37424505.9ae151f613233935.js

js slidexy.ai collected 2026-10-05 12:38:56 UTC 389,415 bytes, 2,746 lines download raw bytes

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23    return ${b.join("+")};
24  }`,w=(...t)=>0===o?"0u":`${c.indices}(${t.map(h).join(",")})`,x=(t,r)=>o<2?`${t}`:`${tm(t,r,o)}`,S={},k=(r,i)=>(()=>

24{if(c.storage===c.value)return`${t}[${r}]=${i};`;if("vec2<u32>"===c.storage&&"i32"===c.value)return`${t}[${r}]=vec2<u32>(u32(${i}), select(0u, 0xFFFFFFFFu, ${i} < 0));`;if("vec2<u32>"===c.storage&&"u32"===c.value)return`${t}[${r}]=vec2<u32>(u32(${i}), 0u);`;if("u32"===c.storage&&"vec4<bool>"===c.value)return`${t}[${r}]=dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(${i}));`;throw Error(`not supported combination of storage type ${c.storage} and value type ${c.value} yet`)})(),T=r=>(()=>{if(c.storage===c.value)return`${t}[${r}]`;if("vec2<u32>"===c.storage&&"i32"===c.value)return`i32(${t}[${r}].x)`;if("vec2<u32>"===c.storage&&"u32"===c.value)return`u32(${t}[${r}].x)`;if("u32"===c.storage&&"vec4<bool>"===c.value)return`vec4<bool>(bool(${t}[${r}] & 0xFFu), bool(${t}[${r}] & 0xFF00u), bool(${t}[${r}] & 0xFF0000u), bool(${t}[${r}] & 0xFF000000u))`;throw Error(`not supported combination of storage type ${c.storage} and value type ${c.value} yet`)})(),C=o<2?"":`
25  fn get_${t}ByIndices(indices: ${c.indices}) -> ${p} {
26    return ${T(`i2o_${t}(indices)`)};
27  }`,I=o<2?"":(()=>{let r=u.map(t=>`d${t}: u32`).join(", "),i=u.map(t=>`d${t}`).join(", ");return`
28  fn get_${t}(${r}) -> ${p} {
29    return get_${t}ByIndices(${w(i)});
30  }`})(),E=o<2?"":`
31  fn set_${t}ByIndices(indices: ${c.indices}, value: ${p}) {
32    ${k(`i2o_${t}(indices)`,"value")}
33  }`,z=o<2?"":(()=>{let r=u.map(t=>`d${t}: u32`).join(", "),i=u.map(t=>`d${t}`).join(", ");return`
34  fn set_${t}(${r}, value: ${p}) {
35    set_${t}ByIndices(${w(i)}, value);
36  }`})();return{impl:()=>{let t=[],r=!1;return f.offsetToIndices&&(t.push($),r=!0),f.indicesToOffset&&(t.push(v),r=!0),f.broadcastedIndicesToOffset&&(Object.values(S).forEac

36h(r=>t.push(r)),r=!0),f.set&&(t.push(z),r=!0),f.setByIndices&&(t.push(E),r=!0),f.get&&(t.push(I),r=!0),f.getByIndices&&(t.push(C),r=!0),!s&&r&&t.unshift(`const ${g} = ${c.indices}(${i.join(",")});`,`const ${y} = ${c.indices}(${tt.computeStrides(i).join(",")});`),t.join(`
37`)},type:c,offsetToIndices:r=>(f.offsetToIndices=!0,o<2?r:`o2i_${t}(${r})`),indicesToOffset:r=>(f.indicesToOffset=!0,o<2?r:`i2o_${t}(${r})`),broadcastedIndicesToOffset:(r,i)=>{f.broadcastedIndicesToOffset=!0;let a=`${i.name}broadcastedIndicesTo${t}Offset`;if(a in S)return`${a}(${r})`;let n=[];for(let t=o-1;t>=0;t--){let r=i.indicesGet("outputIndices",t+i.rank-o);n.push(`${x(y,t)} * (${r} % ${x(g,t)})`)}return S[a]=`fn ${a}(outputIndices: ${i.type.indices}) -> u32 {
38             return ${n.length>0?n.join("+"):"0u"};
39           }`,`${a}(${r})`},indices:w,indicesGet:x,indicesSet:(t,r,i)=>o<2?`${t}=${i};`:`${tm(t,r,o)}=${i};`,set:(...r)=>{if(r.length!==o+1)throw Error(`indices length must be ${o}`);let i=r[o];if("string"!=typeof i)throw Error("value must be string");let a=r.slice(0,o).map(h).join(",");return 0===o?k("0u",i):1===o?k(a[0],i):(f.set=!0,f.setByIndices=!0,f.indicesToOffset=!0,`set_${t}(${a}, ${i})`)},setByOffset:k,setByIndices:(r,i)=>o<2?k(r,i):(f.setByIndices=!0,f.indicesToOffset=!0,`set_${t}ByIndices(${r}, ${i});`),get:(...r)=>{if(r.length!==o)throw Error(`indices length must be ${o}`);let i=r.map(h).join(",");return 0===o?T("0u"):1===o?T(i[0]):(f.get=!0,f.getByIndices=!0,f.indicesToOffset=!0,`get_${t}(${i})`)},getByOffset:T,getByIndices:r=>o<2?T(r):(f.getByIndices=!0,f.indicesToOffset=!0,`get_${t}ByIndices(${r})`),usage:a,name:t,strides:y,shape:g,rank:o}},ty=(t,r,i,a=1)=>tg(t,r,i,"input",a),t_=(t,r,i,a=1)=>tg(t,r,i,"output",a),t$=(t,r,i)=>tg(t,r,i,"atomicOutput",1),tb=(t,r,i,a=1)=>tg(t,r,i,"internal",a),tv=class{constructor(t,r){this.normalizedDispatchGroup=t,this.limits=r,this.internalVariables=[],this.variables=[],this.uniforms=[],this.variableIndex=0}guardAgainstOutOfBoundsWorkgroupSizes(t){return`if (global_idx >= ${"number"==typeof t?`${t}u`:t}) { return; }`}mainStart(t=ts){let r="number"==typeof t?t:t[0],i="number"==typeof t?1:t[1],a="number"==typeof t?1:t[2];if(r>this.limits.maxComputeWorkgroupSizeX||i>this.limits.maxComputeWorkgroupSizeY||a>this.limits.maxComputeWorkgroupSizeZ)throw Error(`workgroup size [${r}, ${i}, ${a}] exceeds the maximum workgroup size [${this.limits.maxComputeWorkgroupSizeX}, ${this.limits.maxComputeWorkgroupSizeY}, ${this.limits.maxComputeWorkgroupSizeZ}].`);if(r*i*a>this.limits.maxComputeInvocationsPerWorkgroup)throw Error(`workgroup size [${r}, ${i}, ${a}] exceeds the maximum workgroup invocations ${this.limits.maxComputeInvocationsPerWorkgroup}.`);let n=1===this.normalizedDispatchGroup[1]&&1===this.normalizedDispatchGroup[2],s=n?`@builtin(global_invocation_id) global_id : vec3<u32>,
40    @builtin(workgroup_id) workgroup_id : vec3<u32>,
41    @builtin(local_invocation_index) local_idx : u32,
42    @builtin(local_invocation_id) local_id : vec3<u32>`:`@builtin(global_invocation_id) global_id : vec3<u32>,
43                                             @builtin(local_invocation_id) local_id : vec3<u32>,
44    @builtin(local_invocation_index) local_idx : u32,
45    @builtin(workgroup_id) workgroup_id : vec3<u32>,
46    @builtin(num_workgroups) num_workgroups : vec3<u32>`,o=n?`let global_idx = global_id.x;
47         let workgroup_index = workgroup_id.x;`:`let workgroup_index = workgroup_id.z * num_workgroups[0] * num_workgroups[1] +
48             workgroup_id.y * num_workgroups[0] + workgroup_id.x;
49         let global_idx = workgroup_index * ${r*i*a}u + local_idx;`;return`@compute @workgroup_size(${r}, ${i}, ${a})
50  fn main(${s}) {
51    ${o}
52  `}appendVariableUniforms(t){0!==t.rank&&(t.shape.startsWith("uniforms.")&&this.uniforms.push({name:t.shape.replace("uniforms.",""),type:"u32",length:t.rank}),t.strides.startsWith("uniforms.")&&this.uniforms.push({name:t.strides.replace("uniforms.",""),type:"u32",length:t.rank}))}declareVariable(t,r){if("internal"===t.usage)throw Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(t),this.appendVariableUniforms(t);let i="input"===t.usage?"read":"read_write",a="atomicOutput"===t.usage?"atomic<i32>":t.type.storage;return`@group(0) @binding(${r}) var<storage, ${i}> ${t.name}: array<${a}>;`}declareVariables(...t){return t.map(t=>this.declareVariable(t,this.variableIndex++)).join(`
53`)}registerInternalVariable(t){if("internal"!==t.usage)throw Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() instead.");this.internalVariables.push(t),this.appendVariableUniforms(t)}registerInternalVariables(...t){return t.forEach(t=>this.registerInternalVariable(t)),this}registerUniform(t,r,i=1){return this.uniforms.push({name:t,type:r,length:i}),this}registerUniforms(t){return this.uniforms=this.uniforms.concat(t),this}uniformDeclaration(){if(0===this.uniforms.length)return"";let t=[];for(let{name:r,type:i,length:a}of this.uniforms)if(a&&a>4)"f16"===i?t.push(`@align(16) ${r}:array<mat2x4<${i}>, ${Math.ceil(a/8)}>`):t.push(`${r}:array<vec4<${i}>, ${Math.ceil(a/4)}>`);else{let n=null==a||1===a?i:`vec${a}<${i}>`;t.push(`${r}:${n}`)}return`
54      struct Uniforms { ${t.join(", ")} };
55      @group(0) @binding(${this.variableIndex}) var<uniform> uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(t=>t.impl()).join(`
56`)+this.internalVariables.map(t=>t.impl()).join(`
57`)}get variablesInfo(){if(0!==this.uniforms.length)return this.uniforms.map(t=>[[12,10,1,6][["u32","f16","f32","i32"].indexOf(t.type)],t.length??1])}},tw=(t,r)=>new tv(t,r)}),n$=W(()=>{nd(),ny(),ng(),n_(),tx=(t,r)=>0!==r.length?r:[...Array(t).keys()].reverse(),tS=(t,r)=>{let i=t.dataType,a=t.dims.length,n=tx(a,r),s=((t,r)=>tt.sortBasedOnPerm(t,tx(t.length,r)))(t.dims,n),o=t.dims,u=s;if(a<2||((t,r)=>{let i=0;for(let a=0;a<t.length;

57++a)if(1!==r[t[a]]){if(t[a]<i)return!1;i=t[a]}return!0})(n,t.dims))return{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let r=tt.size(s);return{outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(r/64/4)},programUniforms:[{type:12,data:Math.ceil(r/4)}]}},getShaderSource:t=>{let r=ty("input",i,o,4),a=t_("output",i,u,4);return`
58  ${t.registerUniform("output_size","u32").declareVariables(r,a)}
59  ${t.mainStart()}
60    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
61    output[global_idx] = input[global_idx];
62  }`}};let{newShape:l,newPerm:d}=((t,r)=>{let i=[],a=[];for(let n=0;n<t.length;++n)1!==t[n]&&i.push(t[n]),1!==t[r[n]]&&a.push(r[n]);return{newShape:i,newPerm:a}})(t.dims,n),p=tt.areEqual(d,[2,3,1]),c=tt.areEqual(d,[3,1,2]);return 2===l.length||p||c?(u=[(o=p?[l[0],l[1]*l[2]]:c?[l[0]*l[1],l[2]]:l)[1],o[0]],{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let r=tt.size(s);return{outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(u[1]/16),y:Math.ceil(u[0]/16)},programUniforms:[{type:12,data:r},...td(o,u)]}},getShaderSource:t=>{let r=ty("a",i,o.length),a=t_("output",i,u.length);return`
63  ${t.registerUniform("output_size","u32").declareVariables(r,a)}
64  var<workgroup> tile : array<array<${a.type.value}, 17>, 16>;
65  ${t.mainStart([16,16,1])}
66    let stride = (uniforms.output_shape[1] - 1) / 16 + 1;
67    let workgroup_id_x = workgroup_index % stride;
68    let workgroup_id_y = workgroup_index / stride;
69    let input_col = workgroup_id_y * 16u + local_id.x;
70    let input_row = workgroup_id_x * 16u + local_id.y;
71    if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) {
72      tile[local_id.y][local_id.x] = ${r.getByIndices(`${r.type.indices}(input_row, input_col)`)};
73    }
74    workgroupBarrier();
75
76    let output_col = workgroup_id_x * 16u + local_id.x;
77    let output_row = workgroup_id_y * 16u + local_id.y;
78    if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) {
79      ${a.setByIndices(`${a.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")}
80    }
81  }`}}):{name:"Transpose",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>{let r=tt.size(s);return{outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(r/64)},programUniforms:[{type:12,data:r},...td(o,u)]}},getShaderSource:t=>{let r=ty("a",i,o.length),s=t_("output",i,u.length);return`
82  ${t.registerUniform("output_size","u32").declareVariables(r,s)}
83
84  ${((t,r,i,a)=>{let n=`fn perm(i: ${a.type.indices}) -> ${i.type.indices} {
85    var a: ${i.type.indices};`;for(let i=0;i<r;++i)n+=`a[${t[i]}]=i[${i}];`;return n+"return a;}"})(n,a,r,s)}
86
87  ${t.mainStart()}
88    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
89
90    let indices = ${s.offsetToIndices("global_idx")};
91    let aIndices = perm(indices);
92
93    ${s.setByOffset("global_idx",r.getByIndices("aIndices"))}
94  }`}}},tk=(t,r)=>{((t,r)=>{if(!t||1!==t.length)throw Error("Transpose requires 1 input.");if(0!==r.length&&r.length!==t[0].dims.length)throw Error(`perm size ${r.length} does not match input rank ${t[0].dims.length}`)})(t.inputs,r.perm),t.compute(tS(t.inputs[0],r.perm))},tT=t=>e7({perm:t.perm})}),nb=W(()=>{nd(),ny(),n_(),nv(),n$(),tC={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},tI={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},tE={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"

940",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},tz={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},tA=(t,r,i,a)=>{let n=1===t.inputs.length?i:tH(t.inputs,i),s=n.axes;0!==s.length||n.noopWithEmptyAxes||(s=t.inputs[0].dims.map((t,r)=>r));let o=tt.normalizeAxes(s,t.inputs[0].dims.length),u=o,l=t.inputs[0],d=((t,r)=>{let i=[];if(!((t,r)=>{for(let i=0;i<t.length;++i)if(t[t.length-i-1]!==r-1-i)return!1;return!0})(t,r)){for(let a=0;a<r;++a)-1===t.indexOf(a)&&i.push(a);t.forEach(t=>i.push(t))}return i})(u,t.inputs[0].dims.length);d.length>0&&(l=t.compute(tS(t.inputs[0],d),{inputs:[0],outputs:[-1]})[0],u=((t,r)=>{let i=[];for(let a=r-t;a<r;++a)i.push(a);return i})(u.length,l.dims.length));let[p,c]=((t,r)=>{let i=[],a=t.length;for(let n=0;n<a;n++)-1===r.indexOf(n)&&i.push(t[n]);return[i,r.map(r=>t[r])]})(l.dims,u),h=p;n.keepDims&&(h=((t,r)=>{let i=t.length+r.length,a=[],n=0;for(let s=0;s<i;s++)-1===r.indexOf(s)?a.push(t[n++]):a.push(1);return a})(p,o)),t.compute(((t,r,i,a,n,s,o)=>{let u=i[0].dims,l=tt.size(s),d=tt.size(o),p=ty("_A",i[0].dataType,u),c=t_("output",n,s),h=64;1===l&&(h=256);let f=`
95          var<workgroup> aBestValues : array<f32, ${h}>;
96       `;return{name:t,shaderCache:{hint:`${r};${h}`,inputDependencies:["type"]},getShaderSource:t=>`
97        ${t.registerUniform("reduceSize","u32").declareVariables(p,c)}
98        ${f}
99        fn DIV_CEIL(a : u32, b : u32) -> u32 {
100          return ((a - 1u) / b + 1u);
101         }
102         ${t.mainStart(h)}
103
104          let outputIndex = global_idx / ${h};
105          let offset = outputIndex * uniforms.reduceSize;
106
107          var bestValue = f32(${tE[a]});
108          let Length = uniforms.reduceSize;
109          for (var k = local_idx; k < Length; k = k + ${h}) {
110           let candidate = f32(${p.getByOffset("offset + k")});
111           bestValue = ${tC[a]};
112          }
113          aBestValues[local_idx] = bestValue;
114          workgroupBarrier();
115
116         var reduceSize = min(Length, ${h}u);
117         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
118             currentSize = reduceSize / 2u) {
119           let interval = DIV_CEIL(reduceSize, 2u);
120           if (local_idx < currentSize) {
121            let candidate = aBestValues[local_idx + interval];
122            bestValue = ${tI[a]};
123            aBestValues[local_idx] = bestValue;
124           }
125           reduceSize = interval;
126           workgroupBarrier();
127         }
128
129         if (local_idx == 0u) {
130          ${c.setByOffset("outputIndex",`${"mean"===a?`${c.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${c.type.storage}(${tz[a]})`}`)};
131         }
132        }`,getRunData:()=>({outputs:[{dims:s,dataType:n}],dispatchGroup:{x:l},programUniforms:[{type:12,data:d}]})}})(r,n.cacheKey,[l],a,t.inputs[0].dataType,h,c),{inputs:[l]})},tO=(t,r)=>{tA(t,"ReduceMeanShared",r,"mean")},tR=(t,r)=>{tA(t,"ReduceL1Shared",r,"l1")},tB=(t,r)=>{tA(t,"ReduceL2Shared",r,"l2")},tN=(t,r)=>{tA(t,"ReduceLogSumExpShared",r,"logSumExp")},tM=(t,r)=>{tA(t,"ReduceMaxShared",r,"max")},tD=(t,r)=>{tA(t,"ReduceMinShared",r,"min")},tP=(t,r)=>{tA(t,"ReduceProdShared",r,"prod")},tU=(t,r)=>{tA(t,"ReduceSumShared",r,"sum")},tW=(t,r)=>{tA(t,"ReduceSumSquareShared",r,"sumSquare")},tq=(t,r)=>{tA(t,"ReduceLogSumShared",r,"logSum")}}),nv=W(()=>{nd(),ny(),ng(),n_(),nb(),tL=t=>{if(!t||0===t.length||t.length>2)throw Error("Reduce op requires 1 or 2 inputs.");if(2===t.length&&1!==t[1].dims.length)throw Error("Invalid axes input dims.")},tV=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],tG=(t,r,i,a,n,s,o=!1,u=!1)=>{let l=[],d=i[0].dims,p=d.length,c=tt.normalizeAxes(n,p),h=!u&&0===c.length;d.forEach((t,r)=>{h||c.indexOf(r)>=0?o&&l.push(1):l.push(t)});let f=l.length,m=tt.size(l);return{name:t,shaderCache:r,getShaderSource:t=>{let r=[],n=ty("_A",i[0].dataType,p),u=t_("output",s,f),l=a(n,u,c),m=l[2];for(let t=0,i=0;t<p;t++)h||c.indexOf(t)>=0?(o&&i++,m=`for(var j${t}: u32 = 0; j${t} < ${d[t]}; j${t}++) {
133                  ${l[2].includes("last_index")?`let last_index = j${t};`:""}
134                  ${n.indicesSet("input_indices",t,`j${t}`)}
135                  ${m}
136                }`):(r.push(`${n.indicesSet("input_indices",t,u.indicesGet("output_indices",

136i))};`),i++);return`
137
138        ${t.registerUniform("output_size","u32").declareVariables(n,u)}
139
140        ${t.mainStart()}
141          ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
142          var input_indices: ${n.type.indices};
143          let output_indices = ${u.offsetToIndices("global_idx")};
144
145          ${r.join(`
146`)}
147          ${l[0]}       // init ops for reduce max/min
148          ${l[1]}
149          ${m}
150          ${l[3]}
151          ${4===l.length?u.setByOffset("global_idx","value"):l.slice(4).join(`
152`)}
153        }`},getRunData:()=>({outputs:[{dims:l,dataType:s}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:[{type:12,data:m},...td(d,l)]})}},tH=(t,r)=>{let i=[];return t[1].dims[0]>0&&t[1].getBigInt64Array().forEach(t=>i.push(Number(t))),e7({axes:i,keepDims:r.keepDims,noopWithEmptyAxes:r.noopWithEmptyAxes})},tF=(t,r,i,a)=>{let n=t.inputs,s=1===n.length?i:tH(n,i);t.compute(tG(r,{hint:s.cacheKey,inputDependencies:["rank"]},[n[0]],s.noopWithEmptyAxes&&0===s.axes.length?tV:a,s.axes,n[0].dataType,s.keepDims,s.noopWithEmptyAxes),{inputs:[0]})},tj=(t,r,i)=>{if(0===r.length)return i;let a=1,n=1;for(let i=0;i<r.length;i++)-1===r.indexOf(i)?a*=t[i]:n*=t[i];return n<32&&a>1024},tK=(t,r)=>{tj(t.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?((t,r)=>{tL(t.inputs),tF(t,"ReduceMean",r,(r,i,a)=>{let n=1;for(let i=0;i<r.rank;i++)(a.indexOf(i)>=0||0===a.length)&&(n*=t.inputs[0].dims[i]);return["var sum = f32(0);","",`sum += f32(${r.getByIndices("input_indices")});`,`let value = ${i.type.value}(sum / ${n});`]})})(t,r):tO(t,r)},tQ=(t,r)=>{tj(t.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?((t,r)=>{tL(t.inputs),tF(t,"ReduceL1",r,(t,r)=>[`var value = ${r.type.storage}(0);`,"",`value += abs(${t.getByIndices("input_indices")});`,""])})(t,r):tR(t,r)},tZ=(t,r)=>{tj(t.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?((t,r)=>{tL(t.inputs),tF(t,"ReduceL2",r,(t,r)=>[`var t = ${r.type.value}(0); var value = ${r.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])})(t,r):tB(t,r)},tX=(t,r)=>{tj(t.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?((t,r)=>{tL(t.inputs),tF(t,"ReduceLogSumExp",r,(t,r)=>[`var value = ${r.type.storage}(0);`,"",`value += exp(${t.getByIndices("input_indices")});`,"value = log(value);"])})(t,r):tN(t,r)},tY=(t,r)=>{tj(t.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?((t,r)=>{tL(t.inputs),tF(t,"ReduceMax",r,(t,r,i)=>{let a=[];for(let r=0;r<t.rank;r++)(i.indexOf(r)>=0||0===i.length)&&a.push(t.indicesSet("input_indices",r,0));return[`${a.join(`
154`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = max(value, ${t.getByIndices("input_indices")});`,""]})})(t,r):tM(t,r)},tJ=(t,r)=>{tj(t.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?((t,r)=>{tL(t.inputs),tF(t,"ReduceMin",r,(t,r,i)=>{let a=[];for(let r=0;r<t.rank;r++)(i.indexOf(r)>=0||0===i.length)&&a.push(`input_indices[${r}] = 0;`);return[`${a.join(`
155`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = min(value, ${t.getByIndices("input_indices")});`,""]})})(t,r):tD(t,r)},t0=(t,r)=>{tj(t.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?((t,r)=>{tL(t.inputs),tF(t,"ReduceProd",r,(t,r)=>[`var value = ${r.type.storage}(1);`,"",`value *= ${t.getByIndices("input_indices")};`,""])})(t,r):tP(t,r)},t1=(t,r)=>{tj(t.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?((t,r)=>{tL(t.inputs),tF(t,"ReduceSum",r,(t,r)=>[`var value = ${r.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,""])})(t,r):tU(t,r)},t2=(t,r)=>{tj(t.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?((t,r)=>{tL(t.inputs),tF(t,"ReduceSumSquare",r,(t,r)=>[`var t = ${r.type.value}(0); var value = ${r.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += t * t;`,""])})(t,r):tW(t,r)},t3=(t,r)=>{tj(t.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?((t,r)=>{tL(t.inputs),tF(t,"ReduceLogSum",r,(t,r)=>[`var value = ${r.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,"value = log(value);"])})(t,r):tq(t,r)}}),nw=W(()=>{nd(),ng(),nv(),t4=t=>{if(!t||0===t.length||t.length>2)throw Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(1!==t[0].dataType)throw Error("Invalid input type.")},t6=(t,r)=>{t4(t.inputs),t.compute(tG("ArgMin",{hint:r.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],(t,i,a)=>{let n=[];for(let r=0;r<t.rank;r++)(a.indexOf(r)>=0||0===a.length)&&n.push(`input_indices[${r}] = 0;`);return[`${n.join(`
156`)}`,`var value = ${t.getByIndices("input_indices")};
157var best_index : i32 = 0;`,`if (${t.getByIndices("input_indices")} ${r.selectLastIndex>0?"<=":"<"} value) {
158         value = ${t.getByIndices("input_indices")};
159         best_index = i32(last_index);
160       }`,"",i.setByOffset("global_idx","best_index")]},[r.axis],7,r.keepDims),{inputs:[0]})},t8=(t,r)=>{t4(t.inputs),t.compute(tG("argMax",{hint:r.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],(t,i,a)=>{let n=[];for(let r=0;r<t.rank;r++)(a.indexOf(r)>=0||0===a.length)&&n.push(`input_indices[${r}] = 0;`);return[`${n.join(`
161`)}`,`var value = ${t.getByIndices("input_indices")};
162var best_index : i32 = 0;`,`if (${t.getByIndices("input_indices")} ${r.selectLastIndex>0?">=":">"} value) {
163         value = ${t.getByIndices("input_indices")};
164         best_index = i32(last_index);
165       }`,"",i.setByOffset("global_idx","best_index")]},[r.axis],7,r.keepDims),{inputs:[0]})},t5=t=>e7(t)}),nx=W(()=>{nd(),ny(),nf(),n_(),t7=(t,r,i)=>r&&t?`
166      let total_sequence_length_input = u32(${r.getByOffset("0")});
167      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
168      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
169      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
170      total_sequence_length = u32(${t?.getByOffset("batchIdx")}) + 1;
171      var past_sequence_length: u32 = 0;
172      if (is_first_prompt == false) {
173        past_sequence_length = total_sequence_length - sequence_length;
174      }
175       `:`
176    ${i?"let past_sequence_length = uniforms.past_sequence_length":""};
177    let present_sequence_length = total_sequence_length;
178    `,t9=(t,r,i,a,n,s,o,u,l,d,p,c)=>{let h=Math.min(t.outputCount,1+ +!!o+ +!!u),f=h>1?d.pastSequenceLength:0,m=f+d.kvSequenceLength,g=l&&tt.size(l.dims)>0?l:void 0,y=[r,i];h>1&&o&&tt.size(o.dims)>0&&y.push(o),g&&y.push(g),p&&y.push(p),c&&y.push(c);let _=t.compute(((t,r,i,a,n,s,o,u,l)=>{let d=o+s.kvSequenceLength,p=[s.batchSize,s.numHeads,s.sequenceLength,d],c=t>1&&a,h=s.kvNumHeads?s.kvNumHeads:s.numHeads,f=c?[s.batchSize,h,d,s.headSize]:void 0,m=s.nReps?s.nReps:1,g=0===s.scale?1/Math.sqrt(s.headSize):s.scale,y=tp(s.headSize),_=s.headSize/y,$={x:Math.ceil(d/12),y:Math.ceil(s.sequenceLength/12),z:s.batchSize*s.numHeads},b=[{type:12,data:s.sequenceLength},{type:12,data:_},{type:12,data:d}

178,{type:12,data:s.numHeads},{type:12,data:s.headSize},{type:1,data:g},{type:12,data:o},{type:12,data:s.kvSequenceLength},{type:12,data:m}],v=c&&a&&tt.size(a.dims)>0,w=["type","type"];v&&w.push("type"),n&&w.push("type"),u&&w.push("type"),l&&w.push("type");let x=[{dims:p,dataType:r.dataType,gpuDataType:0}];return c&&x.push({dims:f,dataType:r.dataType,gpuDataType:0}),{name:"AttentionProbs",shaderCache:{hint:`${y};${void 0!==n};${void 0!==a};${t}`,inputDependencies:w},getRunData:()=>({outputs:x,dispatchGroup:$,programUniforms:b}),getShaderSource:t=>{let s=ty("q",r.dataType,r.dims,y),o=[s,ty("key",i.dataType,i.dims,y)];if(v){let t=ty("past_key",a.dataType,a.dims,y);o.push(t)}n&&o.push(ty("attention_bias",n.dataType,n.dims));let d=u?ty("seq_lens",u.dataType,u.dims):void 0;d&&o.push(d);let h=l?ty("total_sequence_length_input",l.dataType,l.dims):void 0;h&&o.push(h);let g=t_("output",r.dataType,p),_=[g];c&&_.push(t_("present_key",r.dataType,f,y));let $=tl(1,y);return`
179  const TILE_SIZE = 12u;
180
181  var<workgroup> tileQ: array<${s.type.storage}, 144>;
182  var<workgroup> tileK: array<${s.type.storage}, 144>;
183  ${t.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}]).declareVariables(...o,..._)}
184  ${t.mainStart([12,12,1])}
185    // x holds the N and y holds the M
186    let headIdx = workgroup_id.z % uniforms.num_heads;
187    let kvHeadIdx = ${1===m?"headIdx":"headIdx / uniforms.n_reps"};
188    let kv_num_heads = ${1===m?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
189    let batchIdx = workgroup_id.z / uniforms.num_heads;
190    let m = workgroup_id.y * TILE_SIZE;
191    let n = workgroup_id.x * TILE_SIZE;
192    let sequence_length = uniforms.M;
193    var total_sequence_length = uniforms.N;
194    ${t7(d,h,!0)}
195    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
196    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
197    ${v&&c?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
198    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
199    ${c?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
200    var value = ${$}(0);
201    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
202      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
203        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
204      }
205      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
206        var idx = TILE_SIZE * local_id.y + local_id.x;
207      ${v&&c?`
208              if (n + local_id.y < past_sequence_length) {
209                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
210              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
211                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
212              }`:`
213          if (n + local_id.y < uniforms.kv_sequence_length) {
214            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
215          }`}
216      ${c?`if (n + local_id.y < present_sequence_length) {
217        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
218      }`:""}
219      }
220      workgroupBarrier();
221
222      for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
223          value += ${$}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);
224      }
225
226      workgroupBarrier();
227    }
228
229    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
230      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
231      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
232      var sum: f32 = ${(()=>{switch(y){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw Error(`Unsupported components: ${y}`)}})()};
233        output[outputIdx] = ${g.type.value} (sum * uniforms.alpha) + ${n?"attention_bias[outputIdx]":"0.0"};
234    }
235  }`}}})(h,r,i,o,g,d,f,p,c),{inputs:y,outputs:h>1?[-1,1]:[-1]})[0];t.compute(((t,r,i,a,n,s,o,u)=>{let l=tp(o?1:s),d=64,p=s/l;p<64&&(d=32);let c=[{type:12,data:r},{type:12,data:i},{type:12,data:a},{type:12,data:n},{type:12,data:p},{type:12,data:Math.ceil(s/l/d)}],h=tu(t.dataType,l),f=tl(1,l),m=["type"];return o&&m.push("type"),u&&m.push("type"),{name:"AttentionProbsSoftmax",shaderCache:{hint:`${d};${h};${l}`,inputDependencies:m},getShaderSource:r=>{let i=t_("x",t.dataType,t.dims,l),a=[i],n=o?ty("seq_lens",o.dataType,o.dims):void 0;n&&a.push(n);let s=u?ty("total_sequence_length_input",u.dataType,u.dims):void 0;s&&a.push(s);let p=tl(t.dataType);return`

