ(r.shape,s);return a.runWebGLProgram(i,[r],r.dtype)}var RJ={kernelName:ns,backendName:"webgl",kernelFunc:Av},MJ=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));
|
|
}
|
|
}
|
|
`}},$J=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 Rs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function $y(e){let t=1;for(;t<e;)t*=2;return t}function _J(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{k:s,sorted:i}=n,o=V().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=V().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,p=u[u.length-1];if(a.shouldExecuteOnCPU([r])||p<o||s>l){let $=a.readSync(r.dataId),[M,R]=jU($,u,r.dtype,s,i);return[a.makeTensorInfo(M.shape,M.dtype,M.values),a.makeTensorInfo(R.shape,R.dtype,R.values)]}if(s===0)return u[u.length-1]=0,[a.makeTensorInfo(u,r.dtype,[]),a.makeTensorInfo(u,"int32",[])];if(p===1)return[r,gp({attrs:{shape:u,dtype:"int32",value:0},backend:a})];let c=a.texData.get(r.dataId),d=c!==null&&c.isPacked,h=d?a.unpackTensor(r):r,f=v.sizeFromShape(u)/p,m=pe({inputs:{x:h},attrs:{shape:[f,p]},backend:a});d&&Rs(a,h);let g=$y(s),y=$y(p),x=null,A=()=>x===null?[m,m]:[m,x],b=($,M,R)=>{let I=A(),_=new MJ(R),D=[[p],[x===null?1:0],[Number.NEGATIVE_INFINITY],[$],[M]],W=x;x=a.runWebGLProgram(_,I,"int32",D),Rs(a,W)};for(let $=1;$<g;$*=2){let M=$*2;for(let R=$;R>=1;R/=2)b(M,R,[f,y])}for(let $=y;$>g;$/=2){let M=A(),R=new $J([f,$/2]),I=[[p],[x===null?1:0],[g]],_=x;x=a.runWebGLProgram(R,M,"int32",I),Rs(a,_);let D=g/2,W=D*2;for(let P=D;P>=1;P/=2)b(W,P,x.shape)}let k=x;x=gu({inputs:{x},backend:a,attrs:{begin:0,size:[f,s]}}),Rs(a,k);let S=dv({inputs:{x:m,indices:x},backend:a,attrs:{axis:1,batchDims:1}});Rs(a,m);let C=u.slice(0,-1);C.push(s),k=x,x=pe({inputs:{x},attrs:{shape:C},backend:a}),Rs(a,k);let N=S;return S=pe({inputs:{x:S},attrs:{shape:C},backend:a}),Rs(a,N),[S,x]}var PJ={kernelName:fo,backendName:"webgl",kernelFunc:_J},FJ=class{constructor(e,t,a,n,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=a==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${o} == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
inCoord -= sz2 * float(int(float(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${o} == 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 (${o} == 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 < ${t}) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = float(${r});
|
|
}
|
|
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(${r});
|
|
} else {
|
|
float inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
float inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
float mapX = mapCoord(inX, float(${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 OJ(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[p,c,d,h]=r.shape,[f,m]=u!=null?u:[c,d],g=[p,f,m,h],y=new FJ(c,d,i,o,l,g);return a.runWebGLProgram(y,[r,s],"float32")}var DJ={kernelName:mo,backendName:"webgl",kernelFunc:OJ};function zJ(e){let{inputs:t,attrs:a,backend:n}=e,{axis:r}=a,{x:s}=t;uu(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=qU(i,r,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var LJ={kernelName:lh,backendName:"webgl",kernelFunc:zJ};function BJ(e){let{inputs:t,backend:a,attrs:n}=e,{value:r}=t,{axis:s}=n;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let m=0;m<o;m++)m!==s&&(u[p++]=i.shape[m]);let c=[],d=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let g=gu({inputs:{x:i},backend:a,attrs:{begin:d,size:h}}),y=pe({inputs:{x:g},backend:a,attrs:{shape:u}});f[m]=y,c.push(g)}return c.forEach(m=>a.disposeIntermediateTensorInfo(m)),f}var WJ={kernelName:au,backendName:"webgl",kernelFunc:BJ},VJ=class{constructor(e,t){this.variableNames=["x","segmentIds"];let a=e.windowSize,n=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/a);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(a/4)*4,p=a%4,c=`
|
|
sumValue += dot(values, segFilter);
|
|
`,d="";r%a>0&&(d=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`);let h="";r%a>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${o};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${d}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${h}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${s})) * float(${a}));
|
|
int currentSeg = int(mod(float(outIdx), float(${s})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${u}; 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
|
|
);
|
|
|
|
${c}
|
|
}
|
|
|
|
int inIdx = inOffset + ${u};
|
|
if (${p===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
|
|
);
|
|
|
|
${c}
|
|
} else if (${p===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
|
|
);
|
|
|
|
${c}
|
|
} else if (${p===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
|
|
);
|
|
|
|
${c}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function UJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,segmentIds:s}=t,{numSegments:i}=n,o=r.shape.length,l=[],u=0,p=T.getAxesPermutation([u],o),c=r;p!=null&&(c=Ta({inputs:{x:r},backend:a,attrs:{perm:p}}),l.push(c),u=T.getInnerMostAxes(1,o)[0]);let d=T.segment_util.computeOutShape(c.shape,u,i),h=v.sizeFromShape([c.shape[u]]),f=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,h]}});l.push(f);let m=Xd(r.dtype),g=(b,k,S,C,N)=>{let $=b.shape[0],M=b.shape[1],R=T.segment_util.segOpComputeOptimalWindowSize(M,N),I={windowSize:R,inSize:M,batchSize:$,numSegments:N},_=new VJ(I,k),D=a.compileAndRun(_,[b,S],C);if(l.push(D),D.shape[1]===N)return D;let W=yv({backend:a,attrs:{start:0,stop:N,step:1,dtype:"float32"}}),P=Av({inputs:{x:W},backend:a,attrs:{reps:[M/R]}});return l.push(W),l.push(P),g(D,k,P,C,N)},y=g(f,"unsortedSegmentSum",s,m,i),x=pe({inputs:{x:y},backend:a,attrs:{shape:d}}),A=x;if(p!=null){l.push(x);let b=T.getUndoAxesPermutation(p);A=Ta({inputs:{x:A},backend:a,attrs:{perm:b}})}return l.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}var GJ={kernelName:uh,backendName:"webgl",kernelFunc:UJ},HJ=[BG,VG,HG,XG,ZG,QG,tH,nH,oH,uH,cH,mH,xH,kH,SH,CH,EH,_H,FH,DH,WH,XH,ZH,JH,rj,ij,dj,wG,hj,xj,kj,Nj,Rj,$j,Pj,Oj,Lj,Vj,Hj,qj,Kj,Yj,eq,aq,iq,lq,pq,fq,gq,bq,Iq,Nq,Mq,Pq,Fq,Dq,Lq,Wq,Uq,Hq,Kq,Jq,tX,nX,iX,uX,hX,yX,kG,AX,gj,kX,SX,NX,SG,$X,OX,zX,VX,HX,KX,JX,aK,iK,uK,pK,mK,yK,AK,wK,SK,CK,EK,MK,FK,LK,UK,YK,NG,tZ,rZ,oZ,dZ,ej,hZ,mZ,yZ,bZ,IZ,CG,TZ,NZ,RZ,$Z,_Z,tj,qK,OZ,BZ,GZ,RG,XZ,YZ,tY,rY,lY,dY,hY,gY,xY,vY,IY,CY,MY,PY,DY,BY,jH,KK,UY,HY,qY,KY,YY,QY,tJ,nJ,sJ,lJ,dJ,cJ,mJ,yJ,AJ,vJ,XK,DG,IJ,CJ,RJ,PJ,DJ,zG,LJ,WJ,GJ,fZ];for(let e of HJ)yn(e);var Ct;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Ct||(Ct={}));var bd;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(bd||(bd={}));var bv;function jJ(e){bv=e.wasm.cwrap(Hr,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function qJ(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n,d=a.dataIdMap.get(r.dataId).id,h=a.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let N=a.dataIdMap.get(i.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);f=N.id}let m=o==null?0:a.dataIdMap.get(o.dataId).id,g=bd[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],x=u?s.shape[1]:s.shape[2],A=xo.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)),b=a.makeOutput([...A,y,x],r.dtype),k=a.dataIdMap.get(b.dataId).id,S=new Uint8Array(new Int32Array(r.shape).buffer),C=new Uint8Array(new Int32Array(s.shape).buffer);return bv(d,S,r.shape.length,h,C,s.shape.length,l,u,g,f,m,c||0,k),b}var XJ={kernelName:Hr,backendName:"wasm",setupFunc:jJ,kernelFunc:qJ};function Bt(e,t){let a;function n(s){a=s.wasm.cwrap(e,null,["number","number","number"])}function r(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),p=i.dataIdMap.get(u.dataId).id;return v.sizeFromShape(u.shape)===0||a(l,Ct[o.dtype],p),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var KJ=Bt(vl);function ca(e,t,a){let n;function r(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,c=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(p.dataId).id,h=a!=null?a:u.dtype,f=T.assertAndGetBroadcastShape(u.shape,p.shape),m=o.makeOutput(f,h);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(p.shape).buffer),x=o.dataIdMap.get(m.dataId).id;return n(c,g,u.shape.length,d,y,p.shape.length,Ct[u.dtype],x),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var ZJ=!0,YJ=ca(ts,ZJ),vv;function JJ(e){vv=e.wasm.cwrap(Ks,null,["array","number","number","number"])}function QJ(e){let{inputs:t,backend:a}=e,n=a.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(n.shape)===0)return n;let r=t.map(o=>a.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=a.dataIdMap.get(n.dataId).id;return vv(s,r.length,Ct[n.dtype],i),n}var eQ={kernelName:Ks,backendName:"wasm",setupFunc:JJ,kernelFunc:QJ};function Ph(e){let{inputs:{x:t},backend:a}=e;if(t.dtype==="string")return Ue(a.readSync(t.dataId),t.shape,t.dtype);let n=a.makeOutput(t.shape,t.dtype),r=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(n).set(r),n}var tQ={kernelName:wi,backendName:"wasm",kernelFunc:Ph},kv;function aQ(e){kv=e.wasm.cwrap(Ar,null,["number","array","number","number","number","array","number"])}function Qr(e){let{inputs:t,backend:a,attrs:n}=e,[r,s]=rQ(t.x.shape,n.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=nQ(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=Ph({inputs:t,backend:a});return f.shape=o,f}let u=a.makeOutput(o,l.dtype),p=a.dataIdMap.get(l.dataId).id,c=a.dataIdMap.get(u.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return kv(p,h,l.shape.length,Ct[l.dtype],c,d,s.length),u}function nQ(e,t){let a=new Array(e.length);for(let n=0;n<a.length;n++)a[n]=e[t[n]];return a}function rQ(e,t){let a=[],n=[];for(let 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KQ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=T.getAxesPermutation([s],l),p=r;u!==null&&(p=Qr({inputs:{x:r},attrs:{perm:u},backend:a}));let c=T.getInnerMostAxes(1,l)[0];T.assertAxesAreInnerMostDims("cumsum",[c],l);let d=a.makeOutput(p.shape,p.dtype),h=p.shape[c],f=a.dataIdMap.get(p.dataId).id,m=a.dataIdMap.get(d.dataId).id;Pv(f,i?1:0,o?1:0,h,m,Ct[r.dtype]);let g=d;if(u!==null){let y=T.getUndoAxesPermutation(u);g=Qr({inputs:{x:d},attrs:{perm:y},backend:a}),a.disposeData(p.dataId),a.disposeData(d.dataId)}return g}var ZQ={kernelName:li,backendName:"wasm",setupFunc:XQ,kernelFunc:KQ},Fv;function YQ(e){Fv=e.wasm.cwrap(di,null,["number","number","number","array","number","array","array","number","number"])}function 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tee(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:c}=a,d=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,s.shape,l,d,p,c,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,k=h.dilationWidth,S=h.strideHeight,C=h.strideWidth,N=h.inChannels,$=h.outChannels,M=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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b=T.expandShapeToKeepDim(A.shape,d);A.shape=b}return u.dtype!=="float32"&&t.disposeData(x.dataId),A}var ite={kernelName:Fi,backendName:"wasm",setupFunc:rte,kernelFunc:ste},Xv;function ote(e){Xv=e.wasm.cwrap(Oi,null,["number","number","number","number"])}function lte(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=us(i,r,t);if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A)}let f=u.shape.length;T.assertAxesAreInnerMostDims("min",c,f);let[m,g]=T.computeOutAndReduceShapes(u.shape,c),y=v.sizeFromShape(g),x=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;Xv(l,Ct[i.dtype],y,A)}if(h&&t.disposeData(p.dataId),s){let A=T.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var ute={kernelName:Oi,backendName:"wasm",setupFunc:ote,kernelFunc:lte},dte=!1,pte=ca(Di,dte),C2;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(C2||(C2={}));var Kv;function cte(e){Kv=e.wasm.cwrap(zi,null,["number","array","number","number","array","array","number","number"])}function hte(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(f=>f[0]),c=n.map(f=>f[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return Kv(i,u,t.shape.length,Ct[t.dtype],d,h,C2[r],l),o}var fte={kernelName:zi,backendName:"wasm",kernelFunc:hte,setupFunc:cte},mte=!0,gte=ca(Li,mte),yte=Bt(Bl);function P3(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var Zv;function xte(e){Zv=e.wasm.cwrap(Wi,"number",["number","number","number","number","number"])}function Ate(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=a,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=Zv(u,p,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=P3(t,c);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var bte={kernelName:Wi,backendName:"wasm",setupFunc:xte,kernelFunc:Ate},Yv;function vte(e){Yv=e.wasm.cwrap(Wl,"number",["number","number","number","number","number","bool"])}function kte(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Yv(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=P3(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var wte={kernelName:Wl,backendName:"wasm",setupFunc:vte,kernelFunc:kte},Jv;function Ite(e){Jv=e.wasm.cwrap(Vi,"number",["number","number","number","number","number","number"])}function Ste(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Jv(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=P3(t,d);t.wasm._free(g);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[y,x]}var Tte={kernelName:Vi,backendName:"wasm",setupFunc:Ite,kernelFunc:Ste},Cte=!1,Nte=ca(Bi,Cte,"bool"),Qv;function Ete(e){Qv=e.wasm.cwrap(Ui,null,["number","number","number","number","number"])}function Rte(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=a.makeOutput([...r.shape,i],s),p=a.dataIdMap.get(u.dataId).id,c=a.dataIdMap.get(r.dataId).id;return Qv(c,i,o,l,p),u}var Mte={kernelName:Ui,backendName:"wasm",setupFunc:Ete,kernelFunc:Rte};function $te(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(1),n}var _te={kernelName:Vl,backendName:"wasm",kernelFunc:$te};function Pte(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return T2({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching 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g=t.dataIdMap.get(r.dataId),y;g.dtype!=="float32"&&(y=yu({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),g=t.dataIdMap.get(y.dataId));let x=g.id,A=t.dataIdMap.get(m.dataId).id;return s8(x,p,c,d,h,l,u,s?1:0,i?1:0,A),y!=null&&t.disposeData(y.dataId),m}var rae={kernelName:Zi,backendName:"wasm",setupFunc:aae,kernelFunc:nae},i8;function sae(e){i8=e.wasm.cwrap(Qi,null,["number","array","number","array","number","number"])}function iae(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=v.parseAxisParam(s,r.shape);if(r.shape.length===0)return Ph({inputs:{x:r},backend:a});let o=a.makeOutput(r.shape,r.dtype),l=a.dataIdMap.get(r.dataId).id,u=a.dataIdMap.get(o.dataId).id,p=new Uint8Array(new Int32Array(i).buffer),c=new Uint8Array(new Int32Array(r.shape).buffer);i8(l,p,i.length,c,r.shape.length,u);let d=Wa({inputs:{x:o},attrs:{shape:r.shape},backend:a});return a.disposeData(o.dataId),d}var oae={kernelName:Qi,backendName:"wasm",kernelFunc:iae,setupFunc:sae},o8;function lae(e){o8=e.wasm.cwrap(go,null,["number","number","number","number","number","number","number","number","array","number","number"])}function uae(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=n,l=a.makeOutput(r.shape,r.dtype),u=a.dataIdMap.get(r.dataId).id,p=a.dataIdMap.get(l.dataId).id,[c,d,h,f]=r.shape,[m,g]=T.getImageCenter(o,d,h),y=i===0,x=255,A=typeof i=="number"?[i,i,i,y?0:x]:[...i,x],b=new Uint8Array(new Int32Array(A).buffer);return o8(u,c,d,h,f,s,m,g,b,A.length,p),l}var dae={kernelName:go,backendName:"wasm",kernelFunc:uae,setupFunc:lae},pae=Bt(eo),cae=Bt(to),l8;function hae(e){l8=e.wasm.cwrap(ao,null,["number","number","number","number","number","number","array","number","number"])}function fae(e){let{backend:t,inputs:a,attrs:n}=e,{indices:r,updates:s}=a,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return 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u=t8.kernelFunc({inputs:{x:r},backend:a,attrs:{paddings:l,constantValue:0}}),p=T.getReshaped(u.shape,s,o,!1),c=T.getPermuted(p.length,s.length,!1),d=T.getReshapedPermuted(u.shape,s,o,!1),h=Wa({inputs:{x:u},backend:a,attrs:{shape:p}}),f=Qr({inputs:{x:h},backend:a,attrs:{perm:c}}),m=Wa({inputs:{x:f},backend:a,attrs:{shape:d}});return a.disposeData(u.dataId),a.disposeData(h.dataId),a.disposeData(f.dataId),m}var Cae={kernelName:Jl,backendName:"wasm",kernelFunc:Tae},c8;function Nae(e){c8=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function Eae(e){let{backend:t,inputs:a}=e,{indices:n,values:r,denseShape:s,defaultValue:i}=a,o=n.shape[0],l=n.shape[1],u=t.readSync(s.dataId)[0],p=[o+u,l],c=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(p,n.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(p.slice(0,1),r.dtype),y=t.dataIdMap.get(g.dataId).id,x=t.makeOutput([u],"bool"),A=t.dataIdMap.get(x.dataId).id,b=t.makeOutput([o],n.dtype),k=t.dataIdMap.get(b.dataId).id,S=t.makeOutput([4],"int32"),C=t.dataIdMap.get(S.dataId).id,N=c8(c,d,Ct[r.dtype],o,u,l,h,m,y,A,k,C),$=t.readSync(S.dataId),M;switch($[0]){case 1:{M=T.getSparseFillEmptyRowsIndicesDenseShapeMismatch($[1]);break}case 2:{M=T.getSparseFillEmptyRowsNegativeIndexErrorMessage($[1],$[2]);break}case 3:M=T.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage($[1],$[2],$[3]);break;default:M=""}if(t.disposeData(S.dataId),M)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(x.dataId),t.disposeData(b.dataId),new Error(M);let R=f,I=g;return N!==p[0]&&(R=qs({inputs:{x:f},attrs:{begin:0,size:[N,l]},backend:t}),I=qs({inputs:{x:g},attrs:{begin:0,size:N},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[R,I,x,b]}var Rae={kernelName:Ld,backendName:"wasm",setupFunc:Nae,kernelFunc:Eae},h8;function Mae(e){h8=e.wasm.cwrap(eu,null,["number","number","number","number","number","number","number"])}function $ae(e){let{backend:t,inputs:a}=e,{inputIndices:n,inputShape:r,newShape:s}=a;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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|
${n.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
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${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=t.dataIdMap.get(n.dataId).id,o=t.dataIdMap.get(r.dataId).id,l=t.dataIdMap.get(s.dataId).id,u=n.shape[0],p=v.sizeFromShape(s.shape),c=t.makeOutput([u,p],n.dtype),d=t.dataIdMap.get(c.dataId).id,h=t.makeOutput([p],s.dtype),f=t.dataIdMap.get(h.dataId).id,m=t.makeOutput([3],"int32"),g=t.dataIdMap.get(m.dataId).id;h8(i,o,l,u,d,f,g);let y=t.readSync(m.dataId),x;switch(y[0]){case 0:{x=T.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(y[1],y[2]);break}case 1:{x=T.getSparseReshapeNegativeOutputDimErrorMessage(y[1],y[2]);break}case 2:x=T.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();break;case 3:{let A=Array.from(t.readSync(r.dataId)),b=Array.from(t.readSync(h.dataId));x=T.getSparseReshapeInputOutputMultipleErrorMessage(A,b);break}case 4:{let A=Array.from(t.readSync(r.dataId)),b=Array.from(t.readSync(h.dataId));x=T.getSparseReshapeInputOutputMismatchErrorMessage(A,b);break}default:x=""}if(t.disposeData(m.dataId),x)throw t.disposeData(c.dataId),t.disposeData(h.dataId),new Error(x);return[c,h]}var _ae={kernelName:eu,backendName:"wasm",setupFunc:Mae,kernelFunc:$ae},f8;function m8(e){f8=e.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function g8(e,t){let{backend:a,inputs:n}=e,{data:r,indices:s,segmentIds:i}=n,o=s.shape[0],l=a.readSync(i.dataId,o-1,o)[0],u=o>0?l+1:0;if(u<0)throw new Error(T.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=r.shape.slice();p[0]=u;let c=a.dataIdMap.get(r.dataId).id,d=a.dataIdMap.get(s.dataId).id,h=a.dataIdMap.get(i.dataId).id,f=a.makeOutput(p,r.dtype),m=a.dataIdMap.get(f.dataId).id,g=a.makeOutput([4],"int32"),y=a.dataIdMap.get(g.dataId).id;f8(c,Ct[r.dtype],r.shape[0],d,h,m,y,t,0);let x=a.readSync(g.dataId),A;switch(x[0]){case 0:{A=T.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{A=T.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:A=T.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(x[1],x[2]);break;case 3:A=T.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(x[1],x[2],x[3]);break;default:A=""}if(a.disposeData(g.dataId),A)throw a.disposeData(f.dataId),new Error(A);return f}function Pae(e){return g8(e,!0)}var Fae={kernelName:Bd,backendName:"wasm",setupFunc:m8,kernelFunc:Pae};function Oae(e){return g8(e,!1)}var Dae={kernelName:Wd,backendName:"wasm",setupFunc:m8,kernelFunc:Oae};function zae(e){let{inputs:t,attrs:a,backend:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=new Array(r.shape.length).fill(0),p=r.shape.slice();return l.map(c=>{let d=[...p];d[o]=c;let h=qs({inputs:{x:r},attrs:{begin:u,size:d},backend:n});return u[o]+=c,h})}var Lae={kernelName:Ql,backendName:"wasm",kernelFunc:zae},Bae=Bt(so),Wae=Bt(Ud),Vae=!0,Uae=ca(lo,Vae),y8;function Gae(e){y8=e.wasm.cwrap(rs,null,["number","number","number","number"])}function Hae(e){let{backend:t,inputs:a,attrs:n}=e,{alpha:r}=n,{x:s}=a,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return y8(i,r,Ct[s.dtype],l),o}var jae={kernelName:rs,backendName:"wasm",setupFunc:Gae,kernelFunc:Hae},x8;function qae(e){x8=e.wasm.cwrap(uo,null,["number","array","number","array","array","array","array","array","number","number"])}function Xae(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=St.sliceInfo(r.shape,s,i,o,l,u,p,c,d),k;if(m)k=Wa({inputs:{x:r},backend:t,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let S=St.computeOutShape(x,A,b),C=qs({inputs:{x:r},backend:t,attrs:{begin:x,size:S}});k=Wa({inputs:{x:C},backend:t,attrs:{shape:f}}),t.disposeData(C.dataId)}else{let S=t.makeOutput(h,"float32"),C=t.dataIdMap.get(r.dataId).id,N=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),$=new Uint8Array(new Int32Array(x).buffer),M=new Uint8Array(new Int32Array(A).buffer),R=new Uint8Array(new Int32Array(b).buffer),I=new Uint8Array(new Int32Array(h).buffer),_=new Uint8Array(new Int32Array(v.computeStrides(h)).buffer),D=t.dataIdMap.get(S.dataId).id;x8(C,N,r.shape.length,$,M,R,I,_,h.length,D),k=Wa({inputs:{x:S},backend:t,attrs:{shape:f}}),t.disposeData(S.dataId)}return k}var Kae={kernelName:uo,backendName:"wasm",setupFunc:qae,kernelFunc:Xae};function Zae(e){let{backend:t,inputs:a,attrs:n}=e,{data:r,dataSplits:s}=a,{separator:i,nGramWidths:o,leftPad:l,rightPad:u,padWidth:p,preserveShortSequences:c}=n,d=t.readSync(r.dataId),h=t.readSync(s.dataId),[f,m]=h3(d,h,i,o,l,u,p,c),g=t.makeOutput([f.length],"string"),y=t.dataIdMap.get(g.dataId);y.stringBytes=f;let x=t.makeOutput(s.shape,"int32");return t.typedArrayFromHeap(x).set(m),[g,x]}var Yae={kernelName:tu,backendName:"wasm",kernelFunc:Zae};function Jae(e){let{backend:t,inputs:a,attrs:n}=e,{input:r,delimiter:s}=a,{skipEmpty:i}=n,o=t.readSync(r.dataId),l=t.readSync(s.dataId),[u,p,c]=f3(o,l[0],i),d=p.length,h=t.makeOutput([d,2],"int32");t.typedArrayFromHeap(h).set(u);let f=t.makeOutput([d],"string"),m=t.dataIdMap.get(f.dataId);m.stringBytes=p;let g=t.makeOutput([2],"int32");return t.typedArrayFromHeap(g).set(c),[h,f,g]}var Qae={kernelName:Gd,backendName:"wasm",kernelFunc:Jae};function ene(e){let{backend:t,inputs:a,attrs:n}=e,{input:r}=a,{numBuckets:s}=n,i=t.readSync(r.dataId),o=m3(i,s),l=t.makeOutput(r.shape,"int32");return t.typedArrayFromHeap(l).set(o),l}var tne={kernelName:Hd,backendName:"wasm",kernelFunc:ene},ane=!0,nne=ca(po,ane),A8;function rne(e){A8=e.wasm.cwrap(io,null,["number","number","number","number"])}function sne(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=us(i,r,t),f=c;if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A,f=T.getInnerMostAxes(f.length,u.shape.length))}T.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[m,g]=T.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(g),x=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;A8(l,y,Ct[x.dtype],A)}if(h&&t.disposeData(p.dataId),s){let A=T.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var ine={kernelName:io,backendName:"wasm",setupFunc:rne,kernelFunc:sne},one=Bt(co),lne=Bt(ho),b8;function une(e){b8=e.wasm.cwrap(ns,null,["number","array","number","array","number","number"])}function dne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,s=a.dataIdMap.get(r.dataId).id,{reps:i}=n,o=new Array(r.shape.length);for(let d=0;d<o.length;d++)o[d]=r.shape[d]*i[d];let l=new Uint8Array(new Int32Array(r.shape).buffer),u=new Uint8Array(new Int32Array(o).buffer),p=a.makeOutput(o,r.dtype),c=a.dataIdMap.get(p.dataId).id;return b8(s,l,r.shape.length,u,o.length,Ct[p.dtype],c),p}var pne={kernelName:ns,backendName:"wasm",setupFunc:une,kernelFunc:dne},v8;function cne(e){v8=e.wasm.cwrap(fo,null,["number","array","number","number","number","bool","number","number"])}var hne=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{k:r,sorted:s}=a,i=t.dataIdMap.get(n.dataId).id,o=new Uint8Array(new Int32Array(n.shape).buffer),l=n.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,n.dtype),p=t.dataIdMap.get(u.dataId).id,c=t.makeOutput(l,"int32"),d=t.dataIdMap.get(c.dataId).id;return v8(i,o,n.shape.length,Ct[n.dtype],r,s,p,d),[u,c]},fne={kernelName:fo,backendName:"wasm",setupFunc:cne,kernelFunc:hne},k8;function mne(e){k8=e.wasm.cwrap(mo,null,["number","number","bool","number","number","number","number","number","number","array","number","array","number","number","number","number","number"])}function gne(e){let{backend:t,inputs:a,attrs:n}=e,{image:r,transforms:s}=a,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[p,c,d,h]=r.shape,[f,m]=u!=null?u:[c,d],g=[p,f,m,h],y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(g)).buffer),A=t.makeOutput(g,r.dtype),b=t.dataIdMap.get(A.dataId).id,k=t.dataIdMap.get(r.dataId).id,S=t.dataIdMap.get(s.dataId).id,C=i==="nearest"?1:2,N;switch(o){case"constant":N=1;break;case"reflect":N=2;break;case"wrap":N=3;break;case"nearest":N=4;break;default:N=1;break}return k8(k,S,s.shape[0]>1,p,f,m,h,d,c,y,r.shape.length-1,x,g.length-1,C,N,l,b),A}var yne={kernelName:mo,backendName:"wasm",setupFunc:mne,kernelFunc:gne};function xne(e){let{inputs:t,backend:a,attrs:n}=e,{value:r}=t,{axis:s}=n;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,l=new Array(o-1),u=0;for(let h=0;h<o;h++)h!==s&&(l[u++]=r.shape[h]);let p=new Array(i),c=new Array(o).fill(0),d=r.shape.slice();d[s]=1;for(let h=0;h<p.length;h++)c[s]=h,p[h]=qs({inputs:{x:r},attrs:{begin:c,size:d},backend:a});return p.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var Ane={kernelName:au,backendName:"wasm",kernelFunc:xne};function bne(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(0),n}var vne={kernelName:nu,backendName:"wasm",kernelFunc:bne},kne=[XJ,KJ,YJ,eQ,lQ,pQ,fQ,yQ,vQ,CQ,NQ,EQ,$Q,_Q,OQ,LQ,BQ,WQ,GQ,qQ,ZQ,QQ,aee,nee,see,iee,oee,lee,pee,cee,fee,yee,bee,wee,Tee,Eee,Mee,_ee,tQ,Pee,Dee,Lee,Wee,Vee,Gee,Hee,qee,Kee,Jee,ete,nte,ite,ute,pte,fte,gte,yte,bte,wte,Tte,Nte,Mte,_te,Fte,t8,Lte,Vte,Hte,qte,Kte,Zte,Yte,Jte,xQ,tae,rae,oae,dae,pae,cae,mae,xae,vae,kae,SQ,Sae,Cae,Rae,_ae,Fae,Dae,Lae,Bae,Wae,Uae,jae,Kae,Yae,Qae,tne,nne,ine,one,lne,pne,fne,yne,sQ,Ane,vne];for(let e of kne)yn(e);var N2=V();N2.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>{try{return WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11]))}catch(e){return!1}});N2.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(N2.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var _y=xl(aS()),wne=xl(nS()),Py=xl(rS()),Fy=_y.default||_y,Ine=Py.default||Py,w8=class extends Al{constructor(e){super(),this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(I8),E2=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new kd(this,vt())}write(e,t,a){let n={id:this.dataIdNextNumber++};return this.move(n,e,t,a,1),n}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,a,n,r){let s=this.dataIdNextNumber++;if(n==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:a,dtype:n,memoryOffset:null,refCount:r});return}let i=v.sizeFromShape(a),o=i*v.bytesPerElement(n),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:a,dtype:n,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e,t,a){let{memoryOffset:n,dtype:r,shape:s,stringBytes:i}=this.dataIdMap.get(e);if(r==="string")return(t==null||t===0)&&(a==null||a>=i.length)?i:i.slice(t,a);t=t||0,a=a||v.sizeFromShape(s);let o=v.bytesPerElement(r),l=this.wasm.HEAPU8.slice(n+t*o,n+a*o);return Cne(l.buffer,r)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let a=this.dataIdMap.get(e);if(a.refCount--,!t&&a.refCount>0)return!1;this.wasm._free(a.memoryOffset),this.wasm.tfjs.disposeData(a.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,a){let n;if(a==null)n=this.write(null,e,t);else{let r=this.dataIdNextNumber++;n={id:r},this.dataIdMap.set(n,{id:r,memoryOffset:a,shape:e,dtype:t,refCount:1});let s=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,a)}return{dataId:n,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:a}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(a),s=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(n,r,s);case"int32":return new Int32Array(n,r,s);case"bool":return new Uint8Array(n,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function Sne(e){return(t,a)=>(v.fetch(e,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${e}'`),n.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{a(s.instance,s.module)})})}),{})}function Oy(e,t,a){if(Vc!=null)return Vc;let n="tfjs-backend-wasm.wasm";return e&&t?n="tfjs-backend-wasm-threaded-simd.wasm":e&&(n="tfjs-backend-wasm-simd.wasm"),Qu!=null&&Qu[n]!=null?Qu[n]:a+n}async function Tne(){let[e,t]=await Promise.all([V().getAsync("WASM_HAS_SIMD_SUPPORT"),V().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((a,n)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=wne.wasmWorkerContents.replace(/\n/g,"\\n"),p=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(p)}return o.endsWith(".wasm")?Oy(e,t,Zu!=null?Zu:l):l+o},F3&&(r.instantiateWasm=Sne(Oy(e,t,Zu!=null?Zu:"")));let s=!1;r.onAbort=()=>{s||ed||(ed=!0,n({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))};let i;t&&e&&Vc==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+Fy.toString()],{type:"text/javascript"}),i=Fy(r)):i=Ine(r),i.then(o=>{s=!0,ed=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),initWithThreadsCount:o.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:o.cwrap("get_threads_count","number",[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},a({wasm:o})}).catch(n)})}function Cne(e,t){switch(t){case"float32":return new Float32Array(e);case"int32":return new Int32Array(e);case"bool":return new Uint8Array(e);default:throw new Error(`Unknown dtype ${t}`)}}var Nne=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Vc=null,Zu=null,Qu={},ed=!1,F3=!1;function Ene(e,t=!1){if(t1("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),ed)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Vc=e,F3=t}function Fh(e,t=!1){if(ed)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")Zu=e;else{Qu=e;let a=Nne.filter(n=>Qu[n]==null);if(a.length>0)throw new Error(`There were no entries found for the following binaries: ${a.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}F3=t}var I8=-1,E2=-1;function Rne(e){I8=e}function Mne(){if(E2===-1)throw new Error("WASM backend not initialized.");return E2}var $ne="4.2.0",_ne=2;yo("wasm",async()=>{let{wasm:e}=await Tne();return new w8(e)},_ne);var zn=V();zn.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zn.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zn.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE",()=>-1);zn.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zn.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zn.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zn.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zn.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE",()=>!0);zn.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG",()=>!1);zn.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL",()=>0);zn.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);var Pne=class{constructor(e){e&&(this.vendor=e.vendor,this.architecture=e.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith("gen"))return Number(this.architecture.match(/\d+/));if(this.architecture.startsWith("xe"))return 12}return 0}isIntel(){return this.vendor==="intel"}},Fne=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,a=!1){let n=Dy(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let r=this.device.createBuffer({size:e,usage:t,mappedAtCreation:a});return this.usedBuffers.get(n).push(r),r}releaseBuffer(e,t,a){if(this.freeBuffers.size===0)return;let n=Dy(t,a);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(n),s=r.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,a){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,a)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(a=>{a.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(a=>{a.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Dy(e,t){return`${e}_${t}`}var One=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,a,n){let r=Ly(a),s=e*t*r,i=zy(e,t,a,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=s,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let l=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(l),l}this.numBytesAllocated+=s;let o=this.device.createTexture({size:[e,t],format:a,usage:n});return this.usedTextures.get(i).push(o),o}releaseTexture(e,t,a,n,r){if(this.freeTextures.size===0)return;let s=zy(t,a,n,r);this.freeTextures.has(s)||this.freeTextures.set(s,[]),this.freeTextures.get(s).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(s),o=i.indexOf(e);if(o<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(o,1);let l=Ly(n),u=t*a*l;this.numBytesUsed-=u}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(a=>{a.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(a=>{a.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function zy(e,t,a,n){return`${e}_${t}_${a}_${n}`}function Ly(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}function Dne(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}var O3=(e,t,a)=>a==="int32"?`atomicAdd(${e}, bitcast<i32>(${t}));`:`
|
|
{
|
|
var oldValue = 0;
|
|
loop {
|
|
let newValueF32 = bitcast<f32>(oldValue) + (${t});
|
|
let newValue = bitcast<i32>(newValueF32);
|
|
let res = atomicCompareExchangeWeak(${e}, oldValue, newValue);
|
|
if res.exchanged {
|
|
break;
|
|
}
|
|
oldValue = res.old_value;
|
|
}
|
|
}`,zne=(e,t,a,n)=>{let r={dtype:n.dtype,shape:n.shape},s=Bne(a,r,t),i=e.createShaderModule({code:s,label:t.constructor.name});return e.createComputePipeline({compute:{module:i,entryPoint:"_start"},label:t.constructor.name,layout:"auto"})};function la(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function br(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function ke(...e){let t;switch(e.length){case 0:t=`
|
|
fn main()
|
|
`;break;case 1:t=`
|
|
fn main(${e[0]} : i32)
|
|
`;break;default:throw Error("Unreachable")}return t}function By(e,t){let a;return a=`
|
|
${Lne(t)}
|
|
fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
|
|
@builtin(global_invocation_id) GlobalId : vec3<u32>,
|
|
@builtin(local_invocation_index) LocalIndex: u32,
|
|
@builtin(workgroup_id) WorkgroupId : vec3<u32>,
|
|
@builtin(num_workgroups) NumWorkgroups : vec3<u32>) {
|
|
localId = LocalId;
|
|
localIndex = LocalIndex;
|
|
globalId = GlobalId;
|
|
numWorkgroups = NumWorkgroups;
|
|
workgroupId = WorkgroupId;
|
|
${e?"main(getGlobalIndex());":"main();"};
|
|
}
|
|
`,a}function Lne(e){return`
|
|
@compute @workgroup_size(${e.workgroupSize[0]}, ${e.workgroupSize[1]}, ${e.workgroupSize[2]})
|
|
`}function Bne(e,t,a){let n=[],r=a.workgroupSize[0]*a.workgroupSize[1]*a.workgroupSize[2];if(n.push(`
|
|
|
|
var<private> localId: vec3<u32>;
|
|
var<private> localIndex: u32;
|
|
var<private> globalId: vec3<u32>;
|
|
var<private> numWorkgroups: vec3<u32>;
|
|
var<private> workgroupId: vec3<u32>;
|
|
|
|
// Only used when the y/z dimension of workgroup size is 1.
