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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])}},Nu=(t,e)=>new Du(t,e),Zn=(t,e)=>{let r=t.length,n=[];for(let a=0;a1&&i===1&&n.unshift(s)}return n}}),Fu,qs,Lu,Uu,xr,Wu,Vu,Jn=J(()=>{xe(),Me(),pt(),Ie(),Fu=t=>{if(!t||t.length!==1)throw new Error("Transpose requires 1 input.")},qs=(t,e)=>e&&e.length!==t?[...new Array(t).keys()].reverse():e,Lu=(t,e)=>X.sortBasedOnPerm(t,qs(t.length,e)),Uu=(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=qs(n,e),s=Lu(t.dims,a),i=_e("output",r,s.length),o=Q("a",r,n),l=u=>` + ${u.registerUniform("output_size","u32").declareVariables(o,i)} + + ${Uu(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}},Wu=(t,e)=>{Fu(t.inputs),t.compute(xr(t.inputs[0],e.perm))},Vu=t=>qe({perm:t.perm})}),Gu,Hu,ju,qu,Ku,Yu,Xu,Qu,Zu,Ju,rr,ed,td,rd,nd,ad,id,sd,od,ld,ud,xy=J(()=>{xe(),Me(),Ie(),Ys(),Jn(),Gu={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 = ${Hu[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}(${qu[n]})`}`)}; + } + }`,getRunData:()=>({outputs:[{dims:s,dataType:a}],dispatchGroup:{x:l},programUniforms:[{type:12,data:u}]})}},rr=(t,e,r,n)=>{let a=t.inputs.length===1?r:Ks(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.")},dd=t=>["","",`var value = ${t.getByIndices("input_indices")};`,""],fi=(t,e,r,n,a,s,i=!1,o=!1)=>{let l=[],u=r[0].dims,d=u.length,h=X.normalizeAxes(a,d),m=!o&&h.length===0;u.forEach((w,v)=>{m||h.indexOf(v)>=0?i&&l.push(1):l.push(w)});let g=l.length,p=X.size(l);return{name:t,shaderCache:e,getShaderSource:w=>{let v=[],x=Q("_A",r[0].dataType,d),$=_e("output",s,g),E=n(x,$,h),T=E[2];for(let A=0,P=0;A=0?(i&&P++,T=`for(var j${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",P))};`),P++);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)]})}},Ks=(t,e)=>{let r=[];return t[1].dims[0]>0&&t[1].getBigInt64Array().forEach(n=>r.push(Number(n))),qe({axes:r,keepDims:e.keepDims,noopWithEmptyAxes:e.noopWithEmptyAxes})},ar=(t,e,r,n)=>{let a=t.inputs,s=a.length===1?r:Ks(a,r);t.compute(fi(e,{hint:s.cacheKey,inputDependencies:["rank"]},[a[0]],s.noopWithEmptyAxes&&s.axes.length===0?dd:n,s.axes,a[0].dataType,s.keepDims,s.noopWithEmptyAxes),{inputs:[0]})},cd=(t,e)=>{nr(t.inputs),ar(t,"ReduceLogSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},pd=(t,e)=>{nr(t.inputs),ar(t,"ReduceL1",e,(r,n)=>[`var value = 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${s});`]})},_d=(t,e)=>{nr(t.inputs),ar(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")});`,""]})},yd=(t,e)=>{nr(t.inputs),ar(t,"ReduceProd",e,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},wd=(t,e)=>{nr(t.inputs),ar(t,"ReduceSum",e,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},bd=(t,e)=>{nr(t.inputs),ar(t,"ReduceSumSquare",e,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},ir=(t,e,r)=>{if(e.length===0)return r;let n=1,a=1;for(let s=0;s1024},vd=(t,e)=>{ir(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?gd(t,e):ed(t,e)},$d=(t,e)=>{ir(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?pd(t,e):td(t,e)},xd=(t,e)=>{ir(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?hd(t,e):rd(t,e)},Sd=(t,e)=>{ir(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?fd(t,e):nd(t,e)},kd=(t,e)=>{ir(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?md(t,e):ad(t,e)},Ed=(t,e)=>{ir(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?_d(t,e):id(t,e)},Cd=(t,e)=>{ir(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?yd(t,e):sd(t,e)},Td=(t,e)=>{ir(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?wd(t,e):od(t,e)},Ad=(t,e)=>{ir(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?bd(t,e):ld(t,e)},Id=(t,e)=>{ir(t.inputs[0].dims,e.axes,e.noopWithEmptyAxes)?cd(t,e):ud(t,e)}}),Xs,Md,Od,Qs,Sy=J(()=>{xe(),pt(),Ys(),Xs=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.")},Md=(t,e)=>{Xs(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 = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",a.setByOffset("global_idx","best_index")]};t.compute(fi("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Od=(t,e)=>{Xs(t.inputs);let r=(n,a,s)=>{let i=[];for(let o=0;o=0||s.length===0)&&i.push(`input_indices[${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 = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",a.setByOffset("global_idx","best_index")]};t.compute(fi("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[t.inputs[0]],r,[e.axis],7,e.keepDims),{inputs:[0]})},Qs=t=>qe(t)}),zd,Pd,Rd,mi,Bd,Dd,Nd=J(()=>{xe(),Me(),pt(),Ie(),zd=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");let r=0,n=t[r],a=n.dataType,s=n.dims.length;t.forEach((i,o)=>{if(o!==r){if(i.dataType!==a)throw new Error("input tensors should be one type");if(i.dims.length!==s)throw new Error("input tensors should have the same shape");i.dims.forEach((l,u)=>{if(u!==e&&l!==n.dims[u])throw new Error("non concat dimensions must match")})}})},Pd=(t,e)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${e}); + for (var i: u32 = 0u; i < ${t}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${t}u; + }`,Rd=(t,e)=>{let r=t.length,n=[];for(let a=0;a{let a=X.size(r),s=new Array(t.length),i=new Array(t.length),o=0,l=[],u=[],d=[{type:12,data:a}];for(let w=0;w`uniforms.sizeInConcatAxis${w}`).join(","),p=w=>` + + ${(()=>{w.registerUniform("outputSize","u32");for(let v=0;v(${g}); + ${m} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${Rd(i,h)} + }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:p}},Bd=(t,e)=>{let r=t.inputs,n=r[0].dims,a=X.normalizeAxis(e.axis,n.length);zd(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(mi(i,a,s,r[0].dataType),{inputs:i})},Dd=t=>qe({axis:t.axis})}),Fd,Ld,Ud,Wd,gi,Vd,Gd,Hd=J(()=>{xe(),Ls(),Ie(),Nd(),Fd=(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,m=h,g=m;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],m=e.qkvHiddenSizes[1],g=e.qkvHiddenSizes[2]}let p=u;if(h!==m)throw new Error("qkv_hidden_sizes first element should be same as the second");if(a.dims[0]!==h+m+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(m!==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]!==m/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}},Ld=(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; + ${m.registerUniforms(p).declareVariables(g)} + ${m.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})}},Ud=(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,m=12,g={x:Math.ceil(o/m),y:Math.ceil(a.sequenceLength/m),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),P=Ot(1,d),B=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"}];return` + const TILE_SIZE = ${m}u; + + var tileQ: array<${$.type.storage}, ${m*m}>; + var tileK: array<${$.type.storage}, ${m*m}>; + ${x.registerUniforms(B).declareVariables(...T,A)} + ${x.mainStart([m,m,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z; + let m = workgroup_id.y * TILE_SIZE; + let n = workgroup_id.x * TILE_SIZE; + let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K; + let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K; + + var value = ${P}(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 += ${P}(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}},Wd=(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),m=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,m,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 = ${h.type.storage}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + let batchIdx = workgroup_id.z / uniforms.num_heads; + let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads; + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + currentBatchHeadNumber * uniforms.N + n; + output[outputIdx] = value; + } + }`}}},gi=(t,e,r,n,a,s,i,o,l,u,d)=>{let h=t.outputCount>1,m=t.outputCount>2,g=h&&m?u.pastSequenceLength:0,p=g+u.kvSequenceLength,w=[u.batchSize,u.numHeads,p,u.headSize],v=i?[i,r]:[r],x=h?t.compute(mi(v,2,w,r.dataType),{inputs:v,outputs:[1]})[0]:r,$=[u.batchSize,u.numHeads,p,u.headSize],E=o?[o,n]:[n],T=m?t.compute(mi(E,2,$,n.dataType),{inputs:E,outputs:[2]})[0]:n,A=[e,x];l&&A.push(l);let P=t.compute(Ud(t,e,x,l,u,d,g),{inputs:A,outputs:[-1]})[0];t.compute(Ld(t,P,u.batchSize*u.numHeads*u.sequenceLength,p),{inputs:[P],outputs:[]});let B=[P,T];t.compute(Wd(t,P,T,u,g),{inputs:B,outputs:[0]})},Vd=(t,e)=>{let r=[e.batchSize,e.numHeads,e.sequenceLength,e.headSize],n=e.sequenceLength,a=e.inputHiddenSize,s=e.headSize,i=12,o={x:Math.ceil(e.headSize/i),y:Math.ceil(e.sequenceLength/i),z:e.batchSize*e.numHeads},l=[t.inputs[0],t.inputs[1],t.inputs[2]],u=[{type:12,data:n},{type:12,data:a},{type:12,data:s},{type:12,data:e.numHeads},{type:12,data:e.headSize},{type:12,data:e.hiddenSize},{type:12,data:e.hiddenSize+e.hiddenSize+e.vHiddenSize}],d=h=>{let m=_e("output_q",l[0].dataType,r),g=_e("output_k",l[0].dataType,r),p=_e("output_v",l[0].dataType,r),w=Q("input",l[0].dataType,l[0].dims),v=Q("weight",l[1].dataType,l[1].dims),x=Q("bias",l[2].dataType,l[2].dims),$=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,m,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]})},Gd=(t,e)=>{let r=Fd(t.inputs,e),[n,a,s]=Vd(t,r);return gi(t,n,a,s,t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e)}}),jd,qd,Kd,Yd,ky=J(()=>{tr(),xe(),Me(),pt(),Ie(),jd=(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[${l}] do not match`)})};if(t[0].dims.length>1){let 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${v.indicesSet("outputIndices","0","0")} + let cOffset = ${v.indicesToOffset("outputIndices")};`;else{E=`var cIndices = ${m.type.indices}(0); + cIndices[0] = outputIndices[${s.length-1}];`;for(let T=1;T` + const epsilon = ${r}; + ${E.registerUniform("outputSize","u32").declareVariables(h,m,g,p,w,v)} + ${E.mainStart()} + ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${v.offsetToIndices(`global_idx * ${i}`)}; + ${x()} + let scale = ${m.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")}; + ${l.setByOffset("global_idx","value")} + }`}},Zd=t=>{Xd(t.inputs),t.compute(Qd(t.inputs))}}),Jd,De,ec,tc,rc,nc,ac,ic,sc,oc,lc,uc,dc,cc,pc,hc,_i,fc,yi,mc,gc,_c,yc,wc,bc,vc,$c,xc,Sc,kc,Ec,Cc,Tc,Ac,Ic,Zs,Mc,Js,eo,Oc,zc,Pc,to=J(()=>{xe(),Me(),pt(),Ie(),Jd=(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=>Jd(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)}]})}),ec=t=>{t.compute(De(t.inputs[0],"Abs","abs"))},tc=t=>{t.compute(De(t.inputs[0],"Acos","acos"))},rc=t=>{t.compute(De(t.inputs[0],"Acosh","acosh"))},nc=t=>{t.compute(De(t.inputs[0],"Asin","asin"))},ac=t=>{t.compute(De(t.inputs[0],"Asinh","asinh"))},ic=t=>{t.compute(De(t.inputs[0],"Atan","atan"))},sc=t=>{t.compute(De(t.inputs[0],"Atanh","atanh"))},oc=t=>qe(t),lc=(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): ${e.to}`)}t.compute(De(t.inputs[0],"Cast",r,void 0,e.cacheKey,e.to))},uc=t=>{let e=t.length>=2&&t[1].data!==0?t[1].getFloat32Array()[0]:Gs,r=t.length>=3&&t[2].data!==0?t[2].getFloat32Array()[0]:Hs;return qe({min:e,max:r})},dc=(t,e)=>{let r=t.inputs.length===1?e:uc(t.inputs),n=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Clip",a=>`clamp(${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]})},cc=t=>{t.compute(De(t.inputs[0],"Ceil","ceil"))},pc=t=>{t.compute(De(t.inputs[0],"Cos","cos"))},hc=t=>{t.compute(De(t.inputs[0],"Cosh","cosh"))},_i=t=>qe(t),fc=(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))},yi=(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<${t}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,mc=t=>{let e=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"Erf",r=>`erf_vf32(${r})`,yi(e)))},gc=t=>{t.compute(De(t.inputs[0],"Exp","exp"))},_c=t=>{t.compute(De(t.inputs[0],"Floor","floor"))},yc=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))`,yi(e)))},wc=(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))},bc=t=>{t.compute(De(t.inputs[0],"Not",e=>`!${e}`))},vc=t=>{t.compute(De(t.inputs[0],"Neg",e=>`-${e}`))},$c=t=>{t.compute(De(t.inputs[0],"Reciprocal",e=>`1.0/${e}`))},xc=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))`))},Sc=t=>{t.compute(De(t.inputs[0],"Sigmoid",e=>`(1.0 / (1.0 + exp(-${e})))`))},kc=t=>qe(t),Ec=(t,e)=>{let r=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${e.alpha} * ${n} + vec4<${r}>(${e.beta})))`,void 0,e.cacheKey))},Cc=t=>{t.compute(De(t.inputs[0],"Sin","sin"))},Tc=t=>{t.compute(De(t.inputs[0],"Sinh","sinh"))},Ac=t=>{t.compute(De(t.inputs[0],"Sqrt","sqrt"))},Ic=t=>{t.compute(De(t.inputs[0],"Tan","tan"))},Zs=t=>`sign(${t}) * (1 - exp(-2 * abs(${t}))) / (1 + exp(-2 * abs(${t})))`,Mc=t=>{t.compute(De(t.inputs[0],"Tanh",Zs))},Js=(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 ${Zs("v")}; +} +`,eo=t=>`(fast_gelu_a + fast_gelu_a * tanh_v(${t} * (fast_gelu_c * ${t} * ${t} + fast_gelu_b))) * ${t}`,Oc=t=>{let e=Ot(t.inputs[0].dataType);t.compute(De(t.inputs[0],"FastGelu",eo,Js(e),void 0,t.inputs[0].dataType))},zc=(t,e)=>{let r=Ot(t.inputs[0].dataType);return t.compute(De(t.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${e.alpha});`,e.cacheKey)),0},Pc=t=>{t.compute(De(t.inputs[0],"Log","log"))}}),Rc,Bc,Dc,Cy=J(()=>{Me(),Ie(),to(),Rc=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")},Bc=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)} + + ${yi(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); + + ${a.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Dc=t=>{Rc(t.inputs),t.compute(Bc(t.inputs))}}),Nc,Fc,sr,Lc,Uc,Wc,Vc,Gc,Hc,jc,qc,Kc,Yc,Ty=J(()=>{xe(),Me(),Ie(),Nc=(t,e,r,n,a,s,i,o,l,u,d,h)=>{let m,g;typeof o=="string"?m=g=($,E)=>`${o}((${$}),(${E}))`:typeof o=="function"?m=g=o:(m=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 $=(E,T,A="")=>{let P=`aData[indexA${T}][componentA${T}]`,B=`bData[indexB${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}] = ${A}(${m(P,B)}); + `};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")} + ${x} + }`},Fc=(t,e,r,n,a,s,i=r.dataType)=>{let o=!X.areEqual(r.dims,n.dims),l=r.dims,u=X.size(r.dims),d=!1,h=!1,m=[o];if(o){let g=$n.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;m.push(p),m.push(w),m.push(v),m.push(x);let $=1;for(let E=1;Eg.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:g=>Nc(g,r.dims,n.dims,l,d,o,h,a,r.dataType,n.dataType,i,s),getRunData:()=>({outputs:[{dims:l,dataType:i}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:Math.ceil(X.size(l)/4)},...we(r.dims,n.dims,l)]})}},sr=(t,e,r,n,a,s)=>{t.compute(Fc(e,a??"",t.inputs[0],t.inputs[1],r,n,s))},Lc=t=>{sr(t,"Add",(e,r)=>`${e}+${r}`)},Uc=t=>{sr(t,"Div",(e,r)=>`${e}/${r}`)},Wc=t=>{sr(t,"Equal",{scalar:(e,r)=>`u32(${e}==${r})`,vector:(e,r)=>`vec4(${e}==${r})`},void 0,void 0,9)},Vc=t=>{sr(t,"Mul",(e,r)=>`${e}*${r}`)},Gc=t=>{let e=Q("input",t.inputs[0].dataType,t.inputs[0].dims).type.value;sr(t,"Pow",{scalar:(r,n)=>`pow_custom(${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)); + } + `)},Hc=t=>{sr(t,"Sub",(e,r)=>`${e}-${r}`)},jc=t=>{sr(t,"Greater",{scalar:(e,r)=>`u32(${e}>${r})`,vector:(e,r)=>`vec4(${e}>${r})`},void 0,void 0,9)},qc=t=>{sr(t,"Less",{scalar:(e,r)=>`u32(${e}<${r})`,vector:(e,r)=>`vec4(${e}<${r})`},void 0,void 0,9)},Kc=t=>{sr(t,"GreaterOrEqual",{scalar:(e,r)=>`u32(${e}>=${r})`,vector:(e,r)=>`vec4(${e}>=${r})`},void 0,void 0,9)},Yc=t=>{sr(t,"LessOrEqual",{scalar:(e,r)=>`u32(${e}<=${r})`,vector:(e,r)=>`vec4(${e}<=${r})`},void 0,void 0,9)}}),Qr,Zr,Jr,ro,en=J(()=>{xe(),Me(),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 ${t.activation}`)}},Zr=(t,e)=>{t.activation==="Clip"?e.push({type:1,data:t.clipMax},{type:1,data:t.clipMin}):t.activation==="HardSigmoid"?e.push({type:1,data:t.alpha},{type:1,data:t.beta}):t.activation==="LeakyRelu"&&e.push({type:1,data:t.alpha})},Jr=(t,e)=>{t.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):t.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):t.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},ro=t=>{let e=(t==null?void 0:t.activation)||"";if(e==="HardSigmoid"){let[r,n]=(t==null?void 0:t.activation_params)||[.2,.5];return{activation:e,alpha:r,beta:n}}else if(e==="Clip"){let[r,n]=(t==null?void 0:t.activation_params)||[Gs,Hs];return{activation:e,clipMax:n,clipMin:r}}else if(e==="LeakyRelu"){let[r]=(t==null?void 0:t.activation_params)||[.01];return{activation:e,alpha:r}}return{activation:e}}}),Et,no,ao=J(()=>{Et=(t,e)=>{switch(t){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${t}-component is not supported.`)}},no=t=>` + ${t?"value = value + getBiasByOutputCoords(coords);":""} + `}),io,Xc=J(()=>{io=t=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${t}.x), i32(${t}.y), i32(${t}.z), 1)); +} +`}),Qc,Zc,wi,so,Jc,bi,ep,oo,vi=J(()=>{xe(),Me(),Ie(),en(),ao(),Qc=(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":""}); + `,Zc=(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];"} + }`,wi=(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,m=d/e[0],g=s/e[1];if(!((a&&m===4&&t[1]===4||!a&&(m===3||m===4))&&d%e[0]===0&&s%e[1]===0&&t[0]===4))throw new Error(`If transposeA ${a} is true, innerElementSize ${m} and workPerThread[1] ${t[1]} must be 4. + Otherwise, innerElementSize ${m} 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/m}>, ${h}>; +var mm_Bsub: array, ${u/t[0]}>, ${s}>; + +const rowPerThread = ${t[1]}; +const colPerThread = ${t[0]}; +const innerElementSize = ${m}; +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; + ${Qc(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]; + ${m===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${Zc(a,m)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},so=(t,e)=>t?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${e?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${e?", batchIndices":""}); + `,Jc=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",bi=(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,m=a?s:u;if(!(m%e[1]===0&&h%e[0]===0&&s%e[1]===0))throw new Error(`tileAHight ${m} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${h} must be divisible by workgroupSize[0]${e[0]}, tileInner ${s} must be divisible by workgroupSize[1]${e[1]}`);let g=m/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 < ${m}; inputRow = inputRow + ${e[1]}) { + for (var inputCol = localCol; inputCol < ${h}; inputCol = inputCol + ${e[0]}) { + ${so(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; + ${so(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) { + ${Jc(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, ${m}>; + 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} + } +`},ep=(t,e,r,n,a,s=!1)=>{let[i,o,l]=a,[u,d,h,m]=n,g=Zn(i,l),p=Zn(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,P=E-1;A>=0;A--,P--)T+=` +aIndices[${A}] = ${E>1?`batchIndices[${P}]`:"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,P=E-1;A>=0;A--,P--)T+=` +bIndices[${A}] = ${E>1?`batchIndices[${P}]`:"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} + ${m.setByIndices("vec3(coords)","value")} + } + } + `},oo=(t,e,r,n,a=!1)=>{let s=t[0].dims,i=t[1].dims,o=s.slice(0,-2),l=i.slice(0,-2),u=n?n.slice(0,-2):r.slice(0,-2),d=X.size(u),h=s[s.length-2],m=s[s.length-1],g=i[i.length-1],p=m%4===0&&g%4===0,w=h<=8?[4,1,1]:[4,4,1],v=[8,8,1],x=[Math.ceil(g/v[0]/w[0]),Math.ceil(h/v[1]/w[1]),Math.ceil(d/v[2]/w[2])],$=p?4:1,E=[...o,h,m/$],T=E.length,A=[...l,m,g/$],P=A.length,B=[d,h,g/$],L=[{type:6,data:h},{type:6,data:g},{type:6,data:m}];Zr(e,L),L.push(...we(u,E,A));let j=["rank","rank"],q=t.length>2;q&&(L.push(...we(t[2].dims)),j.push("rank")),L.push(...we(B));let ue=ae=>{let ne=u.length,ie=js("batchDims",t[0].dataType,ne,1),N=_t(t[0].dataType),M=Q("a",t[0].dataType,T,$),G=Q("b",t[1].dataType,P,$),K=_e("result",t[0].dataType,B.length,$),ee=[M,G];if(q){let Se=a?