236  var<workgroup> thread_max: array<f32, ${d}>;
237  var<workgroup> thread_sum: array<f32, ${d}>;
238  ${r.registerUniforms([{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}]).declareVariables(...a)}
239  ${r.mainStart([d,1,1])}
240    let batchIdx = workgroup_id.z / uniforms.num_heads;
241    let headIdx = workgroup_id.z % uniforms.num_heads;
242    let sequence_length = uniforms.sequence_length;
243    var total_sequence_length = uniforms.total_sequence_length;
244    ${t7(n,s,!1)}
245    let local_offset = local_idx * uniforms.elements_per_thread;
246    let offset = (global_idx / ${d}) * uniforms.total_sequence_length + local_offset;
247    let seq_causal_length = ${o?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
248    var thread_max_vector = ${f}(-3.402823e+38f);
249    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
250      thread_max_vector = max(${f}(x[offset + i]), thread_max_vector);
251    }
252    thread_max[local_idx] = ${(()=>{switch(l){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw Error(`Unsupported components: ${l}`)}})()};
253    workgroupBarrier();
254
255    var max_value =  f32(-3.402823e+38f);
256    for (var i = 0u; i < ${d}; i++) {
257      max_value = max(thread_max[i], max_value);
258    }
259
260    var sum_vector = ${f}(0);
261    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
262      sum_vector += exp(${f}(x[offset + i]) - max_value);
263    }
264    thread_sum[local_idx] = ${(()=>{switch(l){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw Error(`Unsupported components: ${l}`)}})()};
265    workgroupBarrier();
266
267    var sum: f32 = 0;
268    for (var i = 0u; i < ${d}; i++) {
269      sum += thread_sum[i];
270    }
271
272    if (sum == 0) {
273      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
274        x[offset + i] = ${i.type.value}(${p}(1.0) / ${p}(seq_causal_length));
275      }
276    } else {
277      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
278        var f32input = ${f}(x[offset + i]);
279        x[offset + i] = ${i.type.value}(exp(f32input - max_value) / sum);
280      }
281    }
282      ${o?`
283        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
284          x[offset + total_seq_id] = ${i.type.value}(${p}(0));
285        }`:""};
286  }`},getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(s/d),y:n,z:r*i},programUniforms:c})}})(_,d.batchSize,d.numHeads,f,d.sequenceLength,m,p,c),{inputs:p&&c?[_,p,c]:[_],outputs:[]});let $=[_,a];h>1&&u&&tt.size(u.dims)>0&&$.push(u),p&&$.push(p),c&&$.push(c),t.compute(((t,r,i,a,n,s,o,u)=>{let l=s+n.kvSequenceLength,d=n.nReps?n.nReps:1,p=n.vHiddenSize*d,c=t>1&&a,h=n.kvNumHeads?n.kvNumHeads:n.numHeads,f=c?[n.batchSize,h,l,n.headSize]:void 0,m=[n.batchSize,n.sequenceLength,p],g={x:Math.ceil(n.vHeadSize/12),y:Math.ceil(n.sequenceLength/12),z:n.batchSize*n.numHeads},y=[{type:12,data:n.sequenceLength},{type:12,data:l},{type:12,data:n.vHeadSize},{type:12,data:n.numHeads},{type:12,data:n.headSize},{type:12,data:p},{type:12,data:s},{type:12,data:n.kvSequenceLength},{type:12,data:d}],_=c&&a&&tt.size(a.dims)>0,$=["type","type"];_&&$.push("type"),o&&$.push("type"),u&&$.push("type");let b=[{dims:m,dataType:r.dataType,gpuDataType:0}];return c&&b.push({dims:f,dataType:r.dataType,gpuDataType:0}),{name:"AttentionScore",shaderCache:{hint:`${void 0!==a};${t}`,inputDependencies:$},getRunData:()=>({outputs:b,dispatchGroup:g,programUniforms:y}),getShaderSource:t=>{let n=ty("probs",r.dataType,r.dims),s=[n,ty("v",i.dataType,i.dims)];_&&s.push(ty("past_value",a.dataType,a.dims));let l=o?ty("seq_lens",o.dataType,o.dims):void 0;o&&s.push(l);let p=u?ty("total_sequence_length_input",u.dataType,u.dims):void 0;u&&s.push(p);let h=[t_("output",r.dataType,m)];return c&&h.push(t_("present_value",r.dataType,f)),`
287  const TILE_SIZE = 12u;
288  var<workgroup> tileQ: array<${n.type.value}, 144>;
289  var<workgroup> tileV: array<${n.type.value}, 144>;
290  ${t.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}]).declareVariables(...s,...h)}
291  ${t.mainStart([12,12,1])}
292   let headIdx = workgroup_id.z % uniforms.num_heads;
293   let batchIdx = workgroup_id.z / uniforms.num_heads;
294   let kvHeadIdx = ${1===d?"headIdx":"headIdx / uniforms.n_reps"};
295   let kv_num_heads = ${1===d?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
296   let m = global_id.y;
297   let n = global_id.x;
298   let sequence_length = uniforms.M;
299   var total_sequence_length = uniforms.K;
300   ${t7(l,p,!0)}
301   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
302   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
303   ${_&&c?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
304   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
305   ${c?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
306   var value = ${n.type.storage}(0);
307   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
308      if (m < uniforms.M && w + local_id.x < uniforms.K) {
309        tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
310      }
311      if (n < uniforms.N && w + local_id.y < uniforms.K) {
312        var idx = TILE_SIZE * local_id.y + local_id.x;
313        ${_&&c?`
314        if (w + local_id.y < past_sequence_length) {
315          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
316        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
317          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
318        }
319      `:`
320            if (w + local_id.y < uniforms.kv_sequence_length) {
321              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
322            }`}
323        ${c?`
324            if (w + local_id.y < present_sequence_length) {
325          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
326        }`:""}
327      }
328     workgroupBarrier();
329     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
330       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
331     }
332     workgroupBarrier();
333   }
334
335   // we need to transpose output from BNSH_v to BSND_v
336   if (m < uniforms.M && n < uniforms.N) {
337     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
338       + headIdx * uniforms.N + n;
339     output[outputIdx] = value;
340   }
341  }`}}})(h,_,a,u,d,f,p,c),{inputs:$,outputs:h>1?[0,2]:[0]})},re=(t,r)=>{let i=((t,r)=>{let i=t[0],a=t[1],n=t[2],s=t[3],o=t[4],u=t[5];if(o&&u)throw Error("Attention cannot have both past and attention_bias");if(3!==i.dims.length)throw Error('Input "input" must have 3 dimensions');

341let l=i.dims[0],d=i.dims[1],p=i.dims[2];if(1!==n.dims.length)throw Error('Input "bias" is expected to have 1 dimensions');if(2!==a.dims.length)throw Error('Input "weights" is expected to have 2 dimensions');if(a.dims[0]!==p)throw Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(n.dims[0]!==a.dims[1])throw Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let c=n.dims[0]/3,h=c,f=h;if(r.qkvHiddenSizes.length>0){if(3!==r.qkvHiddenSizes.length)throw Error("qkv_hidden_sizes attribute should have 3 elements");for(let t of r.qkvHiddenSizes)if(t%r.numHeads!=0)throw Error("qkv_hidden_sizes should be divisible by num_heads");c=r.qkvHiddenSizes[0],h=r.qkvHiddenSizes[1],f=r.qkvHiddenSizes[2]}if(c!==h)throw Error("qkv_hidden_sizes first element should be same as the second");if(n.dims[0]!==c+h+f)throw Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let m=0;if(o){if(h!==f)throw Error('Input "past" expect k_hidden_size == v_hidden_size');if(5!==o.dims.length)throw Error('Input "past" must have 5 dimensions');if(2!==o.dims[0])throw Error('Input "past" first dimension must be 2');if(o.dims[1]!==l)throw Error('Input "past" second dimension must be batch_size');if(o.dims[2]!==r.numHeads)throw Error('Input "past" third dimension must be num_heads');if(o.dims[4]!==h/r.numHeads)throw Error('Input "past" fifth dimension must be k_hidden_size / num_heads');r.pastPresentShareBuffer||(m=o.dims[3])}let g=d+m;if(s)throw Error("Mask not supported");if(o)throw Error("past is not supported");if(u){if(4!==u.dims.length)throw Error('Input "attention_bias" must have 4 dimensions');if(u.dims[0]!==l||u.dims[1]!==r.numHeads||u.dims[2]!==d||u.dims[3]!==g)throw Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:l,sequenceLength:d,pastSequenceLength:m,kvSequenceLength:d,totalSequenceLength:g,maxSequenceLength:-1,inputHiddenSize:p,hiddenSize:c,vHiddenSize:f,headSize:Math.floor(c/r.numHeads),vHeadSize:Math.floor(f/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:0,scale:r.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}})(t.inputs,r),[a,n,s]=((t,r)=>{let i=[r.batchSize,r.numHeads,r.sequenceLength,r.headSize],a=r.sequenceLength,n=r.inputHiddenSize,s=r.headSize,o={x:Math.ceil(r.headSize/12),y:Math.ceil(r.sequenceLength/12),z:r.batchSize*r.numHeads},u=[t.inputs[0],t.inputs[1],t.inputs[2]],l=[{type:12,data:a},{type:12,data:n},{type:12,data:s},{type:12,data:r.numHeads},{type:12,data:r.headSize},{type:12,data:r.hiddenSize},{type:12,data:r.hiddenSize+r.hiddenSize+r.vHiddenSize}];return t.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:i,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:i,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:i,dataType:t.inputs[0].dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:l}),getShaderSource:t=>{let r=t_("output_q",u[0].dataType,i),a=t_("output_k",u[0].dataType,i),n=t_("output_v",u[0].dataType,i),s=ty("input",u[0].dataType,u[0].dims),o=ty("weight",u[1].dataType,u[1].dims),l=ty("bias",u[2].dataType,u[2].dims),d=s.type.storage;return`
342  const TILE_SIZE = 12u;
343  var<workgroup> tileInput: array<${d}, 144>;
344  var<workgroup> tileWeightQ: array<${d}, 144>;
345  var<workgroup> tileWeightK: array<${d}, 144>;
346  var<workgroup> tileWeightV: array<${d}, 144>;
347  ${t.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(s,o,l,r,a,n)}
348  ${t.mainStart([12,12,1])}
349    let batchIndex = workgroup_id.z / uniforms.num_heads;
350    let headNumber = workgroup_id.z % uniforms.num_heads;
351    let m = global_id.y;
352    let n = global_id.x;
353
354    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;
355    let biasOffsetQ = headNumber * uniforms.head_size;
356    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;
357    let biasOffsetV = uniforms.hidden_size + biasOffsetK;
358
359    var valueQ = ${d}(0);
360    var valueK = ${d}(0);
361    var valueV = ${d}(0);
362    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
363      if (m < uniforms.M && w + local_id.x < uniforms.K) {

364        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
365      }
366      if (n < uniforms.N && w + local_id.y < uniforms.K) {
367        let offset = n + (w + local_id.y) * uniforms.ldb;
368        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
369        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
370        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
371      }
372      workgroupBarrier();
373      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
374        let inputTileOffset = TILE_SIZE * local_id.y + k;
375        let weightTileOffset = TILE_SIZE * k + local_id.x;
376        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
377        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
378        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
379      }
380
381      workgroupBarrier();
382    }
383
384    let headOffset = (m * uniforms.N + n) % uniforms.head_size;
385    valueQ += bias[headOffset + biasOffsetQ];
386    valueK += bias[headOffset + biasOffsetK];
387    valueV += bias[headOffset + biasOffsetV];
388
389    let offset = workgroup_id.z * uniforms.M * uniforms.N;
390    if (m < uniforms.M && n < uniforms.N) {
391      let outputIdx = offset + m * uniforms.N + n;
392      output_q[outputIdx] = valueQ;
393      output_k[outputIdx] = valueK;
394      output_v[outputIdx] = valueV;
395    }
396  }`}},{inputs:u,outputs:[-1,-1,-1]})})(t,i);return t9(t,a,n,s,t.inputs[4],void 0,void 0,void 0,t.inputs[5],i)}}),nS=W(()=>{eu(),nd(),ny(),ng(),n_(),rt=(t,r)=>{let i,{inputs:a,outputCount:n}=t,s=(i={...r,outputCount:n},e7(i));if(c.webgpu.validateInputContent&&((t,r)=>{if(!t||5!==t.length)throw Error("BatchNormalization requires 5 inputs");let i=(t,r,i)=>{let a=r.length;if(a!==t.length)throw Error(`${i}: num dimensions != ${a}`);r.forEach((r,a)=>{if(r!==t[a])throw Error(`${i}: dim[${a}] do not match`)})};if(t[0].dims.length>1){let a="NHWC"===r.format?r.spatial?t[0].dims.slice(-1):t[0].dims.slice(-1).concat(t[0].dims.slice(1,t[0].dims.length-1)):t[0].dims.slice(1,r.spatial?2:void 0);i(t[1].dims,a,"Invalid input scale"),i(t[2].dims,a,"Invalid input B"),i(t[3].dims,a,"Invalid input mean"),i(t[4].dims,a,"Invalid input var")}else i(t[1].dims,[1],"Invalid input scale"),i(t[2].dims,[1],"Invalid input B"),i(t[3].dims,[1],"Invalid input mean"),i(t[4].dims,[1],"Invalid input var")})(a,s),r.trainingMode)throw Error("BatchNormalization trainingMode is not supported yet.");t.compute(((t,r)=>{let{epsilon:i,spatial:a,format:n}=r,s=t[0].dims,o=a?tp(s[s.length-1]):1,u="NHWC"===n&&s.length>1?o:1,l=tt.size(s)/o,d=a?s.length:s,p=ty("x",t[0].dataType,t[0].dims,o),c=ty("scale",t[1].dataType,t[1].dims,u),h=ty("bias",t[2].dataType,t[2].dims,u),f=ty("inputMean",t[3].dataType,t[3].dims,u),m=ty("inputVar",t[4].dataType,t[4].dims,u),g=t_("y",t[0].dataType,d,o);return{name:"BatchNormalization",shaderCache:{hint:`${r.epsilon}_${r.format}_${a}_${o}`,inputDependencies:a?["rank","type","type","type","type"]:void 0},getShaderSource:t=>`
397  const epsilon = ${i};
398  ${t.registerUniform("outputSize","u32").declareVariables(p,c,h,f,m,g)}
399  ${t.mainStart()}
400  ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
401    var outputIndices = ${g.offsetToIndices(`global_idx * ${o}`)};
402    ${(()=>{let t="";if(a)t=`let cOffset = ${1===s.length?"0u":"NHWC"===n?`outputIndices[${s.length-1}] / ${o}`:"outputIndices[1]"};`;else if("NCHW"===n)t=`
403            ${g.indicesSet("outputIndices","0","0")}
404            let cOffset = ${g.indicesToOffset("outputIndices")};`;else{t=`var cIndices = ${c.type.indices}(0);
405                       cIndices[0] = outputIndices[${s.length-1}];`;for(let r=1;r<c.rank;r++)t+=`cIndices[${r}] = outputIndices[${r}];`;t+=`let cOffset = ${c.indicesToOffset("cIndices")};`}return t})()}
406    let scale = ${c.getByOffset("cOffset")};
407    let bias = ${h.getByOffset("cOffset")};
408    let inputMean = ${f.getByOffset("cOffset")};
409    let inputVar = ${m.getByOffset("cOffset")};
410    let x = ${p.getByOffset("global_idx")};
411    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
412    ${g.setByOffset("global_idx","value")}
413  }`,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:a?[{type:12,data:l},...td(s)]:[{type:12,data:l}]})}})(a,s))}}),nk=W(()=>{ny(),n_(),rr=t=>{(t=>{if(3!==t[0].dims.length)throw Error("input should have 3 dimensions");if(![320,640,1280].includes(t[0].dims[2]))throw Error("number of channels should be 320, 640 or 1280");if(1!==t[1].dims.length)throw Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw Error("last dimension of input and bias are not the same")}

413)(t.inputs),t.compute((t=>{let r=t[0].dims,i=t[0].dims[2],a=tt.size(r)/4,n=t[0].dataType,s=ty("input",n,r,4),o=ty("bias",n,[i],4),u=ty("residual",n,r,4),l=t_("output",n,r,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:t=>`
414  const channels = ${i}u / 4;
415  ${t.declareVariables(s,o,u,l)}
416
417  ${t.mainStart()}
418    ${t.guardAgainstOutOfBoundsWorkgroupSizes(a)}
419    let value = ${s.getByOffset("global_idx")}
420      + ${o.getByOffset("global_idx % channels")} + ${u.getByOffset("global_idx")};
421    ${l.setByOffset("global_idx","value")}
422  }`}})(t.inputs))}}),nT=W(()=>{nd(),ny(),ng(),n_(),ri=(t,r,i,a,n,s=t.dataType,o,u)=>{let l=[{type:12,data:Math.ceil(tt.size(t.dims)/4)}];return o&&l.push(...o),{name:r,shaderCache:{hint:n,inputDependencies:["type"]},getShaderSource:r=>((t,r,i,a,n,s,o)=>{let u=Math.ceil(r/4),l="";l="string"==typeof n?`${n}(a)`:n("a");let d=ty("inputData",i,[u],4),p=t_("outputData",a,[u],4),c=[{name:"vec_size",type:"u32"}];return o&&c.push(...o),`
423      ${t.registerUniforms(c).declareVariables(d,p)}
424
425  ${s??""}
426
427  ${t.mainStart()}
428    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
429
430    let a = ${d.getByOffset("global_idx")};
431    ${p.setByOffset("global_idx",l)}
432  }`})(r,tt.size(t.dims),t.dataType,s,i,a,u),getRunData:r=>({outputs:[{dims:t.dims,dataType:s}],dispatchGroup:{x:Math.ceil(tt.size(r[0].dims)/64/4)},programUniforms:l})}},ra=t=>{t.compute(ri(t.inputs[0],"Abs","abs"))},rn=t=>{t.compute(ri(t.inputs[0],"Acos","acos"))},rs=t=>{t.compute(ri(t.inputs[0],"Acosh","acosh"))},ro=t=>{t.compute(ri(t.inputs[0],"Asin","asin"))},ru=t=>{t.compute(ri(t.inputs[0],"Asinh","asinh"))},rl=t=>{t.compute(ri(t.inputs[0],"Atan","atan"))},rd=t=>{t.compute(ri(t.inputs[0],"Atanh","atanh"))},rp=t=>e7(t),rc=(t,r)=>{let i;switch(r.to){case 10:i="vec4<f16>";break;case 1:i="vec4<f32>";break;case 12:i="vec4<u32>";break;case 6:i="vec4<i32>";break;case 9:i="vec4<bool>";break;default:throw RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${r.to}`)}t.compute(ri(t.inputs[0],"Cast",i,void 0,r.cacheKey,r.to))},rh=(t,r)=>{let i=r||(t=>{let r,i,a=t.length>=2&&0!==t[1].data,n=t.length>=3&&0!==t[2].data;switch(t[0].dataType){case 1:r=a?t[1].getFloat32Array()[0]:-34028234663852886e22,i=n?t[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:r=a?t[1].getUint16Array()[0]:64511,i=n?t[2].getUint16Array()[0]:31743;break;default:throw Error("Unsupport data type")}return e7({min:r,max:i})})(t.inputs),a=tl(t.inputs[0].dataType);t.compute(ri(t.inputs[0],"Clip",t=>`clamp(${t}, vec4<${a}>(uniforms.min), vec4<${a}>(uniforms.max))`,void 0,i.cacheKey,void 0,[{type:t.inputs[0].dataType,data:i.min},{type:t.inputs[0].dataType,data:i.max}],[{name:"min",type:a},{name:"max",type:a}]),{inputs:[0]})},rf=t=>{t.compute(ri(t.inputs[0],"Ceil","ceil"))},rm=t=>{t.compute(ri(t.inputs[0],"Cos","cos"))},rg=t=>{t.compute(ri(t.inputs[0],"Cosh","cosh"))},ry=t=>e7(t),r_=(t,r)=>{let i=tl(t.inputs[0].dataType);t.compute(ri(t.inputs[0],"Elu",t=>`elu_vf32(${t})`,`
433  const elu_alpha_ = ${i}(${r.alpha});
434
435  fn elu_f32(a: ${i}) -> ${i} {
436  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);
437  }
438
439  fn elu_vf32(v: vec4<${i}>) -> vec4<${i}> {
440  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
441  }`,r.cacheKey))},r$=(t="f32")=>`
442const r0: ${t} = 0.3275911;
443const r1: ${t} = 0.254829592;
444const r2: ${t} = -0.284496736;
445const r3: ${t} = 1.421413741;
446const r4: ${t} = -1.453152027;
447const r5: ${t} = 1.061405429;
448
449fn erf_vf32(v: vec4<${t}>) -> vec4<${t}> {
450  let absv = abs(v);
451  let x = 1.0 / (1.0 + r0 * absv);
452  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
453}`,rb=t=>{let r=tl(t.inputs[0].dataType);t.compute(ri(t.inputs[0],"Erf",t=>`erf_vf32(${t})`,r$(r)))},rv=t=>{t.compute(ri(t.inputs[0],"Exp","exp"))},rw=t=>{t.compute(ri(t.inputs[0],"Floor","floor"))},rx=t=>{let r=tl(t.inputs[0].dataType);t.compute(ri(t.inputs[0],"Gelu",t=>`0.5 * ${t} * (1.0 + erf_vf32(${t} * 0.7071067811865475))`,r$(r)))},rS=(t,r)=>{let i=tl(t.inputs[0].dataType);t.compute(ri(t.inputs[0],"LeakyRelu",t=>

vendor: 1,657 bytes, lines 453-461

453`select(leaky_relu_alpha_ * ${t}, ${t}, ${t} >= vec4<${i}>(0.0))`,`const leaky_relu_alpha_ = ${i}(${r.alpha});`,r.cacheKey))},rk=t=>{t.compute(ri(t.inputs[0],"Not",t=>`!${t}`))},rT=t=>{t.compute(ri(t.inputs[0],"Neg",t=>`-${t}`))},rC=t=>{t.compute(ri(t.inputs[0],"Reciprocal",t=>`1.0/${t}`))},rI=t=>{let r=tl(t.inputs[0].dataType);t.compute(ri(t.inputs[0],"Relu",t=>`select(vec4<${r}>(0.0), ${t}, ${t} > vec4<${r}>(0.0))`))},rE=t=>{t.compute(ri(t.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},rz=t=>e7(t),rA=(t,r)=>{let i=tl(t.inputs[0].dataType);t.compute(ri(t.inputs[0],"HardSigmoid",t=>`max(vec4<${i}>(0.0), min(vec4<${i}>(1.0), ${r.alpha} * ${t} + vec4<${i}>(${r.beta})))`,void 0,r.cacheKey))},rO=t=>{t.compute(ri(t.inputs[0],"Sin","sin"))},rR=t=>{t.compute(ri(t.inputs[0],"Sinh","sinh"))},rB=t=>{t.compute(ri(t.inputs[0],"Sqrt","sqrt"))},rN=t=>{t.compute(ri(t.inputs[0],"Tan","tan"))},rM=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,rD=t=>{t.compute(ri(t.inputs[0],"Tanh",rM))},rP=(t="f32")=>`
454const fast_gelu_a: ${t} = 0.5;
455const fast_gelu_b: ${t} = 0.7978845608028654;
456const fast_gelu_c: ${t} = 0.035677408136300125;
457
458fn tanh_v(v: vec4<${t}>) -> vec4<${t}> {
459  return ${rM("v")};
460}
461`,rU=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,rW=t=>{let r=tl(t.inputs[0].dataType);t.compute(ri(t.inputs[0],"FastGelu",rU,rP(r),void 0,t.inputs[0].dataType))},rq=(t,r)=>{let i=tl(t.inputs[0].dataType);return t.compute(ri(t.inputs[0],"ThresholdedRelu",t=>`select(vec4<${i}>(0.0), ${t}, ${t} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${i}>(${r.alpha});

461`,r.cacheKey)),0},rL=t=>{t.compute(ri(t.inputs[0],"Log","log"))},rV=t=>`quick_gelu_impl(${t})`,rG=(t,r)=>{let i=tl(t.inputs[0].dataType);t.compute(ri(t.inputs[0],"QuickGelu",rV,((t,r)=>`
462const alpha = vec4<${t}>(${r});
463const one = ${t}(1.0);
464const zero = ${t}(0.0);
465
466fn quick_gelu_impl(x: vec4<${t}>) -> vec4<${t}> {
467  let v = x *alpha;
468  var x1 : vec4<${t}>;
469  for (var i = 0; i < 4; i = i + 1) {
470    if (v[i] >= zero) {
471      x1[i] = one / (one + exp(-v[i]));
472    } else {
473      x1[i] = one - one / (one + exp(v[i]));
474    }
475  }
476  return x * x1;
477}
478`)(i,r.alpha),r.cacheKey,t.inputs[0].dataType))}}),nC=W(()=>{ny(),n_(),nT(),rH=t=>{(t=>{if(3!==t[0].dims.length)throw Error("input should have 3 dimensions");if(![2560,5120,10240].includes(t[0].dims[2]))throw Error("hidden state should be 2560, 5120 or 10240");if(1!==t[1].dims.length)throw Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw Error("last dimension of input and bias are not the same")})(t.inputs),t.compute((t=>{let r=t[0].dims.slice();r[2]=r[2]/2;let i=ty("input",t[0].dataType,t[0].dims,4),a=ty("bias",t[0].dataType,[t[0].dims[2]],4),n=t_("output",t[0].dataType,r,4),s=tt.size(r)/4,o=tu(t[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:r=>`
479  const M_SQRT2 = sqrt(2.0);
480  const halfChannels = ${t[0].dims[2]/4/2}u;
481
482  ${r.declareVariables(i,a,n)}
483
484  ${r$(o)}
485
486  ${r.mainStart()}
487    ${r.guardAgainstOutOfBoundsWorkgroupSizes(s)}
488    let biasIdx = global_idx % halfChannels;
489    let batchIndex = global_idx / halfChannels;
490    let inputOffset = biasIdx + batchIndex * halfChannels * 2;
491    let valueLeft = input[inputOffset] + bias[biasIdx];
492    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];
493    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);
494
495    ${n.setByOffset("global_idx","valueLeft * geluRight")}
496  }`}})(t.inputs))}}),nI=W(()=>{nd(),ny(),n_(),rF=(t,r,i,a,n,s)=>{t.compute(((t,r,i,a,n,s,o=i.dataType)=>{let u=i.dims.map(t=>Number(t)??1),l=a.dims.map(t=>Number(t)??1),d=!tt.areEqual(u,l),p=u,c=tt.size(u),h=!1,f=!1,m=[d];if(d){let t=te.calcShape(u,l,!1);if(!t)throw Error("Can't perform binary op on the given tensors");p=t.slice(),c=tt.size(p);let r=1===tt.size(u),i=1===tt.size(l),a=u.length>0&&u[u.length-1]%4==0,n=l.length>0&&l[l.length-1]%4==0;m.push(r),m.push(i),m.push(a),m.push(n);let s=1;for(let t=1;t<p.length;t++){let r=u[u.length-t];if(r===l[l.length-t])s*=r;else break}s%4==0?(f=!0,h=!0):(r||i||a||n)&&(h=!0)}else h=!0;return m.push(h),{name:t,shaderCache:{hint:r+m.map(t=>t.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:t=>((t,r,i,a,n,s,o,u,l,d,p,c)=>{let h,f;"string"==typeof u?h=f=(t,r)=>`${u}((${t}),(${r}))`:"function"==typeof u?h=f=u:(h=u.scalar,f=u.vector);let m=t_("outputData",p,a.length,4),g=ty("aData",l,r.length,4),y=ty("bData",d,i.length,4),_;if(n)if(s){let t=1===tt.size(r),a=1===tt.size(i),n=r.length>0&&r[r.length-1]%4==0,s=i.length>0&&i[i.length-1]%4==0;_=t||a?m.setByOffset("global_idx",f(t?`${g.type.value}(${g.getByOffset("0")}.x)`:g.getByOffset("global_idx"),a?`${y.type.value}(${y.getByOffset("0")}.x)`:y.getByOffset("global_idx"))):`
497            let outputIndices = ${m.offsetToIndices("global_idx * 4u")};
498            let offsetA = ${g.broadcastedIndicesToOffset("outputIndices",m)};
499            let offsetB = ${y.broadcastedIndicesToOffset("outputIndices",m)};
500            ${m.setByOffset("global_idx",f(o||n?g.getByOffset("offsetA / 4u"):`${g.type.value}(${g.getByOffset("offsetA / 4u")}[offsetA % 4u])`,o||s?y.getByOffset("offsetB / 4u"):`${y.type.value}(${y.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
501          `}else _=m.setByOffset("global_idx",f(g.getByOffset("global_idx"),y.getByOffset("global_idx")));else{if(!s)throw Error("no necessary to use scalar implementation for element-wise binary op implementation.");let t=(t,r,i="")=>{let a=`aData[indexA${r}][componentA${r}]`,n=`bData[indexB${r}][componentB${r}]`;return`
502            let outputIndices${r} = ${m.offsetToIndices(`global_idx * 4u + ${r}u`)};
503            let offsetA${r} = ${g.broadcastedIndicesToOffset(`outputIndices${r}`,m)};
504            let offsetB${r} = ${y.broadcastedIndicesToOffset(`outputIndices${r}`,m)};
505            let indexA${r} = offsetA${r} / 4u;
506            let indexB${r} = offsetB${r} / 4u;
507            let componentA${r} = offsetA${r} % 4u;
508            let componentB${r} = offsetB${r} % 4u;
509            ${t}[${r}] = ${i}(${h(a,n)});
510          `};_=9===p?`
511            var data = vec4<u32>(0);
512            ${t("data",0,"u32")}
513            ${t("data",1,"u32")}
514            ${t("data",2,"u32")}
515            ${t("data",3,"u32")}

516            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:`
517            ${t("outputData[global_idx]",0)}
518            ${t("outputData[global_idx]",1)}
519            ${t("outputData[global_idx]",2)}
520            ${t("outputData[global_idx]",3)}
521          `}return`
522        ${t.registerUniform("vec_size","u32").declareVariables(g,y,m)}
523
524        ${c??""}
525
526        ${t.mainStart()}
527        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
528        ${_}
529      }`})(t,u,l,p,h,d,f,n,i.dataType,a.dataType,o,s),getRunData:()=>({outputs:[{dims:p,dataType:o}],dispatchGroup:{x:Math.ceil(c/64/4)},programUniforms:[{type:12,data:Math.ceil(tt.size(p)/4)},...td(u,l,p)]})}})(r,n??"",t.inputs[0],t.inputs[1],i,a,s))},rj=t=>{rF(t,"Add",(t,r)=>`${t}+${r}`)},rK=t=>{rF(t,"Div",(t,r)=>`${t}/${r}`)},rQ=t=>{rF(t,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},rZ=t=>{rF(t,"Mul",(t,r)=>`${t}*${r}`)},rX=t=>{let r=ty("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;rF(t,"Pow",{scalar:(t,r)=>`pow_custom(${t},${r})`,vector:(t,r)=>`pow_vector_custom(${t},${r})`},`
530    fn pow_custom(a : ${r}, b : ${r}) -> ${r} {
531      if (b == ${r}(0.0)) {
532        return ${r}(1.0);
533      } else if (a < ${r}(0.0) && f32(b) != floor(f32(b))) {
534        return ${r}(pow(f32(a), f32(b))); // NaN
535      }
536      return select(sign(a), ${r}(1.0), round(f32(abs(b) % ${r}(2.0))) != 1.0) * ${r}(${"i32"===r?"round":""}(pow(f32(abs(a)), f32(b))));
537    }
538    fn pow_vector_custom(a : vec4<${r}>, b : vec4<${r}>) -> vec4<${r}> {
539      // TODO: implement vectorized pow
540      return vec4<${r}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
541    }
542      `)},rY=t=>{rF(t,"Sub",(t,r)=>`${t}-${r}`)},rJ=t=>{rF(t,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},r0=t=>{rF(t,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},r1=t=>{rF(t,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},r2=t=>{rF(t,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}}),nE=W(()=>{nd(),ny(),ng(),n_(),r3=(t,r)=>{let i=t.inputs,a=i[0].dims,n=tt.normalizeAxis(r.axis,a.length);((t,r)=>{if(!t||t.length<1)throw Error("too few inputs");let i=t[0],a=i.dataType,n=i.dims.length;t.forEach((t,s)=>{if(0!==s){if(t.dataType!==a)throw Error("input tensors should be one type");if(t.dims.length!==n)throw Error("input tensors should have the same shape");t.dims.forEach((t,a)=>{if(a!==r&&t!==i.dims[a])throw Error("non concat dimensions must match")})}})})(i,n);let s=a.slice();s[n]=i.reduce((t,r)=>t+(r.dims.length>n?r.dims[n]:0),0);let o=i.filter(t=>tt.size(t.dims)>0);t.compute(((t,r,i,a)=>{let n=tt.size(i),s=Array(t.length),o=Array(t.length),u=0,l=[],d=[],p=[{type:12,data:n}];for(let i=0;i<t.length;++i)u+=t[i].dims[r],s[i]=u,d.push(t[i].dims.length),o[i]=ty(`input${i}`,a,d[i]),l.push("rank"),p.push({type:12,data:s[i]});for(let r=0;r<t.length;++r)p.push(...td(t[r].dims));p.push(...td(i));let c=t_("output",a,i.length),h=c.indicesGet("indices",r),f=Array.from(Array(s.length).keys()).map(t=>`uniforms.sizeInConcatAxis${t}`).join(",");return{name:"Concat",shaderCache:{hint:`${r}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:p}),getShaderSource:r=>`
543
544  ${(()=>{r.registerUniform("outputSize","u32");for(let i=0;i<t.length;i++)r.registerUniform(`sizeInConcatAxis${i}`,"u32");return r.declareVariables(...o,c)})()}
545
546  ${((t,r)=>`
547  fn calculateInputIndex(index: u32) -> u32 {
548    let sizeInConcatAxis = array<u32, ${t}u>(${r});
549    for (var i: u32 = 0u; i < ${t}; i += 1u ) {
550      if (index < sizeInConcatAxis[i]) {
551        return i;
552      }
553    }
554    return ${t}u;
555  }`)(s.length,f)}
556
557  ${r.mainStart()}
558    ${r.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
559
560    var indices = ${c.offsetToIndices("global_idx")};
561
562    let inputIndex = calculateInputIndex(${h});
563    if (inputIndex != 0u) {
564      let sizeInConcatAxis = array<u32, ${s.length}u>(${f});
565      ${h} -= sizeInConcatAxis[inputIndex - 1u];
566    }
567
568    ${((t,r)=>{let i=t.length,a=[];for(let n=0;n<i;++n){let s=r.setByOffset("global_idx",t[n].getByIndices("indices"));1===i?a.push(s):0===n?a.push(`if (inputIndex == ${n}u) { ${s} }`):n===i-1?a.push(`else { ${s} }`):a.push(`else if (inputIndex == ${n}) { ${s} }`)}return a.join(`
569`)})(o,c)}
570  }`}})(o,n,s,i[0].dataType),{inputs:o})},r4=t=>e7({axis:t.axis})}),nz=W(()=>{nd(),ny(),r6=(t,r,i="f32")=>{switch(t.activation){case"Relu":return`value = max(value, ${r}(0.0));`;case"Sigmoid":return`value = (${r}(1.0) / (${r}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${r}(${i}(uniforms.clip_min)), ${r}(${i}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${r}(0.0), min(${r}(1.0), ${i}(uniforms.alpha) * value + ${i}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${i}(uniforms.alpha) * value, value, value >= ${r}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
571              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
572        `;case"":return"";default:throw Error(`Unsupported activation ${t.activation}`)}},r8=(t,r)=>{"Clip"===t.activation?r.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):"HardSigmoid"===t.activation?r.push({type:1,data:t.alpha},{type:1,data:t.beta}):"LeakyRelu"===t.activation&&r.push({type:1,data:t.alpha})},r5=(t,r)=>{"Clip"===t.activation?r.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):"HardSigmoid"===t.activation?r.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):"LeakyRelu"===t.activation&&r.push({name:"alpha",type:"f32"})},r7=t=>{let r=t?.activation||"";if("HardSigmoid"===r){let[i,a]=t?.activation_params||[.2,.5];return{activation:r,alpha:i,beta:a}}if("Clip"===r){let[i,a]=t?.activation_params||[ta,tn];return{activation:r,clipMax:a,clipMin:i}}if("LeakyRelu"===r){let[i]=t?.activation_params||[.01];return{activation:r,alpha:i}}return{activation:r}}}),nA=W(()=>{r9=(t,r)=>{switch(t){case 1:return r;case 2:return`vec2<${r}>`;case 3:return`vec3<${r}>`;case 4:return`vec4<${r}>`;default:throw Error(`${t}-component is not supported.`)}},ie=t=>`
573      ${t?"value = value + getBiasByOutputCoords(coords);":""}
574      `}),nO=W(()=>{it=t=>`
575fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
576  return dot(coords, vec4<i32>(
577      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
578}
579fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
580  return dot(coords, vec4<i32>(
581    i32(${t}.x), i32(${t}.y), i32(${t}.z), 1));
582}
583`}),nR=W(()=>{nd(),ny(),n_(),nz(),ir=(t,r,i,a,n)=>{let s=a-i;return`
584      ${Array.from({length:i}).map((i,o)=>`
585      if (${tm(r.shape,o,r.rank)} != 1) {
586        ${r.indicesSet(t,o,tm(n,o+s,a))}
587      } else {
588        ${r.indicesSet(t,o,0)}
589      }`).join("")}
590`},ii=(t,r,i,a,n=!1,s)=>{let o=t[0].dims,u=t[1].dims,l=o[o.length-2],d=u[u.length-1],p=o[o.length-1],c=tp(d),h=tp(p),f=tp(l),m=tt.size(i)/c/f,g=t.length>2,y=a?a.slice(0,-2):i.slice(0,-2),_=[tt.size(y),l,d],$=[{type:12,data:m},{type:12,data:l},{type:12,data:d}

