shreyask/KittenTTS-WebGPU

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${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,..._)}232  ${e.mainStart([12,12,1])}233    // x holds the N and y holds the M234    let headIdx = workgroup_id.z % uniforms.num_heads;235    let kvHeadIdx = ${m===1?`headIdx`:`headIdx / uniforms.n_reps`};236    let kv_num_heads = ${m===1?`uniforms.num_heads`:`uniforms.num_heads / uniforms.n_reps`};237    let batchIdx = workgroup_id.z / uniforms.num_heads;238    let m = workgroup_id.y * TILE_SIZE;239    let n = workgroup_id.x * TILE_SIZE;240    let sequence_length = uniforms.M;241    var total_sequence_length = uniforms.N;242    ${Br(l,f,!0)}243    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;244    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;245    ${b&&d?`let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;`:``};246    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;247    ${d?`let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;`:``}248    var value = ${v}(0);249    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {250      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {251        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];252      }253      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {254        var idx = TILE_SIZE * local_id.y + local_id.x;255      ${b&&d?`256              if (n + local_id.y < past_sequence_length) {257                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];258              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {259                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];260              }`:`261          if (n + local_id.y < uniforms.kv_sequence_length) {262            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];263          }`}264      ${d?`if (n + local_id.y < present_sequence_length) {265        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];266      }`:``}267      }268      workgroupBarrier();269 270      for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {271          value += ${v}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);272      }273 274      workgroupBarrier();275    }276 277    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {278      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;279      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;280      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}`)}})()};281        output[outputIdx] = ${h.type.value} (sum * uniforms.alpha) + ${i?`attention_bias[outputIdx]`:`0.0`};282    }283  }`}}},Ur=(e,t,n,r,i,a,o=void 0,s=void 0)=>{let c=a+i.kvSequenceLength,l=i.nReps?i.nReps:1,u=i.vHiddenSize*l,d=e>1&&r,f=i.kvNumHeads?i.kvNumHeads:i.numHeads,p=d?[i.batchSize,f,c,i.headSize]:void 0,m=[i.batchSize,i.sequenceLength,u],h={x:Math.ceil(i.vHeadSize/12),y:Math.ceil(i.sequenceLength/12),z:i.batchSize*i.numHeads},g=[{type:12,data:i.sequenceLength},{type:12,data:c},{type:12,data:i.vHeadSize},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:12,data:u},{type:12,data:a},{type:12,data:i.kvSequenceLength},{type:12,data:l}],_=d&&r&&z.size(r.dims)>0,v=[`type`,`type`];_&&v.push(`type`),o&&v.push(`type`),s&&v.push(`type`);let y=[{dims:m,dataType:t.dataType,gpuDataType:0}];return d&&y.push({dims:p,dataType:t.dataType,gpuDataType:0}),{name:`AttentionScore`,shaderCache:{hint:`${r!==void 0};${e}`,inputDependencies:v},getRunData:()=>({outputs:y,dispatchGroup:h,programUniforms:g}),getShaderSource:e=>{let i=q(`probs`,t.dataType,t.dims),a=[i,q(`v`,n.dataType,n.dims)];_&&a.push(q(`past_value`,r.dataType,r.dims));let c=o?q(`seq_lens`,o.dataType,o.dims):void 0;o&&a.push(c);let u=s?q(`total_sequence_length_input`,s.dataType,s.dims):void 0;s&&a.push(u);let f=[J(`output`,t.dataType,m)];return d&&f.push(J(`present_value`,t.dataType,p)),`284  const TILE_SIZE = 12u;285  var<workgroup> tileQ: array<${i.type.value}, 144>;286  var<workgroup> tileV: array<${i.type.value}, 144>;287  ${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)}288  ${e.mainStart([12,12,1])}289   let headIdx = workgroup_id.z % uniforms.num_heads;290   let batchIdx = workgroup_id.z / uniforms.num_heads;291   let kvHeadIdx = ${l===1?`headIdx`:`headIdx / uniforms.n_reps`};292   let kv_num_heads = ${l===1?`uniforms.num_heads`:`uniforms.num_heads / uniforms.n_reps`};293   let m = global_id.y;294   let n = global_id.x;295   let sequence_length = uniforms.M;296   var total_sequence_length = uniforms.K;297   ${Br(c,u,!0)}298   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;299   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch300   ${_&&d?`let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;`:``};301   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;302   ${d?