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Call_vijjjiiji=ht.yf)(e,t,n,i,a,o,s,u,l),Pn=r.dynCall_iiiji=(e,t,n,i,a)=>(Pn=r.dynCall_iiiji=ht.zf)(e,t,n,i,a),Dn=r.dynCall_iiijiiiiiiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h)=>(Dn=r.dynCall_iiijiiiiiiiiii=ht.Af)(e,t,n,i,a,o,s,u,l,d,c,p,f,h),Nn=r.dynCall_vj=(e,t)=>(Nn=r.dynCall_vj=ht.Bf)(e,t),Un=r.dynCall_jjj=(e,t,n)=>(Un=r.dynCall_jjj=ht.Cf)(e,t,n),Wn=r.dynCall_iiijiiiiii=(e,t,n,i,a,o,s,u,l,d)=>(Wn=r.dynCall_iiijiiiiii=ht.Df)(e,t,n,i,a,o,s,u,l,d),Ln=r.dynCall_vfiii=(e,t,n,i,a)=>(Ln=r.dynCall_vfiii=ht.Ef)(e,t,n,i,a),Hn=r.dynCall_viiiiff=(e,t,n,i,a,o,s)=>(Hn=r.dynCall_viiiiff=ht.Ff)(e,t,n,i,a,o,s),Gn=r.dynCall_viiiiiff=(e,t,n,i,a,o,s,u)=>(Gn=r.dynCall_viiiiiff=ht.Gf)(e,t,n,i,a,o,s,u),Vn=r.dynCall_viiff=(e,t,n,i,a)=>(Vn=r.dynCall_viiff=ht.Hf)(e,t,n,i,a),Fn=r.dynCall_viiiiiiiiifiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h)=>(Fn=r.dynCall_viiiiiiiiifiii=ht.If)(e,t,n,i,a,o,s,u,l,d,c,p,f,h),qn=r.dynCall_viiiiiiiijj=(e,t,n,i,a,o,s,u,l,d,c)=>(qn=r.dynCall_viiiiiiiijj=ht.Jf)(e,t,n,i,a,o,s,u,l,d,c),Kn=r.dynCall_iiiiiiiiiiiiiifii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y)=>(Kn=r.dynCall_iiiiiiiiiiiiiifii=ht.Kf)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y),Yn=r.dynCall_viiiiiiiiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f)=>(Yn=r.dynCall_viiiiiiiiiiii=ht.Lf)(e,t,n,i,a,o,s,u,l,d,c,p,f),Zn=r.dynCall_iiiiiiiiiiiiiiiiifii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y,v,w,_)=>(Zn=r.dynCall_iiiiiiiiiiiiiiiiifii=ht.Mf)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y,v,w,_),Qn=r.dynCall_vijjiiiiii=(e,t,n,i,a,o,s,u,l,d)=>(Qn=r.dynCall_vijjiiiiii=ht.Nf)(e,t,n,i,a,o,s,u,l,d),Jn=r.dynCall_iiiijjj=(e,t,n,i,a,o,s)=>(Jn=r.dynCall_iiiijjj=ht.Of)(e,t,n,i,a,o,s),Xn=r.dynCall_viiiiiiiiii=(e,t,n,i,a,o,s,u,l,d,c)=>(Xn=r.dynCall_viiiiiiiiii=ht.Pf)(e,t,n,i,a,o,s,u,l,d,c),ei=r.dynCall_iiijjj=(e,t,n,i,a,o)=>(ei=r.dynCall_iiijjj=ht.Qf)(e,t,n,i,a,o),ti=r.dynCall_fffffff=(e,t,n,i,a,o,s)=>(ti=r.dynCall_fffffff=ht.Rf)(e,t,n,i,a,o,s),ni=r.dynCall_viiiij=(e,t,n,i,a,o)=>(ni=r.dynCall_viiiij=ht.Sf)(e,t,n,i,a,o),ii=r.dynCall_viijj=(e,t,n,i,a)=>(ii=r.dynCall_viijj=ht.Tf)(e,t,n,i,a),ri=r.dynCall_vjjjjjjffiifiiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y,v)=>(ri=r.dynCall_vjjjjjjffiifiiiiii=ht.Uf)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y,v),ai=r.dynCall_viiiiiiffiifiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y)=>(ai=r.dynCall_viiiiiiffiifiiiii=ht.Vf)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y),oi=r.dynCall_viiiiiiffifiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g)=>(oi=r.dynCall_viiiiiiffifiiiii=ht.Wf)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g),si=r.dynCall_viiiiiiffiiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m)=>(si=r.dynCall_viiiiiiffiiiiii=ht.Xf)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m),ui=r.dynCall_vjjjjjjjjfffiifiiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y,v,w,_,$)=>(ui=r.dynCall_vjjjjjjjjfffiifiiiiii=ht.Yf)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y,v,w,_,$),li=r.dynCall_vjjjjjjfffifiiiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y,v,w)=>(li=r.dynCall_vjjjjjjfffifiiiiiii=ht.Zf)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y,v,w),di=r.dynCall_vjjjjjjfffifiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y)=>(di=r.dynCall_vjjjjjjfffifiiiii=ht._f)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y),ci=r.dynCall_vjjjjjjjjfffiifiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y,v,w,_)=>(ci=r.dynCall_vjjjjjjjjfffiifiiiii=ht.$f)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m,g,y,v,w,_),pi=r.dynCall_vijjfffiii=(e,t,n,i,a,o,s,u,l,d)=>(pi=r.dynCall_vijjfffiii=ht.ag)(e,t,n,i,a,o,s,u,l,d),fi=r.dynCall_vijiiiiiiii=(e,t,n,i,a,o,s,u,l,d,c)=>(fi=r.dynCall_vijiiiiiiii=ht.bg)(e,t,n,i,a,o,s,u,l,d,c),hi=r.dynCall_vijjjjjjifiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m)=>(hi=r.dynCall_vijjjjjjifiiiii=ht.cg)(e,t,n,i,a,o,s,u,l,d,c,p,f,h,m),mi=r.dynCall_vjjjjjiiii=(e,t,n,i,a,o,s,u,l,d)=>(mi=r.dynCall_vjjjjjiiii=ht.dg)(e,t,n,i,a,o,s,u,l,d),gi=r.dynCall_vjjjjfiii=(e,t,n,i,a,o,s,u,l)=>(gi=r.dynCall_vjjjjfiii=ht.eg)(e,t,n,i,a,o,s,u,l),yi=r.dynCall_viifi=(e,t,n,i,a)=>(yi=r.dynCall_viifi=ht.fg)(e,t,n,i,a),vi=r.dynCall_vijjii=(e,t,n,i,a,o)=>(vi=r.dynCall_vijjii=ht.gg)(e,t,n,i,a,o),wi=r.dynCall_viiiiijjiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f)=>(wi=r.dynCall_viiiiijjiiiii=ht.hg)(e,t,n,i,a,o,s,u,l,d,c,p,f),_i=r.dynCall_iiiiiji=(e,t,n,i,a,o,s)=>(_i=r.dynCall_iiiiiji=ht.ig)(e,t,n,i,a,o,s),$i=r.dynCall_iiiiji=(e,t,n,i,a,o)=>($i=r.dynCall_iiiiji=ht.jg)(e,t,n,i,a,o),bi=r.dynCall_viiiiijiiiiii=(e,t,n,i,a,o,s,u,l,d,c,p,f)=>(bi=r.dynCall_viiiiijiiiiii=ht.kg)(e,t,n,i,a,o,s,u,l,d,c,p,f),xi=r.dynCall_viiijiiiiii=(e,t,n,i,a,o,s,u,l,d,c)=>(xi=r.dynCall_viiijiiiiii=ht.lg)(e,t,n,i,a,o,s,u,l,d,c),Ci=r.dynCall_viiiijii=(e,t,n,i,a,o,s,u)=>(Ci=r.dynCall_viiiijii=ht.mg)(e,t,n,i,a,o,s,u),Si=r.dynCall_viijjiii=(e,t,n,i,a,o,s,u)=>(Si=r.dynCall_viijjiii=ht.ng)(e,t,n,i,a,o,s,u),Ei=r.dynCall_ijii=(e,t,n,i)=>(Ei=r.dynCall_ijii=ht.og)(e,t,n,i),Ii=r.dynCall_viiiiijjji=(e,t,n,i,a,o,s,u,l,d)=>(Ii=r.dynCall_viiiiijjji=ht.pg)(e,t,n,i,a,o,s,u,l,d),ki=r.dynCall_vijjjjiij=(e,t,n,i,a,o,s,u,l)=>(ki=r.dynCall_vijjjjiij=ht.qg)(e,t,n,i,a,o,s,u,l),Ti=r.dynCall_viiiiijij=(e,t,n,i,a,o,s,u,l)=>(Ti=r.dynCall_viiiiijij=ht.rg)(e,t,n,i,a,o,s,u,l),Ai=r.dynCall_viiiiiijij=(e,t,n,i,a,o,s,u,l,d)=>(Ai=r.dynCall_viiiiiijij=ht.sg)(e,t,n,i,a,o,s,u,l,d),Oi=r.dynCall_vijiii=(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interval];\n bestValue = ${Lt[i]};\n aBestValues[local_idx] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (local_idx == 0u) {\n ${c.setByOffset("outputIndex",""+("mean"===i?`bestValue / ${c.type.storage}(uniforms.reduceSize)`:`${Gt[i]}`))};\n }\n }`,getRunData:()=>({outputs:[{dims:a,dataType:r}],dispatchGroup:{x:u},programUniforms:[{type:12,data:l}]})}},Qt=(e,t,n,i)=>{let r=1===e.inputs.length?n:pn(e.inputs,n),a=r.axes;0===a.length&&!r.noopWithEmptyAxes&&(a=e.inputs[0].dims.map(((e,t)=>t)));let 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new Error("Invalid axes input dims.")},dn=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],cn=(e,t,n,i,r,a,o=!1,s=!1)=>{let u=[],l=n[0].dims,d=l.length,c=pt.normalizeAxes(r,d),p=!s&&0===c.length;l.forEach(((e,t)=>{p||c.indexOf(t)>=0?o&&u.push(1):u.push(e)}));let f=u.length,h=pt.size(u);return{name:e,shaderCache:t,getShaderSource:e=>{let t=[],r=kt("_A",n[0].dataType,d),s=Tt("output",a,f),u=i(r,s,c),h=u[2];for(let e=0,n=0;e=0?(o&&n++,h=`for(var j${e}: u32 = 0; j${e} < ${l[e]}; j${e}++) {\n ${u[2].includes("last_index")?`let last_index = j${e};`:""}\n ${r.indicesSet("input_indices",e,`j${e}`)}\n ${h}\n }`):(t.push(`${r.indicesSet("input_indices",e,s.indicesGet("output_indices",n))};`),n++);return`\n\n ${e.registerUniform("output_size","u32").declareVariables(r,s)}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n var input_indices: ${r.type.indices};\n let output_indices = ${s.offsetToIndices("global_idx")};\n\n ${t.join("\n")}\n 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n=0;n1024},Sn=(e,t)=>{Cn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?wn(e,t):Jt(e,t)},En=(e,t)=>{Cn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?mn(e,t):Xt(e,t)},In=(e,t)=>{Cn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?gn(e,t):en(e,t)},kn=(e,t)=>{Cn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?yn(e,t):tn(e,t)},Tn=(e,t)=>{Cn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?vn(e,t):nn(e,t)},An=(e,t)=>{Cn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?_n(e,t):rn(e,t)},On=(e,t)=>{Cn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?$n(e,t):an(e,t)},zn=(e,t)=>{Cn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?bn(e,t):on(e,t)},Rn=(e,t)=>{Cn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?xn(e,t):sn(e,t)},jn=(e,t)=>{Cn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?hn(e,t):un(e,t)}})),Gs=D((()=>{zs(),Ds(),Hs(),Mn=e=>{if(!e||0===e.length||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(1!==e[0].dataType)throw new Error("Invalid input 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${e.mainStart([a,1,1])}\n let localOffset = local_idx * uniforms.elements_per_wg;\n let offset: u32 = workgroup_id.x * uniforms.d_comp + localOffset;\n\n var thread_max_vector = ${xt("f32",r,"-3.402823e+38f")};\n for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n thread_max_vector = max(${Ct(o,r,"x[offset + i]")}, thread_max_vector);\n }\n wgMax[local_idx] = ${i};\n workgroupBarrier();\n\n var maxValue = -3.402823e+38f;\n for (var i = 0u; i < ${a}; i++) {\n maxValue = max(wgMax[i], maxValue);\n }\n\n var sumVector = ${xt("f32",r,"0")};\n for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n sumVector += exp(${Ct(o,r,"x[offset + i]")} - maxValue);\n }\n wgSum[local_idx] = ${St("sumVector",r)};\n workgroupBarrier();\n\n var sum: f32 = 0;\n for (var i = 0u; i < ${a}; i++) {\n sum += wgSum[i];\n }\n\n if (sum == 0) {\n for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n x[offset + i] = ${xt(o,r,"uniforms.d_inv")};\n }\n } else {\n for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n let f32input = ${Ct(o,r,"x[offset + i]")};\n x[offset + i] = ${n.type.value}(exp(f32input - maxValue) / sum);\n }\n }\n }`},getRunData:()=>({outputs:[],dispatchGroup:{x:n},programUniforms:u})},{inputs:[t],outputs:[]})},Wn=(e,t,n,i,r,a)=>{let o=[r.batchSize,r.numHeads,r.sequenceLength,r.kvSequenceLength+r.pastSequenceLength],s=0===a.scale?1/Math.sqrt(r.headSize):a.scale,u=bt(r.headSize),l=r.headSize/u,d=12,c={x:Math.ceil(r.totalSequenceLength/d),y:Math.ceil(r.sequenceLength/d),z:r.batchSize*r.numHeads},p=[{type:12,data:r.sequenceLength},{type:12,data:l},{type:12,data:r.totalSequenceLength},{type:12,data:r.kvSequenceLength},{type:t.dataType,data:s}],f=[t,n],h=e.compute({name:"AttentionProbs",shaderCache:{hint:`${u}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:t.dataType,gpuDataType:0}],dispatchGroup:c,programUniforms:p}),getShaderSource:e=>{let i=kt("q",t.dataType,t.dims,u),r=kt("key",n.dataType,n.dims,u),a=Tt("output",t.dataType,o),s=wt(t.dataType),l=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"alpha",type:s}];return`\n const beta: ${s} = 1.0;\n const TILE_SIZE = 12u;\n\n var tileQ: array<${i.type.storage}, 144>;\n var tileK: array<${i.type.storage}, 144>;\n ${e.registerUniforms(l).declareVariables(i,r,a)}\n ${e.mainStart([d,d,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 lm = m + local_id.y;\n let ln = n + local_id.x;\n\n let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;\n let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx + n * uniforms.K;\n\n var value = ${xt(s,u)};\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m + local_id.y < uniforms.M && w + local_id.x < uniforms.K) {\n tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];\n }\n if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {\n tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];\n }\n workgroupBarrier();\n\n for (var k: u32 = 0u; k{let r=[i.batchSize,i.sequenceLength,i.vHiddenSize],a=12,o={x:Math.ceil(i.vHeadSize/a),y:Math.ceil(i.sequenceLength/a),z:i.batchSize*i.numHeads},s=[{type:12,data:i.sequenceLength},{type:12,data:i.totalSequenceLength},{type:12,data:i.vHeadSize},{type:12,data:i.numHeads},{type:12,data:i.vHiddenSize}];return e.compute({name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:s}),getShaderSource:e=>{let i=kt("probs",t.dataType,t.dims),o=kt("v",n.dataType,n.dims),s=Tt("output",t.dataType,r);return`\n const TILE_SIZE = 12u;\n var tileQ: array<${i.type.value}, 144>;\n var tileK: array<${i.type.value}, 144>;\n ${e.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}]).declareVariables(i,o,s)}\n ${e.mainStart([a,a,1])}\n let headIdx = workgroup_id.z;\n let m = workgroup_id.y * TILE_SIZE + local_id.y;\n let n = workgroup_id.x * TILE_SIZE + local_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 = ${i.type.storage}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m < uniforms.M && w + local_id.x < uniforms.K) {\n tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n }\n if (n < uniforms.N && w + local_id.y < uniforms.K) {\n tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n }\n workgroupBarrier();\n for (var k: u32 = 0u; k{let c=Wn(e,t,n,0,l,d);Ln(e,c,i,l)},Gn=(e,t)=>{let n=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],i=t.sequenceLength,r=t.inputHiddenSize,a=t.headSize,o=12,s={x:Math.ceil(t.headSize/o),y:Math.ceil(t.sequenceLength/o),z:t.batchSize*t.numHeads},u=[e.inputs[0],e.inputs[1],e.inputs[2]],l=[{type:12,data:i},{type:12,data:r},{type:12,data:a},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}];return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:s,programUniforms:l}),getShaderSource:e=>{let t=Tt("output_q",u[0].dataType,n),i=Tt("output_k",u[0].dataType,n),r=Tt("output_v",u[0].dataType,n),a=kt("input",u[0].dataType,u[0].dims),s=kt("weight",u[1].dataType,u[1].dims),l=kt("bias",u[2].dataType,u[2].dims),d=a.type.storage;return`\n const TILE_SIZE = 12u;\n var tileInput: array<${d}, 144>;\n var tileWeightQ: array<${d}, 144>;\n var tileWeightK: array<${d}, 144>;\n var tileWeightV: array<${d}, 144>;\n ${e.registerUniforms([{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"}]).declareVariables(a,s,l,t,i,r)}\n ${e.mainStart([o,o,1])}\n let batchIndex = workgroup_id.z / uniforms.num_heads;\n let headNumber = workgroup_id.z % uniforms.num_heads;\n let m = workgroup_id.y * TILE_SIZE + local_id.y;\n let n = workgroup_id.x * TILE_SIZE + local_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 = ${d}(0);\n var valueK = ${d}(0);\n var valueV = ${d}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m < uniforms.M && w + local_id.x < uniforms.K) {\n tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n }\n if (n < uniforms.N && w + local_id.y < uniforms.K) {\n let offset = n + (w + local_id.y) * uniforms.ldb;\n tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n }\n workgroupBarrier();\n for (var k: u32 = 0u; k{let n=Nn(e.inputs,t),[i,r,a]=Gn(e,n);return Hn(e,i,r,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],n,t)}})),Fs=D((()=>{oe(),zs(),Ns(),Ds(),Us(),Fn=(e,t)=>{if(!e||5!==e.length)throw new Error("BatchNormalization requires 5 inputs");let n=(e,t,n)=>{let i=t.length;if(i!==e.length)throw new Error(`${n}: num dimensions != ${i}`);t.forEach(((t,i)=>{if(t!==e[i])throw new Error(`${n}: dim[${i}] do not match`)}))};if(e[0].dims.length>1){let i="NHWC"===t.format?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);n(e[1].dims,i,"Invalid input scale"),n(e[2].dims,i,"Invalid input B"),n(e[3].dims,i,"Invalid input mean"),n(e[4].dims,i,"Invalid input var")}else n(e[1].dims,[1],"Invalid input scale"),n(e[2].dims,[1],"Invalid input B"),n(e[3].dims,[1],"Invalid input mean"),n(e[4].dims,[1],"Invalid input var")},qn=(e,t)=>{let{epsilon:n,spatial:i,format:r}=t,a=e[0].dims,o=i?bt(a[a.length-1]):1,s="NHWC"===r&&a.length>1?o:1,u=pt.size(a)/o,l=i,d=l?a.length:a,c=kt("x",e[0].dataType,e[0].dims,o),p=kt("scale",e[1].dataType,e[1].dims,s),f=kt("bias",e[2].dataType,e[2].dims,s),h=kt("inputMean",e[3].dataType,e[3].dims,s),m=kt("inputVar",e[4].dataType,e[4].dims,s),g=Tt("y",e[0].dataType,d,o);return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${i}_${o}`,inputDependencies:l?["rank","type","type","type","type"]:void 0},getShaderSource:e=>`\n const epsilon = ${n};\n ${e.registerUniform("outputSize","u32").declareVariables(c,p,f,h,m,g)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n var outputIndices = ${g.offsetToIndices(`global_idx * ${o}`)};\n ${(()=>{let e="";if(i)e=`let cOffset = ${1===a.length?"0u":"NHWC"===r?`outputIndices[${a.length-1}] / ${o}`:"outputIndices[1]"};`;else if("NCHW"===r)e=`\n ${g.indicesSet("outputIndices","0","0")}\n let cOffset = ${g.indicesToOffset("outputIndices")};`;else{e=`var cIndices = ${p.type.indices}(0);\n cIndices[0] = outputIndices[${a.length-1}];`;for(let t=1;t({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l?[{type:12,data:u},...$t(a)]:[{type:12,data:u}]})}},Kn=e=>lt(e),Yn=(e,t)=>{let{inputs:n,outputCount:i}=e,r=Kn({...t,outputCount:i});if(d.webgpu.validateInputContent&&Fn(n,r),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(qn(n,r))}})),qs=D((()=>{Ns(),Us(),Zn=e=>{if(3!==e[0].dims.length)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(1!==e[1].dims.length)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")},Qn=e=>{let t=e[0].dims,n=e[0].dims[2],i=pt.size(t)/4,r=e[0].dataType,a=kt("input",r,t,4),o=kt("bias",r,[n],4),s=kt("residual",r,t,4),u=Tt("output",r,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)}}),getShaderSource:e=>`\n const channels = ${n}u / 4;\n ${e.declareVariables(a,o,s,u)}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes(i)}\n let value = ${a.getByOffset("global_idx")}\n + ${o.getByOffset("global_idx % channels")} + ${s.getByOffset("global_idx")};\n ${u.setByOffset("global_idx","value")}\n }`}},Jn=e=>{Zn(e.inputs),e.compute(Qn(e.inputs))}})),Ks=D((()=>{zs(),Ns(),Ds(),Us(),Xn=(e,t,n,i,r,a)=>{let o=Math.ceil(t/4),s="";s="string"==typeof r?`${r}(a)`:r("a");let u=kt("inputData",n,[o],4),l=Tt("outputData",i,[o],4);return`\n ${e.registerUniform("vec_size","u32").declareVariables(u,l)}\n\n ${a??""}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n\n let a = ${u.getByOffset("global_idx")};\n ${l.setByOffset("global_idx",s)}\n }`},ei=(e,t,n,i,r,a=e.dataType)=>({name:t,shaderCache:{hint:r,inputDependencies:["type"]},getShaderSource:t=>Xn(t,pt.size(e.dims),e.dataType,a,n,i),getRunData:t=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil(pt.size(t[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(pt.size(e.dims)/4)}]})}),ti=e=>{e.compute(ei(e.inputs[0],"Abs","abs"))},ni=e=>{e.compute(ei(e.inputs[0],"Acos","acos"))},ii=e=>{e.compute(ei(e.inputs[0],"Acosh","acosh"))},ri=e=>{e.compute(ei(e.inputs[0],"Asin","asin"))},ai=e=>{e.compute(ei(e.inputs[0],"Asinh","asinh"))},oi=e=>{e.compute(ei(e.inputs[0],"Atan","atan"))},si=e=>{e.compute(ei(e.inputs[0],"Atanh","atanh"))},ui=e=>lt(e),li=(e,t)=>{let n;switch(t.to){case 10:n="vec4";break;case 1:n="vec4";break;case 12:n="vec4";break;case 6:n="vec4";break;case 9:n="vec4";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute(ei(e.inputs[0],"Cast",n,void 0,t.cacheKey,t.to))},di=e=>{let t=e.length>=2&&0!==e[1].data?e[1].getFloat32Array()[0]:mt,n=e.length>=3&&0!==e[2].data?e[2].getFloat32Array()[0]:gt;return lt({min:t,max:n})},ci=(e,t)=>{let n=1===e.inputs.length?t:di(e.inputs),i=_t(e.inputs[0].dataType);e.compute(ei(e.inputs[0],"Clip",(e=>`clamp(${e}, clip_min_, clip_max_)`),`\n const clip_min_: vec4<${i}> = vec4(${i}(${n.min}));\n const clip_max_: vec4<${i}> = vec4(${i}(${n.max}));\n`,n.cacheKey),{inputs:[0]})},pi=e=>{e.compute(ei(e.inputs[0],"Ceil","ceil"))},fi=e=>{e.compute(ei(e.inputs[0],"Cos","cos"))},hi=e=>{e.compute(ei(e.inputs[0],"Cosh","cosh"))},mi=e=>lt(e),gi=(e,t)=>{let n=_t(e.inputs[0].dataType);e.compute(ei(e.inputs[0],"Elu",(e=>`elu_vf32(${e})`),`\n const elu_alpha_ = ${n}(${t.alpha});\n\n fn elu_f32(a: ${n}) -> ${n} {\n return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);\n }\n\n fn elu_vf32(v: vec4<${n}>) -> vec4<${n}> {\n return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));\n }`,t.cacheKey))},yi=(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<${e}> {\n let absv = abs(v);\n let x = 1.0 / (1.0 + r0 * absv);\n return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));\n}`,vi=e=>{let t=_t(e.inputs[0].dataType);e.compute(ei(e.inputs[0],"Erf",(e=>`erf_vf32(${e})`),yi(t)))},wi=e=>{e.compute(ei(e.inputs[0],"Exp","exp"))},_i=e=>{e.compute(ei(e.inputs[0],"Floor","floor"))},$i=e=>{let t=_t(e.inputs[0].dataType);e.compute(ei(e.inputs[0],"Gelu",(e=>`0.5 * ${e} * (1.0 + erf_vf32(${e} * 0.7071067811865475))`),yi(t)))},bi=(e,t)=>{let n=_t(e.inputs[0].dataType);e.compute(ei(e.inputs[0],"LeakyRelu",(e=>`select(leaky_relu_alpha_ * ${e}, ${e}, ${e} >= vec4<${n}>(0.0))`),`const leaky_relu_alpha_ = ${n}(${t.alpha});`,t.cacheKey))},xi=e=>{e.compute(ei(e.inputs[0],"Not",(e=>`!${e}`)))},Ci=e=>{e.compute(ei(e.inputs[0],"Neg",(e=>`-${e}`)))},Si=e=>{e.compute(ei(e.inputs[0],"Reciprocal",(e=>`1.0/${e}`)))},Ei=e=>{let t=_t(e.inputs[0].dataType);e.compute(ei(e.inputs[0],"Relu",(e=>`select(vec4<${t}>(0.0), ${e}, ${e} > vec4<${t}>(0.0))`)))},Ii=e=>{e.compute(ei(e.inputs[0],"Sigmoid",(e=>`(1.0 / (1.0 + exp(-${e})))`)))},ki=e=>lt(e),Ti=(e,t)=>{let n=_t(e.inputs[0].dataType);e.compute(ei(e.inputs[0],"HardSigmoid",(e=>`max(vec4<${n}>(0.0), min(vec4<${n}>(1.0), ${t.alpha} * ${e} + vec4<${n}>(${t.beta})))`),void 0,t.cacheKey))},Ai=e=>{e.compute(ei(e.inputs[0],"Sin","sin"))},Oi=e=>{e.compute(ei(e.inputs[0],"Sinh","sinh"))},zi=e=>{e.compute(ei(e.inputs[0],"Sqrt","sqrt"))},Ri=e=>{e.compute(ei(e.inputs[0],"Tan","tan"))},ji=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Mi=e=>{e.compute(ei(e.inputs[0],"Tanh",ji))},Bi=(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 ${ji("v")};\n}\n`,Pi=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Di=e=>{let t=_t(e.inputs[0].dataType);e.compute(ei(e.inputs[0],"FastGelu",Pi,Bi(t),void 0,e.inputs[0].dataType))},Ni=(e,t)=>{let n=_t(e.inputs[0].dataType);return e.compute(ei(e.inputs[0],"ThresholdedRelu",(e=>`select(vec4<${n}>(0.0), ${e}, ${e} > thresholded_relu_alpha_)`),`const thresholded_relu_alpha_ = vec4<${n}>(${t.alpha});`,t.cacheKey)),0},Ui=e=>{e.compute(ei(e.inputs[0],"Log","log"))}})),Ys=D((()=>{Ns(),Us(),Ks(),Wi=e=>{if(3!==e[0].dims.length)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(1!==e[1].dims.length)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")},Li=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let n=kt("input",e[0].dataType,e[0].dims,4),i=kt("bias",e[0].dataType,[e[0].dims[2]],4),r=Tt("output",e[0].dataType,t,4),a=pt.size(t)/4,o=wt(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:t=>`\n const M_SQRT2 = sqrt(2.0);\n const halfChannels = ${e[0].dims[2]/4/2}u;\n\n ${t.declareVariables(n,i,r)}\n\n ${yi(o)}\n\n ${t.mainStart()}\n ${t.guardAgainstOutOfBoundsWorkgroupSizes(a)}\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 ${r.setByOffset("global_idx","valueLeft * geluRight")}\n }`}},Hi=e=>{Wi(e.inputs),e.compute(Li(e.inputs))}})),Zs=D((()=>{zs(),Ns(),Us(),Gi=(e,t,n,i,r,a,o,s,u,l,d,c)=>{let p,f;"string"==typeof s?p=f=(e,t)=>`${s}((${e}),(${t}))`:"function"==typeof s?p=f=s:(p=s.scalar,f=s.vector);let h,m=Tt("outputData",d,i.length,4),g=kt("aData",u,t.length,4),y=kt("bData",l,n.length,4);if(r)if(a){let e=1===pt.size(t),i=1===pt.size(n),r=t.length>0&&t[t.length-1]%4==0,a=n.length>0&&n[n.length-1]%4==0;h=e||i?m.setByOffset("global_idx",f(e?`${g.type.value}(${g.getByOffset("0")}.x)`:g.getByOffset("global_idx"),i?`${y.type.value}(${y.getByOffset("0")}.x)`:y.getByOffset("global_idx"))):`\n let outputIndices = ${m.offsetToIndices("global_idx * 4u")};\n let offsetA = ${g.broadcastedIndicesToOffset("outputIndices",m)};\n let offsetB = ${y.broadcastedIndicesToOffset("outputIndices",m)};\n ${m.setByOffset("global_idx",f(o||r?g.getByOffset("offsetA / 4u"):`${g.type.value}(${g.getByOffset("offsetA / 4u")}[offsetA % 4u])`,o||a?y.getByOffset("offsetB / 4u"):`${y.type.value}(${y.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}\n `}else h=m.setByOffset("global_idx",f(g.getByOffset("global_idx"),y.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let e=(e,t,n="")=>{let i=`aData[indexA${t}][componentA${t}]`,r=`bData[indexB${t}][componentB${t}]`;return`\n let outputIndices${t} = ${m.offsetToIndices(`global_idx * 4u + ${t}u`)};\n let offsetA${t} = ${g.broadcastedIndicesToOffset(`outputIndices${t}`,m)};\n let offsetB${t} = ${y.broadcastedIndicesToOffset(`outputIndices${t}`,m)};\n let indexA${t} = offsetA${t} / 4u;\n let indexB${t} = offsetB${t} / 4u;\n let componentA${t} = offsetA${t} % 4u;\n let componentB${t} = offsetB${t} % 4u;\n ${e}[${t}] = ${n}(${p(i,r)});\n `};h=9===d?`\n var data = vec4(0);\n ${e("data",0,"u32")}\n ${e("data",1,"u32")}\n ${e("data",2,"u32")}\n ${e("data",3,"u32")}\n outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:`\n ${e("outputData[global_idx]",0)}\n ${e("outputData[global_idx]",1)}\n ${e("outputData[global_idx]",2)}\n ${e("outputData[global_idx]",3)}\n `}return`\n ${e.registerUniform("vec_size","u32").declareVariables(g,y,m)}\n\n ${c??""}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${h}\n }`},Vi=(e,t,n,i,r,a,o=n.dataType)=>{let s=!pt.areEqual(n.dims,i.dims),u=n.dims,l=pt.size(n.dims),d=!1,c=!1,p=[s];if(s){let e=ct.calcShape(n.dims,i.dims,!1);if(!e)throw new Error("Can't perform binary op on the given tensors");u=e,l=pt.size(u);let t=1===pt.size(n.dims),r=1===pt.size(i.dims),a=n.dims.length>0&&n.dims[n.dims.length-1]%4==0,o=i.dims.length>0&&i.dims[i.dims.length-1]%4==0;p.push(t),p.push(r),p.push(a),p.push(o);let s=1;for(let e=1;ee.toString())).join("_"),inputDependencies:["rank","rank"]},getShaderSource:e=>Gi(e,n.dims,i.dims,u,d,s,c,r,n.dataType,i.dataType,o,a),getRunData:()=>({outputs:[{dims:u,dataType:o}],dispatchGroup:{x:Math.ceil(l/64/4)},programUniforms:[{type:12,data:Math.ceil(pt.size(u)/4)},...$t(n.dims,i.dims,u)]})}},Fi=(e,t,n,i,r,a)=>{e.compute(Vi(t,r??"",e.inputs[0],e.inputs[1],n,i,a))},qi=e=>{Fi(e,"Add",((e,t)=>`${e}+${t}`))},Ki=e=>{Fi(e,"Div",((e,t)=>`${e}/${t}`))},Yi=e=>{Fi(e,"Equal",{scalar:(e,t)=>`u32(${e}==${t})`,vector:(e,t)=>`vec4(${e}==${t})`},void 0,void 0,9)},Zi=e=>{Fi(e,"Mul",((e,t)=>`${e}*${t}`))},Qi=e=>{let t=kt("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Fi(e,"Pow",{scalar:(e,t)=>`pow_custom(${e},${t})`,vector:(e,t)=>`pow_vector_custom(${e},${t})`},`\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) * ${t}(${"i32"===t?"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 `)},Ji=e=>{Fi(e,"Sub",((e,t)=>`${e}-${t}`))},Xi=e=>{Fi(e,"Greater",{scalar:(e,t)=>`u32(${e}>${t})`,vector:(e,t)=>`vec4(${e}>${t})`},void 0,void 0,9)},er=e=>{Fi(e,"Less",{scalar:(e,t)=>`u32(${e}<${t})`,vector:(e,t)=>`vec4(${e}<${t})`},void 0,void 0,9)},tr=e=>{Fi(e,"GreaterOrEqual",{scalar:(e,t)=>`u32(${e}>=${t})`,vector:(e,t)=>`vec4(${e}>=${t})`},void 0,void 0,9)},nr=e=>{Fi(e,"LessOrEqual",{scalar:(e,t)=>`u32(${e}<=${t})`,vector:(e,t)=>`vec4(${e}<=${t})`},void 0,void 0,9)}})),Qs=D((()=>{zs(),Ns(),Ds(),Us(),ir=e=>{if(!e||e.length<1)throw new Error("too few inputs");let t=e[0].dataType,n=e[0].dims.length;for(let i of e){if(i.dataType!==t)throw new Error("input tensors should be one type");if(i.dims.length!==n)throw new Error("input tensors should have the same shape")}},rr=(e,t)=>`\n fn calculateInputIndex(index: u32) -> u32 {\n let sizeInConcatAxis = array(${t});\n for (var i: u32 = 0u; i < ${e}; i += 1u ) {\n if (index < sizeInConcatAxis[i]) {\n return i;\n }\n }\n return ${e}u;\n }`,ar=(e,t)=>{let n=e.length,i=[];for(let r=0;r{let n=e[0].dims.slice();if(t>=n.length||t<-1*n.length)throw new Error("axis specified for concat doesn't match input dimensionality");let i=t<0?n.length+t:t,r=n.slice(0);for(let t=1;t`uniforms.sizeInConcatAxis${e}`)).join(",");return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:t=>`\n\n ${(()=>{t.registerUniform("outputSize","u32");for(let n=0;n(${m});\n ${h} -= sizeInConcatAxis[inputIndex - 1u];\n }\n\n ${ar(s,f)}\n }`}},sr=(e,t)=>{ir(e.inputs);let n=e.inputs.filter((e=>pt.size(e.dims)>0));e.compute(or(n,t.axis),{inputs:n})},ur=e=>lt({axis:e.axis})})),Js=D((()=>{zs(),Ns(),lr=(e,t,n="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, ${t}(${n}(uniforms.clip_min)), ${t}(${n}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${n}(uniforms.alpha) * value + ${n}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${n}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},dr=(e,t)=>{"Clip"===e.activation?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):"HardSigmoid"===e.activation?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):"LeakyRelu"===e.activation&&t.push({type:1,data:e.alpha})},cr=(e,t)=>{"Clip"===e.activation?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):"HardSigmoid"===e.activation?