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Uniforms { ${t.join(", ")} }; + @group(0) @binding(${this.variableIndex}) var uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(t=>t.impl()).join(` +`)+this.internalVariables.map(t=>t.impl()).join(` +`)}get variablesInfo(){if(this.uniforms.length===0)return;let t=e=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(e)];return this.uniforms.map(e=>[t(e.type),e.length??1])}},_p=(t,e)=>new Ru(t,e),Yn=(t,e)=>{let r=t.length,n=[];for(let i=0;i1&&s===1&&n.unshift(a)}return n}}),Pu,ns,Bu,Du,sr,yp,wp,na=Y(()=>{ve(),Ie(),at(),Te(),Pu=t=>{if(!t||t.length!==1)throw new Error("Transpose requires 1 input.")},ns=(t,e)=>e&&e.length!==t?[...new Array(t).keys()].reverse():e,Bu=(t,e)=>G.sortBasedOnPerm(t,ns(t.length,e)),Du=(t,e,r,n)=>{let i=[];i.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} { + var a: ${r.type.indices};`);for(let a=0;a{let r=t.dataType,n=t.dims.length,i=ns(n,e),a=Bu(t.dims,i),s=fe("output",r,a.length),o=q("a",r,n),u=l=>` + ${l.registerUniform("output_size","u32").declareVariables(o,s)} + + ${Du(i,n,o,s)} + + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${s.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${s.setByOffset("global_idx",o.getByIndices("aIndices"))} + }`;return{name:"Transpose",shaderCache:{hint:`${e}`,inputDependencies:["rank"]},getRunData:l=>{let h=G.size(a);return{outputs:[{dims:a,dataType:l[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:[{type:12,data:h},..._e(l[0].dims,a)]}},getShaderSource:u}},yp=(t,e)=>{Pu(t.inputs),t.compute(sr(t.inputs[0],e.perm))},wp=t=>Ge({perm:t.perm})}),Nu,Fu,Lu,Wu,Uu,Vu,Gu,Hu,qu,ju,Nt,bp,vp,$p,xp,Sp,Cp,Ep,Tp,kp,Ip,U0=Y(()=>{ve(),Ie(),Te(),go(),na(),Nu={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * 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currentSize) { + let candidate = aBestValues[local_idx + interval]; + bestValue = ${Fu[n]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${f.setByOffset("outputIndex",`${n==="mean"?`${f.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${f.type.storage}(${Wu[n]})`}`)}; + } + }`,getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:u},programUniforms:[{type:12,data:l}]})}},Nt=(t,e,r,n)=>{let i=t.inputs.length===1?r:Ns(t.inputs,r),a=i.axes;a.length===0&&!i.noopWithEmptyAxes&&(a=t.inputs[0].dims.map((c,y)=>y));let s=G.normalizeAxes(a,t.inputs[0].dims.length),o=s,u=t.inputs[0],l=qu(o,t.inputs[0].dims.length);l.length>0&&(u=t.compute(sr(t.inputs[0],l),{inputs:[0],outputs:[-1]})[0],o=Uu(o.length,u.dims.length));let[h,f]=Vu(u.dims,o),m=h;i.keepDims&&(m=Gu(h,s)),t.compute(ju(e,{hint:i.cacheKey,inputDependencies:["type"]},[u],n,t.inputs[0].dataType,m,f),{inputs:[u]})},bp=(t,e)=>{Nt(t,"ReduceMeanShared",e,"mean")},vp=(t,e)=>{Nt(t,"ReduceL1Shared",e,"l1")},$p=(t,e)=>{Nt(t,"ReduceL2Shared",e,"l2")},xp=(t,e)=>{Nt(t,"ReduceLogSumExpShared",e,"logSumExp")},Sp=(t,e)=>{Nt(t,"ReduceMaxShared",e,"max")},Cp=(t,e)=>{Nt(t,"ReduceMinShared",e,"min")},Ep=(t,e)=>{Nt(t,"ReduceProdShared",e,"prod")},Tp=(t,e)=>{Nt(t,"ReduceSumShared",e,"sum")},kp=(t,e)=>{Nt(t,"ReduceSumSquareShared",e,"sumSquare")},Ip=(t,e)=>{Nt(t,"ReduceLogSumShared",e,"logSum")}}),Ft,Ku,pi,Ns,Lt,Yu,Xu,Qu,Ju,Zu,el,tl,rl,nl,al,Wt,Ap,Mp,Op,zp,Rp,Pp,Bp,Dp,Np,Fp,go=Y(()=>{ve(),Ie(),at(),Te(),U0(),Ft=t=>{if(!t||t.length===0||t.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(t.length===2&&t[1].dims.length!==1)throw new Error("Invalid axes input dims.")},Ku=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],pi=(t,e,r,n,i,a,s=!1,o=!1)=>{let u=[],l=r[0].dims,h=l.length,f=G.normalizeAxes(i,h),m=!o&&f.length===0;l.forEach((b,v)=>{m||f.indexOf(v)>=0?s&&u.push(1):u.push(b)});let c=u.length,y=G.size(u);return{name:t,shaderCache:e,getShaderSource:b=>{let v=[],C=q("_A",r[0].dataType,h),x=fe("output",a,c),T=n(C,x,f),I=T[2];for(let A=0,R=0;A=0?(s&&R++,I=`for(var j${A}: u32 = 0; j${A} < ${l[A]}; j${A}++) { + ${T[2].includes("last_index")?`let last_index = j${A};`:""} + ${C.indicesSet("input_indices",A,`j${A}`)} + ${I} + }`):(v.push(`${C.indicesSet("input_indices",A,x.indicesGet("output_indices",R))};`),R++);return` + + ${b.registerUniform("output_size","u32").declareVariables(C,x)} + + ${b.mainStart()} + ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var input_indices: ${C.type.indices}; + let output_indices = ${x.offsetToIndices("global_idx")}; + + ${v.join(` +`)} + ${T[0]} // init ops for reduce max/min + ${T[1]} + ${I} + ${T[3]} + ${T.length===4?x.setByOffset("global_idx","value"):T.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:u,dataType:a}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:[{type:12,data:y},..._e(l,u)]})}},Ns=(t,e)=>{let r=[];return t[1].dims[0]>0&&t[1].getBigInt64Array().forEach(n=>r.push(Number(n))),Ge({axes:r,keepDims:e.keepDims,noopWithEmptyAxes:e.noopWithEmptyAxes})},Lt=(t,e,r,n)=>{let i=t.inputs,a=i.length===1?r:Ns(i,r);t.compute(pi(e,{hint:a.cacheKey,inputDependencies:["rank"]},[i[0]],a.noopWithEmptyAxes&&a.axes.length===0?Ku:n,a.axes,i[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},Yu=(t,e)=>{Ft(t.inputs),Lt(t,"ReduceLogSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},Xu=(t,e)=>{Ft(t.inputs),Lt(t,"ReduceL1",e,(r,n)=>[`var value = 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${a});`]})},tl=(t,e)=>{Ft(t.inputs),Lt(t,"ReduceMin",e,(r,n,i)=>{let a=[];for(let s=0;s=0||i.length===0)&&a.push(`input_indices[${s}] = 0;`);return[`${a.join(` +`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},rl=(t,e)=>{Ft(t.inputs),Lt(t,"ReduceProd",e,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},nl=(t,e)=>{Ft(t.inputs),Lt(t,"ReduceSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},al=(t,e)=>{Ft(t.inputs),Lt(t,"ReduceSumSquare",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},Wt=(t,e,r)=>{if(e.length===0)return r;let n=1,i=1;for(let a=0;a1024},Ap=(t,e)=>{Wt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?el(t,e):bp(t,e)},Mp=(t,e)=>{Wt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Xu(t,e):vp(t,e)},Op=(t,e)=>{Wt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Qu(t,e):$p(t,e)},zp=(t,e)=>{Wt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Ju(t,e):xp(t,e)},Rp=(t,e)=>{Wt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Zu(t,e):Sp(t,e)},Pp=(t,e)=>{Wt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?tl(t,e):Cp(t,e)},Bp=(t,e)=>{Wt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?rl(t,e):Ep(t,e)},Dp=(t,e)=>{Wt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?nl(t,e):Tp(t,e)},Np=(t,e)=>{Wt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?al(t,e):kp(t,e)},Fp=(t,e)=>{Wt(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?Yu(t,e):Ip(t,e)}}),as,Lp,Wp,Fs,V0=Y(()=>{ve(),at(),go(),as=t=>{if(!t||t.length===0||t.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(t[0].dataType!==1)throw new Error("Invalid input type.")},Lp=(t,e)=>{as(t.inputs);let r=(n,i,a)=>{let s=[];for(let o=0;o=0||a.length===0)&&s.push(`input_indices[${o}] = 0;`);return[`${s.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?"<=":"<"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",i.setByOffset("global_idx","best_index")]};t.compute(pi("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Wp=(t,e)=>{as(t.inputs);let r=(n,i,a)=>{let s=[];for(let o=0;o=0||a.length===0)&&s.push(`input_indices[${o}] = 0;`);return[`${s.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?">=":">"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",i.setByOffset("global_idx","best_index")]};t.compute(pi("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Fs=t=>Ge(t)}),il,sl,ol,hi,Up,Vp,Gp=Y(()=>{ve(),Ie(),at(),Te(),il=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");let r=0,n=t[r],i=n.dataType,a=n.dims.length;t.forEach((s,o)=>{if(o!==r){if(s.dataType!==i)throw new Error("input tensors should be one type");if(s.dims.length!==a)throw new Error("input tensors should have the same shape");s.dims.forEach((u,l)=>{if(l!==e&&u!==n.dims[l])throw new Error("non concat dimensions must match")})}})},sl=(t,e)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${e}); + for (var i: u32 = 0u; i < ${t}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${t}u; + }`,ol=(t,e)=>{let r=t.length,n=[];for(let i=0;i{let i=G.size(r),a=new Array(t.length),s=new Array(t.length),o=0,u=[],l=[],h=[{type:12,data:i}];for(let b=0;b`uniforms.sizeInConcatAxis${b}`).join(","),y=b=>` + + ${(()=>{b.registerUniform("outputSize","u32");for(let v=0;v(${c}); + ${m} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${ol(s,f)} + }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:h}),getShaderSource:y}},Up=(t,e)=>{let r=t.inputs,n=r[0].dims,i=G.normalizeAxis(e.axis,n.length);il(r,i);let a=n.slice();a[i]=r.reduce((o,u)=>o+(u.dims.length>i?u.dims[i]:0),0);let s=r.filter(o=>G.size(o.dims)>0);t.compute(hi(s,i,a,r[0].dataType),{inputs:s})},Vp=t=>Ge({axis:t.axis})}),ul,ll,dl,cl,fi,pl,Hp,qp=Y(()=>{ve(),po(),Te(),Gp(),ul=(t,e)=>{let r=t[0],n=t[1],i=t[2],a=t[3],s=t[4],o=t[5];if(s&&o)throw new Error("Attention cannot have both past and relative_position_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let u=r.dims[0],l=r.dims[1],h=r.dims[2];if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==h)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(i.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let f=i.dims[0]/3,m=f,c=m;if(e.qkvHiddenSizes.length>0){if(e.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let T of e.qkvHiddenSizes)if(T%e.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");f=e.qkvHiddenSizes[0],m=e.qkvHiddenSizes[1],c=e.qkvHiddenSizes[2]}let y=l;if(f!==m)throw new Error("qkv_hidden_sizes first element should be same as the second");if(i.dims[0]!==f+m+c)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let b=0;if(s){if(m!==c)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(s.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(s.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(s.dims[1]!==u)throw new Error('Input "past" second dimension must be batch_size');if(s.dims[2]!==e.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(s.dims[4]!==m/e.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');e.pastPresentShareBuffer||(b=s.dims[3])}let v=y+b,C=-1,x=0;if(a)throw new Error("Mask not supported");if(s)throw new Error("past is not supported");return{batchSize:u,sequenceLength:l,pastSequenceLength:b,kvSequenceLength:y,totalSequenceLength:v,maxSequenceLength:C,inputHiddenSize:h,hiddenSize:f,vHiddenSize:c,headSize:Math.floor(f/e.numHeads),vHeadSize:Math.floor(c/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:x,scale:e.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},ll=(t,e,r,n)=>{let i=et(n),a=64,s=n/i;s{let c=fe("x",e.dataType,e.dims,i),y=[{name:"d_inv",type:vt(e.dataType)},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return` + var thread_max: array; + var thread_sum: array; + ${m.registerUniforms(y).declareVariables(c)} + ${m.mainStart([a,1,1])} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = workgroup_id.x * uniforms.d_comp + local_offset; + + var thread_max_vector = ${h}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + thread_max_vector = max(${h}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(i){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 new Error(`Unsupported components: ${i}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${a}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${h}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + sum_vector += exp(${h}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(i){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 new Error(`Unsupported components: ${i}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${a}; i++) { + sum += thread_sum[i]; + } + + if (sum == 0) { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + x[offset + i] = ${c.type.value}(uniforms.d_inv); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + var f32input = ${h}(x[offset + i]); + x[offset + i] = ${c.type.value}(exp(f32input - max_value) / sum); + } + } + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${a};${l};${i}`},getShaderSource:f,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:u})}},dl=(t,e,r,n,i,a,s)=>{let o=s+i.kvSequenceLength,u=[i.batchSize,i.numHeads,i.sequenceLength,o],l=a.scale===0?1/Math.sqrt(i.headSize):a.scale,h=et(i.headSize),f=i.headSize/h,m=12,c={x:Math.ceil(o/m),y:Math.ceil(i.sequenceLength/m),z:i.batchSize*i.numHeads},y=[{type:12,data:i.sequenceLength},{type:12,data:f},{type:12,data:o},{type:12,data:i.numHeads},{type:1,data:l}],b=n?["type","type","type"]:["type","type"],v=C=>{let x=q("q",e.dataType,e.dims,h),T=q("key",r.dataType,r.dims,h),I=[x,T];n&&I.push(q("relative_position_bias",n.dataType,n.dims));let A=fe("output",e.dataType,u),R=vt(1,h),z=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"}];return` + const TILE_SIZE = ${m}u; + + var tileQ: array<${x.type.storage}, ${m*m}>; + var tileK: array<${x.type.storage}, ${m*m}>; + ${C.registerUniforms(z).declareVariables(...I,A)} + ${C.mainStart([m,m,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z; + let m = workgroup_id.y * TILE_SIZE; + let n = workgroup_id.x * TILE_SIZE; + let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K; + let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K; + + var value = ${R}(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) { + tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x]; + } + workgroupBarrier(); + + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += ${R}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]); + } + + workgroupBarrier(); + } + + let headOffset = headIdx * uniforms.M * uniforms.N; + if (global_id.y < uniforms.M && global_id.x < uniforms.N) { + let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x; + var sum: f32 = ${(()=>{switch(h){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${h}`)}})()}; + output[outputIdx] = ${A.type.value} (sum * uniforms.alpha) + ${n?"relative_position_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${h}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:u,dataType:e.dataType,gpuDataType:0}],dispatchGroup:c,programUniforms:y}),getShaderSource:v}},cl=(t,e,r,n,i)=>{let a=i+n.kvSequenceLength,s=[n.batchSize,n.sequenceLength,n.vHiddenSize],o=12,u={x:Math.ceil(n.vHeadSize/o),y:Math.ceil(n.sequenceLength/o),z:n.batchSize*n.numHeads},l=[{type:12,data:n.sequenceLength},{type:12,data:a},{type:12,data:n.vHeadSize},{type:12,data:n.numHeads},{type:12,data:n.vHiddenSize}];return{name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:l}),getShaderSource:h=>{let f=q("probs",e.dataType,e.dims),m=q("v",r.dataType,r.dims),c=fe("output",e.dataType,s),y=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return` + const TILE_SIZE = ${o}u; + var tileQ: array<${f.type.value}, ${o*o}>; + var tileK: array<${f.type.value}, ${o*o}>; + ${h.registerUniforms(y).declareVariables(f,m,c)} + ${h.mainStart([o,o,1])} + let headIdx = workgroup_id.z; + let m = global_id.y; + let n = global_id.x; + + let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K; + let offsetB = headIdx * (uniforms.N * uniforms.K) + n; + + var value = ${f.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) { + tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + let batchIdx = workgroup_id.z / uniforms.num_heads; + let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads; + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + currentBatchHeadNumber * uniforms.N + n; + output[outputIdx] = value; + } + }`}}},fi=(t,e,r,n,i,a,s,o,u,l,h)=>{let f=t.outputCount>1,m=t.outputCount>2,c=f&&m?l.pastSequenceLength:0,y=c+l.kvSequenceLength,b=[l.batchSize,l.numHeads,y,l.headSize],v=s?[s,r]:[r],C=f?t.compute(hi(v,2,b,r.dataType),{inputs:v,outputs:[1]})[0]:r,x=[l.batchSize,l.numHeads,y,l.headSize],T=o?[o,n]:[n],I=m?t.compute(hi(T,2,x,n.dataType),{inputs:T,outputs:[2]})[0]:n,A=[e,C];u&&A.push(u);let R=t.compute(dl(t,e,C,u,l,h,c),{inputs:A,outputs:[-1]})[0];t.compute(ll(t,R,l.batchSize*l.numHeads*l.sequenceLength,y),{inputs:[R],outputs:[]});let z=[R,I];t.compute(cl(t,R,I,l,c),{inputs:z,outputs:[0]})},pl=(t,e)=>{let r=[e.batchSize,e.numHeads,e.sequenceLength,e.headSize],n=e.sequenceLength,i=e.inputHiddenSize,a=e.headSize,s=12,o={x:Math.ceil(e.headSize/s),y:Math.ceil(e.sequenceLength/s),z:e.batchSize*e.numHeads},u=[t.inputs[0],t.inputs[1],t.inputs[2]],l=[{type:12,data:n},{type:12,data:i},{type:12,data:a},{type:12,data:e.numHeads},{type:12,data:e.headSize},{type:12,data:e.hiddenSize},{type:12,data:e.hiddenSize+e.hiddenSize+e.vHiddenSize}],h=f=>{let m=fe("output_q",u[0].dataType,r),c=fe("output_k",u[0].dataType,r),y=fe("output_v",u[0].dataType,r),b=q("input",u[0].dataType,u[0].dims),v=q("weight",u[1].dataType,u[1].dims),C=q("bias",u[2].dataType,u[2].dims),x=b.type.storage,T=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return` + const TILE_SIZE = ${s}u; + var tileInput: array<${x}, ${s*s}>; + var tileWeightQ: array<${x}, ${s*s}>; + var tileWeightK: array<${x}, ${s*s}>; + var tileWeightV: array<${x}, ${s*s}>; + ${f.registerUniforms(T).declareVariables(b,v,C,m,c,y)} + ${f.mainStart([s,s,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 = ${x}(0); + var valueK = ${x}(0); + var valueV = ${x}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:t.inputs[0].dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:l}),getShaderSource:h},{inputs:u,outputs:[-1,-1,-1]})},Hp=(t,e)=>{let r=ul(t.inputs,e),[n,i,a]=pl(t,r);return fi(t,n,i,a,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),hl,fl,ml,jp,G0=Y(()=>{qt(),ve(),Ie(),at(),Te(),hl=(t,e)=>{if(!t||t.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,i,a)=>{let s=i.length;if(s!==n.length)throw new Error(`${a}: num dimensions != ${s}`);i.forEach((o,u)=>{if(o!==n[u])throw new Error(`${a}: dim[${u}] do not match`)})};if(t[0].dims.length>1){let n=e.format==="NHWC"?e.spatial?t[0].dims.slice(-1):t[0].dims.slice(-1).concat(t[0].dims.slice(1,t[0].dims.length-1)):t[0].dims.slice(1,e.spatial?2:void 0);r(t[1].dims,n,"Invalid input scale"),r(t[2].dims,n,"Invalid input B"),r(t[3].dims,n,"Invalid input mean"),r(t[4].dims,n,"Invalid input var")}else r(t[1].dims,[1],"Invalid input scale"),r(t[2].dims,[1],"Invalid input B"),r(t[3].dims,[1],"Invalid input mean"),r(t[4].dims,[1],"Invalid input var")},fl=(t,e)=>{let{epsilon:r,spatial:n,format:i}=e,a=t[0].dims,s=n?et(a[a.length-1]):1,o=i==="NHWC"&&a.length>1?s:1,u=G.size(a)/s,l=n,h=l?a.length:a,f=q("x",t[0].dataType,t[0].dims,s),m=q("scale",t[1].dataType,t[1].dims,o),c=q("bias",t[2].dataType,t[2].dims,o),y=q("inputMean",t[3].dataType,t[3].dims,o),b=q("inputVar",t[4].dataType,t[4].dims,o),v=fe("y",t[0].dataType,h,s),C=()=>{let T="";if(n)T=`let cOffset = ${a.length===1?"0u":i==="NHWC"?`outputIndices[${a.length-1}] / ${s}`:"outputIndices[1]"};`;else if(i==="NCHW")T=` + ${v.indicesSet("outputIndices","0","0")} + let cOffset = ${v.indicesToOffset("outputIndices")};`;else{T=`var cIndices = ${m.type.indices}(0); + cIndices[0] = outputIndices[${a.length-1}];`;for(let I=1;I` + const epsilon = ${r}; + ${T.registerUniform("outputSize","u32").declareVariables(f,m,c,y,b,v)} + ${T.mainStart()} + ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${v.offsetToIndices(`global_idx * ${s}`)}; + ${C()} + let scale = ${m.getByOffset("cOffset")}; + let bias = ${c.getByOffset("cOffset")}; + let inputMean = ${y.getByOffset("cOffset")}; + let inputVar = ${b.getByOffset("cOffset")}; + let x = ${f.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${v.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${e.epsilon}_${e.format}_${n}_${s}`,inputDependencies:l?["rank","type","type","type","type"]:void 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${a??""} + + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${u.getByOffset("global_idx")}; + ${l.setByOffset("global_idx",o)} + }`},ze=(t,e,r,n,i,a=t.dataType)=>({name:e,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:s=>yl(s,G.size(t.dims),t.dataType,a,r,n),getRunData:s=>({outputs:[{dims:t.dims,dataType:a}],dispatchGroup:{x:Math.ceil(G.size(s[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(G.size(t.dims)/4)}]})}),Yp=t=>{t.compute(ze(t.inputs[0],"Abs","abs"))},Xp=t=>{t.compute(ze(t.inputs[0],"Acos","acos"))},Qp=t=>{t.compute(ze(t.inputs[0],"Acosh","acosh"))},Jp=t=>{t.compute(ze(t.inputs[0],"Asin","asin"))},Zp=t=>{t.compute(ze(t.inputs[0],"Asinh","asinh"))},eh=t=>{t.compute(ze(t.inputs[0],"Atan","atan"))},th=t=>{t.compute(ze(t.inputs[0],"Atanh","atanh"))},rh=t=>Ge(t),nh=(t,e)=>{let r;switch(e.to){case 10:r="vec4";break;case 1:r="vec4";break;case 12:r="vec4";break;case 6:r="vec4";break;case 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a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,e.cacheKey))},ni=(t="f32")=>` +const r0: ${t} = 0.3275911; +const r1: ${t} = 0.254829592; +const r2: ${t} = -0.284496736; +const r3: ${t} = 1.421413741; +const r4: ${t} = -1.453152027; +const r5: ${t} = 1.061405429; + +fn erf_vf32(v: vec4<${t}>) -> vec4<${t}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,lh=t=>{let e=vt(t.inputs[0].dataType);t.compute(ze(t.inputs[0],"Erf",r=>`erf_vf32(${r})`,ni(e)))},dh=t=>{t.compute(ze(t.inputs[0],"Exp","exp"))},ch=t=>{t.compute(ze(t.inputs[0],"Floor","floor"))},ph=t=>{let e=vt(t.inputs[0].dataType);t.compute(ze(t.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,ni(e)))},hh=(t,e)=>{let 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abs(${t}))) / (1 + exp(-2 * abs(${t})))`,Ch=t=>{t.compute(ze(t.inputs[0],"Tanh",is))},Ls=(t="f32")=>` +const fast_gelu_a: ${t} = 0.5; +const fast_gelu_b: ${t} = 0.7978845608028654; +const fast_gelu_c: ${t} = 0.035677408136300125; + +fn tanh_v(v: vec4<${t}>) -> vec4<${t}> { + return ${is("v")}; +} +`,Ws=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,Eh=t=>{let e=vt(t.inputs[0].dataType);t.compute(ze(t.inputs[0],"FastGelu",Ws,Ls(e),void 0,t.inputs[0].dataType))},Th=(t,e)=>{let r=vt(t.inputs[0].dataType);return t.compute(ze(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},kh=t=>{t.compute(ze(t.inputs[0],"Log","log"))}}),bl,vl,Ih,q0=Y(()=>{Ie(),Te(),_o(),bl=t=>{if(t[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(t[0].dims[2]))throw new Error("hidden state should be 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bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${i.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Ih=t=>{bl(t.inputs),t.compute(vl(t.inputs))}}),$l,xl,Ut,Ah,Mh,Oh,zh,Rh,Ph,Bh,Dh,Nh,Fh,j0=Y(()=>{ve(),Ie(),Te(),$l=(t,e,r,n,i,a,s,o,u,l,h,f)=>{let m,c;typeof o=="string"?m=c=(x,T)=>`${o}((${x}),(${T}))`:typeof o=="function"?m=c=o:(m=o.scalar,c=o.vector);let y=fe("outputData",h,n.length,4),b=q("aData",u,e.length,4),v=q("bData",l,r.length,4),C;if(i)if(a){let x=G.size(e)===1,T=G.size(r)===1,I=e.length>0&&e[e.length-1]%4===0,A=r.length>0&&r[r.length-1]%4===0;x||T?C=y.setByOffset("global_idx",c(x?`${b.type.value}(${b.getByOffset("0")}.x)`:b.getByOffset("global_idx"),T?`${v.type.value}(${v.getByOffset("0")}.x)`:v.getByOffset("global_idx"))):C=` + let outputIndices = ${y.offsetToIndices("global_idx * 4u")}; + let offsetA = ${b.broadcastedIndicesToOffset("outputIndices",y)}; + let offsetB = ${v.broadcastedIndicesToOffset("outputIndices",y)}; + ${y.setByOffset("global_idx",c(s||I?b.getByOffset("offsetA / 4u"):`${b.type.value}(${b.getByOffset("offsetA / 4u")}[offsetA % 4u])`,s||A?v.getByOffset("offsetB / 4u"):`${v.type.value}(${v.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else C=y.setByOffset("global_idx",c(b.getByOffset("global_idx"),v.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let x=(T,I,A="")=>{let R=`aData[indexA${I}][componentA${I}]`,z=`bData[indexB${I}][componentB${I}]`;return` + let outputIndices${I} = ${y.offsetToIndices(`global_idx * 4u + ${I}u`)}; + let offsetA${I} = ${b.broadcastedIndicesToOffset(`outputIndices${I}`,y)}; + let offsetB${I} = ${v.broadcastedIndicesToOffset(`outputIndices${I}`,y)}; + let indexA${I} = offsetA${I} / 4u; + let indexB${I} = offsetB${I} / 4u; + let componentA${I} = offsetA${I} % 4u; + let componentB${I} = offsetB${I} % 4u; + ${T}[${I}] = ${A}(${m(R,z)}); + `};h===9?C=` + var data = vec4(0); + ${x("data",0,"u32")} + ${x("data",1,"u32")} + ${x("data",2,"u32")} + ${x("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:C=` + ${x("outputData[global_idx]",0)} + ${x("outputData[global_idx]",1)} + ${x("outputData[global_idx]",2)} + ${x("outputData[global_idx]",3)} + `}return` + ${t.registerUniform("vec_size","u32").declareVariables(b,v,y)} + + ${f??""} + + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${C} + }`},xl=(t,e,r,n,i,a,s=r.dataType)=>{let o=!G.areEqual(r.dims,n.dims),u=r.dims,l=G.size(r.dims),h=!1,f=!1,m=[o];if(o){let c=pn.calcShape(r.dims,n.dims,!1);if(!c)throw new Error("Can't perform binary op on the given tensors");u=c,l=G.size(u);let y=G.size(r.dims)===1,b=G.size(n.dims)===1,v=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,C=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;m.push(y),m.push(b),m.push(v),m.push(C);let 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select(sign(a), ${e}(1.0), round(f32(abs(b) % ${e}(2.0))) != 1.0) * ${e}(${e==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); + } + fn pow_vector_custom(a : vec4<${e}>, b : vec4<${e}>) -> vec4<${e}> { + // TODO: implement vectorized pow + return vec4<${e}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); + } + `)},Ph=t=>{Ut(t,"Sub",(e,r)=>`${e}-${r}`)},Bh=t=>{Ut(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4(${e}>${r})`},void 0,void 0,9)},Dh=t=>{Ut(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4(${e}<${r})`},void 0,void 0,9)},Nh=t=>{Ut(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4(${e}>=${r})`},void 0,void 0,9)},Fh=t=>{Ut(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4(${e}<=${r})`},void 0,void 0,9)}}),Br,Dr,Nr,yo,Wr=Y(()=>{ve(),Ie(),Br=(t,e,r="f32")=>{switch(t.activation){case"Relu":return`value = max(value, ${e}(0.0));`;case"Sigmoid":return`value = (${e}(1.0) / (${e}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${e}(${r}(uniforms.clip_min)), ${e}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${e}(0.0), min(${e}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${e}(0.0));`;case"":return"";default:throw new Error(`Unsupported activation ${t.activation}`)}},Dr=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},Nr=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},yo=t=>{let e=t?.activation||"";if(e==="HardSigmoid"){let[r,n]=t?.activation_params||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=t?.activation_params||[ho,fo];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=t?.activation_params||[.01];return{activation:e,alpha:r}}return{activation:e}}}),ht,wo,bo=Y(()=>{ht=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},wo=t=>` + ${t?"value = value + getBiasByOutputCoords(coords);":""} + `}),vo,Lh=Y(()=>{vo=t=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${t}.x), i32(${t}.y), i32(${t}.z), 1)); +} +`}),Sl,Cl,mi,ss,El,gi,Tl,$o,xi=Y(()=>{ve(),Ie(),Te(),Wr(),bo(),Sl=(t,e)=>t?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart / innerElementSize + inputCol${e?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRow + innerRow, + kStart / innerElementSize + inputCol${e?", batchIndices":""}); + `,Cl=(t,e)=>t?` + let ACached0 = mm_Asub[k * innerElementSize][localRow]; + let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; + let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; + ${e===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"} + for (var i = 0; i < rowPerThread; i = i + 1) { + acc[i] = BCached0 * ACached0[i] + acc[i]; + acc[i] = BCached1 * ACached1[i] + acc[i]; + acc[i] = BCached2 * ACached2[i] + acc[i]; + ${e===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"} + }`:` + for (var i = 0; i < rowPerThread; i = i + 1) { + let ACached = mm_Asub[tileRow + i][k]; + acc[i] = BCached0 * ACached.x + acc[i]; + acc[i] = BCached1 * ACached.y + acc[i]; + acc[i] = BCached2 * ACached.z + acc[i]; + ${e===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} + }`,mi=(t,e,r="f32",n,i=!1,a=32,s=!1,o=32)=>{let u=e[1]*t[1],l=e[0]*t[0],h=i?u:a,f=i?a:u,m=h/e[0],c=a/e[1];if(!((i&&m===4&&t[1]===4||!i&&(m===3||m===4))&&h%e[0]===0&&a%e[1]===0&&t[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${m} and workPerThread[1] ${t[1]} must be 4. + Otherwise, innerElementSize ${m} must be 3 or 4. + tileAWidth ${h} must be divisible by workgroupSize[0]${e[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return` +var mm_Asub: array, ${h/m}>, ${f}>; +var mm_Bsub: array, ${l/t[0]}>, ${a}>; + +const rowPerThread = ${t[1]}; +const colPerThread = ${t[0]}; +const innerElementSize = ${m}; +const tileInner = ${a}; + +@compute @workgroup_size(${e[0]}, ${e[1]}, ${e[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let localRow = i32(localId.y); + let tileRow = localRow * rowPerThread; + let tileCol = i32(localId.x); + + let globalRow =i32(globalId.y) * rowPerThread; + let globalCol = i32(globalId.x); + let batch = ${s?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${u}; + + let num_tiles = ${s?`${Math.ceil(o/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${s?`i32(globalId.z) * ${o}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${c}; + 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; + ${Sl(i,n)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${c}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${n?", batchIndices":""}); + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { + let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; + let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; + let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; + ${m===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${Cl(i,m)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},ss=(t,e)=>t?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${e?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${e?", batchIndices":""}); + `,El=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",gi=(t,e,r="f32",n,i=!1,a=32,s=!1,o=32,u=!1)=>{let l=t[1]*e[1],h=t[0]*e[0],f=i?l:a,m=i?a:l;if(!(m%e[1]===0&&f%e[0]===0&&a%e[1]===0))throw new Error(`tileAHight ${m} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${f} must be divisible by workgroupSize[0]${e[0]}, tileInner ${a} must be divisible by workgroupSize[1]${e[1]}`);let c=m/e[1],y=f/e[0],b=a/e[1],v=u?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${l}; + let globalColStart = i32(workgroupId.x) * ${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 inputRow = localRow; inputRow < ${m}; inputRow = inputRow + ${e[1]}) { + for (var inputCol = localCol; inputCol < ${f}; inputCol = inputCol + ${e[0]}) { + ${ss(i,n)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${e[1]}) { + for (var inputCol = localCol; inputCol < ${h}; inputCol = inputCol + ${e[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalColStart + inputCol${n?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${r}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}]; + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + + ACached * BCached[innerCol]; + } + } + } + workgroupBarrier(); + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let gRow = globalRowStart + localRow + innerRow * ${e[1]}; + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let gCol = globalColStart + localCol + innerCol * ${e[0]}; + mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); + } + } + `:` +let tileRow = i32(localId.y) * rowPerThread; +let tileCol = i32(localId.x) * colPerThread; + +let globalRow = i32(globalId.y) * rowPerThread; +let globalCol = i32(globalId.x) * colPerThread; +let globalRowStart = i32(workgroupId.y) * ${l}; + +let tileRowA = i32(localId.y) * ${c}; +let tileColA = i32(localId.x) * ${y}; +let tileRowB = i32(localId.y) * ${b}; +// 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 < ${c}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${y}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${ss(i,n)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${b}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${n?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${r}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][tileCol + inner]; + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + ${El(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 mm_Asub : array, ${m}>; + var mm_Bsub : array, ${a}>; + const rowPerThread = ${t[1]}; + const colPerThread = ${t[0]}; + const tileInner = ${a}; + +@compute @workgroup_size(${e[0]}, ${e[1]}, ${e[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let batch = ${s?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${s?`${Math.ceil(o/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${s?`i32(globalId.z) * ${o}`:"0"}; + + var acc : array, rowPerThread>; + + // Without this initialization strange values show up in acc. + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = 0.0; + } + } + ${v} + } +`},Tl=(t,e,r,n,i,a=!1)=>{let[s,o,u]=i,[l,h,f,m]=n,c=Yn(s,u),y=Yn(o,u),b=st(n[0].type.tensor),v=()=>{let x=h.rank,T=l.rank,I=`var aIndices: ${h.type.indices};`;for(let A=x-2-1,R=T-1;A>=0;A--,R--)I+=` +aIndices[${A}] = ${T>1?`batchIndices[${R}]`:"batchIndices"};`;return c.forEach(A=>{I+=` +aIndices[${A}] = 0;`}),I+=` +aIndices[${x-2}] = u32(row); + aIndices[${x-1}] = u32(colIn);`,I},C=()=>{let x=f.rank,T=l.rank,I=`var bIndices: ${f.type.indices};`;for(let A=x-2-1,R=T-1;A>=0;A--,R--)I+=` +bIndices[${A}] = ${T>1?`batchIndices[${R}]`:"batchIndices"};`;return y.forEach(A=>{I+=` +bIndices[${A}] = 0;`}),I+=` +bIndices[${x-2}] = u32(row); + bIndices[${x-1}] = u32(colIn);`,I};return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${l.type.indices}) -> ${ht(t,b)} { + var value = ${ht(t,b)}(0.0); + let col = colIn * ${t}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + ${v()} + value = ${h.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${l.type.indices}) -> ${ht(t,b)} { + var value = ${ht(t,b)}(0.0); + let col = colIn * ${t}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + ${C()} + value = ${f.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${ht(t,b)}) { + let col = colIn * ${t}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueIn; + let coords = vec3(batch, row, colIn); + ${e?`value = value + ${a?"bias[colIn]":`${ht(t,b)}(bias[row])`};`:""} + ${r} + ${m.setByIndices("vec3(coords)","value")} + } + } + `},$o=(t,e,r,n,i=!1)=>{let a=t[0].dims,s=t[1].dims,o=a.slice(0,-2),u=s.slice(0,-2),l=n?n.slice(0,-2):r.slice(0,-2),h=G.size(l),f=a[a.length-2],m=a[a.length-1],c=s[s.length-1],y=m%4===0&&c%4===0,b=f<=8?[4,1,1]:[4,4,1],v=[8,8,1],C=[Math.ceil(c/v[0]/b[0]),Math.ceil(f/v[1]/b[1]),Math.ceil(h/v[2]/b[2])],x=y?4:1,T=[...o,f,m/x],I=T.length,A=[...u,m,c/x],R=A.length,z=[h,f,c/x],P=[{type:6,data:f},{type:6,data:c},{type:6,data:m}];Dr(e,P),P.push(..._e(l,T,A));let J=["rank","rank"],K=t.length>2;K&&(P.push(..._e(t[2].dims)),J.push("rank")),P.push(..._e(z));let ue=ie=>{let ge=l.length,he=mo("batchDims",t[0].dataType,ge,1),D=st(t[0].dataType),U=q("a",t[0].dataType,I,x),de=q("b",t[1].dataType,R,x),re=fe("result",t[0].dataType,z.length,x),Q=[U,de];if(K){let Ee=i?x:1;Q.push(q("bias",t[2].dataType,t[2].dims.length,Ee))}let Z=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Nr(e,Z);let L=st(re.type.tensor),ee=Br(e,re.type.value,L),le=Tl(x,K,ee,[he,U,de,re],[o,u,l],i);return` + ${ie.registerUniforms(Z).registerInternalVariables(he).declareVariables(...Q,re)} + ${le} + ${y?mi(b,v,D,he):gi(b,v,D,he)} + `};return{name:"MatMul",shaderCache:{hint:`${b};${e.activation};${y};${i}`,inputDependencies:J},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:P}),getShaderSource:ue}}}),kl,Wh,K0=Y(()=>{ve(),Lr(),Te(),Wr(),bo(),Lh(),xi(),kl=(t,e,r,n,i=!1,a,s=4,o=4,u=4,l="f32")=>{let h=J=>{switch(J){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${l}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${J} is not supported.`)}},f=J=>{switch(J){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${J} is not supported.`)}},m=t?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,c=t?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,y=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",b=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",v=t?"row":"col",C=t?"col":"row",x=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${v} / outWidth; + let outCol = ${v} % outWidth; + + let WRow = ${C} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${C} / 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 = ${C} % inChannels; + var resData = ${ht(s,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 < ${y} && xCol >= 0 && xCol < ${b}) { + ${m} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${h(s)} + } + return resData;`,T=t?e&&n?` + let col = colIn * ${s}; + ${x}`:` + let col = colIn * ${s}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${x} + } + return ${ht(s,l)}(0.0);`:n&&r?` + let col = colIn * ${s}; + ${x}`:` + let col = colIn * ${s}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${x} + } + return ${ht(s,l)}(0.0);`,I=`${f(o)}`,A=ht(u,l),R=ht(t?s:o,l),z=ht(t?o:s,l),P=Br(a,A,l);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} { + ${t?T:I} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${z} { + ${t?I:T} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${A}) { + let col = colIn * ${u}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) + { + var value = valueIn; + let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${c} + ${wo(i)} + ${P} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},Wh=(t,e,r,n,i,a,s,o)=>{let u=e.format==="NHWC",l=u?t[0].dims[3]:t[0].dims[1],h=r[0],f=u?r[2]:r[3],m=u?r[1]:r[2],c=u?r[3]:r[1],y=u&&(l%4===0||l%3===0)&&c%4===0,b=u?c:f*m,v=u?f*m:c,C=[8,8,1],x=n<=8?[4,1,1]:[4,4,1],T=[Math.ceil(b/C[0]/x[0]),Math.ceil(v/C[1]/x[1]),Math.ceil(h/C[2]/x[2])];Qe("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${T}`);let I=y?u&&l%4!==0?3:4:1,A=C[1]*x[1],R=C[0]*x[0],z=Math.max(C[0]*I,C[1]),P=n%A===0,J=i%R===0,K=a%z===0,ue=y?[I,4,4]:[1,1,1],ie=[{type:6,data:n},{type:6,data:i},{type:6,data:a},{type:6,data:[e.pads[0],e.pads[1]]},{type:6,data:e.strides},{type:6,data:e.dilations}];Dr(e,ie),ie.push(..._e(t[0].dims,t[1].dims));let ge=["rank","rank"];s&&(ie.push(..._e(t[2].dims)),ge.push("rank")),ie.push(..._e(r));let he=D=>{let U=[{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}];Nr(e,U);let de=y?4:1,re=st(t[0].dataType),Q=` + fn setOutputAtIndex(flatIndex : i32, value : ${y?`vec4<${re}>`:re}) { + result[flatIndex] = ${y?`vec4<${re}>`:re}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${y?`vec4<${re}>`:re}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${y?"/ 4":""}, value); + }`,Z=q("x",t[0].dataType,t[0].dims.length,I===3?1:I),L=q("w",t[1].dataType,t[1].dims.length,de),ee=[Z,L],le=fe("result",t[0].dataType,r.length,de);if(s){let Ee=q("bias",t[2].dataType,t[2].dims.length,de);ee.push(Ee),Q+=` + fn getBiasByOutputCoords(coords : vec4) -> ${y?`vec4<${re}>`:re} { + return bias[coords.${u?"w":"y"}${y?"