`let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;`:``}303   var value = ${i.type.storage}(0);304   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {305      if (m < uniforms.M && w + local_id.x < uniforms.K) {306        tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];307      }308      if (n < uniforms.N && w + local_id.y < uniforms.K) {309        var idx = TILE_SIZE * local_id.y + local_id.x;310        ${_&&d?`311        if (w + local_id.y < past_sequence_length) {312          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];313        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {314          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];315        }316      `:`317            if (w + local_id.y < uniforms.kv_sequence_length) {318              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];319            }`}320        ${d?`321            if (w + local_id.y < present_sequence_length) {322          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];323        }`:``}324      }325     workgroupBarrier();326     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {327       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];328     }329     workgroupBarrier();330   }331 332   // we need to transpose output from BNSH_v to BSND_v333   if (m < uniforms.M && n < uniforms.N) {334     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size335       + headIdx * uniforms.N + n;336     output[outputIdx] = value;337   }338  }`}}},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?1:0)+(s?1:0)),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`339  const TILE_SIZE = 12u;340  var<workgroup> tileInput: array<${l}, 144>;341  var<workgroup> tileWeightQ: array<${l}, 144>;342  var<workgroup> tileWeightK: array<${l}, 144>;343  var<workgroup> tileWeightV: array<${l}, 144>;344  ${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)}345  ${e.mainStart([12,12,1])}346    let batchIndex = workgroup_id.z / uniforms.num_heads;347    let headNumber = workgroup_id.z % uniforms.num_heads;348    let m = global_id.y;349    let n = global_id.x;350 351    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;352    let biasOffsetQ = headNumber * uniforms.head_size;353    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;354    let biasOffsetV = uniforms.hidden_size + biasOffsetK;355 356    var valueQ = ${l}(0);357    var valueK = ${l}(0);358    var valueV = ${l}(0);359    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {360      if (m < uniforms.M && w + local_id.x < uniforms.K) {361        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];362      }363      if (n < uniforms.N && w + local_id.y < uniforms.K) {364        let offset = n + (w + local_id.y) * uniforms.ldb;365        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];366        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];367        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];368      }369      workgroupBarrier();370      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {371        let inputTileOffset = TILE_SIZE * local_id.y + k;372        let weightTileOffset = TILE_SIZE * k + local_id.x;373        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];374        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];375        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];376      }377 378      workgroupBarrier();379    }380 381    let headOffset = (m * uniforms.N + n) % uniforms.head_size;382    valueQ += bias[headOffset + biasOffsetQ];383    valueK += bias[headOffset + biasOffsetK];384    valueV += bias[headOffset + biasOffsetV];385 386    let offset = workgroup_id.z * uniforms.M * uniforms.N;387    if (m < uniforms.M && n < uniforms.N) {388      let outputIdx = offset + m * uniforms.N + n;389      output_q[outputIdx] = valueQ;390      output_k[outputIdx] = valueK;391      output_v[outputIdx] = valueV;392    }393  }`}},{inputs:s,outputs:[-1,-1,-1]})},Kr=(e,t)=>{let n=zr(e.inputs,t),[r,i,a]=Gr(e,n);return Wr(e,r,i,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],n)}}),Jr,Yr,Xr,Zr,Qr=o(()=>{N(),L(),B(),H(),Y(),Jr=(e,t)=>{if(!e||e.length!==5)throw Error(`BatchNormalization requires 5 inputs`);let n=(e,t,n)=>{let r=t.length;if(r!==e.length)throw Error(`${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=`394            ${g.indicesSet(`outputIndices`,`0`,`0`)}395            let cOffset = ${g.indicesToOffset(`outputIndices`)};`;else{e=`var cIndices = ${f.type.indices}(0);396                       cIndices[0] = outputIndices[${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=>`397  const epsilon = ${n};398  ${e.registerUniform(`outputSize`,`u32`).declareVariables(d,f,p,m,h,g)}399  ${e.mainStart()}400  ${e.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.outputSize`)}401    var outputIndices = ${g.offsetToIndices(`global_idx * ${o}`)};402    ${_()}403    let scale = ${f.getByOffset(`cOffset`)};404    let bias = ${p.getByOffset(`cOffset`)};405    let inputMean = ${m.getByOffset(`cOffset`)};406    let inputVar = ${h.getByOffset(`cOffset`)};407  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if(t===`LeakyRelu`){let[n]=e?.activation_params||[.01];return{activation:t,alpha:n}}return{activation:t}}}),wa,Ta,Ea=o(()=>{wa=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw Error(`${e}-component is not supported.