diff --git "a/assets/llm-worker-CKHJqaHO.js" "b/assets/llm-worker-CKHJqaHO.js" new file mode 100755--- /dev/null +++ "b/assets/llm-worker-CKHJqaHO.js" @@ -0,0 +1,2856 @@ +var e1=Object.defineProperty;var t1=(e,r,t)=>r in e?e1(e,r,{enumerable:!0,configurable:!0,writable:!0,value:t}):e[r]=t;var ce=(e,r,t)=>t1(e,typeof r!="symbol"?r+"":r,t);const Gh=new Map,ci=[],r1=(e,r,t)=>{if(r&&typeof r.init=="function"&&typeof r.createInferenceSessionHandler=="function"){const i=Gh.get(e);if(i===void 0)Gh.set(e,{backend:r,priority:t});else{if(i.priority>t)return;if(i.priority===t&&i.backend!==r)throw new Error(`cannot register backend "${e}" using priority ${t}`)}if(t>=0){const l=ci.indexOf(e);l!==-1&&ci.splice(l,1);for(let n=0;n{const r=Gh.get(e);if(!r)return"backend not found.";if(r.initialized)return r.backend;if(r.aborted)return r.error;{const t=!!r.initPromise;try{return t||(r.initPromise=r.backend.init(e)),await r.initPromise,r.initialized=!0,r.backend}catch(i){return t||(r.error=`${i}`,r.aborted=!0),r.error}finally{delete r.initPromise}}},n1=async e=>{const r=e.executionProviders||[],t=r.map(h=>typeof h=="string"?h:h.name),i=t.length===0?ci:t;let l;const n=[],c=new Set;for(const h of i){const f=await s1(h);typeof f=="string"?n.push({name:h,err:f}):(l||(l=f),l===f&&c.add(h))}if(!l)throw new Error(`no available backend found. 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wa=n.dynCall_iiiiiiiijjjfi=Y.Ai)(s,a,o,u,p,_,y,v,T,j,B,H,ee),uu=n.dynCall_iijiiii=(s,a,o,u,p,_,y)=>(uu=n.dynCall_iijiiii=Y.Bi)(s,a,o,u,p,_,y),cu=n.dynCall_viiiijj=(s,a,o,u,p,_,y)=>(cu=n.dynCall_viiiijj=Y.Ci)(s,a,o,u,p,_,y),As=n.dynCall_iijjjii=(s,a,o,u,p,_,y)=>(As=n.dynCall_iijjjii=Y.Di)(s,a,o,u,p,_,y),du=n.dynCall_jij=(s,a,o)=>(du=n.dynCall_jij=Y.Ei)(s,a,o),pu=n.dynCall_jjj=(s,a,o)=>(pu=n.dynCall_jjj=Y.Fi)(s,a,o),hu=n.dynCall_iiji=(s,a,o,u)=>(hu=n.dynCall_iiji=Y.Gi)(s,a,o,u),fu=n.dynCall_viffiii=(s,a,o,u,p,_,y)=>(fu=n.dynCall_viffiii=Y.Hi)(s,a,o,u,p,_,y),ya=n.dynCall_viifiii=(s,a,o,u,p,_,y)=>(ya=n.dynCall_viifiii=Y.Ii)(s,a,o,u,p,_,y),mu=n.dynCall_viiiiidiidi=(s,a,o,u,p,_,y,v,T,j,B)=>(mu=n.dynCall_viiiiidiidi=Y.Ji)(s,a,o,u,p,_,y,v,T,j,B),_u=n.dynCall_viiiiiiiiidi=(s,a,o,u,p,_,y,v,T,j,B,H)=>(_u=n.dynCall_viiiiiiiiidi=Y.Ki)(s,a,o,u,p,_,y,v,T,j,B,H),gu=n.dynCall_viiiiiiiiiiiiiifi=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We)=>(gu=n.dynCall_viiiiiiiiiiiiiifi=Y.Li)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We),wu=n.dynCall_ijii=(s,a,o,u)=>(wu=n.dynCall_ijii=Y.Mi)(s,a,o,u),qn=n.dynCall_viijiiiijiii=(s,a,o,u,p,_,y,v,T,j,B,H)=>(qn=n.dynCall_viijiiiijiii=Y.Ni)(s,a,o,u,p,_,y,v,T,j,B,H),yu=n.dynCall_vijjjjjjjjjjjjji=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De)=>(yu=n.dynCall_vijjjjjjjjjjjjji=Y.Oi)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De),bu=n.dynCall_viiijii=(s,a,o,u,p,_,y)=>(bu=n.dynCall_viiijii=Y.Pi)(s,a,o,u,p,_,y),Mu=n.dynCall_vijjjiiji=(s,a,o,u,p,_,y,v,T)=>(Mu=n.dynCall_vijjjiiji=Y.Qi)(s,a,o,u,p,_,y,v,T),En=n.dynCall_iiiijiiiiiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe)=>(En=n.dynCall_iiiijiiiiiiiiii=Y.Ri)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe),vu=n.dynCall_iiiiiiiiii=(s,a,o,u,p,_,y,v,T,j)=>(vu=n.dynCall_iiiiiiiiii=Y.Si)(s,a,o,u,p,_,y,v,T,j),xu=n.dynCall_vj=(s,a)=>(xu=n.dynCall_vj=Y.Ti)(s,a),Tu=n.dynCall_vfiii=(s,a,o,u,p)=>(Tu=n.dynCall_vfiii=Y.Ui)(s,a,o,u,p),Cu=n.dynCall_viiiiff=(s,a,o,u,p,_,y)=>(Cu=n.dynCall_viiiiff=Y.Vi)(s,a,o,u,p,_,y),Qn=n.dynCall_viiiiiff=(s,a,o,u,p,_,y,v)=>(Qn=n.dynCall_viiiiiff=Y.Wi)(s,a,o,u,p,_,y,v),Eu=n.dynCall_viiff=(s,a,o,u,p)=>(Eu=n.dynCall_viiff=Y.Xi)(s,a,o,u,p),Pu=n.dynCall_viiiiiiiiifiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe)=>(Pu=n.dynCall_viiiiiiiiifiiii=Y.Yi)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe),Su=n.dynCall_viiiiiiiijj=(s,a,o,u,p,_,y,v,T,j,B)=>(Su=n.dynCall_viiiiiiiijj=Y.Zi)(s,a,o,u,p,_,y,v,T,j,B),Mp=n.dynCall_iiiiiiiiiiiiiifii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We)=>(Mp=n.dynCall_iiiiiiiiiiiiiifii=Y._i)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We),ku=n.dynCall_viiiiiiiiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe)=>(ku=n.dynCall_viiiiiiiiiiiii=Y.$i)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe),$u=n.dynCall_iiiiiiiiiiiiiiiiiiifii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot,Gt,_r)=>($u=n.dynCall_iiiiiiiiiiiiiiiiiiifii=Y.aj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot,Gt,_r),Iu=n.dynCall_vijjiiiiiii=(s,a,o,u,p,_,y,v,T,j,B)=>(Iu=n.dynCall_vijjiiiiiii=Y.bj)(s,a,o,u,p,_,y,v,T,j,B),on=n.dynCall_iiiijjj=(s,a,o,u,p,_,y)=>(on=n.dynCall_iiiijjj=Y.cj)(s,a,o,u,p,_,y),Au=n.dynCall_fffffff=(s,a,o,u,p,_,y)=>(Au=n.dynCall_fffffff=Y.dj)(s,a,o,u,p,_,y),Ou=n.dynCall_viiiiiijiifiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe)=>(Ou=n.dynCall_viiiiiijiifiii=Y.ej)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe),Fu=n.dynCall_vjjjjjjffjifiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt)=>(Fu=n.dynCall_vjjjjjjffjifiiiiii=Y.fj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt),Du=n.dynCall_viiiiiiffjifiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We)=>(Du=n.dynCall_viiiiiiffjifiiiii=Y.gj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We),ju=n.dynCall_viiiiiiffjfiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De)=>(ju=n.dynCall_viiiiiiffjfiiiii=Y.hj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De),Lu=n.dynCall_viiiiiiffjiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe)=>(Lu=n.dynCall_viiiiiiffjiiiii=Y.ij)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe),zu=n.dynCall_vjjjjjjjjfffjifiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot,Gt)=>(zu=n.dynCall_vjjjjjjjjfffjifiiiiii=Y.jj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot,Gt),Xn=n.dynCall_vjjjjjjfffifijiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt)=>(Xn=n.dynCall_vjjjjjjfffifijiiiii=Y.kj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt),vp=n.dynCall_vjjjjjjfffifiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt)=>(vp=n.dynCall_vjjjjjjfffifiiiiii=Y.lj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt),Bu=n.dynCall_vjjjjjjjjfffiiifiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot,Gt)=>(Bu=n.dynCall_vjjjjjjjjfffiiifiiiii=Y.mj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot,Gt),Ru=n.dynCall_vijiiiiiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee)=>(Ru=n.dynCall_vijiiiiiiiiii=Y.nj)(s,a,o,u,p,_,y,v,T,j,B,H,ee),Nu=n.dynCall_vijjfffiii=(s,a,o,u,p,_,y,v,T,j)=>(Nu=n.dynCall_vijjfffiii=Y.oj)(s,a,o,u,p,_,y,v,T,j),Vu=n.dynCall_jiijjiif=(s,a,o,u,p,_,y,v)=>(Vu=n.dynCall_jiijjiif=Y.pj)(s,a,o,u,p,_,y,v),Wu=n.dynCall_vijjjjjjifiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe)=>(Wu=n.dynCall_vijjjjjjifiiiii=Y.qj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe),ba=n.dynCall_vjjjjjiiii=(s,a,o,u,p,_,y,v,T,j)=>(ba=n.dynCall_vjjjjjiiii=Y.rj)(s,a,o,u,p,_,y,v,T,j),Uu=n.dynCall_vjjjjfiii=(s,a,o,u,p,_,y,v,T)=>(Uu=n.dynCall_vjjjjfiii=Y.sj)(s,a,o,u,p,_,y,v,T),Gu=n.dynCall_viiiiiijiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe)=>(Gu=n.dynCall_viiiiiijiiiiii=Y.tj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe),Ma=n.dynCall_vijjii=(s,a,o,u,p,_)=>(Ma=n.dynCall_vijjii=Y.uj)(s,a,o,u,p,_),Ku=n.dynCall_viiiiijjiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee)=>(Ku=n.dynCall_viiiiijjiiiii=Y.vj)(s,a,o,u,p,_,y,v,T,j,B,H,ee),Hu=n.dynCall_iiiiiji=(s,a,o,u,p,_,y)=>(Hu=n.dynCall_iiiiiji=Y.wj)(s,a,o,u,p,_,y),va=n.dynCall_viiiiijiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee)=>(va=n.dynCall_viiiiijiiiiii=Y.xj)(s,a,o,u,p,_,y,v,T,j,B,H,ee),qu=n.dynCall_iiiiiiiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H)=>(qu=n.dynCall_iiiiiiiiiiii=Y.yj)(s,a,o,u,p,_,y,v,T,j,B,H),Qu=n.dynCall_viiijiiiiii=(s,a,o,u,p,_,y,v,T,j,B)=>(Qu=n.dynCall_viiijiiiiii=Y.zj)(s,a,o,u,p,_,y,v,T,j,B),Jn=n.dynCall_viiiijii=(s,a,o,u,p,_,y,v)=>(Jn=n.dynCall_viiiijii=Y.Aj)(s,a,o,u,p,_,y,v),Xu=n.dynCall_viijjiii=(s,a,o,u,p,_,y,v)=>(Xu=n.dynCall_viijjiii=Y.Bj)(s,a,o,u,p,_,y,v),Ju=n.dynCall_viiiji=(s,a,o,u,p,_)=>(Ju=n.dynCall_viiiji=Y.Cj)(s,a,o,u,p,_),Yu=n.dynCall_viiiiiijii=(s,a,o,u,p,_,y,v,T,j)=>(Yu=n.dynCall_viiiiiijii=Y.Dj)(s,a,o,u,p,_,y,v,T,j),Zu=n.dynCall_viiiiijjji=(s,a,o,u,p,_,y,v,T,j)=>(Zu=n.dynCall_viiiiijjji=Y.Ej)(s,a,o,u,p,_,y,v,T,j),xa=n.dynCall_vijiii=(s,a,o,u,p,_)=>(xa=n.dynCall_vijiii=Y.Fj)(s,a,o,u,p,_),Yn=n.dynCall_iiijiiii=(s,a,o,u,p,_,y,v)=>(Yn=n.dynCall_iiijiiii=Y.Gj)(s,a,o,u,p,_,y,v),xp=n.dynCall_viiiiiijjiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe)=>(xp=n.dynCall_viiiiiijjiiiii=Y.Hj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe),ec=n.dynCall_viiiiiiijiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe)=>(ec=n.dynCall_viiiiiiijiiiiii=Y.Ij)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe),tc=n.dynCall_viiiiiji=(s,a,o,u,p,_,y,v)=>(tc=n.dynCall_viiiiiji=Y.Jj)(s,a,o,u,p,_,y,v),Ta=n.dynCall_fiif=(s,a,o,u)=>(Ta=n.dynCall_fiif=Y.Kj)(s,a,o,u),rc=n.dynCall_viijjjiii=(s,a,o,u,p,_,y,v,T)=>(rc=n.dynCall_viijjjiii=Y.Lj)(s,a,o,u,p,_,y,v,T),sc=n.dynCall_viiiiiifiii=(s,a,o,u,p,_,y,v,T,j,B)=>(sc=n.dynCall_viiiiiifiii=Y.Mj)(s,a,o,u,p,_,y,v,T,j,B),Ca=n.dynCall_viijji=(s,a,o,u,p,_)=>(Ca=n.dynCall_viijji=Y.Nj)(s,a,o,u,p,_),nc=n.dynCall_iiiiiiiiiiijijji=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De)=>(nc=n.dynCall_iiiiiiiiiiijijji=Y.Oj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De),ic=n.dynCall_jiijjiii=(s,a,o,u,p,_,y,v)=>(ic=n.dynCall_jiijjiii=Y.Pj)(s,a,o,u,p,_,y,v),Ea=n.dynCall_viifiifijjjii=(s,a,o,u,p,_,y,v,T,j,B,H,ee)=>(Ea=n.dynCall_viifiifijjjii=Y.Qj)(s,a,o,u,p,_,y,v,T,j,B,H,ee),Tp=n.dynCall_viiiiiiiiiiiiiiiiiiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot,Gt,_r,jr,Os)=>(Tp=n.dynCall_viiiiiiiiiiiiiiiiiiiiiii=Y.Rj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot,Gt,_r,jr,Os),Cp=n.dynCall_viiiiifiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee)=>(Cp=n.dynCall_viiiiifiiiiii=Y.Sj)(s,a,o,u,p,_,y,v,T,j,B,H,ee),Pa=n.dynCall_vijjiiiiii=(s,a,o,u,p,_,y,v,T,j)=>(Pa=n.dynCall_vijjiiiiii=Y.Tj)(s,a,o,u,p,_,y,v,T,j),Zn=n.dynCall_vijiiiiiiijjii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe)=>(Zn=n.dynCall_vijiiiiiiijjii=Y.Uj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe),ac=n.dynCall_viiiiiiiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee)=>(ac=n.dynCall_viiiiiiiiiiii=Y.Vj)(s,a,o,u,p,_,y,v,T,j,B,H,ee),oc=n.dynCall_viiiiiiiiiiiiiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt)=>(oc=n.dynCall_viiiiiiiiiiiiiiiiii=Y.Wj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt),lc=n.dynCall_viiiiiiiiiiiiiiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot)=>(lc=n.dynCall_viiiiiiiiiiiiiiiiiii=Y.Xj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot),ei=n.dynCall_viiijiiiiiiiiiiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot)=>(ei=n.dynCall_viiijiiiiiiiiiiiiiii=Y.Yj)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot),uc=n.dynCall_viiiijjj=(s,a,o,u,p,_,y,v)=>(uc=n.dynCall_viiiijjj=Y.Zj)(s,a,o,u,p,_,y,v),cc=n.dynCall_viiiiiiijjj=(s,a,o,u,p,_,y,v,T,j,B)=>(cc=n.dynCall_viiiiiiijjj=Y._j)(s,a,o,u,p,_,y,v,T,j,B),dc=n.dynCall_iiiiiiiiiiiiiiiiiiiifi=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot,Gt,_r)=>(dc=n.dynCall_iiiiiiiiiiiiiiiiiiiifi=Y.$j)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe,De,We,nt,pt,Ot,Gt,_r),ti=n.dynCall_viiijiiiiiiiiii=(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe)=>(ti=n.dynCall_viiijiiiiiiiiii=Y.ak)(s,a,o,u,p,_,y,v,T,j,B,H,ee,fe,xe),pc=n.dynCall_viiiiif=(s,a,o,u,p,_,y)=>(pc=n.dynCall_viiiiif=Y.bk)(s,a,o,u,p,_,y),hc=n.dynCall_viiif=(s,a,o,u,p)=>(hc=n.dynCall_viiif=Y.ck)(s,a,o,u,p),fc=n.dynCall_viiiiiiiiifi=(s,a,o,u,p,_,y,v,T,j,B,H)=>(fc=n.dynCall_viiiiiiiiifi=Y.dk)(s,a,o,u,p,_,y,v,T,j,B,H),ri=n.dynCall_viiiiid=(s,a,o,u,p,_,y)=>(ri=n.dynCall_viiiiid=Y.ek)(s,a,o,u,p,_,y),mc=n.dynCall_viiid=(s,a,o,u,p)=>(mc=n.dynCall_viiid=Y.fk)(s,a,o,u,p),_c=n.dynCall_iiif=(s,a,o,u)=>(_c=n.dynCall_iiif=Y.gk)(s,a,o,u),gc=n.dynCall_iiiij=(s,a,o,u,p)=>(gc=n.dynCall_iiiij=Y.hk)(s,a,o,u,p),wc=n.dynCall_iiiiiif=(s,a,o,u,p,_,y)=>(wc=n.dynCall_iiiiiif=Y.ik)(s,a,o,u,p,_,y),yc=n.dynCall_vijij=(s,a,o,u,p)=>(yc=n.dynCall_vijij=Y.jk)(s,a,o,u,p),bc=n.dynCall_viiijji=(s,a,o,u,p,_,y)=>(bc=n.dynCall_viiijji=Y.kk)(s,a,o,u,p,_,y),Mc=n.dynCall_viiiiiiffii=(s,a,o,u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r=this.tensorTrackersById.get(e);r&&(this.tensorTrackersById.delete(e),r.tensorWrapper&&this.releaseTensor(r.tensorWrapper))}async ensureTensor(e,r,t,i,l){Kt("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${r}, dataType: ${t}, shape: ${i}, copyOld: ${l}}`);let n=this.tensorTrackersById.get(r);if(!n)throw new Error("Tensor not found.");return n.ensureTensor(e,t,i,l)}upload(e,r){let t=this.tensorTrackersById.get(e);if(!t)throw new Error("Tensor not found.");t.upload(r)}async download(e,r){Kt("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${e}, dstBuffer: ${r==null?void 0:r.byteLength}}`);let t=this.tensorTrackersById.get(e);if(!t)throw new Error("Tensor not found.");return t.download(r)}releaseTensorsForSession(e){for(let r of this.freeTensors)r.sessionId===e&&r.destroy();this.freeTensors=this.freeTensors.filter(r=>r.sessionId!==e)}registerTensor(e,r,t,i){let l=this.getMLContext(e),n=Lf(),c=new Bf({sessionId:e,context:l,tensor:r,dataType:t,shape:i});return 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${I.registerUniform("output_size","u32").declareVariables(z,E)} + var tile : array, ${M}>; + ${I.mainStart([M,M,1])} + let stride = (uniforms.output_shape[1] - 1) / ${M} + 1; + let workgroup_id_x = workgroup_index % stride; + let workgroup_id_y = workgroup_index / stride; + let input_col = workgroup_id_y * ${M}u + local_id.x; + let input_row = workgroup_id_x * ${M}u + local_id.y; + if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) { + tile[local_id.y][local_id.x] = ${z.getByIndices(`${z.type.indices}(input_row, input_col)`)}; + } + workgroupBarrier(); + + let output_col = workgroup_id_x * ${M}u + local_id.x; + let output_row = workgroup_id_y * ${M}u + local_id.y; + if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) { + ${E.setByIndices(`${E.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")} + } + }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let 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outputIndex = global_idx / ${g}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${Y_[i]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${g}) { + let candidate = f32(${w.getByOffset("offset + k")}); + bestValue = ${X_[i]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${g}u); + for (var currentSize = reduceSize / 2u; reduceSize > 1u; + currentSize = reduceSize / 2u) { + let interval = DIV_CEIL(reduceSize, 2u); + if (local_idx < currentSize) { + let candidate = aBestValues[local_idx + interval]; + bestValue = ${J_[i]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${m.setByOffset("outputIndex",`${i==="mean"?`${m.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${m.type.storage}(${Z_[i]})`}`)}; + } + }`;return{name:e,shaderCache:{hint:`${r};${g}`,inputDependencies:["type"]},getShaderSource:M,getRunData:()=>({outputs:[{dims:n,dataType:l}],dispatchGroup:{x:h},programUniforms:[{type:12,data:f}]})}},Fs=(e,r,t,i)=>{let l=e.inputs.length===1?t:Pm(e.inputs,t),n=l.axes;n.length===0&&!l.noopWithEmptyAxes&&(n=e.inputs[0].dims.map((x,M)=>M));let 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${C.offsetToIndices("global_idx")}; + + ${z.join(` +`)} + ${D[0]} // init ops for reduce max/min + ${D[1]} + ${A} + ${D[3]} + ${D.length===4?C.setByOffset("global_idx","value"):D.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:h,dataType:n}],dispatchGroup:{x:Math.ceil(M/64)},programUniforms:[{type:12,data:M},...Et(f,h)]})}},Pm=(e,r)=>{let t=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(i=>t.push(Number(i))),Zt({axes:t,keepDims:r.keepDims,noopWithEmptyAxes:r.noopWithEmptyAxes})},js=(e,r,t,i)=>{let l=e.inputs,n=l.length===1?t:Pm(l,t);e.compute(Zh(r,{hint:n.cacheKey,inputDependencies:["rank"]},[l[0]],n.noopWithEmptyAxes&&n.axes.length===0?ag:i,n.axes,l[0].dataType,n.keepDims,n.noopWithEmptyAxes),{inputs:[0]})},og=(e,r)=>{Ds(e.inputs),js(e,"ReduceLogSum",r,(t,i)=>[`var value = ${i.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,"value = log(value);"])},lg=(e,r)=>{Ds(e.inputs),js(e,"ReduceL1",r,(t,i)=>[`var value = ${i.type.storage}(0);`,"",`value 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${n});`]})},hg=(e,r)=>{Ds(e.inputs),js(e,"ReduceMin",r,(t,i,l)=>{let n=[];for(let c=0;c=0||l.length===0)&&n.push(`input_indices[${c}] = 0;`);return[`${n.join(` +`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = min(value, ${t.getByIndices("input_indices")});`,""]})},fg=(e,r)=>{Ds(e.inputs),js(e,"ReduceProd",r,(t,i)=>[`var value = ${i.type.storage}(1);`,"",`value *= ${t.getByIndices("input_indices")};`,""])},mg=(e,r)=>{Ds(e.inputs),js(e,"ReduceSum",r,(t,i)=>[`var value = ${i.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,""])},_g=(e,r)=>{Ds(e.inputs),js(e,"ReduceSumSquare",r,(t,i)=>[`var t = ${i.type.value}(0); var value = ${i.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += t * t;`,""])},Ls=(e,r,t)=>{if(r.length===0)return t;let i=1,l=1;for(let n=0;n1024},Q0=(e,r)=>{Ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?pg(e,r):B0(e,r)},X0=(e,r)=>{Ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?lg(e,r):R0(e,r)},J0=(e,r)=>{Ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?ug(e,r):N0(e,r)},Y0=(e,r)=>{Ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?cg(e,r):V0(e,r)},Z0=(e,r)=>{Ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?dg(e,r):W0(e,r)},eb=(e,r)=>{Ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?hg(e,r):U0(e,r)},tb=(e,r)=>{Ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?fg(e,r):G0(e,r)},rb=(e,r)=>{Ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?mg(e,r):K0(e,r)},sb=(e,r)=>{Ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?_g(e,r):H0(e,r)},nb=(e,r)=>{Ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?og(e,r):q0(e,r)}}),Uf,ib,ab,Sm,K1=Ye(()=>{$t(),br(),Zm(),Uf=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},ib=(e,r)=>{Uf(e.inputs);let t=(i,l,n)=>{let c=[];for(let d=0;d=0||n.length===0)&&c.push(`input_indices[${d}] = 0;`);return[`${c.join(` +`)}`,`var value = ${i.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${i.getByIndices("input_indices")} ${r.selectLastIndex>0?"<=":"<"} value) { + value = ${i.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",l.setByOffset("global_idx","best_index")]};e.compute(Zh("ArgMin",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},ab=(e,r)=>{Uf(e.inputs);let t=(i,l,n)=>{let c=[];for(let d=0;d=0||n.length===0)&&c.push(`input_indices[${d}] = 0;`);return[`${c.join(` +`)}`,`var value = ${i.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${i.getByIndices("input_indices")} ${r.selectLastIndex>0?">=":">"} value) { + value = ${i.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",l.setByOffset("global_idx","best_index")]};e.compute(Zh("argMax",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},Sm=e=>Zt(e)}),gg,zh,wg,yg,bg,yd,Mg,ob,e_=Ye(()=>{$t(),Lt(),Jm(),zt(),gg=(e,r)=>{let t=e[0],i=e[1],l=e[2],n=e[3],c=e[4],d=e[5];if(c&&d)throw new Error("Attention cannot have both past and attention_bias");if(t.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let h=t.dims[0],f=t.dims[1],w=t.dims[2];if(l.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(i.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(i.dims[0]!==w)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(l.dims[0]!==i.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let m=l.dims[0]/3,g=m,x=g;if(r.qkvHiddenSizes.length>0){if(r.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let D of r.qkvHiddenSizes)if(D%r.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");m=r.qkvHiddenSizes[0],g=r.qkvHiddenSizes[1],x=r.qkvHiddenSizes[2]}let M=f;if(m!==g)throw new Error("qkv_hidden_sizes first element should be same as the second");if(l.dims[0]!==m+g+x)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let I=0;if(c){if(g!==x)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(c.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(c.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(c.dims[1]!==h)throw new Error('Input "past" second dimension must be batch_size');if(c.dims[2]!==r.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(c.dims[4]!==g/r.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');r.pastPresentShareBuffer||(I=c.dims[3])}let z=M+I,E=-1,C=0;if(n)throw new Error("Mask not supported");if(c)throw new Error("past is not supported");if(d){if(d.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(d.dims[0]!==h||d.dims[1]!==r.numHeads||d.dims[2]!==f||d.dims[3]!==z)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:h,sequenceLength:f,pastSequenceLength:I,kvSequenceLength:M,totalSequenceLength:z,maxSequenceLength:E,inputHiddenSize:w,hiddenSize:m,vHiddenSize:x,headSize:Math.floor(m/r.numHeads),vHeadSize:Math.floor(x/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:C,scale:r.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},zh=(e,r,t)=>r&&e?` + let total_sequence_length_input = u32(${r.getByOffset("0")}); + let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length); + let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input; + let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input; + total_sequence_length = u32(${e==null?void 0:e.getByOffset("batchIdx")}) + 1; + var past_sequence_length: u32 = 0; + if (is_first_prompt == false) { + past_sequence_length = total_sequence_length - sequence_length; + } + `:` + ${t?"let past_sequence_length = uniforms.past_sequence_length":""}; + let present_sequence_length = total_sequence_length; + `,wg=(e,r,t,i,l,n,c,d)=>{let h=gr(c?1:n),f=64,w=n/h;w{let C=gt("x",e.dataType,e.dims,h),D=[C],A=c?Ne("seq_lens",c.dataType,c.dims):void 0;A&&D.push(A);let $=d?Ne("total_sequence_length_input",d.dataType,d.dims):void 0;$&&D.push($);let P=Qr(e.dataType),k=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return` + var thread_max: array; + var thread_sum: array; + ${E.registerUniforms(k).declareVariables(...D)} + ${E.mainStart([f,1,1])} + let batchIdx = workgroup_id.z / uniforms.num_heads; + let headIdx = workgroup_id.z % uniforms.num_heads; + let sequence_length = uniforms.sequence_length; + var total_sequence_length = uniforms.total_sequence_length; + ${zh(A,$,!1)} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = (global_idx / ${f}) * uniforms.total_sequence_length + local_offset; + let seq_causal_length = ${c?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"}; + var thread_max_vector = ${M}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + thread_max_vector = max(${M}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(h){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${h}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${f}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${M}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + sum_vector += exp(${M}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(h){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${h}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${f}; i++) { + sum += thread_sum[i]; + } + + if (sum == 0) { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + x[offset + i] = ${C.type.value}(${P}(1.0) / ${P}(seq_causal_length)); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + var f32input = ${M}(x[offset + i]); + x[offset + i] = ${C.type.value}(exp(f32input - max_value) / sum); + } + } + ${c?` + for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) { + x[offset + total_seq_id] = ${C.type.value}(${P}(0)); + }`:""}; + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${f};${x};${h}`,inputDependencies:I},getShaderSource:z,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:l,z:r*t},programUniforms:g})}},yg=(e,r,t,i,l,n,c,d,h)=>{let f=c+n.kvSequenceLength,w=[n.batchSize,n.numHeads,n.sequenceLength,f],m=e>1&&i,g=n.kvNumHeads?n.kvNumHeads:n.numHeads,x=m?[n.batchSize,g,f,n.headSize]:void 0,M=n.nReps?n.nReps:1,I=n.scale===0?1/Math.sqrt(n.headSize):n.scale,z=gr(n.headSize),E=n.headSize/z,C=12,D={x:Math.ceil(f/C),y:Math.ceil(n.sequenceLength/C),z:n.batchSize*n.numHeads},A=[{type:12,data:n.sequenceLength},{type:12,data:E},{type:12,data:f},{type:12,data:n.numHeads},{type:12,data:n.headSize},{type:1,data:I},{type:12,data:c},{type:12,data:n.kvSequenceLength},{type:12,data:M}],$=m&&i&&je.size(i.dims)>0,P=["type","type"];$&&P.push("type"),l&&P.push("type"),d&&P.push("type"),h&&P.push("type");let k=[{dims:w,dataType:r.dataType,gpuDataType:0}];m&&k.push({dims:x,dataType:r.dataType,gpuDataType:0});let O=R=>{let U=Ne("q",r.dataType,r.dims,z),te=Ne("key",t.dataType,t.dims,z),se=[U,te];if($){let Se=Ne("past_key",i.dataType,i.dims,z);se.push(Se)}l&&se.push(Ne("attention_bias",l.dataType,l.dims));let K=d?Ne("seq_lens",d.dataType,d.dims):void 0;K&&se.push(K);let pe=h?Ne("total_sequence_length_input",h.dataType,h.dims):void 0;pe&&se.push(pe);let re=gt("output",r.dataType,w),oe=[re];m&&oe.push(gt("present_key",r.dataType,x,z));let ge=Qr(1,z),le=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return` + const TILE_SIZE = ${C}u; + + var tileQ: array<${U.type.storage}, ${C*C}>; + var tileK: array<${U.type.storage}, ${C*C}>; + ${R.registerUniforms(le).declareVariables(...se,...oe)} + ${R.mainStart([C,C,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z % uniforms.num_heads; + let kvHeadIdx = ${M===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${M===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let m = workgroup_id.y * TILE_SIZE; + let n = workgroup_id.x * TILE_SIZE; + let sequence_length = uniforms.M; + var total_sequence_length = uniforms.N; + ${zh(K,pe,!0)} + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; + let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + ${$&&m?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""}; + let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K; + ${m?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""} + var value = ${ge}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) { + tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x]; + } + if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) { + var idx = TILE_SIZE * local_id.y + local_id.x; + ${$&&m?` + if (n + local_id.y < past_sequence_length) { + tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; + } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) { + tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x]; + }`:` + if (n + local_id.y < uniforms.kv_sequence_length) { + tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; + }`} + ${m?`if (n + local_id.y < present_sequence_length) { + present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx]; + }`:""} + } + workgroupBarrier(); + + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += ${ge}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]); + } + + workgroupBarrier(); + } + + if (global_id.y < uniforms.M && global_id.x < total_sequence_length) { + let headOffset = workgroup_id.z * uniforms.M * uniforms.N; + let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x; + var sum: f32 = ${(()=>{switch(z){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${z}`)}})()}; + output[outputIdx] = ${re.type.value} (sum * uniforms.alpha) + ${l?"attention_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${z};${l!==void 0};${i!==void 0};${e}`,inputDependencies:P},getRunData:()=>({outputs:k,dispatchGroup:D,programUniforms:A}),getShaderSource:O}},bg=(e,r,t,i,l,n,c=void 0,d=void 0)=>{let h=n+l.kvSequenceLength,f=l.nReps?l.nReps:1,w=l.vHiddenSize*f,m=e>1&&i,g=l.kvNumHeads?l.kvNumHeads:l.numHeads,x=m?[l.batchSize,g,h,l.headSize]:void 0,M=[l.batchSize,l.sequenceLength,w],I=12,z={x:Math.ceil(l.vHeadSize/I),y:Math.ceil(l.sequenceLength/I),z:l.batchSize*l.numHeads},E=[{type:12,data:l.sequenceLength},{type:12,data:h},{type:12,data:l.vHeadSize},{type:12,data:l.numHeads},{type:12,data:l.headSize},{type:12,data:w},{type:12,data:n},{type:12,data:l.kvSequenceLength},{type:12,data:f}],C=m&&i&&je.size(i.dims)>0,D=["type","type"];C&&D.push("type"),c&&D.push("type"),d&&D.push("type");let A=[{dims:M,dataType:r.dataType,gpuDataType:0}];m&&A.push({dims:x,dataType:r.dataType,gpuDataType:0});let $=P=>{let k=Ne("probs",r.dataType,r.dims),O=Ne("v",t.dataType,t.dims),R=[k,O];C&&R.push(Ne("past_value",i.dataType,i.dims));let U=c?Ne("seq_lens",c.dataType,c.dims):void 0;c&&R.push(U);let te=d?Ne("total_sequence_length_input",d.dataType,d.dims):void 0;d&&R.push(te);let se=[gt("output",r.dataType,M)];m&&se.push(gt("present_value",r.dataType,x));let K=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return` + const TILE_SIZE = ${I}u; + var tileQ: array<${k.type.value}, ${I*I}>; + var tileV: array<${k.type.value}, ${I*I}>; + ${P.registerUniforms(K).declareVariables(...R,...se)} + ${P.mainStart([I,I,1])} + let headIdx = workgroup_id.z % uniforms.num_heads; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let kvHeadIdx = ${f===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${f===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; + let m = global_id.y; + let n = global_id.x; + let sequence_length = uniforms.M; + var total_sequence_length = uniforms.K; + ${zh(U,te,!0)} + let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch + ${C&&m?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""}; + let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n; + ${m?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""} + var value = ${k.type.storage}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + var idx = TILE_SIZE * local_id.y + local_id.x; + ${C&&m?` + if (w + local_id.y < past_sequence_length) { + tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N]; + } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) { + tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N]; + } + `:` + if (w + local_id.y < uniforms.kv_sequence_length) { + tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N]; + }`} + ${m?` + if (w + local_id.y < present_sequence_length) { + present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx]; + }`:""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + headIdx * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${i!==void 0};${e}`,inputDependencies:D},getRunData:()=>({outputs:A,dispatchGroup:z,programUniforms:E}),getShaderSource:$}},yd=(e,r,t,i,l,n,c,d,h,f,w=void 0,m=void 0)=>{let g=Math.min(e.outputCount,1+(c?1:0)+(d?1:0)),x=g>1?f.pastSequenceLength:0,M=x+f.kvSequenceLength,I=h&&je.size(h.dims)>0?h:void 0,z=[r,t];g>1&&c&&je.size(c.dims)>0&&z.push(c),I&&z.push(I),w&&z.push(w),m&&z.push(m);let E=e.compute(yg(g,r,t,c,I,f,x,w,m),{inputs:z,outputs:g>1?[-1,1]:[-1]})[0];e.compute(wg(E,f.batchSize,f.numHeads,x,f.sequenceLength,M,w,m),{inputs:w&&m?[E,w,m]:[E],outputs:[]});let C=[E,i];g>1&&d&&je.size(d.dims)>0&&C.push(d),w&&C.push(w),m&&C.push(m),e.compute(bg(g,E,i,d,f,x,w,m),{inputs:C,outputs:g>1?[0,2]:[0]})},Mg=(e,r)=>{let t=[r.batchSize,r.numHeads,r.sequenceLength,r.headSize],i=r.sequenceLength,l=r.inputHiddenSize,n=r.headSize,c=12,d={x:Math.ceil(r.headSize/c),y:Math.ceil(r.sequenceLength/c),z:r.batchSize*r.numHeads},h=[e.inputs[0],e.inputs[1],e.inputs[2]],f=[{type:12,data:i},{type:12,data:l},{type:12,data:n},{type:12,data:r.numHeads},{type:12,data:r.headSize},{type:12,data:r.hiddenSize},{type:12,data:r.hiddenSize+r.hiddenSize+r.vHiddenSize}],w=m=>{let g=gt("output_q",h[0].dataType,t),x=gt("output_k",h[0].dataType,t),M=gt("output_v",h[0].dataType,t),I=Ne("input",h[0].dataType,h[0].dims),z=Ne("weight",h[1].dataType,h[1].dims),E=Ne("bias",h[2].dataType,h[2].dims),C=I.type.storage,D=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return` + const TILE_SIZE = ${c}u; + var tileInput: array<${C}, ${c*c}>; + var tileWeightQ: array<${C}, ${c*c}>; + var tileWeightK: array<${C}, ${c*c}>; + var tileWeightV: array<${C}, ${c*c}>; + ${m.registerUniforms(D).declareVariables(I,z,E,g,x,M)} + ${m.mainStart([c,c,1])} + let batchIndex = workgroup_id.z / uniforms.num_heads; + let headNumber = workgroup_id.z % uniforms.num_heads; + let m = global_id.y; + let n = global_id.x; + + let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K; + let biasOffsetQ = headNumber * uniforms.head_size; + let biasOffsetK = uniforms.hidden_size + biasOffsetQ; + let biasOffsetV = uniforms.hidden_size + biasOffsetK; + + var valueQ = ${C}(0); + var valueK = ${C}(0); + var valueV = ${C}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:d,programUniforms:f}),getShaderSource:w},{inputs:h,outputs:[-1,-1,-1]})},ob=(e,r)=>{let t=gg(e.inputs,r),[i,l,n]=Mg(e,t);return yd(e,i,l,n,e.inputs[4],void 0,void 0,void 0,e.inputs[5],t)}}),vg,xg,Tg,lb,H1=Ye(()=>{Ws(),$t(),Lt(),br(),zt(),vg=(e,r)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let t=(i,l,n)=>{let c=l.length;if(c!==i.length)throw new Error(`${n}: num dimensions != ${c}`);l.forEach((d,h)=>{if(d!==i[h])throw new Error(`${n}: dim[${h}] do not match`)})};if(e[0].dims.length>1){let i=r.format==="NHWC"?r.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,r.spatial?2:void 0);t(e[1].dims,i,"Invalid input scale"),t(e[2].dims,i,"Invalid input B"),t(e[3].dims,i,"Invalid input mean"),t(e[4].dims,i,"Invalid input var")}else t(e[1].dims,[1],"Invalid input scale"),t(e[2].dims,[1],"Invalid input B"),t(e[3].dims,[1],"Invalid input mean"),t(e[4].dims,[1],"Invalid input var")},xg=(e,r)=>{let{epsilon:t,spatial:i,format:l}=r,n=e[0].dims,c=i?gr(n[n.length-1]):1,d=l==="NHWC"&&n.length>1?c:1,h=je.size(n)/c,f=i,w=f?n.length:n,m=Ne("x",e[0].dataType,e[0].dims,c),g=Ne("scale",e[1].dataType,e[1].dims,d),x=Ne("bias",e[2].dataType,e[2].dims,d),M=Ne("inputMean",e[3].dataType,e[3].dims,d),I=Ne("inputVar",e[4].dataType,e[4].dims,d),z=gt("y",e[0].dataType,w,c),E=()=>{let D="";if(i)D=`let cOffset = ${n.length===1?"0u":l==="NHWC"?`outputIndices[${n.length-1}] / ${c}`:"outputIndices[1]"};`;else if(l==="NCHW")D=` + ${z.indicesSet("outputIndices","0","0")} + let cOffset = ${z.indicesToOffset("outputIndices")};`;else{D=`var cIndices = ${g.type.indices}(0); + cIndices[0] = outputIndices[${n.length-1}];`;for(let A=1;A` + const epsilon = ${t}; + ${D.registerUniform("outputSize","u32").declareVariables(m,g,x,M,I,z)} + ${D.mainStart()} + ${D.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${z.offsetToIndices(`global_idx * ${c}`)}; + ${E()} + let scale = ${g.getByOffset("cOffset")}; + let bias = ${x.getByOffset("cOffset")}; + let inputMean = ${M.getByOffset("cOffset")}; + let inputVar = ${I.getByOffset("cOffset")}; + let x = ${m.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${z.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${r.epsilon}_${r.format}_${i}_${c}`,inputDependencies:f?["rank","type","type","type","type"]:void 0},getShaderSource:C,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:f?[{type:12,data:h},...Et(n)]:[{type:12,data:h}]})}},Tg=e=>Zt(e),lb=(e,r)=>{let{inputs:t,outputCount:i}=e,l=Tg({...r,outputCount:i});if(dr.webgpu.validateInputContent&&vg(t,l),r.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(xg(t,l))}}),Cg,Eg,ub,q1=Ye(()=>{Lt(),zt(),Cg=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Eg=e=>{let r=e[0].dims,t=e[0].dims[2],i=je.size(r)/4,l=e[0].dataType,n=Ne("input",l,r,4),c=Ne("bias",l,[t],4),d=Ne("residual",l,r,4),h=gt("output",l,r,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)}}),getShaderSource:f=>` + const channels = ${t}u / 4; + ${f.declareVariables(n,c,d,h)} + + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes(i)} + let value = ${n.getByOffset("global_idx")} + + ${c.getByOffset("global_idx % channels")} + ${d.getByOffset("global_idx")}; + ${h.setByOffset("global_idx","value")} + }`}},ub=e=>{Cg(e.inputs),e.compute(Eg(e.inputs))}}),Pg,Xt,cb,db,pb,hb,fb,mb,_b,gb,wb,Sg,yb,bb,Mb,vb,fd,xb,qh,Tb,Cb,Eb,Pb,Sb,kb,$b,Ib,Ab,Ob,Fb,Db,jb,Lb,zb,Bb,Gf,Rb,km,$m,Nb,Vb,Wb,kg,$g,Ub,t_=Ye(()=>{$t(),Lt(),br(),zt(),Pg=(e,r,t,i,l,n,c)=>{let d=Math.ceil(r/4),h="";typeof l=="string"?h=`${l}(a)`:h=l("a");let f=Ne("inputData",t,[d],4),w=gt("outputData",i,[d],4),m=[{name:"vec_size",type:"u32"}];return c&&m.push(...c),` + ${e.registerUniforms(m).declareVariables(f,w)} + + ${n??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${f.getByOffset("global_idx")}; + ${w.setByOffset("global_idx",h)} + }`},Xt=(e,r,t,i,l,n=e.dataType,c,d)=>{let h=[{type:12,data:Math.ceil(je.size(e.dims)/4)}];return c&&h.push(...c),{name:r,shaderCache:{hint:l,inputDependencies:["type"]},getShaderSource:f=>Pg(f,je.size(e.dims),e.dataType,n,t,i,d),getRunData:f=>({outputs:[{dims:e.dims,dataType:n}],dispatchGroup:{x:Math.ceil(je.size(f[0].dims)/64/4)},programUniforms:h})}},cb=e=>{e.compute(Xt(e.inputs[0],"Abs","abs"))},db=e=>{e.compute(Xt(e.inputs[0],"Acos","acos"))},pb=e=>{e.compute(Xt(e.inputs[0],"Acosh","acosh"))},hb=e=>{e.compute(Xt(e.inputs[0],"Asin","asin"))},fb=e=>{e.compute(Xt(e.inputs[0],"Asinh","asinh"))},mb=e=>{e.compute(Xt(e.inputs[0],"Atan","atan"))},_b=e=>{e.compute(Xt(e.inputs[0],"Atanh","atanh"))},gb=e=>Zt(e),wb=(e,r)=>{let t;switch(r.to){case 10:t="vec4";break;case 1:t="vec4";break;case 12:t="vec4";break;case 6:t="vec4";break;case 9:t="vec4";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${r.to}`)}e.compute(Xt(e.inputs[0],"Cast",t,void 0,r.cacheKey,r.to))},Sg=e=>{let r,t,i=e.length>=2&&e[1].data!==0,l=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:r=i?e[1].getFloat32Array()[0]:-34028234663852886e22,t=l?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:r=i?e[1].getUint16Array()[0]:64511,t=l?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return Zt({min:r,max:t})},yb=(e,r)=>{let t=r||Sg(e.inputs),i=Qr(e.inputs[0].dataType);e.compute(Xt(e.inputs[0],"Clip",l=>`clamp(${l}, vec4<${i}>(uniforms.min), vec4<${i}>(uniforms.max))`,void 0,t.cacheKey,void 0,[{type:e.inputs[0].dataType,data:t.min},{type:e.inputs[0].dataType,data:t.max}],[{name:"min",type:i},{name:"max",type:i}]),{inputs:[0]})},bb=e=>{e.compute(Xt(e.inputs[0],"Ceil","ceil"))},Mb=e=>{e.compute(Xt(e.inputs[0],"Cos","cos"))},vb=e=>{e.compute(Xt(e.inputs[0],"Cosh","cosh"))},fd=e=>Zt(e),xb=(e,r)=>{let t=Qr(e.inputs[0].dataType);e.compute(Xt(e.inputs[0],"Elu",i=>`elu_vf32(${i})`,` + const elu_alpha_ = ${t}(${r.alpha}); + + fn elu_f32(a: ${t}) -> ${t} { + return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${t}>) -> vec4<${t}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,r.cacheKey))},qh=(e="f32")=>` +const r0: ${e} = 0.3275911; +const r1: ${e} = 0.254829592; +const r2: ${e} = -0.284496736; +const r3: ${e} = 1.421413741; +const r4: ${e} = -1.453152027; +const r5: ${e} = 1.061405429; + +fn erf_vf32(v: vec4<${e}>) -> vec4<${e}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,Tb=e=>{let r=Qr(e.inputs[0].dataType);e.compute(Xt(e.inputs[0],"Erf",t=>`erf_vf32(${t})`,qh(r)))},Cb=e=>{e.compute(Xt(e.inputs[0],"Exp","exp"))},Eb=e=>{e.compute(Xt(e.inputs[0],"Floor","floor"))},Pb=e=>{let r=Qr(e.inputs[0].dataType);e.compute(Xt(e.inputs[0],"Gelu",t=>`0.5 * ${t} * (1.0 + erf_vf32(${t} * 0.7071067811865475))`,qh(r)))},Sb=(e,r)=>{let t=Qr(e.inputs[0].dataType);e.compute(Xt(e.inputs[0],"LeakyRelu",i=>`select(leaky_relu_alpha_ * ${i}, ${i}, ${i} >= vec4<${t}>(0.0))`,`const leaky_relu_alpha_ = ${t}(${r.alpha});`,r.cacheKey))},kb=e=>{e.compute(Xt(e.inputs[0],"Not",r=>`!${r}`))},$b=e=>{e.compute(Xt(e.inputs[0],"Neg",r=>`-${r}`))},Ib=e=>{e.compute(Xt(e.inputs[0],"Reciprocal",r=>`1.0/${r}`))},Ab=e=>{let r=Qr(e.inputs[0].dataType);e.compute(Xt(e.inputs[0],"Relu",t=>`select(vec4<${r}>(0.0), ${t}, ${t} > vec4<${r}>(0.0))`))},Ob=e=>{e.compute(Xt(e.inputs[0],"Sigmoid",r=>`(1.0 / (1.0 + exp(-${r})))`))},Fb=e=>Zt(e),Db=(e,r)=>{let t=Qr(e.inputs[0].dataType);e.compute(Xt(e.inputs[0],"HardSigmoid",i=>`max(vec4<${t}>(0.0), min(vec4<${t}>(1.0), ${r.alpha} * ${i} + vec4<${t}>(${r.beta})))`,void 0,r.cacheKey))},jb=e=>{e.compute(Xt(e.inputs[0],"Sin","sin"))},Lb=e=>{e.compute(Xt(e.inputs[0],"Sinh","sinh"))},zb=e=>{e.compute(Xt(e.inputs[0],"Sqrt","sqrt"))},Bb=e=>{e.compute(Xt(e.inputs[0],"Tan","tan"))},Gf=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Rb=e=>{e.compute(Xt(e.inputs[0],"Tanh",Gf))},km=(e="f32")=>` +const fast_gelu_a: ${e} = 0.5; +const fast_gelu_b: ${e} = 0.7978845608028654; +const fast_gelu_c: ${e} = 0.035677408136300125; + +fn tanh_v(v: vec4<${e}>) -> vec4<${e}> { + return ${Gf("v")}; +} +`,$m=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Nb=e=>{let r=Qr(e.inputs[0].dataType);e.compute(Xt(e.inputs[0],"FastGelu",$m,km(r),void 0,e.inputs[0].dataType))},Vb=(e,r)=>{let t=Qr(e.inputs[0].dataType);return e.compute(Xt(e.inputs[0],"ThresholdedRelu",i=>`select(vec4<${t}>(0.0), ${i}, ${i} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${t}>(${r.alpha});`,r.cacheKey)),0},Wb=e=>{e.compute(Xt(e.inputs[0],"Log","log"))},kg=(e,r)=>` +const alpha = vec4<${e}>(${r}); +const one = ${e}(1.0); +const zero = ${e}(0.0); + +fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> { + let v = x *alpha; + var x1 : vec4<${e}>; + for (var i = 0; i < 4; i = i + 1) { + if (v[i] >= zero) { + x1[i] = one / (one + exp(-v[i])); + } else { + x1[i] = one - one / (one + exp(v[i])); + } + } + return x * x1; +} +`,$g=e=>`quick_gelu_impl(${e})`,Ub=(e,r)=>{let t=Qr(e.inputs[0].dataType);e.compute(Xt(e.inputs[0],"QuickGelu",$g,kg(t,r.alpha),r.cacheKey,e.inputs[0].dataType))}}),Ig,Ag,Gb,Q1=Ye(()=>{Lt(),zt(),t_(),Ig=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Ag=e=>{let r=e[0].dims.slice();r[2]=r[2]/2;let t=Ne("input",e[0].dataType,e[0].dims,4),i=Ne("bias",e[0].dataType,[e[0].dims[2]],4),l=gt("output",e[0].dataType,r,4),n=je.size(r)/4,c=Lr(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:d=>` + const M_SQRT2 = sqrt(2.0); + const halfChannels = ${e[0].dims[2]/4/2}u; + + ${d.declareVariables(t,i,l)} + + ${qh(c)} + + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes(n)} + let biasIdx = global_idx % halfChannels; + let batchIndex = global_idx / halfChannels; + let inputOffset = biasIdx + batchIndex * halfChannels * 2; + let valueLeft = input[inputOffset] + bias[biasIdx]; + let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${l.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Gb=e=>{Ig(e.inputs),e.compute(Ag(e.inputs))}}),Og,Fg,zs,Kb,Hb,qb,Qb,Xb,Jb,Yb,Zb,eM,tM,X1=Ye(()=>{$t(),Lt(),zt(),Og=(e,r,t,i,l,n,c,d,h,f,w,m)=>{let g,x;typeof d=="string"?g=x=(C,D)=>`${d}((${C}),(${D}))`:typeof d=="function"?g=x=d:(g=d.scalar,x=d.vector);let M=gt("outputData",w,i.length,4),I=Ne("aData",h,r.length,4),z=Ne("bData",f,t.length,4),E;if(l)if(n){let C=je.size(r)===1,D=je.size(t)===1,A=r.length>0&&r[r.length-1]%4===0,$=t.length>0&&t[t.length-1]%4===0;C||D?E=M.setByOffset("global_idx",x(C?`${I.type.value}(${I.getByOffset("0")}.x)`:I.getByOffset("global_idx"),D?`${z.type.value}(${z.getByOffset("0")}.x)`:z.getByOffset("global_idx"))):E=` + let outputIndices = ${M.offsetToIndices("global_idx * 4u")}; + let offsetA = ${I.broadcastedIndicesToOffset("outputIndices",M)}; + let offsetB = ${z.broadcastedIndicesToOffset("outputIndices",M)}; + ${M.setByOffset("global_idx",x(c||A?I.getByOffset("offsetA / 4u"):`${I.type.value}(${I.getByOffset("offsetA / 4u")}[offsetA % 4u])`,c||$?z.getByOffset("offsetB / 4u"):`${z.type.value}(${z.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else E=M.setByOffset("global_idx",x(I.getByOffset("global_idx"),z.getByOffset("global_idx")));else{if(!n)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let C=(D,A,$="")=>{let P=`aData[indexA${A}][componentA${A}]`,k=`bData[indexB${A}][componentB${A}]`;return` + let outputIndices${A} = ${M.offsetToIndices(`global_idx * 4u + ${A}u`)}; + let offsetA${A} = ${I.broadcastedIndicesToOffset(`outputIndices${A}`,M)}; + let offsetB${A} = ${z.broadcastedIndicesToOffset(`outputIndices${A}`,M)}; + let indexA${A} = offsetA${A} / 4u; + let indexB${A} = offsetB${A} / 4u; + let componentA${A} = offsetA${A} % 4u; + let componentB${A} = offsetB${A} % 4u; + ${D}[${A}] = ${$}(${g(P,k)}); + `};w===9?E=` + var data = vec4(0); + ${C("data",0,"u32")} + ${C("data",1,"u32")} + ${C("data",2,"u32")} + ${C("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:E=` + ${C("outputData[global_idx]",0)} + ${C("outputData[global_idx]",1)} + ${C("outputData[global_idx]",2)} + ${C("outputData[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(I,z,M)} + + ${m??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${E} + }`},Fg=(e,r,t,i,l,n,c=t.dataType)=>{let d=t.dims.map(I=>Number(I)??1),h=i.dims.map(I=>Number(I)??1),f=!je.areEqual(d,h),w=d,m=je.size(d),g=!1,x=!1,M=[f];if(f){let I=Ra.calcShape(d,h,!1);if(!I)throw new Error("Can't perform binary op on the given tensors");w=I.slice(),m=je.size(w);let z=je.size(d)===1,E=je.size(h)===1,C=d.length>0&&d[d.length-1]%4===0,D=h.length>0&&h[h.length-1]%4===0;M.push(z),M.push(E),M.push(C),M.push(D);let A=1;for(let $=1;$I.