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iiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O)=>(sy=i.dynCall_iiijiiiiiiiiii=N.Cf)(s,l,u,p,f,g,w,b,x,E,T,P,D,O),oy=i.dynCall_vj=(s,l)=>(oy=i.dynCall_vj=N.Df)(s,l),ly=i.dynCall_jjj=(s,l,u)=>(ly=i.dynCall_jjj=N.Ef)(s,l,u),uy=i.dynCall_iiijiiiiii=(s,l,u,p,f,g,w,b,x,E)=>(uy=i.dynCall_iiijiiiiii=N.Ff)(s,l,u,p,f,g,w,b,x,E),dy=i.dynCall_vfiii=(s,l,u,p,f)=>(dy=i.dynCall_vfiii=N.Gf)(s,l,u,p,f),cy=i.dynCall_viiiiff=(s,l,u,p,f,g,w)=>(cy=i.dynCall_viiiiff=N.Hf)(s,l,u,p,f,g,w),py=i.dynCall_viiiiiff=(s,l,u,p,f,g,w,b)=>(py=i.dynCall_viiiiiff=N.If)(s,l,u,p,f,g,w,b),hy=i.dynCall_viiff=(s,l,u,p,f)=>(hy=i.dynCall_viiff=N.Jf)(s,l,u,p,f),fy=i.dynCall_viiiiiiiiifiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O)=>(fy=i.dynCall_viiiiiiiiifiii=N.Kf)(s,l,u,p,f,g,w,b,x,E,T,P,D,O),my=i.dynCall_viiiiiiiijj=(s,l,u,p,f,g,w,b,x,E,T)=>(my=i.dynCall_viiiiiiiijj=N.Lf)(s,l,u,p,f,g,w,b,x,E,T),gy=i.dynCall_iiiiiiiiiiiiiifii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de)=>(gy=i.dynCall_iiiiiiiiiiiiiifii=N.Mf)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de),yy=i.dynCall_viiiiiiiiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D)=>(yy=i.dynCall_viiiiiiiiiiii=N.Nf)(s,l,u,p,f,g,w,b,x,E,T,P,D),_y=i.dynCall_iiiiiiiiiiiiiiiiifii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e,ke,De)=>(_y=i.dynCall_iiiiiiiiiiiiiiiiifii=N.Of)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e,ke,De),wy=i.dynCall_vijjiiiiii=(s,l,u,p,f,g,w,b,x,E)=>(wy=i.dynCall_vijjiiiiii=N.Pf)(s,l,u,p,f,g,w,b,x,E),vy=i.dynCall_iiiijjj=(s,l,u,p,f,g,w)=>(vy=i.dynCall_iiiijjj=N.Qf)(s,l,u,p,f,g,w),by=i.dynCall_viiiiiiiiii=(s,l,u,p,f,g,w,b,x,E,T)=>(by=i.dynCall_viiiiiiiiii=N.Rf)(s,l,u,p,f,g,w,b,x,E,T),$y=i.dynCall_iiijjj=(s,l,u,p,f,g)=>($y=i.dynCall_iiijjj=N.Sf)(s,l,u,p,f,g),xy=i.dynCall_fffffff=(s,l,u,p,f,g,w)=>(xy=i.dynCall_fffffff=N.Tf)(s,l,u,p,f,g,w),Cy=i.dynCall_viiiij=(s,l,u,p,f,g)=>(Cy=i.dynCall_viiiij=N.Uf)(s,l,u,p,f,g),Sy=i.dynCall_viiiiiijiifiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O)=>(Sy=i.dynCall_viiiiiijiifiii=N.Vf)(s,l,u,p,f,g,w,b,x,E,T,P,D,O),Ey=i.dynCall_vjjjjjjffjifiiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e)=>(Ey=i.dynCall_vjjjjjjffjifiiiiii=N.Wf)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e),ky=i.dynCall_viiiiiiffjifiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de)=>(ky=i.dynCall_viiiiiiffjifiiiii=N.Xf)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de),Ty=i.dynCall_viiiiiiffjfiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J)=>(Ty=i.dynCall_viiiiiiffjfiiiii=N.Yf)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J),Iy=i.dynCall_viiiiiiffjiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F)=>(Iy=i.dynCall_viiiiiiffjiiiii=N.Zf)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F),Ay=i.dynCall_vjjjjjjjjfffiiifiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e,ke,De,qe)=>(Ay=i.dynCall_vjjjjjjjjfffiiifiiiii=N._f)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e,ke,De,qe),My=i.dynCall_vjjjjjjfffifijiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e,ke)=>(My=i.dynCall_vjjjjjjfffifijiiiii=N.$f)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e,ke),Oy=i.dynCall_vjjjjjjfffifiiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e)=>(Oy=i.dynCall_vjjjjjjfffifiiiiii=N.ag)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e),zy=i.dynCall_vjjjjjjjjfffjifiiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e,ke,De,qe)=>(zy=i.dynCall_vjjjjjjjjfffjifiiiiii=N.bg)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F,J,de,_e,ke,De,qe),Py=i.dynCall_vijiiiiiiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D)=>(Py=i.dynCall_vijiiiiiiiiii=N.cg)(s,l,u,p,f,g,w,b,x,E,T,P,D),jy=i.dynCall_vijjfffiii=(s,l,u,p,f,g,w,b,x,E)=>(jy=i.dynCall_vijjfffiii=N.dg)(s,l,u,p,f,g,w,b,x,E),Ry=i.dynCall_viiiiiiijiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D)=>(Ry=i.dynCall_viiiiiiijiiii=N.eg)(s,l,u,p,f,g,w,b,x,E,T,P,D),By=i.dynCall_vijjjjjjifiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F)=>(By=i.dynCall_vijjjjjjifiiiii=N.fg)(s,l,u,p,f,g,w,b,x,E,T,P,D,O,F),Dy=i.dynCall_viifi=(s,l,u,p,f)=>(Dy=i.dynCall_viifi=N.gg)(s,l,u,p,f),Ny=i.dynCall_vjjjjjiiii=(s,l,u,p,f,g,w,b,x,E)=>(Ny=i.dynCall_vjjjjjiiii=N.hg)(s,l,u,p,f,g,w,b,x,E),Ly=i.dynCall_vjjjjfiii=(s,l,u,p,f,g,w,b,x)=>(Ly=i.dynCall_vjjjjfiii=N.ig)(s,l,u,p,f,g,w,b,x),Uy=i.dynCall_viiiiiijiiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D,O)=>(Uy=i.dynCall_viiiiiijiiiiii=N.jg)(s,l,u,p,f,g,w,b,x,E,T,P,D,O),Fy=i.dynCall_vijjii=(s,l,u,p,f,g)=>(Fy=i.dynCall_vijjii=N.kg)(s,l,u,p,f,g),Wy=i.dynCall_viiiiijjiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D)=>(Wy=i.dynCall_viiiiijjiiiii=N.lg)(s,l,u,p,f,g,w,b,x,E,T,P,D),Gy=i.dynCall_iiiiiji=(s,l,u,p,f,g,w)=>(Gy=i.dynCall_iiiiiji=N.mg)(s,l,u,p,f,g,w),Vy=i.dynCall_iiiiji=(s,l,u,p,f,g)=>(Vy=i.dynCall_iiiiji=N.ng)(s,l,u,p,f,g),Hy=i.dynCall_viiiiijiiiiii=(s,l,u,p,f,g,w,b,x,E,T,P,D)=>(Hy=i.dynCall_viiiiijiiiiii=N.og)(s,l,u,p,f,g,w,b,x,E,T,P,D),qy=i.dynCall_viiijiiiiii=(s,l,u,p,f,g,w,b,x,E,T)=>(qy=i.dynCall_viiijiiiiii=N.pg)(s,l,u,p,f,g,w,b,x,E,T),Ky=i.dynCall_viijj=(s,l,u,p,f)=>(Ky=i.dynCall_viijj=N.qg)(s,l,u,p,f),Yy=i.dynCall_viiiijii=(s,l,u,p,f,g,w,b)=>(Yy=i.dynCall_viiiijii=N.rg)(s,l,u,p,f,g,w,b),Qy=i.dynCall_viijjiii=(s,l,u,p,f,g,w,b)=>(Qy=i.dynCall_viijjiii=N.sg)(s,l,u,p,f,g,w,b),Xy=i.dynCall_ijii=(s,l,u,p)=>(Xy=i.dynCall_ijii=N.tg)(s,l,u,p),Zy=i.dynCall_viiiiijjji=(s,l,u,p,f,g,w,b,x,E)=>(Zy=i.dynCall_viiiiijjji=N.ug)(s,l,u,p,f,g,w,b,x,E),Jy=i.dynCall_vijjjjiij=(s,l,u,p,f,g,w,b,x)=>(Jy=i.dynC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o=a+n.kvSequenceLength,i=[n.batchSize,n.sequenceLength,n.vHiddenSize],d=12,h={x:Math.ceil(n.vHeadSize/d),y:Math.ceil(n.sequenceLength/d),z:n.batchSize*n.numHeads},m=[{type:12,data:n.sequenceLength},{type:12,data:o},{type:12,data:n.vHeadSize},{type:12,data:n.numHeads},{type:12,data:n.vHiddenSize}];return{name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType,gpuDataType:0}],dispatchGroup:h,programUniforms:m}),getShaderSource:c=>{let _=oe("probs",e.dataType,e.dims),v=oe("v",r.dataType,r.dims),$=$e("output",e.dataType,i),C=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return` - const TILE_SIZE = ${d}u; - var tileQ: array<${_.type.value}, ${d*d}>; - var tileK: array<${_.type.value}, ${d*d}>; - ${c.registerUniforms(C).declareVariables(_,v,$)} - ${c.mainStart([d,d,1])} - let headIdx = workgroup_id.z; - let m = global_id.y; - let n = 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${j}[${R}] = ${L}(${v(Y,Q)}); - `};c===9?z=` - var data = vec4(0); - ${I("data",0,"u32")} - ${I("data",1,"u32")} - ${I("data",2,"u32")} - ${I("data",3,"u32")} - outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:z=` - ${I("outputData[global_idx]",0)} - ${I("outputData[global_idx]",1)} - ${I("outputData[global_idx]",2)} - ${I("outputData[global_idx]",3)} - `}return` - ${t.registerUniform("vec_size","u32").declareVariables(k,M,C)} - - ${_??""} - - ${t.mainStart()} - ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} - ${z} - }`},$c=(t,e,r,n,a,o,i=r.dataType)=>{let d=!se.areEqual(r.dims,n.dims),h=r.dims,m=se.size(r.dims),c=!1,_=!1,v=[d];if(d){let $=hn.calcShape(r.dims,n.dims,!1);if(!$)throw new Error("Can't perform binary op on the given tensors");h=$,m=se.size(h);let C=se.size(r.dims)===1,k=se.size(n.dims)===1,M=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,z=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;v.push(C),v.push(k),v.push(M),v.push(z);let I=1;for(let j=1;j$.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:$=>bc($,r.dims,n.dims,h,c,d,_,a,r.dataType,n.dataType,i,o),getRunData:()=>({outputs:[{dims:h,dataType:i}],dispatchGroup:{x:Math.ceil(m/64/4)},programUniforms:[{type:12,data:Math.ceil(se.size(h)/4)},...Ce(r.dims,n.dims,h)]})}},er=(t,e,r,n,a,o)=>{t.compute($c(e,a??"",t.inputs[0],t.inputs[1],r,n,o))},xc=t=>{er(t,"Add",(e,r)=>`${e}+${r}`)},Cc=t=>{er(t,"Div",(e,r)=>`${e}/${r}`)},Sc=t=>{er(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4(${e}==${r})`},void 0,void 0,9)},Ec=t=>{er(t,"Mul",(e,r)=>`${e}*${r}`)},kc=t=>{let e=oe("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;er(t,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},` - fn pow_custom(a : ${e}, b : ${e}) -> ${e} { - if (b == ${e}(0.0)) { - return ${e}(1.0); - } else if (a < ${e}(0.0) && f32(b) != floor(f32(b))) { - return ${e}(pow(f32(a), f32(b))); // NaN - } - return select(sign(a), ${e}(1.0), round(f32(abs(b) % ${e}(2.0))) != 1.0) * ${e}(${e==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); - } - fn pow_vector_custom(a : vec4<${e}>, b : vec4<${e}>) -> vec4<${e}> { - // TODO: implement vectorized pow - return vec4<${e}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); - } - `)},Tc=t=>{er(t,"Sub",(e,r)=>`${e}-${r}`)},Ic=t=>{er(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4(${e}>${r})`},void 0,void 0,9)},Ac=t=>{er(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4(${e}<${r})`},void 0,void 0,9)},Mc=t=>{er(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4(${e}>=${r})`},void 0,void 0,9)},Oc=t=>{er(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4(${e}<=${r})`},void 0,void 0,9)}}),Fr,Wr,Gr,so,Vr=le(()=>{Oe(),Be(),Fr=(t,e,r="f32")=>{switch(t.activation){case"Relu":return`value = max(value, ${e}(0.0));`;case"Sigmoid":return`value = (${e}(1.0) / (${e}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${e}(${r}(uniforms.clip_min)), ${e}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${e}(0.0), min(${e}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${e}(0.0));`;case"":return"";default:throw new Error(`Unsupported activation ${t.activation}`)}},Wr=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},Gr=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},so=t=>{let e=(t==null?void 0:t.activation)||"";if(e==="HardSigmoid"){let[r,n]=(t==null?void 0:t.activation_params)||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=(t==null?void 0:t.activation_params)||[Ks,Ys];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=(t==null?void 0:t.activation_params)||[.01];return{activation:e,alpha:r}}return{activation:e}}}),xt,oo,lo=le(()=>{xt=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},oo=t=>` - ${t?"value = value + getBiasByOutputCoords(coords);":""} - `}),uo,zc=le(()=>{uo=t=>` -fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { - return dot(coords, vec4( - shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); -} -fn getOutputIndexFromCoords(coords : vec4) -> i32 { - return dot(coords, vec4( - i32(${t}.x), i32(${t}.y), i32(${t}.z), 1)); -} -`}),Pc,jc,ba,co,Rc,$a,Bc,po,xa=le(()=>{Oe(),Be(),Re(),Vr(),lo(),Pc=(t,e)=>t?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - kStart + inputRow, - globalRowStart / innerElementSize + inputCol${e?", batchIndices":""}); - `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - globalRow + innerRow, - kStart / innerElementSize + inputCol${e?", batchIndices":""}); - `,jc=(t,e)=>t?` - let ACached0 = mm_Asub[k * innerElementSize][localRow]; - let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; - let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; - ${e===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]; - ${e===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]; - ${e===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} - }`,ba=(t,e,r="f32",n,a=!1,o=32,i=!1,d=32)=>{let h=e[1]*t[1],m=e[0]*t[0],c=a?h:o,_=a?o:h,v=c/e[0],$=o/e[1];if(!((a&&v===4&&t[1]===4||!a&&(v===3||v===4))&&c%e[0]===0&&o%e[1]===0&&t[0]===4))throw new Error(`If transposeA ${a} is true, innerElementSize ${v} and workPerThread[1] ${t[1]} must be 4. - Otherwise, innerElementSize ${v} must be 3 or 4. - tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return` -var mm_Asub: array, ${c/v}>, ${_}>; -var mm_Bsub: array, ${m/t[0]}>, ${o}>; - -const rowPerThread = ${t[1]}; -const colPerThread = ${t[0]}; -const innerElementSize = ${v}; -const tileInner = ${o}; - -@compute @workgroup_size(${e[0]}, ${e[1]}, ${e[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 = ${i?"0":"i32(globalId.z)"}; - ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} - let globalRowStart = i32(workgroupId.y) * ${h}; - - let num_tiles = ${i?`${Math.ceil(d/o)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; - var kStart = ${i?`i32(globalId.z) * ${d}`:"0"}; - - var acc: array, rowPerThread>; - - // Loop over shared dimension. - let tileRowB = localRow * ${$}; - 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; - ${Pc(a,n)} - } - - // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${$}; innerRow = innerRow + 1) { - let inputRow = tileRowB + innerRow; - let inputCol = tileCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${n?", 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]; - ${v===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} - - ${jc(a,v)} - } - - workgroupBarrier(); - } - - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); - } -}`},co=(t,e)=>t?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - kStart + inputRow, - globalRowStart + inputCol${e?", batchIndices":""}); - `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - globalRowStart + inputRow, - kStart + inputCol${e?", batchIndices":""}); - `,Rc=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",$a=(t,e,r="f32",n,a=!1,o=32,i=!1,d=32,h=!1)=>{let m=t[1]*e[1],c=t[0]*e[0],_=a?m:o,v=a?o:m;if(!(v%e[1]===0&&_%e[0]===0&&o%e[1]===0))throw new Error(`tileAHight ${v} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${_} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let $=v/e[1],C=_/e[0],k=o/e[1],M=h?` - let localRow = i32(localId.y); - let localCol = i32(localId.x); - let globalRowStart = i32(workgroupId.y) * ${m}; - let globalColStart = i32(workgroupId.x) * ${c}; - - // 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 < ${v}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${_}; inputCol = inputCol + ${e[0]}) { - ${co(a,n)} - } - } - // Load one tile of B into local memory. - for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) { - mm_Bsub[inputRow][inputCol] = mm_readB(batch, - kStart + inputRow, - globalColStart + inputCol${n?", batchIndices":""}); - } - } - kStart = kStart + tileInner; - workgroupBarrier(); - - // Compute acc values for a single thread. - var BCached : array<${r}, 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 * ${e[0]}]; - } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - let ACached = ${a?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[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 * ${e[1]}; - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { - let gCol = globalColStart + localCol + innerCol * ${e[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) * ${m}; - -let tileRowA = i32(localId.y) * ${$}; -let tileColA = i32(localId.x) * ${C}; -let tileRowB = i32(localId.y) * ${k}; -// 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 < ${$}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < ${C}; innerCol = innerCol + 1) { - let inputRow = tileRowA + innerRow; - let inputCol = tileColA + innerCol; - ${co(a,n)} - } - } - - // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${k}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { - let inputRow = tileRowB + innerRow; - let inputCol = tileCol + innerCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, - kStart + inputRow, - globalCol + innerCol${n?", batchIndices":""}); - } - } - kStart = kStart + tileInner; - workgroupBarrier(); - - // Compute acc values for a single thread. - var BCached : array<${r}, 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) { - ${Rc(a)} - 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, ${v}>; - var mm_Bsub : array, ${o}>; - const rowPerThread = ${t[1]}; - const colPerThread = ${t[0]}; - const tileInner = ${o}; - -@compute @workgroup_size(${e[0]}, ${e[1]}, ${e[2]}) -fn main(@builtin(local_invocation_id) localId : vec3, - @builtin(global_invocation_id) globalId : vec3, - @builtin(workgroup_id) workgroupId : vec3) { - let batch = ${i?"0":"i32(globalId.z)"}; - ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} - let num_tiles = ${i?`${Math.ceil(d/o)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; - var kStart = ${i?`i32(globalId.z) * ${d}`:"0"}; - - var acc : array, rowPerThread>; - - // Without this initialization strange values show up in acc. - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { - acc[innerRow][innerCol] = 0.0; - } - } - ${M} - } -`},Bc=(t,e,r,n,a,o=!1)=>{let[i,d,h]=a,[m,c,_,v]=n,$=Vn(i,h),C=Vn(d,h),k=mt(n[0].type.tensor),M=()=>{let I=c.rank,j=m.rank,R=`var aIndices: ${c.type.indices};`;for(let L=I-2-1,Y=j-1;L>=0;L--,Y--)R+=` -aIndices[${L}] = ${j>1?`batchIndices[${Y}]`:"batchIndices"};`;return $.forEach(L=>{R+=` -aIndices[${L}] = 0;`}),R+=` -aIndices[${I-2}] = u32(row); - aIndices[${I-1}] = u32(colIn);`,R},z=()=>{let I=_.rank,j=m.rank,R=`var bIndices: ${_.type.indices};`;for(let L=I-2-1,Y=j-1;L>=0;L--,Y--)R+=` -bIndices[${L}] = ${j>1?`batchIndices[${Y}]`:"batchIndices"};`;return C.forEach(L=>{R+=` -bIndices[${L}] = 0;`}),R+=` -bIndices[${I-2}] = u32(row); - bIndices[${I-1}] = u32(colIn);`,R};return` - fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${m.type.indices}) -> ${xt(t,k)} { - var value = ${xt(t,k)}(0.0); - let col = colIn * ${t}; - if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) - { - ${M()} - value = ${c.getByIndices("aIndices")}; - } - return value; - } - - fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${m.type.indices}) -> ${xt(t,k)} { - var value = ${xt(t,k)}(0.0); - let col = colIn * ${t}; - if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) - { - ${z()} - value = ${_.getByIndices("bIndices")}; - } - return value; - } - - fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${xt(t,k)}) { - let col = colIn * ${t}; - if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { - var value = valueIn; - let coords = vec3(batch, row, colIn); - ${e?`value = value + ${o?"bias[colIn]":`${xt(t,k)}(bias[row])`};`:""} - ${r} - ${v.setByIndices("vec3(coords)","value")} - } - } - `},po=(t,e,r,n,a=!1)=>{let o=t[0].dims,i=t[1].dims,d=o.slice(0,-2),h=i.slice(0,-2),m=n?n.slice(0,-2):r.slice(0,-2),c=se.size(m),_=o[o.length-2],v=o[o.length-1],$=i[i.length-1],C=v%4===0&&$%4===0,k=_<=8?[4,1,1]:[4,4,1],M=[8,8,1],z=[Math.ceil($/M[0]/k[0]),Math.ceil(_/M[1]/k[1]),Math.ceil(c/M[2]/k[2])],I=C?4:1,j=[...d,_,v/I],R=j.length,L=[...h,v,$/I],Y=L.length,Q=[c,_,$/I],ie=[{type:6,data:_},{type:6,data:$},{type:6,data:v}];Wr(e,ie),ie.push(...Ce(m,j,L));let pe=["rank","rank"],ce=t.length>2;ce&&(ie.push(...Ce(t[2].dims)),pe.push("rank")),ie.push(...Ce(Q));let Se=fe=>{let me=m.length,ge=Qs("batchDims",t[0].dataType,me,1),X=mt(t[0].dataType),U=oe("a",t[0].dataType,R,I),ne=oe("b",t[1].dataType,Y,I),ee=$e("result",t[0].dataType,Q.length,I),Ee=[U,ne];if(ce){let Le=a?I:1;Ee.push(oe("bias",t[2].dataType,t[2].dims.length,Le))}let Te=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Gr(e,Te);let K=mt(ee.type.tensor),he=Fr(e,ee.type.value,K),ve=Bc(I,ce,he,[ge,U,ne,ee],[d,h,m],a);return` - ${fe.registerUniforms(Te).registerInternalVariables(ge).declareVariables(...Ee,ee)} - ${ve} - ${C?ba(k,M,X,ge):$a(k,M,X,ge)} - `};return{name:"MatMul",shaderCache:{hint:`${k};${e.activation};${C};${a}`,inputDependencies:pe},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:z[0],y:z[1],z:z[2]},programUniforms:ie}),getShaderSource:Se}}}),Dc,Nc,cw=le(()=>{Oe(),Ur(),Re(),Vr(),lo(),zc(),xa(),Dc=(t,e,r,n,a=!1,o,i=4,d=4,h=4,m="f32")=>{let c=pe=>{switch(pe){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${m}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${pe} is not supported.`)}},_=pe=>{switch(pe){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 ${pe} is not supported.`)}},v=t?` - let coord = vec4(batch, xRow, xCol, xCh); - `:` - let coord = vec4(batch, xCh, xRow, xCol); - `,$=t?` - let coords = vec4( - batch, - row / outWidth, - row % outWidth, - col); - `:` - let coords = vec4( - batch, - row, - col / outWidth, - col % outWidth); - `,C=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",k=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",M=t?"row":"col",z=t?"col":"row",I=` - let inChannels = i32(uniforms.w_shape[2]); - let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; - let outRow = ${M} / outWidth; - let outCol = ${M} % outWidth; - - let WRow = ${z} / (i32(uniforms.w_shape[1]) * inChannels); - let WCol = ${z} / 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 = ${z} % inChannels; - var resData = ${xt(i,m)}(0.0); - // The bounds checking is always needed since we use it to pad zero for - // the 'same' padding type. - if (xRow >= 0 && xRow < ${C} && xCol >= 0 && xCol < ${k}) { - ${v} - let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); - ${c(i)} - } - return resData;`,j=t?e&&n?` - let col = colIn * ${i}; - ${I}`:` - let col = colIn * ${i}; - if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { - ${I} - } - return ${xt(i,m)}(0.0);`:n&&r?` - let col = colIn * ${i}; - ${I}`:` - let col = colIn * ${i}; - if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { - ${I} - } - return ${xt(i,m)}(0.0);`,R=`${_(d)}`,L=xt(h,m),Y=xt(t?i:d,m),Q=xt(t?d:i,m),ie=Fr(o,L,m);return` - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Y} { - ${t?j:R} - } - - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Q} { - ${t?R:j} - } - - fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${L}) { - let col = colIn * ${h}; - if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) - { - var value = valueIn; - let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; - ${$} - ${oo(a)} - ${ie} - setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); - } - }`},Nc=(t,e,r,n,a,o,i,d)=>{let h=e.format==="NHWC",m=h?t[0].dims[3]:t[0].dims[1],c=r[0],_=h?r[2]:r[3],v=h?r[1]:r[2],$=h?r[3]:r[1],C=h&&(m%4===0||m%3===0)&&$%4===0,k=h?$:_*v,M=h?_*v:$,z=[8,8,1],I=n<=8?[4,1,1]:[4,4,1],j=[Math.ceil(k/z[0]/I[0]),Math.ceil(M/z[1]/I[1]),Math.ceil(c/z[2]/I[2])];it("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${j}`);let R=C?h&&m%4!==0?3:4:1,L=z[1]*I[1],Y=z[0]*I[0],Q=Math.max(z[0]*R,z[1]),ie=n%L===0,pe=a%Y===0,ce=o%Q===0,Se=C?[R,4,4]:[1,1,1],fe=[{type:6,data:n},{type:6,data:a},{type:6,data:o},{type:6,data:[e.pads[0],e.pads[1]]},{type:6,data:e.strides},{type:6,data:e.dilations}];Wr(e,fe),fe.push(...Ce(t[0].dims,t[1].dims));let me=["rank","rank"];i&&(fe.push(...Ce(t[2].dims)),me.push("rank")),fe.push(...Ce(r));let ge=X=>{let U=[{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}];Gr(e,U);let ne=C?4:1,ee=mt(t[0].dataType),Ee=` - fn setOutputAtIndex(flatIndex : i32, value : ${C?`vec4<${ee}>`:ee}) { - result[flatIndex] = ${C?`vec4<${ee}>`:ee}(value); - } - fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${C?`vec4<${ee}>`:ee}) { - let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); - setOutputAtIndex(flatIndex ${C?"/ 4":""}, value); - }`,Te=oe("x",t[0].dataType,t[0].dims.length,R===3?1:R),K=oe("w",t[1].dataType,t[1].dims.length,ne),he=[Te,K],ve=$e("result",t[0].dataType,r.length,ne);if(i){let Le=oe("bias",t[2].dataType,t[2].dims.length,ne);he.push(Le),Ee+=` - fn getBiasByOutputCoords(coords : vec4) -> ${C?`vec4<${ee}>`:ee} { - return bias[coords.${h?"w":"y"}${C?"/ 4":""}]; - }`}return` - ${uo("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 }; - ${X.registerUniforms(U).declareVariables(...he,ve)} - ${Ee} - ${Dc(h,ie,pe,ce,i,e,Se[0],Se[1],Se[2],ee)} - ${C?ba(I,z,ee,void 0,!h,Q):$a(I,z,ee,void 0,!h,Q,!1,void 0,d)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${R};${C};${ie};${pe};${ce};${L};${Y};${Q}`,inputDependencies:me},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:j[0],y:j[1],z:j[2]},programUniforms:fe}),getShaderSource:ge}}}),ho,Lc,pw=le(()=>{Oe(),Be(),Re(),qc(),Vr(),ho=(t,e,r)=>{let n=t.length>2,a=n?"value += b[output_channel];":"",o=t[0].dims,i=t[1].dims,d=i[0]/e.group,h=e.format==="NHWC",m=Ca(o,i,e.dilations,e.pads,e.strides,h),c=se.size(m),_=[{type:12,data:c},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:d}];Wr(e,_),_.push(...Ce(o,i));let v=["rank","rank"];n&&(_.push(...Ce(t[2].dims)),v.push("rank")),_.push(...Ce(m));let $=C=>{let k=$e("output",t[0].dataType,m.length),M=mt(k.type.tensor),z=Fr(e,k.type.value,M),I=oe("x",t[0].dataType,o.length),j=oe("w",t[1].dataType,i.length),R=[I,j];n&&R.push(oe("b",t[2].dataType,t[2].dims.length));let L=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:e.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return Gr(e,L),` - ${C.registerUniforms(L).declareVariables(...R,k)} - - ${C.mainStart()} - ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - - let outputIndices = ${k.offsetToIndices("global_idx")}; - let batch: u32 = outputIndices[0]; - let output_channel: u32 = outputIndices[${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 / uniforms.output_channels_per_group; - - var value: ${k.type.value} = ${k.type.value}(0); - for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { - let input_channel = group_id * uniforms.w_shape[1] + wInChannel; - for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { - let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; - - if (xHeight < 0u || xHeight >= uniforms.x_shape[${h?1:2}]) { - 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[${h?2:3}]) { - continue; - } - - let xVal = ${h?I.get("batch","xHeight","xWidth","input_channel"):I.get("batch","input_channel","xHeight","xWidth")}; - let wVal = ${j.get("output_channel","wInChannel","wHeight","wWidth")}; - value += xVal*wVal; - } - } - } - ${a} - ${z} - ${k.setByOffset("global_idx","value")} - }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:v},getRunData:()=>({outputs:[{dims:r?r(m):m,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:_}),getShaderSource:$}},Lc=(t,e,r)=>{let n=t.length>2,a=dt(r[3]),o=dt(r[2]),i=se.size(r)/a/o,d=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],h=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],m=[r[0],r[1],r[2],r[3]/a],c=[{type:12,data:i},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];Wr(e,c),c.push(...Ce(d,h,m));let _=(o-1)*e.strides[1]+h[1],v=$=>{let C=$e("output",t[0].dataType,m.length,a),k=mt(C.type.tensor),M=Fr(e,C.type.value,k),z=oe("x",t[0].dataType,d.length,a),I=oe("w",t[1].dataType,h.length,a),j=[z,I];n&&j.push(oe("b",t[2].dataType,t[2].dims,a));let R=n?"value += b[output_channel];":"",L=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Gr(e,L),` - ${$.registerUniforms(L).declareVariables(...j,C)} - ${$.mainStart()} - ${$.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] / ${o}u; - let col = (index1 % width1) * ${o}u; - index1 = index1 / width1; - let row = index1 % uniforms.output_shape[1]; - let batch = index1 / uniforms.output_shape[1]; - - let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; - - var x_vals: array<${z.type.value}, ${_}>; - var values: array<${C.type.value}, ${o}>; - 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 < ${h[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 < ${_}; i++) { - let x_width = x_corner.y + i; - if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { - x_vals[i] = ${z.get("batch","u32(x_height)","u32(x_width)","input_channel")}; - } else { - x_vals[i] = ${z.type.value}(0); - } - } - for (var w_width: u32 = 0u; w_width < ${h[1]}; w_width++) { - let w_val = ${I.get("w_height","w_width","0","output_channel")}; - for (var i = 0u; i < ${o}u; i++) { - values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); - } - } - } - } - - for (var i = 0u; i < ${o}u; i++) { - var value = values[i]; - ${R} - ${M} - ${C.set("batch","row","col + i","output_channel","value")}; - } - }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${a};${o};${_};${h[0]};${h[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:c}),getShaderSource:v}}}),fo,Uc,Fc,Wc=le(()=>{Oe(),Be(),xa(),Re(),Vr(),fo=(t,e,r,n,a=!1)=>{let o=t[0].dims,i=t[1].dims,d=o[o.length-2],h=i[i.length-1],m=o[o.length-1],c=dt(h),_=dt(m),v=dt(d),$=se.size(r)/c/v,C=t.length>2,k=n?n.slice(0,-2):r.slice(0,-2),M=[se.size(k),d,h],z=[{type:12,data:$},{type:12,data:d},{type:12,data:h},{type:12,data:m}];Wr(e,z),z.push(...Ce(k,o,i)),C&&z.push(...Ce(t[2].dims)),z.push(...Ce(M));let I=j=>{let R=Qs("batch_dims",t[0].dataType,k.length),L=oe("a",t[0].dataType,o.length,_),Y=oe("b",t[1].dataType,i.length,c),Q=$e("output",t[0].dataType,M.length,c),ie=mt(Q.type.tensor),pe=Fr(e,Q.type.value,ie),ce=[L,Y],Se="";if(C){let Ee=a?c:1;ce.push(oe("bias",t[2].dataType,t[2].dims.length,Ee)),Se=`${a?`value += bias[col / ${Ee}];`:`value += ${Q.type.value}(bias[row + i]);`}`}let fe=o.slice(0,-2),me=i.slice(0,-2),ge=Vn(fe,k),X=Vn(me,k),U=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Gr(e,U);let ne=(Ee,Te)=>{let K=Ee.rank,he=Ee.name;if(K===2)return`var ${he}_indices = ${Ee.type.indices}(0u, 0u);`;let ve=R.rank,Le=`var ${he}_indices: ${Ee.type.indices};`;for(let Ke=K-2-1,ct=ve-1;Ke>=0;Ke--,ct--)Le+=` -${he}_indices[${Ke}] = ${ve>1?`batch_indices[${ct}]`:"batch_indices"};`;return Te.forEach(Ke=>{Le+=` -${he}_indices[${Ke}] = 0;`}),Le+=`${he}_indices[${K-2}] = 0u; - ${he}_indices[${K-1}] = 0u;`,Le},ee=()=>{let Ee=`var a_data: ${L.type.value};`;for(let Te=0;Te<_;Te++)Ee+=` - let b_data${Te} = b[(b_offset + (k + ${Te}) * uniforms.N + col) / ${c}];`;for(let Te=0;Te; - for (var k: u32 = 0u; k < uniforms.K; k = k + ${_}) { - ${ee()} - } - for (var i = 0u; i < ${v}u; i++) { - var value = values[i]; - ${Se} - ${pe} - let cur_indices = ${Q.type.indices}(batch, row + i, col); - let offset = ${Q.indicesToOffset("cur_indices")}; - ${Q.setByOffset(`offset / ${c}`,"value")}; - } - } - `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${c};${_};${v};${a}`,inputDependencies:C?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil($/64)},programUniforms:z}),getShaderSource:I}},Uc=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.")},Fc=t=>{Uc(t.inputs);let e=hn.calcShape(t.inputs[0].dims,t.inputs[1].dims,!0);if(!e)throw new Error("Can't use matmul on the given tensors");let r=e[e.length-1],n=t.inputs[0].dims[t.inputs[0].dims.length-1];r<8&&n<8?t.compute(fo(t.inputs,{activation:""},e)):t.compute(po(t.inputs,{activation:""},e))}}),Ca,Sa,Gc,mo,go,Vc,Hc,yo,qc=le(()=>{Be(),cw(),xa(),pw(),Vr(),Wc(),Hn(),Ca=(t,e,r,n,a,o)=>{let i=t[0],d=t.slice(o?1:2,o?3:4),h=d.length,m=e[0],c=e.slice(2).map((v,$)=>v+(v-1)*(r[$]-1)),_=d.map((v,$)=>v+n[$]+n[$+h]).map((v,$)=>Math.floor((v-c[$]+a[$])/a[$]));return _.splice(0,0,i),_.splice(o?3:1,0,m),_},Sa=[2,3,1,0],Gc=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let r=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],n=t[1].dims[1]*e.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(t.length===3&&(t[2].dims.length!==1||t[1].dims[0]!==t[2].dims[0]))throw new Error("invalid bias");let a=t[0].dims.length-2;if(e.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(e.strides.length!==a)throw new Error(`strides should be ${a}D`);if(e.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},mo=(t,e)=>{let r=t.kernelShape.slice();for(let o=2;o{let e=so(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,o=t.group,i=t.kernel_shape,d=t.pads,h=t.strides,m=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:o,kernelShape:i,pads:d,strides:h,wIsConst:m,...e,cacheKey:`${t.format};${e.activation};`}},Vc=(t,e,r)=>{let n=mo(r,e),a=r.format==="NHWC";if(r.group!==1){if(!t.adapterInfo.isArchitecture("ampere")&&a&&e[1].dims[0]===r.group&&e[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let Y=Ca(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),Q=t.kernelCustomData.wT??t.compute(yr(e[1],Sa),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=Q);let ie=[e[0],Q];e.length===3&&ie.push(e[2]),t.compute(Lc(ie,n,Y),{inputs:ie})}else t.compute(ho(e,n));return}let o=e.length===3,i=e[0].dims[a?1:2],d=e[0].dims[a?2:3],h=e[0].dims[a?3:1],m=e[1].dims[2],c=e[1].dims[3],_=Ca(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),v=_[a?1:2],$=_[a?2:3],C=_[a?3:1],k=a&&m===i&&c===d&&r.pads[0]===0&&r.pads[1]===0;if(k||m===1&&c===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let Y=_[0],Q,ie,pe,ce=[];if(a){let me=t.kernelCustomData.wT??t.compute(yr(e[1],Sa),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=me),k){let ge=i*d*h;Q=e[0].reshape([1,Y,ge]),ie=me.reshape([1,ge,C]),pe=[1,Y,C]}else Q=e[0].reshape([Y,i*d,h]),ie=me.reshape([1,h,C]),pe=[Y,v*$,C];ce.push(Q),ce.push(ie)}else Q=e[0].reshape([Y,h,i*d]),ie=e[1].reshape([1,C,h]),pe=[Y,C,v*$],ce.push(ie),ce.push(Q);o&&ce.push(e[2]);let Se=pe[2],fe=ce[0].dims[ce[0].dims.length-1];Se<8&&fe<8?t.compute(fo(ce,n,_,pe,a),{inputs:ce}):t.compute(po(ce,n,_,pe,a),{inputs:ce});return}let M=!0,z=t.kernelCustomData.wT??t.compute(yr(e[1],Sa),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=z);let I=[e[0],z];o&&I.push(e[2]);let j=a?v*$:C,R=a?C:v*$,L=m*c*h;t.compute(Nc(I,n,_,j,R,L,o,M),{inputs:I})},Hc=(t,e)=>{let r=e.format==="NHWC",n=[t.inputs[0].reshape(r?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&n.push(t.inputs[2]);let a=[0,e.pads[0],0,e.pads[1]],o=[1].concat(e.strides),i=[1].concat(e.dilations),d=[1].concat(e.kernelShape),h=mo({...e,pads:a,strides:o,dilations:i,kernelShape:d},n);t.compute(ho(n,h,m=>r?[m[0],m[2],m[3]]:[]))},yo=(t,e)=>{Gc(t.inputs,e),t.inputs[0].dims.length===3?Hc(t,e):Vc(t,t.inputs,e)}}),Kc,Yc,hw=le(()=>{Oe(),Ur(),Re(),Vr(),lo(),zc(),xa(),Kc=(t,e=!1,r,n,a=4)=>{let o=M=>{switch(M){case 1:return"return w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];";case 4:return` - let coord1 = vec4(coordX, coordY, col + 1, rowInner); - let coord2 = vec4(coordX, coordY, col + 2, rowInner); - let coord3 = vec4(coordX, coordY, col + 3, rowInner); - let v0 = w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))]; - let v1 = w[getIndexFromCoords4D(coord1, vec4(uniforms.w_shape))]; - let v2 = w[getIndexFromCoords4D(coord2, vec4(uniforms.w_shape))]; - let v3 = w[getIndexFromCoords4D(coord3, vec4(uniforms.w_shape))]; - return ${n}(v0, v1, v2, v3); - `;default:throw new Error(`innerElementSize ${M} is not supported.`)}},i=t?` - let coord = vec4(batch, iXR, iXC, xCh); - `:` - let coord = vec4(batch, xCh, iXR, iXC); - `,d=t?` - let coords = vec4( - batch, - row / outWidth, - row % outWidth, - col); - `:` - let coords = vec4( - batch, - row, - col / outWidth, - col % outWidth); - `,h=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",m=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",c=t?"row":"col",_=t?"col":"row",v=` - let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; - let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; - let outRow = ${c} / outWidth; - let outCol = ${c} % outWidth; - - let WRow = ${_} / (uniforms.filter_dims[1] * inChannels); - let WCol = ${_} / inChannels % uniforms.filter_dims[1]; - let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]); - let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]); - if (xR < 0.0 || xR >= f32(${h}) || fract(xR) > 0.0) { - return ${n}(0.0); - } - if (xC < 0.0 || xC >= f32(${m}) || fract(xC) > 0.0) { - return ${n}(0.0); - } - let iXR = i32(xR); - let iXC = i32(xC); - let xCh = ${_} % inChannels; - ${i} - return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${a}];`,$=t?` - let col = colIn * ${a}; - if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { - ${v} - } - return ${n}(0.0);`:` - let col = colIn * ${a}; - if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { - ${v} - } - return ${n}(0.0);`,C=` - let col = colIn * ${a}; - let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; - let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels); - let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1]; - if (${t?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"} && coordX >= 0 && coordY >= 0) { - let rowInner = row % inChannels; - let coord = vec4(coordX, coordY, col, rowInner); - ${o(a)} - } - return ${n}(0.0); - `,k=Fr(r,n);return` - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} { - ${t?