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set_${e}(${qe}, value: ${g}) { + set_${e}ByIndices(${Ye(vt)}, value); + }`})();return{impl:()=>{let qe=[],vt=!1;return x.offsetToIndices&&(qe.push(ee),vt=!0),x.indicesToOffset&&(qe.push(ke),vt=!0),x.broadcastedIndicesToOffset&&(Object.values(ar).forEach(rr=>qe.push(rr)),vt=!0),x.set&&(qe.push(xr),vt=!0),x.setByIndices&&(qe.push(Qt),vt=!0),x.get&&(qe.push(hr),vt=!0),x.getByIndices&&(qe.push(jr),vt=!0),!a&&vt&&qe.unshift(`const ${D} = ${l.indices}(${r.join(",")});`,`const ${V} = ${l.indices}(${Ee.computeStrides(r).join(",")});`),qe.join(` +`)},type:l,offsetToIndices:te,indicesToOffset:Pe,broadcastedIndicesToOffset:nr,indices:Ye,indicesGet:Ft,indicesSet:Bt,set:(...qe)=>{if(qe.length!==s+1)throw new Error(`indices length must be ${s}`);let vt=qe[s];if(typeof vt!="string")throw new Error("value must be string");let rr=qe.slice(0,s).map(T).join(",");return s===0?Ht("0u",vt):s===1?Ht(rr[0],vt):(x.set=!0,x.setByIndices=!0,x.indicesToOffset=!0,`set_${e}(${rr}, 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workgroup_index * ${t*r*n}u + local_idx;`;return`@compute @workgroup_size(${t}, ${r}, ${n}) + fn main(${a}) { + ${s} + `}appendVariableUniforms(e){e.rank!==0&&(e.shape.startsWith("uniforms.")&&this.uniforms.push({name:e.shape.replace("uniforms.",""),type:"u32",length:e.rank}),e.strides.startsWith("uniforms.")&&this.uniforms.push({name:e.strides.replace("uniforms.",""),type:"u32",length:e.rank}))}declareVariable(e,t){if(e.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(e),this.appendVariableUniforms(e);let r=e.usage==="input"?"read":"read_write",n=e.type.storage;return`@group(0) @binding(${t}) var ${e.name}: array<${n}>;`}declareVariables(...e){return e.map(t=>this.declareVariable(t,this.variableIndex++)).join(` +`)}registerInternalVariable(e){if(e.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() 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: array, ${C}>; + ${V.mainStart([C,C,1])} + let stride = (uniforms.output_shape[1] - 1) / ${C} + 1; + let workgroup_id_x = workgroup_index % stride; + let workgroup_id_y = workgroup_index / stride; + let input_col = workgroup_id_y * ${C}u + local_id.x; + let input_row = workgroup_id_x * ${C}u + local_id.y; + if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) { + tile[local_id.y][local_id.x] = ${T.getByIndices(`${T.type.indices}(input_row, input_col)`)}; + } + workgroupBarrier(); + + let output_col = workgroup_id_x * ${C}u + local_id.x; + let output_row = workgroup_id_y * ${C}u + local_id.y; + if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) { + ${x.setByIndices(`${x.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")} + } + }`:D=V=>` + ${V.registerUniform("output_size","u32").declareVariables(T,x)} + + ${ho(i,n,T,x)} + + ${V.mainStart()} + ${V.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${x.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${x.setByOffset("global_idx",T.getByIndices("aIndices"))} + }`,{name:g?"TransposeShared":"Transpose",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:()=>{let V=Ee.size(a);return{outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:g?{x:Math.ceil(l[1]/C),y:Math.ceil(l[0]/C)}:{x:Math.ceil(V/64)},programUniforms:[{type:12,data:V},...kt(m,l)]}},getShaderSource:D}},Fd=(e,t)=>{po(e.inputs),e.compute(xn(e.inputs[0],t.perm))},fo=e=>or({perm:e.perm})}),mo,_o,go,wo,yo,Ei,bo,Mo,ki,vo,Fn,Si,xo,To,Pi,Co,$o,Ai,Eo,ko,Ii,Od=U(()=>{Yt(),Kt(),pr(),Vi(),jn(),mo={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 * 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u=r[0].dims,d=Ee.size(a),c=Ee.size(s),g=Qe("_A",r[0].dataType,u),m=qt("output",i,a),l=32,T=` + var aBestValues : array; + `;return{name:e,shaderCache:t,getShaderSource:x=>` + ${x.registerUniform("reduceSize","u32").declareVariables(g,m)} + ${T} + fn DIV_CEIL(a : u32, b : u32) -> u32 { + return ((a - 1u) / b + 1u); + } + ${x.mainStart(l)} + + let outputIndex = global_idx / ${l}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${go[n]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${l}) { + let candidate = f32(${g.getByOffset("offset + k")}); + bestValue = ${mo[n]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${l}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 = ${_o[n]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${m.setByOffset("outputIndex",`${n==="mean"?`${m.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${m.type.storage}(${wo[n]})`}`)}; + } + }`,getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:d},programUniforms:[{type:12,data:c}]})}},Fn=(e,t,r,n)=>{let i=e.inputs.length===1?r:Oi(e.inputs,r),a=i.axes;a.length===0&&!i.noopWithEmptyAxes&&(a=e.inputs[0].dims.map((T,x)=>x));let 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Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},Fi=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],ni=(e,t,r,n,i,a,s=!1,u=!1)=>{let d=[],c=r[0].dims,g=c.length,m=Ee.normalizeAxes(i,g),l=!u&&m.length===0;c.forEach((C,D)=>{l||m.indexOf(D)>=0?s&&d.push(1):d.push(C)});let T=d.length,x=Ee.size(d);return{name:e,shaderCache:t,getShaderSource:C=>{let D=[],V=Qe("_A",r[0].dataType,g),A=qt("output",a,T),ee=n(V,A,m),te=ee[2];for(let ie=0,ke=0;ie=0?(s&&ke++,te=`for(var j${ie}: u32 = 0; j${ie} < ${c[ie]}; j${ie}++) { + ${ee[2].includes("last_index")?`let last_index = j${ie};`:""} + ${V.indicesSet("input_indices",ie,`j${ie}`)} + ${te} + }`):(D.push(`${V.indicesSet("input_indices",ie,A.indicesGet("output_indices",ke))};`),ke++);return` + + ${C.registerUniform("output_size","u32").declareVariables(V,A)} + + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var input_indices: ${V.type.indices}; + let output_indices = ${A.offsetToIndices("global_idx")}; + + ${D.join(` +`)} + ${ee[0]} // init ops for reduce max/min + ${ee[1]} + ${te} + ${ee[3]} + ${ee.length===4?A.setByOffset("global_idx","value"):ee.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:d,dataType:a}],dispatchGroup:{x:Math.ceil(x/64)},programUniforms:[{type:12,data:x},...kt(c,d)]})}},Oi=(e,t)=>{let r=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),or({axes:r,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},En=(e,t,r,n)=>{let i=e.inputs,a=i.length===1?r:Oi(i,r);e.compute(ni(t,{hint:a.cacheKey,inputDependencies:["rank"]},[i[0]],a.noopWithEmptyAxes&&a.axes.length===0?Fi:n,a.axes,i[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},So=(e,t)=>{On(e.inputs),En(e,"ReduceLogSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = 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f32(${r.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${a});`]})},Fo=(e,t)=>{On(e.inputs),En(e,"ReduceMin",t,(r,n,i)=>{let a=[];for(let s=0;s=0||i.length===0)&&a.push(`input_indices[${s}] = 0;`);return[`${a.join(` +`)}`,`var value = ${r.getByIndices("input_indices")};`,`value = min(value, ${r.getByIndices("input_indices")});`,""]})},Oo=(e,t)=>{On(e.inputs),En(e,"ReduceProd",t,(r,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${r.getByIndices("input_indices")};`,""])},Li=(e,t)=>{On(e.inputs),En(e,"ReduceSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,""])},zo=(e,t)=>{On(e.inputs),En(e,"ReduceSumSquare",t,(r,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${r.getByIndices("input_indices")}; value += t * t;`,""])},zn=(e,t,r)=>{if(t.length===0)return r;let n=1,i=1;for(let a=0;a1024},Bi=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Di(e,t):Si(e,t)},Do=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Po(e,t):xo(e,t)},Lo=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?zi(e,t):To(e,t)},Ri=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ao(e,t):Pi(e,t)},Bo=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Io(e,t):Co(e,t)},Ro=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Fo(e,t):$o(e,t)},Ni=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Oo(e,t):Ai(e,t)},No=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Li(e,t):Eo(e,t)},jo=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?zo(e,t):ko(e,t)},ji=(e,t)=>{zn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?So(e,t):Ii(e,t)}}),Ui,Wi,Vo,Gi,Uo=U(()=>{Yt(),Pr(),Vi(),Ui=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.")},Wi=(e,t)=>{Ui(e.inputs);let r=(n,i,a)=>{let s=[];for(let u=0;u=0||a.length===0)&&s.push(`input_indices[${u}] = 0;`);return[`${s.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",i.setByOffset("global_idx","best_index")]};e.compute(ni("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Vo=(e,t)=>{Ui(e.inputs);let r=(n,i,a)=>{let s=[];for(let u=0;u=0||a.length===0)&&s.push(`input_indices[${u}] = 0;`);return[`${s.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",i.setByOffset("global_idx","best_index")]};e.compute(ni("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Gi=e=>or(e)}),Wo,si,qi,Go,qo,bs,Ho,Ko,ii=U(()=>{Yt(),Kt(),oe(),pr(),Wo=(e,t)=>{let r=e[0],n=e[1],i=e[2],a=e[3],s=e[4],u=e[5];if(s&&u)throw new Error("Attention cannot have both past and attention_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let d=r.dims[0],c=r.dims[1],g=r.dims[2];if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==g)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(i.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let m=i.dims[0]/3,l=m,T=l;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let ee of t.qkvHiddenSizes)if(ee%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");m=t.qkvHiddenSizes[0],l=t.qkvHiddenSizes[1],T=t.qkvHiddenSizes[2]}let x=c;if(m!==l)throw new Error("qkv_hidden_sizes first element should be same as the second");if(i.dims[0]!==m+l+T)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let C=0;if(s){if(l!==T)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(s.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(s.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(s.dims[1]!==d)throw new Error('Input "past" second dimension must be batch_size');if(s.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(s.dims[4]!==l/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(C=s.dims[3])}let D=x+C,V=-1,A=0;if(a)throw new Error("Mask not supported");if(s)throw new Error("past is not supported");if(u){if(u.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(u.dims[0]!==d||u.dims[1]!==t.numHeads||u.dims[2]!==c||u.dims[3]!==D)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:d,sequenceLength:c,pastSequenceLength:C,kvSequenceLength:x,totalSequenceLength:D,maxSequenceLength:V,inputHiddenSize:g,hiddenSize:m,vHiddenSize:T,headSize:Math.floor(m/t.numHeads),vHeadSize:Math.floor(T/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:A,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},si=(e,t,r)=>t&&e?` + let total_sequence_length_input = u32(${t.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; + } + `:` + ${r?"let past_sequence_length = uniforms.past_sequence_length":""}; + let present_sequence_length = total_sequence_length; + `,qi=(e,t,r,n,i,a,s,u)=>{let d=_r(s?1:a),c=64,g=a/d;g{let A=qt("x",e.dataType,e.dims,d),ee=[A],te=s?Qe("seq_lens",s.dataType,s.dims):void 0;te&&ee.push(te);let ie=u?Qe("total_sequence_length_input",u.dataType,u.dims):void 0;ie&&ee.push(ie);let ke=Or(e.dataType),Pe=[{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(Pe).declareVariables(...ee)} + ${V.mainStart([c,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; + ${si(te,ie,!1)} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = (global_idx / ${c}) * uniforms.total_sequence_length + local_offset; + let seq_causal_length = ${s?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"}; + var thread_max_vector = ${x}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + thread_max_vector = max(${x}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(d){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: ${d}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${c}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${x}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + sum_vector += exp(${x}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(d){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: ${d}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${c}; 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] = ${A.type.value}(${ke}(1.0) / ${ke}(seq_causal_length)); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + var f32input = ${x}(x[offset + i]); + x[offset + i] = ${A.type.value}(exp(f32input - max_value) / sum); + } + } + ${s?` + 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] = ${A.type.value}(${ke}(0)); + }`:""}; + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${c};${T};${d}`,inputDependencies:C},getShaderSource:D,getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(a/c),y:i,z:t*r},programUniforms:l})}},Go=(e,t,r,n,i,a,s,u,d)=>{let c=s+a.kvSequenceLength,g=[a.batchSize,a.numHeads,a.sequenceLength,c],m=e>1&&n,l=a.kvNumHeads?a.kvNumHeads:a.numHeads,T=m?[a.batchSize,l,c,a.headSize]:void 0,x=a.nReps?a.nReps:1,C=a.scale===0?1/Math.sqrt(a.headSize):a.scale,D=_r(a.headSize),V=a.headSize/D,A=12,ee={x:Math.ceil(c/A),y:Math.ceil(a.sequenceLength/A),z:a.batchSize*a.numHeads},te=[{type:12,data:a.sequenceLength},{type:12,data:V},{type:12,data:c},{type:12,data:a.numHeads},{type:12,data:a.headSize},{type:1,data:C},{type:12,data:s},{type:12,data:a.kvSequenceLength},{type:12,data:x}],ie=m&&n&&Ee.size(n.dims)>0,ke=["type","type"];ie&&ke.push("type"),i&&ke.push("type"),u&&ke.push("type"),d&&ke.push("type");let Pe=[{dims:g,dataType:t.dataType,gpuDataType:0}];m&&Pe.push({dims:T,dataType:t.dataType,gpuDataType:0});let Ye=Ft=>{let Bt=Qe("q",t.dataType,t.dims,D),ar=Qe("key",r.dataType,r.dims,D),nr=[Bt,ar];if(ie){let Qt=Qe("past_key",n.dataType,n.dims,D);nr.push(Qt)}i&&nr.push(Qe("attention_bias",i.dataType,i.dims));let Ht=u?Qe("seq_lens",u.dataType,u.dims):void 0;Ht&&nr.push(Ht);let Er=d?Qe("total_sequence_length_input",d.dataType,d.dims):void 0;Er&&nr.push(Er);let jr=qt("output",t.dataType,g),hr=[jr];m&&hr.push(qt("present_key",t.dataType,T,D));let Fr=Or(1,D),Gt=[{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 = ${A}u; + + var tileQ: array<${Bt.type.storage}, ${A*A}>; + var tileK: array<${Bt.type.storage}, ${A*A}>; + ${Ft.registerUniforms(Gt).declareVariables(...nr,...hr)} + ${Ft.mainStart([A,A,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z % uniforms.num_heads; + let kvHeadIdx = ${x===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${x===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; + ${si(Ht,Er,!0)} + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; + let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + ${ie&&m?