assets/ort.bundle.min-BZrO1_VY.js

import{n as e}from"./chunk-DLHVD8vl.js";
/*!
* ONNX Runtime Web v1.24.3
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/
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  fn main(${i?`@builtin(global_invocation_id) global_id : vec3<u32>,
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    @builtin(local_invocation_index) local_idx : u32,
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    @builtin(workgroup_id) workgroup_id : vec3<u32>,
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         let workgroup_index = workgroup_id.x;`:`let workgroup_index = workgroup_id.z * num_workgroups[0] * num_workgroups[1] +
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         let global_idx = workgroup_index * ${t*n*r}u + local_idx;`}
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      struct Uniforms { ${e.join(`, `)} };
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    }
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    }
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  ${Fn(i,r,t,a)}

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    let indices = ${a.offsetToIndices(`global_idx`)};
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          let outputIndex = global_idx / ${f};
          let offset = outputIndex * uniforms.reduceSize;

          var bestValue = f32(${Wn[r]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${f}) {
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           bestValue = ${Hn[r]};
          }
          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

         var reduceSize = min(Length, ${f}u);
         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
             currentSize = reduceSize / 2u) {
           let interval = DIV_CEIL(reduceSize, 2u);
           if (local_idx < currentSize) {
            let candidate = aBestValues[local_idx + interval];
            bestValue = ${Un[r]};
            aBestValues[local_idx] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (local_idx == 0u) {
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                  ${c[2].includes(`last_index`)?`let last_index = j${e};`:``}
                  ${i.indicesSet(`input_indices`,e,`j${e}`)}
                  ${m}
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        ${e.registerUniform(`output_size`,`u32`).declareVariables(i,s)}

        ${e.mainStart()}
          ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
          var input_indices: ${i.type.indices};
          let output_indices = ${s.offsetToIndices(`global_idx`)};

          ${t.join(`
`)}
          ${c[0]}       // init ops for reduce max/min
          ${c[1]}
          ${m}
          ${c[3]}
          ${c.length===4?s.setByOffset(`global_idx`,`value`):c.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:c,dataType:a}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:[{type:12,data:m},...W(l,c)]})}},pr=(e,t)=>{let n=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(e=>n.push(Number(e))),V({axes:n,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},mr=(e,t,n,r)=>{let i=e.inputs,a=i.length===1?n:pr(i,n);e.compute(fr(t,{hint:a.cacheKey,inputDependencies:[`rank`]},[i[0]],a.noopWithEmptyAxes&&a.axes.length===0?dr:r,a.axes,i[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},hr=(e,t)=>{ur(e.inputs),mr(e,`ReduceLogSum`,t,(e,t)=>[`var value = ${t.type.storage}(0);`,``,`value += ${e.getByIndices(`input_indices`)};`,`value = log(value);`])},gr=(e,t)=>{ur(e.inputs),mr(e,`ReduceL1`,t,(e,t)=>[`var value = ${t.type.storage}(0);`,``,`value += abs(${e.getByIndices(`input_indices`)});`,``])},_r=(e,t)=>{ur(e.inputs),mr(e,`ReduceL2`,t,(e,t)=>[`var t = ${t.type.value}(0); var value = ${t.type.value}(0);`,``,`t = ${e.getByIndices(`input_indices`)}; value += (t * t);`,`value = sqrt(value);`])},vr=(e,t)=>{ur(e.inputs),mr(e,`ReduceLogSumExp`,t,(e,t)=>[`var value = ${t.type.storage}(0);`,``,`value += exp(${e.getByIndices(`input_indices`)});`,`value = log(value);`])},yr=(e,t)=>{ur(e.inputs),mr(e,`ReduceMax`,t,(e,t,n)=>{let r=[];for(let t=0;t<e.rank;t++)(n.indexOf(t)>=0||n.length===0)&&r.push(e.indicesSet(`input_indices`,t,0));return[`${r.join(`
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var best_index : i32 = 0;`,`if (${e.getByIndices(`input_indices`)} ${t.selectLastIndex>0?`<=`:`<`} value) {
         value = ${e.getByIndices(`input_indices`)};
         best_index = i32(last_index);
       }`,``,n.setByOffset(`global_idx`,`best_index`)]},[t.axis],7,t.keepDims),{inputs:[0]})},Ir=(e,t)=>{Pr(e.inputs),e.compute(fr(`argMax`,{hint:t.cacheKey,inputDependencies:[`rank`]},[e.inputs[0]],(e,n,r)=>{let i=[];for(let t=0;t<e.rank;t++)(r.indexOf(t)>=0||r.length===0)&&i.push(`input_indices[${t}] = 0;`);return[`${i.join(`
`)}`,`var value = ${e.getByIndices(`input_indices`)};
var best_index : i32 = 0;`,`if (${e.getByIndices(`input_indices`)} ${t.selectLastIndex>0?`>=`:`>`} value) {
         value = ${e.getByIndices(`input_indices`)};
         best_index = i32(last_index);
       }`,``,n.setByOffset(`global_idx`,`best_index`)]},[t.axis],7,t.keepDims),{inputs:[0]})},Lr=e=>V(e)}),zr,Br,Vr,Hr,Ur,Wr,Gr,Kr,qr=o(()=>{L(),B(),ln(),Y(),zr=(e,t)=>{let n=e[0],r=e[1],i=e[2],a=e[3],o=e[4],s=e[5];if(o&&s)throw Error(`Attention cannot have both past and attention_bias`);if(n.dims.length!==3)throw Error(`Input "input" must have 3 dimensions`);let c=n.dims[0],l=n.dims[1],u=n.dims[2];if(i.dims.length!==1)throw Error(`Input "bias" is expected to have 1 dimensions`);if(r.dims.length!==2)throw Error(`Input "weights" is expected to have 2 dimensions`);if(r.dims[0]!==u)throw Error(`Input 1 dimension 0 should have same length as dimension 2 of input 0`);if(i.dims[0]!==r.dims[1])throw Error(`Input "bias" dimension 0 should have same length as dimension 1 of input "weights"`);let d=i.dims[0]/3,f=d,p=f;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw Error(`qkv_hidden_sizes attribute should have 3 elements`);for(let e of t.qkvHiddenSizes)if(e%t.numHeads!==0)throw Error(`qkv_hidden_sizes should be divisible by num_heads`);d=t.qkvHiddenSizes[0],f=t.qkvHiddenSizes[1],p=t.qkvHiddenSizes[2]}let m=l;if(d!==f)throw Error(`qkv_hidden_sizes first element should be same as the second`);if(i.dims[0]!==d+f+p)throw Error(`Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes`);let h=0;if(o){if(f!==p)throw Error(`Input "past" expect k_hidden_size == v_hidden_size`);if(o.dims.length!==5)throw Error(`Input "past" must have 5 dimensions`);if(o.dims[0]!==2)throw Error(`Input "past" first dimension must be 2`);if(o.dims[1]!==c)throw Error(`Input "past" second dimension must be batch_size`);if(o.dims[2]!==t.numHeads)throw Error(`Input "past" third dimension must be num_heads`);if(o.dims[4]!==f/t.numHeads)throw Error(`Input "past" fifth dimension must be k_hidden_size / num_heads`);t.pastPresentShareBuffer||(h=o.dims[3])}let g=m+h;if(a)throw Error(`Mask not supported`);if(o)throw Error(`past is not supported`);if(s){if(s.dims.length!==4)throw Error(`Input "attention_bias" must have 4 dimensions`);if(s.dims[0]!==c||s.dims[1]!==t.numHeads||s.dims[2]!==l||s.dims[3]!==g)throw Error(`Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)`)}return{batchSize:c,sequenceLength:l,pastSequenceLength:h,kvSequenceLength:m,totalSequenceLength:g,maxSequenceLength:-1,inputHiddenSize:u,hiddenSize:d,vHiddenSize:p,headSize:Math.floor(d/t.numHeads),vHeadSize:Math.floor(p/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:0,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Br=(e,t,n)=>t&&e?`
      let total_sequence_length_input = u32(${t.getByOffset(`0`)});
      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
      total_sequence_length = u32(${e?.getByOffset(`batchIdx`)}) + 1;
      var past_sequence_length: u32 = 0;
      if (is_first_prompt == false) {
        past_sequence_length = total_sequence_length - sequence_length;
      }
       `:`
    ${n?`let past_sequence_length = uniforms.past_sequence_length`:``};
    let present_sequence_length = total_sequence_length;
    `,Vr=(e,t,n,r,i,a,o,s)=>{let c=G(o?1:a),l=64,u=a/c;u<l&&(l=32);let d=Math.ceil(a/c/l),f=[{type:12,data:t},{type:12,data:n},{type:12,data:r},{type:12,data:i},{type:12,data:u},{type:12,data:d}],p=U(e.dataType,c),m=Cn(1,c),h=[`type`];return o&&h.push(`type`),s&&h.push(`type`),{name:`AttentionProbsSoftmax`,shaderCache:{hint:`${l};${p};${c}`,inputDependencies:h},getShaderSource:t=>{let n=J(`x`,e.dataType,e.dims,c),r=[n],i=o?q(`seq_lens`,o.dataType,o.dims):void 0;i&&r.push(i);let a=s?q(`total_sequence_length_input`,s.dataType,s.dims):void 0;a&&r.push(a);let u=Cn(e.dataType);return`
  var<workgroup> thread_max: array<f32, ${l}>;
  var<workgroup> thread_sum: array<f32, ${l}>;
  ${t.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(...r)}
  ${t.mainStart([l,1,1])}
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let sequence_length = uniforms.sequence_length;
    var total_sequence_length = uniforms.total_sequence_length;
    ${Br(i,a,!1)}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${l}) * uniforms.total_sequence_length + local_offset;
    let seq_causal_length = ${o?`u32(past_sequence_length + workgroup_id.y + 1)`:`total_sequence_length`};
    var thread_max_vector = ${m}(-3.4028234663852886e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      thread_max_vector = max(${m}(x[offset + i]), thread_max_vector);
    }
    thread_max[local_idx] = ${(()=>{switch(c){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: ${c}`)}})()};
    workgroupBarrier();

    var max_value =  f32(-3.4028234663852886e+38f);
    for (var i = 0u; i < ${l}; i++) {
      max_value = max(thread_max[i], max_value);
    }

    var sum_vector = ${m}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      sum_vector += exp(${m}(x[offset + i]) - max_value);
    }
    thread_sum[local_idx] = ${(()=>{switch(c){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: ${c}`)}})()};
    workgroupBarrier();

    var sum: f32 = 0;
    for (var i = 0u; i < ${l}; i++) {
      sum += thread_sum[i];
    }

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        x[offset + i] = ${n.type.value}(${u}(1.0) / ${u}(seq_causal_length));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        var f32input = ${m}(x[offset + i]);
        x[offset + i] = ${n.type.value}(exp(f32input - max_value) / sum);
      }
    }
      ${o?`
        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
          x[offset + total_seq_id] = ${n.type.value}(${u}(0));
        }`:``};
  }`},getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:i,z:t*n},programUniforms:f})}},Hr=(e,t,n,r,i,a,o,s,c)=>{let l=o+a.kvSequenceLength,u=[a.batchSize,a.numHeads,a.sequenceLength,l],d=e>1&&r,f=a.kvNumHeads?a.kvNumHeads:a.numHeads,p=d?[a.batchSize,f,l,a.headSize]:void 0,m=a.nReps?a.nReps:1,h=a.scale===0?1/Math.sqrt(a.headSize):a.scale,g=G(a.headSize),_=a.headSize/g,v={x:Math.ceil(l/12),y:Math.ceil(a.sequenceLength/12),z:a.batchSize*a.numHeads},y=[{type:12,data:a.sequenceLength},{type:12,data:_},{type:12,data:l},{type:12,data:a.numHeads},{type:12,data:a.headSize},{type:1,data:h},{type:12,data:o},{type:12,data:a.kvSequenceLength},{type:12,data:m}],b=d&&r&&z.size(r.dims)>0,x=[`type`,`type`];b&&x.push(`type`),i&&x.push(`type`),s&&x.push(`type`),c&&x.push(`type`);let S=[{dims:u,dataType:t.dataType,gpuDataType:0}];return d&&S.push({dims:p,dataType:t.dataType,gpuDataType:0}),{name:`AttentionProbs`,shaderCache:{hint:`${g};${i!==void 0};${r!==void 0};${e}`,inputDependencies:x},getRunData:()=>({outputs:S,dispatchGroup:v,programUniforms:y}),getShaderSource:e=>{let a=q(`q`,t.dataType,t.dims,g),o=[a,q(`key`,n.dataType,n.dims,g)];if(b){let e=q(`past_key`,r.dataType,r.dims,g);o.push(e)}i&&o.push(q(`attention_bias`,i.dataType,i.dims));let l=s?q(`seq_lens`,s.dataType,s.dims):void 0;l&&o.push(l);let f=c?q(`total_sequence_length_input`,c.dataType,c.dims):void 0;f&&o.push(f);let h=J(`output`,t.dataType,u),_=[h];d&&_.push(J(`present_key`,t.dataType,p,g));let v=Cn(1,g);return`
  const TILE_SIZE = 12u;

  var<workgroup> tileQ: array<${a.type.storage}, 144>;
  var<workgroup> tileK: array<${a.type.storage}, 144>;
  ${e.registerUniforms([{name:`M`,type:`u32`},{name:`K`,type:`u32`},{name:`N`,type:`u32`},{name:`num_heads`,type:`u32`},{name:`head_size`,type:`u32`},{name:`alpha`,type:`f32`},{name:`past_sequence_length`,type:`u32`},{name:`kv_sequence_length`,type:`u32`},{name:`n_reps`,type:`u32`}]).declareVariables(...o,..._)}
  ${e.mainStart([12,12,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let kvHeadIdx = ${m===1?`headIdx`:`headIdx / uniforms.n_reps`};
    let kv_num_heads = ${m===1?`uniforms.num_heads`:`uniforms.num_heads / uniforms.n_reps`};
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let sequence_length = uniforms.M;
    var total_sequence_length = uniforms.N;
    ${Br(l,f,!0)}
    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
    ${b&&d?`let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;`:``};
    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
    ${d?`let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;`:``}
    var value = ${v}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
      ${b&&d?`
              if (n + local_id.y < past_sequence_length) {
                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
              }`:`
          if (n + local_id.y < uniforms.kv_sequence_length) {
            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
          }`}
      ${d?`if (n + local_id.y < present_sequence_length) {
        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
      }`:``}
      }
      workgroupBarrier();

      for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
          value += ${v}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);
      }

      workgroupBarrier();
    }

    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(g){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: ${g}`)}})()};
        output[outputIdx] = ${h.type.value} (sum * uniforms.alpha) + ${i?`attention_bias[outputIdx]`:`0.0`};
    }
  }`}}},Ur=(e,t,n,r,i,a,o=void 0,s=void 0)=>{let c=a+i.kvSequenceLength,l=i.nReps?i.nReps:1,u=i.vHiddenSize*l,d=e>1&&r,f=i.kvNumHeads?i.kvNumHeads:i.numHeads,p=d?[i.batchSize,f,c,i.headSize]:void 0,m=[i.batchSize,i.sequenceLength,u],h={x:Math.ceil(i.vHeadSize/12),y:Math.ceil(i.sequenceLength/12),z:i.batchSize*i.numHeads},g=[{type:12,data:i.sequenceLength},{type:12,data:c},{type:12,data:i.vHeadSize},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:12,data:u},{type:12,data:a},{type:12,data:i.kvSequenceLength},{type:12,data:l}],_=d&&r&&z.size(r.dims)>0,v=[`type`,`type`];_&&v.push(`type`),o&&v.push(`type`),s&&v.push(`type`);let y=[{dims:m,dataType:t.dataType,gpuDataType:0}];return d&&y.push({dims:p,dataType:t.dataType,gpuDataType:0}),{name:`AttentionScore`,shaderCache:{hint:`${r!==void 0};${e}`,inputDependencies:v},getRunData:()=>({outputs:y,dispatchGroup:h,programUniforms:g}),getShaderSource:e=>{let i=q(`probs`,t.dataType,t.dims),a=[i,q(`v`,n.dataType,n.dims)];_&&a.push(q(`past_value`,r.dataType,r.dims));let c=o?q(`seq_lens`,o.dataType,o.dims):void 0;o&&a.push(c);let u=s?q(`total_sequence_length_input`,s.dataType,s.dims):void 0;s&&a.push(u);let f=[J(`output`,t.dataType,m)];return d&&f.push(J(`present_value`,t.dataType,p)),`
  const TILE_SIZE = 12u;
  var<workgroup> tileQ: array<${i.type.value}, 144>;
  var<workgroup> tileV: array<${i.type.value}, 144>;
  ${e.registerUniforms([{name:`M`,type:`u32`},{name:`K`,type:`u32`},{name:`N`,type:`u32`},{name:`num_heads`,type:`u32`},{name:`head_size`,type:`u32`},{name:`v_hidden_size`,type:`u32`},{name:`past_sequence_length`,type:`u32`},{name:`kv_sequence_length`,type:`u32`},{name:`n_reps`,type:`u32`}]).declareVariables(...a,...f)}
  ${e.mainStart([12,12,1])}
   let headIdx = workgroup_id.z % uniforms.num_heads;
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let kvHeadIdx = ${l===1?`headIdx`:`headIdx / uniforms.n_reps`};
   let kv_num_heads = ${l===1?`uniforms.num_heads`:`uniforms.num_heads / uniforms.n_reps`};
   let m = global_id.y;
   let n = global_id.x;
   let sequence_length = uniforms.M;
   var total_sequence_length = uniforms.K;
   ${Br(c,u,!0)}
   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
   ${_&&d?`let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;`:``};
   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
   ${d?`let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;`:``}
   var value = ${i.type.storage}(0);
   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
        ${_&&d?`
        if (w + local_id.y < past_sequence_length) {
          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
        }
      `:`
            if (w + local_id.y < uniforms.kv_sequence_length) {
              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
            }`}
        ${d?`
            if (w + local_id.y < present_sequence_length) {
          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
        }`:``}
      }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + headIdx * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`}}},Wr=(e,t,n,r,i,a,o,s,c,l,u=void 0,d=void 0)=>{let f=Math.min(e.outputCount,1+ +!!o+ +!!s),p=f>1?l.pastSequenceLength:0,m=p+l.kvSequenceLength,h=c&&z.size(c.dims)>0?c:void 0,g=[t,n];f>1&&o&&z.size(o.dims)>0&&g.push(o),h&&g.push(h),u&&g.push(u),d&&g.push(d);let _=e.compute(Hr(f,t,n,o,h,l,p,u,d),{inputs:g,outputs:f>1?[-1,1]:[-1]})[0];e.compute(Vr(_,l.batchSize,l.numHeads,p,l.sequenceLength,m,u,d),{inputs:u&&d?[_,u,d]:[_],outputs:[]});let v=[_,r];f>1&&s&&z.size(s.dims)>0&&v.push(s),u&&v.push(u),d&&v.push(d),e.compute(Ur(f,_,r,s,l,p,u,d),{inputs:v,outputs:f>1?[0,2]:[0]})},Gr=(e,t)=>{let n=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],r=t.sequenceLength,i=t.inputHiddenSize,a=t.headSize,o={x:Math.ceil(t.headSize/12),y:Math.ceil(t.sequenceLength/12),z:t.batchSize*t.numHeads},s=[e.inputs[0],e.inputs[1],e.inputs[2]],c=[{type:12,data:r},{type:12,data:i},{type:12,data:a},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}];return e.compute({name:`AttentionPrepare`,shaderCache:{inputDependencies:[`type`,`type`,`type`]},getRunData:()=>({outputs:[{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:c}),getShaderSource:e=>{let t=J(`output_q`,s[0].dataType,n),r=J(`output_k`,s[0].dataType,n),i=J(`output_v`,s[0].dataType,n),a=q(`input`,s[0].dataType,s[0].dims),o=q(`weight`,s[1].dataType,s[1].dims),c=q(`bias`,s[2].dataType,s[2].dims),l=a.type.storage;return`
  const TILE_SIZE = 12u;
  var<workgroup> tileInput: array<${l}, 144>;
  var<workgroup> tileWeightQ: array<${l}, 144>;
  var<workgroup> tileWeightK: array<${l}, 144>;
  var<workgroup> tileWeightV: array<${l}, 144>;
  ${e.registerUniforms([{name:`M`,type:`u32`},{name:`K`,type:`u32`},{name:`N`,type:`u32`},{name:`num_heads`,type:`u32`},{name:`head_size`,type:`u32`},{name:`hidden_size`,type:`u32`},{name:`ldb`,type:`u32`}]).declareVariables(a,o,c,t,r,i)}
  ${e.mainStart([12,12,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = global_id.y;
    let n = global_id.x;

    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;
    let biasOffsetQ = headNumber * uniforms.head_size;
    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;
    let biasOffsetV = uniforms.hidden_size + biasOffsetK;

    var valueQ = ${l}(0);
    var valueK = ${l}(0);
    var valueV = ${l}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

    let headOffset = (m * uniforms.N + n) % uniforms.head_size;
    valueQ += bias[headOffset + biasOffsetQ];
    valueK += bias[headOffset + biasOffsetK];
    valueV += bias[headOffset + biasOffsetV];

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`}},{inputs:s,outputs:[-1,-1,-1]})},Kr=(e,t)=>{let n=zr(e.inputs,t),[r,i,a]=Gr(e,n);return Wr(e,r,i,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],n)}}),Jr,Yr,Xr,Zr,Qr=o(()=>{N(),L(),B(),H(),Y(),Jr=(e,t)=>{if(!e||e.length!==5)throw Error(`BatchNormalization requires 5 inputs`);let n=(e,t,n)=>{let r=t.length;if(r!==e.length)throw Error(`${n}: num dimensions != ${r}`);t.forEach((t,r)=>{if(t!==e[r])throw Error(`${n}: dim[${r}] do not match`)})};if(e[0].dims.length>1){let r=t.format===`NHWC`?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);n(e[1].dims,r,`Invalid input scale`),n(e[2].dims,r,`Invalid input B`),n(e[3].dims,r,`Invalid input mean`),n(e[4].dims,r,`Invalid input var`)}else n(e[1].dims,[1],`Invalid input scale`),n(e[2].dims,[1],`Invalid input B`),n(e[3].dims,[1],`Invalid input mean`),n(e[4].dims,[1],`Invalid input var`)},Yr=(e,t)=>{let{epsilon:n,spatial:r,format:i}=t,a=e[0].dims,o=r?G(a[a.length-1]):1,s=i===`NHWC`&&a.length>1?o:1,c=z.size(a)/o,l=r,u=l?a.length:a,d=q(`x`,e[0].dataType,e[0].dims,o),f=q(`scale`,e[1].dataType,e[1].dims,s),p=q(`bias`,e[2].dataType,e[2].dims,s),m=q(`inputMean`,e[3].dataType,e[3].dims,s),h=q(`inputVar`,e[4].dataType,e[4].dims,s),g=J(`y`,e[0].dataType,u,o),_=()=>{let e=``;if(r)e=`let cOffset = ${a.length===1?`0u`:i===`NHWC`?`outputIndices[${a.length-1}] / ${o}`:`outputIndices[1]`};`;else if(i===`NCHW`)e=`
            ${g.indicesSet(`outputIndices`,`0`,`0`)}
            let cOffset = ${g.indicesToOffset(`outputIndices`)};`;else{e=`var cIndices = ${f.type.indices}(0);
                       cIndices[0] = outputIndices[${a.length-1}];`;for(let t=1;t<f.rank;t++)e+=`cIndices[${t}] = outputIndices[${t}];`;e+=`let cOffset = ${f.indicesToOffset(`cIndices`)};`}return e};return{name:`BatchNormalization`,shaderCache:{hint:`${t.epsilon}_${t.format}_${r}_${o}`,inputDependencies:l?[`rank`,`type`,`type`,`type`,`type`]:void 0},getShaderSource:e=>`
  const epsilon = ${n};
  ${e.registerUniform(`outputSize`,`u32`).declareVariables(d,f,p,m,h,g)}
  ${e.mainStart()}
  ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.outputSize`)}
    var outputIndices = ${g.offsetToIndices(`global_idx * ${o}`)};
    ${_()}
    let scale = ${f.getByOffset(`cOffset`)};
    let bias = ${p.getByOffset(`cOffset`)};
    let inputMean = ${m.getByOffset(`cOffset`)};
    let inputVar = ${h.getByOffset(`cOffset`)};
    let x = ${d.getByOffset(`global_idx`)};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${g.setByOffset(`global_idx`,`value`)}
  }`,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:l?[{type:12,data:c},...W(a)]:[{type:12,data:c}]})}},Xr=e=>V(e),Zr=(e,t)=>{let{inputs:n,outputCount:r}=e,i=Xr({...t,outputCount:r});if(S.webgpu.validateInputContent&&Jr(n,i),t.trainingMode)throw Error(`BatchNormalization trainingMode is not supported yet.`);e.compute(Yr(n,i))}}),$r,ei,ti,ni=o(()=>{B(),Y(),$r=e=>{if(e[0].dims.length!==3)throw Error(`input should have 3 dimensions`);if(![320,640,1280].includes(e[0].dims[2]))throw Error(`number of channels should be 320, 640 or 1280`);if(e[1].dims.length!==1)throw Error(`bias is expected to have 1 dimensions`);if(e[0].dims[2]!==e[1].dims[0])throw Error(`last dimension of input and bias are not the same`)},ei=e=>{let t=e[0].dims,n=e[0].dims[2],r=z.size(t)/4,i=e[0].dataType,a=q(`input`,i,t,4),o=q(`bias`,i,[n],4),s=q(`residual`,i,t,4),c=J(`output`,i,t,4);return{name:`BiasAdd`,getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(r/64)}}),getShaderSource:e=>`
  const channels = ${n}u / 4;
  ${e.declareVariables(a,o,s,c)}

  ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes(r)}
    let value = ${a.getByOffset(`global_idx`)}
      + ${o.getByOffset(`global_idx % channels`)} + ${s.getByOffset(`global_idx`)};
    ${c.setByOffset(`global_idx`,`value`)}
  }`}},ti=e=>{$r(e.inputs),e.compute(ei(e.inputs))}}),ri,$,ii,ai,oi,si,ci,li,ui,di,fi,pi,mi,hi,gi,_i,vi,yi,bi,xi,Si,Ci,wi,Ti,Ei,Di,Oi,ki,Ai,ji,Mi,Ni,Pi,Fi,Ii,Li,Ri,zi,Bi,Vi,Hi,Ui,Wi,Gi,Ki,qi=o(()=>{L(),B(),H(),Y(),ri=(e,t,n,r,i,a,o)=>{let s=Math.ceil(t/4),c=``;c=typeof i==`string`?`${i}(a)`:i(`a`);let l=q(`inputData`,n,[s],4),u=J(`outputData`,r,[s],4),d=[{name:`vec_size`,type:`u32`}];return o&&d.push(...o),`
      ${e.registerUniforms(d).declareVariables(l,u)}

