onnxruntime-web/dist/ort.min.js

A Javascript library for running ONNX models on browsers

/*!
 * ONNX Runtime Web v1.23.2
 * Copyright (c) Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License.
 */
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Most likely the range contains negative values in them.");o*=Number(t[i])}return o}static computeStrides(t){let n=t.length;if(n===0)return[];if(n===1)return[1];let r=new Array(n);r[n-1]=1,r[n-2]=t[n-1];for(let o=n-3;o>=0;--o)r[o]=r[o+1]*t[o+1];return r}static normalizeAxis(t,n){if(t<-n&&t>=n)throw new Error("unsupported axis for this operation.");return t<0?t+n:t}static normalizeAxes(t,n){return t.map(r=>this.normalizeAxis(r,n??t.length))}static sortBasedOnPerm(t,n){return n?n.map(r=>t[r]):t.slice().reverse()}static padShape(t,n){let r=t.length;return t.map((o,i)=>o+n[i]+n[i+r])}static areEqual(t,n){return t.length!==n.length?!1:t.every((r,o)=>r===n[o])}},Xe=class e{static adjustPoolAttributes(t,n,r,o,i,s){if(!t&&r.length!==n.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(t)for(let a=0;a<n.length-2;a++)a>=r.length?r.push(n[a+2]):r[a]=n[a+2];for(let a=0;a<r.length;a++)if(a<o.length){if(o[a]<0)throw new Error("strides should be greater than or equal to 1")}else o.push(1);for(let a=0;a<r.length;a++)if(a<i.length){if(i[a]<0)throw new Error("dilations should be greater than or equal to 1")}else i.push(1);for(let a=0;a<r.length*2;a++)if(a<s.length){if(s[a]<0)throw new Error("pad should be greater than or equal to 1")}else s.push(0);for(let a=0;a<r.length;a++){if(r[a]<=0)throw new Error("kernel shapes need to be greater than 0");if(s[a]>=r[a]||s[a+r.length]>=r[a])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(t,n,r,o,i,s,a){if(a){if(i.length!==2*(t.length-2))throw new Error("length of pads should be twice the length of data dimensions");if(n.length!==t.length-2)throw new Error("length of strides should be the length of data dimensions");if(o.length!==t.length-2)throw new Error("length of kernel shapes should be the length of data dimensions");for(let u=0;u<t.length-2;u++)e.adjustPadAndReturnShape(t[u+(s?1:2)],n[u],r[u],o[u],i,u,u+t.length-2,a)}}static computePoolOutputShape(t,n,r,o,i,s,a){if(n.length<=0)throw new Error("input shape must be of size greater than 0");let u=[n[0],n[1]];return e.computeShapeHelper(t,n,u,r,o,i,s,a),u}static computeConvOutputShape(t,n,r,o,i,s,a){if(t.length<=0||n.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let u=[t[0],n[0]];return e.computeShapeHelper(!1,t,u,r,o,i,s,a),u}static computeShapeHelper(t,n,r,o,i,s,a,u){if(t)for(let d=0;d<n.length-2;d++)r.push(1);else for(let d=0;d<n.length-2;d++)r.push(e.adjustPadAndReturnShape(n[d+2],o[d],i[d],s[d],a,d,d+n.length-2,u))}static adjustPadAndReturnShape(t,n,r,o,i,s,a,u){let d=r*(o-1)+1;if(u&&u!=="NOTSET")switch(u){case"VALID":return i[s]=0,i[a]=0,Math.floor((t-d)/n+1);case"SAME_LOWER":case"SAME_UPPER":if(r!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let c=((t+n-1)/n-1)*n+o-t;return i[s]=Math.floor(u==="SAME_LOWER"?(c+1)/2:c/2),i[a]=c-i[s],Math.floor((t+c-o)/n+1)}default:throw new Error("Unsupported AutoPad type")}else return Math.floor((t+i[s]+i[a]-d)/n+1)}},Gt=class{static getShapeOfGemmResult(t,n,r,o,i){if(t.length!==2||r.length!==2)throw new Error("shape need to be of size 2");let s,a,u;n?(s=t[1],a=t[0]):(s=t[0],a=t[1]);let d=-1;if(o?(u=r[0],d=1):(u=r[1],d=0),r[d]!