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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])}},Uu=(t,e)=>new Lu(t,e),aa=(t,e)=>{let r=t.length,n=[];for(let a=0;a1&&i===1&&n.unshift(s)}return n}}),Wu,eo,Vu,Gu,kr,Hu,ju,ia=ee(()=>{xe(),Oe(),pt(),Ie(),Wu=t=>{if(!t||t.length!==1)throw new Error("Transpose requires 1 input.")},eo=(t,e)=>e&&e.length!==t?[...new Array(t).keys()].reverse():e,Vu=(t,e)=>X.sortBasedOnPerm(t,eo(t.length,e)),Gu=(t,e,r,n)=>{let a=[];a.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} { + var a: ${r.type.indices};`);for(let s=0;s{let r=t.dataType,n=t.dims.length,a=eo(n,e),s=Vu(t.dims,a),i=_e("output",r,s.length),o=Q("a",r,n),l=u=>` + ${u.registerUniform("output_size","u32").declareVariables(o,i)} + + ${Gu(a,n,o,i)} + + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${i.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${i.setByOffset("global_idx",o.getByIndices("aIndices"))} + }`;return{name:"Transpose",shaderCache:{hint:`${e}`,inputDependencies:["rank"]},getRunData:u=>{let d=X.size(s);return{outputs:[{dims:s,dataType:u[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:[{type:12,data:d},...we(u[0].dims,s)]}},getShaderSource:l}},Hu=(t,e)=>{Wu(t.inputs),t.compute(kr(t.inputs[0],e.perm))},ju=t=>Ke({perm:t.perm})}),qu,Ku,Yu,Xu,Qu,Zu,Ju,ed,td,rd,nr,nd,ad,id,sd,od,ld,ud,dd,cd,pd,Dy=ee(()=>{xe(),Oe(),Ie(),ro(),ia(),qu={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 = ${Ku[n]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${h.setByOffset("outputIndex",`${n==="mean"?`${h.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${h.type.storage}(${Xu[n]})`}`)}; + } + }`,getRunData:()=>({outputs:[{dims:s,dataType:a}],dispatchGroup:{x:l},programUniforms:[{type:12,data:u}]})}},nr=(t,e,r,n)=>{let a=t.inputs.length===1?r:to(t.inputs,r),s=a.axes;s.length===0&&!a.noopWithEmptyAxes&&(s=t.inputs[0].dims.map((g,p)=>p));let 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Error("Reduce op requires 1 or 2 inputs.");if(t.length===2&&t[1].dims.length!==1)throw new Error("Invalid axes input dims.")},hd=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],gi=(t,e,r,n,a,s,i=!1,o=!1)=>{let l=[],u=r[0].dims,d=u.length,h=X.normalizeAxes(a,d),f=!o&&h.length===0;u.forEach((w,v)=>{f||h.indexOf(v)>=0?i&&l.push(1):l.push(w)});let g=l.length,p=X.size(l);return{name:t,shaderCache:e,getShaderSource:w=>{let v=[],x=Q("_A",r[0].dataType,d),$=_e("output",s,g),E=n(x,$,h),T=E[2];for(let A=0,z=0;A=0?(i&&z++,T=`for(var j${A}: u32 = 0; j${A} < ${u[A]}; j${A}++) { + ${E[2].includes("last_index")?`let last_index = j${A};`:""} + ${x.indicesSet("input_indices",A,`j${A}`)} + ${T} + }`):(v.push(`${x.indicesSet("input_indices",A,$.indicesGet("output_indices",z))};`),z++);return` + + ${w.registerUniform("output_size","u32").declareVariables(x,$)} + + ${w.mainStart()} + ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var input_indices: ${x.type.indices}; + let output_indices = ${$.offsetToIndices("global_idx")}; + + ${v.join(` +`)} + ${E[0]} // init ops for reduce max/min + ${E[1]} + ${T} + ${E[3]} + ${E.length===4?$.setByOffset("global_idx","value"):E.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:l,dataType:s}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...we(u,l)]})}},to=(t,e)=>{let r=[];return t[1].dims[0]>0&&t[1].getBigInt64Array().forEach(n=>r.push(Number(n))),Ke({axes:r,keepDims:e.keepDims,noopWithEmptyAxes:e.noopWithEmptyAxes})},ir=(t,e,r,n)=>{let a=t.inputs,s=a.length===1?r:to(a,r);t.compute(gi(e,{hint:s.cacheKey,inputDependencies:["rank"]},[a[0]],s.noopWithEmptyAxes&&s.axes.length===0?hd:n,s.axes,a[0].dataType,s.keepDims,s.noopWithEmptyAxes),{inputs:[0]})},fd=(t,e)=>{ar(t.inputs),ir(t,"ReduceLogSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},md=(t,e)=>{ar(t.inputs),ir(t,"ReduceL1",e,(r,n)=>[`var value = 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${s});`]})},bd=(t,e)=>{ar(t.inputs),ir(t,"ReduceMin",e,(r,n,a)=>{let s=[];for(let i=0;i=0||a.length===0)&&s.push(`input_indices[${i}] = 0;`);return[`${s.join(` +`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},vd=(t,e)=>{ar(t.inputs),ir(t,"ReduceProd",e,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},$d=(t,e)=>{ar(t.inputs),ir(t,"ReduceSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},xd=(t,e)=>{ar(t.inputs),ir(t,"ReduceSumSquare",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},sr=(t,e,r)=>{if(e.length===0)return r;let n=1,a=1;for(let s=0;s1024},Sd=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?wd(t,e):nd(t,e)},kd=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?md(t,e):ad(t,e)},Ed=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?gd(t,e):id(t,e)},Cd=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?_d(t,e):sd(t,e)},Td=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?yd(t,e):od(t,e)},Ad=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?bd(t,e):ld(t,e)},Id=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?vd(t,e):ud(t,e)},Md=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?$d(t,e):dd(t,e)},Od=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?xd(t,e):cd(t,e)},zd=(t,e)=>{sr(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?fd(t,e):pd(t,e)}}),no,Pd,Rd,ao,Ny=ee(()=>{xe(),pt(),ro(),no=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.")},Pd=(t,e)=>{no(t.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o=0||s.length===0)&&i.push(`input_indices[${o}] = 0;`);return[`${i.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?"<=":"<"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",a.setByOffset("global_idx","best_index")]};t.compute(gi("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Rd=(t,e)=>{no(t.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o=0||s.length===0)&&i.push(`input_indices[${o}] = 0;`);return[`${i.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${e.selectLastIndex>0?">=":">"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",a.setByOffset("global_idx","best_index")]};t.compute(gi("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},ao=t=>Ke(t)}),Bd,Dd,Nd,_i,Fd,Ld,Ud=ee(()=>{xe(),Oe(),pt(),Ie(),Bd=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");let r=0,n=t[r],a=n.dataType,s=n.dims.length;t.forEach((i,o)=>{if(o!==r){if(i.dataType!==a)throw new Error("input tensors should be one type");if(i.dims.length!==s)throw new Error("input tensors should have the same shape");i.dims.forEach((l,u)=>{if(u!==e&&l!==n.dims[u])throw new Error("non concat dimensions must match")})}})},Dd=(t,e)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${e}); + for (var i: u32 = 0u; i < ${t}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${t}u; + }`,Nd=(t,e)=>{let r=t.length,n=[];for(let a=0;a{let a=X.size(r),s=new Array(t.length),i=new Array(t.length),o=0,l=[],u=[],d=[{type:12,data:a}];for(let w=0;w`uniforms.sizeInConcatAxis${w}`).join(","),p=w=>` + + ${(()=>{w.registerUniform("outputSize","u32");for(let v=0;v(${g}); + ${f} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${Nd(i,h)} + }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:p}},Fd=(t,e)=>{let r=t.inputs,n=r[0].dims,a=X.normalizeAxis(e.axis,n.length);Bd(r,a);let s=n.slice();s[a]=r.reduce((o,l)=>o+(l.dims.length>a?l.dims[a]:0),0);let i=r.filter(o=>X.size(o.dims)>0);t.compute(_i(i,a,s,r[0].dataType),{inputs:i})},Ld=t=>Ke({axis:t.axis})}),Wd,Vd,Gd,Hd,yi,jd,qd,Kd=ee(()=>{xe(),qs(),Ie(),Ud(),Wd=(t,e)=>{let r=t[0],n=t[1],a=t[2],s=t[3],i=t[4],o=t[5];if(i&&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 l=r.dims[0],u=r.dims[1],d=r.dims[2];if(a.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]!==d)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(a.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let h=a.dims[0]/3,f=h,g=f;if(e.qkvHiddenSizes.length>0){if(e.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let E of e.qkvHiddenSizes)if(E%e.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");h=e.qkvHiddenSizes[0],f=e.qkvHiddenSizes[1],g=e.qkvHiddenSizes[2]}let p=u;if(h!==f)throw new Error("qkv_hidden_sizes first element should be same as the second");if(a.dims[0]!==h+f+g)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let w=0;if(i){if(f!==g)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==l)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==e.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==f/e.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');e.pastPresentShareBuffer||(w=i.dims[3])}let v=p+w,x=-1,$=0;if(s)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");return{batchSize:l,sequenceLength:u,pastSequenceLength:w,kvSequenceLength:p,totalSequenceLength:v,maxSequenceLength:x,inputHiddenSize:d,hiddenSize:h,vHiddenSize:g,headSize:Math.floor(h/e.numHeads),vHeadSize:Math.floor(g/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:$,scale:e.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Vd=(t,e,r,n)=>{let a=st(n),s=64,i=n/a;i{let g=_e("x",e.dataType,e.dims,a),p=[{name:"d_inv",type:Ot(e.dataType)},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return` + var thread_max: array; + var thread_sum: array; + ${f.registerUniforms(p).declareVariables(g)} + ${f.mainStart([s,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 = ${d}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + thread_max_vector = max(${d}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(a){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: ${a}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${s}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${d}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + sum_vector += exp(${d}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(a){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: ${a}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${s}; 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] = ${g.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 = ${d}(x[offset + i]); + x[offset + i] = ${g.type.value}(exp(f32input - max_value) / sum); + } + } + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${s};${u};${a}`},getShaderSource:h,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:l})}},Gd=(t,e,r,n,a,s,i)=>{let o=i+a.kvSequenceLength,l=[a.batchSize,a.numHeads,a.sequenceLength,o],u=s.scale===0?1/Math.sqrt(a.headSize):s.scale,d=st(a.headSize),h=a.headSize/d,f=12,g={x:Math.ceil(o/f),y:Math.ceil(a.sequenceLength/f),z:a.batchSize*a.numHeads},p=[{type:12,data:a.sequenceLength},{type:12,data:h},{type:12,data:o},{type:12,data:a.numHeads},{type:1,data:u}],w=n?["type","type","type"]:["type","type"],v=x=>{let $=Q("q",e.dataType,e.dims,d),E=Q("key",r.dataType,r.dims,d),T=[$,E];n&&T.push(Q("relative_position_bias",n.dataType,n.dims));let A=_e("output",e.dataType,l),z=Ot(1,d),R=[{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 = ${f}u; + + var tileQ: array<${$.type.storage}, ${f*f}>; + var tileK: array<${$.type.storage}, ${f*f}>; + ${x.registerUniforms(R).declareVariables(...T,A)} + ${x.mainStart([f,f,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 = ${z}(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 += ${z}(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(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}`)}})()}; + output[outputIdx] = ${A.type.value} (sum * uniforms.alpha) + ${n?"relative_position_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${d}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:l,dataType:e.dataType,gpuDataType:0}],dispatchGroup:g,programUniforms:p}),getShaderSource:v}},Hd=(t,e,r,n,a)=>{let s=a+n.kvSequenceLength,i=[n.batchSize,n.sequenceLength,n.vHiddenSize],o=12,l={x:Math.ceil(n.vHeadSize/o),y:Math.ceil(n.sequenceLength/o),z:n.batchSize*n.numHeads},u=[{type:12,data:n.sequenceLength},{type:12,data:s},{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:i,dataType:e.dataType,gpuDataType:0}],dispatchGroup:l,programUniforms:u}),getShaderSource:d=>{let h=Q("probs",e.dataType,e.dims),f=Q("v",r.dataType,r.dims),g=_e("output",e.dataType,i),p=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return` + const TILE_SIZE = ${o}u; + var tileQ: array<${h.type.value}, ${o*o}>; + var tileK: array<${h.type.value}, ${o*o}>; + ${d.registerUniforms(p).declareVariables(h,f,g)} + ${d.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 = ${h.type.storage}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + let batchIdx = workgroup_id.z / uniforms.num_heads; + let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads; + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + currentBatchHeadNumber * uniforms.N + n; + output[outputIdx] = value; + } + }`}}},yi=(t,e,r,n,a,s,i,o,l,u,d)=>{let h=t.outputCount>1,f=t.outputCount>2,g=h&&f?u.pastSequenceLength:0,p=g+u.kvSequenceLength,w=[u.batchSize,u.numHeads,p,u.headSize],v=i?[i,r]:[r],x=h?t.compute(_i(v,2,w,r.dataType),{inputs:v,outputs:[1]})[0]:r,$=[u.batchSize,u.numHeads,p,u.headSize],E=o?[o,n]:[n],T=f?t.compute(_i(E,2,$,n.dataType),{inputs:E,outputs:[2]})[0]:n,A=[e,x];l&&A.push(l);let z=t.compute(Gd(t,e,x,l,u,d,g),{inputs:A,outputs:[-1]})[0];t.compute(Vd(t,z,u.batchSize*u.numHeads*u.sequenceLength,p),{inputs:[z],outputs:[]});let R=[z,T];t.compute(Hd(t,z,T,u,g),{inputs:R,outputs:[0]})},jd=(t,e)=>{let r=[e.batchSize,e.numHeads,e.sequenceLength,e.headSize],n=e.sequenceLength,a=e.inputHiddenSize,s=e.headSize,i=12,o={x:Math.ceil(e.headSize/i),y:Math.ceil(e.sequenceLength/i),z:e.batchSize*e.numHeads},l=[t.inputs[0],t.inputs[1],t.inputs[2]],u=[{type:12,data:n},{type:12,data:a},{type:12,data:s},{type:12,data:e.numHeads},{type:12,data:e.headSize},{type:12,data:e.hiddenSize},{type:12,data:e.hiddenSize+e.hiddenSize+e.vHiddenSize}],d=h=>{let f=_e("output_q",l[0].dataType,r),g=_e("output_k",l[0].dataType,r),p=_e("output_v",l[0].dataType,r),w=Q("input",l[0].dataType,l[0].dims),v=Q("weight",l[1].dataType,l[1].dims),x=Q("bias",l[2].dataType,l[2].dims),$=w.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` + const TILE_SIZE = ${i}u; + var tileInput: array<${$}, ${i*i}>; + var tileWeightQ: array<${$}, ${i*i}>; + var tileWeightK: array<${$}, ${i*i}>; + var tileWeightV: array<${$}, ${i*i}>; + ${h.registerUniforms(E).declareVariables(w,v,x,f,g,p)} + ${h.mainStart([i,i,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 = ${$}(0); + var valueK = ${$}(0); + var valueV = ${$}(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:u}),getShaderSource:d},{inputs:l,outputs:[-1,-1,-1]})},qd=(t,e)=>{let r=Wd(t.inputs,e),[n,a,s]=jd(t,r);return yi(t,n,a,s,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),Yd,Xd,Qd,Zd,Fy=ee(()=>{rr(),xe(),Oe(),pt(),Ie(),Yd=(t,e)=>{if(!t||t.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,a,s)=>{let i=a.length;if(i!==n.length)throw new Error(`${s}: num dimensions != ${i}`);a.forEach((o,l)=>{if(o!==n[l])throw new Error(`${s}: dim[${l}] 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")},Xd=(t,e)=>{let{epsilon:r,spatial:n,format:a}=e,s=t[0].dims,i=n?st(s[s.length-1]):1,o=a==="NHWC"&&s.length>1?i:1,l=X.size(s)/i,u=n,d=u?s.length:s,h=Q("x",t[0].dataType,t[0].dims,i),f=Q("scale",t[1].dataType,t[1].dims,o),g=Q("bias",t[2].dataType,t[2].dims,o),p=Q("inputMean",t[3].dataType,t[3].dims,o),w=Q("inputVar",t[4].dataType,t[4].dims,o),v=_e("y",t[0].dataType,d,i),x=()=>{let E="";if(n)E=`let cOffset = ${s.length===1?"0u":a==="NHWC"?`outputIndices[${s.length-1}] / ${i}`:"outputIndices[1]"};`;else if(a==="NCHW")E=` + ${v.indicesSet("outputIndices","0","0")} + let cOffset = ${v.indicesToOffset("outputIndices")};`;else{E=`var cIndices = ${f.type.indices}(0); + cIndices[0] = outputIndices[${s.length-1}];`;for(let T=1;T` + const epsilon = ${r}; + ${E.registerUniform("outputSize","u32").declareVariables(h,f,g,p,w,v)} + ${E.mainStart()} + ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${v.offsetToIndices(`global_idx * ${i}`)}; + ${x()} + let scale = ${f.getByOffset("cOffset")}; + let bias = ${g.getByOffset("cOffset")}; + let inputMean = ${p.getByOffset("cOffset")}; + let inputVar = ${w.getByOffset("cOffset")}; + let x = ${h.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}_${i}`,inputDependencies:u?["rank","type","type","type","type"]:void 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e=t[0].dims,r=t[0].dims[2],n=X.size(e)/4,a=t[0].dataType,s=Q("input",a,e,4),i=Q("bias",a,[r],4),o=Q("residual",a,e,4),l=_e("output",a,e,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:u=>` + const channels = ${r}u / 4; + ${u.declareVariables(s,i,o,l)} + + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes(n)} + let value = ${s.getByOffset("global_idx")} + + ${i.getByOffset("global_idx % channels")} + ${o.getByOffset("global_idx")}; + ${l.setByOffset("global_idx","value")} + }`}},tc=t=>{Jd(t.inputs),t.compute(ec(t.inputs))}}),rc,De,nc,ac,ic,sc,oc,lc,uc,dc,cc,pc,hc,fc,mc,gc,wi,_c,bi,yc,wc,bc,vc,$c,xc,Sc,kc,Ec,Cc,Tc,Ac,Ic,Mc,Oc,zc,io,Pc,so,oo,Rc,Bc,Dc,lo=ee(()=>{xe(),Oe(),pt(),Ie(),rc=(t,e,r,n,a,s)=>{let i=Math.ceil(e/4),o="";typeof a=="string"?o=`${a}(a)`:o=a("a");let l=Q("inputData",r,[i],4),u=_e("outputData",n,[i],4);return` + ${t.registerUniform("vec_size","u32").declareVariables(l,u)} + + ${s??""} + + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${l.getByOffset("global_idx")}; + ${u.setByOffset("global_idx",o)} + }`},De=(t,e,r,n,a,s=t.dataType)=>({name:e,shaderCache:{hint:a,inputDependencies:["type"]},getShaderSource:i=>rc(i,X.size(t.dims),t.dataType,s,r,n),getRunData:i=>({outputs:[{dims:t.dims,dataType:s}],dispatchGroup:{x:Math.ceil(X.size(i[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(X.size(t.dims)/4)}]})}),nc=t=>{t.compute(De(t.inputs[0],"Abs","abs"))},ac=t=>{t.compute(De(t.inputs[0],"Acos","acos"))},ic=t=>{t.compute(De(t.inputs[0],"Acosh","acosh"))},sc=t=>{t.compute(De(t.inputs[0],"Asin","asin"))},oc=t=>{t.compute(De(t.inputs[0],"Asinh","asinh"))},lc=t=>{t.compute(De(t.inputs[0],"Atan","atan"))},uc=t=>{t.compute(De(t.inputs[0],"Atanh","atanh"))},dc=t=>Ke(t),cc=(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 9:r="vec4";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${e.to}`)}t.compute(De(t.inputs[0],"Cast",r,void 0,e.cacheKey,e.to))},pc=t=>{let e=t.length>=2&&t[1].data!==0?t[1].getFloat32Array()[0]:Qs,r=t.length>=3&&t[2].data!==0?t[2].getFloat32Array()[0]:Zs;return Ke({min:e,max:r})},hc=(t,e)=>{let r=t.inputs.length===1?e:pc(t.inputs),n=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Clip",a=>`clamp(${a}, clip_min_, clip_max_)`,` + const clip_min_: vec4<${n}> = vec4(${n}(${r.min})); + const clip_max_: vec4<${n}> = vec4(${n}(${r.max})); +`,r.cacheKey),{inputs:[0]})},fc=t=>{t.compute(De(t.inputs[0],"Ceil","ceil"))},mc=t=>{t.compute(De(t.inputs[0],"Cos","cos"))},gc=t=>{t.compute(De(t.inputs[0],"Cosh","cosh"))},wi=t=>Ke(t),_c=(t,e)=>{let r=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Elu",n=>`elu_vf32(${n})`,` + const elu_alpha_ = ${r}(${e.alpha}); + + fn elu_f32(a: ${r}) -> ${r} { + return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,e.cacheKey))},bi=(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<${t}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,yc=t=>{let e=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Erf",r=>`erf_vf32(${r})`,bi(e)))},wc=t=>{t.compute(De(t.inputs[0],"Exp","exp"))},bc=t=>{t.compute(De(t.inputs[0],"Floor","floor"))},vc=t=>{let e=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,bi(e)))},$c=(t,e)=>{let r=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${e.alpha});`,e.cacheKey))},xc=t=>{t.compute(De(t.inputs[0],"Not",e=>`!${e}`))},Sc=t=>{t.compute(De(t.inputs[0],"Neg",e=>`-${e}`))},kc=t=>{t.compute(De(t.inputs[0],"Reciprocal",e=>`1.0/${e}`))},Ec=t=>{let e=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Relu",r=>`select(vec4<${e}>(0.0), ${r}, ${r} > vec4<${e}>(0.0))`))},Cc=t=>{t.compute(De(t.inputs[0],"Sigmoid",e=>`(1.0 / (1.0 + exp(-${e})))`))},Tc=t=>Ke(t),Ac=(t,e)=>{let r=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${e.alpha} * ${n} + vec4<${r}>(${e.beta})))`,void 0,e.cacheKey))},Ic=t=>{t.compute(De(t.inputs[0],"Sin","sin"))},Mc=t=>{t.compute(De(t.inputs[0],"Sinh","sinh"))},Oc=t=>{t.compute(De(t.inputs[0],"Sqrt","sqrt"))},zc=t=>{t.compute(De(t.inputs[0],"Tan","tan"))},io=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,Pc=t=>{t.compute(De(t.inputs[0],"Tanh",io))},so=(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 ${io("v")}; +} +`,oo=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,Rc=t=>{let e=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"FastGelu",oo,so(e),void 0,t.inputs[0].dataType))},Bc=(t,e)=>{let r=Ot(t.inputs[0].dataType);return t.compute(De(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},Dc=t=>{t.compute(De(t.inputs[0],"Log","log"))}}),Nc,Fc,Lc,Uy=ee(()=>{Oe(),Ie(),lo(),Nc=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 2560, 5120 or 10240");if(t[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(t[0].dims[2]!==t[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Fc=t=>{let e=t[0].dims.slice();e[2]=e[2]/2;let r=Q("input",t[0].dataType,t[0].dims,4),n=Q("bias",t[0].dataType,[t[0].dims[2]],4),a=_e("output",t[0].dataType,e,4),s=X.size(e)/4,i=_t(t[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:e,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:o=>` + const M_SQRT2 = sqrt(2.0); + const halfChannels = ${t[0].dims[2]/4/2}u; + + ${o.declareVariables(r,n,a)} + + ${bi(i)} + + ${o.mainStart()} + ${o.guardAgainstOutOfBoundsWorkgroupSizes(s)} + let biasIdx = global_idx % halfChannels; + let batchIndex = global_idx / halfChannels; + let inputOffset = biasIdx + batchIndex * halfChannels * 2; + let valueLeft = input[inputOffset] + bias[biasIdx]; + let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${a.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Lc=t=>{Nc(t.inputs),t.compute(Fc(t.inputs))}}),Uc,Wc,or,Vc,Gc,Hc,jc,qc,Kc,Yc,Xc,Qc,Zc,Wy=ee(()=>{xe(),Oe(),Ie(),Uc=(t,e,r,n,a,s,i,o,l,u,d,h)=>{let f,g;typeof o=="string"?f=g=($,E)=>`${o}((${$}),(${E}))`:typeof o=="function"?f=g=o:(f=o.scalar,g=o.vector);let p=_e("outputData",d,n.length,4),w=Q("aData",l,e.length,4),v=Q("bData",u,r.length,4),x;if(a)if(s){let $=X.size(e)===1,E=X.size(r)===1,T=e.length>0&&e[e.length-1]%4===0,A=r.length>0&&r[r.length-1]%4===0;$||E?x=p.setByOffset("global_idx",g($?`${w.type.value}(${w.getByOffset("0")}.x)`:w.getByOffset("global_idx"),E?`${v.type.value}(${v.getByOffset("0")}.x)`:v.getByOffset("global_idx"))):x=` + let outputIndices = ${p.offsetToIndices("global_idx * 4u")}; + let offsetA = ${w.broadcastedIndicesToOffset("outputIndices",p)}; + let offsetB = ${v.broadcastedIndicesToOffset("outputIndices",p)}; + ${p.setByOffset("global_idx",g(i||T?w.getByOffset("offsetA / 4u"):`${w.type.value}(${w.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||A?v.getByOffset("offsetB / 4u"):`${v.type.value}(${v.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else x=p.setByOffset("global_idx",g(w.getByOffset("global_idx"),v.getByOffset("global_idx")));else{if(!s)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let $=(E,T,A="")=>{let z=`aData[indexA${T}][componentA${T}]`,R=`bData[indexB${T}][componentB${T}]`;return` + let outputIndices${T} = ${p.offsetToIndices(`global_idx * 4u + ${T}u`)}; + let offsetA${T} = ${w.broadcastedIndicesToOffset(`outputIndices${T}`,p)}; + let offsetB${T} = ${v.broadcastedIndicesToOffset(`outputIndices${T}`,p)}; + let indexA${T} = offsetA${T} / 4u; + let indexB${T} = offsetB${T} / 4u; + let componentA${T} = offsetA${T} % 4u; + let componentB${T} = offsetB${T} % 4u; + ${E}[${T}] = ${A}(${f(z,R)}); + `};d===9?x=` + var data = vec4(0); + ${$("data",0,"u32")} + ${$("data",1,"u32")} + ${$("data",2,"u32")} + ${$("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:x=` + ${$("outputData[global_idx]",0)} + ${$("outputData[global_idx]",1)} + ${$("outputData[global_idx]",2)} + ${$("outputData[global_idx]",3)} + `}return` + ${t.registerUniform("vec_size","u32").declareVariables(w,v,p)} + + ${h??""} + + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${x} + }`},Wc=(t,e,r,n,a,s,i=r.dataType)=>{let o=!X.areEqual(r.dims,n.dims),l=r.dims,u=X.size(r.dims),d=!1,h=!1,f=[o];if(o){let g=xn.calcShape(r.dims,n.dims,!1);if(!g)throw new Error("Can't perform binary op on the given tensors");l=g,u=X.size(l);let p=X.size(r.dims)===1,w=X.size(n.dims)===1,v=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,x=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;f.push(p),f.push(w),f.push(v),f.push(x);let $=1;for(let E=1;Eg.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:g=>Uc(g,r.dims,n.dims,l,d,o,h,a,r.dataType,n.dataType,i,s),getRunData:()=>({outputs:[{dims:l,dataType:i}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:Math.ceil(X.size(l)/4)},...we(r.dims,n.dims,l)]})}},or=(t,e,r,n,a,s)=>{t.compute(Wc(e,a??"",t.inputs[0],t.inputs[1],r,n,s))},Vc=t=>{or(t,"Add",(e,r)=>`${e}+${r}`)},Gc=t=>{or(t,"Div",(e,r)=>`${e}/${r}`)},Hc=t=>{or(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4(${e}==${r})`},void 0,void 0,9)},jc=t=>{or(t,"Mul",(e,r)=>`${e}*${r}`)},qc=t=>{let e=Q("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;or(t,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},` + fn pow_custom(a : ${e}, b : ${e}) -> ${e} { + if (b == ${e}(0.0)) { + return ${e}(1.0); + } else if (a < ${e}(0.0) && f32(b) != floor(f32(b))) { + return ${e}(pow(f32(a), f32(b))); // NaN + } + return 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)); + } + `)},Kc=t=>{or(t,"Sub",(e,r)=>`${e}-${r}`)},Yc=t=>{or(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4(${e}>${r})`},void 0,void 0,9)},Xc=t=>{or(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4(${e}<${r})`},void 0,void 0,9)},Qc=t=>{or(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4(${e}>=${r})`},void 0,void 0,9)},Zc=t=>{or(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4(${e}<=${r})`},void 0,void 0,9)}}),Qr,Zr,Jr,uo,en=ee(()=>{xe(),Oe(),Qr=(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 ${t.activation}`)}},Zr=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},Jr=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},uo=t=>{let e=(t==null?void 0:t.activation)||"";if(e==="HardSigmoid"){let[r,n]=(t==null?void 0:t.activation_params)||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=(t==null?void 0:t.activation_params)||[Qs,Zs];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=(t==null?void 0:t.activation_params)||[.01];return{activation:e,alpha:r}}return{activation:e}}}),Et,co,po=ee(()=>{Et=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},co=t=>` + ${t?"value = value + getBiasByOutputCoords(coords);":""} + `}),ho,Jc=ee(()=>{ho=t=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${t}.x), i32(${t}.y), i32(${t}.z), 1)); +} +`}),ep,tp,vi,fo,rp,$i,np,mo,xi=ee(()=>{xe(),Oe(),Ie(),en(),po(),ep=(t,e)=>t?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart / innerElementSize + inputCol${e?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRow + innerRow, + kStart / innerElementSize + inputCol${e?", batchIndices":""}); + `,tp=(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];"} + }`,vi=(t,e,r="f32",n,a=!1,s=32,i=!1,o=32)=>{let l=e[1]*t[1],u=e[0]*t[0],d=a?l:s,h=a?s:l,f=d/e[0],g=s/e[1];if(!((a&&f===4&&t[1]===4||!a&&(f===3||f===4))&&d%e[0]===0&&s%e[1]===0&&t[0]===4))throw new Error(`If transposeA ${a} is true, innerElementSize ${f} and workPerThread[1] ${t[1]} must be 4. + Otherwise, innerElementSize ${f} must be 3 or 4. + tileAWidth ${d} must be divisible by workgroupSize[0]${e[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return` +var mm_Asub: array, ${d/f}>, ${h}>; +var mm_Bsub: array, ${u/t[0]}>, ${s}>; + +const rowPerThread = ${t[1]}; +const colPerThread = ${t[0]}; +const innerElementSize = ${f}; +const tileInner = ${s}; + +@compute @workgroup_size(${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 = ${i?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${l}; + + let num_tiles = ${i?`${Math.ceil(o/s)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${i?`i32(globalId.z) * ${o}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${g}; + 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; + ${ep(a,n)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${g}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${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]; + ${f===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${tp(a,f)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},fo=(t,e)=>t?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${e?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${e?", batchIndices":""}); + `,rp=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",$i=(t,e,r="f32",n,a=!1,s=32,i=!1,o=32,l=!1)=>{let u=t[1]*e[1],d=t[0]*e[0],h=a?u:s,f=a?s:u;if(!(f%e[1]===0&&h%e[0]===0&&s%e[1]===0))throw new Error(`tileAHight ${f} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${h} must be divisible by workgroupSize[0]${e[0]}, tileInner ${s} must be divisible by workgroupSize[1]${e[1]}`);let g=f/e[1],p=h/e[0],w=s/e[1],v=l?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${u}; + let globalColStart = i32(workgroupId.x) * ${d}; + + // Loop over shared dimension. + for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var inputRow = localRow; inputRow < ${f}; inputRow = inputRow + ${e[1]}) { + for (var inputCol = localCol; inputCol < ${h}; inputCol = inputCol + ${e[0]}) { + ${fo(a,n)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${e[1]}) { + for (var inputCol = localCol; inputCol < ${d}; 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 = ${a?`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) * ${u}; + +let tileRowA = i32(localId.y) * ${g}; +let tileColA = i32(localId.x) * ${p}; +let tileRowB = i32(localId.y) * ${w}; +// 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 < ${g}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${p}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${fo(a,n)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${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) { + ${rp(a)} + 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, ${f}>; + var mm_Bsub : array, ${s}>; + const rowPerThread = ${t[1]}; + const colPerThread = ${t[0]}; + const tileInner = ${s}; + +@compute @workgroup_size(${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 = ${i?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${i?`${Math.ceil(o/s)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${i?`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} + } +`},np=(t,e,r,n,a,s=!1)=>{let[i,o,l]=a,[u,d,h,f]=n,g=aa(i,l),p=aa(o,l),w=_t(n[0].type.tensor),v=()=>{let $=d.rank,E=u.rank,T=`var aIndices: ${d.type.indices};`;for(let A=$-2-1,z=E-1;A>=0;A--,z--)T+=` +aIndices[${A}] = ${E>1?`batchIndices[${z}]`:"batchIndices"};`;return g.forEach(A=>{T+=` +aIndices[${A}] = 0;`}),T+=` +aIndices[${$-2}] = u32(row); + aIndices[${$-1}] = u32(colIn);`,T},x=()=>{let $=h.rank,E=u.rank,T=`var bIndices: ${h.type.indices};`;for(let A=$-2-1,z=E-1;A>=0;A--,z--)T+=` +bIndices[${A}] = ${E>1?`batchIndices[${z}]`:"batchIndices"};`;return p.forEach(A=>{T+=` +bIndices[${A}] = 0;`}),T+=` +bIndices[${$-2}] = u32(row); + bIndices[${$-1}] = u32(colIn);`,T};return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${u.type.indices}) -> ${Et(t,w)} { + var value = ${Et(t,w)}(0.0); + let col = colIn * ${t}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + ${v()} + value = ${d.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${u.type.indices}) -> ${Et(t,w)} { + var value = ${Et(t,w)}(0.0); + let col = colIn * ${t}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + ${x()} + value = ${h.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Et(t,w)}) { + 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 + ${s?"bias[colIn]":`${Et(t,w)}(bias[row])`};`:""} + ${r} + ${f.setByIndices("vec3(coords)","value")} + } + } + `},mo=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[1].dims,o=s.slice(0,-2),l=i.slice(0,-2),u=n?n.slice(0,-2):r.slice(0,-2),d=X.size(u),h=s[s.length-2],f=s[s.length-1],g=i[i.length-1],p=f%4===0&&g%4===0,w=h<=8?[4,1,1]:[4,4,1],v=[8,8,1],x=[Math.ceil(g/v[0]/w[0]),Math.ceil(h/v[1]/w[1]),Math.ceil(d/v[2]/w[2])],$=p?4:1,E=[...o,h,f/$],T=E.length,A=[...l,f,g/$],z=A.length,R=[d,h,g/$],G=[{type:6,data:h},{type:6,data:g},{type:6,data:f}];Zr(e,G),G.push(...we(u,E,A));let j=["rank","rank"],q=t.length>2;q&&(G.push(...we(t[2].dims)),j.push("rank")),G.push(...we(R));let fe=ne=>{let te=u.length,se=Js("batchDims",t[0].dataType,te,1),N=_t(t[0].dataType),M=Q("a",t[0].dataType,T,$),V=Q("b",t[1].dataType,z,$),K=_e("result",t[0].dataType,R.length,$),pe=[M,V];if(q){let Se=a?