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""}; + let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K; + ${m?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""} + var value = ${Fr}(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; + ${ie&&m?` + 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]; + }`} + ${m?`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 += ${Fr}(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(D){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${D}`)}})()}; + output[outputIdx] = ${jr.type.value} (sum * uniforms.alpha) + ${i?"attention_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${D};${i!==void 0};${n!==void 0};${e}`,inputDependencies:ke},getRunData:()=>({outputs:Pe,dispatchGroup:ee,programUniforms:te}),getShaderSource:Ye}},qo=(e,t,r,n,i,a,s=void 0,u=void 0)=>{let d=a+i.kvSequenceLength,c=i.nReps?i.nReps:1,g=i.vHiddenSize*c,m=e>1&&n,l=i.kvNumHeads?i.kvNumHeads:i.numHeads,T=m?[i.batchSize,l,d,i.headSize]:void 0,x=[i.batchSize,i.sequenceLength,g],C=12,D={x:Math.ceil(i.vHeadSize/C),y:Math.ceil(i.sequenceLength/C),z:i.batchSize*i.numHeads},V=[{type:12,data:i.sequenceLength},{type:12,data:d},{type:12,data:i.vHeadSize},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:12,data:g},{type:12,data:a},{type:12,data:i.kvSequenceLength},{type:12,data:c}],A=m&&n&&Ee.size(n.dims)>0,ee=["type","type"];A&&ee.push("type"),s&&ee.push("type"),u&&ee.push("type");let te=[{dims:x,dataType:t.dataType,gpuDataType:0}];m&&te.push({dims:T,dataType:t.dataType,gpuDataType:0});let ie=ke=>{let Pe=Qe("probs",t.dataType,t.dims),Ye=Qe("v",r.dataType,r.dims),Ft=[Pe,Ye];A&&Ft.push(Qe("past_value",n.dataType,n.dims));let Bt=s?Qe("seq_lens",s.dataType,s.dims):void 0;s&&Ft.push(Bt);let ar=u?Qe("total_sequence_length_input",u.dataType,u.dims):void 0;u&&Ft.push(ar);let nr=[qt("output",t.dataType,x)];m&&nr.push(qt("present_value",t.dataType,T));let Ht=[{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 = ${C}u; + var tileQ: array<${Pe.type.value}, ${C*C}>; + var tileV: array<${Pe.type.value}, ${C*C}>; + ${ke.registerUniforms(Ht).declareVariables(...Ft,...nr)} + ${ke.mainStart([C,C,1])} + let headIdx = workgroup_id.z % uniforms.num_heads; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let kvHeadIdx = ${c===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${c===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; + ${si(Bt,ar,!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 + ${A&&m?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""}; + let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n; + ${m?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""} + var value = ${Pe.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; + ${A&&m?` + 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]; + }`} + ${m?` + if (w + local_id.y < present_sequence_length) { + present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx]; + }`:""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + headIdx * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:ee},getRunData:()=>({outputs:te,dispatchGroup:D,programUniforms:V}),getShaderSource:ie}},bs=(e,t,r,n,i,a,s,u,d,c,g=void 0,m=void 0)=>{let l=Math.min(e.outputCount,1+(s?1:0)+(u?1:0)),T=l>1?c.pastSequenceLength:0,x=T+c.kvSequenceLength,C=d&&Ee.size(d.dims)>0?d:void 0,D=[t,r];l>1&&s&&Ee.size(s.dims)>0&&D.push(s),C&&D.push(C),g&&D.push(g),m&&D.push(m);let V=e.compute(Go(l,t,r,s,C,c,T,g,m),{inputs:D,outputs:l>1?[-1,1]:[-1]})[0];e.compute(qi(V,c.batchSize,c.numHeads,T,c.sequenceLength,x,g,m),{inputs:g&&m?[V,g,m]:[V],outputs:[]});let A=[V,n];l>1&&u&&Ee.size(u.dims)>0&&A.push(u),g&&A.push(g),m&&A.push(m),e.compute(qo(l,V,n,u,c,T,g,m),{inputs:A,outputs:l>1?[0,2]:[0]})},Ho=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,i=t.inputHiddenSize,a=t.headSize,s=12,u={x:Math.ceil(t.headSize/s),y:Math.ceil(t.sequenceLength/s),z:t.batchSize*t.numHeads},d=[e.inputs[0],e.inputs[1],e.inputs[2]],c=[{type:12,data:n},{type:12,data:i},{type:12,data:a},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],g=m=>{let l=qt("output_q",d[0].dataType,r),T=qt("output_k",d[0].dataType,r),x=qt("output_v",d[0].dataType,r),C=Qe("input",d[0].dataType,d[0].dims),D=Qe("weight",d[1].dataType,d[1].dims),V=Qe("bias",d[2].dataType,d[2].dims),A=C.type.storage,ee=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return` + const TILE_SIZE = ${s}u; + var tileInput: array<${A}, ${s*s}>; + var tileWeightQ: array<${A}, ${s*s}>; + var tileWeightK: array<${A}, ${s*s}>; + var tileWeightV: array<${A}, ${s*s}>; + ${m.registerUniforms(ee).declareVariables(C,D,V,l,T,x)} + ${m.mainStart([s,s,1])} + let batchIndex = workgroup_id.z / uniforms.num_heads; + let headNumber = workgroup_id.z % uniforms.num_heads; + let m = global_id.y; + let n = global_id.x; + + let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K; + let biasOffsetQ = headNumber * uniforms.head_size; + let biasOffsetK = uniforms.hidden_size + biasOffsetQ; + let biasOffsetV = uniforms.hidden_size + biasOffsetK; + + var valueQ = ${A}(0); + var valueK = ${A}(0); + var valueV = ${A}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:c}),getShaderSource:g},{inputs:d,outputs:[-1,-1,-1]})},Ko=(e,t)=>{let r=Wo(e.inputs,t),[n,i,a]=Ho(e,r);return bs(e,n,i,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r)}}),Xo,Qo,Hi,Yo,zd=U(()=>{At(),Yt(),Kt(),Pr(),pr(),Xo=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,i,a)=>{let s=i.length;if(s!==n.length)throw new Error(`${a}: num dimensions != ${s}`);i.forEach((u,d)=>{if(u!==n[d])throw new Error(`${a}: dim[${d}] do not match`)})};if(e[0].dims.length>1){let n=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,n,"Invalid input scale"),r(e[2].dims,n,"Invalid input B"),r(e[3].dims,n,"Invalid input mean"),r(e[4].dims,n,"Invalid 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${ee.registerUniform("outputSize","u32").declareVariables(m,l,T,x,C,D)} + ${ee.mainStart()} + ${ee.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${D.offsetToIndices(`global_idx * ${s}`)}; + ${V()} + let scale = ${l.getByOffset("cOffset")}; + let bias = ${T.getByOffset("cOffset")}; + let inputMean = ${x.getByOffset("cOffset")}; + let inputVar = ${C.getByOffset("cOffset")}; + let x = ${m.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${D.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${s}`,inputDependencies:c?["rank","type","type","type","type"]:void 0},getShaderSource:A,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c?[{type:12,data:d},...kt(a)]:[{type:12,data:d}]})}},Hi=e=>or(e),Yo=(e,t)=>{let{inputs:r,outputCount:n}=e,i=Hi({...t,outputCount:n});if(E.webgpu.validateInputContent&&Xo(r,i),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Qo(r,i))}}),Ki,Zo,Jo,el=U(()=>{Kt(),pr(),Ki=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")},Zo=e=>{let t=e[0].dims,r=e[0].dims[2],n=Ee.size(t)/4,i=e[0].dataType,a=Qe("input",i,t,4),s=Qe("bias",i,[r],4),u=Qe("residual",i,t,4),d=qt("output",i,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:c=>` + const channels = ${r}u / 4; + ${c.declareVariables(a,s,u,d)} + + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes(n)} + let value = ${a.getByOffset("global_idx")} + + ${s.getByOffset("global_idx % channels")} + ${u.getByOffset("global_idx")}; + ${d.setByOffset("global_idx","value")} + }`}},Jo=e=>{Ki(e.inputs),e.compute(Zo(e.inputs))}}),tl,vr,Xi,rl,nl,sl,il,Qi,al,ol,ll,ul,Yi,dl,cl,pl,Bs,Zi,ai,hl,Ji,fl,ml,ea,_l,gl,ta,wl,yl,ra,bl,Ml,oi,vl,xl,li,na,sa,ui,Tl,Cl,$l,ia,El,kl,aa=U(()=>{Yt(),Kt(),Pr(),pr(),tl=(e,t,r,n,i,a,s)=>{let u=Math.ceil(t/4),d="";typeof i=="string"?d=`${i}(a)`:d=i("a");let c=Qe("inputData",r,[u],4),g=qt("outputData",n,[u],4),m=[{name:"vec_size",type:"u32"}];return s&&m.push(...s),` + ${e.registerUniforms(m).declareVariables(c,g)} + + ${a??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${c.getByOffset("global_idx")}; + ${g.setByOffset("global_idx",d)} + }`},vr=(e,t,r,n,i,a=e.dataType,s,u)=>{let d=[{type:12,data:Math.ceil(Ee.size(e.dims)/4)}];return s&&d.push(...s),{name:t,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:c=>tl(c,Ee.size(e.dims),e.dataType,a,r,n,u),getRunData:c=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil(Ee.size(c[0].dims)/64/4)},programUniforms:d})}},Xi=e=>{e.compute(vr(e.inputs[0],"Abs","abs"))},rl=e=>{e.compute(vr(e.inputs[0],"Acos","acos"))},nl=e=>{e.compute(vr(e.inputs[0],"Acosh","acosh"))},sl=e=>{e.compute(vr(e.inputs[0],"Asin","asin"))},il=e=>{e.compute(vr(e.inputs[0],"Asinh","asinh"))},Qi=e=>{e.compute(vr(e.inputs[0],"Atan","atan"))},al=e=>{e.compute(vr(e.inputs[0],"Atanh","atanh"))},ol=e=>or(e),ll=(e,t)=>{let r;switch(t.to){case 10:r="vec4";break;case 1:r="vec4";break;case 12:r="vec4";break;case 6:r="vec4";break;case 9:r="vec4";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute(vr(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},ul=e=>{let t,r,n=e.length>=2&&e[1].data!==0,i=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=n?e[1].getFloat32Array()[0]:-34028234663852886e22,r=i?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=n?e[1].getUint16Array()[0]:64511,r=i?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return or({min:t,max:r})},Yi=(e,t)=>{let r=t||ul(e.inputs),n=Or(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"Clip",i=>`clamp(${i}, vec4<${n}>(uniforms.min), vec4<${n}>(uniforms.max))`,void 0,r.cacheKey,void 0,[{type:e.inputs[0].dataType,data:r.min},{type:e.inputs[0].dataType,data:r.max}],[{name:"min",type:n},{name:"max",type:n}]),{inputs:[0]})},dl=e=>{e.compute(vr(e.inputs[0],"Ceil","ceil"))},cl=e=>{e.compute(vr(e.inputs[0],"Cos","cos"))},pl=e=>{e.compute(vr(e.inputs[0],"Cosh","cosh"))},Bs=e=>or(e),Zi=(e,t)=>{let r=Or(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"Elu",n=>`elu_vf32(${n})`,` + const elu_alpha_ = ${r}(${t.alpha}); + + fn elu_f32(a: ${r}) -> ${r} { + return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,t.cacheKey))},ai=(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<${e}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,hl=e=>{let t=Or(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,ai(t)))},Ji=e=>{e.compute(vr(e.inputs[0],"Exp","exp"))},fl=e=>{e.compute(vr(e.inputs[0],"Floor","floor"))},ml=e=>{let t=Or(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,ai(t)))},ea=(e,t)=>{let r=Or(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},_l=e=>{e.compute(vr(e.inputs[0],"Not",t=>`!${t}`))},gl=e=>{e.compute(vr(e.inputs[0],"Neg",t=>`-${t}`))},ta=e=>{e.compute(vr(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},wl=e=>{let t=Or(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},yl=e=>{e.compute(vr(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},ra=e=>or(e),bl=(e,t)=>{let r=Or(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${n} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},Ml=e=>{e.compute(vr(e.inputs[0],"Sin","sin"))},oi=e=>{e.compute(vr(e.inputs[0],"Sinh","sinh"))},vl=e=>{e.compute(vr(e.inputs[0],"Sqrt","sqrt"))},xl=e=>{e.compute(vr(e.inputs[0],"Tan","tan"))},li=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,na=e=>{e.compute(vr(e.inputs[0],"Tanh",li))},sa=(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 ${li("v")}; +} +`,ui=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Tl=e=>{let t=Or(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"FastGelu",ui,sa(t),void 0,e.inputs[0].dataType))},Cl=(e,t)=>{let r=Or(e.inputs[0].dataType);return e.compute(vr(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},$l=e=>{e.compute(vr(e.inputs[0],"Log","log"))},ia=(e,t)=>` +const alpha = vec4<${e}>(${t}); +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; +} +`,El=e=>`quick_gelu_impl(${e})`,kl=(e,t)=>{let r=Or(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"QuickGelu",El,ia(r,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),oa,Sl,Pl,Al=U(()=>{Kt(),pr(),aa(),oa=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")},Sl=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=Qe("input",e[0].dataType,e[0].dims,4),n=Qe("bias",e[0].dataType,[e[0].dims[2]],4),i=qt("output",e[0].dataType,t,4),a=Ee.size(t)/4,s=fr(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:u=>` + const M_SQRT2 = sqrt(2.0); + const halfChannels = ${e[0].dims[2]/4/2}u; + + ${u.declareVariables(r,n,i)} + + ${ai(s)} + + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes(a)} + 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); + + ${i.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Pl=e=>{oa(e.inputs),e.compute(Sl(e.inputs))}}),Il,Fl,kn,Ol,zl,la,Dl,Ll,ua,Bl,Rl,da,Nl,Dd=U(()=>{Yt(),Kt(),pr(),Il=(e,t,r,n,i,a,s,u,d,c,g,m)=>{let l,T;typeof u=="string"?l=T=(A,ee)=>`${u}((${A}),(${ee}))`:typeof u=="function"?l=T=u:(l=u.scalar,T=u.vector);let x=qt("outputData",g,n.length,4),C=Qe("aData",d,t.length,4),D=Qe("bData",c,r.length,4),V;if(i)if(a){let A=Ee.size(t)===1,ee=Ee.size(r)===1,te=t.length>0&&t[t.length-1]%4===0,ie=r.length>0&&r[r.length-1]%4===0;A||ee?V=x.setByOffset("global_idx",T(A?`${C.type.value}(${C.getByOffset("0")}.x)`:C.getByOffset("global_idx"),ee?`${D.type.value}(${D.getByOffset("0")}.x)`:D.getByOffset("global_idx"))):V=` + let outputIndices = ${x.offsetToIndices("global_idx * 4u")}; + let offsetA = ${C.broadcastedIndicesToOffset("outputIndices",x)}; + let offsetB = ${D.broadcastedIndicesToOffset("outputIndices",x)}; + ${x.setByOffset("global_idx",T(s||te?C.getByOffset("offsetA / 4u"):`${C.type.value}(${C.getByOffset("offsetA / 4u")}[offsetA % 4u])`,s||ie?D.getByOffset("offsetB / 4u"):`${D.type.value}(${D.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else V=x.setByOffset("global_idx",T(C.getByOffset("global_idx"),D.