/ 4":""}]; + }`}return` + ${vo("uniforms.result_strides")} + //struct Uniforms { xShape : vec4, wShape : vec4, outShape : vec4, + // outShapeStrides: vec3, filterDims : vec2, pad : vec2, stride : vec2, + // dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32 }; + ${D.registerUniforms(U).declareVariables(...ee,le)} + ${Q} + ${kl(u,P,J,K,s,e,ue[0],ue[1],ue[2],re)} + ${y?mi(x,C,re,void 0,!u,z):gi(x,C,re,void 0,!u,z,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${I};${y};${P};${J};${K};${A};${R};${z}`,inputDependencies:ge},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:T[0],y:T[1],z:T[2]},programUniforms:ie}),getShaderSource:he}}}),Us,Uh,Y0=Y(()=>{ve(),Ie(),Te(),Hh(),Wr(),Us=(t,e,r)=>{let n=t.length>2,i=n?"value += b[output_channel];":"",a=t[0].dims,s=t[1].dims,o=s[0]/e.group,u=e.format==="NHWC",l=ai(a,s,e.dilations,e.pads,e.strides,u),h=G.size(l),f=[{type:12,data:h},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];Dr(e,f),f.push(..._e(a,s));let m=["rank","rank"];n&&(f.push(..._e(t[2].dims)),m.push("rank")),f.push(..._e(l));let c=y=>{let b=fe("output",t[0].dataType,l.length),v=st(b.type.tensor),C=Br(e,b.type.value,v),x=q("x",t[0].dataType,a.length),T=q("w",t[1].dataType,s.length),I=[x,T];n&&I.push(q("b",t[2].dataType,t[2].dims.length));let A=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:e.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return Nr(e,A),` + ${y.registerUniforms(A).declareVariables(...I,b)} + + ${y.mainStart()} + ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${b.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${u?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${u?1:2}], outputIndices[${u?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel / uniforms.output_channels_per_group; + + var value: ${b.type.value} = ${b.type.value}(0); + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { + let input_channel = group_id * uniforms.w_shape[1] + wInChannel; + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[${u?1:2}]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[${u?2:3}]) { + continue; + } + + let xVal = ${u?x.get("batch","xHeight","xWidth","input_channel"):x.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${T.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal*wVal; + } + } + } + ${i} + ${C} + ${b.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:m},getRunData:()=>({outputs:[{dims:r?r(l):l,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:f}),getShaderSource:c}},Uh=(t,e,r)=>{let n=t.length>2,i=et(r[3]),a=et(r[2]),s=G.size(r)/i/a,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/i],u=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/i],l=[r[0],r[1],r[2],r[3]/i],h=[{type:12,data:s},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];Dr(e,h),h.push(..._e(o,u,l));let f=(a-1)*e.strides[1]+u[1],m=c=>{let y=fe("output",t[0].dataType,l.length,i),b=st(y.type.tensor),v=Br(e,y.type.value,b),C=q("x",t[0].dataType,o.length,i),x=q("w",t[1].dataType,u.length,i),T=[C,x];n&&T.push(q("b",t[2].dataType,t[2].dims,i));let I=n?"value += b[output_channel];":"",A=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Nr(e,A),` + ${c.registerUniforms(A).declareVariables(...T,y)} + ${c.mainStart()} + ${c.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] / ${a}u; + let col = (index1 % width1) * ${a}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${C.type.value}, ${f}>; + var values: array<${y.type.value}, ${a}>; + 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 < ${u[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] = ${C.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${C.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${u[1]}; w_width++) { + let w_val = ${x.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${a}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${a}u; i++) { + var value = values[i]; + ${I} + ${v} + ${y.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${i};${a};${f};${u[0]};${u[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:h}),getShaderSource:m}}}),Vs,Il,Vh,Gh=Y(()=>{ve(),Ie(),xi(),Te(),Wr(),Vs=(t,e,r,n,i=!1)=>{let a=t[0].dims,s=t[1].dims,o=a[a.length-2],u=s[s.length-1],l=a[a.length-1],h=et(u),f=et(l),m=et(o),c=G.size(r)/h/m,y=t.length>2,b=n?n.slice(0,-2):r.slice(0,-2),v=[G.size(b),o,u],C=[{type:12,data:c},{type:12,data:o},{type:12,data:u},{type:12,data:l}];Dr(e,C),C.push(..._e(b,a,s)),y&&C.push(..._e(t[2].dims)),C.push(..._e(v));let x=T=>{let I=mo("batch_dims",t[0].dataType,b.length),A=q("a",t[0].dataType,a.length,f),R=q("b",t[1].dataType,s.length,h),z=fe("output",t[0].dataType,v.length,h),P=st(z.type.tensor),J=Br(e,z.type.value,P),K=[A,R],ue="";if(y){let Q=i?h:1;K.push(q("bias",t[2].dataType,t[2].dims.length,Q)),ue=`${i?`value += bias[col / ${Q}];`:`value += ${z.type.value}(bias[row + i]);`}`}let ie=a.slice(0,-2),ge=s.slice(0,-2),he=Yn(ie,b),D=Yn(ge,b),U=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Nr(e,U);let de=(Q,Z)=>{let L=Q.rank,ee=Q.name;if(L===2)return`var ${ee}_indices = ${Q.type.indices}(0u, 0u);`;let le=I.rank,Ee=`var ${ee}_indices: ${Q.type.indices};`;for(let Re=L-2-1,He=le-1;Re>=0;Re--,He--)Ee+=` +${ee}_indices[${Re}] = ${le>1?`batch_indices[${He}]`:"batch_indices"};`;return Z.forEach(Re=>{Ee+=` +${ee}_indices[${Re}] = 0;`}),Ee+=`${ee}_indices[${L-2}] = 0u; + ${ee}_indices[${L-1}] = 0u;`,Ee},re=()=>{let Q=`var a_data: ${A.type.value};`;for(let Z=0;Z; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${f}) { + ${re()} + } + for (var i = 0u; i < ${m}u; i++) { + var value = values[i]; + ${ue} + ${J} + let cur_indices = ${z.type.indices}(batch, row + i, col); + let offset = ${z.indicesToOffset("cur_indices")}; + ${z.setByOffset(`offset / ${h}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${h};${f};${m};${i}`,inputDependencies:y?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:C}),getShaderSource:x}},Il=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.")},Vh=t=>{Il(t.inputs);let e=pn.calcShape(t.inputs[0].dims,t.inputs[1].dims,!0);if(!e)throw new Error("Can't use matmul on the given tensors");let r=e[e.length-1],n=t.inputs[0].dims[t.inputs[0].dims.length-1];r<8&&n<8?t.compute(Vs(t.inputs,{activation:""},e)):t.compute($o(t.inputs,{activation:""},e))}}),ai,Ka,Al,os,Gs,Ml,Ol,Hs,Hh=Y(()=>{Ie(),K0(),xi(),Y0(),Wr(),Gh(),na(),ai=(t,e,r,n,i,a)=>{let s=t[0],o=t.slice(a?1:2,a?3:4),u=o.length,l=e[0],h=e.slice(2).map((m,c)=>m+(m-1)*(r[c]-1)),f=o.map((m,c)=>m+n[c]+n[c+u]).map((m,c)=>Math.floor((m-h[c]+i[c])/i[c]));return f.splice(0,0,s),f.splice(a?3:1,0,l),f},Ka=[2,3,1,0],Al=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let r=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],n=t[1].dims[1]*e.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(t.length===3&&(t[2].dims.length!==1||t[1].dims[0]!==t[2].dims[0]))throw new Error("invalid bias");let i=t[0].dims.length-2;if(e.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(e.strides.length!==i)throw new Error(`strides should be ${i}D`);if(e.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},os=(t,e)=>{let r=t.kernelShape.slice();for(let a=2;a{let e=yo(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],i=t.dilations,a=t.group,s=t.kernel_shape,o=t.pads,u=t.strides,l=t.w_is_const();return{autoPad:n,format:r,dilations:i,group:a,kernelShape:s,pads:o,strides:u,wIsConst:l,...e,cacheKey:`${t.format};${e.activation};`}},Ml=(t,e,r)=>{let n=os(r,e),i=r.format==="NHWC";if(r.group!==1){if(!t.adapterInfo.isArchitecture("ampere")&&i&&e[1].dims[0]===r.group&&e[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let R=ai(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,i),z=t.kernelCustomData.wT??t.compute(sr(e[1],Ka),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=z);let P=[e[0],z];e.length===3&&P.push(e[2]),t.compute(Uh(P,n,R),{inputs:P})}else t.compute(Us(e,n));return}let a=e.length===3,s=e[0].dims[i?1:2],o=e[0].dims[i?2:3],u=e[0].dims[i?3:1],l=e[1].dims[2],h=e[1].dims[3],f=ai(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,i),m=f[i?1:2],c=f[i?2:3],y=f[i?3:1],b=i&&l===s&&h===o&&r.pads[0]===0&&r.pads[1]===0;if(b||l===1&&h===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let R=f[0],z,P,J,K=[];if(i){let ge=t.kernelCustomData.wT??t.compute(sr(e[1],Ka),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=ge),b){let he=s*o*u;z=e[0].reshape([1,R,he]),P=ge.reshape([1,he,y]),J=[1,R,y]}else z=e[0].reshape([R,s*o,u]),P=ge.reshape([1,u,y]),J=[R,m*c,y];K.push(z),K.push(P)}else z=e[0].reshape([R,u,s*o]),P=e[1].reshape([1,y,u]),J=[R,y,m*c],K.push(P),K.push(z);a&&K.push(e[2]);let ue=J[2],ie=K[0].dims[K[0].dims.length-1];ue<8&&ie<8?t.compute(Vs(K,n,f,J,i),{inputs:K}):t.compute($o(K,n,f,J,i),{inputs:K});return}let v=!0,C=t.kernelCustomData.wT??t.compute(sr(e[1],Ka),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=C);let x=[e[0],C];a&&x.push(e[2]);let T=i?m*c:y,I=i?y:m*c,A=l*h*u;t.compute(Wh(x,n,f,T,I,A,a,v),{inputs:x})},Ol=(t,e)=>{let r=e.format==="NHWC",n=[t.inputs[0].reshape(r?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&n.push(t.inputs[2]);let i=[0,e.pads[0],0,e.pads[1]],a=[1].concat(e.strides),s=[1].concat(e.dilations),o=[1].concat(e.kernelShape),u=os({...e,pads:i,strides:a,dilations:s,kernelShape:o},n);t.compute(Us(n,u,l=>r?[l[0],l[2],l[3]]:[]))},Hs=(t,e)=>{Al(t.inputs,e),t.inputs[0].dims.length===3?Ol(t,e):Ml(t,t.inputs,e)}}),zl,qh,X0=Y(()=>{ve(),Lr(),Te(),Wr(),bo(),Lh(),xi(),zl=(t,e=!1,r,n,i=4)=>{let a=v=>{switch(v){case 1:return"return w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];";case 4:return` + let coord1 = vec4(coordX, coordY, col + 1, rowInner); + let coord2 = vec4(coordX, coordY, col + 2, rowInner); + let coord3 = vec4(coordX, coordY, col + 3, rowInner); + let v0 = w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))]; + let v1 = w[getIndexFromCoords4D(coord1, vec4(uniforms.w_shape))]; + let v2 = w[getIndexFromCoords4D(coord2, vec4(uniforms.w_shape))]; + let v3 = w[getIndexFromCoords4D(coord3, vec4(uniforms.w_shape))]; + return ${n}(v0, v1, v2, v3); + `;default:throw new Error(`innerElementSize ${v} is not supported.`)}},s=t?` + let coord = vec4(batch, iXR, iXC, xCh); + `:` + let coord = vec4(batch, xCh, iXR, iXC); + `,o=t?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,u=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",l=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",h=t?"row":"col",f=t?"col":"row",m=` + let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${h} / outWidth; + let outCol = ${h} % outWidth; + + let WRow = ${f} / (uniforms.filter_dims[1] * inChannels); + let WCol = ${f} / inChannels % uniforms.filter_dims[1]; + let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]); + let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]); + if (xR < 0.0 || xR >= f32(${u}) || fract(xR) > 0.0) { + return ${n}(0.0); + } + if (xC < 0.0 || xC >= f32(${l}) || fract(xC) > 0.0) { + return ${n}(0.0); + } + let iXR = i32(xR); + let iXC = i32(xC); + let xCh = ${f} % inChannels; + ${s} + return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${i}];`,c=t?` + let col = colIn * ${i}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${m} + } + return ${n}(0.0);`:` + let col = colIn * ${i}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${m} + } + return ${n}(0.0);`,y=` + let col = colIn * ${i}; + let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels); + let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1]; + if (${t?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"} && coordX >= 0 && coordY >= 0) { + let rowInner = row % inChannels; + let coord = vec4(coordX, coordY, col, rowInner); + ${a(i)} + } + return ${n}(0.0); + `,b=Br(r,n);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} { + ${t?c:y} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} { + ${t?y:c} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) { + let col = colIn * ${i}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueInput; + let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${o} + ${wo(e)} + ${b} + result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${i}] = value; + } + }`},qh=(t,e,r,n,i,a,s,o)=>{let u=e.format==="NHWC",l=u?t[0].dims[3]:t[0].dims[1],h=r[0],f=u?r[2]:r[3],m=u?r[1]:r[2],c=u?r[3]:r[1],y=u&&l%4===0&&l%3&&c%4===0,b=u?c:f*m,v=u?f*m:c,C=[8,8,1],x=n<=8?[4,1,1]:[4,4,1],T=[Math.ceil(b/C[0]/x[0]),Math.ceil(v/C[1]/x[1]),Math.ceil(h/C[2]/x[2])];Qe("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${T}`);let I=y?4:1,A=Math.max(C[0]*I,C[1]),R=y?4:1,z=[e.kernelShape[u?1:2],e.kernelShape[u?2:3]],P=[z[0]+(e.dilations[0]<=1?0:(z[0]-1)*(e.dilations[0]-1)),z[1]+(e.dilations[1]<=1?0:(z[1]-1)*(e.dilations[1]-1))],J=[P[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),P[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],K=[{type:6,data:n},{type:6,data:i},{type:6,data:a},{type:6,data:e.strides},{type:6,data:e.dilations},{type:6,data:z},{type:6,data:J}];Dr(e,K),K.push(..._e(t[0].dims,t[1].dims));let ue=["rank","rank"];s&&(K.push(..._e(t[2].dims)),ue.push("rank")),K.push(..._e(r));let ie=ge=>{let he=q("x",t[0].dataType,t[0].dims.length,R),D=q("w",t[1].dataType,t[1].dims.length,1),U=fe("result",t[0].dataType,r.length,R),de=[he,D],re="";if(s){let L=q("bias",t[2].dataType,t[2].dims.length,R);de.push(L),re+=` + fn getBiasByOutputCoords(coords : vec4) -> ${L.type.value} { + return bias[coords.${u?"w":"y"}${y?"/ 4":""}]; + }`}let Q=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:z.length},{name:"pads",type:"i32",length:J.length}];Nr(e,Q);let Z=st(t[0].dataType,1);if(Z!=="f16"&&Z!=="f32")throw new Error(`elemType ${Z} is not supported.`);return` + ${vo("uniforms.result_strides")} + ${ge.registerUniforms(Q).declareVariables(...de,U)}; + ${re} + ${zl(u,s,e,he.type.value,I)} + ${y?mi(x,C,Z,void 0,!u,A):gi(x,C,Z,void 0,!u,A,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${x};${C};${y}`,inputDependencies:ue},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:T[0],y:T[1],z:T[2]},programUniforms:K}),getShaderSource:ie}}}),Rl,qs,Q0=Y(()=>{ve(),Lr(),Ie(),Te(),Rl=(t,e,r,n,i,a=!1,s,o,u=!1)=>{let l=u?1:2,h=u?2:3,f=u?3:1,m=a?2:1,c=` + fn setOutputAtIndex(flatIndex : u32, value : ${a?`vec4<${s}>`:s}) { + result[flatIndex] = ${a?`vec4<${s}>`:s}(value); + }`;n&&(c+=` + fn getBiasByOutputCoords(coords : vec4) -> ${a?`vec4<${s}>`:s} { + return bias[coords.${u?"w":"y"}${a?"/ 4":""}]; + }`);let y=a?4:1,b=q("W",e[1].dataType,e[1].dims.length,y),v=q("Dy",e[0].dataType,e[0].dims.length,y),C=[v,b];n&&C.push(q("bias",e[2].dataType,[r[f]].length,y));let x=fe("result",e[0].dataType,r.length,y),T=`{ + let batch: u32 = ${i?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1]; + let r = ${i?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1]; + let c = ${i?"global_id.y":"workgroup_id.y"} * ${m}; + let d1: u32 = ${i?"global_id.x":"workgroup_id.x"} * 4; + + let dyCorner = vec2(i32(r), i32(c)) - vec2(uniforms.pads); + + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd: array, ${m}>; + for (var i = 0; i < ${m}; i++) { + dotProd[i] = vec4<${s}>(0.0); + } + for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) { + var dyR = (${s}(dyCorner.x) + ${s}(wR)) / ${s}(uniforms.strides.x); + let wRPerm = uniforms.filter_dims[0] - 1 - wR; + if (dyR < 0.0 || dyR >= ${s}(uniforms.Dy_shape[1]) || + fract(dyR) > 0.0 || wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) { + let dyC = (${s}(dyCorner.y) + ${s}(wC)) / ${s}(uniforms.strides.y); + let dyC2 = (${s}(dyCorner.y) + 1.0 + ${s}(wC)) / ${s}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims[1] - 1 - wC; + if (wCPerm < 0) { + continue; + } + var bDyCVal = true; + var bDyCVal2 = true; + if (dyC < 0.0 || dyC >= ${s}(uniforms.Dy_shape[2]) || + fract(dyC) > 0.0) { + bDyCVal = false; + } + if (dyC2 < 0.0 || dyC2 >= ${s}(uniforms.Dy_shape[2]) || + fract(dyC2) > 0.0) { + bDyCVal2 = false; + } + + let idyC: u32 = u32(dyC); + let idyC2: u32 = u32(dyC2); + if (bDyCVal && bDyCVal2) { + let d2Length = uniforms.Dy_shape[3]; + for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${v.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${s}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + + xValue = ${v.get("batch","idyR","idyC2","d2")}; + + dotProd[1] = dotProd[1] + vec4<${s}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + } + } else if (bDyCVal) { + let d2Length = uniforms.Dy_shape[${f}]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${v.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${s}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + } + } else if (bDyCVal2) { + let d2Length = uniforms.Dy_shape[3]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${v.get("batch","idyR","idyC2","d2")}; + let tmpval = vec4<${s}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[1] = dotProd[1] + tmpval; + } + } + } + } + + for (var i: u32 = 0; i < ${m}; i = i + 1) { + let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${s}>(0.0)`}; + ${x.set("batch","r","c + i","d1","value")}; + } + }`,I=` + let outputIndices = ${x.offsetToIndices("global_idx")}; + let batch = ${x.indicesGet("outputIndices",0)}; + let d1 = ${x.indicesGet("outputIndices",f)}; + let r = ${x.indicesGet("outputIndices",l)}; + let c = ${x.indicesGet("outputIndices",h)}; + let dyCorner = vec2(i32(r), i32(c)) - uniforms.pads; + let dyRCorner = dyCorner.x; + let dyCCorner = dyCorner.y; + let groupId = d1 / uniforms.output_channels_per_group; + let wOutChannel = d1 - groupId * uniforms.output_channels_per_group; + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd = ${s}(0.0); + for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${s}(dyRCorner) + ${s}(wR)) / ${s}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${s}(uniforms.Dy_shape[${l}]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { + if (wC % uniforms.dilations.y != 0) { + continue; + } + let dyC = (${s}(dyCCorner) + ${s}(wC)) / ${s}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${s}(uniforms.Dy_shape[${h}]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC: u32 = u32(dyC); + var inputChannel = groupId * uniforms.input_channels_per_group; + for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) { + let xValue = ${u?v.get("batch","idyR","idyC","inputChannel"):v.get("batch","inputChannel","idyR","idyC")}; + let wValue = ${b.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")}; + dotProd = dotProd + xValue * wValue; + inputChannel = inputChannel + 1; + } + } + } + let value = dotProd + ${n?"bias[d1]":`${s}(0.0)`}; + ${x.setByOffset("global_idx","value")}; + `;return` + ${t.registerUniforms(o).declareVariables(...C,x)} + ${c} + + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${a?T:I}}`},qs=(t,e,r)=>{let n=t.length>2,i=e.outputShape,a=G.size(i),s=[Math.ceil(a/64),1,1];Qe("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${s}`);let o=e.format==="NHWC",u=["rank","rank"],l=[e.strides[0],e.strides[1]],h=[e.kernelShape[o?1:2],e.kernelShape[o?2:3]],f=[e.dilations[0],e.dilations[1]],m=[h[0]+(e.dilations[0]<=1?0:(e.kernelShape[o?1:2]-1)*(e.dilations[0]-1)),h[1]+(e.dilations[1]<=1?0:(e.kernelShape[o?2:3]-1)*(e.dilations[1]-1))],c=[m[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),m[1]-1-Math.floor(e.pads[1]+e.pads[3])/2],y=!1,b=e.group,v=t[1].dims,C=v[0]/b,x=v[1],T=[{type:12,data:a},{type:12,data:l},{type:12,data:h},{type:12,data:f},{type:12,data:m},{type:6,data:c},{type:12,data:C},{type:12,data:x},..._e(t[0].dims,t[1].dims)];n&&(T.push(..._e(t[2].dims)),u.push("rank")),T.push(..._e(i));let I=s[1]===1&&s[2]===1,A=R=>{let z=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:l.length},{name:"filter_dims",type:"u32",length:h.length},{name:"dilations",type:"u32",length:h.length},{name:"effective_filter_dims",type:"u32",length:m.length},{name:"pads",type:"i32",length:c.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],P=st(t[0].dataType);return`${Rl(R,t,i,n,I,y,P,z,o)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${e.cacheKey};`,inputDependencies:u},getRunData:()=>({dispatchGroup:{x:s[0],y:s[1],z:s[2]},outputs:[{dims:r?r(i):i,dataType:t[0].dataType}],programUniforms:T}),getShaderSource:A}}}),Pl,Bl,Dl,us,jh,Nl,Fl,Ll,Wl,Kh,J0=Y(()=>{X0(),Q0(),Wr(),na(),Pl=(t,e,r,n,i,a)=>(t-1)*e+r+(n-1)*i+1-a,Bl=(t,e,r,n,i)=>{let a=Math.floor(t/2);e==="SAME_UPPER"?(r[n]=a,r[i]=t-a):e==="SAME_LOWER"&&(r[n]=t-a,r[i]=a)},Dl=(t,e,r,n,i,a,s,o,u,l)=>{let h=t.length-2,f=l.length===0;if(u.length===0)for(let y=0;y{let r=t.kernelShape.slice();if(t.kernelShape.length===0||t.kernelShape.reduce((f,m)=>f*m,1)===0){r.length=0;for(let f=2;ff+m,0)===0){let f=e[0].dims.length-2;u=new Array(f).fill(1)}let l=t.strides.slice();if(l.reduce((f,m)=>f+m,0)===0){let f=e[0].dims.length-2;l=new Array(f).fill(1)}Dl(o,r,u,t.autoPad,t.group,i,l,n,s,a);let h=Object.assign({},t);return Object.assign(h,{kernelShape:r,pads:i,outputPadding:s,outputShape:a,dilations:u,strides:l}),h},jh=t=>{let e=yo(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof t.autoPad>"u"?0:t.autoPad],i=t.dilations,a=t.group,s=t.kernelShape,o=t.pads,u=t.strides,l=t.wIsConst(),h=t.outputPadding,f=t.outputShape;return{autoPad:n,format:r,dilations:i,group:a,kernelShape:s,outputPadding:h,outputShape:f,pads:o,strides:u,wIsConst:l,...e,cacheKey:`${t.format};${e.activation};`}},Nl=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let r=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],n=t[1].dims[0];if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let i=t[1].dims[1]*e.group;if(t.length===3&&(t[2].dims.length!==1||t[2].dims[0]!==i))throw new Error("invalid bias");let a=t[0].dims.length-2;if(e.dilations.reduce((s,o)=>s+o,0)>0&&e.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(e.strides.reduce((s,o)=>s+o,0)>0&&e.strides.length!==a)throw new Error(`strides should be ${a}D`);if(e.pads.reduce((s,o)=>s+o,0)>0&&e.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(e.outputPadding.length!==a&&e.outputPadding.length!==0)throw new Error(`output_padding should be ${a}D`);if(e.kernelShape.reduce((s,o)=>s+o,0)>0&&e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if(e.outputShape.length!==0&&e.outputShape.length!==t[0].dims.length-2)throw new Error("invalid output shape")},Fl=[2,3,1,0],Ll=(t,e,r)=>{let n=us(r,e),i=r.format==="NHWC",a=n.outputShape,s=a[i?3:1],o=e[0].dims[i?3:1];if(n.group!==1||s===1&&o===1){t.compute(qs(e,n));return}let u=a[i?1:2],l=a[i?2:3],h=e[1].dims[2],f=e[1].dims[3],m=i?u*l:s,c=i?s:u*l,y=h*f*o,b=!0,v=t.kernelCustomData.wT??t.compute(sr(e[1],Fl),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=v);let C=[e[0],v],x=e.length===3;x&&(!i&&e[2].dims.length===1?C.push(e[2].reshape([e[2].dims[0],1,1])):C.push(e[2])),t.compute(qh(C,n,a,m,c,y,x,b),{inputs:C})},Wl=(t,e)=>{let r=e.format==="NHWC",n=[t.inputs[0].reshape(r?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&n.push(t.inputs[2]);let i=e.kernelShape;(i.length===0||i[0]===0)&&(i=[t.inputs[1].dims[2]]);let a=e.dilations;(a.length===0||a[0]===0)&&(a=[1]);let s=e.strides;(s.length===0||s[0]===0)&&(s=[1]);let o=e.pads;o.length===0&&(o=[0,0]),o=[0,o[0],0,o[1]],s=[1].concat(s),a=[1].concat(a),i=[1].concat(i);let u=us({...e,pads:o,strides:s,dilations:a,kernelShape:i},n);t.compute(qs(n,u,l=>r?[l[0],l[2],l[3]]:[l[0],l[1],l[3]]))},Kh=(t,e)=>{Nl(t.inputs,e),t.inputs[0].dims.length===3?Wl(t,e):Ll(t,t.inputs,e)}}),Ul,Yh,Xh,Z0=Y(()=>{ve(),Ie(),at(),Te(),Ul=(t,e,r,n)=>{let i=G.size(e),a=e.length,s=q("input",t,a),o=fe("output",t,a),u=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),l=G.normalizeAxis(u,a),h=f=>{let m=` i32(${s.indicesGet("inputIndices","uniforms.axis")}) `,c=xe("uniforms.input_shape","uniforms.axis",a),y=n.reverse?m+(n.exclusive?" + 1":""):"0",b=n.reverse?c:m+(n.exclusive?"":" + 1");return` + ${f.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(s,o)} + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${o.offsetToIndices("global_idx")}; + var sum = ${o.type.value}(0); + let first : i32 = ${y}; + let last : i32 = ${b}; + for (var i : i32 = first; i < last; i++) { + ${s.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${s.getByIndices("inputIndices")}; + } + ${o.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:e,dataType:t}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},{type:12,data:l},..._e(e,e)]}),getShaderSource:h}},Yh=(t,e)=>{let r=t.inputs[0].dims,n=t.inputs[0].dataType,i=t.inputs[1];t.compute(Ul(n,r,i,e),{inputs:[0]})},Xh=t=>{let e=t.exclusive===1,r=t.reverse===1;return Ge({exclusive:e,reverse:r})}}),Vl,Gl,Hl,Qh,Jh,e_=Y(()=>{ve(),Ie(),at(),Te(),Vl=t=>{if(!t||t.length!==1)throw new Error("DepthToSpace requires 1 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}`;return{name:"DepthToSpace",shaderCache:{hint:`${t.dims};${e.blocksize};${e.mode}`,inputDependencies:["rank"]},getRunData:C=>{let x=u?[r,n*l,i*l,a/l**2]:[r,a/l**2,n*l,i*l],T=G.size(x),I=f.dims,A=G.sortBasedOnPerm(I,o);return{outputs:[{dims:x,dataType:C[0].dataType}],dispatchGroup:{x:Math.ceil(T/64)},programUniforms:[{type:12,data:T},..._e(I,A)]}},getShaderSource:v}},Qh=(t,e)=>{Vl(t.inputs),t.compute(Hl(t.inputs[0],e))},Jh=t=>Ge({blocksize:t.blocksize,mode:t.mode,format:t.format})}),Ya,Dn,ls,ql,jl,Kl,Yl,ds,Xl,Zh,ef,t_=Y(()=>{ve(),Ie(),at(),Te(),Ya="[a-zA-Z]|\\.\\.\\.",Dn="("+Ya+")+",ls="^"+Dn+"$",ql="("+Dn+",)*"+Dn,jl="^"+ql+"$",Kl=class{constructor(t=-1){this.symbolToIndices=new Map,this.inputIndex=t}addSymbol(t,e){let r=this.symbolToIndices.get(t);r===void 0?r=[e]:r.push(e),this.symbolToIndices.set(t,r)}},Yl=class{constructor(t,e){this.equation=e,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new 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n={count:1,dimValue:e,inputIndices:[r]};this.symbolToInfo.set(t,n)}processTerm(t,e,r,n=-1){let i=r.length,a=!1,s=[],o=0;if(!t.match(RegExp(ls))&&!e&&t!=="")throw new Error("Invalid LHS term");let u=t.match(RegExp(Ya,"g")),l=new Kl(n);return u?.forEach((h,f)=>{if(h==="..."){if(a)throw new Error("Only one ellipsis is allowed per input term");a=!0;let m=i-u.length+1;if(m<0)throw new Error("Ellipsis out of bounds");if(s=r.slice(o,o+m),this.hasEllipsis){if(this.ellipsisDims.length!==s.length||this.ellipsisDims.toString()!==s.toString())throw new Error("Ellipsis dimensions mismatch")}else if(e)this.hasEllipsis=!0,this.ellipsisDims=s;else throw new Error("Ellipsis must be specified in the LHS");for(let c=0;ct+"_max",Xl=(t,e,r,n)=>{let i=t.map(l=>l.length).map((l,h)=>q(`input${h}`,e,l)),a=G.size(n),s=fe("output",e,n.length),o=[...r.symbolToInfo.keys()].filter(l=>!r.rhs.symbolToIndices.has(l)),u=l=>{let h=[],f="var prod = 1.0;",m="var sum = 0.0;",c="sum += 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${l.registerUniforms(o.map(I=>({name:`${ds(I)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...i,s)} + + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${s.offsetToIndices("global_idx")}; + ${i.map((I,A)=>`var input${A}Indices: ${i[A].type.indices};`).join(` +`)} + ${T.join(` +`)}; + ${s.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:t.map(()=>"rank")},getRunData:()=>{let l=o.filter(f=>r.symbolToInfo.has(f)).map(f=>({type:12,data:r.symbolToInfo.get(f)?.dimValue||0}));l.push({type:12,data:a});let h=t.map((f,m)=>[..._e(f)]).reduce((f,m)=>f.concat(m),l);return h.push(..._e(n)),{outputs:[{dims:n,dataType:e}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h}},getShaderSource:u}},Zh=(t,e)=>{let r=new Yl(t.inputs,e.equation),n=r.outputDims,i=t.inputs.map((a,s)=>a.dims);t.compute(Xl(i,t.inputs[0].dataType,r,n))},ef=t=>{let e=t.equation.replace(/\s+/g,"");return Ge({equation:e})}}),Ql,cs,Jl,Zl,tf,r_=Y(()=>{ve(),Ie(),Te(),Ql=t=>{if(!t||t.length!==2)throw new Error("Expand requires 2 input.");let e=t[0].dims,r=Array.from(t[1].getBigInt64Array(),Number),n=r.length{let r=t.length-e.length,n=[];for(let i=0;it.length>e.length?cs(t,e):cs(e,t),Zl=t=>{let e=t[0].dims,r=Array.from(t[1].getBigInt64Array(),Number),n=Jl(e,r),i=t[0].dataType,a=i===9?4:1,s=Math.ceil(G.size(n)/a),o=l=>{let h=q("input",i,e.length,a),f=fe("output",i,n.length,a),m;if(i===9){let c=(y,b,v="")=>` + let outputIndices${b} = ${f.offsetToIndices(`outputOffset + ${b}u`)}; + let offset${b} = ${h.broadcastedIndicesToOffset(`outputIndices${b}`,f)}; + let index${b} = offset${b} / 4u; + let component${b} = offset${b} % 4u; + ${y}[${b}] = ${v}(${h.getByOffset(`index${b}`)}[component${b}]); + `;m=` + let outputOffset = global_idx * ${a}; + var data = vec4(0); + ${c("data",0,"u32")} + ${c("data",1,"u32")} + ${c("data",2,"u32")} + ${c("data",3,"u32")} + ${f.setByOffset("global_idx","data")} + }`}else m=` + let outputIndices = ${f.offsetToIndices("global_idx")}; + let inputOffset = ${h.broadcastedIndicesToOffset("outputIndices",f)}; + ${f.setByOffset("global_idx",h.getByOffset("inputOffset"))} + }`;return` + ${l.registerUniform("vec_size","u32").declareVariables(h,f)} + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${m}`},u=[{type:12,data:s},..._e(e,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:o,getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:u})}},tf=t=>{Ql(t.inputs),t.compute(Zl(t.inputs),{inputs:[0]})}}),ed,rf,n_=Y(()=>{ve(),Ie(),Te(),_o(),ed=t=>{let e=t[0].dataType,r=G.size(t[0].dims),n=G.size(t[1].dims),i=n%4===0,a=s=>{let o=q("x",e,[1],4),u=q("bias",e,[1],4),l=fe("y",e,[1],4),h=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],f=c=>` + let bias${c}_offset: u32 = (global_idx * 4 + ${c}) % uniforms.bias_size; + let bias${c} = ${u.getByOffset(`bias${c}_offset / 4`)}[bias${c}_offset % 4];`,m=i?` + let bias = ${u.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${f(0)}${f(1)}${f(2)}${f(3)} + let bias = ${o.type.value}(bias0, bias1, bias2, bias3);`;return`${s.registerUniforms(h).declareVariables(o,u,l)} + + ${Ls(vt(e))} + + ${s.mainStart(hn)} + ${s.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${o.getByOffset("global_idx")}; + ${m} + let x_in = x + bias; + ${l.setByOffset("global_idx",Ws("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${i}`,inputDependencies:["type","type"]},getShaderSource:a,getRunData:s=>({outputs:[{dims:s[0].dims,dataType:s[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/hn/4)}})}},rf=t=>{t.inputs.length<2||G.size(t.inputs[1].dims)===0?Eh(t):t.compute(ed(t.inputs))}}),td,rd,nf,af,a_=Y(()=>{ve(),Ie(),at(),Te(),td=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},rd=(t,e)=>{let r=t[0].dims,n=t[1].dims,i=r.length,a=G.normalizeAxis(e.axis,i),s=r.slice(0);s.splice(a,1,...n);let o=r[a],u=t[0].dataType===9?4:1,l=Math.ceil(G.size(s)/u),h=[{type:12,data:l},{type:6,data:o},{type:12,data:a},..._e(t[0].dims,t[1].dims,s)],f=m=>{let c=q("data",t[0].dataType,t[0].dims.length,u),y=q("inputIndices",t[1].dataType,t[1].dims.length),b=fe("output",t[0].dataType,s.length,u),v=x=>{let T=n.length,I=`var indicesIndices${x} = ${y.type.indices}(0);`;for(let A=0;A1?`indicesIndices${x}[${A}]`:`indicesIndices${x}`} = ${s.length>1?`outputIndices${x}[uniforms.axis + ${A}]`:`outputIndices${x}`};`;I+=` + var idx${x} = ${y.getByIndices(`indicesIndices${x}`)}; + if (idx${x} < 0) { + idx${x} = idx${x} + uniforms.axisDimLimit; + } + var dataIndices${x} : ${c.type.indices}; + `;for(let A=0,R=0;A1?`dataIndices${x}[${A}]`:`dataIndices${x}`} = u32(idx${x});`,R+=T):(I+=`${i>1?`dataIndices${x}[${A}]`:`dataIndices${x}`} = ${s.length>1?`outputIndices${x}[${R}]`:`outputIndices${x}`};`,R++);return I},C;if(t[0].dataType===9){let x=(T,I,A="")=>` + let outputIndices${I} = ${b.offsetToIndices(`outputOffset + ${I}u`)}; + ${v(I)}; + let offset${I} = ${c.indicesToOffset(`dataIndices${I}`)}; + let index${I} = offset${I} / 4u; + let component${I} = offset${I} % 4u; + ${T}[${I}] = ${A}(${c.getByOffset(`index${I}`)}[component${I}]); + `;C=` + let outputOffset = global_idx * ${u}; + var value = vec4(0); + ${x("value",0,"u32")} + ${x("value",1,"u32")} + ${x("value",2,"u32")} + ${x("value",3,"u32")} + ${b.setByOffset("global_idx","value")} + `}else C=` + let outputIndices = ${b.offsetToIndices("global_idx")}; + ${v("")}; + let value = ${c.getByIndices("dataIndices")}; + ${b.setByOffset("global_idx","value")}; + `;return` + ${m.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(c,y,b)} + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${C} + }`};return{name:"Gather",shaderCache:{hint:e.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:h}),getShaderSource:f}},nf=t=>Ge({axis:t.axis}),af=(t,e)=>{let r=t.inputs;td(r),t.compute(rd(t.inputs,e))}}),nd,ad,sf,of,i_=Y(()=>{ve(),Ie(),at(),Te(),nd=t=>{if(!t||t.length!==2)throw new Error("GatherElements requires 2 inputs.");if(t[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(t[0].dims.length!==t[1].dims.length)throw new Error(`GatherElements requires that the data input and + indices input tensors be of same rank.`)},ad=(t,e)=>{let r=t[0].dims,n=t[0].dataType,i=r.length,a=t[1].dims,s=t[1].dataType,o=G.normalizeAxis(e.axis,i),u=r[o],l=a.slice(0),h=G.size(l),f=q("input",n,i),m=q("indicesInput",s,a.length),c=fe("output",n,l.length),y=[{type:12,data:h},{type:6,data:u},{type:12,data:o}];return y.push(..._e(r,a,l)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:l,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:y}),getShaderSource:b=>` + ${b.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(f,m,c)} + ${b.mainStart()} + ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${c.offsetToIndices("global_idx")}; + + var idx = ${m.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${f.type.indices}(outputIndices); + ${f.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${f.getByIndices("inputIndices")}; + + ${c.setByOffset("global_idx","value")}; + }`}},sf=t=>Ge({axis:t.axis}),of=(t,e)=>{let r=t.inputs;nd(r),t.compute(ad(t.inputs,e))}}),id,sd,uf,lf,s_=Y(()=>{ve(),Ie(),Te(),id=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},sd=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[i,a,s]=gp.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),o=[i,a];if(!o)throw new Error("Can't use gemm on the given tensors");let u=G.size(o),l=[{type:12,data:u},{type:12,data:i},{type:12,data:a},{type:12,data:s},{type:1,data:e.alpha},{type:1,data:e.beta}],h=["type","type"];t.length===3&&(l.push(..._e(t[2].dims)),h.push("rank")),l.push(..._e(o));let f=m=>{let c="";e.transA&&e.transB?c="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?c="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?c="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(c="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let y=e.alpha===1?"":"value *= uniforms.alpha;",b=q("a",t[0].dataType,t[0].dims),v=q("b",t[1].dataType,t[1].dims),C=b.type.value,x=null,T=[b,v];t.length===3&&(x=q("c",t[2].dataType,t[2].dims.length),T.push(x));let I=fe("output",t[0].dataType,o.length);T.push(I);let A=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return` + ${m.registerUniforms(A).declareVariables(...T)} + + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${C}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${c} + } + + ${y} + ${x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",I)}; value += ${C}(uniforms.beta) * ${x.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:f}},uf=t=>{let e=t.transA,r=t.transB,n=t.alpha,i=t.beta;return{transA:e,transB:r,alpha:n,beta:i,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},lf=(t,e)=>{id(t.inputs),t.compute(sd(t.inputs,e))}}),od,ud,ld,df,o_=Y(()=>{ve(),Ie(),Te(),od=(t,e)=>{let r=t[0].dims,n=r,i=2,a=G.sizeToDimension(r,i),s=G.sizeFromDimension(r,i),o=et(s),u=s/o,l=[r[0],r[1],u],h=["rank","type","type"],f=[{type:12,data:s},{type:12,data:u}];f.push(..._e(l,l));let m=c=>{let y=q("x",t[0].dataType,l.length,o),b=q("scale",t[1].dataType,t[1].dims),v=q("bias",t[2].dataType,t[2].dims),C=fe("output",t[0].dataType,l.length,o),x=[y,b,v,C],T=y.type.value,I=o===1?"f32":`vec${o}`,A=64,R=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return` + var meanShared : f32; + var squaredNormShared : f32; + var workgroupShared : array<${I}, ${A}>; + const workgroupSize = ${A}u; + ${c.registerUniforms(R).declareVariables(...x)} + ${c.mainStart(A)} + let norm = global_idx / workgroupSize; + let batch = norm / uniforms.x_shape[1]; + let channel = norm % uniforms.x_shape[1]; + let localIndex = local_id.x; + + // initialize workgroup memory + var initial = ${I}(0); + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + initial = initial + ${I}(${y.get("batch","channel","h")}); + } + workgroupShared[localIndex] = initial; + workgroupBarrier(); + + // Calculate the mean of current channel data. + for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) { + if (localIndex < currSize) { + workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize]; + } + workgroupBarrier(); + } + if (localIndex == 0) { + meanShared = ${mr("workgroupShared[0]",o)} / f32(uniforms.normSize); + } + workgroupBarrier(); + + // reinitialize workgroup memory. + initial = ${I}(0); + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + let deviation = ${I}(${y.get("batch","channel","h")}) - ${I}(meanShared); + initial = initial + deviation * deviation; + } + workgroupShared[localIndex] = initial; + workgroupBarrier(); + + // Calculate the sum of square of deviation of current channel data. + for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) { + if (localIndex < currSize) { + workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize]; + } + workgroupBarrier(); + } + if (localIndex == 0) { + squaredNormShared = ${mr("workgroupShared[0]",o)}; + } + workgroupBarrier(); + + let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${e.epsilon})); + let channelScale = invStdDev * f32(${b.getByOffset("channel")}); + let channelShift = f32(${v.getByOffset("channel")}) - meanShared * channelScale; + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + let value = ${y.get("batch","channel","h")} * ${T}(${I}(channelScale)) + ${T}(${I}(channelShift)); + ${C.set("batch","channel","h","value")}; + } + }`};return{name:"InstanceNormalization",shaderCache:{hint:`${e.epsilon};${o}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:a},programUniforms:f}),getShaderSource:m}},ud=(t,e,r,n,i,a,s,o)=>{let u=et(s),l=64,h=u===1?"vec2f":`mat2x${u}f`,f=u===1?"f32":`vec${u}f`,m=(R,z)=>`${h}(${R}, ${z})`,c=i*s/u,y=Math.ceil(a/l),b=["type"],v=[{type:12,data:y},{type:12,data:a},{type:12,data:Math.floor(s/u)},{type:12,data:Math.floor(a*s/u)}],C=R=>{let z=q("input",e.dataType,e.dims,u);return` + ${R.declareVariables(z)} + @group(0) @binding(1) var output : array<${h}>; + struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32}; + @group(0) @binding(2) var uniforms: Uniforms; + + ${R.mainStart(l)} + let currentImageNumber = global_idx / ${l} / uniforms.C; + let currentChannelNumber = (global_idx / ${l}) % uniforms.C; + let wgOffset = local_id.x * uniforms.wg_size; + if (wgOffset >= uniforms.H) { + return; + } + let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H); + + let offset = currentImageNumber * uniforms.image_size + currentChannelNumber; + var sum = ${ir("f32",u)}; + var squaredSum = ${ir("f32",u)}; + for (var i: u32 = wgOffset; i < wgMax; i++) { + let value = ${f}(input[offset + i * uniforms.C]); + sum += value; + squaredSum += value * value; + } + output[global_idx] = ${m("sum","squaredSum")}; + }`},x=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${u}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:[i,s,l,2],dataType:1}],dispatchGroup:{x:i*s/u},programUniforms:v}),getShaderSource:C},{inputs:[e],outputs:[-1]})[0],T=[{type:12,data:c},{type:12,data:a},{type:12,data:Math.floor(s/u)},{type:12,data:Math.floor(l*s/u)}],I=["type","type","type"],A=R=>{let z=q("scale",r.dataType,r.dims,u),P=q("bias",n.dataType,n.dims,u);return` + @group(0) @binding(0) var input : array<${h}>; + @group(0) @binding(1) var scale : array<${z.type.storage}>; + @group(0) @binding(2) var bias : array<${P.type.storage}>; + @group(0) @binding(3) var output : array<${h}>; + struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32}; + @group(0) @binding(4) var uniforms: Uniforms; + + ${R.mainStart()} + ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")} + let currentImageNumber = global_idx / uniforms.C; + let currentChannelNumber = global_idx % uniforms.C; + + let offset = currentImageNumber * uniforms.image_size; + var sum = ${ir("f32",u)}; + var squaredSum = ${ir("f32",u)}; + for (var i: u32 = 0; i < min(${l}, uniforms.H); i++) { + let value = input[offset + i + currentChannelNumber * ${l}]; + sum += value[0]; + squaredSum += value[1]; + } + sum = sum / f32(uniforms.H); + squaredSum = squaredSum / f32(uniforms.H); + let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${o})); + let channelScale = invStdDev * ${f}(scale[currentChannelNumber]); + let channelShift = ${f}(bias[currentChannelNumber]) - sum * channelScale; + + output[global_idx] = ${m("channelScale","channelShift")}; + }`};return t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${u};${o}`,inputDependencies:I},getRunData:()=>({outputs:[{dims:[i,s,2],dataType:1}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:T}),getShaderSource:A},{inputs:[x,r,n],outputs:[-1]})[0]},ld=(t,e,r)=>{let n=e[0].dims,i=n,a=n[0],s=n[n.length-1],o=G.sizeFromDimension(n,1)/s,u=et(s),l=G.size(i)/u,h=[{type:12,data:o},{type:12,data:Math.floor(s/u)}],f=["type","type"],m=ud(t,e[0],e[1],e[2],a,o,s,r.epsilon),c=y=>{let b=st(e[0].dataType),v=u===1?"vec2f":`mat2x${u}f`,C=u===1?b:`vec${u}<${b}>`,x=q("input",e[0].dataType,e[0].dims,u),T=fe("output",e[0].dataType,i,u);return` + @group(0) @binding(0) var input : array<${x.type.storage}>; + @group(0) @binding(1) var scaleInput : array<${v}>; + @group(0) @binding(2) var output : array<${T.