$:1;pe.push(Q("bias",t[2].dataType,t[2].dims.length,Se))}let le=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Jr(e,le);let D=_t(K.type.tensor),ae=Qr(e,K.type.value,D),de=np($,q,ae,[se,M,V,K],[o,l,u],a);return` + ${ne.registerUniforms(le).registerInternalVariables(se).declareVariables(...pe,K)} + ${de} + ${p?vi(w,v,N,se):$i(w,v,N,se)} + `};return{name:"MatMul",shaderCache:{hint:`${w};${e.activation};${p};${a}`,inputDependencies:j},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:x[0],y:x[1],z:x[2]},programUniforms:G}),getShaderSource:fe}}}),ap,ip,Vy=ee(()=>{xe(),Xr(),Ie(),en(),po(),Jc(),xi(),ap=(t,e,r,n,a=!1,s,i=4,o=4,l=4,u="f32")=>{let d=j=>{switch(j){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${u}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${j} is not supported.`)}},h=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 ${j} is not supported.`)}},f=t?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,g=t?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,p=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",w=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",v=t?"row":"col",x=t?"col":"row",$=` + 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 = ${x} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${x} / 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 = ${x} % inChannels; + var resData = ${Et(i,u)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${p} && xCol >= 0 && xCol < ${w}) { + ${f} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${d(i)} + } + return resData;`,E=t?e&&n?` + let col = colIn * ${i}; + ${$}`:` + let col = colIn * ${i}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${$} + } + return ${Et(i,u)}(0.0);`:n&&r?` + let col = colIn * ${i}; + ${$}`:` + let col = colIn * ${i}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${$} + } + return ${Et(i,u)}(0.0);`,T=`${h(o)}`,A=Et(l,u),z=Et(t?i:o,u),R=Et(t?o:i,u),G=Qr(s,A,u);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${z} { + ${t?E:T} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${R} { + ${t?T:E} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${A}) { + let col = colIn * ${l}; + 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])"}; + ${g} + ${co(a)} + ${G} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},ip=(t,e,r,n,a,s,i,o)=>{let l=e.format==="NHWC",u=l?t[0].dims[3]:t[0].dims[1],d=r[0],h=l?r[2]:r[3],f=l?r[1]:r[2],g=l?r[3]:r[1],p=l&&(u%4===0||u%3===0)&&g%4===0,w=l?g:h*f,v=l?h*f:g,x=[8,8,1],$=n<=8?[4,1,1]:[4,4,1],E=[Math.ceil(w/x[0]/$[0]),Math.ceil(v/x[1]/$[1]),Math.ceil(d/x[2]/$[2])];nt("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let T=p?l&&u%4!==0?3:4:1,A=x[1]*$[1],z=x[0]*$[0],R=Math.max(x[0]*T,x[1]),G=n%A===0,j=a%z===0,q=s%R===0,fe=p?[T,4,4]:[1,1,1],ne=[{type:6,data:n},{type:6,data:a},{type:6,data:s},{type:6,data:[e.pads[0],e.pads[1]]},{type:6,data:e.strides},{type:6,data:e.dilations}];Zr(e,ne),ne.push(...we(t[0].dims,t[1].dims));let te=["rank","rank"];i&&(ne.push(...we(t[2].dims)),te.push("rank")),ne.push(...we(r));let se=N=>{let M=[{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}];Jr(e,M);let V=p?4:1,K=_t(t[0].dataType),pe=` + fn setOutputAtIndex(flatIndex : i32, value : ${p?`vec4<${K}>`:K}) { + result[flatIndex] = ${p?`vec4<${K}>`:K}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${p?`vec4<${K}>`:K}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${p?"/ 4":""}, value); + }`,le=Q("x",t[0].dataType,t[0].dims.length,T===3?1:T),D=Q("w",t[1].dataType,t[1].dims.length,V),ae=[le,D],de=_e("result",t[0].dataType,r.length,V);if(i){let Se=Q("bias",t[2].dataType,t[2].dims.length,V);ae.push(Se),pe+=` + fn getBiasByOutputCoords(coords : vec4) -> ${p?`vec4<${K}>`:K} { + return bias[coords.${l?"w":"y"}${p?"/ 4":""}]; + }`}return` + ${ho("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 }; + ${N.registerUniforms(M).declareVariables(...ae,de)} + ${pe} + ${ap(l,G,j,q,i,e,fe[0],fe[1],fe[2],K)} + ${p?vi($,x,K,void 0,!l,R):$i($,x,K,void 0,!l,R,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${T};${p};${G};${j};${q};${A};${z};${R}`,inputDependencies:te},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:ne}),getShaderSource:se}}}),go,sp,Gy=ee(()=>{xe(),Oe(),Ie(),hp(),en(),go=(t,e,r)=>{let n=t.length>2,a=n?"value += b[output_channel];":"",s=t[0].dims,i=t[1].dims,o=i[0]/e.group,l=e.format==="NHWC",u=Si(s,i,e.dilations,e.pads,e.strides,l),d=X.size(u),h=[{type:12,data:d},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];Zr(e,h),h.push(...we(s,i));let f=["rank","rank"];n&&(h.push(...we(t[2].dims)),f.push("rank")),h.push(...we(u));let g=p=>{let w=_e("output",t[0].dataType,u.length),v=_t(w.type.tensor),x=Qr(e,w.type.value,v),$=Q("x",t[0].dataType,s.length),E=Q("w",t[1].dataType,i.length),T=[$,E];n&&T.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 Jr(e,A),` + ${p.registerUniforms(A).declareVariables(...T,w)} + + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${w.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${l?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${l?1:2}], outputIndices[${l?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel / uniforms.output_channels_per_group; + + var value: ${w.type.value} = ${w.type.value}(0); + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { + let input_channel = group_id * uniforms.w_shape[1] + wInChannel; + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[${l?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[${l?2:3}]) { + continue; + } + + let xVal = ${l?$.get("batch","xHeight","xWidth","input_channel"):$.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal*wVal; + } + } + } + ${a} + ${x} + ${w.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:f},getRunData:()=>({outputs:[{dims:r?r(u):u,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:h}),getShaderSource:g}},sp=(t,e,r)=>{let n=t.length>2,a=st(r[3]),s=st(r[2]),i=X.size(r)/a/s,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],l=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],u=[r[0],r[1],r[2],r[3]/a],d=[{type:12,data:i},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];Zr(e,d),d.push(...we(o,l,u));let h=(s-1)*e.strides[1]+l[1],f=g=>{let p=_e("output",t[0].dataType,u.length,a),w=_t(p.type.tensor),v=Qr(e,p.type.value,w),x=Q("x",t[0].dataType,o.length,a),$=Q("w",t[1].dataType,l.length,a),E=[x,$];n&&E.push(Q("b",t[2].dataType,t[2].dims,a));let T=n?"value += b[output_channel];":"",A=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Jr(e,A),` + ${g.registerUniforms(A).declareVariables(...E,p)} + ${g.mainStart()} + ${g.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] / ${s}u; + let col = (index1 % width1) * ${s}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${x.type.value}, ${h}>; + var values: array<${p.type.value}, ${s}>; + let input_channel = output_channel; + // Use constant instead of uniform can give better performance for w's height/width. + for (var w_height: u32 = 0u; w_height < ${l[0]}; w_height++) { + let x_height = x_corner.x + i32(w_height); + if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) { + for (var i = 0; i < ${h}; i++) { + let x_width = x_corner.y + i; + if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { + x_vals[i] = ${x.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${x.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${l[1]}; w_width++) { + let w_val = ${$.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${s}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${s}u; i++) { + var value = values[i]; + ${T} + ${v} + ${p.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${a};${s};${h};${l[0]};${l[1]}`,inputDependencies:n?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:d}),getShaderSource:f}}}),_o,op,lp,up=ee(()=>{xe(),Oe(),xi(),Ie(),en(),_o=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[1].dims,o=s[s.length-2],l=i[i.length-1],u=s[s.length-1],d=st(l),h=st(u),f=st(o),g=X.size(r)/d/f,p=t.length>2,w=n?n.slice(0,-2):r.slice(0,-2),v=[X.size(w),o,l],x=[{type:12,data:g},{type:12,data:o},{type:12,data:l},{type:12,data:u}];Zr(e,x),x.push(...we(w,s,i)),p&&x.push(...we(t[2].dims)),x.push(...we(v));let $=E=>{let T=Js("batch_dims",t[0].dataType,w.length),A=Q("a",t[0].dataType,s.length,h),z=Q("b",t[1].dataType,i.length,d),R=_e("output",t[0].dataType,v.length,d),G=_t(R.type.tensor),j=Qr(e,R.type.value,G),q=[A,z],fe="";if(p){let pe=a?d:1;q.push(Q("bias",t[2].dataType,t[2].dims.length,pe)),fe=`${a?`value += bias[col / ${pe}];`:`value += ${R.type.value}(bias[row + i]);`}`}let ne=s.slice(0,-2),te=i.slice(0,-2),se=aa(ne,w),N=aa(te,w),M=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Jr(e,M);let V=(pe,le)=>{let D=pe.rank,ae=pe.name;if(D===2)return`var ${ae}_indices = ${pe.type.indices}(0u, 0u);`;let de=T.rank,Se=`var ${ae}_indices: ${pe.type.indices};`;for(let Te=D-2-1,Xe=de-1;Te>=0;Te--,Xe--)Se+=` +${ae}_indices[${Te}] = ${de>1?`batch_indices[${Xe}]`:"batch_indices"};`;return le.forEach(Te=>{Se+=` +${ae}_indices[${Te}] = 0;`}),Se+=`${ae}_indices[${D-2}] = 0u; + ${ae}_indices[${D-1}] = 0u;`,Se},K=()=>{let pe=`var a_data: ${A.type.value};`;for(let le=0;le; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${h}) { + ${K()} + } + for (var i = 0u; i < ${f}u; i++) { + var value = values[i]; + ${fe} + ${j} + let cur_indices = ${R.type.indices}(batch, row + i, col); + let offset = ${R.indicesToOffset("cur_indices")}; + ${R.setByOffset(`offset / ${d}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${d};${h};${f};${a}`,inputDependencies:p?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:x}),getShaderSource:$}},op=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.")},lp=t=>{op(t.inputs);let e=xn.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(_o(t.inputs,{activation:""},e)):t.compute(mo(t.inputs,{activation:""},e))}}),Si,ki,dp,yo,wo,cp,pp,bo,hp=ee(()=>{Oe(),Vy(),xi(),Gy(),en(),up(),ia(),Si=(t,e,r,n,a,s)=>{let i=t[0],o=t.slice(s?1:2,s?3:4),l=o.length,u=e[0],d=e.slice(2).map((f,g)=>f+(f-1)*(r[g]-1)),h=o.map((f,g)=>f+n[g]+n[g+l]).map((f,g)=>Math.floor((f-d[g]+a[g])/a[g]));return h.splice(0,0,i),h.splice(s?3:1,0,u),h},ki=[2,3,1,0],dp=(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 a=t[0].dims.length-2;if(e.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(e.strides.length!==a)throw new Error(`strides should be ${a}D`);if(e.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},yo=(t,e)=>{let r=t.kernelShape.slice();for(let s=2;s{let e=uo(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,s=t.group,i=t.kernel_shape,o=t.pads,l=t.strides,u=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:s,kernelShape:i,pads:o,strides:l,wIsConst:u,...e,cacheKey:`${t.format};${e.activation};`}},cp=(t,e,r)=>{let n=yo(r,e),a=r.format==="NHWC";if(r.group!==1){if(!t.adapterInfo.isArchitecture("ampere")&&a&&e[1].dims[0]===r.group&&e[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let z=Si(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),R=t.kernelCustomData.wT??t.compute(kr(e[1],ki),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=R);let G=[e[0],R];e.length===3&&G.push(e[2]),t.compute(sp(G,n,z),{inputs:G})}else t.compute(go(e,n));return}let s=e.length===3,i=e[0].dims[a?1:2],o=e[0].dims[a?2:3],l=e[0].dims[a?3:1],u=e[1].dims[2],d=e[1].dims[3],h=Si(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),f=h[a?1:2],g=h[a?2:3],p=h[a?3:1],w=a&&u===i&&d===o&&r.pads[0]===0&&r.pads[1]===0;if(w||u===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 z=h[0],R,G,j,q=[];if(a){let te=t.kernelCustomData.wT??t.compute(kr(e[1],ki),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=te),w){let se=i*o*l;R=e[0].reshape([1,z,se]),G=te.reshape([1,se,p]),j=[1,z,p]}else R=e[0].reshape([z,i*o,l]),G=te.reshape([1,l,p]),j=[z,f*g,p];q.push(R),q.push(G)}else R=e[0].reshape([z,l,i*o]),G=e[1].reshape([1,p,l]),j=[z,p,f*g],q.push(G),q.push(R);s&&q.push(e[2]);let fe=j[2],ne=q[0].dims[q[0].dims.length-1];fe<8&&ne<8?t.compute(_o(q,n,h,j,a),{inputs:q}):t.compute(mo(q,n,h,j,a),{inputs:q});return}let v=!0,x=t.kernelCustomData.wT??t.compute(kr(e[1],ki),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=x);let $=[e[0],x];s&&$.push(e[2]);let E=a?f*g:p,T=a?p:f*g,A=u*d*l;t.compute(ip($,n,h,E,T,A,s,v),{inputs:$})},pp=(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 a=[0,e.pads[0],0,e.pads[1]],s=[1].concat(e.strides),i=[1].concat(e.dilations),o=[1].concat(e.kernelShape),l=yo({...e,pads:a,strides:s,dilations:i,kernelShape:o},n);t.compute(go(n,l,u=>r?[u[0],u[2],u[3]]:[]))},bo=(t,e)=>{dp(t.inputs,e),t.inputs[0].dims.length===3?pp(t,e):cp(t,t.inputs,e)}}),fp,mp,Hy=ee(()=>{xe(),Xr(),Ie(),en(),po(),Jc(),xi(),fp=(t,e=!1,r,n,a=4)=>{let s=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.`)}},i=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); + `,l=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",u=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",d=t?"row":"col",h=t?"col":"row",f=` + 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 = ${d} / outWidth; + let outCol = ${d} % outWidth; + + let WRow = ${h} / (uniforms.filter_dims[1] * inChannels); + let WCol = ${h} / inChannels % uniforms.filter_dims[1]; + let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]); + let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]); + if (xR < 0.0 || xR >= f32(${l}) || fract(xR) > 0.0) { + return ${n}(0.0); + } + if (xC < 0.0 || xC >= f32(${u}) || fract(xC) > 0.0) { + return ${n}(0.0); + } + let iXR = i32(xR); + let iXC = i32(xC); + let xCh = ${h} % inChannels; + ${i} + return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${a}];`,g=t?` + let col = colIn * ${a}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${f} + } + return ${n}(0.0);`:` + let col = colIn * ${a}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${f} + } + return ${n}(0.0);`,p=` + let col = colIn * ${a}; + 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); + ${s(a)} + } + return ${n}(0.0); + `,w=Qr(r,n);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} { + ${t?g:p} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} { + ${t?p:g} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) { + let col = colIn * ${a}; + 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} + ${co(e)} + ${w} + result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${a}] = value; + } + }`},mp=(t,e,r,n,a,s,i,o)=>{let l=e.format==="NHWC",u=l?t[0].dims[3]:t[0].dims[1],d=r[0],h=l?r[2]:r[3],f=l?r[1]:r[2],g=l?r[3]:r[1],p=l&&u%4===0&&u%3&&g%4===0,w=l?g:h*f,v=l?h*f:g,x=[8,8,1],$=n<=8?[4,1,1]:[4,4,1],E=[Math.ceil(w/x[0]/$[0]),Math.ceil(v/x[1]/$[1]),Math.ceil(d/x[2]/$[2])];nt("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let T=p?4:1,A=Math.max(x[0]*T,x[1]),z=p?4:1,R=[e.kernelShape[l?1:2],e.kernelShape[l?2:3]],G=[R[0]+(e.dilations[0]<=1?0:(R[0]-1)*(e.dilations[0]-1)),R[1]+(e.dilations[1]<=1?0:(R[1]-1)*(e.dilations[1]-1))],j=[G[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),G[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],q=[{type:6,data:n},{type:6,data:a},{type:6,data:s},{type:6,data:e.strides},{type:6,data:e.dilations},{type:6,data:R},{type:6,data:j}];Zr(e,q),q.push(...we(t[0].dims,t[1].dims));let fe=["rank","rank"];i&&(q.push(...we(t[2].dims)),fe.push("rank")),q.push(...we(r));let ne=te=>{let se=Q("x",t[0].dataType,t[0].dims.length,z),N=Q("w",t[1].dataType,t[1].dims.length,1),M=_e("result",t[0].dataType,r.length,z),V=[se,N],K="";if(i){let D=Q("bias",t[2].dataType,t[2].dims.length,z);V.push(D),K+=` + fn getBiasByOutputCoords(coords : vec4) -> ${D.type.value} { + return bias[coords.${l?"w":"y"}${p?"/ 4":""}]; + }`}let pe=[{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:R.length},{name:"pads",type:"i32",length:j.length}];Jr(e,pe);let le=_t(t[0].dataType,1);if(le!=="f16"&&le!=="f32")throw new Error(`elemType ${le} is not supported.`);return` + ${ho("uniforms.result_strides")} + ${te.registerUniforms(pe).declareVariables(...V,M)}; + ${K} + ${fp(l,i,e,se.type.value,T)} + ${p?vi($,x,le,void 0,!l,A):$i($,x,le,void 0,!l,A,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${$};${x};${p}`,inputDependencies:fe},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:q}),getShaderSource:ne}}}),gp,vo,jy=ee(()=>{xe(),Xr(),Oe(),Ie(),gp=(t,e,r,n,a,s=!1,i,o,l=!1)=>{let u=l?1:2,d=l?2:3,h=l?3:1,f=s?2:1,g=` + fn setOutputAtIndex(flatIndex : u32, value : ${s?`vec4<${i}>`:i}) { + result[flatIndex] = ${s?`vec4<${i}>`:i}(value); + }`;n&&(g+=` + fn getBiasByOutputCoords(coords : vec4) -> ${s?`vec4<${i}>`:i} { + return bias[coords.${l?"w":"y"}${s?"/ 4":""}]; + }`);let p=s?4:1,w=Q("W",e[1].dataType,e[1].dims.length,p),v=Q("Dy",e[0].dataType,e[0].dims.length,p),x=[v,w];n&&x.push(Q("bias",e[2].dataType,[r[h]].length,p));let $=_e("result",e[0].dataType,r.length,p),E=`{ + let batch: u32 = ${a?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1]; + let r = ${a?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1]; + let c = ${a?"global_id.y":"workgroup_id.y"} * ${f}; + let d1: u32 = ${a?"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, ${f}>; + for (var i = 0; i < ${f}; i++) { + dotProd[i] = vec4<${i}>(0.0); + } + for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) { + var dyR = (${i}(dyCorner.x) + ${i}(wR)) / ${i}(uniforms.strides.x); + let wRPerm = uniforms.filter_dims[0] - 1 - wR; + if (dyR < 0.0 || dyR >= ${i}(uniforms.Dy_shape[1]) || + fract(dyR) > 0.0 || wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) { + let dyC = (${i}(dyCorner.y) + ${i}(wC)) / ${i}(uniforms.strides.y); + let dyC2 = (${i}(dyCorner.y) + 1.0 + ${i}(wC)) / ${i}(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 >= ${i}(uniforms.Dy_shape[2]) || + fract(dyC) > 0.0) { + bDyCVal = false; + } + if (dyC2 < 0.0 || dyC2 >= ${i}(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 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${v.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${i}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + + xValue = ${v.get("batch","idyR","idyC2","d2")}; + + dotProd[1] = dotProd[1] + vec4<${i}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + } + } else if (bDyCVal) { + let d2Length = uniforms.Dy_shape[${h}]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${v.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${i}>(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 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${w.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${v.get("batch","idyR","idyC2","d2")}; + let tmpval = vec4<${i}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[1] = dotProd[1] + tmpval; + } + } + } + } + + for (var i: u32 = 0; i < ${f}; i = i + 1) { + let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${i}>(0.0)`}; + ${$.set("batch","r","c + i","d1","value")}; + } + }`,T=` + let outputIndices = ${$.offsetToIndices("global_idx")}; + let batch = ${$.indicesGet("outputIndices",0)}; + let d1 = ${$.indicesGet("outputIndices",h)}; + let r = ${$.indicesGet("outputIndices",u)}; + let c = ${$.indicesGet("outputIndices",d)}; + 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 = ${i}(0.0); + for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${i}(dyRCorner) + ${i}(wR)) / ${i}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${i}(uniforms.Dy_shape[${u}]) || 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 = (${i}(dyCCorner) + ${i}(wC)) / ${i}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${i}(uniforms.Dy_shape[${d}]) || + 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 = ${l?v.get("batch","idyR","idyC","inputChannel"):v.get("batch","inputChannel","idyR","idyC")}; + let wValue = ${w.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")}; + dotProd = dotProd + xValue * wValue; + inputChannel = inputChannel + 1; + } + } + } + let value = dotProd + ${n?"bias[d1]":`${i}(0.0)`}; + ${$.setByOffset("global_idx","value")}; + `;return` + ${t.registerUniforms(o).declareVariables(...x,$)} + ${g} + + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${s?E:T}}`},vo=(t,e,r)=>{let n=t.length>2,a=e.outputShape,s=X.size(a),i=[Math.ceil(s/64),1,1];nt("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${i}`);let o=e.format==="NHWC",l=["rank","rank"],u=[e.strides[0],e.strides[1]],d=[e.kernelShape[o?1:2],e.kernelShape[o?2:3]],h=[e.dilations[0],e.dilations[1]],f=[d[0]+(e.dilations[0]<=1?0:(e.kernelShape[o?1:2]-1)*(e.dilations[0]-1)),d[1]+(e.dilations[1]<=1?0:(e.kernelShape[o?2:3]-1)*(e.dilations[1]-1))],g=[f[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),f[1]-1-Math.floor(e.pads[1]+e.pads[3])/2],p=!1,w=e.group,v=t[1].dims,x=v[0]/w,$=v[1],E=[{type:12,data:s},{type:12,data:u},{type:12,data:d},{type:12,data:h},{type:12,data:f},{type:6,data:g},{type:12,data:x},{type:12,data:$},...we(t[0].dims,t[1].dims)];n&&(E.push(...we(t[2].dims)),l.push("rank")),E.push(...we(a));let T=i[1]===1&&i[2]===1,A=z=>{let R=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:u.length},{name:"filter_dims",type:"u32",length:d.length},{name:"dilations",type:"u32",length:d.length},{name:"effective_filter_dims",type:"u32",length:f.length},{name:"pads",type:"i32",length:g.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],G=_t(t[0].dataType);return`${gp(z,t,a,n,T,p,G,R,o)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${e.cacheKey};`,inputDependencies:l},getRunData:()=>({dispatchGroup:{x:i[0],y:i[1],z:i[2]},outputs:[{dims:r?r(a):a,dataType:t[0].dataType}],programUniforms:E}),getShaderSource:A}}}),_p,yp,wp,$o,bp,vp,$p,xp,Sp,kp,qy=ee(()=>{Hy(),jy(),en(),ia(),_p=(t,e,r,n,a,s)=>(t-1)*e+r+(n-1)*a+1-s,yp=(t,e,r,n,a)=>{let s=Math.floor(t/2);e==="SAME_UPPER"?(r[n]=s,r[a]=t-s):e==="SAME_LOWER"&&(r[n]=t-s,r[a]=s)},wp=(t,e,r,n,a,s,i,o,l,u)=>{let d=t.length-2,h=u.length===0;if(l.length===0)for(let p=0;p{let r=t.kernelShape.slice();if(t.kernelShape.length===0||t.kernelShape.reduce((h,f)=>h*f,1)===0){r.length=0;for(let h=2;hh+f,0)===0){let h=e[0].dims.length-2;l=new Array(h).fill(1)}let u=t.strides.slice();if(u.reduce((h,f)=>h+f,0)===0){let h=e[0].dims.length-2;u=new Array(h).fill(1)}wp(o,r,l,t.autoPad,t.group,a,u,n,i,s);let d=Object.assign({},t);return Object.assign(d,{kernelShape:r,pads:a,outputPadding:i,outputShape:s,dilations:l,strides:u}),d},bp=t=>{let e=uo(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof t.autoPad>"u"?0:t.autoPad],a=t.dilations,s=t.group,i=t.kernelShape,o=t.pads,l=t.strides,u=t.wIsConst(),d=t.outputPadding,h=t.outputShape;return{autoPad:n,format:r,dilations:a,group:s,kernelShape:i,outputPadding:d,outputShape:h,pads:o,strides:l,wIsConst:u,...e,cacheKey:`${t.format};${e.activation};`}},vp=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently 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a=e.kernelShape;(a.length===0||a[0]===0)&&(a=[t.inputs[1].dims[2]]);let s=e.dilations;(s.length===0||s[0]===0)&&(s=[1]);let i=e.strides;(i.length===0||i[0]===0)&&(i=[1]);let o=e.pads;o.length===0&&(o=[0,0]),o=[0,o[0],0,o[1]],i=[1].concat(i),s=[1].concat(s),a=[1].concat(a);let l=$o({...e,pads:o,strides:i,dilations:s,kernelShape:a},n);t.compute(vo(n,l,u=>r?[u[0],u[2],u[3]]:[u[0],u[1],u[3]]))},kp=(t,e)=>{vp(t.inputs,e),t.inputs[0].dims.length===3?Sp(t,e):xp(t,t.inputs,e)}}),Ep,Cp,Tp,Ky=ee(()=>{xe(),Oe(),pt(),Ie(),Ep=(t,e,r,n)=>{let a=X.size(e),s=e.length,i=Q("input",t,s),o=_e("output",t,s),l=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),u=X.normalizeAxis(l,s),d=h=>{let f=` i32(${i.indicesGet("inputIndices","uniforms.axis")}) `,g=ke("uniforms.input_shape","uniforms.axis",s),p=n.reverse?f+(n.exclusive?" + 1":""):"0",w=n.reverse?g:f+(n.exclusive?"":" + 1");return` + ${h.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(i,o)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${o.offsetToIndices("global_idx")}; + var sum = ${o.type.value}(0); + let first : i32 = ${p}; + let last : i32 = ${w}; + for (var i : i32 = first; i < last; i++) { + ${i.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${i.getByIndices("inputIndices")}; + } + ${o.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:e,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},{type:12,data:u},...we(e,e)]}),getShaderSource:d}},Cp=(t,e)=>{let r=t.inputs[0].dims,n=t.inputs[0].dataType,a=t.inputs[1];t.compute(Ep(n,r,a,e),{inputs:[0]})},Tp=t=>{let e=t.exclusive===1,r=t.reverse===1;return Ke({exclusive:e,reverse:r})}}),Ap,Ip,Mp,Op,zp,Yy=ee(()=>{xe(),Oe(),pt(),Ie(),Ap=t=>{if(!t||t.length!==1)throw new Error("DepthToSpace requires 1 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}`;return{name:"DepthToSpace",shaderCache:{hint:`${t.dims};${e.blocksize};${e.mode}`,inputDependencies:["rank"]},getRunData:x=>{let $=l?[r,n*u,a*u,s/u**2]:[r,s/u**2,n*u,a*u],E=X.size($),T=h.dims,A=X.sortBasedOnPerm(T,o);return{outputs:[{dims:$,dataType:x[0].dataType}],dispatchGroup:{x:Math.ceil(E/64)},programUniforms:[{type:12,data:E},...we(T,A)]}},getShaderSource:v}},Op=(t,e)=>{Ap(t.inputs),t.compute(Mp(t.inputs[0],e))},zp=t=>Ke({blocksize:t.blocksize,mode:t.mode,format:t.format})}),Ei,sa,xo,Pp,Rp,Bp,Dp,So,Np,Fp,Lp,Xy=ee(()=>{xe(),Oe(),pt(),Ie(),Ei="[a-zA-Z]|\\.\\.\\.",sa="("+Ei+")+",xo="^"+sa+"$",Pp="("+sa+",)*"+sa,Rp="^"+Pp+"$",Bp=class{constructor(t=-1){this.symbolToIndices=new Map,this.inputIndex=t}addSymbol(t,e){let r=this.symbolToIndices.get(t);r===void 0?r=[e]:r.push(e),this.symbolToIndices.set(t,r)}},Dp=class{constructor(t,e){var a;this.equation=e,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new 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n={count:1,dimValue:e,inputIndices:[r]};this.symbolToInfo.set(t,n)}processTerm(t,e,r,n=-1){let a=r.length,s=!1,i=[],o=0;if(!t.match(RegExp(xo))&&!e&&t!=="")throw new Error("Invalid LHS term");let l=t.match(RegExp(Ei,"g")),u=new Bp(n);return l==null||l.forEach((d,h)=>{if(d==="..."){if(s)throw new Error("Only one ellipsis is allowed per input term");s=!0;let f=a-l.length+1;if(f<0)throw new Error("Ellipsis out of bounds");if(i=r.slice(o,o+f),this.hasEllipsis){if(this.ellipsisDims.length!==i.length||this.ellipsisDims.toString()!==i.toString())throw new Error("Ellipsis dimensions mismatch")}else if(e)this.hasEllipsis=!0,this.ellipsisDims=i;else throw new Error("Ellipsis must be specified in the LHS");for(let g=0;gt+"_max",Np=(t,e,r,n)=>{let a=t.map(u=>u.length).map((u,d)=>Q(`input${d}`,e,u)),s=X.size(n),i=_e("output",e,n.length),o=[...r.symbolToInfo.keys()].filter(u=>!r.rhs.symbolToIndices.has(u)),l=u=>{let d=[],h="var prod = 1.0;",f="var sum = 0.0;",g="sum += 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${u.registerUniforms(o.map(T=>({name:`${So(T)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...a,i)} + + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${i.offsetToIndices("global_idx")}; + ${a.map((T,A)=>`var input${A}Indices: ${a[A].type.indices};`).join(` +`)} + ${E.join(` +`)}; + ${i.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:t.map(()=>"rank")},getRunData:()=>{let u=o.filter(h=>r.symbolToInfo.has(h)).map(h=>{var f;return{type:12,data:((f=r.symbolToInfo.get(h))==null?void 0:f.dimValue)||0}});u.push({type:12,data:s});let d=t.map((h,f)=>[...we(h)]).reduce((h,f)=>h.concat(f),u);return d.push(...we(n)),{outputs:[{dims:n,dataType:e}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:d}},getShaderSource:l}},Fp=(t,e)=>{let r=new Dp(t.inputs,e.equation),n=r.outputDims,a=t.inputs.map((s,i)=>s.dims);t.compute(Np(a,t.inputs[0].dataType,r,n))},Lp=t=>{let e=t.equation.replace(/\s+/g,"");return Ke({equation:e})}}),Up,ko,Wp,Vp,Gp,Qy=ee(()=>{xe(),Oe(),Ie(),Up=t=>{if(!t||t.length!==2)throw new Error("Expand requires 2 input.");let e=t[0].dims,r=Array.from(t[1].getBigInt64Array(),Number),n=r.length{let r=t.length-e.length,n=[];for(let a=0;at.length>e.length?ko(t,e):ko(e,t),Vp=t=>{let e=t[0].dims,r=Array.from(t[1].getBigInt64Array(),Number),n=Wp(e,r),a=t[0].dataType,s=a===9?4:1,i=Math.ceil(X.size(n)/s),o=u=>{let d=Q("input",a,e.length,s),h=_e("output",a,n.length,s),f;if(a===9){let g=(p,w,v="")=>` + let outputIndices${w} = ${h.offsetToIndices(`outputOffset + ${w}u`)}; + let offset${w} = ${d.broadcastedIndicesToOffset(`outputIndices${w}`,h)}; + let index${w} = offset${w} / 4u; + let component${w} = offset${w} % 4u; + ${p}[${w}] = ${v}(${d.getByOffset(`index${w}`)}[component${w}]); + `;f=` + let outputOffset = global_idx * ${s}; + var data = vec4(0); + ${g("data",0,"u32")} + ${g("data",1,"u32")} + ${g("data",2,"u32")} + ${g("data",3,"u32")} + ${h.setByOffset("global_idx","data")} + }`}else f=` + let outputIndices = ${h.offsetToIndices("global_idx")}; + let inputOffset = ${d.broadcastedIndicesToOffset("outputIndices",h)}; + ${h.setByOffset("global_idx",d.getByOffset("inputOffset"))} + }`;return` + ${u.registerUniform("vec_size","u32").declareVariables(d,h)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${f}`},l=[{type:12,data:i},...we(e,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:o,getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:l})}},Gp=t=>{Up(t.inputs),t.compute(Vp(t.inputs),{inputs:[0]})}}),Hp,jp,Zy=ee(()=>{xe(),Oe(),Ie(),lo(),Hp=t=>{let e=t[0].dataType,r=X.size(t[0].dims),n=X.size(t[1].dims),a=n%4===0,s=i=>{let o=Q("x",e,[1],4),l=Q("bias",e,[1],4),u=_e("y",e,[1],4),d=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],h=g=>` + let bias${g}_offset: u32 = (global_idx * 4 + ${g}) % uniforms.bias_size; + let bias${g} = ${l.getByOffset(`bias${g}_offset / 4`)}[bias${g}_offset % 4];`,f=a?` + let bias = ${l.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${h(0)}${h(1)}${h(2)}${h(3)} + let bias = ${o.type.value}(bias0, bias1, bias2, bias3);`;return`${i.registerUniforms(d).declareVariables(o,l,u)} + + ${so(Ot(e))} + + ${i.mainStart(Sn)} + ${i.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${o.getByOffset("global_idx")}; + ${f} + let x_in = x + bias; + ${u.setByOffset("global_idx",oo("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${a}`,inputDependencies:["type","type"]},getShaderSource:s,getRunData:i=>({outputs:[{dims:i[0].dims,dataType:i[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/Sn/4)}})}},jp=t=>{t.inputs.length<2||X.size(t.inputs[1].dims)===0?Rc(t):t.compute(Hp(t.inputs))}}),qp,Kp,Yp,Xp,Jy=ee(()=>{xe(),Oe(),pt(),Ie(),qp=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},Kp=(t,e)=>{let r=t[0].dims,n=t[1].dims,a=r.length,s=X.normalizeAxis(e.axis,a),i=r.slice(0);i.splice(s,1,...n);let o=r[s],l=t[0].dataType===9?4:1,u=Math.ceil(X.size(i)/l),d=[{type:12,data:u},{type:6,data:o},{type:12,data:s},...we(t[0].dims,t[1].dims,i)],h=f=>{let g=Q("data",t[0].dataType,t[0].dims.length,l),p=Q("inputIndices",t[1].dataType,t[1].dims.length),w=_e("output",t[0].dataType,i.length,l),v=$=>{let E=n.length,T=`var indicesIndices${$} = ${p.type.indices}(0);`;for(let A=0;A1?`indicesIndices${$}[${A}]`:`indicesIndices${$}`} = ${i.length>1?`outputIndices${$}[uniforms.axis + ${A}]`:`outputIndices${$}`};`;T+=` + var idx${$} = ${p.getByIndices(`indicesIndices${$}`)}; + if (idx${$} < 0) { + idx${$} = idx${$} + uniforms.axisDimLimit; + } + var dataIndices${$} : ${g.type.indices}; + `;for(let A=0,z=0;A1?`dataIndices${$}[${A}]`:`dataIndices${$}`} = u32(idx${$});`,z+=E):(T+=`${a>1?`dataIndices${$}[${A}]`:`dataIndices${$}`} = ${i.length>1?`outputIndices${$}[${z}]`:`outputIndices${$}`};`,z++);return T},x;if(t[0].dataType===9){let $=(E,T,A="")=>` + let outputIndices${T} = ${w.offsetToIndices(`outputOffset + ${T}u`)}; + ${v(T)}; + let offset${T} = ${g.indicesToOffset(`dataIndices${T}`)}; + let index${T} = offset${T} / 4u; + let component${T} = offset${T} % 4u; + ${E}[${T}] = ${A}(${g.getByOffset(`index${T}`)}[component${T}]); + `;x=` + let outputOffset = global_idx * ${l}; + var value = vec4(0); + ${$("value",0,"u32")} + ${$("value",1,"u32")} + ${$("value",2,"u32")} + ${$("value",3,"u32")} + ${w.setByOffset("global_idx","value")} + `}else x=` + let outputIndices = ${w.offsetToIndices("global_idx")}; + ${v("")}; + let value = ${g.getByIndices("dataIndices")}; + ${w.setByOffset("global_idx","value")}; + `;return` + ${f.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(g,p,w)} + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${x} + }`};return{name:"Gather",shaderCache:{hint:e.