$:C} - } - - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} { - ${t?C:$} - } - - fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) { - let col = colIn * ${a}; - if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { - var value = valueInput; - let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; - ${d} - ${oo(e)} - ${k} - result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${a}] = value; - } - }`},Yc=(t,e,r,n,a,o,i,d)=>{let h=e.format==="NHWC",m=h?t[0].dims[3]:t[0].dims[1],c=r[0],_=h?r[2]:r[3],v=h?r[1]:r[2],$=h?r[3]:r[1],C=h&&m%4===0&&m%3&&$%4===0,k=h?$:_*v,M=h?_*v:$,z=[8,8,1],I=n<=8?[4,1,1]:[4,4,1],j=[Math.ceil(k/z[0]/I[0]),Math.ceil(M/z[1]/I[1]),Math.ceil(c/z[2]/I[2])];it("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${j}`);let R=C?4:1,L=Math.max(z[0]*R,z[1]),Y=C?4:1,Q=[e.kernelShape[h?1:2],e.kernelShape[h?2:3]],ie=[Q[0]+(e.dilations[0]<=1?0:(Q[0]-1)*(e.dilations[0]-1)),Q[1]+(e.dilations[1]<=1?0:(Q[1]-1)*(e.dilations[1]-1))],pe=[ie[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),ie[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],ce=[{type:6,data:n},{type:6,data:a},{type:6,data:o},{type:6,data:e.strides},{type:6,data:e.dilations},{type:6,data:Q},{type:6,data:pe}];Wr(e,ce),ce.push(...Ce(t[0].dims,t[1].dims));let Se=["rank","rank"];i&&(ce.push(...Ce(t[2].dims)),Se.push("rank")),ce.push(...Ce(r));let fe=me=>{let ge=oe("x",t[0].dataType,t[0].dims.length,Y),X=oe("w",t[1].dataType,t[1].dims.length,1),U=$e("result",t[0].dataType,r.length,Y),ne=[ge,X],ee="";if(i){let K=oe("bias",t[2].dataType,t[2].dims.length,Y);ne.push(K),ee+=` - fn getBiasByOutputCoords(coords : vec4) -> ${K.type.value} { - return bias[coords.${h?"w":"y"}${C?"/ 4":""}]; - }`}let Ee=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:Q.length},{name:"pads",type:"i32",length:pe.length}];Gr(e,Ee);let Te=mt(t[0].dataType,1);if(Te!=="f16"&&Te!=="f32")throw new Error(`elemType ${Te} is not supported.`);return` - ${uo("uniforms.result_strides")} - ${me.registerUniforms(Ee).declareVariables(...ne,U)}; - ${ee} - ${Kc(h,i,e,ge.type.value,R)} - ${C?ba(I,z,Te,void 0,!h,L):$a(I,z,Te,void 0,!h,L,!1,void 0,d)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${I};${z};${C}`,inputDependencies:Se},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:j[0],y:j[1],z:j[2]},programUniforms:ce}),getShaderSource:fe}}}),Qc,_o,fw=le(()=>{Oe(),Ur(),Be(),Re(),Qc=(t,e,r,n,a,o=!1,i,d,h=!1)=>{let m=h?1:2,c=h?2:3,_=h?3:1,v=o?2:1,$=` - fn setOutputAtIndex(flatIndex : u32, value : ${o?`vec4<${i}>`:i}) { - result[flatIndex] = ${o?`vec4<${i}>`:i}(value); - }`;n&&($+=` - fn getBiasByOutputCoords(coords : vec4) -> ${o?`vec4<${i}>`:i} { - return bias[coords.${h?"w":"y"}${o?"/ 4":""}]; - }`);let C=o?4:1,k=oe("W",e[1].dataType,e[1].dims.length,C),M=oe("Dy",e[0].dataType,e[0].dims.length,C),z=[M,k];n&&z.push(oe("bias",e[2].dataType,[r[_]].length,C));let I=$e("result",e[0].dataType,r.length,C),j=`{ - let batch: u32 = ${a?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1]; - let r = ${a?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1]; - let c = ${a?"global_id.y":"workgroup_id.y"} * ${v}; - let d1: u32 = ${a?"global_id.x":"workgroup_id.x"} * 4; - - let dyCorner = vec2(i32(r), i32(c)) - vec2(uniforms.pads); - - // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). - // ? = to be determined. : = across all values in that axis. - var dotProd: array, ${v}>; - for (var i = 0; i < ${v}; i++) { - dotProd[i] = vec4<${i}>(0.0); - } - for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) { - var dyR = (${i}(dyCorner.x) + ${i}(wR)) / ${i}(uniforms.strides.x); - let wRPerm = uniforms.filter_dims[0] - 1 - wR; - if (dyR < 0.0 || dyR >= ${i}(uniforms.Dy_shape[1]) || - fract(dyR) > 0.0 || wRPerm < 0) { - continue; - } - let idyR: u32 = u32(dyR); - - for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) { - let dyC = (${i}(dyCorner.y) + ${i}(wC)) / ${i}(uniforms.strides.y); - let dyC2 = (${i}(dyCorner.y) + 1.0 + ${i}(wC)) / ${i}(uniforms.strides.y); - let wCPerm = uniforms.filter_dims[1] - 1 - wC; - if (wCPerm < 0) { - continue; - } - var bDyCVal = true; - var bDyCVal2 = true; - if (dyC < 0.0 || dyC >= ${i}(uniforms.Dy_shape[2]) || - fract(dyC) > 0.0) { - bDyCVal = false; - } - if (dyC2 < 0.0 || dyC2 >= ${i}(uniforms.Dy_shape[2]) || - fract(dyC2) > 0.0) { - bDyCVal2 = false; - } - - let idyC: u32 = u32(dyC); - let idyC2: u32 = u32(dyC2); - if (bDyCVal && bDyCVal2) { - let d2Length = uniforms.Dy_shape[3]; - for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) { - let wValue0 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${M.get("batch","idyR","idyC","d2")}; - let tmpval = vec4<${i}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - dotProd[0] = dotProd[0] + tmpval; - - xValue = ${M.get("batch","idyR","idyC2","d2")}; - - dotProd[1] = dotProd[1] + vec4<${i}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - } - } else if (bDyCVal) { - let d2Length = uniforms.Dy_shape[${_}]; - for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { - let wValue0 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${M.get("batch","idyR","idyC","d2")}; - let tmpval = vec4<${i}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - dotProd[0] = dotProd[0] + tmpval; - } - } else if (bDyCVal2) { - let d2Length = uniforms.Dy_shape[3]; - for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { - let wValue0 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${k.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${M.get("batch","idyR","idyC2","d2")}; - let tmpval = vec4<${i}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - dotProd[1] = dotProd[1] + tmpval; - } - } - } - } - - for (var i: u32 = 0; i < ${v}; i = i + 1) { - let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${i}>(0.0)`}; - ${I.set("batch","r","c + i","d1","value")}; - } - }`,R=` - let outputIndices = ${I.offsetToIndices("global_idx")}; - let batch = ${I.indicesGet("outputIndices",0)}; - let d1 = ${I.indicesGet("outputIndices",_)}; - let r = ${I.indicesGet("outputIndices",m)}; - let c = ${I.indicesGet("outputIndices",c)}; - let dyCorner = vec2(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 = ${i}(0.0); - for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { - if (wR % uniforms.dilations.x != 0) { - continue; - } - let dyR = (${i}(dyRCorner) + ${i}(wR)) / ${i}(uniforms.strides[0]); - let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; - if (dyR < 0.0 || dyR >= ${i}(uniforms.Dy_shape[${m}]) || fract(dyR) > 0.0 || - wRPerm < 0) { - continue; - } - let idyR: u32 = u32(dyR); - - for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { - if (wC % uniforms.dilations.y != 0) { - continue; - } - let dyC = (${i}(dyCCorner) + ${i}(wC)) / ${i}(uniforms.strides.y); - let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; - if (dyC < 0.0 || dyC >= ${i}(uniforms.Dy_shape[${c}]) || - fract(dyC) > 0.0 || wCPerm < 0) { - continue; - } - let idyC: u32 = u32(dyC); - var inputChannel = groupId * uniforms.input_channels_per_group; - for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) { - let xValue = ${h?M.get("batch","idyR","idyC","inputChannel"):M.get("batch","inputChannel","idyR","idyC")}; - let wValue = ${k.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")}; - dotProd = dotProd + xValue * wValue; - inputChannel = inputChannel + 1; - } - } - } - let value = dotProd + ${n?"bias[d1]":`${i}(0.0)`}; - ${I.setByOffset("global_idx","value")}; - `;return` - ${t.registerUniforms(d).declareVariables(...z,I)} - ${$} - - ${t.mainStart()} - ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; - ${o?j:R}}`},_o=(t,e,r)=>{let n=t.length>2,a=e.outputShape,o=se.size(a),i=[Math.ceil(o/64),1,1];it("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${i}`);let d=e.format==="NHWC",h=["rank","rank"],m=[e.strides[0],e.strides[1]],c=[e.kernelShape[d?1:2],e.kernelShape[d?2:3]],_=[e.dilations[0],e.dilations[1]],v=[c[0]+(e.dilations[0]<=1?0:(e.kernelShape[d?1:2]-1)*(e.dilations[0]-1)),c[1]+(e.dilations[1]<=1?0:(e.kernelShape[d?2:3]-1)*(e.dilations[1]-1))],$=[v[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),v[1]-1-Math.floor(e.pads[1]+e.pads[3])/2],C=!1,k=e.group,M=t[1].dims,z=M[0]/k,I=M[1],j=[{type:12,data:o},{type:12,data:m},{type:12,data:c},{type:12,data:_},{type:12,data:v},{type:6,data:$},{type:12,data:z},{type:12,data:I},...Ce(t[0].dims,t[1].dims)];n&&(j.push(...Ce(t[2].dims)),h.push("rank")),j.push(...Ce(a));let R=i[1]===1&&i[2]===1,L=Y=>{let Q=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:m.length},{name:"filter_dims",type:"u32",length:c.length},{name:"dilations",type:"u32",length:c.length},{name:"effective_filter_dims",type:"u32",length:v.length},{name:"pads",type:"i32",length:$.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],ie=mt(t[0].dataType);return`${Qc(Y,t,a,n,R,C,ie,Q,d)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${e.cacheKey};`,inputDependencies:h},getRunData:()=>({dispatchGroup:{x:i[0],y:i[1],z:i[2]},outputs:[{dims:r?r(a):a,dataType:t[0].dataType}],programUniforms:j}),getShaderSource:L}}}),Xc,Zc,Jc,wo,ep,tp,rp,np,ip,ap,mw=le(()=>{hw(),fw(),Vr(),Hn(),Xc=(t,e,r,n,a,o)=>(t-1)*e+r+(n-1)*a+1-o,Zc=(t,e,r,n,a)=>{let o=Math.floor(t/2);e==="SAME_UPPER"?(r[n]=o,r[a]=t-o):e==="SAME_LOWER"&&(r[n]=t-o,r[a]=o)},Jc=(t,e,r,n,a,o,i,d,h,m)=>{let c=t.length-2,_=m.length===0;if(h.length===0)for(let C=0;C{let r=t.kernelShape.slice();if(t.kernelShape.length===0||t.kernelShape.reduce((_,v)=>_*v,1)===0){r.length=0;for(let _=2;__+v,0)===0){let _=e[0].dims.length-2;h=new Array(_).fill(1)}let m=t.strides.slice();if(m.reduce((_,v)=>_+v,0)===0){let _=e[0].dims.length-2;m=new Array(_).fill(1)}Jc(d,r,h,t.autoPad,t.group,a,m,n,i,o);let c=Object.assign({},t);return Object.assign(c,{kernelShape:r,pads:a,outputPadding:i,outputShape:o,dilations:h,strides:m}),c},ep=t=>{let e=so(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof t.autoPad>"u"?0:t.autoPad],a=t.dilations,o=t.group,i=t.kernelShape,d=t.pads,h=t.strides,m=t.wIsConst(),c=t.outputPadding,_=t.outputShape;return{autoPad:n,format:r,dilations:a,group:o,kernelShape:i,outputPadding:c,outputShape:_,pads:d,strides:h,wIsConst:m,...e,cacheKey:`${t.format};${e.activation};`}},tp=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently 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the data input be rank >= 1.");if(t[0].dims.length!==t[1].dims.length)throw new Error(`GatherElements requires that the data input and - indices input tensors be of same rank.`)},Op=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,o=t[1].dims,i=t[1].dataType,d=se.normalizeAxis(e.axis,a),h=r[d],m=o.slice(0),c=se.size(m),_=oe("input",n,a),v=oe("indicesInput",i,o.length),$=$e("output",n,m.length),C=[{type:12,data:c},{type:6,data:h},{type:12,data:d}];return C.push(...Ce(r,o,m)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:m,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:C}),getShaderSource:k=>` - ${k.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(_,v,$)} - ${k.mainStart()} - ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - - let outputIndices = ${$.offsetToIndices("global_idx")}; - - var idx = ${v.getByOffset("global_idx")}; - if (idx < 0) { - idx = idx + uniforms.axisDimLimit; - } - var inputIndices = ${_.type.indices}(outputIndices); - ${_.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; - let value = ${_.getByIndices("inputIndices")}; - - ${$.setByOffset("global_idx","value")}; - }`}},zp=t=>Ze({axis:t.axis}),Pp=(t,e)=>{let r=t.inputs;Mp(r),t.compute(Op(t.inputs,e))}}),jp,Rp,Bp,Dp,xw=le(()=>{Oe(),Be(),Re(),jp=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},Rp=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,o,i]=wu.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),d=[a,o];if(!d)throw new Error("Can't use gemm on the given tensors");let h=se.size(d),m=[{type:12,data:h},{type:12,data:a},{type:12,data:o},{type:12,data:i},{type:1,data:e.alpha},{type:1,data:e.beta}],c=["type","type"];t.length===3&&(m.push(...Ce(t[2].dims)),c.push("rank")),m.push(...Ce(d));let _=v=>{let $="";e.transA&&e.transB?$="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?$="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?$="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&($="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let C=e.alpha===1?"":"value *= uniforms.alpha;",k=oe("a",t[0].dataType,t[0].dims),M=oe("b",t[1].dataType,t[1].dims),z=k.type.value,I=null,j=[k,M];t.length===3&&(I=oe("c",t[2].dataType,t[2].dims.length),j.push(I));let R=$e("output",t[0].dataType,d.length);j.push(R);let L=[{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` - ${v.registerUniforms(L).declareVariables(...j)} - - ${v.mainStart()} - ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - - let m = global_idx / uniforms.N; - let n = global_idx % uniforms.N; - - var value = ${z}(0); - for (var k: u32 = 0u; k < uniforms.K; k++) { - ${$} - } - - ${C} - ${I!=null?`let cOffset = ${I.broadcastedIndicesToOffset("vec2(m, n)",R)}; value += ${z}(uniforms.beta) * ${I.getByOffset("cOffset")};`:""} - output[global_idx] = value; - }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:m}),getShaderSource:_}},Bp=t=>{let e=t.transA,r=t.transB,n=t.alpha,a=t.beta;return{transA:e,transB:r,alpha:n,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},Dp=(t,e)=>{jp(t.inputs),t.compute(Rp(t.inputs,e))}}),Np,Lp,Up,Fp,Cw=le(()=>{Oe(),Be(),Re(),Np=(t,e)=>{let r=t[0].dims,n=r,a=2,o=se.sizeToDimension(r,a),i=se.sizeFromDimension(r,a),d=dt(i),h=i/d,m=[r[0],r[1],h],c=["rank","type","type"],_=[{type:12,data:i},{type:12,data:h}];_.push(...Ce(m,m));let v=$=>{let C=oe("x",t[0].dataType,m.length,d),k=oe("scale",t[1].dataType,t[1].dims),M=oe("bias",t[2].dataType,t[2].dims),z=$e("output",t[0].dataType,m.length,d),I=[C,k,M,z],j=C.type.value,R=d===1?"f32":`vec${d}`,L=64,Y=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return` - var meanShared : f32; - var squaredNormShared : f32; - var workgroupShared : array<${R}, ${L}>; - const workgroupSize = ${L}u; - ${$.registerUniforms(Y).declareVariables(...I)} - ${$.mainStart(L)} - let norm = global_idx / workgroupSize; - let batch = norm / uniforms.x_shape[1]; - let channel = norm % uniforms.x_shape[1]; - let localIndex = local_id.x; - - // initialize workgroup memory - var initial = ${R}(0); - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - initial = initial + ${R}(${C.get("batch","channel","h")}); - } - workgroupShared[localIndex] = initial; - workgroupBarrier(); - - // Calculate the mean of current channel data. - for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) { - if (localIndex < currSize) { - workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize]; - } - workgroupBarrier(); - } - if (localIndex == 0) { - meanShared = ${kr("workgroupShared[0]",d)} / f32(uniforms.normSize); - } - workgroupBarrier(); - - // reinitialize workgroup memory. - initial = ${R}(0); - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - let deviation = ${R}(${C.get("batch","channel","h")}) - ${R}(meanShared); - initial = initial + deviation * deviation; - } - workgroupShared[localIndex] = initial; - workgroupBarrier(); - - // Calculate the sum of square of deviation of current channel data. - for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) { - if (localIndex < currSize) { - workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize]; - } - workgroupBarrier(); - } - if (localIndex == 0) { - squaredNormShared = ${kr("workgroupShared[0]",d)}; - } - workgroupBarrier(); - - let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${e.epsilon})); - let channelScale = invStdDev * f32(${k.getByOffset("channel")}); - let channelShift = f32(${M.getByOffset("channel")}) - meanShared * channelScale; - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - let value = ${C.get("batch","channel","h")} * ${j}(${R}(channelScale)) + ${j}(${R}(channelShift)); - ${z.set("batch","channel","h","value")}; - } - }`};return{name:"InstanceNormalization",shaderCache:{hint:`${e.epsilon};${d}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:o},programUniforms:_}),getShaderSource:v}},Lp=(t,e,r,n,a,o,i,d)=>{let h=dt(i),m=64,c=h===1?"vec2f":`mat2x${h}f`,_=h===1?"f32":`vec${h}f`,v=(Y,Q)=>`${c}(${Y}, ${Q})`,$=a*i/h,C=Math.ceil(o/m),k=["type"],M=[{type:12,data:C},{type:12,data:o},{type:12,data:Math.floor(i/h)},{type:12,data:Math.floor(o*i/h)}],z=Y=>{let Q=oe("input",e.dataType,e.dims,h);return` - ${Y.declareVariables(Q)} - @group(0) @binding(1) var output : array<${c}>; - struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32}; - @group(0) @binding(2) var uniforms: Uniforms; - - ${Y.mainStart(m)} - let currentImageNumber = global_idx / ${m} / uniforms.C; - let currentChannelNumber = (global_idx / ${m}) % uniforms.C; - let wgOffset = local_id.x * uniforms.wg_size; - if (wgOffset >= uniforms.H) { - return; - } - let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H); - - let offset = currentImageNumber * uniforms.image_size + currentChannelNumber; - var sum = ${gr("f32",h)}; - var squaredSum = ${gr("f32",h)}; - for (var i: u32 = wgOffset; i < wgMax; i++) { - let value = ${_}(input[offset + i * uniforms.C]); - sum += value; - squaredSum += value * value; - } - output[global_idx] = ${v("sum","squaredSum")}; - }`},I=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${h}`,inputDependencies:k},getRunData:()=>({outputs:[{dims:[a,i,m,2],dataType:1}],dispatchGroup:{x:a*i/h},programUniforms:M}),getShaderSource:z},{inputs:[e],outputs:[-1]})[0],j=[{type:12,data:$},{type:12,data:o},{type:12,data:Math.floor(i/h)},{type:12,data:Math.floor(m*i/h)}],R=["type","type","type"],L=Y=>{let Q=oe("scale",r.dataType,r.dims,h),ie=oe("bias",n.dataType,n.dims,h);return` - @group(0) @binding(0) var input : array<${c}>; - @group(0) @binding(1) var scale : array<${Q.type.storage}>; - @group(0) @binding(2) var bias : array<${ie.type.storage}>; - @group(0) @binding(3) var output : array<${c}>; - struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32}; - @group(0) @binding(4) var uniforms: Uniforms; - - ${Y.mainStart()} - ${Y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")} - let currentImageNumber = global_idx / uniforms.C; - let currentChannelNumber = global_idx % uniforms.C; - - let offset = currentImageNumber * uniforms.image_size; - var sum = ${gr("f32",h)}; - var squaredSum = ${gr("f32",h)}; - for (var i: u32 = 0; i < min(${m}, uniforms.H); i++) { - let value = input[offset + i + currentChannelNumber * ${m}]; - sum += value[0]; - squaredSum += value[1]; - } - sum = sum / f32(uniforms.H); - squaredSum = squaredSum / f32(uniforms.H); - let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${d})); - let channelScale = invStdDev * ${_}(scale[currentChannelNumber]); - let channelShift = ${_}(bias[currentChannelNumber]) - sum * channelScale; - - output[global_idx] = ${v("channelScale","channelShift")}; - }`};return t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${h};${d}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:[a,i,2],dataType:1}],dispatchGroup:{x:Math.ceil($/64)},programUniforms:j}),getShaderSource:L},{inputs:[I,r,n],outputs:[-1]})[0]},Up=(t,e,r)=>{let n=e[0].dims,a=n,o=n[0],i=n[n.length-1],d=se.sizeFromDimension(n,1)/i,h=dt(i),m=se.size(a)/h,c=[{type:12,data:d},{type:12,data:Math.floor(i/h)}],_=["type","type"],v=Lp(t,e[0],e[1],e[2],o,d,i,r.epsilon),$=C=>{let k=mt(e[0].dataType),M=h===1?"vec2f":`mat2x${h}f`,z=h===1?k:`vec${h}<${k}>`,I=oe("input",e[0].dataType,e[0].dims,h),j=$e("output",e[0].dataType,a,h);return` - @group(0) @binding(0) var input : array<${I.type.storage}>; - @group(0) @binding(1) var scaleInput : array<${M}>; - @group(0) @binding(2) var output : array<${j.type.storage}>; - struct Uniforms {H: u32, C : u32}; - @group(0) @binding(3) var uniforms: Uniforms; - - ${C.mainStart()} - let currentImageNumber = global_idx / (uniforms.C * uniforms.H); - let currentChannelNumber = global_idx % uniforms.C; - - let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber; - let scale = scaleInput[scaleOffset]; - output[global_idx] = fma(input[global_idx], ${z}(scale[0]), ${z}(scale[1])); - }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${h}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:c}),getShaderSource:$},{inputs:[e[0],v]})},Fp=(t,e)=>{e.format==="NHWC"?Up(t,t.inputs,e):t.compute(Np(t.inputs,e))}}),Wp,Gp,Vp,Sw=le(()=>{Oe(),Be(),Re(),Wp=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Gp=(t,e,r)=>{let n=e.simplified,a=t[0].dims,o=t[1],i=!n&&t[2],d=a,h=se.normalizeAxis(e.axis,a.length),m=se.sizeToDimension(a,h),c=se.sizeFromDimension(a,h),_=se.size(o.dims),v=i?se.size(i.dims):0;if(_!==c||i&&v!==c)throw new Error(`Size of X.shape()[axis:] == ${c}. - Size of scale and bias (if provided) must match this. - Got scale size of ${_} and bias size of ${v}`);let $=[];for(let L=0;L1,I=r>2,j=L=>{let Y=mt(t[0].dataType),Q=[oe("x",t[0].dataType,t[0].dims,C),oe("scale",o.dataType,o.dims,C)];i&&Q.push(oe("bias",i.dataType,i.dims,C)),Q.push($e("output",t[0].dataType,d,C)),z&&Q.push($e("mean_data_output",1,$)),I&&Q.push($e("inv_std_output",1,$));let ie=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` - ${L.registerUniforms(ie).declareVariables(...Q)} - ${L.mainStart()} - ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} - let offset = global_idx * uniforms.norm_size_vectorized; - var mean_vector = ${gr("f32",C)}; - var mean_square_vector = ${gr("f32",C)}; - - for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { - let value = ${mn(Y,C,"x[h + offset]")}; - mean_vector += value; - mean_square_vector += value * value; - } - let mean = ${kr("mean_vector",C)} / uniforms.norm_size; - let inv_std_dev = inverseSqrt(${kr("mean_square_vector",C)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon); - - for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { - let f32input = ${mn(Y,C,"x[j + offset]")}; - let f32scale = ${mn(Y,C,"scale[j]")}; - output[j + offset] = ${Q[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale - ${i?`+ ${mn(Y,C,"bias[j]")}`:""} - ); - } - - ${z?"mean_data_output[global_idx] = mean":""}; - ${I?"inv_std_output[global_idx] = inv_std_dev":""}; - }`},R=[{dims:d,dataType:t[0].dataType}];return z&&R.push({dims:$,dataType:1}),I&&R.push({dims:$,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${C};${r};${n}`,inputDependencies:k},getRunData:()=>({outputs:R,dispatchGroup:{x:Math.ceil(m/64)},programUniforms:M}),getShaderSource:j}},Vp=(t,e)=>{Wp(t.inputs),t.compute(Gp(t.inputs,e,t.outputCount))}}),Hp,qp,Kp,Yp,Ew=le(()=>{Oe(),Be(),ft(),Re(),Hp=(t,e)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=t[0],n=r.dims.length;if(r.dims[n-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let a=Math.floor((e.k+e.blockSize-1)/e.blockSize),o=e.blockSize/8*e.bits,i=t[1];if(!se.areEqual(i.dims,[e.n,a,o]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let d=t[2].dims;if(se.size(d)!==e.n*a)throw new Error("scales input size error.");if(t.length===4){let h=t[3].dims,m=e.bits>4?e.n*a:e.n*Math.floor((a+1)/2);if(se.size(h)!==m)throw new Error("zeroPoints input size error.")}},qp=(t,e,r,n)=>{let a=t[0].dims,o=a.length,i=Math.floor((e.k+e.blockSize-1)/e.blockSize),d=a[o-2],h=e.k,m=e.n,c=a.slice(0,o-2),_=se.size(c),v=e.blockSize/8*e.bits/4,$=t[0].dataType,C=dt(d),k=dt(e.k),M=dt(v),z=Gn($),I=d*i*z,j=Math.floor(n/I),R=i<=r[0]&&j>0,L=!R||j>=4?dt(m):j>=2&&dt(m)>=2?2:1,Y=c.concat([d,m]),Q=se.size(Y)/L/C,ie=R?[]:[{type:12,data:Q},{type:12,data:e.blockSize}],pe=[_,d,h/k],ce=se.convertShape(t[1].dims).slice();ce.splice(-1,1,v/M),ie.push(...Ce(pe)),ie.push(...Ce(ce)),ie.push(...Ce(t[2].dims)),t.length===4&&ie.push(...Ce(se.convertShape(t[3].dims)));let Se=[_,d,m/L];ie.push(...Ce(Se));let fe=me=>{let ge=pe.length,X=oe("a",t[0].dataType,ge,k),U=oe("b",12,ce.length,M),ne=oe("scales",t[2].dataType,t[2].dims.length),ee=[X,U,ne],Ee=t.length===4?oe("zero_points",12,t[3].dims.length):void 0;Ee&&ee.push(Ee);let Te=Se.length,K=$e("output",t[0].dataType,Te,L),he=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],ve=mt(t[0].dataType),Le=(()=>{switch(k){case 1:return`array<${ve}, 8>`;case 2:return`mat4x2<${ve}>`;case 4:return`mat2x4<${ve}>`;default:throw new Error(`${k}-component is not supported.`)}})(),Ke=` - for (var word: u32 = 0; word < ${v}; word += ${M}) { - ${U.indicesSet("b_indices","2","word")}; - let b_data = ${U.getByIndices("b_indices")}; - for (var i: u32 = 0; i < ${M}; i++) { - let b_value: u32 = ${M===1?"b_data":"b_data[word + i]"}; - let b_mask: u32 = 0x0F0F0F0Fu; - let b_value_lower: vec4 = unpack4xU8(b_value & b_mask); - let b_value_upper: vec4 = unpack4xU8((b_value >> 4) & b_mask); - let b_quantized_values = ${Le}(${Array.from({length:4},(pt,rt)=>`${ve}(b_value_lower[${rt}]), ${ve}(b_value_upper[${rt}])`).join(", ")}); - let b_dequantized_values = ${k===1?`${Le}(${Array.from({length:8},(pt,rt)=>`(b_quantized_values[${rt}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Le}(${Array(8).fill("zero_point").join(",")})) * scale;`}; - // Number of B elements per 32-bit word is 32/bits = 32/4 = 8 - for (var m: u32 = 0; m < ${R?d:C}u; m++) { - ${X.indicesSet("a_indices",ge-2,R?"m":`row * ${C} + m`)}; - ${X.indicesSet("a_indices",ge-1,"word_offset")}; - var input_offset = ${X.indicesToOffset("a_indices")}; - var a_data: ${Le}; - for (var j: u32 = 0; j < ${8/k}; j++) { - a_data[j] = ${X.getByOffset("input_offset")}; - input_offset++; - } - ${R?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${L>1?"[c]":""} += ${Array.from({length:8/k},(pt,rt)=>`${k===1?`a_data[${rt}] * b_dequantized_values[${rt}]`:`dot(a_data[${rt}], b_dequantized_values[${rt}])`}`).join(" + ")}; - } - word_offset += ${8/k}; - } - }`,ct=Ee?` - zero_point_offset += 4; - if (zero_point_offset == 32) { - zero_point_offset = 0; - zero_point_index++; - zero_point_word = ${Ee.getByOffset("zero_point_index")}; - }`:"";return R?` - var workgroup_shared: array<${K.type.value}, ${d*i}>; - ${me.declareVariables(...ee,K)} - ${me.mainStart([i,1,1])} - var a_indices: ${X.type.indices}; - var block = local_id.x; - var col = workgroup_id.y; - var batch = workgroup_id.z; - ${X.indicesSet("a_indices","0","batch")}; - // Two zero points are packed into one byte when uniforms.bits is 4. - for (var c: u32 = 0; c < ${L}; c++) { - let col_times_components_plus_c = col * ${L} + c; - ${Ee?` - var zero_point_bytes_per_col: u32 = (${i} + 1) / 2; - var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u); - var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u; - var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u; - var zero_point_nibble_offset: u32 = block & 0x1u; - var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); - var zero_point_word: u32 = ${Ee.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""} - var b_indices: ${U.type.indices}; - ${U.indicesSet("b_indices","0","col_times_components_plus_c")}; - // The scale and zero points are computed per block. - var scales_index = col_times_components_plus_c * ${i} + block; - let scale = ${ne.getByOffset("scales_index")}; - // The default zero point is 8 for unsigned 4-bit quantization. - let zero_point = ${ve}(${Ee?"(zero_point_word) & 0xFu":8}); - ${U.indicesSet("b_indices","1","block")}; - var word_offset: u32 = block * ${e.blockSize/k}; - var workgroup_shared_offset: u32 = block * ${d}; - ${Ke} - } - workgroupBarrier(); - if (local_id.x == 0u) { - var output_indices: ${K.type.indices}; - ${K.indicesSet("output_indices","0","batch")}; - ${K.indicesSet("output_indices",Te-1,"col")}; - ${K.indicesSet("output_indices",Te-2,"0")}; - var output_offset = ${K.indicesToOffset("output_indices")}; - for (var m: u32 = 0u; m < ${d}u; m++) { - var output_value: ${K.type.value} = ${K.type.value}(0); - var workgroup_shared_offset: u32 = m; - for (var b: u32 = 0u; b < ${i}u; b++) { - output_value += workgroup_shared[workgroup_shared_offset]; - workgroup_shared_offset += ${d}; - } - ${K.setByOffset("output_offset","output_value")}; - output_offset += ${m/L}; - } - } - }`:` - ${me.registerUniforms(he).declareVariables(...ee,K)} - ${me.mainStart()} - ${me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - var output_values: array<${K.type.value}, ${C}>; - var output_indices = ${K.offsetToIndices("global_idx")}; - var col = ${K.indicesGet("output_indices",Te-1)}; - var row = ${K.indicesGet("output_indices",Te-2)}; - var a_indices: ${X.type.indices} = output_indices; - // Two zero points are packed into one byte because uniforms.bits <= 4. - // zero_point_offset is either 0 or 4. It is bit offset within one byte. - // TODO support zero_point_offset for bits > 4 - ${Ee?` - var zero_point_abs_offset = col * ${L} * ((${i} + 1) / 2); - var zero_point_index: u32 = zero_point_abs_offset / 4; - var zero_point_word: u32 = ${Ee.getByOffset("zero_point_index")}; - var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""} - var scale_index = col * ${i*L}; - var b_indices: ${U.type.indices}; - for (var c: u32 = 0; c < ${L}; c++) { - ${U.indicesSet("b_indices","0",`col * ${L} + c`)}; - var block_offset: u32 = 0; - for (var block: u32 = 0; block < ${i}; block++) { - // The scale and zero points are computed per block. - let scale = ${ne.getByOffset("scale_index")}; - // The default zero point is 8 for unsigned 4-bit quantization. - let zero_point = ${ve}(${Ee?"extractBits(zero_point_word, zero_point_offset, 4)":8}); - ${U.indicesSet("b_indices","1","block")}; - var word_offset: u32 = block_offset; - ${Ke} - scale_index++; - ${ct} - block_offset += uniforms.block_size / ${k}; - } - // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte. - ${Ee?`if (zero_point_offset % 8 > 0) { - ${ct} - }`:""} - } - for (var k: u32 = 0u; k < ${C}u; k++) { - ${K.indicesSet("output_indices",Te-2,`${C} * row + k`)}; - ${K.setByIndices("output_indices","output_values[k]")} - } - }`};return{name:R?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${d};${$};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:Y,dataType:$}],name:R?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:R?{x:1,y:Math.ceil(m/L),z:_}:{x:Math.ceil(Q/64)},programUniforms:ie}),getShaderSource:fe}},Kp=(t,e)=>{Hp(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(qp(t.inputs,e,r,n))},Yp=t=>Ze(t)}),Ct,Qp,Xp,xo,Zp,ka,Jp,kw=le(()=>{Oe(),Be(),ft(),Gs(),Td(),Re(),Hn(),Ct=(t,e)=>t.length>e&&t[e].dims.length>0&&se.size(t[e].dims)>0?t[e]:void 0,Qp=(t,e)=>{let r=t[0],n=Ct(t,1),a=Ct(t,2),o=Ct(t,3),i=Ct(t,4),d=Ct(t,5),h=Ct(t,6),m=Ct(t,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let c=!1,_=r.dims[0],v=r.dims[1],$=r.dims.length===3?c?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],C=v,k=0,M=0,z=Math.floor($/e.numHeads);if(h&&m){if(h.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(h.dims[0]!==_||h.dims[1]!==e.numHeads||h.dims[3]!==z)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(m.dims[0]!==_||m.dims[1]!==e.numHeads||m.dims[3]!==z)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(h.dims[2]!==m.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(m.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');k=h.dims[2],M=h.dims[2]}else if(h||m)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let I;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');I=2,C=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==e.numHeads||n.dims[3]!==2||n.dims[4]!==z)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(a)throw new Error('Expect "value" be none when "key" has packed kv format.');I=5,C=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==z)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');I=0,C=n.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(r.dims.length===5&&(r.dims[2]!==e.numHeads||r.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');I=3}if(o){if(o.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(a&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let j=0;if(i){j=8;let ie=i.dims;throw ie.length===1?ie[0]===_?j=1:ie[0]===3*_+2&&(j=3):ie.length===2&&ie[0]===_&&ie[1]===C&&(j=5),j===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let R=!1,L=$;if(a){if(a.dims.length!==3&&a.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==a.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(a.dims.length===3){if(C!==a.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');L=a.dims[2]}else{if(C!==a.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');L=a.dims[1]*a.dims[3],R=!0}}let Y=k+C,Q=!1;if(i)throw new Error("Key padding mask is not supported");if(d){if(d.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(d.dims[0]!==_&&d.dims[0]!==1||d.dims[1]!==e.numHeads||d.dims[2]!==v||d.dims[3]!==Y)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:_,sequenceLength:v,pastSequenceLength:k,kvSequenceLength:C,totalSequenceLength:Y,maxSequenceLength:M,inputHiddenSize:0,hiddenSize:$,vHiddenSize:L,headSize:z,vHeadSize:Math.floor(L/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:j,scale:e.scale,broadcastResPosBias:Q,passPastInKv:R,qkvFormat:I}},Xp=t=>Ze({...t}),xo=Ze({perm:[0,2,1,3]}),Zp=(t,e,r,n,a,o,i)=>{let d=[n,a,o],h=se.size(d),m=[{type:12,data:h},{type:12,data:i},{type:12,data:o}],c=_=>{let v=$e("qkv_with_bias",e.dataType,d),$=oe("qkv",e.dataType,d),C=oe("bias",r.dataType,d),k=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` - ${_.registerUniforms(k).declareVariables($,C,v)} - ${_.mainStart()} - ${_.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 t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:d,dataType:e.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:m}),getShaderSource:c},{inputs:[e,r],outputs:[-1]})[0]},ka=(t,e,r,n,a,o,i,d)=>{let h=o;if(i){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return h=Zp(t,o,i,e,n,r*a,d),h=h.reshape([e,n,r,a]),t.compute(yr(h,xo.perm),{inputs:[h],outputs:[-1]})[0]}else return o.dims.length===3&&(h=o.reshape([e,n,r,a])),t.compute(yr(h,xo.perm),{inputs:[h],outputs:[-1]})[0]},Jp=(t,e)=>{let r=Qp(t.inputs,e),n=t.inputs[0],a=Ct(t.inputs,1),o=Ct(t.inputs,2),i=Ct(t.inputs,3),d=Ct(t.inputs,4),h=Ct(t.inputs,5),m=Ct(t.inputs,6),c=Ct(t.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((a==null?void 0:a.dims.length)===5)throw new Error("Packed KV is not implemented");let _=a&&o&&a.dims.length===4&&o.dims.length===4,v=ka(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(_)return _a(t,v,a,o,d,void 0,m,c,h,r,e);if(!a||!o)throw new Error("key and value must be provided");let $=ka(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,a,i,r.hiddenSize),C=ka(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,o,i,2*r.hiddenSize);_a(t,v,$,C,d,void 0,m,c,h,r,e)}}),eh,th,rh,nh,ih,ah,sh,oh,lh,Tw=le(()=>{Oe(),Be(),Re(),eh=t=>{if(!t||t.length<1)throw new Error("Too few inputs");if(t[0].dataType!==1&&t[0].dataType!==10)throw new Error("Input type must be float or float16.");if(t.length>=2){let e=t[0].dims.length*2===t[1].dims[0];if(t.length===4&&(e=t[3].dims[0]*2===t[1].dims[0]),!e)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},th=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` - k = i32(${t.indicesGet("indices",a)}) - ${ze("uniforms.pads",a,r)}; - if (k < 0) { - break; - } - if (k >= i32(${ze("uniforms.x_shape",a,e)})) { - break; - } - offset += k * i32(${ze("uniforms.x_strides",a,e)}); - `;return` - value = ${t.type.value}(uniforms.constant_value); - for (var i = 0; i < 1; i++) { - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - } - `},rh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` - k = i32(${t.indicesGet("indices",a)}) - ${ze("uniforms.pads",a,r)}; - if (k < 0) { - k = -k; - } - { - let _2n_1 = 2 * (i32(${ze("uniforms.x_shape",a,e)}) - 1); - k = k % _2n_1; - if(k >= i32(${ze("uniforms.x_shape",a,e)})) { - k = _2n_1 - k; - } - } - offset += k * i32(${ze("uniforms.x_strides",a,e)}); - `;return` - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - `},nh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` - k = i32(${t.indicesGet("indices",a)}) - ${ze("uniforms.pads",a,r)}; - if (k < 0) { - k = 0; - } - if (k >= i32(${ze("uniforms.x_shape",a,e)})) { - k = i32(${ze("uniforms.x_shape",a,e)}) - 1; - } - offset += k * i32(${ze("uniforms.x_strides",a,e)}); - `;return` - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - `},ih=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` - k = i32(${t.indicesGet("indices",a)}) - ${ze("uniforms.pads",a,r)}; - if (k < 0) { - k += i32(${ze("uniforms.x_shape",a,e)}]); - } - if (k >= i32(${ze("uniforms.x_shape",a,e)})) { - k -= i32(${ze("uniforms.x_shape",a,e)}); - } - offset += k * i32(${ze("uniforms.x_strides",a,e)}); - `;return` - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - `},ah=(t,e,r)=>{switch(r.mode){case 0:return th(t,e,r.pads.length);case 1:return rh(t,e,r.pads.length);case 2:return nh(t,e,r.pads.length);case 3:return ih(t,e,r.pads.length);default:throw new Error("Invalid mode")}},sh=(t,e)=>{let r=se.