diff --git "a/assets/worker-COJ87XCR.js" "b/assets/worker-COJ87XCR.js" new file mode 100644--- /dev/null +++ "b/assets/worker-COJ87XCR.js" @@ -0,0 +1,2846 @@ +var xx=Object.defineProperty;var Tx=(vs,Sr,zn)=>Sr in vs?xx(vs,Sr,{enumerable:!0,configurable:!0,writable:!0,value:zn}):vs[Sr]=zn;var re=(vs,Sr,zn)=>Tx(vs,typeof Sr!="symbol"?Sr+"":Sr,zn);(function(){"use strict";const vs=new Map,Sr=[],zn=(e,r,t)=>{if(r&&typeof r.init=="function"&&typeof r.createInferenceSessionHandler=="function"){const s=vs.get(e);if(s===void 0)vs.set(e,{backend:r,priority:t});else{if(s.priority>t)return;if(s.priority===t&&s.backend!==r)throw new Error(`cannot register backend "${e}" using priority ${t}`)}if(t>=0){const i=Sr.indexOf(e);i!==-1&&Sr.splice(i,1);for(let n=0;n{const r=vs.get(e);if(!r)return"backend not found.";if(r.initialized)return r.backend;if(r.aborted)return r.error;{const t=!!r.initPromise;try{return t||(r.initPromise=r.backend.init(e)),await r.initPromise,r.initialized=!0,r.backend}catch(s){return t||(r.error=`${s}`,r.aborted=!0),r.error}finally{delete r.initPromise}}},Iu=async e=>{const r=e.executionProviders||[],t=r.map(l=>typeof l=="string"?l:l.name),s=t.length===0?Sr:t;let i;const n=[],o=new Set;for(const l of s){const d=await ub(l);typeof d=="string"?n.push({name:l,err:d}):(i||(i=d),i===d&&o.add(l))}if(!i)throw new Error(`no available backend found. 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r)switch(this.dataLocation=r.location,i=r.type,n=r.dims,r.location){case"cpu-pinned":{const a=Bn.get(i);if(!a)throw new TypeError(`unsupported type "${i}" to create tensor from pinned buffer`);if(!(r.data instanceof a))throw new TypeError(`buffer should be of type ${a.name}`);this.cpuData=r.data;break}case"texture":{if(i!=="float32")throw new TypeError(`unsupported type "${i}" to create tensor from texture`);this.gpuTextureData=r.texture,this.downloader=r.download,this.disposer=r.dispose;break}case"gpu-buffer":{if(i!=="float32"&&i!=="float16"&&i!=="int32"&&i!=="int64"&&i!=="uint32"&&i!=="uint8"&&i!=="bool"&&i!=="uint4"&&i!=="int4")throw new TypeError(`unsupported type "${i}" to create tensor from gpu buffer`);this.gpuBufferData=r.gpuBuffer,this.downloader=r.download,this.disposer=r.dispose;break}case"ml-tensor":{if(i!=="float32"&&i!=="float16"&&i!=="int32"&&i!=="int64"&&i!=="uint32"&&i!=="uint64"&&i!=="int8"&&i!=="uint8"&&i!=="bool")throw new TypeError(`unsupported type "${i}" to create tensor from MLTensor`);this.mlTensorData=r.mlTensor,this.downloader=r.download,this.disposer=r.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let a,l;if(typeof r=="string")if(i=r,l=s,r==="string"){if(!Array.isArray(t))throw new TypeError("A string tensor's data must be a string array.");a=t}else{const d=Bn.get(r);if(d===void 0)throw new TypeError(`Unsupported tensor type: ${r}.`);if(Array.isArray(t)){if(r==="float16"&&d===Uint16Array||r==="uint4"||r==="int4")throw new TypeError(`Creating a ${r} tensor from number array is not supported. 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${Math.ceil(s/4)}>`);else{let i=s==null||s===1?t:`vec${s}<${t}>`;e.push(`${r}:${i}`)}return` + struct Uniforms { ${e.join(", ")} }; + @group(0) @binding(${this.variableIndex}) var uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(e=>e.impl()).join(` +`)+this.internalVariables.map(e=>e.impl()).join(` +`)}get variablesInfo(){if(this.uniforms.length===0)return;let e=r=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(r)];return this.uniforms.map(r=>[e(r.type),r.length??1])}},Rp=(e,r)=>new Bp(e,r)}),Np,Vd,jp,Up,Vp,Wp,Nr,Gp,Kp,Ks=je(()=>{pt(),yt(),Yt(),vt(),Np=(e,r)=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(r.length!==0&&r.length!==e[0].dims.length)throw new Error(`perm size ${r.length} does not match input rank ${e[0].dims.length}`)},Vd=(e,r)=>r.length!==0?r:[...new Array(e).keys()].reverse(),jp=(e,r)=>xe.sortBasedOnPerm(e,Vd(e.length,r)),Up=(e,r,t,s)=>{let i=`fn perm(i: ${s.type.indices}) -> 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${P.registerUniform("output_size","u32").declareVariables(A,v)} + var tile : array, ${_}>; + ${P.mainStart([_,_,1])} + let stride = (uniforms.output_shape[1] - 1) / ${_} + 1; + let workgroup_id_x = workgroup_index % stride; + let workgroup_id_y = workgroup_index / stride; + let input_col = workgroup_id_y * ${_}u + local_id.x; + let input_row = workgroup_id_x * ${_}u + local_id.y; + if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) { + tile[local_id.y][local_id.x] = ${A.getByIndices(`${A.type.indices}(input_row, input_col)`)}; + } + workgroupBarrier(); + + let output_col = workgroup_id_x * ${_}u + local_id.x; + let output_row = workgroup_id_y * ${_}u + local_id.y; + if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) { + ${v.setByIndices(`${v.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")} + } + }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let P=xe.size(n);return{outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(a[1]/_),y:Math.ceil(a[0]/_)},programUniforms:[{type:12,data:P},...nt(o,a)]}},getShaderSource:d}}return d=_=>{let P=$e("a",t,o.length),A=tt("output",t,a.length);return` + ${_.registerUniform("output_size","u32").declareVariables(P,A)} + + ${Up(i,s,P,A)} + + ${_.mainStart()} + ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${A.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${A.setByOffset("global_idx",P.getByIndices("aIndices"))} + }`},{name:"Transpose",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>{let _=xe.size(n);return{outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...nt(o,a)]}},getShaderSource:d}},Gp=(e,r)=>{Np(e.inputs,r.perm),e.compute(Nr(e.inputs[0],r.perm))},Kp=e=>Dt({perm:e.perm})}),Hp,qp,Qp,Xp,Jp,Yp,Zp,eh,th,rh,is,sh,nh,ih,oh,ah,lh,dh,ch,uh,ph,Xb=je(()=>{pt(),yt(),vt(),Gd(),Ks(),Hp={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},qp={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + 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outputIndex = global_idx / ${h}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${Qp[s]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${h}) { + let candidate = f32(${p.getByOffset("offset + k")}); + bestValue = ${Hp[s]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${h}u); + for (var currentSize = reduceSize / 2u; reduceSize > 1u; + currentSize = reduceSize / 2u) { + let interval = DIV_CEIL(reduceSize, 2u); + if (local_idx < currentSize) { + let candidate = aBestValues[local_idx + interval]; + bestValue = ${qp[s]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${u.setByOffset("outputIndex",`${s==="mean"?`${u.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${u.type.storage}(${Xp[s]})`}`)}; + } + 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inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},hh=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],Ta=(e,r,t,s,i,n,o=!1,a=!1)=>{let l=[],d=t[0].dims,p=d.length,u=xe.normalizeAxes(i,p),h=!a&&u.length===0;d.forEach((P,A)=>{h||u.indexOf(A)>=0?o&&l.push(1):l.push(P)});let w=l.length,_=xe.size(l);return{name:e,shaderCache:r,getShaderSource:P=>{let A=[],v=$e("_A",t[0].dataType,p),y=tt("output",n,w),S=s(v,y,u),x=S[2];for(let g=0,M=0;g=0?(o&&M++,x=`for(var j${g}: u32 = 0; j${g} < ${d[g]}; j${g}++) { + ${S[2].includes("last_index")?`let last_index = j${g};`:""} + ${v.indicesSet("input_indices",g,`j${g}`)} + ${x} + }`):(A.push(`${v.indicesSet("input_indices",g,y.indicesGet("output_indices",M))};`),M++);return` + + ${P.registerUniform("output_size","u32").declareVariables(v,y)} + + ${P.mainStart()} + ${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var input_indices: ${v.type.indices}; + let output_indices = ${y.offsetToIndices("global_idx")}; + + ${A.join(` +`)} + ${S[0]} // init ops for reduce max/min + ${S[1]} + ${x} + ${S[3]} + ${S.length===4?y.setByOffset("global_idx","value"):S.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:l,dataType:n}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...nt(d,l)]})}},Wd=(e,r)=>{let t=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(s=>t.push(Number(s))),Dt({axes:t,keepDims:r.keepDims,noopWithEmptyAxes:r.noopWithEmptyAxes})},as=(e,r,t,s)=>{let i=e.inputs,n=i.length===1?t:Wd(i,t);e.compute(Ta(r,{hint:n.cacheKey,inputDependencies:["rank"]},[i[0]],n.noopWithEmptyAxes&&n.axes.length===0?hh:s,n.axes,i[0].dataType,n.keepDims,n.noopWithEmptyAxes),{inputs:[0]})},mh=(e,r)=>{os(e.inputs),as(e,"ReduceLogSum",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,"value = log(value);"])},fh=(e,r)=>{os(e.inputs),as(e,"ReduceL1",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += abs(${t.getByIndices("input_indices")});`,""])},_h=(e,r)=>{os(e.inputs),as(e,"ReduceL2",r,(t,s)=>[`var t = ${s.type.value}(0); var value = ${s.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},gh=(e,r)=>{os(e.inputs),as(e,"ReduceLogSumExp",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += exp(${t.getByIndices("input_indices")});`,"value = log(value);"])},wh=(e,r)=>{os(e.inputs),as(e,"ReduceMax",r,(t,s,i)=>{let n=[];for(let o=0;o=0||i.length===0)&&n.push(t.indicesSet("input_indices",o,0));return[`${n.join(` +`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = max(value, ${t.getByIndices("input_indices")});`,""]})},yh=(e,r)=>{os(e.inputs),as(e,"ReduceMean",r,(t,s,i)=>{let n=1;for(let o=0;o=0||i.length===0)&&(n*=e.inputs[0].dims[o]);return["var sum = f32(0);","",`sum += f32(${t.getByIndices("input_indices")});`,`let value = ${s.type.value}(sum / ${n});`]})},Mh=(e,r)=>{os(e.inputs),as(e,"ReduceMin",r,(t,s,i)=>{let n=[];for(let o=0;o=0||i.length===0)&&n.push(`input_indices[${o}] = 0;`);return[`${n.join(` +`)}`,`var value = ${t.getByIndices("input_indices")};`,`value = min(value, ${t.getByIndices("input_indices")});`,""]})},bh=(e,r)=>{os(e.inputs),as(e,"ReduceProd",r,(t,s)=>[`var value = ${s.type.storage}(1);`,"",`value *= ${t.getByIndices("input_indices")};`,""])},vh=(e,r)=>{os(e.inputs),as(e,"ReduceSum",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,""])},xh=(e,r)=>{os(e.inputs),as(e,"ReduceSumSquare",r,(t,s)=>[`var t = ${s.type.value}(0); var value = ${s.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += t * t;`,""])},ls=(e,r,t)=>{if(r.length===0)return t;let s=1,i=1;for(let n=0;n1024},Th=(e,r)=>{ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?yh(e,r):sh(e,r)},Eh=(e,r)=>{ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?fh(e,r):nh(e,r)},Ph=(e,r)=>{ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?_h(e,r):ih(e,r)},Ch=(e,r)=>{ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?gh(e,r):oh(e,r)},Sh=(e,r)=>{ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?wh(e,r):ah(e,r)},$h=(e,r)=>{ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Mh(e,r):lh(e,r)},kh=(e,r)=>{ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?bh(e,r):dh(e,r)},Ih=(e,r)=>{ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?vh(e,r):ch(e,r)},Ah=(e,r)=>{ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?xh(e,r):uh(e,r)},Oh=(e,r)=>{ls(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?mh(e,r):ph(e,r)}}),Kd,Fh,Dh,Hd,Jb=je(()=>{pt(),Yt(),Gd(),Kd=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},Fh=(e,r)=>{Kd(e.inputs);let t=(s,i,n)=>{let o=[];for(let a=0;a=0||n.length===0)&&o.push(`input_indices[${a}] = 0;`);return[`${o.join(` +`)}`,`var value = ${s.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${s.getByIndices("input_indices")} ${r.selectLastIndex>0?"<=":"<"} value) { + value = ${s.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",i.setByOffset("global_idx","best_index")]};e.compute(Ta("ArgMin",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},Dh=(e,r)=>{Kd(e.inputs);let t=(s,i,n)=>{let o=[];for(let a=0;a=0||n.length===0)&&o.push(`input_indices[${a}] = 0;`);return[`${o.join(` +`)}`,`var value = ${s.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${s.getByIndices("input_indices")} ${r.selectLastIndex>0?">=":">"} value) { + value = ${s.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",i.setByOffset("global_idx","best_index")]};e.compute(Ta("argMax",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},Hd=e=>Dt(e)}),Lh,Ea,zh,Bh,Rh,Oi,Nh,jh,qd=je(()=>{pt(),yt(),zd(),vt(),Lh=(e,r)=>{let t=e[0],s=e[1],i=e[2],n=e[3],o=e[4],a=e[5];if(o&&a)throw new Error("Attention cannot have both past and attention_bias");if(t.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let l=t.dims[0],d=t.dims[1],p=t.dims[2];if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(s.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(s.dims[0]!==p)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(i.dims[0]!==s.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let u=i.dims[0]/3,h=u,w=h;if(r.qkvHiddenSizes.length>0){if(r.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let S of r.qkvHiddenSizes)if(S%r.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");u=r.qkvHiddenSizes[0],h=r.qkvHiddenSizes[1],w=r.qkvHiddenSizes[2]}let _=d;if(u!==h)throw new Error("qkv_hidden_sizes first element should be same as the second");if(i.dims[0]!==u+h+w)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let P=0;if(o){if(h!==w)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(o.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(o.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(o.dims[1]!==l)throw new Error('Input "past" second dimension must be batch_size');if(o.dims[2]!==r.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(o.dims[4]!==h/r.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');r.pastPresentShareBuffer||(P=o.dims[3])}let A=_+P,v=-1,y=0;if(n)throw new Error("Mask not supported");if(o)throw new Error("past is not supported");if(a){if(a.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(a.dims[0]!==l||a.dims[1]!==r.numHeads||a.dims[2]!==d||a.dims[3]!==A)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:l,sequenceLength:d,pastSequenceLength:P,kvSequenceLength:_,totalSequenceLength:A,maxSequenceLength:v,inputHiddenSize:p,hiddenSize:u,vHiddenSize:w,headSize:Math.floor(u/r.numHeads),vHeadSize:Math.floor(w/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:y,scale:r.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Ea=(e,r,t)=>r&&e?` + let total_sequence_length_input = u32(${r.getByOffset("0")}); + let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length); + let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input; + let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input; + total_sequence_length = u32(${e==null?void 0:e.getByOffset("batchIdx")}) + 1; + var past_sequence_length: u32 = 0; + if (is_first_prompt == false) { + past_sequence_length = total_sequence_length - sequence_length; + } + `:` + ${t?"let past_sequence_length = uniforms.past_sequence_length":""}; + let present_sequence_length = total_sequence_length; + `,zh=(e,r,t,s,i,n,o,a)=>{let l=Xt(o?1:n),d=64,p=n/l;p{let y=tt("x",e.dataType,e.dims,l),S=[y],x=o?$e("seq_lens",o.dataType,o.dims):void 0;x&&S.push(x);let g=a?$e("total_sequence_length_input",a.dataType,a.dims):void 0;g&&S.push(g);let M=Tr(e.dataType),E=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return` + var thread_max: array; + var thread_sum: array; + ${v.registerUniforms(E).declareVariables(...S)} + ${v.mainStart([d,1,1])} + let batchIdx = workgroup_id.z / uniforms.num_heads; + let headIdx = workgroup_id.z % uniforms.num_heads; + let sequence_length = uniforms.sequence_length; + var total_sequence_length = uniforms.total_sequence_length; + ${Ea(x,g,!1)} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = (global_idx / ${d}) * uniforms.total_sequence_length + local_offset; + let seq_causal_length = ${o?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"}; + var thread_max_vector = ${_}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + thread_max_vector = max(${_}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(l){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: ${l}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${d}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${_}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + sum_vector += exp(${_}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(l){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: ${l}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${d}; i++) { + sum += thread_sum[i]; + } + + if (sum == 0) { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + x[offset + i] = ${y.type.value}(${M}(1.0) / ${M}(seq_causal_length)); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + var f32input = ${_}(x[offset + i]); + x[offset + i] = ${y.type.value}(exp(f32input - max_value) / sum); + } + } + ${o?` + for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) { + x[offset + total_seq_id] = ${y.type.value}(${M}(0)); + }`:""}; + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${d};${w};${l}`,inputDependencies:P},getShaderSource:A,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:i,z:r*t},programUniforms:h})}},Bh=(e,r,t,s,i,n,o,a,l)=>{let d=o+n.kvSequenceLength,p=[n.batchSize,n.numHeads,n.sequenceLength,d],u=e>1&&s,h=n.kvNumHeads?n.kvNumHeads:n.numHeads,w=u?[n.batchSize,h,d,n.headSize]:void 0,_=n.nReps?n.nReps:1,P=n.scale===0?1/Math.sqrt(n.headSize):n.scale,A=Xt(n.headSize),v=n.headSize/A,y=12,S={x:Math.ceil(d/y),y:Math.ceil(n.sequenceLength/y),z:n.batchSize*n.numHeads},x=[{type:12,data:n.sequenceLength},{type:12,data:v},{type:12,data:d},{type:12,data:n.numHeads},{type:12,data:n.headSize},{type:1,data:P},{type:12,data:o},{type:12,data:n.kvSequenceLength},{type:12,data:_}],g=u&&s&&xe.size(s.dims)>0,M=["type","type"];g&&M.push("type"),i&&M.push("type"),a&&M.push("type"),l&&M.push("type");let E=[{dims:p,dataType:r.dataType,gpuDataType:0}];u&&E.push({dims:w,dataType:r.dataType,gpuDataType:0});let k=B=>{let R=$e("q",r.dataType,r.dims,A),J=$e("key",t.dataType,t.dims,A),q=[R,J];if(g){let ae=$e("past_key",s.dataType,s.dims,A);q.push(ae)}i&&q.push($e("attention_bias",i.dataType,i.dims));let V=a?$e("seq_lens",a.dataType,a.dims):void 0;V&&q.push(V);let Y=l?$e("total_sequence_length_input",l.dataType,l.dims):void 0;Y&&q.push(Y);let H=tt("output",r.dataType,p),Q=[H];u&&Q.push(tt("present_key",r.dataType,w,A));let ie=Tr(1,A),le=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return` + const TILE_SIZE = ${y}u; + + var tileQ: array<${R.type.storage}, ${y*y}>; + var tileK: array<${R.type.storage}, ${y*y}>; + ${B.registerUniforms(le).declareVariables(...q,...Q)} + ${B.mainStart([y,y,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z % uniforms.num_heads; + let kvHeadIdx = ${_===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${_===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let m = workgroup_id.y * TILE_SIZE; + let n = workgroup_id.x * TILE_SIZE; + let sequence_length = uniforms.M; + var total_sequence_length = uniforms.N; + ${Ea(V,Y,!0)} + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; + let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + ${g&&u?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""}; + let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K; + ${u?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""} + var value = ${ie}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) { + tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x]; + } + if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) { + var idx = TILE_SIZE * local_id.y + local_id.x; + ${g&&u?` + if (n + local_id.y < past_sequence_length) { + tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; + } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) { + tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x]; + }`:` + if (n + local_id.y < uniforms.kv_sequence_length) { + tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; + }`} + ${u?`if (n + local_id.y < present_sequence_length) { + present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx]; + }`:""} + } + workgroupBarrier(); + + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += ${ie}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]); + } + + workgroupBarrier(); + } + + if (global_id.y < uniforms.M && global_id.x < total_sequence_length) { + let headOffset = workgroup_id.z * uniforms.M * uniforms.N; + let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x; + var sum: f32 = ${(()=>{switch(A){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: ${A}`)}})()}; + output[outputIdx] = ${H.type.value} (sum * uniforms.alpha) + ${i?"attention_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${A};${i!==void 0};${s!==void 0};${e}`,inputDependencies:M},getRunData:()=>({outputs:E,dispatchGroup:S,programUniforms:x}),getShaderSource:k}},Rh=(e,r,t,s,i,n,o=void 0,a=void 0)=>{let l=n+i.kvSequenceLength,d=i.nReps?i.nReps:1,p=i.vHiddenSize*d,u=e>1&&s,h=i.kvNumHeads?i.kvNumHeads:i.numHeads,w=u?[i.batchSize,h,l,i.headSize]:void 0,_=[i.batchSize,i.sequenceLength,p],P=12,A={x:Math.ceil(i.vHeadSize/P),y:Math.ceil(i.sequenceLength/P),z:i.batchSize*i.numHeads},v=[{type:12,data:i.sequenceLength},{type:12,data:l},{type:12,data:i.vHeadSize},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:12,data:p},{type:12,data:n},{type:12,data:i.kvSequenceLength},{type:12,data:d}],y=u&&s&&xe.size(s.dims)>0,S=["type","type"];y&&S.push("type"),o&&S.push("type"),a&&S.push("type");let x=[{dims:_,dataType:r.dataType,gpuDataType:0}];u&&x.push({dims:w,dataType:r.dataType,gpuDataType:0});let g=M=>{let E=$e("probs",r.dataType,r.dims),k=$e("v",t.dataType,t.dims),B=[E,k];y&&B.push($e("past_value",s.dataType,s.dims));let R=o?$e("seq_lens",o.dataType,o.dims):void 0;o&&B.push(R);let J=a?$e("total_sequence_length_input",a.dataType,a.dims):void 0;a&&B.push(J);let q=[tt("output",r.dataType,_)];u&&q.push(tt("present_value",r.dataType,w));let V=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return` + const TILE_SIZE = ${P}u; + var tileQ: array<${E.type.value}, ${P*P}>; + var tileV: array<${E.type.value}, ${P*P}>; + ${M.registerUniforms(V).declareVariables(...B,...q)} + ${M.mainStart([P,P,1])} + let headIdx = workgroup_id.z % uniforms.num_heads; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let kvHeadIdx = ${d===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${d===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; + let m = global_id.y; + let n = global_id.x; + let sequence_length = uniforms.M; + var total_sequence_length = uniforms.K; + ${Ea(R,J,!0)} + let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch + ${y&&u?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""}; + let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n; + ${u?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""} + var value = ${E.type.storage}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + var idx = TILE_SIZE * local_id.y + local_id.x; + ${y&&u?` + if (w + local_id.y < past_sequence_length) { + tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N]; + } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) { + tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N]; + } + `:` + if (w + local_id.y < uniforms.kv_sequence_length) { + tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N]; + }`} + ${u?` + if (w + local_id.y < present_sequence_length) { + present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx]; + }`:""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + headIdx * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${s!==void 0};${e}`,inputDependencies:S},getRunData:()=>({outputs:x,dispatchGroup:A,programUniforms:v}),getShaderSource:g}},Oi=(e,r,t,s,i,n,o,a,l,d,p=void 0,u=void 0)=>{let h=Math.min(e.outputCount,1+(o?1:0)+(a?1:0)),w=h>1?d.pastSequenceLength:0,_=w+d.kvSequenceLength,P=l&&xe.size(l.dims)>0?l:void 0,A=[r,t];h>1&&o&&xe.size(o.dims)>0&&A.push(o),P&&A.push(P),p&&A.push(p),u&&A.push(u);let v=e.compute(Bh(h,r,t,o,P,d,w,p,u),{inputs:A,outputs:h>1?[-1,1]:[-1]})[0];e.compute(zh(v,d.batchSize,d.numHeads,w,d.sequenceLength,_,p,u),{inputs:p&&u?[v,p,u]:[v],outputs:[]});let y=[v,s];h>1&&a&&xe.size(a.dims)>0&&y.push(a),p&&y.push(p),u&&y.push(u),e.compute(Rh(h,v,s,a,d,w,p,u),{inputs:y,outputs:h>1?[0,2]:[0]})},Nh=(e,r)=>{let t=[r.batchSize,r.numHeads,r.sequenceLength,r.headSize],s=r.sequenceLength,i=r.inputHiddenSize,n=r.headSize,o=12,a={x:Math.ceil(r.headSize/o),y:Math.ceil(r.sequenceLength/o),z:r.batchSize*r.numHeads},l=[e.inputs[0],e.inputs[1],e.inputs[2]],d=[{type:12,data:s},{type:12,data:i},{type:12,data:n},{type:12,data:r.numHeads},{type:12,data:r.headSize},{type:12,data:r.hiddenSize},{type:12,data:r.hiddenSize+r.hiddenSize+r.vHiddenSize}],p=u=>{let h=tt("output_q",l[0].dataType,t),w=tt("output_k",l[0].dataType,t),_=tt("output_v",l[0].dataType,t),P=$e("input",l[0].dataType,l[0].dims),A=$e("weight",l[1].dataType,l[1].dims),v=$e("bias",l[2].dataType,l[2].dims),y=P.type.storage,S=[{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 = ${o}u; + var tileInput: array<${y}, ${o*o}>; + var tileWeightQ: array<${y}, ${o*o}>; + var tileWeightK: array<${y}, ${o*o}>; + var tileWeightV: array<${y}, ${o*o}>; + ${u.registerUniforms(S).declareVariables(P,A,v,h,w,_)} + ${u.mainStart([o,o,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 = ${y}(0); + var valueK = ${y}(0); + var valueV = ${y}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:a,programUniforms:d}),getShaderSource:p},{inputs:l,outputs:[-1,-1,-1]})},jh=(e,r)=>{let t=Lh(e.inputs,r),[s,i,n]=Nh(e,t);return Oi(e,s,i,n,e.inputs[4],void 0,void 0,void 0,e.inputs[5],t)}}),Uh,Vh,Wh,Gh,Yb=je(()=>{ns(),pt(),yt(),Yt(),vt(),Uh=(e,r)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let t=(s,i,n)=>{let o=i.length;if(o!==s.length)throw new Error(`${n}: num dimensions != ${o}`);i.forEach((a,l)=>{if(a!==s[l])throw new Error(`${n}: dim[${l}] do not match`)})};if(e[0].dims.length>1){let s=r.format==="NHWC"?r.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,r.spatial?2:void 0);t(e[1].dims,s,"Invalid input scale"),t(e[2].dims,s,"Invalid input B"),t(e[3].dims,s,"Invalid input mean"),t(e[4].dims,s,"Invalid 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${S.mainStart()} + ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${A.offsetToIndices(`global_idx * ${o}`)}; + ${v()} + let scale = ${h.getByOffset("cOffset")}; + let bias = ${w.getByOffset("cOffset")}; + let inputMean = ${_.getByOffset("cOffset")}; + let inputVar = ${P.getByOffset("cOffset")}; + let x = ${u.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${A.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${r.epsilon}_${r.format}_${s}_${o}`,inputDependencies:d?["rank","type","type","type","type"]:void 0},getShaderSource:y,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d?[{type:12,data:l},...nt(n)]:[{type:12,data:l}]})}},Wh=e=>Dt(e),Gh=(e,r)=>{let{inputs:t,outputCount:s}=e,i=Wh({...r,outputCount:s});if(Vt.webgpu.validateInputContent&&Uh(t,i),r.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Vh(t,i))}}),Kh,Hh,qh,Zb=je(()=>{yt(),vt(),Kh=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Hh=e=>{let r=e[0].dims,t=e[0].dims[2],s=xe.size(r)/4,i=e[0].dataType,n=$e("input",i,r,4),o=$e("bias",i,[t],4),a=$e("residual",i,r,4),l=tt("output",i,r,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:d=>` + const channels = ${t}u / 4; + ${d.declareVariables(n,o,a,l)} + + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes(s)} + let value = ${n.getByOffset("global_idx")} + + ${o.getByOffset("global_idx % channels")} + ${a.getByOffset("global_idx")}; + ${l.setByOffset("global_idx","value")} + }`}},qh=e=>{Kh(e.inputs),e.compute(Hh(e.inputs))}}),Qh,$t,Xh,Jh,Yh,Zh,em,tm,rm,sm,nm,im,om,am,lm,dm,Fi,cm,Pa,um,pm,hm,mm,fm,_m,gm,wm,ym,Mm,bm,vm,xm,Tm,Em,Pm,Qd,Cm,Xd,Jd,Sm,$m,km,Im,Am,Om,Yd=je(()=>{pt(),yt(),Yt(),vt(),Qh=(e,r,t,s,i,n,o)=>{let a=Math.ceil(r/4),l="";typeof i=="string"?l=`${i}(a)`:l=i("a");let d=$e("inputData",t,[a],4),p=tt("outputData",s,[a],4),u=[{name:"vec_size",type:"u32"}];return o&&u.push(...o),` + ${e.registerUniforms(u).declareVariables(d,p)} + + ${n??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${d.getByOffset("global_idx")}; + ${p.setByOffset("global_idx",l)} + }`},$t=(e,r,t,s,i,n=e.dataType,o,a)=>{let l=[{type:12,data:Math.ceil(xe.size(e.dims)/4)}];return o&&l.push(...o),{name:r,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:d=>Qh(d,xe.size(e.dims),e.dataType,n,t,s,a),getRunData:d=>({outputs:[{dims:e.dims,dataType:n}],dispatchGroup:{x:Math.ceil(xe.size(d[0].dims)/64/4)},programUniforms:l})}},Xh=e=>{e.compute($t(e.inputs[0],"Abs","abs"))},Jh=e=>{e.compute($t(e.inputs[0],"Acos","acos"))},Yh=e=>{e.compute($t(e.inputs[0],"Acosh","acosh"))},Zh=e=>{e.compute($t(e.inputs[0],"Asin","asin"))},em=e=>{e.compute($t(e.inputs[0],"Asinh","asinh"))},tm=e=>{e.compute($t(e.inputs[0],"Atan","atan"))},rm=e=>{e.compute($t(e.inputs[0],"Atanh","atanh"))},sm=e=>Dt(e),nm=(e,r)=>{let t;switch(r.to){case 10:t="vec4";break;case 1:t="vec4";break;case 12:t="vec4";break;case 6:t="vec4";break;case 9:t="vec4";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${r.to}`)}e.compute($t(e.inputs[0],"Cast",t,void 0,r.cacheKey,r.to))},im=e=>{let r,t,s=e.length>=2&&e[1].data!==0,i=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:r=s?e[1].getFloat32Array()[0]:-34028234663852886e22,t=i?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:r=s?e[1].getUint16Array()[0]:64511,t=i?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return Dt({min:r,max:t})},om=(e,r)=>{let t=r||im(e.inputs),s=Tr(e.inputs[0].dataType);e.compute($t(e.inputs[0],"Clip",i=>`clamp(${i}, vec4<${s}>(uniforms.min), vec4<${s}>(uniforms.max))`,void 0,t.cacheKey,void 0,[{type:e.inputs[0].dataType,data:t.min},{type:e.inputs[0].dataType,data:t.max}],[{name:"min",type:s},{name:"max",type:s}]),{inputs:[0]})},am=e=>{e.compute($t(e.inputs[0],"Ceil","ceil"))},lm=e=>{e.compute($t(e.inputs[0],"Cos","cos"))},dm=e=>{e.compute($t(e.inputs[0],"Cosh","cosh"))},Fi=e=>Dt(e),cm=(e,r)=>{let t=Tr(e.inputs[0].dataType);e.compute($t(e.inputs[0],"Elu",s=>`elu_vf32(${s})`,` + const elu_alpha_ = ${t}(${r.alpha}); + + fn elu_f32(a: ${t}) -> ${t} { + return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${t}>) -> vec4<${t}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,r.cacheKey))},Pa=(e="f32")=>` +const r0: ${e} = 0.3275911; +const r1: ${e} = 0.254829592; +const r2: ${e} = -0.284496736; +const r3: ${e} = 1.421413741; +const r4: ${e} = -1.453152027; +const r5: ${e} = 1.061405429; + +fn erf_vf32(v: vec4<${e}>) -> vec4<${e}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,um=e=>{let r=Tr(e.inputs[0].dataType);e.compute($t(e.inputs[0],"Erf",t=>`erf_vf32(${t})`,Pa(r)))},pm=e=>{e.compute($t(e.inputs[0],"Exp","exp"))},hm=e=>{e.compute($t(e.inputs[0],"Floor","floor"))},mm=e=>{let r=Tr(e.inputs[0].dataType);e.compute($t(e.inputs[0],"Gelu",t=>`0.5 * ${t} * (1.0 + erf_vf32(${t} * 0.7071067811865475))`,Pa(r)))},fm=(e,r)=>{let t=Tr(e.inputs[0].dataType);e.compute($t(e.inputs[0],"LeakyRelu",s=>`select(leaky_relu_alpha_ * ${s}, ${s}, ${s} >= vec4<${t}>(0.0))`,`const leaky_relu_alpha_ = ${t}(${r.alpha});`,r.cacheKey))},_m=e=>{e.compute($t(e.inputs[0],"Not",r=>`!${r}`))},gm=e=>{e.compute($t(e.inputs[0],"Neg",r=>`-${r}`))},wm=e=>{e.compute($t(e.inputs[0],"Reciprocal",r=>`1.0/${r}`))},ym=e=>{let r=Tr(e.inputs[0].dataType);e.compute($t(e.inputs[0],"Relu",t=>`select(vec4<${r}>(0.0), ${t}, ${t} > vec4<${r}>(0.0))`))},Mm=e=>{e.compute($t(e.inputs[0],"Sigmoid",r=>`(1.0 / (1.0 + exp(-${r})))`))},bm=e=>Dt(e),vm=(e,r)=>{let t=Tr(e.inputs[0].dataType);e.compute($t(e.inputs[0],"HardSigmoid",s=>`max(vec4<${t}>(0.0), min(vec4<${t}>(1.0), ${r.alpha} * ${s} + vec4<${t}>(${r.beta})))`,void 0,r.cacheKey))},xm=e=>{e.compute($t(e.inputs[0],"Sin","sin"))},Tm=e=>{e.compute($t(e.inputs[0],"Sinh","sinh"))},Em=e=>{e.compute($t(e.inputs[0],"Sqrt","sqrt"))},Pm=e=>{e.compute($t(e.inputs[0],"Tan","tan"))},Qd=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Cm=e=>{e.compute($t(e.inputs[0],"Tanh",Qd))},Xd=(e="f32")=>` +const fast_gelu_a: ${e} = 0.5; +const fast_gelu_b: ${e} = 0.7978845608028654; +const fast_gelu_c: ${e} = 0.035677408136300125; + +fn tanh_v(v: vec4<${e}>) -> vec4<${e}> { + return ${Qd("v")}; +} +`,Jd=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Sm=e=>{let r=Tr(e.inputs[0].dataType);e.compute($t(e.inputs[0],"FastGelu",Jd,Xd(r),void 0,e.inputs[0].dataType))},$m=(e,r)=>{let t=Tr(e.inputs[0].dataType);return e.compute($t(e.inputs[0],"ThresholdedRelu",s=>`select(vec4<${t}>(0.0), ${s}, ${s} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${t}>(${r.alpha});`,r.cacheKey)),0},km=e=>{e.compute($t(e.inputs[0],"Log","log"))},Im=(e,r)=>` +const alpha = vec4<${e}>(${r}); +const one = ${e}(1.0); +const zero = ${e}(0.0); + +fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> { + let v = x *alpha; + var x1 : vec4<${e}>; + for (var i = 0; i < 4; i = i + 1) { + if (v[i] >= zero) { + x1[i] = one / (one + exp(-v[i])); + } else { + x1[i] = one - one / (one + exp(v[i])); + } + } + return x * x1; +} +`,Am=e=>`quick_gelu_impl(${e})`,Om=(e,r)=>{let t=Tr(e.inputs[0].dataType);e.compute($t(e.inputs[0],"QuickGelu",Am,Im(t,r.alpha),r.cacheKey,e.inputs[0].dataType))}}),Fm,Dm,Lm,ev=je(()=>{yt(),vt(),Yd(),Fm=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")},Dm=e=>{let r=e[0].dims.slice();r[2]=r[2]/2;let t=$e("input",e[0].dataType,e[0].dims,4),s=$e("bias",e[0].dataType,[e[0].dims[2]],4),i=tt("output",e[0].dataType,r,4),n=xe.size(r)/4,o=lr(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:a=>` + const M_SQRT2 = sqrt(2.0); + const halfChannels = ${e[0].dims[2]/4/2}u; + + ${a.declareVariables(t,s,i)} + + ${Pa(o)} + + ${a.mainStart()} + ${a.guardAgainstOutOfBoundsWorkgroupSizes(n)} + let biasIdx = global_idx % halfChannels; + let batchIndex = global_idx / halfChannels; + let inputOffset = biasIdx + batchIndex * halfChannels * 2; + let valueLeft = input[inputOffset] + bias[biasIdx]; + let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${i.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Lm=e=>{Fm(e.inputs),e.compute(Dm(e.inputs))}}),zm,Bm,ds,Rm,Nm,jm,Um,Vm,Wm,Gm,Km,Hm,qm,tv=je(()=>{pt(),yt(),vt(),zm=(e,r,t,s,i,n,o,a,l,d,p,u)=>{let h,w;typeof a=="string"?h=w=(y,S)=>`${a}((${y}),(${S}))`:typeof a=="function"?h=w=a:(h=a.scalar,w=a.vector);let _=tt("outputData",p,s.length,4),P=$e("aData",l,r.length,4),A=$e("bData",d,t.length,4),v;if(i)if(n){let y=xe.size(r)===1,S=xe.size(t)===1,x=r.length>0&&r[r.length-1]%4===0,g=t.length>0&&t[t.length-1]%4===0;y||S?v=_.setByOffset("global_idx",w(y?`${P.type.value}(${P.getByOffset("0")}.x)`:P.getByOffset("global_idx"),S?`${A.type.value}(${A.getByOffset("0")}.x)`:A.getByOffset("global_idx"))):v=` + let outputIndices = ${_.offsetToIndices("global_idx * 4u")}; + let offsetA = ${P.broadcastedIndicesToOffset("outputIndices",_)}; + let offsetB = ${A.broadcastedIndicesToOffset("outputIndices",_)}; + ${_.setByOffset("global_idx",w(o||x?P.getByOffset("offsetA / 4u"):`${P.type.value}(${P.getByOffset("offsetA / 4u")}[offsetA % 4u])`,o||g?A.getByOffset("offsetB / 4u"):`${A.type.value}(${A.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else v=_.setByOffset("global_idx",w(P.getByOffset("global_idx"),A.getByOffset("global_idx")));else{if(!n)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let y=(S,x,g="")=>{let M=`aData[indexA${x}][componentA${x}]`,E=`bData[indexB${x}][componentB${x}]`;return` + let outputIndices${x} = ${_.offsetToIndices(`global_idx * 4u + ${x}u`)}; + let offsetA${x} = ${P.broadcastedIndicesToOffset(`outputIndices${x}`,_)}; + let offsetB${x} = ${A.broadcastedIndicesToOffset(`outputIndices${x}`,_)}; + let indexA${x} = offsetA${x} / 4u; + let indexB${x} = offsetB${x} / 4u; + let componentA${x} = offsetA${x} % 4u; + let componentB${x} = offsetB${x} % 4u; + ${S}[${x}] = ${g}(${h(M,E)}); + `};p===9?v=` + var data = vec4(0); + ${y("data",0,"u32")} + ${y("data",1,"u32")} + ${y("data",2,"u32")} + ${y("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:v=` + ${y("outputData[global_idx]",0)} + ${y("outputData[global_idx]",1)} + ${y("outputData[global_idx]",2)} + ${y("outputData[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(P,A,_)} + + ${u??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${v} + }`},Bm=(e,r,t,s,i,n,o=t.dataType)=>{let a=t.dims.map(P=>Number(P)??1),l=s.dims.map(P=>Number(P)??1),d=!xe.areEqual(a,l),p=a,u=xe.size(a),h=!1,w=!1,_=[d];if(d){let P=Nn.calcShape(a,l,!1);if(!P)throw new Error("Can't perform binary op on the given tensors");p=P.slice(),u=xe.size(p);let A=xe.size(a)===1,v=xe.size(l)===1,y=a.length>0&&a[a.length-1]%4===0,S=l.length>0&&l[l.length-1]%4===0;_.push(A),_.push(v),_.push(y),_.push(S);let x=1;for(let g=1;gP.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:P=>zm(P,a,l,p,h,d,w,i,t.dataType,s.dataType,o,n),getRunData:()=>({outputs:[{dims:p,dataType:o}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:Math.ceil(xe.size(p)/4)},...nt(a,l,p)]})}},ds=(e,r,t,s,i,n)=>{e.compute(Bm(r,i??"",e.inputs[0],e.inputs[1],t,s,n))},Rm=e=>{ds(e,"Add",(r,t)=>`${r}+${t}`)},Nm=e=>{ds(e,"Div",(r,t)=>`${r}/${t}`)},jm=e=>{ds(e,"Equal",{scalar:(r,t)=>`u32(${r}==${t})`,vector:(r,t)=>`vec4(${r}==${t})`},void 0,void 0,9)},Um=e=>{ds(e,"Mul",(r,t)=>`${r}*${t}`)},Vm=e=>{let r=$e("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;ds(e,"Pow",{scalar:(t,s)=>`pow_custom(${t},${s})`,vector:(t,s)=>`pow_vector_custom(${t},${s})`},` + fn pow_custom(a : ${r}, b : ${r}) -> ${r} { + if (b == ${r}(0.0)) { + return ${r}(1.0); + } else if (a < ${r}(0.0) && f32(b) != floor(f32(b))) { + return ${r}(pow(f32(a), f32(b))); // NaN + } + return select(sign(a), ${r}(1.0), round(f32(abs(b) % ${r}(2.0))) != 1.0) * ${r}(${r==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); + } + fn pow_vector_custom(a : vec4<${r}>, b : vec4<${r}>) -> vec4<${r}> { + // TODO: implement vectorized pow + return vec4<${r}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); + } + `)},Wm=e=>{ds(e,"Sub",(r,t)=>`${r}-${t}`)},Gm=e=>{ds(e,"Greater",{scalar:(r,t)=>`u32(${r}>${t})`,vector:(r,t)=>`vec4(${r}>${t})`},void 0,void 0,9)},Km=e=>{ds(e,"Less",{scalar:(r,t)=>`u32(${r}<${t})`,vector:(r,t)=>`vec4(${r}<${t})`},void 0,void 0,9)},Hm=e=>{ds(e,"GreaterOrEqual",{scalar:(r,t)=>`u32(${r}>=${t})`,vector:(r,t)=>`vec4(${r}>=${t})`},void 0,void 0,9)},qm=e=>{ds(e,"LessOrEqual",{scalar:(r,t)=>`u32(${r}<=${t})`,vector:(r,t)=>`vec4(${r}<=${t})`},void 0,void 0,9)}}),Qm,Xm,Jm,Ym,Zm,ef,rv=je(()=>{pt(),yt(),Yt(),vt(),Qm=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");let t=0,s=e[t],i=s.dataType,n=s.dims.length;e.forEach((o,a)=>{if(a!==t){if(o.dataType!==i)throw new Error("input tensors should be one type");if(o.dims.length!==n)throw new Error("input tensors should have the same shape");o.dims.forEach((l,d)=>{if(d!==r&&l!==s.dims[d])throw new Error("non concat dimensions must match")})}})},Xm=(e,r)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${r}); + for (var i: u32 = 0u; i < ${e}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${e}u; + }`,Jm=(e,r)=>{let t=e.length,s=[];for(let i=0;i{let i=xe.size(t),n=new Array(e.length),o=new Array(e.length),a=0,l=[],d=[],p=[{type:12,data:i}];for(let P=0;P`uniforms.sizeInConcatAxis${P}`).join(","),_=P=>` + + ${(()=>{P.registerUniform("outputSize","u32");for(let A=0;A(${w}); + ${h} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${Jm(o,u)} + }`;return{name:"Concat",shaderCache:{hint:`${r}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:t,dataType:s}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:p}),getShaderSource:_}},Zm=(e,r)=>{let t=e.inputs,s=t[0].dims,i=xe.normalizeAxis(r.axis,s.length);Qm(t,i);let n=s.slice();n[i]=t.reduce((a,l)=>a+(l.dims.length>i?l.dims[i]:0),0);let o=t.filter(a=>xe.size(a.dims)>0);e.compute(Ym(o,i,n,t[0].dataType),{inputs:o})},ef=e=>Dt({axis:e.axis})}),gn,wn,yn,Zd,Mn=je(()=>{pt(),yt(),gn=(e,r,t="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${r}(0.0));`;case"Sigmoid":return`value = (${r}(1.0) / (${r}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${r}(${t}(uniforms.clip_min)), ${r}(${t}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${r}(0.0), min(${r}(1.0), ${t}(uniforms.alpha) * value + ${t}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${t}(uniforms.alpha) * value, value, value >= ${r}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value)); + value = sign(value) * (1.0 - e2x) / (1.0 + e2x); + `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},wn=(e,r)=>{e.activation==="Clip"?r.