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${y.mainStart()} + let currentImageNumber = global_idx / (uniforms.C * uniforms.H); + let currentChannelNumber = global_idx % uniforms.C; + + let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber; + let scale = scaleInput[scaleOffset]; + output[global_idx] = fma(input[global_idx], ${C}(scale[0]), ${C}(scale[1])); + }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${u}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:h}),getShaderSource:c},{inputs:[e[0],m]})},df=(t,e)=>{e.format==="NHWC"?ld(t,t.inputs,e):t.compute(od(t.inputs,e))}}),dd,cd,cf,u_=Y(()=>{ve(),Ie(),Te(),dd=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},cd=(t,e,r)=>{let n=e.simplified,i=t[0].dims,a=t[1],s=!n&&t[2],o=i,u=G.normalizeAxis(e.axis,i.length),l=G.sizeToDimension(i,u),h=G.sizeFromDimension(i,u),f=G.size(a.dims),m=s?G.size(s.dims):0;if(f!==h||s&&m!==h)throw new Error(`Size of X.shape()[axis:] == ${h}. + Size of scale and bias (if provided) must match this. + Got scale size of ${f} and bias size of ${m}`);let c=[];for(let A=0;A1,x=r>2,T=A=>{let R=st(t[0].dataType),z=[q("x",t[0].dataType,t[0].dims,y),q("scale",a.dataType,a.dims,y)];s&&z.push(q("bias",s.dataType,s.dims,y)),z.push(fe("output",t[0].dataType,o,y)),C&&z.push(fe("mean_data_output",1,c)),x&&z.push(fe("inv_std_output",1,c));let P=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${A.registerUniforms(P).declareVariables(...z)} + ${A.mainStart()} + ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${ir("f32",y)}; + var mean_square_vector = ${ir("f32",y)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${un(R,y,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${mr("mean_vector",y)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${mr("mean_square_vector",y)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${un(R,y,"x[j + offset]")}; + let f32scale = ${un(R,y,"scale[j]")}; + output[j + offset] = ${z[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale + ${s?`+ ${un(R,y,"bias[j]")}`:""} + ); + } + + ${C?"mean_data_output[global_idx] = mean":""}; + ${x?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},I=[{dims:o,dataType:t[0].dataType}];return C&&I.push({dims:c,dataType:1}),x&&I.push({dims:c,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${y};${r};${n}`,inputDependencies:b},getRunData:()=>({outputs:I,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:v}),getShaderSource:T}},cf=(t,e)=>{dd(t.inputs),t.compute(cd(t.inputs,e,t.outputCount))}}),pd,hd,pf,hf,l_=Y(()=>{ve(),Ie(),at(),Te(),pd=(t,e)=>{if(t.length<3||t.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=t[0],n=r.dims.length;if(r.dims[n-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let i=Math.floor((e.k+e.blockSize-1)/e.blockSize),a=e.blockSize/8*e.bits,s=t[1];if(!G.areEqual(s.dims,[e.n,i,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(G.size(o)!==e.n*i)throw new Error("scales input size error.");if(t.length===4){let u=t[3].dims,l=e.bits>4?e.n*i:e.n*Math.floor((i+1)/2);if(G.size(u)!==l)throw new Error("zeroPoints input size error.")}},hd=(t,e,r,n)=>{let i=t[0].dims,a=i.length,s=Math.floor((e.k+e.blockSize-1)/e.blockSize),o=i[a-2],u=e.k,l=e.n,h=i.slice(0,a-2),f=G.size(h),m=e.blockSize/8*e.bits/4,c=t[0].dataType,y=et(o),b=et(e.k),v=et(m),C=Kn(c),x=o*s*C,T=Math.floor(n/x),I=s<=r[0]&&T>0,A=!I||T>=4?et(l):T>=2&&et(l)>=2?2:1,R=h.concat([o,l]),z=G.size(R)/A/y,P=I?[]:[{type:12,data:z},{type:12,data:e.blockSize}],J=[f,o,u/b],K=G.convertShape(t[1].dims).slice();K.splice(-1,1,m/v),P.push(..._e(J)),P.push(..._e(K)),P.push(..._e(t[2].dims)),t.length===4&&P.push(..._e(G.convertShape(t[3].dims)));let ue=[f,o,l/A];P.push(..._e(ue));let ie=ge=>{let he=J.length,D=q("a",t[0].dataType,he,b),U=q("b",12,K.length,v),de=q("scales",t[2].dataType,t[2].dims.length),re=[D,U,de],Q=t.length===4?q("zero_points",12,t[3].dims.length):void 0;Q&&re.push(Q);let Z=ue.length,L=fe("output",t[0].dataType,Z,A),ee=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],le=st(t[0].dataType),Ee=(()=>{switch(b){case 1:return`array<${le}, 8>`;case 2:return`mat4x2<${le}>`;case 4:return`mat2x4<${le}>`;default:throw new Error(`${b}-component is not supported.`)}})(),Re=` + for (var word: u32 = 0; word < ${m}; word += ${v}) { + ${U.indicesSet("b_indices","2","word")}; + let b_data = ${U.getByIndices("b_indices")}; + for (var i: u32 = 0; i < ${v}; i++) { + let b_value: u32 = ${v===1?"b_data":"b_data[word + i]"}; + let b_mask: u32 = 0x0F0F0F0Fu; + let b_value_lower: vec4 = unpack4xU8(b_value & b_mask); + let b_value_upper: vec4 = unpack4xU8((b_value >> 4) & b_mask); + let b_quantized_values = ${Ee}(${Array.from({length:4},(Ke,Le)=>`${le}(b_value_lower[${Le}]), ${le}(b_value_upper[${Le}])`).join(", ")}); + let b_dequantized_values = ${b===1?`${Ee}(${Array.from({length:8},(Ke,Le)=>`(b_quantized_values[${Le}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Ee}(${Array(8).fill("zero_point").join(",")})) * scale;`}; + // Number of B elements per 32-bit word is 32/bits = 32/4 = 8 + for (var m: u32 = 0; m < ${I?o:y}u; m++) { + ${D.indicesSet("a_indices",he-2,I?"m":`row * ${y} + m`)}; + ${D.indicesSet("a_indices",he-1,"word_offset")}; + var input_offset = ${D.indicesToOffset("a_indices")}; + var a_data: ${Ee}; + for (var j: u32 = 0; j < ${8/b}; j++) { + a_data[j] = ${D.getByOffset("input_offset")}; + input_offset++; + } + ${I?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${A>1?"[c]":""} += ${Array.from({length:8/b},(Ke,Le)=>`${b===1?`a_data[${Le}] * b_dequantized_values[${Le}]`:`dot(a_data[${Le}], b_dequantized_values[${Le}])`}`).join(" + ")}; + } + word_offset += ${8/b}; + } + }`,He=Q?` + zero_point_offset += 4; + if (zero_point_offset == 32) { + zero_point_offset = 0; + zero_point_index++; + zero_point_word = ${Q.getByOffset("zero_point_index")}; + }`:"";return I?` + var workgroup_shared: array<${L.type.value}, ${o*s}>; + ${ge.declareVariables(...re,L)} + ${ge.mainStart([s,1,1])} + var a_indices: ${D.type.indices}; + var block = local_id.x; + var col = workgroup_id.y; + var batch = workgroup_id.z; + ${D.indicesSet("a_indices","0","batch")}; + // Two zero points are packed into one byte when uniforms.bits is 4. + for (var c: u32 = 0; c < ${A}; c++) { + let col_times_components_plus_c = col * ${A} + c; + ${Q?` + var zero_point_bytes_per_col: u32 = (${s} + 1) / 2; + var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u); + var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u; + var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u; + var zero_point_nibble_offset: u32 = block & 0x1u; + var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + var zero_point_word: u32 = ${Q.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""} + var b_indices: ${U.type.indices}; + ${U.indicesSet("b_indices","0","col_times_components_plus_c")}; + // The scale and zero points are computed per block. + var scales_index = col_times_components_plus_c * ${s} + block; + let scale = ${de.getByOffset("scales_index")}; + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${le}(${Q?"(zero_point_word) & 0xFu":8}); + ${U.indicesSet("b_indices","1","block")}; + var word_offset: u32 = block * ${e.blockSize/b}; + var workgroup_shared_offset: u32 = block * ${o}; + ${Re} + } + workgroupBarrier(); + if (local_id.x == 0u) { + var output_indices: ${L.type.indices}; + ${L.indicesSet("output_indices","0","batch")}; + ${L.indicesSet("output_indices",Z-1,"col")}; + ${L.indicesSet("output_indices",Z-2,"0")}; + var output_offset = ${L.indicesToOffset("output_indices")}; + for (var m: u32 = 0u; m < ${o}u; m++) { + var output_value: ${L.type.value} = ${L.type.value}(0); + var workgroup_shared_offset: u32 = m; + for (var b: u32 = 0u; b < ${s}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${o}; + } + ${L.setByOffset("output_offset","output_value")}; + output_offset += ${l/A}; + } + } + }`:` + ${ge.registerUniforms(ee).declareVariables(...re,L)} + ${ge.mainStart()} + ${ge.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var output_values: array<${L.type.value}, ${y}>; + var output_indices = ${L.offsetToIndices("global_idx")}; + var col = ${L.indicesGet("output_indices",Z-1)}; + var row = ${L.indicesGet("output_indices",Z-2)}; + var a_indices: ${D.type.indices} = output_indices; + // Two zero points are packed into one byte because uniforms.bits <= 4. + // zero_point_offset is either 0 or 4. It is bit offset within one byte. + // TODO support zero_point_offset for bits > 4 + ${Q?` + var zero_point_abs_offset = col * ${A} * ((${s} + 1) / 2); + var zero_point_index: u32 = zero_point_abs_offset / 4; + var zero_point_word: u32 = ${Q.getByOffset("zero_point_index")}; + var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""} + var scale_index = col * ${s*A}; + var b_indices: ${U.type.indices}; + for (var c: u32 = 0; c < ${A}; c++) { + ${U.indicesSet("b_indices","0",`col * ${A} + c`)}; + var block_offset: u32 = 0; + for (var block: u32 = 0; block < ${s}; block++) { + // The scale and zero points are computed per block. + let scale = ${de.getByOffset("scale_index")}; + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${le}(${Q?"extractBits(zero_point_word, zero_point_offset, 4)":8}); + ${U.indicesSet("b_indices","1","block")}; + var word_offset: u32 = block_offset; + ${Re} + scale_index++; + ${He} + block_offset += uniforms.block_size / ${b}; + } + // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte. + ${Q?`if (zero_point_offset % 8 > 0) { + ${He} + }`:""} + } + for (var k: u32 = 0u; k < ${y}u; k++) { + ${L.indicesSet("output_indices",Z-2,`${y} * row + k`)}; + ${L.setByIndices("output_indices","output_values[k]")} + } + }`};return{name:I?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${o};${c};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:R,dataType:c}],name:I?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:I?{x:1,y:Math.ceil(l/A),z:f}:{x:Math.ceil(z/64)},programUniforms:P}),getShaderSource:ie}},pf=(t,e)=>{pd(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(hd(t.inputs,e,r,n))},hf=t=>Ge(t)}),pt,fd,ff,ps,md,Xa,mf,d_=Y(()=>{ve(),Ie(),at(),po(),qp(),Te(),na(),pt=(t,e)=>t.length>e&&t[e].dims.length>0&&G.size(t[e].dims)>0?t[e]:void 0,fd=(t,e)=>{let r=t[0],n=pt(t,1),i=pt(t,2),a=pt(t,3),s=pt(t,4),o=pt(t,5),u=pt(t,6),l=pt(t,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let h=!1,f=r.dims[0],m=r.dims[1],c=r.dims.length===3?h?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],y=m,b=0,v=0,C=Math.floor(c/e.numHeads);if(u&&l){if(u.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(u.dims[0]!==f||u.dims[1]!==e.numHeads||u.dims[3]!==C)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[0]!==f||l.dims[1]!==e.numHeads||l.dims[3]!==C)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[2]!==l.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(l.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');b=u.dims[2],v=u.dims[2]}else if(u||l)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let x;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');x=2,y=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==e.numHeads||n.dims[3]!==2||n.dims[4]!==C)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(i)throw new Error('Expect "value" be none when "key" has packed kv format.');x=5,y=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==C)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');x=0,y=n.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(r.dims.length===5&&(r.dims[2]!==e.numHeads||r.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');x=3}if(a){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(i&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let T=0;if(s){T=8;let P=s.dims;throw P.length===1?P[0]===f?T=1:P[0]===3*f+2&&(T=3):P.length===2&&P[0]===f&&P[1]===y&&(T=5),T===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let I=!1,A=c;if(i){if(i.dims.length!==3&&i.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(i.dims.length===3){if(y!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');A=i.dims[2]}else{if(y!==i.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');A=i.dims[1]*i.dims[3],I=!0}}let R=b+y,z=!1;if(s)throw new Error("Key padding mask is not supported");if(o){if(o.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(o.dims[0]!==f&&o.dims[0]!==1||o.dims[1]!==e.numHeads||o.dims[2]!==m||o.dims[3]!==R)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:f,sequenceLength:m,pastSequenceLength:b,kvSequenceLength:y,totalSequenceLength:R,maxSequenceLength:v,inputHiddenSize:0,hiddenSize:c,vHiddenSize:A,headSize:C,vHeadSize:Math.floor(A/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:T,scale:e.scale,broadcastResPosBias:z,passPastInKv:I,qkvFormat:x}},ff=t=>Ge({...t}),ps=Ge({perm:[0,2,1,3]}),md=(t,e,r,n,i,a,s)=>{let o=[n,i,a],u=G.size(o),l=[{type:12,data:u},{type:12,data:s},{type:12,data:a}],h=f=>{let m=fe("qkv_with_bias",e.dataType,o),c=q("qkv",e.dataType,o),y=q("bias",r.dataType,o),b=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${f.registerUniforms(b).declareVariables(c,y,m)} + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:e.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:h},{inputs:[e,r],outputs:[-1]})[0]},Xa=(t,e,r,n,i,a,s,o)=>{let u=a;if(s){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return u=md(t,a,s,e,n,r*i,o),u=u.reshape([e,n,r,i]),t.compute(sr(u,ps.perm),{inputs:[u],outputs:[-1]})[0]}else return a.dims.length===3&&(u=a.reshape([e,n,r,i])),t.compute(sr(u,ps.perm),{inputs:[u],outputs:[-1]})[0]},mf=(t,e)=>{let r=fd(t.inputs,e),n=t.inputs[0],i=pt(t.inputs,1),a=pt(t.inputs,2),s=pt(t.inputs,3),o=pt(t.inputs,4),u=pt(t.inputs,5),l=pt(t.inputs,6),h=pt(t.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if(i?.dims.length===5)throw new Error("Packed KV is not implemented");let f=i&&a&&i.dims.length===4&&a.dims.length===4,m=Xa(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,s,0);if(f)return fi(t,m,i,a,o,void 0,l,h,u,r,e);if(!i||!a)throw new Error("key and value must be provided");let c=Xa(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,i,s,r.hiddenSize),y=Xa(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,a,s,2*r.hiddenSize);fi(t,m,c,y,o,void 0,l,h,u,r,e)}}),gd,_d,yd,wd,bd,vd,$d,xd,gf,c_=Y(()=>{ve(),Ie(),Te(),gd=t=>{if(!t||t.length<1)throw new Error("Too few inputs");if(t[0].dataType!==1&&t[0].dataType!==10)throw new Error("Input type must be float or float16.");if(t.length>=2){let e=t[0].dims.length*2===t[1].dims[0];if(t.length===4&&(e=t[3].dims[0]*2===t[1].dims[0]),!e)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},_d=(t,e,r)=>{let n="";for(let i=e-1;i>=0;--i)n+=` + k = i32(${t.indicesGet("indices",i)}) - ${xe("uniforms.pads",i,r)}; + if (k < 0) { + break; + } + if (k >= i32(${xe("uniforms.x_shape",i,e)})) { + break; + } + offset += k * i32(${xe("uniforms.x_strides",i,e)}); + `;return` + value = ${t.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + } + `},yd=(t,e,r)=>{let n="";for(let i=e-1;i>=0;--i)n+=` + k = i32(${t.indicesGet("indices",i)}) - ${xe("uniforms.pads",i,r)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${xe("uniforms.x_shape",i,e)}) - 1); + k = k % _2n_1; + if(k >= i32(${xe("uniforms.x_shape",i,e)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${xe("uniforms.x_strides",i,e)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},wd=(t,e,r)=>{let n="";for(let i=e-1;i>=0;--i)n+=` + k = i32(${t.indicesGet("indices",i)}) - ${xe("uniforms.pads",i,r)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${xe("uniforms.x_shape",i,e)})) { + k = i32(${xe("uniforms.x_shape",i,e)}) - 1; + } + offset += k * i32(${xe("uniforms.x_strides",i,e)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},bd=(t,e,r)=>{let n="";for(let i=e-1;i>=0;--i)n+=` + k = i32(${t.indicesGet("indices",i)}) - ${xe("uniforms.pads",i,r)}; + if (k < 0) { + k += i32(${xe("uniforms.x_shape",i,e)}]); + } + if (k >= i32(${xe("uniforms.x_shape",i,e)})) { + k -= i32(${xe("uniforms.x_shape",i,e)}); + } + offset += k * i32(${xe("uniforms.x_strides",i,e)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},vd=(t,e,r)=>{switch(r.mode){case 0:return _d(t,e,r.pads.length);case 1:return yd(t,e,r.pads.length);case 2:return wd(t,e,r.pads.length);case 3:return bd(t,e,r.pads.length);default:throw new Error("Invalid mode")}},$d=(t,e)=>{let r=G.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,i=G.size(r),a=[{type:12,data:i},{type:6,data:e.pads}];e.mode===0&&a.push({type:t[0].dataType,data:e.value}),a.push(..._e(t[0].dims,r));let s=["rank"],o=u=>{let l=fe("output",t[0].dataType,r.length),h=q("x",t[0].dataType,n.length),f=h.type.value,m=vd(l,n.length,e),c=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.mode===0&&c.push({name:"constant_value",type:f}),` + ${u.registerUniforms(c).declareVariables(h,l)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${l.offsetToIndices("global_idx")}; + + var value = ${f}(0); + ${m} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:s},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(G.size(r)/64)},programUniforms:a}),getShaderSource:o}},xd=(t,e)=>{if(t.length>1){let r=t[1].getBigInt64Array(),n=t.length>=3&&t[2].data?t[2].getFloat32Array()[0]:0,i=t[0].dims.length,a=new Int32Array(2*i).fill(0);if(t.length>=4){let o=t[3].getBigInt64Array();for(let u=0;ua[Number(u)]=Number(o));let s=[];return a.forEach(o=>s.push(o)),{mode:e.mode,value:n,pads:s}}else return e},gf=(t,e)=>{gd(t.inputs);let r=xd(t.inputs,e);t.compute($d(t.inputs,r),{inputs:[0]})}}),Nn,hs,fs,ms,gs,Sd,Cd,_s,ys,_f,yf,ws,wf,bf,bs,vf,$f,xf,Sf,p_=Y(()=>{qt(),ve(),Ie(),Te(),Nn=t=>{if(Fe.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},hs=(t,e,r)=>{let n=e.format==="NHWC",i=t.dims.slice();n&&i.splice(1,0,i.pop());let a=Object.hasOwnProperty.call(e,"dilations"),s=e.kernelShape.slice(),o=e.strides.slice(),u=a?e.dilations.slice():[],l=e.pads.slice();ci.adjustPoolAttributes(r,i,s,o,u,l);let h=ci.computePoolOutputShape(r,i,o,u,s,l,e.autoPad),f=Object.assign({},e);a?Object.assign(f,{kernelShape:s,strides:o,pads:l,dilations:u,cacheKey:e.cacheKey}):Object.assign(f,{kernelShape:s,strides:o,pads:l,cacheKey:e.cacheKey});let m=h.slice();return m.push(m.splice(1,1)[0]),[f,n?m:h]},fs=(t,e)=>{let r=e.format==="NHWC",n=G.size(t),i=G.size(e.kernelShape),a=[{type:12,data:n},{type:12,data:i}],s=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(e.kernelShape.length<=2){let o=e.kernelShape[e.kernelShape.length-1],u=e.strides[e.strides.length-1],l=e.pads[e.pads.length/2-1],h=e.pads[e.pads.length-1],f=!!(l+h);a.push({type:12,data:o},{type:12,data:u},{type:12,data:l},{type:12,data:h}),s.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let m=!1;if(e.kernelShape.length===2){let c=e.kernelShape[e.kernelShape.length-2],y=e.strides[e.strides.length-2],b=e.pads[e.pads.length/2-2],v=e.pads[e.pads.length-2];m=!!(b+v),a.push({type:12,data:c},{type:12,data:y},{type:12,data:b},{type:12,data:v}),s.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,s,!0,f,m]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=G.computeStrides(e.kernelShape);a.push({type:12,data:o},{type:12,data:e.pads},{type:12,data:e.strides}),s.push({name:"kernelStrides",type:"u32",length:o.length},{name:"pads",type:"u32",length:e.pads.length},{name:"strides",type:"u32",length:e.strides.length});let u=e.pads.reduce((l,h)=>l+h);return[a,s,!!u,!1,!1]}},ms=(t,e,r,n,i,a,s,o,u,l,h,f)=>{let m=i.format==="NHWC",c=e.type.value,y=fe("output",e.type.tensor,n);if(i.kernelShape.length<=2){let b="",v="",C="",x=r-(m?2:1);if(h?b=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${x}] < 0 || xIndices[${x}] + >= uniforms.x_shape[${x}]) { + pad++; + continue; + } + let x_val = x[${e.indicesToOffset("xIndices")}]; + ${a} + }`:b=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${e.indicesToOffset("xIndices")}]; + ${a} + }`,i.kernelShape.length===2){let T=r-(m?3:2);f?v=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${T}] = indices[${T}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${T}] < 0 || xIndices[${T}] >= uniforms.x_shape[${T}]) { + pad += i32(uniforms.kw); + continue; + } + `:v=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${T}] = indices[${T}] * uniforms.sh - uniforms.phStart + j; + `,C=` + } + `}return` + ${t.registerUniforms(u).declareVariables(e,y)} + + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${y.offsetToIndices("global_idx")}; + var xIndices = ${y.offsetToIndices("global_idx")}; + + var value = ${c}(${o}); + var pad = 0; + ${v} + ${b} + ${C} + ${s} + + output[global_idx] = value; + }`}else{if(m)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let b=i.kernelShape.length,v=i.pads.length,C="";return l?C=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${e.indicesToOffset("xIndices")}]; + ${a} + }`:C=` + } + let x_val = x[${e.indicesToOffset("xIndices")}]; + ${a} + `,` + ${t.registerUniforms(u).declareVariables(e,y)} + + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${y.offsetToIndices("global_idx")}; + var xIndices = ${y.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${c}(${o}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${b-1}u; j++) { + offsets[j] = offset / ${xe("uniforms.kernelStrides","j",b)}; + offset -= offsets[j] * ${xe("uniforms.kernelStrides","j",b)}; + } + offsets[${b-1}] = offset; + + isPad = false; + for (var j = ${r-b}u; j < ${r}u; j++) { + xIndices[j] = indices[j] * ${xe("uniforms.strides",`j - ${r-b}u`,b)} + + offsets[j - ${r-b}u] - ${xe("uniforms.pads","j - 2u",v)}; + ${C} + } + ${s} + + output[global_idx] = value; + }`}},gs=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,Sd=t=>`${gs(t)};${t.countIncludePad}`,Cd=t=>`${gs(t)};${t.storageOrder};${t.dilations}`,_s=t=>({format:t.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],ceilMode:t.ceil_mode,kernelShape:t.kernel_shape,strides:t.strides,pads:t.pads}),ys=(t,e,r,n)=>{let[i,a]=hs(e,n,r),s=q("x",e.dataType,e.dims.length),o=s.type.value,u="value += x_val;",l="";i.countIncludePad?l+=`value /= ${o}(uniforms.kernelSize);`:l+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[h,f,m,c,y]=fs(a,i);h.push(..._e(e.dims,a));let b=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${m};${c};${y}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(G.size(a)/64)},programUniforms:h}),getShaderSource:v=>ms(v,s,e.dims.length,a.length,i,u,l,0,f,m,c,y)}},_f=t=>{let e=t.count_include_pad!==0,r=_s(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:Sd(n)}},yf=(t,e)=>{Nn(t.inputs),t.compute(ys("AveragePool",t.inputs[0],!1,e))},ws={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},wf=t=>{let e=t.format;return{format:e,...ws,cacheKey:e}},bf=(t,e)=>{Nn(t.inputs),t.compute(ys("GlobalAveragePool",t.inputs[0],!0,e))},bs=(t,e,r,n)=>{let[i,a]=hs(e,n,r),s=` + value = max(x_val, value); + `,o="",u=q("x",e.dataType,e.dims.length),l=["rank"],[h,f,m,c,y]=fs(a,i);return h.push(..._e(e.dims,a)),{name:t,shaderCache:{hint:`${n.cacheKey};${m};${c};${y}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(G.size(a)/64)},programUniforms:h}),getShaderSource:b=>ms(b,u,e.dims.length,a.length,i,s,o,e.dataType===10?-65504:-1e5,f,m,c,y)}},vf=(t,e)=>{Nn(t.inputs),t.compute(bs("MaxPool",t.inputs[0],!1,e))},$f=t=>{let e=t.storage_order,r=t.dilations,n=_s(t);if(e!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let i={storageOrder:e,dilations:r,...n,cacheKey:""};return{...i,cacheKey:Cd(i)}},xf=t=>{let e=t.format;return{format:e,...ws,cacheKey:e}},Sf=(t,e)=>{Nn(t.inputs),t.compute(bs("GlobalMaxPool",t.inputs[0],!0,e))}}),Ed,Td,Cf,h_=Y(()=>{qt(),ve(),Te(),Ed=(t,e,r)=>{let n=t===e,i=te&&r>0;if(n||i||a)throw new Error("Range these inputs' contents are invalid.")},Td=(t,e,r,n)=>{let i=Math.abs(Math.ceil((e-t)/r)),a=[i],s=i,o=[{type:12,data:s},{type:n,data:t},{type:n,data:r},..._e(a)],u=l=>{let h=fe("output",n,a.length),f=h.type.value,m=[{name:"outputSize",type:"u32"},{name:"start",type:f},{name:"delta",type:f}];return` + ${l.registerUniforms(m).declareVariables(h)} + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${f}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:u,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:o})}},Cf=t=>{let e=0,r=0,n=0;t.inputs[0].dataType===6?(e=t.inputs[0].getInt32Array()[0],r=t.inputs[1].getInt32Array()[0],n=t.inputs[2].getInt32Array()[0]):t.inputs[0].dataType===1&&(e=t.inputs[0].getFloat32Array()[0],r=t.inputs[1].getFloat32Array()[0],n=t.inputs[2].getFloat32Array()[0]),Fe.webgpu.validateInputContent&&Ed(e,r,n),t.compute(Td(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),kd,Id,Ad,Md,Od,zd,Rd,Pd,Bd,Dd,Nd,vs,Fd,Ld,Wd,Ud,Vd,Ef,Tf,f_=Y(()=>{ve(),Ie(),at(),Te(),kd=(t,e)=>{if(t.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),t.length>0){if(e.mode==="linear"){if(!(t.length===2||t.length===3||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1||t.length===5&&t[0]===1&&t[1]===1))throw new 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(e.mode==="cubic"&&!(t.length===2||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},Id=(t,e,r)=>{e.every(i=>i>=0&&i{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return e.forEach((i,a)=>n[i]=t[a]),n},Ad=(t,e,r,n,i,a)=>{let[s,o,u]=r>10?[1,2,3]:[-1,t.length>1?1:-1,-1],l=t[0].dims.length;if(s>0&&t.length>s&&t[s].dims.length>0)t[s].getFloat32Array().forEach(h=>a.push(h));else if(e.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(o>0&&t.length>o&&t[o].dims.length>0){if(t[o].getFloat32Array().forEach(h=>n.push(h)),n.length!==0&&n.length!==l&&r>=18&&n.length!==e.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");kd(n,e),e.axes.length>0&&Id(n,e.axes,l).forEach((h,f)=>n[f]=h)}if(u>0&&t.length>u&&(t[u].getBigInt64Array().forEach(h=>i.push(Number(h))),i.length!==l||r>=18&&i.length===e.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(e.axes.length>0){if(n.length!==e.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(i.length!==e.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof i<"u"&&n.length>0&&i.length>l)throw new Error("Resize requires only of scales or sizes to be specified")},Md=(t,e)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, + lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${e} { `+(()=>{switch(t){case"asymmetric":return`return ${e}(xResized) / ${e}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) { + return (${e}(xResized) + 0.5) / ${e}(xScale) - 0.5; + } else { + return 0.0; + }`;case"tf_half_pixel_for_nn":return`return (${e}(xResized) + 0.5) / ${e}(xScale);`;case"align_corners":return`if (lengthResized == 1) { + return 0.0; + } else { + // 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 whole = ${e}(xResized * (lengthOriginal - 1) / (lengthResized - 1)); + let fract = + ${e}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${e}(lengthResized - 1); + return whole + fract; + }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { + return ${e}(roiStart) * ${e}(lengthOriginal - 1) + + (${e}(xResized) * ${e}(roiEnd - roiStart) * ${e}(lengthOriginal - 1)) / + ${e}(lengthResized - 1); + } else { + return 0.5 * ${e}(roiStart + roiEnd) * ${e}(lengthOriginal - 1); + }`;case"half_pixel_symmetric":return`const outputWidth = ${e}xScale * ${e}(lengthResized); + const adjustment = ${e}(lengthResized) / outputWidth; + const center = ${e}(lengthOriginal) / 2; + const offset = center * (1 - adjustment); + return offset + ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;case"half_pixel":return`return ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${t} is not supported`)}})()+"}",Od=(t,e,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(t){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(e<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${t} is not supported`)}})()+"}",zd=(t,e,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),i=t.length===0?n:t.slice();return e.length>0?(e.forEach((a,s)=>{n[a]=i[s],n[s+r]=i[e.length+s]}),n):i},Rd=(t,e,r,n)=>{let i=[];if(r.length>0)if(n.length>0){if(t.forEach(a=>i.push(a)),Math.max(...n)>t.length)throw new Error("axes is out of bound");n.forEach((a,s)=>i[a]=r[s])}else r.forEach(a=>i.push(a));else{if(e.length===0)throw new Error("Resize requires either scales or sizes.");i=t.map((a,s)=>Math.round(a*e[s]))}return i},Pd=(t,e,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(a=>e[a]),Number.MAX_VALUE):Math.min(...e,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(a=>e[a]),Number.MIN_VALUE):Math.max(...e,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();e.fill(1,0,e.length);let i=t.slice();return r.axes.length>0?(r.axes.forEach(a=>e[a]=n),r.axes.forEach(a=>i[a]=Math.round(t[a]*e[a]))):(e.fill(n,0,e.length),i.forEach((a,s)=>i[s]=Math.round(a*e[s]))),i},Bd=(t,e,r,n,i)=>` + fn calculateOriginalIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> array<${t.type.value}, ${r.length}> { + var original_indices: array<${t.type.value}, ${r.length}>; + for (var i:u32 = 0; i < ${r.length}; i++) { + var output_index = ${t.indicesGet("output_indices","i")}; + var scale = ${xe("uniforms.scales","i",n)}; + var roi_low = ${xe("uniforms.roi","i",i)}; + var roi_hi = ${xe("uniforms.roi",`i + ${e.length}`,i)}; + if (scale == 1.0) { + original_indices[i] = ${t.type.value}(output_index); + } else { + var input_shape_i = ${xe("uniforms.input_shape","i",e.length)}; + var output_shape_i = ${xe("uniforms.output_shape","i",r.length)}; + original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + } + } + return original_indices; + }`,Dd=(t,e,r,n,i,a,s)=>` + fn calculateInputIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} { + var input_indices: ${t.type.indices}; + for (var i:u32 = 0; i < ${n.length}; i++) { + var output_index = ${e.indicesGet("output_indices","i")}; + var input_index: u32; + var scale = ${xe("uniforms.scales","i",i)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${xe("uniforms.roi","i",a)}; + var roi_hi = ${xe("uniforms.roi",`i + ${r.length}`,a)}; + var input_shape_i = ${xe("uniforms.input_shape","i",r.length)}; + var output_shape_i = ${xe("uniforms.output_shape","i",n.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${s} || (original_idx >= 0 && original_idx < ${e.type.value}(input_shape_i))) { + if (original_idx < 0) { + input_index = 0; + } else if (original_idx > ${e.type.value}(input_shape_i - 1)) { + input_index = input_shape_i - 1; + } else { + input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1)); + } + } else { + input_index = u32(original_idx); + } + } + ${t.indicesSet("input_indices","i"," input_index")} + } + return input_indices; + }`,Nd=(t,e)=>` + fn checkInputIndices(input_indices: ${t.type.indices}) -> bool { + for (var i:u32 = 0; i < ${e.length}; i++) { + var input_index = ${t.indicesGet("input_indices","i")}; + if (input_index < 0 || input_index >= ${xe("uniforms.input_shape","i",e.length)}) { + return false; + } + } + return true; + }`,vs=(t,e,r,n)=>t.rank>n?` + ${t.indicesSet("input_indices",e,"channel")}; + ${t.indicesSet("input_indices",r,"batch")}; +`:"",Fd=(t,e,r,n,i)=>{let[a,s,o,u]=r.length===2?[-1,0,1,-1]:[0,2,3,1],l=t.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${l} { + var input_indices: ${t.type.indices}; + ${t.indicesSet("input_indices",s,`max(0, min(row, ${r[s]} - 1))`)}; + ${t.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)}; + ${vs(t,u,a,2)} + return ${t.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${e.type.indices}) -> ${l} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${l} = originalIndices[${s}]; + var col:${l} = originalIndices[${o}]; + ${n?`if (row < 0 || row > (${r[s]} - 1) || col < 0 || col > (${r[o]} - 1)) { + return ${i}; + }`:""}; + row = max(0, min(row, ${r[s]} - 1)); + col = max(0, min(col, ${r[o]} - 1)); + var row1: u32 = u32(row); + var col1: u32 = u32(col); + var row2: u32 = u32(row + 1); + var col2: u32 = u32(col + 1); + var channel: u32 = ${r.length>2?`u32(originalIndices[${u}])`:"0"}; + var batch: u32 = ${r.length>2?`u32(originalIndices[${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); + }`},Ld=(t,e,r,n,i,a,s,o,u,l)=>{let h=r.length===2,[f,m]=h?[0,1]:[2,3],c=t.type.value,y=b=>{let v=b===f?"row":"col";return` + fn ${v}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${e.type.indices}) -> ${c} { + var output_index = ${e.indicesGet("output_indices",b)}; + var originalIdx: ${c} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[b]}, + ${n[b]}, ${r[b]}, ${a[b]}, ${a[b]} + ${r.length}); + var fractOriginalIdx: ${c} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${o} && (originalIdx < 0 || originalIdx > (${r[b]} - 1))) { + return ${u}; + } + var data: array<${c}, 4> = array<${c}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${v}: ${c} = originalIdx + ${c}(i); + if (${v} < 0 || ${v} >= ${r[b]}) { + ${l?`coefs[i + 1] = 0.0; + continue;`:o?`return ${u};`:`${v} = max(0, min(${v}, ${r[b]} - 1));`}; + } + var input_indices_copy: ${t.type.indices} = input_indices; + ${t.indicesSet("input_indices_copy",b,`u32(${v})`)}; + data[i + 1] = ${b===f?t.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${y(f)}; + ${y(m)}; + fn getCubicInterpolationCoefs(s: ${c}) -> array<${c}, 4> { + var absS = abs(s); + var coeffs: array<${c}, 4> = array<${c}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${c} = 1.0 - absS; + var twoMinusAbsS: ${c} = 2.0 - absS; + var onePlusAbsS: ${c} = 1.0 + absS; + coeffs[0] = ((${s} * onePlusAbsS - 5 * ${s}) * onePlusAbsS + 8 * ${s}) * onePlusAbsS - 4 * ${s}; + coeffs[1] = ((${s} + 2) * absS - (${s} + 3)) * absS * absS + 1; + coeffs[2] = ((${s} + 2) * oneMinusAbsS - (${s} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${s} * twoMinusAbsS - 5 * ${s}) * twoMinusAbsS + 8 * ${s}) * twoMinusAbsS - 4 * ${s}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${c}, 4>, coefs: array<${c}, 4>) -> ${c} { + var coefsSum: ${c} = coefs[0] + coefs[1] + coefs[2] + coefs[3]; + return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum; + } + + fn bicubicInterpolation(output_indices: ${e.type.indices}) -> ${c} { + var input_indices: ${t.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},Wd=(t,e,r,n,i)=>{let[a,s,o,u,l]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],h=t.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${h} { + var input_indices: ${t.type.indices}; + ${t.indicesSet("input_indices",s,`max(0, min(depth, ${r[s]} - 1))`)}; + ${t.indicesSet("input_indices",o,`max(0, min(height, ${r[o]} - 1))`)}; + ${t.indicesSet("input_indices",u,`max(0, min(width, ${r[u]} - 1))`)}; + ${vs(t,l,a,3)} + return ${t.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${e.type.indices}) -> ${h} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${h} = originalIndices[${s}]; + var height:${h} = originalIndices[${o}]; + var width:${h} = originalIndices[${u}]; + ${n?`if (depth < 0 || depth > (${r[s]} - 1) || height < 0 || height > (${r[o]} - 1) || width < 0 || (width > ${r[u]} - 1)) { + return ${i}; + }`:""}; + + depth = max(0, min(depth, ${r[s]} - 1)); + height = max(0, min(height, ${r[o]} - 1)); + width = max(0, min(width, ${r[u]} - 1)); + var depth1: u32 = u32(depth); + var height1: u32 = u32(height); + var width1: u32 = u32(width); + var depth2: u32 = u32(depth + 1); + var height2: u32 = u32(height + 1); + var width2: u32 = u32(width + 1); + var channel: u32 = ${r.length>3?`u32(originalIndices[${l}])`:"0"}; + var batch: u32 = ${r.length>3?`u32(originalIndices[${a}])`:"0"}; + + var x111: ${h} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${h} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${h} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${h} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${h} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${h} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${h} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${h} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${h} = abs(depth - ${h}(depth1)); + var dx2: ${h} = abs(${h}(depth2) - depth); + var dy1: ${h} = abs(height - ${h}(height1)); + var dy2: ${h} = abs(${h}(height2) - height); + var dz1: ${h} = abs(width - ${h}(width1)); + var dz2: ${h} = abs(${h}(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); + }`},Ud=(t,e,r,n,i,a)=>{let s=t.dims,o=zd(a,e.axes,s.length),u=Rd(s,n,i,e.axes),l=n.slice();n.length===0&&(l=s.map((x,T)=>x===0?1:u[T]/x),e.keepAspectRatioPolicy!=="stretch"&&(u=Pd(s,l,e)));let h=fe("output",t.dataType,u.length),f=q("input",t.dataType,s.length),m=G.size(u),c=s.length===u.length&&s.every((x,T)=>x===u[T]),y=e.coordinateTransformMode==="tf_crop_and_resize",b=e.extrapolationValue,v=f.type.value,C=x=>` + ${c?"":` + ${Md(e.coordinateTransformMode,v)}; + ${(()=>{switch(e.mode){case"nearest":return` + ${Nd(f,s)}; + ${Od(e.nearestMode,r,v)}; + ${Dd(f,h,s,u,l.length,o.length,y)}; + `;case"linear":return` + ${Bd(h,s,u,l.length,o.length)}; + ${(()=>{if(s.length===2||s.length===4)return`${Fd(f,h,s,y,b)}`;if(s.length===3||s.length===5)return`${Wd(f,h,s,y,b)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(s.length===2||s.length===4)return`${Ld(f,h,s,u,l,o,e.cubicCoeffA,y,e.extrapolationValue,e.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; + `;default:throw Error("Invalid resize mode")}})()}; + `} + ${x.registerUniform("output_size","u32").registerUniform("scales","f32",l.length).registerUniform("roi","f32",o.length).declareVariables(f,h)} + ${x.mainStart()} + ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${c?"output[global_idx] = input[global_idx];":` + let output_indices = ${h.offsetToIndices("global_idx")}; + var input_indices: ${f.type.indices}; + ${(()=>{switch(e.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${f.getByIndices("input_indices")}; + } else { + output[global_idx] = ${e.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${s.length===2||s.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${e.mode}`)}})()}; +`} + }`;return{name:"Resize",shaderCache:{hint:`${e.cacheKey}|${r}|${l.length>0?l:""}|${i.length>0?i:""}|${o.length>0?o:""}|${c}|${s}`,inputDependencies:["rank"]},getShaderSource:C,getRunData:()=>({outputs:[{dims:u,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:[{type:12,data:m},{type:1,data:l},{type:1,data:o},..._e(s,u)]})}},Vd=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},Ef=(t,e)=>{let r=[],n=[],i=[],a=Vd(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Ad(t.inputs,e,a,r,n,i),t.compute(Ud(t.inputs[0],e,a,r,n,i),{inputs:[0]})},Tf=t=>{let e=t.antialias,r=t.axes,n=t.coordinateTransformMode,i=t.cubicCoeffA,a=t.excludeOutside!