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=se.size(r),o=[{type:12,data:a},{type:6,data:e.pads}];e.mode===0&&o.push({type:t[0].dataType,data:e.value}),o.push(...Ce(t[0].dims,r));let i=["rank"],d=h=>{let m=$e("output",t[0].dataType,r.length),c=oe("x",t[0].dataType,n.length),_=c.type.value,v=ah(m,n.length,e),$=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.mode===0&&$.push({name:"constant_value",type:_}),` - ${h.registerUniforms($).declareVariables(c,m)} - ${h.mainStart()} - ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - - let indices = ${m.offsetToIndices("global_idx")}; - - var value = ${_}(0); - ${v} - output[global_idx] = value; - }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(se.size(r)/64)},programUniforms:o}),getShaderSource:d}},oh=(t,e)=>{if(t.length>1){let r=t[1].getBigInt64Array(),n=t.length>=3&&t[2].data?t[2].getFloat32Array()[0]:0,a=t[0].dims.length,o=new Int32Array(2*a).fill(0);if(t.length>=4){let d=t[3].getBigInt64Array();for(let h=0;ho[Number(h)]=Number(d));let i=[];return o.forEach(d=>i.push(d)),{mode:e.mode,value:n,pads:i}}else return e},lh=(t,e)=>{eh(t.inputs);let r=oh(t.inputs,e);t.compute(sh(t.inputs,r),{inputs:[0]})}}),Kn,Co,So,Eo,ko,uh,dh,To,Io,ch,ph,Ao,hh,fh,Mo,mh,gh,yh,_h,Iw=le(()=>{Yt(),Oe(),Be(),Re(),Kn=t=>{if(He.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},Co=(t,e,r)=>{let n=e.format==="NHWC",a=t.dims.slice();n&&a.splice(1,0,a.pop());let o=Object.hasOwnProperty.call(e,"dilations"),i=e.kernelShape.slice(),d=e.strides.slice(),h=o?e.dilations.slice():[],m=e.pads.slice();ha.adjustPoolAttributes(r,a,i,d,h,m);let c=ha.computePoolOutputShape(r,a,d,h,i,m,e.autoPad),_=Object.assign({},e);o?Object.assign(_,{kernelShape:i,strides:d,pads:m,dilations:h,cacheKey:e.cacheKey}):Object.assign(_,{kernelShape:i,strides:d,pads:m,cacheKey:e.cacheKey});let v=c.slice();return v.push(v.splice(1,1)[0]),[_,n?v:c]},So=(t,e)=>{let r=e.format==="NHWC",n=se.size(t),a=se.size(e.kernelShape),o=[{type:12,data:n},{type:12,data:a}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(e.kernelShape.length<=2){let d=e.kernelShape[e.kernelShape.length-1],h=e.strides[e.strides.length-1],m=e.pads[e.pads.length/2-1],c=e.pads[e.pads.length-1],_=!!(m+c);o.push({type:12,data:d},{type:12,data:h},{type:12,data:m},{type:12,data:c}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let v=!1;if(e.kernelShape.length===2){let $=e.kernelShape[e.kernelShape.length-2],C=e.strides[e.strides.length-2],k=e.pads[e.pads.length/2-2],M=e.pads[e.pads.length-2];v=!!(k+M),o.push({type:12,data:$},{type:12,data:C},{type:12,data:k},{type:12,data:M}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[o,i,!0,_,v]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let d=se.computeStrides(e.kernelShape);o.push({type:12,data:d},{type:12,data:e.pads},{type:12,data:e.strides}),i.push({name:"kernelStrides",type:"u32",length:d.length},{name:"pads",type:"u32",length:e.pads.length},{name:"strides",type:"u32",length:e.strides.length});let h=e.pads.reduce((m,c)=>m+c);return[o,i,!!h,!1,!1]}},Eo=(t,e,r,n,a,o,i,d,h,m,c,_)=>{let v=a.format==="NHWC",$=e.type.value,C=$e("output",e.type.tensor,n);if(a.kernelShape.length<=2){let k="",M="",z="",I=r-(v?2:1);if(c?k=` - for (var i: u32 = 0u; i < uniforms.kw; i++) { - xIndices[${I}] = indices[${I}] * uniforms.sw - uniforms.pwStart + i; - if (xIndices[${I}] < 0 || xIndices[${I}] - >= uniforms.x_shape[${I}]) { - pad++; - continue; - } - let x_val = x[${e.indicesToOffset("xIndices")}]; - ${o} - }`:k=` - for (var i: u32 = 0u; i < uniforms.kw; i++) { - xIndices[${I}] = indices[${I}] * uniforms.sw - uniforms.pwStart + i; - let x_val = x[${e.indicesToOffset("xIndices")}]; - ${o} - }`,a.kernelShape.length===2){let j=r-(v?3:2);_?M=` - for (var j: u32 = 0u; j < uniforms.kh; j++) { - xIndices[${j}] = indices[${j}] * uniforms.sh - uniforms.phStart + j; - if (xIndices[${j}] < 0 || xIndices[${j}] >= uniforms.x_shape[${j}]) { - pad += i32(uniforms.kw); - continue; - } - `:M=` - for (var j: u32 = 0u; j < uniforms.kh; j++) { - xIndices[${j}] = indices[${j}] * uniforms.sh - uniforms.phStart + j; - `,z=` - } - `}return` - ${t.registerUniforms(h).declareVariables(e,C)} - - ${t.mainStart()} - ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - - let indices = ${C.offsetToIndices("global_idx")}; - var xIndices = ${C.offsetToIndices("global_idx")}; - - var value = ${$}(${d}); - var pad = 0; - ${M} - ${k} - ${z} - ${i} - - output[global_idx] = value; - }`}else{if(v)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let k=a.kernelShape.length,M=a.pads.length,z="";return m?z=` - if (xIndices[j] >= uniforms.x_shape[j]) { - pad++; - isPad = true; - break; - } - } - if (!isPad) { - let x_val = x[${e.indicesToOffset("xIndices")}]; - ${o} - }`:z=` - } - let x_val = x[${e.indicesToOffset("xIndices")}]; - ${o} - `,` - ${t.registerUniforms(h).declareVariables(e,C)} - - ${t.mainStart()} - ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - let indices = ${C.offsetToIndices("global_idx")}; - var xIndices = ${C.offsetToIndices("global_idx")}; - - var offsets: array; - - var value = ${$}(${d}); - var pad = 0; - var isPad = false; - - for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { - var offset = i; - for (var j = 0u; j < ${k-1}u; j++) { - offsets[j] = offset / ${ze("uniforms.kernelStrides","j",k)}; - offset -= offsets[j] * ${ze("uniforms.kernelStrides","j",k)}; - } - offsets[${k-1}] = offset; - - isPad = false; - for (var j = ${r-k}u; j < ${r}u; j++) { - xIndices[j] = indices[j] * ${ze("uniforms.strides",`j - ${r-k}u`,k)} - + offsets[j - ${r-k}u] - ${ze("uniforms.pads","j - 2u",M)}; - ${z} - } - ${i} - - output[global_idx] = value; - }`}},ko=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,uh=t=>`${ko(t)};${t.countIncludePad}`,dh=t=>`${ko(t)};${t.storageOrder};${t.dilations}`,To=t=>({format:t.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],ceilMode:t.ceil_mode,kernelShape:t.kernel_shape,strides:t.strides,pads:t.pads}),Io=(t,e,r,n)=>{let[a,o]=Co(e,n,r),i=oe("x",e.dataType,e.dims.length),d=i.type.value,h="value += x_val;",m="";a.countIncludePad?m+=`value /= ${d}(uniforms.kernelSize);`:m+=`value /= ${d}(i32(uniforms.kernelSize) - pad);`;let[c,_,v,$,C]=So(o,a);c.push(...Ce(e.dims,o));let k=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${v};${$};${C}`,inputDependencies:k},getRunData:()=>({outputs:[{dims:o,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(se.size(o)/64)},programUniforms:c}),getShaderSource:M=>Eo(M,i,e.dims.length,o.length,a,h,m,0,_,v,$,C)}},ch=t=>{let e=t.count_include_pad!==0,r=To(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:uh(n)}},ph=(t,e)=>{Kn(t.inputs),t.compute(Io("AveragePool",t.inputs[0],!1,e))},Ao={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},hh=t=>{let e=t.format;return{format:e,...Ao,cacheKey:e}},fh=(t,e)=>{Kn(t.inputs),t.compute(Io("GlobalAveragePool",t.inputs[0],!0,e))},Mo=(t,e,r,n)=>{let[a,o]=Co(e,n,r),i=` - value = max(x_val, value); 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- }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:h,getRunData:()=>({outputs:[{dims:o,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:d})}},bh=t=>{let e=0,r=0,n=0;t.inputs[0].dataType===6?(e=t.inputs[0].getInt32Array()[0],r=t.inputs[1].getInt32Array()[0],n=t.inputs[2].getInt32Array()[0]):t.inputs[0].dataType===1&&(e=t.inputs[0].getFloat32Array()[0],r=t.inputs[1].getFloat32Array()[0],n=t.inputs[2].getFloat32Array()[0]),He.webgpu.validateInputContent&&wh(e,r,n),t.compute(vh(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),$h,xh,Ch,Sh,Eh,kh,Th,Ih,Ah,Mh,Oh,Oo,zh,Ph,jh,Rh,Bh,Dh,Nh,Mw=le(()=>{Oe(),Be(),ft(),Re(),$h=(t,e)=>{if(t.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),t.length>0){if(e.mode==="linear"){if(!(t.length===2||t.length===3||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1||t.length===5&&t[0]===1&&t[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and - one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(e.mode==="cubic"&&!(t.length===2||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},xh=(t,e,r)=>{e.every(a=>a>=0&&a{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return e.forEach((a,o)=>n[a]=t[o]),n},Ch=(t,e,r,n,a,o)=>{let[i,d,h]=r>10?[1,2,3]:[-1,t.length>1?1:-1,-1],m=t[0].dims.length;if(i>0&&t.length>i&&t[i].dims.length>0)t[i].getFloat32Array().forEach(c=>o.push(c));else if(e.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(d>0&&t.length>d&&t[d].dims.length>0){if(t[d].getFloat32Array().forEach(c=>n.push(c)),n.length!==0&&n.length!==m&&r>=18&&n.length!==e.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");$h(n,e),e.axes.length>0&&xh(n,e.axes,m).forEach((c,_)=>n[_]=c)}if(h>0&&t.length>h&&(t[h].getBigInt64Array().forEach(c=>a.push(Number(c))),a.length!==m||r>=18&&a.length===e.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(e.axes.length>0){if(n.length!==e.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(a.length!==e.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof a<"u"&&n.length>0&&a.length>m)throw new Error("Resize requires only of scales or sizes to be specified")},Sh=(t,e)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, - lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${e} { `+(()=>{switch(t){case"asymmetric":return`return ${e}(xResized) / ${e}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) { - return (${e}(xResized) + 0.5) / ${e}(xScale) - 0.5; 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0.5;`;default:throw new Error(`Coordinate transform mode ${t} is not supported`)}})()+"}",Eh=(t,e,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(t){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(e<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${t} is not supported`)}})()+"}",kh=(t,e,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),a=t.length===0?n:t.slice();return e.length>0?(e.forEach((o,i)=>{n[o]=a[i],n[i+r]=a[e.length+i]}),n):a},Th=(t,e,r,n)=>{let a=[];if(r.length>0)if(n.length>0){if(t.forEach(o=>a.push(o)),Math.max(...n)>t.length)throw new 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array<${t.type.value}, ${r.length}>; - for (var i:u32 = 0; i < ${r.length}; i++) { - var output_index = ${t.indicesGet("output_indices","i")}; - var scale = ${ze("uniforms.scales","i",n)}; - var roi_low = ${ze("uniforms.roi","i",a)}; - var roi_hi = ${ze("uniforms.roi",`i + ${e.length}`,a)}; - if (scale == 1.0) { - original_indices[i] = ${t.type.value}(output_index); - } else { - var input_shape_i = ${ze("uniforms.input_shape","i",e.length)}; - var output_shape_i = ${ze("uniforms.output_shape","i",r.length)}; - original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, - input_shape_i, roi_low, roi_hi); - } - } - return original_indices; - }`,Mh=(t,e,r,n,a,o,i)=>` - fn calculateInputIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} { - var input_indices: ${t.type.indices}; - for (var i:u32 = 0; i < ${n.length}; i++) { - var output_index = ${e.indicesGet("output_indices","i")}; - var input_index: u32; - var scale = ${ze("uniforms.scales","i",a)}; - if (scale == 1.0) { - input_index = output_index; - } else { - var roi_low = ${ze("uniforms.roi","i",o)}; - var roi_hi = ${ze("uniforms.roi",`i + ${r.length}`,o)}; - var input_shape_i = ${ze("uniforms.input_shape","i",r.length)}; - var output_shape_i = ${ze("uniforms.output_shape","i",n.length)}; - var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, - input_shape_i, roi_low, roi_hi); - if (!${i} || (original_idx >= 0 && original_idx < ${e.type.value}(input_shape_i))) { - if (original_idx < 0) { - input_index = 0; - } else if (original_idx > ${e.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); - } - } - ${t.indicesSet("input_indices","i"," input_index")} - } - return input_indices; - }`,Oh=(t,e)=>` - fn checkInputIndices(input_indices: ${t.type.indices}) -> bool { - for (var i:u32 = 0; i < ${e.length}; i++) { - var input_index = ${t.indicesGet("input_indices","i")}; - if (input_index < 0 || input_index >= ${ze("uniforms.input_shape","i",e.length)}) { - return false; - } - } - return true; - }`,Oo=(t,e,r,n)=>t.rank>n?` - ${t.indicesSet("input_indices",e,"channel")}; - ${t.indicesSet("input_indices",r,"batch")}; -`:"",zh=(t,e,r,n,a)=>{let[o,i,d,h]=r.length===2?[-1,0,1,-1]:[0,2,3,1],m=t.type.value;return` - fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${m} { - var input_indices: ${t.type.indices}; - ${t.indicesSet("input_indices",i,`max(0, min(row, ${r[i]} - 1))`)}; - ${t.indicesSet("input_indices",d,`max(0, min(col, ${r[d]} - 1))`)}; - ${Oo(t,h,o,2)} - return ${t.getByIndices("input_indices")}; - } - - fn bilinearInterpolation(output_indices: ${e.type.indices}) -> ${m} { - var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); - var row:${m} = originalIndices[${i}]; - var col:${m} = originalIndices[${d}]; - ${n?`if (row < 0 || row > (${r[i]} - 1) || col < 0 || col > (${r[d]} - 1)) { - return ${a}; - }`:""}; - row = max(0, min(row, ${r[i]} - 1)); - col = max(0, min(col, ${r[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 = ${r.length>2?`u32(originalIndices[${h}])`:"0"}; - var batch: u32 = ${r.length>2?`u32(originalIndices[${o}])`:"0"}; - var x11: ${m} = getInputValue(batch, channel, row1, col1); - var x12: ${m} = getInputValue(batch, channel, row1, col2); - var x21: ${m} = getInputValue(batch, channel, row2, col1); - var x22: ${m} = getInputValue(batch, channel, row2, col2); - var dx1: ${m} = abs(row - ${m}(row1)); - var dx2: ${m} = abs(${m}(row2) - row); - var dy1: ${m} = abs(col - ${m}(col1)); - var dy2: ${m} = abs(${m}(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); - }`},Ph=(t,e,r,n,a,o,i,d,h,m)=>{let c=r.length===2,[_,v]=c?[0,1]:[2,3],$=t.type.value,C=k=>{let M=k===_?"row":"col";return` - fn ${M}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${e.type.indices}) -> ${$} { - var output_index = ${e.indicesGet("output_indices",k)}; - var originalIdx: ${$} = getOriginalCoordinateFromResizedCoordinate(output_index, ${a[k]}, - ${n[k]}, ${r[k]}, ${o[k]}, ${o[k]} + ${r.length}); - var fractOriginalIdx: ${$} = originalIdx - floor(originalIdx); - var coefs = getCubicInterpolationCoefs(fractOriginalIdx); - - if (${d} && (originalIdx < 0 || originalIdx > (${r[k]} - 1))) { - return ${h}; - } - var data: array<${$}, 4> = array<${$}, 4>(0.0, 0.0, 0.0, 0.0); - for (var i: i32 = -1; i < 3; i++) { - var ${M}: ${$} = originalIdx + ${$}(i); - if (${M} < 0 || ${M} >= ${r[k]}) { - ${m?`coefs[i + 1] = 0.0; - continue;`:d?`return ${h};`:`${M} = max(0, min(${M}, ${r[k]} - 1));`}; - } - var input_indices_copy: ${t.type.indices} = input_indices; - ${t.indicesSet("input_indices_copy",k,`u32(${M})`)}; - data[i + 1] = ${k===_?t.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; - } - return cubicInterpolation1D(data, coefs); - }`};return` - ${C(_)}; - ${C(v)}; - fn getCubicInterpolationCoefs(s: ${$}) -> array<${$}, 4> { - var absS = abs(s); - var coeffs: array<${$}, 4> = array<${$}, 4>(0.0, 0.0, 0.0, 0.0); - var oneMinusAbsS: ${$} = 1.0 - absS; - var twoMinusAbsS: ${$} = 2.0 - absS; - var onePlusAbsS: ${$} = 1.0 + absS; - coeffs[0] = ((${i} * onePlusAbsS - 5 * ${i}) * onePlusAbsS + 8 * ${i}) * onePlusAbsS - 4 * ${i}; - coeffs[1] = ((${i} + 2) * absS - (${i} + 3)) * absS * absS + 1; - coeffs[2] = ((${i} + 2) * oneMinusAbsS - (${i} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; - coeffs[3] = ((${i} * twoMinusAbsS - 5 * ${i}) * twoMinusAbsS + 8 * ${i}) * twoMinusAbsS - 4 * ${i}; - return coeffs; - } - - fn cubicInterpolation1D(x: array<${$}, 4>, coefs: array<${$}, 4>) -> ${$} { - var coefsSum: ${$} = 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: ${e.type.indices}) -> ${$} { - var input_indices: ${t.type.indices} = output_indices; - return colCubicInterpolation(input_indices, output_indices); - } - `},jh=(t,e,r,n,a)=>{let[o,i,d,h,m]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],c=t.type.value;return` - fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${c} { - var input_indices: ${t.type.indices}; - ${t.indicesSet("input_indices",i,`max(0, min(depth, ${r[i]} - 1))`)}; - ${t.indicesSet("input_indices",d,`max(0, min(height, ${r[d]} - 1))`)}; - ${t.indicesSet("input_indices",h,`max(0, min(width, ${r[h]} - 1))`)}; - ${Oo(t,m,o,3)} - return ${t.getByIndices("input_indices")}; - } - - fn trilinearInterpolation(output_indices: ${e.type.indices}) -> ${c} { - var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); - var depth:${c} = originalIndices[${i}]; - var height:${c} = originalIndices[${d}]; - var width:${c} = originalIndices[${h}]; - ${n?`if (depth < 0 || depth > (${r[i]} - 1) || height < 0 || height > (${r[d]} - 1) || width < 0 || (width > ${r[h]} - 1)) { - return ${a}; - }`:""}; - - depth = max(0, min(depth, ${r[i]} - 1)); - height = max(0, min(height, ${r[d]} - 1)); - width = max(0, min(width, ${r[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 = ${r.length>3?`u32(originalIndices[${m}])`:"0"}; - var batch: u32 = ${r.length>3?`u32(originalIndices[${o}])`:"0"}; - - var x111: ${c} = getInputValue(batch, channel, depth1, height1, width1); - var x112: ${c} = getInputValue(batch, channel, depth1, height1, width2); - var x121: ${c} = getInputValue(batch, channel, depth1, height2, width1); - var x122: ${c} = getInputValue(batch, channel, depth1, height2, width2); - var x211: ${c} = getInputValue(batch, channel, depth2, height1, width1); - var x212: ${c} = getInputValue(batch, channel, depth2, height1, width2); - var x221: ${c} = getInputValue(batch, channel, depth2, height2, width1); - var x222: ${c} = getInputValue(batch, channel, depth2, height2, width2); - var dx1: ${c} = abs(depth - ${c}(depth1)); - var dx2: ${c} = abs(${c}(depth2) - depth); - var dy1: ${c} = abs(height - ${c}(height1)); - var dy2: ${c} = abs(${c}(height2) - height); - var dz1: ${c} = abs(width - ${c}(width1)); - var dz2: ${c} = abs(${c}(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); - }`},Rh=(t,e,r,n,a,o)=>{let i=t.dims,d=kh(o,e.axes,i.length),h=Th(i,n,a,e.axes),m=n.slice();n.length===0&&(m=i.map((I,j)=>I===0?1:h[j]/I),e.keepAspectRatioPolicy!=="stretch"&&(h=Ih(i,m,e)));let c=$e("output",t.dataType,h.length),_=oe("input",t.dataType,i.length),v=se.size(h),$=i.length===h.length&&i.every((I,j)=>I===h[j]),C=e.coordinateTransformMode==="tf_crop_and_resize",k=e.extrapolationValue,M=_.type.value,z=I=>` - ${$?"":` - ${Sh(e.coordinateTransformMode,M)}; - ${(()=>{switch(e.mode){case"nearest":return` - ${Oh(_,i)}; - ${Eh(e.nearestMode,r,M)}; - ${Mh(_,c,i,h,m.length,d.length,C)}; - `;case"linear":return` - ${Ah(c,i,h,m.length,d.length)}; - ${(()=>{if(i.length===2||i.length===4)return`${zh(_,c,i,C,k)}`;if(i.length===3||i.length===5)return`${jh(_,c,i,C,k)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; - `;case"cubic":return` - ${(()=>{if(i.length===2||i.length===4)return`${Ph(_,c,i,h,m,d,e.cubicCoeffA,C,e.extrapolationValue,e.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; - `;default:throw Error("Invalid resize mode")}})()}; - `} - ${I.registerUniform("output_size","u32").registerUniform("scales","f32",m.length).registerUniform("roi","f32",d.length).declareVariables(_,c)} - ${I.mainStart()} - ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - ${$?"output[global_idx] = input[global_idx];":` - let output_indices = ${c.offsetToIndices("global_idx")}; - var input_indices: ${_.type.indices}; - ${(()=>{switch(e.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); - if (checkInputIndices(input_indices)) { - output[global_idx] = ${_.getByIndices("input_indices")}; - } else { - output[global_idx] = ${e.extrapolationValue}; - }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${e.mode}`)}})()}; -`} - }`;return{name:"Resize",shaderCache:{hint:`${e.cacheKey}|${r}|${m.length>0?m:""}|${a.length>0?a:""}|${d.length>0?d:""}|${$}|${i}`,inputDependencies:["rank"]},getShaderSource:z,getRunData:()=>({outputs:[{dims:h,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(v/64)},programUniforms:[{type:12,data:v},{type:1,data:m},{type:1,data:d},...Ce(i,h)]})}},Bh=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},Dh=(t,e)=>{let r=[],n=[],a=[],o=Bh(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Ch(t.inputs,e,o,r,n,a),t.compute(Rh(t.inputs[0],e,o,r,n,a),{inputs:[0]})},Nh=t=>{let e=t.antialias,r=t.axes,n=t.coordinateTransformMode,a=t.cubicCoeffA,o=t.excludeOutside!==0,i=t.extrapolationValue,d=t.keepAspectRatioPolicy,h=t.mode,m=t.nearestMode===""?"simple":t.nearestMode;return Ze({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:o,extrapolationValue:i,keepAspectRatioPolicy:d,mode:h,nearestMode:m})}}),Lh,Uh,Fh,Ow=le(()=>{Oe(),Be(),ft(),Re(),Lh=(t,e)=>{let[r,n,a,o]=t,{numHeads:i,rotaryEmbeddingDim:d}=e;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!se.areEqual(n.dims,[])&&!se.areEqual(n.dims,[1])&&n.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${n.dims.length}`);if(a.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(o.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(!se.areEqual(a.dims,o.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(d>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let h=r.dims[0],m=r.dims[r.dims.length-2],c=a.dims[0],_=se.sizeFromDimension(r.dims,1)/m,v=d===0?a.dims[1]*2:_/i;if(d>v)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(h!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(m!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(v/2!==a.dims[1]&&d/2!==a.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${a.dims[1]}`);if(m>c)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Uh=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:o}=e,i=t[0].dims[0],d=se.sizeFromDimension(t[0].dims,1),h=t[0].dims[t[0].dims.length-2],m=d/h,c=t[2].dims[1],_=a===0?c*2:m/n,v=new Array(i,h,m/_,_-c),$=se.computeStrides(v),C=[{type:1,data:o},{type:12,data:v},{type:12,data:$},...t[0].dims.length===3?new Array({type:12,data:[d,m,_,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[d,_,h*_,1]}):[],...Ce(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],k=M=>{let z=oe("input",t[0].dataType,t[0].dims.length),I=oe("position_ids",t[1].dataType,t[1].dims.length),j=oe("cos_cache",t[2].dataType,t[2].dims.length),R=oe("sin_cache",t[3].dataType,t[3].dims.length),L=$e("output",t[0].dataType,t[0].dims.length);return M.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:v.length},{name:"global_strides",type:"u32",length:$.length},{name:"input_output_strides",type:"u32",length:$.length}]),` - ${M.declareVariables(z,I,j,R,L)} - - ${M.mainStart(fn)} - let half_rotary_emb_dim = uniforms.${j.name}_shape[1]; - let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; - let size = uniforms.global_shape[0] * uniforms.global_strides[0]; - ${M.guardAgainstOutOfBoundsWorkgroupSizes("size")} - - if (bsnh[3] < half_rotary_emb_dim) { - let position_ids_idx = - ${I.broadcastedIndicesToOffset("bsnh.xy",$e("",I.type.tensor,2))}; - let position_id = - u32(${I.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); - let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r}); - let j = i + select(half_rotary_emb_dim, 1, ${r}); - let re = ${z.getByOffset("i")} * ${j.get("position_id","bsnh[3]")} - - ${z.getByOffset("j")} * ${R.get("position_id","bsnh[3]")}; - ${L.setByOffset("i","re")} - let im = ${z.getByOffset("i")} * ${R.get("position_id","bsnh[3]")} + - ${z.getByOffset("j")} * ${j.get("position_id","bsnh[3]")}; - ${L.setByOffset("j","im")} - } else { - let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; - ${L.setByOffset("k",z.getByOffset("k"))} - } - }`};return{name:"RotaryEmbedding",shaderCache:{hint:Ze({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:k,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(se.size(v)/fn)},programUniforms:C})}},Fh=(t,e)=>{Lh(t.inputs,e),t.compute(Uh(t.inputs,e))}}),Wh,Gh,Vh,zw=le(()=>{Oe(),Be(),Re(),Wh=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],r=t[1],n=t[2];if(e.dataType!==r.dataType||e.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let a=e.dims[e.dims.length-1],o=e.dims[e.dims.length-2];if(r.dims[r.dims.length-1]!==a)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==o)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==a)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let i=t[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let i=t[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},Gh=(t,e,r,n)=>{let a=e.simplified,o=t[0].dims,i=se.size(o),d=o,h=i,m=o.slice(-1)[0],c=n?o.slice(0,-1).concat(1):[],_=!a&&t.length>3,v=t.length>4,$=n&&r>1,C=n&&r>2,k=r>3,M=dt(m),z=[{type:12,data:h},{type:12,data:M},{type:12,data:m},{type:1,data:e.epsilon}],I=R=>{let L=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Y=[oe("x",t[0].dataType,t[0].dims,M),oe("skip",t[1].dataType,t[1].dims,M),oe("gamma",t[2].dataType,t[2].dims,M)];_&&Y.push(oe("beta",t[3].dataType,t[3].dims,M)),v&&Y.push(oe("bias",t[4].dataType,t[4].dims,M)),Y.push($e("output",t[0].dataType,d,M)),$&&Y.push($e("mean_output",1,c)),C&&Y.push($e("inv_std_output",1,c)),k&&Y.push($e("input_skip_bias_sum",t[0].dataType,d,M));let Q=mt(t[0].dataType);return` - - ${R.registerUniforms(L).declareVariables(...Y)} - - ${R.mainStart()} - ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")} - let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; - let offset = global_idx * hidden_size_vectorized; - var sum = ${gr("f32",M)}; - var squareSum = ${gr("f32",M)}; - for (var i: u32 = 0; i < hidden_size_vectorized; i++) { - let skip_value = skip[offset + i]; - let bias_value = ${v?"bias[i]":Q+"(0.0)"}; - let input_value = x[offset + i]; - let value = input_value + skip_value + bias_value; - ${k?"input_skip_bias_sum[offset + i] = value;":""} - output[offset + i] = value; - let f32_value = ${mn(Q,M,"value")}; - sum += f32_value; - squareSum += f32_value * f32_value; - } - let mean = ${kr("sum",M)} / f32(uniforms.hidden_size); - let inv_std_dev = inverseSqrt(${kr("squareSum",M)} / f32(uniforms.hidden_size) ${a?"":"- mean * mean"} + uniforms.epsilon); - ${$?"mean_output[global_idx] = mean;":""} - ${C?"inv_std_output[global_idx] = inv_std_dev;":""} - for (var i: u32 = 0; i < hidden_size_vectorized; i++) { - output[offset + i] = (output[offset + i] ${a?"":`- ${Q}(mean)`}) * ${Q}(inv_std_dev) * gamma[i] ${_?"+ beta[i]":""}; - } - }`},j=[{dims:d,dataType:t[0].dataType}];return 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m=r.slice(0);a.forEach((z,I)=>{m[z]=Math.ceil((d[z]-i[z])/o[z])});let c={dims:m,dataType:t[0].dataType},_=$e("output",t[0].dataType,m.length),v=oe("input",t[0].dataType,t[0].dims.length),$=se.size(m),C=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:h.length},{name:"steps",type:"u32",length:o.length}],k=[{type:12,data:$},{type:12,data:i},{type:6,data:h},{type:12,data:o},...Ce(t[0].dims,m)],M=z=>` - ${z.registerUniforms(C).declareVariables(v,_)} - ${Kh(v,_,r)} - ${z.mainStart()} - ${z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - let output_indices = ${_.offsetToIndices("global_idx")}; - let input_indices = calculateInputIndices(output_indices); - ${_.setByOffset("global_idx",v.getByIndices("input_indices"))} - 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max(threadShared[lindex], threadShared[lindex + reduceSize]); - } - workgroupBarrier(); - } - if (lindex == 0) { - rowMaxShared = ${$}(${c("threadShared[0]",h)}); - } - workgroupBarrier(); - - // find the rows sum - var threadSum = ${$}(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 = ${$}(${kr("threadShared[0]",h)}); - } - workgroupBarrier(); - - // calculate final value for each element in the row - for (var col = lindex; col < cols; col += wg) { - let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared; - setValue(row, col, row_stride, value); - } - }`;return{name:"Softmax",shaderCache:{hint:`${h}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:d},programUniforms:[{type:6,data:m}]}),getShaderSource:k}},ef=(t,e)=>{Zh(t.inputs),t.compute(Jh(t.inputs[0],e))},tf=t=>Ze({axis:t.axis})}),rf,nf,af,sf,of,lf,uf,Rw=le(()=>{Oe(),Be(),ft(),Re(),rf=t=>{if(!t||t.length<1)throw new Error("too few inputs")},nf=(t,e)=>{let r=[],n=e.numOutputs;return t[1].dims[0]>0&&(t[1].getBigInt64Array().forEach(a=>r.push(Number(a))),n=r.length),Ze({numOutputs:n,axis:e.axis,splitSizes:r})},af=t=>` -fn calculateOutputIndex(index: u32) -> u32 { - for (var i: u32 = 0u; i < ${t}u; i += 1u ) { - if (index < ${ze("uniforms.size_in_split_axis","i",t)}) { - return i; - } - } - return ${t}u; -}`,sf=t=>{let e=t.length,r=[];for(let n=0;n{let r=t[0].dims,n=se.size(r),a=t[0].dataType,o=se.normalizeAxis(e.axis,r.length),i=new Array(e.numOutputs),d=oe("input",a,r.length),h=new 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o=$e("output_data",a,r.length,4),i=oe("a_data",e[1].dataType,e[1].dims.length,4),d=oe("b_data",e[2].dataType,e[2].dims.length,4),h=oe("c_data",e[0].dataType,e[0].dims.length,4),m,c=(_,v,$)=>`select(${v}, ${_}, ${$})`;if(!n)m=o.setByOffset("global_idx",c(i.getByOffset("global_idx"),d.getByOffset("global_idx"),h.getByOffset("global_idx")));else{let _=(v,$,C="")=>{let k=`a_data[index_a${$}][component_a${$}]`,M=`b_data[index_b${$}][component_b${$}]`,z=`bool(c_data[index_c${$}] & (0xffu << (component_c${$} * 8)))`;return` - let output_indices${$} = ${o.offsetToIndices(`global_idx * 4u + ${$}u`)}; - let offset_a${$} = ${i.broadcastedIndicesToOffset(`output_indices${$}`,o)}; - let offset_b${$} = ${d.broadcastedIndicesToOffset(`output_indices${$}`,o)}; - let offset_c${$} = ${h.broadcastedIndicesToOffset(`output_indices${$}`,o)}; - let index_a${$} = offset_a${$} / 4u; - let index_b${$} = offset_b${$} / 4u; - let index_c${$} = offset_c${$} / 4u; - let component_a${$} = offset_a${$} % 4u; - let 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m={kernelId:this.backend.currentKernelId,computePipeline:t.computePipeline,bindGroup:h,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(m)}i.setPipeline(t.computePipeline),i.setBindGroup(0,h),i.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),Ut(t.programInfo.name)}dispose(){}build(t,e){Kt(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=bu(e,this.backend.device.limits),o=t.getShaderSource(a),i=`${n.join(` -`)} -${a.additionalImplementations} -${o}`,d=r.createShaderModule({code:i,label:t.name});it("verbose",()=>`[WebGPU] ${t.name} shader code: ${i}`);let h=r.createComputePipeline({compute:{module:d,entryPoint:"main"},layout:"auto",label:t.name});return Ut(t.name),{programInfo:t,computePipeline:h,uniformVariablesInfo:a.variablesInfo}}normalizeDispatchGroupSize(t){let e=typeof t=="number"?t:t.x,r=typeof t=="number"?1:t.y||1,n=typeof t=="number"?1:t.z||1,a=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(e<=a&&r<=a&&n<=a)return[e,r,n];let o=e*r*n,i=Math.ceil(Math.sqrt(o));if(i>a){if(i=Math.ceil(Math.cbrt(o)),i>a)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),wf,vf,bf,$f,Uw=le(()=>{Yt(),Oe(),Ur(),nw(),iw(),Nw(),Lw(),wf=(t,e)=>{if(e.length!==t.length)throw new Error(`inputDependencies length ${e.length} is not equal to inputTensors length ${t.length}.`);let r=[];for(let n=0;n{var a,o;let n=t.name;return(a=t.shaderCache)!=null&&a.hint&&(n+="["+t.shaderCache.hint+"]"),n+=":"+r+`:${wf(e,((o=t.shaderCache)==null?void 0:o.inputDependencies)??new Array(e.length).fill("dims"))}`,n},bf=class{constructor(t){t&&(this.architecture=t.architecture,this.vendor=t.vendor)}isArchitecture(t){return this.architecture===t}isVendor(t){return this.vendor===t}},$f=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 t=this.kernelCustomData.get(this.currentKernelId);return t||(t={},this.kernelCustomData.set(this.currentKernelId,t)),t}async initialize(t,e){this.env=t;let r=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:e.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:e.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:e.limits.maxStorageBufferBindingSize,maxBufferSize:e.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:e.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:e.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:e.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:e.limits.maxComputeWorkgroupSizeZ},requiredFeatures:r};e.features.has("chromium-experimental-timestamp-query-inside-passes")?r.push("chromium-experimental-timestamp-query-inside-passes"):e.features.has("timestamp-query")&&r.push("timestamp-query"),e.features.has("shader-f16")&&r.push("shader-f16"),this.device=await e.requestDevice(n),this.adapterInfo=new bf(await e.requestAdapterInfo()),this.gpuDataManager=gu(this),this.programManager=new _f(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,du(t.logLevel,!!t.debug),this.device.onuncapturederror=a=>{a.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${a.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:e,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let t=this.getCommandEncoder(),e={};this.queryType==="at-passes"&&(e.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=t.beginComputePass(e)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Kt(),this.endComputePass();let t;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),t=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(t,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&t.mapAsync(GPUMapMode.READ).then(()=>{var n;let e=new BigUint64Array(t.getMappedRange()),r=this.pendingQueries.get(t);for(let a=0;a"u"&&(this.queryTimeBase=$);let k=Number($-this.queryTimeBase),M=Number(C-this.queryTimeBase);if(!Number.isSafeInteger(k)||!Number.isSafeInteger(M))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:_.map(z=>({dims:z.dims,dataType:Lr(z.dataType)})),outputsMetadata:v.map(z=>({dims:z.dims,dataType:Lr(z.dataType)})),kernelId:i,kernelType:h,kernelName:m,programName:c,startTime:k,endTime:M});else{let z="";_.forEach((j,R)=>{z+=`input[${R}]: [${j.dims}] | ${Lr(j.dataType)}, `});let I="";v.forEach((j,R)=>{I+=`output[${R}]: [${j.dims}] | ${Lr(j.dataType)}, `}),console.log(`[profiling] kernel "${i}|${h}|${m}|${c}" ${z}${I}execution time: ${M-k} ns`)}Un("GPU",`${c}::${$}::${C}`)}t.unmap(),this.pendingQueries.delete(t)}),Ut()}run(t,e,r,n,a,o){Kt(t.name);let i=[];for(let I=0;Ij):r;if(c.length!==d.length)throw new Error(`Output size ${c.length} must be equal to ${d.length}.`);let _=[],v=[];for(let I=0;I=o)throw new Error(`Invalid output index: ${c[I]}`);if(c[I]===-3)continue;let j=c[I]===-1,R=c[I]===-2,L=j||R?a(d[I].dataType,d[I].dims):n(c[I],d[I].dataType,d[I].dims);if(_.push(L),L.data===0)continue;let Y=this.gpuDataManager.get(L.data);if(!Y)throw new Error(`no GPU data for output: ${L.data}`);if(j&&this.temporaryData.push(Y),R){let Q=this.kernelPersistentData.get(this.currentKernelId);Q||(Q=[],this.kernelPersistentData.set(this.currentKernelId,Q)),Q.push(Y)}v.push(Y)}if(i.length!==e.length||v.length!==_.length){if(v.length===0)return Ut(t.name),_;throw new Error(`Program ${t.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let $;if(m){let I=0,j=[];m.forEach(Q=>{let ie=typeof Q.data=="number"?[Q.data]:Q.data;if(ie.length===0)return;let pe=Q.type===10?2:4,ce,Se;Q.type===10?(Se=ie.length>4?16:ie.length>2?8:ie.length*pe,ce=ie.length>4?16:pe*ie.length):(Se=ie.length<=2?ie.length*pe:16,ce=16),I=Math.ceil(I/Se)*Se,j.push(I);let fe=Q.type===10?8:4;I+=ie.length>4?Math.ceil(ie.length/fe)*ce:ie.length*pe});let R=16;I=Math.ceil(I/R)*R;let L=new ArrayBuffer(I);m.forEach((Q,ie)=>{let pe=j[ie],ce=typeof Q.data=="number"?