==a)throw new Error("dimension mismatch");if(s<=0||u<=0||a<=0)throw new Error("invalid shape specified");if(i&&!Pe.isValidBroadcast(i,[s,u]))throw new Error("gemm: invalid bias shape for broadcast");return[s,u,a]}},lo=-34028234663852886e22,co=34028234663852886e22});var Ht,On=k(()=>{"use strict";V();Ht=(e,t)=>new(rt(t))(e)});var mo,Un,fo,sd,po,ad,ho,qt,Ft,Mn,go,yo=k(()=>{"use strict";V();ke();mo=new Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),Un=(e,t)=>{if(t==="int32")return e;let n=mo.get(t);if(!n)throw new Error(`WebNN backend does not support data type: ${t}`);let r=n/8;if(e.byteLength%r!==0)throw new Error(`Invalid Uint8Array length - must be a multiple of ${r}.`);let o=e.byteLength/r,i=new(rt(t))(e.buffer,e.byteOffset,o);switch(t){case"int64":case"uint64":{let s=new Int32Array(o);for(let a=0;a<o;a++){let u=i[a];if(u>2147483647n||u<-2147483648n)throw new Error("Can not convert int64 data to int32 - value out of range.");s[a]=Number(u)}return new Uint8Array(s.buffer)}case"int8":case"uint8":case"uint32":{if(t==="uint32"&&i.some(a=>a>2147483647))throw new Error("Can not convert uint32 data to int32 - value out of range.");let s=Int32Array.from(i,Number);return new Uint8Array(s.buffer)}default:throw new Error(`Unsupported data conversion from ${t} to 'int32'`)}},fo=(e,t)=>{if(t==="int32")return e;if(e.byteLength%4!==0)throw new Error("Invalid Uint8Array length - must be a multiple of 4 (int32).");let n=e.byteLength/4,r=new Int32Array(e.buffer,e.byteOffset,n);switch(t){case"int64":{let o=BigInt64Array.from(r,BigInt);return new Uint8Array(o.buffer)}case"uint64":{if(r.some(i=>i<0))throw new Error("Can not convert int32 data to uin64 - negative value found.");let o=BigUint64Array.from(r,BigInt);return new Uint8Array(o.buffer)}case"int8":{if(r.some(i=>i<-128||i>127))throw new Error("Can not convert int32 data to int8 - value out of range.");let o=Int8Array.from(r,Number);return new Uint8Array(o.buffer)}case"uint8":{if(r.some(o=>o<0||o>255))throw new Error("Can not convert int32 data to uint8 - value out of range.");return Uint8Array.from(r,Number)}case"uint32":{if(r.some(i=>i<0))throw new Error("Can not convert int32 data to uint32 - negative value found.");let o=Uint32Array.from(r,Number);return new Uint8Array(o.buffer)}default:throw new Error(`Unsupported data conversion from 'int32' to ${t}`)}},sd=1,po=()=>sd++,ad=new Map([["int8","int32"],["uint8","int32"],["uint32","int32"],["int64","int32"]]),ho=(e,t)=>{let n=mo.get(e);if(!n)throw new Error(`WebNN backend does not support data type: ${e}`);return t.length>0?Math.ceil(t.reduce((r,o)=>r*o)*n/8):0},qt=class{constructor(t){this.isDataConverted=!1;let{sessionId:n,context:r,tensor:o,dataType:i,shape:s,fallbackDataType:a}=t;this.sessionId=n,this.mlContext=r,this.mlTensor=o,this.dataType=i,this.tensorShape=s,this.fallbackDataType=a}get tensor(){return this.mlTensor}get type(){return this.dataType}get fallbackType(){return this.fallbackDataType}get shape(){return this.tensorShape}get byteLength(){return ho(this.dataType,this.tensorShape)}destroy(){j("verbose",()=>"[WebNN] TensorWrapper.destroy"),this.mlTensor.destroy()}write(t){this.mlContext.writeTensor(this.mlTensor,t)}async read(t){if(this.fallbackDataType){let n=await this.mlContext.readTensor(this.mlTensor),r=fo(new Uint8Array(n),this.dataType);if(t){(t instanceof ArrayBuffer?new Uint8Array(t):new Uint8Array(t.buffer,t.byteOffset,t.byteLength)).set(r);return}else return r.buffer}else return t?this.mlContext.readTensor(this.mlTensor,t):this.mlContext.readTensor(this.mlTensor)}canReuseTensor(t,n,r){return