590,{type:12,data:p}];return r8(r,$),$.push(...td(y,o,u)),g&&$.push(...td(t[2].dims)),$.push(...td(_)),{name:"MatMulNaive",shaderCache:{hint:`${r.activation};${c};${h};${f};${n}`,inputDependencies:g?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:s?s(i):i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:$}),getShaderSource:a=>{let s=tb("batch_dims",t[0].dataType,y.length),l=ty("a",t[0].dataType,o.length,h),d=ty("b",t[1].dataType,u.length,c),p=t_("output",t[0].dataType,_.length,c),m=tu(p.type.tensor),$=r6(r,p.type.value,m),b=[l,d],v="";if(g){let r=n?c:1;b.push(ty("bias",t[2].dataType,t[2].dims.length,r)),v=`${n?`value += bias[col / ${r}];`:`value += ${p.type.value}(bias[row + i]);`}`}let w=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];return r5(r,w),`
591  ${a.registerUniforms(w).registerInternalVariables(s).declareVariables(...b,p)}
592  ${a.mainStart()}
593    ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
594    let col = (global_idx % (uniforms.N / ${c})) * ${c};
595    var index1 = global_idx / (uniforms.N / ${c});
596    let stride1 = uniforms.M / ${f};
597    let row = (index1 % stride1) * ${f};
598    let batch = index1 / stride1;
599
600    ${2===i.length?"":`let batch_indices = ${s.offsetToIndices("batch")};`}
601
602    var a_indices: ${l.type.indices};
603    ${ir("a_indices",l,l.rank-2,s.rank,"batch_indices")}
604    ${l.indicesSet("a_indices",l.rank-2,0)}
605    ${l.indicesSet("a_indices",l.rank-1,0)}
606    let a_offset = ${l.indicesToOffset("a_indices")};
607
608    var b_indices: ${d.type.indices};
609    ${ir("b_indices",d,d.rank-2,s.rank,"batch_indices")}
610    ${d.indicesSet("b_indices",d.rank-2,0)}
611    ${d.indicesSet("b_indices",d.rank-1,0)}
612    let b_offset = ${d.indicesToOffset("b_indices")};
613    var values: array<${p.type.value}, ${f}>;
614    for (var k: u32 = 0u; k < uniforms.K; k = k + ${h}) {
615      ${(()=>{let t=`var a_data: ${l.type.value};`;for(let r=0;r<h;r++)t+=`
616              let b_data${r} = b[(b_offset + (k + ${r}) * uniforms.N + col) / ${c}];`;for(let r=0;r<f;r++){t+=`a_data = a[(a_offset + (row + ${r}) * uniforms.K + k) / ${h}];`;for(let i=0;i<h;i++)t+=`
617            values[${r}] = fma(${d.type.value}(a_data${1===h?"":`[${i}]`}), b_data${i}, values[${r}]);
618`}return t})()}
619    }
620    for (var i = 0u; i < ${f}u; i++) {
621      var value = values[i];
622      ${v}
623      ${$}
624      let cur_indices = ${p.type.indices}(batch, row + i, col);
625      let offset = ${p.indicesToOffset("cur_indices")};
626      ${p.setByOffset(`offset / ${c}`,"value")};
627    }
628  }
629  `}}}}),nB=W(()=>{nd(),ny(),n_(),nz(),nR(),nA(),ia=(t,r,i="f32",a,n=!1,s=32,o=!1,u=32)=>{let l=r[1]*t[1],d=r[0]*t[0],p=n?l:s,c=n?s:l,h=p/r[0],f=s/r[1];if(!((n&&4===h&&4===t[1]||!n&&(3===h||4===h))&&p%r[0]==0&&s%r[1]==0&&4===t[0]))throw Error(`If transposeA ${n} is true, innerElementSize ${h} and workPerThread[1] ${t[1]} must be 4.
630      Otherwise, innerElementSize ${h} must be 3 or 4.
631  tileAWidth ${p} must be divisible by workgroupSize[0]${r[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${r[1]}. colPerThread ${t[0]} must be 4.`);return`
632var<workgroup> mm_Asub: array<array<vec${h}<${i}>, ${p/h}>, ${c}>;
633var<workgroup> mm_Bsub: array<array<vec4<${i}>, ${d/t[0]}>, ${s}>;
634
635const rowPerThread = ${t[1]};
636const colPerThread = ${t[0]};
637const innerElementSize = ${h};
638const tileInner = ${s};
639
640@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]})
641fn main(@builtin(local_invocation_id) localId : vec3<u32>,
642        @builtin(global_invocation_id) globalId : vec3<u32>,
643        @builtin(workgroup_id) workgroupId : vec3<u32>) {
644  let localRow = i32(localId.y);
645  let tileRow = localRow * rowPerThread;
646  let tileCol = i32(localId.x);
647
648  let globalRow =i32(globalId.y) * rowPerThread;
649  let globalCol = i32(globalId.x);
650  let batch = ${o?"0":"i32(globalId.z)"};
651  ${a?`let batchIndices = ${a.offsetToIndices("u32(batch)")};`:""}
652  let globalRowStart = i32(workgroupId.y) * ${l};
653
654  let num_tiles = ${o?`${Math.ceil(u/s)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
655  var kStart = ${o?`i32(globalId.z) * ${u}`:"0"};
656
657  var acc: array<vec4<${i}>, rowPerThread>;
658
659  // Loop over shared dimension.
660  let tileRowB = localRow * ${f};
661  for (var t = 0; t < num_tiles; t = t + 1) {
662      // Load one tile of A into local memory.
663      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
664          let inputRow = tileRow + innerRow;
665          let inputCol = tileCol;
666          ${((t,r)=>t?`
667        mm_Asub[inputRow][inputCol] = mm_readA(batch,
668          kStart + inputRow,
669          globalRowStart / innerElementSize + inputCol${r?", batchIndices":""});
670        `:`
671        mm_Asub[inputRow][inputCol] = mm_readA(batch,
672          globalRow + innerRow,
673          kStart / innerElementSize + inputCol${r?", batchIndices":""});
674        `)(n,a)}
675      }
676
677      // Load one tile of B into local memory.
678      for (var innerRow = 0; innerRow < ${f}; innerRow = innerRow + 1) {
679          let inputRow = tileRowB + innerRow;
680          let inputCol = tileCol;
681          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${a?", batchIndices":""});
682      }
683      kStart = kStart + tileInner;
684      workgroupBarrier();
685
686      // Compute acc values for a single thread.
687      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {
688          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];
689          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];
690          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];
691          ${3===h?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}
692
693          ${((t,r)=>t?`
694        let ACached0 = mm_Asub[k * innerElementSize][localRow];
695        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
696        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
697        ${3===r?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
698        for (var i = 0; i < rowPerThread; i = i + 1) {
699          acc[i] = BCached0 * ACached0[i] + acc[i];
700          acc[i] = BCached1 * ACached1[i] + acc[i];
701          acc[i] = BCached2 * ACached2[i] + acc[i];
702          ${3===r?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
703        }`:`
704        for (var i = 0; i < rowPerThread; i = i + 1) {
705          let ACached = mm_Asub[tileRow + i][k];
706          acc[i] = BCached0 * ACached.x + acc[i];
707          acc[i] = BCached1 * ACached.y + acc[i];
708          acc[i] = BCached2 * ACached.z + acc[i];
709          ${3===r?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
710        }`)(n,h)}
711      }
712
713      workgroupBarrier();
714  }
715
716  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
717      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
718  }
719}`},is=(t,r)=>t?`
720            mm_Asub[inputRow][inputCol] = mm_readA(batch,
721              kStart + inputRow,
722              globalRowStart + inputCol${r?", batchIndices":""});
723            `:`
724            mm_Asub[inputRow][inputCol] = mm_readA(batch,
725              globalRowStart + inputRow,
726              kStart + inputCol${r?", batchIndices":""});
727            `,io=(t,r,i="f32",a,n=!1,s=32,o=!1,u=32,l=!1)=>

727{let d=t[1]*r[1],p=t[0]*r[0],c=n?d:s,h=n?s:d;if(h%r[1]!=0||c%r[0]!=0||s%r[1]!=0)throw Error(`tileAHight ${h} must be divisible by workgroupSize[1]${r[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${r[0]}, tileInner ${s} must be divisible by workgroupSize[1]${r[1]}`);let f=h/r[1],m=c/r[0],g=s/r[1],y=l?`
728    let localRow = i32(localId.y);
729    let localCol = i32(localId.x);
730    let globalRowStart = i32(workgroupId.y) * ${d};
731    let globalColStart = i32(workgroupId.x) * ${p};
732
733    // Loop over shared dimension.
734    for (var t = 0; t < num_tiles; t = t + 1) {
735      // Load one tile of A into local memory.
736      for (var inputRow = localRow; inputRow < ${h}; inputRow = inputRow + ${r[1]}) {
737        for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${r[0]}) {
738          ${is(n,a)}
739        }
740      }
741      // Load one tile of B into local memory.
742      for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${r[1]}) {
743            for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${r[0]}) {
744          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
745            kStart + inputRow,
746            globalColStart + inputCol${a?", batchIndices":""});
747        }
748      }
749      kStart = kStart + tileInner;
750      workgroupBarrier();
751
752      // Compute acc values for a single thread.
753      var BCached : array<${i}, colPerThread>;
754      for (var k = 0; k < tileInner; k = k + 1) {
755        for (var inner = 0; inner < colPerThread; inner = inner + 1) {
756          BCached[inner] = mm_Bsub[k][localCol + inner * ${r[0]}];
757        }
758        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
759          let ACached = ${n?`mm_Asub[k][localRow + innerRow * ${r[1]}];`:`mm_Asub[localRow + innerRow * ${r[1]}][k];`}
760          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
761            acc[innerRow][innerCol] = acc[innerRow][innerCol] +
762                ACached * BCached[innerCol];
763          }
764        }
765      }
766      workgroupBarrier();
767    }
768    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
769      let gRow = globalRowStart + localRow + innerRow * ${r[1]};
770      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
771        let gCol = globalColStart + localCol + innerCol * ${r[0]};
772        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
773      }
774    }
775    `:`
776let tileRow = i32(localId.y) * rowPerThread;
777let tileCol = i32(localId.x) * colPerThread;
778
779let globalRow = i32(globalId.y) * rowPerThread;
780let globalCol = i32(globalId.x) * colPerThread;
781let globalRowStart = i32(workgroupId.y) * ${d};
782
783let tileRowA = i32(localId.y) * ${f};
784let tileColA = i32(localId.x) * ${m};
785let tileRowB = i32(localId.y) * ${g};
786// Loop over shared dimension.
787for (var t = 0; t < num_tiles; t = t + 1) {
788  // Load one tile of A into local memory.
789  for (var innerRow = 0; innerRow < ${f}; innerRow = innerRow + 1) {
790    for (var innerCol = 0; innerCol < ${m}; innerCol = innerCol + 1) {
791      let inputRow = tileRowA + innerRow;
792      let inputCol = tileColA + innerCol;
793      ${is(n,a)}
794    }
795  }
796
797  // Load one tile of B into local memory.
798  for (var innerRow = 0; innerRow < ${g}; innerRow = innerRow + 1) {
799    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
800      let inputRow = tileRowB + innerRow;
801      let inputCol = tileCol + innerCol;
802      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
803        kStart + inputRow,
804        globalCol + innerCol${a?", batchIndices":""});
805    }
806  }
807  kStart = kStart + tileInner;
808  workgroupBarrier();
809
810  // Compute acc values for a single thread.
811  var BCached : array<${i}, colPerThread>;
812  for (var k = 0; k < tileInner; k = k + 1) {
813    for (var inner = 0; inner < colPerThread; inner = inner + 1) {
814      BCached[inner] = mm_Bsub[k][tileCol + inner];
815    }
816
817    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
818      ${n?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];"}
819      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
820        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
821      }
822    }
823  }
824
825  workgroupBarrier();
826}
827
828for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
829  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
830    mm_write(batch, globalRow + innerRow, globalCol + innerCol,
831        acc[innerRow][innerCol]);
832  }
833}
834`;return`

835  var<workgroup> mm_Asub : array<array<${i}, ${c}>, ${h}>;
836  var<workgroup> mm_Bsub : array<array<${i}, ${p}>, ${s}>;
837  const rowPerThread = ${t[1]};
838  const colPerThread = ${t[0]};
839  const tileInner = ${s};
840
841@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]})
842fn main(@builtin(local_invocation_id) localId : vec3<u32>,
843        @builtin(global_invocation_id) globalId : vec3<u32>,
844        @builtin(workgroup_id) workgroupId : vec3<u32>) {
845    let batch = ${o?"0":"i32(globalId.z)"};
846    ${a?`let batchIndices = ${a.offsetToIndices("u32(batch)")};`:""}
847    let num_tiles = ${o?`${Math.ceil(u/s)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
848    var kStart = ${o?`i32(globalId.z) * ${u}`:"0"};
849
850    var acc : array<array<${i}, colPerThread>, rowPerThread>;
851    ${y}
852  }
853`},iu=(t,r,i,a,n=!1,s)=>{let o=t[0].dims,u=t[1].dims,l=o.slice(0,-2),d=u.slice(0,-2),p=a?a.slice(0,-2):i.slice(0,-2),c=tt.size(p),h=o[o.length-2],f=o[o.length-1],m=u[u.length-1],g=f%4==0&&m%4==0,y=h<=8?[4,1,1]:[4,4,1],_=[8,8,1],$=[Math.ceil(m/_[0]/y[0]),Math.ceil(h/_[1]/y[1]),Math.ceil(c/_[2]/y[2])],b=g?4:1,v=[...l,h,f/b],w=v.length,x=[...d,f,m/b],S=x.length,k=[c,h,m/b],T=[{type:6,data:h},{type:6,data:m},{type:6,data:f}];r8(r,T),T.push(...td(p,v,x));let C=["rank","rank"],I=t.length>2;return I&&(T.push(...td(t[2].dims)),C.push("rank")),T.push(...td(k)),{name:"MatMul",shaderCache:{hint:`${y};${r.activation};${g};${n}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:s?s(i):i,dataType:t[0].dataType}],dispatchGroup:{x:$[0],y:$[1],z:$[2]},programUniforms:T}),getShaderSource:i=>{let a=p.length,s=tb("batchDims",t[0].dataType,a,1),o=tu(t[0].dataType),u=ty("a",t[0].dataType,w,b),l=ty("b",t[1].dataType,S,b),d=t_("result",t[0].dataType,k.length,b),c=[u,l];if(I){let r=n?b:1;c.push(ty("bias",t[2].dataType,t[2].dims.length,r))}let h=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];r5(r,h);let f=tu(d.type.tensor),m=((t,r,i,a,n=!1)=>{let[s,o,u,l]=a,d=tu(a[0].type.tensor);return`
854    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${s.type.indices}) -> ${r9(t,d)} {
855      var value = ${r9(t,d)}(0.0);
856      let col = colIn * ${t};
857      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
858      {
859        var aIndices: ${o.type.indices};
860        ${ir("aIndices",o,o.rank-2,s.rank,"batchIndices")}
861        ${o.indicesSet("aIndices",o.rank-2,"u32(row)")}
862        ${o.indicesSet("aIndices",o.rank-1,"u32(colIn)")}
863        value = ${o.getByIndices("aIndices")};
864      }
865      return value;
866    }
867
868    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${s.type.indices}) -> ${r9(t,d)} {
869      var value = ${r9(t,d)}(0.0);
870      let col = colIn * ${t};
871      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
872      {
873        var bIndices: ${u.type.indices};
874        ${ir("bIndices",u,u.rank-2,s.rank,"batchIndices")}
875        ${u.indicesSet("bIndices",u.rank-2,"u32(row)")}
876        ${u.indicesSet("bIndices",u.rank-1,"u32(colIn)")}
877        value = ${u.getByIndices("bIndices")};
878      }
879      return value;
880    }
881
882    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${r9(t,d)}) {
883      let col = colIn * ${t};
884      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
885        var value = valueIn;
886        let coords = vec3<i32>(batch, row, colIn);
887        ${r?`value = value + ${n?"bias[colIn]":`${r9(t,d)}(bias[row])`};`:""}
888        ${i}
889        ${l.setByIndices("vec3<u32>(coords)","value")}
890      }
891    }
892    `})(b,I,r6(r,d.type.value,f),[s,u,l,d],n);return`
893  ${i.registerUniforms(h).registerInternalVariables(s).declareVariables(...c,d)}
894  ${m}
895  ${g?ia(y,_,o,s):io(y,_,o,s)}
896                   `}}}}),nN=W(()=>{nd(),nc(),n_(),nz(),nA(),nO(),nB(),il=(t,r,i,a,n,s,o,u,l)=>{let d="NHWC"===r.format,p=d?t[0].dims[3]:t[0].dims[1],c=i[0],h=d?i[2]:i[3],f=d?i[1]:i[2],m=d?i[3]:i[1],g=d&&(p%4==0||p%3==0)&&m%4==0,y=d?m:h*f,_=d?h*f:m,$=[8,8,1],b=a<=8?[4,1,1]:[4,4,1],v=[Math.ceil(y/$[0]/b[0]),Math.ceil(_/$[1]/b[1]),Math.ceil(c/$[2]/b[2])];eX("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${v}`);let w=g?d&&p%4!=0?3:4:1,x=$[1]*b[1],S=$[0]*b[0],k=Math.max($[0]*w,$[1]),T=a%x==0,C=n%S==0,I=s%k==0,E=g?[w,4,4]:[1,1,1],z=[{type:6,data:a},{type:6,data:n},{type:6,data:s},{type:6,data:[r.pads[0],r.pads[1]]},{type:6,data:r.strides},{type:6,data:r.dilations}];r8(r,z),z.push(...td(t[0].dims,t[1].dims));let A=["rank","rank"];return o&&(z.push(...td(t[2].dims)),A.push("rank")),z.push(...td(i)),{name:"Conv2DMatMul",shaderCache:{hint:`${r.cacheKey};${w};${g};${T};${C};${I};${x};${S};${k}`,inputDependencies:A},getRunData:()=>({outputs:[{dims:l?l(i):i,dataType:t[0].dataType}],dispatchGroup:{x:v[0],y:v[1],z:v[2]},programUniforms:z}),getShaderSource:a=>{let n=[{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}];r5(r,n);let s=g?4:1,l=tu(t[0].dataType),p=`
897      fn setOutputAtIndex(flatIndex : i32, value : ${g?`vec4<${l}>`:l}) {
898        result[flatIndex] = ${g?`vec4<${l}>`:l}(value);
899      }
900      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${g?`vec4<${l}>`:l}) {
901        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
902        setOutputAtIndex(flatIndex ${g?"/ 4":""}, value);
903      }`,c=[ty("x",t[0].dataType,t[0].dims.length,3===w?1:w),ty("w",t[1].dataType,t[1].dims.length,s)],h=t_("result",t[0].dataType,i.length,s);if(o){let r=ty("bias",t[2].dataType,t[2].dims.length,s);c.push(r),p+=`
904        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${g?`vec4<${l}>`:l} {
905          return bias[coords.${d?"w":"y"}${g?"/ 4":""}];
906        }`}return`
907        ${it("uniforms.result_strides")}
908        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,
909        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,
910        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };
911        ${a.registerUniforms(n).declareVariables(...c,h)}
912        ${p}
913        ${((t,r,i,a,n=!1,s,o=4,u=4,l=4,d="f32")=>{let p=t=>{switch(t){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 Error(`innerElementSize ${t} is not supported.`)}},c=t?`

914    let coord = vec4<i32>(batch, xRow, xCol, xCh);
915    `:`
916    let coord = vec4<i32>(batch, xCh, xRow, xCol);
917    `,h=t?`
918    let coords = vec4<i32>(
919      batch,
920      row / outWidth,
921      row % outWidth,
922      col);
923    `:`
924    let coords = vec4<i32>(
925      batch,
926      row,
927      col / outWidth,
928      col % outWidth);
929    `,f=t?"row":"col",m=t?"col":"row",g=`
930    let inChannels = i32(uniforms.w_shape[2]);
931    let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
932    let outRow = ${f} / outWidth;
933    let outCol = ${f} % outWidth;
934
935    let WRow = ${m} / (i32(uniforms.w_shape[1]) * inChannels);
936    let WCol = ${m} / inChannels % i32(uniforms.w_shape[1]);
937    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
938    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
939    let xCh = ${m} % inChannels;
940    var resData = ${r9(o,d)}(0.0);
941    // The bounds checking is always needed since we use it to pad zero for
942    // the 'same' padding type.
943    if (xRow >= 0 && xRow < ${t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])"} && xCol >= 0 && xCol < ${t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])"}) {
944      ${c}
945      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
946      ${(t=>{switch(t){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${d}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw Error(`innerElementSize ${t} is not supported.`)}})(o)}
947    }
948    return resData;`,y=t?r&&a?`
949    let col = colIn * ${o};
950    ${g}`:`
951    let col = colIn * ${o};
952    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
953      ${g}
954    }
955    return ${r9(o,d)}(0.0);`:a&&i?`
956    let col = colIn * ${o};
957    ${g}`:`
958    let col = colIn * ${o};
959    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
960      ${g}
961    }
962    return ${r9(o,d)}(0.0);`,_=t?a&&i?p(u):`
963    let col = colIn * ${u};
964    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
965      ${p(u)}
966    }
967    return ${r9(u,d)}(0.0);`:`
968    let col = colIn * ${u};
969    if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {
970      ${p(u)}
971    }
972    return ${r9(u,d)}(0.0);`,$=r9(l,d),b=t?r9(o,d):r9(u,d),v=t?r9(u,d):r9(o,d),w=r6(s,$,d);return`
973    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${b} {
974      ${t?y:_}
975    }
976
977    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${v} {
978      ${t?_:y}
979    }
980
981    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${$}) {
982      let col = colIn * ${l};
983      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
984      {
985      var value = valueIn;
986      let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
987      ${h}
988      ${ie(n)}
989      ${w}
990      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
991      }
992    }`})(d,T,C,I,o,r,E[0],E[1],E[2],l)}
993        ${g?ia(b,$,l,void 0,!d,k):io(b,$,l,void 0,!d,k,!1,void 0,u)}`}}}}),nM=W(()=>{nd(),nc(),ny(),n_(),nz(),nA(),id=t=>"number"==typeof t?[t,t,t]:t,ip=(t,r)=>r<=1?t:t+(t-1)*(r-1),ic=(t,r,i,a,n)=>{null==n&&(n=((t,r,i,a=1)=>{let n=ip(r,a);return Math.floor((t[0]*(i-1)-i+n)/2)})(t,r[0],a[0]));let s=[0,0,0,i];for(let i=0;i<3;i++)t[i]+2*n>=r[i]&&(s[i]=Math.trunc((t[i]-r[i]+2*n)/a[i]+1));return s},ih=(t,r,i,a,n,s=!1,o="channelsLast")=>{let u,l,d,p,c;if("channelsLast"===o)[u,l,d,p,c]=t;else if("channelsFirst"===o)[u,c,l,d,p]=t;else throw Error(`Unknown dataFormat ${o}`);let[h,,f,m,g]=r,[y,_,$]=id(i),[b,v,w]=id(a),x=ip(f,b),S=ip(m,v),k=ip(g,w),{padInfo:T,outDepth:C,outHeight:I,outWidth:E}=((t,r,i,a,n,s,o,u,l,d)=>{let p,c,h,f;if("VALID"===t&&(t=0),"number"==typeof t){p={top:t,bottom:t,left:t,right:t,front:t,back:t};let m=ic([r,i,a,1],[u,l,d],1,[n,s,o],t);c=m[0],h=m[1],f=m[2]}else if(Array.isArray(t)){if(!t.every((t,r,i)=>t===i[0]))throw Error(`Unsupported padding parameter: ${t}`);p={top:t[0],bottom:t[1],left:t[2],right:t[3],front:t[4],back:t[5]};let m=ic([r,i,a,1],[u,l,d],1,[n,s,o],t[0]);c=m[0],h=m[1],f=m[2]}else if("SAME_UPPER"===t){c=Math.ceil(r/n),h=Math.ceil(i/s),f=Math.ceil(a/o);

993let t=(c-1)*n+u-r,m=(h-1)*s+l-i,g=(f-1)*o+d-a,y=Math.floor(t/2),_=Math.floor(m/2),$=Math.floor(g/2);p={top:_,bottom:m-_,left:$,right:g-$,front:y,back:t-y}}else throw Error(`Unknown padding parameter: ${t}`);return{padInfo:p,outDepth:c,outHeight:h,outWidth:f}})(n,l,d,p,y,_,$,x,S,k),z=s?h*c:h,A=[0,0,0,0,0];return"channelsFirst"===o?A=[u,z,C,I,E]:"channelsLast"===o&&(A=[u,C,I,E,z]),{batchSize:u,dataFormat:o,inDepth:l,inHeight:d,inWidth:p,inChannels:c,outDepth:C,outHeight:I,outWidth:E,outChannels:z,padInfo:T,strideDepth:y,strideHeight:_,strideWidth:$,filterDepth:f,filterHeight:m,filterWidth:g,effectiveFilterDepth:x,effectiveFilterHeight:S,effectiveFilterWidth:k,dilationDepth:b,dilationHeight:v,dilationWidth:w,inShape:t,outShape:A,filterShape:r}},im=(t,r,i,a,n,s)=>{let o="channelsLast"===s,u=(o?t[0].dims[3]:t[0].dims[1],[Math.ceil((t=>{let r=1;for(let i=0;i<t.length;i++)r*=t[i];return r})(({x:i.map((t,r)=>r)}).x.map(t=>i[t]))/64),1,1]);eX("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${u}`);let l=[{type:12,data:tt.size(i)},{type:12,data:a},{type:12,data:n},{type:12,data:r.strides},{type:12,data:r.dilations}];r8(r,l),l.push(...td(t[0].dims,t[1].dims));let d=["rank","rank"],p=3===t.length;return p&&(l.push(...td(t[2].dims)),d.push("rank")),l.push(...td(i)),{name:"Conv3DNaive",shaderCache:{hint:`${r.cacheKey};${o};1;${p}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:u[0],y:u[1],z:u[2]},programUniforms:l}),getShaderSource:s=>{let u=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:a.length},{name:"pads",type:"u32",length:n.length},{name:"strides",type:"u32",length:r.strides.length},{name:"dilations",type:"u32",length:r.dilations.length}];r5(r,u);let l=tu(t[0].dataType),d=ty("x",t[0].dataType,t[0].dims.length,1),c=ty("W",t[1].dataType,t[1].dims.length,1),h=[d,c],f=t_("result",t[0].dataType,i.length,1),m="";if(p){let r=ty("bias",t[2].dataType,t[2].dims.length,1);h.push(r),m+=`
994        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${l} {
995          return bias[${o?tm("coords",4,5):tm("coords",1,5)}];
996        }`}let g=r9(1,l),y=r6(r,g,l);return`
997            ${m}
998            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
999              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
1000              return ${d.getByIndices("aIndices")};
1001            }
1002            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
1003              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
1004              return ${c.getByIndices("aIndices")};
1005            }
1006          ${s.registerUniforms(u).declareVariables(...h,f)}
1007          ${s.mainStart()}
1008          ${s.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
1009              let coords = ${f.offsetToIndices("global_idx")};
1010              let batch = ${tm("coords",0,d.rank)};
1011              let d2 = ${o?tm("coords",d.rank-1,d.rank):tm("coords",1,d.rank)};
1012              let xFRCCorner = vec3<u32>(${o?tm("coords",1,d.rank):tm("coords",2,d.rank)},
1013              ${o?tm("coords",2,d.rank):tm("coords",3,d.rank)},
1014              ${o?tm("coords",3,d.rank):tm("coords",4,d.rank)}) * uniforms.strides - uniforms.pads;
1015              let xFCorner = xFRCCorner.x;
1016              let xRCorner = xFRCCorner.y;
1017              let xCCorner = xFRCCorner.z;
1018              let xShapeY = ${o?tm("uniforms.x_shape",1,d.rank):tm("uniforms.x_shape",2,d.rank)};
1019              let xShapeZ = ${o?tm("uniforms.x_shape",2,d.rank):tm("uniforms.x_shape",3,d.rank)};
1020              let xShapeW = ${o?tm("uniforms.x_shape",3,d.rank):tm("uniforms.x_shape",4,d.rank)};
1021              let xShapeU = ${o?tm("uniforms.x_shape",4,d.rank):tm("uniforms.x_shape",1,d.rank)};
1022              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
1023              let inputDepthVec4Remainder = xShapeU % 4;
1024
1025              var value = 0.0;
1026              for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) {
1027                let xF = xFCorner + wF * uniforms.dilations[0];
1028                if (xF < 0 || xF >= xShapeY) {
1029                  continue;
1030                }
1031
1032                for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) {
1033                  let xR = xRCorner + wR * uniforms.dilations[1];
1034                  if (xR < 0 || xR >= xShapeZ) {
1035                    continue;
1036                  }
1037
1038                  for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) {
1039                    let xC = xCCorner + wC * uniforms.dilations[2];
1040                    if (xC < 0 || xC >= xShapeW) {
1041                      continue;
1042                    }
1043
1044                    for (var d1 = 0u;

1044 d1 < inputDepthNearestVec4; d1 += 4) {
1045                      ${o?`let xValues = vec4<f32>(
1046                               getX(batch, xF, xR, xC, d1),
1047                               getX(batch, xF, xR, xC, d1 + 1),
1048                               getX(batch, xF, xR, xC, d1 + 2),
1049                               getX(batch, xF, xR, xC, d1 + 3));
1050                            `:`let xValues = vec4<f32>(
1051                               getX(batch, d1, xF, xR, xC),
1052                               getX(batch, d1 + 1, xF, xR, xC),
1053                               getX(batch, d1 + 2, xF, xR, xC),
1054                               getX(batch, d1 + 3, xF, xR, xC));
1055                            `}
1056                            let wValues = vec4<f32>(
1057                              getW(d2, d1, wF, wR, wC),
1058                              getW(d2, d1 + 1, wF, wR, wC),
1059                              getW(d2, d1 + 2, wF, wR, wC),
1060                              getW(d2, d1 + 3, wF, wR, wC));
1061                      value += dot(xValues, wValues);
1062                    }
1063                    if (inputDepthVec4Remainder == 1) {
1064                        ${o?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
1065                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
1066                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
1067                    } else if (inputDepthVec4Remainder == 2) {
1068                      ${o?`let xValues = vec2<f32>(
1069                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
1070                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
1071                      `:`let xValues = vec2<f32>(
1072                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
1073                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
1074                    `}
1075                    let wValues = vec2<f32>(
1076                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
1077                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
1078                      value += dot(xValues, wValues);
1079                    } else if (inputDepthVec4Remainder == 3) {
1080                      ${o?`let xValues = vec3<f32>(
1081                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
1082                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
1083                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
1084                      `:`let xValues = vec3<f32>(
1085                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
1086                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
1087                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
1088                    `}
1089                    let wValues = vec3<f32>(
1090                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
1091                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
1092                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
1093                      value += dot(xValues, wValues);
1094                    }
1095                  }
1096                }
1097              }
1098              ${p?"value = value + getBiasByOutputCoords(coords)":""};
1099              ${y}
1100              result[global_idx] = f32(value);
1101          }`}}}}),nD=W(()=>{nd(),ny(),n_(),nz(),ig=(t,r,i,a)=>{let n=t.length>2,s=n?"value += b[output_channel];":"",o=t[0].dims,u=t[1].dims,l="NHWC"===r.format,d=l?i[3]:i[1],p=d/r.group,c=l&&p>=4?tp(d):1,h=tt.size(i)/c,f=[{type:12,data:h},{type:12,data:r.dilations},{type:12,data:[r.strides[0],r.strides[1]]},{type:12,data:[r.pads[0],r.pads[1]]},{type:12,data:p}];return r8(r,f),f.push(...td(o,[u[0],u[1],u[2],u[3]/c])),f.push(...td([i[0],i[1],i[2],i[3]/c])),{name:"GroupedConv",shaderCache:{hint:`${r.cacheKey}_${c}`,inputDependencies:n?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:a?a(i):i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:f}),getShaderSource:a=>{let d=t_("output",t[0].dataType,i.length,c),p=tu(d.type.tensor),h=r6(r,d.type.value,p),f=ty("x",t[0].dataType,o.length),m=ty("w",t[1].dataType,u.length,c),g=[f,m];n&&g.push(ty("b",t[2].dataType,t[2].dims,c));let y=[{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"}];r5(r,y);let _=l?`
1102      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
1103        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];
1104
1105        if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) {
1106          continue;
1107        }
1108
1109        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) {
1110          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
1111          if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) {
1112            continue;
1113          }
1114
1115          for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {
1116            let input_channel = in_channel_offset + wInChannel;
1117            let xVal = ${f.get("batch","xHeight","xWidth","input_channel")};
1118            let wVal = ${m.get("wHeight","wWidth","wInChannel","output_channel")};
1119            value += xVal * wVal;
1120          }
1121        }
1122      }
1123      `:`
1124      for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {
1125        let input_channel = in_channel_offset + wInChannel;
1126        for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {
1127          let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];
1128
1129          if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) {
1130            continue;
1131          }
1132
1133          for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {
1134            let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
1135            if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) {
1136              continue;
1137            }
1138
1139            let xVal = ${f.get("batch","input_channel","xHeight","xWidth")};
1140            let wVal = ${m.get("output_channel","wInChannel","wHeight","wWidth")};
1141            value += xVal * wVal;
1142          }
1143        }
1144      }
1145      `;return`
1146  ${a.registerUniforms(y).declareVariables(...g,d)}
1147
1148  ${a.mainStart()}
1149    ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
1150
1151    let outputIndices = ${d.offsetToIndices("global_idx")};
1152    let batch: u32 = outputIndices[0];
1153    let output_channel: u32 = outputIndices[${l?3:1}];
1154    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${l?1:2}

