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i=a+n.kvSequenceLength,s=[n.batchSize,n.sequenceLength,n.vHiddenSize],o=12,l={x:Math.ceil(n.vHeadSize/o),y:Math.ceil(n.sequenceLength/o),z:n.batchSize*n.numHeads},u=[{type:12,data:n.sequenceLength},{type:12,data:i},{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:s,dataType:e.dataType,gpuDataType:0}],dispatchGroup:l,programUniforms:u}),getShaderSource:p=>{let h=X("probs",e.dataType,e.dims),m=X("v",r.dataType,r.dims),d=ge("output",e.dataType,s),_=[{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 = ${o}u; - var tileQ: array<${h.type.value}, ${o*o}>; - var tileK: array<${h.type.value}, ${o*o}>; - ${p.registerUniforms(_).declareVariables(h,m,d)} - ${p.mainStart([o,o,1])} - let headIdx = workgroup_id.z; - let m = global_id.y; - let n = 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${E}[${T}] = ${A}(${m(P,B)}); - `};p===9?S=` - var data = vec4(0); - ${$("data",0,"u32")} - ${$("data",1,"u32")} - ${$("data",2,"u32")} - ${$("data",3,"u32")} - outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:S=` - ${$("outputData[global_idx]",0)} - ${$("outputData[global_idx]",1)} - ${$("outputData[global_idx]",2)} - ${$("outputData[global_idx]",3)} - `}return` - ${t.registerUniform("vec_size","u32").declareVariables(w,v,_)} - - ${h??""} - - ${t.mainStart()} - ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} - ${S} - }`},md=(t,e,r,n,a,i,s=r.dataType)=>{let o=!K.areEqual(r.dims,n.dims),l=r.dims,u=K.size(r.dims),p=!1,h=!1,m=[o];if(o){let d=zn.calcShape(r.dims,n.dims,!1);if(!d)throw new Error("Can't perform binary op on the given tensors");l=d,u=K.size(l);let _=K.size(r.dims)===1,w=K.size(n.dims)===1,v=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,S=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;m.push(_),m.push(w),m.push(v),m.push(S);let $=1;for(let E=1;Ed.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:d=>fd(d,r.dims,n.dims,l,p,o,h,a,r.dataType,n.dataType,s,i),getRunData:()=>({outputs:[{dims:l,dataType:s}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:Math.ceil(K.size(l)/4)},...ye(r.dims,n.dims,l)]})}},ir=(t,e,r,n,a,i)=>{t.compute(md(e,a??"",t.inputs[0],t.inputs[1],r,n,i))},Rf=t=>{ir(t,"Add",(e,r)=>`${e}+${r}`)},Bf=t=>{ir(t,"Div",(e,r)=>`${e}/${r}`)},Df=t=>{ir(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4(${e}==${r})`},void 0,void 0,9)},Nf=t=>{ir(t,"Mul",(e,r)=>`${e}*${r}`)},Ff=t=>{let e=X("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;ir(t,"Pow",{scalar:(r,n)=>`pow_custom(${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)); - } - `)},Lf=t=>{ir(t,"Sub",(e,r)=>`${e}-${r}`)},Uf=t=>{ir(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4(${e}>${r})`},void 0,void 0,9)},Wf=t=>{ir(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4(${e}<${r})`},void 0,void 0,9)},Vf=t=>{ir(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4(${e}>=${r})`},void 0,void 0,9)},Gf=t=>{ir(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4(${e}<=${r})`},void 0,void 0,9)}}),sn,on,ln,rl,dn=J(()=>{$e(),Me(),sn=(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 ${t.activation}`)}},on=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},ln=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},rl=t=>{let e=t?.activation||"";if(e==="HardSigmoid"){let[r,n]=t?.activation_params||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=t?.activation_params||[Qo,Zo];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=t?.activation_params||[.01];return{activation:e,alpha:r}}return{activation:e}}}),kt,nl,al=J(()=>{kt=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},nl=t=>` - ${t?"value = value + getBiasByOutputCoords(coords);":""} - `}),il,Hf=J(()=>{il=t=>` -fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { - return dot(coords, vec4( - shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); -} -fn getOutputIndexFromCoords(coords : vec4) -> i32 { - return dot(coords, vec4( - i32(${t}.x), i32(${t}.y), i32(${t}.z), 1)); -} -`}),gd,_d,Yi,Ws,yd,Xi,wd,sl,as=J(()=>{$e(),Me(),Te(),dn(),al(),gd=(t,e)=>t?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - kStart + inputRow, - globalRowStart / innerElementSize + inputCol${e?", batchIndices":""}); - `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - globalRow + innerRow, - kStart / innerElementSize + inputCol${e?", batchIndices":""}); - `,_d=(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];"} - }`,Yi=(t,e,r="f32",n,a=!1,i=32,s=!1,o=32)=>{let l=e[1]*t[1],u=e[0]*t[0],p=a?l:i,h=a?i:l,m=p/e[0],d=i/e[1];if(!((a&&m===4&&t[1]===4||!a&&(m===3||m===4))&&p%e[0]===0&&i%e[1]===0&&t[0]===4))throw new Error(`If transposeA ${a} is true, innerElementSize ${m} and workPerThread[1] ${t[1]} must be 4. - Otherwise, innerElementSize ${m} must be 3 or 4. - tileAWidth ${p} must be divisible by workgroupSize[0]${e[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return` -var mm_Asub: array, ${p/m}>, ${h}>; -var mm_Bsub: array, ${u/t[0]}>, ${i}>; - -const rowPerThread = ${t[1]}; -const colPerThread = ${t[0]}; -const innerElementSize = ${m}; -const tileInner = ${i}; - -@compute @workgroup_size(${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 = ${s?"0":"i32(globalId.z)"}; - ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} - let globalRowStart = i32(workgroupId.y) * ${l}; - - let num_tiles = ${s?`${Math.ceil(o/i)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; - var kStart = ${s?`i32(globalId.z) * ${o}`:"0"}; - - var acc: array, rowPerThread>; - - // Loop over shared dimension. - let tileRowB = localRow * ${d}; - 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; - ${gd(a,n)} - } - - // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) { - let inputRow = tileRowB + innerRow; - let inputCol = tileCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${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]; - ${m===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} - - ${_d(a,m)} - } - - workgroupBarrier(); - } - - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); - } -}`},Ws=(t,e)=>t?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - kStart + inputRow, - globalRowStart + inputCol${e?", batchIndices":""}); - `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - globalRowStart + inputRow, - kStart + inputCol${e?", batchIndices":""}); - `,yd=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Xi=(t,e,r="f32",n,a=!1,i=32,s=!1,o=32,l=!1)=>{let u=t[1]*e[1],p=t[0]*e[0],h=a?u:i,m=a?i:u;if(!(m%e[1]===0&&h%e[0]===0&&i%e[1]===0))throw new Error(`tileAHight ${m} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${h} must be divisible by workgroupSize[0]${e[0]}, tileInner ${i} must be divisible by workgroupSize[1]${e[1]}`);let d=m/e[1],_=h/e[0],w=i/e[1],v=l?` - let localRow = i32(localId.y); - let localCol = i32(localId.x); - let globalRowStart = i32(workgroupId.y) * ${u}; - let globalColStart = i32(workgroupId.x) * ${p}; - - // 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 < ${m}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${h}; inputCol = inputCol + ${e[0]}) { - ${Ws(a,n)} - } - } - // Load one tile of B into local memory. - for (var inputRow = localRow; inputRow < ${i}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${p}; 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) * ${u}; - -let tileRowA = i32(localId.y) * ${d}; -let tileColA = i32(localId.x) * ${_}; -let tileRowB = i32(localId.y) * ${w}; -// Loop over shared dimension. -for (var t = 0; t < num_tiles; t = t + 1) { - // Load one tile of A into local memory. - for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < ${_}; innerCol = innerCol + 1) { - let inputRow = tileRowA + innerRow; - let inputCol = tileColA + innerCol; - ${Ws(a,n)} - } - } - - // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { - let inputRow = tileRowB + innerRow; - let inputCol = tileCol + innerCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, - kStart + inputRow, - globalCol + innerCol${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) { - ${yd(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, ${m}>; - var mm_Bsub : array, ${i}>; - const rowPerThread = ${t[1]}; - const colPerThread = ${t[0]}; - const tileInner = ${i}; - -@compute @workgroup_size(${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 = ${s?"0":"i32(globalId.z)"}; - ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} - let num_tiles = ${s?`${Math.ceil(o/i)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; - var kStart = ${s?`i32(globalId.z) * ${o}`:"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; - } - } - ${v} - } -`},wd=(t,e,r,n,a,i=!1)=>{let[s,o,l]=a,[u,p,h,m]=n,d=xa(s,l),_=xa(o,l),w=mt(n[0].type.tensor),v=()=>{let $=p.rank,E=u.rank,T=`var aIndices: ${p.type.indices};`;for(let A=$-2-1,P=E-1;A>=0;A--,P--)T+=` -aIndices[${A}] = ${E>1?`batchIndices[${P}]`:"batchIndices"};`;return d.forEach(A=>{T+=` -aIndices[${A}] = 0;`}),T+=` -aIndices[${$-2}] = u32(row); - aIndices[${$-1}] = u32(colIn);`,T},S=()=>{let $=h.rank,E=u.rank,T=`var bIndices: ${h.type.indices};`;for(let A=$-2-1,P=E-1;A>=0;A--,P--)T+=` -bIndices[${A}] = ${E>1?`batchIndices[${P}]`:"batchIndices"};`;return _.forEach(A=>{T+=` -bIndices[${A}] = 0;`}),T+=` -bIndices[${$-2}] = u32(row); - bIndices[${$-1}] = u32(colIn);`,T};return` - fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${u.type.indices}) -> ${kt(t,w)} { - var value = ${kt(t,w)}(0.0); - let col = colIn * ${t}; - if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) - { - ${v()} - value = ${p.getByIndices("aIndices")}; - } - return value; - } - - fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${u.type.indices}) -> ${kt(t,w)} { - var value = ${kt(t,w)}(0.0); - let col = colIn * ${t}; - if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) - { - ${S()} - value = ${h.getByIndices("bIndices")}; - } - return value; - } - - fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(t,w)}) { - 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 + ${i?"bias[colIn]":`${kt(t,w)}(bias[row])`};`:""} - ${r} - ${m.setByIndices("vec3(coords)","value")} - } - } - `},sl=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i.slice(0,-2),l=s.slice(0,-2),u=n?n.slice(0,-2):r.slice(0,-2),p=K.size(u),h=i[i.length-2],m=i[i.length-1],d=s[s.length-1],_=m%4===0&&d%4===0,w=h<=8?[4,1,1]:[4,4,1],v=[8,8,1],S=[Math.ceil(d/v[0]/w[0]),Math.ceil(h/v[1]/w[1]),Math.ceil(p/v[2]/w[2])],$=_?4:1,E=[...o,h,m/$],T=E.length,A=[...l,m,d/$],P=A.length,B=[p,h,d/$],D=[{type:6,data:h},{type:6,data:d},{type:6,data:m}];on(e,D),D.push(...ye(u,E,A));let q=["rank","rank"],H=t.length>2;H&&(D.push(...ye(t[2].dims)),q.push("rank")),D.push(...ye(B));let ie=te=>{let de=u.length,se=Jo("batchDims",t[0].dataType,de,1),M=mt(t[0].dataType),R=X("a",t[0].dataType,T,$),G=X("b",t[1].dataType,P,$),re=ge("result",t[0].dataType,B.length,$),ee=[R,G];if(H){let Ce=a?$:1;ee.push(X("bias",t[2].dataType,t[2].dims.length,Ce))}let ne=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];ln(e,ne);let W=mt(re.type.tensor),ae=sn(e,re.type.value,W),fe=wd($,H,ae,[se,R,G,re],[o,l,u],a);return` - ${te.registerUniforms(ne).registerInternalVariables(se).declareVariables(...ee,re)} - ${fe} - ${_?Yi(w,v,M,se):Xi(w,v,M,se)} - `};return{name:"MatMul",shaderCache:{hint:`${w};${e.activation};${_};${a}`,inputDependencies:q},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:S[0],y:S[1],z:S[2]},programUniforms:D}),getShaderSource:ie}}}),bd,jf,Cy=J(()=>{$e(),un(),Te(),dn(),al(),Hf(),as(),bd=(t,e,r,n,a=!1,i,s=4,o=4,l=4,u="f32")=>{let p=q=>{switch(q){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${u}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${q} is not supported.`)}},h=q=>{switch(q){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 ${q} is not supported.`)}},m=t?` - let coord = vec4(batch, xRow, xCol, xCh); - `:` - let coord = vec4(batch, xCh, xRow, xCol); - `,d=t?` - let coords = vec4( - batch, - row / outWidth, - row % outWidth, - col); - `:` - let coords = vec4( - batch, - row, - col / outWidth, - col % outWidth); - `,_=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",w=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",v=t?"row":"col",S=t?"col":"row",$=` - let inChannels = i32(uniforms.w_shape[2]); - let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; - let outRow = ${v} / outWidth; - let outCol = ${v} % outWidth; - - let WRow = ${S} / (i32(uniforms.w_shape[1]) * inChannels); - let WCol = ${S} / 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 = ${S} % inChannels; - var resData = ${kt(s,u)}(0.0); - // The bounds checking is always needed since we use it to pad zero for - // the 'same' padding type. - if (xRow >= 0 && xRow < ${_} && xCol >= 0 && xCol < ${w}) { - ${m} - let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); - ${p(s)} - } - return resData;`,E=t?e&&n?` - let col = colIn * ${s}; - ${$}`:` - let col = colIn * ${s}; - if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { - ${$} - } - return ${kt(s,u)}(0.0);`:n&&r?` - let col = colIn * ${s}; - ${$}`:` - let col = colIn * ${s}; - if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { - ${$} - } - return ${kt(s,u)}(0.0);`,T=`${h(o)}`,A=kt(l,u),P=kt(t?s:o,u),B=kt(t?o:s,u),D=sn(i,A,u);return` - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${P} { - ${t?E:T} - } - - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${B} { - ${t?T:E} - } - - fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${A}) { - let col = colIn * ${l}; - 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])"}; - ${d} - ${nl(a)} - ${D} - setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); - } - }`},jf=(t,e,r,n,a,i,s,o)=>{let l=e.format==="NHWC",u=l?t[0].dims[3]:t[0].dims[1],p=r[0],h=l?r[2]:r[3],m=l?r[1]:r[2],d=l?r[3]:r[1],_=l&&(u%4===0||u%3===0)&&d%4===0,w=l?d:h*m,v=l?h*m:d,S=[8,8,1],$=n<=8?[4,1,1]:[4,4,1],E=[Math.ceil(w/S[0]/$[0]),Math.ceil(v/S[1]/$[1]),Math.ceil(p/S[2]/$[2])];at("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let T=_?l&&u%4!==0?3:4:1,A=S[1]*$[1],P=S[0]*$[0],B=Math.max(S[0]*T,S[1]),D=n%A===0,q=a%P===0,H=i%B===0,ie=_?[T,4,4]:[1,1,1],te=[{type:6,data:n},{type:6,data:a},{type:6,data:i},{type:6,data:[e.pads[0],e.pads[1]]},{type:6,data:e.strides},{type:6,data:e.dilations}];on(e,te),te.push(...ye(t[0].dims,t[1].dims));let de=["rank","rank"];s&&(te.push(...ye(t[2].dims)),de.push("rank")),te.push(...ye(r));let se=M=>{let R=[{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}];ln(e,R);let G=_?4:1,re=mt(t[0].dataType),ee=` - fn setOutputAtIndex(flatIndex : i32, value : ${_?`vec4<${re}>`:re}) { - result[flatIndex] = ${_?`vec4<${re}>`:re}(value); - } - fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${_?`vec4<${re}>`:re}) { - let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); - setOutputAtIndex(flatIndex ${_?"/ 4":""}, value); - }`,ne=X("x",t[0].dataType,t[0].dims.length,T===3?1:T),W=X("w",t[1].dataType,t[1].dims.length,G),ae=[ne,W],fe=ge("result",t[0].dataType,r.length,G);if(s){let Ce=X("bias",t[2].dataType,t[2].dims.length,G);ae.push(Ce),ee+=` - fn getBiasByOutputCoords(coords : vec4) -> ${_?`vec4<${re}>`:re} { - return bias[coords.${l?"w":"y"}${_?"/ 4":""}]; - }`}return` - ${il("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 }; - ${M.registerUniforms(R).declareVariables(...ae,fe)} - ${ee} - ${bd(l,D,q,H,s,e,ie[0],ie[1],ie[2],re)} - ${_?Yi($,S,re,void 0,!l,B):Xi($,S,re,void 0,!l,B,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${T};${_};${D};${q};${H};${A};${P};${B}`,inputDependencies:de},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:te}),getShaderSource:se}}}),Co,qf,Ty=J(()=>{$e(),Me(),Te(),Xf(),dn(),Co=(t,e,r)=>{let n=t.length>2,a=n?"value += b[output_channel];":"",i=t[0].dims,s=t[1].dims,o=s[0]/e.group,l=e.format==="NHWC",u=Ri(i,s,e.dilations,e.pads,e.strides,l),p=K.size(u),h=[{type:12,data:p},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];on(e,h),h.push(...ye(i,s));let m=["rank","rank"];n&&(h.push(...ye(t[2].dims)),m.push("rank")),h.push(...ye(u));let d=_=>{let w=ge("output",t[0].dataType,u.length),v=mt(w.type.tensor),S=sn(e,w.type.value,v),$=X("x",t[0].dataType,i.length),E=X("w",t[1].dataType,s.length),T=[$,E];n&&T.push(X("b",t[2].dataType,t[2].dims.length));let A=[{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 ln(e,A),` - ${_.registerUniforms(A).declareVariables(...T,w)} - - ${_.mainStart()} - ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - - let outputIndices = ${w.offsetToIndices("global_idx")}; - let batch: u32 = outputIndices[0]; - let output_channel: u32 = outputIndices[${l?3:1}]; - let xRCCorner: vec2 = vec2(outputIndices[${l?1:2}], outputIndices[${l?2:3}]) * uniforms.strides - uniforms.pads; - let group_id: u32 = output_channel / uniforms.output_channels_per_group; - - var value: ${w.type.value} = ${w.type.value}(0); - for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { - let input_channel = group_id * uniforms.w_shape[1] + wInChannel; - for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { - let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; - - if (xHeight < 0u || xHeight >= uniforms.x_shape[${l?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[${l?2:3}]) { - continue; - } - - let xVal = ${l?$.get("batch","xHeight","xWidth","input_channel"):$.get("batch","input_channel","xHeight","xWidth")}; - let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")}; - value += xVal*wVal; - } - } - } - ${a} - ${S} - ${w.setByOffset("global_idx","value")} - }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:m},getRunData:()=>({outputs:[{dims:r?r(u):u,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:d}},qf=(t,e,r)=>{let n=t.length>2,a=ot(r[3]),i=ot(r[2]),s=K.size(r)/a/i,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],l=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],u=[r[0],r[1],r[2],r[3]/a],p=[{type:12,data:s},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];on(e,p),p.push(...ye(o,l,u));let h=(i-1)*e.strides[1]+l[1],m=d=>{let _=ge("output",t[0].dataType,u.length,a),w=mt(_.type.tensor),v=sn(e,_.type.value,w),S=X("x",t[0].dataType,o.length,a),$=X("w",t[1].dataType,l.length,a),E=[S,$];n&&E.push(X("b",t[2].dataType,t[2].dims,a));let T=n?"value += b[output_channel];":"",A=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return ln(e,A),` - ${d.registerUniforms(A).declareVariables(...E,_)} - ${d.mainStart()} - ${d.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] / ${i}u; - let col = (index1 % width1) * ${i}u; - index1 = index1 / width1; - let row = index1 % uniforms.output_shape[1]; - let batch = index1 / uniforms.output_shape[1]; - - let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; - - var x_vals: array<${S.type.value}, ${h}>; - var values: array<${_.type.value}, ${i}>; - 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 < ${l[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 < ${h}; i++) { - let x_width = x_corner.y + i; - if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { - x_vals[i] = ${S.get("batch","u32(x_height)","u32(x_width)","input_channel")}; - } else { - x_vals[i] = ${S.type.value}(0); - } - } - for (var w_width: u32 = 0u; w_width < ${l[1]}; w_width++) { - let w_val = ${$.get("w_height","w_width","0","output_channel")}; - for (var i = 0u; i < ${i}u; i++) { - values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); - } - } - } - } - - for (var i = 0u; i < ${i}u; i++) { - var value = values[i]; - ${T} - ${v} - ${_.set("batch","row","col + i","output_channel","value")}; - } - }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${a};${i};${h};${l[0]};${l[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:p}),getShaderSource:m}}}),To,vd,Kf,Yf=J(()=>{$e(),Me(),as(),Te(),dn(),To=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i[i.length-2],l=s[s.length-1],u=i[i.length-1],p=ot(l),h=ot(u),m=ot(o),d=K.size(r)/p/m,_=t.length>2,w=n?n.slice(0,-2):r.slice(0,-2),v=[K.size(w),o,l],S=[{type:12,data:d},{type:12,data:o},{type:12,data:l},{type:12,data:u}];on(e,S),S.push(...ye(w,i,s)),_&&S.push(...ye(t[2].dims)),S.push(...ye(v));let $=E=>{let T=Jo("batch_dims",t[0].dataType,w.length),A=X("a",t[0].dataType,i.length,h),P=X("b",t[1].dataType,s.length,p),B=ge("output",t[0].dataType,v.length,p),D=mt(B.type.tensor),q=sn(e,B.type.value,D),H=[A,P],ie="";if(_){let ee=a?p:1;H.push(X("bias",t[2].dataType,t[2].dims.length,ee)),ie=`${a?`value += 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s=t[0],o=t.slice(i?1:2,i?3:4),l=o.length,u=e[0],p=e.slice(2).map((m,d)=>m+(m-1)*(r[d]-1)),h=o.map((m,d)=>m+n[d]+n[d+l]).map((m,d)=>Math.floor((m-p[d]+a[d])/a[d]));return h.splice(0,0,s),h.splice(i?3:1,0,u),h},xi=[2,3,1,0],$d=(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 ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},Vs=(t,e)=>{let r=t.kernelShape.slice();for(let i=2;i{let e=rl(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,i=t.group,s=t.kernel_shape,o=t.pads,l=t.strides,u=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:i,kernelShape:s,pads:o,strides:l,wIsConst:u,...e,cacheKey:`${t.format};${e.activation};`}},xd=(t,e,r)=>{let n=Vs(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 P=Ri(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),B=t.kernelCustomData.wT??t.compute(Er(e[1],xi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=B);let D=[e[0],B];e.length===3&&D.push(e[2]),t.compute(qf(D,n,P),{inputs:D})}else t.compute(Co(e,n));return}let i=e.length===3,s=e[0].dims[a?1:2],o=e[0].dims[a?2:3],l=e[0].dims[a?3:1],u=e[1].dims[2],p=e[1].dims[3],h=Ri(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),m=h[a?1:2],d=h[a?2:3],_=h[a?3:1],w=a&&u===s&&p===o&&r.pads[0]===0&&r.pads[1]===0;if(w||u===1&&p===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 P=h[0],B,D,q,H=[];if(a){let de=t.kernelCustomData.wT??t.compute(Er(e[1],xi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=de),w){let se=s*o*l;B=e[0].reshape([1,P,se]),D=de.reshape([1,se,_]),q=[1,P,_]}else B=e[0].reshape([P,s*o,l]),D=de.reshape([1,l,_]),q=[P,m*d,_];H.push(B),H.push(D)}else B=e[0].reshape([P,l,s*o]),D=e[1].reshape([1,_,l]),q=[P,_,m*d],H.push(D),H.push(B);i&&H.push(e[2]);let ie=q[2],te=H[0].dims[H[0].dims.length-1];ie<8&&te<8?t.compute(To(H,n,h,q,a),{inputs:H}):t.compute(sl(H,n,h,q,a),{inputs:H});return}let v=!0,S=t.kernelCustomData.wT??t.compute(Er(e[1],xi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=S);let $=[e[0],S];i&&$.push(e[2]);let E=a?m*d:_,T=a?_:m*d,A=u*p*l;t.compute(jf($,n,h,E,T,A,i,v),{inputs:$})},Sd=(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]],i=[1].concat(e.strides),s=[1].concat(e.dilations),o=[1].concat(e.kernelShape),l=Vs({...e,pads:a,strides:i,dilations:s,kernelShape:o},n);t.compute(Co(n,l,u=>r?[u[0],u[2],u[3]]:[]))},Io=(t,e)=>{$d(t.inputs,e),t.inputs[0].dims.length===3?Sd(t,e):xd(t,t.inputs,e)}}),kd,Qf,Ay=J(()=>{$e(),un(),Te(),dn(),al(),Hf(),as(),kd=(t,e=!1,r,n,a=4)=>{let i=v=>{switch(v){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 ${v} is not supported.`)}},s=t?` - let coord = vec4(batch, iXR, iXC, xCh); - `:` - let coord = vec4(batch, xCh, iXR, iXC); - `,o=t?` - let coords = vec4( - batch, - row / outWidth, - row % outWidth, - col); - `:` - let coords = vec4( - batch, - row, - col / outWidth, - col % outWidth); - `,l=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",u=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",p=t?"row":"col",h=t?"col":"row",m=` - 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 = ${p} / outWidth; - let outCol = ${p} % outWidth; - - let WRow = ${h} / (uniforms.filter_dims[1] * inChannels); - let WCol = ${h} / inChannels % uniforms.filter_dims[1]; - let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]); - let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]); - if (xR < 0.0 || xR >= f32(${l}) || fract(xR) > 0.0) { - return ${n}(0.0); - } - if (xC < 0.0 || xC >= f32(${u}) || fract(xC) > 0.0) { - return ${n}(0.0); - } - let iXR = i32(xR); - let iXC = i32(xC); - let xCh = ${h} % inChannels; - ${s} - return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${a}];`,d=t?` - let col = colIn * ${a}; - if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { - ${m} - } - return ${n}(0.0);`:` - let col = colIn * ${a}; - if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { - ${m} - } - return ${n}(0.0);`,_=` - 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); - ${i(a)} - } - return ${n}(0.0); - `,w=sn(r,n);return` - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} { - ${t?d:_} - } - - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} { - ${t?_:d} - } - - 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])"}; - ${o} - ${nl(e)} - ${w} - result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${a}] = value; - } - }`},Qf=(t,e,r,n,a,i,s,o)=>{let l=e.format==="NHWC",u=l?t[0].dims[3]:t[0].dims[1],p=r[0],h=l?r[2]:r[3],m=l?r[1]:r[2],d=l?r[3]:r[1],_=l&&u%4===0&&u%3&&d%4===0,w=l?d:h*m,v=l?h*m:d,S=[8,8,1],$=n<=8?[4,1,1]:[4,4,1],E=[Math.ceil(w/S[0]/$[0]),Math.ceil(v/S[1]/$[1]),Math.ceil(p/S[2]/$[2])];at("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let T=_?4:1,A=Math.max(S[0]*T,S[1]),P=_?4:1,B=[e.kernelShape[l?1:2],e.kernelShape[l?2:3]],D=[B[0]+(e.dilations[0]<=1?0:(B[0]-1)*(e.dilations[0]-1)),B[1]+(e.dilations[1]<=1?0:(B[1]-1)*(e.dilations[1]-1))],q=[D[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),D[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],H=[{type:6,data:n},{type:6,data:a},{type:6,data:i},{type:6,data:e.strides},{type:6,data:e.dilations},{type:6,data:B},{type:6,data:q}];on(e,H),H.push(...ye(t[0].dims,t[1].dims));let ie=["rank","rank"];s&&(H.push(...ye(t[2].dims)),ie.push("rank")),H.push(...ye(r));let te=de=>{let se=X("x",t[0].dataType,t[0].dims.length,P),M=X("w",t[1].dataType,t[1].dims.length,1),R=ge("result",t[0].dataType,r.length,P),G=[se,M],re="";if(s){let W=X("bias",t[2].dataType,t[2].dims.length,P);G.push(W),re+=` - fn getBiasByOutputCoords(coords : vec4) -> ${W.type.value} { - return bias[coords.${l?"w":"y"}${_?"/ 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:B.length},{name:"pads",type:"i32",length:q.length}];ln(e,ee);let ne=mt(t[0].dataType,1);if(ne!=="f16"&&ne!=="f32")throw new Error(`elemType ${ne} is not supported.`);return` - ${il("uniforms.result_strides")} - ${de.registerUniforms(ee).declareVariables(...G,R)}; - ${re} - ${kd(l,s,e,se.type.value,T)} - ${_?Yi($,S,ne,void 0,!l,A):Xi($,S,ne,void 0,!l,A,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${$};${S};${_}`,inputDependencies:ie},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:H}),getShaderSource:te}}}),Ed,Mo,Iy=J(()=>{$e(),un(),Me(),Te(),Ed=(t,e,r,n,a,i=!1,s,o,l=!1)=>{let u=l?1:2,p=l?2:3,h=l?3:1,m=i?2:1,d=` - fn setOutputAtIndex(flatIndex : u32, value : ${i?`vec4<${s}>`:s}) { - result[flatIndex] = ${i?`vec4<${s}>`:s}(value); - }`;n&&(d+=` - fn getBiasByOutputCoords(coords : vec4) -> ${i?`vec4<${s}>`:s} { - return bias[coords.${l?"w":"y"}${i?"/ 4":""}]; - }`);let _=i?4:1,w=X("W",e[1].dataType,e[1].dims.length,_),v=X("Dy",e[0].dataType,e[0].dims.length,_),S=[v,w];n&&S.push(X("bias",e[2].dataType,[r[h]].length,_));let $=ge("result",e[0].dataType,r.length,_),E=`{ - 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"} * ${m}; - 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, ${m}>; - for (var i = 0; i < ${m}; i++) { - dotProd[i] = vec4<${s}>(0.0); - } - for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) { - var dyR = (${s}(dyCorner.x) + ${s}(wR)) / ${s}(uniforms.strides.x); - let wRPerm = uniforms.filter_dims[0] - 1 - wR; - if (dyR < 0.0 || dyR >= ${s}(uniforms.Dy_shape[1]) || - fract(dyR) > 0.0 || wRPerm < 0) { - continue; - } - let idyR: u32 = u32(dyR); - - for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) { - let dyC = (${s}(dyCorner.y) + ${s}(wC)) / ${s}(uniforms.strides.y); - let dyC2 = (${s}(dyCorner.y) + 1.0 + ${s}(wC)) / ${s}(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 >= ${s}(uniforms.Dy_shape[2]) || - fract(dyC) > 0.0) { - bDyCVal = false; - } - if (dyC2 < 0.0 || dyC2 >= ${s}(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 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${v.get("batch","idyR","idyC","d2")}; - let tmpval = vec4<${s}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - dotProd[0] = dotProd[0] + tmpval; - - xValue = ${v.get("batch","idyR","idyC2","d2")}; - - dotProd[1] = dotProd[1] + vec4<${s}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - } - } else if (bDyCVal) { - let d2Length = uniforms.Dy_shape[${h}]; - for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { - let wValue0 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${v.get("batch","idyR","idyC","d2")}; - let tmpval = vec4<${s}>(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 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${v.get("batch","idyR","idyC2","d2")}; - let tmpval = vec4<${s}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - dotProd[1] = dotProd[1] + tmpval; - } - } - } - } - - for (var i: u32 = 0; i < ${m}; i = i + 1) { - let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${s}>(0.0)`}; - ${$.set("batch","r","c + i","d1","value")}; - } - }`,T=` - let outputIndices = ${$.offsetToIndices("global_idx")}; - let batch = ${$.indicesGet("outputIndices",0)}; - let d1 = ${$.indicesGet("outputIndices",h)}; - let r = ${$.indicesGet("outputIndices",u)}; - let c = ${$.indicesGet("outputIndices",p)}; - 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 = ${s}(0.0); - for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { - if (wR % uniforms.dilations.x != 0) { - continue; - } - let dyR = (${s}(dyRCorner) + ${s}(wR)) / ${s}(uniforms.strides[0]); - let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; - if (dyR < 0.0 || dyR >= ${s}(uniforms.Dy_shape[${u}]) || 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 = (${s}(dyCCorner) + ${s}(wC)) / ${s}(uniforms.strides.y); - let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; - if (dyC < 0.0 || dyC >= ${s}(uniforms.Dy_shape[${p}]) || - 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 = ${l?v.get("batch","idyR","idyC","inputChannel"):v.get("batch","inputChannel","idyR","idyC")}; - let wValue = ${w.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")}; - dotProd = dotProd + xValue * wValue; - inputChannel = inputChannel + 1; - } - } - } - let value = dotProd + ${n?"bias[d1]":`${s}(0.0)`}; - ${$.setByOffset("global_idx","value")}; - `;return` - ${t.registerUniforms(o).declareVariables(...S,$)} - ${d} - - ${t.mainStart()} - ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; - ${i?E:T}}`},Mo=(t,e,r)=>{let n=t.length>2,a=e.outputShape,i=K.size(a),s=[Math.ceil(i/64),1,1];at("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${s}`);let o=e.format==="NHWC",l=["rank","rank"],u=[e.strides[0],e.strides[1]],p=[e.kernelShape[o?1:2],e.kernelShape[o?2:3]],h=[e.dilations[0],e.dilations[1]],m=[p[0]+(e.dilations[0]<=1?0:(e.kernelShape[o?1:2]-1)*(e.dilations[0]-1)),p[1]+(e.dilations[1]<=1?0:(e.kernelShape[o?2:3]-1)*(e.dilations[1]-1))],d=[m[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),m[1]-1-Math.floor(e.pads[1]+e.pads[3])/2],_=!1,w=e.group,v=t[1].dims,S=v[0]/w,$=v[1],E=[{type:12,data:i},{type:12,data:u},{type:12,data:p},{type:12,data:h},{type:12,data:m},{type:6,data:d},{type:12,data:S},{type:12,data:$},...ye(t[0].dims,t[1].dims)];n&&(E.push(...ye(t[2].dims)),l.push("rank")),E.push(...ye(a));let T=s[1]===1&&s[2]===1,A=P=>{let B=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:u.length},{name:"filter_dims",type:"u32",length:p.length},{name:"dilations",type:"u32",length:p.length},{name:"effective_filter_dims",type:"u32",length:m.length},{name:"pads",type:"i32",length:d.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],D=mt(t[0].dataType);return`${Ed(P,t,a,n,T,_,D,B,o)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${e.cacheKey};`,inputDependencies:l},getRunData:()=>({dispatchGroup:{x:s[0],y:s[1],z:s[2]},outputs:[{dims:r?r(a):a,dataType:t[0].dataType}],programUniforms:E}),getShaderSource:A}}}),Cd,Td,Ad,Gs,Zf,Id,Md,Od,zd,Jf,My=J(()=>{Ay(),Iy(),dn(),Ia(),Cd=(t,e,r,n,a,i)=>(t-1)*e+r+(n-1)*a+1-i,Td=(t,e,r,n,a)=>{let i=Math.floor(t/2);e==="SAME_UPPER"?(r[n]=i,r[a]=t-i):e==="SAME_LOWER"&&(r[n]=t-i,r[a]=i)},Ad=(t,e,r,n,a,i,s,o,l,u)=>{let p=t.length-2,h=u.length===0;if(l.length===0)for(let _=0;_{let r=t.kernelShape.slice();if(t.kernelShape.length===0||t.kernelShape.reduce((h,m)=>h*m,1)===0){r.length=0;for(let h=2;hh+m,0)===0){let h=e[0].dims.length-2;l=new Array(h).fill(1)}let u=t.strides.slice();if(u.reduce((h,m)=>h+m,0)===0){let h=e[0].dims.length-2;u=new Array(h).fill(1)}Ad(o,r,l,t.autoPad,t.group,a,u,n,s,i);let p=Object.assign({},t);return Object.assign(p,{kernelShape:r,pads:a,outputPadding:s,outputShape:i,dilations:l,strides:u}),p},Zf=t=>{let e=rl(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof t.autoPad>"u"?0:t.autoPad],a=t.dilations,i=t.group,s=t.kernelShape,o=t.pads,l=t.strides,u=t.wIsConst(),p=t.outputPadding,h=t.outputShape;return{autoPad:n,format:r,dilations:a,group:i,kernelShape:s,outputPadding:p,outputShape:h,pads:o,strides:l,wIsConst:u,...e,cacheKey:`${t.format};${e.activation};`}},Id=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently 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l=K.size(o),u=[{type:12,data:l},{type:12,data:a},{type:12,data:i},{type:12,data:s},{type:1,data:e.alpha},{type:1,data:e.beta}],p=["type","type"];t.length===3&&(u.push(...ye(t[2].dims)),p.push("rank")),u.push(...ye(o));let h=m=>{let d="";e.transA&&e.transB?d="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?d="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?d="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(d="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let _=e.alpha===1?"":"value *= uniforms.alpha;",w=X("a",t[0].dataType,t[0].dims),v=X("b",t[1].dataType,t[1].dims),S=w.type.value,$=null,E=[w,v];t.length===3&&($=X("c",t[2].dataType,t[2].dims.length),E.push($));let T=ge("output",t[0].dataType,o.length);E.push(T);let A=[{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` - ${m.registerUniforms(A).declareVariables(...E)} - - ${m.mainStart()} - ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - - let m = global_idx / uniforms.N; - let n = global_idx % uniforms.N; - - var value = ${S}(0); - for (var k: u32 = 0u; k < uniforms.K; k++) { - ${d} - } - - ${_} - ${$!=null?`let cOffset = ${$.broadcastedIndicesToOffset("vec2(m, n)",T)}; value += ${S}(uniforms.beta) * ${$.getByOffset("cOffset")};`:""} - output[global_idx] = value; - }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:h}},pm=t=>{let e=t.transA,r=t.transB,n=t.alpha,a=t.beta;return{transA:e,transB:r,alpha:n,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},hm=(t,e)=>{Qd(t.inputs),t.compute(Zd(t.inputs,e))}}),Jd,ec,tc,fm,Ly=J(()=>{$e(),Me(),Te(),Jd=(t,e)=>{let r=t[0].dims,n=r,a=2,i=K.sizeToDimension(r,a),s=K.sizeFromDimension(r,a),o=ot(s),l=s/o,u=[r[0],r[1],l],p=["rank","type","type"],h=[{type:12,data:s},{type:12,data:l}];h.push(...ye(u,u));let m=d=>{let _=X("x",t[0].dataType,u.length,o),w=X("scale",t[1].dataType,t[1].dims),v=X("bias",t[2].dataType,t[2].dims),S=ge("output",t[0].dataType,u.length,o),$=[_,w,v,S],E=_.type.value,T=o===1?"f32":`vec${o}`,A=64,P=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return` - var meanShared : f32; - var squaredNormShared : f32; - var workgroupShared : array<${T}, ${A}>; - const workgroupSize = ${A}u; - ${d.registerUniforms(P).declareVariables(...$)} - ${d.mainStart(A)} - 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 = ${T}(0); - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - initial = initial + ${T}(${_.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 = ${Dr("workgroupShared[0]",o)} / f32(uniforms.normSize); - } - workgroupBarrier(); - - // reinitialize workgroup memory. - initial = ${T}(0); - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - let deviation = ${T}(${_.get("batch","channel","h")}) - ${T}(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 = ${Dr("workgroupShared[0]",o)}; - } - workgroupBarrier(); - - let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${e.epsilon})); - let channelScale = invStdDev * f32(${w.getByOffset("channel")}); - let channelShift = f32(${v.getByOffset("channel")}) - meanShared * channelScale; - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - let value = ${_.get("batch","channel","h")} * ${E}(${T}(channelScale)) + ${E}(${T}(channelShift)); - ${S.set("batch","channel","h","value")}; - } - }`};return{name:"InstanceNormalization",shaderCache:{hint:`${e.epsilon};${o}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:i},programUniforms:h}),getShaderSource:m}},ec=(t,e,r,n,a,i,s,o)=>{let l=ot(s),u=64,p=l===1?"vec2f":`mat2x${l}f`,h=l===1?"f32":`vec${l}f`,m=(P,B)=>`${p}(${P}, ${B})`,d=a*s/l,_=Math.ceil(i/u),w=["type"],v=[{type:12,data:_},{type:12,data:i},{type:12,data:Math.floor(s/l)},{type:12,data:Math.floor(i*s/l)}],S=P=>{let B=X("input",e.dataType,e.dims,l);return` - ${P.declareVariables(B)} - @group(0) @binding(1) var output : array<${p}>; - struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32}; - @group(0) @binding(2) var uniforms: Uniforms; - - ${P.mainStart(u)} - let currentImageNumber = global_idx / ${u} / uniforms.C; - let currentChannelNumber = (global_idx / ${u}) % 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 = ${Sr("f32",l)}; - var squaredSum = ${Sr("f32",l)}; - for (var i: u32 = wgOffset; i < wgMax; i++) { - let value = ${h}(input[offset + i * uniforms.C]); - sum += value; - squaredSum += value * value; - } - output[global_idx] = ${m("sum","squaredSum")}; - }`},$=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${l}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:[a,s,u,2],dataType:1}],dispatchGroup:{x:a*s/l},programUniforms:v}),getShaderSource:S},{inputs:[e],outputs:[-1]})[0],E=[{type:12,data:d},{type:12,data:i},{type:12,data:Math.floor(s/l)},{type:12,data:Math.floor(u*s/l)}],T=["type","type","type"],A=P=>{let B=X("scale",r.dataType,r.dims,l),D=X("bias",n.dataType,n.dims,l);return` - @group(0) @binding(0) var input : array<${p}>; - @group(0) @binding(1) var scale : array<${B.type.storage}>; - @group(0) @binding(2) var bias : array<${D.type.storage}>; - @group(0) @binding(3) var output : array<${p}>; - struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32}; - @group(0) @binding(4) var uniforms: Uniforms; - - ${P.mainStart()} - ${P.