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:I=>Og(I,d,h,w,g,f,x,l,t.dataType,i.dataType,c,n),getRunData:()=>({outputs:[{dims:w,dataType:c}],dispatchGroup:{x:Math.ceil(m/64/4)},programUniforms:[{type:12,data:Math.ceil(je.size(w)/4)},...Et(d,h,w)]})}},zs=(e,r,t,i,l,n)=>{e.compute(Fg(r,l??"",e.inputs[0],e.inputs[1],t,i,n))},Kb=e=>{zs(e,"Add",(r,t)=>`${r}+${t}`)},Hb=e=>{zs(e,"Div",(r,t)=>`${r}/${t}`)},qb=e=>{zs(e,"Equal",{scalar:(r,t)=>`u32(${r}==${t})`,vector:(r,t)=>`vec4(${r}==${t})`},void 0,void 0,9)},Qb=e=>{zs(e,"Mul",(r,t)=>`${r}*${t}`)},Xb=e=>{let r=Ne("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;zs(e,"Pow",{scalar:(t,i)=>`pow_custom(${t},${i})`,vector:(t,i)=>`pow_vector_custom(${t},${i})`},` + fn pow_custom(a : ${r}, b : ${r}) -> ${r} { + if (b == ${r}(0.0)) { + return ${r}(1.0); + } else if (a < ${r}(0.0) && f32(b) != floor(f32(b))) { + return ${r}(pow(f32(a), f32(b))); // NaN + } + return select(sign(a), ${r}(1.0), round(f32(abs(b) % ${r}(2.0))) != 1.0) * ${r}(${r==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); + } + fn pow_vector_custom(a : vec4<${r}>, b : vec4<${r}>) -> vec4<${r}> { + // TODO: implement vectorized pow + return vec4<${r}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); + } + `)},Jb=e=>{zs(e,"Sub",(r,t)=>`${r}-${t}`)},Yb=e=>{zs(e,"Greater",{scalar:(r,t)=>`u32(${r}>${t})`,vector:(r,t)=>`vec4(${r}>${t})`},void 0,void 0,9)},Zb=e=>{zs(e,"Less",{scalar:(r,t)=>`u32(${r}<${t})`,vector:(r,t)=>`vec4(${r}<${t})`},void 0,void 0,9)},eM=e=>{zs(e,"GreaterOrEqual",{scalar:(r,t)=>`u32(${r}>=${t})`,vector:(r,t)=>`vec4(${r}>=${t})`},void 0,void 0,9)},tM=e=>{zs(e,"LessOrEqual",{scalar:(r,t)=>`u32(${r}<=${t})`,vector:(r,t)=>`vec4(${r}<=${t})`},void 0,void 0,9)}}),Dg,jg,Lg,zg,rM,sM,J1=Ye(()=>{$t(),Lt(),br(),zt(),Dg=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");let t=0,i=e[t],l=i.dataType,n=i.dims.length;e.forEach((c,d)=>{if(d!==t){if(c.dataType!==l)throw new Error("input tensors should be one type");if(c.dims.length!==n)throw new Error("input tensors should have the same shape");c.dims.forEach((h,f)=>{if(f!==r&&h!==i.dims[f])throw new Error("non concat dimensions must match")})}})},jg=(e,r)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${r}); + for (var i: u32 = 0u; i < ${e}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${e}u; + }`,Lg=(e,r)=>{let t=e.length,i=[];for(let l=0;l{let l=je.size(t),n=new Array(e.length),c=new Array(e.length),d=0,h=[],f=[],w=[{type:12,data:l}];for(let I=0;I`uniforms.sizeInConcatAxis${I}`).join(","),M=I=>` + + ${(()=>{I.registerUniform("outputSize","u32");for(let z=0;z(${x}); + ${g} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${Lg(c,m)} + }`;return{name:"Concat",shaderCache:{hint:`${r}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:t,dataType:i}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:w}),getShaderSource:M}},rM=(e,r)=>{let t=e.inputs,i=t[0].dims,l=je.normalizeAxis(r.axis,i.length);Dg(t,l);let n=i.slice();n[l]=t.reduce((d,h)=>d+(h.dims.length>l?h.dims[l]:0),0);let c=t.filter(d=>je.size(d.dims)>0);e.compute(zg(c,l,n,t[0].dataType),{inputs:c})},sM=e=>Zt({axis:e.axis})}),mi,_i,gi,r_,yi=Ye(()=>{$t(),Lt(),mi=(e,r,t="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${r}(0.0));`;case"Sigmoid":return`value = (${r}(1.0) / (${r}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${r}(${t}(uniforms.clip_min)), ${r}(${t}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${r}(0.0), min(${r}(1.0), ${t}(uniforms.alpha) * value + ${t}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${t}(uniforms.alpha) * value, value, value >= ${r}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value)); + value = sign(value) * (1.0 - e2x) / (1.0 + e2x); + `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},_i=(e,r)=>{e.activation==="Clip"?r.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?r.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&r.push({type:1,data:e.alpha})},gi=(e,r)=>{e.activation==="Clip"?r.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?r.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&r.push({name:"alpha",type:"f32"})},r_=e=>{let r=(e==null?void 0:e.activation)||"";if(r==="HardSigmoid"){let[t,i]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:r,alpha:t,beta:i}}else if(r==="Clip"){let[t,i]=(e==null?void 0:e.activation_params)||[k0,$0];return{activation:r,clipMax:i,clipMin:t}}else if(r==="LeakyRelu"){let[t]=(e==null?void 0:e.activation_params)||[.01];return{activation:r,alpha:t}}return{activation:r}}}),Vr,nM,s_=Ye(()=>{Vr=(e,r)=>{switch(e){case 1:return r;case 2:return`vec2<${r}>`;case 3:return`vec3<${r}>`;case 4:return`vec4<${r}>`;default:throw new Error(`${e}-component is not supported.`)}},nM=e=>` + ${e?"value = value + getBiasByOutputCoords(coords);":""} + `}),iM,Y1=Ye(()=>{iM=e=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); +} +`}),_d,n_,i_=Ye(()=>{$t(),Lt(),zt(),yi(),_d=(e,r,t,i,l)=>{let n=i-t;return` + ${Array.from({length:t}).map((c,d)=>` + if (${Mt(r.shape,d,r.rank)} != 1) { + ${r.indicesSet(e,d,Mt(l,d+n,i))} + } else { + ${r.indicesSet(e,d,0)} + }`).join("")} +`},n_=(e,r,t,i,l=!1,n)=>{let c=e[0].dims,d=e[1].dims,h=c[c.length-2],f=d[d.length-1],w=c[c.length-1],m=gr(f),g=gr(w),x=gr(h),M=je.size(t)/m/x,I=e.length>2,z=i?i.slice(0,-2):t.slice(0,-2),E=[je.size(z),h,f],C=[{type:12,data:M},{type:12,data:h},{type:12,data:f},{type:12,data:w}];_i(r,C),C.push(...Et(z,c,d)),I&&C.push(...Et(e[2].dims)),C.push(...Et(E));let D=A=>{let $=Ym("batch_dims",e[0].dataType,z.length),P=Ne("a",e[0].dataType,c.length,g),k=Ne("b",e[1].dataType,d.length,m),O=gt("output",e[0].dataType,E.length,m),R=Lr(O.type.tensor),U=mi(r,O.type.value,R),te=[P,k],se="";if(I){let re=l?m:1;te.push(Ne("bias",e[2].dataType,e[2].dims.length,re)),se=`${l?`value += bias[col / ${re}];`:`value += ${O.type.value}(bias[row + i]);`}`}let K=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];gi(r,K);let pe=()=>{let re=`var a_data: ${P.type.value};`;for(let oe=0;oe; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${g}) { + ${pe()} + } + for (var i = 0u; i < ${x}u; i++) { + var value = values[i]; + ${se} + ${U} + let cur_indices = ${O.type.indices}(batch, row + i, col); + let offset = ${O.indicesToOffset("cur_indices")}; + ${O.setByOffset(`offset / ${m}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${r.activation};${m};${g};${x};${l}`,inputDependencies:I?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M/64)},programUniforms:C}),getShaderSource:D}}}),Bg,Rg,Im,Kf,Ng,Am,Vg,ef,a_=Ye(()=>{$t(),Lt(),zt(),yi(),i_(),s_(),Bg=(e,r)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart / innerElementSize + inputCol${r?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRow + innerRow, + kStart / innerElementSize + inputCol${r?", batchIndices":""}); + `,Rg=(e,r)=>e?` + let ACached0 = mm_Asub[k * innerElementSize][localRow]; + let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; + let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; + ${r===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"} + for (var i = 0; i < rowPerThread; i = i + 1) { + acc[i] = BCached0 * ACached0[i] + acc[i]; + acc[i] = BCached1 * ACached1[i] + acc[i]; + acc[i] = BCached2 * ACached2[i] + acc[i]; + ${r===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"} + }`:` + for (var i = 0; i < rowPerThread; i = i + 1) { + 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]; + ${r===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} + }`,Im=(e,r,t="f32",i,l=!1,n=32,c=!1,d=32)=>{let h=r[1]*e[1],f=r[0]*e[0],w=l?h:n,m=l?n:h,g=w/r[0],x=n/r[1];if(!((l&&g===4&&e[1]===4||!l&&(g===3||g===4))&&w%r[0]===0&&n%r[1]===0&&e[0]===4))throw new Error(`If transposeA ${l} is true, innerElementSize ${g} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${g} must be 3 or 4. + tileAWidth ${w} must be divisible by workgroupSize[0]${r[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${r[1]}. colPerThread ${e[0]} must be 4.`);return` +var mm_Asub: array, ${w/g}>, ${m}>; +var mm_Bsub: array, ${f/e[0]}>, ${n}>; + +const rowPerThread = ${e[1]}; +const colPerThread = ${e[0]}; +const innerElementSize = ${g}; +const tileInner = ${n}; + +@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let localRow = i32(localId.y); + let tileRow = localRow * rowPerThread; + let tileCol = i32(localId.x); + + let globalRow =i32(globalId.y) * rowPerThread; + let globalCol = i32(globalId.x); + let batch = ${c?"0":"i32(globalId.z)"}; + ${i?`let batchIndices = ${i.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${h}; + + let num_tiles = ${c?`${Math.ceil(d/n)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${c?`i32(globalId.z) * ${d}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${x}; + for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let inputRow = tileRow + innerRow; + let inputCol = tileCol; + ${Bg(l,i)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${x}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${i?", batchIndices":""}); + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { + let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; + let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; + let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; + ${g===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${Rg(l,g)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},Kf=(e,r)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${r?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${r?", batchIndices":""}); + `,Ng=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Am=(e,r,t="f32",i,l=!1,n=32,c=!1,d=32,h=!1)=>{let f=e[1]*r[1],w=e[0]*r[0],m=l?f:n,g=l?n:f;if(!(g%r[1]===0&&m%r[0]===0&&n%r[1]===0))throw new Error(`tileAHight ${g} must be divisible by workgroupSize[1]${r[1]}, tileAWidth ${m} must be divisible by workgroupSize[0]${r[0]}, tileInner ${n} must be divisible by workgroupSize[1]${r[1]}`);let x=g/r[1],M=m/r[0],I=n/r[1],z=h?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${f}; + let globalColStart = i32(workgroupId.x) * ${w}; + + // Loop over shared dimension. + for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var inputRow = localRow; inputRow < ${g}; inputRow = inputRow + ${r[1]}) { + for (var inputCol = localCol; inputCol < ${m}; inputCol = inputCol + ${r[0]}) { + ${Kf(l,i)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${n}; inputRow = inputRow + ${r[1]}) { + for (var inputCol = localCol; inputCol < ${w}; inputCol = inputCol + ${r[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalColStart + inputCol${i?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${t}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][localCol + inner * ${r[0]}]; + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let ACached = ${l?`mm_Asub[k][localRow + innerRow * ${r[1]}];`:`mm_Asub[localRow + innerRow * ${r[1]}][k];`} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + + ACached * BCached[innerCol]; + } + } + } + workgroupBarrier(); + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let gRow = globalRowStart + localRow + innerRow * ${r[1]}; + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let gCol = globalColStart + localCol + innerCol * ${r[0]}; + mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); + } + } + `:` +let tileRow = i32(localId.y) * rowPerThread; +let tileCol = i32(localId.x) * colPerThread; + +let globalRow = i32(globalId.y) * rowPerThread; +let globalCol = i32(globalId.x) * colPerThread; +let globalRowStart = i32(workgroupId.y) * ${f}; + +let tileRowA = i32(localId.y) * ${x}; +let tileColA = i32(localId.x) * ${M}; +let tileRowB = i32(localId.y) * ${I}; +// Loop over shared dimension. +for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var innerRow = 0; innerRow < ${x}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${M}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${Kf(l,i)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${I}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${i?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${t}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][tileCol + inner]; + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + ${Ng(l)} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; + } + } + } + + workgroupBarrier(); +} + +for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + mm_write(batch, globalRow + innerRow, globalCol + innerCol, + acc[innerRow][innerCol]); + } +} +`;return` + var mm_Asub : array, ${g}>; + var mm_Bsub : array, ${n}>; + const rowPerThread = ${e[1]}; + const colPerThread = ${e[0]}; + const tileInner = ${n}; + +@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let batch = ${c?"0":"i32(globalId.z)"}; + ${i?`let batchIndices = ${i.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${c?`${Math.ceil(d/n)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${c?`i32(globalId.z) * ${d}`:"0"}; + + var acc : array, rowPerThread>; + ${z} + } +`},Vg=(e,r,t,i,l=!1)=>{let[n,c,d,h]=i,f=Lr(i[0].type.tensor);return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${n.type.indices}) -> ${Vr(e,f)} { + var value = ${Vr(e,f)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + var aIndices: ${c.type.indices}; + ${_d("aIndices",c,c.rank-2,n.rank,"batchIndices")} + ${c.indicesSet("aIndices",c.rank-2,"u32(row)")} + ${c.indicesSet("aIndices",c.rank-1,"u32(colIn)")} + value = ${c.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${n.type.indices}) -> ${Vr(e,f)} { + var value = ${Vr(e,f)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + var bIndices: ${d.type.indices}; + ${_d("bIndices",d,d.rank-2,n.rank,"batchIndices")} + ${d.indicesSet("bIndices",d.rank-2,"u32(row)")} + ${d.indicesSet("bIndices",d.rank-1,"u32(colIn)")} + value = ${d.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Vr(e,f)}) { + let col = colIn * ${e}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueIn; + let coords = vec3(batch, row, colIn); + ${r?`value = value + ${l?"bias[colIn]":`${Vr(e,f)}(bias[row])`};`:""} + ${t} + ${h.setByIndices("vec3(coords)","value")} + } + } + `},ef=(e,r,t,i,l=!1,n)=>{let c=e[0].dims,d=e[1].dims,h=c.slice(0,-2),f=d.slice(0,-2),w=i?i.slice(0,-2):t.slice(0,-2),m=je.size(w),g=c[c.length-2],x=c[c.length-1],M=d[d.length-1],I=x%4===0&&M%4===0,z=g<=8?[4,1,1]:[4,4,1],E=[8,8,1],C=[Math.ceil(M/E[0]/z[0]),Math.ceil(g/E[1]/z[1]),Math.ceil(m/E[2]/z[2])],D=I?4:1,A=[...h,g,x/D],$=A.length,P=[...f,x,M/D],k=P.length,O=[m,g,M/D],R=[{type:6,data:g},{type:6,data:M},{type:6,data:x}];_i(r,R),R.push(...Et(w,A,P));let U=["rank","rank"],te=e.length>2;te&&(R.push(...Et(e[2].dims)),U.push("rank")),R.push(...Et(O));let se=K=>{let pe=w.length,re=Ym("batchDims",e[0].dataType,pe,1),oe=Lr(e[0].dataType),ge=Ne("a",e[0].dataType,$,D),le=Ne("b",e[1].dataType,k,D),Se=gt("result",e[0].dataType,O.length,D),Ce=[ge,le];if(te){let Ae=l?D:1;Ce.push(Ne("bias",e[2].dataType,e[2].dims.length,Ae))}let q=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];gi(r,q);let N=Lr(Se.type.tensor),Q=mi(r,Se.type.value,N),de=Vg(D,te,Q,[re,ge,le,Se],l);return` + ${K.registerUniforms(q).registerInternalVariables(re).declareVariables(...Ce,Se)} + ${de} + ${I?Im(z,E,oe,re):Am(z,E,oe,re)} + `};return{name:"MatMul",shaderCache:{hint:`${z};${r.activation};${I};${l}`,inputDependencies:U},getRunData:()=>({outputs:[{dims:n?n(t):t,dataType:e[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:R}),getShaderSource:se}}}),Wg,aM,Z1=Ye(()=>{$t(),_n(),zt(),yi(),s_(),Y1(),a_(),Wg=(e,r,t,i,l=!1,n,c=4,d=4,h=4,f="f32")=>{let w=R=>{switch(R){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${f}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${R} is not supported.`)}},m=R=>{switch(R){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${R} is not supported.`)}},g=e?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,x=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,M=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",I=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",z=e?"row":"col",E=e?"col":"row",C=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${z} / outWidth; + let outCol = ${z} % outWidth; + + let WRow = ${E} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${E} / inChannels % i32(uniforms.w_shape[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 = ${E} % inChannels; + var resData = ${Vr(c,f)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${M} && xCol >= 0 && xCol < ${I}) { + ${g} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${w(c)} + } + return resData;`,D=e?r&&i?` + let col = colIn * ${c}; + ${C}`:` + let col = colIn * ${c}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${C} + } + return ${Vr(c,f)}(0.0);`:i&&t?` + let col = colIn * ${c}; + ${C}`:` + let col = colIn * ${c}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${C} + } + return ${Vr(c,f)}(0.0);`,A=e?i&&t?m(d):` + let col = colIn * ${d}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${m(d)} + } + return ${Vr(d,f)}(0.0);`:` + let col = colIn * ${d}; + if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) { + ${m(d)} + } + return ${Vr(d,f)}(0.0);`,$=Vr(h,f),P=Vr(e?c:d,f),k=Vr(e?d:c,f),O=mi(n,$,f);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${P} { + ${e?D:A} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${k} { + ${e?A:D} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${$}) { + let col = colIn * ${h}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) + { + var value = valueIn; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${x} + ${nM(l)} + ${O} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},aM=(e,r,t,i,l,n,c,d,h)=>{let f=r.format==="NHWC",w=f?e[0].dims[3]:e[0].dims[1],m=t[0],g=f?t[2]:t[3],x=f?t[1]:t[2],M=f?t[3]:t[1],I=f&&(w%4===0||w%3===0)&&M%4===0,z=f?M:g*x,E=f?g*x:M,C=[8,8,1],D=i<=8?[4,1,1]:[4,4,1],A=[Math.ceil(z/C[0]/D[0]),Math.ceil(E/C[1]/D[1]),Math.ceil(m/C[2]/D[2])];Kt("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${A}`);let $=I?f&&w%4!==0?3:4:1,P=C[1]*D[1],k=C[0]*D[0],O=Math.max(C[0]*$,C[1]),R=i%P===0,U=l%k===0,te=n%O===0,se=I?[$,4,4]:[1,1,1],K=[{type:6,data:i},{type:6,data:l},{type:6,data:n},{type:6,data:[r.pads[0],r.pads[1]]},{type:6,data:r.strides},{type:6,data:r.dilations}];_i(r,K),K.push(...Et(e[0].dims,e[1].dims));let pe=["rank","rank"];c&&(K.push(...Et(e[2].dims)),pe.push("rank")),K.push(...Et(t));let re=oe=>{let ge=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];gi(r,ge);let le=I?4:1,Se=Lr(e[0].dataType),Ce=` + fn setOutputAtIndex(flatIndex : i32, value : ${I?`vec4<${Se}>`:Se}) { + result[flatIndex] = ${I?`vec4<${Se}>`:Se}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${I?`vec4<${Se}>`:Se}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${I?"/ 4":""}, value); + }`,q=Ne("x",e[0].dataType,e[0].dims.length,$===3?1:$),N=Ne("w",e[1].dataType,e[1].dims.length,le),Q=[q,N],de=gt("result",e[0].dataType,t.length,le);if(c){let Ae=Ne("bias",e[2].dataType,e[2].dims.length,le);Q.push(Ae),Ce+=` + fn getBiasByOutputCoords(coords : vec4) -> ${I?`vec4<${Se}>`:Se} { + return bias[coords.${f?"w":"y"}${I?"/ 4":""}]; + }`}return` + ${iM("uniforms.result_strides")} + //struct Uniforms { xShape : vec4, wShape : vec4, outShape : vec4, + // outShapeStrides: vec3, filterDims : vec2, pad : vec2, stride : vec2, + // dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32 }; + ${oe.registerUniforms(ge).declareVariables(...Q,de)} + ${Ce} + ${Wg(f,R,U,te,c,r,se[0],se[1],se[2],Se)} + ${I?Im(D,C,Se,void 0,!f,O):Am(D,C,Se,void 0,!f,O,!1,void 0,d)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${r.cacheKey};${$};${I};${R};${U};${te};${P};${k};${O}`,inputDependencies:pe},getRunData:()=>({outputs:[{dims:h?h(t):t,dataType:e[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:K}),getShaderSource:re}}}),Ug,Hf,ad,Gg,qf,Kg,oM,lM,eT=Ye(()=>{$t(),_n(),Lt(),zt(),yi(),s_(),Ug=e=>{let r=1;for(let t=0;ttypeof e=="number"?[e,e,e]:e,ad=(e,r)=>r<=1?e:e+(e-1)*(r-1),Gg=(e,r,t,i=1)=>{let l=ad(r,i);return Math.floor((e[0]*(t-1)-t+l)/2)},qf=(e,r,t,i,l)=>{l==null&&(l=Gg(e,r[0],i[0]));let n=[0,0,0,t];for(let c=0;c<3;c++)e[c]+2*l>=r[c]&&(n[c]=Math.trunc((e[c]-r[c]+2*l)/i[c]+1));return n},Kg=(e,r,t,i,l,n,c,d,h,f)=>{let w,m,g,x;if(e==="VALID"&&(e=0),typeof e=="number"){w={top:e,bottom:e,left:e,right:e,front:e,back:e};let M=qf([r,t,i,1],[d,h,f],1,[l,n,c],e);m=M[0],g=M[1],x=M[2]}else if(Array.isArray(e)){if(!e.every((I,z,E)=>I===E[0]))throw Error(`Unsupported padding parameter: ${e}`);w={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let M=qf([r,t,i,1],[d,h,f],1,[l,n,c],e[0]);m=M[0],g=M[1],x=M[2]}else if(e==="SAME_UPPER"){m=Math.ceil(r/l),g=Math.ceil(t/n),x=Math.ceil(i/c);let M=(m-1)*l+d-r,I=(g-1)*n+h-t,z=(x-1)*c+f-i,E=Math.floor(M/2),C=M-E,D=Math.floor(I/2),A=I-D,$=Math.floor(z/2),P=z-$;w={top:D,bottom:A,left:$,right:P,front:E,back:C}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:w,outDepth:m,outHeight:g,outWidth:x}},oM=(e,r,t,i,l,n=!1,c="channelsLast")=>{let d,h,f,w,m;if(c==="channelsLast")[d,h,f,w,m]=e;else if(c==="channelsFirst")[d,m,h,f,w]=e;else throw new Error(`Unknown dataFormat ${c}`);let[g,,x,M,I]=r,[z,E,C]=Hf(t),[D,A,$]=Hf(i),P=ad(x,D),k=ad(M,A),O=ad(I,$),{padInfo:R,outDepth:U,outHeight:te,outWidth:se}=Kg(l,h,f,w,z,E,C,P,k,O),K=n?g*m:g,pe=[0,0,0,0,0];return c==="channelsFirst"?pe=[d,K,U,te,se]:c==="channelsLast"&&(pe=[d,U,te,se,K]),{batchSize:d,dataFormat:c,inDepth:h,inHeight:f,inWidth:w,inChannels:m,outDepth:U,outHeight:te,outWidth:se,outChannels:K,padInfo:R,strideDepth:z,strideHeight:E,strideWidth:C,filterDepth:x,filterHeight:M,filterWidth:I,effectiveFilterDepth:P,effectiveFilterHeight:k,effectiveFilterWidth:O,dilationDepth:D,dilationHeight:A,dilationWidth:$,inShape:e,outShape:pe,filterShape:r}},lM=(e,r,t,i,l,n)=>{let c=n==="channelsLast";c?e[0].dims[3]:e[0].dims[1];let d=[64,1,1],h={x:t.map((z,E)=>E)},f=[Math.ceil(Ug(h.x.map(z=>t[z]))/d[0]),1,1];Kt("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${f}`);let w=1,m=je.size(t),g=[{type:12,data:m},{type:12,data:i},{type:12,data:l},{type:12,data:r.strides},{type:12,data:r.dilations}];_i(r,g),g.push(...Et(e[0].dims,e[1].dims));let x=["rank","rank"],M=e.length===3;M&&(g.push(...Et(e[2].dims)),x.push("rank")),g.push(...Et(t));let I=z=>{let E=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:i.length},{name:"pads",type:"u32",length:l.length},{name:"strides",type:"u32",length:r.strides.length},{name:"dilations",type:"u32",length:r.dilations.length}];gi(r,E);let C=1,D=Lr(e[0].dataType),A=Ne("x",e[0].dataType,e[0].dims.length,w),$=Ne("W",e[1].dataType,e[1].dims.length,C),P=[A,$],k=gt("result",e[0].dataType,t.length,C),O="";if(M){let te=Ne("bias",e[2].dataType,e[2].dims.length,C);P.push(te),O+=` + fn getBiasByOutputCoords(coords : array) -> ${D} { + return bias[${c?Mt("coords",4,5):Mt("coords",1,5)}]; + }`}let R=Vr(w,D),U=mi(r,R,D);return` + ${O} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${A.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${$.getByIndices("aIndices")}; + } + ${z.registerUniforms(E).declareVariables(...P,k)} + ${z.mainStart()} + ${z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${k.offsetToIndices("global_idx")}; + let batch = ${Mt("coords",0,A.rank)}; + let d2 = ${c?Mt("coords",A.rank-1,A.rank):Mt("coords",1,A.rank)}; + let xFRCCorner = vec3(${c?Mt("coords",1,A.rank):Mt("coords",2,A.rank)}, + ${c?Mt("coords",2,A.rank):Mt("coords",3,A.rank)}, + ${c?Mt("coords",3,A.rank):Mt("coords",4,A.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${c?Mt("uniforms.x_shape",1,A.rank):Mt("uniforms.x_shape",2,A.rank)}; + let xShapeZ = ${c?Mt("uniforms.x_shape",2,A.rank):Mt("uniforms.x_shape",3,A.rank)}; + let xShapeW = ${c?Mt("uniforms.x_shape",3,A.rank):Mt("uniforms.x_shape",4,A.rank)}; + let xShapeU = ${c?Mt("uniforms.x_shape",4,A.rank):Mt("uniforms.x_shape",1,A.rank)}; + let inputDepthNearestVec4 = (xShapeU / 4) * 4; + let inputDepthVec4Remainder = xShapeU % 4; + + var value = 0.0; + for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) { + let xF = xFCorner + wF * uniforms.dilations[0]; + if (xF < 0 || xF >= xShapeY) { + continue; + } + + for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) { + let xR = xRCorner + wR * uniforms.dilations[1]; + if (xR < 0 || xR >= xShapeZ) { + continue; + } + + for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) { + let xC = xCCorner + wC * uniforms.dilations[2]; + if (xC < 0 || xC >= xShapeW) { + continue; + } + + for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) { + ${c?`let xValues = vec4( + getX(batch, xF, xR, xC, d1), + getX(batch, xF, xR, xC, d1 + 1), + getX(batch, xF, xR, xC, d1 + 2), + getX(batch, xF, xR, xC, d1 + 3)); + `:`let xValues = vec4( + getX(batch, d1, xF, xR, xC), + getX(batch, d1 + 1, xF, xR, xC), + getX(batch, d1 + 2, xF, xR, xC), + getX(batch, d1 + 3, xF, xR, xC)); + `} + let wValues = vec4( + getW(d2, d1, wF, wR, wC), + getW(d2, d1 + 1, wF, wR, wC), + getW(d2, d1 + 2, wF, wR, wC), + getW(d2, d1 + 3, wF, wR, wC)); + value += dot(xValues, wValues); + } + if (inputDepthVec4Remainder == 1) { + ${c?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4) + * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC) + * getW(d2, inputDepthNearestVec4, wF, wR, wC);`} + } else if (inputDepthVec4Remainder == 2) { + ${c?`let xValues = vec2( + getX(batch, xF, xR, xC, inputDepthNearestVec4), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)); + `:`let xValues = vec2( + getX(batch, inputDepthNearestVec4, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC)); + `} + let wValues = vec2( + getW(d2, inputDepthNearestVec4, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC)); + value += dot(xValues, wValues); + } else if (inputDepthVec4Remainder == 3) { + ${c?`let xValues = vec3( + getX(batch, xF, xR, xC, inputDepthNearestVec4), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)); + `:`let xValues = vec3( + getX(batch, inputDepthNearestVec4, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC)); + `} + let wValues = vec3( + getW(d2, inputDepthNearestVec4, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC)); + value += dot(xValues, wValues); + } + } + } + } + ${M?"value = value + getBiasByOutputCoords(coords)":""}; + ${U} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${r.cacheKey};${c};${w};${M}`,inputDependencies:x},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:f[0],y:f[1],z:f[2]},programUniforms:g}),getShaderSource:I}}}),uM,cM,tT=Ye(()=>{$t(),Lt(),zt(),yi(),uM=(e,r,t,i)=>{let l=e.length>2,n=l?"value += b[output_channel];":"",c=e[0].dims,d=e[1].dims,h=r.format==="NHWC",f=h?t[3]:t[1],w=f/r.group,m=h&&w>=4?gr(f):1,g=je.size(t)/m,x=[{type:12,data:g},{type:12,data:r.dilations},{type:12,data:[r.strides[0],r.strides[1]]},{type:12,data:[r.pads[0],r.pads[1]]},{type:12,data:w}];_i(r,x),x.push(...Et(c,[d[0],d[1],d[2],d[3]/m]));let M=l?["rank","rank","rank"]:["rank","rank"];x.push(...Et([t[0],t[1],t[2],t[3]/m]));let I=z=>{let E=gt("output",e[0].dataType,t.length,m),C=Lr(E.type.tensor),D=mi(r,E.type.value,C),A=Ne("x",e[0].dataType,c.length),$=Ne("w",e[1].dataType,d.length,m),P=[A,$];l&&P.push(Ne("b",e[2].dataType,e[2].dims,m));let k=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:r.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];gi(r,k);let O=h?` + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) { + continue; + } + + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) { + let input_channel = in_channel_offset + wInChannel; + let xVal = ${A.get("batch","xHeight","xWidth","input_channel")}; + let wVal = ${$.get("wHeight","wWidth","wInChannel","output_channel")}; + value += xVal * wVal; + } + } + } + `:` + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { + let input_channel = in_channel_offset + wInChannel; + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) { + continue; + } + + let xVal = ${A.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${$.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal * wVal; + } + } + } + `;return` + ${z.registerUniforms(k).declareVariables(...P,E)} + + ${z.mainStart()} + ${z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${E.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${h?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${h?1:2}], outputIndices[${h?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel * ${m} / uniforms.output_channels_per_group; + var in_channel_offset = group_id * uniforms.w_shape[${h?2:1}]; + + var value: ${E.type.value} = ${E.type.value}(0); + ${O} + ${n} + ${D} + ${E.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:`${r.cacheKey}_${m}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:i?i(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:x}),getShaderSource:I}},cM=(e,r,t,i)=>{let l=e.length>2,n=gr(t[3]),c=gr(t[2]),d=je.size(t)/n/c,h=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/n],f=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/n],w=[t[0],t[1],t[2],t[3]/n],m=[{type:12,data:d},{type:6,data:[r.strides[0],r.strides[1]]},{type:6,data:[r.pads[0],r.pads[1]]}];_i(r,m),m.push(...Et(h,f,w));let g=(c-1)*r.strides[1]+f[1],x=M=>{let I=gt("output",e[0].dataType,w.length,n),z=Lr(I.type.tensor),E=mi(r,I.type.value,z),C=Ne("x",e[0].dataType,h.length,n),D=Ne("w",e[1].dataType,f.length,n),A=[C,D];l&&A.push(Ne("b",e[2].dataType,e[2].dims,n));let $=l?"value += b[output_channel];":"",P=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return gi(r,P),` + ${M.registerUniforms(P).declareVariables(...A,I)} + ${M.mainStart()} + ${M.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let width0 = uniforms.output_shape[3]; + let output_channel = global_idx % width0; + var index1 = global_idx / width0; + let width1 = uniforms.output_shape[2] / ${c}u; + let col = (index1 % width1) * ${c}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${C.type.value}, ${g}>; + var values: array<${I.type.value}, ${c}>; + let input_channel = output_channel; + // Use constant instead of uniform can give better performance for w's height/width. + for (var w_height: u32 = 0u; w_height < ${f[0]}; w_height++) { + let x_height = x_corner.x + i32(w_height); + if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) { + for (var i = 0; i < ${g}; i++) { + let x_width = x_corner.y + i; + if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { + x_vals[i] = ${C.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${C.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${f[1]}; w_width++) { + let w_val = ${D.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${c}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${c}u; i++) { + var value = values[i]; + ${$} + ${E} + ${I.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${r.cacheKey};${n};${c};${g};${f[0]};${f[1]}`,inputDependencies:l?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:i?i(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:m}),getShaderSource:x}}}),Hg,Bh,qg,Rh,Om,Qf,Qg,Xg,Fm,rT=Ye(()=>{Lt(),Z1(),eT(),a_(),tT(),yi(),i_(),On(),Hg=(e,r,t,i,l,n)=>{let c=e[0],d=e.slice(n?1:2,n?3:4),h=d.length,f=r[0],w=r.slice(2).map((g,x)=>g+(g-1)*(t[x]-1)),m=d.map((g,x)=>g+i[x]+i[x+h]).map((g,x)=>Math.floor((g-w[x]+l[x])/l[x]));return m.splice(0,0,c),m.splice(n?3:1,0,f),m},Bh=[2,3,1,0],qg=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let t=e[0].dims[r.format==="NHWC"?e[0].dims.length-1:1],i=e[1].dims[1]*r.group;if(t!==i)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let l=e[0].dims.length-2;if(r.dilations.length!==l)throw new Error(`dilations should be ${l}D`);if(r.strides.length!==l)throw new Error(`strides should be ${l}D`);if(r.pads.length!==l*2)throw new Error(`pads should be ${l*2}D`);if(r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},Rh=(e,r)=>{let t=e.kernelShape.slice();t.length{let r=r_(e),t=e.format,i=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],l=e.dilations,n=e.group,c=e.kernel_shape,d=e.pads,h=e.strides,f=e.w_is_const();return{autoPad:i,format:t,dilations:l,group:n,kernelShape:c,pads:d,strides:h,wIsConst:f,...r,cacheKey:`${e.format};${r.activation};`}},Qf=(e,r,t,i)=>{let l=t.format==="NHWC",n=Hg(r[0].dims,r[1].dims,t.dilations,t.pads,t.strides,l);if(t.group!==1){let P=[r[0]];if(l){let k=e.kernelCustomData.wT??e.compute(_s(r[1],Bh),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=k),P.push(k)}else P.push(r[1]);r.length===3&&P.push(r[2]),!e.adapterInfo.isArchitecture("ampere")&&l&&r[1].dims[0]===t.group&&r[1].dims[1]===1&&t.dilations[0]===1&&t.dilations[1]===1?e.compute(cM(P,t,n,i),{inputs:P}):e.compute(uM(P,t,n,i),{inputs:P});return}let c=r.length===3,d=r[0].dims[l?1:2],h=r[0].dims[l?2:3],f=r[0].dims[l?3:1],w=r[1].dims[2],m=r[1].dims[3],g=n[l?1:2],x=n[l?2:3],M=n[l?3:1],I=l&&w===d&&m===h&&t.pads[0]===0&&t.pads[1]===0;if(I||w===1&&m===1&&t.dilations[0]===1&&t.dilations[1]===1&&t.strides[0]===1&&t.strides[1]===1&&t.pads[0]===0&&t.pads[1]===0){let P=n[0],k,O,R,U=[];if(l){let K=e.kernelCustomData.wT??e.compute(_s(r[1],Bh),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];if(t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=K),I){let pe=d*h*f;k=r[0].reshape([1,P,pe]),O=K.reshape([1,pe,M]),R=[1,P,M]}else k=r[0].reshape([P,d*h,f]),O=K.reshape([1,f,M]),R=[P,g*x,M];U.push(k),U.push(O)}else k=r[0].reshape([P,f,d*h]),O=r[1].reshape([1,M,f]),R=[P,M,g*x],U.push(O),U.push(k);c&&U.push(r[2]);let te=R[2],se=U[0].dims[U[0].dims.length-1];te<8&&se<8?e.compute(n_(U,t,n,R,l,i),{inputs:U}):e.compute(ef(U,t,n,R,l,i),{inputs:U});return}let z=!0,E=e.kernelCustomData.wT??e.compute(_s(r[1],Bh),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=E);let C=[r[0],E];c&&C.push(r[2]);let D=l?g*x:M,A=l?M:g*x,$=w*m*f;e.compute(aM(C,t,n,D,A,$,c,z,i),{inputs:C})},Qg=(e,r)=>{let t=r.format==="NHWC",i=[e.inputs[0].reshape(t?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&i.push(e.inputs[2]);let l=[0,r.pads[0],0,r.pads[1]],n=[1].concat(r.strides),c=[1].concat(r.dilations),d=[1].concat(r.kernelShape),h=Rh({...r,pads:l,strides:n,dilations:c,kernelShape:d},i);Qf(e,i,h,f=>t?[f[0],f[2],f[3]]:[f[0],f[1],f[3]])},Xg=(e,r,t)=>{let i=t.format==="NHWC"?"channelsLast":"channelsFirst",l=Rh(t,r),n=t.autoPad==="NOTSET"?t.pads:t.autoPad,c=oM(r[0].dims,r[1].dims,t.strides,t.dilations,n,!1,i);e.compute(lM(r,l,c.outShape,[c.filterDepth,c.filterHeight,c.filterWidth],[c.padInfo.front,c.padInfo.top,c.padInfo.left],i))},Fm=(e,r)=>{if(qg(e.inputs,r),e.inputs[0].dims.length===3)Qg(e,r);else if(e.inputs[0].dims.length===5)Xg(e,e.inputs,r);else{let t=Rh(r,e.inputs);Qf(e,e.inputs,t)}}}),dM,sT=Ye(()=>{$t(),_n(),Lt(),zt(),dM=(e,r,t)=>{let i=e.length>2,l=r.outputShape,n=r.format==="NHWC",c=r.group,d=e[1].dims,h=d[2]/c,f=d[3],w=n?gr(h):1,m=n&&f===1&&h>=4,g=m?Math.floor(h/4)*4:Math.floor(h/w)*w,x=h-g,M=n?gr(f):1,I=n?f===1?w:M:1,z=je.size(l)/M,E=[Math.ceil(z/64),1,1];Kt("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${E}`);let C=["rank","rank"],D=[r.strides[0],r.strides[1]],A=[r.kernelShape[n?1:2],r.kernelShape[n?2:3]],$=[r.dilations[0],r.dilations[1]],P=[A[0]+(r.dilations[0]<=1?0:(r.kernelShape[n?1:2]-1)*(r.dilations[0]-1)),A[1]+(r.dilations[1]<=1?0:(r.kernelShape[n?2:3]-1)*(r.dilations[1]-1))],k=[P[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),P[1]-1-Math.floor((r.pads[1]+r.pads[3])/2)],O=[{type:12,data:z},{type:12,data:D},{type:12,data:A},{type:12,data:$},{type:12,data:P},{type:6,data:k},{type:12,data:g},{type:12,data:h},{type:12,data:f},...Et(e[0].dims,e[1].dims)];i&&(O.push(...Et(e[2].dims)),C.push("rank")),O.push(...Et(l));let R=U=>{let te=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:D.length},{name:"filter_dims",type:"u32",length:A.length},{name:"dilations",type:"u32",length:A.length},{name:"effective_filter_dims",type:"u32",length:P.length},{name:"pads",type:"i32",length:k.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],se=Lr(e[0].dataType),K=n?1:2,pe=n?2:3,re=n?3:1,oe=Ne("W",e[1].dataType,e[1].dims.length,I),ge=Ne("Dy",e[0].dataType,e[0].dims.length,w),le=[ge,oe];i&&le.push(Ne("bias",e[2].dataType,[l[re]].length,M));let Se=gt("result",e[0].dataType,l.length,M),Ce=()=>{let Q="";if(m)w===4?Q+=` + let xValue = ${ge.getByOffset("x_offset")}; + let wValue = ${oe.getByOffset("w_offset")}; + dotProd = dotProd + dot(xValue, wValue); + x_offset += 1u; + w_offset += 1u;`:w===2?Q+=` + dotProd = dotProd + dot(vec4<${se}>(${ge.getByOffset("x_offset")}, ${ge.getByOffset("x_offset + 1u")}), vec4<${se}>(${oe.getByOffset("w_offset")}, ${oe.getByOffset("w_offset + 1u")})); + x_offset += 2u; + w_offset += 2u;`:w===1&&(Q+=` + dotProd = dotProd + dot(vec4<${se}>(${ge.getByOffset("x_offset")}, ${ge.getByOffset("x_offset + 1u")}, ${ge.getByOffset("x_offset + 2u")}, ${ge.getByOffset("x_offset + 3u")}), vec4<${se}>(${oe.getByOffset("w_offset")}, ${oe.getByOffset("w_offset + 1u")}, ${oe.getByOffset("w_offset + 2u")}, ${oe.getByOffset("w_offset + 3u")})); + x_offset += 4u; + w_offset += 4u;`);else if(Q+=` + let xValue = ${n?ge.getByOffset(`${ge.indicesToOffset(`${ge.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${w}`):ge.get("batch","inputChannel","idyR","idyC")}; + `,w===1)Q+=` + let w_offset = ${oe.indicesToOffset(`${oe.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)}; + let wValue = ${oe.getByOffset(`w_offset / ${I}`)}; + dotProd = dotProd + xValue * wValue;`;else for(let de=0;de{if(x===0)return"";if(!m)throw new Error(`packInputAs4 ${m} is not true.`);let Q="";if(w===1){Q+="dotProd = dotProd";for(let de=0;de(i32(r), i32(c)) - uniforms.pads; + let dyRCorner = dyCorner.x; + let dyCCorner = dyCorner.y; + let groupId = d1 / uniforms.output_channels_per_group; + let wOutChannel = d1 - groupId * uniforms.output_channels_per_group; + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd = ${Se.type.value}(0.0); + var wR: u32 = 0; + if (uniforms.dilations.x == 1) { + // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0 + wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner); + } + for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${se}(dyRCorner) + ${se}(wR)) / ${se}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${se}(uniforms.Dy_shape[${K}]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + var wC: u32 = 0; + if (uniforms.dilations.y == 1) { + // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0 + wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner); + } + for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { + if (wC % uniforms.dilations.y != 0) { + continue; + } + let dyC = (${se}(dyCCorner) + ${se}(wC)) / ${se}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${se}(uniforms.Dy_shape[${pe}]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC: u32 = u32(dyC); + var inputChannel = groupId * uniforms.input_channels_per_group; + ${m?` + var x_offset = ${ge.indicesToOffset(`${ge.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${w}; + var w_offset = ${oe.indicesToOffset(`${oe.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${I}; + `:""} + for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${m?4:w}) { + ${Ce()} + inputChannel = inputChannel + ${m?4:w}; + } + ${q()} + wC = wC + uniforms.strides.y - 1; + } + wR = wR + uniforms.strides[0] - 1; + } + let value = dotProd${i?` + bias[d1 / ${M}]`:""}; + ${Se.setByOffset("global_idx","value")}; + `;return` + ${U.registerUniforms(te).declareVariables(...le,Se)} + ${U.mainStart()} + ${U.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${N}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${r.cacheKey};${w}${I}${M}${m}${x}`,inputDependencies:C},getRunData:()=>({dispatchGroup:{x:E[0],y:E[1],z:E[2]},outputs:[{dims:t?t(l):l,dataType:e[0].dataType}],programUniforms:O}),getShaderSource:R}}}),Jg,Yg,Zg,Xf,pM,ew,Jf,tw,hM,nT=Ye(()=>{sT(),yi(),On(),Jg=(e,r,t,i,l,n)=>(e-1)*r+t+(i-1)*l+1-n,Yg=(e,r,t,i,l)=>{let 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${n}D`);if(r.strides.reduce((c,d)=>c+d,0)>0&&r.strides.length!==n)throw new Error(`strides should be ${n}D`);if(r.pads.reduce((c,d)=>c+d,0)>0&&r.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(r.outputPadding.length!==n&&r.outputPadding.length!==0)throw new Error(`output_padding should be ${n}D`);if(r.kernelShape.reduce((c,d)=>c+d,0)>0&&r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(r.outputShape.length!==0&&r.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Jf=(e,r,t,i)=>{let l=e.kernelCustomData.wT??e.compute(_s(r[1],[2,3,0,1]),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=l);let n=[r[0],l];r.length===3&&n.push(r[2]),e.compute(dM(n,t,i),{inputs:n})},tw=(e,r)=>{let t=r.format==="NHWC",i=[e.inputs[0].reshape(t?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&i.push(e.inputs[2]);let l=r.kernelShape;(l.length===0||l[0]===0)&&(l=[e.inputs[1].dims[2]]);let n=r.dilations;(n.length===0||n[0]===0)&&(n=[1]);let c=r.strides;(c.length===0||c[0]===0)&&(c=[1]);let d=r.pads;d.length===0&&(d=[0,0]),d=[0,d[0],0,d[1]],c=[1].concat(c),n=[1].concat(n),l=[1].concat(l);let h=r.outputPadding;h=[0].concat(h);let f=Xf({...r,pads:d,strides:c,dilations:n,kernelShape:l,outputPadding:h},i);Jf(e,i,f,w=>t?[w[0],w[2],w[3]]:[w[0],w[1],w[3]])},hM=(e,r)=>{if(ew(e.inputs,r),e.inputs[0].dims.length===3)tw(e,r);else{let t=Xf(r,e.inputs);Jf(e,e.inputs,t)}}}),rw,fM,mM,iT=Ye(()=>{$t(),Lt(),br(),zt(),rw=(e,r,t,i)=>{let l=je.size(r),n=r.length,c=Ne("input",e,n),d=gt("output",e,n),h=t.dataType===6?t.getInt32Array()[0]:Number(t.getBigInt64Array()[0]),f=je.normalizeAxis(h,n),w=m=>{let g=` i32(${c.indicesGet("inputIndices","uniforms.axis")}) `,x=Mt("uniforms.input_shape","uniforms.axis",n),M=i.reverse?g+(i.exclusive?" + 1":""):"0",I=i.reverse?x:g+(i.exclusive?"":" + 1");return` + ${m.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(c,d)} + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${d.offsetToIndices("global_idx")}; + var sum = ${d.type.value}(0); + let first : i32 = ${M}; + let last : i32 = ${I}; + for (var i : i32 = first; i < last; i++) { + ${c.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${c.getByIndices("inputIndices")}; + } + ${d.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:i.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:[{type:12,data:l},{type:12,data:f},...Et(r,r)]}),getShaderSource:w}},fM=(e,r)=>{let t=e.inputs[0].dims,i=e.inputs[0].dataType,l=e.inputs[1];e.compute(rw(i,t,l,r),{inputs:[0]})},mM=e=>{let r=e.exclusive===1,t=e.reverse===1;return Zt({exclusive:r,reverse:t})}}),sw,nw,iw,_M,gM,aT=Ye(()=>{$t(),Lt(),br(),zt(),sw=e=>{if(!e||e.length!==1)throw new Error("DepthToSpace requires 1 input.");if(e[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},nw=(e,r,t,i)=>{let l=[];l.push(`fn perm(i: ${i.type.indices}) -> ${t.type.indices} { + var a: ${t.type.indices};`);for(let n=0;n{let t,i,l,n,c,d,h=r.format==="NHWC",f=r.blocksize,w=r.mode==="DCR";h?([t,i,l,n]=e.dims,c=w?[t,i,l,f,f,n/f**2]:[t,i,l,n/f**2,f,f],d=w?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([t,i,l,n]=[e.dims[0],e.dims[2],e.dims[3],e.dims[1]],c=w?[t,f,f,n/f**2,i,l]:[t,n/f**2,f,f,i,l],d=w?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let m=e.reshape(c),g=m.dims.length,x=e.dataType,M=Ne("a",x,g),I=gt("output",x,g),z=E=>` + ${E.registerUniform("output_size","u32").declareVariables(M,I)} + + ${nw(d,g,M,I)} + + ${E.mainStart()} + ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${I.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${I.setByOffset("global_idx",M.getByIndices("aIndices"))} + }`;return{name:"DepthToSpace",shaderCache:{hint:`${e.dims};${r.blocksize};${r.mode}`,inputDependencies:["rank"]},getRunData:E=>{let C=h?[t,i*f,l*f,n/f**2]:[t,n/f**2,i*f,l*f],D=je.size(C),A=m.dims,$=je.sortBasedOnPerm(A,d);return{outputs:[{dims:C,dataType:E[0].dataType}],dispatchGroup:{x:Math.ceil(D/64)},programUniforms:[{type:12,data:D},...Et(A,$)]}},getShaderSource:z}},_M=(e,r)=>{sw(e.inputs),e.compute(iw(e.inputs[0],r))},gM=e=>Zt({blocksize:e.blocksize,mode:e.mode,format:e.format})}),Nh,od,Yf,aw,ow,lw,uw,Zf,cw,wM,yM,oT=Ye(()=>{$t(),Lt(),br(),zt(),Nh="[a-zA-Z]|\\.\\.\\.",od="("+Nh+")+",Yf="^"+od+"$",aw="("+od+",)*"+od,ow="^"+aw+"$",lw=class{constructor(e=-1){this.symbolToIndices=new Map,this.inputIndex=e}addSymbol(e,r){let t=this.symbolToIndices.get(e);t===void 0?t=[r]:t.push(r),this.symbolToIndices.set(e,t)}},uw=class{constructor(e,r){var l;this.equation=r,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[t,i]=r.includes("->")?r.split("->",2):[r,""];if(!t.match(RegExp(ow)))throw new Error("Invalid LHS term");if(t.split(",").forEach((n,c)=>{let d=e[c].dims.slice();if(!n.match(RegExp(Yf)))throw new Error("Invalid LHS term");let h=this.processTerm(n,!0,d,c);this.lhs.push(h)}),i==="")i+=[...this.symbolToInfo.entries()].filter(([n,c])=>c.count===1||n==="...").map(([n])=>n).join("");else if(!i.match(RegExp(od)))throw new Error("Invalid RHS");(l=i.match(RegExp(Nh,"g")))==null||l.forEach(n=>{if(n==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let c=this.symbolToInfo.get(n);if(c===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(c.dimValue)}}),this.rhs=this.processTerm(i,!1,this.outputDims)}addSymbol(e,r,t){let i=this.symbolToInfo.get(e);if(i!