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):"LeakyRelu"===e.activation&&t.push({name:"alpha",type:"f32"})},pr=e=>{let t=e?.activation||"";if("HardSigmoid"===t){let[n,i]=e?.activation_params||[.2,.5];return{activation:t,alpha:n,beta:i}}if("Clip"===t){let[n,i]=e?.activation_params||[mt,gt];return{activation:t,clipMax:i,clipMin:n}}if("LeakyRelu"===t){let[n]=e?.activation_params||[.01];return{activation:t,alpha:n}}return{activation:t}}})),Xs=D((()=>{fr=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},hr=e=>`\n ${e?"value = value + getBiasByOutputCoords(coords);":""}\n `})),eu=D((()=>{mr=e=>`\nfn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`})),tu=D((()=>{zs(),Ns(),Us(),Js(),Xs(),gr=(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 `,yr=(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 ${3===t?"":"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 ${3===t?"":"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 ${3===t?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n }`,vr=(e,t,n="f32",i,r=!1,a=32,o=!1,s=32)=>{let u=t[1]*e[1],l=t[0]*e[0],d=r?u:a,c=r?a:u,p=d/t[0],f=a/t[1];if((!r||4!==p||4!==e[1])&&(r||3!==p&&4!==p)||d%t[0]!=0||a%t[1]!=0||4!==e[0])throw new Error(`If transposeA ${r} is true, innerElementSize ${p} and workPerThread[1] ${e[1]} must be 4.\n Otherwise, innerElementSize ${p} must be 3 or 4.\n tileAWidth ${d} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar mm_Asub: array, ${d/p}>, ${c}>;\nvar mm_Bsub: array, ${l/e[0]}>, ${a}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${p};\nconst tileInner = ${a};\n\n@compute @workgroup_size(${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 = ${o?"0":"i32(globalId.z)"};\n ${i?`let batchIndices = ${i.offsetToIndices("u32(batch)")};`:""}\n let globalRowStart = i32(workgroupId.y) * ${u};\n\n let num_tiles = ${o?`${Math.ceil(s/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n var kStart = ${o?`i32(globalId.z) * ${s}`:"0"};\n\n var acc: array, rowPerThread>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${f};\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 ${gr(r,i)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow = innerRow + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${i?", 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 ${3===p?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n ${yr(r,p)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n}`},wr=(e,t)=>e?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart + inputCol${t?", batchIndices":""});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRowStart + inputRow,\n kStart + inputCol${t?", batchIndices":""});\n `,_r=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",$r=(e,t,n="f32",i,r=!1,a=32,o=!1,s=32,u=!1)=>{let l=e[1]*t[1],d=e[0]*t[0],c=r?l:a,p=r?a:l;if(p%t[1]!=0||c%t[0]!=0||a%t[1]!=0)throw new Error(`tileAHight ${p} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${t[0]}, tileInner ${a} must be divisible by workgroupSize[1]${t[1]}`);let f=p/t[1],h=c/t[0],m=a/t[1],g=u?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${l};\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 < ${p}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${t[0]}) {\n ${wr(r,i)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${t[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalColStart + inputCol${i?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${n}, 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 = ${r?`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) * ${l};\n\nlet tileRowA = i32(localId.y) * ${f};\nlet tileColA = i32(localId.x) * ${h};\nlet tileRowB = i32(localId.y) * ${m};\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 < ${f}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < ${h}; innerCol = innerCol + 1) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${wr(r,i)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalCol + innerCol${i?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${n}, 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 ${_r(r)}\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, ${p}>;\n var mm_Bsub : array, ${a}>;\n const rowPerThread = ${e[1]};\n const colPerThread = ${e[0]};\n const tileInner = ${a};\n\n@compute @workgroup_size(${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 = ${o?"0":"i32(globalId.z)"};\n ${i?`let batchIndices = ${i.offsetToIndices("u32(batch)")};`:""}\n let num_tiles = ${o?`${Math.ceil(s/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n var kStart = ${o?`i32(globalId.z) * ${s}`:"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 ${g}\n }\n`},br=(e,t,n,i,r,a=!1)=>{let[o,s,u]=r,[l,d,c,p]=i,f=Rt(o,u),h=Rt(s,u),m=wt(i[0].type.tensor);return`\n fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${l.type.indices}) -> ${fr(e,m)} {\n var value = ${fr(e,m)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\n {\n ${(()=>{let e=d.rank,t=l.rank,n=`var aIndices: ${d.type.indices};`;for(let i=e-2-1,r=t-1;i>=0;i--,r--)n+=`\naIndices[${i}] = ${t>1?`batchIndices[${r}]`:"batchIndices"};`;return f.forEach((e=>{n+=`\naIndices[${e}] = 0;`})),n+=`\naIndices[${e-2}] = u32(row);\n aIndices[${e-1}] = u32(colIn);`,n})()}\n value = ${d.getByIndices("aIndices")};\n }\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${l.type.indices}) -> ${fr(e,m)} {\n var value = ${fr(e,m)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\n {\n ${(()=>{let e=c.rank,t=l.rank,n=`var bIndices: ${c.type.indices};`;for(let i=e-2-1,r=t-1;i>=0;i--,r--)n+=`\nbIndices[${i}] = ${t>1?`batchIndices[${r}]`:"batchIndices"};`;return h.forEach((e=>{n+=`\nbIndices[${e}] = 0;`})),n+=`\nbIndices[${e-2}] = u32(row);\n bIndices[${e-1}] = u32(colIn);`,n})()}\n value = ${c.getByIndices("bIndices")};\n }\n return value;\n }\n\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${fr(e,m)}) {\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 + ${a?"bias[colIn]":`${fr(e,m)}(bias[row])`};`:""}\n ${n}\n ${p.setByIndices("vec3(coords)","value")}\n }\n }\n `},xr=(e,t,n,i,r=!1)=>{let a=e[0].dims,o=e[1].dims,s=a.slice(0,-2),u=o.slice(0,-2),l=i?i.slice(0,-2):n.slice(0,-2),d=pt.size(l),c=a[a.length-2],p=a[a.length-1],f=o[o.length-1],h=p%4==0&&f%4==0,m=c<=8?[4,1,1]:[4,4,1],g=[8,8,1],y=[Math.ceil(f/g[0]/m[0]),Math.ceil(c/g[1]/m[1]),Math.ceil(d/g[2]/m[2])],v=h?4:1,w=[...s,c,p/v],_=w.length,$=[...u,p,f/v],b=$.length,x=[d,c,f/v],C=[{type:6,data:c},{type:6,data:f},{type:6,data:p}];dr(t,C),C.push(...$t(l,w,$));let S=["rank","rank"],E=e.length>2;E&&(C.push(...$t(e[2].dims)),S.push("rank")),C.push(...$t(x));return{name:"MatMul",shaderCache:{hint:`${m};${t.activation};${h};${r}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:y[0],y:y[1],z:y[2]},programUniforms:C}),getShaderSource:n=>{let i=l.length,a=At("batchDims",e[0].dataType,i,1),o=wt(e[0].dataType),d=kt("a",e[0].dataType,_,v),c=kt("b",e[1].dataType,b,v),p=Tt("result",e[0].dataType,x.length,v),f=[d,c];if(E){let t=r?v:1;f.push(kt("bias",e[2].dataType,e[2].dims.length,t))}let y=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];cr(t,y);let w=wt(p.type.tensor),$=lr(t,p.type.value,w),C=br(v,E,$,[a,d,c,p],[s,u,l],r);return`\n ${n.registerUniforms(y).registerInternalVariables(a).declareVariables(...f,p)}\n ${C}\n ${h?vr(m,g,o,a):$r(m,g,o,a)}\n `}}}})),nu=D((()=>{zs(),js(),Us(),Js(),Xs(),eu(),tu(),Cr=(e,t,n,i,r=!1,a,o=4,s=4,u=4,l="f32")=>{let d=e?"\n let coord = vec4(batch, xRow, xCol, xCh);\n ":"\n let coord = vec4(batch, xCh, xRow, xCol);\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])",f=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",h=e?"row":"col",m=e?"col":"row",g=`\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 = ${h} / outWidth;\n let outCol = ${h} % outWidth;\n\n let WRow = ${m} / (i32(uniforms.w_shape[1]) * inChannels);\n let WCol = ${m} / 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 = ${m} % inChannels;\n var resData = ${fr(o,l)}(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 < ${p} && xCol >= 0 && xCol < ${f}) {\n ${d}\n let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape));\n ${(e=>{switch(e){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${l}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${e} is not supported.`)}})(o)}\n }\n return resData;`,y=e?t&&i?`\n let col = colIn * ${o};\n ${g}`:`\n let col = colIn * ${o};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n ${g}\n }\n return ${fr(o,l)}(0.0);`:i&&n?`\n let col = colIn * ${o};\n ${g}`:`\n let col = colIn * ${o};\n if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n ${g}\n }\n return ${fr(o,l)}(0.0);`,v=`${(e=>{switch(e){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 ${e} is not supported.`)}})(s)}`,w=fr(u,l),_=fr(e?o:s,l),$=fr(e?s:o,l),b=lr(a,w,l);return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {\n ${e?y:v}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${$} {\n ${e?v:y}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${w}) {\n let col = colIn * ${u};\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 ${c}\n ${hr(r)}\n ${b}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`},Sr=(e,t,n,i,r,a,o,s)=>{let u="NHWC"===t.format,l=u?e[0].dims[3]:e[0].dims[1],d=n[0],c=u?n[2]:n[3],p=u?n[1]:n[2],f=u?n[3]:n[1],h=u&&(l%4==0||l%3==0)&&f%4==0,m=u?f:c*p,g=u?c*p:f,y=[8,8,1],v=i<=8?[4,1,1]:[4,4,1],w=[Math.ceil(m/y[0]/v[0]),Math.ceil(g/y[1]/v[1]),Math.ceil(d/y[2]/v[2])];et("verbose",(()=>`[conv2d_mm_webgpu] dispatch = ${w}`));let _=h?u&&l%4!=0?3:4:1,$=y[1]*v[1],b=y[0]*v[0],x=Math.max(y[0]*_,y[1]),C=i%$==0,S=r%b==0,E=a%x==0,I=h?[_,4,4]:[1,1,1],k=[{type:6,data:i},{type:6,data:r},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];dr(t,k),k.push(...$t(e[0].dims,e[1].dims));let T=["rank","rank"];o&&(k.push(...$t(e[2].dims)),T.push("rank")),k.push(...$t(n));return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${_};${h};${C};${S};${E};${$};${b};${x}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:w[0],y:w[1],z:w[2]},programUniforms:k}),getShaderSource:i=>{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}];cr(t,r);let a=h?4:1,l=wt(e[0].dataType),d=`\n fn setOutputAtIndex(flatIndex : i32, value : ${h?`vec4<${l}>`:l}) {\n result[flatIndex] = ${h?`vec4<${l}>`:l}(value);\n }\n fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${h?`vec4<${l}>`:l}) {\n let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3));\n setOutputAtIndex(flatIndex ${h?"/ 4":""}, value);\n }`,c=[kt("x",e[0].dataType,e[0].dims.length,3===_?1:_),kt("w",e[1].dataType,e[1].dims.length,a)],p=Tt("result",e[0].dataType,n.length,a);if(o){let t=kt("bias",e[2].dataType,e[2].dims.length,a);c.push(t),d+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${h?`vec4<${l}>`:l} {\n return bias[coords.${u?"w":"y"}${h?"/ 4":""}];\n }`}return`\n ${mr("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 ${i.registerUniforms(r).declareVariables(...c,p)}\n ${d}\n ${Cr(u,C,S,E,o,t,I[0],I[1],I[2],l)}\n ${h?vr(v,y,l,void 0,!u,x):$r(v,y,l,void 0,!u,x,!1,void 0,s)}`}}}})),iu=D((()=>{zs(),Ns(),Us(),au(),Js(),Er=(e,t,n)=>{let i=e.length>2,r=i?"value += b[output_channel];":"",a=e[0].dims,o=e[1].dims,s=o[0]/t.group,u="NHWC"===t.format,l=Ar(a,o,t.dilations,t.pads,t.strides,u),d=pt.size(l),c=[{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:s}];dr(t,c),c.push(...$t(a,o,l));let p=["rank","rank"];i&&(c.push(...$t(e[2].dims)),p.push("rank")),c.push(...$t(l));return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:p},getRunData:()=>({outputs:[{dims:n?n(l):l,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:n=>{let s=Tt("output",e[0].dataType,l.length),d=wt(s.type.tensor),c=lr(t,s.type.value,d),p=kt("x",e[0].dataType,a.length),f=kt("w",e[1].dataType,o.length),h=[p,f];i&&h.push(kt("b",e[2].dataType,e[2].dims));let m=[{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 cr(t,m),`\n ${n.registerUniforms(m).declareVariables(...h,s)}\n\n ${n.mainStart()}\n ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let outputIndices = ${s.offsetToIndices("global_idx")};\n let batch: u32 = outputIndices[0];\n let output_channel: u32 = outputIndices[${u?3:1}];\n let xRCCorner: vec2 = vec2(outputIndices[${u?1:2}], outputIndices[${u?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} = ${s.type.value}(0);\n for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {\n let input_channel = group_id * uniforms.w_shape[1] + wInChannel;\n for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {\n let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];\n\n if (xHeight < 0u || xHeight >= uniforms.x_shape[${u?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[${u?2:3}]) {\n continue;\n }\n\n let xVal = ${u?p.get("batch","xHeight","xWidth","input_channel"):p.get("batch","input_channel","xHeight","xWidth")};\n let wVal = ${f.get("output_channel","wInChannel","wHeight","wWidth")};\n value += xVal*wVal;\n }\n }\n }\n ${r}\n ${c}\n ${s.setByOffset("global_idx","value")}\n }`}}}})),ru=D((()=>{zs(),Ns(),tu(),Us(),Js(),Ir=(e,t,n,i,r=!1)=>{let a=e[0].dims,o=e[1].dims,s=a[a.length-2],u=o[o.length-1],l=a[a.length-1],d=bt(u),c=bt(l),p=bt(s),f=pt.size(n)/d/p,h=e.length>2,m=i?i.slice(0,-2):n.slice(0,-2),g=[pt.size(m),s,u],y=[{type:12,data:f},{type:12,data:s},{type:12,data:u},{type:12,data:l}];dr(t,y),y.push(...$t(m,a,o)),h&&y.push(...$t(e[2].dims)),y.push(...$t(g));return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${d};${c};${p};${r}`,inputDependencies:h?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:y}),getShaderSource:i=>{let s=At("batch_dims",e[0].dataType,m.length),u=kt("a",e[0].dataType,a.length,c),l=kt("b",e[1].dataType,o.length,d),f=Tt("output",e[0].dataType,g.length,d),y=wt(f.type.tensor),v=lr(t,f.type.value,y),w=[u,l],_="";if(h){let t=r?d:1;w.push(kt("bias",e[2].dataType,e[2].dims.length,t)),_=""+(r?`value += bias[col / ${t}];`:`value += ${f.type.value}(bias[row + i]);`)}let $=a.slice(0,-2),b=o.slice(0,-2),x=Rt($,m),C=Rt(b,m),S=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];cr(t,S);let E=(e,t)=>{let n=e.rank,i=e.name;if(2===n)return`var ${i}_indices = ${e.type.indices}(0u, 0u);`;let r=s.rank,a=`var ${i}_indices: ${e.type.indices};`;for(let e=n-2-1,t=r-1;e>=0;e--,t--)a+=`\n${i}_indices[${e}] = ${r>1?`batch_indices[${t}]`:"batch_indices"};`;return t.forEach((e=>{a+=`\n${i}_indices[${e}] = 0;`})),a+=`${i}_indices[${n-2}] = 0u;\n ${i}_indices[${n-1}] = 0u;`,a};return`\n ${i.registerUniforms(S).registerInternalVariables(s).declareVariables(...w,f)}\n ${i.mainStart()}\n ${i.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let col = (global_idx % (uniforms.N / ${d})) * ${d};\n var index1 = global_idx / (uniforms.N / ${d});\n let stride1 = uniforms.M / ${p};\n let row = (index1 % stride1) * ${p};\n let batch = index1 / stride1;\n\n ${2===n.length?"":`let batch_indices = ${s.offsetToIndices("batch")};`}\n ${E(u,x)}\n let a_offset = ${u.indicesToOffset("a_indices")};\n ${E(l,C)}\n let b_offset = ${l.indicesToOffset("b_indices")};\n var values: array<${f.type.value}, ${p}>;\n for (var k: u32 = 0u; k < uniforms.K; k = k + ${c}) {\n ${(()=>{let e=`var a_data: ${u.type.value};`;for(let t=0;t{if(!e||2!==e.length)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.")},Tr=e=>{kr(e.inputs);let t=ct.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let n=t[t.length-1],i=e.inputs[0].dims[e.inputs[0].dims.length-1];n<8&&i<8?e.compute(Ir(e.inputs,{activation:""},t)):e.compute(xr(e.inputs,{activation:""},t))}})),au=D((()=>{Ns(),nu(),tu(),iu(),Js(),ru(),Ws(),Ar=(e,t,n,i,r,a)=>{let o=e[0],s=e.slice(a?1:2,a?3:4),u=s.length,l=t[0],d=t.slice(2).map(((e,t)=>e+(e-1)*(n[t]-1))),c=s.map(((e,t)=>e+i[t]+i[t+u])).map(((e,t)=>Math.floor((e-d[t]+r[t])/r[t])));return c.splice(0,0,o),c.splice(a?3:1,0,l),c},Or=[2,3,1,0],zr=(e,t)=>{if(!e||2!==e.length&&3!==e.length)throw new Error("Conv requires 2 or 3 inputs");if(4!==e[0].dims.length&&3!==e[0].dims.length)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");if(e[0].dims["NHWC"===t.format?e[0].dims.length-1:1]!==e[1].dims[1]*t.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(3===e.length&&(1!==e[2].dims.length||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let n=e[0].dims.length-2;if(t.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(t.strides.length!==n)throw new Error(`strides should be ${n}D`);if(t.pads.length!==2*n)throw new Error(`pads should be ${2*n}D`);if(0!==t.kernelShape.length&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},Rr=(e,t)=>{let n=e.kernelShape.slice();for(let e=2;e{let t=pr(e),n=e.format;return{autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],format:n,dilations:e.dilations,group:e.group,kernelShape:e.kernel_shape,pads:e.pads,strides:e.strides,wIsConst:e.w_is_const(),...t,cacheKey:`${e.format};${t.activation};`}},Mr=(e,t,n)=>{let i=Rr(n,t),r="NHWC"===n.format;if(1!==n.group)return void e.compute(Er(t,i));let a=3===t.length,o=t[0].dims[r?1:2],s=t[0].dims[r?2:3],u=t[0].dims[r?3:1],l=t[1].dims[2],d=t[1].dims[3],c=Ar(t[0].dims,t[1].dims,n.dilations,i.pads,n.strides,r),p=c[r?1:2],f=c[r?2:3],h=c[r?3:1],m=r&&l===o&&d===s&&0===n.pads[0]&&0===n.pads[1];if(m||1===l&&1===d&&1===n.dilations[0]&&1===n.dilations[1]&&1===n.strides[0]&&1===n.strides[1]&&0===n.pads[0]&&0===n.pads[1]){let l,d,g,y=c[0],v=[];if(r){let i=e.kernelCustomData.wT??e.compute(Dt(t[1],Or),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];if(n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=i),m){let e=o*s*u;l=t[0].reshape([1,y,e]),d=i.reshape([1,e,h]),g=[1,y,h]}else l=t[0].reshape([y,o*s,u]),d=i.reshape([1,u,h]),g=[y,p*f,h];v.push(l),v.push(d)}else l=t[0].reshape([y,u,o*s]),d=t[1].reshape([1,h,u]),g=[y,h,p*f],v.push(d),v.push(l);a&&v.push(t[2]);let w=g[2],_=v[0].dims[v[0].dims.length-1];return void(w<8&&_<8?e.compute(Ir(v,i,c,g,r),{inputs:v}):e.compute(xr(v,i,c,g,r),{inputs:v}))}let g=e.kernelCustomData.wT??e.compute(Dt(t[1],Or),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=g);let y=[t[0],g];a&&y.push(t[2]);let v=r?p*f:h,w=r?h:p*f,_=l*d*u;e.compute(Sr(y,i,c,v,w,_,a,!0),{inputs:y})},Br=(e,t)=>{let n="NHWC"===t.format,i=[e.inputs[0].reshape(n?[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]])];3===e.inputs.length&&i.push(e.inputs[2]);let r=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),o=[1].concat(t.dilations),s=[1].concat(t.kernelShape),u=Rr({...t,pads:r,strides:a,dilations:o,kernelShape:s},i);e.compute(Er(i,u,(e=>n?[e[0],e[2],e[3]]:[])))},Pr=(e,t)=>{zr(e.inputs,t),3===e.inputs[0].dims.length?Br(e,t):Mr(e,e.inputs,t)}})),ou=D((()=>{zs(),js(),Us(),Js(),Xs(),eu(),tu(),Dr=(e,t=!1,n,i,r=4)=>{let a=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 ",o=e?"row":"col",s=e?"col":"row",u=`\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 = ${o} / outWidth;\n let outCol = ${o} % outWidth;\n\n let WRow = ${s} / (uniforms.filter_dims[1] * inChannels);\n let WCol = ${s} / inChannels % uniforms.filter_dims[1];\n let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(${e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])"}) || fract(xR) > 0.0) {\n return ${i}(0.0);\n }\n if (xC < 0.0 || xC >= f32(${e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])"}) || fract(xC) > 0.0) {\n return ${i}(0.0);\n }\n let iXR = i32(xR);\n let iXC = i32(xC);\n let xCh = ${s} % inChannels;\n ${e?"\n let coord = vec4(batch, iXR, iXC, xCh);\n ":"\n let coord = vec4(batch, xCh, iXR, iXC);\n "}\n return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${r}];`,l=e?`\n let col = colIn * ${r};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n ${u}\n }\n return ${i}(0.0);`:`\n let col = colIn * ${r};\n if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n ${u}\n }\n return ${i}(0.0);`,d=`\n let col = colIn * ${r};\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 ${(e=>{switch(e){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 ${i}(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${e} is not supported.`)}})(r)}\n }\n return ${i}(0.0);\n `,c=lr(n,i);return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${i} {\n ${e?l:d}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${i} {\n ${e?d:l}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${i}) {\n let col = colIn * ${r};\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 ${a}\n ${hr(t)}\n ${c}\n result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${r}] = value;\n }\n }`},Nr=(e,t,n,i,r,a,o,s)=>{let u="NHWC"===t.format,l=u?e[0].dims[3]:e[0].dims[1],d=n[0],c=u?n[2]:n[3],p=u?n[1]:n[2],f=u?n[3]:n[1],h=u?l%4==0&&f%4==0:c%4==0&&f%4==0,m=u?f:c*p,g=u?c*p:f,y=h?[8,8,1]:[m<=4||g<=4?4:16,m>4&&g<=4?4:16,1],v=h?[4,4,1]:[m<=4?1:4,m>4&&g<=4?1:4,1],w=[Math.ceil(m/y[0]/v[0]),Math.ceil(g/y[1]/v[1]),Math.ceil(d/y[2]/v[2])];et("verbose",(()=>`[conv_backprop_mm_webgpu] dispatch = ${w}`));let _=h?4:1,$=Math.max(y[0]*_,y[1]),b=h?4:1,x=[t.kernelShape[u?1:2],t.kernelShape[u?2:3]],C=[x[0]+(t.dilations[0]<=1?0:(x[0]-1)*(t.dilations[0]-1)),x[1]+(t.dilations[1]<=1?0:(x[1]-1)*(t.dilations[1]-1))],S=[C[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),C[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],E=[{type:6,data:i},{type:6,data:r},{type:6,data:a},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:x},{type:6,data:S}];dr(t,E),E.push(...$t(e[0].dims,e[1].dims));let I=["rank","rank"];o&&(E.push(...$t(e[2].dims)),I.push("rank")),E.push(...$t(n));return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${v};${y};${h}`,inputDependencies:I},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:w[0],y:w[1],z:w[2]},programUniforms:E}),getShaderSource:i=>{let r=kt("x",e[0].dataType,e[0].dims.length,b),a=kt("w",e[1].dataType,e[1].dims.length,1),l=Tt("result",e[0].dataType,n.length,b),d=[r,a],c="";if(o){let t=kt("bias",e[2].dataType,e[2].dims.length,b);d.push(t),c+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${t.type.value} {\n return bias[coords.${u?"w":"y"}${h?"/ 4":""}];\n }`}let p=[{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:x.length},{name:"pads",type:"i32",length:S.length}];cr(t,p);let f=wt(e[0].dataType,1);if("f16"!==f&&"f32"!==f)throw new Error(`elemType ${f} is not supported.`);return`\n ${mr("uniforms.result_strides")}\n ${i.registerUniforms(p).declareVariables(...d,l)};\n ${c}\n ${Dr(u,o,t,r.type.value,_)}\n ${h?vr(v,y,f,void 0,!u,$):$r(v,y,f,void 0,!u,$,!1,void 0,s)}`}}}})),su=D((()=>{zs(),js(),Ns(),Us(),Ur=(e,t,n,i,r,a=!1,o,s,u=!1)=>{let l=u?1:2,d=u?2:3,c=u?3:1,p=a?2:1,f=`\n fn setOutputAtIndex(flatIndex : u32, value : ${a?`vec4<${o}>`:o}) {\n result[flatIndex] = ${a?`vec4<${o}>`:o}(value);\n }`;i&&(f+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${a?`vec4<${o}>`:o} {\n return bias[coords.${u?"w":"y"}${a?"/ 4":""}];\n }`);let h=a?4:1,m=kt("W",t[1].dataType,t[1].dims.length,h),g=kt("Dy",t[0].dataType,t[0].dims.length,h),y=[g,m];i&&y.push(kt("bias",t[2].dataType,[n[c]].length,h));let v=Tt("result",t[0].dataType,n.length,h),w=`{\n let batch: u32 = ${r?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];\n let r = ${r?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];\n let c = ${r?"global_id.y":"workgroup_id.y"} * ${p};\n let d1: u32 = ${r?"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, ${p}>;\n for (var i = 0; i < ${p}; i++) {\n dotProd[i] = vec4<${o}>(0.0);\n }\n for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\n var dyR = (${o}(dyCorner.x) + ${o}(wR)) / ${o}(uniforms.strides.x);\n let wRPerm = uniforms.filter_dims[0] - 1 - wR;\n if (dyR < 0.0 || dyR >= ${o}(uniforms.Dy_shape[1]) ||\n fract(dyR) > 0.0 || wRPerm < 0) {\n continue;\n }\n let idyR: u32 = u32(dyR);\n\n for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\n let dyC = (${o}(dyCorner.y) + ${o}(wC)) / ${o}(uniforms.strides.y);\n let dyC2 = (${o}(dyCorner.y) + 1.0 + ${o}(wC)) / ${o}(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 >= ${o}(uniforms.Dy_shape[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= ${o}(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 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${g.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${o}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n\n xValue = ${g.get("batch","idyR","idyC2","d2")};\n\n dotProd[1] = dotProd[1] + vec4<${o}>(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[${c}];\n for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${g.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${o}>(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 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${m.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${g.get("batch","idyR","idyC2","d2")};\n let tmpval = vec4<${o}>(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 < ${p}; i = i + 1) {\n let value = dotProd[i] + ${i?"bias[c+i]":`vec4<${o}>(0.0)`};\n ${v.set("batch","r","c + i","d1","value")};\n }\n }`,_=`\n let outputIndices = ${v.offsetToIndices("global_idx")};\n let batch = ${v.indicesGet("outputIndices",0)};\n let d1 = ${v.indicesGet("outputIndices",c)};\n let r = ${v.indicesGet("outputIndices",l)};\n let c = ${v.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 = ${o}(0.0);\n for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\n if (wR % uniforms.dilations.x != 0) {\n continue;\n }\n let dyR = (${o}(dyRCorner) + ${o}(wR)) / ${o}(uniforms.strides[0]);\n let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\n if (dyR < 0.0 || dyR >= ${o}(uniforms.Dy_shape[${l}]) || 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 = (${o}(dyCCorner) + ${o}(wC)) / ${o}(uniforms.strides.y);\n let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\n if (dyC < 0.0 || dyC >= ${o}(uniforms.Dy_shape[${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 = ${u?g.get("batch","idyR","idyC","inputChannel"):g.get("batch","inputChannel","idyR","idyC")};\n let wValue = ${m.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n dotProd = dotProd + xValue * wValue;\n inputChannel = inputChannel + 1;\n }\n }\n }\n let value = dotProd + ${i?"bias[d1]":`${o}(0.0)`};\n ${v.setByOffset("global_idx","value")};\n `;return`\n ${e.registerUniforms(s).declareVariables(...y,v)}\n ${f}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};\n ${a?w:_}}`},Wr=(e,t,n)=>{let i=e.length>2,r=t.outputShape,a=pt.size(r),o=[Math.ceil(a/64),1,1];et("verbose",(()=>`[conv2d_backprop_webgpu] dispatch = ${o}`));let s="NHWC"===t.format,u=["rank","rank"],l=[t.strides[0],t.strides[1]],d=[t.kernelShape[s?1:2],t.kernelShape[s?2:3]],c=[t.dilations[0],t.dilations[1]],p=[d[0]+(t.dilations[0]<=1?0:(t.kernelShape[s?1:2]-1)*(t.dilations[0]-1)),d[1]+(t.dilations[1]<=1?0:(t.kernelShape[s?2:3]-1)*(t.dilations[1]-1))],f=[p[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),p[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],h=t.group,m=e[1].dims,g=m[0]/h,y=m[1],v=[{type:6,data:a},{type:12,data:l},{type:12,data:d},{type:12,data:c},{type:12,data:p},{type:6,data:f},{type:12,data:g},{type:12,data:y},...$t(e[0].dims,e[1].dims)];i&&(v.push(...$t(e[2].dims)),u.push("rank")),v.push(...$t(r));let w=1===o[1]&&1===o[2];return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:u},getRunData:()=>({dispatchGroup:{x:o[0],y:o[1],z:o[2]},outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],programUniforms:v}),getShaderSource:t=>{let n=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:l.length},{name:"filter_dims",type:"u32",length:d.length},{name:"dilations",type:"u32",length:d.length},{name:"effective_filter_dims",type:"u32",length:p.length},{name:"pads",type:"i32",length:f.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],a=wt(e[0].dataType);return`${Ur(t,e,r,i,w,false,a,n,s)}`}}}})),uu=D((()=>{ou(),su(),Js(),Ws(),Lr=(e,t,n,i,r,a)=>(e-1)*t+n+(i-1)*r+1-a,Hr=(e,t,n,i,r)=>{let a=Math.floor(e/2);"SAME_UPPER"===t?(n[i]=a,n[r]=e-a):"SAME_LOWER"===t&&(n[i]=e-a,n[r]=a)},Gr=(e,t,n,i,r,a,o,s,u,l)=>{let d=e.length-2,c=0===l.length;if(0===u.length)for(let e=0;e{let n=e.kernelShape.slice();if(0===e.kernelShape.length||0===e.kernelShape.reduce(((e,t)=>e*t),1)){n.length=0;for(let e=2;ee+t),0)){let e=t[0].dims.length-2;u=new Array(e).fill(1)}let l=e.strides.slice();if(0===l.reduce(((e,t)=>e+t),0)){let e=t[0].dims.length-2;l=new Array(e).fill(1)}Gr(s,n,u,e.autoPad,e.group,r,l,i,o,a);let d=Object.assign({},e);return Object.assign(d,{kernelShape:n,pads:r,outputPadding:o,outputShape:a,dilations:u,strides:l}),d},Fr=e=>{let t=pr(e),n=e.format,i=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],r=e.dilations,a=e.group,o=e.kernelShape,s=e.pads,u=e.strides,l=e.wIsConst();return{autoPad:i,format:n,dilations:r,group:a,kernelShape:o,outputPadding:e.outputPadding,outputShape:e.outputShape,pads:s,strides:u,wIsConst:l,...t,cacheKey:`${e.format};${t.activation};`}},qr=(e,t)=>{if(!e||2!==e.length&&3!==e.length)throw new Error("Conv requires 2 or 3 inputs");if(4!==e[0].dims.length&&3!==e[0].dims.length)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");if(e[0].dims["NHWC"===t.format?e[0].dims.length-1:1]!