|
|
fn getGlobalIndex() -> i32 {
|
|
${S8(a)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +
|
|
workgroupId.y * numWorkgroups.x + workgroupId.x) * ${r}u +
|
|
localIndex);
|
|
`}
|
|
}
|
|
`),a.isFromPixels){n.push(`
|
|
struct Uniform {
|
|
size : i32,
|
|
numChannels : i32,
|
|
outShapeStrides : vec2<i32>,
|
|
};
|
|
|
|
@group(0) @binding(0) var<storage, read_write> result: array<${td(t.dtype,a.isVec4)}>;
|
|
@group(0) @binding(2) var<uniform> uniforms: Uniform;
|
|
`);let h=Uy(a);return[Wy,n.join(`
|
|
`),Vy(t.shape),a.getUserCode(),By(h,a)].join(`
|
|
`)}let s="struct Uniforms { NAN : f32, INFINITY : f32, ";a.variableNames.forEach((h,f)=>{let m=la(e[f].shape.length);s+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${m}, `});let i=la(t.shape.length);s+=`outShape : ${i}, `;let o=t.shape.length-1,l=la(o);s+=`
|
|
outShapeStrides: ${l}, `,a.size&&(s+="size : i32, "),a.uniforms&&(s+=a.uniforms),s+="};",s=Kne(s),n.push(s),a.atomic?n.push(`
|
|
@group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
|
|
`):n.push(`
|
|
@group(0) @binding(0) var<storage, read_write> result: array<${td(t.dtype,a.isVec4)}>;
|
|
`),a.variableNames.forEach((h,f)=>{n.push(`
|
|
@group(0) @binding(${1+f}) var<storage, read> ${h}: array<${a.variableTypes?a.variableTypes[f]:td(e[f].dtype,a.isVec4)}>;
|
|
`)}),s!==""&&n.push(`
|
|
@group(0) @binding(${1+a.variableNames.length}) var<uniform> uniforms: Uniforms;
|
|
`);let u=jne(t.shape,a.dispatchLayout),p=[Wy,n.join(`
|
|
`)+Vne,Vy(t.shape),u,qne(t.shape.length)];a.atomic||p.push(Xne(t.shape,t.dtype,a.isVec4));let c=e.map((h,f)=>Hne(h,t.shape,a.variableTypes?a.variableTypes[f]==="vec4<f32>":a.isVec4,a.dispatchLayout.x.length===t.shape.length)).join(`
|
|
`);p.push(c),p.push(a.getUserCode());let d=Uy(a);return p.push(By(d,a)),p.join(`
|
|
`)}function Wne(e,t,a,n){let r=e.shaderKey;if(e.isFromPixels)return r;let s=a.map(p=>p.dtype).concat(n.dtype),i=a.map(p=>T.getBroadcastDims(p.shape,n.shape)),o=a.map(p=>v.arraysEqual(p.shape,n.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=S8(e)?"flatDispatch":"";return r+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+t.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,r}var Wy=`
|
|
struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
|
|
struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};
|
|
|
|
// Checks whether coordinates lie within the bounds of the shape.
|
|
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
|
|
return all(coord >= vec2<i32>(0)) && all(coord < shape);
|
|
}
|
|
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
|
|
return all(coord >= vec3<i32>(0)) && all(coord < shape);
|
|
}
|
|
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
|
|
return all(coord >= vec4<i32>(0)) && all(coord < shape);
|
|
}
|
|
|
|
fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
|
|
return coord;
|
|
}
|
|
fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
|
|
return dot(coords, vec2<i32>(shape.y, 1));
|
|
}
|
|
fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
|
|
return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
|
|
}
|
|
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
|
|
return dot(coords, vec4<i32>(
|
|
shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
|
|
}
|
|
fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {
|
|
let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);
|
|
return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;
|
|
}
|
|
fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {
|
|
let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);
|
|
return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;
|
|
}
|
|
|
|
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
|
|
var res: i32 = a / b;
|
|
let modulo: i32 = a % b;
|
|
if (sign < 0. && modulo != 0) {
|
|
res = res - 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
// NaN defination in IEEE 754-1985 is :
|
|
// - sign = either 0 or 1.
|
|
// - biased exponent = all 1 bits.
|
|
// - fraction = anything except all 0 bits (since all 0 bits represents infinity).
|
|
// https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
|
|
fn isnan(val: f32) -> bool {
|
|
let floatToUint: u32 = bitcast<u32>(val);
|
|
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
|
|
}
|
|
fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
|
|
let floatToUint: vec4<u32> = bitcast<vec4<u32>>(val);
|
|
return (floatToUint & vec4<u32>(0x7fffffffu)) > vec4<u32>(0x7f800000u);
|
|
}
|
|
`,Vne=`
|
|
fn isinf(val: f32) -> bool {
|
|
return abs(val) == uniforms.INFINITY;
|
|
}
|
|
`;function Vy(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let a=v.computeStrides(e),n=la(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(a.length===1)return` fn getCoordsFromIndex(index : i32) -> vec2<i32> {
|
|
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
|
|
return vec2<i32>(d0, d1);
|
|
}`;let s;return s="var index2 = index;"+a.map((i,o)=>{let l=`let ${r[o]} = index2 / uniforms.outShapeStrides.${br(o)}`,u=o===a.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides.${br(o)}`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides.${br(o)}`;return`${l}; ${u};`}).join(""),`
|
|
fn getCoordsFromIndex(index : i32) -> ${n} {
|
|
${s}
|
|
return ${n}(${r.join(",")});
|
|
}
|
|
`}function Une(e,t){let a=e.name,n=e.shape.length,r=la(n),s="get"+a.charAt(0).toUpperCase()+a.slice(1),i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=i.map(p=>`${p} : i32`).join(", ");if(n<1)return t?`
|
|
fn ${s}() -> vec4<f32> {
|
|
return vec4<f32>(${a}[0]);
|
|
}
|
|
`:`
|
|
fn ${s}() ->f32 {
|
|
return f32(${a}[0]);
|
|
}
|
|
`;let l=`uniforms.${a.charAt(0).toLowerCase()+a.slice(1)}Shape`,u=`${n}D`;return n===0&&(u="1D"),t?`
|
|
fn ${s}(${o}) -> vec4<f32> {
|
|
return vec4<f32>(${a}[getIndexFromCoords${u}(${r}(${i.join(",")}),
|
|
${l}) / 4]);
|
|
}
|
|
`:`
|
|
fn ${s}(${o}) -> f32 {
|
|
return f32(${a}[getIndexFromCoords${u}(${r}(${i.join(",")}),
|
|
${l})]);
|
|
}
|
|
`}function Gne(e,t,a,n){let r=e.name,s=r.charAt(0).toUpperCase()+r.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=la(l);if(v.arraysEqual(e.shape,t)&&n)return a?`
|
|
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
|
|
return vec4<f32>(${r}[globalIndex]);
|
|
}
|
|
|
|
fn ${i}Coords(coords : ${u}) -> vec4<f32> {
|
|
return vec4<f32>(${r}[${l>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
|
|
}
|
|
`:`
|
|
fn ${i}Index(globalIndex : i32) -> f32 {
|
|
return f32(${r}[globalIndex]);
|
|
}
|
|
|
|
fn ${i}Coords(coords : ${u}) -> f32 {
|
|
return f32(${r}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}]);
|
|
}
|
|
`;let p=T.getBroadcastDims(e.shape,t),c=l-o,d="";if(o===0)return a?`
|
|
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
|
|
return get${s}();
|
|
}
|
|
|
|
fn ${i}Coords(coords : ${u}) -> vec4<f32> {
|
|
return get${s}();
|
|
}
|
|
`:`
|
|
fn ${i}Index(globalIndex : i32) -> f32{
|
|
return get${s}();
|
|
}
|
|
|
|
fn ${i}Coords(coords : ${u}) -> f32{
|
|
return get${s}();
|
|
}
|
|
`;l<2&&p.length>=1?d="coords = 0;":d=p.map(g=>`coords.${br(g+c)} = 0;`).join(`
|
|
`);let h="";if(l<2&&o>0)h="coords";else if(l>1){let g=la(o),y=e.shape.map((x,A)=>`coords.${br(A+c)}`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return a?`
|
|
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
|
|
var coords = getCoordsFromIndex(globalIndex);
|
|
${d}
|
|
return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
|
|
}
|
|
|
|
fn ${i}Coords(coordsIn : ${u}) -> vec4<f32> {
|
|
var coords = coordsIn;
|
|
${d}
|
|
return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
|
|
}
|
|
`:`
|
|
fn ${i}Index(globalIndex : i32) -> f32 {
|
|
var coords = getCoordsFromIndex(globalIndex);
|
|
${d}
|
|
return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
|
|
}
|
|
|
|
fn ${i}Coords(coordsIn : ${u}) -> f32 {
|
|
var coords = coordsIn;
|
|
${d}
|
|
return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
|
|
}
|
|
`}function Hne(e,t,a,n){let r=Une(e,a);return e.shape.length<=t.length&&(r+=Gne(e,t,a,n)),r}function jne(e,t){let{x:a,y:n=[],z:r=[]}=t,s=e.length,i=a.length+n.length+r.length;if(i!==s)return"";if(a.length===s)return`fn getOutputCoords() -> ${la(s)}{
|
|
let globalIndex = getGlobalIndex();
|
|
return getCoordsFromIndex(globalIndex);
|
|
}
|
|
`;let o="",l=[a,n,r];for(let d=0;d<l.length;d++){let h=l[d];if(h.length!==0)if(h.length===1)o+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Dne(h,"uniforms.outShape");o+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:o+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let u=[];for(let d=0;d<i;d++)u.push(`d${d}`);let p=la(i),c=`fn getOutputCoords() -> ${p} {
|
|
${o}
|
|
`;return u.length===0?c+=`return ${p}(0); }`:c+=`return ${p}(${u.join(",")}); }`,c}function qne(e){let t="";switch(e){case 0:case 1:t+=`
|
|
fn getOutputIndexFromCoords(coords : i32) -> i32 {
|
|
return coords;
|
|
}
|
|
`;break;case 2:t+=`
|
|
fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
|
|
return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
|
|
}
|
|
`;break;case 3:t+=`
|
|
fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
|
|
return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
|
|
}
|
|
`;break;case 4:t+=`
|
|
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
|
|
return dot(coords, vec4<i32>(
|
|
uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
|
|
}
|
|
`;break;case 5:t+=`
|
|
fn getOutputIndexFromCoords(coords : vec5) -> i32 {
|
|
return coords.x * uniforms.outShapeStrides.x +
|
|
coords.y * uniforms.outShapeStrides.y +
|
|
coords.z * uniforms.outShapeStrides.z +
|
|
coords.w * uniforms.outShapeStrides.w +
|
|
coords.u;
|
|
}
|
|
`;break;case 6:t+=`
|
|
fn getOutputIndexFromCoords(coords : vec6) -> i32 {
|
|
return coords.x * uniforms.outShapeStrides.x +
|
|
coords.y * uniforms.outShapeStrides.y +
|
|
coords.z * uniforms.outShapeStrides.z +
|
|
coords.w * uniforms.outShapeStrides.w +
|
|
coords.u * uniforms.outShapeStrides.u +
|
|
coords.v;
|
|
}
|
|
`;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function S8(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function td(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Xne(e,t,a){let n=e.length,r=td(t,a),s;if(a?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
|
|
result[flatIndex] = ${r}(value);
|
|
}
|
|
fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
|
|
result[flatIndex] = ${r}(value);
|
|
}`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {
|
|
result[flatIndex] = ${r}(value);
|
|
}
|
|
fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
|
|
result[flatIndex] = ${r}(value);
|
|
}`,n>=2){let i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=la(n);a?s+=`
|
|
fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
|
|
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
|
|
setOutputAtIndex(flatIndex / 4, value);
|
|
}
|
|
fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
|
|
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
|
|
setOutputAtIndexI32(flatIndex / 4, value);
|
|
}
|
|
`:s+=`
|
|
fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : f32) {
|
|
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
|
|
setOutputAtIndex(flatIndex, value);
|
|
}
|
|
fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : i32) {
|
|
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
|
|
setOutputAtIndexI32(flatIndex, value);
|
|
}
|
|
`}return s}function Kne(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function Uy(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var T8={};Ze(T8,{GPUBytesPerElement:()=>R2,MatMulProgramType:()=>Pn,assertNotComplex:()=>N8,computeDispatch:()=>we,computeWorkPerThreadForConv2d:()=>z3,computeWorkgroupInfoForMatMul:()=>C8,computeWorkgroupSizeForConv2d:()=>D3,flatDispatchLayout:()=>$e,isWebGPUSupported:()=>L3,tilesFitEvenlyIntoShape:()=>Zne});var Ds=e=>{let t=1;for(let a=0;a<e.length;a++)t*=e[a];return t};function Zne(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((a,n)=>a%e[n]===0)}function we(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(Ds(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(Ds(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(Ds(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function C8(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function D3(e,t,a=!1){if(a)return[8,8,1];let n=Ds(e.x.map(s=>t[s])),r=Ds(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function z3(e,t,a=!1){if(a)return[4,4,1];let n=Ds(e.x.map(s=>t[s])),r=Ds(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function $e(e){return{x:e.map((t,a)=>a)}}function R2(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function L3(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function N8(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var Pn;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(Pn||(Pn={}));var Yne=V().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Jne=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Oh=class extends Al{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!L3())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query-inside-passes"),this.adapterInfo=new Pne(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Fne(this.device),this.textureManager=new One(this.device),this.tensorMap=new kd(this,vt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),V().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))}nextDataId(){return Oh.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let a=this.tensorMap.get(e);if(this.decRef(e),!t&&a.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),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.resourceInfo)){if(t.external){t.resourceInfo=null;return}if("texture"in t.resourceInfo){let a=t.resourceInfo;a.texture instanceof GPUTexture&&this.textureManager.releaseTexture(a.texture,a.width,a.height,a.format,a.usage),a.texture=null}else{let a=t.resourceInfo;this.bufferManager.releaseBuffer(a.buffer,a.size,a.usage),a.buffer=null}t.resourceInfo=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,a){if(a==="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:a,shape:t,values:e,refCount:1}),n}move(e,t,a,n,r){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:a,values:t,refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,a,0,t),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=a.getMappedRange().slice(0);return a.unmap(),a!=null&&this.bufferManager.releaseBuffer(a,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),V().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let a=this.tensorMap.get(e);return this.releaseResource(e),a.values=t,a.values}readSync(e){let t=this.tensorMap.get(e),{values:a}=t;if(a==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return a}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:a}=t;if(a!=null)return this.convertAndCacheOnCPU(e,a);let n;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=r[0],i=r[1];n=T.mergeRealAndImagArrays(s,i)}else{let r=t.resourceInfo,s=await this.getBufferData(r.buffer,r.size);n=v.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e,t,a){let n=this.bufferManager.acquireBuffer(t,a);return this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,a){let n=e.buffer;if(a==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let r={id:this.nextDataId()};this.tensorMap.set(r,{dtype:a,shape:t,values:null,refCount:1,external:e.zeroCopy});let s=this.tensorMap.get(r),i=R2(s.dtype)*v.sizeFromShape(s.shape);if(e.buffer.size<i)throw new Error(`GPUBuffer size(${e.buffer.size}) is smaller than tensor size(${i})!`);if((e.buffer.usage&(GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC))!==(GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC))throw new Error("GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!");return e.zeroCopy!==!0&&(n=this.copyBuffer(n,i,n.usage)),s.resourceInfo={size:n.size,usage:n.usage,buffer:n},vt().makeTensorFromDataId(r,t,a,this)}readToGPU(e){let t=this.tensorMap.get(e),{values:a,dtype:n,shape:r,resourceInfo:s}=t;if(n==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(s==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=s.size,o=this.bufferManager.acquireBuffer(i,s.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s.buffer,0,o,0,i),this.submitQueue();let l=this.makeTensorInfo(r,n),u=vt().makeTensorFromTensorInfo(l),p=this.tensorMap.get(l.dataId);return p.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:o},{tensorRef:u,buffer:o,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return _e(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,a){return t==="string"&&a!=null&&a.length>0&&v.isString(a[0])&&(a=a.map(n=>v.encodeString(n))),{dataId:this.write(a,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let a=t.resourceInfo;return{offset:0,size:a.size,buffer:a.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let a=R2(t.dtype)*v.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(a,this.defaultGpuBufferUsage());if(t.resourceInfo={size:a,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let r=this.bufferManager.acquireUploadBuffer(a,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,n,0,a);let i={size:a,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,a=0,n=[],r=1;e.forEach(l=>{l.data.length===0&&(l.data=[1]);let u;switch(l.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:v.assert(!1,()=>`Unsupported ${l.data.length}D shape`)}(a===5||a===6)&&(u=16),u>r&&(r=u),t=Math.ceil(t/u)*u,a=l.data.length,n.push(t),t+=l.data.length*4}),t=Math.ceil(t/r)*r;let s=new ArrayBuffer(t);e.forEach((l,u)=>{let p=n[u];l.type==="int32"?new Int32Array(s,p,l.data.length).set(l.data):l.type==="uint32"?new Uint32Array(s,p,l.data.length).set(l.data):new Float32Array(s,p,l.data.length).set(l.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(i,0,s,0,t);let o={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:i};return this.uniformPendingDisposal.push(o),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,a,n,r){if(r||(r=this.makeTensorInfo(e.outputShape,a)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=Jne(this.device,e);let s=[],i=[];if(!e.isFromPixels){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=t.concat(r).map(g=>g.shape);let f="int32";i.map(g=>{s.push({type:f,data:g})});let m=v.computeStrides(r.shape);if(s.push({type:f,data:m}),e.size){let g=v.sizeFromShape(e.outputShape);s.push({type:f,data:[e.isVec4?g/4:g]})}}let o=t.map((f,m)=>{if(f.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(f.dataId),{dtype:this.tensorMap.get(f.dataId).dtype,shape:f.shape,name:e.variableNames[m]}}),l=Wne(e,i,o,r),u;l in this.pipelineCache?u=this.pipelineCache[l]:(u=zne(this.device,e,o,r),this.pipelineCache[l]=u),n&&(s=[...s,...n]);let p=[this.tensorToBinding(r),...t.map(f=>this.tensorToBinding(f)),this.makeUniforms(s)],c=this.device.createBindGroup({layout:u.getBindGroupLayout(0),entries:p.map((f,m)=>({binding:m,resource:f}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),h=this.activeTimers!=null;return h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(u),d.setBindGroup(0,c),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(f=>{this.commandQueueOwnedIds.add(f.dataId)}),this.commandQueueOwnedIds.add(r.dataId),V().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),h&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,a,0,16),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(a.getMappedRange()),r=Number(n[1]-n[0]);return a.unmap(),this.bufferManager.releaseBuffer(a,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Yne){return V().getBool("WEBGPU_CPU_FORWARD")&&e.every(a=>this.tensorMap.get(a.dataId).resourceInfo==null&&v.sizeFromShape(a.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDataPendingDisposal.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}};Oh.nextDataId=0;L3()&&yo("webgpu",async()=>{V().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:V().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={};t.features.has("timestamp-query-inside-passes")&&(a.requiredFeatures=["timestamp-query-inside-passes"]);let n=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize};let r=await t.requestDevice(a),s=await t.requestAdapterInfo();return new Oh(r,s)},3);var Pe;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.EQUAL=5]="EQUAL",e[e.GREATER=6]="GREATER",e[e.GREATER_EQUAL=7]="GREATER_EQUAL",e[e.INT_DIV=8]="INT_DIV",e[e.LESS=9]="LESS",e[e.LESS_EQUAL=10]="LESS_EQUAL",e[e.LOGICAL_AND=11]="LOGICAL_AND",e[e.LOGICAL_OR=12]="LOGICAL_OR",e[e.MAX=13]="MAX",e[e.MIN=14]="MIN",e[e.MOD=15]="MOD",e[e.MUL=16]="MUL",e[e.NOT_EQUAL=17]="NOT_EQUAL",e[e.POW=18]="POW",e[e.PRELU=19]="PRELU",e[e.SQUARED_DIFFERENCE=20]="SQUARED_DIFFERENCE",e[e.SUB=21]="SUB"})(Pe||(Pe={}));var E8=`
|
|
if (isnan(a)) { return a; }
|
|
if (isnan(b)) { return b; }
|
|
`,Dh=`
|
|
resultTemp = select(
|
|
resultTemp, vec4<f32>(valueForNaN),
|
|
vec4<bool>(isNaN) | isnanVec4(a) | isnanVec4(b));
|
|