`)}},Ta=e=>`570      ${e?`value = value + getBiasByOutputCoords(coords);`:``}571      `}),Da,Oa=o(()=>{Da=e=>`572fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {573  return dot(coords, vec4<i32>(574      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));575}576fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {577  return dot(coords, vec4<i32>(578    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));579}580`}),ka,Aa,ja=o(()=>{L(),B(),Y(),Ca(),ka=(e,t,n,r,i)=>{let a=r-n;return`581      ${Array.from({length:n}).map((n,o)=>`582      if (${K(t.shape,o,t.rank)} != 1) {583        ${t.indicesSet(e,o,K(i,o+a,r))}584      } else {585        ${t.indicesSet(e,o,0)}586      }`).join(``)}587`},Aa=(e,t,n,r,i=!1,a)=>{let o=e[0].dims,s=e[1].dims,c=o[o.length-2],l=s[s.length-1],u=o[o.length-1],d=G(l),f=G(u),p=G(c),m=z.size(n)/d/p,h=e.length>2,g=r?r.slice(0,-2):n.slice(0,-2),_=[z.size(g),c,l],v=[{type:12,data:m},{type:12,data:c},{type:12,data:l},{type:12,data:u}];return ba(t,v),v.push(...W(g,o,s)),h&&v.push(...W(e[2].dims)),v.push(...W(_)),{name:`MatMulNaive`,shaderCache:{hint:`${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: ${c.type.value};`;for(let t=0;t<f;t++)e+=`588              let b_data${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) / ${f}];`;for(let n=0;n<f;n++)e+=`589            values[${t}] = fma(${l.type.value}(a_data${f===1?``:`[${n}]`}), b_data${n}, values[${t}]);590`}return e};return`591  ${r.registerUniforms(x).registerInternalVariables(a).declareVariables(...y,u)}592  ${r.mainStart()}593    ${r.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}594    let col = (global_idx % (uniforms.N / ${d})) * ${d};595    var index1 = global_idx / (uniforms.N / ${d});596    let stride1 = uniforms.M / ${p};597    let row = (index1 % stride1) * ${p};598    let batch = index1 / stride1;599 600    ${n.length===2?``:`let batch_indices = ${a.offsetToIndices(`batch`)};`}601 602    var a_indices: ${c.type.indices};603    ${ka(`a_indices`,c,c.rank-2,a.rank,`batch_indices`)}604    ${c.indicesSet(`a_indices`,c.rank-2,0)}605    ${c.indicesSet(`a_indices`,c.rank-1,0)}606    let a_offset = ${c.indicesToOffset(`a_indices`)};607 608    var b_indices: ${l.type.indices};609    ${ka(`b_indices`,l,l.rank-2,a.rank,`batch_indices`)}610    ${l.indicesSet(`b_indices`,l.rank-2,0)}611    ${l.indicesSet(`b_indices`,l.rank-1,0)}612    let b_offset = ${l.indicesToOffset(`b_indices`)};613    var values: array<${u.type.value}, ${p}>;614    for (var k: u32 = 0u; k < uniforms.K; k = k + ${f}) {615      ${S()}616    }617    for (var i = 0u; i < ${p}u; i++) {618      var value = values[i];619      ${b}620      ${v}621      let cur_indices = ${u.type.indices}(batch, row + i, col);622      let offset = ${u.indicesToOffset(`cur_indices`)};623      ${u.setByOffset(`offset / ${d}`,`value`)};624    }625  }626  `}}}}),Ma,Na,Pa,Fa,Ia,La,Ra,za,Ba=o(()=>{L(),B(),Y(),Ca(),ja(),Ea(),Ma=(e,t)=>e?`627        mm_Asub[inputRow][inputCol] = mm_readA(batch,628          kStart + inputRow,629          globalRowStart / innerElementSize + inputCol${t?`, batchIndices`:``});630        `:`631        mm_Asub[inputRow][inputCol] = mm_readA(batch,632          globalRow + innerRow,633          kStart / innerElementSize + inputCol${t?`, batchIndices`:``});634        `,Na=(e,t)=>e?`635        let ACached0 = mm_Asub[k * innerElementSize][localRow];636        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];637        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];638        ${t===3?``:`let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];`}639        for (var i = 0; i < rowPerThread; i = i + 1) {640          acc[i] = BCached0 * ACached0[i] + acc[i];641          acc[i] = BCached1 * ACached1[i] + acc[i];642          acc[i] = BCached2 * ACached2[i] + acc[i];643          ${t===3?``:`acc[i] = BCached3 * ACached3[i] + acc[i];`}644        }`:`645        for (var i = 0; i < rowPerThread; i = i + 1) {646          let ACached = mm_Asub[tileRow + i][k];647          acc[i] = BCached0 * ACached.x + acc[i];648          acc[i] = BCached1 * ACached.y + acc[i];649          acc[i] = BCached2 * ACached.z + acc[i];650          ${t===3?``:`acc[i] = BCached3 * ACached.w + acc[i];`}651        }`,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 4.652      Otherwise, innerElementSize ${f} must be 3 or 4.653  tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`654var<workgroup> mm_Asub: array<array<vec${f}<${n}>, ${u/f}>, ${d}>;655var<workgroup> mm_Bsub: array<array<vec4<${n}>, ${l/e[0]}>, ${a}>;656 657const rowPerThread = ${e[1]};658const colPerThread = ${e[0]};659const innerElementSize = ${f};660const tileInner = ${a};661 662@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})663fn main(@builtin(local_invocation_id) localId : vec3<u32>,664        @builtin(global_invocation_id) globalId : vec3<u32>,665        @builtin(workgroup_id) workgroupId : vec3<u32>) {666  let localRow = i32(localId.y);667  let tileRow = localRow * rowPerThread;668  let tileCol = i32(localId.x);669 670  let globalRow =i32(globalId.y) * rowPerThread;671  let globalCol = i32(globalId.x);672  let batch = ${o?