$:1;ee.push(Q("bias",t[2].dataType,t[2].dims.length,Se))}let de=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Jr(e,de);let R=_t(K.type.tensor),se=Qr(e,K.type.value,R),pe=ep($,q,se,[ie,M,G,K],[o,l,u],a);return` + ${ae.registerUniforms(de).registerInternalVariables(ie).declareVariables(...ee,K)} + ${pe} + ${p?wi(w,v,N,ie):bi(w,v,N,ie)} + `};return{name:"MatMul",shaderCache:{hint:`${w};${e.activation};${p};${a}`,inputDependencies:j},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:x[0],y:x[1],z:x[2]},programUniforms:L}),getShaderSource:ue}}}),tp,rp,Ay=J(()=>{xe(),Xr(),Ie(),en(),ao(),Xc(),vi(),tp=(t,e,r,n,a=!1,s,i=4,o=4,l=4,u="f32")=>{let d=j=>{switch(j){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${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 ${j} is not supported.`)}},m=t?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,g=t?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,p=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",w=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",v=t?"row":"col",x=t?"col":"row",$=` + 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}) { + ${m} + 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),P=Et(t?i:o,u),B=Et(t?o:i,u),L=Qr(s,A,u);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${P} { + ${t?E:T} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${B} { + ${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} + ${no(a)} + ${L} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},rp=(t,e,r,n,a,s,i,o)=>{let l=e.format==="NHWC",u=l?t[0].dims[3]:t[0].dims[1],d=r[0],h=l?r[2]:r[3],m=l?r[1]:r[2],g=l?r[3]:r[1],p=l&&(u%4===0||u%3===0)&&g%4===0,w=l?g:h*m,v=l?h*m:g,x=[8,8,1],$=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],P=x[0]*$[0],B=Math.max(x[0]*T,x[1]),L=n%A===0,j=a%P===0,q=s%B===0,ue=p?[T,4,4]:[1,1,1],ae=[{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,ae),ae.push(...we(t[0].dims,t[1].dims));let ne=["rank","rank"];i&&(ae.push(...we(t[2].dims)),ne.push("rank")),ae.push(...we(r));let ie=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 G=p?4:1,K=_t(t[0].dataType),ee=` + 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); + }`,de=Q("x",t[0].dataType,t[0].dims.length,T===3?1:T),R=Q("w",t[1].dataType,t[1].dims.length,G),se=[de,R],pe=_e("result",t[0].dataType,r.length,G);if(i){let Se=Q("bias",t[2].dataType,t[2].dims.length,G);se.push(Se),ee+=` + fn getBiasByOutputCoords(coords : vec4) -> ${p?`vec4<${K}>`:K} { + return bias[coords.${l?"w":"y"}${p?"/ 4":""}]; + }`}return` + ${io("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(...se,pe)} + ${ee} + ${tp(l,L,j,q,i,e,ue[0],ue[1],ue[2],K)} + ${p?wi($,x,K,void 0,!l,B):bi($,x,K,void 0,!l,B,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${T};${p};${L};${j};${q};${A};${P};${B}`,inputDependencies:ne},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:ae}),getShaderSource:ie}}}),lo,np,Iy=J(()=>{xe(),Me(),Ie(),dp(),en(),lo=(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=$i(s,i,e.dilations,e.pads,e.strides,l),d=X.size(u),h=[{type:12,data:d},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:o}];Zr(e,h),h.push(...we(s,i));let m=["rank","rank"];n&&(h.push(...we(t[2].dims)),m.push("rank")),h.push(...we(u));let g=p=>{let w=_e("output",t[0].dataType,u.length),v=_t(w.type.tensor),x=Qr(e,w.type.value,v),$=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} + ${w.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:e.cacheKey,inputDependencies:m},getRunData:()=>({outputs:[{dims:r?r(u):u,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:h}),getShaderSource:g}},np=(t,e,r)=>{let n=t.length>2,a=st(r[3]),s=st(r[2]),i=X.size(r)/a/s,o=[t[0].dims[0],t[0].dims[1],t[0].dims[2],t[0].dims[3]/a],l=[t[1].dims[0],t[1].dims[1],t[1].dims[2],t[1].dims[3]/a],u=[r[0],r[1],r[2],r[3]/a],d=[{type:12,data:i},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];Zr(e,d),d.push(...we(o,l,u));let h=(s-1)*e.strides[1]+l[1],m=g=>{let p=_e("output",t[0].dataType,u.length,a),w=_t(p.type.tensor),v=Qr(e,p.type.value,w),x=Q("x",t[0].dataType,o.length,a),$=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:m}}}),uo,ap,ip,sp=J(()=>{xe(),Me(),vi(),Ie(),en(),uo=(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),m=st(o),g=X.size(r)/d/m,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 $=E=>{let T=js("batch_dims",t[0].dataType,w.length),A=Q("a",t[0].dataType,s.length,h),P=Q("b",t[1].dataType,i.length,d),B=_e("output",t[0].dataType,v.length,d),L=_t(B.type.tensor),j=Qr(e,B.type.value,L),q=[A,P],ue="";if(p){let ee=a?d:1;q.push(Q("bias",t[2].dataType,t[2].dims.length,ee)),ue=`${a?`value += bias[col / ${ee}];`:`value += ${B.type.value}(bias[row + i]);`}`}let ae=s.slice(0,-2),ne=i.slice(0,-2),ie=Zn(ae,w),N=Zn(ne,w),M=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Jr(e,M);let G=(ee,de)=>{let R=ee.rank,se=ee.name;if(R===2)return`var ${se}_indices = ${ee.type.indices}(0u, 0u);`;let pe=T.rank,Se=`var ${se}_indices: ${ee.type.indices};`;for(let Te=R-2-1,Ye=pe-1;Te>=0;Te--,Ye--)Se+=` +${se}_indices[${Te}] = ${pe>1?`batch_indices[${Ye}]`:"batch_indices"};`;return de.forEach(Te=>{Se+=` +${se}_indices[${Te}] = 0;`}),Se+=`${se}_indices[${R-2}] = 0u; + ${se}_indices[${R-1}] = 0u;`,Se},K=()=>{let ee=`var a_data: ${A.type.value};`;for(let de=0;de; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${h}) { + ${K()} + } + for (var i = 0u; i < ${m}u; i++) { + var value = values[i]; + ${ue} + ${j} + let cur_indices = ${B.type.indices}(batch, row + i, col); + let offset = ${B.indicesToOffset("cur_indices")}; + ${B.setByOffset(`offset / ${d}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${d};${h};${m};${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:$}},ap=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.")},ip=t=>{ap(t.inputs);let e=$n.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(uo(t.inputs,{activation:""},e)):t.compute(oo(t.inputs,{activation:""},e))}}),$i,xi,op,co,po,lp,up,ho,dp=J(()=>{Me(),Ay(),vi(),Iy(),en(),sp(),Jn(),$i=(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((m,g)=>m+(m-1)*(r[g]-1)),h=o.map((m,g)=>m+n[g]+n[g+l]).map((m,g)=>Math.floor((m-d[g]+a[g])/a[g]));return h.splice(0,0,i),h.splice(s?3:1,0,u),h},xi=[2,3,1,0],op=(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 ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},co=(t,e)=>{let r=t.kernelShape.slice();for(let s=2;s{let e=ro(t),r=t.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,s=t.group,i=t.kernel_shape,o=t.pads,l=t.strides,u=t.w_is_const();return{autoPad:n,format:r,dilations:a,group:s,kernelShape:i,pads:o,strides:l,wIsConst:u,...e,cacheKey:`${t.format};${e.activation};`}},lp=(t,e,r)=>{let n=co(r,e),a=r.format==="NHWC";if(r.group!==1){if(!t.adapterInfo.isArchitecture("ampere")&&a&&e[1].dims[0]===r.group&&e[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1){let P=$i(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),B=t.kernelCustomData.wT??t.compute(xr(e[1],xi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=B);let L=[e[0],B];e.length===3&&L.push(e[2]),t.compute(np(L,n,P),{inputs:L})}else t.compute(lo(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=$i(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),m=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 P=h[0],B,L,j,q=[];if(a){let ne=t.kernelCustomData.wT??t.compute(xr(e[1],xi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=ne),w){let ie=i*o*l;B=e[0].reshape([1,P,ie]),L=ne.reshape([1,ie,p]),j=[1,P,p]}else B=e[0].reshape([P,i*o,l]),L=ne.reshape([1,l,p]),j=[P,m*g,p];q.push(B),q.push(L)}else B=e[0].reshape([P,l,i*o]),L=e[1].reshape([1,p,l]),j=[P,p,m*g],q.push(L),q.push(B);s&&q.push(e[2]);let ue=j[2],ae=q[0].dims[q[0].dims.length-1];ue<8&&ae<8?t.compute(uo(q,n,h,j,a),{inputs:q}):t.compute(oo(q,n,h,j,a),{inputs:q});return}let v=!0,x=t.kernelCustomData.wT??t.compute(xr(e[1],xi),{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?m*g:p,T=a?p:m*g,A=u*d*l;t.compute(rp($,n,h,E,T,A,s,v),{inputs:$})},up=(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=co({...e,pads:a,strides:s,dilations:i,kernelShape:o},n);t.compute(lo(n,l,u=>r?[u[0],u[2],u[3]]:[]))},ho=(t,e)=>{op(t.inputs,e),t.inputs[0].dims.length===3?up(t,e):lp(t,t.inputs,e)}}),cp,pp,My=J(()=>{xe(),Xr(),Ie(),en(),ao(),Xc(),vi(),cp=(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",m=` + let inChannels = ${t?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${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) { + ${m} + } + return ${n}(0.0);`:` + let col = colIn * ${a}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${m} + } + 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} + ${no(e)} + ${w} + result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${a}] = value; + } + }`},pp=(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],m=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*m,v=l?h*m: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]),P=p?4:1,B=[e.kernelShape[l?1:2],e.kernelShape[l?2:3]],L=[B[0]+(e.dilations[0]<=1?0:(B[0]-1)*(e.dilations[0]-1)),B[1]+(e.dilations[1]<=1?0:(B[1]-1)*(e.dilations[1]-1))],j=[L[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),L[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:B},{type:6,data:j}];Zr(e,q),q.push(...we(t[0].dims,t[1].dims));let ue=["rank","rank"];i&&(q.push(...we(t[2].dims)),ue.push("rank")),q.push(...we(r));let ae=ne=>{let ie=Q("x",t[0].dataType,t[0].dims.length,P),N=Q("w",t[1].dataType,t[1].dims.length,1),M=_e("result",t[0].dataType,r.length,P),G=[ie,N],K="";if(i){let R=Q("bias",t[2].dataType,t[2].dims.length,P);G.push(R),K+=` + fn getBiasByOutputCoords(coords : vec4) -> ${R.type.value} { + return bias[coords.${l?"w":"y"}${p?"/ 4":""}]; + }`}let ee=[{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:B.length},{name:"pads",type:"i32",length:j.length}];Jr(e,ee);let de=_t(t[0].dataType,1);if(de!=="f16"&&de!=="f32")throw new Error(`elemType ${de} is not supported.`);return` + ${io("uniforms.result_strides")} + ${ne.registerUniforms(ee).declareVariables(...G,M)}; + ${K} + ${cp(l,i,e,ie.type.value,T)} + ${p?wi($,x,de,void 0,!l,A):bi($,x,de,void 0,!l,A,!1,void 0,o)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${e.cacheKey};${$};${x};${p}`,inputDependencies:ue},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:q}),getShaderSource:ae}}}),hp,fo,Oy=J(()=>{xe(),Xr(),Me(),Ie(),hp=(t,e,r,n,a,s=!1,i,o,l=!1)=>{let u=l?1:2,d=l?2:3,h=l?3:1,m=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"} * ${m}; + 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, ${m}>; + for (var i = 0; i < ${m}; i++) { + dotProd[i] = vec4<${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 < ${m}; 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}}`},fo=(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 = ${i}`);let o=e.format==="NHWC",l=["rank","rank"],u=[e.strides[0],e.strides[1]],d=[e.kernelShape[o?1:2],e.kernelShape[o?2:3]],h=[e.dilations[0],e.dilations[1]],m=[d[0]+(e.dilations[0]<=1?0:(e.kernelShape[o?1:2]-1)*(e.dilations[0]-1)),d[1]+(e.dilations[1]<=1?0:(e.kernelShape[o?2:3]-1)*(e.dilations[1]-1))],g=[m[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),m[1]-1-Math.floor(e.pads[1]+e.pads[3])/2],p=!1,w=e.group,v=t[1].dims,x=v[0]/w,$=v[1],E=[{type:12,data:s},{type:12,data:u},{type:12,data:d},{type:12,data:h},{type:12,data:m},{type:6,data:g},{type:12,data:x},{type:12,data:$},...we(t[0].dims,t[1].dims)];n&&(E.push(...we(t[2].dims)),l.push("rank")),E.push(...we(a));let T=i[1]===1&&i[2]===1,A=P=>{let B=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:u.length},{name:"filter_dims",type:"u32",length:d.length},{name:"dilations",type:"u32",length:d.length},{name:"effective_filter_dims",type:"u32",length:m.length},{name:"pads",type:"i32",length:g.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],L=_t(t[0].dataType);return`${hp(P,t,a,n,T,p,L,B,o)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${e.cacheKey};`,inputDependencies:l},getRunData:()=>({dispatchGroup:{x:i[0],y:i[1],z:i[2]},outputs:[{dims:r?r(a):a,dataType:t[0].dataType}],programUniforms:E}),getShaderSource:A}}}),fp,mp,gp,mo,_p,yp,wp,bp,vp,$p,zy=J(()=>{My(),Oy(),en(),Jn(),fp=(t,e,r,n,a,s)=>(t-1)*e+r+(n-1)*a+1-s,mp=(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)},gp=(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,m)=>h*m,1)===0){r.length=0;for(let h=2;hh+m,0)===0){let h=e[0].dims.length-2;l=new Array(h).fill(1)}let u=t.strides.slice();if(u.reduce((h,m)=>h+m,0)===0){let h=e[0].dims.length-2;u=new Array(h).fill(1)}gp(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},_p=t=>{let e=ro(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};`}},yp=(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=mo({...e,pads:o,strides:i,dilations:s,kernelShape:a},n);t.compute(fo(n,l,u=>r?[u[0],u[2],u[3]]:[u[0],u[1],u[3]]))},$p=(t,e)=>{yp(t.inputs,e),t.inputs[0].dims.length===3?vp(t,e):bp(t,t.inputs,e)}}),xp,Sp,kp,Py=J(()=>{xe(),Me(),pt(),Ie(),xp=(t,e,r,n)=>{let a=X.size(e),s=e.length,i=Q("input",t,s),o=_e("output",t,s),l=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),u=X.normalizeAxis(l,s),d=h=>{let m=` i32(${i.indicesGet("inputIndices","uniforms.axis")}) `,g=ke("uniforms.input_shape","uniforms.axis",s),p=n.reverse?m+(n.exclusive?" + 1":""):"0",w=n.reverse?g:m+(n.exclusive?"":" + 1");return` + ${h.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(i,o)} + 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}`;return{name:"DepthToSpace",shaderCache:{hint:`${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:$,dataType:x[0].dataType}],dispatchGroup:{x:Math.ceil(E/64)},programUniforms:[{type:12,data:E},...we(T,A)]}},getShaderSource:v}},Ap=(t,e)=>{Ep(t.inputs),t.compute(Tp(t.inputs[0],e))},Ip=t=>qe({blocksize:t.blocksize,mode:t.mode,format:t.format})}),Si,ea,go,Mp,Op,zp,Pp,_o,Rp,Bp,Dp,By=J(()=>{xe(),Me(),pt(),Ie(),Si="[a-zA-Z]|\\.\\.\\.",ea="("+Si+")+",go="^"+ea+"$",Mp="("+ea+",)*"+ea,Op="^"+Mp+"$",zp=class{constructor(t=-1){this.symbolToIndices=new Map,this.inputIndex=t}addSymbol(t,e){let r=this.symbolToIndices.get(t);r===void 0?r=[e]:r.push(e),this.symbolToIndices.set(t,r)}},Pp=class{constructor(t,e){var a;this.equation=e,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new 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${u.registerUniforms(o.map(T=>({name:`${_o(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(` +`)}; + ${i.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:t.map(()=>"rank")},getRunData:()=>{let u=o.filter(h=>r.symbolToInfo.has(h)).map(h=>{var m;return{type:12,data:((m=r.symbolToInfo.get(h))==null?void 0:m.dimValue)||0}});u.push({type:12,data:s});let d=t.map((h,m)=>[...we(h)]).reduce((h,m)=>h.concat(m),u);return d.push(...we(n)),{outputs:[{dims:n,dataType:e}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:d}},getShaderSource:l}},Bp=(t,e)=>{let r=new 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${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 m=` + 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")} + ${m}`},l=[{type:12,data:i},...we(e,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:o,getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:l})}},Up=t=>{Np(t.inputs),t.compute(Lp(t.inputs),{inputs:[0]})}}),Wp,Vp,Ny=J(()=>{xe(),Me(),Ie(),to(),Wp=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 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}`};return{name:"FastGeluWithBias",shaderCache:{hint:`${a}`,inputDependencies:["type","type"]},getShaderSource:s,getRunData:i=>({outputs:[{dims:i[0].dims,dataType:i[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/xn/4)}})}},Vp=t=>{t.inputs.length<2||X.size(t.inputs[1].dims)===0?Oc(t):t.compute(Wp(t.inputs))}}),Gp,Hp,jp,qp,Fy=J(()=>{xe(),Me(),pt(),Ie(),Gp=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},Hp=(t,e)=>{let r=t[0].dims,n=t[1].dims,a=r.length,s=X.normalizeAxis(e.axis,a),i=r.slice(0);i.splice(s,1,...n);let o=r[s],l=t[0].dataType===9?4:1,u=Math.ceil(X.size(i)/l),d=[{type:12,data:u},{type:6,data:o},{type:12,data:s},...we(t[0].dims,t[1].dims,i)],h=m=>{let g=Q("data",t[0].dataType,t[0].dims.length,l),p=Q("inputIndices",t[1].dataType,t[1].dims.length),w=_e("output",t[0].dataType,i.length,l),v=$=>{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 + ${A}]`:`outputIndices${$}`};`;T+=` + var idx${$} = ${p.getByIndices(`indicesIndices${$}`)}; + if (idx${$} < 0) { + idx${$} = idx${$} + uniforms.axisDimLimit; + } + var dataIndices${$} : ${g.type.indices}; + `;for(let A=0,P=0;A1?`dataIndices${$}[${A}]`:`dataIndices${$}`} = u32(idx${$});`,P+=E):(T+=`${a>1?`dataIndices${$}[${A}]`:`dataIndices${$}`} = ${i.length>1?`outputIndices${$}[${P}]`:`outputIndices${$}`};`,P++);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` + ${m.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(g,p,w)} + ${m.mainStart()} + ${m.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}},jp=t=>qe({axis:t.axis}),qp=(t,e)=>{let r=t.inputs;Gp(r),t.compute(Hp(t.inputs,e))}}),Kp,Yp,Xp,Qp,Ly=J(()=>{xe(),Me(),pt(),Ie(),Kp=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.`)},Yp=(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),m=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,m,g)} + ${w.mainStart()} + ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${g.offsetToIndices("global_idx")}; + + var idx = ${m.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")}; + + ${g.setByOffset("global_idx","value")}; + }`}},Xp=t=>qe({axis:t.axis}),Qp=(t,e)=>{let r=t.inputs;Kp(r),t.compute(Yp(t.inputs,e))}}),Zp,Jp,eh,th,Uy=J(()=>{xe(),Me(),Ie(),Zp=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},Jp=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,s,i]=Bu.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),o=[a,s];if(!o)throw new Error("Can't use gemm on the given tensors");let l=X.size(o),u=[{type:12,data:l},{type:12,data:a},{type:12,data:s},{type:12,data:i},{type:1,data:e.alpha},{type:1,data:e.beta}],d=["type","type"];t.length===3&&(u.push(...we(t[2].dims)),d.push("rank")),u.push(...we(o));let h=m=>{let g="";e.transA&&e.transB?g="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?g="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?g="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(g="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let p=e.alpha===1?"":"value *= uniforms.alpha;",w=Q("a",t[0].dataType,t[0].dims),v=Q("b",t[1].dataType,t[1].dims),x=w.type.value,$=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` + ${m.registerUniforms(A).declareVariables(...E)} + + ${m.mainStart()} + ${m.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:`${e.cacheKey}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:h}},eh=t=>{let e=t.transA,r=t.transB,n=t.alpha,a=t.beta;return{transA:e,transB:r,alpha:n,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},th=(t,e)=>{Zp(t.inputs),t.compute(Jp(t.inputs,e))}}),rh,nh,ah,ih,Wy=J(()=>{xe(),Me(),Ie(),rh=(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 m=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,P=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return` + var meanShared : f32; + var squaredNormShared : f32; + var workgroupShared : array<${T}, ${A}>; + const workgroupSize = ${A}u; + ${g.registerUniforms(P).declareVariables(...