`,Qne="return a + b;",ere="return areal * breal - aimag * bimag;",tre="return areal * bimag + aimag * breal;",are="return a / b;",nre="return f32(a == b);",rre="return vec4<f32>(a == b);",sre="return f32(a > b);",ire="return vec4<f32>(a > b);",ore="return f32(a >= b);",lre="return vec4<f32>(a >= b);",ure=`
|
|
let s = sign(a) * sign(b);
|
|
let ia = i32(round(a));
|
|
let ib = i32(round(b));
|
|
return f32(idiv(ia, ib, s));
|
|
`,dre=`
|
|
let ia = vec4<i32>(round(a));
|
|
let ib = vec4<i32>(round(b));
|
|
let cond = ib != vec4<i32>(0);
|
|
var resultTemp = vec4<i32>(0);
|
|
let s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
resultTemp[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
resultTemp[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
resultTemp[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
resultTemp[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4<f32>(resultTemp);
|
|
`,pre="return f32(a < b);",cre="return vec4<f32>(a < b);",hre="return f32(a <= b);",fre="return vec4<f32>(a <= b);",mre="return f32(a >= 1.0 && b >= 1.0);",gre=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
|
|
vec4<f32>(b >= vec4<f32>(1.0)));`,yre="return f32(a >= 1.0 || b >= 1.0);",xre=`return min(vec4<f32>(a >= vec4<f32>(1.0)) +
|
|
vec4<f32>(b >= vec4<f32>(1.0)), vec4<f32>(1.0));`,Are=`
|
|
${E8}
|
|
if (b == 0.) {
|
|
return uniforms.NAN;
|
|
}
|
|
var resultTemp = a % b;
|
|
if ((a < 0. && b < 0.) || (a >= 0. && b > 0.)) {
|
|
return resultTemp;
|
|
} else {
|
|
return (resultTemp + b) % b;
|
|
}
|
|
`,bre=`
|
|
let isNaN = !vec4<bool>(b);
|
|
let valueForNaN = uniforms.NAN;
|
|
var resultTemp = vec4<f32>(a % b);
|
|
${Dh}
|
|
|
|
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];
|
|
}
|
|
|
|
return resultTemp;
|
|
`,vre="return a * b;",kre=`
|
|
if (isnan(a) || isnan(b)) {
|
|
return 1.0;
|
|
}
|
|
return f32(a != b);
|
|
`,wre=`
|
|
var resultTemp = vec4<f32>(a != b);
|
|
let valueForNaN = 1.0;
|
|
${Dh}
|
|
|
|
return resultTemp;
|
|
`,Ire=`
|
|
if(a < 0.0 && floor(b) < b) {
|
|
return uniforms.NAN;
|
|
}
|
|
if (b == 0.0) {
|
|
return 1.0;
|
|
}
|
|
if (round(abs(b) % 2.0) != 1.0) {
|
|
return pow(abs(a), b);
|
|
}
|
|
return sign(a) * pow(abs(a), b);
|
|
`,Sre=`
|
|
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
|
|
let isModRound1 = vec4<f32>(isModRound1Bool);
|
|
let multiplier = sign(a) * isModRound1 + (vec4<f32>(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<f32>(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<f32>(0.0)) & (floor(b) < b);
|
|
let valueForNaN = uniforms.NAN;
|
|
${Dh}
|
|
return resultTemp;
|
|
`,Tre="if (a < 0.0) { return b * a; } return a;",Cre=`
|
|
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
|
|
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
|
|
`,Nre="return (a - b) * (a - b);",Ere="return a - b;";function Dm(e,t,a="uniforms.NAN"){let n=t?Dh:E8;return t?`
|
|
let valueForNaN = ${a};
|
|
var resultTemp = vec4<f32>(${e}(a, b));
|
|
`+n+`
|
|
return resultTemp;
|
|
`:n+`
|
|
return ${e}(a, b);
|
|
`}function B3(e,t){switch(e){case Pe.ADD:return Qne;case Pe.ATAN2:return Dm("atan2",t);case Pe.COMPLEX_MULTIPLY_IMAG:return tre;case Pe.COMPLEX_MULTIPLY_REAL:return ere;case Pe.DIV:return are;case Pe.EQUAL:return t?rre:nre;case Pe.GREATER:return t?ire:sre;case Pe.GREATER_EQUAL:return t?lre:ore;case Pe.INT_DIV:return t?dre:ure;case Pe.LESS:return t?cre:pre;case Pe.LESS_EQUAL:return t?fre:hre;case Pe.LOGICAL_AND:return t?gre:mre;case Pe.LOGICAL_OR:return t?xre:yre;case Pe.MAX:return Dm("max",t);case Pe.MIN:return Dm("min",t);case Pe.MOD:return t?bre:Are;case Pe.MUL:return vre;case Pe.NOT_EQUAL:return t?wre:kre;case Pe.POW:return t?Sre:Ire;case Pe.PRELU:return t?Cre:Tre;case Pe.SQUARED_DIFFERENCE:return Nre;case Pe.SUB:return Ere;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var le;(function(e){e[e.ABS=0]="ABS",e[e.ACOS=1]="ACOS",e[e.ACOSH=2]="ACOSH",e[e.ASIN=3]="ASIN",e[e.ASINH=4]="ASINH",e[e.ATAN=5]="ATAN",e[e.ATANH=6]="ATANH",e[e.CEIL=7]="CEIL",e[e.COS=8]="COS",e[e.COSH=9]="COSH",e[e.ELU=10]="ELU",e[e.ERF=11]="ERF",e[e.EXP=12]="EXP",e[e.EXPM1=13]="EXPM1",e[e.FLOOR=14]="FLOOR",e[e.IS_FINITE=15]="IS_FINITE",e[e.IS_INF=16]="IS_INF",e[e.IS_NAN=17]="IS_NAN",e[e.LINEAR=18]="LINEAR",e[e.LOG=19]="LOG",e[e.LOG1P=20]="LOG1P",e[e.LOGICAL_NOT=21]="LOGICAL_NOT",e[e.NEG=22]="NEG",e[e.RELU=23]="RELU",e[e.RELU6=24]="RELU6",e[e.LEAKYRELU=25]="LEAKYRELU",e[e.RECIPROCAL=26]="RECIPROCAL",e[e.ROUND=27]="ROUND",e[e.RSQRT=28]="RSQRT",e[e.SELU=29]="SELU",e[e.SIGMOID=30]="SIGMOID",e[e.SIGN=31]="SIGN",e[e.SIN=32]="SIN",e[e.SINH=33]="SINH",e[e.SOFTPLUS=34]="SOFTPLUS",e[e.SQRT=35]="SQRT",e[e.SQUARE=36]="SQUARE",e[e.STEP=37]="STEP",e[e.TAN=38]="TAN",e[e.TANH=39]="TANH",e[e.TO_INT=40]="TO_INT"})(le||(le={}));var Rre="return abs(a);",Mre=`
|
|
if (abs(a) > 1.) {
|
|
return uniforms.NAN;
|
|
}
|
|
return acos(a);
|
|
`,$re=`
|
|
if (a < 1.) {
|
|
return uniforms.NAN;
|
|
}
|
|
return acosh(a);
|
|
`,_re=`
|
|
if (abs(a) > 1.) {
|
|
return uniforms.NAN;
|
|
}
|
|
return asin(a);
|
|
`,Pre="return asinh(a);",Fre=`
|
|
if (isnan(a)) {
|
|
return uniforms.NAN;
|
|
}
|
|
return atan(a);
|
|
`,Ore=`
|
|
if (abs(a) > 1.) {
|
|
return uniforms.NAN;
|
|
}
|
|
if (a == 1.) {
|
|
return uniforms.INFINITY;
|
|
}
|
|
if (a == -1.) {
|
|
return -uniforms.INFINITY;
|
|
}
|
|
return atanh(a);
|
|
`,Dre="return ceil(a);",zre="return cos(a);",Lre=`
|
|
let e2x = exp(-a);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,Bre="return exp(a) - 1.0;",Wre="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Vre=`
|
|
var resFloat = exp(a) - vec4<f32>(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;
|
|
`,Ure=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
let p = ${T.ERF_P};
|
|
let a1 = ${T.ERF_A1};
|
|
let a2 = ${T.ERF_A2};
|
|
let a3 = ${T.ERF_A3};
|
|
let a4 = ${T.ERF_A4};
|
|
let a5 = ${T.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));
|
|
`,Gre="return exp(a);",Hre="return floor(a);",jre="return f32(!isnan(a) && !isinf(a));",qre="return f32(isinf(a));",Xre="return f32(isnan(a));",Kre="return a;",Zre=`if (a < 0.0) { return uniforms.NAN; }
|
|
return log(a);`,Yre=`
|
|
if (isnan(a)) { return a; }
|
|
return log(1.0 + a);
|
|
`,Jre="return f32(!(a >= 1.0));",Qre="return -a;",ese="if (a < 0.0) { return uniforms.alpha * a; } return a;",tse=`
|
|
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
|
|
return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
|
|
`,ase="return 1.0 / a;",nse="return select(a, 0.0, a < 0.0);",rse="return clamp(a, 0.0, 6.0);",sse="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",ise=`
|
|
return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
|
|
`,ose="return round(a);",lse="return inverseSqrt(a);",use=`
|
|
if (a >= 0.0) {
|
|
return ${T.SELU_SCALE} * a;
|
|
} else {
|
|
return ${T.SELU_SCALEALPHA} * (exp(a) - 1.0);
|
|
}
|
|
`,dse="return 1.0 / (1.0 + exp(-1.0 * a));",pse="return sign(a);",cse="return sin(a);",hse=`
|
|
let e2x = exp(a);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,fse=`
|
|
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);
|
|
}
|
|
`,mse="return sqrt(a);",gse="return a * a;",yse=`
|
|
if (isnan(a)) {
|
|
return a;
|
|
}
|
|
|
|
return select(uniforms.stepAlpha, 1.0, a > 0.0);
|
|
`,xse="return tan(a);",Ase=`
|
|
let e2x = exp(-2.0 * abs(a));
|
|
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,bse="return f32(i32((a)));";function $s(e,t){switch(e){case le.ABS:return Rre;case le.ACOS:return Mre;case le.ACOSH:return $re;case le.ASIN:return _re;case le.ASINH:return Pre;case le.ATAN:return Fre;case le.ATANH:return Ore;case le.COS:return zre;case le.COSH:return Lre;case le.CEIL:return Dre;case le.ELU:return t?Vre:Wre;case le.ERF:return Ure;case le.EXP:return Gre;case le.EXPM1:return Bre;case le.FLOOR:return Hre;case le.IS_FINITE:return jre;case le.IS_INF:return qre;case le.IS_NAN:return Xre;case le.LINEAR:return Kre;case le.LOG:return Zre;case le.LOG1P:return Yre;case le.LOGICAL_NOT:return Jre;case le.NEG:return Qre;case le.LEAKYRELU:return t?tse:ese;case le.RECIPROCAL:return ase;case le.RELU:return t?ise:nse;case le.RELU6:return t?sse:rse;case le.ROUND:return ose;case le.RSQRT:return lse;case le.SELU:return use;case le.SIGMOID:return dse;case le.SIGN:return pse;case le.SIN:return cse;case le.SINH:return hse;case le.SOFTPLUS:return fse;case le.SQRT:return mse;case le.SQUARE:return gse;case le.STEP:return yse;case le.TAN:return xse;case le.TANH:return Ase;case le.TO_INT:return bse;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var $t=e=>{switch(e){case 1:return"f32";case 2:return"vec2<f32>";case 3:return"vec3<f32>";case 4:return"vec4<f32>";default:throw new Error(`${e}-component is not supported.`)}};function Cr(e,t=!1,a=!1,n=3){if(e===null)return"";let r="";if(e==="linear")r=$s(le.LINEAR);else if(e==="relu")r=$s(le.RELU,a);else if(e==="elu")r=$s(le.ELU,a);else if(e==="relu6")r=$s(le.RELU6,a);else if(e==="prelu")r=B3(Pe.PRELU,a);else if(e==="sigmoid")r=$s(le.SIGMOID,a);else if(e==="leakyrelu")r=$s(le.LEAKYRELU,a);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let s=$t(a?4:1),i="";return t?i=`
|
|
fn activation(a : ${s}, coords : vec${n}<i32>) -> ${s} {
|
|
let b = getPreluActivationWeightsByOutputCoords(coords);
|
|
${r}
|
|
}`:i=`
|
|
fn activation(a : ${s}, coords : vec${n}<i32>) -> ${s} {
|
|
${r}
|
|
}`,i}function ko(e,t){return`
|
|
${e?"value = value + getBiasByOutputCoords(coords);":""}
|
|
${t?"value = activation(value, coords);":""}
|
|
`}function R8(e,t,a=!1,n=!1,r=!1,s=1){v.assert(e&&s===1||!e,()=>`transposeA ${e} is not compatible with component size ${s}`);let i=`
|
|
${e?"value = getA(batch, col, row);":"value = getA(batch, row, col);"}
|
|
|
|
`,o=t?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return`
|
|
fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${$t(s)} {
|
|
var value = ${$t(s)}(0.0);
|
|
let col = colIn * ${s};
|
|
${a&&r?i:`
|
|
${e?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"}
|
|
{
|
|
${i}
|
|
}
|
|
`}
|
|
return value;
|
|
}
|
|
|
|
fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${$t(s)} {
|
|
let col = colIn * ${s};
|
|
var value = ${$t(s)}(0.0);
|
|
${o}
|
|
return value;
|
|
}
|
|
`}function W3(e,t,a,n,r=!1,s=!1,i=!1,o=1){return`
|
|
${R8(a,n,r,s,i,o)}
|
|
fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${$t(o)}) {
|
|
let col = colIn * ${o};
|
|
${r&&s?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"}
|
|
{
|
|
var value = valueIn;
|
|
let coords = vec3<i32>(batch, row, col);
|
|
${ko(e,t)}
|
|
setOutputAtCoords(coords[0], coords[1], coords[2], value);
|
|
}
|
|
}
|
|
`}var vse=(e,t)=>e?`
|
|
mm_Asub[inputRow][inputCol] = mm_readA(batchA,
|
|
kStart + inputRow,
|
|
globalRowStart / ${t} + inputCol);
|
|
`:`
|
|
mm_Asub[inputRow][inputCol] = mm_readA(batchA,
|
|
globalRow + innerRow,
|
|
kStart / ${t} + inputCol);
|
|
`,kse=(e,t,a)=>e?`
|
|
let ACached0 = mm_Asub[k * ${t}][localRow];
|
|
let ACached1 = mm_Asub[k * ${t} + 1][localRow];
|
|
let ACached2 = mm_Asub[k * ${t} + 2][localRow];
|
|
${t===3?"":`let ACached3 = mm_Asub[k * ${t} + 3][localRow];`}
|
|
for (var i = 0; i < ${a}; i++) {
|
|
acc[i] = BCached0 * ACached0[i] + acc[i];
|
|
acc[i] = BCached1 * ACached1[i] + acc[i];
|
|
acc[i] = BCached2 * ACached2[i] + acc[i];
|
|
${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
|
|
}`:`
|
|
for (var i = 0; i < ${a}; i++) {
|
|
let ACached = mm_Asub[tileRow + i][k];
|
|
acc[i] = BCached0 * ACached.x + acc[i];
|
|
acc[i] = BCached1 * ACached.y + acc[i];
|
|
acc[i] = BCached2 * ACached.z + acc[i];
|
|
${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
|
|
}`;function zh(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=t[1]*e[1],u=t[0]*e[0],p=a?l:n,c=a?n:l,d=p/t[0],h=n/t[1],f=e[1];return v.assert((a&&d===4&&e[1]===4||!a&&(d===3||d===4))&&p%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${d} and workPerThread[1] ${e[1]} must be 4.
|
|
Otherwise, innerElementSize ${d} must be 3 or 4.
|
|
tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`),`
|
|
var<workgroup> mm_Asub : array<array<vec${d}<f32>, ${p/d}>, ${c}>;
|
|
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${u/e[0]}>, ${n}>;
|
|
|
|
${ke()} {
|
|
let localRow = i32(localId.y);
|
|
let tileRow = ${i?"0":`localRow * ${f}`};
|
|
let tileCol = i32(localId.x);
|
|
|
|
let globalRow = ${i?"0":`i32(globalId.y) * ${f}`};
|
|
let globalCol = i32(globalId.x);
|
|
let batch = ${r?"0":"i32(globalId.z)"};
|
|
let batchA = ${r||!o?"batch":"batch % uniforms.aShape[0]"};
|
|
let batchB = ${r||!o?"batch":"batch % uniforms.bShape[0]"};
|
|
let globalRowStart = i32(workgroupId.y) * ${l};
|
|
|
|
let numTiles = ${r?`${Math.ceil(s/n)}`:`(uniforms.dimInner - 1) / ${n} + 1`};
|
|
var kStart = ${r?`i32(globalId.z) * ${s}`:"0"};
|
|
|
|
var acc: array<vec4<f32>, ${f}>;
|
|
|
|
// Loop over shared dimension.
|
|
let tileRowB = localRow * ${h};
|
|
for (var t = 0; t < numTiles; t++) {
|
|
// Load one tile of A into local memory.
|
|
for (var innerRow = 0; innerRow < ${f}; innerRow++) {
|
|
let inputRow = tileRow + innerRow;
|
|
let inputCol = tileCol;
|
|
${vse(a,d)}
|
|
}
|
|
|
|
// Load one tile of B into local memory.
|
|
for (var innerRow = 0; innerRow < ${h}; innerRow++) {
|
|
let inputRow = tileRowB + innerRow;
|
|
let inputCol = tileCol;
|
|
mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);
|
|
}
|
|
kStart = kStart + ${n};
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < ${n/d}; k++) {
|
|
let BCached0 = mm_Bsub[k * ${d}][tileCol];
|
|
let BCached1 = mm_Bsub[k * ${d} + 1][tileCol];
|
|
let BCached2 = mm_Bsub[k * ${d} + 2][tileCol];
|
|
${d===3?"":`let BCached3 = mm_Bsub[k * ${d} + 3][tileCol];`}
|
|
|
|
${kse(a,d,f)}
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
for (var innerRow = 0; innerRow < ${f}; innerRow++) {
|
|
mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
|
|
}
|
|
}`}var Gy=e=>e?`
|
|
mm_Asub[inputRow][inputCol] = mm_readA(batchA,
|
|
kStart + inputRow,
|
|
globalRowStart + inputCol);
|
|
`:`
|
|
mm_Asub[inputRow][inputCol] = mm_readA(batchA,
|
|
globalRowStart + inputRow,
|
|
kStart + inputCol);
|
|
`,wse=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Lh(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=e[1]*t[1],u=e[0]*t[0],p=a?l:n,c=a?n:l;v.assert(c%t[1]===0&&p%t[0]===0&&n%t[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let d=c/t[1],h=p/t[0],f=n/t[1],m=e[1],g=e[0],y=i?`
|
|
let localRow = i32(localId.y);
|
|
let localCol = i32(localId.x);
|
|
let globalRowStart = i32(workgroupId.y) * ${l};
|
|
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 + ${t[1]}) {
|
|
for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${t[0]}) {
|
|
${Gy(a)}
|
|
}
|
|
}
|
|
// Load one tile of B into local memory.
|
|
for (var inputRow = localRow; inputRow < ${n}; inputRow = inputRow + ${t[1]}) {
|
|
for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) {
|
|
mm_Bsub[inputRow][inputCol] = mm_readB(batchB,
|
|
kStart + inputRow,
|
|
globalColStart + inputCol);
|
|
}
|
|
}
|
|
kStart = kStart + ${n};
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
var BCached : array<f32, ${g}>;
|
|
for (var k = 0; k < ${n}; k++) {
|
|
for (var inner = 0; inner < ${g}; inner++) {
|
|
BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];
|
|
}
|
|
for (var innerRow = 0; innerRow < ${m}; innerRow++) {
|
|
let ACached = ${a?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}
|
|
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
|
|
acc[innerRow][innerCol] = acc[innerRow][innerCol] +
|
|
ACached * BCached[innerCol];
|
|
}
|
|
}
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
for (var innerRow = 0; innerRow < ${m}; innerRow++) {
|
|
let gRow = globalRowStart + localRow + innerRow * ${t[1]};
|
|
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
|
|
let gCol = globalColStart + localCol + innerCol * ${t[0]};
|
|
mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
|
|
}
|
|
}
|
|
`:`
|
|
let tileRow = i32(localId.y) * ${m};
|
|
let tileCol = i32(localId.x) * ${g};
|
|
|
|
let globalRow = i32(globalId.y) * ${m};
|
|
let globalCol = i32(globalId.x) * ${g};
|
|
let globalRowStart = i32(workgroupId.y) * ${l};
|
|
|
|
let tileRowA = i32(localId.y) * ${d};
|
|
let tileColA = i32(localId.x) * ${h};
|
|
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 < ${d}; innerRow++) {
|
|
for (var innerCol = 0; innerCol < ${h}; innerCol++) {
|
|
let inputRow = tileRowA + innerRow;
|
|
let inputCol = tileColA + innerCol;
|
|
${Gy(a)}
|
|
}
|
|
}
|
|
|
|
// 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 + ${n};
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
var BCached : array<f32, ${g}>;
|
|
for (var k = 0; k < ${n}; k++) {
|
|
for (var inner = 0; inner < ${g}; inner++) {
|
|
BCached[inner] = mm_Bsub[k][tileCol + inner];
|
|
}
|
|
|
|
for (var innerRow = 0; innerRow < ${m}; innerRow++) {
|
|
${wse(a)}
|
|
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
|
|
acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
|
|
}
|
|
}
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
for (var innerRow = 0; innerRow < ${m}; innerRow++) {
|
|
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
|
|
mm_write(batch, globalRow + innerRow, globalCol + innerCol,
|
|
acc[innerRow][innerCol]);
|
|
}
|
|
}
|
|
`;return`
|
|
var<workgroup> mm_Asub : array<array<f32, ${p}>, ${c}>;
|
|
var<workgroup> mm_Bsub : array<array<f32, ${u}>, ${n}>;
|
|
|
|
${ke()} {
|
|
let batch = ${r?"0":"i32(globalId.z)"};
|
|
let batchA = ${r||!o?"batch":"batch % uniforms.aShape[0]"};
|
|
let batchB = ${r||!o?"batch":"batch % uniforms.bShape[0]"};
|
|
let numTiles = ${r?`${Math.ceil(s/n)}`:`(uniforms.dimInner - 1) / ${n} + 1`};
|
|
var kStart = ${r?`i32(globalId.z) * ${s}`:"0"};
|
|
|
|
var acc : array<array<f32, ${g}>, ${m}>;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
for (var innerRow = 0; innerRow < ${m}; innerRow++) {
|
|
for (var innerCol = 0; innerCol < ${g}; innerCol++) {
|
|
acc[innerRow][innerCol] = 0.0;
|
|
}
|
|
}
|
|
${y}
|
|
}
|
|
`}var Ise=e=>e?`
|
|
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 Sse(e,t=!1){v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`);let a=e[0]*4;return`
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
|
|
${ke()} {
|
|
let tileCol = i32(localId.x);
|
|
let globalCol = i32(globalId.x);
|
|
let globalRow = i32(globalId.y);
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / ${a} + 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 * ${a} + tileCol * 4;
|
|
mm_Asub[tileCol] = vec4<f32>(${Ise(t)});
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < ${a/4}; k++) {
|
|
let rowB = t * ${a} + k * 4;
|
|
let BCached = vec4<f32>(mm_readB(batchB, rowB, globalCol),
|
|
mm_readB(batchB, rowB + 1, globalCol),
|
|
mm_readB(batchB, rowB + 2, globalCol),
|
|
mm_readB(batchB, rowB + 3, globalCol));
|
|
|
|
let ACached = mm_Asub[k];
|
|
acc = acc + dot(ACached, BCached);
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
mm_write(batch, globalRow, globalCol, acc);
|
|
}
|
|
`}var Tse=class{constructor(e,t,a=!1,n=!1,r=null,s=null,i=null,o=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];if(this.isVec4=(l%4===0&&!a||t[1]%4===0&&a)&&t[2]%4===0&&!n,this.isVectorA=t[1]===1&&!a,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let c=C8(t[1],l,t[2],a);this.workgroupSize=c.workgroupSize,this.elementsPerThread=c.elementsPerThread}this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let u=r!=null,p=i!=null;u&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=o,this.transposeA=a,this.transposeB=n,this.addBias=u,this.activation=s,this.hasPreluActivationWeights=p,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${a}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return`
|
|
${Cr(this.activation,this.hasPreluActivationWeights,this.isVec4)}
|
|
${W3(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}
|
|
${this.isVec4?zh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA,!0):this.isVectorA?Sse(this.workgroupSize,this.transposeA):Lh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}
|
|
`}};function Cse(e){return`
|
|
var<workgroup> sumValues : array<f32, ${e}>;
|
|
${ke()} {
|
|
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 + ${e}) {
|
|
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 = ${e/2}u; currentSize > 1u;
|
|
currentSize = currentSize / 2u) {
|
|
if (localId.x < currentSize)
|
|
{
|
|
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (localId.x == 0u) {
|
|
sum = sumValues[0] + sumValues[1];
|
|
mm_write(batch, row, col, sum);
|
|
}
|
|
}
|
|
`}var Nse=class{constructor(e,t=!1,a=!1,n=null,r=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=a,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${a}`}getUserCode(){return`
|
|
${Cr(this.activation,this.hasPreluActivationWeights)}
|
|
${W3(this.addBias,this.activation,this.transposeA,this.transposeB)}
|
|
${Cse(this.workgroupSize[0])}
|
|
`}};function Ese(e){let t=e[1],a=e[0],n=t>a?t:a;return`
|
|
var<workgroup> mm_Asub : array<array<f32, ${n}>, ${t}>;
|
|
var<workgroup> mm_Bsub : array<array<f32, ${a}>, ${n}>;
|
|
|
|
// 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.