==void 0){if(i.dimValue!==r&&i.count!==1)throw new Error("Dimension mismatch");i.count++,i.inputIndices.push(t)}else i={count:1,dimValue:r,inputIndices:[t]};this.symbolToInfo.set(e,i)}processTerm(e,r,t,i=-1){let l=t.length,n=!1,c=[],d=0;if(!e.match(RegExp(Yf))&&!r&&e!=="")throw new Error("Invalid LHS term");let h=e.match(RegExp(Nh,"g")),f=new lw(i);return h==null||h.forEach((w,m)=>{if(w==="..."){if(n)throw new Error("Only one ellipsis is allowed per input term");n=!0;let g=l-h.length+1;if(g<0)throw new Error("Ellipsis out of bounds");if(c=t.slice(d,d+g),this.hasEllipsis){if(this.ellipsisDims.length!==c.length||this.ellipsisDims.toString()!==c.toString())throw new Error("Ellipsis dimensions mismatch")}else if(r)this.hasEllipsis=!0,this.ellipsisDims=c;else throw new Error("Ellipsis must be specified in the LHS");for(let x=0;xe+"_max",cw=(e,r,t,i)=>{let l=e.map(f=>f.length).map((f,w)=>Ne(`input${w}`,r,f)),n=je.size(i),c=gt("output",r,i.length),d=[...t.symbolToInfo.keys()].filter(f=>!t.rhs.symbolToIndices.has(f)),h=f=>{let w=[],m="var prod = 1.0;",g="var sum = 0.0;",x="sum += prod;",M=[],I=[],z=[],E=[],C=t.symbolToInfo.size===t.rhs.symbolToIndices.size;t.symbolToInfo.forEach((A,$)=>{var P;if(t.rhs.symbolToIndices.has($)){let k=(P=t.rhs.symbolToIndices.get($))==null?void 0:P[0];k!==void 0&&t.lhs.forEach((O,R)=>{if(A.inputIndices.includes(R)){let U=O.symbolToIndices.get($);if(U===void 0)throw new Error("Invalid symbol error");U.forEach(te=>{w.push(`${l[R].indicesSet(`input${R}Indices`,te,c.indicesGet("outputIndices",k))}`)})}})}else t.lhs.forEach((k,O)=>{if(A.inputIndices.includes(O)){let R=k.symbolToIndices.get($);if(R===void 0)throw new Error("Invalid symbol error");R.forEach(U=>{M.push(`${l[O].indicesSet(`input${O}Indices`,U,`${$}`)}`)}),E.push(`prod *= ${l[O].getByIndices(`input${O}Indices`)};`)}}),I.push(`for(var ${$}: u32 = 0; ${$} < uniforms.${Zf($)}; ${$}++) {`),z.push("}")});let D=C?[...w,`let sum = ${l.map((A,$)=>A.getByIndices(`input${$}Indices`)).join(" * ")};`]:[...w,g,...I,...M,m,...E,x,...z];return` + ${f.registerUniforms(d.map(A=>({name:`${Zf(A)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...l,c)} + + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${c.offsetToIndices("global_idx")}; + ${l.map((A,$)=>`var input${$}Indices: ${l[$].type.indices};`).join(` +`)} + ${D.join(` +`)}; + ${c.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:t.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let f=d.filter(m=>t.symbolToInfo.has(m)).map(m=>{var g;return{type:12,data:((g=t.symbolToInfo.get(m))==null?void 0:g.dimValue)||0}});f.push({type:12,data:n});let w=e.map((m,g)=>[...Et(m)]).reduce((m,g)=>m.concat(g),f);return w.push(...Et(i)),{outputs:[{dims:i,dataType:r}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:w}},getShaderSource:h}},wM=(e,r)=>{let t=new uw(e.inputs,r.equation),i=t.outputDims,l=e.inputs.map((n,c)=>n.dims);e.compute(cw(l,e.inputs[0].dataType,t,i))},yM=e=>{let r=e.equation.replace(/\s+/g,"");return Zt({equation:r})}}),dw,em,pw,hw,bM,lT=Ye(()=>{$t(),Lt(),zt(),dw=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let r=e[0].dims,t=Array.from(e[1].getBigInt64Array(),Number),i=t.length{let t=e.length-r.length,i=[];for(let l=0;le.length>r.length?em(e,r):em(r,e),hw=e=>{let r=e[0].dims,t=Array.from(e[1].getBigInt64Array(),Number),i=pw(r,t),l=e[0].dataType,n=l===9||je.size(r)===1,c=l===9||r.length>0&&r[r.length-1]%4===0?4:1,d=n||i.length>0&&i[i.length-1]%4===0?4:1,h=Math.ceil(je.size(i)/d),f=m=>{let g=Ne("input",l,r.length,c),x=gt("output",l,i.length,d),M;if(l===9){let I=(z,E,C="")=>` + let outputIndices${E} = ${x.offsetToIndices(`outputOffset + ${E}u`)}; + let offset${E} = ${g.broadcastedIndicesToOffset(`outputIndices${E}`,x)}; + let index${E} = offset${E} / 4u; + let component${E} = offset${E} % 4u; + ${z}[${E}] = ${C}(${g.getByOffset(`index${E}`)}[component${E}]); + `;M=` + let outputOffset = global_idx * ${d}; + var data = vec4(0); + ${I("data",0,"u32")} + ${I("data",1,"u32")} + ${I("data",2,"u32")} + ${I("data",3,"u32")} + ${x.setByOffset("global_idx","data")} + }`}else M=` + let outputIndices = ${x.offsetToIndices(`global_idx * ${d}`)}; + let inputOffset = ${g.broadcastedIndicesToOffset("outputIndices",x)}; + let data = ${x.type.value}(${g.getByOffset(`inputOffset / ${c}`)}); + ${x.setByOffset("global_idx","data")} + }`;return` + ${m.registerUniform("vec_size","u32").declareVariables(g,x)} + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${M}`},w=[{type:12,data:h},...Et(r,i)];return{name:"Expand",shaderCache:{hint:`${i.length};${c}${d}`,inputDependencies:["rank"]},getShaderSource:f,getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:w})}},bM=e=>{dw(e.inputs),e.compute(hw(e.inputs),{inputs:[0]})}}),fw,MM,uT=Ye(()=>{$t(),Lt(),zt(),t_(),fw=e=>{let r=e[0].dataType,t=je.size(e[0].dims),i=je.size(e[1].dims),l=i%4===0,n=c=>{let d=Ne("x",r,[1],4),h=Ne("bias",r,[1],4),f=gt("y",r,[1],4),w=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],m=x=>` + let bias${x}_offset: u32 = (global_idx * 4 + ${x}) % uniforms.bias_size; + let bias${x} = ${h.getByOffset(`bias${x}_offset / 4`)}[bias${x}_offset % 4];`,g=l?` + let bias = ${h.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${m(0)}${m(1)}${m(2)}${m(3)} + let bias = ${d.type.value}(bias0, bias1, bias2, bias3);`;return`${c.registerUniforms(w).declareVariables(d,h,f)} + + ${km(Qr(r))} + + ${c.mainStart(Na)} + ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${d.getByOffset("global_idx")}; + ${g} + let x_in = x + bias; + ${f.setByOffset("global_idx",$m("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${l}`,inputDependencies:["type","type"]},getShaderSource:n,getRunData:c=>({outputs:[{dims:c[0].dims,dataType:c[0].dataType}],programUniforms:[{type:12,data:Math.ceil(t/4)},{type:12,data:i}],dispatchGroup:{x:Math.ceil(t/Na/4)}})}},MM=e=>{e.inputs.length<2||je.size(e.inputs[1].dims)===0?Nb(e):e.compute(fw(e.inputs))}}),mw,_w,vM,xM,cT=Ye(()=>{$t(),Lt(),br(),zt(),mw=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},_w=(e,r)=>{let t=e[0].dims,i=e[1].dims,l=t.length,n=je.normalizeAxis(r.axis,l),c=t.slice(0);c.splice(n,1,...i);let d=t[n],h=e[0].dataType===9?4:1,f=Math.ceil(je.size(c)/h),w=[{type:12,data:f},{type:6,data:d},{type:12,data:n},...Et(e[0].dims,e[1].dims,c)],m=g=>{let x=Ne("data",e[0].dataType,e[0].dims.length,h),M=Ne("inputIndices",e[1].dataType,e[1].dims.length),I=gt("output",e[0].dataType,c.length,h),z=C=>{let D=i.length,A=`var indicesIndices${C} = ${M.type.indices}(0);`;for(let $=0;$1?`indicesIndices${C}[${$}]`:`indicesIndices${C}`} = ${c.length>1?`outputIndices${C}[uniforms.axis + ${$}]`:`outputIndices${C}`};`;A+=` + var idx${C} = ${M.getByIndices(`indicesIndices${C}`)}; + if (idx${C} < 0) { + idx${C} = idx${C} + uniforms.axisDimLimit; + } + var dataIndices${C} : ${x.type.indices}; + `;for(let $=0,P=0;$1?`dataIndices${C}[${$}]`:`dataIndices${C}`} = u32(idx${C});`,P+=D):(A+=`${l>1?`dataIndices${C}[${$}]`:`dataIndices${C}`} = ${c.length>1?`outputIndices${C}[${P}]`:`outputIndices${C}`};`,P++);return A},E;if(e[0].dataType===9){let C=(D,A,$="")=>` + let outputIndices${A} = ${I.offsetToIndices(`outputOffset + ${A}u`)}; + ${z(A)}; + let offset${A} = ${x.indicesToOffset(`dataIndices${A}`)}; + let index${A} = offset${A} / 4u; + let component${A} = offset${A} % 4u; + ${D}[${A}] = ${$}(${x.getByOffset(`index${A}`)}[component${A}]); + `;E=` + let outputOffset = global_idx * ${h}; + var value = vec4(0); + ${C("value",0,"u32")} + ${C("value",1,"u32")} + ${C("value",2,"u32")} + ${C("value",3,"u32")} + ${I.setByOffset("global_idx","value")} + `}else E=` + let outputIndices = ${I.offsetToIndices("global_idx")}; + ${z("")}; + let value = ${x.getByIndices("dataIndices")}; + ${I.setByOffset("global_idx","value")}; + `;return` + ${g.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(x,M,I)} + ${g.mainStart()} + ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${E} + }`};return{name:"Gather",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:w}),getShaderSource:m}},vM=e=>Zt({axis:e.axis}),xM=(e,r)=>{let t=e.inputs;mw(t),e.compute(_w(e.inputs,r))}}),gw,TM,CM,dT=Ye(()=>{$t(),Lt(),zt(),gw=(e,r,t,i,l,n,c,d,h)=>{let f=[{type:12,data:n},{type:12,data:i},{type:12,data:l},{type:12,data:t},{type:12,data:c},{type:12,data:d},{type:12,data:h}],w=[n];f.push(...Et(r.dims,w));let m=g=>{let x=Ne("indices_data",r.dataType,r.dims.length),M=gt("input_slice_offsets_data",12,1,1),I=[x,M],z=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:l.length},{name:"sizes_from_slice_dims_data",type:"u32",length:t.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return` + ${g.registerUniforms(z).declareVariables(...I)} + ${g.mainStart()} + ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let batch_idx = global_idx / uniforms.num_slices_per_batch; + let base_offset = batch_idx * uniforms.input_batch_stride; + + let slice_indices_base_offset = global_idx * uniforms.num_slice_dims; + var relative_slice_offset = 0; + for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) { + var index = i32(indices_data[dim_idx + slice_indices_base_offset].x); + let input_dim_idx = uniforms.batch_dims + dim_idx; + if (index < 0) { + ${l.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"} + } + ${t.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"} + } + + input_slice_offsets_data[global_idx] = base_offset + u32(relative_slice_offset); + }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${l.length}_${t.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:w,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:f}),getShaderSource:m},{inputs:[r],outputs:[-1]})[0]},TM=(e,r)=>{let t=e.inputs,i=t[0].dims,l=t[0].dataType,n=t[1].dims,c=n[n.length-1],d=je.sizeToDimension(n,n.length-1),h=je.sizeFromDimension(i,r.batchDims+c),f=je.sizeToDimension(i,r.batchDims),w=je.sizeFromDimension(i,r.batchDims),m=d/f,g=new Array(c),x=h;for(let A=0;Ai.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let z=n.slice(0,-1).concat(i.slice(I)),E=je.size(z),C=[{type:12,data:E},{type:12,data:h},...Et(t[0].dims,M.dims,z)],D=A=>{let $=Ne("data",t[0].dataType,t[0].dims.length),P=Ne("slice_offsets",12,M.dims.length),k=gt("output",t[0].dataType,z.length);return` + ${A.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables($,P,k)} + ${A.mainStart()} + ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let slice_offset = slice_offsets[global_idx / uniforms.slice_size]; + output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size]; + }`};e.compute({name:"GatherND",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:z,dataType:l}],dispatchGroup:{x:Math.ceil(E/64)},programUniforms:C}),getShaderSource:D},{inputs:[t[0],M]})},CM=e=>({batchDims:e.batch_dims,cacheKey:""})}),ww,yw,EM,PM,pT=Ye(()=>{$t(),Lt(),br(),zt(),ww=(e,r)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let t=je.normalizeAxis(r.quantizeAxis,e[0].dims.length),i=r.blockSize,l=e[0],n=e[2],c=e.length===4?e[3]:void 0;if(n.dims.length!==l.dims.length||!l.dims.map((d,h)=>h===t?Math.ceil(d/i)===n.dims[h]:d===n.dims[h]).reduce((d,h)=>d&&h,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(c){if(c.dataType!==l.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(c.dims.length!==n.dims.length||!c.dims.map((d,h)=>d===n.dims[h]).reduce((d,h)=>d&&h,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},yw=(e,r)=>{let t=e[0].dims,i=e[1].dims,l=t.length,n=je.normalizeAxis(r.gatherAxis,l),c=je.normalizeAxis(r.quantizeAxis,l),d=t.slice(0);d.splice(n,1,...i);let h=je.size(d),f=e[2].dataType,w=e[0].dataType===22,m=[{type:12,data:h},{type:12,data:c},{type:12,data:n},{type:12,data:r.blockSize},...Et(...e.map((x,M)=>x.dims),d)],g=x=>{let M=Ne("data",e[0].dataType,e[0].dims.length),I=Ne("inputIndices",e[1].dataType,e[1].dims.length),z=Ne("scales",e[2].dataType,e[2].dims.length),E=e.length>3?Ne("zeroPoint",e[3].dataType,e[3].dims.length):void 0,C=gt("output",f,d.length),D=[M,I,z];E&&D.push(E);let A=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${x.registerUniforms(A).declareVariables(...D,C)} + ${x.mainStart()} + let output_indices = ${C.offsetToIndices("global_idx")}; + var indices_indices = ${I.type.indices}(0); + ${i.length>1?` + for (var i: u32 = 0; i < ${i.length}; i++) { + let index = ${C.indicesGet("output_indices","uniforms.gather_axis + i")}; + ${I.indicesSet("indices_indices","i","index")}; + }`:`indices_indices = ${C.indicesGet("output_indices","uniforms.gather_axis")};`}; + var data_indices = ${M.type.indices}(0); + for (var i: u32 = 0; i < uniforms.gather_axis; i++) { + let index = ${C.indicesGet("output_indices","i")}; + ${M.indicesSet("data_indices","i","index")}; + } + var index_from_indices = ${I.getByIndices("indices_indices")}; + if (index_from_indices < 0) { + index_from_indices += ${t[n]}; + } + ${M.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")}; + for (var i = uniforms.gather_axis + 1; i < ${d.length}; i++) { + let index = ${C.indicesGet("output_indices",`i + ${i.length} - 1`)}; + ${M.indicesSet("data_indices","i","index")}; + } + let data_offset = ${M.indicesToOffset("data_indices")}; + let data_index = data_offset % 8; + // Convert 4-bit packed data to 8-bit packed data. + let packed_4bit_quantized_data = ${M.getByOffset("data_offset / 8")}; + let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f; + let quantized_data_vec = ${w?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data)); + let quantized_data = quantized_data_vec[data_index / 2]; + var scale_indices = data_indices; + let quantize_axis_index = ${z.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size; + ${z.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")}; + var scale = ${z.getByIndices("scale_indices")}; + ${E?` + let zero_point_indices = scale_indices; + let zero_point_offset = ${E.indicesToOffset("zero_point_indices")}; + let zero_point_index = zero_point_offset % 8; + let packed_4bit_zero_points = ${E.getByOffset("zero_point_offset / 8")}; + let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f; + let zero_point_vec = ${w?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points)); + let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"}; + let dequantized_data = ${Qr(f)}(quantized_data - zero_point) * scale; + ${C.setByOffset("global_idx","dequantized_data")}; + }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${r.cacheKey};${e.filter((x,M)=>M!==1).map(x=>x.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(x,M)=>"rank")},getRunData:()=>({outputs:[{dims:d,dataType:f}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:m}),getShaderSource:g}},EM=(e,r)=>{let t=e.inputs;ww(t,r),e.compute(yw(e.inputs,r))},PM=e=>Zt({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),bw,Mw,SM,kM,hT=Ye(()=>{$t(),Lt(),br(),zt(),bw=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and + indices input tensors be of same rank.`)},Mw=(e,r)=>{let t=e[0].dims,i=e[0].dataType,l=t.length,n=e[1].dims,c=e[1].dataType,d=je.normalizeAxis(r.axis,l),h=t[d],f=n.slice(0),w=je.size(f),m=Ne("input",i,l),g=Ne("indicesInput",c,n.length),x=gt("output",i,f.length),M=[{type:12,data:w},{type:6,data:h},{type:12,data:d}];return M.push(...Et(t,n,f)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:f,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:M}),getShaderSource:I=>` + ${I.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(m,g,x)} + ${I.mainStart()} + ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${x.offsetToIndices("global_idx")}; + + var idx = ${g.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${m.type.indices}(outputIndices); + ${m.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${m.getByIndices("inputIndices")}; + + ${x.setByOffset("global_idx","value")}; + }`}},SM=e=>Zt({axis:e.axis}),kM=(e,r)=>{let t=e.inputs;bw(t),e.compute(Mw(e.inputs,r))}}),vw,xw,$M,IM,fT=Ye(()=>{$t(),Lt(),zt(),vw=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},xw=(e,r)=>{let t=e[0].dims.slice(),i=e[1].dims.slice(),[l,n,c]=S0.getShapeOfGemmResult(t,r.transA,i,r.transB,e.length===3?e[2].dims:void 0),d=[l,n];if(!d)throw new Error("Can't use gemm on the given tensors");let h=16,f=Math.ceil(n/h),w=Math.ceil(l/h),m=!0,g=je.size(d),x=[{type:12,data:m?f:g},{type:12,data:l},{type:12,data:n},{type:12,data:c},{type:1,data:r.alpha},{type:1,data:r.beta}],M=["type","type"];e.length===3&&(x.push(...Et(e[2].dims)),M.push("rank")),x.push(...Et(d));let I=E=>{let C="";r.transA&&r.transB?C="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":r.transA&&!r.transB?C="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!r.transA&&r.transB?C="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!r.transA&&!r.transB&&(C="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let D=r.alpha===1?"":"value *= uniforms.alpha;",A=Ne("a",e[0].dataType,e[0].dims),$=Ne("b",e[1].dataType,e[1].dims),P=A.type.value,k=null,O=[A,$];e.length===3&&(k=Ne("c",e[2].dataType,e[2].dims.length),O.push(k));let R=gt("output",e[0].dataType,d.length);O.push(R);let U=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return` + ${E.registerUniforms(U).declareVariables(...O)} + + ${E.mainStart()} + ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${P}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${C} + } + + ${D} + ${k!=null?`let cOffset = ${k.broadcastedIndicesToOffset("vec2(m, n)",R)}; value += ${P}(uniforms.beta) * ${k.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`},z=E=>{let C=Ne("a",e[0].dataType,e[0].dims),D=Ne("b",e[1].dataType,e[1].dims),A=null,$=[C,D];e.length===3&&(A=Ne("c",e[2].dataType,e[2].dims.length),$.push(A));let P=gt("output",e[0].dataType,d.length);$.push(P);let k=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],O="",R="";r.transA&&r.transB?(R=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${C.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${D.type.value}(0); + } + `,O="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):r.transA&&!r.transB?(R=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${C.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${D.type.value}(0); + } + `,O="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!r.transA&&r.transB?(R=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${C.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${D.type.value}(0); + } + `,O="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!r.transA&&!r.transB&&(R=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${C.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${D.type.value}(0); + } + `,O="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let U=r.alpha===1?"":"value *= uniforms.alpha;";return` + ${E.registerUniforms(k).declareVariables(...$)} + var tile_a: array, ${h}>; + var tile_b: array, ${h}>; + ${E.mainStart([h,h,1])} + let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${h}; + let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${h}; + let num_tiles = (uniforms.K - 1) / ${h} + 1; + var k_start = 0u; + var value = ${P.type.value}(0); + for (var t: u32 = 0u; t < num_tiles; t++) { + ${R} + k_start = k_start + ${h}; + workgroupBarrier(); + + for (var k: u32 = 0u; k < ${h}; k++) { + ${O} + } + workgroupBarrier(); + } + + ${U} + let m = tile_row_start + local_id.y; + let n = tile_col_start + local_id.x; + ${A!=null?`let cOffset = ${A.broadcastedIndicesToOffset("vec2(m, n)",P)}; value += ${P.type.value}(uniforms.beta) * ${A.getByOffset("cOffset")};`:""} + if (m < uniforms.M && n < uniforms.N) { + output[m * uniforms.N + n] = value; + } + }`};return m?{name:"GemmShared",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:f*w},programUniforms:x}),getShaderSource:z}:{name:"Gemm",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:x}),getShaderSource:I}},$M=e=>{let r=e.transA,t=e.transB,i=e.alpha,l=e.beta;return{transA:r,transB:t,alpha:i,beta:l,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},IM=(e,r)=>{vw(e.inputs),e.compute(xw(e.inputs,r))}}),en,hn,ai,oi,Tw,Cw,Ew,Pw,Sw,kw,$w,Iw,AM,OM,mT=Ye(()=>{$t(),Lt(),br(),zt(),[en,hn,ai,oi]=[0,1,2,3],Tw=e=>{if(e[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(e[0].dims.length!==e[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(e[0].dims.length-2!==e[1].dims[e[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},Cw=` + fn gs_get_cubic_coeffs(x: f32) -> vec4 { + let cubic_alpha = -0.75f; + let x_abs = abs(x); + var coeffs: vec4; + coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha); + coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1); + coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1); + coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha); + return coeffs; + } +`,Ew=e=>` + fn gs_bicubic_interpolate(p: mat4x4<${e}>, x: f32, y: f32) -> ${e} { + var v: vec4; + var coeffs = gs_get_cubic_coeffs(x); + for (var i = 0; i < 4; i++) { + v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3]; + } + coeffs = gs_get_cubic_coeffs(y); + let pixel = ${e}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]); + return pixel; + } +`,Pw=e=>` + fn gs_denormalize(n: f32, length: i32) -> f32 { + ${e.alignCorners===0?` + // alignCorners: false => [-1, 1] to [-0.5, length - 0.5] + return ((n + 1.0) * f32(length) - 1.0) / 2.0; + `:` + // alignCorners: true => [-1, 1] to [0, length - 1] + return (n + 1.0) / 2.0 * (f32(length - 1)); + `} + } +`,Sw=e=>` + ${e.paddingMode==="reflection"?` + fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 { + var dx = 0.0; + var fx = f32(x); + let range = x_max - x_min; + if (fx < x_min) { + dx = x_min - fx; + let n = u32(dx / range); + let r = dx - f32(n) * range; + if (n % 2 == 0) { + fx = x_min + r; + } else { + fx = x_max - r; + } + } else if (fx > x_max) { + dx = fx - x_max; + let n = u32(dx / range); + let r = dx - f32(n) * range; + if (n % 2 == 0) { + fx = x_max - r; + } else { + fx = x_min + r; + } + } + return u32(fx); + }`:""} +`,kw=(e,r,t)=>` + fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4) -> ${r} { + var pixel = ${r}(0); + var indices = vec4(0); + indices[${en}] = batch; + indices[${hn}] = channel;`+(()=>{switch(t.paddingMode){case"zeros":return` + if (r >= 0 && r < H && c >=0 && c < W) { + indices[${ai}] = u32(r); + indices[${oi}] = u32(c); + } else { + return ${r}(0); + } + `;case"border":return` + indices[${ai}] = u32(clamp(r, 0, H - 1)); + indices[${oi}] = u32(clamp(c, 0, W - 1)); + `;case"reflection":return` + indices[${ai}] = gs_reflect(r, border[1], border[3]); + indices[${oi}] = gs_reflect(c, border[0], border[2]); + `;default:throw new Error(`padding mode ${t.paddingMode} is not supported`)}})()+` + return ${e.getByIndices("indices")}; + } +`,$w=(e,r,t)=>(()=>{switch(t.mode){case"nearest":return` + let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${en}], indices[${hn}], border); + `;case"bilinear":return` + let x1 = i32(floor(x)); + let y1 = i32(floor(y)); + let x2 = x1 + 1; + let y2 = y1 + 1; + + let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${en}], indices[${hn}], border); + let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${en}], indices[${hn}], border); + let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${en}], indices[${hn}], border); + let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${en}], indices[${hn}], border); + + let dx2 = ${r}(f32(x2) - x); + let dx1 = ${r}(x - f32(x1)); + let dy2 = ${r}(f32(y2) - y); + let dy1 = ${r}(y - f32(y1)); + let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22); + `;case"bicubic":return` + let x0 = i32(floor(x)) - 1; + let y0 = i32(floor(y)) - 1; + var p: mat4x4<${r}>; + for (var h = 0; h < 4; h++) { + for (var w = 0; w < 4; w++) { + p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${en}], indices[${hn}], border); + } + } + + let dx = x - f32(x0 + 1); + let dy = y - f32(y0 + 1); + let result = gs_bicubic_interpolate(p, dx, dy); + `;default:throw new Error(`mode ${t.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,Iw=(e,r)=>{let t=Ne("x",e[0].dataType,e[0].dims.length),i=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],l=Ne("grid",e[1].dataType,i.length,2),n=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];r.format==="NHWC"&&(n=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[en,hn,ai,oi]=[0,3,1,2]);let c=gt("output",e[0].dataType,n.length),d=t.type.value,h=je.size(n),f=[{type:12,data:h},...Et(e[0].dims,i,n)],w=m=>` + ${m.registerUniform("output_size","u32").declareVariables(t,l,c)} + ${Cw} + ${Ew(d)} + ${Pw(r)} + ${Sw(r)} + ${kw(t,d,r)} + + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let H_in = i32(uniforms.x_shape[${ai}]); + let W_in = i32(uniforms.x_shape[${oi}]); + + ${r.alignCorners===0?` + let x_min = -0.5; + let x_max = f32(W_in) - 0.5; + let y_min = -0.5; + let y_max = f32(H_in) - 0.5; + `:` + let x_min = 0.0; + let x_max = f32(W_in) - 1.0; + let y_min = 0.0; + let y_max = f32(H_in) - 1.0; + `}; + let border = vec4(x_min, y_min, x_max, y_max); + + let indices = ${c.offsetToIndices("global_idx")}; + var grid_indices = vec3(indices[${en}], indices[${ai}], indices[${oi}]); + let nxy = ${l.getByIndices("grid_indices")}; + var x = gs_denormalize(f32(nxy[0]), W_in); + var y = gs_denormalize(f32(nxy[1]), H_in); + + ${$w(c,d,r)} + }`;return{name:"GridSample",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:["type","type"]},getRunData:m=>{let g=je.size(n);return{outputs:[{dims:n,dataType:m[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:f}},getShaderSource:w}},AM=(e,r)=>{Tw(e.inputs),e.compute(Iw(e.inputs,r))},OM=e=>Zt({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),ts,Aw,FM,tm,Ow,md,DM,jM=Ye(()=>{$t(),Lt(),br(),Jm(),e_(),zt(),On(),ts=(e,r)=>e.length>r&&e[r].dims.length>0?e[r]:void 0,Aw=(e,r)=>{let t=e[0],i=ts(e,1),l=ts(e,2),n=ts(e,3),c=ts(e,4),d=ts(e,5),h=ts(e,6),f=ts(e,7);if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let w=t.dims[0],m=t.dims[1],g=t.dims.length===3?t.dims[2]:r.numHeads*t.dims[4],x=m,M=0,I=0,z=Math.floor(g/r.numHeads);if(h&&f&&je.size(h.dims)&&je.size(f.dims)){if(h.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(h.dims[0]!==w||h.dims[1]!==r.numHeads||h.dims[3]!==z)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(f.dims[0]!==w||f.dims[1]!==r.numHeads||f.dims[3]!==z)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(h.dims[2]!==f.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(f.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');M=h.dims[2],I=h.dims[2]}else if(h&&je.size(h.dims)||f&&je.size(f.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let E;if(i&&je.size(i.dims)>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(i.dims.length<3||i.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==i.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(i.dims.length===3){if(i.dims[2]!==t.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');E=2,x=i.dims[1]}else if(i.dims.length===5){if(i.dims[2]!==r.numHeads||i.dims[3]!==2||i.dims[4]!==z)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(l)throw new Error('Expect "value" be none when "key" has packed kv format.');E=5,x=i.dims[1]}else{if(i.dims[1]!==r.numHeads||i.dims[3]!==z)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');E=0,x=i.dims[2]}}else{if(t.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(t.dims[2]!==r.numHeads||t.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');E=3}if(n&&je.size(n.dims)>0){if(n.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(i&&i.dims.length===5&&i.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let C=M+x,D=0;if(c&&je.size(c.dims)>0){D=8;let k=c.dims;throw k.length===1?k[0]===w?D=1:k[0]===3*w+2&&(D=3):k.length===2&&k[0]===w&&k[1]===C&&(D=5),D===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let A=!1,$=g;if(l&&je.size(l.dims)>0){if(l.dims.length!==3&&l.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.dims[0]!==l.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(l.dims.length===3){if(x!==l.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');$=l.dims[2]}else{if(x!==l.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');$=l.dims[1]*l.dims[3],A=!0}}let P=!1;if(c&&je.size(c.dims)>0)throw new Error("Key padding mask is not supported");if(d&&je.size(d.dims)>0){if(d.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(d.dims[0]!==w||d.dims[1]!==r.numHeads||d.dims[2]!==m||d.dims[3]!==C)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:w,sequenceLength:m,pastSequenceLength:M,kvSequenceLength:x,totalSequenceLength:C,maxSequenceLength:I,inputHiddenSize:0,hiddenSize:g,vHiddenSize:$,headSize:z,vHeadSize:Math.floor($/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:D,scale:r.scale,broadcastResPosBias:P,passPastInKv:A,qkvFormat:E}},FM=e=>Zt({...e}),tm=Zt({perm:[0,2,1,3]}),Ow=(e,r,t,i,l,n,c)=>{let d=[i,l,n],h=je.size(d),f=[{type:12,data:h},{type:12,data:c},{type:12,data:n}],w=m=>{let g=gt("qkv_with_bias",r.dataType,d),x=Ne("qkv",r.dataType,d),M=Ne("bias",t.dataType,d),I=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${m.registerUniforms(I).declareVariables(x,M,g)} + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:d,dataType:r.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:f}),getShaderSource:w},{inputs:[r,t],outputs:[-1]})[0]},md=(e,r,t,i,l,n,c,d)=>{let h=n;if(c&&je.size(c.dims)>0){if(i===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return h=Ow(e,n,c,r,i,t*l,d),h=h.reshape([r,i,t,l]),t===1||i===1?h:e.compute(_s(h,tm.perm),{inputs:[h],outputs:[-1]})[0]}else return n.dims.length===3&&(h=n.reshape([r,i,t,l])),t===1||i===1?h:e.compute(_s(h,tm.perm),{inputs:[h],outputs:[-1]})[0]},DM=(e,r)=>{let t=Aw(e.inputs,r),i=e.inputs[0],l=ts(e.inputs,1),n=ts(e.inputs,2),c=ts(e.inputs,3),d=ts(e.inputs,4),h=ts(e.inputs,5),f=ts(e.inputs,6),w=ts(e.inputs,7);if(i.dims.length===5)throw new Error("Packed QKV is not implemented");if((l==null?void 0:l.dims.length)===5)throw new Error("Packed KV is not implemented");let m=l&&n&&l.dims.length===4&&n.dims.length===4,g=md(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,i,c,0);if(m)return yd(e,g,l,n,d,void 0,f,w,h,t);if(!l||!n)throw new Error("key and value must be provided");let x=md(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.headSize,l,c,t.hiddenSize),M=md(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.vHeadSize,n,c,2*t.hiddenSize);yd(e,g,x,M,d,void 0,f,w,h,t)}}),Fw,Dw,jw,Lw,Dm,LM,zM,BM=Ye(()=>{$t(),Lt(),br(),zt(),Fw=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Dw=(e,r)=>{let t=[],i=r.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(l=>t.push(Number(l))),i=t.length),Zt({numOutputs:i,axis:r.axis,splitSizes:t})},jw=e=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${e}u; i += 1u ) { + if (index < ${Mt("uniforms.size_in_split_axis","i",e)}) { + return i; + } + } + return ${e}u; +}`,Lw=e=>{let r=e.length,t=[];for(let i=0;i{let t=e[0].dims,i=je.size(t),l=e[0].dataType,n=je.normalizeAxis(r.axis,t.length),c=new Array(r.numOutputs),d=Ne("input",l,t.length),h=new Array(r.numOutputs),f=[],w=[],m=0,g=[{type:12,data:i}];for(let M=0;M` + ${M.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",h.length).declareVariables(d,...c)} + ${jw(h.length)} + ${Lw(c)} + + ${M.mainStart()} + ${M.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${d.offsetToIndices("global_idx")}; + var index = ${d.indicesGet("indices",n)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${Mt("uniforms.size_in_split_axis","output_number - 1u",h.length)}; + ${d.indicesSet("indices",n,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getShaderSource:x,getRunData:()=>({outputs:f,dispatchGroup:{x:Math.ceil(i/64)},programUniforms:g})}},LM=(e,r)=>{Fw(e.inputs);let t=e.inputs.length===1?r:Dw(e.inputs,r);e.compute(Dm(e.inputs,t),{inputs:[0]})},zM=e=>{let r=e.axis,t=e.splitSizes,i=e.numOutputs<0?t.length:e.numOutputs;if(i!==t.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Zt({axis:r,numOutputs:i,splitSizes:t})}}),zw,tf,RM,NM=Ye(()=>{$t(),Lt(),br(),zt(),zw=(e,r)=>{let[t,i,l,n]=e,{numHeads:c,rotaryEmbeddingDim:d}=r;if(t.dims.length!==3&&t.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${t.dims.length}`);if(!je.areEqual(i.dims,[])&&!je.areEqual(i.dims,[1])&&i.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${i.dims.length}`);if(l.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${l.dims.length}`);if(n.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${n.dims.length}`);if(!je.areEqual(l.dims,n.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(d>0&&c===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let h=t.dims[0],f=t.dims[t.dims.length-2],w=l.dims[0],m=je.sizeFromDimension(t.dims,1)/f,g=d===0?l.dims[1]*2:m/c;if(d>g)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(i.dims.length===2){if(h!==i.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${i.dims[0]}`);if(f!==i.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${i.dims[1]}`)}if(g/2!==l.dims[1]&&d/2!==l.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${l.dims[1]}`);if(f>w)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},tf=(e,r)=>{let{interleaved:t,numHeads:i,rotaryEmbeddingDim:l,scale:n}=r,c=e[0].dims[0],d=je.sizeFromDimension(e[0].dims,1),h=e[0].dims[e[0].dims.length-2],f=d/h,w=e[2].dims[1],m=l===0?w*2:f/i,g=new Array(c,h,f/m,m-w),x=je.computeStrides(g),M=[{type:1,data:n},{type:12,data:g},{type:12,data:x},...e[0].dims.length===3?new Array({type:12,data:[d,f,m,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[d,m,h*m,1]}):[],...Et(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],I=z=>{let E=Ne("input",e[0].dataType,e[0].dims.length),C=Ne("position_ids",e[1].dataType,e[1].dims.length),D=Ne("cos_cache",e[2].dataType,e[2].dims.length),A=Ne("sin_cache",e[3].dataType,e[3].dims.length),$=gt("output",e[0].dataType,e[0].dims.length);return z.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:g.length},{name:"global_strides",type:"u32",length:x.length},{name:"input_output_strides",type:"u32",length:x.length}]),` + ${z.declareVariables(E,C,D,A,$)} + + ${z.mainStart(Na)} + let half_rotary_emb_dim = uniforms.${D.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${z.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${C.broadcastedIndicesToOffset("bsnh.xy",gt("",C.type.tensor,2))}; + let position_id = + u32(${C.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${t}); + let j = i + select(half_rotary_emb_dim, 1, ${t}); + let re = ${E.getByOffset("i")} * ${D.get("position_id","bsnh[3]")} - + ${E.getByOffset("j")} * ${A.get("position_id","bsnh[3]")}; + ${$.setByOffset("i","re")} + let im = ${E.getByOffset("i")} * ${A.get("position_id","bsnh[3]")} + + ${E.getByOffset("j")} * ${D.get("position_id","bsnh[3]")}; + ${$.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${$.setByOffset("k",E.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:Zt({interleaved:t}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:I,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(je.size(g)/Na)},programUniforms:M})}},RM=(e,r)=>{zw(e.inputs,r),e.compute(tf(e.inputs,r))}}),Bw,Rw,rm,Nw,VM,_T=Ye(()=>{br(),$t(),e_(),jM(),BM(),On(),NM(),zt(),Bw=(e,r)=>{if(r.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let t=e[0],i=e[1],l=e[2],n=e[3],c=e[4];if(r.doRotary!==0&&e.length<=7)throw new Error("cos_cast and sin_cache are expected if do_rotary attribute is non-zero");if(r.localWindowSize!==-1)throw new Error("Local attention is not supported");if(r.softcap!==0)throw new Error("Softcap is not supported");if(r.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(r.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let d=!1,h=t.dims[0],f=t.dims[1],w=t.dims.length===3?d?t.dims[2]/3:t.dims[2]:r.numHeads*t.dims[4],m=f,g=0,x=!i||i.dims.length===0,M=Math.floor(x?w/(r.numHeads+2*r.kvNumHeads):w/r.numHeads);x&&(w=M*r.numHeads);let I=n&&n.dims.length!==0,z=c&&c.dims.length!==0;if(I&&n.dims.length===4&&n.dims[0]===h&&n.dims[1]!==r.kvNumHeads&&n.dims[2]===r.kvNumHeads&&n.dims[3]===M)throw new Error("BSNH pastKey/pastValue is not supported");if(I&&z){if(n.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(c.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');g=n.dims[2]}else if(I||z)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let E=1;if(i&&i.dims.length>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(i.dims.length<3||i.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==i.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(i.dims.length===3){if(t.dims[2]%i.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');m=i.dims[1]}else if(i.dims.length===5){if(i.dims[2]!==r.numHeads||i.dims[3]!==2||i.dims[4]!==M)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(l)throw new Error('Expect "value" be none when "key" has packed kv format.');m=i.dims[1]}else{if(i.dims[1]!==r.numHeads||i.dims[3]!==M)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');m=i.dims[2]}}else{if(t.dims.length!==3&&t.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(t.dims.length===5&&(t.dims[2]!==r.numHeads||t.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');E=3}let C=0,D=!1,A=r.kvNumHeads?M*r.kvNumHeads:w;if(l&&l.dims.length>0){if(l.dims.length!==3&&l.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.dims[0]!==l.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(l.dims.length===3){if(m!==l.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');A=l.dims[2]}else{if(m!==l.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');A=l.dims[1]*l.dims[3],D=!0}}let $=e.length>4?e[5]:void 0;if($&&$.dims.length!==1&&$.dims[0]!==h)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:h,sequenceLength:f,pastSequenceLength:g,kvSequenceLength:m,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:w,vHiddenSize:A,headSize:M,vHeadSize:Math.floor(A/r.kvNumHeads),numHeads:r.numHeads,kvNumHeads:r.kvNumHeads,nReps:r.numHeads/r.kvNumHeads,pastPresentShareBuffer:!1,maskType:C,scale:r.scale,broadcastResPosBias:!1,passPastInKv:D,qkvFormat:E}},Rw=Zt({perm:[0,2,1,3]}),rm=(e,r,t)=>{let i=r,l=t.kvNumHeads;return r.dims.length===3&&t.kvSequenceLength!==0&&(i=r.reshape([t.batchSize,t.kvSequenceLength,l,t.headSize]),i=e.compute(_s(i,Rw.perm),{inputs:[i],outputs:[-1]})[0]),i},Nw=(e,r,t,i)=>{let l=7,n=["type","type"],c=[e*r],d=e*r,h=[{type:12,data:d},{type:12,data:r},{type:12,data:e}],f=w=>{let m=Ne("seq_lens",t.dataType,t.dims),g=Ne("total_seq_lens",i.dataType,i.dims),x=gt("pos_ids",l,c),M=[{name:"output_size",type:"u32"},{name:"sequence_length",type:"u32"},{name:"batch_size",type:"u32"}];return` + ${w.registerUniforms(M).declareVariables(m,g,x)} + ${w.mainStart()} + ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let total_sequence_length = u32(${g.getByOffset("0")}); + let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length; + let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length; + let batch_idx = global_idx / uniforms.sequence_length; + let sequence_idx = i32(global_idx % uniforms.sequence_length); + var pos_id: i32 = 0; + let seqlen = ${m.getByOffset("batch_idx")}; + let total_seqlen = seqlen + 1; + if (is_first_prompt) { + if (sequence_idx < total_seqlen) { + pos_id = sequence_idx; + } else { + pos_id = 1; + } + ${x.setByOffset("global_idx","pos_id")} + } else if (is_subsequent_prompt) { + let past_seqlen = total_seqlen - i32(uniforms.sequence_length); + if (past_seqlen + sequence_idx < total_seqlen) { + pos_id = past_seqlen + sequence_idx; + } else { + pos_id = 1; + } + ${x.setByOffset("global_idx","pos_id")} + } else if (global_idx < uniforms.batch_size) { + ${x.setByOffset("global_idx","seqlen")} + }; + } + `};return{name:"GeneratePositionIds",shaderCache:{hint:`${e};${r}`,inputDependencies:n},getRunData:()=>({outputs:[{dims:c,dataType:l}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:h}),getShaderSource:f}},VM=(e,r)=>{var A;let t=Bw(e.inputs,r);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((A=e.inputs[1])==null?void 0:A.dims.length)===5)throw new Error("Packed KV is not implemented");let i=e.inputs[0],l=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,n=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,c=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,d=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,h=e.inputs.length>4?e.inputs[5]:void 0,f=e.inputs.length>5?e.inputs[6]:void 0,w=t.kvNumHeads?t.kvNumHeads:t.numHeads,m=Zt({axis:2,numOutputs:3,splitSizes:[t.numHeads*t.headSize,w*t.headSize,w*t.headSize]}),[g,x,M]=!l&&!n?e.compute(Dm([i],m),{inputs:[i],outputs:[-1,-1,-1]}):[i,l,n],I,z;if(r.doRotary){let $=e.compute(Nw(t.batchSize,t.sequenceLength,h,f),{inputs:[h,f],outputs:[-1]})[0],P=e.inputs[7],k=e.inputs[8],O=Zt({interleaved:r.rotaryInterleaved!==0,numHeads:t.numHeads,rotaryEmbeddingDim:0,scale:r.scale}),R=[g,$,P,k],U=[-1];I=e.compute(tf(R,O),{inputs:R,outputs:U})[0],R.splice(0,1,x);let te=Zt({interleaved:r.rotaryInterleaved!==0,numHeads:t.kvNumHeads,rotaryEmbeddingDim:0,scale:r.scale});z=e.compute(tf(R,te),{inputs:R,outputs:U})[0]}let E=md(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,r.doRotary?I:g,void 0,0),C=rm(e,r.doRotary?z:x,t),D=rm(e,M,t);yd(e,E,C,D,void 0,void 0,c,d,void 0,t,h,f)}}),sm,Vw,Ww,WM,gT=Ye(()=>{$t(),Lt(),On(),zt(),sm=(e,r,t,i,l,n,c,d)=>{let h=gr(n),f=h===1?"f32":`vec${h}f`,w=h===1?"vec2f":`mat2x${h}f`,m=l*c,g=64;m===1&&(g=256);let x=[l,c,n/h],M=[l,c,2],I=["rank","type","type"],z=[];z.push(...Et(x,M));let E=C=>{let D=Ne("x",r.dataType,3,h),A=Ne("scale",t.dataType,t.dims),$=Ne("bias",i.dataType,i.dims),P=gt("output",1,3,2),k=[D,A,$,P];return` + var workgroup_shared : array<${w}, ${g}>; + const workgroup_size = ${g}u; + ${C.declareVariables(...k)} + ${C.mainStart(g)} + let batch = workgroup_index / uniforms.x_shape[1]; + let channel = workgroup_index % uniforms.x_shape[1]; + let hight = uniforms.x_shape[2]; + // initialize workgroup memory + var sum = ${f}(0); + var squared_sum = ${f}(0); + for (var h = local_idx; h < hight; h += workgroup_size) { + let value = ${f}(${D.get("batch","channel","h")}); + sum += value; + squared_sum += value * value; + } + workgroup_shared[local_idx] = ${w}(sum, squared_sum); + workgroupBarrier(); + + for (var currSize = workgroup_size >> 1; currSize > 0; currSize = currSize >> 1) { + if (local_idx < currSize) { + workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize]; + } + workgroupBarrier(); + } + if (local_idx == 0) { + let sum_final = ${An("workgroup_shared[0][0]",h)} / f32(hight * ${h}); + let squared_sum_final = ${An("workgroup_shared[0][1]",h)} / f32(hight * ${h}); + + let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${d})); + let channel_scale = inv_std_dev * f32(scale[channel]); + let channel_shift = f32(bias[channel]) - sum_final * channel_scale; + output[workgroup_index] = vec2f(channel_scale, channel_shift); + } + }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${h};${d};${g}`,inputDependencies:I},getRunData:()=>({outputs:[{dims:M,dataType:1}],dispatchGroup:{x:m},programUniforms:z}),getShaderSource:E},{inputs:[r,t,i],outputs:[-1]})[0]},Vw=(e,r,t)=>{let i=r[0].dims,l=i,n=2,c=i[0],d=i[1],h=je.sizeFromDimension(i,n),f=gr(h),w=je.size(l)/f,m=sm(e,r[0],r[1],r[2],c,h,d,t.epsilon),g=[c,d,h/f],x=[c,d],M=["type","none"],I=z=>{let E=Ne("x",r[0].dataType,g.length,f),C=Ne("scale_shift",1,x.length,2),D=gt("output",r[0].dataType,g.length,f),A=[E,C,D];return` + ${z.registerUniform("output_size","u32").declareVariables(...A)} + ${z.mainStart()} + ${z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let outputIndices = ${D.offsetToIndices("global_idx")}; + let batch = outputIndices[0]; + let channel = outputIndices[1]; + let scale_shift = ${C.getByIndices("vec2(batch, channel)")}; + let value = ${E.getByOffset("global_idx")} * ${D.type.value}(scale_shift.x) + ${D.type.value}(scale_shift.y); + ${D.setByOffset("global_idx","value")}; + }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${f}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:l,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:[{type:12,data:w},...Et(g,x,g)]}),getShaderSource:I},{inputs:[r[0],m]})},Ww=(e,r,t)=>{let i=r[0].dims,l=i,n=i[0],c=i[i.length-1],d=je.sizeFromDimension(i,1)/c,h=gr(c),f=je.size(l)/h,w=[{type:12,data:d},{type:12,data:Math.floor(c/h)}],m=["type","type"],g=!1,x=[0,i.length-1];for(let E=0;Ei[x[C]])),I=sm(e,M,r[1],r[2],n,d,c,t.epsilon),z=E=>{let C=Lr(r[0].dataType),D=h===1?"vec2f":`mat${h}x2f`,A=k=>{let O=k===0?"x":"y",R=h===1?"f32":`vec${h}f`;switch(h){case 1:return`${C}(${R}(scale.${O}))`;case 2:return`vec2<${C}>(${R}(scale[0].${O}, scale[1].${O}))`;case 4:return`vec4<${C}>(${R}(scale[0].${O}, scale[1].${O}, scale[2].${O}, scale[3].