==e[1].dims[0])throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let n=e[1].dims[1]*t.group;if(3===e.length&&(1!==e[2].dims.length||e[2].dims[0]!==n))throw new Error("invalid bias");let i=e[0].dims.length-2;if(t.dilations.reduce(((e,t)=>e+t),0)>0&&t.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(t.strides.reduce(((e,t)=>e+t),0)>0&&t.strides.length!==i)throw new Error(`strides should be ${i}D`);if(t.pads.reduce(((e,t)=>e+t),0)>0&&t.pads.length!==2*i)throw new Error(`pads should be ${2*i}D`);if(t.outputPadding.length!==i&&0!==t.outputPadding.length)throw new Error(`output_padding should be ${i}D`);if(t.kernelShape.reduce(((e,t)=>e+t),0)>0&&0!==t.kernelShape.length&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(0!==t.outputShape.length&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Kr=[2,3,1,0],Yr=(e,t,n)=>{let i=Vr(n,t),r="NHWC"===n.format,a=i.outputShape,o=a[r?3:1],s=t[0].dims[r?3:1];if(1!==i.group||1===o&&1===s)return void 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u=Vr({...t,pads:s,strides:o,dilations:a,kernelShape:r},i);e.compute(Wr(i,u,(e=>n?[e[0],e[2],e[3]]:[e[0],e[1],e[3]])))},Qr=(e,t)=>{qr(e.inputs,t),3===e.inputs[0].dims.length?Zr(e,t):Yr(e,e.inputs,t)}})),lu=D((()=>{zs(),Ns(),Ds(),Us(),Jr=(e,t,n,i)=>{let r=pt.size(t),a=t.length,o=kt("input",e,a),s=Tt("output",e,a),u=6===n.dataType?n.getInt32Array()[0]:Number(n.getBigInt64Array()[0]),l=pt.normalizeAxis(u,a);return{name:"CumSum",shaderCache:{hint:i.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(r/64)},programUniforms:[{type:12,data:r},{type:6,data:l},...$t(t,t)]}),getShaderSource:e=>{let t=` i32(${o.indicesGet("inputIndices","uniforms.axis")}) `,n=Et("uniforms.input_shape","uniforms.axis",a),r=i.reverse?t+(i.exclusive?" + 1":""):"0",u=i.reverse?n:t+(i.exclusive?"":" + 1");return`\n ${e.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(o,s)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n var inputIndices = ${s.offsetToIndices("global_idx")};\n var sum = ${s.type.value}(0);\n let first : i32 = ${r};\n let last : i32 = ${u};\n for (var i : i32 = first; i < last; i++) {\n ${o.indicesSet("inputIndices","uniforms.axis","u32(i)")};\n sum = sum + ${o.getByIndices("inputIndices")};\n }\n ${s.setByOffset("global_idx","sum")};\n }`}}},Xr=(e,t)=>{let n=e.inputs[0].dims,i=e.inputs[0].dataType,r=e.inputs[1];e.compute(Jr(i,n,r,t),{inputs:[0]})},ea=e=>{let t=1===e.exclusive,n=1===e.reverse;return lt({exclusive:t,reverse:n})}})),du=D((()=>{zs(),Ns(),Ds(),Us(),ia="^"+(na="("+(ta="[a-zA-Z]|\\.\\.\\.")+")+")+"$",ra="^"+("("+na+",)*"+na)+"$",aa=class{constructor(e=-1){this.symbolToIndices=new Map,this.inputIndex=e}addSymbol(e,t){let n=this.symbolToIndices.get(e);void 0===n?n=[t]:n.push(t),this.symbolToIndices.set(e,n)}},oa=class{constructor(e,t){this.equation=t,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new 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r=e.map((e=>e.length)).map(((e,n)=>kt(`input${n}`,t,e))),a=pt.size(i),o=Tt("output",t,i.length),s=[...n.symbolToInfo.keys()].filter((e=>!n.rhs.symbolToIndices.has(e)));return{name:"Einsum",shaderCache:{hint:n.equation,inputDependencies:e.map((()=>"rank"))},getRunData:()=>{let r=s.filter((e=>n.symbolToInfo.has(e))).map((e=>({type:12,data:n.symbolToInfo.get(e)?.dimValue||0})));r.push({type:12,data:a});let o=e.map(((e,t)=>[...$t(e)])).reduce(((e,t)=>e.concat(t)),r);return o.push(...$t(i)),{outputs:[{dims:i,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:o}},getShaderSource:e=>{let t=[],i=[],a=[],u=[],l=[],d=n.symbolToInfo.size===n.rhs.symbolToIndices.size;n.symbolToInfo.forEach(((e,s)=>{if(n.rhs.symbolToIndices.has(s)){let i=n.rhs.symbolToIndices.get(s)?.[0];void 0!==i&&n.lhs.forEach(((n,a)=>{if(e.inputIndices.includes(a)){let e=n.symbolToIndices.get(s);if(void 0===e)throw new Error("Invalid symbol error");e.forEach((e=>{t.push(`${r[a].indicesSet(`input${a}Indices`,e,o.indicesGet("outputIndices",i))}`)}))}}))}else n.lhs.forEach(((t,n)=>{if(e.inputIndices.includes(n)){let e=t.symbolToIndices.get(s);if(void 0===e)throw new Error("Invalid symbol error");e.forEach((e=>{i.push(`${r[n].indicesSet(`input${n}Indices`,e,`${s}`)}`)})),l.push(`prod *= ${r[n].getByIndices(`input${n}Indices`)};`)}})),a.push(`for(var ${s}: u32 = 0; ${s} < uniforms.${sa(s)}; ${s}++) {`),u.push("}")}));let c=d?[...t,`let sum = ${r.map(((e,t)=>e.getByIndices(`input${t}Indices`))).join(" * ")};`]:[...t,"var sum = 0.0;",...a,...i,"var prod = 1.0;",...l,"sum += prod;",...u];return`\n ${e.registerUniforms(s.map((e=>({name:`${sa(e)}`,type:"u32"})))).registerUniform("outputSize","u32").declareVariables(...r,o)}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n var outputIndices = ${o.offsetToIndices("global_idx")};\n ${r.map(((e,t)=>`var input${t}Indices: 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= ${o.broadcastedIndicesToOffset(`outputIndices${t}`,s)};\n let index${t} = offset${t} / 4u;\n let component${t} = offset${t} % 4u;\n ${e}[${t}] = ${n}(${o.getByOffset(`index${t}`)}[component${t}]);\n `;n=`\n let outputOffset = global_idx * ${a};\n var data = vec4(0);\n ${e("data",0,"u32")}\n ${e("data",1,"u32")}\n ${e("data",2,"u32")}\n ${e("data",3,"u32")}\n ${s.setByOffset("global_idx","data")}\n }`}else n=`\n let outputIndices = ${s.offsetToIndices("global_idx")};\n let inputOffset = ${o.broadcastedIndicesToOffset("outputIndices",s)};\n ${s.setByOffset("global_idx",o.getByOffset("inputOffset"))}\n }`;return`\n ${e.registerUniform("vec_size","u32").declareVariables(o,s)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${n}`},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:s})}},ma=e=>{ca(e.inputs),e.compute(ha(e.inputs),{inputs:[0]})}})),pu=D((()=>{zs(),Ns(),Us(),Ks(),ga=e=>{let t=e[0].dataType,n=pt.size(e[0].dims),i=pt.size(e[1].dims),r=i%4==0;return{name:"FastGeluWithBias",shaderCache:{hint:`${r}`,inputDependencies:["type","type"]},getShaderSource:e=>{let n=kt("x",t,[1],4),i=kt("bias",t,[1],4),a=Tt("y",t,[1],4),o=e=>`\n let bias${e}_offset: u32 = (global_idx * 4 + ${e}) % uniforms.bias_size;\n let bias${e} = ${i.getByOffset(`bias${e}_offset / 4`)}[bias${e}_offset % 4];`,s=r?`\n let bias = ${i.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${o(0)}${o(1)}${o(2)}${o(3)}\n let bias = ${n.type.value}(bias0, bias1, bias2, bias3);`;return`${e.registerUniforms([{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}]).declareVariables(n,i,a)}\n\n ${Bi(_t(t))}\n\n ${e.mainStart(yt)}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}\n\n let x = ${n.getByOffset("global_idx")};\n ${s}\n let x_in = x + bias;\n ${a.setByOffset("global_idx",Pi("x_in"))}\n }`},getRunData:e=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],programUniforms:[{type:12,data:Math.ceil(n/4)},{type:12,data:i}],dispatchGroup:{x:Math.ceil(n/yt/4)}})}},ya=e=>{e.inputs.length<2||0===pt.size(e.inputs[1].dims)?Di(e):e.compute(ga(e.inputs))}})),fu=D((()=>{zs(),Ns(),Ds(),Us(),va=e=>{if(!e||2!==e.length)throw new Error("Gather requires 2 inputs.")},wa=(e,t)=>{let n=e[0].dims,i=e[1].dims,r=n.length,a=pt.normalizeAxis(t.axis,r),o=n.slice(0);o.splice(a,1,...i);let s=n[a],u=9===e[0].dataType?4:1,l=Math.ceil(pt.size(o)/u),d=[{type:12,data:l},{type:6,data:s},{type:12,data:a},...$t(e[0].dims,e[1].dims,o)];return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:t=>{let n,s=kt("data",e[0].dataType,e[0].dims.length,u),l=kt("inputIndices",e[1].dataType,e[1].dims.length),d=Tt("output",e[0].dataType,o.length,u),c=e=>{let t=i.length,n=`var indicesIndices${e} = ${l.type.indices}(0);`;for(let i=0;i1?`indicesIndices${e}[${i}]`:`indicesIndices${e}`} = ${o.length>1?`outputIndices${e}[uniforms.axis + ${i}]`:`outputIndices${e}`};`;n+=`\n var idx${e} = ${l.getByIndices(`indicesIndices${e}`)};\n if (idx${e} < 0) {\n idx${e} = idx${e} + uniforms.axisDimLimit;\n }\n var dataIndices${e} : ${s.type.indices};\n `;for(let i=0,s=0;i1?`dataIndices${e}[${i}]`:`dataIndices${e}`} = u32(idx${e});`,s+=t):(n+=`${r>1?`dataIndices${e}[${i}]`:`dataIndices${e}`} = ${o.length>1?`outputIndices${e}[${s}]`:`outputIndices${e}`};`,s++);return n};if(9===e[0].dataType){let e=(e,t,n="")=>`\n let outputIndices${t} = ${d.offsetToIndices(`outputOffset + ${t}u`)};\n ${c(t)};\n let offset${t} = ${s.indicesToOffset(`dataIndices${t}`)};\n let index${t} = offset${t} / 4u;\n let component${t} = offset${t} % 4u;\n ${e}[${t}] = ${n}(${s.getByOffset(`index${t}`)}[component${t}]);\n `;n=`\n let outputOffset = global_idx * ${u};\n var value = vec4(0);\n ${e("value",0,"u32")}\n ${e("value",1,"u32")}\n ${e("value",2,"u32")}\n ${e("value",3,"u32")}\n ${d.setByOffset("global_idx","value")}\n `}else n=`\n let outputIndices = ${d.offsetToIndices("global_idx")};\n ${c("")};\n let value = ${s.getByIndices("dataIndices")};\n ${d.setByOffset("global_idx","value")};\n `;return`\n ${t.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(s,l,d)}\n ${t.mainStart()}\n ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n ${n}\n }`}}},_a=e=>lt({axis:e.axis}),$a=(e,t)=>{let n=e.inputs;va(n),e.compute(wa(e.inputs,t))}})),hu=D((()=>{zs(),Ns(),Ds(),Us(),ba=e=>{if(!e||2!==e.length)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error("GatherElements requires that the data input and\n indices input tensors be of same rank.")},xa=(e,t)=>{let n=e[0].dims,i=e[0].dataType,r=n.length,a=e[1].dims,o=e[1].dataType,s=pt.normalizeAxis(t.axis,r),u=n[s],l=a.slice(0),d=pt.size(l),c=kt("input",i,r),p=kt("indicesInput",o,a.length),f=Tt("output",i,l.length),h=[{type:12,data:d},{type:6,data:u},{type:12,data:s}];return h.push(...$t(n,a,l)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:l,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:h}),getShaderSource:e=>`\n ${e.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(c,p,f)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n let outputIndices = ${f.offsetToIndices("global_idx")};\n\n var idx = ${p.getByOffset("global_idx")};\n if (idx < 0) {\n idx = idx + uniforms.axisDimLimit;\n }\n var inputIndices = ${c.type.indices}(outputIndices);\n ${c.indicesSet("inputIndices","uniforms.axis","u32(idx)")};\n let value = ${c.getByIndices("inputIndices")};\n\n ${f.setByOffset("global_idx","value")};\n }`}},Ca=e=>lt({axis:e.axis}),Sa=(e,t)=>{let n=e.inputs;ba(n),e.compute(xa(e.inputs,t))}})),mu=D((()=>{zs(),Ns(),Us(),Ea=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(3===e.length&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||3===e.length&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},Ia=(e,t)=>{let n=e[0].dims.slice(),i=e[1].dims.slice(),[r,a,o]=ht.getShapeOfGemmResult(n,t.transA,i,t.transB,3===e.length?e[2].dims:void 0),s=[r,a];if(!s)throw new Error("Can't use gemm on the given tensors");let u=pt.size(s),l=[{type:12,data:u},{type:12,data:r},{type:12,data:a},{type:12,data:o},{type:1,data:t.alpha},{type:1,data:t.beta}],d=["type","type"];3===e.length&&(l.push(...$t(e[2].dims)),d.push("rank")),l.push(...$t(s));return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:n=>{let i="";t.transA&&t.transB?i="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?i="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?i="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(i="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let r=1===t.alpha?"":"value *= uniforms.alpha;",a=kt("a",e[0].dataType,e[0].dims),o=kt("b",e[1].dataType,e[1].dims),u=a.type.value,l=null,d=[a,o];3===e.length&&(l=kt("c",e[2].dataType,e[2].dims.length),d.push(l));let c=Tt("output",e[0].dataType,s.length);d.push(c);return`\n ${n.registerUniforms([{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"}]).declareVariables(...d)}\n\n ${n.mainStart()}\n ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let m = global_idx / uniforms.N;\n let n = global_idx % uniforms.N;\n\n var value = ${u}(0);\n for (var k: u32 = 0u; k < uniforms.K; k++) {\n ${i}\n }\n\n ${r}\n ${null!=l?`let cOffset = ${l.broadcastedIndicesToOffset("vec2(m, n)",c)}; value += ${u}(uniforms.beta) * ${l.getByOffset("cOffset")};`:""}\n output[global_idx] = value;\n }`}}},ka=e=>({transA:e.transA,transB:e.transB,alpha:e.alpha,beta:e.beta,cacheKey:`${e.transA};${e.transB};${1===e.alpha}`}),Ta=(e,t)=>{Ea(e.inputs),e.compute(Ia(e.inputs,t))}})),gu=D((()=>{zs(),Ns(),Us(),Aa=(e,t)=>{let n=e[0].dims,i=n,r=pt.sizeToDimension(n,2),a=pt.sizeFromDimension(n,2),o=bt(a),s=a/o,u=[n[0],n[1],s],l=[{type:12,data:a},{type:12,data:s}];l.push(...$t(u,u));return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${o}`,inputDependencies:["rank","type","type"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:r},programUniforms:l}),getShaderSource:n=>{let i=kt("x",e[0].dataType,u.length,o),r=kt("scale",e[1].dataType,e[1].dims),a=kt("bias",e[2].dataType,e[2].dims),s=Tt("output",e[0].dataType,u.length,o),l=[i,r,a,s],d=i.type.value,c=1===o?"f32":`vec${o}`;return`\n var meanShared : f32;\n var squaredNormShared : f32;\n var workgroupShared : array<${c}, 64>;\n const workgroupSize = 64u;\n ${n.registerUniforms([{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}]).declareVariables(...l)}\n ${n.mainStart(64)}\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 = ${c}(0);\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n initial = initial + ${c}(${i.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 = ${St("workgroupShared[0]",o)} / f32(uniforms.normSize);\n }\n workgroupBarrier();\n\n // reinitialize workgroup memory.\n initial = ${c}(0);\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let deviation = ${c}(${i.get("batch","channel","h")}) - ${c}(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 = ${St("workgroupShared[0]",o)};\n }\n workgroupBarrier();\n\n let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n let channelScale = invStdDev * f32(${r.getByOffset("channel")});\n let channelShift = f32(${a.getByOffset("channel")}) - meanShared * channelScale;\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let value = ${i.get("batch","channel","h")} * ${d}(${c}(channelScale)) + ${d}(${c}(channelShift));\n ${s.set("batch","channel","h","value")};\n }\n }`}}},Oa=(e,t,n,i,r,a,o,s)=>{let u=bt(o),l=64,d=1===u?"vec2f":`mat2x${u}f`,c=1===u?"f32":`vec${u}f`,p=(e,t)=>`${d}(${e}, ${t})`,f=r*o/u,h=[{type:12,data:Math.ceil(a/l)},{type:12,data:a},{type:12,data:Math.floor(o/u)},{type:12,data:Math.floor(a*o/u)}],m=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${u}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:[r,o,l,2],dataType:1}],dispatchGroup:{x:r*o/u},programUniforms:h}),getShaderSource:e=>{let n=kt("input",t.dataType,t.dims,u);return`\n ${e.declareVariables(n)}\n @group(0) @binding(1) var output : array<${d}>;\n struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n @group(0) @binding(2) var uniforms: Uniforms;\n\n ${e.mainStart(l)}\n let currentImageNumber = global_idx / 64 / uniforms.C;\n let currentChannelNumber = (global_idx / 64) % uniforms.C;\n let wgId = global_idx % 64;\n let wgOffset = wgId * uniforms.wg_size;\n if (wgOffset >= uniforms.H) {\n return;\n }\n let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);\n\n let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;\n var sum = ${xt("f32",u)};\n var squaredSum = ${xt("f32",u)};\n for (var i: u32 = wgOffset; i < wgMax; i++) {\n let value = ${c}(input[offset + i * uniforms.C]);\n sum += value;\n squaredSum += value * value;\n }\n output[global_idx] = ${p("sum","squaredSum")};\n }`}},{inputs:[t],outputs:[-1]})[0],g=[{type:12,data:f},{type:12,data:a},{type:12,data:Math.floor(o/u)},{type:12,data:Math.floor(l*o/u)}];return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${u};${s}`,inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:[r,o,2],dataType:1}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:g}),getShaderSource:e=>{let t=kt("scale",n.dataType,n.dims,u),r=kt("bias",i.dataType,i.dims,u);return`\n @group(0) @binding(0) var input : array<${d}>;\n @group(0) @binding(1) var scale : array<${t.type.storage}>;\n @group(0) @binding(2) var bias : array<${r.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 ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}\n let currentImageNumber = global_idx / uniforms.C;\n let currentChannelNumber = global_idx % uniforms.C;\n\n let offset = currentImageNumber * uniforms.image_size;\n var sum = ${xt("f32",u)};\n var squaredSum = ${xt("f32",u)};\n for (var i: u32 = 0; i < 64; i++) {\n let value = input[offset + i + currentChannelNumber * 64];\n sum += value[0];\n squaredSum += value[1];\n }\n sum = sum / f32(uniforms.H);\n squaredSum = squaredSum / f32(uniforms.H);\n let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${s}));\n let channelScale = invStdDev * ${c}(scale[currentChannelNumber]);\n let channelShift = ${c}(bias[currentChannelNumber]) - sum * channelScale;\n\n output[global_idx] = ${p("channelScale","channelShift")};\n }`}},{inputs:[m,n,i],outputs:[-1]})[0]},za=(e,t,n)=>{let i=t[0].dims,r=i,a=i[0],o=i[i.length-1],s=pt.sizeFromDimension(i,1)/o,u=bt(o),l=pt.size(r)/u,d=[{type:12,data:s},{type:12,data:Math.floor(o/u)}],c=Oa(e,t[0],t[1],t[2],a,s,o,n.epsilon);e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${u}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:e=>{let n=wt(t[0].dataType),i=1===u?"vec2f":`mat2x${u}f`,a=1===u?n:`vec${u}<${n}>`,o=kt("input",t[0].dataType,t[0].dims,u),s=Tt("output",t[0].dataType,r,u);return`\n @group(0) @binding(0) var input : array<${o.type.storage}>;\n @group(0) @binding(1) var scaleInput : array<${i}>;\n @group(0) @binding(2) var output : array<${s.type.storage}>;\n struct Uniforms {H: u32, C : u32};\n @group(0) @binding(3) var uniforms: Uniforms;\n\n ${e.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], ${a}(scale[0]), ${a}(scale[1]));\n }`}},{inputs:[t[0],c]})},Ra=(e,t)=>{"NHWC"===t.format?za(e,e.inputs,t):e.compute(Aa(e.inputs,t))}})),yu=D((()=>{zs(),Ns(),Us(),ja=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Ma=(e,t,n)=>{let i=e[0].dims,r=e[1],a=e[2],o=i,s=pt.normalizeAxis(t.axis,i.length),u=pt.sizeToDimension(i,s),l=pt.sizeFromDimension(i,s),d=pt.size(r.dims),c=a?pt.size(a.dims):0;if(d!==l||a&&c!==l)throw new Error(`Size of X.shape()[axis:] == ${l}.\n Size of scale and bias (if provided) must match this.\n Got scale size of ${d} and bias size of ${c}`);let p=[];for(let e=0;e1,y=n>2,v=[{dims:o,dataType:e[0].dataType}];return g&&v.push({dims:p,dataType:1}),y&&v.push({dims:p,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${f};${n}`,inputDependencies:h},getRunData:()=>({outputs:v,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:m}),getShaderSource:t=>{let n=wt(e[0].dataType),i=[kt("x",e[0].dataType,e[0].dims,f),kt("scale",r.dataType,r.dims,f)];a&&i.push(kt("bias",a.dataType,a.dims,f)),i.push(Tt("output",e[0].dataType,o,f)),g&&i.push(Tt("mean_data_output",1,p)),y&&i.push(Tt("inv_std_output",1,p));return`\n ${t.registerUniforms([{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}]).declareVariables(...i)}\n ${t.mainStart()}\n ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n let offset = global_idx * uniforms.norm_size_vectorized;\n var mean_vector = ${xt("f32",f)};\n var mean_square_vector = ${xt("f32",f)};\n\n for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n let value = ${Ct(n,f,"x[h + offset]")};\n mean_vector += value;\n mean_square_vector += value * value;\n }\n let mean = ${St("mean_vector",f)} / uniforms.norm_size;\n let inv_std_dev = inverseSqrt(${St("mean_square_vector",f)} / uniforms.norm_size - mean * mean + uniforms.epsilon);\n\n for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n let f32input = ${Ct(n,f,"x[j + offset]")};\n let f32scale = ${Ct(n,f,"scale[j]")};\n output[j + offset] = ${i[0].type.value}((f32input - mean) * inv_std_dev * f32scale\n ${a?`+ ${Ct(n,f,"bias[j]")}`:""}\n );\n }\n\n ${g?"mean_data_output[global_idx] = mean":""};\n ${y?"inv_std_output[global_idx] = inv_std_dev":""};\n }`}}},Ba=(e,t)=>{ja(e.inputs),e.compute(Ma(e.inputs,t,e.outputCount))}})),vu=D((()=>{zs(),Ns(),Ds(),Us(),Pa=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let n=e[0],i=n.dims.length;if(n.dims[i-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let r=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,o=e[1];if(!pt.areEqual(o.dims,[t.n,r,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let s=e[2].dims;if(pt.size(s)!==t.n*r)throw new Error("scales input size error.");if(4===e.length){let n=e[3].dims,i=t.bits>4?t.n*r:t.n*Math.floor((r+1)/2);if(pt.size(n)!==i)throw new Error("zeroPoints input size error.")}},Da=(e,t)=>{let n=e[0],i=e[1],r=e[2],a=n.dims.length,o=n.dims.slice(0,a-1).concat(t.n),s=pt.size(o),u=[{type:12,data:s},{type:12,data:t.k},{type:12,data:t.n},{type:12,data:t.accuracyLevel},{type:12,data:t.bits},{type:12,data:t.blockSize}];u.push(...$t(n.dims)),u.push(...$t(pt.convertShape(i.dims))),u.push(...$t(r.dims)),4===e.length&&u.push(...$t(pt.convertShape(e[3].dims))),u.push(...$t(o));return{name:"MatMulNBits",shaderCache:{hint:`${t.cacheKey};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:u}),getShaderSource:n=>{let i=kt("a",e[0].dataType,e[0].dims.length),r=kt("b",12,e[1].dims.length),s=kt("scales",e[2].dataType,e[2].dims.length),u=[i,r,s],l=4===e.length?kt("zero_points",12,e[3].dims.length):void 0;l&&u.push(l);let d=Tt("output",e[0].dataType,o.length),c=Math.floor((t.k+t.blockSize-1)/t.blockSize),p=t.blockSize/8*t.bits/4,f=wt(e[0].dataType);return`\n fn ortUnpack8x4snorm(value: u32) -> array<${f}, 8>{\n var result = array<${f}, 8>();\n var offset: u32 = 0;\n let count: u32 = 4;\n for (var i: u32 = 0; i < 8u; i++) {\n result[i] = ${f}(extractBits(value, offset, count));\n offset += count;\n }\n return result;\n }\n ${n.registerUniforms([{name:"output_size",type:"u32"},{name:"k",type:"u32"},{name:"n",type:"u32"},{name:"accuracy_level",type:"u32"},{name:"bits",type:"u32"},{name:"block_size",type:"u32"}]).declareVariables(...u,d)}\n ${n.mainStart()}\n ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n var value: ${f} = 0.0;\n let output_indices = ${d.offsetToIndices("global_idx")};\n var a_indices: ${i.type.indices} = output_indices;\n var n = ${d.indicesGet("output_indices",a-1)};\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 ${l?`\n var zero_point_index: u32 = n * ((${c} + 1) / 2) / 4;\n var zero_point_word: u32 = ${l.getByOffset("zero_point_index")};\n var zero_point_offset: u32 = 0;`:""}\n var scale_idex = n * ${c};\n var b_indices: ${r.type.indices};\n ${r.indicesSet("b_indices","0","n")};\n var block_offset: u32 = 0;\n for (var block: u32 = 0; block < ${c}; block++) {\n // The scale and zero points are computed per block.\n let scale = ${s.getByOffset("scale_idex")};\n // The default zero point is 8 for unsigned 4-bit quantization.\n let zero_point: ${f} = ${l?`${f}(extractBits(zero_point_word, zero_point_offset, 4))`:8};\n ${r.indicesSet("b_indices","1","block")};\n var word_offset: u32 = block_offset;\n for (var word: u32 = 0; word < ${p}; word++) {\n ${r.indicesSet("b_indices","2","word")};\n let b_value = ${r.getByIndices("b_indices")};\n let b_quantized_values: array<${f}, 8> = ortUnpack8x4snorm(b_value);\n // Number of B elements per 32-bit word is 32/bits = 32/4 = 8\n var offset: u32 = word_offset;\n for (var i: u32 = 0; i < 8; i++) {\n ${i.indicesSet("a_indices",a-1,"offset")};\n let a_value = ${i.getByIndices("a_indices")};\n let b_quantized_value = b_quantized_values[i];\n let b_dequantized_value = (b_quantized_value - zero_point) * scale;\n value += a_value * b_dequantized_value;\n offset++;\n }\n word_offset += 8;\n }\n scale_idex++;\n ${l?`\n if (zero_point_offset == 28) {\n zero_point_offset = 0;\n zero_point_index++;\n zero_point_word = ${l.getByOffset("zero_point_index")};\n } else {\n zero_point_offset += 4;\n }`:""}\n block_offset += uniforms.block_size;\n }\n ${d.setByOffset("global_idx","value")};\n }\n `}}},Na=(e,t)=>{Pa(e.inputs,t),e.compute(Da(e.inputs,t))},Ua=e=>lt(e)})),wu=D((()=>{zs(),Ns(),Ds(),Bs(),Vs(),Us(),Ws(),Wa=(e,t)=>{let n=e[0],i=e[1],r=e[2],a=e[3],o=e[4],s=e[5],u=e[6],l=e[7];if(3!==n.dims.length&&5!==n.dims.length)throw new Error("Input query is expected to have 3 or 5 dimensions");let d,c=n.dims[0],p=n.dims[1],f=3===n.dims.length?n.dims[2]:t.numHeads*n.dims[4],h=p,m=0,g=0,y=Math.floor(f/t.numHeads);if(u&&l){if(4!==u.dims.length)throw new Error('Input "past_key" is expected to have 4 dimensions');if(4!==l.dims.length)throw new Error('Input "past_value" is expected to have 4 dimensions');m=u.dims[2],g=u.dims[2]}else if(u||l)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');if(i){if(3!==n.dims.length)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(i.dims.length<3||i.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==i.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(3===i.dims.length){if(i.dims[2]!==n.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');d=2,h=i.dims[1]}else if(5===i.dims.length){if(i.dims[2]!==t.numHeads||2!==i.dims[3]||i.dims[4]!==y)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(r)throw new Error('Expect "value" be none when "key" has packed kv format.');d=5,h=i.dims[1]}else{if(i.dims[1]!==t.numHeads||i.dims[3]!==y)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');d=0,h=i.dims[2]}}else{if(3!==n.dims.length&&5!==n.dims.length)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(5===n.dims.length&&(n.dims[2]!==t.numHeads||3!==n.dims[3]))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');d=3}if(a){if(1!==a.dims.length)throw new Error('Input "bias" is expected to have 1 dimension');if(r&&5===n.dims.length&&2===n.dims[3])throw new Error("bias is not allowed for packed kv.")}let v=0;if(o){v=8;let e=o.dims;throw 1===e.length?e[0]===c?v=1:e[0]===3*c+2&&(v=3):2===e.length&&e[0]===c&&e[1]===h&&(v=5),8===v?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let w=!1,_=f;if(r){if(3!==r.dims.length&&4!==r.dims.length)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==r.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(3===r.dims.length){if(h!==r.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');_=r.dims[2]}else{if(h!==r.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');_=r.dims[1]*r.dims[3],w=!0}}let $=m+h;if(o)throw new Error("Key padding mask is not supported");if(s)throw new Error("extraAddQk is not supported");if(u)throw new Error("pastKey is not supported");if(l)throw new Error("pastValue is not supported");return{batchSize:c,sequenceLength:p,pastSequenceLength:m,kvSequenceLength:h,totalSequenceLength:$,maxSequenceLength:g,inputHiddenSize:0,hiddenSize:f,vHiddenSize:_,headSize:y,vHeadSize:Math.floor(_/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:v,scale:t.scale,broadcastResPosBias:!1,passPastInKv:w,qkvFormat:d}},La=e=>lt({...e}),Ha=lt({perm:[0,2,1,3]}),Ga=(e,t,n,i,r,a,o)=>{let s=[i,r,a],u=pt.size(s),l=[{type:12,data:u},{type:12,data:o},{type:12,data:a}];return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:e=>{let i=Tt("qkv_with_bias",t.dataType,s),r=kt("qkv",t.dataType,s),a=kt("bias",n.dataType,s);return`\n ${e.registerUniforms([{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}]).declareVariables(r,a,i)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n }`}},{inputs:[t,n],outputs:[-1]})[0]},Va=(e,t,n,i,r,a,o,s)=>{let u=a;if(o){if(1===i)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return u=Ga(e,a,o,t,i,n*r,s),u=u.reshape([t,i,n,r]),e.compute(Dt(u,Ha.perm),{inputs:[u],outputs:[-1]})[0]}return 3===a.dims.length&&(u=a.reshape([t,i,n,r])),e.compute(Dt(u,Ha.perm),{inputs:[u],outputs:[-1]})[0]},Fa=(e,t)=>{let n=Wa(e.inputs,t);if(5===e.inputs[0].dims.length)throw new Error("Packed QKV is not implemented");if(5===e.inputs[1]?.dims.length)throw new Error("Packed KV is not implemented");let i=e.inputs[1]&&e.inputs[2]&&4===e.inputs[1].dims.length&&4===e.inputs[2].dims.length,r=Va(e,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,e.inputs[0],e.inputs[3],0);if(i)return Hn(e,r,e.inputs[1],e.inputs[2],e.inputs[4],void 0,void 0,void 0,e.inputs[5],n,t);let a=Va(e,n.batchSize,n.numHeads,n.kvSequenceLength,n.headSize,e.inputs[1],e.inputs[3],n.hiddenSize),o=Va(e,n.batchSize,n.numHeads,n.kvSequenceLength,n.vHeadSize,e.inputs[2],e.inputs[3],2*n.hiddenSize);Hn(e,r,a,o,e.inputs[4],void 0,e.inputs[6],e.inputs[7],e.inputs[5],n,t)}})),_u=D((()=>{zs(),Ns(),Us(),qa=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(1!==e[0].dataType&&10!