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?r.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&r.push({type:1,data:e.alpha})},yn=(e,r)=>{e.activation==="Clip"?r.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?r.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&r.push({name:"alpha",type:"f32"})},Zd=e=>{let r=(e==null?void 0:e.activation)||"";if(r==="HardSigmoid"){let[t,s]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:r,alpha:t,beta:s}}else if(r==="Clip"){let[t,s]=(e==null?void 0:e.activation_params)||[Dp,Lp];return{activation:r,clipMax:s,clipMin:t}}else if(r==="LeakyRelu"){let[t]=(e==null?void 0:e.activation_params)||[.01];return{activation:r,alpha:t}}return{activation:r}}}),hr,tf,ec=je(()=>{hr=(e,r)=>{switch(e){case 1:return r;case 2:return`vec2<${r}>`;case 3:return`vec3<${r}>`;case 4:return`vec4<${r}>`;default:throw new Error(`${e}-component is not supported.`)}},tf=e=>` + ${e?"value = value + getBiasByOutputCoords(coords);":""} + `}),rf,sv=je(()=>{rf=e=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); +} +`}),Di,tc,rc=je(()=>{pt(),yt(),vt(),Mn(),Di=(e,r,t,s,i)=>{let n=s-t;return` + ${Array.from({length:t}).map((o,a)=>` + if (${rt(r.shape,a,r.rank)} != 1) { + ${r.indicesSet(e,a,rt(i,a+n,s))} + } else { + ${r.indicesSet(e,a,0)} + }`).join("")} +`},tc=(e,r,t,s,i=!1,n)=>{let o=e[0].dims,a=e[1].dims,l=o[o.length-2],d=a[a.length-1],p=o[o.length-1],u=Xt(d),h=Xt(p),w=Xt(l),_=xe.size(t)/u/w,P=e.length>2,A=s?s.slice(0,-2):t.slice(0,-2),v=[xe.size(A),l,d],y=[{type:12,data:_},{type:12,data:l},{type:12,data:d},{type:12,data:p}];wn(r,y),y.push(...nt(A,o,a)),P&&y.push(...nt(e[2].dims)),y.push(...nt(v));let S=x=>{let g=Ud("batch_dims",e[0].dataType,A.length),M=$e("a",e[0].dataType,o.length,h),E=$e("b",e[1].dataType,a.length,u),k=tt("output",e[0].dataType,v.length,u),B=lr(k.type.tensor),R=gn(r,k.type.value,B),J=[M,E],q="";if(P){let H=i?u:1;J.push($e("bias",e[2].dataType,e[2].dims.length,H)),q=`${i?`value += bias[col / ${H}];`:`value += ${k.type.value}(bias[row + i]);`}`}let V=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];yn(r,V);let Y=()=>{let H=`var a_data: ${M.type.value};`;for(let Q=0;Q; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${h}) { + ${Y()} + } + for (var i = 0u; i < ${w}u; i++) { + var value = values[i]; + ${q} + ${R} + let cur_indices = ${k.type.indices}(batch, row + i, col); + let offset = ${k.indicesToOffset("cur_indices")}; + ${k.setByOffset(`offset / ${u}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${r.activation};${u};${h};${w};${i}`,inputDependencies:P?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:y}),getShaderSource:S}}}),sf,nf,sc,nc,of,ic,af,Ca,oc=je(()=>{pt(),yt(),vt(),Mn(),rc(),ec(),sf=(e,r)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart / innerElementSize + inputCol${r?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRow + innerRow, + kStart / innerElementSize + inputCol${r?", batchIndices":""}); + `,nf=(e,r)=>e?` + let ACached0 = mm_Asub[k * innerElementSize][localRow]; + let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; + let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; + ${r===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]; + ${r===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]; + ${r===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} + }`,sc=(e,r,t="f32",s,i=!1,n=32,o=!1,a=32)=>{let l=r[1]*e[1],d=r[0]*e[0],p=i?l:n,u=i?n:l,h=p/r[0],w=n/r[1];if(!((i&&h===4&&e[1]===4||!i&&(h===3||h===4))&&p%r[0]===0&&n%r[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${h} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${h} must be 3 or 4. + tileAWidth ${p} must be divisible by workgroupSize[0]${r[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${r[1]}. colPerThread ${e[0]} must be 4.`);return` +var mm_Asub: array, ${p/h}>, ${u}>; +var mm_Bsub: array, ${d/e[0]}>, ${n}>; + +const rowPerThread = ${e[1]}; +const colPerThread = ${e[0]}; +const innerElementSize = ${h}; +const tileInner = ${n}; + +@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[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 = ${o?"0":"i32(globalId.z)"}; + ${s?`let batchIndices = ${s.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${l}; + + let num_tiles = ${o?`${Math.ceil(a/n)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${o?`i32(globalId.z) * ${a}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${w}; + 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; + ${sf(i,s)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${s?", 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]; + ${h===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${nf(i,h)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},nc=(e,r)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${r?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${r?", batchIndices":""}); + `,of=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",ic=(e,r,t="f32",s,i=!1,n=32,o=!1,a=32,l=!1)=>{let d=e[1]*r[1],p=e[0]*r[0],u=i?d:n,h=i?n:d;if(!(h%r[1]===0&&u%r[0]===0&&n%r[1]===0))throw new Error(`tileAHight ${h} must be divisible by workgroupSize[1]${r[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${r[0]}, tileInner ${n} must be divisible by workgroupSize[1]${r[1]}`);let w=h/r[1],_=u/r[0],P=n/r[1],A=l?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${d}; + let globalColStart = i32(workgroupId.x) * ${p}; + + // 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 < ${h}; inputRow = inputRow + ${r[1]}) { + for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${r[0]}) { + ${nc(i,s)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${n}; inputRow = inputRow + ${r[1]}) { + for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${r[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalColStart + inputCol${s?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${t}, 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 * ${r[0]}]; + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${r[1]}];`:`mm_Asub[localRow + innerRow * ${r[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 * ${r[1]}; + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let gCol = globalColStart + localCol + innerCol * ${r[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) * ${d}; + +let tileRowA = i32(localId.y) * ${w}; +let tileColA = i32(localId.x) * ${_}; +let tileRowB = i32(localId.y) * ${P}; +// 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 < ${w}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${_}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${nc(i,s)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${P}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${s?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${t}, 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) { + ${of(i)} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; + } + } + } + + workgroupBarrier(); +} + +for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + mm_write(batch, globalRow + innerRow, globalCol + innerCol, + acc[innerRow][innerCol]); + } +} +`;return` + var mm_Asub : array, ${h}>; + var mm_Bsub : array, ${n}>; + const rowPerThread = ${e[1]}; + const colPerThread = ${e[0]}; + const tileInner = ${n}; + +@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let batch = ${o?"0":"i32(globalId.z)"}; + ${s?`let batchIndices = ${s.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${o?`${Math.ceil(a/n)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${o?`i32(globalId.z) * ${a}`:"0"}; + + var acc : array, rowPerThread>; + ${A} + } +`},af=(e,r,t,s,i=!1)=>{let[n,o,a,l]=s,d=lr(s[0].type.tensor);return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${n.type.indices}) -> ${hr(e,d)} { + var value = ${hr(e,d)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + var aIndices: ${o.type.indices}; + ${Di("aIndices",o,o.rank-2,n.rank,"batchIndices")} + ${o.indicesSet("aIndices",o.rank-2,"u32(row)")} + ${o.indicesSet("aIndices",o.rank-1,"u32(colIn)")} + value = ${o.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${n.type.indices}) -> ${hr(e,d)} { + var value = ${hr(e,d)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + var bIndices: ${a.type.indices}; + ${Di("bIndices",a,a.rank-2,n.rank,"batchIndices")} + ${a.indicesSet("bIndices",a.rank-2,"u32(row)")} + ${a.indicesSet("bIndices",a.rank-1,"u32(colIn)")} + value = ${a.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${hr(e,d)}) { + let col = colIn * ${e}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueIn; + let coords = vec3(batch, row, colIn); + ${r?`value = value + ${i?"bias[colIn]":`${hr(e,d)}(bias[row])`};`:""} + ${t} + ${l.setByIndices("vec3(coords)","value")} + } + } + `},Ca=(e,r,t,s,i=!1,n)=>{let o=e[0].dims,a=e[1].dims,l=o.slice(0,-2),d=a.slice(0,-2),p=s?s.slice(0,-2):t.slice(0,-2),u=xe.size(p),h=o[o.length-2],w=o[o.length-1],_=a[a.length-1],P=w%4===0&&_%4===0,A=h<=8?[4,1,1]:[4,4,1],v=[8,8,1],y=[Math.ceil(_/v[0]/A[0]),Math.ceil(h/v[1]/A[1]),Math.ceil(u/v[2]/A[2])],S=P?4:1,x=[...l,h,w/S],g=x.length,M=[...d,w,_/S],E=M.length,k=[u,h,_/S],B=[{type:6,data:h},{type:6,data:_},{type:6,data:w}];wn(r,B),B.push(...nt(p,x,M));let R=["rank","rank"],J=e.length>2;J&&(B.push(...nt(e[2].dims)),R.push("rank")),B.push(...nt(k));let q=V=>{let Y=p.length,H=Ud("batchDims",e[0].dataType,Y,1),Q=lr(e[0].dataType),ie=$e("a",e[0].dataType,g,S),le=$e("b",e[1].dataType,E,S),ae=tt("result",e[0].dataType,k.length,S),ge=[ie,le];if(J){let X=i?S:1;ge.push($e("bias",e[2].dataType,e[2].dims.length,X))}let N=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];yn(r,N);let F=lr(ae.type.tensor),G=gn(r,ae.type.value,F),ne=af(S,J,G,[H,ie,le,ae],i);return` + ${V.registerUniforms(N).registerInternalVariables(H).declareVariables(...ge,ae)} + ${ne} + ${P?sc(A,v,Q,H):ic(A,v,Q,H)} + `};return{name:"MatMul",shaderCache:{hint:`${A};${r.activation};${P};${i}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:n?n(t):t,dataType:e[0].dataType}],dispatchGroup:{x:y[0],y:y[1],z:y[2]},programUniforms:B}),getShaderSource:q}}}),lf,df,nv=je(()=>{pt(),Is(),vt(),Mn(),ec(),sv(),oc(),lf=(e,r,t,s,i=!1,n,o=4,a=4,l=4,d="f32")=>{let p=B=>{switch(B){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${d}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${B} is not supported.`)}},u=B=>{switch(B){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 ${B} is not supported.`)}},h=e?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,w=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,_=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",P=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",A=e?"row":"col",v=e?"col":"row",y=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${A} / outWidth; + let outCol = ${A} % outWidth; + + let WRow = ${v} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${v} / 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 = ${v} % inChannels; + var resData = ${hr(o,d)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${_} && xCol >= 0 && xCol < ${P}) { + ${h} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${p(o)} + } + return resData;`,S=e?r&&s?` + let col = colIn * ${o}; + ${y}`:` + let col = colIn * ${o}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${y} + } + return ${hr(o,d)}(0.0);`:s&&t?` + let col = colIn * ${o}; + ${y}`:` + let col = colIn * ${o}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${y} + } + return ${hr(o,d)}(0.0);`,x=e?s&&t?u(a):` + let col = colIn * ${a}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${u(a)} + } + return ${hr(a,d)}(0.0);`:` + let col = colIn * ${a}; + if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) { + ${u(a)} + } + return ${hr(a,d)}(0.0);`,g=hr(l,d),M=hr(e?o:a,d),E=hr(e?a:o,d),k=gn(n,g,d);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${M} { + ${e?S:x} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} { + ${e?x:S} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${g}) { + let col = colIn * ${l}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) + { + var value = valueIn; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${w} + ${tf(i)} + ${k} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},df=(e,r,t,s,i,n,o,a,l)=>{let d=r.format==="NHWC",p=d?e[0].dims[3]:e[0].dims[1],u=t[0],h=d?t[2]:t[3],w=d?t[1]:t[2],_=d?t[3]:t[1],P=d&&(p%4===0||p%3===0)&&_%4===0,A=d?_:h*w,v=d?h*w:_,y=[8,8,1],S=s<=8?[4,1,1]:[4,4,1],x=[Math.ceil(A/y[0]/S[0]),Math.ceil(v/y[1]/S[1]),Math.ceil(u/y[2]/S[2])];St("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${x}`);let g=P?d&&p%4!==0?3:4:1,M=y[1]*S[1],E=y[0]*S[0],k=Math.max(y[0]*g,y[1]),B=s%M===0,R=i%E===0,J=n%k===0,q=P?[g,4,4]:[1,1,1],V=[{type:6,data:s},{type:6,data:i},{type:6,data:n},{type:6,data:[r.pads[0],r.pads[1]]},{type:6,data:r.strides},{type:6,data:r.dilations}];wn(r,V),V.push(...nt(e[0].dims,e[1].dims));let Y=["rank","rank"];o&&(V.push(...nt(e[2].dims)),Y.push("rank")),V.push(...nt(t));let H=Q=>{let ie=[{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}];yn(r,ie);let le=P?4:1,ae=lr(e[0].dataType),ge=` + fn setOutputAtIndex(flatIndex : i32, value : ${P?`vec4<${ae}>`:ae}) { + result[flatIndex] = ${P?`vec4<${ae}>`:ae}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${P?`vec4<${ae}>`:ae}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${P?"/ 4":""}, value); + }`,N=$e("x",e[0].dataType,e[0].dims.length,g===3?1:g),F=$e("w",e[1].dataType,e[1].dims.length,le),G=[N,F],ne=tt("result",e[0].dataType,t.length,le);if(o){let X=$e("bias",e[2].dataType,e[2].dims.length,le);G.push(X),ge+=` + fn getBiasByOutputCoords(coords : vec4) -> ${P?`vec4<${ae}>`:ae} { + return bias[coords.${d?"w":"y"}${P?"/ 4":""}]; + }`}return` + ${rf("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 }; + ${Q.registerUniforms(ie).declareVariables(...G,ne)} + ${ge} + ${lf(d,B,R,J,o,r,q[0],q[1],q[2],ae)} + ${P?sc(S,y,ae,void 0,!d,k):ic(S,y,ae,void 0,!d,k,!1,void 0,a)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${r.cacheKey};${g};${P};${B};${R};${J};${M};${E};${k}`,inputDependencies:Y},getRunData:()=>({outputs:[{dims:l?l(t):t,dataType:e[0].dataType}],dispatchGroup:{x:x[0],y:x[1],z:x[2]},programUniforms:V}),getShaderSource:H}}}),cf,ac,Li,uf,lc,pf,hf,mf,iv=je(()=>{pt(),Is(),yt(),vt(),Mn(),ec(),cf=e=>{let r=1;for(let t=0;ttypeof e=="number"?[e,e,e]:e,Li=(e,r)=>r<=1?e:e+(e-1)*(r-1),uf=(e,r,t,s=1)=>{let i=Li(r,s);return Math.floor((e[0]*(t-1)-t+i)/2)},lc=(e,r,t,s,i)=>{i==null&&(i=uf(e,r[0],s[0]));let n=[0,0,0,t];for(let o=0;o<3;o++)e[o]+2*i>=r[o]&&(n[o]=Math.trunc((e[o]-r[o]+2*i)/s[o]+1));return n},pf=(e,r,t,s,i,n,o,a,l,d)=>{let p,u,h,w;if(e==="VALID"&&(e=0),typeof e=="number"){p={top:e,bottom:e,left:e,right:e,front:e,back:e};let _=lc([r,t,s,1],[a,l,d],1,[i,n,o],e);u=_[0],h=_[1],w=_[2]}else if(Array.isArray(e)){if(!e.every((P,A,v)=>P===v[0]))throw Error(`Unsupported padding parameter: ${e}`);p={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let _=lc([r,t,s,1],[a,l,d],1,[i,n,o],e[0]);u=_[0],h=_[1],w=_[2]}else if(e==="SAME_UPPER"){u=Math.ceil(r/i),h=Math.ceil(t/n),w=Math.ceil(s/o);let _=(u-1)*i+a-r,P=(h-1)*n+l-t,A=(w-1)*o+d-s,v=Math.floor(_/2),y=_-v,S=Math.floor(P/2),x=P-S,g=Math.floor(A/2),M=A-g;p={top:S,bottom:x,left:g,right:M,front:v,back:y}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:u,outHeight:h,outWidth:w}},hf=(e,r,t,s,i,n=!1,o="channelsLast")=>{let a,l,d,p,u;if(o==="channelsLast")[a,l,d,p,u]=e;else if(o==="channelsFirst")[a,u,l,d,p]=e;else throw new Error(`Unknown dataFormat ${o}`);let[h,,w,_,P]=r,[A,v,y]=ac(t),[S,x,g]=ac(s),M=Li(w,S),E=Li(_,x),k=Li(P,g),{padInfo:B,outDepth:R,outHeight:J,outWidth:q}=pf(i,l,d,p,A,v,y,M,E,k),V=n?h*u:h,Y=[0,0,0,0,0];return o==="channelsFirst"?Y=[a,V,R,J,q]:o==="channelsLast"&&(Y=[a,R,J,q,V]),{batchSize:a,dataFormat:o,inDepth:l,inHeight:d,inWidth:p,inChannels:u,outDepth:R,outHeight:J,outWidth:q,outChannels:V,padInfo:B,strideDepth:A,strideHeight:v,strideWidth:y,filterDepth:w,filterHeight:_,filterWidth:P,effectiveFilterDepth:M,effectiveFilterHeight:E,effectiveFilterWidth:k,dilationDepth:S,dilationHeight:x,dilationWidth:g,inShape:e,outShape:Y,filterShape:r}},mf=(e,r,t,s,i,n)=>{let o=n==="channelsLast";o?e[0].dims[3]:e[0].dims[1];let a=[64,1,1],l={x:t.map((A,v)=>v)},d=[Math.ceil(cf(l.x.map(A=>t[A]))/a[0]),1,1];St("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${d}`);let p=1,u=xe.size(t),h=[{type:12,data:u},{type:12,data:s},{type:12,data:i},{type:12,data:r.strides},{type:12,data:r.dilations}];wn(r,h),h.push(...nt(e[0].dims,e[1].dims));let w=["rank","rank"],_=e.length===3;_&&(h.push(...nt(e[2].dims)),w.push("rank")),h.push(...nt(t));let P=A=>{let v=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:s.length},{name:"pads",type:"u32",length:i.length},{name:"strides",type:"u32",length:r.strides.length},{name:"dilations",type:"u32",length:r.dilations.length}];yn(r,v);let y=1,S=lr(e[0].dataType),x=$e("x",e[0].dataType,e[0].dims.length,p),g=$e("W",e[1].dataType,e[1].dims.length,y),M=[x,g],E=tt("result",e[0].dataType,t.length,y),k="";if(_){let J=$e("bias",e[2].dataType,e[2].dims.length,y);M.push(J),k+=` + fn getBiasByOutputCoords(coords : array) -> ${S} { + return bias[${o?rt("coords",4,5):rt("coords",1,5)}]; + }`}let B=hr(p,S),R=gn(r,B,S);return` + ${k} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${x.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${g.getByIndices("aIndices")}; + } + ${A.registerUniforms(v).declareVariables(...M,E)} + ${A.mainStart()} + ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${E.offsetToIndices("global_idx")}; + let batch = ${rt("coords",0,x.rank)}; + let d2 = ${o?rt("coords",x.rank-1,x.rank):rt("coords",1,x.rank)}; + let xFRCCorner = vec3(${o?rt("coords",1,x.rank):rt("coords",2,x.rank)}, + ${o?rt("coords",2,x.rank):rt("coords",3,x.rank)}, + ${o?rt("coords",3,x.rank):rt("coords",4,x.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${o?rt("uniforms.x_shape",1,x.rank):rt("uniforms.x_shape",2,x.rank)}; + let xShapeZ = ${o?rt("uniforms.x_shape",2,x.rank):rt("uniforms.x_shape",3,x.rank)}; + let xShapeW = ${o?rt("uniforms.x_shape",3,x.rank):rt("uniforms.x_shape",4,x.rank)}; + let xShapeU = ${o?rt("uniforms.x_shape",4,x.rank):rt("uniforms.x_shape",1,x.rank)}; + let inputDepthNearestVec4 = (xShapeU / 4) * 4; + let inputDepthVec4Remainder = xShapeU % 4; + + var value = 0.0; + for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) { + let xF = xFCorner + wF * uniforms.dilations[0]; + if (xF < 0 || xF >= xShapeY) { + continue; + } + + for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) { + let xR = xRCorner + wR * uniforms.dilations[1]; + if (xR < 0 || xR >= xShapeZ) { + continue; + } + + for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) { + let xC = xCCorner + wC * uniforms.dilations[2]; + if (xC < 0 || xC >= xShapeW) { + continue; + } + + for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) { + ${o?`let xValues = vec4( + getX(batch, xF, xR, xC, d1), + getX(batch, xF, xR, xC, d1 + 1), + getX(batch, xF, xR, xC, d1 + 2), + getX(batch, xF, xR, xC, d1 + 3)); + `:`let xValues = vec4( + getX(batch, d1, xF, xR, xC), + getX(batch, d1 + 1, xF, xR, xC), + getX(batch, d1 + 2, xF, xR, xC), + getX(batch, d1 + 3, xF, xR, xC)); + `} + let wValues = vec4( + getW(d2, d1, wF, wR, wC), + getW(d2, d1 + 1, wF, wR, wC), + getW(d2, d1 + 2, wF, wR, wC), + getW(d2, d1 + 3, wF, wR, wC)); + value += dot(xValues, wValues); + } + if (inputDepthVec4Remainder == 1) { + ${o?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4) + * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC) + * getW(d2, inputDepthNearestVec4, wF, wR, wC);`} + } else if (inputDepthVec4Remainder == 2) { + ${o?`let xValues = vec2( + getX(batch, xF, xR, xC, inputDepthNearestVec4), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)); + `:`let xValues = vec2( + getX(batch, inputDepthNearestVec4, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC)); + `} + let wValues = vec2( + getW(d2, inputDepthNearestVec4, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC)); + value += dot(xValues, wValues); + } else if (inputDepthVec4Remainder == 3) { + ${o?`let xValues = vec3( + getX(batch, xF, xR, xC, inputDepthNearestVec4), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)); + `:`let xValues = vec3( + getX(batch, inputDepthNearestVec4, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC)); + `} + let wValues = vec3( + getW(d2, inputDepthNearestVec4, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC)); + value += dot(xValues, wValues); + } + } + } + } + ${_?"value = value + getBiasByOutputCoords(coords)":""}; + ${R} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${r.cacheKey};${o};${p};${_}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:d[0],y:d[1],z:d[2]},programUniforms:h}),getShaderSource:P}}}),ff,_f,ov=je(()=>{pt(),yt(),vt(),Mn(),ff=(e,r,t,s)=>{let i=e.length>2,n=i?"value += b[output_channel];":"",o=e[0].dims,a=e[1].dims,l=r.format==="NHWC",d=l?t[3]:t[1],p=d/r.group,u=l&&p>=4?Xt(d):1,h=xe.size(t)/u,w=[{type:12,data:h},{type:12,data:r.dilations},{type:12,data:[r.strides[0],r.strides[1]]},{type:12,data:[r.pads[0],r.pads[1]]},{type:12,data:p}];wn(r,w),w.push(...nt(o,[a[0],a[1],a[2],a[3]/u]));let _=i?["rank","rank","rank"]:["rank","rank"];w.push(...nt([t[0],t[1],t[2],t[3]/u]));let P=A=>{let v=tt("output",e[0].dataType,t.length,u),y=lr(v.type.tensor),S=gn(r,v.type.value,y),x=$e("x",e[0].dataType,o.length),g=$e("w",e[1].dataType,a.length,u),M=[x,g];i&&M.push($e("b",e[2].dataType,e[2].dims,u));let E=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:r.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];yn(r,E);let k=l?` + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) { + continue; + } + + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) { + let input_channel = in_channel_offset + wInChannel; + let xVal = ${x.get("batch","xHeight","xWidth","input_channel")}; + let wVal = ${g.get("wHeight","wWidth","wInChannel","output_channel")}; + value += xVal * wVal; + } + } + } + `:` + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { + let input_channel = in_channel_offset + wInChannel; + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) { + continue; + } + + let xVal = ${x.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${g.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal * wVal; + } + } + } + `;return` + ${A.registerUniforms(E).declareVariables(...M,v)} + + ${A.mainStart()} + ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${v.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${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 * ${u} / uniforms.output_channels_per_group; + var in_channel_offset = group_id * uniforms.w_shape[${l?2:1}]; + + var value: ${v.type.value} = ${v.type.value}(0); + ${k} + ${n} + ${S} + ${v.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:`${r.cacheKey}_${u}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:s?s(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:w}),getShaderSource:P}},_f=(e,r,t,s)=>{let i=e.length>2,n=Xt(t[3]),o=Xt(t[2]),a=xe.size(t)/n/o,l=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/n],d=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/n],p=[t[0],t[1],t[2],t[3]/n],u=[{type:12,data:a},{type:6,data:[r.strides[0],r.strides[1]]},{type:6,data:[r.pads[0],r.pads[1]]}];wn(r,u),u.push(...nt(l,d,p));let h=(o-1)*r.strides[1]+d[1],w=_=>{let P=tt("output",e[0].dataType,p.length,n),A=lr(P.type.tensor),v=gn(r,P.type.value,A),y=$e("x",e[0].dataType,l.length,n),S=$e("w",e[1].dataType,d.length,n),x=[y,S];i&&x.push($e("b",e[2].dataType,e[2].dims,n));let g=i?"value += b[output_channel];":"",M=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return yn(r,M),` + ${_.registerUniforms(M).declareVariables(...x,P)} + ${_.mainStart()} + ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let width0 = uniforms.output_shape[3]; + let output_channel = global_idx % width0; + var index1 = global_idx / width0; + let width1 = uniforms.output_shape[2] / ${o}u; + let col = (index1 % width1) * ${o}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${y.type.value}, ${h}>; + var values: array<${P.type.value}, ${o}>; + let input_channel = output_channel; + // Use constant instead of uniform can give better performance for w's height/width. + for (var w_height: u32 = 0u; w_height < ${d[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] = ${y.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${y.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${d[1]}; w_width++) { + let w_val = ${S.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${o}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${o}u; i++) { + var value = values[i]; + ${g} + ${v} + ${P.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${r.cacheKey};${n};${o};${h};${d[0]};${d[1]}`,inputDependencies:i?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:s?s(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:u}),getShaderSource:w}}}),gf,Sa,wf,$a,dc,cc,yf,Mf,uc,av=je(()=>{yt(),nv(),iv(),oc(),ov(),Mn(),rc(),Ks(),gf=(e,r,t,s,i,n)=>{let o=e[0],a=e.slice(n?1:2,n?3:4),l=a.length,d=r[0],p=r.slice(2).map((h,w)=>h+(h-1)*(t[w]-1)),u=a.map((h,w)=>h+s[w]+s[w+l]).map((h,w)=>Math.floor((h-p[w]+i[w])/i[w]));return u.splice(0,0,o),u.splice(n?3:1,0,d),u},Sa=[2,3,1,0],wf=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let t=e[0].dims[r.format==="NHWC"?e[0].dims.length-1:1],s=e[1].dims[1]*r.group;if(t!==s)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(r.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(r.strides.length!==i)throw new Error(`strides should be ${i}D`);if(r.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},$a=(e,r)=>{let t=e.kernelShape.slice();t.length{let r=Zd(e),t=e.format,s=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,n=e.group,o=e.kernel_shape,a=e.pads,l=e.strides,d=e.w_is_const();return{autoPad:s,format:t,dilations:i,group:n,kernelShape:o,pads:a,strides:l,wIsConst:d,...r,cacheKey:`${e.format};${r.activation};`}},cc=(e,r,t,s)=>{let i=t.format==="NHWC",n=gf(r[0].dims,r[1].dims,t.dilations,t.pads,t.strides,i);if(t.group!==1){let M=[r[0]];if(i){let E=e.kernelCustomData.wT??e.compute(Nr(r[1],Sa),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=E),M.push(E)}else M.push(r[1]);r.length===3&&M.push(r[2]),!e.adapterInfo.isArchitecture("ampere")&&i&&r[1].dims[0]===t.group&&r[1].dims[1]===1&&t.dilations[0]===1&&t.dilations[1]===1?e.compute(_f(M,t,n,s),{inputs:M}):e.compute(ff(M,t,n,s),{inputs:M});return}let o=r.length===3,a=r[0].dims[i?1:2],l=r[0].dims[i?2:3],d=r[0].dims[i?3:1],p=r[1].dims[2],u=r[1].dims[3],h=n[i?1:2],w=n[i?2:3],_=n[i?3:1],P=i&&p===a&&u===l&&t.pads[0]===0&&t.pads[1]===0;if(P||p===1&&u===1&&t.dilations[0]===1&&t.dilations[1]===1&&t.strides[0]===1&&t.strides[1]===1&&t.pads[0]===0&&t.pads[1]===0){let M=n[0],E,k,B,R=[];if(i){let V=e.kernelCustomData.wT??e.compute(Nr(r[1],Sa),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];if(t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=V),P){let Y=a*l*d;E=r[0].reshape([1,M,Y]),k=V.reshape([1,Y,_]),B=[1,M,_]}else E=r[0].reshape([M,a*l,d]),k=V.reshape([1,d,_]),B=[M,h*w,_];R.push(E),R.push(k)}else E=r[0].reshape([M,d,a*l]),k=r[1].reshape([1,_,d]),B=[M,_,h*w],R.push(k),R.push(E);o&&R.push(r[2]);let J=B[2],q=R[0].dims[R[0].dims.length-1];J<8&&q<8?e.compute(tc(R,t,n,B,i,s),{inputs:R}):e.compute(Ca(R,t,n,B,i,s),{inputs:R});return}let A=!0,v=e.kernelCustomData.wT??e.compute(Nr(r[1],Sa),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=v);let y=[r[0],v];o&&y.push(r[2]);let S=i?h*w:_,x=i?_:h*w,g=p*u*d;e.compute(df(y,t,n,S,x,g,o,A,s),{inputs:y})},yf=(e,r)=>{let t=r.format==="NHWC",s=[e.inputs[0].reshape(t?[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&&s.push(e.inputs[2]);let i=[0,r.pads[0],0,r.pads[1]],n=[1].concat(r.strides),o=[1].concat(r.dilations),a=[1].concat(r.kernelShape),l=$a({...r,pads:i,strides:n,dilations:o,kernelShape:a},s);cc(e,s,l,d=>t?[d[0],d[2],d[3]]:[d[0],d[1],d[3]])},Mf=(e,r,t)=>{let s=t.format==="NHWC"?"channelsLast":"channelsFirst",i=$a(t,r),n=t.autoPad==="NOTSET"?t.pads:t.autoPad,o=hf(r[0].dims,r[1].dims,t.strides,t.dilations,n,!1,s);e.compute(mf(r,i,o.outShape,[o.filterDepth,o.filterHeight,o.filterWidth],[o.padInfo.front,o.padInfo.top,o.padInfo.left],s))},uc=(e,r)=>{if(wf(e.inputs,r),e.inputs[0].dims.length===3)yf(e,r);else if(e.inputs[0].dims.length===5)Mf(e,e.inputs,r);else{let t=$a(r,e.inputs);cc(e,e.inputs,t)}}}),bf,lv=je(()=>{pt(),Is(),yt(),vt(),bf=(e,r,t)=>{let s=e.length>2,i=r.outputShape,n=r.format==="NHWC",o=r.group,a=e[1].dims,l=a[2]/o,d=a[3],p=n?Xt(l):1,u=n&&d===1&&l>=4,h=u?Math.floor(l/4)*4:Math.floor(l/p)*p,w=l-h,_=n?Xt(d):1,P=n?d===1?p:_:1,A=xe.size(i)/_,v=[Math.ceil(A/64),1,1];St("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${v}`);let y=["rank","rank"],S=[r.strides[0],r.strides[1]],x=[r.kernelShape[n?1:2],r.kernelShape[n?2:3]],g=[r.dilations[0],r.dilations[1]],M=[x[0]+(r.dilations[0]<=1?0:(r.kernelShape[n?1:2]-1)*(r.dilations[0]-1)),x[1]+(r.dilations[1]<=1?0:(r.kernelShape[n?2:3]-1)*(r.dilations[1]-1))],E=[M[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),M[1]-1-Math.floor((r.pads[1]+r.pads[3])/2)],k=[{type:12,data:A},{type:12,data:S},{type:12,data:x},{type:12,data:g},{type:12,data:M},{type:6,data:E},{type:12,data:h},{type:12,data:l},{type:12,data:d},...nt(e[0].dims,e[1].dims)];s&&(k.push(...nt(e[2].dims)),y.push("rank")),k.push(...nt(i));let B=R=>{let J=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:S.length},{name:"filter_dims",type:"u32",length:x.length},{name:"dilations",type:"u32",length:x.length},{name:"effective_filter_dims",type:"u32",length:M.length},{name:"pads",type:"i32",length:E.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],q=lr(e[0].dataType),V=n?1:2,Y=n?2:3,H=n?3:1,Q=$e("W",e[1].dataType,e[1].dims.length,P),ie=$e("Dy",e[0].dataType,e[0].dims.length,p),le=[ie,Q];s&&le.push($e("bias",e[2].dataType,[i[H]].length,_));let ae=tt("result",e[0].dataType,i.length,_),ge=()=>{let G="";if(u)p===4?G+=` + let xValue = ${ie.getByOffset("x_offset")}; + let wValue = ${Q.getByOffset("w_offset")}; + dotProd = dotProd + dot(xValue, wValue); + x_offset += 1u; + w_offset += 1u;`:p===2?G+=` + dotProd = dotProd + dot(vec4<${q}>(${ie.getByOffset("x_offset")}, ${ie.getByOffset("x_offset + 1u")}), vec4<${q}>(${Q.getByOffset("w_offset")}, ${Q.getByOffset("w_offset + 1u")})); + x_offset += 2u; + w_offset += 2u;`:p===1&&(G+=` + dotProd = dotProd + dot(vec4<${q}>(${ie.getByOffset("x_offset")}, ${ie.getByOffset("x_offset + 1u")}, ${ie.getByOffset("x_offset + 2u")}, ${ie.getByOffset("x_offset + 3u")}), vec4<${q}>(${Q.getByOffset("w_offset")}, ${Q.getByOffset("w_offset + 1u")}, ${Q.getByOffset("w_offset + 2u")}, ${Q.getByOffset("w_offset + 3u")})); + x_offset += 4u; + w_offset += 4u;`);else if(G+=` + let xValue = ${n?ie.getByOffset(`${ie.indicesToOffset(`${ie.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}`):ie.get("batch","inputChannel","idyR","idyC")}; + `,p===1)G+=` + let w_offset = ${Q.indicesToOffset(`${Q.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)}; + let wValue = ${Q.getByOffset(`w_offset / ${P}`)}; + dotProd = dotProd + xValue * wValue;`;else for(let ne=0;ne{if(w===0)return"";if(!u)throw new Error(`packInputAs4 ${u} is not true.`);let G="";if(p===1){G+="dotProd = dotProd";for(let ne=0;ne(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 = ${ae.type.value}(0.0); + var wR: u32 = 0; + if (uniforms.dilations.x == 1) { + // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0 + wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner); + } + for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${q}(dyRCorner) + ${q}(wR)) / ${q}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${q}(uniforms.Dy_shape[${V}]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + var wC: u32 = 0; + if (uniforms.dilations.y == 1) { + // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0 + wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner); + } + for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { + if (wC % uniforms.dilations.y != 0) { + continue; + } + let dyC = (${q}(dyCCorner) + ${q}(wC)) / ${q}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${q}(uniforms.Dy_shape[${Y}]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC: u32 = u32(dyC); + var inputChannel = groupId * uniforms.input_channels_per_group; + ${u?` + var x_offset = ${ie.indicesToOffset(`${ie.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}; + var w_offset = ${Q.indicesToOffset(`${Q.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${P}; + `:""} + for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${u?4:p}) { + ${ge()} + inputChannel = inputChannel + ${u?4:p}; + } + ${N()} + wC = wC + uniforms.strides.y - 1; + } + wR = wR + uniforms.strides[0] - 1; + } + let value = dotProd${s?` + bias[d1 / ${_}]`:""}; + ${ae.setByOffset("global_idx","value")}; + `;return` + ${R.registerUniforms(J).declareVariables(...le,ae)} + ${R.mainStart()} + ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${F}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${r.cacheKey};${p}${P}${_}${u}${w}`,inputDependencies:y},getRunData:()=>({dispatchGroup:{x:v[0],y:v[1],z:v[2]},outputs:[{dims:t?t(i):i,dataType:e[0].dataType}],programUniforms:k}),getShaderSource:B}}}),vf,xf,Tf,pc,Ef,Pf,hc,Cf,Sf,dv=je(()=>{lv(),Mn(),Ks(),vf=(e,r,t,s,i,n)=>(e-1)*r+t+(s-1)*i+1-n,xf=(e,r,t,s,i)=>{let n=Math.floor(e/2);r==="SAME_UPPER"?(t[s]=n,t[i]=e-n):r==="SAME_LOWER"&&(t[s]=e-n,t[i]=n)},Tf=(e,r,t,s,i,n,o,a,l,d)=>{let p=e.length-2,u=d.length===0;l.length{let t=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((u,h)=>u*h,1)===0){t.length=0;for(let u=2;uu+h,0)===0){let u=r[0].dims.length-2;l=new Array(u).fill(1)}let d=e.strides.slice();if(d.reduce((u,h)=>u+h,0)===0){let u=r[0].dims.length-2;d=new Array(u).fill(1)}Tf(a,t,l,e.autoPad,e.group,i,d,s,o,n);let p=Object.assign({},e);return Object.assign(p,{kernelShape:t,pads:i,outputPadding:o,outputShape:n,dilations:l,strides:d}),p},Ef=e=>{let r=Zd(e),t=e.format,s=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,n=e.group,o=e.kernelShape,a=e.pads,l=e.strides,d=e.wIsConst(),p=e.outputPadding,u=e.outputShape;return{autoPad:s,format:t,dilations:i,group:n,kernelShape:o,outputPadding:p,outputShape:u,pads:a,strides:l,wIsConst:d,...r,cacheKey:`${e.format};${r.activation};`}},Pf=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let t=e[0].dims[r.format==="NHWC"?e[0].dims.length-1:1],s=e[1].dims[0];if(t!==s)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let i=e[1].dims[1]*r.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==i))throw new Error("invalid bias");let n=e[0].dims.length-2;if(r.dilations.reduce((o,a)=>o+a,0)>0&&r.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(r.strides.reduce((o,a)=>o+a,0)>0&&r.strides.length!==n)throw new Error(`strides should be ${n}D`);if(r.pads.reduce((o,a)=>o+a,0)>0&&r.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(r.outputPadding.length!==n&&r.outputPadding.length!==0)throw new Error(`output_padding should be ${n}D`);if(r.kernelShape.reduce((o,a)=>o+a,0)>0&&r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(r.outputShape.length!==0&&r.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},hc=(e,r,t,s)=>{let i=e.kernelCustomData.wT??e.compute(Nr(r[1],[2,3,0,1]),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=i);let n=[r[0],i];r.length===3&&n.push(r[2]),e.compute(bf(n,t,s),{inputs:n})},Cf=(e,r)=>{let t=r.format==="NHWC",s=[e.inputs[0].reshape(t?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&s.push(e.inputs[2]);let i=r.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let n=r.dilations;(n.length===0||n[0]===0)&&(n=[1]);let o=r.strides;(o.length===0||o[0]===0)&&(o=[1]);let a=r.pads;a.length===0&&(a=[0,0]),a=[0,a[0],0,a[1]],o=[1].concat(o),n=[1].concat(n),i=[1].concat(i);let l=r.outputPadding;l=[0].concat(l);let d=pc({...r,pads:a,strides:o,dilations:n,kernelShape:i,outputPadding:l},s);hc(e,s,d,p=>t?[p[0],p[2],p[3]]:[p[0],p[1],p[3]])},Sf=(e,r)=>{if(Pf(e.inputs,r),e.inputs[0].dims.length===3)Cf(e,r);else{let t=pc(r,e.inputs);hc(e,e.inputs,t)}}}),$f,kf,If,cv=je(()=>{pt(),yt(),Yt(),vt(),$f=(e,r,t,s)=>{let i=xe.size(r),n=r.length,o=$e("input",e,n),a=tt("output",e,n),l=t.dataType===6?t.getInt32Array()[0]:Number(t.getBigInt64Array()[0]),d=xe.normalizeAxis(l,n),p=u=>{let h=` i32(${o.indicesGet("inputIndices","uniforms.axis")}) `,w=rt("uniforms.input_shape","uniforms.axis",n),_=s.reverse?h+(s.exclusive?" + 1":""):"0",P=s.reverse?w:h+(s.exclusive?"":" + 1");return` + ${u.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(o,a)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${a.offsetToIndices("global_idx")}; + var sum = ${a.type.value}(0); + let first : i32 = ${_}; + let last : i32 = ${P}; + for (var i : i32 = first; i < last; i++) { + ${o.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${o.getByIndices("inputIndices")}; + } + ${a.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:s.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},{type:12,data:d},...nt(r,r)]}),getShaderSource:p}},kf=(e,r)=>{let t=e.inputs[0].dims,s=e.inputs[0].dataType,i=e.inputs[1];e.compute($f(s,t,i,r),{inputs:[0]})},If=e=>{let r=e.exclusive===1,t=e.reverse===1;return Dt({exclusive:r,reverse:t})}}),Af,Of,Ff,Df,Lf,uv=je(()=>{pt(),yt(),Yt(),vt(),Af=e=>{if(!e||e.length!==1)throw new Error("DepthToSpace requires 1 input.");if(e[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},Of=(e,r,t,s)=>{let i=[];i.push(`fn perm(i: ${s.type.indices}) -> ${t.type.indices} { + var a: ${t.type.indices};`);for(let n=0;n{let t,s,i,n,o,a,l=r.format==="NHWC",d=r.blocksize,p=r.mode==="DCR";l?([t,s,i,n]=e.dims,o=p?[t,s,i,d,d,n/d**2]:[t,s,i,n/d**2,d,d],a=p?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([t,s,i,n]=[e.dims[0],e.dims[2],e.dims[3],e.dims[1]],o=p?[t,d,d,n/d**2,s,i]:[t,n/d**2,d,d,s,i],a=p?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let u=e.reshape(o),h=u.dims.length,w=e.dataType,_=$e("a",w,h),P=tt("output",w,h),A=v=>` + ${v.registerUniform("output_size","u32").declareVariables(_,P)} + + ${Of(a,h,_,P)} + + ${v.mainStart()} + ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${P.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${P.setByOffset("global_idx",_.getByIndices("aIndices"))} + }`;return{name:"DepthToSpace",shaderCache:{hint:`${e.dims};${r.blocksize};${r.mode}`,inputDependencies:["rank"]},getRunData:v=>{let y=l?[t,s*d,i*d,n/d**2]:[t,n/d**2,s*d,i*d],S=xe.size(y),x=u.dims,g=xe.sortBasedOnPerm(x,a);return{outputs:[{dims:y,dataType:v[0].dataType}],dispatchGroup:{x:Math.ceil(S/64)},programUniforms:[{type:12,data:S},...nt(x,g)]}},getShaderSource:A}},Df=(e,r)=>{Af(e.inputs),e.compute(Ff(e.inputs[0],r))},Lf=e=>Dt({blocksize:e.blocksize,mode:e.mode,format:e.format})}),ka,zi,mc,zf,Bf,Rf,Nf,fc,jf,Uf,Vf,pv=je(()=>{pt(),yt(),Yt(),vt(),ka="[a-zA-Z]|\\.\\.\\.",zi="("+ka+")+",mc="^"+zi+"$",zf="("+zi+",)*"+zi,Bf="^"+zf+"$",Rf=class{constructor(e=-1){this.symbolToIndices=new Map,this.inputIndex=e}addSymbol(e,r){let t=this.symbolToIndices.get(e);t===void 0?t=[r]:t.push(r),this.symbolToIndices.set(e,t)}},Nf=class{constructor(e,r){var i;this.equation=r,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[t,s]=r.includes("->")?r.split("->",2):[r,""];if(!t.match(RegExp(Bf)))throw new Error("Invalid LHS term");if(t.split(",").forEach((n,o)=>{let a=e[o].dims.slice();if(!n.match(RegExp(mc)))throw new Error("Invalid LHS term");let l=this.processTerm(n,!0,a,o);this.lhs.push(l)}),s==="")s+=[...this.symbolToInfo.entries()].filter(([n,o])=>o.count===1||n==="...").map(([n])=>n).join("");else if(!s.match(RegExp(zi)))throw new Error("Invalid RHS");(i=s.match(RegExp(ka,"g")))==null||i.forEach(n=>{if(n==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let o=this.symbolToInfo.get(n);if(o===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(o.dimValue)}}),this.rhs=this.processTerm(s,!1,this.outputDims)}addSymbol(e,r,t){let s=this.symbolToInfo.get(e);if(s!