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let A=(ee,te,ie="")=>{let ke=`aData[indexA${te}][componentA${te}]`,Pe=`bData[indexB${te}][componentB${te}]`;return` + let outputIndices${te} = ${x.offsetToIndices(`global_idx * 4u + ${te}u`)}; + let offsetA${te} = ${C.broadcastedIndicesToOffset(`outputIndices${te}`,x)}; + let offsetB${te} = ${D.broadcastedIndicesToOffset(`outputIndices${te}`,x)}; + let indexA${te} = offsetA${te} / 4u; + let indexB${te} = offsetB${te} / 4u; + let componentA${te} = offsetA${te} % 4u; + let componentB${te} = offsetB${te} % 4u; + ${ee}[${te}] = ${ie}(${l(ke,Pe)}); + `};g===9?V=` + var data = vec4(0); + ${A("data",0,"u32")} + ${A("data",1,"u32")} + ${A("data",2,"u32")} + ${A("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:V=` + ${A("outputData[global_idx]",0)} + ${A("outputData[global_idx]",1)} + ${A("outputData[global_idx]",2)} + ${A("outputData[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(C,D,x)} + + ${m??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${V} + }`},Fl=(e,t,r,n,i,a,s=r.dataType)=>{let u=!Ee.areEqual(r.dims,n.dims),d=r.dims,c=Ee.size(r.dims),g=!1,m=!1,l=[u];if(u){let T=bn.calcShape(r.dims,n.dims,!1);if(!T)throw new Error("Can't perform binary op on the given tensors");d=T,c=Ee.size(d);let x=Ee.size(r.dims)===1,C=Ee.size(n.dims)===1,D=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,V=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;l.push(x),l.push(C),l.push(D),l.push(V);let A=1;for(let ee=1;eeT.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:T=>Il(T,r.dims,n.dims,d,g,u,m,i,r.dataType,n.dataType,s,a),getRunData:()=>({outputs:[{dims:d,dataType:s}],dispatchGroup:{x:Math.ceil(c/64/4)},programUniforms:[{type:12,data:Math.ceil(Ee.size(d)/4)},...kt(r.dims,n.dims,d)]})}},kn=(e,t,r,n,i,a)=>{e.compute(Fl(t,i??"",e.inputs[0],e.inputs[1],r,n,a))},Ol=e=>{kn(e,"Add",(t,r)=>`${t}+${r}`)},zl=e=>{kn(e,"Div",(t,r)=>`${t}/${r}`)},la=e=>{kn(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4(${t}==${r})`},void 0,void 0,9)},Dl=e=>{kn(e,"Mul",(t,r)=>`${t}*${r}`)},Ll=e=>{let t=Qe("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;kn(e,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},` + fn pow_custom(a : ${t}, b : ${t}) -> ${t} { + if (b == ${t}(0.0)) { + return ${t}(1.0); + } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) { + return ${t}(pow(f32(a), f32(b))); // NaN + } + return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); + } + fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> { + // TODO: implement vectorized pow + return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); + } + `)},ua=e=>{kn(e,"Sub",(t,r)=>`${t}-${r}`)},Bl=e=>{kn(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4(${t}>${r})`},void 0,void 0,9)},Rl=e=>{kn(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4(${t}<${r})`},void 0,void 0,9)},da=e=>{kn(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4(${t}>=${r})`},void 0,void 0,9)},Nl=e=>{kn(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4(${t}<=${r})`},void 0,void 0,9)}}),jl,Vl,di,Ul,Wl,Gl,Ld=U(()=>{Yt(),Kt(),Pr(),pr(),jl=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],i=n.dataType,a=n.dims.length;e.forEach((s,u)=>{if(u!==r){if(s.dataType!==i)throw new Error("input tensors should be one type");if(s.dims.length!==a)throw new Error("input tensors should have the same shape");s.dims.forEach((d,c)=>{if(c!==t&&d!==n.dims[c])throw new Error("non concat dimensions must match")})}})},Vl=(e,t)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${t}); + for (var i: u32 = 0u; i < ${e}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${e}u; + }`,di=(e,t)=>{let r=e.length,n=[];for(let i=0;i{let i=Ee.size(r),a=new Array(e.length),s=new Array(e.length),u=0,d=[],c=[],g=[{type:12,data:i}];for(let C=0;C`uniforms.sizeInConcatAxis${C}`).join(","),x=C=>` + + ${(()=>{C.registerUniform("outputSize","u32");for(let D=0;D(${T}); + ${l} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${di(s,m)} + }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:g}),getShaderSource:x}},Wl=(e,t)=>{let r=e.inputs,n=r[0].dims,i=Ee.normalizeAxis(t.axis,n.length);jl(r,i);let a=n.slice();a[i]=r.reduce((u,d)=>u+(d.dims.length>i?d.dims[i]:0),0);let s=r.filter(u=>Ee.size(u.dims)>0);e.compute(Ul(s,i,a,r[0].dataType),{inputs:s})},Gl=e=>or({axis:e.axis})}),Yn,Vn,Zn,ca,Un=U(()=>{Yt(),Kt(),Yn=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"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 ${e.activation}`)}},Vn=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Zn=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},ca=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[r,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=(e==null?void 0:e.activation_params)||[In,Rn];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:r}}return{activation:t}}}),hn,pa,ci=U(()=>{hn=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},pa=e=>` + ${e?"value = value + getBiasByOutputCoords(coords);":""} + `}),ha,fa=U(()=>{ha=e=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); +} +`}),ql,Hl,Rs,ma,Kl,Ns,Xl,_a,js=U(()=>{Yt(),Kt(),pr(),Un(),ci(),ql=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart / innerElementSize + inputCol${t?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRow + innerRow, + kStart / innerElementSize + inputCol${t?", batchIndices":""}); + `,Hl=(e,t)=>e?` + let ACached0 = mm_Asub[k * innerElementSize][localRow]; + let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; + let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; + ${t===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]; + ${t===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]; + ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} + }`,Rs=(e,t,r="f32",n,i=!1,a=32,s=!1,u=32)=>{let d=t[1]*e[1],c=t[0]*e[0],g=i?d:a,m=i?a:d,l=g/t[0],T=a/t[1];if(!((i&&l===4&&e[1]===4||!i&&(l===3||l===4))&&g%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${l} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${l} must be 3 or 4. + tileAWidth ${g} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return` +var mm_Asub: array, ${g/l}>, ${m}>; +var mm_Bsub: array, ${c/e[0]}>, ${a}>; + +const rowPerThread = ${e[1]}; +const colPerThread = ${e[0]}; +const innerElementSize = ${l}; +const tileInner = ${a}; + +@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let localRow = i32(localId.y); + let tileRow = localRow * rowPerThread; + let tileCol = i32(localId.x); + + let globalRow =i32(globalId.y) * rowPerThread; + let globalCol = i32(globalId.x); + let batch = ${s?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${d}; + + let num_tiles = ${s?`${Math.ceil(u/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${s?`i32(globalId.z) * ${u}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${T}; + 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; + ${ql(i,n)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${T}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${n?", batchIndices":""}); + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { + let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; + let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; + let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; + ${l===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${Hl(i,l)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},ma=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${t?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${t?", batchIndices":""}); + `,Kl=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Ns=(e,t,r="f32",n,i=!1,a=32,s=!1,u=32,d=!1)=>{let c=e[1]*t[1],g=e[0]*t[0],m=i?c:a,l=i?a:c;if(!(l%t[1]===0&&m%t[0]===0&&a%t[1]===0))throw new Error(`tileAHight ${l} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${m} must be divisible by workgroupSize[0]${t[0]}, tileInner ${a} must be divisible by workgroupSize[1]${t[1]}`);let T=l/t[1],x=m/t[0],C=a/t[1],D=d?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${c}; + let globalColStart = i32(workgroupId.x) * ${g}; + + // 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 < ${l}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${m}; inputCol = inputCol + ${t[0]}) { + ${ma(i,n)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${g}; inputCol = inputCol + ${t[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalColStart + inputCol${n?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${r}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}]; + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[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 * ${t[1]}; + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let gCol = globalColStart + localCol + innerCol * ${t[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) * ${c}; + +let tileRowA = i32(localId.y) * ${T}; +let tileColA = i32(localId.x) * ${x}; +let tileRowB = i32(localId.y) * ${C}; +// 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 < ${T}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${x}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${ma(i,n)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${C}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${n?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${r}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][tileCol + inner]; + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + ${Kl(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, ${l}>; + var mm_Bsub : array, ${a}>; + const rowPerThread = ${e[1]}; + const colPerThread = ${e[0]}; + const tileInner = ${a}; + +@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let batch = ${s?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${s?`${Math.ceil(u/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${s?`i32(globalId.z) * ${u}`:"0"}; + + var acc : array, rowPerThread>; + ${D} + } +`},Xl=(e,t,r,n,i,a=!1)=>{let[s,u,d]=i,[c,g,m,l]=n,T=ys(s,d),x=ys(u,d),C=fr(n[0].type.tensor),D=()=>{let A=g.rank,ee=c.rank,te=`var aIndices: ${g.type.indices};`;for(let ie=A-2-1,ke=ee-1;ie>=0;ie--,ke--)te+=` +aIndices[${ie}] = ${ee>1?`batchIndices[${ke}]`:"batchIndices"};`;return T.forEach(ie=>{te+=` +aIndices[${ie}] = 0;`}),te+=` +aIndices[${A-2}] = u32(row); + aIndices[${A-1}] = u32(colIn);`,te},V=()=>{let A=m.rank,ee=c.rank,te=`var bIndices: ${m.type.indices};`;for(let ie=A-2-1,ke=ee-1;ie>=0;ie--,ke--)te+=` +bIndices[${ie}] = ${ee>1?`batchIndices[${ke}]`:"batchIndices"};`;return x.forEach(ie=>{te+=` +bIndices[${ie}] = 0;`}),te+=` +bIndices[${A-2}] = u32(row); + bIndices[${A-1}] = u32(colIn);`,te};return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${c.type.indices}) -> ${hn(e,C)} { + var value = ${hn(e,C)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + ${D()} + value = ${g.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${c.type.indices}) -> ${hn(e,C)} { + var value = ${hn(e,C)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + ${V()} + value = ${m.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${hn(e,C)}) { + let col = colIn * ${e}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueIn; + let coords = vec3(batch, row, colIn); + ${t?`value = value + ${a?"bias[colIn]":`${hn(e,C)}(bias[row])`};`:""} + ${r} + ${l.setByIndices("vec3(coords)","value")} + } + } + `},_a=(e,t,r,n,i=!1,a)=>{let s=e[0].dims,u=e[1].dims,d=s.slice(0,-2),c=u.slice(0,-2),g=n?n.slice(0,-2):r.slice(0,-2),m=Ee.size(g),l=s[s.length-2],T=s[s.length-1],x=u[u.length-1],C=T%4===0&&x%4===0,D=l<=8?[4,1,1]:[4,4,1],V=[8,8,1],A=[Math.ceil(x/V[0]/D[0]),Math.ceil(l/V[1]/D[1]),Math.ceil(m/V[2]/D[2])],ee=C?4:1,te=[...d,l,T/ee],ie=te.length,ke=[...c,T,x/ee],Pe=ke.length,Ye=[m,l,x/ee],Ft=[{type:6,data:l},{type:6,data:x},{type:6,data:T}];Vn(t,Ft),Ft.push(...kt(g,te,ke));let Bt=["rank","rank"],ar=e.length>2;ar&&(Ft.push(...kt(e[2].dims)),Bt.push("rank")),Ft.push(...kt(Ye));let nr=Ht=>{let Er=g.length,jr=Ti("batchDims",e[0].dataType,Er,1),hr=fr(e[0].dataType),Fr=Qe("a",e[0].dataType,ie,ee),Gt=Qe("b",e[1].dataType,Pe,ee),Qt=qt("result",e[0].dataType,Ye.length,ee),xr=[Fr,Gt];if(ar){let nn=i?ee:1;xr.push(Qe("bias",e[2].dataType,e[2].dims.length,nn))}let qe=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Zn(t,qe);let vt=fr(Qt.type.tensor),rr=Yn(t,Qt.type.value,vt),Br=Xl(ee,ar,rr,[jr,Fr,Gt,Qt],[d,c,g],i);return` + ${Ht.registerUniforms(qe).registerInternalVariables(jr).declareVariables(...xr,Qt)} + ${Br} + ${C?Rs(D,V,hr,jr):Ns(D,V,hr,jr)} + `};return{name:"MatMul",shaderCache:{hint:`${D};${t.activation};${C};${i}`,inputDependencies:Bt},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:Ft}),getShaderSource:nr}}}),Ql,Yl,Bd=U(()=>{Yt(),_(),pr(),Un(),ci(),fa(),js(),Ql=(e,t,r,n,i=!1,a,s=4,u=4,d=4,c="f32")=>{let g=Ft=>{switch(Ft){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${c}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Ft} is not supported.`)}},m=Ft=>{switch(Ft){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 ${Ft} is not supported.`)}},l=e?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,T=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,x=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",C=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",D=e?"row":"col",V=e?"col":"row",A=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${D} / outWidth; + let outCol = ${D} % 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 = ${hn(s,c)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${x} && xCol >= 0 && xCol < ${C}) { + ${l} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${g(s)} + } + return resData;`,ee=e?t&&n?` + let col = colIn * ${s}; + ${A}`:` + let col = colIn * ${s}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${A} + } + return ${hn(s,c)}(0.0);`:n&&r?` + let col = colIn * ${s}; + ${A}`:` + let col = colIn * ${s}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${A} + } + return ${hn(s,c)}(0.0);`,te=`${m(u)}`,ie=hn(d,c),ke=hn(e?s:u,c),Pe=hn(e?u:s,c),Ye=Yn(a,ie,c);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${ke} { + ${e?ee:te} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Pe} { + ${e?te:ee} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${ie}) { + let col = colIn * ${d}; + 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])"}; + ${T} + ${pa(i)} + ${Ye} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},Yl=(e,t,r,n,i,a,s,u,d)=>{let c=t.format==="NHWC",g=c?e[0].dims[3]:e[0].dims[1],m=r[0],l=c?r[2]:r[3],T=c?r[1]:r[2],x=c?r[3]:r[1],C=c&&(g%4===0||g%3===0)&&x%4===0,D=c?x:l*T,V=c?l*T:x,A=[8,8,1],ee=n<=8?