`0`:`i32(globalId.z)`};673  ${r?`let batchIndices = ${r.offsetToIndices(`u32(batch)`)};`:``}674  let globalRowStart = i32(workgroupId.y) * ${c};675 676  let num_tiles = ${o?`${Math.ceil(s/a)}`:`(uniforms.dim_inner - 1) / tileInner + 1`};677  var kStart = ${o?`i32(globalId.z) * ${s}`:`0`};678 679  var acc: array<vec4<${n}>, rowPerThread>;680 681  // Loop over shared dimension.682  let tileRowB = localRow * ${p};683  for (var t = 0; t < num_tiles; t = t + 1) {684      // Load one tile of A into local memory.685      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {686          let inputRow = tileRow + innerRow;687          let inputCol = tileCol;688          ${Ma(i,r)}689      }690 691      // Load one tile of B into local memory.692      for (var innerRow = 0; innerRow < ${p}; innerRow = innerRow + 1) {693          let inputRow = tileRowB + innerRow;694          let inputCol = tileCol;695          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${r?`, batchIndices`:``});696      }697      kStart = kStart + tileInner;698      workgroupBarrier();699 700      // Compute acc values for a single thread.701      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {702          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];703          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];704          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];705          ${f===3?``:`let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];`}706 707          ${Na(i,f)}708      }709 710      workgroupBarrier();711  }712 713  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {714      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);715  }716}`},Fa=(e,t)=>e?`717            mm_Asub[inputRow][inputCol] = mm_readA(batch,718              kStart + inputRow,719              globalRowStart + inputCol${t?`, batchIndices`:``});720            `:`721            mm_Asub[inputRow][inputCol] = mm_readA(batch,722              globalRowStart + inputRow,723              kStart + inputCol${t?`, batchIndices`:``});724            `,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?`725    let localRow = i32(localId.y);726    let localCol = i32(localId.x);727    let globalRowStart = i32(workgroupId.y) * ${l};728    let globalColStart = i32(workgroupId.x) * ${u};729 730    // Loop over shared dimension.731    for (var t = 0; t < num_tiles; t = t + 1) {732      // Load one tile of A into local memory.733      for (var inputRow = localRow; inputRow < ${f}; inputRow = inputRow + ${t[1]}) {734        for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${t[0]}) {735          ${Fa(i,r)}736        }737      }738      // Load one tile of B into local memory.739      for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) {740            for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) {741          mm_Bsub[inputRow][inputCol] = mm_readB(batch,742            kStart + inputRow,743            globalColStart + inputCol${r?`, batchIndices`:``});744        }745      }746      kStart = kStart + tileInner;747      workgroupBarrier();748 749      // Compute acc values for a single thread.750      var BCached : array<${n}, colPerThread>;751      for (var k = 0; k < tileInner; k = k + 1) {752        for (var inner = 0; inner < colPerThread; inner = inner + 1) {753          BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];754        }755        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {756          let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}757          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {758            acc[innerRow][innerCol] = acc[innerRow][innerCol] +759                ACached * BCached[innerCol];760          }761        }762      }763      workgroupBarrier();764    }765    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {766      let gRow = globalRowStart + localRow + innerRow * ${t[1]};767      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {768        let gCol = globalColStart + localCol + innerCol * ${t[0]};769        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);770      }771    }772    `:`773let tileRow = i32(localId.y) * rowPerThread;774let tileCol = i32(localId.x) * colPerThread;775 776let globalRow = i32(globalId.y) * rowPerThread;777let globalCol = i32(globalId.x) * colPerThread;778let globalRowStart = i32(workgroupId.y) * ${l};779 780let tileRowA = i32(localId.y) * ${p};781let tileColA = i32(localId.x) * ${m};782let tileRowB = i32(localId.y) * ${h};783// Loop over