==void 0){if(s.dimValue!==r&&s.count!==1)throw new Error("Dimension mismatch");s.count++,s.inputIndices.push(t)}else s={count:1,dimValue:r,inputIndices:[t]};this.symbolToInfo.set(e,s)}processTerm(e,r,t,s=-1){let i=t.length,n=!1,o=[],a=0;if(!e.match(RegExp(mc))&&!r&&e!=="")throw new Error("Invalid LHS term");let l=e.match(RegExp(ka,"g")),d=new Rf(s);return l==null||l.forEach((p,u)=>{if(p==="..."){if(n)throw new Error("Only one ellipsis is allowed per input term");n=!0;let h=i-l.length+1;if(h<0)throw new Error("Ellipsis out of bounds");if(o=t.slice(a,a+h),this.hasEllipsis){if(this.ellipsisDims.length!==o.length||this.ellipsisDims.toString()!==o.toString())throw new Error("Ellipsis dimensions mismatch")}else if(r)this.hasEllipsis=!0,this.ellipsisDims=o;else throw new Error("Ellipsis must be specified in the LHS");for(let w=0;we+"_max",jf=(e,r,t,s)=>{let i=e.map(d=>d.length).map((d,p)=>$e(`input${p}`,r,d)),n=xe.size(s),o=tt("output",r,s.length),a=[...t.symbolToInfo.keys()].filter(d=>!t.rhs.symbolToIndices.has(d)),l=d=>{let p=[],u="var prod = 1.0;",h="var sum = 0.0;",w="sum += prod;",_=[],P=[],A=[],v=[],y=t.symbolToInfo.size===t.rhs.symbolToIndices.size;t.symbolToInfo.forEach((x,g)=>{var M;if(t.rhs.symbolToIndices.has(g)){let E=(M=t.rhs.symbolToIndices.get(g))==null?void 0:M[0];E!==void 0&&t.lhs.forEach((k,B)=>{if(x.inputIndices.includes(B)){let R=k.symbolToIndices.get(g);if(R===void 0)throw new Error("Invalid symbol error");R.forEach(J=>{p.push(`${i[B].indicesSet(`input${B}Indices`,J,o.indicesGet("outputIndices",E))}`)})}})}else t.lhs.forEach((E,k)=>{if(x.inputIndices.includes(k)){let B=E.symbolToIndices.get(g);if(B===void 0)throw new Error("Invalid symbol error");B.forEach(R=>{_.push(`${i[k].indicesSet(`input${k}Indices`,R,`${g}`)}`)}),v.push(`prod *= ${i[k].getByIndices(`input${k}Indices`)};`)}}),P.push(`for(var ${g}: u32 = 0; ${g} < uniforms.${fc(g)}; ${g}++) {`),A.push("}")});let S=y?[...p,`let sum = ${i.map((x,g)=>x.getByIndices(`input${g}Indices`)).join(" * ")};`]:[...p,h,...P,..._,u,...v,w,...A];return` + ${d.registerUniforms(a.map(x=>({name:`${fc(x)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...i,o)} + + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${o.offsetToIndices("global_idx")}; + ${i.map((x,g)=>`var input${g}Indices: ${i[g].type.indices};`).join(` +`)} + ${S.join(` +`)}; + ${o.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:t.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let d=a.filter(u=>t.symbolToInfo.has(u)).map(u=>{var h;return{type:12,data:((h=t.symbolToInfo.get(u))==null?void 0:h.dimValue)||0}});d.push({type:12,data:n});let p=e.map((u,h)=>[...nt(u)]).reduce((u,h)=>u.concat(h),d);return p.push(...nt(s)),{outputs:[{dims:s,dataType:r}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:p}},getShaderSource:l}},Uf=(e,r)=>{let t=new Nf(e.inputs,r.equation),s=t.outputDims,i=e.inputs.map((n,o)=>n.dims);e.compute(jf(i,e.inputs[0].dataType,t,s))},Vf=e=>{let r=e.equation.replace(/\s+/g,"");return Dt({equation:r})}}),Wf,_c,Gf,Kf,Hf,hv=je(()=>{pt(),yt(),vt(),Wf=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let r=e[0].dims,t=Array.from(e[1].getBigInt64Array(),Number),s=t.length{let t=e.length-r.length,s=[];for(let i=0;ie.length>r.length?_c(e,r):_c(r,e),Kf=e=>{let r=e[0].dims,t=Array.from(e[1].getBigInt64Array(),Number),s=Gf(r,t),i=e[0].dataType,n=i===9||xe.size(r)===1,o=i===9||r.length>0&&r[r.length-1]%4===0?4:1,a=n||s.length>0&&s[s.length-1]%4===0?4:1,l=Math.ceil(xe.size(s)/a),d=u=>{let h=$e("input",i,r.length,o),w=tt("output",i,s.length,a),_;if(i===9){let P=(A,v,y="")=>` + let outputIndices${v} = ${w.offsetToIndices(`outputOffset + ${v}u`)}; + let offset${v} = ${h.broadcastedIndicesToOffset(`outputIndices${v}`,w)}; + let index${v} = offset${v} / 4u; + let component${v} = offset${v} % 4u; + ${A}[${v}] = ${y}(${h.getByOffset(`index${v}`)}[component${v}]); + `;_=` + let outputOffset = global_idx * ${a}; + var data = vec4(0); + ${P("data",0,"u32")} + ${P("data",1,"u32")} + ${P("data",2,"u32")} + ${P("data",3,"u32")} + ${w.setByOffset("global_idx","data")} + }`}else _=` + let outputIndices = ${w.offsetToIndices(`global_idx * ${a}`)}; + let inputOffset = ${h.broadcastedIndicesToOffset("outputIndices",w)}; + let data = ${w.type.value}(${h.getByOffset(`inputOffset / ${o}`)}); + ${w.setByOffset("global_idx","data")} + }`;return` + ${u.registerUniform("vec_size","u32").declareVariables(h,w)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${_}`},p=[{type:12,data:l},...nt(r,s)];return{name:"Expand",shaderCache:{hint:`${s.length};${o}${a}`,inputDependencies:["rank"]},getShaderSource:d,getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:p})}},Hf=e=>{Wf(e.inputs),e.compute(Kf(e.inputs),{inputs:[0]})}}),qf,Qf,mv=je(()=>{pt(),yt(),vt(),Yd(),qf=e=>{let r=e[0].dataType,t=xe.size(e[0].dims),s=xe.size(e[1].dims),i=s%4===0,n=o=>{let a=$e("x",r,[1],4),l=$e("bias",r,[1],4),d=tt("y",r,[1],4),p=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],u=w=>` + let bias${w}_offset: u32 = (global_idx * 4 + ${w}) % uniforms.bias_size; + let bias${w} = ${l.getByOffset(`bias${w}_offset / 4`)}[bias${w}_offset % 4];`,h=i?` + let bias = ${l.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${u(0)}${u(1)}${u(2)}${u(3)} + let bias = ${a.type.value}(bias0, bias1, bias2, bias3);`;return`${o.registerUniforms(p).declareVariables(a,l,d)} + + ${Xd(Tr(r))} + + ${o.mainStart(jn)} + ${o.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${a.getByOffset("global_idx")}; + ${h} + let x_in = x + bias; + ${d.setByOffset("global_idx",Jd("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${i}`,inputDependencies:["type","type"]},getShaderSource:n,getRunData:o=>({outputs:[{dims:o[0].dims,dataType:o[0].dataType}],programUniforms:[{type:12,data:Math.ceil(t/4)},{type:12,data:s}],dispatchGroup:{x:Math.ceil(t/jn/4)}})}},Qf=e=>{e.inputs.length<2||xe.size(e.inputs[1].dims)===0?Sm(e):e.compute(qf(e.inputs))}}),Xf,Jf,Yf,Zf,fv=je(()=>{pt(),yt(),Yt(),vt(),Xf=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},Jf=(e,r)=>{let t=e[0].dims,s=e[1].dims,i=t.length,n=xe.normalizeAxis(r.axis,i),o=t.slice(0);o.splice(n,1,...s);let a=t[n],l=e[0].dataType===9?4:1,d=Math.ceil(xe.size(o)/l),p=[{type:12,data:d},{type:6,data:a},{type:12,data:n},...nt(e[0].dims,e[1].dims,o)],u=h=>{let w=$e("data",e[0].dataType,e[0].dims.length,l),_=$e("inputIndices",e[1].dataType,e[1].dims.length),P=tt("output",e[0].dataType,o.length,l),A=y=>{let S=s.length,x=`var indicesIndices${y} = ${_.type.indices}(0);`;for(let g=0;g1?`indicesIndices${y}[${g}]`:`indicesIndices${y}`} = ${o.length>1?`outputIndices${y}[uniforms.axis + ${g}]`:`outputIndices${y}`};`;x+=` + var idx${y} = ${_.getByIndices(`indicesIndices${y}`)}; + if (idx${y} < 0) { + idx${y} = idx${y} + uniforms.axisDimLimit; + } + var dataIndices${y} : ${w.type.indices}; + `;for(let g=0,M=0;g1?`dataIndices${y}[${g}]`:`dataIndices${y}`} = u32(idx${y});`,M+=S):(x+=`${i>1?`dataIndices${y}[${g}]`:`dataIndices${y}`} = ${o.length>1?`outputIndices${y}[${M}]`:`outputIndices${y}`};`,M++);return x},v;if(e[0].dataType===9){let y=(S,x,g="")=>` + let outputIndices${x} = ${P.offsetToIndices(`outputOffset + ${x}u`)}; + ${A(x)}; + let offset${x} = ${w.indicesToOffset(`dataIndices${x}`)}; + let index${x} = offset${x} / 4u; + let component${x} = offset${x} % 4u; + ${S}[${x}] = ${g}(${w.getByOffset(`index${x}`)}[component${x}]); + `;v=` + let outputOffset = global_idx * ${l}; + var value = vec4(0); + ${y("value",0,"u32")} + ${y("value",1,"u32")} + ${y("value",2,"u32")} + ${y("value",3,"u32")} + ${P.setByOffset("global_idx","value")} + `}else v=` + let outputIndices = ${P.offsetToIndices("global_idx")}; + ${A("")}; + let value = ${w.getByIndices("dataIndices")}; + ${P.setByOffset("global_idx","value")}; + `;return` + ${h.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(w,_,P)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${v} + }`};return{name:"Gather",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:u}},Yf=e=>Dt({axis:e.axis}),Zf=(e,r)=>{let t=e.inputs;Xf(t),e.compute(Jf(e.inputs,r))}}),e_,t_,r_,_v=je(()=>{pt(),yt(),vt(),e_=(e,r,t,s,i,n,o,a,l)=>{let d=[{type:12,data:n},{type:12,data:s},{type:12,data:i},{type:12,data:t},{type:12,data:o},{type:12,data:a},{type:12,data:l}],p=[n];d.push(...nt(r.dims,p));let u=h=>{let w=$e("indices_data",r.dataType,r.dims.length),_=tt("input_slice_offsets_data",12,1,1),P=[w,_],A=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:i.length},{name:"sizes_from_slice_dims_data",type:"u32",length:t.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return` + ${h.registerUniforms(A).declareVariables(...P)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let batch_idx = global_idx / uniforms.num_slices_per_batch; + let base_offset = batch_idx * uniforms.input_batch_stride; + + let slice_indices_base_offset = global_idx * uniforms.num_slice_dims; + var relative_slice_offset = 0; + for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) { + var index = i32(indices_data[dim_idx + slice_indices_base_offset].x); + let input_dim_idx = uniforms.batch_dims + dim_idx; + if (index < 0) { + ${i.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"} + } + ${t.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"} + } + + input_slice_offsets_data[global_idx] = base_offset + u32(relative_slice_offset); + }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${i.length}_${t.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:p,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:d}),getShaderSource:u},{inputs:[r],outputs:[-1]})[0]},t_=(e,r)=>{let t=e.inputs,s=t[0].dims,i=t[0].dataType,n=t[1].dims,o=n[n.length-1],a=xe.sizeToDimension(n,n.length-1),l=xe.sizeFromDimension(s,r.batchDims+o),d=xe.sizeToDimension(s,r.batchDims),p=xe.sizeFromDimension(s,r.batchDims),u=a/d,h=new Array(o),w=l;for(let x=0;xs.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let A=n.slice(0,-1).concat(s.slice(P)),v=xe.size(A),y=[{type:12,data:v},{type:12,data:l},...nt(t[0].dims,_.dims,A)],S=x=>{let g=$e("data",t[0].dataType,t[0].dims.length),M=$e("slice_offsets",12,_.dims.length),E=tt("output",t[0].dataType,A.length);return` + ${x.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(g,M,E)} + ${x.mainStart()} + ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let slice_offset = slice_offsets[global_idx / uniforms.slice_size]; + output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size]; + }`};e.compute({name:"GatherND",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:A,dataType:i}],dispatchGroup:{x:Math.ceil(v/64)},programUniforms:y}),getShaderSource:S},{inputs:[t[0],_]})},r_=e=>({batchDims:e.batch_dims,cacheKey:""})}),s_,n_,i_,o_,gv=je(()=>{pt(),yt(),Yt(),vt(),s_=(e,r)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let t=xe.normalizeAxis(r.quantizeAxis,e[0].dims.length),s=r.blockSize,i=e[0],n=e[2],o=e.length===4?e[3]:void 0;if(n.dims.length!==i.dims.length||!i.dims.map((a,l)=>l===t?Math.ceil(a/s)===n.dims[l]:a===n.dims[l]).reduce((a,l)=>a&&l,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(o){if(o.dataType!==i.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(o.dims.length!==n.dims.length||!o.dims.map((a,l)=>a===n.dims[l]).reduce((a,l)=>a&&l,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},n_=(e,r)=>{let t=e[0].dims,s=e[1].dims,i=t.length,n=xe.normalizeAxis(r.gatherAxis,i),o=xe.normalizeAxis(r.quantizeAxis,i),a=t.slice(0);a.splice(n,1,...s);let l=xe.size(a),d=e[2].dataType,p=e[0].dataType===22,u=[{type:12,data:l},{type:12,data:o},{type:12,data:n},{type:12,data:r.blockSize},...nt(...e.map((w,_)=>w.dims),a)],h=w=>{let _=$e("data",e[0].dataType,e[0].dims.length),P=$e("inputIndices",e[1].dataType,e[1].dims.length),A=$e("scales",e[2].dataType,e[2].dims.length),v=e.length>3?$e("zeroPoint",e[3].dataType,e[3].dims.length):void 0,y=tt("output",d,a.length),S=[_,P,A];v&&S.push(v);let x=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${w.registerUniforms(x).declareVariables(...S,y)} + ${w.mainStart()} + let output_indices = ${y.offsetToIndices("global_idx")}; + var indices_indices = ${P.type.indices}(0); + ${s.length>1?` + for (var i: u32 = 0; i < ${s.length}; i++) { + let index = ${y.indicesGet("output_indices","uniforms.gather_axis + i")}; + ${P.indicesSet("indices_indices","i","index")}; + }`:`indices_indices = ${y.indicesGet("output_indices","uniforms.gather_axis")};`}; + var data_indices = ${_.type.indices}(0); + for (var i: u32 = 0; i < uniforms.gather_axis; i++) { + let index = ${y.indicesGet("output_indices","i")}; + ${_.indicesSet("data_indices","i","index")}; + } + var index_from_indices = ${P.getByIndices("indices_indices")}; + if (index_from_indices < 0) { + index_from_indices += ${t[n]}; + } + ${_.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")}; + for (var i = uniforms.gather_axis + 1; i < ${a.length}; i++) { + let index = ${y.indicesGet("output_indices",`i + ${s.length} - 1`)}; + ${_.indicesSet("data_indices","i","index")}; + } + let data_offset = ${_.indicesToOffset("data_indices")}; + let data_index = data_offset % 8; + // Convert 4-bit packed data to 8-bit packed data. + let packed_4bit_quantized_data = ${_.getByOffset("data_offset / 8")}; + let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f; + let quantized_data_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data)); + let quantized_data = quantized_data_vec[data_index / 2]; + var scale_indices = data_indices; + let quantize_axis_index = ${A.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size; + ${A.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")}; + var scale = ${A.getByIndices("scale_indices")}; + ${v?` + let zero_point_indices = scale_indices; + let zero_point_offset = ${v.indicesToOffset("zero_point_indices")}; + let zero_point_index = zero_point_offset % 8; + let packed_4bit_zero_points = ${v.getByOffset("zero_point_offset / 8")}; + let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f; + let zero_point_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points)); + let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"}; + let dequantized_data = ${Tr(d)}(quantized_data - zero_point) * scale; + ${y.setByOffset("global_idx","dequantized_data")}; + }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${r.cacheKey};${e.filter((w,_)=>_!==1).map(w=>w.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(w,_)=>"rank")},getRunData:()=>({outputs:[{dims:a,dataType:d}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:h}},i_=(e,r)=>{let t=e.inputs;s_(t,r),e.compute(n_(e.inputs,r))},o_=e=>Dt({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),a_,l_,d_,c_,wv=je(()=>{pt(),yt(),Yt(),vt(),a_=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and + indices input tensors be of same rank.`)},l_=(e,r)=>{let t=e[0].dims,s=e[0].dataType,i=t.length,n=e[1].dims,o=e[1].dataType,a=xe.normalizeAxis(r.axis,i),l=t[a],d=n.slice(0),p=xe.size(d),u=$e("input",s,i),h=$e("indicesInput",o,n.length),w=tt("output",s,d.length),_=[{type:12,data:p},{type:6,data:l},{type:12,data:a}];return _.push(...nt(t,n,d)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:_}),getShaderSource:P=>` + ${P.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(u,h,w)} + ${P.mainStart()} + ${P.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${w.offsetToIndices("global_idx")}; + + var idx = ${h.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${u.type.indices}(outputIndices); + ${u.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${u.getByIndices("inputIndices")}; + + ${w.setByOffset("global_idx","value")}; + }`}},d_=e=>Dt({axis:e.axis}),c_=(e,r)=>{let t=e.inputs;a_(t),e.compute(l_(e.inputs,r))}}),u_,p_,h_,m_,yv=je(()=>{pt(),yt(),vt(),u_=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},p_=(e,r)=>{let t=e[0].dims.slice(),s=e[1].dims.slice(),[i,n,o]=Fp.getShapeOfGemmResult(t,r.transA,s,r.transB,e.length===3?e[2].dims:void 0),a=[i,n];if(!a)throw new Error("Can't use gemm on the given tensors");let l=16,d=Math.ceil(n/l),p=Math.ceil(i/l),u=!0,h=xe.size(a),w=[{type:12,data:u?d:h},{type:12,data:i},{type:12,data:n},{type:12,data:o},{type:1,data:r.alpha},{type:1,data:r.beta}],_=["type","type"];e.length===3&&(w.push(...nt(e[2].dims)),_.push("rank")),w.push(...nt(a));let P=v=>{let y="";r.transA&&r.transB?y="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":r.transA&&!r.transB?y="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!r.transA&&r.transB?y="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!r.transA&&!r.transB&&(y="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let S=r.alpha===1?"":"value *= uniforms.alpha;",x=$e("a",e[0].dataType,e[0].dims),g=$e("b",e[1].dataType,e[1].dims),M=x.type.value,E=null,k=[x,g];e.length===3&&(E=$e("c",e[2].dataType,e[2].dims.length),k.push(E));let B=tt("output",e[0].dataType,a.length);k.push(B);let R=[{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` + ${v.registerUniforms(R).declareVariables(...k)} + + ${v.mainStart()} + ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${M}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${y} + } + + ${S} + ${E!=null?`let cOffset = ${E.broadcastedIndicesToOffset("vec2(m, n)",B)}; value += ${M}(uniforms.beta) * ${E.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`},A=v=>{let y=$e("a",e[0].dataType,e[0].dims),S=$e("b",e[1].dataType,e[1].dims),x=null,g=[y,S];e.length===3&&(x=$e("c",e[2].dataType,e[2].dims.length),g.push(x));let M=tt("output",e[0].dataType,a.length);g.push(M);let E=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],k="",B="";r.transA&&r.transB?(B=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${y.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${S.type.value}(0); + } + `,k="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):r.transA&&!r.transB?(B=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${y.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${S.type.value}(0); + } + `,k="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!r.transA&&r.transB?(B=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${y.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${S.type.value}(0); + } + `,k="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!r.transA&&!r.transB&&(B=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${y.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${S.type.value}(0); + } + `,k="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let R=r.alpha===1?"":"value *= uniforms.alpha;";return` + ${v.registerUniforms(E).declareVariables(...g)} + var tile_a: array, ${l}>; + var tile_b: array, ${l}>; + ${v.mainStart([l,l,1])} + let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${l}; + let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${l}; + let num_tiles = (uniforms.K - 1) / ${l} + 1; + var k_start = 0u; + var value = ${M.type.value}(0); + for (var t: u32 = 0u; t < num_tiles; t++) { + ${B} + k_start = k_start + ${l}; + workgroupBarrier(); + + for (var k: u32 = 0u; k < ${l}; k++) { + ${k} + } + workgroupBarrier(); + } + + ${R} + let m = tile_row_start + local_id.y; + let n = tile_col_start + local_id.x; + ${x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",M)}; value += ${M.type.value}(uniforms.beta) * ${x.getByOffset("cOffset")};`:""} + if (m < uniforms.M && n < uniforms.N) { + output[m * uniforms.N + n] = value; + } + }`};return u?{name:"GemmShared",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:d*p},programUniforms:w}),getShaderSource:A}:{name:"Gemm",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:w}),getShaderSource:P}},h_=e=>{let r=e.transA,t=e.transB,s=e.alpha,i=e.beta;return{transA:r,transB:t,alpha:s,beta:i,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},m_=(e,r)=>{u_(e.inputs),e.compute(p_(e.inputs,r))}}),xs,As,bn,vn,f_,__,g_,w_,y_,M_,b_,v_,x_,T_,Mv=je(()=>{pt(),yt(),Yt(),vt(),[xs,As,bn,vn]=[0,1,2,3],f_=e=>{if(e[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(e[0].dims.length!==e[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(e[0].dims.length-2!==e[1].dims[e[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},__=` + fn gs_get_cubic_coeffs(x: f32) -> vec4 { + let cubic_alpha = -0.75f; + let x_abs = abs(x); + var coeffs: vec4; + coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha); + coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1); + coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1); + coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha); + return coeffs; + } +`,g_=e=>` + fn gs_bicubic_interpolate(p: mat4x4<${e}>, x: f32, y: f32) -> ${e} { + var v: vec4; + var coeffs = gs_get_cubic_coeffs(x); + for (var i = 0; i < 4; i++) { + v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3]; + } + coeffs = gs_get_cubic_coeffs(y); + let pixel = ${e}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]); + return pixel; + } +`,w_=e=>` + fn gs_denormalize(n: f32, length: i32) -> f32 { + ${e.alignCorners===0?` + // alignCorners: false => [-1, 1] to [-0.5, length - 0.5] + return ((n + 1.0) * f32(length) - 1.0) / 2.0; + `:` + // alignCorners: true => [-1, 1] to [0, length - 1] + return (n + 1.0) / 2.0 * (f32(length - 1)); + `} + } +`,y_=e=>` + ${e.paddingMode==="reflection"?` + fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 { + var dx = 0.0; + var fx = f32(x); + let range = x_max - x_min; + if (fx < x_min) { + dx = x_min - fx; + let n = u32(dx / range); + let r = dx - f32(n) * range; + if (n % 2 == 0) { + fx = x_min + r; + } else { + fx = x_max - r; + } + } else if (fx > x_max) { + dx = fx - x_max; + let n = u32(dx / range); + let r = dx - f32(n) * range; + if (n % 2 == 0) { + fx = x_max - r; + } else { + fx = x_min + r; + } + } + return u32(fx); + }`:""} +`,M_=(e,r,t)=>` + fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4) -> ${r} { + var pixel = ${r}(0); + var indices = vec4(0); + indices[${xs}] = batch; + indices[${As}] = channel;`+(()=>{switch(t.paddingMode){case"zeros":return` + if (r >= 0 && r < H && c >=0 && c < W) { + indices[${bn}] = u32(r); + indices[${vn}] = u32(c); + } + `;case"border":return` + indices[${bn}] = u32(clamp(r, 0, H - 1)); + indices[${vn}] = u32(clamp(c, 0, W - 1)); + `;case"reflection":return` + indices[${bn}] = gs_reflect(r, border[1], border[3]); + indices[${vn}] = gs_reflect(c, border[0], border[2]); + `;default:throw new Error(`padding mode ${t.paddingMode} is not supported`)}})()+` + return ${e.getByIndices("indices")}; + } +`,b_=(e,r,t)=>(()=>{switch(t.mode){case"nearest":return` + let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${xs}], indices[${As}], border); + `;case"bilinear":return` + let x1 = i32(floor(x)); + let y1 = i32(floor(y)); + let x2 = x1 + 1; + let y2 = y1 + 1; + + let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${xs}], indices[${As}], border); + let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${xs}], indices[${As}], border); + let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${xs}], indices[${As}], border); + let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${xs}], indices[${As}], border); + + let dx2 = ${r}(f32(x2) - x); + let dx1 = ${r}(x - f32(x1)); + let dy2 = ${r}(f32(y2) - y); + let dy1 = ${r}(y - f32(y1)); + let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22); + `;case"bicubic":return` + let x0 = i32(floor(x)) - 1; + let y0 = i32(floor(y)) - 1; + var p: mat4x4<${r}>; + for (var h = 0; h < 4; h++) { + for (var w = 0; w < 4; w++) { + p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${xs}], indices[${As}], border); + } + } + + let dx = x - f32(x0 + 1); + let dy = y - f32(y0 + 1); + let result = gs_bicubic_interpolate(p, dx, dy); + `;default:throw new Error(`mode ${t.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,v_=(e,r)=>{let t=$e("x",e[0].dataType,e[0].dims.length),s=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],i=$e("grid",e[1].dataType,s.length,2),n=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];r.format==="NHWC"&&(n=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[xs,As,bn,vn]=[0,3,1,2]);let o=tt("output",e[0].dataType,n.length),a=t.type.value,l=xe.size(n),d=[{type:12,data:l},...nt(e[0].dims,s,n)],p=u=>` + ${u.registerUniform("output_size","u32").declareVariables(t,i,o)} + ${__} + ${g_(a)} + ${w_(r)} + ${y_(r)} + ${M_(t,a,r)} + + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let H_in = i32(uniforms.x_shape[${bn}]); + let W_in = i32(uniforms.x_shape[${vn}]); + + ${r.alignCorners===0?` + let x_min = -0.5; + let x_max = f32(W_in) - 0.5; + let y_min = -0.5; + let y_max = f32(H_in) - 0.5; + `:` + let x_min = 0.0; + let x_max = f32(W_in) - 1.0; + let y_min = 0.0; + let y_max = f32(H_in) - 1.0; + `}; + let border = vec4(x_min, y_min, x_max, y_max); + + let indices = ${o.offsetToIndices("global_idx")}; + var grid_indices = vec3(indices[${xs}], indices[${bn}], indices[${vn}]); + let nxy = ${i.getByIndices("grid_indices")}; + var x = gs_denormalize(f32(nxy[0]), W_in); + var y = gs_denormalize(f32(nxy[1]), H_in); + + ${b_(o,a,r)} + }`;return{name:"GridSample",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:["type","type"]},getRunData:u=>{let h=xe.size(n);return{outputs:[{dims:n,dataType:u[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:d}},getShaderSource:p}},x_=(e,r)=>{f_(e.inputs),e.compute(v_(e.inputs,r))},T_=e=>Dt({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),$r,E_,P_,gc,C_,Bi,S_,$_=je(()=>{pt(),yt(),Yt(),zd(),qd(),vt(),Ks(),$r=(e,r)=>e.length>r&&e[r].dims.length>0?e[r]:void 0,E_=(e,r)=>{let t=e[0],s=$r(e,1),i=$r(e,2),n=$r(e,3),o=$r(e,4),a=$r(e,5),l=$r(e,6),d=$r(e,7);if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let p=t.dims[0],u=t.dims[1],h=t.dims.length===3?t.dims[2]:r.numHeads*t.dims[4],w=u,_=0,P=0,A=Math.floor(h/r.numHeads);if(l&&d&&xe.size(l.dims)&&xe.size(d.dims)){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(l.dims[0]!==p||l.dims[1]!==r.numHeads||l.dims[3]!==A)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(d.dims[0]!==p||d.dims[1]!==r.numHeads||d.dims[3]!==A)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[2]!==d.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(d.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');_=l.dims[2],P=l.dims[2]}else if(l&&xe.size(l.dims)||d&&xe.size(d.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let v;if(s&&xe.size(s.dims)>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(s.dims.length<3||s.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==s.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(s.dims.length===3){if(s.dims[2]!==t.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');v=2,w=s.dims[1]}else if(s.dims.length===5){if(s.dims[2]!==r.numHeads||s.dims[3]!==2||s.dims[4]!==A)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.');v=5,w=s.dims[1]}else{if(s.dims[1]!==r.numHeads||s.dims[3]!==A)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');v=0,w=s.dims[2]}}else{if(t.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(t.dims[2]!==r.numHeads||t.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');v=3}if(n&&xe.size(n.dims)>0){if(n.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(s&&s.dims.length===5&&s.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let y=_+w,S=0;if(o&&xe.size(o.dims)>0){S=8;let E=o.dims;throw E.length===1?E[0]===p?S=1:E[0]===3*p+2&&(S=3):E.length===2&&E[0]===p&&E[1]===y&&(S=5),S===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let x=!1,g=h;if(i&&xe.size(i.dims)>0){if(i.dims.length!==3&&i.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.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(w!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');g=i.dims[2]}else{if(w!==i.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');g=i.dims[1]*i.dims[3],x=!0}}let M=!1;if(o&&xe.size(o.dims)>0)throw new Error("Key padding mask is not supported");if(a&&xe.size(a.dims)>0){if(a.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(a.dims[0]!==p||a.dims[1]!==r.numHeads||a.dims[2]!==u||a.dims[3]!==y)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:u,pastSequenceLength:_,kvSequenceLength:w,totalSequenceLength:y,maxSequenceLength:P,inputHiddenSize:0,hiddenSize:h,vHiddenSize:g,headSize:A,vHeadSize:Math.floor(g/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:S,scale:r.scale,broadcastResPosBias:M,passPastInKv:x,qkvFormat:v}},P_=e=>Dt({...e}),gc=Dt({perm:[0,2,1,3]}),C_=(e,r,t,s,i,n,o)=>{let a=[s,i,n],l=xe.size(a),d=[{type:12,data:l},{type:12,data:o},{type:12,data:n}],p=u=>{let h=tt("qkv_with_bias",r.dataType,a),w=$e("qkv",r.dataType,a),_=$e("bias",t.dataType,a),P=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${u.registerUniforms(P).declareVariables(w,_,h)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:r.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:p},{inputs:[r,t],outputs:[-1]})[0]},Bi=(e,r,t,s,i,n,o,a)=>{let l=n;if(o&&xe.size(o.dims)>0){if(s===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=C_(e,n,o,r,s,t*i,a),l=l.reshape([r,s,t,i]),t===1||s===1?l:e.compute(Nr(l,gc.perm),{inputs:[l],outputs:[-1]})[0]}else return n.dims.length===3&&(l=n.reshape([r,s,t,i])),t===1||s===1?l:e.compute(Nr(l,gc.perm),{inputs:[l],outputs:[-1]})[0]},S_=(e,r)=>{let t=E_(e.inputs,r),s=e.inputs[0],i=$r(e.inputs,1),n=$r(e.inputs,2),o=$r(e.inputs,3),a=$r(e.inputs,4),l=$r(e.inputs,5),d=$r(e.inputs,6),p=$r(e.inputs,7);if(s.dims.length===5)throw new Error("Packed QKV is not implemented");if((i==null?void 0:i.dims.length)===5)throw new Error("Packed KV is not implemented");let u=i&&n&&i.dims.length===4&&n.dims.length===4,h=Bi(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,s,o,0);if(u)return Oi(e,h,i,n,a,void 0,d,p,l,t);if(!i||!n)throw new Error("key and value must be provided");let w=Bi(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.headSize,i,o,t.hiddenSize),_=Bi(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.vHeadSize,n,o,2*t.hiddenSize);Oi(e,h,w,_,a,void 0,d,p,l,t)}}),k_,I_,A_,O_,wc,F_,D_,L_=je(()=>{pt(),yt(),Yt(),vt(),k_=e=>{if(!e||e.length<1)throw new Error("too few inputs")},I_=(e,r)=>{let t=[],s=r.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(i=>t.push(Number(i))),s=t.length),Dt({numOutputs:s,axis:r.axis,splitSizes:t})},A_=e=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${e}u; i += 1u ) { + if (index < ${rt("uniforms.size_in_split_axis","i",e)}) { + return i; + } + } + return ${e}u; +}`,O_=e=>{let r=e.length,t=[];for(let s=0;s{let t=e[0].dims,s=xe.size(t),i=e[0].dataType,n=xe.normalizeAxis(r.axis,t.length),o=new Array(r.numOutputs),a=$e("input",i,t.length),l=new Array(r.numOutputs),d=[],p=[],u=0,h=[{type:12,data:s}];for(let _=0;_` + ${_.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(a,...o)} + ${A_(l.length)} + ${O_(o)} + + ${_.mainStart()} + ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${a.offsetToIndices("global_idx")}; + var index = ${a.indicesGet("indices",n)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${rt("uniforms.size_in_split_axis","output_number - 1u",l.length)}; + ${a.indicesSet("indices",n,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getShaderSource:w,getRunData:()=>({outputs:d,dispatchGroup:{x:Math.ceil(s/64)},programUniforms:h})}},F_=(e,r)=>{k_(e.inputs);let t=e.inputs.length===1?r:I_(e.inputs,r);e.compute(wc(e.inputs,t),{inputs:[0]})},D_=e=>{let r=e.axis,t=e.splitSizes,s=e.numOutputs<0?t.length:e.numOutputs;if(s!==t.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Dt({axis:r,numOutputs:s,splitSizes:t})}}),z_,B_,yc,R_,bv=je(()=>{Yt(),qd(),$_(),L_(),Ks(),z_=(e,r)=>{if(r.doRotary)throw new Error("GroupQuerryAttention do_rotary attribute is not supported");if(r.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let t=e[0],s=e[1],i=e[2],n=e[3],o=e[4];if(r.localWindowSize!==-1)throw new Error("Local attention is not supported");if(r.softcap!==0)throw new Error("Softcap is not supported");if(r.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(r.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let a=!1,l=t.dims[0],d=t.dims[1],p=t.dims.length===3?a?t.dims[2]/3:t.dims[2]:r.numHeads*t.dims[4],u=d,h=0,w=!s||s.dims.length===0,_=Math.floor(w?p/(r.numHeads+2*r.kvNumHeads):p/r.numHeads);w&&(p=_*r.numHeads);let P=n&&n.dims.length!==0,A=o&&o.dims.length!==0;if(P&&n.dims.length===4&&n.dims[0]===l&&n.dims[1]!==r.kvNumHeads&&n.dims[2]===r.kvNumHeads&&n.dims[3]===_)throw new Error("BSNH pastKey/pastValue is not supported");if(P&&A){if(n.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(o.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');h=n.dims[2]}else if(P||A)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let v=1;if(s&&s.dims.length>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(s.dims.length<3||s.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==s.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(s.dims.length===3){if(t.dims[2]%s.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');u=s.dims[1]}else if(s.dims.length===5){if(s.dims[2]!==r.numHeads||s.dims[3]!==2||s.dims[4]!==_)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.');u=s.dims[1]}else{if(s.dims[1]!==r.numHeads||s.dims[3]!==_)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');u=s.dims[2]}}else{if(t.dims.length!==3&&t.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(t.dims.length===5&&(t.dims[2]!==r.numHeads||t.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');v=3}let y=0,S=!1,x=r.kvNumHeads?_*r.kvNumHeads:p;if(i&&i.dims.length>0){if(i.dims.length!==3&&i.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.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(u!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');x=i.dims[2]}else{if(u!==i.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');x=i.dims[1]*i.dims[3],S=!0}}let g=e.length>4?e[5]:void 0;if(g&&g.dims.length!==1&&g.dims[0]!==l)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:l,sequenceLength:d,pastSequenceLength:h,kvSequenceLength:u,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:p,vHiddenSize:x,headSize:_,vHeadSize:Math.floor(x/r.kvNumHeads),numHeads:r.numHeads,kvNumHeads:r.kvNumHeads,nReps:r.numHeads/r.kvNumHeads,pastPresentShareBuffer:!1,maskType:y,scale:r.scale,broadcastResPosBias:!1,passPastInKv:S,qkvFormat:v}},B_=Dt({perm:[0,2,1,3]}),yc=(e,r,t)=>{let s=r,i=t.kvNumHeads;return r.dims.length===3&&t.kvSequenceLength!==0&&(s=r.reshape([t.batchSize,t.kvSequenceLength,i,t.headSize]),s=e.compute(Nr(s,B_.perm),{inputs:[s],outputs:[-1]})[0]),s},R_=(e,r)=>{var A;let t=z_(e.inputs,r);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((A=e.inputs[1])==null?void 0:A.dims.length)===5)throw new Error("Packed KV is not implemented");let s=e.inputs[0],i=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,n=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,o=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,a=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,l=e.inputs.length>4?e.inputs[5]:void 0,d=e.inputs.length>5?e.inputs[6]:void 0,p=t.kvNumHeads?t.kvNumHeads:t.numHeads,u=Dt({axis:2,numOutputs:3,splitSizes:[t.numHeads*t.headSize,p*t.headSize,p*t.headSize]}),[h,w,_]=!i&&!n?e.compute(wc([s],u),{inputs:[s],outputs:[-1,-1,-1]}):[s,i,n],P=Bi(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,h,void 0,0);Oi(e,P,yc(e,w,t),yc(e,_,t),void 0,void 0,o,a,void 0,t,l,d)}}),Mc,N_,j_,U_,vv=je(()=>{pt(),yt(),Ks(),vt(),Mc=(e,r,t,s,i,n,o,a)=>{let l=Xt(n),d=l===1?"f32":`vec${l}f`,p=l===1?"vec2f":`mat2x${l}f`,u=i*o,h=64;u===1&&(h=256);let w=[i,o,n/l],_=[i,o,2],P=["rank","type","type"],A=[];A.push(...nt(w,_));let v=y=>{let S=$e("x",r.dataType,3,l),x=$e("scale",t.dataType,t.dims),g=$e("bias",s.dataType,s.dims),M=tt("output",1,3,2),E=[S,x,g,M];return` + var workgroup_shared : array<${p}, ${h}>; + const workgroup_size = ${h}u; + ${y.declareVariables(...E)} + ${y.mainStart(h)} + let batch = workgroup_index / uniforms.x_shape[1]; + let channel = workgroup_index % uniforms.x_shape[1]; + let hight = uniforms.x_shape[2]; + // initialize workgroup memory + var sum = ${d}(0); + var squared_sum = ${d}(0); + for (var h = local_idx; h < hight; h += workgroup_size) { + let value = ${d}(${S.get("batch","channel","h")}); + sum += value; + squared_sum += value * value; + } + workgroup_shared[local_idx] = ${p}(sum, squared_sum); + workgroupBarrier(); + + for (var currSize = workgroup_size >> 1; currSize > 0; currSize = currSize >> 1) { + if (local_idx < currSize) { + workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize]; + } + workgroupBarrier(); + } + if (local_idx == 0) { + let sum_final = ${Gs("workgroup_shared[0][0]",l)} / f32(hight * ${l}); + let squared_sum_final = ${Gs("workgroup_shared[0][1]",l)} / f32(hight * ${l}); + + let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${a})); + let channel_scale = inv_std_dev * f32(scale[channel]); + let channel_shift = f32(bias[channel]) - sum_final * channel_scale; + output[workgroup_index] = vec2f(channel_scale, channel_shift); + } + }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${a};${h}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:_,dataType:1}],dispatchGroup:{x:u},programUniforms:A}),getShaderSource:v},{inputs:[r,t,s],outputs:[-1]})[0]},N_=(e,r,t)=>{let s=r[0].dims,i=s,n=2,o=s[0],a=s[1],l=xe.sizeFromDimension(s,n),d=Xt(l),p=xe.size(i)/d,u=Mc(e,r[0],r[1],r[2],o,l,a,t.epsilon),h=[o,a,l/d],w=[o,a],_=["type","none"],P=A=>{let v=$e("x",r[0].dataType,h.length,d),y=$e("scale_shift",1,w.length,2),S=tt("output",r[0].dataType,h.length,d),x=[v,y,S];return` + ${A.registerUniform("output_size","u32").declareVariables(...x)} + ${A.mainStart()} + ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let outputIndices = ${S.offsetToIndices("global_idx")}; + let batch = outputIndices[0]; + let channel = outputIndices[1]; + let scale_shift = ${y.getByIndices("vec2(batch, channel)")}; + let value = ${v.getByOffset("global_idx")} * ${S.type.value}(scale_shift.x) + ${S.type.value}(scale_shift.y); + ${S.setByOffset("global_idx","value")}; + }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${d}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:i,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...nt(h,w,h)]}),getShaderSource:P},{inputs:[r[0],u]})},j_=(e,r,t)=>{let s=r[0].dims,i=s,n=s[0],o=s[s.length-1],a=xe.sizeFromDimension(s,1)/o,l=Xt(o),d=xe.size(i)/l,p=[{type:12,data:a},{type:12,data:Math.floor(o/l)}],u=["type","type"],h=!1,w=[0,s.length-1];for(let v=0;vs[w[y]])),P=Mc(e,_,r[1],r[2],n,a,o,t.epsilon),A=v=>{let y=lr(r[0].dataType),S=l===1?"vec2f":`mat${l}x2f`,x=E=>{let k=E===0?"x":"y",B=l===1?"f32":`vec${l}f`;switch(l){case 1:return`${y}(${B}(scale.${k}))`;case 2:return`vec2<${y}>(${B}(scale[0].${k}, scale[1].${k}))`;case 4:return`vec4<${y}>(${B}(scale[0].${k}, scale[1].${k}, scale[2].${k}, scale[3].${k}))`;default:throw new Error(`Not supported compoents ${l}`)}},g=$e("input",r[0].dataType,r[0].dims,l),M=tt("output",r[0].dataType,i,l);return` + @group(0) @binding(0) var input : array<${g.type.storage}>; + @group(0) @binding(1) var scale_input : array<${S}>; + @group(0) @binding(2) var output : array<${M.