[4,1,1]:[4,4,1],te=[Math.ceil(D/A[0]/ee[0]),Math.ceil(V/A[1]/ee[1]),Math.ceil(m/A[2]/ee[2])];ae("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${te}`);let ie=C?c&&g%4!==0?3:4:1,ke=A[1]*ee[1],Pe=A[0]*ee[0],Ye=Math.max(A[0]*ie,A[1]),Ft=n%ke===0,Bt=i%Pe===0,ar=a%Ye===0,nr=C?[ie,4,4]:[1,1,1],Ht=[{type:6,data:n},{type:6,data:i},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Vn(t,Ht),Ht.push(...kt(e[0].dims,e[1].dims));let Er=["rank","rank"];s&&(Ht.push(...kt(e[2].dims)),Er.push("rank")),Ht.push(...kt(r));let jr=hr=>{let Fr=[{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}];Zn(t,Fr);let Gt=C?4:1,Qt=fr(e[0].dataType),xr=` + fn setOutputAtIndex(flatIndex : i32, value : ${C?`vec4<${Qt}>`:Qt}) { + result[flatIndex] = ${C?`vec4<${Qt}>`:Qt}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${C?`vec4<${Qt}>`:Qt}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${C?"/ 4":""}, value); + }`,qe=Qe("x",e[0].dataType,e[0].dims.length,ie===3?1:ie),vt=Qe("w",e[1].dataType,e[1].dims.length,Gt),rr=[qe,vt],Br=qt("result",e[0].dataType,r.length,Gt);if(s){let nn=Qe("bias",e[2].dataType,e[2].dims.length,Gt);rr.push(nn),xr+=` + fn getBiasByOutputCoords(coords : vec4) -> ${C?`vec4<${Qt}>`:Qt} { + return bias[coords.${c?"w":"y"}${C?"/ 4":""}]; + }`}return` + ${ha("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 }; + ${hr.registerUniforms(Fr).declareVariables(...rr,Br)} + ${xr} + ${Ql(c,Ft,Bt,ar,s,t,nr[0],nr[1],nr[2],Qt)} + ${C?Rs(ee,A,Qt,void 0,!c,Ye):Ns(ee,A,Qt,void 0,!c,Ye,!1,void 0,u)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${ie};${C};${Ft};${Bt};${ar};${ke};${Pe};${Ye}`,inputDependencies:Er},getRunData:()=>({outputs:[{dims:d?d(r):r,dataType:e[0].dataType}],dispatchGroup:{x:te[0],y:te[1],z:te[2]},programUniforms:Ht}),getShaderSource:jr}}}),Zl,ga,Vs,wa,ya,Jl,ba,eu,Rd=U(()=>{Yt(),_(),Kt(),pr(),Un(),ci(),Zl=e=>{let t=1;for(let r=0;rtypeof e=="number"?[e,e,e]:e,Vs=(e,t)=>t<=1?e:e+(e-1)*(t-1),wa=(e,t,r,n=1)=>{let i=Vs(t,n);return Math.floor((e[0]*(r-1)-r+i)/2)},ya=(e,t,r,n,i)=>{i==null&&(i=wa(e,t[0],n[0]));let a=[0,0,0,r];for(let s=0;s<3;s++)e[s]+2*i>=t[s]&&(a[s]=Math.trunc((e[s]-t[s]+2*i)/n[s]+1));return a},Jl=(e,t,r,n,i,a,s,u,d,c)=>{let g,m,l,T;if(e==="VALID"&&(e=0),typeof e=="number"){g={top:e,bottom:e,left:e,right:e,front:e,back:e};let x=ya([t,r,n,1],[u,d,c],1,[i,a,s],e);m=x[0],l=x[1],T=x[2]}else if(Array.isArray(e)){if(!e.every((C,D,V)=>C===V[0]))throw Error(`Unsupported padding parameter: ${e}`);g={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let x=ya([t,r,n,1],[u,d,c],1,[i,a,s],e[0]);m=x[0],l=x[1],T=x[2]}else if(e==="SAME_UPPER"){m=Math.ceil(t/i),l=Math.ceil(r/a),T=Math.ceil(n/s);let x=(m-1)*i+u-t,C=(l-1)*a+d-r,D=(T-1)*s+c-n,V=Math.floor(x/2),A=x-V,ee=Math.floor(C/2),te=C-ee,ie=Math.floor(D/2),ke=D-ie;g={top:ee,bottom:te,left:ie,right:ke,front:V,back:A}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:g,outDepth:m,outHeight:l,outWidth:T}},ba=(e,t,r,n,i,a=!1,s="channelsLast")=>{let u,d,c,g,m;if(s==="channelsLast")[u,d,c,g,m]=e;else if(s==="channelsFirst")[u,m,d,c,g]=e;else throw new Error(`Unknown dataFormat ${s}`);let[l,,T,x,C]=t,[D,V,A]=ga(r),[ee,te,ie]=ga(n),ke=Vs(T,ee),Pe=Vs(x,te),Ye=Vs(C,ie),{padInfo:Ft,outDepth:Bt,outHeight:ar,outWidth:nr}=Jl(i,d,c,g,D,V,A,ke,Pe,Ye),Ht=a?l*m:l,Er=[0,0,0,0,0];return s==="channelsFirst"?Er=[u,Ht,Bt,ar,nr]:s==="channelsLast"&&(Er=[u,Bt,ar,nr,Ht]),{batchSize:u,dataFormat:s,inDepth:d,inHeight:c,inWidth:g,inChannels:m,outDepth:Bt,outHeight:ar,outWidth:nr,outChannels:Ht,padInfo:Ft,strideDepth:D,strideHeight:V,strideWidth:A,filterDepth:T,filterHeight:x,filterWidth:C,effectiveFilterDepth:ke,effectiveFilterHeight:Pe,effectiveFilterWidth:Ye,dilationDepth:ee,dilationHeight:te,dilationWidth:ie,inShape:e,outShape:Er,filterShape:t}},eu=(e,t,r,n,i,a)=>{let s=a==="channelsLast";s?e[0].dims[3]:e[0].dims[1];let u=[64,1,1],d={x:r.map((D,V)=>V)},c=[Math.ceil(Zl(d.x.map(D=>r[D]))/u[0]),1,1];ae("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${c}`);let g=1,m=Ee.size(r),l=[{type:12,data:m},{type:12,data:n},{type:12,data:i},{type:12,data:t.strides},{type:12,data:t.dilations}];Vn(t,l),l.push(...kt(e[0].dims,e[1].dims));let T=["rank","rank"],x=e.length===3;x&&(l.push(...kt(e[2].dims)),T.push("rank")),l.push(...kt(r));let C=D=>{let V=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:n.length},{name:"pads",type:"u32",length:i.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];Zn(t,V);let A=1,ee=fr(e[0].dataType),te=Qe("x",e[0].dataType,e[0].dims.length,g),ie=Qe("W",e[1].dataType,e[1].dims.length,A),ke=[te,ie],Pe=qt("result",e[0].dataType,r.length,A),Ye="";if(x){let ar=Qe("bias",e[2].dataType,e[2].dims.length,A);ke.push(ar),Ye+=` + fn getBiasByOutputCoords(coords : array) -> ${ee} { + return bias[${s?Wt("coords",4,5):Wt("coords",1,5)}]; + }`}let Ft=hn(g,ee),Bt=Yn(t,Ft,ee);return` + ${Ye} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${te.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${ie.getByIndices("aIndices")}; + } + ${D.registerUniforms(V).declareVariables(...ke,Pe)} + ${D.mainStart()} + ${D.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${Pe.offsetToIndices("global_idx")}; + let batch = ${Wt("coords",0,te.rank)}; + let d2 = ${s?Wt("coords",te.rank-1,te.rank):Wt("coords",1,te.rank)}; + let xFRCCorner = vec3(${s?Wt("coords",1,te.rank):Wt("coords",2,te.rank)}, + ${s?Wt("coords",2,te.rank):Wt("coords",3,te.rank)}, + ${s?Wt("coords",3,te.rank):Wt("coords",4,te.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${s?Wt("uniforms.x_shape",1,te.rank):Wt("uniforms.x_shape",2,te.rank)}; + let xShapeZ = ${s?Wt("uniforms.x_shape",2,te.rank):Wt("uniforms.x_shape",3,te.rank)}; + let xShapeW = ${s?Wt("uniforms.x_shape",3,te.rank):Wt("uniforms.x_shape",4,te.rank)}; + let xShapeU = ${s?Wt("uniforms.x_shape",4,te.rank):Wt("uniforms.x_shape",1,te.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) { + ${s?`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) { + ${s?`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) { + ${s?`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) { + ${s?`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); + } + } + } + } + ${x?"value = value + getBiasByOutputCoords(coords)":""}; + ${Bt} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${s};${g};${x}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:c[0],y:c[1],z:c[2]},programUniforms:l}),getShaderSource:C}}}),Jn,tu,Nd=U(()=>{Yt(),Kt(),pr(),Un(),Jn=(e,t,r,n)=>{let i=e.length>2,a=i?"value += b[output_channel];":"",s=e[0].dims,u=e[1].dims,d=t.format==="NHWC",c=d?r[3]:r[1],g=c/t.group,m=d&&g>=4?_r(c):1,l=Ee.size(r)/m,T=[{type:12,data:l},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:g}];Vn(t,T),T.push(...kt(s,[u[0],u[1],u[2],u[3]/m]));let x=i?["rank","rank","rank"]:["rank","rank"];T.push(...kt([r[0],r[1],r[2],r[3]/m]));let C=D=>{let V=qt("output",e[0].dataType,r.length,m),A=fr(V.type.tensor),ee=Yn(t,V.type.value,A),te=Qe("x",e[0].dataType,s.length),ie=Qe("w",e[1].dataType,u.length,m),ke=[te,ie];i&&ke.push(Qe("b",e[2].dataType,e[2].dims,m));let Pe=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];Zn(t,Pe);let Ye=d?` + 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 = ${te.get("batch","xHeight","xWidth","input_channel")}; + let wVal = ${ie.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 = ${te.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${ie.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal * wVal; + } + } + } + `;return` + ${D.registerUniforms(Pe).declareVariables(...ke,V)} + + ${D.mainStart()} + ${D.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${V.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${d?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${d?1:2}], outputIndices[${d?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel * ${m} / uniforms.output_channels_per_group; + var in_channel_offset = group_id * uniforms.w_shape[${d?2:1}]; + + var value: ${V.type.value} = ${V.type.value}(0); + ${Ye} + ${a} + ${ee} + ${V.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${m}`,inputDependencies:x},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:T}),getShaderSource:C}},tu=(e,t,r,n)=>{let i=e.length>2,a=_r(r[3]),s=_r(r[2]),u=Ee.size(r)/a/s,d=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/a],c=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/a],g=[r[0],r[1],r[2],r[3]/a],m=[{type:12,data:u},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];Vn(t,m),m.push(...kt(d,c,g));let l=(s-1)*t.strides[1]+c[1],T=x=>{let C=qt("output",e[0].dataType,g.length,a),D=fr(C.type.tensor),V=Yn(t,C.type.value,D),A=Qe("x",e[0].dataType,d.length,a),ee=Qe("w",e[1].dataType,c.length,a),te=[A,ee];i&&te.push(Qe("b",e[2].dataType,e[2].dims,a));let ie=i?"value += b[output_channel];":"",ke=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Zn(t,ke),` + ${x.registerUniforms(ke).declareVariables(...te,C)} + ${x.mainStart()} + ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let width0 = uniforms.output_shape[3]; + let output_channel = global_idx % width0; + var index1 = global_idx / width0; + let width1 = uniforms.output_shape[2] / ${s}u; + let col = (index1 % width1) * ${s}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${A.type.value}, ${l}>; + var values: array<${C.type.value}, ${s}>; + let input_channel = output_channel; + // Use constant instead of uniform can give better performance for w's height/width. + for (var w_height: u32 = 0u; w_height < ${c[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 < ${l}; i++) { + let x_width = x_corner.y + i; + if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { + x_vals[i] = ${A.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${A.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${c[1]}; w_width++) { + let w_val = ${ee.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${s}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${s}u; i++) { + var value = values[i]; + ${ie} + ${V} + ${C.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${a};${s};${l};${c[0]};${c[1]}`,inputDependencies:i?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:m}),getShaderSource:T}}}),Ma,ru,va,nu=U(()=>{Yt(),Kt(),js(),pr(),Un(),Ma=(e,t,r,n,i=!1,a)=>{let s=e[0].dims,u=e[1].dims,d=s[s.length-2],c=u[u.length-1],g=s[s.length-1],m=_r(c),l=_r(g),T=_r(d),x=Ee.size(r)/m/T,C=e.length>2,D=n?n.slice(0,-2):r.slice(0,-2),V=[Ee.size(D),d,c],A=[{type:12,data:x},{type:12,data:d},{type:12,data:c},{type:12,data:g}];Vn(t,A),A.push(...kt(D,s,u)),C&&A.push(...kt(e[2].dims)),A.push(...kt(V));let ee=te=>{let ie=Ti("batch_dims",e[0].dataType,D.length),ke=Qe("a",e[0].dataType,s.length,l),Pe=Qe("b",e[1].dataType,u.length,m),Ye=qt("output",e[0].dataType,V.length,m),Ft=fr(Ye.type.tensor),Bt=Yn(t,Ye.type.value,Ft),ar=[ke,Pe],nr="";if(C){let xr=i?m:1;ar.push(Qe("bias",e[2].dataType,e[2].dims.length,xr)),nr=`${i?`value += bias[col / ${xr}];`:`value += ${Ye.type.value}(bias[row + i]);`}`}let Ht=s.slice(0,-2),Er=u.slice(0,-2),jr=ys(Ht,D),hr=ys(Er,D),Fr=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Zn(t,Fr);let Gt=(xr,qe)=>{let vt=xr.rank,rr=xr.name;if(vt===2)return`var ${rr}_indices = ${xr.type.indices}(0u, 0u);`;let Br=ie.rank,nn=`var ${rr}_indices: ${xr.type.indices};`;for(let an=vt-2-1,Xs=Br-1;an>=0;an--,Xs--)nn+=` +${rr}_indices[${an}] = ${Br>1?`batch_indices[${Xs}]`:"batch_indices"};`;return qe.forEach(an=>{nn+=` +${rr}_indices[${an}] = 0;`}),nn+=`${rr}_indices[${vt-2}] = 0u; + ${rr}_indices[${vt-1}] = 0u;`,nn},Qt=()=>{let xr=`var a_data: ${ke.type.value};`;for(let qe=0;qe; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${l}) { + ${Qt()} + } + for (var i = 0u; i < ${T}u; i++) { + var value = values[i]; + ${nr} + ${Bt} + let cur_indices = ${Ye.type.indices}(batch, row + i, col); + let offset = ${Ye.indicesToOffset("cur_indices")}; + ${Ye.setByOffset(`offset / ${m}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${m};${l};${T};${i}`,inputDependencies:C?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(x/64)},programUniforms:A}),getShaderSource:ee}},ru=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.")},va=e=>{ru(e.inputs);let t=bn.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let r=t[t.length-1],n=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&n<8?e.compute(Ma(e.inputs,{activation:""},t)):e.compute(_a(e.inputs,{activation:""},t))}}),su,pi,iu,Ms,xa,Ta,au,Us,Ca,jd=U(()=>{Kt(),Bd(),Rd(),js(),Nd(),Un(),nu(),jn(),su=(e,t,r,n,i,a)=>{let s=e[0],u=e.slice(a?1:2,a?3:4),d=u.length,c=t[0],g=t.slice(2).map((l,T)=>l+(l-1)*(r[T]-1)),m=u.map((l,T)=>l+n[T]+n[T+d]).map((l,T)=>Math.floor((l-g[T]+i[T])/i[T]));return m.splice(0,0,s),m.splice(a?3:1,0,c),m},pi=[2,3,1,0],iu=(e,t)=>{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 r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[1]*t.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let i=e[0].dims.length-2;if(t.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(t.strides.length!==i)throw new Error(`strides should be ${i}D`);if(t.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},Ms=(e,t)=>{let r=e.kernelShape.slice();r.length{let t=ca(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,a=e.group,s=e.kernel_shape,u=e.pads,d=e.strides,c=e.w_is_const();return{autoPad:n,format:r,dilations:i,group:a,kernelShape:s,pads:u,strides:d,wIsConst:c,...t,cacheKey:`${e.format};${t.activation};`}},Ta=(e,t,r,n)=>{let i=r.format==="NHWC",a=su(t[0].dims,t[1].dims,r.dilations,r.pads,r.strides,i);if(r.group!==1){let ke=[t[0]];if(i){let Pe=e.kernelCustomData.wT??e.compute(xn(t[1],pi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Pe),ke.push(Pe)}else ke.push(t[1]);t.length===3&&ke.