[Q.data]:Q.data;if(Q.type===6)new Int32Array(L,pe,ce.length).set(ce);else if(Q.type===12)new Uint32Array(L,pe,ce.length).set(ce);else if(Q.type===10)new Uint16Array(L,pe,ce.length).set(ce);else if(Q.type===1)new Float32Array(L,pe,ce.length).set(ce);else throw new Error(`Unsupported uniform type: ${Lr(Q.type)}`)});let Y=this.gpuDataManager.create(I,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Y.buffer,0,L,0,I),this.gpuDataManager.release(Y.id),$={offset:0,size:I,buffer:Y.buffer}}let C=this.programManager.normalizeDispatchGroupSize(h),k=C[1]===1&&C[2]===1,M=vf(t,e,k),z=this.programManager.getArtifact(M);if(z||(z=this.programManager.build(t,C),this.programManager.setArtifact(M,z),it("info",()=>`[artifact] key: ${M}, programName: ${t.name}`)),m&&z.uniformVariablesInfo){if(m.length!==z.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${z.uniformVariablesInfo.length}, got ${m.length} in program "${z.programInfo.name}".`);for(let I=0;I`[ProgramManager] run "${t.name}" (key=${M}) with ${C[0]}x${C[1]}x${C[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let I={kernelId:this.currentKernelId,programName:z.programInfo.name,inputTensorViews:e,outputTensorViews:_};this.pendingKernels.push(I),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(I)}return this.programManager.run(z,i,v,C,$),Ut(t.name),_}upload(t,e){this.gpuDataManager.upload(t,e)}memcpy(t,e){this.gpuDataManager.memcpy(t,e)}async download(t,e){await this.gpuDataManager.download(t,e)}alloc(t){return this.gpuDataManager.create(t).id}free(t){return this.gpuDataManager.release(t)}createKernel(t,e,r,n){let a=yf.get(t);if(!a)throw new Error(`kernel not implemented: ${t}`);let o={kernelType:t,kernelName:n,kernelEntry:a[0],attributes:[a[1],r]};this.kernels.set(e,o)}releaseKernel(t){let e=this.kernelPersistentData.get(t);if(e){for(let r of e)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(t)}this.kernelCustomData.delete(t),this.kernels.delete(t)}computeKernel(t,e,r){let n=this.kernels.get(t);if(!n)throw new Error(`kernel not created: ${t}`);let a=n.kernelType,o=n.kernelName,i=n.kernelEntry,d=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${a}] ${o}" is not allowed to be called recursively`);this.currentKernelId=t,d[0]&&(d[1]=d[0](d[1]),d[0]=void 0),it("info",()=>`[WebGPU] Start to run kernel "[${a}] ${o}"...`);let h=this.env.debug;this.temporaryData=[];try{return h&&this.device.pushErrorScope("validation"),i(e,d[1]),0}catch(m){return r.push(Promise.resolve(`[WebGPU] Kernel "[${a}] ${o}" failed. ${m}`)),1}finally{h&&r.push(this.device.popErrorScope().then(m=>m?`GPU validation error for kernel "[${a}] ${o}": ${m.message}`:null));for(let m of this.temporaryData)this.gpuDataManager.release(m.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(t,e,r,n){let a=this.sessionExternalDataMapping.get(t);a||(a=new Map,this.sessionExternalDataMapping.set(t,a));let o=a.get(e),i=this.gpuDataManager.registerExternalBuffer(r,n,o==null?void 0:o[1]);return a.set(e,[i,r]),i}unregisterBuffers(t){let e=this.sessionExternalDataMapping.get(t);e&&(e.forEach(r=>this.gpuDataManager.unregisterExternalBuffer(r[1])),this.sessionExternalDataMapping.delete(t))}getBuffer(t){let e=this.gpuDataManager.get(t);if(!e)throw new Error(`no GPU data for buffer: ${t}`);return e.buffer}createDownloader(t,e,r){return async()=>{let n=await qs(this,t,e);return pu(n.buffer,r)}}writeTimestamp(t){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){var t;this.queryType="none",(((t=this.env.webgpu.profiling)==null?void 0:t.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(){it("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(){it("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){it("info","replay"),this.sessionStatus="replaying";let t=this.capturedCommandList.get(this.currentSessionId),e=this.capturedPendingKernels.get(this.currentSessionId),r=t.length;this.pendingKernels=[];for(let n=0;n=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onReleaseSession(t){this.unregisterBuffers(t),this.capturedCommandList.has(t)&&this.capturedCommandList.delete(t),this.capturedPendingKernels.has(t)&&this.capturedPendingKernels.delete(t),this.gpuDataManager.onReleaseSession(t)}onRunStart(t){this.currentSessionId=t,this.setQueryType()}}}),xf={};pn(xf,{init:()=>Sf});var Ta,Cf,Sf,Fw=le(()=>{Oe(),Uw(),Ur(),Be(),Ta=class m0{constructor(e,r,n,a){this.module=e,this.dataType=r,this.data=n,this.dims=a}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let e=se.size(this.dims);return e===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,e)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let e=se.size(this.dims);return e===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,e)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let e=se.size(this.dims);return e===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,e)}reshape(e){if(se.size(e)!==se.size(this.dims))throw new Error("Invalid new shape");return new m0(this.module,this.dataType,this.data,e)}},Cf=class{constructor(t,e,r){this.module=t,this.backend=e,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=e.adapterInfo;let n=t.HEAPU32,a=r>>>2;this.opKernelContext=n[a++];let o=n[a++];this.outputCount=n[a++],this.customDataOffset=n[a++],this.customDataSize=n[a++];let i=[];for(let d=0;dtypeof d=="number"?this.inputs[d]:d))??this.inputs,n=(e==null?void 0:e.outputs)??[],a=(d,h,m)=>new Ta(this.module,h,this.output(d,m),m),o=(d,h)=>{let m=Gn(d);if(!m)throw new Error(`Unsupported data type: ${d}`);let c=m*se.size(h),_=c>0?this.backend.gpuDataManager.create(c).id:0;return new Ta(this.module,d,_,h)};return this.backend.run(t,r,n,a,o,this.outputCount)}output(t,e){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+e.length)*4),a=n>>2;this.module.HEAPU32[a++]=e.length;for(let o=0;o{let a=e.jsepInit;if(!a)throw new Error("Failed to initialize JSEP. 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c=s+u.kvSequenceLength,f=[u.batchSize,u.sequenceLength,u.vHiddenSize],d=12,g={x:Math.ceil(u.vHeadSize/d),y:Math.ceil(u.sequenceLength/d),z:u.batchSize*u.numHeads},w=[{type:12,data:u.sequenceLength},{type:12,data:c},{type:12,data:u.vHeadSize},{type:12,data:u.numHeads},{type:12,data:u.vHiddenSize}];return{name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:f,dataType:r.dataType,gpuDataType:0}],dispatchGroup:g,programUniforms:w}),getShaderSource:A=>{let P=Z("probs",r.dataType,r.dims),x=Z("v",t.dataType,t.dims),E=ne("output",r.dataType,f),O=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`\n const TILE_SIZE = ${d}u;\n var tileQ: array<${P.type.value}, ${d*d}>;\n var tileK: array<${P.type.value}, ${d*d}>;\n ${A.registerUniforms(O).declareVariables(P,x,E)}\n ${A.mainStart([d,d,1])}\n let headIdx = workgroup_id.z;\n let m = global_id.y;\n let n = 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must be 3 or 4.\n tileAWidth ${C} must be divisible by workgroupSize[0]${r[0]}. tileInner ${c} must be divisible by workgroupSize[1] ${r[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar mm_Asub: array, ${C/A}>, ${$}>;\nvar mm_Bsub: array, ${w/e[0]}>, ${c}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${A};\nconst tileInner = ${c};\n\n@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]})\nfn main(@builtin(local_invocation_id) localId : vec3,\n @builtin(global_invocation_id) globalId : vec3,\n @builtin(workgroup_id) workgroupId : vec3) {\n let localRow = i32(localId.y);\n let tileRow = localRow * rowPerThread;\n let tileCol = i32(localId.x);\n\n let globalRow =i32(globalId.y) * rowPerThread;\n let globalCol = i32(globalId.x);\n let batch = ${f?"0":"i32(globalId.z)"};\n ${u?`let batchIndices = ${u.offsetToIndices("u32(batch)")};`:""}\n let globalRowStart = i32(workgroupId.y) * ${g};\n\n let num_tiles = ${f?`${Math.ceil(d/c)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n var kStart = ${f?`i32(globalId.z) * ${d}`:"0"};\n\n var acc: array, rowPerThread>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${P};\n for (var t = 0; t < num_tiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${Lg(s,u)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${P}; innerRow = innerRow + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${u?", batchIndices":""});\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n ${A===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n ${Fg(s,A)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n}`},bd=(e,r)=>e?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart + inputCol${r?", batchIndices":""});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRowStart + inputRow,\n kStart + inputCol${r?", batchIndices":""});\n `,qg=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",fn=(e,r,t="f32",u,s=!1,c=32,f=!1,d=32,g=!1)=>{let w=e[1]*r[1],C=e[0]*r[0],$=s?w:c,A=s?c:w;if(!(A%r[1]===0&&$%r[0]===0&&c%r[1]===0))throw new Error(`tileAHight ${A} must be divisible by workgroupSize[1]${r[1]}, tileAWidth ${$} must be divisible by workgroupSize[0]${r[0]}, tileInner ${c} must be divisible by workgroupSize[1]${r[1]}`);let P=A/r[1],x=$/r[0],E=c/r[1],O=g?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${w};\n let globalColStart = i32(workgroupId.x) * ${C};\n\n // Loop over shared dimension.\n for (var t = 0; t < num_tiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${A}; inputRow = inputRow + ${r[1]}) {\n for (var inputCol = localCol; inputCol < ${$}; inputCol = inputCol + ${r[0]}) {\n ${bd(s,u)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${r[1]}) {\n for (var inputCol = localCol; inputCol < ${C}; inputCol = inputCol + ${r[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalColStart + inputCol${u?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${t}, colPerThread>;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${r[0]}];\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let ACached = ${s?`mm_Asub[k][localRow + innerRow * ${r[1]}];`:`mm_Asub[localRow + innerRow * ${r[1]}][k];`}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n ACached * BCached[innerCol];\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let gRow = globalRowStart + localRow + innerRow * ${r[1]};\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let gCol = globalColStart + localCol + innerCol * ${r[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${w};\n\nlet tileRowA = i32(localId.y) * ${P};\nlet tileColA = i32(localId.x) * ${x};\nlet tileRowB = i32(localId.y) * ${E};\n// Loop over shared dimension.\nfor (var t = 0; t < num_tiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${P}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < ${x}; innerCol = innerCol + 1) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${bd(s,u)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${E}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalCol + innerCol${u?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${t}, colPerThread>;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n ${qg(s)}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n }\n }\n }\n\n workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n}\n`;return`\n var mm_Asub : array, ${A}>;\n var mm_Bsub : array, ${c}>;\n const rowPerThread = ${e[1]};\n const colPerThread = ${e[0]};\n const tileInner = ${c};\n\n@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]})\nfn main(@builtin(local_invocation_id) localId : vec3,\n @builtin(global_invocation_id) globalId : vec3,\n @builtin(workgroup_id) workgroupId : vec3) {\n let batch = ${f?"0":"i32(globalId.z)"};\n ${u?`let batchIndices = ${u.offsetToIndices("u32(batch)")};`:""}\n let num_tiles = ${f?`${Math.ceil(d/c)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n var kStart = ${f?`i32(globalId.z) * ${d}`:"0"};\n\n var acc : array, rowPerThread>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${O}\n }\n`},Kg=(e,r,t,u,s,c=!1)=>{let[f,d,g]=s,[w,C,$,A]=u,P=Br(f,g),x=Br(d,g),E=Ne(u[0].type.tensor),O=()=>{let j=C.rank,U=w.rank,L=`var aIndices: ${C.type.indices};`;for(let F=j-2-1,te=U-1;F>=0;F--,te--)L+=`\naIndices[${F}] = ${U>1?`batchIndices[${te}]`:"batchIndices"};`;return P.forEach(F=>{L+=`\naIndices[${F}] = 0;`}),L+=`\naIndices[${j-2}] = u32(row);\n aIndices[${j-1}] = u32(colIn);`,L},B=()=>{let j=$.rank,U=w.rank,L=`var bIndices: ${$.type.indices};`;for(let F=j-2-1,te=U-1;F>=0;F--,te--)L+=`\nbIndices[${F}] = ${U>1?`batchIndices[${te}]`:"batchIndices"};`;return x.forEach(F=>{L+=`\nbIndices[${F}] = 0;`}),L+=`\nbIndices[${j-2}] = u32(row);\n bIndices[${j-1}] = u32(colIn);`,L};return`\n fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${w.type.indices}) -> ${st(e,E)} {\n var value = ${st(e,E)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\n {\n ${O()}\n value = ${C.getByIndices("aIndices")};\n }\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${w.type.indices}) -> ${st(e,E)} {\n var value = ${st(e,E)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\n {\n ${B()}\n value = ${$.getByIndices("bIndices")};\n }\n return value;\n }\n\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${st(e,E)}) {\n let col = colIn * ${e};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n var value = valueIn;\n let coords = vec3(batch, row, colIn);\n ${r?`value = value + ${c?"bias[colIn]":`${st(e,E)}(bias[row])`};`:""}\n ${t}\n ${A.setByIndices("vec3(coords)","value")}\n }\n }\n `},mi=(e,r,t,u,s=!1)=>{let c=e[0].dims,f=e[1].dims,d=c.slice(0,-2),g=f.slice(0,-2),w=u?u.slice(0,-2):t.slice(0,-2),C=K.size(w),$=c[c.length-2],A=c[c.length-1],P=f[f.length-1],x=A%4===0&&P%4===0,E=$<=8?[4,1,1]:[4,4,1],O=[8,8,1],B=[Math.ceil(P/O[0]/E[0]),Math.ceil($/O[1]/E[1]),Math.ceil(C/O[2]/E[2])],R=x?4:1,j=[...d,$,A/R],U=j.length,L=[...g,A,P/R],F=L.length,te=[C,$,P/R],J=[{type:6,data:$},{type:6,data:P},{type:6,data:A}];Tt(r,J),J.push(...se(w,j,L));let oe=["rank","rank"],le=e.length>2;le&&(J.push(...se(e[2].dims)),oe.push("rank")),J.push(...se(te));let ge=X=>{let pe=w.length,we=ri("batchDims",e[0].dataType,pe,1),ue=Ne(e[0].dataType),me=Z("a",e[0].dataType,U,R),Ee=Z("b",e[1].dataType,F,R),Pe=ne("result",e[0].dataType,te.length,R),Ce=[me,Ee];if(le){let ee=s?R:1;Ce.push(Z("bias",e[2].dataType,e[2].dims.length,ee))}let be=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Et(r,be);let Ae=Ne(Pe.type.tensor),_e=At(r,Pe.type.value,Ae),Je=Kg(R,le,_e,[we,me,Ee,Pe],[d,g,w],s);return`\n ${X.registerUniforms(be).registerInternalVariables(we).declareVariables(...Ce,Pe)}\n ${Je}\n ${x?cn(E,O,ue,we):fn(E,O,ue,we)}\n `};return{name:"MatMul",shaderCache:{hint:`${E};${r.activation};${x};${s}`,inputDependencies:oe},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:B[0],y:B[1],z:B[2]},programUniforms:J}),getShaderSource:ge}}});var Yg,vd,wd=ae(()=>{"use strict";Te();Lt();Re();Ft();fi();eo();pn();Yg=(e,r,t,u,s=!1,c,f=4,d=4,g=4,w="f32")=>{let C=le=>{switch(le){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${w}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${le} is not supported.`)}},$=le=>{switch(le){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 ${le} is not supported.`)}},A=e?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,P=e?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,x=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",E=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",O=e?"row":"col",B=e?"col":"row",R=`\n let inChannels = i32(uniforms.w_shape[2]);\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n let outRow = ${O} / outWidth;\n let outCol = ${O} % outWidth;\n\n let WRow = ${B} / (i32(uniforms.w_shape[1]) * inChannels);\n let WCol = ${B} / inChannels % i32(uniforms.w_shape[1]);\n let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n let xCh = ${B} % inChannels;\n var resData = ${st(f,w)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the \'same\' padding type.\n if (xRow >= 0 && xRow < ${x} && xCol >= 0 && xCol < ${E}) {\n ${A}\n let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape));\n ${C(f)}\n }\n return resData;`,j=e?r&&u?`\n let col = colIn * ${f};\n ${R}`:`\n let col = colIn * ${f};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n ${R}\n }\n return ${st(f,w)}(0.0);`:u&&t?`\n let col = colIn * ${f};\n ${R}`:`\n let col = colIn * ${f};\n if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n ${R}\n }\n return ${st(f,w)}(0.0);`,U=`${$(d)}`,L=st(g,w),F=e?st(f,w):st(d,w),te=e?st(d,w):st(f,w),J=At(c,L,w);return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${F} {\n ${e?j:U}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${te} {\n ${e?U:j}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${L}) {\n let col = colIn * ${g};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)\n {\n var value = valueIn;\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n ${P}\n ${ci(s)}\n ${J}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`},vd=(e,r,t,u,s,c,f,d)=>{let g=r.format==="NHWC",w=g?e[0].dims[3]:e[0].dims[1],C=t[0],$=g?t[2]:t[3],A=g?t[1]:t[2],P=g?t[3]:t[1],x=g&&(w%4===0||w%3===0)&&P%4===0,E=g?P:$*A,O=g?$*A:P,B=[8,8,1],R=u<=8?[4,1,1]:[4,4,1],j=[Math.ceil(E/B[0]/R[0]),Math.ceil(O/B[1]/R[1]),Math.ceil(C/B[2]/R[2])];Fe("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${j}`);let U=x?g&&w%4!==0?3:4:1,L=B[1]*R[1],F=B[0]*R[0],te=Math.max(B[0]*U,B[1]),J=u%L===0,oe=s%F===0,le=c%te===0,ge=x?[U,4,4]:[1,1,1],X=[{type:6,data:u},{type:6,data:s},{type:6,data:c},{type:6,data:[r.pads[0],r.pads[1]]},{type:6,data:r.strides},{type:6,data:r.dilations}];Tt(r,X),X.push(...se(e[0].dims,e[1].dims));let pe=["rank","rank"];f&&(X.push(...se(e[2].dims)),pe.push("rank")),X.push(...se(t));let we=ue=>{let me=[{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}];Et(r,me);let Ee=x?4:1,Pe=Ne(e[0].dataType),Ce=`\n fn setOutputAtIndex(flatIndex : i32, value : ${x?`vec4<${Pe}>`:Pe}) {\n result[flatIndex] = ${x?`vec4<${Pe}>`:Pe}(value);\n }\n fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${x?`vec4<${Pe}>`:Pe}) {\n let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3));\n setOutputAtIndex(flatIndex ${x?"/ 4":""}, value);\n }`,be=Z("x",e[0].dataType,e[0].dims.length,U===3?1:U),Ae=Z("w",e[1].dataType,e[1].dims.length,Ee),_e=[be,Ae],Je=ne("result",e[0].dataType,t.length,Ee);if(f){let ee=Z("bias",e[2].dataType,e[2].dims.length,Ee);_e.push(ee),Ce+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${x?`vec4<${Pe}>`:Pe} {\n return bias[coords.${g?"w":"y"}${x?"/ 4":""}];\n }`}return`\n ${pi("uniforms.result_strides")}\n //struct Uniforms { xShape : vec4, wShape : vec4, outShape : vec4,\n // outShapeStrides: vec3, filterDims : vec2, pad : vec2, stride : vec2,\n // dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n ${ue.registerUniforms(me).declareVariables(..._e,Je)}\n ${Ce}\n ${Yg(g,J,oe,le,f,r,ge[0],ge[1],ge[2],Pe)}\n ${x?cn(R,B,Pe,void 0,!g,te):fn(R,B,Pe,void 0,!g,te,!1,void 0,d)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${r.cacheKey};${U};${x};${J};${oe};${le};${L};${F};${te}`,inputDependencies:pe},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:j[0],y:j[1],z:j[2]},programUniforms:X}),getShaderSource:we}}});var to,_d,$d=ae(()=>{"use strict";Te();De();Re();ro();Ft();to=(e,r,t)=>{let u=e.length>2,s=u?"value += b[output_channel];":"",c=e[0].dims,f=e[1].dims,d=f[0]/r.group,g=r.format==="NHWC",w=hi(c,f,r.dilations,r.pads,r.strides,g),C=K.size(w),$=[{type:12,data:C},{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:d}];Tt(r,$),$.push(...se(c,f));let A=["rank","rank"];u&&($.push(...se(e[2].dims)),A.push("rank")),$.push(...se(w));let P=x=>{let E=ne("output",e[0].dataType,w.length),O=Ne(E.type.tensor),B=At(r,E.type.value,O),R=Z("x",e[0].dataType,c.length),j=Z("w",e[1].dataType,f.length),U=[R,j];u&&U.push(Z("b",e[2].dataType,e[2].dims.length));let L=[{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"}];return Et(r,L),`\n ${x.registerUniforms(L).declareVariables(...U,E)}\n\n ${x.mainStart()}\n ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let outputIndices = ${E.offsetToIndices("global_idx")};\n let batch: u32 = outputIndices[0];\n let output_channel: u32 = outputIndices[${g?3:1}];\n let xRCCorner: vec2 = vec2(outputIndices[${g?1:2}], outputIndices[${g?2:3}]) * uniforms.strides - uniforms.pads;\n let group_id: u32 = output_channel / uniforms.output_channels_per_group;\n\n var value: ${E.type.value} = ${E.type.value}(0);\n for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {\n let input_channel = group_id * uniforms.w_shape[1] + wInChannel;\n for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {\n let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];\n\n if (xHeight < 0u || xHeight >= uniforms.x_shape[${g?1:2}]) {\n continue;\n }\n\n for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {\n let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];\n if (xWidth < 0u || xWidth >= uniforms.x_shape[${g?2:3}]) {\n continue;\n }\n\n let xVal = ${g?R.get("batch","xHeight","xWidth","input_channel"):R.get("batch","input_channel","xHeight","xWidth")};\n let wVal = ${j.get("output_channel","wInChannel","wHeight","wWidth")};\n value += xVal*wVal;\n }\n }\n }\n ${s}\n ${B}\n ${E.setByOffset("global_idx","value")}\n }`};return{name:"GroupedConv",shaderCache:{hint:r.cacheKey,inputDependencies:A},getRunData:()=>({outputs:[{dims:t?t(w):w,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:$}),getShaderSource:P}},_d=(e,r,t)=>{let u=e.length>2,s=He(t[3]),c=He(t[2]),f=K.size(t)/s/c,d=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/s],g=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/s],w=[t[0],t[1],t[2],t[3]/s],C=[{type:12,data:f},{type:6,data:[r.strides[0],r.strides[1]]},{type:6,data:[r.pads[0],r.pads[1]]}];Tt(r,C),C.push(...se(d,g,w));let $=(c-1)*r.strides[1]+g[1],A=P=>{let x=ne("output",e[0].dataType,w.length,s),E=Ne(x.type.tensor),O=At(r,x.type.value,E),B=Z("x",e[0].dataType,d.length,s),R=Z("w",e[1].dataType,g.length,s),j=[B,R];u&&j.push(Z("b",e[2].dataType,e[2].dims,s));let U=u?"value += b[output_channel];":"",L=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Et(r,L),`\n ${P.registerUniforms(L).declareVariables(...j,x)}\n ${P.mainStart()}\n ${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let width0 = uniforms.output_shape[3];\n let output_channel = global_idx % width0;\n var index1 = global_idx / width0;\n let width1 = uniforms.output_shape[2] / ${c}u;\n let col = (index1 % width1) * ${c}u;\n index1 = index1 / width1;\n let row = index1 % uniforms.output_shape[1];\n let batch = index1 / uniforms.output_shape[1];\n\n let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads;\n\n var x_vals: array<${B.type.value}, ${$}>;\n var values: array<${x.type.value}, ${c}>;\n let input_channel = output_channel;\n // Use constant instead of uniform can give better performance for w\'s height/width.\n for (var w_height: u32 = 0u; w_height < ${g[0]}; w_height++) {\n let x_height = x_corner.x + i32(w_height);\n if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {\n for (var i = 0; i < ${$}; i++) {\n let x_width = x_corner.y + i;\n if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {\n x_vals[i] = ${B.get("batch","u32(x_height)","u32(x_width)","input_channel")};\n } else {\n x_vals[i] = ${B.type.value}(0);\n }\n }\n for (var w_width: u32 = 0u; w_width < ${g[1]}; w_width++) {\n let w_val = ${R.get("w_height","w_width","0","output_channel")};\n for (var i = 0u; i < ${c}u; i++) {\n values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);\n }\n }\n }\n }\n\n for (var i = 0u; i < ${c}u; i++) {\n var value = values[i];\n ${U}\n ${O}\n ${x.set("batch","row","col + i","output_channel","value")};\n }\n }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${r.cacheKey};${s};${c};${$};${g[0]};${g[1]}`,inputDependencies:u?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:C}),getShaderSource:A}}});var no,Zg,Cd,io=ae(()=>{"use strict";Te();De();pn();Re();Ft();no=(e,r,t,u,s=!1)=>{let c=e[0].dims,f=e[1].dims,d=c[c.length-2],g=f[f.length-1],w=c[c.length-1],C=He(g),$=He(w),A=He(d),P=K.size(t)/C/A,x=e.length>2,E=u?u.slice(0,-2):t.slice(0,-2),B=[K.size(E),d,g],R=[{type:12,data:P},{type:12,data:d},{type:12,data:g},{type:12,data:w}];Tt(r,R),R.push(...se(E,c,f)),x&&R.push(...se(e[2].dims)),R.push(...se(B));let j=U=>{let L=ri("batch_dims",e[0].dataType,E.length),F=Z("a",e[0].dataType,c.length,$),te=Z("b",e[1].dataType,f.length,C),J=ne("output",e[0].dataType,B.length,C),oe=Ne(J.type.tensor),le=At(r,J.type.value,oe),ge=[F,te],X="";if(x){let be=s?C:1;ge.push(Z("bias",e[2].dataType,e[2].dims.length,be)),X=`${s?`value += bias[col / ${be}];`:`value += ${J.type.value}(bias[row + i]);`}`}let pe=c.slice(0,-2),we=f.slice(0,-2),ue=Br(pe,E),me=Br(we,E),Ee=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Et(r,Ee);let Pe=(be,Ae)=>{let _e=be.rank,Je=be.name;if(_e===2)return`var ${Je}_indices = ${be.type.indices}(0u, 0u);`;let ee=L.rank,ce=`var ${Je}_indices: ${be.type.indices};`;for(let Be=_e-2-1,tt=ee-1;Be>=0;Be--,tt--)ce+=`\n${Je}_indices[${Be}] = ${ee>1?`batch_indices[${tt}]`:"batch_indices"};`;return Ae.forEach(Be=>{ce+=`\n${Je}_indices[${Be}] = 0;`}),ce+=`${Je}_indices[${_e-2}] = 0u;\n ${Je}_indices[${_e-1}] = 0u;`,ce},Ce=()=>{let be=`var a_data: ${F.type.value};`;for(let Ae=0;Ae<$;Ae++)be+=`\n let b_data${Ae} = b[(b_offset + (k + ${Ae}) * uniforms.N + col) / ${C}];`;for(let Ae=0;Ae;\n for (var k: u32 = 0u; k < uniforms.K; k = k + ${$}) {\n ${Ce()}\n }\n for (var i = 0u; i < ${A}u; i++) {\n var value = values[i];\n ${X}\n ${le}\n let cur_indices = ${J.type.indices}(batch, row + i, col);\n let offset = ${J.indicesToOffset("cur_indices")};\n ${J.setByOffset(`offset / ${C}`,"value")};\n }\n }\n `};return{name:"MatMulNaive",shaderCache:{hint:`${r.activation};${C};${$};${A};${s}`,inputDependencies:x?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(P/64)},programUniforms:R}),getShaderSource:j}},Zg=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},Cd=e=>{Zg(e.inputs);let r=Ot.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!r)throw new Error("Can\'t use matmul on the given tensors");let t=r[r.length-1],u=e.inputs[0].dims[e.inputs[0].dims.length-1];t<8&&u<8?e.compute(no(e.inputs,{activation:""},r)):e.compute(mi(e.inputs,{activation:""},r))}});var hi,oo,Qg,Sd,ao,Xg,Jg,so,ro=ae(()=>{"use strict";De();wd();pn();$d();Ft();io();Dr();hi=(e,r,t,u,s,c)=>{let f=e[0],d=e.slice(c?1:2,c?3:4),g=d.length,w=r[0],$=r.slice(2).map((x,E)=>x+(x-1)*(t[E]-1)),P=d.map((x,E)=>x+u[E]+u[E+g]).map((x,E)=>Math.floor((x-$[E]+s[E])/s[E]));return P.splice(0,0,f),P.splice(c?3:1,0,w),P},oo=[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!==4&&e[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");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],u=e[1].dims[1]*r.group;if(t!==u)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 s=e[0].dims.length-2;if(r.dilations.length!==s)throw new Error(`dilations should be ${s}D`);if(r.strides.length!==s)throw new Error(`strides should be ${s}D`);if(r.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},Sd=(e,r)=>{let t=e.kernelShape.slice();for(let c=2;c{let r=di(e),t=e.format,u=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],s=e.dilations,c=e.group,f=e.kernel_shape,d=e.pads,g=e.strides,w=e.w_is_const();return{autoPad:u,format:t,dilations:s,group:c,kernelShape:f,pads:d,strides:g,wIsConst:w,...r,cacheKey:`${e.format};${r.activation};`}},Xg=(e,r,t)=>{let u=Sd(t,r),s=t.format==="NHWC";if(t.group!==1){if(!e.adapterInfo.isArchitecture("ampere")&&s&&r[1].dims[0]===t.group&&r[1].dims[1]===1&&t.dilations[0]===1&&t.dilations[1]===1){let te=hi(r[0].dims,r[1].dims,t.dilations,u.pads,t.strides,s),J=e.kernelCustomData.wT??e.compute($t(r[1],oo),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=J);let oe=[r[0],J];r.length===3&&oe.push(r[2]),e.compute(_d(oe,u,te),{inputs:oe})}else e.compute(to(r,u));return}let c=r.length===3,f=r[0].dims[s?1:2],d=r[0].dims[s?2:3],g=r[0].dims[s?3:1],w=r[1].dims[2],C=r[1].dims[3],$=hi(r[0].dims,r[1].dims,t.dilations,u.pads,t.strides,s),A=$[s?1:2],P=$[s?2:3],x=$[s?3:1],E=s&&w===f&&C===d&&t.pads[0]===0&&t.pads[1]===0;if(E||w===1&&C===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 F=$[0],te,J,oe,le=[];if(s){let pe=e.kernelCustomData.wT??e.compute($t(r[1],oo),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];if(t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=pe),E){let we=f*d*g;te=r[0].reshape([1,F,we]),J=pe.reshape([1,we,x]),oe=[1,F,x]}else te=r[0].reshape([F,f*d,g]),J=pe.reshape([1,g,x]),oe=[F,A*P,x];le.push(te),le.push(J)}else te=r[0].reshape([F,g,f*d]),J=r[1].reshape([1,x,g]),oe=[F,x,A*P],le.push(J),le.push(te);c&&le.push(r[2]);let ge=oe[2],X=le[0].dims[le[0].dims.length-1];ge<8&&X<8?e.compute(no(le,u,$,oe,s),{inputs:le}):e.compute(mi(le,u,$,oe,s),{inputs:le});return}let O=!0,B=e.kernelCustomData.wT??e.compute($t(r[1],oo),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=B);let R=[r[0],B];c&&R.push(r[2]);let j=s?A*P:x,U=s?x:A*P,L=w*C*g;e.compute(vd(R,u,$,j,U,L,c,O),{inputs:R})},Jg=(e,r)=>{let t=r.format==="NHWC",u=[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&&u.push(e.inputs[2]);let s=[0,r.pads[0],0,r.pads[1]],c=[1].concat(r.strides),f=[1].concat(r.dilations),d=[1].concat(r.kernelShape),g=Sd({...r,pads:s,strides:c,dilations:f,kernelShape:d},u);e.compute(to(u,g,w=>t?[w[0],w[2],w[3]]:[]))},so=(e,r)=>{Qg(e.inputs,r),e.inputs[0].dims.length===3?Jg(e,r):Xg(e,e.inputs,r)}});var ey,xd,Id=ae(()=>{"use strict";Te();Lt();Re();Ft();fi();eo();pn();ey=(e,r=!1,t,u,s=4)=>{let c=B=>{switch(B){case 1:return"return w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];\n let v1 = w[getIndexFromCoords4D(coord1, vec4(uniforms.w_shape))];\n let v2 = w[getIndexFromCoords4D(coord2, vec4(uniforms.w_shape))];\n let v3 = w[getIndexFromCoords4D(coord3, vec4(uniforms.w_shape))];\n return ${u}(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${B} is not supported.`)}},f=e?`\n let coord = vec4(batch, iXR, iXC, xCh);\n `:`\n let coord = vec4(batch, xCh, iXR, iXC);\n `,d=e?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,g=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",w=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",C=e?"row":"col",$=e?"col":"row",A=`\n let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n let outRow = ${C} / outWidth;\n let outCol = ${C} % outWidth;\n\n let WRow = ${$} / (uniforms.filter_dims[1] * inChannels);\n let WCol = ${$} / inChannels % uniforms.filter_dims[1];\n let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(${g}) || fract(xR) > 0.0) {\n return ${u}(0.0);\n }\n if (xC < 0.0 || xC >= f32(${w}) || fract(xC) > 0.0) {\n return ${u}(0.0);\n }\n let iXR = i32(xR);\n let iXC = i32(xC);\n let xCh = ${$} % inChannels;\n ${f}\n return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${s}];`,P=e?`\n let col = colIn * ${s};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n ${A}\n }\n return ${u}(0.0);`:`\n let col = colIn * ${s};\n if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n ${A}\n }\n return ${u}(0.0);`,x=`\n let col = colIn * ${s};\n let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);\n let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];\n if (${e?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"} && coordX >= 0 && coordY >= 0) {\n let rowInner = row % inChannels;\n let coord = vec4(coordX, coordY, col, rowInner);\n ${c(s)}\n }\n return ${u}(0.0);\n `,E=At(t,u);return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${u} {\n ${e?P:x}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${u} {\n ${e?x:P}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${u}) {\n let col = colIn * ${s};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n var value = valueInput;\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n ${d}\n ${ci(r)}\n ${E}\n result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${s}] = value;\n }\n }`},xd=(e,r,t,u,s,c,f,d)=>{let g=r.format==="NHWC",w=g?e[0].dims[3]:e[0].dims[1],C=t[0],$=g?t[2]:t[3],A=g?t[1]:t[2],P=g?t[3]:t[1],x=g&&w%4===0&&w%3&&P%4===0,E=g?P:$*A,O=g?$*A:P,B=[8,8,1],R=u<=8?[4,1,1]:[4,4,1],j=[Math.ceil(E/B[0]/R[0]),Math.ceil(O/B[1]/R[1]),Math.ceil(C/B[2]/R[2])];Fe("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${j}`);let U=x?4:1,L=Math.max(B[0]*U,B[1]),F=x?4:1,te=[r.kernelShape[g?1:2],r.kernelShape[g?2:3]],J=[te[0]+(r.dilations[0]<=1?0:(te[0]-1)*(r.dilations[0]-1)),te[1]+(r.dilations[1]<=1?0:(te[1]-1)*(r.dilations[1]-1))],oe=[J[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),J[1]-1-Math.floor((r.pads[1]+r.pads[3])/2)],le=[{type:6,data:u},{type:6,data:s},{type:6,data:c},{type:6,data:r.strides},{type:6,data:r.dilations},{type:6,data:te},{type:6,data:oe}];Tt(r,le),le.push(...se(e[0].dims,e[1].dims));let ge=["rank","rank"];f&&(le.push(...se(e[2].dims)),ge.push("rank")),le.push(...se(t));let X=pe=>{let we=Z("x",e[0].dataType,e[0].dims.length,F),ue=Z("w",e[1].dataType,e[1].dims.length,1),me=ne("result",e[0].dataType,t.length,F),Ee=[we,ue],Pe="";if(f){let Ae=Z("bias",e[2].dataType,e[2].dims.length,F);Ee.push(Ae),Pe+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${Ae.type.value} {\n return bias[coords.${g?"w":"y"}${x?"/ 4":""}];\n }`}let Ce=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:te.length},{name:"pads",type:"i32",length:oe.length}];Et(r,Ce);let be=Ne(e[0].dataType,1);if(be!=="f16"&&be!=="f32")throw new Error(`elemType ${be} is not supported.`);return`\n ${pi("uniforms.result_strides")}\n ${pe.registerUniforms(Ce).declareVariables(...Ee,me)};\n ${Pe}\n ${ey(g,f,r,we.type.value,U)}\n ${x?cn(R,B,be,void 0,!g,L):fn(R,B,be,void 0,!g,L,!1,void 0,d)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${r.cacheKey};${R};${B};${x}`,inputDependencies:ge},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:j[0],y:j[1],z:j[2]},programUniforms:le}),getShaderSource:X}}});var ty,uo,Ad=ae(()=>{"use strict";Te();Lt();De();Re();ty=(e,r,t,u,s,c=!1,f,d,g=!1)=>{let w=g?1:2,C=g?2:3,$=g?3:1,A=c?2:1,P=`\n fn setOutputAtIndex(flatIndex : u32, value : ${c?`vec4<${f}>`:f}) {\n result[flatIndex] = ${c?`vec4<${f}>`:f}(value);\n }`;u&&(P+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${c?`vec4<${f}>`:f} {\n return bias[coords.${g?"w":"y"}${c?"/ 4":""}];\n }`);let x=c?4:1,E=Z("W",r[1].dataType,r[1].dims.length,x),O=Z("Dy",r[0].dataType,r[0].dims.length,x),B=[O,E];u&&B.push(Z("bias",r[2].dataType,[t[$]].length,x));let R=ne("result",r[0].dataType,t.length,x),j=`{\n let batch: u32 = ${s?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];\n let r = ${s?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];\n let c = ${s?"global_id.y":"workgroup_id.y"} * ${A};\n let d1: u32 = ${s?"global_id.x":"workgroup_id.x"} * 4;\n\n let dyCorner = vec2(i32(r), i32(c)) - vec2(uniforms.pads);\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${A}>;\n for (var i = 0; i < ${A}; i++) {\n dotProd[i] = vec4<${f}>(0.0);\n }\n for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\n var dyR = (${f}(dyCorner.x) + ${f}(wR)) / ${f}(uniforms.strides.x);\n let wRPerm = uniforms.filter_dims[0] - 1 - wR;\n if (dyR < 0.0 || dyR >= ${f}(uniforms.Dy_shape[1]) ||\n fract(dyR) > 0.0 || wRPerm < 0) {\n continue;\n }\n let idyR: u32 = u32(dyR);\n\n for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\n let dyC = (${f}(dyCorner.y) + ${f}(wC)) / ${f}(uniforms.strides.y);\n let dyC2 = (${f}(dyCorner.y) + 1.0 + ${f}(wC)) / ${f}(uniforms.strides.y);\n let wCPerm = uniforms.filter_dims[1] - 1 - wC;\n if (wCPerm < 0) {\n continue;\n }\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= ${f}(uniforms.Dy_shape[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= ${f}(uniforms.Dy_shape[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC: u32 = u32(dyC);\n let idyC2: u32 = u32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = uniforms.Dy_shape[3];\n for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${O.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${f}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n\n xValue = ${O.get("batch","idyR","idyC2","d2")};\n\n dotProd[1] = dotProd[1] + vec4<${f}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.Dy_shape[${$}];\n for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${O.