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:d}),getShaderSource:h}},Yp=t=>Ke({axis:t.axis}),Xp=(t,e)=>{let r=t.inputs;qp(r),t.compute(Kp(t.inputs,e))}}),Qp,Zp,Jp,eh,ew=ee(()=>{xe(),Oe(),pt(),Ie(),Qp=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.`)},Zp=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,s=t[1].dims,i=t[1].dataType,o=X.normalizeAxis(e.axis,a),l=r[o],u=s.slice(0),d=X.size(u),h=Q("input",n,a),f=Q("indicesInput",i,s.length),g=_e("output",n,u.length),p=[{type:12,data:d},{type:6,data:l},{type:12,data:o}];return p.push(...we(r,s,u)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:u,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:w=>` + ${w.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(h,f,g)} + ${w.mainStart()} + ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${g.offsetToIndices("global_idx")}; + + var idx = ${f.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${h.type.indices}(outputIndices); + ${h.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${h.getByIndices("inputIndices")}; + + ${g.setByOffset("global_idx","value")}; + }`}},Jp=t=>Ke({axis:t.axis}),eh=(t,e)=>{let r=t.inputs;Qp(r),t.compute(Zp(t.inputs,e))}}),th,rh,nh,ah,tw=ee(()=>{xe(),Oe(),Ie(),th=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},rh=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,s,i]=Fu.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),o=[a,s];if(!o)throw new Error("Can't use gemm on the given tensors");let l=X.size(o),u=[{type:12,data:l},{type:12,data:a},{type:12,data:s},{type:12,data:i},{type:1,data:e.alpha},{type:1,data:e.beta}],d=["type","type"];t.length===3&&(u.push(...we(t[2].dims)),d.push("rank")),u.push(...we(o));let h=f=>{let g="";e.transA&&e.transB?g="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?g="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?g="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(g="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let p=e.alpha===1?"":"value *= uniforms.alpha;",w=Q("a",t[0].dataType,t[0].dims),v=Q("b",t[1].dataType,t[1].dims),x=w.type.value,$=null,E=[w,v];t.length===3&&($=Q("c",t[2].dataType,t[2].dims.length),E.push($));let T=_e("output",t[0].dataType,o.length);E.push(T);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` + ${f.registerUniforms(A).declareVariables(...E)} + + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${x}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${g} + } + + ${p} + ${$!=null?`let cOffset = ${$.broadcastedIndicesToOffset("vec2(m, n)",T)}; value += ${x}(uniforms.beta) * ${$.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:h}},nh=t=>{let e=t.transA,r=t.transB,n=t.alpha,a=t.beta;return{transA:e,transB:r,alpha:n,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},ah=(t,e)=>{th(t.inputs),t.compute(rh(t.inputs,e))}}),ih,sh,oh,lh,rw=ee(()=>{xe(),Oe(),Ie(),ih=(t,e)=>{let r=t[0].dims,n=r,a=2,s=X.sizeToDimension(r,a),i=X.sizeFromDimension(r,a),o=st(i),l=i/o,u=[r[0],r[1],l],d=["rank","type","type"],h=[{type:12,data:i},{type:12,data:l}];h.push(...we(u,u));let f=g=>{let p=Q("x",t[0].dataType,u.length,o),w=Q("scale",t[1].dataType,t[1].dims),v=Q("bias",t[2].dataType,t[2].dims),x=_e("output",t[0].dataType,u.length,o),$=[p,w,v,x],E=p.type.value,T=o===1?"f32":`vec${o}`,A=64,z=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return` + var meanShared : f32; + var squaredNormShared : f32; + var workgroupShared : array<${T}, ${A}>; + const workgroupSize = ${A}u; + ${g.registerUniforms(z).declareVariables(...$)} + ${g.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 = ${T}(0); + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + initial = initial + ${T}(${p.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 = ${Nr("workgroupShared[0]",o)} / f32(uniforms.normSize); + } + workgroupBarrier(); + + // reinitialize workgroup memory. + initial = ${T}(0); + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + let deviation = ${T}(${p.get("batch","channel","h")}) - ${T}(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 = ${Nr("workgroupShared[0]",o)}; + } + workgroupBarrier(); + + let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${e.epsilon})); + let channelScale = invStdDev * f32(${w.getByOffset("channel")}); + let channelShift = f32(${v.getByOffset("channel")}) - meanShared * channelScale; + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + let value = ${p.get("batch","channel","h")} * ${E}(${T}(channelScale)) + ${E}(${T}(channelShift)); + ${x.set("batch","channel","h","value")}; + } + }`};return{name:"InstanceNormalization",shaderCache:{hint:`${e.epsilon};${o}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:s},programUniforms:h}),getShaderSource:f}},sh=(t,e,r,n,a,s,i,o)=>{let l=st(i),u=64,d=l===1?"vec2f":`mat2x${l}f`,h=l===1?"f32":`vec${l}f`,f=(z,R)=>`${d}(${z}, ${R})`,g=a*i/l,p=Math.ceil(s/u),w=["type"],v=[{type:12,data:p},{type:12,data:s},{type:12,data:Math.floor(i/l)},{type:12,data:Math.floor(s*i/l)}],x=z=>{let R=Q("input",e.dataType,e.dims,l);return` + ${z.declareVariables(R)} + @group(0) @binding(1) var output : array<${d}>; + struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32}; + @group(0) @binding(2) var uniforms: Uniforms; + + ${z.mainStart(u)} + let currentImageNumber = global_idx / ${u} / uniforms.C; + let currentChannelNumber = (global_idx / ${u}) % 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 = ${Sr("f32",l)}; + var squaredSum = ${Sr("f32",l)}; + for (var i: u32 = wgOffset; i < wgMax; i++) { + let value = ${h}(input[offset + i * uniforms.C]); + sum += value; + squaredSum += value * value; + } + output[global_idx] = ${f("sum","squaredSum")}; + }`},$=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${l}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:[a,i,u,2],dataType:1}],dispatchGroup:{x:a*i/l},programUniforms:v}),getShaderSource:x},{inputs:[e],outputs:[-1]})[0],E=[{type:12,data:g},{type:12,data:s},{type:12,data:Math.floor(i/l)},{type:12,data:Math.floor(u*i/l)}],T=["type","type","type"],A=z=>{let R=Q("scale",r.dataType,r.dims,l),G=Q("bias",n.dataType,n.dims,l);return` + @group(0) @binding(0) var input : array<${d}>; + @group(0) @binding(1) var scale : array<${R.type.storage}>; + @group(0) @binding(2) var bias : array<${G.type.storage}>; + @group(0) @binding(3) var output : array<${d}>; + struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32}; + @group(0) @binding(4) var uniforms: Uniforms; + + ${z.mainStart()} + ${z.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 = ${Sr("f32",l)}; + var squaredSum = ${Sr("f32",l)}; + for (var i: u32 = 0; i < min(${u}, uniforms.H); i++) { + let value = input[offset + i + currentChannelNumber * ${u}]; + sum += value[0]; + squaredSum += value[1]; + } + sum = sum / f32(uniforms.H); + squaredSum = squaredSum / f32(uniforms.H); + let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${o})); + let channelScale = invStdDev * ${h}(scale[currentChannelNumber]); + let channelShift = ${h}(bias[currentChannelNumber]) - sum * channelScale; + + output[global_idx] = ${f("channelScale","channelShift")}; + }`};return t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${o}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:[a,i,2],dataType:1}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:E}),getShaderSource:A},{inputs:[$,r,n],outputs:[-1]})[0]},oh=(t,e,r)=>{let n=e[0].dims,a=n,s=n[0],i=n[n.length-1],o=X.sizeFromDimension(n,1)/i,l=st(i),u=X.size(a)/l,d=[{type:12,data:o},{type:12,data:Math.floor(i/l)}],h=["type","type"],f=sh(t,e[0],e[1],e[2],s,o,i,r.epsilon),g=p=>{let w=_t(e[0].dataType),v=l===1?"vec2f":`mat2x${l}f`,x=l===1?w:`vec${l}<${w}>`,$=Q("input",e[0].dataType,e[0].dims,l),E=_e("output",e[0].dataType,a,l);return` + @group(0) @binding(0) var input : array<${$.type.storage}>; + @group(0) @binding(1) var scaleInput : array<${v}>; + @group(0) @binding(2) var output : array<${E.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${p.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], ${x}(scale[0]), ${x}(scale[1])); + }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:d}),getShaderSource:g},{inputs:[e[0],f]})},lh=(t,e)=>{e.format==="NHWC"?oh(t,t.inputs,e):t.compute(ih(t.inputs,e))}}),uh,dh,ch,nw=ee(()=>{xe(),Oe(),Ie(),uh=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},dh=(t,e,r)=>{let n=e.simplified,a=t[0].dims,s=t[1],i=!n&&t[2],o=a,l=X.normalizeAxis(e.axis,a.length),u=X.sizeToDimension(a,l),d=X.sizeFromDimension(a,l),h=X.size(s.dims),f=i?X.size(i.dims):0;if(h!==d||i&&f!==d)throw new Error(`Size of X.shape()[axis:] == ${d}. + Size of scale and bias (if provided) must match this. + Got scale size of ${h} and bias size of ${f}`);let g=[];for(let A=0;A1,$=r>2,E=A=>{let z=_t(t[0].dataType),R=[Q("x",t[0].dataType,t[0].dims,p),Q("scale",s.dataType,s.dims,p)];i&&R.push(Q("bias",i.dataType,i.dims,p)),R.push(_e("output",t[0].dataType,o,p)),x&&R.push(_e("mean_data_output",1,g)),$&&R.push(_e("inv_std_output",1,g));let G=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${A.registerUniforms(G).declareVariables(...R)} + ${A.mainStart()} + ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${Sr("f32",p)}; + var mean_square_vector = ${Sr("f32",p)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${kn(z,p,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${Nr("mean_vector",p)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${Nr("mean_square_vector",p)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${kn(z,p,"x[j + offset]")}; + let f32scale = ${kn(z,p,"scale[j]")}; + output[j + offset] = ${R[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale + ${i?`+ ${kn(z,p,"bias[j]")}`:""} + ); + } + + ${x?"mean_data_output[global_idx] = mean":""}; + ${$?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},T=[{dims:o,dataType:t[0].dataType}];return x&&T.push({dims:g,dataType:1}),$&&T.push({dims:g,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${p};${r};${n}`,inputDependencies:w},getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:v}),getShaderSource:E}},ch=(t,e)=>{uh(t.inputs),t.compute(dh(t.inputs,e,t.outputCount))}}),ph,hh,fh,mh,aw=ee(()=>{xe(),Oe(),pt(),Ie(),ph=(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 a=Math.floor((e.k+e.blockSize-1)/e.blockSize),s=e.blockSize/8*e.bits,i=t[1];if(!X.areEqual(i.dims,[e.n,a,s]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=t[2].dims;if(X.size(o)!==e.n*a)throw new Error("scales input size error.");if(t.length===4){let l=t[3].dims,u=e.bits>4?e.n*a:e.n*Math.floor((a+1)/2);if(X.size(l)!==u)throw new Error("zeroPoints input size error.")}},hh=(t,e,r,n)=>{let a=t[0].dims,s=a.length,i=Math.floor((e.k+e.blockSize-1)/e.blockSize),o=a[s-2],l=e.k,u=e.n,d=a.slice(0,s-2),h=X.size(d),f=e.blockSize/8*e.bits/4,g=t[0].dataType,p=st(o),w=st(e.k),v=st(f),x=na(g),$=o*i*x,E=Math.floor(n/$),T=i<=r[0]&&E>0,A=!T||E>=4?st(u):E>=2&&st(u)>=2?2:1,z=d.concat([o,u]),R=X.size(z)/A/p,G=T?[]:[{type:12,data:R},{type:12,data:e.blockSize}],j=[h,o,l/w],q=X.convertShape(t[1].dims).slice();q.splice(-1,1,f/v),G.push(...we(j)),G.push(...we(q)),G.push(...we(t[2].dims)),t.length===4&&G.push(...we(X.convertShape(t[3].dims)));let fe=[h,o,u/A];G.push(...we(fe));let ne=te=>{let se=j.length,N=Q("a",t[0].dataType,se,w),M=Q("b",12,q.length,v),V=Q("scales",t[2].dataType,t[2].dims.length),K=[N,M,V],pe=t.length===4?Q("zero_points",12,t[3].dims.length):void 0;pe&&K.push(pe);let le=fe.length,D=_e("output",t[0].dataType,le,A),ae=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],de=_t(t[0].dataType),Se=(()=>{switch(w){case 1:return`array<${de}, 8>`;case 2:return`mat4x2<${de}>`;case 4:return`mat2x4<${de}>`;default:throw new Error(`${w}-component is not supported.`)}})(),Te=` + for (var word: u32 = 0; word < ${f}; word += ${v}) { + ${M.indicesSet("b_indices","2","word")}; + let b_data = ${M.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 = ${Se}(${Array.from({length:4},(ot,je)=>`${de}(b_value_lower[${je}]), ${de}(b_value_upper[${je}])`).join(", ")}); + let b_dequantized_values = ${w===1?`${Se}(${Array.from({length:8},(ot,je)=>`(b_quantized_values[${je}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Se}(${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 < ${T?o:p}u; m++) { + ${N.indicesSet("a_indices",se-2,T?"m":`row * ${p} + m`)}; + ${N.indicesSet("a_indices",se-1,"word_offset")}; + var input_offset = ${N.indicesToOffset("a_indices")}; + var a_data: ${Se}; + for (var j: u32 = 0; j < ${8/w}; j++) { + a_data[j] = ${N.getByOffset("input_offset")}; + input_offset++; + } + ${T?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${A>1?"[c]":""} += ${Array.from({length:8/w},(ot,je)=>`${w===1?`a_data[${je}] * b_dequantized_values[${je}]`:`dot(a_data[${je}], b_dequantized_values[${je}])`}`).join(" + ")}; + } + word_offset += ${8/w}; + } + }`,Xe=pe?` + zero_point_offset += 4; + if (zero_point_offset == 32) { + zero_point_offset = 0; + zero_point_index++; + zero_point_word = ${pe.getByOffset("zero_point_index")}; + }`:"";return T?` + var workgroup_shared: array<${D.type.value}, ${o*i}>; + ${te.declareVariables(...K,D)} + ${te.mainStart([i,1,1])} + var a_indices: ${N.type.indices}; + var block = local_id.x; + var col = workgroup_id.y; + var batch = workgroup_id.z; + ${N.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; + ${pe?` + var zero_point_bytes_per_col: u32 = (${i} + 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 = ${pe.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""} + var b_indices: ${M.type.indices}; + ${M.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 * ${i} + block; + let scale = ${V.getByOffset("scales_index")}; + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${de}(${pe?"(zero_point_word) & 0xFu":8}); + ${M.indicesSet("b_indices","1","block")}; + var word_offset: u32 = block * ${e.blockSize/w}; + var workgroup_shared_offset: u32 = block * ${o}; + ${Te} + } + workgroupBarrier(); + if (local_id.x == 0u) { + var output_indices: ${D.type.indices}; + ${D.indicesSet("output_indices","0","batch")}; + ${D.indicesSet("output_indices",le-1,"col")}; + ${D.indicesSet("output_indices",le-2,"0")}; + var output_offset = ${D.indicesToOffset("output_indices")}; + for (var m: u32 = 0u; m < ${o}u; m++) { + var output_value: ${D.type.value} = ${D.type.value}(0); + var workgroup_shared_offset: u32 = m; + for (var b: u32 = 0u; b < ${i}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${o}; + } + ${D.setByOffset("output_offset","output_value")}; + output_offset += ${u/A}; + } + } + }`:` + ${te.registerUniforms(ae).declareVariables(...K,D)} + ${te.mainStart()} + ${te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var output_values: array<${D.type.value}, ${p}>; + var output_indices = ${D.offsetToIndices("global_idx")}; + var col = ${D.indicesGet("output_indices",le-1)}; + var row = ${D.indicesGet("output_indices",le-2)}; + var a_indices: ${N.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 + ${pe?` + var zero_point_abs_offset = col * ${A} * ((${i} + 1) / 2); + var zero_point_index: u32 = zero_point_abs_offset / 4; + var zero_point_word: u32 = ${pe.getByOffset("zero_point_index")}; + var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""} + var scale_index = col * ${i*A}; + var b_indices: ${M.type.indices}; + for (var c: u32 = 0; c < ${A}; c++) { + ${M.indicesSet("b_indices","0",`col * ${A} + c`)}; + var block_offset: u32 = 0; + for (var block: u32 = 0; block < ${i}; block++) { + // The scale and zero points are computed per block. + let scale = ${V.getByOffset("scale_index")}; + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${de}(${pe?"extractBits(zero_point_word, zero_point_offset, 4)":8}); + ${M.indicesSet("b_indices","1","block")}; + var word_offset: u32 = block_offset; + ${Te} + scale_index++; + ${Xe} + block_offset += uniforms.block_size / ${w}; + } + // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte. + ${pe?`if (zero_point_offset % 8 > 0) { + ${Xe} + }`:""} + } + for (var k: u32 = 0u; k < ${p}u; k++) { + ${D.indicesSet("output_indices",le-2,`${p} * row + k`)}; + ${D.setByIndices("output_indices","output_values[k]")} + } + }`};return{name:T?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${o};${g};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:z,dataType:g}],name:T?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:T?{x:1,y:Math.ceil(u/A),z:h}:{x:Math.ceil(R/64)},programUniforms:G}),getShaderSource:ne}},fh=(t,e)=>{ph(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(hh(t.inputs,e,r,n))},mh=t=>Ke(t)}),Ct,gh,_h,Eo,yh,Ci,wh,iw=ee(()=>{xe(),Oe(),pt(),qs(),Kd(),Ie(),ia(),Ct=(t,e)=>t.length>e&&t[e].dims.length>0&&X.size(t[e].dims)>0?t[e]:void 0,gh=(t,e)=>{let r=t[0],n=Ct(t,1),a=Ct(t,2),s=Ct(t,3),i=Ct(t,4),o=Ct(t,5),l=Ct(t,6),u=Ct(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 d=!1,h=r.dims[0],f=r.dims[1],g=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],p=f,w=0,v=0,x=Math.floor(g/e.numHeads);if(l&&u){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(l.dims[0]!==h||l.dims[1]!==e.numHeads||l.dims[3]!==x)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[0]!==h||u.dims[1]!==e.numHeads||u.dims[3]!==x)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[2]!==u.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(u.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');w=l.dims[2],v=l.dims[2]}else if(l||u)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let $;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');$=2,p=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==e.numHeads||n.dims[3]!==2||n.dims[4]!==x)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(a)throw new Error('Expect "value" be none when "key" has packed kv format.');$=5,p=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==x)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');$=0,p=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');$=3}if(s){if(s.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(a&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(i){E=8;let G=i.dims;throw G.length===1?G[0]===h?E=1:G[0]===3*h+2&&(E=3):G.length===2&&G[0]===h&&G[1]===p&&(E=5),E===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let T=!1,A=g;if(a){if(a.dims.length!==3&&a.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==a.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(a.dims.length===3){if(p!==a.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');A=a.dims[2]}else{if(p!==a.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');A=a.dims[1]*a.dims[3],T=!0}}let z=w+p,R=!1;if(i)throw new Error("Key padding mask is not supported");if(o){if(o.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(o.dims[0]!==h&&o.dims[0]!==1||o.dims[1]!==e.numHeads||o.dims[2]!==f||o.dims[3]!==z)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:h,sequenceLength:f,pastSequenceLength:w,kvSequenceLength:p,totalSequenceLength:z,maxSequenceLength:v,inputHiddenSize:0,hiddenSize:g,vHiddenSize:A,headSize:x,vHeadSize:Math.floor(A/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:E,scale:e.scale,broadcastResPosBias:R,passPastInKv:T,qkvFormat:$}},_h=t=>Ke({...t}),Eo=Ke({perm:[0,2,1,3]}),yh=(t,e,r,n,a,s,i)=>{let o=[n,a,s],l=X.size(o),u=[{type:12,data:l},{type:12,data:i},{type:12,data:s}],d=h=>{let f=_e("qkv_with_bias",e.dataType,o),g=Q("qkv",e.dataType,o),p=Q("bias",r.dataType,o),w=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${h.registerUniforms(w).declareVariables(g,p,f)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:e.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:d},{inputs:[e,r],outputs:[-1]})[0]},Ci=(t,e,r,n,a,s,i,o)=>{let l=s;if(i){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=yh(t,s,i,e,n,r*a,o),l=l.reshape([e,n,r,a]),t.compute(kr(l,Eo.perm),{inputs:[l],outputs:[-1]})[0]}else return s.dims.length===3&&(l=s.reshape([e,n,r,a])),t.compute(kr(l,Eo.perm),{inputs:[l],outputs:[-1]})[0]},wh=(t,e)=>{let r=gh(t.inputs,e),n=t.inputs[0],a=Ct(t.inputs,1),s=Ct(t.inputs,2),i=Ct(t.inputs,3),o=Ct(t.inputs,4),l=Ct(t.inputs,5),u=Ct(t.inputs,6),d=Ct(t.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((a==null?void 0:a.dims.length)===5)throw new Error("Packed KV is not implemented");let h=a&&s&&a.dims.length===4&&s.dims.length===4,f=Ci(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(h)return yi(t,f,a,s,o,void 0,u,d,l,r,e);if(!a||!s)throw new Error("key and value must be provided");let g=Ci(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,a,i,r.hiddenSize),p=Ci(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,s,i,2*r.hiddenSize);yi(t,f,g,p,o,void 0,u,d,l,r,e)}}),bh,vh,$h,xh,Sh,kh,Eh,Ch,Th,sw=ee(()=>{xe(),Oe(),Ie(),bh=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].")}},vh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` + k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)}; + if (k < 0) { + break; + } + if (k >= i32(${ke("uniforms.x_shape",a,e)})) { + break; + } + offset += k * i32(${ke("uniforms.x_strides",a,e)}); + `;return` + value = ${t.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + } + `},$h=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` + k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${ke("uniforms.x_shape",a,e)}) - 1); + k = k % _2n_1; + if(k >= i32(${ke("uniforms.x_shape",a,e)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${ke("uniforms.x_strides",a,e)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},xh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` + k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${ke("uniforms.x_shape",a,e)})) { + k = i32(${ke("uniforms.x_shape",a,e)}) - 1; + } + offset += k * i32(${ke("uniforms.x_strides",a,e)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},Sh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` + k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)}; + if (k < 0) { + k += i32(${ke("uniforms.x_shape",a,e)}]); + } + if (k >= i32(${ke("uniforms.x_shape",a,e)})) { + k -= i32(${ke("uniforms.x_shape",a,e)}); + } + offset += k * i32(${ke("uniforms.x_strides",a,e)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},kh=(t,e,r)=>{switch(r.mode){case 0:return vh(t,e,r.pads.length);case 1:return $h(t,e,r.pads.length);case 2:return xh(t,e,r.pads.length);case 3:return Sh(t,e,r.pads.length);default:throw new Error("Invalid mode")}},Eh=(t,e)=>{let r=X.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=X.size(r),s=[{type:12,data:a},{type:6,data:e.pads}];e.mode===0&&s.push({type:t[0].dataType,data:e.value}),s.push(...we(t[0].dims,r));let i=["rank"],o=l=>{let u=_e("output",t[0].dataType,r.length),d=Q("x",t[0].dataType,n.length),h=d.type.value,f=kh(u,n.length,e),g=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.mode===0&&g.push({name:"constant_value",type:h}),` + ${l.registerUniforms(g).declareVariables(d,u)} + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${u.offsetToIndices("global_idx")}; + + var value = ${h}(0); + ${f} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(X.size(r)/64)},programUniforms:s}),getShaderSource:o}},Ch=(t,e)=>{if(t.length>1){let r=t[1].getBigInt64Array(),n=t.length>=3&&t[2].data?t[2].getFloat32Array()[0]:0,a=t[0].dims.length,s=new Int32Array(2*a).fill(0);if(t.length>=4){let o=t[3].getBigInt64Array();for(let l=0;ls[Number(l)]=Number(o));let i=[];return s.forEach(o=>i.push(o)),{mode:e.mode,value:n,pads:i}}else return e},Th=(t,e)=>{bh(t.inputs);let r=Ch(t.inputs,e);t.compute(Eh(t.inputs,r),{inputs:[0]})}}),oa,Co,To,Ao,Io,Ah,Ih,Mo,Oo,Mh,Oh,zo,zh,Ph,Po,Rh,Bh,Dh,Nh,ow=ee(()=>{rr(),xe(),Oe(),Ie(),oa=t=>{if(Ue.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},Co=(t,e,r)=>{let n=e.format==="NHWC",a=t.dims.slice();n&&a.splice(1,0,a.pop());let s=Object.hasOwnProperty.call(e,"dilations"),i=e.kernelShape.slice(),o=e.strides.slice(),l=s?e.dilations.slice():[],u=e.pads.slice();hi.adjustPoolAttributes(r,a,i,o,l,u);let d=hi.computePoolOutputShape(r,a,o,l,i,u,e.autoPad),h=Object.assign({},e);s?Object.assign(h,{kernelShape:i,strides:o,pads:u,dilations:l,cacheKey:e.cacheKey}):Object.assign(h,{kernelShape:i,strides:o,pads:u,cacheKey:e.cacheKey});let f=d.slice();return f.push(f.splice(1,1)[0]),[h,n?f:d]},To=(t,e)=>{let r=e.format==="NHWC",n=X.size(t),a=X.size(e.kernelShape),s=[{type:12,data:n},{type:12,data:a}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(e.kernelShape.length<=2){let o=e.kernelShape[e.kernelShape.length-1],l=e.strides[e.strides.length-1],u=e.pads[e.pads.length/2-1],d=e.pads[e.pads.length-1],h=!!(u+d);s.push({type:12,data:o},{type:12,data:l},{type:12,data:u},{type:12,data:d}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let f=!1;if(e.kernelShape.length===2){let g=e.kernelShape[e.kernelShape.length-2],p=e.strides[e.strides.length-2],w=e.pads[e.pads.length/2-2],v=e.pads[e.pads.length-2];f=!!(w+v),s.push({type:12,data:g},{type:12,data:p},{type:12,data:w},{type:12,data:v}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[s,i,!0,h,f]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=X.computeStrides(e.kernelShape);s.push({type:12,data:o},{type:12,data:e.pads},{type:12,data:e.strides}),i.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 l=e.pads.reduce((u,d)=>u+d);return[s,i,!!l,!1,!1]}},Ao=(t,e,r,n,a,s,i,o,l,u,d,h)=>{let f=a.format==="NHWC",g=e.type.value,p=_e("output",e.type.tensor,n);if(a.kernelShape.length<=2){let w="",v="",x="",$=r-(f?2:1);if(d?w=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${$}] < 0 || xIndices[${$}] + >= uniforms.x_shape[${$}]) { + pad++; + continue; + } + let x_val = x[${e.indicesToOffset("xIndices")}]; + ${s} + }`:w=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${e.indicesToOffset("xIndices")}]; + ${s} + }`,a.kernelShape.length===2){let E=r-(f?3:2);h?v=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${E}] = indices[${E}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${E}] < 0 || xIndices[${E}] >= uniforms.x_shape[${E}]) { + pad += i32(uniforms.kw); + continue; + } + `:v=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${E}] = indices[${E}] * uniforms.sh - uniforms.phStart + j; + `,x=` + } + `}return` + ${t.registerUniforms(l).declareVariables(e,p)} + + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${p.offsetToIndices("global_idx")}; + var xIndices = ${p.offsetToIndices("global_idx")}; + + var value = ${g}(${o}); + var pad = 0; + ${v} + ${w} + ${x} + ${i} + + output[global_idx] = value; + }`}else{if(f)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let w=a.kernelShape.length,v=a.pads.length,x="";return u?x=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${e.indicesToOffset("xIndices")}]; + ${s} + }`:x=` + } + let x_val = x[${e.indicesToOffset("xIndices")}]; + ${s} + `,` + ${t.registerUniforms(l).declareVariables(e,p)} + + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${p.offsetToIndices("global_idx")}; + var xIndices = ${p.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${g}(${o}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${w-1}u; j++) { + offsets[j] = offset / ${ke("uniforms.kernelStrides","j",w)}; + offset -= offsets[j] * ${ke("uniforms.kernelStrides","j",w)}; + } + offsets[${w-1}] = offset; + + isPad = false; + for (var j = ${r-w}u; j < ${r}u; j++) { + xIndices[j] = indices[j] * ${ke("uniforms.strides",`j - ${r-w}u`,w)} + + offsets[j - ${r-w}u] - ${ke("uniforms.pads","j - 2u",v)}; + ${x} + } + ${i} + + output[global_idx] = value; + }`}},Io=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,Ah=t=>`${Io(t)};${t.countIncludePad}`,Ih=t=>`${Io(t)};${t.storageOrder};${t.dilations}`,Mo=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}),Oo=(t,e,r,n)=>{let[a,s]=Co(e,n,r),i=Q("x",e.dataType,e.dims.length),o=i.type.value,l="value += x_val;",u="";a.countIncludePad?u+=`value /= ${o}(uniforms.kernelSize);`:u+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[d,h,f,g,p]=To(s,a);d.push(...we(e.dims,s));let w=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${f};${g};${p}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(X.size(s)/64)},programUniforms:d}),getShaderSource:v=>Ao(v,i,e.dims.length,s.length,a,l,u,0,h,f,g,p)}},Mh=t=>{let e=t.count_include_pad!==0,r=Mo(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:Ah(n)}},Oh=(t,e)=>{oa(t.inputs),t.compute(Oo("AveragePool",t.inputs[0],!1,e))},zo={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},zh=t=>{let e=t.format;return{format:e,...zo,cacheKey:e}},Ph=(t,e)=>{oa(t.inputs),t.compute(Oo("GlobalAveragePool",t.inputs[0],!0,e))},Po=(t,e,r,n)=>{let[a,s]=Co(e,n,r),i=` + value = max(x_val, value); + `,o="",l=Q("x",e.dataType,e.dims.length),u=["rank"],[d,h,f,g,p]=To(s,a);return d.push(...we(e.dims,s)),{name:t,shaderCache:{hint:`${n.cacheKey};${f};${g};${p}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(X.size(s)/64)},programUniforms:d}),getShaderSource:w=>Ao(w,l,e.dims.length,s.length,a,i,o,e.dataType===10?-65504:-1e5,h,f,g,p)}},Rh=(t,e)=>{oa(t.inputs),t.compute(Po("MaxPool",t.inputs[0],!1,e))},Bh=t=>{let e=t.storage_order,r=t.dilations,n=Mo(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 a={storageOrder:e,dilations:r,...n,cacheKey:""};return{...a,cacheKey:Ih(a)}},Dh=t=>{let e=t.format;return{format:e,...zo,cacheKey:e}},Nh=(t,e)=>{oa(t.inputs),t.compute(Po("GlobalMaxPool",t.inputs[0],!0,e))}}),Fh,Lh,Uh,lw=ee(()=>{rr(),xe(),Ie(),Fh=(t,e,r)=>{let n=t===e,a=te&&r>0;if(n||a||s)throw new Error("Range these inputs' contents are invalid.")},Lh=(t,e,r,n)=>{let a=Math.abs(Math.ceil((e-t)/r)),s=[a],i=a,o=[{type:12,data:i},{type:n,data:t},{type:n,data:r},...we(s)],l=u=>{let d=_e("output",n,s.length),h=d.type.value,f=[{name:"outputSize",type:"u32"},{name:"start",type:h},{name:"delta",type:h}];return` + ${u.registerUniforms(f).declareVariables(d)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${h}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:s,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:o})}},Uh=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]),Ue.webgpu.validateInputContent&&Fh(e,r,n),t.compute(Lh(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),Wh,Vh,Gh,Hh,jh,qh,Kh,Yh,Xh,Qh,Zh,Ro,Jh,ef,tf,rf,nf,af,sf,uw=ee(()=>{xe(),Oe(),pt(),Ie(),Wh=(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")}},Vh=(t,e,r)=>{e.every(a=>a>=0&&a{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((a,s)=>n[a]=t[s]),n},Gh=(t,e,r,n,a,s)=>{let[i,o,l]=r>10?[1,2,3]:[-1,t.length>1?1:-1,-1],u=t[0].dims.length;if(i>0&&t.length>i&&t[i].dims.length>0)t[i].getFloat32Array().forEach(d=>s.push(d));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(d=>n.push(d)),n.length!==0&&n.length!==u&&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");Wh(n,e),e.axes.length>0&&Vh(n,e.axes,u).forEach((d,h)=>n[h]=d)}if(l>0&&t.length>l&&(t[l].getBigInt64Array().forEach(d=>a.push(Number(d))),a.length!==u||r>=18&&a.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(a.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 a<"u"&&n.length>0&&a.length>u)throw new Error("Resize requires only of scales or sizes to be specified")},Hh=(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`)}})()+"}",jh=(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`)}})()+"}",qh=(t,e,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),a=t.length===0?n:t.slice();return e.length>0?(e.forEach((s,i)=>{n[s]=a[i],n[i+r]=a[e.length+i]}),n):a},Kh=(t,e,r,n)=>{let a=[];if(r.length>0)if(n.length>0){if(t.forEach(s=>a.push(s)),Math.max(...n)>t.length)throw new Error("axes is out of bound");n.forEach((s,i)=>a[s]=r[i])}else r.forEach(s=>a.push(s));else{if(e.length===0)throw new Error("Resize requires either scales or sizes.");a=t.map((s,i)=>Math.round(s*e[i]))}return a},Yh=(t,e,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(s=>e[s]),Number.MAX_VALUE):Math.min(...e,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(s=>e[s]),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 a=t.slice();return r.axes.length>0?(r.axes.forEach(s=>e[s]=n),r.axes.forEach(s=>a[s]=Math.round(t[s]*e[s]))):(e.fill(n,0,e.length),a.forEach((s,i)=>a[i]=Math.round(s*e[i]))),a},Xh=(t,e,r,n,a)=>` + 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 = ${ke("uniforms.scales","i",n)}; + var roi_low = ${ke("uniforms.roi","i",a)}; + var roi_hi = ${ke("uniforms.roi",`i + ${e.length}`,a)}; + if (scale == 1.0) { + original_indices[i] = ${t.type.value}(output_index); + } else { + var input_shape_i = ${ke("uniforms.input_shape","i",e.length)}; + var output_shape_i = ${ke("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; + }`,Qh=(t,e,r,n,a,s,i)=>` + 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 = ${ke("uniforms.scales","i",a)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${ke("uniforms.roi","i",s)}; + var roi_hi = ${ke("uniforms.roi",`i + ${r.length}`,s)}; + var input_shape_i = ${ke("uniforms.input_shape","i",r.length)}; + var output_shape_i = ${ke("uniforms.output_shape","i",n.