==0,s=t.extrapolationValue,o=t.keepAspectRatioPolicy,u=t.mode,l=t.nearestMode===""?"simple":t.nearestMode;return Ge({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:i,excludeOutside:a,extrapolationValue:s,keepAspectRatioPolicy:o,mode:u,nearestMode:l})}}),Gd,Hd,kf,m_=Y(()=>{ve(),Ie(),at(),Te(),Gd=(t,e)=>{let[r,n,i,a]=t,{numHeads:s,rotaryEmbeddingDim:o}=e;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!G.areEqual(n.dims,[])&&!G.areEqual(n.dims,[1])&&n.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${n.dims.length}`);if(i.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(a.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(!G.areEqual(i.dims,a.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(o>0&&s===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let u=r.dims[0],l=r.dims[r.dims.length-2],h=i.dims[0],f=G.sizeFromDimension(r.dims,1)/l,m=o===0?i.dims[1]*2:f/s;if(o>m)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(u!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(l!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(m/2!==i.dims[1]&&o/2!==i.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${i.dims[1]}`);if(l>h)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Hd=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:i,scale:a}=e,s=t[0].dims[0],o=G.sizeFromDimension(t[0].dims,1),u=t[0].dims[t[0].dims.length-2],l=o/u,h=t[2].dims[1],f=i===0?h*2:l/n,m=new Array(s,u,l/f,f-h),c=G.computeStrides(m),y=[{type:1,data:a},{type:12,data:m},{type:12,data:c},...t[0].dims.length===3?new Array({type:12,data:[o,l,f,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[o,f,u*f,1]}):[],..._e(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],b=v=>{let C=q("input",t[0].dataType,t[0].dims.length),x=q("position_ids",t[1].dataType,t[1].dims.length),T=q("cos_cache",t[2].dataType,t[2].dims.length),I=q("sin_cache",t[3].dataType,t[3].dims.length),A=fe("output",t[0].dataType,t[0].dims.length);return v.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:m.length},{name:"global_strides",type:"u32",length:c.length},{name:"input_output_strides",type:"u32",length:c.length}]),` + ${v.declareVariables(C,x,T,I,A)} + + ${v.mainStart(hn)} + let half_rotary_emb_dim = uniforms.${T.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${v.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${x.broadcastedIndicesToOffset("bsnh.xy",fe("",x.type.tensor,2))}; + let position_id = + u32(${x.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r}); + let j = i + select(half_rotary_emb_dim, 1, ${r}); + let re = ${C.getByOffset("i")} * ${T.get("position_id","bsnh[3]")} - + ${C.getByOffset("j")} * ${I.get("position_id","bsnh[3]")}; + ${A.setByOffset("i","re")} + let im = ${C.getByOffset("i")} * ${I.get("position_id","bsnh[3]")} + + ${C.getByOffset("j")} * ${T.get("position_id","bsnh[3]")}; + ${A.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${A.setByOffset("k",C.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:Ge({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:b,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(G.size(m)/hn)},programUniforms:y})}},kf=(t,e)=>{Gd(t.inputs,e),t.compute(Hd(t.inputs,e))}}),qd,jd,If,g_=Y(()=>{ve(),Ie(),Te(),qd=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],r=t[1],n=t[2];if(e.dataType!==r.dataType||e.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let i=e.dims[e.dims.length-1],a=e.dims[e.dims.length-2];if(r.dims[r.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==a)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let s=t[3];if(s.dims.length!==1)throw new Error("Beta must be 1D");if(s.dims[s.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let s=t[4];if(s.dims.length!==1)throw new Error("Bias must be 1D");if(s.dims[s.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},jd=(t,e,r,n)=>{let i=e.simplified,a=t[0].dims,s=G.size(a),o=a,u=s,l=a.slice(-1)[0],h=n?a.slice(0,-1).concat(1):[],f=!i&&t.length>3,m=t.length>4,c=n&&r>1,y=n&&r>2,b=r>3,v=et(l),C=[{type:12,data:u},{type:12,data:v},{type:12,data:l},{type:1,data:e.epsilon}],x=I=>{let A=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],R=[q("x",t[0].dataType,t[0].dims,v),q("skip",t[1].dataType,t[1].dims,v),q("gamma",t[2].dataType,t[2].dims,v)];f&&R.push(q("beta",t[3].dataType,t[3].dims,v)),m&&R.push(q("bias",t[4].dataType,t[4].dims,v)),R.push(fe("output",t[0].dataType,o,v)),c&&R.push(fe("mean_output",1,h)),y&&R.push(fe("inv_std_output",1,h)),b&&R.push(fe("input_skip_bias_sum",t[0].dataType,o,v));let z=st(t[0].dataType);return` + + ${I.registerUniforms(A).declareVariables(...R)} + + ${I.mainStart()} + ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")} + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + let offset = global_idx * hidden_size_vectorized; + var sum = ${ir("f32",v)}; + var squareSum = ${ir("f32",v)}; + for (var i: u32 = 0; i < hidden_size_vectorized; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${m?"bias[i]":z+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${b?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${un(z,v,"value")}; + sum += f32_value; + squareSum += f32_value * f32_value; + } + let mean = ${mr("sum",v)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${mr("squareSum",v)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon); + ${c?"mean_output[global_idx] = mean;":""} + ${y?"inv_std_output[global_idx] = inv_std_dev;":""} + for (var i: u32 = 0; i < hidden_size_vectorized; i++) { + output[offset + i] = (output[offset + i] ${i?"":`- ${z}(mean)`}) * ${z}(inv_std_dev) * gamma[i] ${f?"+ beta[i]":""}; + } + }`},T=[{dims:o,dataType:t[0].dataType}];return r>1&&T.push({dims:h,dataType:1}),r>2&&T.push({dims:h,dataType:1}),r>3&&T.push({dims:a,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${v};${c};${y};${b}`,inputDependencies:t.map((I,A)=>"type")},getShaderSource:x,getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(u/l/64)},programUniforms:C})}},If=(t,e)=>{qd(t.inputs);let r=[0];t.outputCount>1&&r.push(-3),t.outputCount>2&&r.push(-3),t.outputCount>3&&r.push(3),t.compute(jd(t.inputs,e,t.outputCount,!1),{outputs:r})}}),Kd,Fn,Yd,$s,Xd,Qd,Af,Mf,__=Y(()=>{ve(),Ie(),at(),Te(),Kd=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");if(e.axes.length!==0){if(e.axes.length!==e.starts.length||e.axes.length!==e.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(e.starts.length!==e.ends.length)throw new Error("starts and ends must have the same length");t.slice(1).forEach((r,n)=>{if(t[n+1].dataType!==6&&t[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},Fn=(t,e)=>{let r=[];if(t.length>e)if(t[e].dataType===7)t[e].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(t[e].dataType===6)t[e].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${e} must be an array of int32 or int64`);return r},Yd=(t,e)=>{if(t.length>1){let r=Fn(t,1),n=Fn(t,2),i=Fn(t,3);return i.length===0&&(i=[...Array(t[0].dims.length).keys()]),Ge({starts:r,ends:n,axes:i})}else return e},$s=(t,e,r,n,i)=>{let a=t;return t<0&&(a+=r[n[e]]),i[e]<0?Math.max(0,Math.min(a,r[n[e]]-1)):Math.max(0,Math.min(a,r[n[e]]))},Xd=(t,e,r)=>`fn calculateInputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} { + var input_indices: ${t.type.indices}; + var carry = 0u; + for (var i = ${r.length}; i >= 0; i--) { + let input_shape_i = ${xe("uniforms.input_shape","i",r.length)}; + let steps_i = ${xe("uniforms.steps","i",r.length)}; + let signs_i = ${xe("uniforms.signs","i",r.length)}; + let starts_i = ${xe("uniforms.starts","i",r.length)}; + var output_index = ${e.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; + } + ${t.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,Qd=(t,e)=>{let r=t[0].dims,n=G.size(r),i=e.axes.length>0?G.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],a=Fn(t,4);a.forEach(C=>C!==0||(()=>{throw new Error("step cannot be 0")})),a.length===0&&(a=Array(i.length).fill(1));let s=e.starts.map((C,x)=>$s(C,x,r,i,a)),o=e.ends.map((C,x)=>$s(C,x,r,i,a));if(i.length!==s.length||i.length!==o.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==r.length)for(let C=0;CMath.sign(C));a.forEach((C,x,T)=>{if(C<0){let I=(o[x]-s[x])/C,A=s[x],R=A+I*a[x];s[x]=R,o[x]=A,T[x]=-C}});let l=r.slice(0);i.forEach((C,x)=>{l[C]=Math.ceil((o[C]-s[C])/a[C])});let h={dims:l,dataType:t[0].dataType},f=fe("output",t[0].dataType,l.length),m=q("input",t[0].dataType,t[0].dims.length),c=G.size(l),y=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:s.length},{name:"signs",type:"i32",length:u.length},{name:"steps",type:"u32",length:a.length}],b=[{type:12,data:c},{type:12,data:s},{type:6,data:u},{type:12,data:a},..._e(t[0].dims,l)],v=C=>` + ${C.registerUniforms(y).declareVariables(m,f)} + ${Xd(m,f,r)} + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${f.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${f.setByOffset("global_idx",m.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${u.length}_${s.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:v,getRunData:()=>({outputs:[h],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:b})}},Af=(t,e)=>{Kd(t.inputs,e);let r=Yd(t.inputs,e);t.compute(Qd(t.inputs,r),{inputs:[0]})},Mf=t=>{let e=t.starts,r=t.ends,n=t.axes;return Ge({starts:e,ends:r,axes:n})}}),Jd,Zd,Of,zf,y_=Y(()=>{ve(),Ie(),at(),Te(),Jd=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},Zd=(t,e)=>{let r=t.dims,n=G.size(r),i=64,a=e.axis;if(a<0&&(a=r.length+a),aC===4?`max(max(${v}.x, ${v}.y), max(${v}.z, ${v}.w))`:C===2?`max(${v}.x, ${v}.y)`:C===3?`max(max(${v}.x, ${v}.y), ${v}.z)`:v,f=q("x",t.dataType,t.dims,u),m=fe("result",t.dataType,t.dims,u),c=f.type.value,y=st(t.dataType)==="f32"?`var threadMax = ${c}(-3.402823e+38f);`:`var threadMax = ${c}(-65504.0h);`,b=v=>` + var rowMaxShared : ${c}; + var rowSumShared : ${c}; + var threadShared : array<${c}, ${i}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${c} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${c}) { + let index = row * row_stride + col; + result[index] = value; + } + ${v.registerUniform("packedCols","i32").declareVariables(f,m)} + ${v.mainStart()} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${i}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${y} + for (var col = lindex; col < cols; col += wg) { + let value = getValue(row, col, row_stride); + threadMax = max(threadMax, value); + } + if (lindex < cols) { + threadShared[lindex] = threadMax; + } + workgroupBarrier(); + + var reduceSize = min(cols, wg); + for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) { + reduceSize = currSize + (reduceSize & 1); + if (lindex < currSize) { + threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]); + } + workgroupBarrier(); + } + if (lindex == 0) { + rowMaxShared = ${c}(${h("threadShared[0]",u)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${c}(0.0); + for (var col = lindex; col < cols; col += wg) { + let subExp = exp(getValue(row, col, row_stride) - rowMaxShared); + threadSum += subExp; + } + threadShared[lindex] = threadSum; + workgroupBarrier(); + + for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) { + if (lindex < currSize) { + threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize]; + } + workgroupBarrier(); + } + if (lindex == 0) { + rowSumShared = ${c}(${mr("threadShared[0]",u)}); + } + workgroupBarrier(); + + // calculate final value for each element in the row + for (var col = lindex; col < cols; col += wg) { + let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared; + setValue(row, col, row_stride, value); + } + }`;return{name:"Softmax",shaderCache:{hint:`${u}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:6,data:l}]}),getShaderSource:b}},Of=(t,e)=>{Jd(t.inputs),t.compute(Zd(t.inputs[0],e))},zf=t=>Ge({axis:t.axis})}),ec,tc,rc,nc,ac,Rf,Pf,w_=Y(()=>{ve(),Ie(),at(),Te(),ec=t=>{if(!t||t.length<1)throw new Error("too few inputs")},tc=(t,e)=>{let r=[],n=e.numOutputs;return t[1].dims[0]>0&&(t[1].getBigInt64Array().forEach(i=>r.push(Number(i))),n=r.length),Ge({numOutputs:n,axis:e.axis,splitSizes:r})},rc=t=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${t}u; i += 1u ) { + if (index < ${xe("uniforms.size_in_split_axis","i",t)}) { + return i; + } + } + return ${t}u; +}`,nc=t=>{let e=t.length,r=[];for(let n=0;n{let r=t[0].dims,n=G.size(r),i=t[0].dataType,a=G.normalizeAxis(e.axis,r.length),s=new Array(e.numOutputs),o=q("input",i,r.length),u=new Array(e.numOutputs),l=[],h=[],f=0,m=[{type:12,data:n}];for(let y=0;y` + ${y.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",u.length).declareVariables(o,...s)} + ${rc(u.length)} + ${nc(s)} + + ${y.mainStart()} + ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${o.offsetToIndices("global_idx")}; + var index = ${o.indicesGet("indices",a)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${xe("uniforms.size_in_split_axis","output_number - 1u",u.length)}; + ${o.indicesSet("indices",a,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:c,getRunData:()=>({outputs:l,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:m})}},Rf=(t,e)=>{ec(t.inputs);let r=t.inputs.length===1?e:tc(t.inputs,e);t.compute(ac(t.inputs,r),{inputs:[0]})},Pf=t=>{let e=t.axis,r=t.splitSizes,n=t.numOutputs<0?r.length:t.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Ge({axis:e,numOutputs:n,splitSizes:r})}}),xs,ic,sc,oc,Bf,b_=Y(()=>{ve(),Ie(),Te(),xs=t=>Array.from(t.getBigInt64Array(),Number),ic=t=>{if(!t||t.length!==2)throw new Error("Tile requires 2 inputs.");if(t[0].dataType!==1&&t[0].dataType!==6&&t[0].dataType!==12)throw new Error("Tile only support float, int32, and uint32 data types");if(t[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(t[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(xs(t[1]).length!==t[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},sc=(t,e)=>{let r=[];for(let n=0;n{let e=t[0].dims,r=xs(t[1]),n=sc(e,r),i=G.size(n),a=t[0].dataType,s=q("input",a,e.length),o=fe("output",a,n.length),u=l=>` + const inputShape = ${s.indices(...e)}; + ${l.registerUniform("output_size","u32").declareVariables(s,o)} + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${o.offsetToIndices("global_idx")}; + var input_indices: ${s.type.indices}; + for (var i = 0; i < ${e.length}; i++) { + let input_dim_i = ${s.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${o.indicesGet("output_indices","i")} % input_dim_i; + + ${s.indicesSet("input_indices","i","input_dim_value")} + } + ${o.setByOffset("global_idx",s.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},..._e(t[0].dims,n)]}),getShaderSource:u}},Bf=t=>{ic(t.inputs),t.compute(oc(t.inputs),{inputs:[0]})}}),uc,lc,Df,v_=Y(()=>{ve(),Ie(),Te(),uc=(t,e,r,n,i)=>{let a=fe("output_data",i,r.length,4),s=q("a_data",e[1].dataType,e[1].dims.length,4),o=q("b_data",e[2].dataType,e[2].dims.length,4),u=q("c_data",e[0].dataType,e[0].dims.length,4),l,h=(f,m,c)=>`select(${m}, ${f}, ${c})`;if(!n)l=a.setByOffset("global_idx",h(s.getByOffset("global_idx"),o.getByOffset("global_idx"),u.getByOffset("global_idx")));else{let f=(m,c,y="")=>{let b=`a_data[index_a${c}][component_a${c}]`,v=`b_data[index_b${c}][component_b${c}]`,C=`bool(c_data[index_c${c}] & (0xffu << (component_c${c} * 8)))`;return` + let output_indices${c} = ${a.offsetToIndices(`global_idx * 4u + ${c}u`)}; + let offset_a${c} = ${s.broadcastedIndicesToOffset(`output_indices${c}`,a)}; + let offset_b${c} = ${o.broadcastedIndicesToOffset(`output_indices${c}`,a)}; + let offset_c${c} = ${u.broadcastedIndicesToOffset(`output_indices${c}`,a)}; + let index_a${c} = offset_a${c} / 4u; + let index_b${c} = offset_b${c} / 4u; + let index_c${c} = offset_c${c} / 4u; + let component_a${c} = offset_a${c} % 4u; + let component_b${c} = offset_b${c} % 4u; + let component_c${c} = offset_c${c} % 4u; + ${m}[${c}] = ${y}(${h(b,v,C)}); + `};i===9?l=` + var data = vec4(0); + ${f("data",0,"u32")} + ${f("data",1,"u32")} + ${f("data",2,"u32")} + ${f("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:l=` + ${f("output_data[global_idx]",0)} + ${f("output_data[global_idx]",1)} + ${f("output_data[global_idx]",2)} + ${f("output_data[global_idx]",3)} + `}return` + ${t.registerUniform("vec_size","u32").declareVariables(u,s,o,a)} + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${l} + }`},lc=t=>{let e=t[1].dims,r=t[2].dims,n=t[0].dims,i=t[1].dataType,a=!(G.areEqual(e,r)&&G.areEqual(r,n)),s=e,o=G.size(e);if(a){let l=pn.calcShape(pn.calcShape(e,r,!1),n,!1);if(!l)throw new Error("Can't perform where op on the given tensors");s=l,o=G.size(s)}let u=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:l=>uc(l,t,s,a,i),getRunData:()=>({outputs:[{dims:s,dataType:i}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:u},..._e(n,e,r,s)]})}},Df=t=>{t.compute(lc(t.inputs))}}),Nf,$_=Y(()=>{V0(),qp(),G0(),H0(),q0(),j0(),Gp(),Hh(),J0(),Z0(),e_(),t_(),r_(),n_(),a_(),i_(),s_(),o_(),u_(),Gh(),l_(),d_(),c_(),p_(),h_(),go(),f_(),m_(),g_(),__(),y_(),w_(),b_(),na(),_o(),v_(),Nf=new 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l={kernelId:this.backend.currentKernelId,computePipeline:t.computePipeline,bindGroup:u,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(l)}s.setPipeline(t.computePipeline),s.setBindGroup(0,u),s.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),Mt(t.programInfo.name)}dispose(){}build(t,e){Ht(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let i=_p(e,this.backend.device.limits),a=t.getShaderSource(i),s=`${n.join(` +`)} +${i.additionalImplementations} +${a}`,o=r.createShaderModule({code:s,label:t.name});Qe("verbose",()=>`[WebGPU] ${t.name} shader code: ${s}`);let u=r.createComputePipeline({compute:{module:o,entryPoint:"main"},layout:"auto",label:t.name});return Mt(t.name),{programInfo:t,computePipeline:u,uniformVariablesInfo:i.variablesInfo}}normalizeDispatchGroupSize(t){let e=typeof t=="number"?t:t.x,r=typeof t=="number"?1:t.y||1,n=typeof t=="number"?1:t.z||1,i=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(e<=i&&r<=i&&n<=i)return[e,r,n];let a=e*r*n,s=Math.ceil(Math.sqrt(a));if(s>i){if(s=Math.ceil(Math.cbrt(a)),s>i)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[s,s,s]}else return[s,s,1]}}}),dc,cc,pc,Lf,S_=Y(()=>{qt(),ve(),Lr(),L0(),W0(),$_(),x_(),dc=(t,e)=>{if(e.length!==t.length)throw new Error(`inputDependencies length ${e.length} is not equal to inputTensors length ${t.length}.`);let r=[];for(let n=0;n{let n=t.name;return t.shaderCache?.hint&&(n+="["+t.shaderCache.hint+"]"),n+=":"+r+`:${dc(e,t.shaderCache?.inputDependencies??new 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R=this.gpuDataManager.create(x,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(R.buffer,0,A,0,x),this.gpuDataManager.release(R.id),c={offset:0,size:x,buffer:R.buffer}}let y=this.programManager.normalizeDispatchGroupSize(u),b=y[1]===1&&y[2]===1,v=cc(t,e,b),C=this.programManager.getArtifact(v);if(C||(C=this.programManager.build(t,y),this.programManager.setArtifact(v,C),Qe("info",()=>`[artifact] key: ${v}, programName: ${t.name}`)),l&&C.uniformVariablesInfo){if(l.length!==C.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${C.uniformVariablesInfo.length}, got ${l.length} in program "${C.programInfo.name}".`);for(let x=0;x`[ProgramManager] run "${t.name}" (key=${v}) with ${y[0]}x${y[1]}x${y[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let x={kernelId:this.currentKernelId,programName:C.programInfo.name,inputTensorViews:e,outputTensorViews:f};this.pendingKernels.push(x),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(x)}return this.programManager.run(C,s,m,y,c),Mt(t.name),f}upload(t,e){this.gpuDataManager.upload(t,e)}memcpy(t,e){this.gpuDataManager.memcpy(t,e)}async download(t,e){await this.gpuDataManager.download(t,e)}alloc(t){return this.gpuDataManager.create(t).id}free(t){return this.gpuDataManager.release(t)}createKernel(t,e,r,n){let i=Nf.get(t);if(!i)throw new Error(`kernel not implemented: ${t}`);let a={kernelType:t,kernelName:n,kernelEntry:i[0],attributes:[i[1],r]};this.kernels.set(e,a)}releaseKernel(t){let e=this.kernelPersistentData.get(t);if(e){for(let r of e)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(t)}this.kernelCustomData.delete(t),this.kernels.delete(t)}computeKernel(t,e,r){let n=this.kernels.get(t);if(!n)throw new Error(`kernel not 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c=a+i.kvSequenceLength,p=[i.batchSize,i.numHeads,i.sequenceLength,c],h=u.scale===0?1/Math.sqrt(i.headSize):u.scale,d=Me(i.headSize),y=i.headSize/d,w=12,_={x:Math.ceil(c/w),y:Math.ceil(i.sequenceLength/w),z:i.batchSize*i.numHeads},v=[{type:12,data:i.sequenceLength},{type:12,data:y},{type:12,data:c},{type:12,data:i.numHeads},{type:1,data:h}],S=o?["type","type","type"]:["type","type"],A=I=>{let x=U("q",t.dataType,t.dims,d),E=U("key",r.dataType,r.dims,d),P=[x,E];o&&P.push(U("relative_position_bias",o.dataType,o.dims));let O=j("output",t.dataType,p),R=et(1,d),L=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"}];return`\n const TILE_SIZE = ${w}u;\n\n var tileQ: array<${x.type.storage}, ${w*w}>;\n var tileK: array<${x.type.storage}, ${w*w}>;\n ${I.registerUniforms(L).declareVariables(...P,O)}\n ${I.mainStart([w,w,1])}\n // x holds the N and y holds the M\n let headIdx = workgroup_id.z;\n let m = workgroup_id.y * TILE_SIZE;\n let n = workgroup_id.x * TILE_SIZE;\n let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;\n let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;\n\n var value = ${R}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {\n tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];\n }\n if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {\n tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];\n }\n workgroupBarrier();\n\n for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {\n value += ${R}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);\n }\n\n workgroupBarrier();\n }\n\n let headOffset = headIdx * uniforms.M * uniforms.N;\n if (global_id.y < uniforms.M && global_id.x < uniforms.N) {\n let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;\n var sum: f32 = ${(()=>{switch(d){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${d}`)}})()};\n output[outputIdx] = ${O.type.value} (sum * uniforms.alpha) + ${o?"relative_position_bias[outputIdx]":"0.0"};\n }\n }`};return{name:"AttentionProbs",shaderCache:{hint:`${d}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:p,dataType:t.dataType,gpuDataType:0}],dispatchGroup:_,programUniforms:v}),getShaderSource:A}},nc=(e,t,r,o,i)=>{let u=i+o.kvSequenceLength,a=[o.batchSize,o.sequenceLength,o.vHiddenSize],c=12,p={x:Math.ceil(o.vHeadSize/c),y:Math.ceil(o.sequenceLength/c),z:o.batchSize*o.numHeads},h=[{type:12,data:o.sequenceLength},{type:12,data:u},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.vHiddenSize}];return{name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType,gpuDataType:0}],dispatchGroup:p,programUniforms:h}),getShaderSource:w=>{let _=U("probs",t.dataType,t.dims),v=U("v",r.dataType,r.dims),S=j("output",t.dataType,a),A=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`\n const TILE_SIZE = ${c}u;\n var tileQ: array<${_.type.value}, ${c*c}>;\n var tileK: array<${_.type.value}, ${c*c}>;\n ${w.registerUniforms(A).declareVariables(_,v,S)}\n ${w.mainStart([c,c,1])}\n let headIdx = workgroup_id.z;\n let m = global_id.y;\n let n = global_id.x;\n\n let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n let offsetB = headIdx * (uniforms.N * uniforms.K) + n;\n\n var value = ${_.type.storage}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m < uniforms.M && w + local_id.x < uniforms.K) {\n tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n }\n if (n < uniforms.N && w + local_id.y < uniforms.K) {\n tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n }\n workgroupBarrier();\n for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {\n value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];\n }\n workgroupBarrier();\n }\n\n // we need to transpose output from BNSH_v to BSND_v\n let batchIdx = workgroup_id.z / uniforms.num_heads;\n let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;\n if (m < uniforms.M && n < uniforms.N) {\n let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size\n + currentBatchHeadNumber * uniforms.N + n;\n output[outputIdx] = value;\n }\n }`}}},Pn=(e,t,r,o,i,u,a,c,p,h,d)=>{let y=e.outputCount>1,w=e.outputCount>2,_=y&&w?h.pastSequenceLength:0,v=_+h.kvSequenceLength,S=[h.batchSize,h.numHeads,v,h.headSize],A=a?[a,r]:[r],I=y?e.compute(En(A,2,S,r.dataType),{inputs:A,outputs:[1]})[0]:r,x=[h.batchSize,h.numHeads,v,h.headSize],E=c?[c,o]:[o],P=w?e.compute(En(E,2,x,o.dataType),{inputs:E,outputs:[2]})[0]:o,O=[t,I];p&&O.push(p);let R=e.compute(rc(e,t,I,p,h,d,_),{inputs:O,outputs:[-1]})[0];e.compute(tc(e,R,h.batchSize*h.numHeads*h.sequenceLength,v),{inputs:[R],outputs:[]});let L=[R,P];e.compute(nc(e,R,P,h,_),{inputs:L,outputs:[0]})},oc=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],o=t.sequenceLength,i=t.inputHiddenSize,u=t.headSize,a=12,c={x:Math.ceil(t.headSize/a),y:Math.ceil(t.sequenceLength/a),z:t.batchSize*t.numHeads},p=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:o},{type:12,data:i},{type:12,data:u},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],d=y=>{let w=j("output_q",p[0].dataType,r),_=j("output_k",p[0].dataType,r),v=j("output_v",p[0].dataType,r),S=U("input",p[0].dataType,p[0].dims),A=U("weight",p[1].dataType,p[1].dims),I=U("bias",p[2].dataType,p[2].dims),x=S.type.storage,E=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`\n const TILE_SIZE = ${a}u;\n var tileInput: array<${x}, ${a*a}>;\n var tileWeightQ: array<${x}, ${a*a}>;\n var tileWeightK: array<${x}, ${a*a}>;\n var tileWeightV: array<${x}, ${a*a}>;\n ${y.registerUniforms(E).declareVariables(S,A,I,w,_,v)}\n ${y.mainStart([a,a,1])}\n let batchIndex = workgroup_id.z / uniforms.num_heads;\n let headNumber = workgroup_id.z % uniforms.num_heads;\n let m = global_id.y;\n let n = global_id.x;\n\n let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;\n let biasOffsetQ = headNumber * uniforms.head_size;\n let biasOffsetK = uniforms.hidden_size + biasOffsetQ;\n let biasOffsetV = uniforms.hidden_size + biasOffsetK;\n\n var valueQ = ${x}(0);\n var valueK = ${x}(0);\n var valueV = ${x}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m < uniforms.M && w + local_id.x < uniforms.K) {\n tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n }\n if (n < uniforms.N && w + local_id.y < uniforms.K) {\n let offset = n + (w + local_id.y) * uniforms.ldb;\n tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n }\n workgroupBarrier();\n for (var k: u32 = 0u; k({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:c,programUniforms:h}),getShaderSource:d},{inputs:p,outputs:[-1,-1,-1]})},Xa=(e,t)=>{let r=ec(e.inputs,t),[o,i,u]=oc(e,r);return Pn(e,o,i,u,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var ic,ac,sc,Qa,Ja=Y(()=>{"use strict";$r();ye();Se();Ze();_e();ic=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(o,i,u)=>{let a=i.length;if(a!==o.length)throw new Error(`${u}: num dimensions != ${a}`);i.forEach((c,p)=>{if(c!==o[p])throw new Error(`${u}: dim[${p}] do not match`)})};if(e[0].dims.length>1){let o=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,o,"Invalid input scale"),r(e[2].dims,o,"Invalid input B"),r(e[3].dims,o,"Invalid input mean"),r(e[4].dims,o,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input var")},ac=(e,t)=>{let{epsilon:r,spatial:o,format:i}=t,u=e[0].dims,a=o?Me(u[u.length-1]):1,c=i==="NHWC"&&u.length>1?a:1,p=M.size(u)/a,h=o,d=h?u.length:u,y=U("x",e[0].dataType,e[0].dims,a),w=U("scale",e[1].dataType,e[1].dims,c),_=U("bias",e[2].dataType,e[2].dims,c),v=U("inputMean",e[3].dataType,e[3].dims,c),S=U("inputVar",e[4].dataType,e[4].dims,c),A=j("y",e[0].dataType,d,a),I=()=>{let E="";if(o)E=`let cOffset = ${u.length===1?"0u":i==="NHWC"?`outputIndices[${u.length-1}] / ${a}`:"outputIndices[1]"};`;else if(i==="NCHW")E=`\n ${A.indicesSet("outputIndices","0","0")}\n let cOffset = ${A.indicesToOffset("outputIndices")};`;else{E=`var cIndices = ${w.type.indices}(0);\n cIndices[0] = outputIndices[${u.length-1}];`;for(let P=1;P`\n const epsilon = ${r};\n ${E.registerUniform("outputSize","u32").declareVariables(y,w,_,v,S,A)}\n ${E.mainStart()}\n ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n var outputIndices = ${A.offsetToIndices(`global_idx * ${a}`)};\n ${I()}\n let scale = ${w.getByOffset("cOffset")};\n let bias = ${_.getByOffset("cOffset")};\n let inputMean = ${v.getByOffset("cOffset")};\n let inputVar = ${S.getByOffset("cOffset")};\n let x = ${y.getByOffset("global_idx")};\n let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;\n ${A.setByOffset("global_idx","value")}\n }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${o}_${a}`,inputDependencies:h?["rank","type","type","type","type"]:void 0},getShaderSource:x,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h?[{type:12,data:p},...Z(u)]:[{type:12,data:p}]})}},sc=e=>ve(e),Qa=(e,t)=>{let{inputs:r,outputCount:o}=e,i=sc({...t,outputCount:o});if(vr.webgpu.validateInputContent&&ic(r,i),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(ac(r,i))}});var uc,dc,es,ts=Y(()=>{"use strict";Se();_e();uc=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},dc=e=>{let t=e[0].dims,r=e[0].dims[2],o=M.size(t)/4,i=e[0].dataType,u=U("input",i,t,4),a=U("bias",i,[r],4),c=U("residual",i,t,4),p=j("output",i,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:d=>`\n const channels = ${r}u / 4;\n ${d.declareVariables(u,a,c,p)}\n\n ${d.mainStart()}\n ${d.guardAgainstOutOfBoundsWorkgroupSizes(o)}\n let value = ${u.getByOffset("global_idx")}\n + ${a.getByOffset("global_idx % channels")} + ${c.getByOffset("global_idx")};\n ${p.setByOffset("global_idx","value")}\n }`}},es=e=>{uc(e.inputs),e.compute(dc(e.inputs))}});var lc,ke,rs,ns,os,is,as,ss,us,ds,ls,cc,cs,ps,ms,fs,kn,hs,On,gs,ys,bs,ws,vs,$s,_s,Ss,xs,Cs,As,Is,Ts,Es,Ps,ks,Os,Rs,Bo,Do,Bs,Ds,zs,Rn=Y(()=>{"use strict";ye();Se();Ze();_e();lc=(e,t,r,o,i,u)=>{let a=Math.ceil(t/4),c="";typeof i=="string"?c=`${i}(a)`:c=i("a");let p=U("inputData",r,[a],4),h=j("outputData",o,[a],4);return`\n ${e.registerUniform("vec_size","u32").declareVariables(p,h)}\n\n ${u??""}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n\n let a = ${p.getByOffset("global_idx")};\n ${h.setByOffset("global_idx",c)}\n }`},ke=(e,t,r,o,i,u=e.dataType)=>({name:t,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:a=>lc(a,M.size(e.dims),e.dataType,u,r,o),getRunData:a=>({outputs:[{dims:e.dims,dataType:u}],dispatchGroup:{x:Math.ceil(M.size(a[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(e.dims)/4)}]})}),rs=e=>{e.compute(ke(e.inputs[0],"Abs","abs"))},ns=e=>{e.compute(ke(e.inputs[0],"Acos","acos"))},os=e=>{e.compute(ke(e.inputs[0],"Acosh","acosh"))},is=e=>{e.compute(ke(e.inputs[0],"Asin","asin"))},as=e=>{e.compute(ke(e.inputs[0],"Asinh","asinh"))},ss=e=>{e.compute(ke(e.inputs[0],"Atan","atan"))},us=e=>{e.compute(ke(e.inputs[0],"Atanh","atanh"))},ds=e=>ve(e),ls=(e,t)=>{let r;switch(t.to){case 10:r="vec4";break;case 1:r="vec4";break;case 12:r="vec4";break;case 6:r="vec4";break;case 9:r="vec4";break;default:throw new RangeError(`not supported type (specified in attribute \'to\' from \'Cast\' operator): ${t.to}`)}e.compute(ke(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},cc=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:xn,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:Cn;return ve({min:t,max:r})},cs=(e,t)=>{let r=e.inputs.length===1?t:cc(e.inputs),o=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Clip",i=>`clamp(${i}, clip_min_, clip_max_)`,`\n const clip_min_: vec4<${o}> = vec4(${o}(${r.min}));\n const clip_max_: vec4<${o}> = vec4(${o}(${r.max}));\n`,r.cacheKey),{inputs:[0]})},ps=e=>{e.compute(ke(e.inputs[0],"Ceil","ceil"))},ms=e=>{e.compute(ke(e.inputs[0],"Cos","cos"))},fs=e=>{e.compute(ke(e.inputs[0],"Cosh","cosh"))},kn=e=>ve(e),hs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Elu",o=>`elu_vf32(${o})`,`\n const elu_alpha_ = ${r}(${t.alpha});\n\n fn elu_f32(a: ${r}) -> ${r} {\n return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);\n }\n\n fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {\n return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));\n }`,t.cacheKey))},On=(e="f32")=>`\nconst r0: ${e} = 0.3275911;\nconst r1: ${e} = 0.254829592;\nconst r2: ${e} = -0.284496736;\nconst r3: ${e} = 1.421413741;\nconst r4: ${e} = -1.453152027;\nconst r5: ${e} = 1.061405429;\n\nfn erf_vf32(v: vec4<${e}>) -> vec4<${e}> {\n let absv = abs(v);\n let x = 1.0 / (1.0 + r0 * absv);\n return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));\n}`,gs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,On(t)))},ys=e=>{e.compute(ke(e.inputs[0],"Exp","exp"))},bs=e=>{e.compute(ke(e.inputs[0],"Floor","floor"))},ws=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,On(t)))},vs=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"LeakyRelu",o=>`select(leaky_relu_alpha_ * ${o}, ${o}, ${o} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},$s=e=>{e.compute(ke(e.inputs[0],"Not",t=>`!${t}`))},_s=e=>{e.compute(ke(e.inputs[0],"Neg",t=>`-${t}`))},Ss=e=>{e.compute(ke(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},xs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},Cs=e=>{e.compute(ke(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},As=e=>ve(e),Is=(e,t)=>{let r=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"HardSigmoid",o=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${o} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},Ts=e=>{e.compute(ke(e.inputs[0],"Sin","sin"))},Es=e=>{e.compute(ke(e.inputs[0],"Sinh","sinh"))},Ps=e=>{e.compute(ke(e.inputs[0],"Sqrt","sqrt"))},ks=e=>{e.compute(ke(e.inputs[0],"Tan","tan"))},Os=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Rs=e=>{e.compute(ke(e.inputs[0],"Tanh",Os))},Bo=(e="f32")=>`\nconst fast_gelu_a: ${e} = 0.5;\nconst fast_gelu_b: ${e} = 0.7978845608028654;\nconst fast_gelu_c: ${e} = 0.035677408136300125;\n\nfn tanh_v(v: vec4<${e}>) -> vec4<${e}> {\n return ${Os("v")};\n}\n`,Do=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Bs=e=>{let t=et(e.inputs[0].dataType);e.compute(ke(e.inputs[0],"FastGelu",Do,Bo(t),void 0,e.inputs[0].dataType))},Ds=(e,t)=>{let r=et(e.inputs[0].dataType);return e.compute(ke(e.inputs[0],"ThresholdedRelu",o=>`select(vec4<${r}>(0.0), ${o}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},zs=e=>{e.compute(ke(e.inputs[0],"Log","log"))}});var pc,mc,Us,Vs=Y(()=>{"use strict";Se();_e();Rn();pc=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},mc=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=U("input",e[0].dataType,e[0].dims,4),o=U("bias",e[0].dataType,[e[0].dims[2]],4),i=j("output",e[0].dataType,t,4),u=M.size(t)/4,a=De(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)}}),getShaderSource:p=>`\n const M_SQRT2 = sqrt(2.0);\n const halfChannels = ${e[0].dims[2]/4/2}u;\n\n ${p.declareVariables(r,o,i)}\n\n ${On(a)}\n\n ${p.mainStart()}\n ${p.guardAgainstOutOfBoundsWorkgroupSizes(u)}\n let biasIdx = global_idx % halfChannels;\n let batchIndex = global_idx / halfChannels;\n let inputOffset = biasIdx + batchIndex * halfChannels * 2;\n let valueLeft = input[inputOffset] + bias[biasIdx];\n let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];\n let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);\n\n ${i.setByOffset("global_idx","valueLeft * geluRight")}\n }`}},Us=e=>{pc(e.inputs),e.compute(mc(e.inputs))}});var fc,hc,Ot,Ws,Ns,Gs,Hs,Ls,Fs,qs,js,Ks,Ys,Zs=Y(()=>{"use strict";ye();Se();_e();fc=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w,_;typeof c=="string"?w=_=(x,E)=>`${c}((${x}),(${E}))`:typeof c=="function"?w=_=c:(w=c.scalar,_=c.vector);let v=j("outputData",d,o.length,4),S=U("aData",p,t.length,4),A=U("bData",h,r.length,4),I;if(i)if(u){let x=M.size(t)===1,E=M.size(r)===1,P=t.length>0&&t[t.length-1]%4===0,O=r.length>0&&r[r.length-1]%4===0;x||E?I=v.setByOffset("global_idx",_(x?`${S.type.value}(${S.getByOffset("0")}.x)`:S.getByOffset("global_idx"),E?`${A.type.value}(${A.getByOffset("0")}.x)`:A.getByOffset("global_idx"))):I=`\n let outputIndices = ${v.offsetToIndices("global_idx * 4u")};\n let offsetA = ${S.broadcastedIndicesToOffset("outputIndices",v)};\n let offsetB = ${A.broadcastedIndicesToOffset("outputIndices",v)};\n ${v.setByOffset("global_idx",_(a||P?S.getByOffset("offsetA / 4u"):`${S.type.value}(${S.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||O?A.getByOffset("offsetB / 4u"):`${A.type.value}(${A.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}\n `}else I=v.setByOffset("global_idx",_(S.getByOffset("global_idx"),A.getByOffset("global_idx")));else{if(!u)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let x=(E,P,O="")=>{let R=`aData[indexA${P}][componentA${P}]`,L=`bData[indexB${P}][componentB${P}]`;return`\n let outputIndices${P} = ${v.offsetToIndices(`global_idx * 4u + ${P}u`)};\n let offsetA${P} = ${S.broadcastedIndicesToOffset(`outputIndices${P}`,v)};\n let offsetB${P} = ${A.broadcastedIndicesToOffset(`outputIndices${P}`,v)};\n let indexA${P} = offsetA${P} / 4u;\n let indexB${P} = offsetB${P} / 4u;\n let componentA${P} = offsetA${P} % 4u;\n let componentB${P} = offsetB${P} % 4u;\n ${E}[${P}] = ${O}(${w(R,L)});\n `};d===9?I=`\n var data = vec4(0);\n ${x("data",0,"u32")}\n ${x("data",1,"u32")}\n ${x("data",2,"u32")}\n ${x("data",3,"u32")}\n outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:I=`\n ${x("outputData[global_idx]",0)}\n ${x("outputData[global_idx]",1)}\n ${x("outputData[global_idx]",2)}\n ${x("outputData[global_idx]",3)}\n `}return`\n ${e.registerUniform("vec_size","u32").declareVariables(S,A,v)}\n\n ${y??""}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${I}\n }`},hc=(e,t,r,o,i,u,a=r.dataType)=>{let c=!M.areEqual(r.dims,o.dims),p=r.dims,h=M.size(r.dims),d=!1,y=!1,w=[c];if(c){let _=It.calcShape(r.dims,o.dims,!1);if(!_)throw new Error("Can\'t perform binary op on the given tensors");p=_,h=M.size(p);let v=M.size(r.dims)===1,S=M.size(o.dims)===1,A=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,I=o.dims.length>0&&o.dims[o.dims.length-1]%4===0;w.push(v),w.push(S),w.push(A),w.push(I);let x=1;for(let E=1;E_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>fc(_,r.dims,o.dims,p,d,c,y,i,r.dataType,o.dataType,a,u),getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(h/64/4)},programUniforms:[{type:12,data:Math.ceil(M.size(p)/4)},...Z(r.dims,o.dims,p)]})}},Ot=(e,t,r,o,i,u)=>{e.compute(hc(t,i??"",e.inputs[0],e.inputs[1],r,o,u))},Ws=e=>{Ot(e,"Add",(t,r)=>`${t}+${r}`)},Ns=e=>{Ot(e,"Div",(t,r)=>`${t}/${r}`)},Gs=e=>{Ot(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4(${t}==${r})`},void 0,void 0,9)},Hs=e=>{Ot(e,"Mul",(t,r)=>`${t}*${r}`)},Ls=e=>{let t=U("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Ot(e,"Pow",{scalar:(o,i)=>`pow_custom(${o},${i})`,vector:(o,i)=>`pow_vector_custom(${o},${i})`},`\n fn pow_custom(a : ${t}, b : ${t}) -> ${t} {\n if (b == ${t}(0.0)) {\n return ${t}(1.0);\n } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {\n return ${t}(pow(f32(a), f32(b))); // NaN\n }\n return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));\n }\n fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {\n // TODO: implement vectorized pow\n return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));\n }\n `)},Fs=e=>{Ot(e,"Sub",(t,r)=>`${t}-${r}`)},qs=e=>{Ot(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4(${t}>${r})`},void 0,void 0,9)},js=e=>{Ot(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4(${t}<${r})`},void 0,void 0,9)},Ks=e=>{Ot(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4(${t}>=${r})`},void 0,void 0,9)},Ys=e=>{Ot(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4(${t}<=${r})`},void 0,void 0,9)}});var St,xt,Ct,Bn,Ft=Y(()=>{"use strict";ye();Se();St=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},xt=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Ct=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Bn=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[r,o]=e?.activation_params||[.2,.5];return{activation:t,alpha:r,beta:o}}else if(t==="Clip"){let[r,o]=e?.activation_params||[xn,Cn];return{activation:t,clipMax:o,clipMin:r}}else if(t==="LeakyRelu"){let[r]=e?.activation_params||[.01];return{activation:t,alpha:r}}return{activation:t}}});var tt,Dn,zn=Y(()=>{"use strict";tt=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Dn=e=>`\n ${e?"value = value + getBiasByOutputCoords(coords);":""}\n `});var Mn,zo=Y(()=>{"use strict";Mn=e=>`\nfn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`});var yc,bc,Hr,Xs,wc,Lr,vc,Un,Fr=Y(()=>{"use strict";ye();Se();_e();Ft();zn();yc=(e,t)=>e?