1154], outputIndices[${l?2:3}]) * uniforms.strides - uniforms.pads;
1155    let group_id: u32 = output_channel * ${c} / uniforms.output_channels_per_group;
1156    var in_channel_offset = group_id * uniforms.w_shape[${l?2:1}];
1157
1158    var value: ${d.type.value} = ${d.type.value}(0);
1159    ${_}
1160    ${s}
1161    ${h}
1162    ${d.setByOffset("global_idx","value")}
1163  }`}}},iy=(t,r,i,a)=>{let n=t.length>2,s=tp(i[3]),o=tp(i[2]),u=tt.size(i)/s/o,l=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/s],d=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/s],p=[i[0],i[1],i[2],i[3]/s],c=[{type:12,data:u},{type:6,data:[r.strides[0],r.strides[1]]},{type:6,data:[r.pads[0],r.pads[1]]}];r8(r,c),c.push(...td(l,d,p));let h=(o-1)*r.strides[1]+d[1];return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${r.cacheKey};${s};${o};${h};${d[0]};${d[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:a?a(i):i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:c}),getShaderSource:i=>{let a=t_("output",t[0].dataType,p.length,s),u=tu(a.type.tensor),c=r6(r,a.type.value,u),f=ty("x",t[0].dataType,l.length,s),m=ty("w",t[1].dataType,d.length,s),g=[f,m];n&&g.push(ty("b",t[2].dataType,t[2].dims,s));let y=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return r5(r,y),`
1164  ${i.registerUniforms(y).declareVariables(...g,a)}
1165  ${i.mainStart()}
1166    ${i.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
1167    let width0 = uniforms.output_shape[3];
1168    let output_channel = global_idx % width0;
1169    var index1 = global_idx / width0;
1170    let width1 = uniforms.output_shape[2] / ${o}u;
1171    let col = (index1 % width1) * ${o}u;
1172    index1 = index1 / width1;
1173    let row = index1 % uniforms.output_shape[1];
1174    let batch = index1 / uniforms.output_shape[1];
1175
1176    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;
1177
1178    var x_vals: array<${f.type.value}, ${h}>;
1179    var values: array<${a.type.value}, ${o}>;
1180    let input_channel = output_channel;
1181    // Use constant instead of uniform can give better performance for w's height/width.
1182    for (var w_height: u32 = 0u; w_height < ${d[0]}; w_height++) {
1183      let x_height = x_corner.x + i32(w_height);
1184      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
1185        for (var i = 0; i < ${h}; i++) {
1186          let x_width = x_corner.y + i;
1187          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
1188            x_vals[i] = ${f.get("batch","u32(x_height)","u32(x_width)","input_channel")};
1189          } else {
1190            x_vals[i] = ${f.type.value}(0);
1191          }
1192        }
1193        for (var w_width: u32 = 0u; w_width < ${d[1]}; w_width++) {
1194          let w_val = ${m.get("w_height","w_width","0","output_channel")};
1195          for (var i = 0u; i < ${o}u; i++) {
1196            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
1197          }
1198        }
1199      }
1200    }
1201
1202    for (var i = 0u; i < ${o}u; i++) {
1203      var value = values[i];
1204      ${n?"value += b[output_channel];":""}
1205      ${c}
1206      ${a.set("batch","row","col + i","output_channel","value")};
1207    }
1208  }`}}}}),nP=W(()=>{ny(),nN(),nM(),nB(),nD(),nz(),nR(),n$(),i_=[2,3,1,0],i$=(t,r)=>{let i=t.kernelShape.slice();i.length<r[1].dims.length-2&&i.push(...Array(r[1].dims.length-2-i.length).fill(0));for(let t=2;t<r[1].dims.length;++t)0===i[t-2]&&(i[t-2]=r[1].dims[t]);let a=t.pads.slice();tr.adjustPadsBasedOnAutoPad(r[0].dims,t.strides,t.dilations,i,a,"NHWC"===t.format,t.autoPad);let n=Object.assign({},t);return Object.assign(n,{kernelShape:i,pads:a}),n},ib=t=>{let r=r7(t),i=t.format;return{autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],format:i,dilations:t.dilations,group:t.group,kernelShape:t.kernel_shape,pads:t.pads,strides:t.strides,wIsConst:t.w_is_const(),...r,cacheKey:`${t.format};${r.activation};`}},iv=(t,r,i,a)=>{let n="NHWC"===i.format,s=((t,r,i,a,n,s)=>{let o=t[0],u=t.slice(s?1:2,s?3:4),l=u.length,d=r[0],p=r.slice(2).map((t,r)=>t+(t-1)*(i[r]-1)),c=u.map((t,r)=>t+a[r]+a[r+l]).map((t,r)=>Math.floor((t-p[r]+n[r])/n[r]));return c.splice(0,0,o),c.splice(s?3:1,0,d),c})(r[0].dims,r[1].dims,i.dilations,i.pads,i.strides,n);if(1!==i.group){let o=[r[0]];if(n){let a=t.kernelCustomData.wT??t.compute(tS(r[1],i_),{inputs:[1],outputs:[i.wIsConst?-2:-1]})[0];i.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=a),o.push(a)}else o.push(r[1]);3===r.length&&o.push(r[2]),!t.adapterInfo.isArchitecture("ampere")&&n&&r[1].dims[0]===i.group&&1===r[1].dims[1]&&1===i.dilations[0]&&1===i.dilations[1]?t.compute(iy(o,i,s,a),{inputs:o}):t.compute(ig(o,i,s,a),{inputs:o});return}let o=3===r.length,u=r[0].dims[n?1:2],l=r[0].dims[n?2:3],d=r[0].dims[n?3:1],p=r[1].dims[2],c=r[1].dims[3],h=s[n?1:2],f=s[n?2:3],m=s[n?3:1],g=n&&p===u&&c===l&&0===i.pads[0]&&0===i.pads[1];if(g||1===p&&1===c&&1===i.dilations[0]&&1===i.dilations[1]&&1===i.strides[0]&&1===i.strides[1]&&0===i.pads[0]&&0===i.pads[1]){let p=s[0],c,y,_,$=[];if(n){let a=t.kernelCustomData.wT??t.compute(tS(r[1],i_),{inputs:[1],outputs:[i.wIsConst?-2:-1]})[0];if(i.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=a),g){let t=u*l*d;c=r[0].reshape([1,p,t]),y=a.reshape([1,t,m]),_=[1,p,m]}else c=r[0].reshape([p,u*l,d]),y=a.reshape([1,d,m]),_=[p,h*f,m];$.push(c),$.push(y)}else c=r[0].reshape([p,d,u*l]),y=r[1].reshape([1,m,d]),_=[p,m,h*f],$.pu

1208sh(y),$.push(c);o&&$.push(r[2]);let b=_[2],v=$[0].dims[$[0].dims.length-1];b<8&&v<8?t.compute(ii($,i,s,_,n,a),{inputs:$}):t.compute(iu($,i,s,_,n,a),{inputs:$});return}let y=t.kernelCustomData.wT??t.compute(tS(r[1],i_),{inputs:[1],outputs:[i.wIsConst?-2:-1]})[0];i.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=y);let _=[r[0],y];o&&_.push(r[2]);let $=n?h*f:m,b=n?m:h*f,v=p*c*d;t.compute(il(_,i,s,$,b,v,o,!0,a),{inputs:_})},iw=(t,r)=>{if(((t,r)=>{if(!t||2!==t.length&&3!==t.length)throw Error("Conv requires 2 or 3 inputs");if(t[0].dims.length>5)throw Error("greater than 5D is not supported");if(t[0].dims.length!==t[1].dims.length)throw Error("filter does not have same dimension as input");if(t[0].dims["NHWC"===r.format?t[0].dims.length-1:1]!==t[1].dims[1]*r.group)throw Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(3===t.length&&(1!==t[2].dims.length||t[1].dims[0]!==t[2].dims[0]))throw Error("invalid bias");let i=t[0].dims.length-2;if(r.dilations.length!==i)throw Error(`dilations should be ${i}D`);if(r.strides.length!==i)throw Error(`strides should be ${i}D`);if(r.pads.length!==2*i)throw Error(`pads should be ${2*i}D`);if(0!==r.kernelShape.length&&r.kernelShape.length!==t[1].dims.length-2)throw Error("invalid kernel shape")})(t.inputs,r),3===t.inputs[0].dims.length)((t,r)=>{let i="NHWC"===r.format,a=[t.inputs[0].reshape(i?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];3===t.inputs.length&&a.push(t.inputs[2]);let n=[0,r.pads[0],0,r.pads[1]],s=[1].concat(r.strides),o=[1].concat(r.dilations),u=[1].concat(r.kernelShape),l=i$({...r,pads:n,strides:s,dilations:o,kernelShape:u},a);iv(t,a,l,t=>i?[t[0],t[2],t[3]]:[t[0],t[1],t[3]])})(t,r);else if(5===t.inputs[0].dims.length)((t,r,i)=>{let a="NHWC"===i.format?"channelsLast":"channelsFirst",n=i$(i,r),s="NOTSET"===i.autoPad?i.pads:i.autoPad,o=ih(r[0].dims,r[1].dims,i.strides,i.dilations,s,!1,a);t.compute(im(r,n,o.outShape,[o.filterDepth,o.filterHeight,o.filterWidth],[o.padInfo.front,o.padInfo.top,o.padInfo.left],a))})(t,t.inputs,r);else{let i=i$(r,t.inputs);iv(t,t.inputs,i)}}}),nU=W(()=>{nd(),nc(),ny(),n_(),ix=(t,r,i)=>{let a=t.length>2,n=r.outputShape,s="NHWC"===r.format,o=r.group,u=t[1].dims,l=u[2]/o,d=u[3],p=s?tp(l):1,c=s?tp(d):1,h=s?1===d?p:c:1,f=tt.size(n)/c,m=[Math.ceil(f/64),1,1];eX("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${m}`);let g=["rank","rank"],y=[r.strides[0],r.strides[1]],_=[r.kernelShape[s?1:2],r.kernelShape[s?2:3]],$=[r.dilations[0],r.dilations[1]],b=[_[0]+(r.dilations[0]<=1?0:(r.kernelShape[s?1:2]-1)*(r.dilations[0]-1)),_[1]+(r.dilations[1]<=1?0:(r.kernelShape[s?2:3]-1)*(r.dilations[1]-1))],v=[b[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),b[1]-1-Math.floor((r.pads[1]+r.pads[3])/2)],w=[{type:12,data:f},{type:12,data:y},{type:12,data:_},{type:12,data:$},{type:12,data:b},{type:6,data:v},{type:12,data:l},{type:12,data:d},...td(t[0].dims,t[1].dims)];return a&&(w.push(...td(t[2].dims)),g.push("rank")),w.push(...td(n)),{name:"ConvTranspose2D",shaderCache:{hint:`${r.cacheKey};${p}${h}${c}${1===d}`,inputDependencies:g},getRunData:()=>({dispatchGroup:{x:m[0],y:m[1],z:m[2]},outputs:[{dims:i?i(n):n,dataType:t[0].dataType}],programUniforms:w}),getShaderSource:r=>{let i=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:y.length},{name:"filter_dims",type:"u32",length:_.length},{name:"dilations",type:"u32",length:_.length},{name:"effective_filter_dims",type:"u32",length:b.length},{name:"pads",type:"i32",length:v.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],o=tu(t[0].dataType),u=s?1:2,l=s?2:3,f=s?3:1,m=ty("W",t[1].dataType,t[1].dims.length,h),g=ty("Dy",t[0].dataType,t[0].dims.length,p),$=[g,m];a&&$.push(ty("bias",t[2].dataType,[n[f]].length,c));let w=t_("result",t[0].dataType,n.length,c),x=`
1209            let outputIndices = ${w.offsetToIndices(`global_idx * ${c}`)};
1210            let batch = ${w.indicesGet("outputIndices",0)};
1211            let d1 = ${w.indicesGet("outputIndices",f)};
1212            let r = ${w.indicesGet("outputIndices",u)};
1213            let c = ${w.indicesGet("outputIndices",l)};
1214            let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;
1215            let dyRCorner = dyCorner.x;
1216            let dyCCorner = dyCorner.y;
1217            let groupId = d1 / uniforms.output_channels_per_group;
1218            let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;
1219            // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
1220            // ? = to be determined. : = across all values in that axis.
1221            var dotProd = ${w.type.value}(0.0);
1222            var wR: u32 = 0;
1223            if (uniforms.dilations.x == 1) {
1224              // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
1225              wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
1226            }
1227            for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
1228              if (wR % uniforms.dilations.x != 0) {
1229                continue;
1230              }
1231              let dyR = (${o}(dyRCorner) + ${o}(wR)) / ${o}(uniforms.strides[0]);
1232              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
1233              if (dyR < 0.0 || dyR >= ${o}(uniforms.Dy_shape[${u}]) || fract(dyR) > 0.0 ||
1234                  wRPerm < 0) {
1235                continue;
1236              }
1237              let idyR: u32 = u32(dyR);
1238              var wC: u32 = 0;
1239              if (uniforms.dilations.y == 1) {
1240                // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
1241                wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
1242              }
1243
1244              for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
1245                if (wC % uniforms.dilations.y != 0) {
1246                  continue;
1247                }
1248                let dyC = (${o}(dyCCorner) + ${o}(wC)) / ${o}(uniforms.strides.y);
1249                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
1250                if (dyC < 0.0 || dyC >= ${o}(uniforms.Dy_shape[${l}

1250]) ||
1251                    fract(dyC) > 0.0 || wCPerm < 0) {
1252                  continue;
1253                }
1254                let idyC: u32 = u32(dyC);
1255                var inputChannel = groupId * uniforms.input_channels_per_group;
1256                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + ${p}) {
1257                  let xValue = ${s?g.getByOffset(`${g.indicesToOffset(`${g.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}`):g.get("batch","inputChannel","idyR","idyC")};
1258                  ${(()=>{let t="";if(1===p)t+=`
1259        let w_offset = ${m.indicesToOffset(`${m.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
1260        let wValue = ${m.getByOffset(`w_offset / ${h}`)};
1261        dotProd = dotProd + xValue * wValue;`;else if(1===d)t+=`
1262          let wValue = ${m.getByOffset(`${m.indicesToOffset(`${m.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)} / ${h}`)};
1263          dotProd = dotProd + dot(xValue, wValue);`;else for(let r=0;r<p;r++)t+=`
1264            let wValue${r} = ${m.getByOffset(`${m.indicesToOffset(`${m.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${r}, wOutChannel)`)} / ${h}`)};
1265            dotProd = dotProd + xValue[${r}] * wValue${r};`;return t})()}
1266                  inputChannel = inputChannel + ${p};
1267                }
1268                wC = wC + uniforms.strides.y - 1;
1269              }
1270              wR = wR + uniforms.strides[0] - 1;
1271            }
1272            let value = dotProd${a?` + bias[d1 / ${c}]`:""};
1273            ${w.setByOffset("global_idx","value")};
1274          `;return`
1275    ${r.registerUniforms(i).declareVariables(...$,w)}
1276      ${r.mainStart()}
1277      ${r.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
1278    ${x}}`}}}}),nW=W(()=>{nU(),nz(),n$(),iS=(t,r,i,a,n,s)=>(t-1)*r+i+(a-1)*n+1-s,ik=(t,r,i,a,n)=>{let s=Math.floor(t/2);"SAME_UPPER"===r?(i[a]=s,i[n]=t-s):"SAME_LOWER"===r&&(i[a]=t-s,i[n]=s)},iT=(t,r)=>{let i=t.kernelShape.slice();if(0===t.kernelShape.length||0===t.kernelShape.reduce((t,r)=>t*r,1)){i.length=0;for(let t=2;t<r[1].dims.length;++t)i.push(r[1].dims[t])}let a="NHWC"===t.format;i.splice(0,0,r[1].dims[0]),i.splice(a?3:1,0,r[1].dims[1]);let n=t.pads.slice(),s=t.outputShape.slice(),o=t.outputPadding.slice(),u=r[0].dims,l=t.dilations.slice();0===l.reduce((t,r)=>t+r,0)&&(l=Array(r[0].dims.length-2).fill(1));let d=t.strides.slice();0===d.reduce((t,r)=>t+r,0)&&(d=Array(r[0].dims.length-2).fill(1)),((t,r,i,a,n,s,o,u,l,d)=>{let p=t.length-2,c=0===d.length;l.length<p&&l.push(...Array(p-l.length).fill(0));let h=t[0],f=r[u?3:1]*n;for(let n=0,h=t.length-p-!!u;n<p;++n,++h){let u=t[h],f=c?u*o[n]:d[n];ik(iS(u,o[n],s[n],r[h],i[n],f),a,s,n,n+p),c&&d.push(o[n]*(u-1)+l[n]+(r[h]-1)*i[n]+1-s[n]-s[n+p])}d.splice(0,0,h),d.splice(u?3:1,0,f)})(u,i,l,t.autoPad,t.group,n,d,a,o,s);let p=Object.assign({},t);return Object.assign(p,{kernelShape:i,pads:n,outputPadding:o,outputShape:s,dilations:l,strides:d}),p},iC=t=>{let r=r7(t),i=t.format,a=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof t.autoPad>"u"?0:t.autoPad],n=t.dilations,s=t.group,o=t.kernelShape,u=t.pads,l=t.strides,d=t.wIsConst();return{autoPad:a,format:i,dilations:n,group:s,kernelShape:o,outputPadding:t.outputPadding,outputShape:t.outputShape,pads:u,strides:l,wIsConst:d,...r,cacheKey:`${t.format};${r.activation};`}},iI=(t,r,i,a)=>{let n=t.kernelCustomData.wT??t.compute(tS(r[1],[2,3,0,1]),{inputs:[1],outputs:[i.wIsConst?-2:-1]})[0];i.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=n);let s=[r[0],n];3===r.length&&s.push(r[2]),t.compute(ix(s,i,a),{inputs:s})},iE=(t,r)=>{if(((t,r)=>{if(!t||2!==t.length&&3!==t.length)throw Error("Conv requires 2 or 3 inputs");if(4!==t[0].dims.length&&3!==t[0].dims.length)throw Error("currently only support 2-dimensional conv");if(t[0].dims.length!==t[1].dims.length)throw Error("filter does not have same dimension as input");if(t[0].dims["NHWC"===r.format?t[0].dims.length-1:1]!==t[1].dims[0])throw Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let i=t[1].dims[1]*r.group;if(3===t.length&&(1!==t[2].dims.length||t[2].dims[0]!==i))throw Error("invalid bias");let a=t[0].dims.length-2;if(r.dilations.reduce((t,r)=>t+r,0)>0&&r.dilations.length!==a)throw Error(`dilations should be ${a}D`);

1278if(r.strides.reduce((t,r)=>t+r,0)>0&&r.strides.length!==a)throw Error(`strides should be ${a}D`);if(r.pads.reduce((t,r)=>t+r,0)>0&&r.pads.length!==2*a)throw Error(`pads should be ${2*a}D`);if(r.outputPadding.length!==a&&0!==r.outputPadding.length)throw Error(`output_padding should be ${a}D`);if(r.kernelShape.reduce((t,r)=>t+r,0)>0&&0!==r.kernelShape.length&&r.kernelShape.length!==t[1].dims.length-2)throw Error("invalid kernel shape");if(0!==r.outputShape.length&&r.outputShape.length!==t[0].dims.length-2)throw Error("invalid output shape")})(t.inputs,r),3===t.inputs[0].dims.length)((t,r)=>{let i="NHWC"===r.format,a=[t.inputs[0].reshape(i?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];3===t.inputs.length&&a.push(t.inputs[2]);let n=r.kernelShape;(0===n.length||0===n[0])&&(n=[t.inputs[1].dims[2]]);let s=r.dilations;(0===s.length||0===s[0])&&(s=[1]);let o=r.strides;(0===o.length||0===o[0])&&(o=[1]);let u=r.pads;0===u.length&&(u=[0,0]),u=[0,u[0],0,u[1]],o=[1].concat(o),s=[1].concat(s),n=[1].concat(n);let l=r.outputPadding;l=[0].concat(l);let d=iT({...r,pads:u,strides:o,dilations:s,kernelShape:n,outputPadding:l},a);iI(t,a,d,t=>i?[t[0],t[2],t[3]]:[t[0],t[1],t[3]])})(t,r);else{let i=iT(r,t.inputs);iI(t,t.inputs,i)}}}),nq=W(()=>{nd(),ny(),ng(),n_(),iz=(t,r)=>{let i=t.inputs[0].dims,a=t.inputs[0].dataType,n=t.inputs[1];t.compute(((t,r,i,a)=>{let n=tt.size(r),s=r.length,o=ty("input",t,s),u=t_("output",t,s),l=6===i.dataType?i.getInt32Array()[0]:Number(i.getBigInt64Array()[0]),d=tt.normalizeAxis(l,s);return{name:"CumSum",shaderCache:{hint:a.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:[{type:12,data:n},{type:12,data:d},...td(r,r)]}),getShaderSource:t=>{let r=` i32(${o.indicesGet("inputIndices","uniforms.axis")}) `,i=tm("uniforms.input_shape","uniforms.axis",s),n=a.reverse?r+(a.exclusive?" + 1":""):"0",l=a.reverse?i:r+(a.exclusive?"":" + 1");return`
1279                ${t.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(o,u)}
1280                ${t.mainStart()}
1281                  ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
1282                  var inputIndices = ${u.offsetToIndices("global_idx")};
1283                  var sum = ${u.type.value}(0);
1284                  let first : i32 = ${n};
1285                  let last : i32 = ${l};
1286                  for (var i : i32 = first; i < last; i++) {
1287                    ${o.indicesSet("inputIndices","uniforms.axis","u32(i)")};
1288                    sum = sum + ${o.getByIndices("inputIndices")};
1289                  }
1290                  ${u.setByOffset("global_idx","sum")};
1291                }`}}})(a,i,n,r),{inputs:[0]})},iA=t=>{let r=1===t.exclusive,i=1===t.reverse;return e7({exclusive:r,reverse:i})}}),nL=W(()=>{nd(),ny(),ng(),n_(),iO=(t,r)=>{(t=>{if(!t||1!==t.length)throw Error("DepthToSpace requires 1 input.");if(4!==t[0].dims.length)throw Error("DepthToSpace requires 4D input.")})(t.inputs),t.compute(((t,r)=>{let i,a,n,s,o,u,l="NHWC"===r.format,d=r.blocksize,p="DCR"===r.mode;l?([i,a,n,s]=t.dims,o=p?[i,a,n,d,d,s/d**2]:[i,a,n,s/d**2,d,d],u=p?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([i,a,n,s]=[t.dims[0],t.dims[2],t.dims[3],t.dims[1]],o=p?[i,d,d,s/d**2,a,n]:[i,s/d**2,d,d,a,n],u=p?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let c=t.reshape(o),h=c.dims.length,f=t.dataType,m=ty("a",f,h),g=t_("output",f,h);return{name:"DepthToSpace",shaderCache:{hint:`${t.dims};${r.blocksize};${r.mode}`,inputDependencies:["rank"]},getRunData:t=>{let r=l?[i,a*d,n*d,s/d**2]:[i,s/d**2,a*d,n*d],o=tt.size(r),p=c.dims,h=tt.sortBasedOnPerm(p,u);return{outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:[{type:12,data:o},...td(p,h)]}},getShaderSource:t=>`
1292  ${t.registerUniform("output_size","u32").declareVariables(m,g)}
1293
1294  ${((t,r,i,a)=>{let n=[];n.push(`fn perm(i: ${a.type.indices}) -> ${i.type.indices} {
1295    var a: ${i.type.indices};`);for(let a=0;a<r;++a)n.push(i.indicesSet("a",t[a],`i[${a}]`));return n.push("return a;}"),n.join(`
1296`)})(u,h,m,g)}
1297
1298  ${t.mainStart()}
1299    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
1300
1301    let indices = ${g.offsetToIndices("global_idx")};
1302    let aIndices = perm(indices);
1303
1304    ${g.setByOffset("global_idx",m.getByIndices("aIndices"))}
1305  }`}})(t.inputs[0],r))},iR=t=>e7({blocksize:t.blocksize,mode:t.mode,format:t.format})}),nV=W(()=>{nd(),ny(),ng(),n_(),iM="^"+(iN="("+(iB="[a-zA-Z]|\\.\\.\\.")+")+")+"$",iD="^"+("("+iN+",)*")+iN+"$",iP=class{constructor(t=-1){this.symbolToIndices=new Map,this.inputIndex=t}addSymbol(t,r){let i=this.symbolToIndices.get(t);void 0===i?i=[r]:i.push(r),this.symbolToIndices.set(t,i)}},iU=class{constructor(t,r){this.equation=r,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=[],this.outputDims=[];let[i,a]=r.includes("->")?r.split("->",2):[r,""];if(!i.match(RegExp(iD)))throw Error("Invalid LHS term");if(i.split(",").forEach((r,i)=>{let a=t[i].dims.slice();if(!r.match(RegExp(iM)))throw Error("Invalid LHS term");let n=this.processTerm(r,!0,a,i);this.lhs.push(n)}),""===a)a+=[...this.symbolToInfo.entries()].filter(([t,r])=>1===r.count||"..."===t).map(([t])=>t).join("");else if(!a.match(RegExp(iN)))throw Error("Invalid RHS");a.match(RegExp(iB,"g"))?.forEach(t=>{if("..."===t)this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let r=this.symbolToInfo.get(t);if(void 0===r)throw Error("Invalid RHS symbol");this.outputDims.push(r.dimValue)}}),this.rhs=this.processTerm(a,!1,this.outputDims)}addSymbol(t,r,i){let a=this.symbolToInfo.get(t);if(void 0!==a){if(a.dimValue!==r&&1!==a.count)throw Error("Dimension mismatch");a.count++,a.inputIndices.push(i)}else a={count:1,dimValue:r,inputIndices:[i]};this.symbolToInfo.set(t,a)}processTerm(t,r,i,a=-1){let n=i.length,s=!1,o=[],u=0;if(!t.match(RegExp(iM))&&!r&&""!==t)throw Error("Invalid LHS term");let l=t.match(RegExp(iB,"g")),d=new iP(a);return l?.forEach((t,p)=>

1305{if("..."===t){if(s)throw Error("Only one ellipsis is allowed per input term");s=!0;let t=n-l.length+1;if(t<0)throw Error("Ellipsis out of bounds");if(o=i.slice(u,u+t),this.hasEllipsis){if(this.ellipsisDims.length!==o.length||this.ellipsisDims.toString()!==o.toString())throw Error("Ellipsis dimensions mismatch")}else if(r)this.hasEllipsis=!0,this.ellipsisDims=o;else throw Error("Ellipsis must be specified in the LHS");for(let t=0;t<o.length;t++){let r=String.fromCharCode(48+t);d.addSymbol(r,p+t),this.addSymbol(r,i[u++],a)}}else d.addSymbol(t,p+(this.hasEllipsis?this.ellipsisDims.length-1:0)),this.addSymbol(t,i[u++],a)}),d}},iW=(t,r)=>{let i=new iU(t.inputs,r.equation),a=i.outputDims,n=t.inputs.map((t,r)=>t.dims);t.compute(((t,r,i,a)=>{let n=t.map(t=>t.length).map((t,i)=>ty(`input${i}`,r,t)),s=tt.size(a),o=t_("output",r,a.length),u=[...i.symbolToInfo.keys()].filter(t=>!i.rhs.symbolToIndices.has(t));return{name:"Einsum",shaderCache:{hint:i.equation,inputDependencies:t.map(()=>"rank")},getRunData:()=>{let n=u.filter(t=>i.symbolToInfo.has(t)).map(t=>({type:12,data:i.symbolToInfo.get(t)?.dimValue||0}));n.push({type:12,data:s});let o=t.map((t,r)=>[...td(t)]).reduce((t,r)=>t.concat(r),n);return o.push(...td(a)),{outputs:[{dims:a,dataType:r}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:o}},getShaderSource:t=>{let r=[],a=[],s=[],l=[],d=[],p=i.symbolToInfo.size===i.rhs.symbolToIndices.size;i.symbolToInfo.forEach((t,u)=>{if(i.rhs.symbolToIndices.has(u)){let a=i.rhs.symbolToIndices.get(u)?.[0];void 0!==a&&i.lhs.forEach((i,s)=>{if(t.inputIndices.includes(s)){let t=i.symbolToIndices.get(u);if(void 0===t)throw Error("Invalid symbol error");t.forEach(t=>{r.push(`${n[s].indicesSet(`input${s}Indices`,t,o.indicesGet("outputIndices",a))}`)})}})}else i.lhs.forEach((r,i)=>{if(t.inputIndices.includes(i)){let t=r.symbolToIndices.get(u);if(void 0===t)throw Error("Invalid symbol error");t.forEach(t=>{a.push(`${n[i].indicesSet(`input${i}Indices`,t,`${u}`)}`)}),d.push(`prod *= ${n[i].getByIndices(`input${i}Indices`)};`)}}),s.push(`for(var ${u}: u32 = 0; ${u} < uniforms.${(t=>t+"_max")(u)}; ${u}++) {`),l.push("}")});let c=p?[...r,`let sum = ${n.map((t,r)=>t.getByIndices(`input${r}Indices`)).join(" * ")};`]:[...r,"var sum = 0.0;",...s,...a,"var prod = 1.0;",...d,"sum += prod;",...l];return`
1306            ${t.registerUniforms(u.map(t=>({name:`${(t=>t+"_max")(t)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...n,o)}
1307
1308            ${t.mainStart()}
1309            ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
1310            var outputIndices = ${o.offsetToIndices("global_idx")};
1311            ${n.map((t,r)=>`var input${r}Indices: ${n[r].type.indices};`).join(`
1312`)}
1313            ${c.join(`
1314`)};
1315            ${o.setByOffset("global_idx","sum")};
1316          }`}}})(n,t.inputs[0].dataType,i,a))},iq=t=>{let r=t.equation.replace(/\s+/g,"");return e7({equation:r})}}),nG=W(()=>{nd(),ny(),n_(),iL=(t,r)=>{let i=t.length-r.length,a=[];for(let r=0;r<i;++r)a.push(t[r]);for(let n=0;n<r.length;++n)a.push(1===r[n]?t[n+i]:r[n]);return a},iV=t=>{(t=>{if(!t||2!==t.length)throw Error("Expand requires 2 input.");let r=t[0].dims,i=Array.from(t[1].getBigInt64Array(),Number),a=i.length<r.length?0:i.length-r.length,n=r.length<i.length?0:r.length-i.length;for(;a<i.length&&n<r.length;++a,++n)if(i[a]!==r[n]&&1!==i[a]&&1!==r[n])throw Error("Expand requires shape to be broadcastable to input")})(t.inputs),t.compute((t=>{let r=t[0].dims,i=((t,r)=>t.length>r.length?iL(t,r):iL(r,t))(r,Array.from(t[1].getBigInt64Array(),Number)),a=t[0].dataType,n=9===a||1===tt.size(r),s=9===a||r.length>0&&r[r.length-1]%4==0?4:1,o=n||i.length>0&&i[i.length-1]%4==0?4:1,u=Math.ceil(tt.size(i)/o),l=[{type:12,data:u},...td(r,i)];return{name:"Expand",shaderCache:{hint:`${i.length};${s}${o}`,inputDependencies:["rank"]},getShaderSource:t=>{let n=ty("input",a,r.length,s),u=t_("output",a,i.length,o),l;if(9===a){let t=(t,r,i="")=>`
1317          let outputIndices${r} = ${u.offsetToIndices(`outputOffset + ${r}u`)};
1318          let offset${r} = ${n.broadcastedIndicesToOffset(`outputIndices${r}`,u)};
1319          let index${r} = offset${r} / 4u;
1320          let component${r} = offset${r} % 4u;
1321          ${t}[${r}] = ${i}(${n.getByOffset(`index${r}`)}[component${r}]);
1322        `;l=`
1323        let outputOffset = global_idx * ${o};
1324        var data = vec4<u32>(0);
1325        ${t("data",0,"u32")}
1326        ${t("data",1,"u32")}
1327        ${t("data",2,"u32")}
1328        ${t("data",3,"u32")}
1329        ${u.setByOffset("global_idx","data")}
1330      }`}else l=`
1331        let outputIndices = ${u.offsetToIndices(`global_idx * ${o}`)};
1332        let inputOffset = ${n.broadcastedIndicesToOffset("outputIndices",u)};
1333        let data = ${u.type.value}(${n.getByOffset(`inputOffset / ${s}`)});
1334        ${u.setByOffset("global_idx","data")}
1335      }`;return`
1336    ${t.registerUniform("vec_size","u32").declareVariables(n,u)}
1337    ${t.mainStart()}
1338    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
1339    ${l}`},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l})}})(t.inputs),{inputs:[0]})}}),nH=W(()=>{nd(),ny(),n_(),nT(),iG=t=>{t.inputs.length<2||0===tt.size(t.inputs[1].dims)?rW(t):t.compute((t=>{let r=t[0].dataType,i=tt.size(t[0].dims),a=tt.size(t[1].dims),n=a%4==0;return{name:"FastGeluWithBias",shaderCache:{hint:`${n}`,inputDependencies:["type","type"]},getShaderSource:t=>{let i=ty("x",r,[1],4),a=ty("bias",r,[1],4),s=t_("y",r,[1],4),o=t=>`
1340      let bias${t}_offset: u32 = (global_idx * 4 + ${t}