shared dimension.784for (var t = 0; t < num_tiles; t = t + 1) {785  // Load one tile of A into local memory.786  for (var innerRow = 0; innerRow < ${p}; innerRow = innerRow + 1) {787    for (var innerCol = 0; innerCol < ${m}; innerCol = innerCol + 1) {788      let inputRow = tileRowA + innerRow;789      let inputCol = tileColA + innerCol;790      ${Fa(i,r)}791    }792  }793 794  // Load one tile of B into local memory.795  for (var innerRow = 0; innerRow < ${h}; innerRow = innerRow + 1) {796    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {797      let inputRow = tileRowB + innerRow;798      let inputCol = tileCol + innerCol;799      mm_Bsub[inputRow][inputCol] = mm_readB(batch,800        kStart + inputRow,801        globalCol + innerCol${r?`, batchIndices`:``});802    }803  }804  kStart = kStart + tileInner;805  workgroupBarrier();806 807  // Compute acc values for a single thread.808  var BCached : array<${n}, colPerThread>;809  for (var k = 0; k < tileInner; k = k + 1) {810    for (var inner = 0; inner < colPerThread; inner = inner + 1) {811      BCached[inner] = mm_Bsub[k][tileCol + inner];812    }813 814    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {815      ${Ia(i)}816      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {817        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];818      }819    }820  }821 822  workgroupBarrier();823}824 825for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {826  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {827    mm_write(batch, globalRow + innerRow, globalCol + innerCol,828        acc[innerRow][innerCol]);829  }830}831`;return`832  var<workgroup> mm_Asub : array<array<${n}, ${d}>, ${f}>;833  var<workgroup> mm_Bsub : array<array<${n}, ${u}>, ${a}>;834  const rowPerThread = ${e[1]};835  const colPerThread = ${e[0]};836  const tileInner = ${a};837 838@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})839fn main(@builtin(local_invocation_id) localId : vec3<u32>,840        @builtin(global_invocation_id) globalId : vec3<u32>,841        @builtin(workgroup_id) workgroupId : vec3<u32>) {842    let batch = ${o?`0`:`i32(globalId.z)`};843    ${r?`let batchIndices = ${r.offsetToIndices(`u32(batch)`)};`:``}844    let num_tiles = ${o?`${Math.ceil(s/a)}`:`(uniforms.dim_inner - 1) / tileInner + 1`};845    var kStart = ${o?`i32(globalId.z) * ${s}`:`0`};846 847    var acc : array<array<${n}, colPerThread>, rowPerThread>;848    ${g}849  }850`},Ra=(e,t,n,r,i=!1)=>{let[a,o,s,c]=r,l=U(r[0].type.tensor);return`851    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${a.type.indices}) -> ${wa(e,l)} {852      var value = ${wa(e,l)}(0.0);853      let col = colIn * ${e};854      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)855      {856        var aIndices: ${o.type.indices};857        ${ka(`aIndices`,o,o.rank-2,a.rank,`batchIndices`)}858        ${o.indicesSet(`aIndices`,o.rank-2,`u32(row)`)}859        ${o.indicesSet(`aIndices`,o.rank-1,`u32(colIn)`)}860        value = ${o.getByIndices(`aIndices`)};861      }862      return value;863    }864 865    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${a.type.indices}) -> ${wa(e,l)} {866      var value = ${wa(e,l)}(0.0);867      let col = colIn * ${e};868      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)869      {870        var bIndices: ${s.type.indices};871        ${ka(`bIndices`,s,s.rank-2,a.rank,`batchIndices`)}872        ${s.indicesSet(`bIndices`,s.rank-2,`u32(row)`)}873        ${s.indicesSet(`bIndices`,s.rank-1,`u32(colIn)`)}874        value = ${s.getByIndices(`bIndices`)};875      }876      return value;877    }878 879    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${wa(e,l)}) {880      let col = colIn * ${e};881      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {882        var value = valueIn;883        let coords = vec3<i32>(batch, row, colIn);884        ${t?`value = value + ${i?`bias[colIn]`:`${wa(e,l)}(bias[row])`};`:``}885        ${n}886        ${c.setByIndices(`vec3<u32>(coords)`,`value`)}887      }888    }889    `},za=(e,t,n,r,i=!1,a)=>{let o=e[0].dims,s=e[1].dims,c=o.slice(0,-2),l=s.slice(0,-2),u=r?r.slice(0,-2):n.slice(0,-2),d=z.size(u),f=o[o.length-2],p=o[o.length-1],m=s[s.length-1],h=p%4==0&&m%4==0,g=f<=8?[4,1,1]:[4,4,1],_=[8,8,1],v=[Math.ceil(m/_[0]/g[0]),Math.ceil(f/_[1]/g[1]),Math.ceil(d/_[2]/g[2])],y=h?4:1,b=[...c,f,p/y],x=b.length,S=[...l,p,m/y],C=S.length,w=[d,f,m/y],ee=[{type:6,data:f},{type:6,data:m},{type:6,data:p}];ba(t,ee),ee.push(...W(u,b,S));let T=[`rank`,`rank`],E=e.length>2;return E&&(ee.push(...W(e[2].dims)),T.push(`rank`)),ee.push(...W(w)),{name:`MatMul`,shaderCache:{hint:`${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`890  ${n.registerUniforms(f).registerInternalVariables(a).declareVariables(...d,l)}891  ${v}892  ${h?Pa(g,_,o,a):La(g,_,o,a)}893                   `}}}}),Va,Ha,Ua=o(()=>{L(),Lt(),Y(),Ca(),Ea(),Oa(),Ba(),Va=(e,t,n,r,i=!1,a,o=4,s=4,c=4,l=`f32`)=>{let u=e=>{switch(e){case 1:return`resData = x[xIndex];`;case 3:return`resData = vec3<${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?