${O}))`;default:throw new Error(`Not supported compoents ${h}`)}},$=Ne("input",r[0].dataType,r[0].dims,h),P=gt("output",r[0].dataType,l,h);return` + @group(0) @binding(0) var input : array<${$.type.storage}>; + @group(0) @binding(1) var scale_input : array<${D}>; + @group(0) @binding(2) var output : array<${P.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${E.mainStart()} + let current_image_number = global_idx / (uniforms.C * uniforms.H); + let current_channel_number = global_idx % uniforms.C; + + let scale_offset = current_image_number * uniforms.C + current_channel_number; + let scale = scale_input[scale_offset]; + output[global_idx] = fma(input[global_idx], ${A(0)}, ${A(1)}); + }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${h}`,inputDependencies:m},getRunData:()=>({outputs:[{dims:l,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:w}),getShaderSource:z},{inputs:[r[0],I]})},WM=(e,r)=>{r.format==="NHWC"?Ww(e,e.inputs,r):Vw(e,e.inputs,r)}}),Uw,Gw,UM,wT=Ye(()=>{$t(),Lt(),zt(),Uw=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Gw=(e,r,t)=>{let i=r.simplified,l=e[0].dims,n=e[1],c=!i&&e[2],d=l,h=je.normalizeAxis(r.axis,l.length),f=je.sizeToDimension(l,h),w=je.sizeFromDimension(l,h),m=je.size(n.dims),g=c?je.size(c.dims):0;if(m!==w||c&&g!==w)throw new Error(`Size of X.shape()[axis:] == ${w}. + Size of scale and bias (if provided) must match this. + Got scale size of ${m} and bias size of ${g}`);let x=[];for(let $=0;$1,C=t>2,D=$=>{let P=Lr(e[0].dataType),k=[Ne("x",e[0].dataType,e[0].dims,M),Ne("scale",n.dataType,n.dims,M)];c&&k.push(Ne("bias",c.dataType,c.dims,M)),k.push(gt("output",e[0].dataType,d,M)),E&&k.push(gt("mean_data_output",1,x)),C&&k.push(gt("inv_std_output",1,x));let O=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${$.registerUniforms(O).declareVariables(...k)} + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${Em("f32",M)}; + var mean_square_vector = ${Em("f32",M)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${Ba(P,M,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${An("mean_vector",M)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${An("mean_square_vector",M)} / uniforms.norm_size ${i?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${Ba(P,M,"x[j + offset]")}; + let f32scale = ${Ba(P,M,"scale[j]")}; + output[j + offset] = ${k[0].type.value}((f32input ${i?"":"- mean"}) * inv_std_dev * f32scale + ${c?`+ ${Ba(P,M,"bias[j]")}`:""} + ); + } + + ${E?"mean_data_output[global_idx] = mean":""}; + ${C?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},A=[{dims:d,dataType:e[0].dataType}];return E&&A.push({dims:x,dataType:1}),C&&A.push({dims:x,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${M};${t};${i}`,inputDependencies:I},getRunData:()=>({outputs:A,dispatchGroup:{x:Math.ceil(f/64)},programUniforms:z}),getShaderSource:D}},UM=(e,r)=>{Uw(e.inputs),e.compute(Gw(e.inputs,r,e.outputCount))}}),Kw,GM,yT=Ye(()=>{Lt(),i_(),a_(),Kw=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},GM=e=>{Kw(e.inputs);let r=Ra.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!r)throw new Error("Can't use matmul on the given tensors");let t=r[r.length-1],i=e.inputs[0].dims[e.inputs[0].dims.length-1];if(t<8&&i<8)e.compute(n_(e.inputs,{activation:""},r));else{let l=r[r.length-2],n=je.size(e.inputs[0].dims.slice(0,-2)),c=je.size(e.inputs[1].dims.slice(0,-2));if(n!==1&&l===1&&c===1){let d=e.inputs[0].reshape([1,n,i]),h=e.inputs[1].reshape([1,i,t]),f=[1,n,t],w=[d,h];e.compute(ef(w,{activation:""},r,f),{inputs:w})}else e.compute(ef(e.inputs,{activation:""},r))}}}),Hw,qw,Qw,KM,HM,bT=Ye(()=>{$t(),Lt(),br(),zt(),Hw=(e,r)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let t=e[0],i=t.dims.length;if(t.dims[i-1]!==r.k)throw new Error("The last dim of input shape does not match the k value");let l=Math.floor((r.k+r.blockSize-1)/r.blockSize),n=r.blockSize/8*r.bits,c=e[1];if(!je.areEqual(c.dims,[r.n,l,n]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let d=e[2].dims;if(je.size(d)!==r.n*l)throw new Error("scales input size error.");if(e.length===4){let h=e[3].dims,f=r.bits>4?r.n*l:r.n*Math.floor((l+1)/2);if(je.size(h)!==f)throw new Error("zeroPoints input size error.")}},qw=(e,r)=>{let t=e[0].dims,i=t.length,l=t[i-2],n=r.k,c=r.n,d=t.slice(0,i-2),h=je.size(d),f=e[1].dims[2]/4,w=e[0].dataType,m=gr(r.k),g=gr(f),x=gr(c),M=d.concat([l,c]),I=l>1&&c/x%2===0?2:1,z=je.size(M)/x/I,E=64,C=[],D=[h,l,n/m],A=je.convertShape(e[1].dims).slice();A.splice(-1,1,f/g),C.push(...Et(D)),C.push(...Et(A)),C.push(...Et(e[2].dims)),e.length===4&&C.push(...Et(je.convertShape(e[3].dims)));let $=[h,l,c/x];C.push(...Et($));let P=k=>{let O=D.length,R=Ne("a",e[0].dataType,O,m),U=Ne("b",12,A.length,g),te=Ne("scales",e[2].dataType,e[2].dims.length),se=[R,U,te],K=e.length===4?Ne("zero_points",12,e[3].dims.length):void 0;K&&se.push(K);let pe=$.length,re=gt("output",e[0].dataType,pe,x),oe=Lr(e[0].dataType),ge=(()=>{switch(m){case 1:return`array<${oe}, 8>`;case 2:return`mat4x2<${oe}>`;case 4:return`mat2x4<${oe}>`;default:throw new Error(`${m}-component is not supported.`)}})(),le=()=>{let q=` + // reuse a data + var input_offset = ${R.indicesToOffset(`${R.type.indices}(batch, row, word_offset)`)}; + var a_data: ${ge}; + for (var j: u32 = 0; j < ${8/m}; j++) { + a_data[j] = ${R.getByOffset("input_offset")}; + input_offset++; + } + `;for(let N=0;N> 4) & b_mask); + b_quantized_values = ${ge}(${Array.from({length:4},(Q,de)=>`${oe}(b_value_lower[${de}]), ${oe}(b_value_upper[${de}])`).join(", ")}); + b_dequantized_values = ${m===1?`${ge}(${Array.from({length:8},(Q,de)=>`(b_quantized_values[${de}] - ${K?`zero_point${N}`:"zero_point"}) * scale${N}`).join(", ")});`:`(b_quantized_values - ${ge}(${Array(8).fill(`${K?`zero_point${N}`:"zero_point"}`).join(",")})) * scale${N};`}; + workgroup_shared[local_id.x * ${I} + ${Math.floor(N/x)}]${x>1?`[${N%x}]`:""} += ${Array.from({length:8/m},(Q,de)=>`${m===1?`a_data[${de}] * b_dequantized_values[${de}]`:`dot(a_data[${de}], b_dequantized_values[${de}])`}`).join(" + ")}; + `;return q},Se=()=>{let q=` + var col_index = col * ${x}; + ${K?` + let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2; + var zero_point_byte_count: u32; + var zero_point_word_index: u32; + var zero_point_byte_offset: u32; + let zero_point_nibble_offset: u32 = block & 0x1u; + var zero_point_bits_offset: u32; + var zero_point_word: u32;`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${oe}(8);`} + `;for(let N=0;N> 0x1u); + zero_point_word_index = zero_point_byte_count >> 0x2u; + zero_point_byte_offset = zero_point_byte_count & 0x3u; + zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + zero_point_word = ${K.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point${N} = ${oe}((zero_point_word) & 0xFu);`:""} + col_index += 1;`;return q},Ce=()=>{let q=`col_index = col * ${x};`;for(let N=0;N; + var b_value_upper: vec4; + var b_quantized_values: ${ge}; + var b_dequantized_values: ${ge};`,q};return` + var workgroup_shared: array<${re.type.value}, ${I*E}>; + ${k.declareVariables(...se,re)} + ${k.mainStart([E,1,1])} + let output_indices = ${re.offsetToIndices(`(global_idx / ${E}) * ${I}`)}; + let col = output_indices[2]; + let row = output_indices[1]; + let batch = output_indices[0]; + let nBlocksPerCol = uniforms.b_shape[1]; + + for (var block = local_id.x; block < nBlocksPerCol; block += ${E}) { + //process one block + var word_offset: u32 = block * ${r.blockSize/m}; + ${Se()} + for (var word: u32 = 0; word < ${f}; word += ${g}) { + ${Ce()} + for (var i: u32 = 0; i < ${g}; i++) { + ${le()} + word_offset += ${8/m}; + } + } + } + workgroupBarrier(); + + if (local_id.x < ${I}) { + var output_value: ${re.type.value} = ${re.type.value}(0); + var workgroup_shared_offset: u32 = local_id.x; + for (var b: u32 = 0u; b < ${E}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${I}; + } + ${re.setByIndices(`${re.type.indices}(batch, row, col + local_id.x)`,"output_value")}; + } + }`};return{name:"MatMulNBits",shaderCache:{hint:`${r.blockSize};${r.bits};${m};${g};${x};${I};${E}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:M,dataType:w}],dispatchGroup:{x:z},programUniforms:C}),getShaderSource:P}},Qw=(e,r)=>{let t=e[0].dims,i=t.length,l=t[i-2],n=r.k,c=r.n,d=t.slice(0,i-2),h=je.size(d),f=e[1].dims[2]/4,w=e[0].dataType,m=gr(r.k),g=gr(f),x=d.concat([l,c]),M=128,I=c%8===0?8:c%4===0?4:1,z=M/I,E=z*g*8,C=E/m,D=E/r.blockSize,A=je.size(x)/I,$=[],P=[h,l,n/m],k=je.convertShape(e[1].dims).slice();k.splice(-1,1,f/g),$.push(...Et(P)),$.push(...Et(k)),$.push(...Et(e[2].dims)),e.length===4&&$.push(...Et(je.convertShape(e[3].dims)));let O=[h,l,c];$.push(...Et(O));let R=U=>{let te=P.length,se=Ne("a",e[0].dataType,te,m),K=Ne("b",12,k.length,g),pe=Ne("scales",e[2].dataType,e[2].dims.length),re=[se,K,pe],oe=e.length===4?Ne("zero_points",12,e[3].dims.length):void 0;oe&&re.push(oe);let ge=O.length,le=gt("output",e[0].dataType,ge),Se=Lr(e[0].dataType),Ce=()=>{switch(m){case 1:return` + let a_data0 = vec4<${Se}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]); + let a_data1 = vec4<${Se}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return` + let a_data0 = vec4<${Se}>(sub_a[word_offset], sub_a[word_offset + 1]); + let a_data1 = vec4<${Se}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return` + let a_data0 = sub_a[word_offset]; + let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${m}-component is not supported.`)}};return` + var sub_a: array<${se.type.value}, ${C}>; + var inter_results: array, ${I}>; + ${U.declareVariables(...re,le)} + ${U.mainStart([z,I,1])} + let output_indices = ${le.offsetToIndices(`workgroup_index * ${I}`)}; + let col = output_indices[2]; + let row = output_indices[1]; + let batch = output_indices[0]; + let n_blocks_per_col = uniforms.b_shape[1]; + let num_tiles = (n_blocks_per_col - 1) / ${D} + 1; + + // Loop over shared dimension. + for (var tile: u32 = 0; tile < num_tiles; tile += 1) { + let a_col_start = tile * ${C}; + // load one tile A data into shared memory. + for (var a_offset = local_idx; a_offset < ${C}; a_offset += ${M}) + { + let a_col = a_col_start + a_offset; + if (a_col < uniforms.a_shape[2]) + { + sub_a[a_offset] = ${se.getByIndices(`${se.type.indices}(batch, row, a_col)`)}; + } else { + sub_a[a_offset] = ${se.type.value}(0); + } + } + workgroupBarrier(); + + // each thread process one block + let b_row = col + local_id.y; + let block = tile * ${D} + local_id.x; + ${oe?` + let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2; + let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u); + let zero_point_word_index = zero_point_byte_count >> 0x2u; + let zero_point_byte_offset = zero_point_byte_count & 0x3u; + let zero_point_nibble_offset: u32 = block & 0x1u; + let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + let zero_point_word = ${oe.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point = ${Se}((zero_point_word) & 0xFu);`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${Se}(8);`} + let scale = ${pe.getByOffset("b_row * n_blocks_per_col + block")}; + let b_data = ${K.getByIndices(`${K.type.indices}(b_row, block, 0)`)}; + var word_offset = local_id.x * ${r.blockSize/m}; + for (var i: u32 = 0; i < ${g}; i++) { + ${Ce()} + let b_value = ${g===1?"b_data":"b_data[i]"}; + let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu); + let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu); + let b_quantized_values = mat2x4<${Se}>(${Array.from({length:4},(q,N)=>`${Se}(b_value_lower[${N}]), ${Se}(b_value_upper[${N}])`).join(", ")}); + let b_dequantized_values = (b_quantized_values - mat2x4<${Se}>(${Array(8).fill("zero_point").join(",")})) * scale; + inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(q,N)=>`${`dot(a_data${N}, b_dequantized_values[${N}])`}`).join(" + ")}; + word_offset += ${8/m}; + } + workgroupBarrier(); + } + + if (local_idx < ${I}) { + var output_value: ${le.type.value} = ${le.type.value}(0); + for (var b = 0u; b < ${z}; b++) { + output_value += inter_results[local_idx][b]; + } + if (col + local_idx < uniforms.output_shape[2]) + { + ${le.setByIndices(`${le.type.indices}(batch, row, col + local_idx)`,"output_value")} + } + } + }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${r.blockSize};${m};${g};${z};${I}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:x,dataType:w}],dispatchGroup:{x:A},programUniforms:$}),getShaderSource:R}},KM=(e,r)=>{Hw(e.inputs,r),r.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(Qw(e.inputs,r)):e.compute(qw(e.inputs,r))},HM=e=>Zt(e)}),Xw,Jw,Yw,Zw,ey,ty,ry,sy,qM,MT=Ye(()=>{$t(),Lt(),zt(),Xw=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let r=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(r=e[3].dims[0]*2===e[1].dims[0]),!r)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Jw=(e,r,t)=>{let i="";for(let l=r-1;l>=0;--l)i+=` + k = i32(${e.indicesGet("indices",l)}) - ${Mt("uniforms.pads",l,t)}; + if (k < 0) { + break; + } + if (k >= i32(${Mt("uniforms.x_shape",l,r)})) { + break; + } + offset += k * i32(${Mt("uniforms.x_strides",l,r)}); + `;return` + value = ${e.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${i} + value = x[offset]; + } + `},Yw=(e,r,t)=>{let i="";for(let l=r-1;l>=0;--l)i+=` + k = i32(${e.indicesGet("indices",l)}) - ${Mt("uniforms.pads",l,t)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${Mt("uniforms.x_shape",l,r)}) - 1); + k = k % _2n_1; + if(k >= i32(${Mt("uniforms.x_shape",l,r)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${Mt("uniforms.x_strides",l,r)}); + `;return` + var offset = 0; + var k = 0; + ${i} + value = x[offset]; + `},Zw=(e,r,t)=>{let i="";for(let l=r-1;l>=0;--l)i+=` + k = i32(${e.indicesGet("indices",l)}) - ${Mt("uniforms.pads",l,t)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${Mt("uniforms.x_shape",l,r)})) { + k = i32(${Mt("uniforms.x_shape",l,r)}) - 1; + } + offset += k * i32(${Mt("uniforms.x_strides",l,r)}); + `;return` + var offset = 0; + var k = 0; + ${i} + value = x[offset]; + `},ey=(e,r,t)=>{let i="";for(let l=r-1;l>=0;--l)i+=` + k = i32(${e.indicesGet("indices",l)}) - ${Mt("uniforms.pads",l,t)}; + if (k < 0) { + k += i32(${Mt("uniforms.x_shape",l,r)}]); + } + if (k >= i32(${Mt("uniforms.x_shape",l,r)})) { + k -= i32(${Mt("uniforms.x_shape",l,r)}); + } + offset += k * i32(${Mt("uniforms.x_strides",l,r)}); + `;return` + var offset = 0; + var k = 0; + ${i} + value = x[offset]; + `},ty=(e,r,t)=>{switch(t.mode){case 0:return Jw(e,r,t.pads.length);case 1:return Yw(e,r,t.pads.length);case 2:return Zw(e,r,t.pads.length);case 3:return ey(e,r,t.pads.length);default:throw new Error("Invalid mode")}},ry=(e,r)=>{let t=je.padShape(e[0].dims.slice(),r.pads),i=e[0].dims,l=je.size(t),n=[{type:12,data:l},{type:6,data:r.pads}],c=e.length>=3&&e[2].data;r.mode===0&&n.push({type:c?e[2].dataType:1,data:r.value}),n.push(...Et(e[0].dims,t));let d=["rank"],h=f=>{let w=gt("output",e[0].dataType,t.length),m=Ne("x",e[0].dataType,i.length),g=m.type.value,x=ty(w,i.length,r),M=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:r.pads.length}];return r.mode===0&&M.push({name:"constant_value",type:c?g:"f32"}),` + ${f.registerUniforms(M).declareVariables(m,w)} + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${w.offsetToIndices("global_idx")}; + + var value = ${g}(0); + ${x} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${r.mode}${c}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(je.size(t)/64)},programUniforms:n}),getShaderSource:h}},sy=(e,r)=>{if(e.length>1){let t=e[1].getBigInt64Array(),i=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,l=e[0].dims.length,n=new Int32Array(2*l).fill(0);if(e.length>=4){let d=e[3].getBigInt64Array();for(let h=0;hn[Number(h)]=Number(d));let c=[];return n.forEach(d=>c.push(d)),{mode:r.mode,value:i,pads:c}}else return r},qM=(e,r)=>{Xw(e.inputs);let t=sy(e.inputs,r);e.compute(ry(e.inputs,t),{inputs:[0]})}}),ld,nm,im,am,om,ny,iy,lm,um,QM,XM,cm,JM,YM,dm,ZM,ev,tv,rv,vT=Ye(()=>{Ws(),$t(),Lt(),zt(),ld=e=>{if(dr.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},nm=(e,r,t)=>{let i=r.format==="NHWC",l=e.dims.slice();i&&l.splice(1,0,l.pop());let n=Object.hasOwnProperty.call(r,"dilations"),c=r.kernelShape.slice(),d=r.strides.slice(),h=n?r.dilations.slice():[],f=r.pads.slice();Yh.adjustPoolAttributes(t,l,c,d,h,f);let w=Yh.computePoolOutputShape(t,l,d,h,c,f,r.autoPad),m=Object.assign({},r);n?Object.assign(m,{kernelShape:c,strides:d,pads:f,dilations:h,cacheKey:r.cacheKey}):Object.assign(m,{kernelShape:c,strides:d,pads:f,cacheKey:r.cacheKey});let g=w.slice();return g.push(g.splice(1,1)[0]),[m,i?g:w]},im=(e,r)=>{let t=r.format==="NHWC",i=je.size(e),l=je.size(r.kernelShape),n=[{type:12,data:i},{type:12,data:l}],c=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(r.kernelShape.length<=2){let d=r.kernelShape[r.kernelShape.length-1],h=r.strides[r.strides.length-1],f=r.pads[r.pads.length/2-1],w=r.pads[r.pads.length-1],m=!!(f+w);n.push({type:12,data:d},{type:12,data:h},{type:12,data:f},{type:12,data:w}),c.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let g=!1;if(r.kernelShape.length===2){let x=r.kernelShape[r.kernelShape.length-2],M=r.strides[r.strides.length-2],I=r.pads[r.pads.length/2-2],z=r.pads[r.pads.length-2];g=!!(I+z),n.push({type:12,data:x},{type:12,data:M},{type:12,data:I},{type:12,data:z}),c.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[n,c,!0,m,g]}else{if(t)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let d=je.computeStrides(r.kernelShape);n.push({type:12,data:d},{type:12,data:r.pads},{type:12,data:r.strides}),c.push({name:"kernelStrides",type:"u32",length:d.length},{name:"pads",type:"u32",length:r.pads.length},{name:"strides",type:"u32",length:r.strides.length});let h=r.pads.reduce((f,w)=>f+w);return[n,c,!!h,!1,!1]}},am=(e,r,t,i,l,n,c,d,h,f,w,m)=>{let g=l.format==="NHWC",x=r.type.value,M=gt("output",r.type.tensor,i);if(l.kernelShape.length<=2){let I="",z="",E="",C=t-(g?2:1);if(w?I=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${C}] = indices[${C}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${C}] < 0 || xIndices[${C}] + >= uniforms.x_shape[${C}]) { + pad++; + continue; + } + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + }`:I=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${C}] = indices[${C}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + }`,l.kernelShape.length===2){let D=t-(g?3:2);m?z=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${D}] = indices[${D}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${D}] < 0 || xIndices[${D}] >= uniforms.x_shape[${D}]) { + pad += i32(uniforms.kw); + continue; + } + `:z=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${D}] = indices[${D}] * uniforms.sh - uniforms.phStart + j; + `,E=` + } + `}return` + ${e.registerUniforms(h).declareVariables(r,M)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${M.offsetToIndices("global_idx")}; + var xIndices = ${M.offsetToIndices("global_idx")}; + + var value = ${x}(${d}); + var pad = 0; + ${z} + ${I} + ${E} + ${c} + + output[global_idx] = value; + }`}else{if(g)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let I=l.kernelShape.length,z=l.pads.length,E="";return f?E=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + }`:E=` + } + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + `,` + ${e.registerUniforms(h).declareVariables(r,M)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${M.offsetToIndices("global_idx")}; + var xIndices = ${M.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${x}(${d}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${I-1}u; j++) { + offsets[j] = offset / ${Mt("uniforms.kernelStrides","j",I)}; + offset -= offsets[j] * ${Mt("uniforms.kernelStrides","j",I)}; + } + offsets[${I-1}] = offset; + + isPad = false; + for (var j = ${t-I}u; j < ${t}u; j++) { + xIndices[j] = indices[j] * ${Mt("uniforms.strides",`j - ${t-I}u`,I)} + + offsets[j - ${t-I}u] - ${Mt("uniforms.pads","j - 2u",z)}; + ${E} + } + ${c} + + output[global_idx] = value; + }`}},om=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,ny=e=>`${om(e)};${e.countIncludePad}`,iy=e=>`${om(e)};${e.storageOrder};${e.dilations}`,lm=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),um=(e,r,t,i)=>{let[l,n]=nm(r,i,t),c=Ne("x",r.dataType,r.dims.length),d=c.type.value,h="value += x_val;",f="";l.countIncludePad?f+=`value /= ${d}(uniforms.kernelSize);`:f+=`value /= ${d}(i32(uniforms.kernelSize) - pad);`;let[w,m,g,x,M]=im(n,l);w.push(...Et(r.dims,n));let I=["rank"];return{name:e,shaderCache:{hint:`${i.cacheKey};${g};${x};${M}`,inputDependencies:I},getRunData:()=>({outputs:[{dims:n,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(je.size(n)/64)},programUniforms:w}),getShaderSource:z=>am(z,c,r.dims.length,n.length,l,h,f,0,m,g,x,M)}},QM=e=>{let r=e.count_include_pad!==0,t=lm(e);if(t.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let i={countIncludePad:r,...t,cacheKey:""};return{...i,cacheKey:ny(i)}},XM=(e,r)=>{ld(e.inputs),e.compute(um("AveragePool",e.inputs[0],!1,r))},cm={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},JM=e=>{let r=e.format;return{format:r,...cm,cacheKey:r}},YM=(e,r)=>{ld(e.inputs),e.compute(um("GlobalAveragePool",e.inputs[0],!0,r))},dm=(e,r,t,i)=>{let[l,n]=nm(r,i,t),c=` + value = max(x_val, value); + `,d="",h=Ne("x",r.dataType,r.dims.length),f=["rank"],[w,m,g,x,M]=im(n,l);return w.push(...Et(r.dims,n)),{name:e,shaderCache:{hint:`${i.cacheKey};${g};${x};${M}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:n,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(je.size(n)/64)},programUniforms:w}),getShaderSource:I=>am(I,h,r.dims.length,n.length,l,c,d,r.dataType===10?-65504:-1e5,m,g,x,M)}},ZM=(e,r)=>{ld(e.inputs),e.compute(dm("MaxPool",e.inputs[0],!1,r))},ev=e=>{let r=e.storage_order,t=e.dilations,i=lm(e);if(r!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(i.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let l={storageOrder:r,dilations:t,...i,cacheKey:""};return{...l,cacheKey:iy(l)}},tv=e=>{let r=e.format;return{format:r,...cm,cacheKey:r}},rv=(e,r)=>{ld(e.inputs),e.compute(dm("GlobalMaxPool",e.inputs[0],!0,r))}}),ay,oy,sv,nv,xT=Ye(()=>{$t(),Lt(),br(),zt(),ay=(e,r)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((t,i)=>t===e[2].dims[i]).reduce((t,i)=>t&&i,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(r.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((l,n)=>n===r.axis||l===e[0].dims[n]).reduce((l,n)=>l&&n,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let t=e[0].dims[r.axis],i=e[1].dims[r.axis];if(r.blockSizeMath.ceil(t/(i-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},oy=(e,r)=>{let t=je.normalizeAxis(r.axis,e[0].dims.length),i=e[0].dataType,l=i===3,n=e[0].dims,c=e[1].dataType,d=je.size(n),h=i===3||i===2,f=h?[Math.ceil(je.size(e[0].dims)/4)]:e[0].dims,w=e[1].dims,m=e.length>2?e[2]:void 0,g=m?h?[Math.ceil(je.size(m.dims)/4)]:m.dims:void 0,x=w.length===0||w.length===1&&w[0]===1,M=x===!1&&w.length===1,I=gr(d),z=x&&(!h||I===4),E=z?I:1,C=z&&!h?I:1,D=Ne("input",h?12:i,f.length,C),A=Ne("scale",c,w.length),$=m?Ne("zero_point",h?12:i,g.length):void 0,P=gt("output",c,n.length,E),k=[D,A];$&&k.push($);let O=[f,w];m&&O.push(g);let R=[{type:12,data:d/E},{type:12,data:t},{type:12,data:r.blockSize},...Et(...O,n)],U=te=>{let se=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${te.registerUniforms(se).declareVariables(...k,P)} + ${te.mainStart()} + ${te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${P.offsetToIndices("global_idx")}; + + // Set input x + ${h?` + let input = ${D.getByOffset("global_idx / 4")}; + let x_vec = ${l?"unpack4xI8(input)":"unpack4xU8(input)"}; + let x_value = ${E===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${D.getByOffset("global_idx")};`}; + + // Set scale input + ${x?`let scale_value= ${A.getByOffset("0")}`:M?` + let scale_index = ${P.indicesGet("output_indices","uniforms.axis")}; + let scale_value= ${A.getByOffset("scale_index")};`:` + var scale_indices: ${A.type.indices} = output_indices; + let index = ${A.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; + ${A.indicesSet("scale_indices","uniforms.axis","index")}; + let scale_value= ${A.getByIndices("scale_indices")};`}; + + // Set zero-point input + ${$?x?h?` + let zero_point_input = ${$.getByOffset("0")}; + let zero_point_vec = ${l?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${$.getByOffset("0")}`:M?h?` + let zero_point_index = ${P.indicesGet("output_indices","uniforms.axis")}; + let zero_point_input = ${$.getByOffset("zero_point_index / 4")}; + let zero_point_vec = ${l?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_index % 4]`:` + let zero_point_index = ${P.indicesGet("output_indices","uniforms.axis")}; + let zero_point_value = ${$.getByOffset("zero_point_index")};`:h?` + let zero_point_offset = ${A.indicesToOffset("scale_indices")}; + let zero_point_input = ${$.getByOffset("zero_point_offset / 4")}; + let zero_point_vec = ${l?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${$.getByIndices("scale_indices")};`:`let zero_point_value = ${h?l?"i32":"u32":D.type.value}(0);`}; + // Compute and write output + ${P.setByOffset("global_idx",`${P.type.value}(x_value - zero_point_value) * scale_value`)}; + }`};return{name:"DequantizeLinear",shaderCache:{hint:r.cacheKey,inputDependencies:$?["rank","rank","rank"]:["rank","rank"]},getShaderSource:U,getRunData:()=>({outputs:[{dims:n,dataType:c}],dispatchGroup:{x:Math.ceil(d/E/64),y:1,z:1},programUniforms:R})}},sv=(e,r)=>{ay(e.inputs,r),e.compute(oy(e.inputs,r))},nv=e=>Zt({axis:e.axis,blockSize:e.blockSize})}),ly,uy,iv,TT=Ye(()=>{Ws(),$t(),zt(),ly=(e,r,t)=>{let i=e===r,l=er&&t>0;if(i||l||n)throw new Error("Range these inputs' contents are invalid.")},uy=(e,r,t,i)=>{let l=Math.abs(Math.ceil((r-e)/t)),n=[l],c=l,d=[{type:12,data:c},{type:i,data:e},{type:i,data:t},...Et(n)],h=f=>{let w=gt("output",i,n.length),m=w.type.value,g=[{name:"outputSize",type:"u32"},{name:"start",type:m},{name:"delta",type:m}];return` + ${f.registerUniforms(g).declareVariables(w)} + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${m}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${i}`},getShaderSource:h,getRunData:()=>({outputs:[{dims:n,dataType:i}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:d})}},iv=e=>{let r=0,t=0,i=0;e.inputs[0].dataType===6?(r=e.inputs[0].getInt32Array()[0],t=e.inputs[1].getInt32Array()[0],i=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(r=e.inputs[0].getFloat32Array()[0],t=e.inputs[1].getFloat32Array()[0],i=e.inputs[2].getFloat32Array()[0]),dr.webgpu.validateInputContent&&ly(r,t,i),e.compute(uy(r,t,i,e.inputs[0].dataType),{inputs:[]})}}),cy,dy,av,ov,CT=Ye(()=>{$t(),Lt(),br(),zt(),cy=(e,r,t,i)=>{if(e!=="none"&&i!=="i32"&&i!=="u32"&&i!=="f32")throw new Error(`Input ${i} is not supported with reduction ${e}.`);let l=`{ + var oldValue = 0; + loop { + let newValueF32 =`,n=`; + let newValue = bitcast(newValueF32); + let res = atomicCompareExchangeWeak(&${r}, oldValue, newValue); + if res.exchanged { + break; + } + oldValue = res.old_value; + } + }`;switch(e){case"none":return`${r}=${t};`;case"add":return i==="i32"||i==="u32"?`atomicAdd(&${r}, bitcast<${i}>(${t}));`:` + ${l}bitcast<${i}>(oldValue) + (${t})${n}`;case"max":return i==="i32"||i==="u32"?`atomicMax(&${r}, bitcast<${i}>(${t}));`:` + ${l}max(bitcast(oldValue), (${t}))${n}`;case"min":return i==="i32"||i==="u32"?`atomicMin(&${r}, bitcast<${i}>(${t}));`:`${l}min(bitcast<${i}>(oldValue), (${t}))${n}`;case"mul":return`${l}(bitcast<${i}>(oldValue) * (${t}))${n}`;default:throw new Error(`Reduction ${e} is not supported.`)}},dy=(e,r)=>{let t=e[0].dims,i=e[1].dims,l=t,n=1,c=Math.ceil(je.sizeToDimension(i,i.length-1)/n),d=i[i.length-1],h=je.sizeFromDimension(t,d),f=[{type:12,data:c},{type:12,data:d},{type:12,data:h},...Et(e[1].dims,e[2].dims,l)],w=m=>{let g=Ne("indices",e[1].dataType,e[1].dims.length),x=Ne("updates",e[2].dataType,e[2].dims.length,n),M=r.reduction!=="none"&&r.reduction!==""?D0("output",e[0].dataType,l.length):gt("output",e[0].dataType,l.length,n);return` + ${m.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(g,x,M)} + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var data_offset = 0u; + let indices_start = uniforms.last_index_dimension * global_idx; + let indices_end = indices_start + uniforms.last_index_dimension; + for (var i = indices_start; i < indices_end; i++) { + var index = i32(indices[i].x); + ${e[0].dims.length===1?` + let element_count_dim = uniforms.output_strides; + let dim_value = uniforms.output_shape;`:` + let element_count_dim = uniforms.output_strides[i - indices_start]; + let dim_value = uniforms.output_shape[i - indices_start];`} + if (index >= 0) { + if (index >= i32(dim_value)) { + index = i32(dim_value - 1); + } + } else { + if (index < -i32(dim_value)) { + index = 0; + } else { + index += i32(dim_value); + } + } + data_offset += u32((u32(index) * element_count_dim)); + } + + for (var i = 0u; i < uniforms.num_updates_elements; i++) { + let value = updates[uniforms.num_updates_elements * global_idx + i]; + ${cy(r.reduction,"output[data_offset + i]","value",M.type.value)} + } + + }`};return{name:"ScatterND",shaderCache:{hint:`${r.cacheKey}_${r.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:l,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:f}),getShaderSource:w}},av=e=>Zt({reduction:e.reduction}),ov=(e,r)=>{e.compute(dy(e.inputs,r),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),py,hy,fy,pm,my,_y,gy,wy,yy,by,My,vy,hm,xy,Ty,Cy,Ey,Py,lv,uv,ET=Ye(()=>{$t(),Lt(),br(),zt(),py=(e,r)=>{if(e.every(t=>t>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(r.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and + one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(r.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},hy=(e,r,t)=>{r.every(l=>l>=0&&l{throw new Error("Resize requires axes input values to be positive and less than rank")}));let i=new Array(t).fill(1);return r.forEach((l,n)=>i[l]=e[n]),i},fy=(e,r,t,i,l,n)=>{let[c,d,h]=t>10?[1,2,3]:[-1,e.length>1?1:-1,-1],f=e[0].dims.length;if(c>0&&e.length>c&&e[c].dims.length>0)e[c].getFloat32Array().forEach(w=>n.push(w));else if(r.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(d>0&&e.length>d&&e[d].dims.length===1&&e[d].dims[0]>0){if(e[d].getFloat32Array().forEach(w=>i.push(w)),i.length!==0&&i.length!==f&&t>=18&&i.length!==r.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");py(i,r),r.axes.length>0&&hy(i,r.axes,f).forEach((w,m)=>i[m]=w)}if(h>0&&e.length>h&&e[h].dims.length===1&&e[h].dims[0]>0&&(e[h].getBigInt64Array().forEach(w=>l.push(Number(w))),l.length!==0&&l.length!==f&&t>=18&&l.length!==r.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(r.axes.length>0){if(i.length!==0&&i.length!==r.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(l.length!==0&&l.length!==r.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof i<"u"&&typeof l<"u"&&i.length>0&&l.length>f)throw new Error("Resize requires only of scales or sizes to be specified")},pm=(e,r,t,i)=>` + // The whole part and the fractional part are calculated separately due to inaccuracy of floating + // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an + // offset-by-one error later in floor(). + let big = (${e}) * (${r}); + let whole = ${i}(big / (${t})); + let fract = ${i}(big % (${t})) / ${i}(${t}); + return whole + fract; +`,my=(e,r)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, + lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${r} { `+(()=>{switch(e){case"asymmetric":return` + if (xScale < 1.0 || floor(xScale) != xScale) { + return ${r}(xResized) / ${r}(xScale); + } else { + ${pm("xResized","lengthOriginal","lengthResized",r)} + } + `;case"pytorch_half_pixel":return`if (lengthResized > 1) { + return (${r}(xResized) + 0.5) / ${r}(xScale) - 0.5; + } else { + return 0.0; + }`;case"tf_half_pixel_for_nn":return`return (${r}(xResized) + 0.5) / ${r}(xScale);`;case"align_corners":return`if (lengthResized == 1) { + return 0.0; + } else { + ${pm("xResized","lengthOriginal - 1","lengthResized - 1",r)} + }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { + return ${r}(roiStart) * ${r}(lengthOriginal - 1) + + (${r}(xResized) * ${r}(roiEnd - roiStart) * ${r}(lengthOriginal - 1)) / + ${r}(lengthResized - 1); + } else { + return 0.5 * ${r}(roiStart + roiEnd) * ${r}(lengthOriginal - 1); + }`;case"half_pixel_symmetric":return`const outputWidth = ${r}xScale * ${r}(lengthResized); + const adjustment = ${r}(lengthResized) / outputWidth; + const center = ${r}(lengthOriginal) / 2; + const offset = center * (1 - adjustment); + return offset + ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;case"half_pixel":return`return ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",_y=(e,r,t)=>`fn getNearestPixelFromOriginal(xOriginal: ${t}, isDownSample: bool) -> ${t} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(r<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",gy=(e,r,t)=>{let i=new Array(t).fill(0).concat(new Array(t).fill(1)),l=e.length===0?i:e.slice();return r.length>0?(r.forEach((n,c)=>{i[n]=l[c],i[c+t]=l[r.length+c]}),i):l},wy=(e,r,t,i)=>{let l=[];if(t.length>0)if(i.length>0){if(e.forEach(n=>l.push(n)),Math.max(...i)>e.length)throw new Error("axes is out of bound");i.forEach((n,c)=>l[n]=t[c])}else t.forEach(n=>l.push(n));else{if(r.length===0)throw new Error("Resize requires either scales or sizes.");l=e.map((n,c)=>Math.round(n*r[c]))}return l},yy=(e,r,t)=>{let i=(()=>{switch(t.keepAspectRatioPolicy){case"not_larger":return t.axes.length>0?Math.min(...t.axes.map(n=>r[n]),Number.MAX_VALUE):Math.min(...r,Number.MAX_VALUE);case"not_smaller":return t.axes.length>0?Math.max(...t.axes.map(n=>r[n]),Number.MIN_VALUE):Math.max(...r,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${t.keepAspectRatioPolicy} is not supported`)}})();r.fill(1,0,r.length);let l=e.slice();return t.axes.length>0?(t.axes.forEach(n=>r[n]=i),t.axes.forEach(n=>l[n]=Math.round(e[n]*r[n]))):(r.fill(i,0,r.length),l.forEach((n,c)=>l[c]=Math.round(n*r[c]))),l},by=(e,r,t,i,l)=>` + fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${t.length}> { + var original_indices: array<${e.type.value}, ${t.length}>; + for (var i:u32 = 0; i < ${t.length}; i++) { + var output_index = ${e.indicesGet("output_indices","i")}; + var scale = ${Mt("uniforms.scales","i",i)}; + var roi_low = ${Mt("uniforms.roi","i",l)}; + var roi_hi = ${Mt("uniforms.roi",`i + ${r.length}`,l)}; + if (scale == 1.0) { + original_indices[i] = ${e.type.value}(output_index); + } else { + var input_shape_i = ${Mt("uniforms.input_shape","i",r.length)}; + var output_shape_i = ${Mt("uniforms.output_shape","i",t.length)}; + original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + } + } + return original_indices; + }`,My=(e,r,t,i,l,n,c)=>` + fn calculateInputIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + for (var i:u32 = 0; i < ${i.length}; i++) { + var output_index = ${r.indicesGet("output_indices","i")}; + var input_index: u32; + var scale = ${Mt("uniforms.scales","i",l)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${Mt("uniforms.roi","i",n)}; + var roi_hi = ${Mt("uniforms.roi",`i + ${t.length}`,n)}; + var input_shape_i = ${Mt("uniforms.input_shape","i",t.length)}; + var output_shape_i = ${Mt("uniforms.output_shape","i",i.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${c} || (original_idx >= 0 && original_idx < ${r.type.value}(input_shape_i))) { + if (original_idx < 0) { + input_index = 0; + } else if (original_idx > ${r.type.value}(input_shape_i - 1)) { + input_index = input_shape_i - 1; + } else { + input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1)); + } + } else { + input_index = u32(original_idx); + } + } + ${e.indicesSet("input_indices","i","input_index")} + } + return input_indices; + }`,vy=(e,r)=>` + fn checkInputIndices(input_indices: ${e.type.indices}) -> bool { + for (var i:u32 = 0; i < ${r.length}; i++) { + var input_index = ${e.indicesGet("input_indices","i")}; + if (input_index < 0 || input_index >= ${Mt("uniforms.input_shape","i",r.length)}) { + return false; + } + } + return true; + }`,hm=(e,r,t,i)=>e.rank>i?` + ${e.indicesSet("input_indices",r,"channel")}; + ${e.indicesSet("input_indices",t,"batch")}; +`:"",xy=(e,r,t,i,l)=>{let[n,c,d,h]=t.length===2?[-1,0,1,-1]:[0,2,3,1],f=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${f} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",c,`max(0, min(row, ${t[c]} - 1))`)}; + ${e.indicesSet("input_indices",d,`max(0, min(col, ${t[d]} - 1))`)}; + ${hm(e,h,n,2)} + return ${e.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${r.type.indices}) -> ${f} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${f} = originalIndices[${c}]; + var col:${f} = originalIndices[${d}]; + ${i?`if (row < 0 || row > (${t[c]} - 1) || col < 0 || col > (${t[d]} - 1)) { + return ${l}; + }`:""}; + row = max(0, min(row, ${t[c]} - 1)); + col = max(0, min(col, ${t[d]} - 1)); + var row1: u32 = u32(row); + var col1: u32 = u32(col); + var row2: u32 = u32(row + 1); + var col2: u32 = u32(col + 1); + var channel: u32 = ${t.length>2?`u32(originalIndices[${h}])`:"0"}; + var batch: u32 = ${t.length>2?`u32(originalIndices[${n}])`:"0"}; + var x11: ${f} = getInputValue(batch, channel, row1, col1); + var x12: ${f} = getInputValue(batch, channel, row1, col2); + var x21: ${f} = getInputValue(batch, channel, row2, col1); + var x22: ${f} = getInputValue(batch, channel, row2, col2); + var dx1: ${f} = abs(row - ${f}(row1)); + var dx2: ${f} = abs(${f}(row2) - row); + var dy1: ${f} = abs(col - ${f}(col1)); + var dy2: ${f} = abs(${f}(col2) - col); + if (row1 == row2) { + dx1 = 0.5; + dx2 = 0.5; + } + if (col1 == col2) { + dy1 = 0.5; + dy2 = 0.5; + } + return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1); + }`},Ty=(e,r,t,i,l,n,c,d,h,f)=>{let w=t.length===2,[m,g]=w?[0,1]:[2,3],x=e.type.value,M=I=>{let z=I===m?"row":"col";return` + fn ${z}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${r.type.indices}) -> ${x} { + var output_index = ${r.indicesGet("output_indices",I)}; + var originalIdx: ${x} = getOriginalCoordinateFromResizedCoordinate(output_index, ${l[I]}, + ${i[I]}, ${t[I]}, ${n[I]}, ${n[I]} + ${t.length}); + var fractOriginalIdx: ${x} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${d} && (originalIdx < 0 || originalIdx > (${t[I]} - 1))) { + return ${h}; + } + var data: array<${x}, 4> = array<${x}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${z}: ${x} = originalIdx + ${x}(i); + if (${z} < 0 || ${z} >= ${t[I]}) { + ${f?`coefs[i + 1] = 0.0; + continue;`:d?`return ${h};`:`${z} = max(0, min(${z}, ${t[I]} - 1));`}; + } + var input_indices_copy: ${e.type.indices} = input_indices; + ${e.indicesSet("input_indices_copy",I,`u32(${z})`)}; + data[i + 1] = ${I===m?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${M(m)}; + ${M(g)}; + fn getCubicInterpolationCoefs(s: ${x}) -> array<${x}, 4> { + var absS = abs(s); + var coeffs: array<${x}, 4> = array<${x}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${x} = 1.0 - absS; + var twoMinusAbsS: ${x} = 2.0 - absS; + var onePlusAbsS: ${x} = 1.0 + absS; + coeffs[0] = ((${c} * onePlusAbsS - 5 * ${c}) * onePlusAbsS + 8 * ${c}) * onePlusAbsS - 4 * ${c}; + coeffs[1] = ((${c} + 2) * absS - (${c} + 3)) * absS * absS + 1; + coeffs[2] = ((${c} + 2) * oneMinusAbsS - (${c} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${c} * twoMinusAbsS - 5 * ${c}) * twoMinusAbsS + 8 * ${c}) * twoMinusAbsS - 4 * ${c}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${x}, 4>, coefs: array<${x}, 4>) -> ${x} { + var coefsSum: ${x} = coefs[0] + coefs[1] + coefs[2] + coefs[3]; + return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum; + } + + fn bicubicInterpolation(output_indices: ${r.type.indices}) -> ${x} { + var input_indices: ${e.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},Cy=(e,r,t,i,l)=>{let[n,c,d,h,f]=t.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],w=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${w} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",c,`max(0, min(depth, ${t[c]} - 1))`)}; + ${e.indicesSet("input_indices",d,`max(0, min(height, ${t[d]} - 1))`)}; + ${e.indicesSet("input_indices",h,`max(0, min(width, ${t[h]} - 1))`)}; + ${hm(e,f,n,3)} + return ${e.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${r.type.indices}) -> ${w} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${w} = originalIndices[${c}]; + var height:${w} = originalIndices[${d}]; + var width:${w} = originalIndices[${h}]; + ${i?`if (depth < 0 || depth > (${t[c]} - 1) || height < 0 || height > (${t[d]} - 1) || width < 0 || (width > ${t[h]} - 1)) { + return ${l}; + }`:""}; + + depth = max(0, min(depth, ${t[c]} - 1)); + height = max(0, min(height, ${t[d]} - 1)); + width = max(0, min(width, ${t[h]} - 1)); + var depth1: u32 = u32(depth); + var height1: u32 = u32(height); + var width1: u32 = u32(width); + var depth2: u32 = u32(depth + 1); + var height2: u32 = u32(height + 1); + var width2: u32 = u32(width + 1); + var channel: u32 = ${t.length>3?`u32(originalIndices[${f}])`:"0"}; + var batch: u32 = ${t.length>3?`u32(originalIndices[${n}])`:"0"}; + + var x111: ${w} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${w} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${w} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${w} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${w} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${w} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${w} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${w} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${w} = abs(depth - ${w}(depth1)); + var dx2: ${w} = abs(${w}(depth2) - depth); + var dy1: ${w} = abs(height - ${w}(height1)); + var dy2: ${w} = abs(${w}(height2) - height); + var dz1: ${w} = abs(width - ${w}(width1)); + var dz2: ${w} = abs(${w}(width2) - width); + if (depth1 == depth2) { + dx1 = 0.5; + dx2 = 0.5; + } + if (height1 == height2) { + dy1 = 0.5; + dy2 = 0.5; + } + if (width1 == width2) { + dz1 = 0.5; + dz2 = 0.5; + } + return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 + + x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1); + }`},Ey=(e,r,t,i,l,n)=>{let c=e.dims,d=gy(n,r.axes,c.length),h=wy(c,i,l,r.axes),f=i.slice();i.length===0&&(f=c.map((C,D)=>C===0?1:h[D]/C),r.keepAspectRatioPolicy!=="stretch"&&(h=yy(c,f,r)));let w=gt("output",e.dataType,h.length),m=Ne("input",e.dataType,c.length),g=je.size(h),x=c.length===h.length&&c.every((C,D)=>C===h[D]),M=r.coordinateTransformMode==="tf_crop_and_resize",I=r.extrapolationValue,z=m.type.value,E=C=>` + ${x?"":` + ${my(r.coordinateTransformMode,z)}; + ${(()=>{switch(r.mode){case"nearest":return` + ${vy(m,c)}; + ${_y(r.nearestMode,t,z)}; + ${My(m,w,c,h,f.length,d.length,M)}; + `;case"linear":return` + ${by(w,c,h,f.length,d.length)}; + ${(()=>{if(c.length===2||c.length===4)return`${xy(m,w,c,M,I)}`;if(c.length===3||c.length===5)return`${Cy(m,w,c,M,I)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(c.length===2||c.length===4)return`${Ty(m,w,c,h,f,d,r.cubicCoeffA,M,r.extrapolationValue,r.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; + `;default:throw Error("Invalid resize mode")}})()}; + `} + ${C.registerUniform("output_size","u32").registerUniform("scales","f32",f.length).registerUniform("roi","f32",d.length).declareVariables(m,w)} + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${x?"output[global_idx] = input[global_idx];":` + let output_indices = ${w.offsetToIndices("global_idx")}; + var input_indices: ${m.type.indices}; + ${(()=>{switch(r.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${m.getByIndices("input_indices")}; + } else { + output[global_idx] = ${r.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${c.length===2||c.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${r.mode}`)}})()}; +`} + }`;return{name:"Resize",shaderCache:{hint:`${r.cacheKey}|${t}|${f.length>0?r.mode==="cubic"?f:f.length:""}|${l.length>0?l:""}|${d.length>0?d:""}|${x}|${r.mode==="nearest"?c.length:c}`,inputDependencies:["rank"]},getShaderSource:E,getRunData:()=>({outputs:[{dims:h,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:[{type:12,data:g},{type:1,data:f},{type:1,data:d},...Et(c,h)]})}},Py=e=>{let r=e.customDataBuffer;return new Uint32Array(r,r.byteOffset,1)[0]},lv=(e,r)=>{let t=[],i=[],l=[],n=Py(e);if(r.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");fy(e.inputs,r,n,t,i,l),e.compute(Ey(e.inputs[0],r,n,t,i,l),{inputs:[0]})},uv=e=>{let r=e.antialias,t=e.axes,i=e.coordinateTransformMode,l=e.cubicCoeffA,n=e.excludeOutside!==0,c=e.extrapolationValue,d=e.keepAspectRatioPolicy,h=e.mode,f=e.nearestMode===""?"simple":e.nearestMode;return Zt({antialias:r,axes:t,coordinateTransformMode:i,cubicCoeffA:l,excludeOutside:n,extrapolationValue:c,keepAspectRatioPolicy:d,mode:h,nearestMode:f})}}),Sy,ky,cv,PT=Ye(()=>{$t(),Lt(),zt(),Sy=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let r=e[0],t=e[1],i=e[2];if(r.dataType!