this.mlContext===t&&this.dataType===n&&this.tensorShape.length===r.length&&this.tensorShape.every((o,i)=>o===r[i])}setIsDataConverted(t){this.isDataConverted=t}},Ft=class{constructor(t,n){this.tensorManager=t;this.wrapper=n}get tensorWrapper(){return this.wrapper}releaseTensor(){this.tensorWrapper&&(this.tensorManager.releaseTensor(this.tensorWrapper),this.wrapper=void 0)}async ensureTensor(t,n,r,o){let i=this.tensorManager.getMLContext(t),s;if(!i.opSupportLimits().input.dataTypes.includes(n)){if(s=ad.get(n),!s||!i.opSupportLimits().input.dataTypes.includes(s))throw new Error(`WebNN backend does not support data type: ${n}`);j("verbose",()=>`[WebNN] TensorIdTracker.ensureTensor: fallback dataType from ${n} to ${s}`)}if(this.wrapper){if(this.wrapper.canReuseTensor(i,n,r))return this.wrapper.tensor;if(o){if(this.wrapper.byteLength!==ho(n,r))throw new Error("Unable to copy data to tensor with different size.");this.activeUpload=new Uint8Array(await this.wrapper.read())}this.tensorManager.releaseTensor(this.wrapper)}let a=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(t,n,r,a,!0,!0,s),o&&this.activeUpload&&(this.wrapper.write(this.activeUpload),this.activeUpload=void 0),this.wrapper.tensor}upload(t){let n=t;if(this.wrapper){if(this.wrapper.fallbackType)if(this.wrapper.fallbackType==="int32")n=Un(t,this.wrapper.type),this.wrapper.setIsDataConverted(!0);else throw new Error(`Unsupported fallback data type: ${this.wrapper.fallbackType}`);if(t.byteLength===this.wrapper.byteLength){this.wrapper.write(n);return}else j("verbose",()=>"Data size does not match tensor size. Releasing tensor."),this.releaseTensor()}this.activeUpload?this.activeUpload.set(n):this.activeUpload=new Uint8Array(n)}async download(t){if(this.activeUpload){let n=this.wrapper?.isDataConverted?fo(this.activeUpload,this.wrapper?.type):this.activeUpload;if(t){t instanceof ArrayBuffer?new Uint8Array(t).set(n):new Uint8Array(t.buffer,t.byteOffset,t.byteLength).set(n);return}else return n.buffer}if(!this.wrapper)throw new Error("Tensor has not been created.");return t?this.wrapper.read(t):this.wrapper.read()}},Mn=class{constructor(t){this.backend=t;this.tensorTrackersById=new Map;this.freeTensors=[];this.externalTensors=new Set}getMLContext(t){let n=this.backend.getMLContext(t);if(!n)throw new Error("MLContext not found for session.");return n}reserveTensorId(){let t=po();return this.tensorTrackersById.set(t,new Ft(this)),t}releaseTensorId(t){let n=this.tensorTrackersById.get(t);n&&(this.tensorTrackersById.delete(t),n.tensorWrapper&&this.releaseTensor(n.tensorWrapper))}async ensureTensor(t,n,r,o,i){j("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${n}, dataType: ${r}, shape: ${o}, copyOld: ${i}}`);let s=this.tensorTrackersById.get(n);if(!s)throw new Error("Tensor not found.");return s.ensureTensor(t,r,o,i)}upload(t,n){let r=this.tensorTrackersById.get(t);if(!r)throw new Error("Tensor not found.");r.upload(n)}async download(t,n){j("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${t}, dstBuffer: ${n?.byteLength}}`);let r=this.tensorTrackersById.get(t);if(!r)throw new Error("Tensor not found.");return r.download(n)}releaseTensorsForSession(t){for(let n of this.freeTensors)n.sessionId===t&&n.destroy();this.freeTensors=this.freeTensors.filter(n=>n.sessionId!==t)}registerTensor(t,n,r,o){let i=this.getMLContext(t),s=po(),a=new qt({sessionId:t,context:i,tensor:n,dataType:r,shape:o});return this.tensorTrackersById.set(s,new Ft(this,a)),this.externalTensors.add(a),s}async getCachedTensor(t,n,r,o,i,s,a){let u=this.getMLContext(t);for(let[l,c]of this.freeTensors.entries())if(c.canReuseTensor(u,n,r)){j("verbose",()=>`[WebNN] Reusing tensor {dataType: ${n}, ${a?