`894    let coord = vec4<i32>(batch, xRow, xCol, xCh);895    `:`896    let coord = vec4<i32>(batch, xCh, xRow, xCol);897    `,p=e?`898    let coords = vec4<i32>(899      batch,900      row / outWidth,901      row % outWidth,902      col);903    `:`904    let coords = vec4<i32>(905      batch,906      row,907      col / outWidth,908      col % outWidth);909    `,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=`910    let inChannels = i32(uniforms.w_shape[2]);911    let outWidth = ${e?`i32(uniforms.result_shape[2])`:`i32(uniforms.result_shape[3])`};912    let outRow = ${g} / outWidth;913    let outCol = ${g} % outWidth;914 915    let WRow = ${_} / (i32(uniforms.w_shape[1]) * inChannels);916    let WCol = ${_} / inChannels % i32(uniforms.w_shape[1]);917    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];918    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];919    let xCh = ${_} % inChannels;920    var resData = ${wa(o,l)}(0.0);921    // The bounds checking is always needed since we use it to pad zero for922    // the 'same' padding type.923    if (xRow >= 0 && xRow < ${m} && xCol >= 0 && xCol < ${h}) {924      ${f}925      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));926      ${u(o)}927    }928    return resData;`,y=e?t&&r?`929    let col = colIn * ${o};930    ${v}`:`931    let col = colIn * ${o};932    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {933      ${v}934    }935    return ${wa(o,l)}(0.0);`:r&&n?`936    let col = colIn * ${o};937    ${v}`:`938    let col = colIn * ${o};939    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {940      ${v}941    }942    return ${wa(o,l)}(0.0);`,b=e?r&&n?d(s):`943    let col = colIn * ${s};944    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {945      ${d(s)}946    }947    return ${wa(s,l)}(0.0);`:`948    let col = colIn * ${s};949    if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {950      ${d(s)}951    }952    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`953    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${S} {954      ${e?y:b}955    }956 957    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${C} {958      ${e?b:y}959    }960 961    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${x}) {962      let col = colIn * ${c};963      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)964      {965      var value = valueIn;966      let outWidth = ${e?`i32(uniforms.result_shape[2])`:`i32(uniforms.result_shape[3])`};967      ${p}968      ${Ta(i)}969      ${w}970      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);971      }972    }`},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=`973      fn setOutputAtIndex(flatIndex : i32, value : ${h?`vec4<${c}>`:c}) {974        result[flatIndex] = ${h?`vec4<${c}>`:c}(value);975      }976      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${h?`vec4<${c}>`:c}) {977        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));978        setOutputAtIndex(flatIndex ${h?`/ 4`:``}, value);979      }`,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+=`980        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${h?`vec4<${c}>`:c} {981          return bias[coords.${l?`w`:`y`}${h?`/ 4`:``}];982        }`}return`983        ${Da(`uniforms.result_strides`)}984        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,985        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,986        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };987        ${r.registerUniforms(i).declareVariables(...d,f)}988        ${u}989        ${Va(l,ee,T,E,o,t,D[0],D[1],D[2],c)}990        ${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+=`991        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${c} {992          return bias[${o?K(`coords`,4,5):K(`coords`,1,5)}];993        }`}let h=wa(1,c),g=ya(t,h,c);return`994            ${m}995            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {996              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);997              return ${l.getByIndices(`aIndices`)};998            }999            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {1000              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);1001              return ${u.getByIndices(`aIndices`)};1002            }1003          ${a.registerUniforms(s).declareVariables(...d,p)}1004          ${a.mainStart()}1005          ${a.