==e[0].dataType)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=2*e[0].dims.length===e[1].dims[0];if(4===e.length&&(t=2*e[3].dims[0]===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Ka=(e,t,n)=>{let i="";for(let r=t-1;r>=0;--r)i+=`\n k = i32(${e.indicesGet("indices",r)}) - ${Et("uniforms.pads",r,n)};\n if (k < 0) {\n break;\n }\n if (k >= i32(${Et("uniforms.x_shape",r,t)})) {\n break;\n }\n offset += k * i32(${Et("uniforms.x_strides",r,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 ${i}\n value = x[offset];\n }\n `},Ya=(e,t,n)=>{let i="";for(let r=t-1;r>=0;--r)i+=`\n k = i32(${e.indicesGet("indices",r)}) - ${Et("uniforms.pads",r,n)};\n if (k < 0) {\n k = -k;\n }\n {\n let _2n_1 = 2 * (i32(${Et("uniforms.x_shape",r,t)}) - 1);\n k = k % _2n_1;\n if(k >= i32(${Et("uniforms.x_shape",r,t)})) {\n k = _2n_1 - k;\n }\n }\n offset += k * i32(${Et("uniforms.x_strides",r,t)});\n `;return`\n var offset = 0;\n var k = 0;\n ${i}\n value = x[offset];\n `},Za=(e,t,n)=>{let i="";for(let r=t-1;r>=0;--r)i+=`\n k = i32(${e.indicesGet("indices",r)}) - ${Et("uniforms.pads",r,n)};\n if (k < 0) {\n k = 0;\n }\n if (k >= i32(${Et("uniforms.x_shape",r,t)})) {\n k = i32(${Et("uniforms.x_shape",r,t)}) - 1;\n }\n offset += k * i32(${Et("uniforms.x_strides",r,t)});\n `;return`\n var offset = 0;\n var k = 0;\n ${i}\n value = x[offset];\n `},Qa=(e,t,n)=>{let i="";for(let r=t-1;r>=0;--r)i+=`\n k = i32(${e.indicesGet("indices",r)}) - ${Et("uniforms.pads",r,n)};\n if (k < 0) {\n k += i32(${Et("uniforms.x_shape",r,t)}]);\n }\n if (k >= i32(${Et("uniforms.x_shape",r,t)})) {\n k -= i32(${Et("uniforms.x_shape",r,t)});\n }\n offset += k * i32(${Et("uniforms.x_strides",r,t)});\n `;return`\n var offset = 0;\n var k = 0;\n ${i}\n value = x[offset];\n `},Ja=(e,t,n)=>{switch(n.mode){case 0:return Ka(e,t,n.pads.length);case 1:return Ya(e,t,n.pads.length);case 2:return Za(e,t,n.pads.length);case 3:return Qa(e,t,n.pads.length);default:throw new Error("Invalid mode")}},Xa=(e,t)=>{let n=pt.padShape(e[0].dims.slice(),t.pads),i=e[0].dims,r=[{type:12,data:pt.size(n)},{type:12,data:t.pads}];0===t.mode&&r.push({type:e[0].dataType,data:t.value}),r.push(...$t(e[0].dims,n));return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(pt.size(n)/64)},programUniforms:r}),getShaderSource:r=>{let a=Tt("output",e[0].dataType,n.length),o=kt("x",e[0].dataType,i.length),s=o.type.value,u=Ja(a,i.length,t),l=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return 0===t.mode&&l.push({name:"constant_value",type:s}),`\n ${r.registerUniforms(l).declareVariables(o,a)}\n ${r.mainStart()}\n ${r.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let indices = ${a.offsetToIndices("global_idx")};\n\n var value = ${s}(0);\n ${u}\n output[global_idx] = value;\n }`}}},eo=(e,t)=>{if(e.length>1){let n=e[1].getBigInt64Array(),i=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,r=e[0].dims.length,a=new Int32Array(2*r).fill(0);if(e.length>=4){let t=e[3].getBigInt64Array();for(let e=0;ea[Number(t)]=Number(e)));let o=[];return a.forEach((e=>o.push(e))),{mode:t.mode,value:i,pads:o}}return t},to=(e,t)=>{qa(e.inputs);let n=eo(e.inputs,t);e.compute(Xa(e.inputs,n),{inputs:[0]})}})),$u=D((()=>{oe(),zs(),Ns(),Us(),no=e=>{if(d.webgpu.validateInputContent&&(!e||1!==e.length))throw new Error("Pool ops requires 1 input.")},io=(e,t,n)=>{let i="NHWC"===t.format,r=e.dims.slice();i&&r.splice(1,0,r.pop());let a=Object.hasOwnProperty.call(t,"dilations"),o=t.kernelShape.slice(),s=t.strides.slice(),u=a?t.dilations.slice():[],l=t.pads.slice();ft.adjustPoolAttributes(n,r,o,s,u,l);let d=ft.computePoolOutputShape(n,r,s,u,o,l,t.autoPad),c=Object.assign({},t);a?Object.assign(c,{kernelShape:o,strides:s,pads:l,dilations:u,cacheKey:t.cacheKey}):Object.assign(c,{kernelShape:o,strides:s,pads:l,cacheKey:t.cacheKey});let p=d.slice();return p.push(p.splice(1,1)[0]),[c,i?p:d]},ro=(e,t)=>{let n="NHWC"===t.format,i=[{type:12,data:pt.size(e)},{type:12,data:pt.size(t.kernelShape)}],r=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let e=t.kernelShape[t.kernelShape.length-1],n=t.strides[t.strides.length-1],a=t.pads[t.pads.length/2-1],o=t.pads[t.pads.length-1],s=!!(a+o);i.push({type:12,data:e},{type:12,data:n},{type:12,data:a},{type:12,data:o}),r.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let u=!1;if(2===t.kernelShape.length){let e=t.kernelShape[t.kernelShape.length-2],n=t.strides[t.strides.length-2],a=t.pads[t.pads.length/2-2],o=t.pads[t.pads.length-2];u=!!(a+o),i.push({type:12,data:e},{type:12,data:n},{type:12,data:a},{type:12,data:o}),r.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[i,r,!0,s,u]}{if(n)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let e=pt.computeStrides(t.kernelShape);return i.push({type:12,data:e},{type:12,data:t.pads},{type:12,data:t.strides}),r.push({name:"kernelStrides",type:"u32",length:e.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length}),[i,r,!!t.pads.reduce(((e,t)=>e+t)),!1,!1]}},ao=(e,t,n,i,r,a,o,s,u,l,d,c)=>{let p="NHWC"===r.format,f=t.type.value,h=Tt("output",t.type.tensor,i);if(r.kernelShape.length<=2){let i="",l="",m="",g=n-(p?2:1);if(i=d?`\n for (var i: u32 = 0u; i < uniforms.kw; i++) {\n xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;\n if (xIndices[${g}] < 0 || xIndices[${g}]\n >= uniforms.x_shape[${g}]) {\n pad++;\n continue;\n }\n let x_val = x[${t.indicesToOffset("xIndices")}];\n ${a}\n }`:`\n for (var i: u32 = 0u; i < uniforms.kw; i++) {\n xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;\n let x_val = x[${t.indicesToOffset("xIndices")}];\n ${a}\n }`,2===r.kernelShape.length){let e=n-(p?3:2);l=c?`\n for (var j: u32 = 0u; j < uniforms.kh; j++) {\n xIndices[${e}] = indices[${e}] * uniforms.sh - uniforms.phStart + j;\n if (xIndices[${e}] < 0 || xIndices[${e}] >= uniforms.x_shape[${e}]) {\n pad += i32(uniforms.kw);\n continue;\n }\n `:`\n for (var j: u32 = 0u; j < uniforms.kh; j++) {\n xIndices[${e}] = indices[${e}] * uniforms.sh - uniforms.phStart + j;\n `,m="\n }\n "}return`\n ${e.registerUniforms(u).declareVariables(t,h)}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n let indices = ${h.offsetToIndices("global_idx")};\n var xIndices = ${h.offsetToIndices("global_idx")};\n\n var value = ${f}(${s});\n var pad = 0;\n ${l}\n ${i}\n ${m}\n ${o}\n\n output[global_idx] = value;\n }`}{if(p)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let i=r.kernelShape.length,d=r.pads.length,c="";return c=l?`\n if (xIndices[j] >= uniforms.x_shape[j]) {\n pad++;\n isPad = true;\n break;\n }\n }\n if (!isPad) {\n let x_val = x[${t.indicesToOffset("xIndices")}];\n ${a}\n }`:`\n }\n let x_val = x[${t.indicesToOffset("xIndices")}];\n ${a}\n `,`\n ${e.registerUniforms(u).declareVariables(t,h)}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n let indices = ${h.offsetToIndices("global_idx")};\n var xIndices = ${h.offsetToIndices("global_idx")};\n\n var offsets: array;\n\n var value = ${f}(${s});\n var pad = 0;\n var isPad = false;\n\n for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n var offset = i;\n for (var j = 0u; j < ${i-1}u; j++) {\n offsets[j] = offset / ${Et("uniforms.kernelStrides","j",i)};\n offset -= offsets[j] * ${Et("uniforms.kernelStrides","j",i)};\n }\n offsets[${i-1}] = offset;\n\n isPad = false;\n for (var j = ${n-i}u; j < ${n}u; j++) {\n xIndices[j] = indices[j] * ${Et("uniforms.strides",`j - ${n-i}u`,i)}\n + offsets[j - ${n-i}u] - ${Et("uniforms.pads","j - 2u",d)};\n ${c}\n }\n ${o}\n\n output[global_idx] = value;\n }`}},oo=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,so=e=>`${oo(e)};${e.countIncludePad}`,uo=e=>`${oo(e)};${e.storageOrder};${e.dilations}`,lo=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}),co=(e,t,n,i)=>{let[r,a]=io(t,i,n),o=kt("x",t.dataType,t.dims.length),s=o.type.value,u="";r.countIncludePad?u+=`value /= ${s}(uniforms.kernelSize);`:u+=`value /= ${s}(i32(uniforms.kernelSize) - pad);`;let[l,d,c,p,f]=ro(a,r);l.push(...$t(t.dims,a));return{name:e,shaderCache:{hint:`${i.cacheKey};${c};${p};${f}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(pt.size(a)/64)},programUniforms:l}),getShaderSource:e=>ao(e,o,t.dims.length,a.length,r,"value += x_val;",u,0,d,c,p,f)}},po=e=>{let t=0!==e.count_include_pad,n=lo(e);if(0!==n.ceilMode)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let i={countIncludePad:t,...n,cacheKey:""};return{...i,cacheKey:so(i)}},fo=(e,t)=>{no(e.inputs),e.compute(co("AveragePool",e.inputs[0],!1,t))},ho={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},mo=e=>{let t=e.format;return{format:t,...ho,cacheKey:t}},go=(e,t)=>{no(e.inputs),e.compute(co("GlobalAveragePool",e.inputs[0],!0,t))},yo=(e,t,n,i)=>{let[r,a]=io(t,i,n),o=kt("x",t.dataType,t.dims.length),[s,u,l,d,c]=ro(a,r);return s.push(...$t(t.dims,a)),{name:e,shaderCache:{hint:`${i.cacheKey};${l};${d};${c}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(pt.size(a)/64)},programUniforms:s}),getShaderSource:e=>ao(e,o,t.dims.length,a.length,r,"\n value = max(x_val, value);\n ","",-1e5,u,l,d,c)}},vo=(e,t)=>{no(e.inputs),e.compute(yo("MaxPool",e.inputs[0],!1,t))},wo=e=>{let t=e.storage_order,n=e.dilations,i=lo(e);if(0!==t)throw new Error("column major storage order is not yet supported for MaxPool");if(0!==i.ceilMode)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let r={storageOrder:t,dilations:n,...i,cacheKey:""};return{...r,cacheKey:uo(r)}},_o=e=>{let t=e.format;return{format:t,...ho,cacheKey:t}},$o=(e,t)=>{no(e.inputs),e.compute(yo("GlobalMaxPool",e.inputs[0],!0,t))}})),bu=D((()=>{oe(),zs(),Us(),bo=(e,t,n)=>{if(e===t||et&&n>0)throw new Error("Range these inputs' contents are invalid.")},xo=(e,t,n,i)=>{let r=Math.abs(Math.ceil((t-e)/n)),a=[r],o=r,s=[{type:12,data:o},{type:i,data:e},{type:i,data:n},...$t(a)];return{name:"Range",shaderCache:{hint:`${i}`},getShaderSource:e=>{let t=Tt("output",i,a.length),n=t.type.value,r=[{name:"outputSize",type:"u32"},{name:"start",type:n},{name:"delta",type:n}];return`\n ${e.registerUniforms(r).declareVariables(t)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n output[global_idx] = uniforms.start + ${n}(global_idx) * uniforms.delta;\n }`},getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:s})}},Co=e=>{let t=0,n=0,i=0;6===e.inputs[0].dataType?(t=e.inputs[0].getInt32Array()[0],n=e.inputs[1].getInt32Array()[0],i=e.inputs[2].getInt32Array()[0]):1===e.inputs[0].dataType&&(t=e.inputs[0].getFloat32Array()[0],n=e.inputs[1].getFloat32Array()[0],i=e.inputs[2].getFloat32Array()[0]),d.webgpu.validateInputContent&&bo(t,n,i),e.compute(xo(t,n,i,e.inputs[0].dataType),{inputs:[]})}})),xu=D((()=>{zs(),Ns(),Ds(),Us(),So=(e,t)=>{if(e.every((e=>e>0||(()=>{throw new Error("Resize requires scales input values to be positive")}))),e.length>0)if("linear"===t.mode){if(!(2===e.length||3===e.length||4===e.length&&1===e[0]&&1===e[1]||4===e.length&&1===e[0]&&1===e[3]||5===e.length&&1===e[0]&&1===e[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("cubic"===t.mode&&!(2===e.length||4===e.length&&1===e[0]&&1===e[1]||4===e.length&&1===e[0]&&1===e[3]))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")},Eo=(e,t,n)=>{t.every((e=>e>=0&&e{throw new Error("Resize requires axes input values to be positive and less than rank")})));let i=new Array(n).fill(1);return t.forEach(((t,n)=>i[t]=e[n])),i},Io=(e,t,n,i,r,a)=>{let[o,s,u]=n>10?[1,2,3]:[-1,e.length>1?1:-1,-1],l=e[0].dims.length;if(o>0&&e.length>o&&e[o].dims.length>0)e[o].getFloat32Array().forEach((e=>a.push(e)));else if("tf_crop_and_resize"===t.coordinateTransformMode)throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(s>0&&e.length>s&&e[s].dims.length>0){if(e[s].getFloat32Array().forEach((e=>i.push(e))),0!==i.length&&i.length!==l&&n>=18&&i.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");So(i,t),t.axes.length>0&&Eo(i,t.axes,l).forEach(((e,t)=>i[t]=e))}if(u>0&&e.length>u&&(e[u].getBigInt64Array().forEach((e=>r.push(Number(e)))),r.length!==l||n>=18&&r.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(i.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(r.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 i<"u"&&typeof r<"u"&&i.length>0&&r.length>l)throw new Error("Resize requires only of scales or sizes to be specified")},ko=(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`)}})()+"}",To=(e,t,n)=>`fn getNearestPixelFromOriginal(xOriginal: ${n}, isDownSample: bool) -> ${n} {`+(()=>{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); }";default:if(t<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Ao=(e,t,n)=>{let i=new Array(n).fill(0).concat(new Array(n).fill(1)),r=0===e.length?i:e.slice();return t.length>0?(t.forEach(((e,a)=>{i[e]=r[a],i[a+n]=r[t.length+a]})),i):r},Oo=(e,t,n,i)=>{let r=[];if(n.length>0)if(i.length>0){if(e.forEach((e=>r.push(e))),Math.max(...i)>e.length)throw new Error("axes is out of bound");i.forEach(((e,t)=>r[e]=n[t]))}else n.forEach((e=>r.push(e)));else{if(0===t.length)throw new Error("Resize requires either scales or sizes.");r=e.map(((e,n)=>Math.round(e*t[n])))}return r},zo=(e,t,n)=>{let i=(()=>{switch(n.keepAspectRatioPolicy){case"not_larger":return n.axes.length>0?Math.min(...n.axes.map((e=>t[e])),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return n.axes.length>0?Math.max(...n.axes.map((e=>t[e])),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${n.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let r=e.slice();return n.axes.length>0?(n.axes.forEach((e=>t[e]=i)),n.axes.forEach((n=>r[n]=Math.round(e[n]*t[n])))):(t.fill(i,0,t.length),r.forEach(((e,n)=>r[n]=Math.round(e*t[n])))),r},Ro=(e,t,n,i,r)=>`\n fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${n.length}> {\n var original_indices: array<${e.type.value}, ${n.length}>;\n for (var i:u32 = 0; i < ${n.length}; i++) {\n var output_index = ${e.indicesGet("output_indices","i")};\n var scale = ${Et("uniforms.scales","i",i)};\n var roi_low = ${Et("uniforms.roi","i",r)};\n var roi_hi = ${Et("uniforms.roi",`i + ${t.length}`,r)};\n if (scale == 1.0) {\n original_indices[i] = ${e.type.value}(output_index);\n } else {\n var input_shape_i = ${Et("uniforms.input_shape","i",t.length)};\n var output_shape_i = ${Et("uniforms.output_shape","i",n.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 }`,jo=(e,t,n,i,r,a,o)=>`\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 < ${i.length}; i++) {\n var output_index = ${t.indicesGet("output_indices","i")};\n var input_index: u32;\n var scale = ${Et("uniforms.scales","i",r)};\n if (scale == 1.0) {\n input_index = output_index;\n } else {\n var roi_low = ${Et("uniforms.roi","i",a)};\n var roi_hi = ${Et("uniforms.roi",`i + ${n.length}`,a)};\n var input_shape_i = ${Et("uniforms.input_shape","i",n.length)};\n var output_shape_i = ${Et("uniforms.output_shape","i",i.length)};\n var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n input_shape_i, roi_low, roi_hi);\n if (!${o} || (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 }`,Mo=(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 >= ${Et("uniforms.input_shape","i",t.length)}) {\n return false;\n }\n }\n return true;\n }`,Bo=(e,t,n,i)=>e.rank>i?`\n ${e.indicesSet("input_indices",t,"channel")};\n ${e.indicesSet("input_indices",n,"batch")};\n`:"",Po=(e,t,n,i,r)=>{let[a,o,s,u]=2===n.length?[-1,0,1,-1]:[0,2,3,1],l=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${l} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",o,`max(0, min(row, ${n[o]} - 1))`)};\n ${e.indicesSet("input_indices",s,`max(0, min(col, ${n[s]} - 1))`)};\n ${Bo(e,u,a,2)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${l} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var row:${l} = originalIndices[${o}];\n var col:${l} = originalIndices[${s}];\n ${i?`if (row < 0 || row > (${n[o]} - 1) || col < 0 || col > (${n[s]} - 1)) {\n return ${r};\n }`:""};\n row = max(0, min(row, ${n[o]} - 1));\n col = max(0, min(col, ${n[s]} - 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 = ${n.length>2?`u32(originalIndices[${u}])`:"0"};\n var batch: u32 = ${n.length>2?`u32(originalIndices[${a}])`:"0"};\n var x11: ${l} = getInputValue(batch, channel, row1, col1);\n var x12: ${l} = getInputValue(batch, channel, row1, col2);\n var x21: ${l} = getInputValue(batch, channel, row2, col1);\n var x22: ${l} = getInputValue(batch, channel, row2, col2);\n var dx1: ${l} = abs(row - ${l}(row1));\n var dx2: ${l} = abs(${l}(row2) - row);\n var dy1: ${l} = abs(col - ${l}(col1));\n var dy2: ${l} = abs(${l}(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 }`},Do=(e,t,n,i,r,a,o,s,u,l)=>{let d=2===n.length,[c,p]=d?[0,1]:[2,3],f=e.type.value,h=o=>{let d=o===c?"row":"col";return`\n fn ${d}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${f} {\n var output_index = ${t.indicesGet("output_indices",o)};\n var originalIdx: ${f} = getOriginalCoordinateFromResizedCoordinate(output_index, ${r[o]},\n ${i[o]}, ${n[o]}, ${a[o]}, ${a[o]} + ${n.length});\n var fractOriginalIdx: ${f} = originalIdx - floor(originalIdx);\n var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n if (${s} && (originalIdx < 0 || originalIdx > (${n[o]} - 1))) {\n return ${u};\n }\n var data: array<${f}, 4> = array<${f}, 4>(0.0, 0.0, 0.0, 0.0);\n for (var i: i32 = -1; i < 3; i++) {\n var ${d}: ${f} = originalIdx + ${f}(i);\n if (${d} < 0 || ${d} >= ${n[o]}) {\n ${l?"coefs[i + 1] = 0.0;\n continue;":s?`return ${u};`:`${d} = max(0, min(${d}, ${n[o]} - 1));`};\n }\n var input_indices_copy: ${e.type.indices} = input_indices;\n ${e.indicesSet("input_indices_copy",o,`u32(${d})`)};\n data[i + 1] = ${o===c?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n }\n return cubicInterpolation1D(data, coefs);\n }`};return`\n ${h(c)};\n ${h(p)};\n fn getCubicInterpolationCoefs(s: ${f}) -> array<${f}, 4> {\n var absS = abs(s);\n var coeffs: array<${f}, 4> = array<${f}, 4>(0.0, 0.0, 0.0, 0.0);\n var oneMinusAbsS: ${f} = 1.0 - absS;\n var twoMinusAbsS: ${f} = 2.0 - absS;\n var onePlusAbsS: ${f} = 1.0 + absS;\n coeffs[0] = ((${o} * onePlusAbsS - 5 * ${o}) * onePlusAbsS + 8 * ${o}) * onePlusAbsS - 4 * ${o};\n coeffs[1] = ((${o} + 2) * absS - (${o} + 3)) * absS * absS + 1;\n coeffs[2] = ((${o} + 2) * oneMinusAbsS - (${o} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n coeffs[3] = ((${o} * twoMinusAbsS - 5 * ${o}) * twoMinusAbsS + 8 * ${o}) * twoMinusAbsS - 4 * ${o};\n return coeffs;\n }\n\n fn cubicInterpolation1D(x: array<${f}, 4>, coefs: array<${f}, 4>) -> ${f} {\n var coefsSum: ${f} = 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}) -> ${f} {\n var input_indices: ${e.type.indices} = output_indices;\n return colCubicInterpolation(input_indices, output_indices);\n }\n `},No=(e,t,n,i,r)=>{let[a,o,s,u,l]=3===n.length?[-1,0,1,2,-1]:[0,2,3,4,1],d=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${d} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",o,`max(0, min(depth, ${n[o]} - 1))`)};\n ${e.indicesSet("input_indices",s,`max(0, min(height, ${n[s]} - 1))`)};\n ${e.indicesSet("input_indices",u,`max(0, min(width, ${n[u]} - 1))`)};\n ${Bo(e,l,a,3)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${d} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var depth:${d} = originalIndices[${o}];\n var height:${d} = originalIndices[${s}];\n var width:${d} = originalIndices[${u}];\n ${i?`if (depth < 0 || depth > (${n[o]} - 1) || height < 0 || height > (${n[s]} - 1) || width < 0 || (width > ${n[u]} - 1)) {\n return ${r};\n }`:""};\n\n depth = max(0, min(depth, ${n[o]} - 1));\n height = max(0, min(height, ${n[s]} - 1));\n width = max(0, min(width, ${n[u]} - 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 = ${n.length>3?`u32(originalIndices[${l}])`:"0"};\n var batch: u32 = ${n.length>3?`u32(originalIndices[${a}])`:"0"};\n\n var x111: ${d} = getInputValue(batch, channel, depth1, height1, width1);\n var x112: ${d} = getInputValue(batch, channel, depth1, height1, width2);\n var x121: ${d} = getInputValue(batch, channel, depth1, height2, width1);\n var x122: ${d} = getInputValue(batch, channel, depth1, height2, width2);\n var x211: ${d} = getInputValue(batch, channel, depth2, height1, width1);\n var x212: ${d} = getInputValue(batch, channel, depth2, height1, width2);\n var x221: ${d} = getInputValue(batch, channel, depth2, height2, width1);\n var x222: ${d} = getInputValue(batch, channel, depth2, height2, width2);\n var dx1: ${d} = abs(depth - ${d}(depth1));\n var dx2: ${d} = abs(${d}(depth2) - depth);\n var dy1: ${d} = abs(height - ${d}(height1));\n var dy2: ${d} = abs(${d}(height2) - height);\n var dz1: ${d} = abs(width - ${d}(width1));\n var dz2: ${d} = abs(${d}(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 }`},Uo=(e,t,n,i,r,a)=>{let o=e.dims,s=Ao(a,t.axes,o.length),u=Oo(o,i,r,t.axes),l=i.slice();0===i.length&&(l=o.map(((e,t)=>0===e?1:u[t]/e)),"stretch"!==t.keepAspectRatioPolicy&&(u=zo(o,l,t)));let d=Tt("output",e.dataType,u.length),c=kt("input",e.dataType,o.length),p=pt.size(u),f=o.length===u.length&&o.every(((e,t)=>e===u[t])),h="tf_crop_and_resize"===t.coordinateTransformMode,m=t.extrapolationValue,g=c.type.value;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${n}|${l.length>0?l:""}|${r.length>0?r:""}|${s.length>0?s:""}|${f}|${o}`,inputDependencies:["rank"]},getShaderSource:e=>`\n ${f?"":`\n ${ko(t.coordinateTransformMode,g)};\n ${(()=>{switch(t.mode){case"nearest":return`\n ${Mo(c,o)};\n ${To(t.nearestMode,n,g)};\n ${jo(c,d,o,u,l.length,s.length,h)};\n `;case"linear":return`\n ${Ro(d,o,u,l.length,s.length)};\n ${(()=>{if(2===o.length||4===o.length)return`${Po(c,d,o,h,m)}`;if(3===o.length||5===o.length)return`${No(c,d,o,h,m)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n `;case"cubic":return`\n ${(()=>{if(2===o.length||4===o.length)return`${Do(c,d,o,u,l,s,t.cubicCoeffA,h,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 ${e.registerUniform("output_size","u32").registerUniform("scales","f32",l.length).registerUniform("roi","f32",s.length).declareVariables(c,d)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n ${f?"output[global_idx] = input[global_idx];":`\n let output_indices = ${d.offsetToIndices("global_idx")};\n var input_indices: ${c.type.indices};\n ${(()=>{switch(t.mode){case"nearest":return`input_indices = 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t=e.antialias,n=e.axes,i=e.coordinateTransformMode,r=e.cubicCoeffA,a=0!==e.excludeOutside,o=e.extrapolationValue,s=e.keepAspectRatioPolicy,u=e.mode,l=""===e.nearestMode?"simple":e.nearestMode;return lt({antialias:t,axes:n,coordinateTransformMode:i,cubicCoeffA:r,excludeOutside:a,extrapolationValue:o,keepAspectRatioPolicy:s,mode:u,nearestMode:l})}})),Cu=D((()=>{zs(),Ns(),Us(),Go=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],n=e[1],i=e[2];if(t.dataType!==n.dataType||t.dataType!==i.dataType)throw new Error("All inputs must have the same data type");if(3!==t.dims.length&&2!==t.dims.length)throw new Error("Input must be 2D or 3D");if(3!==n.dims.length&&2!==n.dims.length)throw new Error("Skip must be 2D or 3D");let r=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(n.dims[n.dims.length-1]!==r)throw new Error("Skip must have the same hidden size as input");if(n.dims[n.dims.length-2]!==a)throw new Error("Skip must have the same sequence length as input");if(1!==i.dims.length)throw new Error("Gamma must be 1D");if(i.dims[i.dims.length-1]!==r)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let t=e[3];if(1!==t.dims.length)throw new Error("Beta must be 1D");if(t.dims[t.dims.length-1]!==r)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let t=e[4];if(1!==t.dims.length)throw new Error("Bias must be 1D");if(t.dims[t.dims.length-1]!==r)throw new Error("Bias must have the same hidden size as input")}},Vo=(e,t,n,i)=>{let r=e[0].dims,a=pt.size(r),o=r,s=a,u=r.slice(-1)[0],l=i?r.slice(0,-1).concat(1):[],d=e.length>3,c=e.length>4,p=i&&n>1,f=i&&n>2,h=n>3,m=bt(u),g=[{type:12,data:s},{type:12,data:m},{type:12,data:u},{type:1,data:t.epsilon}],y=[{dims:o,dataType:e[0].dataType}];return n>1&&y.push({dims:l,dataType:1}),n>2&&y.push({dims:l,dataType:1}),n>3&&y.push({dims:r,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${m};${p};${f};${h}`,inputDependencies:e.map(((e,t)=>"type"))},getShaderSource:t=>{let n=[kt("x",e[0].dataType,e[0].dims,m),kt("skip",e[1].dataType,e[1].dims,m),kt("gamma",e[2].dataType,e[2].dims,m)];d&&n.push(kt("beta",e[3].dataType,e[3].dims,m)),c&&n.push(kt("bias",e[4].dataType,e[4].dims,m)),n.push(Tt("output",e[0].dataType,o,m)),p&&n.push(Tt("mean_output",1,l)),f&&n.push(Tt("inv_std_output",1,l)),h&&n.push(Tt("input_skip_bias_sum",e[0].dataType,o,m));let i=wt(e[0].dataType);return`\n\n ${t.registerUniforms([{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}]).declareVariables(...n)}\n\n ${t.mainStart()}\n ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")}\n let hidden_size_vectorized: u32 = uniforms.hidden_size / 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d={dims:l,dataType:e[0].dataType},c=Tt("output",e[0].dataType,l.length),p=kt("input",e[0].dataType,e[0].dims.length),f=pt.size(l),h=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:o.length},{name:"signs",type:"i32",length:u.length},{name:"steps",type:"u32",length:a.length}],m=[{type:12,data:f},{type:12,data:o},{type:6,data:u},{type:12,data:a},...$t(e[0].dims,l)];return{name:"Slice",shaderCache:{hint:`${u.length}_${o.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:e=>`\n ${e.registerUniforms(h).declareVariables(p,c)}\n ${Qo(p,c,n)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n let output_indices = ${c.offsetToIndices("global_idx")};\n let input_indices = calculateInputIndices(output_indices);\n ${c.setByOffset("global_idx",p.getByIndices("input_indices"))}\n }`,getRunData:()=>({outputs:[d],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:m})}},Xo=(e,t)=>{qo(e.inputs,t);let n=Yo(e.inputs,t);e.compute(Jo(e.inputs,n),{inputs:[0]})},es=e=>{let t=e.starts,n=e.ends,i=e.axes;return lt({starts:t,ends:n,axes:i})}})),Eu=D((()=>{zs(),Ns(),Ds(),Us(),ts=e=>{if(!e||1!==e.length)throw new Error("Softmax op requires 1 input.")},ns=(e,t)=>{let n=e.dims,i=pt.size(n),r=t.axis;if(r<0&&(r=n.length+r),r({outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:o},programUniforms:[{type:12,data:u}]}),getShaderSource:e=>`\n var rowMaxShared : ${c};\n var rowSumShared : ${c};\n var threadShared : array<${c}, 64>;\n\n fn getValue(row: i32, col: i32, row_stride: i32) -> ${c} {\n let index = row * row_stride + col;\n return x[index];\n }\n\n fn setValue(row: i32, col: i32, row_stride: i32, value: ${c}) {\n let index = row * row_stride + col;\n result[index] = value;\n }\n ${e.registerUniform("packedCols","i32").declareVariables(l,d)}\n ${e.mainStart()}\n let gindex = i32(global_idx);\n let lindex = i32(local_idx);\n const wg = 64;\n let row = gindex / wg;\n let cols = 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e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach((e=>n.push(Number(e)))),i=n.length),lt({numOutputs:i,axis:t.axis,splitSizes:n})},ss=e=>`\nfn calculateOutputIndex(index: u32) -> u32 {\n for (var i: u32 = 0u; i < ${e}u; i += 1u ) {\n if (index < ${Et("uniforms.size_in_split_axis","i",e)}) {\n return i;\n }\n }\n return ${e}u;\n}`,us=e=>{let t=e.length,n=[];for(let i=0;i{let n=e[0].dims,i=pt.size(n),r=e[0].dataType,a=pt.normalizeAxis(t.axis,n.length),o=new Array(t.numOutputs),s=kt("input",r,n.length),u=new Array(t.numOutputs),l=[],d=[],c=0,p=[{type:12,data:i}];for(let i=0;i`\n ${e.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",u.length).declareVariables(s,...o)}\n ${ss(u.length)}\n ${us(o)}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}\n\n var indices = ${s.offsetToIndices("global_idx")};\n var index = ${s.indicesGet("indices",a)};\n let output_number = calculateOutputIndex(index);\n if (output_number != 0) {\n index -= ${Et("uniforms.size_in_split_axis","output_number - 1u",u.length)};\n ${s.indicesSet("indices",a,"index")};\n }\n writeBufferData(output_number, indices, global_idx);\n }`,getRunData:()=>({outputs:l,dispatchGroup:{x:Math.ceil(i/64)},programUniforms:p})}},ds=(e,t)=>{as(e.inputs);let n=1===e.inputs.length?t:os(e.inputs,t);e.compute(ls(e.inputs,n),{inputs:[0]})},cs=e=>{let t=e.axis,n=e.splitSizes,i=e.numOutputs<0?n.length:e.numOutputs;if(i!==n.length)throw new Error("numOutputs and splitSizes lengh must be equal");return lt({axis:t,numOutputs:i,splitSizes:n})}})),ku=D((()=>{zs(),Ns(),Us(),ps=e=>Array.from(e.getBigInt64Array(),Number),fs=e=>{if(!e||2!==e.length)throw new Error("Tile requires 2 inputs.");if(1!==e[0].dataType&&6!==e[0].dataType&&12!==e[0].dataType)throw new Error("Tile only support float, int32, and uint32 data types");if(7!==e[1].dataType)throw new Error("Tile `repeats` input should be of int64 data type");if(1!