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${f}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = uniforms.Dy_shape[3];\n for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${O.get("batch","idyR","idyC2","d2")};\n let tmpval = vec4<${f}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i: u32 = 0; i < ${A}; i = i + 1) {\n let value = dotProd[i] + ${u?"bias[c+i]":`vec4<${f}>(0.0)`};\n ${R.set("batch","r","c + i","d1","value")};\n }\n }`,U=`\n let outputIndices = ${R.offsetToIndices("global_idx")};\n let batch = ${R.indicesGet("outputIndices",0)};\n let d1 = ${R.indicesGet("outputIndices",$)};\n let r = ${R.indicesGet("outputIndices",w)};\n let c = ${R.indicesGet("outputIndices",C)};\n let dyCorner = vec2(i32(r), i32(c)) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n let groupId = d1 / uniforms.output_channels_per_group;\n let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = ${f}(0.0);\n for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\n if (wR % uniforms.dilations.x != 0) {\n continue;\n }\n let dyR = (${f}(dyRCorner) + ${f}(wR)) / ${f}(uniforms.strides[0]);\n let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\n if (dyR < 0.0 || dyR >= ${f}(uniforms.Dy_shape[${w}]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR: u32 = u32(dyR);\n\n for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {\n if (wC % uniforms.dilations.y != 0) {\n continue;\n }\n let dyC = (${f}(dyCCorner) + ${f}(wC)) / ${f}(uniforms.strides.y);\n let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\n if (dyC < 0.0 || dyC >= ${f}(uniforms.Dy_shape[${C}]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC: u32 = u32(dyC);\n var inputChannel = groupId * uniforms.input_channels_per_group;\n for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {\n let xValue = ${g?O.get("batch","idyR","idyC","inputChannel"):O.get("batch","inputChannel","idyR","idyC")};\n let wValue = ${E.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n dotProd = dotProd + xValue * wValue;\n inputChannel = inputChannel + 1;\n }\n }\n }\n let value = dotProd + ${u?"bias[d1]":`${f}(0.0)`};\n ${R.setByOffset("global_idx","value")};\n `;return`\n ${e.registerUniforms(d).declareVariables(...B,R)}\n ${P}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};\n ${c?j:U}}`},uo=(e,r,t)=>{let u=e.length>2,s=r.outputShape,c=K.size(s),f=[Math.ceil(c/64),1,1];Fe("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${f}`);let d=r.format==="NHWC",g=["rank","rank"],w=[r.strides[0],r.strides[1]],C=[r.kernelShape[d?1:2],r.kernelShape[d?2:3]],$=[r.dilations[0],r.dilations[1]],A=[C[0]+(r.dilations[0]<=1?0:(r.kernelShape[d?1:2]-1)*(r.dilations[0]-1)),C[1]+(r.dilations[1]<=1?0:(r.kernelShape[d?2:3]-1)*(r.dilations[1]-1))],P=[A[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),A[1]-1-Math.floor(r.pads[1]+r.pads[3])/2],x=!1,E=r.group,O=e[1].dims,B=O[0]/E,R=O[1],j=[{type:12,data:c},{type:12,data:w},{type:12,data:C},{type:12,data:$},{type:12,data:A},{type:6,data:P},{type:12,data:B},{type:12,data:R},...se(e[0].dims,e[1].dims)];u&&(j.push(...se(e[2].dims)),g.push("rank")),j.push(...se(s));let U=f[1]===1&&f[2]===1,L=F=>{let te=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:w.length},{name:"filter_dims",type:"u32",length:C.length},{name:"dilations",type:"u32",length:C.length},{name:"effective_filter_dims",type:"u32",length:A.length},{name:"pads",type:"i32",length:P.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],J=Ne(e[0].dataType);return`${ty(F,e,s,u,U,x,J,te,d)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${r.cacheKey};`,inputDependencies:g},getRunData:()=>({dispatchGroup:{x:f[0],y:f[1],z:f[2]},outputs:[{dims:t?t(s):s,dataType:e[0].dataType}],programUniforms:j}),getShaderSource:L}}});var ry,ny,iy,Td,Ed,oy,ay,sy,uy,Pd,kd=ae(()=>{"use strict";Id();Ad();Ft();Dr();ry=(e,r,t,u,s,c)=>(e-1)*r+t+(u-1)*s+1-c,ny=(e,r,t,u,s)=>{let c=Math.floor(e/2);r==="SAME_UPPER"?(t[u]=c,t[s]=e-c):r==="SAME_LOWER"&&(t[u]=e-c,t[s]=c)},iy=(e,r,t,u,s,c,f,d,g,w)=>{let C=e.length-2,$=w.length===0;if(g.length===0)for(let x=0;x{let t=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce(($,A)=>$*A,1)===0){t.length=0;for(let $=2;$$+A,0)===0){let $=r[0].dims.length-2;g=new Array($).fill(1)}let w=e.strides.slice();if(w.reduce(($,A)=>$+A,0)===0){let $=r[0].dims.length-2;w=new Array($).fill(1)}iy(d,t,g,e.autoPad,e.group,s,w,u,f,c);let C=Object.assign({},e);return Object.assign(C,{kernelShape:t,pads:s,outputPadding:f,outputShape:c,dilations:g,strides:w}),C},Ed=e=>{let r=di(e),t=e.format,u=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],s=e.dilations,c=e.group,f=e.kernelShape,d=e.pads,g=e.strides,w=e.wIsConst(),C=e.outputPadding,$=e.outputShape;return{autoPad:u,format:t,dilations:s,group:c,kernelShape:f,outputPadding:C,outputShape:$,pads:d,strides:g,wIsConst:w,...r,cacheKey:`${e.format};${r.activation};`}},oy=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently 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shape");if(r.outputShape.length!==0&&r.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},ay=[2,3,1,0],sy=(e,r,t)=>{let u=Td(t,r),s=t.format==="NHWC",c=u.outputShape,f=c[s?3:1],d=r[0].dims[s?3:1];if(u.group!==1||f===1&&d===1){e.compute(uo(r,u));return}let g=c[s?1:2],w=c[s?2:3],C=r[1].dims[2],$=r[1].dims[3],A=s?g*w:f,P=s?f:g*w,x=C*$*d,E=!0,O=e.kernelCustomData.wT??e.compute($t(r[1],ay),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=O);let B=[r[0],O],R=r.length===3;R&&(!s&&r[2].dims.length===1?B.push(r[2].reshape([r[2].dims[0],1,1])):B.push(r[2])),e.compute(xd(B,u,c,A,P,x,R,E),{inputs:B})},uy=(e,r)=>{let t=r.format==="NHWC",u=[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&&u.push(e.inputs[2]);let s=r.kernelShape;(s.length===0||s[0]===0)&&(s=[e.inputs[1].dims[2]]);let c=r.dilations;(c.length===0||c[0]===0)&&(c=[1]);let f=r.strides;(f.length===0||f[0]===0)&&(f=[1]);let d=r.pads;d.length===0&&(d=[0,0]),d=[0,d[0],0,d[1]],f=[1].concat(f),c=[1].concat(c),s=[1].concat(s);let g=Td({...r,pads:d,strides:f,dilations:c,kernelShape:s},u);e.compute(uo(u,g,w=>t?[w[0],w[2],w[3]]:[w[0],w[1],w[3]]))},Pd=(e,r)=>{oy(e.inputs,r),e.inputs[0].dims.length===3?uy(e,r):sy(e,e.inputs,r)}});var ly,Od,Rd,Bd=ae(()=>{"use strict";Te();De();nt();Re();ly=(e,r,t,u)=>{let s=K.size(r),c=r.length,f=Z("input",e,c),d=ne("output",e,c),g=t.dataType===6?t.getInt32Array()[0]:Number(t.getBigInt64Array()[0]),w=K.normalizeAxis(g,c),C=$=>{let A=` i32(${f.indicesGet("inputIndices","uniforms.axis")}) `,P=xe("uniforms.input_shape","uniforms.axis",c),x=u.reverse?A+(u.exclusive?" + 1":""):"0",E=u.reverse?P:A+(u.exclusive?"":" + 1");return`\n ${$.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(f,d)}\n ${$.mainStart()}\n ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n var inputIndices = ${d.offsetToIndices("global_idx")};\n var sum = ${d.type.value}(0);\n let first : i32 = ${x};\n let last : i32 = ${E};\n for (var i : i32 = first; i < last; i++) {\n ${f.indicesSet("inputIndices","uniforms.axis","u32(i)")};\n sum = sum + ${f.getByIndices("inputIndices")};\n }\n ${d.setByOffset("global_idx","sum")};\n }`};return{name:"CumSum",shaderCache:{hint:u.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:[{type:12,data:s},{type:12,data:w},...se(r,r)]}),getShaderSource:C}},Od=(e,r)=>{let t=e.inputs[0].dims,u=e.inputs[0].dataType,s=e.inputs[1];e.compute(ly(u,t,s,r),{inputs:[0]})},Rd=e=>{let r=e.exclusive===1,t=e.reverse===1;return ke({exclusive:r,reverse:t})}});var dy,cy,fy,Dd,zd,Md=ae(()=>{"use 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initial = initial + deviation * deviation;\n }\n workgroupShared[localIndex] = initial;\n workgroupBarrier();\n\n // Calculate the sum of square of deviation of current channel data.\n for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (localIndex < currSize) {\n workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n }\n workgroupBarrier();\n }\n if (localIndex == 0) {\n squaredNormShared = ${It("workgroupShared[0]",d)};\n }\n workgroupBarrier();\n\n let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${r.epsilon}));\n let channelScale = invStdDev * f32(${E.getByOffset("channel")});\n let channelShift = f32(${O.getByOffset("channel")}) - meanShared * channelScale;\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let value = ${x.get("batch","channel","h")} * ${j}(${U}(channelScale)) + ${j}(${U}(channelShift));\n ${B.set("batch","channel","h","value")};\n }\n }`};return{name:"InstanceNormalization",shaderCache:{hint:`${r.epsilon};${d}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:c},programUniforms:$}),getShaderSource:A}},Ay=(e,r,t,u,s,c,f,d)=>{let g=He(f),w=64,C=g===1?"vec2f":`mat2x${g}f`,$=g===1?"f32":`vec${g}f`,A=(F,te)=>`${C}(${F}, ${te})`,P=s*f/g,x=Math.ceil(c/w),E=["type"],O=[{type:12,data:x},{type:12,data:c},{type:12,data:Math.floor(f/g)},{type:12,data:Math.floor(c*f/g)}],B=F=>{let te=Z("input",r.dataType,r.dims,g);return`\n ${F.declareVariables(te)}\n @group(0) @binding(1) var output : array<${C}>;\n struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n @group(0) @binding(2) var uniforms: Uniforms;\n\n ${F.mainStart(w)}\n let currentImageNumber = global_idx / ${w} / uniforms.C;\n let currentChannelNumber = (global_idx / ${w}) % uniforms.C;\n let wgOffset = local_id.x * uniforms.wg_size;\n if (wgOffset >= uniforms.H) {\n return;\n }\n let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);\n\n let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;\n var sum = ${xt("f32",g)};\n var squaredSum = ${xt("f32",g)};\n for (var i: u32 = wgOffset; i < wgMax; i++) {\n let value = ${$}(input[offset + i * uniforms.C]);\n sum += value;\n squaredSum += value * value;\n }\n output[global_idx] = ${A("sum","squaredSum")};\n }`},R=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${g}`,inputDependencies:E},getRunData:()=>({outputs:[{dims:[s,f,w,2],dataType:1}],dispatchGroup:{x:s*f/g},programUniforms:O}),getShaderSource:B},{inputs:[r],outputs:[-1]})[0],j=[{type:12,data:P},{type:12,data:c},{type:12,data:Math.floor(f/g)},{type:12,data:Math.floor(w*f/g)}],U=["type","type","type"],L=F=>{let te=Z("scale",t.dataType,t.dims,g),J=Z("bias",u.dataType,u.dims,g);return`\n @group(0) @binding(0) var input : array<${C}>;\n @group(0) @binding(1) var scale : array<${te.type.storage}>;\n @group(0) @binding(2) var bias : array<${J.type.storage}>;\n @group(0) @binding(3) var output : array<${C}>;\n struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n @group(0) @binding(4) var uniforms: Uniforms;\n\n ${F.mainStart()}\n ${F.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}\n let currentImageNumber = global_idx / uniforms.C;\n let currentChannelNumber = global_idx % uniforms.C;\n\n let offset = currentImageNumber * uniforms.image_size;\n var sum = ${xt("f32",g)};\n var squaredSum = ${xt("f32",g)};\n for (var i: u32 = 0; i < min(${w}, uniforms.H); i++) {\n let value = input[offset + i + currentChannelNumber * ${w}];\n sum += value[0];\n squaredSum += value[1];\n }\n sum = sum / f32(uniforms.H);\n squaredSum = squaredSum / f32(uniforms.H);\n let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${d}));\n let channelScale = invStdDev * ${$}(scale[currentChannelNumber]);\n let channelShift = ${$}(bias[currentChannelNumber]) - sum * channelScale;\n\n output[global_idx] = ${A("channelScale","channelShift")};\n }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${g};${d}`,inputDependencies:U},getRunData:()=>({outputs:[{dims:[s,f,2],dataType:1}],dispatchGroup:{x:Math.ceil(P/64)},programUniforms:j}),getShaderSource:L},{inputs:[R,t,u],outputs:[-1]})[0]},Ty=(e,r,t)=>{let u=r[0].dims,s=u,c=u[0],f=u[u.length-1],d=K.sizeFromDimension(u,1)/f,g=He(f),w=K.size(s)/g,C=[{type:12,data:d},{type:12,data:Math.floor(f/g)}],$=["type","type"],A=Ay(e,r[0],r[1],r[2],c,d,f,t.epsilon),P=x=>{let E=Ne(r[0].dataType),O=g===1?"vec2f":`mat2x${g}f`,B=g===1?E:`vec${g}<${E}>`,R=Z("input",r[0].dataType,r[0].dims,g),j=ne("output",r[0].dataType,s,g);return`\n @group(0) @binding(0) var input : array<${R.type.storage}>;\n @group(0) @binding(1) var scaleInput : array<${O}>;\n @group(0) @binding(2) var output : array<${j.type.storage}>;\n struct Uniforms {H: u32, C : u32};\n @group(0) @binding(3) var uniforms: Uniforms;\n\n ${x.mainStart()}\n let currentImageNumber = global_idx / (uniforms.C * uniforms.H);\n let currentChannelNumber = global_idx % uniforms.C;\n\n let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;\n let scale = scaleInput[scaleOffset];\n output[global_idx] = fma(input[global_idx], ${B}(scale[0]), ${B}(scale[1]));\n }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${g}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:s,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:C}),getShaderSource:P},{inputs:[r[0],A]})},nc=(e,r)=>{r.format==="NHWC"?Ty(e,e.inputs,r):e.compute(Iy(e.inputs,r))}});var Ey,Py,oc,ac=ae(()=>{"use strict";Te();De();Re();Ey=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Py=(e,r,t)=>{let u=r.simplified,s=e[0].dims,c=e[1],f=!u&&e[2],d=s,g=K.normalizeAxis(r.axis,s.length),w=K.sizeToDimension(s,g),C=K.sizeFromDimension(s,g),$=K.size(c.dims),A=f?K.size(f.dims):0;if($!==C||f&&A!==C)throw new Error(`Size of X.shape()[axis:] == ${C}.\n Size of scale and bias (if provided) must match this.\n Got scale size of ${$} and bias size of ${A}`);let P=[];for(let L=0;L1,R=t>2,j=L=>{let F=Ne(e[0].dataType),te=[Z("x",e[0].dataType,e[0].dims,x),Z("scale",c.dataType,c.dims,x)];f&&te.push(Z("bias",f.dataType,f.dims,x)),te.push(ne("output",e[0].dataType,d,x)),B&&te.push(ne("mean_data_output",1,P)),R&&te.push(ne("inv_std_output",1,P));let J=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n ${L.registerUniforms(J).declareVariables(...te)}\n ${L.mainStart()}\n ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n let offset = global_idx * uniforms.norm_size_vectorized;\n var mean_vector = ${xt("f32",x)};\n var mean_square_vector = ${xt("f32",x)};\n\n for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n let value = ${lr(F,x,"x[h + offset]")};\n mean_vector += value;\n mean_square_vector += value * value;\n }\n let mean = ${It("mean_vector",x)} / uniforms.norm_size;\n let inv_std_dev = inverseSqrt(${It("mean_square_vector",x)} / uniforms.norm_size ${u?"":"- mean * mean"} + uniforms.epsilon);\n\n for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n let f32input = ${lr(F,x,"x[j + offset]")};\n let f32scale = ${lr(F,x,"scale[j]")};\n output[j + offset] = ${te[0].type.value}((f32input ${u?"":"- mean"}) * inv_std_dev * f32scale\n ${f?`+ ${lr(F,x,"bias[j]")}`:""}\n );\n }\n\n ${B?"mean_data_output[global_idx] = mean":""};\n ${R?"inv_std_output[global_idx] = inv_std_dev":""};\n }`},U=[{dims:d,dataType:e[0].dataType}];return B&&U.push({dims:P,dataType:1}),R&&U.push({dims:P,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${x};${t};${u}`,inputDependencies:E},getRunData:()=>({outputs:U,dispatchGroup:{x:Math.ceil(w/64)},programUniforms:O}),getShaderSource:j}},oc=(e,r)=>{Ey(e.inputs),e.compute(Py(e.inputs,r,e.outputCount))}});var ky,Oy,sc,uc,lc=ae(()=>{"use strict";Te();De();nt();Re();ky=(e,r)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let t=e[0],u=t.dims.length;if(t.dims[u-1]!==r.k)throw new Error("The last dim of input shape does not match the k value");let s=Math.floor((r.k+r.blockSize-1)/r.blockSize),c=r.blockSize/8*r.bits,f=e[1];if(!K.areEqual(f.dims,[r.n,s,c]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let g=e[2].dims;if(K.size(g)!==r.n*s)throw new Error("scales input size error.");if(e.length===4){let C=e[3].dims,$=r.bits>4?r.n*s:r.n*Math.floor((s+1)/2);if(K.size(C)!==$)throw new Error("zeroPoints input size error.")}},Oy=(e,r,t,u)=>{let s=e[0].dims,c=s.length,f=Math.floor((r.k+r.blockSize-1)/r.blockSize),d=s[c-2],g=r.k,w=r.n,C=s.slice(0,c-2),$=K.size(C),P=r.blockSize/8*r.bits/4,x=e[0].dataType,E=He(d),O=He(r.k),B=He(P),R=or(x),j=d*f*R,U=Math.floor(u/j),L=f<=t[0]&&U>0,F=!L||U>=4?He(w):U>=2&&He(w)>=2?2:1,te=C.concat([d,w]),J=K.size(te)/F/E,oe=L?[]:[{type:12,data:J},{type:12,data:r.blockSize}],le=[$,d,g/O],ge=K.convertShape(e[1].dims).slice();ge.splice(-1,1,P/B),oe.push(...se(le)),oe.push(...se(ge)),oe.push(...se(e[2].dims)),e.length===4&&oe.push(...se(K.convertShape(e[3].dims)));let X=[$,d,w/F];oe.push(...se(X));let pe=we=>{let ue=le.length,me=Z("a",e[0].dataType,ue,O),Ee=Z("b",12,ge.length,B),Pe=Z("scales",e[2].dataType,e[2].dims.length),Ce=[me,Ee,Pe],be=e.length===4?Z("zero_points",12,e[3].dims.length):void 0;be&&Ce.push(be);let Ae=X.length,_e=ne("output",e[0].dataType,Ae,F),Je=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],ee=Ne(e[0].dataType),ce=(()=>{switch(O){case 1:return`array<${ee}, 8>`;case 2:return`mat4x2<${ee}>`;case 4:return`mat2x4<${ee}>`;default:throw new Error(`${O}-component is not supported.`)}})(),Be=`\n for (var word: u32 = 0; word < ${P}; word += ${B}) {\n ${Ee.indicesSet("b_indices","2","word")};\n let b_data = ${Ee.getByIndices("b_indices")};\n for (var i: u32 = 0; i < ${B}; i++) {\n let b_value: u32 = ${B===1?"b_data":"b_data[word + i]"};\n let b_mask: u32 = 0x0F0F0F0Fu;\n let b_value_lower: vec4 = unpack4xU8(b_value & b_mask);\n let b_value_upper: vec4 = unpack4xU8((b_value >> 4) & b_mask);\n let b_quantized_values = ${ce}(${Array.from({length:4},(Ye,Ke)=>`${ee}(b_value_lower[${Ke}]), ${ee}(b_value_upper[${Ke}])`).join(", ")});\n let b_dequantized_values = ${(()=>O===1?`${ce}(${Array.from({length:8},(Ye,Ke)=>`(b_quantized_values[${Ke}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${ce}(${Array(8).fill("zero_point").join(",")})) * scale;`)()};\n // Number of B elements per 32-bit word is 32/bits = 32/4 = 8\n for (var m: u32 = 0; m < ${L?d:E}u; m++) {\n ${me.indicesSet("a_indices",ue-2,L?"m":`row * ${E} + m`)};\n ${me.indicesSet("a_indices",ue-1,"word_offset")};\n var input_offset = ${me.indicesToOffset("a_indices")};\n var a_data: ${ce};\n for (var j: u32 = 0; j < ${8/O}; j++) {\n a_data[j] = ${me.getByOffset("input_offset")};\n input_offset++;\n }\n ${L?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${F>1?"[c]":""} += ${Array.from({length:8/O},(Ye,Ke)=>`${O===1?`a_data[${Ke}] * b_dequantized_values[${Ke}]`:`dot(a_data[${Ke}], b_dequantized_values[${Ke}])`}`).join(" + ")};\n }\n word_offset += ${8/O};\n }\n }`,tt=be?`\n zero_point_offset += 4;\n if (zero_point_offset == 32) {\n zero_point_offset = 0;\n zero_point_index++;\n zero_point_word = ${be.getByOffset("zero_point_index")};\n }`:"";return L?`\n var workgroup_shared: array<${_e.type.value}, ${d*f}>;\n ${we.declareVariables(...Ce,_e)}\n ${we.mainStart([f,1,1])}\n var a_indices: ${me.type.indices};\n var block = local_id.x;\n var col = workgroup_id.y;\n var batch = workgroup_id.z;\n ${me.indicesSet("a_indices","0","batch")};\n // Two zero points are packed into one byte when uniforms.bits is 4.\n for (var c: u32 = 0; c < ${F}; c++) {\n let col_times_components_plus_c = col * ${F} + c;\n ${be?`\n var zero_point_bytes_per_col: u32 = (${f} + 1) / 2;\n var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u);\n var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u;\n var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u;\n var zero_point_nibble_offset: u32 = block & 0x1u;\n var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);\n var zero_point_word: u32 = ${be.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}\n var b_indices: ${Ee.type.indices};\n ${Ee.indicesSet("b_indices","0","col_times_components_plus_c")};\n // The scale and zero points are computed per block.\n var scales_index = col_times_components_plus_c * ${f} + block;\n let scale = ${Pe.getByOffset("scales_index")};\n // The default zero point is 8 for unsigned 4-bit quantization.\n let zero_point = ${ee}(${be?"(zero_point_word) & 0xFu":8});\n ${Ee.indicesSet("b_indices","1","block")};\n var word_offset: u32 = block * ${r.blockSize/O};\n var workgroup_shared_offset: u32 = block * ${d};\n ${Be}\n }\n workgroupBarrier();\n if (local_id.x == 0u) {\n var output_indices: ${_e.type.indices};\n ${_e.indicesSet("output_indices","0","batch")};\n ${_e.indicesSet("output_indices",Ae-1,"col")};\n ${_e.indicesSet("output_indices",Ae-2,"0")};\n var output_offset = ${_e.indicesToOffset("output_indices")};\n for (var m: u32 = 0u; m < ${d}u; m++) {\n var output_value: ${_e.type.value} = ${_e.type.value}(0);\n var workgroup_shared_offset: u32 = m;\n for (var b: u32 = 0u; b < ${f}u; b++) {\n output_value += workgroup_shared[workgroup_shared_offset];\n workgroup_shared_offset += ${d};\n }\n ${_e.setByOffset("output_offset","output_value")};\n output_offset += ${w/F};\n }\n }\n }`:`\n ${we.registerUniforms(Je).declareVariables(...Ce,_e)}\n ${we.mainStart()}\n ${we.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n var output_values: array<${_e.type.value}, ${E}>;\n var output_indices = ${_e.offsetToIndices("global_idx")};\n var col = ${_e.indicesGet("output_indices",Ae-1)};\n var row = ${_e.indicesGet("output_indices",Ae-2)};\n var a_indices: ${me.type.indices} = output_indices;\n // Two zero points are packed into one byte because uniforms.bits <= 4.\n // zero_point_offset is either 0 or 4. It is bit offset within one byte.\n // TODO support zero_point_offset for bits > 4\n ${be?`\n var zero_point_abs_offset = col * ${F} * ((${f} + 1) / 2);\n var zero_point_index: u32 = zero_point_abs_offset / 4;\n var zero_point_word: u32 = ${be.getByOffset("zero_point_index")};\n var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}\n var scale_index = col * ${f*F};\n var b_indices: ${Ee.type.indices};\n for (var c: u32 = 0; c < ${F}; c++) {\n ${Ee.indicesSet("b_indices","0",`col * ${F} + c`)};\n var block_offset: u32 = 0;\n for (var block: u32 = 0; block < ${f}; block++) {\n // The scale and zero points are computed per block.\n let scale = ${Pe.getByOffset("scale_index")};\n // The default zero point is 8 for unsigned 4-bit quantization.\n let zero_point = ${ee}(${be?"extractBits(zero_point_word, zero_point_offset, 4)":8});\n ${Ee.indicesSet("b_indices","1","block")};\n var word_offset: u32 = block_offset;\n ${Be}\n scale_index++;\n ${tt}\n block_offset += uniforms.block_size / ${O};\n }\n // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.\n ${be?`if (zero_point_offset % 8 > 0) {\n ${tt}\n }`:""}\n }\n for (var k: u32 = 0u; k < ${E}u; k++) {\n ${_e.indicesSet("output_indices",Ae-2,`${E} * row + k`)};\n ${_e.setByIndices("output_indices","output_values[k]")}\n }\n }`};return{name:L?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${r.cacheKey};${d};${x};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:te,dataType:x}],name:L?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:L?{x:1,y:Math.ceil(w/F),z:$}:{x:Math.ceil(J/64)},programUniforms:oe}),getShaderSource:pe}},sc=(e,r)=>{ky(e.inputs,r);let t=e.getMaxComputeWorkgroupSizes(),u=e.getMaxComputeWorkgroupStoragesize();e.compute(Oy(e.inputs,r,t,u))},uc=e=>ke(e)});var ft,Ry,cc,dc,By,po,fc,pc=ae(()=>{"use strict";Te();De();nt();Xn();Qi();Re();Dr();ft=(e,r)=>e.length>r&&e[r].dims.length>0&&K.size(e[r].dims)>0?e[r]:void 0,Ry=(e,r)=>{let t=e[0],u=ft(e,1),s=ft(e,2),c=ft(e,3),f=ft(e,4),d=ft(e,5),g=ft(e,6),w=ft(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 C=!1,$=t.dims[0],A=t.dims[1],P=t.dims.length===3?C?t.dims[2]/3:t.dims[2]:r.numHeads*t.dims[4],x=A,E=0,O=0,B=Math.floor(P/r.numHeads);if(g&&w){if(g.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(g.dims[0]!==$||g.dims[1]!==r.numHeads||g.dims[3]!==B)throw new Error(\'Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(w.dims[0]!==$||w.dims[1]!==r.numHeads||w.dims[3]!==B)throw new Error(\'Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(g.dims[2]!==w.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)\');if(w.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');E=g.dims[2],O=g.dims[2]}else if(g||w)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let R;if(u){if(t.dims.length!==3)throw new Error(\'Input "query" is expected to have 3 dimensions when key is given\');if(u.dims.length<3||u.dims.length>5)throw new Error(\'Input "key" is expected to have 3, 4, or 5 dimensions\');if(t.dims[0]!==u.dims[0])throw new Error(\'Input "query" and "key" shall have same dim 0 (batch size)\');if(u.dims.length===3){if(u.dims[2]!==t.dims[2])throw new Error(\'Input "query" and "key" shall have same dim 2 (hidden_size)\');R=2,x=u.dims[1]}else if(u.dims.length===5){if(u.dims[2]!==r.numHeads||u.dims[3]!==2||u.dims[4]!==B)throw new Error(\'Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\');if(s)throw new Error(\'Expect "value" be none when "key" has packed kv format.\');R=5,x=u.dims[1]}else{if(u.dims[1]!==r.numHeads||u.dims[3]!==B)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');R=0,x=u.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\');R=3}if(c){if(c.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(s&&t.dims.length===5&&t.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let j=0;if(f){j=8;let J=f.dims;throw J.length===1?J[0]===$?j=1:J[0]===3*$+2&&(j=3):J.length===2&&J[0]===$&&J[1]===x&&(j=5),j===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let U=!1,L=P;if(s){if(s.dims.length!==3&&s.dims.length!==4)throw new Error(\'Input "value" is expected to have 3 or 4 dimensions\');if(t.dims[0]!==s.dims[0])throw new Error(\'Input "query" and "value" shall have same dim 0 (batch_size)\');if(s.dims.length===3){if(x!==s.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');L=s.dims[2]}else{if(x!==s.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');L=s.dims[1]*s.dims[3],U=!0}}let F=E+x,te=!1;if(f)throw new Error("Key padding mask is not supported");if(d){if(d.dims.length!==4)throw new Error(\'Input "relative_position_bias" is expected to have 4 dimensions\');if(d.dims[0]!==$&&d.dims[0]!==1||d.dims[1]!==r.numHeads||d.dims[2]!==A||d.dims[3]!==F)throw new Error(\'Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)\')}return{batchSize:$,sequenceLength:A,pastSequenceLength:E,kvSequenceLength:x,totalSequenceLength:F,maxSequenceLength:O,inputHiddenSize:0,hiddenSize:P,vHiddenSize:L,headSize:B,vHeadSize:Math.floor(L/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:j,scale:r.scale,broadcastResPosBias:te,passPastInKv:U,qkvFormat:R}},cc=e=>ke({...e}),dc=ke({perm:[0,2,1,3]}),By=(e,r,t,u,s,c,f)=>{let d=[u,s,c],g=K.size(d),w=[{type:12,data:g},{type:12,data:f},{type:12,data:c}],C=$=>{let A=ne("qkv_with_bias",r.dataType,d),P=Z("qkv",r.dataType,d),x=Z("bias",t.dataType,d),E=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n ${$.registerUniforms(E).declareVariables(P,x,A)}\n ${$.mainStart()}\n ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:d,dataType:r.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:w}),getShaderSource:C},{inputs:[r,t],outputs:[-1]})[0]},po=(e,r,t,u,s,c,f,d)=>{let g=c;if(f){if(u===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return g=By(e,c,f,r,u,t*s,d),g=g.reshape([r,u,t,s]),e.compute($t(g,dc.perm),{inputs:[g],outputs:[-1]})[0]}else return c.dims.length===3&&(g=c.reshape([r,u,t,s])),e.compute($t(g,dc.perm),{inputs:[g],outputs:[-1]})[0]},fc=(e,r)=>{let t=Ry(e.inputs,r),u=e.inputs[0],s=ft(e.inputs,1),c=ft(e.inputs,2),f=ft(e.inputs,3),d=ft(e.inputs,4),g=ft(e.inputs,5),w=ft(e.inputs,6),C=ft(e.inputs,7);if(u.dims.length===5)throw new Error("Packed QKV is not implemented");if(s?.dims.length===5)throw new Error("Packed KV is not implemented");let $=s&&c&&s.dims.length===4&&c.dims.length===4,A=po(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,u,f,0);if($)return ai(e,A,s,c,d,void 0,w,C,g,t,r);if(!s||!c)throw new Error("key and value must be provided");let P=po(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.headSize,s,f,t.hiddenSize),x=po(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.vHeadSize,c,f,2*t.hiddenSize);ai(e,A,P,x,d,void 0,w,C,g,t,r)}});var Dy,zy,My,jy,Uy,Vy,Wy,Ny,mc,hc=ae(()=>{"use strict";Te();De();Re();Dy=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].")}},zy=(e,r,t)=>{let u="";for(let s=r-1;s>=0;--s)u+=`\n k = i32(${e.indicesGet("indices",s)}) - ${xe("uniforms.pads",s,t)};\n if (k < 0) {\n break;\n }\n if (k >= i32(${xe("uniforms.x_shape",s,r)})) {\n break;\n }\n offset += k * i32(${xe("uniforms.x_strides",s,r)});\n `;return`\n value = ${e.type.value}(uniforms.constant_value);\n for (var i = 0; i < 1; i++) {\n var offset = 0;\n var k = 0;\n ${u}\n value = x[offset];\n }\n `},My=(e,r,t)=>{let u="";for(let s=r-1;s>=0;--s)u+=`\n k = i32(${e.indicesGet("indices",s)}) - ${xe("uniforms.pads",s,t)};\n if (k < 0) {\n k = 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i32(${xe("uniforms.x_strides",s,r)});\n `;return`\n var offset = 0;\n var k = 0;\n ${u}\n value = x[offset];\n `},Vy=(e,r,t)=>{switch(t.mode){case 0:return zy(e,r,t.pads.length);case 1:return My(e,r,t.pads.length);case 2:return jy(e,r,t.pads.length);case 3:return Uy(e,r,t.pads.length);default:throw new Error("Invalid mode")}},Wy=(e,r)=>{let t=K.padShape(e[0].dims.slice(),r.pads),u=e[0].dims,s=K.size(t),c=[{type:12,data:s},{type:6,data:r.pads}];r.mode===0&&c.push({type:e[0].dataType,data:r.value}),c.push(...se(e[0].dims,t));let f=["rank"],d=g=>{let w=ne("output",e[0].dataType,t.length),C=Z("x",e[0].dataType,u.length),$=C.type.value,A=Vy(w,u.length,r),P=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:r.pads.length}];return r.mode===0&&P.push({name:"constant_value",type:$}),`\n ${g.registerUniforms(P).declareVariables(C,w)}\n ${g.mainStart()}\n ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let indices = ${w.offsetToIndices("global_idx")};\n\n var value = ${$}(0);\n ${A}\n output[global_idx] = value;\n }`};return{name:"Pad",shaderCache:{hint:`${r.mode}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(K.size(t)/64)},programUniforms:c}),getShaderSource:d}},Ny=(e,r)=>{if(e.length>1){let t=e[1].getBigInt64Array(),u=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,s=e[0].dims.length,c=new Int32Array(2*s).fill(0);if(e.length>=4){let d=e[3].getBigInt64Array();for(let g=0;gc[Number(g)]=Number(d));let f=[];return c.forEach(d=>f.push(d)),{mode:r.mode,value:u,pads:f}}else return r},mc=(e,r)=>{Dy(e.inputs);let t=Ny(e.inputs,r);e.compute(Wy(e.inputs,t),{inputs:[0]})}});var yi,gc,yc,bc,vc,Gy,Hy,wc,_c,$c,Cc,Sc,xc,Ic,Ac,Tc,Ec,Pc,kc,Oc=ae(()=>{"use strict";Rr();Te();De();Re();yi=e=>{if(Or.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},gc=(e,r,t)=>{let u=r.format==="NHWC",s=e.dims.slice();u&&s.splice(1,0,s.pop());let c=Object.hasOwnProperty.call(r,"dilations"),f=r.kernelShape.slice(),d=r.strides.slice(),g=c?r.dilations.slice():[],w=r.pads.slice();sr.adjustPoolAttributes(t,s,f,d,g,w);let C=sr.computePoolOutputShape(t,s,d,g,f,w,r.autoPad),$=Object.assign({},r);c?Object.assign($,{kernelShape:f,strides:d,pads:w,dilations:g,cacheKey:r.cacheKey}):Object.assign($,{kernelShape:f,strides:d,pads:w,cacheKey:r.cacheKey});let A=C.slice();return A.push(A.splice(1,1)[0]),[$,u?A:C]},yc=(e,r)=>{let t=r.format==="NHWC",u=K.size(e),s=K.size(r.kernelShape),c=[{type:12,data:u},{type:12,data:s}],f=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(r.kernelShape.length<=2){let d=r.kernelShape[r.kernelShape.length-1],g=r.strides[r.strides.length-1],w=r.pads[r.pads.length/2-1],C=r.pads[r.pads.length-1],$=!!(w+C);c.push({type:12,data:d},{type:12,data:g},{type:12,data:w},{type:12,data:C}),f.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let A=!1;if(r.kernelShape.length===2){let P=r.kernelShape[r.kernelShape.length-2],x=r.strides[r.strides.length-2],E=r.pads[r.pads.length/2-2],O=r.pads[r.pads.length-2];A=!!(E+O),c.push({type:12,data:P},{type:12,data:x},{type:12,data:E},{type:12,data:O}),f.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[c,f,!0,$,A]}else{if(t)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let d=K.computeStrides(r.kernelShape);c.push({type:12,data:d},{type:12,data:r.pads},{type:12,data:r.strides}),f.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 g=r.pads.reduce((w,C)=>w+C);return[c,f,!!g,!1,!1]}},bc=(e,r,t,u,s,c,f,d,g,w,C,$)=>{let A=s.format==="NHWC",P=r.type.value,x=ne("output",r.type.tensor,u);if(s.kernelShape.length<=2){let E="",O="",B="",R=t-(A?2:1);if(C?E=`\n for (var i: u32 = 0u; i < uniforms.kw; i++) {\n xIndices[${R}] = indices[${R}] * uniforms.sw - uniforms.pwStart + i;\n if (xIndices[${R}] < 0 || xIndices[${R}]\n >= uniforms.x_shape[${R}]) {\n pad++;\n continue;\n }\n let x_val = x[${r.indicesToOffset("xIndices")}];\n ${c}\n }`:E=`\n for (var i: u32 = 0u; i < uniforms.kw; i++) {\n xIndices[${R}] = indices[${R}] * uniforms.sw - uniforms.pwStart + i;\n let x_val = x[${r.indicesToOffset("xIndices")}];\n ${c}\n }`,s.kernelShape.length===2){let U=t-(A?3:2);$?O=`\n for (var j: u32 = 0u; j < uniforms.kh; j++) {\n xIndices[${U}] = indices[${U}] * uniforms.sh - uniforms.phStart + j;\n if (xIndices[${U}] < 0 || xIndices[${U}] >= uniforms.x_shape[${U}]) {\n pad += i32(uniforms.kw);\n continue;\n }\n `:O=`\n for (var j: u32 = 0u; j < uniforms.kh; j++) {\n xIndices[${U}] = indices[${U}] * uniforms.sh - uniforms.phStart + j;\n `,B=`\n }\n `}return`\n ${e.registerUniforms(g).declareVariables(r,x)}\n\n ${e.mainStart()}\n 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max(x_val, value);\n `,d="",g=Z("x",r.dataType,r.dims.length),w=["rank"],[C,$,A,P,x]=yc(c,s);return C.push(...se(r.dims,c)),{name:e,shaderCache:{hint:`${u.cacheKey};${A};${P};${x}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:c,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(K.size(c)/64)},programUniforms:C}),getShaderSource:E=>bc(E,g,r.dims.length,c.length,s,f,d,r.dataType===10?-65504:-1e5,$,A,P,x)}},Tc=(e,r)=>{yi(e.inputs),e.compute(Ac("MaxPool",e.inputs[0],!1,r))},Ec=e=>{let r=e.storage_order,t=e.dilations,u=wc(e);if(r!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(u.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let s={storageOrder:r,dilations:t,...u,cacheKey:""};return{...s,cacheKey:Hy(s)}},Pc=e=>{let r=e.format;return{format:r,...Sc,cacheKey:r}},kc=(e,r)=>{yi(e.inputs),e.compute(Ac("GlobalMaxPool",e.inputs[0],!0,r))}});var Fy,qy,Rc,Bc=ae(()=>{"use strict";Rr();Te();Re();Fy=(e,r,t)=>{let u=e===r,s=er&&t>0;if(u||s||c)throw new Error("Range these inputs\' contents are invalid.")},qy=(e,r,t,u)=>{let s=Math.abs(Math.ceil((r-e)/t)),c=[s],f=s,d=[{type:12,data:f},{type:u,data:e},{type:u,data:t},...se(c)],g=w=>{let C=ne("output",u,c.length),$=C.type.value,A=[{name:"outputSize",type:"u32"},{name:"start",type:$},{name:"delta",type:$}];return`\n ${w.registerUniforms(A).declareVariables(C)}\n ${w.mainStart()}\n ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n output[global_idx] = uniforms.start + ${$}(global_idx) * uniforms.delta;\n }`};return{name:"Range",shaderCache:{hint:`${u}`},getShaderSource:g,getRunData:()=>({outputs:[{dims:c,dataType:u}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:d})}},Rc=e=>{let r=0,t=0,u=0;e.inputs[0].dataType===6?(r=e.inputs[0].getInt32Array()[0],t=e.inputs[1].getInt32Array()[0],u=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(r=e.inputs[0].getFloat32Array()[0],t=e.inputs[1].getFloat32Array()[0],u=e.inputs[2].getFloat32Array()[0]),Or.webgpu.validateInputContent&&Fy(r,t,u),e.compute(qy(r,t,u,e.inputs[0].dataType),{inputs:[]})}});var Ky,Yy,Zy,Qy,Xy,Jy,e0,t0,r0,n0,i0,Dc,o0,a0,s0,u0,l0,zc,Mc,jc=ae(()=>{"use strict";Te();De();nt();Re();Ky=(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\n 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")}},Yy=(e,r,t)=>{r.every(s=>s>=0&&s{throw new Error("Resize requires axes input values to be positive and less than rank")}));let u=new Array(t).fill(1);return r.forEach((s,c)=>u[s]=e[c]),u},Zy=(e,r,t,u,s,c)=>{let[f,d,g]=t>10?[1,2,3]:[-1,e.length>1?1:-1,-1],w=e[0].dims.length;if(f>0&&e.length>f&&e[f].dims.length>0)e[f].getFloat32Array().forEach(C=>c.push(C));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>0){if(e[d].getFloat32Array().forEach(C=>u.push(C)),u.length!==0&&u.length!==w&&t>=18&&u.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");Ky(u,r),r.axes.length>0&&Yy(u,r.axes,w).forEach((C,$)=>u[$]=C)}if(g>0&&e.length>g&&(e[g].getBigInt64Array().forEach(C=>s.push(Number(C))),s.length!==w||t>=18&&s.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(u.length!==r.axes.length)throw new Error(\'Resize requires "scales" input size to be of axes rank when axes attributes is specified\');if(s.