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 = ${Sr("f32",l)}; - var squaredSum = ${Sr("f32",l)}; - for (var i: u32 = 0; i < min(${u}, uniforms.H); i++) { - let value = input[offset + i + currentChannelNumber * ${u}]; - sum += value[0]; - squaredSum += value[1]; - } - sum = sum / f32(uniforms.H); - squaredSum = squaredSum / f32(uniforms.H); - let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${o})); - let channelScale = invStdDev * ${h}(scale[currentChannelNumber]); - let channelShift = ${h}(bias[currentChannelNumber]) - sum * channelScale; - - output[global_idx] = ${m("channelScale","channelShift")}; - }`};return t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${o}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:[a,s,2],dataType:1}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:E}),getShaderSource:A},{inputs:[$,r,n],outputs:[-1]})[0]},tc=(t,e,r)=>{let n=e[0].dims,a=n,i=n[0],s=n[n.length-1],o=K.sizeFromDimension(n,1)/s,l=ot(s),u=K.size(a)/l,p=[{type:12,data:o},{type:12,data:Math.floor(s/l)}],h=["type","type"],m=ec(t,e[0],e[1],e[2],i,o,s,r.epsilon),d=_=>{let w=mt(e[0].dataType),v=l===1?"vec2f":`mat2x${l}f`,S=l===1?w:`vec${l}<${w}>`,$=X("input",e[0].dataType,e[0].dims,l),E=ge("output",e[0].dataType,a,l);return` - @group(0) @binding(0) var input : array<${$.type.storage}>; - @group(0) @binding(1) var scaleInput : array<${v}>; - @group(0) @binding(2) var output : array<${E.type.storage}>; - struct Uniforms {H: u32, C : u32}; - @group(0) @binding(3) var uniforms: Uniforms; - - ${_.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], ${S}(scale[0]), ${S}(scale[1])); - }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:p}),getShaderSource:d},{inputs:[e[0],m]})},fm=(t,e)=>{e.format==="NHWC"?tc(t,t.inputs,e):t.compute(Jd(t.inputs,e))}}),rc,nc,mm,Uy=J(()=>{$e(),Me(),Te(),rc=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},nc=(t,e,r)=>{let n=e.simplified,a=t[0].dims,i=t[1],s=!n&&t[2],o=a,l=K.normalizeAxis(e.axis,a.length),u=K.sizeToDimension(a,l),p=K.sizeFromDimension(a,l),h=K.size(i.dims),m=s?K.size(s.dims):0;if(h!==p||s&&m!==p)throw new Error(`Size of X.shape()[axis:] == ${p}. - Size of scale and bias (if provided) must match this. - Got scale size of ${h} and bias size of ${m}`);let d=[];for(let A=0;A1,$=r>2,E=A=>{let P=mt(t[0].dataType),B=[X("x",t[0].dataType,t[0].dims,_),X("scale",i.dataType,i.dims,_)];s&&B.push(X("bias",s.dataType,s.dims,_)),B.push(ge("output",t[0].dataType,o,_)),S&&B.push(ge("mean_data_output",1,d)),$&&B.push(ge("inv_std_output",1,d));let D=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` - ${A.registerUniforms(D).declareVariables(...B)} - ${A.mainStart()} - ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} - let offset = global_idx * uniforms.norm_size_vectorized; - var mean_vector = ${Sr("f32",_)}; - var mean_square_vector = ${Sr("f32",_)}; - - for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { - let value = ${Mn(P,_,"x[h + offset]")}; - mean_vector += value; - mean_square_vector += value * value; - } - let mean = ${Dr("mean_vector",_)} / uniforms.norm_size; - let inv_std_dev = inverseSqrt(${Dr("mean_square_vector",_)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon); - - for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { - let f32input = ${Mn(P,_,"x[j + offset]")}; - let f32scale = ${Mn(P,_,"scale[j]")}; - output[j + offset] = ${B[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale - ${s?`+ ${Mn(P,_,"bias[j]")}`:""} - ); - } - - ${S?"mean_data_output[global_idx] = mean":""}; - ${$?"inv_std_output[global_idx] = inv_std_dev":""}; - }`},T=[{dims:o,dataType:t[0].dataType}];return S&&T.push({dims:d,dataType:1}),$&&T.push({dims:d,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${_};${r};${n}`,inputDependencies:w},getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:v}),getShaderSource:E}},mm=(t,e)=>{rc(t.inputs),t.compute(nc(t.inputs,e,t.outputCount))}}),ac,ic,gm,_m,Wy=J(()=>{$e(),Me(),ct(),Te(),ac=(t,e)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=t[0],n=r.dims.length;if(r.dims[n-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let a=Math.floor((e.k+e.blockSize-1)/e.blockSize),i=e.blockSize/8*e.bits,s=t[1];if(!K.areEqual(s.dims,[e.n,a,i]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(K.size(o)!==e.n*a)throw new Error("scales input size error.");if(t.length===4){let l=t[3].dims,u=e.bits>4?e.n*a:e.n*Math.floor((a+1)/2);if(K.size(l)!==u)throw new Error("zeroPoints input size error.")}},ic=(t,e,r,n)=>{let a=t[0].dims,i=a.length,s=Math.floor((e.k+e.blockSize-1)/e.blockSize),o=a[i-2],l=e.k,u=e.n,p=a.slice(0,i-2),h=K.size(p),m=e.blockSize/8*e.bits/4,d=t[0].dataType,_=ot(o),w=ot(e.k),v=ot(m),S=$a(d),$=o*s*S,E=Math.floor(n/$),T=s<=r[0]&&E>0,A=!T||E>=4?ot(u):E>=2&&ot(u)>=2?2:1,P=p.concat([o,u]),B=K.size(P)/A/_,D=T?[]:[{type:12,data:B},{type:12,data:e.blockSize}],q=[h,o,l/w],H=K.convertShape(t[1].dims).slice();H.splice(-1,1,m/v),D.push(...ye(q)),D.push(...ye(H)),D.push(...ye(t[2].dims)),t.length===4&&D.push(...ye(K.convertShape(t[3].dims)));let ie=[h,o,u/A];D.push(...ye(ie));let te=de=>{let se=q.length,M=X("a",t[0].dataType,se,w),R=X("b",12,H.length,v),G=X("scales",t[2].dataType,t[2].dims.length),re=[M,R,G],ee=t.length===4?X("zero_points",12,t[3].dims.length):void 0;ee&&re.push(ee);let ne=ie.length,W=ge("output",t[0].dataType,ne,A),ae=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],fe=mt(t[0].dataType),Ce=(()=>{switch(w){case 1:return`array<${fe}, 8>`;case 2:return`mat4x2<${fe}>`;case 4:return`mat2x4<${fe}>`;default:throw new Error(`${w}-component is not supported.`)}})(),Be=` - for (var word: u32 = 0; word < ${m}; word += ${v}) { - ${R.indicesSet("b_indices","2","word")}; - let b_data = ${R.getByIndices("b_indices")}; - for (var i: u32 = 0; i < ${v}; i++) { - let b_value: u32 = ${v===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 = ${Ce}(${Array.from({length:4},(Qe,We)=>`${fe}(b_value_lower[${We}]), ${fe}(b_value_upper[${We}])`).join(", ")}); - let b_dequantized_values = ${w===1?`${Ce}(${Array.from({length:8},(Qe,We)=>`(b_quantized_values[${We}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Ce}(${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 < ${T?o:_}u; m++) { - ${M.indicesSet("a_indices",se-2,T?"m":`row * ${_} + m`)}; - ${M.indicesSet("a_indices",se-1,"word_offset")}; - var input_offset = ${M.indicesToOffset("a_indices")}; - var a_data: ${Ce}; - for (var j: u32 = 0; j < ${8/w}; j++) { - a_data[j] = ${M.getByOffset("input_offset")}; - input_offset++; - } - ${T?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${A>1?"[c]":""} += ${Array.from({length:8/w},(Qe,We)=>`${w===1?`a_data[${We}] * b_dequantized_values[${We}]`:`dot(a_data[${We}], b_dequantized_values[${We}])`}`).join(" + ")}; - } - word_offset += ${8/w}; - } - }`,Ye=ee?` - zero_point_offset += 4; - if (zero_point_offset == 32) { - zero_point_offset = 0; - zero_point_index++; - zero_point_word = ${ee.getByOffset("zero_point_index")}; - }`:"";return T?` - var workgroup_shared: array<${W.type.value}, ${o*s}>; - ${de.declareVariables(...re,W)} - ${de.mainStart([s,1,1])} - var a_indices: ${M.type.indices}; - var block = local_id.x; - var col = workgroup_id.y; - var batch = workgroup_id.z; - ${M.indicesSet("a_indices","0","batch")}; - // Two zero points are packed into one byte when uniforms.bits is 4. - for (var c: u32 = 0; c < ${A}; c++) { - let col_times_components_plus_c = col * ${A} + c; - ${ee?` - var zero_point_bytes_per_col: u32 = (${s} + 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: ${R.type.indices}; - ${R.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 * ${s} + block; - let scale = ${G.getByOffset("scales_index")}; - // The default zero point is 8 for unsigned 4-bit quantization. - let zero_point = ${fe}(${ee?"(zero_point_word) & 0xFu":8}); - ${R.indicesSet("b_indices","1","block")}; - var word_offset: u32 = block * ${e.blockSize/w}; - var workgroup_shared_offset: u32 = block * ${o}; - ${Be} - } - workgroupBarrier(); - if (local_id.x == 0u) { - var output_indices: ${W.type.indices}; - ${W.indicesSet("output_indices","0","batch")}; - ${W.indicesSet("output_indices",ne-1,"col")}; - ${W.indicesSet("output_indices",ne-2,"0")}; - var output_offset = ${W.indicesToOffset("output_indices")}; - for (var m: u32 = 0u; m < ${o}u; m++) { - var output_value: ${W.type.value} = ${W.type.value}(0); - var workgroup_shared_offset: u32 = m; - for (var b: u32 = 0u; b < ${s}u; b++) { - output_value += workgroup_shared[workgroup_shared_offset]; - workgroup_shared_offset += ${o}; - } - ${W.setByOffset("output_offset","output_value")}; - output_offset += ${u/A}; - } - } - }`:` - ${de.registerUniforms(ae).declareVariables(...re,W)} - ${de.mainStart()} - ${de.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - var output_values: array<${W.type.value}, ${_}>; - var output_indices = ${W.offsetToIndices("global_idx")}; - var col = ${W.indicesGet("output_indices",ne-1)}; - var row = ${W.indicesGet("output_indices",ne-2)}; - var a_indices: ${M.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 * ${A} * ((${s} + 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 * ${s*A}; - var b_indices: ${R.type.indices}; - for (var c: u32 = 0; c < ${A}; c++) { - ${R.indicesSet("b_indices","0",`col * ${A} + c`)}; - var block_offset: u32 = 0; - for (var block: u32 = 0; block < ${s}; block++) { - // The scale and zero points are computed per block. - let scale = ${G.getByOffset("scale_index")}; - // The default zero point is 8 for unsigned 4-bit quantization. - let zero_point = ${fe}(${ee?"extractBits(zero_point_word, zero_point_offset, 4)":8}); - ${R.indicesSet("b_indices","1","block")}; - var word_offset: u32 = block_offset; - ${Be} - scale_index++; - ${Ye} - block_offset += uniforms.block_size / ${w}; - } - // 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) { - ${Ye} - }`:""} - } - for (var k: u32 = 0u; k < ${_}u; k++) { - ${W.indicesSet("output_indices",ne-2,`${_} * row + k`)}; - ${W.setByIndices("output_indices","output_values[k]")} - } - }`};return{name:T?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${o};${d};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:P,dataType:d}],name:T?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:T?{x:1,y:Math.ceil(u/A),z:h}:{x:Math.ceil(B/64)},programUniforms:D}),getShaderSource:te}},gm=(t,e)=>{ac(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(ic(t.inputs,e,r,n))},_m=t=>Ke(t)}),St,sc,ym,Ks,oc,ki,wm,Vy=J(()=>{$e(),Me(),ct(),Xo(),Xh(),Te(),Ia(),St=(t,e)=>t.length>e&&t[e].dims.length>0&&K.size(t[e].dims)>0?t[e]:void 0,sc=(t,e)=>{let r=t[0],n=St(t,1),a=St(t,2),i=St(t,3),s=St(t,4),o=St(t,5),l=St(t,6),u=St(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 p=!1,h=r.dims[0],m=r.dims[1],d=r.dims.length===3?p?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],_=m,w=0,v=0,S=Math.floor(d/e.numHeads);if(l&&u){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(l.dims[0]!==h||l.dims[1]!==e.numHeads||l.dims[3]!==S)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[0]!==h||u.dims[1]!==e.numHeads||u.dims[3]!==S)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[2]!==u.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(u.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');w=l.dims[2],v=l.dims[2]}else if(l||u)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let $;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');$=2,_=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==e.numHeads||n.dims[3]!==2||n.dims[4]!==S)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.');$=5,_=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==S)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for 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0.5;`;default:throw new Error(`Coordinate transform mode ${t} is not supported`)}})()+"}",Sc=(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 ${t} is not supported`)}})()+"}",kc=(t,e,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),a=t.length===0?n:t.slice();return e.length>0?(e.forEach((i,s)=>{n[i]=a[s],n[s+r]=a[e.length+s]}),n):a},Ec=(t,e,r,n)=>{let a=[];if(r.length>0)if(n.length>0){if(t.forEach(i=>a.push(i)),Math.max(...n)>t.length)throw new 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array<${t.type.value}, ${r.length}>; - for (var i:u32 = 0; i < ${r.length}; i++) { - var output_index = ${t.indicesGet("output_indices","i")}; - var scale = ${Se("uniforms.scales","i",n)}; - var roi_low = ${Se("uniforms.roi","i",a)}; - var roi_hi = ${Se("uniforms.roi",`i + ${e.length}`,a)}; - if (scale == 1.0) { - original_indices[i] = ${t.type.value}(output_index); - } else { - var input_shape_i = ${Se("uniforms.input_shape","i",e.length)}; - var output_shape_i = ${Se("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; - }`,Ac=(t,e,r,n,a,i,s)=>` - 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 = ${Se("uniforms.scales","i",a)}; - if (scale == 1.0) { - input_index = output_index; - } else { - var roi_low = ${Se("uniforms.roi","i",i)}; - var roi_hi = ${Se("uniforms.roi",`i + ${r.length}`,i)}; - var input_shape_i = ${Se("uniforms.input_shape","i",r.length)}; - var output_shape_i = ${Se("uniforms.output_shape","i",n.length)}; - var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, - input_shape_i, roi_low, roi_hi); - if (!${s} || (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; - }`,Ic=(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 >= ${Se("uniforms.input_shape","i",e.length)}) { - return false; - } - } - return true; - }`,no=(t,e,r,n)=>t.rank>n?` - ${t.indicesSet("input_indices",e,"channel")}; - ${t.indicesSet("input_indices",r,"batch")}; -`:"",Mc=(t,e,r,n,a)=>{let[i,s,o,l]=r.length===2?[-1,0,1,-1]:[0,2,3,1],u=t.type.value;return` - fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${u} { - var input_indices: ${t.type.indices}; - ${t.indicesSet("input_indices",s,`max(0, min(row, ${r[s]} - 1))`)}; - ${t.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)}; - ${no(t,l,i,2)} - return ${t.getByIndices("input_indices")}; - } - - fn bilinearInterpolation(output_indices: ${e.type.indices}) -> ${u} { - var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); - var row:${u} = originalIndices[${s}]; - var col:${u} = originalIndices[${o}]; - ${n?`if (row < 0 || row > (${r[s]} - 1) || col < 0 || col > (${r[o]} - 1)) { - return ${a}; - }`:""}; - row = max(0, min(row, ${r[s]} - 1)); - col = max(0, min(col, ${r[o]} - 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 = ${r.length>2?`u32(originalIndices[${l}])`:"0"}; - var batch: u32 = ${r.length>2?`u32(originalIndices[${i}])`:"0"}; - var x11: ${u} = getInputValue(batch, channel, row1, col1); - var x12: ${u} = getInputValue(batch, channel, row1, col2); - var x21: ${u} = getInputValue(batch, channel, row2, col1); - var x22: ${u} = getInputValue(batch, channel, row2, col2); - var dx1: ${u} = abs(row - ${u}(row1)); - var dx2: ${u} = abs(${u}(row2) - row); - var dy1: ${u} = abs(col - ${u}(col1)); - var dy2: ${u} = abs(${u}(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); - }`},Oc=(t,e,r,n,a,i,s,o,l,u)=>{let p=r.length===2,[h,m]=p?[0,1]:[2,3],d=t.type.value,_=w=>{let v=w===h?"row":"col";return` - fn ${v}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${e.type.indices}) -> ${d} { - var output_index = ${e.indicesGet("output_indices",w)}; - var originalIdx: ${d} = getOriginalCoordinateFromResizedCoordinate(output_index, ${a[w]}, - ${n[w]}, ${r[w]}, ${i[w]}, ${i[w]} + ${r.length}); - var fractOriginalIdx: ${d} = originalIdx - floor(originalIdx); - var coefs = getCubicInterpolationCoefs(fractOriginalIdx); - - if (${o} && (originalIdx < 0 || originalIdx > (${r[w]} - 1))) { - return ${l}; - } - var data: array<${d}, 4> = array<${d}, 4>(0.0, 0.0, 0.0, 0.0); - for (var i: i32 = -1; i < 3; i++) { - var ${v}: ${d} = originalIdx + ${d}(i); - if (${v} < 0 || ${v} >= ${r[w]}) { - ${u?`coefs[i + 1] = 0.0; - continue;`:o?`return ${l};`:`${v} = max(0, min(${v}, ${r[w]} - 1));`}; - } - var input_indices_copy: ${t.type.indices} = input_indices; - ${t.indicesSet("input_indices_copy",w,`u32(${v})`)}; - data[i + 1] = ${w===h?t.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; - } - return cubicInterpolation1D(data, coefs); - }`};return` - ${_(h)}; - ${_(m)}; - fn getCubicInterpolationCoefs(s: ${d}) -> array<${d}, 4> { - var absS = abs(s); - var coeffs: array<${d}, 4> = array<${d}, 4>(0.0, 0.0, 0.0, 0.0); - var oneMinusAbsS: ${d} = 1.0 - absS; - var twoMinusAbsS: ${d} = 2.0 - absS; - var onePlusAbsS: ${d} = 1.0 + absS; - coeffs[0] = ((${s} * onePlusAbsS - 5 * ${s}) * onePlusAbsS + 8 * ${s}) * onePlusAbsS - 4 * ${s}; - coeffs[1] = ((${s} + 2) * absS - (${s} + 3)) * absS * absS + 1; - coeffs[2] = ((${s} + 2) * oneMinusAbsS - (${s} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; - coeffs[3] = ((${s} * twoMinusAbsS - 5 * ${s}) * twoMinusAbsS + 8 * ${s}) * twoMinusAbsS - 4 * ${s}; - return coeffs; - } - - fn cubicInterpolation1D(x: array<${d}, 4>, coefs: array<${d}, 4>) -> ${d} { - var coefsSum: ${d} = 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}) -> ${d} { - var input_indices: ${t.type.indices} = output_indices; - return colCubicInterpolation(input_indices, output_indices); - } - `},zc=(t,e,r,n,a)=>{let[i,s,o,l,u]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],p=t.type.value;return` - fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${p} { - var input_indices: ${t.type.indices}; - ${t.indicesSet("input_indices",s,`max(0, min(depth, ${r[s]} - 1))`)}; - ${t.indicesSet("input_indices",o,`max(0, min(height, ${r[o]} - 1))`)}; - ${t.indicesSet("input_indices",l,`max(0, min(width, ${r[l]} - 1))`)}; - ${no(t,u,i,3)} - return ${t.getByIndices("input_indices")}; - } - - fn trilinearInterpolation(output_indices: ${e.type.indices}) -> ${p} { - var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); - var depth:${p} = originalIndices[${s}]; - var height:${p} = originalIndices[${o}]; - var width:${p} = originalIndices[${l}]; - ${n?`if (depth < 0 || depth > (${r[s]} - 1) || height < 0 || height > (${r[o]} - 1) || width < 0 || (width > ${r[l]} - 1)) { - return ${a}; - }`:""}; - - depth = max(0, min(depth, ${r[s]} - 1)); - height = max(0, min(height, ${r[o]} - 1)); - width = max(0, min(width, ${r[l]} - 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 = ${r.length>3?`u32(originalIndices[${u}])`:"0"}; - var batch: u32 = ${r.length>3?`u32(originalIndices[${i}])`:"0"}; - - var x111: ${p} = getInputValue(batch, channel, depth1, height1, width1); - var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2); - var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1); - var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2); - var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1); - var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2); - var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1); - var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2); - var dx1: ${p} = abs(depth - ${p}(depth1)); - var dx2: ${p} = abs(${p}(depth2) - depth); - var dy1: ${p} = abs(height - ${p}(height1)); - var dy2: ${p} = abs(${p}(height2) - height); - var dz1: ${p} = abs(width - ${p}(width1)); - var dz2: ${p} = abs(${p}(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); - }`},Pc=(t,e,r,n,a,i)=>{let s=t.dims,o=kc(i,e.axes,s.length),l=Ec(s,n,a,e.axes),u=n.slice();n.length===0&&(u=s.map(($,E)=>$===0?1:l[E]/$),e.keepAspectRatioPolicy!=="stretch"&&(l=Cc(s,u,e)));let p=ge("output",t.dataType,l.length),h=X("input",t.dataType,s.length),m=K.size(l),d=s.length===l.length&&s.every(($,E)=>$===l[E]),_=e.coordinateTransformMode==="tf_crop_and_resize",w=e.extrapolationValue,v=h.type.value,S=$=>` - ${d?"":` - ${xc(e.coordinateTransformMode,v)}; - ${(()=>{switch(e.mode){case"nearest":return` - ${Ic(h,s)}; - ${Sc(e.nearestMode,r,v)}; - ${Ac(h,p,s,l,u.length,o.length,_)}; - `;case"linear":return` - ${Tc(p,s,l,u.length,o.length)}; - ${(()=>{if(s.length===2||s.length===4)return`${Mc(h,p,s,_,w)}`;if(s.length===3||s.length===5)return`${zc(h,p,s,_,w)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; - `;case"cubic":return` - ${(()=>{if(s.length===2||s.length===4)return`${Oc(h,p,s,l,u,o,e.cubicCoeffA,_,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")}})()}; - `} - ${$.registerUniform("output_size","u32").registerUniform("scales","f32",u.length).registerUniform("roi","f32",o.length).declareVariables(h,p)} - ${$.mainStart()} - ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - ${d?"output[global_idx] = input[global_idx];":` - let output_indices = ${p.offsetToIndices("global_idx")}; - var input_indices: ${h.type.indices}; - ${(()=>{switch(e.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); - if (checkInputIndices(input_indices)) { - output[global_idx] = ${h.getByIndices("input_indices")}; - } else { - output[global_idx] = ${e.extrapolationValue}; - }`;case"linear":return`output[global_idx] = ${s.length===2||s.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}|${r}|${u.length>0?u:""}|${a.length>0?a:""}|${o.length>0?o:""}|${d}|${s}`,inputDependencies:["rank"]},getShaderSource:S,getRunData:()=>({outputs:[{dims:l,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:[{type:12,data:m},{type:1,data:u},{type:1,data:o},...ye(s,l)]})}},Rc=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},Im=(t,e)=>{let r=[],n=[],a=[],i=Rc(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");$c(t.inputs,e,i,r,n,a),t.compute(Pc(t.inputs[0],e,i,r,n,a),{inputs:[0]})},Mm=t=>{let e=t.antialias,r=t.axes,n=t.coordinateTransformMode,a=t.cubicCoeffA,i=t.excludeOutside!==0,s=t.extrapolationValue,o=t.keepAspectRatioPolicy,l=t.mode,u=t.nearestMode===""?"simple":t.nearestMode;return Ke({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:i,extrapolationValue:s,keepAspectRatioPolicy:o,mode:l,nearestMode:u})}}),Bc,Dc,Om,Ky=J(()=>{$e(),Me(),ct(),Te(),Bc=(t,e)=>{let[r,n,a,i]=t,{numHeads:s,rotaryEmbeddingDim:o}=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(!K.areEqual(n.dims,[])&&!K.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(i.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(!K.areEqual(a.dims,i.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(o>0&&s===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let l=r.dims[0],u=r.dims[r.dims.length-2],p=a.dims[0],h=K.sizeFromDimension(r.dims,1)/u,m=o===0?a.dims[1]*2:h/s;if(o>m)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(l!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(u!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(m/2!==a.dims[1]&&o/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 ${a.dims[1]}`);if(u>p)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Dc=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:i}=e,s=t[0].dims[0],o=K.sizeFromDimension(t[0].dims,1),l=t[0].dims[t[0].dims.length-2],u=o/l,p=t[2].dims[1],h=a===0?p*2:u/n,m=new Array(s,l,u/h,h-p),d=K.computeStrides(m),_=[{type:1,data:i},{type:12,data:m},{type:12,data:d},...t[0].dims.length===3?new Array({type:12,data:[o,u,h,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[o,h,l*h,1]}):[],...ye(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],w=v=>{let S=X("input",t[0].dataType,t[0].dims.length),$=X("position_ids",t[1].dataType,t[1].dims.length),E=X("cos_cache",t[2].dataType,t[2].dims.length),T=X("sin_cache",t[3].dataType,t[3].dims.length),A=ge("output",t[0].dataType,t[0].dims.length);return v.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:m.length},{name:"global_strides",type:"u32",length:d.length},{name:"input_output_strides",type:"u32",length:d.length}]),` - ${v.declareVariables(S,$,E,T,A)} - - ${v.mainStart(Pn)} - let half_rotary_emb_dim = uniforms.${E.name}_shape[1]; - let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; - let size = uniforms.global_shape[0] * uniforms.global_strides[0]; - ${v.guardAgainstOutOfBoundsWorkgroupSizes("size")} - - if (bsnh[3] < half_rotary_emb_dim) { - let position_ids_idx = - ${$.broadcastedIndicesToOffset("bsnh.xy",ge("",$.type.tensor,2))}; - let position_id = - u32(${$.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 = ${S.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} - - ${S.getByOffset("j")} * ${T.get("position_id","bsnh[3]")}; - ${A.setByOffset("i","re")} - let im = ${S.getByOffset("i")} * ${T.get("position_id","bsnh[3]")} + - ${S.getByOffset("j")} * ${E.get("position_id","bsnh[3]")}; - ${A.setByOffset("j","im")} - } else { - let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; - ${A.setByOffset("k",S.getByOffset("k"))} - } - }`};return{name:"RotaryEmbedding",shaderCache:{hint:Ke({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(K.size(m)/Pn)},programUniforms:_})}},Om=(t,e)=>{Bc(t.inputs,e),t.compute(Dc(t.inputs,e))}}),Nc,Fc,zm,Yy=J(()=>{$e(),Me(),Te(),Nc=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],i=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]!==i)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 s=t[3];if(s.dims.length!==1)throw new Error("Beta must be 1D");if(s.dims[s.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let s=t[4];if(s.dims.length!==1)throw new Error("Bias must be 1D");if(s.dims[s.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},Fc=(t,e,r,n)=>{let a=e.simplified,i=t[0].dims,s=K.size(i),o=i,l=s,u=i.slice(-1)[0],p=n?i.slice(0,-1).concat(1):[],h=!a&&t.length>3,m=t.length>4,d=n&&r>1,_=n&&r>2,w=r>3,v=ot(u),S=[{type:12,data:l},{type:12,data:v},{type:12,data:u},{type:1,data:e.epsilon}],$=T=>{let A=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],P=[X("x",t[0].dataType,t[0].dims,v),X("skip",t[1].dataType,t[1].dims,v),X("gamma",t[2].dataType,t[2].dims,v)];h&&P.push(X("beta",t[3].dataType,t[3].dims,v)),m&&P.push(X("bias",t[4].dataType,t[4].dims,v)),P.push(ge("output",t[0].dataType,o,v)),d&&P.push(ge("mean_output",1,p)),_&&P.push(ge("inv_std_output",1,p)),w&&P.push(ge("input_skip_bias_sum",t[0].dataType,o,v));let B=mt(t[0].dataType);return` - - ${T.registerUniforms(A).declareVariables(...P)} - - ${T.mainStart()} - ${T.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 = ${Sr("f32",v)}; - var squareSum = ${Sr("f32",v)}; - for (var i: u32 = 0; i < hidden_size_vectorized; i++) { - let skip_value = skip[offset + i]; - let bias_value = ${m?"bias[i]":B+"(0.0)"}; - let input_value = x[offset + i]; - let value = input_value + skip_value + bias_value; - ${w?"input_skip_bias_sum[offset + i] = value;":""} - output[offset + i] = value; - let f32_value = ${Mn(B,v,"value")}; - sum += f32_value; - squareSum += f32_value * f32_value; - } - let mean = ${Dr("sum",v)} / f32(uniforms.hidden_size); - let inv_std_dev = inverseSqrt(${Dr("squareSum",v)} / f32(uniforms.hidden_size) ${a?"":"- mean * mean"} + uniforms.epsilon); - ${d?"mean_output[global_idx] = mean;":""} - ${_?"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?"":`- ${B}(mean)`}) * ${B}(inv_std_dev) * gamma[i] ${h?"+ beta[i]":""}; - } - }`},E=[{dims:o,dataType:t[0].dataType}];return r>1&&E.push({dims:p,dataType:1}),r>2&&E.push({dims:p,dataType:1}),r>3&&E.push({dims:i,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${v};${d};${_};${w}`,inputDependencies:t.map((T,A)=>"type")},getShaderSource:$,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/u/64)},programUniforms:S})}},zm=(t,e)=>{Nc(t.inputs);let r=[0];t.outputCount>1&&r.push(-3),t.outputCount>2&&r.push(-3),t.outputCount>3&&r.push(3),t.compute(Fc(t.inputs,e,t.outputCount,!1),{outputs:r})}}),Lc,la,Uc,ao,Wc,Vc,Pm,Rm,Xy=J(()=>{$e(),Me(),ct(),Te(),Lc=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");if(e.axes.length!==0){if(e.axes.length!==e.starts.length||e.axes.length!==e.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(e.starts.length!==e.ends.length)throw new Error("starts and ends must have the same length");t.slice(1).forEach((r,n)=>{if(t[n+1].dataType!==6&&t[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or 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u=r.slice(0);a.forEach((S,$)=>{u[S]=Math.ceil((o[S]-s[S])/i[S])});let p={dims:u,dataType:t[0].dataType},h=ge("output",t[0].dataType,u.length),m=X("input",t[0].dataType,t[0].dims.length),d=K.size(u),_=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:s.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:i.length}],w=[{type:12,data:d},{type:12,data:s},{type:6,data:l},{type:12,data:i},...ye(t[0].dims,u)],v=S=>` - ${S.registerUniforms(_).declareVariables(m,h)} - ${Wc(m,h,r)} - ${S.mainStart()} - ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - let output_indices = ${h.offsetToIndices("global_idx")}; - let input_indices = calculateInputIndices(output_indices); - ${h.setByOffset("global_idx",m.getByIndices("input_indices"))} - 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i=ge("output_data",a,r.length,4),s=X("a_data",e[1].dataType,e[1].dims.length,4),o=X("b_data",e[2].dataType,e[2].dims.length,4),l=X("c_data",e[0].dataType,e[0].dims.length,4),u,p=(h,m,d)=>`select(${m}, ${h}, ${d})`;if(!n)u=i.setByOffset("global_idx",p(s.getByOffset("global_idx"),o.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let h=(m,d,_="")=>{let w=`a_data[index_a${d}][component_a${d}]`,v=`b_data[index_b${d}][component_b${d}]`,S=`bool(c_data[index_c${d}] & (0xffu << (component_c${d} * 8)))`;return` - let output_indices${d} = ${i.offsetToIndices(`global_idx * 4u + ${d}u`)}; - let offset_a${d} = ${s.broadcastedIndicesToOffset(`output_indices${d}`,i)}; - let offset_b${d} = ${o.broadcastedIndicesToOffset(`output_indices${d}`,i)}; - let offset_c${d} = ${l.broadcastedIndicesToOffset(`output_indices${d}`,i)}; - let index_a${d} = offset_a${d} / 4u; - let index_b${d} = offset_b${d} / 4u; - let index_c${d} = offset_c${d} / 4u; - let component_a${d} = offset_a${d} % 4u; - let 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u={kernelId:this.backend.currentKernelId,computePipeline:t.computePipeline,bindGroup:l,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(u)}s.setPipeline(t.computePipeline),s.setBindGroup(0,l),s.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(),jt(t.programInfo.name)}dispose(){}build(t,e){ur(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=vh(e,this.backend.device.limits),i=t.getShaderSource(a),s=`${n.join(` -`)} -${a.additionalImplementations} -${i}`,o=r.createShaderModule({code:s,label:t.name});at("verbose",()=>`[WebGPU] ${t.name} shader code: ${s}`);let l=r.createComputePipeline({compute:{module:o,entryPoint:"main"},layout:"auto",label:t.name});return jt(t.name),{programInfo:t,computePipeline:l,uniformVariablesInfo:a.variablesInfo}}normalizeDispatchGroupSize(t){let e=typeof t=="number"?t:t.x,r=typeof t=="number"?1:t.y||1,n=typeof t=="number"?1:t.z||1,a=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(e<=a&&r<=a&&n<=a)return[e,r,n];let i=e*r*n,s=Math.ceil(Math.sqrt(i));if(s>a){if(s=Math.ceil(Math.cbrt(i)),s>a)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[s,s,s]}else return[s,s,1]}}}),rp,np,ap,Gm,nw=J(()=>{dr(),$e(),un(),wy(),by(),tw(),rw(),rp=(t,e)=>{if(e.length!==t.length)throw new Error(`inputDependencies length ${e.length} is not equal to inputTensors length ${t.length}.`);let r=[];for(let n=0;n{let n=t.name;return t.shaderCache?.hint&&(n+="["+t.shaderCache.hint+"]"),n+=":"+r+`:${rp(e,t.shaderCache?.inputDependencies??new Array(e.length).fill("dims"))}`,n},ap=class{constructor(t){t&&(this.architecture=t.architecture,this.vendor=t.vendor)}isArchitecture(t){return this.architecture===t}isVendor(t){return this.vendor===t}},Gm=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 ap(await e.requestAdapterInfo()),this.gpuDataManager=wh(this),this.programManager=new Vm(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,_h(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;ur(),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(()=>{let e=new BigUint64Array(t.getMappedRange()),r=this.pendingQueries.get(t);for(let n=0;n"u"&&(this.queryTimeBase=m);let _=Number(m-this.queryTimeBase),w=Number(d-this.queryTimeBase);if(!Number.isSafeInteger(_)||!Number.isSafeInteger(w))throw new RangeError("incorrect timestamp range");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:p.map(v=>({dims:v.dims,dataType:Jr(v.dataType)})),outputsMetadata:h.map(v=>({dims:v.dims,dataType:Jr(v.dataType)})),kernelId:i,kernelType:o,kernelName:l,programName:u,startTime:_,endTime:w});else{let v="";p.forEach(($,E)=>{v+=`input[${E}]: [${$.dims}] | ${Jr($.dataType)}, `});let S="";h.forEach(($,E)=>{S+=`output[${E}]: [${$.dims}] | ${Jr($.dataType)}, `}),console.log(`[profiling] kernel "${i}|${o}|${l}|${u}" ${v}${S}execution time: ${w-_} ns`)}va("GPU",`${u}::${m}::${d}`)}t.unmap(),this.pendingQueries.delete(t)}),jt()}run(t,e,r,n,a,i){ur(t.name);let s=[];for(let $=0;$E):r;if(p.length!==o.length)throw new Error(`Output size ${p.length} must be equal to ${o.length}.`);let h=[],m=[];for(let $=0;$=i)throw new Error(`Invalid output index: ${p[$]}`);if(p[$]===-3)continue;let E=p[$]===-1,T=p[$]===-2,A=E||T?a(o[$].dataType,o[$].dims):n(p[$],o[$].dataType,o[$].dims);if(h.push(A),A.data===0)continue;let P=this.gpuDataManager.get(A.data);if(!P)throw new Error(`no GPU data for output: ${A.data}`);if(E&&this.temporaryData.push(P),T){let B=this.kernelPersistentData.get(this.currentKernelId);B||(B=[],this.kernelPersistentData.set(this.currentKernelId,B)),B.push(P)}m.push(P)}if(s.length!==e.length||m.length!==h.length){if(m.length===0)return jt(t.name),h;throw new Error(`Program ${t.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let d;if(u){let $=0,E=[];u.forEach(B=>{let D=typeof B.data=="number"?[B.data]:B.data;if(D.length===0)return;let q=B.type===10?2:4,H,ie;B.type===10?(ie=D.length>4?16:D.length>2?8:D.length*q,H=D.length>4?16:q*D.length):(ie=D.length<=2?D.length*q:16,H=16),$=Math.ceil($/ie)*ie,E.push($);let te=B.type===10?8:4;$+=D.length>4?Math.ceil(D.length/te)*H:D.length*q});let T=16;$=Math.ceil($/T)*T;let A=new ArrayBuffer($);u.forEach((B,D)=>{let q=E[D],H=typeof B.data=="number"?[B.data]:B.data;if(B.type===6)new Int32Array(A,q,H.length).set(H);else if(B.type===12)new Uint32Array(A,q,H.length).set(H);else if(B.type===10)new Uint16Array(A,q,H.length).set(H);else if(B.type===1)new Float32Array(A,q,H.length).set(H);else throw new Error(`Unsupported uniform type: ${Jr(B.type)}`)});let P=this.gpuDataManager.create($,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(P.buffer,0,A,0,$),this.gpuDataManager.release(P.id),d={offset:0,size:$,buffer:P.buffer}}let _=this.programManager.normalizeDispatchGroupSize(l),w=_[1]===1&&_[2]===1,v=np(t,e,w),S=this.programManager.getArtifact(v);if(S||(S=this.programManager.build(t,_),this.programManager.setArtifact(v,S),at("info",()=>`[artifact] key: ${v}, programName: ${t.name}`)),u&&S.uniformVariablesInfo){if(u.length!==S.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${S.uniformVariablesInfo.length}, got ${u.length} in program "${S.programInfo.name}".`);for(let $=0;$`[ProgramManager] run "${t.name}" (key=${v}) with ${_[0]}x${_[1]}x${_[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let $={kernelId:this.currentKernelId,programName:S.programInfo.name,inputTensorViews:e,outputTensorViews:h};this.pendingKernels.push($),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push($)}return this.programManager.run(S,s,m,_,d),jt(t.name),h}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=Wm.get(t);if(!a)throw new Error(`kernel not implemented: ${t}`);let i={kernelType:t,kernelName:n,kernelEntry:a[0],attributes:[a[1],r]};this.kernels.set(e,i)}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: ${t}`);let a=n.kernelType,i=n.kernelName,s=n.kernelEntry,o=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${a}] ${i}" is not allowed to be called recursively`);this.currentKernelId=t,o[0]&&(o[1]=o[0](o[1]),o[0]=void 0),at("info",()=>`[WebGPU] Start to run kernel "[${a}] ${i}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),s(e,o[1]),0}catch(u){return r.push(Promise.resolve(`[WebGPU] Kernel "[${a}] ${i}" failed. ${u}`)),1}finally{l&&r.push(this.device.popErrorScope().then(u=>u?`GPU validation error for kernel "[${a}] ${i}": ${u.message}`:null));for(let u of this.temporaryData)this.gpuDataManager.release(u.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 i=a.get(e),s=this.gpuDataManager.registerExternalBuffer(r,n,i?.[1]);return a.set(e,[s,r]),s}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 $o(this,t,e);return yh(n.buffer,r)}}writeTimestamp(t){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){this.queryType="none",(this.env.webgpu.profiling?.mode==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){at("info","captureBegin"),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus="capturing"}captureEnd(){at("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){at("info","replay"),this.sessionStatus="replaying";let t=this.capturedCommandList.get(this.currentSessionId),e=this.capturedPendingKernels.get(this.currentSessionId),r=t.length;this.pendingKernels=[];for(let n=0;n=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onReleaseSession(t){this.unregisterBuffers(t),this.capturedCommandList.has(t)&&this.capturedCommandList.delete(t),this.capturedPendingKernels.has(t)&&this.capturedPendingKernels.delete(t),this.gpuDataManager.onReleaseSession(t)}onRunStart(t){this.currentSessionId=t,this.setQueryType()}}}),Hm={};Dn(Hm,{init:()=>jm});var Ei,ip,jm,aw=J(()=>{$e(),nw(),un(),Me(),Ei=class qm{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=K.