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&i&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1?e.compute(tu(ke,r,a,n),{inputs:ke}):e.compute(Jn(ke,r,a,n),{inputs:ke});return}let s=t.length===3,u=t[0].dims[i?1:2],d=t[0].dims[i?2:3],c=t[0].dims[i?3:1],g=t[1].dims[2],m=t[1].dims[3],l=a[i?1:2],T=a[i?2:3],x=a[i?3:1],C=i&&g===u&&m===d&&r.pads[0]===0&&r.pads[1]===0;if(C||g===1&&m===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let ke=a[0],Pe,Ye,Ft,Bt=[];if(i){let Ht=e.kernelCustomData.wT??e.compute(xn(t[1],pi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ht),C){let Er=u*d*c;Pe=t[0].reshape([1,ke,Er]),Ye=Ht.reshape([1,Er,x]),Ft=[1,ke,x]}else Pe=t[0].reshape([ke,u*d,c]),Ye=Ht.reshape([1,c,x]),Ft=[ke,l*T,x];Bt.push(Pe),Bt.push(Ye)}else Pe=t[0].reshape([ke,c,u*d]),Ye=t[1].reshape([1,x,c]),Ft=[ke,x,l*T],Bt.push(Ye),Bt.push(Pe);s&&Bt.push(t[2]);let ar=Ft[2],nr=Bt[0].dims[Bt[0].dims.length-1];ar<8&&nr<8?e.compute(Ma(Bt,r,a,Ft,i,n),{inputs:Bt}):e.compute(_a(Bt,r,a,Ft,i,n),{inputs:Bt});return}let D=!0,V=e.kernelCustomData.wT??e.compute(xn(t[1],pi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=V);let A=[t[0],V];s&&A.push(t[2]);let ee=i?l*T:x,te=i?x:l*T,ie=g*m*c;e.compute(Yl(A,r,a,ee,te,ie,s,D,n),{inputs:A})},au=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let i=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),s=[1].concat(t.dilations),u=[1].concat(t.kernelShape),d=Ms({...t,pads:i,strides:a,dilations:s,kernelShape:u},n);Ta(e,n,d,c=>r?[c[0],c[2],c[3]]:[c[0],c[1],c[3]])},Us=(e,t,r)=>{let n=r.format==="NHWC"?"channelsLast":"channelsFirst",i=Ms(r,t),a=r.autoPad==="NOTSET"?r.pads:r.autoPad,s=ba(t[0].dims,t[1].dims,r.strides,r.dilations,a,!1,n);e.compute(eu(t,i,s.outShape,[s.filterDepth,s.filterHeight,s.filterWidth],[s.padInfo.front,s.padInfo.top,s.padInfo.left],n))},Ca=(e,t)=>{if(iu(e.inputs,t),e.inputs[0].dims.length===3)au(e,t);else if(e.inputs[0].dims.length===5)Us(e,e.inputs,t);else{let r=Ms(t,e.inputs);Ta(e,e.inputs,r)}}}),ou,lu,$a=U(()=>{Yt(),_(),pr(),Un(),ci(),fa(),js(),ou=(e,t=!1,r,n,i=4)=>{let a=D=>{switch(D){case 1:return"return w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];";case 4:return` + let coord1 = vec4(coordX, coordY, col + 1, rowInner); + let coord2 = vec4(coordX, coordY, col + 2, rowInner); + let coord3 = vec4(coordX, coordY, col + 3, rowInner); + let v0 = w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))]; + let v1 = w[getIndexFromCoords4D(coord1, vec4(uniforms.w_shape))]; + let v2 = w[getIndexFromCoords4D(coord2, vec4(uniforms.w_shape))]; + let v3 = w[getIndexFromCoords4D(coord3, vec4(uniforms.w_shape))]; + return ${n}(v0, v1, v2, v3); + `;default:throw new Error(`innerElementSize ${D} is not supported.`)}},s=e?` + let coord = vec4(batch, iXR, iXC, xCh); + `:` + let coord = vec4(batch, xCh, iXR, iXC); + `,u=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,d=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",c=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",g=e?"row":"col",m=e?"col":"row",l=` + let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${g} / outWidth; + let outCol = ${g} % outWidth; + + let WRow = ${m} / (uniforms.filter_dims[1] * inChannels); + let WCol = ${m} / inChannels % uniforms.filter_dims[1]; + let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]); + let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]); + if (xR < 0.0 || xR >= f32(${d}) || fract(xR) > 0.0) { + return ${n}(0.0); + } + if (xC < 0.0 || xC >= f32(${c}) || fract(xC) > 0.0) { + return ${n}(0.0); + } + let iXR = i32(xR); + let iXC = i32(xC); + let xCh = ${m} % inChannels; + ${s} + return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${i}];`,T=e?` + let col = colIn * ${i}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${l} + } + return ${n}(0.0);`:` + let col = colIn * ${i}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${l} + } + return ${n}(0.0);`,x=` + let col = colIn * ${i}; + let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels); + let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1]; + if (${e?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"} && coordX >= 0 && coordY >= 0) { + let rowInner = row % inChannels; + let coord = vec4(coordX, coordY, col, rowInner); + ${a(i)} + } + return ${n}(0.0); + `,C=Yn(r,n);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} { + ${e?T:x} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} { + ${e?x:T} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) { + let col = colIn * ${i}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueInput; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${u} + ${pa(t)} + ${C} + result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${i}] = value; + } + }`},lu=(e,t,r,n,i,a,s,u)=>{let d=t.format==="NHWC",c=d?e[0].dims[3]:e[0].dims[1],g=r[0],m=d?r[2]:r[3],l=d?r[1]:r[2],T=d?r[3]:r[1],x=d&&c%4===0&&c%3&&T%4===0,C=d?T:m*l,D=d?m*l:T,V=[8,8,1],A=n<=8?[4,1,1]:[4,4,1],ee=[Math.ceil(C/V[0]/A[0]),Math.ceil(D/V[1]/A[1]),Math.ceil(g/V[2]/A[2])];ae("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${ee}`);let te=x?4:1,ie=Math.max(V[0]*te,V[1]),ke=x?4:1,Pe=[t.kernelShape[d?1:2],t.kernelShape[d?2:3]],Ye=[Pe[0]+(t.dilations[0]<=1?0:(Pe[0]-1)*(t.dilations[0]-1)),Pe[1]+(t.dilations[1]<=1?0:(Pe[1]-1)*(t.dilations[1]-1))],Ft=[Ye[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),Ye[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Bt=[{type:6,data:n},{type:6,data:i},{type:6,data:a},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:Pe},{type:6,data:Ft}];Vn(t,Bt),Bt.push(...kt(e[0].dims,e[1].dims));let ar=["rank","rank"];s&&(Bt.push(...kt(e[2].dims)),ar.push("rank")),Bt.push(...kt(r));let nr=Ht=>{let Er=Qe("x",e[0].dataType,e[0].dims.length,ke),jr=Qe("w",e[1].dataType,e[1].dims.length,1),hr=qt("result",e[0].dataType,r.length,ke),Fr=[Er,jr],Gt="";if(s){let qe=Qe("bias",e[2].dataType,e[2].dims.length,ke);Fr.push(qe),Gt+=` + fn getBiasByOutputCoords(coords : vec4) -> ${qe.type.value} { + return bias[coords.${d?"w":"y"}${x?"/ 4":""}]; + }`}let Qt=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:Pe.length},{name:"pads",type:"i32",length:Ft.length}];Zn(t,Qt);let xr=fr(e[0].dataType,1);if(xr!=="f16"&&xr!=="f32")throw new Error(`elemType ${xr} is not supported.`);return` + ${ha("uniforms.result_strides")} + ${Ht.registerUniforms(Qt).declareVariables(...Fr,hr)}; + ${Gt} + ${ou(d,s,t,Er.type.value,te)} + ${x?Rs(A,V,xr,void 0,!d,ie):Ns(A,V,xr,void 0,!d,ie,!1,void 0,u)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${A};${V};${x}`,inputDependencies:ar},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:ee[0],y:ee[1],z:ee[2]},programUniforms:Bt}),getShaderSource:nr}}}),uu,Ea,Vd=U(()=>{Yt(),_(),Kt(),pr(),uu=(e,t,r,n,i,a=!1,s,u,d=!1)=>{let c=d?1:2,g=d?2:3,m=d?3:1,l=a?2:1,T=` + fn setOutputAtIndex(flatIndex : u32, value : ${a?`vec4<${s}>`:s}) { + result[flatIndex] = ${a?`vec4<${s}>`:s}(value); + }`;n&&(T+=` + fn getBiasByOutputCoords(coords : vec4) -> ${a?`vec4<${s}>`:s} { + return bias[coords.${d?"w":"y"}${a?"/ 4":""}]; + }`);let x=a?4:1,C=Qe("W",t[1].dataType,t[1].dims.length,x),D=Qe("Dy",t[0].dataType,t[0].dims.length,x),V=[D,C];n&&V.push(Qe("bias",t[2].dataType,[r[m]].length,x));let A=qt("result",t[0].dataType,r.length,x),ee=`{ + let batch: u32 = ${i?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1]; + let r = ${i?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1]; + let c = ${i?"global_id.y":"workgroup_id.y"} * ${l}; + let d1: u32 = ${i?"global_id.x":"workgroup_id.x"} * 4; + + let dyCorner = vec2(i32(r), i32(c)) - vec2(uniforms.pads); + + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd: array, ${l}>; + for (var i = 0; i < ${l}; i++) { + dotProd[i] = vec4<${s}>(0.0); + } + for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) { + var dyR = (${s}(dyCorner.x) + ${s}(wR)) / ${s}(uniforms.strides.x); + let wRPerm = uniforms.filter_dims[0] - 1 - wR; + if (dyR < 0.0 || dyR >= ${s}(uniforms.Dy_shape[1]) || + fract(dyR) > 0.0 || wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) { + let dyC = (${s}(dyCorner.y) + ${s}(wC)) / ${s}(uniforms.strides.y); + let dyC2 = (${s}(dyCorner.y) + 1.0 + ${s}(wC)) / ${s}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims[1] - 1 - wC; + if (wCPerm < 0) { + continue; + } + var bDyCVal = true; + var bDyCVal2 = true; + if (dyC < 0.0 || dyC >= ${s}(uniforms.Dy_shape[2]) || + fract(dyC) > 0.0) { + bDyCVal = false; + } + if (dyC2 < 0.0 || dyC2 >= ${s}(uniforms.Dy_shape[2]) || + fract(dyC2) > 0.0) { + bDyCVal2 = false; + } + + let idyC: u32 = u32(dyC); + let idyC2: u32 = u32(dyC2); + if (bDyCVal && bDyCVal2) { + let d2Length = uniforms.Dy_shape[3]; + for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${D.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${s}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + + xValue = ${D.get("batch","idyR","idyC2","d2")}; + + dotProd[1] = dotProd[1] + vec4<${s}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + } + } else if (bDyCVal) { + let d2Length = uniforms.Dy_shape[${m}]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${D.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${s}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + } + } else if (bDyCVal2) { + let d2Length = uniforms.Dy_shape[3]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${C.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${D.get("batch","idyR","idyC2","d2")}; + let tmpval = vec4<${s}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[1] = dotProd[1] + tmpval; + } + } + } + } + + for (var i: u32 = 0; i < ${l}; i = i + 1) { + let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${s}>(0.0)`}; + ${A.set("batch","r","c + i","d1","value")}; + } + }`,te=` + let outputIndices = ${A.offsetToIndices("global_idx")}; + let batch = ${A.indicesGet("outputIndices",0)}; + let d1 = ${A.indicesGet("outputIndices",m)}; + let r = ${A.indicesGet("outputIndices",c)}; + let c = ${A.indicesGet("outputIndices",g)}; + let dyCorner = vec2(i32(r), i32(c)) - uniforms.pads; + let dyRCorner = dyCorner.x; + let dyCCorner = dyCorner.y; + let groupId = d1 / uniforms.output_channels_per_group; + let wOutChannel = d1 - groupId * uniforms.output_channels_per_group; + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd = ${s}(0.0); + for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${s}(dyRCorner) + ${s}(wR)) / ${s}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${s}(uniforms.Dy_shape[${c}]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { + if (wC % uniforms.dilations.y != 0) { + continue; + } + let dyC = (${s}(dyCCorner) + ${s}(wC)) / ${s}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${s}(uniforms.Dy_shape[${g}]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC: u32 = u32(dyC); + var inputChannel = groupId * uniforms.input_channels_per_group; + for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) { + let xValue = ${d?D.get("batch","idyR","idyC","inputChannel"):D.get("batch","inputChannel","idyR","idyC")}; + let wValue = ${C.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")}; + dotProd = dotProd + xValue * wValue; + inputChannel = inputChannel + 1; + } + } + } + let value = dotProd + ${n?"bias[d1]":`${s}(0.0)`}; + ${A.setByOffset("global_idx","value")}; + `;return` + ${e.registerUniforms(u).declareVariables(...V,A)} + ${T} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${a?ee:te}}`},Ea=(e,t,r)=>{let n=e.length>2,i=t.outputShape,a=Ee.size(i),s=[Math.ceil(a/64),1,1];ae("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${s}`);let u=t.format==="NHWC",d=["rank","rank"],c=[t.strides[0],t.strides[1]],g=[t.kernelShape[u?1:2],t.kernelShape[u?2:3]],m=[t.dilations[0],t.dilations[1]],l=[g[0]+(t.dilations[0]<=1?0:(t.kernelShape[u?1:2]-1)*(t.dilations[0]-1)),g[1]+(t.dilations[1]<=1?0:(t.kernelShape[u?2:3]-1)*(t.dilations[1]-1))],T=[l[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),l[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],x=!1,C=t.group,D=e[1].dims,V=D[0]/C,A=D[1],ee=[{type:12,data:a},{type:12,data:c},{type:12,data:g},{type:12,data:m},{type:12,data:l},{type:6,data:T},{type:12,data:V},{type:12,data:A},...kt(e[0].dims,e[1].dims)];n&&(ee.push(...kt(e[2].dims)),d.push("rank")),ee.push(...kt(i));let te=s[1]===1&&s[2]===1,ie=ke=>{let Pe=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:c.length},{name:"filter_dims",type:"u32",length:g.length},{name:"dilations",type:"u32",length:g.length},{name:"effective_filter_dims",type:"u32",length:l.length},{name:"pads",type:"i32",length:T.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],Ye=fr(e[0].dataType);return`${uu(ke,e,i,n,te,x,Ye,Pe,u)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:d},getRunData:()=>({dispatchGroup:{x:s[0],y:s[1],z:s[2]},outputs:[{dims:r?r(i):i,dataType:e[0].dataType}],programUniforms:ee}),getShaderSource:ie}}}),Ud,du,cu,ka,vs,pu,hu,fu,mu,_u,Sa=U(()=>{$a(),Vd(),Un(),jn(),Ud=(e,t,r,n,i,a)=>(e-1)*t+r+(n-1)*i+1-a,du=(e,t,r,n,i)=>{let a=Math.floor(e/2);t==="SAME_UPPER"?(r[n]=a,r[i]=e-a):t==="SAME_LOWER"&&(r[n]=e-a,r[i]=a)},cu=(e,t,r,n,i,a,s,u,d,c)=>{let g=e.length-2,m=c.length===0;d.length{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((m,l)=>m*l,1)===0){r.length=0;for(let m=2;mm+l,0)===0){let m=t[0].dims.length-2;d=new Array(m).fill(1)}let c=e.strides.slice();if(c.reduce((m,l)=>m+l,0)===0){let m=t[0].dims.length-2;c=new Array(m).fill(1)}cu(u,r,d,e.autoPad,e.group,i,c,n,s,a);let g=Object.assign({},e);return Object.assign(g,{kernelShape:r,pads:i,outputPadding:s,outputShape:a,dilations:d,strides:c}),g},vs=e=>{let t=ca(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,a=e.group,s=e.kernelShape,u=e.pads,d=e.strides,c=e.wIsConst(),g=e.outputPadding,m=e.outputShape;return{autoPad:n,format:r,dilations:i,group:a,kernelShape:s,outputPadding:g,outputShape:m,pads:u,strides:d,wIsConst:c,...t,cacheKey:`${e.format};${t.activation};`}},pu=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[0];if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let i=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==i))throw new Error("invalid bias");let a=e[0].dims.length-2;if(t.dilations.reduce((s,u)=>s+u,0)>0&&t.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(t.strides.reduce((s,u)=>s+u,0)>0&&t.strides.length!==a)throw new Error(`strides should be ${a}D`);if(t.pads.reduce((s,u)=>s+u,0)>0&&t.