==e[1].dims.length)throw new Error("Tile `repeats` input should be 1-D");if(ps(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},hs=(e,t)=>{let n=[];for(let i=0;i{let t=e[0].dims,n=ps(e[1]),i=hs(t,n),r=pt.size(i),a=e[0].dataType,o=kt("input",a,t.length),s=Tt("output",a,i.length);return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(r/64)},programUniforms:[{type:12,data:r},...$t(e[0].dims,i)]}),getShaderSource:e=>`\n const inputShape = ${o.indices(...t)};\n ${e.registerUniform("output_size","u32").declareVariables(o,s)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let output_indices = ${s.offsetToIndices("global_idx")};\n var input_indices: ${o.type.indices};\n for (var i = 0; i < ${t.length}; i++) {\n let input_dim_i = ${o.indicesGet("uniforms.input_shape","i")};\n let input_dim_value = 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f=this.programManager.normalizeDispatchGroupSize(s),h=1===f[1]&&1===f[2],m=xs(e,t,h),g=this.programManager.getArtifact(m);if(g||(g=this.programManager.build(e,f),this.programManager.setArtifact(m,g),et("info",(()=>`[artifact] key: ${m}, programName: ${e.name}`))),et("info",(()=>`[ProgramManager] run "${e.name}" (key=${m}) with ${f[0]}x${f[1]}x${f[2]}`)),"none"!==this.queryType||"capturing"===this.sessionStatus){let e={kernelId:this.currentKernelId,programName:g.programInfo.name,inputTensorViews:t,outputTensorViews:c};this.pendingKernels.push(e),"capturing"===this.sessionStatus&&this.capturedPendingKernels.get(this.currentSessionId).push(e)}return this.programManager.run(g,a,p,f,d),T(e.name),c}upload(e,t){this.gpuDataManager.upload(e,t)}memcpy(e,t){this.gpuDataManager.memcpy(e,t)}async download(e,t){await this.gpuDataManager.download(e,t)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,t,n,i){let r=_s.get(e);if(!r)throw new 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tt(i.buffer,n)}}writeTimestamp(e){"inside-passes"===this.queryType&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){this.queryType="none",("default"===this.env.webgpu.profiling?.mode||this.env.wasm.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"),"none"!==this.queryType&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2*this.maxDispatchNumber}),this.queryResolveBuffer=this.device.createBuffer({size:2*this.maxDispatchNumber*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){et("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(){et("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){et("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),n=e.length;this.pendingKernels=[];for(let i=0;i=this.maxDispatchNumber||"at-passes"===this.queryType)&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}})),Ru={};U(Ru,{init:()=>Bu});var ju,Mu,Bu,Pu,Du,Nu,Uu,Wu,Lu,Hu,Gu,Vu,Fu,qu,Ku,Yu,Zu,Qu,Ju,Xu,el,tl,nl,il,rl,al,ol,sl,ul,ll,dl,cl,pl,fl,hl,ml,gl,yl,vl=D((()=>{zs(),zu(),js(),Ns(),ju=class e{constructor(e,t,n,i){this.module=e,this.dataType=t,this.data=n,this.dims=i}getFloat32Array(){if(1!==this.dataType)throw new Error("Invalid data type");let e=pt.size(this.dims);return 0===e?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,e)}getBigInt64Array(){if(7!==this.dataType)throw new Error("Invalid data 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m=[s.batchSize,s.numHeads,s.sequenceLength,s.kvSequenceLength+s.pastSequenceLength],d=f.scale===0?1/Math.sqrt(s.headSize):f.scale,y=Je(s.headSize),w=s.headSize/y,C=12,$={x:Math.ceil(s.totalSequenceLength/C),y:Math.ceil(s.sequenceLength/C),z:s.batchSize*s.numHeads},T=[{type:12,data:s.sequenceLength},{type:12,data:w},{type:12,data:s.totalSequenceLength},{type:12,data:s.kvSequenceLength},{type:r.dataType,data:d}],P=[r,t],A=R=>{let D=Q("q",r.dataType,r.dims,y),k=Q("key",t.dataType,t.dims,y),G=ne("output",r.dataType,m),V=Ve(r.dataType),q=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"alpha",type:V}];return`\n const beta: ${V} = 1.0;\n const TILE_SIZE = ${C}u;\n\n var tileQ: array<${D.type.storage}, ${C*C}>;\n var tileK: array<${D.type.storage}, ${C*C}>;\n ${R.registerUniforms(q).declareVariables(D,k,G)}\n ${R.mainStart([C,C,1])}\n // x holds the N and y holds the M\n let headIdx = workgroup_id.z;\n let m = workgroup_id.y * 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s=[u.batchSize,u.sequenceLength,u.vHiddenSize],f=12,m={x:Math.ceil(u.vHeadSize/f),y:Math.ceil(u.sequenceLength/f),z:u.batchSize*u.numHeads},d=[{type:12,data:u.sequenceLength},{type:12,data:u.totalSequenceLength},{type:12,data:u.vHeadSize},{type:12,data:u.numHeads},{type:12,data:u.vHiddenSize}],y=w=>{let C=Q("probs",r.dataType,r.dims),$=Q("v",t.dataType,t.dims),T=ne("output",r.dataType,s),P=[{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 = ${f}u;\n var tileQ: array<${C.type.value}, ${f*f}>;\n var tileK: array<${C.type.value}, ${f*f}>;\n ${w.registerUniforms(P).declareVariables(C,$,T)}\n ${w.mainStart([f,f,1])}\n let headIdx = workgroup_id.z;\n let m = workgroup_id.y * TILE_SIZE + local_id.y;\n let n = workgroup_id.x * TILE_SIZE + local_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 = ${C.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({outputs:[{dims:s,dataType:r.dataType,gpuDataType:0}],dispatchGroup:m,programUniforms:d}),getShaderSource:y},{inputs:[r,t],outputs:[0]})[0]},si=(e,r,t,u,s,f,m,d,y,w,C)=>{let $=ag(e,r,t,y,w,C);sg(e,$,u,w)},ug=(e,r)=>{let t=[r.batchSize,r.numHeads,r.sequenceLength,r.headSize],u=r.sequenceLength,s=r.inputHiddenSize,f=r.headSize,m=12,d={x:Math.ceil(r.headSize/m),y:Math.ceil(r.sequenceLength/m),z:r.batchSize*r.numHeads},y=[e.inputs[0],e.inputs[1],e.inputs[2]],w=[{type:12,data:u},{type:12,data:s},{type:12,data:f},{type:12,data:r.numHeads},{type:12,data:r.headSize},{type:12,data:r.hiddenSize},{type:12,data:r.hiddenSize+r.hiddenSize+r.vHiddenSize}],C=$=>{let T=ne("output_q",y[0].dataType,t),P=ne("output_k",y[0].dataType,t),A=ne("output_v",y[0].dataType,t),O=Q("input",y[0].dataType,y[0].dims),R=Q("weight",y[1].dataType,y[1].dims),D=Q("bias",y[2].dataType,y[2].dims),k=O.type.storage,G=[{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 = ${m}u;\n var tileInput: array<${k}, ${m*m}>;\n var tileWeightQ: array<${k}, ${m*m}>;\n var tileWeightK: array<${k}, ${m*m}>;\n var tileWeightV: array<${k}, ${m*m}>;\n ${$.registerUniforms(G).declareVariables(O,R,D,T,P,A)}\n ${$.mainStart([m,m,1])}\n let batchIndex = workgroup_id.z / uniforms.num_heads;\n let headNumber = workgroup_id.z % uniforms.num_heads;\n let m = workgroup_id.y * TILE_SIZE + local_id.y;\n let n = workgroup_id.x * TILE_SIZE + local_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 = ${k}(0);\n var valueK = ${k}(0);\n var valueV = ${k}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m < uniforms.M && w + local_id.x < uniforms.K) {\n tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n }\n if (n < uniforms.N && w + local_id.y < uniforms.K) {\n let offset = n + (w + local_id.y) * uniforms.ldb;\n tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n }\n workgroupBarrier();\n for (var k: u32 = 0u; k({outputs:[{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:d,programUniforms:w}),getShaderSource:C},{inputs:y,outputs:[-1,-1,-1]})},nl=(e,r)=>{let t=ig(e.inputs,r),[u,s,f]=ug(e,t);return si(e,u,s,f,e.inputs[4],void 0,void 0,void 0,e.inputs[5],t,r)}});var lg,dg,cg,il,ol=oe(()=>{"use strict";Ar();Ce();Oe();nt();Ee();lg=(e,r)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let t=(u,s,f)=>{let m=s.length;if(m!==u.length)throw new Error(`${f}: num dimensions != ${m}`);s.forEach((d,y)=>{if(d!==u[y])throw new Error(`${f}: dim[${y}] do not 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if(s==="NCHW")G=`\n ${R.indicesSet("outputIndices","0","0")}\n let cOffset = ${R.indicesToOffset("outputIndices")};`;else{G=`var cIndices = ${T.type.indices}(0);\n cIndices[0] = outputIndices[${f.length-1}];`;for(let V=1;V`\n const epsilon = ${t};\n ${G.registerUniform("outputSize","u32").declareVariables($,T,P,A,O,R)}\n ${G.mainStart()}\n ${G.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n var outputIndices = ${R.offsetToIndices(`global_idx * ${m}`)};\n ${D()}\n let scale = ${T.getByOffset("cOffset")};\n let bias = ${P.getByOffset("cOffset")};\n let inputMean = ${A.getByOffset("cOffset")};\n let inputVar = ${O.getByOffset("cOffset")};\n let x = ${$.getByOffset("global_idx")};\n let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;\n ${R.setByOffset("global_idx","value")}\n }`;return{name:"BatchNormalization",shaderCache:{hint:`${r.epsilon}_${r.format}_${u}_${m}`,inputDependencies:w?["rank","type","type","type","type"]:void 0},getShaderSource:k,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:w?[{type:12,data:y},...se(f)]:[{type:12,data:y}]})}},cg=e=>Pe(e),il=(e,r)=>{let{inputs:t,outputCount:u}=e,s=cg({...r,outputCount:u});if(Ir.webgpu.validateInputContent&&lg(t,s),r.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(dg(t,s))}});var fg,pg,al,sl=oe(()=>{"use strict";Oe();Ee();fg=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},pg=e=>{let 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k=1;for(let G=1;GP.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:P=>bg(P,t.dims,u.dims,y,C,d,$,s,t.dataType,u.dataType,m,f),getRunData:()=>({outputs:[{dims:y,dataType:m}],dispatchGroup:{x:Math.ceil(w/64/4)},programUniforms:[{type:12,data:Math.ceil(X.size(y)/4)},...se(t.dims,u.dims,y)]})}},Mt=(e,r,t,u,s,f)=>{e.compute(vg(r,s??"",e.inputs[0],e.inputs[1],t,u,f))},Fl=e=>{Mt(e,"Add",(r,t)=>`${r}+${t}`)},ql=e=>{Mt(e,"Div",(r,t)=>`${r}/${t}`)},Kl=e=>{Mt(e,"Equal",{scalar:(r,t)=>`u32(${r}==${t})`,vector:(r,t)=>`vec4(${r}==${t})`},void 0,void 0,9)},Yl=e=>{Mt(e,"Mul",(r,t)=>`${r}*${t}`)},Zl=e=>{let r=Q("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Mt(e,"Pow",{scalar:(u,s)=>`pow_custom(${u},${s})`,vector:(u,s)=>`pow_vector_custom(${u},${s})`},`\n fn pow_custom(a : ${r}, b : ${r}) -> ${r} {\n if (b == ${r}(0.0)) {\n return ${r}(1.0);\n } else if (a < ${r}(0.0) && f32(b) != floor(f32(b))) {\n return ${r}(pow(f32(a), f32(b))); // NaN\n }\n return 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${e.activation}`)}},Ut=(e,r)=>{e.activation==="Clip"?r.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?r.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&r.push({type:1,data:e.alpha})},jt=(e,r)=>{e.activation==="Clip"?r.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?r.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&r.push({name:"alpha",type:"f32"})},ci=e=>{let r=e?.activation||"";if(r==="HardSigmoid"){let[t,u]=e?.activation_params||[.2,.5];return{activation:r,alpha:t,beta:u}}else if(r==="Clip"){let[t,u]=e?.activation_params||[ti,ri];return{activation:r,clipMax:u,clipMin:t}}else if(r==="LeakyRelu"){let[t]=e?.activation_params||[.01];return{activation:r,alpha:t}}return{activation:r}}});var at,fi,pi=oe(()=>{"use strict";at=(e,r)=>{switch(e){case 1:return r;case 2:return`vec2<${r}>`;case 3:return`vec3<${r}>`;case 4:return`vec4<${r}>`;default:throw new Error(`${e}-component is not supported.`)}},fi=e=>`\n ${e?"value = value + getBiasByOutputCoords(coords);":""}\n `});var mi,Yi=oe(()=>{"use strict";mi=e=>`\nfn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`});var xg,Ig,cn,ad,Ag,fn,Tg,hi,pn=oe(()=>{"use strict";Ce();Oe();Ee();qt();pi();xg=(e,r)=>e?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart / innerElementSize + inputCol${r?", batchIndices":""});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRow + innerRow,\n kStart / innerElementSize + inputCol${r?", batchIndices":""});\n `,Ig=(e,r)=>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 ${r===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 ${r===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 ${r===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n }`,cn=(e,r,t="f32",u,s=!1,f=32,m=!1,d=32)=>{let y=r[1]*e[1],w=r[0]*e[0],C=s?y:f,$=s?f:y,T=C/r[0],P=f/r[1];if(!((s&&T===4&&e[1]===4||!s&&(T===3||T===4))&&C%r[0]===0&&f%r[1]===0&&e[0]===4))throw new Error(`If transposeA ${s} is true, innerElementSize ${T} and workPerThread[1] ${e[1]} must be 4.\n Otherwise, innerElementSize ${T} must be 3 or 4.\n tileAWidth ${C} must be divisible by workgroupSize[0]${r[0]}. tileInner ${f} must be divisible by workgroupSize[1] ${r[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar mm_Asub: array, ${C/T}>, ${$}>;\nvar mm_Bsub: array, ${w/e[0]}>, ${f}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${T};\nconst tileInner = ${f};\n\n@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]})\nfn main(@builtin(local_invocation_id) localId : vec3,\n @builtin(global_invocation_id) globalId : vec3,\n @builtin(workgroup_id) workgroupId : vec3) {\n let localRow = i32(localId.y);\n let tileRow = localRow * rowPerThread;\n let tileCol = i32(localId.x);\n\n let globalRow =i32(globalId.y) * rowPerThread;\n let globalCol = i32(globalId.x);\n let batch = ${m?"0":"i32(globalId.z)"};\n ${u?`let batchIndices = ${u.offsetToIndices("u32(batch)")};`:""}\n let globalRowStart = i32(workgroupId.y) * ${y};\n\n let num_tiles = ${m?`${Math.ceil(d/f)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n var kStart = ${m?`i32(globalId.z) * ${d}`:"0"};\n\n var acc: array, rowPerThread>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${P};\n for (var t = 0; t < num_tiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${xg(s,u)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${P}; innerRow = innerRow + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${u?", batchIndices":""});\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n ${T===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n ${Ig(s,T)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n}`},ad=(e,r)=>e?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart + inputCol${r?", batchIndices":""});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRowStart + inputRow,\n kStart + inputCol${r?", batchIndices":""});\n `,Ag=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",fn=(e,r,t="f32",u,s=!1,f=32,m=!1,d=32,y=!1)=>{let w=e[1]*r[1],C=e[0]*r[0],$=s?w:f,T=s?f:w;if(!(T%r[1]===0&&$%r[0]===0&&f%r[1]===0))throw new Error(`tileAHight ${T} must be divisible by workgroupSize[1]${r[1]}, tileAWidth ${$} must be divisible by workgroupSize[0]${r[0]}, tileInner ${f} must be divisible by workgroupSize[1]${r[1]}`);let P=T/r[1],A=$/r[0],O=f/r[1],R=y?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${w};\n let globalColStart = i32(workgroupId.x) * ${C};\n\n // Loop over shared dimension.\n for (var t = 0; t < num_tiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${T}; inputRow = inputRow + ${r[1]}) {\n for (var inputCol = localCol; inputCol < ${$}; inputCol = inputCol + ${r[0]}) {\n ${ad(s,u)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${f}; inputRow = inputRow + ${r[1]}) {\n for (var inputCol = localCol; inputCol < ${C}; inputCol = inputCol + ${r[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalColStart + inputCol${u?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${t}, colPerThread>;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${r[0]}];\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let ACached = ${s?`mm_Asub[k][localRow + innerRow * ${r[1]}];`:`mm_Asub[localRow + innerRow * ${r[1]}][k];`}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n ACached * BCached[innerCol];\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let gRow = globalRowStart + localRow + innerRow * ${r[1]};\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let gCol = globalColStart + localCol + innerCol * ${r[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${w};\n\nlet tileRowA = i32(localId.y) * ${P};\nlet tileColA = i32(localId.x) * ${A};\nlet tileRowB = i32(localId.y) * ${O};\n// Loop over shared dimension.\nfor (var t = 0; t < num_tiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${P}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < ${A}; innerCol = innerCol + 1) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${ad(s,u)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${O}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalCol + innerCol${u?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${t}, colPerThread>;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n ${Ag(s)}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n }\n }\n }\n\n workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n}\n`;return`\n var mm_Asub : array, ${T}>;\n var mm_Bsub : array, ${f}>;\n const rowPerThread = ${e[1]};\n const colPerThread = ${e[0]};\n const tileInner = ${f};\n\n@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]})\nfn main(@builtin(local_invocation_id) localId : vec3,\n @builtin(global_invocation_id) globalId : vec3,\n @builtin(workgroup_id) workgroupId : vec3) {\n let batch = ${m?"0":"i32(globalId.z)"};\n ${u?`let batchIndices = ${u.offsetToIndices("u32(batch)")};`:""}\n let num_tiles = ${m?`${Math.ceil(d/f)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n var kStart = ${m?`i32(globalId.z) * ${d}`:"0"};\n\n var acc : array, rowPerThread>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${R}\n }\n`},Tg=(e,r,t,u,s,f=!1)=>{let[m,d,y]=s,[w,C,$,T]=u,P=Tr(m,y),A=Tr(d,y),O=Ve(u[0].type.tensor),R=()=>{let G=C.rank,V=w.rank,q=`var aIndices: ${C.type.indices};`;for(let Y=G-2-1,J=V-1;Y>=0;Y--,J--)q+=`\naIndices[${Y}] = ${V>1?`batchIndices[${J}]`:"batchIndices"};`;return P.forEach(Y=>{q+=`\naIndices[${Y}] = 0;`}),q+=`\naIndices[${G-2}] = u32(row);\n aIndices[${G-1}] = u32(colIn);`,q},D=()=>{let G=$.rank,V=w.rank,q=`var bIndices: ${$.type.indices};`;for(let Y=G-2-1,J=V-1;Y>=0;Y--,J--)q+=`\nbIndices[${Y}] = ${V>1?`batchIndices[${J}]`:"batchIndices"};`;return A.forEach(Y=>{q+=`\nbIndices[${Y}] = 0;`}),q+=`\nbIndices[${G-2}] = u32(row);\n bIndices[${G-1}] = u32(colIn);`,q};return`\n fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${w.type.indices}) -> ${at(e,O)} {\n var value = ${at(e,O)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\n {\n ${R()}\n value = ${C.getByIndices("aIndices")};\n }\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${w.type.indices}) -> ${at(e,O)} {\n var value = ${at(e,O)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\n {\n ${D()}\n value = ${$.getByIndices("bIndices")};\n }\n return value;\n }\n\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${at(e,O)}) {\n let col = colIn * ${e};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n var value = valueIn;\n let coords = vec3(batch, row, colIn);\n ${r?`value = value + ${f?"bias[colIn]":`${at(e,O)}(bias[row])`};`:""}\n ${t}\n ${T.setByIndices("vec3(coords)","value")}\n }\n }\n `},hi=(e,r,t,u,s=!1)=>{let f=e[0].dims,m=e[1].dims,d=f.slice(0,-2),y=m.slice(0,-2),w=u?u.slice(0,-2):t.slice(0,-2),C=X.size(w),$=f[f.length-2],T=f[f.length-1],P=m[m.length-1],A=T%4===0&&P%4===0,O=$<=8?[4,1,1]:[4,4,1],R=[8,8,1],D=[Math.ceil(P/R[0]/O[0]),Math.ceil($/R[1]/O[1]),Math.ceil(C/R[2]/O[2])],k=A?4:1,G=[...d,$,T/k],V=G.length,q=[...y,T,P/k],Y=q.length,J=[C,$,P/k],ie=[{type:6,data:$},{type:6,data:P},{type:6,data:T}];Ut(r,ie),ie.push(...se(w,G,q));let ge=["rank","rank"],L=e.length>2;L&&(ie.push(...se(e[2].dims)),ge.push("rank")),ie.push(...se(J));let ue=je=>{let Ne=w.length,_e=ii("batchDims",e[0].dataType,Ne,1),ae=Ve(e[0].dataType),ye=Q("a",e[0].dataType,V,k),Me=Q("b",e[1].dataType,Y,k),Ae=ne("result",e[0].dataType,J.length,k),$e=[ye,Me];if(L){let te=s?k:1;$e.push(Q("bias",e[2].dataType,e[2].dims.length,te))}let Se=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];jt(r,Se);let xe=Ve(Ae.type.tensor),De=zt(r,Ae.type.value,xe),Ye=Tg(k,L,De,[_e,ye,Me,Ae],[d,y,w],s);return`\n ${je.registerUniforms(Se).registerInternalVariables(_e).declareVariables(...$e,Ae)}\n ${Ye}\n ${A?cn(O,R,ae,_e):fn(O,R,ae,_e)}\n `};return{name:"MatMul",shaderCache:{hint:`${O};${r.activation};${A};${s}`,inputDependencies:ge},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:D[0],y:D[1],z:D[2]},programUniforms:ie}),getShaderSource:ue}}});var Eg,sd,ud=oe(()=>{"use strict";Ce();Ft();Ee();qt();pi();Yi();pn();Eg=(e,r,t,u,s=!1,f,m=4,d=4,y=4,w="f32")=>{let C=L=>{switch(L){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${w}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${L} is not supported.`)}},$=L=>{switch(L){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 ${L} is not supported.`)}},T=e?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,P=e?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,A=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",O=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",R=e?"row":"col",D=e?"col":"row",k=`\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 = ${R} / outWidth;\n let outCol = ${R} % outWidth;\n\n let WRow = ${D} / (i32(uniforms.w_shape[1]) * inChannels);\n let WCol = ${D} / 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 = ${D} % inChannels;\n var resData = ${at(m,w)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the \'same\' padding type.\n if (xRow >= 0 && xRow < ${A} && xCol >= 0 && xCol < ${O}) {\n ${T}\n let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape));\n ${C(m)}\n }\n return resData;`,G=e?r&&u?`\n let col = colIn * ${m};\n ${k}`:`\n let col = colIn * ${m};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n ${k}\n }\n return ${at(m,w)}(0.0);`:u&&t?`\n let col = colIn * ${m};\n ${k}`:`\n let col = colIn * ${m};\n if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n ${k}\n }\n return ${at(m,w)}(0.0);`,V=`${$(d)}`,q=at(y,w),Y=e?at(m,w):at(d,w),J=e?at(d,w):at(m,w),ie=zt(f,q,w);return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Y} {\n ${e?G:V}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${J} {\n ${e?V:G}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${q}) {\n let col = colIn * ${y};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)\n {\n var value = valueIn;\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n ${P}\n ${fi(s)}\n ${ie}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`},sd=(e,r,t,u,s,f,m,d)=>{let y=r.format==="NHWC",w=y?e[0].dims[3]:e[0].dims[1],C=t[0],$=y?t[2]:t[3],T=y?t[1]:t[2],P=y?t[3]:t[1],A=y&&(w%4===0||w%3===0)&&P%4===0,O=y?P:$*T,R=y?$*T:P,D=[8,8,1],k=u<=8?[4,1,1]:[4,4,1],G=[Math.ceil(O/D[0]/k[0]),Math.ceil(R/D[1]/k[1]),Math.ceil(C/D[2]/k[2])];He("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${G}`);let V=A?y&&w%4!==0?3:4:1,q=D[1]*k[1],Y=D[0]*k[0],J=Math.max(D[0]*V,D[1]),ie=u%q===0,ge=s%Y===0,L=f%J===0,ue=A?[V,4,4]:[1,1,1],je=[{type:6,data:u},{type:6,data:s},{type:6,data:f},{type:6,data:[r.pads[0],r.pads[1]]},{type:6,data:r.strides},{type:6,data:r.dilations}];Ut(r,je),je.push(...se(e[0].dims,e[1].dims));let Ne=["rank","rank"];m&&(je.push(...se(e[2].dims)),Ne.push("rank")),je.push(...se(t));let _e=ae=>{let ye=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];jt(r,ye);let Me=A?4:1,Ae=Ve(e[0].dataType),$e=`\n fn setOutputAtIndex(flatIndex : i32, value : ${A?`vec4<${Ae}>`:Ae}) {\n result[flatIndex] = ${A?`vec4<${Ae}>`:Ae}(value);\n }\n fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${A?`vec4<${Ae}>`:Ae}) {\n let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3));\n setOutputAtIndex(flatIndex ${A?"/ 4":""}, value);\n }`,Se=Q("x",e[0].dataType,e[0].dims.length,V===3?1:V),xe=Q("w",e[1].dataType,e[1].dims.length,Me),De=[Se,xe],Ye=ne("result",e[0].dataType,t.length,Me);if(m){let te=Q("bias",e[2].dataType,e[2].dims.length,Me);De.push(te),$e+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${A?`vec4<${Ae}>`:Ae} {\n return bias[coords.${y?"w":"y"}${A?"/ 4":""}];\n }`}return`\n ${mi("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 ${ae.registerUniforms(ye).declareVariables(...De,Ye)}\n ${$e}\n ${Eg(y,ie,ge,L,m,r,ue[0],ue[1],ue[2],Ae)}\n ${A?cn(k,D,Ae,void 0,!y,J):fn(k,D,Ae,void 0,!y,J,!1,void 0,d)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${r.cacheKey};${V};${A};${ie};${ge};${L};${q};${Y};${J}`,inputDependencies:Ne},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:G[0],y:G[1],z:G[2]},programUniforms:je}),getShaderSource:_e}}});var Zi,ld=oe(()=>{"use strict";Ce();Oe();Ee();Xi();qt();Zi=(e,r,t)=>{let u=e.length>2,s=u?"value += b[output_channel];":"",f=e[0].dims,m=e[1].dims,d=m[0]/r.group,y=r.format==="NHWC",w=Qi(f,m,r.dilations,r.pads,r.strides,y),C=X.size(w),$=[{type:12,data:C},{type:12,data:r.dilations},{type:12,data:[r.strides[0],r.strides[1]]},{type:12,data:[r.pads[0],r.pads[1]]},{type:12,data:d}];Ut(r,$),$.push(...se(f,m,w));let T=["rank","rank"];u&&($.push(...se(e[2].dims)),T.push("rank")),$.push(...se(w));let P=A=>{let O=ne("output",e[0].dataType,w.length),R=Ve(O.type.tensor),D=zt(r,O.type.value,R),k=Q("x",e[0].dataType,f.length),G=Q("w",e[1].dataType,m.length),V=[k,G];u&&V.push(Q("b",e[2].dataType,e[2].dims));let q=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:r.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return jt(r,q),`\n ${A.registerUniforms(q).declareVariables(...V,O)}\n\n ${A.mainStart()}\n ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let outputIndices = ${O.offsetToIndices("global_idx")};\n let batch: u32 = outputIndices[0];\n let output_channel: u32 = outputIndices[${y?3:1}];\n let xRCCorner: vec2 = vec2(outputIndices[${y?1:2}], outputIndices[${y?2:3}]) * uniforms.strides - uniforms.pads;\n let group_id: u32 = output_channel / uniforms.output_channels_per_group;\n\n var value: ${O.type.value} = ${O.type.value}(0);\n for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {\n let input_channel = group_id * uniforms.w_shape[1] + wInChannel;\n for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {\n let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];\n\n if (xHeight < 0u || xHeight >= uniforms.x_shape[${y?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[${y?2:3}]) {\n continue;\n }\n\n let xVal = ${y?k.get("batch","xHeight","xWidth","input_channel"):k.get("batch","input_channel","xHeight","xWidth")};\n let wVal = ${G.get("output_channel","wInChannel","wHeight","wWidth")};\n value += xVal*wVal;\n }\n }\n }\n ${s}\n ${D}\n ${O.setByOffset("global_idx","value")}\n }`};return{name:"GroupedConv",shaderCache:{hint:r.cacheKey,inputDependencies:T},getRunData:()=>({outputs:[{dims:t?t(w):w,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:$}),getShaderSource:P}}});var Ji,Pg,dd,eo=oe(()=>{"use strict";Ce();Oe();pn();Ee();qt();Ji=(e,r,t,u,s=!1)=>{let f=e[0].dims,m=e[1].dims,d=f[f.length-2],y=m[m.length-1],w=f[f.length-1],C=Je(y),$=Je(w),T=Je(d),P=X.size(t)/C/T,A=e.length>2,O=u?u.slice(0,-2):t.slice(0,-2),D=[X.size(O),d,y],k=[{type:12,data:P},{type:12,data:d},{type:12,data:y},{type:12,data:w}];Ut(r,k),k.push(...se(O,f,m)),A&&k.push(...se(e[2].dims)),k.push(...se(D));let G=V=>{let q=ii("batch_dims",e[0].dataType,O.length),Y=Q("a",e[0].dataType,f.length,$),J=Q("b",e[1].dataType,m.length,C),ie=ne("output",e[0].dataType,D.length,C),ge=Ve(ie.type.tensor),L=zt(r,ie.type.value,ge),ue=[Y,J],je="";if(A){let Se=s?C:1;ue.push(Q("bias",e[2].dataType,e[2].dims.length,Se)),je=`${s?`value += bias[col / ${Se}];`:`value += ${ie.type.value}(bias[row + i]);`}`}let Ne=f.slice(0,-2),_e=m.slice(0,-2),ae=Tr(Ne,O),ye=Tr(_e,O),Me=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];jt(r,Me);let Ae=(Se,xe)=>{let De=Se.rank,Ye=Se.name;if(De===2)return`var ${Ye}_indices = ${Se.type.indices}(0u, 0u);`;let te=q.rank,fe=`var ${Ye}_indices: ${Se.type.indices};`;for(let Ie=De-2-1,st=te-1;Ie>=0;Ie--,st--)fe+=`\n${Ye}_indices[${Ie}] = ${te>1?`batch_indices[${st}]`:"batch_indices"};`;return xe.forEach(Ie=>{fe+=`\n${Ye}_indices[${Ie}] = 0;`}),fe+=`${Ye}_indices[${De-2}] = 0u;\n ${Ye}_indices[${De-1}] = 0u;`,fe},$e=()=>{let Se=`var a_data: ${Y.type.value};`;for(let xe=0;xe<$;xe++)Se+=`\n let b_data${xe} = b[(b_offset + (k + ${xe}) * uniforms.N + col) / ${C}];`;for(let xe=0;xe;\n for (var k: u32 = 0u; k < uniforms.K; k = k + ${$}) {\n ${$e()}\n }\n for (var i = 0u; i < ${T}u; i++) {\n var value = values[i];\n ${je}\n ${L}\n let cur_indices = ${ie.type.indices}(batch, row + i, col);\n let offset = ${ie.indicesToOffset("cur_indices")};\n ${ie.setByOffset(`offset / ${C}`,"value")};\n }\n }\n `};return{name:"MatMulNaive",shaderCache:{hint:`${r.activation};${C};${$};${T};${s}`,inputDependencies:A?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(P/64)},programUniforms:k}),getShaderSource:G}},Pg=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},dd=e=>{Pg(e.inputs);let r=Ot.