1340) % uniforms.bias_size;
1341      let bias${t} = ${a.getByOffset(`bias${t}_offset / 4`)}[bias${t}_offset % 4];`,u=n?`
1342      let bias = ${a.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${o(0)}${o(1)}${o(2)}${o(3)}
1343      let bias = ${i.type.value}(bias0, bias1, bias2, bias3);`;return`${t.registerUniforms([{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}]).declareVariables(i,a,s)}
1344
1345    ${rP(tl(r))}
1346
1347    ${t.mainStart(ts)}
1348      ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}
1349
1350      let x = ${i.getByOffset("global_idx")};
1351      ${u}
1352      let x_in = x + bias;
1353      ${s.setByOffset("global_idx",rU("x_in"))}
1354    }`},getRunData:t=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],programUniforms:[{type:12,data:Math.ceil(i/4)},{type:12,data:a}],dispatchGroup:{x:Math.ceil(i/ts/4)}})}})(t.inputs))}}),nF=W(()=>{nd(),ny(),ng(),n_(),iH=t=>e7({axis:t.axis}),iF=(t,r)=>{(t=>{if(!t||2!==t.length)throw Error("Gather requires 2 inputs.")})(t.inputs),t.compute(((t,r)=>{let i=t[0].dims,a=t[1].dims,n=i.length,s=tt.normalizeAxis(r.axis,n),o=i.slice(0);o.splice(s,1,...a);let u=i[s],l=9===t[0].dataType?4:1,d=Math.ceil(tt.size(o)/l),p=[{type:12,data:d},{type:6,data:u},{type:12,data:s},...td(t[0].dims,t[1].dims,o)];return{name:"Gather",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:r=>{let i=ty("data",t[0].dataType,t[0].dims.length,l),u=ty("inputIndices",t[1].dataType,t[1].dims.length),d=t_("output",t[0].dataType,o.length,l),p=t=>{let r=a.length,l=`var indicesIndices${t}  = ${u.type.indices}(0);`;for(let i=0;i<r;i++)l+=`${r>1?`indicesIndices${t}[${i}]`:`indicesIndices${t}`} = ${o.length>1?`outputIndices${t}[uniforms.axis + ${i}]`:`outputIndices${t}`};`;l+=`
1355          var idx${t} = ${u.getByIndices(`indicesIndices${t}`)};
1356          if (idx${t} < 0) {
1357            idx${t} = idx${t} + uniforms.axisDimLimit;
1358          }
1359          var dataIndices${t} : ${i.type.indices};
1360        `;for(let i=0,a=0;i<n;i++)i===s?(l+=`${n>1?`dataIndices${t}[${i}]`:`dataIndices${t}`} = u32(idx${t});`,a+=r):(l+=`${n>1?`dataIndices${t}[${i}]`:`dataIndices${t}`} = ${o.length>1?`outputIndices${t}[${a}]`:`outputIndices${t}`};`,a++);return l},c;if(9===t[0].dataType){let t=(t,r,a="")=>`
1361          let outputIndices${r} = ${d.offsetToIndices(`outputOffset + ${r}u`)};
1362          ${p(r)};
1363          let offset${r} = ${i.indicesToOffset(`dataIndices${r}`)};
1364          let index${r} = offset${r} / 4u;
1365          let component${r} = offset${r} % 4u;
1366          ${t}[${r}] = ${a}(${i.getByOffset(`index${r}`)}[component${r}]);
1367        `;c=`
1368        let outputOffset = global_idx * ${l};
1369        var value = vec4<u32>(0);
1370        ${t("value",0,"u32")}
1371        ${t("value",1,"u32")}
1372        ${t("value",2,"u32")}
1373        ${t("value",3,"u32")}
1374        ${d.setByOffset("global_idx","value")}
1375      `}else c=`
1376      let outputIndices = ${d.offsetToIndices("global_idx")};
1377      ${p("")};
1378      let value = ${i.getByIndices("dataIndices")};
1379      ${d.setByOffset("global_idx","value")};
1380      `;return`
1381      ${r.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(i,u,d)}
1382      ${r.mainStart()}
1383        ${r.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
1384        ${c}
1385      }`}}})(t.inputs,r))}}),nj=W(()=>{nd(),ny(),n_(),ij=(t,r)=>{let i=t.inputs,a=i[0].dims,n=i[0].dataType,s=i[1].dims,o=s[s.length-1],u=tt.sizeToDimension(s,s.length-1),l=tt.sizeFromDimension(a,r.batchDims+o),d=tt.sizeToDimension(a,r.batchDims),p=tt.sizeFromDimension(a,r.batchDims),c=Array(o),h=l;for(let t=0;t<o;++t)c[o-1-t]=h,h*=a[r.batchDims+o-1-t];let f=((t,r,i,a,n,s,o,u,l)=>{let d=[{type:12,data:s},{type:12,data:a},{type:12,data:n},{type:12,data:i},{type:12,data:o},{type:12,data:u},{type:12,data:l}],p=[s];return d.push(...td(r.dims,p)),t.compute({name:"computeSliceOffsets",shaderCache:{hint:`${n.length}_${i.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:p,dataType:t.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:d}),getShaderSource:t=>{let a=ty("indices_data",r.dataType,r.dims.length),s=t_("input_slice_offsets_data",12,1,1),o=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:n.length},{name:"sizes_from_slice_dims_data",type:"u32",length:i.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return`
1386  ${t.registerUniforms(o).declareVariables(a,s)}
1387  ${t.mainStart()}
1388    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
1389    let batch_idx = global_idx / uniforms.num_slices_per_batch;
1390    let base_offset = batch_idx * uniforms.input_batch_stride;
1391
1392    let slice_indices_base_offset = global_idx * uniforms.num_slice_dims;
1393    var relative_slice_offset = 0;
1394    for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) {
1395      var index = i32(indices_data[dim_idx + slice_indices_base_offset].x);
1396      let input_dim_idx = uniforms.batch_dims + dim_idx;
1397      if (index < 0) {
1398        ${1===n.length?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"}
1399      }
1400      ${1===i.length?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"}
1401    }
1402
1403    input_slice_offsets_data[global_idx] =  base_offset + u32(relative_slice_offset);
1404  }`}},{inputs:[r],outputs:[-1]})[0]})(t,i[1],c,r.batchDims,a,u,u/d,p,o),m=r.batchDims+o;if(m>a.length)throw Error("last dimension of indices must not be larger than rank of input tensor");let g=s.slice(0,-1).concat(a.slice(m)),y=tt.size(g),_=[{type:12,data:y},{type:12,data:l},...td(i[0].dims,f.dims,g)];t.compute({name:"GatherND",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:g,dataType:n}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:_}),getShaderSource:t=>{let r=ty("data",i[0].dataType,i[0].dims.length),a=ty("slice_offsets",12,f.dims.length),n=t_("output",i[0].dataType,g.length);return`
1405          ${t.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(r,a,n)}
1406            ${t.mainStart()}
1407            ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
1408          let slice_offset = slice_offsets[global_idx / uniforms.slice_size];

1409          output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
1410        }`}},{inputs:[i[0],f]})},iK=t=>({batchDims:t.batch_dims,cacheKey:""})}),nK=W(()=>{nd(),ny(),ng(),n_(),iQ=(t,r)=>{((t,r)=>{if(t.length<3||t.length>4)throw Error("GatherBlockQuantized requires 3 or 4 inputs.");let i=tt.normalizeAxis(r.quantizeAxis,t[0].dims.length),a=r.blockSize,n=t[0],s=t[2],o=4===t.length?t[3]:void 0;if(s.dims.length!==n.dims.length||!n.dims.map((t,r)=>r===i?Math.ceil(t/a)===s.dims[r]:t===s.dims[r]).reduce((t,r)=>t&&r,!0))throw Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(o){if(o.dataType!==n.dataType)throw Error("Zero point must have the same data type as the input tensor.");if(o.dims.length!==s.dims.length||!o.dims.map((t,r)=>t===s.dims[r]).reduce((t,r)=>t&&r,!0))throw Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}})(t.inputs,r),t.compute(((t,r)=>{let i=t[0].dims,a=t[1].dims,n=i.length,s=tt.normalizeAxis(r.gatherAxis,n),o=tt.normalizeAxis(r.quantizeAxis,n),u=i.slice(0);u.splice(s,1,...a);let l=tt.size(u),d=t[2].dataType,p=22===t[0].dataType,c=[{type:12,data:l},{type:12,data:o},{type:12,data:s},{type:12,data:r.blockSize},...td(...t.map((t,r)=>t.dims),u)];return{name:"GatherBlockQuantized",shaderCache:{hint:`${r.cacheKey};${t.filter((t,r)=>1!==r).map(t=>t.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:t.length},(t,r)=>"rank")},getRunData:()=>({outputs:[{dims:u,dataType:d}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:c}),getShaderSource:r=>{let n=ty("data",t[0].dataType,t[0].dims.length),o=ty("inputIndices",t[1].dataType,t[1].dims.length),l=ty("scales",t[2].dataType,t[2].dims.length),c=t.length>3?ty("zeroPoint",t[3].dataType,t[3].dims.length):void 0,h=t_("output",d,u.length),f=[n,o,l];return c&&f.push(c),`
1411        ${r.registerUniforms([{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}]).declareVariables(...f,h)}
1412        ${r.mainStart()}
1413        let output_indices = ${h.offsetToIndices("global_idx")};
1414        var indices_indices = ${o.type.indices}(0);
1415        ${a.length>1?`
1416          for (var i: u32 = 0; i < ${a.length}; i++) {
1417            let index = ${h.indicesGet("output_indices","uniforms.gather_axis + i")};
1418            ${o.indicesSet("indices_indices","i","index")};
1419          }`:`indices_indices = ${h.indicesGet("output_indices","uniforms.gather_axis")};`};
1420        var data_indices = ${n.type.indices}(0);
1421        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
1422          let index = ${h.indicesGet("output_indices","i")};
1423          ${n.indicesSet("data_indices","i","index")};
1424        }
1425        var index_from_indices = ${o.getByIndices("indices_indices")};
1426        if (index_from_indices < 0) {
1427          index_from_indices += ${i[s]};
1428        }
1429        ${n.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
1430        for (var i = uniforms.gather_axis + 1; i < ${u.length}; i++) {
1431          let index = ${h.indicesGet("output_indices",`i + ${a.length} - 1`)};
1432          ${n.indicesSet("data_indices","i","index")};
1433        }
1434        let data_offset = ${n.indicesToOffset("data_indices")};
1435        let data_index = data_offset % 8;
1436        // Convert 4-bit packed data to 8-bit packed data.
1437        let packed_4bit_quantized_data = ${n.getByOffset("data_offset / 8")};
1438        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
1439        let quantized_data_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
1440        let quantized_data = quantized_data_vec[data_index / 2];
1441        var scale_indices = data_indices;
1442        let quantize_axis_index = ${l.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
1443        ${l.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
1444        var scale = ${l.getByIndices("scale_indices")};
1445        ${c?`
1446              let zero_point_indices = scale_indices;
1447              let zero_point_offset = ${c.indicesToOffset("zero_point_indices")};
1448              let zero_point_index = zero_point_offset % 8;
1449              let packed_4bit_zero_points = ${c.getByOffset("zero_point_offset / 8")};
1450              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
1451              let zero_point_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
1452              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
1453        let dequantized_data = ${tl(d)}(quantized_data - zero_point) * scale;
1454        ${h.setByOffset("global_idx","dequantized_data")};
1455    }`}}})(t.inputs,r))},iZ=t=>e7({blockSize:t.blockSize,gatherAxis:t.gatherAxis,quantizeAxis:t.quantizeAxis})}),nQ=W(()=>{nd(),ny(),ng(),n_(),iX=t=>e7({axis:t.axis}),iY=(t,r)=>{(t=>{if(!t||2!==t.length)throw Error("GatherElements requires 2 inputs.");if(t[0].dims.length<1)throw Error("GatherElements requires that the data input be rank >= 1.");if(t[0].dims.length!==t[1].dims.length)throw Error(`GatherElements requires that the data input and
1456                     indices input tensors be of same rank.`)})(t.inputs),t.compute(((t,r)=>{let i=t[0].dims,a=t[0].dataType,n=i.length,s=t[1].dims,o=t[1].dataType,u=tt.normalizeAxis(r.axis,n),l=i[u],d=s.slice(0),p=tt.size(d),c=ty("input",a,n),h=ty("indicesInput",o,s.length),f=t_("output",a,d.length),m=[{type:12,data:p},{type:6,data:l},{type:12,data:u}];return m.push(...td(i,s,d)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:m}),getShaderSource:t=>`
1457      ${t.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(c,h,f)}
1458      ${t.mainStart()}
1459      ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
1460
1461      let outputIndices = ${f.offsetToIndices("global_idx")};
1462
1463      var idx = ${h.getByOffset("global_idx")};
1464      if (idx < 0) {
1465        idx = idx + uniforms.axisDimLimit;
1466      }
1467      var inputIndices = ${c.type.indices}(outputIndices);
1468      ${c.indicesSet("inputIndices","uniforms.axis","u32(idx)")};
1469      let value = ${c.getByIndices("inputIndices")};
1470
1471      ${f.setByOffset("global_idx","value")};
1472  }`}})(t.inputs,r))}}),nZ=W(()=>{nd(),ny(),n_(),iJ=t=>({transA:t.transA,transB:t.transB,alpha:t.alpha,beta:t.beta,cacheKey:`${t.transA};${t.transB};${1===t.alpha}`}),i0=(t,r)=>{(t=>{if(!t)throw Error("Input is missing");if(t.length<2||t.length>3)throw Error("Invaid input number.");if(3===t.length&&t[2].dims.length>2)throw Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||3===t.length&&t[0].dataType!==t[2].dataType)throw Error("Input types are mismatched")})(t.inputs),t.compute(((t,r)=>{let i=t[0].dims.slice(),a=t[1].dims.slice(),[n,s,o]=ti.getShapeOfGemmResult(i,r.transA,a,r.transB,3===t.length?t[2].dims:void 0),u=[n,s];if(!u)throw Error("Can't use gemm on the given tensors");let l=Math.ceil(s/16),d=Math.ceil(n/16),p=(tt.size(u),[{type:12,data:l},{type:12,data:n},{type:12,data:s},{type:12,data:o},{type:1,data:r.alpha},{type:1,data:r.beta}]),c=["type","type"];return 3===t.length&&(p.push(...td(t[2].dims)),c.push("rank")),p.push(...td(u)),{name:"GemmShared",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:u,dataType:t[0].dataType}],dispatchGroup:{x:l*d},programUniforms:p}),getShaderSource:i=>{let a=ty("a",t[0].dataType,t[0].dims),n=ty("b",t[1].dataType,t[1].dims),s=null,o=[a,n];3===t.length&&(s=ty("c",t[2].dataType,t[2].dims.length),o.push(s));let l=t_("output",t[0].dataType,u.length);o.push(l);let d="",p="";r.transA&&r.transB?(p=`
1473      var col = tile_row_start + local_id.x;
1474      var row = k_start + local_id.y;
1475      if (col < uniforms.M && row < uniforms.K) {
1476        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
1477      } else {
1478        tile_a[local_id.y][local_id.x] = ${a.type.value}(0);
1479      }
1480
1481      col = k_start + local_id.x;
1482      row = tile_col_start + local_id.y;
1483      if (col < uniforms.K && row < uniforms.N) {
1484        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
1485      } else {
1486        tile_b[local_id.y][local_id.x] = ${n.type.value}(0);
1487      }
1488      `,d="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):r.transA&&!r.transB?(p=`
1489      var col = tile_row_start + local_id.x;
1490      var row = k_start + local_id.y;
1491      if (col < uniforms.M && row < uniforms.K) {
1492        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
1493      } else {
1494        tile_a[local_id.y][local_id.x] = ${a.type.value}(0);
1495      }
1496
1497      col = tile_col_start + local_id.x;
1498      row = k_start + local_id.y;
1499      if (col < uniforms.N && row < uniforms.K) {
1500        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
1501      } else {
1502        tile_b[local_id.y][local_id.x] = ${n.type.value}(0);
1503      }
1504      `,d="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!r.transA&&r.transB?(p=`
1505      var col = k_start + local_id.x;
1506      var row = tile_row_start + local_id.y;
1507      if (col < uniforms.K && row < uniforms.M) {
1508        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
1509      } else {
1510        tile_a[local_id.y][local_id.x] = ${a.type.value}(0);
1511      }
1512
1513      col = k_start + local_id.x;
1514      row = tile_col_start + local_id.y;
1515      if (col < uniforms.K && row < uniforms.N) {
1516        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
1517      } else {
1518        tile_b[local_id.y][local_id.x] = ${n.type.value}(0);
1519      }
1520      `,d="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):r.transA||r.transB||(p=`
1521      var col = k_start + local_id.x;
1522      var row = tile_row_start + local_id.y;
1523      if (col < uniforms.K && row < uniforms.M) {
1524        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
1525      } else {
1526        tile_a[local_id.y][local_id.x] = ${a.type.value}(0);
1527      }
1528
1529      col = tile_col_start + local_id.x;
1530      row = k_start + local_id.y;
1531      if (col < uniforms.N && row < uniforms.K) {
1532        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
1533      } else {
1534        tile_b[local_id.y][local_id.x] = ${n.type.value}(0);
1535      }
1536      `,d="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let c=1===r.alpha?"":"value *= uniforms.alpha;";return`
1537  ${i.registerUniforms([{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}]).declareVariables(...o)}
1538  var<workgroup> tile_a: array<array<${a.type.storage}, 16>, 16>;

1539  var<workgroup> tile_b: array<array<${n.type.storage}, 16>, 16>;
1540  ${i.mainStart([16,16,1])}
1541    let tile_col_start = (workgroup_index % uniforms.num_tile_n) * 16;
1542    let tile_row_start = (workgroup_index / uniforms.num_tile_n) * 16;
1543    let num_tiles = (uniforms.K - 1) / 16 + 1;
1544    var k_start = 0u;
1545    var value = ${l.type.value}(0);
1546    for (var t: u32 = 0u; t < num_tiles; t++) {
1547      ${p}
1548      k_start = k_start + 16;
1549      workgroupBarrier();
1550
1551      for (var k: u32 = 0u; k < 16; k++) {
1552        ${d}
1553      }
1554      workgroupBarrier();
1555    }
1556
1557    ${c}
1558    let m = tile_row_start + local_id.y;
1559    let n = tile_col_start + local_id.x;
1560    ${null!=s?`let cOffset = ${s.broadcastedIndicesToOffset("vec2(m, n)",l)}; value += ${l.type.value}(uniforms.beta) * ${s.getByOffset("cOffset")};`:""}
1561    if (m < uniforms.M && n < uniforms.N) {
1562      output[m * uniforms.N + n] = value;
1563    }
1564  }`}}})(t.inputs,r))}}),nX=W(()=>{nd(),ny(),ng(),n_(),[i1,i2,i3,i4]=[0,1,2,3],i6=`
1565  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
1566    let cubic_alpha = -0.75f;
1567    let x_abs = abs(x);
1568    var coeffs: vec4<f32>;
1569    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
1570    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
1571    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
1572    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
1573    return coeffs;
1574  }
1575`,i8=(t,r)=>{(t=>{if(4!==t[0].dims.length)throw Error("only 4-D tensor is supported.");if(t[0].dims.length!==t[1].dims.length)throw Error("input dimensions must be equal to grid dimensions");if(t[0].dims.length-2!==t[1].dims[t[1].dims.length-1])throw Error(`last dimension of grid must be equal to ${t[0].dims.length-2}`);if(t[0].dims[0]!==t[1].dims[0])throw Error("grid batch size must match input batch size")})(t.inputs),t.compute(((t,r)=>{let i=ty("x",t[0].dataType,t[0].dims.length),a=[t[1].dims[0],t[1].dims[1],t[1].dims[2]],n=ty("grid",t[1].dataType,a.length,2),s=[t[0].dims[0],t[0].dims[1],t[1].dims[1],t[1].dims[2]];"NHWC"===r.format&&(s=[t[0].dims[0],t[1].dims[1],t[1].dims[2],t[0].dims[3]],[i1,i2,i3,i4]=[0,3,1,2]);let o=t_("output",t[0].dataType,s.length),u=i.type.value,l=[{type:12,data:tt.size(s)},...td(t[0].dims,a,s)];return{name:"GridSample",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:["type","type"]},getRunData:t=>{let r=tt.size(s);return{outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(r/64)},programUniforms:l}},getShaderSource:t=>{let a,s;return`
1576  ${t.registerUniform("output_size","u32").declareVariables(i,n,o)}
1577  ${i6}
1578  ${(t=>`
1579  fn gs_bicubic_interpolate(p: mat4x4<${t}>, x: f32, y: f32) -> ${t} {
1580    var v: vec4<f32>;
1581    var coeffs = gs_get_cubic_coeffs(x);
1582    for (var i = 0; i < 4; i++) {
1583      v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3];
1584    }
1585    coeffs = gs_get_cubic_coeffs(y);
1586    let pixel = ${t}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]);
1587    return pixel;
1588  }
1589`)(u)}
1590  ${a=r,`
1591  fn gs_denormalize(n: f32, length: i32) -> f32 {
1592    ${0===a.alignCorners?`
1593    // alignCorners: false => [-1, 1] to [-0.5, length - 0.5]
1594    return ((n + 1.0) * f32(length) - 1.0) / 2.0;
1595    `:`
1596    // alignCorners: true => [-1, 1] to [0, length - 1]
1597    return (n + 1.0) / 2.0 * (f32(length - 1));
1598    `}
1599  }
1600`}
1601  ${s=r,`
1602  ${"reflection"===s.paddingMode?`
1603      fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 {
1604        var dx = 0.0;
1605        var fx = f32(x);
1606        let range = x_max - x_min;
1607        if (fx < x_min) {
1608          dx = x_min - fx;
1609          let n = u32(dx / range);
1610          let r = dx - f32(n) * range;
1611          if (n % 2 == 0) {
1612            fx = x_min + r;
1613          } else {
1614            fx = x_max - r;
1615          }
1616        } else if (fx > x_max) {
1617          dx = fx - x_max;
1618          let n = u32(dx / range);
1619          let r = dx - f32(n) * range;
1620          if (n % 2 == 0) {
1621            fx = x_max - r;
1622          } else {
1623            fx = x_min + r;
1624          }
1625        }
1626        return u32(fx);
1627      }`:""}
1628`}
1629  ${((t,r,i)=>`
1630  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${r} {
1631     var pixel = ${r}(0);
1632     var indices = vec4<u32>(0);
1633     indices[${i1}] = batch;
1634     indices[${i2}] = channel;`+(()=>{switch(i.paddingMode){case"zeros":return`
1635          if (r >= 0 && r < H && c >=0 && c < W) {
1636            indices[${i3}] = u32(r);
1637            indices[${i4}] = u32(c);
1638          }
1639        `;case"border":return`
1640          indices[${i3}] = u32(clamp(r, 0, H - 1));
1641          indices[${i4}] = u32(clamp(c, 0, W - 1));
1642        `;case"reflection":return`
1643          indices[${i3}] = gs_reflect(r, border[1], border[3]);
1644          indices[${i4}] = gs_reflect(c, border[0], border[2]);
1645        `;default:throw Error(`padding mode ${i.paddingMode} is not supported`)}})()+`
1646    return ${t.getByIndices("indices")};
1647  }
1648`)(i,u,r)}
1649
1650  ${t.mainStart()}
1651    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
1652      let H_in = i32(uniforms.x_shape[${i3}]);
1653      let W_in = i32(uniforms.x_shape[${i4}]);
1654
1655      ${0===r.alignCorners?`
1656      let x_min = -0.5;
1657      let x_max = f32(W_in) - 0.5;
1658      let y_min = -0.5;
1659      let y_max = f32(H_in) - 0.5;
1660      `:`
1661      let x_min = 0.0;
1662      let x_max = f32(W_in) - 1.0;
1663      let y_min = 0.0;
1664      let y_max = f32(H_in) - 1.0;
1665      `};
1666      let border = vec4<f32>(x_min, y_min, x_max, y_max);
1667
1668      let indices = ${o.offsetToIndices("global_idx")};
1669      var grid_indices = vec3<u32>(indices[${i1}], indices[${i3}], indices[${i4}]);
1670      let nxy = ${n.getByIndices("grid_indices")};
1671      var x = gs_denormalize(f32(nxy[0]), W_in);
1672      var y = gs_denormalize(f32(nxy[1]), H_in);
1673
1674      ${((t,r,i)=>(()=>{switch(i.mode){case"nearest":return`
1675          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${i1}], indices[${i2}], border);
1676        `;case"bilinear":return`
1677          let x1 = i32(floor(x));
1678          let y1 = i32(floor(y));
1679          let x2 = x1 + 1;
1680          let y2 = y1 + 1;
1681
1682          let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${i1}], indices[${i2}], border);
1683          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${i1}], indices[${i2}], border);
1684          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${i1}], indices[${i2}], border);
1685          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${i1}], indices[${i2}], border);
1686
1687          let dx2 = ${r}(f32(x2) - x);
1688          let dx1 = ${r}(x - f32(x1));
1689          let dy2 = ${r}(f32(y2) - y);
1690          let dy1 = ${r}(y - f32(y1));
1691          let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22);
1692        `;case"bicubic":return`
1693          let x0 = i32(floor(x)) - 1;
1694          let y0 = i32(floor(y)) - 1;
1695          var p: mat4x4<${r}>;
1696          for (var h = 0; h < 4; h++) {
1697            for (var w = 0; w < 4; w++) {

1698              p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${i1}], indices[${i2}], border);
1699            }
1700          }
1701
1702          let dx = x - f32(x0 + 1);
1703          let dy = y - f32(y0 + 1);
1704          let result = gs_bicubic_interpolate(p, dx, dy);
1705        `;default:throw Error(`mode ${i.mode} is not supported`)}})()+`${t.setByOffset("global_idx","result")}`)(o,u,r)}
1706  }`}}})(t.inputs,r))},i5=t=>e7({alignCorners:t.align_corners,mode:t.mode,paddingMode:t.padding_mode,format:t.format})}),nY=W(()=>{nd(),ny(),ng(),nf(),nx(),n_(),n$(),i7=(t,r)=>t.length>r&&t[r].dims.length>0?t[r]:void 0,i9=t=>e7({...t}),ae=e7({perm:[0,2,1,3]}),at=(t,r,i,a,n,s,o,u)=>{let l=s;if(!(o&&tt.size(o.dims)>0))return 3===s.dims.length&&(l=s.reshape([r,a,i,n])),1===i||1===a?l:t.compute(tS(l,ae.perm),{inputs:[l],outputs:[-1]})[0];if(1===a)throw Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=(l=((t,r,i,a,n,s,o)=>{let u=[a,n,s],l=tt.size(u),d=[{type:12,data:l},{type:12,data:o},{type:12,data:s}];return t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:u,dataType:r.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:t=>{let a=t_("qkv_with_bias",r.dataType,u),n=ty("qkv",r.dataType,u),s=ty("bias",i.dataType,u);return`
1707  ${t.registerUniforms([{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}]).declareVariables(n,s,a)}
1708  ${t.mainStart()}
1709    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
1710    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;
1711
1712    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
1713  }`}},{inputs:[r,i],outputs:[-1]})[0]})(t,s,o,r,a,i*n,u)).reshape([r,a,i,n]),1===i||1===a?l:t.compute(tS(l,ae.perm),{inputs:[l],outputs:[-1]})[0]},ar=(t,r)=>{let i=((t,r)=>{let i,a=t[0],n=i7(t,1),s=i7(t,2),o=i7(t,3),u=i7(t,4),l=i7(t,5),d=i7(t,6),p=i7(t,7);if(3!==a.dims.length&&5!==a.dims.length)throw Error("Input query is expected to have 3 or 5 dimensions");let c=a.dims[0],h=a.dims[1],f=3===a.dims.length?a.dims[2]:r.numHeads*a.dims[4],m=h,g=0,y=0,_=Math.floor(f/r.numHeads);if(d&&p&&tt.size(d.dims)&&tt.size(p.dims)){if(4!==d.dims.length)throw Error('Input "past_key" is expected to have 4 dimensions');if(d.dims[0]!==c||d.dims[1]!==r.numHeads||d.dims[3]!==_)throw Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(p.dims[0]!==c||p.dims[1]!==r.numHeads||p.dims[3]!==_)throw Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(d.dims[2]!==p.dims[2])throw Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(4!==p.dims.length)throw Error('Input "past_value" is expected to have 4 dimensions');g=d.dims[2],y=d.dims[2]}else if(d&&tt.size(d.dims)||p&&tt.size(p.dims))throw Error('Input "past_key" and "past_value" shall be both present or both absent');if(n&&tt.size(n.dims)>0){if(3!==a.dims.length)throw Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(a.dims[0]!==n.dims[0])throw Error('Input "query" and "key" shall have same dim 0 (batch size)');if(3===n.dims.length){if(n.dims[2]!==a.dims[2])throw Error('Input "query" and "key" shall have same dim 2 (hidden_size)');i=2,m=n.dims[1]}else if(5===n.dims.length){if(n.dims[2]!==r.numHeads||2!==n.dims[3]||n.dims[4]!==_)throw Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(s)throw Error('Expect "value" be none when "key" has packed kv format.');i=5,m=n.dims[1]}else{if(n.dims[1]!==r.numHeads||n.dims[3]!==_)throw Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');i=0,m=n.dims[2]}}else{if(5!==a.dims.length)throw Error('Input "query" is expected to have 5 dimensions when key is empty');if(a.dims[2]!==r.numHeads||3!==a.dims[3])throw Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');i=3}if(o&&tt.size(o.dims)>0){if(1!==o.dims.length)throw Error('Input "bias" is expected to have 1 dimension');

1713if(n&&5===n.dims.length&&2===n.dims[3])throw Error("bias is not allowed for packed kv.")}let $=g+m,b=0;if(u&&tt.size(u.dims)>0){b=8;let t=u.dims;throw 1===t.length?t[0]===c?b=1:t[0]===3*c+2&&(b=3):2===t.length&&t[0]===c&&t[1]===$&&(b=5),8===b?Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):Error("Mask not supported")}let v=!1,w=f;if(s&&tt.size(s.dims)>0){if(3!==s.dims.length&&4!==s.dims.length)throw Error('Input "value" is expected to have 3 or 4 dimensions');if(a.dims[0]!==s.dims[0])throw Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(3===s.dims.length){if(m!==s.dims[1])throw Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');w=s.dims[2]}else{if(m!==s.dims[2])throw Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');w=s.dims[1]*s.dims[3],v=!0}}if(u&&tt.size(u.dims)>0)throw Error("Key padding mask is not supported");if(l&&tt.size(l.dims)>0){if(4!==l.dims.length)throw Error('Input "attention_bias" is expected to have 4 dimensions');if(l.dims[0]!==c||l.dims[1]!==r.numHeads||l.dims[2]!==h||l.dims[3]!==$)throw Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:c,sequenceLength:h,pastSequenceLength:g,kvSequenceLength:m,totalSequenceLength:$,maxSequenceLength:y,inputHiddenSize:0,hiddenSize:f,vHiddenSize:w,headSize:_,vHeadSize:Math.floor(w/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:b,scale:r.scale,broadcastResPosBias:!1,passPastInKv:v,qkvFormat:i}})(t.inputs,r),a=t.inputs[0],n=i7(t.inputs,1),s=i7(t.inputs,2),o=i7(t.inputs,3),u=i7(t.inputs,4),l=i7(t.inputs,5),d=i7(t.inputs,6),p=i7(t.inputs,7);if(5===a.dims.length)throw Error("Packed QKV is not implemented");if(n?.dims.length===5)throw Error("Packed KV is not implemented");let c=n&&s&&4===n.dims.length&&4===s.dims.length,h=at(t,i.batchSize,i.numHeads,i.sequenceLength,i.headSize,a,o,0);if(c)return t9(t,h,n,s,u,void 0,d,p,l,i);if(!n||!s)throw Error("key and value must be provided");let f=at(t,i.batchSize,i.numHeads,i.kvSequenceLength,i.headSize,n,o,i.hiddenSize),m=at(t,i.batchSize,i.numHeads,i.kvSequenceLength,i.vHeadSize,s,o,2*i.hiddenSize);t9(t,h,f,m,u,void 0,d,p,l,i)}}),nJ=W(()=>{nd(),ny(),ng(),n_(),ai=(t,r)=>{let i=t[0].dims,a=tt.size(i),n=t[0].dataType,s=tt.normalizeAxis(r.axis,i.length),o=Array(r.numOutputs),u=ty("input",n,i.length),l=Array(r.numOutputs),d=[],p=[],c=0,h=[{type:12,data:a}];for(let a=0;a<r.numOutputs;a++){c+=r.splitSizes[a],l[a]=c;let u=i.slice();u[s]=r.splitSizes[a],p.push(u),o[a]=t_(`output${a}`,n,u.length),d.push({dims:p[a],dataType:t[0].dataType})}return h.push({type:12,data:l},...td(i,...p)),{name:"Split",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getShaderSource:t=>`
1714  ${t.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(u,...o)}
1715  ${(t=>`
1716fn calculateOutputIndex(index: u32) -> u32 {
1717    for (var i: u32 = 0u; i < ${t}u; i += 1u ) {
1718    if (index < ${tm("uniforms.size_in_split_axis","i",t)}) {
1719        return i;
1720    }
1721    }
1722    return ${t}u;
1723}`)(l.length)}
1724  ${(t=>{let r=t.length,i=[];for(let a=0;a<r;++a){let n=t[a].setByIndices("indices","input[global_idx]");1===r?i.push(n):0===a?i.push(`if (output_number == ${a}u) { ${n} }`):a===r-1?i.push(`else { ${n} }`):i.push(`else if (output_number == ${a}) { ${n} }`)}return`
1725      fn writeBufferData(output_number: u32, indices: ${t[0].type.indices}, global_idx: u32) {
1726        ${i.join(`
1727`)}
1728      }`})(o)}
1729
1730  ${t.mainStart()}
1731    ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}
1732
1733    var indices = ${u.offsetToIndices("global_idx")};
1734    var index = ${u.indicesGet("indices",s)};
1735    let output_number = calculateOutputIndex(index);
1736    if (output_number != 0) {
1737      index -= ${tm("uniforms.size_in_split_axis","output_number - 1u",l.length)};
1738      ${u.indicesSet("indices",s,"index")};
1739    }
1740    writeBufferData(output_number, indices, global_idx);
1741  }`,getRunData:()=>({outputs:d,dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h})}},aa=(t,r)=>{(t=>{if(!t||t.length<1)throw Error("too few inputs")})(t.inputs);let i=1===t.inputs.length?r:((t,r)=>{let i=[],a=r.numOutputs;return t[1].dims[0]>