  ${a??``}

  ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.vec_size`)}

    let a = ${l.getByOffset(`global_idx`)};
    ${u.setByOffset(`global_idx`,c)}
  }`},$=(e,t,n,r,i,a=e.dataType,o,s)=>{let c=[{type:12,data:Math.ceil(z.size(e.dims)/4)}];return o&&c.push(...o),{name:t,shaderCache:{hint:i,inputDependencies:[`type`]},getShaderSource:t=>ri(t,z.size(e.dims),e.dataType,a,n,r,s),getRunData:t=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil(z.size(t[0].dims)/64/4)},programUniforms:c})}},ii=e=>{e.compute($(e.inputs[0],`Abs`,`abs`))},ai=e=>{e.compute($(e.inputs[0],`Acos`,`acos`))},oi=e=>{e.compute($(e.inputs[0],`Acosh`,`acosh`))},si=e=>{e.compute($(e.inputs[0],`Asin`,`asin`))},ci=e=>{e.compute($(e.inputs[0],`Asinh`,`asinh`))},li=e=>{e.compute($(e.inputs[0],`Atan`,`atan`))},ui=e=>{e.compute($(e.inputs[0],`Atanh`,`atanh`))},di=e=>V(e),fi=(e,t)=>{let n;switch(t.to){case 10:n=`vec4<f16>`;break;case 1:n=`vec4<f32>`;break;case 12:n=`vec4<u32>`;break;case 6:n=`vec4<i32>`;break;case 9:n=`vec4<bool>`;break;default:throw RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute($(e.inputs[0],`Cast`,n,void 0,t.cacheKey,t.to))},pi=e=>{let t,n,r=e.length>=2&&e[1].data!==0,i=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=r?e[1].getFloat32Array()[0]:-34028234663852886e22,n=i?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=r?e[1].getUint16Array()[0]:64511,n=i?e[2].getUint16Array()[0]:31743;break;default:throw Error(`Unsupport data type`)}return V({min:t,max:n})},mi=(e,t)=>{let n=t||pi(e.inputs),r=Cn(e.inputs[0].dataType);e.compute($(e.inputs[0],`Clip`,e=>`clamp(${e}, vec4<${r}>(uniforms.min), vec4<${r}>(uniforms.max))`,void 0,n.cacheKey,void 0,[{type:e.inputs[0].dataType,data:n.min},{type:e.inputs[0].dataType,data:n.max}],[{name:`min`,type:r},{name:`max`,type:r}]),{inputs:[0]})},hi=e=>{e.compute($(e.inputs[0],`Ceil`,`ceil`))},gi=e=>{e.compute($(e.inputs[0],`Cos`,`cos`))},_i=e=>{e.compute($(e.inputs[0],`Cosh`,`cosh`))},vi=e=>V(e),yi=(e,t)=>{let n=Cn(e.inputs[0].dataType);e.compute($(e.inputs[0],`Elu`,e=>`elu_vf32(${e})`,`
  const elu_alpha_ = ${n}(${t.alpha});

  fn elu_f32(a: ${n}) -> ${n} {
  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);
  }

  fn elu_vf32(v: vec4<${n}>) -> vec4<${n}> {
  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
  }`,t.cacheKey))},bi=(e=`f32`)=>`
const r0: ${e} = 0.3275911;
const r1: ${e} = 0.254829592;
const r2: ${e} = -0.284496736;
const r3: ${e} = 1.421413741;
const r4: ${e} = -1.453152027;
const r5: ${e} = 1.061405429;

fn erf_vf32(v: vec4<${e}>) -> vec4<${e}> {
  let absv = abs(v);
  let x = 1.0 / (1.0 + r0 * absv);
  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
}`,xi=e=>{let t=Cn(e.inputs[0].dataType);e.compute($(e.inputs[0],`Erf`,e=>`erf_vf32(${e})`,bi(t)))},Si=e=>{e.compute($(e.inputs[0],`Exp`,`exp`))},Ci=e=>{e.compute($(e.inputs[0],`Floor`,`floor`))},wi=e=>{let t=Cn(e.inputs[0].dataType);e.compute($(e.inputs[0],`Gelu`,e=>`0.5 * ${e} * (1.0 + erf_vf32(${e} * 0.7071067811865475))`,bi(t)))},Ti=(e,t)=>{let n=Cn(e.inputs[0].dataType);e.compute($(e.inputs[0],`LeakyRelu`,e=>`select(leaky_relu_alpha_ * ${e}, ${e}, ${e} >= vec4<${n}>(0.0))`,`const leaky_relu_alpha_ = ${n}(${t.alpha});`,t.cacheKey))},Ei=e=>{e.compute($(e.inputs[0],`Not`,e=>`!${e}`))},Di=e=>{e.compute($(e.inputs[0],`Neg`,e=>`-${e}`))},Oi=e=>{e.compute($(e.inputs[0],`Reciprocal`,e=>`1.0/${e}`))},ki=e=>{let t=Cn(e.inputs[0].dataType);e.compute($(e.inputs[0],`Relu`,e=>`select(vec4<${t}>(0.0), ${e}, ${e} > vec4<${t}>(0.0))`))},Ai=e=>{e.compute($(e.inputs[0],`Sigmoid`,e=>`(1.0 / (1.0 + exp(-${e})))`))},ji=e=>V(e),Mi=(e,t)=>{let n=Cn(e.inputs[0].dataType);e.compute($(e.inputs[0],`HardSigmoid`,e=>`max(vec4<${n}>(0.0), min(vec4<${n}>(1.0), ${t.alpha} * ${e} + vec4<${n}>(${t.beta})))`,void 0,t.cacheKey))},Ni=e=>{e.compute($(e.inputs[0],`Sin`,`sin`))},Pi=e=>{e.compute($(e.inputs[0],`Sinh`,`sinh`))},Fi=e=>{e.compute($(e.inputs[0],`Sqrt`,`sqrt`))},Ii=e=>{e.compute($(e.inputs[0],`Tan`,`tan`))},Li=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Ri=e=>{e.compute($(e.inputs[0],`Tanh`,Li))},zi=(e=`f32`)=>`
const fast_gelu_a: ${e} = 0.5;
const fast_gelu_b: ${e} = 0.7978845608028654;
const fast_gelu_c: ${e} = 0.035677408136300125;

fn tanh_v(v: vec4<${e}>) -> vec4<${e}> {
  return ${Li(`v`)};
}
`,Bi=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Vi=e=>{let t=Cn(e.inputs[0].dataType);e.compute($(e.inputs[0],`FastGelu`,Bi,zi(t),void 0,e.inputs[0].dataType))},Hi=(e,t)=>{let n=Cn(e.inputs[0].dataType);return e.compute($(e.inputs[0],`ThresholdedRelu`,e=>`select(vec4<${n}>(0.0), ${e}, ${e} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${n}>(${t.alpha});`,t.cacheKey)),0},Ui=e=>{e.compute($(e.inputs[0],`Log`,`log`))},Wi=(e,t)=>`
const alpha = vec4<${e}>(${t});
const one = ${e}(1.0);
const zero = ${e}(0.0);

fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> {
  let v = x *alpha;
  var x1 : vec4<${e}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,Gi=e=>`quick_gelu_impl(${e})`,Ki=(e,t)=>{let n=Cn(e.inputs[0].dataType);e.compute($(e.inputs[0],`QuickGelu`,Gi,Wi(n,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),Ji,Yi,Xi,Zi=o(()=>{B(),Y(),qi(),Ji=e=>{if(e[0].dims.length!==3)throw Error(`input should have 3 dimensions`);if(![2560,5120,10240].includes(e[0].dims[2]))throw Error(`hidden state should be 2560, 5120 or 10240`);if(e[1].dims.length!==1)throw Error(`bias is expected to have 1 dimensions`);if(e[0].dims[2]!==e[1].dims[0])throw Error(`last dimension of input and bias are not the same`)},Yi=e=>{let t=e[0].dims.slice();t[2]/=2;let n=q(`input`,e[0].dataType,e[0].dims,4),r=q(`bias`,e[0].dataType,[e[0].dims[2]],4),i=J(`output`,e[0].dataType,t,4),a=z.size(t)/4,o=U(e[0].dataType);return{name:`BiasSplitGelu`,getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:t=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

  ${t.declareVariables(n,r,i)}

  ${bi(o)}

  ${t.mainStart()}
    ${t.guardAgainstOutOfBoundsWorkgroupSizes(a)}
    let biasIdx = global_idx % halfChannels;
    let batchIndex = global_idx / halfChannels;
    let inputOffset = biasIdx + batchIndex * halfChannels * 2;
    let valueLeft = input[inputOffset] + bias[biasIdx];
    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];
    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);

    ${i.setByOffset(`global_idx`,`valueLeft * geluRight`)}
  }`}},Xi=e=>{Ji(e.inputs),e.compute(Yi(e.inputs))}}),Qi,$i,ea,ta,na,ra,ia,aa,oa,sa,ca,la,ua,da=o(()=>{L(),B(),Y(),Qi=(e,t,n,r,i,a,o,s,c,l,u,d)=>{let f,p;typeof s==`string`?f=p=(e,t)=>`${s}((${e}),(${t}))`:typeof s==`function`?f=p=s:(f=s.scalar,p=s.vector);let m=J(`outputData`,u,r.length,4),h=q(`aData`,c,t.length,4),g=q(`bData`,l,n.length,4),_;if(i)if(a){let e=z.size(t)===1,r=z.size(n)===1,i=t.length>0&&t[t.length-1]%4==0,a=n.length>0&&n[n.length-1]%4==0;_=e||r?m.setByOffset(`global_idx`,p(e?`${h.type.value}(${h.getByOffset(`0`)}.x)`:h.getByOffset(`global_idx`),r?`${g.type.value}(${g.getByOffset(`0`)}.x)`:g.getByOffset(`global_idx`))):`
            let outputIndices = ${m.offsetToIndices(`global_idx * 4u`)};
            let offsetA = ${h.broadcastedIndicesToOffset(`outputIndices`,m)};
            let offsetB = ${g.broadcastedIndicesToOffset(`outputIndices`,m)};
            ${m.setByOffset(`global_idx`,p(o||i?h.getByOffset(`offsetA / 4u`):`${h.type.value}(${h.getByOffset(`offsetA / 4u`)}[offsetA % 4u])`,o||a?g.getByOffset(`offsetB / 4u`):`${g.type.value}(${g.getByOffset(`offsetB / 4u`)}[offsetB % 4u])`))}
          `}else _=m.setByOffset(`global_idx`,p(h.getByOffset(`global_idx`),g.getByOffset(`global_idx`)));else{if(!a)throw Error(`no necessary to use scalar implementation for element-wise binary op implementation.`);let e=(e,t,n=``)=>{let r=`aData[indexA${t}][componentA${t}]`,i=`bData[indexB${t}][componentB${t}]`;return`
            let outputIndices${t} = ${m.offsetToIndices(`global_idx * 4u + ${t}u`)};
            let offsetA${t} = ${h.broadcastedIndicesToOffset(`outputIndices${t}`,m)};
            let offsetB${t} = ${g.broadcastedIndicesToOffset(`outputIndices${t}`,m)};
            let indexA${t} = offsetA${t} / 4u;
            let indexB${t} = offsetB${t} / 4u;
            let componentA${t} = offsetA${t} % 4u;
            let componentB${t} = offsetB${t} % 4u;
            ${e}[${t}] = ${n}(${f(r,i)});
          `};_=u===9?`
            var data = vec4<u32>(0);
            ${e(`data`,0,`u32`)}
            ${e(`data`,1,`u32`)}
            ${e(`data`,2,`u32`)}
            ${e(`data`,3,`u32`)}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:`
            ${e(`outputData[global_idx]`,0)}
            ${e(`outputData[global_idx]`,1)}
            ${e(`outputData[global_idx]`,2)}
            ${e(`outputData[global_idx]`,3)}
          `}return`
        ${e.registerUniform(`vec_size`,`u32`).declareVariables(h,g,m)}

        ${d??``}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.vec_size`)}
        ${_}
      }`},$i=(e,t,n,r,i,a,o=n.dataType)=>{let s=n.dims.map(Number),c=r.dims.map(Number),l=!z.areEqual(s,c),u=s,d=z.size(s),f=!1,p=!1,m=[l];if(l){let e=zt.calcShape(s,c,!1);if(!e)throw Error(`Can't perform binary op on the given tensors`);u=e.slice(),d=z.size(u);let t=z.size(s)===1,n=z.size(c)===1,r=s.length>0&&s[s.length-1]%4==0,i=c.length>0&&c[c.length-1]%4==0;m.push(t),m.push(n),m.push(r),m.push(i);let a=1;for(let e=1;e<u.length;e++){let t=s[s.length-e];if(t===c[c.length-e])a*=t;else break}a%4==0?(p=!0,f=!0):(t||n||r||i)&&(f=!0)}else f=!0;return m.push(f),{name:e,shaderCache:{hint:t+m.map(e=>e.toString()).join(`_`),inputDependencies:[`rank`,`rank`]},getShaderSource:e=>Qi(e,s,c,u,f,l,p,i,n.dataType,r.dataType,o,a),getRunData:()=>({outputs:[{dims:u,dataType:o}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:[{type:12,data:Math.ceil(z.size(u)/4)},...W(s,c,u)]})}},ea=(e,t,n,r,i,a)=>{e.compute($i(t,i??``,e.inputs[0],e.inputs[1],n,r,a))},ta=e=>{ea(e,`Add`,(e,t)=>`${e}+${t}`)},na=e=>{ea(e,`Div`,(e,t)=>`${e}/${t}`)},ra=e=>{ea(e,`Equal`,{scalar:(e,t)=>`u32(${e}==${t})`,vector:(e,t)=>`vec4<u32>(${e}==${t})`},void 0,void 0,9)},ia=e=>{ea(e,`Mul`,(e,t)=>`${e}*${t}`)},aa=e=>{let t=q(`input`,e.inputs[0].dataType,e.inputs[0].dims).type.value;ea(e,`Pow`,{scalar:(e,t)=>`pow_custom(${e},${t})`,vector:(e,t)=>`pow_vector_custom(${e},${t})`},`
    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {
      if (b == ${t}(0.0)) {
        return ${t}(1.0);
      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {
        return ${t}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t===`i32`?`round`:``}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {
      // TODO: implement vectorized pow
      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},oa=e=>{ea(e,`Sub`,(e,t)=>`${e}-${t}`)},sa=e=>{ea(e,`Greater`,{scalar:(e,t)=>`u32(${e}>${t})`,vector:(e,t)=>`vec4<u32>(${e}>${t})`},void 0,void 0,9)},ca=e=>{ea(e,`Less`,{scalar:(e,t)=>`u32(${e}<${t})`,vector:(e,t)=>`vec4<u32>(${e}<${t})`},void 0,void 0,9)},la=e=>{ea(e,`GreaterOrEqual`,{scalar:(e,t)=>`u32(${e}>=${t})`,vector:(e,t)=>`vec4<u32>(${e}>=${t})`},void 0,void 0,9)},ua=e=>{ea(e,`LessOrEqual`,{scalar:(e,t)=>`u32(${e}<=${t})`,vector:(e,t)=>`vec4<u32>(${e}<=${t})`},void 0,void 0,9)}}),fa,pa,ma,ha,ga,_a,va=o(()=>{L(),B(),H(),Y(),fa=(e,t)=>{if(!e||e.length<1)throw Error(`too few inputs`);let n=e[0],r=n.dataType,i=n.dims.length;e.forEach((e,a)=>{if(a!==0){if(e.dataType!==r)throw Error(`input tensors should be one type`);if(e.dims.length!==i)throw Error(`input tensors should have the same shape`);e.dims.forEach((e,r)=>{if(r!==t&&e!==n.dims[r])throw Error(`non concat dimensions must match`)})}})},pa=(e,t)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${e}u>(${t});
    for (var i: u32 = 0u; i < ${e}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${e}u;
  }`,ma=(e,t)=>{let n=e.length,r=[];for(let i=0;i<n;++i){let a=t.setByOffset(`global_idx`,e[i].getByIndices(`indices`));n===1?r.push(a):i===0?r.push(`if (inputIndex == ${i}u) { ${a} }`):i===n-1?r.push(`else { ${a} }`):r.push(`else if (inputIndex == ${i}) { ${a} }`)}return r.join(`
`)},ha=(e,t,n,r)=>{let i=z.size(n),a=Array(e.length),o=Array(e.length),s=0,c=[],l=[],u=[{type:12,data:i}];for(let n=0;n<e.length;++n)s+=e[n].dims[t],a[n]=s,l.push(e[n].dims.length),o[n]=q(`input${n}`,r,l[n]),c.push(`rank`),u.push({type:12,data:a[n]});for(let t=0;t<e.length;++t)u.push(...W(e[t].dims));u.push(...W(n));let d=J(`output`,r,n.length),f=d.indicesGet(`indices`,t),p=Array.from(Array(a.length).keys()).map(e=>`uniforms.sizeInConcatAxis${e}`).join(`,`);return{name:`Concat`,shaderCache:{hint:`${t}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:n,dataType:r}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:u}),getShaderSource:t=>`

  ${(()=>{t.registerUniform(`outputSize`,`u32`);for(let n=0;n<e.length;n++)t.registerUniform(`sizeInConcatAxis${n}`,`u32`);return t.declareVariables(...o,d)})()}

  ${pa(a.length,p)}

  ${t.mainStart()}
    ${t.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.outputSize`)}

    var indices = ${d.offsetToIndices(`global_idx`)};

    let inputIndex = calculateInputIndex(${f});
    if (inputIndex != 0u) {
      let sizeInConcatAxis = array<u32, ${a.length}u>(${p});
      ${f} -= sizeInConcatAxis[inputIndex - 1u];
    }

    ${ma(o,d)}
  }`}},ga=(e,t)=>{let n=e.inputs,r=n[0].dims,i=z.normalizeAxis(t.axis,r.length);fa(n,i);let a=r.slice();a[i]=n.reduce((e,t)=>e+(t.dims.length>i?t.dims[i]:0),0);let o=n.filter(e=>z.size(e.dims)>0);e.compute(ha(o,i,a,n[0].dataType),{inputs:o})},_a=e=>V({axis:e.axis})}),ya,ba,xa,Sa,Ca=o(()=>{L(),B(),ya=(e,t,n=`f32`)=>{switch(e.activation){case`Relu`:return`value = max(value, ${t}(0.0));`;case`Sigmoid`:return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case`Clip`:return`value = clamp(value, ${t}(${n}(uniforms.clip_min)), ${t}(${n}(uniforms.clip_max)));`;case`HardSigmoid`:return`value = max(${t}(0.0), min(${t}(1.0), ${n}(uniforms.alpha) * value + ${n}(uniforms.beta)));`;case`LeakyRelu`:return`value = select(${n}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case`Tanh`:return`let e2x = exp(-2.0 * abs(value));
              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
        `;case``:return``;default:throw Error(`Unsupported activation ${e.activation}`)}},ba=(e,t)=>{e.activation===`Clip`?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation===`HardSigmoid`?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation===`LeakyRelu`&&t.push({type:1,data:e.alpha})},xa=(e,t)=>{e.activation===`Clip`?t.push({name:`clip_max`,type:`f32`},{name:`clip_min`,type:`f32`}):e.activation===`HardSigmoid`?t.push({name:`alpha`,type:`f32`},{name:`beta`,type:`f32`}):e.activation===`LeakyRelu`&&t.push({name:`alpha`,type:`f32`})},Sa=e=>{let t=e?.activation||``;if(t===`HardSigmoid`){let[n,r]=e?.activation_params||[.2,.5];return{activation:t,alpha:n,beta:r}}else if(t===`Clip`){let[n,r]=e?.activation_params||[Ht,Ut];return{activation:t,clipMax:r,clipMin:n}}else if(t===`LeakyRelu`){let[n]=e?.activation_params||[.01];return{activation:t,alpha:n}}return{activation:t}}}),wa,Ta,Ea=o(()=>{wa=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw Error(`${e}-component is not supported.`)}},Ta=e=>`
      ${e?`value = value + getBiasByOutputCoords(coords);`:``}
      `}),Da,Oa=o(()=>{Da=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),ka,Aa,ja=o(()=>{L(),B(),Y(),Ca(),ka=(e,t,n,r,i)=>{let a=r-n;return`
      ${Array.from({length:n}).map((n,o)=>`
      if (${K(t.shape,o,t.rank)} != 1) {
        ${t.indicesSet(e,o,K(i,o+a,r))}
      } else {
        ${t.indicesSet(e,o,0)}
      }`).join(``)}
`},Aa=(e,t,n,r,i=!1,a)=>{let o=e[0].dims,s=e[1].dims,c=o[o.length-2],l=s[s.length-1],u=o[o.length-1],d=G(l),f=G(u),p=G(c),m=z.size(n)/d/p,h=e.length>2,g=r?r.slice(0,-2):n.slice(0,-2),_=[z.size(g),c,l],v=[{type:12,data:m},{type:12,data:c},{type:12,data:l},{type:12,data:u}];return ba(t,v),v.push(...W(g,o,s)),h&&v.push(...W(e[2].dims)),v.push(...W(_)),{name:`MatMulNaive`,shaderCache:{hint:`${t.activation};${d};${f};${p};${i}`,inputDependencies:h?[`rank`,`rank`,`rank`]:[`rank`,`rank`]},getRunData:()=>({outputs:[{dims:a?a(n):n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:v}),getShaderSource:r=>{let a=kn(`batch_dims`,e[0].dataType,g.length),c=q(`a`,e[0].dataType,o.length,f),l=q(`b`,e[1].dataType,s.length,d),u=J(`output`,e[0].dataType,_.length,d),m=U(u.type.tensor),v=ya(t,u.type.value,m),y=[c,l],b=``;if(h){let t=i?d:1;y.push(q(`bias`,e[2].dataType,e[2].dims.length,t)),b=`${i?`value += bias[col / ${t}];`:`value += ${u.type.value}(bias[row + i]);`}`}let x=[{name:`output_size`,type:`u32`},{name:`M`,type:`u32`},{name:`N`,type:`u32`},{name:`K`,type:`u32`}];xa(t,x);let S=()=>{let e=`var a_data: ${c.type.value};`;for(let t=0;t<f;t++)e+=`
              let b_data${t} = b[(b_offset + (k + ${t}) * uniforms.N + col) / ${d}];`;for(let t=0;t<p;t++){e+=`a_data = a[(a_offset + (row + ${t}) * uniforms.K + k) / ${f}];`;for(let n=0;n<f;n++)e+=`
            values[${t}] = fma(${l.type.value}(a_data${f===1?``:`[${n}]`}), b_data${n}, values[${t}]);
`}return e};return`
  ${r.registerUniforms(x).registerInternalVariables(a).declareVariables(...y,u)}
  ${r.mainStart()}
    ${r.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
    let col = (global_idx % (uniforms.N / ${d})) * ${d};
    var index1 = global_idx / (uniforms.N / ${d});
    let stride1 = uniforms.M / ${p};
    let row = (index1 % stride1) * ${p};
    let batch = index1 / stride1;

    ${n.length===2?``:`let batch_indices = ${a.offsetToIndices(`batch`)};`}

    var a_indices: ${c.type.indices};
    ${ka(`a_indices`,c,c.rank-2,a.rank,`batch_indices`)}
    ${c.indicesSet(`a_indices`,c.rank-2,0)}
    ${c.indicesSet(`a_indices`,c.rank-1,0)}
    let a_offset = ${c.indicesToOffset(`a_indices`)};

    var b_indices: ${l.type.indices};
    ${ka(`b_indices`,l,l.rank-2,a.rank,`batch_indices`)}
    ${l.indicesSet(`b_indices`,l.rank-2,0)}
    ${l.indicesSet(`b_indices`,l.rank-1,0)}
    let b_offset = ${l.indicesToOffset(`b_indices`)};
    var values: array<${u.type.value}, ${p}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${f}) {
      ${S()}
    }
    for (var i = 0u; i < ${p}u; i++) {
      var value = values[i];
      ${b}
      ${v}
      let cur_indices = ${u.type.indices}(batch, row + i, col);
      let offset = ${u.indicesToOffset(`cur_indices`)};
      ${u.setByOffset(`offset / ${d}`,`value`)};
    }
  }
  `}}}}),Ma,Na,Pa,Fa,Ia,La,Ra,za,Ba=o(()=>{L(),B(),Y(),Ca(),ja(),Ea(),Ma=(e,t)=>e?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${t?`, batchIndices`:``});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${t?`, batchIndices`:``});
        `,Na=(e,t)=>e?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${t===3?``:`let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];`}
        for (var i = 0; i < rowPerThread; i = i + 1) {
          acc[i] = BCached0 * ACached0[i] + acc[i];
          acc[i] = BCached1 * ACached1[i] + acc[i];
          acc[i] = BCached2 * ACached2[i] + acc[i];
          ${t===3?``:`acc[i] = BCached3 * ACached3[i] + acc[i];`}
        }`:`
        for (var i = 0; i < rowPerThread; i = i + 1) {
          let ACached = mm_Asub[tileRow + i][k];
          acc[i] = BCached0 * ACached.x + acc[i];
          acc[i] = BCached1 * ACached.y + acc[i];
          acc[i] = BCached2 * ACached.z + acc[i];
          ${t===3?``:`acc[i] = BCached3 * ACached.w + acc[i];`}
        }`,Pa=(e,t,n=`f32`,r,i=!1,a=32,o=!1,s=32)=>{let c=t[1]*e[1],l=t[0]*e[0],u=i?c:a,d=i?a:c,f=u/t[0],p=a/t[1];if(!((i&&f===4&&e[1]===4||!i&&(f===3||f===4))&&u%t[0]===0&&a%t[1]===0&&e[0]===4))throw Error(`If transposeA ${i} is true, innerElementSize ${f} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${f} must be 3 or 4.
  tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${f}<${n}>, ${u/f}>, ${d}>;
var<workgroup> mm_Bsub: array<array<vec4<${n}>, ${l/e[0]}>, ${a}>;

const rowPerThread = ${e[1]};
const colPerThread = ${e[0]};
const innerElementSize = ${f};
const tileInner = ${a};

@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
        @builtin(global_invocation_id) globalId : vec3<u32>,
        @builtin(workgroup_id) workgroupId : vec3<u32>) {
  let localRow = i32(localId.y);
  let tileRow = localRow * rowPerThread;
  let tileCol = i32(localId.x);

  let globalRow =i32(globalId.y) * rowPerThread;
  let globalCol = i32(globalId.x);
  let batch = ${o?`0`:`i32(globalId.z)`};
  ${r?`let batchIndices = ${r.offsetToIndices(`u32(batch)`)};`:``}
  let globalRowStart = i32(workgroupId.y) * ${c};

  let num_tiles = ${o?`${Math.ceil(s/a)}`:`(uniforms.dim_inner - 1) / tileInner + 1`};
  var kStart = ${o?`i32(globalId.z) * ${s}`:`0`};

  var acc: array<vec4<${n}>, rowPerThread>;

  // Loop over shared dimension.
  let tileRowB = localRow * ${p};
  for (var t = 0; t < num_tiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let inputRow = tileRow + innerRow;
          let inputCol = tileCol;
          ${Ma(i,r)}
      }

      // Load one tile of B into local memory.
      for (var innerRow = 0; innerRow < ${p}; innerRow = innerRow + 1) {
          let inputRow = tileRowB + innerRow;
          let inputCol = tileCol;
          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${r?`, batchIndices`:``});
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

      // Compute acc values for a single thread.
      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {
          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];
          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];
          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];
          ${f===3?``:`let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];`}

          ${Na(i,f)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},Fa=(e,t)=>e?`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              kStart + inputRow,
              globalRowStart + inputCol${t?`, batchIndices`:``});
            `:`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              globalRowStart + inputRow,
              kStart + inputCol${t?`, batchIndices`:``});
            `,Ia=e=>e?`let ACached = mm_Asub[k][tileRow + innerRow];`:`let ACached = mm_Asub[tileRow + innerRow][k];`,La=(e,t,n=`f32`,r,i=!1,a=32,o=!1,s=32,c=!1)=>{let l=e[1]*t[1],u=e[0]*t[0],d=i?l:a,f=i?a:l;if(!(f%t[1]===0&&d%t[0]===0&&a%t[1]===0))throw Error(`tileAHight ${f} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${d} must be divisible by workgroupSize[0]${t[0]}, tileInner ${a} must be divisible by workgroupSize[1]${t[1]}`);let p=f/t[1],m=d/t[0],h=a/t[1],g=c?`
    let localRow = i32(localId.y);
    let localCol = i32(localId.x);
    let globalRowStart = i32(workgroupId.y) * ${l};
    let globalColStart = i32(workgroupId.x) * ${u};

    // Loop over shared dimension.
    for (var t = 0; t < num_tiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var inputRow = localRow; inputRow < ${f}; inputRow = inputRow + ${t[1]}) {
        for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${t[0]}) {
          ${Fa(i,r)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) {
            for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) {
          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
            kStart + inputRow,
            globalColStart + inputCol${r?`, batchIndices`:``});
        }
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

      // Compute acc values for a single thread.
      var BCached : array<${n}, colPerThread>;
      for (var k = 0; k < tileInner; k = k + 1) {
        for (var inner = 0; inner < colPerThread; inner = inner + 1) {
          BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];
        }
        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}
          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
            acc[innerRow][innerCol] = acc[innerRow][innerCol] +
                ACached * BCached[innerCol];
          }
        }
      }
      workgroupBarrier();
    }
    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      let gRow = globalRowStart + localRow + innerRow * ${t[1]};
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        let gCol = globalColStart + localCol + innerCol * ${t[0]};
        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
      }
    }
    `:`
let tileRow = i32(localId.y) * rowPerThread;
let tileCol = i32(localId.x) * colPerThread;

let globalRow = i32(globalId.y) * rowPerThread;
let globalCol = i32(globalId.x) * colPerThread;
let globalRowStart = i32(workgroupId.y) * ${l};

let tileRowA = i32(localId.y) * ${p};
let tileColA = i32(localId.x) * ${m};
let tileRowB = i32(localId.y) * ${h};
// Loop over shared dimension.
for (var t = 0; t < num_tiles; t = t + 1) {
  // Load one tile of A into local memory.
  for (var innerRow = 0; innerRow < ${p}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${m}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${Fa(i,r)}
    }
  }

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${h}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${r?`, batchIndices`:``});
    }
  }
  kStart = kStart + tileInner;
  workgroupBarrier();

  // Compute acc values for a single thread.
  var BCached : array<${n}, colPerThread>;
  for (var k = 0; k < tileInner; k = k + 1) {
    for (var inner = 0; inner < colPerThread; inner = inner + 1) {
      BCached[inner] = mm_Bsub[k][tileCol + inner];
    }

    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      ${Ia(i)}
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
      }
    }
  }

  workgroupBarrier();
}

for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
    mm_write(batch, globalRow + innerRow, globalCol + innerCol,
        acc[innerRow][innerCol]);
  }
}
`;return`
  var<workgroup> mm_Asub : array<array<${n}, ${d}>, ${f}>;
  var<workgroup> mm_Bsub : array<array<${n}, ${u}>, ${a}>;
  const rowPerThread = ${e[1]};
  const colPerThread = ${e[0]};
  const tileInner = ${a};

@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
        @builtin(global_invocation_id) globalId : vec3<u32>,
        @builtin(workgroup_id) workgroupId : vec3<u32>) {
    let batch = ${o?`0`:`i32(globalId.z)`};
    ${r?`let batchIndices = ${r.offsetToIndices(`u32(batch)`)};`:``}
    let num_tiles = ${o?`${Math.ceil(s/a)}`:`(uniforms.dim_inner - 1) / tileInner + 1`};
    var kStart = ${o?`i32(globalId.z) * ${s}`:`0`};

    var acc : array<array<${n}, colPerThread>, rowPerThread>;
    ${g}
  }
`},Ra=(e,t,n,r,i=!1)=>{let[a,o,s,c]=r,l=U(r[0].type.tensor);return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${a.type.indices}) -> ${wa(e,l)} {
      var value = ${wa(e,l)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        var aIndices: ${o.type.indices};
        ${ka(`aIndices`,o,o.rank-2,a.rank,`batchIndices`)}
        ${o.indicesSet(`aIndices`,o.rank-2,`u32(row)`)}
        ${o.indicesSet(`aIndices`,o.rank-1,`u32(colIn)`)}
        value = ${o.getByIndices(`aIndices`)};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${a.type.indices}) -> ${wa(e,l)} {
      var value = ${wa(e,l)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
      {
        var bIndices: ${s.type.indices};
        ${ka(`bIndices`,s,s.rank-2,a.rank,`batchIndices`)}
        ${s.indicesSet(`bIndices`,s.rank-2,`u32(row)`)}
        ${s.indicesSet(`bIndices`,s.rank-1,`u32(colIn)`)}
        value = ${s.getByIndices(`bIndices`)};
      }
      return value;
    }