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=K.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=K.size(this.dims);return e===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,e)}reshape(e){if(K.size(e)!==K.size(this.dims))throw new Error("Invalid new shape");return new qm(this.module,this.dataType,this.data,e)}},ip=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 i=n[a++];this.outputCount=n[a++],this.customDataOffset=n[a++],this.customDataSize=n[a++];let s=[];for(let o=0;otypeof s=="number"?this.inputs[s]:s)??this.inputs,n=e?.outputs??[],a=(s,o,l)=>new Ei(this.module,o,this.output(s,l),l),i=(s,o)=>{let l=$a(s);if(!l)throw new Error(`Unsupported data type: ${s}`);let u=l*K.size(o),p=u>0?this.backend.gpuDataManager.create(u).id:0;return new Ei(this.module,s,p,o)};return this.backend.run(t,r,n,a,i,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 i=0;i{let a=e.jsepInit;if(!a)throw new Error("Failed to initialize JSEP. 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c=a+i.kvSequenceLength,p=[i.batchSize,i.numHeads,i.sequenceLength,c],h=u.scale===0?1/Math.sqrt(i.headSize):u.scale,d=Me(i.headSize),y=i.headSize/d,w=12,_={x:Math.ceil(c/w),y:Math.ceil(i.sequenceLength/w),z:i.batchSize*i.numHeads},v=[{type:12,data:i.sequenceLength},{type:12,data:y},{type:12,data:c},{type:12,data:i.numHeads},{type:1,data:h}],S=o?["type","type","type"]:["type","type"],A=I=>{let x=U("q",t.dataType,t.dims,d),E=U("key",r.dataType,r.dims,d),P=[x,E];o&&P.push(U("relative_position_bias",o.dataType,o.dims));let O=j("output",t.dataType,p),R=et(1,d),L=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"}];return`\n const TILE_SIZE = ${w}u;\n\n var tileQ: array<${x.type.storage}, ${w*w}>;\n var tileK: array<${x.type.storage}, ${w*w}>;\n ${I.registerUniforms(L).declareVariables(...P,O)}\n ${I.mainStart([w,w,1])}\n // x holds the N and y holds the M\n let headIdx = workgroup_id.z;\n let m = workgroup_id.y * TILE_SIZE;\n let n = workgroup_id.x * TILE_SIZE;\n let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;\n let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;\n\n var value = ${R}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {\n tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];\n }\n if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {\n tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];\n }\n workgroupBarrier();\n\n for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {\n value += ${R}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);\n }\n\n workgroupBarrier();\n }\n\n let headOffset = headIdx * uniforms.M * uniforms.N;\n if (global_id.y < uniforms.M && global_id.x < uniforms.N) {\n let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;\n var sum: f32 = ${(()=>{switch(d){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${d}`)}})()};\n output[outputIdx] = ${O.type.value} (sum * uniforms.alpha) + ${o?"relative_position_bias[outputIdx]":"0.0"};\n }\n }`};return{name:"AttentionProbs",shaderCache:{hint:`${d}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:p,dataType:t.dataType,gpuDataType:0}],dispatchGroup:_,programUniforms:v}),getShaderSource:A}},nc=(e,t,r,o,i)=>{let u=i+o.kvSequenceLength,a=[o.batchSize,o.sequenceLength,o.vHiddenSize],c=12,p={x:Math.ceil(o.vHeadSize/c),y:Math.ceil(o.sequenceLength/c),z:o.batchSize*o.numHeads},h=[{type:12,data:o.sequenceLength},{type:12,data:u},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.vHiddenSize}];return{name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType,gpuDataType:0}],dispatchGroup:p,programUniforms:h}),getShaderSource:w=>{let _=U("probs",t.dataType,t.dims),v=U("v",r.dataType,r.dims),S=j("output",t.dataType,a),A=[{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 = ${c}u;\n var tileQ: array<${_.type.value}, ${c*c}>;\n var tileK: array<${_.type.value}, ${c*c}>;\n ${w.registerUniforms(A).declareVariables(_,v,S)}\n ${w.mainStart([c,c,1])}\n let headIdx = workgroup_id.z;\n let m = global_id.y;\n let n = global_id.x;\n\n let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n let offsetB = headIdx * (uniforms.N * uniforms.K) + n;\n\n var value = ${_.type.storage}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m < uniforms.M && w + local_id.x < uniforms.K) {\n tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n }\n if (n < uniforms.N && w + local_id.y < uniforms.K) {\n tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n }\n workgroupBarrier();\n for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {\n value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];\n }\n workgroupBarrier();\n }\n\n // we need to transpose output from BNSH_v to BSND_v\n let batchIdx = workgroup_id.z / uniforms.num_heads;\n let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;\n if (m < uniforms.M && n < uniforms.N) {\n let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size\n + currentBatchHeadNumber * uniforms.N + n;\n output[outputIdx] = value;\n }\n }`}}},Pn=(e,t,r,o,i,u,a,c,p,h,d)=>{let y=e.outputCount>1,w=e.outputCount>2,_=y&&w?h.pastSequenceLength:0,v=_+h.kvSequenceLength,S=[h.batchSize,h.numHeads,v,h.headSize],A=a?[a,r]:[r],I=y?e.compute(En(A,2,S,r.dataType),{inputs:A,outputs:[1]})[0]:r,x=[h.batchSize,h.numHeads,v,h.headSize],E=c?[c,o]:[o],P=w?e.compute(En(E,2,x,o.dataType),{inputs:E,outputs:[2]})[0]:o,O=[t,I];p&&O.push(p);let R=e.compute(rc(e,t,I,p,h,d,_),{inputs:O,outputs:[-1]})[0];e.compute(tc(e,R,h.batchSize*h.numHeads*h.sequenceLength,v),{inputs:[R],outputs:[]});let L=[R,P];e.compute(nc(e,R,P,h,_),{inputs:L,outputs:[0]})},oc=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],o=t.sequenceLength,i=t.inputHiddenSize,u=t.headSize,a=12,c={x:Math.ceil(t.headSize/a),y:Math.ceil(t.sequenceLength/a),z:t.batchSize*t.numHeads},p=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:o},{type:12,data:i},{type:12,data:u},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],d=y=>{let w=j("output_q",p[0].dataType,r),_=j("output_k",p[0].dataType,r),v=j("output_v",p[0].dataType,r),S=U("input",p[0].dataType,p[0].dims),A=U("weight",p[1].dataType,p[1].dims),I=U("bias",p[2].dataType,p[2].dims),x=S.type.storage,E=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`\n const TILE_SIZE = ${a}u;\n var tileInput: array<${x}, ${a*a}>;\n var tileWeightQ: array<${x}, ${a*a}>;\n var tileWeightK: array<${x}, ${a*a}>;\n var tileWeightV: array<${x}, ${a*a}>;\n ${y.registerUniforms(E).declareVariables(S,A,I,w,_,v)}\n ${y.mainStart([a,a,1])}\n let batchIndex = workgroup_id.z / uniforms.num_heads;\n let headNumber = workgroup_id.z % uniforms.num_heads;\n let m = global_id.y;\n let n = global_id.x;\n\n let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;\n let biasOffsetQ = headNumber * uniforms.head_size;\n let biasOffsetK = uniforms.hidden_size + biasOffsetQ;\n let biasOffsetV = uniforms.hidden_size + biasOffsetK;\n\n var valueQ = ${x}(0);\n var valueK = ${x}(0);\n var valueV = ${x}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m < uniforms.M && w + local_id.x < uniforms.K) {\n tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n }\n if (n < uniforms.N && w + local_id.y < uniforms.K) {\n let offset = n + (w + local_id.y) * uniforms.ldb;\n tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n }\n workgroupBarrier();\n for (var k: u32 = 0u; k({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:c,programUniforms:h}),getShaderSource:d},{inputs:p,outputs:[-1,-1,-1]})},Xa=(e,t)=>{let r=ec(e.inputs,t),[o,i,u]=oc(e,r);return Pn(e,o,i,u,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var ic,ac,sc,Qa,Ja=Y(()=>{"use strict";$r();ye();Se();Ze();_e();ic=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(o,i,u)=>{let a=i.length;if(a!==o.length)throw new Error(`${u}: num dimensions != ${a}`);i.forEach((c,p)=>{if(c!==o[p])throw new Error(`${u}: dim[${p}] do not match`)})};if(e[0].dims.length>1){let o=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,o,"Invalid input scale"),r(e[2].dims,o,"Invalid input B"),r(e[3].dims,o,"Invalid input mean"),r(e[4].dims,o,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input var")},ac=(e,t)=>{let{epsilon:r,spatial:o,format:i}=t,u=e[0].dims,a=o?Me(u[u.length-1]):1,c=i==="NHWC"&&u.length>1?a:1,p=M.size(u)/a,h=o,d=h?u.length:u,y=U("x",e[0].dataType,e[0].dims,a),w=U("scale",e[1].dataType,e[1].dims,c),_=U("bias",e[2].dataType,e[2].dims,c),v=U("inputMean",e[3].dataType,e[3].dims,c),S=U("inputVar",e[4].dataType,e[4].dims,c),A=j("y",e[0].dataType,d,a),I=()=>{let E="";if(o)E=`let cOffset = ${u.length===1?"0u":i==="NHWC"?`outputIndices[${u.length-1}] / ${a}`:"outputIndices[1]"};`;else if(i==="NCHW")E=`\n ${A.indicesSet("outputIndices","0","0")}\n let cOffset = ${A.indicesToOffset("outputIndices")};`;else{E=`var cIndices = ${w.type.indices}(0);\n cIndices[0] = outputIndices[${u.length-1}];`;for(let P=1;P`\n const epsilon = ${r};\n ${E.registerUniform("outputSize","u32").declareVariables(y,w,_,v,S,A)}\n ${E.mainStart()}\n ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n var outputIndices = ${A.offsetToIndices(`global_idx * ${a}`)};\n ${I()}\n let scale = ${w.getByOffset("cOffset")};\n let bias = ${_.getByOffset("cOffset")};\n let inputMean = ${v.getByOffset("cOffset")};\n let inputVar = ${S.getByOffset("cOffset")};\n let x = ${y.getByOffset("global_idx")};\n let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;\n ${A.setByOffset("global_idx","value")}\n }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${o}_${a}`,inputDependencies:h?["rank","type","type","type","type"]:void 0},getShaderSource:x,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h?[{type:12,data:p},...Z(u)]:[{type:12,data:p}]})}},sc=e=>ve(e),Qa=(e,t)=>{let{inputs:r,outputCount:o}=e,i=sc({...t,outputCount:o});if(vr.webgpu.validateInputContent&&ic(r,i),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(ac(r,i))}});var uc,dc,es,ts=Y(()=>{"use strict";Se();_e();uc=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},dc=e=>{let t=e[0].dims,r=e[0].dims[2],o=M.size(t)/4,i=e[0].dataType,u=U("input",i,t,4),a=U("bias",i,[r],4),c=U("residual",i,t,4),p=j("output",i,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:d=>`\n const channels = ${r}u / 4;\n ${d.declareVariables(u,a,c,p)}\n\n ${d.mainStart()}\n ${d.guardAgainstOutOfBoundsWorkgroupSizes(o)}\n let value = ${u.getByOffset("global_idx")}\n + ${a.getByOffset("global_idx % channels")} + ${c.getByOffset("global_idx")};\n ${p.setByOffset("global_idx","value")}\n }`}},es=e=>{uc(e.inputs),e.compute(dc(e.inputs))}});var lc,ke,rs,ns,os,is,as,ss,us,ds,ls,cc,cs,ps,ms,fs,kn,hs,On,gs,ys,bs,ws,vs,$s,_s,Ss,xs,Cs,As,Is,Ts,Es,Ps,ks,Os,Rs,Bo,Do,Bs,Ds,zs,Rn=Y(()=>{"use strict";ye();Se();Ze();_e();lc=(e,t,r,o,i,u)=>{let a=Math.ceil(t/4),c="";typeof i=="string"?c=`${i}(a)`:c=i("a");let p=U("inputData",r,[a],4),h=j("outputData",o,[a],4);return`\n ${e.registerUniform("vec_size","u32").declareVariables(p,h)}\n\n ${u??""}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n\n let a = ${p.getByOffset("global_idx")};\n ${h.setByOffset("global_idx",c)}\n }`},ke=(e,t,r,o,i,u=e.dataType)=>({name:t,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:a=>lc(a,M.size(e.dims),e.dataType,u,r,o),getRunData:a=>({outputs:[{dims:e.dims,dataType:u}],dispatchGroup:{x:Math.ceil(M.size(a[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(e.dims)/4)}]})}),rs=e=>{e.compute(ke(e.inputs[0],"Abs","abs"))},ns=e=>{e.compute(ke(e.inputs[0],"Acos","acos"))},os=e=>{e.compute(ke(e.inputs[0],"Acosh","acosh"))},is=e=>{e.compute(ke(e.inputs[0],"Asin","asin"))},as=e=>{e.compute(ke(e.inputs[0],"Asinh","asinh"))},ss=e=>{e.compute(ke(e.inputs[0],"Atan","atan"))},us=e=>{e.compute(ke(e.inputs[0],"Atanh","atanh"))},ds=e=>ve(e),ls=(e,t)=>{let r;switch(t.to){case 10:r="vec4";break;case 1:r="vec4";break;case 12:r="vec4";break;case 6:r="vec4";break;case 9:r="vec4";break;default:throw new RangeError(`not supported type (specified in attribute \'to\' from \'Cast\' operator): ${t.to}`)}e.compute(ke(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},cc=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:xn,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:Cn;return ve({min:t,max:r})},cs=(e,t)=>{let r=e.inputs.length===1?t:cc(e.inputs),o=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Clip",i=>`clamp(${i}, clip_min_, clip_max_)`,`\n const clip_min_: vec4<${o}> = vec4(${o}(${r.min}));\n const clip_max_: vec4<${o}> = vec4(${o}(${r.max}));\n`,r.cacheKey),{inputs:[0]})},ps=e=>{e.compute(ke(e.inputs[0],"Ceil","ceil"))},ms=e=>{e.compute(ke(e.inputs[0],"Cos","cos"))},fs=e=>{e.compute(ke(e.inputs[0],"Cosh","cosh"))},kn=e=>ve(e),hs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Elu",o=>`elu_vf32(${o})`,`\n const elu_alpha_ = ${r}(${t.alpha});\n\n fn elu_f32(a: ${r}) -> ${r} {\n return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);\n }\n\n fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {\n return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));\n }`,t.cacheKey))},On=(e="f32")=>`\nconst r0: ${e} = 0.3275911;\nconst r1: ${e} = 0.254829592;\nconst r2: ${e} = -0.284496736;\nconst r3: ${e} = 1.421413741;\nconst r4: ${e} = -1.453152027;\nconst r5: ${e} = 1.061405429;\n\nfn erf_vf32(v: vec4<${e}>) -> vec4<${e}> {\n let absv = abs(v);\n let x = 1.0 / (1.0 + r0 * absv);\n return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));\n}`,gs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,On(t)))},ys=e=>{e.compute(ke(e.inputs[0],"Exp","exp"))},bs=e=>{e.compute(ke(e.inputs[0],"Floor","floor"))},ws=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,On(t)))},vs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"LeakyRelu",o=>`select(leaky_relu_alpha_ * ${o}, ${o}, ${o} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},$s=e=>{e.compute(ke(e.inputs[0],"Not",t=>`!${t}`))},_s=e=>{e.compute(ke(e.inputs[0],"Neg",t=>`-${t}`))},Ss=e=>{e.compute(ke(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},xs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Cs=e=>{e.compute(ke(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},As=e=>ve(e),Is=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"HardSigmoid",o=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${o} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},Ts=e=>{e.compute(ke(e.inputs[0],"Sin","sin"))},Es=e=>{e.compute(ke(e.inputs[0],"Sinh","sinh"))},Ps=e=>{e.compute(ke(e.inputs[0],"Sqrt","sqrt"))},ks=e=>{e.compute(ke(e.inputs[0],"Tan","tan"))},Os=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Rs=e=>{e.compute(ke(e.inputs[0],"Tanh",Os))},Bo=(e="f32")=>`\nconst fast_gelu_a: ${e} = 0.5;\nconst fast_gelu_b: ${e} = 0.7978845608028654;\nconst fast_gelu_c: ${e} = 0.035677408136300125;\n\nfn tanh_v(v: vec4<${e}>) -> vec4<${e}> {\n return ${Os("v")};\n}\n`,Do=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Bs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"FastGelu",Do,Bo(t),void 0,e.inputs[0].dataType))},Ds=(e,t)=>{let r=et(e.inputs[0].dataType);return e.compute(ke(e.inputs[0],"ThresholdedRelu",o=>`select(vec4<${r}>(0.0), ${o}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},zs=e=>{e.compute(ke(e.inputs[0],"Log","log"))}});var pc,mc,Us,Vs=Y(()=>{"use strict";Se();_e();Rn();pc=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},mc=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=U("input",e[0].dataType,e[0].dims,4),o=U("bias",e[0].dataType,[e[0].dims[2]],4),i=j("output",e[0].dataType,t,4),u=M.size(t)/4,a=De(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)}}),getShaderSource:p=>`\n const M_SQRT2 = sqrt(2.0);\n const halfChannels = ${e[0].dims[2]/4/2}u;\n\n ${p.declareVariables(r,o,i)}\n\n ${On(a)}\n\n ${p.mainStart()}\n ${p.guardAgainstOutOfBoundsWorkgroupSizes(u)}\n let biasIdx = global_idx % halfChannels;\n let batchIndex = global_idx / halfChannels;\n let inputOffset = biasIdx + batchIndex * halfChannels * 2;\n let valueLeft = input[inputOffset] + bias[biasIdx];\n let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];\n let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);\n\n ${i.setByOffset("global_idx","valueLeft * geluRight")}\n }`}},Us=e=>{pc(e.inputs),e.compute(mc(e.inputs))}});var fc,hc,Ot,Ws,Ns,Gs,Hs,Ls,Fs,qs,js,Ks,Ys,Zs=Y(()=>{"use strict";ye();Se();_e();fc=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w,_;typeof c=="string"?w=_=(x,E)=>`${c}((${x}),(${E}))`:typeof c=="function"?w=_=c:(w=c.scalar,_=c.vector);let v=j("outputData",d,o.length,4),S=U("aData",p,t.length,4),A=U("bData",h,r.length,4),I;if(i)if(u){let x=M.size(t)===1,E=M.size(r)===1,P=t.length>0&&t[t.length-1]%4===0,O=r.length>0&&r[r.length-1]%4===0;x||E?I=v.setByOffset("global_idx",_(x?`${S.type.value}(${S.getByOffset("0")}.x)`:S.getByOffset("global_idx"),E?`${A.type.value}(${A.getByOffset("0")}.x)`:A.getByOffset("global_idx"))):I=`\n let outputIndices = ${v.offsetToIndices("global_idx * 4u")};\n let offsetA = ${S.broadcastedIndicesToOffset("outputIndices",v)};\n let offsetB = ${A.broadcastedIndicesToOffset("outputIndices",v)};\n ${v.setByOffset("global_idx",_(a||P?S.getByOffset("offsetA / 4u"):`${S.type.value}(${S.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||O?A.getByOffset("offsetB / 4u"):`${A.type.value}(${A.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}\n `}else I=v.setByOffset("global_idx",_(S.getByOffset("global_idx"),A.getByOffset("global_idx")));else{if(!u)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let x=(E,P,O="")=>{let R=`aData[indexA${P}][componentA${P}]`,L=`bData[indexB${P}][componentB${P}]`;return`\n let outputIndices${P} = ${v.offsetToIndices(`global_idx * 4u + ${P}u`)};\n let offsetA${P} = ${S.broadcastedIndicesToOffset(`outputIndices${P}`,v)};\n let offsetB${P} = ${A.broadcastedIndicesToOffset(`outputIndices${P}`,v)};\n let indexA${P} = offsetA${P} / 4u;\n let indexB${P} = offsetB${P} / 4u;\n let componentA${P} = offsetA${P} % 4u;\n let componentB${P} = offsetB${P} % 4u;\n ${E}[${P}] = ${O}(${w(R,L)});\n `};d===9?I=`\n var data = vec4(0);\n ${x("data",0,"u32")}\n ${x("data",1,"u32")}\n ${x("data",2,"u32")}\n ${x("data",3,"u32")}\n outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:I=`\n ${x("outputData[global_idx]",0)}\n ${x("outputData[global_idx]",1)}\n ${x("outputData[global_idx]",2)}\n ${x("outputData[global_idx]",3)}\n `}return`\n ${e.registerUniform("vec_size","u32").declareVariables(S,A,v)}\n\n ${y??""}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${I}\n }`},hc=(e,t,r,o,i,u,a=r.dataType)=>{let c=!M.areEqual(r.dims,o.dims),p=r.dims,h=M.size(r.dims),d=!1,y=!1,w=[c];if(c){let _=It.calcShape(r.dims,o.dims,!1);if(!_)throw new Error("Can\'t perform binary op on the given tensors");p=_,h=M.size(p);let v=M.size(r.dims)===1,S=M.size(o.dims)===1,A=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,I=o.dims.length>0&&o.dims[o.dims.length-1]%4===0;w.push(v),w.push(S),w.push(A),w.push(I);let x=1;for(let E=1;E_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>fc(_,r.dims,o.dims,p,d,c,y,i,r.dataType,o.dataType,a,u),getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(h/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(p)/4)},...Z(r.dims,o.dims,p)]})}},Ot=(e,t,r,o,i,u)=>{e.compute(hc(t,i??"",e.inputs[0],e.inputs[1],r,o,u))},Ws=e=>{Ot(e,"Add",(t,r)=>`${t}+${r}`)},Ns=e=>{Ot(e,"Div",(t,r)=>`${t}/${r}`)},Gs=e=>{Ot(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4(${t}==${r})`},void 0,void 0,9)},Hs=e=>{Ot(e,"Mul",(t,r)=>`${t}*${r}`)},Ls=e=>{let t=U("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Ot(e,"Pow",{scalar:(o,i)=>`pow_custom(${o},${i})`,vector:(o,i)=>`pow_vector_custom(${o},${i})`},`\n fn pow_custom(a : ${t}, b : ${t}) -> ${t} {\n if (b == ${t}(0.0)) {\n return ${t}(1.0);\n } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {\n return ${t}(pow(f32(a), f32(b))); // NaN\n }\n return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));\n }\n fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {\n // TODO: implement vectorized pow\n return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));\n }\n `)},Fs=e=>{Ot(e,"Sub",(t,r)=>`${t}-${r}`)},qs=e=>{Ot(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4(${t}>${r})`},void 0,void 0,9)},js=e=>{Ot(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4(${t}<${r})`},void 0,void 0,9)},Ks=e=>{Ot(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4(${t}>=${r})`},void 0,void 0,9)},Ys=e=>{Ot(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4(${t}<=${r})`},void 0,void 0,9)}});var St,xt,Ct,Bn,Ft=Y(()=>{"use strict";ye();Se();St=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},xt=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Ct=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Bn=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[r,o]=e?.activation_params||[.2,.5];return{activation:t,alpha:r,beta:o}}else if(t==="Clip"){let[r,o]=e?.activation_params||[xn,Cn];return{activation:t,clipMax:o,clipMin:r}}else if(t==="LeakyRelu"){let[r]=e?.activation_params||[.01];return{activation:t,alpha:r}}return{activation:t}}});var tt,Dn,zn=Y(()=>{"use strict";tt=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Dn=e=>`\n ${e?"value = value + getBiasByOutputCoords(coords);":""}\n `});var Mn,zo=Y(()=>{"use strict";Mn=e=>`\nfn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`});var yc,bc,Hr,Xs,wc,Lr,vc,Un,Fr=Y(()=>{"use strict";ye();Se();_e();Ft();zn();yc=(e,t)=>e?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRow + innerRow,\n kStart / innerElementSize + inputCol${t?", batchIndices":""});\n `,bc=(e,t)=>e?`\n let ACached0 = mm_Asub[k * innerElementSize][localRow];\n let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n for (var i = 0; i < rowPerThread; i = i + 1) {\n acc[i] = BCached0 * ACached0[i] + acc[i];\n acc[i] = BCached1 * ACached1[i] + acc[i];\n acc[i] = BCached2 * ACached2[i] + acc[i];\n ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n }`:`\n for (var i = 0; i < rowPerThread; i = i + 1) {\n let ACached = mm_Asub[tileRow + i][k];\n acc[i] = BCached0 * ACached.x + acc[i];\n acc[i] = BCached1 * ACached.y + acc[i];\n acc[i] = BCached2 * ACached.z + acc[i];\n ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n }`,Hr=(e,t,r="f32",o,i=!1,u=32,a=!1,c=32)=>{let p=t[1]*e[1],h=t[0]*e[0],d=i?p:u,y=i?u:p,w=d/t[0],_=u/t[1];if(!((i&&w===4&&e[1]===4||!i&&(w===3||w===4))&&d%t[0]===0&&u%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${w} and workPerThread[1] ${e[1]} must be 4.\n Otherwise, innerElementSize ${w} must be 3 or 4.\n tileAWidth ${d} must be divisible by workgroupSize[0]${t[0]}. tileInner ${u} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar mm_Asub: array, ${d/w}>, ${y}>;\nvar mm_Bsub: array, ${h/e[0]}>, ${u}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${w};\nconst tileInner = ${u};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[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 = ${a?"0":"i32(globalId.z)"};\n ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let num_tiles = ${a?`${Math.ceil(c/u)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n var kStart = ${a?`i32(globalId.z) * ${c}`:"0"};\n\n var acc: array, rowPerThread>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${_};\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 ${yc(i,o)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${o?", 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 ${w===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n ${bc(i,w)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n}`},Xs=(e,t)=>e?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart + inputCol${t?", batchIndices":""});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRowStart + inputRow,\n kStart + inputCol${t?", batchIndices":""});\n `,wc=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Lr=(e,t,r="f32",o,i=!1,u=32,a=!1,c=32,p=!1)=>{let h=e[1]*t[1],d=e[0]*t[0],y=i?h:u,w=i?u:h;if(!(w%t[1]===0&&y%t[0]===0&&u%t[1]===0))throw new Error(`tileAHight ${w} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${y} must be divisible by workgroupSize[0]${t[0]}, tileInner ${u} must be divisible by workgroupSize[1]${t[1]}`);let _=w/t[1],v=y/t[0],S=u/t[1],A=p?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${h};\n let globalColStart = i32(workgroupId.x) * ${d};\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 < ${w}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${y}; inputCol = inputCol + ${t[0]}) {\n ${Xs(i,o)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${u}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${t[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalColStart + inputCol${o?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${r}, 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 * ${t[0]}];\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[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 * ${t[1]};\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let gCol = globalColStart + localCol + innerCol * ${t[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) * ${h};\n\nlet tileRowA = i32(localId.y) * ${_};\nlet tileColA = i32(localId.x) * ${v};\nlet tileRowB = i32(localId.y) * ${S};\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 < ${_}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < ${v}; innerCol = innerCol + 1) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${Xs(i,o)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${S}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalCol + innerCol${o?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${r}, 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 ${wc(i)}\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, ${w}>;\n var mm_Bsub : array, ${u}>;\n const rowPerThread = ${e[1]};\n const colPerThread = ${e[0]};\n const tileInner = ${u};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[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 = ${a?"0":"i32(globalId.z)"};\n ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n let num_tiles = ${a?`${Math.ceil(c/u)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n var kStart = ${a?`i32(globalId.z) * ${c}`:"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 ${A}\n }\n`},vc=(e,t,r,o,i,u=!1)=>{let[a,c,p]=i,[h,d,y,w]=o,_=_r(a,p),v=_r(c,p),S=De(o[0].type.tensor),A=()=>{let E=d.rank,P=h.rank,O=`var aIndices: ${d.type.indices};`;for(let R=E-2-1,L=P-1;R>=0;R--,L--)O+=`\naIndices[${R}] = ${P>1?`batchIndices[${L}]`:"batchIndices"};`;return _.forEach(R=>{O+=`\naIndices[${R}] = 0;`}),O+=`\naIndices[${E-2}] = u32(row);\n aIndices[${E-1}] = u32(colIn);`,O},I=()=>{let E=y.rank,P=h.rank,O=`var bIndices: ${y.type.indices};`;for(let R=E-2-1,L=P-1;R>=0;R--,L--)O+=`\nbIndices[${R}] = ${P>1?`batchIndices[${L}]`:"batchIndices"};`;return v.forEach(R=>{O+=`\nbIndices[${R}] = 0;`}),O+=`\nbIndices[${E-2}] = u32(row);\n bIndices[${E-1}] = u32(colIn);`,O};return`\n fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${tt(e,S)} {\n var value = ${tt(e,S)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\n {\n ${A()}\n value = ${d.getByIndices("aIndices")};\n }\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${tt(e,S)} {\n var value = ${tt(e,S)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\n {\n ${I()}\n value = ${y.getByIndices("bIndices")};\n }\n return value;\n }\n\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${tt(e,S)}) {\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 ${t?`value = value + ${u?"bias[colIn]":`${tt(e,S)}(bias[row])`};`:""}\n ${r}\n ${w.setByIndices("vec3(coords)","value")}\n }\n }\n `},Un=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u.slice(0,-2),p=a.slice(0,-2),h=o?o.slice(0,-2):r.slice(0,-2),d=M.size(h),y=u[u.length-2],w=u[u.length-1],_=a[a.length-1],v=w%4===0&&_%4===0,S=y<=8?[4,1,1]:[4,4,1],A=[8,8,1],I=[Math.ceil(_/A[0]/S[0]),Math.ceil(y/A[1]/S[1]),Math.ceil(d/A[2]/S[2])],x=v?4:1,E=[...c,y,w/x],P=E.length,O=[...p,w,_/x],R=O.length,L=[d,y,_/x],N=[{type:6,data:y},{type:6,data:_},{type:6,data:w}];xt(t,N),N.push(...Z(h,E,O));let K=["rank","rank"],Q=e.length>2;Q&&(N.push(...Z(e[2].dims)),K.push("rank")),N.push(...Z(L));let he=W=>{let se=h.length,Ce=An("batchDims",e[0].dataType,se,1),We=De(e[0].dataType),ee=U("a",e[0].dataType,P,x),ae=U("b",e[1].dataType,R,x),Ae=j("result",e[0].dataType,L.length,x),me=[ee,ae];if(Q){let G=i?x:1;me.push(U("bias",e[2].dataType,e[2].dims.length,G))}let ie=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Ct(t,ie);let ue=De(Ae.type.tensor),le=St(t,Ae.type.value,ue),qe=vc(x,Q,le,[Ce,ee,ae,Ae],[c,p,h],i);return`\n ${W.registerUniforms(ie).registerInternalVariables(Ce).declareVariables(...me,Ae)}\n ${qe}\n ${v?Hr(S,A,We,Ce):Lr(S,A,We,Ce)}\n `};return{name:"MatMul",shaderCache:{hint:`${S};${t.activation};${v};${i}`,inputDependencies:K},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:I[0],y:I[1],z:I[2]},programUniforms:N}),getShaderSource:he}}});var $c,Qs,Js=Y(()=>{"use strict";ye();Lt();_e();Ft();zn();zo();Fr();$c=(e,t,r,o,i=!1,u,a=4,c=4,p=4,h="f32")=>{let d=Q=>{switch(Q){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Q} is not supported.`)}},y=Q=>{switch(Q){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 ${Q} is not supported.`)}},w=e?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,_=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 `,v=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",S=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",A=e?"row":"col",I=e?"col":"row",x=`\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 = ${A} / outWidth;\n let outCol = ${A} % outWidth;\n\n let WRow = ${I} / (i32(uniforms.w_shape[1]) * inChannels);\n let WCol = ${I} / 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 = ${I} % inChannels;\n var resData = ${tt(a,h)}(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 < ${v} && xCol >= 0 && xCol < ${S}) {\n ${w}\n let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape));\n ${d(a)}\n }\n return resData;`,E=e?t&&o?`\n let col = colIn * ${a};\n ${x}`:`\n let col = colIn * ${a};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n ${x}\n }\n return ${tt(a,h)}(0.0);`:o&&r?`\n let col = colIn * ${a};\n ${x}`:`\n let col = colIn * ${a};\n if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n ${x}\n }\n return ${tt(a,h)}(0.0);`,P=`${y(c)}`,O=tt(p,h),R=e?tt(a,h):tt(c,h),L=e?tt(c,h):tt(a,h),N=St(u,O,h);return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} {\n ${e?E:P}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${L} {\n ${e?P:E}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${O}) {\n let col = colIn * ${p};\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 ${_}\n ${Dn(i)}\n ${N}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`},Qs=(e,t,r,o,i,u,a,c)=>{let p=t.format==="NHWC",h=p?e[0].dims[3]:e[0].dims[1],d=r[0],y=p?r[2]:r[3],w=p?r[1]:r[2],_=p?r[3]:r[1],v=p&&(h%4===0||h%3===0)&&_%4===0,S=p?_:y*w,A=p?y*w:_,I=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/I[0]/x[0]),Math.ceil(A/I[1]/x[1]),Math.ceil(d/I[2]/x[2])];Ve("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let P=v?p&&h%4!==0?3:4:1,O=I[1]*x[1],R=I[0]*x[0],L=Math.max(I[0]*P,I[1]),N=o%O===0,K=i%R===0,Q=u%L===0,he=v?[P,4,4]:[1,1,1],W=[{type:6,data:o},{type:6,data:i},{type:6,data:u},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];xt(t,W),W.push(...Z(e[0].dims,e[1].dims));let se=["rank","rank"];a&&(W.push(...Z(e[2].dims)),se.push("rank")),W.push(...Z(r));let Ce=We=>{let ee=[{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}];Ct(t,ee);let ae=v?4:1,Ae=De(e[0].dataType),me=`\n fn setOutputAtIndex(flatIndex : i32, value : ${v?`vec4<${Ae}>`:Ae}) {\n result[flatIndex] = ${v?`vec4<${Ae}>`:Ae}(value);\n }\n fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${v?`vec4<${Ae}>`:Ae}) {\n let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3));\n setOutputAtIndex(flatIndex ${v?"/ 4":""}, value);\n }`,ie=U("x",e[0].dataType,e[0].dims.length,P===3?1:P),ue=U("w",e[1].dataType,e[1].dims.length,ae),le=[ie,ue],qe=j("result",e[0].dataType,r.length,ae);if(a){let G=U("bias",e[2].dataType,e[2].dims.length,ae);le.push(G),me+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${v?`vec4<${Ae}>`:Ae} {\n return bias[coords.${p?"w":"y"}${v?"/ 4":""}];\n }`}return`\n ${Mn("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 ${We.registerUniforms(ee).declareVariables(...le,qe)}\n ${me}\n ${$c(p,N,K,Q,a,t,he[0],he[1],he[2],Ae)}\n ${v?Hr(x,I,Ae,void 0,!p,L):Lr(x,I,Ae,void 0,!p,L,!1,void 0,c)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${P};${v};${N};${K};${Q};${O};${R};${L}`,inputDependencies:se},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:W}),getShaderSource:Ce}}});var Mo,eu,tu=Y(()=>{"use strict";ye();Se();_e();Uo();Ft();Mo=(e,t,r)=>{let o=e.length>2,i=o?"value += b[output_channel];":"",u=e[0].dims,a=e[1].dims,c=a[0]/t.group,p=t.format==="NHWC",h=Vn(u,a,t.dilations,t.pads,t.strides,p),d=M.size(h),y=[{type:12,data:d},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:c}];xt(t,y),y.push(...Z(u,a));let w=["rank","rank"];o&&(y.push(...Z(e[2].dims)),w.push("rank")),y.push(...Z(h));let _=v=>{let S=j("output",e[0].dataType,h.length),A=De(S.type.tensor),I=St(t,S.type.value,A),x=U("x",e[0].dataType,u.length),E=U("w",e[1].dataType,a.length),P=[x,E];o&&P.push(U("b",e[2].dataType,e[2].dims.length));let O=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return Ct(t,O),`\n ${v.registerUniforms(O).declareVariables(...P,S)}\n\n ${v.mainStart()}\n ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let outputIndices = ${S.offsetToIndices("global_idx")};\n let batch: u32 = outputIndices[0];\n let output_channel: u32 = outputIndices[${p?3:1}];\n let xRCCorner: vec2 = vec2(outputIndices[${p?1:2}], outputIndices[${p?2:3}]) * uniforms.strides - uniforms.pads;\n let group_id: u32 = output_channel / uniforms.output_channels_per_group;\n\n var value: ${S.type.value} = ${S.type.value}(0);\n for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {\n let input_channel = group_id * uniforms.w_shape[1] + wInChannel;\n for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {\n let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];\n\n if (xHeight < 0u || xHeight >= uniforms.x_shape[${p?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[${p?2:3}]) {\n continue;\n }\n\n let xVal = ${p?x.get("batch","xHeight","xWidth","input_channel"):x.get("batch","input_channel","xHeight","xWidth")};\n let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")};\n value += xVal*wVal;\n }\n }\n }\n ${i}\n ${I}\n ${S.setByOffset("global_idx","value")}\n }`};return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:w},getRunData:()=>({outputs:[{dims:r?r(h):h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:y}),getShaderSource:_}},eu=(e,t,r)=>{let o=e.length>2,i=Me(r[3]),u=Me(r[2]),a=M.size(r)/i/u,c=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/i],p=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/i],h=[r[0],r[1],r[2],r[3]/i],d=[{type:12,data:a},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];xt(t,d),d.push(...Z(c,p,h));let y=(u-1)*t.strides[1]+p[1],w=_=>{let v=j("output",e[0].dataType,h.length,i),S=De(v.type.tensor),A=St(t,v.type.value,S),I=U("x",e[0].dataType,c.length,i),x=U("w",e[1].dataType,p.length,i),E=[I,x];o&&E.push(U("b",e[2].dataType,e[2].dims,i));let P=o?"value += b[output_channel];":"",O=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Ct(t,O),`\n ${_.registerUniforms(O).declareVariables(...E,v)}\n ${_.mainStart()}\n ${_.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] / ${u}u;\n let col = (index1 % width1) * ${u}u;\n index1 = index1 / width1;\n let row = index1 % uniforms.output_shape[1];\n let batch = index1 / uniforms.output_shape[1];\n\n let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads;\n\n var x_vals: array<${I.type.value}, ${y}>;\n var values: array<${v.type.value}, ${u}>;\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 < ${p[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 < ${y}; 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] = ${I.get("batch","u32(x_height)","u32(x_width)","input_channel")};\n } else {\n x_vals[i] = ${I.type.value}(0);\n }\n }\n for (var w_width: u32 = 0u; w_width < ${p[1]}; w_width++) {\n let w_val = ${x.get("w_height","w_width","0","output_channel")};\n for (var i = 0u; i < ${u}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 < ${u}u; i++) {\n var value = values[i];\n ${P}\n ${A}\n ${v.set("batch","row","col + i","output_channel","value")};\n }\n }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${i};${u};${y};${p[0]};${p[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:w}}});var Vo,_c,ru,Wo=Y(()=>{"use strict";ye();Se();Fr();_e();Ft();Vo=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u[u.length-2],p=a[a.length-1],h=u[u.length-1],d=Me(p),y=Me(h),w=Me(c),_=M.size(r)/d/w,v=e.length>2,S=o?o.slice(0,-2):r.slice(0,-2),I=[M.size(S),c,p],x=[{type:12,data:_},{type:12,data:c},{type:12,data:p},{type:12,data:h}];xt(t,x),x.push(...Z(S,u,a)),v&&x.push(...Z(e[2].dims)),x.push(...Z(I));let E=P=>{let O=An("batch_dims",e[0].dataType,S.length),R=U("a",e[0].dataType,u.length,y),L=U("b",e[1].dataType,a.length,d),N=j("output",e[0].dataType,I.length,d),K=De(N.type.tensor),Q=St(t,N.type.value,K),he=[R,L],W="";if(v){let ie=i?d:1;he.push(U("bias",e[2].dataType,e[2].dims.length,ie)),W=`${i?`value += bias[col / ${ie}];`:`value += ${N.type.value}(bias[row + i]);`}`}let se=u.slice(0,-2),Ce=a.slice(0,-2),We=_r(se,S),ee=_r(Ce,S),ae=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Ct(t,ae);let Ae=(ie,ue)=>{let le=ie.rank,qe=ie.name;if(le===2)return`var ${qe}_indices = ${ie.type.indices}(0u, 0u);`;let G=O.rank,ne=`var ${qe}_indices: ${ie.type.indices};`;for(let xe=le-2-1,Ke=G-1;xe>=0;xe--,Ke--)ne+=`\n${qe}_indices[${xe}] = ${G>1?