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${v.mainStart()} + let current_image_number = global_idx / (uniforms.C * uniforms.H); + let current_channel_number = global_idx % uniforms.C; + + let scale_offset = current_image_number * uniforms.C + current_channel_number; + let scale = scale_input[scale_offset]; + output[global_idx] = fma(input[global_idx], ${x(0)}, ${x(1)}); + }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:i,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:A},{inputs:[r[0],P]})},U_=(e,r)=>{r.format==="NHWC"?j_(e,e.inputs,r):N_(e,e.inputs,r)}}),V_,W_,G_,xv=je(()=>{pt(),yt(),vt(),V_=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},W_=(e,r,t)=>{let s=r.simplified,i=e[0].dims,n=e[1],o=!s&&e[2],a=i,l=xe.normalizeAxis(r.axis,i.length),d=xe.sizeToDimension(i,l),p=xe.sizeFromDimension(i,l),u=xe.size(n.dims),h=o?xe.size(o.dims):0;if(u!==p||o&&h!==p)throw new Error(`Size of X.shape()[axis:] == ${p}. + Size of scale and bias (if provided) must match this. + Got scale size of ${u} and bias size of ${h}`);let w=[];for(let g=0;g1,y=t>2,S=g=>{let M=lr(e[0].dataType),E=[$e("x",e[0].dataType,e[0].dims,_),$e("scale",n.dataType,n.dims,_)];o&&E.push($e("bias",o.dataType,o.dims,_)),E.push(tt("output",e[0].dataType,a,_)),v&&E.push(tt("mean_data_output",1,w)),y&&E.push(tt("inv_std_output",1,w));let k=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${g.registerUniforms(k).declareVariables(...E)} + ${g.mainStart()} + ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${jd("f32",_)}; + var mean_square_vector = ${jd("f32",_)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${Un(M,_,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${Gs("mean_vector",_)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${Gs("mean_square_vector",_)} / uniforms.norm_size ${s?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${Un(M,_,"x[j + offset]")}; + let f32scale = ${Un(M,_,"scale[j]")}; + output[j + offset] = ${E[0].type.value}((f32input ${s?"":"- mean"}) * inv_std_dev * f32scale + ${o?`+ ${Un(M,_,"bias[j]")}`:""} + ); + } + + ${v?"mean_data_output[global_idx] = mean":""}; + ${y?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},x=[{dims:a,dataType:e[0].dataType}];return v&&x.push({dims:w,dataType:1}),y&&x.push({dims:w,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${_};${t};${s}`,inputDependencies:P},getRunData:()=>({outputs:x,dispatchGroup:{x:Math.ceil(d/64)},programUniforms:A}),getShaderSource:S}},G_=(e,r)=>{V_(e.inputs),e.compute(W_(e.inputs,r,e.outputCount))}}),K_,H_,Tv=je(()=>{yt(),rc(),oc(),K_=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.")},H_=e=>{K_(e.inputs);let r=Nn.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!r)throw new Error("Can't use matmul on the given tensors");let t=r[r.length-1],s=e.inputs[0].dims[e.inputs[0].dims.length-1];if(t<8&&s<8)e.compute(tc(e.inputs,{activation:""},r));else{let i=r[r.length-2],n=xe.size(e.inputs[0].dims.slice(0,-2)),o=xe.size(e.inputs[1].dims.slice(0,-2));if(n!==1&&i===1&&o===1){let a=e.inputs[0].reshape([1,n,s]),l=e.inputs[1].reshape([1,s,t]),d=[1,n,t],p=[a,l];e.compute(Ca(p,{activation:""},r,d),{inputs:p})}else e.compute(Ca(e.inputs,{activation:""},r))}}}),q_,Q_,X_,J_,Y_,Ev=je(()=>{pt(),yt(),Yt(),vt(),q_=(e,r)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let t=e[0],s=t.dims.length;if(t.dims[s-1]!==r.k)throw new Error("The last dim of input shape does not match the k value");let i=Math.floor((r.k+r.blockSize-1)/r.blockSize),n=r.blockSize/8*r.bits,o=e[1];if(!xe.areEqual(o.dims,[r.n,i,n]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let a=e[2].dims;if(xe.size(a)!==r.n*i)throw new Error("scales input size error.");if(e.length===4){let l=e[3].dims,d=r.bits>4?r.n*i:r.n*Math.floor((i+1)/2);if(xe.size(l)!==d)throw new Error("zeroPoints input size error.")}},Q_=(e,r)=>{let t=e[0].dims,s=t.length,i=t[s-2],n=r.k,o=r.n,a=t.slice(0,s-2),l=xe.size(a),d=e[1].dims[2]/4,p=e[0].dataType,u=Xt(r.k),h=Xt(d),w=Xt(o),_=a.concat([i,o]),P=i>1&&o/w%2===0?2:1,A=xe.size(_)/w/P,v=64,y=[],S=[l,i,n/u],x=xe.convertShape(e[1].dims).slice();x.splice(-1,1,d/h),y.push(...nt(S)),y.push(...nt(x)),y.push(...nt(e[2].dims)),e.length===4&&y.push(...nt(xe.convertShape(e[3].dims)));let g=[l,i,o/w];y.push(...nt(g));let M=E=>{let k=S.length,B=$e("a",e[0].dataType,k,u),R=$e("b",12,x.length,h),J=$e("scales",e[2].dataType,e[2].dims.length),q=[B,R,J],V=e.length===4?$e("zero_points",12,e[3].dims.length):void 0;V&&q.push(V);let Y=g.length,H=tt("output",e[0].dataType,Y,w),Q=lr(e[0].dataType),ie=(()=>{switch(u){case 1:return`array<${Q}, 8>`;case 2:return`mat4x2<${Q}>`;case 4:return`mat2x4<${Q}>`;default:throw new Error(`${u}-component is not supported.`)}})(),le=()=>{let N=` + // reuse a data + var input_offset = ${B.indicesToOffset(`${B.type.indices}(batch, row, word_offset)`)}; + var a_data: ${ie}; + for (var j: u32 = 0; j < ${8/u}; j++) { + a_data[j] = ${B.getByOffset("input_offset")}; + input_offset++; + } + `;for(let F=0;F> 4) & b_mask); + b_quantized_values = ${ie}(${Array.from({length:4},(G,ne)=>`${Q}(b_value_lower[${ne}]), ${Q}(b_value_upper[${ne}])`).join(", ")}); + b_dequantized_values = ${u===1?`${ie}(${Array.from({length:8},(G,ne)=>`(b_quantized_values[${ne}] - ${V?`zero_point${F}`:"zero_point"}) * scale${F}`).join(", ")});`:`(b_quantized_values - ${ie}(${Array(8).fill(`${V?`zero_point${F}`:"zero_point"}`).join(",")})) * scale${F};`}; + workgroup_shared[local_id.x * ${P} + ${Math.floor(F/w)}]${w>1?`[${F%w}]`:""} += ${Array.from({length:8/u},(G,ne)=>`${u===1?`a_data[${ne}] * b_dequantized_values[${ne}]`:`dot(a_data[${ne}], b_dequantized_values[${ne}])`}`).join(" + ")}; + `;return N},ae=()=>{let N=` + var col_index = col * ${w}; + ${V?` + let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2; + var zero_point_byte_count: u32; + var zero_point_word_index: u32; + var zero_point_byte_offset: u32; + let zero_point_nibble_offset: u32 = block & 0x1u; + var zero_point_bits_offset: u32; + var zero_point_word: u32;`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${Q}(8);`} + `;for(let F=0;F> 0x1u); + zero_point_word_index = zero_point_byte_count >> 0x2u; + zero_point_byte_offset = zero_point_byte_count & 0x3u; + zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + zero_point_word = ${V.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point${F} = ${Q}((zero_point_word) & 0xFu);`:""} + col_index += 1;`;return N},ge=()=>{let N=`col_index = col * ${w};`;for(let F=0;F; + var b_value_upper: vec4; + var b_quantized_values: ${ie}; + var b_dequantized_values: ${ie};`,N};return` + var workgroup_shared: array<${H.type.value}, ${P*v}>; + ${E.declareVariables(...q,H)} + ${E.mainStart([v,1,1])} + let output_indices = ${H.offsetToIndices(`(global_idx / ${v}) * ${P}`)}; + let col = output_indices[2]; + let row = output_indices[1]; + let batch = output_indices[0]; + let nBlocksPerCol = uniforms.b_shape[1]; + + for (var block = local_id.x; block < nBlocksPerCol; block += ${v}) { + //process one block + var word_offset: u32 = block * ${r.blockSize/u}; + ${ae()} + for (var word: u32 = 0; word < ${d}; word += ${h}) { + ${ge()} + for (var i: u32 = 0; i < ${h}; i++) { + ${le()} + word_offset += ${8/u}; + } + } + } + workgroupBarrier(); + + if (local_id.x < ${P}) { + var output_value: ${H.type.value} = ${H.type.value}(0); + var workgroup_shared_offset: u32 = local_id.x; + for (var b: u32 = 0u; b < ${v}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${P}; + } + ${H.setByIndices(`${H.type.indices}(batch, row, col + local_id.x)`,"output_value")}; + } + }`};return{name:"MatMulNBits",shaderCache:{hint:`${r.blockSize};${r.bits};${u};${h};${w};${P};${v}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:_,dataType:p}],dispatchGroup:{x:A},programUniforms:y}),getShaderSource:M}},X_=(e,r)=>{let t=e[0].dims,s=t.length,i=t[s-2],n=r.k,o=r.n,a=t.slice(0,s-2),l=xe.size(a),d=e[1].dims[2]/4,p=e[0].dataType,u=Xt(r.k),h=Xt(d),w=a.concat([i,o]),_=128,P=o%8===0?8:o%4===0?4:1,A=_/P,v=A*h*8,y=v/u,S=v/r.blockSize,x=xe.size(w)/P,g=[],M=[l,i,n/u],E=xe.convertShape(e[1].dims).slice();E.splice(-1,1,d/h),g.push(...nt(M)),g.push(...nt(E)),g.push(...nt(e[2].dims)),e.length===4&&g.push(...nt(xe.convertShape(e[3].dims)));let k=[l,i,o];g.push(...nt(k));let B=R=>{let J=M.length,q=$e("a",e[0].dataType,J,u),V=$e("b",12,E.length,h),Y=$e("scales",e[2].dataType,e[2].dims.length),H=[q,V,Y],Q=e.length===4?$e("zero_points",12,e[3].dims.length):void 0;Q&&H.push(Q);let ie=k.length,le=tt("output",e[0].dataType,ie),ae=lr(e[0].dataType),ge=()=>{switch(u){case 1:return` + let a_data0 = vec4<${ae}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]); + let a_data1 = vec4<${ae}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return` + let a_data0 = vec4<${ae}>(sub_a[word_offset], sub_a[word_offset + 1]); + let a_data1 = vec4<${ae}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return` + let a_data0 = sub_a[word_offset]; + let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${u}-component is not supported.`)}};return` + var sub_a: array<${q.type.value}, ${y}>; + var inter_results: array, ${P}>; + ${R.declareVariables(...H,le)} + ${R.mainStart([A,P,1])} + let output_indices = ${le.offsetToIndices(`workgroup_index * ${P}`)}; + let col = output_indices[2]; + let row = output_indices[1]; + let batch = output_indices[0]; + let n_blocks_per_col = uniforms.b_shape[1]; + let num_tiles = (n_blocks_per_col - 1) / ${S} + 1; + + // Loop over shared dimension. + for (var tile: u32 = 0; tile < num_tiles; tile += 1) { + let a_col_start = tile * ${y}; + // load one tile A data into shared memory. + for (var a_offset = local_idx; a_offset < ${y}; a_offset += ${_}) + { + let a_col = a_col_start + a_offset; + if (a_col < uniforms.a_shape[2]) + { + sub_a[a_offset] = ${q.getByIndices(`${q.type.indices}(batch, row, a_col)`)}; + } else { + sub_a[a_offset] = ${q.type.value}(0); + } + } + workgroupBarrier(); + + // each thread process one block + let b_row = col + local_id.y; + let block = tile * ${S} + local_id.x; + ${Q?` + let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2; + let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u); + let zero_point_word_index = zero_point_byte_count >> 0x2u; + let zero_point_byte_offset = zero_point_byte_count & 0x3u; + let zero_point_nibble_offset: u32 = block & 0x1u; + let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + let zero_point_word = ${Q.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point = ${ae}((zero_point_word) & 0xFu);`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${ae}(8);`} + let scale = ${Y.getByOffset("b_row * n_blocks_per_col + block")}; + let b_data = ${V.getByIndices(`${V.type.indices}(b_row, block, 0)`)}; + var word_offset = local_id.x * ${r.blockSize/u}; + for (var i: u32 = 0; i < ${h}; i++) { + ${ge()} + let b_value = ${h===1?"b_data":"b_data[i]"}; + let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu); + let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu); + let b_quantized_values = mat2x4<${ae}>(${Array.from({length:4},(N,F)=>`${ae}(b_value_lower[${F}]), ${ae}(b_value_upper[${F}])`).join(", ")}); + let b_dequantized_values = (b_quantized_values - mat2x4<${ae}>(${Array(8).fill("zero_point").join(",")})) * scale; + inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(N,F)=>`${`dot(a_data${F}, b_dequantized_values[${F}])`}`).join(" + ")}; + word_offset += ${8/u}; + } + workgroupBarrier(); + } + + if (local_idx < ${P}) { + var output_value: ${le.type.value} = ${le.type.value}(0); + for (var b = 0u; b < ${A}; b++) { + output_value += inter_results[local_idx][b]; + } + if (col + local_idx < uniforms.output_shape[2]) + { + ${le.setByIndices(`${le.type.indices}(batch, row, col + local_idx)`,"output_value")} + } + } + }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${r.blockSize};${u};${h};${A};${P}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:w,dataType:p}],dispatchGroup:{x},programUniforms:g}),getShaderSource:B}},J_=(e,r)=>{q_(e.inputs,r),r.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(X_(e.inputs,r)):e.compute(Q_(e.inputs,r))},Y_=e=>Dt(e)}),Z_,eg,tg,rg,sg,ng,ig,og,ag,Pv=je(()=>{pt(),yt(),vt(),Z_=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let r=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(r=e[3].dims[0]*2===e[1].dims[0]),!r)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},eg=(e,r,t)=>{let s="";for(let i=r-1;i>=0;--i)s+=` + k = i32(${e.indicesGet("indices",i)}) - ${rt("uniforms.pads",i,t)}; + if (k < 0) { + break; + } + if (k >= i32(${rt("uniforms.x_shape",i,r)})) { + break; + } + offset += k * i32(${rt("uniforms.x_strides",i,r)}); + `;return` + value = ${e.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${s} + value = x[offset]; + } + `},tg=(e,r,t)=>{let s="";for(let i=r-1;i>=0;--i)s+=` + k = i32(${e.indicesGet("indices",i)}) - ${rt("uniforms.pads",i,t)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${rt("uniforms.x_shape",i,r)}) - 1); + k = k % _2n_1; + if(k >= i32(${rt("uniforms.x_shape",i,r)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${rt("uniforms.x_strides",i,r)}); + `;return` + var offset = 0; + var k = 0; + ${s} + value = x[offset]; + `},rg=(e,r,t)=>{let s="";for(let i=r-1;i>=0;--i)s+=` + k = i32(${e.indicesGet("indices",i)}) - ${rt("uniforms.pads",i,t)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${rt("uniforms.x_shape",i,r)})) { + k = i32(${rt("uniforms.x_shape",i,r)}) - 1; + } + offset += k * i32(${rt("uniforms.x_strides",i,r)}); + `;return` + var offset = 0; + var k = 0; + ${s} + value = x[offset]; + `},sg=(e,r,t)=>{let s="";for(let i=r-1;i>=0;--i)s+=` + k = i32(${e.indicesGet("indices",i)}) - ${rt("uniforms.pads",i,t)}; + if (k < 0) { + k += i32(${rt("uniforms.x_shape",i,r)}]); + } + if (k >= i32(${rt("uniforms.x_shape",i,r)})) { + k -= i32(${rt("uniforms.x_shape",i,r)}); + } + offset += k * i32(${rt("uniforms.x_strides",i,r)}); + `;return` + var offset = 0; + var k = 0; + ${s} + value = x[offset]; + `},ng=(e,r,t)=>{switch(t.mode){case 0:return eg(e,r,t.pads.length);case 1:return tg(e,r,t.pads.length);case 2:return rg(e,r,t.pads.length);case 3:return sg(e,r,t.pads.length);default:throw new Error("Invalid mode")}},ig=(e,r)=>{let t=xe.padShape(e[0].dims.slice(),r.pads),s=e[0].dims,i=xe.size(t),n=[{type:12,data:i},{type:6,data:r.pads}],o=e.length>=3&&e[2].data;r.mode===0&&n.push({type:o?e[2].dataType:1,data:r.value}),n.push(...nt(e[0].dims,t));let a=["rank"],l=d=>{let p=tt("output",e[0].dataType,t.length),u=$e("x",e[0].dataType,s.length),h=u.type.value,w=ng(p,s.length,r),_=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:r.pads.length}];return r.mode===0&&_.push({name:"constant_value",type:o?h:"f32"}),` + ${d.registerUniforms(_).declareVariables(u,p)} + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${p.offsetToIndices("global_idx")}; + + var value = ${h}(0); + ${w} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${r.mode}${o}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(xe.size(t)/64)},programUniforms:n}),getShaderSource:l}},og=(e,r)=>{if(e.length>1){let t=e[1].getBigInt64Array(),s=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,i=e[0].dims.length,n=new Int32Array(2*i).fill(0);if(e.length>=4){let a=e[3].getBigInt64Array();for(let l=0;ln[Number(l)]=Number(a));let o=[];return n.forEach(a=>o.push(a)),{mode:r.mode,value:s,pads:o}}else return r},ag=(e,r)=>{Z_(e.inputs);let t=og(e.inputs,r);e.compute(ig(e.inputs,t),{inputs:[0]})}}),Ri,bc,vc,xc,Tc,lg,dg,Ec,Pc,cg,ug,Cc,pg,hg,Sc,mg,fg,_g,gg,Cv=je(()=>{ns(),pt(),yt(),vt(),Ri=e=>{if(Vt.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},bc=(e,r,t)=>{let s=r.format==="NHWC",i=e.dims.slice();s&&i.splice(1,0,i.pop());let n=Object.hasOwnProperty.call(r,"dilations"),o=r.kernelShape.slice(),a=r.strides.slice(),l=n?r.dilations.slice():[],d=r.pads.slice();va.adjustPoolAttributes(t,i,o,a,l,d);let p=va.computePoolOutputShape(t,i,a,l,o,d,r.autoPad),u=Object.assign({},r);n?Object.assign(u,{kernelShape:o,strides:a,pads:d,dilations:l,cacheKey:r.cacheKey}):Object.assign(u,{kernelShape:o,strides:a,pads:d,cacheKey:r.cacheKey});let h=p.slice();return h.push(h.splice(1,1)[0]),[u,s?h:p]},vc=(e,r)=>{let t=r.format==="NHWC",s=xe.size(e),i=xe.size(r.kernelShape),n=[{type:12,data:s},{type:12,data:i}],o=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(r.kernelShape.length<=2){let a=r.kernelShape[r.kernelShape.length-1],l=r.strides[r.strides.length-1],d=r.pads[r.pads.length/2-1],p=r.pads[r.pads.length-1],u=!!(d+p);n.push({type:12,data:a},{type:12,data:l},{type:12,data:d},{type:12,data:p}),o.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let h=!1;if(r.kernelShape.length===2){let w=r.kernelShape[r.kernelShape.length-2],_=r.strides[r.strides.length-2],P=r.pads[r.pads.length/2-2],A=r.pads[r.pads.length-2];h=!!(P+A),n.push({type:12,data:w},{type:12,data:_},{type:12,data:P},{type:12,data:A}),o.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[n,o,!0,u,h]}else{if(t)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let a=xe.computeStrides(r.kernelShape);n.push({type:12,data:a},{type:12,data:r.pads},{type:12,data:r.strides}),o.push({name:"kernelStrides",type:"u32",length:a.length},{name:"pads",type:"u32",length:r.pads.length},{name:"strides",type:"u32",length:r.strides.length});let l=r.pads.reduce((d,p)=>d+p);return[n,o,!!l,!1,!1]}},xc=(e,r,t,s,i,n,o,a,l,d,p,u)=>{let h=i.format==="NHWC",w=r.type.value,_=tt("output",r.type.tensor,s);if(i.kernelShape.length<=2){let P="",A="",v="",y=t-(h?2:1);if(p?P=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${y}] = indices[${y}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${y}] < 0 || xIndices[${y}] + >= uniforms.x_shape[${y}]) { + pad++; + continue; + } + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + }`:P=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${y}] = indices[${y}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + }`,i.kernelShape.length===2){let S=t-(h?3:2);u?A=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${S}] = indices[${S}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${S}] < 0 || xIndices[${S}] >= uniforms.x_shape[${S}]) { + pad += i32(uniforms.kw); + continue; + } + `:A=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${S}] = indices[${S}] * uniforms.sh - uniforms.phStart + j; + `,v=` + } + `}return` + ${e.registerUniforms(l).declareVariables(r,_)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${_.offsetToIndices("global_idx")}; + var xIndices = ${_.offsetToIndices("global_idx")}; + + var value = ${w}(${a}); + var pad = 0; + ${A} + ${P} + ${v} + ${o} + + output[global_idx] = value; + }`}else{if(h)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let P=i.kernelShape.length,A=i.pads.length,v="";return d?v=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + }`:v=` + } + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + `,` + ${e.registerUniforms(l).declareVariables(r,_)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${_.offsetToIndices("global_idx")}; + var xIndices = ${_.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${w}(${a}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${P-1}u; j++) { + offsets[j] = offset / ${rt("uniforms.kernelStrides","j",P)}; + offset -= offsets[j] * ${rt("uniforms.kernelStrides","j",P)}; + } + offsets[${P-1}] = offset; + + isPad = false; + for (var j = ${t-P}u; j < ${t}u; j++) { + xIndices[j] = indices[j] * ${rt("uniforms.strides",`j - ${t-P}u`,P)} + + offsets[j - ${t-P}u] - ${rt("uniforms.pads","j - 2u",A)}; + ${v} + } + ${o} + + output[global_idx] = value; + }`}},Tc=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,lg=e=>`${Tc(e)};${e.countIncludePad}`,dg=e=>`${Tc(e)};${e.storageOrder};${e.dilations}`,Ec=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}),Pc=(e,r,t,s)=>{let[i,n]=bc(r,s,t),o=$e("x",r.dataType,r.dims.length),a=o.type.value,l="value += x_val;",d="";i.countIncludePad?d+=`value /= ${a}(uniforms.kernelSize);`:d+=`value /= ${a}(i32(uniforms.kernelSize) - pad);`;let[p,u,h,w,_]=vc(n,i);p.push(...nt(r.dims,n));let P=["rank"];return{name:e,shaderCache:{hint:`${s.cacheKey};${h};${w};${_}`,inputDependencies:P},getRunData:()=>({outputs:[{dims:n,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(xe.size(n)/64)},programUniforms:p}),getShaderSource:A=>xc(A,o,r.dims.length,n.length,i,l,d,0,u,h,w,_)}},cg=e=>{let r=e.count_include_pad!==0,t=Ec(e);if(t.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let s={countIncludePad:r,...t,cacheKey:""};return{...s,cacheKey:lg(s)}},ug=(e,r)=>{Ri(e.inputs),e.compute(Pc("AveragePool",e.inputs[0],!1,r))},Cc={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},pg=e=>{let r=e.format;return{format:r,...Cc,cacheKey:r}},hg=(e,r)=>{Ri(e.inputs),e.compute(Pc("GlobalAveragePool",e.inputs[0],!0,r))},Sc=(e,r,t,s)=>{let[i,n]=bc(r,s,t),o=` + value = max(x_val, value); + `,a="",l=$e("x",r.dataType,r.dims.length),d=["rank"],[p,u,h,w,_]=vc(n,i);return p.push(...nt(r.dims,n)),{name:e,shaderCache:{hint:`${s.cacheKey};${h};${w};${_}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:n,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(xe.size(n)/64)},programUniforms:p}),getShaderSource:P=>xc(P,l,r.dims.length,n.length,i,o,a,r.dataType===10?-65504:-1e5,u,h,w,_)}},mg=(e,r)=>{Ri(e.inputs),e.compute(Sc("MaxPool",e.inputs[0],!1,r))},fg=e=>{let r=e.storage_order,t=e.dilations,s=Ec(e);if(r!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let i={storageOrder:r,dilations:t,...s,cacheKey:""};return{...i,cacheKey:dg(i)}},_g=e=>{let r=e.format;return{format:r,...Cc,cacheKey:r}},gg=(e,r)=>{Ri(e.inputs),e.compute(Sc("GlobalMaxPool",e.inputs[0],!0,r))}}),wg,yg,Mg,bg,Sv=je(()=>{pt(),yt(),Yt(),vt(),wg=(e,r)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((t,s)=>t===e[2].dims[s]).reduce((t,s)=>t&&s,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(r.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((i,n)=>n===r.axis||i===e[0].dims[n]).reduce((i,n)=>i&&n,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let t=e[0].dims[r.axis],s=e[1].dims[r.axis];if(r.blockSizeMath.ceil(t/(s-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},yg=(e,r)=>{let t=xe.normalizeAxis(r.axis,e[0].dims.length),s=e[0].dataType,i=s===3,n=e[0].dims,o=e[1].dataType,a=xe.size(n),l=s===3||s===2,d=l?[Math.ceil(xe.size(e[0].dims)/4)]:e[0].dims,p=e[1].dims,u=e.length>2?e[2]:void 0,h=u?l?[Math.ceil(xe.size(u.dims)/4)]:u.dims:void 0,w=p.length===0||p.length===1&&p[0]===1,_=w===!1&&p.length===1,P=Xt(a),A=w&&(!l||P===4),v=A?P:1,y=A&&!l?P:1,S=$e("input",l?12:s,d.length,y),x=$e("scale",o,p.length),g=u?$e("zero_point",l?12:s,h.length):void 0,M=tt("output",o,n.length,v),E=[S,x];g&&E.push(g);let k=[d,p];u&&k.push(h);let B=[{type:12,data:a/v},{type:12,data:t},{type:12,data:r.blockSize},...nt(...k,n)],R=J=>{let q=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${J.registerUniforms(q).declareVariables(...E,M)} + ${J.mainStart()} + ${J.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${M.offsetToIndices("global_idx")}; + + // Set input x + ${l?` + let input = ${S.getByOffset("global_idx / 4")}; + let x_vec = ${i?"unpack4xI8(input)":"unpack4xU8(input)"}; + let x_value = ${v===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${S.getByOffset("global_idx")};`}; + + // Set scale input + ${w?`let scale_value= ${x.getByOffset("0")}`:_?` + let scale_index = ${M.indicesGet("output_indices","uniforms.axis")}; + let scale_value= ${x.getByOffset("scale_index")};`:` + var scale_indices: ${x.type.indices} = output_indices; + let index = ${x.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; + ${x.indicesSet("scale_indices","uniforms.axis","index")}; + let scale_value= ${x.getByIndices("scale_indices")};`}; + + // Set zero-point input + ${g?w?l?` + let zero_point_input = ${g.getByOffset("0")}; + let zero_point_vec = ${i?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${g.getByOffset("0")}`:_?l?` + let zero_point_index = ${M.indicesGet("output_indices","uniforms.axis")}; + let zero_point_input = ${g.getByOffset("zero_point_index / 4")}; + let zero_point_vec = ${i?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_index % 4]`:` + let zero_point_index = ${M.indicesGet("output_indices","uniforms.axis")}; + let zero_point_value = ${g.getByOffset("zero_point_index")};`:l?` + let zero_point_offset = ${x.indicesToOffset("scale_indices")}; + let zero_point_input = ${g.getByOffset("zero_point_offset / 4")}; + let zero_point_vec = ${i?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${g.getByIndices("scale_indices")};`:`let zero_point_value = ${l?i?"i32":"u32":S.type.value}(0);`}; + // Compute and write output + ${M.setByOffset("global_idx",`${M.type.value}(x_value - zero_point_value) * scale_value`)}; + }`};return{name:"DequantizeLinear",shaderCache:{hint:r.cacheKey,inputDependencies:g?["rank","rank","rank"]:["rank","rank"]},getShaderSource:R,getRunData:()=>({outputs:[{dims:n,dataType:o}],dispatchGroup:{x:Math.ceil(a/v/64),y:1,z:1},programUniforms:B})}},Mg=(e,r)=>{wg(e.inputs,r),e.compute(yg(e.inputs,r))},bg=e=>Dt({axis:e.axis,blockSize:e.blockSize})}),vg,xg,Tg,$v=je(()=>{ns(),pt(),vt(),vg=(e,r,t)=>{let s=e===r,i=er&&t>0;if(s||i||n)throw new Error("Range these inputs' contents are invalid.")},xg=(e,r,t,s)=>{let i=Math.abs(Math.ceil((r-e)/t)),n=[i],o=i,a=[{type:12,data:o},{type:s,data:e},{type:s,data:t},...nt(n)],l=d=>{let p=tt("output",s,n.length),u=p.type.value,h=[{name:"outputSize",type:"u32"},{name:"start",type:u},{name:"delta",type:u}];return` + ${d.registerUniforms(h).declareVariables(p)} + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${u}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${s}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:n,dataType:s}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:a})}},Tg=e=>{let r=0,t=0,s=0;e.inputs[0].dataType===6?(r=e.inputs[0].getInt32Array()[0],t=e.inputs[1].getInt32Array()[0],s=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(r=e.inputs[0].getFloat32Array()[0],t=e.inputs[1].getFloat32Array()[0],s=e.inputs[2].getFloat32Array()[0]),Vt.webgpu.validateInputContent&&vg(r,t,s),e.compute(xg(r,t,s,e.inputs[0].dataType),{inputs:[]})}}),Eg,Pg,Cg,Sg,kv=je(()=>{pt(),yt(),Yt(),vt(),Eg=(e,r,t,s)=>{if(e!=="none"&&s!=="i32"&&s!=="u32"&&s!=="f32")throw new Error(`Input ${s} is not supported with reduction ${e}.`);let i=`{ + var oldValue = 0; + loop { + let newValueF32 =`,n=`; + let newValue = bitcast(newValueF32); + let res = atomicCompareExchangeWeak(&${r}, oldValue, newValue); + if res.exchanged { + break; + } + oldValue = res.old_value; + } + }`;switch(e){case"none":return`${r}=${t};`;case"add":return s==="i32"||s==="u32"?`atomicAdd(&${r}, bitcast<${s}>(${t}));`:` + ${i}bitcast<${s}>(oldValue) + (${t})${n}`;case"max":return s==="i32"||s==="u32"?`atomicMax(&${r}, bitcast<${s}>(${t}));`:` + ${i}max(bitcast(oldValue), (${t}))${n}`;case"min":return s==="i32"||s==="u32"?`atomicMin(&${r}, bitcast<${s}>(${t}));`:`${i}min(bitcast<${s}>(oldValue), (${t}))${n}`;case"mul":return`${i}(bitcast<${s}>(oldValue) * (${t}))${n}`;default:throw new Error(`Reduction ${e} is not supported.`)}},Pg=(e,r)=>{let t=e[0].dims,s=e[1].dims,i=t,n=1,o=Math.ceil(xe.size(s)/n),a=s[s.length-1],l=xe.sizeFromDimension(t,a),d=[{type:12,data:o},{type:12,data:a},{type:12,data:l},...nt(e[1].dims,e[2].dims,i)],p=u=>{let h=$e("indices",e[1].dataType,e[1].dims.length),w=$e("updates",e[2].dataType,e[2].dims.length,n),_=r.reduction!=="none"&&r.reduction!==""?zp("output",e[0].dataType,i.length):tt("output",e[0].dataType,i.length,n);return` + ${u.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(h,w,_)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var hasDuplicates = false; + if (${r.reduction==="none"}) { + let n = ${xe.size(s)}; + for (var i = 0; i < n; i = i + 1) { + for (var j = i + 1; j < n; j = j + 1) { + var index_i = i32(indices[i].x); + var index_j = i32(indices[j].x); + if (index_i == index_j) { + hasDuplicates = true; + break; + } + } + if (hasDuplicates) { + break; + } + } + } + + var data_offset = 0u; + var indices_start = uniforms.last_index_dimension * global_idx; + if (${r.reduction==="none"} && hasDuplicates) { + if (global_idx != 0u) { + return; + } + indices_start = 0u; + } + let indices_end = indices_start + uniforms.last_index_dimension; + for (var i = indices_start; i < indices_end; i++) { + var index = i32(indices[i].x); + ${e[0].dims.length===1?` + let element_count_dim = uniforms.output_strides; + let dim_value = uniforms.output_shape;`:` + let element_count_dim = uniforms.output_strides[i - indices_start]; + let dim_value = uniforms.output_shape[i - indices_start + uniforms.last_index_dimension];`} + if (index >= 0) { + if (index >= i32(dim_value)) { + index = i32(dim_value - 1); + } + } else { + if (index < -i32(dim_value)) { + index = 0; + } else { + index += i32(dim_value); + } + } + data_offset += u32((u32(index) * element_count_dim)); + } + + for (var i = 0u; i < uniforms.num_updates_elements; i++) { + let value = updates[uniforms.num_updates_elements * global_idx + i]; + ${Eg(r.reduction,"output[data_offset + i]","value",_.type.value)} + } + + }`};return{name:"ScatterND",shaderCache:{hint:`${r.cacheKey}_${r.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:d}),getShaderSource:p}},Cg=e=>Dt({reduction:e.reduction}),Sg=(e,r)=>{e.compute(Pg(e.inputs,r),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),$g,kg,Ig,$c,Ag,Og,Fg,Dg,Lg,zg,Bg,Rg,kc,Ng,jg,Ug,Vg,Wg,Gg,Kg,Iv=je(()=>{pt(),yt(),Yt(),vt(),$g=(e,r)=>{if(e.every(t=>t>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(r.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 + one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(r.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")}},kg=(e,r,t)=>{r.every(i=>i>=0&&i{throw new Error("Resize requires axes input values to be positive and less than rank")}));let s=new Array(t).fill(1);return r.forEach((i,n)=>s[i]=e[n]),s},Ig=(e,r,t,s,i,n)=>{let[o,a,l]=t>10?[1,2,3]:[-1,e.length>1?1:-1,-1],d=e[0].dims.length;if(o>0&&e.length>o&&e[o].dims.length>0)e[o].getFloat32Array().forEach(p=>n.push(p));else if(r.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(a>0&&e.length>a&&e[a].dims.length===1&&e[a].dims[0]>0){if(e[a].getFloat32Array().forEach(p=>s.push(p)),s.length!==0&&s.length!==d&&t>=18&&s.length!==r.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");$g(s,r),r.axes.length>0&&kg(s,r.axes,d).forEach((p,u)=>s[u]=p)}if(l>0&&e.length>l&&e[l].dims.length===1&&e[l].dims[0]>0&&(e[l].getBigInt64Array().forEach(p=>i.push(Number(p))),i.length!==0&&i.length!==d&&t>=18&&i.length!==r.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(r.axes.length>0){if(s.length!==0&&s.length!==r.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(i.length!==0&&i.length!==r.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof s<"u"&&typeof i<"u"&&s.length>0&&i.length>d)throw new Error("Resize requires only of scales or sizes to be specified")},$c=(e,r,t,s)=>` + // The whole part and the fractional part are calculated separately due to inaccuracy of floating + // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an + // offset-by-one error later in floor(). + let big = (${e}) * (${r}); + let whole = ${s}(big / (${t})); + let fract = ${s}(big % (${t})) / ${s}(${t}); + return whole + fract; +`,Ag=(e,r)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, + lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${r} { `+(()=>{switch(e){case"asymmetric":return` + if (xScale < 1.0 || floor(xScale) != xScale) { + return ${r}(xResized) / ${r}(xScale); + } else { + ${$c("xResized","lengthOriginal","lengthResized",r)} + } + `;case"pytorch_half_pixel":return`if (lengthResized > 1) { + return (${r}(xResized) + 0.5) / ${r}(xScale) - 0.5; + } else { + return 0.0; + }`;case"tf_half_pixel_for_nn":return`return (${r}(xResized) + 0.5) / ${r}(xScale);`;case"align_corners":return`if (lengthResized == 1) { + return 0.0; + } else { + ${$c("xResized","lengthOriginal - 1","lengthResized - 1",r)} + }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { + return ${r}(roiStart) * ${r}(lengthOriginal - 1) + + (${r}(xResized) * ${r}(roiEnd - roiStart) * ${r}(lengthOriginal - 1)) / + ${r}(lengthResized - 1); + } else { + return 0.5 * ${r}(roiStart + roiEnd) * ${r}(lengthOriginal - 1); + }`;case"half_pixel_symmetric":return`const outputWidth = ${r}xScale * ${r}(lengthResized); + const adjustment = ${r}(lengthResized) / outputWidth; + const center = ${r}(lengthOriginal) / 2; + const offset = center * (1 - adjustment); + return offset + ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;case"half_pixel":return`return ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",Og=(e,r,t)=>`fn getNearestPixelFromOriginal(xOriginal: ${t}, isDownSample: bool) -> ${t} {`+(()=>{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(r<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Fg=(e,r,t)=>{let s=new Array(t).fill(0).concat(new Array(t).fill(1)),i=e.length===0?s:e.slice();return r.length>0?(r.forEach((n,o)=>{s[n]=i[o],s[o+t]=i[r.length+o]}),s):i},Dg=(e,r,t,s)=>{let i=[];if(t.length>0)if(s.length>0){if(e.forEach(n=>i.push(n)),Math.max(...s)>e.length)throw new Error("axes is out of bound");s.forEach((n,o)=>i[n]=t[o])}else t.forEach(n=>i.push(n));else{if(r.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((n,o)=>Math.round(n*r[o]))}return i},Lg=(e,r,t)=>{let s=(()=>{switch(t.keepAspectRatioPolicy){case"not_larger":return t.axes.length>0?Math.min(...t.axes.map(n=>r[n]),Number.MAX_VALUE):Math.min(...r,Number.MAX_VALUE);case"not_smaller":return t.axes.length>0?Math.max(...t.axes.map(n=>r[n]),Number.MIN_VALUE):Math.max(...r,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${t.keepAspectRatioPolicy} is not supported`)}})();r.fill(1,0,r.length);let i=e.slice();return t.axes.length>0?(t.axes.forEach(n=>r[n]=s),t.axes.forEach(n=>i[n]=Math.round(e[n]*r[n]))):(r.fill(s,0,r.length),i.forEach((n,o)=>i[o]=Math.round(n*r[o]))),i},zg=(e,r,t,s,i)=>` + fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${t.length}> { + var original_indices: array<${e.type.value}, ${t.length}>; + for (var i:u32 = 0; i < ${t.length}; i++) { + var output_index = ${e.indicesGet("output_indices","i")}; + var scale = ${rt("uniforms.scales","i",s)}; + var roi_low = ${rt("uniforms.roi","i",i)}; + var roi_hi = ${rt("uniforms.roi",`i + ${r.length}`,i)}; + if (scale == 1.0) { + original_indices[i] = ${e.type.value}(output_index); + } else { + var input_shape_i = ${rt("uniforms.input_shape","i",r.length)}; + var output_shape_i = ${rt("uniforms.output_shape","i",t.length)}; + original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + } + } + return original_indices; + }`,Bg=(e,r,t,s,i,n,o)=>` + fn calculateInputIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + for (var i:u32 = 0; i < ${s.length}; i++) { + var output_index = ${r.indicesGet("output_indices","i")}; + var input_index: u32; + var scale = ${rt("uniforms.scales","i",i)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${rt("uniforms.roi","i",n)}; + var roi_hi = ${rt("uniforms.roi",`i + ${t.length}`,n)}; + var input_shape_i = ${rt("uniforms.input_shape","i",t.length)}; + var output_shape_i = ${rt("uniforms.output_shape","i",s.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${o} || (original_idx >= 0 && original_idx < ${r.type.value}(input_shape_i))) { + if (original_idx < 0) { + input_index = 0; + } else if (original_idx > ${r.type.value}(input_shape_i - 1)) { + input_index = input_shape_i - 1; + } else { + input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1)); + } + } else { + input_index = u32(original_idx); + } + } + ${e.indicesSet("input_indices","i","input_index")} + } + return input_indices; + }`,Rg=(e,r)=>` + fn checkInputIndices(input_indices: ${e.type.indices}) -> bool { + for (var i:u32 = 0; i < ${r.length}; i++) { + var input_index = ${e.indicesGet("input_indices","i")}; + if (input_index < 0 || input_index >= ${rt("uniforms.input_shape","i",r.length)}) { + return false; + } + } + return true; + }`,kc=(e,r,t,s)=>e.rank>s?` + ${e.indicesSet("input_indices",r,"channel")}; + ${e.indicesSet("input_indices",t,"batch")}; +`:"",Ng=(e,r,t,s,i)=>{let[n,o,a,l]=t.length===2?[-1,0,1,-1]:[0,2,3,1],d=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",o,`max(0, min(row, ${t[o]} - 1))`)}; + ${e.indicesSet("input_indices",a,`max(0, min(col, ${t[a]} - 1))`)}; + ${kc(e,l,n,2)} + return ${e.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${r.type.indices}) -> ${d} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${d} = originalIndices[${o}]; + var col:${d} = originalIndices[${a}]; + ${s?`if (row < 0 || row > (${t[o]} - 1) || col < 0 || col > (${t[a]} - 1)) { + return ${i}; + }`:""}; + row = max(0, min(row, ${t[o]} - 1)); + col = max(0, min(col, ${t[a]} - 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 = ${t.length>2?`u32(originalIndices[${l}])`:"0"}; + var batch: u32 = ${t.length>2?`u32(originalIndices[${n}])`:"0"}; + var x11: ${d} = getInputValue(batch, channel, row1, col1); + var x12: ${d} = getInputValue(batch, channel, row1, col2); + var x21: ${d} = getInputValue(batch, channel, row2, col1); + var x22: ${d} = getInputValue(batch, channel, row2, col2); + var dx1: ${d} = abs(row - ${d}(row1)); + var dx2: ${d} = abs(${d}(row2) - row); + var dy1: ${d} = abs(col - ${d}(col1)); + var dy2: ${d} = abs(${d}(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); + }`},jg=(e,r,t,s,i,n,o,a,l,d)=>{let p=t.length===2,[u,h]=p?[0,1]:[2,3],w=e.type.value,_=P=>{let A=P===u?"row":"col";return` + fn ${A}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${r.type.indices}) -> ${w} { + var output_index = ${r.indicesGet("output_indices",P)}; + var originalIdx: ${w} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[P]}, + ${s[P]}, ${t[P]}, ${n[P]}, ${n[P]} + ${t.length}); + var fractOriginalIdx: ${w} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${a} && (originalIdx < 0 || originalIdx > (${t[P]} - 1))) { + return ${l}; + } + var data: array<${w}, 4> = array<${w}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${A}: ${w} = originalIdx + ${w}(i); + if (${A} < 0 || ${A} >= ${t[P]}) { + ${d?`coefs[i + 1] = 0.0; + continue;`:a?`return ${l};`:`${A} = max(0, min(${A}, ${t[P]} - 1));`}; + } + var input_indices_copy: ${e.type.indices} = input_indices; + ${e.indicesSet("input_indices_copy",P,`u32(${A})`)}; + data[i + 1] = ${P===u?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${_(u)}; + ${_(h)}; + fn getCubicInterpolationCoefs(s: ${w}) -> array<${w}, 4> { + var absS = abs(s); + var coeffs: array<${w}, 4> = array<${w}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${w} = 1.0 - absS; + var twoMinusAbsS: ${w} = 2.0 - absS; + var onePlusAbsS: ${w} = 1.0 + absS; + coeffs[0] = ((${o} * onePlusAbsS - 5 * ${o}) * onePlusAbsS + 8 * ${o}) * onePlusAbsS - 4 * ${o}; + coeffs[1] = ((${o} + 2) * absS - (${o} + 3)) * absS * absS + 1; + coeffs[2] = ((${o} + 2) * oneMinusAbsS - (${o} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${o} * twoMinusAbsS - 5 * ${o}) * twoMinusAbsS + 8 * ${o}) * twoMinusAbsS - 4 * ${o}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${w}, 4>, coefs: array<${w}, 4>) -> ${w} { + var coefsSum: ${w} = 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: ${r.type.indices}) -> ${w} { + var input_indices: ${e.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},Ug=(e,r,t,s,i)=>{let[n,o,a,l,d]=t.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],p=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${p} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",o,`max(0, min(depth, ${t[o]} - 1))`)}; + ${e.indicesSet("input_indices",a,`max(0, min(height, ${t[a]} - 1))`)}; + ${e.indicesSet("input_indices",l,`max(0, min(width, ${t[l]} - 1))`)}; + ${kc(e,d,n,3)} + return ${e.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${r.type.indices}) -> ${p} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${p} = originalIndices[${o}]; + var height:${p} = originalIndices[${a}]; + var width:${p} = originalIndices[${l}]; + ${s?`if (depth < 0 || depth > (${t[o]} - 1) || height < 0 || height > (${t[a]} - 1) || width < 0 || (width > ${t[l]} - 1)) { + return ${i}; + }`:""}; + + depth = max(0, min(depth, ${t[o]} - 1)); + height = max(0, min(height, ${t[a]} - 1)); + width = max(0, min(width, ${t[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 = ${t.length>3?