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}1006              let coords = ${p.offsetToIndices(`global_idx`)};1007              let batch = ${K(`coords`,0,l.rank)};1008              let d2 = ${o?K(`coords`,l.rank-1,l.rank):K(`coords`,1,l.rank)};1009              let xFRCCorner = vec3<u32>(${o?K(`coords`,1,l.rank):K(`coords`,2,l.rank)},1010              ${o?K(`coords`,2,l.rank):K(`coords`,3,l.rank)},1011              ${o?K(`coords`,3,l.rank):K(`coords`,4,l.rank)}) * uniforms.strides - uniforms.pads;1012              let xFCorner = xFRCCorner.x;1013              let xRCorner = xFRCCorner.y;1014              let xCCorner = xFRCCorner.z;1015              let xShapeY = ${o?K(`uniforms.x_shape`,1,l.rank):K(`uniforms.x_shape`,2,l.rank)};1016              let xShapeZ = ${o?K(`uniforms.x_shape`,2,l.rank):K(`uniforms.x_shape`,3,l.rank)};1017              let xShapeW = ${o?K(`uniforms.x_shape`,3,l.rank):K(`uniforms.x_shape`,4,l.rank)};1018              let xShapeU = ${o?K(`uniforms.x_shape`,4,l.rank):K(`uniforms.x_shape`,1,l.rank)};1019              let inputDepthNearestVec4 = (xShapeU / 4) * 4;1020              let inputDepthVec4Remainder = xShapeU % 4;1021 1022              var value = 0.0;1023              for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) {1024                let xF = xFCorner + wF * uniforms.dilations[0];1025                if (xF < 0 || xF >= xShapeY) {1026                  continue;1027                }1028 1029                for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) {1030                  let xR = xRCorner + wR * uniforms.dilations[1];1031                  if (xR < 0 || xR >= xShapeZ) {1032                    continue;1033                  }1034 1035                  for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) {1036                    let xC = xCCorner + wC * uniforms.dilations[2];1037                    if (xC < 0 || xC >= xShapeW) {1038                      continue;1039                    }1040 1041                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {1042                      ${o?`let xValues = vec4<f32>(1043                               getX(batch, xF, xR, xC, d1),1044                               getX(batch, xF, xR, xC, d1 + 1),1045                               getX(batch, xF, xR, xC, d1 + 2),1046                               getX(batch, xF, xR, xC, d1 + 3));1047                            `:`let xValues = vec4<f32>(1048                               getX(batch, d1, xF, xR, xC),1049                               getX(batch, d1 + 1, xF, xR, xC),1050                               getX(batch, d1 + 2, xF, xR, xC),1051                               getX(batch, d1 + 3, xF, xR, xC));1052                            `}1053                            let wValues = vec4<f32>(1054                              getW(d2, d1, wF, wR, wC),1055                              getW(d2, d1 + 1, wF, wR, wC),1056                              getW(d2, d1 + 2, wF, wR, wC),1057                              getW(d2, d1 + 3, wF, wR, wC));1058                      value += dot(xValues, wValues);1059                    }1060                    if (inputDepthVec4Remainder == 1) {1061                        ${o?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)1062                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)1063                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}1064                    } else if (inputDepthVec4Remainder == 2) {1065                      ${o?`let xValues = vec2<f32>(1066                        getX(batch, xF, xR, xC, inputDepthNearestVec4),1067                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));1068                      `:`let xValues = vec2<f32>(1069                        getX(batch, inputDepthNearestVec4, xF, xR, xC),1070                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));1071                    `}1072                    let wValues = vec2<f32>(1073                      getW(d2, inputDepthNearestVec4, wF, wR, wC),1074                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));1075                      value += dot(xValues, wValues);1076                    } else if (inputDepthVec4Remainder == 3) {1077                      ${o?`let xValues = vec3<f32>(1078                        getX(batch, xF, xR, xC, inputDepthNearestVec4),1079                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),1080                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));1081                      `:`let xValues = vec3<f32>(1082                        getX(batch, inputDepthNearestVec4, xF, xR, xC),1083                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),1084                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));1085                    `}1086                    let wValues = vec3<f32>(1087                      getW(d2, inputDepthNearestVec4, wF, wR, wC),1088                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),1089                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));1090                      value += dot(xValues, wValues);1091                    }1092                  }1093                }1094              }1095              ${f?