`batch_indices[${Ke}]`:"batch_indices"};`;return ue.forEach(xe=>{ne+=`\n${qe}_indices[${xe}] = 0;`}),ne+=`${qe}_indices[${le-2}] = 0u;\n ${qe}_indices[${le-1}] = 0u;`,ne},me=()=>{let ie=`var a_data: ${R.type.value};`;for(let ue=0;ue;\n for (var k: u32 = 0u; k < uniforms.K; k = k + ${y}) {\n ${me()}\n }\n for (var i = 0u; i < ${w}u; i++) {\n var value = values[i];\n ${W}\n ${Q}\n let cur_indices = ${N.type.indices}(batch, row + i, col);\n let offset = ${N.indicesToOffset("cur_indices")};\n ${N.setByOffset(`offset / ${d}`,"value")};\n }\n }\n `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${d};${y};${w};${i}`,inputDependencies:v?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:x}),getShaderSource:E}},_c=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.")},ru=e=>{_c(e.inputs);let t=It.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can\'t use matmul on the given tensors");let r=t[t.length-1],o=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&o<8?e.compute(Vo(e.inputs,{activation:""},t)):e.compute(Un(e.inputs,{activation:""},t))}});var Vn,No,Sc,nu,Go,xc,Cc,Ho,Uo=Y(()=>{"use strict";Se();Js();Fr();tu();Ft();Wo();Sr();Vn=(e,t,r,o,i,u)=>{let a=e[0],c=e.slice(u?1:2,u?3:4),p=c.length,h=t[0],y=t.slice(2).map((v,S)=>v+(v-1)*(r[S]-1)),_=c.map((v,S)=>v+o[S]+o[S+p]).map((v,S)=>Math.floor((v-y[S]+i[S])/i[S]));return _.splice(0,0,a),_.splice(u?3:1,0,h),_},No=[2,3,1,0],Sc=(e,t)=>{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 r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[1]*t.group;if(r!==o)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 i=e[0].dims.length-2;if(t.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(t.strides.length!==i)throw new Error(`strides should be ${i}D`);if(t.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},nu=(e,t)=>{let r=e.kernelShape.slice();for(let u=2;u{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,u=e.group,a=e.kernel_shape,c=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},xc=(e,t,r)=>{let o=nu(r,t),i=r.format==="NHWC";if(r.group!==1){if(!e.adapterInfo.isArchitecture("ampere")&&i&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let L=Vn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,i),N=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=N);let K=[t[0],N];t.length===3&&K.push(t[2]),e.compute(eu(K,o,L),{inputs:K})}else e.compute(Mo(t,o));return}let u=t.length===3,a=t[0].dims[i?1:2],c=t[0].dims[i?2:3],p=t[0].dims[i?3:1],h=t[1].dims[2],d=t[1].dims[3],y=Vn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,i),w=y[i?1:2],_=y[i?2:3],v=y[i?3:1],S=i&&h===a&&d===c&&r.pads[0]===0&&r.pads[1]===0;if(S||h===1&&d===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 R=y[0],L,N,K,Q=[];if(i){let se=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=se),S){let Ce=a*c*p;L=t[0].reshape([1,R,Ce]),N=se.reshape([1,Ce,v]),K=[1,R,v]}else L=t[0].reshape([R,a*c,p]),N=se.reshape([1,p,v]),K=[R,w*_,v];Q.push(L),Q.push(N)}else L=t[0].reshape([R,p,a*c]),N=t[1].reshape([1,v,p]),K=[R,v,w*_],Q.push(N),Q.push(L);u&&Q.push(t[2]);let he=K[2],W=Q[0].dims[Q[0].dims.length-1];he<8&&W<8?e.compute(Vo(Q,o,y,K,i),{inputs:Q}):e.compute(Un(Q,o,y,K,i),{inputs:Q});return}let A=!0,I=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=I);let x=[t[0],I];u&&x.push(t[2]);let E=i?w*_:v,P=i?v:w*_,O=h*d*p;e.compute(Qs(x,o,y,E,P,O,u,A),{inputs:x})},Cc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[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&&o.push(e.inputs[2]);let i=[0,t.pads[0],0,t.pads[1]],u=[1].concat(t.strides),a=[1].concat(t.dilations),c=[1].concat(t.kernelShape),p=nu({...t,pads:i,strides:u,dilations:a,kernelShape:c},o);e.compute(Mo(o,p,h=>r?[h[0],h[2],h[3]]:[]))},Ho=(e,t)=>{Sc(e.inputs,t),e.inputs[0].dims.length===3?Cc(e,t):xc(e,e.inputs,t)}});var Ac,ou,iu=Y(()=>{"use strict";ye();Lt();_e();Ft();zn();zo();Fr();Ac=(e,t=!1,r,o,i=4)=>{let u=I=>{switch(I){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 ${o}(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${I} is not supported.`)}},a=e?`\n let coord = vec4(batch, iXR, iXC, xCh);\n `:`\n let coord = vec4(batch, xCh, iXR, iXC);\n `,c=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 `,p=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",h=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",d=e?"row":"col",y=e?"col":"row",w=`\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 = ${d} / outWidth;\n let outCol = ${d} % outWidth;\n\n let WRow = ${y} / (uniforms.filter_dims[1] * inChannels);\n let WCol = ${y} / inChannels % uniforms.filter_dims[1];\n let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(${p}) || fract(xR) > 0.0) {\n return ${o}(0.0);\n }\n if (xC < 0.0 || xC >= f32(${h}) || fract(xC) > 0.0) {\n return ${o}(0.0);\n }\n let iXR = i32(xR);\n let iXC = i32(xC);\n let xCh = ${y} % inChannels;\n ${a}\n return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${i}];`,_=e?`\n let col = colIn * ${i};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n ${w}\n }\n return ${o}(0.0);`:`\n let col = colIn * ${i};\n if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n ${w}\n }\n return ${o}(0.0);`,v=`\n let col = colIn * ${i};\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 ${u(i)}\n }\n return ${o}(0.0);\n `,S=St(r,o);return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${o} {\n ${e?_:v}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${o} {\n ${e?v:_}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${o}) {\n let col = colIn * ${i};\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 ${c}\n ${Dn(t)}\n ${S}\n result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${i}] = value;\n }\n }`},ou=(e,t,r,o,i,u,a,c)=>{let p=t.format==="NHWC",h=p?e[0].dims[3]:e[0].dims[1],d=r[0],y=p?r[2]:r[3],w=p?r[1]:r[2],_=p?r[3]:r[1],v=p&&h%4===0&&h%3&&_%4===0,S=p?_:y*w,A=p?y*w:_,I=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/I[0]/x[0]),Math.ceil(A/I[1]/x[1]),Math.ceil(d/I[2]/x[2])];Ve("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let P=v?4:1,O=Math.max(I[0]*P,I[1]),R=v?4:1,L=[t.kernelShape[p?1:2],t.kernelShape[p?2:3]],N=[L[0]+(t.dilations[0]<=1?0:(L[0]-1)*(t.dilations[0]-1)),L[1]+(t.dilations[1]<=1?0:(L[1]-1)*(t.dilations[1]-1))],K=[N[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),N[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Q=[{type:6,data:o},{type:6,data:i},{type:6,data:u},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:L},{type:6,data:K}];xt(t,Q),Q.push(...Z(e[0].dims,e[1].dims));let he=["rank","rank"];a&&(Q.push(...Z(e[2].dims)),he.push("rank")),Q.push(...Z(r));let W=se=>{let Ce=U("x",e[0].dataType,e[0].dims.length,R),We=U("w",e[1].dataType,e[1].dims.length,1),ee=j("result",e[0].dataType,r.length,R),ae=[Ce,We],Ae="";if(a){let ue=U("bias",e[2].dataType,e[2].dims.length,R);ae.push(ue),Ae+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${ue.type.value} {\n return bias[coords.${p?"w":"y"}${v?"/ 4":""}];\n }`}let me=[{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:L.length},{name:"pads",type:"i32",length:K.length}];Ct(t,me);let ie=De(e[0].dataType,1);if(ie!=="f16"&&ie!=="f32")throw new Error(`elemType ${ie} is not supported.`);return`\n ${Mn("uniforms.result_strides")}\n ${se.registerUniforms(me).declareVariables(...ae,ee)};\n ${Ae}\n ${Ac(p,a,t,Ce.type.value,P)}\n ${v?Hr(x,I,ie,void 0,!p,O):Lr(x,I,ie,void 0,!p,O,!1,void 0,c)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${x};${I};${v}`,inputDependencies:he},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:Q}),getShaderSource:W}}});var Ic,Lo,au=Y(()=>{"use strict";ye();Lt();Se();_e();Ic=(e,t,r,o,i,u=!1,a,c,p=!1)=>{let h=p?1:2,d=p?2:3,y=p?3:1,w=u?2:1,_=`\n fn setOutputAtIndex(flatIndex : u32, value : ${u?`vec4<${a}>`:a}) {\n result[flatIndex] = ${u?`vec4<${a}>`:a}(value);\n }`;o&&(_+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${u?`vec4<${a}>`:a} {\n return bias[coords.${p?"w":"y"}${u?"/ 4":""}];\n }`);let v=u?4:1,S=U("W",t[1].dataType,t[1].dims.length,v),A=U("Dy",t[0].dataType,t[0].dims.length,v),I=[A,S];o&&I.push(U("bias",t[2].dataType,[r[y]].length,v));let x=j("result",t[0].dataType,r.length,v),E=`{\n let batch: u32 = ${i?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];\n let r = ${i?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];\n let c = ${i?"global_id.y":"workgroup_id.y"} * ${w};\n let d1: u32 = ${i?"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, ${w}>;\n for (var i = 0; i < ${w}; i++) {\n dotProd[i] = vec4<${a}>(0.0);\n }\n for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\n var dyR = (${a}(dyCorner.x) + ${a}(wR)) / ${a}(uniforms.strides.x);\n let wRPerm = uniforms.filter_dims[0] - 1 - wR;\n if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[1]) ||\n fract(dyR) > 0.0 || wRPerm < 0) {\n continue;\n }\n let idyR: u32 = u32(dyR);\n\n for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\n let dyC = (${a}(dyCorner.y) + ${a}(wC)) / ${a}(uniforms.strides.y);\n let dyC2 = (${a}(dyCorner.y) + 1.0 + ${a}(wC)) / ${a}(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 >= ${a}(uniforms.Dy_shape[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= ${a}(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 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${A.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${a}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n\n xValue = ${A.get("batch","idyR","idyC2","d2")};\n\n dotProd[1] = dotProd[1] + vec4<${a}>(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[${y}];\n for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${A.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${a}>(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 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${A.get("batch","idyR","idyC2","d2")};\n let tmpval = vec4<${a}>(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 < ${w}; i = i + 1) {\n let value = dotProd[i] + ${o?"bias[c+i]":`vec4<${a}>(0.0)`};\n ${x.set("batch","r","c + i","d1","value")};\n }\n }`,P=`\n let outputIndices = ${x.offsetToIndices("global_idx")};\n let batch = ${x.indicesGet("outputIndices",0)};\n let d1 = ${x.indicesGet("outputIndices",y)};\n let r = ${x.indicesGet("outputIndices",h)};\n let c = ${x.indicesGet("outputIndices",d)};\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 = ${a}(0.0);\n for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\n if (wR % uniforms.dilations.x != 0) {\n continue;\n }\n let dyR = (${a}(dyRCorner) + ${a}(wR)) / ${a}(uniforms.strides[0]);\n let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\n if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[${h}]) || 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 = (${a}(dyCCorner) + ${a}(wC)) / ${a}(uniforms.strides.y);\n let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\n if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[${d}]) ||\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 = ${p?A.get("batch","idyR","idyC","inputChannel"):A.get("batch","inputChannel","idyR","idyC")};\n let wValue = ${S.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n dotProd = dotProd + xValue * wValue;\n inputChannel 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c=t.format==="NHWC",p=["rank","rank"],h=[t.strides[0],t.strides[1]],d=[t.kernelShape[c?1:2],t.kernelShape[c?2:3]],y=[t.dilations[0],t.dilations[1]],w=[d[0]+(t.dilations[0]<=1?0:(t.kernelShape[c?1:2]-1)*(t.dilations[0]-1)),d[1]+(t.dilations[1]<=1?0:(t.kernelShape[c?2:3]-1)*(t.dilations[1]-1))],_=[w[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),w[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],v=!1,S=t.group,A=e[1].dims,I=A[0]/S,x=A[1],E=[{type:12,data:u},{type:12,data:h},{type:12,data:d},{type:12,data:y},{type:12,data:w},{type:6,data:_},{type:12,data:I},{type:12,data:x},...Z(e[0].dims,e[1].dims)];o&&(E.push(...Z(e[2].dims)),p.push("rank")),E.push(...Z(i));let P=a[1]===1&&a[2]===1,O=R=>{let L=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:h.length},{name:"filter_dims",type:"u32",length:d.length},{name:"dilations",type:"u32",length:d.length},{name:"effective_filter_dims",type:"u32",length:w.length},{name:"pads",type:"i32",length:_.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],N=De(e[0].dataType);return`${Ic(R,e,i,o,P,v,N,L,c)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:p},getRunData:()=>({dispatchGroup:{x:a[0],y:a[1],z:a[2]},outputs:[{dims:r?r(i):i,dataType:e[0].dataType}],programUniforms:E}),getShaderSource:O}}});var Tc,Ec,Pc,su,uu,kc,Oc,Rc,Bc,du,lu=Y(()=>{"use strict";iu();au();Ft();Sr();Tc=(e,t,r,o,i,u)=>(e-1)*t+r+(o-1)*i+1-u,Ec=(e,t,r,o,i)=>{let u=Math.floor(e/2);t==="SAME_UPPER"?(r[o]=u,r[i]=e-u):t==="SAME_LOWER"&&(r[o]=e-u,r[i]=u)},Pc=(e,t,r,o,i,u,a,c,p,h)=>{let d=e.length-2,y=h.length===0;if(p.length===0)for(let v=0;v{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((y,w)=>y*w,1)===0){r.length=0;for(let y=2;yy+w,0)===0){let y=t[0].dims.length-2;p=new Array(y).fill(1)}let h=e.strides.slice();if(h.reduce((y,w)=>y+w,0)===0){let y=t[0].dims.length-2;h=new Array(y).fill(1)}Pc(c,r,p,e.autoPad,e.group,i,h,o,a,u);let d=Object.assign({},e);return Object.assign(d,{kernelShape:r,pads:i,outputPadding:a,outputShape:u,dilations:p,strides:h}),d},uu=e=>{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,u=e.group,a=e.kernelShape,c=e.pads,p=e.strides,h=e.wIsConst(),d=e.outputPadding,y=e.outputShape;return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,outputPadding:d,outputShape:y,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},kc=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently 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shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Oc=[2,3,1,0],Rc=(e,t,r)=>{let o=su(r,t),i=r.format==="NHWC",u=o.outputShape,a=u[i?3:1],c=t[0].dims[i?3:1];if(o.group!==1||a===1&&c===1){e.compute(Lo(t,o));return}let p=u[i?1:2],h=u[i?2:3],d=t[1].dims[2],y=t[1].dims[3],w=i?p*h:a,_=i?a:p*h,v=d*y*c,S=!0,A=e.kernelCustomData.wT??e.compute(yt(t[1],Oc),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=A);let I=[t[0],A],x=t.length===3;x&&(!i&&t[2].dims.length===1?I.push(t[2].reshape([t[2].dims[0],1,1])):I.push(t[2])),e.compute(ou(I,o,u,w,_,v,x,S),{inputs:I})},Bc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let u=t.dilations;(u.length===0||u[0]===0)&&(u=[1]);let a=t.strides;(a.length===0||a[0]===0)&&(a=[1]);let c=t.pads;c.length===0&&(c=[0,0]),c=[0,c[0],0,c[1]],a=[1].concat(a),u=[1].concat(u),i=[1].concat(i);let p=su({...t,pads:c,strides:a,dilations:u,kernelShape:i},o);e.compute(Lo(o,p,h=>r?[h[0],h[2],h[3]]:[h[0],h[1],h[3]]))},du=(e,t)=>{kc(e.inputs,t),e.inputs[0].dims.length===3?Bc(e,t):Rc(e,e.inputs,t)}});var Dc,cu,pu,mu=Y(()=>{"use strict";ye();Se();Ze();_e();Dc=(e,t,r,o)=>{let i=M.size(t),u=t.length,a=U("input",e,u),c=j("output",e,u),p=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),h=M.normalizeAxis(p,u),d=y=>{let w=` i32(${a.indicesGet("inputIndices","uniforms.axis")}) `,_=fe("uniforms.input_shape","uniforms.axis",u),v=o.reverse?w+(o.exclusive?" + 1":""):"0",S=o.reverse?_:w+(o.exclusive?"":" + 1");return`\n 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${_("data",3,"u32")}\n ${y.setByOffset("global_idx","data")}\n }`}else w=`\n let outputIndices = ${y.offsetToIndices("global_idx")};\n let inputOffset = ${d.broadcastedIndicesToOffset("outputIndices",y)};\n ${y.setByOffset("global_idx",d.getByOffset("inputOffset"))}\n }`;return`\n ${h.registerUniform("vec_size","u32").declareVariables(d,y)}\n ${h.mainStart()}\n ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${w}`},p=[{type:12,data:a},...Z(t,o)];return{name:"Expand",shaderCache:{hint:`${o.length}`,inputDependencies:["rank"]},getShaderSource:c,getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p})}},Su=e=>{Gc(e.inputs),e.compute(Lc(e.inputs),{inputs:[0]})}});var Fc,Cu,Au=Y(()=>{"use strict";ye();Se();_e();Rn();Fc=e=>{let t=e[0].dataType,r=M.size(e[0].dims),o=M.size(e[1].dims),i=o%4===0,u=a=>{let c=U("x",t,[1],4),p=U("bias",t,[1],4),h=j("y",t,[1],4),d=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],y=_=>`\n let bias${_}_offset: u32 = (global_idx * 4 + ${_}) % uniforms.bias_size;\n let bias${_} = ${p.getByOffset(`bias${_}_offset / 4`)}[bias${_}_offset % 4];`,w=i?`\n let bias = ${p.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${y(0)}${y(1)}${y(2)}${y(3)}\n let bias = ${c.type.value}(bias0, bias1, bias2, bias3);`;return`${a.registerUniforms(d).declareVariables(c,p,h)}\n\n ${Bo(et(t))}\n\n ${a.mainStart(or)}\n ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}\n\n let x = ${c.getByOffset("global_idx")};\n ${w}\n let x_in = x + bias;\n ${h.setByOffset("global_idx",Do("x_in"))}\n }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${i}`,inputDependencies:["type","type"]},getShaderSource:u,getRunData:a=>({outputs:[{dims:a[0].dims,dataType:a[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:o}],dispatchGroup:{x:Math.ceil(r/or/4)}})}},Cu=e=>{e.inputs.length<2||M.size(e.inputs[1].dims)===0?Bs(e):e.compute(Fc(e.inputs))}});var qc,jc,Iu,Tu,Eu=Y(()=>{"use strict";ye();Se();Ze();_e();qc=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},jc=(e,t)=>{let r=e[0].dims,o=e[1].dims,i=r.length,u=M.normalizeAxis(t.axis,i),a=r.slice(0);a.splice(u,1,...o);let c=r[u],p=e[0].dataType===9?4:1,h=Math.ceil(M.size(a)/p),d=[{type:12,data:h},{type:6,data:c},{type:12,data:u},...Z(e[0].dims,e[1].dims,a)],y=w=>{let _=U("data",e[0].dataType,e[0].dims.length,p),v=U("inputIndices",e[1].dataType,e[1].dims.length),S=j("output",e[0].dataType,a.length,p),A=x=>{let E=o.length,P=`var indicesIndices${x} = ${v.type.indices}(0);`;for(let 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gemm on the given tensors");let p=M.size(c),h=[{type:12,data:p},{type:12,data:i},{type:12,data:u},{type:12,data:a},{type:1,data:t.alpha},{type:1,data:t.beta}],d=["type","type"];e.length===3&&(h.push(...Z(e[2].dims)),d.push("rank")),h.push(...Z(c));let y=w=>{let _="";t.transA&&t.transB?_="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?_="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?_="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(_="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let v=t.alpha===1?"":"value *= uniforms.alpha;",S=U("a",e[0].dataType,e[0].dims),A=U("b",e[1].dataType,e[1].dims),I=S.type.value,x=null,E=[S,A];e.length===3&&(x=U("c",e[2].dataType,e[2].dims.length),E.push(x));let P=j("output",e[0].dataType,c.length);E.push(P);let O=[{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`\n ${w.registerUniforms(O).declareVariables(...E)}\n\n ${w.mainStart()}\n ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let m = global_idx / uniforms.N;\n let n = global_idx % uniforms.N;\n\n var value = ${I}(0);\n for (var k: u32 = 0u; k < uniforms.K; k++) {\n ${_}\n }\n\n ${v}\n ${(()=>x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",P)}; value += ${I}(uniforms.beta) * ${x.getByOffset("cOffset")};`:"")()}\n output[global_idx] = value;\n }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:y}},Ru=e=>{let t=e.transA,r=e.transB,o=e.alpha,i=e.beta;return{transA:t,transB:r,alpha:o,beta:i,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},Bu=(e,t)=>{Zc(e.inputs),e.compute(Xc(e.inputs,t))}});var Qc,Jc,ep,zu,Mu=Y(()=>{"use strict";ye();Se();_e();Qc=(e,t)=>{let r=e[0].dims,o=r,i=2,u=M.sizeToDimension(r,i),a=M.sizeFromDimension(r,i),c=Me(a),p=a/c,h=[r[0],r[1],p],d=["rank","type","type"],y=[{type:12,data:a},{type:12,data:p}];y.push(...Z(h,h));let w=_=>{let v=U("x",e[0].dataType,h.length,c),S=U("scale",e[1].dataType,e[1].dims),A=U("bias",e[2].dataType,e[2].dims),I=j("output",e[0].dataType,h.length,c),x=[v,S,A,I],E=v.type.value,P=c===1?"f32":`vec${c}`,O=64,R=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`\n var meanShared : f32;\n var squaredNormShared : f32;\n var workgroupShared : array<${P}, ${O}>;\n const workgroupSize = ${O}u;\n ${_.registerUniforms(R).declareVariables(...x)}\n ${_.mainStart(O)}\n let norm = global_idx / workgroupSize;\n let batch = norm / uniforms.x_shape[1];\n let channel = norm % uniforms.x_shape[1];\n let localIndex = local_id.x;\n\n // initialize workgroup memory\n var initial = ${P}(0);\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n initial = initial + ${P}(${v.get("batch","channel","h")});\n }\n workgroupShared[localIndex] = initial;\n workgroupBarrier();\n\n // Calculate the mean 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 meanShared = ${_t("workgroupShared[0]",c)} / f32(uniforms.normSize);\n }\n workgroupBarrier();\n\n // reinitialize workgroup memory.\n initial = ${P}(0);\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let deviation = ${P}(${v.get("batch","channel","h")}) - ${P}(meanShared);\n 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 = ${_t("workgroupShared[0]",c)};\n }\n workgroupBarrier();\n\n let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n let channelScale = invStdDev * f32(${S.getByOffset("channel")});\n let channelShift = f32(${A.getByOffset("channel")}) - meanShared * channelScale;\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let value = ${v.get("batch","channel","h")} * ${E}(${P}(channelScale)) + ${E}(${P}(channelShift));\n ${I.set("batch","channel","h","value")};\n }\n }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${c}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:u},programUniforms:y}),getShaderSource:w}},Jc=(e,t,r,o,i,u,a,c)=>{let p=Me(a),h=64,d=p===1?"vec2f":`mat2x${p}f`,y=p===1?"f32":`vec${p}f`,w=(R,L)=>`${d}(${R}, ${L})`,_=i*a/p,v=Math.ceil(u/h),S=["type"],A=[{type:12,data:v},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(u*a/p)}],I=R=>{let L=U("input",t.dataType,t.dims,p);return`\n ${R.declareVariables(L)}\n @group(0) @binding(1) var output : array<${d}>;\n struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n @group(0) @binding(2) var uniforms: Uniforms;\n\n ${R.mainStart(h)}\n let currentImageNumber = global_idx / ${h} / uniforms.C;\n let currentChannelNumber = (global_idx / ${h}) % 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 = ${$t("f32",p)};\n var squaredSum = ${$t("f32",p)};\n for (var i: u32 = wgOffset; i < wgMax; i++) {\n let value = ${y}(input[offset + i * uniforms.C]);\n sum += value;\n squaredSum += value * value;\n }\n output[global_idx] = ${w("sum","squaredSum")};\n }`},x=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${p}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:[i,a,h,2],dataType:1}],dispatchGroup:{x:i*a/p},programUniforms:A}),getShaderSource:I},{inputs:[t],outputs:[-1]})[0],E=[{type:12,data:_},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(h*a/p)}],P=["type","type","type"],O=R=>{let L=U("scale",r.dataType,r.dims,p),N=U("bias",o.dataType,o.dims,p);return`\n @group(0) @binding(0) var input : array<${d}>;\n @group(0) @binding(1) var scale : array<${L.type.storage}>;\n @group(0) @binding(2) var bias : array<${N.type.storage}>;\n @group(0) @binding(3) var output : array<${d}>;\n struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n @group(0) @binding(4) var uniforms: Uniforms;\n\n ${R.mainStart()}\n ${R.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 = ${$t("f32",p)};\n var squaredSum = ${$t("f32",p)};\n for (var i: u32 = 0; i < min(${h}, uniforms.H); i++) {\n let value = input[offset + i + currentChannelNumber * ${h}];\n sum += value[0];\n squaredSum += value[1];\n }\n sum = sum / f32(uniforms.H);\n squaredSum = squaredSum / f32(uniforms.H);\n let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${c}));\n let channelScale = invStdDev * ${y}(scale[currentChannelNumber]);\n let channelShift = ${y}(bias[currentChannelNumber]) - sum * channelScale;\n\n output[global_idx] = ${w("channelScale","channelShift")};\n }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${p};${c}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:[i,a,2],dataType:1}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:E}),getShaderSource:O},{inputs:[x,r,o],outputs:[-1]})[0]},ep=(e,t,r)=>{let o=t[0].dims,i=o,u=o[0],a=o[o.length-1],c=M.sizeFromDimension(o,1)/a,p=Me(a),h=M.size(i)/p,d=[{type:12,data:c},{type:12,data:Math.floor(a/p)}],y=["type","type"],w=Jc(e,t[0],t[1],t[2],u,c,a,r.epsilon),_=v=>{let S=De(t[0].dataType),A=p===1?"vec2f":`mat2x${p}f`,I=p===1?S:`vec${p}<${S}>`,x=U("input",t[0].dataType,t[0].dims,p),E=j("output",t[0].dataType,i,p);return`\n @group(0) @binding(0) var input : array<${x.type.storage}>;\n @group(0) @binding(1) var scaleInput : array<${A}>;\n @group(0) @binding(2) var output : array<${E.type.storage}>;\n struct Uniforms {H: u32, C : u32};\n @group(0) @binding(3) var uniforms: Uniforms;\n\n ${v.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], ${I}(scale[0]), ${I}(scale[1]));\n }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:d}),getShaderSource:_},{inputs:[t[0],w]})},zu=(e,t)=>{t.format==="NHWC"?ep(e,e.inputs,t):e.compute(Qc(e.inputs,t))}});var tp,rp,Uu,Vu=Y(()=>{"use strict";ye();Se();_e();tp=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},rp=(e,t,r)=>{let o=t.simplified,i=e[0].dims,u=e[1],a=!o&&e[2],c=i,p=M.normalizeAxis(t.axis,i.length),h=M.sizeToDimension(i,p),d=M.sizeFromDimension(i,p),y=M.size(u.dims),w=a?M.size(a.dims):0;if(y!==d||a&&w!==d)throw new Error(`Size of X.shape()[axis:] == ${d}.\n Size of scale and bias (if provided) must match this.\n Got scale size of ${y} and bias size of ${w}`);let _=[];for(let O=0;O1,x=r>2,E=O=>{let R=De(e[0].dataType),L=[U("x",e[0].dataType,e[0].dims,v),U("scale",u.dataType,u.dims,v)];a&&L.push(U("bias",a.dataType,a.dims,v)),L.push(j("output",e[0].dataType,c,v)),I&&L.push(j("mean_data_output",1,_)),x&&L.push(j("inv_std_output",1,_));let N=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n ${O.registerUniforms(N).declareVariables(...L)}\n ${O.mainStart()}\n ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n let offset = global_idx * uniforms.norm_size_vectorized;\n var mean_vector = ${$t("f32",v)};\n var mean_square_vector = ${$t("f32",v)};\n\n for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n let value = ${ir(R,v,"x[h + offset]")};\n mean_vector += value;\n mean_square_vector += value * value;\n }\n let mean = ${_t("mean_vector",v)} / uniforms.norm_size;\n let inv_std_dev = inverseSqrt(${_t("mean_square_vector",v)} / uniforms.norm_size ${o?"":"- mean * mean"} + uniforms.epsilon);\n\n for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n let f32input = ${ir(R,v,"x[j + offset]")};\n let f32scale = ${ir(R,v,"scale[j]")};\n output[j + offset] = ${L[0].type.value}((f32input ${o?"":"- mean"}) * inv_std_dev * f32scale\n ${a?`+ ${ir(R,v,"bias[j]")}`:""}\n );\n }\n\n ${I?"mean_data_output[global_idx] = mean":""};\n ${x?"inv_std_output[global_idx] = inv_std_dev":""};\n }`},P=[{dims:c,dataType:e[0].dataType}];return I&&P.push({dims:_,dataType:1}),x&&P.push({dims:_,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${v};${r};${o}`,inputDependencies:S},getRunData:()=>({outputs:P,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:A}),getShaderSource:E}},Uu=(e,t)=>{tp(e.inputs),e.compute(rp(e.inputs,t,e.outputCount))}});var np,op,Wu,Nu,Gu=Y(()=>{"use strict";ye();Se();Ze();_e();np=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],o=r.dims.length;if(r.dims[o-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let i=Math.floor((t.k+t.blockSize-1)/t.blockSize),u=t.blockSize/8*t.bits,a=e[1];if(!M.areEqual(a.dims,[t.n,i,u]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let p=e[2].dims;if(M.size(p)!==t.n*i)throw new Error("scales input size error.");if(e.length===4){let d=e[3].dims,y=t.bits>4?t.n*i:t.n*Math.floor((i+1)/2);if(M.size(d)!==y)throw new Error("zeroPoints input size error.")}},op=(e,t,r,o)=>{let i=e[0].dims,u=i.length,a=Math.floor((t.k+t.blockSize-1)/t.blockSize),c=i[u-2],p=t.k,h=t.n,d=i.slice(0,u-2),y=M.size(d),_=t.blockSize/8*t.bits/4,v=e[0].dataType,S=Me(c),A=Me(t.k),I=Me(_),x=tr(v),E=c*a*x,P=Math.floor(o/E),O=a<=r[0]&&P>0,R=!O||P>=4?Me(h):P>=2&&Me(h)>=2?2:1,L=d.concat([c,h]),N=M.size(L)/R/S,K=O?[]:[{type:12,data:N},{type:12,data:t.blockSize}],Q=[y,c,p/A],he=M.convertShape(e[1].dims).slice();he.splice(-1,1,_/I),K.push(...Z(Q)),K.push(...Z(he)),K.push(...Z(e[2].dims)),e.length===4&&K.push(...Z(M.convertShape(e[3].dims)));let W=[y,c,h/R];K.push(...Z(W));let se=Ce=>{let We=Q.length,ee=U("a",e[0].dataType,We,A),ae=U("b",12,he.length,I),Ae=U("scales",e[2].dataType,e[2].dims.length),me=[ee,ae,Ae],ie=e.length===4?U("zero_points",12,e[3].dims.length):void 0;ie&&me.push(ie);let ue=W.length,le=j("output",e[0].dataType,ue,R),qe=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],G=De(e[0].dataType),ne=(()=>{switch(A){case 1:return`array<${G}, 8>`;case 2:return`mat4x2<${G}>`;case 4:return`mat2x4<${G}>`;default:throw new Error(`${A}-component is not supported.`)}})(),xe=`\n for (var word: u32 = 0; word < ${_}; word += ${I}) {\n ${ae.indicesSet("b_indices","2","word")};\n let b_data = ${ae.getByIndices("b_indices")};\n for (var i: u32 = 0; i < ${I}; i++) {\n let b_value: u32 = ${I===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 = ${ne}(${Array.from({length:4},(Be,Ge)=>`${G}(b_value_lower[${Ge}]), ${G}(b_value_upper[${Ge}])`).join(", ")});\n let b_dequantized_values = ${(()=>A===1?`${ne}(${Array.from({length:8},(Be,Ge)=>`(b_quantized_values[${Ge}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${ne}(${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 < ${O?c:S}u; m++) {\n ${ee.indicesSet("a_indices",We-2,O?"m":`row * ${S} + m`)};\n ${ee.indicesSet("a_indices",We-1,"word_offset")};\n var input_offset = ${ee.indicesToOffset("a_indices")};\n var a_data: ${ne};\n for (var j: u32 = 0; j < ${8/A}; j++) {\n a_data[j] = ${ee.getByOffset("input_offset")};\n input_offset++;\n }\n ${O?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${R>1?"[c]":""} += ${Array.from({length:8/A},(Be,Ge)=>`${A===1?`a_data[${Ge}] * b_dequantized_values[${Ge}]`:`dot(a_data[${Ge}], b_dequantized_values[${Ge}])`}`).join(" + ")};\n }\n word_offset += ${8/A};\n }\n }`,Ke=ie?`\n zero_point_offset += 4;\n if (zero_point_offset == 32) {\n zero_point_offset = 0;\n zero_point_index++;\n zero_point_word = ${ie.getByOffset("zero_point_index")};\n }`:"";return O?`\n var workgroup_shared: array<${le.type.value}, ${c*a}>;\n ${Ce.declareVariables(...me,le)}\n ${Ce.mainStart([a,1,1])}\n var a_indices: ${ee.type.indices};\n var block = local_id.x;\n var col = workgroup_id.y;\n var batch = workgroup_id.z;\n ${ee.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 < ${R}; c++) {\n let col_times_components_plus_c = col * ${R} + c;\n ${ie?`\n var zero_point_bytes_per_col: u32 = (${a} + 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 = ${ie.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}\n var b_indices: ${ae.type.indices};\n ${ae.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 * ${a} + block;\n let scale = ${Ae.getByOffset("scales_index")};\n // The default zero point is 8 for unsigned 4-bit quantization.\n let zero_point = ${G}(${ie?"(zero_point_word) & 0xFu":8});\n ${ae.indicesSet("b_indices","1","block")};\n var word_offset: u32 = block * ${t.blockSize/A};\n var workgroup_shared_offset: u32 = block * ${c};\n ${xe}\n }\n workgroupBarrier();\n if (local_id.x == 0u) {\n var output_indices: ${le.type.indices};\n ${le.indicesSet("output_indices","0","batch")};\n ${le.indicesSet("output_indices",ue-1,"col")};\n ${le.indicesSet("output_indices",ue-2,"0")};\n var output_offset = ${le.indicesToOffset("output_indices")};\n for (var m: u32 = 0u; m < ${c}u; m++) {\n var output_value: ${le.type.value} = ${le.type.value}(0);\n var workgroup_shared_offset: u32 = m;\n for (var b: u32 = 0u; b < ${a}u; b++) {\n output_value += workgroup_shared[workgroup_shared_offset];\n workgroup_shared_offset += ${c};\n }\n ${le.setByOffset("output_offset","output_value")};\n output_offset += ${h/R};\n }\n }\n }`:`\n ${Ce.registerUniforms(qe).declareVariables(...me,le)}\n ${Ce.mainStart()}\n ${Ce.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n var output_values: array<${le.type.value}, ${S}>;\n var output_indices = ${le.offsetToIndices("global_idx")};\n var col = ${le.indicesGet("output_indices",ue-1)};\n var row = ${le.indicesGet("output_indices",ue-2)};\n var a_indices: ${ee.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 ${ie?`\n var zero_point_abs_offset = col * ${R} * ((${a} + 1) / 2);\n var zero_point_index: u32 = zero_point_abs_offset / 4;\n var zero_point_word: u32 = ${ie.getByOffset("zero_point_index")};\n var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}\n var scale_index = col * ${a*R};\n var b_indices: ${ae.type.indices};\n for (var c: u32 = 0; c < ${R}; c++) {\n ${ae.indicesSet("b_indices","0",`col * ${R} + c`)};\n var block_offset: u32 = 0;\n for (var block: u32 = 0; block < ${a}; block++) {\n // The scale and zero points are computed per block.\n let scale = ${Ae.getByOffset("scale_index")};\n // The default zero point is 8 for unsigned 4-bit quantization.\n let zero_point = ${G}(${ie?"extractBits(zero_point_word, zero_point_offset, 4)":8});\n ${ae.indicesSet("b_indices","1","block")};\n var word_offset: u32 = block_offset;\n ${xe}\n scale_index++;\n ${Ke}\n block_offset += uniforms.block_size / ${A};\n }\n // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.\n ${ie?`if (zero_point_offset % 8 > 0) {\n ${Ke}\n }`:""}\n }\n for (var k: u32 = 0u; k < ${S}u; k++) {\n ${le.indicesSet("output_indices",ue-2,`${S} * row + k`)};\n ${le.setByIndices("output_indices","output_values[k]")}\n }\n }`};return{name:O?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${t.cacheKey};${c};${v};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:L,dataType:v}],name:O?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:O?{x:1,y:Math.ceil(h/R),z:y}:{x:Math.ceil(N/64)},programUniforms:K}),getShaderSource:se}},Wu=(e,t)=>{np(e.inputs,t);let r=e.getMaxComputeWorkgroupSizes(),o=e.getMaxComputeWorkgroupStoragesize();e.compute(op(e.inputs,t,r,o))},Nu=e=>ve(e)});var it,ip,Lu,Hu,ap,Ko,Fu,qu=Y(()=>{"use strict";ye();Se();Ze();_n();Ro();_e();Sr();it=(e,t)=>e.length>t&&e[t].dims.length>0&&M.size(e[t].dims)>0?e[t]:void 0,ip=(e,t)=>{let r=e[0],o=it(e,1),i=it(e,2),u=it(e,3),a=it(e,4),c=it(e,5),p=it(e,6),h=it(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let d=!1,y=r.dims[0],w=r.dims[1],_=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],v=w,S=0,A=0,I=Math.floor(_/t.numHeads);if(p&&h){if(p.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(p.dims[0]!==y||p.dims[1]!==t.numHeads||p.dims[3]!==I)throw new Error(\'Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(h.dims[0]!==y||h.dims[1]!==t.numHeads||h.dims[3]!==I)throw new Error(\'Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(p.dims[2]!==h.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)\');if(h.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');S=p.dims[2],A=p.dims[2]}else if(p||h)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let x;if(o){if(r.dims.length!==3)throw new Error(\'Input "query" is expected to have 3 dimensions when key is given\');if(o.dims.length<3||o.dims.length>5)throw new Error(\'Input "key" is expected to have 3, 4, or 5 dimensions\');if(r.dims[0]!==o.dims[0])throw new Error(\'Input "query" and "key" shall have same dim 0 (batch size)\');if(o.dims.length===3){if(o.dims[2]!==r.dims[2])throw new Error(\'Input "query" and "key" shall have same dim 2 (hidden_size)\');x=2,v=o.dims[1]}else if(o.dims.length===5){if(o.dims[2]!==t.numHeads||o.dims[3]!==2||o.dims[4]!==I)throw new Error(\'Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\');if(i)throw new Error(\'Expect "value" be none when "key" has packed kv format.\');x=5,v=o.dims[1]}else{if(o.dims[1]!==t.numHeads||o.dims[3]!==I)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');x=0,v=o.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]!==t.numHeads||r.dims[3]!==3))throw new Error(\'Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv\');x=3}if(u){if(u.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(i&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(a){E=8;let N=a.dims;throw N.length===1?N[0]===y?E=1:N[0]===3*y+2&&(E=3):N.length===2&&N[0]===y&&N[1]===v&&(E=5),E===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let P=!1,O=_;if(i){if(i.dims.length!==3&&i.dims.length!==4)throw new Error(\'Input "value" is expected to have 3 or 4 dimensions\');if(r.dims[0]!==i.dims[0])throw new Error(\'Input "query" and "value" shall have same dim 0 (batch_size)\');if(i.dims.length===3){if(v!==i.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');O=i.dims[2]}else{if(v!==i.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');O=i.dims[1]*i.dims[3],P=!0}}let R=S+v,L=!1;if(a)throw new Error("Key padding mask is not supported");if(c){if(c.dims.length!==4)throw new Error(\'Input "relative_position_bias" is expected to have 4 dimensions\');if(c.dims[0]!==y&&c.dims[0]!==1||c.dims[1]!==t.numHeads||c.dims[2]!==w||c.dims[3]!==R)throw new Error(\'Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)\')}return{batchSize:y,sequenceLength:w,pastSequenceLength:S,kvSequenceLength:v,totalSequenceLength:R,maxSequenceLength:A,inputHiddenSize:0,hiddenSize:_,vHiddenSize:O,headSize:I,vHeadSize:Math.floor(O/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:E,scale:t.scale,broadcastResPosBias:L,passPastInKv:P,qkvFormat:x}},Lu=e=>ve({...e}),Hu=ve({perm:[0,2,1,3]}),ap=(e,t,r,o,i,u,a)=>{let c=[o,i,u],p=M.size(c),h=[{type:12,data:p},{type:12,data:a},{type:12,data:u}],d=y=>{let w=j("qkv_with_bias",t.dataType,c),_=U("qkv",t.dataType,c),v=U("bias",r.dataType,c),S=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n ${y.registerUniforms(S).declareVariables(_,v,w)}\n ${y.mainStart()}\n ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:c,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:d},{inputs:[t,r],outputs:[-1]})[0]},Ko=(e,t,r,o,i,u,a,c)=>{let p=u;if(a){if(o===1)throw new Error("AddBiasReshape is not implemented. 