$)} + ${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 = ${Rr("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 = ${Rr("workgroupShared[0]",o)}; + } + workgroupBarrier(); + + let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${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};${o}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:s},programUniforms:h}),getShaderSource:m}},nh=(t,e,r,n,a,s,i,o)=>{let l=st(i),u=64,d=l===1?"vec2f":`mat2x${l}f`,h=l===1?"f32":`vec${l}f`,m=(P,B)=>`${d}(${P}, ${B})`,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=P=>{let B=Q("input",e.dataType,e.dims,l);return` + ${P.declareVariables(B)} + @group(0) @binding(1) var output : array<${d}>; + struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32}; + @group(0) @binding(2) var uniforms: Uniforms; + + ${P.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 = ${$r("f32",l)}; + var squaredSum = ${$r("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] = ${m("sum","squaredSum")}; + }`},$=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${l}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:[a,i,u,2],dataType:1}],dispatchGroup:{x:a*i/l},programUniforms:v}),getShaderSource:x},{inputs:[e],outputs:[-1]})[0],E=[{type:12,data:g},{type:12,data:s},{type:12,data:Math.floor(i/l)},{type:12,data:Math.floor(u*i/l)}],T=["type","type","type"],A=P=>{let B=Q("scale",r.dataType,r.dims,l),L=Q("bias",n.dataType,n.dims,l);return` + @group(0) @binding(0) var input : array<${d}>; + @group(0) @binding(1) var scale : array<${B.type.storage}>; + @group(0) @binding(2) var bias : array<${L.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; + + ${P.mainStart()} + ${P.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 = ${$r("f32",l)}; + var squaredSum = ${$r("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] = ${m("channelScale","channelShift")}; + }`};return t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${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]},ah=(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"],m=nh(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],m]})},ih=(t,e)=>{e.format==="NHWC"?ah(t,t.inputs,e):t.compute(rh(t.inputs,e))}}),sh,oh,lh,Vy=J(()=>{xe(),Me(),Ie(),sh=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},oh=(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),m=i?X.size(i.dims):0;if(h!==d||i&&m!==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 ${m}`);let g=[];for(let A=0;A1,$=r>2,E=A=>{let P=_t(t[0].dataType),B=[Q("x",t[0].dataType,t[0].dims,p),Q("scale",s.dataType,s.dims,p)];i&&B.push(Q("bias",i.dataType,i.dims,p)),B.push(_e("output",t[0].dataType,o,p)),x&&B.push(_e("mean_data_output",1,g)),$&&B.push(_e("inv_std_output",1,g));let L=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${A.registerUniforms(L).declareVariables(...B)} + ${A.mainStart()} + ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${$r("f32",p)}; + var mean_square_vector = ${$r("f32",p)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${Sn(P,p,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${Rr("mean_vector",p)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${Rr("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 = ${Sn(P,p,"x[j + offset]")}; + let f32scale = ${Sn(P,p,"scale[j]")}; + output[j + offset] = ${B[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale + ${i?`+ ${Sn(P,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}},lh=(t,e)=>{sh(t.inputs),t.compute(oh(t.inputs,e,t.outputCount))}}),uh,dh,ch,ph,Gy=J(()=>{xe(),Me(),pt(),Ie(),uh=(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.")}},dh=(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),m=e.blockSize/8*e.bits/4,g=t[0].dataType,p=st(o),w=st(e.k),v=st(m),x=Qn(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,P=d.concat([o,u]),B=X.size(P)/A/p,L=T?[]:[{type:12,data:B},{type:12,data:e.blockSize}],j=[h,o,l/w],q=X.convertShape(t[1].dims).slice();q.splice(-1,1,m/v),L.push(...we(j)),L.push(...we(q)),L.push(...we(t[2].dims)),t.length===4&&L.push(...we(X.convertShape(t[3].dims)));let ue=[h,o,u/A];L.push(...we(ue));let ae=ne=>{let ie=j.length,N=Q("a",t[0].dataType,ie,w),M=Q("b",12,q.length,v),G=Q("scales",t[2].dataType,t[2].dims.length),K=[N,M,G],ee=t.length===4?Q("zero_points",12,t[3].dims.length):void 0;ee&&K.push(ee);let de=ue.length,R=_e("output",t[0].dataType,de,A),se=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],pe=_t(t[0].dataType),Se=(()=>{switch(w){case 1:return`array<${pe}, 8>`;case 2:return`mat4x2<${pe}>`;case 4:return`mat2x4<${pe}>`;default:throw new Error(`${w}-component is not supported.`)}})(),Te=` + for (var word: u32 = 0; word < ${m}; 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,He)=>`${pe}(b_value_lower[${He}]), ${pe}(b_value_upper[${He}])`).join(", ")}); + let b_dequantized_values = ${(()=>w===1?`${Se}(${Array.from({length:8},(ot,He)=>`(b_quantized_values[${He}] - 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",ie-2,T?"m":`row * ${p} + m`)}; + ${N.indicesSet("a_indices",ie-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]":""} += ${Array.from({length:8/w},(ot,He)=>`${w===1?`a_data[${He}] * b_dequantized_values[${He}]`:`dot(a_data[${He}], b_dequantized_values[${He}])`}`).join(" + ")}; + } + word_offset += ${8/w}; + } + }`,Ye=ee?` + zero_point_offset += 4; + if (zero_point_offset == 32) { + zero_point_offset = 0; + zero_point_index++; + zero_point_word = ${ee.getByOffset("zero_point_index")}; + }`:"";return T?` + var workgroup_shared: array<${R.type.value}, ${o*i}>; + ${ne.declareVariables(...K,R)} + ${ne.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; + ${ee?` + var zero_point_bytes_per_col: u32 = (${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 = ${ee.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 = ${G.getByOffset("scales_index")}; + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${pe}(${ee?"(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: ${R.type.indices}; + ${R.indicesSet("output_indices","0","batch")}; + ${R.indicesSet("output_indices",de-1,"col")}; + ${R.indicesSet("output_indices",de-2,"0")}; + var output_offset = ${R.indicesToOffset("output_indices")}; + for (var m: u32 = 0u; m < ${o}u; m++) { + var output_value: ${R.type.value} = ${R.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}; + } + ${R.setByOffset("output_offset","output_value")}; + output_offset += ${u/A}; + } + } + }`:` + ${ne.registerUniforms(se).declareVariables(...K,R)} + ${ne.mainStart()} + ${ne.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var output_values: array<${R.type.value}, ${p}>; + var output_indices = ${R.offsetToIndices("global_idx")}; + var col = ${R.indicesGet("output_indices",de-1)}; + var row = ${R.indicesGet("output_indices",de-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 + ${ee?` + 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 = ${ee.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 = ${G.getByOffset("scale_index")}; + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${pe}(${ee?"extractBits(zero_point_word, zero_point_offset, 4)":8}); + ${M.indicesSet("b_indices","1","block")}; + var word_offset: u32 = block_offset; + ${Te} + scale_index++; + ${Ye} + 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. + ${ee?`if (zero_point_offset % 8 > 0) { + ${Ye} + }`:""} + } + for (var k: u32 = 0u; k < ${p}u; k++) { + ${R.indicesSet("output_indices",de-2,`${p} * row + k`)}; + ${R.setByIndices("output_indices","output_values[k]")} + } + }`};return{name:T?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${e.cacheKey};${o};${g};${t.length}`,inputDependencies:Array(t.length).fill("rank")},getRunData:()=>({outputs:[{dims:P,dataType:g}],name:T?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:T?{x:1,y:Math.ceil(u/A),z:h}:{x:Math.ceil(B/64)},programUniforms:L}),getShaderSource:ae}},ch=(t,e)=>{uh(t.inputs,e);let r=t.getMaxComputeWorkgroupSizes(),n=t.getMaxComputeWorkgroupStoragesize();t.compute(dh(t.inputs,e,r,n))},ph=t=>qe(t)}),Ct,hh,fh,wo,mh,ki,gh,Hy=J(()=>{xe(),Me(),pt(),Ls(),Hd(),Ie(),Jn(),Ct=(t,e)=>t.length>e&&t[e].dims.length>0&&X.size(t[e].dims)>0?t[e]:void 0,hh=(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],m=r.dims[1],g=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],p=m,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');$=3}if(s){if(s.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(a&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(i){E=8;let L=i.dims;throw L.length===1?L[0]===h?E=1:L[0]===3*h+2&&(E=3):L.length===2&&L[0]===h&&L[1]===p&&(E=5),E===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let T=!1,A=g;if(a){if(a.dims.length!==3&&a.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==a.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(a.dims.length===3){if(p!==a.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');A=a.dims[2]}else{if(p!==a.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');A=a.dims[1]*a.dims[3],T=!0}}let P=w+p,B=!1;if(i)throw new Error("Key padding mask is not supported");if(o){if(o.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(o.dims[0]!==h&&o.dims[0]!==1||o.dims[1]!==e.numHeads||o.dims[2]!==m||o.dims[3]!==P)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:h,sequenceLength:m,pastSequenceLength:w,kvSequenceLength:p,totalSequenceLength:P,maxSequenceLength:v,inputHiddenSize:0,hiddenSize:g,vHiddenSize:A,headSize:x,vHeadSize:Math.floor(A/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:E,scale:e.scale,broadcastResPosBias:B,passPastInKv:T,qkvFormat:$}},fh=t=>qe({...t}),wo=qe({perm:[0,2,1,3]}),mh=(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 m=_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,m)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:e.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:d},{inputs:[e,r],outputs:[-1]})[0]},ki=(t,e,r,n,a,s,i,o)=>{let l=s;if(i){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=mh(t,s,i,e,n,r*a,o),l=l.reshape([e,n,r,a]),t.compute(xr(l,wo.perm),{inputs:[l],outputs:[-1]})[0]}else return s.dims.length===3&&(l=s.reshape([e,n,r,a])),t.compute(xr(l,wo.perm),{inputs:[l],outputs:[-1]})[0]},gh=(t,e)=>{let r=hh(t.inputs,e),n=t.inputs[0],a=Ct(t.inputs,1),s=Ct(t.inputs,2),i=Ct(t.inputs,3),o=Ct(t.inputs,4),l=Ct(t.inputs,5),u=Ct(t.inputs,6),d=Ct(t.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((a==null?void 0:a.dims.length)===5)throw new Error("Packed KV is not implemented");let h=a&&s&&a.dims.length===4&&s.dims.length===4,m=ki(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(h)return gi(t,m,a,s,o,void 0,u,d,l,r,e);if(!a||!s)throw new Error("key and value must be provided");let g=ki(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,a,i,r.hiddenSize),p=ki(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,s,i,2*r.hiddenSize);gi(t,m,g,p,o,void 0,u,d,l,r,e)}}),_h,yh,wh,bh,vh,$h,xh,Sh,kh,jy=J(()=>{xe(),Me(),Ie(),_h=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].")}},yh=(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; + ${n} + value = x[offset]; + } + `},wh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` + k = i32(${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; + ${n} + value = x[offset]; + `},bh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` + k = i32(${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; + ${n} + value = x[offset]; + `},vh=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` + k = i32(${t.indicesGet("indices",a)}) - ${ke("uniforms.pads",a,r)}; + if (k < 0) { + k += i32(${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; + ${n} + value = x[offset]; + `},$h=(t,e,r)=>{switch(r.mode){case 0:return yh(t,e,r.pads.length);case 1:return wh(t,e,r.pads.length);case 2:return bh(t,e,r.pads.length);case 3:return vh(t,e,r.pads.length);default:throw new Error("Invalid mode")}},xh=(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,m=$h(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); + ${m} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(X.size(r)/64)},programUniforms:s}),getShaderSource:o}},Sh=(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},kh=(t,e)=>{_h(t.inputs);let r=Sh(t.inputs,e);t.compute(xh(t.inputs,r),{inputs:[0]})}}),ta,bo,vo,$o,xo,Eh,Ch,So,ko,Th,Ah,Eo,Ih,Mh,Co,Oh,zh,Ph,Rh,qy=J(()=>{tr(),xe(),Me(),Ie(),ta=t=>{if(Ue.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},bo=(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();ci.adjustPoolAttributes(r,a,i,o,l,u);let d=ci.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 m=d.slice();return m.push(m.splice(1,1)[0]),[h,n?m:d]},vo=(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 m=!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];m=!!(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,m]}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]}},$o=(t,e,r,n,a,s,i,o,l,u,d,h)=>{let m=a.format==="NHWC",g=e.type.value,p=_e("output",e.type.tensor,n);if(a.kernelShape.length<=2){let w="",v="",x="",$=r-(m?2:1);if(d?w=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${$}] < 0 || xIndices[${$}] + >= uniforms.x_shape[${$}]) { + pad++; + continue; + } + let x_val = x[${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")}]; + ${s} + }`,a.kernelShape.length===2){let E=r-(m?3:2);h?v=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${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} + ${i} + + output[global_idx] = value; + }`}else{if(m)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let w=a.kernelShape.length,v=a.pads.length,x="";return u?x=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${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} + } + ${i} + + output[global_idx] = value; + }`}},xo=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,Eh=t=>`${xo(t)};${t.countIncludePad}`,Ch=t=>`${xo(t)};${t.storageOrder};${t.dilations}`,So=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}),ko=(t,e,r,n)=>{let[a,s]=bo(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 /= ${o}(i32(uniforms.kernelSize) - pad);`;let[d,h,m,g,p]=vo(s,a);d.push(...we(e.dims,s));let w=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${m};${g};${p}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(X.size(s)/64)},programUniforms:d}),getShaderSource:v=>$o(v,i,e.dims.length,s.length,a,l,u,0,h,m,g,p)}},Th=t=>{let e=t.count_include_pad!==0,r=So(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:Eh(n)}},Ah=(t,e)=>{ta(t.inputs),t.compute(ko("AveragePool",t.inputs[0],!1,e))},Eo={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Ih=t=>{let e=t.format;return{format:e,...Eo,cacheKey:e}},Mh=(t,e)=>{ta(t.inputs),t.compute(ko("GlobalAveragePool",t.inputs[0],!0,e))},Co=(t,e,r,n)=>{let[a,s]=bo(e,n,r),i=` + value = max(x_val, value); + `,o="",l=Q("x",e.dataType,e.dims.length),u=["rank"],[d,h,m,g,p]=vo(s,a);return d.push(...we(e.dims,s)),{name:t,shaderCache:{hint:`${n.cacheKey};${m};${g};${p}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(X.size(s)/64)},programUniforms:d}),getShaderSource:w=>$o(w,l,e.dims.length,s.length,a,i,o,e.dataType===10?-65504:-1e5,h,m,g,p)}},Oh=(t,e)=>{ta(t.inputs),t.compute(Co("MaxPool",t.inputs[0],!1,e))},zh=t=>{let e=t.storage_order,r=t.dilations,n=So(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:Ch(a)}},Ph=t=>{let e=t.format;return{format:e,...Eo,cacheKey:e}},Rh=(t,e)=>{ta(t.inputs),t.compute(Co("GlobalMaxPool",t.inputs[0],!0,e))}}),Bh,Dh,Nh,Ky=J(()=>{tr(),xe(),Ie(),Bh=(t,e,r)=>{let n=t===e,a=te&&r>0;if(n||a||s)throw new Error("Range these inputs' contents are invalid.")},Dh=(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,m=[{name:"outputSize",type:"u32"},{name:"start",type:h},{name:"delta",type:h}];return` + ${u.registerUniforms(m).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})}},Nh=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&&Bh(e,r,n),t.compute(Dh(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),Fh,Lh,Uh,Wh,Vh,Gh,Hh,jh,qh,Kh,Yh,To,Xh,Qh,Zh,Jh,ef,tf,rf,Yy=J(()=>{xe(),Me(),pt(),Ie(),Fh=(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")}},Lh=(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},Uh=(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");Fh(n,e),e.axes.length>0&&Lh(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")},Wh=(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`)}})()+"}",Vh=(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`)}})()+"}",Gh=(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},Hh=(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},jh=(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},qh=(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; + }`,Kh=(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; + }`,Yh=(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; + }`,To=(t,e,r,n)=>t.rank>n?` + ${t.indicesSet("input_indices",e,"channel")}; + ${t.indicesSet("input_indices",r,"batch")}; +`:"",Xh=(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))`)}; + ${To(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); + }`},Qh=(t,e,r,n,a,s,i,o,l,u)=>{let d=r.length===2,[h,m]=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(m)}; + 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); + } + `},Zh=(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))`)}; + ${To(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); + }`},Jh=(t,e,r,n,a,s)=>{let i=t.dims,o=Gh(s,e.axes,i.length),l=Hh(i,n,a,e.axes),u=n.slice();n.length===0&&(u=i.map(($,E)=>$===0?1:l[E]/$),e.keepAspectRatioPolicy!=="stretch"&&(l=jh(i,u,e)));let d=_e("output",t.dataType,l.length),h=Q("input",t.dataType,i.length),m=X.size(l),g=i.length===l.length&&i.every(($,E)=>$===l[E]),p=e.coordinateTransformMode==="tf_crop_and_resize",w=e.extrapolationValue,v=h.type.value,x=$=>` + ${g?"":` + ${Wh(e.coordinateTransformMode,v)}; + ${(()=>{switch(e.mode){case"nearest":return` + ${Yh(h,i)}; + ${Vh(e.nearestMode,r,v)}; + ${Kh(h,d,i,l,u.length,o.length,p)}; + `;case"linear":return` + ${qh(d,i,l,u.length,o.length)}; + ${(()=>{if(i.length===2||i.length===4)return`${Xh(h,d,i,p,w)}`;if(i.length===3||i.length===5)return`${Zh(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` + ${(()=>{if(i.length===2||i.length===4)return`${Qh(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(m/64)},programUniforms:[{type:12,data:m},{type:1,data:u},{type:1,data:o},...we(i,l)]})}},ef=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},tf=(t,e)=>{let r=[],n=[],a=[],s=ef(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Uh(t.inputs,e,s,r,n,a),t.compute(Jh(t.inputs[0],e,s,r,n,a),{inputs:[0]})},rf=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 qe({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:s,extrapolationValue:i,keepAspectRatioPolicy:o,mode:l,nearestMode:u})}}),nf,af,sf,Xy=J(()=>{xe(),Me(),pt(),Ie(),nf=(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,m=o===0?a.dims[1]*2:h/i;if(o>m)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(m/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 ${a.dims[1]}`);if(u>d)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},af=(t,e)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:a,scale:s}=e,i=t[0].dims[0],o=X.sizeFromDimension(t[0].dims,1),l=t[0].dims[t[0].dims.length-2],u=o/l,d=t[2].dims[1],h=a===0?d*2:u/n,m=new Array(i,l,u/h,h-d),g=X.computeStrides(m),p=[{type:1,data:s},{type:12,data:m},{type:12,data:g},...t[0].dims.length===3?new Array({type:12,data:[o,u,h,1]}):[],...t[0].dims.length===4?new Array({type:12,data:[o,h,l*h,1]}):[],...we(t[0].dims,t[1].dims,t[2].dims,t[3].dims,t[0].dims)],w=v=>{let x=Q("input",t[0].dataType,t[0].dims.length),$=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:m.length},{name:"global_strides",type:"u32",length:g.length},{name:"input_output_strides",type:"u32",length:g.length}]),` + ${v.declareVariables(x,$,E,T,A)} + + ${v.mainStart(xn)} + let half_rotary_emb_dim = uniforms.