@tensorflow/tfjs-core/dist/tf-core.es2017.min.js

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2024 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
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Rr(e){const t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}class Br{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=c(e),null!=n){const e=n.length;i(e===this.size,(()=>`Length of values '${e}' does not match the size inferred by the shape '${this.size}'.`))}if("complex64"===t)throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||y(t,this.size),this.strides=N(e)}set(e,...t){0===t.length&&(t=[0]),i(t.length===this.rank,(()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`));const n=this.locToIndex(t);this.values[n]=e}get(...e){0===e.length&&(e=[0]);let t=0;for(const n of e){if(n<0||n>=this.shape[t]){const t=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(t)}t++}let n=e[e.length-1];for(let t=0;t<e.length-1;++t)n+=this.strides[t]*e[t];return this.values[n]}locToIndex(e){if(0===this.rank)return 0;if(1===this.rank)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(0===this.rank)return[];if(1===this.rank)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Fr().makeTensor(this.values,this.shape,this.dtype)}}let Fr=null,Cr=null;class Pr{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=c(e),this.strides=N(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return Cr.buffer(this.shape,this.dtype,e)}bufferSync(){return Cr.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return T(this.shape,e,"complex64"===this.dtype)}arraySync(){return T(this.shape,this.dataSync(),"complex64"===this.dtype)}async data(){this.throwIfDisposed();const e=Fr().read(this.dataId);if("string"===this.dtype){const t=await e;try{return t.map((e=>xr(e)))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataToGPU(e){return this.throwIfDisposed(),Fr().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();const e=Fr().readSync(this.dataId);if("string"===this.dtype)try{return e.map((e=>xr(e)))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await Fr().read(this.dataId);return"string"===this.dtype?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(this.kerasMask&&this.kerasMask.dispose(),Fr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Cr.print(this,e)}clone(){return this.throwIfDisposed(),Cr.clone(this)}toString(e=!1){return Nr(this.dataSync(),this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Cr.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Fr().makeVariable(this,e,t,n)}}function Or(){return W("Tensor",(()=>Pr))}Object.defineProperty(Pr,Symbol.hasInstance,{value:e=>!!e&&null!=e.data&&null!=e.dataSync&&null!=e.throwIfDisposed}),Or();class Lr extends Pr{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r),this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!d(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Fr().disposeTensor(this),this.dataId=e.dataId,Fr().incRef(this,null)}dispose(){Fr().disposeVariable(this),this.isDisposedInternal=!0}}var zr,Ur,Wr,Gr,qr;Object.defineProperty(Lr,Symbol.hasInstance,{value:e=>e instanceof Pr&&null!=e.assign&&e.assign instanceof Function}),e.Rank=void 0,(zr=e.Rank||(e.Rank={})).R0="R0",zr.R1="R1",zr.R2="R2",zr.R3="R3",zr.R4="R4",zr.R5="R5",zr.R6="R6",function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"}(Ur||(Ur={})),function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"}(Wr||(Wr={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"}(Gr||(Gr={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"}(qr||(qr={}));const Kr={float32:Gr,int32:Ur,bool:Wr,complex64:qr};function Vr(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Kr[e][t]}function Hr(e){return null!=e&&"object"==typeof e&&"texture"in e&&e.texture instanceof WebGLTexture}function jr(e){return"undefined"!=typeof GPUBuffer&&null!=e&&"object"==typeof e&&"buffer"in e&&e.buffer instanceof GPUBuffer}function Jr(e,t){if(e.dtype===t.dtype)return[e,t];const n=Vr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Zr(e,t){i(e.dtype===t.dtype,(()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`))}function Yr(e){const t=[];return Xr(e,t,new Set),t}function Xr(e,t,n){if(null==e)return;if(e instanceof Pr)return void t.push(e);if(r=e,!Array.isArray(r)&&"object"!=typeof r)return;var r;const s=e;for(const e in s){const r=s[e];n.has(r)||(n.add(r),Xr(r,t,n))}}var Qr={__proto__:null,assertTypesMatch:Zr,getTensorsInContainer:Yr,isTensorInList:function(e,t){return t.some((t=>t.id===e.id))},makeTypesMatch:Jr};function es(e){return null!=e.kernelName}class ts{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map((e=>e.name))))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class ns{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ts}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then((()=>{}));if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t];if(await this.initializeBackend(n).success)return void await this.setBackend(n)}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(null==this.backendInstance){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{const{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(Sn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(null==this.registryFactory[e])throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,null==this.registry[e]){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new Ar(this.backendInstance),!0}setupRegisteredKernels(){Nn(this.backendName).forEach((e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)}))}disposeRegisteredKernels(e){Nn(e).forEach((t=>{null!=t.disposeFunc&&t.disposeFunc(this.registry[e])}))}initializeBackend(e){const t=this.registryFactory[e];if(null==t)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const r=t.factory();if(!r||r instanceof n||"function"!=typeof r.then)return this.registry[e]=r,{success:!0,asyncInit:!1};{const t=++this.pendingBackendInitId,n=r.then((n=>!