17410&&(t[1].getBigInt64Array().forEach(t=>i.push(Number(t))),a=i.length),e7({numOutputs:a,axis:r.axis,splitSizes:i})})(t.inputs,r);t.compute(ai(t.inputs,i),{inputs:[0]})},an=t=>{let r=t.axis,i=t.splitSizes,a=t.numOutputs<0?i.length:t.numOutputs;if(a!==i.length)throw Error("numOutputs and splitSizes lengh must be equal");return e7({axis:r,numOutputs:a,splitSizes:i})}}),n0=W(()=>{ng(),nx(),nY(),nJ(),n$(),as=e7({perm:[0,2,1,3]}),ao=(t,r,i)=>{let a=r,n=i.kvNumHeads;return 3===r.dims.length&&0!==i.kvSequenceLength&&(a=r.reshape([i.batchSize,i.kvSequenceLength,n,i.headSize]),a=t.compute(tS(a,as.perm),{inputs:[a],outputs:[-1]})[0]),a},au=(t,r)=>{let i=((t,r)=>{if(r.doRotary)throw Error("GroupQuerryAttention do_rotary attribute is not supported");if(r.doRotary&&t.length<=7)throw Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let i=t[0],a=t[1],n=t[2],s=t[3],o=t[4];if(-1!==r.localWindowSize)throw Error("Local attention is not supported");if(0!==r.softcap)throw Error("Softcap is not supported");if(0!==r.rotaryInterleaved)throw Error("Rotary interleaved is not supported");if(r.smoothSoftmax)throw Error("Smooth softmax is not supported");if(3!==i.dims.length&&5!==i.dims.length)throw Error("Input query is expected to have 3 or 5 dimensions");let u=i.dims[0],l=i.dims[1],d=3===i.dims.length?i.dims[2]:r.numHeads*i.dims[4],p=l,c=0,h=!a||0===a.dims.length,f=Math.floor(h?d/(r.numHeads+2*r.kvNumHeads):d/r.numHeads);h&&(d=f*r.numHeads);let m=s&&0!==s.dims.length,g=o&&0!==o.dims.length;if(m&&4===s.dims.length&&s.dims[0]===u&&s.dims[1]!==r.kvNumHeads&&s.dims[2]===r.kvNumHeads&&s.dims[3]===f)throw Error("BSNH pastKey/pastValue is not supported");if(m&&g){if(4!==s.dims.length)throw Error('Input "past_key" is expected to have 4 dimensions');if(4!==o.dims.length)throw Error('Input "past_value" is expected to have 4 dimensions');c=s.dims[2]}else if(m||g)throw Error('Input "past_key" and "past_value" shall be both present or both absent');let y=1;if(a&&a.dims.length>0){if(3!==i.dims.length)throw Error('Input "query" is expected to have 3 dimensions when key is given');if(a.dims.length<3||a.dims.length>5)throw Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(i.dims[0]!==a.dims[0])throw Error('Input "query" and "key" shall have same dim 0 (batch size)');if(3===a.dims.length){if(i.dims[2]%a.dims[2]!=0)throw Error('Dimension 2 of "query" should be a multiple of "key"');p=a.dims[1]}else if(5===a.dims.length){if(a.dims[2]!==r.numHeads||2!==a.dims[3]||a.dims[4]!==f)throw Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(n)throw Error('Expect "value" be none when "key" has packed kv format.');p=a.dims[1]}else{if(a.dims[1]!==r.numHeads||a.dims[3]!==f)throw Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');p=a.dims[2]}}else{if(3!==i.dims.length&&5!==i.dims.length)throw Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(5===i.dims.length&&(i.dims[2]!==r.numHeads||3!==i.dims[3]))throw Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');y=3}let _=!1,$=r.kvNumHeads?f*r.kvNumHeads:d;if(n&&n.dims.length>0){if(3!==n.dims.length&&4!==n.dims.length)throw Error('Input "value" is expected to have 3 or 4 dimensions');if(i.dims[0]!==n.dims[0])throw Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(3===n.dims.length){if(p!==n.dims[1])throw Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');$=n.dims[2]}else{if(p!==n.dims[2])throw Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');$=n.dims[1]*n.dims[3],_=!0}}let b=t.length>4?t[5]:void 0;if(b&&1!==b.dims.length&&b.dims[0]!==u)throw Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:u,sequenceLength:l,pastSequenceLength:c,kvSequenceLength:p,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:d,vHiddenSize:$,headSize:f,vHeadSize:Math.floor($/r.kvNumHeads),numHeads:r.numHeads,kvNumHeads:r.kvNumHeads,nReps:r.numHeads/r.kvNumHeads,pastPresentShareBuffer:!1,maskType:0,scale:r.scale,broadcastResPosBias:!1,passPastInKv:_,qkvFormat:y}})(t.inputs,r);if(5===t.inputs[0].dims.length)throw Error("Packed QKV is not implemented");if(t.inputs[1]?.dims.length===5)throw Error("Packed KV is not implemented");

1741let a=t.inputs[0],n=t.inputs[1]&&t.inputs[1].dims.length>0?t.inputs[1]:void 0,s=t.inputs[2]&&t.inputs[2].dims.length>0?t.inputs[2]:void 0,o=t.inputs[3]&&0!==t.inputs[3].dims.length?t.inputs[3]:void 0,u=t.inputs[4]&&0!==t.inputs[4].dims.length?t.inputs[4]:void 0,l=t.inputs.length>4?t.inputs[5]:void 0,d=t.inputs.length>5?t.inputs[6]:void 0,p=i.kvNumHeads?i.kvNumHeads:i.numHeads,c=e7({axis:2,numOutputs:3,splitSizes:[i.numHeads*i.headSize,p*i.headSize,p*i.headSize]}),[h,f,m]=n||s?[a,n,s]:t.compute(ai([a],c),{inputs:[a],outputs:[-1,-1,-1]}),g=at(t,i.batchSize,i.numHeads,i.sequenceLength,i.headSize,h,void 0,0);t9(t,g,ao(t,f,i),ao(t,m,i),void 0,void 0,o,u,void 0,i,l,d)}}),n1=W(()=>{nd(),ny(),n$(),n_(),al=(t,r,i,a,n,s,o,u)=>{let l=tp(s),d=1===l?"f32":`vec${l}f`,p=1===l?"vec2f":`mat2x${l}f`,c=n*o,h=64;1===c&&(h=256);let f=[n,o,s/l],m=[n,o,2],g=[];return g.push(...td(f,m)),t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${u};${h}`,inputDependencies:["rank","type","type"]},getRunData:()=>({outputs:[{dims:m,dataType:1}],dispatchGroup:{x:c},programUniforms:g}),getShaderSource:t=>{let n=ty("x",r.dataType,3,l),s=[n,ty("scale",i.dataType,i.dims),ty("bias",a.dataType,a.dims),t_("output",1,3,2)];return`
1742  var<workgroup> workgroup_shared : array<${p}, ${h}>;
1743  const workgroup_size = ${h}u;
1744  ${t.declareVariables(...s)}
1745  ${t.mainStart(h)}
1746    let batch = workgroup_index / uniforms.x_shape[1];
1747    let channel = workgroup_index % uniforms.x_shape[1];
1748    let hight = uniforms.x_shape[2];
1749    // initialize workgroup memory
1750    var sum = ${d}(0);
1751    var squared_sum = ${d}(0);
1752    for (var h = local_idx; h < hight; h += workgroup_size) {
1753      let value = ${d}(${n.get("batch","channel","h")});
1754      sum += value;
1755      squared_sum += value * value;
1756    }
1757    workgroup_shared[local_idx] = ${p}(sum, squared_sum);
1758    workgroupBarrier();
1759
1760    for (var currSize = workgroup_size >> 1;  currSize > 0; currSize = currSize >> 1) {
1761      if (local_idx < currSize) {
1762        workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize];
1763      }
1764      workgroupBarrier();
1765    }
1766    if (local_idx == 0) {
1767      let sum_final = ${tf("workgroup_shared[0][0]",l)} / f32(hight * ${l});
1768      let squared_sum_final = ${tf("workgroup_shared[0][1]",l)} / f32(hight * ${l});
1769
1770      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${u}));
1771      let channel_scale = inv_std_dev * f32(scale[channel]);
1772      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
1773      output[workgroup_index] = vec2f(channel_scale, channel_shift);
1774    }
1775  }`}},{inputs:[r,i,a],outputs:[-1]})[0]},ad=(t,r)=>{"NHWC"===r.format?((t,r,i)=>{let a=r[0].dims,n=a[0],s=a[a.length-1],o=tt.sizeFromDimension(a,1)/s,u=tp(s),l=tt.size(a)/u,d=[{type:12,data:o},{type:12,data:Math.floor(s/u)}],p=!1,c=[0,a.length-1];for(let t=0;t<a.length-2;t++)p=p||1!==a[t+1],c.push(t+1);let h=(p=p&&1!==a[a.length-1])?t.compute(tS(t.inputs[0],c),{inputs:[t.inputs[0]],outputs:[-1]})[0]:t.inputs[0].reshape(Array.from({length:a.length},(t,r)=>a[c[r]])),f=al(t,h,r[1],r[2],n,o,s,i.epsilon);t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${u}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:t=>{let i=tu(r[0].dataType),n=1===u?"vec2f":`mat${u}x2f`,s=t=>{let r=0===t?"x":"y",a=1===u?"f32":`vec${u}f`;switch(u){case 1:return`${i}(${a}(scale.${r}))`;case 2:return`vec2<${i}>(${a}(scale[0].${r}, scale[1].${r}))`;case 4:return`vec4<${i}>(${a}(scale[0].${r}, scale[1].${r}, scale[2].${r}, scale[3].${r}))`;default:throw Error(`Not supported compoents ${u}`)}},o=ty("input",r[0].dataType,r[0].dims,u),l=t_("output",r[0].dataType,a,u);return`
1776  @group(0) @binding(0) var<storage, read> input : array<${o.type.storage}>;
1777  @group(0) @binding(1) var<storage, read> scale_input : array<${n}>;
1778  @group(0) @binding(2) var<storage, read_write> output : array<${l.type.storage}>;
1779  struct Uniforms {H: u32, C : u32};
1780  @group(0) @binding(3) var<uniform> uniforms: Uniforms;
1781
1782  ${t.mainStart()}

1783    let current_image_number = global_idx / (uniforms.C * uniforms.H);
1784    let current_channel_number = global_idx % uniforms.C;
1785
1786    let scale_offset = current_image_number * uniforms.C + current_channel_number;
1787    let scale = scale_input[scale_offset];
1788    output[global_idx] = fma(input[global_idx], ${s(0)}, ${s(1)});
1789  }`}},{inputs:[r[0],f]})})(t,t.inputs,r):((t,r,i)=>{let a=r[0].dims,n=a[0],s=a[1],o=tt.sizeFromDimension(a,2),u=tp(o),l=tt.size(a)/u,d=al(t,r[0],r[1],r[2],n,o,s,i.epsilon),p=[n,s,o/u],c=[n,s];t.compute({name:"InstanceNormalization",shaderCache:{hint:`${u}`,inputDependencies:["type","none"]},getRunData:()=>({outputs:[{dims:a,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:[{type:12,data:l},...td(p,c,p)]}),getShaderSource:t=>{let i=ty("x",r[0].dataType,p.length,u),a=ty("scale_shift",1,c.length,2),n=t_("output",r[0].dataType,p.length,u),s=[i,a,n];return`
1790  ${t.registerUniform("output_size","u32").declareVariables(...s)}
1791  ${t.mainStart()}
1792  ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
1793      let outputIndices = ${n.offsetToIndices("global_idx")};
1794      let batch = outputIndices[0];
1795      let channel = outputIndices[1];
1796      let scale_shift = ${a.getByIndices("vec2<u32>(batch, channel)")};
1797      let value = ${i.getByOffset("global_idx")} * ${n.type.value}(scale_shift.x) + ${n.type.value}(scale_shift.y);
1798      ${n.setByOffset("global_idx","value")};
1799  }`}},{inputs:[r[0],d]})})(t,t.inputs,r)}}),n2=W(()=>{nd(),ny(),n_(),ap=(t,r)=>{(t=>{if(!t||t.length<2)throw Error("layerNorm requires at least 2 inputs.")})(t.inputs),t.compute(((t,r,i)=>{let a=r.simplified,n=t[0].dims,s=t[1],o=!a&&t[2],u=tt.normalizeAxis(r.axis,n.length),l=tt.sizeToDimension(n,u),d=tt.sizeFromDimension(n,u),p=tt.size(s.dims),c=o?tt.size(o.dims):0;if(p!==d||o&&c!==d)throw Error(`Size of X.shape()[axis:] == ${d}.
1800       Size of scale and bias (if provided) must match this.
1801       Got scale size of ${p} and bias size of ${c}`);let h=[];for(let t=0;t<n.length;++t)t<u?h.push(n[t]):h.push(1);let f=tp(d),m=["type","type"],g=[{type:12,data:l},{type:1,data:d},{type:12,data:Math.floor(d/f)},{type:1,data:r.epsilon}];o&&m.push("type");let y=i>1,_=i>2,$=[{dims:n,dataType:t[0].dataType}];return y&&$.push({dims:h,dataType:1}),_&&$.push({dims:h,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${f};${i};${a}`,inputDependencies:m},getRunData:()=>({outputs:$,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:g}),getShaderSource:r=>{let i=tu(t[0].dataType),u=[ty("x",t[0].dataType,t[0].dims,f),ty("scale",s.dataType,s.dims,f)];return o&&u.push(ty("bias",o.dataType,o.dims,f)),u.push(t_("output",t[0].dataType,n,f)),y&&u.push(t_("mean_data_output",1,h)),_&&u.push(t_("inv_std_output",1,h)),`
1802  ${r.registerUniforms([{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}]).declareVariables(...u)}
1803  ${r.mainStart()}
1804    ${r.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
1805    let offset = global_idx * uniforms.norm_size_vectorized;
1806    var mean_vector = ${tc("f32",f)};
1807    var mean_square_vector = ${tc("f32",f)};
1808
1809    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
1810      let value = ${th(i,f,"x[h + offset]")};
1811      mean_vector += value;
1812      mean_square_vector += value * value;
1813    }
1814    let mean = ${tf("mean_vector",f)} / uniforms.norm_size;
1815    let inv_std_dev = inverseSqrt(${tf("mean_square_vector",f)} / uniforms.norm_size ${a?"":"- mean * mean"} + uniforms.epsilon);
1816
1817    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
1818      let f32input = ${th(i,f,"x[j + offset]")};
1819      let f32scale = ${th(i,f,"scale[j]")};
1820      output[j + offset] = ${u[0].type.value}((f32input ${a?"":"- mean"}) * inv_std_dev * f32scale
1821        ${o?`+ ${th(i,f,"bias[j]")}`:""}
1822      );
1823    }
1824
1825    ${y?"mean_data_output[global_idx] = mean":""};
1826    ${_?"inv_std_output[global_idx] = inv_std_dev":""};
1827  }`}}})(t.inputs,r,t.outputCount))}}),n3=W(()=>{ny(),nR(),nB(),ac=t=>{(t=>{if(!t||2!==t.length)throw Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw Error("shared dimension does not match.")})(t.inputs);

1827let r=te.calcShape(t.inputs[0].dims,t.inputs[1].dims,!0);if(!r)throw Error("Can't use matmul on the given tensors");let i=r[r.length-1],a=t.inputs[0].dims[t.inputs[0].dims.length-1];if(i<8&&a<8)t.compute(ii(t.inputs,{activation:""},r));else{let n=r[r.length-2],s=tt.size(t.inputs[0].dims.slice(0,-2)),o=tt.size(t.inputs[1].dims.slice(0,-2));if(1!==s&&1===n&&1===o){let n=t.inputs[0].reshape([1,s,a]),o=t.inputs[1].reshape([1,a,i]),u=[1,s,i],l=[n,o];t.compute(iu(l,{activation:""},r,u),{inputs:l})}else t.compute(iu(t.inputs,{activation:""},r))}}}),n4=W(()=>{nd(),ny(),ng(),n_(),ah=(t,r)=>{((t,r)=>{if(t.length<3||t.length>4)throw Error("MatMulNBits requires 3 or 4 inputs");let i=t[0],a=i.dims.length;if(i.dims[a-1]!==r.k)throw Error("The last dim of input shape does not match the k value");let n=Math.floor((r.k+r.blockSize-1)/r.blockSize),s=r.blockSize/8*r.bits,o=t[1];if(!tt.areEqual(o.dims,[r.n,n,s]))throw Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let u=t[2].dims;if(tt.size(u)!==r.n*n)throw Error("scales input size error.");if(4===t.length){let i=t[3].dims,a=r.bits>4?r.n*n:r.n*Math.floor((n+1)/2);if(tt.size(i)!==a)throw Error("zeroPoints input size error.")}})(t.inputs,r),32===r.blockSize&&t.adapterInfo.isVendor("intel")&&t.adapterInfo.isArchitecture("gen-12lp")?t.compute(((t,r)=>{let i=t[0].dims,a=i.length,n=i[a-2],s=r.k,o=r.n,u=i.slice(0,a-2),l=tt.size(u),d=t[1].dims[2]/4,p=t[0].dataType,c=tp(r.k),h=tp(d),f=u.concat([n,o]),m=o%8==0?8:o%4==0?4:1,g=128/m,y=g*h*8,_=y/c,$=y/r.blockSize,b=tt.size(f)/m,v=[],w=[l,n,s/c],x=tt.convertShape(t[1].dims).slice();x.splice(-1,1,d/h),v.push(...td(w)),v.push(...td(x)),v.push(...td(t[2].dims)),4===t.length&&v.push(...td(tt.convertShape(t[3].dims)));let S=[l,n,o];return v.push(...td(S)),{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${r.blockSize};${c};${h};${g};${m}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:f,dataType:p}],dispatchGroup:{x:b},programUniforms:v}),getShaderSource:i=>{let a=w.length,n=ty("a",t[0].dataType,a,c),s=ty("b",12,x.length,h),o=ty("scales",t[2].dataType,t[2].dims.length),u=[n,s,o],l=4===t.length?ty("zero_points",12,t[3].dims.length):void 0;l&&u.push(l);let d=S.length,p=t_("output",t[0].dataType,d),f=tu(t[0].dataType);return`
1828        var<workgroup> sub_a: array<${n.type.value}, ${_}>;
1829        var<workgroup> inter_results: array<array<${p.type.value}, ${g}>, ${m}>;
1830        ${i.declareVariables(...u,p)}
1831        ${i.mainStart([g,m,1])}
1832          let output_indices = ${p.offsetToIndices(`workgroup_index * ${m}`)};
1833          let col = output_indices[2];
1834          let row = output_indices[1];
1835          let batch = output_indices[0];
1836          let n_blocks_per_col = uniforms.b_shape[1];
1837          let num_tiles =  (n_blocks_per_col - 1) / ${$} + 1;
1838
1839          // Loop over shared dimension.
1840          for (var tile: u32 = 0; tile < num_tiles; tile += 1) {
1841            let a_col_start = tile * ${_};
1842            // load one tile A data into shared memory.
1843            for (var a_offset = local_idx; a_offset < ${_}; a_offset += 128)
1844            {
1845              let a_col = a_col_start + a_offset;
1846              if (a_col < uniforms.a_shape[2])
1847              {
1848                sub_a[a_offset] = ${n.getByIndices(`${n.type.indices}(batch, row, a_col)`)};
1849              } else {
1850                sub_a[a_offset] = ${n.type.value}(0);
1851              }
1852            }
1853            workgroupBarrier();
1854
1855            // each thread process one block
1856            let b_row = col + local_id.y;
1857            let block = tile * ${$} + local_id.x;
1858            ${l?`
1859            let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2;
1860            let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u);
1861            let zero_point_word_index = zero_point_byte_count >> 0x2u;
1862            let zero_point_byte_offset = zero_point_byte_count & 0x3u;
1863            let zero_point_nibble_offset: u32 = block & 0x1u;
1864            let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
1865            let zero_point_word = ${l.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
1866            let zero_point = ${f}((zero_point_word) & 0xFu);`:`
1867            // The default zero point is 8 for unsigned 4-bit quantization.
1868            let zero_point = ${f}(8);`}
1869            let scale = ${o.getByOffset("b_row * n_blocks_per_col + block")};
1870            let b_data = ${s.getByIndices(`${s.type.indices}(b_row, block, 0)`)};
1871            var word_offset = local_id.x * ${r.blockSize/c};
1872            for (var i: u32 = 0; i < ${h}; i++) {
1873              ${(()=>{switch(c){case 1:return`
1874          let a_data0 = vec4<${f}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
1875          let a_data1 = vec4<${f}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
1876          let a_data0 = vec4<${f}>(sub_a[word_offset], sub_a[word_offset + 1]);
1877          let a_data1 = vec4<${f}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
1878          let a_data0 = sub_a[word_offset];
1879          let a_data1 = sub_a[word_offset + 1];`;default:throw Error(`${c}-component is not supported.`)}})()}
1880              let b_value = ${1===h?"b_data":"b_data[i]"};
1881              let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu);
1882              let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu);
1883              let b_quantized_values = mat2x4<${f}>(${Array.from({length:4},(t,r)=>`${f}(b_value_lower[${r}]), ${f}(b_value_upper[${r}])`).join(", ")});
1884              let b_dequantized_values = (b_quantized_values - mat2x4<${f}>(${Array(8).fill("zero_point").join(",")})) * scale;
1885              inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(t,r)=>`dot(a_data${r}, b_dequantized_values[${r}])`).join(" + ")};
1886              word_offset += ${8/c};
1887            }
1888            workgroupBarrier();
1889          }
1890
1891          if (local_idx < ${m}) {
1892            var output_value: ${p.type.value}

1892 = ${p.type.value}(0);
1893            for (var b = 0u; b < ${g}; b++) {
1894              output_value += inter_results[local_idx][b];
1895            }
1896            if (col + local_idx < uniforms.output_shape[2])
1897            {
1898              ${p.setByIndices(`${p.type.indices}(batch, row, col + local_idx)`,"output_value")}
1899            }
1900          }
1901        }`}}})(t.inputs,r)):t.compute(((t,r)=>{let i=t[0].dims,a=i.length,n=i[a-2],s=r.k,o=r.n,u=i.slice(0,a-2),l=tt.size(u),d=t[1].dims[2]/4,p=t[0].dataType,c=tp(r.k),h=tp(d),f=tp(o),m=u.concat([n,o]),g=n>1&&o/f%2==0?2:1,y=tt.size(m)/f/g,_=[],$=[l,n,s/c],b=tt.convertShape(t[1].dims).slice();b.splice(-1,1,d/h),_.push(...td($)),_.push(...td(b)),_.push(...td(t[2].dims)),4===t.length&&_.push(...td(tt.convertShape(t[3].dims)));let v=[l,n,o/f];return _.push(...td(v)),{name:"MatMulNBits",shaderCache:{hint:`${r.blockSize};${r.bits};${c};${h};${f};${g};64`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:m,dataType:p}],dispatchGroup:{x:y},programUniforms:_}),getShaderSource:i=>{let a=$.length,n=ty("a",t[0].dataType,a,c),s=ty("b",12,b.length,h),o=ty("scales",t[2].dataType,t[2].dims.length),u=[n,s,o],l=4===t.length?ty("zero_points",12,t[3].dims.length):void 0;l&&u.push(l);let p=v.length,m=t_("output",t[0].dataType,p,f),y=tu(t[0].dataType),_=(()=>{switch(c){case 1:return`array<${y}, 8>`;case 2:return`mat4x2<${y}>`;case 4:return`mat2x4<${y}>`;default:throw Error(`${c}-component is not supported.`)}})();return`
1902        var<workgroup> workgroup_shared: array<${m.type.value}, ${64*g}>;
1903        ${i.declareVariables(...u,m)}
1904        ${i.mainStart([64,1,1])}
1905          let output_indices = ${m.offsetToIndices(`(global_idx / 64) * ${g}`)};
1906          let col = output_indices[2];
1907          let row = output_indices[1];
1908          let batch = output_indices[0];
1909          let nBlocksPerCol = uniforms.b_shape[1];
1910
1911          for (var block = local_id.x; block < nBlocksPerCol; block += 64) {
1912            //process one block
1913            var word_offset: u32 = block * ${r.blockSize/c};
1914            ${(()=>{let t=`
1915            var col_index = col * ${f};
1916            ${l?`
1917            let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2;
1918            var zero_point_byte_count: u32;
1919            var zero_point_word_index: u32;
1920            var zero_point_byte_offset: u32;
1921            let zero_point_nibble_offset: u32 = block & 0x1u;
1922            var zero_point_bits_offset: u32;
1923            var zero_point_word: u32;`:`
1924            // The default zero point is 8 for unsigned 4-bit quantization.
1925            let zero_point = ${y}(8);`}
1926            `;for(let r=0;r<f*g;r++)t+=`
1927            let scale${r} = ${o.getByOffset("col_index * nBlocksPerCol + block")};
1928            ${l?`
1929            zero_point_byte_count = col_index * zero_point_bytes_per_col + (block >> 0x1u);
1930            zero_point_word_index = zero_point_byte_count >> 0x2u;
1931            zero_point_byte_offset = zero_point_byte_count & 0x3u;
1932            zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
1933            zero_point_word = ${l.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
1934            let zero_point${r} = ${y}((zero_point_word) & 0xFu);`:""}
1935            col_index += 1;`;return t})()}
1936            for (var word: u32 = 0; word < ${d}; word += ${h}) {
1937              ${(()=>{let t=`col_index = col * ${f};`;for(let r=0;r<f*g;r++)t+=`
1938            let b${r}_data = ${s.getByIndices(`${s.type.indices}(col_index, block, word)`)};
1939            col_index += 1;`;return t+`
1940            var b_value: u32;
1941            let b_mask: u32 = 0x0F0F0F0Fu;
1942            var b_value_lower: vec4<u32>;
1943            var b_value_upper: vec4<u32>;
1944            var b_quantized_values: ${_};
1945            var b_dequantized_values: ${_};`})()}
1946              for (var i: u32 = 0; i < ${h}; i++) {
1947                ${(()=>{let t=`
1948          // reuse a data
1949            var input_offset = ${n.indicesToOffset(`${n.type.indices}(batch, row, word_offset)`)};
1950            var a_data: ${_};
1951            for (var j: u32 = 0; j < ${8/c}; j++) {
1952              a_data[j] = ${n.getByOffset("input_offset")};
1953              input_offset++;
1954            }
1955          `;

1955for(let r=0;r<f*g;r++)t+=`
1956            b_value = ${1===h?`b${r}_data`:`b${r}_data[i]`};
1957            b_value_lower = unpack4xU8(b_value & b_mask);
1958            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
1959            b_quantized_values = ${_}(${Array.from({length:4},(t,r)=>`${y}(b_value_lower[${r}]), ${y}(b_value_upper[${r}])`).join(", ")});
1960            b_dequantized_values = ${1===c?`${_}(${Array.from({length:8},(t,i)=>`(b_quantized_values[${i}] - ${l?`zero_point${r}`:"zero_point"}) * scale${r}`).join(", ")});`:`(b_quantized_values - ${_}(${Array(8).fill(`${l?`zero_point${r}`:"zero_point"}`).join(",")})) * scale${r};`};
1961            workgroup_shared[local_id.x * ${g} + ${Math.floor(r/f)}]${f>1?`[${r%f}]`:""} += ${Array.from({length:8/c},(t,r)=>`${1===c?`a_data[${r}] * b_dequantized_values[${r}]`:`dot(a_data[${r}], b_dequantized_values[${r}])`}`).join(" + ")};
1962          `;return t})()}
1963                word_offset += ${8/c};
1964              }
1965            }
1966          }
1967          workgroupBarrier();
1968
1969          if (local_id.x < ${g}) {
1970            var output_value: ${m.type.value} = ${m.type.value}(0);
1971            var workgroup_shared_offset: u32 = local_id.x;
1972            for (var b: u32 = 0u; b < 64u; b++) {
1973              output_value += workgroup_shared[workgroup_shared_offset];
1974              workgroup_shared_offset += ${g};
1975            }
1976            ${m.setByIndices(`${m.type.indices}(batch, row, col + local_id.x)`,"output_value")};
1977          }
1978        }`}}})(t.inputs,r))},af=t=>e7(t)}),n6=W(()=>{nd(),ny(),n_(),am=(t,r)=>{(t=>{if(!t||t.length<1)throw Error("Too few inputs");if(1!==t[0].dataType&&10!==t[0].dataType)throw Error("Input type must be float or float16.");if(t.length>=2){let r=2*t[0].dims.length===t[1].dims[0];if(4===t.length&&(r=2*t[3].dims[0]===t[1].dims[0]),!r)throw Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}})(t.inputs);let i=((t,r)=>{if(!(t.length>1))return r;{let i=t[1].getBigInt64Array(),a=t.length>=3&&t[2].data?10===t[2].dataType?t[2].getUint16Array()[0]:t[2].getFloat32Array()[0]:0,n=t[0].dims.length,s=new Int32Array(2*n).fill(0);if(t.length>=4){let r=t[3].getBigInt64Array();for(let t=0;t<r.length;t++)s[Number(r[t])]=Number(i[t]),s[Number(r[t])+n]=Number(i[t+r.length])}else i.forEach((t,r)=>s[Number(r)]=Number(t));let o=[];return s.forEach(t=>o.push(t)),{mode:r.mode,value:a,pads:o}}})(t.inputs,r);t.compute(((t,r)=>{let i=tt.padShape(t[0].dims.slice(),r.pads),a=t[0].dims,n=[{type:12,data:tt.size(i)},{type:6,data:r.pads}],s=t.length>=3&&t[2].data;return 0===r.mode&&n.push({type:s?t[2].dataType:1,data:r.value}),n.push(...td(t[0].dims,i)),{name:"Pad",shaderCache:{hint:`${r.mode}${s}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(tt.size(i)/64)},programUniforms:n}),getShaderSource:n=>{let o=t_("output",t[0].dataType,i.length),u=ty("x",t[0].dataType,a.length),l=u.type.value,d=((t,r,i)=>{switch(i.mode){case 0:return((t,r,i)=>{let a="";for(let n=r-1;n>=0;--n)a+=`
1979            k = i32(${t.indicesGet("indices",n)}) - ${tm("uniforms.pads",n,i)};
1980            if (k < 0) {
1981              break;
1982            }
1983            if (k >= i32(${tm("uniforms.x_shape",n,r)})) {
1984              break;
1985            }
1986            offset += k * i32(${tm("uniforms.x_strides",n,r)});
1987        `;return`
1988          value = ${t.type.value}(uniforms.constant_value);
1989          for (var i = 0; i < 1; i++) {
1990            var offset = 0;
1991            var k = 0;
1992            ${a}
1993            value = x[offset];
1994          }
1995      `})(t,r,i.pads.length);case 1:return((t,r,i)=>{let a="";for(let n=r-1;n>=0;--n)a+=`
1996                k = i32(${t.indicesGet("indices",n)}) - ${tm("uniforms.pads",n,i)};
1997                if (k < 0) {
1998                  k = -k;
1999                }
2000                {
2001                  let _2n_1 = 2 * (i32(${tm("uniforms.x_shape",n,r)}) - 1);
2002                  k = k % _2n_1;
2003                  if(k >= i32(${tm("uniforms.x_shape",n,r)})) {
2004                    k = _2n_1 - k;
2005                  }
2006                }
2007                offset += k * i32(${tm("uniforms.x_strides",n,r)});
2008            `;return`
2009              var offset = 0;
2010              var k = 0;
2011              ${a}
2012              value = x[offset];
2013          `})(t,r,i.pads.length);case 2:return((t,r,i)=>{let a="";for(let n=r-1;n>=0;--n)a+=`
2014                k = i32(${t.indicesGet("indices",n)}) - ${tm("uniforms.pads",n,i)};
2015                if (k < 0) {
2016                  k = 0;
2017                }
2018                if (k >= i32(${tm("uniforms.x_shape",n,r)})) {
2019                  k = i32(${tm("uniforms.x_shape",n,r)}) - 1;
2020                }
2021                offset += k * i32(${tm("uniforms.x_strides",n,r)});
2022            `;return`
2023              var offset = 0;
2024              var k = 0;
2025              ${a}
2026              value = x[offset];
2027          `})(t,r,i.pads.length);case 3:return((t,r,i)=>{let a="";for(let n=r-1;n>=0;--n)a+=`
2028                k = i32(${t.indicesGet("indices",n)}) - ${tm("uniforms.pads",n,i)};
2029                if (k < 0)  {
2030                  k += i32(${tm("uniforms.x_shape",n,r)}]);
2031                }
2032                if (k >= i32(${tm("uniforms.x_shape",n,r)})) {
2033                  k -= i32(${tm("uniforms.x_shape",n,r)});
2034                }
2035                offset += k * i32(${tm("uniforms.x_strides",n,r)});
2036            `;return`
2037              var offset = 0;
2038              var k = 0;
2039              ${a}
2040              value = x[offset];
2041          `})(t,r,i.pads.length);default:throw Error("Invalid mode")}})(o,a.length,r),p=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:r.pads.length}];return 0===r.mode&&p.push({name:"constant_value",type:s?l:"f32"}),`
2042            ${n.registerUniforms(p).declareVariables(u,o)}
2043            ${n.mainStart()}
2044            ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
2045
2046            let indices = ${o.offsetToIndices("global_idx")};
2047
2048            var value = ${l}(0);
2049            ${d}
2050            output[global_idx] = value;
2051        }`}}})(t.inputs,i),{inputs:[0]})}}),n8=W(()=>{eu(),nd(),ny(),n_(),ag=t=>{if(c.webgpu.validateInputContent&&(!t||1!==t.length))throw Error("Pool ops requires 1 input.")},ay=(t,r,i)=>{let a="NHWC"===r.format,n=t.dims.slice();a&&n.splice(1,0,n.pop());let s=Object.hasOwnProperty.call(r,"dilations"),o=r.kernelShape.slice(),u=r.strides.slice(),l=s?r.dilations.slice():[],d=r.pads.slice();tr.adjustPoolAttributes(i,n,o,u,l,d);let p=tr.computePoolOutputShape(i,n,u,l,o,d,r.autoPad),c=Object.assign({},r);s?Object.assign(c,{kernelShape:o,strides:u,pads:d,dilations:l,cacheKey:r.cacheKey}):Object.assign(c,{kernelShape:o,strides:u,pads:d,cacheKey:r.cacheKey});let h=p.slice();return h.push(h.splice(1,1)[0]),[c,a?h:p]},a_=(t,r)=>{let i="NHWC"===r.format,a=[{type:12,data:tt.size(t)}

vendor: 4,242 bytes, lines 2051-2121

2051,{type:12,data:tt.size(r.kernelShape)}],n=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(r.kernelShape.length<=2){let t=r.kernelShape[r.kernelShape.length-1],i=r.strides[r.strides.length-1],s=r.pads[r.pads.length/2-1],o=r.pads[r.pads.length-1],u=!!(s+o);a.push({type:12,data:t},{type:12,data:i},{type:12,data:s},{type:12,data:o}),n.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let l=!1;if(2===r.kernelShape.length){let t=r.kernelShape[r.kernelShape.length-2],i=r.strides[r.strides.length-2],s=r.pads[r.pads.length/2-2],o=r.pads[r.pads.length-2];l=!!(s+o),a.push({type:12,data:t},{type:12,data:i},{type:12,data:s},{type:12,data:o}),n.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,n,!0,u,l]}{if(i)throw Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let t=tt.computeStrides(r.kernelShape);return a.push({type:12,data:t},{type:12,data:r.pads},{type:12,data:r.strides}),n.push({name:"kernelStrides",type:"u32",length:t.length},{name:"pads",type:"u32",length:r.pads.length},{name:"strides",type:"u32",length:r.strides.length}),[a,n,!!r.pads.reduce((t,r)=>t+r),!1,!1]}},a$=(t,r,i,a,n,s,o,u,l,d,p,c)=>{let h="NHWC"===n.format,f=r.type.value,m=t_("output",r.type.tensor,a);if(n.kernelShape.length<=2){let a="",d="",g="",y=i-(h?2:1);if(a=p?`
2052                for (var i: u32 = 0u; i < uniforms.kw; i++) {
2053                  xIndices[${y}] = indices[${y}] * uniforms.sw - uniforms.pwStart + i;
2054                  if (xIndices[${y}] < 0 || xIndices[${y}]
2055                      >= uniforms.x_shape[${y}]) {
2056                    pad++;
2057                    continue;
2058                  }
2059                  let x_val = x[${r.indicesToOffset("xIndices")}];
2060                  ${s}
2061                }`:`
2062                for (var i: u32 = 0u; i < uniforms.kw; i++) {
2063                  xIndices[${y}] = indices[${y}] * uniforms.sw - uniforms.pwStart + i;
2064                  let x_val = x[${r.indicesToOffset("xIndices")}];
2065                  ${s}
2066                }`,2===n.kernelShape.length){let t=i-(h?3:2);d=c?`
2067                for (var j: u32 = 0u; j < uniforms.kh; j++) {
2068                  xIndices[${t}] = indices[${t}] * uniforms.sh - uniforms.phStart + j;
2069                  if (xIndices[${t}] < 0 || xIndices[${t}] >= uniforms.x_shape[${t}]) {
2070                    pad += i32(uniforms.kw);
2071                    continue;
2072                  }
2073              `:`
2074                for (var j: u32 = 0u; j < uniforms.kh; j++) {
2075                  xIndices[${t}] = indices[${t}] * uniforms.sh - uniforms.phStart + j;
2076                `,g=`
2077              }
2078            `}return`
2079            ${t.registerUniforms(l).declareVariables(r,m)}
2080
2081            ${t.mainStart()}
2082              ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
2083
2084              let indices = ${m.offsetToIndices("global_idx")};
2085              var xIndices = ${m.offsetToIndices("global_idx")};
2086
2087              var value = ${f}(${u});
2088              var pad = 0;
2089              ${d}
2090              ${a}
2091              ${g}
2092              ${o}
2093
2094              output[global_idx] = value;
2095            }`}{if(h)throw Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let a=n.kernelShape.length,p=n.pads.length,c="";return c=d?`
2096                if (xIndices[j] >= uniforms.x_shape[j]) {
2097                  pad++;
2098                  isPad = true;
2099                  break;
2100                }
2101              }
2102              if (!isPad) {
2103                let x_val = x[${r.indicesToOffset("xIndices")}];
2104                ${s}
2105              }`:`
2106              }
2107              let x_val = x[${r.indicesToOffset("xIndices")}];
2108              ${s}
2109            `,`
2110            ${t.registerUniforms(l).declareVariables(r,m)}
2111
2112            ${t.mainStart()}
2113              ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
2114              let indices = ${m.offsetToIndices("global_idx")};
2115              var xIndices = ${m.offsetToIndices("global_idx")};
2116
2117              var offsets: array<u32, ${a}>;
2118
2119              var value = ${f}(${u});
2120              var pad = 0;
2121              var isPad = false;