${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; + ${A.setByOffset("k",x.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:qe({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:t[0].dims,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(X.size(m)/xn)},programUniforms:p})}},sf=(t,e)=>{nf(t.inputs,e),t.compute(af(t.inputs,e))}}),of,lf,uf,Qy=J(()=>{xe(),Me(),Ie(),of=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],r=t[1],n=t[2];if(e.dataType!==r.dataType||e.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let a=e.dims[e.dims.length-1],s=e.dims[e.dims.length-2];if(r.dims[r.dims.length-1]!==a)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==a)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let i=t[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let i=t[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},lf=(t,e,r,n)=>{let a=e.simplified,s=t[0].dims,i=X.size(s),o=s,l=i,u=s.slice(-1)[0],d=n?s.slice(0,-1).concat(1):[],h=!a&&t.length>3,m=t.length>4,g=n&&r>1,p=n&&r>2,w=r>3,v=st(u),x=[{type:12,data:l},{type:12,data:v},{type:12,data:u},{type:1,data:e.epsilon}],$=T=>{let A=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],P=[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&&P.push(Q("beta",t[3].dataType,t[3].dims,v)),m&&P.push(Q("bias",t[4].dataType,t[4].dims,v)),P.push(_e("output",t[0].dataType,o,v)),g&&P.push(_e("mean_output",1,d)),p&&P.push(_e("inv_std_output",1,d)),w&&P.push(_e("input_skip_bias_sum",t[0].dataType,o,v));let B=_t(t[0].dataType);return` + + ${T.registerUniforms(A).declareVariables(...P)} + + ${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 = ${$r("f32",v)}; + var squareSum = ${$r("f32",v)}; + for (var i: u32 = 0; i < hidden_size_vectorized; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${m?"bias[i]":B+"(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 = ${Sn(B,v,"value")}; + sum += f32_value; + squareSum += f32_value * f32_value; + } + let mean = ${Rr("sum",v)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${Rr("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?"":`- ${B}(mean)`}) * ${B}(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})}},uf=(t,e)=>{of(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(lf(t.inputs,e,t.outputCount,!1),{outputs:r})}}),df,ra,cf,Ao,pf,hf,ff,mf,Zy=J(()=>{xe(),Me(),pt(),Ie(),df=(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`)})},ra=(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},cf=(t,e)=>{if(t.length>1){let r=ra(t,1),n=ra(t,2),a=ra(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),qe({starts:r,ends:n,axes:a})}else return e},Ao=(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]]))},pf=(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; + } + ${t.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,hf=(t,e)=>{let r=t[0].dims,n=X.size(r),a=e.axes.length>0?X.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],s=ra(t,4);s.forEach(x=>x!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(a.length).fill(1));let i=e.starts.map((x,$)=>Ao(x,$,r,a,s)),o=e.ends.map((x,$)=>Ao(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[$],P=A+T*s[$];i[$]=P,o[$]=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),m=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(m,h)} + ${pf(m,h,r)} + ${x.mainStart()} + ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${h.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${h.setByOffset("global_idx",m.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${i.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:v,getRunData:()=>({outputs:[d],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:w})}},ff=(t,e)=>{df(t.inputs,e);let r=cf(t.inputs,e);t.compute(hf(t.inputs,r),{inputs:[0]})},mf=t=>{let e=t.starts,r=t.ends,n=t.axes;return qe({starts:e,ends:r,axes:n})}}),gf,_f,yf,wf,Jy=J(()=>{xe(),Me(),pt(),Ie(),gf=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},_f=(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),m=_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,m)} + ${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 = ${g}(${Rr("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:`${l}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:6,data:u}]}),getShaderSource:w}},yf=(t,e)=>{gf(t.inputs),t.compute(_f(t.inputs[0],e))},wf=t=>qe({axis:t.axis})}),bf,vf,$f,xf,Sf,kf,Ef,ew=J(()=>{xe(),Me(),pt(),Ie(),bf=t=>{if(!t||t.length<1)throw new Error("too few inputs")},vf=(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),qe({numOutputs:n,axis:e.axis,splitSizes:r})},$f=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; +}`,xf=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,m=[{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)} + ${$f(l.length)} + ${xf(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:m})}},kf=(t,e)=>{bf(t.inputs);let r=t.inputs.length===1?e:vf(t.inputs,e);t.compute(Sf(t.inputs,r),{inputs:[0]})},Ef=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 qe({axis:e,numOutputs:n,splitSizes:r})}}),Io,Cf,Tf,Af,If,tw=J(()=>{xe(),Me(),Ie(),Io=t=>Array.from(t.getBigInt64Array(),Number),Cf=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(Io(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")},Tf=(t,e)=>{let r=[];for(let n=0;n{let e=t[0].dims,r=Io(t[1]),n=Tf(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}},If=t=>{Cf(t.inputs),t.compute(Af(t.inputs),{inputs:[0]})}}),Mf,Of,zf,rw=J(()=>{xe(),Me(),Ie(),Mf=(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,m,g)=>`select(${m}, ${h}, ${g})`;if(!n)u=s.setByOffset("global_idx",d(i.getByOffset("global_idx"),o.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let h=(m,g,p="")=>{let w=`a_data[index_a${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; + ${m}[${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")} + ${u} + }`},Of=t=>{let e=t[1].dims,r=t[2].dims,n=t[0].dims,a=t[1].dataType,s=!(X.areEqual(e,r)&&X.areEqual(r,n)),i=e,o=X.size(e);if(s){let u=$n.calcShape($n.calcShape(e,r,!1),n,!1);if(!u)throw new Error("Can't perform where op on the given tensors");i=u,o=X.size(i)}let l=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:u=>Mf(u,t,i,s,a),getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:l},...we(n,e,r,i)]})}},zf=t=>{t.compute(Of(t.inputs))}}),Pf,nw=J(()=>{Sy(),Hd(),ky(),Ey(),Cy(),Ty(),Nd(),dp(),zy(),Py(),Ry(),By(),Dy(),Ny(),Fy(),Ly(),Uy(),Wy(),Vy(),sp(),Gy(),Hy(),jy(),qy(),Ky(),Ys(),Yy(),Xy(),Qy(),Zy(),Jy(),ew(),tw(),Jn(),to(),rw(),Pf=new 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u={kernelId:this.backend.currentKernelId,computePipeline:t.computePipeline,bindGroup:l,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(u)}i.setPipeline(t.computePipeline),i.setBindGroup(0,l),i.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),Kt(t.programInfo.name)}dispose(){}build(t,e){er(t.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let a=Nu(e,this.backend.device.limits),s=t.getShaderSource(a),i=`${n.join(` +`)} +${a.additionalImplementations} +${s}`,o=r.createShaderModule({code:i,label:t.name});nt("verbose",()=>`[WebGPU] ${t.name} shader code: ${i}`);let l=r.createComputePipeline({compute:{module:o,entryPoint:"main"},layout:"auto",label:t.name});return Kt(t.name),{programInfo:t,computePipeline:l,uniformVariablesInfo:a.variablesInfo}}normalizeDispatchGroupSize(t){let e=typeof t=="number"?t:t.x,r=typeof t=="number"?1:t.y||1,n=typeof t=="number"?1:t.z||1,a=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(e<=a&&r<=a&&n<=a)return[e,r,n];let s=e*r*n,i=Math.ceil(Math.sqrt(s));if(i>a){if(i=Math.ceil(Math.cbrt(s)),i>a)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),Bf,Df,Nf,Ff,iw=J(()=>{tr(),xe(),Xr(),vy(),$y(),nw(),aw(),Bf=(t,e)=>{if(e.length!==t.length)throw new Error(`inputDependencies length ${e.length} is not equal to inputTensors length ${t.length}.`);let r=[];for(let n=0;n{var a,s;let n=t.name;return(a=t.shaderCache)!=null&&a.hint&&(n+="["+t.shaderCache.hint+"]"),n+=":"+r+`:${Bf(e,((s=t.shaderCache)==null?void 0:s.inputDependencies)??new Array(e.length).fill("dims"))}`,n},Nf=class{constructor(t){t&&(this.architecture=t.architecture,this.vendor=t.vendor)}isArchitecture(t){return this.architecture===t}isVendor(t){return this.vendor===t}},Ff=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 Nf(await e.requestAdapterInfo()),this.gpuDataManager=zu(this),this.programManager=new Rf(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Cu(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;er(),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:m.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 $="";m.forEach((E,T)=>{$+=`output[${T}]: [${E.dims}] | ${Yr(E.dataType)}, `}),console.log(`[profiling] kernel "${i}|${l}|${u}|${d}" ${x}${$}execution time: ${v-w} ns`)}Kn("GPU",`${d}::${g}::${p}`)}t.unmap(),this.pendingQueries.delete(t)}),Kt()}run(t,e,r,n,a,s){er(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=[],m=[];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 P=this.gpuDataManager.get(A.data);if(!P)throw new Error(`no GPU data for output: ${A.data}`);if(E&&this.temporaryData.push(P),T){let B=this.kernelPersistentData.get(this.currentKernelId);B||(B=[],this.kernelPersistentData.set(this.currentKernelId,B)),B.push(P)}m.push(P)}if(i.length!==e.length||m.length!==h.length){if(m.length===0)return Kt(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 $=0,E=[];u.forEach(B=>{let L=typeof B.data=="number"?[B.data]:B.data;if(L.length===0)return;let j=B.type===10?2:4,q,ue;B.type===10?(ue=L.length>4?16:L.length>2?8:L.length*j,q=L.length>4?16:j*L.length):(ue=L.length<=2?L.length*j:16,q=16),$=Math.ceil($/ue)*ue,E.push($);let ae=B.type===10?8:4;$+=L.length>4?Math.ceil(L.length/ae)*q:L.length*j});let T=16;$=Math.ceil($/T)*T;let A=new ArrayBuffer($);u.forEach((B,L)=>{let j=E[L],q=typeof B.data=="number"?[B.data]:B.data;if(B.type===6)new Int32Array(A,j,q.length).set(q);else if(B.type===12)new Uint32Array(A,j,q.length).set(q);else if(B.type===10)new Uint16Array(A,j,q.length).set(q);else if(B.type===1)new Float32Array(A,j,q.length).set(q);else throw new Error(`Unsupported uniform type: ${Yr(B.type)}`)});let P=this.gpuDataManager.create($,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(P.buffer,0,A,0,$),this.gpuDataManager.release(P.id),g={offset:0,size:$,buffer:P.buffer}}let p=this.programManager.normalizeDispatchGroupSize(l),w=p[1]===1&&p[2]===1,v=Df(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,m,p,g),Kt(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=Pf.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 Vs(this,t,e);return Au(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()}}}),Lf={};vn(Lf,{init:()=>Wf});var Ei,Uf,Wf,sw=J(()=>{xe(),iw(),Xr(),Me(),Ei=class T0{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 T0(this.module,this.dataType,this.data,e)}},Uf=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 Ei(this.module,l,this.output(o,u),u),s=(o,l)=>{let u=Qn(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 Ei(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'}),Dr,zt,kn,na,aa,zo,Ci,tn,rn,Zf,ia,Jf,em,tm,rm,nm,am,im,sm=J(()=>{var t;tr(),ow(),Xn(),Dr=()=>!!Ue.wasm.proxy&&typeof document<"u",kn=!1,na=!1,aa=!1,Ci=new Map,tn=(e,r)=>{let n=Ci.get(e);n?n.push(r):Ci.set(e,[r])},rn=()=>{if(kn||!na||aa||!zt)throw new Error("worker not 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t=(cw(),jr(cm)).wasmBackend;qr("webgpu",t,5),qr("webnn",t,5),qr("cpu",t,10),qr("wasm",t,10)}Object.defineProperty(Ue.versions,"web",{value:pw,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 fw=Object.freeze({__proto__:null,get InferenceSession(){return As},get TRACE(){return Kn},get TRACE_FUNC_BEGIN(){return er},get TRACE_FUNC_END(){return Kt},get Tensor(){return kt},get TrainingSession(){return Is},default:hw,get env(){return Ue},get registerBackend(){return qr}});const mw=(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 m=l();m.width=h,m.height=d;const g=u(m);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 m=t.height,g=t.width;return h.drawImage(t,0,0,g,m),i=h.getImageData(0,0,g,m).data,o.height=m,o.width=g,Ro(i,o)}else throw new Error("Can not access image data")}else{if(s)return new Promise((d,h)=>{const m=l(),g=u(m);if(!t||!g)return h();const p=new Image;p.crossOrigin="Anonymous",p.src=t,p.onload=()=>{m.width=p.width,m.height=p.height,g.drawImage(p,0,0,m.width,m.height);const w=g.getImageData(0,0,m.width,m.height);o.height=m.height,o.width=m.width,d(Ro(w.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(i!==void 0)return Ro(i,o);throw new Error("Input data provided is not supported - aborted tensor creation")},yw=(t,e)=>{const{width:r,height:n,download:a,dispose:s}=e,i=[1,n,r,4];return new fr({location:"texture",type:"float32",texture:t,dims:i,download:a,dispose:s})},ww=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:s}=e;return new fr({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:s})},bw=(t,e,r)=>new fr({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),En=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),Ti=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let hm=!1;const vw=()=>{if(!hm){hm=!0;const t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,r=typeof Float16Array<"u"&&Float16Array.from;t&&(En.set("int64",BigInt64Array),Ti.set(BigInt64Array,"int64")),e&&(En.set("uint64",BigUint64Array),Ti.set(BigUint64Array,"uint64")),r?(En.set("float16",Float16Array),Ti.set(Float16Array,"float16")):En.set("float16",Uint16Array)}},$w=t=>{let e=1;for(let r=0;r{switch(t.location){case"cpu":return new fr(t.type,t.data,e);case"cpu-pinned":return new fr({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new fr({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new fr({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let fr=class{constructor(e,r,n){vw();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=En.get(a);if(!o)throw new TypeError(`unsupported type 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Should be one of: ${sa.join(", ")}.`);e=[t]}return e}async function fm(t,e){return await kw.create(t,e)}function mm(t){return t instanceof nn.Tensor}const or=nn==null?void 0:nn.env;or!=null&&or.wasm&&(or.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.18.0/dist/",or.wasm.proxy=!Gr.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(or.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(or.wasm.simd=!1));function Cw(){var t;return(t=or==null?void 0:or.wasm)==null?void 0:t.proxy}Mt.backends.onnx=or;const Do=async(t,e,r)=>{const n=await fm(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 new fe(i[r])}};class No{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=Do(new 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Uint8Array([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=Do(new Uint8Array([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}}D(No,"session_options",{});const gm=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 fe{constructor(...e){D(this,"ort_tensor");return mm(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new Sw(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 fe(this.type,[this.data[e]],n)}indexOf(e){const r=this.data;for(let n=0;nm)throw new Error(`Invalid slice: ${d}`);let g=[Math.max(h,0),Math.min(m,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+=(m%g+n[h][0])*l[h],m=Math.floor(m/g)}o[u]=i[d]}return new fe(this.type,o,r)}permute(...e){return Aw(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 fe(this.type,[o],[])}r=mr(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 m=this.dims[u];if(u!==r){const g=d%m;l+=g*h,h*=s[u]}d=Math.floor(d/m)}i[l]+=a[o]**e}if(e!==1)for(let o=0;o=0;--o){const d=this.dims[o];if(o!==r){const h=l%d;i+=h*u,u*=this.dims[o]}l=Math.floor(l/d)}a[s]/=n.data[i]}return this}normalize(e=2,r=1){return this.clone().normalize_(e,r)}stride(){return zw(this.dims)}squeeze(e=null){return new fe(this.type,this.data,ym(this.dims,e))}squeeze_(e=null){return this.dims=ym(this.dims,e),this}unsqueeze(e=null){return new fe(this.type,this.data,wm(this.dims,e))}unsqueeze_(e=null){return this.dims=wm(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 n=0;ni!==r?a*s:a,1);e[r]=this.data.length/n}return new fe(this.type,this.data,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 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u=t.data.reduce((g,p)=>g+p,0)/t.data.length,d=Math.sqrt(t.data.reduce((g,p)=>g+(p-u)**2,0)/(t.data.length-r)),h=new fe(t.type,[u],[]);return[new fe(t.type,[d],[]),h]}e=mr(e,t.dims.length);const a=Lo(t,e,n),s=t.dims.slice();s[e]=1;const i=new t.data.constructor(t.data.length/t.dims[e]);for(let l=0;l=0;--d){const g=t.dims[d];if(d!==e){const p=h%g;u+=p*m,m*=s[d]}h=Math.floor(h/g)}i[u]+=(t.data[l]-a.data[u])**2}for(let l=0;li+o,0);return new fe(t.type,[s/t.data.length],[])}e=mr(e,t.dims.length);const n=t.dims.slice();n[e]=1;const a=new t.data.constructor(t.data.length/t.dims[e]);for(let s=0;s=0;--o){const d=t.dims[o];if(o!==e){const h=l%d;i+=h*u,u*=n[o]}l=Math.floor(l/d)}a[i]+=t.data[s]}if(t.dims[e]!==1)for(let s=0;s0||o>0;)switch(l.push(i-1),u.push(o-1),s[i][o].item()){case 0:--i,--o;break;case 1:--i;break;case 2:--o;break;default:throw new Error(`Internal error in dynamic time warping. 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W=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"}),bm=Object.freeze({set:W.Set,for:W.For,in:W.In,is:W.Is,if:W.If,else:W.Else,endif:W.EndIf,elif:W.ElseIf,endfor:W.EndFor,and:W.And,or:W.Or,not:W.Not,"not 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Tm=[["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"]],Mi=new Map(Tm),cb=new Map([...Tm.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 Am(t){t=t.toLowerCase();let e=cb.get(t);if(e===void 0)if(Mi.has(t))e=t;else{const n=t.length===2?Mi.keys():Mi.values();throw new Error(`Language "${t}" is not supported. Must be one of: ${JSON.stringify(n)}`)}return e}const Go="https://github.com/xenova/transformers.js/issues/new/choose";async function Im(t,e){const r=await Promise.all([zr(t,"tokenizer.json",!0,e),zr(t,"tokenizer_config.json",!0,e)]);return e.legacy!