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${wa(e,l)}) {
      let col = colIn * ${e};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${t?`value = value + ${i?`bias[colIn]`:`${wa(e,l)}(bias[row])`};`:``}
        ${n}
        ${c.setByIndices(`vec3<u32>(coords)`,`value`)}
      }
    }
    `},za=(e,t,n,r,i=!1,a)=>{let o=e[0].dims,s=e[1].dims,c=o.slice(0,-2),l=s.slice(0,-2),u=r?r.slice(0,-2):n.slice(0,-2),d=z.size(u),f=o[o.length-2],p=o[o.length-1],m=s[s.length-1],h=p%4==0&&m%4==0,g=f<=8?[4,1,1]:[4,4,1],_=[8,8,1],v=[Math.ceil(m/_[0]/g[0]),Math.ceil(f/_[1]/g[1]),Math.ceil(d/_[2]/g[2])],y=h?4:1,b=[...c,f,p/y],x=b.length,S=[...l,p,m/y],C=S.length,w=[d,f,m/y],ee=[{type:6,data:f},{type:6,data:m},{type:6,data:p}];ba(t,ee),ee.push(...W(u,b,S));let T=[`rank`,`rank`],E=e.length>2;return E&&(ee.push(...W(e[2].dims)),T.push(`rank`)),ee.push(...W(w)),{name:`MatMul`,shaderCache:{hint:`${g};${t.activation};${h};${i}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:a?a(n):n,dataType:e[0].dataType}],dispatchGroup:{x:v[0],y:v[1],z:v[2]},programUniforms:ee}),getShaderSource:n=>{let r=u.length,a=kn(`batchDims`,e[0].dataType,r,1),o=U(e[0].dataType),s=q(`a`,e[0].dataType,x,y),c=q(`b`,e[1].dataType,C,y),l=J(`result`,e[0].dataType,w.length,y),d=[s,c];if(E){let t=i?y:1;d.push(q(`bias`,e[2].dataType,e[2].dims.length,t))}let f=[{name:`dim_a_outer`,type:`i32`},{name:`dim_b_outer`,type:`i32`},{name:`dim_inner`,type:`i32`}];xa(t,f);let p=U(l.type.tensor),m=ya(t,l.type.value,p),v=Ra(y,E,m,[a,s,c,l],i);return`
  ${n.registerUniforms(f).registerInternalVariables(a).declareVariables(...d,l)}
  ${v}
  ${h?Pa(g,_,o,a):La(g,_,o,a)}
                   `}}}}),Va,Ha,Ua=o(()=>{L(),Lt(),Y(),Ca(),Ea(),Oa(),Ba(),Va=(e,t,n,r,i=!1,a,o=4,s=4,c=4,l=`f32`)=>{let u=e=>{switch(e){case 1:return`resData = x[xIndex];`;case 3:return`resData = vec3<${l}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return`resData = x[xIndex / 4];`;default:throw Error(`innerElementSize ${e} is not supported.`)}},d=e=>{switch(e){case 1:return`return w[row * i32(uniforms.w_shape[3]) + colIn];`;case 4:return`return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];`;default:throw Error(`innerElementSize ${e} is not supported.`)}},f=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,p=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,m=e?`i32(uniforms.x_shape[1])`:`i32(uniforms.x_shape[2])`,h=e?`i32(uniforms.x_shape[2])`:`i32(uniforms.x_shape[3])`,g=e?`row`:`col`,_=e?`col`:`row`,v=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?`i32(uniforms.result_shape[2])`:`i32(uniforms.result_shape[3])`};
    let outRow = ${g} / outWidth;
    let outCol = ${g} % outWidth;

    let WRow = ${_} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${_} / inChannels % i32(uniforms.w_shape[1]);
    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
    let xCh = ${_} % inChannels;
    var resData = ${wa(o,l)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${m} && xCol >= 0 && xCol < ${h}) {
      ${f}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${u(o)}
    }
    return resData;`,y=e?t&&r?`
    let col = colIn * ${o};
    ${v}`:`
    let col = colIn * ${o};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${v}
    }
    return ${wa(o,l)}(0.0);`:r&&n?`
    let col = colIn * ${o};
    ${v}`:`
    let col = colIn * ${o};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${v}
    }
    return ${wa(o,l)}(0.0);`,b=e?r&&n?d(s):`
    let col = colIn * ${s};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${d(s)}
    }
    return ${wa(s,l)}(0.0);`:`
    let col = colIn * ${s};
    if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {
      ${d(s)}
    }
    return ${wa(s,l)}(0.0);`,x=wa(c,l),S=wa(e?o:s,l),C=wa(e?s:o,l),w=ya(a,x,l);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${S} {
      ${e?y:b}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${C} {
      ${e?b:y}
    }

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${x}) {
      let col = colIn * ${c};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${e?`i32(uniforms.result_shape[2])`:`i32(uniforms.result_shape[3])`};
      ${p}
      ${Ta(i)}
      ${w}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Ha=(e,t,n,r,i,a,o,s,c)=>{let l=t.format===`NHWC`,u=l?e[0].dims[3]:e[0].dims[1],d=n[0],f=l?n[2]:n[3],p=l?n[1]:n[2],m=l?n[3]:n[1],h=l&&(u%4==0||u%3==0)&&m%4==0,g=l?m:f*p,_=l?f*p:m,v=[8,8,1],y=r<=8?[4,1,1]:[4,4,1],b=[Math.ceil(g/v[0]/y[0]),Math.ceil(_/v[1]/y[1]),Math.ceil(d/v[2]/y[2])];R(`verbose`,()=>`[conv2d_mm_webgpu] dispatch = ${b}`);let x=h?l&&u%4!=0?3:4:1,S=v[1]*y[1],C=v[0]*y[0],w=Math.max(v[0]*x,v[1]),ee=r%S===0,T=i%C===0,E=a%w===0,D=h?[x,4,4]:[1,1,1],O=[{type:6,data:r},{type:6,data:i},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];ba(t,O),O.push(...W(e[0].dims,e[1].dims));let k=[`rank`,`rank`];return o&&(O.push(...W(e[2].dims)),k.push(`rank`)),O.push(...W(n)),{name:`Conv2DMatMul`,shaderCache:{hint:`${t.cacheKey};${x};${h};${ee};${T};${E};${S};${C};${w}`,inputDependencies:k},getRunData:()=>({outputs:[{dims:c?c(n):n,dataType:e[0].dataType}],dispatchGroup:{x:b[0],y:b[1],z:b[2]},programUniforms:O}),getShaderSource:r=>{let i=[{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}];xa(t,i);let a=h?4:1,c=U(e[0].dataType),u=`
      fn setOutputAtIndex(flatIndex : i32, value : ${h?`vec4<${c}>`:c}) {
        result[flatIndex] = ${h?`vec4<${c}>`:c}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${h?`vec4<${c}>`:c}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${h?`/ 4`:``}, value);
      }`,d=[q(`x`,e[0].dataType,e[0].dims.length,x===3?1:x),q(`w`,e[1].dataType,e[1].dims.length,a)],f=J(`result`,e[0].dataType,n.length,a);if(o){let t=q(`bias`,e[2].dataType,e[2].dims.length,a);d.push(t),u+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${h?`vec4<${c}>`:c} {
          return bias[coords.${l?`w`:`y`}${h?`/ 4`:``}];
        }`}return`
        ${Da(`uniforms.result_strides`)}
        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,
        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,
        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };
        ${r.registerUniforms(i).declareVariables(...d,f)}
        ${u}
        ${Va(l,ee,T,E,o,t,D[0],D[1],D[2],c)}
        ${h?Pa(y,v,c,void 0,!l,w):La(y,v,c,void 0,!l,w,!1,void 0,s)}`}}}}),Wa,Ga,Ka,qa,Ja,Ya,Xa,Za,Qa=o(()=>{L(),Lt(),B(),Y(),Ca(),Ea(),Wa=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t},Ga=e=>typeof e==`number`?[e,e,e]:e,Ka=(e,t)=>t<=1?e:e+(e-1)*(t-1),qa=(e,t,n,r=1)=>{let i=Ka(t,r);return Math.floor((e[0]*(n-1)-n+i)/2)},Ja=(e,t,n,r,i)=>{i??=qa(e,t[0],r[0]);let a=[0,0,0,n];for(let n=0;n<3;n++)e[n]+2*i>=t[n]&&(a[n]=Math.trunc((e[n]-t[n]+2*i)/r[n]+1));return a},Ya=(e,t,n,r,i,a,o,s,c,l)=>{let u,d,f,p;if(e===`VALID`&&(e=0),typeof e==`number`){u={top:e,bottom:e,left:e,right:e,front:e,back:e};let m=Ja([t,n,r,1],[s,c,l],1,[i,a,o],e);d=m[0],f=m[1],p=m[2]}else if(Array.isArray(e)){if(!e.every((e,t,n)=>e===n[0]))throw Error(`Unsupported padding parameter: ${e}`);u={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let m=Ja([t,n,r,1],[s,c,l],1,[i,a,o],e[0]);d=m[0],f=m[1],p=m[2]}else if(e===`SAME_UPPER`){d=Math.ceil(t/i),f=Math.ceil(n/a),p=Math.ceil(r/o);let e=(d-1)*i+s-t,m=(f-1)*a+c-n,h=(p-1)*o+l-r,g=Math.floor(e/2),_=e-g,v=Math.floor(m/2),y=m-v,b=Math.floor(h/2);u={top:v,bottom:y,left:b,right:h-b,front:g,back:_}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:u,outDepth:d,outHeight:f,outWidth:p}},Xa=(e,t,n,r,i,a=!1,o=`channelsLast`)=>{let s,c,l,u,d;if(o===`channelsLast`)[s,c,l,u,d]=e;else if(o===`channelsFirst`)[s,d,c,l,u]=e;else throw Error(`Unknown dataFormat ${o}`);let[f,,p,m,h]=t,[g,_,v]=Ga(n),[y,b,x]=Ga(r),S=Ka(p,y),C=Ka(m,b),w=Ka(h,x),{padInfo:ee,outDepth:T,outHeight:E,outWidth:D}=Ya(i,c,l,u,g,_,v,S,C,w),O=a?f*d:f,k=[0,0,0,0,0];return o===`channelsFirst`?k=[s,O,T,E,D]:o===`channelsLast`&&(k=[s,T,E,D,O]),{batchSize:s,dataFormat:o,inDepth:c,inHeight:l,inWidth:u,inChannels:d,outDepth:T,outHeight:E,outWidth:D,outChannels:O,padInfo:ee,strideDepth:g,strideHeight:_,strideWidth:v,filterDepth:p,filterHeight:m,filterWidth:h,effectiveFilterDepth:S,effectiveFilterHeight:C,effectiveFilterWidth:w,dilationDepth:y,dilationHeight:b,dilationWidth:x,inShape:e,outShape:k,filterShape:t}},Za=(e,t,n,r,i,a)=>{let o=a===`channelsLast`;o?e[0].dims[3]:e[0].dims[1];let s=[64,1,1],c={x:n.map((e,t)=>t)},l=[Math.ceil(Wa(c.x.map(e=>n[e]))/s[0]),1,1];R(`verbose`,()=>`[conv3d_naive_webgpu] dispatch = ${l}`);let u=[{type:12,data:z.size(n)},{type:12,data:r},{type:12,data:i},{type:12,data:t.strides},{type:12,data:t.dilations}];ba(t,u),u.push(...W(e[0].dims,e[1].dims));let d=[`rank`,`rank`],f=e.length===3;return f&&(u.push(...W(e[2].dims)),d.push(`rank`)),u.push(...W(n)),{name:`Conv3DNaive`,shaderCache:{hint:`${t.cacheKey};${o};1;${f}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:l[0],y:l[1],z:l[2]},programUniforms:u}),getShaderSource:a=>{let s=[{name:`output_size`,type:`u32`},{name:`filter_dims`,type:`u32`,length:r.length},{name:`pads`,type:`u32`,length:i.length},{name:`strides`,type:`u32`,length:t.strides.length},{name:`dilations`,type:`u32`,length:t.dilations.length}];xa(t,s);let c=U(e[0].dataType),l=q(`x`,e[0].dataType,e[0].dims.length,1),u=q(`W`,e[1].dataType,e[1].dims.length,1),d=[l,u],p=J(`result`,e[0].dataType,n.length,1),m=``;if(f){let t=q(`bias`,e[2].dataType,e[2].dims.length,1);d.push(t),m+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${c} {
          return bias[${o?K(`coords`,4,5):K(`coords`,1,5)}];
        }`}let h=wa(1,c),g=ya(t,h,c);return`
            ${m}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${l.getByIndices(`aIndices`)};
            }
            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${u.getByIndices(`aIndices`)};
            }
          ${a.registerUniforms(s).declareVariables(...d,p)}
          ${a.mainStart()}
          ${a.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
              let coords = ${p.offsetToIndices(`global_idx`)};
              let batch = ${K(`coords`,0,l.rank)};
              let d2 = ${o?K(`coords`,l.rank-1,l.rank):K(`coords`,1,l.rank)};
              let xFRCCorner = vec3<u32>(${o?K(`coords`,1,l.rank):K(`coords`,2,l.rank)},
              ${o?K(`coords`,2,l.rank):K(`coords`,3,l.rank)},
              ${o?K(`coords`,3,l.rank):K(`coords`,4,l.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${o?K(`uniforms.x_shape`,1,l.rank):K(`uniforms.x_shape`,2,l.rank)};
              let xShapeZ = ${o?K(`uniforms.x_shape`,2,l.rank):K(`uniforms.x_shape`,3,l.rank)};
              let xShapeW = ${o?K(`uniforms.x_shape`,3,l.rank):K(`uniforms.x_shape`,4,l.rank)};
              let xShapeU = ${o?K(`uniforms.x_shape`,4,l.rank):K(`uniforms.x_shape`,1,l.rank)};
              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
              let inputDepthVec4Remainder = xShapeU % 4;

              var value = 0.0;
              for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) {
                let xF = xFCorner + wF * uniforms.dilations[0];
                if (xF < 0 || xF >= xShapeY) {
                  continue;
                }

                for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) {
                  let xR = xRCorner + wR * uniforms.dilations[1];
                  if (xR < 0 || xR >= xShapeZ) {
                    continue;
                  }

                  for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) {
                    let xC = xCCorner + wC * uniforms.dilations[2];
                    if (xC < 0 || xC >= xShapeW) {
                      continue;
                    }

                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
                      ${o?`let xValues = vec4<f32>(
                               getX(batch, xF, xR, xC, d1),
                               getX(batch, xF, xR, xC, d1 + 1),
                               getX(batch, xF, xR, xC, d1 + 2),
                               getX(batch, xF, xR, xC, d1 + 3));
                            `:`let xValues = vec4<f32>(
                               getX(batch, d1, xF, xR, xC),
                               getX(batch, d1 + 1, xF, xR, xC),
                               getX(batch, d1 + 2, xF, xR, xC),
                               getX(batch, d1 + 3, xF, xR, xC));
                            `}
                            let wValues = vec4<f32>(
                              getW(d2, d1, wF, wR, wC),
                              getW(d2, d1 + 1, wF, wR, wC),
                              getW(d2, d1 + 2, wF, wR, wC),
                              getW(d2, d1 + 3, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                    if (inputDepthVec4Remainder == 1) {
                        ${o?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
                    } else if (inputDepthVec4Remainder == 2) {
                      ${o?`let xValues = vec2<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
                      `:`let xValues = vec2<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
                    `}
                    let wValues = vec2<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
                      value += dot(xValues, wValues);
                    } else if (inputDepthVec4Remainder == 3) {
                      ${o?`let xValues = vec3<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
                      `:`let xValues = vec3<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
                    `}
                    let wValues = vec3<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                  }
                }
              }
              ${f?`value = value + getBiasByOutputCoords(coords)`:``};
              ${g}
              result[global_idx] = f32(value);
          }`}}}}),$a,eo,to=o(()=>{L(),B(),Y(),Ca(),$a=(e,t,n,r)=>{let i=e.length>2,a=i?`value += b[output_channel];`:``,o=e[0].dims,s=e[1].dims,c=t.format===`NHWC`,l=c?n[3]:n[1],u=l/t.group,d=c&&u>=4?G(l):1,f=z.size(n)/d,p=[{type:12,data:f},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:u}];ba(t,p),p.push(...W(o,[s[0],s[1],s[2],s[3]/d]));let m=i?[`rank`,`rank`,`rank`]:[`rank`,`rank`];return p.push(...W([n[0],n[1],n[2],n[3]/d])),{name:`GroupedConv`,shaderCache:{hint:`${t.cacheKey}_${d}`,inputDependencies:m},getRunData:()=>({outputs:[{dims:r?r(n):n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:p}),getShaderSource:r=>{let l=J(`output`,e[0].dataType,n.length,d),u=U(l.type.tensor),f=ya(t,l.type.value,u),p=q(`x`,e[0].dataType,o.length),m=q(`w`,e[1].dataType,s.length,d),h=[p,m];i&&h.push(q(`b`,e[2].dataType,e[2].dims,d));let g=[{name:`output_size`,type:`u32`},{name:`dilations`,type:`u32`,length:t.dilations.length},{name:`strides`,type:`u32`,length:2},{name:`pads`,type:`u32`,length:2},{name:`output_channels_per_group`,type:`u32`}];xa(t,g);let _=c?`
      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

        if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) {
          continue;
        }

        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) {
          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
          if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) {
            continue;
          }

          for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {
            let input_channel = in_channel_offset + wInChannel;
            let xVal = ${p.get(`batch`,`xHeight`,`xWidth`,`input_channel`)};
            let wVal = ${m.get(`wHeight`,`wWidth`,`wInChannel`,`output_channel`)};
            value += xVal * wVal;
          }
        }
      }
      `:`
      for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {
        let input_channel = in_channel_offset + wInChannel;
        for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {
          let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

          if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) {
            continue;
          }

          for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {
            let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
            if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) {
              continue;
            }

            let xVal = ${p.get(`batch`,`input_channel`,`xHeight`,`xWidth`)};
            let wVal = ${m.get(`output_channel`,`wInChannel`,`wHeight`,`wWidth`)};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${r.registerUniforms(g).declareVariables(...h,l)}

  ${r.mainStart()}
    ${r.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}

    let outputIndices = ${l.offsetToIndices(`global_idx`)};
    let batch: u32 = outputIndices[0];
    let output_channel: u32 = outputIndices[${c?3:1}];
    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${c?1:2}], outputIndices[${c?2:3}]) * uniforms.strides - uniforms.pads;
    let group_id: u32 = output_channel * ${d} / uniforms.output_channels_per_group;
    var in_channel_offset = group_id * uniforms.w_shape[${c?2:1}];

    var value: ${l.type.value} = ${l.type.value}(0);
    ${_}
    ${a}
    ${f}
    ${l.setByOffset(`global_idx`,`value`)}
  }`}}},eo=(e,t,n,r)=>{let i=e.length>2,a=G(n[3]),o=G(n[2]),s=z.size(n)/a/o,c=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/a],l=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/a],u=[n[0],n[1],n[2],n[3]/a],d=[{type:12,data:s},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];ba(t,d),d.push(...W(c,l,u));let f=(o-1)*t.strides[1]+l[1];return{name:`GroupedConv-Vectorize`,shaderCache:{hint:`${t.cacheKey};${a};${o};${f};${l[0]};${l[1]}`,inputDependencies:i?[`rank`,`rank`,`type`]:[`rank`,`rank`]},getRunData:()=>({outputs:[{dims:r?r(n):n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:d}),getShaderSource:n=>{let r=J(`output`,e[0].dataType,u.length,a),s=U(r.type.tensor),d=ya(t,r.type.value,s),p=q(`x`,e[0].dataType,c.length,a),m=q(`w`,e[1].dataType,l.length,a),h=[p,m];i&&h.push(q(`b`,e[2].dataType,e[2].dims,a));let g=i?`value += b[output_channel];`:``,_=[{name:`output_size`,type:`u32`},{name:`strides`,type:`i32`,length:2},{name:`pads`,type:`i32`,length:2}];return xa(t,_),`
  ${n.registerUniforms(_).declareVariables(...h,r)}
  ${n.mainStart()}
    ${n.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
    let width0 = uniforms.output_shape[3];
    let output_channel = global_idx % width0;
    var index1 = global_idx / width0;
    let width1 = uniforms.output_shape[2] / ${o}u;
    let col = (index1 % width1) * ${o}u;
    index1 = index1 / width1;
    let row = index1 % uniforms.output_shape[1];
    let batch = index1 / uniforms.output_shape[1];

    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;

    var x_vals: array<${p.type.value}, ${f}>;
    var values: array<${r.type.value}, ${o}>;
    let input_channel = output_channel;
    // Use constant instead of uniform can give better performance for w's height/width.
    for (var w_height: u32 = 0u; w_height < ${l[0]}; w_height++) {
      let x_height = x_corner.x + i32(w_height);
      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
        for (var i = 0; i < ${f}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${p.get(`batch`,`u32(x_height)`,`u32(x_width)`,`input_channel`)};
          } else {
            x_vals[i] = ${p.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${l[1]}; w_width++) {
          let w_val = ${m.get(`w_height`,`w_width`,`0`,`output_channel`)};
          for (var i = 0u; i < ${o}u; i++) {
            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
          }
        }
      }
    }

    for (var i = 0u; i < ${o}u; i++) {
      var value = values[i];
      ${g}
      ${d}
      ${r.set(`batch`,`row`,`col + i`,`output_channel`,`value`)};
    }
  }`}}}}),no,ro,io,ao,oo,so,co,lo,uo,fo=o(()=>{B(),Ua(),Qa(),Ba(),to(),Ca(),ja(),Vn(),no=(e,t,n,r,i,a)=>{let o=e[0],s=e.slice(a?1:2,a?3:4),c=s.length,l=t[0],u=t.slice(2).map((e,t)=>e+(e-1)*(n[t]-1)),d=s.map((e,t)=>e+r[t]+r[t+c]).map((e,t)=>Math.floor((e-u[t]+i[t])/i[t]));return d.splice(0,0,o),d.splice(a?3:1,0,l),d},ro=[2,3,1,0],io=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw Error(`Conv requires 2 or 3 inputs`);if(e[0].dims.length>5)throw Error(`greater than 5D is not supported`);if(e[0].dims.length!==e[1].dims.length)throw Error(`filter does not have same dimension as input`);if(e[0].dims[t.format===`NHWC`?e[0].dims.length-1:1]!==e[1].dims[1]*t.group)throw Error(`FILTER_IN_CHANNEL should be equal to DATA_CHANNEL`);if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw Error(`invalid bias`);let n=e[0].dims.length-2;if(t.dilations.length!==n)throw Error(`dilations should be ${n}D`);if(t.strides.length!==n)throw Error(`strides should be ${n}D`);if(t.pads.length!==n*2)throw Error(`pads should be ${n*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw Error(`invalid kernel shape`)},ao=(e,t)=>{let n=e.kernelShape.slice();n.length<t[1].dims.length-2&&n.push(...Array(t[1].dims.length-2-n.length).fill(0));for(let e=2;e<t[1].dims.length;++e)n[e-2]===0&&(n[e-2]=t[1].dims[e]);let r=e.pads.slice();Bt.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,n,r,e.format===`NHWC`,e.autoPad);let i=Object.assign({},e);return Object.assign(i,{kernelShape:n,pads:r}),i},oo=e=>{let t=Sa(e),n=e.format;return{autoPad:[`NOTSET`,`VALID`,`SAME_UPPER`,`SAME_LOWER`][e.auto_pad],format:n,dilations:e.dilations,group:e.group,kernelShape:e.kernel_shape,pads:e.pads,strides:e.strides,wIsConst:e.w_is_const(),...t,cacheKey:`${e.format};${t.activation};`}},so=(e,t,n,r)=>{let i=n.format===`NHWC`,a=no(t[0].dims,t[1].dims,n.dilations,n.pads,n.strides,i);if(n.group!==1){let o=[t[0]];if(i){let r=e.kernelCustomData.wT??e.compute(Rn(t[1],ro),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=r),o.push(r)}else o.push(t[1]);t.length===3&&o.push(t[2]),!e.adapterInfo.isArchitecture(`ampere`)&&i&&t[1].dims[0]===n.group&&t[1].dims[1]===1&&n.dilations[0]===1&&n.dilations[1]===1?e.compute(eo(o,n,a,r),{inputs:o}):e.compute($a(o,n,a,r),{inputs:o});return}let o=t.length===3,s=t[0].dims[i?1:2],c=t[0].dims[i?2:3],l=t[0].dims[i?3:1],u=t[1].dims[2],d=t[1].dims[3],f=a[i?1:2],p=a[i?2:3],m=a[i?3:1],h=i&&u===s&&d===c&&n.pads[0]===0&&n.pads[1]===0;if(h||u===1&&d===1&&n.dilations[0]===1&&n.dilations[1]===1&&n.strides[0]===1&&n.strides[1]===1&&n.pads[0]===0&&n.pads[1]===0){let u=a[0],d,g,_,v=[];if(i){let r=e.kernelCustomData.wT??e.compute(Rn(t[1],ro),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];if(n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=r),h){let e=s*c*l;d=t[0].reshape([1,u,e]),g=r.reshape([1,e,m]),_=[1,u,m]}else d=t[0].reshape([u,s*c,l]),g=r.reshape([1,l,m]),_=[u,f*p,m];v.push(d),v.push(g)}else d=t[0].reshape([u,l,s*c]),g=t[1].reshape([1,m,l]),_=[u,m,f*p],v.push(g),v.push(d);o&&v.push(t[2]);let y=_[2],b=v[0].dims[v[0].dims.length-1];y<8&&b<8?e.compute(Aa(v,n,a,_,i,r),{inputs:v}):e.compute(za(v,n,a,_,i,r),{inputs:v});return}let g=e.kernelCustomData.wT??e.compute(Rn(t[1],ro),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=g);let _=[t[0],g];o&&_.push(t[2]);let v=i?f*p:m,y=i?m:f*p,b=u*d*l;e.compute(Ha(_,n,a,v,y,b,o,!0,r),{inputs:_})},co=(e,t)=>{let n=t.format===`NHWC`,r=[e.inputs[0].reshape(n?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&r.push(e.inputs[2]);let i=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),o=[1].concat(t.dilations),s=[1].concat(t.kernelShape),c=ao({...t,pads:i,strides:a,dilations:o,kernelShape:s},r);so(e,r,c,e=>n?[e[0],e[2],e[3]]:[e[0],e[1],e[3]])},lo=(e,t,n)=>{let r=n.format===`NHWC`?`channelsLast`:`channelsFirst`,i=ao(n,t),a=n.autoPad===`NOTSET`?n.pads:n.autoPad,o=Xa(t[0].dims,t[1].dims,n.strides,n.dilations,a,!1,r);e.compute(Za(t,i,o.outShape,[o.filterDepth,o.filterHeight,o.filterWidth],[o.padInfo.front,o.padInfo.top,o.padInfo.left],r))},uo=(e,t)=>{if(io(e.inputs,t),e.inputs[0].dims.length===3)co(e,t);else if(e.inputs[0].dims.length===5)lo(e,e.inputs,t);else{let n=ao(t,e.inputs);so(e,e.inputs,n)}}}),po,mo=o(()=>{L(),Lt(),B(),Y(),po=(e,t,n)=>{let r=e.length>2,i=t.outputShape,a=t.format===`NHWC`,o=t.group,s=e[1].dims,c=s[2]/o,l=s[3],u=a?G(c):1,d=a&&l===1&&c>=4,f=d?Math.floor(c/4)*4:Math.floor(c/u)*u,p=c-f,m=a?G(l):1,h=a?l===1?u:m:1,g=z.size(i)/m,_=[Math.ceil(g/64),1,1];R(`verbose`,()=>`[conv2d_backprop_webgpu] dispatch = ${_}`);let v=[`rank`,`rank`],y=[t.strides[0],t.strides[1]],b=[t.kernelShape[a?1:2],t.kernelShape[a?2:3]],x=[t.dilations[0],t.dilations[1]],S=[b[0]+(t.dilations[0]<=1?0:(t.kernelShape[a?1:2]-1)*(t.dilations[0]-1)),b[1]+(t.dilations[1]<=1?0:(t.kernelShape[a?2:3]-1)*(t.dilations[1]-1))],C=[S[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),S[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],w=[{type:12,data:g},{type:12,data:y},{type:12,data:b},{type:12,data:x},{type:12,data:S},{type:6,data:C},{type:12,data:f},{type:12,data:c},{type:12,data:l},...W(e[0].dims,e[1].dims)];return r&&(w.push(...W(e[2].dims)),v.push(`rank`)),w.push(...W(i)),{name:`ConvTranspose2D`,shaderCache:{hint:`${t.cacheKey};${u}${h}${m}${d}${p}`,inputDependencies:v},getRunData:()=>({dispatchGroup:{x:_[0],y:_[1],z:_[2]},outputs:[{dims:n?n(i):i,dataType:e[0].dataType}],programUniforms:w}),getShaderSource:t=>{let n=[{name:`output_size`,type:`u32`},{name:`strides`,type:`u32`,length:y.length},{name:`filter_dims`,type:`u32`,length:b.length},{name:`dilations`,type:`u32`,length:b.length},{name:`effective_filter_dims`,type:`u32`,length:S.length},{name:`pads`,type:`i32`,length:C.length},{name:`input_channels_per_group_int`,type:`u32`},{name:`input_channels_per_group`,type:`u32`},{name:`output_channels_per_group`,type:`u32`}],o=U(e[0].dataType),s=a?1:2,c=a?2:3,l=a?3:1,f=q(`W`,e[1].dataType,e[1].dims.length,h),g=q(`Dy`,e[0].dataType,e[0].dims.length,u),_=[g,f];r&&_.push(q(`bias`,e[2].dataType,[i[l]].length,m));let v=J(`result`,e[0].dataType,i.length,m),x=`
            let outputIndices = ${v.offsetToIndices(`global_idx * ${m}`)};
            let batch = ${v.indicesGet(`outputIndices`,0)};
            let d1 = ${v.indicesGet(`outputIndices`,l)};
            let r = ${v.indicesGet(`outputIndices`,s)};
            let c = ${v.indicesGet(`outputIndices`,c)};
            let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;
            let dyRCorner = dyCorner.x;
            let dyCCorner = dyCorner.y;
            let groupId = d1 / uniforms.output_channels_per_group;
            let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;
            // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
            // ? = to be determined. : = across all values in that axis.
            var dotProd = ${v.type.value}(0.0);
            var wR: u32 = 0;
            if (uniforms.dilations.x == 1) {
              // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
              wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
            }
            for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
              if (wR % uniforms.dilations.x != 0) {
                continue;
              }
              let dyR = (${o}(dyRCorner) + ${o}(wR)) / ${o}(uniforms.strides[0]);
              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
              if (dyR < 0.0 || dyR >= ${o}(uniforms.Dy_shape[${s}]) || fract(dyR) > 0.0 ||
                  wRPerm < 0) {
                continue;
              }
              let idyR: u32 = u32(dyR);
              var wC: u32 = 0;
              if (uniforms.dilations.y == 1) {
                // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
                wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
              }
              for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
                if (wC % uniforms.dilations.y != 0) {
                  continue;
                }
                let dyC = (${o}(dyCCorner) + ${o}(wC)) / ${o}(uniforms.strides.y);
                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
                if (dyC < 0.0 || dyC >= ${o}(uniforms.Dy_shape[${c}]) ||
                    fract(dyC) > 0.0 || wCPerm < 0) {
                  continue;
                }
                let idyC: u32 = u32(dyC);
                var inputChannel = groupId * uniforms.input_channels_per_group;
                ${d?`
                var x_offset = ${g.indicesToOffset(`${g.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${u};
                var w_offset = ${f.indicesToOffset(`${f.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${h};
                  `:``}
                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${d?4:u}) {
                  ${(()=>{let e=``;if(d)u===4?e+=`
        let xValue = ${g.getByOffset(`x_offset`)};
        let wValue = ${f.getByOffset(`w_offset`)};
        dotProd = dotProd + dot(xValue, wValue);
        x_offset += 1u;
        w_offset += 1u;`:u===2?e+=`
          dotProd = dotProd + dot(vec4<${o}>(${g.getByOffset(`x_offset`)}, ${g.getByOffset(`x_offset + 1u`)}), vec4<${o}>(${f.getByOffset(`w_offset`)}, ${f.getByOffset(`w_offset + 1u`)}));
          x_offset += 2u;
          w_offset += 2u;`:u===1&&(e+=`
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      let x_in = x + bias;
      ${a.setByOffset(`global_idx`,Bi(`x_in`))}
    }`},getRunData:e=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],programUniforms:[{type:12,data:Math.ceil(n/4)},{type:12,data:r}],dispatchGroup:{x:Math.ceil(n/xn/4)}})}},es=e=>{e.inputs.length<2||z.size(e.inputs[1].dims)===0?Vi(e):e.compute($o(e.inputs))}}),ns,rs,is,as,os=o(()=>{L(),B(),H(),Y(),ns=e=>{if(!e||e.length!==2)throw Error(`Gather requires 2 inputs.`)},rs=(e,t)=>{let n=e[0].dims,r=e[1].dims,i=n.length,a=z.normalizeAxis(t.axis,i),o=n.slice(0);o.splice(a,1,...r);let s=n[a],c=e[0].dataType===9?4:1,l=Math.ceil(z.size(o)/c),u=[{type:12,data:l},{type:6,data:s},{type:12,data:a},...W(e[0].dims,e[1].dims,o)];return{name:`Gather`,shaderCache:{hint:t.cacheKey,inputDependencies:[`rank`,`rank`]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:t=>{let n=q(`data`,e[0].dataType,e[0].dims.length,c),s=q(`inputIndices`,e[1].dataType,e[1].dims.length),l=J(`output`,e[0].dataType,o.length,c),u=e=>{let t=r.length,c=`var indicesIndices${e}  = ${s.type.indices}(0);`;for(let n=0;n<t;n++)c+=`${t>1?`indicesIndices${e}[${n}]`:`indicesIndices${e}`} = ${o.length>1?`outputIndices${e}[uniforms.axis + ${n}]`:`outputIndices${e}`};`;c+=`
          var idx${e} = ${s.getByIndices(`indicesIndices${e}`)};
          if (idx${e} < 0) {
            idx${e} = idx${e} + uniforms.axisDimLimit;
          }
          var dataIndices${e} : ${n.type.indices};
        `;for(let n=0,r=0;n<i;n++)n===a?(c+=`${i>1?`dataIndices${e}[${n}]`:`dataIndices${e}`} = u32(idx${e});`,r+=t):(c+=`${i>1?`dataIndices${e}[${n}]`:`dataIndices${e}`} = ${o.length>1?`outputIndices${e}[${r}]`:`outputIndices${e}`};`,r++);return c},d;if(e[0].dataType===9){let e=(e,t,r=``)=>`
          let outputIndices${t} = ${l.offsetToIndices(`outputOffset + ${t}u`)};
          ${u(t)};
          let offset${t} = ${n.indicesToOffset(`dataIndices${t}`)};
          let index${t} = offset${t} / 4u;
          let component${t} = offset${t} % 4u;
          ${e}[${t}] = ${r}(${n.getByOffset(`index${t}`)}[component${t}]);
        `;d=`
        let outputOffset = global_idx * ${c};
        var value = vec4<u32>(0);
        ${e(`value`,0,`u32`)}
        ${e(`value`,1,`u32`)}
        ${e(`value`,2,`u32`)}
        ${e(`value`,3,`u32`)}
        ${l.setByOffset(`global_idx`,`value`)}
      `}else d=`
      let outputIndices = ${l.offsetToIndices(`global_idx`)};
      ${u(``)};
      let value = ${n.getByIndices(`dataIndices`)};
      ${l.setByOffset(`global_idx`,`value`)};
      `;return`
      ${t.registerUniform(`outputSize`,`u32`).registerUniform(`axisDimLimit`,`i32`).registerUniform(`axis`,`u32`).declareVariables(n,s,l)}
      ${t.mainStart()}
        ${t.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.outputSize`)}
        ${d}
      }`}}},is=e=>V({axis:e.axis}),as=(e,t)=>{let n=e.inputs;ns(n),e.compute(rs(e.inputs,t))}}),ss,cs,ls,us=o(()=>{L(),B(),Y(),ss=(e,t,n,r,i,a,o,s,c)=>{let l=[{type:12,data:a},{type:12,data:r},{type:12,data:i},{type:12,data:n},{type:12,data:o},{type:12,data:s},{type:12,data:c}],u=[a];return l.push(...W(t.dims,u)),e.compute({name:`computeSliceOffsets`,shaderCache:{hint:`${i.length}_${n.length}`,inputDependencies:[`rank`]},getRunData:()=>({outputs:[{dims:u,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:l}),getShaderSource:e=>{let r=[q(`indices_data`,t.dataType,t.dims.length),J(`input_slice_offsets_data`,12,1,1)],a=[{name:`output_size`,type:`u32`},{name:`batch_dims`,type:`u32`},{name:`input_dims`,type:`u32`,length:i.length},{name:`sizes_from_slice_dims_data`,type:`u32`,length:n.length},{name:`num_slices_per_batch`,type:`u32`},{name:`input_batch_stride`,type:`u32`},{name:`num_slice_dims`,type:`u32`}];return`
  ${e.registerUniforms(a).declareVariables(...r)}
  ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
    let batch_idx = global_idx / uniforms.num_slices_per_batch;
    let base_offset = batch_idx * uniforms.input_batch_stride;