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!r)throw new Error("Can\'t use matmul on the given tensors");let t=r[r.length-1],u=e.inputs[0].dims[e.inputs[0].dims.length-1];t<8&&u<8?e.compute(Ji(e.inputs,{activation:""},r)):e.compute(hi(e.inputs,{activation:""},r))}});var Qi,cd,Og,fd,to,kg,Rg,ro,Xi=oe(()=>{"use strict";Oe();ud();pn();ld();qt();eo();Er();Qi=(e,r,t,u,s,f)=>{let m=e[0],d=e.slice(f?1:2,f?3:4),y=d.length,w=r[0],$=r.slice(2).map((A,O)=>A+(A-1)*(t[O]-1)),P=d.map((A,O)=>A+u[O]+u[O+y]).map((A,O)=>Math.floor((A-$[O]+s[O])/s[O]));return P.splice(0,0,m),P.splice(f?3:1,0,w),P},cd=[2,3,1,0],Og=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let t=e[0].dims[r.format==="NHWC"?e[0].dims.length-1:1],u=e[1].dims[1]*r.group;if(t!==u)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let s=e[0].dims.length-2;if(r.dilations.length!==s)throw new Error(`dilations should be ${s}D`);if(r.strides.length!==s)throw new Error(`strides should be ${s}D`);if(r.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},fd=(e,r)=>{let t=e.kernelShape.slice();for(let f=2;f{let r=ci(e),t=e.format,u=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],s=e.dilations,f=e.group,m=e.kernel_shape,d=e.pads,y=e.strides,w=e.w_is_const();return{autoPad:u,format:t,dilations:s,group:f,kernelShape:m,pads:d,strides:y,wIsConst:w,...r,cacheKey:`${e.format};${r.activation};`}},kg=(e,r,t)=>{let u=fd(t,r),s=t.format==="NHWC";if(t.group!==1){e.compute(Zi(r,u));return}let f=r.length===3,m=r[0].dims[s?1:2],d=r[0].dims[s?2:3],y=r[0].dims[s?3:1],w=r[1].dims[2],C=r[1].dims[3],$=Qi(r[0].dims,r[1].dims,t.dilations,u.pads,t.strides,s),T=$[s?1:2],P=$[s?2:3],A=$[s?3:1],O=s&&w===m&&C===d&&t.pads[0]===0&&t.pads[1]===0;if(O||w===1&&C===1&&t.dilations[0]===1&&t.dilations[1]===1&&t.strides[0]===1&&t.strides[1]===1&&t.pads[0]===0&&t.pads[1]===0){let Y=$[0],J,ie,ge,L=[];if(s){let Ne=e.kernelCustomData.wT??e.compute(xt(r[1],cd),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];if(t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ne),O){let _e=m*d*y;J=r[0].reshape([1,Y,_e]),ie=Ne.reshape([1,_e,A]),ge=[1,Y,A]}else J=r[0].reshape([Y,m*d,y]),ie=Ne.reshape([1,y,A]),ge=[Y,T*P,A];L.push(J),L.push(ie)}else J=r[0].reshape([Y,y,m*d]),ie=r[1].reshape([1,A,y]),ge=[Y,A,T*P],L.push(ie),L.push(J);f&&L.push(r[2]);let ue=ge[2],je=L[0].dims[L[0].dims.length-1];ue<8&&je<8?e.compute(Ji(L,u,$,ge,s),{inputs:L}):e.compute(hi(L,u,$,ge,s),{inputs:L});return}let R=!0,D=e.kernelCustomData.wT??e.compute(xt(r[1],cd),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=D);let k=[r[0],D];f&&k.push(r[2]);let G=s?T*P:A,V=s?A:T*P,q=w*C*y;e.compute(sd(k,u,$,G,V,q,f,R),{inputs:k})},Rg=(e,r)=>{let t=r.format==="NHWC",u=[e.inputs[0].reshape(t?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&u.push(e.inputs[2]);let s=[0,r.pads[0],0,r.pads[1]],f=[1].concat(r.strides),m=[1].concat(r.dilations),d=[1].concat(r.kernelShape),y=fd({...r,pads:s,strides:f,dilations:m,kernelShape:d},u);e.compute(Zi(u,y,w=>t?[w[0],w[2],w[3]]:[]))},ro=(e,r)=>{Og(e.inputs,r),e.inputs[0].dims.length===3?Rg(e,r):kg(e,e.inputs,r)}});var Dg,pd,md=oe(()=>{"use strict";Ce();Ft();Ee();qt();pi();Yi();pn();Dg=(e,r=!1,t,u,s=4)=>{let f=D=>{switch(D){case 1:return"return w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];\n let v1 = w[getIndexFromCoords4D(coord1, vec4(uniforms.w_shape))];\n let v2 = w[getIndexFromCoords4D(coord2, vec4(uniforms.w_shape))];\n let v3 = w[getIndexFromCoords4D(coord3, vec4(uniforms.w_shape))];\n return ${u}(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=e?`\n let coord = vec4(batch, iXR, iXC, xCh);\n `:`\n let coord = vec4(batch, xCh, iXR, iXC);\n `,d=e?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,y=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",w=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",C=e?"row":"col",$=e?"col":"row",T=`\n let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n let outRow = ${C} / outWidth;\n let outCol = ${C} % outWidth;\n\n let WRow = ${$} / (uniforms.filter_dims[1] * inChannels);\n let WCol = ${$} / inChannels % uniforms.filter_dims[1];\n let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(${y}) || fract(xR) > 0.0) {\n return ${u}(0.0);\n }\n if (xC < 0.0 || xC >= f32(${w}) || fract(xC) > 0.0) {\n return ${u}(0.0);\n }\n let iXR = i32(xR);\n let iXC = i32(xC);\n let xCh = ${$} % inChannels;\n ${m}\n return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${s}];`,P=e?`\n let col = colIn * ${s};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n ${T}\n }\n return ${u}(0.0);`:`\n let col = colIn * ${s};\n if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n ${T}\n }\n return ${u}(0.0);`,A=`\n let col = colIn * ${s};\n let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);\n let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];\n if (${e?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"} && coordX >= 0 && coordY >= 0) {\n let rowInner = row % inChannels;\n let coord = vec4(coordX, coordY, col, rowInner);\n ${f(s)}\n }\n return ${u}(0.0);\n `,O=zt(t,u);return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${u} {\n ${e?P:A}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${u} {\n ${e?A:P}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${u}) {\n let col = colIn * ${s};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n var value = valueInput;\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n ${d}\n ${fi(r)}\n ${O}\n result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${s}] = value;\n }\n }`},pd=(e,r,t,u,s,f,m,d)=>{let y=r.format==="NHWC",w=y?e[0].dims[3]:e[0].dims[1],C=t[0],$=y?t[2]:t[3],T=y?t[1]:t[2],P=y?t[3]:t[1],A=y?w%4===0&&P%4===0:$%4===0&&P%4===0,O=y?P:$*T,R=y?$*T:P,D=A?[8,8,1]:[O<=4||R<=4?4:16,O>4&&R<=4?4:16,1],k=A?[4,4,1]:[O<=4?1:4,O>4&&R<=4?1:4,1],G=[Math.ceil(O/D[0]/k[0]),Math.ceil(R/D[1]/k[1]),Math.ceil(C/D[2]/k[2])];He("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${G}`);let V=A?4:1,q=Math.max(D[0]*V,D[1]),Y=A?4:1,J=[r.kernelShape[y?1:2],r.kernelShape[y?2:3]],ie=[J[0]+(r.dilations[0]<=1?0:(J[0]-1)*(r.dilations[0]-1)),J[1]+(r.dilations[1]<=1?0:(J[1]-1)*(r.dilations[1]-1))],ge=[ie[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),ie[1]-1-Math.floor((r.pads[1]+r.pads[3])/2)],L=[{type:6,data:u},{type:6,data:s},{type:6,data:f},{type:6,data:r.strides},{type:6,data:r.dilations},{type:6,data:J},{type:6,data:ge}];Ut(r,L),L.push(...se(e[0].dims,e[1].dims));let ue=["rank","rank"];m&&(L.push(...se(e[2].dims)),ue.push("rank")),L.push(...se(t));let je=Ne=>{let _e=Q("x",e[0].dataType,e[0].dims.length,Y),ae=Q("w",e[1].dataType,e[1].dims.length,1),ye=ne("result",e[0].dataType,t.length,Y),Me=[_e,ae],Ae="";if(m){let xe=Q("bias",e[2].dataType,e[2].dims.length,Y);Me.push(xe),Ae+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${xe.type.value} {\n return bias[coords.${y?"w":"y"}${A?"/ 4":""}];\n }`}let $e=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:J.length},{name:"pads",type:"i32",length:ge.length}];jt(r,$e);let Se=Ve(e[0].dataType,1);if(Se!=="f16"&&Se!=="f32")throw new Error(`elemType ${Se} is not supported.`);return`\n ${mi("uniforms.result_strides")}\n ${Ne.registerUniforms($e).declareVariables(...Me,ye)};\n ${Ae}\n ${Dg(y,m,r,_e.type.value,V)}\n ${A?cn(k,D,Se,void 0,!y,q):fn(k,D,Se,void 0,!y,q,!1,void 0,d)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${r.cacheKey};${k};${D};${A}`,inputDependencies:ue},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:G[0],y:G[1],z:G[2]},programUniforms:L}),getShaderSource:je}}});var Bg,no,hd=oe(()=>{"use strict";Ce();Ft();Oe();Ee();Bg=(e,r,t,u,s,f=!1,m,d,y=!1)=>{let w=y?1:2,C=y?2:3,$=y?3:1,T=f?2:1,P=`\n fn setOutputAtIndex(flatIndex : u32, value : ${f?`vec4<${m}>`:m}) {\n result[flatIndex] = ${f?`vec4<${m}>`:m}(value);\n }`;u&&(P+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${f?`vec4<${m}>`:m} {\n return bias[coords.${y?"w":"y"}${f?"/ 4":""}];\n }`);let A=f?4:1,O=Q("W",r[1].dataType,r[1].dims.length,A),R=Q("Dy",r[0].dataType,r[0].dims.length,A),D=[R,O];u&&D.push(Q("bias",r[2].dataType,[t[$]].length,A));let k=ne("result",r[0].dataType,t.length,A),G=`{\n let batch: u32 = ${s?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];\n let r = ${s?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];\n let c = ${s?"global_id.y":"workgroup_id.y"} * ${T};\n let d1: u32 = ${s?"global_id.x":"workgroup_id.x"} * 4;\n\n let dyCorner = vec2(i32(r), i32(c)) - vec2(uniforms.pads);\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${T}>;\n for (var i = 0; i < ${T}; i++) {\n dotProd[i] = vec4<${m}>(0.0);\n }\n for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\n var dyR = (${m}(dyCorner.x) + ${m}(wR)) / ${m}(uniforms.strides.x);\n let wRPerm = uniforms.filter_dims[0] - 1 - wR;\n if (dyR < 0.0 || dyR >= ${m}(uniforms.Dy_shape[1]) ||\n fract(dyR) > 0.0 || wRPerm < 0) {\n continue;\n }\n let idyR: u32 = u32(dyR);\n\n for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\n let dyC = (${m}(dyCorner.y) + ${m}(wC)) / ${m}(uniforms.strides.y);\n let dyC2 = (${m}(dyCorner.y) + 1.0 + ${m}(wC)) / ${m}(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 >= ${m}(uniforms.Dy_shape[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= ${m}(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 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${R.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${m}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n\n xValue = ${R.get("batch","idyR","idyC2","d2")};\n\n dotProd[1] = dotProd[1] + vec4<${m}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.Dy_shape[${$}];\n for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${R.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${m}>(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 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${O.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${R.get("batch","idyR","idyC2","d2")};\n let tmpval = vec4<${m}>(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 < ${T}; i = i + 1) {\n let value = dotProd[i] + ${u?"bias[c+i]":`vec4<${m}>(0.0)`};\n ${k.set("batch","r","c + i","d1","value")};\n }\n }`,V=`\n let outputIndices = ${k.offsetToIndices("global_idx")};\n let batch = ${k.indicesGet("outputIndices",0)};\n let d1 = ${k.indicesGet("outputIndices",$)};\n let r = ${k.indicesGet("outputIndices",w)};\n let c = ${k.indicesGet("outputIndices",C)};\n let dyCorner = vec2(i32(r), i32(c)) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n let groupId = d1 / uniforms.output_channels_per_group;\n let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = ${m}(0.0);\n for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\n if (wR % uniforms.dilations.x != 0) {\n continue;\n }\n let dyR = (${m}(dyRCorner) + ${m}(wR)) / ${m}(uniforms.strides[0]);\n let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\n if (dyR < 0.0 || dyR >= ${m}(uniforms.Dy_shape[${w}]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR: u32 = u32(dyR);\n\n for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {\n if (wC % uniforms.dilations.y != 0) {\n continue;\n }\n let dyC = (${m}(dyCCorner) + ${m}(wC)) / ${m}(uniforms.strides.y);\n let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\n if (dyC < 0.0 || dyC >= ${m}(uniforms.Dy_shape[${C}]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC: u32 = u32(dyC);\n var inputChannel = groupId * uniforms.input_channels_per_group;\n for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {\n let xValue = ${y?R.get("batch","idyR","idyC","inputChannel"):R.get("batch","inputChannel","idyR","idyC")};\n let wValue = ${O.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n dotProd = dotProd + xValue * wValue;\n inputChannel = inputChannel + 1;\n }\n }\n }\n let value = dotProd + ${u?"bias[d1]":`${m}(0.0)`};\n ${k.setByOffset("global_idx","value")};\n `;return`\n ${e.registerUniforms(d).declareVariables(...D,k)}\n ${P}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};\n ${f?G:V}}`},no=(e,r,t)=>{let u=e.length>2,s=r.outputShape,f=X.size(s),m=[Math.ceil(f/64),1,1];He("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${m}`);let d=r.format==="NHWC",y=["rank","rank"],w=[r.strides[0],r.strides[1]],C=[r.kernelShape[d?1:2],r.kernelShape[d?2:3]],$=[r.dilations[0],r.dilations[1]],T=[C[0]+(r.dilations[0]<=1?0:(r.kernelShape[d?1:2]-1)*(r.dilations[0]-1)),C[1]+(r.dilations[1]<=1?0:(r.kernelShape[d?2:3]-1)*(r.dilations[1]-1))],P=[T[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),T[1]-1-Math.floor(r.pads[1]+r.pads[3])/2],A=!1,O=r.group,R=e[1].dims,D=R[0]/O,k=R[1],G=[{type:6,data:f},{type:12,data:w},{type:12,data:C},{type:12,data:$},{type:12,data:T},{type:6,data:P},{type:12,data:D},{type:12,data:k},...se(e[0].dims,e[1].dims)];u&&(G.push(...se(e[2].dims)),y.push("rank")),G.push(...se(s));let V=m[1]===1&&m[2]===1,q=Y=>{let J=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:w.length},{name:"filter_dims",type:"u32",length:C.length},{name:"dilations",type:"u32",length:C.length},{name:"effective_filter_dims",type:"u32",length:T.length},{name:"pads",type:"i32",length:P.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],ie=Ve(e[0].dataType);return`${Bg(Y,e,s,u,V,A,ie,J,d)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${r.cacheKey};`,inputDependencies:y},getRunData:()=>({dispatchGroup:{x:m[0],y:m[1],z:m[2]},outputs:[{dims:t?t(s):s,dataType:e[0].dataType}],programUniforms:G}),getShaderSource:q}}});var Mg,zg,Ug,gd,yd,jg,Vg,Ng,Wg,bd,vd=oe(()=>{"use strict";md();hd();qt();Er();Mg=(e,r,t,u,s,f)=>(e-1)*r+t+(u-1)*s+1-f,zg=(e,r,t,u,s)=>{let f=Math.floor(e/2);r==="SAME_UPPER"?(t[u]=f,t[s]=e-f):r==="SAME_LOWER"&&(t[u]=e-f,t[s]=f)},Ug=(e,r,t,u,s,f,m,d,y,w)=>{let C=e.length-2,$=w.length===0;if(y.length===0)for(let A=0;A{let t=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce(($,T)=>$*T,1)===0){t.length=0;for(let $=2;$$+T,0)===0){let $=r[0].dims.length-2;y=new Array($).fill(1)}let w=e.strides.slice();if(w.reduce(($,T)=>$+T,0)===0){let $=r[0].dims.length-2;w=new Array($).fill(1)}Ug(d,t,y,e.autoPad,e.group,s,w,u,m,f);let C=Object.assign({},e);return Object.assign(C,{kernelShape:t,pads:s,outputPadding:m,outputShape:f,dilations:y,strides:w}),C},yd=e=>{let r=ci(e),t=e.format,u=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],s=e.dilations,f=e.group,m=e.kernelShape,d=e.pads,y=e.strides,w=e.wIsConst(),C=e.outputPadding,$=e.outputShape;return{autoPad:u,format:t,dilations:s,group:f,kernelShape:m,outputPadding:C,outputShape:$,pads:d,strides:y,wIsConst:w,...r,cacheKey:`${e.format};${r.activation};`}},jg=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let t=e[0].dims[r.format==="NHWC"?e[0].dims.length-1:1],u=e[1].dims[0];if(t!==u)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let s=e[1].dims[1]*r.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==s))throw new Error("invalid bias");let f=e[0].dims.length-2;if(r.dilations.reduce((C,$)=>C+$,0)>0&&r.dilations.length!==f)throw new Error(`dilations should be ${f}D`);if(r.strides.reduce((C,$)=>C+$,0)>0&&r.strides.length!==f)throw new Error(`strides should be ${f}D`);if(r.pads.reduce((C,$)=>C+$,0)>0&&r.pads.length!==f*2)throw new Error(`pads should be ${f*2}D`);if(r.outputPadding.length!==f&&r.outputPadding.length!==0)throw new Error(`output_padding should be ${f}D`);if(r.kernelShape.reduce((C,$)=>C+$,0)>0&&r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel 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s=r.kernelShape;(s.length===0||s[0]===0)&&(s=[e.inputs[1].dims[2]]);let f=r.dilations;(f.length===0||f[0]===0)&&(f=[1]);let m=r.strides;(m.length===0||m[0]===0)&&(m=[1]);let d=r.pads;d.length===0&&(d=[0,0]),d=[0,d[0],0,d[1]],m=[1].concat(m),f=[1].concat(f),s=[1].concat(s);let y=gd({...r,pads:d,strides:m,dilations:f,kernelShape:s},u);e.compute(no(u,y,w=>t?[w[0],w[2],w[3]]:[w[0],w[1],w[3]]))},bd=(e,r)=>{jg(e.inputs,r),e.inputs[0].dims.length===3?Wg(e,r):Ng(e,e.inputs,r)}});var Gg,wd,_d,$d=oe(()=>{"use strict";Ce();Oe();nt();Ee();Gg=(e,r,t,u)=>{let s=X.size(r),f=r.length,m=Q("input",e,f),d=ne("output",e,f),y=t.dataType===6?t.getInt32Array()[0]:Number(t.getBigInt64Array()[0]),w=X.normalizeAxis(y,f),C=$=>{let T=` i32(${m.indicesGet("inputIndices","uniforms.axis")}) `,P=ve("uniforms.input_shape","uniforms.axis",f),A=u.reverse?T+(u.exclusive?" + 1":""):"0",O=u.reverse?P:T+(u.exclusive?"":" + 1");return`\n 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io,gi,Cd,Hg,Lg,oo,ao,Sd,Fg,xd,Id,Ad=oe(()=>{"use strict";Ce();Oe();nt();Ee();io="[a-zA-Z]|\\\\.\\\\.\\\\.",gi="("+io+")+",Cd="^"+gi+"$",Hg="("+gi+",)*"+gi,Lg="^"+Hg+"$",oo=class{constructor(r=-1){this.symbolToIndices=new Map,this.inputIndex=r}addSymbol(r,t){let u=this.symbolToIndices.get(r);u===void 0?u=[t]:u.push(t),this.symbolToIndices.set(r,u)}},ao=class{constructor(r,t){this.equation=t;this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[u,s]=t.includes("->")?t.split("->",2):[t,""];if(!u.match(RegExp(Lg)))throw new Error("Invalid LHS term");if(u.split(",").forEach((d,y)=>{let w=r[y].dims.slice();if(!d.match(RegExp(Cd)))throw new Error("Invalid LHS term");let C=this.processTerm(d,!0,w,y);this.lhs.push(C)}),s==="")s+=[...this.symbolToInfo.entries()].filter(([d,y])=>y.count===1||d==="...").map(([d])=>d).join("");else if(!s.match(RegExp(gi)))throw new Error("Invalid 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error");ge.forEach(L=>{O.push(`${f[ie].indicesSet(`input${ie}Indices`,L,`${Y}`)}`)}),k.push(`prod *= ${f[ie].getByIndices(`input${ie}Indices`)};`)}}),R.push(`for(var ${Y}: u32 = 0; ${Y} < uniforms.${Sd(Y)}; ${Y}++) {`),D.push("}")});let V=G?[...$,`let sum = ${f.map((q,Y)=>q.getByIndices(`input${Y}Indices`)).join(" * ")};`]:[...$,P,...R,...O,T,...k,A,...D];return`\n ${C.registerUniforms(y.map(q=>({name:`${Sd(q)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...f,d)}\n\n ${C.mainStart()}\n ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n var outputIndices = ${d.offsetToIndices("global_idx")};\n ${f.map((q,Y)=>`var input${Y}Indices: ${f[Y].type.indices};`).join(`\n`)}\n ${V.join(`\n`)};\n ${d.setByOffset("global_idx","sum")};\n }`};return{name:"Einsum",shaderCache:{hint:t.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let C=y.filter(T=>t.symbolToInfo.has(T)).map(T=>({type:12,data:t.symbolToInfo.get(T)?.dimValue||0}));C.push({type:12,data:m});let $=e.map((T,P)=>[...se(T)]).reduce((T,P)=>T.concat(P),C);return $.push(...se(u)),{outputs:[{dims:u,dataType:r}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:$}},getShaderSource:w}},xd=(e,r)=>{let t=new ao(e.inputs,r.equation),u=t.outputDims,s=e.inputs.map((f,m)=>f.dims);e.compute(Fg(s,e.inputs[0].dataType,t,u))},Id=e=>{let r=e.equation.replace(/\\s+/g,"");return Pe({equation:r})}});var qg,Td,Kg,Yg,Ed,Pd=oe(()=>{"use strict";Ce();Oe();Ee();qg=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let r=e[0].dims,t=Array.from(e[1].getBigInt64Array(),Number),u=t.length{let t=e.length-r.length,u=[];for(let s=0;se.length>r.length?Td(e,r):Td(r,e),Yg=e=>{let r=e[0].dims,t=Array.from(e[1].getBigInt64Array(),Number),u=Kg(r,t),s=e[0].dataType,f=s===9?4:1,m=Math.ceil(X.size(u)/f),d=w=>{let C=Q("input",s,r.length,f),$=ne("output",s,u.length,f),T;if(s===9){let P=(A,O,R="")=>`\n let outputIndices${O} = ${$.offsetToIndices(`outputOffset + ${O}u`)};\n let offset${O} = ${C.broadcastedIndicesToOffset(`outputIndices${O}`,$)};\n let index${O} = offset${O} / 4u;\n let component${O} = offset${O} % 4u;\n ${A}[${O}] = ${R}(${C.getByOffset(`index${O}`)}[component${O}]);\n `;T=`\n let outputOffset = global_idx * ${f};\n var data = vec4(0);\n ${P("data",0,"u32")}\n ${P("data",1,"u32")}\n ${P("data",2,"u32")}\n ${P("data",3,"u32")}\n ${$.setByOffset("global_idx","data")}\n }`}else T=`\n let outputIndices = ${$.offsetToIndices("global_idx")};\n let inputOffset = ${C.broadcastedIndicesToOffset("outputIndices",$)};\n ${$.setByOffset("global_idx",C.getByOffset("inputOffset"))}\n }`;return`\n ${w.registerUniform("vec_size","u32").declareVariables(C,$)}\n ${w.mainStart()}\n ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${T}`},y=[{type:12,data:m},...se(r,u)];return{name:"Expand",shaderCache:{hint:`${u.length}`,inputDependencies:["rank"]},getShaderSource:d,getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:y})}},Ed=e=>{qg(e.inputs),e.compute(Yg(e.inputs),{inputs:[0]})}});var Zg,Od,kd=oe(()=>{"use strict";Ce();Oe();Ee();di();Zg=e=>{let r=e[0].dataType,t=X.size(e[0].dims),u=X.size(e[1].dims),s=u%4===0,f=m=>{let d=Q("x",r,[1],4),y=Q("bias",r,[1],4),w=ne("y",r,[1],4),C=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],$=P=>`\n let bias${P}_offset: u32 = (global_idx * 4 + ${P}) % uniforms.bias_size;\n let bias${P} = ${y.getByOffset(`bias${P}_offset / 4`)}[bias${P}_offset % 4];`,T=s?`\n let bias = ${y.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${$(0)}${$(1)}${$(2)}${$(3)}\n let bias = ${d.type.value}(bias0, bias1, bias2, bias3);`;return`${m.registerUniforms(C).declareVariables(d,y,w)}\n\n ${qi(ct(r))}\n\n ${m.mainStart(ni)}\n ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}\n\n let x = ${d.getByOffset("global_idx")};\n ${T}\n let x_in = x + bias;\n ${w.setByOffset("global_idx",Ki("x_in"))}\n }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${s}`,inputDependencies:["type","type"]},getShaderSource:f,getRunData:m=>({outputs:[{dims:m[0].dims,dataType:m[0].dataType}],programUniforms:[{type:12,data:Math.ceil(t/4)},{type:12,data:u}],dispatchGroup:{x:Math.ceil(t/ni/4)}})}},Od=e=>{e.inputs.length<2||X.size(e.inputs[1].dims)===0?Vl(e):e.compute(Zg(e.inputs))}});var Qg,Xg,Rd,Dd,Bd=oe(()=>{"use strict";Ce();Oe();nt();Ee();Qg=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},Xg=(e,r)=>{let t=e[0].dims,u=e[1].dims,s=t.length,f=X.normalizeAxis(r.axis,s),m=t.slice(0);m.splice(f,1,...u);let d=t[f],y=e[0].dataType===9?4:1,w=Math.ceil(X.size(m)/y),C=[{type:12,data:w},{type:6,data:d},{type:12,data:f},...se(e[0].dims,e[1].dims,m)],$=T=>{let P=Q("data",e[0].dataType,e[0].dims.length,y),A=Q("inputIndices",e[1].dataType,e[1].dims.length),O=ne("output",e[0].dataType,m.length,y),R=k=>{let G=u.length,V=`var indicesIndices${k} = ${A.type.indices}(0);`;for(let q=0;q1?`indicesIndices${k}[${q}]`:`indicesIndices${k}`} = ${m.length>1?`outputIndices${k}[uniforms.axis + ${q}]`:`outputIndices${k}`};`;V+=`\n var idx${k} = ${A.getByIndices(`indicesIndices${k}`)};\n if (idx${k} < 0) {\n idx${k} = idx${k} + uniforms.axisDimLimit;\n }\n var dataIndices${k} : ${P.type.indices};\n `;for(let q=0,Y=0;q1?`dataIndices${k}[${q}]`:`dataIndices${k}`} = u32(idx${k});`,Y+=G):(V+=`${s>1?`dataIndices${k}[${q}]`:`dataIndices${k}`} = ${m.length>1?`outputIndices${k}[${Y}]`:`outputIndices${k}`};`,Y++);return V},D;if(e[0].dataType===9){let k=(G,V,q="")=>`\n let outputIndices${V} = ${O.offsetToIndices(`outputOffset + ${V}u`)};\n ${R(V)};\n let offset${V} = ${P.indicesToOffset(`dataIndices${V}`)};\n let index${V} = offset${V} / 4u;\n let component${V} = offset${V} % 4u;\n ${G}[${V}] = ${q}(${P.getByOffset(`index${V}`)}[component${V}]);\n `;D=`\n let outputOffset = global_idx * ${y};\n var value = vec4(0);\n ${k("value",0,"u32")}\n ${k("value",1,"u32")}\n ${k("value",2,"u32")}\n ${k("value",3,"u32")}\n ${O.setByOffset("global_idx","value")}\n `}else D=`\n let outputIndices = ${O.offsetToIndices("global_idx")};\n ${R("")};\n let value = ${P.getByIndices("dataIndices")};\n ${O.setByOffset("global_idx","value")};\n `;return`\n ${T.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(P,A,O)}\n ${T.mainStart()}\n ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n ${D}\n }`};return{name:"Gather",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:m,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:C}),getShaderSource:$}},Rd=e=>Pe({axis:e.axis}),Dd=(e,r)=>{let t=e.inputs;Qg(t),e.compute(Xg(e.inputs,r))}});var Jg,ey,Md,zd,Ud=oe(()=>{"use strict";Ce();Oe();nt();Ee();Jg=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and\n indices input tensors be of same rank.`)},ey=(e,r)=>{let t=e[0].dims,u=e[0].dataType,s=t.length,f=e[1].dims,m=e[1].dataType,d=X.normalizeAxis(r.axis,s),y=t[d],w=f.slice(0),C=X.size(w),$=Q("input",u,s),T=Q("indicesInput",m,f.length),P=ne("output",u,w.length),A=[{type:12,data:C},{type:6,data:y},{type:12,data:d}];return A.push(...se(t,f,w)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:w,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:A}),getShaderSource:D=>`\n ${D.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables($,T,P)}\n ${D.mainStart()}\n ${D.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n let outputIndices = ${P.offsetToIndices("global_idx")};\n\n var idx = ${T.getByOffset("global_idx")};\n if (idx < 0) {\n idx = idx + uniforms.axisDimLimit;\n }\n var inputIndices = ${$.type.indices}(outputIndices);\n ${$.indicesSet("inputIndices","uniforms.axis","u32(idx)")};\n let value = ${$.getByIndices("inputIndices")};\n\n ${P.setByOffset("global_idx","value")};\n }`}},Md=e=>Pe({axis:e.axis}),zd=(e,r)=>{let t=e.inputs;Jg(t),e.compute(ey(e.inputs,r))}});var ty,ry,jd,Vd,Nd=oe(()=>{"use strict";Ce();Oe();Ee();ty=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},ry=(e,r)=>{let t=e[0].dims.slice(),u=e[1].dims.slice(),[s,f,m]=ei.getShapeOfGemmResult(t,r.transA,u,r.transB,e.length===3?e[2].dims:void 0),d=[s,f];if(!d)throw new Error("Can\'t use gemm on the given tensors");let y=X.size(d),w=[{type:12,data:y},{type:12,data:s},{type:12,data:f},{type:12,data:m},{type:1,data:r.alpha},{type:1,data:r.beta}],C=["type","type"];e.length===3&&(w.push(...se(e[2].dims)),C.push("rank")),w.push(...se(d));let $=T=>{let P="";r.transA&&r.transB?P="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":r.transA&&!r.transB?P="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!r.transA&&r.transB?P="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!r.transA&&!r.transB&&(P="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let A=r.alpha===1?"":"value *= uniforms.alpha;",O=Q("a",e[0].dataType,e[0].dims),R=Q("b",e[1].dataType,e[1].dims),D=O.type.value,k=null,G=[O,R];e.length===3&&(k=Q("c",e[2].dataType,e[2].dims.length),G.push(k));let V=ne("output",e[0].dataType,d.length);G.push(V);let q=[{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 ${T.registerUniforms(q).declareVariables(...G)}\n\n ${T.mainStart()}\n ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let m = global_idx / uniforms.N;\n let n = global_idx % uniforms.N;\n\n var value = ${D}(0);\n for (var k: u32 = 0u; k < uniforms.K; k++) {\n ${P}\n }\n\n ${A}\n ${(()=>k!=null?`let cOffset = ${k.broadcastedIndicesToOffset("vec2(m, n)",V)}; value += ${D}(uniforms.beta) * ${k.getByOffset("cOffset")};`:"")()}\n output[global_idx] = value;\n }`};return{name:"Gemm",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:w}),getShaderSource:$}},jd=e=>{let r=e.transA,t=e.transB,u=e.alpha,s=e.beta;return{transA:r,transB:t,alpha:u,beta:s,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},Vd=(e,r)=>{ty(e.inputs),e.compute(ry(e.inputs,r))}});var ny,iy,oy,Wd,Gd=oe(()=>{"use strict";Ce();Oe();Ee();ny=(e,r)=>{let t=e[0].dims,u=t,s=2,f=X.sizeToDimension(t,s),m=X.sizeFromDimension(t,s),d=Je(m),y=m/d,w=[t[0],t[1],y],C=["rank","type","type"],$=[{type:12,data:m},{type:12,data:y}];$.push(...se(w,w));let T=P=>{let A=Q("x",e[0].dataType,w.length,d),O=Q("scale",e[1].dataType,e[1].dims),R=Q("bias",e[2].dataType,e[2].dims),D=ne("output",e[0].dataType,w.length,d),k=[A,O,R,D],G=A.type.value,V=d===1?"f32":`vec${d}`,q=64,Y=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`\n var meanShared : f32;\n var squaredNormShared : f32;\n var workgroupShared : array<${V}, ${q}>;\n const workgroupSize = ${q}u;\n ${P.registerUniforms(Y).declareVariables(...k)}\n ${P.mainStart(q)}\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 = ${V}(0);\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n initial = initial + ${V}(${A.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 = ${ft("workgroupShared[0]",d)} / f32(uniforms.normSize);\n }\n workgroupBarrier();\n\n // reinitialize workgroup memory.