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(t.outputPadding.length!==a&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${a}D`);if(t.kernelShape.reduce((s,u)=>s+u,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},hu=[2,3,1,0],fu=(e,t,r)=>{let n=ka(r,t),i=r.format==="NHWC",a=n.outputShape,s=a[i?3:1],u=t[0].dims[i?3:1];if(n.group!==1||s===1&&u===1){e.compute(Ea(t,n));return}let d=a[i?1:2],c=a[i?2:3],g=t[1].dims[2],m=t[1].dims[3],l=i?d*c:s,T=i?s:d*c,x=g*m*u,C=!0,D=e.kernelCustomData.wT??e.compute(xn(t[1],hu),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=D);let V=[t[0],D],A=t.length===3;A&&(!i&&t[2].dims.length===1?V.push(t[2].reshape([t[2].dims[0],1,1])):V.push(t[2])),e.compute(lu(V,n,a,l,T,x,A,C),{inputs:V})},mu=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let a=t.dilations;(a.length===0||a[0]===0)&&(a=[1]);let s=t.strides;(s.length===0||s[0]===0)&&(s=[1]);let u=t.pads;u.length===0&&(u=[0,0]),u=[0,u[0],0,u[1]],s=[1].concat(s),a=[1].concat(a),i=[1].concat(i);let d=ka({...t,pads:u,strides:s,dilations:a,kernelShape:i},n);e.compute(Ea(n,d,c=>r?[c[0],c[2],c[3]]:[c[0],c[1],c[3]]))},_u=(e,t)=>{pu(e.inputs,t),e.inputs[0].dims.length===3?mu(e,t):fu(e,e.inputs,t)}}),Wd,gu,wu,Gd=U(()=>{Yt(),Kt(),Pr(),pr(),Wd=(e,t,r,n)=>{let i=Ee.size(t),a=t.length,s=Qe("input",e,a),u=qt("output",e,a),d=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),c=Ee.normalizeAxis(d,a),g=m=>{let l=` i32(${s.indicesGet("inputIndices","uniforms.axis")}) `,T=Wt("uniforms.input_shape","uniforms.axis",a),x=n.reverse?l+(n.exclusive?" + 1":""):"0",C=n.reverse?T:l+(n.exclusive?"":" + 1");return` + ${m.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(s,u)} + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${u.offsetToIndices("global_idx")}; + var sum = ${u.type.value}(0); + let first : i32 = ${x}; + let last : i32 = ${C}; + for (var i : i32 = first; i < last; i++) { + ${s.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${s.getByIndices("inputIndices")}; + } + ${u.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},{type:12,data:c},...kt(t,t)]}),getShaderSource:g}},gu=(e,t)=>{let r=e.inputs[0].dims,n=e.inputs[0].dataType,i=e.inputs[1];e.compute(Wd(n,r,i,t),{inputs:[0]})},wu=e=>{let t=e.exclusive===1,r=e.reverse===1;return or({exclusive:t,reverse:r})}}),yu,bu,Pa,Mu,vu,xu=U(()=>{Yt(),Kt(),Pr(),pr(),yu=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.")},bu=(e,t,r,n)=>{let i=[];i.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} { + var a: ${r.type.indices};`);for(let a=0;a{let r,n,i,a,s,u,d=t.format==="NHWC",c=t.blocksize,g=t.mode==="DCR";d?([r,n,i,a]=e.dims,s=g?[r,n,i,c,c,a/c**2]:[r,n,i,a/c**2,c,c],u=g?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([r,n,i,a]=[e.dims[0],e.dims[2],e.dims[3],e.dims[1]],s=g?[r,c,c,a/c**2,n,i]:[r,a/c**2,c,c,n,i],u=g?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let m=e.reshape(s),l=m.dims.length,T=e.dataType,x=Qe("a",T,l),C=qt("output",T,l),D=V=>` + ${V.registerUniform("output_size","u32").declareVariables(x,C)} + + ${bu(u,l,x,C)} + + ${V.mainStart()} + ${V.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${C.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${C.setByOffset("global_idx",x.getByIndices("aIndices"))} + }`;return{name:"DepthToSpace",shaderCache:{hint:`${e.dims};${t.blocksize};${t.mode}`,inputDependencies:["rank"]},getRunData:V=>{let A=d?[r,n*c,i*c,a/c**2]:[r,a/c**2,n*c,i*c],ee=Ee.size(A),te=m.dims,ie=Ee.sortBasedOnPerm(te,u);return{outputs:[{dims:A,dataType:V[0].dataType}],dispatchGroup:{x:Math.ceil(ee/64)},programUniforms:[{type:12,data:ee},...kt(te,ie)]}},getShaderSource:D}},Mu=(e,t)=>{yu(e.inputs),e.compute(Pa(e.inputs[0],t))},vu=e=>or({blocksize:e.blocksize,mode:e.mode,format:e.format})}),hi,Ws,fi,Tu,Cu,mi,$u,Aa,Eu,ku,Ia,Fa=U(()=>{Yt(),Kt(),Pr(),pr(),hi="[a-zA-Z]|\\.\\.\\.",Ws="("+hi+")+",fi="^"+Ws+"$",Tu="("+Ws+",)*"+Ws,Cu="^"+Tu+"$",mi=class{constructor(e=-1){this.symbolToIndices=new Map,this.inputIndex=e}addSymbol(e,t){let r=this.symbolToIndices.get(e);r===void 0?r=[t]:r.push(t),this.symbolToIndices.set(e,r)}},$u=class{constructor(e,t){var i;this.equation=t,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[r,n]=t.includes("->")?t.split("->",2):[t,""];if(!r.match(RegExp(Cu)))throw new Error("Invalid LHS term");if(r.split(",").forEach((a,s)=>{let u=e[s].dims.slice();if(!a.match(RegExp(fi)))throw new Error("Invalid LHS term");let d=this.processTerm(a,!0,u,s);this.lhs.push(d)}),n==="")n+=[...this.symbolToInfo.entries()].filter(([a,s])=>s.count===1||a==="...").map(([a])=>a).join("");else if(!n.match(RegExp(Ws)))throw new Error("Invalid RHS");(i=n.match(RegExp(hi,"g")))==null||i.forEach(a=>{if(a==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let s=this.symbolToInfo.get(a);if(s===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(s.dimValue)}}),this.rhs=this.processTerm(n,!1,this.outputDims)}addSymbol(e,t,r){let n=this.symbolToInfo.get(e);if(n!==void 0){if(n.dimValue!==t&&n.count!==1)throw new Error("Dimension mismatch");n.count++,n.inputIndices.push(r)}else n={count:1,dimValue:t,inputIndices:[r]};this.symbolToInfo.set(e,n)}processTerm(e,t,r,n=-1){let i=r.length,a=!1,s=[],u=0;if(!e.match(RegExp(fi))&&!t&&e!=="")throw new Error("Invalid LHS term");let d=e.match(RegExp(hi,"g")),c=new mi(n);return d==null||d.forEach((g,m)=>{if(g==="..."){if(a)throw new Error("Only one ellipsis is allowed per input term");a=!0;let l=i-d.length+1;if(l<0)throw new Error("Ellipsis out of bounds");if(s=r.slice(u,u+l),this.hasEllipsis){if(this.ellipsisDims.length!==s.length||this.ellipsisDims.toString()!==s.toString())throw new Error("Ellipsis dimensions mismatch")}else if(t)this.hasEllipsis=!0,this.ellipsisDims=s;else throw new Error("Ellipsis must be specified in the LHS");for(let T=0;Te+"_max",Eu=(e,t,r,n)=>{let i=e.map(c=>c.length).map((c,g)=>Qe(`input${g}`,t,c)),a=Ee.size(n),s=qt("output",t,n.length),u=[...r.symbolToInfo.keys()].filter(c=>!r.rhs.symbolToIndices.has(c)),d=c=>{let g=[],m="var prod = 1.0;",l="var sum = 0.0;",T="sum += prod;",x=[],C=[],D=[],V=[],A=r.symbolToInfo.size===r.rhs.symbolToIndices.size;r.symbolToInfo.forEach((te,ie)=>{var ke;if(r.rhs.symbolToIndices.has(ie)){let Pe=(ke=r.rhs.symbolToIndices.get(ie))==null?void 0:ke[0];Pe!==void 0&&r.lhs.forEach((Ye,Ft)=>{if(te.inputIndices.includes(Ft)){let Bt=Ye.symbolToIndices.get(ie);if(Bt===void 0)throw new Error("Invalid symbol error");Bt.forEach(ar=>{g.push(`${i[Ft].indicesSet(`input${Ft}Indices`,ar,s.indicesGet("outputIndices",Pe))}`)})}})}else r.lhs.forEach((Pe,Ye)=>{if(te.inputIndices.includes(Ye)){let Ft=Pe.symbolToIndices.get(ie);if(Ft===void 0)throw new Error("Invalid symbol error");Ft.forEach(Bt=>{x.push(`${i[Ye].indicesSet(`input${Ye}Indices`,Bt,`${ie}`)}`)}),V.push(`prod *= ${i[Ye].getByIndices(`input${Ye}Indices`)};`)}}),C.push(`for(var ${ie}: u32 = 0; ${ie} < uniforms.${Aa(ie)}; ${ie}++) {`),D.push("}")});let ee=A?[...g,`let sum = ${i.map((te,ie)=>te.getByIndices(`input${ie}Indices`)).join(" * ")};`]:[...g,l,...C,...x,m,...V,T,...D];return` + ${c.registerUniforms(u.map(te=>({name:`${Aa(te)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...i,s)} + + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${s.offsetToIndices("global_idx")}; + ${i.map((te,ie)=>`var input${ie}Indices: ${i[ie].type.indices};`).join(` +`)} + ${ee.join(` +`)}; + ${s.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let c=u.filter(m=>r.symbolToInfo.has(m)).map(m=>{var l;return{type:12,data:((l=r.symbolToInfo.get(m))==null?void 0:l.dimValue)||0}});c.push({type:12,data:a});let g=e.map((m,l)=>[...kt(m)]).reduce((m,l)=>m.concat(l),c);return g.push(...kt(n)),{outputs:[{dims:n,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:g}},getShaderSource:d}},ku=(e,t)=>{let r=new $u(e.inputs,t.equation),n=r.outputDims,i=e.inputs.map((a,s)=>a.dims);e.compute(Eu(i,e.inputs[0].dataType,r,n))},Ia=e=>{let t=e.equation.replace(/\s+/g,"");return or({equation:t})}}),qd,Oa,Su,za,Pu,Hd=U(()=>{Yt(),Kt(),pr(),qd=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let t=e[0].dims,r=Array.from(e[1].getBigInt64Array(),Number),n=r.length{let r=e.length-t.length,n=[];for(let i=0;ie.length>t.length?Oa(e,t):Oa(t,e),za=e=>{let t=e[0].dims,r=Array.from(e[1].getBigInt64Array(),Number),n=Su(t,r),i=e[0].dataType,a=i===9?4:1,s=Math.ceil(Ee.size(n)/a),u=c=>{let g=Qe("input",i,t.length,a),m=qt("output",i,n.length,a),l;if(i===9){let T=(x,C,D="")=>` + let outputIndices${C} = ${m.offsetToIndices(`outputOffset + ${C}u`)}; + let offset${C} = ${g.broadcastedIndicesToOffset(`outputIndices${C}`,m)}; + let index${C} = offset${C} / 4u; + let component${C} = offset${C} % 4u; + ${x}[${C}] = ${D}(${g.getByOffset(`index${C}`)}[component${C}]); + `;l=` + let outputOffset = global_idx * ${a}; + var data = vec4(0); + ${T("data",0,"u32")} + ${T("data",1,"u32")} + ${T("data",2,"u32")} + ${T("data",3,"u32")} + ${m.setByOffset("global_idx","data")} + }`}else l=` + let outputIndices = ${m.offsetToIndices("global_idx")}; + let inputOffset = ${g.broadcastedIndicesToOffset("outputIndices",m)}; + ${m.setByOffset("global_idx",g.getByOffset("inputOffset"))} + }`;return` + ${c.registerUniform("vec_size","u32").declareVariables(g,m)} + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${l}`},d=[{type:12,data:s},...kt(t,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:u,getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:d})}},Pu=e=>{qd(e.inputs),e.compute(za(e.inputs),{inputs:[0]})}}),Da,Au,Kd=U(()=>{Yt(),Kt(),pr(),aa(),Da=e=>{let t=e[0].dataType,r=Ee.size(e[0].dims),n=Ee.size(e[1].dims),i=n%4===0,a=s=>{let u=Qe("x",t,[1],4),d=Qe("bias",t,[1],4),c=qt("y",t,[1],4),g=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],m=T=>` + let bias${T}_offset: u32 = (global_idx * 4 + ${T}) % uniforms.bias_size; + let bias${T} = ${d.getByOffset(`bias${T}_offset / 4`)}[bias${T}_offset % 4];`,l=i?` + let bias = ${d.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${m(0)}${m(1)}${m(2)}${m(3)} + let bias = ${u.type.value}(bias0, bias1, bias2, bias3);`;return`${s.registerUniforms(g).declareVariables(u,d,c)} + + ${sa(Or(t))} + + ${s.mainStart(pn)} + ${s.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${u.getByOffset("global_idx")}; + ${l} + let x_in = x + bias; + ${c.setByOffset("global_idx",ui("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${i}`,inputDependencies:["type","type"]},getShaderSource:a,getRunData:s=>({outputs:[{dims:s[0].dims,dataType:s[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/pn/4)}})}},Au=e=>{e.inputs.length<2||Ee.size(e.inputs[1].dims)===0?Tl(e):e.compute(Da(e.inputs))}}),La,Xd,Qd,Ba,Iu=U(()=>{Yt(),Kt(),Pr(),pr(),La=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},Xd=(e,t)=>{let r=e[0].dims,n=e[1].dims,i=r.length,a=Ee.normalizeAxis(t.axis,i),s=r.slice(0);s.splice(a,1,...n);let u=r[a],d=e[0].dataType===9?4:1,c=Math.ceil(Ee.size(s)/d),g=[{type:12,data:c},{type:6,data:u},{type:12,data:a},...kt(e[0].dims,e[1].dims,s)],m=l=>{let T=Qe("data",e[0].dataType,e[0].dims.length,d),x=Qe("inputIndices",e[1].dataType,e[1].dims.length),C=qt("output",e[0].dataType,s.length,d),D=A=>{let ee=n.length,te=`var indicesIndices${A} = ${x.type.indices}(0);`;for(let ie=0;ie1?`indicesIndices${A}[${ie}]`:`indicesIndices${A}`} = ${s.length>1?`outputIndices${A}[uniforms.axis + ${ie}]`:`outputIndices${A}`};`;te+=` + var idx${A} = ${x.getByIndices(`indicesIndices${A}`)}; + if (idx${A} < 0) { + idx${A} = idx${A} + uniforms.axisDimLimit; + } + var dataIndices${A} : ${T.type.indices}; + `;for(let ie=0,ke=0;ie1?`dataIndices${A}[${ie}]`:`dataIndices${A}`} = u32(idx${A});`,ke+=ee):(te+=`${i>1?`dataIndices${A}[${ie}]`:`dataIndices${A}`} = ${s.length>1?`outputIndices${A}[${ke}]`:`outputIndices${A}`};`,ke++);return te},V;if(e[0].dataType===9){let A=(ee,te,ie="")=>` + let outputIndices${te} = ${C.offsetToIndices(`outputOffset + ${te}u`)}; + ${D(te)}; + let offset${te} = ${T.indicesToOffset(`dataIndices${te}`)}; + let index${te} = offset${te} / 4u; + let component${te} = offset${te} % 4u; + ${ee}[${te}] = ${ie}(${T.getByOffset(`index${te}`)}[component${te}]); + `;V=` + let outputOffset = global_idx * ${d}; + var value = vec4(0); + ${A("value",0,"u32")} + ${A("value",1,"u32")} + ${A("value",2,"u32")} + ${A("value",3,"u32")} + ${C.setByOffset("global_idx","value")} + `}else V=` + let outputIndices = ${C.offsetToIndices("global_idx")}; + ${D("")}; + let value = ${T.getByIndices("dataIndices")}; + ${C.setByOffset("global_idx","value")}; + `;return` + ${l.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(T,x,C)} + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${V} + }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:g}),getShaderSource:m}},Qd=e=>or({axis:e.axis}),Ba=(e,t)=>{let r=e.inputs;La(r),e.compute(Xd(e.inputs,t))}}),Fu,Ou,Ra,zu,Yd=U(()=>{Yt(),Kt(),Pr(),pr(),Fu=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let r=Ee.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,i=e[0],a=e[2],s=e.length===4?e[3]:void 0;if(a.dims.length!==i.dims.length||!i.dims.map((u,d)=>d===r?Math.ceil(u/n)===a.dims[d]:u===a.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(s){if(s.dataType!==i.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(s.dims.length!==a.dims.length||!s.dims.map((u,d)=>u===a.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},Ou=(e,t)=>{let r=e[0].dims,n=e[1].dims,i=r.length,a=Ee.normalizeAxis(t.gatherAxis,i),s=Ee.normalizeAxis(t.quantizeAxis,i),u=r.slice(0);u.splice(a,1,...n);let d=Ee.size(u),c=e[2].dataType,g=e[0].dataType===22,m=[{type:12,data:d},{type:12,data:s},{type:12,data:a},{type:12,data:t.blockSize},...kt(...e.map((T,x)=>T.dims),u)],l=T=>{let x=Qe("data",e[0].dataType,e[0].dims.length),C=Qe("inputIndices",e[1].dataType,e[1].dims.length),D=Qe("scales",e[2].dataType,e[2].dims.length),V=e.length>3?Qe("zeroPoint",e[3].dataType,e[3].dims.length):void 0,A=qt("output",c,u.length),ee=[x,C,D];V&&ee.push(V);let te=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${T.registerUniforms(te).declareVariables(...ee,A)} + ${T.mainStart()} + let output_indices = ${A.offsetToIndices("global_idx")}; + var indices_indices = ${C.type.indices}(0); + ${n.length>1?