    let slice_indices_base_offset = global_idx * uniforms.num_slice_dims;
    var relative_slice_offset = 0;
    for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) {
      var index = i32(indices_data[dim_idx + slice_indices_base_offset].x);
      let input_dim_idx = uniforms.batch_dims + dim_idx;
      if (index < 0) {
        ${i.length===1?`index += i32(uniforms.input_dims);`:`index += i32(uniforms.input_dims[input_dim_idx]);`}
      }
      ${n.length===1?`relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);`:`relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);`}
    }

    input_slice_offsets_data[global_idx] =  base_offset + u32(relative_slice_offset);
  }`}},{inputs:[t],outputs:[-1]})[0]},cs=(e,t)=>{let n=e.inputs,r=n[0].dims,i=n[0].dataType,a=n[1].dims,o=a[a.length-1],s=z.sizeToDimension(a,a.length-1),c=z.sizeFromDimension(r,t.batchDims+o),l=z.sizeToDimension(r,t.batchDims),u=z.sizeFromDimension(r,t.batchDims),d=s/l,f=Array(o),p=c;for(let e=0;e<o;++e)f[o-1-e]=p,p*=r[t.batchDims+o-1-e];let m=ss(e,n[1],f,t.batchDims,r,s,d,u,o),h=t.batchDims+o;if(h>r.length)throw Error(`last dimension of indices must not be larger than rank of input tensor`);let g=a.slice(0,-1).concat(r.slice(h)),_=z.size(g),v=[{type:12,data:_},{type:12,data:c},...W(n[0].dims,m.dims,g)];e.compute({name:`GatherND`,shaderCache:{hint:t.cacheKey,inputDependencies:[`rank`,`rank`]},getRunData:()=>({outputs:[{dims:g,dataType:i}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:v}),getShaderSource:e=>{let t=q(`data`,n[0].dataType,n[0].dims.length),r=q(`slice_offsets`,12,m.dims.length),i=J(`output`,n[0].dataType,g.length);return`
          ${e.registerUniform(`output_size`,`u32`).registerUniform(`slice_size`,`u32`).declareVariables(t,r,i)}
            ${e.mainStart()}
            ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
          let slice_offset = slice_offsets[global_idx / uniforms.slice_size];
          output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
        }`}},{inputs:[n[0],m]})},ls=e=>({batchDims:e.batch_dims,cacheKey:``})}),ds,fs,ps,ms,hs=o(()=>{L(),B(),H(),Y(),ds=(e,t)=>{if(e.length<3||e.length>4)throw Error(`GatherBlockQuantized requires 3 or 4 inputs.`);let n=z.normalizeAxis(t.quantizeAxis,e[0].dims.length),r=t.blockSize,i=e[0],a=e[2],o=e.length===4?e[3]:void 0;if(a.dims.length!==i.dims.length||!i.dims.map((e,t)=>t===n?Math.ceil(e/r)===a.dims[t]:e===a.dims[t]).reduce((e,t)=>e&&t,!0))throw Error(`Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.`);if(o){if(o.dataType!==i.dataType)throw Error(`Zero point must have the same data type as the input tensor.`);if(o.dims.length!==a.dims.length||!o.dims.map((e,t)=>e===a.dims[t]).reduce((e,t)=>e&&t,!0))throw Error(`Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.`)}},fs=(e,t)=>{let n=e[0].dims,r=e[1].dims,i=n.length,a=z.normalizeAxis(t.gatherAxis,i),o=z.normalizeAxis(t.quantizeAxis,i),s=n.slice(0);s.splice(a,1,...r);let c=z.size(s),l=e[2].dataType,u=e[0].dataType===22,d=[{type:12,data:c},{type:12,data:o},{type:12,data:a},{type:12,data:t.blockSize},...W(...e.map((e,t)=>e.dims),s)];return{name:`GatherBlockQuantized`,shaderCache:{hint:`${t.cacheKey};${e.filter((e,t)=>t!==1).map(e=>e.dims.join(`_`)).join(`;`)}`,inputDependencies:Array.from({length:e.length},(e,t)=>`rank`)},getRunData:()=>({outputs:[{dims:s,dataType:l}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:d}),getShaderSource:t=>{let i=q(`data`,e[0].dataType,e[0].dims.length),o=q(`inputIndices`,e[1].dataType,e[1].dims.length),c=q(`scales`,e[2].dataType,e[2].dims.length),d=e.length>3?q(`zeroPoint`,e[3].dataType,e[3].dims.length):void 0,f=J(`output`,l,s.length),p=[i,o,c];return d&&p.push(d),`
        ${t.registerUniforms([{name:`output_size`,type:`u32`},{name:`quantize_axis`,type:`u32`},{name:`gather_axis`,type:`u32`},{name:`block_size`,type:`u32`}]).declareVariables(...p,f)}
        ${t.mainStart()}
        let output_indices = ${f.offsetToIndices(`global_idx`)};
        var indices_indices = ${o.type.indices}(0);
        ${r.length>1?`
          for (var i: u32 = 0; i < ${r.length}; i++) {
            let index = ${f.indicesGet(`output_indices`,`uniforms.gather_axis + i`)};
            ${o.indicesSet(`indices_indices`,`i`,`index`)};
          }`:`indices_indices = ${f.indicesGet(`output_indices`,`uniforms.gather_axis`)};`};
        var data_indices = ${i.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${f.indicesGet(`output_indices`,`i`)};
          ${i.indicesSet(`data_indices`,`i`,`index`)};
        }
        var index_from_indices = ${o.getByIndices(`indices_indices`)};
        if (index_from_indices < 0) {
          index_from_indices += ${n[a]};
        }
        ${i.indicesSet(`data_indices`,`uniforms.gather_axis`,`u32(index_from_indices)`)};
        for (var i = uniforms.gather_axis + 1; i < ${s.length}; i++) {
          let index = ${f.indicesGet(`output_indices`,`i + ${r.length} - 1`)};
          ${i.indicesSet(`data_indices`,`i`,`index`)};
        }
        let data_offset = ${i.indicesToOffset(`data_indices`)};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${i.getByOffset(`data_offset / 8`)};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${u?`unpack4xI8`:`unpack4xU8`}(u32(packed_8bit_quantized_data));
        let quantized_data = quantized_data_vec[data_index / 2];
        var scale_indices = data_indices;
        let quantize_axis_index = ${c.indicesGet(`data_indices`,`uniforms.quantize_axis`)} / uniforms.block_size;
        ${c.indicesSet(`scale_indices`,`uniforms.quantize_axis`,`quantize_axis_index`)};
        var scale = ${c.getByIndices(`scale_indices`)};
        ${d?`
              let zero_point_indices = scale_indices;
              let zero_point_offset = ${d.indicesToOffset(`zero_point_indices`)};
              let zero_point_index = zero_point_offset % 8;
              let packed_4bit_zero_points = ${d.getByOffset(`zero_point_offset / 8`)};
              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
              let zero_point_vec = ${u?`unpack4xI8`:`unpack4xU8`}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:`var zero_point = 0`};
        let dequantized_data = ${Cn(l)}(quantized_data - zero_point) * scale;
        ${f.setByOffset(`global_idx`,`dequantized_data`)};
    }`}}},ps=(e,t)=>{let n=e.inputs;ds(n,t),e.compute(fs(e.inputs,t))},ms=e=>V({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),gs,_s,vs,ys,bs=o(()=>{L(),B(),H(),Y(),gs=e=>{if(!e||e.length!==2)throw Error(`GatherElements requires 2 inputs.`);if(e[0].dims.length<1)throw Error(`GatherElements requires that the data input be rank >= 1.`);if(e[0].dims.length!==e[1].dims.length)throw Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},_s=(e,t)=>{let n=e[0].dims,r=e[0].dataType,i=n.length,a=e[1].dims,o=e[1].dataType,s=z.normalizeAxis(t.axis,i),c=n[s],l=a.slice(0),u=z.size(l),d=q(`input`,r,i),f=q(`indicesInput`,o,a.length),p=J(`output`,r,l.length),m=[{type:12,data:u},{type:6,data:c},{type:12,data:s}];return m.push(...W(n,a,l)),{name:`GatherElements`,shaderCache:{inputDependencies:[`rank`,`rank`]},getRunData:()=>({outputs:[{dims:l,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:m}),getShaderSource:e=>`
      ${e.registerUniform(`outputSize`,`u32`).registerUniform(`axisDimLimit`,`i32`).registerUniform(`axis`,`u32`).declareVariables(d,f,p)}
      ${e.mainStart()}
      ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.outputSize`)}

      let outputIndices = ${p.offsetToIndices(`global_idx`)};

      var idx = ${f.getByOffset(`global_idx`)};
      if (idx < 0) {
        idx = idx + uniforms.axisDimLimit;
      }
      var inputIndices = ${d.type.indices}(outputIndices);
      ${d.indicesSet(`inputIndices`,`uniforms.axis`,`u32(idx)`)};
      let value = ${d.getByIndices(`inputIndices`)};

      ${p.setByOffset(`global_idx`,`value`)};
  }`}},vs=e=>V({axis:e.axis}),ys=(e,t)=>{let n=e.inputs;gs(n),e.compute(_s(e.inputs,t))}}),xs,Ss,Cs,ws,Ts=o(()=>{L(),B(),Y(),xs=e=>{if(!e)throw Error(`Input is missing`);if(e.length<2||e.length>3)throw Error(`Invaid input number.`);if(e.length===3&&e[2].dims.length>2)throw Error(`Invalid input shape of C`);if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw Error(`Input types are mismatched`)},Ss=(e,t)=>{let n=e[0].dims.slice(),r=e[1].dims.slice(),[i,a,o]=Vt.getShapeOfGemmResult(n,t.transA,r,t.transB,e.length===3?e[2].dims:void 0),s=[i,a];if(!s)throw Error(`Can't use gemm on the given tensors`);let c=Math.ceil(a/16),l=Math.ceil(i/16);z.size(s);let u=[{type:12,data:c},{type:12,data:i},{type:12,data:a},{type:12,data:o},{type:1,data:t.alpha},{type:1,data:t.beta}],d=[`type`,`type`];return e.length===3&&(u.push(...W(e[2].dims)),d.push(`rank`)),u.push(...W(s)),{name:`GemmShared`,shaderCache:{hint:`${t.cacheKey}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:c*l},programUniforms:u}),getShaderSource:n=>{let r=q(`a`,e[0].dataType,e[0].dims),i=q(`b`,e[1].dataType,e[1].dims),a=null,o=[r,i];e.length===3&&(a=q(`c`,e[2].dataType,e[2].dims.length),o.push(a));let c=J(`output`,e[0].dataType,s.length);o.push(c);let l=[{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`}],u=``,d=``;t.transA&&t.transB?(d=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${r.type.value}(0);
      }

      col = k_start + local_id.x;
      row = tile_col_start + local_id.y;
      if (col < uniforms.K && row < uniforms.N) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${i.type.value}(0);
      }
      `,u=`value += tile_a[k][local_id.y] * tile_b[local_id.x][k];`):t.transA&&!t.transB?(d=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${r.type.value}(0);
      }

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${i.type.value}(0);
      }
      `,u=`value += tile_a[k][local_id.y] * tile_b[k][local_id.x];`):!t.transA&&t.transB?(d=`
      var col = k_start + local_id.x;
      var row = tile_row_start + local_id.y;
      if (col < uniforms.K && row < uniforms.M) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${r.type.value}(0);
      }

      col = k_start + local_id.x;
      row = tile_col_start + local_id.y;
      if (col < uniforms.K && row < uniforms.N) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${i.type.value}(0);
      }
      `,u=`value += tile_a[local_id.y][k] * tile_b[local_id.x][k];`):!t.transA&&!t.transB&&(d=`
      var col = k_start + local_id.x;
      var row = tile_row_start + local_id.y;
      if (col < uniforms.K && row < uniforms.M) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${r.type.value}(0);
      }

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${i.type.value}(0);
      }
      `,u=`value += tile_a[local_id.y][k] * tile_b[k][local_id.x];`);let f=t.alpha===1?``:`value *= uniforms.alpha;`;return`
  ${n.registerUniforms(l).declareVariables(...o)}
  var<workgroup> tile_a: array<array<${r.type.storage}, 16>, 16>;
  var<workgroup> tile_b: array<array<${i.type.storage}, 16>, 16>;
  ${n.mainStart([16,16,1])}
    let tile_col_start = (workgroup_index % uniforms.num_tile_n) * 16;
    let tile_row_start = (workgroup_index / uniforms.num_tile_n) * 16;
    let num_tiles = (uniforms.K - 1) / 16 + 1;
    var k_start = 0u;
    var value = ${c.type.value}(0);
    for (var t: u32 = 0u; t < num_tiles; t++) {
      ${d}
      k_start = k_start + 16;
      workgroupBarrier();

      for (var k: u32 = 0u; k < 16; k++) {
        ${u}
      }
      workgroupBarrier();
    }

    ${f}
    let m = tile_row_start + local_id.y;
    let n = tile_col_start + local_id.x;
    ${a==null?``:`let cOffset = ${a.broadcastedIndicesToOffset(`vec2(m, n)`,c)}; value += ${c.type.value}(uniforms.beta) * ${a.getByOffset(`cOffset`)};`}
    if (m < uniforms.M && n < uniforms.N) {
      output[m * uniforms.N + n] = value;
    }
  }`}}},Cs=e=>({transA:e.transA,transB:e.transB,alpha:e.alpha,beta:e.beta,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}),ws=(e,t)=>{xs(e.inputs),e.compute(Ss(e.inputs,t))}}),Es,Ds,Os,ks,As,js,Ms,Ns,Ps,Fs,Is,Ls,Rs,zs,Bs=o(()=>{L(),B(),H(),Y(),[Es,Ds,Os,ks]=[0,1,2,3],As=e=>{if(e[0].dims.length!==4)throw Error(`only 4-D tensor is supported.`);if(e[0].dims.length!==e[1].dims.length)throw Error(`input dimensions must be equal to grid dimensions`);if(e[0].dims.length-2!==e[1].dims[e[1].dims.length-1])throw Error(`last dimension of grid must be equal to ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw Error(`grid batch size must match input batch size`)},js=`
  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
    let cubic_alpha = -0.75f;
    let x_abs = abs(x);
    var coeffs: vec4<f32>;
    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
    return coeffs;
  }
`,Ms=e=>`
  fn gs_bicubic_interpolate(p: mat4x4<${e}>, x: f32, y: f32) -> ${e} {
    var v: vec4<f32>;
    var coeffs = gs_get_cubic_coeffs(x);
    for (var i = 0; i < 4; i++) {
      v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3];
    }
    coeffs = gs_get_cubic_coeffs(y);
    let pixel = ${e}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]);
    return pixel;
  }
`,Ns=e=>`
  fn gs_denormalize(n: f32, length: i32) -> f32 {
    ${e.alignCorners===0?`
    // alignCorners: false => [-1, 1] to [-0.5, length - 0.5]
    return ((n + 1.0) * f32(length) - 1.0) / 2.0;
    `:`
    // alignCorners: true => [-1, 1] to [0, length - 1]
    return (n + 1.0) / 2.0 * (f32(length - 1));
    `}
  }
`,Ps=e=>`
  ${e.paddingMode===`reflection`?`
      fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 {
        var dx = 0.0;
        var fx = f32(x);
        let range = x_max - x_min;
        if (fx < x_min) {
          dx = x_min - fx;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_min + r;
          } else {
            fx = x_max - r;
          }
        } else if (fx > x_max) {
          dx = fx - x_max;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_max - r;
          } else {
            fx = x_min + r;
          }
        }
        return u32(fx);
      }`:``}
`,Fs=(e,t,n)=>`
  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${t} {
     var pixel = ${t}(0);
     var indices = vec4<u32>(0);
     indices[${Es}] = batch;
     indices[${Ds}] = channel;`+(()=>{switch(n.paddingMode){case`zeros`:return`
          if (r >= 0 && r < H && c >=0 && c < W) {
            indices[${Os}] = u32(r);
            indices[${ks}] = u32(c);
          } else {
            return ${t}(0);
          }
        `;case`border`:return`
          indices[${Os}] = u32(clamp(r, 0, H - 1));
          indices[${ks}] = u32(clamp(c, 0, W - 1));
        `;case`reflection`:return`
          indices[${Os}] = gs_reflect(r, border[1], border[3]);
          indices[${ks}] = gs_reflect(c, border[0], border[2]);
        `;default:throw Error(`padding mode ${n.paddingMode} is not supported`)}})()+`
    return ${e.getByIndices(`indices`)};
  }
`,Is=(e,t,n)=>(()=>{switch(n.mode){case`nearest`:return`
          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${Es}], indices[${Ds}], border);
        `;case`bilinear`:return`
          let x1 = i32(floor(x));
          let y1 = i32(floor(y));
          let x2 = x1 + 1;
          let y2 = y1 + 1;

          let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${Es}], indices[${Ds}], border);
          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${Es}], indices[${Ds}], border);
          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${Es}], indices[${Ds}], border);
          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${Es}], indices[${Ds}], border);

          let dx2 = ${t}(f32(x2) - x);
          let dx1 = ${t}(x - f32(x1));
          let dy2 = ${t}(f32(y2) - y);
          let dy1 = ${t}(y - f32(y1));
          let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22);
        `;case`bicubic`:return`
          let x0 = i32(floor(x)) - 1;
          let y0 = i32(floor(y)) - 1;
          var p: mat4x4<${t}>;
          for (var h = 0; h < 4; h++) {
            for (var w = 0; w < 4; w++) {
              p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${Es}], indices[${Ds}], border);
            }
          }

          let dx = x - f32(x0 + 1);
          let dy = y - f32(y0 + 1);
          let result = gs_bicubic_interpolate(p, dx, dy);
        `;default:throw Error(`mode ${n.mode} is not supported`)}})()+`${e.setByOffset(`global_idx`,`result`)}`,Ls=(e,t)=>{let n=q(`x`,e[0].dataType,e[0].dims.length),r=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],i=q(`grid`,e[1].dataType,r.length,2),a=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];t.format===`NHWC`&&(a=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[Es,Ds,Os,ks]=[0,3,1,2]);let o=J(`output`,e[0].dataType,a.length),s=n.type.value,c=[{type:12,data:z.size(a)},...W(e[0].dims,r,a)];return{name:`GridSample`,shaderCache:{hint:`${t.cacheKey}`,inputDependencies:[`type`,`type`]},getRunData:e=>{let t=z.size(a);return{outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(t/64)},programUniforms:c}},getShaderSource:e=>`
  ${e.registerUniform(`output_size`,`u32`).declareVariables(n,i,o)}
  ${js}
  ${Ms(s)}
  ${Ns(t)}
  ${Ps(t)}
  ${Fs(n,s,t)}

  ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
      let H_in = i32(uniforms.x_shape[${Os}]);
      let W_in = i32(uniforms.x_shape[${ks}]);

      ${t.alignCorners===0?`
      let x_min = -0.5;
      let x_max = f32(W_in) - 0.5;
      let y_min = -0.5;
      let y_max = f32(H_in) - 0.5;
      `:`
      let x_min = 0.0;
      let x_max = f32(W_in) - 1.0;
      let y_min = 0.0;
      let y_max = f32(H_in) - 1.0;
      `};
      let border = vec4<f32>(x_min, y_min, x_max, y_max);

      let indices = ${o.offsetToIndices(`global_idx`)};
      var grid_indices = vec3<u32>(indices[${Es}], indices[${Os}], indices[${ks}]);
      let nxy = ${i.getByIndices(`grid_indices`)};
      var x = gs_denormalize(f32(nxy[0]), W_in);
      var y = gs_denormalize(f32(nxy[1]), H_in);

      ${Is(o,s,t)}
  }`}},Rs=(e,t)=>{As(e.inputs),e.compute(Ls(e.inputs,t))},zs=e=>V({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),Vs,Hs,Us,Ws,Gs,Ks,qs,Js=o(()=>{L(),B(),H(),ln(),qr(),Y(),Vn(),Vs=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,Hs=(e,t)=>{let n=e[0],r=Vs(e,1),i=Vs(e,2),a=Vs(e,3),o=Vs(e,4),s=Vs(e,5),c=Vs(e,6),l=Vs(e,7);if(n.dims.length!==3&&n.dims.length!==5)throw Error(`Input query is expected to have 3 or 5 dimensions`);let u=n.dims[0],d=n.dims[1],f=n.dims.length===3?n.dims[2]:t.numHeads*n.dims[4],p=d,m=0,h=0,g=Math.floor(f/t.numHeads);if(c&&l&&z.size(c.dims)&&z.size(l.dims)){if(c.dims.length!==4)throw Error(`Input "past_key" is expected to have 4 dimensions`);if(c.dims[0]!==u||c.dims[1]!==t.numHeads||c.dims[3]!==g)throw Error(`Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)`);if(l.dims[0]!==u||l.dims[1]!==t.numHeads||l.dims[3]!==g)throw Error(`Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)`);if(c.dims[2]!==l.dims[2])throw Error(`Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)`);if(l.dims.length!==4)throw Error(`Input "past_value" is expected to have 4 dimensions`);m=c.dims[2],h=c.dims[2]}else if(c&&z.size(c.dims)||l&&z.size(l.dims))throw Error(`Input "past_key" and "past_value" shall be both present or both absent`);let _;if(r&&z.size(r.dims)>0){if(n.dims.length!==3)throw Error(`Input "query" is expected to have 3 dimensions when key is given`);if(r.dims.length<3||r.dims.length>5)throw Error(`Input "key" is expected to have 3, 4, or 5 dimensions`);if(n.dims[0]!==r.dims[0])throw Error(`Input "query" and "key" shall have same dim 0 (batch size)`);if(r.dims.length===3){if(r.dims[2]!==n.dims[2])throw Error(`Input "query" and "key" shall have same dim 2 (hidden_size)`);_=2,p=r.dims[1]}else if(r.dims.length===5){if(r.dims[2]!==t.numHeads||r.dims[3]!==2||r.dims[4]!==g)throw Error(`Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv`);if(i)throw Error(`Expect "value" be none when "key" has packed kv format.`);_=5,p=r.dims[1]}else{if(r.dims[1]!==t.numHeads||r.dims[3]!==g)throw Error(`Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key`);_=0,p=r.dims[2]}}else{if(n.dims.length!==5)throw Error(`Input "query" is expected to have 5 dimensions when key is empty`);if(n.dims[2]!==t.numHeads||n.dims[3]!==3)throw Error(`Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv`);_=3}if(a&&z.size(a.dims)>0){if(a.dims.length!==1)throw Error(`Input "bias" is expected to have 1 dimension`);if(r&&r.dims.length===5&&r.dims[3]===2)throw Error(`bias is not allowed for packed kv.`)}let v=m+p,y=0;if(o&&z.size(o.dims)>0){y=8;let e=o.dims;throw e.length===1?e[0]===u?y=1:e[0]===3*u+2&&(y=3):e.length===2&&e[0]===u&&e[1]===v&&(y=5),Error(y===8?`Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)`:`Mask not supported`)}let b=!1,x=f;if(i&&z.size(i.dims)>0){if(i.dims.length!==3&&i.dims.length!==4)throw Error(`Input "value" is expected to have 3 or 4 dimensions`);if(n.dims[0]!==i.dims[0])throw Error(`Input "query" and "value" shall have same dim 0 (batch_size)`);if(i.dims.length===3){if(p!==i.dims[1])throw Error(`Input "key" and "value" shall have the same dim 1 (kv_sequence_length)`);x=i.dims[2]}else{if(p!==i.dims[2])throw Error(`Input "key" and "value" shall have the same dim 2 (kv_sequence_length)`);x=i.dims[1]*i.dims[3],b=!0}}if(o&&z.size(o.dims)>0)throw Error(`Key padding mask is not supported`);if(s&&z.size(s.dims)>0){if(s.dims.length!==4)throw Error(`Input "attention_bias" is expected to have 4 dimensions`);if(s.dims[0]!==u||s.dims[1]!==t.numHeads||s.dims[2]!==d||s.dims[3]!==v)throw Error(`Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)`)}return{batchSize:u,sequenceLength:d,pastSequenceLength:m,kvSequenceLength:p,totalSequenceLength:v,maxSequenceLength:h,inputHiddenSize:0,hiddenSize:f,vHiddenSize:x,headSize:g,vHeadSize:Math.floor(x/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:y,scale:t.scale,broadcastResPosBias:!1,passPastInKv:b,qkvFormat:_}},Us=e=>V({...e}),Ws=V({perm:[0,2,1,3]}),Gs=(e,t,n,r,i,a,o)=>{let s=[r,i,a],c=z.size(s),l=[{type:12,data:c},{type:12,data:o},{type:12,data:a}];return e.compute({name:`MultiHeadAttentionAddBias`,shaderCache:{inputDependencies:[`type`,`type`]},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:l}),getShaderSource:e=>{let r=J(`qkv_with_bias`,t.dataType,s),i=q(`qkv`,t.dataType,s),a=q(`bias`,n.dataType,s);return`
  ${e.registerUniforms([{name:`output_size`,type:`u32`},{name:`bias_offset`,type:`u32`},{name:`hidden_size`,type:`u32`}]).declareVariables(i,a,r)}
  ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`}},{inputs:[t,n],outputs:[-1]})[0]},Ks=(e,t,n,r,i,a,o,s)=>{let c=a;if(o&&z.size(o.dims)>0){if(r===1)throw Error(`AddBiasReshape is not implemented. Please export your model with packed QKV or KV`);return c=Gs(e,a,o,t,r,n*i,s),c=c.reshape([t,r,n,i]),n===1||r===1?c:e.compute(Rn(c,Ws.perm),{inputs:[c],outputs:[-1]})[0]}else return a.dims.length===3&&(c=a.reshape([t,r,n,i])),n===1||r===1?c:e.compute(Rn(c,Ws.perm),{inputs:[c],outputs:[-1]})[0]},qs=(e,t)=>{let n=Hs(e.inputs,t),r=e.inputs[0],i=Vs(e.inputs,1),a=Vs(e.inputs,2),o=Vs(e.inputs,3),s=Vs(e.inputs,4),c=Vs(e.inputs,5),l=Vs(e.inputs,6),u=Vs(e.inputs,7);if(r.dims.length===5)throw Error(`Packed QKV is not implemented`);if(i?.dims.length===5)throw Error(`Packed KV is not implemented`);let d=i&&a&&i.dims.length===4&&a.dims.length===4,f=Ks(e,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,r,o,0);if(d)return Wr(e,f,i,a,s,void 0,l,u,c,n);if(!i||!a)throw Error(`key and value must be provided`);let p=Ks(e,n.batchSize,n.numHeads,n.kvSequenceLength,n.headSize,i,o,n.hiddenSize),m=Ks(e,n.batchSize,n.numHeads,n.kvSequenceLength,n.vHeadSize,a,o,2*n.hiddenSize);Wr(e,f,p,m,s,void 0,l,u,c,n)}}),Ys,Xs,Zs,Qs,$s,ec,tc,nc=o(()=>{L(),B(),H(),Y(),Ys=e=>{if(!e||e.length<1)throw Error(`too few inputs`)},Xs=(e,t)=>{let n=[],r=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(e=>n.push(Number(e))),r=n.length),V({numOutputs:r,axis:t.axis,splitSizes:n})},Zs=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${K(`uniforms.size_in_split_axis`,`i`,e)}) {
        return i;
    }
    }
    return ${e}u;
}`,Qs=e=>{let t=e.length,n=[];for(let r=0;r<t;++r){let i=e[r].setByIndices(`indices`,`input[global_idx]`);t===1?n.push(i):r===0?n.push(`if (output_number == ${r}u) { ${i} }`):r===t-1?n.push(`else { ${i} }`):n.push(`else if (output_number == ${r}) { ${i} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${n.join(`
`)}
      }`},$s=(e,t)=>{let n=e[0].dims,r=z.size(n),i=e[0].dataType,a=z.normalizeAxis(t.axis,n.length),o=Array(t.numOutputs),s=q(`input`,i,n.length),c=Array(t.numOutputs),l=[],u=[],d=0,f=[{type:12,data:r}];for(let r=0;r<t.numOutputs;r++){d+=t.splitSizes[r],c[r]=d;let s=n.slice();s[a]=t.splitSizes[r],u.push(s),o[r]=J(`output${r}`,i,s.length),l.push({dims:u[r],dataType:e[0].dataType})}return f.push({type:12,data:c},...W(n,...u)),{name:`Split`,shaderCache:{hint:t.cacheKey,inputDependencies:[`rank`]},getShaderSource:e=>`
  ${e.registerUniform(`input_size`,`u32`).registerUniform(`size_in_split_axis`,`u32`,c.length).declareVariables(s,...o)}
  ${Zs(c.length)}
  ${Qs(o)}

  ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.input_size`)}

    var indices = ${s.offsetToIndices(`global_idx`)};
    var index = ${s.indicesGet(`indices`,a)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${K(`uniforms.size_in_split_axis`,`output_number - 1u`,c.length)};
      ${s.indicesSet(`indices`,a,`index`)};
    }
    writeBufferData(output_number, indices, global_idx);
  }`,getRunData:()=>({outputs:l,dispatchGroup:{x:Math.ceil(r/64)},programUniforms:f})}},ec=(e,t)=>{Ys(e.inputs);let n=e.inputs.length===1?t:Xs(e.inputs,t);e.compute($s(e.inputs,n),{inputs:[0]})},tc=e=>{let t=e.axis,n=e.splitSizes,r=e.numOutputs<0?n.length:e.numOutputs;if(r!==n.length)throw Error(`numOutputs and splitSizes length must be equal`);return V({axis:t,numOutputs:r,splitSizes:n})}}),rc,ic,ac,oc=o(()=>{L(),B(),H(),Y(),rc=(e,t)=>{let[n,r,i,a]=e,{numHeads:o,rotaryEmbeddingDim:s}=t;if(n.dims.length!==3&&n.dims.length!==4)throw Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${n.dims.length}`);if(!z.areEqual(r.dims,[])&&!z.areEqual(r.dims,[1])&&r.dims.length!==2)throw Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${r.dims.length}`);if(i.dims.length!==2)throw Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(a.dims.length!==2)throw Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(!z.areEqual(i.dims,a.dims))throw Error(`Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape`);if(s>0&&o===0)throw Error(`num_heads must be provided if rotary_embedding_dim is specified`);let c=n.dims[0],l=n.dims[n.dims.length-2],u=i.dims[0],d=z.sizeFromDimension(n.dims,1)/l,f=s===0?i.dims[1]*2:d/o;if(s>f)throw Error(`rotary_embedding_dim must be less than or equal to head_size`);if(r.dims.length===2){if(c!==r.dims[0])throw Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${r.dims[0]}`);if(l!==r.dims[1])throw Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${r.dims[1]}`)}if(f/2!==i.dims[1]&&s/2!==i.dims[1])throw Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${i.dims[1]}`);if(l>u)throw Error(`Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported`)},ic=(e,t)=>{let{interleaved:n,numHeads:r,rotaryEmbeddingDim:i,scale:a}=t,o=e[0].dims[0],s=z.sizeFromDimension(e[0].dims,1),c=e[0].dims[e[0].dims.length-2],l=s/c,u=e[2].dims[1],d=i===0?u*2:l/r,f=[o,c,l/d,d-u],p=z.computeStrides(f),m=[{type:1,data:a},{type:12,data:f},{type:12,data:p},...e[0].dims.length===3?Array({type:12,data:[s,l,d,1]}):[],...e[0].dims.length===4?Array({type:12,data:[s,d,c*d,1]}):[],...W(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)];return{name:`RotaryEmbedding`,shaderCache:{hint:V({interleaved:n}).cacheKey,inputDependencies:[`rank`,`rank`,`rank`,`rank`]},getShaderSource:t=>{let r=q(`input`,e[0].dataType,e[0].dims.length),i=q(`position_ids`,e[1].dataType,e[1].dims.length),a=q(`cos_cache`,e[2].dataType,e[2].dims.length),o=q(`sin_cache`,e[3].dataType,e[3].dims.length),s=J(`output`,e[0].dataType,e[0].dims.length);return t.registerUniforms([{name:`scale`,type:`f32`},{name:`global_shape`,type:`u32`,length:f.length},{name:`global_strides`,type:`u32`,length:p.length},{name:`input_output_strides`,type:`u32`,length:p.length}]),`
        ${t.declareVariables(r,i,a,o,s)}

        ${t.mainStart(xn)}
          let half_rotary_emb_dim = uniforms.${a.name}_shape[1];
          let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;
          let size = uniforms.global_shape[0] * uniforms.global_strides[0];
          ${t.guardAgainstOutOfBoundsWorkgroupSizes(`size`)}

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${i.broadcastedIndicesToOffset(`bsnh.xy`,J(``,i.type.tensor,2))};
            let position_id =
                u32(${i.getByOffset(`position_ids_idx`)}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${n});
            let j = i + select(half_rotary_emb_dim, 1, ${n});
            let re = ${r.getByOffset(`i`)} * ${a.get(`position_id`,`bsnh[3]`)} -
                ${r.getByOffset(`j`)} * ${o.get(`position_id`,`bsnh[3]`)};
            ${s.setByOffset(`i`,`re`)}
            let im = ${r.getByOffset(`i`)} * ${o.get(`position_id`,`bsnh[3]`)} +
                ${r.getByOffset(`j`)} * ${a.get(`position_id`,`bsnh[3]`)};
            ${s.setByOffset(`j`,`im`)}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${s.setByOffset(`k`,r.getByOffset(`k`))}
          }
        }`},getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(z.size(f)/xn)},programUniforms:m})}},ac=(e,t)=>{rc(e.inputs,t),e.compute(ic(e.inputs,t))}}),sc,cc,lc,uc,dc,fc=o(()=>{H(),L(),qr(),Js(),nc(),Vn(),oc(),Y(),sc=(e,t)=>{if(t.doRotary&&e.length<=7)throw Error(`cos_cache and sin_cache inputs are required if do_rotary is specified`);let n=e[0],r=e[1],i=e[2],a=e[3],o=e[4];if(t.doRotary!==0&&e.length<=7)throw Error(`cos_cast and sin_cache are expected if do_rotary attribute is non-zero`);if(t.localWindowSize!==-1)throw Error(`Local attention is not supported`);if(t.softcap!==0)throw Error(`Softcap is not supported`);if(t.rotaryInterleaved!==0)throw Error(`Rotary interleaved is not supported`);if(t.smoothSoftmax)throw Error(`Smooth softmax is not supported`);if(n.dims.length!==3&&n.dims.length!==5)throw Error(`Input query is expected to have 3 or 5 dimensions`);let s=n.dims[0],c=n.dims[1],l=n.dims.length===3?n.dims[2]:t.numHeads*n.dims[4],u=c,d=0,f=!r||r.dims.length===0,p=Math.floor(f?l/(t.numHeads+2*t.kvNumHeads):l/t.numHeads);f&&(l=p*t.numHeads);let m=a&&a.dims.length!==0,h=o&&o.dims.length!==0;if(m&&a.dims.length===4&&a.dims[0]===s&&a.dims[1]!==t.kvNumHeads&&a.dims[2]===t.kvNumHeads&&a.dims[3]===p)throw Error(`BSNH pastKey/pastValue is not supported`);if(m&&h){if(a.dims.length!==4)throw Error(`Input "past_key" is expected to have 4 dimensions`);if(o.dims.length!==4)throw Error(`Input "past_value" is expected to have 4 dimensions`);d=a.dims[2]}else if(m||h)throw Error(`Input "past_key" and "past_value" shall be both present or both absent`);let g=1;if(r&&r.dims.length>0){if(n.dims.length!==3)throw Error(`Input "query" is expected to have 3 dimensions when key is given`);if(r.dims.length<3||r.dims.length>5)throw Error(`Input "key" is expected to have 3, 4, or 5 dimensions`);if(n.dims[0]!==r.dims[0])throw Error(`Input "query" and "key" shall have same dim 0 (batch size)`);if(r.dims.length===3){if(n.dims[2]%r.dims[2]!==0)throw Error(`Dimension 2 of "query" should be a multiple of "key"`);u=r.dims[1]}else if(r.dims.length===5){if(r.dims[2]!==t.numHeads||r.dims[3]!==2||r.dims[4]!==p)throw Error(`Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv`);if(i)throw Error(`Expect "value" be none when "key" has packed kv format.`);u=r.dims[1]}else{if(r.dims[1]!==t.numHeads||r.dims[3]!==p)throw Error(`Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key`);u=r.dims[2]}}else{if(n.dims.length!==3&&n.dims.length!==5)throw Error(`Input "query" is expected to have 3 or 5 dimensions when key is empty`);if(n.dims.length===5&&(n.dims[2]!==t.numHeads||n.dims[3]!==3))throw Error(`Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv`);g=3}let _=!1,v=t.kvNumHeads?p*t.kvNumHeads:l;if(i&&i.dims.length>0){if(i.dims.length!==3&&i.dims.length!==4)throw Error(`Input "value" is expected to have 3 or 4 dimensions`);if(n.dims[0]!==i.dims[0])throw Error(`Input "query" and "value" shall have same dim 0 (batch_size)`);if(i.dims.length===3){if(u!==i.dims[1])throw Error(`Input "key" and "value" shall have the same dim 1 (kv_sequence_length)`);v=i.dims[2]}else{if(u!==i.dims[2])throw Error(`Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)`);v=i.dims[1]*i.dims[3],_=!0}}let y=e.length>4?e[5]:void 0;if(y&&y.dims.length!==1&&y.dims[0]!==s)throw Error(`Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size`);return{batchSize:s,sequenceLength:c,pastSequenceLength:d,kvSequenceLength:u,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:l,vHiddenSize:v,headSize:p,vHeadSize:Math.floor(v/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:0,scale:t.scale,broadcastResPosBias:!1,passPastInKv:_,qkvFormat:g}},cc=V({perm:[0,2,1,3]}),lc=(e,t,n)=>{let r=t,i=n.kvNumHeads;return t.dims.length===3&&n.kvSequenceLength!==0&&(r=t.reshape([n.batchSize,n.kvSequenceLength,i,n.headSize]),r=e.compute(Rn(r,cc.perm),{inputs:[r],outputs:[-1]})[0]),r},uc=(e,t,n,r)=>{let i=[`type`,`type`],a=[e*t],o=e*t,s=[{type:12,data:o},{type:12,data:t},{type:12,data:e}];return{name:`GeneratePositionIds`,shaderCache:{hint:`${e};${t}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:a,dataType:7}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:s}),getShaderSource:e=>{let t=q(`seq_lens`,n.dataType,n.dims),i=q(`total_seq_lens`,r.dataType,r.dims),o=J(`pos_ids`,7,a);return`
  ${e.registerUniforms([{name:`output_size`,type:`u32`},{name:`sequence_length`,type:`u32`},{name:`batch_size`,type:`u32`}]).declareVariables(t,i,o)}
  ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
    let total_sequence_length = u32(${i.getByOffset(`0`)});
    let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length;
    let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length;
    let batch_idx = global_idx / uniforms.sequence_length;
    let sequence_idx = i32(global_idx % uniforms.sequence_length);
    var pos_id: i32 = 0;
    let seqlen = ${t.getByOffset(`batch_idx`)};
    let total_seqlen = seqlen + 1;
    if (is_first_prompt) {
      if (sequence_idx < total_seqlen) {
        pos_id = sequence_idx;
      } else {
        pos_id = 1;
      }
      ${o.setByOffset(`global_idx`,`pos_id`)}
    } else if (is_subsequent_prompt) {
      let past_seqlen = total_seqlen - i32(uniforms.sequence_length);
      if (past_seqlen + sequence_idx < total_seqlen) {
        pos_id = past_seqlen + sequence_idx;
      } else {
        pos_id = 1;
      }
      ${o.setByOffset(`global_idx`,`pos_id`)}
    } else if (global_idx < uniforms.batch_size) {
      ${o.setByOffset(`global_idx`,`seqlen`)}
    };
  }
  `}}},dc=(e,t)=>{let n=sc(e.inputs,t);if(e.inputs[0].dims.length===5)throw Error(`Packed QKV is not implemented`);if(e.inputs[1]?.dims.length===5)throw Error(`Packed KV is not implemented`);let r=e.inputs[0],i=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,a=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,o=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,s=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,c=e.inputs.length>4?e.inputs[5]:void 0,l=e.inputs.length>5?e.inputs[6]:void 0,u=n.kvNumHeads?n.kvNumHeads:n.numHeads,d=V({axis:2,numOutputs:3,splitSizes:[n.numHeads*n.headSize,u*n.headSize,u*n.headSize]}),[f,p,m]=!i&&!a?e.compute($s([r],d),{inputs:[r],outputs:[-1,-1,-1]}):[r,i,a],h,g;if(t.doRotary){let r=e.compute(uc(n.batchSize,n.sequenceLength,c,l),{inputs:[c,l],outputs:[-1]})[0],i=e.inputs[7],a=e.inputs[8],o=V({interleaved:t.rotaryInterleaved!==0,numHeads:n.numHeads,rotaryEmbeddingDim:0,scale:t.scale}),s=[f,r,i,a],u=[-1];h=e.compute(ic(s,o),{inputs:s,outputs:u})[0],s.splice(0,1,p);let d=V({interleaved:t.rotaryInterleaved!==0,numHeads:n.kvNumHeads,rotaryEmbeddingDim:0,scale:t.scale});g=e.compute(ic(s,d),{inputs:s,outputs:u})[0]}let _=Ks(e,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,t.doRotary?h:f,void 0,0),v=lc(e,t.doRotary?g:p,n),y=lc(e,m,n);Wr(e,_,v,y,void 0,void 0,o,s,void 0,n,c,l)}}),pc,mc,hc,gc,_c=o(()=>{L(),B(),Vn(),Y(),pc=(e,t,n,r,i,a,o,s)=>{let c=G(a),l=c===1?`f32`:`vec${c}f`,u=c===1?`vec2f`:`mat2x${c}f`,d=i*o,f=64;d===1&&(f=256);let p=[i,o,a/c],m=[i,o,2],h=[`rank`,`type`,`type`],g=[];return g.push(...W(p,m)),e.compute({name:`InstanceNormComputeChannelScaleShift`,shaderCache:{hint:`${c};${s};${f}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:m,dataType:1}],dispatchGroup:{x:d},programUniforms:g}),getShaderSource:e=>{let i=q(`x`,t.dataType,3,c),a=[i,q(`scale`,n.dataType,n.dims),q(`bias`,r.dataType,r.dims),J(`output`,1,3,2)];return`
  var<workgroup> workgroup_shared : array<${u}, ${f}>;
  const workgroup_size = ${f}u;
  ${e.declareVariables(...a)}
  ${e.mainStart(f)}
    let batch = workgroup_index / uniforms.x_shape[1];
    let channel = workgroup_index % uniforms.x_shape[1];
    let hight = uniforms.x_shape[2];
    // initialize workgroup memory
    var sum = ${l}(0);
    var squared_sum = ${l}(0);
    for (var h = local_idx; h < hight; h += workgroup_size) {
      let value = ${l}(${i.get(`batch`,`channel`,`h`)});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${u}(sum, squared_sum);
    workgroupBarrier();

    for (var currSize = workgroup_size >> 1;  currSize > 0; currSize = currSize >> 1) {
      if (local_idx < currSize) {
        workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize];
      }
      workgroupBarrier();
    }
    if (local_idx == 0) {
      let sum_final = ${En(`workgroup_shared[0][0]`,c)} / f32(hight * ${c});
      let squared_sum_final = ${En(`workgroup_shared[0][1]`,c)} / f32(hight * ${c});

      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${s}));
      let channel_scale = inv_std_dev * f32(scale[channel]);
      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
      output[workgroup_index] = vec2f(channel_scale, channel_shift);
    }
  }`}},{inputs:[t,n,r],outputs:[-1]})[0]},mc=(e,t,n)=>{let r=t[0].dims,i=r,a=r[0],o=r[1],s=z.sizeFromDimension(r,2),c=G(s),l=z.size(i)/c,u=pc(e,t[0],t[1],t[2],a,s,o,n.epsilon),d=[a,o,s/c],f=[a,o];e.compute({name:`InstanceNormalization`,shaderCache:{hint:`${c}`,inputDependencies:[`type`,`none`]},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:[{type:12,data:l},...W(d,f,d)]}),getShaderSource:e=>{let n=q(`x`,t[0].dataType,d.length,c),r=q(`scale_shift`,1,f.length,2),i=J(`output`,t[0].dataType,d.length,c),a=[n,r,i];return`
  ${e.registerUniform(`output_size`,`u32`).declareVariables(...a)}
  ${e.mainStart()}
  ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
      let outputIndices = ${i.offsetToIndices(`global_idx`)};
      let batch = outputIndices[0];
      let channel = outputIndices[1];
      let scale_shift = ${r.getByIndices(`vec2<u32>(batch, channel)`)};
      let value = ${n.getByOffset(`global_idx`)} * ${i.type.value}(scale_shift.x) + ${i.type.value}(scale_shift.y);
      ${i.setByOffset(`global_idx`,`value`)};
  }`}},{inputs:[t[0],u]})},hc=(e,t,n)=>{let r=t[0].dims,i=r,a=r[0],o=r[r.length-1],s=z.sizeFromDimension(r,1)/o,c=G(o),l=z.size(i)/c,u=[{type:12,data:s},{type:12,data:Math.floor(o/c)}],d=[`type`,`type`],f=!1,p=[0,r.length-1];for(let e=0;e<r.length-2;e++)f||=r[e+1]!==1,p.push(e+1);f&&=r[r.length-1]!==1;let m=f?e.compute(Rn(e.inputs[0],p),{inputs:[e.inputs[0]],outputs:[-1]})[0]:e.inputs[0].reshape(Array.from({length:r.length},(e,t)=>r[p[t]])),h=pc(e,m,t[1],t[2],a,s,o,n.epsilon);e.compute({name:`InstanceNormalizationNHWC`,shaderCache:{hint:`${c}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:e=>{let n=U(t[0].dataType),r=c===1?`vec2f`:`mat${c}x2f`,a=e=>{let t=e===0?`x`:`y`,r=c===1?`f32`:`vec${c}f`;switch(c){case 1:return`${n}(${r}(scale.${t}))`;case 2:return`vec2<${n}>(${r}(scale[0].${t}, scale[1].${t}))`;case 4:return`vec4<${n}>(${r}(scale[0].${t}, scale[1].${t}, scale[2].${t}, scale[3].${t}))`;default:throw Error(`Not supported compoents ${c}`)}},o=q(`input`,t[0].dataType,t[0].dims,c),s=J(`output`,t[0].dataType,i,c);return`
  @group(0) @binding(0) var<storage, read> input : array<${o.type.storage}>;
  @group(0) @binding(1) var<storage, read> scale_input : array<${r}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${s.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

  ${e.mainStart()}
    let current_image_number = global_idx / (uniforms.C * uniforms.H);
    let current_channel_number = global_idx % uniforms.C;

    let scale_offset = current_image_number * uniforms.C + current_channel_number;
    let scale = scale_input[scale_offset];
    output[global_idx] = fma(input[global_idx], ${a(0)}, ${a(1)});
  }`}},{inputs:[t[0],h]})},gc=(e,t)=>{t.format===`NHWC`?hc(e,e.inputs,t):mc(e,e.inputs,t)}}),vc,yc,bc,xc=o(()=>{L(),B(),Y(),vc=e=>{if(!e||e.length<2)throw Error(`layerNorm requires at least 2 inputs.`)},yc=(e,t,n)=>{let r=t.simplified,i=e[0].dims,a=e[1],o=!r&&e[2],s=i,c=z.normalizeAxis(t.axis,i.length),l=z.sizeToDimension(i,c),u=z.sizeFromDimension(i,c),d=z.size(a.dims),f=o?z.size(o.dims):0;if(d!==u||o&&f!==u)throw Error(`Size of X.shape()[axis:] == ${u}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${d} and bias size of ${f}`);let p=[];for(let e=0;e<i.length;++e)e<c?p.push(i[e]):p.push(1);let m=G(u),h=[`type`,`type`],g=[{type:12,data:l},{type:1,data:u},{type:12,data:Math.floor(u/m)},{type:1,data:t.epsilon}];o&&h.push(`type`);let _=n>1,v=n>2,y=t=>{let n=U(e[0].dataType),i=[q(`x`,e[0].dataType,e[0].dims,m),q(`scale`,a.dataType,a.dims,m)];return o&&i.push(q(`bias`,o.dataType,o.dims,m)),i.push(J(`output`,e[0].dataType,s,m)),_&&i.push(J(`mean_data_output`,1,p)),v&&i.push(J(`inv_std_output`,1,p)),`
  ${t.registerUniforms([{name:`norm_count`,type:`u32`},{name:`norm_size`,type:`f32`},{name:`norm_size_vectorized`,type:`u32`},{name:`epsilon`,type:`f32`}]).declareVariables(...i)}
  ${t.mainStart()}
    ${t.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.norm_count`)}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${wn(`f32`,m)};
    var mean_square_vector = ${wn(`f32`,m)};

    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
      let value = ${Tn(n,m,`x[h + offset]`)};
      mean_vector += value;
      mean_square_vector += value * value;
    }
    let mean = ${En(`mean_vector`,m)} / uniforms.norm_size;
    let inv_std_dev = inverseSqrt(${En(`mean_square_vector`,m)} / uniforms.norm_size ${r?``:`- mean * mean`} + uniforms.epsilon);

    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
      let f32input = ${Tn(n,m,`x[j + offset]`)};
      let f32scale = ${Tn(n,m,`scale[j]`)};
      output[j + offset] = ${i[0].type.value}((f32input ${r?``:`- mean`}) * inv_std_dev * f32scale
        ${o?`+ ${Tn(n,m,`bias[j]`)}`:``}
      );
    }

    ${_?`mean_data_output[global_idx] = mean`:``};
    ${v?`inv_std_output[global_idx] = inv_std_dev`:``};
  }`},b=[{dims:s,dataType:e[0].dataType}];return _&&b.push({dims:p,dataType:1}),v&&b.push({dims:p,dataType:1}),{name:`LayerNormalization`,shaderCache:{hint:`${m};${n};${r}`,inputDependencies:h},getRunData:()=>({outputs:b,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:g}),getShaderSource:y}},bc=(e,t)=>{vc(e.inputs),e.compute(yc(e.inputs,t,e.outputCount))}}),Sc,Cc,wc=o(()=>{B(),ja(),Ba(),Sc=e=>{if(!e||e.length!==2)throw Error(`MatMul requires 2 inputs.`);if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw Error(`shared dimension does not match.`)},Cc=e=>{Sc(e.inputs);let t=zt.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw Error(`Can't use matmul on the given tensors`);let n=t[t.length-1],r=e.inputs[0].dims[e.inputs[0].dims.length-1];if(n<8&&r<8)e.compute(Aa(e.inputs,{activation:``},t));else{let i=t[t.length-2],a=z.size(e.inputs[0].dims.slice(0,-2)),o=z.size(e.inputs[1].dims.slice(0,-2));if(a!==1&&i===1&&o===1){let i=e.inputs[0].reshape([1,a,r]),o=e.inputs[1].reshape([1,r,n]),s=[1,a,n],c=[i,o];e.compute(za(c,{activation:``},t,s),{inputs:c})}else e.compute(za(e.inputs,{activation:``},t))}}}),Tc,Ec,Dc,Oc,kc,Ac=o(()=>{L(),B(),H(),Y(),Tc=(e,t)=>{if(e.length<3||e.length>4)throw Error(`MatMulNBits requires 3 or 4 inputs`);let n=e[0],r=n.dims.length;if(n.dims[r-1]!==t.k)throw Error(`The last dim of input shape does not match the k value`);let i=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,o=e[1];if(!z.areEqual(o.dims,[t.n,i,a]))throw Error(`The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize`);let s=e[2].dims;if(z.size(s)!==t.n*i)throw Error(`scales input size error.`);if(e.length===4){let n=e[3].dims,r=t.n*(t.bits===8?i:Math.floor((i*t.bits+7)/8));if(z.size(n)!==r)throw Error(`zeroPoints input size error.`)}},Ec=(e,t)=>{let n=e[0].dims,r=n.length,i=n[r-2],a=t.k,o=t.n,s=n.slice(0,r-2),c=z.size(s),l=e[1].dims[2]/4,u=e[0].dataType,d=G(t.k),f=G(l),p=G(o),m=s.concat([i,o]),h=i>1&&o/p%2==0?2:1,g=z.size(m)/p/h,_=[],v=[c,i,a/d],y=z.convertShape(e[1].dims).slice();y.splice(-1,1,l/f),_.push(...W(v)),_.push(...W(y)),_.push(...W(e[2].dims)),e.length===4&&_.push(...W(z.convertShape(e[3].dims)));let b=[c,i,o/p];return _.push(...W(b)),{name:`MatMulNBits`,shaderCache:{hint:`${t.blockSize};${t.bits};${d};${f};${p};${h};64`,inputDependencies:Array(e.length).fill(`rank`)},getRunData:()=>({outputs:[{dims:m,dataType:u}],dispatchGroup:{x:g},programUniforms:_}),getShaderSource:n=>{let r=v.length,i=q(`a`,e[0].dataType,r,d),a=q(`b`,12,y.length,f),o=q(`scales`,e[2].dataType,e[2].dims.length),s=[i,a,o],c=e.length===4?q(`zero_points`,12,e[3].dims.length):void 0;c&&s.push(c);let u=b.length,m=J(`output`,e[0].dataType,u,p),g=U(e[0].dataType),_=(()=>{switch(d){case 1:return`array<${g}, 8>`;case 2:return`mat4x2<${g}>`;case 4:return`mat2x4<${g}>`;default:throw Error(`${d}-component is not supported.`)}})(),x=()=>{let e=`
          // reuse a data
            var input_offset = ${i.indicesToOffset(`${i.type.indices}(batch, row, word_offset)`)};
            var a_data: ${_};
            for (var j: u32 = 0; j < ${8/d}; j++) {
              a_data[j] = ${i.getByOffset(`input_offset`)};
              input_offset++;
            }
          `;for(let t=0;t<p*h;t++)e+=`
            b_value = ${f===1?`b${t}_data`:`b${t}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${_}(${Array.from({length:4},(e,t)=>`${g}(b_value_lower[${t}]), ${g}(b_value_upper[${t}])`).join(`, `)});
            b_dequantized_values = ${d===1?`${_}(${Array.from({length:8},(e,n)=>`(b_quantized_values[${n}] - ${c?`zero_point${t}`:`zero_point`}) * scale${t}`).join(`, `)});`:`(b_quantized_values - ${_}(${Array(8).fill(`${c?`zero_point${t}`:`zero_point`}`).join(`,`)})) * scale${t};`};
            workgroup_shared[local_id.x * ${h} + ${Math.floor(t/p)}]${p>1?`[${t%p}]`:``} += ${Array.from({length:8/d},(e,t)=>`${d===1?`a_data[${t}] * b_dequantized_values[${t}]`:`dot(a_data[${t}], b_dequantized_values[${t}])`}`).join(` + `)};
          `;return e},S=()=>{let e=`
            var col_index = col * ${p};
            ${c?`
            let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2;
            var zero_point_byte_count: u32;
            var zero_point_word_index: u32;
            var zero_point_byte_offset: u32;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32;
            var zero_point_word: u32;`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${g}(8);`}
            `;for(let t=0;t<p*h;t++)e+=`
            let scale${t} = ${o.getByOffset(`col_index * nBlocksPerCol + block`)};
            ${c?`
            zero_point_byte_count = col_index * zero_point_bytes_per_col + (block >> 0x1u);
            zero_point_word_index = zero_point_byte_count >> 0x2u;
            zero_point_byte_offset = zero_point_byte_count & 0x3u;
            zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            zero_point_word = ${c.getByOffset(`zero_point_word_index`)} >> zero_point_bits_offset;
            let zero_point${t} = ${g}((zero_point_word) & 0xFu);`:``}
            col_index += 1;`;return e},C=()=>{let e=`col_index = col * ${p};`;for(let t=0;t<p*h;t++)e+=`
            let b${t}_data = ${a.getByIndices(`${a.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return e+=`
            var b_value: u32;
            let b_mask: u32 = 0x0F0F0F0Fu;
            var b_value_lower: vec4<u32>;
            var b_value_upper: vec4<u32>;
            var b_quantized_values: ${_};
            var b_dequantized_values: ${_};`,e};return`
        var<workgroup> workgroup_shared: array<${m.type.value}, ${h*64}>;
        ${n.declareVariables(...s,m)}
        ${n.mainStart([64,1,1])}
          let output_indices = ${m.offsetToIndices(`(global_idx / 64) * ${h}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let nBlocksPerCol = uniforms.b_shape[1];