==t.dataType||r.dataType!==i.dataType)throw new Error("All inputs must have the same data type");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Input must be 2D or 3D");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Skip must be 2D or 3D");let l=r.dims[r.dims.length-1],n=r.dims[r.dims.length-2];if(t.dims[t.dims.length-1]!==l)throw new Error("Skip must have the same hidden size as input");if(t.dims[t.dims.length-2]!==n)throw new Error("Skip must have the same sequence length as input");if(i.dims.length!==1)throw new Error("Gamma must be 1D");if(i.dims[i.dims.length-1]!==l)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let c=e[3];if(c.dims.length!==1)throw new Error("Beta must be 1D");if(c.dims[c.dims.length-1]!==l)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let c=e[4];if(c.dims.length!==1)throw new Error("Bias must be 1D");if(c.dims[c.dims.length-1]!==l)throw new Error("Bias must have the same hidden size as input")}},ky=(e,r,t,i)=>{let l=r.simplified,n=e[0].dims,c=je.size(n),d=n,h=c,f=n.slice(-1)[0],w=i?n.slice(0,-1).concat(1):[],m=!l&&e.length>3,g=e.length>4,x=i&&t>1,M=i&&t>2,I=t>3,z=64,E=gr(f),C=[{type:12,data:h},{type:12,data:E},{type:12,data:f},{type:1,data:r.epsilon}],D=$=>{let P=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],k=[Ne("x",e[0].dataType,e[0].dims,E),Ne("skip",e[1].dataType,e[1].dims,E),Ne("gamma",e[2].dataType,e[2].dims,E)];m&&k.push(Ne("beta",e[3].dataType,e[3].dims,E)),g&&k.push(Ne("bias",e[4].dataType,e[4].dims,E)),k.push(gt("output",e[0].dataType,d,E)),x&&k.push(gt("mean_output",1,w)),M&&k.push(gt("inv_std_output",1,w)),I&&k.push(gt("input_skip_bias_sum",e[0].dataType,d,E));let O=Lr(e[0].dataType),R=Lr(1,E);return` + + ${$.registerUniforms(P).declareVariables(...k)} + var sum_shared : array<${R}, ${z}>; + var sum_squared_shared : array<${R}, ${z}>; + + ${$.mainStart([z,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${z}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${z}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${z-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${g?"bias[offset1d + i]":O+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${I?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${Ba(O,E,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${z}; + for (var curr_size = reduce_size >> 1; curr_size > 0; curr_size = reduce_size >> 1) { + reduce_size = curr_size + (reduce_size & 1); + if (ix < curr_size) { + sum_shared[ix] += sum_shared[ix + reduce_size]; + sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size]; + } + workgroupBarrier(); + } + + let sum = sum_shared[0]; + let square_sum = sum_squared_shared[0]; + let mean = ${An("sum",E)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${An("square_sum",E)} / f32(uniforms.hidden_size) ${l?"":"- mean * mean"} + uniforms.epsilon); + ${x?"mean_output[global_idx] = mean;":""} + ${M?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${l?"":`- ${O}(mean)`}) * + ${O}(inv_std_dev) * gamma[offset1d + i] + ${m?"+ beta[offset1d + i]":""}; + } + }`},A=[{dims:d,dataType:e[0].dataType}];return t>1&&A.push({dims:w,dataType:1}),t>2&&A.push({dims:w,dataType:1}),t>3&&A.push({dims:n,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${E};${x};${M};${I}`,inputDependencies:e.map(($,P)=>"type")},getShaderSource:D,getRunData:()=>({outputs:A,dispatchGroup:{x:Math.ceil(h/f)},programUniforms:C})}},cv=(e,r)=>{Sy(e.inputs);let t=[0];e.outputCount>1&&t.push(-3),e.outputCount>2&&t.push(-3),e.outputCount>3&&t.push(3),e.compute(ky(e.inputs,r,e.outputCount,!1),{outputs:t})}}),$y,ud,Iy,fm,Ay,Oy,dv,pv,ST=Ye(()=>{$t(),Lt(),br(),zt(),$y=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");if(r.axes.length!==0){if(r.axes.length!==r.starts.length||r.axes.length!==r.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(r.starts.length!==r.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((t,i)=>{if(e[i+1].dataType!==6&&e[i+1].dataType!==7)throw new Error(`Input ${i} must be an array of int32 or int64`)})},ud=(e,r)=>{let t=[];if(e.length>r)if(e[r].dataType===7)e[r].getBigInt64Array().forEach(i=>t.push(Number(i)));else if(e[r].dataType===6)e[r].getInt32Array().forEach(i=>t.push(Number(i)));else throw new Error(`Input ${r} must be an array of int32 or int64`);return t},Iy=(e,r)=>{if(e.length>1){let t=ud(e,1),i=ud(e,2),l=ud(e,3);return l.length===0&&(l=[...Array(e[0].dims.length).keys()]),Zt({starts:t,ends:i,axes:l})}else return r},fm=(e,r,t,i,l)=>{let n=e;return e<0&&(n+=t[i[r]]),l[r]<0?Math.max(0,Math.min(n,t[i[r]]-1)):Math.max(0,Math.min(n,t[i[r]]))},Ay=(e,r,t)=>`fn calculateInputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + var carry = 0u; + for (var i = ${t.length}; i >= 0; i--) { + let input_shape_i = ${Mt("uniforms.input_shape","i",t.length)}; + let steps_i = ${Mt("uniforms.steps","i",t.length)}; + let signs_i = ${Mt("uniforms.signs","i",t.length)}; + let starts_i = ${Mt("uniforms.starts","i",t.length)}; + var output_index = ${r.indicesGet("output_indices","i")}; + var input_index = output_index * steps_i + starts_i + carry; + carry = input_index / input_shape_i; + input_index = input_index % input_shape_i; + if (signs_i < 0) { + input_index = input_shape_i - input_index - 1u + starts_i; + } + ${e.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,Oy=(e,r)=>{let t=e[0].dims,i=je.size(t),l=r.axes.length>0?je.normalizeAxes(r.axes,t.length):[...Array(t.length).keys()],n=ud(e,4);n.forEach(E=>E!==0||(()=>{throw new Error("step cannot be 0")})),n.length===0&&(n=Array(l.length).fill(1));let c=r.starts.map((E,C)=>fm(E,C,t,l,n)),d=r.ends.map((E,C)=>fm(E,C,t,l,n));if(l.length!==c.length||l.length!==d.length)throw new Error("start, ends and axes should have the same number of elements");if(l.length!==t.length)for(let E=0;EMath.sign(E));n.forEach((E,C,D)=>{if(E<0){let A=(d[C]-c[C])/E,$=c[C],P=$+A*n[C];c[C]=P,d[C]=$,D[C]=-E}});let f=t.slice(0);l.forEach((E,C)=>{f[E]=Math.ceil((d[E]-c[E])/n[E])});let w={dims:f,dataType:e[0].dataType},m=gt("output",e[0].dataType,f.length),g=Ne("input",e[0].dataType,e[0].dims.length),x=je.size(f),M=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:c.length},{name:"signs",type:"i32",length:h.length},{name:"steps",type:"u32",length:n.length}],I=[{type:12,data:x},{type:12,data:c},{type:6,data:h},{type:12,data:n},...Et(e[0].dims,f)],z=E=>` + ${E.registerUniforms(M).declareVariables(g,m)} + ${Ay(g,m,t)} + ${E.mainStart()} + ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${m.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${m.setByOffset("global_idx",g.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${h.length}_${c.length}_${n.length}`,inputDependencies:["rank"]},getShaderSource:z,getRunData:()=>({outputs:[w],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:I})}},dv=(e,r)=>{$y(e.inputs,r);let t=Iy(e.inputs,r);e.compute(Oy(e.inputs,t),{inputs:[0]})},pv=e=>{let r=e.starts,t=e.ends,i=e.axes;return Zt({starts:r,ends:t,axes:i})}}),Fy,Dy,hv,fv,kT=Ye(()=>{$t(),Lt(),br(),On(),zt(),Fy=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Dy=(e,r)=>{let t=e.inputs[0],i=t.dims,l=je.size(i),n=i.length,c=je.normalizeAxis(r.axis,n),d=cO),f[c]=n-1,f[n-1]=c,h=e.compute(_s(t,f),{inputs:[t],outputs:[-1]})[0]):h=t;let w=h.dims,m=w[n-1],g=l/m,x=gr(m),M=m/x,I=64;g===1&&(I=256);let z=(k,O)=>O===4?`max(max(${k}.x, ${k}.y), max(${k}.z, ${k}.w))`:O===2?`max(${k}.x, ${k}.y)`:O===3?`max(max(${k}.x, ${k}.y), ${k}.z)`:k,E=Ne("x",h.dataType,h.dims,x),C=gt("result",h.dataType,h.dims,x),D=E.type.value,A=Lr(h.dataType)==="f32"?`var threadMax = ${D}(-3.402823e+38f);`:`var threadMax = ${D}(-65504.0h);`,$=k=>` + var rowMaxShared : ${D}; + var rowSumShared : ${D}; + var threadShared : array<${D}, ${I}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${D} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${D}) { + let index = row * row_stride + col; + result[index] = value; + } + ${k.registerUniform("packedCols","i32").declareVariables(E,C)} + ${k.mainStart(I)} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${I}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${A} + for (var col = lindex; col < cols; col += wg) { + let value = getValue(row, col, row_stride); + threadMax = max(threadMax, value); + } + if (lindex < cols) { + threadShared[lindex] = threadMax; + } + workgroupBarrier(); + + var reduceSize = min(cols, wg); + for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) { + reduceSize = currSize + (reduceSize & 1); + if (lindex < currSize) { + threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]); + } + workgroupBarrier(); + } + if (lindex == 0) { + rowMaxShared = ${D}(${z("threadShared[0]",x)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${D}(0.0); + for (var col = lindex; col < cols; col += wg) { + let subExp = exp(getValue(row, col, row_stride) - rowMaxShared); + threadSum += subExp; + } + threadShared[lindex] = threadSum; + workgroupBarrier(); + + for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) { + if (lindex < currSize) { + threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize]; + } + workgroupBarrier(); + } + if (lindex == 0) { + rowSumShared = ${D}(${An("threadShared[0]",x)}); + } + workgroupBarrier(); + + // calculate final value for each element in the row + for (var col = lindex; col < cols; col += wg) { + let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared; + setValue(row, col, row_stride, value); + } + }`,P=e.compute({name:"Softmax",shaderCache:{hint:`${x};${I}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:w,dataType:h.dataType}],dispatchGroup:{x:g},programUniforms:[{type:6,data:M}]}),getShaderSource:$},{inputs:[h],outputs:[d?-1:0]})[0];d&&e.compute(_s(P,f),{inputs:[P]})},hv=(e,r)=>{Fy(e.inputs),Dy(e,r)},fv=e=>Zt({axis:e.axis})}),mm,jy,Ly,zy,mv,$T=Ye(()=>{$t(),Lt(),zt(),mm=e=>Array.from(e.getBigInt64Array(),Number),jy=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==10&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(mm(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Ly=(e,r)=>{let t=[];for(let i=0;i{let t=e[0].dims,i=r??mm(e[1]),l=Ly(t,i),n=je.size(l),c=e[0].dataType,d=Ne("input",c,t.length),h=gt("output",c,l.length),f=w=>` + const inputShape = ${d.indices(...t)}; + ${w.registerUniform("output_size","u32").declareVariables(d,h)} + ${w.mainStart()} + ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${h.offsetToIndices("global_idx")}; + var input_indices: ${d.type.indices}; + for (var i = 0; i < ${t.length}; i++) { + let input_dim_i = ${d.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${h.indicesGet("output_indices","i")} % input_dim_i; + + ${d.indicesSet("input_indices","i","input_dim_value")} + } + ${h.setByOffset("global_idx",d.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${i}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:l,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:[{type:12,data:n},...Et(e[0].dims,l)]}),getShaderSource:f}},mv=e=>{jy(e.inputs),e.compute(zy(e.inputs),{inputs:[0]})}}),By,Ry,_v,IT=Ye(()=>{$t(),Lt(),zt(),By=(e,r,t,i,l)=>{let n=gt("output_data",l,t.length,4),c=Ne("a_data",r[1].dataType,r[1].dims.length,4),d=Ne("b_data",r[2].dataType,r[2].dims.length,4),h=Ne("c_data",r[0].dataType,r[0].dims.length,4),f,w=(m,g,x)=>`select(${g}, ${m}, ${x})`;if(!i)f=n.setByOffset("global_idx",w(c.getByOffset("global_idx"),d.getByOffset("global_idx"),h.getByOffset("global_idx")));else{let m=(g,x,M="")=>{let I=`a_data[index_a${x}][component_a${x}]`,z=`b_data[index_b${x}][component_b${x}]`,E=`bool(c_data[index_c${x}] & (0xffu << (component_c${x} * 8)))`;return` + let output_indices${x} = ${n.offsetToIndices(`global_idx * 4u + ${x}u`)}; + let offset_a${x} = ${c.broadcastedIndicesToOffset(`output_indices${x}`,n)}; + let offset_b${x} = ${d.broadcastedIndicesToOffset(`output_indices${x}`,n)}; + let offset_c${x} = ${h.broadcastedIndicesToOffset(`output_indices${x}`,n)}; + let index_a${x} = offset_a${x} / 4u; + let index_b${x} = offset_b${x} / 4u; + let index_c${x} = offset_c${x} / 4u; + let component_a${x} = offset_a${x} % 4u; + let component_b${x} = offset_b${x} % 4u; + let component_c${x} = offset_c${x} % 4u; + ${g}[${x}] = ${M}(${w(I,z,E)}); + `};l===9?f=` + var data = vec4(0); + ${m("data",0,"u32")} + ${m("data",1,"u32")} + ${m("data",2,"u32")} + ${m("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:f=` + ${m("output_data[global_idx]",0)} + ${m("output_data[global_idx]",1)} + ${m("output_data[global_idx]",2)} + ${m("output_data[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(h,c,d,n)} + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${f} + }`},Ry=e=>{let r=e[1].dims,t=e[2].dims,i=e[0].dims,l=e[1].dataType,n=!(je.areEqual(r,t)&&je.areEqual(t,i)),c=r,d=je.size(r);if(n){let f=Ra.calcShape(Ra.calcShape(r,t,!1),i,!1);if(!f)throw new Error("Can't perform where op on the given tensors");c=f,d=je.size(c)}let h=Math.ceil(d/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:f=>By(f,e,c,n,l),getRunData:()=>({outputs:[{dims:c,dataType:l}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:[{type:12,data:h},...Et(i,r,t,c)]})}},_v=e=>{e.compute(Ry(e.inputs))}}),gv,AT=Ye(()=>{K1(),e_(),H1(),q1(),Q1(),X1(),J1(),rT(),nT(),iT(),aT(),oT(),lT(),uT(),cT(),dT(),pT(),hT(),fT(),mT(),_T(),gT(),wT(),yT(),bT(),jM(),MT(),vT(),xT(),TT(),CT(),Zm(),ET(),NM(),PT(),ST(),kT(),BM(),$T(),On(),t_(),IT(),gv=new Map([["Abs",[cb]],["Acos",[db]],["Acosh",[pb]],["Add",[Kb]],["ArgMax",[ab,Sm]],["ArgMin",[ib,Sm]],["Asin",[hb]],["Asinh",[fb]],["Atan",[mb]],["Atanh",[_b]],["Attention",[ob]],["AveragePool",[XM,QM]],["BatchNormalization",[lb]],["BiasAdd",[ub]],["BiasSplitGelu",[Gb]],["Cast",[wb,gb]],["Ceil",[bb]],["Clip",[yb]],["Concat",[rM,sM]],["Conv",[Fm,Om]],["ConvTranspose",[hM,pM]],["Cos",[Mb]],["Cosh",[vb]],["CumSum",[fM,mM]],["DepthToSpace",[_M,gM]],["DequantizeLinear",[sv,nv]],["Div",[Hb]],["Einsum",[wM,yM]],["Elu",[xb,fd]],["Equal",[qb]],["Erf",[Tb]],["Exp",[Cb]],["Expand",[bM]],["FastGelu",[MM]],["Floor",[Eb]],["FusedConv",[Fm,Om]],["Gather",[xM,vM]],["GatherElements",[kM,SM]],["GatherBlockQuantized",[EM,PM]],["GatherND",[TM,CM]],["Gelu",[Pb]],["Gemm",[IM,$M]],["GlobalAveragePool",[YM,JM]],["GlobalMaxPool",[rv,tv]],["Greater",[Yb]],["GreaterOrEqual",[eM]],["GridSample",[AM,OM]],["GroupQueryAttention",[VM]],["HardSigmoid",[Db,Fb]],["InstanceNormalization",[WM]],["LayerNormalization",[UM]],["LeakyRelu",[Sb,fd]],["Less",[Zb]],["LessOrEqual",[tM]],["Log",[Wb]],["MatMul",[GM]],["MatMulNBits",[KM,HM]],["MaxPool",[ZM,ev]],["Mul",[Qb]],["MultiHeadAttention",[DM,FM]],["Neg",[$b]],["Not",[kb]],["Pad",[qM]],["Pow",[Xb]],["QuickGelu",[Ub,fd]],["Range",[iv]],["Reciprocal",[Ib]],["ReduceMin",[eb]],["ReduceMean",[Q0]],["ReduceMax",[Z0]],["ReduceSum",[rb]],["ReduceProd",[tb]],["ReduceL1",[X0]],["ReduceL2",[J0]],["ReduceLogSum",[nb]],["ReduceLogSumExp",[Y0]],["ReduceSumSquare",[sb]],["Relu",[Ab]],["Resize",[lv,uv]],["RotaryEmbedding",[RM]],["ScatterND",[ov,av]],["Sigmoid",[Ob]],["Sin",[jb]],["Sinh",[Lb]],["Slice",[dv,pv]],["SkipLayerNormalization",[cv]],["Split",[LM,zM]],["Sqrt",[zb]],["Softmax",[hv,fv]],["Sub",[Jb]],["Tan",[Bb]],["Tanh",[Rb]],["ThresholdedRelu",[Vb,fd]],["Tile",[mv]],["Transpose",[L0,z0]],["Where",[_v]]])}),wv,OT=Ye(()=>{Ws(),_n(),zt(),wv=class{constructor(e){this.backend=e,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,r){this.repo.set(e,r)}run(e,r,t,i,l){Vs(e.programInfo.name);let n=this.backend.device,c=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let d=[];for(let f of r)d.push({binding:d.length,resource:{buffer:f.buffer}});for(let f of t)d.push({binding:d.length,resource:{buffer:f.buffer}});l&&d.push({binding:d.length,resource:l});let h=n.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:d,label:e.programInfo.name});if(this.backend.sessionStatus==="capturing"){let f={kernelId:this.backend.currentKernelId,computePipeline:e.computePipeline,bindGroup:h,dispatchGroup:i};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(f)}c.setPipeline(e.computePipeline),c.setBindGroup(0,h),c.dispatchWorkgroups(...i),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),xs(e.programInfo.name)}dispose(){}build(e,r){Vs(e.name);let t=this.backend.device,i=[];[{feature:"shader-f16",extension:"f16"},{feature:"subgroups",extension:"subgroups"}].forEach(f=>{t.features.has(f.feature)&&i.push(`enable ${f.extension};`)});let l=j0(r,this.backend.device.limits),n=e.getShaderSource(l),c=`${i.join(` +`)} +${l.additionalImplementations} +${n}`,d=t.createShaderModule({code:c,label:e.name});Kt("verbose",()=>`[WebGPU] ${e.name} shader code: ${c}`);let h=t.createComputePipeline({compute:{module:d,entryPoint:"main"},layout:"auto",label:e.name});return xs(e.name),{programInfo:e,computePipeline:h,uniformVariablesInfo:l.variablesInfo}}normalizeDispatchGroupSize(e){let r=typeof e=="number"?e:e.x,t=typeof e=="number"?1:e.y||1,i=typeof e=="number"?1:e.z||1,l=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(r<=l&&t<=l&&i<=l)return[r,t,i];let n=r*t*i,c=Math.ceil(Math.sqrt(n));if(c>l){if(c=Math.ceil(Math.cbrt(n)),c>l)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[c,c,c]}else return[c,c,1]}}}),yv={};Va(yv,{WebGpuBackend:()=>bv});var Ny,Vy,Wy,bv,FT=Ye(()=>{Ws(),$t(),_n(),I0(),U1(),AT(),OT(),Ny=(e,r)=>{if(r.length!==e.length)throw new Error(`inputDependencies length ${r.length} is not equal to inputTensors length ${e.length}.`);let t=[];for(let i=0;i{var l,n;let i=e.name;return(l=e.shaderCache)!=null&&l.hint&&(i+="["+e.shaderCache.hint+"]"),i+=":"+t+`:${Ny(r,((n=e.shaderCache)==null?void 0:n.inputDependencies)??new Array(r.length).fill("dims"))}`,i},Wy=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},bv=class{constructor(){this.currentSessionId=null,this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.maxDispatchNumber=16,this.pendingDispatchNumber=0,this.pendingKernels=[],this.pendingQueries=new Map,this.sessionStatus="default",this.capturedCommandList=new Map,this.capturedPendingKernels=new Map,this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. (should not happen)");let e=this.kernelCustomData.get(this.currentKernelId);return e||(e={},this.kernelCustomData.set(this.currentKernelId,e)),e}async initialize(e,r){this.env=e;let t=[],i={requiredLimits:{maxComputeWorkgroupStorageSize:r.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.limits.maxStorageBufferBindingSize,maxBufferSize:r.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:r.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:r.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:r.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:r.limits.maxComputeWorkgroupSizeZ},requiredFeatures:t},l=n=>r.features.has(n)&&t.push(n)&&!0;l("chromium-experimental-timestamp-query-inside-passes")||l("timestamp-query"),l("shader-f16"),l("subgroups"),this.device=await r.requestDevice(i),this.adapterInfo=new Wy(r.info||await r.requestAdapterInfo()),this.gpuDataManager=F0(this),this.programManager=new wv(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Qm(e.logLevel,!!e.debug),this.device.onuncapturederror=n=>{n.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${n.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:r,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e=this.getCommandEncoder(),r={};this.queryType==="at-passes"&&(r.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(r)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Vs(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{var i;let r=new BigUint64Array(e.getMappedRange()),t=this.pendingQueries.get(e);for(let l=0;l"u"&&(this.queryTimeBase=x);let I=Number(x-this.queryTimeBase),z=Number(M-this.queryTimeBase);if(!Number.isSafeInteger(I)||!Number.isSafeInteger(z))throw new RangeError("incorrect timestamp range");if((i=this.env.webgpu.profiling)!=null&&i.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:m.map(E=>({dims:E.dims,dataType:mn(E.dataType)})),outputsMetadata:g.map(E=>({dims:E.dims,dataType:mn(E.dataType)})),kernelId:c,kernelType:h,kernelName:f,programName:w,startTime:I,endTime:z});else{let E="";m.forEach((D,A)=>{E+=`input[${A}]: [${D.dims}] | ${mn(D.dataType)}, `});let C="";g.forEach((D,A)=>{C+=`output[${A}]: [${D.dims}] | ${mn(D.dataType)}, `}),console.log(`[profiling] kernel "${c}|${h}|${f}|${w}" ${E}${C}execution time: ${z-I} ns`)}wd("GPU",`${w}::${x}::${M}`)}e.unmap(),this.pendingQueries.delete(e)}),xs()}run(e,r,t,i,l,n){Vs(e.name);let c=[];for(let C=0;CD):t;if(w.length!==d.length)throw new Error(`Output size ${w.length} must be equal to ${d.length}.`);let m=[],g=[];for(let C=0;C=n)throw new Error(`Invalid output index: ${w[C]}`);if(w[C]===-3)continue;let D=w[C]===-1,A=w[C]===-2,$=D||A?l(d[C].dataType,d[C].dims):i(w[C],d[C].dataType,d[C].dims);if(m.push($),$.data===0)continue;let P=this.gpuDataManager.get($.data);if(!P)throw new Error(`no GPU data for output: ${$.data}`);if(D&&this.temporaryData.push(P),A){let k=this.kernelPersistentData.get(this.currentKernelId);k||(k=[],this.kernelPersistentData.set(this.currentKernelId,k)),k.push(P)}g.push(P)}if(c.length!==r.length||g.length!==m.length){if(g.length===0)return xs(e.name),m;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let x;if(f){let C=0,D=[];f.forEach(k=>{let O=typeof k.data=="number"?[k.data]:k.data;if(O.length===0)return;let R=k.type===10?2:4,U,te;k.type===10?(te=O.length>4?16:O.length>2?8:O.length*R,U=O.length>4?16:R*O.length):(te=O.length<=2?O.length*R:16,U=16),C=Math.ceil(C/te)*te,D.push(C);let se=k.type===10?8:4;C+=O.length>4?Math.ceil(O.length/se)*U:O.length*R});let A=16;C=Math.ceil(C/A)*A;let $=new ArrayBuffer(C);f.forEach((k,O)=>{let R=D[O],U=typeof k.data=="number"?[k.data]:k.data;if(k.type===6)new Int32Array($,R,U.length).set(U);else if(k.type===12)new Uint32Array($,R,U.length).set(U);else if(k.type===10)new Uint16Array($,R,U.length).set(U);else if(k.type===1)new Float32Array($,R,U.length).set(U);else throw new Error(`Unsupported uniform type: ${mn(k.type)}`)});let P=this.gpuDataManager.create(C,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(P.buffer,0,$,0,C),this.gpuDataManager.release(P.id),x={offset:0,size:C,buffer:P.buffer}}let M=this.programManager.normalizeDispatchGroupSize(h),I=M[1]===1&&M[2]===1,z=Vy(e,r,I),E=this.programManager.getArtifact(z);if(E||(E=this.programManager.build(e,M),this.programManager.setArtifact(z,E),Kt("info",()=>`[artifact] key: ${z}, programName: ${e.name}`)),f&&E.uniformVariablesInfo){if(f.length!==E.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${E.uniformVariablesInfo.length}, got ${f.length} in program "${E.programInfo.name}".`);for(let C=0;C`[ProgramManager] run "${e.name}" (key=${z}) with ${M[0]}x${M[1]}x${M[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let C={kernelId:this.currentKernelId,programName:E.programInfo.name,inputTensorViews:r,outputTensorViews:m};this.pendingKernels.push(C),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(C)}return this.programManager.run(E,c,g,M,x),xs(e.name),m}upload(e,r){this.gpuDataManager.upload(e,r)}memcpy(e,r){this.gpuDataManager.memcpy(e,r)}async download(e,r){await this.gpuDataManager.download(e,r)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,r,t,i){let l=gv.get(e);if(!l)throw new Error(`kernel not implemented: ${e}`);let n={kernelType:e,kernelName:i,kernelEntry:l[0],attributes:[l[1],t]};this.kernels.set(r,n)}releaseKernel(e){let r=this.kernelPersistentData.get(e);if(r){for(let t of r)this.gpuDataManager.release(t.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,r,t){let i=this.kernels.get(e);if(!i)throw new Error(`kernel not created: ${e}`);let l=i.kernelType,n=i.kernelName,c=i.kernelEntry,d=i.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${l}] ${n}" is not allowed to be called recursively`);this.currentKernelId=e,d[0]&&(d[1]=d[0](d[1]),d[0]=void 0),Kt("info",()=>`[WebGPU] Start to run kernel "[${l}] ${n}"...`);let h=this.env.debug;this.temporaryData=[];try{return h&&this.device.pushErrorScope("validation"),c(r,d[1]),0}catch(f){return t.push(Promise.resolve(`[WebGPU] Kernel "[${l}] ${n}" failed. ${f}`)),1}finally{h&&t.push(this.device.popErrorScope().then(f=>f?`GPU validation error for kernel "[${l}] ${n}": ${f.message}`:null));for(let f of this.temporaryData)this.gpuDataManager.release(f.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,r,t,i){let l=this.sessionExternalDataMapping.get(e);l||(l=new Map,this.sessionExternalDataMapping.set(e,l));let n=l.get(r),c=this.gpuDataManager.registerExternalBuffer(t,i,n);return l.set(r,[c,t]),c}unregisterBuffers(e){let r=this.sessionExternalDataMapping.get(e);r&&(r.forEach(t=>this.gpuDataManager.unregisterExternalBuffer(t[0])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let r=this.gpuDataManager.get(e);if(!r)throw new Error(`no GPU data for buffer: ${e}`);return r.buffer}createDownloader(e,r,t){return async()=>{let i=await Cm(this,e,r);return Xm(i.buffer,t)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){var e;this.queryType="none",(((e=this.env.webgpu.profiling)==null?void 0:e.mode)==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){Kt("info","captureBegin"),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus="capturing"}captureEnd(){Kt("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){Kt("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),r=this.capturedPendingKernels.get(this.currentSessionId),t=e.length;this.pendingKernels=[];for(let i=0;i=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),Mv={};Va(Mv,{init:()=>vv});var Vh,Uy,vv,DT=Ye(()=>{$t(),_n(),Lt(),W1(),Vh=class xv{constructor(r,t,i,l){this.module=r,this.dataType=t,this.data=i,this.dims=l}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let r=je.size(this.dims);return r===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,r)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let r=je.size(this.dims);return r===0?new 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m=0;mLm,initializeFlags:()=>jm,wasmBackend:()=>Dv});var jm,Lm,Dv,LT=Ye(()=>{Ws(),Av(),jT(),jm=()=>{(typeof dr.wasm.initTimeout!="number"||dr.wasm.initTimeout<0)&&(dr.wasm.initTimeout=0);let e=dr.wasm.simd;if(typeof e!="boolean"&&e!==void 0&&e!=="fixed"&&e!=="relaxed"&&(console.warn(`Property "env.wasm.simd" is set to unknown value "${e}". Reset it to \`false\` and ignore SIMD feature checking.`),dr.wasm.simd=!1),typeof dr.wasm.proxy!="boolean"&&(dr.wasm.proxy=!1),typeof dr.wasm.trace!="boolean"&&(dr.wasm.trace=!1),typeof dr.wasm.numThreads!="number"||!Number.isInteger(dr.wasm.numThreads)||dr.wasm.numThreads<=0)if(typeof self<"u"&&!self.crossOriginIsolated)dr.wasm.numThreads=1;else{let r=typeof navigator>"u"?x1("node:os").cpus().length:navigator.hardwareConcurrency;dr.wasm.numThreads=Math.min(4,Math.ceil((r||1)/2))}},Lm=class{async init(e){jm(),await Cv(),await Ev(e)}async createInferenceSessionHandler(e,r){let t=new Ov;return await t.loadModel(e,r),t}},Dv=new Lm});Ws();Ws();Ws();var zT="1.23.0",BT=g0;{let e=(LT(),gd(Fv)).wasmBackend;fi("webgpu",e,5),fi("webnn",e,5),fi("cpu",e,10),fi("wasm",e,10)}Object.defineProperty(dr.versions,"web",{value:zT,enumerable:!0});/** +* @license +* Copyright 2021 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. +* ============================================================================= +*//** + * @license + * Copyright 2020 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. + * ============================================================================= + *//** + * @license + * Copyright 2019 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. + * ============================================================================= + */var RT=Object.freeze({__proto__:null,get InferenceSession(){return Nm},get TRACE(){return wd},get TRACE_FUNC_BEGIN(){return Vs},get TRACE_FUNC_END(){return xs},get Tensor(){return Rs},default:BT,get env(){return dr},get registerBackend(){return 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this(O)}}},"./src/base/processing_utils.js":(e,r,t)=>{t.r(r),t.d(r,{Processor:()=>c});var i=t("./src/utils/constants.js"),l=t("./src/utils/generic.js"),n=t("./src/utils/hub.js");class c extends l.Callable{constructor(h,f){super(),this.config=h,this.components=f}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(h,f={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(h,{tokenize:!1,...f})}batch_decode(...h){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...h)}decode(...h){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.decode(...h)}async _call(h,...f){for(const w of[this.image_processor,this.feature_extractor,this.tokenizer])if(w)return w(h,...f);throw 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i=t("./src/utils/generic.js");t("./src/utils/tensor.js");var l=t("./src/utils/maths.js");class n extends i.Callable{_call(P,k){throw Error("`_call` should be implemented in a subclass")}}class c extends i.Callable{_call(P,k){throw Error("`_call` should be implemented in a subclass")}}class d extends i.Callable{constructor(){super(),this.processors=[]}push(P){this.processors.push(P)}extend(P){this.processors.push(...P)}_call(P,k){let O=k;for(const R of this.processors)O=R(P,O);return O}[Symbol.iterator](){return this.processors.values()}}class h extends n{constructor(P){super(),this.bos_token_id=P}_call(P,k){for(let O=0;O=1&&U[U.length-1]>=this.timestamp_begin,se=U.length<2||U[U.length-2]>=this.timestamp_begin;if(te&&(se?R.subarray(this.timestamp_begin).fill(-1/0):R.subarray(0,this.eos_token_id).fill(-1/0)),P[O].length===this.begin_index&&this.max_initial_timestamp_index!==null){const oe=this.timestamp_begin+this.max_initial_timestamp_index;R.subarray(oe+1).fill(-1/0)}const K=(0,l.log_softmax)(R),pe=Math.log(K.subarray(this.timestamp_begin).map(Math.exp).reduce((oe,ge)=>oe+ge)),re=(0,l.max)(K.subarray(0,this.timestamp_begin))[0];pe>re&&R.subarray(0,this.timestamp_begin).fill(-1/0)}return k}}class g extends n{constructor(P){super(),this.no_repeat_ngram_size=P}getNgrams(P){const k=P.length,O=[];for(let U=0;U1 to use the classifier free guidance processor, got guidance scale ${P}.`);this.guidance_scale=P}_call(P,k){if(k.dims[0]!==2*P.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. 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Error("sample should be implemented in subclasses.")}getLogits(m,g){let x=m.dims.at(-1),M=m.data;if(g===-1)M=M.slice(-x);else{let I=g*x;M=M.slice(I,I+x)}return M}randomSelect(m){let g=0;for(let M=0;M1)return new f(m);if(m.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${m.num_return_sequences}.`);return new d(m)}}class d extends c{async sample(m){const g=(0,n.max)(m.data)[1];return[[BigInt(g),0]]}}class h extends c{async sample(m){let g=m.dims.at(-1);this.generation_config.top_k>0&&(g=Math.min(this.generation_config.top_k,g));const[x,M]=await(0,l.topk)(m,g),I=(0,n.softmax)(x.data);return Array.from({length:this.generation_config.num_beams},()=>{const z=this.randomSelect(I);return[M.data[z],Math.log(I[z])]})}}class f extends c{async sample(m){let g=m.dims.at(-1);this.generation_config.top_k>0&&(g=Math.min(this.generation_config.top_k,g));const[x,M]=await(0,l.topk)(m,g),I=(0,n.softmax)(x.data);return 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i=t("./src/configs.js"),l=t("./src/backends/onnx.js"),n=t("./src/utils/dtypes.js"),c=t("./src/utils/generic.js"),d=t("./src/utils/core.js"),h=t("./src/utils/hub.js"),f=t("./src/utils/constants.js"),w=t("./src/generation/logits_process.js"),m=t("./src/generation/configuration_utils.js"),g=t("./src/utils/tensor.js"),x=t("./src/utils/image.js"),M=t("./src/utils/maths.js"),I=t("./src/generation/stopping_criteria.js"),z=t("./src/generation/logits_sampler.js"),E=t("./src/env.js"),C=t("./src/models/whisper/generation_whisper.js"),D=t("./src/models/whisper/common_whisper.js");const A={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7,MultiModality:8,Phi3V:9,AudioTextToText:10,AutoEncoder:11},$=new Map,P=new Map,k=new Map;async function O(S,F,V){var kr;let _e=((kr=V.config)==null?void 0:kr["transformers.js_config"])??{},Ie=V.device??_e.device;Ie&&typeof Ie!="string"&&(Ie.hasOwnProperty(F)?Ie=Ie[F]:(console.warn(`device not specified for "${F}". Using the default device.`),Ie=null));const ke=Ie??(E.apis.IS_NODE_ENV?"cpu":"wasm"),Be=(0,l.deviceToExecutionProviders)(ke),Qe=_e.device_config??{};Qe.hasOwnProperty(ke)&&(_e={..._e,...Qe[ke]});let rt=V.dtype??_e.dtype;if(typeof rt!="string"&&(rt&&rt.hasOwnProperty(F)?rt=rt[F]:(rt=n.DEFAULT_DEVICE_DTYPE_MAPPING[ke]??n.DATA_TYPES.fp32,console.warn(`dtype not specified for "${F}". Using the default dtype (${rt}) for this device (${ke}).`))),rt===n.DATA_TYPES.auto){let Rt=_e.dtype;typeof Rt!="string"&&(Rt=Rt==null?void 0:Rt[F]),Rt&&Rt!==n.DATA_TYPES.auto&&n.DATA_TYPES.hasOwnProperty(Rt)?rt=Rt:rt=n.DEFAULT_DEVICE_DTYPE_MAPPING[ke]??n.DATA_TYPES.fp32}const lt=rt;if(n.DEFAULT_DTYPE_SUFFIX_MAPPING.hasOwnProperty(lt)){if(lt===n.DATA_TYPES.fp16&&ke==="webgpu"&&!await(0,n.isWebGpuFp16Supported)())throw new Error(`The device (${ke}) does not support fp16.`)}else throw new Error(`Invalid dtype: ${lt}. Should be one of: ${Object.keys(n.DATA_TYPES).join(", ")}`);const Pt=_e.kv_cache_dtype,It=Pt?typeof Pt=="string"?Pt:Pt[lt]??"float32":void 0;if(It&&!["float32","float16"].includes(It))throw new Error(`Invalid kv_cache_dtype: ${It}. Should be one of: float32, float16`);const Ct={dtype:lt,kv_cache_dtype:It,device:ke},Bt=n.DEFAULT_DTYPE_SUFFIX_MAPPING[lt],kt=`${F}${Bt}.onnx`,jt=`${V.subfolder??""}/${kt}`,_t={...V.session_options};_t.executionProviders??(_t.executionProviders=Be);const Wt=_e.free_dimension_overrides;Wt?_t.freeDimensionOverrides??(_t.freeDimensionOverrides=Wt):ke.startsWith("webnn")&&!_t.freeDimensionOverrides&&console.warn(`WebNN does not currently support dynamic shapes and requires 'free_dimension_overrides' to be set in config.json, preferably as a field within config["transformers.js_config"]["device_config"]["${ke}"]. When 'free_dimension_overrides' is not set, you may experience significant performance degradation.`);const nr=E.apis.IS_NODE_ENV&&E.env.useFSCache,ar=(0,h.getModelFile)(S,jt,!0,V,nr),mr=V.use_external_data_format??_e.use_external_data_format;let hr=[];if(mr){let Rt;typeof mr=="object"?mr.hasOwnProperty(kt)?Rt=mr[kt]:mr.hasOwnProperty(F)?Rt=mr[F]:Rt=!1:Rt=mr;const xr=+Rt;if(xr>h.MAX_EXTERNAL_DATA_CHUNKS)throw new Error(`The number of external data chunks (${xr}) exceeds the maximum allowed value (${h.MAX_EXTERNAL_DATA_CHUNKS}).`);for(let es=0;es{const cn=await(0,h.getModelFile)(S,Gr,!0,V,nr);ns(cn instanceof Uint8Array?{path:un,data:cn}:un)}))}}else _t.externalData!==void 0&&(hr=_t.externalData.map(async Rt=>{if(typeof Rt.data=="string"){const xr=await(0,h.getModelFile)(S,Rt.data,!0,V);return{...Rt,data:xr}}return Rt}));if(hr.length>0){const Rt=await Promise.all(hr);E.apis.IS_NODE_ENV||(_t.externalData=Rt)}if(ke==="webgpu"){const Rt=(0,i.getKeyValueShapes)(V.config,{prefix:"present"});if(Object.keys(Rt).length>0&&!(0,l.isONNXProxy)()){const xr={};for(const es in Rt)xr[es]="gpu-buffer";_t.preferredOutputLocation=xr}}return{buffer_or_path:await ar,session_options:_t,session_config:Ct}}async function R(S,F,V){return Object.fromEntries(await Promise.all(Object.keys(F).map(async _e=>{const{buffer_or_path:Ie,session_options:ke,session_config:Be}=await O(S,F[_e],V),Qe=await(0,l.createInferenceSession)(Ie,ke,Be);return[_e,Qe]})))}async function U(S,F,V){return Object.fromEntries(await Promise.all(Object.keys(F).map(async _e=>{const Ie=await(0,h.getModelJSON)(S,F[_e],!1,V);return[_e,Ie]})))}function te(S,F){const V=Object.create(null),_e=[];for(const Be of S.inputNames){const Qe=F[Be];if(!(Qe instanceof g.Tensor)){_e.push(Be);continue}V[Be]=(0,l.isONNXProxy)()?Qe.clone():Qe}if(_e.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${_e.join(", ")}.`);const Ie=Object.keys(F).length,ke=S.inputNames.length;if(Ie>ke){let Be=Object.keys(F).filter(Qe=>!S.inputNames.includes(Qe));console.warn(`WARNING: Too many inputs were provided (${Ie} > ${ke}). The following inputs will be ignored: "${Be.join(", ")}".`)}return V}let se=Promise.resolve();async function K(S,F){const V=te(S,F);try{const _e=Object.fromEntries(Object.entries(V).map(([Be,Qe])=>[Be,Qe.ort_tensor])),Ie=()=>S.run(_e),ke=await(E.apis.IS_BROWSER_ENV||E.apis.IS_WEBWORKER_ENV?se=se.then(Ie):Ie());return pe(ke)}catch(_e){const Ie=Object.fromEntries(Object.entries(V).map(([ke,Be])=>{const Qe={type:Be.type,dims:Be.dims,location:Be.location};return Qe.location!=="gpu-buffer"&&(Qe.data=Be.data),[ke,Qe]}));throw console.error(`An error occurred during model execution: "${_e}".`),console.error("Inputs given to model:",Ie),_e}}function pe(S){for(let F in S)(0,l.isONNXTensor)(S[F])?S[F]=new g.Tensor(S[F]):typeof S[F]=="object"&&pe(S[F]);return S}function re(S){if(S instanceof g.Tensor)return S;if(S.length===0)throw Error("items must be non-empty");if(Array.isArray(S[0])){if(S.some(F=>F.length!==S[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new g.Tensor("int64",BigInt64Array.from(S.flat().map(F=>BigInt(F))),[S.length,S[0].length])}else return new g.Tensor("int64",BigInt64Array.from(S.map(F=>BigInt(F))),[1,S.length])}function oe(S){return new g.Tensor("bool",[S],[1])}async function ge(S,F){let{encoder_outputs:V,input_ids:_e,decoder_input_ids:Ie,...ke}=F;if(!V){const Qe=(0,d.pick)(F,S.sessions.model.inputNames);V=(await le(S,Qe)).last_hidden_state}return ke.input_ids=Ie,ke.encoder_hidden_states=V,S.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(ke.encoder_attention_mask=F.attention_mask),await Ce(S,ke,!0)}async function le(S,F){const V=S.sessions.model,_e=(0,d.pick)(F,V.inputNames);if(V.inputNames.includes("inputs_embeds")&&!_e.inputs_embeds){if(!F.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");_e.inputs_embeds=await S.encode_text({input_ids:F.input_ids})}if(V.inputNames.includes("token_type_ids")&&!_e.token_type_ids){if(!_e.input_ids)throw new Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");_e.token_type_ids=(0,g.zeros_like)(_e.input_ids)}if(V.inputNames.includes("pixel_mask")&&!_e.pixel_mask){if(!_e.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const Ie=_e.pixel_values.dims;_e.pixel_mask=(0,g.ones)([Ie[0],Ie[2],Ie[3]])}return await K(V,_e)}async function Se(S,F){const V=await S.encode(F);return await S.decode(V)}async function Ce(S,F,V=!1){const _e=S.sessions[V?"decoder_model_merged":"model"],{past_key_values:Ie,...ke}=F;if(_e.inputNames.includes("use_cache_branch")&&(ke.use_cache_branch=oe(!!Ie)),_e.inputNames.includes("position_ids")&&ke.attention_mask&&!ke.position_ids){const Qe=["paligemma","gemma3_text","gemma3"].includes(S.config.model_type)?1:0;ke.position_ids=Ze(ke,Ie,Qe)}S.addPastKeyValues(ke,Ie);const Be=(0,d.pick)(ke,_e.inputNames);return await K(_e,Be)}function q({modality_token_id:S,inputs_embeds:F,modality_features:V,input_ids:_e,attention_mask:Ie}){const ke=_e.tolist().map(lt=>lt.reduce((Pt,It,Ct)=>(It==S&&Pt.push(Ct),Pt),[])),Be=ke.reduce((lt,Pt)=>lt+Pt.length,0),Qe=V.dims[0];if(Be!==Qe)throw new Error(`Number of tokens and features do not match: tokens: ${Be}, features ${Qe}`);let rt=0;for(let lt=0;ltke.dims[1])){if(IeQe==S.config.image_token_index)){const Qe=S.config.num_image_tokens;if(!Qe)throw new Error("`num_image_tokens` is missing in the model configuration.");const rt=ke.dims[1]-(Ie-Qe);V.input_ids=ke.slice(null,[-rt,null]),V.attention_mask=(0,g.ones)([1,Ie+rt])}}}return V}function Ee(S,F,V,_e){return V.past_key_values&&(F=F.map(Ie=>[Ie.at(-1)])),{...V,decoder_input_ids:re(F)}}function Z(S,...F){return S.config.is_encoder_decoder?Ee(S,...F):et(S,...F)}function me(S,F,V,_e){const Ie=!!V.past_key_values;return _e.guidance_scale!==null&&_e.guidance_scale>1&&(Ie?V.input_ids=(0,g.cat)([V.input_ids,V.input_ids],0):(V.input_ids=(0,g.cat)([V.input_ids,(0,g.full_like)(V.input_ids,BigInt(_e.pad_token_id))],0),V.attention_mask=(0,g.cat)([V.attention_mask,(0,g.full_like)(V.attention_mask,0n)],0))),(Ie||!V.pixel_values)&&(V.pixel_values=(0,g.full)([0,0,3,384,384],1)),Ie&&(V.images_seq_mask=new g.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),V.images_emb_mask=new g.Tensor("bool",new Array(0).fill(!1),[1,1,0])),V}class X extends c.Callable{constructor(V,_e,Ie){super();ce(this,"main_input_name","input_ids");ce(this,"forward_params",["input_ids","attention_mask"]);this.config=V,this.sessions=_e,this.configs=Ie;const ke=k.get(this.constructor),Be=$.get(ke);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Be){case A.DecoderOnly:this.can_generate=!0,this._forward=Ce,this._prepare_inputs_for_generation=et;break;case A.Seq2Seq:case A.Vision2Seq:case A.Musicgen:this.can_generate=!0,this._forward=ge,this._prepare_inputs_for_generation=Ee;break;case A.EncoderDecoder:this._forward=ge;break;case A.ImageTextToText:this.can_generate=!0,this._forward=ye,this._prepare_inputs_for_generation=Z;break;case A.AudioTextToText:this.can_generate=!0,this._forward=Ae,this._prepare_inputs_for_generation=Z;break;case A.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=Z;break;case A.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=me;break;case A.AutoEncoder:this._forward=Se;break;default:this._forward=le;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var _e;const V=[];for(const Ie of Object.values(this.sessions))(_e=Ie==null?void 0:Ie.handler)!=null&&_e.dispose&&V.push(Ie.handler.dispose());return await Promise.all(V)}static async from_pretrained(V,{progress_callback:_e=null,config:Ie=null,cache_dir:ke=null,local_files_only:Be=!1,revision:Qe="main",model_file_name:rt=null,subfolder:lt="onnx",device:Pt=null,dtype:It=null,use_external_data_format:Ct=null,session_options:Bt={}}={}){let kt={progress_callback:_e,config:Ie,cache_dir:ke,local_files_only:Be,revision:Qe,model_file_name:rt,subfolder:lt,device:Pt,dtype:It,use_external_data_format:Ct,session_options:Bt};const jt=k.get(this),_t=$.get(jt);Ie=kt.config=await i.AutoConfig.from_pretrained(V,kt);let Wt;if(_t===A.DecoderOnly)Wt=await Promise.all([R(V,{model:kt.model_file_name??"model"},kt),U(V,{generation_config:"generation_config.json"},kt)]);else if(_t===A.Seq2Seq||_t===A.Vision2Seq)Wt=await Promise.all([R(V,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},kt),U(V,{generation_config:"generation_config.json"},kt)]);else if(_t===A.MaskGeneration)Wt=await Promise.all([R(V,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},kt)]);else if(_t===A.EncoderDecoder)Wt=await Promise.all([R(V,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},kt)]);else if(_t===A.ImageTextToText){const nr={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Ie.is_encoder_decoder&&(nr.model="encoder_model"),Wt=await Promise.all([R(V,nr,kt),U(V,{generation_config:"generation_config.json"},kt)])}else if(_t===A.AudioTextToText){const nr={embed_tokens:"embed_tokens",audio_encoder:"audio_encoder",decoder_model_merged:"decoder_model_merged"};Wt=await Promise.all([R(V,nr,kt),U(V,{generation_config:"generation_config.json"},kt)])}else if(_t===A.Musicgen)Wt=await Promise.all([R(V,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},kt),U(V,{generation_config:"generation_config.json"},kt)]);else if(_t===A.MultiModality)Wt=await Promise.all([R(V,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},kt),U(V,{generation_config:"generation_config.json"},kt)]);else if(_t===A.Phi3V)Wt=await Promise.all([R(V,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},kt),U(V,{generation_config:"generation_config.json"},kt)]);else if(_t===A.AutoEncoder)Wt=await Promise.all([R(V,{encoder_model:"encoder_model",decoder_model:"decoder_model"},kt)]);else{if(_t!==A.EncoderOnly){const nr=jt??(Ie==null?void 0:Ie.model_type);nr!=="custom"&&console.warn(`Model type for '${nr}' not found, assuming encoder-only architecture. Please report this at ${f.GITHUB_ISSUE_URL}.`)}Wt=await Promise.all([R(V,{model:kt.model_file_name??"model"},kt)])}return new this(Ie,...Wt)}async _call(V){return await this.forward(V)}async forward(V){return await this._forward(this,V)}get generation_config(){var V;return((V=this.configs)==null?void 0:V.generation_config)??null}_get_logits_warper(V){const _e=new w.LogitsProcessorList;return V.temperature!==null&&V.temperature!==1&&_e.push(new w.TemperatureLogitsWarper(V.temperature)),V.top_k!==null&&V.top_k!==0&&_e.push(new w.TopKLogitsWarper(V.top_k)),V.top_p!==null&&V.top_p<1&&_e.push(new w.TopPLogitsWarper(V.top_p)),_e}_get_logits_processor(V,_e,Ie=null){const ke=new w.LogitsProcessorList;if(V.repetition_penalty!==null&&V.repetition_penalty!==1&&ke.push(new w.RepetitionPenaltyLogitsProcessor(V.repetition_penalty)),V.no_repeat_ngram_size!==null&&V.no_repeat_ngram_size>0&&ke.push(new w.NoRepeatNGramLogitsProcessor(V.no_repeat_ngram_size)),V.bad_words_ids!==null&&ke.push(new w.NoBadWordsLogitsProcessor(V.bad_words_ids,V.eos_token_id)),V.min_length!