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sp,up,dp,lp,cp,pp,mp,fp,ju,Ku=Y(()=>{"use strict";ye();Se();_e();sp=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},up=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n if (k < 0) {\n break;\n }\n if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n break;\n }\n offset += k * i32(${fe("uniforms.x_strides",i,t)});\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 ${o}\n value = x[offset];\n }\n `},dp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n if (k < 0) {\n k = 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i32(${fe("uniforms.x_strides",i,t)});\n `;return`\n var offset = 0;\n var k = 0;\n ${o}\n value = x[offset];\n `},pp=(e,t,r)=>{switch(r.mode){case 0:return up(e,t,r.pads.length);case 1:return dp(e,t,r.pads.length);case 2:return lp(e,t,r.pads.length);case 3:return cp(e,t,r.pads.length);default:throw new Error("Invalid mode")}},mp=(e,t)=>{let r=M.padShape(e[0].dims.slice(),t.pads),o=e[0].dims,i=M.size(r),u=[{type:12,data:i},{type:6,data:t.pads}];t.mode===0&&u.push({type:e[0].dataType,data:t.value}),u.push(...Z(e[0].dims,r));let a=["rank"],c=p=>{let h=j("output",e[0].dataType,r.length),d=U("x",e[0].dataType,o.length),y=d.type.value,w=pp(h,o.length,t),_=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&_.push({name:"constant_value",type:y}),`\n ${p.registerUniforms(_).declareVariables(d,h)}\n ${p.mainStart()}\n ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let indices = ${h.offsetToIndices("global_idx")};\n\n var value = ${y}(0);\n ${w}\n output[global_idx] = value;\n }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(r)/64)},programUniforms:u}),getShaderSource:c}},fp=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),o=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,i=e[0].dims.length,u=new Int32Array(2*i).fill(0);if(e.length>=4){let c=e[3].getBigInt64Array();for(let p=0;pu[Number(p)]=Number(c));let a=[];return u.forEach(c=>a.push(c)),{mode:t.mode,value:o,pads:a}}else return t},ju=(e,t)=>{sp(e.inputs);let r=fp(e.inputs,t);e.compute(mp(e.inputs,r),{inputs:[0]})}});var Nn,Yu,Zu,Xu,Qu,hp,gp,Ju,ed,td,rd,nd,od,id,ad,sd,ud,dd,ld,cd=Y(()=>{"use strict";$r();ye();Se();_e();Nn=e=>{if(vr.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Yu=(e,t,r)=>{let o=t.format==="NHWC",i=e.dims.slice();o&&i.splice(1,0,i.pop());let u=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),c=t.strides.slice(),p=u?t.dilations.slice():[],h=t.pads.slice();nr.adjustPoolAttributes(r,i,a,c,p,h);let d=nr.computePoolOutputShape(r,i,c,p,a,h,t.autoPad),y=Object.assign({},t);u?Object.assign(y,{kernelShape:a,strides:c,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(y,{kernelShape:a,strides:c,pads:h,cacheKey:t.cacheKey});let w=d.slice();return w.push(w.splice(1,1)[0]),[y,o?w:d]},Zu=(e,t)=>{let r=t.format==="NHWC",o=M.size(e),i=M.size(t.kernelShape),u=[{type:12,data:o},{type:12,data:i}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let c=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],d=t.pads[t.pads.length-1],y=!!(h+d);u.push({type:12,data:c},{type:12,data:p},{type:12,data:h},{type:12,data:d}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let w=!1;if(t.kernelShape.length===2){let _=t.kernelShape[t.kernelShape.length-2],v=t.strides[t.strides.length-2],S=t.pads[t.pads.length/2-2],A=t.pads[t.pads.length-2];w=!!(S+A),u.push({type:12,data:_},{type:12,data:v},{type:12,data:S},{type:12,data:A}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[u,a,!0,y,w]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let c=M.computeStrides(t.kernelShape);u.push({type:12,data:c},{type:12,data:t.pads},{type:12,data:t.strides}),a.push({name:"kernelStrides",type:"u32",length:c.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let p=t.pads.reduce((h,d)=>h+d);return[u,a,!!p,!1,!1]}},Xu=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w=i.format==="NHWC",_=t.type.value,v=j("output",t.type.tensor,o);if(i.kernelShape.length<=2){let S="",A="",I="",x=r-(w?2:1);if(d?S=`\n for (var i: u32 = 0u; i < 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= false;\n\n for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n var offset = i;\n for (var j = 0u; j < ${S-1}u; j++) {\n offsets[j] = offset / ${fe("uniforms.kernelStrides","j",S)};\n offset -= offsets[j] * ${fe("uniforms.kernelStrides","j",S)};\n }\n offsets[${S-1}] = offset;\n\n isPad = false;\n for (var j = ${r-S}u; j < ${r}u; j++) {\n xIndices[j] = indices[j] * ${fe("uniforms.strides",`j - ${r-S}u`,S)}\n + offsets[j - ${r-S}u] - ${fe("uniforms.pads","j - 2u",A)};\n ${I}\n }\n ${a}\n\n output[global_idx] = value;\n }`}},Qu=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,hp=e=>`${Qu(e)};${e.countIncludePad}`,gp=e=>`${Qu(e)};${e.storageOrder};${e.dilations}`,Ju=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),ed=(e,t,r,o)=>{let[i,u]=Yu(t,o,r),a=U("x",t.dataType,t.dims.length),c=a.type.value,p="value += x_val;",h="";i.countIncludePad?h+=`value /= 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strict";$r();ye();_e();bp=(e,t,r)=>{let o=e===t,i=et&&r>0;if(o||i||u)throw new Error("Range these inputs\' contents are invalid.")},wp=(e,t,r,o)=>{let i=Math.abs(Math.ceil((t-e)/r)),u=[i],a=i,c=[{type:12,data:a},{type:o,data:e},{type:o,data:r},...Z(u)],p=h=>{let d=j("output",o,u.length),y=d.type.value,w=[{name:"outputSize",type:"u32"},{name:"start",type:y},{name:"delta",type:y}];return`\n ${h.registerUniforms(w).declareVariables(d)}\n ${h.mainStart()}\n ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n output[global_idx] = uniforms.start + ${y}(global_idx) * uniforms.delta;\n }`};return{name:"Range",shaderCache:{hint:`${o}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:u,dataType:o}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c})}},pd=e=>{let t=0,r=0,o=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],o=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],o=e.inputs[2].getFloat32Array()[0]),vr.webgpu.validateInputContent&&bp(t,r,o),e.compute(wp(t,r,o,e.inputs[0].dataType),{inputs:[]})}});var vp,$p,_p,Sp,xp,Cp,Ap,Ip,Tp,Ep,Pp,fd,kp,Op,Rp,Bp,Dp,hd,gd,yd=Y(()=>{"use strict";ye();Se();Ze();_e();vp=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},$p=(e,t,r)=>{t.every(i=>i>=0&&i{throw new Error("Resize requires axes input values to be positive and less than rank")}));let o=new Array(r).fill(1);return t.forEach((i,u)=>o[i]=e[u]),o},_p=(e,t,r,o,i,u)=>{let[a,c,p]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach(d=>u.push(d));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(c>0&&e.length>c&&e[c].dims.length>0){if(e[c].getFloat32Array().forEach(d=>o.push(d)),o.length!==0&&o.length!==h&&r>=18&&o.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");vp(o,t),t.axes.length>0&&$p(o,t.axes,h).forEach((d,y)=>o[y]=d)}if(p>0&&e.length>p&&(e[p].getBigInt64Array().forEach(d=>i.push(Number(d))),i.length!==h||r>=18&&i.length===t.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(t.axes.length>0){if(o.length!==t.axes.length)throw new Error(\'Resize requires "scales" input size to be of axes rank when axes attributes is specified\');if(i.length!==t.axes.length)throw new Error(\'Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified\')}if(typeof o<"u"&&typeof i<"u"&&o.length>0&&i.length>h)throw new Error("Resize requires only of scales or sizes to be specified")},Sp=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;\n } else {\n return 0.0;\n }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(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 = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n let fract =\n ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);\n return whole + fract;\n }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n return ${t}(roiStart) * ${t}(lengthOriginal - 1) +\n (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /\n ${t}(lengthResized - 1);\n } else {\n return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);\n }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);\n const adjustment = ${t}(lengthResized) / outputWidth;\n const center = ${t}(lengthOriginal) / 2;\n const offset = center * (1 - adjustment);\n return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",xp=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{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(t<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Cp=(e,t,r)=>{let o=new Array(r).fill(0).concat(new Array(r).fill(1)),i=e.length===0?o:e.slice();return t.length>0?(t.forEach((u,a)=>{o[u]=i[a],o[a+r]=i[t.length+a]}),o):i},Ap=(e,t,r,o)=>{let i=[];if(r.length>0)if(o.length>0){if(e.forEach(u=>i.push(u)),Math.max(...o)>e.length)throw new Error("axes is out of bound");o.forEach((u,a)=>i[u]=r[a])}else r.forEach(u=>i.push(u));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((u,a)=>Math.round(u*t[a]))}return i},Ip=(e,t,r)=>{let o=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(u=>t[u]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(u=>t[u]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let i=e.slice();return r.axes.length>0?(r.axes.forEach(u=>t[u]=o),r.axes.forEach(u=>i[u]=Math.round(e[u]*t[u]))):(t.fill(o,0,t.length),i.forEach((u,a)=>i[a]=Math.round(u*t[a]))),i},Tp=(e,t,r,o,i)=>`\n fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {\n var original_indices: array<${e.type.value}, ${r.length}>;\n for (var i:u32 = 0; i < ${r.length}; i++) {\n var output_index = ${e.indicesGet("output_indices","i")};\n var scale = ${fe("uniforms.scales","i",o)};\n var roi_low = ${fe("uniforms.roi","i",i)};\n var roi_hi = ${fe("uniforms.roi",`i + ${t.length}`,i)};\n if (scale == 1.0) {\n original_indices[i] = ${e.type.value}(output_index);\n } else {\n var input_shape_i = ${fe("uniforms.input_shape","i",t.length)};\n var output_shape_i = ${fe("uniforms.output_shape","i",r.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 }`,Ep=(e,t,r,o,i,u,a)=>`\n fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n var input_indices: ${e.type.indices};\n for (var i:u32 = 0; i < ${o.length}; i++) {\n var output_index = ${t.indicesGet("output_indices","i")};\n var input_index: u32;\n var scale = ${fe("uniforms.scales","i",i)};\n if (scale == 1.0) {\n input_index = output_index;\n } else {\n var roi_low = ${fe("uniforms.roi","i",u)};\n var roi_hi = ${fe("uniforms.roi",`i + ${r.length}`,u)};\n var input_shape_i = ${fe("uniforms.input_shape","i",r.length)};\n var output_shape_i = ${fe("uniforms.output_shape","i",o.length)};\n var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n input_shape_i, roi_low, roi_hi);\n if (!${a} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {\n if (original_idx < 0) {\n input_index = 0;\n } else if (original_idx > ${t.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 }`,Pp=(e,t)=>`\n fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n for (var i:u32 = 0; i < ${t.length}; i++) {\n var input_index = ${e.indicesGet("input_indices","i")};\n if (input_index < 0 || input_index >= ${fe("uniforms.input_shape","i",t.length)}) {\n return false;\n }\n }\n return true;\n }`,fd=(e,t,r,o)=>e.rank>o?`\n ${e.indicesSet("input_indices",t,"channel")};\n ${e.indicesSet("input_indices",r,"batch")};\n`:"",kp=(e,t,r,o,i)=>{let[a,c,p,h]=r.length===2?[-1,0,1,-1]:[0,2,3,1],d=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",c,`max(0, min(row, ${r[c]} - 1))`)};\n ${e.indicesSet("input_indices",p,`max(0, min(col, ${r[p]} - 1))`)};\n ${fd(e,h,a,2)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${d} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var row:${d} = originalIndices[${c}];\n var col:${d} = originalIndices[${p}];\n ${o?`if (row < 0 || row > (${r[c]} - 1) || col < 0 || col > (${r[p]} - 1)) {\n return ${i};\n }`:""};\n row = max(0, min(row, ${r[c]} - 1));\n col = max(0, min(col, ${r[p]} - 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 = ${r.length>2?`u32(originalIndices[${h}])`:"0"};\n var batch: u32 = ${r.length>2?`u32(originalIndices[${a}])`:"0"};\n var x11: ${d} = getInputValue(batch, channel, row1, col1);\n var x12: ${d} = getInputValue(batch, channel, row1, col2);\n var x21: ${d} = getInputValue(batch, channel, row2, col1);\n var x22: ${d} = getInputValue(batch, channel, row2, col2);\n var dx1: ${d} = abs(row - ${d}(row1));\n var dx2: ${d} = abs(${d}(row2) - row);\n var dy1: ${d} = abs(col - ${d}(col1));\n var dy2: ${d} = abs(${d}(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 }`},Op=(e,t,r,o,i,u,a,c,p,h)=>{let d=r.length===2,y=!0,[w,_]=d?[0,1]:y?[2,3]:[1,2],v=e.type.value,S=A=>{let I=A===w?"row":"col";return`\n fn ${I}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${v} {\n var output_index = ${t.indicesGet("output_indices",A)};\n var originalIdx: ${v} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[A]},\n ${o[A]}, ${r[A]}, ${u[A]}, ${u[A]} + ${r.length});\n var fractOriginalIdx: ${v} = originalIdx - floor(originalIdx);\n var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n if (${c} && (originalIdx < 0 || originalIdx > (${r[A]} - 1))) {\n return ${p};\n }\n var data: array<${v}, 4> = array<${v}, 4>(0.0, 0.0, 0.0, 0.0);\n for (var i: i32 = -1; i < 3; i++) {\n var ${I}: ${v} = originalIdx + ${v}(i);\n if (${I} < 0 || ${I} >= ${r[A]}) {\n ${(()=>h?`coefs[i + 1] = 0.0;\n continue;`:c?`return ${p};`:`${I} = max(0, min(${I}, ${r[A]} - 1));`)()};\n }\n var input_indices_copy: ${e.type.indices} = input_indices;\n ${e.indicesSet("input_indices_copy",A,`u32(${I})`)};\n data[i + 1] = ${A===w?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n }\n return cubicInterpolation1D(data, coefs);\n }`};return`\n ${S(w)};\n ${S(_)};\n fn getCubicInterpolationCoefs(s: ${v}) -> array<${v}, 4> {\n var absS = abs(s);\n var coeffs: array<${v}, 4> = array<${v}, 4>(0.0, 0.0, 0.0, 0.0);\n var oneMinusAbsS: ${v} = 1.0 - absS;\n var twoMinusAbsS: ${v} = 2.0 - absS;\n var onePlusAbsS: ${v} = 1.0 + absS;\n coeffs[0] = ((${a} * onePlusAbsS - 5 * ${a}) * onePlusAbsS + 8 * ${a}) * onePlusAbsS - 4 * ${a};\n coeffs[1] = ((${a} + 2) * absS - (${a} + 3)) * absS * absS + 1;\n coeffs[2] = ((${a} + 2) * oneMinusAbsS - (${a} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n coeffs[3] = ((${a} * twoMinusAbsS - 5 * ${a}) * twoMinusAbsS + 8 * ${a}) * twoMinusAbsS - 4 * ${a};\n return coeffs;\n }\n\n fn cubicInterpolation1D(x: array<${v}, 4>, coefs: array<${v}, 4>) -> ${v} {\n var coefsSum: ${v} = 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: ${t.type.indices}) -> ${v} {\n var input_indices: ${e.type.indices} = output_indices;\n return colCubicInterpolation(input_indices, output_indices);\n }\n `},Rp=(e,t,r,o,i)=>{let[a,c,p,h,d]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],y=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${y} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",c,`max(0, min(depth, ${r[c]} - 1))`)};\n ${e.indicesSet("input_indices",p,`max(0, min(height, ${r[p]} - 1))`)};\n ${e.indicesSet("input_indices",h,`max(0, min(width, ${r[h]} - 1))`)};\n ${fd(e,d,a,3)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${y} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var depth:${y} = originalIndices[${c}];\n var height:${y} = originalIndices[${p}];\n var width:${y} = originalIndices[${h}];\n ${o?`if (depth < 0 || depth > (${r[c]} - 1) || height < 0 || height > (${r[p]} - 1) || width < 0 || (width > ${r[h]} - 1)) {\n return ${i};\n }`:""};\n\n depth = max(0, min(depth, ${r[c]} - 1));\n height = max(0, min(height, ${r[p]} - 1));\n width = max(0, min(width, ${r[h]} - 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 = ${r.length>3?`u32(originalIndices[${d}])`:"0"};\n var batch: u32 = ${r.length>3?`u32(originalIndices[${a}])`:"0"};\n\n var x111: ${y} = getInputValue(batch, channel, depth1, height1, width1);\n var x112: ${y} = getInputValue(batch, channel, depth1, height1, width2);\n var x121: ${y} = getInputValue(batch, channel, depth1, height2, width1);\n var x122: ${y} = getInputValue(batch, channel, depth1, height2, width2);\n var x211: ${y} = getInputValue(batch, channel, depth2, height1, width1);\n var x212: ${y} = getInputValue(batch, channel, depth2, height1, width2);\n var x221: ${y} = getInputValue(batch, channel, depth2, height2, width1);\n var x222: ${y} = getInputValue(batch, channel, depth2, height2, width2);\n var dx1: ${y} = abs(depth - ${y}(depth1));\n var dx2: ${y} = abs(${y}(depth2) - depth);\n var dy1: ${y} = abs(height - ${y}(height1));\n var dy2: ${y} = abs(${y}(height2) - height);\n var dz1: ${y} = abs(width - ${y}(width1));\n var dz2: ${y} = abs(${y}(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 }`},Bp=(e,t,r,o,i,u)=>{let a=e.dims,c=Cp(u,t.axes,a.length),p=Ap(a,o,i,t.axes),h=o.slice();o.length===0&&(h=a.map((x,E)=>x===0?1:p[E]/x),t.keepAspectRatioPolicy!=="stretch"&&(p=Ip(a,h,t)));let d=j("output",e.dataType,p.length),y=U("input",e.dataType,a.length),w=M.size(p),_=a.length===p.length&&a.every((x,E)=>x===p[E]),v=t.coordinateTransformMode==="tf_crop_and_resize",S=t.extrapolationValue,A=y.type.value,I=x=>`\n ${_?"":`\n ${Sp(t.coordinateTransformMode,A)};\n ${(()=>{switch(t.mode){case"nearest":return`\n ${Pp(y,a)};\n ${xp(t.nearestMode,r,A)};\n ${Ep(y,d,a,p,h.length,c.length,v)};\n `;case"linear":return`\n ${Tp(d,a,p,h.length,c.length)};\n ${(()=>{if(a.length===2||a.length===4)return`${kp(y,d,a,v,S)}`;if(a.length===3||a.length===5)return`${Rp(y,d,a,v,S)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n `;case"cubic":return`\n ${(()=>{if(a.length===2||a.length===4)return`${Op(y,d,a,p,h,c,t.cubicCoeffA,v,t.extrapolationValue,t.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 ${x.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",c.length).declareVariables(y,d)}\n ${x.mainStart()}\n ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n ${_?"output[global_idx] = input[global_idx];":`\n let output_indices = ${d.offsetToIndices("global_idx")};\n var input_indices: ${y.type.indices};\n ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n if (checkInputIndices(input_indices)) {\n output[global_idx] = ${y.getByIndices("input_indices")};\n } else {\n output[global_idx] = ${t.extrapolationValue};\n }`;case"linear":return`output[global_idx] = ${a.length===2||a.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};\n`}\n }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${h.length>0?h:""}|${i.length>0?i:""}|${c.length>0?c:""}|${_}|${a}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:[{type:12,data:w},{type:1,data:h},{type:1,data:c},...Z(a,p)]})}},Dp=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},hd=(e,t)=>{let r=[],o=[],i=[],u=Dp(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");_p(e.inputs,t,u,r,o,i),e.compute(Bp(e.inputs[0],t,u,r,o,i),{inputs:[0]})},gd=e=>{let t=e.antialias,r=e.axes,o=e.coordinateTransformMode,i=e.cubicCoeffA,u=e.excludeOutside!==0,a=e.extrapolationValue,c=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return ve({antialias:t,axes:r,coordinateTransformMode:o,cubicCoeffA:i,excludeOutside:u,extrapolationValue:a,keepAspectRatioPolicy:c,mode:p,nearestMode:h})}});var zp,Mp,bd,wd=Y(()=>{"use strict";ye();Se();Ze();_e();zp=(e,t)=>{let[r,o,i,u]=e,{numHeads:a,rotaryEmbeddingDim:c}=t;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(!M.areEqual(o.dims,[])&&!M.areEqual(o.dims,[1])&&o.dims.length!==2)throw new Error(`Input \'position_ids\' is expected to have 0, 1, or 2 dimensions, got ${o.dims.length}`);if(i.dims.length!==2)throw new Error(`Input \'cos_cache\' is expected to have 2 dimensions, got ${i.dims.length}`);if(u.dims.length!==2)throw new Error(`Input \'sin_cache\' is expected to have 2 dimensions, got ${u.dims.length}`);if(!M.areEqual(i.dims,u.dims))throw new Error("Inputs \'cos_cache\' and \'sin_cache\' are expected to have the same shape");if(c>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let p=r.dims[0],h=r.dims[r.dims.length-2],d=i.dims[0],y=M.sizeFromDimension(r.dims,1)/h,w=c===0?i.dims[1]*2:y/a;if(c>w)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(o.dims.length===2){if(p!==o.dims[0])throw new Error(`Input \'position_ids\' dimension 0 should be of size batch_size, got ${o.dims[0]}`);if(h!==o.dims[1])throw new Error(`Input \'position_ids\' dimension 1 should be of size sequence_length, got ${o.dims[1]}`)}if(w/2!==i.dims[1]&&c/2!==i.dims[1])throw new Error(`Input \'cos_cache\' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${i.dims[1]}`);if(h>d)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Mp=(e,t)=>{let{interleaved:r,numHeads:o,rotaryEmbeddingDim:i,scale:u}=t,a=e[0].dims[0],c=M.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=c/p,d=e[2].dims[1],y=i===0?d*2:h/o,w=new Array(a,p,h/y,y-d),_=M.computeStrides(w),v=[{type:1,data:u},{type:12,data:w},{type:12,data:_},...e[0].dims.length===3?new Array({type:12,data:[c,h,y,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[c,y,p*y,1]}):[],...Z(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],S=A=>{let I=U("input",e[0].dataType,e[0].dims.length),x=U("position_ids",e[1].dataType,e[1].dims.length),E=U("cos_cache",e[2].dataType,e[2].dims.length),P=U("sin_cache",e[3].dataType,e[3].dims.length),O=j("output",e[0].dataType,e[0].dims.length);return A.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:w.length},{name:"global_strides",type:"u32",length:_.length},{name:"input_output_strides",type:"u32",length:_.length}]),`\n ${A.declareVariables(I,x,E,P,O)}\n\n ${A.mainStart(or)}\n let half_rotary_emb_dim = uniforms.${E.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 ${A.guardAgainstOutOfBoundsWorkgroupSizes("size")}\n\n if (bsnh[3] < half_rotary_emb_dim) {\n let position_ids_idx =\n ${x.broadcastedIndicesToOffset("bsnh.xy",j("",x.type.tensor,2))};\n let position_id =\n u32(${x.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], ${r});\n let j = i + select(half_rotary_emb_dim, 1, ${r});\n let re = ${I.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} -\n ${I.getByOffset("j")} * ${P.get("position_id","bsnh[3]")};\n ${O.setByOffset("i","re")}\n let im = ${I.getByOffset("i")} * ${P.get("position_id","bsnh[3]")} +\n ${I.getByOffset("j")} * ${E.get("position_id","bsnh[3]")};\n ${O.setByOffset("j","im")}\n } else {\n let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;\n ${O.setByOffset("k",I.getByOffset("k"))}\n }\n }`};return{name:"RotaryEmbedding",shaderCache:{hint:ve({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:S,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(w)/or)},programUniforms:v})}},bd=(e,t)=>{zp(e.inputs,t),e.compute(Mp(e.inputs,t))}});var Up,Vp,vd,$d=Y(()=>{"use strict";ye();Se();_e();Up=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],o=e[2];if(t.dataType!==r.dataType||t.dataType!==o.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.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 i=t.dims[t.dims.length-1],u=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==u)throw new Error("Skip must have the same sequence length as input");if(o.dims.length!==1)throw new Error("Gamma must be 1D");if(o.dims[o.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let a=e[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let a=e[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},Vp=(e,t,r,o)=>{let i=t.simplified,u=e[0].dims,a=M.size(u),c=u,p=a,h=u.slice(-1)[0],d=o?u.slice(0,-1).concat(1):[],y=!i&&e.length>3,w=e.length>4,_=o&&r>1,v=o&&r>2,S=r>3,A=Me(h),I=[{type:12,data:p},{type:12,data:A},{type:12,data:h},{type:1,data:t.epsilon}],x=P=>{let O=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],R=[U("x",e[0].dataType,e[0].dims,A),U("skip",e[1].dataType,e[1].dims,A),U("gamma",e[2].dataType,e[2].dims,A)];y&&R.push(U("beta",e[3].dataType,e[3].dims,A)),w&&R.push(U("bias",e[4].dataType,e[4].dims,A)),R.push(j("output",e[0].dataType,c,A)),_&&R.push(j("mean_output",1,d)),v&&R.push(j("inv_std_output",1,d)),S&&R.push(j("input_skip_bias_sum",e[0].dataType,c,A));let L=De(e[0].dataType);return`\n\n ${P.registerUniforms(O).declareVariables(...R)}\n\n ${P.mainStart()}\n ${P.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 = ${$t("f32",A)};\n var squareSum = ${$t("f32",A)};\n for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n let skip_value = skip[offset + i];\n let bias_value = ${w?"bias[i]":L+"(0.0)"};\n let input_value = x[offset + i];\n let value = input_value + skip_value + bias_value;\n ${S?"input_skip_bias_sum[offset + i] = value;":""}\n output[offset + i] = value;\n let f32_value = ${ir(L,A,"value")};\n sum += f32_value;\n squareSum += f32_value * f32_value;\n }\n let mean = ${_t("sum",A)} / f32(uniforms.hidden_size);\n let inv_std_dev = inverseSqrt(${_t("squareSum",A)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon);\n ${_?"mean_output[global_idx] = mean;":""}\n ${v?"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] ${i?"":`- ${L}(mean)`}) * ${L}(inv_std_dev) * gamma[i] ${y?"+ beta[i]":""};\n }\n }`},E=[{dims:c,dataType:e[0].dataType}];return r>1&&E.push({dims:d,dataType:1}),r>2&&E.push({dims:d,dataType:1}),r>3&&E.push({dims:u,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${A};${_};${v};${S}`,inputDependencies:e.map((P,O)=>"type")},getShaderSource:x,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(p/h/64)},programUniforms:I})}},vd=(e,t)=>{Up(e.inputs);let o=[0];e.outputCount>1&&o.push(-3),e.outputCount>2&&o.push(-3),e.outputCount>3&&o.push(3),e.compute(Vp(e.inputs,t,e.outputCount,!1),{outputs:o})}});var Wp,Gn,Np,_d,Gp,Hp,Sd,xd,Cd=Y(()=>{"use strict";ye();Se();Ze();_e();Wp=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,o)=>{if(e[o+1].dataType!==6&&e[o+1].dataType!==7)throw new Error(`Input ${o} must be an array of int32 or int64`)})},Gn=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(o=>r.push(Number(o)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(o=>r.push(Number(o)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},Np=(e,t)=>{if(e.length>1){let r=Gn(e,1),o=Gn(e,2),i=Gn(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),ve({starts:r,ends:o,axes:i})}else return t},_d=(e,t,r,o,i)=>{let u=e;return e<0&&(u+=r[o[t]]),i[t]<0?Math.max(0,Math.min(u,r[o[t]]-1)):Math.max(0,Math.min(u,r[o[t]]))},Gp=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n var input_indices: ${e.type.indices};\n var carry = 0u;\n for (var i = ${r.length}; i >= 0; i--) {\n let input_shape_i = ${fe("uniforms.input_shape","i",r.length)};\n let steps_i = ${fe("uniforms.steps","i",r.length)};\n let signs_i = ${fe("uniforms.signs","i",r.length)};\n let starts_i = ${fe("uniforms.starts","i",r.length)};\n var output_index = ${t.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 }`,Hp=(e,t)=>{let r=e[0].dims,o=M.size(r),i=t.axes.length>0?M.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],u=Gn(e,4);u.forEach(I=>I!==0||(()=>{throw new Error("step cannot be 0")})),u.length===0&&(u=Array(i.length).fill(1));let a=t.starts.map((I,x)=>_d(I,x,r,i,u)),c=t.ends.map((I,x)=>_d(I,x,r,i,u));if(i.length!==a.length||i.length!==c.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==r.length)for(let I=0;IMath.sign(I));u.forEach((I,x,E)=>{if(I<0){let P=(c[x]-a[x])/I,O=a[x],R=O+P*u[x];a[x]=R,c[x]=O,E[x]=-I}});let h=r.slice(0);i.forEach((I,x)=>{h[I]=Math.ceil((c[I]-a[I])/u[I])});let d={dims:h,dataType:e[0].dataType},y=j("output",e[0].dataType,h.length),w=U("input",e[0].dataType,e[0].dims.length),_=M.size(h),v=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:a.length},{name:"signs",type:"i32",length:p.length},{name:"steps",type:"u32",length:u.length}],S=[{type:12,data:_},{type:12,data:a},{type:6,data:p},{type:12,data:u},...Z(e[0].dims,h)],A=I=>`\n ${I.registerUniforms(v).declareVariables(w,y)}\n ${Gp(w,y,r)}\n ${I.mainStart()}\n ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n let output_indices = ${y.offsetToIndices("global_idx")};\n let input_indices = calculateInputIndices(output_indices);\n ${y.setByOffset("global_idx",w.getByIndices("input_indices"))}\n }`;return{name:"Slice",shaderCache:{hint:`${p.length}_${a.length}_${u.length}`,inputDependencies:["rank"]},getShaderSource:A,getRunData:()=>({outputs:[d],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:S})}},Sd=(e,t)=>{Wp(e.inputs,t);let r=Np(e.inputs,t);e.compute(Hp(e.inputs,r),{inputs:[0]})},xd=e=>{let t=e.starts,r=e.ends,o=e.axes;return ve({starts:t,ends:r,axes:o})}});var Lp,Fp,Ad,Id,Td=Y(()=>{"use strict";ye();Se();Ze();_e();Lp=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Fp=(e,t)=>{let r=e.dims,o=M.size(r),i=64,u=t.axis;if(u<0&&(u=r.length+u),uI===4?`max(max(${A}.x, ${A}.y), max(${A}.z, ${A}.w))`:I===2?`max(${A}.x, ${A}.y)`:I===3?`max(max(${A}.x, ${A}.y), ${A}.z)`:A,y=U("x",e.dataType,e.dims,p),w=j("result",e.dataType,e.dims,p),_=y.type.value,v=De(e.dataType)==="f32"?`var threadMax = ${_}(-3.402823e+38f);`:`var threadMax = ${_}(-65504.0h);`,S=A=>`\n var rowMaxShared : ${_};\n var rowSumShared : ${_};\n var threadShared : array<${_}, ${i}>;\n\n fn getValue(row: i32, col: i32, row_stride: i32) -> ${_} {\n let index = row * row_stride + col;\n return x[index];\n }\n\n fn setValue(row: i32, col: i32, row_stride: i32, value: ${_}) {\n let index = row * row_stride + col;\n result[index] = value;\n }\n ${A.registerUniform("packedCols","i32").declareVariables(y,w)}\n ${A.mainStart()}\n let gindex = i32(global_idx);\n let lindex = i32(local_idx);\n const wg = ${i};\n let row = gindex / wg;\n let cols = uniforms.packedCols;\n let row_stride : i32 = uniforms.packedCols;\n\n // find the rows max\n ${v}\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 = ${_}(${d("threadShared[0]",p)});\n }\n workgroupBarrier();\n\n // find the rows sum\n var threadSum = ${_}(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 = ${_}(${_t("threadShared[0]",p)});\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:`${p}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:c},programUniforms:[{type:6,data:h}]}),getShaderSource:S}},Ad=(e,t)=>{Lp(e.inputs),e.compute(Fp(e.inputs[0],t))},Id=e=>ve({axis:e.axis})});var qp,jp,Kp,Yp,Zp,Ed,Pd,kd=Y(()=>{"use strict";ye();Se();Ze();_e();qp=e=>{if(!e||e.length<1)throw new Error("too few inputs")},jp=(e,t)=>{let r=[],o=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(i=>r.push(Number(i))),o=r.length),ve({numOutputs:o,axis:t.axis,splitSizes:r})},Kp=e=>`\nfn calculateOutputIndex(index: u32) -> u32 {\n for (var i: u32 = 0u; i < ${e}u; i += 1u ) {\n if (index < ${fe("uniforms.size_in_split_axis","i",e)}) {\n return i;\n }\n }\n return ${e}u;\n}`,Yp=e=>{let t=e.length,r=[];for(let o=0;o{let r=e[0].dims,o=M.size(r),i=e[0].dataType,u=M.normalizeAxis(t.axis,r.length),a=new Array(t.numOutputs),c=U("input",i,r.length),p=new Array(t.numOutputs),h=[],d=[],y=0,w=[{type:12,data:o}];for(let v=0;v`\n ${v.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",p.length).declareVariables(c,...a)}\n ${Kp(p.length)}\n ${Yp(a)}\n\n ${v.mainStart()}\n ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}\n\n var indices = ${c.offsetToIndices("global_idx")};\n var index = ${c.indicesGet("indices",u)};\n let output_number = calculateOutputIndex(index);\n if (output_number != 0) {\n index -= ${fe("uniforms.size_in_split_axis","output_number - 1u",p.length)};\n ${c.indicesSet("indices",u,"index")};\n }\n writeBufferData(output_number, indices, global_idx);\n }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:_,getRunData:()=>({outputs:h,dispatchGroup:{x:Math.ceil(o/64)},programUniforms:w})}},Ed=(e,t)=>{qp(e.inputs);let r=e.inputs.length===1?t:jp(e.inputs,t);e.compute(Zp(e.inputs,r),{inputs:[0]})},Pd=e=>{let t=e.axis,r=e.splitSizes,o=e.numOutputs<0?r.length:e.numOutputs;if(o!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return ve({axis:t,numOutputs:o,splitSizes:r})}});var Od,Xp,Qp,Jp,Rd,Bd=Y(()=>{"use strict";ye();Se();_e();Od=e=>Array.from(e.getBigInt64Array(),Number),Xp=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(Od(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")},Qp=(e,t)=>{let r=[];for(let o=0;o{let t=e[0].dims,r=Od(e[1]),o=Qp(t,r),i=M.size(o),u=e[0].dataType,a=U("input",u,t.length),c=j("output",u,o.length),p=h=>`\n const inputShape = ${a.indices(...t)};\n ${h.registerUniform("output_size","u32").declareVariables(a,c)}\n ${h.mainStart()}\n ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let output_indices = ${c.offsetToIndices("global_idx")};\n var input_indices: ${a.type.indices};\n for (var i = 0; i < ${t.length}; i++) {\n let input_dim_i = ${a.indicesGet("uniforms.input_shape","i")};\n let input_dim_value = ${c.indicesGet("output_indices","i")} % input_dim_i;\n\n ${a.indicesSet("input_indices","i","input_dim_value")}\n }\n ${c.setByOffset("global_idx",a.getByIndices("input_indices"))}\n }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},...Z(e[0].dims,o)]}),getShaderSource:p}},Rd=e=>{Xp(e.inputs),e.compute(Jp(e.inputs),{inputs:[0]})}});var em,tm,Dd,zd=Y(()=>{"use strict";ye();Se();_e();em=(e,t,r,o,i)=>{let u=j("output_data",i,r.length,4),a=U("a_data",t[1].dataType,t[1].dims.length,4),c=U("b_data",t[2].dataType,t[2].dims.length,4),p=U("c_data",t[0].dataType,t[0].dims.length,4),h,d=(y,w,_)=>`select(${w}, ${y}, ${_})`;if(!o)h=u.setByOffset("global_idx",d(a.getByOffset("global_idx"),c.getByOffset("global_idx"),p.getByOffset("global_idx")));else{let y=(w,_,v="")=>{let S=`a_data[index_a${_}][component_a${_}]`,A=`b_data[index_b${_}][component_b${_}]`,I=`bool(c_data[index_c${_}] & (0xffu << (component_c${_} * 8)))`;return`\n let output_indices${_} = ${u.offsetToIndices(`global_idx * 4u + ${_}u`)};\n let offset_a${_} = ${a.broadcastedIndicesToOffset(`output_indices${_}`,u)};\n let offset_b${_} = ${c.broadcastedIndicesToOffset(`output_indices${_}`,u)};\n let offset_c${_} = ${p.broadcastedIndicesToOffset(`output_indices${_}`,u)};\n let index_a${_} = offset_a${_} / 4u;\n let index_b${_} = offset_b${_} / 4u;\n let index_c${_} = offset_c${_} / 4u;\n let component_a${_} = offset_a${_} % 4u;\n let component_b${_} = offset_b${_} % 4u;\n let component_c${_} = offset_c${_} % 4u;\n ${w}[${_}] = ${v}(${d(S,A,I)});\n `};i===9?h=`\n var data = vec4(0);\n ${y("data",0,"u32")}\n ${y("data",1,"u32")}\n ${y("data",2,"u32")}\n ${y("data",3,"u32")}\n output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:h=`\n ${y("output_data[global_idx]",0)}\n ${y("output_data[global_idx]",1)}\n ${y("output_data[global_idx]",2)}\n ${y("output_data[global_idx]",3)}\n `}return`\n ${e.registerUniform("vec_size","u32").declareVariables(p,a,c,u)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${h}\n }`},tm=e=>{let t=e[1].dims,r=e[2].dims,o=e[0].dims,i=e[1].dataType,u=!(M.areEqual(t,r)&&M.areEqual(r,o)),a=t,c=M.size(t);if(u){let h=It.calcShape(It.calcShape(t,r,!1),o,!1);if(!h)throw new Error("Can\'t perform where op on the given tensors");a=h,c=M.size(a)}let p=Math.ceil(c/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:h=>em(h,e,a,u,i),getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(c/64/4)},programUniforms:[{type:12,data:p},...Z(o,t,r,a)]})}},Dd=e=>{e.compute(tm(e.inputs))}});var Md,Ud=Y(()=>{"use strict";Ka();Ro();Ja();ts();Vs();Zs();Oo();Uo();lu();mu();gu();$u();xu();Au();Eu();Ou();Du();Mu();Vu();Wo();Gu();qu();Ku();cd();md();In();yd();wd();$d();Cd();Td();kd();Bd();Sr();Rn();zd();Md=new 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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'}),Pr,Vt,En,ua,da,oo,Ci,jr,qr,lp,ca,tg,rg,ng,ag,ig,sg,og,lg=J(()=>{dr(),iw(),Aa(),Pr=()=>!!Ue.wasm.proxy&&typeof document<"u",En=!1,ua=!1,da=!1,Ci=new Map,jr=(t,e)=>{let r=Ci.get(t);r?r.push(e):Ci.set(t,[e])},qr=()=>{if(En||!ua||da||!Vt)throw new Error("worker not 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fetchModelAndCopyToWasmMemory(t){return ng(await Gi(t))}async loadModel(t,e){ur();let r;typeof t=="string"?typeof process<"u"&&process.versions&&process.versions.node?r=await Gi(t):r=await this.fetchModelAndCopyToWasmMemory(t):r=t,[this.sessionId,this.inputNames,this.outputNames]=await ag(r,e),jt()}async dispose(){return ig(this.sessionId)}async run(t,e,r){ur();let n=[],a=[];Object.entries(t).forEach(h=>{let m=h[0],d=h[1],_=this.inputNames.indexOf(m);if(_===-1)throw new Error(`invalid input '${m}'`);n.push(d),a.push(_)});let i=[],s=[];Object.entries(e).forEach(h=>{let m=h[0],d=h[1],_=this.outputNames.indexOf(m);if(_===-1)throw new Error(`invalid output '${m}'`);i.push(d),s.push(_)});let o=n.map((h,m)=>lo(h,()=>`input "${this.inputNames[a[m]]}"`)),l=i.map((h,m)=>h?lo(h,()=>`output "${this.outputNames[s[m]]}"`):null),u=await sg(this.sessionId,a,o,s,l,r),p={};for(let h=0;h{dr(),lg(),ow(),dp=()=>{if((typeof 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t=(uw(),nn(cg)).wasmBackend;tn("webgpu",t,5),tn("webnn",t,5),tn("cpu",t,10),tn("wasm",t,10)}Object.defineProperty(Ue.versions,"web",{value:dw,enumerable:!0});/** -* @license -* Copyright 2021 Google LLC. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 (the "License"); -* you may not use this file except in compliance with the License. -* You may obtain a copy of the License at -* -* http://www.apache.org/licenses/LICENSE-2.0 -* -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. -* ============================================================================= -*//** - * @license - * Copyright 2020 Google LLC. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - *//** - * @license - * Copyright 2019 Google LLC. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */const pw=Object.freeze(Object.defineProperty({__proto__:null,get InferenceSession(){return Vo},get TRACE(){return va},get TRACE_FUNC_BEGIN(){return ur},get TRACE_FUNC_END(){return jt},get Tensor(){return Et},get TrainingSession(){return Go},default:cw,get env(){return Ue},get registerBackend(){return 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s,o=e??{};const l=()=>{if(typeof document<"u")return document.createElement("canvas");if(typeof OffscreenCanvas<"u")return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},u=p=>p instanceof HTMLCanvasElement||p instanceof OffscreenCanvas?p.getContext("2d"):null;if(r){const p=l();p.width=t.width,p.height=t.height;const h=u(p);if(h!=null){let m=t.height,d=t.width;if(e!==void 0&&e.resizedHeight!==void 0&&e.resizedWidth!==void 0&&(m=e.resizedHeight,d=e.resizedWidth),e!==void 0){if(o=e,e.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");o.tensorFormat="RGBA",o.height=m,o.width=d}else o.tensorFormat="RGBA",o.height=m,o.width=d;h.drawImage(t,0,0),s=h.getImageData(0,0,d,m).data}else throw new Error("Can not access image data")}else if(n){let p,h;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(p=e.resizedHeight,h=e.resizedWidth):(p=t.height,h=t.width),e!==void 0&&(o=e),o.format="RGBA",o.height=p,o.width=h,e!==void 0){const m=l();m.width=h,m.height=p;const d=u(m);if(d!=null)d.putImageData(t,0,0),s=d.getImageData(0,0,h,p).data;else throw new Error("Can not access image data")}else s=t.data}else if(a){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const p=l();p.width=t.width,p.height=t.height;const h=u(p);if(h!=null){const m=t.height,d=t.width;return h.drawImage(t,0,0,d,m),s=h.getImageData(0,0,d,m).data,o.height=m,o.width=d,uo(s,o)}else throw new Error("Can not access image data")}else{if(i)return new Promise((p,h)=>{const m=l(),d=u(m);if(!t||!d)return h();const _=new Image;_.crossOrigin="Anonymous",_.src=t,_.onload=()=>{m.width=_.width,m.height=_.height,d.drawImage(_,0,0,m.width,m.height);const w=d.getImageData(0,0,m.width,m.height);o.height=m.height,o.width=m.width,p(uo(w.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return uo(s,o);throw new Error("Input data provided is not supported - aborted tensor creation")},gw=(t,e)=>{const{width:r,height:n,download:a,dispose:i}=e,s=[1,n,r,4];return new mr({location:"texture",type:"float32",texture:t,dims:s,download:a,dispose:i})},_w=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:i}=e;return new mr({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:i})},yw=(t,e,r)=>new mr({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),On=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),Bi=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let cp=!1;const ww=()=>{if(!cp){cp=!0;const t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,r=typeof Float16Array<"u"&&Float16Array.from;t&&(On.set("int64",BigInt64Array),Bi.set(BigInt64Array,"int64")),e&&(On.set("uint64",BigUint64Array),Bi.set(BigUint64Array,"uint64")),r?(On.set("float16",Float16Array),Bi.set(Float16Array,"float16")):On.set("float16",Uint16Array)}},bw=t=>{let e=1;for(let r=0;r{switch(t.location){case"cpu":return new mr(t.type,t.data,e);case"cpu-pinned":return new mr({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new mr({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new mr({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let mr=class{constructor(e,r,n){ww();let a,i;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,a=e.type,i=e.dims,e.location){case"cpu-pinned":{const o=On.get(a);if(!o)throw new TypeError(`unsupported type "${a}" to create tensor from pinned buffer`);if(!(e.data instanceof o))throw new TypeError(`buffer should be of type ${o.name}`);this.cpuData=e.data;break}case"texture":{if(a!=="float32")throw new TypeError(`unsupported type "${a}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break}case"gpu-buffer":{if(a!=="float32"&&a!=="float16"&&a!=="int32"&&a!=="int64"&&a!=="uint32"&&a!=="uint8"&&a!=="bool")throw new TypeError(`unsupported type "${a}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let o,l;if(typeof e=="string")if(a=e,l=n,e==="string"){if(!Array.isArray(r))throw new TypeError("A string tensor's data must be a string array.");o=r}else{const u=On.get(e);if(u===void 0)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(r)){if(e==="float16"&&u===Uint16Array)throw new TypeError("Creating a float16 tensor from number array is not supported. 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Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":{if(!this.downloader)throw new Error("The current tensor is not created with a specified data downloader.");if(this.isDownloading)throw new Error("The current tensor is being downloaded.");try{this.isDownloading=!0;const r=await this.downloader();return this.downloader=void 0,this.dataLocation="cpu",this.cpuData=r,e&&this.disposer&&(this.disposer(),this.disposer=void 0),r}finally{this.isDownloading=!1}}default:throw new Error(`cannot get data from location: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return vw(this,e)}};const $w=mr,_a=[];let zo,Sa;an.IS_NODE_ENV?(Sa=Je??k0,_a.push("cpu"),zo=["cpu"]):(Sa=pw,an.IS_WEBGPU_AVAILABLE&&_a.push("webgpu"),_a.push("wasm"),zo=["wasm"]);const xw=Sa.InferenceSession;function Sw(t){let e=zo;if(t){if(!_a.includes(t))throw new Error(`Unsupported device: "${t}". Should be one of: ${_a.join(", ")}.`);e=[t]}return e}async function hg(t,e){return await xw.create(t,e)}function fg(t){return t instanceof Sa.Tensor}const en=Sa?.env;en?.wasm&&(en.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.18.0/dist/",en.wasm.proxy=!an.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(en.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(en.wasm.simd=!1));function kw(){return en?.wasm?.proxy}zt.backends.onnx=en;const Cn=async(t,e,r)=>{const n=await hg(new Uint8Array(t),e);return async a=>{const i=Object.fromEntries(Object.entries(a).map(([o,l])=>[o,l.ort_tensor])),s=await n.run(i);return Array.isArray(r)?r.map(o=>new ue(s[o])):new ue(s[r])}};class Qi{static session_options={};static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=Cn([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=Cn([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=Cn([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=Cn([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=Cn([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=Cn([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}}const pp=Object.freeze({float32:Float32Array,float16:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array});class ue{get dims(){return this.ort_tensor.dims}set dims(e){this.ort_tensor.dims=e}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}ort_tensor;constructor(...e){return fg(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new $w(e[0],e[1],e[2]),new Proxy(this,{get:(r,n)=>{if(typeof n=="string"){let a=Number(n);if(Number.isInteger(a))return r._getitem(a)}return r[n]},set:(r,n,a)=>r[n]=a})}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...r]=this.dims;if(r.length>0){const n=r.reduce((a,i)=>a*i);for(let a=0;a0){const a=n.reduce((i,s)=>i*s);return this._subarray(e,a,n)}else return new ue(this.type,[this.data[e]],n)}indexOf(e){const r=this.data;for(let n=0;nm)throw new Error(`Invalid slice: ${p}`);let d=[Math.max(h,0),Math.min(m,this.dims[u])];n.push(d),r.push(d[1]-d[0])}else throw new Error(`Invalid slice: ${p}`)}let a=n.map(([u,p])=>p-u),i=a.reduce((u,p)=>u*p);const s=this.data;let o=new s.constructor(i);const l=this.stride();for(let u=0;u=0;--h){const d=a[h];p+=(m%d+n[h][0])*l[h],m=Math.floor(m/d)}o[u]=s[p]}return new ue(this.type,o,r)}permute(...e){return Cw(this,e)}transpose(...e){return this.permute(...e)}sum(e=null,r=!1){return this.norm(1,e,r)}norm(e="fro",r=null,n=!1){if(e==="fro")e=2;else if(typeof e=="string")throw Error(`Unsupported norm: ${e}`);const a=this.data;if(r===null){let o=a.reduce((l,u)=>l+u**e,0)**(1/e);return new ue(this.type,[o],[])}r=fr(r,this.dims.length);const i=this.dims.slice();i[r]=1;const s=new a.constructor(a.length/this.dims[r]);for(let o=0;o=0;--u){const m=this.dims[u];if(u!==r){const d=p%m;l+=d*h,h*=i[u]}p=Math.floor(p/m)}s[l]+=a[o]**e}if(e!==1)for(let o=0;o=0;--o){const p=this.dims[o];if(o!==r){const h=l%p;s+=h*u,u*=this.dims[o]}l=Math.floor(l/p)}a[i]/=n.data[s]}return this}normalize(e=2,r=1){return this.clone().normalize_(e,r)}stride(){return Ow(this.dims)}squeeze(e=null){return new ue(this.type,this.data,fp(this.dims,e))}squeeze_(e=null){return this.dims=fp(this.dims,e),this}unsqueeze(e=null){return new ue(this.type,this.data,mp(this.dims,e))}unsqueeze_(e=null){return this.dims=mp(this.dims,e),this}flatten_(e=0,r=-1){r=(r+this.dims.length)%this.dims.length;let n=this.dims.slice(0,e),a=this.dims.slice(e,r+1),i=this.dims.slice(r+1);return this.dims=[...n,a.reduce((s,o)=>s*o,1),...i],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let n=0;ns!==r?a*i:a,1);e[r]=this.data.length/n}return new ue(this.type,this.data,e)}neg_(){const e=this.data;for(let r=0;ri*s);if(r!==n)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let a=t;for(let i=e.length-1;i>=0;i--)a=a.reduce((s,o)=>{let l=s[s.length-1];return l.lengthr!==1):typeof e=="number"?t[e]===1&&t.splice(e,1):Array.isArray(e)&&(t=t.filter((r,n)=>r!==1||!e.includes(n))),t}function mp(t,e){return e=fr(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function fr(t,e,r=null,n=!0){if(n&&(t<-e||t>=e))throw new Error(`IndexError: index ${t} is out of bounds for dimension${r===null?"":" "+r} with size ${e}`);return t<0&&(t=(t%e+e)%e),t}function gr(t,e=0){e=fr(e,t[0].dims.length);const r=t[0].dims.slice();r[e]=t.reduce((s,o)=>s+o.dims[e],0);const n=r.reduce((s,o)=>s*o,1),a=new t[0].data.constructor(n),i=t[0].type;if(e===0){let s=0;for(let o of t)a.set(o.data,s),s+=o.data.length}else{let s=0;for(let o=0;o=0;--h){const _=l.dims[h];let w=m%_;h===e&&(w+=s),p+=w*d,d*=r[h],m=Math.floor(m/_)}a[p]=l.data[u]}s+=l.dims[e]}}return new ue(i,a,r)}function ka(t,e=0){return gr(t.map(r=>r.unsqueeze(e)),e)}function Iw(t,e=null,r=1,n=!1){if(e===null){const u=t.data.reduce((d,_)=>d+_,0)/t.data.length,p=Math.sqrt(t.data.reduce((d,_)=>d+(_-u)**2,0)/(t.data.length-r)),h=new ue(t.type,[u],[]);return[new ue(t.type,[p],[]),h]}e=fr(e,t.dims.length);const a=ll(t,e,n),i=t.dims.slice();i[e]=1;const s=new t.data.constructor(t.data.length/t.dims[e]);for(let l=0;l=0;--p){const d=t.dims[p];if(p!==e){const _=h%d;u+=_*m,m*=i[p]}h=Math.floor(h/d)}s[u]+=(t.data[l]-a.data[u])**2}for(let l=0;ls+o,0);return new ue(t.type,[i/t.data.length],[])}e=fr(e,t.dims.length);const n=t.dims.slice();n[e]=1;const a=new t.data.constructor(t.data.length/t.dims[e]);for(let i=0;i=0;--o){const p=t.dims[o];if(o!==e){const h=l%p;s+=h*u,u*=n[o]}l=Math.floor(l/p)}a[s]+=t.data[i]}if(t.dims[e]!==1)for(let i=0;i0||o>0;)switch(l.push(s-1),u.push(o-1),i[s][o].item()){case 0:--s,--o;break;case 1:--s;break;case 2:--o;break;default:throw new Error(`Internal error in dynamic time warping. 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Please file a bug report.`)}return l.reverse(),u.reverse(),[l,u]}function Ow(t){const e=new Array(t.length);for(let r=t.length-1,n=1;r>=0;--r)e[r]=n,n*=t[r];return e}function ul(t,e,r,n){const a=t.reduce((i,s)=>i*s,1);return new ue(r,new n(a).fill(e),t)}function zw(t,e){let r,n;return r="float32",n=Float32Array,ul(t,e,r,n)}function Pw(t,e){return zw(t.dims,e)}function Ma(t){return ul(t,1n,"int64",BigInt64Array)}function Rw(t){return Ma(t.dims)}function Bw(t){return ul(t,0n,"int64",BigInt64Array)}function Dw(t){return Bw(t.dims)}function Nw(t,e){if(t.dims.length!==2)throw new Error("The tensor must have 2 dimensions");if(t.dims.at(-1)%8!==0)throw new Error("The last dimension of the tensor must be a multiple of 8");if(!["binary","ubinary"].includes(e))throw new Error("The precision must be either 'binary' or 'ubinary'");const r=e==="binary",n=r?"int8":"uint8",a=r?Int8Array:Uint8Array,i=t.data,s=new a(i.length/8);for(let o=0;o0?1:0,u=Math.floor(o/8),p=o%8;s[u]|=l<<7-p,r&&p===0&&(s[u]-=128)}return new ue(n,s,[t.dims[0],t.dims[1]/8])}class Fw{constructor(e=(n,a)=>n>a,r=1/0){this._heap=[],this._comparator=e,this._maxSize=r}get size(){return this._heap.length}isEmpty(){return this.size===0}peek(){return this._heap[0]}push(...e){return this.extend(e)}extend(e){for(const r of e)if(this.size0&&this._swap(0,r),this._heap.pop(),this._siftDown(),e}replace(e){const r=this.peek();return this._heap[0]=e,this._siftDown(),r}_parent(e){return(e+1>>>1)-1}_left(e){return(e<<1)+1}_right(e){return e+1<<1}_greater(e,r){return this._comparator(this._heap[e],this._heap[r])}_swap(e,r){const n=this._heap[e];this._heap[e]=this._heap[r],this._heap[r]=n}_siftUp(){this._siftUpFrom(this.size-1)}_siftUpFrom(e){for(;e>0&&this._greater(e,this._parent(e));)this._swap(e,this._parent(e)),e=this._parent(e)}_siftDown(){let e=0;for(;this._left(e)[]),this.endNodes=Array.from({length:this.len+1},()=>[]);const a=new ya(this.bosTokenId,0,0,0,0),i=new ya(this.eosTokenId,1,this.len,0,0);this.nodes.push(a.clone()),this.nodes.push(i.clone()),this.beginNodes[this.len].push(i),this.endNodes[0].push(a)}insert(e,r,n,a){const i=this.nodes.length,s=new ya(a,i,e,r,n);this.beginNodes[e].push(s),this.endNodes[e+r].push(s),this.nodes.push(s)}viterbi(){const e=this.len;let r=0;for(;r<=e;){if(this.beginNodes[r].length==0)return[];for(let o of this.beginNodes[r]){o.prev=null;let l=0,u=null;for(let p of this.endNodes[r]){const h=p.backtraceScore+o.score;(u===null||h>l)&&(u=p.clone(),l=h)}if(u!==null)o.prev=u,o.backtraceScore=l;else return[]}++r}const n=[],i=this.beginNodes[e][0].prev;if(i===null)return[];let s=i.clone();for(;s.prev!==null;)n.push(s.clone()),s=s.clone().prev.clone();return n.reverse(),n}piece(e){return this.sentence.slice(e.pos,e.pos+e.length)}tokens(){return this.viterbi().map(r=>this.piece(r))}tokenIds(){return this.viterbi().map(r=>r.tokenId)}}class ya{constructor(e,r,n,a,i){this.tokenId=e,this.nodeId=r,this.pos=n,this.length=a,this.score=i,this.prev=null,this.backtraceScore=0}clone(){const e=new ya(this.tokenId,this.nodeId,this.pos,this.length,this.score);return e.prev=this.prev,e.backtraceScore=this.backtraceScore,e}}var U=Object.freeze({Text:"Text",NumericLiteral:"NumericLiteral",BooleanLiteral:"BooleanLiteral",StringLiteral:"StringLiteral",Identifier:"Identifier",Equals:"Equals",OpenParen:"OpenParen",CloseParen:"CloseParen",OpenStatement:"OpenStatement",CloseStatement:"CloseStatement",OpenExpression:"OpenExpression",CloseExpression:"CloseExpression",OpenSquareBracket:"OpenSquareBracket",CloseSquareBracket:"CloseSquareBracket",OpenCurlyBracket:"OpenCurlyBracket",CloseCurlyBracket:"CloseCurlyBracket",Comma:"Comma",Dot:"Dot",Colon:"Colon",Pipe:"Pipe",CallOperator:"CallOperator",AdditiveBinaryOperator:"AdditiveBinaryOperator",MultiplicativeBinaryOperator:"MultiplicativeBinaryOperator",ComparisonBinaryOperator:"ComparisonBinaryOperator",UnaryOperator:"UnaryOperator",Set:"Set",If:"If",For:"For",In:"In",Is:"Is",NotIn:"NotIn",Else:"Else",EndIf:"EndIf",ElseIf:"ElseIf",EndFor:"EndFor",And:"And",Or:"Or",Not:"UnaryOperator"}),gp=Object.freeze({set:U.Set,for:U.For,in:U.In,is:U.Is,if:U.If,else:U.Else,endif:U.EndIf,elif:U.ElseIf,endfor:U.EndFor,and:U.And,or:U.Or,not:U.Not,"not 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Gw(t,e={}){return t.endsWith(` -`)&&(t=t.slice(0,-1)),t=t.replace(/{#.*?#}/gs,"{##}"),e.lstrip_blocks&&(t=t.replace(/^[ \t]*({[#%])/gm,"$1")),e.trim_blocks&&(t=t.replace(/([#%]})\n/g,"$1")),t.replace(/{##}/g,"").replace(/-%}\s*/g,"%}").replace(/\s*{%-/g,"{%").replace(/-}}\s*/g,"}}").replace(/\s*{{-/g,"{{")}function Hw(t,e={}){const r=[],n=Gw(t,e);let a=0;const i=s=>{let o="";for(;s(n[a]);){if(n[a]==="\\"){if(++a,a>=n.length)throw new SyntaxError("Unexpected end of input");const l=n[a++],u=Vw.get(l);if(u===void 0)throw new SyntaxError(`Unexpected escaped character: ${l}`);o+=u;continue}if(o+=n[a++],a>=n.length)throw new SyntaxError("Unexpected end of input")}return o};e:for(;a0){r.push(new Kr(l,U.Text));continue}}i(l=>/\s/.test(l));const o=n[a];if(o==="-"||o==="+"){const l=r.at(-1)?.type;if(l===U.Text||l===void 0)throw new SyntaxError(`Unexpected character: ${o}`);switch(l){case U.Identifier:case U.NumericLiteral:case U.BooleanLiteral:case U.StringLiteral:case U.CloseParen:case 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iterable type in for loop: got ${n.type}`);let a="";for(let i=0;i0?n.value[i-1]:new sr],["nextitem",ip.value.length?"few":"many"} items to unpack`);for(let h=0;hthis.evaluate(r,e)));case"TupleLiteral":return new ob(t.value.map(r=>this.evaluate(r,e)));case"ObjectLiteral":{const r=new Map;for(const[n,a]of t.value){const i=this.evaluate(n,e);if(!(i instanceof Pe))throw new Error(`Object keys must be strings: got ${i.type}`);r.set(i.value,this.evaluate(a,e))}return new or(r)}case"Identifier":return this.evaluateIdentifier(t,e);case"CallExpression":return this.evaluateCallExpression(t,e);case"MemberExpression":return this.evaluateMemberExpression(t,e);case"UnaryExpression":return this.evaluateUnaryExpression(t,e);case"BinaryExpression":return this.evaluateBinaryExpression(t,e);case"FilterExpression":return this.evaluateFilterExpression(t,e);case"TestExpression":return this.evaluateTestExpression(t,e);default:throw new SyntaxError(`Unknown node type: ${t.type}`)}}};function 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gg=[["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"]],Ni=new Map(gg),db=new Map([...gg.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"]]);function _g(t){t=t.toLowerCase();let e=db.get(t);if(e===void 0)if(Ni.has(t))e=t;else{const n=t.length===2?Ni.keys():Ni.values();throw new Error(`Language "${t}" is not supported. Must be one of: ${JSON.stringify(n)}`)}return e}const po="https://github.com/xenova/transformers.js/issues/new/choose";async function yg(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 cb(t,e){const r=[];let n=0;for(const a of t.matchAll(e)){const i=a[0];n0&&r.push(i),n=a.index+i.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 fb(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 yb extends za{constructor(e){super(e),this.tokens_to_ids=dl(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 i=!1,s=0;const o=[];for(;s0&&(p=this.config.continuing_subword_prefix+p),this.tokens_to_ids.has(p)){u=p;break}--l}if(u===null){i=!0;break}o.push(u),s=l}i?r.push(this.unk_token):r.push(...o)}return r}}class wb extends za{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,i])),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=Fp(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new Lw,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,i)=>i+33),...Array.from({length:12},(a,i)=>i+161),...Array.from({length:82},(a,i)=>i+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,i)=>[a,n[i]]))})(),bb=O0(vg);class vb extends za{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=dl(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.ignore_merges=this.config.ignore_merges??!1,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 i=new Fw((l,u)=>l.score`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):r.push(this.unk_token)}return r}}class $b extends za{constructor(e,r){super(e),this.tokens_to_ids=dl(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 Lt extends bt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new Ob(e);case"Precompiled":return new Qb(e);case"Sequence":return new Mb(e);case"Replace":return new xb(e);case"NFC":return new Sb(e);case"NFKC":return new kb(e);case"NFKD":return new Eb(e);case"Strip":return new Cb(e);case"StripAccents":return new Tb(e);case"Lowercase":return new Ab(e);case"Prepend":return new Ib(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class xb extends Lt{normalize(e){const r=is(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class Sb extends Lt{normalize(e){return e=e.normalize("NFC"),e}}class kb extends Lt{normalize(e){return e=e.normalize("NFKC"),e}}class Eb extends Lt{normalize(e){return e=e.normalize("NFKD"),e}}class Cb extends Lt{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class Tb extends Lt{normalize(e){return e=bg(e),e}}class Ab extends Lt{normalize(e){return e=e.toLowerCase(),e}}class Ib extends Lt{normalize(e){return e=this.config.prepend+e,e}}class Mb extends Lt{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>Lt.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class Ob extends Lt{_tokenize_chinese_chars(e){const r=[];for(let n=0;nthis.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class zb extends qt{constructor(e){super(),this.pattern=new RegExp(`[^\\s${Ea}]+|[${Ea}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class Pb extends qt{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=vg,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),i=>this.byte_encoder[i]).join(""))}}class Rb extends qt{constructor(e){super(),this.config=e,this.pattern=is(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:cb(e,this.pattern)}}class Bb extends qt{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${Ea}]+|[${Ea}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Db extends qt{constructor(e){super(),this.config=e;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Rn extends bt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new Nb(e);case"ByteLevel":return new Sg(e);case"RobertaProcessing":return new xg(e);case"BertProcessing":return new $g(e);case"Sequence":return new Fb(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 $g extends Rn{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=ft([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const i=n&&this instanceof xg?[this.sep]:[],s=n?[this.sep]:[];e=ft(e,i,r,s),a=ft(a,new Array(r.length+i.length+s.length).fill(1))}return{tokens:e,token_type_ids:a}}}class xg extends $g{}class Nb extends Rn{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 i=[],s=[];for(const o of a)"SpecialToken"in o?n&&(i.push(o.SpecialToken.id),s.push(o.SpecialToken.type_id)):"Sequence"in o&&(o.Sequence.id==="A"?(i=ft(i,e),s=ft(s,new Array(e.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(i=ft(i,r),s=ft(s,new Array(r.length).fill(o.Sequence.type_id))));return{tokens:i,token_type_ids:s}}}class Sg extends Rn{post_process(e,r=null){return r&&(e=ft(e,r)),{tokens:e}}}class Fb extends Rn{constructor(e){super(e),this.processors=e.processors.map(r=>Rn.fromConfig(r))}post_process(e,r=null,n={}){let a;for(const i of this.processors)if(i instanceof Sg)e=i.post_process(e).tokens,r&&(r=i.post_process(r).tokens);else{const s=i.post_process(e,r,n);e=s.tokens,a=s.token_type_ids}return{tokens:e,token_type_ids:a}}}class Ut extends bt{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 Gb(e);case"Metaspace":return new Xb(e);case"ByteLevel":return new Hb(e);case"Replace":return new Lb(e);case"ByteFallback":return new Ub(e);case"Fuse":return new Wb(e);case"Strip":return new Vb(e);case"Sequence":return new qb(e);case"CTC":return new jb(e);case"BPEDecoder":return new Kb(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 Lb extends Ut{decode_chain(e){const r=is(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class Ub extends Ut{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const r=[];let n=[];for(const a of e){let i=null;if(a.length===6&&a.startsWith("<0x")&&a.endsWith(">")){const s=parseInt(a.slice(3,5),16);isNaN(s)||(i=s)}if(i!==null)n.push(i);else{if(n.length>0){const s=this.text_decoder.decode(Uint8Array.from(n));r.push(s),n=[]}r.push(a)}}if(n.length>0){const a=this.text_decoder.decode(Uint8Array.from(n));r.push(a),n=[]}return r}}class Wb extends Ut{decode_chain(e){return[e.join("")]}}class Vb extends Ut{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 i=0;i(n!==0&&(r.startsWith(this.config.prefix)?r=r.replace(this.config.prefix,""):r=" "+r),this.cleanup&&(r=cl(r)),r))}}class Hb extends Ut{constructor(e){super(e),this.byte_decoder=bb,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(i=>this.byte_decoder[i]));return this.text_decoder.decode(n)}decode_chain(e){const r=[];let n=[];for(const a of e)this.added_tokens.find(i=>i.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 jb extends Ut{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 i=1;ii!==this.pad_token).join("");return this.cleanup&&(a=cl(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class qb extends Ut{constructor(e){super(e),this.decoders=e.decoders.map(r=>Ut.fromConfig(r))}decode_chain(e){return this.decoders.reduce((r,n)=>n.decode_chain(r),e)}}class Kb extends Ut{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 Yb extends Ut{decode_chain(e){let r="";for(let n=1;nn.normalize("NFKC")).join("～"):e=e.normalize("NFKC"),e}}class Zb extends qt{constructor(e){super(),this.tokenizers=e.pretokenizers.map(r=>qt.fromConfig(r))}pre_tokenize_text(e,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[e])}}class Jb extends qt{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class ev extends qt{constructor(e){super()}pre_tokenize_text(e,r){return mb(e)}}class tv extends qt{constructor(e){super(),this.config=e,this.pattern=is(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 rv=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function nv(t,e,r,n){for(const a of Object.keys(t)){const i=e-t[a].length,s=r(a),o=new Array(i).fill(s);t[a]=n==="right"?ft(t[a],o):ft(o,t[a])}}function av(t,e){for(const r of Object.keys(t))t[r].length=e}class Ee extends bt{return_token_type_ids=!1;_default_chat_template=`{% for message in messages %}{{'<|im_start|>' + message['role'] + ' -' + message['content'] + '<|im_end|>' + ' -'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant -' }}{% endif %}`;padding_side="right";constructor(e,r){super(),this._tokenizer_config=r,this.normalizer=Lt.fromConfig(e.normalizer),this.pre_tokenizer=qt.fromConfig(e.pre_tokenizer),this.model=za.fromConfig(e.model,r),this.post_processor=Rn.fromConfig(e.post_processor),this.decoder=Ut.fromConfig(e.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const n of e.added_tokens){const a=new _b(n);this.added_tokens.push(a),this.model.tokens_to_ids.set(a.content,a.id),this.model.vocab[a.id]=a.content,a.special&&(this.special_tokens.push(a.content),this.all_special_ids.push(a.id))}if(this.additional_special_tokens=r.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(n=>`${n.lstrip?"\\s*":""}(${Np(n.content)})${n.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=r.model_max_length,this.remove_space=r.remove_space,this.clean_up_tokenization_spaces=r.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=r.do_lowercase_and_remove_accent??!1,r.padding_side&&(this.padding_side=r.padding_side),this.legacy=!1,this.chat_template=r.chat_template??null,Array.isArray(this.chat_template)){const n=Object.create(null);for(const{name:a,template:i}of this.chat_template){if(typeof a!="string"||typeof i!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');n[a]=i}this.chat_template=n}this._compiled_template_cache=new Map}getToken(...e){for(const r of e){const n=this._tokenizer_config[r];if(n)if(typeof n=="object"){if(n.__type==="AddedToken")return n.content;throw Error(`Unknown token: ${n}`)}else return n}return null}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",legacy:o=null}={}){const l=await yg(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,legacy:o});return new this(...l)}_call(e,{text_pair:r=null,add_special_tokens:n=!0,padding:a=!1,truncation:i=null,max_length:s=null,return_tensor:o=!0,return_token_type_ids:l=null}={}){const u=Array.isArray(e);let p;if(u){if(e.length===0)throw Error("text array must be non-empty");if(r!==null){if(Array.isArray(r)){if(e.length!==r.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");p=e.map((m,d)=>this._encode_plus(m,{text_pair:r[d],add_special_tokens:n,return_token_type_ids:l}))}else p=e.map(m=>this._encode_plus(m,{add_special_tokens:n,return_token_type_ids:l}))}else{if(e==null)throw Error("text may not be null or undefined");if(Array.isArray(r))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");p=[this._encode_plus(e,{text_pair:r,add_special_tokens:n,return_token_type_ids:l})]}if(s===null?a==="max_length"?s=this.model_max_length:s=Kt(p.map(m=>m.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."),s=Math.min(s,this.model_max_length??1/0),a||i)for(let m=0;ms?i&&av(p[m],s):a&&nv(p[m],s,d=>d==="input_ids"?this.pad_token_id:0,this.padding_side));const h={};if(o){if(!(a&&i)&&p.some(d=>{for(const _ of Object.keys(d))if(d[_].length!==p[0][_]?.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 m=[p.length,p[0].input_ids.length];for(const d of Object.keys(p[0]))h[d]=new ue("int64",BigInt64Array.from(p.flatMap(_=>_[d]).map(BigInt)),m)}else{for(const m of Object.keys(p[0]))h[m]=p.map(d=>d[m]);if(!u)for(const m of Object.keys(h))h[m]=h[m][0]}return h}_encode_text(e){return e===null?null:(this.added_tokens_regex?e.split(this.added_tokens_regex).filter(a=>a):[e]).map((a,i)=>{if(this.added_tokens.find(o=>o.content===a)!==void 0)return a;{if(this.remove_space===!0&&(a=a.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(a=pb(a)),this.normalizer!==null&&(a=this.normalizer(a)),a.length===0)return[];const o=this.pre_tokenizer!==null?this.pre_tokenizer(a,{section_index:i}):[a];return this.model(o)}}).flat()}_encode_plus(e,{text_pair:r=null,add_special_tokens:n=!0,return_token_type_ids:a=null}={}){const{tokens:i,token_type_ids:s}=this._tokenize_helper(e,{pair:r,add_special_tokens:n}),o=this.model.convert_tokens_to_ids(i),l={input_ids:o,attention_mask:new Array(o.length).fill(1)};return(a??this.return_token_type_ids)&&s&&(l.token_type_ids=s),l}_tokenize_helper(e,{pair:r=null,add_special_tokens:n=!1}={}){const a=this._encode_text(e),i=this._encode_text(r);return this.post_processor?this.post_processor(a,i,{add_special_tokens:n}):{tokens:ft(a??[],i??[])}}tokenize(e,{pair:r=null,add_special_tokens:n=!1}={}){return this._tokenize_helper(e,{pair:r,add_special_tokens:n}).tokens}encode(e,{text_pair:r=null,add_special_tokens:n=!0,return_token_type_ids:a=null}={}){return this._encode_plus(e,{text_pair:r,add_special_tokens:n,return_token_type_ids:a}).input_ids}batch_decode(e,r={}){return e instanceof ue&&(e=e.tolist()),e.map(n=>this.decode(n,r))}decode(e,r={}){if(e instanceof ue&&(e=wg(e)),!Array.isArray(e)||e.length===0||!z0(e[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(e,r)}decode_single(e,{skip_special_tokens:r=!1,clean_up_tokenization_spaces:n=null}){let a=this.model.convert_ids_to_tokens(e);r&&(a=a.filter(s=>!this.special_tokens.includes(s)));let i=this.decoder?this.decoder(a):a.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(i=i.replaceAll(this.decoder.end_of_word_suffix," "),r&&(i=i.trim())),(n??this.clean_up_tokenization_spaces)&&(i=cl(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(e,{chat_template:r=null,add_generation_prompt:n=!1,tokenize:a=!0,padding:i=!1,truncation:s=!1,max_length:o=null,return_tensor:l=!0,return_dict:u=!1,tokenizer_kwargs:p={},...h}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null&&this.default_chat_template&&typeof this.default_chat_template=="object"){const w=this.chat_template??this.default_chat_template;if(r!==null&&Object.hasOwn(w,r))r=w[r];else if(r===null&&"default"in w)r=w.default;else if(r===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(w).sort()}.`)}else r??=this.chat_template??this.default_chat_template;if(typeof r!="string")throw Error(`chat_template must be a string, but got ${typeof r}`);let m=this._compiled_template_cache.get(r);m===void 0&&(m=new ub(r),this._compiled_template_cache.set(r,m));const d=Object.create(null);for(const w of rv){const v=this.getToken(w);v&&(d[w]=v)}const _=m.render({messages:e,add_generation_prompt:n,...d,...h});if(a){const w=this._call(_,{add_special_tokens:!1,padding:i,truncation:s,max_length:o,return_tensor:l,...p});return u?w:w.input_ids}return _}}class iv extends Ee{return_token_type_ids=!0}class sv extends Ee{return_token_type_ids=!0}class ov extends Ee{return_token_type_ids=!0}class lv extends Ee{return_token_type_ids=!0}class uv extends Ee{return_token_type_ids=!0}class dv extends Ee{return_token_type_ids=!0}class cv extends Ee{return_token_type_ids=!0}class pv extends Ee{return_token_type_ids=!0}class hv extends Ee{return_token_type_ids=!0}class fv extends Ee{}class mv extends Ee{}class gv extends Ee{return_token_type_ids=!0;constructor(e,r){super(e,r),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class _v extends Ee{return_token_type_ids=!0}class yv extends Ee{}class Eg extends Ee{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}'}class wv extends Ee{}class Cg extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,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 pl(this,e,r,n)}}class bv extends Cg{}class vv extends Ee{}class $v extends Eg{constructor(e,r){const n=".,!?…。，、।۔،",a=e.pre_tokenizer?.pretokenizers[0]?.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(e,r)}}const Ti="▁";class Tg extends Ee{_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 %}`;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.`;padding_side="left";constructor(e,r){super(e,r),this.use_default_system_prompt=r.use_default_system_prompt??!1,this.legacy=r.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new kg({replacement:Ti,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(e){if(e===null)return null;if(this.legacy||e.length===0)return super._encode_text(e);let r=super._encode_text(Ti+e.replaceAll(Ti," "));return r.length>1&&r[0]===Ti&&this.special_tokens.includes(r[1])&&(r=r.slice(1)),r}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 xv extends Tg{}class Sv extends Ee{}class kv extends Ee{}class Ev extends Ee{}class Cv extends Ee{}class Tv extends Ee{}class Av extends Ee{}class Iv extends Ee{_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 Mv extends Ee{}function pl(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,i=n.tgt_lang;if(!t.language_codes.includes(i))throw new Error(`Target language code "${i}" 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: {${t.language_codes.join(", ")}}`);for(const s of t.post_processor.config.single)if("SpecialToken"in s&&t.languageRegex.test(s.SpecialToken.id)){s.SpecialToken.id=t.lang_to_token(a);break}}return n.forced_bos_token_id=t.model.convert_tokens_to_ids([t.lang_to_token(i)])[0],t._call(e,r)}class Ov extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,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 pl(this,e,r,n)}}class zv extends Ee{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 pl(this,e,r,n)}}class Pv extends Ee{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}';get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(e,{return_timestamps:r=!1,return_language:n=!1,time_precision:a=null,force_full_sequences:i=!0}={}){if(a===null)throw Error("Must specify time_precision");let s=null;const o=r==="word";function l(){return{language:s,timestamp:[null,null],text:""}}const u=[];let p=l(),h=0;const m=this.timestamp_begin;let d=[],_=[],w=!1,v=null;const S=new Set(this.all_special_ids);for(const T of e){const A=T.tokens,P=o?T.token_timestamps:null;let B=null,D=m;if("stride"in T){const[ie,te,de]=T.stride;if(h-=te,v=ie-de,te&&(D=te/a+m),de)for(let se=A.length-1;se>=0;--se){const M=Number(A[se]);if(M>=m){if(B!==null&&(M-m)*a=m){const de=(te-m)*a+h,se=Mi(de,2);if(B!==null&&te>=B)w=!0;else if(w||d.length>0&&te0?(d.push(q),o&&_.push(H)):d.every(ie=>ie.length===0)&&(p=l(),d=[],q=[],_=[],H=[])}if(d.length>0){if(i&&r)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[T,A]=this.findLongestCommonSequence(d,_),P=this.decode(T);p.text=P,o&&(p.words=this.collateWordTimestamps(T,A,s)),u.push(p)}let $=Object.create(null);const E=u.map(T=>T.text).join("");if(r||n){for(let T=0;T0;let o=s?[]:null,l=s?r[0]:null;for(let u=1;use===ie[M]).length,de=te/T+A;te>1&&de>h&&(h=de,m=[P,B,q,H])}const[_,w,v,S]=m,$=Math.floor((w+_)/2),E=Math.floor((S+v)/2);i.push(...n.slice(0,$)),n=p.slice(E),a=n.length,s&&(o.push(...l.slice(0,$)),l=r[u].slice(E))}return i.push(...n),s?(o.push(...l),[i,o]):[i,[]]}collateWordTimestamps(e,r,n){const[a,i,s]=this.combineTokensIntoWords(e,n),o=[];for(let l=0;l=a){const o=((s-a)*n).toFixed(2);i.push(`<|${o}|>`),i.push([])}else i[i.length-1].push(s);return i=i.map(s=>typeof s=="string"?s:super.decode(s,r)),i.join("")}splitTokensOnUnicode(e){const r=this.decode(e,{decode_with_timestamps:!0}),n="�",a=[],i=[],s=[];let o=[],l=[],u=0;for(let p=0;p=this.model.tokens_to_ids.get("<|endoftext|>"),_=p.startsWith(" "),w=p.trim(),v=l.test(w);if(d||_||v||i.length===0)i.push(p),s.push(h),o.push(m);else{const S=i.length-1;i[S]+=p,s[S].push(...h),o[S].push(...m)}}return[i,s,o]}mergePunctuations(e,r,n,a,i){const s=structuredClone(e),o=structuredClone(r),l=structuredClone(n);let u=s.length-2,p=s.length-1;for(;u>=0;)s[u].startsWith(" ")&&a.includes(s[u].trim())?(s[p]=s[u]+s[p],o[p]=ft(o[u],o[p]),l[p]=ft(l[u],l[p]),s[u]="",o[u]=[],l[u]=[]):p=u,--u;for(u=0,p=1;ph),o.filter(h=>h.length>0),l.filter(h=>h.length>0)]}get_decoder_prompt_ids({language:e=null,task:r=null,no_timestamps:n=!0}={}){const a=[];if(e){const i=_g(e),s=this.model.tokens_to_ids.get(`<|${i}|>`);if(s===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at ${po}.`);a.push(s)}else a.push(null);if(r){if(r=r.toLowerCase(),r!=="transcribe"&&r!=="translate")throw new Error(`Task "${r}" is not supported. Must be one of: ["transcribe", "translate"]`);const i=this.model.tokens_to_ids.get(`<|${r}|>`);if(i===void 0)throw new Error(`Unable to find task "${r}" in model vocabulary. Please report this issue at ${po}.`);a.push(i)}else a.push(null);if(n){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 ${po}.`);a.push(i)}return a.map((i,s)=>[s+1,i]).filter(i=>i[1]!