          for (var block = local_id.x; block < nBlocksPerCol; block += 64) {
            //process one block
            var word_offset: u32 = block * ${t.blockSize/d};
            ${S()}
            for (var word: u32 = 0; word < ${l}; word += ${f}) {
              ${C()}
              for (var i: u32 = 0; i < ${f}; i++) {
                ${x()}
                word_offset += ${8/d};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${h}) {
            var output_value: ${m.type.value} = ${m.type.value}(0);
            var workgroup_shared_offset: u32 = local_id.x;
            for (var b: u32 = 0u; b < 64u; b++) {
              output_value += workgroup_shared[workgroup_shared_offset];
              workgroup_shared_offset += ${h};
            }
            ${m.setByIndices(`${m.type.indices}(batch, row, col + local_id.x)`,`output_value`)};
          }
        }`}}},Dc=(e,t)=>{let n=e[0].dims,r=n.length,i=n[r-2],a=t.k,o=t.n,s=n.slice(0,r-2),c=z.size(s),l=e[1].dims[2]/4,u=e[0].dataType,d=G(t.k),f=G(l),p=s.concat([i,o]),m=o%8==0?8:o%4==0?4:1,h=128/m,g=h*f*8,_=g/d,v=g/t.blockSize,y=z.size(p)/m,b=[],x=[c,i,a/d],S=z.convertShape(e[1].dims).slice();S.splice(-1,1,l/f),b.push(...W(x)),b.push(...W(S)),b.push(...W(e[2].dims)),e.length===4&&b.push(...W(z.convertShape(e[3].dims)));let C=[c,i,o];return b.push(...W(C)),{name:`BlockwiseMatMulNBits32`,shaderCache:{hint:`${t.blockSize};${d};${f};${h};${m}`,inputDependencies:Array(e.length).fill(`rank`)},getRunData:()=>({outputs:[{dims:p,dataType:u}],dispatchGroup:{x:y},programUniforms:b}),getShaderSource:n=>{let r=x.length,i=q(`a`,e[0].dataType,r,d),a=q(`b`,12,S.length,f),o=q(`scales`,e[2].dataType,e[2].dims.length),s=[i,a,o],c=e.length===4?q(`zero_points`,12,e[3].dims.length):void 0;c&&s.push(c);let l=C.length,u=J(`output`,e[0].dataType,l),p=U(e[0].dataType),g=()=>{switch(d){case 1:return`
          let a_data0 = vec4<${p}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
          let a_data1 = vec4<${p}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
          let a_data0 = vec4<${p}>(sub_a[word_offset], sub_a[word_offset + 1]);
          let a_data1 = vec4<${p}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
          let a_data0 = sub_a[word_offset];
          let a_data1 = sub_a[word_offset + 1];`;default:throw Error(`${d}-component is not supported.`)}};return`
        var<workgroup> sub_a: array<${i.type.value}, ${_}>;
        var<workgroup> inter_results: array<array<${u.type.value}, ${h}>, ${m}>;
        ${n.declareVariables(...s,u)}
        ${n.mainStart([h,m,1])}
          let output_indices = ${u.offsetToIndices(`workgroup_index * ${m}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let n_blocks_per_col = uniforms.b_shape[1];
          let num_tiles =  (n_blocks_per_col - 1) / ${v} + 1;

          // Loop over shared dimension.
          for (var tile: u32 = 0; tile < num_tiles; tile += 1) {
            let a_col_start = tile * ${_};
            // load one tile A data into shared memory.
            for (var a_offset = local_idx; a_offset < ${_}; a_offset += 128)
            {
              let a_col = a_col_start + a_offset;
              if (a_col < uniforms.a_shape[2])
              {
                sub_a[a_offset] = ${i.getByIndices(`${i.type.indices}(batch, row, a_col)`)};
              } else {
                sub_a[a_offset] = ${i.type.value}(0);
              }
            }
            workgroupBarrier();

            // each thread process one block
            let b_row = col + local_id.y;
            let block = tile * ${v} + local_id.x;
            ${c?`
            let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2;
            let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u);
            let zero_point_word_index = zero_point_byte_count >> 0x2u;
            let zero_point_byte_offset = zero_point_byte_count & 0x3u;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            let zero_point_word = ${c.getByOffset(`zero_point_word_index`)} >> zero_point_bits_offset;
            let zero_point = ${p}((zero_point_word) & 0xFu);`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${p}(8);`}
            let scale = ${o.getByOffset(`b_row * n_blocks_per_col + block`)};
            let b_data = ${a.getByIndices(`${a.type.indices}(b_row, block, 0)`)};
            var word_offset = local_id.x * ${t.blockSize/d};
            for (var i: u32 = 0; i < ${f}; i++) {
              ${g()}
              let b_value = ${f===1?`b_data`:`b_data[i]`};
              let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu);
              let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu);
              let b_quantized_values = mat2x4<${p}>(${Array.from({length:4},(e,t)=>`${p}(b_value_lower[${t}]), ${p}(b_value_upper[${t}])`).join(`, `)});
              let b_dequantized_values = (b_quantized_values - mat2x4<${p}>(${Array(8).fill(`zero_point`).join(`,`)})) * scale;
              inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(e,t)=>`${`dot(a_data${t}, b_dequantized_values[${t}])`}`).join(` + `)};
              word_offset += ${8/d};
            }
            workgroupBarrier();
          }

          if (local_idx < ${m}) {
            var output_value: ${u.type.value} = ${u.type.value}(0);
            for (var b = 0u; b < ${h}; b++) {
              output_value += inter_results[local_idx][b];
            }
            if (col + local_idx < uniforms.output_shape[2])
            {
              ${u.setByIndices(`${u.type.indices}(batch, row, col + local_idx)`,`output_value`)}
            }
          }
        }`}}},Oc=(e,t)=>{Tc(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor(`intel`)&&e.adapterInfo.isArchitecture(`gen-12lp`)?e.compute(Dc(e.inputs,t)):e.compute(Ec(e.inputs,t))},kc=e=>V(e)}),jc,Mc,Nc,Pc,Fc,Ic,Lc,Rc,zc,Bc=o(()=>{L(),B(),Y(),jc=e=>{if(!e||e.length<1)throw Error(`Too few inputs`);if(e[0].dataType!==1&&e[0].dataType!==10)throw Error(`Input type must be float or float16.`);if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw Error(`The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].`)}},Mc=(e,t,n)=>{let r=``;for(let i=t-1;i>=0;--i)r+=`
            k = i32(${e.indicesGet(`indices`,i)}) - ${K(`uniforms.pads`,i,n)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${K(`uniforms.x_shape`,i,t)})) {
              break;
            }
            offset += k * i32(${K(`uniforms.x_strides`,i,t)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${r}
            value = x[offset];
          }
      `},Nc=(e,t,n)=>{let r=``;for(let i=t-1;i>=0;--i)r+=`
                k = i32(${e.indicesGet(`indices`,i)}) - ${K(`uniforms.pads`,i,n)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${K(`uniforms.x_shape`,i,t)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${K(`uniforms.x_shape`,i,t)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${K(`uniforms.x_strides`,i,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${r}
              value = x[offset];
          `},Pc=(e,t,n)=>{let r=``;for(let i=t-1;i>=0;--i)r+=`
                k = i32(${e.indicesGet(`indices`,i)}) - ${K(`uniforms.pads`,i,n)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${K(`uniforms.x_shape`,i,t)})) {
                  k = i32(${K(`uniforms.x_shape`,i,t)}) - 1;
                }
                offset += k * i32(${K(`uniforms.x_strides`,i,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${r}
              value = x[offset];
          `},Fc=(e,t,n)=>{let r=``;for(let i=t-1;i>=0;--i)r+=`
                k = i32(${e.indicesGet(`indices`,i)}) - ${K(`uniforms.pads`,i,n)};
                if (k < 0)  {
                  k += i32(${K(`uniforms.x_shape`,i,t)}]);
                }
                if (k >= i32(${K(`uniforms.x_shape`,i,t)})) {
                  k -= i32(${K(`uniforms.x_shape`,i,t)});
                }
                offset += k * i32(${K(`uniforms.x_strides`,i,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${r}
              value = x[offset];
          `},Ic=(e,t,n)=>{switch(n.mode){case 0:return Mc(e,t,n.pads.length);case 1:return Nc(e,t,n.pads.length);case 2:return Pc(e,t,n.pads.length);case 3:return Fc(e,t,n.pads.length);default:throw Error(`Invalid mode`)}},Lc=(e,t)=>{let n=z.padShape(e[0].dims.slice(),t.pads),r=e[0].dims,i=[{type:12,data:z.size(n)},{type:6,data:t.pads}],a=e.length>=3&&e[2].data;return t.mode===0&&i.push({type:a?e[2].dataType:1,data:t.value}),i.push(...W(e[0].dims,n)),{name:`Pad`,shaderCache:{hint:`${t.mode}${a}`,inputDependencies:[`rank`]},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(z.size(n)/64)},programUniforms:i}),getShaderSource:i=>{let o=J(`output`,e[0].dataType,n.length),s=q(`x`,e[0].dataType,r.length),c=s.type.value,l=Ic(o,r.length,t),u=[{name:`output_size`,type:`u32`},{name:`pads`,type:`i32`,length:t.pads.length}];return t.mode===0&&u.push({name:`constant_value`,type:a?c:`f32`}),`
            ${i.registerUniforms(u).declareVariables(s,o)}
            ${i.mainStart()}
            ${i.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}

            let indices = ${o.offsetToIndices(`global_idx`)};

            var value = ${c}(0);
            ${l}
            output[global_idx] = value;
        }`}}},Rc=(e,t)=>{if(e.length>1){let n=e[1].getBigInt64Array(),r=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,i=e[0].dims.length,a=new Int32Array(2*i).fill(0);if(e.length>=4){let t=e[3].getBigInt64Array();for(let e=0;e<t.length;e++)a[Number(t[e])]=Number(n[e]),a[Number(t[e])+i]=Number(n[e+t.length])}else n.forEach((e,t)=>a[Number(t)]=Number(e));let o=[];return a.forEach(e=>o.push(e)),{mode:t.mode,value:r,pads:o}}else return t},zc=(e,t)=>{jc(e.inputs);let n=Rc(e.inputs,t);e.compute(Lc(e.inputs,n),{inputs:[0]})}}),Vc,Hc,Uc,Wc,Gc,Kc,qc,Jc,Yc,Xc,Zc,Qc,$c,el,tl,nl,rl,il,al,ol=o(()=>{N(),L(),B(),Y(),Vc=e=>{if(S.webgpu.validateInputContent&&(!e||e.length!==1))throw Error(`Pool ops requires 1 input.`)},Hc=(e,t,n)=>{let r=t.format===`NHWC`,i=e.dims.slice();r&&i.splice(1,0,i.pop());let a=Object.hasOwnProperty.call(t,`dilations`),o=t.kernelShape.slice(),s=t.strides.slice(),c=a?t.dilations.slice():[],l=t.pads.slice();Bt.adjustPoolAttributes(n,i,o,s,c,l);let u=Bt.computePoolOutputShape(n,i,s,c,o,l,t.autoPad),d=Object.assign({},t);a?Object.assign(d,{kernelShape:o,strides:s,pads:l,dilations:c,cacheKey:t.cacheKey}):Object.assign(d,{kernelShape:o,strides:s,pads:l,cacheKey:t.cacheKey});let f=u.slice();return f.push(f.splice(1,1)[0]),[d,r?f:u]},Uc=(e,t)=>{let n=t.format===`NHWC`,r=z.size(e),i=z.size(t.kernelShape),a=[{type:12,data:r},{type:12,data:i}],o=[{name:`outputSize`,type:`u32`},{name:`kernelSize`,type:`u32`}];if(t.kernelShape.length<=2){let e=t.kernelShape[t.kernelShape.length-1],n=t.strides[t.strides.length-1],r=t.pads[t.pads.length/2-1],i=t.pads[t.pads.length-1],s=!!(r+i);a.push({type:12,data:e},{type:12,data:n},{type:12,data:r},{type:12,data:i}),o.push({name:`kw`,type:`u32`},{name:`sw`,type:`u32`},{name:`pwStart`,type:`u32`},{name:`pwEnd`,type:`u32`});let c=!1;if(t.kernelShape.length===2){let e=t.kernelShape[t.kernelShape.length-2],n=t.strides[t.strides.length-2],r=t.pads[t.pads.length/2-2],i=t.pads[t.pads.length-2];c=!!(r+i),a.push({type:12,data:e},{type:12,data:n},{type:12,data:r},{type:12,data:i}),o.push({name:`kh`,type:`u32`},{name:`sh`,type:`u32`},{name:`phStart`,type:`u32`},{name:`phEnd`,type:`u32`})}return[a,o,!0,s,c]}else{if(n)throw Error(`Pooling with kernelShape.length > 2 is not supported for NHWC format.`);let e=z.computeStrides(t.kernelShape);return a.push({type:12,data:e},{type:12,data:t.pads},{type:12,data:t.strides}),o.push({name:`kernelStrides`,type:`u32`,length:e.length},{name:`pads`,type:`u32`,length:t.pads.length},{name:`strides`,type:`u32`,length:t.strides.length}),[a,o,!!t.pads.reduce((e,t)=>e+t),!1,!1]}},Wc=(e,t,n,r,i,a,o,s,c,l,u,d)=>{let f=i.format===`NHWC`,p=t.type.value,m=J(`output`,t.type.tensor,r);if(i.kernelShape.length<=2){let r=``,l=``,h=``,g=n-(f?2:1);if(r=u?`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${g}] < 0 || xIndices[${g}]
                      >= uniforms.x_shape[${g}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${t.indicesToOffset(`xIndices`)}];
                  ${a}
                }`:`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${t.indicesToOffset(`xIndices`)}];
                  ${a}
                }`,i.kernelShape.length===2){let e=n-(f?3:2);l=d?`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${e}] = indices[${e}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${e}] < 0 || xIndices[${e}] >= uniforms.x_shape[${e}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${e}] = indices[${e}] * uniforms.sh - uniforms.phStart + j;
                `,h=`
              }
            `}return`
            ${e.registerUniforms(c).declareVariables(t,m)}

            ${e.mainStart()}
              ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.outputSize`)}

              let indices = ${m.offsetToIndices(`global_idx`)};
              var xIndices = ${m.offsetToIndices(`global_idx`)};

              var value = ${p}(${s});
              var pad = 0;
              ${l}
              ${r}
              ${h}
              ${o}

              output[global_idx] = value;
            }`}else{if(f)throw Error(`Pooling with kernelShape.length > 2 is not supported for NHWC format.`);let r=i.kernelShape.length,u=i.pads.length,d=``;return d=l?`
                if (xIndices[j] >= uniforms.x_shape[j]) {
                  pad++;
                  isPad = true;
                  break;
                }
              }
              if (!isPad) {
                let x_val = x[${t.indicesToOffset(`xIndices`)}];
                ${a}
              }`:`
              }
              let x_val = x[${t.indicesToOffset(`xIndices`)}];
              ${a}
            `,`
            ${e.registerUniforms(c).declareVariables(t,m)}

            ${e.mainStart()}
              ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.outputSize`)}
              let indices = ${m.offsetToIndices(`global_idx`)};
              var xIndices = ${m.offsetToIndices(`global_idx`)};

              var offsets: array<u32, ${r}>;

              var value = ${p}(${s});
              var pad = 0;
              var isPad = false;

              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {
                var offset = i;
                for (var j = 0u; j < ${r-1}u; j++) {
                  offsets[j] = offset / ${K(`uniforms.kernelStrides`,`j`,r)};
                  offset -= offsets[j] * ${K(`uniforms.kernelStrides`,`j`,r)};
                }
                offsets[${r-1}] = offset;

                isPad = false;
                for (var j = ${n-r}u; j < ${n}u; j++) {
                  xIndices[j] = indices[j] * ${K(`uniforms.strides`,`j - ${n-r}u`,r)}
                    + offsets[j - ${n-r}u] - ${K(`uniforms.pads`,`j - 2u`,u)};
                  ${d}
              }
              ${o}

              output[global_idx] = value;
            }`}},Gc=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Kc=e=>`${Gc(e)};${e.countIncludePad}`,qc=e=>`${Gc(e)};${e.storageOrder};${e.dilations}`,Jc=e=>({format:e.format,autoPad:[`NOTSET`,`VALID`,`SAME_UPPER`,`SAME_LOWER`][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),Yc=(e,t,n,r)=>{let[i,a]=Hc(t,r,n),o=q(`x`,t.dataType,t.dims.length),s=o.type.value,c=``;i.countIncludePad?c+=`value /= ${s}(uniforms.kernelSize);`:c+=`value /= ${s}(i32(uniforms.kernelSize) - pad);`;let[l,u,d,f,p]=Uc(a,i);return l.push(...W(t.dims,a)),{name:e,shaderCache:{hint:`${r.cacheKey};${d};${f};${p}`,inputDependencies:[`rank`]},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(z.size(a)/64)},programUniforms:l}),getShaderSource:e=>Wc(e,o,t.dims.length,a.length,i,`value += x_val;`,c,0,u,d,f,p)}},Xc=e=>{let t=e.count_include_pad!==0,n=Jc(e);if(n.ceilMode!==0)throw Error(`using ceil() in shape computation is not yet supported for AveragePool`);let r={countIncludePad:t,...n,cacheKey:``};return{...r,cacheKey:Kc(r)}},Zc=(e,t)=>{Vc(e.inputs),e.compute(Yc(`AveragePool`,e.inputs[0],!1,t))},Qc={autoPad:``,ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},$c=e=>{let t=e.format;return{format:t,...Qc,cacheKey:t}},el=(e,t)=>{Vc(e.inputs),e.compute(Yc(`GlobalAveragePool`,e.inputs[0],!0,t))},tl=(e,t,n,r)=>{let[i,a]=Hc(t,r,n),o=q(`x`,t.dataType,t.dims.length),s=[`rank`],[c,l,u,d,f]=Uc(a,i);return c.push(...W(t.dims,a)),{name:e,shaderCache:{hint:`${r.cacheKey};${u};${d};${f}`,inputDependencies:s},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(z.size(a)/64)},programUniforms:c}),getShaderSource:e=>Wc(e,o,t.dims.length,a.length,i,`
      value = max(x_val, value);
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      ${e.registerUniforms([{name:`output_size`,type:`u32`},{name:`axis`,type:`u32`},{name:`block_size`,type:`u32`}]).declareVariables(...C,S)}
      ${e.mainStart()}
          ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
          let output_indices = ${S.offsetToIndices(`global_idx`)};

          // Set input x
          ${c?`
            let input = ${y.getByOffset(`global_idx / 4`)};
            let x_vec = ${i?`unpack4xI8(input)`:`unpack4xU8(input)`};
            let x_value = ${_===1?`x_vec[global_idx % 4]`:`x_vec`};`:`let x_value = ${y.getByOffset(`global_idx`)};`};

          // Set scale input
          ${p?`let scale_value= ${b.getByOffset(`0`)}`:m?`
            let scale_index = ${S.indicesGet(`output_indices`,`uniforms.axis`)};
            let scale_value= ${b.getByOffset(`scale_index`)};`:`
            var scale_indices: ${b.type.indices} = output_indices;
            let index = ${b.indicesGet(`scale_indices`,`uniforms.axis`)} / uniforms.block_size;
            ${b.indicesSet(`scale_indices`,`uniforms.axis`,`index`)};
            let scale_value= ${b.getByIndices(`scale_indices`)};`};

          // Set zero-point input
          ${x?p?c?`
                let zero_point_input = ${x.getByOffset(`0`)};
                let zero_point_vec =  ${i?`unpack4xI8(zero_point_input)`:`unpack4xU8(zero_point_input)`};
                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${x.getByOffset(`0`)}`:m?c?`
                let zero_point_index = ${S.indicesGet(`output_indices`,`uniforms.axis`)};
                let zero_point_input = ${x.getByOffset(`zero_point_index / 4`)};
                let zero_point_vec =  ${i?`unpack4xI8(zero_point_input)`:`unpack4xU8(zero_point_input)`};
                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
                let zero_point_index = ${S.indicesGet(`output_indices`,`uniforms.axis`)};
                let zero_point_value = ${x.getByOffset(`zero_point_index`)};`:c?`
                let zero_point_offset = ${b.indicesToOffset(`scale_indices`)};
                let zero_point_input = ${x.getByOffset(`zero_point_offset / 4`)};
                let zero_point_vec = ${i?`unpack4xI8(zero_point_input)`:`unpack4xU8(zero_point_input)`};
                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${x.getByIndices(`scale_indices`)};`:`let zero_point_value = ${c?i?`i32`:`u32`:y.type.value}(0);`};
      // Compute and write output
      ${S.setByOffset(`global_idx`,`${S.type.value}(x_value - zero_point_value) * scale_value`)};
      }`,getRunData:()=>({outputs:[{dims:a,dataType:o}],dispatchGroup:{x:Math.ceil(s/_/64),y:1,z:1},programUniforms:ee})}},ll=(e,t)=>{sl(e.inputs,t),e.compute(cl(e.inputs,t))},ul=e=>V({axis:e.axis,blockSize:e.blockSize})}),fl,pl,ml,hl=o(()=>{N(),L(),Y(),fl=(e,t,n)=>{if(e===t||e<t&&n<0||e>t&&n>0)throw Error(`Range these inputs' contents are invalid.`)},pl=(e,t,n,r)=>{let i=Math.abs(Math.ceil((t-e)/n)),a=[i],o=i,s=[{type:12,data:o},{type:r,data:e},{type:r,data:n},...W(a)];return{name:`Range`,shaderCache:{hint:`${r}`},getShaderSource:e=>{let t=J(`output`,r,a.length),n=t.type.value,i=[{name:`outputSize`,type:`u32`},{name:`start`,type:n},{name:`delta`,type:n}];return`
        ${e.registerUniforms(i).declareVariables(t)}
        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.outputSize`)}
        output[global_idx] = uniforms.start + ${n}(global_idx) * uniforms.delta;
      }`},getRunData:()=>({outputs:[{dims:a,dataType:r}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:s})}},ml=e=>{let t=0,n=0,r=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],n=e.inputs[1].getInt32Array()[0],r=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],n=e.inputs[1].getFloat32Array()[0],r=e.inputs[2].getFloat32Array()[0]),S.webgpu.validateInputContent&&fl(t,n,r),e.compute(pl(t,n,r,e.inputs[0].dataType),{inputs:[]})}}),gl,_l,vl,yl,bl=o(()=>{L(),B(),H(),Y(),gl=(e,t,n,r)=>{if(e!==`none`&&r!==`i32`&&r!==`u32`&&r!==`f32`)throw Error(`Input ${r} is not supported with reduction ${e}.`);let i=`{
                var oldValue = 0;
                loop {
                  let newValueF32 =`,a=`;
                  let newValue = bitcast<i32>(newValueF32);
                  let res = atomicCompareExchangeWeak(&${t}, oldValue, newValue);
                  if res.exchanged {
                    break;
                  }
                  oldValue = res.old_value;
                }
              }`;switch(e){case`none`:return`${t}=${n};`;case`add`:return r===`i32`||r===`u32`?`atomicAdd(&${t}, bitcast<${r}>(${n}));`:`
              ${i}bitcast<${r}>(oldValue) + (${n})${a}`;case`max`:return r===`i32`||r===`u32`?`atomicMax(&${t}, bitcast<${r}>(${n}));`:`
                ${i}max(bitcast<f32>(oldValue), (${n}))${a}`;case`min`:return r===`i32`||r===`u32`?`atomicMin(&${t}, bitcast<${r}>(${n}));`:`${i}min(bitcast<${r}>(oldValue), (${n}))${a}`;case`mul`:return`${i}(bitcast<${r}>(oldValue) * (${n}))${a}`;default:throw Error(`Reduction ${e} is not supported.`)}},_l=(e,t)=>{let n=e[0].dims,r=e[1].dims,i=n,a=Math.ceil(z.sizeToDimension(r,r.length-1)/1),o=r[r.length-1],s=z.sizeFromDimension(n,o),c=[{type:12,data:a},{type:12,data:o},{type:12,data:s},...W(e[1].dims,e[2].dims,i)];return{name:`ScatterND`,shaderCache:{hint:`${t.cacheKey}_${t.reduction}`,inputDependencies:[`rank`,`rank`]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c}),getShaderSource:n=>{let r=q(`indices`,e[1].dataType,e[1].dims.length),a=q(`updates`,e[2].dataType,e[2].dims.length,1),o=t.reduction!==`none`&&t.reduction!==``?On(`output`,e[0].dataType,i.length):J(`output`,e[0].dataType,i.length,1);return`
      ${n.registerUniform(`output_size`,`u32`).registerUniform(`last_index_dimension`,`u32`).registerUniform(`num_updates_elements`,`u32`).declareVariables(r,a,o)}
      ${n.mainStart()}
        ${n.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
  var data_offset = 0u;
  let indices_start = uniforms.last_index_dimension * global_idx;
  let indices_end = indices_start + uniforms.last_index_dimension;
  for (var i = indices_start; i < indices_end; i++) {
    var index = i32(indices[i].x);
    ${e[0].dims.length===1?`
    let element_count_dim = uniforms.output_strides;
    let dim_value = uniforms.output_shape;`:`
    let element_count_dim = uniforms.output_strides[i - indices_start];
    let dim_value = uniforms.output_shape[i - indices_start];`}
    if (index >= 0) {
      if (index >= i32(dim_value)) {
        index = i32(dim_value - 1);
      }
    } else {
      if (index < -i32(dim_value)) {
        index = 0;
      } else {
        index += i32(dim_value);
      }
    }
    data_offset += u32((u32(index) * element_count_dim));
  }

  for (var i = 0u; i < uniforms.num_updates_elements; i++) {
    let value = updates[uniforms.num_updates_elements * global_idx + i];
    ${gl(t.reduction,`output[data_offset + i]`,`value`,o.type.value)}
  }