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${i} || (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; + }`,Zh=(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 >= ${ke("uniforms.input_shape","i",e.length)}) { + return false; + } + } + return true; + }`,Ro=(t,e,r,n)=>t.rank>n?` + ${t.indicesSet("input_indices",e,"channel")}; + ${t.indicesSet("input_indices",r,"batch")}; +`:"",Jh=(t,e,r,n,a)=>{let[s,i,o,l]=r.length===2?[-1,0,1,-1]:[0,2,3,1],u=t.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${u} { + var input_indices: ${t.type.indices}; + ${t.indicesSet("input_indices",i,`max(0, min(row, ${r[i]} - 1))`)}; + ${t.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)}; + ${Ro(t,l,s,2)} + return ${t.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${e.type.indices}) -> ${u} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${u} = originalIndices[${i}]; + var col:${u} = originalIndices[${o}]; + ${n?`if (row < 0 || row > (${r[i]} - 1) || col < 0 || col > (${r[o]} - 1)) { + return ${a}; + }`:""}; + row = max(0, min(row, ${r[i]} - 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 = ${r.length>2?`u32(originalIndices[${l}])`:"0"}; + var batch: u32 = ${r.length>2?`u32(originalIndices[${s}])`:"0"}; + var x11: ${u} = getInputValue(batch, channel, row1, col1); + var x12: ${u} = getInputValue(batch, channel, row1, col2); + var x21: ${u} = getInputValue(batch, channel, row2, col1); + var x22: ${u} = getInputValue(batch, channel, row2, col2); + var dx1: ${u} = abs(row - ${u}(row1)); + var dx2: ${u} = abs(${u}(row2) - row); + var dy1: ${u} = abs(col - ${u}(col1)); + var dy2: ${u} = abs(${u}(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); + }`},ef=(t,e,r,n,a,s,i,o,l,u)=>{let d=r.length===2,[h,f]=d?[0,1]:[2,3],g=t.type.value,p=w=>{let v=w===h?"row":"col";return` + fn ${v}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${e.type.indices}) -> ${g} { + var output_index = ${e.indicesGet("output_indices",w)}; + var originalIdx: ${g} = getOriginalCoordinateFromResizedCoordinate(output_index, ${a[w]}, + ${n[w]}, ${r[w]}, ${s[w]}, ${s[w]} + ${r.length}); + var fractOriginalIdx: ${g} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${o} && (originalIdx < 0 || originalIdx > (${r[w]} - 1))) { + return ${l}; + } + var data: array<${g}, 4> = array<${g}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${v}: ${g} = originalIdx + ${g}(i); + if (${v} < 0 || ${v} >= ${r[w]}) { + ${u?`coefs[i + 1] = 0.0; + continue;`:o?`return ${l};`:`${v} = max(0, min(${v}, ${r[w]} - 1));`}; + } + var input_indices_copy: ${t.type.indices} = input_indices; + ${t.indicesSet("input_indices_copy",w,`u32(${v})`)}; + data[i + 1] = ${w===h?t.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${p(h)}; + ${p(f)}; + fn getCubicInterpolationCoefs(s: ${g}) -> array<${g}, 4> { + var absS = abs(s); + var coeffs: array<${g}, 4> = array<${g}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${g} = 1.0 - absS; + var twoMinusAbsS: ${g} = 2.0 - absS; + var onePlusAbsS: ${g} = 1.0 + absS; + coeffs[0] = ((${i} * onePlusAbsS - 5 * ${i}) * onePlusAbsS + 8 * ${i}) * onePlusAbsS - 4 * ${i}; + coeffs[1] = ((${i} + 2) * absS - (${i} + 3)) * absS * absS + 1; + coeffs[2] = ((${i} + 2) * oneMinusAbsS - (${i} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${i} * twoMinusAbsS - 5 * ${i}) * twoMinusAbsS + 8 * ${i}) * twoMinusAbsS - 4 * ${i}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${g}, 4>, coefs: array<${g}, 4>) -> ${g} { + var coefsSum: ${g} = 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}) -> ${g} { + var input_indices: ${t.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},tf=(t,e,r,n,a)=>{let[s,i,o,l,u]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],d=t.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${d} { + var input_indices: ${t.type.indices}; + ${t.indicesSet("input_indices",i,`max(0, min(depth, ${r[i]} - 1))`)}; + ${t.indicesSet("input_indices",o,`max(0, min(height, ${r[o]} - 1))`)}; + ${t.indicesSet("input_indices",l,`max(0, min(width, ${r[l]} - 1))`)}; + ${Ro(t,u,s,3)} + return ${t.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${e.type.indices}) -> ${d} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${d} = originalIndices[${i}]; + var height:${d} = originalIndices[${o}]; + var width:${d} = originalIndices[${l}]; + ${n?`if (depth < 0 || depth > (${r[i]} - 1) || height < 0 || height > (${r[o]} - 1) || width < 0 || (width > ${r[l]} - 1)) { + return ${a}; + }`:""}; + + depth = max(0, min(depth, ${r[i]} - 1)); + height = max(0, min(height, ${r[o]} - 1)); + width = max(0, min(width, ${r[l]} - 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 = ${r.length>3?`u32(originalIndices[${u}])`:"0"}; + var batch: u32 = ${r.length>3?`u32(originalIndices[${s}])`:"0"}; + + var x111: ${d} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${d} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${d} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${d} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${d} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${d} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${d} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${d} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${d} = abs(depth - ${d}(depth1)); + var dx2: ${d} = abs(${d}(depth2) - depth); + var dy1: ${d} = abs(height - ${d}(height1)); + var dy2: ${d} = abs(${d}(height2) - height); + var dz1: ${d} = abs(width - ${d}(width1)); + var dz2: ${d} = abs(${d}(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); + }`},rf=(t,e,r,n,a,s)=>{let i=t.dims,o=qh(s,e.axes,i.length),l=Kh(i,n,a,e.axes),u=n.slice();n.length===0&&(u=i.map(($,E)=>$===0?1:l[E]/$),e.keepAspectRatioPolicy!=="stretch"&&(l=Yh(i,u,e)));let d=_e("output",t.dataType,l.length),h=Q("input",t.dataType,i.length),f=X.size(l),g=i.length===l.length&&i.every(($,E)=>$===l[E]),p=e.coordinateTransformMode==="tf_crop_and_resize",w=e.extrapolationValue,v=h.type.value,x=$=>` + ${g?"":` + ${Hh(e.coordinateTransformMode,v)}; + ${(()=>{switch(e.mode){case"nearest":return` + ${Zh(h,i)}; + ${jh(e.nearestMode,r,v)}; + ${Qh(h,d,i,l,u.length,o.length,p)}; + `;case"linear":return` + ${Xh(d,i,l,u.length,o.length)}; + ${(()=>{if(i.length===2||i.length===4)return`${Jh(h,d,i,p,w)}`;if(i.length===3||i.length===5)return`${tf(h,d,i,p,w)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(i.length===2||i.length===4)return`${ef(h,d,i,l,u,o,e.cubicCoeffA,p,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")}})()}; + `} + ${$.registerUniform("output_size","u32").registerUniform("scales","f32",u.length).registerUniform("roi","f32",o.length).declareVariables(h,d)} + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${g?"output[global_idx] = input[global_idx];":` + let output_indices = ${d.offsetToIndices("global_idx")}; + var input_indices: ${h.type.indices}; + ${(()=>{switch(e.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${h.getByIndices("input_indices")}; + } else { + output[global_idx] = ${e.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${i.length===2||i.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}|${r}|${u.length>0?u:""}|${a.length>0?a:""}|${o.length>0?o:""}|${g}|${i}`,inputDependencies:["rank"]},getShaderSource:x,getRunData:()=>({outputs:[{dims:l,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:[{type:12,data:f},{type:1,data:u},{type:1,data:o},...we(i,l)]})}},nf=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},af=(t,e)=>{let r=[],n=[],a=[],s=nf(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Gh(t.inputs,e,s,r,n,a),t.compute(rf(t.inputs[0],e,s,r,n,a),{inputs:[0]})},sf=t=>{let e=t.antialias,r=t.axes,n=t.coordinateTransformMode,a=t.cubicCoeffA,s=t.excludeOutside!==0,i=t.extrapolationValue,o=t.keepAspectRatioPolicy,l=t.mode,u=t.nearestMode===""?"simple":t.nearestMode;return Ke({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:s,extrapolationValue:i,keepAspectRatioPolicy:o,mode:l,nearestMode:u})}}),of,lf,uf,dw=ee(()=>{xe(),Oe(),pt(),Ie(),of=(t,e)=>{let[r,n,a,s]=t,{numHeads:i,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(!X.areEqual(n.dims,[])&&!X.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(a.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(s.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${s.dims.length}`);if(!X.areEqual(a.dims,s.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(o>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let l=r.dims[0],u=r.dims[r.dims.length-2],d=a.dims[0],h=X.sizeFromDimension(r.dims,1)/u,f=o===0?a.dims[1]*2:h/i;if(o>f)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(l!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(u!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(f/2!==a.dims[1]&&o/2!==a.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${a.dims[1]}`);if(u>d)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},lf=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:s}=e,i=t[0].dims[0],o=X.sizeFromDimension(t[0].dims,1),l=t[0].dims[t[0].dims.length-2],u=o/l,d=t[2].dims[1],h=a===0?d*2:u/n,f=new Array(i,l,u/h,h-d),g=X.computeStrides(f),p=[{type:1,data:s},{type:12,data:f},{type:12,data:g},...t[0].dims.length===3?new Array({type:12,data:[o,u,h,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[o,h,l*h,1]}):[],...we(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],w=v=>{let x=Q("input",t[0].dataType,t[0].dims.length),$=Q("position_ids",t[1].dataType,t[1].dims.length),E=Q("cos_cache",t[2].dataType,t[2].dims.length),T=Q("sin_cache",t[3].dataType,t[3].dims.length),A=_e("output",t[0].dataType,t[0].dims.length);return v.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:f.length},{name:"global_strides",type:"u32",length:g.length},{name:"input_output_strides",type:"u32",length:g.length}]),` + ${v.declareVariables(x,$,E,T,A)} + + ${v.mainStart(Sn)} + let half_rotary_emb_dim = uniforms.${E.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 = + ${$.broadcastedIndicesToOffset("bsnh.xy",_e("",$.type.tensor,2))}; + let position_id = + u32(${$.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 = ${x.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} - + ${x.getByOffset("j")} * ${T.get("position_id","bsnh[3]")}; + ${A.setByOffset("i","re")} + let im = ${x.getByOffset("i")} * ${T.get("position_id","bsnh[3]")} + + ${x.getByOffset("j")} * ${E.get("position_id","bsnh[3]")}; + ${A.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${A.setByOffset("k",x.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:Ke({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(X.size(f)/Sn)},programUniforms:p})}},uf=(t,e)=>{of(t.inputs,e),t.compute(lf(t.inputs,e))}}),df,cf,pf,cw=ee(()=>{xe(),Oe(),Ie(),df=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],r=t[1],n=t[2];if(e.dataType!==r.dataType||e.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let a=e.dims[e.dims.length-1],s=e.dims[e.dims.length-2];if(r.dims[r.dims.length-1]!==a)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==a)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let i=t[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let i=t[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},cf=(t,e,r,n)=>{let a=e.simplified,s=t[0].dims,i=X.size(s),o=s,l=i,u=s.slice(-1)[0],d=n?s.slice(0,-1).concat(1):[],h=!a&&t.length>3,f=t.length>4,g=n&&r>1,p=n&&r>2,w=r>3,v=st(u),x=[{type:12,data:l},{type:12,data:v},{type:12,data:u},{type:1,data:e.epsilon}],$=T=>{let A=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],z=[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)];h&&z.push(Q("beta",t[3].dataType,t[3].dims,v)),f&&z.push(Q("bias",t[4].dataType,t[4].dims,v)),z.push(_e("output",t[0].dataType,o,v)),g&&z.push(_e("mean_output",1,d)),p&&z.push(_e("inv_std_output",1,d)),w&&z.push(_e("input_skip_bias_sum",t[0].dataType,o,v));let R=_t(t[0].dataType);return` + + ${T.registerUniforms(A).declareVariables(...z)} + + ${T.mainStart()} + ${T.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 = ${Sr("f32",v)}; + var squareSum = ${Sr("f32",v)}; + for (var i: u32 = 0; i < hidden_size_vectorized; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${f?"bias[i]":R+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${w?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${kn(R,v,"value")}; + sum += f32_value; + squareSum += f32_value * f32_value; + } + let mean = ${Nr("sum",v)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${Nr("squareSum",v)} / f32(uniforms.hidden_size) ${a?"":"- mean * mean"} + uniforms.epsilon); + ${g?"mean_output[global_idx] = mean;":""} + ${p?"inv_std_output[global_idx] = inv_std_dev;":""} + for (var i: u32 = 0; i < hidden_size_vectorized; i++) { + output[offset + i] = (output[offset + i] ${a?"":`- ${R}(mean)`}) * ${R}(inv_std_dev) * gamma[i] ${h?"+ beta[i]":""}; + } + }`},E=[{dims:o,dataType:t[0].dataType}];return r>1&&E.push({dims:d,dataType:1}),r>2&&E.push({dims:d,dataType:1}),r>3&&E.push({dims:s,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${v};${g};${p};${w}`,inputDependencies:t.map((T,A)=>"type")},getShaderSource:$,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/u/64)},programUniforms:x})}},pf=(t,e)=>{df(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(cf(t.inputs,e,t.outputCount,!1),{outputs:r})}}),hf,la,ff,Bo,mf,gf,_f,yf,pw=ee(()=>{xe(),Oe(),pt(),Ie(),hf=(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`)})},la=(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},ff=(t,e)=>{if(t.length>1){let r=la(t,1),n=la(t,2),a=la(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),Ke({starts:r,ends:n,axes:a})}else return e},Bo=(t,e,r,n,a)=>{let s=t;return t<0&&(s+=r[n[e]]),a[e]<0?Math.max(0,Math.min(s,r[n[e]]-1)):Math.max(0,Math.min(s,r[n[e]]))},mf=(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 = ${ke("uniforms.input_shape","i",r.length)}; + let steps_i = ${ke("uniforms.steps","i",r.length)}; + let signs_i = ${ke("uniforms.signs","i",r.length)}; + let starts_i = ${ke("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; + } + ${t.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,gf=(t,e)=>{let r=t[0].dims,n=X.size(r),a=e.axes.length>0?X.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],s=la(t,4);s.forEach(x=>x!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(a.length).fill(1));let i=e.starts.map((x,$)=>Bo(x,$,r,a,s)),o=e.ends.map((x,$)=>Bo(x,$,r,a,s));if(a.length!==i.length||a.length!==o.length)throw new Error("start, ends and axes should have the same number of elements");if(a.length!==r.length)for(let x=0;xMath.sign(x));s.forEach((x,$,E)=>{if(x<0){let T=(o[$]-i[$])/x,A=i[$],z=A+T*s[$];i[$]=z,o[$]=A,E[$]=-x}});let u=r.slice(0);a.forEach((x,$)=>{u[x]=Math.ceil((o[x]-i[x])/s[x])});let d={dims:u,dataType:t[0].dataType},h=_e("output",t[0].dataType,u.length),f=Q("input",t[0].dataType,t[0].dims.length),g=X.size(u),p=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:s.length}],w=[{type:12,data:g},{type:12,data:i},{type:6,data:l},{type:12,data:s},...we(t[0].dims,u)],v=x=>` + ${x.registerUniforms(p).declareVariables(f,h)} + ${mf(f,h,r)} + ${x.mainStart()} + ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${h.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${h.setByOffset("global_idx",f.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${i.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:v,getRunData:()=>({outputs:[d],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:w})}},_f=(t,e)=>{hf(t.inputs,e);let r=ff(t.inputs,e);t.compute(gf(t.inputs,r),{inputs:[0]})},yf=t=>{let e=t.starts,r=t.ends,n=t.axes;return Ke({starts:e,ends:r,axes:n})}}),wf,bf,vf,$f,hw=ee(()=>{xe(),Oe(),pt(),Ie(),wf=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},bf=(t,e)=>{let r=t.dims,n=X.size(r),a=64,s=e.axis;if(s<0&&(s=r.length+s),sx===4?`max(max(${v}.x, ${v}.y), max(${v}.z, ${v}.w))`:x===2?`max(${v}.x, ${v}.y)`:x===3?`max(max(${v}.x, ${v}.y), ${v}.z)`:v,h=Q("x",t.dataType,t.dims,l),f=_e("result",t.dataType,t.dims,l),g=h.type.value,p=_t(t.dataType)==="f32"?`var threadMax = ${g}(-3.402823e+38f);`:`var threadMax = ${g}(-65504.0h);`,w=v=>` + var rowMaxShared : ${g}; + var rowSumShared : ${g}; + var threadShared : array<${g}, ${a}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${g} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${g}) { + let index = row * row_stride + col; + result[index] = value; + } + ${v.registerUniform("packedCols","i32").declareVariables(h,f)} + ${v.mainStart()} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${a}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${p} + 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 = ${g}(${d("threadShared[0]",l)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${g}(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 = ${g}(${Nr("threadShared[0]",l)}); + } + 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:`${l}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:6,data:u}]}),getShaderSource:w}},vf=(t,e)=>{wf(t.inputs),t.compute(bf(t.inputs[0],e))},$f=t=>Ke({axis:t.axis})}),xf,Sf,kf,Ef,Cf,Tf,Af,fw=ee(()=>{xe(),Oe(),pt(),Ie(),xf=t=>{if(!t||t.length<1)throw new Error("too few inputs")},Sf=(t,e)=>{let r=[],n=e.numOutputs;return t[1].dims[0]>0&&(t[1].getBigInt64Array().forEach(a=>r.push(Number(a))),n=r.length),Ke({numOutputs:n,axis:e.axis,splitSizes:r})},kf=t=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${t}u; i += 1u ) { + if (index < ${ke("uniforms.size_in_split_axis","i",t)}) { + return i; + } + } + return ${t}u; +}`,Ef=t=>{let e=t.length,r=[];for(let n=0;n{let r=t[0].dims,n=X.size(r),a=t[0].dataType,s=X.normalizeAxis(e.axis,r.length),i=new Array(e.numOutputs),o=Q("input",a,r.length),l=new Array(e.numOutputs),u=[],d=[],h=0,f=[{type:12,data:n}];for(let p=0;p` + ${p.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(o,...i)} + ${kf(l.length)} + ${Ef(i)} + + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${o.offsetToIndices("global_idx")}; + var index = ${o.indicesGet("indices",s)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${ke("uniforms.size_in_split_axis","output_number - 1u",l.length)}; + ${o.indicesSet("indices",s,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:g,getRunData:()=>({outputs:u,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:f})}},Tf=(t,e)=>{xf(t.inputs);let r=t.inputs.length===1?e:Sf(t.inputs,e);t.compute(Cf(t.inputs,r),{inputs:[0]})},Af=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 Ke({axis:e,numOutputs:n,splitSizes:r})}}),Do,If,Mf,Of,zf,mw=ee(()=>{xe(),Oe(),Ie(),Do=t=>Array.from(t.getBigInt64Array(),Number),If=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(Do(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")},Mf=(t,e)=>{let r=[];for(let n=0;n{let e=t[0].dims,r=Do(t[1]),n=Mf(e,r),a=X.size(n),s=t[0].dataType,i=Q("input",s,e.length),o=_e("output",s,n.length),l=u=>` + const inputShape = ${i.indices(...e)}; + ${u.registerUniform("output_size","u32").declareVariables(i,o)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${o.offsetToIndices("global_idx")}; + var input_indices: ${i.type.indices}; + for (var i = 0; i < ${e.length}; i++) { + let input_dim_i = ${i.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${o.indicesGet("output_indices","i")} % input_dim_i; + + ${i.indicesSet("input_indices","i","input_dim_value")} + } + ${o.setByOffset("global_idx",i.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...we(t[0].dims,n)]}),getShaderSource:l}},zf=t=>{If(t.inputs),t.compute(Of(t.inputs),{inputs:[0]})}}),Pf,Rf,Bf,gw=ee(()=>{xe(),Oe(),Ie(),Pf=(t,e,r,n,a)=>{let s=_e("output_data",a,r.length,4),i=Q("a_data",e[1].dataType,e[1].dims.length,4),o=Q("b_data",e[2].dataType,e[2].dims.length,4),l=Q("c_data",e[0].dataType,e[0].dims.length,4),u,d=(h,f,g)=>`select(${f}, ${h}, ${g})`;if(!n)u=s.setByOffset("global_idx",d(i.getByOffset("global_idx"),o.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let h=(f,g,p="")=>{let w=`a_data[index_a${g}][component_a${g}]`,v=`b_data[index_b${g}][component_b${g}]`,x=`bool(c_data[index_c${g}] & (0xffu << (component_c${g} * 8)))`;return` + let output_indices${g} = ${s.offsetToIndices(`global_idx * 4u + ${g}u`)}; + let offset_a${g} = ${i.broadcastedIndicesToOffset(`output_indices${g}`,s)}; + let offset_b${g} = ${o.broadcastedIndicesToOffset(`output_indices${g}`,s)}; + let offset_c${g} = ${l.broadcastedIndicesToOffset(`output_indices${g}`,s)}; + let index_a${g} = offset_a${g} / 4u; + let index_b${g} = offset_b${g} / 4u; + let index_c${g} = offset_c${g} / 4u; + let component_a${g} = offset_a${g} % 4u; + let component_b${g} = offset_b${g} % 4u; + let component_c${g} = offset_c${g} % 4u; + ${f}[${g}] = ${p}(${d(w,v,x)}); + `};a===9?u=` + var data = vec4(0); + ${h("data",0,"u32")} + ${h("data",1,"u32")} + ${h("data",2,"u32")} + ${h("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:u=` + ${h("output_data[global_idx]",0)} + ${h("output_data[global_idx]",1)} + ${h("output_data[global_idx]",2)} + ${h("output_data[global_idx]",3)} + `}return` + ${t.registerUniform("vec_size","u32").declareVariables(l,i,o,s)} + ${t.mainStart()} + ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${u} + }`},Rf=t=>{let e=t[1].dims,r=t[2].dims,n=t[0].dims,a=t[1].dataType,s=!(X.areEqual(e,r)&&X.areEqual(r,n)),i=e,o=X.size(e);if(s){let u=xn.calcShape(xn.calcShape(e,r,!1),n,!1);if(!u)throw new Error("Can't perform where op on the given tensors");i=u,o=X.size(i)}let l=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:u=>Pf(u,t,i,s,a),getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:l},...we(n,e,r,i)]})}},Bf=t=>{t.compute(Rf(t.inputs))}}),Df,_w=ee(()=>{Ny(),Kd(),Fy(),Ly(),Uy(),Wy(),Ud(),hp(),qy(),Ky(),Yy(),Xy(),Qy(),Zy(),Jy(),ew(),tw(),rw(),nw(),up(),aw(),iw(),sw(),ow(),lw(),ro(),uw(),dw(),cw(),pw(),hw(),fw(),mw(),ia(),lo(),gw(),Df=new 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Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,e){this.repo.set(t,e)}run(t,e,r,n,a){tr(t.programInfo.name);let s=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let o=[];for(let u of e)o.push({binding:o.length,resource:{buffer:u.buffer}});for(let u of r)o.push({binding:o.length,resource:{buffer:u.buffer}});a&&o.push({binding:o.length,resource:a});let l=s.createBindGroup({layout:t.computePipeline.getBindGroupLayout(0),entries:o,label:t.programInfo.name});if(this.backend.sessionStatus==="capturing"){let u={kernelId:this.backend.currentKernelId,computePipeline:t.computePipeline,bindGroup:l,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(u)}i.setPipeline(t.computePipeline),i.setBindGroup(0,l),i.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(),Yt(t.programInfo.name)}dispose(){}build(t,e){tr(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=Uu(e,this.backend.device.limits),s=t.getShaderSource(a),i=`${n.join(` +`)} +${a.additionalImplementations} +${s}`,o=r.createShaderModule({code:i,label:t.name});nt("verbose",()=>`[WebGPU] ${t.name} shader code: ${i}`);let l=r.createComputePipeline({compute:{module:o,entryPoint:"main"},layout:"auto",label:t.name});return Yt(t.name),{programInfo:t,computePipeline:l,uniformVariablesInfo:a.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,a=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(e<=a&&r<=a&&n<=a)return[e,r,n];let s=e*r*n,i=Math.ceil(Math.sqrt(s));if(i>a){if(i=Math.ceil(Math.cbrt(s)),i>a)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),Ff,Lf,Uf,Wf,ww=ee(()=>{rr(),xe(),Xr(),Ry(),By(),_w(),yw(),Ff=(t,e)=>{if(e.length!==t.length)throw new Error(`inputDependencies length ${e.length} is not equal to inputTensors length ${t.length}.`);let r=[];for(let n=0;n{var a,s;let n=t.name;return(a=t.shaderCache)!=null&&a.hint&&(n+="["+t.shaderCache.hint+"]"),n+=":"+r+`:${Ff(e,((s=t.shaderCache)==null?void 0:s.inputDependencies)??new Array(e.length).fill("dims"))}`,n},Uf=class{constructor(t){t&&(this.architecture=t.architecture,this.vendor=t.vendor)}isArchitecture(t){return this.architecture===t}isVendor(t){return this.vendor===t}},Wf=class{constructor(){this.currentSessionId=null,this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.maxDispatchNumber=16,this.pendingDispatchNumber=0,this.pendingKernels=[],this.pendingQueries=new Map,this.sessionStatus="default",this.capturedCommandList=new Map,this.capturedPendingKernels=new Map,this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. (should not happen)");let t=this.kernelCustomData.get(this.currentKernelId);return t||(t={},this.kernelCustomData.set(this.currentKernelId,t)),t}async initialize(t,e){this.env=t;let r=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:e.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:e.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:e.limits.maxStorageBufferBindingSize,maxBufferSize:e.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:e.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:e.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:e.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:e.limits.maxComputeWorkgroupSizeZ},requiredFeatures:r};e.features.has("chromium-experimental-timestamp-query-inside-passes")?r.push("chromium-experimental-timestamp-query-inside-passes"):e.features.has("timestamp-query")&&r.push("timestamp-query"),e.features.has("shader-f16")&&r.push("shader-f16"),this.device=await e.requestDevice(n),this.adapterInfo=new Uf(await e.requestAdapterInfo()),this.gpuDataManager=Bu(this),this.programManager=new Nf(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Iu(t.logLevel,!!t.debug),this.device.onuncapturederror=a=>{a.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${a.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:e,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let t=this.getCommandEncoder(),e={};this.queryType==="at-passes"&&(e.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=t.beginComputePass(e)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;tr(),this.endComputePass();let t;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),t=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(t,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&t.mapAsync(GPUMapMode.READ).then(()=>{var n;let e=new BigUint64Array(t.getMappedRange()),r=this.pendingQueries.get(t);for(let a=0;a"u"&&(this.queryTimeBase=g);let w=Number(g-this.queryTimeBase),v=Number(p-this.queryTimeBase);if(!Number.isSafeInteger(w)||!Number.isSafeInteger(v))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:h.map(x=>({dims:x.dims,dataType:Yr(x.dataType)})),outputsMetadata:f.map(x=>({dims:x.dims,dataType:Yr(x.dataType)})),kernelId:i,kernelType:l,kernelName:u,programName:d,startTime:w,endTime:v});else{let x="";h.forEach((E,T)=>{x+=`input[${T}]: [${E.dims}] | ${Yr(E.dataType)}, `});let $="";f.forEach((E,T)=>{$+=`output[${T}]: [${E.dims}] | ${Yr(E.dataType)}, `}),console.log(`[profiling] kernel "${i}|${l}|${u}|${d}" ${x}${$}execution time: ${v-w} ns`)}ea("GPU",`${d}::${g}::${p}`)}t.unmap(),this.pendingQueries.delete(t)}),Yt()}run(t,e,r,n,a,s){tr(t.name);let i=[];for(let $=0;$E):r;if(d.length!==o.length)throw new Error(`Output size ${d.length} must be equal to ${o.length}.`);let h=[],f=[];for(let $=0;$=s)throw new Error(`Invalid output index: ${d[$]}`);if(d[$]===-3)continue;let E=d[$]===-1,T=d[$]===-2,A=E||T?a(o[$].dataType,o[$].dims):n(d[$],o[$].dataType,o[$].dims);if(h.push(A),A.data===0)continue;let z=this.gpuDataManager.get(A.data);if(!z)throw new Error(`no GPU data for output: ${A.data}`);if(E&&this.temporaryData.push(z),T){let R=this.kernelPersistentData.get(this.currentKernelId);R||(R=[],this.kernelPersistentData.set(this.currentKernelId,R)),R.push(z)}f.push(z)}if(i.length!==e.length||f.length!==h.length){if(f.length===0)return Yt(t.name),h;throw new Error(`Program ${t.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let g;if(u){let $=0,E=[];u.forEach(R=>{let G=typeof R.data=="number"?[R.data]:R.data;if(G.length===0)return;let j=R.type===10?2:4,q,fe;R.type===10?(fe=G.length>4?16:G.length>2?8:G.length*j,q=G.length>4?16:j*G.length):(fe=G.length<=2?G.length*j:16,q=16),$=Math.ceil($/fe)*fe,E.push($);let ne=R.type===10?8:4;$+=G.length>4?Math.ceil(G.length/ne)*q:G.length*j});let T=16;$=Math.ceil($/T)*T;let A=new ArrayBuffer($);u.forEach((R,G)=>{let j=E[G],q=typeof R.data=="number"?[R.data]:R.data;if(R.type===6)new Int32Array(A,j,q.length).set(q);else if(R.type===12)new Uint32Array(A,j,q.length).set(q);else if(R.type===10)new Uint16Array(A,j,q.length).set(q);else if(R.type===1)new Float32Array(A,j,q.length).set(q);else throw new Error(`Unsupported uniform type: ${Yr(R.type)}`)});let z=this.gpuDataManager.create($,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(z.buffer,0,A,0,$),this.gpuDataManager.release(z.id),g={offset:0,size:$,buffer:z.buffer}}let p=this.programManager.normalizeDispatchGroupSize(l),w=p[1]===1&&p[2]===1,v=Lf(t,e,w),x=this.programManager.getArtifact(v);if(x||(x=this.programManager.build(t,p),this.programManager.setArtifact(v,x),nt("info",()=>`[artifact] key: ${v}, programName: ${t.name}`)),u&&x.uniformVariablesInfo){if(u.length!==x.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${x.uniformVariablesInfo.length}, got ${u.length} in program "${x.programInfo.name}".`);for(let $=0;$`[ProgramManager] run "${t.name}" (key=${v}) with ${p[0]}x${p[1]}x${p[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let $={kernelId:this.currentKernelId,programName:x.programInfo.name,inputTensorViews:e,outputTensorViews:h};this.pendingKernels.push($),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push($)}return this.programManager.run(x,i,f,p,g),Yt(t.name),h}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 a=Df.get(t);if(!a)throw new Error(`kernel not implemented: ${t}`);let s={kernelType:t,kernelName:n,kernelEntry:a[0],attributes:[a[1],r]};this.kernels.set(e,s)}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 created: ${t}`);let a=n.kernelType,s=n.kernelName,i=n.kernelEntry,o=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${a}] ${s}" is not allowed to be called recursively`);this.currentKernelId=t,o[0]&&(o[1]=o[0](o[1]),o[0]=void 0),nt("info",()=>`[WebGPU] Start to run kernel "[${a}] ${s}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),i(e,o[1]),0}catch(u){return r.push(Promise.resolve(`[WebGPU] Kernel "[${a}] ${s}" failed. ${u}`)),1}finally{l&&r.push(this.device.popErrorScope().then(u=>u?`GPU validation error for kernel "[${a}] ${s}": ${u.message}`:null));for(let u of this.temporaryData)this.gpuDataManager.release(u.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(t,e,r,n){let a=this.sessionExternalDataMapping.get(t);a||(a=new Map,this.sessionExternalDataMapping.set(t,a));let s=a.get(e),i=this.gpuDataManager.registerExternalBuffer(r,n,s==null?void 0:s[1]);return a.set(e,[i,r]),i}unregisterBuffers(t){let e=this.sessionExternalDataMapping.get(t);e&&(e.forEach(r=>this.gpuDataManager.unregisterExternalBuffer(r[1])),this.sessionExternalDataMapping.delete(t))}getBuffer(t){let e=this.gpuDataManager.get(t);if(!e)throw new Error(`no GPU data for buffer: ${t}`);return e.buffer}createDownloader(t,e,r){return async()=>{let n=await Xs(this,t,e);return Ou(n.buffer,r)}}writeTimestamp(t){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){var t;this.queryType="none",(((t=this.env.webgpu.profiling)==null?void 0:t.mode)==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){nt("info","captureBegin"),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus="capturing"}captureEnd(){nt("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){nt("info","replay"),this.sessionStatus="replaying";let t=this.capturedCommandList.get(this.currentSessionId),e=this.capturedPendingKernels.get(this.currentSessionId),r=t.length;this.pendingKernels=[];for(let n=0;n=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onReleaseSession(t){this.unregisterBuffers(t),this.capturedCommandList.has(t)&&this.capturedCommandList.delete(t),this.capturedPendingKernels.has(t)&&this.capturedPendingKernels.delete(t),this.gpuDataManager.onReleaseSession(t)}onRunStart(t){this.currentSessionId=t,this.setQueryType()}}}),Vf={};$n(Vf,{init:()=>Hf});var Ti,Gf,Hf,bw=ee(()=>{xe(),ww(),Xr(),Oe(),Ti=class L0{constructor(e,r,n,a){this.module=e,this.dataType=r,this.data=n,this.dims=a}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let e=X.size(this.dims);return e===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,e)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let e=X.size(this.dims);return e===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,e)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let e=X.size(this.dims);return e===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,e)}reshape(e){if(X.size(e)!