(t<this.pendingBackendInitId)&&(this.registry[e]=n,this.pendingBackendInit=null,!0))).catch((n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,Sn(`Initialization of backend ${e} failed`),Sn(n.stack||n.message)),!1)));return this.pendingBackendInit=n,{success:n,asyncInit:!0}}}catch(t){return Sn(`Initialization of backend ${e} failed`),Sn(t.stack||t.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&null!=this.pendingBackendInit&&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(0===Object.keys(this.registryFactory).length)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(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:r,asyncInit:s}=this.initializeBackend(n);if(s||r)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),r=n.backend,s=this.readSync(t),o=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,o),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n,r=null;if(null==t){if("function"!=typeof e)throw new Error("Please provide a function to tidy()");t=e}else{if("string"!=typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!=typeof t)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=e}return this.scopedRun((()=>this.startScope(r)),(()=>this.endScope(n)),(()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n)))}scopedRun(e,t,n){e();try{const e=n();return t(),e}catch(e){throw t(),e}}nextTensorId(){return ns.nextTensorId++}nextVariableId(){return ns.nextVariableId++}clone(e){const t=ss.runKernel(qe,{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],(e=>({x:()=>{const t={x:e},n={dtype:"float32"};return ss.runKernel(ie,t,n)}})),[],{}),t}runKernel(e,t,n){null==this.backendName&&this.backend;if(!(null!=_n(e,this.backendName)))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const r=this.backend.numDataIds();let s=0;n.forEach((e=>{s+="complex64"===e.dtype?3:1}));const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=r-t-s-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const r=this.isTapeOn(),s=this.state.numBytes,o=this.state.numTensors;let a,l;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const u=es(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(es(e)){const{kernelName:t,inputs:s,attrs:o}=e;null==this.backendName&&this.backend;const u=_n(t,this.backendName);i(null!=u,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),a=()=>{const e=this.backend.numDataIds();l=u.kernelFunc({inputs:s,attrs:o,backend:this.backend});const a=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,a);const i=a.map((e=>null!=e.rank?e:this.makeTensorFromTensorInfo(e)));if(r){const e=this.getTensorsForGradient(t,s,i);n=this.saveTensorsForBackwardMode(e)}return i}}else{const{forwardFunc:t}=e,s=e=>{r&&(n=e.map((e=>this.keep(this.clone(e)))))};a=()=>{const e=this.backend.numDataIds();l=this.tidy((()=>t(this.backend,s)));const n=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,e,n),n}}const{inputs:c,attrs:h}=e,d=es(e)?null:e.backwardsFunc;let p;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(p=this.profiler.profileKernel(u,c,(()=>a())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs):t=a()})),r&&this.addTapeNode(u,c,t,d,n,h),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-o,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map((e=>null!=c[e]?c[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){const t=e.map((e=>this.keep(this.clone(e))));return t}getTensorsForGradient(e,t,n){const r=In(e);if(null!=r){const e=r.inputsToSave||[],s=r.outputsToSave||[];let o;r.saveAllInputs?(i(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),o=Object.keys(t).map((e=>t[e]))):o=e.map((e=>t[e]));const a=n.filter(((e,t)=>s[t]));return o.concat(a)}return[]}makeTensor(e,t,n,r){if(null==e)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let s=e;"string"===n&&E(e[0])&&(s=e.map((e=>vr(e))));const o=r.write(s,t,n),a=new Pr(t,n,o,this.nextTensorId());if(this.trackTensor(a,r),"string"===n){const e=this.state.tensorInfo.get(o),t=x(s);this.state.numBytes+=t-e.bytes,e.bytes=t}return a}makeTensorFromDataId(e,t,n,r){const s={dataId:e,shape:t,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(s,r)}makeTensorFromTensorInfo(e,t){const{dataId:n,shape:r,dtype:s}=e,o=new Pr(r,s,n,this.nextTensorId());return this.trackTensor(o,t),o}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));const s=new Lr(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[s.name])throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*v(e.dtype)),this.state.numBytes+=n,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:n})),e instanceof Lr||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;const t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,"string"===e.dtype&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),"complex64"!==e.dtype&&"string"!==e.dtype){const t=e.size*v(e.dtype);this.state.numBytes-=t}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),null!=this.state.registeredVariables[e.name]&&delete this.state.registeredVariables[e.name]}memory(){const 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,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=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((e=>e.totalBytesSnapshot))),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const e of this.state.activeProfile.kernels)e.kernelTimeMs=await e.kernelTimeMs,e.extraInfo=await e.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&0===this.state.kernelDepth}addTapeNode(e,t,n,r,s,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:s},i=In(e);null!=i&&(r=i.gradFunc),null!=r&&(a.gradient=e=>(e=e.map(((e,t)=>{if(null==e){const e=n[t],r=R(e.size,e.dtype);return this.makeTensor(r,e.shape,e.dtype)}return e})),r(e.length>1?e:e[0],s,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=Yr(e),n=new Set(t.map((e=>e.id)));for(let e=0;e<this.state.activeScope.track.length;e++){const t=this.state.activeScope.track[e];t.kept||n.has(t.id)||t.dispose()}const r=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach((e=>{e.kept||e.scopeId!==r.id||this.track(e)}))}gradients(e,t,n,r=!1){if(i(t.length>0,(()=>"gradients() received an empty list of xs.")),null!=n&&"float32"!==n.dtype)throw new Error(`dy must have 'float32' dtype, but