==null)}}class Rv extends Ee{}class Bv extends Ee{}class Dv extends Ee{}class Nv extends Ee{constructor(e,r){super(e,r),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(n=>this.languageRegex.test(n)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;const[r,...n]=e.trim().split(this.languageRegex);if(n.length===0)return super._encode_text(r);if(n.length===2){const[a,i]=n;return this.supported_language_codes.includes(a)||console.warn(`Unsupported language code "${a}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),ft([a],super._encode_text(i))}}}class Fv extends Ee{}class Ag extends Ee{_default_chat_template="{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ ' ' }}{% endif %}{% endfor %}{{ eos_token }}"}class Lv extends Ag{}class Uv extends Ee{}class Wv extends Ee{}class Vv extends Ee{constructor(e,r){super(e,r),this.decoder=new Yb({})}}class Gv extends Ee{}class ht{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:yv,DistilBertTokenizer:fv,CamembertTokenizer:mv,DebertaTokenizer:uv,DebertaV2Tokenizer:dv,BertTokenizer:iv,HerbertTokenizer:cv,ConvBertTokenizer:pv,RoFormerTokenizer:hv,XLMTokenizer:gv,ElectraTokenizer:_v,MobileBertTokenizer:ov,SqueezeBertTokenizer:lv,AlbertTokenizer:sv,GPT2Tokenizer:Eg,BartTokenizer:wv,MBartTokenizer:Cg,MBart50Tokenizer:bv,RobertaTokenizer:vv,WhisperTokenizer:Pv,CodeGenTokenizer:Rv,CLIPTokenizer:Bv,SiglipTokenizer:Dv,MarianTokenizer:Nv,BloomTokenizer:$v,NllbTokenizer:Ov,M2M100Tokenizer:zv,LlamaTokenizer:Tg,CodeLlamaTokenizer:xv,XLMRobertaTokenizer:Sv,MPNetTokenizer:kv,FalconTokenizer:Ev,GPTNeoXTokenizer:Cv,EsmTokenizer:Tv,Wav2Vec2CTCTokenizer:Fv,BlenderbotTokenizer:Ag,BlenderbotSmallTokenizer:Lv,SpeechT5Tokenizer:Uv,NougatTokenizer:Wv,VitsTokenizer:Vv,Qwen2Tokenizer:Av,GemmaTokenizer:Iv,Grok1Tokenizer:Mv,CohereTokenizer:Gv,PreTrainedTokenizer:Ee};static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",legacy:o=null}={}){const[l,u]=await yg(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,legacy:o}),p=u.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let h=this.TOKENIZER_CLASS_MAPPING[p];return h||(console.warn(`Unknown tokenizer class "${p}", attempting to construct from base class.`),h=Ee),new h(l,u)}}async function Hv(t,e){return await Br(t,"config.json",!0,e)}function In(t){const e={};let r={};switch(t.model_type){case"llava":case"paligemma":r=In(t.text_config);break;case"moondream1":r=In(t.phi_config);break;case"musicgen":r=In(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"cohere":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"gemma":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.dim_kv="head_dim";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;case"vision-encoder-decoder":const a=In(t.encoder),i=In(t.decoder),s="num_decoder_layers"in i,o={};return s?(o.num_decoder_layers=i.num_layers,o.num_decoder_heads=i.num_heads,o.decoder_hidden_size=i.hidden_size,o.num_encoder_layers=a.num_layers,o.num_encoder_heads=a.num_heads,o.encoder_hidden_size=a.hidden_size):(o.num_layers=i.num_layers,o.num_heads=i.num_heads,o.hidden_size=i.hidden_size),o}const n={...r,...rn(t,["model_type","multi_query","is_encoder_decoder"])};for(const a in e)n[a]=t[e[a]];return n}function Ig(t,{prefix:e="past_key_values",encoder_add_pkv:r=!0}={}){const n={},a=t.normalized_config,i=1;if(a.is_encoder_decoder&&r){const s=a.encoder_dim_kv??a.encoder_hidden_size/a.num_encoder_heads,o=a.decoder_dim_kv??a.decoder_hidden_size/a.num_decoder_heads,l=[i,a.num_encoder_heads,0,s],u=[i,a.num_decoder_heads,0,o];for(let p=0;p=1&&s[s.length-1]>=this.timestamp_begin,l=s.length<2||s[s.length-2]>=this.timestamp_begin;if(o&&(l?i.subarray(this.timestamp_begin).fill(-1/0):i.subarray(0,this.eos_token_id).fill(-1/0)),e[n].length===this.begin_index&&this.max_initial_timestamp_index!==null){const m=this.timestamp_begin+this.max_initial_timestamp_index;i.subarray(m+1).fill(-1/0)}const u=U0(i),p=Math.log(u.subarray(this.timestamp_begin).map(Math.exp).reduce((m,d)=>m+d)),h=Kt(u.subarray(0,this.timestamp_begin))[0];p>h&&i.subarray(0,this.timestamp_begin).fill(-1/0)}return r}}class Zv extends yr{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const r=e.length,n=[];for(let i=0;i1 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),i=r.slice([n,r.dims[0]],null);for(let s=0;s1)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 s2 extends hl{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 Og{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(e){Object.assign(this,rn(e,Object.getOwnPropertyNames(this)))}}class fl extends bt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class ml extends bt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof ml?e=e.criteria:e instanceof fl&&(e=[e]),this.criteria.push(...e)}_call(e,r){const n=new Array(e.length).fill(!1);for(const a of this.criteria){const i=a(e,r);for(let s=0;sr.length>=this.max_length)}}class l2 extends fl{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(i=>a==i)})}}class ss extends bt{constructor(e){super(),this.generation_config=e}async _call(e){return this.sample(e)}async sample(e){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 i=r*n;a=a.slice(i,i+n)}return a}randomSelect(e){let r=0;for(let a=0;a1)return new c2(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 u2(e)}}class u2 extends ss{async sample(e){const r=Kt(e.data)[1];return[[BigInt(r),0]]}}class d2 extends ss{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const[n,a]=await Nn(e,r),i=wt(n.data);return Array.from({length:this.generation_config.num_beams},()=>{const s=this.randomSelect(i);return[a.data[s],Math.log(i[s])]})}}class c2 extends ss{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const[n,a]=await Nn(e,r),i=wt(n.data);return Array.from({length:this.generation_config.num_beams},(s,o)=>[a.data[o],Math.log(i[o])])}}class p2 extends Og{return_timestamps=null;return_token_timestamps=null;num_frames=null;alignment_heads=null;task=null;language=null;no_timestamps_token_id=null;prompt_ids=null;is_multilingual=null;lang_to_id=null;task_to_id=null;max_initial_timestamp_index=1}const ve={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},es=new Map,zg=new Map,ba=new Map;async function h2(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=Sw(n);let i=r.dtype;if(typeof i!="string"&&(i&&i.hasOwnProperty(e)?i=i[e]:(i=qv[a[0]],console.warn(`Dtype not specified for ${e}. Using the default dtype: ${i}.`))),xp.hasOwnProperty(i)){if(i===Rt.fp16&&!await jv())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${i}. Should be one of: ${Object.keys(Rt).join(", ")}`);const s=xp[i],o=`${r.subfolder??""}/${e}${s}.onnx`,l={...r.session_options};l.executionProviders??=a;const u=Ii(t,o,!0,r);let p=[];if(r.use_external_data_format){if(an.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const m=`${e}${s}.onnx_data`,d=`${r.subfolder??""}/${m}`;p.push(new Promise(async(_,w)=>{const v=await Ii(t,d,!0,r);_({path:m,data:v})}))}else l.externalData!==void 0&&(p=l.externalData.map(async m=>{if(typeof m.data=="string"){const d=await Ii(t,m.data,!0,r);return{...m,data:d}}return m}));if(p.length>0&&(l.externalData=await Promise.all(p)),n==="webgpu"){const m=Ig(r.config,{prefix:"present"});if(Object.keys(m).length>0){const d={};for(const _ in m)d[_]="gpu-buffer";l.preferredOutputLocation=d}}return{buffer:await u,session_options:l}}async function Yr(t,e,r){const n=Object.keys(e),a=await Promise.all(n.map(async s=>h2(t,e[s],r))),i={};for(let s=0;s0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${n.join(", ")}.`);const a=Object.keys(e).length,i=t.inputNames.length;if(a>i){let s=Object.keys(e).filter(o=>!t.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${a} > ${i}). The following inputs will be ignored: "${s.join(", ")}".`)}return r}async function Nr(t,e){const r=f2(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([i,s])=>[i,s.ort_tensor]));let a=await t.run(n);return a=Pg(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function Pg(t){for(let e in t)fg(t[e])?t[e]=new ue(t[e]):typeof t[e]=="object"&&Pg(t[e]);return t}function Rg(t){if(t instanceof ue)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 ue("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new ue("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function Bg(t){return new ue("bool",[t],[1])}async function Sp(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const l=rn(e,t.sessions.model.inputNames);r=(await Ca(t,l)).last_hidden_state}const{input_ids:a,decoder_input_ids:i,...s}=e;return s.input_ids=i,s.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(s.encoder_attention_mask=e.attention_mask),await gl(t,s,!0)}async function Ca(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 ue("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await Nr(r,n)}async function gl(t,e,r=!1){const n=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:a,...i}=e;n.inputNames.includes("use_cache_branch")&&(i.use_cache_branch=Bg(!!a)),n.inputNames.includes("position_ids")&&i.attention_mask&&!i.position_ids&&(i.position_ids=g2(i,a)),t.addPastKeyValues(i,a);const s=rn(i,n.inputNames);return await Nr(n,s)}async function m2(t,{input_ids:e=null,attention_mask:r=null,pixel_values:n=null,position_ids:a=null,inputs_embeds:i=null,past_key_values:s=null,generation_config:o=null,logits_processor:l=null,...u}){if(!i){if(i=await t.encode_text({input_ids:e}),n&&e.dims[1]!==1){const h=await t.encode_image({pixel_values:n});({inputs_embeds:i,attention_mask:r}=t._merge_input_ids_with_image_features({image_features:h,inputs_embeds:i,input_ids:e,attention_mask:r}))}else if(s&&n&&e.dims[1]===1){const h=e.dims[1],m=Object.values(s)[0].dims.at(-2);r=gr([Ma([e.dims[0],m]),r.slice(null,[r.dims[1]-h,r.dims[1]])],1)}}return await gl(t,{inputs_embeds:i,past_key_values:s,attention_mask:r,position_ids:a,generation_config:o,logits_processor:l},!0)}function g2(t,e=null){const{input_ids:r,inputs_embeds:n,attention_mask:a}=t,[i,s]=a.dims,o=new BigInt64Array(a.data.length);for(let u=0;ui.dims[1])){if(ao==t.config.image_token_index)){const o=t.config.num_image_tokens;if(!o)throw new Error("`num_image_tokens` is missing in the model configuration.");const l=i.dims[1]-(a-o);r.input_ids=i.slice(null,[-l,null]),r.attention_mask=Ma([1,a+l])}}}return r}function _2(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(s=>[s.at(-1)])),a.decoder_input_ids=Rg(e),a}class Q extends bt{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(e,r){super(),this.config=e,this.sessions=r;const n=ba.get(this.constructor),a=es.get(n);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,a===ve.DecoderOnly?(this.can_generate=!0,this._forward=gl,this._prepare_inputs_for_generation=kp):a===ve.Seq2Seq||a===ve.Vision2Seq||a===ve.Musicgen?(this.can_generate=!0,this._forward=Sp,this._prepare_inputs_for_generation=_2):a===ve.EncoderDecoder?this._forward=Sp:a===ve.ImageTextToText?(this.can_generate=!0,this._forward=m2,this._prepare_inputs_for_generation=kp):this._forward=Ca,this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const e=[];for(const r of Object.values(this.sessions))r?.handler?.dispose&&e.push(r.handler.dispose());return await Promise.all(e)}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",model_file_name:o=null,subfolder:l="onnx",device:u=null,dtype:p=null,use_external_data_format:h=null,session_options:m={}}={}){let d={progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,model_file_name:o,subfolder:l,device:u,dtype:p,use_external_data_format:h,session_options:m};const _=ba.get(this),w=es.get(_);d.config=await Mg.from_pretrained(e,d);let v;return w===ve.DecoderOnly?v=await Promise.all([Yr(e,{model:d.model_file_name??"model"},d),Br(e,"generation_config.json",!1,d)]):w===ve.Seq2Seq||w===ve.Vision2Seq?v=await Promise.all([Yr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},d),Br(e,"generation_config.json",!1,d)]):w===ve.MaskGeneration?v=await Promise.all([Yr(e,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},d)]):w===ve.EncoderDecoder?v=await Promise.all([Yr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},d)]):w===ve.ImageTextToText?v=await Promise.all([Yr(e,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},d),Br(e,"generation_config.json",!1,d)]):w===ve.Musicgen?v=await Promise.all([Yr(e,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},d),Br(e,"generation_config.json",!1,d)]):(w!==ve.EncoderOnly&&console.warn(`Model type for '${_??n?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),v=await Promise.all([Yr(e,{model:d.model_file_name??"model"},d)])),new this(d.config,...v)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}_get_logits_warper(e){const r=new Ro;return e.temperature!==null&&e.temperature!==1&&r.push(new a2(e.temperature)),e.top_k!==null&&e.top_k!==0&&r.push(new s2(e.top_k)),e.top_p!==null&&e.top_p<1&&r.push(new i2(e.top_p)),r}_get_logits_processor(e,r,n=null){const a=new Ro;if(e.repetition_penalty!==null&&e.repetition_penalty!==1&&a.push(new Jv(e.repetition_penalty)),e.no_repeat_ngram_size!==null&&e.no_repeat_ngram_size>0&&a.push(new Zv(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&a.push(new r2(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&a.push(new e2(e.min_length,e.eos_token_id)),e.min_new_tokens!==null&&e.eos_token_id!==null&&e.min_new_tokens>0&&a.push(new t2(r,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&a.push(new Kv(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&a.push(new Yv(e.max_length,e.forced_eos_token_id)),e.begin_suppress_tokens!==null){const i=r>1||e.forced_bos_token_id===null?r:r+1;a.push(new Xv(e.begin_suppress_tokens,i))}return e.guidance_scale!==null&&e.guidance_scale>1&&a.push(new n2(e.guidance_scale)),n!==null&&a.extend(n),a}_prepare_generation_config(e,r,n=Og){const a={...this.config};for(const s of["decoder","generator","text_config"])s in a&&Object.assign(a,a[s]);const i=new n(a);return"generation_config"in this&&Object.assign(i,this.generation_config),e&&Object.assign(i,e),r&&Object.assign(i,rn(r,Object.getOwnPropertyNames(i))),i}_get_stopping_criteria(e,r=null){const n=new ml;return e.max_length!==null&&n.push(new o2(e.max_length,this.config.max_position_embeddings??null)),e.eos_token_id!==null&&n.push(new l2(e.eos_token_id)),r&&n.extend(r),n}_validate_model_class(){if(!this.can_generate){const e=[Sl,kl,xl,$l],r=ba.get(this.constructor),n=new Set,a=this.config.model_type;for(const s of e){const o=s.get(a);o&&n.add(o[0])}let i=`The current model class (${r}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw n.size>0&&(i+=` Please use the following class instead: ${[...n].join(", ")}`),Error(i)}}prepare_inputs_for_generation(...e){return this._prepare_inputs_for_generation(this,...e)}_update_model_kwargs_for_generation({generated_input_ids:e,outputs:r,model_inputs:n,is_encoder_decoder:a}){return n.past_key_values=this.getPastKeyValues(r,n.past_key_values),n.input_ids=new ue("int64",e.flat(),[e.length,1]),a||(n.attention_mask=gr([n.attention_mask,Ma([n.attention_mask.dims[0],1])],1)),n.position_ids=null,n}_prepare_model_inputs({inputs:e,bos_token_id:r,model_kwargs:n}){const a=rn(n,this.forward_params),i=this.main_input_name;if(i in a){if(e)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else a[i]=e;return{inputs_tensor:a[i],model_inputs:a,model_input_name:i}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:e,model_inputs:r,model_input_name:n,generation_config:a}){const i=rn(r,this.sessions.model.inputNames);let{last_hidden_state:s}=await Ca(this,i);return a.guidance_scale!==null&&a.guidance_scale>1&&(s=gr([s,Pw(s,0)],0),"attention_mask"in r&&(r.attention_mask=gr([r.attention_mask,Dw(r.attention_mask)],0))),r.encoder_outputs=s,r}_prepare_decoder_input_ids_for_generation({batch_size:e,model_input_name:r,model_kwargs:n,decoder_start_token_id:a,bos_token_id:i,generation_config:s}){let{decoder_input_ids:o,...l}=n;if(!o)if(a??=i,this.config.model_type==="musicgen")o=Array.from({length:e*this.config.decoder.num_codebooks},()=>[a]);else if(Array.isArray(a)){if(a.length!==e)throw new Error(`\`decoder_start_token_id\` expcted to have length ${e} but got ${a.length}`);o=a}else o=Array.from({length:e},()=>[a]);return o=Rg(o),n.decoder_attention_mask=Rw(o),{input_ids:o,model_inputs:l}}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,streamer:i=null,...s}){this._validate_model_class(),r=this._prepare_generation_config(r,s);let{inputs_tensor:o,model_inputs:l,model_input_name:u}=this._prepare_model_inputs({inputs:e,model_kwargs:s});const p=this.config.is_encoder_decoder;p&&("encoder_outputs"in l||(l=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:o,model_inputs:l,model_input_name:u,generation_config:r})));let h;p?{input_ids:h,model_inputs:l}=this._prepare_decoder_input_ids_for_generation({batch_size:l[u].dims.at(0),model_input_name:u,model_kwargs:l,decoder_start_token_id:r.decoder_start_token_id,bos_token_id:r.bos_token_id,generation_config:r}):h=l[u];let m=h.dims.at(-1);r.max_new_tokens!==null&&(r.max_length=m+r.max_new_tokens);const d=this._get_logits_processor(r,m,n),_=this._get_stopping_criteria(r,a),w=l[u].dims.at(0),v=ss.getSampler(r),S=new Array(w).fill(0),$=h.tolist();i&&i.put($);let E=null;for(;;){l=this.prepare_inputs_for_generation($,l,r);const A=await this.forward(l),P=A.logits.slice(null,-1,null),B=d($,P),D=[];for(let H=0;HH)){r.return_dict_in_generate&&(E=this.getPastKeyValues(A,l.past_key_values,!1));break}l=this._update_model_kwargs_for_generation({generated_input_ids:D,outputs:A,model_inputs:l,is_encoder_decoder:p})}i&&i.end();const T=new ue("int64",$.flat(),[$.length,$[0].length]);return r.return_dict_in_generate?{sequences:T,past_key_values:E}:T}addAttentionsToBeam(e,r){if(this.config.is_encoder_decoder){if(!r.cross_attentions||r.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`.");e.cross_attentions||(e.cross_attentions=[]),e.cross_attentions.push(r.cross_attentions)}if(!r.decoder_attentions||r.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`.");e.decoder_attentions||(e.decoder_attentions=[]),e.decoder_attentions.push(r.decoder_attentions)}groupBeams(e){const r=Object.create(null);for(const n of e)r[n.id]===void 0?r[n.id]=[n]:r[n.id].push(n);return Object.values(r)}getPastKeyValues(e,r,n=!0){const a=Object.create(null);for(const i in e)if(i.startsWith("present")){let s=i.replace("present","past_key_values");if(r&&i.includes("encoder"))a[s]=r[s];else{if(n&&r){const o=r[s];o.location==="gpu-buffer"&&o.dispose()}a[s]=e[i]}}return a}getAttentions(e){const r=Object.create(null);for(const n of["cross_attentions","decoder_attentions"]){const a=[];for(const i in e)if(i.startsWith(n)){const s=i.split(".").pop();a[s]=e[i]}r[n]=a}return r}addPastKeyValues(e,r){if(r)Object.assign(e,r);else{const n=this.custom_config.kv_cache_dtype??"float32",a=n==="float16"?new Uint16Array:[],i=Ig(this.config);for(const s in i)e[s]=new ue(n,a,i[s])}}}class Yt{}class Pa extends Q{}class y2 extends Pa{}class w2 extends Pa{async _call(e){return new $t(await super._call(e))}}class b2 extends Pa{async _call(e){return new Ae(await super._call(e))}}class v2 extends Pa{async _call(e){return new vt(await super._call(e))}}class $2 extends Pa{async _call(e){return new Ct(await super._call(e))}}class x2 extends Q{}class S2 extends x2{}class Ra extends Q{}class k2 extends Ra{}class E2 extends Ra{async _call(e){return new $t(await super._call(e))}}class C2 extends Ra{async _call(e){return new Ae(await super._call(e))}}class T2 extends Ra{async _call(e){return new vt(await super._call(e))}}class A2 extends Ra{async _call(e){return new Ct(await super._call(e))}}class Ba extends Q{}class I2 extends Ba{}class M2 extends Ba{async _call(e){return new $t(await super._call(e))}}class O2 extends Ba{async _call(e){return new Ae(await super._call(e))}}class z2 extends Ba{async _call(e){return new vt(await super._call(e))}}class P2 extends Ba{async _call(e){return new Ct(await super._call(e))}}class Da extends Q{}class R2 extends Da{}class B2 extends Da{async _call(e){return new $t(await super._call(e))}}class D2 extends Da{async _call(e){return new Ae(await super._call(e))}}class N2 extends Da{async _call(e){return new vt(await super._call(e))}}class F2 extends Da{async _call(e){return new Ct(await super._call(e))}}class Na extends Q{}class L2 extends Na{}class U2 extends Na{async _call(e){return new $t(await super._call(e))}}class W2 extends Na{async _call(e){return new Ae(await super._call(e))}}class V2 extends Na{async _call(e){return new vt(await super._call(e))}}class G2 extends Na{async _call(e){return new Ct(await super._call(e))}}class Fa extends Q{}class H2 extends Fa{}class j2 extends Fa{async _call(e){return new $t(await super._call(e))}}class q2 extends Fa{async _call(e){return new Ae(await super._call(e))}}class K2 extends Fa{async _call(e){return new vt(await super._call(e))}}class Y2 extends Fa{async _call(e){return new Ct(await super._call(e))}}class La extends Q{}class X2 extends La{}class Q2 extends La{async _call(e){return new $t(await super._call(e))}}class Z2 extends La{async _call(e){return new Ae(await super._call(e))}}class J2 extends La{async _call(e){return new vt(await super._call(e))}}class e1 extends La{async _call(e){return new Ct(await super._call(e))}}class Ua extends Q{}class t1 extends Ua{}class r1 extends Ua{async _call(e){return new Ae(await super._call(e))}}class n1 extends Ua{async _call(e){return new vt(await super._call(e))}}class a1 extends Ua{async _call(e){return new Ct(await super._call(e))}}class i1 extends Ua{async _call(e){return new $t(await super._call(e))}}class os extends Q{}class s1 extends os{}class o1 extends os{async _call(e){return new $t(await super._call(e))}}class l1 extends os{async _call(e){return new Ae(await super._call(e))}}class u1 extends os{async _call(e){return new vt(await super._call(e))}}class ls extends Q{}class d1 extends ls{}class c1 extends ls{async _call(e){return new $t(await super._call(e))}}class p1 extends ls{async _call(e){return new Ae(await super._call(e))}}class h1 extends ls{async _call(e){return new Ct(await super._call(e))}}class Wa extends Q{}class f1 extends Wa{}class m1 extends Wa{async _call(e){return new $t(await super._call(e))}}class g1 extends Wa{async _call(e){return new Ae(await super._call(e))}}class _1 extends Wa{async _call(e){return new vt(await super._call(e))}}class y1 extends Wa{async _call(e){return new Ct(await super._call(e))}}class us extends Q{}class w1 extends us{}class b1 extends us{async _call(e){return new $t(await super._call(e))}}class v1 extends us{async _call(e){return new Ae(await super._call(e))}}class $1 extends us{async _call(e){return new Ct(await super._call(e))}}class ds extends Q{}class x1 extends ds{}class S1 extends ds{async _call(e){return new Ae(await super._call(e))}}class k1 extends ds{async _call(e){return new Ct(await super._call(e))}}class E1 extends ds{async _call(e){return new $t(await super._call(e))}}class Dg extends Q{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class C1 extends Dg{}class T1 extends Dg{}class Ng extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class A1 extends Ng{}class I1 extends Ng{}class Fg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class M1 extends Fg{}class O1 extends Fg{}class _l extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class z1 extends _l{}class P1 extends _l{}class R1 extends _l{async _call(e){return new Ae(await super._call(e))}}class cs extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class B1 extends cs{}class D1 extends cs{}class N1 extends cs{async _call(e){return new Ae(await super._call(e))}}class F1 extends cs{}class Lg extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class L1 extends Lg{}class U1 extends Lg{}class Ug extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class W1 extends Ug{}class V1 extends Ug{}class Va extends Q{}class G1 extends Va{}class H1 extends Va{async _call(e){return new $t(await super._call(e))}}class j1 extends Va{async _call(e){return new Ae(await super._call(e))}}class q1 extends Va{async _call(e){return new vt(await super._call(e))}}class K1 extends Va{async _call(e){return new Ct(await super._call(e))}}class Ga extends Q{}class Y1 extends Ga{}class X1 extends Ga{async _call(e){return new $t(await super._call(e))}}class Q1 extends Ga{async _call(e){return new Ae(await super._call(e))}}class Z1 extends Ga{async _call(e){return new vt(await super._call(e))}}class J1 extends Ga{async _call(e){return new Ct(await super._call(e))}}class Ha extends Q{}class e$ extends Ha{}class t$ extends Ha{async _call(e){return new $t(await super._call(e))}}class r$ extends Ha{async _call(e){return new Ae(await super._call(e))}}class n$ extends Ha{async _call(e){return new vt(await super._call(e))}}class a$ extends Ha{async _call(e){return new Ct(await super._call(e))}}class Wg extends Q{}class i$ extends Wg{}class s$ extends Wg{}class Vg extends Q{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class o$ extends Vg{}class l$ extends Vg{_prepare_generation_config(e,r){return super._prepare_generation_config(e,r,p2)}_retrieve_init_tokens(e){const r=[e.decoder_start_token_id];let n=e.language;const a=e.task;if(e.is_multilingual){n||(console.warn("No language specified - defaulting to English (en)."),n="en");const s=`<|${_g(n)}|>`;r.push(e.lang_to_id[s]),r.push(e.task_to_id[a??"transcribe"])}else if(n||a)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!e.return_timestamps&&e.no_timestamps_token_id&&r.at(-1)!==e.no_timestamps_token_id?r.push(e.no_timestamps_token_id):e.return_timestamps&&r.at(-1)===e.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),r.pop()),r.filter(i=>i!=null)}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,...i}){r=this._prepare_generation_config(r,i);const s=this._retrieve_init_tokens(r);return r.return_timestamps&&(n??=new Ro,n.push(new Qv(r,s))),await super.generate({inputs:e,generation_config:r,logits_processor:n,decoder_input_ids:s,...i})}_extract_token_timestamps(e,r,n=null,a=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let i=this.config.median_filter_width;i===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),i=7);const s=e.cross_attentions.map(u=>{let p=Array.from({length:this.config.decoder_layers},(v,S)=>gr(u.map($=>$[S]),2)),h=ka(r.map(([v,S])=>n?p[v].slice(null,S,null,[0,n]):p[v].slice(null,S)));h=h.transpose(1,0,2,3);let[m,d]=Iw(h,-2,0,!0),_=h.clone();for(let v=0;v<_.dims[0];++v){let S=_[v];for(let $=0;$h[S+1]-h[S]),_=ft([1],d).map(v=>!!v),w=[];for(let v=0;v<_.length;++v)_[v]&&w.push(m[v]*a);l[u].data.set(w,1)}return l}}class Gg extends Q{main_input_name="pixel_values";forward_params=["pixel_values","input_ids","encoder_hidden_states","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class u$ extends Q{forward_params=["input_ids","pixel_values","attention_mask","position_ids","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class Hg extends u${async encode_image({pixel_values:e}){const r=(await Nr(this.sessions.vision_encoder,{pixel_values:e})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${r.dims[1]}).`),this.config.num_image_tokens=r.dims[1]),r}async encode_text({input_ids:e}){return(await Nr(this.sessions.embed_tokens,{input_ids:e})).inputs_embeds}_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:n,attention_mask:a}){const i=this.config.image_token_index,o=n.tolist().map(m=>m.findIndex(d=>d==i)),l=o.every(m=>m===-1),u=o.every(m=>m!==-1);if(!l&&!u)throw new Error("Every input should contain either 0 or 1 image token.");if(l)return{inputs_embeds:e,attention_mask:a};const p=[],h=[];for(let m=0;mi*s,1);e.input_labels=new ue("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 Nr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new Yx(await super._call(e))}}class Yx extends Yt{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class A_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class Xx extends A_{}class Qx extends A_{}class I_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class Zx extends I_{}class Jx extends I_{}class cn extends Q{}class eS extends cn{}class tS extends cn{async _call(e){return new Ln(await super._call(e))}}class rS extends cn{async _call(e){return new Ae(await super._call(e))}}class nS extends cn{async _call(e){return new vt(await super._call(e))}}class wl extends Q{}class aS extends wl{}class iS extends wl{async _call(e){return new Ln(await super._call(e))}}class sS extends wl{async _call(e){return new Ae(await super._call(e))}}class hs extends Q{}class oS extends hs{}class lS extends hs{async _call(e){return new Ln(await super._call(e))}}class uS extends hs{async _call(e){return new Ae(await super._call(e))}}class dS extends hs{async _call(e){return new vt(await super._call(e))}}class bl extends Q{}class cS extends bl{}class pS extends bl{async _call(e){return new Ln(await super._call(e))}}class hS extends bl{async _call(e){return new Ae(await super._call(e))}}class fS extends cn{}class mS extends cn{async _call(e){return new Ln(await super._call(e))}}class gS extends cn{async _call(e){return new Ae(await super._call(e))}}class ja extends Q{}class _S extends ja{}class yS extends ja{async _call(e){return new Ln(await super._call(e))}}class wS extends ja{async _call(e){return new Ae(await super._call(e))}}class bS extends ja{async _call(e){return new E3(await super._call(e))}}class vS extends ja{async _call(e){return new vt(await super._call(e))}}class M_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class $S extends M_{}class xS extends M_{async generate_speech(e,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:i=20,vocoder:s=null}={}){const o={input_ids:e},{encoder_outputs:l,encoder_attention_mask:u}=await Ca(this,o),p=l.dims[1]/this.config.reduction_factor,h=Math.floor(p*i),m=Math.floor(p*a),d=this.config.num_mel_bins;let _=[],w=null,v=null,S=0;for(;;){++S;const T=Bg(!!v);let A;v?A=v.output_sequence_out:A=new ue("float32",new Float32Array(d),[1,1,d]);let P={use_cache_branch:T,output_sequence:A,encoder_attention_mask:u,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(P,w),v=await Nr(this.sessions.decoder_model_merged,P),w=this.getPastKeyValues(v,w);const{prob:B,spectrum:D}=v;if(_.push(D),S>=m&&(Array.from(B.data).filter(q=>q>=n).length>0||S>=h))break}const $=gr(_),{waveform:E}=await Nr(s.sessions.model,{spectrogram:$});return{spectrogram:$,waveform:E}}}class SS extends Q{main_input_name="spectrogram"}class kS extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class ES extends kS{}class O_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class CS extends O_{}class TS extends O_{}class z_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class AS extends z_{}class IS extends z_{}class P_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class MS extends P_{}class OS extends P_{}class vl extends Q{}class zS extends vl{}class PS extends vl{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class RS extends vl{static async from_pretrained(e,r={}){return r.model_file_name??="audio_model",super.from_pretrained(e,r)}}class BS extends Q{}class R_ extends BS{async _call(e){return new T3(await super._call(e))}}class B_ extends Q{}class DS extends B_{}class NS extends B_{}class D_ extends Q{constructor(e,r,n){super(e,r),this.generation_config=n}}class FS extends D_{}class LS extends D_{}class N_ extends Q{}class US extends N_{}class WS extends N_{async _call(e){return new Ae(await super._call(e))}}class F_ extends Q{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}_apply_and_filter_by_delay_pattern_mask(e){const[r,n]=e.dims,a=this.config.decoder.num_codebooks,i=n-a;let s=0;for(let u=0;u0&&m<=i&&(e.data[s++]=e.data[u])}const o=Math.floor(r/a),l=s/(o*a);return new ue(e.type,e.data.slice(0,s),[o,a,l])}prepare_inputs_for_generation(e,r,n){let a=structuredClone(e);for(let s=0;s=o&&(a[s][o]=BigInt(this.config.decoder.pad_token_id));return n.guidance_scale!==null&&n.guidance_scale>1&&(a=a.concat(a)),super.prepare_inputs_for_generation(a,r,n)}async generate(e){const r=await super.generate(e),n=this._apply_and_filter_by_delay_pattern_mask(r).unsqueeze_(0),{audio_values:a}=await Nr(this.sessions.encodec_decode,{audio_codes:n});return a}}class L_ extends Q{}class VS extends L_{}class GS extends L_{async _call(e){return new Ae(await super._call(e))}}class U_ extends Q{}class HS extends U_{}class jS extends U_{async _call(e){return new Ae(await super._call(e))}}class W_ extends Q{}class qS extends W_{}class KS extends W_{async _call(e){return new Ae(await super._call(e))}}class V_ extends Q{}class YS extends V_{}class XS extends V_{async _call(e){return new Ae(await super._call(e))}}class et{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",model_file_name:o=null,subfolder:l="onnx",device:u=null,dtype:p=null,use_external_data_format:h=null,session_options:m={}}={}){let d={progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,model_file_name:o,subfolder:l,device:u,dtype:p,use_external_data_format:h,session_options:m};if(d.config=await Mg.from_pretrained(e,d),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let _ of this.MODEL_CLASS_MAPPINGS){const w=_.get(d.config.model_type);if(w)return await w[1].from_pretrained(e,d)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${d.config.model_type}", attempting to construct from base class.