==X.size(this.dims))throw new Error("Invalid new shape");return new L0(this.module,this.dataType,this.data,e)}},Gf=class{constructor(t,e,r){this.module=t,this.backend=e,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=e.adapterInfo;let n=t.HEAPU32,a=r>>>2;this.opKernelContext=n[a++];let s=n[a++];this.outputCount=n[a++],this.customDataOffset=n[a++],this.customDataSize=n[a++];let i=[];for(let o=0;otypeof o=="number"?this.inputs[o]:o))??this.inputs,n=(e==null?void 0:e.outputs)??[],a=(o,l,u)=>new Ti(this.module,l,this.output(o,u),u),s=(o,l)=>{let u=na(o);if(!u)throw new Error(`Unsupported data type: ${o}`);let d=u*X.size(l),h=d>0?this.backend.gpuDataManager.create(d).id:0;return new Ti(this.module,o,h,l)};return this.backend.run(t,r,n,a,s,this.outputCount)}output(t,e){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+e.length)*4),a=n>>2;this.module.HEAPU32[a++]=e.length;for(let s=0;s{let a=e.jsepInit;if(!a)throw new Error("Failed to initialize JSEP. 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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};${t.activation};`}},kc=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently 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shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Oc=[2,3,1,0],Rc=(e,t,r)=>{let o=su(r,t),i=r.format==="NHWC",u=o.outputShape,a=u[i?3:1],c=t[0].dims[i?3:1];if(o.group!==1||a===1&&c===1){e.compute(Lo(t,o));return}let p=u[i?1:2],h=u[i?2:3],d=t[1].dims[2],y=t[1].dims[3],w=i?p*h:a,_=i?a:p*h,v=d*y*c,S=!0,A=e.kernelCustomData.wT??e.compute(yt(t[1],Oc),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=A);let I=[t[0],A],x=t.length===3;x&&(!i&&t[2].dims.length===1?I.push(t[2].reshape([t[2].dims[0],1,1])):I.push(t[2])),e.compute(ou(I,o,u,w,_,v,x,S),{inputs:I})},Bc=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let u=t.dilations;(u.length===0||u[0]===0)&&(u=[1]);let a=t.strides;(a.length===0||a[0]===0)&&(a=[1]);let c=t.pads;c.length===0&&(c=[0,0]),c=[0,c[0],0,c[1]],a=[1].concat(a),u=[1].concat(u),i=[1].concat(i);let p=su({...t,pads:c,strides:a,dilations:u,kernelShape:i},o);e.compute(Lo(o,p,h=>r?[h[0],h[2],h[3]]:[h[0],h[1],h[3]]))},du=(e,t)=>{kc(e.inputs,t),e.inputs[0].dims.length===3?Bc(e,t):Rc(e,e.inputs,t)}});var Dc,cu,pu,mu=Y(()=>{"use strict";ye();Se();Ze();_e();Dc=(e,t,r,o)=>{let i=M.size(t),u=t.length,a=U("input",e,u),c=j("output",e,u),p=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),h=M.normalizeAxis(p,u),d=y=>{let w=` i32(${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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= false;\n\n for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n var offset = i;\n for (var j = 0u; j < ${S-1}u; j++) {\n offsets[j] = offset / ${fe("uniforms.kernelStrides","j",S)};\n offset -= offsets[j] * ${fe("uniforms.kernelStrides","j",S)};\n }\n offsets[${S-1}] = offset;\n\n isPad = false;\n for (var j = ${r-S}u; j < ${r}u; j++) {\n xIndices[j] = indices[j] * ${fe("uniforms.strides",`j - ${r-S}u`,S)}\n + offsets[j - ${r-S}u] - ${fe("uniforms.pads","j - 2u",A)};\n ${I}\n }\n ${a}\n\n output[global_idx] = value;\n }`}},Qu=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,hp=e=>`${Qu(e)};${e.countIncludePad}`,gp=e=>`${Qu(e)};${e.storageOrder};${e.dilations}`,Ju=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),ed=(e,t,r,o)=>{let[i,u]=Yu(t,o,r),a=U("x",t.dataType,t.dims.length),c=a.type.value,p="value += x_val;",h="";i.countIncludePad?h+=`value /= 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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'}),Lr,zt,En,ua,da,Lo,Ai,tn,rn,tm,ca,rm,nm,am,im,sm,om,lm,um=ee(()=>{var t;rr(),vw(),ra(),Lr=()=>!!Ue.wasm.proxy&&typeof document<"u",En=!1,ua=!1,da=!1,Ai=new Map,tn=(e,r)=>{let n=Ai.get(e);n?n.push(r):Ai.set(e,[r])},rn=()=>{if(En||!ua||da||!zt)throw new Error("worker not 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${t[3]}`)}},cm=class{async fetchModelAndCopyToWasmMemory(t){return am(await di(t))}async loadModel(t,e){tr();let r;typeof t=="string"?typeof process<"u"&&process.versions&&process.versions.node?r=await di(t):r=await this.fetchModelAndCopyToWasmMemory(t):r=t,[this.sessionId,this.inputNames,this.outputNames]=await im(r,e),Yt()}async dispose(){return sm(this.sessionId)}async run(t,e,r){tr();let n=[],a=[];Object.entries(t).forEach(h=>{let f=h[0],g=h[1],p=this.inputNames.indexOf(f);if(p===-1)throw new Error(`invalid input '${f}'`);n.push(g),a.push(p)});let s=[],i=[];Object.entries(e).forEach(h=>{let f=h[0],g=h[1],p=this.outputNames.indexOf(f);if(p===-1)throw new Error(`invalid output '${f}'`);s.push(g),i.push(p)});let o=n.map((h,f)=>Uo(h,()=>`input "${this.inputNames[a[f]]}"`)),l=s.map((h,f)=>h?Uo(h,()=>`output "${this.outputNames[i[f]]}"`):null),u=await om(this.sessionId,a,o,i,l,r),d={};for(let h=0;h{rr(),um(),xw(),pm=()=>{if((typeof Ue.wasm.initTimeout!="number"||Ue.wasm.initTimeout<0)&&(Ue.wasm.initTimeout=0),typeof Ue.wasm.simd!="boolean"&&(Ue.wasm.simd=!0),typeof Ue.wasm.proxy!="boolean"&&(Ue.wasm.proxy=!1),typeof Ue.wasm.trace!="boolean"&&(Ue.wasm.trace=!1),typeof Ue.wasm.numThreads!="number"||!Number.isInteger(Ue.wasm.numThreads)||Ue.wasm.numThreads<=0){(typeof self<"u"&&!self.crossOriginIsolated||typeof process<"u"&&process.versions&&process.versions.node)&&(Ue.wasm.numThreads=1);let t=typeof navigator>"u"?(void 0)().length:navigator.hardwareConcurrency;Ue.wasm.numThreads=Math.min(4,Math.ceil((t||1)/2))}},hm=class{async init(t){pm(),await rm(),await nm(t)}async createInferenceSessionHandler(t,e){let r=new cm;return await r.loadModel(t,e),Promise.resolve(r)}}}),fm={};$n(fm,{wasmBackend:()=>mm});var mm,kw=ee(()=>{Sw(),mm=new hm});rr(),rr(),rr();var Ew="1.18.0",Cw=au;{let t=(kw(),jr(fm)).wasmBackend;qr("webgpu",t,5),qr("webnn",t,5),qr("cpu",t,10),qr("wasm",t,10)}Object.defineProperty(Ue.versions,"web",{value:Ew,enumerable:!0});/** +* @license +* Copyright 2021 Google LLC. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================================= +*//** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */var Tw=Object.freeze({__proto__:null,get InferenceSession(){return Bs},get TRACE(){return ea},get TRACE_FUNC_BEGIN(){return tr},get TRACE_FUNC_END(){return Yt},get Tensor(){return kt},get TrainingSession(){return Ds},default:Cw,get env(){return Ue},get registerBackend(){return qr}});const Aw=(t,e)=>{const r=typeof 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0&&(o=e),o.format="RGBA",o.height=d,o.width=h,e!==void 0){const f=l();f.width=h,f.height=d;const g=u(f);if(g!=null)g.putImageData(t,0,0),i=g.getImageData(0,0,h,d).data;else throw new Error("Can not access image data")}else i=t.data}else if(a){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const d=l();d.width=t.width,d.height=t.height;const h=u(d);if(h!=null){const f=t.height,g=t.width;return h.drawImage(t,0,0,g,f),i=h.getImageData(0,0,g,f).data,o.height=f,o.width=g,Wo(i,o)}else throw new Error("Can not access image data")}else{if(s)return new Promise((d,h)=>{const f=l(),g=u(f);if(!t||!g)return h();const p=new Image;p.crossOrigin="Anonymous",p.src=t,p.onload=()=>{f.width=p.width,f.height=p.height,g.drawImage(p,0,0,f.width,f.height);const w=g.getImageData(0,0,f.width,f.height);o.height=f.height,o.width=f.width,d(Wo(w.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(i!==void 0)return Wo(i,o);throw new Error("Input data provided is not supported - aborted tensor creation")},Ow=(t,e)=>{const{width:r,height:n,download:a,dispose:s}=e,i=[1,n,r,4];return new mr({location:"texture",type:"float32",texture:t,dims:i,download:a,dispose:s})},zw=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:s}=e;return new mr({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:s})},Pw=(t,e,r)=>new mr({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),Cn=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 gm=!1;const Rw=()=>{if(!gm){gm=!0;const t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,r=typeof Float16Array<"u"&&Float16Array.from;t&&(Cn.set("int64",BigInt64Array),Ii.set(BigInt64Array,"int64")),e&&(Cn.set("uint64",BigUint64Array),Ii.set(BigUint64Array,"uint64")),r?(Cn.set("float16",Float16Array),Ii.set(Float16Array,"float16")):Cn.set("float16",Uint16Array)}},Bw=t=>{let e=1;for(let r=0;r{switch(t.location){case"cpu":return new mr(t.type,t.data,e);case"cpu-pinned":return new mr({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new mr({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new mr({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let mr=class{constructor(e,r,n){Rw();let a,s;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,a=e.type,s=e.dims,e.location){case"cpu-pinned":{const o=Cn.get(a);if(!o)throw new TypeError(`unsupported type "${a}" to create tensor from pinned buffer`);if(!(e.data instanceof o))throw new TypeError(`buffer should be of type ${o.name}`);this.cpuData=e.data;break}case"texture":{if(a!=="float32")throw new TypeError(`unsupported type "${a}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break}case"gpu-buffer":{if(a!=="float32"&&a!=="float16"&&a!=="int32"&&a!=="int64"&&a!=="uint32"&&a!=="uint8"&&a!=="bool")throw new TypeError(`unsupported type "${a}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let o,l;if(typeof e=="string")if(a=e,l=n,e==="string"){if(!Array.isArray(r))throw new TypeError("A string tensor's data must be a string array.");o=r}else{const u=Cn.get(e);if(u===void 0)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(r)){if(e==="float16"&&u===Uint16Array)throw new TypeError("Creating a float16 tensor from number array is not supported. Please use Uint16Array as data.");e==="uint64"||e==="int64"?o=u.from(r,BigInt):o=u.from(r)}else if(r instanceof u)o=r;else throw new TypeError(`A ${a} tensor's data must be type of ${u}`)}else if(l=r,Array.isArray(e)){if(e.length===0)throw new TypeError("Tensor type cannot be inferred from an empty array.");const u=typeof e[0];if(u==="string")a="string",o=e;else if(u==="boolean")a="bool",o=Uint8Array.from(e);else throw new TypeError(`Invalid element type of data array: ${u}.`)}else{const u=Ii.get(e.constructor);if(u===void 0)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);a=u,o=e}if(l===void 0)l=[o.length];else if(!Array.isArray(l))throw new TypeError("A tensor's dims must be a number array");s=l,this.cpuData=o,this.dataLocation="cpu"}const i=Bw(s);if(this.cpuData&&i!==this.cpuData.length)throw new Error(`Tensor's size(${i}) does not match data length(${this.cpuData.length}).`);this.type=a,this.dims=s,this.size=i}static async fromImage(e,r){return Mw(e,r)}static fromTexture(e,r){return Ow(e,r)}static fromGpuBuffer(e,r){return zw(e,r)}static fromPinnedBuffer(e,r,n){return Pw(e,r,n)}toDataURL(e){return Aw(this,e)}toImageData(e){return Iw(this,e)}get data(){if(this.ensureValid(),!this.cpuData)throw new Error("The data is not on CPU. Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":{if(!this.downloader)throw new Error("The current tensor is not created with a specified data downloader.");if(this.isDownloading)throw new Error("The current tensor is being downloaded.");try{this.isDownloading=!0;const r=await this.downloader();return this.downloader=void 0,this.dataLocation="cpu",this.cpuData=r,e&&this.disposer&&(this.disposer(),this.disposer=void 0),r}finally{this.isDownloading=!1}}default:throw new Error(`cannot get data from location: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return Dw(this,e)}};const Nw=mr,pa=[];let Vo,nn;Rr.IS_NODE_ENV?(nn=Ve??vr,pa.push("cpu"),Vo=["cpu"]):(nn=Tw,Rr.IS_WEBGPU_AVAILABLE&&pa.push("webgpu"),pa.push("wasm"),Vo=["wasm"]);const Fw=nn.InferenceSession;function Lw(t){let e=Vo;if(t){if(!pa.includes(t))throw new Error(`Unsupported device: "${t}". Should be one of: ${pa.join(", ")}.`);e=[t]}return e}async function _m(t,e){return await Fw.create(t,e)}function ym(t){return t instanceof nn.Tensor}const lr=nn==null?void 0:nn.env;lr!=null&&lr.wasm&&(lr.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.18.0/dist/",lr.wasm.proxy=!Rr.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(lr.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(lr.wasm.simd=!1));function Uw(){var t;return(t=lr==null?void 0:lr.wasm)==null?void 0:t.proxy}Mt.backends.onnx=lr;const Tn=async(t,e,r)=>{const n=await _m(new Uint8Array(t),e);return async a=>{const s=Object.fromEntries(Object.entries(a).map(([o,l])=>[o,l.ort_tensor])),i=await n.run(s);return Array.isArray(r)?r.map(o=>new ce(i[o])):new ce(i[r])}};class ha{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=Tn([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=Tn([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=Tn([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=Tn([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=Tn([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=Tn([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}}B(ha,"session_options",{});const wm=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{constructor(...e){B(this,"ort_tensor");return ym(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new Nw(e[0],e[1],e[2]),new Proxy(this,{get:(r,n)=>{if(typeof n=="string"){let a=Number(n);if(Number.isInteger(a))return r._getitem(a)}return r[n]},set:(r,n,a)=>r[n]=a})}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}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...r]=this.dims;if(r.length>0){const n=r.reduce((a,s)=>a*s);for(let a=0;a0){const a=n.reduce((s,i)=>s*i);return this._subarray(e,a,n)}else return new ce(this.type,[this.data[e]],n)}indexOf(e){const r=this.data;for(let n=0;nf)throw new Error(`Invalid slice: ${d}`);let g=[Math.max(h,0),Math.min(f,this.dims[u])];n.push(g),r.push(g[1]-g[0])}else throw new Error(`Invalid slice: ${d}`)}let a=n.map(([u,d])=>d-u),s=a.reduce((u,d)=>u*d);const i=this.data;let o=new i.constructor(s);const l=this.stride();for(let u=0;u=0;--h){const g=a[h];d+=(f%g+n[h][0])*l[h],f=Math.floor(f/g)}o[u]=i[d]}return new ce(this.type,o,r)}permute(...e){return Vw(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: ${e}`);const a=this.data;if(r===null){let o=a.reduce((l,u)=>l+u**e,0)**(1/e);return new ce(this.type,[o],[])}r=gr(r,this.dims.length);const s=this.dims.slice();s[r]=1;const i=new a.constructor(a.length/this.dims[r]);for(let o=0;o=0;--u){const f=this.dims[u];if(u!==r){const g=d%f;l+=g*h,h*=s[u]}d=Math.floor(d/f)}i[l]+=a[o]**e}if(e!==1)for(let o=0;o=0;--l){const h=this.dims[l];if(l!==r){const f=u%h;o+=f*d,d*=this.dims[l]}u=Math.floor(u/h)}a[i]/=s[o]}return this}normalize(e=2,r=1){return this.clone().normalize_(e,r)}stride(){return qw(this.dims)}squeeze(e=null){return new ce(this.type,this.data,vm(this.dims,e))}squeeze_(e=null){return this.dims=vm(this.dims,e),this}unsqueeze(e=null){return new ce(this.type,this.data,$m(this.dims,e))}unsqueeze_(e=null){return this.dims=$m(this.dims,e),this}flatten_(e=0,r=-1){r=(r+this.dims.length)%this.dims.length;let n=this.dims.slice(0,e),a=this.dims.slice(e,r+1),s=this.dims.slice(r+1);return this.dims=[...n,a.reduce((i,o)=>i*o,1),...s],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let a=0;ao!==r?s*i:s,1);e[r]=n.length/a}return new ce(this.type,n,e)}neg_(){const e=this.data;for(let r=0;rs*i);if(r!==n)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let a=t;for(let s=e.length-1;s>=0;s--)a=a.reduce((i,o)=>{let l=i[i.length-1];return l.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 $m(t,e){return e=gr(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function gr(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 Xt(t,e=0){e=gr(e,t[0].dims.length);const r=t[0].dims.slice();r[e]=t.reduce((i,o)=>i+o.dims[e],0);const n=r.reduce((i,o)=>i*o,1),a=new t[0].data.constructor(n),s=t[0].type;if(e===0){let i=0;for(const o of t){const l=o.data;a.set(l,i),i+=l.length}}else{let i=0;for(let o=0;o=0;--f){const w=u[f];let v=g%w;f===e&&(v+=i),h+=v*p,p*=r[f],g=Math.floor(g/w)}a[h]=l[d]}i+=u[e]}}return new ce(s,a,r)}function fa(t,e=0){return Xt(t.map(r=>r.unsqueeze(e)),e)}function jw(t,e=null,r=1,n=!1){const a=t.data,s=t.dims;if(e===null){const f=a.reduce((v,x)=>v+x,0)/a.length,g=Math.sqrt(a.reduce((v,x)=>v+(x-f)**2,0)/(a.length-r)),p=new ce(t.type,[f],[]);return[new ce(t.type,[g],[]),p]}e=gr(e,s.length);const i=Ho(t,e,n),o=i.data,l=s.slice();l[e]=1;const u=new a.constructor(a.length/s[e]);for(let h=0;h=0;--g){const v=s[g];if(g!==e){const x=p%v;f+=x*w,w*=l[g]}p=Math.floor(p/v)}u[f]+=(a[h]-o[f])**2}for(let h=0;hl+u,0);return new ce(t.type,[o/n.length],[])}const a=t.dims;e=gr(e,a.length);const s=a.slice();s[e]=1;const i=new n.constructor(n.length/a[e]);for(let o=0;o=0;--u){const f=a[u];if(u!==e){const g=d%f;l+=g*h,h*=s[u]}d=Math.floor(d/f)}i[l]+=n[o]}if(a[e]!==1)for(let o=0;o=0;--r)e[r]=n,n*=t[r];return e}function jo(t,e,r,n){const a=t.reduce((s,i)=>s*i,1);return new ce(r,new n(a).fill(e),t)}function Kw(t,e){let r,n;return r="float32",n=Float32Array,jo(t,e,r,n)}function Yw(t,e){return Kw(t.dims,e)}function In(t){return jo(t,1n,"int64",BigInt64Array)}function Xw(t){return In(t.dims)}function Qw(t){return jo(t,0n,"int64",BigInt64Array)}function Zw(t){return Qw(t.dims)}function Jw(t,e){if(t.dims.length!==2)throw new Error("The tensor must have 2 dimensions");if(t.dims.at(-1)%8!==0)throw new Error("The last dimension of the tensor must be a multiple of 8");if(!["binary","ubinary"].includes(e))throw new Error("The precision must be either 'binary' or 'ubinary'");const r=e==="binary",n=r?"int8":"uint8",a=r?Int8Array:Uint8Array,s=t.data,i=new a(s.length/8);for(let o=0;o0?1:0,u=Math.floor(o/8),d=o%8;i[u]|=l<<7-d,r&&d===0&&(i[u]-=128)}return new ce(n,i,[t.dims[0],t.dims[1]/8])}class eb{constructor(e=(n,a)=>n>a,r=1/0){this._heap=[],this._comparator=e,this._maxSize=r}get size(){return this._heap.length}isEmpty(){return this.size===0}peek(){return this._heap[0]}push(...e){return this.extend(e)}extend(e){for(const r of e)if(this.size0&&this._swap(0,r),this._heap.pop(),this._siftDown(),e}replace(e){const r=this.peek();return this._heap[0]=e,this._siftDown(),r}_parent(e){return(e+1>>>1)-1}_left(e){return(e<<1)+1}_right(e){return e+1<<1}_greater(e,r){return this._comparator(this._heap[e],this._heap[r])}_swap(e,r){const n=this._heap[e];this._heap[e]=this._heap[r],this._heap[r]=n}_siftUp(){this._siftUpFrom(this.size-1)}_siftUpFrom(e){for(;e>0&&this._greater(e,this._parent(e));)this._swap(e,this._parent(e)),e=this._parent(e)}_siftDown(){let e=0;for(;this._left(e)[]),this.endNodes=Array.from({length:this.len+1},()=>[]);const a=new ma(this.bosTokenId,0,0,0,0),s=new ma(this.eosTokenId,1,this.len,0,0);this.nodes.push(a.clone()),this.nodes.push(s.clone()),this.beginNodes[this.len].push(s),this.endNodes[0].push(a)}insert(e,r,n,a){const s=this.nodes.length,i=new ma(a,s,e,r,n);this.beginNodes[e].push(i),this.endNodes[e+r].push(i),this.nodes.push(i)}viterbi(){const e=this.len;let r=0;for(;r<=e;){if(this.beginNodes[r].length==0)return[];for(let o of this.beginNodes[r]){o.prev=null;let l=0,u=null;for(let d of this.endNodes[r]){const h=d.backtraceScore+o.score;(u===null||h>l)&&(u=d.clone(),l=h)}if(u!==null)o.prev=u,o.backtraceScore=l;else return[]}++r}const n=[],s=this.beginNodes[e][0].prev;if(s===null)return[];let i=s.clone();for(;i.prev!==null;)n.push(i.clone()),i=i.clone().prev.clone();return n.reverse(),n}piece(e){return this.sentence.slice(e.pos,e.pos+e.length)}tokens(){return this.viterbi().map(r=>this.piece(r))}tokenIds(){return this.viterbi().map(r=>r.tokenId)}}class ma{constructor(e,r,n,a,s){this.tokenId=e,this.nodeId=r,this.pos=n,this.length=a,this.score=s,this.prev=null,this.backtraceScore=0}clone(){const e=new ma(this.tokenId,this.nodeId,this.pos,this.length,this.score);return e.prev=this.prev,e.backtraceScore=this.backtraceScore,e}}var U=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"}),xm=Object.freeze({set:U.Set,for:U.For,in:U.In,is:U.Is,if:U.If,else:U.Else,endif:U.EndIf,elif:U.ElseIf,endfor:U.EndFor,and:U.And,or:U.Or,not:U.Not,"not 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ib(t,e={}){return t.endsWith(` +`)&&(t=t.slice(0,-1)),t=t.replace(/{#.*?#}/gs,"{##}"),e.lstrip_blocks&&(t=t.replace(/^[ \t]*({[#%])/gm,"$1")),e.trim_blocks&&(t=t.replace(/([#%]})\n/g,"$1")),t.replace(/{##}/g,"").replace(/-%}\s*/g,"%}").replace(/\s*{%-/g,"{%").replace(/-}}\s*/g,"}}").replace(/\s*{{-/g,"{{")}function sb(t,e={}){var i,o,l;const r=[],n=ib(t,e);let a=0;const s=u=>{let d="";for(;u(n[a]);){if(n[a]==="\\"){if(++a,a>=n.length)throw new SyntaxError("Unexpected end of input");const h=n[a++],f=ab.get(h);if(f===void 0)throw new SyntaxError(`Unexpected escaped character: ${h}`);d+=f;continue}if(d+=n[a++],a>=n.length)throw new SyntaxError("Unexpected end of input")}return d};e:for(;a0){r.push(new an(h,U.Text));continue}}s(h=>/\s/.test(h));const d=n[a];if(d==="-"||d==="+"){const h=(o=r.at(-1))==null?void 0:o.type;if(h===U.Text||h===void 0)throw new SyntaxError(`Unexpected character: ${d}`);switch(h){case U.Identifier:case U.NumericLiteral:case U.BooleanLiteral:case 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this.evaluateIdentifier(t,e);case"CallExpression":return this.evaluateCallExpression(t,e);case"MemberExpression":return this.evaluateMemberExpression(t,e);case"UnaryExpression":return this.evaluateUnaryExpression(t,e);case"BinaryExpression":return this.evaluateBinaryExpression(t,e);case"FilterExpression":return this.evaluateFilterExpression(t,e);case"TestExpression":return this.evaluateTestExpression(t,e);default:throw new SyntaxError(`Unknown node type: ${t.type}`)}}};function Oi(t){switch(typeof t){case"number":return new Ye(t);case"string":return new Re(t);case"boolean":return new at(t);case"object":return t===null?new ya:Array.isArray(t)?new et(t.map(Oi)):new ur(new Map(Object.entries(t).map(([e,r])=>[e,Oi(r)])));case"function":return new Cr((e,r)=>{const n=t(...e.map(a=>a.value))??null;return Oi(n)});default:throw new Error(`Cannot convert to runtime value: ${t}`)}}var Sb=class{constructor(t){B(this,"parsed");const e=sb(t,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=bb(e)}render(t){const e=new Ko;e.set("false",!1),e.set("true",!0),e.set("raise_exception",a=>{throw new Error(a)}),e.set("range",vb);for(const[a,s]of Object.entries(t))e.set(a,s);return new xb(e).run(this.parsed).value}};const Mm=[["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"]],zi=new Map(Mm),kb=new Map([...Mm.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 Om(t){t=t.toLowerCase();let e=kb.get(t);if(e===void 0)if(zi.has(t))e=t;else{const n=t.length===2?zi.keys():zi.values();throw new Error(`Language "${t}" is not supported. Must be one of: ${JSON.stringify(n)}`)}return e}const Yo="https://github.com/xenova/transformers.js/issues/new/choose";async function zm(t,e){const r=await Promise.all([Br(t,"tokenizer.json",!0,e),Br(t,"tokenizer_config.json",!0,e)]);return e.legacy!==null&&(r[1].legacy=e.legacy),r}function Eb(t,e){const r=[];let n=0;for(const a of t.matchAll(e)){const s=a[0];n0&&r.push(s),n=a.index+s.length}return n=19968&&t<=40959||t>=13312&&t<=19903||t>=131072&&t<=173791||t>=173824&&t<=177983||t>=177984&&t<=178207||t>=178208&&t<=183983||t>=63744&&t<=64255||t>=194560&&t<=195103}function Ab(t,e,r){const n=[];let a=0;for(;athis.tokens_to_ids.get(r)??this.unk_token_id)}convert_ids_to_tokens(e){return e.map(r=>this.vocab[r]??this.unk_token)}}class Pb extends wa{constructor(e){super(e),this.tokens_to_ids=Xo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.max_input_chars_per_word=e.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r}encode(e){const r=[];for(const n of e){const a=[...n];if(a.length>this.max_input_chars_per_word){r.push(this.unk_token);continue}let s=!1,i=0;const o=[];for(;i0&&(d=this.config.continuing_subword_prefix+d),this.tokens_to_ids.has(d)){u=d;break}--l}if(u===null){s=!0;break}o.push(u),i=l}s?r.push(this.unk_token):r.push(...o)}return r}}class Rb extends wa{constructor(e,r){super(e);const n=e.vocab.length;this.vocab=new Array(n),this.scores=new Array(n);for(let a=0;a[a,s])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=r.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=Fl(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new tb,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(e){const r=e.sentence,n=r.length;let a=0;for(;a{const t=[...Array.from({length:94},(a,s)=>s+33),...Array.from({length:12},(a,s)=>s+161),...Array.from({length:82},(a,s)=>s+174)],e=t.slice();let r=0;for(let a=0;a<256;++a)t.includes(a)||(t.push(a),e.push(256+r),r+=1);const n=e.map(a=>String.fromCharCode(a));return Object.fromEntries(t.map((a,s)=>[a,n[s]]))})(),Bb=q0(Bm);class Db extends wa{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=Xo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[r,n]of this.tokens_to_ids)this.vocab[n]=r;this.bpe_ranks=new Map(e.merges.map((r,n)=>[r,n])),this.merges=e.merges.map(r=>r.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=e.end_of_word_suffix,this.continuing_subword_suffix=e.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(e){if(e.length===0)return[];const r=this.cache.get(e);if(r!==void 0)return r;const n=Array.from(e);this.end_of_word_suffix&&(n[n.length-1]+=this.end_of_word_suffix);let a=[];if(n.length>1){const s=new eb((l,u)=>l.score`<0x${i.toString(16).toUpperCase().padStart(2,"0")}>`)):r.push(this.unk_token)}return r}}class Nb extends wa{constructor(e,r){super(e),this.tokens_to_ids=Xo(r.target_lang?e.vocab[r.target_lang]:e.vocab),this.bos_token=r.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=r.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=r.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=r.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[n,a]of this.tokens_to_ids)this.vocab[a]=n}encode(e){return e}}class Lt extends wt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new Kb(e);case"Precompiled":return new hv(e);case"Sequence":return new qb(e);case"Replace":return new Fb(e);case"NFC":return new Lb(e);case"NFKC":return new Ub(e);case"NFKD":return new Wb(e);case"Strip":return new Vb(e);case"StripAccents":return new Gb(e);case"Lowercase":return new Hb(e);case"Prepend":return new jb(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class Fb extends Lt{normalize(e){const r=Pi(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class Lb extends Lt{normalize(e){return e=e.normalize("NFC"),e}}class Ub extends Lt{normalize(e){return e=e.normalize("NFKC"),e}}class Wb extends Lt{normalize(e){return e=e.normalize("NFKD"),e}}class Vb extends Lt{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class Gb extends Lt{normalize(e){return e=Rm(e),e}}class Hb extends Lt{normalize(e){return e=e.toLowerCase(),e}}class jb extends Lt{normalize(e){return e=this.config.prepend+e,e}}class qb extends Lt{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>Lt.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class Kb extends Lt{_tokenize_chinese_chars(e){const r=[];for(let n=0;nthis.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class Yb extends Qt{constructor(e){super(),this.pattern=new RegExp(`[^\\s${zn}]+|[${zn}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class Xb extends Qt{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=Bm,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),s=>this.byte_encoder[s]).join(""))}}class Qb extends Qt{constructor(e){super(),this.config=e,this.pattern=Pi(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:Eb(e,this.pattern)}}class Zb extends Qt{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${zn}]+|[${zn}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Jb extends Qt{constructor(e){super(),this.config=e;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Pn extends wt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new ev(e);case"ByteLevel":return new Fm(e);case"RobertaProcessing":return new Nm(e);case"BertProcessing":return new Dm(e);case"Sequence":return new tv(e);default:throw new Error(`Unknown PostProcessor type: ${e.type}`)}}post_process(e,...r){throw Error("post_process should be implemented in subclass.")}_call(e,...r){return this.post_process(e,...r)}}class Dm extends Pn{constructor(e){super(e),this.cls=e.cls[0],this.sep=e.sep[0]}post_process(e,r=null,{add_special_tokens:n=!0}={}){n&&(e=gt([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const s=n&&this instanceof Nm?[this.sep]:[],i=n?[this.sep]:[];e=gt(e,s,r,i),a=gt(a,new Array(r.length+s.length+i.length).fill(1))}return{tokens:e,token_type_ids:a}}}class Nm extends Dm{}class ev extends Pn{constructor(e){super(e),this.single=e.single,this.pair=e.pair}post_process(e,r=null,{add_special_tokens:n=!0}={}){const a=r===null?this.single:this.pair;let s=[],i=[];for(const o of a)"SpecialToken"in o?n&&(s.push(o.SpecialToken.id),i.push(o.SpecialToken.type_id)):"Sequence"in o&&(o.Sequence.id==="A"?(s=gt(s,e),i=gt(i,new Array(e.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(s=gt(s,r),i=gt(i,new Array(r.length).fill(o.Sequence.type_id))));return{tokens:s,token_type_ids:i}}}class Fm extends Pn{post_process(e,r=null){return r&&(e=gt(e,r)),{tokens:e}}}class tv extends Pn{constructor(e){super(e),this.processors=e.processors.map(r=>Pn.fromConfig(r))}post_process(e,r=null,n={}){let a;for(const s of this.processors)if(s instanceof Fm)e=s.post_process(e).tokens,r&&(r=s.post_process(r).tokens);else{const i=s.post_process(e,r,n);e=i.tokens,a=i.token_type_ids}return{tokens:e,token_type_ids:a}}}class Ut extends wt{constructor(e){super(),this.config=e,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=e.trim_offsets}static fromConfig(e){if(e===null)return null;switch(e.type){case"WordPiece":return new sv(e);case"Metaspace":return new pv(e);case"ByteLevel":return new ov(e);case"Replace":return new rv(e);case"ByteFallback":return new nv(e);case"Fuse":return new av(e);case"Strip":return new iv(e);case"Sequence":return new uv(e);case"CTC":return new lv(e);case"BPEDecoder":return new dv(e);default:throw new Error(`Unknown Decoder type: ${e.type}`)}}_call(e){return this.decode(e)}decode(e){return this.decode_chain(e).join("")}decode_chain(e){throw Error("`decode_chain` should be implemented in subclass.")}}class rv extends Ut{decode_chain(e){const r=Pi(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class nv extends Ut{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const r=[];let n=[];for(const a of e){let s=null;if(a.length===6&&a.startsWith("<0x")&&a.endsWith(">")){const i=parseInt(a.slice(3,5),16);isNaN(i)||(s=i)}if(s!==null)n.push(s);else{if(n.length>0){const i=this.text_decoder.decode(Uint8Array.from(n));r.push(i),n=[]}r.push(a)}}if(n.length>0){const a=this.text_decoder.decode(Uint8Array.from(n));r.push(a),n=[]}return r}}class av extends Ut{decode_chain(e){return[e.join("")]}}class iv extends Ut{constructor(e){super(e),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(e){return e.map(r=>{let n=0;for(let s=0;s(n!==0&&(r.startsWith(this.config.prefix)?r=r.replace(this.config.prefix,""):r=" "+r),this.cleanup&&(r=Qo(r)),r))}}class ov extends Ut{constructor(e){super(e),this.byte_decoder=Bb,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(e){const r=e.join(""),n=new Uint8Array([...r].map(s=>this.byte_decoder[s]));return this.text_decoder.decode(n)}decode_chain(e){const r=[];let n=[];for(const a of e)this.added_tokens.find(s=>s.content===a)!==void 0?(n.length>0&&(r.push(this.convert_tokens_to_string(n)),n=[]),r.push(a)):n.push(a);return n.length>0&&r.push(this.convert_tokens_to_string(n)),r}}class lv extends Ut{constructor(e){super(e),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(e){if(e.length===0)return"";const r=[e[0]];for(let s=1;ss!