\n initial = ${V}(0);\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let deviation = ${V}(${A.get("batch","channel","h")}) - ${V}(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 = ${ft("workgroupShared[0]",d)};\n }\n workgroupBarrier();\n\n let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${r.epsilon}));\n let channelScale = invStdDev * f32(${O.getByOffset("channel")});\n let channelShift = f32(${R.getByOffset("channel")}) - meanShared * channelScale;\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let value = ${A.get("batch","channel","h")} * ${G}(${V}(channelScale)) + ${G}(${V}(channelShift));\n ${D.set("batch","channel","h","value")};\n }\n }`};return{name:"InstanceNormalization",shaderCache:{hint:`${r.epsilon};${d}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:f},programUniforms:$}),getShaderSource:T}},iy=(e,r,t,u,s,f,m,d)=>{let y=Je(m),w=64,C=y===1?"vec2f":`mat2x${y}f`,$=y===1?"f32":`vec${y}f`,T=(Y,J)=>`${C}(${Y}, ${J})`,P=s*m/y,A=Math.ceil(f/w),O=["type"],R=[{type:12,data:A},{type:12,data:f},{type:12,data:Math.floor(m/y)},{type:12,data:Math.floor(f*m/y)}],D=Y=>{let J=Q("input",r.dataType,r.dims,y);return`\n ${Y.declareVariables(J)}\n @group(0) @binding(1) var output : array<${C}>;\n struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n @group(0) @binding(2) var uniforms: Uniforms;\n\n ${Y.mainStart(w)}\n let currentImageNumber = global_idx / ${w} / uniforms.C;\n let currentChannelNumber = (global_idx / ${w}) % uniforms.C;\n let wgId = global_idx % ${w};\n let wgOffset = wgId * 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 = ${ot("f32",y)};\n var squaredSum = ${ot("f32",y)};\n for (var i: u32 = wgOffset; i < wgMax; i++) {\n let value = ${$}(input[offset + i * uniforms.C]);\n sum += value;\n squaredSum += value * value;\n }\n output[global_idx] = ${T("sum","squaredSum")};\n }`},k=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${y}`,inputDependencies:O},getRunData:()=>({outputs:[{dims:[s,m,w,2],dataType:1}],dispatchGroup:{x:s*m/y},programUniforms:R}),getShaderSource:D},{inputs:[r],outputs:[-1]})[0],G=[{type:12,data:P},{type:12,data:f},{type:12,data:Math.floor(m/y)},{type:12,data:Math.floor(w*m/y)}],V=["type","type","type"],q=Y=>{let J=Q("scale",t.dataType,t.dims,y),ie=Q("bias",u.dataType,u.dims,y);return`\n @group(0) @binding(0) var input : array<${C}>;\n @group(0) @binding(1) var scale : array<${J.type.storage}>;\n @group(0) @binding(2) var bias : array<${ie.type.storage}>;\n @group(0) @binding(3) var output : array<${C}>;\n struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n @group(0) @binding(4) var uniforms: Uniforms;\n\n ${Y.mainStart()}\n ${Y.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 = ${ot("f32",y)};\n var squaredSum = ${ot("f32",y)};\n for (var i: u32 = 0; i < ${w}; i++) {\n let value = input[offset + i + currentChannelNumber * ${w}];\n sum += value[0];\n squaredSum += value[1];\n }\n sum = sum / f32(uniforms.H);\n squaredSum = squaredSum / f32(uniforms.H);\n let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${d}));\n let channelScale = invStdDev * ${$}(scale[currentChannelNumber]);\n let channelShift = ${$}(bias[currentChannelNumber]) - sum * channelScale;\n\n output[global_idx] = ${T("channelScale","channelShift")};\n }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${y};${d}`,inputDependencies:V},getRunData:()=>({outputs:[{dims:[s,m,2],dataType:1}],dispatchGroup:{x:Math.ceil(P/64)},programUniforms:G}),getShaderSource:q},{inputs:[k,t,u],outputs:[-1]})[0]},oy=(e,r,t)=>{let u=r[0].dims,s=u,f=u[0],m=u[u.length-1],d=X.sizeFromDimension(u,1)/m,y=Je(m),w=X.size(s)/y,C=[{type:12,data:d},{type:12,data:Math.floor(m/y)}],$=["type","type"],T=iy(e,r[0],r[1],r[2],f,d,m,t.epsilon),P=A=>{let O=Ve(r[0].dataType),R=y===1?"vec2f":`mat2x${y}f`,D=y===1?O:`vec${y}<${O}>`,k=Q("input",r[0].dataType,r[0].dims,y),G=ne("output",r[0].dataType,s,y);return`\n @group(0) @binding(0) var input : array<${k.type.storage}>;\n @group(0) @binding(1) var scaleInput : array<${R}>;\n @group(0) @binding(2) var output : array<${G.type.storage}>;\n struct Uniforms {H: u32, C : u32};\n @group(0) @binding(3) var uniforms: Uniforms;\n\n ${A.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], ${D}(scale[0]), ${D}(scale[1]));\n }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${y}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:s,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:C}),getShaderSource:P},{inputs:[r[0],T]})},Wd=(e,r)=>{r.format==="NHWC"?oy(e,e.inputs,r):e.compute(ny(e.inputs,r))}});var ay,sy,Hd,Ld=oe(()=>{"use strict";Ce();Oe();Ee();ay=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},sy=(e,r,t)=>{let u=e[0].dims,s=e[1],f=e[2],m=u,d=X.normalizeAxis(r.axis,u.length),y=X.sizeToDimension(u,d),w=X.sizeFromDimension(u,d),C=X.size(s.dims),$=f?X.size(f.dims):0;if(C!==w||f&&$!==w)throw new Error(`Size of X.shape()[axis:] == ${w}.\n Size of scale and bias (if provided) must match this.\n Got scale size of ${C} and bias size of ${$}`);let T=[];for(let V=0;V1,D=t>2,k=V=>{let q=Ve(e[0].dataType),Y=[Q("x",e[0].dataType,e[0].dims,P),Q("scale",s.dataType,s.dims,P)];f&&Y.push(Q("bias",f.dataType,f.dims,P)),Y.push(ne("output",e[0].dataType,m,P)),R&&Y.push(ne("mean_data_output",1,T)),D&&Y.push(ne("inv_std_output",1,T));let J=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n ${V.registerUniforms(J).declareVariables(...Y)}\n ${V.mainStart()}\n ${V.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n let offset = global_idx * uniforms.norm_size_vectorized;\n var mean_vector = ${ot("f32",P)};\n var mean_square_vector = ${ot("f32",P)};\n\n for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n let value = ${St(q,P,"x[h + offset]")};\n mean_vector += value;\n mean_square_vector += value * value;\n }\n let mean = ${ft("mean_vector",P)} / uniforms.norm_size;\n let inv_std_dev = inverseSqrt(${ft("mean_square_vector",P)} / uniforms.norm_size - mean * mean + uniforms.epsilon);\n\n for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n let f32input = ${St(q,P,"x[j + offset]")};\n let f32scale = ${St(q,P,"scale[j]")};\n output[j + offset] = ${Y[0].type.value}((f32input - mean) * inv_std_dev * f32scale\n ${f?`+ ${St(q,P,"bias[j]")}`:""}\n );\n }\n\n ${R?"mean_data_output[global_idx] = mean":""};\n ${D?"inv_std_output[global_idx] = inv_std_dev":""};\n }`},G=[{dims:m,dataType:e[0].dataType}];return R&&G.push({dims:T,dataType:1}),D&&G.push({dims:T,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${P};${t}`,inputDependencies:A},getRunData:()=>({outputs:G,dispatchGroup:{x:Math.ceil(y/64)},programUniforms:O}),getShaderSource:k}},Hd=(e,r)=>{ay(e.inputs),e.compute(sy(e.inputs,r,e.outputCount))}});var uy,ly,Fd,qd,Kd=oe(()=>{"use strict";Ce();Oe();nt();Ee();uy=(e,r)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let t=e[0],u=t.dims.length;if(t.dims[u-1]!==r.k)throw new Error("The last dim of input shape does not match the k value");let s=Math.floor((r.k+r.blockSize-1)/r.blockSize),f=r.blockSize/8*r.bits,m=e[1];if(!X.areEqual(m.dims,[r.n,s,f]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let y=e[2].dims;if(X.size(y)!==r.n*s)throw new Error("scales input size error.");if(e.length===4){let C=e[3].dims,$=r.bits>4?r.n*s:r.n*Math.floor((s+1)/2);if(X.size(C)!==$)throw new Error("zeroPoints input size error.")}},ly=(e,r)=>{let t=e[0],u=e[1],s=e[2],f=t.dims.length,m=t.dims.slice(0,f-1).concat(r.n),d=X.size(m),y=[{type:12,data:d},{type:12,data:r.k},{type:12,data:r.n},{type:12,data:r.accuracyLevel},{type:12,data:r.bits},{type:12,data:r.blockSize}];y.push(...se(t.dims)),y.push(...se(X.convertShape(u.dims))),y.push(...se(s.dims)),e.length===4&&y.push(...se(X.convertShape(e[3].dims))),y.push(...se(m));let w=C=>{let $=Q("a",e[0].dataType,e[0].dims.length),T=Q("b",12,e[1].dims.length),P=Q("scales",e[2].dataType,e[2].dims.length),A=[$,T,P],O=e.length===4?Q("zero_points",12,e[3].dims.length):void 0;O&&A.push(O);let R=ne("output",e[0].dataType,m.length),D=[{name:"output_size",type:"u32"},{name:"k",type:"u32"},{name:"n",type:"u32"},{name:"accuracy_level",type:"u32"},{name:"bits",type:"u32"},{name:"block_size",type:"u32"}],k=Math.floor((r.k+r.blockSize-1)/r.blockSize),V=r.blockSize/8*r.bits/4,q=Ve(e[0].dataType);return`\n fn ortUnpack8x4snorm(value: u32) -> array<${q}, 8>{\n var result = array<${q}, 8>();\n var offset: u32 = 0;\n let count: u32 = 4;\n for (var i: u32 = 0; i < 8u; i++) {\n result[i] = ${q}(extractBits(value, offset, count));\n offset += count;\n }\n return result;\n }\n ${C.registerUniforms(D).declareVariables(...A,R)}\n ${C.mainStart()}\n ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n var value: ${q} = 0.0;\n let output_indices = ${R.offsetToIndices("global_idx")};\n var a_indices: ${$.type.indices} = output_indices;\n var n = ${R.indicesGet("output_indices",f-1)};\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 ${O?`\n var zero_point_index: u32 = n * ((${k} + 1) / 2) / 4;\n var zero_point_word: u32 = ${O.getByOffset("zero_point_index")};\n var zero_point_offset: u32 = 0;`:""}\n var scale_idex = n * ${k};\n var b_indices: ${T.type.indices};\n ${T.indicesSet("b_indices","0","n")};\n var block_offset: u32 = 0;\n for (var block: u32 = 0; block < ${k}; block++) {\n // The scale and zero points are computed per block.\n let scale = ${P.getByOffset("scale_idex")};\n // The default zero point is 8 for unsigned 4-bit quantization.\n let zero_point: ${q} = ${O?`${q}(extractBits(zero_point_word, zero_point_offset, 4))`:8};\n ${T.indicesSet("b_indices","1","block")};\n var word_offset: u32 = block_offset;\n for (var word: u32 = 0; word < ${V}; word++) {\n ${T.indicesSet("b_indices","2","word")};\n let b_value = ${T.getByIndices("b_indices")};\n let b_quantized_values: array<${q}, 8> = ortUnpack8x4snorm(b_value);\n // Number of B elements per 32-bit word is 32/bits = 32/4 = 8\n var offset: u32 = word_offset;\n for (var i: u32 = 0; i < 8; i++) {\n ${$.indicesSet("a_indices",f-1,"offset")};\n let a_value = ${$.getByIndices("a_indices")};\n let b_quantized_value = b_quantized_values[i];\n let b_dequantized_value = (b_quantized_value - zero_point) * scale;\n value += a_value * b_dequantized_value;\n offset++;\n }\n word_offset += 8;\n }\n scale_idex++;\n ${O?`\n if (zero_point_offset == 28) {\n zero_point_offset = 0;\n zero_point_index++;\n zero_point_word = ${O.getByOffset("zero_point_index")};\n } else {\n zero_point_offset += 4;\n }`:""}\n block_offset += uniforms.block_size;\n }\n ${R.setByOffset("global_idx","value")};\n }\n `};return{name:"MatMulNBits",shaderCache:{hint:`${r.cacheKey};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:m,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:y}),getShaderSource:w}},Fd=(e,r)=>{uy(e.inputs,r),e.compute(ly(e.inputs,r))},qd=e=>Pe(e)});var dy,Zd,Yd,cy,so,Qd,Xd=oe(()=>{"use strict";Ce();Oe();nt();Xn();Fi();Ee();Er();dy=(e,r)=>{let t=e[0],u=e[1],s=e[2],f=e[3],m=e[4],d=e[5],y=e[6],w=e[7];if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let C=!1,$=t.dims[0],T=t.dims[1],P=t.dims.length===3?C?t.dims[2]/3:t.dims[2]:r.numHeads*t.dims[4],A=T,O=0,R=0,D=Math.floor(P/r.numHeads);if(y&&w){if(y.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(w.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');O=y.dims[2],R=y.dims[2]}else if(y||w)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let k;if(u){if(t.dims.length!==3)throw new Error(\'Input "query" is expected to have 3 dimensions when key is given\');if(u.dims.length<3||u.dims.length>5)throw new Error(\'Input "key" is expected to have 3, 4, or 5 dimensions\');if(t.dims[0]!==u.dims[0])throw new Error(\'Input "query" and "key" shall have same dim 0 (batch size)\');if(u.dims.length===3){if(u.dims[2]!==t.dims[2])throw new Error(\'Input "query" and "key" shall have same dim 2 (hidden_size)\');k=2,A=u.dims[1]}else if(u.dims.length===5){if(u.dims[2]!==r.numHeads||u.dims[3]!==2||u.dims[4]!==D)throw new Error(\'Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\');if(s)throw new Error(\'Expect "value" be none when "key" has packed kv format.\');k=5,A=u.dims[1]}else{if(u.dims[1]!==r.numHeads||u.dims[3]!==D)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');k=0,A=u.dims[2]}}else{if(t.dims.length!==3&&t.dims.length!==5)throw new Error(\'Input "query" is expected to have 3 or 5 dimensions when key is empty\');if(t.dims.length===5&&(t.dims[2]!==r.numHeads||t.dims[3]!==3))throw new Error(\'Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv\');k=3}if(f){if(f.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(s&&t.dims.length===5&&t.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let G=0;if(m){G=8;let ie=m.dims;throw ie.length===1?ie[0]===$?G=1:ie[0]===3*$+2&&(G=3):ie.length===2&&ie[0]===$&&ie[1]===A&&(G=5),G===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let V=!1,q=P;if(s){if(s.dims.length!==3&&s.dims.length!==4)throw new Error(\'Input "value" is expected to have 3 or 4 dimensions\');if(t.dims[0]!==s.dims[0])throw new Error(\'Input "query" and "value" shall have same dim 0 (batch_size)\');if(s.dims.length===3){if(A!==s.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');q=s.dims[2]}else{if(A!==s.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');q=s.dims[1]*s.dims[3],V=!0}}let Y=O+A,J=!1;if(m)throw new Error("Key padding mask is not supported");if(d)throw new Error("extraAddQk is not supported");if(y)throw new Error("pastKey is not supported");if(w)throw new Error("pastValue is not supported");return{batchSize:$,sequenceLength:T,pastSequenceLength:O,kvSequenceLength:A,totalSequenceLength:Y,maxSequenceLength:R,inputHiddenSize:0,hiddenSize:P,vHiddenSize:q,headSize:D,vHeadSize:Math.floor(q/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:G,scale:r.scale,broadcastResPosBias:J,passPastInKv:V,qkvFormat:k}},Zd=e=>Pe({...e}),Yd=Pe({perm:[0,2,1,3]}),cy=(e,r,t,u,s,f,m)=>{let d=[u,s,f],y=X.size(d),w=[{type:12,data:y},{type:12,data:m},{type:12,data:f}],C=$=>{let T=ne("qkv_with_bias",r.dataType,d),P=Q("qkv",r.dataType,d),A=Q("bias",t.dataType,d),O=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n ${$.registerUniforms(O).declareVariables(P,A,T)}\n ${$.mainStart()}\n ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:d,dataType:r.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:w}),getShaderSource:C},{inputs:[r,t],outputs:[-1]})[0]},so=(e,r,t,u,s,f,m,d)=>{let y=f;if(m){if(u===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return y=cy(e,f,m,r,u,t*s,d),y=y.reshape([r,u,t,s]),e.compute(xt(y,Yd.perm),{inputs:[y],outputs:[-1]})[0]}else return f.dims.length===3&&(y=f.reshape([r,u,t,s])),e.compute(xt(y,Yd.perm),{inputs:[y],outputs:[-1]})[0]},Qd=(e,r)=>{let t=dy(e.inputs,r);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let u=e.inputs[1]&&e.inputs[2]&&e.inputs[1].dims.length===4&&e.inputs[2].dims.length===4,s=so(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,e.inputs[0],e.inputs[3],0);if(u)return si(e,s,e.inputs[1],e.inputs[2],e.inputs[4],void 0,void 0,void 0,e.inputs[5],t,r);let f=so(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.headSize,e.inputs[1],e.inputs[3],t.hiddenSize),m=so(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.vHeadSize,e.inputs[2],e.inputs[3],2*t.hiddenSize);si(e,s,f,m,e.inputs[4],void 0,e.inputs[6],e.inputs[7],e.inputs[5],t,r)}});var fy,py,my,hy,gy,yy,by,vy,Jd,ec=oe(()=>{"use strict";Ce();Oe();Ee();fy=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let r=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(r=e[3].dims[0]*2===e[1].dims[0]),!r)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},py=(e,r,t)=>{let u="";for(let s=r-1;s>=0;--s)u+=`\n k = i32(${e.indicesGet("indices",s)}) - ${ve("uniforms.pads",s,t)};\n if (k < 0) {\n break;\n }\n if (k >= i32(${ve("uniforms.x_shape",s,r)})) {\n break;\n }\n offset += k * i32(${ve("uniforms.x_strides",s,r)});\n `;return`\n value = ${e.type.value}(uniforms.constant_value);\n for (var i = 0; i < 1; i++) {\n var offset = 0;\n var k = 0;\n ${u}\n value = x[offset];\n }\n `},my=(e,r,t)=>{let u="";for(let s=r-1;s>=0;--s)u+=`\n k = i32(${e.indicesGet("indices",s)}) - ${ve("uniforms.pads",s,t)};\n if (k < 0) {\n k = -k;\n }\n {\n let _2n_1 = 2 * (i32(${ve("uniforms.x_shape",s,r)}) - 1);\n k = k % _2n_1;\n if(k >= i32(${ve("uniforms.x_shape",s,r)})) {\n k = _2n_1 - k;\n }\n }\n offset += k * i32(${ve("uniforms.x_strides",s,r)});\n `;return`\n var offset = 0;\n var k = 0;\n ${u}\n value = x[offset];\n `},hy=(e,r,t)=>{let u="";for(let s=r-1;s>=0;--s)u+=`\n k = i32(${e.indicesGet("indices",s)}) - ${ve("uniforms.pads",s,t)};\n if (k < 0) {\n k = 0;\n }\n if (k >= i32(${ve("uniforms.x_shape",s,r)})) {\n k = i32(${ve("uniforms.x_shape",s,r)}) - 1;\n }\n offset += k * i32(${ve("uniforms.x_strides",s,r)});\n `;return`\n var offset = 0;\n var k = 0;\n ${u}\n value = x[offset];\n `},gy=(e,r,t)=>{let u="";for(let s=r-1;s>=0;--s)u+=`\n k = i32(${e.indicesGet("indices",s)}) - ${ve("uniforms.pads",s,t)};\n if (k < 0) {\n k += i32(${ve("uniforms.x_shape",s,r)}]);\n }\n if (k >= i32(${ve("uniforms.x_shape",s,r)})) {\n k -= i32(${ve("uniforms.x_shape",s,r)});\n }\n offset += k * i32(${ve("uniforms.x_strides",s,r)});\n `;return`\n var offset = 0;\n var k = 0;\n ${u}\n value = x[offset];\n `},yy=(e,r,t)=>{switch(t.mode){case 0:return py(e,r,t.pads.length);case 1:return my(e,r,t.pads.length);case 2:return hy(e,r,t.pads.length);case 3:return gy(e,r,t.pads.length);default:throw new Error("Invalid mode")}},by=(e,r)=>{let t=X.padShape(e[0].dims.slice(),r.pads),u=e[0].dims,s=X.size(t),f=[{type:12,data:s},{type:12,data:r.pads}];r.mode===0&&f.push({type:e[0].dataType,data:r.value}),f.push(...se(e[0].dims,t));let m=["rank"],d=y=>{let w=ne("output",e[0].dataType,t.length),C=Q("x",e[0].dataType,u.length),$=C.type.value,T=yy(w,u.length,r),P=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:r.pads.length}];return r.mode===0&&P.push({name:"constant_value",type:$}),`\n ${y.registerUniforms(P).declareVariables(C,w)}\n ${y.mainStart()}\n ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let indices = ${w.offsetToIndices("global_idx")};\n\n var value = ${$}(0);\n ${T}\n output[global_idx] = value;\n }`};return{name:"Pad",shaderCache:{hint:`${r.mode}`,inputDependencies:m},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(X.size(t)/64)},programUniforms:f}),getShaderSource:d}},vy=(e,r)=>{if(e.length>1){let t=e[1].getBigInt64Array(),u=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,s=e[0].dims.length,f=new Int32Array(2*s).fill(0);if(e.length>=4){let d=e[3].getBigInt64Array();for(let y=0;yf[Number(y)]=Number(d));let m=[];return f.forEach(d=>m.push(d)),{mode:r.mode,value:u,pads:m}}else return r},Jd=(e,r)=>{fy(e.inputs);let t=vy(e.inputs,r);e.compute(by(e.inputs,t),{inputs:[0]})}});var yi,tc,rc,nc,ic,wy,_y,oc,ac,sc,uc,lc,dc,cc,fc,pc,mc,hc,gc,yc=oe(()=>{"use strict";Ar();Ce();Oe();Ee();yi=e=>{if(Ir.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},tc=(e,r,t)=>{let u=r.format==="NHWC",s=e.dims.slice();u&&s.splice(1,0,s.pop());let f=Object.hasOwnProperty.call(r,"dilations"),m=r.kernelShape.slice(),d=r.strides.slice(),y=f?r.dilations.slice():[],w=r.pads.slice();sr.adjustPoolAttributes(t,s,m,d,y,w);let C=sr.computePoolOutputShape(t,s,d,y,m,w,r.autoPad),$=Object.assign({},r);f?Object.assign($,{kernelShape:m,strides:d,pads:w,dilations:y,cacheKey:r.cacheKey}):Object.assign($,{kernelShape:m,strides:d,pads:w,cacheKey:r.cacheKey});let T=C.slice();return T.push(T.splice(1,1)[0]),[$,u?T:C]},rc=(e,r)=>{let t=r.format==="NHWC",u=X.size(e),s=X.size(r.kernelShape),f=[{type:12,data:u},{type:12,data:s}],m=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(r.kernelShape.length<=2){let d=r.kernelShape[r.kernelShape.length-1],y=r.strides[r.strides.length-1],w=r.pads[r.pads.length/2-1],C=r.pads[r.pads.length-1],$=!!(w+C);f.push({type:12,data:d},{type:12,data:y},{type:12,data:w},{type:12,data:C}),m.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let T=!1;if(r.kernelShape.length===2){let P=r.kernelShape[r.kernelShape.length-2],A=r.strides[r.strides.length-2],O=r.pads[r.pads.length/2-2],R=r.pads[r.pads.length-2];T=!!(O+R),f.push({type:12,data:P},{type:12,data:A},{type:12,data:O},{type:12,data:R}),m.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[f,m,!0,$,T]}else{if(t)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let d=X.computeStrides(r.kernelShape);f.push({type:12,data:d},{type:12,data:r.pads},{type:12,data:r.strides}),m.push({name:"kernelStrides",type:"u32",length:d.length},{name:"pads",type:"u32",length:r.pads.length},{name:"strides",type:"u32",length:r.strides.length});let y=r.pads.reduce((w,C)=>w+C);return[f,m,!!y,!1,!1]}},nc=(e,r,t,u,s,f,m,d,y,w,C,$)=>{let T=s.format==="NHWC",P=r.type.value,A=ne("output",r.type.tensor,u);if(s.kernelShape.length<=2){let O="",R="",D="",k=t-(T?2:1);if(C?O=`\n for (var i: u32 = 0u; i < uniforms.kw; i++) {\n xIndices[${k}] = indices[${k}] * uniforms.sw - uniforms.pwStart + i;\n if (xIndices[${k}] < 0 || xIndices[${k}]\n >= uniforms.x_shape[${k}]) {\n pad++;\n continue;\n }\n let x_val = x[${r.indicesToOffset("xIndices")}];\n ${f}\n }`:O=`\n for (var i: u32 = 0u; i < uniforms.kw; i++) {\n xIndices[${k}] = indices[${k}] * uniforms.sw - uniforms.pwStart + i;\n let x_val = x[${r.indicesToOffset("xIndices")}];\n ${f}\n }`,s.kernelShape.length===2){let V=t-(T?3:2);$?R=`\n for (var j: u32 = 0u; j < uniforms.kh; j++) {\n xIndices[${V}] = indices[${V}] * uniforms.sh - uniforms.phStart + j;\n if (xIndices[${V}] < 0 || xIndices[${V}] >= uniforms.x_shape[${V}]) {\n pad += i32(uniforms.kw);\n continue;\n }\n `:R=`\n for (var j: u32 = 0u; j < uniforms.kh; j++) {\n xIndices[${V}] = indices[${V}] * uniforms.sh - uniforms.phStart + j;\n `,D=`\n }\n `}return`\n ${e.registerUniforms(y).declareVariables(r,A)}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n let indices = ${A.offsetToIndices("global_idx")};\n var xIndices = ${A.offsetToIndices("global_idx")};\n\n var value = ${P}(${d});\n var pad = 0;\n ${R}\n ${O}\n ${D}\n ${m}\n\n output[global_idx] = value;\n }`}else{if(T)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let O=s.kernelShape.length,R=s.pads.length,D="";return w?D=`\n if (xIndices[j] >= uniforms.x_shape[j]) {\n pad++;\n isPad = true;\n break;\n }\n }\n if (!isPad) {\n let x_val = x[${r.indicesToOffset("xIndices")}];\n ${f}\n }`:D=`\n }\n let x_val = x[${r.indicesToOffset("xIndices")}];\n ${f}\n `,`\n ${e.registerUniforms(y).declareVariables(r,A)}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n let indices = ${A.offsetToIndices("global_idx")};\n var xIndices = ${A.offsetToIndices("global_idx")};\n\n var offsets: array;\n\n var value = ${P}(${d});\n var pad = 0;\n var isPad = false;\n\n for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n var offset = i;\n for (var j = 0u; j < ${O-1}u; j++) {\n offsets[j] = offset / ${ve("uniforms.kernelStrides","j",O)};\n offset -= offsets[j] * ${ve("uniforms.kernelStrides","j",O)};\n }\n offsets[${O-1}] = offset;\n\n isPad = false;\n for (var j = ${t-O}u; j < ${t}u; j++) {\n xIndices[j] = indices[j] * ${ve("uniforms.strides",`j - ${t-O}u`,O)}\n + offsets[j - ${t-O}u] - ${ve("uniforms.pads","j - 2u",R)};\n ${D}\n }\n ${m}\n\n output[global_idx] = value;\n }`}},ic=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,wy=e=>`${ic(e)};${e.countIncludePad}`,_y=e=>`${ic(e)};${e.storageOrder};${e.dilations}`,oc=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}),ac=(e,r,t,u)=>{let[s,f]=tc(r,u,t),m=Q("x",r.dataType,r.dims.length),d=m.type.value,y="value += x_val;",w="";s.countIncludePad?w+=`value /= ${d}(uniforms.kernelSize);`:w+=`value /= ${d}(i32(uniforms.kernelSize) - pad);`;let[C,$,T,P,A]=rc(f,s);C.push(...se(r.dims,f));let O=["rank"];return{name:e,shaderCache:{hint:`${u.cacheKey};${T};${P};${A}`,inputDependencies:O},getRunData:()=>({outputs:[{dims:f,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(X.size(f)/64)},programUniforms:C}),getShaderSource:R=>nc(R,m,r.dims.length,f.length,s,y,w,0,$,T,P,A)}},sc=e=>{let r=e.count_include_pad!==0,t=oc(e);if(t.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let u={countIncludePad:r,...t,cacheKey:""};return{...u,cacheKey:wy(u)}},uc=(e,r)=>{yi(e.inputs),e.compute(ac("AveragePool",e.inputs[0],!1,r))},lc={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},dc=e=>{let r=e.format;return{format:r,...lc,cacheKey:r}},cc=(e,r)=>{yi(e.inputs),e.compute(ac("GlobalAveragePool",e.inputs[0],!0,r))},fc=(e,r,t,u)=>{let[s,f]=tc(r,u,t),m=`\n value = max(x_val, value);\n `,d="",y=Q("x",r.dataType,r.dims.length),w=["rank"],[C,$,T,P,A]=rc(f,s);return C.push(...se(r.dims,f)),{name:e,shaderCache:{hint:`${u.cacheKey};${T};${P};${A}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:f,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(X.size(f)/64)},programUniforms:C}),getShaderSource:O=>nc(O,y,r.dims.length,f.length,s,m,d,-1e5,$,T,P,A)}},pc=(e,r)=>{yi(e.inputs),e.compute(fc("MaxPool",e.inputs[0],!1,r))},mc=e=>{let r=e.storage_order,t=e.dilations,u=oc(e);if(r!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(u.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let s={storageOrder:r,dilations:t,...u,cacheKey:""};return{...s,cacheKey:_y(s)}},hc=e=>{let r=e.format;return{format:r,...lc,cacheKey:r}},gc=(e,r)=>{yi(e.inputs),e.compute(fc("GlobalMaxPool",e.inputs[0],!0,r))}});var Cy,Sy,bc,vc=oe(()=>{"use strict";Ar();Ce();Ee();Cy=(e,r,t)=>{let u=e===r,s=er&&t>0;if(u||s||f)throw new Error("Range these inputs\' contents are invalid.")},Sy=(e,r,t,u)=>{let s=Math.abs(Math.ceil((r-e)/t)),f=[s],m=s,d=[{type:12,data:m},{type:u,data:e},{type:u,data:t},...se(f)],y=w=>{let C=ne("output",u,f.length),$=C.type.value,T=[{name:"outputSize",type:"u32"},{name:"start",type:$},{name:"delta",type:$}];return`\n ${w.registerUniforms(T).declareVariables(C)}\n ${w.mainStart()}\n ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n output[global_idx] = uniforms.start + ${$}(global_idx) * uniforms.delta;\n }`};return{name:"Range",shaderCache:{hint:`${u}`},getShaderSource:y,getRunData:()=>({outputs:[{dims:f,dataType:u}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:d})}},bc=e=>{let r=0,t=0,u=0;e.inputs[0].dataType===6?(r=e.inputs[0].getInt32Array()[0],t=e.inputs[1].getInt32Array()[0],u=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(r=e.inputs[0].getFloat32Array()[0],t=e.inputs[1].getFloat32Array()[0],u=e.inputs[2].getFloat32Array()[0]),Ir.webgpu.validateInputContent&&Cy(r,t,u),e.compute(Sy(r,t,u,e.inputs[0].dataType),{inputs:[]})}});var xy,Iy,Ay,Ty,Ey,Py,Oy,ky,Ry,Dy,By,wc,My,zy,Uy,jy,Vy,_c,$c,Cc=oe(()=>{"use strict";Ce();Oe();nt();Ee();xy=(e,r)=>{if(e.every(t=>t>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(r.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(r.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},Iy=(e,r,t)=>{r.every(s=>s>=0&&s{throw new Error("Resize requires axes input values to be positive and less than rank")}));let u=new Array(t).fill(1);return r.forEach((s,f)=>u[s]=e[f]),u},Ay=(e,r,t,u,s,f)=>{let[m,d,y]=t>10?[1,2,3]:[-1,e.length>1?1:-1,-1],w=e[0].dims.length;if(m>0&&e.length>m&&e[m].dims.length>0)e[m].getFloat32Array().forEach(C=>f.push(C));else if(r.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(d>0&&e.length>d&&e[d].dims.length>0){if(e[d].getFloat32Array().forEach(C=>u.push(C)),u.length!==0&&u.length!==w&&t>=18&&u.length!==r.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");xy(u,r),r.axes.length>0&&Iy(u,r.axes,w).forEach((C,$)=>u[$]=C)}if(y>0&&e.length>y&&(e[y].getBigInt64Array().forEach(C=>s.push(Number(C))),s.length!==w||t>=18&&s.length===r.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(r.axes.length>0){if(u.length!==r.axes.length)throw new Error(\'Resize requires "scales" input size to be of axes rank when axes attributes is specified\');if(s.length!==r.axes.length)throw new Error(\'Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified\')}if(typeof u<"u"&&typeof s<"u"&&u.length>0&&s.length>w)throw new Error("Resize requires only of scales or sizes to be specified")},Ty=(e,r)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${r} { `+(()=>{switch(e){case"asymmetric":return`return ${r}(xResized) / ${r}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n return (${r}(xResized) + 0.5) / ${r}(xScale) - 0.5;\n } else {\n return 0.0;\n }`;case"tf_half_pixel_for_nn":return`return (${r}(xResized) + 0.5) / ${r}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n return 0.0;\n } else {\n // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n // offset-by-one error later in floor().\n let whole = ${r}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n let fract =\n ${r}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${r}(lengthResized - 1);\n return whole + fract;\n }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n return ${r}(roiStart) * ${r}(lengthOriginal - 1) +\n (${r}(xResized) * ${r}(roiEnd - roiStart) * ${r}(lengthOriginal - 1)) /\n ${r}(lengthResized - 1);\n } else {\n return 0.5 * ${r}(roiStart + roiEnd) * ${r}(lengthOriginal - 1);\n }`;case"half_pixel_symmetric":return`const outputWidth = ${r}xScale * ${r}(lengthResized);\n const adjustment = ${r}(lengthResized) / outputWidth;\n const center = ${r}(lengthOriginal) / 2;\n const offset = center * (1 - adjustment);\n return offset + ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;case"half_pixel":return`return ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",Ey=(e,r,t)=>`fn getNearestPixelFromOriginal(xOriginal: ${t}, isDownSample: bool) -> ${t} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(r<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Py=(e,r,t)=>{let u=new Array(t).fill(0).concat(new Array(t).fill(1)),s=e.length===0?u:e.slice();return r.length>0?