==null&&V.eos_token_id!==null&&V.min_length>0&&ke.push(new w.MinLengthLogitsProcessor(V.min_length,V.eos_token_id)),V.min_new_tokens!==null&&V.eos_token_id!==null&&V.min_new_tokens>0&&ke.push(new w.MinNewTokensLengthLogitsProcessor(_e,V.min_new_tokens,V.eos_token_id)),V.forced_bos_token_id!==null&&ke.push(new w.ForcedBOSTokenLogitsProcessor(V.forced_bos_token_id)),V.forced_eos_token_id!==null&&ke.push(new w.ForcedEOSTokenLogitsProcessor(V.max_length,V.forced_eos_token_id)),V.begin_suppress_tokens!==null){const Be=_e>1||V.forced_bos_token_id===null?_e:_e+1;ke.push(new w.SuppressTokensAtBeginLogitsProcessor(V.begin_suppress_tokens,Be))}return V.guidance_scale!==null&&V.guidance_scale>1&&ke.push(new w.ClassifierFreeGuidanceLogitsProcessor(V.guidance_scale)),Ie!==null&&ke.extend(Ie),ke}_prepare_generation_config(V,_e,Ie=m.GenerationConfig){const ke={...this.config};for(const Qe of["decoder","generator","text_config"])Qe in ke&&Object.assign(ke,ke[Qe]);const Be=new Ie(ke);return Object.assign(Be,this.generation_config??{}),V&&Object.assign(Be,V),_e&&Object.assign(Be,(0,d.pick)(_e,Object.getOwnPropertyNames(Be))),Be}_get_stopping_criteria(V,_e=null){const Ie=new I.StoppingCriteriaList;return V.max_length!==null&&Ie.push(new I.MaxLengthCriteria(V.max_length,this.config.max_position_embeddings??null)),V.eos_token_id!==null&&Ie.push(new I.EosTokenCriteria(V.eos_token_id)),_e&&Ie.extend(_e),Ie}_validate_model_class(){if(!this.can_generate){const V=[Kc,Hc,Gc,Uc],_e=k.get(this.constructor),Ie=new Set,ke=this.config.model_type;for(const Qe of V){const rt=Qe.get(ke);rt&&Ie.add(rt[0])}let Be=`The current model class (${_e}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Ie.size>0&&(Be+=` Please use the following class instead: ${[...Ie].join(", ")}`),Error(Be)}}prepare_inputs_for_generation(...V){return this._prepare_inputs_for_generation(this,...V)}_update_model_kwargs_for_generation({generated_input_ids:V,outputs:_e,model_inputs:Ie,is_encoder_decoder:ke}){return Ie.past_key_values=this.getPastKeyValues(_e,Ie.past_key_values),Ie.input_ids=new g.Tensor("int64",V.flat(),[V.length,1]),ke||(Ie.attention_mask=(0,g.cat)([Ie.attention_mask,(0,g.ones)([Ie.attention_mask.dims[0],1])],1)),Ie.position_ids=null,Ie}_prepare_model_inputs({inputs:V,bos_token_id:_e,model_kwargs:Ie}){const ke=(0,d.pick)(Ie,this.forward_params),Be=this.main_input_name;if(Be in ke){if(V)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else ke[Be]=V;return{inputs_tensor:ke[Be],model_inputs:ke,model_input_name:Be}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:V,model_inputs:_e,model_input_name:Ie,generation_config:ke}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!_e.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:Qe,pixel_values:rt,attention_mask:lt,...Pt}=_e,It=await this._prepare_inputs_embeds(_e);_e={...Pt,...(0,d.pick)(It,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Be}=await le(this,_e);if(ke.guidance_scale!==null&&ke.guidance_scale>1)Be=(0,g.cat)([Be,(0,g.full_like)(Be,0)],0),"attention_mask"in _e&&(_e.attention_mask=(0,g.cat)([_e.attention_mask,(0,g.zeros_like)(_e.attention_mask)],0));else if(_e.decoder_input_ids){const Qe=re(_e.decoder_input_ids).dims[0];if(Qe!==Be.dims[0]){if(Be.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Be.dims[0]}) than the decoder inputs (${Qe}).`);Be=(0,g.cat)(Array.from({length:Qe},()=>Be),0)}}return _e.encoder_outputs=Be,_e}_prepare_decoder_input_ids_for_generation({batch_size:V,model_input_name:_e,model_kwargs:Ie,decoder_start_token_id:ke,bos_token_id:Be,generation_config:Qe}){let{decoder_input_ids:rt,...lt}=Ie;if(!(rt instanceof g.Tensor)){if(rt)Array.isArray(rt[0])||(rt=Array.from({length:V},()=>rt));else if(ke??(ke=Be),this.config.model_type==="musicgen")rt=Array.from({length:V*this.config.decoder.num_codebooks},()=>[ke]);else if(Array.isArray(ke)){if(ke.length!==V)throw new Error(`\`decoder_start_token_id\` expcted to have length ${V} but got ${ke.length}`);rt=ke}else rt=Array.from({length:V},()=>[ke]);rt=re(rt)}return Ie.decoder_attention_mask=(0,g.ones_like)(rt),{input_ids:rt,model_inputs:lt}}async generate({inputs:V=null,generation_config:_e=null,logits_processor:Ie=null,stopping_criteria:ke=null,streamer:Be=null,...Qe}){this._validate_model_class(),_e=this._prepare_generation_config(_e,Qe);let{inputs_tensor:rt,model_inputs:lt,model_input_name:Pt}=this._prepare_model_inputs({inputs:V,model_kwargs:Qe});const It=this.config.is_encoder_decoder;It&&("encoder_outputs"in lt||(lt=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:rt,model_inputs:lt,model_input_name:Pt,generation_config:_e})));let Ct;It?{input_ids:Ct,model_inputs:lt}=this._prepare_decoder_input_ids_for_generation({batch_size:lt[Pt].dims.at(0),model_input_name:Pt,model_kwargs:lt,decoder_start_token_id:_e.decoder_start_token_id,bos_token_id:_e.bos_token_id,generation_config:_e}):Ct=lt[Pt];let Bt=Ct.dims.at(-1);_e.max_new_tokens!==null&&(_e.max_length=Bt+_e.max_new_tokens);const kt=this._get_logits_processor(_e,Bt,Ie),jt=this._get_stopping_criteria(_e,ke),_t=lt[Pt].dims.at(0),Wt=z.LogitsSampler.getSampler(_e),nr=new Array(_t).fill(0),ar=Ct.tolist();Be&&Be.put(ar);let mr,hr={};for(;;){if(lt=this.prepare_inputs_for_generation(ar,lt,_e),mr=await this.forward(lt),_e.output_attentions&&_e.return_dict_in_generate){const Gr=this.getAttentions(mr);for(const ns in Gr)ns in hr||(hr[ns]=[]),hr[ns].push(Gr[ns])}const Rt=mr.logits.slice(null,-1,null),xr=kt(ar,Rt),es=[];for(let Gr=0;GrGr))break;lt=this._update_model_kwargs_for_generation({generated_input_ids:es,outputs:mr,model_inputs:lt,is_encoder_decoder:It})}Be&&Be.end();const yr=this.getPastKeyValues(mr,lt.past_key_values,!0),kr=new g.Tensor("int64",ar.flat(),[ar.length,ar[0].length]);if(_e.return_dict_in_generate)return{sequences:kr,past_key_values:yr,...hr};for(const Rt of Object.values(mr))Rt.location==="gpu-buffer"&&Rt.dispose();return kr}getPastKeyValues(V,_e,Ie=!1){const ke=Object.create(null);for(const Be in V)if(Be.startsWith("present")){const Qe=Be.replace("present","past_key_values"),rt=Be.includes("encoder");if(rt&&_e?ke[Qe]=_e[Qe]:ke[Qe]=V[Be],_e&&(!rt||Ie)){const lt=_e[Qe];lt.location==="gpu-buffer"&<.dispose()}}return ke}getAttentions(V){const _e={};for(const Ie of["cross_attentions","encoder_attentions","decoder_attentions"])for(const ke in V)ke.startsWith(Ie)&&(Ie in _e||(_e[Ie]=[]),_e[Ie].push(V[ke]));return _e}addPastKeyValues(V,_e){var Ie,ke,Be;if(_e)Object.assign(V,_e);else{const Qe=this.sessions.decoder_model_merged??this.sessions.model,rt=((Ie=Qe==null?void 0:Qe.config)==null?void 0:Ie.kv_cache_dtype)??"float32",lt=rt==="float16"?new g.DataTypeMap.float16:[],Pt=((Be=(ke=V[this.main_input_name]??V.attention_mask)==null?void 0:ke.dims)==null?void 0:Be[0])??1,It=(0,i.getKeyValueShapes)(this.config,{batch_size:Pt});for(const Ct in It)V[Ct]=new g.Tensor(rt,lt,It[Ct])}}async encode_image({pixel_values:V}){const _e=(await K(this.sessions.vision_encoder,{pixel_values:V})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${_e.dims[1]}).`),this.config.num_image_tokens=_e.dims[1]),_e}async encode_text({input_ids:V}){return(await K(this.sessions.embed_tokens,{input_ids:V})).inputs_embeds}async encode_audio({audio_values:V}){return(await K(this.sessions.audio_encoder,{audio_values:V})).audio_features}}class Oe{}class Fe extends Oe{constructor({last_hidden_state:F,hidden_states:V=null,attentions:_e=null}){super(),this.last_hidden_state=F,this.hidden_states=V,this.attentions=_e}}class Ve extends X{}class Re extends Ve{}class Ue extends Ve{async _call(F){return new Or(await super._call(F))}}class Ge extends Ve{async _call(F){return new Dt(await super._call(F))}}class Je extends Ve{async _call(F){return new Sr(await super._call(F))}}class at extends Ve{async _call(F){return new Nr(await super._call(F))}}class ot extends X{}class Ke extends ot{}class st extends ot{async _call(F){return new Or(await super._call(F))}}class ft extends ot{async _call(F){return new Dt(await super._call(F))}}class wt extends ot{async _call(F){return new Sr(await super._call(F))}}class St extends X{}class vt extends St{}class He extends X{}class xt extends He{}class Vt extends He{async _call(F){return new Or(await super._call(F))}}class he extends He{async _call(F){return new Dt(await super._call(F))}}class ue extends He{async _call(F){return new Sr(await super._call(F))}}class Cr extends He{async _call(F){return new Nr(await super._call(F))}}class er extends X{}class ht extends er{}class as extends er{async _call(F){return new Or(await super._call(F))}}class W extends er{async _call(F){return new Dt(await super._call(F))}}class we extends er{async _call(F){return new Sr(await super._call(F))}}class G extends er{async _call(F){return new Nr(await super._call(F))}}class ne extends X{}class $e extends ne{}class Xe extends ne{async _call(F){return new Or(await super._call(F))}}class Le extends ne{async _call(F){return new Dt(await super._call(F))}}class Ft extends ne{async _call(F){return new Sr(await super._call(F))}}class Qt extends ne{async _call(F){return new Nr(await super._call(F))}}class yt extends X{}class Nt extends yt{}class mt extends yt{async _call(F){return new Or(await super._call(F))}}class tr extends yt{async _call(F){return new Dt(await super._call(F))}}class rr extends yt{async _call(F){return new Sr(await super._call(F))}}class Fr extends yt{async _call(F){return new Nr(await super._call(F))}}class pr extends X{}class Kr extends pr{}class Ts extends pr{async _call(F){return new Or(await super._call(F))}}class Us extends pr{async _call(F){return new Dt(await super._call(F))}}class Cs extends pr{async _call(F){return new Sr(await super._call(F))}}class tn extends pr{async _call(F){return new Nr(await super._call(F))}}class rs extends X{}class rn extends rs{}class os extends rs{async _call(F){return new Or(await super._call(F))}}class Gs extends rs{async _call(F){return new Dt(await super._call(F))}}class Ks extends rs{async _call(F){return new Sr(await super._call(F))}}class Hs extends rs{async _call(F){return new Nr(await super._call(F))}}class Wr extends X{}class Jt extends Wr{}class Es extends Wr{async _call(F){return new Dt(await super._call(F))}}class qs extends Wr{async _call(F){return new Sr(await super._call(F))}}class Er extends Wr{async _call(F){return new Nr(await super._call(F))}}class qe extends Wr{async _call(F){return new Or(await super._call(F))}}class ct extends X{}class bt extends ct{}class ur extends ct{async _call(F){return new Or(await super._call(F))}}class ls extends ct{async _call(F){return new Dt(await super._call(F))}}class Ps extends ct{async _call(F){return new Sr(await super._call(F))}}class Xr extends X{}class Ss extends Xr{}class ks extends Xr{async _call(F){return new Or(await super._call(F))}}class $s extends Xr{async _call(F){return new Dt(await super._call(F))}}class Is extends Xr{async _call(F){return new Nr(await super._call(F))}}class gs extends X{}class gn extends gs{}class wn extends gs{async _call(F){return new Or(await super._call(F))}}class Fn extends gs{async _call(F){return new Dt(await super._call(F))}}class Dn extends gs{async _call(F){return new Sr(await super._call(F))}}class Hr extends gs{async _call(F){return new Nr(await super._call(F))}}class ws extends X{}class sn extends ws{}class us extends ws{async _call(F){return new Or(await super._call(F))}}class Jr extends ws{async _call(F){return new Dt(await super._call(F))}}class yn extends ws{async _call(F){return new Nr(await super._call(F))}}class Qs extends X{}class Te extends Qs{}class L extends Qs{async _call(F){return new Dt(await super._call(F))}}class J extends Qs{async _call(F){return new Nr(await super._call(F))}}class ie extends Qs{async _call(F){return new Or(await super._call(F))}}class ve extends X{constructor(){super(...arguments);ce(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class be extends ve{}class ze extends ve{}class tt extends X{}class ut extends tt{}class it extends tt{}class dt extends X{}class At extends dt{}class qt extends dt{}class Ht extends X{}class fr extends Ht{}class sr extends Ht{}class Pr extends Ht{async _call(F){return new Dt(await super._call(F))}}class $r extends X{}class qr extends $r{}class ys extends $r{}class Ur extends $r{async _call(F){return new Dt(await super._call(F))}}class Xs extends $r{}class cr extends X{}class Ir extends cr{}class zr extends cr{}class Yr extends X{}class ss extends Yr{}class Br extends Yr{}class Dr extends X{}class Ar extends Dr{}class wr extends Dr{async _call(F){return new Or(await super._call(F))}}class Mr extends Dr{async _call(F){return new Dt(await super._call(F))}}class vr extends Dr{async _call(F){return new Sr(await super._call(F))}}class Rr extends Dr{async _call(F){return new Nr(await super._call(F))}}class cs extends X{}class bi extends cs{}class Md extends cs{async _call(F){return new Or(await super._call(F))}}class jn extends cs{async _call(F){return new Dt(await super._call(F))}}class Js extends cs{async _call(F){return new Sr(await super._call(F))}}class ds extends cs{async _call(F){return new Nr(await super._call(F))}}class Tt extends X{}class Wa extends Tt{}class Ln extends Tt{async _call(F){return new Or(await super._call(F))}}class vd extends Tt{async _call(F){return new Dt(await super._call(F))}}class xd extends Tt{async _call(F){return new Sr(await super._call(F))}}class zn extends Tt{async _call(F){return new Nr(await super._call(F))}}class Ua extends X{}class Td extends Ua{}class Ga extends Ua{}class Ka extends X{constructor(){super(...arguments);ce(this,"requires_attention_mask",!1);ce(this,"main_input_name","input_features");ce(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Cd extends Ka{}class Ha extends Ka{_prepare_generation_config(F,V){return super._prepare_generation_config(F,V,C.WhisperGenerationConfig)}_retrieve_init_tokens(F){const V=[F.decoder_start_token_id];let _e=F.language;const Ie=F.task;if(F.is_multilingual){_e||(console.warn("No language specified - defaulting to English (en)."),_e="en");const Be=`<|${(0,D.whisper_language_to_code)(_e)}|>`;V.push(F.lang_to_id[Be]),V.push(F.task_to_id[Ie??"transcribe"])}else if(_e||Ie)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!F.return_timestamps&&F.no_timestamps_token_id&&V.at(-1)!==F.no_timestamps_token_id?V.push(F.no_timestamps_token_id):F.return_timestamps&&V.at(-1)===F.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),V.pop()),V.filter(ke=>ke!=null)}async generate({inputs:F=null,generation_config:V=null,logits_processor:_e=null,stopping_criteria:Ie=null,...ke}){V=this._prepare_generation_config(V,ke);const Be=ke.decoder_input_ids??this._retrieve_init_tokens(V);if(V.return_timestamps&&(_e??(_e=new w.LogitsProcessorList),_e.push(new w.WhisperTimeStampLogitsProcessor(V,Be))),V.begin_suppress_tokens&&(_e??(_e=new w.LogitsProcessorList),_e.push(new w.SuppressTokensAtBeginLogitsProcessor(V.begin_suppress_tokens,Be.length))),V.return_token_timestamps){if(!V.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");V.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),V.output_attentions=!0,V.return_dict_in_generate=!0}const Qe=await super.generate({inputs:F,generation_config:V,logits_processor:_e,decoder_input_ids:Be,...ke});return V.return_token_timestamps&&(Qe.token_timestamps=this._extract_token_timestamps(Qe,V.alignment_heads,V.num_frames)),Qe}_extract_token_timestamps(F,V,_e=null,Ie=.02){if(!F.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");_e==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let ke=this.config.median_filter_width;ke===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),ke=7);const Be=F.cross_attentions,Qe=Array.from({length:this.config.decoder_layers},(jt,_t)=>(0,g.cat)(Be.map(Wt=>Wt[_t]),2)),rt=(0,g.stack)(V.map(([jt,_t])=>{if(jt>=Qe.length)throw new Error(`Layer index ${jt} is out of bounds for cross attentions (length ${Qe.length}).`);return _e?Qe[jt].slice(null,_t,null,[0,_e]):Qe[jt].slice(null,_t)})).transpose(1,0,2,3),[lt,Pt]=(0,g.std_mean)(rt,-2,0,!0),It=rt.clone();for(let jt=0;jtWt[kr+1]-Wt[kr]),mr=(0,d.mergeArrays)([1],ar).map(yr=>!!yr),hr=[];for(let yr=0;yrCt.findIndex(Bt=>Bt==ke)),rt=Qe.every(Ct=>Ct===-1),lt=Qe.every(Ct=>Ct!==-1);if(!rt&&!lt)throw new Error("Every input should contain either 0 or 1 image token.");if(rt)return{inputs_embeds:F,attention_mask:Ie};const Pt=[],It=[];for(let Ct=0;CtArray.from({length:F.dims[0]},ar=>Array.from({length:F.dims[1]},mr=>1))),kt=V?V.tolist():[],jt=_e?_e.tolist():[];let _t=0,Wt=0;for(let nr=0;nrCt[nr][Tr]==1),hr=ar.reduce((or,Tr,Zs)=>(Tr==rt&&or.push(Zs),or),[]).map(or=>ar[or+1]),yr=hr.filter(or=>or==Be).length,kr=hr.filter(or=>or==Qe).length;let Rt=[],xr=0,es=yr,un=kr;for(let or=0;orhs>xr&&pn==Be),Zs=ar.findIndex((pn,hs)=>hs>xr&&pn==Qe),dn=es>0&&Tr!==-1?Tr:ar.length+1,Sn=un>0&&Zs!==-1?Zs:ar.length+1;let Oa,Qc,Xc,Jc;dn0?(0,M.max)(Rt.at(-1))[0]+1:0;Rt.push(Array.from({length:3*Zc},(pn,hs)=>Ih+hs%Zc));const ed=Zc+Ih,Da=_f*Yc*Fa,gf=Array.from({length:Da},(pn,hs)=>ed+Math.floor(hs/(Yc*Fa))),wf=Array.from({length:Da},(pn,hs)=>ed+Math.floor(hs/Fa)%Yc),yf=Array.from({length:Da},(pn,hs)=>ed+hs%Fa);Rt.push([gf,wf,yf].flat()),xr=Oa+Da}if(xr0?(0,M.max)(Rt.at(-1))[0]+1:0,Tr=ar.length-xr;Rt.push(Array.from({length:3*Tr},(Zs,dn)=>or+dn%Tr))}const Gr=Rt.reduce((or,Tr)=>or+Tr.length,0),ns=new Array(Gr);let $a=0;for(let or=0;or<3;++or)for(let Tr=0;TrIt[_t%It.length]),kt=Array.from({length:Ct[0]},(jt,_t)=>(0,M.max)(It.subarray(Ct[1]*_t,Ct[1]*(_t+1)))[0]+1n+BigInt(Ct[1]));return[new g.Tensor("int64",Bt,[3,...Ct]),new g.Tensor("int64",kt,[kt.length,1])]}else{const[It,Ct]=F.dims,Bt=BigInt64Array.from({length:3*It*Ct},(kt,jt)=>BigInt(Math.floor(jt%Ct/It)));return[new g.Tensor("int64",Bt,[3,...F.dims]),(0,g.zeros)([It,1])]}}async encode_image({pixel_values:F,image_grid_thw:V}){return(await K(this.sessions.vision_encoder,{pixel_values:F,grid_thw:V})).image_features}_merge_input_ids_with_image_features(F){return N({image_token_id:this.config.image_token_id,...F})}prepare_inputs_for_generation(F,V,_e){if(V.attention_mask&&!V.position_ids)if(!V.past_key_values)[V.position_ids,V.rope_deltas]=this.get_rope_index(V.input_ids,V.image_grid_thw,V.video_grid_thw,V.attention_mask);else{V.pixel_values=null;const Ie=BigInt(Object.values(V.past_key_values)[0].dims.at(-2)),ke=V.rope_deltas.map(Be=>Ie+Be);V.position_ids=(0,g.stack)([ke,ke,ke],0)}return V}}class $i extends X{}class Ao extends $i{}class Ii extends $i{}class xn extends X{}class Zr extends xn{}class an extends xn{}class Ai extends X{}class Oo extends Ai{}class Oi extends Ai{}class Rn extends X{}class Fi extends Rn{}class Di extends Rn{}class Nn extends X{}class ji extends Nn{}class Fo extends Nn{}class Vn extends X{}class Do extends Vn{}class jo extends Vn{async _call(F){return new Dt(await super._call(F))}}class Li extends X{}class Lo extends Li{}class zo extends Li{async _call(F){return new Dt(await super._call(F))}}class Bo extends X{}class Ro extends Bo{}class zi extends X{}class No extends zi{}class Vo extends zi{async _call(F){return new Dt(await super._call(F))}}class Wo extends X{}class Bi extends Wo{}class Ri extends X{}class Ni extends Ri{}class Vi extends Ri{async _call(F){return new Dt(await super._call(F))}}class Wi extends X{}class Tn extends Wi{}class Wn extends X{}class Uo extends Wn{}class Ui extends Wn{async _call(F){return new Dt(await super._call(F))}}class Go extends X{}class Ko extends Go{async _call(F){return new kh(await super._call(F))}}class Gi extends X{}class Ki extends Gi{}class Ho extends Gi{async _call(F){return new Dt(await super._call(F))}}class Hi extends X{}class qi extends Hi{}class Me extends Hi{async _call(F){return new Dt(await super._call(F))}}class Qi extends X{}class qo extends Qi{}class Qo extends Qi{}class Xi extends X{}class Xo extends Xi{}class Jo extends Xi{}class Ji extends X{}class Yo extends Ji{}class Zo extends Ji{async _call(F){return new Dt(await super._call(F))}}class Un extends X{}class el extends Un{}class tl extends Un{async _call(F){return new Yi(await super._call(F))}}class Gn extends Un{async _call(F){return new rl(await super._call(F))}}class Yi extends Oe{constructor({logits:F,pred_boxes:V}){super(),this.logits=F,this.pred_boxes=V}}class rl extends Oe{constructor({logits:F,pred_boxes:V,pred_masks:_e}){super(),this.logits=F,this.pred_boxes=V,this.pred_masks=_e}}class Zi extends X{}class sl extends Zi{}class ea extends Zi{async _call(F){return new Cn(await super._call(F))}}class Cn extends Oe{constructor({logits:F,pred_boxes:V}){super(),this.logits=F,this.pred_boxes=V}}class ta extends X{}class nl extends ta{}class il extends ta{async _call(F){return new al(await super._call(F))}}class al extends Cn{}class ra extends X{}class ol extends ra{}class ll extends ra{async _call(F){return new ul(await super._call(F))}}class ul extends Cn{}class sa extends X{}class cl extends sa{}class dl extends sa{async _call(F){return new Cn(await super._call(F))}}class na extends X{}class pl extends na{}class hl extends na{async _call(F){return new fl(await super._call(F))}}class fl extends Yi{}class ia extends X{}class ml extends ia{}class _l extends ia{async _call(F){return new Dt(await super._call(F))}}class aa extends X{}class gl extends aa{}class wl extends aa{async _call(F){return new Dt(await super._call(F))}}class oa extends X{}class yl extends oa{}class bl extends oa{async _call(F){return new Dt(await super._call(F))}}class Kn extends X{}class Ml extends Kn{}class vl extends Kn{async _call(F){return new Dt(await super._call(F))}}class xl extends Kn{}class la extends X{}class Tl extends la{}class Cl extends la{}class ua extends X{}class El extends ua{}class Pl extends ua{}class Sl extends X{}class kl extends Sl{}class Hn extends X{}class $l extends Hn{}class Il extends Hn{}class Al extends Hn{}class Ol extends X{}class Fl extends Ol{}class Dl extends X{}class jl extends Dl{}class Ll extends X{}class zl extends Ll{}class ca extends X{}class Bl extends ca{}class Rl extends ca{}class da extends X{}class Nl extends da{}class Vl extends da{}class Wl extends X{}class Ul extends Wl{}class pa extends X{}class Gl extends pa{}class Kl extends pa{async _call(F){return new Dt(await super._call(F))}}class ha extends X{}class Hl extends ha{}class ql extends ha{async _call(F){return new Dt(await super._call(F))}}class fa extends X{}class Ql extends fa{}class Xl extends fa{async _call(F){return new Dt(await super._call(F))}}class ma extends X{}class Jl extends ma{}class Yl extends ma{async _call(F){return new Dt(await super._call(F))}}class Zl extends X{}class eu extends Zl{}class _a extends X{}class tu extends _a{}class ru extends _a{async _call(F){return new su(await super._call(F))}}class su extends Oe{constructor({logits:F,pred_boxes:V}){super(),this.logits=F,this.pred_boxes=V}}class nu extends X{}class iu extends nu{async get_image_embeddings({pixel_values:F}){return await le(this,{pixel_values:F})}async forward(F){if((!F.image_embeddings||!F.image_positional_embeddings)&&(F={...F,...await this.get_image_embeddings(F)}),!F.input_labels&&F.input_points){const _e=F.input_points.dims.slice(0,-1),Ie=_e.reduce((ke,Be)=>ke*Be,1);F.input_labels=new g.Tensor("int64",new BigInt64Array(Ie).fill(1n),_e)}const V={image_embeddings:F.image_embeddings,image_positional_embeddings:F.image_positional_embeddings};return F.input_points&&(V.input_points=F.input_points),F.input_labels&&(V.input_labels=F.input_labels),F.input_boxes&&(V.input_boxes=F.input_boxes),await K(this.sessions.prompt_encoder_mask_decoder,V)}async _call(F){return new au(await super._call(F))}}class au extends Oe{constructor({iou_scores:F,pred_masks:V}){super(),this.iou_scores=F,this.pred_masks=V}}class ga extends X{}class ou extends ga{}class lu extends ga{}class wa extends X{}class uu extends wa{}class cu extends wa{}class As extends X{}class du extends As{}class pu extends As{async _call(F){return new ln(await super._call(F))}}class hu extends As{async _call(F){return new Dt(await super._call(F))}}class fu extends As{async _call(F){return new Sr(await super._call(F))}}class ya extends X{}class mu extends ya{}class _u extends ya{async _call(F){return new Sr(await super._call(F))}}class gu extends X{}class wu extends gu{}class qn extends X{}class yu extends qn{}class bu extends qn{async _call(F){return new ln(await super._call(F))}}class Mu extends qn{async _call(F){return new Dt(await super._call(F))}}class En extends X{}class vu extends En{}class xu extends En{async _call(F){return new ln(await super._call(F))}}class Tu extends En{async _call(F){return new Dt(await super._call(F))}}class Cu extends En{async _call(F){return new Sr(await super._call(F))}}class Qn extends X{}class Eu extends Qn{}class Pu extends Qn{async _call(F){return new ln(await super._call(F))}}class Su extends Qn{async _call(F){return new Dt(await super._call(F))}}class Mp extends X{}class ku extends As{}class $u extends As{async _call(F){return new ln(await super._call(F))}}class Iu extends As{async _call(F){return new Dt(await super._call(F))}}class on extends X{}class Au extends on{}class Ou extends on{async _call(F){return new ln(await super._call(F))}}class Fu extends on{async _call(F){return new Dt(await super._call(F))}}class Du extends on{async _call(F){return new Sh(await super._call(F))}}class ju extends on{async _call(F){return new Sr(await super._call(F))}}class Lu extends X{}class zu extends Lu{}class Xn extends X{}class vp extends Xn{}class Bu extends Xn{}class Ru extends Xn{async generate_speech(F,V,{threshold:_e=.5,minlenratio:Ie=0,maxlenratio:ke=20,vocoder:Be=null}={}){const Qe={input_ids:F},{encoder_outputs:rt,encoder_attention_mask:lt}=await le(this,Qe),Pt=rt.dims[1]/this.config.reduction_factor,It=Math.floor(Pt*ke),Ct=Math.floor(Pt*Ie),Bt=this.config.num_mel_bins;let kt=[],jt=null,_t=null,Wt=0;for(;;){++Wt;const mr=oe(!!_t);let hr;_t?hr=_t.output_sequence_out:hr=new g.Tensor("float32",new Float32Array(Bt),[1,1,Bt]);let yr={use_cache_branch:mr,output_sequence:hr,encoder_attention_mask:lt,speaker_embeddings:V,encoder_hidden_states:rt};this.addPastKeyValues(yr,jt),_t=await K(this.sessions.decoder_model_merged,yr),jt=this.getPastKeyValues(_t,jt);const{prob:kr,spectrum:Rt}=_t;if(kt.push(Rt),Wt>=Ct&&(Array.from(kr.data).filter(xr=>xr>=_e).length>0||Wt>=It))break}const nr=(0,g.cat)(kt),{waveform:ar}=await K(Be.sessions.model,{spectrogram:nr});return{spectrogram:nr,waveform:ar}}}class Nu extends X{constructor(){super(...arguments);ce(this,"main_input_name","spectrogram")}}class Vu extends X{}class Wu extends Vu{}class ba extends X{}class Uu extends ba{}class Gu extends ba{}class Ma extends X{}class Ku extends Ma{}class Hu extends Ma{}class va extends X{}class qu extends va{}class Qu extends va{}class Jn extends X{}class Xu extends Jn{}class Ju extends Jn{static async from_pretrained(F,V={}){return super.from_pretrained(F,{...V,model_file_name:V.model_file_name??"text_model"})}}class Yu extends Jn{static async from_pretrained(F,V={}){return super.from_pretrained(F,{...V,model_file_name:V.model_file_name??"audio_model"})}}class Zu extends X{}class xa extends Zu{async _call(F){return new $h(await super._call(F))}}class Yn extends X{}class xp extends Yn{}class ec extends Yn{}class tc extends Yn{}class Ta extends X{}class rc extends Ta{}class sc extends Ta{}class Ca extends X{}class nc extends Ca{}class ic extends Ca{async _call(F){return new Dt(await super._call(F))}}class Ea extends X{}class Tp extends Ea{}class Cp extends Ea{}class Pa extends X{constructor(){super(...arguments);ce(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}_apply_and_filter_by_delay_pattern_mask(V){const[_e,Ie]=V.dims,ke=this.config.decoder.num_codebooks,Be=Ie-ke;let Qe=0;for(let Pt=0;Pt0&&Bt<=Be&&(V.data[Qe++]=V.data[Pt])}const rt=Math.floor(_e/ke),lt=Qe/(rt*ke);return new g.Tensor(V.type,V.data.slice(0,Qe),[rt,ke,lt])}prepare_inputs_for_generation(V,_e,Ie){let ke=structuredClone(V);for(let Qe=0;Qe=rt&&(ke[Qe][rt]=BigInt(this.config.decoder.pad_token_id));return Ie.guidance_scale!==null&&Ie.guidance_scale>1&&(ke=ke.concat(ke)),super.prepare_inputs_for_generation(ke,_e,Ie)}async generate(V){const _e=await super.generate(V),Ie=this._apply_and_filter_by_delay_pattern_mask(_e).unsqueeze_(0),{audio_values:ke}=await K(this.sessions.encodec_decode,{audio_codes:Ie});return ke}}class Zn extends X{}class ac extends Zn{}class oc extends Zn{async _call(F){return new Dt(await super._call(F))}}class lc extends Zn{}class ei extends X{}class uc extends ei{}class cc extends ei{async _call(F){return new Dt(await super._call(F))}}class dc extends ei{}class ti extends X{}class pc extends ti{}class hc extends ti{async _call(F){return new Dt(await super._call(F))}}class fc extends ti{}class ri extends X{}class mc extends ri{}class _c extends ri{async _call(F){return new Dt(await super._call(F))}}class gc extends ri{}class wc extends X{}class yc extends wc{}class bc extends X{}class Mc extends bc{constructor(...V){super(...V);ce(this,"forward_params",["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"]);this._generation_mode="text"}async forward(V){const _e=this._generation_mode??"text";let Ie;if(_e==="text"||!V.past_key_values){const lt=this.sessions.prepare_inputs_embeds,Pt=(0,d.pick)(V,lt.inputNames);Ie=await K(lt,Pt)}else{const lt=this.sessions.gen_img_embeds,Pt=(0,d.pick)({image_ids:V.input_ids},lt.inputNames);Ie=await K(lt,Pt)}const ke={...V,...Ie},Be=await Ce(this,ke),Qe=this.sessions[_e==="text"?"lm_head":"gen_head"];if(!Qe)throw new Error(`Unable to find "${Qe}" generation head`);const rt=await K(Qe,(0,d.pick)(Be,Qe.inputNames));return{...Ie,...Be,...rt}}async generate(V){return this._generation_mode="text",super.generate(V)}async generate_images(V){this._generation_mode="image";const _e=(V.inputs??V[this.main_input_name]).dims[1],ke=(await super.generate(V)).slice(null,[_e,null]),Be=this.sessions.image_decode,{decoded_image:Qe}=await K(Be,{generated_tokens:ke}),rt=Qe.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),lt=[];for(const Pt of rt){const It=x.RawImage.fromTensor(Pt);lt.push(It)}return lt}}class vc extends Oe{constructor({char_logits:F,bpe_logits:V,wp_logits:_e}){super(),this.char_logits=F,this.bpe_logits=V,this.wp_logits=_e}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class xc extends X{}class Tc extends xc{async _call(F){return new vc(await super._call(F))}}class Sa extends X{}class Cc extends Sa{}class Ec extends Sa{}class ka extends X{}class Pc extends ka{}class Sc extends ka{}class kc extends X{constructor(){super(...arguments);ce(this,"forward_params",["input_ids","attention_mask","position_ids","audio_values","past_key_values"])}}class $c extends kc{_merge_input_ids_with_audio_features(F){const V=F.audio_features.dims.at(-1),_e=F.audio_features.view(-1,V);return Q({audio_token_id:this.config.ignore_index,...F,audio_features:_e})}}class si extends X{constructor(){super(...arguments);ce(this,"main_input_name","input_values");ce(this,"forward_params",["input_values"])}}class Ic extends Oe{constructor({audio_codes:F}){super(),this.audio_codes=F}}class Ac extends Oe{constructor({audio_values:F}){super(),this.audio_values=F}}class Oc extends si{async encode(F){return new Ic(await K(this.sessions.encoder_model,F))}async decode(F){return new Ac(await K(this.sessions.decoder_model,F))}}class Fc extends si{static async from_pretrained(F,V={}){return super.from_pretrained(F,{...V,model_file_name:V.model_file_name??"encoder_model"})}}class Dc extends si{static async from_pretrained(F,V={}){return super.from_pretrained(F,{...V,model_file_name:V.model_file_name??"decoder_model"})}}class ni extends X{constructor(){super(...arguments);ce(this,"main_input_name","input_values");ce(this,"forward_params",["input_values"])}}class jc extends Oe{constructor({audio_codes:F}){super(),this.audio_codes=F}}class Lc extends Oe{constructor({audio_values:F}){super(),this.audio_values=F}}class zc extends ni{async encode(F){return new jc(await K(this.sessions.encoder_model,F))}async decode(F){return new Lc(await K(this.sessions.decoder_model,F))}}class Bc extends ni{static async from_pretrained(F,V={}){return super.from_pretrained(F,{...V,model_file_name:V.model_file_name??"encoder_model"})}}class Rc extends ni{static async from_pretrained(F,V={}){return super.from_pretrained(F,{...V,model_file_name:V.model_file_name??"decoder_model"})}}class ii extends X{constructor(){super(...arguments);ce(this,"main_input_name","input_values");ce(this,"forward_params",["input_values"])}}class Nc extends ii{async encode(F){return await K(this.sessions.encoder_model,F)}async decode(F){return await K(this.sessions.decoder_model,F)}}class Vc extends ii{static async from_pretrained(F,V={}){return super.from_pretrained(F,{...V,model_file_name:V.model_file_name??"encoder_model"})}}class Wc extends ii{static async from_pretrained(F,V={}){return super.from_pretrained(F,{...V,model_file_name:V.model_file_name??"decoder_model"})}}class Ut{static async from_pretrained(F,{progress_callback:V=null,config:_e=null,cache_dir:Ie=null,local_files_only:ke=!1,revision:Be="main",model_file_name:Qe=null,subfolder:rt="onnx",device:lt=null,dtype:Pt=null,use_external_data_format:It=null,session_options:Ct={}}={}){const Bt={progress_callback:V,config:_e,cache_dir:Ie,local_files_only:ke,revision:Be,model_file_name:Qe,subfolder:rt,device:lt,dtype:Pt,use_external_data_format:It,session_options:Ct};if(Bt.config=await i.AutoConfig.from_pretrained(F,Bt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);const kt=Bt.config.model_type;for(const jt of this.MODEL_CLASS_MAPPINGS){let _t=jt.get(kt);if(!_t){for(const Wt of jt.values())if(Wt[0]===kt){_t=Wt;break}if(!_t)continue}return await _t[1].from_pretrained(F,Bt)}if(this.BASE_IF_FAIL)return Jp.has(kt)||console.warn(`Unknown model class "${kt}", attempting to construct from base class.`),await X.from_pretrained(F,Bt);throw Error(`Unsupported model type: ${kt}`)}}ce(Ut,"MODEL_CLASS_MAPPINGS",null),ce(Ut,"BASE_IF_FAIL",!1);const af=new Map([["bert",["BertModel",Re]],["modernbert",["ModernBertModel",Ke]],["nomic_bert",["NomicBertModel",vt]],["roformer",["RoFormerModel",xt]],["electra",["ElectraModel",$e]],["esm",["EsmModel",bt]],["convbert",["ConvBertModel",ht]],["camembert",["CamembertModel",Nt]],["deberta",["DebertaModel",Kr]],["deberta-v2",["DebertaV2Model",rn]],["mpnet",["MPNetModel",gn]],["albert",["AlbertModel",Te]],["distilbert",["DistilBertModel",Jt]],["roberta",["RobertaModel",Ar]],["xlm",["XLMModel",bi]],["xlm-roberta",["XLMRobertaModel",Wa]],["clap",["ClapModel",Xu]],["clip",["CLIPModel",eo]],["clipseg",["CLIPSegModel",Rd]],["chinese_clip",["ChineseCLIPModel",io]],["siglip",["SiglipModel",no]],["jina_clip",["JinaCLIPModel",zd]],["mobilebert",["MobileBertModel",Ss]],["squeezebert",["SqueezeBertModel",sn]],["wav2vec2",["Wav2Vec2Model",du]],["wav2vec2-bert",["Wav2Vec2BertModel",Eu]],["unispeech",["UniSpeechModel",yu]],["unispeech-sat",["UniSpeechSatModel",vu]],["hubert",["HubertModel",ku]],["wavlm",["WavLMModel",Au]],["audio-spectrogram-transformer",["ASTModel",Td]],["vits",["VitsModel",xa]],["pyannote",["PyAnnoteModel",mu]],["wespeaker-resnet",["WeSpeakerResNetModel",wu]],["detr",["DetrModel",el]],["rt_detr",["RTDetrModel",sl]],["rt_detr_v2",["RTDetrV2Model",nl]],["rf_detr",["RFDetrModel",ol]],["d_fine",["DFineModel",cl]],["table-transformer",["TableTransformerModel",pl]],["vit",["ViTModel",Do]],["ijepa",["IJepaModel",Lo]],["pvt",["PvtModel",No]],["vit_msn",["ViTMSNModel",Ni]],["vit_mae",["ViTMAEModel",Bi]],["groupvit",["GroupViTModel",Tn]],["fastvit",["FastViTModel",Uo]],["mobilevit",["MobileViTModel",Ki]],["mobilevitv2",["MobileViTV2Model",qi]],["owlvit",["OwlViTModel",qo]],["owlv2",["Owlv2Model",Xo]],["beit",["BeitModel",Yo]],["deit",["DeiTModel",ml]],["hiera",["HieraModel",gl]],["convnext",["ConvNextModel",Gl]],["convnextv2",["ConvNextV2Model",Hl]],["dinov2",["Dinov2Model",Ql]],["dinov2_with_registers",["Dinov2WithRegistersModel",Jl]],["resnet",["ResNetModel",yl]],["swin",["SwinModel",Ml]],["swin2sr",["Swin2SRModel",Tl]],["donut-swin",["DonutSwinModel",Ul]],["yolos",["YolosModel",tu]],["dpt",["DPTModel",El]],["glpn",["GLPNModel",Nl]],["hifigan",["SpeechT5HifiGan",Nu]],["efficientnet",["EfficientNetModel",nc]],["decision_transformer",["DecisionTransformerModel",yc]],["patchtst",["PatchTSTForPrediction",Cc]],["patchtsmixer",["PatchTSMixerForPrediction",Pc]],["mobilenet_v1",["MobileNetV1Model",ac]],["mobilenet_v2",["MobileNetV2Model",uc]],["mobilenet_v3",["MobileNetV3Model",pc]],["mobilenet_v4",["MobileNetV4Model",mc]],["maskformer",["MaskFormerModel",Bl]],["mgp-str",["MgpstrForSceneTextRecognition",Tc]],["style_text_to_speech_2",["StyleTextToSpeech2Model",zu]]]),of=new Map([["t5",["T5Model",be]],["longt5",["LongT5Model",ut]],["mt5",["MT5Model",At]],["bart",["BartModel",fr]],["mbart",["MBartModel",qr]],["marian",["MarianModel",ou]],["whisper",["WhisperModel",Cd]],["m2m_100",["M2M100Model",uu]],["blenderbot",["BlenderbotModel",Ir]],["blenderbot-small",["BlenderbotSmallModel",ss]]]),lf=new Map([["mimi",["MimiModel",Oc]],["dac",["DacModel",zc]],["snac",["SnacModel",Nc]]]),uf=new Map([["bloom",["BloomModel",Oo]],["jais",["JAISModel",Wd]],["gpt2",["GPT2Model",Nd]],["gptj",["GPTJModel",ps]],["gpt_bigcode",["GPTBigCodeModel",Kd]],["gpt_neo",["GPTNeoModel",Bn]],["gpt_neox",["GPTNeoXModel",po]],["codegen",["CodeGenModel",qd]],["llama",["LlamaModel",Xd]],["exaone",["ExaoneModel",wo]],["olmo",["OlmoModel",vo]],["olmo2",["Olmo2Model",rp]],["mobilellm",["MobileLLMModel",bo]],["granite",["GraniteModel",np]],["cohere",["CohereModel",ap]],["gemma",["GemmaModel",lp]],["gemma2",["Gemma2Model",cp]],["gemma3_text",["Gemma3Model",pp]],["helium",["HeliumModel",Yd]],["glm",["GlmModel",Pi]],["openelm",["OpenELMModel",fp]],["qwen2",["Qwen2Model",_p]],["qwen3",["Qwen3Model",wp]],["phi",["PhiModel",Ao]],["phi3",["Phi3Model",Zr]],["mpt",["MptModel",Fi]],["opt",["OPTModel",ji]],["mistral",["MistralModel",Uu]],["starcoder2",["Starcoder2Model",Ku]],["falcon",["FalconModel",qu]],["stablelm",["StableLmModel",rc]]]),Uc=new Map([["speecht5",["SpeechT5ForSpeechToText",Bu]],["whisper",["WhisperForConditionalGeneration",Ha]],["lite-whisper",["LiteWhisperForConditionalGeneration",qa]],["moonshine",["MoonshineForConditionalGeneration",Ed]]]),Ep=new Map([["speecht5",["SpeechT5ForTextToSpeech",Ru]]]),Pp=new Map([["vits",["VitsModel",xa]],["musicgen",["MusicgenForConditionalGeneration",Pa]]]),Sp=new Map([["bert",["BertForSequenceClassification",Ge]],["modernbert",["ModernBertForSequenceClassification",ft]],["roformer",["RoFormerForSequenceClassification",he]],["electra",["ElectraForSequenceClassification",Le]],["esm",["EsmForSequenceClassification",ls]],["convbert",["ConvBertForSequenceClassification",W]],["camembert",["CamembertForSequenceClassification",tr]],["deberta",["DebertaForSequenceClassification",Us]],["deberta-v2",["DebertaV2ForSequenceClassification",Gs]],["mpnet",["MPNetForSequenceClassification",Fn]],["albert",["AlbertForSequenceClassification",L]],["distilbert",["DistilBertForSequenceClassification",Es]],["roberta",["RobertaForSequenceClassification",Mr]],["xlm",["XLMForSequenceClassification",jn]],["xlm-roberta",["XLMRobertaForSequenceClassification",vd]],["bart",["BartForSequenceClassification",Pr]],["mbart",["MBartForSequenceClassification",Ur]],["mobilebert",["MobileBertForSequenceClassification",$s]],["squeezebert",["SqueezeBertForSequenceClassification",Jr]]]),kp=new Map([["bert",["BertForTokenClassification",Je]],["modernbert",["ModernBertForTokenClassification",wt]],["roformer",["RoFormerForTokenClassification",ue]],["electra",["ElectraForTokenClassification",Ft]],["esm",["EsmForTokenClassification",Ps]],["convbert",["ConvBertForTokenClassification",we]],["camembert",["CamembertForTokenClassification",rr]],["deberta",["DebertaForTokenClassification",Cs]],["deberta-v2",["DebertaV2ForTokenClassification",Ks]],["mpnet",["MPNetForTokenClassification",Dn]],["distilbert",["DistilBertForTokenClassification",qs]],["roberta",["RobertaForTokenClassification",vr]],["xlm",["XLMForTokenClassification",Js]],["xlm-roberta",["XLMRobertaForTokenClassification",xd]]]),Gc=new Map([["t5",["T5ForConditionalGeneration",ze]],["longt5",["LongT5ForConditionalGeneration",it]],["mt5",["MT5ForConditionalGeneration",qt]],["bart",["BartForConditionalGeneration",sr]],["mbart",["MBartForConditionalGeneration",ys]],["marian",["MarianMTModel",lu]],["m2m_100",["M2M100ForConditionalGeneration",cu]],["blenderbot",["BlenderbotForConditionalGeneration",zr]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Br]]]),Kc=new Map([["bloom",["BloomForCausalLM",Oi]],["gpt2",["GPT2LMHeadModel",Vd]],["jais",["JAISLMHeadModel",Ud]],["gptj",["GPTJForCausalLM",Pe]],["gpt_bigcode",["GPTBigCodeForCausalLM",Hd]],["gpt_neo",["GPTNeoForCausalLM",Mn]],["gpt_neox",["GPTNeoXForCausalLM",Gd]],["codegen",["CodeGenForCausalLM",Qd]],["llama",["LlamaForCausalLM",Jd]],["exaone",["ExaoneForCausalLM",yo]],["olmo",["OlmoForCausalLM",tp]],["olmo2",["Olmo2ForCausalLM",sp]],["mobilellm",["MobileLLMForCausalLM",ep]],["granite",["GraniteForCausalLM",ip]],["cohere",["CohereForCausalLM",op]],["gemma",["GemmaForCausalLM",up]],["gemma2",["Gemma2ForCausalLM",dp]],["gemma3_text",["Gemma3ForCausalLM",hp]],["helium",["HeliumForCausalLM",Zd]],["glm",["GlmForCausalLM",vn]],["openelm",["OpenELMForCausalLM",mp]],["qwen2",["Qwen2ForCausalLM",gp]],["qwen3",["Qwen3ForCausalLM",yp]],["phi",["PhiForCausalLM",Ii]],["phi3",["Phi3ForCausalLM",an]],["mpt",["MptForCausalLM",Di]],["opt",["OPTForCausalLM",Fo]],["mbart",["MBartForCausalLM",Xs]],["mistral",["MistralForCausalLM",Gu]],["starcoder2",["Starcoder2ForCausalLM",Hu]],["falcon",["FalconForCausalLM",Qu]],["trocr",["TrOCRForCausalLM",Wu]],["stablelm",["StableLmForCausalLM",sc]],["phi3_v",["Phi3VForCausalLM",Za]]]),cf=new Map([["multi_modality",["MultiModalityCausalLM",Mc]]]),$p=new Map([["bert",["BertForMaskedLM",Ue]],["modernbert",["ModernBertForMaskedLM",st]],["roformer",["RoFormerForMaskedLM",Vt]],["electra",["ElectraForMaskedLM",Xe]],["esm",["EsmForMaskedLM",ur]],["convbert",["ConvBertForMaskedLM",as]],["camembert",["CamembertForMaskedLM",mt]],["deberta",["DebertaForMaskedLM",Ts]],["deberta-v2",["DebertaV2ForMaskedLM",os]],["mpnet",["MPNetForMaskedLM",wn]],["albert",["AlbertForMaskedLM",ie]],["distilbert",["DistilBertForMaskedLM",qe]],["roberta",["RobertaForMaskedLM",wr]],["xlm",["XLMWithLMHeadModel",Md]],["xlm-roberta",["XLMRobertaForMaskedLM",Ln]],["mobilebert",["MobileBertForMaskedLM",ks]],["squeezebert",["SqueezeBertForMaskedLM",us]]]),Ip=new Map([["bert",["BertForQuestionAnswering",at]],["roformer",["RoFormerForQuestionAnswering",Cr]],["electra",["ElectraForQuestionAnswering",Qt]],["convbert",["ConvBertForQuestionAnswering",G]],["camembert",["CamembertForQuestionAnswering",Fr]],["deberta",["DebertaForQuestionAnswering",tn]],["deberta-v2",["DebertaV2ForQuestionAnswering",Hs]],["mpnet",["MPNetForQuestionAnswering",Hr]],["albert",["AlbertForQuestionAnswering",J]],["distilbert",["DistilBertForQuestionAnswering",Er]],["roberta",["RobertaForQuestionAnswering",Rr]],["xlm",["XLMForQuestionAnswering",ds]],["xlm-roberta",["XLMRobertaForQuestionAnswering",zn]],["mobilebert",["MobileBertForQuestionAnswering",Is]],["squeezebert",["SqueezeBertForQuestionAnswering",yn]]]),Hc=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Xa]],["idefics3",["Idefics3ForConditionalGeneration",vi]],["smolvlm",["SmolVLMForConditionalGeneration",Ja]]]),Ap=new Map([["llava",["LlavaForConditionalGeneration",Mi]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",Sd]],["moondream1",["Moondream1ForConditionalGeneration",kd]],["florence2",["Florence2ForConditionalGeneration",Id]],["qwen2-vl",["Qwen2VLForConditionalGeneration",Y]],["idefics3",["Idefics3ForConditionalGeneration",vi]],["smolvlm",["SmolVLMForConditionalGeneration",Ja]],["paligemma",["PaliGemmaForConditionalGeneration",Od]]]),Op=new Map([["ultravox",["UltravoxModel",$c]]]),df=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Xa]]]),Fp=new Map([["vit",["ViTForImageClassification",jo]],["ijepa",["IJepaForImageClassification",zo]],["pvt",["PvtForImageClassification",Vo]],["vit_msn",["ViTMSNForImageClassification",Vi]],["fastvit",["FastViTForImageClassification",Ui]],["mobilevit",["MobileViTForImageClassification",Ho]],["mobilevitv2",["MobileViTV2ForImageClassification",Me]],["beit",["BeitForImageClassification",Zo]],["deit",["DeiTForImageClassification",_l]],["hiera",["HieraForImageClassification",wl]],["convnext",["ConvNextForImageClassification",Kl]],["convnextv2",["ConvNextV2ForImageClassification",ql]],["dinov2",["Dinov2ForImageClassification",Xl]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",Yl]],["resnet",["ResNetForImageClassification",bl]],["swin",["SwinForImageClassification",vl]],["segformer",["SegformerForImageClassification",ec]],["efficientnet",["EfficientNetForImageClassification",ic]],["mobilenet_v1",["MobileNetV1ForImageClassification",oc]],["mobilenet_v2",["MobileNetV2ForImageClassification",cc]],["mobilenet_v3",["MobileNetV3ForImageClassification",hc]],["mobilenet_v4",["MobileNetV4ForImageClassification",_c]]]),Dp=new