2122
2123              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {
2124                var offset = i;
2125                for (var j = 0u; j < ${a-1}u; j++) {
2126                  offsets[j] = offset / ${tm("uniforms.kernelStrides","j",a)};
2127                  offset -= offsets[j] * ${tm("uniforms.kernelStrides","j",a)};
2128                }
2129                offsets[${a-1}] = offset;
2130
2131                isPad = false;
2132                for (var j = ${i-a}u; j < ${i}u; j++) {
2133                  xIndices[j] = indices[j] * ${tm("uniforms.strides",`j - ${i-a}u`,a)}
2134                    + offsets[j - ${i-a}u] - ${tm("uniforms.pads","j - 2u",p)};
2135                  ${c}
2136              }
2137              ${o}
2138
2139              output[global_idx] = value;
2140            }`}},ab=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,av=t=>({format:t.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],ceilMode:t.ceil_mode,kernelShape:t.kernel_shape,strides:t.strides,pads:t.pads}),aw=(t,r,i,a)=>{let[n,s]=ay(r,a,i),o=ty("x",r.dataType,r.dims.length),u=o.type.value,l="";n.countIncludePad?l+=`value /= ${u}(uniforms.kernelSize);`:l+=`value /= ${u}(i32(uniforms.kernelSize) - pad);`;let[d,p,c,h,f]=a_(s,n);return d.push(...td(r.dims,s)),{name:t,shaderCache:{hint:`${a.cacheKey};${c};${h};${f}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:s,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(tt.size(s)/64)},programUniforms:d}),getShaderSource:t=>a$(t,o,r.dims.length,s.length,n,"value += x_val;",l,0,p,c,h,f)}},ax=t=>{let r=0!==t.count_include_pad,i=av(t);if(0!==i.ceilMode)throw Error("using ceil() in shape computation is not yet supported for AveragePool");let a={countIncludePad:r,...i,cacheKey:""};return{...a,cacheKey:(t=>`${ab(t)};${t.countIncludePad}`)(a)}},aS=(t,r)=>{ag(t.inputs),t.compute(aw("AveragePool",t.inputs[0],!1,r))},ak={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},aT=t=>{let r=t.format;return{format:r,...ak,cacheKey:r}},aC=(t,r)=>{ag(t.inputs),t.compute(aw("GlobalAveragePool",t.inputs[0],!0,r))},aI=(t,r,i,a)=>{let[n,s]=ay(r,a,i),o=`
2141      value = max(x_val, value);
2142    `,u=ty("x",r.dataType,r.dims.length),[l,d,p,c,h]=a_(s,n);return l.push(...td(r.dims,s)),{name:t,shaderCache:{hint:`${a.cacheKey};${p};${c};${h}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:s,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(tt.size(s)/64)},programUniforms:l}),getShaderSource:t=>a$(t,u,r.dims.length,s.length,n,o,"",10===r.dataType?-65504:-1e5,d,p,c,h)}},aE=(t,r)=>{ag(t.inputs),t.compute(aI("MaxPool",t.inputs[0],!1,r))},az=t=>{let r=t.storage_order,i=t.dilations,a=av(t);if(0!==r)throw Error("column major storage order is not yet supported for MaxPool");if(0!==a.ceilMode)throw Error("using ceil() in shape computation is not yet supported for MaxPool");let n={storageOrder:r,dilations:i,...a,cacheKey:""};return{...n,cacheKey:(t=>`${ab(t)};${t.storageOrder};${t.dilations}`)(n)}},aA=t=>{let r=t.format;return{format:r,...ak,cacheKey:r}},aO=(t,r)=>{ag(t.inputs),t.compute(aI("GlobalMaxPool",t.inputs[0],!0,r))}}),n5=W(()=>{nd(),ny(),ng(),n_(),aR=(t,r)=>{((t,r)=>{if(t.length<2||t.length>3)throw Error("DequantizeLinear requires 2 or 3 inputs.");if(3===t.length&&t[1].dims===t[2].dims)throw Error("x-scale and x-zero-point must have the same shape.");if(3===t.length&&t[0].dataType!==t[2].dataType)throw Error("x and x-zero-point must have the same data type.");if(6===t[0].dataType&&t.length>2)throw Error("In the case of dequantizing int32 there is no zero point.");if(0!==t[1].dims.length&&1!==t[1].dims.length&&t[1].dims.length!==t[0].dims.length)throw Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(t.length>2){if(t[0].dataType!==t[2].dataType)throw Error("x and x-zero-point must have the same data type.");if(t[1].dims.length!==t[2].dims.length)throw Error("scale and zero-point inputs must have the same rank.");if(!t[1].dims.map((r,i)=>r===t[2].dims[i]).reduce((t,r)=>t&&r,!0))throw Error("scale and zero-point inputs must have the same shape.")}if(r.blockSize>0){if(0===t[1].dims.length||1===t[1].dims.length&&1===t[1].dims[0])throw Error("blockSize must be set only for block quantization.");

2142if(!t[1].dims.map((i,a)=>a===r.axis||i===t[0].dims[a]).reduce((t,r)=>t&&r,!0))throw Error("For block qunatization, scale input shape to match the input shape except for the axis");if(t[1].dims.length!==t[0].dims.length)throw Error("For block qunatization the scale input rank must be the same as the x rank.");let i=t[0].dims[r.axis],a=t[1].dims[r.axis];if(r.blockSize<Math.ceil(i/a)||r.blockSize>Math.ceil(i/(a-1)-1))throw Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}})(t.inputs,r),t.compute(((t,r)=>{let i=tt.normalizeAxis(r.axis,t[0].dims.length),a=t[0].dataType,n=3===a,s=t[0].dims,o=t[1].dataType,u=tt.size(s),l=3===a||2===a,d=l?[Math.ceil(tt.size(t[0].dims)/4)]:t[0].dims,p=t[1].dims,c=t.length>2?t[2]:void 0,h=c?l?[Math.ceil(tt.size(c.dims)/4)]:c.dims:void 0,f=0===p.length||1===p.length&&1===p[0],m=!1===f&&1===p.length,g=tp(u),y=f&&(!l||4===g),_=y?g:1,$=ty("input",l?12:a,d.length,y&&!l?g:1),b=ty("scale",o,p.length),v=c?ty("zero_point",l?12:a,h.length):void 0,w=t_("output",o,s.length,_),x=[$,b];v&&x.push(v);let S=[d,p];c&&S.push(h);let k=[{type:12,data:u/_},{type:12,data:i},{type:12,data:r.blockSize},...td(...S,s)];return{name:"DequantizeLinear",shaderCache:{hint:r.cacheKey,inputDependencies:v?["rank","rank","rank"]:["rank","rank"]},getShaderSource:t=>`
2143      ${t.registerUniforms([{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}]).declareVariables(...x,w)}
2144      ${t.mainStart()}
2145          ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
2146          let output_indices = ${w.offsetToIndices("global_idx")};
2147
2148          // Set input x
2149          ${l?`
2150            let input = ${$.getByOffset("global_idx / 4")};
2151            let x_vec = ${n?"unpack4xI8(input)":"unpack4xU8(input)"};
2152            let x_value = ${1===_?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${$.getByOffset("global_idx")};`};
2153
2154          // Set scale input
2155          ${f?`let scale_value= ${b.getByOffset("0")}`:m?`
2156            let scale_index = ${w.indicesGet("output_indices","uniforms.axis")};
2157            let scale_value= ${b.getByOffset("scale_index")};`:`
2158            var scale_indices: ${b.type.indices} = output_indices;
2159            let index = ${b.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size;
2160            ${b.indicesSet("scale_indices","uniforms.axis","index")};
2161            let scale_value= ${b.getByIndices("scale_indices")};`};
2162
2163          // Set zero-point input
2164          ${v?f?l?`
2165                let zero_point_input = ${v.getByOffset("0")};
2166                let zero_point_vec =  ${n?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
2167                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${v.getByOffset("0")}`:m?l?`
2168                let zero_point_index = ${w.indicesGet("output_indices","uniforms.axis")};
2169                let zero_point_input = ${v.getByOffset("zero_point_index / 4")};
2170                let zero_point_vec =  ${n?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
2171                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
2172                let zero_point_index = ${w.indicesGet("output_indices","uniforms.axis")};
2173                let zero_point_value = ${v.getByOffset("zero_point_index")};`:l?`
2174                let zero_point_offset = ${b.indicesToOffset("scale_indices")};
2175                let zero_point_input = ${v.getByOffset("zero_point_offset / 4")};
2176                let zero_point_vec = ${n?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
2177                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${v.getByIndices("scale_indices")};`:`let zero_point_value = ${l?n?"i32":"u32":$.type.value}(0);`};
2178      // Compute and write output
2179      ${w.setByOffset("global_idx",`${w.type.value}(x_value - zero_point_value) * scale_value`)};
2180      }`,getRunData:()=>({outputs:[{dims:s,dataType:o}],dispatchGroup:{x:Math.ceil(u/_/64),y:1,z:1},programUniforms:k})}})(t.inputs,r))},aB=t=>e7({axis:t.axis,blockSize:t.blockSize})}),n7=W(()=>{eu(),nd(),n_(),aN=t=>{let r=0,i=0,a=0;6===t.inputs[0].dataType?(r=t.inputs[0].getInt32Array()[0],i=t.inputs[1].getInt32Array()[0],a=t.inputs[2].getInt32Array()[0]):1===t.inputs[0].dataType&&(r=t.inputs[0].getFloat32Array()[0],i=t.inputs[1].getFloat32Array()[0],a=t.inputs[2].getFloat32Array()[0]),c.webgpu.validateInputContent&&((t,r,i)=>{if(t===r||t<r&&i<0||t>r&&i>0)throw Error("Range these inputs' contents are invalid.")})(r,i,a),t.compute(((t,r,i,a)=>{let n=Math.abs(Math.ceil((r-t)/i)),s=[n],o=[{type:12,data:n},{type:a,data:t},{type:a,data:i},...td(s)];return{name:"Range",shaderCache:{hint:`${a}`}

2180,getShaderSource:t=>{let r=t_("output",a,s.length),i=r.type.value;return`
2181        ${t.registerUniforms([{name:"outputSize",type:"u32"},{name:"start",type:i},{name:"delta",type:i}]).declareVariables(r)}
2182        ${t.mainStart()}
2183        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
2184        output[global_idx] = uniforms.start + ${i}(global_idx) * uniforms.delta;
2185      }`},getRunData:()=>({outputs:[{dims:s,dataType:a}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:o})}})(r,i,a,t.inputs[0].dataType),{inputs:[]})}}),n9=W(()=>{nd(),ny(),ng(),n_(),aM=t=>e7({reduction:t.reduction}),aD=(t,r)=>{t.compute(((t,r)=>{let i=t[0].dims,a=t[1].dims,n=Math.ceil(tt.size(a)/1),s=a[a.length-1],o=tt.sizeFromDimension(i,s),u=[{type:12,data:n},{type:12,data:s},{type:12,data:o},...td(t[1].dims,t[2].dims,i)];return{name:"ScatterND",shaderCache:{hint:`${r.cacheKey}_${r.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:u}),getShaderSource:n=>{let s=ty("indices",t[1].dataType,t[1].dims.length),o=ty("updates",t[2].dataType,t[2].dims.length,1),u="none"!==r.reduction&&""!==r.reduction?t$("output",t[0].dataType,i.length):t_("output",t[0].dataType,i.length,1);return`
2186      ${n.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(s,o,u)}
2187      ${n.mainStart()}
2188        ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
2189  var hasDuplicates = false;
2190  if (${"none"===r.reduction}) {
2191    let n = ${tt.size(a)};
2192    for (var i = 0; i < n; i = i + 1) {
2193      for (var j = i + 1; j < n; j = j + 1) {
2194        var index_i = i32(indices[i].x);
2195        var index_j = i32(indices[j].x);
2196        if (index_i == index_j) {
2197          hasDuplicates = true;
2198          break;
2199        }
2200      }
2201      if (hasDuplicates) {
2202        break;
2203      }
2204    }
2205  }
2206
2207  var data_offset = 0u;
2208  var indices_start = uniforms.last_index_dimension * global_idx;
2209  if (${"none"===r.reduction} && hasDuplicates) {
2210    if (global_idx != 0u) {
2211      return;
2212    }
2213    indices_start = 0u;
2214  }
2215  let indices_end = indices_start + uniforms.last_index_dimension;
2216  for (var i = indices_start; i < indices_end; i++) {
2217    var index = i32(indices[i].x);
2218    ${1===t[0].dims.length?`
2219    let element_count_dim = uniforms.output_strides;
2220    let dim_value = uniforms.output_shape;`:`
2221    let element_count_dim = uniforms.output_strides[i - indices_start];
2222    let dim_value = uniforms.output_shape[i - indices_start + uniforms.last_index_dimension];`}
2223    if (index >= 0) {
2224      if (index >= i32(dim_value)) {
2225        index = i32(dim_value - 1);
2226      }
2227    } else {
2228      if (index < -i32(dim_value)) {
2229        index = 0;
2230      } else {
2231        index += i32(dim_value);
2232      }
2233    }
2234    data_offset += u32((u32(index) * element_count_dim));
2235  }
2236
2237  for (var i = 0u; i < uniforms.num_updates_elements; i++) {
2238    let value = updates[uniforms.num_updates_elements * global_idx + i];
2239    ${((t,r,i,a)=>{if("none"!==t&&"i32"!==a&&"u32"!==a&&"f32"!==a)throw Error(`Input ${a} is not supported with reduction ${t}.`);let n=`{
2240                var oldValue = 0;
2241                loop {
2242                  let newValueF32 =`,s=`;
2243                  let newValue = bitcast<i32>(newValueF32);
2244                  let res = atomicCompareExchangeWeak(&${r}, oldValue, newValue);
2245                  if res.exchanged {
2246                    break;
2247                  }
2248                  oldValue = res.old_value;
2249                }
2250              }`;switch(t){case"none":return`${r}=${i};`;case"add":return"i32"===a||"u32"===a?`atomicAdd(&${r}, bitcast<${a}>(${i}));`:`
2251              ${n}bitcast<${a}>(oldValue) + (${i})${s}`;case"max":return"i32"===a||"u32"===a?`atomicMax(&${r}, bitcast<${a}>(${i}));`:`
2252                ${n}max(bitcast<f32>(oldValue), (${i}))${s}`;case"min":return"i32"===a||"u32"===a?`atomicMin(&${r}, bitcast<${a}>(${i}));`:`${n}min(bitcast<${a}>(oldValue), (${i}))${s}`;case"mul":return`${n}(bitcast<${a}>(oldValue) * (${i}))${s}`;default:throw Error(`Reduction ${t} is not supported.`)}})(r.reduction,"output[data_offset + i]","value",u.type.value)}
2253  }
2254
2255      }`}}}

2255)(t.inputs,r),{inputs:[t.inputs[1],t.inputs[2]],outputs:[]})}}),se=W(()=>{nd(),ny(),ng(),n_(),aP=(t,r,i,a)=>`
2256  // The whole part and the fractional part are calculated separately due to inaccuracy of floating
2257  // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
2258  // offset-by-one error later in floor().
2259  let big = (${t}) * (${r});
2260  let whole = ${a}(big / (${i}));
2261  let fract = ${a}(big % (${i})) / ${a}(${i});
2262  return whole + fract;
2263`,aU=(t,r,i,a)=>t.rank>a?`
2264    ${t.indicesSet("input_indices",r,"channel")};
2265    ${t.indicesSet("input_indices",i,"batch")};
2266`:"",aW=(t,r)=>{let i=[],a=[],n=[],s=(t=>{let r=t.customDataBuffer;return new Uint32Array(r,r.byteOffset,1)[0]})(t);if(0!==r.antialias)throw Error("Only default value (0) for Antialias attribute is supported");((t,r,i,a,n,s)=>{let[o,u,l]=i>10?[1,2,3]:[-1,t.length>1?1:-1,-1],d=t[0].dims.length;if(o>0&&t.length>o&&t[o].dims.length>0)t[o].getFloat32Array().forEach(t=>s.push(t));else if("tf_crop_and_resize"===r.coordinateTransformMode)throw Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(u>0&&t.length>u&&1===t[u].dims.length&&t[u].dims[0]>0){if(t[u].getFloat32Array().forEach(t=>a.push(t)),0!==a.length&&a.length!==d&&i>=18&&a.length!==r.axes.length)throw Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");((t,r)=>{if(t.every(t=>t>0||(()=>{throw Error("Resize requires scales input values to be positive")})),t.length>0){if("linear"===r.mode){if(2!==t.length&&3!==t.length&&(4!==t.length||1!==t[0]||1!==t[1])&&(4!==t.length||1!==t[0]||1!==t[3])&&(5!==t.length||1!==t[0]||1!==t[1]))throw Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
2267            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if("cubic"===r.mode&&2!==t.length&&(4!==t.length||1!==t[0]||1!==t[1])&&(4!==t.length||1!==t[0]||1!==t[3]))throw Error("Resize requires scales input size to be 2 or 4 for cubic mode")}})(a,r),r.axes.length>0&&((t,r,i)=>{r.every(t=>t>=0&&t<i||(()=>{throw Error("Resize requires axes input values to be positive and less than rank")}));let a=Array(i).fill(1);return r.forEach((r,i)=>a[r]=t[i]),a})(a,r.axes,d).forEach((t,r)=>a[r]=t)}if(l>0&&t.length>l&&1===t[l].dims.length&&t[l].dims[0]>0&&(t[l].getBigInt64Array().forEach(t=>n.push(Number(t))),0!==n.length&&n.length!==d&&i>=18&&n.length!==r.axes.length))throw 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(0!==a.length&&a.length!==r.axes.length)throw Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(0!==n.length&&n.length!==r.axes.length)throw Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if("u">typeof a&&"u">typeof n&&a.length>0&&n.length>d)throw Error("Resize requires only of scales or sizes to be specified")})(t.inputs,r,s,i,a,n),t.compute(((t,r,i,a,n,s)=>{let o=t.dims,u=((t,r,i)=>{let a=Array(i).fill(0).concat(Array(i).fill(1)),n=0===t.length?a:t.slice();return r.length>0?(r.forEach((t,s)=>{a[t]=n[s],a[s+i]=n[r.length+s]}),a):n})(s,r.axes,o.length),l=((t,r,i,a)=>{let n=[];if(i.length>0)if(a.length>0){if(t.forEach(t=>n.push(t)),Math.max(...a)>t.length)throw Error("axes is out of bound");a.forEach((t,r)=>n[t]=i[r])}else i.forEach(t=>n.push(t));else{if(0===r.length)throw Error("Resize requires either scales or sizes.");n=t.map((t,i)=>Math.round(t*r[i]))}return n})(o,a,n,r.axes),d=a.slice();0===a.length&&(d=o.map((t,r)=>0===t?1:l[r]/t),"stretch"!==r.keepAspectRatioPolicy&&(l=((t,r,i)=>{let a=(()=>{switch(i.keepAspectRatioPolicy){case"not_larger":return i.axes.length>0?Math.min(...i.axes.map(t=>r[t]),Number.MAX_VALUE):Math.min(...r,Number.MAX_VALUE);case"not_smaller":return i.axes.length>0?Math.max(...i.axes.map(t=>r[t]),5e-324):Math.max(...r,5e-324);default:throw Error(`Keep aspect ratio policy ${i.keepAspectRatioPolicy} is not supported`)}})();r.fill(1,0,r.length);let n=t.slice();return i.axes.length>

22670?(i.axes.forEach(t=>r[t]=a),i.axes.forEach(i=>n[i]=Math.round(t[i]*r[i]))):(r.fill(a,0,r.length),n.forEach((t,i)=>n[i]=Math.round(t*r[i]))),n})(o,d,r)));let p=t_("output",t.dataType,l.length),c=ty("input",t.dataType,o.length),h=tt.size(l),f=o.length===l.length&&o.every((t,r)=>t===l[r]),m="tf_crop_and_resize"===r.coordinateTransformMode,g=r.extrapolationValue,y=c.type.value;return{name:"Resize",shaderCache:{hint:`${r.cacheKey}|${i}|${d.length>0?"cubic"===r.mode?d:d.length:""}|${n.length>0?n:""}|${u.length>0?u:""}|${f}|${"nearest"===r.mode?o.length:o}`,inputDependencies:["rank"]},getShaderSource:t=>`
2268      ${f?"":`
2269      ${((t,r)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
2270     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${r} { `+(()=>{switch(t){case"asymmetric":return`
2271          if (xScale < 1.0 || floor(xScale) != xScale) {
2272            return ${r}(xResized) / ${r}(xScale);
2273          } else {
2274            ${aP("xResized","lengthOriginal","lengthResized",r)}
2275          }
2276        `;case"pytorch_half_pixel":return`if (lengthResized > 1) {
2277                    return (${r}(xResized) + 0.5) / ${r}(xScale) - 0.5;
2278                  } else {
2279                    return 0.0;
2280                  }`;case"tf_half_pixel_for_nn":return`return (${r}(xResized) + 0.5) / ${r}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
2281                    return 0.0;
2282                  } else {
2283                    ${aP("xResized","lengthOriginal - 1","lengthResized - 1",r)}
2284                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
2285                    return ${r}(roiStart) * ${r}(lengthOriginal - 1) +
2286                        (${r}(xResized) * ${r}(roiEnd - roiStart) * ${r}(lengthOriginal - 1)) /
2287                        ${r}(lengthResized - 1);
2288                  } else {
2289                    return 0.5 * ${r}(roiStart + roiEnd) * ${r}(lengthOriginal - 1);
2290                  }`;case"half_pixel_symmetric":return`const outputWidth = ${r}xScale * ${r}(lengthResized);
2291                  const adjustment = ${r}(lengthResized) / outputWidth;
2292                  const center = ${r}(lengthOriginal) / 2;
2293                  const offset = center * (1 - adjustment);
2294                  return offset + ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;case"half_pixel":return`return ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;default:throw Error(`Coordinate transform mode ${t} is not supported`)}})()+"}")(r.coordinateTransformMode,y)};
2295      ${(()=>{switch(r.mode){case"nearest":return`
2296              ${((t,r)=>`
2297    fn checkInputIndices(input_indices: ${t.type.indices}) -> bool {
2298      for (var i:u32 = 0; i < ${r.length}; i++) {
2299        var input_index = ${t.indicesGet("input_indices","i")};
2300        if (input_index < 0 || input_index >= ${tm("uniforms.input_shape","i",r.length)}) {
2301          return false;
2302        }
2303      }
2304      return true;
2305    }`)(c,o)};
2306              ${((t,r,i)=>`fn getNearestPixelFromOriginal(xOriginal: ${i}, isDownSample: bool) -> ${i} {`+(()=>{switch(t){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";default:if(r<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw Error(`Nearest mode ${t} is not supported`)}})()+"}")(r.nearestMode,i,y)};
2307              ${((t,r,i,a,n,s,o)=>`
2308    fn calculateInputIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> ${t.type.indices} {
2309      var input_indices: ${t.type.indices};
2310      for (var i:u32 = 0; i < ${a.length}; i++) {
2311        var output_index = ${r.indicesGet("output_indices","i")};
2312        var input_index: u32;

2313        var scale = ${tm("uniforms.scales","i",n)};
2314        if (scale == 1.0) {
2315          input_index = output_index;
2316        } else {
2317          var roi_low = ${tm("uniforms.roi","i",s)};
2318          var roi_hi = ${tm("uniforms.roi",`i + ${i.length}`,s)};
2319          var input_shape_i = ${tm("uniforms.input_shape","i",i.length)};
2320          var output_shape_i = ${tm("uniforms.output_shape","i",a.length)};
2321          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
2322                                                                        input_shape_i, roi_low, roi_hi);
2323          if (!${o} || (original_idx >= 0 && original_idx < ${r.type.value}(input_shape_i))) {
2324            if (original_idx < 0) {
2325              input_index = 0;
2326            } else if (original_idx > ${r.type.value}(input_shape_i - 1)) {
2327              input_index = input_shape_i - 1;
2328            } else {
2329              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
2330            }
2331          } else {
2332            input_index = u32(original_idx);
2333          }
2334        }
2335        ${t.indicesSet("input_indices","i","input_index")}
2336      }
2337      return input_indices;
2338    }`)(c,p,o,l,d.length,u.length,m)};
2339              `;case"linear":return`
2340              ${((t,r,i,a,n)=>`
2341    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> array<${t.type.value}, ${i.length}> {
2342      var original_indices: array<${t.type.value}, ${i.length}>;
2343      for (var i:u32 = 0; i < ${i.length}; i++) {
2344        var output_index = ${t.indicesGet("output_indices","i")};
2345        var scale = ${tm("uniforms.scales","i",a)};
2346        var roi_low = ${tm("uniforms.roi","i",n)};
2347        var roi_hi = ${tm("uniforms.roi",`i + ${r.length}`,n)};
2348        if (scale == 1.0) {
2349          original_indices[i] = ${t.type.value}(output_index);
2350        } else {
2351          var input_shape_i = ${tm("uniforms.input_shape","i",r.length)};
2352          var output_shape_i = ${tm("uniforms.output_shape","i",i.length)};
2353          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
2354                                                                           input_shape_i, roi_low, roi_hi);
2355        }
2356      }
2357      return original_indices;
2358    }`)(p,o,l,d.length,u.length)};
2359              ${(()=>{if(2===o.length||4===o.length)return`${((t,r,i,a,n)=>{let[s,o,u,l]=2===i.length?[-1,0,1,-1]:[0,2,3,1],d=t.type.value;return`
2360    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {
2361      var input_indices: ${t.type.indices};
2362      ${t.indicesSet("input_indices",o,`max(0, min(row, ${i[o]} - 1))`)};
2363      ${t.indicesSet("input_indices",u,`max(0, min(col, ${i[u]} - 1))`)};
2364      ${aU(t,l,s,2)}
2365      return ${t.getByIndices("input_indices")};
2366    }
2367
2368    fn bilinearInterpolation(output_indices: ${r.type.indices}) -> ${d} {
2369      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
2370      var row:${d} = originalIndices[${o}];
2371      var col:${d} = originalIndices[${u}];
2372      ${a?`if (row < 0 || row > (${i[o]} - 1) || col < 0 || col > (${i[u]} - 1)) {
2373        return ${n};
2374      }`:""};
2375      row = max(0, min(row, ${i[o]} - 1));
2376      col = max(0, min(col, ${i[u]} - 1));
2377      var row1: u32 = u32(row);
2378      var col1: u32 = u32(col);
2379      var row2: u32 = u32(row + 1);
2380      var col2: u32 = u32(col + 1);
2381      var channel: u32 = ${i.length>2?`u32(originalIndices[${l}])`:"0"};
2382      var batch: u32 =  ${i.length>2?`u32(originalIndices[${s}])`:"0"};
2383      var x11: ${d} = getInputValue(batch, channel, row1, col1);
2384      var x12: ${d} = getInputValue(batch, channel, row1, col2);
2385      var x21: ${d} = getInputValue(batch, channel, row2, col1);
2386      var x22: ${d} = getInputValue(batch, channel, row2, col2);
2387      var dx1: ${d} = abs(row - ${d}(row1));
2388      var dx2: ${d} = abs(${d}(row2) - row);
2389      var dy1: ${d} = abs(col - ${d}(col1));
2390      var dy2: ${d} = abs(${d}(col2) - col);
2391      if (row1 == row2) {
2392        dx1 = 0.5;
2393        dx2 = 0.5;
2394      }
2395      if (col1 == col2) {
2396        dy1 = 0.5;
2397        dy2 = 0.5;
2398      }
2399      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);
2400    }`})(c,p,o,m,g)}`;if(3===o.length||5===o.length)return`${((t,r,i,a,n)=>{let[s,o,u,l,d]=3===i.length?[-1,0,1,2,-1]:[0,2,3,4,1],p=t.type.value;return`
2401    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${p} {
2402      var input_indices: ${t.type.indices};
2403      ${t.indicesSet("input_indices",o,`max(0, min(depth, ${i[o]} - 1))`)};
2404      ${t.indicesSet("input_indices",u,`max(0, min(height, ${i[u]} - 1))`)};
2405      ${t.indicesSet("input_indices",l,`max(0, min(width, ${i[l]} - 1))`)};
2406      ${aU(t,d,s,3)}
2407      return ${t.getByIndices("input_indices")};
2408    }
2409
2410    fn trilinearInterpolation(output_indices: ${r.type.indices}) -> ${p} {
2411      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
2412      var depth:${p} = originalIndices[${o}];
2413      var height:${p} = originalIndices[${u}];
2414      var width:${p} = originalIndices[${l}];
2415      ${a?`if (depth < 0 || depth > (${i[o]} - 1) || height < 0 || height > (${i[u]} - 1) || width < 0 || (width > ${i[l]} - 1)) {
2416      return ${n};
2417        }`:""};
2418
2419    depth = max(0, min(depth, ${i[o]} - 1));
2420      height = max(0, min(height, ${i[u]} - 1));
2421      width = max(0, min(width, ${i[l]} - 1));
2422      var depth1: u32 = u32(depth);
2423      var height1: u32 = u32(height);
2424      var width1: u32 = u32(width);
2425      var depth2: u32 = u32(depth + 1);
2426      var height2: u32 = u32(height + 1);
2427      var width2: u32 = u32(width + 1);
2428      var channel: u32 = ${i.length>3?`u32(originalIndices[${d}])`:"0"};
2429      var batch: u32 =  ${i.length>3?`u32(originalIndices[${s}])`:"0"};
2430
2431      var x111: ${p} = getInputValue(batch, channel, depth1, height1, width1);
2432      var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2);
2433      var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1);
2434      var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2);
2435      var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1);
2436      var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2);
2437      var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1);
2438      var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2);
2439      var dx1: ${p} = abs(depth - ${p}(depth1));
2440      var dx2: ${p} = abs(${p}(depth2) - depth);
2441      var dy1: ${p} = abs(height - ${p}(height1));
2442      var dy2: ${p} = abs(${p}(height2) - height);
2443      var dz1: ${p} = abs(width - ${p}(width1));
2444      var dz2: ${p} = abs(${p}(width2) - width);
2445      if (depth1 == depth2) {
2446        dx1 = 0.5;
2447        dx2 = 0.5;
2448      }
2449      if (height1 == height2) {
2450        dy1 = 0.5;
2451        dy2 = 0.5;
2452      }
2453      if (width1 == width2) {
2454        dz1 = 0.5;
2455        dz2 = 0.5;
2456      }
2457      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
2458              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
2459    }`})(c,p,o,m,g)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
2460            `;case"cubic":return`
2461            ${(()=>{if(2===o.length||4===o.length)return`${((t,r,i,a,n,s,o,u,l,d)=>{let[p,c]=2===i.length?[0,1]:[2,3],h=t.type.value,f=o=>{let c=o===p?"row":"col";return`
2462      fn ${c}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${r.type.indices}) -> ${h} {
2463        var output_index = ${r.indicesGet("output_indices",o)};
2464        var originalIdx: ${h} = getOriginalCoordinateFromResizedCoordinate(output_index, ${n[o]},
2465        ${a[o]}, ${i[o]}, ${s[o]}, ${s[o]} + ${i.length});
2466        var fractOriginalIdx: ${h} = originalIdx - floor(originalIdx);
2467        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);
2468
2469        if (${u} && (originalIdx < 0 || originalIdx > (${i[o]} - 1))) {
2470          return ${l};
2471        }
2472        var data: array<${h}, 4> = array<${h}, 4>(0.0, 0.0, 0.0, 0.0);
2473        for (var i: i32 = -1; i < 3; i++) {
2474          var ${c}: ${h} = originalIdx + ${h}(i);
2475          if (${c} < 0 || ${c} >= ${i[o]}) {
2476            ${d?`coefs[i + 1] = 0.0;
2477                        continue;`:u?`return ${l};`:`${c} = max(0, min(${c}, ${i[o]} - 1));`};
2478          }
2479        var input_indices_copy: ${t.type.indices} = input_indices;
2480          ${t.indicesSet("input_indices_copy",o,`u32(${c})`)};
2481          data[i + 1] = ${o===p?t.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};
2482        }
2483        return cubicInterpolation1D(data, coefs);
2484      }`};return`
2485    ${f(p)};
2486    ${f(c)};
2487  fn getCubicInterpolationCoefs(s: ${h}) -> array<${h}, 4> {
2488    var absS = abs(s);
2489    var coeffs: array<${h}, 4> = array<${h}, 4>(0.0, 0.0, 0.0, 0.0);
2490    var oneMinusAbsS: ${h} = 1.0 - absS;
2491    var twoMinusAbsS: ${h} = 2.0 - absS;
2492    var onePlusAbsS: ${h} = 1.0 + absS;
2493    coeffs[0] = ((${o}