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRow + innerRow,\n kStart / innerElementSize + inputCol${t?", batchIndices":""});\n `,bc=(e,t)=>e?`\n let ACached0 = mm_Asub[k * innerElementSize][localRow];\n let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n for (var i = 0; i < rowPerThread; i = i + 1) {\n acc[i] = BCached0 * ACached0[i] + acc[i];\n acc[i] = BCached1 * ACached1[i] + acc[i];\n acc[i] = BCached2 * ACached2[i] + acc[i];\n ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n }`:`\n for (var i = 0; i < rowPerThread; i = i + 1) {\n let ACached = mm_Asub[tileRow + i][k];\n acc[i] = BCached0 * ACached.x + acc[i];\n acc[i] = BCached1 * ACached.y + acc[i];\n acc[i] = BCached2 * ACached.z + acc[i];\n ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n }`,Hr=(e,t,r="f32",o,i=!1,u=32,a=!1,c=32)=>{let p=t[1]*e[1],h=t[0]*e[0],d=i?p:u,y=i?u:p,w=d/t[0],_=u/t[1];if(!((i&&w===4&&e[1]===4||!i&&(w===3||w===4))&&d%t[0]===0&&u%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${w} and workPerThread[1] ${e[1]} must be 4.\n Otherwise, innerElementSize ${w} must be 3 or 4.\n tileAWidth ${d} must be divisible by workgroupSize[0]${t[0]}. tileInner ${u} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar mm_Asub: array, ${d/w}>, ${y}>;\nvar mm_Bsub: array, ${h/e[0]}>, ${u}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${w};\nconst tileInner = ${u};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3,\n @builtin(global_invocation_id) globalId : vec3,\n @builtin(workgroup_id) workgroupId : vec3) {\n let localRow = i32(localId.y);\n let tileRow = localRow * rowPerThread;\n let tileCol = i32(localId.x);\n\n let globalRow =i32(globalId.y) * rowPerThread;\n let globalCol = i32(globalId.x);\n let batch = ${a?"0":"i32(globalId.z)"};\n ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let num_tiles = ${a?`${Math.ceil(c/u)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n var kStart = ${a?`i32(globalId.z) * ${c}`:"0"};\n\n var acc: array, rowPerThread>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${_};\n for (var t = 0; t < num_tiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${yc(i,o)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${o?", batchIndices":""});\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n ${w===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n ${bc(i,w)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n}`},Xs=(e,t)=>e?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart + inputCol${t?", batchIndices":""});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRowStart + inputRow,\n kStart + inputCol${t?", batchIndices":""});\n `,wc=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Lr=(e,t,r="f32",o,i=!1,u=32,a=!1,c=32,p=!1)=>{let h=e[1]*t[1],d=e[0]*t[0],y=i?h:u,w=i?u:h;if(!(w%t[1]===0&&y%t[0]===0&&u%t[1]===0))throw new Error(`tileAHight ${w} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${y} must be divisible by workgroupSize[0]${t[0]}, tileInner ${u} must be divisible by workgroupSize[1]${t[1]}`);let _=w/t[1],v=y/t[0],S=u/t[1],A=p?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${h};\n let globalColStart = i32(workgroupId.x) * ${d};\n\n // Loop over shared dimension.\n for (var t = 0; t < num_tiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${w}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${y}; inputCol = inputCol + ${t[0]}) {\n ${Xs(i,o)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${u}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${t[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalColStart + inputCol${o?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${r}, colPerThread>;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n ACached * BCached[innerCol];\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let gCol = globalColStart + localCol + innerCol * ${t[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${h};\n\nlet tileRowA = i32(localId.y) * ${_};\nlet tileColA = i32(localId.x) * ${v};\nlet tileRowB = i32(localId.y) * ${S};\n// Loop over shared dimension.\nfor (var t = 0; t < num_tiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < ${v}; innerCol = innerCol + 1) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${Xs(i,o)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${S}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalCol + innerCol${o?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${r}, colPerThread>;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n ${wc(i)}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n }\n }\n }\n\n workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n}\n`;return`\n var mm_Asub : array, ${w}>;\n var mm_Bsub : array, ${u}>;\n const rowPerThread = ${e[1]};\n const colPerThread = ${e[0]};\n const tileInner = ${u};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3,\n @builtin(global_invocation_id) globalId : vec3,\n @builtin(workgroup_id) workgroupId : vec3) {\n let batch = ${a?"0":"i32(globalId.z)"};\n ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n let num_tiles = ${a?`${Math.ceil(c/u)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};\n var kStart = ${a?`i32(globalId.z) * ${c}`:"0"};\n\n var acc : array, rowPerThread>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${A}\n }\n`},vc=(e,t,r,o,i,u=!1)=>{let[a,c,p]=i,[h,d,y,w]=o,_=_r(a,p),v=_r(c,p),S=De(o[0].type.tensor),A=()=>{let E=d.rank,P=h.rank,O=`var aIndices: ${d.type.indices};`;for(let R=E-2-1,L=P-1;R>=0;R--,L--)O+=`\naIndices[${R}] = ${P>1?`batchIndices[${L}]`:"batchIndices"};`;return _.forEach(R=>{O+=`\naIndices[${R}] = 0;`}),O+=`\naIndices[${E-2}] = u32(row);\n aIndices[${E-1}] = u32(colIn);`,O},I=()=>{let E=y.rank,P=h.rank,O=`var bIndices: ${y.type.indices};`;for(let R=E-2-1,L=P-1;R>=0;R--,L--)O+=`\nbIndices[${R}] = ${P>1?`batchIndices[${L}]`:"batchIndices"};`;return v.forEach(R=>{O+=`\nbIndices[${R}] = 0;`}),O+=`\nbIndices[${E-2}] = u32(row);\n bIndices[${E-1}] = u32(colIn);`,O};return`\n fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${tt(e,S)} {\n var value = ${tt(e,S)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)\n {\n ${A()}\n value = ${d.getByIndices("aIndices")};\n }\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${h.type.indices}) -> ${tt(e,S)} {\n var value = ${tt(e,S)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)\n {\n ${I()}\n value = ${y.getByIndices("bIndices")};\n }\n return value;\n }\n\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${tt(e,S)}) {\n let col = colIn * ${e};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n var value = valueIn;\n let coords = vec3(batch, row, colIn);\n ${t?`value = value + ${u?"bias[colIn]":`${tt(e,S)}(bias[row])`};`:""}\n ${r}\n ${w.setByIndices("vec3(coords)","value")}\n }\n }\n `},Un=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u.slice(0,-2),p=a.slice(0,-2),h=o?o.slice(0,-2):r.slice(0,-2),d=M.size(h),y=u[u.length-2],w=u[u.length-1],_=a[a.length-1],v=w%4===0&&_%4===0,S=y<=8?[4,1,1]:[4,4,1],A=[8,8,1],I=[Math.ceil(_/A[0]/S[0]),Math.ceil(y/A[1]/S[1]),Math.ceil(d/A[2]/S[2])],x=v?4:1,E=[...c,y,w/x],P=E.length,O=[...p,w,_/x],R=O.length,L=[d,y,_/x],N=[{type:6,data:y},{type:6,data:_},{type:6,data:w}];xt(t,N),N.push(...Z(h,E,O));let K=["rank","rank"],Q=e.length>2;Q&&(N.push(...Z(e[2].dims)),K.push("rank")),N.push(...Z(L));let he=W=>{let se=h.length,Ce=An("batchDims",e[0].dataType,se,1),We=De(e[0].dataType),ee=U("a",e[0].dataType,P,x),ae=U("b",e[1].dataType,R,x),Ae=j("result",e[0].dataType,L.length,x),me=[ee,ae];if(Q){let G=i?x:1;me.push(U("bias",e[2].dataType,e[2].dims.length,G))}let ie=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Ct(t,ie);let ue=De(Ae.type.tensor),le=St(t,Ae.type.value,ue),qe=vc(x,Q,le,[Ce,ee,ae,Ae],[c,p,h],i);return`\n ${W.registerUniforms(ie).registerInternalVariables(Ce).declareVariables(...me,Ae)}\n ${qe}\n ${v?Hr(S,A,We,Ce):Lr(S,A,We,Ce)}\n `};return{name:"MatMul",shaderCache:{hint:`${S};${t.activation};${v};${i}`,inputDependencies:K},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:I[0],y:I[1],z:I[2]},programUniforms:N}),getShaderSource:he}}});var $c,Qs,Js=Y(()=>{"use strict";ye();Lt();_e();Ft();zn();zo();Fr();$c=(e,t,r,o,i=!1,u,a=4,c=4,p=4,h="f32")=>{let d=Q=>{switch(Q){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Q} is not supported.`)}},y=Q=>{switch(Q){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${Q} is not supported.`)}},w=e?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,_=e?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,v=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",S=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",A=e?"row":"col",I=e?"col":"row",x=`\n let inChannels = i32(uniforms.w_shape[2]);\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n let outRow = ${A} / outWidth;\n let outCol = ${A} % outWidth;\n\n let WRow = ${I} / (i32(uniforms.w_shape[1]) * inChannels);\n let WCol = ${I} / inChannels % i32(uniforms.w_shape[1]);\n let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n let xCh = ${I} % inChannels;\n var resData = ${tt(a,h)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the \'same\' padding type.\n if (xRow >= 0 && xRow < ${v} && xCol >= 0 && xCol < ${S}) {\n ${w}\n let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape));\n ${d(a)}\n }\n return resData;`,E=e?t&&o?`\n let col = colIn * ${a};\n ${x}`:`\n let col = colIn * ${a};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n ${x}\n }\n return ${tt(a,h)}(0.0);`:o&&r?`\n let col = colIn * ${a};\n ${x}`:`\n let col = colIn * ${a};\n if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n ${x}\n }\n return ${tt(a,h)}(0.0);`,P=`${y(c)}`,O=tt(p,h),R=e?tt(a,h):tt(c,h),L=e?tt(c,h):tt(a,h),N=St(u,O,h);return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} {\n ${e?E:P}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${L} {\n ${e?P:E}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${O}) {\n let col = colIn * ${p};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)\n {\n var value = valueIn;\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n ${_}\n ${Dn(i)}\n ${N}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`},Qs=(e,t,r,o,i,u,a,c)=>{let p=t.format==="NHWC",h=p?e[0].dims[3]:e[0].dims[1],d=r[0],y=p?r[2]:r[3],w=p?r[1]:r[2],_=p?r[3]:r[1],v=p&&(h%4===0||h%3===0)&&_%4===0,S=p?_:y*w,A=p?y*w:_,I=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/I[0]/x[0]),Math.ceil(A/I[1]/x[1]),Math.ceil(d/I[2]/x[2])];Ve("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let P=v?p&&h%4!==0?3:4:1,O=I[1]*x[1],R=I[0]*x[0],L=Math.max(I[0]*P,I[1]),N=o%O===0,K=i%R===0,Q=u%L===0,he=v?[P,4,4]:[1,1,1],W=[{type:6,data:o},{type:6,data:i},{type:6,data:u},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];xt(t,W),W.push(...Z(e[0].dims,e[1].dims));let se=["rank","rank"];a&&(W.push(...Z(e[2].dims)),se.push("rank")),W.push(...Z(r));let Ce=We=>{let ee=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Ct(t,ee);let ae=v?4:1,Ae=De(e[0].dataType),me=`\n fn setOutputAtIndex(flatIndex : i32, value : ${v?`vec4<${Ae}>`:Ae}) {\n result[flatIndex] = ${v?`vec4<${Ae}>`:Ae}(value);\n }\n fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${v?`vec4<${Ae}>`:Ae}) {\n let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3));\n setOutputAtIndex(flatIndex ${v?"/ 4":""}, value);\n }`,ie=U("x",e[0].dataType,e[0].dims.length,P===3?1:P),ue=U("w",e[1].dataType,e[1].dims.length,ae),le=[ie,ue],qe=j("result",e[0].dataType,r.length,ae);if(a){let G=U("bias",e[2].dataType,e[2].dims.length,ae);le.push(G),me+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${v?`vec4<${Ae}>`:Ae} {\n return bias[coords.${p?"w":"y"}${v?"/ 4":""}];\n }`}return`\n ${Mn("uniforms.result_strides")}\n //struct Uniforms { xShape : vec4, wShape : vec4, outShape : vec4,\n // outShapeStrides: vec3, filterDims : vec2, pad : vec2, stride : vec2,\n // dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n ${We.registerUniforms(ee).declareVariables(...le,qe)}\n ${me}\n ${$c(p,N,K,Q,a,t,he[0],he[1],he[2],Ae)}\n ${v?Hr(x,I,Ae,void 0,!p,L):Lr(x,I,Ae,void 0,!p,L,!1,void 0,c)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${P};${v};${N};${K};${Q};${O};${R};${L}`,inputDependencies:se},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:W}),getShaderSource:Ce}}});var Mo,eu,tu=Y(()=>{"use strict";ye();Se();_e();Uo();Ft();Mo=(e,t,r)=>{let o=e.length>2,i=o?"value += b[output_channel];":"",u=e[0].dims,a=e[1].dims,c=a[0]/t.group,p=t.format==="NHWC",h=Vn(u,a,t.dilations,t.pads,t.strides,p),d=M.size(h),y=[{type:12,data:d},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:c}];xt(t,y),y.push(...Z(u,a));let w=["rank","rank"];o&&(y.push(...Z(e[2].dims)),w.push("rank")),y.push(...Z(h));let _=v=>{let S=j("output",e[0].dataType,h.length),A=De(S.type.tensor),I=St(t,S.type.value,A),x=U("x",e[0].dataType,u.length),E=U("w",e[1].dataType,a.length),P=[x,E];o&&P.push(U("b",e[2].dataType,e[2].dims.length));let O=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return Ct(t,O),`\n ${v.registerUniforms(O).declareVariables(...P,S)}\n\n ${v.mainStart()}\n ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let outputIndices = ${S.offsetToIndices("global_idx")};\n let batch: u32 = outputIndices[0];\n let output_channel: u32 = outputIndices[${p?3:1}];\n let xRCCorner: vec2 = vec2(outputIndices[${p?1:2}], outputIndices[${p?2:3}]) * uniforms.strides - uniforms.pads;\n let group_id: u32 = output_channel / uniforms.output_channels_per_group;\n\n var value: ${S.type.value} = ${S.type.value}(0);\n for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {\n let input_channel = group_id * uniforms.w_shape[1] + wInChannel;\n for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {\n let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];\n\n if (xHeight < 0u || xHeight >= uniforms.x_shape[${p?1:2}]) {\n continue;\n }\n\n for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {\n let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];\n if (xWidth < 0u || xWidth >= uniforms.x_shape[${p?2:3}]) {\n continue;\n }\n\n let xVal = ${p?x.get("batch","xHeight","xWidth","input_channel"):x.get("batch","input_channel","xHeight","xWidth")};\n let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")};\n value += xVal*wVal;\n }\n }\n }\n ${i}\n ${I}\n ${S.setByOffset("global_idx","value")}\n }`};return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:w},getRunData:()=>({outputs:[{dims:r?r(h):h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:y}),getShaderSource:_}},eu=(e,t,r)=>{let o=e.length>2,i=Me(r[3]),u=Me(r[2]),a=M.size(r)/i/u,c=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/i],p=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/i],h=[r[0],r[1],r[2],r[3]/i],d=[{type:12,data:a},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];xt(t,d),d.push(...Z(c,p,h));let y=(u-1)*t.strides[1]+p[1],w=_=>{let v=j("output",e[0].dataType,h.length,i),S=De(v.type.tensor),A=St(t,v.type.value,S),I=U("x",e[0].dataType,c.length,i),x=U("w",e[1].dataType,p.length,i),E=[I,x];o&&E.push(U("b",e[2].dataType,e[2].dims,i));let P=o?"value += b[output_channel];":"",O=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Ct(t,O),`\n ${_.registerUniforms(O).declareVariables(...E,v)}\n ${_.mainStart()}\n ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let width0 = uniforms.output_shape[3];\n let output_channel = global_idx % width0;\n var index1 = global_idx / width0;\n let width1 = uniforms.output_shape[2] / ${u}u;\n let col = (index1 % width1) * ${u}u;\n index1 = index1 / width1;\n let row = index1 % uniforms.output_shape[1];\n let batch = index1 / uniforms.output_shape[1];\n\n let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads;\n\n var x_vals: array<${I.type.value}, ${y}>;\n var values: array<${v.type.value}, ${u}>;\n let input_channel = output_channel;\n // Use constant instead of uniform can give better performance for w\'s height/width.\n for (var w_height: u32 = 0u; w_height < ${p[0]}; w_height++) {\n let x_height = x_corner.x + i32(w_height);\n if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {\n for (var i = 0; i < ${y}; i++) {\n let x_width = x_corner.y + i;\n if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {\n x_vals[i] = ${I.get("batch","u32(x_height)","u32(x_width)","input_channel")};\n } else {\n x_vals[i] = ${I.type.value}(0);\n }\n }\n for (var w_width: u32 = 0u; w_width < ${p[1]}; w_width++) {\n let w_val = ${x.get("w_height","w_width","0","output_channel")};\n for (var i = 0u; i < ${u}u; i++) {\n values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);\n }\n }\n }\n }\n\n for (var i = 0u; i < ${u}u; i++) {\n var value = values[i];\n ${P}\n ${A}\n ${v.set("batch","row","col + i","output_channel","value")};\n }\n }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${i};${u};${y};${p[0]};${p[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:w}}});var Vo,_c,ru,Wo=Y(()=>{"use strict";ye();Se();Fr();_e();Ft();Vo=(e,t,r,o,i=!1)=>{let u=e[0].dims,a=e[1].dims,c=u[u.length-2],p=a[a.length-1],h=u[u.length-1],d=Me(p),y=Me(h),w=Me(c),_=M.size(r)/d/w,v=e.length>2,S=o?o.slice(0,-2):r.slice(0,-2),I=[M.size(S),c,p],x=[{type:12,data:_},{type:12,data:c},{type:12,data:p},{type:12,data:h}];xt(t,x),x.push(...Z(S,u,a)),v&&x.push(...Z(e[2].dims)),x.push(...Z(I));let E=P=>{let O=An("batch_dims",e[0].dataType,S.length),R=U("a",e[0].dataType,u.length,y),L=U("b",e[1].dataType,a.length,d),N=j("output",e[0].dataType,I.length,d),K=De(N.type.tensor),Q=St(t,N.type.value,K),he=[R,L],W="";if(v){let ie=i?d:1;he.push(U("bias",e[2].dataType,e[2].dims.length,ie)),W=`${i?`value += bias[col / ${ie}];`:`value += ${N.type.value}(bias[row + i]);`}`}let se=u.slice(0,-2),Ce=a.slice(0,-2),We=_r(se,S),ee=_r(Ce,S),ae=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Ct(t,ae);let Ae=(ie,ue)=>{let le=ie.rank,qe=ie.name;if(le===2)return`var ${qe}_indices = ${ie.type.indices}(0u, 0u);`;let G=O.rank,ne=`var ${qe}_indices: ${ie.type.indices};`;for(let xe=le-2-1,Ke=G-1;xe>=0;xe--,Ke--)ne+=`\n${qe}_indices[${xe}] = ${G>1?`batch_indices[${Ke}]`:"batch_indices"};`;return ue.forEach(xe=>{ne+=`\n${qe}_indices[${xe}] = 0;`}),ne+=`${qe}_indices[${le-2}] = 0u;\n ${qe}_indices[${le-1}] = 0u;`,ne},me=()=>{let ie=`var a_data: ${R.type.value};`;for(let ue=0;ue;\n for (var k: u32 = 0u; k < uniforms.K; k = k + ${y}) {\n ${me()}\n }\n for (var i = 0u; i < ${w}u; i++) {\n var value = values[i];\n ${W}\n ${Q}\n let cur_indices = ${N.type.indices}(batch, row + i, col);\n let offset = ${N.indicesToOffset("cur_indices")};\n ${N.setByOffset(`offset / ${d}`,"value")};\n }\n }\n `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${d};${y};${w};${i}`,inputDependencies:v?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:x}),getShaderSource:E}},_c=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},ru=e=>{_c(e.inputs);let t=It.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can\'t use matmul on the given tensors");let r=t[t.length-1],o=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&o<8?e.compute(Vo(e.inputs,{activation:""},t)):e.compute(Un(e.inputs,{activation:""},t))}});var Vn,No,Sc,nu,Go,xc,Cc,Ho,Uo=Y(()=>{"use strict";Se();Js();Fr();tu();Ft();Wo();Sr();Vn=(e,t,r,o,i,u)=>{let a=e[0],c=e.slice(u?1:2,u?3:4),p=c.length,h=t[0],y=t.slice(2).map((v,S)=>v+(v-1)*(r[S]-1)),_=c.map((v,S)=>v+o[S]+o[S+p]).map((v,S)=>Math.floor((v-y[S]+i[S])/i[S]));return _.splice(0,0,a),_.splice(u?3:1,0,h),_},No=[2,3,1,0],Sc=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[1]*t.group;if(r!==o)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let i=e[0].dims.length-2;if(t.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(t.strides.length!==i)throw new Error(`strides should be ${i}D`);if(t.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},nu=(e,t)=>{let r=e.kernelShape.slice();for(let u=2;u{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,u=e.group,a=e.kernel_shape,c=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},xc=(e,t,r)=>{let o=nu(r,t),i=r.format==="NHWC";if(r.group!==1){if(!e.adapterInfo.isArchitecture("ampere")&&i&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let L=Vn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,i),N=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=N);let K=[t[0],N];t.length===3&&K.push(t[2]),e.compute(eu(K,o,L),{inputs:K})}else e.compute(Mo(t,o));return}let u=t.length===3,a=t[0].dims[i?1:2],c=t[0].dims[i?2:3],p=t[0].dims[i?3:1],h=t[1].dims[2],d=t[1].dims[3],y=Vn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,i),w=y[i?1:2],_=y[i?2:3],v=y[i?3:1],S=i&&h===a&&d===c&&r.pads[0]===0&&r.pads[1]===0;if(S||h===1&&d===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let R=y[0],L,N,K,Q=[];if(i){let se=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=se),S){let Ce=a*c*p;L=t[0].reshape([1,R,Ce]),N=se.reshape([1,Ce,v]),K=[1,R,v]}else L=t[0].reshape([R,a*c,p]),N=se.reshape([1,p,v]),K=[R,w*_,v];Q.push(L),Q.push(N)}else L=t[0].reshape([R,p,a*c]),N=t[1].reshape([1,v,p]),K=[R,v,w*_],Q.push(N),Q.push(L);u&&Q.push(t[2]);let he=K[2],W=Q[0].dims[Q[0].dims.length-1];he<8&&W<8?e.compute(Vo(Q,o,y,K,i),{inputs:Q}):e.compute(Un(Q,o,y,K,i),{inputs:Q});return}let A=!0,I=e.kernelCustomData.wT??e.compute(yt(t[1],No),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=I);let x=[t[0],I];u&&x.push(t[2]);let E=i?w*_:v,P=i?v:w*_,O=h*d*p;e.compute(Qs(x,o,y,E,P,O,u,A),{inputs:x})},Cc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let i=[0,t.pads[0],0,t.pads[1]],u=[1].concat(t.strides),a=[1].concat(t.dilations),c=[1].concat(t.kernelShape),p=nu({...t,pads:i,strides:u,dilations:a,kernelShape:c},o);e.compute(Mo(o,p,h=>r?[h[0],h[2],h[3]]:[]))},Ho=(e,t)=>{Sc(e.inputs,t),e.inputs[0].dims.length===3?Cc(e,t):xc(e,e.inputs,t)}});var Ac,ou,iu=Y(()=>{"use strict";ye();Lt();_e();Ft();zn();zo();Fr();Ac=(e,t=!1,r,o,i=4)=>{let u=I=>{switch(I){case 1:return"return w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];\n let v1 = w[getIndexFromCoords4D(coord1, vec4(uniforms.w_shape))];\n let v2 = w[getIndexFromCoords4D(coord2, vec4(uniforms.w_shape))];\n let v3 = w[getIndexFromCoords4D(coord3, vec4(uniforms.w_shape))];\n return ${o}(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${I} is not supported.`)}},a=e?`\n let coord = vec4(batch, iXR, iXC, xCh);\n `:`\n let coord = vec4(batch, xCh, iXR, iXC);\n `,c=e?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,p=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",h=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",d=e?"row":"col",y=e?"col":"row",w=`\n let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n let outRow = ${d} / outWidth;\n let outCol = ${d} % outWidth;\n\n let WRow = ${y} / (uniforms.filter_dims[1] * inChannels);\n let WCol = ${y} / inChannels % uniforms.filter_dims[1];\n let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(${p}) || fract(xR) > 0.0) {\n return ${o}(0.0);\n }\n if (xC < 0.0 || xC >= f32(${h}) || fract(xC) > 0.0) {\n return ${o}(0.0);\n }\n let iXR = i32(xR);\n let iXC = i32(xC);\n let xCh = ${y} % inChannels;\n ${a}\n return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${i}];`,_=e?`\n let col = colIn * ${i};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {\n ${w}\n }\n return ${o}(0.0);`:`\n let col = colIn * ${i};\n if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {\n ${w}\n }\n return ${o}(0.0);`,v=`\n let col = colIn * ${i};\n let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"};\n let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels);\n let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1];\n if (${e?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"} && coordX >= 0 && coordY >= 0) {\n let rowInner = row % inChannels;\n let coord = vec4(coordX, coordY, col, rowInner);\n ${u(i)}\n }\n return ${o}(0.0);\n `,S=St(r,o);return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${o} {\n ${e?_:v}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${o} {\n ${e?v:_}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${o}) {\n let col = colIn * ${i};\n if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {\n var value = valueInput;\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n ${c}\n ${Dn(t)}\n ${S}\n result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${i}] = value;\n }\n }`},ou=(e,t,r,o,i,u,a,c)=>{let p=t.format==="NHWC",h=p?e[0].dims[3]:e[0].dims[1],d=r[0],y=p?r[2]:r[3],w=p?r[1]:r[2],_=p?r[3]:r[1],v=p&&h%4===0&&h%3&&_%4===0,S=p?_:y*w,A=p?y*w:_,I=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(S/I[0]/x[0]),Math.ceil(A/I[1]/x[1]),Math.ceil(d/I[2]/x[2])];Ve("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let P=v?4:1,O=Math.max(I[0]*P,I[1]),R=v?4:1,L=[t.kernelShape[p?1:2],t.kernelShape[p?2:3]],N=[L[0]+(t.dilations[0]<=1?0:(L[0]-1)*(t.dilations[0]-1)),L[1]+(t.dilations[1]<=1?0:(L[1]-1)*(t.dilations[1]-1))],K=[N[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),N[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Q=[{type:6,data:o},{type:6,data:i},{type:6,data:u},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:L},{type:6,data:K}];xt(t,Q),Q.push(...Z(e[0].dims,e[1].dims));let he=["rank","rank"];a&&(Q.push(...Z(e[2].dims)),he.push("rank")),Q.push(...Z(r));let W=se=>{let Ce=U("x",e[0].dataType,e[0].dims.length,R),We=U("w",e[1].dataType,e[1].dims.length,1),ee=j("result",e[0].dataType,r.length,R),ae=[Ce,We],Ae="";if(a){let ue=U("bias",e[2].dataType,e[2].dims.length,R);ae.push(ue),Ae+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${ue.type.value} {\n return bias[coords.${p?"w":"y"}${v?"/ 4":""}];\n }`}let me=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:L.length},{name:"pads",type:"i32",length:K.length}];Ct(t,me);let ie=De(e[0].dataType,1);if(ie!=="f16"&&ie!=="f32")throw new Error(`elemType ${ie} is not supported.`);return`\n ${Mn("uniforms.result_strides")}\n ${se.registerUniforms(me).declareVariables(...ae,ee)};\n ${Ae}\n ${Ac(p,a,t,Ce.type.value,P)}\n ${v?Hr(x,I,ie,void 0,!p,O):Lr(x,I,ie,void 0,!p,O,!1,void 0,c)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${x};${I};${v}`,inputDependencies:he},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:Q}),getShaderSource:W}}});var Ic,Lo,au=Y(()=>{"use strict";ye();Lt();Se();_e();Ic=(e,t,r,o,i,u=!1,a,c,p=!1)=>{let h=p?1:2,d=p?2:3,y=p?3:1,w=u?2:1,_=`\n fn setOutputAtIndex(flatIndex : u32, value : ${u?`vec4<${a}>`:a}) {\n result[flatIndex] = ${u?`vec4<${a}>`:a}(value);\n }`;o&&(_+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${u?`vec4<${a}>`:a} {\n return bias[coords.${p?"w":"y"}${u?"/ 4":""}];\n }`);let v=u?4:1,S=U("W",t[1].dataType,t[1].dims.length,v),A=U("Dy",t[0].dataType,t[0].dims.length,v),I=[A,S];o&&I.push(U("bias",t[2].dataType,[r[y]].length,v));let x=j("result",t[0].dataType,r.length,v),E=`{\n let batch: u32 = ${i?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1];\n let r = ${i?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1];\n let c = ${i?"global_id.y":"workgroup_id.y"} * ${w};\n let d1: u32 = ${i?"global_id.x":"workgroup_id.x"} * 4;\n\n let dyCorner = vec2(i32(r), i32(c)) - vec2(uniforms.pads);\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${w}>;\n for (var i = 0; i < ${w}; i++) {\n dotProd[i] = vec4<${a}>(0.0);\n }\n for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) {\n var dyR = (${a}(dyCorner.x) + ${a}(wR)) / ${a}(uniforms.strides.x);\n let wRPerm = uniforms.filter_dims[0] - 1 - wR;\n if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[1]) ||\n fract(dyR) > 0.0 || wRPerm < 0) {\n continue;\n }\n let idyR: u32 = u32(dyR);\n\n for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) {\n let dyC = (${a}(dyCorner.y) + ${a}(wC)) / ${a}(uniforms.strides.y);\n let dyC2 = (${a}(dyCorner.y) + 1.0 + ${a}(wC)) / ${a}(uniforms.strides.y);\n let wCPerm = uniforms.filter_dims[1] - 1 - wC;\n if (wCPerm < 0) {\n continue;\n }\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= ${a}(uniforms.Dy_shape[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC: u32 = u32(dyC);\n let idyC2: u32 = u32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = uniforms.Dy_shape[3];\n for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${A.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${a}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n\n xValue = ${A.get("batch","idyR","idyC2","d2")};\n\n dotProd[1] = dotProd[1] + vec4<${a}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.Dy_shape[${y}];\n for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${A.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${a}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = uniforms.Dy_shape[3];\n for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${A.get("batch","idyR","idyC2","d2")};\n let tmpval = vec4<${a}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i: u32 = 0; i < ${w}; i = i + 1) {\n let value = dotProd[i] + ${o?"bias[c+i]":`vec4<${a}>(0.0)`};\n ${x.set("batch","r","c + i","d1","value")};\n }\n }`,P=`\n let outputIndices = ${x.offsetToIndices("global_idx")};\n let batch = ${x.indicesGet("outputIndices",0)};\n let d1 = ${x.indicesGet("outputIndices",y)};\n let r = ${x.indicesGet("outputIndices",h)};\n let c = ${x.indicesGet("outputIndices",d)};\n let dyCorner = vec2(i32(r), i32(c)) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n let groupId = d1 / uniforms.output_channels_per_group;\n let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = ${a}(0.0);\n for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {\n if (wR % uniforms.dilations.x != 0) {\n continue;\n }\n let dyR = (${a}(dyRCorner) + ${a}(wR)) / ${a}(uniforms.strides[0]);\n let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;\n if (dyR < 0.0 || dyR >= ${a}(uniforms.Dy_shape[${h}]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR: u32 = u32(dyR);\n\n for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {\n if (wC % uniforms.dilations.y != 0) {\n continue;\n }\n let dyC = (${a}(dyCCorner) + ${a}(wC)) / ${a}(uniforms.strides.y);\n let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;\n if (dyC < 0.0 || dyC >= ${a}(uniforms.Dy_shape[${d}]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC: u32 = u32(dyC);\n var inputChannel = groupId * uniforms.input_channels_per_group;\n for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) {\n let xValue = ${p?A.get("batch","idyR","idyC","inputChannel"):A.get("batch","inputChannel","idyR","idyC")};\n let wValue = ${S.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n dotProd = dotProd + xValue * wValue;\n inputChannel 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c=t.format==="NHWC",p=["rank","rank"],h=[t.strides[0],t.strides[1]],d=[t.kernelShape[c?1:2],t.kernelShape[c?2:3]],y=[t.dilations[0],t.dilations[1]],w=[d[0]+(t.dilations[0]<=1?0:(t.kernelShape[c?1:2]-1)*(t.dilations[0]-1)),d[1]+(t.dilations[1]<=1?0:(t.kernelShape[c?2:3]-1)*(t.dilations[1]-1))],_=[w[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),w[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],v=!1,S=t.group,A=e[1].dims,I=A[0]/S,x=A[1],E=[{type:12,data:u},{type:12,data:h},{type:12,data:d},{type:12,data:y},{type:12,data:w},{type:6,data:_},{type:12,data:I},{type:12,data:x},...Z(e[0].dims,e[1].dims)];o&&(E.push(...Z(e[2].dims)),p.push("rank")),E.push(...Z(i));let P=a[1]===1&&a[2]===1,O=R=>{let L=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:h.length},{name:"filter_dims",type:"u32",length:d.length},{name:"dilations",type:"u32",length:d.length},{name:"effective_filter_dims",type:"u32",length:w.length},{name:"pads",type:"i32",length:_.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],N=De(e[0].dataType);return`${Ic(R,e,i,o,P,v,N,L,c)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:p},getRunData:()=>({dispatchGroup:{x:a[0],y:a[1],z:a[2]},outputs:[{dims:r?r(i):i,dataType:e[0].dataType}],programUniforms:E}),getShaderSource:O}}});var Tc,Ec,Pc,su,uu,kc,Oc,Rc,Bc,du,lu=Y(()=>{"use strict";iu();au();Ft();Sr();Tc=(e,t,r,o,i,u)=>(e-1)*t+r+(o-1)*i+1-u,Ec=(e,t,r,o,i)=>{let u=Math.floor(e/2);t==="SAME_UPPER"?(r[o]=u,r[i]=e-u):t==="SAME_LOWER"&&(r[o]=e-u,r[i]=u)},Pc=(e,t,r,o,i,u,a,c,p,h)=>{let d=e.length-2,y=h.length===0;if(p.length===0)for(let v=0;v{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((y,w)=>y*w,1)===0){r.length=0;for(let y=2;yy+w,0)===0){let y=t[0].dims.length-2;p=new Array(y).fill(1)}let h=e.strides.slice();if(h.reduce((y,w)=>y+w,0)===0){let y=t[0].dims.length-2;h=new Array(y).fill(1)}Pc(c,r,p,e.autoPad,e.group,i,h,o,a,u);let d=Object.assign({},e);return Object.assign(d,{kernelShape:r,pads:i,outputPadding:a,outputShape:u,dilations:p,strides:h}),d},uu=e=>{let t=Bn(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,u=e.group,a=e.kernelShape,c=e.pads,p=e.strides,h=e.wIsConst(),d=e.outputPadding,y=e.outputShape;return{autoPad:o,format:r,dilations:i,group:u,kernelShape:a,outputPadding:d,outputShape:y,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},kc=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[0];if(r!==o)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let i=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==i))throw new Error("invalid bias");let u=e[0].dims.length-2;if(t.dilations.reduce((d,y)=>d+y,0)>0&&t.dilations.length!==u)throw new Error(`dilations should be ${u}D`);if(t.strides.reduce((d,y)=>d+y,0)>0&&t.strides.length!==u)throw new Error(`strides should be ${u}D`);if(t.pads.reduce((d,y)=>d+y,0)>0&&t.pads.length!==u*2)throw new Error(`pads should be ${u*2}D`);if(t.outputPadding.length!==u&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${u}D`);if(t.kernelShape.reduce((d,y)=>d+y,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Oc=[2,3,1,0],Rc=(e,t,r)=>{let o=su(r,t),i=r.format==="NHWC",u=o.outputShape,a=u[i?3:1],c=t[0].dims[i?3:1];if(o.group!==1||a===1&&c===1){e.compute(Lo(t,o));return}let p=u[i?1:2],h=u[i?2:3],d=t[1].dims[2],y=t[1].dims[3],w=i?p*h:a,_=i?a:p*h,v=d*y*c,S=!0,A=e.kernelCustomData.wT??e.compute(yt(t[1],Oc),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=A);let I=[t[0],A],x=t.length===3;x&&(!i&&t[2].dims.length===1?I.push(t[2].reshape([t[2].dims[0],1,1])):I.push(t[2])),e.compute(ou(I,o,u,w,_,v,x,S),{inputs:I})},Bc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let u=t.dilations;(u.length===0||u[0]===0)&&(u=[1]);let a=t.strides;(a.length===0||a[0]===0)&&(a=[1]);let c=t.pads;c.length===0&&(c=[0,0]),c=[0,c[0],0,c[1]],a=[1].concat(a),u=[1].concat(u),i=[1].concat(i);let p=su({...t,pads:c,strides:a,dilations:u,kernelShape:i},o);e.compute(Lo(o,p,h=>r?[h[0],h[2],h[3]]:[h[0],h[1],h[3]]))},du=(e,t)=>{kc(e.inputs,t),e.inputs[0].dims.length===3?Bc(e,t):Rc(e,e.inputs,t)}});var Dc,cu,pu,mu=Y(()=>{"use strict";ye();Se();Ze();_e();Dc=(e,t,r,o)=>{let i=M.size(t),u=t.length,a=U("input",e,u),c=j("output",e,u),p=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),h=M.normalizeAxis(p,u),d=y=>{let w=` i32(${a.indicesGet("inputIndices","uniforms.axis")}) `,_=fe("uniforms.input_shape","uniforms.axis",u),v=o.reverse?w+(o.exclusive?" + 1":""):"0",S=o.reverse?_:w+(o.exclusive?"":" + 1");return`\n 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${_("data",3,"u32")}\n ${y.setByOffset("global_idx","data")}\n }`}else w=`\n let outputIndices = ${y.offsetToIndices("global_idx")};\n let inputOffset = ${d.broadcastedIndicesToOffset("outputIndices",y)};\n ${y.setByOffset("global_idx",d.getByOffset("inputOffset"))}\n }`;return`\n ${h.registerUniform("vec_size","u32").declareVariables(d,y)}\n ${h.mainStart()}\n ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${w}`},p=[{type:12,data:a},...Z(t,o)];return{name:"Expand",shaderCache:{hint:`${o.length}`,inputDependencies:["rank"]},getShaderSource:c,getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p})}},Su=e=>{Gc(e.inputs),e.compute(Lc(e.inputs),{inputs:[0]})}});var Fc,Cu,Au=Y(()=>{"use strict";ye();Se();_e();Rn();Fc=e=>{let t=e[0].dataType,r=M.size(e[0].dims),o=M.size(e[1].dims),i=o%4===0,u=a=>{let c=U("x",t,[1],4),p=U("bias",t,[1],4),h=j("y",t,[1],4),d=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],y=_=>`\n let bias${_}_offset: u32 = (global_idx * 4 + ${_}) % uniforms.bias_size;\n let bias${_} = ${p.getByOffset(`bias${_}_offset / 4`)}[bias${_}_offset % 4];`,w=i?`\n let bias = ${p.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${y(0)}${y(1)}${y(2)}${y(3)}\n let bias = ${c.type.value}(bias0, bias1, bias2, bias3);`;return`${a.registerUniforms(d).declareVariables(c,p,h)}\n\n ${Bo(et(t))}\n\n ${a.mainStart(or)}\n ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}\n\n let x = ${c.getByOffset("global_idx")};\n ${w}\n let x_in = x + bias;\n ${h.setByOffset("global_idx",Do("x_in"))}\n }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${i}`,inputDependencies:["type","type"]},getShaderSource:u,getRunData:a=>({outputs:[{dims:a[0].dims,dataType:a[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:o}],dispatchGroup:{x:Math.ceil(r/or/4)}})}},Cu=e=>{e.inputs.length<2||M.size(e.inputs[1].dims)===0?Bs(e):e.compute(Fc(e.inputs))}});var qc,jc,Iu,Tu,Eu=Y(()=>{"use strict";ye();Se();Ze();_e();qc=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},jc=(e,t)=>{let r=e[0].dims,o=e[1].dims,i=r.length,u=M.normalizeAxis(t.axis,i),a=r.slice(0);a.splice(u,1,...o);let c=r[u],p=e[0].dataType===9?4:1,h=Math.ceil(M.size(a)/p),d=[{type:12,data:h},{type:6,data:c},{type:12,data:u},...Z(e[0].dims,e[1].dims,a)],y=w=>{let _=U("data",e[0].dataType,e[0].dims.length,p),v=U("inputIndices",e[1].dataType,e[1].dims.length),S=j("output",e[0].dataType,a.length,p),A=x=>{let E=o.length,P=`var indicesIndices${x} = ${v.type.indices}(0);`;for(let 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gemm on the given tensors");let p=M.size(c),h=[{type:12,data:p},{type:12,data:i},{type:12,data:u},{type:12,data:a},{type:1,data:t.alpha},{type:1,data:t.beta}],d=["type","type"];e.length===3&&(h.push(...Z(e[2].dims)),d.push("rank")),h.push(...Z(c));let y=w=>{let _="";t.transA&&t.transB?_="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?_="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?