Map([["detr",["DetrForObjectDetection",tl]],["rt_detr",["RTDetrForObjectDetection",ea]],["rt_detr_v2",["RTDetrV2ForObjectDetection",il]],["rf_detr",["RFDetrForObjectDetection",ll]],["d_fine",["DFineForObjectDetection",dl]],["table-transformer",["TableTransformerForObjectDetection",hl]],["yolos",["YolosForObjectDetection",ru]]]),jp=new Map([["owlvit",["OwlViTForObjectDetection",Qo]],["owlv2",["Owlv2ForObjectDetection",Jo]],["grounding-dino",["GroundingDinoForObjectDetection",eu]]]),Pn=new Map([["detr",["DetrForSegmentation",Gn]],["clipseg",["CLIPSegForImageSegmentation",oo]]]),Lp=new Map([["segformer",["SegformerForSemanticSegmentation",tc]],["sapiens",["SapiensForSemanticSegmentation",$l]],["swin",["SwinForSemanticSegmentation",xl]],["mobilenet_v1",["MobileNetV1ForSemanticSegmentation",lc]],["mobilenet_v2",["MobileNetV2ForSemanticSegmentation",dc]],["mobilenet_v3",["MobileNetV3ForSemanticSegmentation",fc]],["mobilenet_v4",["MobileNetV4ForSemanticSegmentation",gc]]]),zp=new Map([["detr",["DetrForSegmentation",Gn]],["maskformer",["MaskFormerForInstanceSegmentation",Rl]]]),Bp=new Map([["sam",["SamModel",iu]]]),Rp=new Map([["wav2vec2",["Wav2Vec2ForCTC",pu]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Pu]],["unispeech",["UniSpeechForCTC",bu]],["unispeech-sat",["UniSpeechSatForCTC",xu]],["wavlm",["WavLMForCTC",Ou]],["hubert",["HubertForCTC",$u]]]),Np=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",hu]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Su]],["unispeech",["UniSpeechForSequenceClassification",Mu]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Tu]],["wavlm",["WavLMForSequenceClassification",Fu]],["hubert",["HubertForSequenceClassification",Iu]],["audio-spectrogram-transformer",["ASTForAudioClassification",Ga]]]),Vp=new Map([["wavlm",["WavLMForXVector",Du]]]),Wp=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Cu]],["wavlm",["WavLMForAudioFrameClassification",ju]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",fu]],["pyannote",["PyAnnoteForAudioFrameClassification",_u]]]),Up=new Map([["vitmatte",["VitMatteForImageMatting",Ko]]]),pf=new Map([["patchtst",["PatchTSTForPrediction",Ec]],["patchtsmixer",["PatchTSMixerForPrediction",Sc]]]),Gp=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Cl]]]),Kp=new Map([["dpt",["DPTForDepthEstimation",Pl]],["depth_anything",["DepthAnythingForDepthEstimation",kl]],["glpn",["GLPNForDepthEstimation",Vl]],["sapiens",["SapiensForDepthEstimation",Il]],["depth_pro",["DepthProForDepthEstimation",Fl]],["metric3d",["Metric3DForDepthEstimation",jl]],["metric3dv2",["Metric3Dv2ForDepthEstimation",zl]]]),Hp=new Map([["sapiens",["SapiensForNormalEstimation",Al]]]),qp=new Map([["vitpose",["VitPoseForPoseEstimation",Ro]]]),Qp=new Map([["clip",["CLIPVisionModelWithProjection",ro]],["siglip",["SiglipVisionModel",Ld]],["jina_clip",["JinaCLIPVisionModel",Bd]]]),Xp=[[af,A.EncoderOnly],[of,A.EncoderDecoder],[uf,A.DecoderOnly],[lf,A.AutoEncoder],[Sp,A.EncoderOnly],[kp,A.EncoderOnly],[Gc,A.Seq2Seq],[Uc,A.Seq2Seq],[Kc,A.DecoderOnly],[cf,A.MultiModality],[$p,A.EncoderOnly],[Ip,A.EncoderOnly],[Hc,A.Vision2Seq],[Ap,A.ImageTextToText],[Op,A.AudioTextToText],[Fp,A.EncoderOnly],[Pn,A.EncoderOnly],[zp,A.EncoderOnly],[Lp,A.EncoderOnly],[Up,A.EncoderOnly],[pf,A.EncoderOnly],[Gp,A.EncoderOnly],[Kp,A.EncoderOnly],[Hp,A.EncoderOnly],[qp,A.EncoderOnly],[Dp,A.EncoderOnly],[jp,A.EncoderOnly],[Bp,A.MaskGeneration],[Rp,A.EncoderOnly],[Np,A.EncoderOnly],[Ep,A.Seq2Seq],[Pp,A.EncoderOnly],[Vp,A.EncoderOnly],[Wp,A.EncoderOnly],[Qp,A.EncoderOnly]];for(const[S,F]of Xp)for(const[V,_e]of S.values())$.set(V,F),k.set(_e,V),P.set(V,_e);const hf=[["MusicgenForConditionalGeneration",Pa,A.Musicgen],["Phi3VForCausalLM",Za,A.Phi3V],["CLIPTextModelWithProjection",bn,A.EncoderOnly],["SiglipTextModel",jd,A.EncoderOnly],["JinaCLIPTextModel",Ys,A.EncoderOnly],["ClapTextModelWithProjection",Ju,A.EncoderOnly],["ClapAudioModelWithProjection",Yu,A.EncoderOnly],["DacEncoderModel",Bc,A.EncoderOnly],["DacDecoderModel",Rc,A.EncoderOnly],["MimiEncoderModel",Fc,A.EncoderOnly],["MimiDecoderModel",Dc,A.EncoderOnly],["SnacEncoderModel",Vc,A.EncoderOnly],["SnacDecoderModel",Wc,A.EncoderOnly]];for(const[S,F,V]of hf)$.set(S,V),k.set(F,S),P.set(S,F);const Jp=new Map([["modnet",Pn],["birefnet",Pn],["isnet",Pn],["ben",Pn]]);for(const[S,F]of Jp.entries())F.set(S,["PreTrainedModel",X]),$.set(S,A.EncoderOnly),k.set(X,S),P.set(S,X);class qc extends Ut{}ce(qc,"MODEL_CLASS_MAPPINGS",Xp.map(F=>F[0])),ce(qc,"BASE_IF_FAIL",!0);class Yp extends Ut{}ce(Yp,"MODEL_CLASS_MAPPINGS",[Sp]);class Zp extends Ut{}ce(Zp,"MODEL_CLASS_MAPPINGS",[kp]);class eh extends Ut{}ce(eh,"MODEL_CLASS_MAPPINGS",[Gc]);class th extends Ut{}ce(th,"MODEL_CLASS_MAPPINGS",[Uc]);class rh extends Ut{}ce(rh,"MODEL_CLASS_MAPPINGS",[Ep]);class sh extends Ut{}ce(sh,"MODEL_CLASS_MAPPINGS",[Pp]);class nh extends Ut{}ce(nh,"MODEL_CLASS_MAPPINGS",[Kc]);class ih extends Ut{}ce(ih,"MODEL_CLASS_MAPPINGS",[$p]);class ah extends Ut{}ce(ah,"MODEL_CLASS_MAPPINGS",[Ip]);class oh extends Ut{}ce(oh,"MODEL_CLASS_MAPPINGS",[Hc]);class lh extends Ut{}ce(lh,"MODEL_CLASS_MAPPINGS",[Fp]);class uh extends Ut{}ce(uh,"MODEL_CLASS_MAPPINGS",[Pn]);class ch extends Ut{}ce(ch,"MODEL_CLASS_MAPPINGS",[Lp]);class dh extends Ut{}ce(dh,"MODEL_CLASS_MAPPINGS",[zp]);class ph extends Ut{}ce(ph,"MODEL_CLASS_MAPPINGS",[Dp]);class hh extends Ut{}ce(hh,"MODEL_CLASS_MAPPINGS",[jp]);class fh extends Ut{}ce(fh,"MODEL_CLASS_MAPPINGS",[Bp]);class mh extends Ut{}ce(mh,"MODEL_CLASS_MAPPINGS",[Rp]);class _h extends Ut{}ce(_h,"MODEL_CLASS_MAPPINGS",[Np]);class gh extends Ut{}ce(gh,"MODEL_CLASS_MAPPINGS",[Vp]);class wh extends Ut{}ce(wh,"MODEL_CLASS_MAPPINGS",[Wp]);class yh extends Ut{}ce(yh,"MODEL_CLASS_MAPPINGS",[df]);class bh extends Ut{}ce(bh,"MODEL_CLASS_MAPPINGS",[Up]);class Mh extends Ut{}ce(Mh,"MODEL_CLASS_MAPPINGS",[Gp]);class vh extends Ut{}ce(vh,"MODEL_CLASS_MAPPINGS",[Kp]);class xh extends Ut{}ce(xh,"MODEL_CLASS_MAPPINGS",[Hp]);class Th extends Ut{}ce(Th,"MODEL_CLASS_MAPPINGS",[qp]);class Ch extends Ut{}ce(Ch,"MODEL_CLASS_MAPPINGS",[Qp]);class Eh extends Ut{}ce(Eh,"MODEL_CLASS_MAPPINGS",[Ap]);class Ph extends Ut{}ce(Ph,"MODEL_CLASS_MAPPINGS",[Op]);class ff extends Oe{constructor({logits:F,past_key_values:V,encoder_outputs:_e,decoder_attentions:Ie=null,cross_attentions:ke=null}){super(),this.logits=F,this.past_key_values=V,this.encoder_outputs=_e,this.decoder_attentions=Ie,this.cross_attentions=ke}}class Dt extends Oe{constructor({logits:F,...V}){super(),this.logits=F;const _e=Object.values(V);_e.length>0&&(this.attentions=_e)}}class Sh extends Oe{constructor({logits:F,embeddings:V}){super(),this.logits=F,this.embeddings=V}}class Sr extends Oe{constructor({logits:F}){super(),this.logits=F}}class Or extends Oe{constructor({logits:F}){super(),this.logits=F}}class Nr extends Oe{constructor({start_logits:F,end_logits:V}){super(),this.start_logits=F,this.end_logits=V}}class ln extends Oe{constructor({logits:F}){super(),this.logits=F}}class mf extends Oe{constructor({logits:F,past_key_values:V}){super(),this.logits=F,this.past_key_values=V}}class kh extends Oe{constructor({alphas:F}){super(),this.alphas=F}}class $h extends Oe{constructor({waveform:F,spectrogram:V}){super(),this.waveform=F,this.spectrogram=V}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(e,r,t)=>{t.r(r),t.d(r,{ASTFeatureExtractor:()=>n});var i=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var l=t("./src/utils/audio.js");class n extends i.FeatureExtractor{constructor(d){super(d);const h=this.config.sampling_rate,f=(0,l.mel_filter_bank)(257,this.config.num_mel_bins,20,Math.floor(h/2),h,null,"kaldi",!0);this.mel_filters=f,this.window=(0,l.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(d,h){return(0,l.spectrogram)(d,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:h,transpose:!0})}async _call(d){(0,i.validate_audio_inputs)(d,"ASTFeatureExtractor");const h=await this._extract_fbank_features(d,this.config.max_length);if(this.config.do_normalize){const f=this.std*2,w=h.data;for(let m=0;m{t.r(r),t.d(r,{AutoFeatureExtractor:()=>c});var i=t("./src/utils/constants.js"),l=t("./src/utils/hub.js");t("./src/base/feature_extraction_utils.js");var n=t("./src/models/feature_extractors.js");class c{static async from_pretrained(h,f={}){const w=await(0,l.getModelJSON)(h,i.FEATURE_EXTRACTOR_NAME,!0,f),m=w.feature_extractor_type,g=n[m];if(!g)throw new Error(`Unknown feature_extractor_type: '${m}'. Please report this at ${i.GITHUB_ISSUE_URL}.`);return new g(w)}}},"./src/models/auto/image_processing_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoImageProcessor:()=>d});var i=t("./src/utils/constants.js"),l=t("./src/utils/hub.js"),n=t("./src/base/image_processors_utils.js"),c=t("./src/models/image_processors.js");class d{static async from_pretrained(f,w={}){const m=await(0,l.getModelJSON)(f,i.IMAGE_PROCESSOR_NAME,!0,w),g=m.image_processor_type??m.feature_extractor_type;let x=c[g];return x||(g!==void 0&&console.warn(`Image processor type '${g}' not found, assuming base ImageProcessor. Please report this at ${i.GITHUB_ISSUE_URL}.`),x=n.ImageProcessor),new x(m)}}},"./src/models/auto/processing_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoProcessor:()=>f});var i=t("./src/utils/constants.js"),l=t("./src/utils/hub.js"),n=t("./src/base/processing_utils.js"),c=t("./src/models/processors.js"),d=t("./src/models/image_processors.js"),h=t("./src/models/feature_extractors.js");class f{static async from_pretrained(m,g={}){const x=await(0,l.getModelJSON)(m,i.IMAGE_PROCESSOR_NAME,!0,g),{image_processor_type:M,feature_extractor_type:I,processor_class:z}=x;if(z&&c[z])return c[z].from_pretrained(m,g);if(!M&&!I)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const E={};if(M){const D=d[M];if(!D)throw new Error(`Unknown image_processor_type: '${M}'.`);E.image_processor=new D(x)}if(I){const D=d[I];if(D)E.image_processor=new D(x);else{const A=h[I];if(!A)throw new Error(`Unknown feature_extractor_type: '${I}'.`);E.feature_extractor=new A(x)}}const C={};return new n.Processor(C,E)}}},"./src/models/beit/image_processing_beit.js":(e,r,t)=>{t.r(r),t.d(r,{BeitFeatureExtractor:()=>l});var i=t("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}},"./src/models/bit/image_processing_bit.js":(e,r,t)=>{t.r(r),t.d(r,{BitImageProcessor:()=>l});var i=t("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}},"./src/models/chinese_clip/image_processing_chinese_clip.js":(e,r,t)=>{t.r(r),t.d(r,{ChineseCLIPFeatureExtractor:()=>l});var i=t("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}},"./src/models/clap/feature_extraction_clap.js":(e,r,t)=>{t.r(r),t.d(r,{ClapFeatureExtractor:()=>n});var i=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var l=t("./src/utils/audio.js");class n extends i.FeatureExtractor{constructor(d){super(d),this.mel_filters=(0,l.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,l.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,l.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(d,h,f,w){let m;const g=d.length-h;if(g>0)if(f==="rand_trunc"){const x=Math.floor(Math.random()*(g+1));d=d.subarray(x,x+h),m=await this._extract_fbank_features(d,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${f}" not implemented`);else{if(g<0){let x=new Float64Array(h);if(x.set(d),w==="repeat")for(let M=d.length;M{t.r(r),t.d(r,{CLIPFeatureExtractor:()=>n,CLIPImageProcessor:()=>l});var 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i.EncodecFeatureExtractor{}},"./src/models/deit/image_processing_deit.js":(e,r,t)=>{t.r(r),t.d(r,{DeiTFeatureExtractor:()=>n,DeiTImageProcessor:()=>l});var i=t("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}class n extends l{}},"./src/models/detr/image_processing_detr.js":(e,r,t)=>{t.r(r),t.d(r,{DetrFeatureExtractor:()=>c,DetrImageProcessor:()=>n});var i=t("./src/base/image_processors_utils.js"),l=t("./src/utils/tensor.js");class n extends i.ImageProcessor{async _call(h){const f=await super._call(h),w=[f.pixel_values.dims[0],64,64],m=(0,l.full)(w,1n);return{...f,pixel_mask:m}}post_process_object_detection(...h){return(0,i.post_process_object_detection)(...h)}post_process_panoptic_segmentation(...h){return(0,i.post_process_panoptic_segmentation)(...h)}post_process_instance_segmentation(...h){return(0,i.post_process_instance_segmentation)(...h)}}class c extends n{}},"./src/models/donut/image_processing_donut.js":(e,r,t)=>{t.r(r),t.d(r,{DonutFeatureExtractor:()=>n,DonutImageProcessor:()=>l});var i=t("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{pad_image(d,h,f,w={}){const[m,g,x]=h;let M=this.image_mean;Array.isArray(this.image_mean)||(M=new Array(x).fill(M));let I=this.image_std;Array.isArray(I)||(I=new Array(x).fill(M));const z=M.map((E,C)=>-E/I[C]);return super.pad_image(d,h,f,{center:!0,constant_values:z,...w})}}class n extends l{}},"./src/models/dpt/image_processing_dpt.js":(e,r,t)=>{t.r(r),t.d(r,{DPTFeatureExtractor:()=>n,DPTImageProcessor:()=>l});var i=t("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}class n extends l{}},"./src/models/efficientnet/image_processing_efficientnet.js":(e,r,t)=>{t.r(r),t.d(r,{EfficientNetImageProcessor:()=>l});var i=t("./src/base/image_processors_utils.js");class l extends 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i=t("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),l=t("./src/models/encodec/feature_extraction_encodec.js"),n=t("./src/models/clap/feature_extraction_clap.js"),c=t("./src/models/dac/feature_extraction_dac.js"),d=t("./src/models/moonshine/feature_extraction_moonshine.js"),h=t("./src/models/pyannote/feature_extraction_pyannote.js"),f=t("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),w=t("./src/models/snac/feature_extraction_snac.js"),m=t("./src/models/speecht5/feature_extraction_speecht5.js"),g=t("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),x=t("./src/models/wespeaker/feature_extraction_wespeaker.js"),M=t("./src/models/whisper/feature_extraction_whisper.js"),I=t("./src/base/image_processors_utils.js")},"./src/models/florence2/processing_florence2.js":(e,r,t)=>{t.r(r),t.d(r,{Florence2Processor:()=>c});var i=t("./src/base/processing_utils.js"),l=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");class c extends i.Processor{constructor(h,f){super(h,f);const{tasks_answer_post_processing_type:w,task_prompts_without_inputs:m,task_prompts_with_input:g}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(w??{})),this.task_prompts_without_inputs=new Map(Object.entries(m??{})),this.task_prompts_with_input=new Map(Object.entries(g??{})),this.regexes={quad_boxes:/(.+?)/gm,bboxes:/([^<]+)?/gm},this.size_per_bin=1e3}construct_prompts(h){typeof h=="string"&&(h=[h]);const f=[];for(const w of h)if(this.task_prompts_without_inputs.has(w))f.push(this.task_prompts_without_inputs.get(w));else{for(const[m,g]of this.task_prompts_with_input)if(w.includes(m)){f.push(g.replaceAll("{input}",w).replaceAll(m,""));break}f.length!==h.length&&f.push(w)}return f}post_process_generation(h,f,w){const m=this.tasks_answer_post_processing_type.get(f)??"pure_text";h=h.replaceAll("","").replaceAll("","");let g;switch(m){case"pure_text":g=h;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const x=m==="ocr"?"quad_boxes":"bboxes",M=h.matchAll(this.regexes[x]),I=[],z=[];for(const[E,C,...D]of M)I.push(C?C.trim():I.at(-1)??""),z.push(D.map((A,$)=>(Number(A)+.5)/this.size_per_bin*w[$%2]));g={labels:I,[x]:z};break;default:throw new Error(`Task "${f}" (of type "${m}") not yet implemented.`)}return{[f]:g}}async _call(h,f=null,w={}){if(!h&&!f)throw new Error("Either text or images must be provided");const m=await this.image_processor(h,w),g=f?this.tokenizer(f,w):{};return{...m,...g}}}ce(c,"tokenizer_class",n.AutoTokenizer),ce(c,"image_processor_class",l.AutoImageProcessor)},"./src/models/glpn/image_processing_glpn.js":(e,r,t)=>{t.r(r),t.d(r,{GLPNFeatureExtractor:()=>l});var i=t("./src/base/image_processors_utils.js");class l extends i.ImageProcessor{}},"./src/models/grounding_dino/image_processing_grounding_dino.js":(e,r,t)=>{t.r(r),t.d(r,{GroundingDinoImageProcessor:()=>n});var i=t("./src/base/image_processors_utils.js"),l=t("./src/utils/tensor.js");class n extends i.ImageProcessor{async _call(d){const h=await super._call(d),f=h.pixel_values.dims,w=(0,l.ones)([f[0],f[2],f[3]]);return{...h,pixel_mask:w}}}},"./src/models/grounding_dino/processing_grounding_dino.js":(e,r,t)=>{t.r(r),t.d(r,{GroundingDinoProcessor:()=>h});var i=t("./src/base/processing_utils.js"),l=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js"),c=t("./src/base/image_processors_utils.js");function d(f,w){const g=f.dims.at(-1)-1,x=f.tolist();x.fill(!1,0,1),x.fill(!1,g);const M=w.tolist();return x.map((I,z)=>I?z:null).filter(I=>I!==null).map(I=>M[I])}class h extends i.Processor{async _call(w,m,g={}){const x=w?await this.image_processor(w,g):{};return{...m?this.tokenizer(m,g):{},...x}}post_process_grounded_object_detection(w,m,{box_threshold:g=.25,text_threshold:x=.25,target_sizes:M=null}={}){const{logits:I,pred_boxes:z}=w,E=I.dims[0];if(M!==null&&M.length!==E)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const C=I.dims.at(1),D=I.sigmoid(),A=D.max(-1).tolist(),$=z.tolist().map(k=>k.map(O=>(0,c.center_to_corners_format)(O))),P=[];for(let k=0;kK.map((pe,re)=>pe*O[(re+1)%2])));const R=A[k],U=[],te=[],se=[];for(let K=0;K{t.r(r),t.d(r,{Idefics3ImageProcessor:()=>n});var i=t("./src/base/image_processors_utils.js"),l=t("./src/utils/tensor.js");class n extends i.ImageProcessor{constructor(d){super(d),this.do_image_splitting=d.do_image_splitting??!0,this.max_image_size=d.max_image_size}get_resize_for_vision_encoder(d,h){let[f,w]=d.dims.slice(-2);const m=w/f;return w>=f?(w=Math.ceil(w/h)*h,f=Math.floor(w/m),f=Math.ceil(f/h)*h):(f=Math.ceil(f/h)*h,w=Math.floor(f*m),w=Math.ceil(w/h)*h),{height:f,width:w}}async _call(d,{do_image_splitting:h=null,return_row_col_info:f=!1}={}){let w;if(!Array.isArray(d))w=[[d]];else{if(d.length===0||!d[0])throw new Error("No images provided.");Array.isArray(d[0])?w=d:w=[d]}let m=[],g=[],x=[];const M=[],I=[];for(const k of w){let O=await Promise.all(k.map(te=>this.preprocess(te)));M.push(...O.map(te=>te.original_size)),I.push(...O.map(te=>te.reshaped_input_size)),O.forEach(te=>te.pixel_values.unsqueeze_(0));const{longest_edge:R}=this.max_image_size;let U;if(h??this.do_image_splitting){let te=new Array(O.length),se=new Array(O.length);U=await Promise.all(O.map(async(K,pe)=>{const re=this.get_resize_for_vision_encoder(K.pixel_values,R),oe=await(0,l.interpolate_4d)(K.pixel_values,{size:[re.height,re.width]}),{frames:ge,num_splits_h:le,num_splits_w:Se}=await this.split_image(oe,this.max_image_size);return te[pe]=le,se[pe]=Se,(0,l.cat)(ge,0)})),g.push(te),x.push(se)}else{const te=[R,R];U=await Promise.all(O.map(se=>(0,l.interpolate_4d)(se.pixel_values,{size:te}))),g.push(new Array(O.length).fill(0)),x.push(new Array(O.length).fill(0))}m.push((0,l.cat)(U,0))}const z=m.length,[E,C,D,A]=m[0].dims;let $,P;if(z===1)$=m[0].unsqueeze_(0),P=(0,l.full)([z,E,D,A],!0);else{const k=Math.max(...m.map(U=>U.dims.at(0)));P=(0,l.full)([z,k,D,A],!0);const O=P.data,R=k*D*A;for(let U=0;Uf||x>w){M=Math.ceil(g/f),I=Math.ceil(x/w);const z=Math.ceil(g/M),E=Math.ceil(x/I);for(let A=0;A{t.r(r),t.d(r,{Idefics3Processor:()=>w});var i=t("./src/base/processing_utils.js"),l=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");t("./src/utils/image.js");var c=t("./src/utils/core.js");function d(m,g,x,M,I,z){let E="";for(let C=0;C`+I.repeat(m);E+=` +`}return E+=` +${M}${z}`+I.repeat(m)+`${M}`,E}function h(m,g,x,M){return`${g}${M}`+x.repeat(m)+`${g}`}function f(m,g,x,M,I,z){return m===0&&g===0?h(x,M,I,z):d(x,m,g,M,I,z)}class w extends i.Processor{constructor(){super(...arguments);ce(this,"fake_image_token","");ce(this,"image_token","");ce(this,"global_img_token","")}async _call(x,M=null,I={}){I.return_row_col_info??(I.return_row_col_info=!0);let z;M&&(z=await this.image_processor(M,I)),Array.isArray(x)||(x=[x]);const E=z.rows??[new Array(x.length).fill(0)],C=z.cols??[new Array(x.length).fill(0)],D=this.config.image_seq_len,A=[],$=[];for(let k=0;kf(pe,U[re],D,this.fake_image_token,this.image_token,this.global_img_token)),se=O.split(this.image_token);if(se.length===0)throw new Error("The image token should be present in the text.");let K=se[0];for(let pe=0;pe{t.r(r),t.d(r,{BeitFeatureExtractor:()=>i.BeitFeatureExtractor,BitImageProcessor:()=>l.BitImageProcessor,CLIPFeatureExtractor:()=>c.CLIPFeatureExtractor,CLIPImageProcessor:()=>c.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>n.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>d.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>d.ConvNextImageProcessor,DPTFeatureExtractor:()=>m.DPTFeatureExtractor,DPTImageProcessor:()=>m.DPTImageProcessor,DeiTFeatureExtractor:()=>h.DeiTFeatureExtractor,DeiTImageProcessor:()=>h.DeiTImageProcessor,DetrFeatureExtractor:()=>f.DetrFeatureExtractor,DetrImageProcessor:()=>f.DetrImageProcessor,DonutFeatureExtractor:()=>w.DonutFeatureExtractor,DonutImageProcessor:()=>w.DonutImageProcessor,EfficientNetImageProcessor:()=>g.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>x.GLPNFeatureExtractor,GroundingDinoImageProcessor:()=>M.GroundingDinoImageProcessor,Idefics3ImageProcessor:()=>I.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>E.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>C.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>D.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>A.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>A.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>$.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>$.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>P.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>P.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>k.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>k.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>O.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>O.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>R.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>R.MobileViTImageProcessor,NougatImageProcessor:()=>U.NougatImageProcessor,OwlViTFeatureExtractor:()=>se.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>se.OwlViTImageProcessor,Owlv2ImageProcessor:()=>te.Owlv2ImageProcessor,Phi3VImageProcessor:()=>K.Phi3VImageProcessor,PvtImageProcessor:()=>pe.PvtImageProcessor,Qwen2VLImageProcessor:()=>re.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>oe.RTDetrImageProcessor,SamImageProcessor:()=>ge.SamImageProcessor,SegformerFeatureExtractor:()=>le.SegformerFeatureExtractor,SegformerImageProcessor:()=>le.SegformerImageProcessor,SiglipImageProcessor:()=>Se.SiglipImageProcessor,SmolVLMImageProcessor:()=>Ce.SmolVLMImageProcessor,Swin2SRImageProcessor:()=>q.Swin2SRImageProcessor,VLMImageProcessor:()=>z.VLMImageProcessor,ViTFeatureExtractor:()=>N.ViTFeatureExtractor,ViTImageProcessor:()=>N.ViTImageProcessor,VitMatteImageProcessor:()=>Q.VitMatteImageProcessor,VitPoseImageProcessor:()=>de.VitPoseImageProcessor,YolosFeatureExtractor:()=>Ae.YolosFeatureExtractor,YolosImageProcessor:()=>Ae.YolosImageProcessor});var 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I=(0,i.isONNXProxy)(),z=Object.fromEntries(Object.entries(M).map(([C,D])=>[C,(I?D.clone():D).ort_tensor])),E=await(x=c?x.then(()=>g.run(z)):g.run(z));return Array.isArray(m)?m.map(C=>new l.Tensor(E[C])):new l.Tensor(E[m])}};class h{static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=d([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,"y")),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=d([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=d([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=d([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=d([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=d([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=d([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=d([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}ce(h,"session_options",{})},"./src/pipelines.js":(e,r,t)=>{t.r(r),t.d(r,{AudioClassificationPipeline:()=>se,AutomaticSpeechRecognitionPipeline:()=>pe,BackgroundRemovalPipeline:()=>le,DepthEstimationPipeline:()=>Ae,DocumentQuestionAnsweringPipeline:()=>N,FeatureExtractionPipeline:()=>U,FillMaskPipeline:()=>D,ImageClassificationPipeline:()=>oe,ImageFeatureExtractionPipeline:()=>te,ImageSegmentationPipeline:()=>ge,ImageToImagePipeline:()=>de,ImageToTextPipeline:()=>re,ObjectDetectionPipeline:()=>Ce,Pipeline:()=>I,QuestionAnsweringPipeline:()=>C,SummarizationPipeline:()=>$,Text2TextGenerationPipeline:()=>A,TextClassificationPipeline:()=>z,TextGenerationPipeline:()=>O,TextToAudioPipeline:()=>Q,TokenClassificationPipeline:()=>E,TranslationPipeline:()=>P,ZeroShotAudioClassificationPipeline:()=>K,ZeroShotClassificationPipeline:()=>R,ZeroShotImageClassificationPipeline:()=>Se,ZeroShotObjectDetectionPipeline:()=>q,pipeline:()=>Ze});var i=t("./src/tokenizers.js"),l=t("./src/models.js"),n=t("./src/models/auto/processing_auto.js");t("./src/base/processing_utils.js");var c=t("./src/utils/generic.js"),d=t("./src/utils/core.js"),h=t("./src/utils/maths.js"),f=t("./src/utils/audio.js"),w=t("./src/utils/tensor.js"),m=t("./src/utils/image.js");async function g(Ee){return Array.isArray(Ee)||(Ee=[Ee]),await Promise.all(Ee.map(Z=>m.RawImage.read(Z)))}async function x(Ee,Z){return Array.isArray(Ee)||(Ee=[Ee]),await Promise.all(Ee.map(me=>typeof me=="string"||me instanceof URL?(0,f.read_audio)(me,Z):me instanceof Float64Array?new Float32Array(me):me))}function M(Ee,Z){Z&&(Ee=Ee.map(Ve=>Ve|0));const[me,X,Oe,Fe]=Ee;return{xmin:me,ymin:X,xmax:Oe,ymax:Fe}}class I extends c.Callable{constructor({task:Z,model:me,tokenizer:X=null,processor:Oe=null}){super(),this.task=Z,this.model=me,this.tokenizer=X,this.processor=Oe}async dispose(){await this.model.dispose()}}class z extends I{constructor(Z){super(Z)}async _call(Z,{top_k:me=1}={}){const X=this.tokenizer(Z,{padding:!0,truncation:!0}),Oe=await this.model(X),Fe=this.model.config.problem_type==="multi_label_classification"?Ue=>Ue.sigmoid():Ue=>new w.Tensor("float32",(0,h.softmax)(Ue.data),Ue.dims),Ve=this.model.config.id2label,Re=[];for(const Ue of Oe.logits){const Ge=Fe(Ue),Je=await(0,w.topk)(Ge,me),at=Je[0].tolist(),Ke=Je[1].tolist().map((st,ft)=>({label:Ve?Ve[st]:`LABEL_${st}`,score:at[ft]}));me===1?Re.push(...Ke):Re.push(Ke)}return Array.isArray(Z)||me===1?Re:Re[0]}}class E extends I{constructor(Z){super(Z)}async _call(Z,{ignore_labels:me=["O"]}={}){const X=Array.isArray(Z),Oe=this.tokenizer(X?Z:[Z],{padding:!0,truncation:!0}),Ve=(await this.model(Oe)).logits,Re=this.model.config.id2label,Ue=[];for(let Ge=0;GeHe==this.tokenizer.sep_token_id);Ue[at].map((He,xt)=>He==1&&(xt===0||xt>Ke&&Ge.findIndex(Vt=>Vt==ot[xt])===-1));const st=Fe[at].tolist(),ft=Ve[at].tolist();for(let He=1;Hext==ot[He])!==-1)&&(st[He]=-1/0,ft[He]=-1/0);const wt=(0,h.softmax)(st).map((He,xt)=>[He,xt]),St=(0,h.softmax)(ft).map((He,xt)=>[He,xt]);wt[0][0]=0,St[0][0]=0;const vt=(0,d.product)(wt,St).filter(He=>He[0][1]<=He[1][1]).map(He=>[He[0][1],He[1][1],He[0][0]*He[1][0]]).sort((He,xt)=>xt[2]-He[2]);for(let He=0;Hest==this.tokenizer.mask_token_id);if(Ge===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const Je=Oe[Re][Ge],at=await(0,w.topk)(new w.Tensor("float32",(0,h.softmax)(Je.data),Je.dims),me),ot=at[0].tolist(),Ke=at[1].tolist();Fe.push(Ke.map((st,ft)=>{const wt=Ue.slice();return wt[Ge]=st,{score:ot[ft],token:Number(st),token_str:this.tokenizer.decode([st]),sequence:this.tokenizer.decode(wt,{skip_special_tokens:!0})}}))}return Array.isArray(Z)?Fe:Fe[0]}}class A extends I{constructor(me){super(me);ce(this,"_key","generated_text")}async _call(me,X={}){Array.isArray(me)||(me=[me]),this.model.config.prefix&&(me=me.map(Ge=>this.model.config.prefix+Ge));const Oe=this.model.config.task_specific_params;Oe&&Oe[this.task]&&Oe[this.task].prefix&&(me=me.map(Ge=>Oe[this.task].prefix+Ge));const Fe=this.tokenizer,Ve={padding:!0,truncation:!0};let Re;this instanceof P&&"_build_translation_inputs"in Fe?Re=Fe._build_translation_inputs(me,Ve,X):Re=Fe(me,Ve);const Ue=await this.model.generate({...Re,...X});return Fe.batch_decode(Ue,{skip_special_tokens:!0}).map(Ge=>({[this._key]:Ge}))}}class $ extends A{constructor(me){super(me);ce(this,"_key","summary_text")}}class P extends A{constructor(me){super(me);ce(this,"_key","translation_text")}}function k(Ee){return Array.isArray(Ee)&&Ee.every(Z=>"role"in Z&&"content"in Z)}class O extends I{constructor(Z){super(Z)}async _call(Z,me={}){let X=!1,Oe=!1,Fe;if(typeof Z=="string")Fe=Z=[Z];else if(Array.isArray(Z)&&Z.every(Ke=>typeof Ke=="string"))X=!0,Fe=Z;else{if(k(Z))Z=[Z];else if(Array.isArray(Z)&&Z.every(k))X=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");Oe=!0,Fe=Z.map(Ke=>this.tokenizer.apply_chat_template(Ke,{tokenize:!1,add_generation_prompt:!0}))}const Ve=me.add_special_tokens??!1,Re=Oe?!1:me.return_full_text??!0;this.tokenizer.padding_side="left";const Ue=this.tokenizer(Fe,{add_special_tokens:Ve,padding:!0,truncation:!0}),Ge=await this.model.generate({...Ue,...me}),Je=this.tokenizer.batch_decode(Ge,{skip_special_tokens:!0});let at;!Re&&Ue.input_ids.dims.at(-1)>0&&(at=this.tokenizer.batch_decode(Ue.input_ids,{skip_special_tokens:!0}).map(Ke=>Ke.length));const ot=Array.from({length:Z.length},Ke=>[]);for(let Ke=0;Ke[me.toLowerCase(),X])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(Z,me,{hypothesis_template:X="This example is {}.",multi_label:Oe=!1}={}){const Fe=Array.isArray(Z);Fe||(Z=[Z]),Array.isArray(me)||(me=[me]);const Ve=me.map(Ge=>X.replace("{}",Ge)),Re=Oe||me.length===1,Ue=[];for(const Ge of Z){const Je=[];for(const Ke of Ve){const st=this.tokenizer(Ge,{text_pair:Ke,padding:!0,truncation:!0}),ft=await this.model(st);Re?Je.push([ft.logits.data[this.contradiction_id],ft.logits.data[this.entailment_id]]):Je.push(ft.logits.data[this.entailment_id])}const ot=(Re?Je.map(Ke=>(0,h.softmax)(Ke)[1]):(0,h.softmax)(Je)).map((Ke,st)=>[Ke,st]).sort((Ke,st)=>st[0]-Ke[0]);Ue.push({sequence:Ge,labels:ot.map(Ke=>me[Ke[1]]),scores:ot.map(Ke=>Ke[0])})}return Fe?Ue:Ue[0]}}class U extends I{constructor(Z){super(Z)}async _call(Z,{pooling:me="none",normalize:X=!1,quantize:Oe=!1,precision:Fe="binary"}={}){const Ve=this.tokenizer(Z,{padding:!0,truncation:!0}),Re=await this.model(Ve);let Ue=Re.last_hidden_state??Re.logits??Re.token_embeddings;if(me!=="none")if(me==="mean")Ue=(0,w.mean_pooling)(Ue,Ve.attention_mask);else if(me==="cls")Ue=Ue.slice(null,0);else throw Error(`Pooling method '${me}' not supported.`);return X&&(Ue=Ue.normalize(2,-1)),Oe&&(Ue=(0,w.quantize_embeddings)(Ue,Fe)),Ue}}class te extends I{constructor(Z){super(Z)}async _call(Z,{pool:me=null}={}){const X=await g(Z),{pixel_values:Oe}=await this.processor(X),Fe=await this.model({pixel_values:Oe});let Ve;if(me){if(!("pooler_output"in Fe))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Ve=Fe.pooler_output}else Ve=Fe.last_hidden_state??Fe.logits??Fe.image_embeds;return Ve}}class se extends I{constructor(Z){super(Z)}async _call(Z,{top_k:me=5}={}){const X=this.processor.feature_extractor.config.sampling_rate,Oe=await x(Z,X),Fe=this.model.config.id2label,Ve=[];for(const Re of Oe){const Ue=await this.processor(Re),Je=(await this.model(Ue)).logits[0],at=await(0,w.topk)(new w.Tensor("float32",(0,h.softmax)(Je.data),Je.dims),me),ot=at[0].tolist(),st=at[1].tolist().map((ft,wt)=>({label:Fe?Fe[ft]:`LABEL_${ft}`,score:ot[wt]}));Ve.push(st)}return Array.isArray(Z)?Ve:Ve[0]}}class K extends I{constructor(Z){super(Z)}async _call(Z,me,{hypothesis_template:X="This is a sound of {}."}={}){const Oe=!Array.isArray(Z);Oe&&(Z=[Z]);const Fe=me.map(Je=>X.replace("{}",Je)),Ve=this.tokenizer(Fe,{padding:!0,truncation:!0}),Re=this.processor.feature_extractor.config.sampling_rate,Ue=await x(Z,Re),Ge=[];for(const Je of Ue){const at=await this.processor(Je),ot=await this.model({...Ve,...at}),Ke=(0,h.softmax)(ot.logits_per_audio.data);Ge.push([...Ke].map((st,ft)=>({score:st,label:me[ft]})))}return Oe?Ge[0]:Ge}}class pe extends I{constructor(Z){super(Z)}async _call(Z,me={}){switch(this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(Z,me);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(Z,me);case"moonshine":return this._call_moonshine(Z,me);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(Z,me){me.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),me.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const X=!Array.isArray(Z);X&&(Z=[Z]);const Oe=this.processor.feature_extractor.config.sampling_rate,Fe=await x(Z,Oe),Ve=[];for(const Re of Fe){const Ue=await this.processor(Re),Je=(await this.model(Ue)).logits[0],at=[];for(const Ke of Je)at.push((0,h.max)(Ke.data)[1]);const ot=this.tokenizer.decode(at);Ve.push({text:ot})}return X?Ve[0]:Ve}async _call_whisper(Z,me){const X=me.return_timestamps??!1,Oe=me.chunk_length_s??0,Fe=me.force_full_sequences??!1;let Ve=me.stride_length_s??null;const Re={...me};X==="word"&&(Re.return_token_timestamps=!0,Re.return_timestamps=!1);const Ue=!Array.isArray(Z);Ue&&(Z=[Z]);const Ge=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,Je=this.processor.feature_extractor.config.hop_length,at=this.processor.feature_extractor.config.sampling_rate,ot=await x(Z,at),Ke=[];for(const st of ot){let ft=[];if(Oe>0){if(Ve===null)Ve=Oe/6;else if(Oe<=Ve)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const vt=at*Oe,He=at*Ve,xt=vt-2*He;let Vt=0;for(;;){const he=Vt+vt,ue=st.subarray(Vt,he),Cr=await this.processor(ue),er=Vt===0,ht=he>=st.length;if(ft.push({stride:[ue.length,er?0:He,ht?0:He],input_features:Cr.input_features,is_last:ht}),ht)break;Vt+=xt}}else ft=[{stride:[st.length,0,0],input_features:(await this.processor(st)).input_features,is_last:!0}];for(const vt of ft){Re.num_frames=Math.floor(vt.stride[0]/Je);const He=await this.model.generate({inputs:vt.input_features,...Re});X==="word"?(vt.tokens=He.sequences.tolist()[0],vt.token_timestamps=He.token_timestamps.tolist()[0].map(xt=>(0,h.round)(xt,2))):vt.tokens=He[0].tolist(),vt.stride=vt.stride.map(xt=>xt/at)}const[wt,St]=this.tokenizer._decode_asr(ft,{time_precision:Ge,return_timestamps:X,force_full_sequences:Fe});Ke.push({text:wt,...St})}return Ue?Ke[0]:Ke}async _call_moonshine(Z,me){const X=!Array.isArray(Z);X&&(Z=[Z]);const Oe=this.processor.feature_extractor.config.sampling_rate,Fe=await x(Z,Oe),Ve=[];for(const Re of Fe){const Ue=await this.processor(Re),Ge=Math.floor(Re.length/Oe)*6,Je=await this.model.generate({max_new_tokens:Ge,...me,...Ue}),at=this.processor.batch_decode(Je,{skip_special_tokens:!0})[0];Ve.push({text:at})}return X?Ve[0]:Ve}}class re extends I{constructor(Z){super(Z)}async _call(Z,me={}){const X=Array.isArray(Z),Oe=await g(Z),{pixel_values:Fe}=await this.processor(Oe),Ve=[];for(const Re of Fe){Re.dims=[1,...Re.dims];const Ue=await this.model.generate({inputs:Re,...me}),Ge=this.tokenizer.batch_decode(Ue,{skip_special_tokens:!0}).map(Je=>({generated_text:Je.trim()}));Ve.push(Ge)}return X?Ve:Ve[0]}}class oe extends I{constructor(Z){super(Z)}async _call(Z,{top_k:me=5}={}){const X=await g(Z),{pixel_values:Oe}=await this.processor(X),Fe=await this.model({pixel_values:Oe}),Ve=this.model.config.id2label,Re=[];for(const Ue of Fe.logits){const Ge=await(0,w.topk)(new w.Tensor("float32",(0,h.softmax)(Ue.data),Ue.dims),me),Je=Ge[0].tolist(),ot=Ge[1].tolist().map((Ke,st)=>({label:Ve?Ve[Ke]:`LABEL_${Ke}`,score:Je[st]}));Re.push(ot)}return Array.isArray(Z)?Re:Re[0]}}class ge extends I{constructor(Z){super(Z),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(Z,{threshold:me=.5,mask_threshold:X=.5,overlap_mask_area_threshold:Oe=.8,label_ids_to_fuse:Fe=null,target_sizes:Ve=null,subtask:Re=null}={}){if(Array.isArray(Z)&&Z.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const Ge=await g(Z),Je=Ge.map(vt=>[vt.height,vt.width]),at=await this.processor(Ge),{inputNames:ot,outputNames:Ke}=this.model.sessions.model;if(!ot.includes("pixel_values")){if(ot.length!==1)throw Error(`Expected a single input name, but got ${ot.length} inputs: ${ot}.`);const vt=ot[0];if(vt in at)throw Error(`Input name ${vt} already exists in the inputs.`);at[vt]=at.pixel_values}const st=await this.model(at);let ft=null;if(Re!==null)ft=this.subtasks_mapping[Re];else if(this.processor.image_processor){for(const[vt,He]of Object.entries(this.subtasks_mapping))if(He in this.processor.image_processor){ft=this.processor.image_processor[He].bind(this.processor.image_processor),Re=vt;break}}const wt=this.model.config.id2label,St=[];if(Re)if(Re==="panoptic"||Re==="instance"){const vt=ft(st,me,X,Oe,Fe,Ve??Je)[0],He=vt.segmentation;for(const xt of vt.segments_info){const Vt=new Uint8ClampedArray(He.data.length);for(let ue=0;ueCr<-1e-5||Cr>1+1e-5)&&he.sigmoid_();const ue=await m.RawImage.fromTensor(he.mul_(255).to("uint8")).resize(Vt[1],Vt[0]);St.push({label:null,score:null,mask:ue})}}return St}}class le extends ge{constructor(Z){super(Z)}async _call(Z,me={}){if(Array.isArray(Z)&&Z.length!==1)throw Error("Background removal pipeline currently only supports a batch size of 1.");const Oe=await g(Z),Fe=await super._call(Z,me);return Oe.map((Re,Ue)=>{const Ge=Re.clone();return Ge.putAlpha(Fe[Ue].mask),Ge})}}class Se extends I{constructor(Z){super(Z)}async _call(Z,me,{hypothesis_template:X="This is a photo of {}"}={}){const Oe=Array.isArray(Z),Fe=await g(Z),Ve=me.map(ot=>X.replace("{}",ot)),Re=this.tokenizer(Ve,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:Ue}=await this.processor(Fe),Ge=await this.model({...Re,pixel_values:Ue}),Je=this.model.config.model_type==="siglip"?ot=>ot.sigmoid().data:ot=>(0,h.softmax)(ot.data),at=[];for(const ot of Ge.logits_per_image){const st=[...Je(ot)].map((ft,wt)=>({score:ft,label:me[wt]}));st.sort((ft,wt)=>wt.score-ft.score),at.push(st)}return Oe?at:at[0]}}class Ce extends I{constructor(Z){super(Z)}async _call(Z,{threshold:me=.9,percentage:X=!1}={}){const Oe=Array.isArray(Z);if(Oe&&Z.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Fe=await g(Z),Ve=X?null:Fe.map(Ke=>[Ke.height,Ke.width]),{pixel_values:Re,pixel_mask:Ue}=await this.processor(Fe),Ge=await this.model({pixel_values:Re,pixel_mask:Ue}),Je=this.processor.image_processor.post_process_object_detection(Ge,me,Ve),at=this.model.config.id2label,ot=Je.map(Ke=>Ke.boxes.map((st,ft)=>({score:Ke.scores[ft],label:at[Ke.classes[ft]],box:M(st,!X)})));return Oe?ot:ot[0]}}class q extends I{constructor(Z){super(Z)}async _call(Z,me,{threshold:X=.1,top_k:Oe=null,percentage:Fe=!1}={}){const Ve=Array.isArray(Z),Re=await g(Z),Ue=this.tokenizer(me,{padding:!0,truncation:!0}),Ge=await this.processor(Re),Je=[];for(let at=0;at({score:St.scores[He],label:St.labels[He],box:M(vt,!Fe)}))}else{const St=this.processor.image_processor.post_process_object_detection(ft,X,Ke,!0)[0];wt=St.boxes.map((vt,He)=>({score:St.scores[He],label:me[St.classes[He]],box:M(vt,!Fe)}))}wt.sort((St,vt)=>vt.score-St.score),Oe!==null&&(wt=wt.slice(0,Oe)),Je.push(wt)}return Ve?Je:Je[0]}}class N extends I{constructor(Z){super(Z)}async _call(Z,me,X={}){const Oe=(await g(Z))[0],{pixel_values:Fe}=await this.processor(Oe),Ve=`${me}`,Re=this.tokenizer(Ve,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,Ue=await this.model.generate({inputs:Fe,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Re,...X}),Je=this.tokenizer.batch_decode(Ue)[0].match(/(.*?)<\/s_answer>/);let at=null;return Je&&Je.length>=2&&(at=Je[1].trim()),[{answer:at}]}}class Q extends I{constructor(me){super(me);ce(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=me.vocoder??null}async _call(me,{speaker_embeddings:X=null}={}){return this.processor?this._call_text_to_spectrogram(me,{speaker_embeddings:X}):this._call_text_to_waveform(me)}async _call_text_to_waveform(me){const X=this.tokenizer(me,{padding:!0,truncation:!0}),{waveform:Oe}=await this.model(X),Fe=this.model.config.sampling_rate;return new f.RawAudio(Oe.data,Fe)}async _call_text_to_spectrogram(me,{speaker_embeddings:X}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await l.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof X=="string"||X instanceof URL)&&(X=new Float32Array(await(await fetch(X)).arrayBuffer())),X instanceof Float32Array)X=new w.Tensor("float32",X,[1,X.length]);else if(!(X instanceof w.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:Oe}=this.tokenizer(me,{padding:!0,truncation:!0}),{waveform:Fe}=await this.model.generate_speech(Oe,X,{vocoder:this.vocoder}),Ve=this.processor.feature_extractor.config.sampling_rate;return new f.RawAudio(Fe.data,Ve)}}class de extends I{constructor(Z){super(Z)}async _call(Z){const me=await g(Z),X=await this.processor(me),Oe=await this.model(X),Fe=[];for(const Ve of Oe.reconstruction){const Re=Ve.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Fe.push(m.RawImage.fromTensor(Re))}return Fe.length>1?Fe:Fe[0]}}class Ae extends I{constructor(Z){super(Z)}async _call(Z){const me=await g(Z),X=await this.processor(me),{predicted_depth:Oe}=await this.model(X),Fe=[];for(let Ve=0;Ve1?Fe:Fe[0]}}const ye=Object.freeze({"text-classification":{tokenizer:i.AutoTokenizer,pipeline:z,model:l.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:i.AutoTokenizer,pipeline:E,model:l.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:i.AutoTokenizer,pipeline:C,model:l.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:i.AutoTokenizer,pipeline:D,model:l.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:i.AutoTokenizer,pipeline:$,model:l.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:i.AutoTokenizer,pipeline:P,model:l.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:i.AutoTokenizer,pipeline:A,model:l.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:i.AutoTokenizer,pipeline:O,model:l.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:i.AutoTokenizer,pipeline:R,model:l.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:se,model:l.AutoModelForAudioClassification,processor:n.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:i.AutoTokenizer,pipeline:K,model:l.AutoModel,processor:n.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:i.AutoTokenizer,pipeline:pe,model:[l.AutoModelForSpeechSeq2Seq,l.AutoModelForCTC],processor:n.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:i.AutoTokenizer,pipeline:Q,model:[l.AutoModelForTextToWaveform,l.AutoModelForTextToSpectrogram],processor:[n.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:i.AutoTokenizer,pipeline:re,model:l.AutoModelForVision2Seq,processor:n.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:oe,model:l.AutoModelForImageClassification,processor:n.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ge,model:[l.AutoModelForImageSegmentation,l.AutoModelForSemanticSegmentation,l.AutoModelForUniversalSegmentation],processor:n.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"background-removal":{pipeline:le,model:[l.AutoModelForImageSegmentation,l.AutoModelForSemanticSegmentation,l.AutoModelForUniversalSegmentation],processor:n.AutoProcessor,default:{model:"Xenova/modnet"},type:"image"},"zero-shot-image-classification":{tokenizer:i.AutoTokenizer,pipeline:Se,model:l.AutoModel,processor:n.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:Ce,model:l.AutoModelForObjectDetection,processor:n.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:i.AutoTokenizer,pipeline:q,model:l.AutoModelForZeroShotObjectDetection,processor:n.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:i.AutoTokenizer,pipeline:N,model:l.AutoModelForDocumentQuestionAnswering,processor:n.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:de,model:l.AutoModelForImageToImage,processor:n.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Ae,model:l.AutoModelForDepthEstimation,processor:n.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:i.AutoTokenizer,pipeline:U,model:l.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:n.AutoProcessor,pipeline:te,model:[l.AutoModelForImageFeatureExtraction,l.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),ae=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function Ze(Ee,Z=null,{progress_callback:me=null,config:X=null,cache_dir:Oe=null,local_files_only:Fe=!1,revision:Ve="main",device:Re=null,dtype:Ue=null,subfolder:Ge="onnx",use_external_data_format:Je=null,model_file_name:at=null,session_options:ot={}}={}){Ee=ae[Ee]??Ee;const Ke=ye[Ee.split("_",1)[0]];if(!Ke)throw Error(`Unsupported pipeline: ${Ee}. Must be one of [${Object.keys(ye)}]`);Z||(Z=Ke.default.model,console.log(`No model specified. Using default model: "${Z}".