==this.pad_token).join("");return this.cleanup&&(a=Qo(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class uv extends Ut{constructor(e){super(e),this.decoders=e.decoders.map(r=>Ut.fromConfig(r))}decode_chain(e){return this.decoders.reduce((r,n)=>n.decode_chain(r),e)}}class dv extends Ut{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map((r,n)=>r.replaceAll(this.suffix,n===e.length-1?"":" "))}}class cv extends Ut{decode_chain(e){let r="";for(let n=1;nn.normalize("NFKC")).join("～"):e=e.normalize("NFKC"),e}}class fv extends Qt{constructor(e){super(),this.tokenizers=e.pretokenizers.map(r=>Qt.fromConfig(r))}pre_tokenize_text(e,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[e])}}class mv extends Qt{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class gv extends Qt{constructor(e){super()}pre_tokenize_text(e,r){return Ib(e)}}class _v extends Qt{constructor(e){super(),this.config=e,this.pattern=Pi(this.config.pattern),this.content=this.config.content}pre_tokenize_text(e,r){return this.pattern===null?[e]:[e.replaceAll(this.pattern,this.config.content)]}}const yv=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function wv(t,e,r,n){for(const a of Object.keys(t)){const s=e-t[a].length,i=r(a),o=new Array(s).fill(i);t[a]=n==="right"?gt(t[a],o):gt(o,t[a])}}function bv(t,e){for(const r of Object.keys(t))t[r].length=e}class Ce extends wt{constructor(r,n){super();B(this,"return_token_type_ids",!1);B(this,"_default_chat_template",`{% for message in messages %}{{'<|im_start|>' + message['role'] + ' +' + message['content'] + '<|im_end|>' + ' +'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant +' }}{% endif %}`);B(this,"padding_side","right");this._tokenizer_config=n,this.normalizer=Lt.fromConfig(r.normalizer),this.pre_tokenizer=Qt.fromConfig(r.pre_tokenizer),this.model=wa.fromConfig(r.model,n),this.post_processor=Pn.fromConfig(r.post_processor),this.decoder=Ut.fromConfig(r.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const a of r.added_tokens){const s=new zb(a);this.added_tokens.push(s),this.model.tokens_to_ids.set(s.content,s.id),this.model.vocab[s.id]=s.content,s.special&&(this.special_tokens.push(s.content),this.all_special_ids.push(s.id))}if(this.additional_special_tokens=n.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.toSorted((a,s)=>s.content.length-a.content.length).map(a=>`${a.lstrip?"\\s*":""}(${Bl(a.content)})${a.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.model_max_length=n.model_max_length,this.remove_space=n.remove_space,this.clean_up_tokenization_spaces=n.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=n.do_lowercase_and_remove_accent??!1,n.padding_side&&(this.padding_side=n.padding_side),this.legacy=!1,this.chat_template=n.chat_template??null,Array.isArray(this.chat_template)){const a=Object.create(null);for(const{name:s,template:i}of this.chat_template){if(typeof s!="string"||typeof i!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');a[s]=i}this.chat_template=a}this._compiled_template_cache=new Map}getToken(...r){for(const n of r){const a=this._tokenizer_config[n];if(a)if(typeof a=="object"){if(a.__type==="AddedToken")return a.content;throw Error(`Unknown token: ${a}`)}else return a}return null}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:s=null,local_files_only:i=!1,revision:o="main",legacy:l=null}={}){const u=await zm(r,{progress_callback:n,config:a,cache_dir:s,local_files_only:i,revision:o,legacy:l});return new this(...u)}_call(r,{text_pair:n=null,add_special_tokens:a=!0,padding:s=!1,truncation:i=null,max_length:o=null,return_tensor:l=!0,return_token_type_ids:u=null}={}){const d=Array.isArray(r);let h;if(d){if(r.length===0)throw Error("text array must be non-empty");if(n!==null){if(Array.isArray(n)){if(r.length!==n.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");h=r.map((g,p)=>this._encode_plus(g,{text_pair:n[p],add_special_tokens:a,return_token_type_ids:u}))}else h=r.map(g=>this._encode_plus(g,{add_special_tokens:a,return_token_type_ids:u}))}else{if(r==null)throw Error("text may not be null or undefined");if(Array.isArray(n))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");h=[this._encode_plus(r,{text_pair:n,add_special_tokens:a,return_token_type_ids:u})]}if(o===null?s==="max_length"?o=this.model_max_length:o=qt(h.map(g=>g.input_ids.length))[0]:i||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),o=Math.min(o,this.model_max_length??1/0),s||i)for(let g=0;go?i&&bv(h[g],o):s&&wv(h[g],o,p=>p==="input_ids"?this.pad_token_id:0,this.padding_side));const f={};if(l){if(!(s&&i)&&h.some(p=>{var w;for(const v of Object.keys(p))if(p[v].length!==((w=h[0][v])==null?void 0:w.length))return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const g=[h.length,h[0].input_ids.length];for(const p of Object.keys(h[0]))f[p]=new ce("int64",BigInt64Array.from(h.flatMap(w=>w[p]).map(BigInt)),g)}else{for(const g of Object.keys(h[0]))f[g]=h.map(p=>p[g]);if(!d)for(const g of Object.keys(f))f[g]=f[g][0]}return f}_encode_text(r){return r===null?null:(this.added_tokens_regex?r.split(this.added_tokens_regex).filter(s=>s):[r]).map((s,i)=>{if(this.added_tokens.find(l=>l.content===s)!==void 0)return s;{if(this.remove_space===!0&&(s=s.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(s=Cb(s)),this.normalizer!==null&&(s=this.normalizer(s)),s.length===0)return[];const l=this.pre_tokenizer!==null?this.pre_tokenizer(s,{section_index:i}):[s];return this.model(l)}}).flat()}_encode_plus(r,{text_pair:n=null,add_special_tokens:a=!0,return_token_type_ids:s=null}={}){const{tokens:i,token_type_ids:o}=this._tokenize_helper(r,{pair:n,add_special_tokens:a}),l=this.model.convert_tokens_to_ids(i),u={input_ids:l,attention_mask:new Array(l.length).fill(1)};return(s??this.return_token_type_ids)&&o&&(u.token_type_ids=o),u}_tokenize_helper(r,{pair:n=null,add_special_tokens:a=!1}={}){const s=this._encode_text(r),i=this._encode_text(n);return this.post_processor?this.post_processor(s,i,{add_special_tokens:a}):{tokens:gt(s??[],i??[])}}tokenize(r,{pair:n=null,add_special_tokens:a=!1}={}){return this._tokenize_helper(r,{pair:n,add_special_tokens:a}).tokens}encode(r,{text_pair:n=null,add_special_tokens:a=!0,return_token_type_ids:s=null}={}){return this._encode_plus(r,{text_pair:n,add_special_tokens:a,return_token_type_ids:s}).input_ids}batch_decode(r,n={}){return r instanceof ce&&(r=r.tolist()),r.map(a=>this.decode(a,n))}decode(r,n={}){if(r instanceof ce&&(r=Pm(r)),!Array.isArray(r)||r.length===0||!K0(r[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(r,n)}decode_single(r,{skip_special_tokens:n=!1,clean_up_tokenization_spaces:a=null}){let s=this.model.convert_ids_to_tokens(r);n&&(s=s.filter(o=>!this.special_tokens.includes(o)));let i=this.decoder?this.decoder(s):s.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(i=i.replaceAll(this.decoder.end_of_word_suffix," "),n&&(i=i.trim())),(a??this.clean_up_tokenization_spaces)&&(i=Qo(i)),i}get default_chat_template(){return this._warned_about_chat_template||(console.warn("No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format. If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information."),this._warned_about_chat_template=!0),this._default_chat_template}apply_chat_template(r,{chat_template:n=null,add_generation_prompt:a=!1,tokenize:s=!0,padding:i=!1,truncation:o=!1,max_length:l=null,return_tensor:u=!0,return_dict:d=!1,tokenizer_kwargs:h={},...f}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null&&this.default_chat_template&&typeof this.default_chat_template=="object"){const v=this.chat_template??this.default_chat_template;if(n!==null&&Object.hasOwn(v,n))n=v[n];else if(n===null&&"default"in v)n=v.default;else if(n===null)throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(v).sort()}.`)}else n??(n=this.chat_template??this.default_chat_template);if(typeof n!="string")throw Error(`chat_template must be a string, but got ${typeof n}`);let g=this._compiled_template_cache.get(n);g===void 0&&(g=new Sb(n),this._compiled_template_cache.set(n,g));const p=Object.create(null);for(const v of yv){const x=this.getToken(v);x&&(p[v]=x)}const w=g.render({messages:r,add_generation_prompt:a,...p,...f});if(s){const v=this._call(w,{add_special_tokens:!1,padding:i,truncation:o,max_length:l,return_tensor:u,...h});return d?v:v.input_ids}return w}}class vv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class $v extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class xv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class Sv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class kv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class Ev extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class Cv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class Tv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class Av extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class Iv extends Ce{}class Mv extends Ce{}class Ov extends Ce{constructor(r,n){super(r,n);B(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class zv extends Ce{constructor(){super(...arguments);B(this,"return_token_type_ids",!0)}}class Pv extends Ce{}class Um extends Ce{constructor(){super(...arguments);B(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}}class Rv extends Ce{}class Wm extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return Zo(this,e,r,n)}}class Bv extends Wm{}class Dv extends Ce{}class Nv extends Um{constructor(e,r){var s,i;const n=".,!?…。，、।۔،",a=(i=(s=e.pre_tokenizer)==null?void 0:s.pretokenizers[0])==null?void 0:i.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(e,r)}}const Ri="▁";class Vm extends Ce{constructor(r,n){super(r,n);B(this,"_default_chat_template",`{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<> +' + system_message + ' +<> + +' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<> +' + content.strip() + ' +<> + +' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}`);B(this,"DEFAULT_SYSTEM_PROMPT",`You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. + +If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.`);B(this,"padding_side","left");this.use_default_system_prompt=n.use_default_system_prompt??!1,this.legacy=n.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new Lm({replacement:Ri,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(r){if(r===null)return null;if(this.legacy||r.length===0)return super._encode_text(r);let n=super._encode_text(Ri+r.replaceAll(Ri," "));return n.length>1&&n[0]===Ri&&this.special_tokens.includes(n[1])&&(n=n.slice(1)),n}get default_chat_template(){return super.default_chat_template.replaceAll("USE_DEFAULT_PROMPT",this.use_default_system_prompt?"true":"false").replaceAll("DEFAULT_SYSTEM_MESSAGE",this.DEFAULT_SYSTEM_PROMPT.replaceAll(` +`,"\\n").replaceAll("'","\\'"))}}class Fv extends Vm{}class Lv extends Ce{}class Uv extends Ce{}class Wv extends Ce{}class Vv extends Ce{}class Gv extends Ce{}class Hv extends Ce{}class jv extends Ce{constructor(){super(...arguments);B(this,"_default_chat_template",`{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + ' +' + message['content'] | trim + ' +' }}{% endfor %}{% if add_generation_prompt %}{{'model +'}}{% endif %}`)}}class qv extends Ce{}function Zo(t,e,r,n){if(!("language_codes"in t)||!Array.isArray(t.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in t)||!(t.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in t)||typeof t.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const a=n.src_lang,s=n.tgt_lang;if(!t.language_codes.includes(s))throw new Error(`Target language code "${s}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);if(a!==void 0){if(!t.language_codes.includes(a))throw new Error(`Source language code "${a}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);for(const i of t.post_processor.config.single)if("SpecialToken"in i&&t.languageRegex.test(i.SpecialToken.id)){i.SpecialToken.id=t.lang_to_token(a);break}}return n.forced_bos_token_id=t.model.convert_tokens_to_ids([t.lang_to_token(s)])[0],t._call(e,r)}class Kv extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)),this.lang_to_token=n=>n}_build_translation_inputs(e,r,n){return Zo(this,e,r,n)}}class Yv extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(n=>this.languageRegex.test(n)).map(n=>n.slice(2,-2)),this.lang_to_token=n=>`__${n}__`}_build_translation_inputs(e,r,n){return Zo(this,e,r,n)}}class Xv extends Ce{constructor(){super(...arguments);B(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(r,{return_timestamps:n=!1,return_language:a=!1,time_precision:s=null,force_full_sequences:i=!0}={}){if(s===null)throw Error("Must specify time_precision");let o=null;const l=n==="word";function u(){return{language:o,timestamp:[null,null],text:""}}const d=[];let h=u(),f=0;const g=this.timestamp_begin;let p=[],w=[],v=!1,x=null;const $=new Set(this.all_special_ids);for(const A of r){const z=A.tokens,R=l?A.token_timestamps:null;let G=null,j=g;if("stride"in A){const[ne,te,se]=A.stride;if(f-=te,x=ne-se,te&&(j=te/s+g),se)for(let N=z.length-1;N>=0;--N){const M=Number(z[N]);if(M>=g){if(G!==null&&(M-g)*s=g){const se=(te-g)*s+f,N=Qn(se,2);if(G!==null&&te>=G)v=!0;else if(v||p.length>0&&te0?(p.push(q),l&&w.push(fe)):p.every(ne=>ne.length===0)&&(h=u(),p=[],q=[],w=[],fe=[])}if(p.length>0){if(i&&n)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[A,z]=this.findLongestCommonSequence(p,w),R=this.decode(A);h.text=R,l&&(h.words=this.collateWordTimestamps(A,z,o)),d.push(h)}let E=Object.create(null);const T=d.map(A=>A.text).join("");if(n||a){for(let A=0;A0;let l=o?[]:null,u=o?n[0]:null;for(let d=1;dN===ne[M]).length,se=te/A+z;te>1&&se>f&&(f=se,g=[R,G,q,fe])}const[w,v,x,$]=g,E=Math.floor((v+w)/2),T=Math.floor(($+x)/2);i.push(...a.slice(0,E)),a=h.slice(T),s=a.length,o&&(l.push(...u.slice(0,E)),u=n[d].slice(T))}return i.push(...a),o?(l.push(...u),[i,l]):[i,[]]}collateWordTimestamps(r,n,a){const[s,i,o]=this.combineTokensIntoWords(r,a),l=[];for(let u=0;u=s){const l=((o-s)*a).toFixed(2);i.push(`<|${l}|>`),i.push([])}else i[i.length-1].push(o);return i=i.map(o=>typeof o=="string"?o:super.decode(o,n)),i.join("")}splitTokensOnUnicode(r){const n=this.decode(r,{decode_with_timestamps:!0}),a="�",s=[],i=[],o=[];let l=[],u=[],d=0;for(let h=0;h=this.model.tokens_to_ids.get("<|endoftext|>"),w=h.startsWith(" "),v=h.trim(),x=u.test(v);if(p||w||x||i.length===0)i.push(h),o.push(f),l.push(g);else{const $=i.length-1;i[$]+=h,o[$].push(...f),l[$].push(...g)}}return[i,o,l]}mergePunctuations(r,n,a,s,i){const o=structuredClone(r),l=structuredClone(n),u=structuredClone(a);let d=o.length-2,h=o.length-1;for(;d>=0;)o[d].startsWith(" ")&&s.includes(o[d].trim())?(o[h]=o[d]+o[h],l[h]=gt(l[d],l[h]),u[h]=gt(u[d],u[h]),o[d]="",l[d]=[],u[d]=[]):h=d,--d;for(d=0,h=1;hf),l.filter(f=>f.length>0),u.filter(f=>f.length>0)]}get_decoder_prompt_ids({language:r=null,task:n=null,no_timestamps:a=!0}={}){const s=[];if(r){const i=Om(r),o=this.model.tokens_to_ids.get(`<|${i}|>`);if(o===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at ${Yo}.`);s.push(o)}else s.push(null);if(n){if(n=n.toLowerCase(),n!=="transcribe"&&n!=="translate")throw new Error(`Task "${n}" is not supported. Must be one of: ["transcribe", "translate"]`);const i=this.model.tokens_to_ids.get(`<|${n}|>`);if(i===void 0)throw new Error(`Unable to find task "${n}" in model vocabulary. Please report this issue at ${Yo}.`);s.push(i)}else s.push(null);if(a){const i=this.model.tokens_to_ids.get("<|notimestamps|>");if(i===void 0)throw new Error(`Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at ${Yo}.`);s.push(i)}return s.map((i,o)=>[o+1,i]).filter(i=>i[1]!==null)}}class Qv extends Ce{}class Zv extends Ce{}class Jv extends Ce{}class e1 extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(n=>this.languageRegex.test(n)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;const[r,...n]=e.trim().split(this.languageRegex);if(n.length===0)return super._encode_text(r);if(n.length===2){const[a,s]=n;return this.supported_language_codes.includes(a)||console.warn(`Unsupported language code "${a}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),gt([a],super._encode_text(s))}}}class t1 extends Ce{}class Gm extends Ce{constructor(){super(...arguments);B(this,"_default_chat_template","{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ ' ' }}{% endif %}{% endfor %}{{ eos_token }}")}}class r1 extends Gm{}class n1 extends Ce{}class a1 extends Ce{}class i1 extends Ce{constructor(e,r){super(e,r),this.decoder=new cv({})}}class s1 extends Ce{}class ut{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",legacy:o=null}={}){var f;const[l,u]=await zm(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,legacy:o}),d=((f=u.tokenizer_class)==null?void 0:f.replace(/Fast$/,""))??"PreTrainedTokenizer";let h=this.TOKENIZER_CLASS_MAPPING[d];return h||(console.warn(`Unknown tokenizer class "${d}", attempting to construct from base class.`),h=Ce),new h(l,u)}}B(ut,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:Pv,DistilBertTokenizer:Iv,CamembertTokenizer:Mv,DebertaTokenizer:kv,DebertaV2Tokenizer:Ev,BertTokenizer:vv,HerbertTokenizer:Cv,ConvBertTokenizer:Tv,RoFormerTokenizer:Av,XLMTokenizer:Ov,ElectraTokenizer:zv,MobileBertTokenizer:xv,SqueezeBertTokenizer:Sv,AlbertTokenizer:$v,GPT2Tokenizer:Um,BartTokenizer:Rv,MBartTokenizer:Wm,MBart50Tokenizer:Bv,RobertaTokenizer:Dv,WhisperTokenizer:Xv,CodeGenTokenizer:Qv,CLIPTokenizer:Zv,SiglipTokenizer:Jv,MarianTokenizer:e1,BloomTokenizer:Nv,NllbTokenizer:Kv,M2M100Tokenizer:Yv,LlamaTokenizer:Vm,CodeLlamaTokenizer:Fv,XLMRobertaTokenizer:Lv,MPNetTokenizer:Uv,FalconTokenizer:Wv,GPTNeoXTokenizer:Vv,EsmTokenizer:Gv,Wav2Vec2CTCTokenizer:t1,BlenderbotTokenizer:Gm,BlenderbotSmallTokenizer:r1,SpeechT5Tokenizer:n1,NougatTokenizer:a1,VitsTokenizer:i1,Qwen2Tokenizer:Hv,GemmaTokenizer:jv,Grok1Tokenizer:qv,CohereTokenizer:s1,PreTrainedTokenizer:Ce});async function o1(t,e){return await Br(t,"config.json",!0,e)}function ba(t){const e={};let r={};switch(t.model_type){case"llava":case"paligemma":case"florence2":r=ba(t.text_config);break;case"moondream1":r=ba(t.phi_config);break;case"musicgen":r=ba(t.decoder);break;case"gpt2":case"gptj":case"codegen":case"gpt_bigcode":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"phi":case"phi3":case"falcon":e.num_heads="num_attention_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size";break;case"llama":case"cohere":case"mistral":case"starcoder2":case"qwen2":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size",e.num_attention_heads="num_attention_heads";break;case"gemma":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.dim_kv="head_dim";break;case"openelm":e.num_heads="num_kv_heads",e.num_layers="num_transformer_layers",e.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":e.num_heads="num_heads",e.num_layers="num_layers",e.hidden_size="hidden_size";break;case"bloom":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="hidden_size";break;case"mpt":e.num_heads="n_heads",e.num_layers="n_layers",e.hidden_size="d_model";break;case"t5":case"mt5":case"longt5":e.num_decoder_layers="num_decoder_layers",e.num_decoder_heads="num_heads",e.decoder_dim_kv="d_kv",e.num_encoder_layers="num_layers",e.num_encoder_heads="num_heads",e.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="d_model",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="d_model";break;case"speecht5":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="hidden_size",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="hidden_size";break;case"trocr":e.num_encoder_layers=e.num_decoder_layers="decoder_layers",e.num_encoder_heads=e.num_decoder_heads="decoder_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="d_model";break;case"musicgen_decoder":e.num_encoder_layers=e.num_decoder_layers="num_hidden_layers",e.num_encoder_heads=e.num_decoder_heads="num_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const a=ba(t.decoder),s="num_decoder_layers"in a,i=$r(t,["model_type","is_encoder_decoder"]);return s?(i.num_decoder_layers=a.num_decoder_layers,i.num_decoder_heads=a.num_decoder_heads,i.decoder_hidden_size=a.decoder_hidden_size,i.num_encoder_layers=a.num_encoder_layers,i.num_encoder_heads=a.num_encoder_heads,i.encoder_hidden_size=a.encoder_hidden_size):(i.num_layers=a.num_layers,i.num_heads=a.num_heads,i.hidden_size=a.hidden_size),i}const n={...r,...$r(t,["model_type","multi_query","is_encoder_decoder"])};for(const a in e)n[a]=t[e[a]];return n}function Hm(t,{prefix:e="past_key_values"}={}){const r={},n=t.normalized_config,a=1;if(n.is_encoder_decoder&&"num_encoder_heads"in n&&"num_decoder_heads"in n){const s=n.encoder_dim_kv??n.encoder_hidden_size/n.num_encoder_heads,i=n.decoder_dim_kv??n.decoder_hidden_size/n.num_decoder_heads,o=[a,n.num_encoder_heads,0,s],l=[a,n.num_decoder_heads,0,i];for(let u=0;u=1&&i[i.length-1]>=this.timestamp_begin,l=i.length<2||i[i.length-2]>=this.timestamp_begin;if(o&&(l?s.subarray(this.timestamp_begin).fill(-1/0):s.subarray(0,this.eos_token_id).fill(-1/0)),e[n].length===this.begin_index&&this.max_initial_timestamp_index!==null){const f=this.timestamp_begin+this.max_initial_timestamp_index;s.subarray(f+1).fill(-1/0)}const u=ry(s),d=Math.log(u.subarray(this.timestamp_begin).map(Math.exp).reduce((f,g)=>f+g)),h=qt(u.subarray(0,this.timestamp_begin))[0];d>h&&s.subarray(0,this.timestamp_begin).fill(-1/0)}return r}}class h1 extends yr{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const r=e.length,n=[];for(let s=0;s1 to use the classifier free guidance processor, got guidance scale ${e}.`);this.guidance_scale=e}_call(e,r){if(r.dims[0]!==2*e.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${r.dims[0]} for the logits and ${e.length} for the input ids.`);const n=e.length,a=r.slice([0,n],null),s=r.slice([n,r.dims[0]],null);for(let i=0;i1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${e}`);if(!Number.isInteger(n)||n<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${n}`);this.top_p=e,this.filter_value=r,this.min_tokens_to_keep=n}}class v1 extends el{constructor(e,{filter_value:r=-1/0,min_tokens_to_keep:n=1}={}){if(super(),!Number.isInteger(e)||e<0)throw new Error(`\`top_k\` must be a positive integer, but is ${e}`);this.top_k=Math.max(e,n),this.filter_value=r}}class Km{constructor(e){B(this,"max_length",20);B(this,"max_new_tokens",null);B(this,"min_length",0);B(this,"min_new_tokens",null);B(this,"early_stopping",!1);B(this,"max_time",null);B(this,"do_sample",!1);B(this,"num_beams",1);B(this,"num_beam_groups",1);B(this,"penalty_alpha",null);B(this,"use_cache",!0);B(this,"temperature",1);B(this,"top_k",50);B(this,"top_p",1);B(this,"typical_p",1);B(this,"epsilon_cutoff",0);B(this,"eta_cutoff",0);B(this,"diversity_penalty",0);B(this,"repetition_penalty",1);B(this,"encoder_repetition_penalty",1);B(this,"length_penalty",1);B(this,"no_repeat_ngram_size",0);B(this,"bad_words_ids",null);B(this,"force_words_ids",null);B(this,"renormalize_logits",!1);B(this,"constraints",null);B(this,"forced_bos_token_id",null);B(this,"forced_eos_token_id",null);B(this,"remove_invalid_values",!1);B(this,"exponential_decay_length_penalty",null);B(this,"suppress_tokens",null);B(this,"begin_suppress_tokens",null);B(this,"forced_decoder_ids",null);B(this,"guidance_scale",null);B(this,"num_return_sequences",1);B(this,"output_attentions",!1);B(this,"output_hidden_states",!1);B(this,"output_scores",!1);B(this,"return_dict_in_generate",!1);B(this,"pad_token_id",null);B(this,"bos_token_id",null);B(this,"eos_token_id",null);B(this,"encoder_no_repeat_ngram_size",0);B(this,"decoder_start_token_id",null);B(this,"generation_kwargs",{});Object.assign(this,$r(e,Object.getOwnPropertyNames(this)))}}class Fi extends wt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class tl extends wt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof tl?e=e.criteria:e instanceof Fi&&(e=[e]),this.criteria.push(...e)}_call(e,r){const n=new Array(e.length).fill(!1);for(const a of this.criteria){const s=a(e,r);for(let i=0;ir.length>=this.max_length)}}class x1 extends Fi{constructor(e){super(),Array.isArray(e)||(e=[e]),this.eos_token_id=e}_call(e,r){return e.map(n=>{const a=n.at(-1);return this.eos_token_id.some(s=>a==s)})}}class S1 extends Fi{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(e,r){return new Array(e.length).fill(this.interrupted)}}class Li extends wt{constructor(e){super(),this.generation_config=e}async _call(e){return this.sample(e)}async sample(e){throw Error("sample should be implemented in subclasses.")}getLogits(e,r){let n=e.dims.at(-1),a=e.data;if(r===-1)a=a.slice(-n);else{let s=r*n;a=a.slice(s,s+n)}return a}randomSelect(e){let r=0;for(let a=0;a1)return new C1(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new k1(e)}}class k1 extends Li{async sample(e){const r=qt(e.data)[1];return[[BigInt(r),0]]}}class E1 extends Li{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const[n,a]=await An(e,r),s=bt(n.data);return Array.from({length:this.generation_config.num_beams},()=>{const i=this.randomSelect(s);return[a.data[i],Math.log(s[i])]})}}class C1 extends Li{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const[n,a]=await An(e,r),s=bt(n.data);return Array.from({length:this.generation_config.num_beams},(i,o)=>[a.data[o],Math.log(s[o])])}}class T1 extends Km{constructor(){super(...arguments);B(this,"return_timestamps",null);B(this,"return_token_timestamps",null);B(this,"num_frames",null);B(this,"alignment_heads",null);B(this,"task",null);B(this,"language",null);B(this,"no_timestamps_token_id",null);B(this,"prompt_ids",null);B(this,"is_multilingual",null);B(this,"lang_to_id",null);B(this,"task_to_id",null);B(this,"max_initial_timestamp_index",1)}}const $e={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},Ui=new Map,Ym=new Map,va=new Map;async function A1(t,e,r){let n=r.device;n&&typeof n!="string"&&(n.hasOwnProperty(e)?n=n[e]:(console.warn(`device not specified for "${e}". Using the default device.`),n=null));const a=Lw(n);let s=r.dtype;if(typeof s!="string"&&(s&&s.hasOwnProperty(e)?s=s[e]:(s=u1[a[0]],console.warn(`dtype not specified for "${e}". Using the default dtype for this device (${s}).`))),jm.hasOwnProperty(s)){if(s===Pt.fp16&&!await l1())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${s}. Should be one of: ${Object.keys(Pt).join(", ")}`);const i=jm[s],o=`${r.subfolder??""}/${e}${i}.onnx`,l={...r.session_options};l.executionProviders??(l.executionProviders=a);const u=ai(t,o,!0,r);let d=[];if(r.use_external_data_format&&(r.use_external_data_format===!0||typeof r.use_external_data_format=="object"&&r.use_external_data_format.hasOwnProperty(e)&&r.use_external_data_format[e]===!0)){if(Rr.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const f=`${e}${i}.onnx_data`,g=`${r.subfolder??""}/${f}`;d.push(new Promise(async(p,w)=>{const v=await ai(t,g,!0,r);p({path:f,data:v})}))}else l.externalData!==void 0&&(d=l.externalData.map(async f=>{if(typeof f.data=="string"){const g=await ai(t,f.data,!0,r);return{...f,data:g}}return f}));if(d.length>0&&(l.externalData=await Promise.all(d)),n==="webgpu"){const f=Hm(r.config,{prefix:"present"});if(Object.keys(f).length>0){const g={};for(const p in f)g[p]="gpu-buffer";l.preferredOutputLocation=g}}return{buffer:await u,session_options:l}}async function sn(t,e,r){const n=Object.keys(e),a=await Promise.all(n.map(async i=>A1(t,e[i],r))),s={};for(let i=0;i0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${n.join(", ")}.`);const a=Object.keys(e).length,s=t.inputNames.length;if(a>s){let i=Object.keys(e).filter(o=>!t.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${a} > ${s}). The following inputs will be ignored: "${i.join(", ")}".`)}return r}async function Ur(t,e){const r=I1(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([s,i])=>[s,i.ort_tensor]));let a=await t.run(n);return a=Xm(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function Xm(t){for(let e in t)ym(t[e])?t[e]=new ce(t[e]):typeof t[e]=="object"&&Xm(t[e]);return t}function Qm(t){if(t instanceof ce)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new ce("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new ce("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function Zm(t){return new ce("bool",[t],[1])}async function Jm(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const l=$r(e,t.sessions.model.inputNames);r=(await Rn(t,l)).last_hidden_state}const{input_ids:a,decoder_input_ids:s,...i}=e;return i.input_ids=s,i.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(i.encoder_attention_mask=e.attention_mask),await Wi(t,i,!0)}async function Rn(t,e){const r=t.sessions.model,n=$r(e,r.inputNames);if(r.inputNames.includes("inputs_embeds")&&!n.inputs_embeds){if(!e.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");n.inputs_embeds=await t.encode_text({input_ids:e.input_ids})}return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new ce("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await Ur(r,n)}async function Wi(t,e,r=!1){const n=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:a,...s}=e;n.inputNames.includes("use_cache_branch")&&(s.use_cache_branch=Zm(!!a)),n.inputNames.includes("position_ids")&&s.attention_mask&&!s.position_ids&&(s.position_ids=O1(s,a)),t.addPastKeyValues(s,a);const i=$r(s,n.inputNames);return await Ur(n,i)}async function M1(t,{input_ids:e=null,attention_mask:r=null,pixel_values:n=null,position_ids:a=null,inputs_embeds:s=null,past_key_values:i=null,generation_config:o=null,logits_processor:l=null,...u}){if(!s){if(s=await t.encode_text({input_ids:e}),n&&e.dims[1]!==1){const h=await t.encode_image({pixel_values:n});({inputs_embeds:s,attention_mask:r}=t._merge_input_ids_with_image_features({image_features:h,inputs_embeds:s,input_ids:e,attention_mask:r}))}else if(i&&n&&e.dims[1]===1){const h=e.dims[1],f=Object.values(i)[0].dims.at(-2);r=Xt([In([e.dims[0],f]),r.slice(null,[r.dims[1]-h,r.dims[1]])],1)}}return await Wi(t,{inputs_embeds:s,past_key_values:i,attention_mask:r,position_ids:a,generation_config:o,logits_processor:l},!0)}function O1(t,e=null){const{input_ids:r,inputs_embeds:n,attention_mask:a}=t,[s,i]=a.dims,o=new BigInt64Array(a.data.length);for(let u=0;us.dims[1])){if(ao==t.config.image_token_index)){const o=t.config.num_image_tokens;if(!o)throw new Error("`num_image_tokens` is missing in the model configuration.");const l=s.dims[1]-(a-o);r.input_ids=s.slice(null,[-l,null]),r.attention_mask=In([1,a+l])}}}return r}function tg(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(i=>[i.at(-1)])),a.decoder_input_ids=Qm(e),a}function z1(t,...e){return t.config.is_encoder_decoder?tg(t,...e):eg(t,...e)}class Z extends wt{constructor(r,n){super();B(this,"main_input_name","input_ids");B(this,"forward_params",["input_ids","attention_mask"]);this.config=r,this.sessions=n;const a=va.get(this.constructor),s=Ui.get(a);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,s){case $e.DecoderOnly:this.can_generate=!0,this._forward=Wi,this._prepare_inputs_for_generation=eg;break;case $e.Seq2Seq:case $e.Vision2Seq:case $e.Musicgen:this.can_generate=!0,this._forward=Jm,this._prepare_inputs_for_generation=tg;break;case $e.EncoderDecoder:this._forward=Jm;break;case $e.ImageTextToText:this.can_generate=!0,this._forward=M1,this._prepare_inputs_for_generation=z1;break;default:this._forward=Rn;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var n;const r=[];for(const a of Object.values(this.sessions))(n=a==null?void 0:a.handler)!=null&&n.dispose&&r.push(a.handler.dispose());return await Promise.all(r)}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:s=null,local_files_only:i=!1,revision:o="main",model_file_name:l=null,subfolder:u="onnx",device:d=null,dtype:h=null,use_external_data_format:f=null,session_options:g={}}={}){let p={progress_callback:n,config:a,cache_dir:s,local_files_only:i,revision:o,model_file_name:l,subfolder:u,device:d,dtype:h,use_external_data_format:f,session_options:g};const w=va.get(this),v=Ui.get(w);a=p.config=await Jo.from_pretrained(r,p);let x;if(v===$e.DecoderOnly)x=await Promise.all([sn(r,{model:p.model_file_name??"model"},p),Br(r,"generation_config.json",!1,p)]);else if(v===$e.Seq2Seq||v===$e.Vision2Seq)x=await Promise.all([sn(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p),Br(r,"generation_config.json",!1,p)]);else if(v===$e.MaskGeneration)x=await Promise.all([sn(r,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},p)]);else if(v===$e.EncoderDecoder)x=await Promise.all([sn(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p)]);else if(v===$e.ImageTextToText){const $={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};a.is_encoder_decoder&&($.model="encoder_model"),x=await Promise.all([sn(r,$,p),Br(r,"generation_config.json",!1,p)])}else v===$e.Musicgen?x=await Promise.all([sn(r,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},p),Br(r,"generation_config.json",!1,p)]):(v!