|
|
${ke()} {
|
|
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) / ${n} + 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 + ${n};
|
|
globalRowB = globalRowB + ${n};
|
|
|
|
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 + ${n};
|
|
globalRowB = globalRowB + ${n};
|
|
|
|
for (var k = 0; k < ${n}; k = k + 1) {
|
|
acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
|
|
mm_write(batch, globalRow, globalCol, acc);
|
|
}
|
|
`}var Rse=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=a,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(a[2]/this.workgroupSize[0]),Math.ceil(a[1]/this.workgroupSize[1]),a[0]];let l=s!=null;l&&this.variableNames.push("bias");let u=o!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${r}`}getUserCode(){return`
|
|
${Cr(this.activation,this.hasPreluActivationWeights)}
|
|
${W3(this.addBias,this.activation,this.transposeA,this.transposeB)}
|
|
${Ese(this.workgroupSize)}
|
|
`}},Mse=class{constructor(e,t,a=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(a&&this.outputShape[1]%4===0||!a&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=we(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=a,this.transposeB=n,this.shaderKey=`matMulSplitK_${a}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=this.isVec4?4:1;return`
|
|
${R8(!1,this.transposeB,!1,!1,!1,e)}
|
|
fn mm_write(batch: i32, row : i32, colIn : i32, value : ${$t(e)}) {
|
|
let col = colIn * ${e};
|
|
if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {
|
|
let coords = vec3<i32>(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) {
|
|
${O3("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")}
|
|
}
|
|
}
|
|
}
|
|
${this.isVec4?zh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Lh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}
|
|
`}},$se=class{constructor(e,t=null,a=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${a}`}getUserCode(){return`
|
|
${Cr(this.activation,this.hasPreluActivationWeights)}
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromIndex(index);
|
|
var value = getXByOutputIndex(index);
|
|
${ko(this.addBias,this.activation)}
|
|
setOutputAtIndex(index, value);
|
|
}
|
|
}
|
|
`}},_se=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
setOutputAtIndex(index, uniforms.value);
|
|
}
|
|
}
|
|
`}};function Nr(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new _se(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var Pse={kernelName:Pl,backendName:"webgpu",kernelFunc:Nr};function Ie(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Fse={kernelName:Hl,backendName:"webgpu",kernelFunc:Ie};function Bh({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],f=n?t.shape[p-2]:t.shape[p-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),A=xo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],k=n?[x,f,d]:[x,d,f],S=Ie({inputs:{x:e},backend:r,attrs:{shape:b}}),C=Ie({inputs:{x:t},backend:r,attrs:{shape:k}}),N=[S,C],$=Math.max(y,x),M=[S,C],R=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[c]}],I,_,D=[$,h,f],W=V().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(W<0){let U=V().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),G=U>0?U:r.thresholdToIncreaseWorkgroups,q=$*Math.ceil(h/32)*Math.ceil(f/32);q<=G||h<=8&&q<=G*2?$*h*f<=128?W=Pn.MatMulReduceProgram:$===1&&d>=2e3?W=Pn.MatMulSplitKProgram:W=Pn.MatMulSmallOutputSizeProgram:W=Pn.MatMulPackedProgram}switch(W){case Pn.MatMulReduceProgram:I=new Nse(D,a,n,s,l,i);break;case Pn.MatMulSplitKProgram:{if(_=Nr({backend:r,attrs:{shape:D,value:0,dtype:e.dtype}}),I=new Mse(D,d,a,n),s||l){_=r.runWebGPUProgram(I,M,e.dtype,R,_);let G=new $se(_.shape,s,l,i),q=null,H=[_];s&&H.push(s),i&&H.push(i),l==="leakyrelu"&&(q=[{type:"float32",data:[o]}],G.uniforms+=" alpha : f32,");let B=r.runWebGPUProgram(G,H,_.dtype,q);N.push(_);let Z=Ie({inputs:{x:B},backend:r,attrs:{shape:A}});N.push(B);for(let X of N)r.disposeData(X.dataId);return Z}break}case Pn.MatMulSmallOutputSizeProgram:I=new Rse(b,k,D,a,n,s,l,i);break;case Pn.MatMulPackedProgram:let U=r.adapterInfo.isIntel();I=new Tse(b,D,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${W}.`)}s&&M.push(s),i&&M.push(i),l==="leakyrelu"&&(R.push({type:"float32",data:[o]}),I.uniforms+=" alpha : f32,"),_=r.runWebGPUProgram(I,M,e.dtype,R,_);let P=Ie({inputs:{x:_},backend:r,attrs:{shape:A}});N.push(_);for(let U of N)r.disposeData(U.dataId);return P}function Ose(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return Bh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var Dse={kernelName:Hr,backendName:"webgpu",kernelFunc:Ose},Hy=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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 {
|
|
${B3(this.op,!1)}
|
|
}
|
|
|
|
${ke("index")} {
|
|
if(index < uniforms.size) {
|
|
let areal = getARealByOutputIndex(index);
|
|
let aimag = getAImagByOutputIndex(index);
|
|
let breal = getBRealByOutputIndex(index);
|
|
let bimag = getBImagByOutputIndex(index);
|
|
setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
}
|
|
`}},M2=class{constructor(e,t,a){this.size=!0,this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.dispatchLayout=$e(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&a.length>1&&t[0]<128,this.useSharedMemoryWithB=a.length<=1&&t.length>1&&a[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?a[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type="shared",this.workgroupSize=[256,1,1],this.workPerThread=1):(v.arraysEqual(t,a)&&v.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type="vec4",this.workPerThread=4):(this.isVec4=!1,this.type="plain",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?"vec4<f32>":"f32",a=`
|
|
fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {
|
|
let isNaN = false;
|
|
{
|
|
${B3(this.op,this.isVec4)}
|
|
}
|
|
};
|
|
`;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",r=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);
|
|
let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];
|
|
let b = getBByOutputIndex(index);`;e=`
|
|
${a}
|
|
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
|
|
${ke("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);
|
|
${r}
|
|
setOutputAtIndex(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}else e=`
|
|
${a}
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let a = getAByOutputIndex(index);
|
|
let b = getBByOutputIndex(index);
|
|
setOutputAtIndex(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`;return e}};function en(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var zse={kernelName:wi,backendName:"webgpu",kernelFunc:en};function wo(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=en({inputs:{x:n},backend:a}),l=en({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Lse={kernelName:Td,backendName:"webgpu",kernelFunc:wo},xu=class{constructor(e,t,a=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,a!==""&&(this.uniforms=a),this.shaderKey=`unary_${t}`}getUserCode(){return`
|
|
fn unaryOperation(a : f32) -> f32 {
|
|
${$s(this.op,!1)}
|
|
}
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let a = getAByOutputIndex(index);
|
|
setOutputAtIndex(index, unaryOperation(a));
|
|
}
|
|
}
|
|
`}};function et({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new xu(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Jt({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,f;if(e!==Pe.MUL)[h,f]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:x.dataId,dtype:x.dtype,shape:o.shape},k=new M2(e,i.shape,o.shape);return l.runWebGPUProgram(k,[A,b],ma(y.dtype,x.dtype))});else{let g=new Hy(Pe.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),y=new Hy(Pe.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),x=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(y,x,"float32")}let m=wo({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let u=n||ma(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?T.fromUint8ToStringArray(c):c,f=i.dtype==="string"?T.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,u);return l.makeTensorInfo(g,u,m)}let p=new M2(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:Bse,castImpl:Wse,ceilImpl:Vse,concatImpl:Use,equalImpl:Gse,expImpl:Hse,expm1Impl:jse,floorImpl:qse,gatherNdImpl:Xse,gatherV2Impl:Kse,greaterEqualImpl:Zse,greaterImpl:Yse,lessEqualImpl:Jse,lessImpl:Qse,logImpl:eie,maxImpl:tie,maximumImpl:aie,minimumImpl:nie,multiplyImpl:rie,negImpl:sie,notEqualImpl:iie,prodImpl:oie,rangeImpl:lie,rsqrtImpl:uie,scatterImpl:die,simpleAbsImpl:pie,sliceImpl:cie,stridedSliceImpl:hie,stringNGramsImpl:fie,subImpl:mie,tileImpl:gie,topKImpl:yie,transposeImpl:xie,uniqueImpl:cfe}=Ch,Aie=et({opType:le.ABS,cpuKernelImpl:pie}),bie={kernelName:vl,backendName:"webgpu",kernelFunc:Aie},vie=et({opType:le.ACOS}),kie={kernelName:kl,backendName:"webgpu",kernelFunc:vie},wie=et({opType:le.ACOSH}),Iie={kernelName:wl,backendName:"webgpu",kernelFunc:wie},Sie=Jt({opType:Pe.ADD,cpuKernelImpl:Bse,supportsComplex:!0}),Tie={kernelName:ts,backendName:"webgpu",kernelFunc:Sie},Cie=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return`
|
|
${ke("index")} {
|
|
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if (flatIndex < uniforms.size) {
|
|
let coords = getCoordsFromIndex(flatIndex);
|
|
${e.join(`
|
|
`)}
|
|
setOutputAtIndex(flatIndex, ${t});
|
|
}
|
|
}
|
|
}
|
|
`}};function Nie(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return en({inputs:{x:n[0]},backend:a});let r=n.map(o=>o.dtype).reduce((o,l)=>ma(o,l)),s=n.map(o=>o.shape),i=new Cie(s);return a.runWebGPUProgram(i,n,r)}var Eie={kernelName:Ks,backendName:"webgpu",kernelFunc:Nie},Rie=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let a=new Array(e.length);for(let n=0;n<a.length;n++)a[n]=e[t[n]];this.outputShape=a,this.dispatchLayout={x:[0],y:[1]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){v.assert(this.workgroupSize[0]===this.workgroupSize[1],()=>`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return`
|
|
var<workgroup> tile : array<array<f32, ${this.workgroupSize[0]+1}>, ${this.workgroupSize[0]}>;
|
|
${ke()} {
|
|
var x = i32(workgroupId.x) * ${e} + i32(localId.x);
|
|
var y = i32(workgroupId.y) * ${e} + i32(localId.y);
|
|
let width = uniforms.outShape[0];
|
|
let height = uniforms.outShape[1];
|
|
if (x < width && y < height) {
|
|
tile[localId.y][localId.x] = f32(A[y * width + x]);
|
|
}
|
|
workgroupBarrier();
|
|
|
|
x = i32(workgroupId.y) * ${e} + i32(localId.x);
|
|
y = i32(workgroupId.x) * ${e} + i32(localId.y);
|
|
if (x < height && y < width) {
|
|
setOutputAtIndex((y * height + x), tile[localId.x]
|
|
[localId.y]);
|
|
}
|
|
}
|
|
`}},Mie=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n<a.length;n++)a[n]=e[t[n]];this.outputShape=a,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=la(this.outputShape.length),t=$ie(this.newDim);return`
|
|
${ke("index")} {
|
|
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if(flatIndex < uniforms.size) {
|
|
let resRC = getCoordsFromIndex(flatIndex);
|
|
setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(
|
|
${e}(${t}), uniforms.aShape)]);
|
|
}
|
|
}
|
|
}
|
|
`}};function $ie(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=new Array(t);for(let n=0;n<e.length;n++)a[e[n]]=`resRC.${br(n)}`;return a.join()}function kr(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{perm:s}=n,i=a,o=r.shape.length,l=new Array(o);for(let p=0;p<l.length;p++)l[p]=r.shape[s[p]];if(a.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,c=xie(p,r.shape,r.dtype,s,l);return a.makeTensorInfo(l,r.dtype,c)}if(r.shape.length===2&&v.arraysEqual(s,[1,0])){let p=new Rie(r.shape,s);return i.runWebGPUProgram(p,[r],r.dtype)}let u=new Mie(r.shape,s);return i.runWebGPUProgram(u,[r],r.dtype)}var _ie={kernelName:Ar,backendName:"webgpu",kernelFunc:kr},Pie=class{constructor(e,t){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[a]=T.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0",a=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<workgroup> xBestValues : array<f32, ${a}>;
|
|
`}
|
|
fn getOffset(outputIndex : i32) -> i32 {
|
|
let outputCoords = getCoordsFromIndex(outputIndex);
|
|
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
|
|
return offset;
|
|
}
|
|
${ke("index")} {
|
|
let outputIndex = index / ${a};
|
|
let offset = getOffset(outputIndex);
|
|
var bestValue = ${t};
|
|
let Length = uniforms.reduceSize;
|
|
let WorkPerThread = DIV_CEIL(u32(Length), ${a}u);
|
|
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
|
|
k = k + ${a}) {
|
|
let candidate = f32(x[offset + k]);
|
|
${e}
|
|
}
|
|
xBestValues[localId.x] = bestValue;
|
|
workgroupBarrier();
|
|
|
|
var reduceSize = min(u32(Length), ${a}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) {
|
|
${n}
|
|
}
|
|
}
|
|
`}};function Io(e,t,a,n,r){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=T.getAxesPermutation(l,s),p=e;u!=null&&(p=kr({inputs:{x:e},attrs:{perm:u},backend:r}),l=T.getInnerMostAxes(l.length,s),i.push(p)),T.assertAxesAreInnerMostDims(n,l,s);let[c,d]=T.computeOutAndReduceShapes(p.shape,l),h=c;a&&(h=T.expandShapeToKeepDim(c,o));let f;if((n==="max"||n==="prod")&&r.shouldExecuteOnCPU([p])){let m=r.tensorMap.get(p.dataId).values;switch(n){case"max":let g=tie(m,v.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=oie(p.shape,p.dtype,m,l);f=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(d),g=v.sizeFromShape(p.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},x=n==="mean"?"float32":Xd(e.dtype),A=[{type:"int32",data:[m]}],b=new Pie(y,n),k=r.runWebGPUProgram(b,[p],x,A);i.push(k),f=Ie({inputs:{x:k},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Fie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"all",a)}var Oie={kernelName:Zs,backendName:"webgpu",kernelFunc:Fie};function Die(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"any",a)}var zie={kernelName:Ys,backendName:"webgpu",kernelFunc:Die},M8=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=T.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=$e(this.outputShape),v.sizeFromShape(s)<32||v.sizeFromShape(r)>1e3?(this.type="plain",this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=we(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${br(this.inputShape.length-1)}`,a=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let r=0;r<this.outputShape.length;r++)n+=`outputCoords.${br(r)},`;return n};return this.type==="shared"?`
|
|
fn DIV_CEIL(a : u32, b : u32) -> u32 {
|
|
return ((a - 1u) / b + 1u);
|
|
}
|
|
|
|
${`
|
|
var<workgroup> xBestIndices : array<i32, ${e}>;
|
|
var<workgroup> xBestValues : array<f32, ${e}>;
|
|
`}
|
|
|
|
${ke("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 + ${e}) {
|
|
let candidate = getX(${a()} 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]);
|
|
}
|
|
}
|
|
`:`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let outputCoords = getCoordsFromIndex(index);
|
|
var bestIndex = 0;
|
|
var bestValue = getX(${a()} 0);
|
|
let reduceLength = ${t()};
|
|
for (var i = 1; i < reduceLength; i++) {
|
|
let candidate = getX(${a()} i);
|
|
if (candidate ${this.op} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = i;
|
|
}
|
|
}
|
|
setOutputAtIndexI32(index, bestIndex);
|
|
}
|
|
}
|
|
`}};function Lie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=kr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new M8(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Bie={kernelName:Js,backendName:"webgpu",kernelFunc:Lie};function Wie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=kr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new M8(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var Vie={kernelName:Id,backendName:"webgpu",kernelFunc:Wie},Uie=et({opType:le.ASIN}),Gie={kernelName:Il,backendName:"webgpu",kernelFunc:Uie},Hie=et({opType:le.ASINH}),jie={kernelName:Sl,backendName:"webgpu",kernelFunc:Hie},qie=et({opType:le.ATAN}),Xie={kernelName:Tl,backendName:"webgpu",kernelFunc:qie},Kie=Jt({opType:Pe.ATAN2}),Zie={kernelName:Nl,backendName:"webgpu",kernelFunc:Kie},Yie=et({opType:le.ATANH}),Jie={kernelName:Cl,backendName:"webgpu",kernelFunc:Yie},jy=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / max(count, 1.0)"),`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromIndex(index);
|
|
let batch = coords[0];
|
|
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
|
|
let xRCorner = xRCCorner.x;
|
|
let xCCorner = xRCCorner.y;
|
|
|
|
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.dilation.x) {
|
|
let xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= uniforms.convDims.x) {
|
|
continue;
|
|
}
|
|
|
|
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
|
|
let xC = xCCorner + wC;
|
|
if (xC < 0 || xC >= uniforms.convDims.y) {
|
|
continue;
|
|
}
|
|
|
|
let value = getX(batch, xR, xC, coords[3]);
|
|
${e}
|
|
}
|
|
}
|
|
|
|
setOutputAtIndex(index, ${t});
|
|
}
|
|
}
|
|
`}},Qie=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromIndex(index);
|
|
let batch = coords[0];
|
|
let d = coords[3];
|
|
|
|
let xRCCorner = coords.yz * uniforms.stride;
|
|
let xRCorner = xRCCorner.x;
|
|
let xCCorner = xRCCorner.y;
|
|
|
|
let value = getX(batch, xRCorner, xCCorner, d);
|
|
setOutputAtIndex(index, value);
|
|
}
|
|
}
|
|
`}};function V3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return Io(r,s,i,"max",a)}var eoe={kernelName:$i,backendName:"webgpu",kernelFunc:V3};function $8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return Io(r,i,s,"mean",a)}var toe={kernelName:Fi,backendName:"webgpu",kernelFunc:$8};function _8(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return en({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=Ie({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=$8({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=V3({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=Ie({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new Qie(t):(a==="avg"?r=new jy(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new jy(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function aoe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=T.computePool2DInfo(r.shape,s,i,u,o,l);return _8(r,p,"avg",a)}var noe={kernelName:Qs,backendName:"webgpu",kernelFunc:aoe},roe=class{constructor(e){this.variableNames=["dy"],this.uniforms=`stride : vec2<i32>, pads : vec2<i32>, dilation : vec2<i32>, filterDims : vec2<i32>,
|
|
outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avg_pool2d_backprop"}getUserCode(){return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromIndex(index);
|
|
let batch = coords[0];
|
|
let d = coords[3];
|
|
|
|
let dyRCCorner = vec2<i32>(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.dilation[0]) {
|
|
let dyR = f32(dyRCorner + wR) / f32(uniforms.stride[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.dilation[1]) {
|
|
let dyC = f32(dyCCorner + wC) / f32(uniforms.stride[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);
|
|
}
|
|
}
|
|
`}};function soe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;N8([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=T.computePool2DInfo(i.shape,o,l,1,u),c=new roe(p),d=1/(p.filterHeight*p.filterWidth),h=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.effectiveFilterHeight-1-p.padInfo.top,p.effectiveFilterWidth-1-p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]},{type:"int32",data:[p.outHeight]},{type:"int32",data:[p.outWidth]},{type:"float32",data:[d]}];return a.runWebGPUProgram(c,[r],i.dtype,h)}var ioe={kernelName:Xc,backendName:"webgpu",kernelFunc:soe};function ooe(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Bh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var loe={kernelName:ei,backendName:"webgpu",kernelFunc:ooe},uoe=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=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${la(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=la(this.rank),t=doe(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${$2[r]} = uniforms.start.${br(r)} + coords.${$2[r]};`),`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
var sourceLoc : ${e};
|
|
let coords = getCoordsFromIndex(index);
|
|
${a.join(`
|
|
`)}
|
|
setOutputAtIndex(index, getSource(${t}));
|
|
}
|
|
}
|
|
`}},$2=["x","y","z","w","u","v"];function doe(e){if(e===1)return"sourceLoc";if(e<=6)return $2.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Au(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=St.parseSliceParams(r,s,i);if(St.assertParamsValid(r,o,l),a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.tensorMap.get(r.dataId),d=cie(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);let u=new uoe(o,l),p=[{type:"int32",data:o}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var poe={kernelName:Xl,backendName:"webgpu",kernelFunc:Au},coe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),d=T.getSliceSize(p,i,s.length),h=[],f=Ie({inputs:{x:r},backend:a,attrs:{shape:l}}),m=kr({inputs:{x:f},backend:a,attrs:{perm:u}}),g=Ie({inputs:{x:m},backend:a,attrs:{shape:p}}),y=Au({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>a.disposeData(x.dataId)),y},hoe={kernelName:El,backendName:"webgpu",kernelFunc:coe},foe=`
|
|
fn bincount_write(index: i32, value: f32) {
|
|
${O3("&result[index]","value","float32")}
|
|
}
|
|
`,moe=`
|
|
fn bincount_write(index: i32, value: f32) {
|
|
atomicStore(&result[index], bitcast<i32>(value));
|
|
}
|
|
`,P8=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(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?moe:foe}
|
|
${ke("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 goe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Nr({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new P8([o],l),h=[{type:"int32",data:[i]}],f=l?[r,s]:[r];return a.runWebGPUProgram(d,f,p,h,c)}var yoe={kernelName:Sd,backendName:"webgpu",kernelFunc:goe},F8=Jt({opType:Pe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:iie}),xoe={kernelName:Bi,backendName:"webgpu",kernelFunc:F8};function yp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.real},backend:a})}var Aoe={kernelName:Dd,backendName:"webgpu",kernelFunc:yp};function boe(e,t){let a=new xu(e.shape,le.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function _2(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return en({inputs:{x:r},backend:a});let i=gn(r.shape),o=_2({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=wo({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=yp({inputs:{input:r},backend:a}),o=_2({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=en({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=Wse(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return boe(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=F8({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var voe={kernelName:ti,backendName:"webgpu",kernelFunc:_2},koe=et({opType:le.CEIL,cpuKernelImpl:Vse}),woe={kernelName:ai,backendName:"webgpu",kernelFunc:koe},Ioe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
|
|
${ke("index")} {
|
|
if(index < uniforms.size) {
|
|
let value = getAByOutputIndex(index);
|
|
var clampedValue = clamp(
|
|
value, vec4<f32>(uniforms.minVal), vec4<f32>(uniforms.maxVal));
|
|
clampedValue = select(clampedValue, value, isnanVec4(value));
|
|
setOutputAtIndex(index, clampedValue);
|
|
}
|
|
}
|
|
`}},Soe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return`
|
|
${ke("index")} {
|
|
if(index < uniforms.size) {
|
|
let value = getAByOutputIndex(index);
|
|
if (isnan(value)) {
|
|
setOutputAtIndex(index, value);
|
|
return;
|
|
}
|
|
setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
|
|
}
|
|
}
|
|
`}};function Toe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(r.shape)%4===0?o=new Ioe(r.shape):o=new Soe(r.shape),a.runWebGPUProgram(o,[r],r.dtype,l)}var Coe={kernelName:as,backendName:"webgpu",kernelFunc:Toe},Noe=class{constructor(e){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n<this.offsetLength;n++)e.push(`else if (yC < uniforms.offset${[n]}){ setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${n-1})); }`);let t=this.offsetLength,a=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${a})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
|
|
${ke("index")} {
|
|
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if(flatIndex < uniforms.size) {
|
|
let coords = getCoordsFromIndex(flatIndex);
|
|
let yR = coords.x;
|
|
let yC = coords.y;
|
|
|
|
${e.join(`
|
|
`)}
|
|
}
|
|
}
|
|
}
|
|
`}};function Wh(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.imag},backend:a})}var Eoe={kernelName:Pd,backendName:"webgpu",kernelFunc:Wh};function Yu(e,t,a){let n=e[0].dtype;if(n==="complex64"){let f=e.map(A=>yp({inputs:{input:A},backend:a})),m=e.map(A=>Wh({inputs:{input:A},backend:a})),g=Yu(f,t,a),y=Yu(m,t,a),x=wo({inputs:{real:g,imag:y},backend:a});return f.forEach(A=>a.disposeData(A.dataId)),m.forEach(A=>a.disposeData(A.dataId)),a.disposeData(g.dataId),a.disposeData(y.dataId),x}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let f=e.map(k=>{let S=[-1,v.sizeFromShape(k.shape.slice(t))];return Ie({inputs:{x:k},backend:a,attrs:{shape:S}})}),m=f.map(k=>({vals:a.readSync(k.dataId),shape:k.shape})),g=T.computeOutShape(f.map(k=>k.shape),1),y=f[0].shape[0]===1,x=Use(m,g,n,y),A=T.computeOutShape(e.map(k=>k.shape),t),b=a.makeTensorInfo(A,n,x);return f.forEach(k=>a.disposeData(k.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let f=[];for(let g=0;g<e.length;g+=s){let y=e.slice(g,g+s);f.push(Yu(y,t,a))}let m=Yu(f,t,a);for(let g of f)a.disposeData(g.dataId);return m}let{tensors2D:i,outShape:o}=Roe(e,t,a),l=i.map(f=>f.shape),u=new Noe(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let f=1;f<c.length;f++)c[f]=c[f-1]+l[f][1],p.push({type:"int32",data:[c[f]]})}let d=a.runWebGPUProgram(u,i,i[0].dtype,p);i.forEach(f=>a.disposeData(f.dataId));let h=Ie({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function Roe(e,t,a){let n=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ie({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function O8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?en({inputs:{x:l[0]},backend:a}):Yu(l,s,a)}var Moe={kernelName:Rl,backendName:"webgpu",kernelFunc:O8};function $oe(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=N=>{switch(N){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},c=N=>{switch(N){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},d=e?`
|
|
let coord = vec4<i32>(batch, xRow, xCol, xCh);
|
|
`:`
|
|
let coord = vec4<i32>(batch, xCh, xRow, xCol);
|
|
`,h=e?`
|
|
let coords = vec4<i32>(
|
|
batch,
|
|
row / outWidth,
|
|
row % outWidth,
|
|
col);
|
|
`:`
|
|
let coords = vec4<i32>(
|
|
batch,
|
|
row,
|
|
col / outWidth,
|
|
col % outWidth);
|
|
`,f=e?"uniforms.xShape[1]":"uniforms.xShape[2]",m=e?"uniforms.xShape[2]":"uniforms.xShape[3]",g=e?"row":"col",y=e?"col":"row",x=`
|
|
let inChannels = uniforms.wShape[2];
|
|
let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"};
|
|
let outRow = ${g} / outWidth;
|
|
let outCol = ${g} % outWidth;
|
|
|
|
let WRow = ${y} / (uniforms.filterDims[1] * inChannels);
|
|
let WCol = ${y} / inChannels % uniforms.filterDims[1];
|
|
let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
|
|
let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
|
|
let xCh = ${y} % inChannels;
|
|
var resData = ${$t(o)}(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 < ${m}) {
|
|
${d}
|
|
let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
|
|
${p(o)}
|
|
}
|
|
return resData;`,A=e?t&&n?`
|
|
let col = colIn * ${o};
|
|
${x}`:`
|
|
let col = colIn * ${o};
|
|
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
|
|
${x}
|
|
}
|
|
return ${$t(o)}(0.0);`:n&&a?`
|
|
let col = colIn * ${o};
|
|
${x}`:`
|
|
let col = colIn * ${o};
|
|
if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
|
|
${x}
|
|
}
|
|
return ${$t(o)}(0.0);`,b=`${c(l)}`,k=$t(u),S=$t(e?o:l),C=$t(e?l:o);return`
|
|
${Cr(s,i,u===4,4)}
|
|
fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${S} {
|
|
${e?A:b}
|
|
}
|
|
|
|
fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${C} {
|
|
${e?b:A}
|
|
}
|
|
|
|
fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {
|
|
let col = colIn * ${u};
|
|
if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
|
|
{
|
|
var value = valueIn;
|
|
let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"};
|
|
${h}
|
|
${ko(r,s)}
|
|
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
|
|
}
|
|
}`}var _oe=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=D3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=z3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4<f32>"]):(this.innerElementSize=4,this.variableTypes=["vec4<f32>","vec4<f32>"]),r&&(this.variableNames.push("bias"),this.variableTypes.push("vec4<f32>")),i&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4<f32>"))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?zh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Lh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`
|
|
${$oe(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}
|
|
${e}
|
|
`}},Poe=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=a,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`
|
|
${Cr(this.activation,this.hasPreluActivationWeights,!1,4)}
|
|
fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{
|
|
let coords = vec4<i32>(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<i32>(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<i32>(batch, row, col, chan);":"vec4<i32>(batch, chan, row, col);"}
|
|
if (coordsInBounds4D(coords, uniforms.outShape)) {
|
|
var value = valueIn;
|
|
${ko(this.addBias,this.activation)}
|
|
setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);
|
|
}
|
|
}
|
|
${ke("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.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
|
|
let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
|
|
for (var xChannel = 0; xChannel < ${this.isChannelsLast?"uniforms.xShape[3];":"uniforms.xShape[1];"} xChannel = xChannel + 1) {
|
|
${this.isChannelsLast?"let v = readInp(batch, xRow, xCol, xChannel);":"let v = readInp(batch, xChannel, xRow, xCol);"}
|
|
let f = readFilt(row, col, xChannel, outChannel);
|
|
acc = acc + v * f;
|
|
}
|
|
}
|
|
}
|
|
writeResult(batch, outRow, outCol, outChannel, acc);
|
|
}
|
|
`}},Foe=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
|
|
inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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,a=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",r=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return`
|
|
${ke("index")} {
|
|
let coords = getCoordsFromIndex(index);
|
|
if(index < uniforms.size) {
|
|
let batch = coords[0];
|
|
let row = ${a};
|
|
let col = ${n};
|
|
let offsetY = (row / uniforms.outWidth) * uniforms.stride[0] - uniforms.pad[0];
|
|
let xRow = offsetY + uniforms.dilation[0] * (col / uniforms.itemsPerBlockRow);
|
|
var value = 0.0;
|
|
if(xRow < uniforms.xShape[${e}] && xRow >= 0) {
|
|
let offsetX = (row % uniforms.outWidth) * uniforms.stride[1] -
|
|
uniforms.pad[1];
|
|
let xCol = offsetX + uniforms.dilation[1] * ((col %
|
|
uniforms.itemsPerBlockRow) / uniforms.inChannels);
|
|
let ch = col % uniforms.inChannels;
|
|
if(xCol < uniforms.xShape[${t}] && xCol >= 0) {
|
|
value = ${r};
|
|
}
|
|
}
|
|
setOutputAtIndex(index, value);
|
|
}
|
|
}
|
|
`}};function Uc(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function Ooe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,f;if(c){let y=a.inHeight*a.inWidth*a.inChannels;h=Ie({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,y]}}),f=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,y,a.outChannels]}})}else h=Ie({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),f=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(f),s!=null){let y=Uc(s.shape,l);y!=null&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:y}}),d.push(s))}if(r!=null){let y=Uc(r.shape,l);y!=null&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:y}}),d.push(r))}let m=Bh({a:l?h:f,b:l?f:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=Ie({inputs:{x:m},backend:n,attrs:{shape:a.outShape}});d.push(m);for(let y of d)n.disposeData(y.dataId);return g}function Doe({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:x}=a,A=x==="channelsLast",b=l*u*p,k=m*f,S=A?[a.batchSize,k,b]:[a.batchSize,b,k],C=new Foe(S,A),N=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[y,g]},{type:"int32",data:[f]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],$=n.runWebGPUProgram(C,[e],e.dtype,N),M=[];M.push($);let R=Ie({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if(M.push(R),s!=null){let D=Uc(s.shape,A);D!=null&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:D}}),M.push(s))}if(r!=null){let D=Uc(r.shape,A);D!=null&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:D}}),M.push(r))}let I=Bh({a:A?$:R,b:A?R:$,transposeA:!A,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),_=Ie({inputs:{x:I},backend:n,attrs:{shape:a.outShape}});M.push(I);for(let D of M)n.disposeData(D.dataId);return _}function D8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=V().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return Ooe({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=V().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=h>0?h:n.thresholdToIncreaseWorkgroups,m=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(V().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||m<=f)return Doe({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,y=[a.padInfo.top,a.padInfo.left],x=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...y]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new Poe(a,l,o,u);else{let S=p?a.outHeight*a.outWidth:a.outChannels,C=p?a.outChannels:a.outHeight*a.outWidth,N=a.filterHeight*a.filterWidth*a.inChannels;x.push({type:"int32",data:[S]},{type:"int32",data:[C]},{type:"int32",data:[N]});let $=n.adapterInfo.isIntel();g=new _oe(a,S,C,N,l,o,u,$)}let A=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=Ie({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),A.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=Ie({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),b.push(s)),o==="leakyrelu"&&(x.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let k=n.runWebGPUProgram(g,b,e.dtype,x);for(let S of A)n.disposeData(S.dataId);return k}function zoe(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return D8({x:r,filter:s,convInfo:d,backend:n})}var Loe={kernelName:ni,backendName:"webgpu",kernelFunc:zoe},Boe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1;return`
|
|
${ke("index")} {
|
|
if(index < uniforms.size) {
|
|
let coords = getCoordsFromIndex(index);
|
|
let batch = coords[0];
|
|
let d1 = coords[${a}];
|
|
|
|
let dyCorner = vec2<i32>(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.stride.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.stride.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) {
|
|
if (${this.isChannelsLast}) {
|
|
let xValue = getDy(batch, idyR, idyC, d2);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
} else {
|
|
let xValue = getDy(batch, d2, idyR, idyC);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutputAtIndex(index, dotProd);
|
|
}
|
|
}
|
|
`}},Woe=class{constructor(e){this.variableNames=["x","dy"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`
|
|
${ke("index")} {
|
|
if(index < uniforms.size) {
|
|
let coords = getCoordsFromIndex(index);
|
|
let wR = coords[0];
|
|
let wC = coords[1];
|
|
let d1 = coords[2];
|
|
let d2 = coords[3];
|
|
|
|
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
var dotProd = 0.0;
|
|
for (var b = 0; b < uniforms.batchSize; b = b + 1) {
|
|
for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {
|
|
let xR = wR + yR * uniforms.stride[0] - uniforms.pad[0];
|
|
if (xR < 0 || xR >= uniforms.inHeight) {
|
|
continue;
|
|
}
|
|
|
|
for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {
|
|
let xC = wC + yC * uniforms.stride[1] - uniforms.pad[1];
|
|
|
|
if (xC < 0 || xC >= uniforms.inWidth) {
|
|
continue;
|
|
}
|
|
|
|
if (${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);
|
|
}
|
|
}
|
|
`}};function Voe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new Woe(d),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]},{type:"int32",data:[d.inHeight]},{type:"int32",data:[d.inWidth]}];return a.runWebGPUProgram(h,[r,s],r.dtype,f)}var Uoe={kernelName:Cd,backendName:"webgpu",kernelFunc:Voe};function Goe(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return`
|
|
let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
|
|
let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
|
|
let coord3 = vec4<i32>(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<f32>(v0, v1, v2, v3);
|
|
`;default:throw new Error(`innerElementSize ${n} is not supported.`)}},a=`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.stride[0]);
|
|
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
|
|
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
|
|
return ${$t(e)}(0.0);
|
|
}
|
|
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
|
|
return ${$t(e)}(0.0);
|
|
}
|
|
let coord = vec4<i32>(
|
|
batch,
|
|
i32(xR),
|
|
i32(xC),
|
|
col % uniforms.outBackprop[3]);
|
|
return x[getIndexFromCoords4D(coord, uniforms.xShape)/${e}];`}
|
|
}
|
|
return ${$t(e)}(0.0);`;return`
|
|
fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${$t(e)} {
|
|
let col = colIn * ${e};
|
|
${a}
|
|
}
|
|
|
|
fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${$t(e)} {
|
|
let col = colIn * ${e};
|
|
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<i32>(coordX, coordY, col, rowInner);
|
|
${t(e)}
|
|
}
|
|
return ${$t(e)}(0.0);
|
|
}
|
|
|
|
fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${$t(e)}) {
|
|
let col = colIn * ${e};
|
|
if (row < uniforms.dimAOuter && (col + ${e-1}) < uniforms.dimBOuter) {
|
|
var value = valueInput;
|
|
let outCoord = vec4<i32>(
|
|
batch,
|
|
row / uniforms.outShape[2],
|
|
row % uniforms.outShape[2],
|
|
col);
|
|
result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${e}] = value;
|
|
}
|
|
}`}var Hoe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.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=D3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=z3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4<f32>","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?zh(this.elementsPerThread,this.workgroupSize):Lh(this.elementsPerThread,this.workgroupSize);return`
|
|
${Goe(this.isVec4?4:1)}
|
|
${e}
|
|
`}};function joe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(V().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||d.filterHeight<=2&&d.filterWidth<=2&&d.outChannels<=16&&d.inChannels===1)f=new Boe(d);else{f=new Hoe(d);let m=d.inHeight*d.inWidth,g=d.inChannels,y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return a.runWebGPUProgram(f,[r,s],"float32",h)}var qoe={kernelName:ri,backendName:"webgpu",kernelFunc:joe},Xoe=et({opType:le.COS}),Koe={kernelName:si,backendName:"webgpu",kernelFunc:Xoe},Zoe=et({opType:le.COSH}),Yoe={kernelName:ii,backendName:"webgpu",kernelFunc:Zoe},Joe=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${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`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromIndex(index);
|
|
let height_ratio = f32(${a});
|
|
let width_ratio = f32(${s});
|
|
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 = ${r};
|
|
if( in_y < 0.0 || in_y > ${e} ) {
|
|
setOutputAtIndex(index, uniforms.extrapolationValue);
|
|
return;
|
|
}
|
|
let in_x = ${o};
|
|
if( in_x < 0.0 || in_x > ${t} ) {
|
|
setOutputAtIndex(index, uniforms.extrapolationValue);
|
|
return;
|
|
}
|
|
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
|
|
if(${this.methodId} == 1) {
|
|
// Compute the four integer indices.