_="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(_="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let v=t.alpha===1?"":"value *= uniforms.alpha;",S=U("a",e[0].dataType,e[0].dims),A=U("b",e[1].dataType,e[1].dims),I=S.type.value,x=null,E=[S,A];e.length===3&&(x=U("c",e[2].dataType,e[2].dims.length),E.push(x));let P=j("output",e[0].dataType,c.length);E.push(P);let O=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`\n ${w.registerUniforms(O).declareVariables(...E)}\n\n ${w.mainStart()}\n ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let m = global_idx / uniforms.N;\n let n = global_idx % uniforms.N;\n\n var value = ${I}(0);\n for (var k: u32 = 0u; k < uniforms.K; k++) {\n ${_}\n }\n\n ${v}\n ${(()=>x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",P)}; value += ${I}(uniforms.beta) * ${x.getByOffset("cOffset")};`:"")()}\n output[global_idx] = value;\n }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:y}},Ru=e=>{let t=e.transA,r=e.transB,o=e.alpha,i=e.beta;return{transA:t,transB:r,alpha:o,beta:i,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},Bu=(e,t)=>{Zc(e.inputs),e.compute(Xc(e.inputs,t))}});var Qc,Jc,ep,zu,Mu=Y(()=>{"use strict";ye();Se();_e();Qc=(e,t)=>{let r=e[0].dims,o=r,i=2,u=M.sizeToDimension(r,i),a=M.sizeFromDimension(r,i),c=Me(a),p=a/c,h=[r[0],r[1],p],d=["rank","type","type"],y=[{type:12,data:a},{type:12,data:p}];y.push(...Z(h,h));let w=_=>{let v=U("x",e[0].dataType,h.length,c),S=U("scale",e[1].dataType,e[1].dims),A=U("bias",e[2].dataType,e[2].dims),I=j("output",e[0].dataType,h.length,c),x=[v,S,A,I],E=v.type.value,P=c===1?"f32":`vec${c}`,O=64,R=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`\n var meanShared : f32;\n var squaredNormShared : f32;\n var workgroupShared : array<${P}, ${O}>;\n const workgroupSize = ${O}u;\n ${_.registerUniforms(R).declareVariables(...x)}\n ${_.mainStart(O)}\n let norm = global_idx / workgroupSize;\n let batch = norm / uniforms.x_shape[1];\n let channel = norm % uniforms.x_shape[1];\n let localIndex = local_id.x;\n\n // initialize workgroup memory\n var initial = ${P}(0);\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n initial = initial + ${P}(${v.get("batch","channel","h")});\n }\n workgroupShared[localIndex] = initial;\n workgroupBarrier();\n\n // Calculate the mean of current channel data.\n for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (localIndex < currSize) {\n workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n }\n workgroupBarrier();\n }\n if (localIndex == 0) {\n meanShared = ${_t("workgroupShared[0]",c)} / f32(uniforms.normSize);\n }\n workgroupBarrier();\n\n // reinitialize workgroup memory.\n initial = ${P}(0);\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let deviation = ${P}(${v.get("batch","channel","h")}) - ${P}(meanShared);\n initial = initial + deviation * deviation;\n }\n workgroupShared[localIndex] = initial;\n workgroupBarrier();\n\n // Calculate the sum of square of deviation of current channel data.\n for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (localIndex < currSize) {\n workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n }\n workgroupBarrier();\n }\n if (localIndex == 0) {\n squaredNormShared = ${_t("workgroupShared[0]",c)};\n }\n workgroupBarrier();\n\n let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n let channelScale = invStdDev * f32(${S.getByOffset("channel")});\n let channelShift = f32(${A.getByOffset("channel")}) - meanShared * channelScale;\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let value = ${v.get("batch","channel","h")} * ${E}(${P}(channelScale)) + ${E}(${P}(channelShift));\n ${I.set("batch","channel","h","value")};\n }\n }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${c}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:u},programUniforms:y}),getShaderSource:w}},Jc=(e,t,r,o,i,u,a,c)=>{let p=Me(a),h=64,d=p===1?"vec2f":`mat2x${p}f`,y=p===1?"f32":`vec${p}f`,w=(R,L)=>`${d}(${R}, ${L})`,_=i*a/p,v=Math.ceil(u/h),S=["type"],A=[{type:12,data:v},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(u*a/p)}],I=R=>{let L=U("input",t.dataType,t.dims,p);return`\n ${R.declareVariables(L)}\n @group(0) @binding(1) var output : array<${d}>;\n struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n @group(0) @binding(2) var uniforms: Uniforms;\n\n ${R.mainStart(h)}\n let currentImageNumber = global_idx / ${h} / uniforms.C;\n let currentChannelNumber = (global_idx / ${h}) % uniforms.C;\n let wgOffset = local_id.x * uniforms.wg_size;\n if (wgOffset >= uniforms.H) {\n return;\n }\n let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);\n\n let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;\n var sum = ${$t("f32",p)};\n var squaredSum = ${$t("f32",p)};\n for (var i: u32 = wgOffset; i < wgMax; i++) {\n let value = ${y}(input[offset + i * uniforms.C]);\n sum += value;\n squaredSum += value * value;\n }\n output[global_idx] = ${w("sum","squaredSum")};\n }`},x=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${p}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:[i,a,h,2],dataType:1}],dispatchGroup:{x:i*a/p},programUniforms:A}),getShaderSource:I},{inputs:[t],outputs:[-1]})[0],E=[{type:12,data:_},{type:12,data:u},{type:12,data:Math.floor(a/p)},{type:12,data:Math.floor(h*a/p)}],P=["type","type","type"],O=R=>{let L=U("scale",r.dataType,r.dims,p),N=U("bias",o.dataType,o.dims,p);return`\n @group(0) @binding(0) var input : array<${d}>;\n @group(0) @binding(1) var scale : array<${L.type.storage}>;\n @group(0) @binding(2) var bias : array<${N.type.storage}>;\n @group(0) @binding(3) var output : array<${d}>;\n struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n @group(0) @binding(4) var uniforms: Uniforms;\n\n ${R.mainStart()}\n ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}\n let currentImageNumber = global_idx / uniforms.C;\n let currentChannelNumber = global_idx % uniforms.C;\n\n let offset = currentImageNumber * uniforms.image_size;\n var sum = ${$t("f32",p)};\n var squaredSum = ${$t("f32",p)};\n for (var i: u32 = 0; i < min(${h}, uniforms.H); i++) {\n let value = input[offset + i + currentChannelNumber * ${h}];\n sum += value[0];\n squaredSum += value[1];\n }\n sum = sum / f32(uniforms.H);\n squaredSum = squaredSum / f32(uniforms.H);\n let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${c}));\n let channelScale = invStdDev * ${y}(scale[currentChannelNumber]);\n let channelShift = ${y}(bias[currentChannelNumber]) - sum * channelScale;\n\n output[global_idx] = ${w("channelScale","channelShift")};\n }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${p};${c}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:[i,a,2],dataType:1}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:E}),getShaderSource:O},{inputs:[x,r,o],outputs:[-1]})[0]},ep=(e,t,r)=>{let o=t[0].dims,i=o,u=o[0],a=o[o.length-1],c=M.sizeFromDimension(o,1)/a,p=Me(a),h=M.size(i)/p,d=[{type:12,data:c},{type:12,data:Math.floor(a/p)}],y=["type","type"],w=Jc(e,t[0],t[1],t[2],u,c,a,r.epsilon),_=v=>{let S=De(t[0].dataType),A=p===1?"vec2f":`mat2x${p}f`,I=p===1?S:`vec${p}<${S}>`,x=U("input",t[0].dataType,t[0].dims,p),E=j("output",t[0].dataType,i,p);return`\n @group(0) @binding(0) var input : array<${x.type.storage}>;\n @group(0) @binding(1) var scaleInput : array<${A}>;\n @group(0) @binding(2) var output : array<${E.type.storage}>;\n struct Uniforms {H: u32, C : u32};\n @group(0) @binding(3) var uniforms: Uniforms;\n\n ${v.mainStart()}\n let currentImageNumber = global_idx / (uniforms.C * uniforms.H);\n let currentChannelNumber = global_idx % uniforms.C;\n\n let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;\n let scale = scaleInput[scaleOffset];\n output[global_idx] = fma(input[global_idx], ${I}(scale[0]), ${I}(scale[1]));\n }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:d}),getShaderSource:_},{inputs:[t[0],w]})},zu=(e,t)=>{t.format==="NHWC"?ep(e,e.inputs,t):e.compute(Qc(e.inputs,t))}});var tp,rp,Uu,Vu=Y(()=>{"use strict";ye();Se();_e();tp=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},rp=(e,t,r)=>{let o=t.simplified,i=e[0].dims,u=e[1],a=!o&&e[2],c=i,p=M.normalizeAxis(t.axis,i.length),h=M.sizeToDimension(i,p),d=M.sizeFromDimension(i,p),y=M.size(u.dims),w=a?M.size(a.dims):0;if(y!==d||a&&w!==d)throw new Error(`Size of X.shape()[axis:] == ${d}.\n Size of scale and bias (if provided) must match this.\n Got scale size of ${y} and bias size of ${w}`);let _=[];for(let O=0;O1,x=r>2,E=O=>{let R=De(e[0].dataType),L=[U("x",e[0].dataType,e[0].dims,v),U("scale",u.dataType,u.dims,v)];a&&L.push(U("bias",a.dataType,a.dims,v)),L.push(j("output",e[0].dataType,c,v)),I&&L.push(j("mean_data_output",1,_)),x&&L.push(j("inv_std_output",1,_));let N=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n ${O.registerUniforms(N).declareVariables(...L)}\n ${O.mainStart()}\n ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n let offset = global_idx * uniforms.norm_size_vectorized;\n var mean_vector = ${$t("f32",v)};\n var mean_square_vector = ${$t("f32",v)};\n\n for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n let value = ${ir(R,v,"x[h + offset]")};\n mean_vector += value;\n mean_square_vector += value * value;\n }\n let mean = ${_t("mean_vector",v)} / uniforms.norm_size;\n let inv_std_dev = inverseSqrt(${_t("mean_square_vector",v)} / uniforms.norm_size ${o?"":"- mean * mean"} + uniforms.epsilon);\n\n for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n let f32input = ${ir(R,v,"x[j + offset]")};\n let f32scale = ${ir(R,v,"scale[j]")};\n output[j + offset] = ${L[0].type.value}((f32input ${o?"":"- mean"}) * inv_std_dev * f32scale\n ${a?`+ ${ir(R,v,"bias[j]")}`:""}\n );\n }\n\n ${I?"mean_data_output[global_idx] = mean":""};\n ${x?"inv_std_output[global_idx] = inv_std_dev":""};\n }`},P=[{dims:c,dataType:e[0].dataType}];return I&&P.push({dims:_,dataType:1}),x&&P.push({dims:_,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${v};${r};${o}`,inputDependencies:S},getRunData:()=>({outputs:P,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:A}),getShaderSource:E}},Uu=(e,t)=>{tp(e.inputs),e.compute(rp(e.inputs,t,e.outputCount))}});var np,op,Wu,Nu,Gu=Y(()=>{"use strict";ye();Se();Ze();_e();np=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],o=r.dims.length;if(r.dims[o-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let i=Math.floor((t.k+t.blockSize-1)/t.blockSize),u=t.blockSize/8*t.bits,a=e[1];if(!M.areEqual(a.dims,[t.n,i,u]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let p=e[2].dims;if(M.size(p)!==t.n*i)throw new Error("scales input size error.");if(e.length===4){let d=e[3].dims,y=t.bits>4?t.n*i:t.n*Math.floor((i+1)/2);if(M.size(d)!==y)throw new Error("zeroPoints input size error.")}},op=(e,t,r,o)=>{let i=e[0].dims,u=i.length,a=Math.floor((t.k+t.blockSize-1)/t.blockSize),c=i[u-2],p=t.k,h=t.n,d=i.slice(0,u-2),y=M.size(d),_=t.blockSize/8*t.bits/4,v=e[0].dataType,S=Me(c),A=Me(t.k),I=Me(_),x=tr(v),E=c*a*x,P=Math.floor(o/E),O=a<=r[0]&&P>0,R=!O||P>=4?Me(h):P>=2&&Me(h)>=2?2:1,L=d.concat([c,h]),N=M.size(L)/R/S,K=O?[]:[{type:12,data:N},{type:12,data:t.blockSize}],Q=[y,c,p/A],he=M.convertShape(e[1].dims).slice();he.splice(-1,1,_/I),K.push(...Z(Q)),K.push(...Z(he)),K.push(...Z(e[2].dims)),e.length===4&&K.push(...Z(M.convertShape(e[3].dims)));let W=[y,c,h/R];K.push(...Z(W));let se=Ce=>{let We=Q.length,ee=U("a",e[0].dataType,We,A),ae=U("b",12,he.length,I),Ae=U("scales",e[2].dataType,e[2].dims.length),me=[ee,ae,Ae],ie=e.length===4?U("zero_points",12,e[3].dims.length):void 0;ie&&me.push(ie);let ue=W.length,le=j("output",e[0].dataType,ue,R),qe=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],G=De(e[0].dataType),ne=(()=>{switch(A){case 1:return`array<${G}, 8>`;case 2:return`mat4x2<${G}>`;case 4:return`mat2x4<${G}>`;default:throw new Error(`${A}-component is not supported.`)}})(),xe=`\n for (var word: u32 = 0; word < ${_}; word += ${I}) {\n ${ae.indicesSet("b_indices","2","word")};\n let b_data = ${ae.getByIndices("b_indices")};\n for (var i: u32 = 0; i < ${I}; i++) {\n let b_value: u32 = ${I===1?"b_data":"b_data[word + i]"};\n let b_mask: u32 = 0x0F0F0F0Fu;\n let b_value_lower: vec4 = unpack4xU8(b_value & b_mask);\n let b_value_upper: vec4 = unpack4xU8((b_value >> 4) & b_mask);\n let b_quantized_values = ${ne}(${Array.from({length:4},(Be,Ge)=>`${G}(b_value_lower[${Ge}]), ${G}(b_value_upper[${Ge}])`).join(", ")});\n let b_dequantized_values = ${(()=>A===1?`${ne}(${Array.from({length:8},(Be,Ge)=>`(b_quantized_values[${Ge}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${ne}(${Array(8).fill("zero_point").join(",")})) * scale;`)()};\n // Number of B elements per 32-bit word is 32/bits = 32/4 = 8\n for (var m: u32 = 0; m < ${O?c:S}u; m++) {\n ${ee.indicesSet("a_indices",We-2,O?"m":`row * ${S} + m`)};\n ${ee.indicesSet("a_indices",We-1,"word_offset")};\n var input_offset = ${ee.indicesToOffset("a_indices")};\n var a_data: ${ne};\n for (var j: u32 = 0; j < ${8/A}; j++) {\n a_data[j] = ${ee.getByOffset("input_offset")};\n input_offset++;\n }\n ${O?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${R>1?"[c]":""} += ${Array.from({length:8/A},(Be,Ge)=>`${A===1?`a_data[${Ge}] * b_dequantized_values[${Ge}]`:`dot(a_data[${Ge}], b_dequantized_values[${Ge}])`}`).join(" + ")};\n }\n word_offset += ${8/A};\n }\n }`,Ke=ie?`\n zero_point_offset += 4;\n if (zero_point_offset == 32) {\n zero_point_offset = 0;\n zero_point_index++;\n zero_point_word = ${ie.getByOffset("zero_point_index")};\n }`:"";return O?`\n var workgroup_shared: array<${le.type.value}, ${c*a}>;\n ${Ce.declareVariables(...me,le)}\n ${Ce.mainStart([a,1,1])}\n var a_indices: ${ee.type.indices};\n var block = local_id.x;\n var col = workgroup_id.y;\n var batch = workgroup_id.z;\n ${ee.indicesSet("a_indices","0","batch")};\n // Two zero points are packed into one byte when uniforms.bits is 4.\n for (var c: u32 = 0; c < ${R}; c++) {\n let col_times_components_plus_c = col * ${R} + c;\n ${ie?`\n var zero_point_bytes_per_col: u32 = (${a} + 1) / 2;\n var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u);\n var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u;\n var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u;\n var zero_point_nibble_offset: u32 = block & 0x1u;\n var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);\n var zero_point_word: u32 = ${ie.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""}\n var b_indices: ${ae.type.indices};\n ${ae.indicesSet("b_indices","0","col_times_components_plus_c")};\n // The scale and zero points are computed per block.\n var scales_index = col_times_components_plus_c * ${a} + block;\n let scale = ${Ae.getByOffset("scales_index")};\n // The default zero point is 8 for unsigned 4-bit quantization.\n let zero_point = ${G}(${ie?"(zero_point_word) & 0xFu":8});\n ${ae.indicesSet("b_indices","1","block")};\n var word_offset: u32 = block * ${t.blockSize/A};\n var workgroup_shared_offset: u32 = block * ${c};\n ${xe}\n }\n workgroupBarrier();\n if (local_id.x == 0u) {\n var output_indices: ${le.type.indices};\n ${le.indicesSet("output_indices","0","batch")};\n ${le.indicesSet("output_indices",ue-1,"col")};\n ${le.indicesSet("output_indices",ue-2,"0")};\n var output_offset = ${le.indicesToOffset("output_indices")};\n for (var m: u32 = 0u; m < ${c}u; m++) {\n var output_value: ${le.type.value} = ${le.type.value}(0);\n var workgroup_shared_offset: u32 = m;\n for (var b: u32 = 0u; b < ${a}u; b++) {\n output_value += workgroup_shared[workgroup_shared_offset];\n workgroup_shared_offset += ${c};\n }\n ${le.setByOffset("output_offset","output_value")};\n output_offset += ${h/R};\n }\n }\n }`:`\n ${Ce.registerUniforms(qe).declareVariables(...me,le)}\n ${Ce.mainStart()}\n ${Ce.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n var output_values: array<${le.type.value}, ${S}>;\n var output_indices = ${le.offsetToIndices("global_idx")};\n var col = ${le.indicesGet("output_indices",ue-1)};\n var row = ${le.indicesGet("output_indices",ue-2)};\n var a_indices: ${ee.type.indices} = output_indices;\n // Two zero points are packed into one byte because uniforms.bits <= 4.\n // zero_point_offset is either 0 or 4. It is bit offset within one byte.\n // TODO support zero_point_offset for bits > 4\n ${ie?`\n var zero_point_abs_offset = col * ${R} * ((${a} + 1) / 2);\n var zero_point_index: u32 = zero_point_abs_offset / 4;\n var zero_point_word: u32 = ${ie.getByOffset("zero_point_index")};\n var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""}\n var scale_index = col * ${a*R};\n var b_indices: ${ae.type.indices};\n for (var c: u32 = 0; c < ${R}; c++) {\n ${ae.indicesSet("b_indices","0",`col * ${R} + c`)};\n var block_offset: u32 = 0;\n for (var block: u32 = 0; block < ${a}; block++) {\n // The scale and zero points are computed per block.\n let scale = ${Ae.getByOffset("scale_index")};\n // The default zero point is 8 for unsigned 4-bit quantization.\n let zero_point = ${G}(${ie?"extractBits(zero_point_word, zero_point_offset, 4)":8});\n ${ae.indicesSet("b_indices","1","block")};\n var word_offset: u32 = block_offset;\n ${xe}\n scale_index++;\n ${Ke}\n block_offset += uniforms.block_size / ${A};\n }\n // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.\n ${ie?`if (zero_point_offset % 8 > 0) {\n ${Ke}\n }`:""}\n }\n for (var k: u32 = 0u; k < ${S}u; k++) {\n ${le.indicesSet("output_indices",ue-2,`${S} * row + k`)};\n ${le.setByIndices("output_indices","output_values[k]")}\n }\n }`};return{name:O?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${t.cacheKey};${c};${v};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:L,dataType:v}],name:O?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:O?{x:1,y:Math.ceil(h/R),z:y}:{x:Math.ceil(N/64)},programUniforms:K}),getShaderSource:se}},Wu=(e,t)=>{np(e.inputs,t);let r=e.getMaxComputeWorkgroupSizes(),o=e.getMaxComputeWorkgroupStoragesize();e.compute(op(e.inputs,t,r,o))},Nu=e=>ve(e)});var it,ip,Lu,Hu,ap,Ko,Fu,qu=Y(()=>{"use strict";ye();Se();Ze();_n();Ro();_e();Sr();it=(e,t)=>e.length>t&&e[t].dims.length>0&&M.size(e[t].dims)>0?e[t]:void 0,ip=(e,t)=>{let r=e[0],o=it(e,1),i=it(e,2),u=it(e,3),a=it(e,4),c=it(e,5),p=it(e,6),h=it(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let d=!1,y=r.dims[0],w=r.dims[1],_=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],v=w,S=0,A=0,I=Math.floor(_/t.numHeads);if(p&&h){if(p.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(p.dims[0]!==y||p.dims[1]!==t.numHeads||p.dims[3]!==I)throw new Error(\'Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(h.dims[0]!==y||h.dims[1]!==t.numHeads||h.dims[3]!==I)throw new Error(\'Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)\');if(p.dims[2]!==h.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)\');if(h.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');S=p.dims[2],A=p.dims[2]}else if(p||h)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let x;if(o){if(r.dims.length!==3)throw new Error(\'Input "query" is expected to have 3 dimensions when key is given\');if(o.dims.length<3||o.dims.length>5)throw new Error(\'Input "key" is expected to have 3, 4, or 5 dimensions\');if(r.dims[0]!==o.dims[0])throw new Error(\'Input "query" and "key" shall have same dim 0 (batch size)\');if(o.dims.length===3){if(o.dims[2]!==r.dims[2])throw new Error(\'Input "query" and "key" shall have same dim 2 (hidden_size)\');x=2,v=o.dims[1]}else if(o.dims.length===5){if(o.dims[2]!==t.numHeads||o.dims[3]!==2||o.dims[4]!==I)throw new Error(\'Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\');if(i)throw new Error(\'Expect "value" be none when "key" has packed kv format.\');x=5,v=o.dims[1]}else{if(o.dims[1]!==t.numHeads||o.dims[3]!==I)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');x=0,v=o.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error(\'Input "query" is expected to have 3 or 5 dimensions when key is empty\');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error(\'Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv\');x=3}if(u){if(u.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(i&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(a){E=8;let N=a.dims;throw N.length===1?N[0]===y?E=1:N[0]===3*y+2&&(E=3):N.length===2&&N[0]===y&&N[1]===v&&(E=5),E===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let P=!1,O=_;if(i){if(i.dims.length!==3&&i.dims.length!==4)throw new Error(\'Input "value" is expected to have 3 or 4 dimensions\');if(r.dims[0]!==i.dims[0])throw new Error(\'Input "query" and "value" shall have same dim 0 (batch_size)\');if(i.dims.length===3){if(v!==i.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');O=i.dims[2]}else{if(v!==i.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');O=i.dims[1]*i.dims[3],P=!0}}let R=S+v,L=!1;if(a)throw new Error("Key padding mask is not supported");if(c){if(c.dims.length!==4)throw new Error(\'Input "relative_position_bias" is expected to have 4 dimensions\');if(c.dims[0]!==y&&c.dims[0]!==1||c.dims[1]!==t.numHeads||c.dims[2]!==w||c.dims[3]!==R)throw new Error(\'Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)\')}return{batchSize:y,sequenceLength:w,pastSequenceLength:S,kvSequenceLength:v,totalSequenceLength:R,maxSequenceLength:A,inputHiddenSize:0,hiddenSize:_,vHiddenSize:O,headSize:I,vHeadSize:Math.floor(O/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:E,scale:t.scale,broadcastResPosBias:L,passPastInKv:P,qkvFormat:x}},Lu=e=>ve({...e}),Hu=ve({perm:[0,2,1,3]}),ap=(e,t,r,o,i,u,a)=>{let c=[o,i,u],p=M.size(c),h=[{type:12,data:p},{type:12,data:a},{type:12,data:u}],d=y=>{let w=j("qkv_with_bias",t.dataType,c),_=U("qkv",t.dataType,c),v=U("bias",r.dataType,c),S=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n ${y.registerUniforms(S).declareVariables(_,v,w)}\n ${y.mainStart()}\n ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:c,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:d},{inputs:[t,r],outputs:[-1]})[0]},Ko=(e,t,r,o,i,u,a,c)=>{let p=u;if(a){if(o===1)throw new Error("AddBiasReshape is not implemented. 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sp,up,dp,lp,cp,pp,mp,fp,ju,Ku=Y(()=>{"use strict";ye();Se();_e();sp=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},up=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n if (k < 0) {\n break;\n }\n if (k >= i32(${fe("uniforms.x_shape",i,t)})) {\n break;\n }\n offset += k * i32(${fe("uniforms.x_strides",i,t)});\n `;return`\n value = ${e.type.value}(uniforms.constant_value);\n for (var i = 0; i < 1; i++) {\n var offset = 0;\n var k = 0;\n ${o}\n value = x[offset];\n }\n `},dp=(e,t,r)=>{let o="";for(let i=t-1;i>=0;--i)o+=`\n k = i32(${e.indicesGet("indices",i)}) - ${fe("uniforms.pads",i,r)};\n if (k < 0) {\n k = 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i32(${fe("uniforms.x_strides",i,t)});\n `;return`\n var offset = 0;\n var k = 0;\n ${o}\n value = x[offset];\n `},pp=(e,t,r)=>{switch(r.mode){case 0:return up(e,t,r.pads.length);case 1:return dp(e,t,r.pads.length);case 2:return lp(e,t,r.pads.length);case 3:return cp(e,t,r.pads.length);default:throw new Error("Invalid mode")}},mp=(e,t)=>{let r=M.padShape(e[0].dims.slice(),t.pads),o=e[0].dims,i=M.size(r),u=[{type:12,data:i},{type:6,data:t.pads}];t.mode===0&&u.push({type:e[0].dataType,data:t.value}),u.push(...Z(e[0].dims,r));let a=["rank"],c=p=>{let h=j("output",e[0].dataType,r.length),d=U("x",e[0].dataType,o.length),y=d.type.value,w=pp(h,o.length,t),_=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&_.push({name:"constant_value",type:y}),`\n ${p.registerUniforms(_).declareVariables(d,h)}\n ${p.mainStart()}\n ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let indices = ${h.offsetToIndices("global_idx")};\n\n var value = ${y}(0);\n ${w}\n output[global_idx] = value;\n }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(r)/64)},programUniforms:u}),getShaderSource:c}},fp=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),o=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,i=e[0].dims.length,u=new Int32Array(2*i).fill(0);if(e.length>=4){let c=e[3].getBigInt64Array();for(let p=0;pu[Number(p)]=Number(c));let a=[];return u.forEach(c=>a.push(c)),{mode:t.mode,value:o,pads:a}}else return t},ju=(e,t)=>{sp(e.inputs);let r=fp(e.inputs,t);e.compute(mp(e.inputs,r),{inputs:[0]})}});var Nn,Yu,Zu,Xu,Qu,hp,gp,Ju,ed,td,rd,nd,od,id,ad,sd,ud,dd,ld,cd=Y(()=>{"use strict";$r();ye();Se();_e();Nn=e=>{if(vr.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Yu=(e,t,r)=>{let o=t.format==="NHWC",i=e.dims.slice();o&&i.splice(1,0,i.pop());let u=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),c=t.strides.slice(),p=u?t.dilations.slice():[],h=t.pads.slice();nr.adjustPoolAttributes(r,i,a,c,p,h);let d=nr.computePoolOutputShape(r,i,c,p,a,h,t.autoPad),y=Object.assign({},t);u?Object.assign(y,{kernelShape:a,strides:c,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(y,{kernelShape:a,strides:c,pads:h,cacheKey:t.cacheKey});let w=d.slice();return w.push(w.splice(1,1)[0]),[y,o?w:d]},Zu=(e,t)=>{let r=t.format==="NHWC",o=M.size(e),i=M.size(t.kernelShape),u=[{type:12,data:o},{type:12,data:i}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let c=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],d=t.pads[t.pads.length-1],y=!!(h+d);u.push({type:12,data:c},{type:12,data:p},{type:12,data:h},{type:12,data:d}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let w=!1;if(t.kernelShape.length===2){let _=t.kernelShape[t.kernelShape.length-2],v=t.strides[t.strides.length-2],S=t.pads[t.pads.length/2-2],A=t.pads[t.pads.length-2];w=!!(S+A),u.push({type:12,data:_},{type:12,data:v},{type:12,data:S},{type:12,data:A}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[u,a,!0,y,w]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let c=M.computeStrides(t.kernelShape);u.push({type:12,data:c},{type:12,data:t.pads},{type:12,data:t.strides}),a.push({name:"kernelStrides",type:"u32",length:c.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let p=t.pads.reduce((h,d)=>h+d);return[u,a,!!p,!1,!1]}},Xu=(e,t,r,o,i,u,a,c,p,h,d,y)=>{let w=i.format==="NHWC",_=t.type.value,v=j("output",t.type.tensor,o);if(i.kernelShape.length<=2){let S="",A="",I="",x=r-(w?2:1);if(d?S=`\n for (var i: u32 = 0u; i < 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strict";$r();ye();_e();bp=(e,t,r)=>{let o=e===t,i=et&&r>0;if(o||i||u)throw new Error("Range these inputs\' contents are invalid.")},wp=(e,t,r,o)=>{let i=Math.abs(Math.ceil((t-e)/r)),u=[i],a=i,c=[{type:12,data:a},{type:o,data:e},{type:o,data:r},...Z(u)],p=h=>{let d=j("output",o,u.length),y=d.type.value,w=[{name:"outputSize",type:"u32"},{name:"start",type:y},{name:"delta",type:y}];return`\n ${h.registerUniforms(w).declareVariables(d)}\n ${h.mainStart()}\n ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n output[global_idx] = uniforms.start + ${y}(global_idx) * uniforms.delta;\n }`};return{name:"Range",shaderCache:{hint:`${o}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:u,dataType:o}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c})}},pd=e=>{let t=0,r=0,o=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],o=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],o=e.inputs[2].getFloat32Array()[0]),vr.webgpu.validateInputContent&&bp(t,r,o),e.compute(wp(t,r,o,e.inputs[0].dataType),{inputs:[]})}});var vp,$p,_p,Sp,xp,Cp,Ap,Ip,Tp,Ep,Pp,fd,kp,Op,Rp,Bp,Dp,hd,gd,yd=Y(()=>{"use strict";ye();Se();Ze();_e();vp=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},$p=(e,t,r)=>{t.every(i=>i>=0&&i{throw new Error("Resize requires axes input values to be positive and less than rank")}));let o=new Array(r).fill(1);return t.forEach((i,u)=>o[i]=e[u]),o},_p=(e,t,r,o,i,u)=>{let[a,c,p]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach(d=>u.push(d));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(c>0&&e.length>c&&e[c].dims.length>0){if(e[c].getFloat32Array().forEach(d=>o.push(d)),o.length!==0&&o.length!==h&&r>=18&&o.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");vp(o,t),t.axes.length>0&&$p(o,t.axes,h).forEach((d,y)=>o[y]=d)}if(p>0&&e.length>p&&(e[p].getBigInt64Array().forEach(d=>i.push(Number(d))),i.length!==h||r>=18&&i.length===t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(o.length!==t.axes.length)throw new Error(\'Resize requires "scales" input size to be of axes rank when axes attributes is specified\');if(i.length!==t.axes.length)throw new Error(\'Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified\')}if(typeof o<"u"&&typeof i<"u"&&o.length>0&&i.length>h)throw new Error("Resize requires only of scales or sizes to be specified")},Sp=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;\n } else {\n return 0.0;\n }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n return 0.0;\n } else {\n // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n // offset-by-one error later in floor().\n let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n let fract =\n ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);\n return whole + fract;\n }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n return ${t}(roiStart) * ${t}(lengthOriginal - 1) +\n (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /\n ${t}(lengthResized - 1);\n } else {\n return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);\n }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);\n const adjustment = ${t}(lengthResized) / outputWidth;\n const center = ${t}(lengthOriginal) / 2;\n const offset = center * (1 - adjustment);\n return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",xp=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(t<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Cp=(e,t,r)=>{let o=new Array(r).fill(0).concat(new Array(r).fill(1)),i=e.length===0?o:e.slice();return t.length>0?(t.forEach((u,a)=>{o[u]=i[a],o[a+r]=i[t.length+a]}),o):i},Ap=(e,t,r,o)=>{let i=[];if(r.length>0)if(o.length>0){if(e.forEach(u=>i.push(u)),Math.max(...o)>e.length)throw new Error("axes is out of bound");o.forEach((u,a)=>i[u]=r[a])}else r.forEach(u=>i.push(u));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((u,a)=>Math.round(u*t[a]))}return i},Ip=(e,t,r)=>{let o=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(u=>t[u]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(u=>t[u]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let i=e.slice();return r.axes.length>0?(r.axes.forEach(u=>t[u]=o),r.axes.forEach(u=>i[u]=Math.round(e[u]*t[u]))):(t.fill(o,0,t.length),i.forEach((u,a)=>i[a]=Math.round(u*t[a]))),i},Tp=(e,t,r,o,i)=>`\n fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {\n var original_indices: array<${e.type.value}, ${r.length}>;\n for (var i:u32 = 0; i < ${r.length}; i++) {\n var output_index = ${e.indicesGet("output_indices","i")};\n var scale = ${fe("uniforms.scales","i",o)};\n var roi_low = ${fe("uniforms.roi","i",i)};\n var roi_hi = ${fe("uniforms.roi",`i + ${t.length}`,i)};\n if (scale == 1.0) {\n original_indices[i] = ${e.type.value}(output_index);\n } else {\n var input_shape_i = ${fe("uniforms.input_shape","i",t.length)};\n var output_shape_i = ${fe("uniforms.output_shape","i",r.length)};\n original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n input_shape_i, roi_low, roi_hi);\n }\n }\n return original_indices;\n }`,Ep=(e,t,r,o,i,u,a)=>`\n fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n var input_indices: ${e.type.indices};\n for (var i:u32 = 0; i < ${o.length}; i++) {\n var output_index = ${t.indicesGet("output_indices","i")};\n var input_index: u32;\n var scale = ${fe("uniforms.scales","i",i)};\n if (scale == 1.0) {\n input_index = output_index;\n } else {\n var roi_low = ${fe("uniforms.roi","i",u)};\n var roi_hi = ${fe("uniforms.roi",`i + ${r.length}`,u)};\n var input_shape_i = ${fe("uniforms.input_shape","i",r.length)};\n var output_shape_i = ${fe("uniforms.output_shape","i",o.length)};\n var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n input_shape_i, roi_low, roi_hi);\n if (!${a} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {\n if (original_idx < 0) {\n input_index = 0;\n } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {\n input_index = input_shape_i - 1;\n } else {\n input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n }\n } else {\n input_index = u32(original_idx);\n }\n }\n ${e.indicesSet("input_indices","i"," input_index")}\n }\n return input_indices;\n }`,Pp=(e,t)=>`\n fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n for (var i:u32 = 0; i < ${t.length}; i++) {\n var input_index = ${e.indicesGet("input_indices","i")};\n if (input_index < 0 || input_index >= ${fe("uniforms.input_shape","i",t.length)}) {\n return false;\n }\n }\n return true;\n }`,fd=(e,t,r,o)=>e.rank>o?`\n ${e.indicesSet("input_indices",t,"channel")};\n ${e.indicesSet("input_indices",r,"batch")};\n`:"",kp=(e,t,r,o,i)=>{let[a,c,p,h]=r.length===2?[-1,0,1,-1]:[0,2,3,1],d=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",c,`max(0, min(row, ${r[c]} - 1))`)};\n ${e.indicesSet("input_indices",p,`max(0, min(col, ${r[p]} - 1))`)};\n ${fd(e,h,a,2)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${d} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var row:${d} = originalIndices[${c}];\n var col:${d} = originalIndices[${p}];\n ${o?`if (row < 0 || row > (${r[c]} - 1) || col < 0 || col > (${r[p]} - 1)) {\n return ${i};\n }`:""};\n row = max(0, min(row, ${r[c]} - 1));\n col = max(0, min(col, ${r[p]} - 1));\n var row1: u32 = u32(row);\n var col1: u32 = u32(col);\n var row2: u32 = u32(row + 1);\n var col2: u32 = u32(col + 1);\n var channel: u32 = ${r.length>2?`u32(originalIndices[${h}])`:"0"};\n var batch: u32 = ${r.length>2?`u32(originalIndices[${a}])`:"0"};\n var x11: ${d} = getInputValue(batch, channel, row1, col1);\n var x12: ${d} = getInputValue(batch, channel, row1, col2);\n var x21: ${d} = getInputValue(batch, channel, row2, col1);\n var x22: ${d} = getInputValue(batch, channel, row2, col2);\n var dx1: ${d} = abs(row - ${d}(row1));\n var dx2: ${d} = abs(${d}(row2) - row);\n var dy1: ${d} = abs(col - ${d}(col1));\n var dy2: ${d} = abs(${d}(col2) - col);\n if (row1 == row2) {\n dx1 = 0.5;\n dx2 = 0.5;\n }\n if (col1 == col2) {\n dy1 = 0.5;\n dy2 = 0.5;\n }\n return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n }`},Op=(e,t,r,o,i,u,a,c,p,h)=>{let d=r.length===2,y=!0,[w,_]=d?[0,1]:y?[2,3]:[1,2],v=e.type.value,S=A=>{let I=A===w?"row":"col";return`\n fn ${I}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${v} {\n var output_index = ${t.indicesGet("output_indices",A)};\n var originalIdx: ${v} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[A]},\n ${o[A]}, ${r[A]}, ${u[A]}, ${u[A]} + ${r.length});\n var fractOriginalIdx: ${v} = originalIdx - floor(originalIdx);\n var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n if (${c} && (originalIdx < 0 || originalIdx > (${r[A]} - 1))) {\n return ${p};\n }\n var data: array<${v}, 4> = array<${v}, 4>(0.0, 0.0, 0.0, 0.0);\n for (var i: i32 = -1; i < 3; i++) {\n var ${I}: ${v} = originalIdx + ${v}(i);\n if (${I} < 0 || ${I} >= ${r[A]}) {\n ${(()=>h?`coefs[i + 1] = 0.0;\n continue;`:c?`return ${p};`:`${I} = max(0, min(${I}, ${r[A]} - 1));`)()};\n }\n var input_indices_copy: ${e.type.indices} = input_indices;\n ${e.indicesSet("input_indices_copy",A,`u32(${I})`)};\n data[i + 1] = ${A===w?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n }\n return cubicInterpolation1D(data, coefs);\n }`};return`\n ${S(w)};\n ${S(_)};\n fn getCubicInterpolationCoefs(s: ${v}) -> array<${v}, 4> {\n var absS = abs(s);\n var coeffs: array<${v}, 4> = array<${v}, 4>(0.0, 0.0, 0.0, 0.0);\n var oneMinusAbsS: ${v} = 1.0 - absS;\n var twoMinusAbsS: ${v} = 2.0 - absS;\n var onePlusAbsS: ${v} = 1.0 + absS;\n coeffs[0] = ((${a} * onePlusAbsS - 5 * ${a}) * onePlusAbsS + 8 * ${a}) * onePlusAbsS - 4 * ${a};\n coeffs[1] = ((${a} + 2) * absS - (${a} + 3)) * absS * absS + 1;\n coeffs[2] = ((${a} + 2) * oneMinusAbsS - (${a} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n coeffs[3] = ((${a} * twoMinusAbsS - 5 * ${a}) * twoMinusAbsS + 8 * ${a}) * twoMinusAbsS - 4 * ${a};\n return coeffs;\n }\n\n fn cubicInterpolation1D(x: array<${v}, 4>, coefs: array<${v}, 4>) -> ${v} {\n var coefsSum: ${v} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n }\n\n fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${v} {\n var input_indices: ${e.type.indices} = output_indices;\n return colCubicInterpolation(input_indices, output_indices);\n }\n `},Rp=(e,t,r,o,i)=>{let[a,c,p,h,d]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],y=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${y} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",c,`max(0, min(depth, ${r[c]} - 1))`)};\n ${e.indicesSet("input_indices",p,`max(0, min(height, ${r[p]} - 1))`)};\n ${e.indicesSet("input_indices",h,`max(0, min(width, ${r[h]} - 1))`)};\n ${fd(e,d,a,3)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${y} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var depth:${y} = originalIndices[${c}];\n var height:${y} = originalIndices[${p}];\n var width:${y} = originalIndices[${h}];\n ${o?`if (depth < 0 || depth > (${r[c]} - 1) || height < 0 || height > (${r[p]} - 1) || width < 0 || (width > ${r[h]} - 1)) {\n return ${i};\n }`:""};\n\n depth = max(0, min(depth, ${r[c]} - 1));\n height = max(0, min(height, ${r[p]} - 1));\n width = max(0, min(width, ${r[h]} - 1));\n var depth1: u32 = u32(depth);\n var height1: u32 = u32(height);\n var width1: u32 = u32(width);\n var depth2: u32 = u32(depth + 1);\n var height2: u32 = u32(height + 1);\n var width2: u32 = u32(width + 1);\n var channel: u32 = ${r.length>3?`u32(originalIndices[${d}])`:"0"};\n var batch: u32 = ${r.length>3?`u32(originalIndices[${a}])`:"0"};\n\n var x111: ${y} = getInputValue(batch, channel, depth1, height1, width1);\n var x112: ${y} = getInputValue(batch, channel, depth1, height1, width2);\n var x121: ${y} = getInputValue(batch, channel, depth1, height2, width1);\n var x122: ${y} = getInputValue(batch, channel, depth1, height2, width2);\n var x211: ${y} = getInputValue(batch, channel, depth2, height1, width1);\n var x212: ${y} = getInputValue(batch, channel, depth2, height1, width2);\n var x221: ${y} = getInputValue(batch, channel, depth2, height2, width1);\n var x222: ${y} = getInputValue(batch, channel, depth2, height2, width2);\n var dx1: ${y} = abs(depth - ${y}(depth1));\n var dx2: ${y} = abs(${y}(depth2) - depth);\n var dy1: ${y} = abs(height - ${y}(height1));\n var dy2: ${y} = abs(${y}(height2) - height);\n var dz1: ${y} = abs(width - ${y}(width1));\n var dz2: ${y} = abs(${y}(width2) - width);\n if (depth1 == depth2) {\n dx1 = 0.5;\n dx2 = 0.5;\n }\n if (height1 == height2) {\n dy1 = 0.5;\n dy2 = 0.5;\n }\n if (width1 == width2) {\n dz1 = 0.5;\n dz2 = 0.5;\n }\n return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n }`},Bp=(e,t,r,o,i,u)=>{let a=e.dims,c=Cp(u,t.axes,a.length),p=Ap(a,o,i,t.axes),h=o.slice();o.length===0&&(h=a.map((x,E)=>x===0?1:p[E]/x),t.keepAspectRatioPolicy!=="stretch"&&(p=Ip(a,h,t)));let d=j("output",e.dataType,p.length),y=U("input",e.dataType,a.length),w=M.size(p),_=a.length===p.length&&a.every((x,E)=>x===p[E]),v=t.coordinateTransformMode==="tf_crop_and_resize",S=t.extrapolationValue,A=y.type.value,I=x=>`\n ${_?"":`\n ${Sp(t.coordinateTransformMode,A)};\n ${(()=>{switch(t.mode){case"nearest":return`\n ${Pp(y,a)};\n ${xp(t.nearestMode,r,A)};\n ${Ep(y,d,a,p,h.length,c.length,v)};\n `;case"linear":return`\n ${Tp(d,a,p,h.length,c.length)};\n ${(()=>{if(a.length===2||a.length===4)return`${kp(y,d,a,v,S)}`;if(a.length===3||a.length===5)return`${Rp(y,d,a,v,S)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n `;case"cubic":return`\n ${(()=>{if(a.length===2||a.length===4)return`${Op(y,d,a,p,h,c,t.cubicCoeffA,v,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n `;default:throw Error("Invalid resize mode")}})()};\n `}\n ${x.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",c.length).declareVariables(y,d)}\n ${x.mainStart()}\n ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n ${_?"output[global_idx] = input[global_idx];":`\n let output_indices = ${d.offsetToIndices("global_idx")};\n var input_indices: ${y.type.indices};\n ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n if (checkInputIndices(input_indices)) {\n output[global_idx] = ${y.getByIndices("input_indices")};\n } else {\n output[global_idx] = ${t.extrapolationValue};\n }`;case"linear":return`output[global_idx] = ${a.length===2||a.