==$e.EncoderOnly&&console.warn(`Model type for '${w??(a==null?void 0:a.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),x=await Promise.all([sn(r,{model:p.model_file_name??"model"},p)]));return new this(a,...x)}async _call(r){return await this.forward(r)}async forward(r){return await this._forward(this,r)}_get_logits_warper(r){const n=new Ni;return r.temperature!==null&&r.temperature!==1&&n.push(new w1(r.temperature)),r.top_k!==null&&r.top_k!==0&&n.push(new v1(r.top_k)),r.top_p!==null&&r.top_p<1&&n.push(new b1(r.top_p)),n}_get_logits_processor(r,n,a=null){const s=new Ni;if(r.repetition_penalty!==null&&r.repetition_penalty!==1&&s.push(new f1(r.repetition_penalty)),r.no_repeat_ngram_size!==null&&r.no_repeat_ngram_size>0&&s.push(new h1(r.no_repeat_ngram_size)),r.bad_words_ids!==null&&s.push(new _1(r.bad_words_ids,r.eos_token_id)),r.min_length!==null&&r.eos_token_id!==null&&r.min_length>0&&s.push(new m1(r.min_length,r.eos_token_id)),r.min_new_tokens!==null&&r.eos_token_id!==null&&r.min_new_tokens>0&&s.push(new g1(n,r.min_new_tokens,r.eos_token_id)),r.forced_bos_token_id!==null&&s.push(new d1(r.forced_bos_token_id)),r.forced_eos_token_id!==null&&s.push(new c1(r.max_length,r.forced_eos_token_id)),r.begin_suppress_tokens!==null){const i=n>1||r.forced_bos_token_id===null?n:n+1;s.push(new qm(r.begin_suppress_tokens,i))}return r.guidance_scale!==null&&r.guidance_scale>1&&s.push(new y1(r.guidance_scale)),a!==null&&s.extend(a),s}_prepare_generation_config(r,n,a=Km){const s={...this.config};for(const o of["decoder","generator","text_config"])o in s&&Object.assign(s,s[o]);const i=new a(s);return"generation_config"in this&&Object.assign(i,this.generation_config),r&&Object.assign(i,r),n&&Object.assign(i,$r(n,Object.getOwnPropertyNames(i))),i}_get_stopping_criteria(r,n=null){const a=new tl;return r.max_length!==null&&a.push(new $1(r.max_length,this.config.max_position_embeddings??null)),r.eos_token_id!==null&&a.push(new x1(r.eos_token_id)),n&&a.extend(n),a}_validate_model_class(){if(!this.can_generate){const r=[ul,dl,ll,ol],n=va.get(this.constructor),a=new Set,s=this.config.model_type;for(const o of r){const l=o.get(s);l&&a.add(l[0])}let i=`The current model class (${n}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw a.size>0&&(i+=` Please use the following class instead: ${[...a].join(", ")}`),Error(i)}}prepare_inputs_for_generation(...r){return this._prepare_inputs_for_generation(this,...r)}_update_model_kwargs_for_generation({generated_input_ids:r,outputs:n,model_inputs:a,is_encoder_decoder:s}){return a.past_key_values=this.getPastKeyValues(n,a.past_key_values),a.input_ids=new ce("int64",r.flat(),[r.length,1]),s||(a.attention_mask=Xt([a.attention_mask,In([a.attention_mask.dims[0],1])],1)),a.position_ids=null,a}_prepare_model_inputs({inputs:r,bos_token_id:n,model_kwargs:a}){const s=$r(a,this.forward_params),i=this.main_input_name;if(i in s){if(r)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else s[i]=r;return{inputs_tensor:s[i],model_inputs:s,model_input_name:i}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:r,model_inputs:n,model_input_name:a,generation_config:s}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!n.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:o,pixel_values:l,attention_mask:u,...d}=n,h=await this._prepare_inputs_embeds(n);n={...d,...$r(h,["inputs_embeds","attention_mask"])}}let{last_hidden_state:i}=await Rn(this,n);return s.guidance_scale!==null&&s.guidance_scale>1&&(i=Xt([i,Yw(i,0)],0),"attention_mask"in n&&(n.attention_mask=Xt([n.attention_mask,Zw(n.attention_mask)],0))),n.encoder_outputs=i,n}_prepare_decoder_input_ids_for_generation({batch_size:r,model_input_name:n,model_kwargs:a,decoder_start_token_id:s,bos_token_id:i,generation_config:o}){let{decoder_input_ids:l,...u}=a;if(l)Array.isArray(l[0])||(l=Array.from({length:r},()=>l));else if(s??(s=i),this.config.model_type==="musicgen")l=Array.from({length:r*this.config.decoder.num_codebooks},()=>[s]);else if(Array.isArray(s)){if(s.length!==r)throw new Error(`\`decoder_start_token_id\` expcted to have length ${r} but got ${s.length}`);l=s}else l=Array.from({length:r},()=>[s]);return l=Qm(l),a.decoder_attention_mask=Xw(l),{input_ids:l,model_inputs:u}}async generate({inputs:r=null,generation_config:n=null,logits_processor:a=null,stopping_criteria:s=null,streamer:i=null,...o}){this._validate_model_class(),n=this._prepare_generation_config(n,o);let{inputs_tensor:l,model_inputs:u,model_input_name:d}=this._prepare_model_inputs({inputs:r,model_kwargs:o});const h=this.config.is_encoder_decoder;h&&("encoder_outputs"in u||(u=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:l,model_inputs:u,model_input_name:d,generation_config:n})));let f;h?{input_ids:f,model_inputs:u}=this._prepare_decoder_input_ids_for_generation({batch_size:u[d].dims.at(0),model_input_name:d,model_kwargs:u,decoder_start_token_id:n.decoder_start_token_id,bos_token_id:n.bos_token_id,generation_config:n}):f=u[d];let g=f.dims.at(-1);n.max_new_tokens!==null&&(n.max_length=g+n.max_new_tokens);const p=this._get_logits_processor(n,g,a),w=this._get_stopping_criteria(n,s),v=u[d].dims.at(0),x=Li.getSampler(n),$=new Array(v).fill(0),E=f.tolist();i&&i.put(E);let T=null,A={};for(;;){u=this.prepare_inputs_for_generation(E,u,n);const R=await this.forward(u);if(n.output_attentions&&n.return_dict_in_generate){const ne=this.getAttentions(R);for(const te in ne)te in A||(A[te]=[]),A[te].push(ne[te])}const G=R.logits.slice(null,-1,null),j=p(E,G),q=[];for(let ne=0;nene)){n.return_dict_in_generate&&(T=this.getPastKeyValues(R,u.past_key_values,!1));break}u=this._update_model_kwargs_for_generation({generated_input_ids:q,outputs:R,model_inputs:u,is_encoder_decoder:h})}i&&i.end();const z=new ce("int64",E.flat(),[E.length,E[0].length]);return n.return_dict_in_generate?{sequences:z,past_key_values:T,...A}:z}getPastKeyValues(r,n,a=!0){const s=Object.create(null);for(const i in r)if(i.startsWith("present")){const o=i.replace("present","past_key_values");if(n&&i.includes("encoder"))s[o]=n[o];else{if(a&&n){const l=n[o];l.location==="gpu-buffer"&&l.dispose()}s[o]=r[i]}}return s}getAttentions(r){const n={};for(const a of["cross_attentions","encoder_attentions","decoder_attentions"])for(const s in r)s.startsWith(a)&&(a in n||(n[a]=[]),n[a].push(r[s]));return n}addPastKeyValues(r,n){if(n)Object.assign(r,n);else{const a=this.custom_config.kv_cache_dtype??"float32",s=a==="float16"?new Uint16Array:[],i=Hm(this.config);for(const o in i)r[o]=new ce(a,s,i[o])}}async encode_image({pixel_values:r}){const n=(await Ur(this.sessions.vision_encoder,{pixel_values:r})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${n.dims[1]}).`),this.config.num_image_tokens=n.dims[1]),n}async encode_text({input_ids:r}){return(await Ur(this.sessions.embed_tokens,{input_ids:r})).inputs_embeds}}class Wt{}class $a extends Z{}class P1 extends $a{}class R1 extends $a{async _call(e){return new $t(await super._call(e))}}class B1 extends $a{async _call(e){return new Me(await super._call(e))}}class D1 extends $a{async _call(e){return new vt(await super._call(e))}}class N1 extends $a{async _call(e){return new Tt(await super._call(e))}}class F1 extends Z{}class L1 extends F1{}class xa extends Z{}class U1 extends xa{}class W1 extends xa{async _call(e){return new $t(await super._call(e))}}class V1 extends xa{async _call(e){return new Me(await super._call(e))}}class G1 extends xa{async _call(e){return new vt(await super._call(e))}}class H1 extends xa{async _call(e){return new Tt(await super._call(e))}}class Sa extends Z{}class j1 extends Sa{}class q1 extends Sa{async _call(e){return new $t(await super._call(e))}}class K1 extends Sa{async _call(e){return new Me(await super._call(e))}}class Y1 extends Sa{async _call(e){return new vt(await super._call(e))}}class X1 extends Sa{async _call(e){return new Tt(await super._call(e))}}class ka extends Z{}class Q1 extends ka{}class Z1 extends ka{async _call(e){return new $t(await super._call(e))}}class J1 extends ka{async _call(e){return new Me(await super._call(e))}}class e2 extends ka{async _call(e){return new vt(await super._call(e))}}class t2 extends ka{async _call(e){return new Tt(await super._call(e))}}class Ea extends Z{}class r2 extends Ea{}class n2 extends Ea{async _call(e){return new $t(await super._call(e))}}class a2 extends Ea{async _call(e){return new Me(await super._call(e))}}class i2 extends Ea{async _call(e){return new vt(await super._call(e))}}class s2 extends Ea{async _call(e){return new Tt(await super._call(e))}}class Ca extends Z{}class o2 extends Ca{}class l2 extends Ca{async _call(e){return new $t(await super._call(e))}}class u2 extends Ca{async _call(e){return new Me(await super._call(e))}}class d2 extends Ca{async _call(e){return new vt(await super._call(e))}}class c2 extends Ca{async _call(e){return new Tt(await super._call(e))}}class Ta extends Z{}class p2 extends Ta{}class h2 extends Ta{async _call(e){return new $t(await super._call(e))}}class f2 extends Ta{async _call(e){return new Me(await super._call(e))}}class m2 extends Ta{async _call(e){return new vt(await super._call(e))}}class g2 extends Ta{async _call(e){return new Tt(await super._call(e))}}class Aa extends Z{}class _2 extends Aa{}class y2 extends Aa{async _call(e){return new Me(await super._call(e))}}class w2 extends Aa{async _call(e){return new vt(await super._call(e))}}class b2 extends Aa{async _call(e){return new Tt(await super._call(e))}}class v2 extends Aa{async _call(e){return new $t(await super._call(e))}}class Vi extends Z{}class $2 extends Vi{}class x2 extends Vi{async _call(e){return new $t(await super._call(e))}}class S2 extends Vi{async _call(e){return new Me(await super._call(e))}}class k2 extends Vi{async _call(e){return new vt(await super._call(e))}}class Gi extends Z{}class E2 extends Gi{}class C2 extends Gi{async _call(e){return new $t(await super._call(e))}}class T2 extends Gi{async _call(e){return new Me(await super._call(e))}}class A2 extends Gi{async _call(e){return new Tt(await super._call(e))}}class Ia extends Z{}class I2 extends Ia{}class M2 extends Ia{async _call(e){return new $t(await super._call(e))}}class O2 extends Ia{async _call(e){return new Me(await super._call(e))}}class z2 extends Ia{async _call(e){return new vt(await super._call(e))}}class P2 extends Ia{async _call(e){return new Tt(await super._call(e))}}class Hi extends Z{}class R2 extends Hi{}class B2 extends Hi{async _call(e){return new $t(await super._call(e))}}class D2 extends Hi{async _call(e){return new Me(await super._call(e))}}class N2 extends Hi{async _call(e){return new Tt(await super._call(e))}}class ji extends Z{}class F2 extends ji{}class L2 extends ji{async _call(e){return new Me(await super._call(e))}}class U2 extends ji{async _call(e){return new Tt(await super._call(e))}}class W2 extends ji{async _call(e){return new $t(await super._call(e))}}class rg extends Z{constructor(r,n,a){super(r,n);B(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class V2 extends rg{}class G2 extends rg{}class ng extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class H2 extends ng{}class j2 extends ng{}class ag extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class q2 extends ag{}class K2 extends ag{}class rl extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class Y2 extends rl{}class X2 extends rl{}class Q2 extends rl{async _call(e){return new Me(await super._call(e))}}class qi extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class Z2 extends qi{}class J2 extends qi{}class e$ extends qi{async _call(e){return new Me(await super._call(e))}}class t$ extends qi{}class ig extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class r$ extends ig{}class n$ extends ig{}class sg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class a$ extends sg{}class i$ extends sg{}class Ma extends Z{}class s$ extends Ma{}class o$ extends Ma{async _call(e){return new $t(await super._call(e))}}class l$ extends Ma{async _call(e){return new Me(await super._call(e))}}class u$ extends Ma{async _call(e){return new vt(await super._call(e))}}class d$ extends Ma{async _call(e){return new Tt(await super._call(e))}}class Oa extends Z{}class c$ extends Oa{}class p$ extends Oa{async _call(e){return new $t(await super._call(e))}}class h$ extends Oa{async _call(e){return new Me(await super._call(e))}}class f$ extends Oa{async _call(e){return new vt(await super._call(e))}}class m$ extends Oa{async _call(e){return new Tt(await super._call(e))}}class za extends Z{}class g$ extends za{}class _$ extends za{async _call(e){return new $t(await super._call(e))}}class y$ extends za{async _call(e){return new Me(await super._call(e))}}class w$ extends za{async _call(e){return new vt(await super._call(e))}}class b$ extends za{async _call(e){return new Tt(await super._call(e))}}class og extends Z{}class v$ extends og{}class $$ extends og{}class lg extends Z{constructor(r,n,a){super(r,n);B(this,"requires_attention_mask",!1);B(this,"main_input_name","input_features");B(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class x$ extends lg{}class S$ extends lg{_prepare_generation_config(e,r){return super._prepare_generation_config(e,r,T1)}_retrieve_init_tokens(e){const r=[e.decoder_start_token_id];let n=e.language;const a=e.task;if(e.is_multilingual){n||(console.warn("No language specified - defaulting to English (en)."),n="en");const i=`<|${Om(n)}|>`;r.push(e.lang_to_id[i]),r.push(e.task_to_id[a??"transcribe"])}else if(n||a)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(s=>s!=null)}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,...s}){r=this._prepare_generation_config(r,s);const i=this._retrieve_init_tokens(r);if(r.return_timestamps&&(n??(n=new Ni),n.push(new p1(r,i))),r.begin_suppress_tokens&&(n??(n=new Ni),n.push(new qm(r.begin_suppress_tokens,i.length))),r.return_token_timestamps){if(!r.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");r.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),r.output_attentions=!0,r.return_dict_in_generate=!0}const o=await super.generate({inputs:e,generation_config:r,logits_processor:n,decoder_input_ids:i,...s});return r.return_token_timestamps&&(o.token_timestamps=this._extract_token_timestamps(o,r.alignment_heads,r.num_frames)),o}_extract_token_timestamps(e,r,n=null,a=.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`.");n==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let s=this.config.median_filter_width;s===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),s=7);const i=e.cross_attentions,o=Array.from({length:this.config.decoder_layers},(w,v)=>Xt(i.map(x=>x[v]),2)),l=fa(r.map(([w,v])=>{if(w>=o.length)throw new Error(`Layer index ${w} is out of bounds for cross attentions (length ${o.length}).`);return n?o[w].slice(null,v,null,[0,n]):o[w].slice(null,v)})).transpose(1,0,2,3),[u,d]=jw(l,-2,0,!0),h=l.clone();for(let w=0;wx[R+1]-x[R]),T=gt([1],E).map(z=>!!z),A=[];for(let z=0;zf.findIndex(g=>g==s)),l=o.every(f=>f===-1),u=o.every(f=>f!==-1);if(!l&&!u)throw new Error("Every input should contain either 0 or 1 image token.");if(l)return{inputs_embeds:e,attention_mask:a};const d=[],h=[];for(let f=0;fs*i,1);e.input_labels=new ce("int64",new BigInt64Array(a).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 Ur(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new gS(await super._call(e))}}class gS extends Wt{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class Yg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class _S extends Yg{}class yS extends Yg{}class Xg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class wS extends Xg{}class bS extends Xg{}class on extends Z{}class vS extends on{}class $S extends on{async _call(e){return new Bn(await super._call(e))}}class xS extends on{async _call(e){return new Me(await super._call(e))}}class SS extends on{async _call(e){return new vt(await super._call(e))}}class al extends Z{}class kS extends al{}class ES extends al{async _call(e){return new Bn(await super._call(e))}}class CS extends al{async _call(e){return new Me(await super._call(e))}}class Yi extends Z{}class TS extends Yi{}class AS extends Yi{async _call(e){return new Bn(await super._call(e))}}class IS extends Yi{async _call(e){return new Me(await super._call(e))}}class MS extends Yi{async _call(e){return new vt(await super._call(e))}}class il extends Z{}class OS extends il{}class zS extends il{async _call(e){return new Bn(await super._call(e))}}class PS extends il{async _call(e){return new Me(await super._call(e))}}class RS extends on{}class BS extends on{async _call(e){return new Bn(await super._call(e))}}class DS extends on{async _call(e){return new Me(await super._call(e))}}class Pa extends Z{}class NS extends Pa{}class FS extends Pa{async _call(e){return new Bn(await super._call(e))}}class LS extends Pa{async _call(e){return new Me(await super._call(e))}}class US extends Pa{async _call(e){return new Tk(await super._call(e))}}class WS extends Pa{async _call(e){return new vt(await super._call(e))}}class Qg extends Z{constructor(e,r,n){super(e,r),this.generation_config=n}}class VS extends Qg{}class GS extends Qg{async generate_speech(e,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:s=20,vocoder:i=null}={}){const o={input_ids:e},{encoder_outputs:l,encoder_attention_mask:u}=await Rn(this,o),d=l.dims[1]/this.config.reduction_factor,h=Math.floor(d*s),f=Math.floor(d*a),g=this.config.num_mel_bins;let p=[],w=null,v=null,x=0;for(;;){++x;const T=Zm(!!v);let A;v?A=v.output_sequence_out:A=new ce("float32",new Float32Array(g),[1,1,g]);let z={use_cache_branch:T,output_sequence:A,encoder_attention_mask:u,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(z,w),v=await Ur(this.sessions.decoder_model_merged,z),w=this.getPastKeyValues(v,w);const{prob:R,spectrum:G}=v;if(p.push(G),x>=f&&(Array.from(R.data).filter(j=>j>=n).length>0||x>=h))break}const $=Xt(p),{waveform:E}=await 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from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",model_file_name:o=null,subfolder:l="onnx",device:u=null,dtype:d=null,use_external_data_format:h=null,session_options:f={}}={}){let g={progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,model_file_name:o,subfolder:l,device:u,dtype:d,use_external_data_format:h,session_options:f};if(g.config=await Jo.from_pretrained(e,g),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let p of this.MODEL_CLASS_MAPPINGS){const w=p.get(g.config.model_type);if(w)return await w[1].from_pretrained(e,g)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${g.config.model_type}", attempting to construct from base class.`),await Z.from_pretrained(e,g);throw Error(`Unsupported model type: ${g.config.model_type}`)}}B(Qe,"MODEL_CLASS_MAPPINGS",null),B(Qe,"BASE_IF_FAIL",!1);const yk=new 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Map([["t5",["T5Model",V2]],["longt5",["LongT5Model",H2]],["mt5",["MT5Model",q2]],["bart",["BartModel",Y2]],["mbart",["MBartModel",Z2]],["marian",["MarianModel",_S]],["whisper",["WhisperModel",x$]],["m2m_100",["M2M100Model",wS]],["blenderbot",["BlenderbotModel",r$]],["blenderbot-small",["BlenderbotSmallModel",a$]]]),bk=new Map([["bloom",["BloomModel",cx]],["gpt2",["GPT2Model",F$]],["gptj",["GPTJModel",H$]],["gpt_bigcode",["GPTBigCodeModel",q$]],["gpt_neo",["GPTNeoModel",U$]],["gpt_neox",["GPTNeoXModel",V$]],["codegen",["CodeGenModel",Y$]],["llama",["LlamaModel",Q$]],["cohere",["CohereModel",J$]],["gemma",["GemmaModel",tx]],["openelm",["OpenELMModel",nx]],["qwen2",["Qwen2Model",ix]],["phi",["PhiModel",ox]],["phi3",["Phi3Model",ux]],["mpt",["MptModel",hx]],["opt",["OPTModel",mx]],["mistral",["MistralModel",KS]],["starcoder2",["Starcoder2Model",XS]],["falcon",["FalconModel",ZS]],["stablelm",["StableLmModel",sk]]]),ol=new Map([["speecht5",["SpeechT5ForSpeechToText",VS]],["whisper",["WhisperForConditionalGeneration",S$]]]),d_=new Map([["speecht5",["SpeechT5ForTextToSpeech",GS]]]),c_=new Map([["vits",["VitsModel",t_]],["musicgen",["MusicgenForConditionalGeneration",i_]]]),p_=new Map([["bert",["BertForSequenceClassification",B1]],["roformer",["RoFormerForSequenceClassification",V1]],["electra",["ElectraForSequenceClassification",J1]],["esm",["EsmForSequenceClassification",S2]],["convbert",["ConvBertForSequenceClassification",K1]],["camembert",["CamembertForSequenceClassification",a2]],["deberta",["DebertaForSequenceClassification",u2]],["deberta-v2",["DebertaV2ForSequenceClassification",f2]],["mpnet",["MPNetForSequenceClassification",O2]],["albert",["AlbertForSequenceClassification",L2]],["distilbert",["DistilBertForSequenceClassification",y2]],["roberta",["RobertaForSequenceClassification",l$]],["xlm",["XLMForSequenceClassification",h$]],["xlm-roberta",["XLMRobertaForSequenceClassification",y$]],["bart",["BartForSequenceClassification",Q2]],["mbart",["MBartForSequenceClassification",e$]],["mobilebert",["MobileBertForSequenceClassification",T2]],["squeezebert",["SqueezeBertForSequenceClassification",D2]]]),h_=new Map([["bert",["BertForTokenClassification",D1]],["roformer",["RoFormerForTokenClassification",G1]],["electra",["ElectraForTokenClassification",e2]],["esm",["EsmForTokenClassification",k2]],["convbert",["ConvBertForTokenClassification",Y1]],["camembert",["CamembertForTokenClassification",i2]],["deberta",["DebertaForTokenClassification",d2]],["deberta-v2",["DebertaV2ForTokenClassification",m2]],["mpnet",["MPNetForTokenClassification",z2]],["distilbert",["DistilBertForTokenClassification",w2]],["roberta",["RobertaForTokenClassification",u$]],["xlm",["XLMForTokenClassification",f$]],["xlm-roberta",["XLMRobertaForTokenClassification",w$]]]),ll=new Map([["t5",["T5ForConditionalGeneration",G2]],["longt5",["LongT5ForConditionalGeneration",j2]],["mt5",["MT5ForConditionalGeneration",K2]],["bart",["BartForConditionalGeneration",X2]],["mbart",["MBartForConditionalGeneration",J2]],["marian",["MarianMTModel",yS]],["m2m_100",["M2M100ForConditionalGeneration",bS]],["blenderbot",["BlenderbotForConditionalGeneration",n$]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",i$]]]),ul=new Map([["bloom",["BloomForCausalLM",px]],["gpt2",["GPT2LMHeadModel",L$]],["gptj",["GPTJForCausalLM",j$]],["gpt_bigcode",["GPTBigCodeForCausalLM",K$]],["gpt_neo",["GPTNeoForCausalLM",W$]],["gpt_neox",["GPTNeoXForCausalLM",G$]],["codegen",["CodeGenForCausalLM",X$]],["llama",["LlamaForCausalLM",Z$]],["cohere",["CohereForCausalLM",ex]],["gemma",["GemmaForCausalLM",rx]],["openelm",["OpenELMForCausalLM",ax]],["qwen2",["Qwen2ForCausalLM",sx]],["phi",["PhiForCausalLM",lx]],["phi3",["Phi3ForCausalLM",dx]],["mpt",["MptForCausalLM",fx]],["opt",["OPTForCausalLM",gx]],["mbart",["MBartForCausalLM",t$]],["mistral",["MistralForCausalLM",YS]],["starcoder2",["Starcoder2ForCausalLM",QS]],["falcon",["FalconForCausalLM",JS]],["trocr",["TrOCRForCausalLM",qS]],["stablelm",["StableLmForCausalLM",ok]]]),f_=new Map([["bert",["BertForMaskedLM",R1]],["roformer",["RoFormerForMaskedLM",W1]],["electra",["ElectraForMaskedLM",Z1]],["esm",["EsmForMaskedLM",x2]],["convbert",["ConvBertForMaskedLM",q1]],["camembert",["CamembertForMaskedLM",n2]],["deberta",["DebertaForMaskedLM",l2]],["deberta-v2",["DebertaV2ForMaskedLM",h2]],["mpnet",["MPNetForMaskedLM",M2]],["albert",["AlbertForMaskedLM",W2]],["distilbert",["DistilBertForMaskedLM",v2]],["roberta",["RobertaForMaskedLM",o$]],["xlm",["XLMWithLMHeadModel",p$]],["xlm-roberta",["XLMRobertaForMaskedLM",_$]],["mobilebert",["MobileBertForMaskedLM",C2]],["squeezebert",["SqueezeBertForMaskedLM",B2]]]),m_=new Map([["bert",["BertForQuestionAnswering",N1]],["roformer",["RoFormerForQuestionAnswering",H1]],["electra",["ElectraForQuestionAnswering",t2]],["convbert",["ConvBertForQuestionAnswering",X1]],["camembert",["CamembertForQuestionAnswering",s2]],["deberta",["DebertaForQuestionAnswering",c2]],["deberta-v2",["DebertaV2ForQuestionAnswering",g2]],["mpnet",["MPNetForQuestionAnswering",P2]],["albert",["AlbertForQuestionAnswering",U2]],["distilbert",["DistilBertForQuestionAnswering",b2]],["roberta",["RobertaForQuestionAnswering",d$]],["xlm",["XLMForQuestionAnswering",m$]],["xlm-roberta",["XLMRobertaForQuestionAnswering",b$]],["mobilebert",["MobileBertForQuestionAnswering",A2]],["squeezebert",["SqueezeBertForQuestionAnswering",N2]]]),dl=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",ug]]]),vk=new Map([["llava",["LlavaForConditionalGeneration",dg]],["moondream1",["Moondream1ForConditionalGeneration",E$]],["florence2",["Florence2ForConditionalGeneration",T$]]]),$k=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",ug]]]),g_=new Map([["vit",["ViTForImageClassification",yx]],["fastvit",["FastViTForImageClassification",bx]],["mobilevit",["MobileViTForImageClassification",Sx]],["mobilevitv2",["MobileViTV2ForImageClassification",Ex]],["beit",["BeitForImageClassification",Ox]],["deit",["DeiTForImageClassification",Gx]],["convnext",["ConvNextForImageClassification",sS]],["convnextv2",["ConvNextV2ForImageClassification",lS]],["dinov2",["Dinov2ForImageClassification",dS]],["resnet",["ResNetForImageClassification",jx]],["swin",["SwinForImageClassification",Kx]],["segformer",["SegformerForImageClassification",ak]],["efficientnet",["EfficientNetForImageClassification",uk]],["mobilenet_v1",["MobileNetV1ForImageClassification",ck]],["mobilenet_v2",["MobileNetV2ForImageClassification",hk]],["mobilenet_v3",["MobileNetV3ForImageClassification",mk]],["mobilenet_v4",["MobileNetV4ForImageClassification",_k]]]),__=new Map([["detr",["DetrForObjectDetection",Px]],["rt_detr",["RTDetrForObjectDetection",Nx]],["table-transformer",["TableTransformerForObjectDetection",Ux]],["yolos",["YolosForObjectDetection",pS]]]),y_=new Map([["owlvit",["OwlViTForObjectDetection",Tx]],["owlv2",["Owlv2ForObjectDetection",Ix]]]),w_=new Map([["detr",["DetrForSegmentation",Rx]],["clipseg",["CLIPSegForImageSegmentation",N$]]]),b_=new Map([["segformer",["SegformerForSemanticSegmentation",ik]]]),xk=new Map([["sam",["SamModel",mS]]]),v_=new Map([["wav2vec2",["Wav2Vec2ForCTC",$S]],["wav2vec2-bert",["Wav2Vec2BertForCTC",zS]],["unispeech",["UniSpeechForCTC",ES]],["unispeech-sat",["UniSpeechSatForCTC",AS]],["wavlm",["WavLMForCTC",FS]],["hubert",["HubertForCTC",BS]]]),$_=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",xS]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",PS]],["unispeech",["UniSpeechForSequenceClassification",CS]],["unispeech-sat",["UniSpeechSatForSequenceClassification",IS]],["wavlm",["WavLMForSequenceClassification",LS]],["hubert",["HubertForSequenceClassification",DS]],["audio-spectrogram-transformer",["ASTForAudioClassification",$$]]]),Sk=new Map([["wavlm",["WavLMForXVector",US]]]),kk=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",MS]],["wavlm",["WavLMForAudioFrameClassification",WS]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",SS]]]),Ek=new Map([["vitmatte",["VitMatteForImageMatting",$x]]]),x_=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Xx]]]),S_=new Map([["dpt",["DPTForDepthEstimation",Zx]],["depth_anything",["DepthAnythingForDepthEstimation",eS]],["glpn",["GLPNForDepthEstimation",rS]]]),k_=new Map([["clip",["CLIPVisionModelWithProjection",M$]],["siglip",["SiglipVisionModel",P$]]]),E_=[[yk,$e.EncoderOnly],[wk,$e.EncoderDecoder],[bk,$e.DecoderOnly],[p_,$e.EncoderOnly],[h_,$e.EncoderOnly],[ll,$e.Seq2Seq],[ol,$e.Seq2Seq],[ul,$e.DecoderOnly],[f_,$e.EncoderOnly],[m_,$e.EncoderOnly],[dl,$e.Vision2Seq],[vk,$e.ImageTextToText],[g_,$e.EncoderOnly],[w_,$e.EncoderOnly],[b_,$e.EncoderOnly],[Ek,$e.EncoderOnly],[x_,$e.EncoderOnly],[S_,$e.EncoderOnly],[__,$e.EncoderOnly],[y_,$e.EncoderOnly],[xk,$e.MaskGeneration],[v_,$e.EncoderOnly],[$_,$e.EncoderOnly],[d_,$e.Seq2Seq],[c_,$e.EncoderOnly],[Sk,$e.EncoderOnly],[kk,$e.EncoderOnly],[k_,$e.EncoderOnly]];for(const[t,e]of E_)for(const[r,n]of t.values())Ui.set(r,e),va.set(n,r),Ym.set(r,n);const Ck=[["MusicgenForConditionalGeneration",i_,$e.Musicgen],["CLIPTextModelWithProjection",I$,$e.EncoderOnly],["SiglipTextModel",z$,$e.EncoderOnly],["ClapTextModelWithProjection",tk,$e.EncoderOnly],["ClapAudioModelWithProjection",rk,$e.EncoderOnly]];for(const[t,e,r]of Ck)Ui.set(t,r),va.set(e,t),Ym.set(t,e);class ln extends Qe{}B(ln,"MODEL_CLASS_MAPPINGS",E_.map(e=>e[0])),B(ln,"BASE_IF_FAIL",!0);class cl extends Qe{}B(cl,"MODEL_CLASS_MAPPINGS",[p_]);class C_ extends Qe{}B(C_,"MODEL_CLASS_MAPPINGS",[h_]);class Xi extends Qe{}B(Xi,"MODEL_CLASS_MAPPINGS",[ll]);class T_ extends Qe{}B(T_,"MODEL_CLASS_MAPPINGS",[ol]);class A_ extends Qe{}B(A_,"MODEL_CLASS_MAPPINGS",[d_]);class I_ extends Qe{}B(I_,"MODEL_CLASS_MAPPINGS",[c_]);class M_ extends Qe{}B(M_,"MODEL_CLASS_MAPPINGS",[ul]);class O_ extends Qe{}B(O_,"MODEL_CLASS_MAPPINGS",[f_]);class z_ extends Qe{}B(z_,"MODEL_CLASS_MAPPINGS",[m_]);class P_ extends Qe{}B(P_,"MODEL_CLASS_MAPPINGS",[dl]);class R_ extends Qe{}B(R_,"MODEL_CLASS_MAPPINGS",[g_]);class B_ extends Qe{}B(B_,"MODEL_CLASS_MAPPINGS",[w_]);class D_ extends Qe{}B(D_,"MODEL_CLASS_MAPPINGS",[b_]);class N_ extends Qe{}B(N_,"MODEL_CLASS_MAPPINGS",[__]);class F_ extends Qe{}B(F_,"MODEL_CLASS_MAPPINGS",[y_]);class L_ extends Qe{}B(L_,"MODEL_CLASS_MAPPINGS",[v_]);class U_ extends Qe{}B(U_,"MODEL_CLASS_MAPPINGS",[$_]);class W_ extends Qe{}B(W_,"MODEL_CLASS_MAPPINGS",[$k]);class V_ extends Qe{}B(V_,"MODEL_CLASS_MAPPINGS",[x_]);class G_ extends Qe{}B(G_,"MODEL_CLASS_MAPPINGS",[S_]);class H_ extends Qe{}B(H_,"MODEL_CLASS_MAPPINGS",[k_]);class Me extends Wt{constructor({logits:e}){super(),this.logits=e}}class Tk extends Wt{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class vt extends Wt{constructor({logits:e}){super(),this.logits=e}}class $t extends Wt{constructor({logits:e}){super(),this.logits=e}}class Tt extends Wt{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class Bn extends Wt{constructor({logits:e}){super(),this.logits=e}}class Ak extends Wt{constructor({alphas:e}){super(),this.alphas=e}}class Ik extends Wt{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const Zt=typeof self<"u",Mk=Zt&&self.constructor.name==="DedicatedWorkerGlobalScope";let un,j_,Wr;if(Zt)un=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},Wr=self.createImageBitmap,j_=self.ImageData;else if(Ve)Wr=async t=>{const r=(await t.metadata()).channels,{data:n,info:a}=await t.rotate().raw().toBuffer({resolveWithObject:!0}),s=new At(new Uint8ClampedArray(n),a.width,a.height,a.channels);return r!==void 0&&r!==a.channels&&s.convert(r),s};else throw new Error("Unable to load image processing library.");const Ok={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},zk=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class At{constructor(e,r,n,a){this.data=e,this.width=r,this.height=n,this.channels=a}get size(){return[this.width,this.height]}static async read(e){if(e instanceof At)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!Zt)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new At(n,e.width,e.height,4)}static async fromURL(e){const r=await ni(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(Zt){const r=await Wr(e),n=un(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=Ve(await e.arrayBuffer());return await Wr(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 At(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?l=n:d=-n,a>=0?u=a:h=-a,o.drawImage(i,l,u,e,r,d,h,e,r),new At(o.getImageData(0,0,e,r).data,e,r,4).convert(s)}else{let s=this.toSharp();if(n>=0&&a>=0)s=s.extract({left:Math.floor(n),top:Math.floor(a),width:e,height:r});else if(n<=0&&a<=0){const i=Math.floor(-a),o=Math.floor(-n);s=s.extend({top:i,left:o,right:e-this.width-o,bottom:r-this.height-i})}else{let i=[0,0],o=0;a<0?(i[0]=Math.floor(-a),i[1]=r-this.height-i[0]):o=Math.floor(a);let l=[0,0],u=0;n<0?(l[0]=Math.floor(-n),l[1]=e-this.width-l[0]):u=Math.floor(n),s=s.extend({top:i[0],bottom:i[1],left:l[0],right:l[1]}).extract({left:u,top:o,width:e,height:r})}return await Wr(s)}}async toBlob(e="image/png",r=1){if(!Zt)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(!Zt)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=un(e.width,e.height),n=new j_(e.data,e.width,e.height);return r.getContext("2d").putImageData(n,0,0),r}_update(e,r,n,a=null){return this.data=e,this.width=r,this.height=n,a!