` + for (var i: u32 = 0; i < ${n.length}; i++) { + let index = ${A.indicesGet("output_indices","uniforms.gather_axis + i")}; + ${C.indicesSet("indices_indices","i","index")}; + }`:`indices_indices = ${A.indicesGet("output_indices","uniforms.gather_axis")};`}; + var data_indices = ${x.type.indices}(0); + for (var i: u32 = 0; i < uniforms.gather_axis; i++) { + let index = ${A.indicesGet("output_indices","i")}; + ${x.indicesSet("data_indices","i","index")}; + } + var index_from_indices = ${C.getByIndices("indices_indices")}; + if (index_from_indices < 0) { + index_from_indices += ${r[a]}; + } + ${x.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")}; + for (var i = uniforms.gather_axis + 1; i < ${u.length}; i++) { + let index = ${A.indicesGet("output_indices",`i + ${n.length} - 1`)}; + ${x.indicesSet("data_indices","i","index")}; + } + let data_offset = ${x.indicesToOffset("data_indices")}; + let data_index = data_offset % 8; + // Convert 4-bit packed data to 8-bit packed data. + let packed_4bit_quantized_data = ${x.getByOffset("data_offset / 8")}; + let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f; + let quantized_data_vec = ${g?"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 = ${D.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size; + ${D.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")}; + var scale = ${D.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 = ${g?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points)); + let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"}; + let dequantized_data = ${Or(c)}(quantized_data - zero_point) * scale; + ${A.setByOffset("global_idx","dequantized_data")}; + }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((T,x)=>x!==1).map(T=>T.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(T,x)=>"rank")},getRunData:()=>({outputs:[{dims:u,dataType:c}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:m}),getShaderSource:l}},Ra=(e,t)=>{let r=e.inputs;Fu(r,t),e.compute(Ou(e.inputs,t))},zu=e=>or({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),Na,Du,Lu,ja,Zd=U(()=>{Yt(),Kt(),Pr(),pr(),Na=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.`)},Du=(e,t)=>{let r=e[0].dims,n=e[0].dataType,i=r.length,a=e[1].dims,s=e[1].dataType,u=Ee.normalizeAxis(t.axis,i),d=r[u],c=a.slice(0),g=Ee.size(c),m=Qe("input",n,i),l=Qe("indicesInput",s,a.length),T=qt("output",n,c.length),x=[{type:12,data:g},{type:6,data:d},{type:12,data:u}];return x.push(...kt(r,a,c)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:x}),getShaderSource:C=>` + ${C.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(m,l,T)} + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${T.offsetToIndices("global_idx")}; + + var idx = ${l.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${m.type.indices}(outputIndices); + ${m.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${m.getByIndices("inputIndices")}; + + ${T.setByOffset("global_idx","value")}; + }`}},Lu=e=>or({axis:e.axis}),ja=(e,t)=>{let r=e.inputs;Na(r),e.compute(Du(e.inputs,t))}}),Bu,Ru,Nu,zr,Ic=U(()=>{Yt(),Kt(),pr(),Bu=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")},Ru=(e,t)=>{let r=e[0].dims.slice(),n=e[1].dims.slice(),[i,a,s]=ln.getShapeOfGemmResult(r,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),u=[i,a];if(!u)throw new Error("Can't use gemm on the given tensors");let d=Ee.size(u),c=[{type:12,data:d},{type:12,data:i},{type:12,data:a},{type:12,data:s},{type:1,data:t.alpha},{type:1,data:t.beta}],g=["type","type"];e.length===3&&(c.push(...kt(e[2].dims)),g.push("rank")),c.push(...kt(u));let m=l=>{let T="";t.transA&&t.transB?T="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?T="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?T="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(T="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let x=t.alpha===1?"":"value *= uniforms.alpha;",C=Qe("a",e[0].dataType,e[0].dims),D=Qe("b",e[1].dataType,e[1].dims),V=C.type.value,A=null,ee=[C,D];e.length===3&&(A=Qe("c",e[2].dataType,e[2].dims.length),ee.push(A));let te=qt("output",e[0].dataType,u.length);ee.push(te);let ie=[{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` + ${l.registerUniforms(ie).declareVariables(...ee)} + + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${V}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${T} + } + + ${x} + ${A!=null?`let cOffset = ${A.broadcastedIndicesToOffset("vec2(m, n)",te)}; value += ${V}(uniforms.beta) * ${A.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:m}},Nu=e=>{let t=e.transA,r=e.transB,n=e.alpha,i=e.beta;return{transA:t,transB:r,alpha:n,beta:i,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},zr=(e,t)=>{Bu(e.inputs),e.compute(Ru(e.inputs,t))}}),Mn,Jd,Va,Ua,ju,Gs,Vu,Wa=U(()=>{Yt(),Kt(),Pr(),oe(),ii(),pr(),jn(),Mn=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,Jd=(e,t)=>{let r=e[0],n=Mn(e,1),i=Mn(e,2),a=Mn(e,3),s=Mn(e,4),u=Mn(e,5),d=Mn(e,6),c=Mn(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let g=r.dims[0],m=r.dims[1],l=r.dims.length===3?r.dims[2]:t.numHeads*r.dims[4],T=m,x=0,C=0,D=Math.floor(l/t.numHeads);if(d&&c&&Ee.size(d.dims)&&Ee.size(c.dims)){if(d.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(d.dims[0]!==g||d.dims[1]!==t.numHeads||d.dims[3]!==D)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(c.dims[0]!==g||c.dims[1]!==t.numHeads||c.dims[3]!==D)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(d.dims[2]!==c.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(c.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');x=d.dims[2],C=d.dims[2]}else if(d&&Ee.size(d.dims)||c&&Ee.size(c.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let V;if(n&&Ee.size(n.dims)>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');V=2,T=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==D)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,T=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==D)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');V=0,T=n.dims[2]}}else{if(r.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(r.dims[2]!==t.numHeads||r.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');V=3}if(a&&Ee.size(a.dims)>0){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(n&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let A=x+T,ee=0;if(s&&Ee.size(s.dims)>0){ee=8;let Pe=s.dims;throw Pe.length===1?Pe[0]===g?ee=1:Pe[0]===3*g+2&&(ee=3):Pe.length===2&&Pe[0]===g&&Pe[1]===A&&(ee=5),ee===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let te=!1,ie=l;if(i&&Ee.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(r.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(i.dims.length===3){if(T!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');ie=i.dims[2]}else{if(T!==i.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');ie=i.dims[1]*i.dims[3],te=!0}}let ke=!1;if(s&&Ee.size(s.dims)>0)throw new Error("Key padding mask is not supported");if(u&&Ee.size(u.dims)>0){if(u.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(u.dims[0]!==g||u.dims[1]!==t.numHeads||u.dims[2]!==m||u.dims[3]!==A)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:g,sequenceLength:m,pastSequenceLength:x,kvSequenceLength:T,totalSequenceLength:A,maxSequenceLength:C,inputHiddenSize:0,hiddenSize:l,vHiddenSize:ie,headSize:D,vHeadSize:Math.floor(ie/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:ee,scale:t.scale,broadcastResPosBias:ke,passPastInKv:te,qkvFormat:V}},Va=e=>or({...e}),Ua=or({perm:[0,2,1,3]}),ju=(e,t,r,n,i,a,s)=>{let u=[n,i,a],d=Ee.size(u),c=[{type:12,data:d},{type:12,data:s},{type:12,data:a}],g=m=>{let l=qt("qkv_with_bias",t.dataType,u),T=Qe("qkv",t.dataType,u),x=Qe("bias",r.dataType,u),C=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${m.registerUniforms(C).declareVariables(T,x,l)} + ${m.mainStart()} + ${m.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:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:g},{inputs:[t,r],outputs:[-1]})[0]},Gs=(e,t,r,n,i,a,s,u)=>{let d=a;if(s&&Ee.size(s.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return d=ju(e,a,s,t,n,r*i,u),d=d.reshape([t,n,r,i]),r===1||n===1?d:e.compute(xn(d,Ua.perm),{inputs:[d],outputs:[-1]})[0]}else return a.dims.length===3&&(d=a.reshape([t,n,r,i])),r===1||n===1?d:e.compute(xn(d,Ua.perm),{inputs:[d],outputs:[-1]})[0]},Vu=(e,t)=>{let r=Jd(e.inputs,t),n=e.inputs[0],i=Mn(e.inputs,1),a=Mn(e.inputs,2),s=Mn(e.inputs,3),u=Mn(e.inputs,4),d=Mn(e.inputs,5),c=Mn(e.inputs,6),g=Mn(e.inputs,7);if(n.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 m=i&&a&&i.dims.length===4&&a.dims.length===4,l=Gs(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,s,0);if(m)return bs(e,l,i,a,u,void 0,c,g,d,r);if(!i||!a)throw new Error("key and value must be provided");let T=Gs(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,i,s,r.hiddenSize),x=Gs(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,a,s,2*r.hiddenSize);bs(e,l,T,x,u,void 0,c,g,d,r)}}),Ga,Uu,Wu,qa,Gu,ec,qu,Hu=U(()=>{Yt(),Kt(),Pr(),pr(),Ga=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Uu=(e,t)=>{let r=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(i=>r.push(Number(i))),n=r.length),or({numOutputs:n,axis:t.axis,splitSizes:r})},Wu=e=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${e}u; i += 1u ) { + if (index < ${Wt("uniforms.size_in_split_axis","i",e)}) { + return i; + } + } + return ${e}u; +}`,qa=e=>{let t=e.length,r=[];for(let n=0;n{let r=e[0].dims,n=Ee.size(r),i=e[0].dataType,a=Ee.normalizeAxis(t.axis,r.length),s=new Array(t.numOutputs),u=Qe("input",i,r.length),d=new Array(t.numOutputs),c=[],g=[],m=0,l=[{type:12,data:n}];for(let x=0;x` + ${x.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",d.length).declareVariables(u,...s)} + ${Wu(d.length)} + ${qa(s)} + + ${x.mainStart()} + ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${u.offsetToIndices("global_idx")}; + var index = ${u.indicesGet("indices",a)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${Wt("uniforms.size_in_split_axis","output_number - 1u",d.length)}; + ${u.indicesSet("indices",a,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:T,getRunData:()=>({outputs:c,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:l})}},ec=(e,t)=>{Ga(e.inputs);let r=e.inputs.length===1?t:Uu(e.inputs,t);e.compute(Gu(e.inputs,r),{inputs:[0]})},qu=e=>{let t=e.axis,r=e.splitSizes,n=e.numOutputs<0?r.length:e.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return or({axis:t,numOutputs:n,splitSizes:r})}}),Ku,Xu,Ha,Qu,tc=U(()=>{Pr(),ii(),Wa(),Hu(),jn(),Ku=(e,t)=>{if(t.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let r=e[0],n=e[1],i=e[2],a=e[3],s=e[4];if(t.localWindowSize!==-1)throw new Error("Local attention is not supported");if(t.softcap!==0)throw new Error("Softcap is not supported");if(t.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(t.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let u=!1,d=r.dims[0],c=r.dims[1],g=r.dims.length===3?u?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],m=c,l=0,T=!n||n.dims.length===0,x=Math.floor(T?g/(t.numHeads+2*t.kvNumHeads):g/t.numHeads);T&&(g=x*t.numHeads);let C=a&&a.dims.length!==0,D=s&&s.dims.length!==0;if(C&&a.dims.length===4&&a.dims[0]===d&&a.dims[1]!==t.kvNumHeads&&a.dims[2]===t.kvNumHeads&&a.dims[3]===x)throw new Error("BSNH pastKey/pastValue is not supported");if(C&&D){if(a.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(s.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');l=a.dims[2]}else if(C||D)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let V=1;if(n&&n.dims.length>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(r.dims[2]%n.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');m=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==x)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(i)throw new Error('Expect "value" be none when "key" has packed kv format.');m=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==x)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');m=n.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');V=3}let A=0,ee=!1,te=t.kvNumHeads?x*t.kvNumHeads:g;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(r.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(i.dims.length===3){if(m!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');te=i.dims[2]}else{if(m!==i.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');te=i.dims[1]*i.dims[3],ee=!0}}let ie=e.length>4?e[5]:void 0;if(ie&&ie.dims.length!==1&&ie.dims[0]!==d)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:d,sequenceLength:c,pastSequenceLength:l,kvSequenceLength:m,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:g,vHiddenSize:te,headSize:x,vHeadSize:Math.floor(te/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:A,scale:t.scale,broadcastResPosBias:!1,passPastInKv:ee,qkvFormat:V}},Xu=or({perm:[0,2,1,3]}),Ha=(e,t,r)=>{let n=t,i=r.kvNumHeads;return t.dims.length===3&&r.kvSequenceLength!