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 u<"u"&&typeof s<"u"&&u.length>0&&s.length>w)throw new Error("Resize requires only of scales or sizes to be specified")},Qy=(e,r)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${r} { `+(()=>{switch(e){case"asymmetric":return`return ${r}(xResized) / ${r}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n return (${r}(xResized) + 0.5) / ${r}(xScale) - 0.5;\n } else {\n return 0.0;\n }`;case"tf_half_pixel_for_nn":return`return (${r}(xResized) + 0.5) / ${r}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n return 0.0;\n } else {\n // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n // offset-by-one error later in floor().\n let whole = ${r}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n let fract =\n ${r}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${r}(lengthResized - 1);\n return whole + fract;\n }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n return ${r}(roiStart) * ${r}(lengthOriginal - 1) +\n (${r}(xResized) * ${r}(roiEnd - roiStart) * ${r}(lengthOriginal - 1)) /\n ${r}(lengthResized - 1);\n } else {\n return 0.5 * ${r}(roiStart + roiEnd) * ${r}(lengthOriginal - 1);\n }`;case"half_pixel_symmetric":return`const outputWidth = ${r}xScale * ${r}(lengthResized);\n const adjustment = ${r}(lengthResized) / outputWidth;\n const center = ${r}(lengthOriginal) / 2;\n const offset = center * (1 - adjustment);\n 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`)}})()+"}",Xy=(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`)}})()+"}",Jy=(e,r,t)=>{let u=new Array(t).fill(0).concat(new Array(t).fill(1)),s=e.length===0?u:e.slice();return r.length>0?(r.forEach((c,f)=>{u[c]=s[f],u[f+t]=s[r.length+f]}),u):s},e0=(e,r,t,u)=>{let s=[];if(t.length>0)if(u.length>0){if(e.forEach(c=>s.push(c)),Math.max(...u)>e.length)throw new Error("axes is out of bound");u.forEach((c,f)=>s[c]=t[f])}else t.forEach(c=>s.push(c));else{if(r.length===0)throw new Error("Resize requires either scales or sizes.");s=e.map((c,f)=>Math.round(c*r[f]))}return s},t0=(e,r,t)=>{let u=(()=>{switch(t.keepAspectRatioPolicy){case"not_larger":return t.axes.length>0?Math.min(...t.axes.map(c=>r[c]),Number.MAX_VALUE):Math.min(...r,Number.MAX_VALUE);case"not_smaller":return t.axes.length>0?Math.max(...t.axes.map(c=>r[c]),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 s=e.slice();return t.axes.length>0?(t.axes.forEach(c=>r[c]=u),t.axes.forEach(c=>s[c]=Math.round(e[c]*r[c]))):(r.fill(u,0,r.length),s.forEach((c,f)=>s[f]=Math.round(c*r[f]))),s},r0=(e,r,t,u,s)=>`\n fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${t.length}> {\n var original_indices: array<${e.type.value}, ${t.length}>;\n for (var i:u32 = 0; i < ${t.length}; i++) {\n var output_index = ${e.indicesGet("output_indices","i")};\n var scale = ${xe("uniforms.scales","i",u)};\n var roi_low = ${xe("uniforms.roi","i",s)};\n var roi_hi = ${xe("uniforms.roi",`i + ${r.length}`,s)};\n if (scale == 1.0) {\n original_indices[i] = ${e.type.value}(output_index);\n } else {\n var input_shape_i = ${xe("uniforms.input_shape","i",r.length)};\n var output_shape_i = ${xe("uniforms.output_shape","i",t.length)};\n original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n input_shape_i, roi_low, roi_hi);\n }\n }\n return original_indices;\n }`,n0=(e,r,t,u,s,c,f)=>`\n fn calculateInputIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {\n var input_indices: ${e.type.indices};\n for (var i:u32 = 0; i < ${u.length}; i++) {\n var output_index = ${r.indicesGet("output_indices","i")};\n var input_index: u32;\n var scale = ${xe("uniforms.scales","i",s)};\n if (scale == 1.0) {\n input_index = output_index;\n } else {\n var roi_low = ${xe("uniforms.roi","i",c)};\n var roi_hi = ${xe("uniforms.roi",`i + ${t.length}`,c)};\n var input_shape_i = ${xe("uniforms.input_shape","i",t.length)};\n var output_shape_i = ${xe("uniforms.output_shape","i",u.length)};\n var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n input_shape_i, roi_low, roi_hi);\n if (!${f} || (original_idx >= 0 && original_idx < ${r.type.value}(input_shape_i))) {\n if (original_idx < 0) {\n input_index = 0;\n } else if (original_idx > ${r.type.value}(input_shape_i - 1)) {\n input_index = input_shape_i - 1;\n } else {\n input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n }\n } else {\n input_index = u32(original_idx);\n }\n }\n ${e.indicesSet("input_indices","i"," input_index")}\n }\n return input_indices;\n }`,i0=(e,r)=>`\n fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n for (var i:u32 = 0; i < ${r.length}; i++) {\n var input_index = ${e.indicesGet("input_indices","i")};\n if (input_index < 0 || input_index >= ${xe("uniforms.input_shape","i",r.length)}) {\n return false;\n }\n }\n return true;\n }`,Dc=(e,r,t,u)=>e.rank>u?`\n ${e.indicesSet("input_indices",r,"channel")};\n ${e.indicesSet("input_indices",t,"batch")};\n`:"",o0=(e,r,t,u,s)=>{let[f,d,g,w]=t.length===2?[-1,0,1,-1]:[0,2,3,1],C=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${C} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",d,`max(0, min(row, ${t[d]} - 1))`)};\n ${e.indicesSet("input_indices",g,`max(0, min(col, ${t[g]} - 1))`)};\n ${Dc(e,w,f,2)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn bilinearInterpolation(output_indices: ${r.type.indices}) -> ${C} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var row:${C} = originalIndices[${d}];\n var col:${C} = originalIndices[${g}];\n ${u?`if (row < 0 || row > (${t[d]} - 1) || col < 0 || col > (${t[g]} - 1)) {\n return ${s};\n }`:""};\n row = max(0, min(row, ${t[d]} - 1));\n col = max(0, min(col, ${t[g]} - 1));\n var row1: u32 = u32(row);\n var col1: u32 = u32(col);\n var row2: u32 = u32(row + 1);\n var col2: u32 = u32(col + 1);\n var channel: u32 = ${t.length>2?`u32(originalIndices[${w}])`:"0"};\n var batch: u32 = ${t.length>2?`u32(originalIndices[${f}])`:"0"};\n var x11: ${C} = getInputValue(batch, channel, row1, col1);\n var x12: ${C} = getInputValue(batch, channel, row1, col2);\n var x21: ${C} = getInputValue(batch, channel, row2, col1);\n var x22: ${C} = getInputValue(batch, channel, row2, col2);\n var dx1: ${C} = abs(row - ${C}(row1));\n var dx2: ${C} = abs(${C}(row2) - row);\n var dy1: ${C} = abs(col - ${C}(col1));\n var dy2: ${C} = abs(${C}(col2) - col);\n if (row1 == row2) {\n dx1 = 0.5;\n dx2 = 0.5;\n }\n if (col1 == col2) {\n dy1 = 0.5;\n dy2 = 0.5;\n }\n return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n }`},a0=(e,r,t,u,s,c,f,d,g,w)=>{let C=t.length===2,$=!0,[A,P]=C?[0,1]:$?[2,3]:[1,2],x=e.type.value,E=O=>{let B=O===A?"row":"col";return`\n fn ${B}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${r.type.indices}) -> ${x} {\n var output_index = ${r.indicesGet("output_indices",O)};\n var originalIdx: ${x} = getOriginalCoordinateFromResizedCoordinate(output_index, ${s[O]},\n ${u[O]}, ${t[O]}, ${c[O]}, ${c[O]} + ${t.length});\n var fractOriginalIdx: ${x} = originalIdx - floor(originalIdx);\n var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n if (${d} && (originalIdx < 0 || originalIdx > (${t[O]} - 1))) {\n return ${g};\n }\n var data: array<${x}, 4> = array<${x}, 4>(0.0, 0.0, 0.0, 0.0);\n for (var i: i32 = -1; i < 3; i++) {\n var ${B}: ${x} = originalIdx + ${x}(i);\n if (${B} < 0 || ${B} >= ${t[O]}) {\n ${(()=>w?`coefs[i + 1] = 0.0;\n continue;`:d?`return ${g};`:`${B} = max(0, min(${B}, ${t[O]} - 1));`)()};\n }\n var input_indices_copy: ${e.type.indices} = input_indices;\n ${e.indicesSet("input_indices_copy",O,`u32(${B})`)};\n data[i + 1] = ${O===A?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n }\n return cubicInterpolation1D(data, coefs);\n }`};return`\n ${E(A)};\n ${E(P)};\n fn getCubicInterpolationCoefs(s: ${x}) -> array<${x}, 4> {\n var absS = abs(s);\n var coeffs: array<${x}, 4> = array<${x}, 4>(0.0, 0.0, 0.0, 0.0);\n var oneMinusAbsS: ${x} = 1.0 - absS;\n var twoMinusAbsS: ${x} = 2.0 - absS;\n var onePlusAbsS: ${x} = 1.0 + absS;\n coeffs[0] = ((${f} * onePlusAbsS - 5 * ${f}) * onePlusAbsS + 8 * ${f}) * onePlusAbsS - 4 * ${f};\n coeffs[1] = ((${f} + 2) * absS - (${f} + 3)) * absS * absS + 1;\n coeffs[2] = ((${f} + 2) * oneMinusAbsS - (${f} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n coeffs[3] = ((${f} * twoMinusAbsS - 5 * ${f}) * twoMinusAbsS + 8 * ${f}) * twoMinusAbsS - 4 * ${f};\n return coeffs;\n }\n\n fn cubicInterpolation1D(x: array<${x}, 4>, coefs: array<${x}, 4>) -> ${x} {\n var coefsSum: ${x} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n }\n\n fn bicubicInterpolation(output_indices: ${r.type.indices}) -> ${x} {\n var input_indices: ${e.type.indices} = output_indices;\n return colCubicInterpolation(input_indices, output_indices);\n }\n `},s0=(e,r,t,u,s)=>{let[f,d,g,w,C]=t.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],$=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${$} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",d,`max(0, min(depth, ${t[d]} - 1))`)};\n ${e.indicesSet("input_indices",g,`max(0, min(height, ${t[g]} - 1))`)};\n ${e.indicesSet("input_indices",w,`max(0, min(width, ${t[w]} - 1))`)};\n ${Dc(e,C,f,3)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn trilinearInterpolation(output_indices: ${r.type.indices}) -> ${$} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var depth:${$} = originalIndices[${d}];\n var height:${$} = originalIndices[${g}];\n var width:${$} = originalIndices[${w}];\n ${u?`if (depth < 0 || depth > (${t[d]} - 1) || height < 0 || height > (${t[g]} - 1) || width < 0 || (width > ${t[w]} - 1)) {\n return ${s};\n }`:""};\n\n depth = max(0, min(depth, ${t[d]} - 1));\n height = max(0, min(height, ${t[g]} - 1));\n width = max(0, min(width, ${t[w]} - 1));\n var depth1: u32 = u32(depth);\n var height1: u32 = u32(height);\n var width1: u32 = u32(width);\n var depth2: u32 = u32(depth + 1);\n var height2: u32 = u32(height + 1);\n var width2: u32 = u32(width + 1);\n var channel: u32 = ${t.length>3?`u32(originalIndices[${C}])`:"0"};\n var batch: u32 = ${t.length>3?`u32(originalIndices[${f}])`:"0"};\n\n var x111: ${$} = getInputValue(batch, channel, depth1, height1, width1);\n var x112: ${$} = getInputValue(batch, channel, depth1, height1, width2);\n var x121: ${$} = getInputValue(batch, channel, depth1, height2, width1);\n var x122: ${$} = getInputValue(batch, channel, depth1, height2, width2);\n var x211: ${$} = getInputValue(batch, channel, depth2, height1, width1);\n var x212: ${$} = getInputValue(batch, channel, depth2, height1, width2);\n var x221: ${$} = getInputValue(batch, channel, depth2, height2, width1);\n var x222: ${$} = getInputValue(batch, channel, depth2, height2, width2);\n var dx1: ${$} = abs(depth - ${$}(depth1));\n var dx2: ${$} = abs(${$}(depth2) - depth);\n var dy1: ${$} = abs(height - ${$}(height1));\n var dy2: ${$} = abs(${$}(height2) - height);\n var dz1: ${$} = abs(width - ${$}(width1));\n var dz2: ${$} = abs(${$}(width2) - width);\n if (depth1 == depth2) {\n dx1 = 0.5;\n dx2 = 0.5;\n }\n if (height1 == height2) {\n dy1 = 0.5;\n dy2 = 0.5;\n }\n if (width1 == width2) {\n dz1 = 0.5;\n dz2 = 0.5;\n }\n return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n }`},u0=(e,r,t,u,s,c)=>{let f=e.dims,d=Jy(c,r.axes,f.length),g=e0(f,u,s,r.axes),w=u.slice();u.length===0&&(w=f.map((R,j)=>R===0?1:g[j]/R),r.keepAspectRatioPolicy!=="stretch"&&(g=t0(f,w,r)));let C=ne("output",e.dataType,g.length),$=Z("input",e.dataType,f.length),A=K.size(g),P=f.length===g.length&&f.every((R,j)=>R===g[j]),x=r.coordinateTransformMode==="tf_crop_and_resize",E=r.extrapolationValue,O=$.type.value,B=R=>`\n ${P?"":`\n ${Qy(r.coordinateTransformMode,O)};\n ${(()=>{switch(r.mode){case"nearest":return`\n ${i0($,f)};\n ${Xy(r.nearestMode,t,O)};\n ${n0($,C,f,g,w.length,d.length,x)};\n `;case"linear":return`\n ${r0(C,f,g,w.length,d.length)};\n ${(()=>{if(f.length===2||f.length===4)return`${o0($,C,f,x,E)}`;if(f.length===3||f.length===5)return`${s0($,C,f,x,E)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n `;case"cubic":return`\n ${(()=>{if(f.length===2||f.length===4)return`${a0($,C,f,g,w,d,r.cubicCoeffA,x,r.extrapolationValue,r.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n `;default:throw Error("Invalid resize mode")}})()};\n `}\n ${R.registerUniform("output_size","u32").registerUniform("scales","f32",w.length).registerUniform("roi","f32",d.length).declareVariables($,C)}\n ${R.mainStart()}\n ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n ${P?"output[global_idx] = input[global_idx];":`\n let output_indices = ${C.offsetToIndices("global_idx")};\n var input_indices: ${$.type.indices};\n ${(()=>{switch(r.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n if (checkInputIndices(input_indices)) {\n output[global_idx] = ${$.getByIndices("input_indices")};\n } else {\n output[global_idx] = ${r.extrapolationValue};\n }`;case"linear":return`output[global_idx] = ${f.length===2||f.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${r.mode}`)}})()};\n`}\n }`;return{name:"Resize",shaderCache:{hint:`${r.cacheKey}|${t}|${w.length>0?w:""}|${s.length>0?s:""}|${d.length>0?d:""}|${P}|${f}`,inputDependencies:["rank"]},getShaderSource:B,getRunData:()=>({outputs:[{dims:g,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(A/64)},programUniforms:[{type:12,data:A},{type:1,data:w},{type:1,data:d},...se(f,g)]})}},l0=e=>{let r=e.customDataBuffer;return new Uint32Array(r,r.byteOffset,1)[0]},zc=(e,r)=>{let t=[],u=[],s=[],c=l0(e);if(r.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Zy(e.inputs,r,c,t,u,s),e.compute(u0(e.inputs[0],r,c,t,u,s),{inputs:[0]})},Mc=e=>{let r=e.antialias,t=e.axes,u=e.coordinateTransformMode,s=e.cubicCoeffA,c=e.excludeOutside!==0,f=e.extrapolationValue,d=e.keepAspectRatioPolicy,g=e.mode,w=e.nearestMode===""?"simple":e.nearestMode;return ke({antialias:r,axes:t,coordinateTransformMode:u,cubicCoeffA:s,excludeOutside:c,extrapolationValue:f,keepAspectRatioPolicy:d,mode:g,nearestMode:w})}});var d0,c0,Uc,Vc=ae(()=>{"use strict";Te();De();nt();Re();d0=(e,r)=>{let[t,u,s,c]=e,{numHeads:f,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(!K.areEqual(u.dims,[])&&!K.areEqual(u.dims,[1])&&u.dims.length!==2)throw new Error(`Input \'position_ids\' is expected to have 0, 1, or 2 dimensions, got ${u.dims.length}`);if(s.dims.length!==2)throw new Error(`Input \'cos_cache\' is expected to have 2 dimensions, got ${s.dims.length}`);if(c.dims.length!==2)throw new Error(`Input \'sin_cache\' is expected to have 2 dimensions, got ${c.dims.length}`);if(!K.areEqual(s.dims,c.dims))throw new Error("Inputs \'cos_cache\' and \'sin_cache\' are expected to have the same shape");if(d>0&&f===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let g=t.dims[0],w=t.dims[t.dims.length-2],C=s.dims[0],$=K.sizeFromDimension(t.dims,1)/w,A=d===0?s.dims[1]*2:$/f;if(d>A)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(u.dims.length===2){if(g!==u.dims[0])throw new Error(`Input \'position_ids\' dimension 0 should be of size batch_size, got ${u.dims[0]}`);if(w!==u.dims[1])throw new Error(`Input \'position_ids\' dimension 1 should be of size sequence_length, got ${u.dims[1]}`)}if(A/2!==s.dims[1]&&d/2!==s.dims[1])throw new Error(`Input \'cos_cache\' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${s.dims[1]}`);if(w>C)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},c0=(e,r)=>{let{interleaved:t,numHeads:u,rotaryEmbeddingDim:s,scale:c}=r,f=e[0].dims[0],d=K.sizeFromDimension(e[0].dims,1),g=e[0].dims[e[0].dims.length-2],w=d/g,C=e[2].dims[1],$=s===0?C*2:w/u,A=new Array(f,g,w/$,$-C),P=K.computeStrides(A),x=[{type:1,data:c},{type:12,data:A},{type:12,data:P},...e[0].dims.length===3?new Array({type:12,data:[d,w,$,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[d,$,g*$,1]}):[],...se(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],E=O=>{let B=Z("input",e[0].dataType,e[0].dims.length),R=Z("position_ids",e[1].dataType,e[1].dims.length),j=Z("cos_cache",e[2].dataType,e[2].dims.length),U=Z("sin_cache",e[3].dataType,e[3].dims.length),L=ne("output",e[0].dataType,e[0].dims.length);return O.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:A.length},{name:"global_strides",type:"u32",length:P.length},{name:"input_output_strides",type:"u32",length:P.length}]),`\n ${O.declareVariables(B,R,j,U,L)}\n\n ${O.mainStart(ur)}\n let half_rotary_emb_dim = uniforms.${j.name}_shape[1];\n let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;\n let size = uniforms.global_shape[0] * uniforms.global_strides[0];\n ${O.guardAgainstOutOfBoundsWorkgroupSizes("size")}\n\n if (bsnh[3] < half_rotary_emb_dim) {\n let position_ids_idx =\n ${R.broadcastedIndicesToOffset("bsnh.xy",ne("",R.type.tensor,2))};\n let position_id =\n u32(${R.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);\n let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${t});\n let j = i + select(half_rotary_emb_dim, 1, ${t});\n let re = ${B.getByOffset("i")} * ${j.get("position_id","bsnh[3]")} -\n ${B.getByOffset("j")} * ${U.get("position_id","bsnh[3]")};\n ${L.setByOffset("i","re")}\n let im = ${B.getByOffset("i")} * ${U.get("position_id","bsnh[3]")} +\n ${B.getByOffset("j")} * ${j.get("position_id","bsnh[3]")};\n ${L.setByOffset("j","im")}\n } else {\n let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;\n ${L.setByOffset("k",B.getByOffset("k"))}\n }\n }`};return{name:"RotaryEmbedding",shaderCache:{hint:ke({interleaved:t}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:E,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(K.size(A)/ur)},programUniforms:x})}},Uc=(e,r)=>{d0(e.inputs,r),e.compute(c0(e.inputs,r))}});var f0,p0,Wc,Nc=ae(()=>{"use strict";Te();De();Re();f0=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let r=e[0],t=e[1],u=e[2];if(r.dataType!==t.dataType||r.dataType!==u.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 s=r.dims[r.dims.length-1],c=r.dims[r.dims.length-2];if(t.dims[t.dims.length-1]!==s)throw new Error("Skip must have the same hidden size as input");if(t.dims[t.dims.length-2]!==c)throw new Error("Skip must have the same sequence length as input");if(u.dims.length!==1)throw new Error("Gamma must be 1D");if(u.dims[u.dims.length-1]!==s)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let f=e[3];if(f.dims.length!==1)throw new Error("Beta must be 1D");if(f.dims[f.dims.length-1]!==s)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let f=e[4];if(f.dims.length!==1)throw new Error("Bias must be 1D");if(f.dims[f.dims.length-1]!==s)throw new Error("Bias must have the same hidden size as input")}},p0=(e,r,t,u)=>{let s=r.simplified,c=e[0].dims,f=K.size(c),d=c,g=f,w=c.slice(-1)[0],C=u?c.slice(0,-1).concat(1):[],$=!s&&e.length>3,A=e.length>4,P=u&&t>1,x=u&&t>2,E=t>3,O=He(w),B=[{type:12,data:g},{type:12,data:O},{type:12,data:w},{type:1,data:r.epsilon}],R=U=>{let L=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],F=[Z("x",e[0].dataType,e[0].dims,O),Z("skip",e[1].dataType,e[1].dims,O),Z("gamma",e[2].dataType,e[2].dims,O)];$&&F.push(Z("beta",e[3].dataType,e[3].dims,O)),A&&F.push(Z("bias",e[4].dataType,e[4].dims,O)),F.push(ne("output",e[0].dataType,d,O)),P&&F.push(ne("mean_output",1,C)),x&&F.push(ne("inv_std_output",1,C)),E&&F.push(ne("input_skip_bias_sum",e[0].dataType,d,O));let te=Ne(e[0].dataType);return`\n\n ${U.registerUniforms(L).declareVariables(...F)}\n\n ${U.mainStart()}\n ${U.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")}\n let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;\n let offset = global_idx * hidden_size_vectorized;\n var sum = ${xt("f32",O)};\n var squareSum = ${xt("f32",O)};\n for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n let skip_value = skip[offset + i];\n let bias_value = ${A?"bias[i]":te+"(0.0)"};\n let input_value = x[offset + i];\n let value = input_value + skip_value + bias_value;\n ${E?"input_skip_bias_sum[offset + i] = value;":""}\n output[offset + i] = value;\n let f32_value = ${lr(te,O,"value")};\n sum += f32_value;\n squareSum += f32_value * f32_value;\n }\n let mean = ${It("sum",O)} / f32(uniforms.hidden_size);\n let inv_std_dev = inverseSqrt(${It("squareSum",O)} / f32(uniforms.hidden_size) ${s?"":"- mean * mean"} + uniforms.epsilon);\n ${P?"mean_output[global_idx] = mean;":""}\n ${x?"inv_std_output[global_idx] = inv_std_dev;":""}\n for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n output[offset + i] = (output[offset + i] ${s?"":`- ${te}(mean)`}) * ${te}(inv_std_dev) * gamma[i] ${$?"+ beta[i]":""};\n }\n }`},j=[{dims:d,dataType:e[0].dataType}];return t>1&&j.push({dims:C,dataType:1}),t>2&&j.push({dims:C,dataType:1}),t>3&&j.push({dims:c,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${O};${P};${x};${E}`,inputDependencies:e.map((U,L)=>"type")},getShaderSource:R,getRunData:()=>({outputs:j,dispatchGroup:{x:Math.ceil(g/w/64)},programUniforms:B})}},Wc=(e,r)=>{f0(e.inputs);let u=[0];e.outputCount>1&&u.push(-3),e.outputCount>2&&u.push(-3),e.outputCount>3&&u.push(3),e.compute(p0(e.inputs,r,e.outputCount,!1),{outputs:u})}});var m0,bi,h0,Gc,g0,y0,Hc,Lc,Fc=ae(()=>{"use strict";Te();De();nt();Re();m0=(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,u)=>{if(e[u+1].dataType!==6&&e[u+1].dataType!==7)throw new Error(`Input ${u} must be an array of int32 or int64`)})},bi=(e,r)=>{let t=[];if(e.length>r)if(e[r].dataType===7)e[r].getBigInt64Array().forEach(u=>t.push(Number(u)));else if(e[r].dataType===6)e[r].getInt32Array().forEach(u=>t.push(Number(u)));else throw new Error(`Input ${r} must be an array of int32 or int64`);return t},h0=(e,r)=>{if(e.length>1){let t=bi(e,1),u=bi(e,2),s=bi(e,3);return s.length===0&&(s=[...Array(e[0].dims.length).keys()]),ke({starts:t,ends:u,axes:s})}else return r},Gc=(e,r,t,u,s)=>{let c=e;return e<0&&(c+=t[u[r]]),s[r]<0?Math.max(0,Math.min(c,t[u[r]]-1)):Math.max(0,Math.min(c,t[u[r]]))},g0=(e,r,t)=>`fn calculateInputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {\n var input_indices: ${e.type.indices};\n var carry = 0u;\n for (var i = ${t.length}; i >= 0; i--) {\n let input_shape_i = ${xe("uniforms.input_shape","i",t.length)};\n let steps_i = ${xe("uniforms.steps","i",t.length)};\n let signs_i = ${xe("uniforms.signs","i",t.length)};\n let starts_i = ${xe("uniforms.starts","i",t.length)};\n var output_index = ${r.indicesGet("output_indices","i")};\n var input_index = output_index * steps_i + starts_i + carry;\n carry = input_index / input_shape_i;\n input_index = input_index % input_shape_i;\n if (signs_i < 0) {\n input_index = input_shape_i - input_index - 1u + starts_i;\n }\n ${e.indicesSet("input_indices","i","input_index")};\n }\n return input_indices;\n }`,y0=(e,r)=>{let t=e[0].dims,u=K.size(t),s=r.axes.length>0?K.normalizeAxes(r.axes,t.length):[...Array(t.length).keys()],c=bi(e,4);c.forEach(B=>B!==0||(()=>{throw new Error("step cannot be 0")})),c.length===0&&(c=Array(s.length).fill(1));let f=r.starts.map((B,R)=>Gc(B,R,t,s,c)),d=r.ends.map((B,R)=>Gc(B,R,t,s,c));if(s.length!==f.length||s.length!==d.length)throw new Error("start, ends and axes should have the same number of elements");if(s.length!==t.length)for(let B=0;BMath.sign(B));c.forEach((B,R,j)=>{if(B<0){let U=(d[R]-f[R])/B,L=f[R],F=L+U*c[R];f[R]=F,d[R]=L,j[R]=-B}});let w=t.slice(0);s.forEach((B,R)=>{w[B]=Math.ceil((d[B]-f[B])/c[B])});let C={dims:w,dataType:e[0].dataType},$=ne("output",e[0].dataType,w.length),A=Z("input",e[0].dataType,e[0].dims.length),P=K.size(w),x=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:f.length},{name:"signs",type:"i32",length:g.length},{name:"steps",type:"u32",length:c.length}],E=[{type:12,data:P},{type:12,data:f},{type:6,data:g},{type:12,data:c},...se(e[0].dims,w)],O=B=>`\n ${B.registerUniforms(x).declareVariables(A,$)}\n ${g0(A,$,t)}\n ${B.mainStart()}\n ${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n let output_indices = ${$.offsetToIndices("global_idx")};\n let input_indices = calculateInputIndices(output_indices);\n ${$.setByOffset("global_idx",A.getByIndices("input_indices"))}\n }`;return{name:"Slice",shaderCache:{hint:`${g.length}_${f.length}_${c.length}`,inputDependencies:["rank"]},getShaderSource:O,getRunData:()=>({outputs:[C],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:E})}},Hc=(e,r)=>{m0(e.inputs,r);let t=h0(e.inputs,r);e.compute(y0(e.inputs,t),{inputs:[0]})},Lc=e=>{let r=e.starts,t=e.ends,u=e.axes;return ke({starts:r,ends:t,axes:u})}});var b0,v0,qc,Kc,Yc=ae(()=>{"use strict";Te();De();nt();Re();b0=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},v0=(e,r)=>{let t=e.dims,u=K.size(t),s=64,c=r.axis;if(c<0&&(c=t.length+c),cB===4?`max(max(${O}.x, ${O}.y), max(${O}.z, ${O}.w))`:B===2?`max(${O}.x, ${O}.y)`:B===3?`max(max(${O}.x, ${O}.y), ${O}.z)`:O,$=Z("x",e.dataType,e.dims,g),A=ne("result",e.dataType,e.dims,g),P=$.type.value,x=Ne(e.dataType)==="f32"?`var threadMax = ${P}(-3.402823e+38f);`:`var threadMax = ${P}(-65504.0h);`,E=O=>`\n var rowMaxShared : ${P};\n var rowSumShared : ${P};\n var threadShared : array<${P}, ${s}>;\n\n fn getValue(row: i32, col: i32, row_stride: i32) -> ${P} {\n let index = row * row_stride + col;\n return x[index];\n }\n\n fn setValue(row: i32, col: i32, row_stride: i32, value: ${P}) {\n let index = row * row_stride + col;\n result[index] = value;\n }\n ${O.registerUniform("packedCols","i32").declareVariables($,A)}\n ${O.mainStart()}\n let gindex = i32(global_idx);\n let lindex = i32(local_idx);\n const wg = ${s};\n let row = gindex / wg;\n let cols = uniforms.packedCols;\n let row_stride : i32 = uniforms.packedCols;\n\n // find the rows max\n ${x}\n for (var col = lindex; col < cols; col += wg) {\n let value = getValue(row, col, row_stride);\n threadMax = max(threadMax, value);\n }\n if (lindex < cols) {\n threadShared[lindex] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, wg);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (lindex < currSize) {\n threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);\n }\n workgroupBarrier();\n }\n if (lindex == 0) {\n rowMaxShared = ${P}(${C("threadShared[0]",g)});\n }\n workgroupBarrier();\n\n // find the rows sum\n var threadSum = ${P}(0.0);\n for (var col = lindex; col < cols; col += wg) {\n let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);\n threadSum += subExp;\n }\n threadShared[lindex] = threadSum;\n workgroupBarrier();\n\n for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) {\n if (lindex < currSize) {\n threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];\n }\n workgroupBarrier();\n }\n if (lindex == 0) {\n rowSumShared = ${P}(${It("threadShared[0]",g)});\n }\n workgroupBarrier();\n\n // calculate final value for each element in the row\n for (var col = lindex; col < cols; col += wg) {\n let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;\n setValue(row, col, row_stride, value);\n }\n }`;return{name:"Softmax",shaderCache:{hint:`${g}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:t,dataType:e.dataType}],dispatchGroup:{x:d},programUniforms:[{type:6,data:w}]}),getShaderSource:E}},qc=(e,r)=>{b0(e.inputs),e.compute(v0(e.inputs[0],r))},Kc=e=>ke({axis:e.axis})});var w0,_0,$0,C0,S0,Zc,Qc,Xc=ae(()=>{"use strict";Te();De();nt();Re();w0=e=>{if(!e||e.length<1)throw new Error("too few inputs")},_0=(e,r)=>{let t=[],u=r.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(s=>t.push(Number(s))),u=t.length),ke({numOutputs:u,axis:r.axis,splitSizes:t})},$0=e=>`\nfn calculateOutputIndex(index: u32) -> u32 {\n for (var i: u32 = 0u; i < ${e}u; i += 1u ) {\n if (index < ${xe("uniforms.size_in_split_axis","i",e)}) {\n return i;\n }\n }\n return ${e}u;\n}`,C0=e=>{let r=e.length,t=[];for(let u=0;u{let t=e[0].dims,u=K.size(t),s=e[0].dataType,c=K.normalizeAxis(r.axis,t.length),f=new Array(r.numOutputs),d=Z("input",s,t.length),g=new Array(r.numOutputs),w=[],C=[],$=0,A=[{type:12,data:u}];for(let x=0;x`\n ${x.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",g.length).declareVariables(d,...f)}\n ${$0(g.length)}\n ${C0(f)}\n\n ${x.mainStart()}\n ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}\n\n var indices = ${d.offsetToIndices("global_idx")};\n var index = ${d.indicesGet("indices",c)};\n let output_number = calculateOutputIndex(index);\n if (output_number != 0) {\n index -= ${xe("uniforms.size_in_split_axis","output_number - 1u",g.length)};\n ${d.indicesSet("indices",c,"index")};\n }\n writeBufferData(output_number, indices, global_idx);\n }`;return{name:"Split",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getShaderSource:P,getRunData:()=>({outputs:w,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:A})}},Zc=(e,r)=>{w0(e.inputs);let t=e.inputs.length===1?r:_0(e.inputs,r);e.compute(S0(e.inputs,t),{inputs:[0]})},Qc=e=>{let r=e.axis,t=e.splitSizes,u=e.numOutputs<0?t.length:e.numOutputs;if(u!==t.length)throw new Error("numOutputs and splitSizes lengh must be equal");return ke({axis:r,numOutputs:u,splitSizes:t})}});var Jc,x0,I0,A0,ef,tf=ae(()=>{"use strict";Te();De();Re();Jc=e=>Array.from(e.getBigInt64Array(),Number),x0=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, 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(Jc(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")},I0=(e,r)=>{let t=[];for(let u=0;u{let r=e[0].dims,t=Jc(e[1]),u=I0(r,t),s=K.size(u),c=e[0].dataType,f=Z("input",c,r.length),d=ne("output",c,u.length),g=w=>`\n const inputShape = 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C={kernelId:this.backend.currentKernelId,computePipeline:r.computePipeline,bindGroup:w,dispatchGroup:s};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(C)}d.setPipeline(r.computePipeline),d.setBindGroup(0,w),d.dispatchWorkgroups(...s),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(),Ht(r.programInfo.name)}dispose(){}build(r,t){ar(r.name);let u=this.backend.device,s=[];u.features.has("shader-f16")&&s.push("enable f16;");let c=Wu(t,this.backend.device.limits),f=r.getShaderSource(c),d=`${s.join(`\n`)}\n${c.additionalImplementations}\n${f}`,g=u.createShaderModule({code:d,label:r.name});Fe("verbose",()=>`[WebGPU] ${r.name} shader code: ${d}`);let w=u.createComputePipeline({compute:{module:g,entryPoint:"main"},layout:"auto",label:r.name});return Ht(r.name),{programInfo:r,computePipeline:w,uniformVariablesInfo:c.variablesInfo}}normalizeDispatchGroupSize(r){let t=typeof r=="number"?r:r.x,u=typeof r=="number"?1:r.y||1,s=typeof r=="number"?1:r.z||1,c=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(t<=c&&u<=c&&s<=c)return[t,u,s];let f=t*u*s,d=Math.ceil(Math.sqrt(f));if(d>c){if(d=Math.ceil(Math.cbrt(f)),d>c)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[d,d,d]}else return[d,d,1]}}});var P0,k0,mo,wi,uf=ae(()=>{"use strict";Rr();Te();Lt();Du();Vu();af();sf();P0=(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 u=0;u{let u=e.name;return e.shaderCache?.hint&&(u+="["+e.shaderCache.hint+"]"),u+=":"+t+`:${P0(r,e.shaderCache?.inputDependencies??new 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This is not supported now.`)}let x;if(C){let j=0,U=[];C.forEach(J=>{let oe=typeof J.data=="number"?[J.data]:J.data;if(oe.length===0)return;let le=J.type===10?2:4,ge,X;J.type===10?(X=oe.length>4?16:oe.length>2?8:oe.length*le,ge=oe.length>4?16:le*oe.length):(X=oe.length<=2?oe.length*le:16,ge=16),j=Math.ceil(j/X)*X,U.push(j);let pe=J.type===10?8:4;j+=oe.length>4?Math.ceil(oe.length/pe)*ge:oe.length*le});let L=16;j=Math.ceil(j/L)*L;let F=new ArrayBuffer(j);C.forEach((J,oe)=>{let le=U[oe],ge=typeof J.data=="number"?[J.data]:J.data;if(J.type===6)new Int32Array(F,le,ge.length).set(ge);else if(J.type===12)new Uint32Array(F,le,ge.length).set(ge);else if(J.type===10)new Uint16Array(F,le,ge.length).set(ge);else if(J.type===1)new Float32Array(F,le,ge.length).set(ge);else throw new Error(`Unsupported uniform type: ${Gt(J.type)}`)});let te=this.gpuDataManager.create(j,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(te.buffer,0,F,0,j),this.gpuDataManager.release(te.id),x={offset:0,size:j,buffer:te.buffer}}let E=this.programManager.normalizeDispatchGroupSize(w),O=E[1]===1&&E[2]===1,B=k0(r,t,O),R=this.programManager.getArtifact(B);if(R||(R=this.programManager.build(r,E),this.programManager.setArtifact(B,R),Fe("info",()=>`[artifact] key: ${B}, programName: ${r.name}`)),C&&R.uniformVariablesInfo){if(C.length!==R.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${R.uniformVariablesInfo.length}, got ${C.length} in program "${R.programInfo.name}".`);for(let j=0;j`[ProgramManager] run "${r.name}" (key=${B}) with ${E[0]}x${E[1]}x${E[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let j={kernelId:this.currentKernelId,programName:R.programInfo.name,inputTensorViews:t,outputTensorViews:A};this.pendingKernels.push(j),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(j)}return this.programManager.run(R,d,P,E,x),Ht(r.name),A}upload(r,t){this.gpuDataManager.upload(r,t)}memcpy(r,t){this.gpuDataManager.memcpy(r,t)}async download(r,t){await this.gpuDataManager.download(r,t)}alloc(r){return this.gpuDataManager.create(r).id}free(r){return this.gpuDataManager.release(r)}createKernel(r,t,u,s){let c=of.get(r);if(!c)throw new Error(`kernel not implemented: ${r}`);let f={kernelType:r,kernelName:s,kernelEntry:c[0],attributes:[c[1],u]};this.kernels.set(t,f)}releaseKernel(r){let t=this.kernelPersistentData.get(r);if(t){for(let u of t)this.gpuDataManager.release(u.id);this.kernelPersistentData.delete(r)}this.kernelCustomData.delete(r),this.kernels.delete(r)}computeKernel(r,t,u){let s=this.kernels.get(r);if(!s)throw new Error(`kernel not created: ${r}`);let c=s.kernelType,f=s.kernelName,d=s.kernelEntry,g=s.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${c}] ${f}" is not allowed to be called recursively`);this.currentKernelId=r,g[0]&&(g[1]=g[0](g[1]),g[0]=void 0),Fe("info",()=>`[WebGPU] Start to run kernel "[${c}] ${f}"...`);let w=this.env.debug;this.temporaryData=[];try{return w&&this.device.pushErrorScope("validation"),d(t,g[1]),0}catch(C){return u.push(Promise.resolve(`[WebGPU] Kernel "[${c}] ${f}" failed. ${C}`)),1}finally{w&&u.push(this.device.popErrorScope().then(C=>C?`GPU validation error for kernel "[${c}] ${f}": ${C.message}`:null));for(let C of this.temporaryData)this.gpuDataManager.release(C.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(r,t,u,s){let c=this.sessionExternalDataMapping.get(r);c||(c=new Map,this.sessionExternalDataMapping.set(r,c));let f=c.get(t),d=this.gpuDataManager.registerExternalBuffer(u,s,f?.[1]);return c.set(t,[d,u]),d}unregisterBuffers(r){let 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r};self.onmessage=e=>{let{type:r,in:t}=e.data;try{switch(r){case"init-wasm":su(t.wasm).then(()=>{ff(t).then(()=>{postMessage({type:r})},u=>{postMessage({type:r,err:u})})},u=>{postMessage({type:r,err:u})});break;case"init-ep":{let{epName:u,env:s}=t;pf(s,u).then(()=>{postMessage({type:r})},c=>{postMessage({type:r,err:c})});break}case"copy-from":{let{buffer:u}=t,s=go(u);postMessage({type:r,out:s});break}case"create":{let{model:u,options:s}=t;mf(u,s).then(c=>{postMessage({type:r,out:c})},c=>{postMessage({type:r,err:c})});break}case"release":hf(t),postMessage({type:r});break;case"run":{let{sessionId:u,inputIndices:s,inputs:c,outputIndices:f,options:d}=t;gf(u,s,c,f,new Array(f.length).fill(null),d).then(g=>{g.some(w=>w[3]!=="cpu")?postMessage({type:r,err:"Proxy does not support non-cpu tensor 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n'}),Ir,Tt,gn,Qn,Xn,Bo,Ia,Hr,qr,jf,Zn,Rf,Bf,Df,Nf,Lf,Uf,Ff,Wf=le(()=>{var t;Yt(),Ww(),Wn(),Ir=()=>!!He.wasm.proxy&&typeof document<"u",gn=!1,Qn=!1,Xn=!1,Ia=new Map,Hr=(e,r)=>{let n=Ia.get(e);n?n.push(r):Ia.set(e,[r])},qr=()=>{if(gn||!Qn||Xn||!Tt)throw new Error("worker not 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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 Qw=Object.freeze({__proto__:null,get InferenceSession(){return zs},get TRACE(){return Un},get TRACE_FUNC_BEGIN(){return Kt},get TRACE_FUNC_END(){return Ut},get Tensor(){return $t},get TrainingSession(){return Ps},default:Yw,get env(){return He},get registerBackend(){return Dr}});const Xw=(t,e)=>{const r=typeof 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e=kv(t,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=Uv(e)}render(t){const e=new Ho;e.set("false",!1),e.set("true",!0),e.set("raise_exception",a=>{throw new Error(a)}),e.set("range",Fv);for(const[a,o]of Object.entries(t))e.set(a,o);return new Gv(e).run(this.parsed).value}};async function um(t,e){const r=await Promise.all([Br(t,"tokenizer.json",!0,e),Br(t,"tokenizer_config.json",!0,e)]);return e.legacy!