(r.forEach((f,m)=>{u[f]=s[m],u[m+t]=s[r.length+m]}),u):s},Oy=(e,r,t,u)=>{let s=[];if(t.length>0)if(u.length>0){if(e.forEach(f=>s.push(f)),Math.max(...u)>e.length)throw new Error("axes is out of bound");u.forEach((f,m)=>s[f]=t[m])}else t.forEach(f=>s.push(f));else{if(r.length===0)throw new Error("Resize requires either scales or sizes.");s=e.map((f,m)=>Math.round(f*r[m]))}return s},ky=(e,r,t)=>{let u=(()=>{switch(t.keepAspectRatioPolicy){case"not_larger":return t.axes.length>0?Math.min(...t.axes.map(f=>r[f]),Number.MAX_VALUE):Math.min(...r,Number.MAX_VALUE);case"not_smaller":return t.axes.length>0?Math.max(...t.axes.map(f=>r[f]),Number.MIN_VALUE):Math.max(...r,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${t.keepAspectRatioPolicy} is not supported`)}})();r.fill(1,0,r.length);let s=e.slice();return t.axes.length>0?(t.axes.forEach(f=>r[f]=u),t.axes.forEach(f=>s[f]=Math.round(e[f]*r[f]))):(r.fill(u,0,r.length),s.forEach((f,m)=>s[m]=Math.round(f*r[m]))),s},Ry=(e,r,t,u,s)=>`\n fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${t.length}> {\n var original_indices: array<${e.type.value}, ${t.length}>;\n for (var i:u32 = 0; i < ${t.length}; i++) {\n var output_index = ${e.indicesGet("output_indices","i")};\n var scale = ${ve("uniforms.scales","i",u)};\n var roi_low = ${ve("uniforms.roi","i",s)};\n var roi_hi = ${ve("uniforms.roi",`i + ${r.length}`,s)};\n if (scale == 1.0) {\n original_indices[i] = ${e.type.value}(output_index);\n } else {\n var input_shape_i = ${ve("uniforms.input_shape","i",r.length)};\n var output_shape_i = ${ve("uniforms.output_shape","i",t.length)};\n original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n input_shape_i, roi_low, roi_hi);\n }\n }\n return original_indices;\n }`,Dy=(e,r,t,u,s,f,m)=>`\n fn calculateInputIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {\n var input_indices: ${e.type.indices};\n for (var i:u32 = 0; i < ${u.length}; i++) {\n var output_index = ${r.indicesGet("output_indices","i")};\n var input_index: u32;\n var scale = ${ve("uniforms.scales","i",s)};\n if (scale == 1.0) {\n input_index = output_index;\n } else {\n var roi_low = ${ve("uniforms.roi","i",f)};\n var roi_hi = ${ve("uniforms.roi",`i + ${t.length}`,f)};\n var input_shape_i = ${ve("uniforms.input_shape","i",t.length)};\n var output_shape_i = ${ve("uniforms.output_shape","i",u.length)};\n var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n input_shape_i, roi_low, roi_hi);\n if (!${m} || (original_idx >= 0 && original_idx < ${r.type.value}(input_shape_i))) {\n if (original_idx < 0) {\n input_index = 0;\n } else if (original_idx > ${r.type.value}(input_shape_i - 1)) {\n input_index = input_shape_i - 1;\n } else {\n input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n }\n } else {\n input_index = u32(original_idx);\n }\n }\n ${e.indicesSet("input_indices","i"," input_index")}\n }\n return input_indices;\n }`,By=(e,r)=>`\n fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n for (var i:u32 = 0; i < ${r.length}; i++) {\n var input_index = ${e.indicesGet("input_indices","i")};\n if (input_index < 0 || input_index >= ${ve("uniforms.input_shape","i",r.length)}) {\n return false;\n }\n }\n return true;\n }`,wc=(e,r,t,u)=>e.rank>u?`\n ${e.indicesSet("input_indices",r,"channel")};\n ${e.indicesSet("input_indices",t,"batch")};\n`:"",My=(e,r,t,u,s)=>{let[m,d,y,w]=t.length===2?[-1,0,1,-1]:[0,2,3,1],C=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${C} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",d,`max(0, min(row, ${t[d]} - 1))`)};\n ${e.indicesSet("input_indices",y,`max(0, min(col, ${t[y]} - 1))`)};\n ${wc(e,w,m,2)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn bilinearInterpolation(output_indices: ${r.type.indices}) -> ${C} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var row:${C} = originalIndices[${d}];\n var col:${C} = originalIndices[${y}];\n ${u?`if (row < 0 || row > (${t[d]} - 1) || col < 0 || col > (${t[y]} - 1)) {\n return ${s};\n }`:""};\n row = max(0, min(row, ${t[d]} - 1));\n col = max(0, min(col, ${t[y]} - 1));\n var row1: u32 = u32(row);\n var col1: u32 = u32(col);\n var row2: u32 = u32(row + 1);\n var col2: u32 = u32(col + 1);\n var channel: u32 = ${t.length>2?`u32(originalIndices[${w}])`:"0"};\n var batch: u32 = ${t.length>2?`u32(originalIndices[${m}])`:"0"};\n var x11: ${C} = getInputValue(batch, channel, row1, col1);\n var x12: ${C} = getInputValue(batch, channel, row1, col2);\n var x21: ${C} = getInputValue(batch, channel, row2, col1);\n var x22: ${C} = getInputValue(batch, channel, row2, col2);\n var dx1: ${C} = abs(row - ${C}(row1));\n var dx2: ${C} = abs(${C}(row2) - row);\n var dy1: ${C} = abs(col - ${C}(col1));\n var dy2: ${C} = abs(${C}(col2) - col);\n if (row1 == row2) {\n dx1 = 0.5;\n dx2 = 0.5;\n }\n if (col1 == col2) {\n dy1 = 0.5;\n dy2 = 0.5;\n }\n return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n }`},zy=(e,r,t,u,s,f,m,d,y,w)=>{let C=t.length===2,$=!0,[T,P]=C?[0,1]:$?[2,3]:[1,2],A=e.type.value,O=R=>{let D=R===T?"row":"col";return`\n fn ${D}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${r.type.indices}) -> ${A} {\n var output_index = ${r.indicesGet("output_indices",R)};\n var originalIdx: ${A} = getOriginalCoordinateFromResizedCoordinate(output_index, ${s[R]},\n ${u[R]}, ${t[R]}, ${f[R]}, ${f[R]} + ${t.length});\n var fractOriginalIdx: ${A} = originalIdx - floor(originalIdx);\n var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n if (${d} && (originalIdx < 0 || originalIdx > (${t[R]} - 1))) {\n return ${y};\n }\n var data: array<${A}, 4> = array<${A}, 4>(0.0, 0.0, 0.0, 0.0);\n for (var i: i32 = -1; i < 3; i++) {\n var ${D}: ${A} = originalIdx + ${A}(i);\n if (${D} < 0 || ${D} >= ${t[R]}) {\n ${(()=>w?`coefs[i + 1] = 0.0;\n continue;`:d?`return ${y};`:`${D} = max(0, min(${D}, ${t[R]} - 1));`)()};\n }\n var input_indices_copy: ${e.type.indices} = input_indices;\n ${e.indicesSet("input_indices_copy",R,`u32(${D})`)};\n data[i + 1] = ${R===T?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n }\n return cubicInterpolation1D(data, coefs);\n }`};return`\n ${O(T)};\n ${O(P)};\n fn getCubicInterpolationCoefs(s: ${A}) -> array<${A}, 4> {\n var absS = abs(s);\n var coeffs: array<${A}, 4> = array<${A}, 4>(0.0, 0.0, 0.0, 0.0);\n var oneMinusAbsS: ${A} = 1.0 - absS;\n var twoMinusAbsS: ${A} = 2.0 - absS;\n var onePlusAbsS: ${A} = 1.0 + absS;\n coeffs[0] = ((${m} * onePlusAbsS - 5 * ${m}) * onePlusAbsS + 8 * ${m}) * onePlusAbsS - 4 * ${m};\n coeffs[1] = ((${m} + 2) * absS - (${m} + 3)) * absS * absS + 1;\n coeffs[2] = ((${m} + 2) * oneMinusAbsS - (${m} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n coeffs[3] = ((${m} * twoMinusAbsS - 5 * ${m}) * twoMinusAbsS + 8 * ${m}) * twoMinusAbsS - 4 * ${m};\n return coeffs;\n }\n\n fn cubicInterpolation1D(x: array<${A}, 4>, coefs: array<${A}, 4>) -> ${A} {\n var coefsSum: ${A} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n }\n\n fn bicubicInterpolation(output_indices: ${r.type.indices}) -> ${A} {\n var input_indices: ${e.type.indices} = output_indices;\n return colCubicInterpolation(input_indices, output_indices);\n }\n `},Uy=(e,r,t,u,s)=>{let[m,d,y,w,C]=t.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],$=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${$} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",d,`max(0, min(depth, ${t[d]} - 1))`)};\n ${e.indicesSet("input_indices",y,`max(0, min(height, ${t[y]} - 1))`)};\n ${e.indicesSet("input_indices",w,`max(0, min(width, ${t[w]} - 1))`)};\n ${wc(e,C,m,3)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn trilinearInterpolation(output_indices: ${r.type.indices}) -> ${$} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var depth:${$} = originalIndices[${d}];\n var height:${$} = originalIndices[${y}];\n var width:${$} = originalIndices[${w}];\n ${u?`if (depth < 0 || depth > (${t[d]} - 1) || height < 0 || height > (${t[y]} - 1) || width < 0 || (width > ${t[w]} - 1)) {\n return ${s};\n }`:""};\n\n depth = max(0, min(depth, ${t[d]} - 1));\n height = max(0, min(height, ${t[y]} - 1));\n width = max(0, min(width, ${t[w]} - 1));\n var depth1: u32 = u32(depth);\n var height1: u32 = u32(height);\n var width1: u32 = u32(width);\n var depth2: u32 = u32(depth + 1);\n var height2: u32 = u32(height + 1);\n var width2: u32 = u32(width + 1);\n var channel: u32 = ${t.length>3?`u32(originalIndices[${C}])`:"0"};\n var batch: u32 = ${t.length>3?`u32(originalIndices[${m}])`:"0"};\n\n var x111: ${$} = getInputValue(batch, channel, depth1, height1, width1);\n var x112: ${$} = getInputValue(batch, channel, depth1, height1, width2);\n var x121: ${$} = getInputValue(batch, channel, depth1, height2, width1);\n var x122: ${$} = getInputValue(batch, channel, depth1, height2, width2);\n var x211: ${$} = getInputValue(batch, channel, depth2, height1, width1);\n var x212: ${$} = getInputValue(batch, channel, depth2, height1, width2);\n var x221: ${$} = getInputValue(batch, channel, depth2, height2, width1);\n var x222: ${$} = getInputValue(batch, channel, depth2, height2, width2);\n var dx1: ${$} = abs(depth - ${$}(depth1));\n var dx2: ${$} = abs(${$}(depth2) - depth);\n var dy1: ${$} = abs(height - ${$}(height1));\n var dy2: ${$} = abs(${$}(height2) - height);\n var dz1: ${$} = abs(width - ${$}(width1));\n var dz2: ${$} = abs(${$}(width2) - width);\n if (depth1 == depth2) {\n dx1 = 0.5;\n dx2 = 0.5;\n }\n if (height1 == height2) {\n dy1 = 0.5;\n dy2 = 0.5;\n }\n if (width1 == width2) {\n dz1 = 0.5;\n dz2 = 0.5;\n }\n return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n }`},jy=(e,r,t,u,s,f)=>{let m=e.dims,d=Py(f,r.axes,m.length),y=Oy(m,u,s,r.axes),w=u.slice();u.length===0&&(w=m.map((k,G)=>k===0?1:y[G]/k),r.keepAspectRatioPolicy!=="stretch"&&(y=ky(m,w,r)));let C=ne("output",e.dataType,y.length),$=Q("input",e.dataType,m.length),T=X.size(y),P=m.length===y.length&&m.every((k,G)=>k===y[G]),A=r.coordinateTransformMode==="tf_crop_and_resize",O=r.extrapolationValue,R=$.type.value,D=k=>`\n ${P?"":`\n ${Ty(r.coordinateTransformMode,R)};\n ${(()=>{switch(r.mode){case"nearest":return`\n ${By($,m)};\n ${Ey(r.nearestMode,t,R)};\n ${Dy($,C,m,y,w.length,d.length,A)};\n `;case"linear":return`\n ${Ry(C,m,y,w.length,d.length)};\n ${(()=>{if(m.length===2||m.length===4)return`${My($,C,m,A,O)}`;if(m.length===3||m.length===5)return`${Uy($,C,m,A,O)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n `;case"cubic":return`\n ${(()=>{if(m.length===2||m.length===4)return`${zy($,C,m,y,w,d,r.cubicCoeffA,A,r.extrapolationValue,r.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n `;default:throw Error("Invalid resize mode")}})()};\n `}\n ${k.registerUniform("output_size","u32").registerUniform("scales","f32",w.length).registerUniform("roi","f32",d.length).declareVariables($,C)}\n ${k.mainStart()}\n ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n ${P?"output[global_idx] = input[global_idx];":`\n let output_indices = ${C.offsetToIndices("global_idx")};\n var input_indices: ${$.type.indices};\n ${(()=>{switch(r.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n if (checkInputIndices(input_indices)) {\n output[global_idx] = ${$.getByIndices("input_indices")};\n } else {\n output[global_idx] = ${r.extrapolationValue};\n }`;case"linear":return`output[global_idx] = ${m.length===2||m.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${r.mode}`)}})()};\n`}\n }`;return{name:"Resize",shaderCache:{hint:`${r.cacheKey}|${t}|${w.length>0?w:""}|${s.length>0?s:""}|${d.length>0?d:""}|${P}|${m}`,inputDependencies:["rank"]},getShaderSource:D,getRunData:()=>({outputs:[{dims:y,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(T/64)},programUniforms:[{type:12,data:T},{type:1,data:w},{type:1,data:d},...se(m,y)]})}},Vy=e=>{let r=e.customDataBuffer;return new Uint32Array(r,r.byteOffset,1)[0]},_c=(e,r)=>{let t=[],u=[],s=[],f=Vy(e);if(r.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Ay(e.inputs,r,f,t,u,s),e.compute(jy(e.inputs[0],r,f,t,u,s),{inputs:[0]})},$c=e=>{let r=e.antialias,t=e.axes,u=e.coordinateTransformMode,s=e.cubicCoeffA,f=e.excludeOutside!==0,m=e.extrapolationValue,d=e.keepAspectRatioPolicy,y=e.mode,w=e.nearestMode===""?"simple":e.nearestMode;return Pe({antialias:r,axes:t,coordinateTransformMode:u,cubicCoeffA:s,excludeOutside:f,extrapolationValue:m,keepAspectRatioPolicy:d,mode:y,nearestMode:w})}});var Ny,Wy,Sc,xc=oe(()=>{"use strict";Ce();Oe();Ee();Ny=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let r=e[0],t=e[1],u=e[2];if(r.dataType!==t.dataType||r.dataType!==u.dataType)throw new Error("All inputs must have the same data type");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Input must be 2D or 3D");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Skip must be 2D or 3D");let s=r.dims[r.dims.length-1],f=r.dims[r.dims.length-2];if(t.dims[t.dims.length-1]!==s)throw new Error("Skip must have the same hidden size as input");if(t.dims[t.dims.length-2]!==f)throw new Error("Skip must have the same sequence length as input");if(u.dims.length!==1)throw new Error("Gamma must be 1D");if(u.dims[u.dims.length-1]!==s)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let m=e[3];if(m.dims.length!==1)throw new Error("Beta must be 1D");if(m.dims[m.dims.length-1]!==s)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let m=e[4];if(m.dims.length!==1)throw new Error("Bias must be 1D");if(m.dims[m.dims.length-1]!==s)throw new Error("Bias must have the same hidden size as input")}},Wy=(e,r,t,u)=>{let s=e[0].dims,f=X.size(s),m=s,d=f,y=s.slice(-1)[0],w=u?s.slice(0,-1).concat(1):[],C=e.length>3,$=e.length>4,T=u&&t>1,P=u&&t>2,A=t>3,O=Je(y),R=[{type:12,data:d},{type:12,data:O},{type:12,data:y},{type:1,data:r.epsilon}],D=G=>{let V=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],q=[Q("x",e[0].dataType,e[0].dims,O),Q("skip",e[1].dataType,e[1].dims,O),Q("gamma",e[2].dataType,e[2].dims,O)];C&&q.push(Q("beta",e[3].dataType,e[3].dims,O)),$&&q.push(Q("bias",e[4].dataType,e[4].dims,O)),q.push(ne("output",e[0].dataType,m,O)),T&&q.push(ne("mean_output",1,w)),P&&q.push(ne("inv_std_output",1,w)),A&&q.push(ne("input_skip_bias_sum",e[0].dataType,m,O));let Y=Ve(e[0].dataType);return`\n\n ${G.registerUniforms(V).declareVariables(...q)}\n\n ${G.mainStart()}\n ${G.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 = ${ot("f32",O)};\n var squareSum = ${ot("f32",O)};\n for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n let skip_value = skip[offset + i];\n let bias_value = 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t>1&&k.push({dims:w,dataType:1}),t>2&&k.push({dims:w,dataType:1}),t>3&&k.push({dims:s,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${O};${T};${P};${A}`,inputDependencies:e.map((G,V)=>"type")},getShaderSource:D,getRunData:()=>({outputs:k,dispatchGroup:{x:Math.ceil(d/y/64)},programUniforms:R})}},Sc=(e,r)=>{Ny(e.inputs);let u=[0];e.outputCount>1&&u.push(-3),e.outputCount>2&&u.push(-3),e.outputCount>3&&u.push(3),e.compute(Wy(e.inputs,r,e.outputCount,!1),{outputs:u})}});var Gy,bi,Hy,Ic,Ly,Fy,Ac,Tc,Ec=oe(()=>{"use strict";Ce();Oe();nt();Ee();Gy=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");if(r.axes.length!==0){if(r.axes.length!==r.starts.length||r.axes.length!==r.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(r.starts.length!==r.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((t,u)=>{if(e[u+1].dataType!==6&&e[u+1].dataType!==7)throw new Error(`Input ${u} must be an array of int32 or int64`)})},bi=(e,r)=>{let t=[];if(e.length>r)if(e[r].dataType===7)e[r].getBigInt64Array().forEach(u=>t.push(Number(u)));else if(e[r].dataType===6)e[r].getInt32Array().forEach(u=>t.push(Number(u)));else throw new Error(`Input ${r} must be an array of int32 or int64`);return t},Hy=(e,r)=>{if(e.length>1){let t=bi(e,1),u=bi(e,2),s=bi(e,3);return s.length===0&&(s=[...Array(e[0].dims.length).keys()]),Pe({starts:t,ends:u,axes:s})}else return r},Ic=(e,r,t,u,s)=>{let f=e;return e<0&&(f+=t[u[r]]),s[r]<0?Math.max(0,Math.min(f,t[u[r]]-1)):Math.max(0,Math.min(f,t[u[r]]))},Ly=(e,r,t)=>`fn calculateInputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} {\n var input_indices: ${e.type.indices};\n var carry = 0u;\n for (var i = ${t.length}; i >= 0; i--) {\n let input_shape_i = ${ve("uniforms.input_shape","i",t.length)};\n let steps_i = ${ve("uniforms.steps","i",t.length)};\n let signs_i = ${ve("uniforms.signs","i",t.length)};\n let starts_i = ${ve("uniforms.starts","i",t.length)};\n var output_index = ${r.indicesGet("output_indices","i")};\n var input_index = output_index * steps_i + starts_i + carry;\n carry = input_index / input_shape_i;\n input_index = input_index % input_shape_i;\n if (signs_i < 0) {\n input_index = input_shape_i - input_index - 1u + starts_i;\n }\n ${e.indicesSet("input_indices","i","input_index")};\n }\n return input_indices;\n }`,Fy=(e,r)=>{let t=e[0].dims,u=X.size(t),s=r.axes.length>0?X.normalizeAxes(r.axes,t.length):[...Array(t.length).keys()],f=bi(e,4);f.forEach(D=>D!==0||(()=>{throw new Error("step cannot be 0")})),f.length===0&&(f=Array(s.length).fill(1));let m=r.starts.map((D,k)=>Ic(D,k,t,s,f)),d=r.ends.map((D,k)=>Ic(D,k,t,s,f));if(s.length!==m.length||s.length!==d.length)throw new Error("start, ends and axes should have the same number of elements");if(s.length!==t.length)for(let D=0;DMath.sign(D));f.forEach((D,k,G)=>{if(D<0){let V=(d[k]-m[k])/D,q=m[k],Y=q+V*f[k];m[k]=Y,d[k]=q,G[k]=-D}});let w=t.slice(0);s.forEach((D,k)=>{w[D]=Math.ceil((d[D]-m[D])/f[D])});let C={dims:w,dataType:e[0].dataType},$=ne("output",e[0].dataType,w.length),T=Q("input",e[0].dataType,e[0].dims.length),P=X.size(w),A=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:m.length},{name:"signs",type:"i32",length:y.length},{name:"steps",type:"u32",length:f.length}],O=[{type:12,data:P},{type:12,data:m},{type:6,data:y},{type:12,data:f},...se(e[0].dims,w)],R=D=>`\n ${D.registerUniforms(A).declareVariables(T,$)}\n ${Ly(T,$,t)}\n ${D.mainStart()}\n ${D.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n let output_indices = ${$.offsetToIndices("global_idx")};\n let input_indices = calculateInputIndices(output_indices);\n ${$.setByOffset("global_idx",T.getByIndices("input_indices"))}\n }`;return{name:"Slice",shaderCache:{hint:`${y.length}_${m.length}_${f.length}`,inputDependencies:["rank"]},getShaderSource:R,getRunData:()=>({outputs:[C],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:O})}},Ac=(e,r)=>{Gy(e.inputs,r);let t=Hy(e.inputs,r);e.compute(Fy(e.inputs,t),{inputs:[0]})},Tc=e=>{let r=e.starts,t=e.ends,u=e.axes;return Pe({starts:r,ends:t,axes:u})}});var qy,Ky,Pc,Oc,kc=oe(()=>{"use strict";Ce();Oe();nt();Ee();qy=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Ky=(e,r)=>{let t=e.dims,u=X.size(t),s=64,f=r.axis;if(f<0&&(f=t.length+f),fD===4?`max(max(${R}.x, ${R}.y), max(${R}.z, ${R}.w))`:D===2?`max(${R}.x, ${R}.y)`:D===3?`max(max(${R}.x, ${R}.y), ${R}.z)`:R,$=Q("x",e.dataType,e.dims,y),T=ne("result",e.dataType,e.dims,y),P=$.type.value,A=Ve(e.dataType)==="f32"?`var threadMax = ${P}(-3.402823e+38f);`:`var threadMax = ${P}(-65504.0h);`,O=R=>`\n var rowMaxShared : ${P};\n var rowSumShared : ${P};\n var threadShared : array<${P}, ${s}>;\n\n fn getValue(row: i32, col: i32, row_stride: i32) -> ${P} {\n let index = row * row_stride + col;\n return x[index];\n }\n\n fn setValue(row: i32, col: i32, row_stride: i32, value: ${P}) {\n let index = row * row_stride + col;\n result[index] = value;\n }\n ${R.registerUniform("packedCols","i32").declareVariables($,T)}\n ${R.mainStart()}\n let gindex = i32(global_idx);\n let lindex = i32(local_idx);\n const wg = ${s};\n let row = gindex / wg;\n let cols = uniforms.packedCols;\n let row_stride : i32 = uniforms.packedCols;\n\n // find the rows max\n ${A}\n for (var col = lindex; col < cols; col += wg) {\n let value = getValue(row, col, row_stride);\n threadMax = max(threadMax, value);\n }\n if (lindex < cols) {\n threadShared[lindex] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, wg);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (lindex < currSize) {\n threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);\n }\n workgroupBarrier();\n }\n if (lindex == 0) {\n rowMaxShared = ${P}(${C("threadShared[0]",y)});\n }\n workgroupBarrier();\n\n // find the rows sum\n var threadSum = ${P}(0.0);\n for (var col = lindex; col < cols; col += wg) {\n let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);\n threadSum += subExp;\n }\n threadShared[lindex] = threadSum;\n workgroupBarrier();\n\n for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) {\n if (lindex < currSize) {\n threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];\n }\n workgroupBarrier();\n }\n if (lindex == 0) {\n rowSumShared = ${P}(${ft("threadShared[0]",y)});\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:`${y}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:t,dataType:e.dataType}],dispatchGroup:{x:d},programUniforms:[{type:12,data:w}]}),getShaderSource:O}},Pc=(e,r)=>{qy(e.inputs),e.compute(Ky(e.inputs[0],r))},Oc=e=>Pe({axis:e.axis})});var Yy,Zy,Qy,Xy,Jy,Rc,Dc,Bc=oe(()=>{"use strict";Ce();Oe();nt();Ee();Yy=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Zy=(e,r)=>{let t=[],u=r.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(s=>t.push(Number(s))),u=t.length),Pe({numOutputs:u,axis:r.axis,splitSizes:t})},Qy=e=>`\nfn calculateOutputIndex(index: u32) -> u32 {\n for (var i: u32 = 0u; i < ${e}u; i += 1u ) {\n if (index < ${ve("uniforms.size_in_split_axis","i",e)}) {\n return i;\n }\n }\n return ${e}u;\n}`,Xy=e=>{let r=e.length,t=[];for(let u=0;u{let t=e[0].dims,u=X.size(t),s=e[0].dataType,f=X.normalizeAxis(r.axis,t.length),m=new Array(r.numOutputs),d=Q("input",s,t.length),y=new Array(r.numOutputs),w=[],C=[],$=0,T=[{type:12,data:u}];for(let A=0;A`\n ${A.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",y.length).declareVariables(d,...m)}\n ${Qy(y.length)}\n ${Xy(m)}\n\n ${A.mainStart()}\n ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}\n\n var indices = ${d.offsetToIndices("global_idx")};\n var index = ${d.indicesGet("indices",f)};\n let output_number = calculateOutputIndex(index);\n if (output_number != 0) {\n index -= ${ve("uniforms.size_in_split_axis","output_number - 1u",y.length)};\n ${d.indicesSet("indices",f,"index")};\n }\n writeBufferData(output_number, indices, global_idx);\n }`;return{name:"Split",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getShaderSource:P,getRunData:()=>({outputs:w,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:T})}},Rc=(e,r)=>{Yy(e.inputs);let t=e.inputs.length===1?r:Zy(e.inputs,r);e.compute(Jy(e.inputs,t),{inputs:[0]})},Dc=e=>{let r=e.axis,t=e.splitSizes,u=e.numOutputs<0?t.length:e.numOutputs;if(u!==t.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Pe({axis:r,numOutputs:u,splitSizes:t})}});var Mc,e0,t0,r0,zc,Uc=oe(()=>{"use strict";Ce();Oe();Ee();Mc=e=>Array.from(e.getBigInt64Array(),Number),e0=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Mc(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},t0=(e,r)=>{let t=[];for(let u=0;u{let r=e[0].dims,t=Mc(e[1]),u=t0(r,t),s=X.size(u),f=e[0].dataType,m=Q("input",f,r.length),d=ne("output",f,u.length),y=w=>`\n const inputShape = 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f=ne("output_data",s,t.length,4),m=Q("a_data",r[1].dataType,r[1].dims.length,4),d=Q("b_data",r[2].dataType,r[2].dims.length,4),y=Q("c_data",r[0].dataType,r[0].dims.length,4),w,C=($,T,P)=>`select(${T}, ${$}, ${P})`;if(!u)w=f.setByOffset("global_idx",C(m.getByOffset("global_idx"),d.getByOffset("global_idx"),y.getByOffset("global_idx")));else{let $=(T,P,A="")=>{let O=`a_data[index_a${P}][component_a${P}]`,R=`b_data[index_b${P}][component_b${P}]`,D=`bool(c_data[index_c${P}] & (0xffu << (component_c${P} * 8)))`;return`\n let output_indices${P} = ${f.offsetToIndices(`global_idx * 4u + ${P}u`)};\n let offset_a${P} = ${m.broadcastedIndicesToOffset(`output_indices${P}`,f)};\n let offset_b${P} = ${d.broadcastedIndicesToOffset(`output_indices${P}`,f)};\n let offset_c${P} = ${y.broadcastedIndicesToOffset(`output_indices${P}`,f)};\n let index_a${P} = offset_a${P} / 4u;\n let index_b${P} = offset_b${P} / 4u;\n let index_c${P} = offset_c${P} / 4u;\n let component_a${P} = offset_a${P} % 4u;\n let component_b${P} = offset_b${P} % 4u;\n let component_c${P} = offset_c${P} % 4u;\n ${T}[${P}] = ${A}(${C(O,R,D)});\n `};s===9?w=`\n var data = vec4(0);\n ${$("data",0,"u32")}\n ${$("data",1,"u32")}\n ${$("data",2,"u32")}\n ${$("data",3,"u32")}\n output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:w=`\n ${$("output_data[global_idx]",0)}\n ${$("output_data[global_idx]",1)}\n ${$("output_data[global_idx]",2)}\n ${$("output_data[global_idx]",3)}\n `}return`\n ${e.registerUniform("vec_size","u32").declareVariables(y,m,d,f)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${w}\n }`},i0=e=>{let r=e[1].dims,t=e[2].dims,u=e[0].dims,s=e[1].dataType,f=!(X.areEqual(r,t)&&X.areEqual(t,u)),m=r,d=X.size(r);if(f){let w=Ot.calcShape(Ot.calcShape(r,t,!1),u,!1);if(!w)throw new Error("Can\'t perform where op on the given tensors");m=w,d=X.size(m)}let y=Math.ceil(d/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:w=>n0(w,e,m,f,s),getRunData:()=>({outputs:[{dims:m,dataType:s}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:[{type:12,data:y},...se(u,r,t,m)]})}},jc=e=>{e.compute(i0(e.inputs))}});var Nc,Wc=oe(()=>{"use strict";rl();Fi();ol();sl();Ll();rd();od();Xi();vd();$d();Ad();Pd();kd();Bd();Ud();Nd();Gd();Ld();eo();Kd();Xd();ec();yc();vc();oi();Cc();xc();Ec();kc();Bc();Uc();Er();di();Vc();Nc=new 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vi,Gc=oe(()=>{"use strict";Ar();Ft();Ee();vi=class{constructor(r){this.backend=r;this.repo=new Map,this.attributesBound=!1}getArtifact(r){return this.repo.get(r)}setArtifact(r,t){this.repo.set(r,t)}run(r,t,u,s,f){ar(r.programInfo.name);let m=this.backend.device,d=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let y=[];for(let C of t)y.push({binding:y.length,resource:{buffer:C.buffer}});for(let C of u)y.push({binding:y.length,resource:{buffer:C.buffer}});f&&y.push({binding:y.length,resource:f});let w=m.createBindGroup({layout:r.computePipeline.getBindGroupLayout(0),entries:y,label:r.programInfo.name});if(this.backend.sessionStatus==="capturing"){let C={kernelId:this.backend.currentKernelId,computePipeline:r.computePipeline,bindGroup:w,dispatchGroup:s};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(C)}d.setPipeline(r.computePipeline),d.setBindGroup(0,w),d.dispatchWorkgroups(...s),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),Lt(r.programInfo.name)}dispose(){}build(r,t){ar(r.name);let u=this.backend.device,s=[];u.features.has("shader-f16")&&s.push("enable f16;");let f=Tu(t),m=r.getShaderSource(f),d=`${s.join(`\n`)}\n${f.additionalImplementations}\n${m}`,y=u.createShaderModule({code:d,label:r.name});He("verbose",()=>`[WebGPU] ${r.name} shader code: ${d}`);let 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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'})),$l=D((()=>{oe(),wl(),ks(),Yu=()=>!!d.wasm.proxy&&typeof document<"u",Qu=!1,Ju=!1,Xu=!1,tl=new Map,nl=(e,t)=>{let n=tl.get(e);n?n.push(t):tl.set(e,[t])},il=()=>{if(Qu||!Ju||Xu||!Zu)throw new Error("worker not 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All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +/** + * @license + * Copyright 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. + * ============================================================================= + */},t={};function n(i){var r=t[i];if(void 0!==r)return r.exports;var a=t[i]={exports:{}};return e[i](a,a.exports,n),a.exports}n.d=(e,t)=>{for(var i in t)n.o(t,i)&&!n.o(e,i)&&Object.defineProperty(e,i,{enumerable:!0,get:t[i]})},n.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),n.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})};var i={};(()=>{ +/*!******************!*\ + !*** ./index.js ***! 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