==null&&(r[1].legacy=e.legacy),r}function pb(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 fb(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 yb extends fa{constructor(e){super(e),this.tokens_to_ids=Ho(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 wb extends fa{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=Bl(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new Uw,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:"~".charCodeAt(0)-"!".charCodeAt(0)+1},(a,s)=>s+"!".charCodeAt(0)),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},(a,s)=>s+"¡".charCodeAt(0)),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},(a,s)=>s+"®".charCodeAt(0))],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=P0(Pm);class vb extends fa{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=Ho(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 Lw((l,u)=>l.score`<0x${i.toString(16).toUpperCase().padStart(2,"0")}>`)):r.push(this.unk_token)}return r}}class $b extends fa{constructor(e,r){super(e),this.tokens_to_ids=Ho(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 Ob(e);case"Precompiled":return new Qb(e);case"Sequence":return new Mb(e);case"Replace":return new xb(e);case"NFC":return new Sb(e);case"NFKC":return new kb(e);case"NFKD":return new Eb(e);case"Strip":return new Cb(e);case"StripAccents":return new Tb(e);case"Lowercase":return new Ab(e);case"Prepend":return new Ib(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class xb extends Lt{normalize(e){const r=Oi(this.config.pattern);return r===null?e:e.replaceAll(r,this.config.content)}}class Sb extends Lt{normalize(e){return e=e.normalize("NFC"),e}}class kb extends Lt{normalize(e){return e=e.normalize("NFKC"),e}}class Eb extends Lt{normalize(e){return e=e.normalize("NFKD"),e}}class Cb extends Lt{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class Tb extends Lt{normalize(e){return e=Om(e),e}}class Ab extends Lt{normalize(e){return e=e.toLowerCase(),e}}class Ib extends Lt{normalize(e){return e=this.config.prepend+e,e}}class Mb extends Lt{constructor(e){super(e),this.normalizers=e.normalizers.map(r=>Lt.fromConfig(r))}normalize(e){return this.normalizers.reduce((r,n)=>n.normalize(r),e)}}class Ob extends Lt{_tokenize_chinese_chars(e){const r=[];for(let n=0;nthis.pre_tokenize_text(n,r)):this.pre_tokenize_text(e,r)).flat()}_call(e,r){return this.pre_tokenize(e,r)}}class zb extends Yt{constructor(e){super(),this.pattern=new RegExp(`[^\\s${ha}]+|[${ha}]`,"gu")}pre_tokenize_text(e,r){return e.trim().match(this.pattern)||[]}}class Pb extends Yt{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=Pm,this.text_encoder=new TextEncoder}pre_tokenize_text(e,r){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),s=>this.byte_encoder[s]).join(""))}}class Rb extends Yt{constructor(e){super(),this.config=e,this.pattern=Oi(this.config.pattern,this.config.invert)}pre_tokenize_text(e,r){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:pb(e,this.pattern)}}class Bb extends Yt{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${ha}]+|[${ha}]+`,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class Db extends Yt{constructor(e){super(),this.config=e;const r=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(r,"gu")}pre_tokenize_text(e,r){return e.match(this.pattern)||[]}}class An extends wt{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new Nb(e);case"ByteLevel":return new Dm(e);case"RobertaProcessing":return new Bm(e);case"BertProcessing":return new Rm(e);case"Sequence":return new Fb(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 Rm extends An{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=ct([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(r!==null){const s=n&&this instanceof Bm?[this.sep]:[],i=n?[this.sep]:[];e=ct(e,s,r,i),a=ct(a,new Array(r.length+s.length+i.length).fill(1))}return{tokens:e,token_type_ids:a}}}class Bm extends Rm{}class Nb extends An{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=ct(s,e),i=ct(i,new Array(e.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(s=ct(s,r),i=ct(i,new Array(r.length).fill(o.Sequence.type_id))));return{tokens:s,token_type_ids:i}}}class Dm extends An{post_process(e,r=null){return r&&(e=ct(e,r)),{tokens:e}}}class Fb extends An{constructor(e){super(e),this.processors=e.processors.map(r=>An.fromConfig(r))}post_process(e,r=null,n={}){let a;for(const s of this.processors)if(s instanceof Dm)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 Gb(e);case"Metaspace":return new Xb(e);case"ByteLevel":return new Hb(e);case"Replace":return new Lb(e);case"ByteFallback":return new Ub(e);case"Fuse":return new Wb(e);case"Strip":return new Vb(e);case"Sequence":return new qb(e);case"CTC":return new jb(e);case"BPEDecoder":return new Kb(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 Lb extends Ut{decode_chain(e){const r=Oi(this.config.pattern);return r===null?e:e.map(n=>n.replaceAll(r,this.config.content))}}class Ub 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 Wb extends Ut{decode_chain(e){return[e.join("")]}}class Vb 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=jo(r)),r))}}class Hb 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 jb 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=jo(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class qb 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 Kb 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 Yb extends Ut{decode_chain(e){let r="";for(let n=1;nn.normalize("NFKC")).join("～"):e=e.normalize("NFKC"),e}}class Zb extends Yt{constructor(e){super(),this.tokenizers=e.pretokenizers.map(r=>Yt.fromConfig(r))}pre_tokenize_text(e,r){return this.tokenizers.reduce((n,a)=>a.pre_tokenize(n,r),[e])}}class Jb extends Yt{constructor(e){super()}pre_tokenize_text(e,r){return e.match(/\w+|[^\w\s]+/g)||[]}}class ev extends Yt{constructor(e){super()}pre_tokenize_text(e,r){return mb(e)}}class tv extends Yt{constructor(e){super(),this.config=e,this.pattern=Oi(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 rv=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function nv(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"?ct(t[a],o):ct(o,t[a])}}function av(t,e){for(const r of Object.keys(t))t[r].length=e}class Ce extends wt{constructor(r,n){super();D(this,"return_token_type_ids",!1);D(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 %}`);D(this,"padding_side","right");this._tokenizer_config=n,this.normalizer=Lt.fromConfig(r.normalizer),this.pre_tokenizer=Yt.fromConfig(r.pre_tokenizer),this.model=fa.fromConfig(r.model,n),this.post_processor=An.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 _b(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.map(a=>`${a.lstrip?"\\s*":""}(${zl(a.content)})${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 Im(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=jt(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&&av(h[g],o):s&&nv(h[g],o,p=>p==="input_ids"?this.pad_token_id:0,this.padding_side));const m={};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]))m[p]=new fe("int64",BigInt64Array.from(h.flatMap(w=>w[p]).map(BigInt)),g)}else{for(const g of Object.keys(h[0]))m[g]=h.map(p=>p[g]);if(!d)for(const g of Object.keys(m))m[g]=m[g][0]}return m}_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=hb(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:ct(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 fe&&(r=r.tolist()),r.map(a=>this.decode(a,n))}decode(r,n={}){if(r instanceof fe&&(r=Mm(r)),!Array.isArray(r)||r.length===0||!R0(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=jo(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={},...m}={}){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 ${typeof n}`);let g=this._compiled_template_cache.get(n);g===void 0&&(g=new db(n),this._compiled_template_cache.set(n,g));const p=Object.create(null);for(const v of rv){const x=this.getToken(v);x&&(p[v]=x)}const w=g.render({messages:r,add_generation_prompt:a,...p,...m});if(s){const v=this._call(w,{add_special_tokens:!1,padding:i,truncation:o,max_length:l,return_tensor:u,...h});return d?v:v.input_ids}return w}}class iv extends Ce{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class sv extends Ce{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class ov extends Ce{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class lv extends Ce{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class uv extends Ce{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class dv extends Ce{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class cv extends Ce{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class pv extends Ce{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class hv extends Ce{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class fv extends Ce{}class mv extends Ce{}class gv extends Ce{constructor(r,n){super(r,n);D(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class _v extends Ce{constructor(){super(...arguments);D(this,"return_token_type_ids",!0)}}class yv extends Ce{}class Fm extends Ce{constructor(){super(...arguments);D(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}}class wv extends Ce{}class Lm extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,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 qo(this,e,r,n)}}class bv extends Lm{}class vv extends Ce{}class $v extends Fm{constructor(e,r){var s,i;const n=".,!?…。，、।۔،",a=(i=(s=e.pre_tokenizer)==null?void 0:s.pretokenizers[0])==null?void 0:i.pattern;a&&a.Regex===` ?[^(\\s|[${n}])]+`&&(a.Regex=` ?[^\\s${n}]+`),super(e,r)}}const zi="▁";class Um extends Ce{constructor(r,n){super(r,n);D(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 %}`);D(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.`);D(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 Nm({replacement:zi,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(zi+r.replaceAll(zi," "));return n.length>1&&n[0]===zi&&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 xv extends Um{}class Sv extends Ce{}class kv extends Ce{}class Ev extends Ce{}class Cv extends Ce{}class Tv extends Ce{}class Av extends Ce{}class Iv extends Ce{constructor(){super(...arguments);D(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 Mv extends Ce{}function qo(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: {${t.language_codes.join(", ")}}`);for(const i of t.post_processor.config.single)if("SpecialToken"in i&&t.languageRegex.test(i.SpecialToken.id)){i.SpecialToken.id=t.lang_to_token(a);break}}return n.forced_bos_token_id=t.model.convert_tokens_to_ids([t.lang_to_token(s)])[0],t._call(e,r)}class Ov extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,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 qo(this,e,r,n)}}class zv 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 qo(this,e,r,n)}}class Pv extends Ce{constructor(){super(...arguments);D(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(),m=0;const g=this.timestamp_begin;let p=[],w=[],v=!1,x=null;const $=new Set(this.all_special_ids);for(const A of r){const P=A.tokens,B=l?A.token_timestamps:null;let L=null,j=g;if("stride"in A){const[ae,ne,ie]=A.stride;if(m-=ne,x=ae-ie,ne&&(j=ne/s+g),ie)for(let N=P.length-1;N>=0;--N){const M=Number(P[N]);if(M>=g){if(L!==null&&(M-g)*s=g){const ie=(ne-g)*s+m,N=ni(ie,2);if(L!==null&&ne>=L)v=!0;else if(v||p.length>0&&ne0?(p.push(q),l&&w.push(ue)):p.every(ae=>ae.length===0)&&(h=u(),p=[],q=[],w=[],ue=[])}if(p.length>0){if(i&&n)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[A,P]=this.findLongestCommonSequence(p,w),B=this.decode(A);h.text=B,l&&(h.words=this.collateWordTimestamps(A,P,o)),d.push(h)}let E=Object.create(null);const T=d.map(A=>A.text).join("");if(n||a){for(let A=0;A0;let l=o?[]:null,u=o?n[0]:null;for(let d=1;dN===ae[M]).length,ie=ne/A+P;ne>1&&ie>m&&(m=ie,g=[B,L,q,ue])}const[w,v,x,$]=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(m),l.push(g);else{const $=i.length-1;i[$]+=h,o[$].push(...m),l[$].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]=ct(l[d],l[h]),u[h]=ct(u[d],u[h]),o[d]="",l[d]=[],u[d]=[]):h=d,--d;for(d=0,h=1;hm),l.filter(m=>m.length>0),u.filter(m=>m.length>0)]}get_decoder_prompt_ids({language:r=null,task:n=null,no_timestamps:a=!0}={}){const s=[];if(r){const i=Am(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 ${Go}.`);s.push(o)}else s.push(null);if(n){if(n=n.toLowerCase(),n!=="transcribe"&&n!=="translate")throw new Error(`Task "${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 ${Go}.`);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 ${Go}.`);s.push(i)}return s.map((i,o)=>[o+1,i]).filter(i=>i[1]!==null)}}class Rv extends Ce{}class Bv extends Ce{}class Dv extends Ce{}class Nv extends Ce{constructor(e,r){super(e,r),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(n=>this.languageRegex.test(n)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;const[r,...n]=e.trim().split(this.languageRegex);if(n.length===0)return super._encode_text(r);if(n.length===2){const[a,s]=n;return this.supported_language_codes.includes(a)||console.warn(`Unsupported language code "${a}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),ct([a],super._encode_text(s))}}}class Fv extends Ce{}class Wm extends Ce{constructor(){super(...arguments);D(this,"_default_chat_template","{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ ' ' }}{% endif %}{% endfor %}{{ eos_token }}")}}class Lv extends Wm{}class Uv extends Ce{}class Wv extends Ce{}class Vv extends Ce{constructor(e,r){super(e,r),this.decoder=new Yb({})}}class Gv extends Ce{}class ht{static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:s=!1,revision:i="main",legacy:o=null}={}){var m;const[l,u]=await Im(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:s,revision:i,legacy:o}),d=((m=u.tokenizer_class)==null?void 0:m.replace(/Fast$/,""))??"PreTrainedTokenizer";let h=this.TOKENIZER_CLASS_MAPPING[d];return h||(console.warn(`Unknown tokenizer class "${d}", attempting to construct from base class.`),h=Ce),new h(l,u)}}D(ht,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:yv,DistilBertTokenizer:fv,CamembertTokenizer:mv,DebertaTokenizer:uv,DebertaV2Tokenizer:dv,BertTokenizer:iv,HerbertTokenizer:cv,ConvBertTokenizer:pv,RoFormerTokenizer:hv,XLMTokenizer:gv,ElectraTokenizer:_v,MobileBertTokenizer:ov,SqueezeBertTokenizer:lv,AlbertTokenizer:sv,GPT2Tokenizer:Fm,BartTokenizer:wv,MBartTokenizer:Lm,MBart50Tokenizer:bv,RobertaTokenizer:vv,WhisperTokenizer:Pv,CodeGenTokenizer:Rv,CLIPTokenizer:Bv,SiglipTokenizer:Dv,MarianTokenizer:Nv,BloomTokenizer:$v,NllbTokenizer:Ov,M2M100Tokenizer:zv,LlamaTokenizer:Um,CodeLlamaTokenizer:xv,XLMRobertaTokenizer:Sv,MPNetTokenizer:kv,FalconTokenizer:Ev,GPTNeoXTokenizer:Cv,EsmTokenizer:Tv,Wav2Vec2CTCTokenizer:Fv,BlenderbotTokenizer:Wm,BlenderbotSmallTokenizer:Lv,SpeechT5Tokenizer:Uv,NougatTokenizer:Wv,VitsTokenizer:Vv,Qwen2Tokenizer:Av,GemmaTokenizer:Iv,Grok1Tokenizer:Mv,CohereTokenizer:Gv,PreTrainedTokenizer:Ce});async function Hv(t,e){return await zr(t,"config.json",!0,e)}function In(t){const e={};let r={};switch(t.model_type){case"llava":case"paligemma":r=In(t.text_config);break;case"moondream1":r=In(t.phi_config);break;case"musicgen":r=In(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"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":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":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=In(t.encoder),s=In(t.decoder),i="num_decoder_layers"in s,o={};return i?(o.num_decoder_layers=s.num_layers,o.num_decoder_heads=s.num_heads,o.decoder_hidden_size=s.hidden_size,o.num_encoder_layers=a.num_layers,o.num_encoder_heads=a.num_heads,o.encoder_hidden_size=a.hidden_size):(o.num_layers=s.num_layers,o.num_heads=s.num_heads,o.hidden_size=s.hidden_size),o}const n={...r,...Hr(t,["model_type","multi_query","is_encoder_decoder"])};for(const a in e)n[a]=t[e[a]];return n}function Vm(t,{prefix:e="past_key_values",encoder_add_pkv:r=!0}={}){const n={},a=t.normalized_config,s=1;if(a.is_encoder_decoder&&r){const i=a.encoder_dim_kv??a.encoder_hidden_size/a.num_encoder_heads,o=a.decoder_dim_kv??a.decoder_hidden_size/a.num_decoder_heads,l=[s,a.num_encoder_heads,0,i],u=[s,a.num_decoder_heads,0,o];for(let d=0;d=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 m=this.timestamp_begin+this.max_initial_timestamp_index;s.subarray(m+1).fill(-1/0)}const u=V0(s),d=Math.log(u.subarray(this.timestamp_begin).map(Math.exp).reduce((m,g)=>m+g)),h=jt(u.subarray(0,this.timestamp_begin))[0];d>h&&s.subarray(0,this.timestamp_begin).fill(-1/0)}return r}}class Zv 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 s2 extends Ko{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 jm{constructor(e){D(this,"max_length",20);D(this,"max_new_tokens",null);D(this,"min_length",0);D(this,"min_new_tokens",null);D(this,"early_stopping",!1);D(this,"max_time",null);D(this,"do_sample",!1);D(this,"num_beams",1);D(this,"num_beam_groups",1);D(this,"penalty_alpha",null);D(this,"use_cache",!0);D(this,"temperature",1);D(this,"top_k",50);D(this,"top_p",1);D(this,"typical_p",1);D(this,"epsilon_cutoff",0);D(this,"eta_cutoff",0);D(this,"diversity_penalty",0);D(this,"repetition_penalty",1);D(this,"encoder_repetition_penalty",1);D(this,"length_penalty",1);D(this,"no_repeat_ngram_size",0);D(this,"bad_words_ids",null);D(this,"force_words_ids",null);D(this,"renormalize_logits",!1);D(this,"constraints",null);D(this,"forced_bos_token_id",null);D(this,"forced_eos_token_id",null);D(this,"remove_invalid_values",!1);D(this,"exponential_decay_length_penalty",null);D(this,"suppress_tokens",null);D(this,"begin_suppress_tokens",null);D(this,"forced_decoder_ids",null);D(this,"guidance_scale",null);D(this,"num_return_sequences",1);D(this,"output_attentions",!1);D(this,"output_hidden_states",!1);D(this,"output_scores",!1);D(this,"return_dict_in_generate",!1);D(this,"pad_token_id",null);D(this,"bos_token_id",null);D(this,"eos_token_id",null);D(this,"encoder_no_repeat_ngram_size",0);D(this,"decoder_start_token_id",null);D(this,"generation_kwargs",{});Object.assign(this,Hr(e,Object.getOwnPropertyNames(this)))}}class Xo extends wt{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}}class Qo extends wt{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof Qo?e=e.criteria:e instanceof Xo&&(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 l2 extends Xo{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 Bi extends wt{constructor(e){super(),this.generation_config=e}_call(e,r=-1){return this.sample(e,r)}sample(e,r){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=e.reduce((a,s)=>a+s,0),n=Math.random()*r;for(let a=0;a1)return new c2(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 u2(e)}}class u2 extends Bi{sample(e,r=-1){let n=this.getLogits(e,r);return[[jt(n)[1],0]]}}class d2 extends Bi{sample(e,r=-1){let n=e.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(e,r),s=wn(a,n),i=bt(s.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},()=>{const o=this.randomSelect(i);return[s[o][0],Math.log(i[o])]})}}class c2 extends Bi{sample(e,r=-1){let n=e.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(e,r),s=wn(a,n),i=bt(s.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},(o,l)=>[s[l][0],Math.log(i[l])])}}class p2 extends jm{constructor(){super(...arguments);D(this,"return_timestamps",null);D(this,"return_token_timestamps",null);D(this,"num_frames",null);D(this,"alignment_heads",null);D(this,"task",null);D(this,"language",null);D(this,"no_timestamps_token_id",null);D(this,"prompt_ids",null);D(this,"is_multilingual",null);D(this,"lang_to_id",null);D(this,"task_to_id",null);D(this,"max_initial_timestamp_index",1)}}const $e={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},Di=new Map,qm=new Map,ma=new Map;async function h2(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=Ew(n);let s=r.dtype;if(typeof s!