      }`}}},vl=e=>V({reduction:e.reduction}),yl=(e,t)=>{e.compute(_l(e.inputs,t),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),xl,Sl,Cl,wl,Tl,El,Dl,Ol,kl,Al,jl,Ml,Nl,Pl,Fl,Il,Ll,Rl,zl,Bl,Vl=o(()=>{L(),B(),H(),Y(),xl=(e,t)=>{if(e.every(e=>e>0||(()=>{throw Error(`Resize requires scales input values to be positive`)})),e.length>0){if(t.mode===`linear`){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode===`cubic`&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw Error(`Resize requires scales input size to be 2 or 4 for cubic mode`)}},Sl=(e,t,n)=>{t.every(e=>e>=0&&e<n||(()=>{throw Error(`Resize requires axes input values to be positive and less than rank`)}));let r=Array(n).fill(1);return t.forEach((t,n)=>r[t]=e[n]),r},Cl=(e,t,n,r,i,a)=>{let[o,s,c]=n>10?[1,2,3]:[-1,e.length>1?1:-1,-1],l=e[0].dims.length;if(o>0&&e.length>o&&e[o].dims.length>0)e[o].getFloat32Array().forEach(e=>a.push(e));else if(t.coordinateTransformMode===`tf_crop_and_resize`)throw Error(`Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize`);if(s>0&&e.length>s&&e[s].dims.length===1&&e[s].dims[0]>0){if(e[s].getFloat32Array().forEach(e=>r.push(e)),r.length!==0&&r.length!==l&&n>=18&&r.length!==t.axes.length)throw Error(`Resize requires scales input size to be same as input rank or axes size for opset 18 and up`);xl(r,t),t.axes.length>0&&Sl(r,t.axes,l).forEach((e,t)=>r[t]=e)}if(c>0&&e.length>c&&e[c].dims.length===1&&e[c].dims[0]>0&&(e[c].getBigInt64Array().forEach(e=>i.push(Number(e))),i.length!==0&&i.length!==l&&n>=18&&i.length!==t.axes.length))throw Error(`Resize requires sizes input size to be same as input rank or axes size for opset 18 and up`);if(t.axes.length>0){if(r.length!==0&&r.length!==t.axes.length)throw Error(`Resize requires "scales" input size to be of axes rank when axes attributes is specified`);if(i.length!==0&&i.length!==t.axes.length)throw Error(`Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified`)}if(typeof r<`u`&&typeof i<`u`&&r.length>0&&i.length>l)throw Error(`Resize requires only of scales or sizes to be specified`)},wl=(e,t,n,r)=>`
  // The whole part and the fractional part are calculated separately due to inaccuracy of floating
  // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
  // offset-by-one error later in floor().
  let big = (${e}) * (${t});
  let whole = ${r}(big / (${n}));
  let fract = ${r}(big % (${n})) / ${r}(${n});
  return whole + fract;
`,Tl=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case`asymmetric`:return`
          if (xScale < 1.0 || floor(xScale) != xScale) {
            return ${t}(xResized) / ${t}(xScale);
          } else {
            ${wl(`xResized`,`lengthOriginal`,`lengthResized`,t)}
          }
        `;case`pytorch_half_pixel`:return`if (lengthResized > 1) {
                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case`tf_half_pixel_for_nn`:return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case`align_corners`:return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    ${wl(`xResized`,`lengthOriginal - 1`,`lengthResized - 1`,t)}
                  }`;case`tf_crop_and_resize`:return`if (lengthResized > 1) {
                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +
                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /
                        ${t}(lengthResized - 1);
                  } else {
                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);
                  }`;case`half_pixel_symmetric`:return`const outputWidth = ${t}xScale * ${t}(lengthResized);
                  const adjustment = ${t}(lengthResized) / outputWidth;
                  const center = ${t}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case`half_pixel`:return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw Error(`Coordinate transform mode ${e} is not supported`)}})()+`}`,El=(e,t,n)=>`fn getNearestPixelFromOriginal(xOriginal: ${n}, isDownSample: bool) -> ${n} {`+(()=>{switch(e){case`round_prefer_ceil`:return`if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }`;case`floor`:return`return floor(xOriginal);`;case`ceil`:return`return ceil(xOriginal);`;case`round_prefer_floor`:return`if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }`;default:if(t<11)return`if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }`;throw Error(`Nearest mode ${e} is not supported`)}})()+`}`,Dl=(e,t,n)=>{let r=Array(n).fill(0).concat(Array(n).fill(1)),i=e.length===0?r:e.slice();return t.length>0?(t.forEach((e,a)=>{r[e]=i[a],r[a+n]=i[t.length+a]}),r):i},Ol=(e,t,n,r)=>{let i=[];if(n.length>0)if(r.length>0){if(e.forEach(e=>i.push(e)),Math.max(...r)>e.length)throw Error(`axes is out of bound`);r.forEach((e,t)=>i[e]=n[t])}else n.forEach(e=>i.push(e));else{if(t.length===0)throw Error(`Resize requires either scales or sizes.`);i=e.map((e,n)=>Math.round(e*t[n]))}return i},kl=(e,t,n)=>{let r=(()=>{switch(n.keepAspectRatioPolicy){case`not_larger`:return n.axes.length>0?Math.min(...n.axes.map(e=>t[e]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case`not_smaller`:return n.axes.length>0?Math.max(...n.axes.map(e=>t[e]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw Error(`Keep aspect ratio policy ${n.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let i=e.slice();return n.axes.length>0?(n.axes.forEach(e=>t[e]=r),n.axes.forEach(n=>i[n]=Math.round(e[n]*t[n]))):(t.fill(r,0,t.length),i.forEach((e,n)=>i[n]=Math.round(e*t[n]))),i},Al=(e,t,n,r,i)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${n.length}> {
      var original_indices: array<${e.type.value}, ${n.length}>;
      for (var i:u32 = 0; i < ${n.length}; i++) {
        var output_index = ${e.indicesGet(`output_indices`,`i`)};
        var scale = ${K(`uniforms.scales`,`i`,r)};
        var roi_low = ${K(`uniforms.roi`,`i`,i)};
        var roi_hi = ${K(`uniforms.roi`,`i + ${t.length}`,i)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${K(`uniforms.input_shape`,`i`,t.length)};
          var output_shape_i = ${K(`uniforms.output_shape`,`i`,n.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,jl=(e,t,n,r,i,a,o)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
      var input_indices: ${e.type.indices};
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var output_index = ${t.indicesGet(`output_indices`,`i`)};
        var input_index: u32;
        var scale = ${K(`uniforms.scales`,`i`,i)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${K(`uniforms.roi`,`i`,a)};
          var roi_hi = ${K(`uniforms.roi`,`i + ${n.length}`,a)};
          var input_shape_i = ${K(`uniforms.input_shape`,`i`,n.length)};
          var output_shape_i = ${K(`uniforms.output_shape`,`i`,r.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${o} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${e.indicesSet(`input_indices`,`i`,`input_index`)}
      }
      return input_indices;
    }`,Ml=(e,t)=>`
    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${t.length}; i++) {
        var input_index = ${e.indicesGet(`input_indices`,`i`)};
        if (input_index < 0 || input_index >= ${K(`uniforms.input_shape`,`i`,t.length)}) {
          return false;
        }
      }
      return true;
    }`,Nl=(e,t,n,r)=>e.rank>r?`
    ${e.indicesSet(`input_indices`,t,`channel`)};
    ${e.indicesSet(`input_indices`,n,`batch`)};
`:``,Pl=(e,t,n,r,i)=>{let[a,o,s,c]=n.length===2?[-1,0,1,-1]:[0,2,3,1],l=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${l} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet(`input_indices`,o,`max(0, min(row, ${n[o]} - 1))`)};
      ${e.indicesSet(`input_indices`,s,`max(0, min(col, ${n[s]} - 1))`)};
      ${Nl(e,c,a,2)}
      return ${e.getByIndices(`input_indices`)};
    }

    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${l} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var row:${l} = originalIndices[${o}];
      var col:${l} = originalIndices[${s}];
      ${r?`if (row < 0 || row > (${n[o]} - 1) || col < 0 || col > (${n[s]} - 1)) {
        return ${i};
      }`:``};
      row = max(0, min(row, ${n[o]} - 1));
      col = max(0, min(col, ${n[s]} - 1));
      var row1: u32 = u32(row);
      var col1: u32 = u32(col);
      var row2: u32 = u32(row + 1);
      var col2: u32 = u32(col + 1);
      var channel: u32 = ${n.length>2?`u32(originalIndices[${c}])`:`0`};
      var batch: u32 =  ${n.length>2?`u32(originalIndices[${a}])`:`0`};
      var x11: ${l} = getInputValue(batch, channel, row1, col1);
      var x12: ${l} = getInputValue(batch, channel, row1, col2);
      var x21: ${l} = getInputValue(batch, channel, row2, col1);
      var x22: ${l} = getInputValue(batch, channel, row2, col2);
      var dx1: ${l} = abs(row - ${l}(row1));
      var dx2: ${l} = abs(${l}(row2) - row);
      var dy1: ${l} = abs(col - ${l}(col1));
      var dy2: ${l} = abs(${l}(col2) - col);
      if (row1 == row2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (col1 == col2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);
    }`},Fl=(e,t,n,r,i,a,o,s,c,l)=>{let[u,d]=n.length===2?[0,1]:[2,3],f=e.type.value,p=o=>{let d=o===u?`row`:`col`;return`
      fn ${d}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${f} {
        var output_index = ${t.indicesGet(`output_indices`,o)};
        var originalIdx: ${f} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[o]},
        ${r[o]}, ${n[o]}, ${a[o]}, ${a[o]} + ${n.length});
        var fractOriginalIdx: ${f} = originalIdx - floor(originalIdx);
        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);

        if (${s} && (originalIdx < 0 || originalIdx > (${n[o]} - 1))) {
          return ${c};
        }
        var data: array<${f}, 4> = array<${f}, 4>(0.0, 0.0, 0.0, 0.0);
        for (var i: i32 = -1; i < 3; i++) {
          var ${d}: ${f} = originalIdx + ${f}(i);
          if (${d} < 0 || ${d} >= ${n[o]}) {
            ${l?`coefs[i + 1] = 0.0;
                        continue;`:s?`return ${c};`:`${d} = max(0, min(${d}, ${n[o]} - 1));`};
          }
        var input_indices_copy: ${e.type.indices} = input_indices;
          ${e.indicesSet(`input_indices_copy`,o,`u32(${d})`)};
          data[i + 1] = ${o===u?e.getByIndices(`input_indices_copy`):`rowCubicInterpolation(input_indices_copy, output_indices)`};
        }
        return cubicInterpolation1D(data, coefs);
      }`};return`
    ${p(u)};
    ${p(d)};
  fn getCubicInterpolationCoefs(s: ${f}) -> array<${f}, 4> {
    var absS = abs(s);
    var coeffs: array<${f}, 4> = array<${f}, 4>(0.0, 0.0, 0.0, 0.0);
    var oneMinusAbsS: ${f} = 1.0 - absS;
    var twoMinusAbsS: ${f} = 2.0 - absS;
    var onePlusAbsS: ${f} = 1.0 + absS;
    coeffs[0] = ((${o} * onePlusAbsS - 5 * ${o}) * onePlusAbsS + 8 * ${o}) * onePlusAbsS - 4 * ${o};
    coeffs[1] = ((${o} + 2) * absS - (${o} + 3)) * absS * absS + 1;
    coeffs[2] = ((${o} + 2) * oneMinusAbsS - (${o} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;
    coeffs[3] = ((${o} * twoMinusAbsS - 5 * ${o}) * twoMinusAbsS + 8 * ${o}) * twoMinusAbsS - 4 * ${o};
    return coeffs;
  }

  fn cubicInterpolation1D(x: array<${f}, 4>, coefs: array<${f}, 4>) -> ${f} {
    var coefsSum: ${f} = coefs[0] + coefs[1] + coefs[2] + coefs[3];
    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;
  }

  fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${f} {
    var input_indices: ${e.type.indices} = output_indices;
    return colCubicInterpolation(input_indices, output_indices);
  }
    `},Il=(e,t,n,r,i)=>{let[a,o,s,c,l]=n.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],u=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${u} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet(`input_indices`,o,`max(0, min(depth, ${n[o]} - 1))`)};
      ${e.indicesSet(`input_indices`,s,`max(0, min(height, ${n[s]} - 1))`)};
      ${e.indicesSet(`input_indices`,c,`max(0, min(width, ${n[c]} - 1))`)};
      ${Nl(e,l,a,3)}
      return ${e.getByIndices(`input_indices`)};
    }

    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${u} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var depth:${u} = originalIndices[${o}];
      var height:${u} = originalIndices[${s}];
      var width:${u} = originalIndices[${c}];
      ${r?`if (depth < 0 || depth > (${n[o]} - 1) || height < 0 || height > (${n[s]} - 1) || width < 0 || (width > ${n[c]} - 1)) {
      return ${i};
        }`:``};

    depth = max(0, min(depth, ${n[o]} - 1));
      height = max(0, min(height, ${n[s]} - 1));
      width = max(0, min(width, ${n[c]} - 1));
      var depth1: u32 = u32(depth);
      var height1: u32 = u32(height);
      var width1: u32 = u32(width);
      var depth2: u32 = u32(depth + 1);
      var height2: u32 = u32(height + 1);
      var width2: u32 = u32(width + 1);
      var channel: u32 = ${n.length>3?`u32(originalIndices[${l}])`:`0`};
      var batch: u32 =  ${n.length>3?`u32(originalIndices[${a}])`:`0`};

      var x111: ${u} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${u} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${u} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${u} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${u} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${u} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${u} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${u} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${u} = abs(depth - ${u}(depth1));
      var dx2: ${u} = abs(${u}(depth2) - depth);
      var dy1: ${u} = abs(height - ${u}(height1));
      var dy2: ${u} = abs(${u}(height2) - height);
      var dz1: ${u} = abs(width - ${u}(width1));
      var dz2: ${u} = abs(${u}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},Ll=(e,t,n,r,i,a)=>{let o=e.dims,s=Dl(a,t.axes,o.length),c=Ol(o,r,i,t.axes),l=r.slice();r.length===0&&(l=o.map((e,t)=>e===0?1:c[t]/e),t.keepAspectRatioPolicy!==`stretch`&&(c=kl(o,l,t)));let u=J(`output`,e.dataType,c.length),d=q(`input`,e.dataType,o.length),f=z.size(c),p=o.length===c.length&&o.every((e,t)=>e===c[t]),m=t.coordinateTransformMode===`tf_crop_and_resize`,h=t.extrapolationValue,g=d.type.value;return{name:`Resize`,shaderCache:{hint:`${t.cacheKey}|${n}|${l.length>0?t.mode===`cubic`?l:l.length:``}|${i.length>0?i:``}|${s.length>0?s:``}|${p}|${t.mode===`nearest`?o.length:o}`,inputDependencies:[`rank`]},getShaderSource:e=>`
      ${p?``:`
      ${Tl(t.coordinateTransformMode,g)};
      ${(()=>{switch(t.mode){case`nearest`:return`
              ${Ml(d,o)};
              ${El(t.nearestMode,n,g)};
              ${jl(d,u,o,c,l.length,s.length,m)};
              `;case`linear`:return`
              ${Al(u,o,c,l.length,s.length)};
              ${(()=>{if(o.length===2||o.length===4)return`${Pl(d,u,o,m,h)}`;if(o.length===3||o.length===5)return`${Il(d,u,o,m,h)}`;throw Error(`Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.`)})()};
            `;case`cubic`:return`
            ${(()=>{if(o.length===2||o.length===4)return`${Fl(d,u,o,c,l,s,t.cubicCoeffA,m,t.extrapolationValue,t.excludeOutside)}`;throw Error(`Cubic mode only supports input dims 2 and 4 are supported in linear mode.`)})()};
            `;default:throw Error(`Invalid resize mode`)}})()};
      `}
      ${e.registerUniform(`output_size`,`u32`).registerUniform(`scales`,`f32`,l.length).registerUniform(`roi`,`f32`,s.length).declareVariables(d,u)}
      ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}
        ${p?`output[global_idx] = input[global_idx];`:`
        let output_indices = ${u.offsetToIndices(`global_idx`)};
        var input_indices: ${d.type.indices};
        ${(()=>{switch(t.mode){case`nearest`:return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${d.getByIndices(`input_indices`)};
                } else {
                  output[global_idx] = ${t.extrapolationValue};
                }`;case`linear`:return`output[global_idx] = ${o.length===2||o.length===4?`bilinearInterpolation`:`trilinearInterpolation`}(output_indices);`;case`cubic`:return`output[global_idx] = bicubicInterpolation(output_indices);`;default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};
`}
      }`,getRunData:()=>({outputs:[{dims:c,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:[{type:12,data:f},{type:1,data:l},{type:1,data:s},...W(o,c)]})}},Rl=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},zl=(e,t)=>{let n=[],r=[],i=[],a=Rl(e);if(t.antialias!==0)throw Error(`Only default value (0) for Antialias attribute is supported`);Cl(e.inputs,t,a,n,r,i),e.compute(Ll(e.inputs[0],t,a,n,r,i),{inputs:[0]})},Bl=e=>{let t=e.antialias,n=e.axes,r=e.coordinateTransformMode,i=e.cubicCoeffA,a=e.excludeOutside!==0,o=e.extrapolationValue,s=e.keepAspectRatioPolicy,c=e.mode,l=e.nearestMode===``?`simple`:e.nearestMode;return V({antialias:t,axes:n,coordinateTransformMode:r,cubicCoeffA:i,excludeOutside:a,extrapolationValue:o,keepAspectRatioPolicy:s,mode:c,nearestMode:l})}}),Hl,Ul,Wl,Gl=o(()=>{L(),B(),Y(),Hl=e=>{if(!e||e.length<3)throw Error(`layerNorm requires at least 3 inputs.`);let t=e[0],n=e[1],r=e[2];if(t.dataType!==n.dataType||t.dataType!==r.dataType)throw Error(`All inputs must have the same data type`);if(t.dims.length!==3&&t.dims.length!==2)throw Error(`Input must be 2D or 3D`);if(n.dims.length!==3&&n.dims.length!==2)throw Error(`Skip must be 2D or 3D`);let i=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(n.dims[n.dims.length-1]!==i)throw Error(`Skip must have the same hidden size as input`);if(n.dims[n.dims.length-2]!==a)throw Error(`Skip must have the same sequence length as input`);if(r.dims.length!==1)throw Error(`Gamma must be 1D`);if(r.dims[r.dims.length-1]!==i)throw Error(`Gamma must have the same hidden size as input`);if(e.length>3){let t=e[3];if(t.dims.length!==1)throw Error(`Beta must be 1D`);if(t.dims[t.dims.length-1]!==i)throw Error(`Beta must have the same hidden size as input`)}if(e.length>4){let t=e[4];if(t.dims.length!==1)throw Error(`Bias must be 1D`);if(t.dims[t.dims.length-1]!==i)throw Error(`Bias must have the same hidden size as input`)}},Ul=(e,t,n,r)=>{let i=t.simplified,a=e[0].dims,o=z.size(a),s=a,c=o,l=a.slice(-1)[0],u=r?a.slice(0,-1).concat(1):[],d=!i&&e.length>3,f=e.length>4,p=r&&n>1,m=r&&n>2,h=n>3,g=G(l),_=[{type:12,data:c},{type:12,data:g},{type:12,data:l},{type:1,data:t.epsilon}],v=t=>{let n=[{name:`output_size`,type:`u32`},{name:`components`,type:`u32`},{name:`hidden_size`,type:`u32`},{name:`epsilon`,type:`f32`}],r=[q(`x`,e[0].dataType,e[0].dims,g),q(`skip`,e[1].dataType,e[1].dims,g),q(`gamma`,e[2].dataType,e[2].dims,g)];d&&r.push(q(`beta`,e[3].dataType,e[3].dims,g)),f&&r.push(q(`bias`,e[4].dataType,e[4].dims,g)),r.push(J(`output`,e[0].dataType,s,g)),p&&r.push(J(`mean_output`,1,u)),m&&r.push(J(`inv_std_output`,1,u)),h&&r.push(J(`input_skip_bias_sum`,e[0].dataType,s,g));let a=U(e[0].dataType),o=U(1,g);return`

      ${t.registerUniforms(n).declareVariables(...r)}
      var<workgroup> sum_shared : array<${o}, 64>;
      var<workgroup> sum_squared_shared : array<${o}, 64>;

      ${t.mainStart([64,1,1])}
        let ix = local_id.x;
        let iy = global_id.x / 64;

        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
        var stride = hidden_size_vectorized / 64;
        let offset = ix * stride + iy * hidden_size_vectorized;
        let offset1d = stride * ix;
        if (ix == 63) {
          stride = hidden_size_vectorized - stride * ix;
        }
        for (var i: u32 = 0; i < stride; i++) {
          let skip_value = skip[offset + i];
          let bias_value = ${f?`bias[offset1d + i]`:a+`(0.0)`};
          let input_value = x[offset + i];
          let value = input_value + skip_value + bias_value;
          ${h?`input_skip_bias_sum[offset + i] = value;`:``}
          output[offset + i] = value;
          let f32_value = ${Tn(a,g,`value`)};
          sum_shared[ix] += f32_value;
          sum_squared_shared[ix] += f32_value * f32_value;
        }
        workgroupBarrier();

        var reduce_size : u32 = 64;
        for (var curr_size = reduce_size >> 1;  curr_size > 0; curr_size = reduce_size >> 1) {
          reduce_size = curr_size + (reduce_size & 1);
          if (ix < curr_size) {
            sum_shared[ix] += sum_shared[ix + reduce_size];
            sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];
          }
          workgroupBarrier();
        }