==null&&(r[1].legacy=e.legacy),r}function Hv(t,e){const r=[];let n=0;for(const a of t.matchAll(e)){const o=a[0];n0&&r.push(o),n=a.index+o.length}return n=19968&&t<=40959||t>=13312&&t<=19903||t>=131072&&t<=173791||t>=173824&&t<=177983||t>=177984&&t<=178207||t>=178208&&t<=183983||t>=63744&&t<=64255||t>=194560&&t<=195103}function Kv(t,e,r){const n=[];let a=0;for(;athis.tokens_to_ids.get(r)??this.unk_token_id)}convert_ids_to_tokens(e){return e.map(r=>this.vocab[r]??this.unk_token)}}class Zv extends ai{constructor(e){super(e),this.tokens_to_ids=qo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.max_input_chars_per_word=e.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r}encode(e){const r=[];for(const n of e){const a=[...n];if(a.length>this.max_input_chars_per_word){r.push(this.unk_token);continue}let o=!1,i=0;const d=[];for(;i0&&(c=this.config.continuing_subword_prefix+c),this.tokens_to_ids.has(c)){m=c;break}--h}if(m===null){o=!0;break}d.push(m),i=h}o?r.push(this.unk_token):r.push(...d)}return r}}class Jv extends ai{constructor(e,r){super(e);const n=e.vocab.length;this.vocab=new Array(n),this.scores=new Array(n);for(let a=0;a[a,o])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=r.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=T0(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new $v,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(e){const r=e.sentence,n=r.length;let a=0;for(;a{const t=[...Array.from({length:94},(a,o)=>o+33),...Array.from({length:12},(a,o)=>o+161),...Array.from({length:82},(a,o)=>o+174)],e=t.slice();let r=0;for(let a=0;a<256;++a)t.includes(a)||(t.push(a),e.push(256+r),r+=1);const n=e.map(a=>String.fromCharCode(a));return Object.fromEntries(t.map((a,o)=>[a,n[o]]))})(),eb=b0(hm);class tb extends ai{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=qo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r;this.bpe_ranks=new Map(e.merges.map((r,n)=>[r,n])),this.merges=e.merges.map(r=>r.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=e.end_of_word_suffix,this.continuing_subword_suffix=e.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=new Map}bpe(e){if(e.length===0)return[];const r=this.cache.get(e);if(r!==void 0)return r;const n=Array.from(e);this.end_of_word_suffix&&(n[n.length-1]+=this.end_of_word_suffix);let a=[];if(n.length>1){const o=new bv((h,m)=>h.score`<0x${i.toString(16).toUpperCase().padStart(2,"0")}>`)):r.push(this.unk_token)}return r}}class rb extends ai{constructor(e,r){super(e),this.tokens_to_ids=qo(r.target_lang?e.vocab[r.target_lang]:e.vocab),this.bos_token=r.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=r.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=r.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=r.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[n,a]of this.tokens_to_ids)this.vocab[a]=n}encode(e){return e}}class zt extends Ot{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new pb(e);case"Precompiled":return new Mb(e);case"Sequence":return new cb(e);case"Replace":return new nb(e);case"NFC":return new ib(e);case"NFKC":return new ab(e);case"NFKD":return new sb(e);case"Strip":return new ob(e);case"StripAccents":return new lb(e);case"Lowercase":return new ub(e);case"Prepend":return new db(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class nb extends zt{normalize(e){const r=Pa(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class ib extends zt{normalize(e){return e=e.normalize("NFC"),e}}class ab extends zt{normalize(e){return e=e.normalize("NFKC"),e}}class sb extends zt{normalize(e){return e=e.normalize("NFKD"),e}}class ob extends zt{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class lb extends zt{normalize(e){return e=cm(e),e}}class ub extends zt{normalize(e){return e=e.toLowerCase(),e}}class db extends zt{normalize(e){return e=this.config.prepend+e,e}}class cb extends zt{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>zt.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class pb extends zt{_tokenize_chinese_chars(e){const r=[];for(let n=0;nthis.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class hb extends Ft{constructor(e){super(),this.pattern=new RegExp(`[^\\s${ii}]+|[${ii}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class fb extends Ft{constructor(e){super(),this.config=e,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=hm,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),o=>this.byte_encoder[o]).join(""))}}class mb extends Ft{constructor(e){super(),this.config=e,this.pattern=Pa(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:Hv(e,this.pattern)}}class gb extends Ft{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${ii}]+|[${ii}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class yb extends Ft{constructor(e){super(),this.config=e;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class ja extends Ot{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new _b(e);case"ByteLevel":return new wb(e);case"RobertaProcessing":return new mm(e);case"BertProcessing":return new fm(e);default:throw new Error(`Unknown PostProcessor type: ${e.type}`)}}post_process(e,...r){throw Error("post_process should be implemented in subclass.")}_call(e,...r){return this.post_process(e,...r)}}class fm extends ja{constructor(e){super(e),this.cls=e.cls[0],this.sep=e.sep[0]}post_process(e,r=null,{add_special_tokens:n=!0}={}){n&&(e=ht([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const o=n&&this instanceof mm?[this.sep]:[],i=n?[this.sep]:[];e=ht(e,o,r,i),a=ht(a,new Array(r.length+o.length+i.length).fill(1))}return{tokens:e,token_type_ids:a}}}class mm extends fm{}class _b extends ja{constructor(e){super(e),this.single=e.single,this.pair=e.pair}post_process(e,r=null,{add_special_tokens:n=!0}={}){const a=r===null?this.single:this.pair;let o=[],i=[];for(const d of a)"SpecialToken"in d?n&&(o.push(d.SpecialToken.id),i.push(d.SpecialToken.type_id)):"Sequence"in d&&(d.Sequence.id==="A"?(o=ht(o,e),i=ht(i,new Array(e.length).fill(d.Sequence.type_id))):d.Sequence.id==="B"&&(o=ht(o,r),i=ht(i,new Array(r.length).fill(d.Sequence.type_id))));return{tokens:o,token_type_ids:i}}}class wb extends ja{post_process(e,r=null){return r&&(e=ht(e,r)),{tokens:e}}}class Pt extends Ot{constructor(e){super(),this.config=e,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=e.trim_offsets}static fromConfig(e){if(e===null)return null;switch(e.type){case"WordPiece":return new Cb(e);case"Metaspace":return new Ab(e);case"ByteLevel":return new Sb(e);case"Replace":return new vb(e);case"ByteFallback":return new bb(e);case"Fuse":return new $b(e);case"Strip":return new xb(e);case"Sequence":return new kb(e);case"CTC":return new Eb(e);case"BPEDecoder":return new Tb(e);default:throw new Error(`Unknown Decoder type: ${e.type}`)}}_call(e){return this.decode(e)}decode(e){return this.decode_chain(e).join("")}decode_chain(e){throw Error("`decode_chain` should be implemented in subclass.")}}class vb extends Pt{decode_chain(e){const r=Pa(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class bb extends Pt{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const r=[];let n=[];for(const a of e){let o=null;if(a.length===6&&a.startsWith("<0x")&&a.endsWith(">")){const i=parseInt(a.slice(3,5),16);isNaN(i)||(o=i)}if(o!==null)n.push(o);else{if(n.length>0){const i=this.text_decoder.decode(Uint8Array.from(n));r.push(i),n=[]}r.push(a)}}if(n.length>0){const a=this.text_decoder.decode(Uint8Array.from(n));r.push(a),n=[]}return r}}class $b extends Pt{decode_chain(e){return[e.join("")]}}class xb extends Pt{constructor(e){super(e),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(e){return e.map(r=>{let n=0;for(let o=0;o(n!==0&&(r.startsWith(this.config.prefix)?r=r.replace(this.config.prefix,""):r=" "+r),this.cleanup&&(r=Ko(r)),r))}}class Sb extends Pt{constructor(e){super(e),this.byte_decoder=eb,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(e){const r=e.join(""),n=new Uint8Array([...r].map(o=>this.byte_decoder[o]));return this.text_decoder.decode(n)}decode_chain(e){const r=[];let n=[];for(const a of e)this.added_tokens.find(o=>o.content===a)!==void 0?(n.length>0&&(r.push(this.convert_tokens_to_string(n)),n=[]),r.push(a)):n.push(a);return n.length>0&&r.push(this.convert_tokens_to_string(n)),r}}class Eb extends Pt{constructor(e){super(e),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(e){if(e.length===0)return"";const r=[e[0]];for(let o=1;oo!==this.pad_token).join("");return this.cleanup&&(a=Ko(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class kb extends Pt{constructor(e){super(e),this.decoders=e.decoders.map(r=>Pt.fromConfig(r))}decode_chain(e){return this.decoders.reduce((r,n)=>n.decode_chain(r),e)}}class Tb extends Pt{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map((r,n)=>r.replaceAll(this.suffix,n===e.length-1?"":" "))}}class Ib extends Pt{decode_chain(e){let r="";for(let n=1;nn.normalize("NFKC")).join("～"):e=e.normalize("NFKC"),e}}class Ob extends Ft{constructor(e){super(),this.tokenizers=e.pretokenizers.map(r=>Ft.fromConfig(r))}pre_tokenize_text(e,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[e])}}class zb extends Ft{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class Pb extends Ft{constructor(e){super()}pre_tokenize_text(e,r){return Yv(e)}}class jb extends Ft{constructor(e){super(),this.config=e,this.pattern=Pa(this.config.pattern),this.content=this.config.content}pre_tokenize_text(e,r){return this.pattern===null?[e]:[e.replaceAll(this.pattern,this.config.content)]}}const Rb=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Bb(t,e,r,n){for(const a of Object.keys(t)){const o=e-t[a].length,i=r(a),d=new Array(o).fill(i);t[a]=n==="right"?ht(t[a],d):ht(d,t[a])}}function Db(t,e){for(const r of Object.keys(t))t[r].length=e}class Pe extends Ot{constructor(r,n){super();te(this,"return_token_type_ids",!1);te(this,"_default_chat_template",`{% for message in messages %}{{'<|im_start|>' + message['role'] + ' -' + message['content'] + '<|im_end|>' + ' -'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant -' }}{% endif %}`);te(this,"padding_side","right");this._tokenizer_config=n,this.normalizer=zt.fromConfig(r.normalizer),this.pre_tokenizer=Ft.fromConfig(r.pre_tokenizer),this.model=ai.fromConfig(r.model,n),this.post_processor=ja.fromConfig(r.post_processor),this.decoder=Pt.fromConfig(r.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const a of r.added_tokens){const o=new Xv(a);this.added_tokens.push(o),this.model.tokens_to_ids.set(o.content,o.id),this.model.vocab[o.id]=o.content,o.special&&(this.special_tokens.push(o.content),this.all_special_ids.push(o.id))}if(this.additional_special_tokens=n.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_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.map(a=>`${a.lstrip?"\\s*":""}(${_l(a.content)})${a.rstrip?"\\s*":""}`).join("|")):null,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.model_max_length=n.model_max_length,this.remove_space=n.remove_space,this.clean_up_tokenization_spaces=n.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=n.do_lowercase_and_remove_accent??!1,n.padding_side&&(this.padding_side=n.padding_side),this.legacy=!1,this.chat_template=n.chat_template??null,Array.isArray(this.chat_template)){const a=Object.create(null);for(const{name:o,template:i}of this.chat_template){if(typeof o!="string"||typeof i!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');a[o]=i}this.chat_template=a}this._compiled_template_cache=new Map}getToken(...r){for(const n of r){const a=this._tokenizer_config[n];if(a)if(typeof a=="object"){if(a.__type==="AddedToken")return a.content;throw Error(`Unknown token: ${a}`)}else return a}return null}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:o=null,local_files_only:i=!1,revision:d="main",legacy:h=null}={}){const m=await um(r,{progress_callback:n,config:a,cache_dir:o,local_files_only:i,revision:d,legacy:h});return new this(...m)}_call(r,{text_pair:n=null,add_special_tokens:a=!0,padding:o=!1,truncation:i=null,max_length:d=null,return_tensor:h=!0}={}){const m=Array.isArray(r);let c;if(m){if(r.length===0)throw Error("text array must be non-empty");if(n!==null){if(Array.isArray(n)){if(r.length!==n.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");c=r.map((v,$)=>this._encode_plus(v,{text_pair:n[$],add_special_tokens:a}))}else c=r.map(v=>this._encode_plus(v,{add_special_tokens:a}))}else{if(r==null)throw Error("text may not be null or undefined");if(Array.isArray(n))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");c=[this._encode_plus(r,{text_pair:n,add_special_tokens:a})]}if(d===null?o==="max_length"?d=this.model_max_length:d=Cs(c.map(v=>v.input_ids.length))[0]:i||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."),d=Math.min(d,this.model_max_length),o||i)for(let v=0;vd?i&&Db(c[v],d):o&&Bb(c[v],d,$=>$==="input_ids"?this.pad_token_id:0,this.padding_side));const _={};if(h){if(!(o&&i)&&c.some($=>{var C;for(const k of Object.keys($))if($[k].length!==((C=c[0][k])==null?void 0:C.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 v=[c.length,c[0].input_ids.length];for(const $ of Object.keys(c[0]))_[$]=new je("int64",BigInt64Array.from(c.flatMap(C=>C[$]).map(BigInt)),v)}else{for(const v of Object.keys(c[0]))_[v]=c.map($=>$[v]);if(!m)for(const v of Object.keys(_))_[v]=_[v][0]}return _}_encode_text(r){return r===null?null:(this.added_tokens_regex?r.split(this.added_tokens_regex).filter(o=>o):[r]).map((o,i)=>{if(this.added_tokens.find(h=>h.content===o)!==void 0)return o;{if(this.remove_space===!0&&(o=o.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(o=qv(o)),this.normalizer!==null&&(o=this.normalizer(o)),o.length===0)return[];const h=this.pre_tokenizer!==null?this.pre_tokenizer(o,{section_index:i}):[o];return this.model(h)}}).flat()}_encode_plus(r,{text_pair:n=null,add_special_tokens:a=!0}={}){const{tokens:o,token_type_ids:i}=this._tokenize_helper(r,{pair:n,add_special_tokens:a}),d=this.model.convert_tokens_to_ids(o),h={input_ids:d,attention_mask:new Array(d.length).fill(1)};return this.return_token_type_ids&&i&&(h.token_type_ids=i),h}_tokenize_helper(r,{pair:n=null,add_special_tokens:a=!1}={}){const o=this._encode_text(r),i=this._encode_text(n);return this.post_processor?this.post_processor(o,i,{add_special_tokens:a}):{tokens:ht(o??[],i??[])}}tokenize(r,{pair:n=null,add_special_tokens:a=!1}={}){return this._tokenize_helper(r,{pair:n,add_special_tokens:a}).tokens}encode(r,{text_pair:n=null,add_special_tokens:a=!0}={}){return this._encode_plus(r,{text_pair:n,add_special_tokens:a}).input_ids}batch_decode(r,n={}){return r instanceof je&&(r=r.tolist()),r.map(a=>this.decode(a,n))}decode(r,n={}){if(r instanceof je&&(r=dm(r)),!Array.isArray(r)||r.length===0||!$0(r[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(r,n)}decode_single(r,{skip_special_tokens:n=!1,clean_up_tokenization_spaces:a=null}){let o=this.model.convert_ids_to_tokens(r);n&&(o=o.filter(d=>!this.special_tokens.includes(d)));let i=this.decoder?this.decoder(o):o.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(i=i.replaceAll(this.decoder.end_of_word_suffix," "),n&&(i=i.trim())),(a??this.clean_up_tokenization_spaces)&&(i=Ko(i)),i}get default_chat_template(){return this._warned_about_chat_template||(console.warn("No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format. If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information."),this._warned_about_chat_template=!0),this._default_chat_template}apply_chat_template(r,{chat_template:n=null,add_generation_prompt:a=!1,tokenize:o=!0,padding:i=!1,truncation:d=!1,max_length:h=null,return_tensor:m=!0,return_dict:c=!1,tokenizer_kwargs:_={},...v}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null&&this.default_chat_template&&typeof this.default_chat_template=="object"){const M=this.chat_template??this.default_chat_template;if(n!==null&&Object.hasOwn(M,n))n=M[n];else if(n===null&&"default"in M)n=M.default;else if(n===null)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(M).sort()}.`)}else n??(n=this.chat_template??this.default_chat_template);if(typeof n!="string")throw Error(`chat_template must be a string, but got ${typeof n}`);let $=this._compiled_template_cache.get(n);$===void 0&&($=new Vv(n),this._compiled_template_cache.set(n,$));const C=Object.create(null);for(const M of Rb){const z=this.getToken(M);z&&(C[M]=z)}const k=$.render({messages:r,add_generation_prompt:a,...C,...v});if(o){const M=this._call(k,{add_special_tokens:!1,padding:i,truncation:d,max_length:h,return_tensor:m,..._});return c?M:M.input_ids}return k}}class Nb extends Pe{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Lb extends Pe{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Ub extends Pe{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Fb extends Pe{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Wb extends Pe{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Gb extends Pe{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Vb extends Pe{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Hb extends Pe{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class qb extends Pe{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Kb extends Pe{}class Yb extends Pe{}class Qb extends Pe{constructor(r,n){super(r,n);te(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 Xb extends Pe{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Zb extends Pe{}class ym extends Pe{constructor(){super(...arguments);te(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}}class Jb extends Pe{}class _m extends Pe{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return Yo(this,e,r,n)}}class e1 extends _m{}class t1 extends Pe{}class r1 extends ym{constructor(e,r){var o,i;const n=".,!?…。，、।۔،",a=(i=(o=e.pre_tokenizer)==null?void 0:o.pretokenizers[0])==null?void 0:i.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(e,r)}}const Ra="▁";class wm extends Pe{constructor(r,n){super(r,n);te(this,"_default_chat_template",`{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<> -' + system_message + ' -<> - -' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<> -' + content.strip() + ' -<> - -' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}`);te(this,"DEFAULT_SYSTEM_PROMPT",`You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. - -If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.`);te(this,"padding_side","left");this.use_default_system_prompt=n.use_default_system_prompt??!1,this.legacy=n.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new gm({replacement:Ra,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(r){if(r===null)return null;if(this.legacy||r.length===0)return super._encode_text(r);let n=super._encode_text(Ra+r.replaceAll(Ra," "));return n.length>1&&n[0]===Ra&&this.special_tokens.includes(n[1])&&(n=n.slice(1)),n}get default_chat_template(){return super.default_chat_template.replaceAll("USE_DEFAULT_PROMPT",this.use_default_system_prompt?"true":"false").replaceAll("DEFAULT_SYSTEM_MESSAGE",this.DEFAULT_SYSTEM_PROMPT.replaceAll(` -`,"\\n").replaceAll("'","\\'"))}}class n1 extends wm{}class i1 extends Pe{}class a1 extends Pe{}class s1 extends Pe{}class o1 extends Pe{}class l1 extends Pe{}class u1 extends Pe{}class d1 extends Pe{constructor(){super(...arguments);te(this,"_default_chat_template",`{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + ' -' + message['content'] | trim + ' -' }}{% endfor %}{% if add_generation_prompt %}{{'model -'}}{% endif %}`)}}class c1 extends Pe{}function Yo(t,e,r,n){if(!("language_codes"in t)||!Array.isArray(t.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in t)||!(t.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in t)||typeof t.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const a=n.src_lang,o=n.tgt_lang;if(!t.language_codes.includes(o))throw new Error(`Target language code "${o}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);if(a!==void 0){if(!t.language_codes.includes(a))throw new Error(`Source language code "${a}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);for(const i of t.post_processor.config.single)if("SpecialToken"in i&&t.languageRegex.test(i.SpecialToken.id)){i.SpecialToken.id=t.lang_to_token(a);break}}return n.forced_bos_token_id=t.model.convert_tokens_to_ids([t.lang_to_token(o)])[0],t._call(e,r)}class p1 extends Pe{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return Yo(this,e,r,n)}}class h1 extends Pe{constructor(e,r){super(e,r),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)).map(n=>n.slice(2,-2)),this.lang_to_token=n=>`__${n}__`}_build_translation_inputs(e,r,n){return Yo(this,e,r,n)}}const vm=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],Ba=new Map(vm),f1=new Map([...vm.map(([t,e])=>[e,t]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class m1 extends Pe{constructor(){super(...arguments);te(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}_decode_asr(r,{return_timestamps:n=!1,return_language:a=!1,time_precision:o=null,force_full_sequences:i=!0}={}){if(o===null)throw Error("Must specify time_precision");let d=null;const h=n==="word";function m(){return{language:d,timestamp:[null,null],text:""}}const c=[];let _=m(),v=0;const $=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let C=[],k=[],M=!1,z=null;const I=new Set(this.all_special_ids);for(const L of r){const Y=L.tokens,Q=h?L.token_timestamps:null;let ie=null,pe=$;if("stride"in L){const[fe,me,ge]=L.stride;if(v-=me,z=fe-ge,me&&(pe=me/o+$),ge)for(let X=Y.length-1;X>=0;--X){const U=Y[X];if(U>=$){if(ie!==null&&(U-$)*o=$){const ge=(me-$)*o+v,X=ia(ge,2);if(ie!==null&&me>=ie)M=!0;else if(M||C.length>0&&me0?(C.push(ce),h&&k.push(Se)):C.every(fe=>fe.length===0)&&(_=m(),C=[],ce=[],k=[],Se=[])}if(C.length>0){if(i&&n)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[L,Y]=this.findLongestCommonSequence(C,k),Q=this.decode(L);_.text=Q,h&&(_.words=this.collateWordTimestamps(L,Y,d)),c.push(_)}let j=Object.create(null);const R=c.map(L=>L.text).join("");if(n||a){for(let L=0;L0;let h=d?[]:null,m=d?n[0]:null;for(let c=1;cX===fe[U]).length,ge=me/L+Y;me>1&&ge>v&&(v=ge,$=[Q,ie,ce,Se])}const[k,M,z,I]=$,j=Math.floor((M+k)/2),R=Math.floor((I+z)/2);i.push(...a.slice(0,j)),a=_.slice(R),o=a.length,d&&(h.push(...m.slice(0,j)),m=n[c].slice(R))}return i.push(...a),d?(h.push(...m),[i,h]):[i,[]]}collateWordTimestamps(r,n,a){const[o,i,d]=this.combineTokensIntoWords(r,a),h=[];for(let m=0;m=o){const h=ia((Number(d)-o)*a,2);i.push(`<|${h}|>`),i.push([])}else i[i.length-1].push(d);return i=i.map(d=>typeof d=="string"?d:super.decode(d,n)),i.join("")}splitTokensOnUnicode(r){const n=this.decode(r,{decode_with_timestamps:!0}),a="�",o=[],i=[],d=[];let h=[],m=[],c=0;for(let _=0;_=this.model.tokens_to_ids.get("<|endoftext|>"),k=_.startsWith(" "),M=_.trim(),z=m.test(M);if(C||k||z||i.length===0)i.push(_),d.push(v),h.push($);else{const I=i.length-1;i[I]+=_,d[I].push(...v),h[I].push(...$)}}return[i,d,h]}mergePunctuations(r,n,a,o,i){const d=structuredClone(r),h=structuredClone(n),m=structuredClone(a);let c=d.length-2,_=d.length-1;for(;c>=0;)d[c].startsWith(" ")&&o.includes(d[c].trim())?(d[_]=d[c]+d[_],h[_]=ht(h[c],h[_]),m[_]=ht(m[c],m[_]),d[c]="",h[c]=[],m[c]=[]):_=c,--c;for(c=0,_=1;_v),h.filter(v=>v.length>0),m.filter(v=>v.length>0)]}get_decoder_prompt_ids({language:r=null,task:n=null,no_timestamps:a=!0}={}){const o=[];if(r){r=r.toLowerCase();let i=f1.get(r);if(i===void 0)if(Ba.has(r))i=r;else{const m=r.length===2?Ba.keys():Ba.values();throw new Error(`Language "${r}" is not supported. Must be one of: ${JSON.stringify(m)}`)}const d=this.model.tokens_to_ids.get(`<|${i}|>`);if(d===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);o.push(d)}else o.push(null);if(n){if(n=n.toLowerCase(),n!=="transcribe"&&n!=="translate")throw new Error(`Task "${n}" is not supported. Must be one of: ["transcribe", "translate"]`);const i=this.model.tokens_to_ids.get(`<|${n}|>`);if(i===void 0)throw new Error(`Unable to find task "${n}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);o.push(i)}else o.push(null);if(a){const i=this.model.tokens_to_ids.get("<|notimestamps|>");if(i===void 0)throw new Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');o.push(i)}return o.map((i,d)=>[d+1,i]).filter(i=>i[1]!==null)}}class g1 extends Pe{}class y1 extends Pe{}class _1 extends Pe{}class w1 extends Pe{constructor(e,r){super(e,r),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(n=>this.languageRegex.test(n)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;const[r,...n]=e.trim().split(this.languageRegex);if(n.length===0)return super._encode_text(r);if(n.length===2){const[a,o]=n;return this.supported_language_codes.includes(a)||console.warn(`Unsupported language code "${a}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),ht([a],super._encode_text(o))}}}class v1 extends Pe{}class bm extends Pe{constructor(){super(...arguments);te(this,"_default_chat_template","{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ ' ' }}{% endif %}{% endfor %}{{ eos_token }}")}}class b1 extends bm{}class $1 extends Pe{}class x1 extends Pe{}class C1 extends Pe{constructor(e,r){super(e,r),this.decoder=new Ib({})}}class S1 extends Pe{}class $m{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:o=!1,revision:i="main",legacy:d=null}={}){var v;const[h,m]=await um(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:o,revision:i,legacy:d}),c=((v=m.tokenizer_class)==null?void 0:v.replace(/Fast$/,""))??"PreTrainedTokenizer";let _=this.TOKENIZER_CLASS_MAPPING[c];return _||(console.warn(`Unknown tokenizer class "${c}", attempting to construct from base class.`),_=Pe),new _(h,m)}}te($m,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:Zb,DistilBertTokenizer:Kb,CamembertTokenizer:Yb,DebertaTokenizer:Wb,DebertaV2Tokenizer:Gb,BertTokenizer:Nb,HerbertTokenizer:Vb,ConvBertTokenizer:Hb,RoFormerTokenizer:qb,XLMTokenizer:Qb,ElectraTokenizer:Xb,MobileBertTokenizer:Ub,SqueezeBertTokenizer:Fb,AlbertTokenizer:Lb,GPT2Tokenizer:ym,BartTokenizer:Jb,MBartTokenizer:_m,MBart50Tokenizer:e1,RobertaTokenizer:t1,WhisperTokenizer:m1,CodeGenTokenizer:g1,CLIPTokenizer:y1,SiglipTokenizer:_1,MarianTokenizer:w1,BloomTokenizer:r1,NllbTokenizer:p1,M2M100Tokenizer:h1,LlamaTokenizer:wm,CodeLlamaTokenizer:n1,XLMRobertaTokenizer:i1,MPNetTokenizer:a1,FalconTokenizer:s1,GPTNeoXTokenizer:o1,EsmTokenizer:l1,Wav2Vec2CTCTokenizer:v1,BlenderbotTokenizer:bm,BlenderbotSmallTokenizer:b1,SpeechT5Tokenizer:$1,NougatTokenizer:x1,VitsTokenizer:C1,Qwen2Tokenizer:u1,GemmaTokenizer:d1,Grok1Tokenizer:c1,CohereTokenizer:S1,PreTrainedTokenizer:Pe});async function E1(t,e){return await Br(t,"config.json",!0,e)}function Qo(t){const e={};let r={};switch(t.model_type){case"llava":r=Qo(t.text_config);break;case"musicgen":r=Qo(t.decoder);break;case"gpt2":case"gptj":case"codegen":case"gpt_bigcode":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"phi":case"phi3":case"falcon":e.num_heads="num_attention_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size";break;case"llama":case"mistral":case"starcoder2":case"qwen2":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size",e.num_attention_heads="num_attention_heads";break;case"openelm":e.num_heads="num_kv_heads",e.num_layers="num_transformer_layers",e.dim_kv="head_dim";break;case"gpt_neo":e.num_heads="num_heads",e.num_layers="num_layers",e.hidden_size="hidden_size";break;case"bloom":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="hidden_size";break;case"mpt":e.num_heads="n_heads",e.num_layers="n_layers",e.hidden_size="d_model";break;case"t5":case"mt5":case"longt5":e.num_decoder_layers="num_decoder_layers",e.num_decoder_heads="num_heads",e.decoder_dim_kv="d_kv",e.num_encoder_layers="num_layers",e.num_encoder_heads="num_heads",e.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="d_model",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="d_model";break;case"speecht5":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="hidden_size",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="hidden_size";break;case"trocr":e.num_encoder_layers=e.num_decoder_layers="decoder_layers",e.num_encoder_heads=e.num_decoder_heads="decoder_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="d_model";break;case"musicgen_decoder":e.num_encoder_layers=e.num_decoder_layers="num_hidden_layers",e.num_encoder_heads=e.num_decoder_heads="num_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="hidden_size";break}const n={...r,model_type:t.model_type,multi_query:t.multi_query,is_encoder_decoder:t.is_encoder_decoder};for(const a in e)n[a]=t[e[a]];return n}function xm(t,{prefix:e="past_key_values",encoder_add_pkv:r=!0}={}){const n={},a=t.normalized_config,o=1;if(a.is_encoder_decoder&&r){const i=a.encoder_dim_kv??a.encoder_hidden_size/a.num_encoder_heads,d=a.decoder_dim_kv??a.decoder_hidden_size/a.num_decoder_heads,h=[o,a.num_encoder_heads,0,i],m=[o,a.num_decoder_heads,0,d];for(let c=0;c1 to use the classifier free guidance processor, got guidance scale ${e}.`);this.guidance_scale=e}_call(e,r){if(r.dims[0]!==2*e.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. Got batch size ${r.dims[0]} for the logits and ${e.length} for the input ids.`);const n=e.length,a=r.slice([0,n],null),o=r.slice([n,r.dims[0]],null);for(let i=0;i1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${e}`);if(!Number.isInteger(n)||n<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${n}`);this.top_p=e,this.filter_value=r,this.min_tokens_to_keep=n}}class U1 extends Xo{constructor(e,{filter_value:r=-1/0,min_tokens_to_keep:n=1}={}){if(super(),!Number.isInteger(e)||e<0)throw new Error(`\`top_k\` must be a positive integer, but is ${e}`);this.top_k=Math.max(e,n),this.filter_value=r}}class F1{constructor(e){te(this,"max_length",20);te(this,"max_new_tokens",null);te(this,"min_length",0);te(this,"min_new_tokens",null);te(this,"early_stopping",!1);te(this,"max_time",null);te(this,"do_sample",!1);te(this,"num_beams",1);te(this,"num_beam_groups",1);te(this,"penalty_alpha",null);te(this,"use_cache",!0);te(this,"temperature",1);te(this,"top_k",50);te(this,"top_p",1);te(this,"typical_p",1);te(this,"epsilon_cutoff",0);te(this,"eta_cutoff",0);te(this,"diversity_penalty",0);te(this,"repetition_penalty",1);te(this,"encoder_repetition_penalty",1);te(this,"length_penalty",1);te(this,"no_repeat_ngram_size",0);te(this,"bad_words_ids",null);te(this,"force_words_ids",null);te(this,"renormalize_logits",!1);te(this,"constraints",null);te(this,"forced_bos_token_id",null);te(this,"forced_eos_token_id",null);te(this,"remove_invalid_values",!1);te(this,"exponential_decay_length_penalty",null);te(this,"suppress_tokens",null);te(this,"begin_suppress_tokens",null);te(this,"forced_decoder_ids",null);te(this,"guidance_scale",null);te(this,"num_return_sequences",1);te(this,"output_attentions",!1);te(this,"output_hidden_states",!1);te(this,"output_scores",!1);te(this,"return_dict_in_generate",!1);te(this,"pad_token_id",null);te(this,"bos_token_id",null);te(this,"eos_token_id",null);te(this,"encoder_no_repeat_ngram_size",0);te(this,"decoder_start_token_id",null);te(this,"generation_kwargs",{});Object.assign(this,dn(e,Object.getOwnPropertyNames(this)))}}class Na extends Ot{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class Zo extends Ot{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof Zo?e=e.criteria:e instanceof Na&&(e=[e]),this.criteria.push(...e)}_call(e,r){const n=new Array(e.length).fill(!1);for(const a of this.criteria){const o=a(e,r);for(let i=0;ir.length>=this.max_length)}}class G1 extends Na{constructor(e){super(),Array.isArray(e)||(e=[e]),this.eos_token_id=e}_call(e,r){return e.map(n=>{const a=n.at(-1);return this.eos_token_id.some(o=>a==o)})}}class La extends Ot{constructor(e){super(),this.generation_config=e}_call(e,r=-1){return this.sample(e,r)}sample(e,r){throw Error("sample should be implemented in subclasses.")}getLogits(e,r){let n=e.dims.at(-1),a=e.data;if(r===-1)a=a.slice(-n);else{let o=r*n;a=a.slice(o,o+n)}return a}randomSelect(e){let r=e.reduce((a,o)=>a+o,0),n=Math.random()*r;for(let a=0;a1)return new q1(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new V1(e)}}class V1 extends La{sample(e,r=-1){let n=this.getLogits(e,r);return[[Cs(n)[1],0]]}}class H1 extends La{sample(e,r=-1){let n=e.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(e,r),o=$l(a,n),i=bl(o.map(d=>d[1]));return Array.from({length:this.generation_config.num_beams},()=>{const d=this.randomSelect(i);return[o[d][0],Math.log(i[d])]})}}class q1 extends La{sample(e,r=-1){let n=e.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(e,r),o=$l(a,n),i=bl(o.map(d=>d[1]));return Array.from({length:this.generation_config.num_beams},(d,h)=>[o[h][0],Math.log(i[h])])}}const Ae={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},Ua=new Map,km=new Map,si=new Map;async function K1(t,e,r){let n=r.device;n&&typeof n!="string"&&(n.hasOwnProperty(e)?n=n[e]:(console.warn(`Device not specified for ${e}. Using the default device.`),n=null));const a=lv(n);let o=r.dtype;if(typeof o!="string"&&(o&&o.hasOwnProperty(e)?o=o[e]:(o=I1[a[0]],console.warn(`Dtype not specified for ${e}. Using the default dtype: ${o}.`))),Sm.hasOwnProperty(o)){if(o===jt.fp16&&!await T1())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${o}. Should be one of: ${Object.keys(jt).join(", ")}`);const i=Sm[o],d=`${r.subfolder??""}/${e}${i}.onnx`,h={...r.session_options};h.executionProviders??(h.executionProviders=a);const m=ra(t,d,!0,r);let c=[];if(r.use_external_data_format){if(Rr.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const v=`${e}${i}.onnx_data`,$=`${r.subfolder??""}/${v}`;c.push(new Promise(async(C,k)=>{const M=await ra(t,$,!0,r);C({path:v,data:M})}))}else h.externalData!==void 0&&(c=h.externalData.map(async v=>{if(typeof v.data=="string"){const $=await ra(t,v.data,!0,r);return{...v,data:$}}return v}));if(c.length>0&&(h.externalData=await Promise.all(c)),n==="webgpu"){const v=xm(r.config,{prefix:"present"}),$={};for(const C in v)$[C]="gpu-buffer";h.preferredOutputLocation=$}return{buffer:await m,session_options:h}}async function Qr(t,e,r){const n=Object.keys(e),a=await Promise.all(n.map(async i=>K1(t,e[i],r))),o={};for(let i=0;i0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${n.join(", ")}.`);const a=Object.keys(e).length,o=t.inputNames.length;if(a>o){let i=Object.keys(e).filter(d=>!t.inputNames.includes(d));console.warn(`WARNING: Too many inputs were provided (${a} > ${o}). The following inputs will be ignored: "${i.join(", ")}".`)}return r}async function Mr(t,e){const r=Y1(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([o,i])=>[o,i.ort_tensor]));let a=await t.run(n);return a=Tm(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function Tm(t){for(let e in t)Xf(t[e])?t[e]=new je(t[e]):typeof t[e]=="object"&&Tm(t[e]);return t}function Q1(t){if(t instanceof je)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[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 je("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new je("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function Im(t){return new je("bool",[t],[1])}async function Am(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const h=dn(e,t.sessions.model.inputNames);r=(await oi(t,h)).last_hidden_state}const{input_ids:a,decoder_input_ids:o,...i}=e;return i.input_ids=o,i.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(i.encoder_attention_mask=e.attention_mask),await Jo(t,i,!0)}async function oi(t,e){const r=t.sessions.model,n=Object.create(null);for(const a of r.inputNames)n[a]=e[a];return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new je("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await Mr(r,n)}async function Jo(t,e,r=!1){const n=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:a,...o}=e;n.inputNames.includes("use_cache_branch")&&(o.use_cache_branch=Im(!!a)),t.addPastKeyValues(o,a);const i=dn(o,n.inputNames);return await Mr(n,i)}function X1(t,e,r,n){if(t.sessions.model.inputNames.includes("position_ids")&&r.attention_mask&&!r.position_ids){const[o,i]=r.attention_mask.dims,d=new BigInt64Array(r.attention_mask.data.length);for(let h=0;h[i.at(-1)])),a.decoder_input_ids=Q1(e),a}class ue extends Ot{constructor(r,n){super();te(this,"main_input_name","input_ids");te(this,"forward_params",["input_ids","attention_mask"]);this.config=r,this.sessions=n;const a=si.get(this.constructor),o=Ua.get(a);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,o===Ae.DecoderOnly?