="string"&&(s&&s.hasOwnProperty(e)?s=s[e]:(s=qv[a[0]],console.warn(`Dtype not specified for ${e}. Using the default dtype: ${s}.`))),Hm.hasOwnProperty(s)){if(s===Pt.fp16&&!await jv())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${s}. Should be one of: ${Object.keys(Pt).join(", ")}`);const i=Hm[s],o=`${r.subfolder??""}/${e}${i}.onnx`,l={...r.session_options};l.executionProviders??(l.executionProviders=a);const u=ti(t,o,!0,r);let d=[];if(r.use_external_data_format){if(Gr.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const m=`${e}${i}.onnx_data`,g=`${r.subfolder??""}/${m}`;d.push(new Promise(async(p,w)=>{const v=await ti(t,g,!0,r);p({path:m,data:v})}))}else l.externalData!==void 0&&(d=l.externalData.map(async m=>{if(typeof m.data=="string"){const g=await ti(t,m.data,!0,r);return{...m,data:g}}return m}));if(d.length>0&&(l.externalData=await Promise.all(d)),n==="webgpu"){const m=Vm(r.config,{prefix:"present"});if(Object.keys(m).length>0){const g={};for(const p in m)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=>h2(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 Nr(t,e){const r=f2(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=Km(a),a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function Km(t){for(let e in t)mm(t[e])?t[e]=new fe(t[e]):typeof t[e]=="object"&&Km(t[e]);return t}function Ym(t){if(t instanceof fe)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 fe("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new fe("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function Xm(t){return new fe("bool",[t],[1])}async function Qm(t,e){let{encoder_outputs:r,past_key_values:n}=e;if(!r){const l=Hr(e,t.sessions.model.inputNames);r=(await ga(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 Zo(t,i,!0)}async function ga(t,e){const r=t.sessions.model,n=Object.create(null);for(const a of r.inputNames)n[a]=e[a];return r.inputNames.includes("token_type_ids")&&!n.token_type_ids&&(n.token_type_ids=new fe("int64",new BigInt64Array(n.input_ids.data.length),n.input_ids.dims)),await Nr(r,n)}async function Zo(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=Xm(!!a)),n.inputNames.includes("position_ids")&&s.attention_mask&&!s.position_ids&&(s.position_ids=g2(s,a)),t.addPastKeyValues(s,a);const i=Hr(s,n.inputNames);return await Nr(n,i)}async function m2(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],m=Object.values(i)[0].dims.at(-2);r=gr([la([e.dims[0],m]),r.slice(null,[r.dims[1]-h,r.dims[1]])],1)}}return await Zo(t,{inputs_embeds:s,past_key_values:i,attention_mask:r,position_ids:a,generation_config:o,logits_processor:l},!0)}function g2(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=la([1,a+l])}}}return r}function _2(t,e,r,n){const{...a}=r;return r.past_key_values&&(e=e.map(i=>[i.at(-1)])),a.decoder_input_ids=Ym(e),a}class re extends wt{constructor(r,n){super();D(this,"main_input_name","input_ids");D(this,"forward_params",["input_ids","attention_mask"]);this.config=r,this.sessions=n;const a=ma.get(this.constructor),s=Di.get(a);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,s===$e.DecoderOnly?(this.can_generate=!0,this._forward=Zo,this._prepare_inputs_for_generation=Zm):s===$e.Seq2Seq||s===$e.Vision2Seq||s===$e.Musicgen?(this.can_generate=!0,this._forward=Qm,this._prepare_inputs_for_generation=_2):s===$e.EncoderDecoder?this._forward=Qm:s===$e.ImageTextToText?(this.can_generate=!0,this._forward=m2,this._prepare_inputs_for_generation=Zm):this._forward=ga,this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var n;const r=[];for(const a of Object.values(this.sessions))(n=a==null?void 0:a.handler)!=null&&n.dispose&&r.push(a.handler.dispose());return await Promise.all(r)}static async from_pretrained(r,{progress_callback:n=null,config:a=null,cache_dir:s=null,local_files_only:i=!1,revision:o="main",model_file_name:l=null,subfolder:u="onnx",device:d=null,dtype:h=null,use_external_data_format:m=null,session_options:g={}}={}){let p={progress_callback:n,config:a,cache_dir:s,local_files_only:i,revision:o,model_file_name:l,subfolder:u,device:d,dtype:h,use_external_data_format:m,session_options:g};const w=ma.get(this),v=Di.get(w);p.config=await Gm.from_pretrained(r,p);let x;return v===$e.DecoderOnly?x=await Promise.all([sn(r,{model:p.model_file_name??"model"},p),zr(r,"generation_config.json",!1,p)]):v===$e.Seq2Seq||v===$e.Vision2Seq?x=await Promise.all([sn(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p),zr(r,"generation_config.json",!1,p)]):v===$e.MaskGeneration?x=await Promise.all([sn(r,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},p)]):v===$e.EncoderDecoder?x=await Promise.all([sn(r,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},p)]):v===$e.ImageTextToText?x=await Promise.all([sn(r,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},p),zr(r,"generation_config.json",!1,p)]):v===$e.Musicgen?x=await Promise.all([sn(r,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},p),zr(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)])),new this(p.config,...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 Yo;return r.temperature!==null&&r.temperature!==1&&n.push(new a2(r.temperature)),r.top_k!==null&&r.top_k!==0&&n.push(new s2(r.top_k)),r.top_p!==null&&r.top_p<1&&n.push(new i2(r.top_p)),n}_get_logits_processor(r,n,a=null){const s=new Yo;if(r.repetition_penalty!==null&&r.repetition_penalty!==1&&s.push(new Jv(r.repetition_penalty)),r.no_repeat_ngram_size!==null&&r.no_repeat_ngram_size>0&&s.push(new Zv(r.no_repeat_ngram_size)),r.bad_words_ids!==null&&s.push(new r2(r.bad_words_ids,r.eos_token_id)),r.min_length!==null&&r.eos_token_id!==null&&r.min_length>0&&s.push(new e2(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 t2(n,r.min_new_tokens,r.eos_token_id)),r.forced_bos_token_id!==null&&s.push(new Kv(r.forced_bos_token_id)),r.forced_eos_token_id!==null&&s.push(new Yv(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 Xv(r.begin_suppress_tokens,i))}return r.guidance_scale!==null&&r.guidance_scale>1&&s.push(new n2(r.guidance_scale)),a!==null&&s.extend(a),s}_prepare_generation_config(r,n,a=jm){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,Hr(n,Object.getOwnPropertyNames(i))),i}_get_stopping_criteria(r,n=null){const a=new Qo;return r.max_length!==null&&a.push(new o2(r.max_length,this.config.max_position_embeddings??null)),r.eos_token_id!==null&&a.push(new l2(r.eos_token_id)),n&&a.extend(n),a}_validate_model_class(){if(!this.can_generate){const r=[sl,ol,il,al],n=ma.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 fe("int64",r.flat(),[r.length,1]),s||(a.attention_mask=gr([a.attention_mask,la([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=Hr(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}){const i=Hr(n,this.sessions.model.inputNames);let{last_hidden_state:o}=await ga(this,i);return s.guidance_scale!==null&&s.guidance_scale>1&&(o=gr([o,Rw(o,0)],0),"attention_mask"in n&&(n.attention_mask=gr([n.attention_mask,Nw(n.attention_mask)],0))),n.encoder_outputs=o,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)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 ${s.length}`);l=s}else l=Array.from({length:r},()=>[s]);return l=Ym(l),a.decoder_attention_mask=Bw(l),{input_ids:l,model_inputs:u}}async generate({inputs:r=null,generation_config:n=null,logits_processor:a=null,stopping_criteria:s=null,streamer:i=null,...o}){this._validate_model_class(),n=this._prepare_generation_config(n,o);let{inputs_tensor:l,model_inputs:u,model_input_name:d}=this._prepare_model_inputs({inputs:r,model_kwargs:o});const h=this.config.is_encoder_decoder;h&&("encoder_outputs"in u||(u=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:l,model_inputs:u,model_input_name:d,generation_config:n})));let m;h?{input_ids:m,model_inputs:u}=this._prepare_decoder_input_ids_for_generation({batch_size:u[d].dims.at(0),model_input_name:d,model_kwargs:u,decoder_start_token_id:n.decoder_start_token_id,bos_token_id:n.bos_token_id,generation_config:n}):m=u[d];let g=m.dims.at(-1);n.max_new_tokens!==null&&(n.max_length=g+n.max_new_tokens);const p=this._get_logits_processor(n,g,a),w=this._get_stopping_criteria(n,s),v=u[d].dims.at(0),x=Bi.getSampler(n),$=new Array(v).fill(0),E=m.tolist();i&&i.put(E);let T=null;for(;;){u=this.prepare_inputs_for_generation(E,u,n);const P=await this.forward(u),B=P.logits.slice(null,-1,null),L=p(E,B),j=[];for(let ue=0;ueue)){n.return_dict_in_generate&&(T=this.getPastKeyValues(P,u.past_key_values,!1));break}u=this._update_model_kwargs_for_generation({generated_input_ids:j,outputs:P,model_inputs:u,is_encoder_decoder:h})}i&&i.end();const A=new fe("int64",E.flat(),[E.length,E[0].length]);return n.return_dict_in_generate?{sequences:A,past_key_values:T}:A}addAttentionsToBeam(r,n){if(this.config.is_encoder_decoder){if(!n.cross_attentions||n.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");r.cross_attentions||(r.cross_attentions=[]),r.cross_attentions.push(n.cross_attentions)}if(!n.decoder_attentions||n.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");r.decoder_attentions||(r.decoder_attentions=[]),r.decoder_attentions.push(n.decoder_attentions)}groupBeams(r){const n=Object.create(null);for(const a of r)n[a.id]===void 0?n[a.id]=[a]:n[a.id].push(a);return Object.values(n)}getPastKeyValues(r,n,a=!0){const s=Object.create(null);for(const i in r)if(i.startsWith("present")){let 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=Object.create(null);for(const a of["cross_attentions","decoder_attentions"]){const s=[];for(const i in r)if(i.startsWith(a)){const o=i.split(".").pop();s[o]=r[i]}n[a]=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=Vm(this.config);for(const o in i)r[o]=new fe(a,s,i[o])}}}class Xt{}class _a extends re{}class y2 extends _a{}class w2 extends _a{async _call(e){return new $t(await super._call(e))}}class b2 extends _a{async _call(e){return new ze(await super._call(e))}}class v2 extends _a{async _call(e){return new vt(await super._call(e))}}class $2 extends _a{async _call(e){return new Tt(await super._call(e))}}class x2 extends re{}class S2 extends x2{}class ya extends re{}class k2 extends ya{}class E2 extends ya{async _call(e){return new $t(await super._call(e))}}class C2 extends ya{async _call(e){return new ze(await super._call(e))}}class T2 extends ya{async _call(e){return new vt(await super._call(e))}}class A2 extends ya{async _call(e){return new Tt(await super._call(e))}}class wa extends re{}class I2 extends wa{}class M2 extends wa{async _call(e){return new $t(await super._call(e))}}class O2 extends wa{async _call(e){return new ze(await super._call(e))}}class z2 extends wa{async _call(e){return new vt(await super._call(e))}}class P2 extends wa{async _call(e){return new Tt(await super._call(e))}}class ba extends re{}class R2 extends ba{}class B2 extends ba{async _call(e){return new $t(await super._call(e))}}class D2 extends ba{async _call(e){return new ze(await super._call(e))}}class N2 extends ba{async _call(e){return new vt(await super._call(e))}}class F2 extends ba{async _call(e){return new Tt(await super._call(e))}}class va extends re{}class L2 extends va{}class U2 extends va{async _call(e){return new $t(await super._call(e))}}class W2 extends va{async _call(e){return new ze(await super._call(e))}}class V2 extends va{async _call(e){return new vt(await super._call(e))}}class G2 extends va{async _call(e){return new Tt(await super._call(e))}}class $a extends re{}class H2 extends $a{}class j2 extends $a{async _call(e){return new $t(await super._call(e))}}class q2 extends $a{async _call(e){return new ze(await super._call(e))}}class K2 extends $a{async _call(e){return new vt(await super._call(e))}}class Y2 extends $a{async _call(e){return new Tt(await super._call(e))}}class xa extends re{}class X2 extends xa{}class Q2 extends xa{async _call(e){return new $t(await super._call(e))}}class Z2 extends xa{async _call(e){return new ze(await super._call(e))}}class J2 extends xa{async _call(e){return new vt(await super._call(e))}}class e1 extends xa{async _call(e){return new Tt(await super._call(e))}}class Sa extends re{}class t1 extends Sa{}class r1 extends Sa{async _call(e){return new ze(await super._call(e))}}class n1 extends Sa{async _call(e){return new vt(await super._call(e))}}class a1 extends Sa{async _call(e){return new Tt(await super._call(e))}}class i1 extends Sa{async _call(e){return new $t(await super._call(e))}}class Ni extends re{}class s1 extends Ni{}class o1 extends Ni{async _call(e){return new $t(await super._call(e))}}class l1 extends Ni{async _call(e){return new ze(await super._call(e))}}class u1 extends Ni{async _call(e){return new vt(await super._call(e))}}class Fi extends re{}class d1 extends Fi{}class c1 extends Fi{async _call(e){return new $t(await super._call(e))}}class p1 extends Fi{async _call(e){return new ze(await super._call(e))}}class h1 extends Fi{async _call(e){return new Tt(await super._call(e))}}class ka extends re{}class f1 extends ka{}class m1 extends ka{async _call(e){return new $t(await super._call(e))}}class g1 extends ka{async _call(e){return new ze(await super._call(e))}}class _1 extends ka{async _call(e){return new vt(await super._call(e))}}class y1 extends ka{async _call(e){return new Tt(await super._call(e))}}class Li extends re{}class w1 extends Li{}class b1 extends Li{async _call(e){return new $t(await super._call(e))}}class v1 extends Li{async _call(e){return new ze(await super._call(e))}}class $1 extends Li{async _call(e){return new Tt(await super._call(e))}}class Ui extends re{}class x1 extends Ui{}class S1 extends Ui{async _call(e){return new ze(await super._call(e))}}class k1 extends Ui{async _call(e){return new Tt(await super._call(e))}}class E1 extends Ui{async _call(e){return new $t(await super._call(e))}}class Jm extends re{constructor(r,n,a){super(r,n);D(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class C1 extends Jm{}class T1 extends Jm{}class eg extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class A1 extends eg{}class I1 extends eg{}class tg extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class M1 extends tg{}class O1 extends tg{}class Jo extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class z1 extends Jo{}class P1 extends Jo{}class R1 extends Jo{async _call(e){return new ze(await super._call(e))}}class Wi extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class B1 extends Wi{}class D1 extends Wi{}class N1 extends Wi{async _call(e){return new ze(await super._call(e))}}class F1 extends Wi{}class rg extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class L1 extends rg{}class U1 extends rg{}class ng extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class W1 extends ng{}class V1 extends ng{}class Ea extends re{}class G1 extends Ea{}class H1 extends Ea{async _call(e){return new $t(await super._call(e))}}class j1 extends Ea{async _call(e){return new ze(await super._call(e))}}class q1 extends Ea{async _call(e){return new vt(await super._call(e))}}class K1 extends Ea{async _call(e){return new Tt(await super._call(e))}}class Ca extends re{}class Y1 extends Ca{}class X1 extends Ca{async _call(e){return new $t(await super._call(e))}}class Q1 extends Ca{async _call(e){return new ze(await super._call(e))}}class Z1 extends Ca{async _call(e){return new vt(await super._call(e))}}class J1 extends Ca{async _call(e){return new Tt(await super._call(e))}}class Ta extends re{}class e$ extends Ta{}class t$ extends Ta{async _call(e){return new $t(await super._call(e))}}class r$ extends Ta{async _call(e){return new ze(await super._call(e))}}class n$ extends Ta{async _call(e){return new vt(await super._call(e))}}class a$ extends Ta{async _call(e){return new Tt(await super._call(e))}}class ag extends re{}class i$ extends ag{}class s$ extends ag{}class ig extends re{constructor(r,n,a){super(r,n);D(this,"requires_attention_mask",!1);D(this,"main_input_name","input_features");D(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}}class o$ extends ig{}class l$ extends ig{_prepare_generation_config(e,r){return super._prepare_generation_config(e,r,p2)}_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=`<|${Am(n)}|>`;r.push(e.lang_to_id[i]),r.push(e.task_to_id[a??"transcribe"])}else if(n||a)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!e.return_timestamps&&e.no_timestamps_token_id&&r.at(-1)!==e.no_timestamps_token_id?r.push(e.no_timestamps_token_id):e.return_timestamps&&r.at(-1)===e.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),r.pop()),r.filter(s=>s!=null)}async generate({inputs:e=null,generation_config:r=null,logits_processor:n=null,stopping_criteria:a=null,...s}){r=this._prepare_generation_config(r,s);const i=this._retrieve_init_tokens(r);return r.return_timestamps&&(n??(n=new Yo),n.push(new Qv(r,i))),await super.generate({inputs:e,generation_config:r,logits_processor:n,decoder_input_ids:i,...s})}_extract_token_timestamps(e,r,n=null,a=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let s=this.config.median_filter_width;s===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),s=7);const i=e.cross_attentions.map(u=>{let d=Array.from({length:this.config.decoder_layers},(v,x)=>gr(u.map($=>$[x]),2)),h=oa(r.map(([v,x])=>n?d[v].slice(null,x,null,[0,n]):d[v].slice(null,x)));h=h.transpose(1,0,2,3);let[m,g]=Mw(h,-2,0,!0),p=h.clone();for(let v=0;vh[x+1]-h[x]),p=ct([1],g).map(v=>!!v),w=[];for(let v=0;vm.findIndex(g=>g==s)),l=o.every(m=>m===-1),u=o.every(m=>m!==-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 m=0;ms*i,1);e.input_labels=new fe("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 Nr(this.sessions.prompt_encoder_mask_decoder,r)}async _call(e){return new qx(await super._call(e))}}class qx extends Xt{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class Gg extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class Kx extends Gg{}class Yx extends Gg{}class Hg extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class Xx extends Hg{}class Qx extends Hg{}class on extends re{}class Zx extends on{}class Jx extends on{async _call(e){return new Mn(await super._call(e))}}class eS extends on{async _call(e){return new ze(await super._call(e))}}class tS extends on{async _call(e){return new vt(await super._call(e))}}class tl extends re{}class rS extends tl{}class nS extends tl{async _call(e){return new Mn(await super._call(e))}}class aS extends tl{async _call(e){return new ze(await super._call(e))}}class Gi extends re{}class iS extends Gi{}class sS extends Gi{async _call(e){return new Mn(await super._call(e))}}class oS extends Gi{async _call(e){return new ze(await super._call(e))}}class lS extends Gi{async _call(e){return new vt(await super._call(e))}}class rl extends re{}class uS extends rl{}class dS extends rl{async _call(e){return new Mn(await super._call(e))}}class cS extends rl{async _call(e){return new ze(await super._call(e))}}class pS extends on{}class hS extends on{async _call(e){return new Mn(await super._call(e))}}class fS extends on{async _call(e){return new ze(await super._call(e))}}class Aa extends re{}class mS extends Aa{}class gS extends Aa{async _call(e){return new Mn(await super._call(e))}}class _S extends Aa{async _call(e){return new ze(await super._call(e))}}class yS extends Aa{async _call(e){return new QS(await super._call(e))}}class wS extends Aa{async _call(e){return new vt(await super._call(e))}}class jg extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class bS extends jg{}class vS extends jg{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 ga(this,o),d=l.dims[1]/this.config.reduction_factor,h=Math.floor(d*s),m=Math.floor(d*a),g=this.config.num_mel_bins;let p=[],w=null,v=null,x=0;for(;;){++x;const T=Xm(!!v);let A;v?A=v.output_sequence_out:A=new fe("float32",new Float32Array(g),[1,1,g]);let P={use_cache_branch:T,output_sequence:A,encoder_attention_mask:u,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(P,w),v=await Nr(this.sessions.decoder_model_merged,P),w=this.getPastKeyValues(v,w);const{prob:B,spectrum:L}=v;if(p.push(L),x>=m&&(Array.from(B.data).filter(j=>j>=n).length>0||x>=h))break}const $=gr(p),{waveform:E}=await Nr(i.sessions.model,{spectrogram:$});return{spectrogram:$,waveform:E}}}class $S extends re{constructor(){super(...arguments);D(this,"main_input_name","spectrogram")}}class xS extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class SS extends xS{}class qg extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class kS extends qg{}class ES extends qg{}class Kg extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class CS extends Kg{}class TS extends Kg{}class Yg extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class AS extends Yg{}class IS extends Yg{}class nl extends re{}class MS extends nl{}class OS extends nl{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="text_model"),super.from_pretrained(e,r)}}class zS extends nl{static async from_pretrained(e,r={}){return r.model_file_name??