|
|
let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
|
|
let sourceCeilCR = vec2<i32>(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<f32>(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<i32>(floor(
|
|
sourceFracIndexCR + vec2<f32>(0.5,0.5)));
|
|
let newValue = getImage(
|
|
bInd, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutputAtIndex(index, newValue);
|
|
}
|
|
}
|
|
}
|
|
`}},Qoe=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Joe(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},ele={kernelName:ui,backendName:"webgpu",kernelFunc:Qoe},vd;(function(e){e.Prod="*",e.Sum="+"})(vd||(vd={}));var qy=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===vd.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${Xy(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
var coords = getCoordsFromIndex(index);
|
|
|
|
let end = ${Ky(e,"coords",this.op)};
|
|
var val = ${a};
|
|
let pow2 = i32(pow(2.0, uniforms.index));
|
|
if (${r}) {
|
|
let idx = ${s};
|
|
${Ky(e,"coords",this.op)} = idx;
|
|
val ${this.op}= getX(${Xy(e,"coords",this.op)});
|
|
}
|
|
setOutputAtIndex(index, val);
|
|
}
|
|
}
|
|
`}};function Xy(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Ky(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function z8(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=kr({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=en({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new qy(e,l.shape,!1,s),f=c,m=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,m),a.disposeData(f.dataId)}if(r){let d=new qy(e,l.shape,r,s),h=c,f=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,f),a.disposeData(h.dataId)}if(o!=null){let d=T.getUndoAxesPermutation(o),h=kr({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function tle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return z8(vd.Prod,r,a,s,i,o)}var ale={kernelName:oi,backendName:"webgpu",kernelFunc:tle};function nle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return z8(vd.Sum,r,a,s,i,o)}var rle={kernelName:li,backendName:"webgpu",kernelFunc:nle};function sle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Nr({backend:a,attrs:{shape:d,value:0,dtype:p}}),f=new P8(c,u,o),m=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(f,g,p,m,h)}var ile={kernelName:Nd,backendName:"webgpu",kernelFunc:sle},ole=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
|
|
${ke("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 = ${this.getInputSamplingString()};
|
|
setOutputAtIndex(index, rlt);
|
|
}
|
|
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function lle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),f=i==="NHWC"?[o,c,d,h]:[o,h,c,d],m=[{type:"int32",data:[s]}],g=new ole(f,i);return a.runWebGPUProgram(g,[r],r.dtype,m)}var ule={kernelName:di,backendName:"webgpu",kernelFunc:lle},dle=class{constructor(e,t,a,n=!1,r=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, inDims : vec2<i32>,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=r,this.hasPreluActivation=s,this.filterHeight=t,this.filterWidth=a,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],a=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`
|
|
${Cr(this.activation,this.hasPreluActivation,!1,4)}
|
|
|
|
var<workgroup> mm_Asub : array<array<f32, ${n}>, ${a}>;
|
|
var<workgroup> mm_Bsub : array<array<f32, ${this.filterWidth}>, ${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;
|
|
}
|
|
|
|
${ke()} {
|
|
let coords = getOutputCoords();
|
|
let batch = coords[0];
|
|
let xRCCorner = vec2<i32>(coords.zw) - uniforms.pad;
|
|
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 < ${a}; 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<t?`if (wIndex < ${e})`:`for(; wIndex < ${e}; wIndex = wIndex + ${t})`}
|
|
|
|
{
|
|
let wRow = wIndex / ${this.filterWidth};
|
|
let wCol = wIndex % ${this.filterWidth};
|
|
mm_Bsub[wRow][wCol] = getW(wRow, wCol, d1, q);
|
|
}
|
|
|
|
workgroupBarrier();
|
|
|
|
var value = 0.0;
|
|
for (var wR = 0; wR < ${this.filterHeight}; wR = wR + 1) {
|
|
for (var wC = 0; wC < ${this.filterWidth}; wC = wC + 1) {
|
|
let xVal = mm_Asub[localRow + wR][localCol + wC];
|
|
let wVal = mm_Bsub[wR][wC];
|
|
value = fma(xVal, wVal, value);
|
|
}
|
|
}
|
|
${ko(this.addBias,this.activation)}
|
|
if (coordsInBounds4D(coords, uniforms.outShape)) {
|
|
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
|
|
}
|
|
}
|
|
`}},L8=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, inDims : vec2<i32>,",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${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,a=this.convInfo.strideWidth;return`
|
|
${Cr(this.activation,this.hasPreluActivation,!0,4)}
|
|
fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4<f32> {
|
|
var value = vec4<f32>(0.0);
|
|
if (col >=0 && col < uniforms.inDims[1]) {
|
|
value = getX(batch, row, col, channel);
|
|
}
|
|
return value;
|
|
}
|
|
|
|
${ke()} {
|
|
let batch = i32(globalId.z) / uniforms.outShape[1];
|
|
let r = i32(globalId.z) % uniforms.outShape[1];
|
|
let c = i32(globalId.y) * ${this.workPerThread};
|
|
let d1 = i32(globalId.x) * 4;
|
|
let xRCCorner = vec2<i32>(r, c) * vec2<i32>(${t}, ${a}) - uniforms.pad;
|
|
|
|
let xRCorner = xRCCorner.x;
|
|
let xCCorner = xRCCorner.y;
|
|
var xVals : array<vec4<f32>, ${e}>;
|
|
var dotProd : array<vec4<f32>, ${this.workPerThread}>;
|
|
for (var i = 0; i < ${this.workPerThread}; i++) {
|
|
dotProd[i] = vec4<f32>(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 * ${a} + wC], wValue, dotProd[i]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let coords = vec4<i32>(batch, r, c + i, d1);
|
|
if (coordsInBounds4D(coords, uniforms.outShape)) {
|
|
var value = dotProd[i];
|
|
${ko(this.addBias,this.activation)}
|
|
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
|
|
}
|
|
}
|
|
}
|
|
`}},B8=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,
|
|
filterWidth : i32, stride : vec2<i32>, dilation : vec2<i32>,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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=a,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`
|
|
${Cr(this.activation,this.hasPreluActivation,!1,4)}
|
|
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getOutputCoords();
|
|
let batch = coords[0];
|
|
let xRCCorner = vec2<i32>(coords.${this.isChannelsLast?"yz":"zw"}) * uniforms.stride - uniforms.pad;
|
|
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.dilation[0];
|
|
let inputColEnd = inputColStart + uniforms.filterWidth *
|
|
uniforms.dilation[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.dilation[0];
|
|
|
|
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
|
|
let xC = inputColStart + wC * uniforms.dilation[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.dilation[0];
|
|
|
|
if (xR < 0 || xR >= uniforms.inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
|
|
let xC = inputColStart + wC * uniforms.dilation[1];
|
|
|
|
if (xC < 0 || xC >= uniforms.inDims[1]) {
|
|
continue;
|
|
}
|
|
|
|
let xVal = ${e};
|
|
let wVal = getW(wR, wC, d1, q);
|
|
value = value + xVal * wVal;
|
|
}
|
|
}
|
|
}
|
|
${ko(this.addBias,this.activation)}
|
|
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
|
|
}
|
|
}
|
|
`}};function ple(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=T.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=T.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),f=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],m=h.dataFormat==="channelsLast",g;return!m&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new dle(h.outShape,h.filterHeight,h.filterWidth):m&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?g=new L8(h):(g=new B8(h),f.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,f)}var cle={kernelName:pi,backendName:"webgpu",kernelFunc:ple},hle=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getOutputCoords();
|
|
let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);
|
|
setOutputAtIndex(index, value);
|
|
}
|
|
}
|
|
`}};function fle(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=Ie({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new hle(s),l=a.runWebGPUProgram(o,[i],i.dtype),u=Ie({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var mle={kernelName:Ed,backendName:"webgpu",kernelFunc:fle},gle=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return`
|
|
${ke("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.stride - uniforms.pad;
|
|
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.dilation[0];
|
|
|
|
if (hIn >= 0 && hIn < uniforms.xShape[1]) {
|
|
for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {
|
|
let wIn = wBeg + w * uniforms.dilation[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 yle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],d=new gle(u);return a.runWebGPUProgram(d,[r,s],r.dtype,c)}var xle={kernelName:Rd,backendName:"webgpu",kernelFunc:yle},W8=Jt({opType:Pe.MUL,cpuKernelImpl:rie,supportsComplex:!0}),Ale={kernelName:Li,backendName:"webgpu",kernelFunc:W8};function U3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"sum",a)}var ble={kernelName:io,backendName:"webgpu",kernelFunc:U3};function vle(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=T.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,f=[];for(let m=0;m<c;++m){for(let g of p[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=s[g]:(A=kr({inputs:{x:s[g]},backend:a,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let k=0;k<x.length;++k)b.splice(x[k],0,1);v.arraysEqual(A.shape,b)||(A=Ie({inputs:{x:A},backend:a,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=W8({inputs:{a:A,b:d},backend:a}),f.push(d))}m<c-1&&(u[m]>=0&&(d=U3({inputs:{x:d},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&a.disposeData(m.dataId);return d}var kle={kernelName:Md,backendName:"webgpu",kernelFunc:vle},wle=et({opType:le.ELU}),Ile={kernelName:hi,backendName:"webgpu",kernelFunc:wle},Sle=Jt({opType:Pe.EQUAL,dtype:"bool",cpuKernelImpl:Gse}),Tle={kernelName:fi,backendName:"webgpu",kernelFunc:Sle},Cle=et({opType:le.ERF}),Nle={kernelName:Ml,backendName:"webgpu",kernelFunc:Cle},V8=et({opType:le.EXP,cpuKernelImpl:Hse,dtype:"float32"}),Ele={kernelName:mi,backendName:"webgpu",kernelFunc:V8};function P2(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Ie({inputs:{x:s},backend:n,attrs:{shape:o}})}var Rle={kernelName:$l,backendName:"webgpu",kernelFunc:P2},Mle=et({opType:le.EXPM1,cpuKernelImpl:jse}),$le={kernelName:_l,backendName:"webgpu",kernelFunc:Mle},Zy=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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;
|
|
}
|
|
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getOutputCoords();
|
|
setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
}
|
|
`}};function U8(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=Ie({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new Zy("real",u),c=new Zy("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,f=t?u[1]:1,m=[{type:"float32",data:[h]},{type:"float32",data:[f]}],g=a.runWebGPUProgram(p,d,"float32",m);o.push(g);let y=a.runWebGPUProgram(c,d,"float32",m);o.push(y);let x=wo({inputs:{real:g,imag:y},backend:a});o.push(x);let A=Ie({inputs:{x},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),A}function _le(e){let{inputs:t,backend:a}=e,{input:n}=t;return U8(n,!1,a)}var Ple={kernelName:$d,backendName:"webgpu",kernelFunc:_le},Fle=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromIndex(index);
|
|
let coordX = uniforms.xShape[2] - coords[2] - 1;
|
|
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
|
|
setOutputAtIndex(index, outputValue);
|
|
}
|
|
}
|
|
`}},Ole={kernelName:gi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Fle(a.shape);return n.runWebGPUProgram(r,[a],a.dtype)}},Dle=et({opType:le.FLOOR,cpuKernelImpl:qse}),zle={kernelName:yi,backendName:"webgpu",kernelFunc:Dle},Lle=Jt({opType:Pe.INT_DIV,dtype:"int32"}),Ble={kernelName:xi,backendName:"webgpu",kernelFunc:Lle},Wle=class{constructor(e,t,a=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=a,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
|
|
@binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d<f32>"};
|
|
${ke("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]));
|
|
}
|
|
}
|
|
}
|
|
`}},Vle={kernelName:nd,backendName:"webgpu",kernelFunc:Ule},Zo,zm=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"),mc=new Map;function Ule(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[p,c]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[c,p,s],h=!1,f=i||o;if(u||l||f){let x;if(h){let M=r;if(!mc.has(M)||mc.get(M).expired){let R={source:M};mc.set(M,a.device.importExternalTexture(R))}x={width:p,height:c,format:null,usage:null,texture:mc.get(M)}}else{if(f){let _=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Zo==null||_!==zm)&&(zm=_,Zo=document.createElement("canvas").getContext("2d",{willReadFrequently:zm})),Zo.canvas.width=p,Zo.canvas.height=c,Zo.drawImage(r,0,0,p,c),r=Zo.canvas}let M=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,R="rgba8unorm",I=a.textureManager.acquireTexture(d[1],d[0],R,M);a.queue.copyExternalImageToTexture({source:r},{texture:I},[d[1],d[0]]),x={width:p,height:c,format:R,usage:M,texture:I}}let A=v.sizeFromShape(d),b=v.computeStrides(d),k=new Wle(d,s,h),S=[{type:"uint32",data:[A]},{type:"uint32",data:[s]},{type:"uint32",data:[...b]}],C=a.makeTensorInfo([c,p],"int32"),N=a.tensorMap.get(C.dataId);N.resourceInfo=x;let $=a.runWebGPUProgram(k,[C],"int32",S);return a.disposeData(C.dataId),$}let m=r.data,g=m;if(s!=null&&s!==4){g=new Uint8Array(r.width*r.height*s);let x=m.length,A=0;for(let b=0;b<x;b++)b%4<s&&(g[A++]=m[b])}let y=a.makeTensorInfo(d,"int32",new Int32Array(g));return a.uploadToGPU(y.dataId),y}var Gle=class{constructor(e,t,a,n,r){this.uniforms="varianceEpsilon : f32,",this.workgroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a),this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
|
|
${ke("index")} {
|
|
if (index < uniforms.size)
|
|
{
|
|
let xValue = getXByOutputIndex(index);
|
|
let meanValue = getMeanByOutputIndex(index);
|
|
let varianValue = getVarianceByOutputIndex(index);
|
|
let offsetValue = ${e};
|
|
let scaleValue = ${t};
|
|
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
|
|
setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
|
|
}
|
|
}
|
|
`}},Hle={kernelName:Ai,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new Gle(n.shape,i.shape,o.shape,c,d),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,f)}};function jle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=T.convertConv2DDataFormat(p),g=T.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,m);return D8({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var qle={kernelName:jr,backendName:"webgpu",kernelFunc:jle};function Xle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,f=p;f==null&&(f=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=T.computeConv2DInfo(r.shape,s.shape,l,f,u,c,!0),g=[r,s],y=i!=null,x=o!=null;y&&g.push(i),x&&g.push(o);let A=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.outHeight>4&&m.outWidth>4&&m.strideWidth<=2&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.inChannels%4===0?b=new L8(m,y,d,x):(b=new B8(m,y,d,x),A.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",A)}var Kle={kernelName:qr,backendName:"webgpu",kernelFunc:Xle},Zle=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${la(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
|
|
${ke("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 = ${e};
|
|
flattenIndex = flattenIndex + indexTemp * strideNum;
|
|
}
|
|
|
|
setOutputAtIndex(index, getA(flattenIndex, coords[1]));
|
|
}
|
|
}
|
|
`}};function Yle(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,p,c]=T.prepareAndValidate(n,r),d=Ie({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=Ie({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/p,p]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let x=a.readSync(r.dataId),A=a.bufferSync(n),b=Xse(x,A,n.dtype,u,i,p,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,b.values)}let f=new Zle(i,[u,p]),m=[{type:"int32",data:[i]},{type:"int32",data:c}],g=a.runWebGPUProgram(f,[h,d],h.dtype,m),y=Ie({inputs:{x:g},backend:a,attrs:{shape:l}});return a.disposeData(d.dataId),a.disposeData(h.dataId),a.disposeData(g.dataId),y}var Jle={kernelName:bi,backendName:"webgpu",kernelFunc:Yle},Qle=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=eue(this.aShape);return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromIndex(index);
|
|
let indexZ = i32(getIndices(resRC.x, resRC.z));
|
|
let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
|
|
setOutputAtIndex(index, inBounds * getA(${e}));
|
|
}
|
|
}
|
|
`}};function eue(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;n<e.length;n++)n===2?a.push("indexZ"):a.push(`${t[n]}`);return a.join()}function G8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0],u=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),p=v.sizeFromShape(s.shape),c=[],d=Ie({inputs:{x:r},backend:a,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=Ie({inputs:{x:s},backend:a,attrs:{shape:[u.batchSize,p/u.batchSize]}});c.push(d),c.push(h);let f=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize];if(a.shouldExecuteOnCPU([r,s])){let x=a.tensorMap.get(h.dataId).values,A=_e(h.shape,h.dtype,x),b=a.tensorMap.get(d.dataId).values,k=_e(d.shape,d.dtype,b),S=Kse(k,A,f);return c.forEach(C=>a.disposeData(C.dataId)),a.makeTensorInfo(u.outputShape,S.dtype,S.values)}let m=new Qle(d.shape,f),g=a.runWebGPUProgram(m,[d,h],d.dtype);c.push(g);let y=Ie({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeData(x.dataId)),y}var tue={kernelName:Fl,backendName:"webgpu",kernelFunc:G8},aue=Jt({opType:Pe.GREATER,cpuKernelImpl:Yse,dtype:"bool"}),nue={kernelName:vi,backendName:"webgpu",kernelFunc:aue},rue=Jt({opType:Pe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Zse}),sue={kernelName:ki,backendName:"webgpu",kernelFunc:rue};function iue(e){let{inputs:t,backend:a}=e,{input:n}=t;return U8(n,!0,a)}var oue={kernelName:_d,backendName:"webgpu",kernelFunc:iue},lue=et({opType:le.IS_FINITE,dtype:"bool"}),uue={kernelName:Ol,backendName:"webgpu",kernelFunc:lue},due=et({opType:le.IS_INF,dtype:"bool"}),pue={kernelName:Dl,backendName:"webgpu",kernelFunc:due},cue=et({opType:le.IS_NAN,dtype:"bool"}),hue={kernelName:Ii,backendName:"webgpu",kernelFunc:cue};function fue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new xu(r.shape,le.LEAKYRELU,"alpha : f32,");return a.runWebGPUProgram(o,[r],"float32",i)}var mue={kernelName:Si,backendName:"webgpu",kernelFunc:fue},gue=Jt({opType:Pe.LESS,dtype:"bool",cpuKernelImpl:Qse}),yue={kernelName:Ti,backendName:"webgpu",kernelFunc:gue},xue=Jt({opType:Pe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Jse}),Aue={kernelName:Ci,backendName:"webgpu",kernelFunc:xue},bue=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=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);
|
|
}
|
|
}
|
|
`}};function vue(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=(r-n)/(s-1),o=new bue(s),l=[{type:"float32",data:[n]},{type:"float32",data:[i]}];return t.runWebGPUProgram(o,[],"float32",l)}var kue={kernelName:Fd,backendName:"webgpu",kernelFunc:vue},wue=et({opType:le.LOG,cpuKernelImpl:eie}),Iue={kernelName:Ni,backendName:"webgpu",kernelFunc:wue},Sue=et({opType:le.LOG1P}),Tue={kernelName:zl,backendName:"webgpu",kernelFunc:Sue},Cue=Jt({opType:Pe.LOGICAL_AND,dtype:"bool"}),Nue={kernelName:Ei,backendName:"webgpu",kernelFunc:Cue},Eue=et({opType:le.LOGICAL_NOT}),Rue={kernelName:Ri,backendName:"webgpu",kernelFunc:Eue},Mue=Jt({opType:Pe.LOGICAL_OR}),$ue={kernelName:Mi,backendName:"webgpu",kernelFunc:Mue},H8=`
|
|
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));
|
|
}
|
|
`,_ue=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=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn"}getUserCode(){return`
|
|
${ke("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;
|
|
}
|
|
}
|
|
${H8}
|
|
|
|
setOutputAtIndex(index, x * powValue);
|
|
}
|
|
}
|
|
`}},Pue=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,v.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=we(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey="lrn_shared"}getUserCode(){return`
|
|
var <workgroup>lrnSub: array<f32, ${this.workgroupSize[0]}>;
|
|
const elementsPerWorkgroup = ${this.elementsPerWorkgroup};
|
|
const maxAllowRadius = ${this.maxAllowRadius};
|
|
|
|
${ke()} {
|
|
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;
|
|
}
|
|
${H8}
|
|
|
|
setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);
|
|
}
|
|
} `}};function Fue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u;s>16?u=new _ue(r.shape):u=new Pue(r.shape,s);let p=[{type:"int32",data:[s]},{type:"float32",data:[i]},{type:"float32",data:[o]},{type:"float32",data:[l]}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var Oue={kernelName:Od,backendName:"webgpu",kernelFunc:Fue},Due=Jt({opType:Pe.MAX,cpuKernelImpl:aie}),zue={kernelName:_i,backendName:"webgpu",kernelFunc:Due};function Lue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,p=T.computePool2DInfo(r.shape,s,i,u,o,l);return _8(r,p,"max",a)}var Bue={kernelName:Pi,backendName:"webgpu",kernelFunc:Lue};function Wue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"min",a)}var Vue={kernelName:Oi,backendName:"webgpu",kernelFunc:Wue},Uue=Jt({opType:Pe.MIN,cpuKernelImpl:nie}),Gue={kernelName:Di,backendName:"webgpu",kernelFunc:Uue},Hue=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=la(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let start = ${i}(${t});
|
|
let end = ${i}(${a});
|
|
var outC = getCoordsFromIndex(index);
|
|
for (var i = 0; i < ${e}; i = i + 1) {
|
|
if (${s} < ${n}) {
|
|
${s} = ${n} * 2 - ${s} - ${this.offset};
|
|
} else if(${s} >= ${r}) {
|
|
${s} = (${r} - 1) * 2 - ${s} + ${this.offset};
|
|
}
|
|
}
|
|
let coords = outC - start;
|
|
setOutputAtIndex(index, getX(${o}));
|
|
}
|
|
}
|
|
`}},jue={kernelName:zi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{paddings:r,mode:s}=t,i=a,o=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new Hue(n.shape,r,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}},que=Jt({opType:Pe.MOD}),Xue={kernelName:Ll,backendName:"webgpu",kernelFunc:que};function Kue(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.tensorMap.get(n.dataId),[i,o]=sie(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r=new xu(n.shape,le.NEG);return a.runWebGPUProgram(r,[n],n.dtype)}var Zue={kernelName:Bl,backendName:"webgpu",kernelFunc:Kue};function Yue(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=Nn.nonMaxSuppressionV3Impl(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var Jue={kernelName:Wi,backendName:"webgpu",kernelFunc:Yue};function Que(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=Nn.nonMaxSuppressionV5Impl(p,c,d,h,f,m);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var ede={kernelName:Vi,backendName:"webgpu",kernelFunc:Que},tde=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=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return`
|
|
${ke("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 ade(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new tde(u,i),c=Ie({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let f=[...r.shape,i],m=Ie({inputs:{x:h},backend:a,attrs:{shape:f}});return a.disposeData(h.dataId),m}var nde={kernelName:Ui,backendName:"webgpu",kernelFunc:ade};function Gc(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=yp({inputs:{input:n},backend:a}),s=Gc({inputs:{x:r},backend:a}),i=Wh({inputs:{input:n},backend:a}),o=Gc({inputs:{x:i},backend:a}),l=wo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Nr({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var rde={kernelName:nu,backendName:"webgpu",kernelFunc:Gc};function j8(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=yp({inputs:{input:n},backend:a}),s=j8({inputs:{x:r},backend:a}),i=Wh({inputs:{input:n},backend:a}),o=Gc({inputs:{x:i},backend:a}),l=wo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Nr({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var sde={kernelName:Vl,backendName:"webgpu",kernelFunc:j8};function ide(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return P2({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=P2({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=O8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var ode={kernelName:Ul,backendName:"webgpu",kernelFunc:ide},lde=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((a,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=la(e),a=this.xShape.map((u,p)=>`uniforms.pad${p}[0]`).join(","),n=this.xShape.map((u,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${a})`:`${a}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let start = ${r};
|
|
let end = ${s};
|
|
let outC = getCoordsFromIndex(index);
|
|
|
|
if (${i} || ${o}) {
|
|
setOutputAtIndex(index, uniforms.constantValue);
|
|
} else {
|
|
let coords = outC - start;
|
|
setOutputAtIndex(index, getX(${l}));
|
|
}
|
|
}
|
|
}
|
|
`}},q8=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return en({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Nr({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new lde(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},ude={kernelName:Gi,backendName:"webgpu",kernelFunc:q8},dde=Jt({opType:Pe.POW}),pde={kernelName:Hi,backendName:"webgpu",kernelFunc:dde};function cde(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new M2(Pe.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var hde={kernelName:ji,backendName:"webgpu",kernelFunc:cde};function fde(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return Io(r,s,i,"prod",a)}var mde={kernelName:qi,backendName:"webgpu",kernelFunc:fde},gde=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=lie(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},yde={kernelName:Gl,backendName:"webgpu",kernelFunc:gde},X8=Jt({opType:Pe.DIV}),xde={kernelName:ci,backendName:"webgpu",kernelFunc:X8},Ade=et({opType:le.RECIPROCAL}),bde={kernelName:Xi,backendName:"webgpu",kernelFunc:Ade},vde=et({opType:le.RELU}),kde={kernelName:Ki,backendName:"webgpu",kernelFunc:vde},wde=et({opType:le.RELU6}),Ide={kernelName:Ji,backendName:"webgpu",kernelFunc:wde},Sde=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
|
|
${ke("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>(
|
|
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
|
|
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
|
|
|
|
let effectiveOutSize = vec2<f32>(
|
|
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
|
|
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
|
|
|
|
let effectiveInputOverOutputRatioRC =
|
|
effectiveInSize / effectiveOutSize;
|
|
|
|
// Fractional source index
|
|
let sourceFracIndexRC =
|
|
(vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
|
|
effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
|
|
|
|
// Compute the four integer indices.
|
|
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
|
|
let sourceCeilRC = vec2<i32>(
|
|
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(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<f32>(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 Tde(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[o?.5:0]}],h=new Sde(r.shape,l,u);return a.runWebGPUProgram(h,[r],"float32",d)}var Cde={kernelName:Yi,backendName:"webgpu",kernelFunc:Tde},Nde=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
|
|
${ke("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>(
|
|
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
|
|
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
|
|
|
|
let effectiveOutSize = vec2<f32>(
|
|
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
|
|
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
|
|
|
|
let effectiveInputOverOutputRatioRC =
|
|
effectiveInSize / effectiveOutSize;
|
|
|
|
// Fractional source index
|
|
let sourceFracIndexRC = ${e};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
|
|
let sourceNearestRC = vec2<i32>(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
|
|
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutputAtIndex(index, newValue);
|
|
}
|
|
}
|
|
`}};function Ede(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[s?.5:0]}],h=new Nde(r.shape,l,u,i);return a.runWebGPUProgram(h,[r],r.dtype,d)}var Rde={kernelName:Zi,backendName:"webgpu",kernelFunc:Ede},Mde=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4<i32>,",this.shaderKey="reverse"}getUserCode(){return`
|
|
|
|
// Using uniform variables as judging conditions, so the function has
|
|
// coherent execution within all threads.