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};\n`}\n }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${h.length>0?h:""}|${i.length>0?i:""}|${c.length>0?c:""}|${_}|${a}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:[{type:12,data:w},{type:1,data:h},{type:1,data:c},...Z(a,p)]})}},Dp=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},hd=(e,t)=>{let r=[],o=[],i=[],u=Dp(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");_p(e.inputs,t,u,r,o,i),e.compute(Bp(e.inputs[0],t,u,r,o,i),{inputs:[0]})},gd=e=>{let t=e.antialias,r=e.axes,o=e.coordinateTransformMode,i=e.cubicCoeffA,u=e.excludeOutside!==0,a=e.extrapolationValue,c=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return ve({antialias:t,axes:r,coordinateTransformMode:o,cubicCoeffA:i,excludeOutside:u,extrapolationValue:a,keepAspectRatioPolicy:c,mode:p,nearestMode:h})}});var zp,Mp,bd,wd=Y(()=>{"use strict";ye();Se();Ze();_e();zp=(e,t)=>{let[r,o,i,u]=e,{numHeads:a,rotaryEmbeddingDim:c}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input \'x\' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!M.areEqual(o.dims,[])&&!M.areEqual(o.dims,[1])&&o.dims.length!==2)throw new Error(`Input \'position_ids\' is expected to have 0, 1, or 2 dimensions, got ${o.dims.length}`);if(i.dims.length!==2)throw new Error(`Input \'cos_cache\' is expected to have 2 dimensions, got ${i.dims.length}`);if(u.dims.length!==2)throw new Error(`Input \'sin_cache\' is expected to have 2 dimensions, got ${u.dims.length}`);if(!M.areEqual(i.dims,u.dims))throw new Error("Inputs \'cos_cache\' and \'sin_cache\' are expected to have the same shape");if(c>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let p=r.dims[0],h=r.dims[r.dims.length-2],d=i.dims[0],y=M.sizeFromDimension(r.dims,1)/h,w=c===0?i.dims[1]*2:y/a;if(c>w)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(o.dims.length===2){if(p!==o.dims[0])throw new Error(`Input \'position_ids\' dimension 0 should be of size batch_size, got ${o.dims[0]}`);if(h!==o.dims[1])throw new Error(`Input \'position_ids\' dimension 1 should be of size sequence_length, got ${o.dims[1]}`)}if(w/2!==i.dims[1]&&c/2!==i.dims[1])throw new Error(`Input \'cos_cache\' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${i.dims[1]}`);if(h>d)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Mp=(e,t)=>{let{interleaved:r,numHeads:o,rotaryEmbeddingDim:i,scale:u}=t,a=e[0].dims[0],c=M.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=c/p,d=e[2].dims[1],y=i===0?d*2:h/o,w=new Array(a,p,h/y,y-d),_=M.computeStrides(w),v=[{type:1,data:u},{type:12,data:w},{type:12,data:_},...e[0].dims.length===3?new Array({type:12,data:[c,h,y,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[c,y,p*y,1]}):[],...Z(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],S=A=>{let I=U("input",e[0].dataType,e[0].dims.length),x=U("position_ids",e[1].dataType,e[1].dims.length),E=U("cos_cache",e[2].dataType,e[2].dims.length),P=U("sin_cache",e[3].dataType,e[3].dims.length),O=j("output",e[0].dataType,e[0].dims.length);return A.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:w.length},{name:"global_strides",type:"u32",length:_.length},{name:"input_output_strides",type:"u32",length:_.length}]),`\n ${A.declareVariables(I,x,E,P,O)}\n\n ${A.mainStart(or)}\n let half_rotary_emb_dim = uniforms.${E.name}_shape[1];\n let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;\n let size = uniforms.global_shape[0] * uniforms.global_strides[0];\n ${A.guardAgainstOutOfBoundsWorkgroupSizes("size")}\n\n if (bsnh[3] < half_rotary_emb_dim) {\n let position_ids_idx =\n ${x.broadcastedIndicesToOffset("bsnh.xy",j("",x.type.tensor,2))};\n let position_id =\n u32(${x.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);\n let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r});\n let j = i + select(half_rotary_emb_dim, 1, ${r});\n let re = ${I.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} -\n ${I.getByOffset("j")} * ${P.get("position_id","bsnh[3]")};\n ${O.setByOffset("i","re")}\n let im = ${I.getByOffset("i")} * ${P.get("position_id","bsnh[3]")} +\n ${I.getByOffset("j")} * ${E.get("position_id","bsnh[3]")};\n ${O.setByOffset("j","im")}\n } else {\n let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;\n ${O.setByOffset("k",I.getByOffset("k"))}\n }\n }`};return{name:"RotaryEmbedding",shaderCache:{hint:ve({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:S,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(M.size(w)/or)},programUniforms:v})}},bd=(e,t)=>{zp(e.inputs,t),e.compute(Mp(e.inputs,t))}});var Up,Vp,vd,$d=Y(()=>{"use strict";ye();Se();_e();Up=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],o=e[2];if(t.dataType!==r.dataType||t.dataType!==o.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let i=t.dims[t.dims.length-1],u=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==u)throw new Error("Skip must have the same sequence length as input");if(o.dims.length!==1)throw new Error("Gamma must be 1D");if(o.dims[o.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let a=e[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let a=e[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},Vp=(e,t,r,o)=>{let i=t.simplified,u=e[0].dims,a=M.size(u),c=u,p=a,h=u.slice(-1)[0],d=o?u.slice(0,-1).concat(1):[],y=!i&&e.length>3,w=e.length>4,_=o&&r>1,v=o&&r>2,S=r>3,A=Me(h),I=[{type:12,data:p},{type:12,data:A},{type:12,data:h},{type:1,data:t.epsilon}],x=P=>{let O=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],R=[U("x",e[0].dataType,e[0].dims,A),U("skip",e[1].dataType,e[1].dims,A),U("gamma",e[2].dataType,e[2].dims,A)];y&&R.push(U("beta",e[3].dataType,e[3].dims,A)),w&&R.push(U("bias",e[4].dataType,e[4].dims,A)),R.push(j("output",e[0].dataType,c,A)),_&&R.push(j("mean_output",1,d)),v&&R.push(j("inv_std_output",1,d)),S&&R.push(j("input_skip_bias_sum",e[0].dataType,c,A));let L=De(e[0].dataType);return`\n\n ${P.registerUniforms(O).declareVariables(...R)}\n\n ${P.mainStart()}\n ${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size / uniforms.hidden_size")}\n let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;\n let offset = global_idx * hidden_size_vectorized;\n var sum = ${$t("f32",A)};\n var squareSum = ${$t("f32",A)};\n for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n let skip_value = skip[offset + i];\n let bias_value = ${w?"bias[i]":L+"(0.0)"};\n let input_value = x[offset + i];\n let value = input_value + skip_value + bias_value;\n ${S?"input_skip_bias_sum[offset + i] = value;":""}\n output[offset + i] = value;\n let f32_value = ${ir(L,A,"value")};\n sum += f32_value;\n squareSum += f32_value * f32_value;\n }\n let mean = ${_t("sum",A)} / f32(uniforms.hidden_size);\n let inv_std_dev = inverseSqrt(${_t("squareSum",A)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon);\n ${_?"mean_output[global_idx] = mean;":""}\n ${v?"inv_std_output[global_idx] = inv_std_dev;":""}\n for (var i: u32 = 0; i < hidden_size_vectorized; i++) {\n output[offset + i] = (output[offset + i] ${i?"":`- ${L}(mean)`}) * ${L}(inv_std_dev) * gamma[i] ${y?"+ beta[i]":""};\n }\n }`},E=[{dims:c,dataType:e[0].dataType}];return r>1&&E.push({dims:d,dataType:1}),r>2&&E.push({dims:d,dataType:1}),r>3&&E.push({dims:u,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${A};${_};${v};${S}`,inputDependencies:e.map((P,O)=>"type")},getShaderSource:x,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(p/h/64)},programUniforms:I})}},vd=(e,t)=>{Up(e.inputs);let o=[0];e.outputCount>1&&o.push(-3),e.outputCount>2&&o.push(-3),e.outputCount>3&&o.push(3),e.compute(Vp(e.inputs,t,e.outputCount,!1),{outputs:o})}});var Wp,Gn,Np,_d,Gp,Hp,Sd,xd,Cd=Y(()=>{"use strict";ye();Se();Ze();_e();Wp=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,o)=>{if(e[o+1].dataType!==6&&e[o+1].dataType!==7)throw new Error(`Input ${o} must be an array of int32 or int64`)})},Gn=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(o=>r.push(Number(o)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(o=>r.push(Number(o)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},Np=(e,t)=>{if(e.length>1){let r=Gn(e,1),o=Gn(e,2),i=Gn(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),ve({starts:r,ends:o,axes:i})}else return t},_d=(e,t,r,o,i)=>{let u=e;return e<0&&(u+=r[o[t]]),i[t]<0?Math.max(0,Math.min(u,r[o[t]]-1)):Math.max(0,Math.min(u,r[o[t]]))},Gp=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n var input_indices: ${e.type.indices};\n var carry = 0u;\n for (var i = ${r.length}; i >= 0; i--) {\n let input_shape_i = ${fe("uniforms.input_shape","i",r.length)};\n let steps_i = ${fe("uniforms.steps","i",r.length)};\n let signs_i = ${fe("uniforms.signs","i",r.length)};\n let starts_i = ${fe("uniforms.starts","i",r.length)};\n var output_index = ${t.indicesGet("output_indices","i")};\n var input_index = output_index * steps_i + starts_i + carry;\n carry = input_index / input_shape_i;\n input_index = input_index % input_shape_i;\n if (signs_i < 0) {\n input_index = input_shape_i - input_index - 1u + starts_i;\n }\n ${e.indicesSet("input_indices","i","input_index")};\n }\n return input_indices;\n }`,Hp=(e,t)=>{let r=e[0].dims,o=M.size(r),i=t.axes.length>0?M.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],u=Gn(e,4);u.forEach(I=>I!==0||(()=>{throw new Error("step cannot be 0")})),u.length===0&&(u=Array(i.length).fill(1));let a=t.starts.map((I,x)=>_d(I,x,r,i,u)),c=t.ends.map((I,x)=>_d(I,x,r,i,u));if(i.length!==a.length||i.length!==c.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==r.length)for(let I=0;IMath.sign(I));u.forEach((I,x,E)=>{if(I<0){let P=(c[x]-a[x])/I,O=a[x],R=O+P*u[x];a[x]=R,c[x]=O,E[x]=-I}});let h=r.slice(0);i.forEach((I,x)=>{h[I]=Math.ceil((c[I]-a[I])/u[I])});let d={dims:h,dataType:e[0].dataType},y=j("output",e[0].dataType,h.length),w=U("input",e[0].dataType,e[0].dims.length),_=M.size(h),v=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:a.length},{name:"signs",type:"i32",length:p.length},{name:"steps",type:"u32",length:u.length}],S=[{type:12,data:_},{type:12,data:a},{type:6,data:p},{type:12,data:u},...Z(e[0].dims,h)],A=I=>`\n ${I.registerUniforms(v).declareVariables(w,y)}\n ${Gp(w,y,r)}\n ${I.mainStart()}\n ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n let output_indices = ${y.offsetToIndices("global_idx")};\n let input_indices = calculateInputIndices(output_indices);\n ${y.setByOffset("global_idx",w.getByIndices("input_indices"))}\n }`;return{name:"Slice",shaderCache:{hint:`${p.length}_${a.length}_${u.length}`,inputDependencies:["rank"]},getShaderSource:A,getRunData:()=>({outputs:[d],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:S})}},Sd=(e,t)=>{Wp(e.inputs,t);let r=Np(e.inputs,t);e.compute(Hp(e.inputs,r),{inputs:[0]})},xd=e=>{let t=e.starts,r=e.ends,o=e.axes;return ve({starts:t,ends:r,axes:o})}});var Lp,Fp,Ad,Id,Td=Y(()=>{"use strict";ye();Se();Ze();_e();Lp=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Fp=(e,t)=>{let r=e.dims,o=M.size(r),i=64,u=t.axis;if(u<0&&(u=r.length+u),uI===4?`max(max(${A}.x, ${A}.y), max(${A}.z, ${A}.w))`:I===2?`max(${A}.x, ${A}.y)`:I===3?`max(max(${A}.x, ${A}.y), ${A}.z)`:A,y=U("x",e.dataType,e.dims,p),w=j("result",e.dataType,e.dims,p),_=y.type.value,v=De(e.dataType)==="f32"?`var threadMax = ${_}(-3.402823e+38f);`:`var threadMax = ${_}(-65504.0h);`,S=A=>`\n var rowMaxShared : ${_};\n var rowSumShared : ${_};\n var threadShared : array<${_}, ${i}>;\n\n fn getValue(row: i32, col: i32, row_stride: i32) -> ${_} {\n let index = row * row_stride + col;\n return x[index];\n }\n\n fn setValue(row: i32, col: i32, row_stride: i32, value: ${_}) {\n let index = row * row_stride + col;\n result[index] = value;\n }\n ${A.registerUniform("packedCols","i32").declareVariables(y,w)}\n ${A.mainStart()}\n let gindex = i32(global_idx);\n let lindex = i32(local_idx);\n const wg = ${i};\n let row = gindex / wg;\n let cols = uniforms.packedCols;\n let row_stride : i32 = uniforms.packedCols;\n\n // find the rows max\n ${v}\n for (var col = lindex; col < cols; col += wg) {\n let value = getValue(row, col, row_stride);\n threadMax = max(threadMax, value);\n }\n if (lindex < cols) {\n threadShared[lindex] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, wg);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (lindex < currSize) {\n threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);\n }\n workgroupBarrier();\n }\n if (lindex == 0) {\n rowMaxShared = ${_}(${d("threadShared[0]",p)});\n }\n workgroupBarrier();\n\n // find the rows sum\n var threadSum = ${_}(0.0);\n for (var col = lindex; col < cols; col += wg) {\n let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);\n threadSum += subExp;\n }\n threadShared[lindex] = threadSum;\n workgroupBarrier();\n\n for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) {\n if (lindex < currSize) {\n threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];\n }\n workgroupBarrier();\n }\n if (lindex == 0) {\n rowSumShared = ${_}(${_t("threadShared[0]",p)});\n }\n workgroupBarrier();\n\n // calculate final value for each element in the row\n for (var col = lindex; col < cols; col += wg) {\n let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;\n setValue(row, col, row_stride, value);\n }\n }`;return{name:"Softmax",shaderCache:{hint:`${p}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:c},programUniforms:[{type:6,data:h}]}),getShaderSource:S}},Ad=(e,t)=>{Lp(e.inputs),e.compute(Fp(e.inputs[0],t))},Id=e=>ve({axis:e.axis})});var qp,jp,Kp,Yp,Zp,Ed,Pd,kd=Y(()=>{"use strict";ye();Se();Ze();_e();qp=e=>{if(!e||e.length<1)throw new Error("too few inputs")},jp=(e,t)=>{let r=[],o=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(i=>r.push(Number(i))),o=r.length),ve({numOutputs:o,axis:t.axis,splitSizes:r})},Kp=e=>`\nfn calculateOutputIndex(index: u32) -> u32 {\n for (var i: u32 = 0u; i < ${e}u; i += 1u ) {\n if (index < ${fe("uniforms.size_in_split_axis","i",e)}) {\n return i;\n }\n }\n return ${e}u;\n}`,Yp=e=>{let t=e.length,r=[];for(let o=0;o{let r=e[0].dims,o=M.size(r),i=e[0].dataType,u=M.normalizeAxis(t.axis,r.length),a=new Array(t.numOutputs),c=U("input",i,r.length),p=new Array(t.numOutputs),h=[],d=[],y=0,w=[{type:12,data:o}];for(let v=0;v`\n ${v.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",p.length).declareVariables(c,...a)}\n ${Kp(p.length)}\n ${Yp(a)}\n\n ${v.mainStart()}\n ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}\n\n var indices = ${c.offsetToIndices("global_idx")};\n var index = ${c.indicesGet("indices",u)};\n let output_number = calculateOutputIndex(index);\n if (output_number != 0) {\n index -= ${fe("uniforms.size_in_split_axis","output_number - 1u",p.length)};\n ${c.indicesSet("indices",u,"index")};\n }\n writeBufferData(output_number, indices, global_idx);\n }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:_,getRunData:()=>({outputs:h,dispatchGroup:{x:Math.ceil(o/64)},programUniforms:w})}},Ed=(e,t)=>{qp(e.inputs);let r=e.inputs.length===1?t:jp(e.inputs,t);e.compute(Zp(e.inputs,r),{inputs:[0]})},Pd=e=>{let t=e.axis,r=e.splitSizes,o=e.numOutputs<0?r.length:e.numOutputs;if(o!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return ve({axis:t,numOutputs:o,splitSizes:r})}});var Od,Xp,Qp,Jp,Rd,Bd=Y(()=>{"use strict";ye();Se();_e();Od=e=>Array.from(e.getBigInt64Array(),Number),Xp=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Od(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Qp=(e,t)=>{let r=[];for(let o=0;o{let t=e[0].dims,r=Od(e[1]),o=Qp(t,r),i=M.size(o),u=e[0].dataType,a=U("input",u,t.length),c=j("output",u,o.length),p=h=>`\n const inputShape = ${a.indices(...t)};\n ${h.registerUniform("output_size","u32").declareVariables(a,c)}\n ${h.mainStart()}\n ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let output_indices = ${c.offsetToIndices("global_idx")};\n var input_indices: ${a.type.indices};\n for (var i = 0; i < ${t.length}; i++) {\n let input_dim_i = ${a.indicesGet("uniforms.input_shape","i")};\n let input_dim_value = ${c.indicesGet("output_indices","i")} % input_dim_i;\n\n ${a.indicesSet("input_indices","i","input_dim_value")}\n }\n ${c.setByOffset("global_idx",a.getByIndices("input_indices"))}\n }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},...Z(e[0].dims,o)]}),getShaderSource:p}},Rd=e=>{Xp(e.inputs),e.compute(Jp(e.inputs),{inputs:[0]})}});var em,tm,Dd,zd=Y(()=>{"use strict";ye();Se();_e();em=(e,t,r,o,i)=>{let u=j("output_data",i,r.length,4),a=U("a_data",t[1].dataType,t[1].dims.length,4),c=U("b_data",t[2].dataType,t[2].dims.length,4),p=U("c_data",t[0].dataType,t[0].dims.length,4),h,d=(y,w,_)=>`select(${w}, ${y}, ${_})`;if(!o)h=u.setByOffset("global_idx",d(a.getByOffset("global_idx"),c.getByOffset("global_idx"),p.getByOffset("global_idx")));else{let y=(w,_,v="")=>{let S=`a_data[index_a${_}][component_a${_}]`,A=`b_data[index_b${_}][component_b${_}]`,I=`bool(c_data[index_c${_}] & (0xffu << (component_c${_} * 8)))`;return`\n let output_indices${_} = ${u.offsetToIndices(`global_idx * 4u + ${_}u`)};\n let offset_a${_} = ${a.broadcastedIndicesToOffset(`output_indices${_}`,u)};\n let offset_b${_} = ${c.broadcastedIndicesToOffset(`output_indices${_}`,u)};\n let offset_c${_} = ${p.broadcastedIndicesToOffset(`output_indices${_}`,u)};\n let index_a${_} = offset_a${_} / 4u;\n let index_b${_} = offset_b${_} / 4u;\n let index_c${_} = offset_c${_} / 4u;\n let component_a${_} = offset_a${_} % 4u;\n let component_b${_} = offset_b${_} % 4u;\n let component_c${_} = offset_c${_} % 4u;\n ${w}[${_}] = ${v}(${d(S,A,I)});\n `};i===9?h=`\n var data = vec4(0);\n ${y("data",0,"u32")}\n ${y("data",1,"u32")}\n ${y("data",2,"u32")}\n ${y("data",3,"u32")}\n output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:h=`\n ${y("output_data[global_idx]",0)}\n ${y("output_data[global_idx]",1)}\n ${y("output_data[global_idx]",2)}\n ${y("output_data[global_idx]",3)}\n `}return`\n ${e.registerUniform("vec_size","u32").declareVariables(p,a,c,u)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${h}\n }`},tm=e=>{let t=e[1].dims,r=e[2].dims,o=e[0].dims,i=e[1].dataType,u=!(M.areEqual(t,r)&&M.areEqual(r,o)),a=t,c=M.size(t);if(u){let h=It.calcShape(It.calcShape(t,r,!1),o,!1);if(!h)throw new Error("Can\'t perform where op on the given tensors");a=h,c=M.size(a)}let p=Math.ceil(c/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:h=>em(h,e,a,u,i),getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(c/64/4)},programUniforms:[{type:12,data:p},...Z(o,t,r,a)]})}},Dd=e=>{e.compute(tm(e.inputs))}});var Md,Ud=Y(()=>{"use strict";Ka();Ro();Ja();ts();Vs();Zs();Oo();Uo();lu();mu();gu();$u();xu();Au();Eu();Ou();Du();Mu();Vu();Wo();Gu();qu();Ku();cd();md();In();yd();wd();$d();Cd();Td();kd();Bd();Sr();Rn();zd();Md=new 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n'}),hr,kt,nn,Ln,Wn,Cs,Ja,Cr,Er,gc,Un,Qf,Jf,Zf,em,tm,rm,nm,am=Y(()=>{qt(),E_(),ra(),hr=()=>!!Fe.wasm.proxy&&typeof document<"u",nn=!1,Ln=!1,Wn=!1,Ja=new Map,Cr=(t,e)=>{let r=Ja.get(t);r?r.push(e):Ja.set(t,[e])},Er=()=>{if(nn||!Ln||Wn||!kt)throw new Error("worker not 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Fe.wasm.initTimeout!="number"||Fe.wasm.initTimeout<0)&&(Fe.wasm.initTimeout=0),typeof Fe.wasm.simd!="boolean"&&(Fe.wasm.simd=!0),typeof Fe.wasm.proxy!="boolean"&&(Fe.wasm.proxy=!1),typeof Fe.wasm.trace!="boolean"&&(Fe.wasm.trace=!1),typeof Fe.wasm.numThreads!="number"||!Number.isInteger(Fe.wasm.numThreads)||Fe.wasm.numThreads<=0){(typeof self<"u"&&!self.crossOriginIsolated||typeof process<"u"&&process.versions&&process.versions.node)&&(Fe.wasm.numThreads=1);let t=typeof navigator>"u"?(void 0)().length:navigator.hardwareConcurrency;Fe.wasm.numThreads=Math.min(4,Math.ceil((t||1)/2))}},sm=class{async init(t){yc(),await Qf(),await Jf(t)}async createInferenceSessionHandler(t,e){let r=new im;return await r.loadModel(t,e),Promise.resolve(r)}}}),om={};mn(om,{wasmBackend:()=>um});var um,A_=Y(()=>{I_(),um=new sm});qt();qt();qt();var M_="1.18.0",O_=ep;{let 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All Rights Reserved. +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================================= +*//** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */const z_=Object.freeze(Object.defineProperty({__proto__:null,get InferenceSession(){return ao},get TRACE(){return jn},get TRACE_FUNC_BEGIN(){return Ht},get TRACE_FUNC_END(){return Mt},get Tensor(){return ft},get TrainingSession(){return io},default:O_,get env(){return Fe},get registerBackend(){return 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0&&(o=e),o.format="RGBA",o.height=h,o.width=f,e!==void 0){const m=u();m.width=f,m.height=h;const c=l(m);if(c!=null)c.putImageData(t,0,0),s=c.getImageData(0,0,f,h).data;else throw new Error("Can not access image data")}else s=t.data}else if(i){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const h=u();h.width=t.width,h.height=t.height;const f=l(h);if(f!=null){const m=t.height,c=t.width;return f.drawImage(t,0,0,c,m),s=f.getImageData(0,0,c,m).data,o.height=m,o.width=c,Ts(s,o)}else throw new Error("Can not access image data")}else{if(a)return new Promise((h,f)=>{const m=u(),c=l(m);if(!t||!c)return f();const y=new Image;y.crossOrigin="Anonymous",y.src=t,y.onload=()=>{m.width=y.width,m.height=y.height,c.drawImage(y,0,0,m.width,m.height);const b=c.getImageData(0,0,m.width,m.height);o.height=m.height,o.width=m.width,h(Ts(b.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return Ts(s,o);throw new Error("Input data provided is not supported - aborted tensor creation")},D_=(t,e)=>{const{width:r,height:n,download:i,dispose:a}=e,s=[1,n,r,4];return new Qt({location:"texture",type:"float32",texture:t,dims:s,download:i,dispose:a})},N_=(t,e)=>{const{dataType:r,dims:n,download:i,dispose:a}=e;return new Qt({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:i,dispose:a})},F_=(t,e,r)=>new Qt({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),ln=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),ii=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let wc=!1;const L_=()=>{if(!wc){wc=!0;const t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,r=typeof Float16Array<"u"&&Float16Array.from;t&&(ln.set("int64",BigInt64Array),ii.set(BigInt64Array,"int64")),e&&(ln.set("uint64",BigUint64Array),ii.set(BigUint64Array,"uint64")),r?(ln.set("float16",Float16Array),ii.set(Float16Array,"float16")):ln.set("float16",Uint16Array)}},W_=t=>{let e=1;for(let r=0;r{switch(t.location){case"cpu":return new Qt(t.type,t.data,e);case"cpu-pinned":return new Qt({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new Qt({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new Qt({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let Qt=class{constructor(e,r,n){L_();let i,a;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,i=e.type,a=e.dims,e.location){case"cpu-pinned":{const o=ln.get(i);if(!o)throw new TypeError(`unsupported type 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Should be one of: ${Hn.join(", ")}.`);e=[t]}return e}async function lm(t,e){return await G_.create(t,e)}function dm(t){return t instanceof Xn.Tensor}const Mr=Xn?.env;Mr?.wasm&&(Mr.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.18.0/dist/",Mr.wasm.proxy=!Pr.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(Mr.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(Mr.wasm.simd=!1));function q_(){return Mr?.wasm?.proxy}bt.backends.onnx=Mr;const an=async(t,e,r)=>{const n=await lm(new Uint8Array(t),e);return async i=>{const a=Object.fromEntries(Object.entries(i).map(([o,u])=>[o,u.ort_tensor])),s=await n.run(a);return Array.isArray(r)?r.map(o=>new ce(s[o])):new ce(s[r])}};class _i{static session_options={};static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=an([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=an([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=an([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=an([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=an([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=an([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}}const bc=Object.freeze({float32:Float32Array,float16:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array});class ce{get dims(){return this.ort_tensor.dims}set dims(e){this.ort_tensor.dims=e}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}ort_tensor;constructor(...e){return dm(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new V_(e[0],e[1],e[2]),new Proxy(this,{get:(r,n)=>{if(typeof n=="string"){let i=Number(n);if(Number.isInteger(i))return r._getitem(i)}return r[n]},set:(r,n,i)=>r[n]=i})}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...r]=this.dims;if(r.length>0){const n=r.reduce((i,a)=>i*a);for(let i=0;i0){const i=n.reduce((a,s)=>a*s);return this._subarray(e,i,n)}else return new ce(this.type,[this.data[e]],n)}indexOf(e){const r=this.data;for(let n=0;nm)throw new Error(`Invalid slice: ${h}`);let c=[Math.max(f,0),Math.min(m,this.dims[l])];n.push(c),r.push(c[1]-c[0])}else throw new Error(`Invalid slice: ${h}`)}let i=n.map(([l,h])=>h-l),a=i.reduce((l,h)=>l*h);const s=this.data;let o=new s.constructor(a);const u=this.stride();for(let l=0;l=0;--f){const c=i[f];h+=(m%c+n[f][0])*u[f],m=Math.floor(m/c)}o[l]=s[h]}return new ce(this.type,o,r)}permute(...e){return K_(this,e)}transpose(...e){return this.permute(...e)}sum(e=null,r=!1){return this.norm(1,e,r)}norm(e="fro",r=null,n=!1){if(e==="fro")e=2;else if(typeof e=="string")throw Error(`Unsupported norm: ${e}`);const i=this.data;if(r===null){let o=i.reduce((u,l)=>u+l**e,0)**(1/e);return new ce(this.type,[o],[])}r=Xt(r,this.dims.length);const a=this.dims.slice();a[r]=1;const s=new i.constructor(i.length/this.dims[r]);for(let o=0;o=0;--l){const m=this.dims[l];if(l!==r){const c=h%m;u+=c*f,f*=a[l]}h=Math.floor(h/m)}s[u]+=i[o]**e}if(e!==1)for(let o=0;o=0;--o){const h=this.dims[o];if(o!==r){const f=u%h;s+=f*l,l*=this.dims[o]}u=Math.floor(u/h)}i[a]/=n.data[s]}return this}normalize(e=2,r=1){return this.clone().normalize_(e,r)}stride(){return J_(this.dims)}squeeze(e=null){return new ce(this.type,this.data,$c(this.dims,e))}squeeze_(e=null){return this.dims=$c(this.dims,e),this}unsqueeze(e=null){return new ce(this.type,this.data,xc(this.dims,e))}unsqueeze_(e=null){return this.dims=xc(this.dims,e),this}flatten_(e=0,r=-1){r=(r+this.dims.length)%this.dims.length;let n=this.dims.slice(0,e),i=this.dims.slice(e,r+1),a=this.dims.slice(r+1);return this.dims=[...n,i.reduce((s,o)=>s*o,1),...a],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let n=0;ns!==r?i*a:i,1);e[r]=this.data.length/n}return new ce(this.type,this.data,e)}neg_(){const e=this.data;for(let r=0;ra*s);if(r!==n)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let i=t;for(let a=e.length-1;a>=0;a--)i=i.reduce((s,o)=>{let u=s[s.length-1];return u.lengthr!==1):typeof e=="number"?t[e]===1&&t.splice(e,1):Array.isArray(e)&&(t=t.filter((r,n)=>r!==1||!e.includes(n))),t}function xc(t,e){return e=Xt(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function Xt(t,e,r=null,n=!0){if(n&&(t<-e||t>=e))throw new Error(`IndexError: index ${t} is out of bounds for dimension${r===null?"":" "+r} with size ${e}`);return t<0&&(t=(t%e+e)%e),t}function Jt(t,e=0){e=Xt(e,t[0].dims.length);const r=t[0].dims.slice();r[e]=t.reduce((s,o)=>s+o.dims[e],0);const n=r.reduce((s,o)=>s*o,1),i=new t[0].data.constructor(n),a=t[0].type;if(e===0){let s=0;for(let o of t)i.set(o.data,s),s+=o.data.length}else{let s=0;for(let o=0;o=0;--f){const y=u.dims[f];let b=m%y;f===e&&(b+=s),h+=b*c,c*=r[f],m=Math.floor(m/y)}i[h]=u.data[l]}s+=u.dims[e]}}return new ce(a,i,r)}function Qn(t,e=0){return Jt(t.map(r=>r.unsqueeze(e)),e)}function X_(t,e=null,r=1,n=!1){if(e===null){const l=t.data.reduce((c,y)=>c+y,0)/t.data.length,h=Math.sqrt(t.data.reduce((c,y)=>c+(y-l)**2,0)/(t.data.length-r)),f=new ce(t.type,[l],[]);return[new ce(t.type,[h],[]),f]}e=Xt(e,t.dims.length);const i=xo(t,e,n),a=t.dims.slice();a[e]=1;const s=new t.data.constructor(t.data.length/t.dims[e]);for(let u=0;u=0;--h){const c=t.dims[h];if(h!==e){const y=f%c;l+=y*m,m*=a[h]}f=Math.floor(f/c)}s[l]+=(t.data[u]-i.data[l])**2}for(let u=0;us+o,0);return new ce(t.type,[a/t.data.length],[])}e=Xt(e,t.dims.length);const n=t.dims.slice();n[e]=1;const i=new t.data.constructor(t.data.length/t.dims[e]);for(let a=0;a=0;--o){const h=t.dims[o];if(o!==e){const f=u%h;s+=f*l,l*=n[o]}u=Math.floor(u/h)}i[s]+=t.data[a]}if(t.dims[e]!==1)for(let a=0;a0||o>0;)switch(u.push(s-1),l.push(o-1),a[s][o].item()){case 0:--s,--o;break;case 1:--s;break;case 2:--o;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${o}]. Please file a bug report.`)}return u.reverse(),l.reverse(),[u,l]}function J_(t){const e=new Array(t.length);for(let r=t.length-1,n=1;r>=0;--r)e[r]=n,n*=t[r];return e}function So(t,e,r,n){const i=t.reduce((a,s)=>a*s,1);return new ce(r,new n(i).fill(e),t)}function Z_(t,e){let r,n;return r="float32",n=Float32Array,So(t,e,r,n)}function ey(t,e){return Z_(t.dims,e)}function aa(t){return So(t,1n,"int64",BigInt64Array)}function ty(t){return aa(t.dims)}function ry(t){return So(t,0n,"int64",BigInt64Array)}function ny(t){return ry(t.dims)}var Ce=Object.freeze({Text:"Text",NumericLiteral:"NumericLiteral",BooleanLiteral:"BooleanLiteral",StringLiteral:"StringLiteral",Identifier:"Identifier",Equals:"Equals",OpenParen:"OpenParen",CloseParen:"CloseParen",OpenStatement:"OpenStatement",CloseStatement:"CloseStatement",OpenExpression:"OpenExpression",CloseExpression:"CloseExpression",OpenSquareBracket:"OpenSquareBracket",CloseSquareBracket:"CloseSquareBracket",OpenCurlyBracket:"OpenCurlyBracket",CloseCurlyBracket:"CloseCurlyBracket",Comma:"Comma",Dot:"Dot",Colon:"Colon",Pipe:"Pipe",CallOperator:"CallOperator",AdditiveBinaryOperator:"AdditiveBinaryOperator",MultiplicativeBinaryOperator:"MultiplicativeBinaryOperator",ComparisonBinaryOperator:"ComparisonBinaryOperator",UnaryOperator:"UnaryOperator",Set:"Set",If:"If",For:"For",In:"In",Is:"Is",NotIn:"NotIn",Else:"Else",EndIf:"EndIf",ElseIf:"ElseIf",EndFor:"EndFor",And:"And",Or:"Or",Not:"UnaryOperator"});Object.freeze({set:Ce.Set,for:Ce.For,in:Ce.In,is:Ce.Is,if:Ce.If,else:Ce.Else,endif:Ce.EndIf,elif:Ce.ElseIf,endfor:Ce.EndFor,and:Ce.And,or:Ce.Or,not:Ce.Not,"not in":Ce.NotIn,true:Ce.BooleanLiteral,false:Ce.BooleanLiteral});Ce.OpenStatement,Ce.CloseStatement,Ce.OpenExpression,Ce.CloseExpression,Ce.OpenParen,Ce.CloseParen,Ce.OpenCurlyBracket,Ce.CloseCurlyBracket,Ce.OpenSquareBracket,Ce.CloseSquareBracket,Ce.Comma,Ce.Dot,Ce.Colon,Ce.Pipe,Ce.ComparisonBinaryOperator,Ce.ComparisonBinaryOperator,Ce.ComparisonBinaryOperator,Ce.ComparisonBinaryOperator,Ce.ComparisonBinaryOperator,Ce.ComparisonBinaryOperator,Ce.AdditiveBinaryOperator,Ce.AdditiveBinaryOperator,Ce.MultiplicativeBinaryOperator,Ce.MultiplicativeBinaryOperator,Ce.MultiplicativeBinaryOperator,Ce.Equals;const hm=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],ks=new Map(hm),ay=new Map([...hm.map(([t,e])=>[e,t]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function iy(t){t=t.toLowerCase();let e=ay.get(t);if(e===void 0)if(ks.has(t))e=t;else{const n=t.length===2?ks.keys():ks.values();throw new Error(`Language "${t}" is not supported. 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ce("bool",[t],[1])}async function Cc(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const u=zr(e,t.sessions.model.inputNames);r=(await Jn(t,u)).last_hidden_state}const{input_ids:i,decoder_input_ids:a,...s}=e;return s.input_ids=a,s.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(s.encoder_attention_mask=e.attention_mask),await ko(t,s,!0)}async function Jn(t,e){const r=t.sessions.model,n=Object.create(null);for(const i of r.inputNames)n[i]=e[i];return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new ce("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await gr(r,n)}async function ko(t,e,r=!1){const n=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:i,...a}=e;n.inputNames.includes("use_cache_branch")&&(a.use_cache_branch=bm(!!i)),n.inputNames.includes("position_ids")&&a.attention_mask&&!a.position_ids&&(a.position_ids=Oy(a,i)),t.addPastKeyValues(a,i);const s=zr(a,n.inputNames);return await gr(n,s)}async function My(t,{input_ids:e=null,attention_mask:r=null,pixel_values:n=null,position_ids:i=null,inputs_embeds:a=null,past_key_values:s=null,generation_config:o=null,logits_processor:u=null,...l}){if(!a){if(a=await t.encode_text({input_ids:e}),n&&e.dims[1]!==1){const f=await t.encode_image({pixel_values:n});({inputs_embeds:a,attention_mask:r}=t._merge_input_ids_with_image_features({image_features:f,inputs_embeds:a,input_ids:e,attention_mask:r}))}else if(s&&n&&e.dims[1]===1){const f=e.dims[1],m=Object.values(s)[0].dims.at(-2);r=Jt([aa([e.dims[0],m]),r.slice(null,[r.dims[1]-f,r.dims[1]])],1)}}return await ko(t,{inputs_embeds:a,past_key_values:s,attention_mask:r,position_ids:i,generation_config:o,logits_processor:u},!0)}function Oy(t,e=null){const{input_ids:r,inputs_embeds:n,attention_mask:i}=t,[a,s]=i.dims,o=new BigInt64Array(i.data.length);for(let l=0;la.dims[1])){if(io==t.config.image_token_index)){const 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n=qn.get(this.constructor),i=wi.get(n);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,i===be.DecoderOnly?(this.can_generate=!0,this._forward=ko,this._prepare_inputs_for_generation=Ec):i===be.Seq2Seq||i===be.Vision2Seq||i===be.Musicgen?(this.can_generate=!0,this._forward=Cc,this._prepare_inputs_for_generation=zy):i===be.EncoderDecoder?this._forward=Cc:i===be.ImageTextToText?(this.can_generate=!0,this._forward=My,this._prepare_inputs_for_generation=Ec):this._forward=Jn,this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const e=[];for(const r of Object.values(this.sessions))r?.handler?.dispose&&e.push(r.handler.dispose());return await Promise.all(e)}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:i=null,local_files_only:a=!1,revision:s="main",model_file_name:o=null,subfolder:u="onnx",device:l=null,dtype:h=null,use_external_data_format:f=null,session_options:m={}}={}){let c={progress_callback:r,config:n,cache_dir:i,local_files_only:a,revision:s,model_file_name:o,subfolder:u,device:l,dtype:h,use_external_data_format:f,session_options:m};const y=qn.get(this),b=wi.get(y);c.config=await mm.from_pretrained(e,c);let v;return b===be.DecoderOnly?v=await Promise.all([Tr(e,{model:c.model_file_name??"model"},c),on(e,"generation_config.json",!1,c)]):b===be.Seq2Seq||b===be.Vision2Seq?v=await Promise.all([Tr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},c),on(e,"generation_config.json",!1,c)]):b===be.MaskGeneration?v=await Promise.all([Tr(e,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},c)]):b===be.EncoderDecoder?v=await Promise.all([Tr(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},c)]):b===be.ImageTextToText?v=await Promise.all([Tr(e,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},c),on(e,"generation_config.json",!1,c)]):b===be.Musicgen?v=await Promise.all([Tr(e,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},c),on(e,"generation_config.json",!1,c)]):(b!==be.EncoderOnly&&console.warn(`Model type for '${y??n?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),v=await Promise.all([Tr(e,{model:c.model_file_name??"model"},c)])),new this(c.config,...v)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}_get_logits_warper(e){const r=new Ys;return e.temperature!==null&&e.temperature!==1&&r.push(new by(e.temperature)),e.top_k!==null&&e.top_k!==0&&r.push(new $y(e.top_k)),e.top_p!==null&&e.top_p<1&&r.push(new vy(e.top_p)),r}_get_logits_processor(e,r,n=null){const i=new Ys;if(e.repetition_penalty!==null&&e.repetition_penalty!==1&&i.push(new my(e.repetition_penalty)),e.no_repeat_ngram_size!==null&&e.no_repeat_ngram_size>0&&i.push(new fy(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&i.push(new yy(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&i.push(new gy(e.min_length,e.eos_token_id)),e.min_new_tokens!==null&&e.eos_token_id!==null&&e.min_new_tokens>0&&i.push(new _y(r,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&i.push(new dy(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&i.push(new cy(e.max_length,e.forced_eos_token_id)),e.begin_suppress_tokens!==null){const a=r>1||e.forced_bos_token_id===null?r:r+1;i.push(new py(e.begin_suppress_tokens,a))}return e.guidance_scale!==null&&e.guidance_scale>1&&i.push(new wy(e.guidance_scale)),n!==null&&i.extend(n),i}_prepare_generation_config(e,r,n=gm){const i={...this.config};for(const s of["decoder","generator","text_config"])s in i&&Object.assign(i,i[s]);const a=new n(i);return"generation_config"in this&&Object.assign(a,this.generation_config),e&&Object.assign(a,e),r&&Object.assign(a,zr(r,Object.getOwnPropertyNames(a))),a}_get_stopping_criteria(e,r=null){const n=new To;return e.max_length!==null&&n.push(new xy(e.max_length,this.config.max_position_embeddings??null)),e.eos_token_id!==null&&n.push(new Sy(e.eos_token_id)),r&&n.extend(r),n}_validate_model_class(){if(!this.can_generate){const e=[Cg,Eg,Sg,xg],r=qn.get(this.constructor),n=new Set,i=this.config.model_type;for(const s of e){const o=s.get(i);o&&n.add(o[0])}let a=`The current model class (${r}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw n.size>0&&(a+=` Please use the following class instead: ${[...n].join(", ")}`),Error(a)}}prepare_inputs_for_generation(...e){return this._prepare_inputs_for_generation(this,...e)}_update_model_kwargs_for_generation({generated_input_ids:e,outputs:r,model_inputs:n,is_encoder_decoder:i}){return n.past_key_values=this.getPastKeyValues(r,n.past_key_values),n.input_ids=new ce("int64",e.flat(),[e.length,1]),i||(n.attention_mask=Jt([n.attention_mask,aa([n.attention_mask.dims[0],1])],1)),n.position_ids=null,n}_prepare_model_inputs({inputs:e,bos_token_id:r,model_kwargs:n}){const i=zr(n,this.forward_params),a=this.main_input_name;if(a in i){if(e)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else i[a]=e;return{inputs_tensor:i[a],model_inputs:i,model_input_name:a}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:e,model_inputs:r,model_input_name:n,generation_config:i}){const a=zr(r,this.sessions.model.inputNames);let{last_hidden_state:s}=await Jn(this,a);return i.guidance_scale!==null&&i.guidance_scale>1&&(s=Jt([s,ey(s,0)],0),"attention_mask"in r&&(r.attention_mask=Jt([r.attention_mask,ny(r.attention_mask)],0))),r.encoder_outputs=s,r}_prepare_decoder_input_ids_for_generation({batch_size:e,model_input_name:r,model_kwargs:n,decoder_start_token_id:i,bos_token_id:a,generation_config:s}){let{decoder_input_ids:o,...u}=n;if(!o)if(i??=a,this.config.model_type==="musicgen")o=Array.from({length:e*this.config.decoder.num_codebooks},()=>[i]);else if(Array.isArray(i)){if(i.length!==e)throw new Error(`\`decoder_start_token_id\` expcted to have length ${e} but got ${i.length}`);o=i}else o=Array.from({length:e},()=>[i]);return o=wm(o),n.decoder_attention_mask=ty(o),{input_ids:o,model_inputs:u}}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:i=null,streamer:a=null,...s}){this._validate_model_class(),r=this._prepare_generation_config(r,s);let{inputs_tensor:o,model_inputs:u,model_input_name:l}=this._prepare_model_inputs({inputs:e,model_kwargs:s});const h=this.config.is_encoder_decoder;h&&("encoder_outputs"in u||(u=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:o,model_inputs:u,model_input_name:l,generation_config:r})));let f;h?{input_ids:f,model_inputs:u}=this._prepare_decoder_input_ids_for_generation({batch_size:u[l].dims.at(0),model_input_name:l,model_kwargs:u,decoder_start_token_id:r.decoder_start_token_id,bos_token_id:r.bos_token_id,generation_config:r}):f=u[l];let m=f.dims.at(-1);r.max_new_tokens!