(r.model_file_name="audio_model"),super.from_pretrained(e,r)}}class PS extends re{}class Xg extends PS{async _call(e){return new JS(await super._call(e))}}class Qg extends re{}class RS extends Qg{}class BS extends Qg{}class Zg extends re{constructor(e,r,n){super(e,r),this.generation_config=n}}class DS extends Zg{}class NS extends Zg{}class Jg extends re{}class FS extends Jg{}class LS extends Jg{async _call(e){return new ze(await super._call(e))}}class e_ extends re{constructor(r,n,a){super(r,n);D(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=a}_apply_and_filter_by_delay_pattern_mask(r){const[n,a]=r.dims,s=this.config.decoder.num_codebooks,i=a-s;let o=0;for(let d=0;d0&&g<=i&&(r.data[o++]=r.data[d])}const l=Math.floor(n/s),u=o/(l*s);return new fe(r.type,r.data.slice(0,o),[l,s,u])}prepare_inputs_for_generation(r,n,a){let s=structuredClone(r);for(let o=0;o=l&&(s[o][l]=BigInt(this.config.decoder.pad_token_id));return a.guidance_scale!==null&&a.guidance_scale>1&&(s=s.concat(s)),super.prepare_inputs_for_generation(s,n,a)}async generate(r){const n=await super.generate(r),a=this._apply_and_filter_by_delay_pattern_mask(n).unsqueeze_(0),{audio_values:s}=await Nr(this.sessions.encodec_decode,{audio_codes:a});return s}}class Xe{static async 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:m={}}={}){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:m};if(g.config=await Gm.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 re.from_pretrained(e,g);throw Error(`Unsupported model type: ${g.config.model_type}`)}}D(Xe,"MODEL_CLASS_MAPPINGS",null),D(Xe,"BASE_IF_FAIL",!1);const US=new Map([["bert",["BertModel",y2]],["nomic_bert",["NomicBertModel",S2]],["roformer",["RoFormerModel",k2]],["electra",["ElectraModel",R2]],["esm",["EsmModel",s1]],["convbert",["ConvBertModel",I2]],["camembert",["CamembertModel",L2]],["deberta",["DebertaModel",H2]],["deberta-v2",["DebertaV2Model",X2]],["mpnet",["MPNetModel",f1]],["albert",["AlbertModel",x1]],["distilbert",["DistilBertModel",t1]],["roberta",["RobertaModel",G1]],["xlm",["XLMModel",Y1]],["xlm-roberta",["XLMRobertaModel",e$]],["clap",["ClapModel",MS]],["clip",["CLIPModel",c$]],["clipseg",["CLIPSegModel",w$]],["chinese_clip",["ChineseCLIPModel",y$]],["siglip",["SiglipModel",f$]],["mobilebert",["MobileBertModel",d1]],["squeezebert",["SqueezeBertModel",w1]],["wav2vec2",["Wav2Vec2Model",Zx]],["wav2vec2-bert",["Wav2Vec2BertModel",uS]],["unispeech",["UniSpeechModel",rS]],["unispeech-sat",["UniSpeechSatModel",iS]],["hubert",["HubertModel",pS]],["wavlm",["WavLMModel",mS]],["audio-spectrogram-transformer",["ASTModel",i$]],["vits",["VitsModel",Xg]],["detr",["DetrModel",hx]],["table-transformer",["TableTransformerModel",_x]],["vit",["ViTModel",Q$]],["fastvit",["FastViTModel",J$]],["mobilevit",["MobileViTModel",nx]],["mobilevitv2",["MobileViTV2Model",ix]],["owlvit",["OwlViTModel",ox]],["owlv2",["Owlv2Model",ux]],["beit",["BeitModel",cx]],["deit",["DeiTModel",bx]],["convnext",["ConvNextModel",Bx]],["convnextv2",["ConvNextV2Model",Nx]],["dinov2",["Dinov2Model",Lx]],["resnet",["ResNetModel",$x]],["swin",["SwinModel",Sx]],["swin2sr",["Swin2SRModel",Ex]],["donut-swin",["DonutSwinModel",Rx]],["yolos",["YolosModel",Wx]],["dpt",["DPTModel",Tx]],["glpn",["GLPNModel",Ox]],["hifigan",["SpeechT5HifiGan",$S]],["efficientnet",["EfficientNetModel",FS]]]),WS=new Map([["t5",["T5Model",C1]],["longt5",["LongT5Model",A1]],["mt5",["MT5Model",M1]],["bart",["BartModel",z1]],["mbart",["MBartModel",B1]],["marian",["MarianModel",Kx]],["whisper",["WhisperModel",o$]],["m2m_100",["M2M100Model",Xx]],["blenderbot",["BlenderbotModel",L1]],["blenderbot-small",["BlenderbotSmallModel",W1]]]),VS=new Map([["bloom",["BloomModel",H$]],["gpt2",["GPT2Model",v$]],["gptj",["GPTJModel",C$]],["gpt_bigcode",["GPTBigCodeModel",A$]],["gpt_neo",["GPTNeoModel",x$]],["gpt_neox",["GPTNeoXModel",k$]],["codegen",["CodeGenModel",M$]],["llama",["LlamaModel",z$]],["gemma",["GemmaModel",R$]],["openelm",["OpenELMModel",D$]],["qwen2",["Qwen2Model",F$]],["phi",["PhiModel",U$]],["phi3",["Phi3Model",V$]],["mpt",["MptModel",q$]],["opt",["OPTModel",Y$]],["mistral",["MistralModel",kS]],["starcoder2",["Starcoder2Model",CS]],["falcon",["FalconModel",AS]],["stablelm",["StableLmModel",DS]]]),al=new Map([["speecht5",["SpeechT5ForSpeechToText",bS]],["whisper",["WhisperForConditionalGeneration",l$]]]),t_=new Map([["speecht5",["SpeechT5ForTextToSpeech",vS]]]),r_=new Map([["vits",["VitsModel",Xg]],["musicgen",["MusicgenForConditionalGeneration",e_]]]),n_=new Map([["bert",["BertForSequenceClassification",b2]],["roformer",["RoFormerForSequenceClassification",C2]],["electra",["ElectraForSequenceClassification",D2]],["esm",["EsmForSequenceClassification",l1]],["convbert",["ConvBertForSequenceClassification",O2]],["camembert",["CamembertForSequenceClassification",W2]],["deberta",["DebertaForSequenceClassification",q2]],["deberta-v2",["DebertaV2ForSequenceClassification",Z2]],["mpnet",["MPNetForSequenceClassification",g1]],["albert",["AlbertForSequenceClassification",S1]],["distilbert",["DistilBertForSequenceClassification",r1]],["roberta",["RobertaForSequenceClassification",j1]],["xlm",["XLMForSequenceClassification",Q1]],["xlm-roberta",["XLMRobertaForSequenceClassification",r$]],["bart",["BartForSequenceClassification",R1]],["mbart",["MBartForSequenceClassification",N1]],["mobilebert",["MobileBertForSequenceClassification",p1]],["squeezebert",["SqueezeBertForSequenceClassification",v1]]]),a_=new Map([["bert",["BertForTokenClassification",v2]],["roformer",["RoFormerForTokenClassification",T2]],["electra",["ElectraForTokenClassification",N2]],["esm",["EsmForTokenClassification",u1]],["convbert",["ConvBertForTokenClassification",z2]],["camembert",["CamembertForTokenClassification",V2]],["deberta",["DebertaForTokenClassification",K2]],["deberta-v2",["DebertaV2ForTokenClassification",J2]],["mpnet",["MPNetForTokenClassification",_1]],["distilbert",["DistilBertForTokenClassification",n1]],["roberta",["RobertaForTokenClassification",q1]],["xlm",["XLMForTokenClassification",Z1]],["xlm-roberta",["XLMRobertaForTokenClassification",n$]]]),il=new Map([["t5",["T5ForConditionalGeneration",T1]],["longt5",["LongT5ForConditionalGeneration",I1]],["mt5",["MT5ForConditionalGeneration",O1]],["bart",["BartForConditionalGeneration",P1]],["mbart",["MBartForConditionalGeneration",D1]],["marian",["MarianMTModel",Yx]],["m2m_100",["M2M100ForConditionalGeneration",Qx]],["blenderbot",["BlenderbotForConditionalGeneration",U1]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",V1]]]),sl=new Map([["bloom",["BloomForCausalLM",j$]],["gpt2",["GPT2LMHeadModel",$$]],["gptj",["GPTJForCausalLM",T$]],["gpt_bigcode",["GPTBigCodeForCausalLM",I$]],["gpt_neo",["GPTNeoForCausalLM",S$]],["gpt_neox",["GPTNeoXForCausalLM",E$]],["codegen",["CodeGenForCausalLM",O$]],["llama",["LlamaForCausalLM",P$]],["gemma",["GemmaForCausalLM",B$]],["openelm",["OpenELMForCausalLM",N$]],["qwen2",["Qwen2ForCausalLM",L$]],["phi",["PhiForCausalLM",W$]],["phi3",["Phi3ForCausalLM",G$]],["mpt",["MptForCausalLM",K$]],["opt",["OPTForCausalLM",X$]],["mbart",["MBartForCausalLM",F1]],["mistral",["MistralForCausalLM",ES]],["starcoder2",["Starcoder2ForCausalLM",TS]],["falcon",["FalconForCausalLM",IS]],["trocr",["TrOCRForCausalLM",SS]],["stablelm",["StableLmForCausalLM",NS]]]),i_=new Map([["bert",["BertForMaskedLM",w2]],["roformer",["RoFormerForMaskedLM",E2]],["electra",["ElectraForMaskedLM",B2]],["esm",["EsmForMaskedLM",o1]],["convbert",["ConvBertForMaskedLM",M2]],["camembert",["CamembertForMaskedLM",U2]],["deberta",["DebertaForMaskedLM",j2]],["deberta-v2",["DebertaV2ForMaskedLM",Q2]],["mpnet",["MPNetForMaskedLM",m1]],["albert",["AlbertForMaskedLM",E1]],["distilbert",["DistilBertForMaskedLM",i1]],["roberta",["RobertaForMaskedLM",H1]],["xlm",["XLMWithLMHeadModel",X1]],["xlm-roberta",["XLMRobertaForMaskedLM",t$]],["mobilebert",["MobileBertForMaskedLM",c1]],["squeezebert",["SqueezeBertForMaskedLM",b1]]]),s_=new Map([["bert",["BertForQuestionAnswering",$2]],["roformer",["RoFormerForQuestionAnswering",A2]],["electra",["ElectraForQuestionAnswering",F2]],["convbert",["ConvBertForQuestionAnswering",P2]],["camembert",["CamembertForQuestionAnswering",G2]],["deberta",["DebertaForQuestionAnswering",Y2]],["deberta-v2",["DebertaV2ForQuestionAnswering",e1]],["mpnet",["MPNetForQuestionAnswering",y1]],["albert",["AlbertForQuestionAnswering",k1]],["distilbert",["DistilBertForQuestionAnswering",a1]],["roberta",["RobertaForQuestionAnswering",K1]],["xlm",["XLMForQuestionAnswering",J1]],["xlm-roberta",["XLMRobertaForQuestionAnswering",a$]],["mobilebert",["MobileBertForQuestionAnswering",h1]],["squeezebert",["SqueezeBertForQuestionAnswering",$1]]]),ol=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",sg]]]),GS=new Map([["llava",["LlavaForConditionalGeneration",og]],["moondream1",["Moondream1ForConditionalGeneration",d$]]]),HS=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",sg]]]),o_=new Map([["vit",["ViTForImageClassification",Z$]],["fastvit",["FastViTForImageClassification",ex]],["mobilevit",["MobileViTForImageClassification",ax]],["mobilevitv2",["MobileViTV2ForImageClassification",sx]],["beit",["BeitForImageClassification",px]],["deit",["DeiTForImageClassification",vx]],["convnext",["ConvNextForImageClassification",Dx]],["convnextv2",["ConvNextV2ForImageClassification",Fx]],["dinov2",["Dinov2ForImageClassification",Ux]],["resnet",["ResNetForImageClassification",xx]],["swin",["SwinForImageClassification",kx]],["segformer",["SegformerForImageClassification",RS]],["efficientnet",["EfficientNetForImageClassification",LS]]]),l_=new Map([["detr",["DetrForObjectDetection",fx]],["table-transformer",["TableTransformerForObjectDetection",yx]],["yolos",["YolosForObjectDetection",Vx]]]),u_=new Map([["owlvit",["OwlViTForObjectDetection",lx]],["owlv2",["Owlv2ForObjectDetection",dx]]]),d_=new Map([["detr",["DetrForSegmentation",mx]],["clipseg",["CLIPSegForImageSegmentation",b$]]]),c_=new Map([["segformer",["SegformerForSemanticSegmentation",BS]]]),jS=new Map([["sam",["SamModel",jx]]]),p_=new Map([["wav2vec2",["Wav2Vec2ForCTC",Jx]],["wav2vec2-bert",["Wav2Vec2BertForCTC",dS]],["unispeech",["UniSpeechForCTC",nS]],["unispeech-sat",["UniSpeechSatForCTC",sS]],["wavlm",["WavLMForCTC",gS]],["hubert",["HubertForCTC",hS]]]),h_=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",eS]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",cS]],["unispeech",["UniSpeechForSequenceClassification",aS]],["unispeech-sat",["UniSpeechSatForSequenceClassification",oS]],["wavlm",["WavLMForSequenceClassification",_S]],["hubert",["HubertForSequenceClassification",fS]],["audio-spectrogram-transformer",["ASTForAudioClassification",s$]]]),qS=new Map([["wavlm",["WavLMForXVector",yS]]]),KS=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",lS]],["wavlm",["WavLMForAudioFrameClassification",wS]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",tS]]]),YS=new Map([["vitmatte",["VitMatteForImageMatting",rx]]]),f_=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Cx]]]),m_=new Map([["dpt",["DPTForDepthEstimation",Ax]],["depth_anything",["DepthAnythingForDepthEstimation",Mx]],["glpn",["GLPNForDepthEstimation",zx]]]),g_=new Map([["clip",["CLIPVisionModelWithProjection",h$]],["siglip",["SiglipVisionModel",g$]]]),__=[[US,$e.EncoderOnly],[WS,$e.EncoderDecoder],[VS,$e.DecoderOnly],[n_,$e.EncoderOnly],[a_,$e.EncoderOnly],[il,$e.Seq2Seq],[al,$e.Seq2Seq],[sl,$e.DecoderOnly],[i_,$e.EncoderOnly],[s_,$e.EncoderOnly],[ol,$e.Vision2Seq],[GS,$e.ImageTextToText],[o_,$e.EncoderOnly],[d_,$e.EncoderOnly],[c_,$e.EncoderOnly],[YS,$e.EncoderOnly],[f_,$e.EncoderOnly],[m_,$e.EncoderOnly],[l_,$e.EncoderOnly],[u_,$e.EncoderOnly],[jS,$e.MaskGeneration],[p_,$e.EncoderOnly],[h_,$e.EncoderOnly],[t_,$e.Seq2Seq],[r_,$e.EncoderOnly],[qS,$e.EncoderOnly],[KS,$e.EncoderOnly],[g_,$e.EncoderOnly]];for(const[t,e]of __)for(const[r,n]of t.values())Di.set(r,e),ma.set(n,r),qm.set(r,n);const XS=[["MusicgenForConditionalGeneration",e_,$e.Musicgen],["CLIPTextModelWithProjection",p$,$e.EncoderOnly],["SiglipTextModel",m$,$e.EncoderOnly],["ClapTextModelWithProjection",OS,$e.EncoderOnly],["ClapAudioModelWithProjection",zS,$e.EncoderOnly]];for(const[t,e,r]of XS)Di.set(t,r),ma.set(e,t),qm.set(t,e);class ln extends Xe{}D(ln,"MODEL_CLASS_MAPPINGS",__.map(e=>e[0])),D(ln,"BASE_IF_FAIL",!0);class ll extends Xe{}D(ll,"MODEL_CLASS_MAPPINGS",[n_]);class y_ extends Xe{}D(y_,"MODEL_CLASS_MAPPINGS",[a_]);class Hi extends Xe{}D(Hi,"MODEL_CLASS_MAPPINGS",[il]);class w_ extends Xe{}D(w_,"MODEL_CLASS_MAPPINGS",[al]);class b_ extends Xe{}D(b_,"MODEL_CLASS_MAPPINGS",[t_]);class v_ extends Xe{}D(v_,"MODEL_CLASS_MAPPINGS",[r_]);class $_ extends Xe{}D($_,"MODEL_CLASS_MAPPINGS",[sl]);class x_ extends Xe{}D(x_,"MODEL_CLASS_MAPPINGS",[i_]);class S_ extends Xe{}D(S_,"MODEL_CLASS_MAPPINGS",[s_]);class k_ extends Xe{}D(k_,"MODEL_CLASS_MAPPINGS",[ol]);class E_ extends Xe{}D(E_,"MODEL_CLASS_MAPPINGS",[o_]);class C_ extends Xe{}D(C_,"MODEL_CLASS_MAPPINGS",[d_]);class T_ extends Xe{}D(T_,"MODEL_CLASS_MAPPINGS",[c_]);class A_ extends Xe{}D(A_,"MODEL_CLASS_MAPPINGS",[l_]);class I_ extends Xe{}D(I_,"MODEL_CLASS_MAPPINGS",[u_]);class M_ extends Xe{}D(M_,"MODEL_CLASS_MAPPINGS",[p_]);class O_ extends Xe{}D(O_,"MODEL_CLASS_MAPPINGS",[h_]);class z_ extends Xe{}D(z_,"MODEL_CLASS_MAPPINGS",[HS]);class P_ extends Xe{}D(P_,"MODEL_CLASS_MAPPINGS",[f_]);class R_ extends Xe{}D(R_,"MODEL_CLASS_MAPPINGS",[m_]);class B_ extends Xe{}D(B_,"MODEL_CLASS_MAPPINGS",[g_]);class ze extends Xt{constructor({logits:e}){super(),this.logits=e}}class QS extends Xt{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class vt extends Xt{constructor({logits:e}){super(),this.logits=e}}class $t extends Xt{constructor({logits:e}){super(),this.logits=e}}class Tt extends Xt{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class Mn extends Xt{constructor({logits:e}){super(),this.logits=e}}class ZS extends Xt{constructor({alphas:e}){super(),this.alphas=e}}class JS extends Xt{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const Qt=typeof self<"u",ek=Qt&&self.constructor.name==="DedicatedWorkerGlobalScope";let un,D_,Fr;if(Qt)un=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},Fr=self.createImageBitmap,D_=self.ImageData;else if(Ve)Fr=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 tk={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},rk=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: ${typeof e}`)}static fromCanvas(e){if(!Qt)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new At(n,e.width,e.height,4)}static async fromURL(e){const r=await ei(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(Qt){const r=await Fr(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 Fr(r)}}static fromTensor(e,r="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(r==="CHW")e=e.transpose(1,2,0);else if(r!=="HWC")throw new Error(`Unsupported channel format: ${r}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new 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 Fr(s)}}async toBlob(e="image/png",r=1){if(!Qt)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 fe("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(!Qt)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=un(e.width,e.height),n=new D_(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(Qt){if(ek)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=rk.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(Qt)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 nk(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 ei(t)).arrayBuffer(),n=new AudioContext({sampleRate:e});typeof e>"u"&&console.warn(`No sampling rate provided, using default of ${n.sampleRate}Hz.`);const a=await n.decodeAudioData(r);let s;if(a.numberOfChannels===2){const i=Math.sqrt(2),o=a.getChannelData(0),l=a.getChannelData(1);s=new Float32Array(o.length);for(let u=0;u2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,r=15,n=27/Math.log(6.4))=>t>=e?r+Math.log(t/e)*n:3*t/200};function ul(t,e="htk"){const r=ak[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}const ik={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,r=15,n=Math.log(6.4)/27)=>t>=r?e*Math.exp(n*(t-r)):200*t/3};function sk(t,e="htk"){const r=ik[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(n=>r(n))}function ok(t,e){const r=Float64Array.from({length:e.length-1},(i,o)=>e[o+1]-e[o]),n=Array.from({length:t.length},()=>new Array(e.length));for(let i=0;inew Array(t.length));for(let i=0;it+n*s)}function Ia(t,e,r,n,a,s=null,i="htk",o=!1){if(s!==null&&s!=="slaney")throw new Error('norm must be one of null or "slaney"');const l=ul(r,i),u=ul(n,i),d=F_(l,u,e+2);let h=sk(d,i),m;if(o){const p=a/(t*2);m=ul(Float64Array.from({length:t},(w,v)=>v*p),i),h=d}else m=F_(0,Math.floor(a/2),t);const g=ok(m,h);if(s!==null&&s==="slaney")for(let p=0;pa)throw Error(`frame_length (${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=lk(t,M,M)}const A=Math.floor(1+Math.floor((t.length-r)/n)),P=l?Math.floor(a/2)+1:a;let B=A,L=A;x!==null&&(x>A?$&&(L=x):L=B=x);const j=new H0(a),q=new Float64Array(a),ue=new Float64Array(j.outputBufferSize),ae=new Array(B);for(let M=0;M=1;--ee)q[ee]-=u*q[ee-1];q[0]*=1-u}for(let ee=0;eeMath.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 (${n})`);return i}function ck([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function dl(t,e=.5,r=null,n=!1){const a=t.logits,s=t.pred_boxes,[i,o,l]=a.dims;if(r!==null&&r.length!==i)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let u=[];for(let d=0;de&&x.push(E)}else{let E=jt(v.data)[1];if(E===l-1||($=bt(v.data),$[E]A*h[(P+1)%2])),m.boxes.push(T),m.classes.push(E),m.scores.push($[E])}}u.push(m)}return u}function Ma(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 ${((r=t==null?void 0:t.constructor)==null?void 0:r.name)??typeof t} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function U_(t,e,r=0,n=null){const a=t/e;let s=q0(a)*e;return n!