|
|
fn getReverseCoords(coords : vec4<i32>) -> vec4<i32> {
|
|
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;
|
|
}
|
|
|
|
${ke("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 $de(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length;if(i===0)return en({inputs:{x:r},backend:a});let o=r.shape,l=[1,1,1,1];o.forEach((g,y)=>{let x=y+4-i;l[x]=g});let u=v.parseAxisParam(s,r.shape),p=[0,0,0,0];u.forEach(g=>{let y=g+4-i;p[y]=1});let c=[{type:"int32",data:p}],d=Ie({inputs:{x:r},backend:a,attrs:{shape:l}}),h=new Mde(l),f=a.runWebGPUProgram(h,[d],d.dtype,c);a.disposeData(d.dataId);let m=Ie({inputs:{x:f},backend:a,attrs:{shape:o}});return a.disposeData(f.dataId),m}var _de={kernelName:Qi,backendName:"webgpu",kernelFunc:$de},Pde=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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<f32>,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
|
|
${ke("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));
|
|
${this.fillSnippet}
|
|
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
|
|
coordY < uniforms.xShape[1]) {
|
|
outputValue = getX(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutputAtIndex(index, outputValue);
|
|
}
|
|
}
|
|
`}},Fde={kernelName:go,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new Pde(n.shape,s),[u,p]=T.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},Ode=et({opType:le.ROUND}),Dde={kernelName:eo,backendName:"webgpu",kernelFunc:Ode},zde=et({opType:le.RSQRT,cpuKernelImpl:uie}),Lde={kernelName:to,backendName:"webgpu",kernelFunc:zde},Ic=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=$e(e),this.dispatch=we(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}`;let l=la(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=`
|
|
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
|
|
return index;
|
|
}
|
|
`):this.dispatchLayout.x.length===2&&(n="vec2<i32>(flattenedIndex, coords[1])",r=`
|
|
fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
|
|
// 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<i32>(d0, d1);
|
|
}
|
|
`);let s=`getUpdates(${Array.from({length:this.updatesRank},(i,o)=>`coords[${o}]`).join(", ")})`;return`
|
|
${r}
|
|
${ke("index")} {
|
|
if (index < uniforms.updatesSize) {
|
|
let coords = getUpdatesCoordsFromFlatIndex(index);
|
|
var flattenedIndex = 0;
|
|
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
|
|
let indexInside = i32(round(${t}));
|
|
flattenedIndex = flattenedIndex + indexInside * ${a};
|
|
}
|
|
let updateValue =
|
|
${td(this.type,!1)}(${s});
|
|
let flatIndex = getOutputIndexFromCoords(${n});
|
|
|
|
${this.sumDupeIndices?O3("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast<i32>(updateValue));"}
|
|
}
|
|
}`}};function Bde(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=T.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=Ie({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=Ie({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=f.dtype,g=Nr({backend:a,attrs:{shape:d,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),x=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[y]}],A=new Ic(f.shape,o,h.shape.length,f.shape.length,p,d,m),b=a.runWebGPUProgram(A,[f,h],m,x,g),k=Ie({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(f.dataId),a.disposeData(b.dataId),k}var Wde={kernelName:ao,backendName:"webgpu",kernelFunc:Bde},Vde=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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;
|
|
}
|
|
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromIndex(index);
|
|
let value = getValuesByOutputIndex(index);
|
|
setOutputAtIndexI32(index, findBound(coords[0], value));
|
|
}
|
|
}
|
|
`}};function Ude(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new Vde([s.shape[0],s.shape[1]],i),l=[{type:"int32",data:[r.shape[1]]}];return a.runWebGPUProgram(o,[r,s],"int32",l)}var Gde={kernelName:zd,backendName:"webgpu",kernelFunc:Ude},Hde=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=a,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s<this.outputShape.length;s++)r.push(`${a[s]}`),s<this.cRank&&n.push(`${a[s]}`);e=n.join(),t=r.join()}return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromIndex(index);
|
|
let cVal = getC(${e});
|
|
if (cVal >= 1.0) {
|
|
setOutputAtIndex(index, getA(${t}));
|
|
} else {
|
|
setOutputAtIndex(index, getB(${t}));
|
|
}
|
|
}
|
|
}
|
|
`}};function jde(e){let{inputs:t,backend:a}=e,{condition:n,t:r,e:s}=t,i=new Hde(n.shape.length,r.shape,r.shape.length);return a.runWebGPUProgram(i,[n,r,s],ma(r.dtype,s.dtype))}var qde={kernelName:jl,backendName:"webgpu",kernelFunc:jde},Xde=et({opType:le.SELU}),Kde={kernelName:ql,backendName:"webgpu",kernelFunc:Xde},Zde=et({opType:le.SIGMOID}),Yde={kernelName:ro,backendName:"webgpu",kernelFunc:Zde},Jde=et({opType:le.SIGN}),Qde={kernelName:Zl,backendName:"webgpu",kernelFunc:Jde},epe=et({opType:le.SIN}),tpe={kernelName:no,backendName:"webgpu",kernelFunc:epe},ape=et({opType:le.SINH}),npe={kernelName:Kl,backendName:"webgpu",kernelFunc:ape},K8=Jt({opType:Pe.SUB,cpuKernelImpl:mie,supportsComplex:!0}),rpe={kernelName:po,backendName:"webgpu",kernelFunc:K8};function spe(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=V3({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=Ie({inputs:{x:o},backend:a,attrs:{shape:l}}),p=K8({inputs:{a:r,b:u},backend:a}),c=V8({inputs:{x:p},backend:a}),d=U3({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=Ie({inputs:{x:d},backend:a,attrs:{shape:l}}),f=X8({inputs:{a:c,b:h},backend:a});return a.disposeData(o.dataId),a.disposeData(u.dataId),a.disposeData(p.dataId),a.disposeData(c.dataId),a.disposeData(d.dataId),a.disposeData(h.dataId),f}var ipe={kernelName:oo,backendName:"webgpu",kernelFunc:spe},ope=et({opType:le.SOFTPLUS}),lpe={kernelName:Yl,backendName:"webgpu",kernelFunc:ope},upe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let u=[],p=q8({inputs:{x:r},backend:a,attrs:{paddings:l,constantValue:0}}),c=T.getReshaped(p.shape,s,o,!1),d=T.getPermuted(c.length,s.length,!1),h=T.getReshapedPermuted(p.shape,s,o,!1),f=Ie({inputs:{x:p},backend:a,attrs:{shape:c}}),m=kr({inputs:{x:f},backend:a,attrs:{perm:d}}),g=Ie({inputs:{x:m},backend:a,attrs:{shape:h}});return u.push(p),u.push(f),u.push(m),u.forEach(y=>a.disposeData(y.dataId)),g},dpe={kernelName:Jl,backendName:"webgpu",kernelFunc:upe},ppe=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n<a.length;n++)a[n]=e[n]*t[n];this.outputShape=a,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=cpe(this.rank,"uniforms.");return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromIndex(index);
|
|
setOutputAtIndex(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function cpe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r<e;r++)n.push(`(${a[r]} % ${t}aShape[${r}])`);return n.join()}function Z8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reps:s}=n;if(a.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=gie(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new ppe(r.shape,s);return a.runWebGPUProgram(i,[r],r.dtype)}var hpe={kernelName:ns,backendName:"webgpu",kernelFunc:Z8};function fpe(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let N=a.bufferSync(r),$=a.bufferSync(s),M=v.decodeString(a.readSync(i.dataId)[0]),R=die(N,$,o,d,p,u,l,c,M,h);return a.makeTensorInfo(o,R.dtype,R.values)}let f=[d/p,p],m=Ie({inputs:{x:r},backend:a,attrs:{shape:[u,l]}}),g=s.shape.length?Ie({inputs:{x:s},backend:a,attrs:{shape:[u,p]}}):en({inputs:{x:s},backend:a}),y=g.dtype,x=a.makeTensorInfo([],y,v.makeZerosTypedArray(1,y)),A=Ie({inputs:{x:i},backend:a,attrs:{shape:Array(f.length).fill(1)}}),b=Z8({inputs:{x:A},backend:a,attrs:{reps:f}}),k=v.sizeFromShape([u,p]),S=[{type:"int32",data:[l]},{type:"int32",data:c},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let N=new Ic([u,p],l,m.shape.length,g.shape.length,c,f,y,h);a.runWebGPUProgram(N,[g,m],y,S,b)}break;default:{let N=new Ic([u,p],l,m.shape.length,x.shape.length,c,f,y,h);a.runWebGPUProgram(N,[x,m],y,S,b)}{let N=new Ic([u,p],l,m.shape.length,g.shape.length,c,f,y);a.runWebGPUProgram(N,[g,m],y,S,b)}}let C=Ie({inputs:{x:b},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),a.disposeData(g.dataId),a.disposeData(A.dataId),a.disposeData(x.dataId),a.disposeData(b.dataId),C}var mpe={kernelName:Vd,backendName:"webgpu",kernelFunc:fpe};function gpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let f=Au({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,f})}var ype={kernelName:Ql,backendName:"webgpu",kernelFunc:gpe},xpe=et({opType:le.SQRT}),Ape={kernelName:so,backendName:"webgpu",kernelFunc:xpe},bpe={kernelName:Ud,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new xu(a.shape,le.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},vpe=Jt({opType:Pe.SQUARED_DIFFERENCE}),kpe={kernelName:lo,backendName:"webgpu",kernelFunc:vpe};function wpe({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=new xu(n.shape,le.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return a.runWebGPUProgram(r,[n],n.dtype,s)}var Ipe={kernelName:rs,backendName:"webgpu",kernelFunc:wpe},Spe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=la(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return`
|
|
${ke("index")} {
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromIndex(index);
|
|
setOutputAtIndex(index, getX(${t}));
|
|
}
|
|
}
|
|
`}};function Tpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=St.sliceInfo(r.shape,s,i,o,l,u,p,c,d),k;if(m)k=Ie({inputs:{x:r},backend:a,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let S=St.computeOutShape(x,A,b),C=Au({inputs:{x:r},backend:a,attrs:{begin:x,size:S}});k=Ie({inputs:{x:C},backend:a,attrs:{shape:f}}),a.disposeData(C.dataId)}else if(a.shouldExecuteOnCPU([r])){let S=a.readSync(r.dataId),C=_e(r.shape,r.dtype,S),N=hie(h,C,b,x);k=a.makeTensorInfo(f,r.dtype,N.values)}else{let S=new Spe(h),C=[{type:"int32",data:x},{type:"int32",data:b}],N=a.runWebGPUProgram(S,[r],r.dtype,C);k=Ie({inputs:{x:N},backend:a,attrs:{shape:f}}),a.disposeData(N.dataId)}return k}var Cpe={kernelName:uo,backendName:"webgpu",kernelFunc:Tpe};function Npe(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[f,m]=fie(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([f.length],"string",f),a.makeTensorInfo(c.shape,"int32",m)]}var Epe={kernelName:tu,backendName:"webgpu",kernelFunc:Npe},Rpe=et({opType:le.TAN}),Mpe={kernelName:co,backendName:"webgpu",kernelFunc:Rpe},$pe=et({opType:le.TANH}),_pe={kernelName:ho,backendName:"webgpu",kernelFunc:$pe},Ppe=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
|
|
dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
|
|
${ke("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));
|
|
}
|
|
}
|
|
}
|
|
`}},Fpe=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
|
|
${ke("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 Yo(e,t){t!==null&&e.disposeData(t.dataId)}function Yy(e){let t=1;for(;t<e;)t*=2;return t}function Ope(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{k:s,sorted:i}=n,o=r.shape,l=o[o.length-1];if(a.shouldExecuteOnCPU([r])){let b=a.readSync(r.dataId),[k,S]=yie(b,o,r.dtype,s,i);return[a.makeTensorInfo(k.shape,k.dtype,k.values),a.makeTensorInfo(S.shape,S.dtype,S.values)]}if(s===0)return o[o.length-1]=0,[a.makeTensorInfo(o,r.dtype,[]),a.makeTensorInfo(o,"int32",[])];if(l===1)return[r,Nr({attrs:{shape:o,dtype:"int32",value:0},backend:a})];let u=v.sizeFromShape(o)/l,p=Ie({inputs:{x:r},attrs:{shape:[u,l]},backend:a}),c=Yy(s),d=Yy(l),h=null,f=()=>h===null?[p,p]:[p,h],m=(b,k,S)=>{let C=f(),N=new Ppe(S),$=[{type:"int32",data:[l]},{type:"int32",data:[h===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[k]}],M=h;h=a.runWebGPUProgram(N,C,"int32",$),Yo(a,M)};for(let b=1;b<c;b*=2){let k=b*2;for(let S=b;S>=1;S/=2)m(k,S,[u,d])}for(let b=d;b>c;b/=2){let k=f(),S=new Fpe([u,b/2]),C=[{type:"int32",data:[l]},{type:"int32",data:[h===null?1:0]},{type:"int32",data:[c]}],N=h;h=a.runWebGPUProgram(S,k,"int32",C),Yo(a,N);let $=c/2,M=$*2;for(let R=$;R>=1;R/=2)m(M,R,h.shape)}let g=h;h=Au({inputs:{x:h},backend:a,attrs:{begin:0,size:[u,s]}}),Yo(a,g);let y=G8({inputs:{x:p,indices:h},backend:a,attrs:{axis:1,batchDims:1}});Yo(a,p);let x=o.slice(0,-1);x.push(s),g=h,h=Ie({inputs:{x:h},attrs:{shape:x},backend:a}),Yo(a,g);let A=y;return y=Ie({inputs:{x:y},attrs:{shape:x},backend:a}),Yo(a,A),[y,h]}var Dpe={kernelName:fo,backendName:"webgpu",kernelFunc:Ope},zpe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=$e(this.outputShape),this.dispatch=we(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;
|
|
}
|
|
|
|
${ke("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 Lpe(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[p,c,d,h]=r.shape,[f,m]=u!=null?u:[c,d],g=[p,f,m,h],y=new zpe(g),x=i==="nearest"?1:2,A;switch(o){case"constant":A=1;break;case"reflect":A=2;break;case"wrap":A=3;break;case"nearest":A=4;break;default:A=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[A]},{type:"float32",data:[l]}];return a.runWebGPUProgram(y,[r,s],"float32",b)}var Bpe={kernelName:mo,backendName:"webgpu",kernelFunc:Lpe};function Wpe(e){let{inputs:t,backend:a,attrs:n}=e,{value:r}=t,{axis:s}=n;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let m=0;m<o;m++)m!==s&&(u[p++]=i.shape[m]);let c=[],d=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let g=Au({inputs:{x:i},backend:a,attrs:{begin:d,size:h}}),y=Ie({inputs:{x:g},backend:a,attrs:{shape:u}});f[m]=y,c.push(g)}return c.forEach(m=>a.disposeData(m.dataId)),f}var Vpe={kernelName:au,backendName:"webgpu",kernelFunc:Wpe},Upe=[Dse,bie,kie,Iie,Tie,Eie,Oie,zie,Bie,Vie,Gie,jie,Xie,Zie,Jie,noe,ioe,loe,hoe,yoe,voe,woe,Coe,Lse,Moe,Loe,Uoe,qoe,Koe,Yoe,ele,ale,rle,ile,ule,cle,mle,xle,kle,Ile,Tle,Nle,Ele,Rle,$le,Ple,Pse,Ole,Vle,zle,Ble,Hle,qle,Kle,Jle,tue,nue,sue,zse,oue,Eoe,uue,pue,hue,mue,yue,Aue,kue,Tue,Iue,Nue,Rue,$ue,Oue,eoe,zue,Bue,toe,Vue,Gue,jue,Xue,Ale,Zue,Jue,ede,xoe,nde,sde,ode,ude,pde,hde,mde,yde,Aoe,xde,bde,kde,Ide,Fse,Cde,Rde,_de,Fde,Dde,Lde,Wde,Gde,qde,Kde,Yde,Qde,tpe,npe,poe,Ipe,Cpe,Epe,ipe,lpe,dpe,mpe,ype,Ape,bpe,kpe,rpe,ble,Mpe,_pe,hpe,Dpe,Bpe,_ie,Vpe,rde];for(let e of Upe)yn(e);var Jy="4.2.0",Gpe="4.2.0",Hpe="4.2.0",jpe="4.2.0",qpe="4.2.0",Xpe="0.0.1-alpha.17",xp={tfjs:Jy,"tfjs-core":Jy,"tfjs-converter":Gpe,"tfjs-backend-cpu":Hpe,"tfjs-backend-webgl":jpe,"tfjs-backend-wasm":qpe,"tfjs-backend-webgpu":Xpe};function K(...e){let t=new Date,a=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(a,"Human:",...e)}function Y8(e,t){let a=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${a}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var ae=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function G3(e,t,a="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")G3(e[r],t[r],r,n);else{let s=e&&typeof e[r]!="undefined";s||n.push({reason:"unknown property",where:`${a}.${r} = ${t[r]}`});let i=e&&typeof e[r]==typeof t[r];s&&!i&&n.push({reason:"property type mismatch",where:`${a}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&a==="config"&&n.length>0&&K("invalid configuration",n),n}function Nt(...e){let t=a=>a&&typeof a=="object";return e.reduce((a,n)=>(Object.keys(n||{}).forEach(r=>{let s=a[r],i=n[r];Array.isArray(s)&&Array.isArray(i)?a[r]=s.concat(...i):t(s)&&t(i)?a[r]=Nt(s,i):a[r]=i}),a),{})}var So={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var J8=`
|
|
precision highp float;
|
|
attribute vec2 pos;
|
|
attribute vec2 uv;
|
|
varying vec2 vUv;
|
|
uniform float flipY;
|
|
void main(void) {
|
|
vUv = uv;
|
|
gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
|
|
}
|
|
`;var Q8=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform float m[20];
|
|
void main(void) {
|
|
vec4 c = texture2D(texture, vUv);
|
|
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
|
|
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
|
|
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
|
|
gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
|
|
}
|
|
`,e9=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform float m[20];
|
|
void main(void) {
|
|
vec4 c = texture2D(texture, vUv);
|
|
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
|
|
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
|
|
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
|
|
gl_FragColor.a = c.a;
|
|
}
|
|
`,t9=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform vec2 size;
|
|
uniform sampler2D texture;
|
|
vec2 pixelate(vec2 coord, vec2 size) {
|
|
return floor( coord / size ) * size;
|
|
}
|
|
void main(void) {
|
|
gl_FragColor = vec4(0.0);
|
|
vec2 coord = pixelate(vUv, size);
|
|
gl_FragColor += texture2D(texture, coord);
|
|
}
|
|
`,a9=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform vec2 px;
|
|
void main(void) {
|
|
gl_FragColor = vec4(0.0);
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
|
|
gl_FragColor += texture2D(texture, vUv )*0.159576912161;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;
|
|
}
|
|
`,n9=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform vec2 px;
|
|
uniform float m[9];
|
|
void main(void) {
|
|
vec4 c11 = texture2D(texture, vUv - px); // top left
|
|
vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
|
|
vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
|
|
vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
|
|
vec4 c22 = texture2D(texture, vUv); // mid center
|
|
vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
|
|
vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
|
|
vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
|
|
vec4 c33 = texture2D(texture, vUv + px ); // bottom right
|
|
gl_FragColor =
|
|
c11 * m[0] + c12 * m[1] + c22 * m[2] +
|
|
c21 * m[3] + c22 * m[4] + c23 * m[5] +
|
|
c31 * m[6] + c32 * m[7] + c33 * m[8];
|
|
gl_FragColor.a = c22.a;
|
|
}
|
|
`;var H3=(e,t,a)=>{let n=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(n,(r,s)=>(a[s]=0,r))},j3=class{constructor(t,a,n){de(this,"uniform",{});de(this,"attribute",{});de(this,"gl");de(this,"id");de(this,"compile",(t,a)=>{let n=this.gl.createShader(a);return n?(this.gl.shaderSource(n,t),this.gl.compileShader(n),this.gl.getShaderParameter(n,this.gl.COMPILE_STATUS)?n:(K(`filter: gl compile failed: ${this.gl.getShaderInfoLog(n)||"unknown"}`),null)):(K("filter: could not create shader"),null)});this.gl=t;let r=this.compile(a,this.gl.VERTEX_SHADER),s=this.compile(n,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!s)){if(!this.id){K("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,s),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){K(`filter: gl link failed: 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k=c.createRenderbuffer();c.bindRenderbuffer(c.RENDERBUFFER,k);let S=c.createTexture();return c.bindTexture(c.TEXTURE_2D,S),c.texImage2D(c.TEXTURE_2D,0,c.RGBA,x,A,0,c.RGBA,c.UNSIGNED_BYTE,null),c.texParameteri(c.TEXTURE_2D,c.TEXTURE_MAG_FILTER,c.LINEAR),c.texParameteri(c.TEXTURE_2D,c.TEXTURE_MIN_FILTER,c.LINEAR),c.texParameteri(c.TEXTURE_2D,c.TEXTURE_WRAP_S,c.CLAMP_TO_EDGE),c.texParameteri(c.TEXTURE_2D,c.TEXTURE_WRAP_T,c.CLAMP_TO_EDGE),c.framebufferTexture2D(c.FRAMEBUFFER,c.COLOR_ATTACHMENT0,c.TEXTURE_2D,S,0),c.bindTexture(c.TEXTURE_2D,null),c.bindFramebuffer(c.FRAMEBUFFER,null),{fbo:b,texture:S}}function f(x){return r[x]=r[x]||h(l.width,l.height),r[x]}function m(x=0){if(!o)return;let A=null,b=null,k=!1;e===0?A=t:A=f(n).texture||null,e++,a&&!(x&p.INTERMEDIATE)?(b=null,k=e%2===0):(n=(n+1)%2,b=f(n).fbo||null),c.bindTexture(c.TEXTURE_2D,A),c.bindFramebuffer(c.FRAMEBUFFER,b),c.uniform1f(o.uniform.flipY,k?-1:1),c.drawArrays(c.TRIANGLES,0,6)}function g(x){if(u[x])return o=u[x],c.useProgram((o?o.id:null)||null),o;if(o=new j3(c,J8,x),!o)return K("filter: could not get webgl program"),null;let A=Float32Array.BYTES_PER_ELEMENT,b=4*A;return c.enableVertexAttribArray(o.attribute.pos),c.vertexAttribPointer(o.attribute.pos,2,c.FLOAT,!1,b,0*A),c.enableVertexAttribArray(o.attribute.uv),c.vertexAttribPointer(o.attribute.uv,2,c.FLOAT,!1,b,2*A),u[x]=o,o}let y={colorMatrix:x=>{let A=new Float32Array(x);A[4]/=255,A[9]/=255,A[14]/=255,A[19]/=255;let b=A[18]===1&&A[3]===0&&A[8]===0&&A[13]===0&&A[15]===0&&A[16]===0&&A[17]===0&&A[19]===0?e9:Q8,k=g(b);k&&(c.uniform1fv(k.uniform.m,A),m())},brightness:x=>{let A=(x||0)+1;y.colorMatrix([A,0,0,0,0,0,A,0,0,0,0,0,A,0,0,0,0,0,1,0])},saturation:x=>{let A=(x||0)*2/3+1,b=(A-1)*-.5;y.colorMatrix([A,b,b,0,0,b,A,b,0,0,b,b,A,0,0,0,0,0,1,0])},desaturate:()=>{y.saturation(-1)},contrast:x=>{let A=(x||0)+1,b=-128*(A-1);y.colorMatrix([A,0,0,0,b,0,A,0,0,b,0,0,A,0,b,0,0,0,1,0])},negative:()=>{y.contrast(-2)},hue:x=>{x=(x||0)/180*Math.PI;let A=Math.cos(x),b=Math.sin(x),k=.213,S=.715,C=.072;y.colorMatrix([k+A*(1-k)+b*-k,S+A*-S+b*-S,C+A*-C+b*(1-C),0,0,k+A*-k+b*.143,S+A*(1-S)+b*.14,C+A*-C+b*-.283,0,0,k+A*-k+b*-(1-k),S+A*-S+b*S,C+A*(1-C)+b*C,0,0,0,0,0,1,0])},desaturateLuminance:()=>{y.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{y.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{y.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{y.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{y.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{y.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{y.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{y.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:x=>{let 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phe=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],che=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],hhe=[33,133,362,263,1,78,308],I3e=phe.map(e=>Ip[e]),S3e=che.map(e=>Ip[e]),T3e=hhe.map(e=>Ip[e]);function ds(e){let t=e.map(a=>a[0]);return t.push(e[e.length-1][1]),t}var fhe=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],mhe=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],ghe=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],yhe=[[474,475],[475,476],[476,477],[477,474]],xhe=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],Ahe=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],bhe=[[469,470],[470,471],[471,472],[472,469]],vhe=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]],C3e={lips:ds(fhe),leftEye:ds(mhe),leftEyebrow:ds(ghe),leftIris:ds(yhe),rightEye:ds(xhe),rightEyebrow:ds(Ahe),rightIris:ds(bhe),faceOval:ds(vhe)};var khe=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],whe=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],Ihe=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],She=[[474,475],[475,476],[476,477],[477,474]],The=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],Che=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],Nhe=[[469,470],[470,471],[471,472],[472,469]],Ehe=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function ps(e){let t=e.map(a=>a[0]);return t.push(e[e.length-1][1]),t}var Rhe={lips:ps(khe),leftEye:ps(whe),leftEyebrow:ps(Ihe),leftIris:ps(She),rightEye:ps(The),rightEyebrow:ps(Che),rightIris:ps(Nhe),faceOval:ps(Ehe)},Mhe=Object.entries(Rhe).map(([e,t])=>t.map(a=>[a,e])).flat(),N3e=new Map(Mhe),Sp=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],Ro=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],Mo=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];var tt;function $he(e,t){var n,r,s,i,o,l,u,p,c;if(!tt.drawLabels||((n=tt.faceLabels)==null?void 0:n.length)===0)return;let a=tt.faceLabels.slice();if(a=it(a,"[id]",e.id.toFixed(0)),e.score&&(a=it(a,"[score]",100*e.score)),e.gender&&(a=it(a,"[gender]",e.gender)),e.genderScore&&(a=it(a,"[genderScore]",100*e.genderScore)),e.age&&(a=it(a,"[age]",e.age)),e.distance&&(a=it(a,"[distance]",100*e.distance)),e.real&&(a=it(a,"[real]",100*e.real)),e.live&&(a=it(a,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let d=e.emotion.map(h=>`${Math.trunc(100*h.score)}% ${h.emotion}`);d.length>3&&(d.length=3),a=it(a,"[emotions]",d.join(" "))}(s=(r=e.rotation)==null?void 0:r.angle)!=null&&s.roll&&(a=it(a,"[roll]",To(e.rotation.angle.roll))),(o=(i=e.rotation)==null?void 0:i.angle)!=null&&o.yaw&&(a=it(a,"[yaw]",To(e.rotation.angle.yaw))),(u=(l=e.rotation)==null?void 0:l.angle)!=null&&u.pitch&&(a=it(a,"[pitch]",To(e.rotation.angle.pitch))),(c=(p=e.rotation)==null?void 0:p.gaze)!=null&&c.bearing&&(a=it(a,"[gaze]",To(e.rotation.gaze.bearing))),bn(t,a,e.box[0],e.box[1],tt)}function _he(e,t){var a,n,r,s;if((a=e.annotations)!=null&&a.leftEyeIris&&((n=e.annotations)!=null&&n.leftEyeIris[0])){t.strokeStyle=tt.useDepth?"rgba(255, 200, 255, 0.3)":tt.color,t.beginPath();let i=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,o=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],i,o,0,0,2*Math.PI),t.stroke(),tt.fillPolygons&&(t.fillStyle=tt.useDepth?"rgba(255, 255, 200, 0.3)":tt.color,t.fill())}if((r=e.annotations)!=null&&r.rightEyeIris&&((s=e.annotations)!=null&&s.rightEyeIris[0])){t.strokeStyle=tt.useDepth?"rgba(255, 200, 255, 0.3)":tt.color,t.beginPath();let i=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,o=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],i,o,0,0,2*Math.PI),t.stroke(),tt.fillPolygons&&(t.fillStyle=tt.useDepth?"rgba(255, 255, 200, 0.3)":tt.color,t.fill())}}function Phe(e,t){var a;if(tt.drawGaze&&((a=e.rotation)!=null&&a.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*To(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*To(e.rotation.angle.pitch)/90,s=new Path2D(`
|
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M ${e.box[0]+e.box[2]/2} ${e.box[1]}
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|
C
|
|
${n} ${e.box[1]},
|
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${n} ${e.box[1]+e.box[3]},
|
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${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
|
|
`),i=new Path2D(`
|
|
M ${e.box[0]} ${e.box[1]+e.box[3]/2}
|
|
C
|
|
${e.box[0]} ${r},
|
|
${e.box[0]+e.box[2]} ${r},
|
|
${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
|
|