2493 * onePlusAbsS - 5 * ${o}) * onePlusAbsS + 8 * ${o}) * onePlusAbsS - 4 * ${o};
2494    coeffs[1] = ((${o} + 2) * absS - (${o} + 3)) * absS * absS + 1;
2495    coeffs[2] = ((${o} + 2) * oneMinusAbsS - (${o} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;
2496    coeffs[3] = ((${o} * twoMinusAbsS - 5 * ${o}) * twoMinusAbsS + 8 * ${o}) * twoMinusAbsS - 4 * ${o};
2497    return coeffs;
2498  }
2499
2500  fn cubicInterpolation1D(x: array<${h}, 4>, coefs: array<${h}, 4>) -> ${h} {
2501    var coefsSum: ${h} = coefs[0] + coefs[1] + coefs[2] + coefs[3];
2502    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;
2503  }
2504
2505  fn bicubicInterpolation(output_indices: ${r.type.indices}) -> ${h} {
2506    var input_indices: ${t.type.indices} = output_indices;
2507    return colCubicInterpolation(input_indices, output_indices);
2508  }
2509    `})(c,p,o,l,d,u,r.cubicCoeffA,m,r.extrapolationValue,r.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
2510            `;default:throw Error("Invalid resize mode")}})()};
2511      `}
2512      ${t.registerUniform("output_size","u32").registerUniform("scales","f32",d.length).registerUniform("roi","f32",u.length).declareVariables(c,p)}
2513      ${t.mainStart()}
2514        ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
2515        ${f?"output[global_idx] = input[global_idx];":`
2516        let output_indices = ${p.offsetToIndices("global_idx")};
2517        var input_indices: ${c.type.indices};
2518        ${(()=>{switch(r.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
2519                if (checkInputIndices(input_indices)) {
2520                  output[global_idx] = ${c.getByIndices("input_indices")};
2521                } else {
2522                  output[global_idx] = ${r.extrapolationValue};
2523                }`;case"linear":return`output[global_idx] = ${2===o.length||4===o.length?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${r.mode}`)}})()};
2524`}
2525      }`,getRunData:()=>({outputs:[{dims:l,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:[{type:12,data:h},{type:1,data:d},{type:1,data:u},...td(o,l)]})}})(t.inputs[0],r,s,i,a,n),{inputs:[0]})},aq=t=>{let r=t.antialias,i=t.axes,a=t.coordinateTransformMode,n=t.cubicCoeffA,s=0!==t.excludeOutside,o=t.extrapolationValue,u=t.keepAspectRatioPolicy,l=t.mode,d=""===t.nearestMode?"simple":t.nearestMode;return e7({antialias:r,axes:i,coordinateTransformMode:a,cubicCoeffA:n,excludeOutside:s,extrapolationValue:o,keepAspectRatioPolicy:u,mode:l,nearestMode:d})}}),st=W(()=>{nd(),ny(),ng(),n_(),aL=(t,r)=>{((t,r)=>{let[i,a,n,s]=t,{numHeads:o,rotaryEmbeddingDim:u}=r;if(3!==i.dims.length&&4!==i.dims.length)throw Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${i.dims.length}`);if(!tt.areEqual(a.dims,[])&&!tt.areEqual(a.dims,[1])&&2!==a.dims.length)throw Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${a.dims.length}`);if(2!==n.dims.length)throw Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${n.dims.length}`);if(2!==s.dims.length)throw Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${s.dims.length}`);if(!tt.areEqual(n.dims,s.dims))throw Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(u>0&&0===o)throw Error("num_heads must be provided if rotary_embedding_dim is specified");let l=i.dims[0],d=i.dims[i.dims.length-2],p=n.dims[0],c=tt.sizeFromDimension(i.dims,1)/d,h=0===u?2*n.dims[1]:c/o;if(u>h)throw Error("rotary_embedding_dim must be less than or equal to head_size");if(2===a.dims.length){if(l!==a.dims[0])throw Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${a.dims[0]}`);if(d!==a.dims[1])throw Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${a.dims[1]}`)}if(h/2!==n.dims[1]&&u/2!==n.dims[1])throw Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${n.dims[1]}`);if(d>p)throw Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")})(t.inputs,r),t.compute(((t,r)=>{let{interleaved:i,numHeads:a,rotaryEmbeddingDim:n,scale:s}=r,o=t[0].dims[0],u=tt.sizeFromDimension(t[0].dims,1),l=t[0].dims[t[0].dims.length-2],d=u/l,p=t[2].dims[1],c=0===n?2*p:d/a,h=[o,l,d/c,c-p],f=tt.computeStrides(h),m=[{type:1,data:s},{type:12,data:h},{type:12,data:f},...3===t[0].dims.length?Array({type:12,data:[u,d,c,1]}):[],...4===t[0].dims.length?Array({type:12,data:[u,c,l*c,1]}):[],...td(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)];return{name:"RotaryEmbedding",shaderCache:{hint:e7({interleaved:i}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:r=>{let a=ty("input",t[0].dataType,t[0].dims.length),n=ty("position_ids",t[1].dataType,t[1].dims.length),s=ty("cos_cache",t[2].dataType,t[2].dims.length),o=ty("sin_cache",t[3].dataType,t[3].dims.length),u=t_("output",t[0].dataType,t[0].dims.length);return r.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:h.length},{name:"global_strides",type:"u32",length:f.length},{name:"input_output_strides",type:"u32",length:f.length}]),`
2526        ${r.declareVariables(a,n,s,o,u)}
2527
2528        ${r.mainStart(ts)}
2529          let half_rotary_emb_dim = uniforms.${s.name}_shape[1];
2530          let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;
2531          let size = uniforms.global_shape[0] * uniforms.global_strides[0];
2532          ${r.guardAgainstOutOfBoundsWorkgroupSizes("size")}
2533
2534          if (bsnh[3] < half_rotary_emb_dim) {
2535            let position_ids_idx =
2536                ${n.broadcastedIndicesToOffset("bsnh.xy",t_("",n.type.tensor,2))};
2537            let position_id =
2538                u32(${n.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);

2539            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${i});
2540            let j = i + select(half_rotary_emb_dim, 1, ${i});
2541            let re = ${a.getByOffset("i")} * ${s.get("position_id","bsnh[3]")} -
2542                ${a.getByOffset("j")} * ${o.get("position_id","bsnh[3]")};
2543            ${u.setByOffset("i","re")}
2544            let im = ${a.getByOffset("i")} * ${o.get("position_id","bsnh[3]")} +
2545                ${a.getByOffset("j")} * ${s.get("position_id","bsnh[3]")};
2546            ${u.setByOffset("j","im")}
2547          } else {
2548            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
2549            ${u.setByOffset("k",a.getByOffset("k"))}
2550          }
2551        }`},getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(tt.size(h)/ts)},programUniforms:m})}})(t.inputs,r))}}),sr=W(()=>{nd(),ny(),n_(),aV=(t,r)=>{(t=>{if(!t||t.length<3)throw Error("layerNorm requires at least 3 inputs.");let r=t[0],i=t[1],a=t[2];if(r.dataType!==i.dataType||r.dataType!==a.dataType)throw Error("All inputs must have the same data type");if(3!==r.dims.length&&2!==r.dims.length)throw Error("Input must be 2D or 3D");if(3!==i.dims.length&&2!==i.dims.length)throw Error("Skip must be 2D or 3D");let n=r.dims[r.dims.length-1],s=r.dims[r.dims.length-2];if(i.dims[i.dims.length-1]!==n)throw Error("Skip must have the same hidden size as input");if(i.dims[i.dims.length-2]!==s)throw Error("Skip must have the same sequence length as input");if(1!==a.dims.length)throw Error("Gamma must be 1D");if(a.dims[a.dims.length-1]!==n)throw Error("Gamma must have the same hidden size as input");if(t.length>3){let r=t[3];if(1!==r.dims.length)throw Error("Beta must be 1D");if(r.dims[r.dims.length-1]!==n)throw Error("Beta must have the same hidden size as input")}if(t.length>4){let r=t[4];if(1!==r.dims.length)throw Error("Bias must be 1D");if(r.dims[r.dims.length-1]!==n)throw Error("Bias must have the same hidden size as input")}})(t.inputs);let i=[0];t.outputCount>1&&i.push(-3),t.outputCount>2&&i.push(-3),t.outputCount>3&&i.push(3),t.compute(((t,r,i,a)=>{let n=r.simplified,s=t[0].dims,o=tt.size(s),u=s.slice(-1)[0],l=a?s.slice(0,-1).concat(1):[],d=!n&&t.length>3,p=t.length>4,c=a&&i>1,h=a&&i>2,f=i>3,m=tp(u),g=[{type:12,data:o},{type:12,data:m},{type:12,data:u},{type:1,data:r.epsilon}],y=[{dims:s,dataType:t[0].dataType}];return i>1&&y.push({dims:l,dataType:1}),i>2&&y.push({dims:l,dataType:1}),i>3&&y.push({dims:s,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${m};${c};${h};${f}`,inputDependencies:t.map((t,r)=>"type")},getShaderSource:r=>{let i=[ty("x",t[0].dataType,t[0].dims,m),ty("skip",t[1].dataType,t[1].dims,m),ty("gamma",t[2].dataType,t[2].dims,m)];d&&i.push(ty("beta",t[3].dataType,t[3].dims,m)),p&&i.push(ty("bias",t[4].dataType,t[4].dims,m)),i.push(t_("output",t[0].dataType,s,m)),c&&i.push(t_("mean_output",1,l)),h&&i.push(t_("inv_std_output",1,l)),f&&i.push(t_("input_skip_bias_sum",t[0].dataType,s,m));let a=tu(t[0].dataType),o=tu(1,m);return`
2552
2553      ${r.registerUniforms([{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}]).declareVariables(...i)}
2554      var<workgroup> sum_shared : array<${o}, 64>;
2555      var<workgroup> sum_squared_shared : array<${o}, 64>;
2556
2557      ${r.mainStart([64,1,1])}
2558        let ix = local_id.x;
2559        let iy = global_id.x / 64;
2560
2561        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
2562        var stride = hidden_size_vectorized / 64;
2563        let offset = ix * stride + iy * hidden_size_vectorized;
2564        let offset1d = stride * ix;
2565        if (ix == 63) {
2566          stride = hidden_size_vectorized - stride * ix;
2567        }
2568        for (var i: u32 = 0; i < stride; i++) {
2569          let skip_value = skip[offset + i];
2570          let bias_value = ${p?"bias[offset1d + i]":a+"(0.0)"};
2571          let input_value = x[offset + i];
2572          let value = input_value + skip_value + bias_value;
2573          ${f?"input_skip_bias_sum[offset + i] = value;":""}
2574          output[offset + i] = value;
2575          let f32_value = ${th(a,m,"value")};
2576          sum_shared[ix] += f32_value;
2577          sum_squared_shared[ix] += f32_value * f32_value;
2578        }
2579        workgroupBarrier();
2580
2581        var reduce_size : u32 = 64;
2582        for (var curr_size = reduce_size >> 1;  curr_size > 0; curr_size = reduce_size >> 1) {
2583          reduce_size = curr_size + (reduce_size & 1);
2584          if (ix < curr_size) {
2585            sum_shared[ix] += sum_shared[ix + reduce_size];
2586            sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];
2587          }
2588          workgroupBarrier();
2589        }
2590
2591        let sum = sum_shared[0];
2592        let square_sum = sum_squared_shared[0];
2593        let mean = ${tf("sum",m)} / f32(uniforms.hidden_size);
2594        let inv_std_dev = inverseSqrt(${tf("square_sum",m)} / f32(uniforms.hidden_size) ${n?"":"- mean * mean"} + uniforms.epsilon);
2595        ${c?"mean_output[global_idx] = mean;":""}
2596        ${h?"inv_std_output[global_idx] = inv_std_dev;":""}
2597
2598        for (var i: u32 = 0; i < stride; i++) {
2599          output[offset + i] = (output[offset + i] ${n?"":`- ${a}(mean)`}) *
2600            ${a}(inv_std_dev) * gamma[offset1d + i]
2601            ${d?"+ beta[offset1d + i]":""};
2602        }
2603      }`},getRunData:()=>({outputs:y,dispatchGroup:{x:Math.ceil(o/u)},programUniforms:g})}})(t.inputs,r,t.outputCount,!1),{outputs:i})}}),si=W(()=>{nd(),ny(),ng(),n_(),aG=(t,r)=>{let i=[];if(t.length>

2603r)if(7===t[r].dataType)t[r].getBigInt64Array().forEach(t=>i.push(Number(t)));else if(6===t[r].dataType)t[r].getInt32Array().forEach(t=>i.push(Number(t)));else throw Error(`Input ${r} must be an array of int32 or int64`);return i},aH=(t,r,i,a,n)=>{let s=t;return t<0&&(s+=i[a[r]]),n[r]<0?Math.max(0,Math.min(s,i[a[r]]-1)):Math.max(0,Math.min(s,i[a[r]]))},aF=(t,r)=>{((t,r)=>{if(!t||t.length<1)throw Error("too few inputs");if(0!==r.axes.length){if(r.axes.length!==r.starts.length||r.axes.length!==r.ends.length)throw Error("axes, starts and ends must have the same length")}else if(r.starts.length!==r.ends.length)throw Error("starts and ends must have the same length");t.slice(1).forEach((r,i)=>{if(6!==t[i+1].dataType&&7!==t[i+1].dataType)throw Error(`Input ${i} must be an array of int32 or int64`)})})(t.inputs,r);let i=((t,r)=>{if(!(t.length>1))return r;{let r=aG(t,1),i=aG(t,2),a=aG(t,3);return 0===a.length&&(a=[...Array(t[0].dims.length).keys()]),e7({starts:r,ends:i,axes:a})}})(t.inputs,r);t.compute(((t,r)=>{let i=t[0].dims,a=tt.size(i),n=r.axes.length>0?tt.normalizeAxes(r.axes,i.length):[...Array(i.length).keys()],s=aG(t,4);s.forEach(t=>0!==t||(()=>{throw Error("step cannot be 0")})),0===s.length&&(s=Array(n.length).fill(1));let o=r.starts.map((t,r)=>aH(t,r,i,n,s)),u=r.ends.map((t,r)=>aH(t,r,i,n,s));if(n.length!==o.length||n.length!==u.length)throw Error("start, ends and axes should have the same number of elements");if(n.length!==i.length)for(let t=0;t<i.length;++t)n.includes(t)||(o.splice(t,0,0),u.splice(t,0,i[t]),s.splice(t,0,1));let l=s.map(t=>Math.sign(t));s.forEach((t,r,i)=>{if(t<0){let a=(u[r]-o[r])/t,n=o[r],l=n+a*s[r];o[r]=l,u[r]=n,i[r]=-t}});let d=i.slice(0);n.forEach((t,r)=>{d[t]=Math.ceil((u[t]-o[t])/s[t])});let p={dims:d,dataType:t[0].dataType},c=t_("output",t[0].dataType,d.length),h=ty("input",t[0].dataType,t[0].dims.length),f=tt.size(d),m=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:o.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:s.length}],g=[{type:12,data:f},{type:12,data:o},{type:6,data:l},{type:12,data:s},...td(t[0].dims,d)];return{name:"Slice",shaderCache:{hint:`${l.length}_${o.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:t=>`
2604      ${t.registerUniforms(m).declareVariables(h,c)}
2605        ${((t,r,i)=>`fn calculateInputIndices(output_indices: ${r.type.indices}) -> ${t.type.indices} {
2606          var input_indices: ${t.type.indices};
2607          var carry = 0u;
2608          for (var i = ${i.length}; i >= 0; i--) {
2609            let input_shape_i = ${tm("uniforms.input_shape","i",i.length)};
2610            let steps_i = ${tm("uniforms.steps","i",i.length)};
2611            let signs_i = ${tm("uniforms.signs","i",i.length)};
2612            let starts_i = ${tm("uniforms.starts","i",i.length)};
2613            var output_index = ${r.indicesGet("output_indices","i")};
2614            var input_index = output_index * steps_i + starts_i + carry;
2615            carry = input_index / input_shape_i;
2616            input_index = input_index % input_shape_i;
2617            if (signs_i < 0) {
2618              input_index = input_shape_i - input_index - 1u + starts_i;
2619            }
2620            ${t.indicesSet("input_indices","i","input_index")};
2621          }
2622          return input_indices;
2623      }`)(h,c,i)}
2624        ${t.mainStart()}
2625          ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
2626          let output_indices = ${c.offsetToIndices("global_idx")};
2627          let input_indices = calculateInputIndices(output_indices);
2628          ${c.setByOffset("global_idx",h.getByIndices("input_indices"))}
2629      }`,getRunData:()=>({outputs:[p],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:g})}})(t.inputs,i),{inputs:[0]})},aj=t=>{let r=t.starts,i=t.ends,a=t.axes;return e7({starts:r,ends:i,axes:a})}}),sa=W(()=>{nd(),ny(),ng(),n$(),n_(),aK=(t,r)=>{(t=>{if(!t||1!==t.length)throw Error("Softmax op requires 1 input.")})(t.inputs),((t,r)=>{let i=t.inputs[0],a=i.dims,n=tt.size(a),s=a.length,o=tt.normalizeAxis(r.axis,s),u=o<a.length-1,l,d=[];u?((d=Array.from({length:s},(t,r)=>r))[o]=s-1,d[s-1]=o,l=t.compute(tS(i,d),{inputs:[i],outputs:[-1]})[0]):l=i;let p=l.dims,c=p[s-1],h=n/c,f=tp(c),m=c/f,g=64;1===h&&(g=256);let y=ty("x",l.dataType,l.dims,f),_=t_("result",l.dataType,l.dims,f),$=y.type.value,b="f32"===tu(l.dataType)?`var threadMax = ${$}(-3.402823e+38f);`:`var threadMax = ${$}(-65504.0h);`,v=t.compute({name:"Softmax",shaderCache:{hint:`${f};${g}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:p,dataType:l.dataType}],dispatchGroup:{x:h},programUniforms:[{type:6,data:m}]}),getShaderSource:t=>`
2630      var<workgroup> rowMaxShared : ${$};
2631      var<workgroup> rowSumShared : ${$};
2632      var<workgroup> threadShared : array<${$}, ${g}>;
2633
2634      fn getValue(row: i32, col: i32, row_stride: i32) -> ${$} {
2635        let index = row * row_stride + col;
2636        return x[index];
2637      }
2638
2639      fn setValue(row: i32, col: i32, row_stride: i32, value: ${$}) {
2640        let index = row * row_stride + col;
2641        result[index] = value;
2642      }
2643      ${t.registerUniform("packedCols","i32").declareVariables(y,_)}
2644      ${t.mainStart(g)}
2645        let gindex = i32(global_idx);
2646        let lindex = i32(local_idx);
2647        const wg = ${g};
2648        let row = gindex / wg;
2649        let cols = uniforms.packedCols;
2650        let row_stride : i32 = uniforms.packedCols;
2651
2652        // find the rows max
2653        ${b}
2654        for (var col = lindex; col < cols; col += wg) {
2655          let value = getValue(row, col, row_stride);
2656          threadMax = max(threadMax, value);
2657        }
2658        if (lindex < cols) {
2659          threadShared[lindex] = threadMax;
2660        }
2661        workgroupBarrier();
2662
2663        var reduceSize = min(cols, wg);
2664        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {

2665          reduceSize = currSize + (reduceSize & 1);
2666          if (lindex < currSize) {
2667            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);
2668          }
2669          workgroupBarrier();
2670        }
2671        if (lindex == 0) {
2672          rowMaxShared = ${$}(${((t,r)=>4===r?`max(max(${t}.x, ${t}.y), max(${t}.z, ${t}.w))`:2===r?`max(${t}.x, ${t}.y)`:3===r?`max(max(${t}.x, ${t}.y), ${t}.z)`:t)("threadShared[0]",f)});
2673        }
2674        workgroupBarrier();
2675
2676        // find the rows sum
2677        var threadSum = ${$}(0.0);
2678        for (var col = lindex; col < cols; col += wg) {
2679          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);
2680          threadSum += subExp;
2681        }
2682        threadShared[lindex] = threadSum;
2683        workgroupBarrier();
2684
2685        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
2686          if (lindex < currSize) {
2687            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];
2688          }
2689          workgroupBarrier();
2690        }
2691        if (lindex == 0) {
2692          rowSumShared = ${$}(${tf("threadShared[0]",f)});
2693        }
2694        workgroupBarrier();
2695
2696        // calculate final value for each element in the row
2697        for (var col = lindex; col < cols; col += wg) {
2698          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
2699          setValue(row, col, row_stride, value);
2700        }
2701      }`},{inputs:[l],outputs:[u?-1:0]})[0];u&&t.compute(tS(v,d),{inputs:[v]})})(t,r)},aQ=t=>e7({axis:t.axis})}),sn=W(()=>{nd(),ny(),n_(),aZ=t=>Array.from(t.getBigInt64Array(),Number),aX=t=>{(t=>{if(!t||2!==t.length)throw Error("Tile requires 2 inputs.");if(1!==t[0].dataType&&10!==t[0].dataType&&6!==t[0].dataType&&12!==t[0].dataType)throw Error("Tile only support float, float16, int32, and uint32 data types");if(7!==t[1].dataType)throw Error("Tile `repeats` input should be of int64 data type");if(1!==t[1].dims.length)throw Error("Tile `repeats` input should be 1-D");if(aZ(t[1]).length!==t[0].dims.length)throw Error("Tile `repeats` input should have same number of elements as rank of input data tensor")})(t.inputs),t.compute(((t,r)=>{let i=t[0].dims,a=r??aZ(t[1]),n=((t,r)=>{let i=[];for(let a=0;a<t.length;++a)i.push(t[a]*r[a]);return i})(i,a),s=tt.size(n),o=t[0].dataType,u=ty("input",o,i.length),l=t_("output",o,n.length);return{name:"Tile",shaderCache:{hint:`${a}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:[{type:12,data:s},...td(t[0].dims,n)]}),getShaderSource:t=>`
2702      const inputShape = ${u.indices(...i)};
2703      ${t.registerUniform("output_size","u32").declareVariables(u,l)}
2704      ${t.mainStart()}
2705      ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
2706      let output_indices = ${l.offsetToIndices("global_idx")};
2707      var input_indices: ${u.type.indices};
2708      for (var i = 0; i < ${i.length}; i++) {
2709        let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")};
2710        let input_dim_value = ${l.indicesGet("output_indices","i")}  % input_dim_i;
2711
2712        ${u.indicesSet("input_indices","i","input_dim_value")}
2713      }
2714      ${l.setByOffset("global_idx",u.getByIndices("input_indices"))}
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Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),a5=(t,r)=>{let i=a8.get(t);if(!i)throw Error("Unsupported data type.");return r.length>0?Math.ceil(r.reduce((t,r)=>t*r)*i/8):0},a7=class{constructor(t){this.sessionId=t.sessionId,this.mlContext=t.context,this.mlTensor=t.tensor,this.dataType=t.dataType,this.tensorShape=t.shape}get tensor(){return this.mlTensor}get type(){return this.dataType}get shape(){return this.tensorShape}get byteLength(){return a5(this.dataType,this.tensorShape)}destroy(){eX("verbose",()=>"[WebNN] TensorWrapper.destroy"),this.mlTensor.destroy()}write(t){this.mlContext.writeTensor(this.mlTensor,t)}async read(t){return t?this.mlContext.readTensor(this.mlTensor,t):this.mlContext.readTensor(this.mlTensor)}canReuseTensor(t,r,i){return this.mlContext===t&&this.dataType===r&&this.tensorShape.length===i.length&&this.tensorShape.every((t,r)=>t===i[r])}},a9=class{constructor(t,r){this.tensorManager=t,this.wrapper=r}get tensorWrapper(){return this.wrapper}releaseTensor(){this.tensorWrapper&&(this.tensorManager.releaseTensor(this.tensorWrapper),this.wrapper=void 0)}async ensureTensor(t,r,i,a){let n=this.tensorManager.getMLContext(t);if(this.wrapper){if(this.wrapper.canReuseTensor(n,r,i))return this.wrapper.tensor;if(a){if(this.wrapper.byteLength!==a5(r,i))throw Error("Unable to copy data to tensor with different size.");this.activeUpload=new Uint8Array(await this.wrapper.read())}this.tensorManager.releaseTensor(this.wrapper)}let s=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(t,r,i,s,!0,!0),a&&this.activeUpload&&(this.wrapper.write(this.activeUpload),this.activeUpload=void 0),this.wrapper.tensor}upload(t){if(this.wrapper)if(t.byteLength===this.wrapper.byteLength)return void this.wrapper.write(t);else eX("verbose",()=>"Data size does not match tensor size. Releasing tensor."),this.releaseTensor();this.activeUpload?this.activeUpload.set(t):this.activeUpload=new Uint8Array(t)}async download(t){if(this.activeUpload)if(t)return void(t instanceof ArrayBuffer?new Uint8Array(t).set(this.activeUpload):new Uint8Array(t.buffer,t.byteOffset,t.byteLength).set(this.activeUpload));else return this.activeUpload.buffer;if(!this.wrapper)throw Error("Tensor has not been created.");return t?this.wrapper.read(t):this.wrapper.read()}},ne=class{constructor(t){this.backend=t,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}getMLContext(t){let r=this.backend.getMLContext(t);if(!r)throw Error("MLContext not found for session.");return r}reserveTensorId(){let t=a6();return this.tensorTrackersById.set(t,new a9(this)),t}releaseTensorId(t){let r=this.tensorTrackersById.get(t);r&&(this.tensorTrackersById.delete(t),r.tensorWrapper&&this.releaseTensor(r.tensorWrapper))}async ensureTensor(t,r,i,a,n){eX("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${r}, dataType: ${i}, shape: ${a}, copyOld: ${n}}`);let s=this.tensorTrackersById.get(r);if(!s)throw Error("Tensor not found.");return s.ensureTensor(t,i,a,n)}upload(t,r){let i=this.tensorTrackersById.get(t);if(!i)throw Error("Tensor not found.");i.upload(r)}async download(t,r){eX("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${t}, dstBuffer: ${r?.byteLength}}`);let i=this.tensorTrackersById.get(t);if(!i)throw Error("Tensor not found.");return i.download(r)}releaseTensorsForSession(t){for(let r of this.freeTensors)r.sessionId===t&&r.destroy();this.freeTensors=this.freeTensors.filter(r=>r.sessionId!==t)}registerTensor(t,r,i,a){let n=this.getMLContext(t),s=a6(),o=new a7({sessionId:t,context:n,tensor:r,dataType:i,shape:a});return this.tensorTrackersById.set(s,new a9(this,o)),this.externalTensors.add(o),s}async getCachedTensor(t,r,i,a,n,s){let o=this.getMLContext(t);for(let[a,n]of this.freeTensors.entries())if(n.canReuseTensor(o,r,i)){eX("verbose",()=>`[WebNN] Reusing tensor {dataType: ${r}, shape: ${i}}`);let n=this.freeTensors.splice(a,1)[0];return n.sessionId=t,n}eX("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${r}, shape: ${i}}`);let u=await o.createTensor({dataType:r,shape:i,dimensions:i,usage:a,writable:n,readable:s});return new a7({sessionId:t,context:o,tensor:u,dataType:r,shape:i})}releaseTensor(t){this.externalTensors.has(t)&&this.externalTensors.delete(t),this.freeTensors.push(t)}},nt=(...t)=>new ne(...t)}),sp=W(()=>{nd(),ns(),nh(),sd(),nc(),nr=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),ni=class{constructor(t){this.tensorManager=nt(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],this.sessionGraphInputs=new Map,this.temporaryGraphInputs=[],this.temporarySessionTensorIds=new Map,eZ(t.logLevel,!!t.debug)}get currentSessionId(){if(void 0===this.activeSessionId)throw Error("No active session");return this.activeSessionId}onRunStart(t){eX("verbose",()=>`[WebNN] onRunStart {sessionId: ${t}}`),this.activeSessionId=t}onRunEnd(t){eX("verbose",()=>`[WebNN] onRunEnd {sessionId: ${t}}`);let r=this.temporarySessionTensorIds.get(t);if(r){for(let t of r)eX("verbose",()=>`[WebNN] releasing temporary tensor {tensorId: ${t}}`),this.tensorManager.releaseTensorId(t);this.temporarySessionTensorIds.delete(t),this.activeSessionId=void 0}}async createMLContext(t){if(t instanceof GPUDevice){let r=this.mlContextCache.findIndex(r=>r.gpuDevice===t);if(-1!==r)return this.mlContextCache[r].mlContext;{let r=await navigator.ml.createContext(t);return this.mlContextCache.push({gpuDevice:t,mlContext:r}),r}}if(void 0===t){let t=this.mlContextCache.findIndex(t=>void 0===t.options&&void 0===t.gpuDevice);if(-1!==t)return this.mlContextCache[t].mlContext;{let t=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:t}),t}}let r=this.mlContextCache.findIndex(r=>((t,r)=>{if(t===r)return!0;

2746if(void 0===t||void 0===r)return!1;let i=Object.keys(t).sort(),a=Object.keys(r).sort();return i.length===a.length&&i.every((i,n)=>i===a[n]&&t[i]===r[i])})(r.options,t));if(-1!==r)return this.mlContextCache[r].mlContext;{let r=await navigator.ml.createContext(t);return this.mlContextCache.push({options:t,mlContext:r}),r}}registerMLContext(t,r){this.mlContextBySessionId.set(t,r);let i=this.sessionIdsByMLContext.get(r);i||(i=new Set,this.sessionIdsByMLContext.set(r,i)),i.add(t),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(t,this.temporaryGraphInputs),this.temporaryGraphInputs=[])}onReleaseSession(t){this.sessionGraphInputs.delete(t);let r=this.mlContextBySessionId.get(t);if(!r)return;this.tensorManager.releaseTensorsForSession(t),this.mlContextBySessionId.delete(t);let i=this.sessionIdsByMLContext.get(r);if(i.delete(t),0===i.size){this.sessionIdsByMLContext.delete(r);let t=this.mlContextCache.findIndex(t=>t.mlContext===r);-1!==t&&this.mlContextCache.splice(t,1)}}getMLContext(t){return this.mlContextBySessionId.get(t)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(t){eX("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${t}}`),this.tensorManager.releaseTensorId(t)}async ensureTensor(t,r,i,a,n){let s=nr.get(i);if(!s)throw Error(`Unsupported ONNX data type: ${i}`);return this.tensorManager.ensureTensor(t??this.currentSessionId,r,s,a,n)}async createTemporaryTensor(t,r,i){eX("verbose",()=>`[WebNN] createTemporaryTensor {onnxDataType: ${r}, shape: ${i}}`);let a=nr.get(r);if(!a)throw Error(`Unsupported ONNX data type: ${r}`);let n=this.tensorManager.reserveTensorId();await this.tensorManager.ensureTensor(t,n,a,i,!1);let s=this.temporarySessionTensorIds.get(t);return s?s.push(n):this.temporarySessionTensorIds.set(t,[n]),n}uploadTensor(t,r){if(!eO().shouldTransferToMLTensor)throw Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");eX("verbose",()=>`[WebNN] uploadTensor {tensorId: ${t}, data: ${r.byteLength}}`),this.tensorManager.upload(t,r)}async downloadTensor(t,r){return this.tensorManager.download(t,r)}createMLTensorDownloader(t,r){return async()=>{let i=await this.tensorManager.download(t);return eY(i,r)}}registerMLTensor(t,r,i,a){let n=nr.get(i);if(!n)throw Error(`Unsupported ONNX data type: ${i}`);let s=this.tensorManager.registerTensor(t,r,n,a);return eX("verbose",()=>`[WebNN] registerMLTensor {tensor: ${r}, dataType: ${n}, dimensions: ${a}} -> {tensorId: ${s}}`),s}registerMLConstant(t,r,i,a,n,s){if(!s)throw Error("External mounted files are not available.");let o=t;t.startsWith("./")&&(o=t.substring(2));let u=s.get(o);if(!u)throw Error(`File with name ${o} not found in preloaded files.`);if(r+i>u.byteLength)throw Error("Out of bounds: data offset and length exceed the external file data size.");let l=u.slice(r,r+i).buffer,d;switch(n.dataType){case"float32":d=new Float32Array(l);break;case"float16":d=new Uint16Array(l);break;case"int32":d=new Int32Array(l);break;case"uint32":d=new Uint32Array(l);break;case"int64":d=new BigInt64Array(l);break;case"uint64":d=new BigUint64Array(l);break;case"int8":d=new Int8Array(l);break;case"int4":case"uint4":case"uint8":d=new Uint8Array(l);break;default:throw Error(`Unsupported data type: ${n.dataType} in creating WebNN Constant from external data.`)}return eX("verbose",()=>`[WebNN] registerMLConstant {dataType: ${n.dataType}, shape: ${n.shape}}}`),a.constant(n,d)}registerGraphInput(t){this.temporaryGraphInputs.push(t)}isGraphInput(t,r){let i=this.sessionGraphInputs.get(t);return!!i&&i.includes(r)}flush(){}}}),sc={};q(sc,{init:()=>sm});var sh,sf,sm,sg,sy,s_,s$,sb,sv,sw,sx,sS,sk,sT,sC,sI,sE,sz,sA,sO,sR,sB,sN,sM,sD,sP,sU,sW,sq,sL,sV,sG,sH,sF,sj=W(()=>{nd(),sl(),nc(),ny(),sp(),sh=class t{constructor(t,r,i,a){this.module=t,this.dataType=r,this.data=i,this.dims=a}getFloat32Array(){if(1!==this.dataType)throw Error("Invalid data type");let t=tt.size(this.dims);return 0===t?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,t)}getBigInt64Array(){if(7!==this.dataType)throw Error("Invalid data type");let t=tt.size(this.dims);return 0===t?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,t)}getInt32Array(){if(6!==this.dataType)throw Error("Invalid data type");let t=tt.size(this.dims);return 0===t?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,t)}getUint16Array(){if(10!==this.dataType&&4!==this.dataType)throw Error("Invalid data type");let t=tt.size(this.dims);return 0===t?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,t)}reshape(r){if(tt.size(r)!==tt.size(this.dims))throw Error("Invalid new shape");return new t(this.module,this.dataType,this.data,r)}},sf=class{constructor(t,r,i){this.module=t,this.backend=r,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=r.adapterInfo,this.deviceInfo=r.deviceInfo;let 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