`u32(originalIndices[${d}])`:"0"}; + var batch: u32 = ${t.length>3?`u32(originalIndices[${n}])`:"0"}; + + var x111: ${p} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${p} = abs(depth - ${p}(depth1)); + var dx2: ${p} = abs(${p}(depth2) - depth); + var dy1: ${p} = abs(height - ${p}(height1)); + var dy2: ${p} = abs(${p}(height2) - height); + var dz1: ${p} = abs(width - ${p}(width1)); + var dz2: ${p} = abs(${p}(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); + }`},Vg=(e,r,t,s,i,n)=>{let o=e.dims,a=Fg(n,r.axes,o.length),l=Dg(o,s,i,r.axes),d=s.slice();s.length===0&&(d=o.map((y,S)=>y===0?1:l[S]/y),r.keepAspectRatioPolicy!=="stretch"&&(l=Lg(o,d,r)));let p=tt("output",e.dataType,l.length),u=$e("input",e.dataType,o.length),h=xe.size(l),w=o.length===l.length&&o.every((y,S)=>y===l[S]),_=r.coordinateTransformMode==="tf_crop_and_resize",P=r.extrapolationValue,A=u.type.value,v=y=>` + ${w?"":` + ${Ag(r.coordinateTransformMode,A)}; + ${(()=>{switch(r.mode){case"nearest":return` + ${Rg(u,o)}; + ${Og(r.nearestMode,t,A)}; + ${Bg(u,p,o,l,d.length,a.length,_)}; + `;case"linear":return` + ${zg(p,o,l,d.length,a.length)}; + ${(()=>{if(o.length===2||o.length===4)return`${Ng(u,p,o,_,P)}`;if(o.length===3||o.length===5)return`${Ug(u,p,o,_,P)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(o.length===2||o.length===4)return`${jg(u,p,o,l,d,a,r.cubicCoeffA,_,r.extrapolationValue,r.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; + `;default:throw Error("Invalid resize mode")}})()}; + `} + ${y.registerUniform("output_size","u32").registerUniform("scales","f32",d.length).registerUniform("roi","f32",a.length).declareVariables(u,p)} + ${y.mainStart()} + ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${w?"output[global_idx] = input[global_idx];":` + let output_indices = ${p.offsetToIndices("global_idx")}; + var input_indices: ${u.type.indices}; + ${(()=>{switch(r.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${u.getByIndices("input_indices")}; + } else { + output[global_idx] = ${r.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${o.length===2||o.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${r.mode}`)}})()}; +`} + }`;return{name:"Resize",shaderCache:{hint:`${r.cacheKey}|${t}|${d.length>0?r.mode==="cubic"?d:d.length:""}|${i.length>0?i:""}|${a.length>0?a:""}|${w}|${r.mode==="nearest"?o.length:o}`,inputDependencies:["rank"]},getShaderSource:v,getRunData:()=>({outputs:[{dims:l,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:[{type:12,data:h},{type:1,data:d},{type:1,data:a},...nt(o,l)]})}},Wg=e=>{let r=e.customDataBuffer;return new Uint32Array(r,r.byteOffset,1)[0]},Gg=(e,r)=>{let t=[],s=[],i=[],n=Wg(e);if(r.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Ig(e.inputs,r,n,t,s,i),e.compute(Vg(e.inputs[0],r,n,t,s,i),{inputs:[0]})},Kg=e=>{let r=e.antialias,t=e.axes,s=e.coordinateTransformMode,i=e.cubicCoeffA,n=e.excludeOutside!==0,o=e.extrapolationValue,a=e.keepAspectRatioPolicy,l=e.mode,d=e.nearestMode===""?"simple":e.nearestMode;return Dt({antialias:r,axes:t,coordinateTransformMode:s,cubicCoeffA:i,excludeOutside:n,extrapolationValue:o,keepAspectRatioPolicy:a,mode:l,nearestMode:d})}}),Hg,qg,Qg,Av=je(()=>{pt(),yt(),Yt(),vt(),Hg=(e,r)=>{let[t,s,i,n]=e,{numHeads:o,rotaryEmbeddingDim:a}=r;if(t.dims.length!==3&&t.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${t.dims.length}`);if(!xe.areEqual(s.dims,[])&&!xe.areEqual(s.dims,[1])&&s.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${s.dims.length}`);if(i.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(n.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${n.dims.length}`);if(!xe.areEqual(i.dims,n.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(a>0&&o===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let l=t.dims[0],d=t.dims[t.dims.length-2],p=i.dims[0],u=xe.sizeFromDimension(t.dims,1)/d,h=a===0?i.dims[1]*2:u/o;if(a>h)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(s.dims.length===2){if(l!==s.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${s.dims[0]}`);if(d!==s.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${s.dims[1]}`)}if(h/2!==i.dims[1]&&a/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 ${i.dims[1]}`);if(d>p)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},qg=(e,r)=>{let{interleaved:t,numHeads:s,rotaryEmbeddingDim:i,scale:n}=r,o=e[0].dims[0],a=xe.sizeFromDimension(e[0].dims,1),l=e[0].dims[e[0].dims.length-2],d=a/l,p=e[2].dims[1],u=i===0?p*2:d/s,h=new Array(o,l,d/u,u-p),w=xe.computeStrides(h),_=[{type:1,data:n},{type:12,data:h},{type:12,data:w},...e[0].dims.length===3?new Array({type:12,data:[a,d,u,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[a,u,l*u,1]}):[],...nt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],P=A=>{let v=$e("input",e[0].dataType,e[0].dims.length),y=$e("position_ids",e[1].dataType,e[1].dims.length),S=$e("cos_cache",e[2].dataType,e[2].dims.length),x=$e("sin_cache",e[3].dataType,e[3].dims.length),g=tt("output",e[0].dataType,e[0].dims.length);return A.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:h.length},{name:"global_strides",type:"u32",length:w.length},{name:"input_output_strides",type:"u32",length:w.length}]),` + ${A.declareVariables(v,y,S,x,g)} + + ${A.mainStart(jn)} + let half_rotary_emb_dim = uniforms.${S.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${A.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${y.broadcastedIndicesToOffset("bsnh.xy",tt("",y.type.tensor,2))}; + let position_id = + u32(${y.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${t}); + let j = i + select(half_rotary_emb_dim, 1, ${t}); + let re = ${v.getByOffset("i")} * ${S.get("position_id","bsnh[3]")} - + ${v.getByOffset("j")} * ${x.get("position_id","bsnh[3]")}; + ${g.setByOffset("i","re")} + let im = ${v.getByOffset("i")} * ${x.get("position_id","bsnh[3]")} + + ${v.getByOffset("j")} * ${S.get("position_id","bsnh[3]")}; + ${g.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${g.setByOffset("k",v.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:Dt({interleaved:t}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:P,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(xe.size(h)/jn)},programUniforms:_})}},Qg=(e,r)=>{Hg(e.inputs,r),e.compute(qg(e.inputs,r))}}),Xg,Jg,Yg,Ov=je(()=>{pt(),yt(),vt(),Xg=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let r=e[0],t=e[1],s=e[2];if(r.dataType!==t.dataType||r.dataType!==s.dataType)throw new Error("All inputs must have the same data type");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Input must be 2D or 3D");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Skip must be 2D or 3D");let i=r.dims[r.dims.length-1],n=r.dims[r.dims.length-2];if(t.dims[t.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(t.dims[t.dims.length-2]!==n)throw new Error("Skip must have the same sequence length as input");if(s.dims.length!==1)throw new Error("Gamma must be 1D");if(s.dims[s.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let o=e[3];if(o.dims.length!==1)throw new Error("Beta must be 1D");if(o.dims[o.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let o=e[4];if(o.dims.length!==1)throw new Error("Bias must be 1D");if(o.dims[o.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},Jg=(e,r,t,s)=>{let i=r.simplified,n=e[0].dims,o=xe.size(n),a=n,l=o,d=n.slice(-1)[0],p=s?n.slice(0,-1).concat(1):[],u=!i&&e.length>3,h=e.length>4,w=s&&t>1,_=s&&t>2,P=t>3,A=64,v=Xt(d),y=[{type:12,data:l},{type:12,data:v},{type:12,data:d},{type:1,data:r.epsilon}],S=g=>{let M=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],E=[$e("x",e[0].dataType,e[0].dims,v),$e("skip",e[1].dataType,e[1].dims,v),$e("gamma",e[2].dataType,e[2].dims,v)];u&&E.push($e("beta",e[3].dataType,e[3].dims,v)),h&&E.push($e("bias",e[4].dataType,e[4].dims,v)),E.push(tt("output",e[0].dataType,a,v)),w&&E.push(tt("mean_output",1,p)),_&&E.push(tt("inv_std_output",1,p)),P&&E.push(tt("input_skip_bias_sum",e[0].dataType,a,v));let k=lr(e[0].dataType),B=lr(1,v);return` + + ${g.registerUniforms(M).declareVariables(...E)} + var sum_shared : array<${B}, ${A}>; + var sum_squared_shared : array<${B}, ${A}>; + + ${g.mainStart([A,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${A}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${A}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${A-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${h?"bias[offset1d + i]":k+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${P?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${Un(k,v,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${A}; + for (var curr_size = reduce_size >> 1; curr_size > 0; curr_size = reduce_size >> 1) { + reduce_size = curr_size + (reduce_size & 1); + if (ix < curr_size) { + sum_shared[ix] += sum_shared[ix + reduce_size]; + sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size]; + } + workgroupBarrier(); + } + + let sum = sum_shared[0]; + let square_sum = sum_squared_shared[0]; + let mean = ${Gs("sum",v)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${Gs("square_sum",v)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon); + ${w?"mean_output[global_idx] = mean;":""} + ${_?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${i?"":`- ${k}(mean)`}) * + ${k}(inv_std_dev) * gamma[offset1d + i] + ${u?"+ beta[offset1d + i]":""}; + } + }`},x=[{dims:a,dataType:e[0].dataType}];return t>1&&x.push({dims:p,dataType:1}),t>2&&x.push({dims:p,dataType:1}),t>3&&x.push({dims:n,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${v};${w};${_};${P}`,inputDependencies:e.map((g,M)=>"type")},getShaderSource:S,getRunData:()=>({outputs:x,dispatchGroup:{x:Math.ceil(l/d)},programUniforms:y})}},Yg=(e,r)=>{Xg(e.inputs);let t=[0];e.outputCount>1&&t.push(-3),e.outputCount>2&&t.push(-3),e.outputCount>3&&t.push(3),e.compute(Jg(e.inputs,r,e.outputCount,!1),{outputs:t})}}),Zg,Ni,ew,Ic,tw,rw,sw,nw,Fv=je(()=>{pt(),yt(),Yt(),vt(),Zg=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");if(r.axes.length!==0){if(r.axes.length!==r.starts.length||r.axes.length!==r.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(r.starts.length!==r.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((t,s)=>{if(e[s+1].dataType!==6&&e[s+1].dataType!==7)throw new Error(`Input ${s} must be an array of int32 or int64`)})},Ni=(e,r)=>{let t=[];if(e.length>r)if(e[r].dataType===7)e[r].getBigInt64Array().forEach(s=>t.push(Number(s)));else if(e[r].dataType===6)e[r].getInt32Array().forEach(s=>t.push(Number(s)));else throw new Error(`Input ${r} must be an array of int32 or int64`);return t},ew=(e,r)=>{if(e.length>1){let t=Ni(e,1),s=Ni(e,2),i=Ni(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),Dt({starts:t,ends:s,axes:i})}else return r},Ic=(e,r,t,s,i)=>{let n=e;return e<0&&(n+=t[s[r]]),i[r]<0?Math.max(0,Math.min(n,t[s[r]]-1)):Math.max(0,Math.min(n,t[s[r]]))},tw=(e,r,t)=>`fn calculateInputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + var carry = 0u; + for (var i = ${t.length}; i >= 0; i--) { + let input_shape_i = ${rt("uniforms.input_shape","i",t.length)}; + let steps_i = ${rt("uniforms.steps","i",t.length)}; + let signs_i = ${rt("uniforms.signs","i",t.length)}; + let starts_i = ${rt("uniforms.starts","i",t.length)}; + var output_index = ${r.indicesGet("output_indices","i")}; + var input_index = output_index * steps_i + starts_i + carry; + carry = input_index / input_shape_i; + input_index = input_index % input_shape_i; + if (signs_i < 0) { + input_index = input_shape_i - input_index - 1u + starts_i; + } + ${e.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,rw=(e,r)=>{let t=e[0].dims,s=xe.size(t),i=r.axes.length>0?xe.normalizeAxes(r.axes,t.length):[...Array(t.length).keys()],n=Ni(e,4);n.forEach(v=>v!==0||(()=>{throw new Error("step cannot be 0")})),n.length===0&&(n=Array(i.length).fill(1));let o=r.starts.map((v,y)=>Ic(v,y,t,i,n)),a=r.ends.map((v,y)=>Ic(v,y,t,i,n));if(i.length!==o.length||i.length!==a.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==t.length)for(let v=0;vMath.sign(v));n.forEach((v,y,S)=>{if(v<0){let x=(a[y]-o[y])/v,g=o[y],M=g+x*n[y];o[y]=M,a[y]=g,S[y]=-v}});let d=t.slice(0);i.forEach((v,y)=>{d[v]=Math.ceil((a[v]-o[v])/n[v])});let p={dims:d,dataType:e[0].dataType},u=tt("output",e[0].dataType,d.length),h=$e("input",e[0].dataType,e[0].dims.length),w=xe.size(d),_=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:o.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:n.length}],P=[{type:12,data:w},{type:12,data:o},{type:6,data:l},{type:12,data:n},...nt(e[0].dims,d)],A=v=>` + ${v.registerUniforms(_).declareVariables(h,u)} + ${tw(h,u,t)} + ${v.mainStart()} + ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${u.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${u.setByOffset("global_idx",h.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${o.length}_${n.length}`,inputDependencies:["rank"]},getShaderSource:A,getRunData:()=>({outputs:[p],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:P})}},sw=(e,r)=>{Zg(e.inputs,r);let t=ew(e.inputs,r);e.compute(rw(e.inputs,t),{inputs:[0]})},nw=e=>{let r=e.starts,t=e.ends,s=e.axes;return Dt({starts:r,ends:t,axes:s})}}),iw,ow,aw,lw,Dv=je(()=>{pt(),yt(),Yt(),Ks(),vt(),iw=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},ow=(e,r)=>{let t=e.inputs[0],s=t.dims,i=xe.size(s),n=s.length,o=xe.normalizeAxis(r.axis,n),a=ok),d[o]=n-1,d[n-1]=o,l=e.compute(Nr(t,d),{inputs:[t],outputs:[-1]})[0]):l=t;let p=l.dims,u=p[n-1],h=i/u,w=Xt(u),_=u/w,P=64;h===1&&(P=256);let A=(E,k)=>k===4?`max(max(${E}.x, ${E}.y), max(${E}.z, ${E}.w))`:k===2?`max(${E}.x, ${E}.y)`:k===3?`max(max(${E}.x, ${E}.y), ${E}.z)`:E,v=$e("x",l.dataType,l.dims,w),y=tt("result",l.dataType,l.dims,w),S=v.type.value,x=lr(l.dataType)==="f32"?`var threadMax = ${S}(-3.402823e+38f);`:`var threadMax = ${S}(-65504.0h);`,g=E=>` + var rowMaxShared : ${S}; + var rowSumShared : ${S}; + var threadShared : array<${S}, ${P}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${S} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${S}) { + let index = row * row_stride + col; + result[index] = value; + } + ${E.registerUniform("packedCols","i32").declareVariables(v,y)} + ${E.mainStart(P)} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${P}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${x} + 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 = ${S}(${A("threadShared[0]",w)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${S}(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 = ${S}(${Gs("threadShared[0]",w)}); + } + 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); + } + }`,M=e.compute({name:"Softmax",shaderCache:{hint:`${w};${P}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:p,dataType:l.dataType}],dispatchGroup:{x:h},programUniforms:[{type:6,data:_}]}),getShaderSource:g},{inputs:[l],outputs:[a?-1:0]})[0];a&&e.compute(Nr(M,d),{inputs:[M]})},aw=(e,r)=>{iw(e.inputs),ow(e,r)},lw=e=>Dt({axis:e.axis})}),Ac,dw,cw,uw,pw,Lv=je(()=>{pt(),yt(),vt(),Ac=e=>Array.from(e.getBigInt64Array(),Number),dw=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==10&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, float16, 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(Ac(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")},cw=(e,r)=>{let t=[];for(let s=0;s{let t=e[0].dims,s=r??Ac(e[1]),i=cw(t,s),n=xe.size(i),o=e[0].dataType,a=$e("input",o,t.length),l=tt("output",o,i.length),d=p=>` + const inputShape = ${a.indices(...t)}; + ${p.registerUniform("output_size","u32").declareVariables(a,l)} + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${l.offsetToIndices("global_idx")}; + var input_indices: ${a.type.indices}; + for (var i = 0; i < ${t.length}; i++) { + let input_dim_i = ${a.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${l.indicesGet("output_indices","i")} % input_dim_i; + + ${a.indicesSet("input_indices","i","input_dim_value")} + } + ${l.setByOffset("global_idx",a.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${s}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:[{type:12,data:n},...nt(e[0].dims,i)]}),getShaderSource:d}},pw=e=>{dw(e.inputs),e.compute(uw(e.inputs),{inputs:[0]})}}),hw,mw,fw,zv=je(()=>{pt(),yt(),vt(),hw=(e,r,t,s,i)=>{let n=tt("output_data",i,t.length,4),o=$e("a_data",r[1].dataType,r[1].dims.length,4),a=$e("b_data",r[2].dataType,r[2].dims.length,4),l=$e("c_data",r[0].dataType,r[0].dims.length,4),d,p=(u,h,w)=>`select(${h}, ${u}, ${w})`;if(!s)d=n.setByOffset("global_idx",p(o.getByOffset("global_idx"),a.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let u=(h,w,_="")=>{let P=`a_data[index_a${w}][component_a${w}]`,A=`b_data[index_b${w}][component_b${w}]`,v=`bool(c_data[index_c${w}] & (0xffu << (component_c${w} * 8)))`;return` + let output_indices${w} = ${n.offsetToIndices(`global_idx * 4u + ${w}u`)}; + let offset_a${w} = ${o.broadcastedIndicesToOffset(`output_indices${w}`,n)}; + let offset_b${w} = ${a.broadcastedIndicesToOffset(`output_indices${w}`,n)}; + let offset_c${w} = ${l.broadcastedIndicesToOffset(`output_indices${w}`,n)}; + let index_a${w} = offset_a${w} / 4u; + let index_b${w} = offset_b${w} / 4u; + let index_c${w} = offset_c${w} / 4u; + let component_a${w} = offset_a${w} % 4u; + let component_b${w} = offset_b${w} % 4u; + let component_c${w} = offset_c${w} % 4u; + ${h}[${w}] = ${_}(${p(P,A,v)}); + `};i===9?d=` + var data = vec4(0); + ${u("data",0,"u32")} + ${u("data",1,"u32")} + ${u("data",2,"u32")} + ${u("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:d=` + ${u("output_data[global_idx]",0)} + ${u("output_data[global_idx]",1)} + ${u("output_data[global_idx]",2)} + ${u("output_data[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(l,o,a,n)} + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${d} + }`},mw=e=>{let r=e[1].dims,t=e[2].dims,s=e[0].dims,i=e[1].dataType,n=!(xe.areEqual(r,t)&&xe.areEqual(t,s)),o=r,a=xe.size(r);if(n){let d=Nn.calcShape(Nn.calcShape(r,t,!1),s,!1);if(!d)throw new Error("Can't perform where op on the given tensors");o=d,a=xe.size(o)}let l=Math.ceil(a/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:d=>hw(d,e,o,n,i),getRunData:()=>({outputs:[{dims:o,dataType:i}],dispatchGroup:{x:Math.ceil(a/64/4)},programUniforms:[{type:12,data:l},...nt(s,r,t,o)]})}},fw=e=>{e.compute(mw(e.inputs))}}),_w,Bv=je(()=>{Jb(),qd(),Yb(),Zb(),ev(),tv(),rv(),av(),dv(),cv(),uv(),pv(),hv(),mv(),fv(),_v(),gv(),wv(),yv(),Mv(),bv(),vv(),xv(),Tv(),Ev(),$_(),Pv(),Cv(),Sv(),$v(),kv(),Gd(),Iv(),Av(),Ov(),Fv(),Dv(),L_(),Lv(),Ks(),Yd(),zv(),_w=new Map([["Abs",[Xh]],["Acos",[Jh]],["Acosh",[Yh]],["Add",[Rm]],["ArgMax",[Dh,Hd]],["ArgMin",[Fh,Hd]],["Asin",[Zh]],["Asinh",[em]],["Atan",[tm]],["Atanh",[rm]],["Attention",[jh]],["AveragePool",[ug,cg]],["BatchNormalization",[Gh]],["BiasAdd",[qh]],["BiasSplitGelu",[Lm]],["Cast",[nm,sm]],["Ceil",[am]],["Clip",[om]],["Concat",[Zm,ef]],["Conv",[uc,dc]],["ConvTranspose",[Sf,Ef]],["Cos",[lm]],["Cosh",[dm]],["CumSum",[kf,If]],["DepthToSpace",[Df,Lf]],["DequantizeLinear",[Mg,bg]],["Div",[Nm]],["Einsum",[Uf,Vf]],["Elu",[cm,Fi]],["Equal",[jm]],["Erf",[um]],["Exp",[pm]],["Expand",[Hf]],["FastGelu",[Qf]],["Floor",[hm]],["FusedConv",[uc,dc]],["Gather",[Zf,Yf]],["GatherElements",[c_,d_]],["GatherBlockQuantized",[i_,o_]],["GatherND",[t_,r_]],["Gelu",[mm]],["Gemm",[m_,h_]],["GlobalAveragePool",[hg,pg]],["GlobalMaxPool",[gg,_g]],["Greater",[Gm]],["GreaterOrEqual",[Hm]],["GridSample",[x_,T_]],["GroupQueryAttention",[R_]],["HardSigmoid",[vm,bm]],["InstanceNormalization",[U_]],["LayerNormalization",[G_]],["LeakyRelu",[fm,Fi]],["Less",[Km]],["LessOrEqual",[qm]],["Log",[km]],["MatMul",[H_]],["MatMulNBits",[J_,Y_]],["MaxPool",[mg,fg]],["Mul",[Um]],["MultiHeadAttention",[S_,P_]],["Neg",[gm]],["Not",[_m]],["Pad",[ag]],["Pow",[Vm]],["QuickGelu",[Om,Fi]],["Range",[Tg]],["Reciprocal",[wm]],["ReduceMin",[$h]],["ReduceMean",[Th]],["ReduceMax",[Sh]],["ReduceSum",[Ih]],["ReduceProd",[kh]],["ReduceL1",[Eh]],["ReduceL2",[Ph]],["ReduceLogSum",[Oh]],["ReduceLogSumExp",[Ch]],["ReduceSumSquare",[Ah]],["Relu",[ym]],["Resize",[Gg,Kg]],["RotaryEmbedding",[Qg]],["ScatterND",[Sg,Cg]],["Sigmoid",[Mm]],["Sin",[xm]],["Sinh",[Tm]],["Slice",[sw,nw]],["SkipLayerNormalization",[Yg]],["Split",[F_,D_]],["Sqrt",[Em]],["Softmax",[aw,lw]],["Sub",[Wm]],["Tan",[Pm]],["Tanh",[Cm]],["ThresholdedRelu",[$m,Fi]],["Tile",[pw]],["Transpose",[Gp,Kp]],["Where",[fw]]])}),gw,Rv=je(()=>{ns(),Is(),vt(),gw=class{constructor(e){this.backend=e,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,r){this.repo.set(e,r)}run(e,r,t,s,i){ss(e.programInfo.name);let n=this.backend.device,o=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let a=[];for(let d of r)a.push({binding:a.length,resource:{buffer:d.buffer}});for(let d of t)a.push({binding:a.length,resource:{buffer:d.buffer}});i&&a.push({binding:a.length,resource:i});let l=n.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:a,label:e.programInfo.name});if(this.backend.sessionStatus==="capturing"){let d={kernelId:this.backend.currentKernelId,computePipeline:e.computePipeline,bindGroup:l,dispatchGroup:s};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(d)}o.setPipeline(e.computePipeline),o.setBindGroup(0,l),o.dispatchWorkgroups(...s),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(),Hr(e.programInfo.name)}dispose(){}build(e,r){ss(e.name);let t=this.backend.device,s=[];[{feature:"shader-f16",extension:"f16"},{feature:"subgroups",extension:"subgroups"}].forEach(d=>{t.features.has(d.feature)&&s.push(`enable ${d.extension};`)});let i=Rp(r,this.backend.device.limits),n=e.getShaderSource(i),o=`${s.join(` +`)} +${i.additionalImplementations} +${n}`,a=t.createShaderModule({code:o,label:e.name});St("verbose",()=>`[WebGPU] ${e.name} shader code: ${o}`);let l=t.createComputePipeline({compute:{module:a,entryPoint:"main"},layout:"auto",label:e.name});return Hr(e.name),{programInfo:e,computePipeline:l,uniformVariablesInfo:i.variablesInfo}}normalizeDispatchGroupSize(e){let r=typeof e=="number"?e:e.x,t=typeof e=="number"?1:e.y||1,s=typeof e=="number"?1:e.z||1,i=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(r<=i&&t<=i&&s<=i)return[r,t,s];let n=r*t*s,o=Math.ceil(Math.sqrt(n));if(o>i){if(o=Math.ceil(Math.cbrt(n)),o>i)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[o,o,o]}else return[o,o,1]}}}),ww,yw,Mw,bw,Nv=je(()=>{ns(),pt(),Is(),Cp(),Qb(),Bv(),Rv(),ww=(e,r)=>{if(r.length!==e.length)throw new Error(`inputDependencies length ${r.length} is not equal to inputTensors length ${e.length}.`);let t=[];for(let s=0;s{var i,n;let s=e.name;return(i=e.shaderCache)!=null&&i.hint&&(s+="["+e.shaderCache.hint+"]"),s+=":"+t+`:${ww(r,((n=e.shaderCache)==null?void 0:n.inputDependencies)??new Array(r.length).fill("dims"))}`,s},Mw=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},bw=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 e=this.kernelCustomData.get(this.currentKernelId);return e||(e={},this.kernelCustomData.set(this.currentKernelId,e)),e}async initialize(e,r){this.env=e;let t=[],s={requiredLimits:{maxComputeWorkgroupStorageSize:r.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.limits.maxStorageBufferBindingSize,maxBufferSize:r.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:r.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:r.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:r.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:r.limits.maxComputeWorkgroupSizeZ},requiredFeatures:t},i=n=>r.features.has(n)&&t.push(n)&&!0;i("chromium-experimental-timestamp-query-inside-passes")||i("timestamp-query"),i("shader-f16"),i("subgroups"),this.device=await r.requestDevice(s),this.adapterInfo=new Mw(r.info||await r.requestAdapterInfo()),this.gpuDataManager=Ip(this),this.programManager=new gw(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Dd(e.logLevel,!!e.debug),this.device.onuncapturederror=n=>{n.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${n.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:r,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 e=this.getCommandEncoder(),r={};this.queryType==="at-passes"&&(r.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(r)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;ss(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{var s;let r=new BigUint64Array(e.getMappedRange()),t=this.pendingQueries.get(e);for(let i=0;i"u"&&(this.queryTimeBase=w);let P=Number(w-this.queryTimeBase),A=Number(_-this.queryTimeBase);if(!Number.isSafeInteger(P)||!Number.isSafeInteger(A))throw new RangeError("incorrect timestamp range");if((s=this.env.webgpu.profiling)!=null&&s.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:u.map(v=>({dims:v.dims,dataType:fn(v.dataType)})),outputsMetadata:h.map(v=>({dims:v.dims,dataType:fn(v.dataType)})),kernelId:o,kernelType:l,kernelName:d,programName:p,startTime:P,endTime:A});else{let v="";u.forEach((S,x)=>{v+=`input[${x}]: [${S.dims}] | ${fn(S.dataType)}, `});let y="";h.forEach((S,x)=>{y+=`output[${x}]: [${S.dims}] | ${fn(S.dataType)}, `}),console.log(`[profiling] kernel "${o}|${l}|${d}|${p}" ${v}${y}execution time: ${A-P} ns`)}ki("GPU",`${p}::${w}::${_}`)}e.unmap(),this.pendingQueries.delete(e)}),Hr()}run(e,r,t,s,i,n){ss(e.name);let o=[];for(let y=0;yS):t;if(p.length!==a.length)throw new Error(`Output size ${p.length} must be equal to ${a.length}.`);let u=[],h=[];for(let y=0;y=n)throw new Error(`Invalid output index: ${p[y]}`);if(p[y]===-3)continue;let S=p[y]===-1,x=p[y]===-2,g=S||x?i(a[y].dataType,a[y].dims):s(p[y],a[y].dataType,a[y].dims);if(u.push(g),g.data===0)continue;let M=this.gpuDataManager.get(g.data);if(!M)throw new Error(`no GPU data for output: ${g.data}`);if(S&&this.temporaryData.push(M),x){let E=this.kernelPersistentData.get(this.currentKernelId);E||(E=[],this.kernelPersistentData.set(this.currentKernelId,E)),E.push(M)}h.push(M)}if(o.length!==r.length||h.length!==u.length){if(h.length===0)return Hr(e.name),u;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let w;if(d){let y=0,S=[];d.forEach(E=>{let k=typeof E.data=="number"?[E.data]:E.data;if(k.length===0)return;let B=E.type===10?2:4,R,J;E.type===10?(J=k.length>4?16:k.length>2?8:k.length*B,R=k.length>4?16:B*k.length):(J=k.length<=2?k.length*B:16,R=16),y=Math.ceil(y/J)*J,S.push(y);let q=E.type===10?8:4;y+=k.length>4?Math.ceil(k.length/q)*R:k.length*B});let x=16;y=Math.ceil(y/x)*x;let g=new ArrayBuffer(y);d.forEach((E,k)=>{let B=S[k],R=typeof E.data=="number"?[E.data]:E.data;if(E.type===6)new Int32Array(g,B,R.length).set(R);else if(E.type===12)new Uint32Array(g,B,R.length).set(R);else if(E.type===10)new Uint16Array(g,B,R.length).set(R);else if(E.type===1)new Float32Array(g,B,R.length).set(R);else throw new Error(`Unsupported uniform type: ${fn(E.type)}`)});let M=this.gpuDataManager.create(y,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(M.buffer,0,g,0,y),this.gpuDataManager.release(M.id),w={offset:0,size:y,buffer:M.buffer}}let _=this.programManager.normalizeDispatchGroupSize(l),P=_[1]===1&&_[2]===1,A=yw(e,r,P),v=this.programManager.getArtifact(A);if(v||(v=this.programManager.build(e,_),this.programManager.setArtifact(A,v),St("info",()=>`[artifact] key: ${A}, programName: ${e.name}`)),d&&v.uniformVariablesInfo){if(d.length!==v.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${v.uniformVariablesInfo.length}, got ${d.length} in program "${v.programInfo.name}".`);for(let y=0;y`[ProgramManager] run "${e.name}" (key=${A}) with ${_[0]}x${_[1]}x${_[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let y={kernelId:this.currentKernelId,programName:v.programInfo.name,inputTensorViews:r,outputTensorViews:u};this.pendingKernels.push(y),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(y)}return this.programManager.run(v,o,h,_,w),Hr(e.name),u}upload(e,r){this.gpuDataManager.upload(e,r)}memcpy(e,r){this.gpuDataManager.memcpy(e,r)}async download(e,r){await this.gpuDataManager.download(e,r)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,r,t,s){let i=_w.get(e);if(!i)throw new Error(`kernel not implemented: ${e}`);let n={kernelType:e,kernelName:s,kernelEntry:i[0],attributes:[i[1],t]};this.kernels.set(r,n)}releaseKernel(e){let r=this.kernelPersistentData.get(e);if(r){for(let t of r)this.gpuDataManager.release(t.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,r,t){let s=this.kernels.get(e);if(!s)throw new Error(`kernel not created: ${e}`);let i=s.kernelType,n=s.kernelName,o=s.kernelEntry,a=s.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${i}] ${n}" is not allowed to be called recursively`);this.currentKernelId=e,a[0]&&(a[1]=a[0](a[1]),a[0]=void 0),St("info",()=>`[WebGPU] Start to run kernel "[${i}] ${n}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),o(r,a[1]),0}catch(d){return t.push(Promise.resolve(`[WebGPU] Kernel "[${i}] ${n}" failed. ${d}`)),1}finally{l&&t.push(this.device.popErrorScope().then(d=>d?`GPU validation error for kernel "[${i}] ${n}": ${d.message}`:null));for(let d of this.temporaryData)this.gpuDataManager.release(d.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,r,t,s){let i=this.sessionExternalDataMapping.get(e);i||(i=new Map,this.sessionExternalDataMapping.set(e,i));let n=i.get(r),o=this.gpuDataManager.registerExternalBuffer(t,s,n);return i.set(r,[o,t]),o}unregisterBuffers(e){let r=this.sessionExternalDataMapping.get(e);r&&(r.forEach(t=>this.gpuDataManager.unregisterExternalBuffer(t[0])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let r=this.gpuDataManager.get(e);if(!r)throw new Error(`no GPU data for buffer: ${e}`);return r.buffer}createDownloader(e,r,t){return async()=>{let s=await Nd(this,e,r);return Ld(s.buffer,t)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){var e;this.queryType="none",(((e=this.env.webgpu.profiling)==null?void 0:e.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(){St("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(){St("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){St("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),r=this.capturedPendingKernels.get(this.currentSessionId),t=e.length;this.pendingKernels=[];for(let s=0;s=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),vw,Oc,xw,Fc,Dc,Lc,Tw,Ew,jv=je(()=>{Is(),vw=1,Oc=()=>vw++,xw=new Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),Fc=(e,r)=>{let t=xw.get(e);if(!t)throw new Error("Unsupported data type.");return r.length>0?Math.ceil(r.reduce((s,i)=>s*i)*t/8):0},Dc=class{constructor(e){this.sessionId=e.sessionId,this.mlContext=e.context,this.mlTensor=e.tensor,this.dataType=e.dataType,this.tensorShape=e.shape}get tensor(){return this.mlTensor}get type(){return this.dataType}get shape(){return this.tensorShape}get byteLength(){return Fc(this.dataType,this.tensorShape)}destroy(){St("verbose",()=>"[WebNN] TensorWrapper.destroy"),this.mlTensor.destroy()}write(e){this.mlContext.writeTensor(this.mlTensor,e)}async read(e){return e?this.mlContext.readTensor(this.mlTensor,e):this.mlContext.readTensor(this.mlTensor)}canReuseTensor(e,r,t){return this.mlContext===e&&this.dataType===r&&this.tensorShape.length===t.length&&this.tensorShape.every((s,i)=>s===t[i])}},Lc=class{constructor(e,r){this.tensorManager=e,this.wrapper=r}get tensorWrapper(){return this.wrapper}releaseTensor(){this.tensorWrapper&&(this.tensorManager.releaseTensor(this.tensorWrapper),this.wrapper=void 0)}async ensureTensor(e,r,t,s){let i=this.tensorManager.getMLContext(e);if(this.wrapper){if(this.wrapper.canReuseTensor(i,r,t))return this.wrapper.tensor;if(s){if(this.wrapper.byteLength!==Fc(r,t))throw new Error("Unable to copy data to tensor with different size.");this.activeUpload=new Uint8Array(await this.wrapper.read())}this.tensorManager.releaseTensor(this.wrapper)}let n=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(e,r,t,n,!0,!0),s&&this.activeUpload&&(this.wrapper.write(this.activeUpload),this.activeUpload=void 0),this.wrapper.tensor}upload(e){if(this.wrapper)if(e.byteLength===this.wrapper.byteLength){this.wrapper.write(e);return}else St("verbose",()=>"Data size does not match tensor size. 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this.mlContextCache[t].mlContext;{let s=await navigator.ml.createContext(e);return this.mlContextCache.push({gpuDevice:e,mlContext:s}),s}}else if(e===void 0){let t=this.mlContextCache.findIndex(s=>s.options===void 0&&s.gpuDevice===void 0);if(t!==-1)return this.mlContextCache[t].mlContext;{let s=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:s}),s}}let r=this.mlContextCache.findIndex(t=>Pw(t.options,e));if(r!==-1)return this.mlContextCache[r].mlContext;{let t=await navigator.ml.createContext(e);return this.mlContextCache.push({options:e,mlContext:t}),t}}registerMLContext(e,r){this.mlContextBySessionId.set(e,r);let t=this.sessionIdsByMLContext.get(r);t||(t=new Set,this.sessionIdsByMLContext.set(r,t)),t.add(e),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(e,this.temporaryGraphInputs),this.temporaryGraphInputs=[])}onReleaseSession(e){this.sessionGraphInputs.delete(e);let 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a=n.get(o);if(!a)throw new Error(`File with name ${o} not found in preloaded files.`);if(r+t>a.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let l=a.slice(r,r+t).buffer,d;switch(i.dataType){case"float32":d=new Float32Array(l);break;case"float16":d=typeof Float16Array<"u"&&Float16Array.from?new Float16Array(l):new Uint16Array(l);break;case"int32":d=new Int32Array(l);break;case"uint32":d=new Uint32Array(l);break;case"int64":d=new BigInt64Array(l);break;case"uint64":d=new BigUint64Array(l);break;case"int8":d=new Int8Array(l);break;case"int4":case"uint4":case"uint8":d=new Uint8Array(l);break;default:throw new Error(`Unsupported data type: ${i.dataType} in creating WebNN Constant from external data.`)}return St("verbose",()=>`[WebNN] registerMLConstant {dataType: ${i.dataType}, shape: ${i.shape}}}`),s.constant(i,d)}registerGraphInput(e){this.temporaryGraphInputs.push(e)}isGraphInput(e,r){let t=this.sessionGraphInputs.get(e);return t?t.includes(r):!1}flush(){}}}),Sw={};Ci(Sw,{init:()=>kw});var Aa,$w,kw,Vv=je(()=>{pt(),Nv(),Is(),yt(),Uv(),Aa=class cb{constructor(r,t,s,i){this.module=r,this.dataType=t,this.data=s,this.dims=i}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let r=xe.size(this.dims);return r===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,r)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let r=xe.size(this.dims);return r===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,r)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let r=xe.size(this.dims);return r===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,r)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let r=xe.size(this.dims);return r===0?new Uint16Array:new 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t=this.module.stackSave();try{let s=this.module.PTR_SIZE,i=s===4?"i32":"i64",n=this.module.stackAlloc((1+r.length)*s);this.module.setValue(n,r.length,i);for(let o=0;o{let i=r.jsepInit;if(!i)throw new Error("Failed to initialize JSEP. 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r.externalData){let E=typeof M=="string"?M:M.path;g.push(Fd(typeof M=="string"?M:M.data).then(k=>{i.mountExternalData(E,k)}))}await Promise.all(g)}for(let g of(r==null?void 0:r.executionProviders)??[])if((typeof g=="string"?g:g.name)==="webnn"){if(i.shouldTransferToMLTensor=!1,typeof g!="string"){let M=g,E=M==null?void 0:M.context,k=M==null?void 0:M.gpuDevice,B=M==null?void 0:M.deviceType,R=M==null?void 0:M.powerPreference;E?i.currentContext=E:k?i.currentContext=await i.jsepCreateMLContext(k):i.currentContext=await i.jsepCreateMLContext({deviceType:B,powerPreference:R})}else i.currentContext=await i.jsepCreateMLContext();break}n=await i._OrtCreateSession(t,s,o),n===0&&At("Can't create a session."),(u=i.jsepOnCreateSession)==null||u.call(i),i.currentContext&&(i.jsepRegisterMLContext(n,i.currentContext),i.currentContext=void 0,i.shouldTransferToMLTensor=!0);let[_,P]=Aw(n),A=!!(r!=null&&r.enableGraphCapture),v=[],y=[],S=[];for(let g=0;g<_;g++){let M=i._OrtGetInputName(n,g);M===0&&At("Can't get an input name."),d.push(M),v.push(i.UTF8ToString(M))}for(let g=0;gg==="gpu-buffer"||g==="ml-tensor")&&(a=i._OrtCreateBinding(n),a===0&&At("Can't create IO binding."),x={handle:a,outputPreferredLocations:S,outputPreferredLocationsEncoded:S.map(g=>Od(g))}),Hs.set(n,[n,d,p,x,A,!1]),[n,v,y]}catch(_){throw d.forEach(P=>i._OrtFree(P)),p.forEach(P=>i._OrtFree(P)),a!==0&&i._OrtReleaseBinding(a)!==0&&At("Can't release IO binding."),n!==0&&i._OrtReleaseSession(n)!==0&&At("Can't release session."),_}finally{i._free(t),o!==0&&i._OrtReleaseSessionOptions(o)!==0&&At("Can't release session options."),l.forEach(_=>i._free(_)),(w=i.unmountExternalData)==null||w.call(i)}},Nc=e=>{var l;let r=ar(),t=Hs.get(e);if(!t)throw new Error(`cannot release session. invalid session id: ${e}`);let[s,i,n,o,a]=t;o&&(a&&r._OrtClearBoundOutputs(o.handle)!==0&&At("Can't clear bound outputs."),r._OrtReleaseBinding(o.handle)!==0&&At("Can't release IO 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A=e[2];if(Array.isArray(A)){w=a*A.length,h=o._malloc(w),t.push(h);for(let v=0;vo.setValue(P+y*a,v,a===4?"i32":"i64"));let A=o._OrtCreateTensor(Rn(l),h,w,P,d.length,Od(u));A===0&&At(`Can't create tensor for input/output. session=${s}, index=${i}.`),r.push(A)}finally{o.stackRestore(_)}},Uc=async(e,r,t,s,i,n)=>{var J,q,V;let o=ar(),a=o.PTR_SIZE,l=Hs.get(e);if(!l)throw new Error(`cannot run inference. invalid session id: ${e}`);let d=l[0],p=l[1],u=l[2],h=l[3],w=l[4],_=l[5],P=r.length,A=s.length,v=0,y=[],S=[],x=[],g=[],M=o.stackSave(),E=o.stackAlloc(P*a),k=o.stackAlloc(P*a),B=o.stackAlloc(A*a),R=o.stackAlloc(A*a);try{[v,y]=fp(n);for(let Q=0;QMe*K,1);N=fn(ne);let He=h==null?void 0:h.outputPreferredLocations[s[Q]];if(N==="string"){if(He==="gpu-buffer"||He==="ml-tensor")throw new Error("String tensor is not supported on GPU.");let Me=[];for(let K=0;K0){let Me=o.jsepGetBuffer;if(!Me)throw new Error('preferredLocation "gpu-buffer" is not supported without using WebGPU.');let 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All Rights Reserved. +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================================= +*//** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */var qv=Object.freeze({__proto__:null,get InferenceSession(){return md},get TRACE(){return ki},get TRACE_FUNC_BEGIN(){return ss},get TRACE_FUNC_END(){return Hr},get Tensor(){return rs},default:Hv,get env(){return Vt},get registerBackend(){return 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Array.from({length:this.generation_config.num_beams},(A,v)=>[_.data[v],Math.log(P[v])])}}},"./src/generation/stopping_criteria.js":(e,r,t)=>{t.r(r),t.d(r,{EosTokenCriteria:()=>a,InterruptableStoppingCriteria:()=>l,MaxLengthCriteria:()=>o,StoppingCriteria:()=>i,StoppingCriteriaList:()=>n});var s=t("./src/utils/generic.js");class i extends s.Callable{_call(p,u){throw Error("StoppingCriteria needs to be subclassed")}}class n extends s.Callable{constructor(){super(),this.criteria=[]}push(p){this.criteria.push(p)}extend(p){p instanceof n?p=p.criteria:p instanceof i&&(p=[p]),this.criteria.push(...p)}_call(p,u){const h=new Array(p.length).fill(!1);for(const w of this.criteria){const _=w(p,u);for(let P=0;Pu.length>=this.max_length)}}class a extends i{constructor(p){super(),Array.isArray(p)||(p=[p]),this.eos_token_id=p}_call(p,u){return p.map(h=>{const w=h.at(-1);return this.eos_token_id.some(_=>w==_)})}}class l extends 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w,_;u.length>0&&((w=this.callback_function)==null||w.call(this,u)),h&&this.callback_function===a&&n.apis.IS_PROCESS_AVAILABLE&&((_=this.callback_function)==null||_.call(this,` +`))}}class d extends l{constructor(u,{skip_prompt:h=!1,callback_function:w=null,token_callback_function:_=null,on_chunk_start:P=null,on_chunk_end:A=null,on_finalize:v=null,time_precision:y=.02,skip_special_tokens:S=!0,decode_kwargs:x={}}={}){super(u,{skip_prompt:h,skip_special_tokens:S,callback_function:w,token_callback_function:_,decode_kwargs:x}),this.timestamp_begin=u.timestamp_begin,this.on_chunk_start=P,this.on_chunk_end=A,this.on_finalize=v,this.time_precision=y,this.waiting_for_timestamp=!1}put(u){var w,_;if(u.