`);t.stroke(i),t.stroke(s)}}function Fhe(e,t){var a;if(tt.drawGaze&&((a=e.rotation)!=null&&a.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let n=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Y3(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[n[0],n[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Y3(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Ohe(e,t){if(tt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let a=0;a<Eo.length/3;a++){let n=[Eo[a*3+0],Eo[a*3+1],Eo[a*3+2]].map(r=>e.mesh[r]);Z3(t,n,tt)}_he(e,t)}}function Dhe(e,t){if(tt.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let a=0;a<e.mesh.length;a++)sr(t,e.mesh[a][0],e.mesh[a][1],e.mesh[a][2],tt),tt.drawAttention&&(Sp.includes(a)&&sr(t,e.mesh[a][0],e.mesh[a][1],e.mesh[a][2]+127,tt),Ro.includes(a)&&sr(t,e.mesh[a][0],e.mesh[a][1],e.mesh[a][2]-127,tt),Mo.includes(a)&&sr(t,e.mesh[a][0],e.mesh[a][1],e.mesh[a][2]-127,tt));else for(let[a,n]of Object.entries((e==null?void 0:e.annotations)||{})){if(!(n!=null&&n[0]))continue;let r=n[0];sr(t,r[0],r[1],0,tt),tt.drawLabels&&bn(t,a,r[0],r[1],tt)}}function zhe(e,t){tt.drawBoxes&&ir(t,e.box[0],e.box[1],e.box[2],e.box[3],tt)}function Kh(e,t,a){if(tt=Nt(_t,a),!t||!e)return;let n=An(e);if(n){n.font=tt.font,n.strokeStyle=tt.color,n.fillStyle=tt.color;for(let r of t)zhe(r,n),$he(r,n),r.mesh&&r.mesh.length>0&&(Dhe(r,n),Ohe(r,n),Phe(r,n),Fhe(r,n))}}function Zh(e,t,a){var s,i;let n=Nt(_t,a);if(!t||!e)return;let r=An(e);if(r){r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=n.color,r.fillStyle=n.color,r.lineWidth=n.lineWidth,r.font=n.font,n.drawBoxes&&t[o].box&&t[o].box.length===4&&(ir(r,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],n),n.drawLabels&&((s=n.bodyLabels)==null?void 0:s.length)>0)){let l=n.bodyLabels.slice();l=it(l,"[id]",t[o].id.toFixed(0)),l=it(l,"[score]",100*t[o].score),bn(r,l,t[o].box[0],t[o].box[1],n)}if(n.drawPoints&&t[o].keypoints)for(let l=0;l<t[o].keypoints.length;l++)!t[o].keypoints[l].score||t[o].keypoints[l].score===0||(r.fillStyle=Co(t[o].keypoints[l].position[2],n),sr(r,t[o].keypoints[l].position[0],t[o].keypoints[l].position[1],0,n));if(n.drawLabels&&((i=n.bodyPartLabels)==null?void 0:i.length)>0&&t[o].keypoints){r.font=n.font;for(let l of t[o].keypoints){if(!l.score||l.score===0)continue;let u=n.bodyPartLabels.slice();u=it(u,"[label]",l.part),u=it(u,"[score]",100*l.score),bn(r,u,l.position[0],l.position[1],n)}}if(n.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let l of Object.values(t[o].annotations))for(let u of l)c9(r,u,n)}}}function Yh(e,t,a){var s,i;let n=Nt(_t,a);if(!t||!e)return;let r=An(e);if(r){r.lineJoin="round",r.font=n.font;for(let o of t){if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,ir(r,o.box[0],o.box[1],o.box[2],o.box[3],n),n.drawLabels&&((s=n.handLabels)==null?void 0:s.length)>0){let l=n.handLabels.slice();l=it(l,"[id]",o.id.toFixed(0)),l=it(l,"[label]",o.label),l=it(l,"[score]",100*o.score),bn(r,l,o.box[0],o.box[1],n)}r.stroke()}if(n.drawPoints&&o.keypoints&&o.keypoints.length>0)for(let l of o.keypoints)r.fillStyle=Co(l[2],n),sr(r,l[0],l[1],0,n);if(n.drawLabels&&o.annotations&&((i=n.fingerLabels)==null?void 0:i.length)>0)for(let[l,u]of Object.entries(o.annotations)){let p=n.fingerLabels.slice();p=it(p,"[label]",l),bn(r,p,u[u.length-1][0],u[u.length-1][1],n)}if(n.drawPolygons&&o.annotations){let l=u=>{if(!(!u||u.length===0||!u[0]))for(let p=0;p<u.length;p++){r.beginPath();let c=u[p][2]||0;r.strokeStyle=Co(p*c,n),r.moveTo(u[p>0?p-1:0][0],u[p>0?p-1:0][1]),r.lineTo(u[p][0],u[p][1]),r.stroke()}};r.lineWidth=n.lineWidth,l(o.annotations.index),l(o.annotations.middle),l(o.annotations.ring),l(o.annotations.pinky),l(o.annotations.thumb)}}}}function Jh(e,t,a){var s;let n=Nt(_t,a);if(!t||!e)return;let r=An(e);if(r){r.lineJoin="round",r.font=n.font;for(let i of t)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,ir(r,i.box[0],i.box[1],i.box[2],i.box[3],n),n.drawLabels&&((s=n.objectLabels)==null?void 0:s.length)>0){let o=n.objectLabels.slice();o=it(o,"[id]",i.id.toFixed(0)),o=it(o,"[label]",i.label),o=it(o,"[score]",100*i.score),bn(r,o,i.box[0],i.box[1],n)}r.stroke()}}}function Qh(e,t,a){var r;let n=Nt(_t,a);if(!(!t||!e)&&n.drawGestures&&((r=n.gestureLabels)==null?void 0:r.length)>0){let s=An(e);if(!s)return;s.font=n.font,s.fillStyle=n.color;let i=1;for(let o=0;o<t.length;o++){let[l,u]=Object.entries(t[o]);if(u.length>1&&u[1].length>0){let p=l[1]>0?`#${l[1]}`:"",c=n.gestureLabels.slice();c=it(c,"[where]",l[0]),c=it(c,"[who]",p),c=it(c,"[what]",u[1]),bn(s,c,8,2+i*n.lineHeight,n),i+=1}}}}var cs={face:`face
|
|
confidence: [score]%
|
|
[gender] [genderScore]%
|
|
age: [age] years
|
|
distance: [distance]cm
|
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real: [real]%
|
|
live: [live]%
|
|
[emotions]
|
|
roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0
|
|
gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var tg=0;function Lhe(e,t,a){let n=Nt(_t,a);if(!t||!e)return;let r=An(e);if(r){r.lineJoin="round",r.font=n.font;for(let s=0;s<t.length;s++)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,ir(r,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],n),n.drawLabels){let i=`person #${s}`;n.shadowColor&&n.shadowColor!==""&&(r.fillStyle=n.shadowColor,r.fillText(i,t[s].box[0]+3,1+t[s].box[1]+n.lineHeight,t[s].box[2])),r.fillStyle=n.labelColor,r.fillText(i,t[s].box[0]+2,0+t[s].box[1]+n.lineHeight,t[s].box[2])}r.stroke()}}}function Bhe(e,t){if(!e||!t)return;let a=An(t);a&&a.drawImage(e,0,0)}async function Whe(e,t,a){if(!(t!=null&&t.performance)||!e)return null;let n=ae(),r=Nt(_t,a),s=Promise.all([Kh(e,t.face,r),Zh(e,t.body,r),Yh(e,t.hand,r),Jh(e,t.object,r),Qh(e,t.gesture,r)]);return tg=ne.perfadd?tg+Math.round(ae()-n):Math.round(ae()-n),t.performance.draw=tg,s}function ag(){_t.faceLabels=cs.face,_t.bodyLabels=cs.body,_t.bodyPartLabels=cs.bodyPart,_t.handLabels=cs.hand,_t.fingerLabels=cs.finger,_t.objectLabels=cs.object,_t.gestureLabels=cs.gesture}var t0={};fr(t0,{connected:()=>rg,kpt:()=>ng});var ng=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],rg={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var vn,$o=224,m9,Vhe=5,a0=[8,16,32,32,32];function Uhe(){let e=[],t=0;for(;t<Vhe;){let a=0,n=t;for(;n<a0.length&&a0[n]===a0[t];)a+=2,n++;let r=a0[t],s=Math.ceil($o/r),i=Math.ceil($o/r);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<a;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}m9={x:Ht(e.map(a=>a.x)),y:Ht(e.map(a=>a.y))}}async function g9(e){if(ne.initial&&(vn=null),!vn&&e.body.detector&&e.body.detector.modelPath){vn=await Me(e.body.detector.modelPath);let t=vn!=null&&vn.executor?Object.values(vn.modelSignature.inputs):void 0;$o=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&vn&&K("cached model:",vn.modelUrl);return Uhe(),vn}var f9=[5,5];function Ghe(e,t){return Oe(()=>{let a=Sa(e,12,1),n=De(a[0]),r=De(a[1]),s=De(a[2]),i=De(a[3]);n=be(xe(n,$o),t.x),r=be(xe(r,$o),t.y),s=te(xe(s,$o),f9[0]),i=te(xe(i,$o),f9[1]);let o=me(n,xe(s,2)),l=me(r,xe(i,2)),u=be(o,s),p=be(l,i);return ua([o,l,u,p],1)})}async function Hhe(e,t,a,n){var u,p;let r=[],s={};s.boxes=Ghe(e,m9),s.scores=Ba(t),s.nms=await ce.nonMaxSuppressionAsync(s.boxes,s.scores,1,((u=a.body.detector)==null?void 0:u.minConfidence)||.1,((p=a.body.detector)==null?void 0:p.iouThreshold)||.1);let i=await s.nms.data(),o=await s.scores.data(),l=await s.boxes.array();for(let c of Array.from(i)){let d=o[c],h=l[c],f=[Math.round(h[0]*n[0]),Math.round(h[1]*n[1]),Math.round(h[2]*n[0]),Math.round(h[3]*n[1])],m={score:d,boxRaw:h,box:f};r.push(m)}return Object.keys(s).forEach(c=>J(s[c])),r}async function y9(e,t,a){let n={};n.res=vn==null?void 0:vn.execute(e,["Identity"]),n.logitsRaw=Fe(n.res,[0,0,0],[1,-1,1]),n.boxesRaw=Fe(n.res,[0,0,1],[1,-1,-1]),n.logits=De(n.logitsRaw),n.boxes=De(n.boxesRaw);let r=await Hhe(n.boxes,n.logits,t,a);return Object.keys(n).forEach(s=>J(n[s])),r}function hs(e,t=[1,1]){let a=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...a[0]),Math.min(...a[1])],r=[Math.max(...a[0]),Math.max(...a[1])],s=[n[0],n[1],r[0]-n[0],r[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function x9(e,t=[1,1]){let a=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...a[0]),Math.min(...a[1])],r=[Math.max(...a[0]),Math.max(...a[1])],s=[(n[0]+r[0])/2,(n[1]+r[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+r[0],-s[1]+r[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function n0(e,t){let a=[e[2]*t,e[3]*t];return[e[0]-(a[0]-e[2])/2,e[1]-(a[1]-e[3])/2,a[0],a[1]]}var Ua,ig=256,sg=Number.MAX_SAFE_INTEGER,jhe={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},s0=[],fs=[[0,0],[0,0],[0,0],[0,0]],A9=0,b9=e=>1-1/(1+Math.exp(e)),k9=e=>g9(e);async function w9(e){if(ne.initial&&(Ua=null),Ua)e.debug&&K("cached model:",Ua.modelUrl);else{Ua=await Me(e.body.modelPath);let t=Ua!=null&&Ua.executor?Object.values(Ua.modelSignature.inputs):void 0;ig=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Ua}function v9(e,t,a){var s,i;let n={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((i=e==null?void 0:e.shape)!=null&&i[2]))return e;let r;if(a&&(n.cropped=ce.cropAndResize(e,[a],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let o=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];fs=[[0,0],o,l,[0,0]],n.pad=rr(n.cropped||e,fs),n.resize=ce.resizeBilinear(n.pad,[t,t]),r=xe(n.resize,Le.tf255)}else e.shape[1]!==t?(n.resize=ce.resizeBilinear(n.cropped||e,[t,t]),r=xe(n.resize,Le.tf255)):r=xe(n.cropped||e,Le.tf255);return Object.keys(n).forEach(o=>J(n[o])),r}function qhe(e,t,a){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+fs[2][0]+fs[2][1])/t[0]-fs[2][0]),Math.trunc(n.position[1]*(t[1]+fs[1][0]+fs[1][1])/t[1]-fs[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(a){let n=a[2]-a[0],r=a[3]-a[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+a[1],s.positionRaw[1]/n+a[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function Xhe(e){let t=e.find(o=>o.part==="leftPalm"),a=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((a.position[2]||0)+(n.position[2]||0))/2;let r=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");r.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function Khe(e,t,a){if(!(Ua!=null&&Ua.executor))return null;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=Ua==null?void 0:Ua.execute(e,jhe.landmarks);let r=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(f=>J(n[f]));let o=[],l=5;for(let f=0;f<s.length/l;f++){let m=b9(s[l*f+3]),g=b9(s[l*f+4]),y=Math.trunc(100*m*g*r)/100,x=[s[l*f+0]/ig,s[l*f+1]/ig,s[l*f+2]+0],A=[Math.trunc(a[0]*x[0]),Math.trunc(a[1]*x[1]),x[2]],b=[i[l*f+0],i[l*f+1],i[l*f+2]+0];o.push({part:ng[f],positionRaw:x,position:A,distance:b,score:y})}if(r<(t.body.minConfidence||0))return null;Xhe(o);let u=qhe(o,a),p=u.map(f=>f.position),c=hs(p,[a[0],a[1]]),d={};for(let[f,m]of Object.entries(rg)){let g=[];for(let y=0;y<m.length-1;y++){let x=u.find(b=>b.part===m[y]),A=u.find(b=>b.part===m[y+1]);x&&A&&g.push([x.position,A.position])}d[f]=g}return{id:0,score:Math.trunc(100*r)/100,box:c.box,boxRaw:c.boxRaw,keypoints:u,annotations:d}}async function og(e,t){var s,i,o;let a=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>ae()-A9,r=sg<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&s0!==null)sg++;else{let l=[];if((i=(s=t.body)==null?void 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t={};t.boxStarts=Fe(e,[0,1],[-1,2]),t.centers=be(t.boxStarts,c0),t.boxSizes=Fe(e,[0,3],[-1,2]),t.boxSizesNormalized=xe(t.boxSizes,wu),t.centersNormalized=xe(t.centers,wu),t.halfBoxSize=xe(t.boxSizesNormalized,Le.tf2),t.starts=me(t.centersNormalized,t.halfBoxSize),t.ends=be(t.centersNormalized,t.halfBoxSize),t.startNormalized=te(t.starts,wu),t.endNormalized=te(t.ends,wu);let a=ru([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>J(t[n])),a}async function U9(e,t){var o,l,u,p,c,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let a={};a.resized=ce.resizeBilinear(e,[ms,ms]),a.div=xe(a.resized,Le.tf127),a.normalized=me(a.div,Le.tf05);let n=Ln==null?void 0:Ln.execute(a.normalized);if(Array.isArray(n)&&n.length>2){let h=n.sort((f,m)=>f.size-m.size);a.concat384=st([h[0],h[2]],2),a.concat512=st([h[1],h[3]],2),a.concat=st([a.concat512,a.concat384],1),a.batch=De(a.concat,[0])}else Array.isArray(n)?a.batch=De(n[0]):a.batch=De(n);J(n),a.boxes=s0e(a.batch),a.logits=Fe(a.batch,[0,0],[-1,1]),a.sigmoid=Ba(a.logits),a.scores=De(a.sigmoid),a.nms=await ce.nonMaxSuppressionAsync(a.boxes,a.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let r=await a.nms.array(),s=[],i=await a.scores.data();for(let h=0;h<r.length;h++){let f=i[r[h]];if(f>(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let m={};m.bbox=Fe(a.boxes,[r[h],0],[1,-1]),m.slice=Fe(a.batch,[r[h],B9-1],[1,-1]),m.squeeze=De(m.slice),m.landmarks=Q(m.squeeze,[B9,-1]);let g=await m.bbox.data(),y={startPoint:[g[0],g[1]],endPoint:[g[2],g[3]],landmarks:await m.landmarks.array(),confidence:f};m.anchor=Fe(c0,[r[h],0],[1,2]);let x=await m.anchor.data(),A=_9(y,[(e.shape[2]||0)/ms,(e.shape[1]||0)/ms],x),b=d0(A,t.face.scale||r0e),k=p0(b);k.size[0]>(((c=t.face.detector)==null?void 0:c.minSize)||0)&&k.size[1]>(((d=t.face.detector)==null?void 0:d.minSize)||0)&&s.push(k),Object.keys(m).forEach(S=>J(m[S]))}}return Object.keys(a).forEach(h=>J(a[h])),s}var an,gs=0,i0e=2.3,xg=Mn.leftEyeLower0,Ag=Mn.rightEyeLower0,Iu={leftBounds:[xg[0],xg[xg.length-1]],rightBounds:[Ag[0],Ag[Ag.length-1]]},Su={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function X9(e){var t,a;return ne.initial&&(an=null),an?e.debug&&K("cached model:",an.modelUrl):an=await Me((t=e.face.iris)==null?void 0:t.modelPath),gs=an!=null&&an.executor&&((a=an.inputs)!=null&&a[0].shape)?an.inputs[0].shape[2]:0,gs===-1&&(gs=64),an}function h0(e,t,a,n){for(let r=0;r<Q3.length;r++){let{key:s,indices:i}=Q3[r],o=Mn[`${a}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var o0e=e=>{let t=e[Iu.leftBounds[0]][2],a=e[Iu.rightBounds[0]][2];return t-a},H9=(e,t,a,n,r,s=!1)=>{let i=p0(d0(P9([e[a],e[n]]),i0e)),o=ku(i),l=ce.cropAndResize(t,[[i.startPoint[1]/r,i.startPoint[0]/r,i.endPoint[1]/r,i.endPoint[0]/r]],[0],[gs,gs]);if(s&&ne.kernels.includes("flipleftright")){let u=ce.flipLeftRight(l);J(l),l=u}return{box:i,boxSize:o,crop:l}},j9=(e,t,a,n=!1)=>{let r=[];for(let s=0;s<Su.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];r.push([(n?1-i/gs:i/gs)*a[0]+t.startPoint[0],o/gs*a[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(Su.index)}},q9=(e,t,a)=>{let n=e[Mn[`${a}EyeUpper0`][Su.upperCenter]][2],r=e[Mn[`${a}EyeLower0`][Su.lowerCenter]][2],s=(n+r)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=r),[i[0],i[1],l]})};async function K9(e,t,a){if(!(an!=null&&an.executor))return e;let{box:n,boxSize:r,crop:s}=H9(e,t,Iu.leftBounds[0],Iu.leftBounds[1],a,!0),{box:i,boxSize:o,crop:l}=H9(e,t,Iu.rightBounds[0],Iu.rightBounds[1],a,!0),u=st([s,l]);J(s),J(l);let p=an.execute(u);J(u);let c=await p.data();J(p);let d=c.slice(0,Su.numCoordinates*3),{rawCoords:h,iris:f}=j9(d,n,r,!0),m=c.slice(Su.numCoordinates*3),{rawCoords:g,iris:y}=j9(m,i,o,!1),x=o0e(e);Math.abs(x)<30?(h0(e,h,"left",null),h0(e,g,"right",null)):x<1?h0(e,h,"left",["EyeUpper0","EyeLower0"]):h0(e,g,"right",["EyeUpper0","EyeLower0"]);let A=q9(e,f,"left"),b=q9(e,y,"right");return e.concat(A).concat(b)}async function Y9(e,t){var s,i,o,l,u,p,c,d,h,f;let a={lips:await((i=(s=t.filter(m=>m.size===160))==null?void 0:s[0])==null?void 0:i.data()),irisL:await((l=(o=t.filter(m=>m.size===10))==null?void 0:o[0])==null?void 0:l.data()),eyeL:await((p=(u=t.filter(m=>m.size===142))==null?void 0:u[0])==null?void 0:p.data()),irisR:await((d=(c=t.filter(m=>m.size===10))==null?void 0:c[1])==null?void 0:d.data()),eyeR:await((f=(h=t.filter(m=>m.size===142))==null?void 0:h[1])==null?void 0:f.data())};for(let m of Object.values(a))if(!m)return e;let n=Ro.reduce((m,g)=>m+=e[g][2],0)/Ro.length;for(let m=0;m<a.irisL.length/2;m++)e.push([a.irisL[2*m+0],a.irisL[2*m+1],n]);let r=Mo.reduce((m,g)=>m+=e[g][2],0)/Mo.length;for(let m=0;m<a.irisR.length/2;m++)e.push([a.irisR[2*m+0],a.irisR[2*m+1],r]);for(let m=0;m<a.eyeL.length/2;m++)e[Ro[m]]=[a.eyeL[2*m+0],a.eyeL[2*m+1],e[Ro[m]][2]];for(let m=0;m<a.eyeR.length/2;m++)e[Mo[m]]=[a.eyeR[2*m+0],a.eyeR[2*m+1],e[Mo[m]][2]];for(let m=0;m<a.lips.length/2;m++)e[Sp[m]]=[a.lips[2*m+0],a.lips[2*m+1],e[Sp[m]][2]];return e}var or={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},wt=null,Tp=0;async function J9(e,t){var l,u,p,c,d,h,f,m,g,y;let a=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>ae()-or.timestamp,n=or.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!a||!n||or.boxes.length===0?(or.boxes=await U9(e,t),or.timestamp=ae(),or.skipped=0):or.skipped++;let r=[],s=[],i=0,o=Tp;for(let x=0;x<or.boxes.length;x++){let A=or.boxes[x],b=0,k,S={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,size:[0,0],annotations:{}};if([b,k,S.tensor]=z9((p=t.face.detector)==null?void 0:p.rotation,A,e,(c=t.face.mesh)!=null&&c.enabled?Tp:W9()),t.filter.equalization){let C=S.tensor?await Vh(S.tensor):void 0;J(S.tensor),C&&(S.tensor=C)}if(S.boxScore=Math.round(100*A.confidence)/100,!((d=t.face.mesh)!=null&&d.enabled)||!(wt!=null&&wt.executor)){S.box=l0(A,e),S.boxRaw=u0(A,e),S.score=S.boxScore,S.size=A.size,S.mesh=A.landmarks,S.meshRaw=S.mesh.map(C=>[C[0]/(e.shape[2]||0),C[1]/(e.shape[1]||0),(C[2]||0)/o]);for(let C of Object.keys(No))S.annotations[C]=[S.mesh[No[C]]]}else if(!wt)t.debug&&K("face mesh detection requested, but model is not loaded");else{if((h=t.face.attention)!=null&&h.enabled&&!ne.kernels.includes("atan2"))return t.face.attention.enabled=!1,J(S.tensor),r;let C=wt.execute(S.tensor),$=await C.find(M=>M.shape[M.shape.length-1]===1).data();if(S.faceScore=Math.round(100*$[0])/100,S.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(A.confidence=S.faceScore,t.face.mesh.keepInvalid){S.box=l0(A,e),S.boxRaw=u0(A,e),S.score=S.boxScore,S.mesh=A.landmarks,S.meshRaw=S.mesh.map(M=>[M[0]/(e.shape[2]||1),M[1]/(e.shape[1]||1),(M[2]||0)/o]);for(let M of Object.keys(No))S.annotations[M]=[S.mesh[No[M]]]}}else{let M=C.find(D=>D.shape[D.shape.length-1]===1404),R=Q(M,[-1,3]),I=await R.array();J(R),(m=t.face.attention)!=null&&m.enabled?I=await Y9(I,C):(g=t.face.iris)!=null&&g.enabled&&(I=await K9(I,S.tensor,Tp)),S.mesh=D9(I,A,b,k,Tp),S.meshRaw=S.mesh.map(D=>[D[0]/(e.shape[2]||0),D[1]/(e.shape[1]||0),(D[2]||0)/o]);for(let D of Object.keys(Mn))S.annotations[D]=Mn[D].map(W=>S.mesh[W]);S.score=S.faceScore;let _={...L9(S.mesh,A),confidence:A.confidence,landmarks:A.landmarks,size:A.size};S.box=l0(_,e),S.boxRaw=u0(_,e),s.push(_)}J(C)}S.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?r.push(S):J(S.tensor)}return or.boxes=s,r}async function Q9(e){var t,a,n,r,s,i;return ne.initial&&(wt=null),(t=e.face.attention)!=null&&t.enabled&&(wt!=null&&wt.signature)&&Object.keys(((a=wt==null?void 0:wt.signature)==null?void 0:a.outputs)||{}).length<6&&(wt=null),wt?e.debug&&K("cached model:",wt.modelUrl):(n=e.face.attention)!=null&&n.enabled?wt=await Me(e.face.attention.modelPath):wt=await Me((r=e.face.mesh)==null?void 0:r.modelPath),Tp=wt.executor&&((s=wt==null?void 0:wt.inputs)!=null&&s[0].shape)?(i=wt==null?void 0:wt.inputs)==null?void 0:i[0].shape[2]:256,wt}var ek=Eo,tk=Ip;var kg=[],ta,f0=[],ak=0,nk=0,vg=Number.MAX_SAFE_INTEGER,wg=!1;async function rk(e){var t,a,n;return ne.initial&&(ta=null),ta?e.debug&&K("cached model:",ta.modelUrl):(ta=await Me((t=e.face.emotion)==null?void 0:t.modelPath),wg=((n=(a=ta==null?void 0:ta.inputs)==null?void 0:a[0].shape)==null?void 0:n[3])===3,wg?kg=["angry","disgust","fear","happy","neutral","sad","surprise"]:kg=["angry","disgust","fear","happy","sad","surprise","neutral"]),ta}async function Ig(e,t,a,n){var i,o;if(!ta)return[];let r=vg<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>ae()-nk;return t.skipAllowed&&s&&r&&ak===n&&f0[a]&&f0[a].length>0?(vg++,f0[a]):(vg=0,new Promise(async l=>{var p,c,d;let u=[];if((p=t.face.emotion)!=null&&p.enabled){let h={},f=ta!=null&&ta.inputs[0].shape?ta.inputs[0].shape[2]:0;if(((c=t.face.emotion)==null?void 0:c.crop)>0){let g=(d=t.face.emotion)==null?void 0:d.crop,y=[[g,g,1-g,1-g]];h.resize=ce.cropAndResize(e,y,[0],[f,f])}else h.resize=ce.resizeBilinear(e,[f,f],!1);wg?(h.mul=te(h.resize,255),h.normalize=me(h.mul,[103.939,116.779,123.68]),h.emotion=ta==null?void 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n=ce.resizeBilinear(a,[aa.inputs[0].shape[2],aa.inputs[0].shape[1]],!1);let r=te(n,Le.tf255);return J(n),r}async function Tg(e,t,a,n){var o,l,u,p;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(aa!=null&&aa.executor))return r;let s=Sg<(((o=t.face.description)==null?void 0:o.skipFrames)||0),i=(((l=t.face.description)==null?void 0:l.skipTime)||0)>ae()-ik;return t.skipAllowed&&s&&i&&ok===n&&((u=ys==null?void 0:ys[a])==null?void 0:u.age)>0&&((p=ys==null?void 0:ys[a])==null?void 0:p.genderScore)>0?(Sg++,ys[a]):(Sg=0,new Promise(async c=>{var d;if((d=t.face.description)!=null&&d.enabled){let h=u0e(e,t),f=aa==null?void 0:aa.execute(h);ik=ae(),J(h);let g=await f.find(N=>N.shape[1]===1).data(),y=Math.trunc(200*Math.abs(g[0]-.5))/100;y>(t.face.description.minConfidence||0)&&(r.gender=g[0]<=.5?"female":"male",r.genderScore=Math.min(.99,y));let x=ar(f.find(N=>N.shape[1]===100),1),A=(await x.data())[0];J(x);let k=await 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n.toTensor()}var na,m0=[],Ng=Number.MAX_SAFE_INTEGER,pk=0,ck=0;async function hk(e){var t;return ne.initial&&(na=null),na?e.debug&&K("cached model:",na.modelUrl):na=await Me((t=e.face.antispoof)==null?void 0:t.modelPath),na}async function Eg(e,t,a,n){var i,o;if(!(na!=null&&na.executor))return 0;let r=(((i=t.face.antispoof)==null?void 0:i.skipTime)||0)>ae()-ck,s=Ng<(((o=t.face.antispoof)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&pk===n&&m0[a]?(Ng++,m0[a]):(Ng=0,new Promise(async l=>{let u=ce.resizeBilinear(e,[na!=null&&na.inputs[0].shape?na.inputs[0].shape[2]:0,na!=null&&na.inputs[0].shape?na.inputs[0].shape[1]:0],!1),p=na==null?void 0:na.execute(u),c=(await p.data())[0];m0[a]=Math.round(100*c)/100,pk=n,ck=ae(),J([u,p]),l(m0[a])}))}var ra,g0=[],Rg=Number.MAX_SAFE_INTEGER,mk=0,gk=0;async function yk(e){var t;return ne.initial&&(ra=null),ra?e.debug&&K("cached model:",ra.modelUrl):ra=await Me((t=e.face.liveness)==null?void 0:t.modelPath),ra}async function Mg(e,t,a,n){var i,o;if(!(ra!=null&&ra.executor))return 0;let r=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>ae()-gk,s=Rg<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&mk===n&&g0[a]?(Rg++,g0[a]):(Rg=0,new Promise(async l=>{let u=ce.resizeBilinear(e,[ra!=null&&ra.inputs[0].shape?ra.inputs[0].shape[2]:0,ra!=null&&ra.inputs[0].shape?ra.inputs[0].shape[1]:0],!1),p=ra==null?void 0:ra.execute(u),c=(await p.data())[0];g0[a]=Math.round(100*c)/100,mk=n,gk=ae(),J([u,p]),l(g0[a])}))}var $n,$g=[],c0e=["white","black","asian","indian","other"],h0e=[15,23,28,35.5,45.5,55.5,65],Ak=0,bk=0,_g=Number.MAX_SAFE_INTEGER;async function vk(e){var t;return ne.initial&&($n=null),$n?e.debug&&K("cached model:",$n.modelUrl):$n=await Me((t=e.face.gear)==null?void 0:t.modelPath),$n}async function Pg(e,t,a,n){var i,o;if(!$n)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=_g<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>ae()-bk;return t.skipAllowed&&s&&r&&Ak===n&&$g[a]?(_g++,$g[a]):(_g=0,new Promise(async l=>{var y,x,A,b;if(!($n!=null&&$n.inputs[0].shape))return;let u={},p=[[0,.1,.9,.9]];if(((y=t.face.gear)==null?void 0:y.crop)>0){let k=(x=t.face.gear)==null?void 0:x.crop;p=[[k,k,1-k,1-k]]}u.resize=ce.cropAndResize(e,p,[0],[$n.inputs[0].shape[2],$n.inputs[0].shape[1]]);let c={age:0,gender:"unknown",genderScore:0,race:[]};(A=t.face.gear)!=null&&A.enabled&&([u.age,u.gender,u.race]=$n.execute(u.resize,["age_output","gender_output","race_output"]));let d=await u.gender.data();c.gender=d[0]>d[1]?"male":"female",c.genderScore=Math.round(100*(d[0]>d[1]?d[0]:d[1]))/100;let h=await u.race.data();for(let k=0;k<h.length;k++)h[k]>(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&c.race.push({score:Math.round(100*h[k])/100,race:c0e[k]});c.race.sort((k,S)=>S.score-k.score);let m=Array.from(await u.age.data()).map((k,S)=>[h0e[S],k]).sort((k,S)=>S[1]-k[1]),g=m[0][0];for(let k=1;k<m.length;k++)g+=m[k][1]*(m[k][0]-g);c.age=Math.round(10*g)/10,Object.keys(u).forEach(k=>J(u[k])),$g[a]=c,Ak=n,bk=ae(),l(c)}))}var _a,y0=[],wk=0,Ik=0,Fg=Number.MAX_SAFE_INTEGER;async function Sk(e){return ne.initial&&(_a=null),_a?e.debug&&K("cached model:",_a.modelUrl):_a=await Me(e.face.ssrnet.modelPathAge),_a}async function Og(e,t,a,n){var i,o,l,u;if(!_a)return{age:0};let r=Fg<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-Ik;return t.skipAllowed&&r&&s&&wk===n&&((l=y0[a])!=null&&l.age)&&((u=y0[a])==null?void 0:u.age)>0?(Fg++,y0[a]):(Fg=0,new Promise(async p=>{var h,f,m;if(!(_a!=null&&_a.inputs)||!_a.inputs[0]||!_a.inputs[0].shape)return;let c={};if(((h=t.face.ssrnet)==null?void 0:h.crop)>0){let g=(f=t.face.ssrnet)==null?void 0:f.crop,y=[[g,g,1-g,1-g]];c.resize=ce.cropAndResize(e,y,[0],[_a.inputs[0].shape[2],_a.inputs[0].shape[1]])}else c.resize=ce.resizeBilinear(e,[_a.inputs[0].shape[2],_a.inputs[0].shape[1]],!1);c.enhance=te(c.resize,Le.tf255);let d={age:0};if((m=t.face.ssrnet)!=null&&m.enabled&&(c.age=_a.execute(c.enhance)),c.age){let g=await c.age.data();d.age=Math.trunc(10*g[0])/10}Object.keys(c).forEach(g=>J(c[g])),y0[a]=d,wk=n,Ik=ae(),p(d)}))}var ya,x0=[],Ck=0,Nk=0,Dg=Number.MAX_SAFE_INTEGER,zg=[.2989,.587,.114];async function Ek(e){var t;return ne.initial&&(ya=null),ya?e.debug&&K("cached model:",ya.modelUrl):ya=await Me((t=e.face.ssrnet)==null?void 0:t.modelPathGender),ya}async function Lg(e,t,a,n){var i,o,l,u;if(!ya)return{gender:"unknown",genderScore:0};let r=Dg<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-Nk;return t.skipAllowed&&r&&s&&Ck===n&&((l=x0[a])!=null&&l.gender)&&((u=x0[a])==null?void 0:u.genderScore)>0?(Dg++,x0[a]):(Dg=0,new Promise(async p=>{var f,m,g;if(!(ya!=null&&ya.inputs[0].shape))return;let c={};if(((f=t.face.ssrnet)==null?void 0:f.crop)>0){let y=(m=t.face.ssrnet)==null?void 0:m.crop,x=[[y,y,1-y,1-y]];c.resize=ce.cropAndResize(e,x,[0],[ya.inputs[0].shape[2],ya.inputs[0].shape[1]])}else c.resize=ce.resizeBilinear(e,[ya.inputs[0].shape[2],ya.inputs[0].shape[1]],!1);c.enhance=Oe(()=>{var x,A;let y;if(((A=(x=ya==null?void 0:ya.inputs)==null?void 0:x[0].shape)==null?void 0:A[3])===1){let[b,k,S]=Sa(c.resize,3,3),C=te(b,zg[0]),N=te(k,zg[1]),$=te(S,zg[2]),M=ph([C,N,$]);y=te(me(M,Le.tf05),2)}else y=te(me(c.resize,Le.tf05),2);return y});let d={gender:"unknown",genderScore:0};(g=t.face.ssrnet)!