        let sum = sum_shared[0];
        let square_sum = sum_squared_shared[0];
        let mean = ${En(`sum`,g)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${En(`square_sum`,g)} / f32(uniforms.hidden_size) ${i?``:`- mean * mean`} + uniforms.epsilon);
        ${p?`mean_output[global_idx] = mean;`:``}
        ${m?`inv_std_output[global_idx] = inv_std_dev;`:``}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${i?``:`- ${a}(mean)`}) *
            ${a}(inv_std_dev) * gamma[offset1d + i]
            ${d?`+ beta[offset1d + i]`:``};
        }
      }`},y=[{dims:s,dataType:e[0].dataType}];return n>1&&y.push({dims:u,dataType:1}),n>2&&y.push({dims:u,dataType:1}),n>3&&y.push({dims:a,dataType:e[0].dataType}),{name:`SkipLayerNormalization`,shaderCache:{hint:`${g};${p};${m};${h}`,inputDependencies:e.map((e,t)=>`type`)},getShaderSource:v,getRunData:()=>({outputs:y,dispatchGroup:{x:Math.ceil(c/l)},programUniforms:_})}},Wl=(e,t)=>{Hl(e.inputs);let n=[0];e.outputCount>1&&n.push(-3),e.outputCount>2&&n.push(-3),e.outputCount>3&&n.push(3),e.compute(Ul(e.inputs,t,e.outputCount,!1),{outputs:n})}}),Kl,ql,Jl,Yl,Xl,Zl,Ql,$l,eu=o(()=>{L(),B(),H(),Y(),Kl=(e,t)=>{if(!e||e.length<1)throw Error(`too few inputs`);if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw Error(`axes, starts and ends must have the same length`)}else if(t.starts.length!==t.ends.length)throw Error(`starts and ends must have the same length`);e.slice(1).forEach((t,n)=>{if(e[n+1].dataType!==6&&e[n+1].dataType!==7)throw Error(`Input ${n} must be an array of int32 or int64`)})},ql=(e,t)=>{let n=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(e=>n.push(Number(e)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(e=>n.push(Number(e)));else throw Error(`Input ${t} must be an array of int32 or int64`);return n},Jl=(e,t)=>{if(e.length>1){let t=ql(e,1),n=ql(e,2),r=ql(e,3);return r.length===0&&(r=[...Array(e[0].dims.length).keys()]),V({starts:t,ends:n,axes:r})}else return t},Yl=(e,t,n,r,i)=>{let a=e;return e<0&&(a+=n[r[t]]),i[t]<0?Math.max(0,Math.min(a,n[r[t]]-1)):Math.max(0,Math.min(a,n[r[t]]))},Xl=(e,t,n)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
          var input_indices: ${e.type.indices};
          var carry = 0u;
          for (var i = ${n.length-1}; i >= 0; i--) {
            let input_shape_i = ${K(`uniforms.input_shape`,`i`,n.length)};
            let steps_i = ${K(`uniforms.steps`,`i`,n.length)};
            let signs_i = ${K(`uniforms.signs`,`i`,n.length)};
            let starts_i = ${K(`uniforms.starts`,`i`,n.length)};
            var output_index = ${t.indicesGet(`output_indices`,`i`)};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${e.indicesSet(`input_indices`,`i`,`input_index`)};
          }
          return input_indices;
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e;this.queryType!==`none`&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!==`none`&&e.mapAsync(GPUMapMode.READ).then(()=>{let t=new BigUint64Array(e.getMappedRange()),n=this.pendingQueries.get(e);for(let e=0;e<t.length/2;e++){let r=n[e],i=r.kernelId,a=this.kernels.get(i),o=a.kernelType,s=a.kernelName,c=r.programName,l=r.inputTensorViews,u=r.outputTensorViews,d=t[e*2],f=t[e*2+1];typeof this.queryTimeBase>`u`&&(this.queryTimeBase=d);let p=Number(d-this.queryTimeBase),m=Number(f-this.queryTimeBase);if(!Number.isSafeInteger(p)||!Number.isSafeInteger(m))throw RangeError(`incorrect timestamp range`);if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:l.map(e=>({dims:e.dims,dataType:St(e.dataType)})),outputsMetadata:u.map(e=>({dims:e.dims,dataType:St(e.dataType)})),kernelId:i,kernelType:o,kernelName:s,programName:c,startTime:p,endTime:m});else{let e=``;l.forEach((t,n)=>{e+=`input[${n}]: [${t.dims}] | ${St(t.dataType)}, `});let t=``;u.forEach((e,n)=>{t+=`output[${n}]: [${e.dims}] | ${St(e.dataType)}, `}),console.log(`[profiling] kernel "${i}|${o}|${s}|${c}" ${e}${t}start time: ${p} ns, execution time: ${m-p} ns`)}he(`GPU`,`${c}::${d}::${f}`)}e.unmap(),this.pendingQueries.delete(e)}),j()}run(e,t,n,r,i,a){_e(e.name);let o=[];for(let e=0;e<t.length;++e){let n=t[e].data;if(n===0)continue;let r=this.gpuDataManager.get(n);if(!r)throw Error(`no GPU data for input: ${n}`);o.push(r)}let{outputs:s,dispatchGroup:c,programUniforms:l}=e.getRunData(t),u=n.length===0?s.map((e,t)=>t):n;if(u.length!==s.length)throw Error(`Output size ${u.length} must be equal to ${s.length}.`);let d=[],f=[];for(let e=0;e<s.length;++e){if(!Number.isInteger(u[e])||u[e]<-3||u[e]>=a)throw Error(`Invalid output index: ${u[e]}`);if(u[e]===-3)continue;let t=u[e]===-1,n=u[e]===-2,o=t||n?i(s[e].dataType,s[e].dims):r(u[e],s[e].dataType,s[e].dims);if(d.push(o),o.data===0)continue;let c=this.gpuDataManager.get(o.data);if(!c)throw Error(`no GPU data for output: ${o.data}`);if(t&&this.temporaryData.push(c),n){let e=this.kernelPersistentData.get(this.currentKernelId);e||(e=[],this.kernelPersistentData.set(this.currentKernelId,e)),e.push(c)}f.push(c)}if(o.length!==t.length||f.length!==d.length){if(f.length===0)return j(e.name),d;throw Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let p;if(l){let e=0,t=[];l.forEach(n=>{let r=typeof n.data==`number`?[n.data]:n.data;if(r.length===0)return;let i=n.type===10?2:4,a,o;n.type===10?(o=r.length>4?16:r.length>2?8:r.length*i,a=r.length>4?16:i*r.length):(o=r.length<=2?r.length*i:16,a=16),e=Math.ceil(e/o)*o,t.push(e);let s=n.type===10?8:4;e+=r.length>4?Math.ceil(r.length/s)*a:r.length*i}),e=Math.ceil(e/16)*16;let n=new ArrayBuffer(e);l.forEach((e,r)=>{let i=t[r],a=typeof e.data==`number`?[e.data]:e.data;if(e.type===6)new Int32Array(n,i,a.length).set(a);else if(e.type===12)new Uint32Array(n,i,a.length).set(a);else if(e.type===10)new Uint16Array(n,i,a.length).set(a);else if(e.type===1)new Float32Array(n,i,a.length).set(a);else throw Error(`Unsupported uniform type: ${St(e.type)}`)});let r=this.gpuDataManager.create(e,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(r.buffer,0,n,0,e),this.gpuDataManager.release(r.id),p={offset:0,size:e,buffer:r.buffer}}let m=this.programManager.normalizeDispatchGroupSize(c),h=m[1]===1&&m[2]===1,g=Su(e,t,h),_=this.programManager.getArtifact(g);if(_||(_=this.programManager.build(e,m),this.programManager.setArtifact(g,_),R(`info`,()=>`[artifact] key: ${g}, programName: ${e.name}`)),l&&_.uniformVariablesInfo){if(l.length!==_.uniformVariablesInfo.length)throw Error(`Uniform variables count mismatch: expect ${_.uniformVariablesInfo.length}, got ${l.length} in program "${_.programInfo.name}".`);for(let e=0;e<l.length;e++){let t=l[e],n=t.type,r=typeof t.data==`number`?1:t.data.length,[i,a]=_.uniformVariablesInfo[e];if(n!==i||r!==a)throw Error(`Uniform variable ${e} mismatch: expect type ${i} with size ${a}, got type ${n} with size ${r} in program "${_.programInfo.name}".`)}}if(R(`info`,()=>`[ProgramManager] run "${e.name}" (key=${g}) with ${m[0]}x${m[1]}x${m[2]}`),this.queryType!==`none`||this.sessionStatus===`capturing`){let e={kernelId:this.currentKernelId,programName:_.programInfo.name,inputTensorViews:t,outputTensorViews:d};this.pendingKernels.push(e),this.sessionStatus===`capturing`&&this.capturedPendingKernels.get(this.currentSessionId).push(e)}return this.programManager.run(_,o,f,m,p),j(e.name),d}upload(e,t){this.gpuDataManager.upload(e,t)}memcpy(e,t){this.gpuDataManager.memcpy(e,t)}async download(e,t){await this.gpuDataManager.download(e,t)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,t,n,r){let i=gu.get(e);if(!i)throw Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:r,kernelEntry:i[0],attributes:[i[1],n]};this.kernels.set(t,a)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let e of t)this.gpuDataManager.release(e.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,n){let r=this.kernels.get(e);if(!r)throw Error(`kernel not created: ${e}`);let i=r.kernelType,a=r.kernelName,o=r.kernelEntry,s=r.attributes;if(this.currentKernelId!==null)throw Error(`kernel "[${i}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,s[0]&&=(s[1]=s[0](s[1]),void 0),R(`info`,()=>`[WebGPU] Start to run kernel "[${i}] ${a}"...`);let c=this.env.debug;this.temporaryData=[];try{return c&&this.device.pushErrorScope(`validation`),o(t,s[1]),0}catch(e){return n.push(Promise.resolve(`[WebGPU] Kernel "[${i}] ${a}" failed. ${e}`)),1}finally{c&&n.push(this.device.popErrorScope().then(e=>e?`GPU validation error for kernel "[${i}] ${a}": ${e.message}`:null));for(let e of this.temporaryData)this.gpuDataManager.release(e.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,n,r){let i=this.sessionExternalDataMapping.get(e);i||(i=new Map,this.sessionExternalDataMapping.set(e,i));let a=i.get(t),o=this.gpuDataManager.registerExternalBuffer(n,r,a);return i.set(t,[o,n]),o}unregisterBuffers(e){let t=this.sessionExternalDataMapping.get(e);t&&(t.forEach(e=>this.gpuDataManager.unregisterExternalBuffer(e[0])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let t=this.gpuDataManager.get(e);if(!t)throw Error(`no GPU data for buffer: ${e}`);return t.buffer}createDownloader(e,t,n){return async()=>{let r=await gn(this,e,t);return Wt(r.buffer,n)}}writeTimestamp(e){this.queryType===`inside-passes`&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){this.queryType=`none`,(this.env.webgpu.profiling?.mode===`default`||(typeof this.env.trace>`u`?this.env.wasm.trace:this.env.trace))&&(this.device.features.has(`chromium-experimental-timestamp-query-inside-passes`)?this.queryType=`inside-passes`:this.device.features.has(`timestamp-query`)&&(this.queryType=`at-passes`),this.queryType!==`none`&&typeof this.querySet>`u`&&(this.querySet=this.device.createQuerySet({type:`timestamp`,count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){R(`info`,`captureBegin`),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus=`capturing`}captureEnd(){R(`info`,`captureEnd`),this.flush(),this.sessionStatus=`default`}replay(){R(`info`,`replay`),this.sessionStatus=`replaying`;let e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),n=e.length;this.pendingKernels=[];for(let r=0;r<n;r++){let n=this.getComputePassEncoder(),i=e[r];this.writeTimestamp(this.pendingDispatchNumber*2),n.setPipeline(i.computePipeline),n.setBindGroup(0,i.bindGroup),n.dispatchWorkgroups(...i.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!==`none`&&this.pendingKernels.push(t[r]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType===`at-passes`)&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus=`default`}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),Eu={};s(Eu,{init:()=>ku});var Du,Ou,ku,Au=o(()=>{L(),Lt(),B(),cn(),Du=class e{constructor(e,t,n,r){this.module=e,this.dataType=t,this.data=n,this.dims=r}getFloat32Array(){if(this.dataType!==1)throw Error(`Invalid data type`);let e=z.size(this.dims);return e===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,e)}getBigInt64Array(){if(this.dataType!==7)throw Error(`Invalid data type`);let e=z.size(this.dims);return e===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,e)}getInt32Array(){if(this.dataType!==6)throw Error(`Invalid data type`);let e=z.size(this.dims);return e===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,e)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw Error(`Invalid data type`);let e=z.size(this.dims);return e===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,e)}reshape(t){if(z.size(t)!==z.size(this.dims))throw Error(`Invalid new shape`);return new e(this.module,this.dataType,this.data,t)}},Ou=class{constructor(e,t,n){this.module=e,this.backend=t,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=t.adapterInfo;let r=e.PTR_SIZE,i=n/e.PTR_SIZE,a=r===4?`i32`:`i64`;this.opKernelContext=Number(e.getValue(r*i++,a));let o=Number(e.getValue(r*i++,a));this.outputCount=Number(e.getValue(r*i++,a)),this.customDataOffset=Number(e.getValue(r*i++,`*`)),this.customDataSize=Number(e.getValue(r*i++,a));let s=[];for(let t=0;t<o;t++){let t=Number(e.getValue(r*i++,a)),n=Number(e.getValue(r*i++,`*`)),o=Number(e.getValue(r*i++,a)),c=[];for(let t=0;t<o;t++)c.push(Number(e.getValue(r*i++,a)));s.push(new Du(e,t,n,c))}this.inputs=s}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(e,t){let n=t?.inputs?.map(e=>typeof e==`number`?this.inputs[e]:e)??this.inputs,r=t?.outputs??[];return this.backend.run(e,n,r,(e,t,n)=>new Du(this.module,t,this.output(e,n),n),(e,t)=>{let n=Ct(e,t);if(!n)throw Error(`Unsupported data type: ${e}`);let r=n>0?this.backend.gpuDataManager.create(n).id:0;return new Du(this.module,e,r,t)},this.outputCount)}output(e,t){let n=this.module.stackSave();try{let n=this.module.PTR_SIZE,r=n===4?`i32`:`i64`,i=this.module.stackAlloc((1+t.length)*n);this.module.setValue(i,t.length,r);for(let e=0;e<t.length;e++)this.module.setValue(i+n*(e+1),t[e],r);return this.module._JsepOutput(this.opKernelContext,e,i)}catch(n){throw Error(`Failed to generate kernel's output[${e}] with dims [${t}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(n)}}},ku=async(e,t,n,r)=>{let i=t.jsepInit;if(!i)throw Error(`Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.`);if(e===`webgpu`){let e=(Tu(),l(bu)).WebGpuBackend,a=new e;await a.initialize(n,r),i(`webgpu`,[a,e=>a.alloc(Number(e)),e=>a.free(e),(e,n,r,i=!1)=>{if(i)R(`verbose`,()=>`[WebGPU] jsepCopyGpuToGpu: src=${Number(e)}, dst=${Number(n)}, size=${Number(r)}`),a.memcpy(Number(e),Number(n));else{R(`verbose`,()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${Number(e)}, gpuDataId=${Number(n)}, size=${Number(r)}`);let i=t.HEAPU8.subarray(Number(e>>>0),Number(e>>>0)+Number(r));a.upload(Number(n),i)}},async(e,n,r)=>{R(`verbose`,()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${e}, dataOffset=${n}, size=${r}`),await a.download(Number(e),()=>t.HEAPU8.subarray(Number(n)>>>0,Number(n+r)>>>0))},(e,n,r)=>a.createKernel(e,Number(n),r,t.UTF8ToString(t._JsepGetNodeName(Number(n)))),e=>a.releaseKernel(e),(e,n,r,i)=>{R(`verbose`,()=>`[WebGPU] jsepRun: sessionHandle=${r}, kernel=${e}, contextDataOffset=${n}`);let o=new Ou(t,a,Number(n));return a.computeKernel(Number(e),o,i)},()=>a.captureBegin(),()=>a.captureEnd(),()=>a.replay()])}else{let e=new sn(n);i(`webnn`,[e,()=>e.reserveTensorId(),t=>e.releaseTensorId(t),async(t,n,r,i,a)=>e.ensureTensor(t,n,r,i,a),(t,n)=>{e.uploadTensor(t,n)},async(t,n)=>e.downloadTensor(t,n),(t,n)=>e.registerMLContext(t,n),!!n.trace])}}}),ju,Mu,Nu,Pu,Fu,Iu,Lu,Ru,zu,Bu,Vu,Hu,Uu,Wu=o(()=>{N(),pt(),bt(),L(),ct(),dt(),At(),ju=(e,t)=>{F()._OrtInit(e,t)!==0&&I(`Can't initialize onnxruntime.`)},Mu=async e=>{ju(e.wasm.numThreads,Tt(e.logLevel))},Nu=async(e,t)=>{F().asyncInit?.();let n=e.webgpu.adapter;if(t===`webgpu`){if(typeof navigator>`u`||!navigator.gpu)throw Error(`WebGPU is not supported in current environment`);if(n){if(typeof n.limits!=`object`||typeof n.features!=`object`||typeof n.requestDevice!=`function`)throw Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let t=e.webgpu.powerPreference;if(t!==void 0&&t!==`low-power`&&t!==`high-performance`)throw Error(`Invalid powerPreference setting: "${t}"`);let r=e.webgpu.forceFallbackAdapter;if(r!==void 0&&typeof r!=`boolean`)throw Error(`Invalid forceFallbackAdapter setting: "${r}"`);if(n=await navigator.gpu.requestAdapter({powerPreference:t,forceFallbackAdapter:r}),!n)throw Error(`Failed to get GPU adapter. You may need to enable flag "--enable-unsafe-webgpu" if you are using Chrome.`)}}if(t===`webnn`&&(typeof navigator>`u`||!navigator.ml))throw Error(`WebNN is not supported in current environment`);{let r=(Au(),l(Eu)).init;t===`webgpu`&&await r(`webgpu`,F(),e,n),t===`webnn`&&await r(`webnn`,F(),e)}},Pu=new Map,Fu=e=>{let t=F(),n=t.stackSave();try{let n=t.PTR_SIZE,r=t.stackAlloc(2*n);t._OrtGetInputOutputCount(e,r,r+n)!==0&&I(`Can't get session input/output count.`);let i=n===4?`i32`:`i64`;return[Number(t.getValue(r,i)),Number(t.getValue(r+n,i))]}finally{t.stackRestore(n)}},Iu=(e,t)=>{let n=F(),r=n.stackSave(),i=0;try{let r=n.PTR_SIZE,a=n.stackAlloc(2*r);n._OrtGetInputOutputMetadata(e,t,a,a+r)!==0&&I(`Can't get session input/output metadata.`);let o=Number(n.getValue(a,`*`));i=Number(n.getValue(a+r,`*`));let s=n.HEAP32[i/4];if(s===0)return[o,0];let c=n.HEAPU32[i/4+1],l=[];for(let e=0;e<c;e++){let t=Number(n.getValue(i+8+e*r,`*`));l.push(t===0?Number(n.getValue(i+8+(e+c)*r,`*`)):n.UTF8ToString(t))}return[o,s,l]}finally{n.stackRestore(r),i!==0&&n._OrtFree(i)}},Lu=e=>{let t=F(),n=t._malloc(e.byteLength);if(n===0)throw Error(`Can't create a session. failed to allocate a buffer of size ${e.byteLength}.`);return t.HEAPU8.set(e,n),[n,e.byteLength]},Ru=async(e,t)=>{let n,r,i=F();Array.isArray(e)?[n,r]=e:e.buffer===i.HEAPU8.buffer?[n,r]=[e.byteOffset,e.byteLength]:[n,r]=Lu(e);let a=0,o=0,s=0,c=[],l=[],u=[];try{if([o,c]=await yt(t),t?.externalData&&i.mountExternalData){let e=[];for(let n of t.externalData){let t=typeof n==`string`?n:n.path;e.push(kt(typeof n==`string`?n:n.data).then(e=>{i.mountExternalData(t,e)}))}await Promise.all(e)}for(let e of t?.executionProviders??[])if((typeof e==`string`?e:e.name)===`webnn`){if(i.shouldTransferToMLTensor=!1,typeof e!=`string`){let t=e,n=t?.context,r=t?.gpuDevice,a=t?.deviceType,o=t?.powerPreference;n?i.currentContext=n:r?i.currentContext=await i.webnnCreateMLContext(r):i.currentContext=await i.webnnCreateMLContext({deviceType:a,powerPreference:o})}else i.currentContext=await i.webnnCreateMLContext();break}a=await i._OrtCreateSession(n,r,o),i.webgpuOnCreateSession?.(a),a===0&&I(`Can't create a session.`),i.jsepOnCreateSession?.(),i.currentContext&&(i.webnnRegisterMLContext(a,i.currentContext),i.currentContext=void 0,i.shouldTransferToMLTensor=!0);let[e,d]=Fu(a),f=!!t?.enableGraphCapture,p=[],m=[],h=[],g=[],_=[];for(let t=0;t<e;t++){let[e,n,r]=Iu(a,t);e===0&&I(`Can't get an input name.`),l.push(e);let o=i.UTF8ToString(e);p.push(o),h.push(n===0?{name:o,isTensor:!1}:{name:o,isTensor:!0,type:St(n),shape:r})}for(let n=0;n<d;n++){let[r,o,s]=Iu(a,n+e);r===0&&I(`Can't get an output name.`),u.push(r);let c=i.UTF8ToString(r);m.push(c),g.push(o===0?{name:c,isTensor:!1}:{name:c,isTensor:!0,type:St(o),shape:s});{if(f&&t?.preferredOutputLocation===void 0){_.push(`gpu-buffer`);continue}let e=typeof t?.preferredOutputLocation==`string`?t.preferredOutputLocation:t?.preferredOutputLocation?.[c]??`cpu`,n=i.webnnIsGraphOutput;if(e===`cpu`&&n&&n(a,c)){_.push(`ml-tensor-cpu-output`);continue}if(e!==`cpu`&&e!==`cpu-pinned`&&e!==`gpu-buffer`&&e!==`ml-tensor`)throw Error(`Not supported preferred output location: ${e}.`);if(f&&e!==`gpu-buffer`)throw Error(`Not supported preferred output location: ${e}. Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);_.push(e)}}let v=null;return _.some(e=>e===`gpu-buffer`||e===`ml-tensor`||e===`ml-tensor-cpu-output`)&&(s=i._OrtCreateBinding(a),s===0&&I(`Can't create IO binding.`),v={handle:s,outputPreferredLocations:_,outputPreferredLocationsEncoded:_.map(e=>e===`ml-tensor-cpu-output`?`ml-tensor`:e).map(e=>Ot(e))}),Pu.set(a,[a,l,u,v,f,!1]),[a,p,m,h,g]}catch(e){throw l.forEach(e=>i._OrtFree(e)),u.forEach(e=>i._OrtFree(e)),s!==0&&i._OrtReleaseBinding(s)!==0&&I(`Can't release IO binding.`),a!==0&&i._OrtReleaseSession(a)!==0&&I(`Can't release session.`),e}finally{i._free(n),o!==0&&i._OrtReleaseSessionOptions(o)!==0&&I(`Can't release session options.`),c.forEach(e=>i._free(e)),i.unmountExternalData?.()}},zu=e=>{let t=F(),n=Pu.get(e);if(!n)throw Error(`cannot release session. invalid session id: ${e}`);let[r,i,a,o,s]=n;o&&(s&&t._OrtClearBoundOutputs(o.handle)!==0&&I(`Can't clear bound outputs.`),t._OrtReleaseBinding(o.handle)!==0&&I(`Can't release IO binding.`)),t.jsepOnReleaseSession?.(e),t.webnnOnReleaseSession?.(e),t.webgpuOnReleaseSession?.(e),i.forEach(e=>t._OrtFree(e)),a.forEach(e=>t._OrtFree(e)),t._OrtReleaseSession(r)!==0&&I(`Can't release session.`),Pu.delete(e)},Bu=async(e,t,n,r,i,a,o=!1)=>{if(!e){t.push(0);return}let s=F(),c=s.PTR_SIZE,l=e[0],u=e[1],d=e[3],f=d,p,m;if(l===`string`&&(d===`gpu-buffer`||d===`ml-tensor`))throw Error(`String tensor is not supported on GPU.`);if(o&&d!==`gpu-buffer`)throw Error(`External buffer must be provided for input/output index ${a} when enableGraphCapture is true.`);if(d===`gpu-buffer`){let t=e[2].gpuBuffer;m=Ct(xt(l),u);{let e=s.jsepRegisterBuffer;if(!e)throw Error(`Tensor location "gpu-buffer" is not supported without using WebGPU.`);p=e(r,a,t,m)}}else if(d===`ml-tensor`){let t=e[2].mlTensor;m=Ct(xt(l),u);let n=s.webnnRegisterMLTensor;if(!n)throw Error(`Tensor location "ml-tensor" is not supported without using WebNN.`);p=n(r,t,xt(l),u)}else{let t=e[2];if(Array.isArray(t)){m=c*t.length,p=s._malloc(m),n.push(p);for(let e=0;e<t.length;e++){if(typeof t[e]!=`string`)throw TypeError(`tensor data at index ${e} is not a string`);s.setValue(p+e*c,lt(t[e],n),`*`)}}else{let e=s.webnnIsGraphInput,a=s.webnnIsGraphOutput;if(l!==`string`&&e&&a){let o=s.UTF8ToString(i);if(e(r,o)||a(r,o)){let e=xt(l);m=Ct(e,u),f=`ml-tensor`;let n=s.webnnCreateTemporaryTensor,i=s.webnnUploadTensor;if(!n||!i)throw Error(`Tensor location "ml-tensor" is not supported without using WebNN.`);let a=await n(r,e,u);i(a,new Uint8Array(t.buffer,t.byteOffset,t.byteLength)),p=a}else m=t.byteLength,p=s._malloc(m),n.push(p),s.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,m),p)}else m=t.byteLength,p=s._malloc(m),n.push(p),s.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,m),p)}}let h=s.stackSave(),g=s.stackAlloc(4*u.length);try{u.forEach((e,t)=>s.setValue(g+t*c,e,c===4?`i32`:`i64`));let e=s._OrtCreateTensor(xt(l),p,m,g,u.length,Ot(f));e===0&&I(`Can't create tensor for input/output. session=${r}, index=${a}.`),t.push(e)}finally{s.stackRestore(h)}},Vu=async(e,t,n,r,i,a)=>{let o=F(),s=o.PTR_SIZE,c=Pu.get(e);if(!c)throw Error(`cannot run inference. invalid session id: ${e}`);let l=c[0],u=c[1],d=c[2],f=c[3],p=c[4],m=c[5],h=t.length,g=r.length,_=0,v=[],y=[],b=[],x=[],S=[],C=o.stackSave(),w=o.stackAlloc(h*s),ee=o.stackAlloc(h*s),T=o.stackAlloc(g*s),E=o.stackAlloc(g*s);try{[_,v]=ft(a),M(`wasm prepareInputOutputTensor`);for(let r=0;r<h;r++)await Bu(n[r],y,x,e,u[t[r]],t[r],p);for(let t=0;t<g;t++)await Bu(i[t],b,x,e,d[r[t]],h+r[t],p);ve(`wasm prepareInputOutputTensor`);for(let e=0;e<h;e++)o.setValue(w+e*s,y[e],`*`),o.setValue(ee+e*s,u[t[e]],`*`);for(let e=0;e<g;e++)o.setValue(T+e*s,b[e],`*`),o.setValue(E+e*s,d[r[e]],`*`);if(f&&!m){let{handle:n,outputPreferredLocations:a,outputPreferredLocationsEncoded:s}=f;if(u.length!==h)throw Error(`input count from feeds (${h}) is expected to be always equal to model's input count (${u.length}).`);M(`wasm bindInputsOutputs`);for(let r=0;r<h;r++){let i=t[r];await o._OrtBindInput(n,u[i],y[r])!==0&&I(`Can't bind input[${r}] for session=${e}.`)}for(let t=0;t<g;t++){let c=r[t];i[t]?.[3]?(S.push(b[t]),o._OrtBindOutput(n,d[c],b[t],0)!==0&&I(`Can't bind pre-allocated output[${t}] for session=${e}.`)):o._OrtBindOutput(n,d[c],0,s[c])!==0&&I(`Can't bind output[${t}] to ${a[t]} for session=${e}.`)}ve(`wasm bindInputsOutputs`),Pu.set(e,[l,u,d,f,p,!0])}o.jsepOnRunStart?.(l),o.webnnOnRunStart?.(l);let c;c=f?await o._OrtRunWithBinding(l,f.handle,g,T,_):await o._OrtRun(l,ee,w,h,E,g,T,_),c!==0&&I(`failed to call OrtRun().`);let C=[],D=[];M(`wasm ProcessOutputTensor`);for(let t=0;t<g;t++){let n=Number(o.getValue(T+t*s,`*`));if(n===b[t]||S.includes(b[t])){C.push(i[t]),n!==b[t]&&o._OrtReleaseTensor(n)!==0&&I(`Can't release tensor.`);continue}let a=o.stackSave(),c=o.stackAlloc(4*s),l=!1,u,d=0;try{o._OrtGetTensorData(n,c,c+s,c+2*s,c+3*s)!==0&&I(`Can't access output tensor data on index ${t}.`);let i=s===4?`i32`:`i64`,a=Number(o.getValue(c,i));d=o.getValue(c+s,`*`);let p=o.getValue(c+s*2,`*`),m=Number(o.getValue(c+s*3,i)),h=[];for(let e=0;e<m;e++)h.push(Number(o.getValue(p+e*s,i)));o._OrtFree(p)!==0&&I(`Can't free memory for tensor dims.`);let g=h.reduce((e,t)=>e*t,1);u=St(a);let _=f?.outputPreferredLocations[r[t]];if(u===`string`){if(_===`gpu-buffer`||_===`ml-tensor`)throw Error(`String tensor is not supported on GPU.`);let e=[];for(let t=0;t<g;t++){let n=o.getValue(d+t*s,`*`),r=o.getValue(d+(t+1)*s,`*`),i=t===g-1?void 0:r-n;e.push(o.UTF8ToString(n,i))}C.push([u,h,e,`cpu`])}else if(_===`gpu-buffer`&&g>0){let e=o.jsepGetBuffer;if(!e)throw Error(`preferredLocation "gpu-buffer" is not supported without using WebGPU.`);let t=e(d),r=Ct(a,g);if(r===void 0||!Et(u))throw Error(`Unsupported data type: ${u}`);l=!0,C.push([u,h,{gpuBuffer:t,download:o.jsepCreateDownloader(t,r,u),dispose:()=>{o._OrtReleaseTensor(n)!==0&&I(`Can't release tensor.`)}},`gpu-buffer`])}else if(_===`ml-tensor`&&g>0){let t=o.webnnEnsureTensor,r=o.webnnIsGraphInputOutputTypeSupported;if(!t||!r)throw Error(`preferredLocation "ml-tensor" is not supported without using WebNN.`);if(Ct(a,g)===void 0||!Dt(u))throw Error(`Unsupported data type: ${u}`);if(!r(e,u,!1))throw Error(`preferredLocation "ml-tensor" for ${u} output is not supported by current WebNN Context.`);let i=await t(e,d,a,h,!1);l=!0,C.push([u,h,{mlTensor:i,download:o.webnnCreateMLTensorDownloader(d,u),dispose:()=>{o.webnnReleaseTensorId(d),o._OrtReleaseTensor(n)}},`ml-tensor`])}else if(_===`ml-tensor-cpu-output`&&g>0){let e=o.webnnCreateMLTensorDownloader(d,u)(),t=C.length;l=!0,D.push((async()=>{let r=[t,await e];return o.webnnReleaseTensorId(d),o._OrtReleaseTensor(n),r})()),C.push([u,h,[],`cpu`])}else{let e=new(wt(u))(g);new Uint8Array(e.buffer,e.byteOffset,e.byteLength).set(o.HEAPU8.subarray(d,d+e.byteLength)),C.push([u,h,e,`cpu`])}}finally{o.stackRestore(a),u===`string`&&d&&o._free(d),l||o._OrtReleaseTensor(n)}}f&&!p&&(o._OrtClearBoundOutputs(f.handle)!==0&&I(`Can't clear bound outputs.`),Pu.set(e,[l,u,d,f,p,!1]));for(let[e,t]of await Promise.all(D))C[e][2]=t;return ve(`wasm ProcessOutputTensor`),C}finally{o.webnnOnRunEnd?.(l),o.stackRestore(C),y.forEach(e=>o._OrtReleaseTensor(e)),b.forEach(e=>o._OrtReleaseTensor(e)),x.forEach(e=>o._free(e)),_!==0&&o._OrtReleaseRunOptions(_),v.forEach(e=>o._free(e))}},Hu=e=>{let t=F(),n=Pu.get(e);if(!n)throw Error(`invalid session id`);let r=n[0],i=t._OrtEndProfiling(r);i===0&&I(`Can't get an profile file name.`),t._OrtFree(i)},Uu=e=>{let t=[];for(let n of e){let e=n[2];!Array.isArray(e)&&`buffer`in e&&t.push(e.buffer)}return t}}),Gu,Ku,qu,Ju,Yu,Xu,Zu,Qu,$u,ed,td,nd,rd,id,ad,od,sd,cd,ld=o(()=>{N(),Wu(),ct(),$e(),Gu=()=>!!S.wasm.proxy&&typeof document<`u`,qu=!1,Ju=!1,Yu=!1,Qu=new Map,$u=(e,t)=>{let n=Qu.get(e);n?n.push(t):Qu.set(e,[t])},ed=()=>{if(qu||!Ju||Yu||!Ku)throw Error(`worker not ready`)},td=e=>{switch(e.data.type){case`init-wasm`:qu=!1,e.data.err?(Yu=!0,Zu[1](e.data.err)):(Ju=!0,Zu[0]()),Xu&&=(URL.revokeObjectURL(Xu),void 0);break;case`init-ep`:case`copy-from`:case`create`:case`release`:case`run`:case`end-profiling`:{let t=Qu.get(e.data.type);e.data.err?t.shift()[1](e.data.err):t.shift()[0](e.data.out);break}default:}},nd=async()=>{if(!Ju){if(qu)throw Error(`multiple calls to 'initWasm()' detected.`);if(Yu)throw Error(`previous call to 'initWasm()' failed.`);if(qu=!0,Gu())return new Promise((e,t)=>{Ku?.terminate(),P().then(([n,r])=>{try{Ku=r,Ku.onerror=e=>t(e),Ku.onmessage=td,Zu=[e,t];let i={type:`init-wasm`,in:S};!i.in.wasm.wasmPaths&&(n||Ve)&&(i.in.wasm.wasmPaths={wasm:new URL(`/assets/ort-wasm-simd-threaded.jsep-C887KxcQ.wasm`,``+import.meta.url).href}),Ku.postMessage(i),Xu=n}catch(e){t(e)}},t)});try{await st(S.wasm),await Mu(S),Ju=!0}catch(e){throw Yu=!0,e}finally{qu=!1}}},rd=async e=>{if(Gu())return ed(),new Promise((t,n)=>{$u(`init-ep`,[t,n]);let r={type:`init-ep`,in:{epName:e,env:S}};Ku.postMessage(r)});await Nu(S,e)},id=async e=>Gu()?(ed(),new Promise((t,n)=>{$u(`copy-from`,[t,n]);let r={type:`copy-from`,in:{buffer:e}};Ku.postMessage(r,[e.buffer])})):Lu(e),ad=async(e,t)=>{if(Gu()){if(t?.preferredOutputLocation)throw Error(`session option "preferredOutputLocation" is not supported for proxy.`);return ed(),new Promise((n,r)=>{$u(`create`,[n,r]);let i={type:`create`,in:{model:e,options:{...t}}},a=[];e instanceof Uint8Array&&a.push(e.buffer),Ku.postMessage(i,a)})}else return Ru(e,t)},od=async e=>{if(Gu())return ed(),new Promise((t,n)=>{$u(`release`,[t,n]);let r={type:`release`,in:e};Ku.postMessage(r)});zu(e)},sd=async(e,t,n,r,i,a)=>{if(Gu()){if(n.some(e=>e[3]!==`cpu`))throw Error(`input tensor on GPU is not supported for proxy.`);if(i.some(e=>e))throw Error(`pre-allocated output tensor is not supported for proxy.`);return ed(),new Promise((i,o)=>{$u(`run`,[i,o]);let s=n,c={type:`run`,in:{sessionId:e,inputIndices:t,inputs:s,outputIndices:r,options:a}};Ku.postMessage(c,Uu(s))})}else return Vu(e,t,n,r,i,a)},cd=async e=>{if(Gu())return ed(),new Promise((t,n)=>{$u(`end-profiling`,[t,n]);let r={type:`end-profiling`,in:e};Ku.postMessage(r)});Hu(e)}}),ud,dd,fd,pd=o(()=>{N(),ld(),L(),ke(),At(),ud=(e,t)=>{switch(e.location){case`cpu`:return[e.type,e.dims,e.data,`cpu`];case`gpu-buffer`:return[e.type,e.dims,{gpuBuffer:e.gpuBuffer},`gpu-buffer`];case`ml-tensor`:return[e.type,e.dims,{mlTensor:e.mlTensor},`ml-tensor`];default:throw Error(`invalid data location: ${e.location} for ${t()}`)}},dd=e=>{switch(e[3]){case`cpu`:return new pe(e[0],e[2],e[1]);case`gpu-buffer`:{let t=e[0];if(!Et(t))throw Error(`not supported data type: ${t} for deserializing GPU tensor`);let{gpuBuffer:n,download:r,dispose:i}=e[2];return pe.fromGpuBuffer(n,{dataType:t,dims:e[1],download:r,dispose:i})}case`ml-tensor`:{let t=e[0];if(!Dt(t))throw Error(`not supported data type: ${t} for deserializing MLTensor tensor`);let{mlTensor:n,download:r,dispose:i}=e[2];return pe.fromMLTensor(n,{dataType:t,dims:e[1],download:r,dispose:i})}default:throw Error(`invalid data location: ${e[3]}`)}},fd=class{async fetchModelAndCopyToWasmMemory(e){return id(await kt(e))}async loadModel(e,t){_e();let n;n=typeof e==`string`?await this.fetchModelAndCopyToWasmMemory(e):e,[this.sessionId,this.inputNames,this.outputNames,this.inputMetadata,this.outputMetadata]=await ad(n,t),j()}async dispose(){return od(this.sessionId)}async run(e,t,n){_e();let r=[],i=[];Object.entries(e).forEach(e=>{let t=e[0],n=e[1],a=this.inputNames.indexOf(t);if(a===-1)throw Error(`invalid input '${t}'`);r.push(n),i.push(a)});let a=[],o=[];Object.entries(t).forEach(e=>{let t=e[0],n=e[1],r=this.outputNames.indexOf(t);if(r===-1)throw Error(`invalid output '${t}'`);a.push(n),o.push(r)});let s=r.map((e,t)=>ud(e,()=>`input "${this.inputNames[i[t]]}"`)),c=a.map((e,t)=>e?ud(e,()=>`output "${this.outputNames[o[t]]}"`):null),l=await sd(this.sessionId,i,s,o,c,n),u={};for(let e=0;e<l.length;e++)u[this.outputNames[o[e]]]=a[e]??dd(l[e]);return j(),u}startProfiling(){}endProfiling(){cd(this.sessionId)}}}),md={};s(md,{OnnxruntimeWebAssemblyBackend:()=>gd,initializeFlags:()=>hd,wasmBackend:()=>_d});var hd,gd,_d,vd=o(()=>{N(),ld(),pd(),hd=()=>{(typeof S.wasm.initTimeout!=`number`||S.wasm.initTimeout<0)&&(S.wasm.initTimeout=0);let e=S.wasm.simd;if(typeof e!=`boolean`&&e!==void 0&&e!==`fixed`&&e!==`relaxed`&&(console.warn(`Property "env.wasm.simd" is set to unknown value "${e}". Reset it to \`false\` and ignore SIMD feature checking.`),S.wasm.simd=!1),typeof S.wasm.proxy!=`boolean`&&(S.wasm.proxy=!1),typeof S.wasm.trace!=`boolean`&&(S.wasm.trace=!1),typeof S.wasm.numThreads!=`number`||!Number.isInteger(S.wasm.numThreads)||S.wasm.numThreads<=0)if(typeof self<`u`&&!self.crossOriginIsolated)S.wasm.numThreads=1;else{let e=typeof navigator>`u`?a(`node:os`).cpus().length:navigator.hardwareConcurrency;S.wasm.numThreads=Math.min(4,Math.ceil((e||1)/2))}},gd=class{async init(e){hd(),await nd(),await rd(e)}async createInferenceSessionHandler(e,t){let n=new fd;return await n.loadModel(e,t),n}},_d=new gd});N(),N(),N();var yd=`1.24.3`,bd=Oe;{let e=(vd(),l(md)).wasmBackend;f(`webgpu`,e,5),f(`webnn`,e,5),f(`cpu`,e,10),f(`wasm`,e,10)}Object.defineProperty(S.versions,`web`,{value:yd,enumerable:!0});
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
export{Se as InferenceSession,he as TRACE,M as TRACE_EVENT_BEGIN,ve as TRACE_EVENT_END,_e as TRACE_FUNC_BEGIN,j as TRACE_FUNC_END,pe as Tensor,bd as default,S as env,f as registerBackend};