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const h=u[0];if(h.length===1){const P=Number(h[0])-this.timestamp_begin;if(P>=0){const A=P*this.time_precision;this.waiting_for_timestamp?(w=this.on_chunk_end)==null||w.call(this,A):(_=this.on_chunk_start)==null||_.call(this,A),this.waiting_for_timestamp=!this.waiting_for_timestamp,u=[[]]}}return super.put(u)}end(){var u;super.end(),(u=this.on_finalize)==null||u.call(this)}}},"./src/models.js":(e,r,t)=>{t.r(r),t.d(r,{ASTForAudioClassification:()=>Ua,ASTModel:()=>ja,ASTPreTrainedModel:()=>Gi,AlbertForMaskedLM:()=>j,AlbertForQuestionAnswering:()=>O,AlbertForSequenceClassification:()=>ue,AlbertModel:()=>Sn,AlbertPreTrainedModel:()=>$s,AutoModel:()=>xu,AutoModelForAudioClassification:()=>G0,AutoModelForAudioFrameClassification:()=>H0,AutoModelForAudioTextToText:()=>rb,AutoModelForCTC:()=>W0,AutoModelForCausalLM:()=>O0,AutoModelForDepthEstimation:()=>J0,AutoModelForDocumentQuestionAnswering:()=>q0,AutoModelForImageClassification:()=>z0,AutoModelForImageFeatureExtraction:()=>eb,AutoModelForImageMatting:()=>Q0,AutoModelForImageSegmentation:()=>B0,AutoModelForImageTextToText:()=>tb,AutoModelForImageToImage:()=>X0,AutoModelForMaskGeneration:()=>V0,AutoModelForMaskedLM:()=>F0,AutoModelForNormalEstimation:()=>Y0,AutoModelForObjectDetection:()=>j0,AutoModelForPoseEstimation:()=>Z0,AutoModelForQuestionAnswering:()=>D0,AutoModelForSemanticSegmentation:()=>R0,AutoModelForSeq2SeqLM:()=>$0,AutoModelForSequenceClassification:()=>C0,AutoModelForSpeechSeq2Seq:()=>k0,AutoModelForTextToSpectrogram:()=>I0,AutoModelForTextToWaveform:()=>A0,AutoModelForTokenClassification:()=>S0,AutoModelForUniversalSegmentation:()=>N0,AutoModelForVision2Seq:()=>L0,AutoModelForXVector:()=>K0,AutoModelForZeroShotObjectDetection:()=>U0,BartForConditionalGeneration:()=>Wt,BartForSequenceClassification:()=>Ft,BartModel:()=>kt,BartPretrainedModel:()=>Ct,BaseModelOutput:()=>Pe,BeitForImageClassification:()=>Sl,BeitModel:()=>Cl,BeitPreTrainedModel:()=>sa,BertForMaskedLM:()=>Ie,BertForQuestionAnswering:()=>Ve,BertForSequenceClassification:()=>Le,BertForTokenClassification:()=>Ne,BertModel:()=>Oe,BertPreTrainedModel:()=>Ee,BlenderbotForConditionalGeneration:()=>Zt,BlenderbotModel:()=>Gt,BlenderbotPreTrainedModel:()=>ws,BlenderbotSmallForConditionalGeneration:()=>ys,BlenderbotSmallModel:()=>Zr,BlenderbotSmallPreTrainedModel:()=>gr,BloomForCausalLM:()=>Bo,BloomModel:()=>zo,BloomPreTrainedModel:()=>gi,CLIPModel:()=>Zi,CLIPPreTrainedModel:()=>Us,CLIPSegForImageSegmentation:()=>eo,CLIPSegModel:()=>on,CLIPSegPreTrainedModel:()=>nn,CLIPTextModel:()=>Jc,CLIPTextModelWithProjection:()=>Ja,CLIPVisionModel:()=>Yc,CLIPVisionModelWithProjection:()=>Qn,CamembertForMaskedLM:()=>Ts,CamembertForQuestionAnswering:()=>Ls,CamembertForSequenceClassification:()=>Es,CamembertForTokenClassification:()=>Ps,CamembertModel:()=>ps,CamembertPreTrainedModel:()=>Ur,CausalLMOutput:()=>Fn,CausalLMOutputWithPast:()=>wx,ChineseCLIPModel:()=>rl,ChineseCLIPPreTrainedModel:()=>tl,ClapAudioModelWithProjection:()=>pM,ClapModel:()=>cM,ClapPreTrainedModel:()=>Wl,ClapTextModelWithProjection:()=>uM,CodeGenForCausalLM:()=>Vr,CodeGenModel:()=>An,CodeGenPreTrainedModel:()=>ti,CohereForCausalLM:()=>vo,CohereModel:()=>bo,CoherePreTrainedModel:()=>ci,ConvBertForMaskedLM:()=>jt,ConvBertForQuestionAnswering:()=>Fs,ConvBertForSequenceClassification:()=>dr,ConvBertForTokenClassification:()=>cs,ConvBertModel:()=>nr,ConvBertPreTrainedModel:()=>xt,ConvNextForImageClassification:()=>oy,ConvNextModel:()=>iy,ConvNextPreTrainedModel:()=>tu,ConvNextV2ForImageClassification:()=>ly,ConvNextV2Model:()=>ay,ConvNextV2PreTrainedModel:()=>ru,DPTForDepthEstimation:()=>Bt,DPTModel:()=>gt,DPTPreTrainedModel:()=>ot,DacDecoderModel:()=>e0,DacDecoderOutput:()=>JM,DacEncoderModel:()=>ZM,DacEncoderOutput:()=>XM,DacModel:()=>YM,DacPreTrainedModel:()=>Jl,DebertaForMaskedLM:()=>et,DebertaForQuestionAnswering:()=>zt,DebertaForSequenceClassification:()=>it,DebertaForTokenClassification:()=>rr,DebertaModel:()=>Be,DebertaPreTrainedModel:()=>mr,DebertaV2ForMaskedLM:()=>ms,DebertaV2ForQuestionAnswering:()=>_s,DebertaV2ForSequenceClassification:()=>Er,DebertaV2ForTokenClassification:()=>fs,DebertaV2Model:()=>hs,DebertaV2PreTrainedModel:()=>kr,DecisionTransformerModel:()=>OM,DecisionTransformerPreTrainedModel:()=>AM,DeiTForImageClassification:()=>Rl,DeiTModel:()=>Bl,DeiTPreTrainedModel:()=>la,DepthAnythingForDepthEstimation:()=>ln,DepthAnythingPreTrainedModel:()=>Or,DepthProForDepthEstimation:()=>Yw,DepthProPreTrainedModel:()=>Jw,DetrForObjectDetection:()=>kl,DetrForSegmentation:()=>na,DetrModel:()=>$l,DetrObjectDetectionOutput:()=>ia,DetrPreTrainedModel:()=>xi,DetrSegmentationOutput:()=>Il,Dinov2ForImageClassification:()=>cy,Dinov2Model:()=>dy,Dinov2PreTrainedModel:()=>su,Dinov2WithRegistersForImageClassification:()=>py,Dinov2WithRegistersModel:()=>uy,Dinov2WithRegistersPreTrainedModel:()=>nu,DistilBertForMaskedLM:()=>Ys,DistilBertForQuestionAnswering:()=>Js,DistilBertForSequenceClassification:()=>Qs,DistilBertForTokenClassification:()=>Xs,DistilBertModel:()=>Cs,DistilBertPreTrainedModel:()=>Qr,DonutSwinModel:()=>ny,DonutSwinPreTrainedModel:()=>sy,EfficientNetForImageClassification:()=>yM,EfficientNetModel:()=>wM,EfficientNetPreTrainedModel:()=>mu,ElectraForMaskedLM:()=>Ds,ElectraForQuestionAnswering:()=>qr,ElectraForSequenceClassification:()=>jr,ElectraFor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oe.guidance_scale!==null&&oe.guidance_scale>1&&(_e?L.input_ids=(0,h.cat)([L.input_ids,L.input_ids],0):(L.input_ids=(0,h.cat)([L.input_ids,(0,h.full_like)(L.input_ids,BigInt(oe.pad_token_id))],0),L.attention_mask=(0,h.cat)([L.attention_mask,(0,h.full_like)(L.attention_mask,0n)],0))),(_e||!L.pixel_values)&&(L.pixel_values=(0,h.full)([0,0,3,384,384],1)),_e&&(L.images_seq_mask=new h.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),L.images_emb_mask=new h.Tensor("bool",new Array(0).fill(!1),[1,1,0])),L}class U extends o.Callable{constructor(L,oe,_e){super();re(this,"main_input_name","input_ids");re(this,"forward_params",["input_ids","attention_mask"]);this.config=L,this.sessions=oe,this.configs=_e;const me=E.get(this.constructor),Se=g.get(me);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Se){case x.DecoderOnly:this.can_generate=!0,this._forward=ae,this._prepare_inputs_for_generation=ke;break;case x.Seq2Seq:case x.Vision2Seq:case x.Musicgen:this.can_generate=!0,this._forward=Q,this._prepare_inputs_for_generation=He;break;case x.EncoderDecoder:this._forward=Q;break;case x.ImageTextToText:this.can_generate=!0,this._forward=X,this._prepare_inputs_for_generation=Me;break;case x.AudioTextToText:this.can_generate=!0,this._forward=ne,this._prepare_inputs_for_generation=Me;break;case x.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=Me;break;case x.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=K;break;case x.AutoEncoder:this._forward=le;break;default:this._forward=ie;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var oe;const L=[];for(const _e of Object.values(this.sessions))(oe=_e==null?void 0:_e.handler)!=null&&oe.dispose&&L.push(_e.handler.dispose());return await Promise.all(L)}static async from_pretrained(L,{progress_callback:oe=null,config:_e=null,cache_dir:me=null,local_files_only:Se=!1,revision:De="main",model_file_name:Ge=null,subfolder:Je="onnx",device:lt=null,dtype:wt=null,use_external_data_format:st=null,session_options:Et={}}={}){let at={progress_callback:oe,config:_e,cache_dir:me,local_files_only:Se,revision:De,model_file_name:Ge,subfolder:Je,device:lt,dtype:wt,use_external_data_format:st,session_options:Et};const bt=E.get(this),ut=g.get(bt);_e=at.config=await s.AutoConfig.from_pretrained(L,at);let Tt;if(ut===x.DecoderOnly)Tt=await Promise.all([B(L,{model:at.model_file_name??"model"},at),R(L,{generation_config:"generation_config.json"},at)]);else if(ut===x.Seq2Seq||ut===x.Vision2Seq)Tt=await Promise.all([B(L,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},at),R(L,{generation_config:"generation_config.json"},at)]);else if(ut===x.MaskGeneration)Tt=await Promise.all([B(L,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},at)]);else if(ut===x.EncoderDecoder)Tt=await Promise.all([B(L,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},at)]);else if(ut===x.ImageTextToText){const Lt={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};_e.is_encoder_decoder&&(Lt.model="encoder_model"),Tt=await Promise.all([B(L,Lt,at),R(L,{generation_config:"generation_config.json"},at)])}else if(ut===x.AudioTextToText){const Lt={embed_tokens:"embed_tokens",audio_encoder:"audio_encoder",decoder_model_merged:"decoder_model_merged"};Tt=await Promise.all([B(L,Lt,at),R(L,{generation_config:"generation_config.json"},at)])}else if(ut===x.Musicgen)Tt=await Promise.all([B(L,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},at),R(L,{generation_config:"generation_config.json"},at)]);else if(ut===x.MultiModality)Tt=await Promise.all([B(L,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},at),R(L,{generation_config:"generation_config.json"},at)]);else if(ut===x.Phi3V)Tt=await Promise.all([B(L,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},at),R(L,{generation_config:"generation_config.json"},at)]);else if(ut===x.AutoEncoder)Tt=await Promise.all([B(L,{encoder_model:"encoder_model",decoder_model:"decoder_model"},at)]);else{if(ut!==x.EncoderOnly){const Lt=bt??(_e==null?void 0:_e.model_type);Lt!=="custom"&&console.warn(`Model type for '${Lt}' not found, assuming encoder-only architecture. Please report this at ${d.GITHUB_ISSUE_URL}.`)}Tt=await Promise.all([B(L,{model:at.model_file_name??"model"},at)])}return new this(_e,...Tt)}async _call(L){return await this.forward(L)}async forward(L){return await this._forward(this,L)}get generation_config(){var L;return((L=this.configs)==null?void 0:L.generation_config)??null}_get_logits_warper(L){const oe=new p.LogitsProcessorList;return L.temperature!==null&&L.temperature!==1&&oe.push(new p.TemperatureLogitsWarper(L.temperature)),L.top_k!==null&&L.top_k!==0&&oe.push(new p.TopKLogitsWarper(L.top_k)),L.top_p!==null&&L.top_p<1&&oe.push(new p.TopPLogitsWarper(L.top_p)),oe}_get_logits_processor(L,oe,_e=null){const me=new p.LogitsProcessorList;if(L.repetition_penalty!==null&&L.repetition_penalty!==1&&me.push(new p.RepetitionPenaltyLogitsProcessor(L.repetition_penalty)),L.no_repeat_ngram_size!==null&&L.no_repeat_ngram_size>0&&me.push(new p.NoRepeatNGramLogitsProcessor(L.no_repeat_ngram_size)),L.bad_words_ids!==null&&me.push(new p.NoBadWordsLogitsProcessor(L.bad_words_ids,L.eos_token_id)),L.min_length!==null&&L.eos_token_id!==null&&L.min_length>0&&me.push(new p.MinLengthLogitsProcessor(L.min_length,L.eos_token_id)),L.min_new_tokens!==null&&L.eos_token_id!==null&&L.min_new_tokens>0&&me.push(new p.MinNewTokensLengthLogitsProcessor(oe,L.min_new_tokens,L.eos_token_id)),L.forced_bos_token_id!==null&&me.push(new p.ForcedBOSTokenLogitsProcessor(L.forced_bos_token_id)),L.forced_eos_token_id!==null&&me.push(new p.ForcedEOSTokenLogitsProcessor(L.max_length,L.forced_eos_token_id)),L.begin_suppress_tokens!==null){const Se=oe>1||L.forced_bos_token_id===null?oe:oe+1;me.push(new p.SuppressTokensAtBeginLogitsProcessor(L.begin_suppress_tokens,Se))}return L.guidance_scale!==null&&L.guidance_scale>1&&me.push(new p.ClassifierFreeGuidanceLogitsProcessor(L.guidance_scale)),_e!==null&&me.extend(_e),me}_prepare_generation_config(L,oe,_e=u.GenerationConfig){const me={...this.config};for(const De of["decoder","generator","text_config"])De in me&&Object.assign(me,me[De]);const Se=new _e(me);return Object.assign(Se,this.generation_config??{}),L&&Object.assign(Se,L),oe&&Object.assign(Se,(0,a.pick)(oe,Object.getOwnPropertyNames(Se))),Se}_get_stopping_criteria(L,oe=null){const _e=new P.StoppingCriteriaList;return L.max_length!==null&&_e.push(new P.MaxLengthCriteria(L.max_length,this.config.max_position_embeddings??null)),L.eos_token_id!==null&&_e.push(new P.EosTokenCriteria(L.eos_token_id)),oe&&_e.extend(oe),_e}_validate_model_class(){if(!this.can_generate){const L=[bu,vu,Mu,yu],oe=E.get(this.constructor),_e=new Set,me=this.config.model_type;for(const De of L){const Ge=De.get(me);Ge&&_e.add(Ge[0])}let Se=`The current model class (${oe}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw _e.size>0&&(Se+=` Please use the following class instead: ${[..._e].join(", ")}`),Error(Se)}}prepare_inputs_for_generation(...L){return this._prepare_inputs_for_generation(this,...L)}_update_model_kwargs_for_generation({generated_input_ids:L,outputs:oe,model_inputs:_e,is_encoder_decoder:me}){return _e.past_key_values=this.getPastKeyValues(oe,_e.past_key_values),_e.input_ids=new h.Tensor("int64",L.flat(),[L.length,1]),me||(_e.attention_mask=(0,h.cat)([_e.attention_mask,(0,h.ones)([_e.attention_mask.dims[0],1])],1)),_e.position_ids=null,_e}_prepare_model_inputs({inputs:L,bos_token_id:oe,model_kwargs:_e}){const me=(0,a.pick)(_e,this.forward_params),Se=this.main_input_name;if(Se in me){if(L)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else me[Se]=L;return{inputs_tensor:me[Se],model_inputs:me,model_input_name:Se}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:L,model_inputs:oe,model_input_name:_e,generation_config:me}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!oe.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:De,pixel_values:Ge,attention_mask:Je,...lt}=oe,wt=await this._prepare_inputs_embeds(oe);oe={...lt,...(0,a.pick)(wt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Se}=await ie(this,oe);if(me.guidance_scale!==null&&me.guidance_scale>1)Se=(0,h.cat)([Se,(0,h.full_like)(Se,0)],0),"attention_mask"in oe&&(oe.attention_mask=(0,h.cat)([oe.attention_mask,(0,h.zeros_like)(oe.attention_mask)],0));else if(oe.decoder_input_ids){const De=Y(oe.decoder_input_ids).dims[0];if(De!==Se.dims[0]){if(Se.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Se.dims[0]}) than the decoder inputs (${De}).`);Se=(0,h.cat)(Array.from({length:De},()=>Se),0)}}return oe.encoder_outputs=Se,oe}_prepare_decoder_input_ids_for_generation({batch_size:L,model_input_name:oe,model_kwargs:_e,decoder_start_token_id:me,bos_token_id:Se,generation_config:De}){let{decoder_input_ids:Ge,...Je}=_e;if(!(Ge instanceof h.Tensor)){if(Ge)Array.isArray(Ge[0])||(Ge=Array.from({length:L},()=>Ge));else if(me??(me=Se),this.config.model_type==="musicgen")Ge=Array.from({length:L*this.config.decoder.num_codebooks},()=>[me]);else if(Array.isArray(me)){if(me.length!==L)throw new Error(`\`decoder_start_token_id\` expcted to have length ${L} but got ${me.length}`);Ge=me}else Ge=Array.from({length:L},()=>[me]);Ge=Y(Ge)}return _e.decoder_attention_mask=(0,h.ones_like)(Ge),{input_ids:Ge,model_inputs:Je}}async generate({inputs:L=null,generation_config:oe=null,logits_processor:_e=null,stopping_criteria:me=null,streamer:Se=null,...De}){this._validate_model_class(),oe=this._prepare_generation_config(oe,De);let{inputs_tensor:Ge,model_inputs:Je,model_input_name:lt}=this._prepare_model_inputs({inputs:L,model_kwargs:De});const wt=this.config.is_encoder_decoder;wt&&("encoder_outputs"in Je||(Je=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Ge,model_inputs:Je,model_input_name:lt,generation_config:oe})));let st;wt?{input_ids:st,model_inputs:Je}=this._prepare_decoder_input_ids_for_generation({batch_size:Je[lt].dims.at(0),model_input_name:lt,model_kwargs:Je,decoder_start_token_id:oe.decoder_start_token_id,bos_token_id:oe.bos_token_id,generation_config:oe}):st=Je[lt];let Et=st.dims.at(-1);oe.max_new_tokens!==null&&(oe.max_length=Et+oe.max_new_tokens);const at=this._get_logits_processor(oe,Et,_e),bt=this._get_stopping_criteria(oe,me),ut=Je[lt].dims.at(0),Tt=A.LogitsSampler.getSampler(oe),Lt=new Array(ut).fill(0),Qt=st.tolist();Se&&Se.put(Qt);let yr,Pt={};for(;;){if(Je=this.prepare_inputs_for_generation(Qt,Je,oe),yr=await this.forward(Je),oe.output_attentions&&oe.return_dict_in_generate){const Cr=this.getAttentions(yr);for(const ks in Cr)ks in Pt||(Pt[ks]=[]),Pt[ks].push(Cr[ks])}const sr=yr.logits.slice(null,-1,null),Wr=at(Qt,sr),cn=[];for(let Cr=0;CrCr))break;Je=this._update_model_kwargs_for_generation({generated_input_ids:cn,outputs:yr,model_inputs:Je,is_encoder_decoder:wt})}Se&&Se.end();const Nt=this.getPastKeyValues(yr,Je.past_key_values,!0),tr=new h.Tensor("int64",Qt.flat(),[Qt.length,Qt[0].length]);if(oe.return_dict_in_generate)return{sequences:tr,past_key_values:Nt,...Pt};for(const sr of Object.values(yr))sr.location==="gpu-buffer"&&sr.dispose();return tr}getPastKeyValues(L,oe,_e=!1){const me=Object.create(null);for(const Se in L)if(Se.startsWith("present")){const De=Se.replace("present","past_key_values"),Ge=Se.includes("encoder");if(Ge&&oe?me[De]=oe[De]:me[De]=L[Se],oe&&(!Ge||_e)){const Je=oe[De];Je.location==="gpu-buffer"&&Je.dispose()}}return me}getAttentions(L){const oe={};for(const _e of["cross_attentions","encoder_attentions","decoder_attentions"])for(const me in L)me.startsWith(_e)&&(_e in oe||(oe[_e]=[]),oe[_e].push(L[me]));return oe}addPastKeyValues(L,oe){var _e,me,Se;if(oe)Object.assign(L,oe);else{const De=this.sessions.decoder_model_merged??this.sessions.model,Ge=((_e=De==null?void 0:De.config)==null?void 0:_e.kv_cache_dtype)??"float32",Je=Ge==="float16"?new h.DataTypeMap.float16:[],lt=((Se=(me=L[this.main_input_name]??L.attention_mask)==null?void 0:me.dims)==null?void 0:Se[0])??1,wt=(0,s.getKeyValueShapes)(this.config,{batch_size:lt});for(const st in wt)L[st]=new h.Tensor(Ge,Je,wt[st])}}async encode_image({pixel_values:L}){const oe=(await q(this.sessions.vision_encoder,{pixel_values:L})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${oe.dims[1]}).`),this.config.num_image_tokens=oe.dims[1]),oe}async encode_text({input_ids:L}){return(await q(this.sessions.embed_tokens,{input_ids:L})).inputs_embeds}async encode_audio({audio_values:L}){return(await q(this.sessions.audio_encoder,{audio_values:L})).audio_features}}class pe{}class Pe extends pe{constructor({last_hidden_state:$,hidden_states:L=null,attentions:oe=null}){super(),this.last_hidden_state=$,this.hidden_states=L,this.attentions=oe}}class Ee extends U{}class Oe extends Ee{}class Ie extends Ee{async _call($){return new wr(await super._call($))}}class Le extends Ee{async _call($){return new Mt(await super._call($))}}class Ne extends Ee{async _call($){return new pr(await super._call($))}}class Ve extends Ee{async _call($){return new Pr(await super._call($))}}class D extends U{}class Z extends D{}class z extends D{async _call($){return new wr(await super._call($))}}class ee extends D{async _call($){return new Mt(await super._call($))}}class ce extends D{async _call($){return new pr(await super._call($))}}class be extends U{}class ve extends be{}class Re extends U{}class Ae extends Re{}class Ue extends Re{async _call($){return new wr(await super._call($))}}class Qe extends Re{async _call($){return new Mt(await super._call($))}}class Xe extends Re{async _call($){return new pr(await super._call($))}}class ct extends Re{async _call($){return new Pr(await super._call($))}}class xt extends U{}class nr extends xt{}class jt extends xt{async _call($){return new wr(await super._call($))}}class dr extends xt{async _call($){return new Mt(await super._call($))}}class cs extends xt{async _call($){return new pr(await super._call($))}}class Fs extends xt{async _call($){return new Pr(await super._call($))}}class Dr extends U{}class us extends Dr{}class Ds extends Dr{async _call($){return new wr(await super._call($))}}class jr extends Dr{async _call($){return new Mt(await super._call($))}}class dt extends Dr{async _call($){return new pr(await super._call($))}}class qr extends Dr{async _call($){return new Pr(await super._call($))}}class Ur extends U{}class ps extends Ur{}class Ts extends Ur{async _call($){return new wr(await super._call($))}}class Es extends Ur{async _call($){return new Mt(await super._call($))}}class Ps extends Ur{async _call($){return new pr(await super._call($))}}class Ls extends Ur{async _call($){return new Pr(await super._call($))}}class mr extends U{}class Be extends mr{}class et extends mr{async _call($){return new wr(await super._call($))}}class it extends mr{async _call($){return new Mt(await super._call($))}}class rr extends mr{async _call($){return new pr(await super._call($))}}class zt extends mr{async _call($){return new Pr(await super._call($))}}class kr extends U{}class hs extends kr{}class ms extends kr{async _call($){return new wr(await super._call($))}}class Er extends kr{async _call($){return new Mt(await super._call($))}}class fs extends kr{async _call($){return new pr(await super._call($))}}class _s extends kr{async _call($){return new Pr(await super._call($))}}class Qr extends U{}class Cs extends Qr{}class Qs extends Qr{async _call($){return new Mt(await super._call($))}}class Xs extends Qr{async _call($){return new pr(await super._call($))}}class Js extends Qr{async _call($){return new Pr(await super._call($))}}class Ys extends Qr{async _call($){return new wr(await super._call($))}}class gs extends U{}class Zs extends gs{}class en extends gs{async _call($){return new wr(await super._call($))}}class tn extends gs{async _call($){return new Mt(await super._call($))}}class Lr extends gs{async _call($){return new pr(await super._call($))}}class Ss extends U{}class br extends Ss{}class zs extends Ss{async _call($){return new wr(await super._call($))}}class Bs extends Ss{async _call($){return new Mt(await super._call($))}}class Ir extends Ss{async _call($){return new Pr(await super._call($))}}class Xr extends U{}class En extends Xr{}class zr extends Xr{async _call($){return new wr(await super._call($))}}class Pn extends Xr{async _call($){return new Mt(await super._call($))}}class Rs extends Xr{async _call($){return new pr(await super._call($))}}class Ar extends Xr{async _call($){return new Pr(await super._call($))}}class Jr extends U{}class cr extends Jr{}class fr extends Jr{async _call($){return new wr(await super._call($))}}class Ns extends Jr{async _call($){return new Mt(await super._call($))}}class Cn extends Jr{async _call($){return new Pr(await super._call($))}}class $s extends U{}class Sn extends $s{}class ue extends $s{async _call($){return new Mt(await super._call($))}}class O extends $s{async _call($){return new Pr(await super._call($))}}class j extends $s{async _call($){return new wr(await super._call($))}}class te extends U{constructor(){super(...arguments);re(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class de extends te{}class he extends te{}class Ce extends U{}class We extends Ce{}class qe extends Ce{}class Ke extends U{}class Ze extends Ke{}class ht extends Ke{}class Ct extends U{}class kt extends Ct{}class Wt extends Ct{}class Ft extends Ct{async _call($){return new Mt(await super._call($))}}class Ut extends U{}class vr extends Ut{}class _r extends Ut{}class Yr extends Ut{async _call($){return new Mt(await super._call($))}}class xr extends Ut{}class ws extends U{}class Gt extends ws{}class Zt extends ws{}class gr extends U{}class Zr extends gr{}class ys extends gr{}class Ht extends U{}class ur extends Ht{}class Rt extends Ht{async _call($){return new wr(await super._call($))}}class Jt extends Ht{async _call($){return new Mt(await super._call($))}}class qt extends Ht{async _call($){return new pr(await super._call($))}}class er extends Ht{async _call($){return new Pr(await super._call($))}}class ir extends U{}class rn extends ir{}class sn extends ir{async _call($){return new wr(await super._call($))}}class La extends ir{async _call($){return new Mt(await super._call($))}}class Vi extends ir{async _call($){return new pr(await super._call($))}}class za extends ir{async _call($){return new Pr(await super._call($))}}class Ms extends U{}class Ba extends Ms{}class Ra extends Ms{async _call($){return new wr(await super._call($))}}class $n extends Ms{async _call($){return new Mt(await super._call($))}}class Na extends Ms{async _call($){return new pr(await super._call($))}}class Wi extends Ms{async _call($){return new Pr(await super._call($))}}class Gi extends U{}class ja extends Gi{}class Ua extends Gi{}class Gn extends U{constructor(){super(...arguments);re(this,"requires_attention_mask",!1);re(this,"main_input_name","input_features");re(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Va extends Gn{}class Ki extends Gn{_prepare_generation_config($,L){return super._prepare_generation_config($,L,y.WhisperGenerationConfig)}_retrieve_init_tokens($){const L=[$.decoder_start_token_id];let oe=$.language;const _e=$.task;if($.is_multilingual){oe||(console.warn("No language specified - defaulting to English (en)."),oe="en");const Se=`<|${(0,S.whisper_language_to_code)(oe)}|>`;L.push($.lang_to_id[Se]),L.push($.task_to_id[_e??"transcribe"])}else if(oe||_e)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!$.return_timestamps&&$.no_timestamps_token_id&&L.at(-1)!==$.no_timestamps_token_id?L.push($.no_timestamps_token_id):$.return_timestamps&&L.at(-1)===$.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),L.pop()),L.filter(me=>me!=null)}async generate({inputs:$=null,generation_config:L=null,logits_processor:oe=null,stopping_criteria:_e=null,...me}){L=this._prepare_generation_config(L,me);const Se=me.decoder_input_ids??this._retrieve_init_tokens(L);if(L.return_timestamps&&(oe??(oe=new p.LogitsProcessorList),oe.push(new p.WhisperTimeStampLogitsProcessor(L,Se))),L.begin_suppress_tokens&&(oe??(oe=new p.LogitsProcessorList),oe.push(new p.SuppressTokensAtBeginLogitsProcessor(L.begin_suppress_tokens,Se.length))),L.return_token_timestamps){if(!L.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");L.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),L.output_attentions=!0,L.return_dict_in_generate=!0}const De=await super.generate({inputs:$,generation_config:L,logits_processor:oe,decoder_input_ids:Se,...me});return L.return_token_timestamps&&(De.token_timestamps=this._extract_token_timestamps(De,L.alignment_heads,L.num_frames)),De}_extract_token_timestamps($,L,oe=null,_e=.02){if(!$.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");oe==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let me=this.config.median_filter_width;me===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),me=7);const Se=$.cross_attentions,De=Array.from({length:this.config.decoder_layers},(bt,ut)=>(0,h.cat)(Se.map(Tt=>Tt[ut]),2)),Ge=(0,h.stack)(L.map(([bt,ut])=>{if(bt>=De.length)throw new Error(`Layer index ${bt} is out of bounds for cross attentions (length ${De.length}).`);return oe?De[bt].slice(null,ut,null,[0,oe]):De[bt].slice(null,ut)})).transpose(1,0,2,3),[Je,lt]=(0,h.std_mean)(Ge,-2,0,!0),wt=Ge.clone();for(let bt=0;btTt[tr+1]-Tt[tr]),yr=(0,a.mergeArrays)([1],Qt).map(Nt=>!!Nt),Pt=[];for(let Nt=0;Ntst.findIndex(Et=>Et==me)),Ge=De.every(st=>st===-1),Je=De.every(st=>st!==-1);if(!Ge&&!Je)throw new Error("Every input should contain either 0 or 1 image token.");if(Ge)return{inputs_embeds:$,attention_mask:_e};const lt=[],wt=[];for(let st=0;stArray.from({length:$.dims[0]},Qt=>Array.from({length:$.dims[1]},yr=>1))),at=L?L.tolist():[],bt=oe?oe.tolist():[];let ut=0,Tt=0;for(let Lt=0;Ltst[Lt][or]==1),Pt=Qt.reduce((Kt,or,un)=>(or==Ge&&Kt.push(un),Kt),[]).map(Kt=>Qt[Kt+1]),Nt=Pt.filter(Kt=>Kt==Se).length,tr=Pt.filter(Kt=>Kt==De).length;let sr=[],Wr=0,cn=Nt,Yl=tr;for(let Kt=0;Ktbs>Wr&&Ln==Se),un=Qt.findIndex((Ln,bs)=>bs>Wr&&Ln==De),Dn=cn>0&&or!==-1?or:Qt.length+1,Pi=Yl>0&&un!==-1?un:Qt.length+1;let rd,Eu,Pu,Cu;Dn0?(0,_.max)(sr.at(-1))[0]+1:0;sr.push(Array.from({length:3*$u},(Ln,bs)=>ob+bs%$u));const ku=$u+ob,nd=yx*Su*sd,Mx=Array.from({length:nd},(Ln,bs)=>ku+Math.floor(bs/(Su*sd))),bx=Array.from({length:nd},(Ln,bs)=>ku+Math.floor(bs/sd)%Su),vx=Array.from({length:nd},(Ln,bs)=>ku+bs%sd);sr.push([Mx,bx,vx].flat()),Wr=rd+nd}if(Wr0?(0,_.max)(sr.at(-1))[0]+1:0,or=Qt.length-Wr;sr.push(Array.from({length:3*or},(un,Dn)=>Kt+Dn%or))}const Cr=sr.reduce((Kt,or)=>Kt+or.length,0),ks=new Array(Cr);let Tu=0;for(let Kt=0;Kt<3;++Kt)for(let or=0;orwt[ut%wt.length]),at=Array.from({length:st[0]},(bt,ut)=>(0,_.max)(wt.subarray(st[1]*ut,st[1]*(ut+1)))[0]+1n+BigInt(st[1]));return[new h.Tensor("int64",Et,[3,...st]),new h.Tensor("int64",at,[at.length,1])]}else{const[wt,st]=$.dims,Et=BigInt64Array.from({length:3*wt*st},(at,bt)=>BigInt(Math.floor(bt%st/wt)));return[new h.Tensor("int64",Et,[3,...$.dims]),(0,h.zeros)([wt,1])]}}async encode_image({pixel_values:$,image_grid_thw:L}){return(await q(this.sessions.vision_encoder,{pixel_values:$,grid_thw:L})).image_features}_merge_input_ids_with_image_features($){return N({image_token_id:this.config.image_token_id,...$})}prepare_inputs_for_generation($,L,oe){if(L.attention_mask&&!L.position_ids)if(!L.past_key_values)[L.position_ids,L.rope_deltas]=this.get_rope_index(L.input_ids,L.image_grid_thw,L.video_grid_thw,L.attention_mask);else{L.pixel_values=null;const _e=BigInt(Object.values(L.past_key_values)[0].dims.at(-2)),me=L.rope_deltas.map(Se=>_e+Se);L.position_ids=(0,h.stack)([me,me,me],0)}return L}}class fi extends U{}class Oo extends fi{}class Fo extends fi{}class _i extends U{}class Do extends _i{}class Lo extends _i{}class gi extends U{}class zo extends gi{}class Bo extends gi{}class wi extends U{}class Ro extends wi{}class No extends wi{}class yi extends U{}class jo extends yi{}class Uo extends yi{}class Mi extends U{}class Vo extends Mi{}class Wo extends Mi{async _call($){return new Mt(await super._call($))}}class bi extends U{}class Go extends bi{}class Ko extends bi{async _call($){return new Mt(await super._call($))}}class Ho extends U{}class qo extends Ho{}class vi extends U{}class Qo extends vi{}class Xo extends vi{async _call($){return new Mt(await super._call($))}}class dl extends U{}class cl extends dl{}class Jo extends U{}class ul extends Jo{}class pl extends Jo{async _call($){return new Mt(await super._call($))}}class hl extends U{}class ml extends hl{}class Yo extends U{}class fl extends Yo{}class _l extends Yo{async _call($){return new Mt(await super._call($))}}class gl extends U{}class wl extends gl{async _call($){return new nb(await super._call($))}}class Zo extends U{}class yl extends Zo{}class Ml extends Zo{async _call($){return new Mt(await super._call($))}}class ea extends U{}class bl extends ea{}class vl extends ea{async _call($){return new Mt(await super._call($))}}class ta extends U{}class xl extends ta{}class Tl extends ta{}class ra extends U{}class El extends ra{}class Pl extends ra{}class sa extends U{}class Cl extends sa{}class Sl extends sa{async _call($){return new Mt(await super._call($))}}class xi extends U{}class $l extends xi{}class kl extends xi{async _call($){return new ia(await super._call($))}}class na extends xi{async _call($){return new Il(await super._call($))}}class ia extends pe{constructor({logits:$,pred_boxes:L}){super(),this.logits=$,this.pred_boxes=L}}class Il extends pe{constructor({logits:$,pred_boxes:L,pred_masks:oe}){super(),this.logits=$,this.pred_boxes=L,this.pred_masks=oe}}class oa extends U{}class Al extends oa{}class Ol extends oa{async _call($){return new Fl(await super._call($))}}class Fl extends pe{constructor({logits:$,pred_boxes:L}){super(),this.logits=$,this.pred_boxes=L}}class aa extends U{}class Dl extends aa{}class Ll extends aa{async _call($){return new zl(await super._call($))}}class zl extends ia{}class la extends U{}class Bl extends la{}class Rl extends la{async _call($){return new Mt(await super._call($))}}class da extends U{}class ca extends da{}class c extends da{async _call($){return new Mt(await super._call($))}}class m extends U{}class b extends m{}class C extends m{async _call($){return new Mt(await super._call($))}}class I extends U{}class W extends I{}class se extends I{async _call($){return new Mt(await super._call($))}}class ye extends I{}class Te extends U{}class ze extends Te{}class Ye extends Te{}class ot extends U{}class gt extends ot{}class Bt extends ot{}class Or extends U{}class ln extends Or{}class Nl extends U{}class qw extends Nl{}class Qw extends Nl{}class Xw extends Nl{}class Jw extends U{}class Yw extends Jw{}class Zc extends U{}class Zw extends Zc{}class ey extends Zc{}class eu extends U{}class ty extends eu{}class ry extends eu{}class sy extends U{}class ny extends sy{}class tu extends U{}class iy extends tu{}class oy extends tu{async _call($){return new Mt(await super._call($))}}class ru extends U{}class ay extends ru{}class ly extends ru{async _call($){return new Mt(await super._call($))}}class su extends U{}class dy extends su{}class cy extends su{async _call($){return new Mt(await super._call($))}}class nu extends U{}class uy extends nu{}class py extends nu{async _call($){return new Mt(await super._call($))}}class hy extends U{}class my extends hy{}class iu extends U{}class fy extends iu{}class _y extends iu{async _call($){return new gy(await super._call($))}}class gy extends pe{constructor({logits:$,pred_boxes:L}){super(),this.logits=$,this.pred_boxes=L}}class wy extends U{}class yy extends wy{async get_image_embeddings({pixel_values:$}){return await ie(this,{pixel_values:$})}async forward($){if((!$.image_embeddings||!$.image_positional_embeddings)&&($={...$,...await this.get_image_embeddings($)}),!$.input_labels&&$.input_points){const oe=$.input_points.dims.slice(0,-1),_e=oe.reduce((me,Se)=>me*Se,1);$.input_labels=new h.Tensor("int64",new BigInt64Array(_e).fill(1n),oe)}const L={image_embeddings:$.image_embeddings,image_positional_embeddings:$.image_positional_embeddings};return $.input_points&&(L.input_points=$.input_points),$.input_labels&&(L.input_labels=$.input_labels),$.input_boxes&&(L.input_boxes=$.input_boxes),await q(this.sessions.prompt_encoder_mask_decoder,L)}async _call($){return new My(await super._call($))}}class My extends pe{constructor({iou_scores:$,pred_masks:L}){super(),this.iou_scores=$,this.pred_masks=L}}class ou extends U{}class by extends ou{}class vy extends ou{}class au extends U{}class xy extends au{}class Ty extends au{}class dn extends U{}class Ey extends dn{}class Py extends dn{async _call($){return new Fn(await super._call($))}}class Cy extends dn{async _call($){return new Mt(await super._call($))}}class Sy extends dn{async _call($){return new pr(await super._call($))}}class lu extends U{}class $y extends lu{}class ky extends lu{async _call($){return new pr(await super._call($))}}class Iy extends U{}class Ay extends Iy{}class jl extends U{}class Oy extends jl{}class Fy extends jl{async _call($){return new Fn(await super._call($))}}class Dy extends jl{async _call($){return new Mt(await super._call($))}}class ua extends U{}class Ly extends ua{}class zy extends ua{async _call($){return new Fn(await super._call($))}}class By extends ua{async _call($){return new Mt(await super._call($))}}class Ry extends ua{async _call($){return new pr(await super._call($))}}class Ul extends U{}class Ny extends Ul{}class jy extends Ul{async _call($){return new Fn(await super._call($))}}class Uy extends Ul{async _call($){return new Mt(await super._call($))}}class nx extends U{}class Vy extends dn{}class Wy extends dn{async _call($){return new Fn(await super._call($))}}class Gy extends dn{async _call($){return new Mt(await super._call($))}}class Ti extends U{}class Ky extends Ti{}class Hy extends Ti{async _call($){return new Fn(await super._call($))}}class qy extends Ti{async _call($){return new Mt(await super._call($))}}class Qy extends Ti{async _call($){return new sb(await super._call($))}}class Xy extends Ti{async _call($){return new pr(await super._call($))}}class Jy extends U{}class Yy extends Jy{}class Vl extends U{}class ix extends Vl{}class Zy extends Vl{}class eM extends Vl{async generate_speech($,L,{threshold:oe=.5,minlenratio:_e=0,maxlenratio:me=20,vocoder:Se=null}={}){const De={input_ids:$},{encoder_outputs:Ge,encoder_attention_mask:Je}=await ie(this,De),lt=Ge.dims[1]/this.config.reduction_factor,wt=Math.floor(lt*me),st=Math.floor(lt*_e),Et=this.config.num_mel_bins;let at=[],bt=null,ut=null,Tt=0;for(;;){++Tt;const yr=H(!!ut);let Pt;ut?Pt=ut.output_sequence_out:Pt=new h.Tensor("float32",new Float32Array(Et),[1,1,Et]);let Nt={use_cache_branch:yr,output_sequence:Pt,encoder_attention_mask:Je,speaker_embeddings:L,encoder_hidden_states:Ge};this.addPastKeyValues(Nt,bt),ut=await q(this.sessions.decoder_model_merged,Nt),bt=this.getPastKeyValues(ut,bt);const{prob:tr,spectrum:sr}=ut;if(at.push(sr),Tt>=st&&(Array.from(tr.data).filter(Wr=>Wr>=oe).length>0||Tt>=wt))break}const Lt=(0,h.cat)(at),{waveform:Qt}=await q(Se.sessions.model,{spectrogram:Lt});return{spectrogram:Lt,waveform:Qt}}}class tM extends U{constructor(){super(...arguments);re(this,"main_input_name","spectrogram")}}class rM extends U{}class sM extends rM{}class du extends U{}class nM extends du{}class iM extends du{}class cu extends U{}class oM extends cu{}class aM extends cu{}class uu extends U{}class lM extends uu{}class dM extends uu{}class Wl extends U{}class cM extends Wl{}class uM extends Wl{static async from_pretrained($,L={}){return super.from_pretrained($,{...L,model_file_name:L.model_file_name??"text_model"})}}class pM extends Wl{static async from_pretrained($,L={}){return super.from_pretrained($,{...L,model_file_name:L.model_file_name??"audio_model"})}}class hM extends U{}class pu extends hM{async _call($){return new ib(await super._call($))}}class Gl extends U{}class ox extends Gl{}class mM extends Gl{}class fM extends Gl{}class hu extends U{}class _M extends hu{}class gM extends hu{}class mu extends U{}class wM extends mu{}class yM extends mu{async _call($){return new Mt(await super._call($))}}class fu extends U{}class ax extends fu{}class lx extends fu{}class _u extends U{constructor(){super(...arguments);re(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}_apply_and_filter_by_delay_pattern_mask(L){const[oe,_e]=L.dims,me=this.config.decoder.num_codebooks,Se=_e-me;let De=0;for(let lt=0;lt0&&Et<=Se&&(L.data[De++]=L.data[lt])}const Ge=Math.floor(oe/me),Je=De/(Ge*me);return new h.Tensor(L.type,L.data.slice(0,De),[Ge,me,Je])}prepare_inputs_for_generation(L,oe,_e){let me=structuredClone(L);for(let De=0;De=Ge&&(me[De][Ge]=BigInt(this.config.decoder.pad_token_id));return _e.guidance_scale!==null&&_e.guidance_scale>1&&(me=me.concat(me)),super.prepare_inputs_for_generation(me,oe,_e)}async generate(L){const oe=await super.generate(L),_e=this._apply_and_filter_by_delay_pattern_mask(oe).unsqueeze_(0),{audio_values:me}=await q(this.sessions.encodec_decode,{audio_codes:_e});return me}}class Kl extends U{}class MM extends Kl{}class bM extends Kl{async _call($){return new Mt(await super._call($))}}class vM extends Kl{}class Hl extends U{}class xM extends Hl{}class TM extends Hl{async _call($){return new Mt(await super._call($))}}class EM extends Hl{}class ql extends U{}class PM extends ql{}class CM extends ql{async _call($){return new Mt(await super._call($))}}class SM extends ql{}class Ql extends U{}class $M extends Ql{}class kM extends Ql{async _call($){return new Mt(await super._call($))}}class IM extends Ql{}class AM extends U{}class OM extends AM{}class FM extends U{}class DM extends FM{constructor(...L){super(...L);re(this,"forward_params",["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"]);this._generation_mode="text"}async forward(L){const oe=this._generation_mode??"text";let _e;if(oe==="text"||!L.past_key_values){const Je=this.sessions.prepare_inputs_embeds,lt=(0,a.pick)(L,Je.inputNames);_e=await q(Je,lt)}else{const Je=this.sessions.gen_img_embeds,lt=(0,a.pick)({image_ids:L.input_ids},Je.inputNames);_e=await q(Je,lt)}const me={...L,..._e},Se=await ae(this,me),De=this.sessions[oe==="text"?"lm_head":"gen_head"];if(!De)throw new Error(`Unable to find "${De}" generation head`);const Ge=await q(De,(0,a.pick)(Se,De.inputNames));return{..._e,...Se,...Ge}}async generate(L){return this._generation_mode="text",super.generate(L)}async generate_images(L){this._generation_mode="image";const oe=(L.inputs??L[this.main_input_name]).dims[1],me=(await super.generate(L)).slice(null,[oe,null]),Se=this.sessions.image_decode,{decoded_image:De}=await q(Se,{generated_tokens:me}),Ge=De.