`),await Q.from_pretrained(e,d);throw Error(`Unsupported model type: ${d.config.model_type}`)}}const QS=new Map([["bert",["BertModel",y2]],["nomic_bert",["NomicBertModel",S2]],["roformer",["RoFormerModel",k2]],["electra",["ElectraModel",R2]],["esm",["EsmModel",s1]],["convbert",["ConvBertModel",I2]],["camembert",["CamembertModel",L2]],["deberta",["DebertaModel",H2]],["deberta-v2",["DebertaV2Model",X2]],["mpnet",["MPNetModel",f1]],["albert",["AlbertModel",x1]],["distilbert",["DistilBertModel",t1]],["roberta",["RobertaModel",G1]],["xlm",["XLMModel",Y1]],["xlm-roberta",["XLMRobertaModel",e$]],["clap",["ClapModel",zS]],["clip",["CLIPModel",c$]],["clipseg",["CLIPSegModel",w$]],["chinese_clip",["ChineseCLIPModel",y$]],["siglip",["SiglipModel",f$]],["mobilebert",["MobileBertModel",d1]],["squeezebert",["SqueezeBertModel",w1]],["wav2vec2",["Wav2Vec2Model",eS]],["wav2vec2-bert",["Wav2Vec2BertModel",cS]],["unispeech",["UniSpeechModel",aS]],["unispeech-sat",["UniSpeechSatModel",oS]],["hubert",["HubertModel",fS]],["wavlm",["WavLMModel",_S]],["audio-spectrogram-transformer",["ASTModel",i$]],["vits",["VitsModel",R_]],["detr",["DetrModel",mx]],["table-transformer",["TableTransformerModel",wx]],["vit",["ViTModel",J$]],["fastvit",["FastViTModel",tx]],["mobilevit",["MobileViTModel",ix]],["mobilevitv2",["MobileViTV2Model",ox]],["owlvit",["OwlViTModel",ux]],["owlv2",["Owlv2Model",cx]],["beit",["BeitModel",hx]],["deit",["DeiTModel",$x]],["convnext",["ConvNextModel",Nx]],["convnextv2",["ConvNextV2Model",Lx]],["dinov2",["Dinov2Model",Wx]],["resnet",["ResNetModel",Sx]],["swin",["SwinModel",Ex]],["swin2sr",["Swin2SRModel",Tx]],["donut-swin",["DonutSwinModel",Dx]],["yolos",["YolosModel",Gx]],["dpt",["DPTModel",Ix]],["glpn",["GLPNModel",Px]],["hifigan",["SpeechT5HifiGan",SS]],["efficientnet",["EfficientNetModel",US]],["mobilenet_v1",["MobileNetV1Model",VS]],["mobilenet_v2",["MobileNetV2Model",HS]],["mobilenet_v3",["MobileNetV3Model",qS]],["mobilenet_v4",["MobileNetV4Model",YS]]]),ZS=new Map([["t5",["T5Model",C1]],["longt5",["LongT5Model",A1]],["mt5",["MT5Model",M1]],["bart",["BartModel",z1]],["mbart",["MBartModel",B1]],["marian",["MarianModel",Xx]],["whisper",["WhisperModel",o$]],["m2m_100",["M2M100Model",Zx]],["blenderbot",["BlenderbotModel",L1]],["blenderbot-small",["BlenderbotSmallModel",W1]]]),JS=new Map([["bloom",["BloomModel",q$]],["gpt2",["GPT2Model",v$]],["gptj",["GPTJModel",C$]],["gpt_bigcode",["GPTBigCodeModel",A$]],["gpt_neo",["GPTNeoModel",x$]],["gpt_neox",["GPTNeoXModel",k$]],["codegen",["CodeGenModel",M$]],["llama",["LlamaModel",z$]],["cohere",["CohereModel",R$]],["gemma",["GemmaModel",D$]],["openelm",["OpenELMModel",F$]],["qwen2",["Qwen2Model",U$]],["phi",["PhiModel",V$]],["phi3",["Phi3Model",H$]],["mpt",["MptModel",Y$]],["opt",["OPTModel",Q$]],["mistral",["MistralModel",CS]],["starcoder2",["Starcoder2Model",AS]],["falcon",["FalconModel",MS]],["stablelm",["StableLmModel",FS]]]),$l=new Map([["speecht5",["SpeechT5ForSpeechToText",$S]],["whisper",["WhisperForConditionalGeneration",l$]]]),G_=new Map([["speecht5",["SpeechT5ForTextToSpeech",xS]]]),H_=new Map([["vits",["VitsModel",R_]],["musicgen",["MusicgenForConditionalGeneration",F_]]]),j_=new Map([["bert",["BertForSequenceClassification",b2]],["roformer",["RoFormerForSequenceClassification",C2]],["electra",["ElectraForSequenceClassification",D2]],["esm",["EsmForSequenceClassification",l1]],["convbert",["ConvBertForSequenceClassification",O2]],["camembert",["CamembertForSequenceClassification",W2]],["deberta",["DebertaForSequenceClassification",q2]],["deberta-v2",["DebertaV2ForSequenceClassification",Z2]],["mpnet",["MPNetForSequenceClassification",g1]],["albert",["AlbertForSequenceClassification",S1]],["distilbert",["DistilBertForSequenceClassification",r1]],["roberta",["RobertaForSequenceClassification",j1]],["xlm",["XLMForSequenceClassification",Q1]],["xlm-roberta",["XLMRobertaForSequenceClassification",r$]],["bart",["BartForSequenceClassification",R1]],["mbart",["MBartForSequenceClassification",N1]],["mobilebert",["MobileBertForSequenceClassification",p1]],["squeezebert",["SqueezeBertForSequenceClassification",v1]]]),q_=new Map([["bert",["BertForTokenClassification",v2]],["roformer",["RoFormerForTokenClassification",T2]],["electra",["ElectraForTokenClassification",N2]],["esm",["EsmForTokenClassification",u1]],["convbert",["ConvBertForTokenClassification",z2]],["camembert",["CamembertForTokenClassification",V2]],["deberta",["DebertaForTokenClassification",K2]],["deberta-v2",["DebertaV2ForTokenClassification",J2]],["mpnet",["MPNetForTokenClassification",_1]],["distilbert",["DistilBertForTokenClassification",n1]],["roberta",["RobertaForTokenClassification",q1]],["xlm",["XLMForTokenClassification",Z1]],["xlm-roberta",["XLMRobertaForTokenClassification",n$]]]),xl=new Map([["t5",["T5ForConditionalGeneration",T1]],["longt5",["LongT5ForConditionalGeneration",I1]],["mt5",["MT5ForConditionalGeneration",O1]],["bart",["BartForConditionalGeneration",P1]],["mbart",["MBartForConditionalGeneration",D1]],["marian",["MarianMTModel",Qx]],["m2m_100",["M2M100ForConditionalGeneration",Jx]],["blenderbot",["BlenderbotForConditionalGeneration",U1]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",V1]]]),Sl=new Map([["bloom",["BloomForCausalLM",K$]],["gpt2",["GPT2LMHeadModel",$$]],["gptj",["GPTJForCausalLM",T$]],["gpt_bigcode",["GPTBigCodeForCausalLM",I$]],["gpt_neo",["GPTNeoForCausalLM",S$]],["gpt_neox",["GPTNeoXForCausalLM",E$]],["codegen",["CodeGenForCausalLM",O$]],["llama",["LlamaForCausalLM",P$]],["cohere",["CohereForCausalLM",B$]],["gemma",["GemmaForCausalLM",N$]],["openelm",["OpenELMForCausalLM",L$]],["qwen2",["Qwen2ForCausalLM",W$]],["phi",["PhiForCausalLM",G$]],["phi3",["Phi3ForCausalLM",j$]],["mpt",["MptForCausalLM",X$]],["opt",["OPTForCausalLM",Z$]],["mbart",["MBartForCausalLM",F1]],["mistral",["MistralForCausalLM",TS]],["starcoder2",["Starcoder2ForCausalLM",IS]],["falcon",["FalconForCausalLM",OS]],["trocr",["TrOCRForCausalLM",ES]],["stablelm",["StableLmForCausalLM",LS]]]),K_=new Map([["bert",["BertForMaskedLM",w2]],["roformer",["RoFormerForMaskedLM",E2]],["electra",["ElectraForMaskedLM",B2]],["esm",["EsmForMaskedLM",o1]],["convbert",["ConvBertForMaskedLM",M2]],["camembert",["CamembertForMaskedLM",U2]],["deberta",["DebertaForMaskedLM",j2]],["deberta-v2",["DebertaV2ForMaskedLM",Q2]],["mpnet",["MPNetForMaskedLM",m1]],["albert",["AlbertForMaskedLM",E1]],["distilbert",["DistilBertForMaskedLM",i1]],["roberta",["RobertaForMaskedLM",H1]],["xlm",["XLMWithLMHeadModel",X1]],["xlm-roberta",["XLMRobertaForMaskedLM",t$]],["mobilebert",["MobileBertForMaskedLM",c1]],["squeezebert",["SqueezeBertForMaskedLM",b1]]]),Y_=new Map([["bert",["BertForQuestionAnswering",$2]],["roformer",["RoFormerForQuestionAnswering",A2]],["electra",["ElectraForQuestionAnswering",F2]],["convbert",["ConvBertForQuestionAnswering",P2]],["camembert",["CamembertForQuestionAnswering",G2]],["deberta",["DebertaForQuestionAnswering",Y2]],["deberta-v2",["DebertaV2ForQuestionAnswering",e1]],["mpnet",["MPNetForQuestionAnswering",y1]],["albert",["AlbertForQuestionAnswering",k1]],["distilbert",["DistilBertForQuestionAnswering",a1]],["roberta",["RobertaForQuestionAnswering",K1]],["xlm",["XLMForQuestionAnswering",J1]],["xlm-roberta",["XLMRobertaForQuestionAnswering",a$]],["mobilebert",["MobileBertForQuestionAnswering",h1]],["squeezebert",["SqueezeBertForQuestionAnswering",$1]]]),kl=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Gg]]]),e3=new Map([["llava",["LlavaForConditionalGeneration",Hg]],["moondream1",["Moondream1ForConditionalGeneration",d$]]]),t3=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Gg]]]),X_=new Map([["vit",["ViTForImageClassification",ex]],["fastvit",["FastViTForImageClassification",rx]],["mobilevit",["MobileViTForImageClassification",sx]],["mobilevitv2",["MobileViTV2ForImageClassification",lx]],["beit",["BeitForImageClassification",fx]],["deit",["DeiTForImageClassification",xx]],["convnext",["ConvNextForImageClassification",Fx]],["convnextv2",["ConvNextV2ForImageClassification",Ux]],["dinov2",["Dinov2ForImageClassification",Vx]],["resnet",["ResNetForImageClassification",kx]],["swin",["SwinForImageClassification",Cx]],["segformer",["SegformerForImageClassification",DS]],["efficientnet",["EfficientNetForImageClassification",WS]],["mobilenet_v1",["MobileNetV1ForImageClassification",GS]],["mobilenet_v2",["MobileNetV2ForImageClassification",jS]],["mobilenet_v3",["MobileNetV3ForImageClassification",KS]],["mobilenet_v4",["MobileNetV4ForImageClassification",XS]]]),Q_=new Map([["detr",["DetrForObjectDetection",gx]],["table-transformer",["TableTransformerForObjectDetection",bx]],["yolos",["YolosForObjectDetection",Hx]]]),Z_=new Map([["owlvit",["OwlViTForObjectDetection",dx]],["owlv2",["Owlv2ForObjectDetection",px]]]),J_=new Map([["detr",["DetrForSegmentation",_x]],["clipseg",["CLIPSegForImageSegmentation",b$]]]),e0=new Map([["segformer",["SegformerForSemanticSegmentation",NS]]]),r3=new Map([["sam",["SamModel",Kx]]]),t0=new Map([["wav2vec2",["Wav2Vec2ForCTC",tS]],["wav2vec2-bert",["Wav2Vec2BertForCTC",pS]],["unispeech",["UniSpeechForCTC",iS]],["unispeech-sat",["UniSpeechSatForCTC",lS]],["wavlm",["WavLMForCTC",yS]],["hubert",["HubertForCTC",mS]]]),r0=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",rS]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",hS]],["unispeech",["UniSpeechForSequenceClassification",sS]],["unispeech-sat",["UniSpeechSatForSequenceClassification",uS]],["wavlm",["WavLMForSequenceClassification",wS]],["hubert",["HubertForSequenceClassification",gS]],["audio-spectrogram-transformer",["ASTForAudioClassification",s$]]]),n3=new Map([["wavlm",["WavLMForXVector",bS]]]),a3=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",dS]],["wavlm",["WavLMForAudioFrameClassification",vS]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",nS]]]),i3=new Map([["vitmatte",["VitMatteForImageMatting",ax]]]),n0=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Ax]]]),a0=new Map([["dpt",["DPTForDepthEstimation",Mx]],["depth_anything",["DepthAnythingForDepthEstimation",zx]],["glpn",["GLPNForDepthEstimation",Rx]]]),i0=new Map([["clip",["CLIPVisionModelWithProjection",h$]],["siglip",["SiglipVisionModel",g$]]]),s0=[[QS,ve.EncoderOnly],[ZS,ve.EncoderDecoder],[JS,ve.DecoderOnly],[j_,ve.EncoderOnly],[q_,ve.EncoderOnly],[xl,ve.Seq2Seq],[$l,ve.Seq2Seq],[Sl,ve.DecoderOnly],[K_,ve.EncoderOnly],[Y_,ve.EncoderOnly],[kl,ve.Vision2Seq],[e3,ve.ImageTextToText],[X_,ve.EncoderOnly],[J_,ve.EncoderOnly],[e0,ve.EncoderOnly],[i3,ve.EncoderOnly],[n0,ve.EncoderOnly],[a0,ve.EncoderOnly],[Q_,ve.EncoderOnly],[Z_,ve.EncoderOnly],[r3,ve.MaskGeneration],[t0,ve.EncoderOnly],[r0,ve.EncoderOnly],[G_,ve.Seq2Seq],[H_,ve.EncoderOnly],[n3,ve.EncoderOnly],[a3,ve.EncoderOnly],[i0,ve.EncoderOnly]];for(const[t,e]of s0)for(const[r,n]of t.values())es.set(r,e),ba.set(n,r),zg.set(r,n);const s3=[["MusicgenForConditionalGeneration",F_,ve.Musicgen],["CLIPTextModelWithProjection",p$,ve.EncoderOnly],["SiglipTextModel",m$,ve.EncoderOnly],["ClapTextModelWithProjection",PS,ve.EncoderOnly],["ClapAudioModelWithProjection",RS,ve.EncoderOnly]];for(const[t,e,r]of s3)es.set(t,r),ba.set(e,t),zg.set(t,e);class ma extends et{static MODEL_CLASS_MAPPINGS=s0.map(e=>e[0]);static BASE_IF_FAIL=!0}class Ep extends et{static MODEL_CLASS_MAPPINGS=[j_]}class o3 extends et{static MODEL_CLASS_MAPPINGS=[q_]}class ho extends et{static MODEL_CLASS_MAPPINGS=[xl]}class l3 extends et{static MODEL_CLASS_MAPPINGS=[$l]}class u3 extends et{static MODEL_CLASS_MAPPINGS=[G_]}class d3 extends et{static MODEL_CLASS_MAPPINGS=[H_]}class c3 extends et{static MODEL_CLASS_MAPPINGS=[Sl]}class p3 extends et{static MODEL_CLASS_MAPPINGS=[K_]}class h3 extends et{static MODEL_CLASS_MAPPINGS=[Y_]}class f3 extends et{static MODEL_CLASS_MAPPINGS=[kl]}class m3 extends et{static MODEL_CLASS_MAPPINGS=[X_]}class g3 extends et{static MODEL_CLASS_MAPPINGS=[J_]}class _3 extends et{static MODEL_CLASS_MAPPINGS=[e0]}class y3 extends et{static MODEL_CLASS_MAPPINGS=[Q_]}class w3 extends et{static MODEL_CLASS_MAPPINGS=[Z_]}class b3 extends et{static MODEL_CLASS_MAPPINGS=[t0]}class v3 extends et{static MODEL_CLASS_MAPPINGS=[r0]}class $3 extends et{static MODEL_CLASS_MAPPINGS=[t3]}class x3 extends et{static MODEL_CLASS_MAPPINGS=[n0]}class S3 extends et{static MODEL_CLASS_MAPPINGS=[a0]}class k3 extends et{static MODEL_CLASS_MAPPINGS=[i0]}class Ae extends Yt{constructor({logits:e}){super(),this.logits=e}}class E3 extends Yt{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class vt extends Yt{constructor({logits:e}){super(),this.logits=e}}class $t extends Yt{constructor({logits:e}){super(),this.logits=e}}class Ct extends Yt{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class Ln extends Yt{constructor({logits:e}){super(),this.logits=e}}class C3 extends Yt{constructor({alphas:e}){super(),this.alphas=e}}class T3 extends Yt{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const Gt=typeof self<"u",A3=Gt&&self.constructor.name==="DedicatedWorkerGlobalScope";let Xr,o0,Rr;if(Gt)Xr=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},Rr=self.createImageBitmap,o0=self.ImageData;else if(Je)Rr=async t=>{const r=(await t.metadata()).channels,{data:n,info:a}=await t.rotate().raw().toBuffer({resolveWithObject:!0}),i=new yt(new Uint8ClampedArray(n),a.width,a.height,a.channels);return r!==void 0&&r!==a.channels&&i.convert(r),i};else throw new Error("Unable to load image processing library.");const I3={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},M3=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class yt{constructor(e,r,n,a){this.data=e,this.width=r,this.height=n,this.channels=a}get size(){return[this.width,this.height]}static async read(e){if(e instanceof yt)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!Gt)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new yt(n,e.width,e.height,4)}static async fromURL(e){const r=await Ui(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(Gt){const r=await Rr(e),n=Xr(r.width,r.height).getContext("2d");return n.drawImage(r,0,0),new this(n.getImageData(0,0,r.width,r.height).data,r.width,r.height,4)}else{const r=Je(await e.arrayBuffer());return await Rr(r)}}static fromTensor(e,r="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(r==="CHW")e=e.transpose(1,2,0);else if(r!=="HWC")throw new Error(`Unsupported channel format: ${r}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new yt(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let r=0,n=0;r=0?l=n:p=-n,a>=0?u=a:h=-a,o.drawImage(s,l,u,e,r,p,h,e,r),new yt(o.getImageData(0,0,e,r).data,e,r,4).convert(i)}else{let i=this.toSharp();if(n>=0&&a>=0)i=i.extract({left:Math.floor(n),top:Math.floor(a),width:e,height:r});else if(n<=0&&a<=0){const s=Math.floor(-a),o=Math.floor(-n);i=i.extend({top:s,left:o,right:e-this.width-o,bottom:r-this.height-s})}else{let s=[0,0],o=0;a<0?(s[0]=Math.floor(-a),s[1]=r-this.height-s[0]):o=Math.floor(a);let l=[0,0],u=0;n<0?(l[0]=Math.floor(-n),l[1]=e-this.width-l[0]):u=Math.floor(n),i=i.extend({top:s[0],bottom:s[1],left:l[0],right:l[1]}).extract({left:u,top:o,width:e,height:r})}return await Rr(i)}}async toBlob(e="image/png",r=1){if(!Gt)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:r})}toTensor(e="CHW"){let r=new ue("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(e!=="HWC")if(e==="CHW")r=r.permute(2,0,1);else throw new Error(`Unsupported channel format: ${e}`);return r}toCanvas(){if(!Gt)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=Xr(e.width,e.height),n=new o0(e.data,e.width,e.height);return r.getContext("2d").putImageData(n,0,0),r}_update(e,r,n,a=null){return this.data=e,this.width=r,this.height=n,a!==null&&(this.channels=a),this}clone(){return new yt(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(Gt){if(A3)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=M3.get(r)??"image/png",a=await this.toBlob(n),i=URL.createObjectURL(a),s=document.createElement("a");s.href=i,s.download=e,s.click(),s.remove()}else{if(zt.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(Gt)throw new Error("toSharp() is only supported in server-side environments.");return Je(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}async function O3(t,e){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");const r=await(await Ui(t)).arrayBuffer(),n=new AudioContext({sampleRate:e});typeof e>"u"&&console.warn(`No sampling rate provided, using default of ${n.sampleRate}Hz.`);const a=await n.decodeAudioData(r);let i;if(a.numberOfChannels===2){const s=Math.sqrt(2),o=a.getChannelData(0),l=a.getChannelData(1);i=new Float32Array(o.length);for(let u=0;u2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,r=15,n=27/Math.log(6.4))=>t>=e?r+Math.log(t/e)*n:3*t/200};function fo(t,e="htk"){const r=z3[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}const P3={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,r=15,n=Math.log(6.4)/27)=>t>=r?e*Math.exp(n*(t-r)):200*t/3};function R3(t,e="htk"){const r=P3[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}function B3(t,e){const r=Float64Array.from({length:e.length-1},(s,o)=>e[o+1]-e[o]),n=Array.from({length:t.length},()=>new Array(e.length));for(let s=0;snew Array(t.length));for(let s=0;st+n*i)}function Ta(t,e,r,n,a,i=null,s="htk",o=!1){if(i!==null&&i!=="slaney")throw new Error('norm must be one of null or "slaney"');const l=fo(r,s),u=fo(n,s),p=Tp(l,u,e+2);let h=R3(p,s),m;if(o){const _=a/(t*2);m=fo(Float64Array.from({length:t},(w,v)=>v*_),s),h=p}else m=Tp(0,Math.floor(a/2),t);const d=B3(m,h);if(i!==null&&i==="slaney")for(let _=0;_a)throw Error(`frame_length (${r}) may not be larger than fft_length (${a})`);if(T!==r)throw new Error(`Length of the window (${T}) must equal frame_length (${r})`);if(n<=0)throw new Error("hop_length must be greater than zero");if(i===null&&p!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. Specify `power` to fix this issue.");if(s){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const R=Math.floor((a-1)/2)+1;t=D3(t,R,R)}const A=Math.floor(1+Math.floor((t.length-r)/n)),P=l?Math.floor(a/2)+1:a;let B=A,D=A;S!==null&&(S>A?$&&(D=S):D=B=S);const q=new V0(a),H=new Float64Array(a),ie=new Float64Array(q.outputBufferSize),te=new Float32Array(P*D);for(let R=0;R=1;--re)H[re]-=u*H[re-1];H[0]*=1-u}for(let re=0;reMath.pow(o,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(r&&(s=s.subarray(0,t)),n===null)return s;if(t>n)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${n})`);return s}function L3([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function El(t,e=.5,r=null,n=!1){const a=t.logits,i=t.pred_boxes,[s,o,l]=a.dims;if(r!==null&&r.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let u=[];for(let p=0;pe&&S.push(E)}else{let E=Kt(v.data)[1];if(E===l-1||($=wt(v.data),$[E]A*h[(P+1)%2])),m.boxes.push(T),m.classes.push(E),m.scores.push($[E])}}u.push(m)}return u}function qa(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${t?.constructor?.name??typeof t} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function Ap(t,e,r=0,n=null){const a=t/e;let i=H0(a)*e;return n!==null&&i>n&&(i=Math.floor(a)*e),ii?u=Math.floor(i*l/a):i>a&&(l=Math.floor(a*u/i)),await e.resize(u,l,{resample:n}))}async crop_margin(e,r=200){const n=e.clone().grayscale(),a=Fp(n.data)[0],s=Kt(n.data)[0]-a;if(s===0)return e;const o=r/255;let l=n.width,u=n.height,p=0,h=0;const m=n.data;for(let d=0;dthis.preprocess(i)));return{pixel_values:ka(n.map(i=>i.pixel_values),0),original_sizes:n.map(i=>i.original_size),reshaped_input_sizes:n.map(i=>i.reshaped_input_size)}}}class U3 extends He{post_process_semantic_segmentation(e,r=null){const n=e.logits,a=n.dims[0];if(r!==null&&r.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const i=[];for(let s=0;sm[E]&&(m[E]=$[E],d[E]=S)}const _=new Array(l.dims[0]),w=h.data;for(let S=0;SS!==void 0);i.push({segmentation:h,labels:v})}return i}}class u0 extends He{}class W3 extends u0{}class V3 extends He{}class G3 extends He{}class d0 extends He{}class H3 extends d0{}class j3 extends He{}class q3 extends He{}class c0 extends He{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){const r=this.size?.shortest_edge;if(r===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(r<384){const n=Math.floor(r/this.crop_pct),[a,i]=this.get_resize_output_image_size(e,{shortest_edge:n});e=await e.resize(a,i,{resample:this.resample}),e=await e.center_crop(r,r)}else e=await e.resize(r,r,{resample:this.resample});return e}}class K3 extends c0{}class Y3 extends He{}class X3 extends He{}class Q3 extends He{constructor(e){super(e),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(r=>r*r))}}class Z3 extends He{}class J3 extends He{}class ek extends He{}class tk extends He{}class p0 extends He{}class rk extends p0{}class h0 extends He{post_process_object_detection(...e){return El(...e)}}class nk extends h0{}class ak extends He{}class ik extends He{}class f0 extends He{pad_image(e,r,n,a={}){const[i,s,o]=r;let l=this.image_mean;Array.isArray(this.image_mean)||(l=new Array(o).fill(l));let u=this.image_std;Array.isArray(u)||(u=new Array(o).fill(l));const p=l.map((h,m)=>-h/u[m]);return super.pad_image(e,r,n,{center:!0,constant_values:p,...a})}}class sk extends f0{}class ok extends He{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],a=new ue("int64",new BigInt64Array(n.reduce((i,s)=>i*s)).fill(1n),n);return{...r,pixel_mask:a}}post_process_object_detection(...e){return El(...e)}remove_low_and_no_objects(e,r,n,a){let i=[],s=[],o=[];for(let l=0;ln&&(i.push(p),s.push(d),o.push(h))}return[i,s,o]}check_segment_validity(e,r,n,a=.5,i=.8){let s=[],o=0,l=0;const u=r[n].data;for(let h=0;h=a&&++l;let p=o>0&&l>0;return p&&(p=o/l>i),[p,s]}compute_segments(e,r,n,a,i,s=null,o=null){let[l,u]=o??e[0].dims,p=new ue("int32",new Int32Array(l*u),[l,u]),h=[];if(o!==null)for(let v=0;vd[E]&&(m[E]=v,d[E]=$[E])}let _=0;const w=p.data;for(let v=0;va!==r.dims[i]))throw Error(`The first ${n.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new ue("int64",e.flat(1/0).map(BigInt),n)}async _call(e,{input_points:r=null,input_labels:n=null,input_boxes:a=null}={}){const i=await super._call(e);if(r&&(i.input_points=this.reshape_input_points(r,i.original_sizes,i.reshaped_input_sizes)),n){if(!i.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");i.input_labels=this.add_input_labels(n,i.input_points)}return a&&(i.input_boxes=this.reshape_input_points(a,i.original_sizes,i.reshaped_input_sizes,!0)),i}async post_process_masks(e,r,n,{mask_threshold:a=0,binarize:i=!0,pad_size:s=null}={}){const o=[];s=s??this.pad_size;const l=[s.height,s.width];for(let u=0;ua&&(_[w]=1);m=new ue("bool",_,m.dims)}o.push(m)}return o}generate_crop_boxes(e,r,{crop_n_layers:n=0,overlap_ratio:a=512/1500,points_per_crop:i=32,crop_n_points_downscale_factor:s=1}={}){}}class dk extends He{pad_image(e,r,n,a={}){const[i,s,o]=r;return super.pad_image(e,r,{width:s+(n-s%n)%n,height:i+(n-i%n)%n},{mode:"symmetric",center:!1,constant_values:-1,...a})}}class ck extends He{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);const n=await Promise.all(e.map(s=>this.preprocess(s))),a=await Promise.all(r.map(s=>this.preprocess(s,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:ka(n.map((s,o)=>gr([s.pixel_values,a[o].pixel_values],0)),0),original_sizes:n.map(s=>s.original_size),reshaped_input_sizes:n.map(s=>s.reshaped_input_size)}}}class pk extends pn{constructor(e){super(e),this.config.mel_filters??=Ta(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=ms(this.config.n_fft,"hann")}async _extract_fbank_features(e){const r=await fs(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),n=r.data,a=Kt(n)[0];for(let i=0;ithis.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),r=e.slice(0,this.config.n_samples)):(r=new Float32Array(this.config.n_samples),r.set(e)),{input_features:(await this._extract_fbank_features(r)).unsqueeze_(0)}}}class hk extends pn{_zero_mean_unit_var_norm(e){const n=e.reduce((i,s)=>i+s,0)/e.length,a=e.reduce((i,s)=>i+(s-n)**2,0)/e.length;return e.map(i=>(i-n)/Math.sqrt(a+1e-7))}async _call(e){qa(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=e;this.config.do_normalize&&(r=this._zero_mean_unit_var_norm(r));const n=[1,r.length];return{input_values:new ue("float32",r,n),attention_mask:new ue("int64",new BigInt64Array(r.length).fill(1n),n)}}}class fk extends pn{constructor(e){super(e);const r=this.config.sampling_rate,n=Ta(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;an*32768),fs(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e,{padding:r=!0,pad_to_multiple_of:n=2,do_normalize_per_mel_bins:a=!0,return_attention_mask:i=!0}={}){qa(e,"SeamlessM4TFeatureExtractor");let s=await this._extract_fbank_features(e,this.config.max_length);if(a){const[_,w]=s.dims,v=s.data;for(let S=0;S0){const $=new Float32Array(w*(_+S));$.set(v),$.fill(this.config.padding_value,v.length);const E=_+S;s=new ue(s.type,$,[E,w]),i&&(o=new ue("int64",new BigInt64Array(E),[1,E]),o.data.fill(1n,0,_))}}const[l,u]=s.dims,p=this.config.stride;if(l%p!==0)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${p}).`);const m=s.view(1,Math.floor(l/p),u*p),d={input_features:m};if(i){const _=m.dims[1],w=new BigInt64Array(_);if(o){const v=o.data;for(let S=1,$=0;S0)if(n==="rand_trunc"){const o=Math.floor(Math.random()*(s+1));e=e.subarray(o,o+r),i=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${n}" not implemented`);else{if(s<0){let o=new Float64Array(r);if(o.set(e),a==="repeat")for(let l=e.length;lyt.read(e)))}async function ts(t,e){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(r=>typeof r=="string"||r instanceof URL?O3(r,e):r instanceof Float64Array?new Float32Array(r):r))}function m0(t,e){e&&(t=t.map(s=>s|0));const[r,n,a,i]=t;return{xmin:r,ymin:n,xmax:a,ymax:i}}class tt extends bt{constructor({task:e,model:r,tokenizer:n=null,processor:a=null}){super(),this.task=e,this.model=r,this.tokenizer=n,this.processor=a}async dispose(){await this.model.dispose()}}class xk extends tt{constructor(e){super(e)}async _call(e,{top_k:r=1}={}){const n=this.tokenizer(e,{padding:!0,truncation:!0}),a=await this.model(n),i=this.model.config.problem_type==="multi_label_classification"?l=>l.sigmoid():l=>new ue("float32",wt(l.data),l.dims),s=this.model.config.id2label,o=[];for(const l of a.logits){const u=i(l),p=await Nn(u,r),h=p[0].tolist(),d=p[1].tolist().map((_,w)=>({label:s?s[_]:`LABEL_${_}`,score:h[w]}));r===1?o.push(...d):o.push(d)}return Array.isArray(e)||r===1?o:o[0]}}class Sk extends tt{constructor(e){super(e)}async _call(e,{ignore_labels:r=["O"]}={}){const n=Array.isArray(e),a=this.tokenizer(n?e:[e],{padding:!0,truncation:!0}),s=(await this.model(a)).logits,o=this.model.config.id2label,l=[];for(let u=0;uE==this.tokenizer.sep_token_id);l[h].map((E,T)=>E==1&&(T===0||T>d&&u.findIndex(A=>A==m[T])===-1));const _=i[h].tolist(),w=s[h].tolist();for(let E=1;E<_.length;++E)(l[h]==0||E<=d||u.findIndex(T=>T==m[E])!==-1)&&(_[E]=-1/0,w[E]=-1/0);const v=wt(_).map((E,T)=>[E,T]),S=wt(w).map((E,T)=>[E,T]);v[0][0]=0,S[0][0]=0;const $=P0(v,S).filter(E=>E[0][1]<=E[1][1]).map(E=>[E[0][1],E[1][1],E[0][0]*E[1][0]]).sort((E,T)=>T[2]-E[2]);for(let E=0;E_==this.tokenizer.mask_token_id);if(u===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const p=a[o][u],h=await Nn(new ue("float32",wt(p.data),p.dims),r),m=h[0].tolist(),d=h[1].tolist();i.push(d.map((_,w)=>{const v=l.slice();return v[u]=_,{score:m[w],token:Number(_),token_str:this.tokenizer.model.vocab[_],sequence:this.tokenizer.decode(v,{skip_special_tokens:!0})}}))}return Array.isArray(e)?i:i[0]}}class Cl extends tt{_key="generated_text";constructor(e){super(e)}async _call(e,r={}){Array.isArray(e)||(e=[e]),this.model.config.prefix&&(e=e.map(l=>this.model.config.prefix+l));const n=this.model.config.task_specific_params;n&&n[this.task]&&n[this.task].prefix&&(e=e.map(l=>n[this.task].prefix+l));const a=this.tokenizer,i={padding:!0,truncation:!0};let s;this instanceof g0&&"_build_translation_inputs"in a?s=a._build_translation_inputs(e,i,r):s=a(e,i);const o=await this.model.generate({...s,...r});return a.batch_decode(o,{skip_special_tokens:!0}).map(l=>({[this._key]:l}))}}class Ck extends Cl{_key="summary_text";constructor(e){super(e)}}class g0 extends Cl{_key="translation_text";constructor(e){super(e)}}class Tk extends tt{constructor(e){super(e)}async _call(e,r={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class Ak extends tt{constructor(e){super(e),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([r,n])=>[r.toLowerCase(),n])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(e,r,{hypothesis_template:n="This example is {}.",multi_label:a=!1}={}){const i=Array.isArray(e);i||(e=[e]),Array.isArray(r)||(r=[r]);const s=r.map(u=>n.replace("{}",u)),o=a||r.length===1,l=[];for(const u of e){const p=[];for(const d of s){const _=this.tokenizer(u,{text_pair:d,padding:!0,truncation:!0}),w=await this.model(_);o?p.push([w.logits.data[this.contradiction_id],w.logits.data[this.entailment_id]]):p.push(w.logits.data[this.entailment_id])}const m=(o?p.map(d=>wt(d)[1]):wt(p)).map((d,_)=>[d,_]).sort((d,_)=>_[0]-d[0]);l.push({sequence:u,labels:m.map(d=>r[d[1]]),scores:m.map(d=>d[0])})}return i?l:l[0]}}class Ik extends tt{constructor(e){super(e)}async _call(e,{pooling:r="none",normalize:n=!1,quantize:a=!1,precision:i="binary"}={}){const s=this.tokenizer(e,{padding:!0,truncation:!0}),o=await this.model(s);let l=o.last_hidden_state??o.logits??o.token_embeddings;if(r!=="none")if(r==="mean")l=Aw(l,s.attention_mask);else if(r==="cls")l=l.slice(null,0);else throw Error(`Pooling method '${r}' not supported.`);return n&&(l=l.normalize(2,-1)),a&&(l=Nw(l,i)),l}}class Mk extends tt{constructor(e){super(e)}async _call(e,{pool:r=null}={}){const n=await Tr(e),{pixel_values:a}=await this.processor(n),i=await this.model({pixel_values:a});let s;if(r){if(!("pooler_output"in i))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");s=i.pooler_output}else s=i.last_hidden_state??i.logits??i.image_embeds;return s}}class Ok extends tt{constructor(e){super(e)}async _call(e,{top_k:r=5}={}){const n=this.processor.feature_extractor.config.sampling_rate,a=await ts(e,n),i=this.model.config.id2label,s=[];for(const o of a){const l=await this.processor(o),p=(await this.model(l)).logits[0],h=await Nn(new ue("float32",wt(p.data),p.dims),r),m=h[0].tolist(),_=h[1].tolist().map((w,v)=>({label:i?i[w]:`LABEL_${w}`,score:m[v]}));s.push(_)}return Array.isArray(e)?s:s[0]}}class zk extends tt{constructor(e){super(e)}async _call(e,r,{hypothesis_template:n="This is a sound of {}."}={}){const a=!Array.isArray(e);a&&(e=[e]);const i=r.map(p=>n.replace("{}",p)),s=this.tokenizer(i,{padding:!0,truncation:!0}),o=this.processor.feature_extractor.config.sampling_rate,l=await ts(e,o),u=[];for(const p of l){const h=await this.processor(p),m=await this.model({...s,...h}),d=wt(m.logits_per_audio.data);u.push([...d].map((_,w)=>({score:_,label:r[w]})))}return a?u[0]:u}}class Pk extends tt{constructor(e){super(e)}async _call(e,r={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(e,r);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(e,r);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(e,r){r.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),r.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const n=!Array.isArray(e);n&&(e=[e]);const a=this.processor.feature_extractor.config.sampling_rate,i=await ts(e,a),s=[];for(const o of i){const l=await this.processor(o),p=(await this.model(l)).logits[0],h=[];for(const d of p)h.push(Kt(d.data)[1]);const m=this.tokenizer.decode(h);s.push({text:m})}return n?s[0]:s}async _call_whisper(e,r){const n=r.return_timestamps??!1,a=r.chunk_length_s??0,i=r.force_full_sequences??!1;let s=r.stride_length_s??null;n==="word"&&(r.return_token_timestamps=!0);const o=!Array.isArray(e);o&&(e=[e]);const l=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,u=this.processor.feature_extractor.config.hop_length,p=this.processor.feature_extractor.config.sampling_rate,h=await ts(e,p),m=[];for(const d of h){let _=[];if(a>0){if(s===null)s=a/6;else if(a<=s)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const S=p*a,$=p*s,E=S-2*$;let T=0;for(;T=d.length;_.push({stride:[A.length,B?0:$,D?0:$],input_features:P.input_features,is_last:D}),T+=E}}else _=[{stride:[d.length,0,0],input_features:(await this.processor(d)).input_features,is_last:!0}];for(const S of _){r.num_frames=Math.floor(S.stride[0]/u);const $=await this.model.generate({inputs:S.input_features,...r});n==="word"?(S.tokens=$.sequences[0].tolist(),S.token_timestamps=$.token_timestamps.tolist()[0].map(E=>Mi(E,2))):S.tokens=$[0].tolist(),S.stride=S.stride.map(E=>E/p)}const[w,v]=this.tokenizer._decode_asr(_,{time_precision:l,return_timestamps:n,force_full_sequences:i});m.push({text:w,...v})}return o?m[0]:m}}class Rk extends tt{constructor(e){super(e)}async _call(e,r={}){const n=Array.isArray(e),a=await Tr(e),{pixel_values:i}=await this.processor(a),s=[];for(const o of i){o.dims=[1,...o.dims];const l=await this.model.generate({inputs:o,...r}),u=this.tokenizer.batch_decode(l,{skip_special_tokens:!0}).map(p=>({generated_text:p.trim()}));s.push(u)}return n?s:s[0]}}class Bk extends tt{constructor(e){super(e)}async _call(e,{top_k:r=5}={}){const n=await Tr(e),{pixel_values:a}=await this.processor(n),i=await this.model({pixel_values:a}),s=this.model.config.id2label,o=[];for(const l of i.logits){const u=await Nn(new ue("float32",wt(l.data),l.dims),r),p=u[0].tolist(),m=u[1].tolist().map((d,_)=>({label:s?s[d]:`LABEL_${d}`,score:p[_]}));o.push(m)}return Array.isArray(e)?o:o[0]}}class Dk extends tt{constructor(e){super(e),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(e,{threshold:r=.5,mask_threshold:n=.5,overlap_mask_area_threshold:a=.8,label_ids_to_fuse:i=null,target_sizes:s=null,subtask:o=null}={}){if(Array.isArray(e)&&e.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const u=await Tr(e),p=u.map(S=>[S.height,S.width]),{pixel_values:h,pixel_mask:m}=await this.processor(u),d=await this.model({pixel_values:h,pixel_mask:m});let _=null;if(o!==null)_=this.subtasks_mapping[o];else for(let[S,$]of Object.entries(this.subtasks_mapping))if($ in this.processor.feature_extractor){_=this.processor.feature_extractor[$].bind(this.processor.feature_extractor),o=S;break}const w=this.model.config.id2label,v=[];if(o==="panoptic"||o==="instance"){const S=_(d,r,n,a,i,s??p)[0],$=S.segmentation;for(const E of S.segments_info){const T=new Uint8ClampedArray($.data.length);for(let P=0;P<$.data.length;++P)$.data[P]===E.id&&(T[P]=255);const A=new yt(T,$.dims[1],$.dims[0],1);v.push({score:E.score,label:w[E.label_id],mask:A})}}else if(o==="semantic"){const{segmentation:S,labels:$}=_(d,s??p)[0];for(const E of $){const T=new Uint8ClampedArray(S.data.length);for(let P=0;Pn.replace("{}",m)),o=this.tokenizer(s,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:l}=await this.processor(i),u=await this.model({...o,pixel_values:l}),p=this.model.config.model_type==="siglip"?m=>m.sigmoid().data:m=>wt(m.data),h=[];for(const m of u.logits_per_image){const _=[...p(m)].map((w,v)=>({score:w,label:r[v]}));_.sort((w,v)=>v.score-w.score),h.push(_)}return a?h:h[0]}}class Fk extends tt{constructor(e){super(e)}async _call(e,{threshold:r=.9,percentage:n=!1}={}){const a=Array.isArray(e);if(a&&e.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const i=await Tr(e),s=n?null:i.map(d=>[d.height,d.width]),{pixel_values:o,pixel_mask:l}=await this.processor(i),u=await this.model({pixel_values:o,pixel_mask:l}),p=this.processor.feature_extractor.post_process_object_detection(u,r,s),h=this.model.config.id2label,m=p.map(d=>d.boxes.map((_,w)=>({score:d.scores[w],label:h[d.classes[w]],box:m0(_,!n)})));return a?m:m[0]}}class Lk extends tt{constructor(e){super(e)}async _call(e,r,{threshold:n=.1,top_k:a=null,percentage:i=!1}={}){const s=Array.isArray(e),o=await Tr(e),l=this.tokenizer(r,{padding:!0,truncation:!0}),u=await this.processor(o),p=[];for(let h=0;h({score:v.scores[E],label:r[v.classes[E]],box:m0($,!i)})).sort(($,E)=>E.score-$.score);a!==null&&(S=S.slice(0,a)),p.push(S)}return s?p:p[0]}}class Uk extends tt{constructor(e){super(e)}async _call(e,r,n={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class Wk extends tt{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(e){super(e),this.vocoder=e.vocoder??null}async _call(e,{speaker_embeddings:r=null}={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}async _call_text_to_waveform(e){const r=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:n}=await this.model(r),a=this.model.config.sampling_rate;return{audio:n.data,sampling_rate:a}}async _call_text_to_spectrogram(e,{speaker_embeddings:r}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await ma.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof r=="string"||r instanceof URL)&&(r=new Float32Array(await(await fetch(r)).arrayBuffer())),r instanceof Float32Array)r=new ue("float32",r,[1,r.length]);else if(!(r instanceof ue))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:n}=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:a}=await this.model.generate_speech(n,r,{vocoder:this.vocoder}),i=this.processor.feature_extractor.config.sampling_rate;return{audio:a.data,sampling_rate:i}}}class Vk extends tt{constructor(e){super(e)}async _call(e){const r=await Tr(e),n=await this.processor(r),a=await this.model(n),i=[];for(const s of a.reconstruction){const o=s.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");i.push(yt.fromTensor(o))}return i.length>1?i:i[0]}}class Gk extends tt{constructor(e){super(e)}async _call(e){const r=await Tr(e),n=await this.processor(r),{predicted_depth:a}=await this.model(n),i=[];for(let s=0;s1?i:i[0]}}const Ip=Object.freeze({"text-classification":{tokenizer:ht,pipeline:xk,model:Ep,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:ht,pipeline:Sk,model:o3,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:ht,pipeline:kk,model:h3,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:ht,pipeline:Ek,model:p3,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:ht,pipeline:Ck,model:ho,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:ht,pipeline:g0,model:ho,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:ht,pipeline:Cl,model:ho,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:ht,pipeline:Tk,model:c3,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:ht,pipeline:Ak,model:Ep,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:Ok,model:v3,processor:Ot,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:ht,pipeline:zk,model:ma,processor:Ot,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:ht,pipeline:Pk,model:[l3,b3],processor:Ot,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:ht,pipeline:Wk,model:[d3,u3],processor:[Ot,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:ht,pipeline:Rk,model:f3,processor:Ot,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Bk,model:m3,processor:Ot,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:Dk,model:[g3,_3],processor:Ot,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:ht,pipeline:Nk,model:ma,processor:Ot,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:Fk,model:y3,processor:Ot,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:ht,pipeline:Lk,model:w3,processor:Ot,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:ht,pipeline:Uk,model:$3,processor:Ot,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:Vk,model:x3,processor:Ot,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Gk,model:S3,processor:Ot,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:ht,pipeline:Ik,model:ma,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:Ot,pipeline:Mk,model:[k3,ma],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Hk=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function jk(t,e=null,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",device:o=null,dtype:l=null,model_file_name:u=null,session_options:p={}}={}){t=Hk[t]??t;const h=Ip[t.split("_",1)[0]];if(!h)throw Error(`Unsupported pipeline: ${t}. 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Using default model: "${e}".`));const m={progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s,device:o,dtype:l,model_file_name:u,session_options:p},d=new Map([["tokenizer",h.tokenizer],["model",h.model],["processor",h.processor]]),_=await qk(d,e,m);_.task=t,An(r,{status:"ready",task:t,model:e});const w=h.pipeline;return new w(_)}async function qk(t,e,r){const n=Object.create(null),a=[];for(let[i,s]of t.entries()){if(!s)continue;let o;Array.isArray(s)?o=new Promise(async(l,u)=>{let p;for(let h of s){if(h===null){l(null);return}try{l(await h.from_pretrained(e,r));return}catch(m){if(m.message?.includes("Unsupported model type"))p=m;else{u(m);return}}}u(p)}):o=s.from_pretrained(e,r),n[i]=o,a.push(o)}await Promise.all(a);for(let[i,s]of Object.entries(n))n[i]=await s;return n}an.IS_PROCESS_AVAILABLE;const gs=document.getElementById("status"),Mp=document.getElementById("container"),ga=document.getElementById("video"),go=document.getElementById("overlay");gs.textContent="Loading model (8 MB)...";async function Kk(){try{return(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{return!1}}const Yk="onnx-community/mobilenetv4_conv_small.e1200_r224_in1k";let _0;try{_0=await jk("image-classification",Yk,{dtype:await Kk()?"fp16":"fp32",device:"webgpu"})}catch(t){throw gs.textContent=t.message,alert(t.message),t}gs.textContent="Ready";const Qr=256,Bo=document.createElement("canvas");Bo.width=Bo.height=Qr;const Op=Bo.getContext("2d",{willReadFrequently:!0});let _o=!1,yo;function y0(){_o||(_o=!0,async function(){Op.drawImage(ga,0,0,Qr,Qr);const t=Op.getImageData(0,0,Qr,Qr).data,e=new yt(t,Qr,Qr,4),r=await _0(e);go.innerHTML="";for(const{label:n,score:a}of r)go.appendChild(document.createTextNode(`${n}: ${a.toFixed(2)}`)),go.appendChild(document.createElement("br"));if(yo!==void 0){const n=1e3/(performance.now()-yo);gs.textContent=`FPS: ${n.toFixed(2)}`}yo=performance.now(),_o=!1}()),window.requestAnimationFrame(y0)}navigator.mediaDevices.getUserMedia({video:!0}).then(t=>{ga.srcObject=t,ga.play();const e=t.getVideoTracks()[0],{width:r,height:n}=e.getSettings();ga.width=r,ga.height=n;const a=r/n,[i,s]=a>720/405?[720,720/a]:[405*a,405];Mp.style.width=`${i}px`,Mp.style.height=`${s}px`,window.requestAnimationFrame(y0)}).catch(t=>{alert(t)});