==null&&(r.max_length=m+r.max_new_tokens);const c=this._get_logits_processor(r,m,n),y=this._get_stopping_criteria(r,i),b=u[l].dims.at(0),v=Si.getSampler(r),C=new Array(b).fill(0),x=f.tolist();a&&a.put(x);let T=null;for(;;){u=this.prepare_inputs_for_generation(x,u,r);const A=await this.forward(u),R=A.logits.slice(null,-1,null),z=c(x,R),P=[];for(let K=0;KK)){r.return_dict_in_generate&&(T=this.getPastKeyValues(A,u.past_key_values,!1));break}u=this._update_model_kwargs_for_generation({generated_input_ids:P,outputs:A,model_inputs:u,is_encoder_decoder:h})}a&&a.end();const I=new ce("int64",x.flat(),[x.length,x[0].length]);return r.return_dict_in_generate?{sequences:I,past_key_values:T}:I}addAttentionsToBeam(e,r){if(this.config.is_encoder_decoder){if(!r.cross_attentions||r.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.cross_attentions||(e.cross_attentions=[]),e.cross_attentions.push(r.cross_attentions)}if(!r.decoder_attentions||r.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.decoder_attentions||(e.decoder_attentions=[]),e.decoder_attentions.push(r.decoder_attentions)}groupBeams(e){const r=Object.create(null);for(const n of e)r[n.id]===void 0?r[n.id]=[n]:r[n.id].push(n);return Object.values(r)}getPastKeyValues(e,r,n=!0){const i=Object.create(null);for(const a in e)if(a.startsWith("present")){let s=a.replace("present","past_key_values");if(r&&a.includes("encoder"))i[s]=r[s];else{if(n&&r){const o=r[s];o.location==="gpu-buffer"&&o.dispose()}i[s]=e[a]}}return i}getAttentions(e){const r=Object.create(null);for(const n of["cross_attentions","decoder_attentions"]){const i=[];for(const a in e)if(a.startsWith(n)){const s=a.split(".").pop();i[s]=e[a]}r[n]=i}return r}addPastKeyValues(e,r){if(r)Object.assign(e,r);else{const n=this.custom_config.kv_cache_dtype??"float32",i=n==="float16"?new Uint16Array:[],a=fm(this.config);for(const s in a)e[s]=new ce(n,i,a[s])}}}class Ot{}class ia extends X{}class Ry extends ia{}class Py extends ia{async _call(e){return new dt(await super._call(e))}}class By extends ia{async _call(e){return new Ae(await super._call(e))}}class Dy extends ia{async _call(e){return new lt(await super._call(e))}}class Ny extends ia{async _call(e){return new mt(await super._call(e))}}class Fy extends X{}class Ly extends Fy{}class sa extends X{}class Wy extends sa{}class Uy extends sa{async _call(e){return new dt(await super._call(e))}}class Vy extends sa{async _call(e){return new Ae(await super._call(e))}}class Gy extends sa{async _call(e){return new lt(await super._call(e))}}class Hy extends sa{async _call(e){return new mt(await super._call(e))}}class oa extends X{}class qy extends oa{}class jy extends oa{async _call(e){return new dt(await super._call(e))}}class Ky extends oa{async _call(e){return new Ae(await super._call(e))}}class Yy extends oa{async _call(e){return new lt(await super._call(e))}}class Xy extends oa{async _call(e){return new mt(await super._call(e))}}class ua extends X{}class Qy extends ua{}class Jy extends ua{async _call(e){return new dt(await super._call(e))}}class Zy extends ua{async _call(e){return new Ae(await super._call(e))}}class ew extends ua{async _call(e){return new lt(await super._call(e))}}class tw extends ua{async _call(e){return new mt(await super._call(e))}}class la extends X{}class rw extends la{}class nw extends la{async _call(e){return new dt(await super._call(e))}}class aw extends la{async _call(e){return new Ae(await super._call(e))}}class iw extends la{async _call(e){return new lt(await super._call(e))}}class sw extends la{async _call(e){return new mt(await super._call(e))}}class da extends X{}class ow extends da{}class uw extends da{async _call(e){return new dt(await super._call(e))}}class lw extends da{async _call(e){return new Ae(await super._call(e))}}class dw extends da{async _call(e){return new lt(await super._call(e))}}class cw extends da{async _call(e){return new mt(await super._call(e))}}class ca extends X{}class pw extends ca{}class hw extends ca{async _call(e){return new dt(await super._call(e))}}class fw extends ca{async _call(e){return new Ae(await super._call(e))}}class mw extends ca{async _call(e){return new lt(await super._call(e))}}class gw extends ca{async _call(e){return new mt(await super._call(e))}}class pa extends X{}class _w extends pa{}class yw extends pa{async _call(e){return new Ae(await super._call(e))}}class ww extends pa{async _call(e){return new lt(await super._call(e))}}class bw extends pa{async _call(e){return new mt(await super._call(e))}}class vw extends pa{async _call(e){return new dt(await super._call(e))}}class Ci extends X{}class $w extends Ci{}class xw extends Ci{async _call(e){return new dt(await super._call(e))}}class Sw extends Ci{async _call(e){return new Ae(await super._call(e))}}class Cw extends Ci{async _call(e){return new lt(await super._call(e))}}class Ei extends X{}class Ew extends Ei{}class Tw extends Ei{async _call(e){return new dt(await super._call(e))}}class kw extends Ei{async _call(e){return new Ae(await super._call(e))}}class Iw extends Ei{async _call(e){return new mt(await super._call(e))}}class ha extends X{}class Aw extends ha{}class Mw extends ha{async _call(e){return new dt(await super._call(e))}}class Ow extends ha{async _call(e){return new Ae(await super._call(e))}}class zw extends ha{async _call(e){return new lt(await super._call(e))}}class Rw extends ha{async _call(e){return new mt(await super._call(e))}}class Ti extends X{}class Pw extends Ti{}class Bw extends Ti{async _call(e){return new dt(await super._call(e))}}class Dw extends Ti{async _call(e){return new Ae(await super._call(e))}}class Nw extends Ti{async _call(e){return new mt(await super._call(e))}}class ki extends X{}class Fw extends ki{}class Lw extends ki{async _call(e){return new Ae(await super._call(e))}}class Ww extends ki{async _call(e){return new mt(await super._call(e))}}class Uw extends ki{async _call(e){return new dt(await super._call(e))}}class vm extends X{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class Vw extends vm{}class Gw extends vm{}class $m extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class Hw extends $m{}class qw extends $m{}class xm extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class jw extends xm{}class Kw extends xm{}class Io extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class Yw extends Io{}class Xw extends Io{}class Qw extends Io{async _call(e){return new Ae(await super._call(e))}}class Ii extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class Jw extends Ii{}class Zw extends Ii{}class eb extends Ii{async _call(e){return new Ae(await super._call(e))}}class tb extends Ii{}class Sm extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class rb extends Sm{}class nb extends Sm{}class Cm extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class ab extends Cm{}class ib extends Cm{}class fa extends X{}class sb extends fa{}class ob extends fa{async _call(e){return new dt(await super._call(e))}}class ub extends fa{async _call(e){return new Ae(await super._call(e))}}class lb extends fa{async _call(e){return new lt(await super._call(e))}}class db extends fa{async _call(e){return new mt(await super._call(e))}}class ma extends X{}class cb extends ma{}class pb extends ma{async _call(e){return new dt(await super._call(e))}}class hb extends ma{async _call(e){return new Ae(await super._call(e))}}class fb extends ma{async _call(e){return new lt(await super._call(e))}}class mb extends ma{async _call(e){return new mt(await super._call(e))}}class ga extends X{}class gb extends ga{}class _b extends ga{async _call(e){return new dt(await super._call(e))}}class yb extends ga{async _call(e){return new Ae(await super._call(e))}}class wb extends ga{async _call(e){return new lt(await super._call(e))}}class bb extends ga{async _call(e){return new mt(await super._call(e))}}class Em extends X{}class vb extends Em{}class $b extends Em{}class Tm extends X{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}}class xb extends Tm{}class Sb extends Tm{_prepare_generation_config(e,r){return super._prepare_generation_config(e,r,ky)}_retrieve_init_tokens(e){const r=[e.decoder_start_token_id];let n=e.language;const i=e.task;if(e.is_multilingual){n||(console.warn("No language specified - defaulting to English (en)."),n="en");const s=`<|${iy(n)}|>`;r.push(e.lang_to_id[s]),r.push(e.task_to_id[i??"transcribe"])}else if(n||i)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!e.return_timestamps&&e.no_timestamps_token_id&&r.at(-1)!==e.no_timestamps_token_id?r.push(e.no_timestamps_token_id):e.return_timestamps&&r.at(-1)===e.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),r.pop()),r.filter(a=>a!=null)}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:i=null,...a}){r=this._prepare_generation_config(r,a);const s=this._retrieve_init_tokens(r);return r.return_timestamps&&(n??=new Ys,n.push(new hy(r,s))),await super.generate({inputs:e,generation_config:r,logits_processor:n,decoder_input_ids:s,...a})}_extract_token_timestamps(e,r,n=null,i=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let a=this.config.median_filter_width;a===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),a=7);const s=e.cross_attentions.map(l=>{let h=Array.from({length:this.config.decoder_layers},(v,C)=>Jt(l.map(x=>x[C]),2)),f=Qn(r.map(([v,C])=>n?h[v].slice(null,C,null,[0,n]):h[v].slice(null,C)));f=f.transpose(1,0,2,3);let[m,c]=X_(f,-2,0,!0),y=f.clone();for(let v=0;vf[C+1]-f[C]),y=e0([1],c).map(v=>!!v),b=[];for(let v=0;vm.findIndex(c=>c==a)),u=o.every(m=>m===-1),l=o.every(m=>m!==-1);if(!u&&!l)throw new Error("Every input should contain either 0 or 1 image token.");if(u)return{inputs_embeds:e,attention_mask:i};const h=[],f=[];for(let m=0;ma*s,1);e.input_labels=new ce("int64",new BigInt64Array(i).fill(1n),n)}const r={image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings};return e.input_points&&(r.input_points=e.input_points),e.input_labels&&(r.input_labels=e.input_labels),e.input_boxes&&(r.input_boxes=e.input_boxes),await gr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new p1(await super._call(e))}}class p1 extends Ot{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class pg extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class h1 extends pg{}class f1 extends pg{}class hg extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class m1 extends hg{}class g1 extends hg{}class Ur extends X{}class _1 extends Ur{}class y1 extends Ur{async _call(e){return new gn(await super._call(e))}}class w1 extends Ur{async _call(e){return new Ae(await super._call(e))}}class b1 extends Ur{async _call(e){return new lt(await super._call(e))}}class Mo extends X{}class v1 extends Mo{}class $1 extends Mo{async _call(e){return new gn(await super._call(e))}}class x1 extends Mo{async _call(e){return new Ae(await super._call(e))}}class Mi extends X{}class S1 extends Mi{}class C1 extends Mi{async _call(e){return new gn(await super._call(e))}}class E1 extends Mi{async _call(e){return new Ae(await super._call(e))}}class T1 extends Mi{async _call(e){return new lt(await super._call(e))}}class Oo extends X{}class k1 extends Oo{}class I1 extends Oo{async _call(e){return new gn(await super._call(e))}}class A1 extends Oo{async _call(e){return new Ae(await super._call(e))}}class M1 extends Ur{}class O1 extends Ur{async _call(e){return new gn(await super._call(e))}}class z1 extends Ur{async _call(e){return new Ae(await super._call(e))}}class _a extends X{}class R1 extends _a{}class P1 extends _a{async _call(e){return new gn(await super._call(e))}}class B1 extends _a{async _call(e){return new Ae(await super._call(e))}}class D1 extends _a{async _call(e){return new zv(await super._call(e))}}class N1 extends _a{async _call(e){return new lt(await super._call(e))}}class fg extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class F1 extends fg{}class L1 extends fg{async generate_speech(e,r,{threshold:n=.5,minlenratio:i=0,maxlenratio:a=20,vocoder:s=null}={}){const o={input_ids:e},{encoder_outputs:u,encoder_attention_mask:l}=await Jn(this,o),h=u.dims[1]/this.config.reduction_factor,f=Math.floor(h*a),m=Math.floor(h*i),c=this.config.num_mel_bins;let y=[],b=null,v=null,C=0;for(;;){++C;const I=bm(!!v);let A;v?A=v.output_sequence_out:A=new ce("float32",new Float32Array(c),[1,1,c]);let R={use_cache_branch:I,output_sequence:A,encoder_attention_mask:l,speaker_embeddings:r,encoder_hidden_states:u};this.addPastKeyValues(R,b),v=await gr(this.sessions.decoder_model_merged,R),b=this.getPastKeyValues(v,b);const{prob:z,spectrum:P}=v;if(y.push(P),C>=m&&(Array.from(z.data).filter(J=>J>=n).length>0||C>=f))break}const x=Jt(y),{waveform:T}=await gr(s.sessions.model,{spectrogram:x});return{spectrogram:x,waveform:T}}}class W1 extends X{main_input_name="spectrogram"}class U1 extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class V1 extends U1{}class mg extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class G1 extends mg{}class H1 extends mg{}class gg extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class q1 extends gg{}class j1 extends gg{}class _g extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class K1 extends _g{}class Y1 extends _g{}class zo extends X{}class X1 extends zo{}class Q1 extends zo{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class J1 extends zo{static async from_pretrained(e,r={}){return r.model_file_name??="audio_model",super.from_pretrained(e,r)}}class Z1 extends X{}class yg extends Z1{async _call(e){return new Pv(await super._call(e))}}class wg extends X{}class ev extends wg{}class tv extends wg{}class bg extends X{constructor(e,r,n){super(e,r),this.generation_config=n}}class rv extends bg{}class nv extends bg{}class vg extends X{}class av extends vg{}class iv extends vg{async _call(e){return new Ae(await super._call(e))}}class $g extends X{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];constructor(e,r,n){super(e,r),this.generation_config=n}_apply_and_filter_by_delay_pattern_mask(e){const[r,n]=e.dims,i=this.config.decoder.num_codebooks,a=n-i;let s=0;for(let l=0;l0&&m<=a&&(e.data[s++]=e.data[l])}const o=Math.floor(r/i),u=s/(o*i);return new ce(e.type,e.data.slice(0,s),[o,i,u])}prepare_inputs_for_generation(e,r,n){let i=structuredClone(e);for(let s=0;s=o&&(i[s][o]=BigInt(this.config.decoder.pad_token_id));return n.guidance_scale!==null&&n.guidance_scale>1&&(i=i.concat(i)),super.prepare_inputs_for_generation(i,r,n)}async generate(e){const r=await super.generate(e),n=this._apply_and_filter_by_delay_pattern_mask(r).unsqueeze_(0),{audio_values:i}=await gr(this.sessions.encodec_decode,{audio_codes:n});return i}}class sv{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:i=null,local_files_only:a=!1,revision:s="main",model_file_name:o=null,subfolder:u="onnx",device:l=null,dtype:h=null,use_external_data_format:f=null,session_options:m={}}={}){let c={progress_callback:r,config:n,cache_dir:i,local_files_only:a,revision:s,model_file_name:o,subfolder:u,device:l,dtype:h,use_external_data_format:f,session_options:m};if(c.config=await mm.from_pretrained(e,c),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let y of this.MODEL_CLASS_MAPPINGS){const b=y.get(c.config.model_type);if(b)return await b[1].from_pretrained(e,c)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${c.config.model_type}", attempting to construct from base class.`),await X.from_pretrained(e,c);throw Error(`Unsupported model type: ${c.config.model_type}`)}}const ov=new Map([["bert",["BertModel",Ry]],["nomic_bert",["NomicBertModel",Ly]],["roformer",["RoFormerModel",Wy]],["electra",["ElectraModel",Qy]],["esm",["EsmModel",$w]],["convbert",["ConvBertModel",qy]],["camembert",["CamembertModel",rw]],["deberta",["DebertaModel",ow]],["deberta-v2",["DebertaV2Model",pw]],["mpnet",["MPNetModel",Aw]],["albert",["AlbertModel",Fw]],["distilbert",["DistilBertModel",_w]],["roberta",["RobertaModel",sb]],["xlm",["XLMModel",cb]],["xlm-roberta",["XLMRobertaModel",gb]],["clap",["ClapModel",X1]],["clip",["CLIPModel",kb]],["clipseg",["CLIPSegModel",Bb]],["chinese_clip",["ChineseCLIPModel",Pb]],["siglip",["SiglipModel",Mb]],["mobilebert",["MobileBertModel",Ew]],["squeezebert",["SqueezeBertModel",Pw]],["wav2vec2",["Wav2Vec2Model",_1]],["wav2vec2-bert",["Wav2Vec2BertModel",k1]],["unispeech",["UniSpeechModel",v1]],["unispeech-sat",["UniSpeechSatModel",S1]],["hubert",["HubertModel",M1]],["wavlm",["WavLMModel",R1]],["audio-spectrogram-transformer",["ASTModel",vb]],["vits",["VitsModel",yg]],["detr",["DetrModel",O2]],["table-transformer",["TableTransformerModel",B2]],["vit",["ViTModel",g2]],["fastvit",["FastViTModel",y2]],["mobilevit",["MobileViTModel",$2]],["mobilevitv2",["MobileViTV2Model",S2]],["owlvit",["OwlViTModel",E2]],["owlv2",["Owlv2Model",k2]],["beit",["BeitModel",A2]],["deit",["DeiTModel",F2]],["convnext",["ConvNextModel",t1]],["convnextv2",["ConvNextV2Model",n1]],["dinov2",["Dinov2Model",i1]],["resnet",["ResNetModel",W2]],["swin",["SwinModel",V2]],["swin2sr",["Swin2SRModel",H2]],["donut-swin",["DonutSwinModel",e1]],["yolos",["YolosModel",o1]],["dpt",["DPTModel",j2]],["glpn",["GLPNModel",Q2]],["hifigan",["SpeechT5HifiGan",W1]],["efficientnet",["EfficientNetModel",av]]]),uv=new Map([["t5",["T5Model",Vw]],["longt5",["LongT5Model",Hw]],["mt5",["MT5Model",jw]],["bart",["BartModel",Yw]],["mbart",["MBartModel",Jw]],["marian",["MarianModel",h1]],["whisper",["WhisperModel",xb]],["m2m_100",["M2M100Model",m1]],["blenderbot",["BlenderbotModel",rb]],["blenderbot-small",["BlenderbotSmallModel",ab]]]),lv=new Map([["bloom",["BloomModel",d2]],["gpt2",["GPT2Model",Nb]],["gptj",["GPTJModel",Gb]],["gpt_bigcode",["GPTBigCodeModel",qb]],["gpt_neo",["GPTNeoModel",Lb]],["gpt_neox",["GPTNeoXModel",Ub]],["codegen",["CodeGenModel",Kb]],["llama",["LlamaModel",Xb]],["cohere",["CohereModel",Jb]],["gemma",["GemmaModel",e2]],["openelm",["OpenELMModel",r2]],["qwen2",["Qwen2Model",a2]],["phi",["PhiModel",s2]],["phi3",["Phi3Model",u2]],["mpt",["MptModel",p2]],["opt",["OPTModel",f2]],["mistral",["MistralModel",G1]],["starcoder2",["Starcoder2Model",q1]],["falcon",["FalconModel",K1]],["stablelm",["StableLmModel",rv]]]),xg=new Map([["speecht5",["SpeechT5ForSpeechToText",F1]],["whisper",["WhisperForConditionalGeneration",Sb]]]),dv=new Map([["speecht5",["SpeechT5ForTextToSpeech",L1]]]),cv=new Map([["vits",["VitsModel",yg]],["musicgen",["MusicgenForConditionalGeneration",$g]]]),pv=new Map([["bert",["BertForSequenceClassification",By]],["roformer",["RoFormerForSequenceClassification",Vy]],["electra",["ElectraForSequenceClassification",Zy]],["esm",["EsmForSequenceClassification",Sw]],["convbert",["ConvBertForSequenceClassification",Ky]],["camembert",["CamembertForSequenceClassification",aw]],["deberta",["DebertaForSequenceClassification",lw]],["deberta-v2",["DebertaV2ForSequenceClassification",fw]],["mpnet",["MPNetForSequenceClassification",Ow]],["albert",["AlbertForSequenceClassification",Lw]],["distilbert",["DistilBertForSequenceClassification",yw]],["roberta",["RobertaForSequenceClassification",ub]],["xlm",["XLMForSequenceClassification",hb]],["xlm-roberta",["XLMRobertaForSequenceClassification",yb]],["bart",["BartForSequenceClassification",Qw]],["mbart",["MBartForSequenceClassification",eb]],["mobilebert",["MobileBertForSequenceClassification",kw]],["squeezebert",["SqueezeBertForSequenceClassification",Dw]]]),hv=new Map([["bert",["BertForTokenClassification",Dy]],["roformer",["RoFormerForTokenClassification",Gy]],["electra",["ElectraForTokenClassification",ew]],["esm",["EsmForTokenClassification",Cw]],["convbert",["ConvBertForTokenClassification",Yy]],["camembert",["CamembertForTokenClassification",iw]],["deberta",["DebertaForTokenClassification",dw]],["deberta-v2",["DebertaV2ForTokenClassification",mw]],["mpnet",["MPNetForTokenClassification",zw]],["distilbert",["DistilBertForTokenClassification",ww]],["roberta",["RobertaForTokenClassification",lb]],["xlm",["XLMForTokenClassification",fb]],["xlm-roberta",["XLMRobertaForTokenClassification",wb]]]),Sg=new Map([["t5",["T5ForConditionalGeneration",Gw]],["longt5",["LongT5ForConditionalGeneration",qw]],["mt5",["MT5ForConditionalGeneration",Kw]],["bart",["BartForConditionalGeneration",Xw]],["mbart",["MBartForConditionalGeneration",Zw]],["marian",["MarianMTModel",f1]],["m2m_100",["M2M100ForConditionalGeneration",g1]],["blenderbot",["BlenderbotForConditionalGeneration",nb]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",ib]]]),Cg=new Map([["bloom",["BloomForCausalLM",c2]],["gpt2",["GPT2LMHeadModel",Fb]],["gptj",["GPTJForCausalLM",Hb]],["gpt_bigcode",["GPTBigCodeForCausalLM",jb]],["gpt_neo",["GPTNeoForCausalLM",Wb]],["gpt_neox",["GPTNeoXForCausalLM",Vb]],["codegen",["CodeGenForCausalLM",Yb]],["llama",["LlamaForCausalLM",Qb]],["cohere",["CohereForCausalLM",Zb]],["gemma",["GemmaForCausalLM",t2]],["openelm",["OpenELMForCausalLM",n2]],["qwen2",["Qwen2ForCausalLM",i2]],["phi",["PhiForCausalLM",o2]],["phi3",["Phi3ForCausalLM",l2]],["mpt",["MptForCausalLM",h2]],["opt",["OPTForCausalLM",m2]],["mbart",["MBartForCausalLM",tb]],["mistral",["MistralForCausalLM",H1]],["starcoder2",["Starcoder2ForCausalLM",j1]],["falcon",["FalconForCausalLM",Y1]],["trocr",["TrOCRForCausalLM",V1]],["stablelm",["StableLmForCausalLM",nv]]]),fv=new Map([["bert",["BertForMaskedLM",Py]],["roformer",["RoFormerForMaskedLM",Uy]],["electra",["ElectraForMaskedLM",Jy]],["esm",["EsmForMaskedLM",xw]],["convbert",["ConvBertForMaskedLM",jy]],["camembert",["CamembertForMaskedLM",nw]],["deberta",["DebertaForMaskedLM",uw]],["deberta-v2",["DebertaV2ForMaskedLM",hw]],["mpnet",["MPNetForMaskedLM",Mw]],["albert",["AlbertForMaskedLM",Uw]],["distilbert",["DistilBertForMaskedLM",vw]],["roberta",["RobertaForMaskedLM",ob]],["xlm",["XLMWithLMHeadModel",pb]],["xlm-roberta",["XLMRobertaForMaskedLM",_b]],["mobilebert",["MobileBertForMaskedLM",Tw]],["squeezebert",["SqueezeBertForMaskedLM",Bw]]]),mv=new Map([["bert",["BertForQuestionAnswering",Ny]],["roformer",["RoFormerForQuestionAnswering",Hy]],["electra",["ElectraForQuestionAnswering",tw]],["convbert",["ConvBertForQuestionAnswering",Xy]],["camembert",["CamembertForQuestionAnswering",sw]],["deberta",["DebertaForQuestionAnswering",cw]],["deberta-v2",["DebertaV2ForQuestionAnswering",gw]],["mpnet",["MPNetForQuestionAnswering",Rw]],["albert",["AlbertForQuestionAnswering",Ww]],["distilbert",["DistilBertForQuestionAnswering",bw]],["roberta",["RobertaForQuestionAnswering",db]],["xlm",["XLMForQuestionAnswering",mb]],["xlm-roberta",["XLMRobertaForQuestionAnswering",bb]],["mobilebert",["MobileBertForQuestionAnswering",Iw]],["squeezebert",["SqueezeBertForQuestionAnswering",Nw]]]),Eg=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Cb]]]),gv=new Map([["llava",["LlavaForConditionalGeneration",km]],["moondream1",["Moondream1ForConditionalGeneration",Tb]]]),_v=new Map([["vit",["ViTForImageClassification",_2]],["fastvit",["FastViTForImageClassification",w2]],["mobilevit",["MobileViTForImageClassification",x2]],["mobilevitv2",["MobileViTV2ForImageClassification",C2]],["beit",["BeitForImageClassification",M2]],["deit",["DeiTForImageClassification",L2]],["convnext",["ConvNextForImageClassification",r1]],["convnextv2",["ConvNextV2ForImageClassification",a1]],["dinov2",["Dinov2ForImageClassification",s1]],["resnet",["ResNetForImageClassification",U2]],["swin",["SwinForImageClassification",G2]],["segformer",["SegformerForImageClassification",ev]],["efficientnet",["EfficientNetForImageClassification",iv]]]),yv=new Map([["detr",["DetrForObjectDetection",z2]],["table-transformer",["TableTransformerForObjectDetection",D2]],["yolos",["YolosForObjectDetection",u1]]]),wv=new Map([["owlvit",["OwlViTForObjectDetection",T2]],["owlv2",["Owlv2ForObjectDetection",I2]]]),bv=new Map([["detr",["DetrForSegmentation",R2]],["clipseg",["CLIPSegForImageSegmentation",Db]]]),vv=new Map([["segformer",["SegformerForSemanticSegmentation",tv]]]),$v=new Map([["sam",["SamModel",c1]]]),xv=new Map([["wav2vec2",["Wav2Vec2ForCTC",y1]],["wav2vec2-bert",["Wav2Vec2BertForCTC",I1]],["unispeech",["UniSpeechForCTC",$1]],["unispeech-sat",["UniSpeechSatForCTC",C1]],["wavlm",["WavLMForCTC",P1]],["hubert",["HubertForCTC",O1]]]),Sv=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",w1]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",A1]],["unispeech",["UniSpeechForSequenceClassification",x1]],["unispeech-sat",["UniSpeechSatForSequenceClassification",E1]],["wavlm",["WavLMForSequenceClassification",B1]],["hubert",["HubertForSequenceClassification",z1]],["audio-spectrogram-transformer",["ASTForAudioClassification",$b]]]),Cv=new Map([["wavlm",["WavLMForXVector",D1]]]),Ev=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",T1]],["wavlm",["WavLMForAudioFrameClassification",N1]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",b1]]]),Tv=new Map([["vitmatte",["VitMatteForImageMatting",v2]]]),kv=new Map([["swin2sr",["Swin2SRForImageSuperResolution",q2]]]),Iv=new Map([["dpt",["DPTForDepthEstimation",K2]],["depth_anything",["DepthAnythingForDepthEstimation",X2]],["glpn",["GLPNForDepthEstimation",J2]]]),Av=new Map([["clip",["CLIPVisionModelWithProjection",Ab]],["siglip",["SiglipVisionModel",zb]]]),Tg=[[ov,be.EncoderOnly],[uv,be.EncoderDecoder],[lv,be.DecoderOnly],[pv,be.EncoderOnly],[hv,be.EncoderOnly],[Sg,be.Seq2Seq],[xg,be.Seq2Seq],[Cg,be.DecoderOnly],[fv,be.EncoderOnly],[mv,be.EncoderOnly],[Eg,be.Vision2Seq],[gv,be.ImageTextToText],[_v,be.EncoderOnly],[bv,be.EncoderOnly],[vv,be.EncoderOnly],[Tv,be.EncoderOnly],[kv,be.EncoderOnly],[Iv,be.EncoderOnly],[yv,be.EncoderOnly],[wv,be.EncoderOnly],[$v,be.MaskGeneration],[xv,be.EncoderOnly],[Sv,be.EncoderOnly],[dv,be.Seq2Seq],[cv,be.EncoderOnly],[Cv,be.EncoderOnly],[Ev,be.EncoderOnly],[Av,be.EncoderOnly]];for(const[t,e]of Tg)for(const[r,n]of t.values())wi.set(r,e),qn.set(n,r),_m.set(r,n);const Mv=[["MusicgenForConditionalGeneration",$g,be.Musicgen],["CLIPTextModelWithProjection",Ib,be.EncoderOnly],["SiglipTextModel",Ob,be.EncoderOnly],["ClapTextModelWithProjection",Q1,be.EncoderOnly],["ClapAudioModelWithProjection",J1,be.EncoderOnly]];for(const[t,e,r]of Mv)wi.set(t,r),qn.set(e,t),_m.set(t,e);class Ov extends sv{static MODEL_CLASS_MAPPINGS=Tg.map(e=>e[0]);static BASE_IF_FAIL=!0}class Ae extends Ot{constructor({logits:e}){super(),this.logits=e}}class zv extends Ot{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class lt extends Ot{constructor({logits:e}){super(),this.logits=e}}class dt extends Ot{constructor({logits:e}){super(),this.logits=e}}class mt extends Ot{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class gn extends Ot{constructor({logits:e}){super(),this.logits=e}}class Rv extends Ot{constructor({alphas:e}){super(),this.alphas=e}}class Pv extends Ot{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const It=typeof self<"u",Bv=It&&self.constructor.name==="DedicatedWorkerGlobalScope";let kr,kg,fr;if(It)kr=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},fr=self.createImageBitmap,kg=self.ImageData;else if(Ye)fr=async t=>{const r=(await t.metadata()).channels,{data:n,info:i}=await t.rotate().raw().toBuffer({resolveWithObject:!0}),a=new Vt(new Uint8ClampedArray(n),i.width,i.height,i.channels);return r!==void 0&&r!==i.channels&&a.convert(r),a};else throw new Error("Unable to load image processing library.");const Dv={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},Nv=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class Vt{constructor(e,r,n,i){this.data=e,this.width=r,this.height=n,this.channels=i}get size(){return[this.width,this.height]}static async read(e){if(e instanceof Vt)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!It)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new Vt(n,e.width,e.height,4)}static async fromURL(e){const r=await Rs(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(It){const r=await fr(e),n=kr(r.width,r.height).getContext("2d");return n.drawImage(r,0,0),new this(n.getImageData(0,0,r.width,r.height).data,r.width,r.height,4)}else{const r=Ye(await e.arrayBuffer());return await fr(r)}}static fromTensor(e,r="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(r==="CHW")e=e.transpose(1,2,0);else if(r!=="HWC")throw new Error(`Unsupported channel format: ${r}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new Vt(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let r=0,n=0;r=0?u=n:h=-n,i>=0?l=i:f=-i,o.drawImage(s,u,l,e,r,h,f,e,r),new Vt(o.getImageData(0,0,e,r).data,e,r,4).convert(a)}else{let a=this.toSharp();if(n>=0&&i>=0)a=a.extract({left:Math.floor(n),top:Math.floor(i),width:e,height:r});else if(n<=0&&i<=0){const s=Math.floor(-i),o=Math.floor(-n);a=a.extend({top:s,left:o,right:e-this.width-o,bottom:r-this.height-s})}else{let s=[0,0],o=0;i<0?(s[0]=Math.floor(-i),s[1]=r-this.height-s[0]):o=Math.floor(i);let u=[0,0],l=0;n<0?(u[0]=Math.floor(-n),u[1]=e-this.width-u[0]):l=Math.floor(n),a=a.extend({top:s[0],bottom:s[1],left:u[0],right:u[1]}).extract({left:l,top:o,width:e,height:r})}return await fr(a)}}async toBlob(e="image/png",r=1){if(!It)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:r})}toTensor(e="CHW"){let r=new ce("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(e!=="HWC")if(e==="CHW")r=r.permute(2,0,1);else throw new Error(`Unsupported channel format: ${e}`);return r}toCanvas(){if(!It)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=kr(e.width,e.height),n=new kg(e.data,e.width,e.height);return r.getContext("2d").putImageData(n,0,0),r}_update(e,r,n,i=null){return this.data=e,this.width=r,this.height=n,i!==null&&(this.channels=i),this}clone(){return new Vt(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(It){if(Bv)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=Nv.get(r)??"image/png",i=await this.toBlob(n),a=URL.createObjectURL(i),s=document.createElement("a");s.href=a,s.download=e,s.click(),s.remove()}else{if(bt.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(It)throw new Error("toSharp() is only supported in server-side environments.");return Ye(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}function Tc(t){if(t<1)return new Float64Array;if(t===1)return new Float64Array([1]);const e=t-1,r=Math.PI/e,n=new Float64Array(t);for(let i=0;i2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,r=15,n=27/Math.log(6.4))=>t>=e?r+Math.log(t/e)*n:3*t/200};function Is(t,e="htk"){const r=Fv[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}const Lv={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,r=15,n=Math.log(6.4)/27)=>t>=r?e*Math.exp(n*(t-r)):200*t/3};function Wv(t,e="htk"){const r=Lv[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}function Uv(t,e){const r=Float64Array.from({length:e.length-1},(s,o)=>e[o+1]-e[o]),n=Array.from({length:t.length},()=>new Array(e.length));for(let s=0;snew Array(t.length));for(let s=0;st+n*a)}function Zn(t,e,r,n,i,a=null,s="htk",o=!1){if(a!==null&&a!=="slaney")throw new Error('norm must be one of null or "slaney"');const u=Is(r,s),l=Is(n,s),h=kc(u,l,e+2);let f=Wv(h,s),m;if(o){const y=i/(t*2);m=Is(Float64Array.from({length:t},(b,v)=>v*y),s),f=h}else m=kc(0,Math.floor(i/2),t);const c=Uv(m,f);if(a!==null&&a==="slaney")for(let y=0;yi)throw Error(`frame_length (${r}) may not be larger than fft_length (${i})`);if(I!==r)throw new Error(`Length of the window (${I}) must equal frame_length (${r})`);if(n<=0)throw new Error("hop_length must be greater than zero");if(a===null&&h!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. Specify `power` to fix this issue.");if(s){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const U=Math.floor((i-1)/2)+1;t=Vv(t,U,U)}const A=Math.floor(1+Math.floor((t.length-r)/n)),R=u?Math.floor(i/2)+1:i;let z=A,P=A;C!==null&&(C>A?x&&(P=C):P=z=C);const J=new d0(i),K=new Float64Array(i),ue=new Float64Array(J.outputBufferSize),ie=new Float32Array(R*P);for(let U=0;U=1;--re)K[re]-=l*K[re-1];K[0]*=1-l}for(let re=0;reMath.pow(o,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(r&&(s=s.subarray(0,t)),n===null)return s;if(t>n)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${n})`);return s}function qv([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function Ro(t,e=.5,r=null,n=!1){const i=t.logits,a=t.pred_boxes,[s,o,u]=i.dims;if(r!==null&&r.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let l=[];for(let h=0;he&&C.push(T)}else{let T=_r(v.data)[1];if(T===u-1||(x=ta(v.data),x[T]A*f[(R+1)%2])),m.boxes.push(I),m.classes.push(T),m.scores.push(x[T])}}l.push(m)}return l}function ya(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${t?.constructor?.name??typeof t} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function Ic(t,e,r=0,n=null){const i=t/e;let a=p0(i)*e;return n!==null&&a>n&&(a=Math.floor(i)*e),aa?l=Math.floor(a*u/i):a>i&&(u=Math.floor(i*l/a)),await e.resize(l,u,{resample:n}))}async crop_margin(e,r=200){const n=e.clone().grayscale(),i=u0(n.data)[0],s=_r(n.data)[0]-i;if(s===0)return e;const o=r/255;let u=n.width,l=n.height,h=0,f=0;const m=n.data;for(let c=0;cthis.preprocess(a)));return{pixel_values:Qn(n.map(a=>a.pixel_values),0),original_sizes:n.map(a=>a.original_size),reshaped_input_sizes:n.map(a=>a.reshaped_input_size)}}}class jv extends je{post_process_semantic_segmentation(e,r=null){const n=e.logits,i=n.dims[0];if(r!==null&&r.length!==i)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const a=[];for(let s=0;sm[T]&&(m[T]=x[T],c[T]=C)}const y=new Array(u.dims[0]),b=f.data;for(let C=0;CC!==void 0);a.push({segmentation:f,labels:v})}return a}}class Ag extends je{}class Kv extends Ag{}class Yv extends je{}class Xv extends je{}class Mg extends je{}class Qv extends Mg{}class Jv extends je{}class Zv extends je{}class Og extends je{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){const r=this.size?.shortest_edge;if(r===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(r<384){const n=Math.floor(r/this.crop_pct),[i,a]=this.get_resize_output_image_size(e,{shortest_edge:n});e=await e.resize(i,a,{resample:this.resample}),e=await e.center_crop(r,r)}else e=await e.resize(r,r,{resample:this.resample});return e}}class e$ extends Og{}class t$ extends je{}class r$ extends je{}class n$ extends je{constructor(e){super(e),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(r=>r*r))}}class zg extends je{}class a$ extends zg{}class Rg extends je{post_process_object_detection(...e){return Ro(...e)}}class i$ extends Rg{}class s$ extends je{}class o$ extends je{}class Pg extends je{pad_image(e,r,n,i={}){const[a,s,o]=r;let u=this.image_mean;Array.isArray(this.image_mean)||(u=new Array(o).fill(u));let l=this.image_std;Array.isArray(l)||(l=new Array(o).fill(u));const h=u.map((f,m)=>-f/l[m]);return super.pad_image(e,r,n,{center:!0,constant_values:h,...i})}}class u$ extends Pg{}class l$ extends je{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],i=new ce("int64",new BigInt64Array(n.reduce((a,s)=>a*s)).fill(1n),n);return{...r,pixel_mask:i}}post_process_object_detection(...e){return Ro(...e)}remove_low_and_no_objects(e,r,n,i){let a=[],s=[],o=[];for(let u=0;un&&(a.push(h),s.push(c),o.push(f))}return[a,s,o]}check_segment_validity(e,r,n,i=.5,a=.8){let s=[],o=0,u=0;const l=r[n].data;for(let f=0;f=i&&++u;let h=o>0&&u>0;return h&&(h=o/u>a),[h,s]}compute_segments(e,r,n,i,a,s=null,o=null){let[u,l]=o??e[0].dims,h=new ce("int32",new Int32Array(u*l),[u,l]),f=[];if(o!==null)for(let v=0;vc[T]&&(m[T]=v,c[T]=x[T])}let y=0;const b=h.data;for(let v=0;vi!==r.dims[a]))throw Error(`The first ${n.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new ce("int64",e.flat(1/0).map(BigInt),n)}async _call(e,{input_points:r=null,input_labels:n=null,input_boxes:i=null}={}){const a=await super._call(e);if(r&&(a.input_points=this.reshape_input_points(r,a.original_sizes,a.reshaped_input_sizes)),n){if(!a.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");a.input_labels=this.add_input_labels(n,a.input_points)}return i&&(a.input_boxes=this.reshape_input_points(i,a.original_sizes,a.reshaped_input_sizes,!0)),a}async post_process_masks(e,r,n,{mask_threshold:i=0,binarize:a=!0,pad_size:s=null}={}){const o=[];s=s??this.pad_size;const u=[s.height,s.width];for(let l=0;li&&(y[b]=1);m=new ce("bool",y,m.dims)}o.push(m)}return o}generate_crop_boxes(e,r,{crop_n_layers:n=0,overlap_ratio:i=512/1500,points_per_crop:a=32,crop_n_points_downscale_factor:s=1}={}){}}class p$ extends je{pad_image(e,r,n,i={}){const[a,s,o]=r;return super.pad_image(e,r,{width:s+(n-s%n)%n,height:a+(n-a%n)%n},{mode:"symmetric",center:!1,constant_values:-1,...i})}}class h$ extends je{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);const n=await Promise.all(e.map(s=>this.preprocess(s))),i=await Promise.all(r.map(s=>this.preprocess(s,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:Qn(n.map((s,o)=>Jt([s.pixel_values,i[o].pixel_values],0)),0),original_sizes:n.map(s=>s.original_size),reshaped_input_sizes:n.map(s=>s.reshaped_input_size)}}}class f$ extends Vr{constructor(e){super(e),this.config.mel_filters??=Zn(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=zi(this.config.n_fft,"hann")}async _extract_fbank_features(e){const r=await Oi(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),n=r.data,i=_r(n)[0];for(let a=0;athis.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),r=e.slice(0,this.config.n_samples)):(r=new Float32Array(this.config.n_samples),r.set(e)),{input_features:(await this._extract_fbank_features(r)).unsqueeze_(0)}}}class m$ extends Vr{_zero_mean_unit_var_norm(e){const n=e.reduce((a,s)=>a+s,0)/e.length,i=e.reduce((a,s)=>a+(s-n)**2,0)/e.length;return e.map(a=>(a-n)/Math.sqrt(i+1e-7))}async _call(e){ya(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=e;this.config.do_normalize&&(r=this._zero_mean_unit_var_norm(r));const n=[1,r.length];return{input_values:new ce("float32",r,n),attention_mask:new ce("int64",new BigInt64Array(r.length).fill(1n),n)}}}class g$ extends Vr{constructor(e){super(e);const r=this.config.sampling_rate,n=Zn(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let i=0;in*32768),Oi(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e,{padding:r=!0,pad_to_multiple_of:n=2,do_normalize_per_mel_bins:i=!0,return_attention_mask:a=!0}={}){ya(e,"SeamlessM4TFeatureExtractor");let s=await this._extract_fbank_features(e,this.config.max_length);if(i){const[y,b]=s.dims,v=s.data;for(let C=0;C0){const x=new Float32Array(b*(y+C));x.set(v),x.fill(this.config.padding_value,v.length);const T=y+C;s=new ce(s.type,x,[T,b]),a&&(o=new ce("int64",new BigInt64Array(T),[1,T]),o.data.fill(1n,0,y))}}const[u,l]=s.dims,h=this.config.stride;if(u%h!==0)throw new Error(`The number of frames (${u}) must be a multiple of the stride (${h}).`);const m=s.view(1,Math.floor(u/h),l*h),c={input_features:m};if(a){const y=m.dims[1],b=new BigInt64Array(y);if(o){const v=o.data;for(let C=1,x=0;C0)if(n==="rand_trunc"){const o=Math.floor(Math.random()*(s+1));e=e.subarray(o,o+r),a=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${n}" not implemented`);else{if(s<0){let o=new Float64Array(r);if(o.set(e),i==="repeat")for(let u=e.length;u{dn=Number(Xs.value),Po.feature_extractor.size={width:dn,height:dn},T$.textContent=dn});Xs.disabled=!1;let cn=.4;Qs.addEventListener("input",()=>{cn=Number(Qs.value),Bg(Fr.videoWidth*cn,Fr.videoHeight*cn),k$.textContent=cn});Qs.disabled=!1;Ri.textContent="Ready";let Ms=!1,Os;const Ac=bi.getContext("2d",{willReadFrequently:!0}),I$=ea.getContext("2d",{willReadFrequently:!0});function Fg(){const{width:t,height:e}=bi;Ms||(Ms=!0,async function(){Ac.drawImage(Fr,0,0,t,e);const r=Ac.getImageData(0,0,t,e),n=new Vt(r.data,t,e,4),i=await Po(n),{predicted_depth:a}=await Ng(i),s=a.data,[o,u,l]=a.dims;let h=1/0,f=-1/0;ea.width=l,ea.height=u;for(let b=0;bf&&(f=v)}const m=f-h,c=new Uint8ClampedArray(4*s.length);for(let b=0;b{Fr.srcObject=t,Fr.play();const e=t.getVideoTracks()[0],{width:r,height:n}=e.getSettings();Bg(r*cn,n*cn),setTimeout(Fg,50)}).catch(t=>{alert(t)});