=null&&g.enabled&&(c.gender=ya.execute(c.enhance));let h=await c.gender.data();d.gender=h[0]>h[1]?"female":"male",d.genderScore=h[0]>h[1]?Math.trunc(100*h[0])/100:Math.trunc(100*h[1])/100,Object.keys(c).forEach(y=>J(c[y])),x0[a]=d,Ck=n,Nk=ae(),p(d)}))}var nn,Bg=[],Mk=0,$k=0,_k=Number.MAX_SAFE_INTEGER;async function Pk(e){var t;return ne.initial&&(nn=null),nn?e.debug&&K("cached model:",nn.modelUrl):nn=await 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a=[0,-.1],n=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],i=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-a[0],n*(s[1]-i[1])/o[1]-a[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},Wk=(e,t)=>{let a=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},n=(m,g)=>{let y=m[0]-g[0],x=m[1]-g[1],A=m[2]-g[2];return[y,x,A]},r=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],x=m[2]*g[0]-m[0]*g[2],A=m[0]*g[1]-m[1]*g[0];return[y,x,A]},s=m=>{let[g,y,x,A,b,k,S,C,N]=m,$,M,R;return 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f,m,g,y,x,A,b,k,S,C,N,$,M,R,I,_,D,W,P,U,G,q,H;let a=ae(),n,r,s,i,o,l,u,p,c,d=[];e.state="run:face";let h=await J9(t,e.config);if(e.performance.face=ne.perfadd?(e.performance.face||0)+Math.trunc(ae()-a):Math.trunc(ae()-a),!t.shape||t.shape.length!==4)return[];if(!h)return[];for(let B=0;B<h.length;B++){if(e.analyze("Get Face"),!h[B].tensor||h[B].tensor.isDisposedInternal){K("Face object is disposed:",h[B].tensor);continue}if((f=e.config.face.detector)!=null&&f.mask){let he=await dk(h[B]);J(h[B].tensor),he&&(h[B].tensor=he)}let Z=h[B].mesh&&h[B].mesh.length>200?Wk(h[B],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=(m=e.config.face.emotion)!=null&&m.enabled?Ig(h[B].tensor||Ue([]),e.config,B,h.length):[]:(e.state="run:emotion",a=ae(),i=(g=e.config.face.emotion)!=null&&g.enabled?await Ig(h[B].tensor||Ue([]),e.config,B,h.length):[],e.performance.emotion=ne.perfadd?(e.performance.emotion||0)+Math.trunc(ae()-a):Math.trunc(ae()-a)),e.analyze("End 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b0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Cp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function nw(e,t,a){let n=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/r,e.endPoint[1]/n,e.endPoint[0]/r]];return ce.cropAndResize(t,s,[0],a)}function rw(e,t){let a=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:a,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function v0(e,t=1.5){let a=Cp(e),n=b0(e),r=[t*n[0]/2,t*n[1]/2],s=[a[0]-r[0],a[1]-r[1]],i=[a[0]+r[0],a[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function k0(e){let t=Cp(e),a=b0(e),r=Math.max(...a)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function I0e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function sw(e,t){let a=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return I0e(a)}var tw=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ws(e,t){let a=0;for(let n=0;n<e.length;n++)a+=e[n]*t[n];return a}function S0e(e,t){let a=[];for(let n=0;n<e.length;n++)a.push(e[n][t]);return a}function aw(e,t){let a=[],n=e.length;for(let r=0;r<n;r++){a.push([]);for(let s=0;s<n;s++)a[r].push(ws(e[r],S0e(t,s)))}return a}function qg(e,t){let a=Math.cos(e),n=Math.sin(e),r=[[a,-n,0],[n,a,0],[0,0,1]],s=tw(t[0],t[1]),i=aw(s,r),o=tw(-t[0],-t[1]);return aw(i,o)}function iw(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],a=[e[0][2],e[1][2]],n=[-ws(t[0],a),-ws(t[1],a)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]}function Xg(e,t){return[ws(e,t[0]),ws(e,t[1])]}var 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Object.keys(a).forEach(r=>J(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=Q(t,[-1,7,2]),n.div=xe(n.reshape,this.inputSizeTensor),n.landmarks=be(n.div,this.anchors[a]?this.anchors[a]:0);let r=te(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>J(n[s])),r}async predict(t,a){var o;let n={};n.resize=ce.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=xe(n.resize,Le.tf127),n.image=me(n.div,Le.tf1),n.batched=this.model.execute(n.image),n.predictions=De(n.batched),n.slice=Fe(n.predictions,[0,0],[-1,1]),n.sigmoid=Ba(n.slice),n.scores=De(n.sigmoid);let r=await n.scores.data();n.boxes=Fe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await ce.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=Fe(n.norm,[l,0],[1,-1]),u.slice=Fe(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=Q(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),f={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},m=rw(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(m),Object.keys(u).forEach(g=>J(u[g]))}return Object.keys(n).forEach(l=>J(n[l])),i}};var N0e=5,uw=1.65,dw=[0,5,9,13,17,1,2],E0e=0,R0e=2,pw=0,I0=class{constructor(t,a){de(this,"handDetector");de(this,"handPoseModel");de(this,"inputSize");de(this,"storedBoxes");de(this,"skipped");de(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>Xg([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return v0(k0(r),N0e)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=v0(k0(a),uw);n.palmLandmarks=[];for(let r=0;r<dw.length;r++)n.palmLandmarks.push(t[dw[r]].slice(0,2));return n}transformRawCoords(t,a,n,r){let s=b0(a),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(h=>[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=qg(n,[0,0]),u=o.map(h=>[...Xg(h,l),h[2]]),p=iw(r),c=[...Cp(a),1],d=[ws(c,p[0]),ws(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>ae()-pw,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i&&(r=await this.handDetector.predict(t,a),this.skipped=0),a.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(u)if(a.hand.landmarks){let p=a.hand.rotation?sw(u.palmLandmarks[E0e],u.palmLandmarks[R0e]):0,c=Cp(u),d=[c[0]/t.shape[2],c[1]/t.shape[1]],h=a.hand.rotation&&ne.kernels.includes("rotatewithoffset")?ce.rotateWithOffset(t,p,0,d):t.clone(),f=qg(-p,c),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=nw(m,h,[this.inputSize,this.inputSize]),y=xe(g,Le.tf255);J(g),J(h);let[x,A]=this.handPoseModel.execute(y);pw=ae(),J(y);let b=(await x.data())[0];if(J(x),b>=a.hand.minConfidence/4){let k=Q(A,[-1,3]),S=await k.array();J(A),J(k);let C=this.transformRawCoords(S,m,p,f),N=this.getBoxForHandLandmarks(C);this.storedBoxes[l]={...N,confidence:b};let $={landmarks:C,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};o.push($)}else this.storedBoxes[l]=null;J(A)}else{let p=v0(k0(u),uw),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var cw={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},zo,Lo,hw;async function Kg(e,t){let a=await hw.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;r<a.length;r++){let s={};if(a[r].landmarks)for(let p of Object.keys(cw))s[p]=cw[p].map(c=>a[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]<o[0]&&(o[0]=p[0]),p[1]<o[1]&&(o[1]=p[1]),p[0]>o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=A0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function fw(e){var a,n;ne.initial&&(zo=null,Lo=null),!zo||!Lo?[zo,Lo]=await Promise.all([e.hand.enabled?Me((a=e.hand.detector)==null?void 0:a.modelPath):null,e.hand.landmarks?Me((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&K("cached model:",zo.modelUrl),e.debug&&K("cached model:",Lo.modelUrl));let t=zo?new w0(zo):void 0;return t&&Lo&&(hw=new I0(t,Lo)),[zo,Lo]}var Ft=[null,null],$0e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Is=[[0,0],[0,0]],_0e=["hand","fist","pinch","point","face","tip","pinchtip"],gw=4,yw=1.6,P0e=512,F0e=1.4,S0=Number.MAX_SAFE_INTEGER,Zg=0,Mr=[0,0],Pt={boxes:[],hands:[]},xw={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function Aw(e){var t;if(ne.initial&&(Ft[0]=null),Ft[0])e.debug&&K("cached model:",Ft[0].modelUrl);else{Xh(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Ft[0]=await Me((t=e.hand.detector)==null?void 0:t.modelPath);let a=Ft[0].executor?Object.values(Ft[0].modelSignature.inputs):void 0;Is[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Is[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ft[0]}async function bw(e){var t;if(ne.initial&&(Ft[1]=null),Ft[1])e.debug&&K("cached model:",Ft[1].modelUrl);else{Ft[1]=await Me((t=e.hand.skeleton)==null?void 0:t.modelPath);let a=Ft[1].executor?Object.values(Ft[1].modelSignature.inputs):void 0;Is[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Is[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ft[1]}async function O0e(e,t){let a=[];if(!e||!Ft[0])return a;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,P0e),i=Math.round(s*r/8)*8;n.resize=ce.resizeBilinear(e,[s,i]),n.cast=Xe(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await Ft[0].executeAsync(n.cast,$0e),n.boxes=De(n.rawBoxes,[0,2]),n.scores=De(n.rawScores,[0]);let o=Na(n.scores,1);J(o[gw]),o.splice(gw,1),n.filtered=ua(o,1),J(o),n.max=fa(n.filtered,1),n.argmax=ar(n.filtered,1);let l=0;n.nms=await ce.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=Fe(n.boxes,d,1),f=await h.data();J(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=n0(m,F0e),y=[Math.trunc(m[0]*Mr[0]),Math.trunc(m[1]*Mr[1]),Math.trunc(m[2]*Mr[0]),Math.trunc(m[3]*Mr[1])],x=p[d],A=_0e[c[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};a.push(b)}return Object.keys(n).forEach(d=>J(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function Yg(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Ft[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=ce.cropAndResize(e,[s],[0],[Is[1][0],Is[1][1]],"bilinear"),r.div=xe(r.crop,Le.tf255),[r.score,r.keypoints]=Ft[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=Q(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/Is[1][1],c[1]/Is[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[Mr[0]*(c[0]+t.boxRaw[0]),Mr[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=A0(n.keypoints);for(let c of Object.keys(xw))n.annotations[c]=xw[c].map(d=>n.landmarks&&n.keypoints[d]?n.keypoints[d]:null)}Object.keys(r).forEach(l=>J(r[l]))}return n}async function Jg(e,t){var r,s;if(!((r=Ft[0])!=null&&r.executor)||!((s=Ft[1])!=null&&s.executor)||!Ft[0].inputs[0].shape||!Ft[1].inputs[0].shape)return[];Mr=[e.shape[2]||0,e.shape[1]||0],S0++;let a=(t.hand.skipTime||0)>ae()-Zg,n=S0<(t.hand.skipFrames||0);return t.skipAllowed&&a&&n?Pt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>ae()-Zg,l=S0<3*(t.hand.skipFrames||0);t.skipAllowed&&Pt.hands.length===t.hand.maxDetected?Pt.hands=await Promise.all(Pt.boxes.map(p=>Yg(e,p,t))):t.skipAllowed&&o&&l&&Pt.hands.length>0?Pt.hands=await Promise.all(Pt.boxes.map(p=>Yg(e,p,t))):(Pt.boxes=await O0e(e,t),Zg=ae(),Pt.hands=await Promise.all(Pt.boxes.map(p=>Yg(e,p,t))),S0=0);let u=[...Pt.boxes];if(Pt.boxes.length=0,t.cacheSensitivity>0)for(let p=0;p<Pt.hands.length;p++){let c=x9(Pt.hands[p].keypoints,Mr);if(c.box[2]/(e.shape[2]||1)>.05&&c.box[3]/(e.shape[1]||1)>.05&&Pt.hands[p].fingerScore&&Pt.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=n0(c.box,yw),h=n0(c.boxRaw,yw);Pt.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p<Pt.hands.length;p++){let c=hs(Pt.hands[p].keypoints,Mr);Pt.hands[p].box=c.box,Pt.hands[p].boxRaw=c.boxRaw}i(Pt.hands)})}var lr=(e=null)=>({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var Np={};fr(Np,{connected:()=>C0,horizontal:()=>Qg,kpt:()=>T0,relative:()=>t5,vertical:()=>e5});var T0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Qg=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],e5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],t5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],C0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var ge=lr(),a5=0;function kw(e,t){var i,o,l,u,p,c,d,h,f,m,g,y,x,A,b,k,S,C,N,$,M,R,I,_,D,W;let a=ae();if(!e)return lr();let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(ge.canvas=e.canvas),e.error&&(ge.error=e.error),!ge.body||e.body.length!==ge.body.length)ge.body=JSON.parse(JSON.stringify(e.body));else for(let P=0;P<e.body.length;P++){let U=e.body[P].box.map((Z,X)=>((r-1)*ge.body[P].box[X]+Z)/r),G=e.body[P].boxRaw.map((Z,X)=>((r-1)*ge.body[P].boxRaw[X]+Z)/r),q=e.body[P].keypoints.map((Z,X)=>{var re,ee,he,ie,ye,Se,Ne,Be,qe;return{score:Z.score,part:Z.part,position:[ge.body[P].keypoints[X]?((r-1)*(ge.body[P].keypoints[X].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],ge.body[P].keypoints[X]?((r-1)*(ge.body[P].keypoints[X].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],ge.body[P].keypoints[X]?((r-1)*(ge.body[P].keypoints[X].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[ge.body[P].keypoints[X]?((r-1)*(ge.body[P].keypoints[X].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.positionRaw[0],ge.body[P].keypoints[X]?((r-1)*(ge.body[P].keypoints[X].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.positionRaw[1],ge.body[P].keypoints[X]?((r-1)*(ge.body[P].keypoints[X].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.positionRaw[2]],distance:[ge.body[P].keypoints[X]?((r-1)*(((re=ge.body[P].keypoints[X].distance)==null?void 0:re[0])||0)+(((ee=Z.distance)==null?void 0:ee[0])||0))/r:(he=Z.distance)==null?void 0:he[0],ge.body[P].keypoints[X]?((r-1)*(((ie=ge.body[P].keypoints[X].distance)==null?void 0:ie[1])||0)+(((ye=Z.distance)==null?void 0:ye[1])||0))/r:(Se=Z.distance)==null?void 0:Se[1],ge.body[P].keypoints[X]?((r-1)*(((Ne=ge.body[P].keypoints[X].distance)==null?void 0:Ne[2])||0)+(((Be=Z.distance)==null?void 0:Be[2])||0))/r:(qe=Z.distance)==null?void 0:qe[2]]}}),H={},B={connected:{}};(i=t.body.modelPath)!=null&&i.includes("efficientpose")?B=i0:(o=t.body.modelPath)!=null&&o.includes("blazepose")?B=t0:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(B=Np);for(let[Z,X]of Object.entries(B.connected)){let re=[];for(let ee=0;ee<X.length-1;ee++){let he=q.find(ye=>ye.part===X[ee]),ie=q.find(ye=>ye.part===X[ee+1]);he&&ie&&re.push([he.position,ie.position])}H[Z]=re}ge.body[P]={...e.body[P],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!ge.hand||e.hand.length!==ge.hand.length)ge.hand=JSON.parse(JSON.stringify(e.hand));else for(let P=0;P<e.hand.length;P++){let U=e.hand[P].box.map((B,Z)=>((r-1)*ge.hand[P].box[Z]+B)/r),G=e.hand[P].boxRaw.map((B,Z)=>((r-1)*ge.hand[P].boxRaw[Z]+B)/r);ge.hand[P].keypoints.length!==e.hand[P].keypoints.length&&(ge.hand[P].keypoints=e.hand[P].keypoints);let q=e.hand[P].keypoints&&e.hand[P].keypoints.length>0?e.hand[P].keypoints.map((B,Z)=>B.map((X,re)=>((r-1)*(ge.hand[P].keypoints[Z][re]||1)+(X||0))/r)):[],H={};if(Object.keys(ge.hand[P].annotations).length!==Object.keys(e.hand[P].annotations).length)ge.hand[P].annotations=e.hand[P].annotations,H=ge.hand[P].annotations;else if(e.hand[P].annotations)for(let B of Object.keys(e.hand[P].annotations))H[B]=(c=(p=(u=e.hand[P])==null?void 0:u.annotations)==null?void 0:p[B])!=null&&c[0]?e.hand[P].annotations[B].map((Z,X)=>Z.map((re,ee)=>((r-1)*ge.hand[P].annotations[B][X][ee]+re)/r)):null;ge.hand[P]={...e.hand[P],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!ge.face||e.face.length!==ge.face.length)ge.face=JSON.parse(JSON.stringify(e.face));else for(let P=0;P<e.face.length;P++){let U=e.face[P].box.map((H,B)=>((r-1)*ge.face[P].box[B]+H)/r),G=e.face[P].boxRaw.map((H,B)=>((r-1)*ge.face[P].boxRaw[B]+H)/r),q=e.face[P].annotations;if(Object.keys(ge.face[P].annotations).length!==Object.keys(e.face[P].annotations).length)ge.face[P].annotations=e.face[P].annotations,q=ge.face[P].annotations;else if(e.face[P].annotations)for(let H of Object.keys(e.face[P].annotations))q[H]=(f=(h=(d=e.face[P])==null?void 0:d.annotations)==null?void 0:h[H])!=null&&f[0]?e.face[P].annotations[H].map((B,Z)=>B.map((X,re)=>((r-1)*ge.face[P].annotations[H][Z][re]+X)/r)):null;if(e.face[P].rotation){let H={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};H.matrix=(m=e.face[P].rotation)==null?void 0:m.matrix,H.angle={roll:((r-1)*(((y=(g=ge.face[P].rotation)==null?void 0:g.angle)==null?void 0:y.roll)||0)+(((A=(x=e.face[P].rotation)==null?void 0:x.angle)==null?void 0:A.roll)||0))/r,yaw:((r-1)*(((k=(b=ge.face[P].rotation)==null?void 0:b.angle)==null?void 0:k.yaw)||0)+(((C=(S=e.face[P].rotation)==null?void 0:S.angle)==null?void 0:C.yaw)||0))/r,pitch:((r-1)*((($=(N=ge.face[P].rotation)==null?void 0:N.angle)==null?void 0:$.pitch)||0)+(((R=(M=e.face[P].rotation)==null?void 0:M.angle)==null?void 0:R.pitch)||0))/r},H.gaze={bearing:((r-1)*(((I=ge.face[P].rotation)==null?void 0:I.gaze.bearing)||0)+(((_=e.face[P].rotation)==null?void 0:_.gaze.bearing)||0))/r,strength:((r-1)*(((D=ge.face[P].rotation)==null?void 0:D.gaze.strength)||0)+(((W=e.face[P].rotation)==null?void 0:W.gaze.strength)||0))/r},ge.face[P]={...e.face[P],rotation:H,box:U,boxRaw:G,annotations:q}}else ge.face[P]={...e.face[P],box:U,boxRaw:G,annotations:q}}if(!ge.object||e.object.length!==ge.object.length)ge.object=JSON.parse(JSON.stringify(e.object));else for(let P=0;P<e.object.length;P++){let 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c=[n[p][1],n[p][0]];r.push({score:Math.round(100*s)/100,part:T0[p],positionRaw:c,position:[Math.round((a.shape[2]||0)*c[0]),Math.round((a.shape[1]||0)*c[1])]})}s=r.reduce((p,c)=>c.score>p?c.score:p,0);let i=[],o=hs(r.map(p=>p.position),[a.shape[2],a.shape[1]]),l={};for(let[p,c]of Object.entries(C0)){let d=[];for(let h=0;h<c.length-1;h++){let f=r.find(g=>g.part===c[h]),m=r.find(g=>g.part===c[h+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&d.push([f.position,m.position])}l[p]=d}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:r,annotations:l};return s5(u),i.push(u),i}function V0e(e,t,a){let n=[];for(let r=0;r<e[0].length;r++){let s=e[0][r],i=Math.round(100*s[51+4])/100;if(i>t.body.minConfidence){let o=[];for(let d=0;d<17;d++){let h=s[3*d+2];if(h>t.body.minConfidence){let f=[s[3*d+1],s[3*d+0]];o.push({part:T0[d],score:Math.round(100*h)/100,positionRaw:f,position:[Math.round((a.shape[2]||0)*f[0]),Math.round((a.shape[1]||0)*f[1])]})}}let 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S=(.5+Math.trunc(A%d))/d,C=(.5+Math.trunc(A/d))/d,N=x[A].map(P=>P*(d/c/s)),[$,M]=[S-M0/c*N[0],C-M0/c*N[1]],[R,I]=[S+M0/c*N[2]-$,C+M0/c*N[3]-M],_=[$,M,R,I];_=_.map(P=>Math.max(0,Math.min(P,1)));let D=[_[0]*t[0],_[1]*t[1],_[2]*t[0],_[3]*t[1]],W={id:n++,score:Math.round(100*k)/100,class:b+1,label:vu[b].label,box:D.map(P=>Math.trunc(P)),boxRaw:_};r.push(W)}}J([h,m,g,y])}let i=r.map(c=>[c.boxRaw[1],c.boxRaw[0],c.boxRaw[3],c.boxRaw[2]]),o=r.map(c=>c.score),l=[];if(i&&i.length>0){let c=await ce.nonMaxSuppressionAsync(i,o,a.object.maxDetected||0,a.object.iouThreshold,a.object.minConfidence);l=Array.from(await c.data()),J(c)}return r=r.filter((c,d)=>l.includes(d)).sort((c,d)=>d.score-c.score),r}async function u5(e,t){if(!(_n!=null&&_n.executor))return[];let a=(t.object.skipTime||0)>ae()-$w,n=l5<(t.object.skipFrames||0);return t.skipAllowed&&a&&n&&R0.length>0?(l5++,R0):(l5=0,!ne.kernels.includes("mod")||!ne.kernels.includes("sparsetodense")?R0:new Promise(async r=>{let 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g=d.execute(m);r.push(c),Array.isArray(g)?g.forEach(y=>J(y)):J(g)}catch(g){e.config.debug&&K("compile fail model:",c)}J(m)}let s=await a.checkCompileCompletionAsync();a.getUniformLocations(),e.config.debug&&K("compile pass:",{models:r,kernels:s.length}),V().set("ENGINE_COMPILE_ONLY",!1);let i=vt().state.numTensors;i-n>0&&K("tensor leak:",i-n)}async function Zw(e,t){await wp(e,!1);let a=ae();return e.state="warmup",t&&(e.config=Nt(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?lr():new Promise(async n=>{await e.models.load(),await lfe(e);let r=await ofe(e),s=ae();e.config.debug&&K("warmup",e.config.warmup,Math.round(s-a),"ms"),e.emit("warmup"),n(r)})}var Mu,$p,_p,z0,Ss,b5=class{constructor(t){de(this,"version");de(this,"config");de(this,"result");de(this,"state");de(this,"process");de(this,"tf");de(this,"env",ne);de(this,"draw",e0);de(this,"match",N0);de(this,"models");de(this,"events");de(this,"faceTriangulation");de(this,"faceUVMap");de(this,"performance");Gn(this,Mu,void 0);Gn(this,$p,void 0);Gn(this,_p,void 0);de(this,"analyze",(...t)=>{if(!qa(this,$p))return;let a=this.tf.engine().state.numTensors,n=qa(this,Mu);mr(this,Mu,a);let r=a-n;r!==0&&K(...t,r)});Gn(this,z0,t=>{if(!qa(this,_p))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ct))return"input must be a tensor";try{this.tf.getBackend()}catch(a){return"backend not loaded"}return null});de(this,"webcam",new qh);de(this,"emit",t=>{var a;(a=this.events)!=null&&a.dispatchEvent&&this.events.dispatchEvent(new Event(t))});Gn(this,Ss,{});let a=(xp.tfjs||t3).replace(/-(.*)/,"");So.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${a}/dist/`,So.modelBasePath=ne.browser?"../models/":"file://models/",this.version=K3,Object.defineProperty(this,"version",{value:K3}),this.config=JSON.parse(JSON.stringify(So)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Nt(this.config,t)),o9(this.config),this.tf=je,this.state="idle",mr(this,Mu,0),mr(this,$p,!1),mr(this,_p,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new Mp(this),ag(),this.result=lr(),this.process={tensor:null,canvas:null},this.faceTriangulation=ek,this.faceUVMap=tk,F0(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&K(`version: ${this.version}`),this.config.debug&&K(`tfjs version: ${this.tf.version["tfjs-core"]}`);let n=JSON.parse(JSON.stringify(this.env));delete n.kernels,delete n.initial,delete n.perfadd,this.config.debug&&K("environment:",n)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(So)),this.config.backend=t,q3(),ne.initial=!0}validate(t){let a=G3(So,t||this.config);return a.length===0&&(this.config=Nt(this.config,t)),a}now(){return ae()}image(t,a=!1){return Hh(t,this.config,a)}async segmentation(t,a){var s,i,o;if(a&&(this.config=Nt(this.config,a)),!this.config.segmentation.enabled)return null;let n=await Hh(t,this.config);if(!n.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Hw(n.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("meet")&&(r=await ww(n.tensor,this.config)),(o=this.config.segmentation.modelPath)!=null&&o.includes("selfie")&&(r=await qw(n.tensor,this.config)),J(n.tensor),r}compare(t,a){return i9(this.config,t,a)}async init(){await wp(this,!0),await this.tf.ready(),q3()}async load(t){this.state="load";let a=ae(),n=Object.values(this.models.models).filter(i=>i).length;t&&(this.config=Nt(this.config,t)),this.env.initial&&(await wp(this,!1)||K("error: backend check failed"),await Jd(),this.env.browser&&(this.config.debug&&K("configuration:",this.config),this.config.debug&&K("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&K("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(i=>i).length!==n&&(this.models.validate(),this.emit("load"));let s=Math.trunc(ae()-a);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return kw(t,this.config)}async warmup(t){let a=ae(),n=await Zw(this,t),r=ae();return this.performance.warmup=Math.trunc(r-a),n}async profile(t,a){let n=await this.tf.profile(()=>this.detect(t,a)),r={},s=0;for(let o of n.kernels){let l=Number(o.kernelTimeMs)||0;r[o.name]?r[o.name]+=l:r[o.name]=l,s+=l}let i=[];Object.entries(r).forEach(o=>i.push({kernel:o[0],time:o[1],perc:0}));for(let o of i)o.perc=Math.round(1e3*o.time/s)/1e3,o.time=Math.round(1e3*o.time)/1e3;return i.sort((o,l)=>l.time-o.time),i.length=20,i}async detect(t,a){return this.state="detect",new Promise(async n=>{var g,y,x,A,b,k,S,C,N,$,M,R,I,_,D,W,P,U,G,q,H;this.state="config";let r;this.config=Nt(this.config,a),this.state="check";let s=qa(this,z0).call(this,t);s&&(K(s,t),this.emit("error"),n(lr(s)));let i=ae();await this.load(),r=ae(),this.state="image";let o=await Hh(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Get Image:"),!o.tensor){this.config.debug&&K("could not convert input to tensor"),this.emit("error"),n(lr("could not convert input to tensor"));return}this.emit("image"),r=ae(),this.config.skipAllowed=await s9(this.config,o.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Check Changed:");let l=[],u=[],p=[],c=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Gg(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=ae(),l=this.config.face.enabled?await Gg(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Nt(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?f5(o.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?og(o.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?fg(o.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?o5(o.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ae(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await f5(o.tensor,d):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?u=this.config.body.enabled?await og(o.tensor,d):[]:(S=this.config.body.modelPath)!=null&&S.includes("efficientpose")?u=this.config.body.enabled?await fg(o.tensor,d):[]:(C=this.config.body.modelPath)!=null&&C.includes("movenet")&&(u=this.config.body.enabled?await o5(o.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Nt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(($=(N=this.config.hand.detector)==null?void 0:N.modelPath)!=null&&$.includes("handdetect")?p=this.config.hand.enabled?Kg(o.tensor,h):[]:(R=(M=this.config.hand.detector)==null?void 0:M.modelPath)!=null&&R.includes("handtrack")&&(p=this.config.hand.enabled?Jg(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ae(),(_=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&_.includes("handdetect")?p=this.config.hand.enabled?await Kg(o.tensor,h):[]:(W=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&W.includes("handtrack")&&(p=this.config.hand.enabled?await Jg(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((P=this.config.object.modelPath)!=null&&P.includes("nanodet")?c=this.config.object.enabled?u5(o.tensor,this.config):[]:(U=this.config.object.modelPath)!=null&&U.includes("centernet")&&(c=this.config.object.enabled?dg(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ae(),(G=this.config.object.modelPath)!=null&&G.includes("nanodet")?c=this.config.object.enabled?await u5(o.tensor,this.config):[]:(q=this.config.object.modelPath)!=null&&q.includes("centernet")&&(c=this.config.object.enabled?await dg(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,p,c]=await Promise.all([l,u,p,c])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=ae(),f=[...Jk(l),...Yk(u),...ew(p),...Qk(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ae()-i):Math.trunc(ae()-i);let m=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:p,gesture:f,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:m[2],height:m[1],get persons(){return Kw(l,u,p,f,m)}},J(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(qa(this,Ss)[t.id]||(this.config.debug&&K("video start",t.id),qa(this,Ss)[t.id]=!0),!t.paused&&qa(this,Ss)[t.id]&&t.readyState>=2&&await this.detect(t),n>0&&await this.sleep(n),qa(this,Ss)[t.id]&&requestAnimationFrame(()=>this.video(t,a,n))):(this.config.debug&&K("video stop",t.id),qa(this,Ss)[t.id]=!1)}};Mu=new WeakMap,$p=new WeakMap,_p=new WeakMap,z0=new WeakMap,Ss=new WeakMap;return FI(dfe);})();
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