==null&&(this.channels=a),this}clone(){return new At(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(Zt){if(Mk)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=zk.get(r)??"image/png",a=await this.toBlob(n),s=URL.createObjectURL(a),i=document.createElement("a");i.href=s,i.download=e,i.click(),i.remove()}else{if(Mt.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(Zt)throw new Error("toSharp() is only supported in server-side environments.");return Ve(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}async function Pk(t,e){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");const r=await(await ni(t)).arrayBuffer(),n=new AudioContext({sampleRate:e});typeof e>"u"&&console.warn(`No sampling rate provided, using default of ${n.sampleRate}Hz.`);const a=await n.decodeAudioData(r);let s;if(a.numberOfChannels===2){const i=Math.sqrt(2),o=a.getChannelData(0),l=a.getChannelData(1);s=new Float32Array(o.length);for(let u=0;u2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,r=15,n=27/Math.log(6.4))=>t>=e?r+Math.log(t/e)*n:3*t/200};function pl(t,e="htk"){const r=Rk[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}const Bk={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,r=15,n=Math.log(6.4)/27)=>t>=r?e*Math.exp(n*(t-r)):200*t/3};function Dk(t,e="htk"){const r=Bk[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}function Nk(t,e){const r=Float64Array.from({length:e.length-1},(i,o)=>e[o+1]-e[o]),n=Array.from({length:t.length},()=>new Array(e.length));for(let i=0;inew Array(t.length));for(let i=0;it+n*s)}function Ra(t,e,r,n,a,s=null,i="htk",o=!1){if(s!==null&&s!=="slaney")throw new Error('norm must be one of null or "slaney"');const l=pl(r,i),u=pl(n,i),d=K_(l,u,e+2);let h=Dk(d,i),f;if(o){const p=a/(t*2);f=pl(Float64Array.from({length:t},(w,v)=>v*p),i),h=d}else f=K_(0,Math.floor(a/2),t);const g=Nk(f,h);if(s!==null&&s==="slaney")for(let p=0;pa)throw Error(`frame_length (${r}) may not be larger than fft_length (${a})`);if(T!==r)throw new Error(`Length of the window (${T}) must equal frame_length (${r})`);if(n<=0)throw new Error("hop_length must be greater than zero");if(s===null&&d!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. Specify `power` to fix this issue.");if(i){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const M=Math.floor((a-1)/2)+1;t=Fk(t,M,M)}const A=Math.floor(1+Math.floor((t.length-r)/n)),z=l?Math.floor(a/2)+1:a;let R=A,G=A;x!==null&&(x>A?$&&(G=x):G=R=x);const j=new ay(a),q=new Float64Array(a),fe=new Float64Array(j.outputBufferSize),ne=new Float32Array(z*G);for(let M=0;M=1;--K)q[K]-=u*q[K-1];q[0]*=1-u}for(let K=0;KMath.pow(o,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(r&&(i=i.subarray(0,t)),n===null)return i;if(t>n)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${n})`);return i}function Wk([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function Ji(t,e=.5,r=null,n=!1){const a=t.logits,s=t.pred_boxes,[i,o,l]=a.dims;if(r!==null&&r.length!==i)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let u=[];for(let d=0;de&&x.push(E)}else{let E=qt(v.data)[1];if(E===l-1||($=bt(v.data),$[E]A*h[(z+1)%2])),f.boxes.push(T),f.classes.push(E),f.scores.push($[E])}}u.push(f)}return u}function Ba(t,e){var r;if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${((r=t==null?void 0:t.constructor)==null?void 0:r.name)??typeof t} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function X_(t,e,r=0,n=null){const a=t/e;let s=sy(a)*e;return n!==null&&s>n&&(s=Math.floor(a)*e),ss?u=Math.floor(s*l/a):s>a&&(l=Math.floor(a*u/s)),await e.resize(u,l,{resample:n}))}async crop_margin(e,r=200){const n=e.clone().grayscale(),a=Fl(n.data)[0],i=qt(n.data)[0]-a;if(i===0)return e;const o=r/255;let l=n.width,u=n.height,d=0,h=0;const f=n.data;for(let g=0;gthis.preprocess(s)));return{pixel_values:fa(n.map(s=>s.pixel_values),0),original_sizes:n.map(s=>s.original_size),reshaped_input_sizes:n.map(s=>s.reshaped_input_size)}}}class Vk extends Ge{post_process_semantic_segmentation(e,r=null){const n=e.logits,a=n.dims[0];if(r!==null&&r.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const s=[];for(let i=0;if[E]&&(f[E]=$[E],g[E]=x)}const p=new Array(l.dims[0]),w=h.data;for(let x=0;xx!==void 0);s.push({segmentation:h,labels:v})}return s}}class Q_ extends Ge{}class Gk extends Q_{}class Hk extends Ge{}class jk extends Ge{}class Z_ extends Ge{}class qk extends Z_{}class Kk extends Ge{}class Yk extends Ge{}class J_ extends Ge{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){var n;const r=(n=this.size)==null?void 0:n.shortest_edge;if(r===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(r<384){const a=Math.floor(r/this.crop_pct),[s,i]=this.get_resize_output_image_size(e,{shortest_edge:a});e=await e.resize(s,i,{resample:this.resample}),e=await e.center_crop(r,r)}else e=await e.resize(r,r,{resample:this.resample});return e}}class Xk extends J_{}class Qk extends Ge{}class Zk extends Ge{}class Jk extends Ge{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 e3 extends Ge{}class t3 extends Ge{}class r3 extends Ge{}class n3 extends Ge{}class e0 extends Ge{}class a3 extends e0{}class t0 extends Ge{post_process_object_detection(...e){return Ji(...e)}}class i3 extends t0{}class s3 extends Ge{post_process_object_detection(...e){return Ji(...e)}}class o3 extends Ge{}class l3 extends Ge{}class r0 extends Ge{pad_image(e,r,n,a={}){const[s,i,o]=r;let l=this.image_mean;Array.isArray(this.image_mean)||(l=new Array(o).fill(l));let u=this.image_std;Array.isArray(u)||(u=new Array(o).fill(l));const d=l.map((h,f)=>-h/u[f]);return super.pad_image(e,r,n,{center:!0,constant_values:d,...a})}}class u3 extends r0{}class d3 extends Ge{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],a=new ce("int64",new BigInt64Array(n.reduce((s,i)=>s*i)).fill(1n),n);return{...r,pixel_mask:a}}post_process_object_detection(...e){return Ji(...e)}remove_low_and_no_objects(e,r,n,a){let s=[],i=[],o=[];for(let l=0;ln&&(s.push(d),i.push(g),o.push(h))}return[s,i,o]}check_segment_validity(e,r,n,a=.5,s=.8){let i=[],o=0,l=0;const u=r[n].data;for(let h=0;h=a&&++l;let d=o>0&&l>0;return d&&(d=o/l>s),[d,i]}compute_segments(e,r,n,a,s,i=null,o=null){let[l,u]=o??e[0].dims,d=new ce("int32",new Int32Array(l*u),[l,u]),h=[];if(o!==null)for(let v=0;vg[E]&&(f[E]=v,g[E]=$[E])}let p=0;const w=d.data;for(let v=0;va!==r.dims[s]))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:a=null}={}){const s=await super._call(e);if(r&&(s.input_points=this.reshape_input_points(r,s.original_sizes,s.reshaped_input_sizes)),n){if(!s.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");s.input_labels=this.add_input_labels(n,s.input_points)}return a&&(s.input_boxes=this.reshape_input_points(a,s.original_sizes,s.reshaped_input_sizes,!0)),s}async post_process_masks(e,r,n,{mask_threshold:a=0,binarize:s=!0,pad_size:i=null}={}){const o=[];i=i??this.pad_size;const l=[i.height,i.width];for(let u=0;ua&&(p[w]=1);f=new ce("bool",p,f.dims)}o.push(f)}return o}generate_crop_boxes(e,r,{crop_n_layers:n=0,overlap_ratio:a=512/1500,points_per_crop:s=32,crop_n_points_downscale_factor:i=1}={}){}}class h3 extends Ge{pad_image(e,r,n,a={}){const[s,i,o]=r;return super.pad_image(e,r,{width:i+(n-i%n)%n,height:s+(n-s%n)%n},{mode:"symmetric",center:!1,constant_values:-1,...a})}}class f3 extends Ge{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);const n=await Promise.all(e.map(i=>this.preprocess(i))),a=await Promise.all(r.map(i=>this.preprocess(i,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:fa(n.map((i,o)=>Xt([i.pixel_values,a[o].pixel_values],0)),0),original_sizes:n.map(i=>i.original_size),reshaped_input_sizes:n.map(i=>i.reshaped_input_size)}}}class m3 extends dn{constructor(e){var r;super(e),(r=this.config).mel_filters??(r.mel_filters=Ra(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 Qi(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,a=qt(n)[0];for(let s=0;sthis.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 g3 extends dn{_zero_mean_unit_var_norm(e){const n=e.reduce((s,i)=>s+i,0)/e.length,a=e.reduce((s,i)=>s+(i-n)**2,0)/e.length;return e.map(s=>(s-n)/Math.sqrt(a+1e-7))}async _call(e){Ba(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 _3 extends dn{constructor(e){super(e);const r=this.config.sampling_rate,n=Ra(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;an*32768),Qi(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:a=!0,return_attention_mask:s=!0}={}){Ba(e,"SeamlessM4TFeatureExtractor");let i=await this._extract_fbank_features(e,this.config.max_length);if(a){const[p,w]=i.dims,v=i.data;for(let x=0;x0){const $=new Float32Array(w*(p+x));$.set(v),$.fill(this.config.padding_value,v.length);const E=p+x;i=new ce(i.type,$,[E,w]),s&&(o=new ce("int64",new BigInt64Array(E),[1,E]),o.data.fill(1n,0,p))}}const[l,u]=i.dims,d=this.config.stride;if(l%d!==0)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${d}).`);const f=i.view(1,Math.floor(l/d),u*d),g={input_features:f};if(s){const p=f.dims[1],w=new BigInt64Array(p);if(o){const v=o.data;for(let x=1,$=0;x0)if(n==="rand_trunc"){const o=Math.floor(Math.random()*(i+1));e=e.subarray(o,o+r),s=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${n}" not implemented`);else{if(i<0){let o=new Float64Array(r);if(o.set(e),a==="repeat")for(let l=e.length;l/gm,bboxes:/([^<]+)?/gm},this.size_per_bin=1e3}construct_prompts(e){typeof e=="string"&&(e=[e]);const r=[];for(const n of e)if(this.task_prompts_without_inputs.has(n))r.push(this.task_prompts_without_inputs.get(n));else{for(const[a,s]of this.task_prompts_with_input)if(n.includes(a)){r.push(s.replaceAll("{input}",n).replaceAll(a,""));break}r.length!==e.length&&r.push(n)}return r}post_process_generation(e,r,n){const a=this.tasks_answer_post_processing_type.get(r)??"pure_text";e=e.replaceAll("","").replaceAll("","");let s;switch(a){case"pure_text":s=e;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const i=a==="ocr"?"quad_boxes":"bboxes",o=e.matchAll(this.regexes[i]),l=[],u=[];for(const[d,h,...f]of o)l.push(h?h.trim():l.at(-1)??""),u.push(f.map((g,p)=>(Number(g)+.5)/this.size_per_bin*n[p%2]));s={labels:l,[i]:u};break;default:throw new Error(`Task "${r}" (of type "${a}") not yet implemented.`)}return{[r]:s}}}class xt{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main"}={}){let o=n??await Br(e,"preprocessor_config.json",!0,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i}),l=o.feature_extractor_type??o.image_processor_type,u=this.FEATURE_EXTRACTOR_CLASS_MAPPING[l];if(!u)if(o.size!==void 0)console.warn(`Feature extractor type "${l}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),u=Ge;else throw new Error(`Unknown Feature Extractor type: ${l}`);let d=this.PROCESSOR_CLASS_MAPPING[o.processor_class]??cn,h=new u(o);return new d(h)}}B(xt,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:Ge,WhisperFeatureExtractor:m3,ViTFeatureExtractor:Qk,MobileViTFeatureExtractor:e0,MobileViTImageProcessor:a3,MobileNetV1FeatureExtractor:e3,MobileNetV2FeatureExtractor:t3,MobileNetV3FeatureExtractor:r3,MobileNetV4FeatureExtractor:n3,OwlViTFeatureExtractor:t0,Owlv2ImageProcessor:i3,CLIPFeatureExtractor:Z_,CLIPImageProcessor:qk,Florence2Processor:n0,ChineseCLIPFeatureExtractor:Kk,SiglipImageProcessor:Yk,ConvNextFeatureExtractor:J_,ConvNextImageProcessor:Xk,SegformerFeatureExtractor:Vk,BitImageProcessor:Hk,DPTImageProcessor:Gk,DPTFeatureExtractor:Q_,GLPNFeatureExtractor:jk,BeitFeatureExtractor:l3,DeiTFeatureExtractor:o3,DetrFeatureExtractor:d3,RTDetrImageProcessor:s3,YolosFeatureExtractor:c3,DonutFeatureExtractor:r0,NougatImageProcessor:u3,EfficientNetImageProcessor:Jk,ViTImageProcessor:Zk,VitMatteImageProcessor:f3,SamImageProcessor:p3,Swin2SRImageProcessor:h3,Wav2Vec2FeatureExtractor:g3,SeamlessM4TFeatureExtractor:_3,SpeechT5FeatureExtractor:b3,ASTFeatureExtractor:y3,ClapFeatureExtractor:w3}),B(xt,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:$3,Wav2Vec2ProcessorWithLM:x3,SamProcessor:v3,SpeechT5Processor:S3,OwlViTProcessor:k3,Florence2Processor:n0});class E3{put(e){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const C3=Rr.IS_PROCESS_AVAILABLE?t=>process.stdout.write(t):t=>console.log(t);class a0 extends E3{constructor(e){super(),this.callback_function=e??C3}}async function Tr(t){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(e=>At.read(e)))}async function es(t,e){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(r=>typeof r=="string"||r instanceof URL?Pk(r,e):r instanceof Float64Array?new Float32Array(r):r))}function i0(t,e){e&&(t=t.map(i=>i|0));const[r,n,a,s]=t;return{xmin:r,ymin:n,xmax:a,ymax:s}}class tt extends wt{constructor({task:e,model:r,tokenizer:n=null,processor:a=null}){super(),this.task=e,this.model=r,this.tokenizer=n,this.processor=a}async dispose(){await this.model.dispose()}}class T3 extends tt{constructor(e){super(e)}async _call(e,{top_k:r=1}={}){const n=this.tokenizer(e,{padding:!0,truncation:!0}),a=await this.model(n),s=this.model.config.problem_type==="multi_label_classification"?l=>l.sigmoid():l=>new ce("float32",bt(l.data),l.dims),i=this.model.config.id2label,o=[];for(const l of a.logits){const u=s(l),d=await An(u,r),h=d[0].tolist(),g=d[1].tolist().map((p,w)=>({label:i?i[p]:`LABEL_${p}`,score:h[w]}));r===1?o.push(...g):o.push(g)}return Array.isArray(e)||r===1?o:o[0]}}class A3 extends tt{constructor(e){super(e)}async _call(e,{ignore_labels:r=["O"]}={}){const n=Array.isArray(e),a=this.tokenizer(n?e:[e],{padding:!0,truncation:!0}),i=(await this.model(a)).logits,o=this.model.config.id2label,l=[];for(let u=0;uE==this.tokenizer.sep_token_id);l[h].map((E,T)=>E==1&&(T===0||T>g&&u.findIndex(A=>A==f[T])===-1));const p=s[h].tolist(),w=i[h].tolist();for(let E=1;ET==f[E])!==-1)&&(p[E]=-1/0,w[E]=-1/0);const v=bt(p).map((E,T)=>[E,T]),x=bt(w).map((E,T)=>[E,T]);v[0][0]=0,x[0][0]=0;const $=Y0(v,x).filter(E=>E[0][1]<=E[1][1]).map(E=>[E[0][1],E[1][1],E[0][0]*E[1][0]]).sort((E,T)=>T[2]-E[2]);for(let E=0;Ep==this.tokenizer.mask_token_id);if(u===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const d=a[o][u],h=await An(new ce("float32",bt(d.data),d.dims),r),f=h[0].tolist(),g=h[1].tolist();s.push(g.map((p,w)=>{const v=l.slice();return v[u]=p,{score:f[w],token:Number(p),token_str:this.tokenizer.model.vocab[p],sequence:this.tokenizer.decode(v,{skip_special_tokens:!0})}}))}return Array.isArray(e)?s:s[0]}}class fl extends tt{constructor(r){super(r);B(this,"_key","generated_text")}async _call(r,n={}){Array.isArray(r)||(r=[r]),this.model.config.prefix&&(r=r.map(u=>this.model.config.prefix+u));const a=this.model.config.task_specific_params;a&&a[this.task]&&a[this.task].prefix&&(r=r.map(u=>a[this.task].prefix+u));const s=this.tokenizer,i={padding:!0,truncation:!0};let o;this instanceof s0&&"_build_translation_inputs"in s?o=s._build_translation_inputs(r,i,n):o=s(r,i);const l=await this.model.generate({...o,...n});return s.batch_decode(l,{skip_special_tokens:!0}).map(u=>({[this._key]:u}))}}class O3 extends fl{constructor(r){super(r);B(this,"_key","summary_text")}}class s0 extends fl{constructor(r){super(r);B(this,"_key","translation_text")}}function ml(t){return Array.isArray(t)&&t.every(e=>"role"in e&&"content"in e)}let o0=class extends tt{constructor(e){super(e)}async _call(e,r={}){let n=!1,a=!1,s;if(typeof e=="string")s=e=[e];else if(Array.isArray(e)&&e.every(g=>typeof g=="string"))n=!0,s=e;else{if(ml(e))e=[e];else if(Array.isArray(e)&&e.every(ml))n=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");a=!0,s=e.map(g=>this.tokenizer.apply_chat_template(g,{tokenize:!1,add_generation_prompt:!0}))}const i=r.add_special_tokens??!1,o=a?!1:r.return_full_text??!0;this.tokenizer.padding_side="left";const l=this.tokenizer(s,{add_special_tokens:i,padding:!0,truncation:!0}),u=await this.model.generate({...l,...r}),d=this.tokenizer.batch_decode(u,{skip_special_tokens:!0});let h;!o&&l.input_ids.dims.at(-1)>0&&(h=this.tokenizer.batch_decode(l.input_ids,{skip_special_tokens:!0}).map(g=>g.length));const f=Array.from({length:e.length},g=>[]);for(let g=0;gs.role==="system")||(e=[{role:"system",content:"You are a helpful assistant."},...e]);let a="";for(const s of e){const i=s.content.trim();switch(s.role){case"system":a+=`${i} +`;break;case"user":a+=`user: +${i} +`;break;case"assistant":a+=`model: +${i}${r} +`;break;default:throw new Error(`Invalid role: ${s.role}. Must be one of 'system', 'user', or 'assistant'.`)}}return a+=`model: +`,a}async _call(e,r={}){var g;const n=self.ai,a=r.top_k,s=r.temperature;(!this._session||this._temperature!==s||this._top_k!==a)&&((g=this._session)==null||g.destroy(),this._temperature=s,this._top_k=a,this._session=await n.createTextSession({temperature:s,topK:a}));let i;if(typeof e=="string")i=e;else if(ml(e)){const p=e;this.tokenizer?i=this.tokenizer.apply_chat_template(p,{add_generation_prompt:!0,tokenize:!1}):i=this._apply_chat_template(p)}else throw new Error("Input must be a string or a Chat item.");const o=r.streamer??null,l=o instanceof a0;if(o&&!l)if(this.tokenizer){const p=this.tokenizer.encode(i,{add_special_tokens:!1});o.put([p])}else throw new Error("The `BuiltInTextGenerationPipeline` only supports `RawTextStreamer` when `load_tokenizer` is false.");const u=r.stopping_criteria??null,d=await this._session.promptStreaming(i);let h="",f;for await(f of d){if(f.startsWith(h)){const p=f.slice(h.length);if(p.length===0)continue;if(o)if(l)o.callback_function(p);else{const w=this.tokenizer.encode(p,{add_special_tokens:!1});if(w.length!==1)throw new Error(`An error occurred while encoding the token "${p}": expected a single token, but got ${w.length} tokens. Please report this bug.`);o.put([BigInt(w[0])])}}if(h=f,u&&u([0]).every(w=>w))break}return o&&!l&&o.end(),d.cancel(),[{generated_text:f}]}}class P3 extends tt{constructor(e){super(e),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([r,n])=>[r.toLowerCase(),n])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(e,r,{hypothesis_template:n="This example is {}.",multi_label:a=!1}={}){const s=Array.isArray(e);s||(e=[e]),Array.isArray(r)||(r=[r]);const i=r.map(u=>n.replace("{}",u)),o=a||r.length===1,l=[];for(const u of e){const d=[];for(const g of i){const p=this.tokenizer(u,{text_pair:g,padding:!0,truncation:!0}),w=await this.model(p);o?d.push([w.logits.data[this.contradiction_id],w.logits.data[this.entailment_id]]):d.push(w.logits.data[this.entailment_id])}const f=(o?d.map(g=>bt(g)[1]):bt(d)).map((g,p)=>[g,p]).sort((g,p)=>p[0]-g[0]);l.push({sequence:u,labels:f.map(g=>r[g[1]]),scores:f.map(g=>g[0])})}return s?l:l[0]}}class R3 extends tt{constructor(e){super(e)}async _call(e,{pooling:r="none",normalize:n=!1,quantize:a=!1,precision:s="binary"}={}){const i=this.tokenizer(e,{padding:!0,truncation:!0}),o=await this.model(i);let l=o.last_hidden_state??o.logits??o.token_embeddings;if(r!=="none")if(r==="mean")l=Hw(l,i.attention_mask);else if(r==="cls")l=l.slice(null,0);else throw Error(`Pooling method '${r}' not supported.`);return n&&(l=l.normalize(2,-1)),a&&(l=Jw(l,s)),l}}class B3 extends tt{constructor(e){super(e)}async _call(e,{pool:r=null}={}){const n=await Tr(e),{pixel_values:a}=await this.processor(n),s=await this.model({pixel_values:a});let i;if(r){if(!("pooler_output"in s))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");i=s.pooler_output}else i=s.last_hidden_state??s.logits??s.image_embeds;return i}}class D3 extends tt{constructor(e){super(e)}async _call(e,{top_k:r=5}={}){const n=this.processor.feature_extractor.config.sampling_rate,a=await es(e,n),s=this.model.config.id2label,i=[];for(const o of a){const l=await this.processor(o),d=(await this.model(l)).logits[0],h=await An(new ce("float32",bt(d.data),d.dims),r),f=h[0].tolist(),p=h[1].tolist().map((w,v)=>({label:s?s[w]:`LABEL_${w}`,score:f[v]}));i.push(p)}return Array.isArray(e)?i:i[0]}}class N3 extends tt{constructor(e){super(e)}async _call(e,r,{hypothesis_template:n="This is a sound of {}."}={}){const a=!Array.isArray(e);a&&(e=[e]);const s=r.map(d=>n.replace("{}",d)),i=this.tokenizer(s,{padding:!0,truncation:!0}),o=this.processor.feature_extractor.config.sampling_rate,l=await es(e,o),u=[];for(const d of l){const h=await this.processor(d),f=await this.model({...i,...h}),g=bt(f.logits_per_audio.data);u.push([...g].map((p,w)=>({score:p,label:r[w]})))}return a?u[0]:u}}class F3 extends tt{constructor(e){super(e)}async _call(e,r={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(e,r);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(e,r);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(e,r){r.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),r.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const n=!Array.isArray(e);n&&(e=[e]);const a=this.processor.feature_extractor.config.sampling_rate,s=await es(e,a),i=[];for(const o of s){const l=await this.processor(o),d=(await this.model(l)).logits[0],h=[];for(const g of d)h.push(qt(g.data)[1]);const f=this.tokenizer.decode(h);i.push({text:f})}return n?i[0]:i}async _call_whisper(e,r){const n=r.return_timestamps??!1,a=r.chunk_length_s??0,s=r.force_full_sequences??!1;let i=r.stride_length_s??null;const o={...r};n==="word"&&(o.return_token_timestamps=!0,o.return_timestamps=!1);const l=!Array.isArray(e);l&&(e=[e]);const u=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,d=this.processor.feature_extractor.config.hop_length,h=this.processor.feature_extractor.config.sampling_rate,f=await es(e,h),g=[];for(const p of f){let w=[];if(a>0){if(i===null)i=a/6;else if(a<=i)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const $=h*a,E=h*i,T=$-2*E;let A=0;for(;;){const z=A+$,R=p.subarray(A,z),G=await this.processor(R),j=A===0,q=z>=p.length;if(w.push({stride:[R.length,j?0:E,q?0:E],input_features:G.input_features,is_last:q}),q)break;A+=T}}else w=[{stride:[p.length,0,0],input_features:(await this.processor(p)).input_features,is_last:!0}];for(const $ of w){o.num_frames=Math.floor($.stride[0]/d);const E=await this.model.generate({inputs:$.input_features,...o});n==="word"?($.tokens=E.sequences.tolist()[0],$.token_timestamps=E.token_timestamps.tolist()[0].map(T=>Qn(T,2))):$.tokens=E[0].tolist(),$.stride=$.stride.map(T=>T/h)}const[v,x]=this.tokenizer._decode_asr(w,{time_precision:u,return_timestamps:n,force_full_sequences:s});g.push({text:v,...x})}return l?g[0]:g}}class L3 extends tt{constructor(e){super(e)}async _call(e,r={}){const n=Array.isArray(e),a=await Tr(e),{pixel_values:s}=await this.processor(a),i=[];for(const o of s){o.dims=[1,...o.dims];const l=await this.model.generate({inputs:o,...r}),u=this.tokenizer.batch_decode(l,{skip_special_tokens:!0}).map(d=>({generated_text:d.trim()}));i.push(u)}return n?i:i[0]}}class U3 extends tt{constructor(e){super(e)}async _call(e,{top_k:r=5}={}){const n=await Tr(e),{pixel_values:a}=await this.processor(n),s=await this.model({pixel_values:a}),i=this.model.config.id2label,o=[];for(const l of s.logits){const u=await An(new ce("float32",bt(l.data),l.dims),r),d=u[0].tolist(),f=u[1].tolist().map((g,p)=>({label:i?i[g]:`LABEL_${g}`,score:d[p]}));o.push(f)}return Array.isArray(e)?o:o[0]}}class W3 extends tt{constructor(e){super(e),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(e,{threshold:r=.5,mask_threshold:n=.5,overlap_mask_area_threshold:a=.8,label_ids_to_fuse:s=null,target_sizes:i=null,subtask:o=null}={}){if(Array.isArray(e)&&e.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const u=await Tr(e),d=u.map(x=>[x.height,x.width]),{pixel_values:h,pixel_mask:f}=await this.processor(u),g=await this.model({pixel_values:h,pixel_mask:f});let p=null;if(o!==null)p=this.subtasks_mapping[o];else for(let[x,$]of Object.entries(this.subtasks_mapping))if($ in this.processor.feature_extractor){p=this.processor.feature_extractor[$].bind(this.processor.feature_extractor),o=x;break}const w=this.model.config.id2label,v=[];if(o==="panoptic"||o==="instance"){const x=p(g,r,n,a,s,i??d)[0],$=x.segmentation;for(const E of x.segments_info){const T=new Uint8ClampedArray($.data.length);for(let z=0;z<$.data.length;++z)$.data[z]===E.id&&(T[z]=255);const A=new At(T,$.dims[1],$.dims[0],1);v.push({score:E.score,label:w[E.label_id],mask:A})}}else if(o==="semantic"){const{segmentation:x,labels:$}=p(g,i??d)[0];for(const E of $){const T=new Uint8ClampedArray(x.data.length);for(let z=0;zn.replace("{}",f)),o=this.tokenizer(i,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:l}=await this.processor(s),u=await this.model({...o,pixel_values:l}),d=this.model.config.model_type==="siglip"?f=>f.sigmoid().data:f=>bt(f.data),h=[];for(const f of u.logits_per_image){const p=[...d(f)].map((w,v)=>({score:w,label:r[v]}));p.sort((w,v)=>v.score-w.score),h.push(p)}return a?h:h[0]}}class G3 extends tt{constructor(e){super(e)}async _call(e,{threshold:r=.9,percentage:n=!1}={}){const a=Array.isArray(e);if(a&&e.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const s=await Tr(e),i=n?null:s.map(g=>[g.height,g.width]),{pixel_values:o,pixel_mask:l}=await this.processor(s),u=await this.model({pixel_values:o,pixel_mask:l}),d=this.processor.feature_extractor.post_process_object_detection(u,r,i),h=this.model.config.id2label,f=d.map(g=>g.boxes.map((p,w)=>({score:g.scores[w],label:h[g.classes[w]],box:i0(p,!n)})));return a?f:f[0]}}class H3 extends tt{constructor(e){super(e)}async _call(e,r,{threshold:n=.1,top_k:a=null,percentage:s=!1}={}){const i=Array.isArray(e),o=await Tr(e),l=this.tokenizer(r,{padding:!0,truncation:!0}),u=await this.processor(o),d=[];for(let h=0;h({score:v.scores[E],label:r[v.classes[E]],box:i0($,!s)})).sort(($,E)=>E.score-$.score);a!==null&&(x=x.slice(0,a)),d.push(x)}return i?d:d[0]}}class j3 extends tt{constructor(e){super(e)}async _call(e,r,n={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class q3 extends tt{constructor(r){super(r);B(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=r.vocoder??null}async _call(r,{speaker_embeddings:n=null}={}){return this.processor?this._call_text_to_spectrogram(r,{speaker_embeddings:n}):this._call_text_to_waveform(r)}async _call_text_to_waveform(r){const n=this.tokenizer(r,{padding:!0,truncation:!0}),{waveform:a}=await this.model(n),s=this.model.config.sampling_rate;return{audio:a.data,sampling_rate:s}}async _call_text_to_spectrogram(r,{speaker_embeddings:n}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await ln.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof n=="string"||n instanceof URL)&&(n=new Float32Array(await(await fetch(n)).arrayBuffer())),n instanceof Float32Array)n=new ce("float32",n,[1,n.length]);else if(!(n instanceof ce))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:a}=this.tokenizer(r,{padding:!0,truncation:!0}),{waveform:s}=await this.model.generate_speech(a,n,{vocoder:this.vocoder}),i=this.processor.feature_extractor.config.sampling_rate;return{audio:s.data,sampling_rate:i}}}class K3 extends tt{constructor(e){super(e)}async _call(e){const r=await Tr(e),n=await this.processor(r),a=await this.model(n),s=[];for(const i of a.reconstruction){const o=i.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");s.push(At.fromTensor(o))}return s.length>1?s:s[0]}}class Y3 extends tt{constructor(e){super(e)}async _call(e){const r=await Tr(e),n=await this.processor(r),{predicted_depth:a}=await this.model(n),s=[];for(let i=0;i1?s:s[0]}}const l0=Object.freeze({"text-classification":{tokenizer:ut,pipeline:T3,model:cl,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:ut,pipeline:A3,model:C_,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:ut,pipeline:I3,model:z_,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:ut,pipeline:M3,model:O_,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:ut,pipeline:O3,model:Xi,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:ut,pipeline:s0,model:Xi,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:ut,pipeline:fl,model:Xi,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:ut,pipeline:o0,model:M_,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:ut,pipeline:P3,model:cl,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:D3,model:U_,processor:xt,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:ut,pipeline:N3,model:ln,processor:xt,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:ut,pipeline:F3,model:[T_,L_],processor:xt,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:ut,pipeline:q3,model:[I_,A_],processor:[xt,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:ut,pipeline:L3,model:P_,processor:xt,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:U3,model:R_,processor:xt,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:W3,model:[B_,D_],processor:xt,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:ut,pipeline:V3,model:ln,processor:xt,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:G3,model:N_,processor:xt,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:ut,pipeline:H3,model:F_,processor:xt,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:ut,pipeline:j3,model:W_,processor:xt,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:K3,model:V_,processor:xt,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Y3,model:G_,processor:xt,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:ut,pipeline:R3,model:ln,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:xt,pipeline:B3,model:[H_,ln],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),X3=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function Q3(t,e=null,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",device:o=null,dtype:l=null,model_file_name:u=null,session_options:d={}}={}){t=X3[t]??t;const h=l0[t.split("_",1)[0]];if(!h)throw Error(`Unsupported pipeline: ${t}. Must be one of [${Object.keys(l0)}]`);e||(e=h.default.model,console.log(`No model specified. Using default model: "${e}".`));const f={progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,device:o,dtype:l,model_file_name:u,session_options:d};if(n=f.config=await Jo.from_pretrained(e,f),t==="text-generation"){const v=n["transformers.js_config"]??{};if(v.use_built_in_ai){if(!Rr.IS_BUILT_IN_AI_AVAILABLE)throw new Error("The built-in AI API is not available in this environment.");if(n.model_type!=="gemini")throw new Error('The built-in AI API is only available for "gemini" models.');const x=v.load_tokenizer?await ut.from_pretrained(e,f):null;return new z3({task:t,model:null,tokenizer:x})}}const g=new Map([["tokenizer",h.tokenizer],["model",h.model],["processor",h.processor]]),p=await Z3(g,e,f);p.task=t,bn(r,{status:"ready",task:t,model:e});const w=h.pipeline;return new w(p)}async function Z3(t,e,r){const n=Object.create(null),a=[];for(let[s,i]of t.entries()){if(!i)continue;let o;Array.isArray(i)?o=new Promise(async(l,u)=>{var h,f;let d;for(let g of i){if(g===null){l(null);return}try{l(await g.from_pretrained(e,r));return}catch(p){if((h=p.message)!=null&&h.includes("Unsupported model type"))d=p;else if((f=p.message)!=null&&f.includes("Could not locate file"))d=p;else{u(p);return}}}u(d)}):o=i.from_pretrained(e,r),n[s]=o,a.push(o)}await Promise.all(a);for(let[s,i]of Object.entries(n))n[s]=await i;return n}class ts{static async getInstance(){return this.instance??(this.instance=Q3("text-generation",this.model_id)),this.instance}}B(ts,"model_id","Xenova/gemini-nano"),B(ts,"instance",null);const rs=new S1;async function J3(t){const e=await ts.getInstance();let r,n=0;const a=o=>{r??(r=performance.now());let l;n++>0&&(l=n/(performance.now()-r)*1e3),self.postMessage({status:"update",output:o,tps:l,numTokens:n})},s=new a0(a);self.postMessage({status:"start"});const i=await e(t,{streamer:s,stopping_criteria:rs,top_k:1,temperature:0});i[0].generated_text.length===0&&self.postMessage({status:"update",output:" ",tps:null,numTokens:0}),self.postMessage({status:"complete",output:i[0].generated_text})}async function eE(){self.postMessage({status:"loading",data:"Loading model..."});const t=await ts.getInstance(e=>{self.postMessage(e)});self.postMessage({status:"loading",data:"Loading the model..."}),await t("1+1="),self.postMessage({status:"ready"})}self.addEventListener("message",async t=>{const{type:e,data:r}=t.data;switch(e){case"load":eE().catch(n=>{self.postMessage({status:"error",data:n})});break;case"generate":rs.reset(),J3(r);break;case"interrupt":rs.interrupt();break;case"reset":rs.reset();break}})})();