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),Je=[];for(const lt of Ge){const wt=w.RawImage.fromTensor(lt);Je.push(wt)}return Je}}class LM extends pe{constructor({char_logits:$,bpe_logits:L,wp_logits:oe}){super(),this.char_logits=$,this.bpe_logits=L,this.wp_logits=oe}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class zM extends U{}class BM extends zM{async _call($){return new LM(await super._call($))}}class gu extends U{}class RM extends gu{}class NM extends gu{}class wu extends U{}class jM extends wu{}class UM extends wu{}class VM extends U{constructor(){super(...arguments);re(this,"forward_params",["input_ids","attention_mask","position_ids","audio_values","past_key_values"])}}class WM extends VM{_merge_input_ids_with_audio_features($){const L=$.audio_features.dims.at(-1),oe=$.audio_features.view(-1,L);return F({audio_token_id:this.config.ignore_index,...$,audio_features:oe})}}class Xl extends U{constructor(){super(...arguments);re(this,"main_input_name","input_values");re(this,"forward_params",["input_values"])}}class GM extends pe{constructor({audio_codes:$}){super(),this.audio_codes=$}}class KM extends pe{constructor({audio_values:$}){super(),this.audio_values=$}}class HM extends Xl{async encode($){return new GM(await q(this.sessions.encoder_model,$))}async decode($){return new KM(await q(this.sessions.decoder_model,$))}}class qM extends Xl{static async from_pretrained($,L={}){return super.from_pretrained($,{...L,model_file_name:L.model_file_name??"encoder_model"})}}class QM extends Xl{static async from_pretrained($,L={}){return super.from_pretrained($,{...L,model_file_name:L.model_file_name??"decoder_model"})}}class Jl extends U{constructor(){super(...arguments);re(this,"main_input_name","input_values");re(this,"forward_params",["input_values"])}}class XM extends pe{constructor({audio_codes:$}){super(),this.audio_codes=$}}class JM extends pe{constructor({audio_values:$}){super(),this.audio_values=$}}class YM extends Jl{async encode($){return new XM(await q(this.sessions.encoder_model,$))}async decode($){return new JM(await q(this.sessions.decoder_model,$))}}class ZM extends Jl{static async from_pretrained($,L={}){return super.from_pretrained($,{...L,model_file_name:L.model_file_name??"encoder_model"})}}class e0 extends Jl{static async from_pretrained($,L={}){return super.from_pretrained($,{...L,model_file_name:L.model_file_name??"decoder_model"})}}class It{static async from_pretrained($,{progress_callback:L=null,config:oe=null,cache_dir:_e=null,local_files_only:me=!1,revision:Se="main",model_file_name:De=null,subfolder:Ge="onnx",device:Je=null,dtype:lt=null,use_external_data_format:wt=null,session_options:st={}}={}){const Et={progress_callback:L,config:oe,cache_dir:_e,local_files_only:me,revision:Se,model_file_name:De,subfolder:Ge,device:Je,dtype:lt,use_external_data_format:wt,session_options:st};if(Et.config=await s.AutoConfig.from_pretrained($,Et),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);const at=Et.config.model_type;for(const bt of this.MODEL_CLASS_MAPPINGS){let ut=bt.get(at);if(!ut){for(const Tt of bt.values())if(Tt[0]===at){ut=Tt;break}if(!ut)continue}return await ut[1].from_pretrained($,Et)}if(this.BASE_IF_FAIL)return P0.has(at)||console.warn(`Unknown model class "${at}", attempting to construct from base class.`),await U.from_pretrained($,Et);throw Error(`Unsupported model type: ${at}`)}}re(It,"MODEL_CLASS_MAPPINGS",null),re(It,"BASE_IF_FAIL",!1);const dx=new Map([["bert",["BertModel",Oe]],["modernbert",["ModernBertModel",Z]],["nomic_bert",["NomicBertModel",ve]],["roformer",["RoFormerModel",Ae]],["electra",["ElectraModel",us]],["esm",["EsmModel",Zs]],["convbert",["ConvBertModel",nr]],["camembert",["CamembertModel",ps]],["deberta",["DebertaModel",Be]],["deberta-v2",["DebertaV2Model",hs]],["mpnet",["MPNetModel",En]],["albert",["AlbertModel",Sn]],["distilbert",["DistilBertModel",Cs]],["roberta",["RobertaModel",ur]],["xlm",["XLMModel",rn]],["xlm-roberta",["XLMRobertaModel",Ba]],["clap",["ClapModel",cM]],["clip",["CLIPModel",Zi]],["clipseg",["CLIPSegModel",on]],["chinese_clip",["ChineseCLIPModel",rl]],["siglip",["SiglipModel",Ya]],["jina_clip",["JinaCLIPModel",sl]],["mobilebert",["MobileBertModel",br]],["squeezebert",["SqueezeBertModel",cr]],["wav2vec2",["Wav2Vec2Model",Ey]],["wav2vec2-bert",["Wav2Vec2BertModel",Ny]],["unispeech",["UniSpeechModel",Oy]],["unispeech-sat",["UniSpeechSatModel",Ly]],["hubert",["HubertModel",Vy]],["wavlm",["WavLMModel",Ky]],["audio-spectrogram-transformer",["ASTModel",ja]],["vits",["VitsModel",pu]],["pyannote",["PyAnnoteModel",$y]],["wespeaker-resnet",["WeSpeakerResNetModel",Ay]],["detr",["DetrModel",$l]],["rt_detr",["RTDetrModel",Al]],["table-transformer",["TableTransformerModel",Dl]],["vit",["ViTModel",Vo]],["ijepa",["IJepaModel",Go]],["pvt",["PvtModel",Qo]],["vit_msn",["ViTMSNModel",ul]],["vit_mae",["ViTMAEModel",cl]],["groupvit",["GroupViTModel",ml]],["fastvit",["FastViTModel",fl]],["mobilevit",["MobileViTModel",yl]],["mobilevitv2",["MobileViTV2Model",bl]],["owlvit",["OwlViTModel",xl]],["owlv2",["Owlv2Model",El]],["beit",["BeitModel",Cl]],["deit",["DeiTModel",Bl]],["hiera",["HieraModel",ca]],["convnext",["ConvNextModel",iy]],["convnextv2",["ConvNextV2Model",ay]],["dinov2",["Dinov2Model",dy]],["dinov2_with_registers",["Dinov2WithRegistersModel",uy]],["resnet",["ResNetModel",b]],["swin",["SwinModel",W]],["swin2sr",["Swin2SRModel",ze]],["donut-swin",["DonutSwinModel",ny]],["yolos",["YolosModel",fy]],["dpt",["DPTModel",gt]],["glpn",["GLPNModel",ty]],["hifigan",["SpeechT5HifiGan",tM]],["efficientnet",["EfficientNetModel",wM]],["decision_transformer",["DecisionTransformerModel",OM]],["patchtst",["PatchTSTForPrediction",RM]],["patchtsmixer",["PatchTSMixerForPrediction",jM]],["mobilenet_v1",["MobileNetV1Model",MM]],["mobilenet_v2",["MobileNetV2Model",xM]],["mobilenet_v3",["MobileNetV3Model",PM]],["mobilenet_v4",["MobileNetV4Model",$M]],["maskformer",["MaskFormerModel",Zw]],["mgp-str",["MgpstrForSceneTextRecognition",BM]],["style_text_to_speech_2",["StyleTextToSpeech2Model",Yy]]]),cx=new Map([["t5",["T5Model",de]],["longt5",["LongT5Model",We]],["mt5",["MT5Model",Ze]],["bart",["BartModel",kt]],["mbart",["MBartModel",vr]],["marian",["MarianModel",by]],["whisper",["WhisperModel",Va]],["m2m_100",["M2M100Model",xy]],["blenderbot",["BlenderbotModel",Gt]],["blenderbot-small",["BlenderbotSmallModel",Zr]]]),ux=new Map([["mimi",["MimiModel",HM]],["dac",["DacModel",YM]]]),px=new 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Map([["speecht5",["SpeechT5ForSpeechToText",Zy]],["whisper",["WhisperForConditionalGeneration",Ki]],["lite-whisper",["LiteWhisperForConditionalGeneration",Wa]],["moonshine",["MoonshineForConditionalGeneration",Ga]]]),t0=new Map([["speecht5",["SpeechT5ForTextToSpeech",eM]]]),r0=new Map([["vits",["VitsModel",pu]],["musicgen",["MusicgenForConditionalGeneration",_u]]]),s0=new Map([["bert",["BertForSequenceClassification",Le]],["modernbert",["ModernBertForSequenceClassification",ee]],["roformer",["RoFormerForSequenceClassification",Qe]],["electra",["ElectraForSequenceClassification",jr]],["esm",["EsmForSequenceClassification",tn]],["convbert",["ConvBertForSequenceClassification",dr]],["camembert",["CamembertForSequenceClassification",Es]],["deberta",["DebertaForSequenceClassification",it]],["deberta-v2",["DebertaV2ForSequenceClassification",Er]],["mpnet",["MPNetForSequenceClassification",Pn]],["albert",["AlbertForSequenceClassification",ue]],["distilbert",["DistilBertForSequenceClassification",Qs]],["roberta",["RobertaForSequenceClassification",Jt]],["xlm",["XLMForSequenceClassification",La]],["xlm-roberta",["XLMRobertaForSequenceClassification",$n]],["bart",["BartForSequenceClassification",Ft]],["mbart",["MBartForSequenceClassification",Yr]],["mobilebert",["MobileBertForSequenceClassification",Bs]],["squeezebert",["SqueezeBertForSequenceClassification",Ns]]]),n0=new Map([["bert",["BertForTokenClassification",Ne]],["modernbert",["ModernBertForTokenClassification",ce]],["roformer",["RoFormerForTokenClassification",Xe]],["electra",["ElectraForTokenClassification",dt]],["esm",["EsmForTokenClassification",Lr]],["convbert",["ConvBertForTokenClassification",cs]],["camembert",["CamembertForTokenClassification",Ps]],["deberta",["DebertaForTokenClassification",rr]],["deberta-v2",["DebertaV2ForTokenClassification",fs]],["mpnet",["MPNetForTokenClassification",Rs]],["distilbert",["DistilBertForTokenClassification",Xs]],["roberta",["RobertaForTokenClassification",qt]],["xlm",["XLMForTokenClassification",Vi]],["xlm-roberta",["XLMRobertaForTokenClassification",Na]]]),Mu=new Map([["t5",["T5ForConditionalGeneration",he]],["longt5",["LongT5ForConditionalGeneration",qe]],["mt5",["MT5ForConditionalGeneration",ht]],["bart",["BartForConditionalGeneration",Wt]],["mbart",["MBartForConditionalGeneration",_r]],["marian",["MarianMTModel",vy]],["m2m_100",["M2M100ForConditionalGeneration",Ty]],["blenderbot",["BlenderbotForConditionalGeneration",Zt]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",ys]]]),bu=new Map([["bloom",["BloomForCausalLM",Bo]],["gpt2",["GPT2LMHeadModel",Yn]],["jais",["JAISLMHeadModel",so]],["gptj",["GPTJForCausalLM",In]],["gpt_bigcode",["GPTBigCodeForCausalLM",co]],["gpt_neo",["GPTNeoForCausalLM",io]],["gpt_neox",["GPTNeoXForCausalLM",oo]],["codegen",["CodeGenForCausalLM",Vr]],["llama",["LlamaForCausalLM",uo]],["exaone",["ExaoneForCausalLM",mt]],["olmo",["OlmoForCausalLM",go]],["olmo2",["Olmo2ForCausalLM",li]],["mobilellm",["MobileLLMForCausalLM",fo]],["granite",["GraniteForCausalLM",Mo]],["cohere",["CohereForCausalLM",vo]],["gemma",["GemmaForCausalLM",To]],["gemma2",["Gemma2ForCausalLM",Po]],["helium",["HeliumForCausalLM",ni]],["glm",["GlmForCausalLM",_t]],["openelm",["OpenELMForCausalLM",So]],["qwen2",["Qwen2ForCausalLM",ko]],["phi",["PhiForCausalLM",Fo]],["phi3",["Phi3ForCausalLM",Lo]],["mpt",["MptForCausalLM",No]],["opt",["OPTForCausalLM",Uo]],["mbart",["MBartForCausalLM",xr]],["mistral",["MistralForCausalLM",iM]],["starcoder2",["Starcoder2ForCausalLM",aM]],["falcon",["FalconForCausalLM",dM]],["trocr",["TrOCRForCausalLM",sM]],["stablelm",["StableLmForCausalLM",gM]],["phi3_v",["Phi3VForCausalLM",js]]]),hx=new Map([["multi_modality",["MultiModalityCausalLM",DM]]]),i0=new Map([["bert",["BertForMaskedLM",Ie]],["modernbert",["ModernBertForMaskedLM",z]],["roformer",["RoFormerForMaskedLM",Ue]],["electra",["ElectraForMaskedLM",Ds]],["esm",["EsmForMaskedLM",en]],["convbert",["ConvBertForMaskedLM",jt]],["camembert",["CamembertForMaskedLM",Ts]],["deberta",["DebertaForMaskedLM",et]],["deberta-v2",["DebertaV2ForMaskedLM",ms]],["mpnet",["MPNetForMaskedLM",zr]],["albert",["AlbertForMaskedLM",j]],["distilbert",["DistilBertForMaskedLM",Ys]],["roberta",["RobertaForMaskedLM",Rt]],["xlm",["XLMWithLMHeadModel",sn]],["xlm-roberta",["XLMRobertaForMaskedLM",Ra]],["mobilebert",["MobileBertForMaskedLM",zs]],["squeezebert",["SqueezeBertForMaskedLM",fr]]]),o0=new Map([["bert",["BertForQuestionAnswering",Ve]],["roformer",["RoFormerForQuestionAnswering",ct]],["electra",["ElectraForQuestionAnswering",qr]],["convbert",["ConvBertForQuestionAnswering",Fs]],["camembert",["CamembertForQuestionAnswering",Ls]],["deberta",["DebertaForQuestionAnswering",zt]],["deberta-v2",["DebertaV2ForQuestionAnswering",_s]],["mpnet",["MPNetForQuestionAnswering",Ar]],["albert",["AlbertForQuestionAnswering",O]],["distilbert",["DistilBertForQuestionAnswering",Js]],["roberta",["RobertaForQuestionAnswering",er]],["xlm",["XLMForQuestionAnswering",za]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Wi]],["mobilebert",["MobileBertForQuestionAnswering",Ir]],["squeezebert",["SqueezeBertForQuestionAnswering",Cn]]]),vu=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",qi]],["idefics3",["Idefics3ForConditionalGeneration",Hn]],["smolvlm",["SmolVLMForConditionalGeneration",qn]]]),a0=new 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Map([["vit",["ViTForImageClassification",Wo]],["ijepa",["IJepaForImageClassification",Ko]],["pvt",["PvtForImageClassification",Xo]],["vit_msn",["ViTMSNForImageClassification",pl]],["fastvit",["FastViTForImageClassification",_l]],["mobilevit",["MobileViTForImageClassification",Ml]],["mobilevitv2",["MobileViTV2ForImageClassification",vl]],["beit",["BeitForImageClassification",Sl]],["deit",["DeiTForImageClassification",Rl]],["hiera",["HieraForImageClassification",c]],["convnext",["ConvNextForImageClassification",oy]],["convnextv2",["ConvNextV2ForImageClassification",ly]],["dinov2",["Dinov2ForImageClassification",cy]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",py]],["resnet",["ResNetForImageClassification",C]],["swin",["SwinForImageClassification",se]],["segformer",["SegformerForImageClassification",mM]],["efficientnet",["EfficientNetForImageClassification",yM]],["mobilenet_v1",["MobileNetV1ForImageClassification",bM]],["mobilenet_v2",["MobileNetV2ForImageClassification",TM]],["mobilenet_v3",["MobileNetV3ForImageClassification",CM]],["mobilenet_v4",["MobileNetV4ForImageClassification",kM]]]),c0=new Map([["detr",["DetrForObjectDetection",kl]],["rt_detr",["RTDetrForObjectDetection",Ol]],["table-transformer",["TableTransformerForObjectDetection",Ll]],["yolos",["YolosForObjectDetection",_y]]]),u0=new Map([["owlvit",["OwlViTForObjectDetection",Tl]],["owlv2",["Owlv2ForObjectDetection",Pl]],["grounding-dino",["GroundingDinoForObjectDetection",my]]]),Ei=new Map([["detr",["DetrForSegmentation",na]],["clipseg",["CLIPSegForImageSegmentation",eo]]]),p0=new Map([["segformer",["SegformerForSemanticSegmentation",fM]],["sapiens",["SapiensForSemanticSegmentation",qw]],["swin",["SwinForSemanticSegmentation",ye]],["mobilenet_v1",["MobileNetV1ForSemanticSegmentation",vM]],["mobilenet_v2",["MobileNetV2ForSemanticSegmentation",EM]],["mobilenet_v3",["MobileNetV3ForSemanticSegmentation",SM]],["mobilenet_v4",["MobileNetV4ForSemanticSegmentation",IM]]]),h0=new Map([["detr",["DetrForSegmentation",na]],["maskformer",["MaskFormerForInstanceSegmentation",ey]]]),m0=new Map([["sam",["SamModel",yy]]]),f0=new Map([["wav2vec2",["Wav2Vec2ForCTC",Py]],["wav2vec2-bert",["Wav2Vec2BertForCTC",jy]],["unispeech",["UniSpeechForCTC",Fy]],["unispeech-sat",["UniSpeechSatForCTC",zy]],["wavlm",["WavLMForCTC",Hy]],["hubert",["HubertForCTC",Wy]]]),_0=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Cy]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Uy]],["unispeech",["UniSpeechForSequenceClassification",Dy]],["unispeech-sat",["UniSpeechSatForSequenceClassification",By]],["wavlm",["WavLMForSequenceClassification",qy]],["hubert",["HubertForSequenceClassification",Gy]],["audio-spectrogram-transformer",["ASTForAudioClassification",Ua]]]),g0=new Map([["wavlm",["WavLMForXVector",Qy]]]),w0=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Ry]],["wavlm",["WavLMForAudioFrameClassification",Xy]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Sy]],["pyannote",["PyAnnoteForAudioFrameClassification",ky]]]),y0=new Map([["vitmatte",["VitMatteForImageMatting",wl]]]),fx=new Map([["patchtst",["PatchTSTForPrediction",NM]],["patchtsmixer",["PatchTSMixerForPrediction",UM]]]),M0=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Ye]]]),b0=new Map([["dpt",["DPTForDepthEstimation",Bt]],["depth_anything",["DepthAnythingForDepthEstimation",ln]],["glpn",["GLPNForDepthEstimation",ry]],["sapiens",["SapiensForDepthEstimation",Qw]],["depth_pro",["DepthProForDepthEstimation",Yw]]]),v0=new Map([["sapiens",["SapiensForNormalEstimation",Xw]]]),x0=new Map([["vitpose",["VitPoseForPoseEstimation",qo]]]),T0=new Map([["clip",["CLIPVisionModelWithProjection",Qn]],["siglip",["SiglipVisionModel",el]],["jina_clip",["JinaCLIPVisionModel",il]]]),E0=[[dx,x.EncoderOnly],[cx,x.EncoderDecoder],[px,x.DecoderOnly],[ux,x.AutoEncoder],[s0,x.EncoderOnly],[n0,x.EncoderOnly],[Mu,x.Seq2Seq],[yu,x.Seq2Seq],[bu,x.DecoderOnly],[hx,x.MultiModality],[i0,x.EncoderOnly],[o0,x.EncoderOnly],[vu,x.Vision2Seq],[a0,x.ImageTextToText],[l0,x.AudioTextToText],[d0,x.EncoderOnly],[Ei,x.EncoderOnly],[h0,x.EncoderOnly],[p0,x.EncoderOnly],[y0,x.EncoderOnly],[fx,x.EncoderOnly],[M0,x.EncoderOnly],[b0,x.EncoderOnly],[v0,x.EncoderOnly],[x0,x.EncoderOnly],[c0,x.EncoderOnly],[u0,x.EncoderOnly],[m0,x.MaskGeneration],[f0,x.EncoderOnly],[_0,x.EncoderOnly],[t0,x.Seq2Seq],[r0,x.EncoderOnly],[g0,x.EncoderOnly],[w0,x.EncoderOnly],[T0,x.EncoderOnly]];for(const[T,$]of E0)for(const[L,oe]of T.values())g.set(L,$),E.set(oe,L),M.set(L,oe);const _x=[["MusicgenForConditionalGeneration",_u,x.Musicgen],["Phi3VForCausalLM",js,x.Phi3V],["CLIPTextModelWithProjection",Ja,x.EncoderOnly],["SiglipTextModel",Za,x.EncoderOnly],["JinaCLIPTextModel",nl,x.EncoderOnly],["ClapTextModelWithProjection",uM,x.EncoderOnly],["ClapAudioModelWithProjection",pM,x.EncoderOnly],["DacEncoderModel",ZM,x.EncoderOnly],["DacDecoderModel",e0,x.EncoderOnly],["MimiEncoderModel",qM,x.EncoderOnly],["MimiDecoderModel",QM,x.EncoderOnly]];for(const[T,$,L]of _x)g.set(T,L),E.set($,T),M.set(T,$);const P0=new Map([["modnet",Ei],["birefnet",Ei],["isnet",Ei],["ben",Ei]]);for(const[T,$]of P0.entries())$.set(T,["PreTrainedModel",U]),g.set(T,x.EncoderOnly),E.set(U,T),M.set(T,U);class xu extends It{}re(xu,"MODEL_CLASS_MAPPINGS",E0.map($=>$[0])),re(xu,"BASE_IF_FAIL",!0);class C0 extends It{}re(C0,"MODEL_CLASS_MAPPINGS",[s0]);class S0 extends It{}re(S0,"MODEL_CLASS_MAPPINGS",[n0]);class $0 extends It{}re($0,"MODEL_CLASS_MAPPINGS",[Mu]);class k0 extends It{}re(k0,"MODEL_CLASS_MAPPINGS",[yu]);class I0 extends It{}re(I0,"MODEL_CLASS_MAPPINGS",[t0]);class A0 extends It{}re(A0,"MODEL_CLASS_MAPPINGS",[r0]);class O0 extends It{}re(O0,"MODEL_CLASS_MAPPINGS",[bu]);class F0 extends It{}re(F0,"MODEL_CLASS_MAPPINGS",[i0]);class D0 extends It{}re(D0,"MODEL_CLASS_MAPPINGS",[o0]);class L0 extends It{}re(L0,"MODEL_CLASS_MAPPINGS",[vu]);class z0 extends It{}re(z0,"MODEL_CLASS_MAPPINGS",[d0]);class B0 extends It{}re(B0,"MODEL_CLASS_MAPPINGS",[Ei]);class R0 extends It{}re(R0,"MODEL_CLASS_MAPPINGS",[p0]);class N0 extends It{}re(N0,"MODEL_CLASS_MAPPINGS",[h0]);class j0 extends It{}re(j0,"MODEL_CLASS_MAPPINGS",[c0]);class U0 extends It{}re(U0,"MODEL_CLASS_MAPPINGS",[u0]);class V0 extends It{}re(V0,"MODEL_CLASS_MAPPINGS",[m0]);class W0 extends It{}re(W0,"MODEL_CLASS_MAPPINGS",[f0]);class G0 extends It{}re(G0,"MODEL_CLASS_MAPPINGS",[_0]);class K0 extends It{}re(K0,"MODEL_CLASS_MAPPINGS",[g0]);class H0 extends It{}re(H0,"MODEL_CLASS_MAPPINGS",[w0]);class q0 extends It{}re(q0,"MODEL_CLASS_MAPPINGS",[mx]);class Q0 extends It{}re(Q0,"MODEL_CLASS_MAPPINGS",[y0]);class X0 extends It{}re(X0,"MODEL_CLASS_MAPPINGS",[M0]);class J0 extends It{}re(J0,"MODEL_CLASS_MAPPINGS",[b0]);class Y0 extends It{}re(Y0,"MODEL_CLASS_MAPPINGS",[v0]);class Z0 extends It{}re(Z0,"MODEL_CLASS_MAPPINGS",[x0]);class eb extends It{}re(eb,"MODEL_CLASS_MAPPINGS",[T0]);class tb extends It{}re(tb,"MODEL_CLASS_MAPPINGS",[a0]);class rb extends It{}re(rb,"MODEL_CLASS_MAPPINGS",[l0]);class gx extends pe{constructor({logits:$,past_key_values:L,encoder_outputs:oe,decoder_attentions:_e=null,cross_attentions:me=null}){super(),this.logits=$,this.past_key_values=L,this.encoder_outputs=oe,this.decoder_attentions=_e,this.cross_attentions=me}}class Mt extends pe{constructor({logits:$,...L}){super(),this.logits=$;const oe=Object.values(L);oe.length>0&&(this.attentions=oe)}}class sb extends pe{constructor({logits:$,embeddings:L}){super(),this.logits=$,this.embeddings=L}}class pr extends pe{constructor({logits:$}){super(),this.logits=$}}class wr extends pe{constructor({logits:$}){super(),this.logits=$}}class Pr extends pe{constructor({start_logits:$,end_logits:L}){super(),this.start_logits=$,this.end_logits=L}}class Fn extends pe{constructor({logits:$}){super(),this.logits=$}}class wx extends pe{constructor({logits:$,past_key_values:L}){super(),this.logits=$,this.past_key_values=L}}class nb extends pe{constructor({alphas:$}){super(),this.alphas=$}}class ib extends pe{constructor({waveform:$,spectrogram:L}){super(),this.waveform=$,this.spectrogram=L}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(e,r,t)=>{t.r(r),t.d(r,{ASTFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var i=t("./src/utils/audio.js");class n extends s.FeatureExtractor{constructor(a){super(a);const l=this.config.sampling_rate,d=(0,i.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(l/2),l,null,"kaldi",!0);for(let p=0;p{t.r(r),t.d(r,{AutoFeatureExtractor:()=>o});var s=t("./src/utils/constants.js"),i=t("./src/utils/hub.js");t("./src/base/feature_extraction_utils.js");var n=t("./src/models/feature_extractors.js");class o{static async from_pretrained(l,d={}){const p=await(0,i.getModelJSON)(l,s.FEATURE_EXTRACTOR_NAME,!0,d),u=p.feature_extractor_type,h=n[u];if(!h)throw new Error(`Unknown feature_extractor_type: '${u}'. Please report this at ${s.GITHUB_ISSUE_URL}.`);return new h(p)}}},"./src/models/auto/image_processing_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoImageProcessor:()=>a});var s=t("./src/utils/constants.js"),i=t("./src/utils/hub.js"),n=t("./src/base/image_processors_utils.js"),o=t("./src/models/image_processors.js");class a{static async from_pretrained(d,p={}){const u=await(0,i.getModelJSON)(d,s.IMAGE_PROCESSOR_NAME,!0,p),h=u.image_processor_type??u.feature_extractor_type;let w=o[h];return w||(h!==void 0&&console.warn(`Image processor type '${h}' not found, assuming base ImageProcessor. 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this.image_processor(p,h):{};return{...u?this.tokenizer(u,h):{},...w}}post_process_grounded_object_detection(p,u,{box_threshold:h=.25,text_threshold:w=.25,target_sizes:_=null}={}){const{logits:P,pred_boxes:A}=p,v=P.dims[0];if(_!==null&&_.length!==v)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const y=P.dims.at(1),S=P.sigmoid(),x=S.max(-1).tolist(),g=A.tolist().map(E=>E.map(k=>(0,o.center_to_corners_format)(k))),M=[];for(let E=0;EV.map((Y,H)=>Y*k[(H+1)%2])));const B=x[E],R=[],J=[],q=[];for(let V=0;V{t.r(r),t.d(r,{Idefics3ImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js"),i=t("./src/utils/tensor.js");class n extends s.ImageProcessor{constructor(a){super(a),this.do_image_splitting=a.do_image_splitting??!0,this.max_image_size=a.max_image_size}get_resize_for_vision_encoder(a,l){let[d,p]=a.dims.slice(-2);const u=p/d;return 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P=(0,s.isONNXProxy)(),A=Object.fromEntries(Object.entries(_).map(([y,S])=>[y,(P?S.clone():S).ort_tensor])),v=await(w=o?w.then(()=>h.run(A)):h.run(A));return Array.isArray(u)?u.map(y=>new i.Tensor(v[y])):new i.Tensor(v[u])}};class l{static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=a([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,"y")),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=a([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=a([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=a([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=a([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=a([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=a([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=a([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}re(l,"session_options",{})},"./src/pipelines.js":(e,r,t)=>{t.r(r),t.d(r,{AudioClassificationPipeline:()=>q,AutomaticSpeechRecognitionPipeline:()=>Y,BackgroundRemovalPipeline:()=>le,DepthEstimationPipeline:()=>X,DocumentQuestionAnsweringPipeline:()=>F,FeatureExtractionPipeline:()=>R,FillMaskPipeline:()=>S,ImageClassificationPipeline:()=>Q,ImageFeatureExtractionPipeline:()=>J,ImageSegmentationPipeline:()=>ie,ImageToImagePipeline:()=>ne,ImageToTextPipeline:()=>H,ObjectDetectionPipeline:()=>ge,Pipeline:()=>P,QuestionAnsweringPipeline:()=>y,SummarizationPipeline:()=>g,Text2TextGenerationPipeline:()=>x,TextClassificationPipeline:()=>A,TextGenerationPipeline:()=>k,TextToAudioPipeline:()=>G,TokenClassificationPipeline:()=>v,TranslationPipeline:()=>M,ZeroShotAudioClassificationPipeline:()=>V,ZeroShotClassificationPipeline:()=>B,ZeroShotImageClassificationPipeline:()=>ae,ZeroShotObjectDetectionPipeline:()=>N,pipeline:()=>ke});var s=t("./src/tokenizers.js"),i=t("./src/models.js"),n=t("./src/models/auto/processing_auto.js");t("./src/base/processing_utils.js");var o=t("./src/utils/generic.js"),a=t("./src/utils/core.js"),l=t("./src/utils/maths.js"),d=t("./src/utils/audio.js"),p=t("./src/utils/tensor.js"),u=t("./src/utils/image.js");async function h(Me){return Array.isArray(Me)||(Me=[Me]),await Promise.all(Me.map(K=>u.RawImage.read(K)))}async function w(Me,K){return Array.isArray(Me)||(Me=[Me]),await Promise.all(Me.map(U=>typeof U=="string"||U instanceof URL?(0,d.read_audio)(U,K):U instanceof Float64Array?new Float32Array(U):U))}function _(Me,K){K&&(Me=Me.map(Oe=>Oe|0));const[U,pe,Pe,Ee]=Me;return{xmin:U,ymin:pe,xmax:Pe,ymax:Ee}}class P extends o.Callable{constructor({task:K,model:U,tokenizer:pe=null,processor:Pe=null}){super(),this.task=K,this.model=U,this.tokenizer=pe,this.processor=Pe}async dispose(){await this.model.dispose()}}class A extends P{constructor(K){super(K)}async _call(K,{top_k:U=1}={}){const pe=this.tokenizer(K,{padding:!0,truncation:!0}),Pe=await this.model(pe),Ee=this.model.config.problem_type==="multi_label_classification"?Le=>Le.sigmoid():Le=>new p.Tensor("float32",(0,l.softmax)(Le.data),Le.dims),Oe=this.model.config.id2label,Ie=[];for(const Le of Pe.logits){const Ne=Ee(Le),Ve=await(0,p.topk)(Ne,U),D=Ve[0].tolist(),z=Ve[1].tolist().map((ee,ce)=>({label:Oe?Oe[ee]:`LABEL_${ee}`,score:D[ce]}));U===1?Ie.push(...z):Ie.push(z)}return Array.isArray(K)||U===1?Ie:Ie[0]}}class v extends P{constructor(K){super(K)}async _call(K,{ignore_labels:U=["O"]}={}){const pe=Array.isArray(K),Pe=this.tokenizer(pe?K:[K],{padding:!0,truncation:!0}),Oe=(await this.model(Pe)).logits,Ie=this.model.config.id2label,Le=[];for(let Ne=0;NeAe==this.tokenizer.sep_token_id);Le[D].map((Ae,Ue)=>Ae==1&&(Ue===0||Ue>z&&Ne.findIndex(Qe=>Qe==Z[Ue])===-1));const ee=Ee[D].tolist(),ce=Oe[D].tolist();for(let Ae=1;AeUe==Z[Ae])!==-1)&&(ee[Ae]=-1/0,ce[Ae]=-1/0);const be=(0,l.softmax)(ee).map((Ae,Ue)=>[Ae,Ue]),ve=(0,l.softmax)(ce).map((Ae,Ue)=>[Ae,Ue]);be[0][0]=0,ve[0][0]=0;const Re=(0,a.product)(be,ve).filter(Ae=>Ae[0][1]<=Ae[1][1]).map(Ae=>[Ae[0][1],Ae[1][1],Ae[0][0]*Ae[1][0]]).sort((Ae,Ue)=>Ue[2]-Ae[2]);for(let Ae=0;Aeee==this.tokenizer.mask_token_id);if(Ne===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const Ve=Pe[Ie][Ne],D=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Ve.data),Ve.dims),U),Z=D[0].tolist(),z=D[1].tolist();Ee.push(z.map((ee,ce)=>{const be=Le.slice();return be[Ne]=ee,{score:Z[ce],token:Number(ee),token_str:this.tokenizer.decode([ee]),sequence:this.tokenizer.decode(be,{skip_special_tokens:!0})}}))}return Array.isArray(K)?Ee:Ee[0]}}class x extends P{constructor(U){super(U);re(this,"_key","generated_text")}async _call(U,pe={}){Array.isArray(U)||(U=[U]),this.model.config.prefix&&(U=U.map(Ne=>this.model.config.prefix+Ne));const Pe=this.model.config.task_specific_params;Pe&&Pe[this.task]&&Pe[this.task].prefix&&(U=U.map(Ne=>Pe[this.task].prefix+Ne));const Ee=this.tokenizer,Oe={padding:!0,truncation:!0};let Ie;this instanceof M&&"_build_translation_inputs"in Ee?Ie=Ee._build_translation_inputs(U,Oe,pe):Ie=Ee(U,Oe);const Le=await this.model.generate({...Ie,...pe});return Ee.batch_decode(Le,{skip_special_tokens:!0}).map(Ne=>({[this._key]:Ne}))}}class g extends x{constructor(U){super(U);re(this,"_key","summary_text")}}class M extends x{constructor(U){super(U);re(this,"_key","translation_text")}}function E(Me){return Array.isArray(Me)&&Me.every(K=>"role"in K&&"content"in K)}class k extends P{constructor(K){super(K)}async _call(K,U={}){let pe=!1,Pe=!1,Ee;if(typeof K=="string")Ee=K=[K];else if(Array.isArray(K)&&K.every(z=>typeof z=="string"))pe=!0,Ee=K;else{if(E(K))K=[K];else if(Array.isArray(K)&&K.every(E))pe=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");Pe=!0,Ee=K.map(z=>this.tokenizer.apply_chat_template(z,{tokenize:!1,add_generation_prompt:!0}))}const Oe=U.add_special_tokens??!1,Ie=Pe?!1:U.return_full_text??!0;this.tokenizer.padding_side="left";const Le=this.tokenizer(Ee,{add_special_tokens:Oe,padding:!0,truncation:!0}),Ne=await this.model.generate({...Le,...U}),Ve=this.tokenizer.batch_decode(Ne,{skip_special_tokens:!0});let D;!Ie&&Le.input_ids.dims.at(-1)>0&&(D=this.tokenizer.batch_decode(Le.input_ids,{skip_special_tokens:!0}).map(z=>z.length));const Z=Array.from({length:K.length},z=>[]);for(let z=0;z[U.toLowerCase(),pe])),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(K,U,{hypothesis_template:pe="This example is {}.",multi_label:Pe=!1}={}){const Ee=Array.isArray(K);Ee||(K=[K]),Array.isArray(U)||(U=[U]);const Oe=U.map(Ne=>pe.replace("{}",Ne)),Ie=Pe||U.length===1,Le=[];for(const Ne of K){const Ve=[];for(const z of Oe){const ee=this.tokenizer(Ne,{text_pair:z,padding:!0,truncation:!0}),ce=await this.model(ee);Ie?Ve.push([ce.logits.data[this.contradiction_id],ce.logits.data[this.entailment_id]]):Ve.push(ce.logits.data[this.entailment_id])}const Z=(Ie?Ve.map(z=>(0,l.softmax)(z)[1]):(0,l.softmax)(Ve)).map((z,ee)=>[z,ee]).sort((z,ee)=>ee[0]-z[0]);Le.push({sequence:Ne,labels:Z.map(z=>U[z[1]]),scores:Z.map(z=>z[0])})}return Ee?Le:Le[0]}}class R extends P{constructor(K){super(K)}async _call(K,{pooling:U="none",normalize:pe=!1,quantize:Pe=!1,precision:Ee="binary"}={}){const Oe=this.tokenizer(K,{padding:!0,truncation:!0}),Ie=await this.model(Oe);let Le=Ie.last_hidden_state??Ie.logits??Ie.token_embeddings;if(U!=="none")if(U==="mean")Le=(0,p.mean_pooling)(Le,Oe.attention_mask);else if(U==="cls")Le=Le.slice(null,0);else throw Error(`Pooling method '${U}' not supported.`);return pe&&(Le=Le.normalize(2,-1)),Pe&&(Le=(0,p.quantize_embeddings)(Le,Ee)),Le}}class J extends P{constructor(K){super(K)}async _call(K,{pool:U=null}={}){const pe=await h(K),{pixel_values:Pe}=await this.processor(pe),Ee=await this.model({pixel_values:Pe});let Oe;if(U){if(!("pooler_output"in Ee))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Oe=Ee.pooler_output}else Oe=Ee.last_hidden_state??Ee.logits??Ee.image_embeds;return Oe}}class q extends P{constructor(K){super(K)}async _call(K,{top_k:U=5}={}){const pe=this.processor.feature_extractor.config.sampling_rate,Pe=await w(K,pe),Ee=this.model.config.id2label,Oe=[];for(const Ie of Pe){const Le=await this.processor(Ie),Ve=(await this.model(Le)).logits[0],D=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Ve.data),Ve.dims),U),Z=D[0].tolist(),ee=D[1].tolist().map((ce,be)=>({label:Ee?Ee[ce]:`LABEL_${ce}`,score:Z[be]}));Oe.push(ee)}return Array.isArray(K)?Oe:Oe[0]}}class V extends P{constructor(K){super(K)}async _call(K,U,{hypothesis_template:pe="This is a sound of {}."}={}){const Pe=!Array.isArray(K);Pe&&(K=[K]);const Ee=U.map(Ve=>pe.replace("{}",Ve)),Oe=this.tokenizer(Ee,{padding:!0,truncation:!0}),Ie=this.processor.feature_extractor.config.sampling_rate,Le=await w(K,Ie),Ne=[];for(const Ve of Le){const D=await this.processor(Ve),Z=await this.model({...Oe,...D}),z=(0,l.softmax)(Z.logits_per_audio.data);Ne.push([...z].map((ee,ce)=>({score:ee,label:U[ce]})))}return Pe?Ne[0]:Ne}}class Y extends P{constructor(K){super(K)}async _call(K,U={}){switch(this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(K,U);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(K,U);case"moonshine":return this._call_moonshine(K,U);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(K,U){U.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),U.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const pe=!Array.isArray(K);pe&&(K=[K]);const Pe=this.processor.feature_extractor.config.sampling_rate,Ee=await w(K,Pe),Oe=[];for(const Ie of Ee){const Le=await this.processor(Ie),Ve=(await this.model(Le)).logits[0],D=[];for(const z of Ve)D.push((0,l.max)(z.data)[1]);const Z=this.tokenizer.decode(D);Oe.push({text:Z})}return pe?Oe[0]:Oe}async _call_whisper(K,U){const pe=U.return_timestamps??!1,Pe=U.chunk_length_s??0,Ee=U.force_full_sequences??!1;let Oe=U.stride_length_s??null;const Ie={...U};pe==="word"&&(Ie.return_token_timestamps=!0,Ie.return_timestamps=!1);const Le=!Array.isArray(K);Le&&(K=[K]);const Ne=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,Ve=this.processor.feature_extractor.config.hop_length,D=this.processor.feature_extractor.config.sampling_rate,Z=await w(K,D),z=[];for(const ee of Z){let ce=[];if(Pe>0){if(Oe===null)Oe=Pe/6;else if(Pe<=Oe)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const Re=D*Pe,Ae=D*Oe,Ue=Re-2*Ae;let Qe=0;for(;;){const Xe=Qe+Re,ct=ee.subarray(Qe,Xe),xt=await this.processor(ct),nr=Qe===0,jt=Xe>=ee.length;if(ce.push({stride:[ct.length,nr?0:Ae,jt?0:Ae],input_features:xt.input_features,is_last:jt}),jt)break;Qe+=Ue}}else ce=[{stride:[ee.length,0,0],input_features:(await this.processor(ee)).input_features,is_last:!0}];for(const Re of ce){Ie.num_frames=Math.floor(Re.stride[0]/Ve);const Ae=await this.model.generate({inputs:Re.input_features,...Ie});pe==="word"?(Re.tokens=Ae.sequences.tolist()[0],Re.token_timestamps=Ae.token_timestamps.tolist()[0].map(Ue=>(0,l.round)(Ue,2))):Re.tokens=Ae[0].tolist(),Re.stride=Re.stride.map(Ue=>Ue/D)}const[be,ve]=this.tokenizer._decode_asr(ce,{time_precision:Ne,return_timestamps:pe,force_full_sequences:Ee});z.push({text:be,...ve})}return Le?z[0]:z}async _call_moonshine(K,U){const pe=!Array.isArray(K);pe&&(K=[K]);const Pe=this.processor.feature_extractor.config.sampling_rate,Ee=await w(K,Pe),Oe=[];for(const Ie of Ee){const Le=await this.processor(Ie),Ne=Math.floor(Ie.length/Pe)*6,Ve=await this.model.generate({max_new_tokens:Ne,...U,...Le}),D=this.processor.batch_decode(Ve,{skip_special_tokens:!0})[0];Oe.push({text:D})}return pe?Oe[0]:Oe}}class H extends P{constructor(K){super(K)}async _call(K,U={}){const pe=Array.isArray(K),Pe=await h(K),{pixel_values:Ee}=await this.processor(Pe),Oe=[];for(const Ie of Ee){Ie.dims=[1,...Ie.dims];const Le=await this.model.generate({inputs:Ie,...U}),Ne=this.tokenizer.batch_decode(Le,{skip_special_tokens:!0}).map(Ve=>({generated_text:Ve.trim()}));Oe.push(Ne)}return pe?Oe:Oe[0]}}class Q extends P{constructor(K){super(K)}async _call(K,{top_k:U=5}={}){const pe=await h(K),{pixel_values:Pe}=await this.processor(pe),Ee=await this.model({pixel_values:Pe}),Oe=this.model.config.id2label,Ie=[];for(const Le of Ee.logits){const Ne=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Le.data),Le.dims),U),Ve=Ne[0].tolist(),Z=Ne[1].tolist().map((z,ee)=>({label:Oe?Oe[z]:`LABEL_${z}`,score:Ve[ee]}));Ie.push(Z)}return Array.isArray(K)?Ie:Ie[0]}}class ie extends P{constructor(K){super(K),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(K,{threshold:U=.5,mask_threshold:pe=.5,overlap_mask_area_threshold:Pe=.8,label_ids_to_fuse:Ee=null,target_sizes:Oe=null,subtask:Ie=null}={}){if(Array.isArray(K)&&K.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const Ne=await h(K),Ve=Ne.map(Re=>[Re.height,Re.width]),D=await this.processor(Ne),{inputNames:Z,outputNames:z}=this.model.sessions.model;if(!Z.includes("pixel_values")){if(Z.length!==1)throw Error(`Expected a single input name, but got ${Z.length} inputs: ${Z}.`);const Re=Z[0];if(Re in D)throw Error(`Input name ${Re} already exists in the inputs.`);D[Re]=D.pixel_values}const ee=await this.model(D);let ce=null;if(Ie!==null)ce=this.subtasks_mapping[Ie];else if(this.processor.image_processor){for(const[Re,Ae]of Object.entries(this.subtasks_mapping))if(Ae in this.processor.image_processor){ce=this.processor.image_processor[Ae].bind(this.processor.image_processor),Ie=Re;break}}const be=this.model.config.id2label,ve=[];if(Ie)if(Ie==="panoptic"||Ie==="instance"){const Re=ce(ee,U,pe,Pe,Ee,Oe??Ve)[0],Ae=Re.segmentation;for(const Ue of Re.segments_info){const Qe=new Uint8ClampedArray(Ae.data.length);for(let ct=0;ctct<0||ct>1)&&Qe.sigmoid_();const Xe=await u.RawImage.fromTensor(Qe.mul_(255).to("uint8")).resize(Ue[1],Ue[0]);ve.push({label:null,score:null,mask:Xe})}}return ve}}class le extends ie{constructor(K){super(K)}async _call(K,U={}){if(Array.isArray(K)&&K.length!==1)throw Error("Background removal pipeline currently only supports a batch size of 1.");const Pe=await h(K),Ee=await super._call(K,U);return Pe.map((Ie,Le)=>{const Ne=Ie.clone();return Ne.putAlpha(Ee[Le].mask),Ne})}}class ae extends P{constructor(K){super(K)}async _call(K,U,{hypothesis_template:pe="This is a photo of {}"}={}){const Pe=Array.isArray(K),Ee=await h(K),Oe=U.map(Z=>pe.replace("{}",Z)),Ie=this.tokenizer(Oe,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:Le}=await this.processor(Ee),Ne=await this.model({...Ie,pixel_values:Le}),Ve=this.model.config.model_type==="siglip"?Z=>Z.sigmoid().data:Z=>(0,l.softmax)(Z.data),D=[];for(const Z of Ne.logits_per_image){const ee=[...Ve(Z)].map((ce,be)=>({score:ce,label:U[be]}));ee.sort((ce,be)=>be.score-ce.score),D.push(ee)}return Pe?D:D[0]}}class ge extends P{constructor(K){super(K)}async _call(K,{threshold:U=.9,percentage:pe=!1}={}){const Pe=Array.isArray(K);if(Pe&&K.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ee=await h(K),Oe=pe?null:Ee.map(z=>[z.height,z.width]),{pixel_values:Ie,pixel_mask:Le}=await this.processor(Ee),Ne=await this.model({pixel_values:Ie,pixel_mask:Le}),Ve=this.processor.image_processor.post_process_object_detection(Ne,U,Oe),D=this.model.config.id2label,Z=Ve.map(z=>z.boxes.map((ee,ce)=>({score:z.scores[ce],label:D[z.classes[ce]],box:_(ee,!pe)})));return Pe?Z:Z[0]}}class N extends P{constructor(K){super(K)}async _call(K,U,{threshold:pe=.1,top_k:Pe=null,percentage:Ee=!1}={}){const Oe=Array.isArray(K),Ie=await h(K),Le=this.tokenizer(U,{padding:!0,truncation:!0}),Ne=await this.processor(Ie),Ve=[];for(let D=0;D({score:ve.scores[Ae],label:ve.labels[Ae],box:_(Re,!Ee)}))}else{const ve=this.processor.image_processor.post_process_object_detection(ce,pe,z,!0)[0];be=ve.boxes.map((Re,Ae)=>({score:ve.scores[Ae],label:U[ve.classes[Ae]],box:_(Re,!Ee)}))}be.sort((ve,Re)=>Re.score-ve.score),Pe!==null&&(be=be.slice(0,Pe)),Ve.push(be)}return Oe?Ve:Ve[0]}}class F extends P{constructor(K){super(K)}async _call(K,U,pe={}){const Pe=(await h(K))[0],{pixel_values:Ee}=await this.processor(Pe),Oe=`${U}`,Ie=this.tokenizer(Oe,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,Le=await this.model.generate({inputs:Ee,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Ie,...pe}),Ve=this.tokenizer.batch_decode(Le)[0].match(/(.*?)<\/s_answer>/);let D=null;return Ve&&Ve.length>=2&&(D=Ve[1].trim()),[{answer:D}]}}class G extends P{constructor(U){super(U);re(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=U.vocoder??null}async _call(U,{speaker_embeddings:pe=null}={}){return this.processor?this._call_text_to_spectrogram(U,{speaker_embeddings:pe}):this._call_text_to_waveform(U)}async _call_text_to_waveform(U){const pe=this.tokenizer(U,{padding:!0,truncation:!0}),{waveform:Pe}=await this.model(pe),Ee=this.model.config.sampling_rate;return new d.RawAudio(Pe.data,Ee)}async _call_text_to_spectrogram(U,{speaker_embeddings:pe}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await i.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof pe=="string"||pe instanceof URL)&&(pe=new Float32Array(await(await fetch(pe)).arrayBuffer())),pe instanceof Float32Array)pe=new p.Tensor("float32",pe,[1,pe.length]);else if(!(pe instanceof p.