==null&&s>n&&(s=Math.floor(a)*e),ss?u=Math.floor(s*l/a):s>a&&(l=Math.floor(a*u/s)),await e.resize(u,l,{resample:n}))}async crop_margin(e,r=200){const n=e.clone().grayscale(),a=Bl(n.data)[0],i=jt(n.data)[0]-a;if(i===0)return e;const o=r/255;let l=n.width,u=n.height,d=0,h=0;const m=n.data;for(let g=0;gthis.preprocess(s)));return{pixel_values:oa(n.map(s=>s.pixel_values),0),original_sizes:n.map(s=>s.original_size),reshaped_input_sizes:n.map(s=>s.reshaped_input_size)}}}class pk extends Qe{post_process_semantic_segmentation(e,r=null){const n=e.logits,a=n.dims[0];if(r!==null&&r.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const s=[];for(let i=0;im[E]&&(m[E]=$[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 W_ extends Qe{}class hk extends W_{}class fk extends Qe{}class mk extends Qe{}class V_ extends Qe{}class gk extends V_{}class _k extends Qe{}class yk extends Qe{}class G_ extends Qe{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 wk extends G_{}class bk extends Qe{}class vk extends Qe{}class $k extends Qe{constructor(e){super(e),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(r=>r*r))}}class H_ extends Qe{}class xk extends H_{}class j_ extends Qe{post_process_object_detection(...e){return dl(...e)}}class Sk extends j_{}class kk extends Qe{}class Ek extends Qe{}class q_ extends Qe{pad_image(e,r,n,a={}){const[s,i,o]=r;let l=this.image_mean;Array.isArray(this.image_mean)||(l=new Array(o).fill(l));let u=this.image_std;Array.isArray(u)||(u=new Array(o).fill(l));const d=l.map((h,m)=>-h/u[m]);return super.pad_image(e,r,n,{center:!0,constant_values:d,...a})}}class Ck extends q_{}class Tk extends Qe{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],a=new fe("int64",new BigInt64Array(n.reduce((s,i)=>s*i)).fill(1n),n);return{...r,pixel_mask:a}}post_process_object_detection(...e){return dl(...e)}remove_low_and_no_objects(e,r,n,a){let s=[],i=[],o=[];for(let l=0;ln&&(s.push(d),i.push(g),o.push(h))}return[s,i,o]}check_segment_validity(e,r,n,a=.5,s=.8){let i=[],o=0,l=0;const u=r[n].data;for(let h=0;h=a&&++l;let d=o>0&&l>0;return d&&(d=o/l>s),[d,i]}compute_segments(e,r,n,a,s,i=null,o=null){let[l,u]=o??e[0].dims,d=new fe("int32",new Int32Array(l*u),[l,u]),h=[];if(o!==null)for(let v=0;vg[E]&&(m[E]=v,g[E]=$[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 fe("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);m=new fe("bool",p,m.dims)}o.push(m)}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 Mk extends Qe{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 Ok extends Qe{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:oa(n.map((i,o)=>gr([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 zk extends dn{constructor(e){var r;super(e),(r=this.config).mel_filters??(r.mel_filters=Ia(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney")),this.window=qi(this.config.n_fft,"hann")}_extract_fbank_features(e){const{data:r,dims:n}=ji(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}),a=jt(r)[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));const{data:n,dims:a}=this._extract_fbank_features(r);return{input_features:new fe("float32",n,[1,...a])}}}class Pk 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){Ma(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 fe("float32",r,n),attention_mask:new fe("int64",new BigInt64Array(r.length).fill(1n),n)}}}class Rk extends dn{constructor(e){super(e);const r=this.config.sampling_rate,n=Ia(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;an*32768),ji(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}={}){Ma(e,"SeamlessM4TFeatureExtractor");let{data:i,dims:o}=this._extract_fbank_features(e,this.config.max_length);if(a){const[w,v]=o;for(let x=0;x0){const $=new Float32Array(v*(w+x));$.set(i),$.fill(this.config.padding_value,i.length);const E=w+x;i=$,o=[E,v],s&&(l=new fe("int64",new BigInt64Array(E),[1,E]),l.data.fill(1n,0,w))}}const[u,d]=o,h=this.config.stride;if(u%h!==0)throw new Error(`The number of frames (${u}) must be a multiple of the stride (${h}).`);const g=new fe("float32",i,o).view(1,Math.floor(u/h),d*h),p={input_features:g};if(s){const w=g.dims[1],v=new BigInt64Array(w);if(l){const x=l.data;for(let $=1,E=0;$0)if(n==="rand_trunc"){i=!0;const l=Math.floor(Math.random()*(o+1));e=e.subarray(l,l+r),s=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),s.dims=[1,...s.dims]}else throw new Error(`Truncation strategy "${n}" not implemented`);else{if(o<0){let l=new Float64Array(r);if(l.set(e),a==="repeat")for(let u=e.length;uAt.read(e)))}async function Ki(t,e){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(r=>typeof r=="string"||r instanceof URL?nk(r,e):r instanceof Float64Array?new Float32Array(r):r))}function K_(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 Gk extends tt{constructor(e){super(e)}async _call(e,{topk: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().data:l=>bt(l.data),i=this.model.config.id2label,o=[];for(const l of a.logits){const u=s(l),h=wn(u,r).map(m=>({label:i[m[0]],score:m[1]}));r===1?o.push(...h):o.push(h)}return Array.isArray(e)||r===1?o:o[0]}}class Hk 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;u[g,p]).filter(g=>g[1]>u),h=Array.from(bt(s.end_logits[o].data)).map((g,p)=>[g,p]).filter(g=>g[1]>u),m=B0(d,h).filter(g=>g[0][1]<=g[1][1]).map(g=>[g[0][1],g[1][1],g[0][0]*g[1][0]]).sort((g,p)=>p[2]-g[2]);for(let g=0;g{const g=[...o];return g[l]=m[0],{score:m[1],token:m[0],token_str:this.tokenizer.model.vocab[m[0]],sequence:this.tokenizer.decode(g,{skip_special_tokens:!0})}}))}return Array.isArray(e)?s:s[0]}}class pl extends tt{constructor(r){super(r);D(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 Y_&&"_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 Kk extends pl{constructor(r){super(r);D(this,"_key","summary_text")}}class Y_ extends pl{constructor(r){super(r);D(this,"_key","translation_text")}}class Yk extends tt{constructor(e){super(e)}async _call(e,r={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class Xk 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 m=(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:m.map(g=>r[g[1]]),scores:m.map(g=>g[0])})}return s?l:l[0]}}class Qk 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=Iw(l,i.attention_mask);else if(r==="cls")l=l.slice(null,0);else throw Error(`Pooling method '${r}' not supported.`);return n&&(l=l.normalize(2,-1)),a&&(l=Fw(l,s)),l}}class Zk extends tt{constructor(e){super(e)}async _call(e,{pool:r=null}={}){const n=await Er(e),{pixel_values:a}=await this.processor(n),s=await this.model({pixel_values:a});let i;if(r){if(!("pooler_output"in s))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");i=s.pooler_output}else i=s.last_hidden_state??s.logits??s.image_embeds;return i}}class Jk extends tt{constructor(e){super(e)}async _call(e,{topk:r=null}={}){const n=!Array.isArray(e),a=this.processor.feature_extractor.config.sampling_rate,s=await Ki(e,a),i=this.model.config.id2label,o=[];for(const l of s){const u=await this.processor(l),h=(await this.model(u)).logits[0],g=wn(bt(h.data),r).map(p=>({label:i[p[0]],score:p[1]}));r===1?o.push(...g):o.push(g)}return!n||r===1?o:o[0]}}class e3 extends tt{constructor(e){super(e)}async _call(e,r,{hypothesis_template:n="This is a sound of {}."}={}){const a=!Array.isArray(e);a&&(e=[e]);const s=r.map(d=>n.replace("{}",d)),i=this.tokenizer(s,{padding:!0,truncation:!0}),o=this.processor.feature_extractor.config.sampling_rate,l=await Ki(e,o),u=[];for(const d of l){const h=await this.processor(d),m=await this.model({...i,...h}),g=bt(m.logits_per_audio.data);u.push([...g].map((p,w)=>({score:p,label:r[w]})))}return a?u[0]:u}}class t3 extends tt{constructor(e){super(e)}async _call(e,r={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(e,r);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(e,r);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${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 Ki(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(jt(g.data)[1]);const m=this.tokenizer.decode(h);i.push({text:m})}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;n==="word"&&(r.return_token_timestamps=!0);const o=!Array.isArray(e);o&&(e=[e]);const l=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,u=this.processor.feature_extractor.config.hop_length,d=this.processor.feature_extractor.config.sampling_rate,h=await Ki(e,d),m=[];for(const g of h){let p=[];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 x=d*a,$=d*i,E=x-2*$;let T=0;for(;T=g.length;p.push({stride:[A.length,B?0:$,L?0:$],input_features:P.input_features,is_last:L}),T+=E}}else p=[{stride:[g.length,0,0],input_features:(await this.processor(g)).input_features,is_last:!0}];for(const x of p){r.num_frames=Math.floor(x.stride[0]/u);const $=await this.model.generate({inputs:x.input_features,...r});n==="word"?(x.tokens=$.sequences[0].tolist(),x.token_timestamps=$.token_timestamps.tolist()[0].map(E=>ni(E,2))):x.tokens=$[0].tolist(),x.stride=x.stride.map(E=>E/d)}const[w,v]=this.tokenizer._decode_asr(p,{time_precision:l,return_timestamps:n,force_full_sequences:s});m.push({text:w,...v})}return o?m[0]:m}}class r3 extends tt{constructor(e){super(e)}async _call(e,r={}){const n=Array.isArray(e),a=await Er(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 n3 extends tt{constructor(e){super(e)}async _call(e,{topk:r=1}={}){const n=Array.isArray(e),a=await Er(e),{pixel_values:s}=await this.processor(a),i=await this.model({pixel_values:s}),o=this.model.config.id2label,l=[];for(const u of i.logits){const h=wn(bt(u.data),r).map(m=>({label:o[m[0]],score:m[1]}));r===1?l.push(...h):l.push(h)}return n||r===1?l:l[0]}}class a3 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 Er(e),d=u.map(x=>[x.height,x.width]),{pixel_values:h,pixel_mask:m}=await this.processor(u),g=await this.model({pixel_values:h,pixel_mask:m});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($.data.length);for(let P=0;P<$.data.length;++P)$.data[P]===E.id&&(T[P]=255);const A=new At(T,$.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 $){const T=new Uint8ClampedArray(x.data.length);for(let P=0;Pn.replace("{}",m)),o=this.tokenizer(i,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:l}=await this.processor(s),u=await this.model({...o,pixel_values:l}),d=this.model.config.model_type==="siglip"?m=>m.sigmoid().data:m=>bt(m.data),h=[];for(const m of u.logits_per_image){const p=[...d(m)].map((w,v)=>({score:w,label:r[v]}));p.sort((w,v)=>v.score-w.score),h.push(p)}return a?h:h[0]}}class s3 extends tt{constructor(e){super(e)}async _call(e,{threshold:r=.9,percentage:n=!1}={}){const a=Array.isArray(e);if(a&&e.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const s=await Er(e),i=n?null:s.map(g=>[g.height,g.width]),{pixel_values:o,pixel_mask:l}=await this.processor(s),u=await this.model({pixel_values:o,pixel_mask:l}),d=this.processor.feature_extractor.post_process_object_detection(u,r,i),h=this.model.config.id2label,m=d.map(g=>g.boxes.map((p,w)=>({score:g.scores[w],label:h[g.classes[w]],box:K_(p,!n)})));return a?m:m[0]}}class o3 extends tt{constructor(e){super(e)}async _call(e,r,{threshold:n=.1,topk:a=null,percentage:s=!1}={}){const i=Array.isArray(e),o=await Er(e),l=this.tokenizer(r,{padding:!0,truncation:!0}),u=await this.processor(o),d=[];for(let h=0;h({score:v.scores[E],label:r[v.classes[E]],box:K_($,!s)})).sort(($,E)=>E.score-$.score);a!==null&&(x=x.slice(0,a)),d.push(x)}return i?d:d[0]}}class l3 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 u3 extends tt{constructor(r){super(r);D(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=r.vocoder??null}async _call(r,{speaker_embeddings:n=null}={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}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 fe("float32",n,[1,n.length]);else if(!(n instanceof fe))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 d3 extends tt{constructor(e){super(e)}async _call(e){const r=await Er(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 c3 extends tt{constructor(e){super(e)}async _call(e){const r=await Er(e),n=await this.processor(r),{predicted_depth:a}=await this.model(n),s=[];for(let i=0;i1?s:s[0]}}const X_=Object.freeze({"text-classification":{tokenizer:ht,pipeline:Gk,model:ll,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:ht,pipeline:Hk,model:y_,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:ht,pipeline:jk,model:S_,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:ht,pipeline:qk,model:x_,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:ht,pipeline:Kk,model:Hi,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:ht,pipeline:Y_,model:Hi,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:ht,pipeline:pl,model:Hi,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:ht,pipeline:Yk,model:$_,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:ht,pipeline:Xk,model:ll,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:Jk,model:O_,processor:xt,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:ht,pipeline:e3,model:ln,processor:xt,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:ht,pipeline:t3,model:[w_,M_],processor:xt,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:ht,pipeline:u3,model:[v_,b_],processor:[xt,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:ht,pipeline:r3,model:k_,processor:xt,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:n3,model:E_,processor:xt,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:a3,model:[C_,T_],processor:xt,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:ht,pipeline:i3,model:ln,processor:xt,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:s3,model:A_,processor:xt,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:ht,pipeline:o3,model:I_,processor:xt,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:ht,pipeline:l3,model:z_,processor:xt,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:d3,model:P_,processor:xt,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:c3,model:R_,processor:xt,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:ht,pipeline:Qk,model:ln,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:xt,pipeline:Zk,model:[B_,ln],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),p3=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function h3(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=p3[t]??t;const h=X_[t.split("_",1)[0]];if(!h)throw Error(`Unsupported pipeline: ${t}. Must be one of [${Object.keys(X_)}]`);e||(e=h.default.model,console.log(`No model specified. Using default model: "${e}".`));const m={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},g=new Map([["tokenizer",h.tokenizer],["model",h.model],["processor",h.processor]]),p=await f3(g,e,m);p.task=t,yn(r,{status:"ready",task:t,model:e});const w=h.pipeline;return new w(p)}async function f3(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;let d;for(let m of i){if(m===null){l(null);return}try{l(await m.from_pretrained(e,r));return}catch(g){if((h=g.message)!=null&&h.includes("Unsupported model type"))d=g;else{u(g);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 m3{put(e){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const g3=Gr.IS_PROCESS_AVAILABLE?t=>process.stdout.write(t):t=>console.log(t);class _3 extends m3{constructor(e,{skip_prompt:r=!1,callback_function:n=null,token_callback_function:a=null,decode_kwargs:s={},...i}={}){super(),this.tokenizer=e,this.skip_prompt=r,this.callback_function=n??g3,this.token_callback_function=a,this.decode_kwargs={...s,...i},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(e){var s;if(e.length>1)throw Error("TextStreamer only supports batch size of 1");const r=e[0];if((s=this.token_callback_function)==null||s.call(this,r),this.skip_prompt&&this.next_tokens_are_prompt){this.next_tokens_are_prompt=!1;return}this.token_cache=ct(this.token_cache,r);const n=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let a;n.endsWith(` +`)?(a=n.slice(this.print_len),this.token_cache=[],this.print_len=0):n.length>0&&zm(n.charCodeAt(n.length-1))?(a=n.slice(this.print_len),this.print_len+=a.length):(a=n.slice(this.print_len,n.lastIndexOf(" ")+1),this.print_len+=a.length),this.on_finalized_text(a,!1)}end(){let e;this.token_cache.length>0?(e=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):e="",this.next_tokens_are_prompt=!0,this.on_finalized_text(e,!0)}on_finalized_text(e,r){var n,a;e.length>0&&((n=this.callback_function)==null||n.call(this,e)),r&&((a=this.callback_function)==null||a.call(this,` +`))}}class y3 extends _3{constructor(e,{skip_prompt:r=!1,callback_function:n=null,token_callback_function:a=null,on_chunk_start:s=null,on_chunk_end:i=null,on_finalize:o=null,time_precision:l=.02,skip_special_tokens:u=!0,decode_kwargs:d={}}={}){super(e,{skip_prompt:r,callback_function:n,token_callback_function:a,decode_kwargs:{skip_special_tokens:u,...d}}),this.timestamp_begin=e.timestamp_begin,this.on_chunk_start=s,this.on_chunk_end=i,this.on_finalize=o,this.time_precision=l,this.waiting_for_timestamp=!1}put(e){var n,a;if(e.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const r=e[0];if(r.length===1){const s=Number(r[0])-this.timestamp_begin;if(s>=0){const i=s*this.time_precision;this.waiting_for_timestamp?(n=this.on_chunk_end)==null||n.call(this,i):(a=this.on_chunk_start)==null||a.call(this,i),this.waiting_for_timestamp=!this.waiting_for_timestamp,e=[[]]}}return super.put(e)}end(){var e;super.end(),(e=this.on_finalize)==null||e.call(this)}}class Oa{constructor(e,r,n){this.tokenizer=e,this.model=r}static async getInstance(e=null){return this.instance===null&&(this.instance=h3(this.task,this.model,{dtype:{encoder_model:"fp32",decoder_model_merged:"q4"},device:"webgpu",progress_callback:e})),this.instance}}D(Oa,"task",null),D(Oa,"model",null),D(Oa,"quantized",null),D(Oa,"instance",null),self.addEventListener("message",async t=>{const e=t.data;let r=await w3(e);r!==null&&self.postMessage({status:"complete",data:r})});class Yi extends Oa{}D(Yi,"task","automatic-speech-recognition"),D(Yi,"model",null),D(Yi,"quantized",null);const w3=async({audio:t,model:e,quantized:r,subtask:n,language:a})=>{const s=e.startsWith("distil-whisper/"),i=Yi;(i.model!==e||i.quantized!==r)&&(i.model=e,i.quantized=r,i.instance!==null&&((await i.getInstance()).dispose(),i.instance=null));const o=await i.getInstance($=>{self.postMessage($)}),l=o.processor.feature_extractor.config.chunk_length/o.model.config.max_source_positions,u=[],d=s?20:30,h=s?3:5;let m=0,g,p=0,w;const v=new y3(o.tokenizer,{time_precision:l,on_chunk_start:$=>{const E=(d-h)*m;u.push({text:"",timestamp:[E+$,null],finalised:!1,offset:E})},token_callback_function:$=>{g??(g=performance.now()),p++>0&&(w=p/(performance.now()-g)*1e3)},callback_function:$=>{u.length!==0&&(u.at(-1).text+=$,self.postMessage({status:"update",data:{text:"",chunks:u,tps:w}}))},on_chunk_end:$=>{const E=u.at(-1);E.timestamp[1]=$+E.offset,E.finalised=!0},on_finalize:()=>{console.log("finalize",m),g=null,p=0,++m}}),x=await o(t,{top_k:0,do_sample:!1,chunk_length_s:d,stride_length_s:h,language:a,task:n,return_timestamps:!0,force_full_sequences:!1,streamer:v}).catch($=>(console.error($),self.postMessage({status:"error",data:$}),null));return{tps:w,...x}}})();