(this.can_generate=!0,this._forward=Jo,this._prepare_inputs_for_generation=X1):o===Ae.Seq2Seq||o===Ae.Vision2Seq||o===Ae.Musicgen?(this.can_generate=!0,this._forward=Am,this._prepare_inputs_for_generation=Z1):o===Ae.EncoderDecoder?this._forward=Am:o===Ae.ImageTextToText?(this.can_generate=!0,console.warn("TODO: Implement visionDecoderForward")):this._forward=oi,this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var n;const r=[];for(const a of Object.values(this.sessions))(n=a==null?void 0:a.handler)!=null&&n.dispose&&r.push(a.handler.dispose());return await Promise.all(r)}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:o=null,local_files_only:i=!1,revision:d="main",model_file_name:h=null,subfolder:m="onnx",device:c=null,dtype:_=null,use_external_data_format:v=null,session_options:$={}}={}){let C={progress_callback:n,config:a,cache_dir:o,local_files_only:i,revision:d,model_file_name:h,subfolder:m,device:c,dtype:_,use_external_data_format:v,session_options:$};const k=si.get(this),M=Ua.get(k),z=await Cm.from_pretrained(r,C);C.config??(C.config=z);let I;return M===Ae.DecoderOnly?I=await Promise.all([Qr(r,{model:C.model_file_name??"model"},C),Br(r,"generation_config.json",!1,C)]):M===Ae.Seq2Seq||M===Ae.Vision2Seq?I=await Promise.all([Qr(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},C),Br(r,"generation_config.json",!1,C)]):M===Ae.MaskGeneration?I=await Promise.all([Qr(r,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},C)]):M===Ae.EncoderDecoder?I=await Promise.all([Qr(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},C)]):M===Ae.ImageTextToText?I=await Promise.all([Qr(r,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},C),Br(r,"generation_config.json",!1,C)]):M===Ae.Musicgen?I=await Promise.all([Qr(r,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},C),Br(r,"generation_config.json",!1,C)]):(M!==Ae.EncoderOnly&&console.warn(`Model type for '${k??(a==null?void 0:a.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),I=await Promise.all([Qr(r,{model:C.model_file_name??"model"},C)])),new this(z,...I)}async _call(r){return await this.forward(r)}async forward(r){return await this._forward(this,r)}_get_logits_warper(r){const n=new Em;return r.temperature!==null&&r.temperature!==1&&n.push(new N1(r.temperature)),r.top_k!==null&&r.top_k!==0&&n.push(new U1(r.top_k)),r.top_p!==null&&r.top_p<1&&n.push(new L1(r.top_p)),n}_get_logits_processor(r,n,a=null){const o=new Em;if(r.repetition_penalty!==null&&r.repetition_penalty!==1&&o.push(new P1(r.repetition_penalty)),r.no_repeat_ngram_size!==null&&r.no_repeat_ngram_size>0&&o.push(new z1(r.no_repeat_ngram_size)),r.bad_words_ids!==null&&o.push(new B1(r.bad_words_ids,r.eos_token_id)),r.min_length!==null&&r.eos_token_id!==null&&r.min_length>0&&o.push(new j1(r.min_length,r.eos_token_id)),r.min_new_tokens!==null&&r.eos_token_id!==null&&r.min_new_tokens>0&&o.push(new R1(n,r.min_new_tokens,r.eos_token_id)),r.forced_bos_token_id!==null&&o.push(new A1(r.forced_bos_token_id)),r.forced_eos_token_id!==null&&o.push(new M1(r.max_length,r.forced_eos_token_id)),r.begin_suppress_tokens!==null){let i=n>1||r.forced_bos_token_id===null?n:n+1;r.forced_decoder_ids!==null&&(i+=r.forced_decoder_ids[r.forced_decoder_ids.length-1][0]),o.push(new O1(r.begin_suppress_tokens,i))}return r.guidance_scale!==null&&r.guidance_scale>1&&o.push(new D1(r.guidance_scale)),a!==null&&o.extend(a),o}_prepare_generation_config(r,n){const a=new F1(this.config);return"generation_config"in this&&Object.assign(a,this.generation_config),r&&Object.assign(a,r),n&&Object.assign(a,dn(n,Object.getOwnPropertyNames(a))),a}_get_stopping_criteria(r,n=null){const a=new Zo;return r.max_length!==null&&a.push(new W1(r.max_length,this.config.max_position_embeddings??null)),r.eos_token_id!==null&&a.push(new G1(r.eos_token_id)),n&&a.extend(n),a}_validate_model_class(){if(!this.can_generate){const r=[al,Ag,Ig,Tg],n=si.get(this.constructor),a=new Set,o=this.config.model_type;for(const d of r){const h=d.get(o);h&&a.add(h[0])}let i=`The current model class (${n}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw a.size>0&&(i+=` Please use the following class instead: ${[...a].join(", ")}`),Error(i)}}prepare_inputs_for_generation(...r){return this._prepare_inputs_for_generation(this,...r)}_update_model_kwargs_for_generation({generated_input_ids:r,outputs:n,model_inputs:a,is_encoder_decoder:o}){return a.past_key_values=this.getPastKeyValues(n,a.past_key_values),a.input_ids=new je("int64",r.flat(),[r.length,1]),o||(a.attention_mask=_r([a.attention_mask,Ma([a.attention_mask.dims[0],1])],1)),a.position_ids=null,a}_prepare_model_inputs({inputs:r,bos_token_id:n,model_kwargs:a}){const o=dn(a,this.forward_params),i=this.main_input_name;if(i in o){if(r)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else o[i]=r;return{inputs_tensor:o[i],model_inputs:o,model_input_name:i}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:r,model_inputs:n,model_input_name:a,generation_config:o}){const i=dn(n,this.sessions.model.inputNames);let{last_hidden_state:d}=await oi(this,i);return o.guidance_scale!==null&&o.guidance_scale>1&&(d=_r([d,yv(d,0)],0),"attention_mask"in n&&(n.attention_mask=_r([n.attention_mask,vv(n.attention_mask)],0))),n.encoder_outputs=d,n}_prepare_decoder_input_ids_for_generation({batch_size:r,model_input_name:n,model_kwargs:a,decoder_start_token_id:o,bos_token_id:i,generation_config:d}){o=o??i;let h;if(this.config.model_type==="musicgen")h=new Array(r*this.config.decoder.num_codebooks).fill(o);else if(Array.isArray(o)){if(o.length!==r)throw new Error(`\`decoder_start_token_id\` expcted to have length ${r} but got ${o.length}`);h=o}else h=new Array(r).fill(o);const c=new je("int64",h,[h.length,1]);return a.decoder_attention_mask=_v(c),{input_ids:c,model_inputs:a}}async generate({inputs:r=null,generation_config:n=null,logits_processor:a=null,stopping_criteria:o=null,streamer:i=null,...d}){this._validate_model_class(),n=this._prepare_generation_config(n,d);let{inputs_tensor:h,model_inputs:m,model_input_name:c}=this._prepare_model_inputs({inputs:r,model_kwargs:d});const _=this.config.is_encoder_decoder;_&&("encoder_outputs"in m||(m=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:h,model_inputs:m,model_input_name:c,generation_config:n})));let v;_?{input_ids:v,model_inputs:m}=this._prepare_decoder_input_ids_for_generation({batch_size:m[c].dims.at(0),model_input_name:c,model_kwargs:m,decoder_start_token_id:n.decoder_start_token_id,bos_token_id:n.bos_token_id,generation_config:n}):v=m[c];let $=v.dims.at(-1);n.max_new_tokens!==null&&(n.max_length=$+n.max_new_tokens);const C=this._get_logits_processor(n,$,a),k=this._get_stopping_criteria(n,o),M=m[c].dims.at(0),z=La.getSampler(n),I=new Array(M).fill(0),j=v.tolist();for(i&&i.put(j);;){m=this.prepare_inputs_for_generation(j,m,n);const R=await this.forward(m),L=R.logits.slice(null,-1,null),Y=C(j,L),Q=[];for(let pe=0;pepe))break;m=this._update_model_kwargs_for_generation({generated_input_ids:Q,outputs:R,model_inputs:m,is_encoder_decoder:_})}return i&&i.end(),new je("int64",j.flat(),[j.length,j[0].length])}addAttentionsToBeam(r,n){if(this.config.is_encoder_decoder){if(!n.cross_attentions||n.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");r.cross_attentions||(r.cross_attentions=[]),r.cross_attentions.push(n.cross_attentions)}if(!n.decoder_attentions||n.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");r.decoder_attentions||(r.decoder_attentions=[]),r.decoder_attentions.push(n.decoder_attentions)}groupBeams(r){const n=Object.create(null);for(const a of r)n[a.id]===void 0?n[a.id]=[a]:n[a.id].push(a);return Object.values(n)}getPastKeyValues(r,n){const a=Object.create(null);for(const o in r)if(o.startsWith("present")){let i=o.replace("present","past_key_values");if(n&&o.includes("encoder"))a[i]=n[i];else{if(n){const d=n[i];d.location==="gpu-buffer"&&d.dispose()}a[i]=r[o]}}return a}getAttentions(r){const n=Object.create(null);for(const a of["cross_attentions","decoder_attentions"]){const o=[];for(const i in r)if(i.startsWith(a)){const d=i.split(".").pop();o[d]=r[i]}n[a]=o}return n}addPastKeyValues(r,n){if(n)Object.assign(r,n);else{const a=this.custom_config.kv_cache_dtype??"float32",o=a==="float16"?new Uint16Array:[],i=xm(this.config,{encoder_add_pkv:this.add_encoder_pkv??!0});for(const d in i)r[d]=new je(a,o,i[d])}}}class Wt{}class li extends ue{}class J1 extends li{}class e$ extends li{async _call(e){return new wt(await super._call(e))}}class t$ extends li{async _call(e){return new Fe(await super._call(e))}}class r$ extends li{async _call(e){return new _t(await super._call(e))}}class n$ extends li{async _call(e){return new St(await super._call(e))}}class i$ extends ue{}class a$ extends i${}class ui extends ue{}class s$ extends ui{}class o$ extends ui{async _call(e){return new wt(await super._call(e))}}class l$ extends ui{async _call(e){return new Fe(await super._call(e))}}class u$ extends ui{async _call(e){return new _t(await super._call(e))}}class d$ extends ui{async _call(e){return new St(await super._call(e))}}class di extends ue{}class c$ extends di{}class p$ extends di{async _call(e){return new wt(await super._call(e))}}class h$ extends di{async _call(e){return new Fe(await super._call(e))}}class f$ extends di{async _call(e){return new _t(await super._call(e))}}class m$ extends di{async _call(e){return new St(await super._call(e))}}class ci extends ue{}class g$ extends ci{}class y$ extends ci{async _call(e){return new wt(await super._call(e))}}class _$ extends ci{async _call(e){return new Fe(await super._call(e))}}class w$ extends ci{async _call(e){return new _t(await super._call(e))}}class v$ extends ci{async _call(e){return new St(await super._call(e))}}class pi extends ue{}class b$ extends pi{}class $$ extends pi{async _call(e){return new wt(await super._call(e))}}class x$ extends pi{async _call(e){return new Fe(await super._call(e))}}class C$ extends pi{async _call(e){return new _t(await super._call(e))}}class S$ extends pi{async _call(e){return new St(await super._call(e))}}class hi extends ue{}class E$ extends hi{}class k$ extends hi{async _call(e){return new wt(await super._call(e))}}class T$ extends hi{async _call(e){return new Fe(await super._call(e))}}class I$ extends hi{async _call(e){return new _t(await super._call(e))}}class A$ extends hi{async _call(e){return new St(await super._call(e))}}class fi extends ue{}class M$ extends fi{}class O$ extends fi{async _call(e){return new wt(await super._call(e))}}class z$ extends fi{async _call(e){return new Fe(await super._call(e))}}class P$ extends fi{async _call(e){return new _t(await super._call(e))}}class j$ extends fi{async _call(e){return new St(await super._call(e))}}class mi extends ue{}class R$ extends mi{}class B$ extends mi{async _call(e){return new Fe(await super._call(e))}}class D$ extends mi{async _call(e){return new _t(await super._call(e))}}class N$ extends mi{async _call(e){return new St(await super._call(e))}}class L$ extends mi{async _call(e){return new wt(await super._call(e))}}class Fa extends ue{}class U$ extends Fa{}class F$ extends Fa{async _call(e){return new wt(await super._call(e))}}class W$ extends Fa{async _call(e){return new Fe(await super._call(e))}}class G$ extends Fa{async _call(e){return new _t(await super._call(e))}}class Wa extends ue{}class V$ extends Wa{}class H$ extends Wa{async _call(e){return new wt(await super._call(e))}}class q$ extends Wa{async _call(e){return new Fe(await super._call(e))}}class K$ extends Wa{async _call(e){return new St(await super._call(e))}}class gi extends ue{}class Y$ extends gi{}class Q$ extends gi{async _call(e){return new wt(await super._call(e))}}class X$ extends gi{async _call(e){return new Fe(await super._call(e))}}class Z$ extends gi{async _call(e){return new _t(await super._call(e))}}class J$ extends gi{async _call(e){return new St(await super._call(e))}}class Ga extends ue{}class e2 extends Ga{}class t2 extends Ga{async _call(e){return new wt(await super._call(e))}}class r2 extends Ga{async _call(e){return new Fe(await super._call(e))}}class n2 extends Ga{async _call(e){return new St(await super._call(e))}}class Va extends ue{}class i2 extends Va{}class a2 extends Va{async _call(e){return new Fe(await super._call(e))}}class s2 extends Va{async _call(e){return new St(await super._call(e))}}class o2 extends Va{async _call(e){return new wt(await super._call(e))}}class Mm extends ue{constructor(r,n,a){super(r,n);te(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class l2 extends Mm{}class u2 extends Mm{}class Om extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class d2 extends Om{}class c2 extends Om{}class zm extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class p2 extends zm{}class h2 extends zm{}class el extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class f2 extends el{}class m2 extends el{}class g2 extends el{async _call(e){return new Fe(await super._call(e))}}class Ha extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class y2 extends Ha{}class _2 extends Ha{}class w2 extends Ha{async _call(e){return new Fe(await super._call(e))}}class v2 extends Ha{}class Pm extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class b2 extends Pm{}class $2 extends Pm{}class jm extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class x2 extends jm{}class C2 extends jm{}class yi extends ue{}class S2 extends yi{}class E2 extends yi{async _call(e){return new wt(await super._call(e))}}class k2 extends yi{async _call(e){return new Fe(await super._call(e))}}class T2 extends yi{async _call(e){return new _t(await super._call(e))}}class I2 extends yi{async _call(e){return new St(await super._call(e))}}class _i extends ue{}class A2 extends _i{}class M2 extends _i{async _call(e){return new wt(await super._call(e))}}class O2 extends _i{async _call(e){return new Fe(await super._call(e))}}class z2 extends _i{async _call(e){return new _t(await super._call(e))}}class P2 extends _i{async _call(e){return new St(await super._call(e))}}class wi extends ue{}class j2 extends wi{}class R2 extends wi{async _call(e){return new wt(await super._call(e))}}class B2 extends wi{async _call(e){return new Fe(await super._call(e))}}class D2 extends wi{async _call(e){return new _t(await super._call(e))}}class N2 extends wi{async _call(e){return new St(await super._call(e))}}class Rm extends ue{}class L2 extends Rm{}class U2 extends Rm{}class Bm extends ue{constructor(r,n,a){super(r,n);te(this,"requires_attention_mask",!1);te(this,"main_input_name","input_features");te(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class F2 extends Bm{}class W2 extends Bm{_retrieve_init_tokens(e){throw e.decoder_start_token_id,new Error("Not implemented yet")}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,language:o=null,task:i=null,...d}){throw new Error("WhisperForConditionalGeneration.generate is not yet in Transformers.js v3.")}_extract_token_timestamps(e,r,n=null,a=.02){if(!e.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`.");let o=this.config.median_filter_width;o===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),o=7);const i=e.cross_attentions.map(m=>{let c=Array.from({length:this.config.decoder_layers},(M,z)=>_r(m.map(I=>I[z]),2)),_=Fo(r.map(([M,z])=>n?c[M].slice(null,z,null,[0,n]):c[M].slice(null,z)));_=_.transpose(1,0,2,3);let[v,$]=hv(_,-2,0,!0),C=_.clone();for(let M=0;M_[z+1]-_[z]),C=ht([1],$).map(M=>!!M),k=[];for(let M=0;Mv.findIndex($=>$==o)),h=d.every(v=>v===-1),m=d.every(v=>v!==-1);if(!h&&!m)throw new Error("Every input should contain either 0 or 1 image token.");if(h)return{inputs_embeds:e,attention_mask:a,position_ids:null};let c=[],_=[];for(let v=0;vo*i,1);e.input_labels=new je("int64",new BigInt64Array(a).fill(1n),n)}const r={image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings};return e.input_points&&(r.input_points=e.input_points),e.input_labels&&(r.input_labels=e.input_labels),e.input_boxes&&(r.input_boxes=e.input_boxes),await Mr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new xC(await super._call(e))}}class xC extends Wt{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class yg extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class CC extends yg{}class SC extends yg{}class _g extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class EC extends _g{}class kC extends _g{}class Xr extends ue{}class TC extends Xr{}class IC extends Xr{async _call(e){return new wn(await super._call(e))}}class AC extends Xr{async _call(e){return new Fe(await super._call(e))}}class MC extends Xr{async _call(e){return new _t(await super._call(e))}}class rl extends ue{}class OC extends rl{}class zC extends rl{async _call(e){return new wn(await super._call(e))}}class PC extends rl{async _call(e){return new Fe(await super._call(e))}}class Ka extends ue{}class jC extends Ka{}class RC extends Ka{async _call(e){return new wn(await super._call(e))}}class BC extends Ka{async _call(e){return new Fe(await super._call(e))}}class DC extends Ka{async _call(e){return new _t(await super._call(e))}}class nl extends ue{}class NC extends nl{}class LC extends nl{async _call(e){return new wn(await super._call(e))}}class UC extends nl{async _call(e){return new Fe(await super._call(e))}}class FC extends Xr{}class WC extends Xr{async _call(e){return new wn(await super._call(e))}}class GC extends Xr{async _call(e){return new Fe(await super._call(e))}}class vi extends ue{}class VC extends vi{}class HC extends vi{async _call(e){return new wn(await super._call(e))}}class qC extends vi{async _call(e){return new Fe(await super._call(e))}}class KC extends vi{async _call(e){return new FS(await super._call(e))}}class YC extends vi{async _call(e){return new _t(await super._call(e))}}class wg extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class QC extends wg{}class XC extends wg{async generate_speech(e,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:o=20,vocoder:i=null}={}){const d={input_ids:e},{encoder_outputs:h,encoder_attention_mask:m}=await oi(this,d),c=h.dims[1]/this.config.reduction_factor,_=Math.floor(c*o),v=Math.floor(c*a),$=this.config.num_mel_bins;let C=[],k=null,M=null,z=0;for(;;){++z;const R=Im(!!M);let L;M?L=M.output_sequence_out:L=new je("float32",new Float32Array($),[1,1,$]);let Y={use_cache_branch:R,output_sequence:L,encoder_attention_mask:m,speaker_embeddings:r,encoder_hidden_states:h};this.addPastKeyValues(Y,k),M=await Mr(this.sessions.decoder_model_merged,Y),k=this.getPastKeyValues(M,k);const{prob:Q,spectrum:ie}=M;if(C.push(ie),z>=v&&(Array.from(Q.data).filter(pe=>pe>=n).length>0||z>=_))break}const I=_r(C),{waveform:j}=await Mr(i.sessions.model,{spectrogram:I});return{spectrogram:I,waveform:j}}}class ZC extends ue{constructor(){super(...arguments);te(this,"main_input_name","spectrogram")}}class JC extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class eS extends JC{}class vg extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class tS extends vg{}class rS extends vg{}class bg extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class nS extends bg{}class iS extends bg{}class $g extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class aS extends $g{}class sS extends $g{}class il extends ue{}class oS extends il{}class lS extends il{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class uS extends il{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="audio_model"),super.from_pretrained(e,r)}}class dS extends ue{}class xg extends dS{async _call(e){return new GS(await super._call(e))}}class Cg extends ue{}class cS extends Cg{}class pS extends Cg{}class Sg extends ue{constructor(e,r,n){super(e,r),this.generation_config=n}}class hS extends Sg{}class fS extends Sg{}class Eg extends ue{}class mS extends Eg{}class gS extends Eg{async _call(e){return new Fe(await super._call(e))}}class kg extends ue{constructor(r,n,a){super(r,n);te(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}_apply_and_filter_by_delay_pattern_mask(r){const[n,a]=r.dims,o=this.config.decoder.num_codebooks,i=a-o;let d=0;for(let c=0;c0&&$<=i&&(r.data[d++]=r.data[c])}const h=Math.floor(n/o),m=d/(h*o);return new je(r.type,r.data.slice(0,d),[h,o,m])}prepare_inputs_for_generation(r,n,a){let o=structuredClone(r);for(let d=0;d=h&&(o[d][h]=BigInt(this.config.decoder.pad_token_id));return a.guidance_scale!==null&&a.guidance_scale>1&&(o=o.concat(o)),super.prepare_inputs_for_generation(o,n,a)}async generate(r){const n=await super.generate(r),a=this._apply_and_filter_by_delay_pattern_mask(n).unsqueeze_(0),{audio_values:o}=await Mr(this.sessions.encodec_decode,{audio_codes:a});return o}}class Ya{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:o=!1,revision:i="main",model_file_name:d=null,subfolder:h="onnx",device:m=null,dtype:c=null,use_external_data_format:_=null,session_options:v={}}={}){let $={progress_callback:r,config:n,cache_dir:a,local_files_only:o,revision:i,model_file_name:d,subfolder:h,device:m,dtype:c,use_external_data_format:_,session_options:v};if(n=await Cm.from_pretrained(e,$),$.config||($.config=n),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let C of this.MODEL_CLASS_MAPPINGS){const k=C.get(n.model_type);if(k)return await k[1].from_pretrained(e,$)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${n.model_type}", attempting to construct from base class.`),await ue.from_pretrained(e,$);throw Error(`Unsupported model type: ${n.model_type}`)}}te(Ya,"MODEL_CLASS_MAPPINGS",null),te(Ya,"BASE_IF_FAIL",!1);const yS=new Map([["bert",["BertModel",J1]],["nomic_bert",["NomicBertModel",a$]],["roformer",["RoFormerModel",s$]],["electra",["ElectraModel",g$]],["esm",["EsmModel",U$]],["convbert",["ConvBertModel",c$]],["camembert",["CamembertModel",b$]],["deberta",["DebertaModel",E$]],["deberta-v2",["DebertaV2Model",M$]],["mpnet",["MPNetModel",Y$]],["albert",["AlbertModel",i2]],["distilbert",["DistilBertModel",R$]],["roberta",["RobertaModel",S2]],["xlm",["XLMModel",A2]],["xlm-roberta",["XLMRobertaModel",j2]],["clap",["ClapModel",oS]],["clip",["CLIPModel",q2]],["clipseg",["CLIPSegModel",tx]],["chinese_clip",["ChineseCLIPModel",ex]],["siglip",["SiglipModel",Q2]],["mobilebert",["MobileBertModel",V$]],["squeezebert",["SqueezeBertModel",e2]],["wav2vec2",["Wav2Vec2Model",TC]],["wav2vec2-bert",["Wav2Vec2BertModel",NC]],["unispeech",["UniSpeechModel",OC]],["unispeech-sat",["UniSpeechSatModel",jC]],["hubert",["HubertModel",FC]],["wavlm",["WavLMModel",VC]],["audio-spectrogram-transformer",["ASTModel",L2]],["vits",["VitsModel",xg]],["detr",["DetrModel",Wx]],["table-transformer",["TableTransformerModel",qx]],["vit",["ViTModel",Mx]],["mobilevit",["MobileViTModel",jx]],["owlvit",["OwlViTModel",Bx]],["owlv2",["Owlv2Model",Nx]],["beit",["BeitModel",Ux]],["deit",["DeiTModel",Qx]],["convnext",["ConvNextModel",pC]],["convnextv2",["ConvNextV2Model",fC]],["dinov2",["Dinov2Model",gC]],["resnet",["ResNetModel",Zx]],["swin",["SwinModel",eC]],["swin2sr",["Swin2SRModel",rC]],["donut-swin",["DonutSwinModel",cC]],["yolos",["YolosModel",_C]],["dpt",["DPTModel",iC]],["glpn",["GLPNModel",lC]],["hifigan",["SpeechT5HifiGan",ZC]],["efficientnet",["EfficientNetModel",mS]]]),_S=new Map([["t5",["T5Model",l2]],["longt5",["LongT5Model",d2]],["mt5",["MT5Model",p2]],["bart",["BartModel",f2]],["mbart",["MBartModel",y2]],["marian",["MarianModel",CC]],["whisper",["WhisperModel",F2]],["m2m_100",["M2M100Model",EC]],["blenderbot",["BlenderbotModel",b2]],["blenderbot-small",["BlenderbotSmallModel",x2]]]),wS=new Map([["bloom",["BloomModel",Sx]],["gpt2",["GPT2Model",nx]],["gptj",["GPTJModel",ux]],["gpt_bigcode",["GPTBigCodeModel",cx]],["gpt_neo",["GPTNeoModel",ax]],["gpt_neox",["GPTNeoXModel",ox]],["codegen",["CodeGenModel",hx]],["llama",["LlamaModel",mx]],["openelm",["OpenELMModel",yx]],["qwen2",["Qwen2Model",wx]],["phi",["PhiModel",bx]],["phi3",["Phi3Model",xx]],["mpt",["MptModel",kx]],["opt",["OPTModel",Ix]],["mistral",["MistralModel",tS]],["starcoder2",["Starcoder2Model",nS]],["falcon",["FalconModel",aS]],["stablelm",["StableLmModel",hS]]]),Tg=new Map([["speecht5",["SpeechT5ForSpeechToText",QC]],["whisper",["WhisperForConditionalGeneration",W2]]]),vS=new Map([["speecht5",["SpeechT5ForTextToSpeech",XC]]]),bS=new Map([["vits",["VitsModel",xg]],["musicgen",["MusicgenForConditionalGeneration",kg]]]),$S=new Map([["bert",["BertForSequenceClassification",t$]],["roformer",["RoFormerForSequenceClassification",l$]],["electra",["ElectraForSequenceClassification",_$]],["esm",["EsmForSequenceClassification",W$]],["convbert",["ConvBertForSequenceClassification",h$]],["camembert",["CamembertForSequenceClassification",x$]],["deberta",["DebertaForSequenceClassification",T$]],["deberta-v2",["DebertaV2ForSequenceClassification",z$]],["mpnet",["MPNetForSequenceClassification",X$]],["albert",["AlbertForSequenceClassification",a2]],["distilbert",["DistilBertForSequenceClassification",B$]],["roberta",["RobertaForSequenceClassification",k2]],["xlm",["XLMForSequenceClassification",O2]],["xlm-roberta",["XLMRobertaForSequenceClassification",B2]],["bart",["BartForSequenceClassification",g2]],["mbart",["MBartForSequenceClassification",w2]],["mobilebert",["MobileBertForSequenceClassification",q$]],["squeezebert",["SqueezeBertForSequenceClassification",r2]]]),xS=new Map([["bert",["BertForTokenClassification",r$]],["roformer",["RoFormerForTokenClassification",u$]],["electra",["ElectraForTokenClassification",w$]],["esm",["EsmForTokenClassification",G$]],["convbert",["ConvBertForTokenClassification",f$]],["camembert",["CamembertForTokenClassification",C$]],["deberta",["DebertaForTokenClassification",I$]],["deberta-v2",["DebertaV2ForTokenClassification",P$]],["mpnet",["MPNetForTokenClassification",Z$]],["distilbert",["DistilBertForTokenClassification",D$]],["roberta",["RobertaForTokenClassification",T2]],["xlm",["XLMForTokenClassification",z2]],["xlm-roberta",["XLMRobertaForTokenClassification",D2]]]),Ig=new Map([["t5",["T5ForConditionalGeneration",u2]],["longt5",["LongT5ForConditionalGeneration",c2]],["mt5",["MT5ForConditionalGeneration",h2]],["bart",["BartForConditionalGeneration",m2]],["mbart",["MBartForConditionalGeneration",_2]],["marian",["MarianMTModel",SC]],["m2m_100",["M2M100ForConditionalGeneration",kC]],["blenderbot",["BlenderbotForConditionalGeneration",$2]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",C2]]]),al=new Map([["bloom",["BloomForCausalLM",Ex]],["gpt2",["GPT2LMHeadModel",ix]],["gptj",["GPTJForCausalLM",dx]],["gpt_bigcode",["GPTBigCodeForCausalLM",px]],["gpt_neo",["GPTNeoForCausalLM",sx]],["gpt_neox",["GPTNeoXForCausalLM",lx]],["codegen",["CodeGenForCausalLM",fx]],["llama",["LlamaForCausalLM",gx]],["openelm",["OpenELMForCausalLM",_x]],["qwen2",["Qwen2ForCausalLM",vx]],["phi",["PhiForCausalLM",$x]],["phi3",["Phi3ForCausalLM",Cx]],["mpt",["MptForCausalLM",Tx]],["opt",["OPTForCausalLM",Ax]],["mbart",["MBartForCausalLM",v2]],["mistral",["MistralForCausalLM",rS]],["starcoder2",["Starcoder2ForCausalLM",iS]],["falcon",["FalconForCausalLM",sS]],["trocr",["TrOCRForCausalLM",eS]],["stablelm",["StableLmForCausalLM",fS]]]),CS=new Map([["bert",["BertForMaskedLM",e$]],["roformer",["RoFormerForMaskedLM",o$]],["electra",["ElectraForMaskedLM",y$]],["esm",["EsmForMaskedLM",F$]],["convbert",["ConvBertForMaskedLM",p$]],["camembert",["CamembertForMaskedLM",$$]],["deberta",["DebertaForMaskedLM",k$]],["deberta-v2",["DebertaV2ForMaskedLM",O$]],["mpnet",["MPNetForMaskedLM",Q$]],["albert",["AlbertForMaskedLM",o2]],["distilbert",["DistilBertForMaskedLM",L$]],["roberta",["RobertaForMaskedLM",E2]],["xlm",["XLMWithLMHeadModel",M2]],["xlm-roberta",["XLMRobertaForMaskedLM",R2]],["mobilebert",["MobileBertForMaskedLM",H$]],["squeezebert",["SqueezeBertForMaskedLM",t2]]]),SS=new Map([["bert",["BertForQuestionAnswering",n$]],["roformer",["RoFormerForQuestionAnswering",d$]],["electra",["ElectraForQuestionAnswering",v$]],["convbert",["ConvBertForQuestionAnswering",m$]],["camembert",["CamembertForQuestionAnswering",S$]],["deberta",["DebertaForQuestionAnswering",A$]],["deberta-v2",["DebertaV2ForQuestionAnswering",j$]],["mpnet",["MPNetForQuestionAnswering",J$]],["albert",["AlbertForQuestionAnswering",s2]],["distilbert",["DistilBertForQuestionAnswering",N$]],["roberta",["RobertaForQuestionAnswering",I2]],["xlm",["XLMForQuestionAnswering",P2]],["xlm-roberta",["XLMRobertaForQuestionAnswering",N2]],["mobilebert",["MobileBertForQuestionAnswering",K$]],["squeezebert",["SqueezeBertForQuestionAnswering",n2]]]),Ag=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",G2]]]),ES=new Map([["llava",["LlavaForConditionalGeneration",H2]]]),kS=new Map([["vit",["ViTForImageClassification",Ox]],["mobilevit",["MobileViTForImageClassification",Rx]],["beit",["BeitForImageClassification",Fx]],["deit",["DeiTForImageClassification",Xx]],["convnext",["ConvNextForImageClassification",hC]],["convnextv2",["ConvNextV2ForImageClassification",mC]],["dinov2",["Dinov2ForImageClassification",yC]],["resnet",["ResNetForImageClassification",Jx]],["swin",["SwinForImageClassification",tC]],["segformer",["SegformerForImageClassification",cS]],["efficientnet",["EfficientNetForImageClassification",gS]]]),TS=new Map([["detr",["DetrForObjectDetection",Gx]],["table-transformer",["TableTransformerForObjectDetection",Kx]],["yolos",["YolosForObjectDetection",wC]]]),IS=new Map([["owlvit",["OwlViTForObjectDetection",Dx]],["owlv2",["Owlv2ForObjectDetection",Lx]]]),AS=new Map([["detr",["DetrForSegmentation",Vx]],["clipseg",["CLIPSegForImageSegmentation",rx]]]),MS=new Map([["segformer",["SegformerForSemanticSegmentation",pS]]]),OS=new Map([["sam",["SamModel",$C]]]),zS=new Map([["wav2vec2",["Wav2Vec2ForCTC",IC]],["wav2vec2-bert",["Wav2Vec2BertForCTC",LC]],["unispeech",["UniSpeechForCTC",zC]],["unispeech-sat",["UniSpeechSatForCTC",RC]],["wavlm",["WavLMForCTC",HC]],["hubert",["HubertForCTC",WC]]]),PS=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",AC]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",UC]],["unispeech",["UniSpeechForSequenceClassification",PC]],["unispeech-sat",["UniSpeechSatForSequenceClassification",BC]],["wavlm",["WavLMForSequenceClassification",qC]],["hubert",["HubertForSequenceClassification",GC]],["audio-spectrogram-transformer",["ASTForAudioClassification",U2]]]),jS=new Map([["wavlm",["WavLMForXVector",KC]]]),RS=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",DC]],["wavlm",["WavLMForAudioFrameClassification",YC]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",MC]]]),BS=new Map([["vitmatte",["VitMatteForImageMatting",Px]]]),DS=new Map([["swin2sr",["Swin2SRForImageSuperResolution",nC]]]),NS=new Map([["dpt",["DPTForDepthEstimation",aC]],["depth_anything",["DepthAnythingForDepthEstimation",oC]],["glpn",["GLPNForDepthEstimation",uC]]]),LS=new Map([["clip",["CLIPVisionModelWithProjection",Y2]],["siglip",["SiglipVisionModel",Z2]]]),Mg=[[yS,Ae.EncoderOnly],[_S,Ae.EncoderDecoder],[wS,Ae.DecoderOnly],[$S,Ae.EncoderOnly],[xS,Ae.EncoderOnly],[Ig,Ae.Seq2Seq],[Tg,Ae.Seq2Seq],[al,Ae.DecoderOnly],[CS,Ae.EncoderOnly],[SS,Ae.EncoderOnly],[Ag,Ae.Vision2Seq],[ES,Ae.ImageTextToText],[kS,Ae.EncoderOnly],[AS,Ae.EncoderOnly],[MS,Ae.EncoderOnly],[BS,Ae.EncoderOnly],[DS,Ae.EncoderOnly],[NS,Ae.EncoderOnly],[TS,Ae.EncoderOnly],[IS,Ae.EncoderOnly],[OS,Ae.MaskGeneration],[zS,Ae.EncoderOnly],[PS,Ae.EncoderOnly],[vS,Ae.Seq2Seq],[bS,Ae.EncoderOnly],[jS,Ae.EncoderOnly],[RS,Ae.EncoderOnly],[LS,Ae.EncoderOnly]];for(const[t,e]of Mg)for(const[r,n]of t.values())Ua.set(r,e),si.set(n,r),km.set(r,n);const US=[["MusicgenForConditionalGeneration",kg,Ae.Musicgen],["CLIPTextModelWithProjection",K2,Ae.EncoderOnly],["SiglipTextModel",X2,Ae.EncoderOnly],["ClapTextModelWithProjection",lS,Ae.EncoderOnly],["ClapAudioModelWithProjection",uS,Ae.EncoderOnly]];for(const[t,e,r]of US)Ua.set(t,r),si.set(e,t),km.set(t,e);class Og extends Ya{}te(Og,"MODEL_CLASS_MAPPINGS",Mg.map(e=>e[0])),te(Og,"BASE_IF_FAIL",!0);class zg extends Ya{}te(zg,"MODEL_CLASS_MAPPINGS",[al]);class Fe extends Wt{constructor({logits:e}){super(),this.logits=e}}class FS extends Wt{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class _t extends Wt{constructor({logits:e}){super(),this.logits=e}}class wt extends Wt{constructor({logits:e}){super(),this.logits=e}}class St extends Wt{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class wn extends Wt{constructor({logits:e}){super(),this.logits=e}}class WS extends Wt{constructor({alphas:e}){super(),this.alphas=e}}class GS extends Wt{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}if(!(typeof self<"u")){if(!Ye)throw new Error("Unable to load image processing library.")}class VS{put(e){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const Pg=Rr.IS_PROCESS_AVAILABLE?t=>process.stdout.write(t):t=>console.log(t);class HS extends VS{constructor(e,{skip_prompt:r=!1,...n}={}){super(),this.tokenizer=e,this.skip_prompt=r,this.decode_kwargs=n,this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(e){if(e.length>1)throw Error("TextStreamer only supports batch size of 1");const r=e[0];if(this.skip_prompt&&this.next_tokens_are_prompt){this.next_tokens_are_prompt=!1;return}this.token_cache=ht(this.token_cache,r);const n=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let a;n.endsWith(` -`)?(a=n.slice(this.print_len),this.token_cache=[],this.print_len=0):n.length>0&&pm(n.charCodeAt(n.length-1))?(a=n.slice(this.print_len),this.print_len+=a.length):(a=n.slice(this.print_len,n.lastIndexOf(" ")+1),this.print_len+=a.length),this.on_finalized_text(a,!1)}end(){let e;this.token_cache.length>0?(e=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):e="",this.next_tokens_are_prompt=!0,this.on_finalized_text(e,!0)}on_finalized_text(e,r){Pg(e),r&&Pg(` -`)}}bt.backends.onnx.wasm.wasmPaths="/",bt.backends.onnx.wasm.proxy=!1;class qS extends HS{constructor(e,r){super(e,{skip_prompt:!0,skip_special_tokens:!0}),this.cb=r}on_finalized_text(e){this.cb(e)}}class KS extends Na{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(e,r){return new Array(e.length).fill(this.interrupted)}}const Qa=new KS;async function YS(){try{return(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{return!1}}class vn{static async getInstance(e=null){return this.model_id??(this.model_id=await YS()?"Xenova/Phi-3-mini-4k-instruct_fp16":"Xenova/Phi-3-mini-4k-instruct"),this.tokenizer??(this.tokenizer=$m.from_pretrained(this.model_id,{legacy:!0,progress_callback:e})),this.model??(this.model=zg.from_pretrained(this.model_id,{dtype:"q4",device:"webgpu",use_external_data_format:!0,progress_callback:e})),Promise.all([this.tokenizer,this.model])}}te(vn,"model_id",null),te(vn,"model",null),te(vn,"tokenizer",null),te(vn,"streamer",null);async function QS(t){const[e,r]=await vn.getInstance(),n=e.apply_chat_template(t,{add_generation_prompt:!0,return_dict:!0});{let a,o=0;const i=c=>{a??(a=performance.now());let _;o++>0&&(_=o/(performance.now()-a)*1e3),self.postMessage({status:"update",output:c,tps:_,numTokens:o})},d=new qS(e,i);self.postMessage({status:"start"});const h=await r.generate({...n,max_new_tokens:512,streamer:d,stopping_criteria:Qa}),m=e.batch_decode(h,{skip_special_tokens:!1});self.postMessage({status:"complete",output:m})}}async function XS(){self.postMessage({status:"loading",data:"Loading model..."});const[t,e]=await vn.getInstance(n=>{self.postMessage(n)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const r=t("a");await e.generate({...r,max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async t=>{const{type:e,data:r}=t.data;switch(e){case"load":XS();break;case"generate":Qa.reset(),QS(r);break;case"interrupt":Qa.interrupt();break;case"reset":Qa.reset();break}})})();