`fallbackDataType: ${a},`:""} shape: ${r}`);let p=this.freeTensors.splice(l,1)[0];return p.sessionId=t,p}j("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${n}, ${a?`fallbackDataType: ${a},`:""} shape: ${r}}`);let d=await u.createTensor({dataType:a??n,shape:r,dimensions:r,usage:o,writable:i,readable:s});return new qt({sessionId:t,context:u,tensor:d,dataType:n,shape:r,fallbackDataType:a})}releaseTensor(t){this.externalTensors.has(t)&&this.externalTensors.delete(t),this.freeTensors.push(t)}},go=(...e)=>new Mn(...e)});var Kt,ud,jt,bo=k(()=>{"use strict";V();Ge();On();yo();ke();Kt=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),ud=(e,t)=>{if(e===t)return!0;if(e===void 0||t===void 0)return!1;let n=Object.keys(e).sort(),r=Object.keys(t).sort();return n.length===r.length&&n.every((o,i)=>o===r[i]&&e[o]===t[o])},jt=class{constructor(t){this.tensorManager=go(this);this.mlContextBySessionId=new Map;this.sessionIdsByMLContext=new Map;this.mlContextCache=[];this.sessionGraphInputs=new Map;this.sessionGraphOutputs=new Map;this.temporaryGraphInputs=[];this.temporaryGraphOutputs=[];this.temporarySessionTensorIds=new Map;Wt(t.logLevel,!!t.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw new Error("No active session");return this.activeSessionId}onRunStart(t){j("verbose",()=>`[WebNN] onRunStart {sessionId: ${t}}`),this.activeSessionId=t}onRunEnd(t){j("verbose",()=>`[WebNN] onRunEnd {sessionId: ${t}}`);let n=this.temporarySessionTensorIds.get(t);if(n){for(let r of n)j("verbose",()=>`[WebNN] releasing temporary tensor {tensorId: ${r}}`),this.tensorManager.releaseTensorId(r);this.temporarySessionTensorIds.delete(t),this.activeSessionId=void 0}}async createMLContext(t){if(t instanceof GPUDevice){let r=this.mlContextCache.findIndex(o=>o.gpuDevice===t);if(r!==-1)return this.mlContextCache[r].mlContext;{let o=await navigator.ml.createContext(t);return this.mlContextCache.push({gpuDevice:t,mlContext:o}),o}}else if(t===void 0){let r=this.mlContextCache.findIndex(o=>o.options===void 0&&o.gpuDevice===void 0);if(r!==-1)return this.mlContextCache[r].mlContext;{let o=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:o}),o}}let n=this.mlContextCache.findIndex(r=>ud(r.options,t));if(n!==-1)return this.mlContextCache[n].mlContext;{let r=await navigator.ml.createContext(t);return this.mlContextCache.push({options:t,mlContext:r}),r}}registerMLContext(t,n){this.mlContextBySessionId.set(t,n);let r=this.sessionIdsByMLContext.get(n);r||(r=new Set,this.sessionIdsByMLContext.set(n,r)),r.add(t),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(t,this.temporaryGraphInputs),this.temporaryGraphInputs=[]),this.temporaryGraphOutputs.length>0&&(this.sessionGraphOutputs.set(t,this.temporaryGraphOutputs),this.temporaryGraphOutputs=[])}onReleaseSession(t){this.sessionGraphInputs.delete(t),this.sessionGraphOutputs.delete(t);let n=this.mlContextBySessionId.get(t);if(!n)return;this.tensorManager.releaseTensorsForSession(t),this.mlContextBySessionId.delete(t);let r=this.sessionIdsByMLContext.get(n);if(r.delete(t),r.size===0){this.sessionIdsByMLContext.delete(n);let o=this.mlContextCache.findIndex(i=>i.mlContext===n);o!==-1&&this.mlContextCache.splice(o,1)}}getMLContext(t){return this.mlContextBySessionId.get(t)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(t){j("verbose",()=>`[WebNN] releaseTensor