`));const st={progress_callback:me,config:X,cache_dir:Oe,local_files_only:Fe,revision:Ve,device:Re,dtype:Ue,subfolder:Ge,use_external_data_format:Je,model_file_name:at,session_options:ot},ft=new Map([["tokenizer",Ke.tokenizer],["model",Ke.model],["processor",Ke.processor]]),wt=await et(ft,Z,st);wt.task=Ee,(0,d.dispatchCallback)(me,{status:"ready",task:Ee,model:Z});const St=Ke.pipeline;return new St(wt)}async function et(Ee,Z,me){const X=Object.create(null),Oe=[];for(const[Fe,Ve]of Ee.entries()){if(!Ve)continue;let Re;Array.isArray(Ve)?Re=new Promise(async(Ue,Ge)=>{var at,ot;let Je;for(const Ke of Ve){if(Ke===null){Ue(null);return}try{Ue(await Ke.from_pretrained(Z,me));return}catch(st){if((at=st.message)!=null&&at.includes("Unsupported model type"))Je=st;else if((ot=st.message)!=null&&ot.includes("Could not locate file"))Je=st;else{Ge(st);return}}}Ge(Je)}):Re=Ve.from_pretrained(Z,me),X[Fe]=Re,Oe.push(Re)}await Promise.all(Oe);for(const[Fe,Ve]of Object.entries(X))X[Fe]=await Ve;return X}},"./src/tokenizers.js":(e,r,t)=>{t.r(r),t.d(r,{AlbertTokenizer:()=>Qt,AutoTokenizer:()=>Qs,BartTokenizer:()=>rn,BertTokenizer:()=>Ft,BlenderbotSmallTokenizer:()=>Hr,BlenderbotTokenizer:()=>Dn,BloomTokenizer:()=>Hs,CLIPTokenizer:()=>gs,CamembertTokenizer:()=>Ts,CodeGenTokenizer:()=>Is,CodeLlamaTokenizer:()=>Es,CohereTokenizer:()=>Jr,ConvBertTokenizer:()=>Fr,DebertaTokenizer:()=>mt,DebertaV2Tokenizer:()=>tr,DistilBertTokenizer:()=>Kr,ElectraTokenizer:()=>Cs,EsmTokenizer:()=>bt,FalconTokenizer:()=>qe,GPT2Tokenizer:()=>rs,GPTNeoXTokenizer:()=>ct,GemmaTokenizer:()=>ls,Grok1Tokenizer:()=>Ps,HerbertTokenizer:()=>rr,LlamaTokenizer:()=>Jt,M2M100Tokenizer:()=>ks,MBart50Tokenizer:()=>Gs,MBartTokenizer:()=>os,MPNetTokenizer:()=>Er,MarianTokenizer:()=>wn,MgpstrTokenizer:()=>yn,MobileBertTokenizer:()=>yt,NllbTokenizer:()=>Ss,NougatTokenizer:()=>sn,PreTrainedTokenizer:()=>Le,Qwen2Tokenizer:()=>ur,RoFormerTokenizer:()=>pr,RobertaTokenizer:()=>Ks,SiglipTokenizer:()=>gn,SpeechT5Tokenizer:()=>ws,SqueezeBertTokenizer:()=>Nt,T5Tokenizer:()=>tn,TokenizerModel:()=>te,VitsTokenizer:()=>us,Wav2Vec2CTCTokenizer:()=>Fn,WhisperTokenizer:()=>$s,XLMRobertaTokenizer:()=>qs,XLMTokenizer:()=>Us,is_chinese_char:()=>D});var i=t("./src/utils/generic.js"),l=t("./src/utils/core.js"),n=t("./src/utils/hub.js"),c=t("./src/utils/maths.js"),d=t("./src/utils/tensor.js"),h=t("./src/utils/data-structures.js"),f=t("./node_modules/@huggingface/jinja/dist/index.js"),w=t("./src/models/whisper/common_whisper.js");async function m(Te,L){const J=await Promise.all([(0,n.getModelJSON)(Te,"tokenizer.json",!0,L),(0,n.getModelJSON)(Te,"tokenizer_config.json",!0,L)]);return L.legacy!==null&&(J[1].legacy=L.legacy),J}function g(Te,L){const J=[];let ie=0;for(const ve of Te.matchAll(L)){const be=ve[0];ie0&&J.push(be),ie=ve.index+be.length}return ie=19968&&Te<=40959||Te>=13312&&Te<=19903||Te>=131072&&Te<=173791||Te>=173824&&Te<=177983||Te>=177984&&Te<=178207||Te>=178208&&Te<=183983||Te>=63744&&Te<=64255||Te>=194560&&Te<=195103}function A(Te,L,J){const ie=[];let ve=0;for(;vethis.tokens_to_ids.get(J)??this.unk_token_id)}convert_ids_to_tokens(L){return L.map(J=>this.vocab[J]??this.unk_token)}}class se extends te{constructor(L){super(L),this.tokens_to_ids=M(L.vocab),this.unk_token_id=this.tokens_to_ids.get(L.unk_token),this.unk_token=L.unk_token,this.max_input_chars_per_word=L.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[J,ie]of this.tokens_to_ids)this.vocab[ie]=J}encode(L){const J=[];for(const ie of L){const ve=[...ie];if(ve.length>this.max_input_chars_per_word){J.push(this.unk_token);continue}let be=!1,ze=0;const tt=[];for(;ze0&&(dt=this.config.continuing_subword_prefix+dt),this.tokens_to_ids.has(dt)){it=dt;break}--ut}if(it===null){be=!0;break}tt.push(it),ze=ut}be?J.push(this.unk_token):J.push(...tt)}return J}}class K extends te{constructor(L,J){super(L);const ie=L.vocab.length;this.vocab=new Array(ie),this.scores=new Array(ie);for(let ve=0;ve[ve,be])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=J.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,c.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new h.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(L){const J=L.chars,ie=1;let ve=0;for(;ve{const Te=[...Array.from({length:94},(ve,be)=>be+33),...Array.from({length:12},(ve,be)=>be+161),...Array.from({length:82},(ve,be)=>be+174)],L=Te.slice();let J=0;for(let ve=0;ve<256;++ve)Te.includes(ve)||(Te.push(ve),L.push(256+J),J+=1);const ie=L.map(ve=>String.fromCharCode(ve));return Object.fromEntries(Te.map((ve,be)=>[ve,ie[be]]))})(),re=(0,l.reverseDictionary)(pe);class oe extends te{constructor(L){super(L),this.tokens_to_ids=M(L.vocab),this.unk_token_id=this.tokens_to_ids.get(L.unk_token),this.unk_token=L.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[ie,ve]of this.tokens_to_ids)this.vocab[ve]=ie;const J=Array.isArray(L.merges[0]);this.merges=J?L.merges:L.merges.map(ie=>ie.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((ie,ve)=>[JSON.stringify(ie),ve])),this.end_of_word_suffix=L.end_of_word_suffix,this.continuing_subword_suffix=L.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.max_length_to_cache=256,this.cache_capacity=1e4,this.cache=new h.LRUCache(this.cache_capacity)}clear_cache(){this.cache.clear()}bpe(L){if(L.length===0)return[];const J=this.cache.get(L);if(J!==void 0)return J;const ie=Array.from(L);this.end_of_word_suffix&&(ie[ie.length-1]+=this.end_of_word_suffix);let ve=[];if(ie.length>1){const be=new h.PriorityQueue((ut,it)=>ut.score`<0x${tt.toString(16).toUpperCase().padStart(2,"0")}>`);ze.every(tt=>this.tokens_to_ids.has(tt))?J.push(...ze):J.push(this.unk_token)}else J.push(this.unk_token)}return J}}class ge extends te{constructor(L,J){super(L),this.tokens_to_ids=M(J.target_lang?L.vocab[J.target_lang]:L.vocab),this.bos_token=J.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=J.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=J.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=J.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[ie,ve]of this.tokens_to_ids)this.vocab[ve]=ie}encode(L){return L}}class le extends i.Callable{constructor(L){super(),this.config=L}static fromConfig(L){if(L===null)return null;switch(L.type){case"BertNormalizer":return new Ee(L);case"Precompiled":return new ht(L);case"Sequence":return new et(L);case"Replace":return new Se(L);case"NFC":return new q(L);case"NFD":return new N(L);case"NFKC":return new Q(L);case"NFKD":return new de(L);case"Strip":return new Ae(L);case"StripAccents":return new ye(L);case"Lowercase":return new ae(L);case"Prepend":return new Ze(L);default:throw new Error(`Unknown Normalizer type: ${L.type}`)}}normalize(L){throw Error("normalize should be implemented in subclass.")}_call(L){return this.normalize(L)}}class Se extends le{normalize(L){const J=x(this.config.pattern);return J===null?L:L.replaceAll(J,this.config.content)}}class Ce extends le{constructor(){super(...arguments);ce(this,"form")}normalize(J){return J=J.normalize(this.form),J}}class q extends Ce{constructor(){super(...arguments);ce(this,"form","NFC")}}class N extends Ce{constructor(){super(...arguments);ce(this,"form","NFD")}}class Q extends Ce{constructor(){super(...arguments);ce(this,"form","NFKC")}}class de extends Ce{constructor(){super(...arguments);ce(this,"form","NFKD")}}class Ae extends le{normalize(L){return this.config.strip_left&&this.config.strip_right?L=L.trim():(this.config.strip_left&&(L=L.trimStart()),this.config.strip_right&&(L=L.trimEnd())),L}}class ye extends le{normalize(L){return L=E(L),L}}class ae extends le{normalize(L){return L=L.toLowerCase(),L}}class Ze extends le{normalize(L){return L=this.config.prepend+L,L}}class et extends le{constructor(L){super(L),this.normalizers=L.normalizers.map(J=>le.fromConfig(J))}normalize(L){return this.normalizers.reduce((J,ie)=>ie.normalize(J),L)}}class Ee extends le{_tokenize_chinese_chars(L){const J=[];for(let ie=0;iethis.pre_tokenize_text(ie,J)):this.pre_tokenize_text(L,J)).flat()}_call(L,J){return this.pre_tokenize(L,J)}}class me extends Z{constructor(L){super(),this.pattern=new RegExp(`[^\\s${P}]+|[${P}]`,"gu")}pre_tokenize_text(L,J){return L.trim().match(this.pattern)||[]}}class X extends Z{constructor(L){super(),this.config=L,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=pe,this.text_encoder=new TextEncoder}pre_tokenize_text(L,J){return this.add_prefix_space&&!L.startsWith(" ")&&(L=" "+L),(this.use_regex?L.match(this.pattern)||[]:[L]).map(ve=>Array.from(this.text_encoder.encode(ve),be=>this.byte_encoder[be]).join(""))}}class Oe extends Z{constructor(L){super(),this.config=L,this.pattern=x(this.config.pattern,this.config.invert)}pre_tokenize_text(L,J){var ie;return this.pattern===null?[]:this.config.invert?L.match(this.pattern)||[]:((ie=this.config.behavior)==null?void 0:ie.toLowerCase())==="removed"?L.split(this.pattern).filter(ve=>ve):g(L,this.pattern)}}class Fe extends Z{constructor(L){super(),this.config=L,this.pattern=new RegExp(`[^${P}]+|[${P}]+`,"gu")}pre_tokenize_text(L,J){return L.match(this.pattern)||[]}}class Ve extends Z{constructor(L){super(),this.config=L;const J=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(J,"gu")}pre_tokenize_text(L,J){return L.match(this.pattern)||[]}}class Re extends i.Callable{constructor(L){super(),this.config=L}static fromConfig(L){if(L===null)return null;switch(L.type){case"TemplateProcessing":return new Je(L);case"ByteLevel":return new at(L);case"RobertaProcessing":return new Ge(L);case"BertProcessing":return new Ue(L);case"Sequence":return new ot(L);default:throw new Error(`Unknown PostProcessor type: ${L.type}`)}}post_process(L,...J){throw Error("post_process should be implemented in subclass.")}_call(L,...J){return this.post_process(L,...J)}}class Ue extends Re{constructor(L){super(L),this.cls=L.cls[0],this.sep=L.sep[0]}post_process(L,J=null,{add_special_tokens:ie=!0}={}){ie&&(L=(0,l.mergeArrays)([this.cls],L,[this.sep]));let ve=new Array(L.length).fill(0);if(J!==null){const be=ie&&this instanceof Ge?[this.sep]:[],ze=ie?[this.sep]:[];L=(0,l.mergeArrays)(L,be,J,ze),ve=(0,l.mergeArrays)(ve,new Array(J.length+be.length+ze.length).fill(1))}return{tokens:L,token_type_ids:ve}}}class Ge extends Ue{}class Je extends Re{constructor(L){super(L),this.single=L.single,this.pair=L.pair}post_process(L,J=null,{add_special_tokens:ie=!0}={}){const ve=J===null?this.single:this.pair;let be=[],ze=[];for(const tt of ve)"SpecialToken"in tt?ie&&(be.push(tt.SpecialToken.id),ze.push(tt.SpecialToken.type_id)):"Sequence"in tt&&(tt.Sequence.id==="A"?(be=(0,l.mergeArrays)(be,L),ze=(0,l.mergeArrays)(ze,new Array(L.length).fill(tt.Sequence.type_id))):tt.Sequence.id==="B"&&(be=(0,l.mergeArrays)(be,J),ze=(0,l.mergeArrays)(ze,new Array(J.length).fill(tt.Sequence.type_id))));return{tokens:be,token_type_ids:ze}}}class at extends Re{post_process(L,J=null){return J&&(L=(0,l.mergeArrays)(L,J)),{tokens:L}}}class ot extends Re{constructor(L){super(L),this.processors=L.processors.map(J=>Re.fromConfig(J))}post_process(L,J=null,ie={}){let ve;for(const be of this.processors)if(be instanceof at)L=be.post_process(L).tokens,J&&(J=be.post_process(J).tokens);else{const ze=be.post_process(L,J,ie);L=ze.tokens,ve=ze.token_type_ids}return{tokens:L,token_type_ids:ve}}}class Ke extends i.Callable{constructor(L){super(),this.config=L,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=L.trim_offsets}static fromConfig(L){if(L===null)return null;switch(L.type){case"WordPiece":return new vt(L);case"Metaspace":return new er(L);case"ByteLevel":return new He(L);case"Replace":return new st(L);case"ByteFallback":return new ft(L);case"Fuse":return new wt(L);case"Strip":return new St(L);case"Sequence":return new Vt(L);case"CTC":return new xt(L);case"BPEDecoder":return new he(L);default:throw new Error(`Unknown Decoder type: ${L.type}`)}}_call(L){return this.decode(L)}decode(L){return this.decode_chain(L).join("")}decode_chain(L){throw Error("`decode_chain` should be implemented in subclass.")}}class st extends Ke{decode_chain(L){const J=x(this.config.pattern);return J===null?L:L.map(ie=>ie.replaceAll(J,this.config.content))}}class ft extends Ke{constructor(L){super(L),this.text_decoder=new TextDecoder}decode_chain(L){const J=[];let ie=[];for(const ve of L){let be=null;if(ve.length===6&&ve.startsWith("<0x")&&ve.endsWith(">")){const ze=parseInt(ve.slice(3,5),16);isNaN(ze)||(be=ze)}if(be!==null)ie.push(be);else{if(ie.length>0){const ze=this.text_decoder.decode(Uint8Array.from(ie));J.push(ze),ie=[]}J.push(ve)}}if(ie.length>0){const ve=this.text_decoder.decode(Uint8Array.from(ie));J.push(ve),ie=[]}return J}}class wt extends Ke{decode_chain(L){return[L.join("")]}}class St extends Ke{constructor(L){super(L),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(L){return L.map(J=>{let ie=0;for(let be=0;be(ie!==0&&(J.startsWith(this.config.prefix)?J=J.replace(this.config.prefix,""):J=" "+J),this.cleanup&&(J=z(J)),J))}}class He extends Ke{constructor(L){super(L),this.byte_decoder=re,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(L){const J=L.join(""),ie=new Uint8Array([...J].map(be=>this.byte_decoder[be]));return this.text_decoder.decode(ie)}decode_chain(L){const J=[];let ie=[];for(const ve of L)this.added_tokens.find(be=>be.content===ve)!==void 0?(ie.length>0&&(J.push(this.convert_tokens_to_string(ie)),ie=[]),J.push(ve)):ie.push(ve);return ie.length>0&&J.push(this.convert_tokens_to_string(ie)),J}}class xt extends Ke{constructor(L){super(L),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(L){if(L.length===0)return"";const J=[L[0]];for(let be=1;bebe!==this.pad_token).join("");return this.cleanup&&(ve=z(ve).replaceAll(this.word_delimiter_token," ").trim()),ve}decode_chain(L){return[this.convert_tokens_to_string(L)]}}class Vt extends Ke{constructor(L){super(L),this.decoders=L.decoders.map(J=>Ke.fromConfig(J))}decode_chain(L){return this.decoders.reduce((J,ie)=>ie.decode_chain(J),L)}}class he extends Ke{constructor(L){super(L),this.suffix=this.config.suffix}decode_chain(L){return L.map((J,ie)=>J.replaceAll(this.suffix,ie===L.length-1?"":" "))}}class ue extends Ke{decode_chain(L){let J="";for(let ie=1;ieie.normalize("NFKC")).join("～"):L=L.normalize("NFKC"),L}}class as extends Z{constructor(L){super(),this.tokenizers=L.pretokenizers.map(J=>Z.fromConfig(J))}pre_tokenize_text(L,J){return this.tokenizers.reduce((ie,ve)=>ve.pre_tokenize(ie,J),[L])}}class W extends Z{constructor(L){super()}pre_tokenize_text(L,J){return L.match(/\w+|[^\w\s]+/g)||[]}}class we extends Z{constructor(L){super()}pre_tokenize_text(L,J){return $(L)}}class G extends Z{constructor(L){super(),this.config=L,this.pattern=x(this.config.pattern),this.content=this.config.content}pre_tokenize_text(L,J){return this.pattern===null?[L]:[L.replaceAll(this.pattern,this.config.content)]}}const ne=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function $e(Te,L,J,ie){for(const ve of Object.keys(Te)){const be=L-Te[ve].length,ze=J(ve),tt=new Array(be).fill(ze);Te[ve]=ie==="right"?(0,l.mergeArrays)(Te[ve],tt):(0,l.mergeArrays)(tt,Te[ve])}}function Xe(Te,L){for(const J of Object.keys(Te))Te[J].length=L}class Le extends i.Callable{constructor(J,ie){super();ce(this,"return_token_type_ids",!1);ce(this,"padding_side","right");this._tokenizer_config=ie,this.normalizer=le.fromConfig(J.normalizer),this.pre_tokenizer=Z.fromConfig(J.pre_tokenizer),this.model=te.fromConfig(J.model,ie),this.post_processor=Re.fromConfig(J.post_processor),this.decoder=Ke.fromConfig(J.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const ve of J.added_tokens){const be=new U(ve);this.added_tokens.push(be),this.model.tokens_to_ids.set(be.content,be.id),this.model.vocab[be.id]=be.content,be.special&&(this.special_tokens.push(be.content),this.all_special_ids.push(be.id))}if(this.additional_special_tokens=ie.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_splitter=new h.DictionarySplitter(this.added_tokens.map(ve=>ve.content)),this.added_tokens_map=new Map(this.added_tokens.map(ve=>[ve.content,ve])),this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=ie.model_max_length,this.remove_space=ie.remove_space,this.clean_up_tokenization_spaces=ie.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=ie.do_lowercase_and_remove_accent??!1,ie.padding_side&&(this.padding_side=ie.padding_side),this.legacy=!1,this.chat_template=ie.chat_template??null,Array.isArray(this.chat_template)){const ve=Object.create(null);for(const{name:be,template:ze}of this.chat_template){if(typeof be!="string"||typeof ze!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');ve[be]=ze}this.chat_template=ve}this._compiled_template_cache=new Map}getToken(...J){for(const ie of J){const ve=this._tokenizer_config[ie];if(ve)if(typeof ve=="object"){if(ve.__type==="AddedToken")return ve.content;throw Error(`Unknown token: ${ve}`)}else return ve}return null}static async from_pretrained(J,{progress_callback:ie=null,config:ve=null,cache_dir:be=null,local_files_only:ze=!1,revision:tt="main",legacy:ut=null}={}){const it=await m(J,{progress_callback:ie,config:ve,cache_dir:be,local_files_only:ze,revision:tt,legacy:ut});return new this(...it)}_call(J,{text_pair:ie=null,add_special_tokens:ve=!0,padding:be=!1,truncation:ze=null,max_length:tt=null,return_tensor:ut=!0,return_token_type_ids:it=null}={}){const dt=Array.isArray(J);let At;if(dt){if(J.length===0)throw Error("text array must be non-empty");if(ie!==null){if(Array.isArray(ie)){if(J.length!==ie.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");At=J.map((Ht,fr)=>this._encode_plus(Ht,{text_pair:ie[fr],add_special_tokens:ve,return_token_type_ids:it}))}else At=J.map(Ht=>this._encode_plus(Ht,{add_special_tokens:ve,return_token_type_ids:it}))}else{if(J==null)throw Error("text may not be null or undefined");if(Array.isArray(ie))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");At=[this._encode_plus(J,{text_pair:ie,add_special_tokens:ve,return_token_type_ids:it})]}if(tt===null?tt=this.model_max_length:ze===null&&(be===!0?(console.warn("`max_length` is ignored when `padding: true` and there is no truncation strategy. To pad to max length, use `padding: 'max_length'`."),tt=this.model_max_length):be===!1&&(console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation: true` to explicitly truncate examples to max length."),ze=!0)),be===!0&&(tt=Math.min((0,c.max)(At.map(Ht=>Ht.input_ids.length))[0],tt??1/0)),tt=Math.min(tt,this.model_max_length??1/0),be||ze)for(let Ht=0;Httt?ze&&Xe(At[Ht],tt):be&&$e(At[Ht],tt,fr=>fr==="input_ids"?this.pad_token_id:0,this.padding_side));const qt={};if(ut){if(!(be&&ze)&&At.some(fr=>{var sr;for(const Pr of Object.keys(fr))if(fr[Pr].length!==((sr=At[0][Pr])==null?void 0:sr.length))return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const Ht=[At.length,At[0].input_ids.length];for(const fr of Object.keys(At[0]))qt[fr]=new d.Tensor("int64",BigInt64Array.from(At.flatMap(sr=>sr[fr]).map(BigInt)),Ht)}else{for(const Ht of Object.keys(At[0]))qt[Ht]=At.map(fr=>fr[Ht]);if(!dt)for(const Ht of Object.keys(qt))qt[Ht]=qt[Ht][0]}return qt}_encode_text(J){if(J===null)return null;const ie=this.added_tokens_splitter.split(J);for(let be=0;be0&&(ie[be-1]=ie[be-1].trimEnd()),ze.rstrip&&be{if(be.length===0)return[];if(this.added_tokens_map.has(be))return[be];if(this.remove_space===!0&&(be=be.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(be=C(be)),this.normalizer!==null&&(be=this.normalizer(be)),be.length===0)return[];const tt=this.pre_tokenizer!==null?this.pre_tokenizer(be,{section_index:ze}):[be];return this.model(tt)})}_encode_plus(J,{text_pair:ie=null,add_special_tokens:ve=!0,return_token_type_ids:be=null}={}){const{tokens:ze,token_type_ids:tt}=this._tokenize_helper(J,{pair:ie,add_special_tokens:ve}),ut=this.model.convert_tokens_to_ids(ze),it={input_ids:ut,attention_mask:new Array(ut.length).fill(1)};return(be??this.return_token_type_ids)&&tt&&(it.token_type_ids=tt),it}_tokenize_helper(J,{pair:ie=null,add_special_tokens:ve=!1}={}){const be=this._encode_text(J),ze=this._encode_text(ie);return this.post_processor?this.post_processor(be,ze,{add_special_tokens:ve}):{tokens:(0,l.mergeArrays)(be??[],ze??[])}}tokenize(J,{pair:ie=null,add_special_tokens:ve=!1}={}){return this._tokenize_helper(J,{pair:ie,add_special_tokens:ve}).tokens}encode(J,{text_pair:ie=null,add_special_tokens:ve=!0,return_token_type_ids:be=null}={}){return this._encode_plus(J,{text_pair:ie,add_special_tokens:ve,return_token_type_ids:be}).input_ids}batch_decode(J,ie={}){return J instanceof d.Tensor&&(J=J.tolist()),J.map(ve=>this.decode(ve,ie))}decode(J,ie={}){if(J instanceof d.Tensor&&(J=I(J)),!Array.isArray(J)||J.length===0||!(0,l.isIntegralNumber)(J[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(J,ie)}decode_single(J,{skip_special_tokens:ie=!1,clean_up_tokenization_spaces:ve=null}){let be=this.model.convert_ids_to_tokens(J);ie&&(be=be.filter(tt=>!this.special_tokens.includes(tt)));let ze=this.decoder?this.decoder(be):be.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(ze=ze.replaceAll(this.decoder.end_of_word_suffix," "),ie&&(ze=ze.trim())),(ve??this.clean_up_tokenization_spaces)&&(ze=z(ze)),ze}get_chat_template({chat_template:J=null,tools:ie=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const ve=this.chat_template;if(J!==null&&Object.hasOwn(ve,J))J=ve[J];else if(J===null)if(ie!==null&&"tool_use"in ve)J=ve.tool_use;else if("default"in ve)J=ve.default;else throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(ve).sort()}.`)}else if(J===null)if(this.chat_template)J=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return J}apply_chat_template(J,{tools:ie=null,documents:ve=null,chat_template:be=null,add_generation_prompt:ze=!1,tokenize:tt=!0,padding:ut=!1,truncation:it=!1,max_length:dt=null,return_tensor:At=!0,return_dict:qt=!1,tokenizer_kwargs:Ht={},...fr}={}){if(be=this.get_chat_template({chat_template:be,tools:ie}),typeof be!="string")throw Error(`chat_template must be a string, but got ${typeof be}`);let sr=this._compiled_template_cache.get(be);sr===void 0&&(sr=new f.Template(be),this._compiled_template_cache.set(be,sr));const Pr=Object.create(null);for(const qr of ne){const ys=this.getToken(qr);ys&&(Pr[qr]=ys)}const $r=sr.render({messages:J,add_generation_prompt:ze,tools:ie,documents:ve,...Pr,...fr});if(tt){const qr=this._call($r,{add_special_tokens:!1,padding:ut,truncation:it,max_length:dt,return_tensor:At,...Ht});return qt?qr:qr.input_ids}return $r}}class Ft extends Le{constructor(){super(...arguments);ce(this,"return_token_type_ids",!0)}}class Qt extends Le{constructor(){super(...arguments);ce(this,"return_token_type_ids",!0)}}class yt extends Le{constructor(){super(...arguments);ce(this,"return_token_type_ids",!0)}}class Nt extends Le{constructor(){super(...arguments);ce(this,"return_token_type_ids",!0)}}class mt extends Le{constructor(){super(...arguments);ce(this,"return_token_type_ids",!0)}}class tr extends Le{constructor(){super(...arguments);ce(this,"return_token_type_ids",!0)}}class rr extends Le{constructor(){super(...arguments);ce(this,"return_token_type_ids",!0)}}class Fr extends Le{constructor(){super(...arguments);ce(this,"return_token_type_ids",!0)}}class pr extends Le{constructor(){super(...arguments);ce(this,"return_token_type_ids",!0)}}class Kr extends Le{}class Ts extends Le{}class Us extends Le{constructor(J,ie){super(J,ie);ce(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Cs extends Le{constructor(){super(...arguments);ce(this,"return_token_type_ids",!0)}}class tn extends Le{}class rs extends Le{}class rn extends Le{}class os extends Le{constructor(L,J){super(L,J),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(ie=>this.languageRegex.test(ie)),this.lang_to_token=ie=>ie}_build_translation_inputs(L,J,ie){return Xr(this,L,J,ie)}}class Gs extends os{}class Ks extends Le{}class Hs extends Le{}const Wr="▁";class Jt extends Le{constructor(J,ie){super(J,ie);ce(this,"padding_side","left");this.legacy=ie.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new Cr({replacement:Wr,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(J){if(J===null)return null;if(this.legacy||J.length===0)return super._encode_text(J);let ie=super._encode_text(Wr+J.replaceAll(Wr," "));return ie.length>1&&ie[0]===Wr&&this.special_tokens.includes(ie[1])&&(ie=ie.slice(1)),ie}}class Es extends Le{}class qs extends Le{}class Er extends Le{}class qe extends Le{}class ct extends Le{}class bt extends Le{}class ur extends Le{}class ls extends Le{}class Ps extends Le{}function Xr(Te,L,J,ie){if(!("language_codes"in Te)||!Array.isArray(Te.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Te)||!(Te.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Te)||typeof Te.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const ve=ie.src_lang,be=ie.tgt_lang;if(!Te.language_codes.includes(be))throw new Error(`Target language code "${be}" is not valid. Must be one of: {${Te.language_codes.join(", ")}}`);if(ve!==void 0){if(!Te.language_codes.includes(ve))throw new Error(`Source language code "${ve}" is not valid. 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UT=b.AutoTokenizer;b.AutomaticSpeechRecognitionPipeline;b.BackgroundRemovalPipeline;b.BartForConditionalGeneration;b.BartForSequenceClassification;b.BartModel;b.BartPretrainedModel;b.BartTokenizer;b.BaseModelOutput;b.BaseStreamer;b.BeitFeatureExtractor;b.BeitForImageClassification;b.BeitModel;b.BeitPreTrainedModel;b.BertForMaskedLM;b.BertForQuestionAnswering;b.BertForSequenceClassification;b.BertForTokenClassification;b.BertModel;b.BertPreTrainedModel;b.BertTokenizer;b.BitImageProcessor;b.BlenderbotForConditionalGeneration;b.BlenderbotModel;b.BlenderbotPreTrainedModel;b.BlenderbotSmallForConditionalGeneration;b.BlenderbotSmallModel;b.BlenderbotSmallPreTrainedModel;b.BlenderbotSmallTokenizer;b.BlenderbotTokenizer;b.BloomForCausalLM;b.BloomModel;b.BloomPreTrainedModel;b.BloomTokenizer;b.CLIPFeatureExtractor;b.CLIPImageProcessor;b.CLIPModel;b.CLIPPreTrainedModel;b.CLIPSegForImageSegmentation;b.CLIPSegModel;b.CLIPSegPreTrainedModel;b.CLIPTextModel;b.CLIPTextModelWithProjection;b.CLIPTokenizer;b.CLIPVisionModel;b.CLIPVisionModelWithProjection;b.CamembertForMaskedLM;b.CamembertForQuestionAnswering;b.CamembertForSequenceClassification;b.CamembertForTokenClassification;b.CamembertModel;b.CamembertPreTrainedModel;b.CamembertTokenizer;b.CausalLMOutput;b.CausalLMOutputWithPast;b.ChineseCLIPFeatureExtractor;b.ChineseCLIPModel;b.ChineseCLIPPreTrainedModel;b.ClapAudioModelWithProjection;b.ClapFeatureExtractor;b.ClapModel;b.ClapPreTrainedModel;b.ClapTextModelWithProjection;b.ClassifierFreeGuidanceLogitsProcessor;b.CodeGenForCausalLM;b.CodeGenModel;b.CodeGenPreTrainedModel;b.CodeGenTokenizer;b.CodeLlamaTokenizer;b.CohereForCausalLM;b.CohereModel;b.CoherePreTrainedModel;b.CohereTokenizer;b.ConvBertForMaskedLM;b.ConvBertForQuestionAnswering;b.ConvBertForSequenceClassification;b.ConvBertForTokenClassification;b.ConvBertModel;b.ConvBertPreTrainedModel;b.ConvBertTokenizer;b.ConvNextFeatureExtractor;b.ConvNextForImageClassification;b.ConvNextImageProcessor;b.ConvNextModel;b.ConvNextPreTrainedModel;b.ConvNextV2ForImageClassification;b.ConvNextV2Model;b.ConvNextV2PreTrainedModel;b.DFineForObjectDetection;b.DFineModel;b.DFinePreTrainedModel;b.DPTFeatureExtractor;b.DPTForDepthEstimation;b.DPTImageProcessor;b.DPTModel;b.DPTPreTrainedModel;b.DacDecoderModel;b.DacDecoderOutput;b.DacEncoderModel;b.DacEncoderOutput;b.DacFeatureExtractor;b.DacModel;b.DacPreTrainedModel;b.DataTypeMap;b.DebertaForMaskedLM;b.DebertaForQuestionAnswering;b.DebertaForSequenceClassification;b.DebertaForTokenClassification;b.DebertaModel;b.DebertaPreTrainedModel;b.DebertaTokenizer;b.DebertaV2ForMaskedLM;b.DebertaV2ForQuestionAnswering;b.DebertaV2ForSequenceClassification;b.DebertaV2ForTokenClassification;b.DebertaV2Model;b.DebertaV2PreTrainedModel;b.DebertaV2Tokenizer;b.DecisionTransformerModel;b.DecisionTransformerPreTrainedModel;b.DeiTFeatureExtractor;b.DeiTForImageClassification;b.DeiTImageProcessor;b.DeiTModel;b.DeiTPreTrainedModel;b.DepthAnythingForDepthEstimation;b.DepthAnythingPreTrainedModel;b.DepthEstimationPipeline;b.DepthProForDepthEstimation;b.DepthProPreTrainedModel;b.DetrFeatureExtractor;b.DetrForObjectDetection;b.DetrForSegmentation;b.DetrImageProcessor;b.DetrModel;b.DetrObjectDetectionOutput;b.DetrPreTrainedModel;b.DetrSegmentationOutput;b.Dinov2ForImageClassification;b.Dinov2Model;b.Dinov2PreTrainedModel;b.Dinov2WithRegistersForImageClassification;b.Dinov2WithRegistersModel;b.Dinov2WithRegistersPreTrainedModel;b.DistilBertForMaskedLM;b.DistilBertForQuestionAnswering;b.DistilBertForSequenceClassification;b.DistilBertForTokenClassification;b.DistilBertModel;b.DistilBertPreTrainedModel;b.DistilBertTokenizer;b.DocumentQuestionAnsweringPipeline;b.DonutFeatureExtractor;b.DonutImageProcessor;b.DonutSwinModel;b.DonutSwinPreTrainedModel;b.EfficientNetForImageClassification;b.EfficientNetImageProcessor;b.EfficientNetModel;b.EfficientNetPreTrainedModel;b.ElectraForMaskedLM;b.ElectraForQuestionAnswering;b.ElectraForSequenceClassification;b.ElectraForTokenClassification;b.ElectraModel;b.ElectraPreTrainedModel;b.ElectraTokenizer;b.EncodecFeatureExtractor;b.EosTokenCriteria;b.EsmForMaskedLM;b.EsmForSequenceClassification;b.EsmForTokenClassification;b.EsmModel;b.EsmPreTrainedModel;b.EsmTokenizer;b.ExaoneForCausalLM;b.ExaoneModel;b.ExaonePreTrainedModel;b.FFT;b.FalconForCausalLM;b.FalconModel;b.FalconPreTrainedModel;b.FalconTokenizer;b.FastViTForImageClassification;b.FastViTModel;b.FastViTPreTrainedModel;b.FeatureExtractionPipeline;b.FeatureExtractor;b.FillMaskPipeline;b.Florence2ForConditionalGeneration;b.Florence2PreTrainedModel;b.Florence2Processor;b.ForcedBOSTokenLogitsProcessor;b.ForcedEOSTokenLogitsProcessor;b.GLPNFeatureExtractor;b.GLPNForDepthEstimation;b.GLPNModel;b.GLPNPreTrainedModel;b.GPT2LMHeadModel;b.GPT2Model;b.GPT2PreTrainedModel;b.GPT2Tokenizer;b.GPTBigCodeForCausalLM;b.GPTBigCodeModel;b.GPTBigCodePreTrainedModel;b.GPTJForCausalLM;b.GPTJModel;b.GPTJPreTrainedModel;b.GPTNeoForCausalLM;b.GPTNeoModel;b.GPTNeoPreTrainedModel;b.GPTNeoXForCausalLM;b.GPTNeoXModel;b.GPTNeoXPreTrainedModel;b.GPTNeoXTokenizer;b.Gemma2ForCausalLM;b.Gemma2Model;b.Gemma2PreTrainedModel;b.Gemma3ForCausalLM;b.Gemma3Model;b.Gemma3PreTrainedModel;b.GemmaForCausalLM;b.GemmaModel;b.GemmaPreTrainedModel;b.GemmaTokenizer;b.GlmForCausalLM;b.GlmModel;b.GlmPreTrainedModel;b.GraniteForCausalLM;b.GraniteModel;b.GranitePreTrainedModel;b.Grok1Tokenizer;b.GroundingDinoForObjectDetection;b.GroundingDinoImageProcessor;b.GroundingDinoPreTrainedModel;b.GroundingDinoProcessor;b.GroupViTModel;b.GroupViTPreTrainedModel;b.HeliumForCausalLM;b.HeliumModel;b.HeliumPreTrainedModel;b.HerbertTokenizer;b.HieraForImageClassification;b.HieraModel;b.HieraPreTrainedModel;b.HubertForCTC;b.HubertForSequenceClassification;b.HubertModel;b.HubertPreTrainedModel;b.IJepaForImageClassification;b.IJepaModel;b.IJepaPreTrainedModel;b.Idefics3ForConditionalGeneration;b.Idefics3ImageProcessor;b.Idefics3PreTrainedModel;b.Idefics3Processor;b.ImageClassificationPipeline;b.ImageFeatureExtractionPipeline;b.ImageFeatureExtractor;b.ImageMattingOutput;b.ImageProcessor;b.ImageSegmentationPipeline;b.ImageToImagePipeline;b.ImageToTextPipeline;var GT=b.InterruptableStoppingCriteria;b.JAISLMHeadModel;b.JAISModel;b.JAISPreTrainedModel;b.JinaCLIPImageProcessor;b.JinaCLIPModel;b.JinaCLIPPreTrainedModel;b.JinaCLIPProcessor;b.JinaCLIPTextModel;b.JinaCLIPVisionModel;b.LiteWhisperForConditionalGeneration;b.LlamaForCausalLM;b.LlamaModel;b.LlamaPreTrainedModel;b.LlamaTokenizer;b.LlavaForConditionalGeneration;b.LlavaOnevisionForConditionalGeneration;b.LlavaOnevisionImageProcessor;b.LlavaPreTrainedModel;b.LogitsProcessor;b.LogitsProcessorList;b.LogitsWarper;b.LongT5ForConditionalGeneration;b.LongT5Model;b.LongT5PreTrainedModel;b.M2M100ForConditionalGeneration;b.M2M100Model;b.M2M100PreTrainedModel;b.M2M100Tokenizer;b.MBart50Tokenizer;b.MBartForCausalLM;b.MBartForConditionalGeneration;b.MBartForSequenceClassification;b.MBartModel;b.MBartPreTrainedModel;b.MBartTokenizer;b.MPNetForMaskedLM;b.MPNetForQuestionAnswering;b.MPNetForSequenceClassification;b.MPNetForTokenClassification;b.MPNetModel;b.MPNetPreTrainedModel;b.MPNetTokenizer;b.MT5ForConditionalGeneration;b.MT5Model;b.MT5PreTrainedModel;b.MarianMTModel;b.MarianModel;b.MarianPreTrainedModel;b.MarianTokenizer;b.Mask2FormerImageProcessor;b.MaskFormerFeatureExtractor;b.MaskFormerForInstanceSegmentation;b.MaskFormerImageProcessor;b.MaskFormerModel;b.MaskFormerPreTrainedModel;b.MaskedLMOutput;b.MaxLengthCriteria;b.Metric3DForDepthEstimation;b.Metric3DPreTrainedModel;b.Metric3Dv2ForDepthEstimation;b.Metric3Dv2PreTrainedModel;b.MgpstrForSceneTextRecognition;b.MgpstrModelOutput;b.MgpstrPreTrainedModel;b.MgpstrProcessor;b.MgpstrTokenizer;b.MimiDecoderModel;b.MimiDecoderOutput;b.MimiEncoderModel;b.MimiEncoderOutput;b.MimiModel;b.MimiPreTrainedModel;b.MinLengthLogitsProcessor;b.MinNewTokensLengthLogitsProcessor;b.MistralForCausalLM;b.MistralModel;b.MistralPreTrainedModel;b.MobileBertForMaskedLM;b.MobileBertForQuestionAnswering;b.MobileBertForSequenceClassification;b.MobileBertModel;b.MobileBertPreTrainedModel;b.MobileBertTokenizer;b.MobileLLMForCausalLM;b.MobileLLMModel;b.MobileLLMPreTrainedModel;b.MobileNetV1FeatureExtractor;b.MobileNetV1ForImageClassification;b.MobileNetV1ForSemanticSegmentation;b.MobileNetV1ImageProcessor;b.MobileNetV1Model;b.MobileNetV1PreTrainedModel;b.MobileNetV2FeatureExtractor;b.MobileNetV2ForImageClassification;b.MobileNetV2ForSemanticSegmentation;b.MobileNetV2ImageProcessor;b.MobileNetV2Model;b.MobileNetV2PreTrainedModel;b.MobileNetV3FeatureExtractor;b.MobileNetV3ForImageClassification;b.MobileNetV3ForSemanticSegmentation;b.MobileNetV3ImageProcessor;b.MobileNetV3Model;b.MobileNetV3PreTrainedModel;b.MobileNetV4FeatureExtractor;b.MobileNetV4ForImageClassification;b.MobileNetV4ForSemanticSegmentation;b.MobileNetV4ImageProcessor;b.MobileNetV4Model;b.MobileNetV4PreTrainedModel;b.MobileViTFeatureExtractor;b.MobileViTForImageClassification;b.MobileViTImageProcessor;b.MobileViTModel;b.MobileViTPreTrainedModel;b.MobileViTV2ForImageClassification;b.MobileViTV2Model;b.MobileViTV2PreTrainedModel;b.ModelOutput;b.ModernBertForMaskedLM;b.ModernBertForSequenceClassification;b.ModernBertForTokenClassification;b.ModernBertModel;b.ModernBertPreTrainedModel;b.Moondream1ForConditionalGeneration;b.MoonshineFeatureExtractor;b.MoonshineForConditionalGeneration;b.MoonshineModel;b.MoonshinePreTrainedModel;b.MoonshineProcessor;b.MptForCausalLM;b.MptModel;b.MptPreTrainedModel;b.MultiModalityCausalLM;b.MultiModalityPreTrainedModel;b.MusicgenForCausalLM;b.MusicgenForConditionalGeneration;b.MusicgenModel;b.MusicgenPreTrainedModel;b.NllbTokenizer;b.NoBadWordsLogitsProcessor;b.NoRepeatNGramLogitsProcessor;b.NomicBertModel;b.NomicBertPreTrainedModel;b.NougatImageProcessor;b.NougatTokenizer;b.OPTForCausalLM;b.OPTModel;b.OPTPreTrainedModel;b.ObjectDetectionPipeline;b.Olmo2ForCausalLM;b.Olmo2Model;b.Olmo2PreTrainedModel;b.OlmoForCausalLM;b.OlmoModel;b.OlmoPreTrainedModel;b.OpenELMForCausalLM;b.OpenELMModel;b.OpenELMPreTrainedModel;b.OwlViTFeatureExtractor;b.OwlViTForObjectDetection;b.OwlViTImageProcessor;b.OwlViTModel;b.OwlViTPreTrainedModel;b.OwlViTProcessor;b.Owlv2ForObjectDetection;b.Owlv2ImageProcessor;b.Owlv2Model;b.Owlv2PreTrainedModel;b.PaliGemmaForConditionalGeneration;b.PaliGemmaPreTrainedModel;b.PaliGemmaProcessor;b.PatchTSMixerForPrediction;b.PatchTSMixerModel;b.PatchTSMixerPreTrainedModel;b.PatchTSTForPrediction;b.PatchTSTModel;b.PatchTSTPreTrainedModel;b.Phi3ForCausalLM;b.Phi3Model;b.Phi3PreTrainedModel;b.Phi3VForCausalLM;b.Phi3VImageProcessor;b.Phi3VPreTrainedModel;b.Phi3VProcessor;b.PhiForCausalLM;b.PhiModel;b.PhiPreTrainedModel;b.Pipeline;b.PreTrainedModel;b.PreTrainedTokenizer;b.PretrainedConfig;b.PretrainedMixin;b.Processor;b.PvtForImageClassification;b.PvtImageProcessor;b.PvtModel;b.PvtPreTrainedModel;b.PyAnnoteFeatureExtractor;b.PyAnnoteForAudioFrameClassification;b.PyAnnoteModel;b.PyAnnotePreTrainedModel;b.PyAnnoteProcessor;b.QuestionAnsweringModelOutput;b.QuestionAnsweringPipeline;b.Qwen2ForCausalLM;b.Qwen2Model;b.Qwen2PreTrainedModel;b.Qwen2Tokenizer;b.Qwen2VLForConditionalGeneration;b.Qwen2VLImageProcessor;b.Qwen2VLPreTrainedModel;b.Qwen2VLProcessor;b.Qwen3ForCausalLM;b.Qwen3Model;b.Qwen3PreTrainedModel;b.RFDetrForObjectDetection;b.RFDetrModel;b.RFDetrObjectDetectionOutput;b.RFDetrPreTrainedModel;b.RTDetrForObjectDetection;b.RTDetrImageProcessor;b.RTDetrModel;b.RTDetrObjectDetectionOutput;b.RTDetrPreTrainedModel;b.RTDetrV2ForObjectDetection;b.RTDetrV2Model;b.RTDetrV2ObjectDetectionOutput;b.RTDetrV2PreTrainedModel;b.RawAudio;b.RawImage;b.RawVideo;b.RawVideoFrame;b.RepetitionPenaltyLogitsProcessor;b.ResNetForImageClassification;b.ResNetModel;b.ResNetPreTrainedModel;b.RoFormerForMaskedLM;b.RoFormerForQuestionAnswering;b.RoFormerForSequenceClassification;b.RoFormerForTokenClassification;b.RoFormerModel;b.RoFormerPreTrainedModel;b.RoFormerTokenizer;b.RobertaForMaskedLM;b.RobertaForQuestionAnswering;b.RobertaForSequenceClassification;b.RobertaForTokenClassification;b.RobertaModel;b.RobertaPreTrainedModel;b.RobertaTokenizer;b.SamImageProcessor;b.SamImageSegmentationOutput;b.SamModel;b.SamPreTrainedModel;b.SamProcessor;b.SapiensForDepthEstimation;b.SapiensForNormalEstimation;b.SapiensForSemanticSegmentation;b.SapiensPreTrainedModel;b.SeamlessM4TFeatureExtractor;b.SegformerFeatureExtractor;b.SegformerForImageClassification;b.SegformerForSemanticSegmentation;b.SegformerImageProcessor;b.SegformerModel;b.SegformerPreTrainedModel;b.Seq2SeqLMOutput;b.SequenceClassifierOutput;b.SiglipImageProcessor;b.SiglipModel;b.SiglipPreTrainedModel;b.SiglipTextModel;b.SiglipTokenizer;b.SiglipVisionModel;b.SmolVLMForConditionalGeneration;b.SmolVLMImageProcessor;b.SmolVLMProcessor;b.SnacDecoderModel;b.SnacEncoderModel;b.SnacFeatureExtractor;b.SnacModel;b.SnacPreTrainedModel;b.SpeechT5FeatureExtractor;b.SpeechT5ForSpeechToText;b.SpeechT5ForTextToSpeech;b.SpeechT5HifiGan;b.SpeechT5Model;b.SpeechT5PreTrainedModel;b.SpeechT5Processor;b.SpeechT5Tokenizer;b.SqueezeBertForMaskedLM;b.SqueezeBertForQuestionAnswering;b.SqueezeBertForSequenceClassification;b.SqueezeBertModel;b.SqueezeBertPreTrainedModel;b.SqueezeBertTokenizer;b.StableLmForCausalLM;b.StableLmModel;b.StableLmPreTrainedModel;b.Starcoder2ForCausalLM;b.Starcoder2Model;b.Starcoder2PreTrainedModel;b.StoppingCriteria;b.StoppingCriteriaList;b.StyleTextToSpeech2Model;b.StyleTextToSpeech2PreTrainedModel;b.SummarizationPipeline;b.SuppressTokensAtBeginLogitsProcessor;b.Swin2SRForImageSuperResolution;b.Swin2SRImageProcessor;b.Swin2SRModel;b.Swin2SRPreTrainedModel;b.SwinForImageClassification;b.SwinForSemanticSegmentation;b.SwinModel;b.SwinPreTrainedModel;b.T5ForConditionalGeneration;b.T5Model;b.T5PreTrainedModel;b.T5Tokenizer;b.TableTransformerForObjectDetection;b.TableTransformerModel;b.TableTransformerObjectDetectionOutput;b.TableTransformerPreTrainedModel;b.TemperatureLogitsWarper;b.Tensor;b.Text2TextGenerationPipeline;b.TextClassificationPipeline;b.TextGenerationPipeline;var KT=b.TextStreamer;b.TextToAudioPipeline;b.TokenClassificationPipeline;b.TokenClassifierOutput;b.TokenizerModel;b.TopKLogitsWarper;b.TopPLogitsWarper;b.TrOCRForCausalLM;b.TrOCRPreTrainedModel;b.TranslationPipeline;b.UltravoxModel;b.UltravoxPreTrainedModel;b.UltravoxProcessor;b.UniSpeechForCTC;b.UniSpeechForSequenceClassification;b.UniSpeechModel;b.UniSpeechPreTrainedModel;b.UniSpeechSatForAudioFrameClassification;b.UniSpeechSatForCTC;b.UniSpeechSatForSequenceClassification;b.UniSpeechSatModel;b.UniSpeechSatPreTrainedModel;b.VLChatProcessor;b.VLMImageProcessor;b.ViTFeatureExtractor;b.ViTForImageClassification;b.ViTImageProcessor;b.ViTMAEModel;b.ViTMAEPreTrainedModel;b.ViTMSNForImageClassification;b.ViTMSNModel;b.ViTMSNPreTrainedModel;b.ViTModel;b.ViTPreTrainedModel;b.VisionEncoderDecoderModel;b.VitMatteForImageMatting;b.VitMatteImageProcessor;b.VitMattePreTrainedModel;b.VitPoseForPoseEstimation;b.VitPoseImageProcessor;b.VitPosePreTrainedModel;b.VitsModel;b.VitsModelOutput;b.VitsPreTrainedModel;b.VitsTokenizer;b.Wav2Vec2BertForCTC;b.Wav2Vec2BertForSequenceClassification;b.Wav2Vec2BertModel;b.Wav2Vec2BertPreTrainedModel;b.Wav2Vec2CTCTokenizer;b.Wav2Vec2FeatureExtractor;b.Wav2Vec2ForAudioFrameClassification;b.Wav2Vec2ForCTC;b.Wav2Vec2ForSequenceClassification;b.Wav2Vec2Model;b.Wav2Vec2PreTrainedModel;b.Wav2Vec2Processor;b.Wav2Vec2ProcessorWithLM;b.WavLMForAudioFrameClassification;b.WavLMForCTC;b.WavLMForSequenceClassification;b.WavLMForXVector;b.WavLMModel;b.WavLMPreTrainedModel;b.WeSpeakerFeatureExtractor;b.WeSpeakerResNetModel;b.WeSpeakerResNetPreTrainedModel;b.WhisperFeatureExtractor;b.WhisperForConditionalGeneration;b.WhisperModel;b.WhisperPreTrainedModel;b.WhisperProcessor;b.WhisperTextStreamer;b.WhisperTimeStampLogitsProcessor;b.WhisperTokenizer;b.XLMForQuestionAnswering;b.XLMForSequenceClassification;b.XLMForTokenClassification;b.XLMModel;b.XLMPreTrainedModel;b.XLMRobertaForMaskedLM;b.XLMRobertaForQuestionAnswering;b.XLMRobertaForSequenceClassification;b.XLMRobertaForTokenClassification;b.XLMRobertaModel;b.XLMRobertaPreTrainedModel;b.XLMRobertaTokenizer;b.XLMTokenizer;b.XLMWithLMHeadModel;b.XVectorOutput;b.YolosFeatureExtractor;b.YolosForObjectDetection;b.YolosImageProcessor;b.YolosModel;b.YolosObjectDetectionOutput;b.YolosPreTrainedModel;b.ZeroShotAudioClassificationPipeline;b.ZeroShotClassificationPipeline;b.ZeroShotImageClassificationPipeline;b.ZeroShotObjectDetectionPipeline;b.bankers_round;b.cat;b.cos_sim;b.dot;b.dynamic_time_warping;var bd=b.env;b.full;b.full_like;b.getKeyValueShapes;b.hamming;b.hanning;b.interpolate;b.interpolate_4d;b.interpolate_data;b.is_chinese_char;b.layer_norm;b.load_image;b.load_video;b.log_softmax;b.magnitude;b.matmul;b.max;b.mean;b.mean_pooling;b.medianFilter;b.mel_filter_bank;b.min;b.ones;b.ones_like;b.permute;b.permute_data;b.pipeline;b.quantize_embeddings;b.rand;b.read_audio;b.rfft;b.round;b.slice;b.softmax;b.spectrogram;b.stack;b.std_mean;b.topk;b.window_function;b.zeros;b.zeros_like;async function HT(){try{if(!await navigator.gpu.requestAdapter())throw new Error("WebGPU is not supported (no adapter found)");return!0}catch(e){self.postMessage({status:"error",data:e.toString()})}}const qT="/models/",QT="/dist/";bd.localModelPath=qT;bd.allowRemoteModels=!0;bd.allowLocalModels=!0;bd.backends.onnx.wasm.wasmPaths=QT;bd.backends.onnx.wasm.proxy=!1;class vs{static async Init(r,t=null){if(!(this.llm&&(r===void 0||this.model_id.path===r.path))){this.model_id=r;const i=this.model_id.path;this.config??(this.config=await VT.from_pretrained(i,{progress_callback:t})),this.config["transformers.js_config"]===void 0&&(this.config["transformers.js_config"]={kv_cache_dtype:{q4f16:"float16",fp16:"float16"},use_external_data_format:{"model.onnx":!0,"model_q4f16.onnx":!0}}),this.tokenizer=await UT.from_pretrained(i,{config:this.config,progress_callback:t}),this.llm=await WT.from_pretrained(i,{config:this.config,dtype:"q4f16",device:"webgpu",use_external_data_format:this.model_id.use_external_data_format,progress_callback:t})}}}ce(vs,"model_id",""),ce(vs,"tokenizer",null),ce(vs,"llm",null),ce(vs,"config",null);const Qh=new GT;let zm=null;async function XT({messages:e,reasonEnabled:r}){const t=vs.tokenizer.apply_chat_template(e,{add_generation_prompt:!0,return_dict:!0,enable_thinking:r}),[i,l]=vs.tokenizer.encode("",{add_special_tokens:!1});let n="answering",c,d=0,h;const f=I=>{switch(c??(c=performance.now()),d++>0&&(h=d/(performance.now()-c)*1e3),Number(I[0])){case i:n="thinking";break;case l:n="answering";break}},w=I=>{self.postMessage({status:"update",output:I,tps:h,numTokens:d,state:n})},m=new KT(vs.tokenizer,{skip_prompt:!0,skip_special_tokens:!0,callback_function:w,token_callback_function:f});self.postMessage({status:"start"});const{past_key_values:g,sequences:x}=await vs.llm.generate({...t,past_key_values:zm,do_sample:!1,top_k:20,temperature:r?.5:.4,max_new_tokens:16384,streamer:m,stopping_criteria:Qh,return_dict_in_generate:!0});zm=g;const M=vs.tokenizer.batch_decode(x,{skip_special_tokens:!0});self.postMessage({status:"complete",output:M})}async function JT(e){self.postMessage({status:"loading",data:"Loading model..."}),await vs.Init(e,t=>{t.file=t.name+"/"+t.file,self.postMessage(t)}),self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const r=vs.tokenizer("a");await vs.llm.generate({...r,max_new_tokens:1}),self.postMessage({status:"ready"}),console.log("LLM Worker: Ready.")}self.addEventListener("message",async e=>{const{type:r,data:t}=e.data;switch(r){case"check":HT();break;case"load_llm":JT(t);break;case"generate":Qh.reset(),XT(t);break;case"interrupt":Qh.interrupt();break;case"reset":zm=null,Qh.reset();break}});