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:Pe}=this.tokenizer(U,{padding:!0,truncation:!0}),{waveform:Ee}=await this.model.generate_speech(Pe,pe,{vocoder:this.vocoder}),Oe=this.processor.feature_extractor.config.sampling_rate;return new d.RawAudio(Ee.data,Oe)}}class ne extends P{constructor(K){super(K)}async _call(K){const U=await h(K),pe=await this.processor(U),Pe=await this.model(pe),Ee=[];for(const Oe of Pe.reconstruction){const Ie=Oe.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Ee.push(u.RawImage.fromTensor(Ie))}return Ee.length>1?Ee:Ee[0]}}class X extends P{constructor(K){super(K)}async _call(K){const U=await h(K),pe=await this.processor(U),{predicted_depth:Pe}=await this.model(pe),Ee=[];for(let Oe=0;Oe1?Ee:Ee[0]}}const we=Object.freeze({"text-classification":{tokenizer:s.AutoTokenizer,pipeline:A,model:i.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:s.AutoTokenizer,pipeline:v,model:i.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:s.AutoTokenizer,pipeline:y,model:i.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:s.AutoTokenizer,pipeline:S,model:i.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:s.AutoTokenizer,pipeline:g,model:i.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:s.AutoTokenizer,pipeline:M,model:i.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:s.AutoTokenizer,pipeline:x,model:i.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:s.AutoTokenizer,pipeline:k,model:i.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:s.AutoTokenizer,pipeline:B,model:i.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:q,model:i.AutoModelForAudioClassification,processor:n.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:s.AutoTokenizer,pipeline:V,model:i.AutoModel,processor:n.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:s.AutoTokenizer,pipeline:Y,model:[i.AutoModelForSpeechSeq2Seq,i.AutoModelForCTC],processor:n.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:s.AutoTokenizer,pipeline:G,model:[i.AutoModelForTextToWaveform,i.AutoModelForTextToSpectrogram],processor:[n.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:s.AutoTokenizer,pipeline:H,model:i.AutoModelForVision2Seq,processor:n.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Q,model:i.AutoModelForImageClassification,processor:n.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ie,model:[i.AutoModelForImageSegmentation,i.AutoModelForSemanticSegmentation,i.AutoModelForUniversalSegmentation],processor:n.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"background-removal":{pipeline:le,model:[i.AutoModelForImageSegmentation,i.AutoModelForSemanticSegmentation,i.AutoModelForUniversalSegmentation],processor:n.AutoProcessor,default:{model:"Xenova/modnet"},type:"image"},"zero-shot-image-classification":{tokenizer:s.AutoTokenizer,pipeline:ae,model:i.AutoModel,processor:n.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:ge,model:i.AutoModelForObjectDetection,processor:n.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:s.AutoTokenizer,pipeline:N,model:i.AutoModelForZeroShotObjectDetection,processor:n.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:s.AutoTokenizer,pipeline:F,model:i.AutoModelForDocumentQuestionAnswering,processor:n.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:ne,model:i.AutoModelForImageToImage,processor:n.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:X,model:i.AutoModelForDepthEstimation,processor:n.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:s.AutoTokenizer,pipeline:R,model:i.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:n.AutoProcessor,pipeline:J,model:[i.AutoModelForImageFeatureExtraction,i.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),fe=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function ke(Me,K=null,{progress_callback:U=null,config:pe=null,cache_dir:Pe=null,local_files_only:Ee=!1,revision:Oe="main",device:Ie=null,dtype:Le=null,subfolder:Ne="onnx",use_external_data_format:Ve=null,model_file_name:D=null,session_options:Z={}}={}){Me=fe[Me]??Me;const z=we[Me.split("_",1)[0]];if(!z)throw Error(`Unsupported pipeline: ${Me}. Must be one of [${Object.keys(we)}]`);K||(K=z.default.model,console.log(`No model specified. Using default model: "${K}".`));const ee={progress_callback:U,config:pe,cache_dir:Pe,local_files_only:Ee,revision:Oe,device:Ie,dtype:Le,subfolder:Ne,use_external_data_format:Ve,model_file_name:D,session_options:Z},ce=new Map([["tokenizer",z.tokenizer],["model",z.model],["processor",z.processor]]),be=await He(ce,K,ee);be.task=Me,(0,a.dispatchCallback)(U,{status:"ready",task:Me,model:K});const ve=z.pipeline;return new ve(be)}async function He(Me,K,U){const pe=Object.create(null),Pe=[];for(const[Ee,Oe]of Me.entries()){if(!Oe)continue;let Ie;Array.isArray(Oe)?Ie=new Promise(async(Le,Ne)=>{var D,Z;let Ve;for(const z of Oe){if(z===null){Le(null);return}try{Le(await z.from_pretrained(K,U));return}catch(ee){if((D=ee.message)!=null&&D.includes("Unsupported model type"))Ve=ee;else if((Z=ee.message)!=null&&Z.includes("Could not locate file"))Ve=ee;else{Ne(ee);return}}}Ne(Ve)}):Ie=Oe.from_pretrained(K,U),pe[Ee]=Ie,Pe.push(Ie)}await Promise.all(Pe);for(const[Ee,Oe]of Object.entries(pe))pe[Ee]=await Oe;return pe}},"./src/tokenizers.js":(e,r,t)=>{t.r(r),t.d(r,{AlbertTokenizer:()=>Ur,AutoTokenizer:()=>Sn,BartTokenizer:()=>ms,BertTokenizer:()=>qr,BlenderbotSmallTokenizer:()=>Jr,BlenderbotTokenizer:()=>Ar,BloomTokenizer:()=>Qr,CLIPTokenizer:()=>En,CamembertTokenizer:()=>it,CodeGenTokenizer:()=>Xr,CodeLlamaTokenizer:()=>Xs,CohereTokenizer:()=>Cn,ConvBertTokenizer:()=>mr,DebertaTokenizer:()=>Es,DebertaV2Tokenizer:()=>Ps,DistilBertTokenizer:()=>et,ElectraTokenizer:()=>zt,EsmTokenizer:()=>en,FalconTokenizer:()=>gs,GPT2Tokenizer:()=>hs,GPTNeoXTokenizer:()=>Zs,GemmaTokenizer:()=>Lr,Grok1Tokenizer:()=>Ss,HerbertTokenizer:()=>Ls,LlamaTokenizer:()=>Qs,M2M100Tokenizer:()=>Bs,MBart50Tokenizer:()=>fs,MBartTokenizer:()=>Er,MPNetTokenizer:()=>Ys,MarianTokenizer:()=>Pn,MgpstrTokenizer:()=>$s,MobileBertTokenizer:()=>ps,NllbTokenizer:()=>zs,NougatTokenizer:()=>fr,PreTrainedTokenizer:()=>dt,Qwen2Tokenizer:()=>tn,RoFormerTokenizer:()=>Be,RobertaTokenizer:()=>_s,SiglipTokenizer:()=>zr,SpeechT5Tokenizer:()=>cr,SqueezeBertTokenizer:()=>Ts,T5Tokenizer:()=>kr,TokenizerModel:()=>J,VitsTokenizer:()=>Ns,Wav2Vec2CTCTokenizer:()=>Rs,WhisperTokenizer:()=>Ir,XLMRobertaTokenizer:()=>Js,XLMTokenizer:()=>rr,is_chinese_char:()=>S});var s=t("./src/utils/generic.js"),i=t("./src/utils/core.js"),n=t("./src/utils/hub.js"),o=t("./src/utils/maths.js"),a=t("./src/utils/tensor.js"),l=t("./src/utils/data-structures.js"),d=t("./node_modules/@huggingface/jinja/dist/index.js"),p=t("./src/models/whisper/common_whisper.js");async function u(ue,O){const j=await Promise.all([(0,n.getModelJSON)(ue,"tokenizer.json",!0,O),(0,n.getModelJSON)(ue,"tokenizer_config.json",!0,O)]);return O.legacy!==null&&(j[1].legacy=O.legacy),j}function h(ue,O){const j=[];let te=0;for(const de of ue.matchAll(O)){const he=de[0];te0&&j.push(he),te=de.index+he.length}return te=19968&&ue<=40959||ue>=13312&&ue<=19903||ue>=131072&&ue<=173791||ue>=173824&&ue<=177983||ue>=177984&&ue<=178207||ue>=178208&&ue<=183983||ue>=63744&&ue<=64255||ue>=194560&&ue<=195103}function x(ue,O,j){const te=[];let de=0;for(;dethis.tokens_to_ids.get(j)??this.unk_token_id)}convert_ids_to_tokens(O){return O.map(j=>this.vocab[j]??this.unk_token)}}class q extends J{constructor(O){super(O),this.tokens_to_ids=_(O.vocab),this.unk_token_id=this.tokens_to_ids.get(O.unk_token),this.unk_token=O.unk_token,this.max_input_chars_per_word=O.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[j,te]of this.tokens_to_ids)this.vocab[te]=j}encode(O){const j=[];for(const te of O){const de=[...te];if(de.length>this.max_input_chars_per_word){j.push(this.unk_token);continue}let he=!1,Ce=0;const We=[];for(;Ce0&&(Ze=this.config.continuing_subword_prefix+Ze),this.tokens_to_ids.has(Ze)){Ke=Ze;break}--qe}if(Ke===null){he=!0;break}We.push(Ke),Ce=qe}he?j.push(this.unk_token):j.push(...We)}return j}}class V extends J{constructor(O,j){super(O);const te=O.vocab.length;this.vocab=new Array(te),this.scores=new Array(te);for(let de=0;de[de,he])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=j.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,o.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new l.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(O){const j=O.chars,te=1;let de=0;for(;de{const ue=[...Array.from({length:94},(de,he)=>he+33),...Array.from({length:12},(de,he)=>he+161),...Array.from({length:82},(de,he)=>he+174)],O=ue.slice();let j=0;for(let de=0;de<256;++de)ue.includes(de)||(ue.push(de),O.push(256+j),j+=1);const te=O.map(de=>String.fromCharCode(de));return Object.fromEntries(ue.map((de,he)=>[de,te[he]]))})(),H=(0,i.reverseDictionary)(Y);class Q extends J{constructor(O){super(O),this.tokens_to_ids=_(O.vocab),this.unk_token_id=this.tokens_to_ids.get(O.unk_token),this.unk_token=O.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[te,de]of this.tokens_to_ids)this.vocab[de]=te;const j=Array.isArray(O.merges[0]);this.merges=j?O.merges:O.merges.map(te=>te.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((te,de)=>[JSON.stringify(te),de])),this.end_of_word_suffix=O.end_of_word_suffix,this.continuing_subword_suffix=O.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(O){if(O.length===0)return[];const j=this.cache.get(O);if(j!==void 0)return j;const te=Array.from(O);this.end_of_word_suffix&&(te[te.length-1]+=this.end_of_word_suffix);let de=[];if(te.length>1){const he=new l.PriorityQueue((qe,Ke)=>qe.score`<0x${We.toString(16).toUpperCase().padStart(2,"0")}>`);Ce.every(We=>this.tokens_to_ids.has(We))?j.push(...Ce):j.push(this.unk_token)}else j.push(this.unk_token)}return j}}class ie extends J{constructor(O,j){super(O),this.tokens_to_ids=_(j.target_lang?O.vocab[j.target_lang]:O.vocab),this.bos_token=j.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=j.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=j.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=j.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[te,de]of this.tokens_to_ids)this.vocab[de]=te}encode(O){return O}}class le extends s.Callable{constructor(O){super(),this.config=O}static fromConfig(O){if(O===null)return null;switch(O.type){case"BertNormalizer":return new Me(O);case"Precompiled":return new jt(O);case"Sequence":return new He(O);case"Replace":return new ae(O);case"NFC":return new N(O);case"NFD":return new F(O);case"NFKC":return new G(O);case"NFKD":return new ne(O);case"Strip":return new X(O);case"StripAccents":return new we(O);case"Lowercase":return new fe(O);case"Prepend":return new ke(O);default:throw new Error(`Unknown Normalizer type: ${O.type}`)}}normalize(O){throw Error("normalize should be implemented in subclass.")}_call(O){return this.normalize(O)}}class ae extends le{normalize(O){const j=w(this.config.pattern);return j===null?O:O.replaceAll(j,this.config.content)}}class ge extends le{constructor(){super(...arguments);re(this,"form")}normalize(j){return j=j.normalize(this.form),j}}class N extends ge{constructor(){super(...arguments);re(this,"form","NFC")}}class F extends ge{constructor(){super(...arguments);re(this,"form","NFD")}}class G extends ge{constructor(){super(...arguments);re(this,"form","NFKC")}}class ne extends ge{constructor(){super(...arguments);re(this,"form","NFKD")}}class X extends le{normalize(O){return this.config.strip_left&&this.config.strip_right?O=O.trim():(this.config.strip_left&&(O=O.trimStart()),this.config.strip_right&&(O=O.trimEnd())),O}}class we extends le{normalize(O){return O=v(O),O}}class fe extends le{normalize(O){return O=O.toLowerCase(),O}}class ke extends le{normalize(O){return O=this.config.prepend+O,O}}class He extends le{constructor(O){super(O),this.normalizers=O.normalizers.map(j=>le.fromConfig(j))}normalize(O){return this.normalizers.reduce((j,te)=>te.normalize(j),O)}}class Me extends le{_tokenize_chinese_chars(O){const j=[];for(let te=0;tethis.pre_tokenize_text(te,j)):this.pre_tokenize_text(O,j)).flat()}_call(O,j){return this.pre_tokenize(O,j)}}class U extends K{constructor(O){super(),this.pattern=new RegExp(`[^\\s${M}]+|[${M}]`,"gu")}pre_tokenize_text(O,j){return O.trim().match(this.pattern)||[]}}class pe extends K{constructor(O){super(),this.config=O,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=Y,this.text_encoder=new TextEncoder}pre_tokenize_text(O,j){return this.add_prefix_space&&!O.startsWith(" ")&&(O=" "+O),(this.use_regex?O.match(this.pattern)||[]:[O]).map(de=>Array.from(this.text_encoder.encode(de),he=>this.byte_encoder[he]).join(""))}}class Pe extends K{constructor(O){super(),this.config=O,this.pattern=w(this.config.pattern,this.config.invert)}pre_tokenize_text(O,j){var te;return this.pattern===null?[]:this.config.invert?O.match(this.pattern)||[]:((te=this.config.behavior)==null?void 0:te.toLowerCase())==="removed"?O.split(this.pattern).filter(de=>de):h(O,this.pattern)}}class Ee extends K{constructor(O){super(),this.config=O,this.pattern=new RegExp(`[^${M}]+|[${M}]+`,"gu")}pre_tokenize_text(O,j){return O.match(this.pattern)||[]}}class Oe extends K{constructor(O){super(),this.config=O;const j=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(j,"gu")}pre_tokenize_text(O,j){return O.match(this.pattern)||[]}}class Ie extends s.Callable{constructor(O){super(),this.config=O}static fromConfig(O){if(O===null)return null;switch(O.type){case"TemplateProcessing":return new Ve(O);case"ByteLevel":return new D(O);case"RobertaProcessing":return new Ne(O);case"BertProcessing":return new Le(O);case"Sequence":return new Z(O);default:throw new Error(`Unknown PostProcessor type: ${O.type}`)}}post_process(O,...j){throw Error("post_process should be implemented in subclass.")}_call(O,...j){return this.post_process(O,...j)}}class Le extends Ie{constructor(O){super(O),this.cls=O.cls[0],this.sep=O.sep[0]}post_process(O,j=null,{add_special_tokens:te=!0}={}){te&&(O=(0,i.mergeArrays)([this.cls],O,[this.sep]));let de=new Array(O.length).fill(0);if(j!==null){const he=te&&this instanceof Ne?[this.sep]:[],Ce=te?[this.sep]:[];O=(0,i.mergeArrays)(O,he,j,Ce),de=(0,i.mergeArrays)(de,new Array(j.length+he.length+Ce.length).fill(1))}return{tokens:O,token_type_ids:de}}}class Ne extends Le{}class Ve extends Ie{constructor(O){super(O),this.single=O.single,this.pair=O.pair}post_process(O,j=null,{add_special_tokens:te=!0}={}){const de=j===null?this.single:this.pair;let he=[],Ce=[];for(const We of de)"SpecialToken"in We?te&&(he.push(We.SpecialToken.id),Ce.push(We.SpecialToken.type_id)):"Sequence"in We&&(We.Sequence.id==="A"?(he=(0,i.mergeArrays)(he,O),Ce=(0,i.mergeArrays)(Ce,new Array(O.length).fill(We.Sequence.type_id))):We.Sequence.id==="B"&&(he=(0,i.mergeArrays)(he,j),Ce=(0,i.mergeArrays)(Ce,new Array(j.length).fill(We.Sequence.type_id))));return{tokens:he,token_type_ids:Ce}}}class D extends Ie{post_process(O,j=null){return j&&(O=(0,i.mergeArrays)(O,j)),{tokens:O}}}class Z extends Ie{constructor(O){super(O),this.processors=O.processors.map(j=>Ie.fromConfig(j))}post_process(O,j=null,te={}){let de;for(const he of this.processors)if(he instanceof D)O=he.post_process(O).tokens,j&&(j=he.post_process(j).tokens);else{const Ce=he.post_process(O,j,te);O=Ce.tokens,de=Ce.token_type_ids}return{tokens:O,token_type_ids:de}}}class z extends s.Callable{constructor(O){super(),this.config=O,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=O.trim_offsets}static fromConfig(O){if(O===null)return null;switch(O.type){case"WordPiece":return new Re(O);case"Metaspace":return new nr(O);case"ByteLevel":return new Ae(O);case"Replace":return new ee(O);case"ByteFallback":return new ce(O);case"Fuse":return new be(O);case"Strip":return new ve(O);case"Sequence":return new Qe(O);case"CTC":return new Ue(O);case"BPEDecoder":return new Xe(O);default:throw new Error(`Unknown Decoder type: ${O.type}`)}}_call(O){return this.decode(O)}decode(O){return this.decode_chain(O).join("")}decode_chain(O){throw Error("`decode_chain` should be implemented in subclass.")}}class ee extends z{decode_chain(O){const j=w(this.config.pattern);return j===null?O:O.map(te=>te.replaceAll(j,this.config.content))}}class ce extends z{constructor(O){super(O),this.text_decoder=new TextDecoder}decode_chain(O){const j=[];let te=[];for(const de of O){let he=null;if(de.length===6&&de.startsWith("<0x")&&de.endsWith(">")){const Ce=parseInt(de.slice(3,5),16);isNaN(Ce)||(he=Ce)}if(he!==null)te.push(he);else{if(te.length>0){const Ce=this.text_decoder.decode(Uint8Array.from(te));j.push(Ce),te=[]}j.push(de)}}if(te.length>0){const de=this.text_decoder.decode(Uint8Array.from(te));j.push(de),te=[]}return j}}class be extends z{decode_chain(O){return[O.join("")]}}class ve extends z{constructor(O){super(O),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(O){return O.map(j=>{let te=0;for(let he=0;he(te!==0&&(j.startsWith(this.config.prefix)?j=j.replace(this.config.prefix,""):j=" "+j),this.cleanup&&(j=A(j)),j))}}class Ae extends z{constructor(O){super(O),this.byte_decoder=H,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(O){const j=O.join(""),te=new Uint8Array([...j].map(he=>this.byte_decoder[he]));return this.text_decoder.decode(te)}decode_chain(O){const j=[];let te=[];for(const de of O)this.added_tokens.find(he=>he.content===de)!==void 0?(te.length>0&&(j.push(this.convert_tokens_to_string(te)),te=[]),j.push(de)):te.push(de);return te.length>0&&j.push(this.convert_tokens_to_string(te)),j}}class Ue extends z{constructor(O){super(O),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(O){if(O.length===0)return"";const j=[O[0]];for(let he=1;hehe!==this.pad_token).join("");return this.cleanup&&(de=A(de).replaceAll(this.word_delimiter_token," ").trim()),de}decode_chain(O){return[this.convert_tokens_to_string(O)]}}class Qe extends z{constructor(O){super(O),this.decoders=O.decoders.map(j=>z.fromConfig(j))}decode_chain(O){return this.decoders.reduce((j,te)=>te.decode_chain(j),O)}}class Xe extends z{constructor(O){super(O),this.suffix=this.config.suffix}decode_chain(O){return O.map((j,te)=>j.replaceAll(this.suffix,te===O.length-1?"":" "))}}class ct extends z{decode_chain(O){let j="";for(let te=1;tete.normalize("NFKC")).join("～"):O=O.normalize("NFKC"),O}}class dr extends K{constructor(O){super(),this.tokenizers=O.pretokenizers.map(j=>K.fromConfig(j))}pre_tokenize_text(O,j){return this.tokenizers.reduce((te,de)=>de.pre_tokenize(te,j),[O])}}class cs extends K{constructor(O){super()}pre_tokenize_text(O,j){return O.match(/\w+|[^\w\s]+/g)||[]}}class Fs extends K{constructor(O){super()}pre_tokenize_text(O,j){return g(O)}}class Dr extends K{constructor(O){super(),this.config=O,this.pattern=w(this.config.pattern),this.content=this.config.content}pre_tokenize_text(O,j){return this.pattern===null?[O]:[O.replaceAll(this.pattern,this.config.content)]}}const us=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Ds(ue,O,j,te){for(const de of Object.keys(ue)){const he=O-ue[de].length,Ce=j(de),We=new Array(he).fill(Ce);ue[de]=te==="right"?(0,i.mergeArrays)(ue[de],We):(0,i.mergeArrays)(We,ue[de])}}function jr(ue,O){for(const j of Object.keys(ue))ue[j].length=O}class dt extends s.Callable{constructor(j,te){super();re(this,"return_token_type_ids",!1);re(this,"padding_side","right");this._tokenizer_config=te,this.normalizer=le.fromConfig(j.normalizer),this.pre_tokenizer=K.fromConfig(j.pre_tokenizer),this.model=J.fromConfig(j.model,te),this.post_processor=Ie.fromConfig(j.post_processor),this.decoder=z.fromConfig(j.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const de of j.added_tokens){const he=new R(de);this.added_tokens.push(he),this.model.tokens_to_ids.set(he.content,he.id),this.model.vocab[he.id]=he.content,he.special&&(this.special_tokens.push(he.content),this.all_special_ids.push(he.id))}if(this.additional_special_tokens=te.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.slice().sort((de,he)=>he.content.length-de.content.length).map(de=>`${de.lstrip?"\\s*":""}(${(0,i.escapeRegExp)(de.content)})${de.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.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=te.model_max_length,this.remove_space=te.remove_space,this.clean_up_tokenization_spaces=te.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=te.do_lowercase_and_remove_accent??!1,te.padding_side&&(this.padding_side=te.padding_side),this.legacy=!1,this.chat_template=te.chat_template??null,Array.isArray(this.chat_template)){const de=Object.create(null);for(const{name:he,template:Ce}of this.chat_template){if(typeof he!="string"||typeof Ce!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');de[he]=Ce}this.chat_template=de}this._compiled_template_cache=new Map}getToken(...j){for(const te of j){const de=this._tokenizer_config[te];if(de)if(typeof de=="object"){if(de.__type==="AddedToken")return de.content;throw Error(`Unknown token: ${de}`)}else return de}return null}static async from_pretrained(j,{progress_callback:te=null,config:de=null,cache_dir:he=null,local_files_only:Ce=!1,revision:We="main",legacy:qe=null}={}){const Ke=await u(j,{progress_callback:te,config:de,cache_dir:he,local_files_only:Ce,revision:We,legacy:qe});return new this(...Ke)}_call(j,{text_pair:te=null,add_special_tokens:de=!0,padding:he=!1,truncation:Ce=null,max_length:We=null,return_tensor:qe=!0,return_token_type_ids:Ke=null}={}){const Ze=Array.isArray(j);let ht;if(Ze){if(j.length===0)throw Error("text array must be non-empty");if(te!==null){if(Array.isArray(te)){if(j.length!==te.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");ht=j.map((kt,Wt)=>this._encode_plus(kt,{text_pair:te[Wt],add_special_tokens:de,return_token_type_ids:Ke}))}else ht=j.map(kt=>this._encode_plus(kt,{add_special_tokens:de,return_token_type_ids:Ke}))}else{if(j==null)throw Error("text may not be null or undefined");if(Array.isArray(te))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");ht=[this._encode_plus(j,{text_pair:te,add_special_tokens:de,return_token_type_ids:Ke})]}if(We===null?he==="max_length"?We=this.model_max_length:We=(0,o.max)(ht.map(kt=>kt.input_ids.length))[0]:Ce||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."),We=Math.min(We,this.model_max_length??1/0),he||Ce)for(let kt=0;ktWe?Ce&&jr(ht[kt],We):he&&Ds(ht[kt],We,Wt=>Wt==="input_ids"?this.pad_token_id:0,this.padding_side));const Ct={};if(qe){if(!(he&&Ce)&&ht.some(Wt=>{var Ft;for(const Ut of Object.keys(Wt))if(Wt[Ut].length!==((Ft=ht[0][Ut])==null?void 0:Ft.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 kt=[ht.length,ht[0].input_ids.length];for(const Wt of Object.keys(ht[0]))Ct[Wt]=new a.Tensor("int64",BigInt64Array.from(ht.flatMap(Ft=>Ft[Wt]).map(BigInt)),kt)}else{for(const kt of Object.keys(ht[0]))Ct[kt]=ht.map(Wt=>Wt[kt]);if(!Ze)for(const kt of Object.keys(Ct))Ct[kt]=Ct[kt][0]}return Ct}_encode_text(j){return j===null?null:(this.added_tokens_regex?j.split(this.added_tokens_regex).filter(he=>he):[j]).map((he,Ce)=>{if(this.added_tokens.find(qe=>qe.content===he)!==void 0)return he;{if(this.remove_space===!0&&(he=he.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(he=y(he)),this.normalizer!==null&&(he=this.normalizer(he)),he.length===0)return[];const qe=this.pre_tokenizer!==null?this.pre_tokenizer(he,{section_index:Ce}):[he];return this.model(qe)}}).flat()}_encode_plus(j,{text_pair:te=null,add_special_tokens:de=!0,return_token_type_ids:he=null}={}){const{tokens:Ce,token_type_ids:We}=this._tokenize_helper(j,{pair:te,add_special_tokens:de}),qe=this.model.convert_tokens_to_ids(Ce),Ke={input_ids:qe,attention_mask:new Array(qe.length).fill(1)};return(he??this.return_token_type_ids)&&We&&(Ke.token_type_ids=We),Ke}_tokenize_helper(j,{pair:te=null,add_special_tokens:de=!1}={}){const he=this._encode_text(j),Ce=this._encode_text(te);return this.post_processor?this.post_processor(he,Ce,{add_special_tokens:de}):{tokens:(0,i.mergeArrays)(he??[],Ce??[])}}tokenize(j,{pair:te=null,add_special_tokens:de=!1}={}){return this._tokenize_helper(j,{pair:te,add_special_tokens:de}).tokens}encode(j,{text_pair:te=null,add_special_tokens:de=!0,return_token_type_ids:he=null}={}){return this._encode_plus(j,{text_pair:te,add_special_tokens:de,return_token_type_ids:he}).input_ids}batch_decode(j,te={}){return j instanceof a.Tensor&&(j=j.tolist()),j.map(de=>this.decode(de,te))}decode(j,te={}){if(j instanceof a.Tensor&&(j=P(j)),!Array.isArray(j)||j.length===0||!(0,i.isIntegralNumber)(j[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(j,te)}decode_single(j,{skip_special_tokens:te=!1,clean_up_tokenization_spaces:de=null}){let he=this.model.convert_ids_to_tokens(j);te&&(he=he.filter(We=>!this.special_tokens.includes(We)));let Ce=this.decoder?this.decoder(he):he.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Ce=Ce.replaceAll(this.decoder.end_of_word_suffix," "),te&&(Ce=Ce.trim())),(de??this.clean_up_tokenization_spaces)&&(Ce=A(Ce)),Ce}get_chat_template({chat_template:j=null,tools:te=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const de=this.chat_template;if(j!==null&&Object.hasOwn(de,j))j=de[j];else if(j===null)if(te!==null&&"tool_use"in de)j=de.tool_use;else if("default"in de)j=de.default;else 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(de).sort()}.`)}else if(j===null)if(this.chat_template)j=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return j}apply_chat_template(j,{tools:te=null,documents:de=null,chat_template:he=null,add_generation_prompt:Ce=!1,tokenize:We=!0,padding:qe=!1,truncation:Ke=!1,max_length:Ze=null,return_tensor:ht=!0,return_dict:Ct=!1,tokenizer_kwargs:kt={},...Wt}={}){if(he=this.get_chat_template({chat_template:he,tools:te}),typeof he!="string")throw Error(`chat_template must be a string, but got ${typeof he}`);let Ft=this._compiled_template_cache.get(he);Ft===void 0&&(Ft=new d.Template(he),this._compiled_template_cache.set(he,Ft));const Ut=Object.create(null);for(const _r of us){const Yr=this.getToken(_r);Yr&&(Ut[_r]=Yr)}const vr=Ft.render({messages:j,add_generation_prompt:Ce,tools:te,documents:de,...Ut,...Wt});if(We){const _r=this._call(vr,{add_special_tokens:!1,padding:qe,truncation:Ke,max_length:Ze,return_tensor:ht,...kt});return Ct?_r:_r.input_ids}return vr}}class qr extends dt{constructor(){super(...arguments);re(this,"return_token_type_ids",!0)}}class Ur extends dt{constructor(){super(...arguments);re(this,"return_token_type_ids",!0)}}class ps extends dt{constructor(){super(...arguments);re(this,"return_token_type_ids",!0)}}class Ts extends dt{constructor(){super(...arguments);re(this,"return_token_type_ids",!0)}}class Es extends dt{constructor(){super(...arguments);re(this,"return_token_type_ids",!0)}}class Ps extends dt{constructor(){super(...arguments);re(this,"return_token_type_ids",!0)}}class Ls extends dt{constructor(){super(...arguments);re(this,"return_token_type_ids",!0)}}class mr extends dt{constructor(){super(...arguments);re(this,"return_token_type_ids",!0)}}class Be extends dt{constructor(){super(...arguments);re(this,"return_token_type_ids",!0)}}class et extends dt{}class it extends dt{}class rr extends dt{constructor(j,te){super(j,te);re(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class zt extends dt{constructor(){super(...arguments);re(this,"return_token_type_ids",!0)}}class kr extends dt{}class hs extends dt{}class ms extends dt{}class Er extends dt{constructor(O,j){super(O,j),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(te=>this.languageRegex.test(te)),this.lang_to_token=te=>te}_build_translation_inputs(O,j,te){return br(this,O,j,te)}}class fs extends Er{}class _s extends dt{}class Qr extends dt{}const Cs="▁";class Qs extends dt{constructor(j,te){super(j,te);re(this,"padding_side","left");this.legacy=te.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new xt({replacement:Cs,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(j){if(j===null)return null;if(this.legacy||j.length===0)return super._encode_text(j);let te=super._encode_text(Cs+j.replaceAll(Cs," "));return te.length>1&&te[0]===Cs&&this.special_tokens.includes(te[1])&&(te=te.slice(1)),te}}class Xs extends dt{}class Js extends dt{}class Ys extends dt{}class gs extends dt{}class Zs extends dt{}class en extends dt{}class tn extends dt{}class Lr extends dt{}class Ss extends dt{}function br(ue,O,j,te){if(!("language_codes"in ue)||!Array.isArray(ue.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in ue)||!(ue.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in ue)||typeof ue.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const de=te.src_lang,he=te.tgt_lang;if(!ue.language_codes.includes(he))throw new Error(`Target language code "${he}" is not valid. Must be one of: {${ue.language_codes.join(", ")}}`);if(de!==void 0){if(!ue.language_codes.includes(de))throw new Error(`Source language code "${de}" is not valid. Must be one of: {${ue.language_codes.join(", ")}}`);for(const Ce of ue.post_processor.config.single)if("SpecialToken"in Ce&&ue.languageRegex.test(Ce.SpecialToken.id)){Ce.SpecialToken.id=ue.lang_to_token(de);break}}return te.forced_bos_token_id=ue.model.convert_tokens_to_ids([ue.lang_to_token(he)])[0],ue._call(O,j)}class zs extends dt{constructor(O,j){super(O,j),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(te=>this.languageRegex.test(te)),this.lang_to_token=te=>te}_build_translation_inputs(O,j,te){return br(this,O,j,te)}}class Bs extends dt{constructor(O,j){super(O,j),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(te=>this.languageRegex.test(te)).map(te=>te.slice(2,-2)),this.lang_to_token=te=>`__${te}__`}_build_translation_inputs(O,j,te){return br(this,O,j,te)}}class Ir extends dt{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(O,{return_timestamps:j=!1,return_language:te=!1,time_precision:de=null,force_full_sequences:he=!0}={}){if(de===null)throw Error("Must specify time_precision");let Ce=null;const We=j==="word";function qe(){return{language:Ce,timestamp:[null,null],text:""}}const Ke=[];let Ze=qe(),ht=0;const Ct=this.timestamp_begin,Wt=Ct+1500;let Ft=[],Ut=[],vr=!1,_r=null;const Yr=new Set(this.all_special_ids);for(const Gt of O){const Zt=Gt.tokens,gr=We?Gt.token_timestamps:null;let Zr=null,ys=Ct;if("stride"in Gt){const[Rt,Jt,qt]=Gt.stride;if(ht-=Jt,_r=Rt-qt,Jt&&(ys=Jt/de+Ct),qt)for(let er=Zt.length-1;er>=0;--er){const ir=Number(Zt[er]);if(ir>=Ct){if(Zr!==null&&(ir-Ct)*de<_r)break;Zr=ir}}}let Ht=[],ur=[];for(let Rt=0;Rt=Ct&&Jt<=Wt){const qt=(Jt-Ct)*de+ht,er=(0,o.round)(qt,2);if(Zr!==null&&Jt>=Zr)vr=!0;else if(vr||Ft.length>0&&Jt0?(Ft.push(Ht),We&&Ut.push(ur)):Ft.every(Rt=>Rt.length===0)&&(Ze=qe(),Ft=[],Ht=[],Ut=[],ur=[])}if(Ft.length>0){if(he&&j)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[Gt,Zt]=this.findLongestCommonSequence(Ft,Ut),gr=this.decode(Gt);Ze.text=gr,We&&(Ze.words=this.collateWordTimestamps(Gt,Zt,Ce)),Ke.push(Ze)}let xr=Object.create(null);const ws=Ke.map(Gt=>Gt.text).join("");if(j||te){for(let Gt=0;Gt0;let We=Ce?[]:null,qe=Ce?j[0]:null;for(let Ke=1;KeJt===ys[qt]&&qe[ws+qt]<=j[Ke][gr+qt]).length:Ht=Zt.filter((Jt,qt)=>Jt===ys[qt]).length;const ur=xr/1e4,Rt=Ht/xr+ur;Ht>1&&Rt>ht&&(ht=Rt,Ct=[ws,Gt,gr,Zr])}const[Wt,Ft,Ut,vr]=Ct,_r=Math.floor((Ft+Wt)/2),Yr=Math.floor((vr+Ut)/2);he.push(...te.slice(0,_r)),te=Ze.slice(Yr),de=te.length,Ce&&(We.push(...qe.slice(0,_r)),qe=j[Ke].slice(Yr))}return he.push(...te),Ce?(We.push(...qe),[he,We]):[he,[]]}collateWordTimestamps(O,j,te){const[de,he,Ce]=this.combineTokensIntoWords(O,te),We=[];for(let qe=0;qe=de){const We=((Ce-de)*te).toFixed(2);he.push(`<|${We}|>`),he.push([])}else he[he.length-1].push(Ce);return he=he.map(Ce=>typeof Ce=="string"?Ce:super.decode(Ce,j)),he.join("")}splitTokensOnUnicode(O){const j=this.decode(O,{decode_with_timestamps:!0}),te="�",de=[],he=[],Ce=[];let We=[],qe=[],Ke=0;for(let Ze=0;Ze=this.model.tokens_to_ids.get("<|endoftext|>"),Wt=Ze.startsWith(" "),Ft=Ze.trim(),Ut=qe.test(Ft);if(kt||Wt||Ut||he.length===0)he.push(Ze),Ce.push(ht),We.push(Ct);else{const vr=he.length-1;he[vr]+=Ze,Ce[vr].push(...ht),We[vr].push(...Ct)}}return[he,Ce,We]}mergePunctuations(O,j,te,de,he){const Ce=structuredClone(O),We=structuredClone(j),qe=structuredClone(te);let Ke=Ce.length-2,Ze=Ce.length-1;for(;Ke>=0;)Ce[Ke].startsWith(" ")&&de.includes(Ce[Ke].trim())?(Ce[Ze]=Ce[Ke]+Ce[Ze],We[Ze]=(0,i.mergeArrays)(We[Ke],We[Ze]),qe[Ze]=(0,i.mergeArrays)(qe[Ke],qe[Ze]),Ce[Ke]="",We[Ke]=[],qe[Ke]=[]):Ze=Ke,--Ke;for(Ke=0,Ze=1;Zeht),We.filter(ht=>ht.length>0),qe.filter(ht=>ht.length>0)]}}class Xr extends dt{}class En extends dt{}class zr extends dt{}class Pn extends dt{constructor(O,j){super(O,j),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(te=>this.languageRegex.test(te)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(O){if(O===null)return null;const[j,...te]=O.trim().split(this.languageRegex);if(te.length===0)return super._encode_text(j);if(te.length===2){const[de,he]=te;return this.supported_language_codes.includes(de)||console.warn(`Unsupported language code "${de}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),(0,i.mergeArrays)([de],super._encode_text(he))}}}class Rs extends dt{}class Ar extends dt{}class Jr extends dt{}class cr extends dt{}class fr extends dt{}class Ns extends dt{constructor(O,j){super(O,j),this.decoder=new ct({})}}class Cn extends dt{}class $s extends dt{}class Sn{static async from_pretrained(O,{progress_callback:j=null,config:te=null,cache_dir:de=null,local_files_only:he=!1,revision:Ce="main",legacy:We=null}={}){var Ct;const[qe,Ke]=await u(O,{progress_callback:j,config:te,cache_dir:de,local_files_only:he,revision:Ce,legacy:We}),Ze=((Ct=Ke.tokenizer_class)==null?void 0:Ct.replace(/Fast$/,""))??"PreTrainedTokenizer";let ht=this.TOKENIZER_CLASS_MAPPING[Ze];return ht||(console.warn(`Unknown tokenizer class "${Ze}", attempting to construct from base class.`),ht=dt),new ht(qe,Ke)}}re(Sn,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:kr,DistilBertTokenizer:et,CamembertTokenizer:it,DebertaTokenizer:Es,DebertaV2Tokenizer:Ps,BertTokenizer:qr,HerbertTokenizer:Ls,ConvBertTokenizer:mr,RoFormerTokenizer:Be,XLMTokenizer:rr,ElectraTokenizer:zt,MobileBertTokenizer:ps,SqueezeBertTokenizer:Ts,AlbertTokenizer:Ur,GPT2Tokenizer:hs,BartTokenizer:ms,MBartTokenizer:Er,MBart50Tokenizer:fs,RobertaTokenizer:_s,WhisperTokenizer:Ir,CodeGenTokenizer:Xr,CLIPTokenizer:En,SiglipTokenizer:zr,MarianTokenizer:Pn,BloomTokenizer:Qr,NllbTokenizer:zs,M2M100Tokenizer:Bs,LlamaTokenizer:Qs,CodeLlamaTokenizer:Xs,XLMRobertaTokenizer:Js,MPNetTokenizer:Ys,FalconTokenizer:gs,GPTNeoXTokenizer:Zs,EsmTokenizer:en,Wav2Vec2CTCTokenizer:Rs,BlenderbotTokenizer:Ar,BlenderbotSmallTokenizer:Jr,SpeechT5Tokenizer:cr,NougatTokenizer:fr,VitsTokenizer:Ns,Qwen2Tokenizer:tn,GemmaTokenizer:Lr,Grok1Tokenizer:Ss,CohereTokenizer:Cn,MgpstrTokenizer:$s,PreTrainedTokenizer:dt})},"./src/utils/audio.js":(e,r,t)=>{t.r(r),t.d(r,{RawAudio:()=>q,hamming:()=>h,hanning:()=>u,mel_filter_bank:()=>S,read_audio:()=>d,spectrogram:()=>k,window_function:()=>B});var s=t("./src/utils/hub.js"),i=t("./src/utils/maths.js"),n=t("./src/utils/core.js"),o=t("./src/env.js"),a=t("?7a2c"),l=t("./src/utils/tensor.js");async function d(V,Y){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. 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For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");const H=await(await(0,s.getFile)(V)).arrayBuffer(),Q=new AudioContext({sampleRate:Y});typeof Y>"u"&&console.warn(`No sampling rate provided, using default of ${Q.sampleRate}Hz.`);const ie=await Q.decodeAudioData(H);let le;if(ie.numberOfChannels===2){const ae=Math.sqrt(2),ge=ie.getChannelData(0),N=ie.getChannelData(1);le=new Float32Array(ge.length);for(let F=0;F2595*Math.log10(1+V/700),kaldi:V=>1127*Math.log(1+V/700),slaney:(V,Y=1e3,H=15,Q=27/Math.log(6.4))=>V>=Y?H+Math.log(V/Y)*Q:3*V/200};function _(V,Y="htk"){const H=w[Y];if(!H)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof V=="number"?H(V):V.map(Q=>H(Q))}const P={htk:V=>700*(10**(V/2595)-1),kaldi:V=>700*(Math.exp(V/1127)-1),slaney:(V,Y=1e3,H=15,Q=Math.log(6.4)/27)=>V>=H?Y*Math.exp(Q*(V-H)):200*V/3};function A(V,Y="htk"){const H=P[Y];if(!H)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof V=="number"?H(V):V.map(Q=>H(Q))}function v(V,Y){const H=Float64Array.from({length:Y.length-1},(ae,ge)=>Y[ge+1]-Y[ge]),Q=Array.from({length:V.length},()=>new Array(Y.length));for(let ae=0;aenew Array(V.length));for(let ae=0;aeV+Q*le)}function S(V,Y,H,Q,ie,le=null,ae="htk",ge=!1){if(le!==null&&le!=="slaney")throw new Error('norm must be one of null or "slaney"');const N=_(H,ae),F=_(Q,ae),G=y(N,F,Y+2);let ne=A(G,ae),X;if(ge){const fe=ie/(V*2);X=_(Float64Array.from({length:V},(ke,He)=>He*fe),ae),ne=G}else X=y(0,Math.floor(ie/2),V);const we=v(X,ne);if(le!==null&&le==="slaney")for(let fe=0;feie)throw Error(`frame_length (${H}) may not be larger than fft_length (${ie})`);if(Pe!==H)throw new Error(`Length of the window (${Pe}) must equal frame_length (${H})`);if(Q<=0)throw new Error("hop_length must be greater than zero");if(le===null&&G!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. 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Zv=f.WhisperForConditionalGeneration;f.WhisperModel,f.WhisperPreTrainedModel,f.WhisperProcessor,f.WhisperTextStreamer,f.WhisperTimeStampLogitsProcessor,f.WhisperTokenizer,f.XLMForQuestionAnswering,f.XLMForSequenceClassification,f.XLMForTokenClassification,f.XLMModel,f.XLMPreTrainedModel,f.XLMRobertaForMaskedLM,f.XLMRobertaForQuestionAnswering,f.XLMRobertaForSequenceClassification,f.XLMRobertaForTokenClassification,f.XLMRobertaModel,f.XLMRobertaPreTrainedModel,f.XLMRobertaTokenizer,f.XLMTokenizer,f.XLMWithLMHeadModel,f.XVectorOutput,f.YolosFeatureExtractor,f.YolosForObjectDetection,f.YolosImageProcessor,f.YolosModel,f.YolosObjectDetectionOutput,f.YolosPreTrainedModel,f.ZeroShotAudioClassificationPipeline,f.ZeroShotClassificationPipeline,f.ZeroShotImageClassificationPipeline,f.ZeroShotObjectDetectionPipeline,f.bankers_round,f.cat,f.cos_sim,f.dot,f.dynamic_time_warping,f.env;var ex=f.full;f.full_like,f.getKeyValueShapes,f.hamming,f.hanning,f.interpolate,f.interpolate_4d,f.interpolate_data,f.is_chinese_char,f.layer_norm,f.load_image,f.load_video,f.log_softmax,f.magnitude,f.matmul,f.max,f.mean,f.mean_pooling,f.medianFilter,f.mel_filter_bank,f.min,f.ones,f.ones_like,f.permute,f.permute_data,f.pipeline,f.quantize_embeddings,f.rand,f.read_audio,f.rfft,f.round,f.slice,f.softmax,f.spectrogram,f.stack,f.std_mean,f.topk,f.window_function,f.zeros,f.zeros_like;const tx=64;class Wn{static async getInstance(r){return this.model_id="onnx-community/whisper-base",this.tokenizer??(this.tokenizer=Jv.from_pretrained(this.model_id,{progress_callback:r})),this.processor??(this.processor=Xv.from_pretrained(this.model_id,{progress_callback:r})),this.model??(this.model=Zv.from_pretrained(this.model_id,{dtype:{encoder_model:"fp32",decoder_model_merged:"q4"},device:"webgpu",progress_callback:r})),Promise.all([this.tokenizer,this.processor,this.model])}}re(Wn,"model_id",null),re(Wn,"tokenizer"),re(Wn,"processor"),re(Wn,"model");let Qc=!1;async function rx({audio:e,language:r}){if(Qc)return;Qc=!0,self.postMessage({status:"start"});const[t,s,i]=await Wn.getInstance();let n,o=0;const a=h=>{n??(n=performance.now());let w;o++>0&&(w=o/(performance.now()-n)*1e3),self.postMessage({status:"update",output:h,tps:w,numTokens:o})},l=new Yv(t,{skip_prompt:!0,decode_kwargs:{skip_special_tokens:!0},callback_function:a}),d=await s(e),p=await i.generate({...d,max_new_tokens:tx,language:r,streamer:l}),u=t.batch_decode(p,{skip_special_tokens:!0});self.postMessage({status:"complete",output:u}),Qc=!1}async function sx(){self.postMessage({status:"loading",data:"Loading model..."});const[e,r,t]=await Wn.getInstance(s=>{self.postMessage(s)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."}),await t.generate({input_features:ex([1,80,3e3],0),max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async e=>{const{type:r,data:t}=e.data;switch(r){case"load":sx();break;case"generate":rx(t);break}})})();