`value = value + getBiasByOutputCoords(coords)`:``};1096              ${g}1097              result[global_idx] = f32(value);1098          }`}}}}),$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?`1099      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {1100        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];1101 1102        if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) {1103          continue;1104        }1105 1106        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) {1107          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];1108          if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) {1109            continue;1110          }1111 1112          for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {1113            let input_channel = in_channel_offset + wInChannel;1114            let xVal = ${p.get(`batch`,`xHeight`,`xWidth`,`input_channel`)};1115            let wVal = ${m.get(`wHeight`,`wWidth`,`wInChannel`,`output_channel`)};1116            value += xVal * wVal;1117          }1118        }1119      }1120      `:`1121      for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {1122        let input_channel = in_channel_offset + wInChannel;1123        for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {1124          let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];1125 1126          if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) {1127            continue;1128          }1129 1130          for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {1131            let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];1132            if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) {1133              continue;1134            }1135 1136            let xVal = ${p.get(`batch`,`input_channel`,`xHeight`,`xWidth`)};1137            let wVal = ${m.get(`output_channel`,`wInChannel`,`wHeight`,`wWidth`)};1138            value += xVal * wVal;1139          }1140        }1141      }1142      `;return`1143  ${r.registerUniforms(g).declareVariables(...h,l)}1144 1145  ${r.mainStart()}1146    ${r.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}1147 1148    let outputIndices = ${l.offsetToIndices(`global_idx`)};1149    let batch: u32 = outputIndices[0];1150    let output_channel: u32 = outputIndices[${c?3:1}];1151    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${c?1:2}], outputIndices[${c?2:3}]) * uniforms.strides - uniforms.pads;1152    let group_id: u32 = output_channel * ${d} / uniforms.output_channels_per_group;1153    var in_channel_offset = group_id * uniforms.w_shape[${c?2:1}];1154 1155    var value: ${l.type.value} = ${l.type.value}(0);1156    ${_}1157    ${a}1158    ${f}1159    ${l.setByOffset(`global_idx`,`value`)}1160  }`}}},eo=(e,t,n,r)=>{let i=e.length>2,a=G(n[3]),o=G(n[2]),s=z.size(n)/a/o,c=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/a],l=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/a],u=[n[0],n[1],n[2],n[3]/a],d=[{type:12,data:s},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];ba(t,d),d.push(...W(c,l,u));let f=(o-1)*t.strides[1]+l[1];return{name:`GroupedConv-Vectorize`,shaderCache:{hint:`${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,_),`1161  ${n.registerUniforms(_).declareVariables(...h,r)}1162  ${n.mainStart()}1163    ${n.guardAgainstOutOfBoundsWorkgroupSizes(`uniforms.output_size`)}1164    let width0 = uniforms.output_shape[3];1165    let output_channel = global_idx % width0;1166    var index1 = global_idx / width0;1167    let width1 = uniforms.output_shape[2] / ${o}u;1168    let col = (index1 % width1) * ${o}u;1169    index1 = index1 / width1;1170    let row = index1 % uniforms.output_shape[1];1171    let batch = index1 / uniforms.output_shape[1];1172 1173    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;1174 1175    var x_vals: array<${p.type.value}, ${f}>;1176    var values: array<${r.type.value}, ${o}>;1177    let input_channel = output_channel;1178    // Use constant instead of uniform can give better performance for w's height/width.1179    for (var w_height: u32 = 0u; w_height < ${l[0]}; w_height++) {1180      let x_height = x_corner.x + i32(w_height);1181      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {1182        for (var i = 0; i < ${f}; i++) {1183          let x_width = x_corner.y + i;1184          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {1185            x_vals[i] = ${p.get(`batch`,`u32(x_height)`,`u32(x_width)`,`input_channel`)};1186          } else {1187            x_vals[i] = ${p.type.value}(0);1188          }1189        }1190        for (var w_width: u32 = 0u; w_width < ${l[1]}; w_width++) {1191          let w_val = ${m.get(`w_height`,`w_width`,`0`,`output_channel`)};1192          for (var i = 0u; i < ${o}u; i++) {1193            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);1194          }1195        }1196      }1197    }1198 1199    for (var i = 0u; i < ${o}u; i++) {1200      var value = values[i];

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