==0&&(n=t.reshape([r.batchSize,r.kvSequenceLength,i,r.headSize]),n=e.compute(xn(n,Xu.perm),{inputs:[n],outputs:[-1]})[0]),n},Qu=(e,t)=>{var D;let r=Ku(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((D=e.inputs[1])==null?void 0:D.dims.length)===5)throw new Error("Packed KV is not implemented");let n=e.inputs[0],i=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,a=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,s=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,u=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,d=e.inputs.length>4?e.inputs[5]:void 0,c=e.inputs.length>5?e.inputs[6]:void 0,g=r.kvNumHeads?r.kvNumHeads:r.numHeads,m=or({axis:2,numOutputs:3,splitSizes:[r.numHeads*r.headSize,g*r.headSize,g*r.headSize]}),[l,T,x]=!i&&!a?e.compute(Gu([n],m),{inputs:[n],outputs:[-1,-1,-1]}):[n,i,a],C=Gs(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,l,void 0,0);bs(e,C,Ha(e,T,r),Ha(e,x,r),void 0,void 0,s,u,void 0,r,d,c)}}),Ka,Yu,Zu,Ju,rc=U(()=>{Yt(),Kt(),jn(),pr(),Ka=(e,t,r,n,i,a,s,u)=>{let d=_r(a),c=d===1?"f32":`vec${d}f`,g=d===1?"vec2f":`mat2x${d}f`,m=i*s,l=[i,s,a/d],T=[i,s,2],x=["rank","type","type"],C=[];C.push(...kt(l,T));let D=V=>{let A=Qe("x",t.dataType,3,d),ee=Qe("scale",r.dataType,r.dims),te=Qe("bias",n.dataType,n.dims),ie=qt("output",1,3,2),ke=[A,ee,te,ie],Pe=64;return` + var workgroup_shared : array<${g}, ${Pe}>; + const workgroup_size = ${Pe}u; + ${V.declareVariables(...ke)} + ${V.mainStart(Pe)} + 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 = ${c}(0); + var squared_sum = ${c}(0); + for (var h = local_idx; h < hight; h += workgroup_size) { + let value = ${c}(${A.get("batch","channel","h")}); + sum += value; + squared_sum += value * value; + } + workgroup_shared[local_idx] = ${g}(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 = ${Nn("workgroup_shared[0][0]",d)} / f32(hight * ${d}); + let squared_sum_final = ${Nn("workgroup_shared[0][1]",d)} / f32(hight * ${d}); + + let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${u})); + 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:`${d};${u}`,inputDependencies:x},getRunData:()=>({outputs:[{dims:T,dataType:1}],dispatchGroup:{x:m},programUniforms:C}),getShaderSource:D},{inputs:[t,r,n],outputs:[-1]})[0]},Yu=(e,t,r)=>{let n=t[0].dims,i=n,a=2,s=n[0],u=n[1],d=Ee.sizeFromDimension(n,a),c=_r(d),g=Ee.size(i)/c,m=Ka(e,t[0],t[1],t[2],s,d,u,r.epsilon),l=[s,u,d/c],T=[s,u],x=["type","none"],C=D=>{let V=Qe("x",t[0].dataType,l.length,c),A=Qe("scale_shift",1,T.length,2),ee=qt("output",t[0].dataType,l.length,c),te=[V,A,ee];return` + ${D.registerUniform("output_size","u32").declareVariables(...te)} + ${D.mainStart()} + ${D.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let outputIndices = ${ee.offsetToIndices("global_idx")}; + let batch = outputIndices[0]; + let channel = outputIndices[1]; + let scale_shift = ${A.getByIndices("vec2(batch, channel)")}; + let value = ${V.getByOffset("global_idx")} * ${ee.type.value}(scale_shift.x) + ${ee.type.value}(scale_shift.y); + ${ee.setByOffset("global_idx","value")}; + }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${c}`,inputDependencies:x},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:[{type:12,data:g},...kt(l,T,l)]}),getShaderSource:C},{inputs:[t[0],m]})},Zu=(e,t,r)=>{let n=t[0].dims,i=n,a=n[0],s=n[n.length-1],u=Ee.sizeFromDimension(n,1)/s,d=_r(s),c=Ee.size(i)/d,g=[{type:12,data:u},{type:12,data:Math.floor(s/d)}],m=["type","type"],l=[0,n.length-1];for(let D=0;D{let V=fr(t[0].dataType),A=d===1?"vec2f":`mat${d}x2f`,ee=ke=>{let Pe=ke===0?"x":"y",Ye=d===1?"f32":`vec${d}f`;switch(d){case 1:return`${V}(${Ye}(scale.${Pe}))`;case 2:return`vec2<${V}>(${Ye}(scale[0].${Pe}, scale[1].${Pe}))`;case 4:return`vec4<${V}>(${Ye}(scale[0].${Pe}, scale[1].${Pe}, scale[2].${Pe}, scale[3].${Pe}))`;default:throw new Error(`Not supported compoents ${d}`)}},te=Qe("input",t[0].dataType,t[0].dims,d),ie=qt("output",t[0].dataType,i,d);return` + @group(0) @binding(0) var input : array<${te.type.storage}>; + @group(0) @binding(1) var scale_input : array<${A}>; + @group(0) @binding(2) var output : array<${ie.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${D.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], ${ee(0)}, ${ee(1)}); + }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${d}`,inputDependencies:m},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:g}),getShaderSource:C},{inputs:[t[0],x]})},Ju=(e,t)=>{t.format==="NHWC"?Zu(e,e.inputs,t):Yu(e,e.inputs,t)}}),ed,td,rd,nc=U(()=>{Yt(),Kt(),pr(),ed=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},td=(e,t,r)=>{let n=t.simplified,i=e[0].dims,a=e[1],s=!n&&e[2],u=i,d=Ee.normalizeAxis(t.axis,i.length),c=Ee.sizeToDimension(i,d),g=Ee.sizeFromDimension(i,d),m=Ee.size(a.dims),l=s?Ee.size(s.dims):0;if(m!==g||s&&l!==g)throw new Error(`Size of X.shape()[axis:] == ${g}. + Size of scale and bias (if provided) must match this. + Got scale size of ${m} and bias size of ${l}`);let T=[];for(let ie=0;ie1,A=r>2,ee=ie=>{let ke=fr(e[0].dataType),Pe=[Qe("x",e[0].dataType,e[0].dims,x),Qe("scale",a.dataType,a.dims,x)];s&&Pe.push(Qe("bias",s.dataType,s.dims,x)),Pe.push(qt("output",e[0].dataType,u,x)),V&&Pe.push(qt("mean_data_output",1,T)),A&&Pe.push(qt("inv_std_output",1,T));let Ye=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${ie.registerUniforms(Ye).declareVariables(...Pe)} + ${ie.mainStart()} + ${ie.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${ss("f32",x)}; + var mean_square_vector = ${ss("f32",x)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${Qn(ke,x,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${Nn("mean_vector",x)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${Nn("mean_square_vector",x)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${Qn(ke,x,"x[j + offset]")}; + let f32scale = ${Qn(ke,x,"scale[j]")}; + output[j + offset] = ${Pe[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale + ${s?`+ ${Qn(ke,x,"bias[j]")}`:""} + ); + } + + ${V?"mean_data_output[global_idx] = mean":""}; + ${A?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},te=[{dims:u,dataType:e[0].dataType}];return V&&te.push({dims:T,dataType:1}),A&&te.push({dims:T,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${x};${r};${n}`,inputDependencies:C},getRunData:()=>({outputs:te,dispatchGroup:{x:Math.ceil(c/64)},programUniforms:D}),getShaderSource:ee}},rd=(e,t)=>{ed(e.inputs),e.compute(td(e.inputs,t,e.outputCount))}}),nd,sc,Xa,sd,id,ic=U(()=>{Yt(),Kt(),Pr(),pr(),nd=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],n=r.dims.length;if(r.dims[n-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let i=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,s=e[1];if(!Ee.areEqual(s.dims,[t.n,i,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let u=e[2].dims;if(Ee.size(u)!==t.n*i)throw new Error("scales input size error.");if(e.length===4){let d=e[3].dims,c=t.bits>4?t.n*i:t.n*Math.floor((i+1)/2);if(Ee.size(d)!==c)throw new Error("zeroPoints input size error.")}},sc=(e,t)=>{let r=e[0].dims,n=r.length,i=r[n-2],a=t.k,s=t.n,u=r.slice(0,n-2),d=Ee.size(u),c=e[1].dims[2]/4,g=e[0].dataType,m=_r(t.k),l=_r(c),T=_r(s),x=u.concat([i,s]),C=i>1&&s/T%2===0?2:1,D=Ee.size(x)/T/C,V=64,A=[],ee=[d,i,a/m],te=Ee.convertShape(e[1].dims).slice();te.splice(-1,1,c/l),A.push(...kt(ee)),A.push(...kt(te)),A.push(...kt(e[2].dims)),e.length===4&&A.push(...kt(Ee.convertShape(e[3].dims)));let ie=[d,i,s/T];A.push(...kt(ie));let ke=Pe=>{let Ye=ee.length,Ft=Qe("a",e[0].dataType,Ye,m),Bt=Qe("b",12,te.length,l),ar=Qe("scales",e[2].dataType,e[2].dims.length),nr=[Ft,Bt,ar],Ht=e.length===4?Qe("zero_points",12,e[3].dims.length):void 0;Ht&&nr.push(Ht);let Er=ie.length,jr=qt("output",e[0].dataType,Er,T),hr=fr(e[0].dataType),Fr=(()=>{switch(m){case 1:return`array<${hr}, 8>`;case 2:return`mat4x2<${hr}>`;case 4:return`mat2x4<${hr}>`;default:throw new Error(`${m}-component is not supported.`)}})(),Gt=()=>{let qe=` + // reuse a data + var input_offset = ${Ft.indicesToOffset(`${Ft.type.indices}(batch, row, word_offset)`)}; + var a_data: ${Fr}; + for (var j: u32 = 0; j < ${8/m}; j++) { + a_data[j] = ${Ft.getByOffset("input_offset")}; + input_offset++; + } + `;for(let vt=0;vt> 4) & b_mask); + b_quantized_values = ${Fr}(${Array.from({length:4},(rr,Br)=>`${hr}(b_value_lower[${Br}]), ${hr}(b_value_upper[${Br}])`).join(", ")}); + b_dequantized_values = ${m===1?`${Fr}(${Array.from({length:8},(rr,Br)=>`(b_quantized_values[${Br}] - ${Ht?`zero_point${vt}`:"zero_point"}) * scale${vt}`).join(", ")});`:`(b_quantized_values - ${Fr}(${Array(8).fill(`${Ht?`zero_point${vt}`:"zero_point"}`).join(",")})) * scale${vt};`}; + workgroup_shared[local_id.x * ${C} + ${Math.floor(vt/T)}]${T>1?`[${vt%T}]`:""} += ${Array.from({length:8/m},(rr,Br)=>`${m===1?`a_data[${Br}] * b_dequantized_values[${Br}]`:`dot(a_data[${Br}], b_dequantized_values[${Br}])`}`).join(" + ")}; + `;return qe},Qt=()=>{let qe=` + var col_index = col * ${T}; + ${Ht?` + 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 = ${hr}(8);`} + `;for(let vt=0;vt> 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 = ${Ht.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point${vt} = ${hr}((zero_point_word) & 0xFu);`:""} + col_index += 1;`;return qe},xr=()=>{let qe=`col_index = col * ${T};`;for(let vt=0;vt; + var b_value_upper: vec4; + var b_quantized_values: ${Fr}; + var b_dequantized_values: ${Fr};`,qe};return` + var workgroup_shared: array<${jr.type.value}, ${C*V}>; + ${Pe.declareVariables(...nr,jr)} + ${Pe.mainStart([V,1,1])} + let output_indices = ${jr.offsetToIndices(`(global_idx / ${V}) * ${C}`)}; + 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 * ${t.blockSize/m}; + ${Qt()} + for (var word: u32 = 0; word < ${c}; word += ${l}) { + ${xr()} + for (var i: u32 = 0; i < ${l}; i++) { + ${Gt()} + word_offset += ${8/m}; + } + } + } + workgroupBarrier(); + + if (local_id.x < ${C}) { + var output_value: ${jr.type.value} = ${jr.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 += ${C}; + } + ${jr.setByIndices(`${jr.type.indices}(batch, row, col + local_id.x)`,"output_value")}; + } + }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${m};${l};${T};${C};${V}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:x,dataType:g}],dispatchGroup:{x:D},programUniforms:A}),getShaderSource:ke}},Xa=(e,t)=>{let r=e[0].dims,n=r.length,i=r[n-2],a=t.k,s=t.n,u=r.slice(0,n-2),d=Ee.size(u),c=e[1].dims[2]/4,g=e[0].dataType,m=_r(t.k),l=_r(c),T=u.concat([i,s]),x=128,C=s%8===0?8:s%4===0?4:1,D=x/C,V=D*l*8,A=V/m,ee=V/t.blockSize,te=Ee.size(T)/C,ie=[],ke=[d,i,a/m],Pe=Ee.convertShape(e[1].dims).slice();Pe.splice(-1,1,c/l),ie.push(...kt(ke)),ie.push(...kt(Pe)),ie.push(...kt(e[2].dims)),e.length===4&&ie.push(...kt(Ee.convertShape(e[3].dims)));let Ye=[d,i,s];ie.push(...kt(Ye));let Ft=Bt=>{let ar=ke.length,nr=Qe("a",e[0].dataType,ar,m),Ht=Qe("b",12,Pe.length,l),Er=Qe("scales",e[2].dataType,e[2].dims.length),jr=[nr,Ht,Er],hr=e.length===4?Qe("zero_points",12,e[3].dims.length):void 0;hr&&jr.push(hr);let Fr=Ye.length,Gt=qt("output",e[0].dataType,Fr),Qt=fr(e[0].dataType),xr=()=>{switch(m){case 1:return` + let a_data0 = vec4<${Qt}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]); + let a_data1 = vec4<${Qt}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return` + let a_data0 = vec4<${Qt}>(sub_a[word_offset], sub_a[word_offset + 1]); + let a_data1 = vec4<${Qt}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return` + let a_data0 = sub_a[word_offset]; + let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${m}-component is not supported.`)}};return` + var sub_a: array<${nr.type.value}, ${A}>; + var inter_results: array, ${C}>; + ${Bt.declareVariables(...jr,Gt)} + ${Bt.mainStart([D,C,1])} + let output_indices = ${Gt.offsetToIndices(`workgroup_index * ${C}`)}; + 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) / ${ee} + 1; + + // Loop over shared dimension. + for (var tile: u32 = 0; tile < num_tiles; tile += 1) { + let a_col_start = tile * ${A}; + // load one tile A data into shared memory. + for (var a_offset = local_idx; a_offset < ${A}; a_offset += ${x}) + { + let a_col = a_col_start + a_offset; + if (a_col < uniforms.a_shape[2]) + { + sub_a[a_offset] = ${nr.getByIndices(`${nr.type.indices}(batch, row, a_col)`)}; + } else { + sub_a[a_offset] = ${nr.type.value}(0); + } + } + workgroupBarrier(); + + // each thread process one block + let b_row = col + local_id.y; + let block = tile * ${ee} + local_id.x; + ${hr?` + 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 = ${hr.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point = ${Qt}((zero_point_word) & 0xFu);`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${Qt}(8);`} + let scale = ${Er.getByOffset("b_row * n_blocks_per_col + block")}; + let b_data = ${Ht.getByIndices(`${Ht.type.indices}(b_row, block, 0)`)}; + var word_offset = local_id.x * ${t.blockSize/m}; + for (var i: u32 = 0; i < ${l}; i++) { + ${xr()} + let b_value = ${l===1?"b_data":"b_data[i]"}; + let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu); + let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu); + let b_quantized_values = mat2x4<${Qt}>(${Array.from({length:4},(qe,vt)=>`${Qt}(b_value_lower[${vt}]), ${Qt}(b_value_upper[${vt}])`).join(", ")}); + let b_dequantized_values = (b_quantized_values - mat2x4<${Qt}>(${Array(8).fill("zero_point").join(",")})) * scale; + inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(qe,vt)=>`${`dot(a_data${vt}, b_dequantized_values[${vt}])`}`).join(" + ")}; + word_offset += ${8/m}; + } + workgroupBarrier(); + } + + if (local_idx < ${C}) { + var output_value: ${Gt.type.value} = ${Gt.type.value}(0); + for (var b = 0u; b < ${D}; b++) { + output_value += inter_results[local_idx][b]; + } + if (col + local_idx < uniforms.output_shape[2]) + { + ${Gt.setByIndices(`${Gt.type.indices}(batch, row, col + local_idx)`,"output_value")} + } + } + }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${t.blockSize};${m};${l};${D};${C}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:T,dataType:g}],dispatchGroup:{x:te},programUniforms:ie}),getShaderSource:Ft}},sd=(e,t)=>{nd(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(Xa(e.inputs,t)):e.compute(sc(e.inputs,t))},id=e=>or(e)}),ad,od,ld,ud,dd,cd,pd,hd,fd,ac=U(()=>{Yt(),Kt(),pr(),ad=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},od=(e,t,r)=>{let n="";for(let i=t-1;i>=0;--i)n+=` + k = i32(${e.indicesGet("indices",i)}) - ${Wt("uniforms.pads",i,r)}; + if (k < 0) { + break; + } + if (k >= i32(${Wt("uniforms.x_shape",i,t)})) { + break; + } + offset += k * i32(${Wt("uniforms.x_strides",i,t)}); + `;return` + value = ${e.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + } + `},ld=(e,t,r)=>{let n="";for(let i=t-1;i>=0;--i)n+=` + k = i32(${e.indicesGet("indices",i)}) - ${Wt("uniforms.pads",i,r)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${Wt("uniforms.x_shape",i,t)}) - 1); + k = k % _2n_1; + if(k >= i32(${Wt("uniforms.x_shape",i,t)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${Wt("uniforms.x_strides",i,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},ud=(e,t,r)=>{let n="";for(let i=t-1;i>=0;--i)n+=` + k = i32(${e.indicesGet("indices",i)}) - ${Wt("uniforms.pads",i,r)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${Wt("uniforms.x_shape",i,t)})) { + k = i32(${Wt("uniforms.x_shape",i,t)}) - 1; + } + offset += k * i32(${Wt("uniforms.x_strides",i,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},dd=(e,t,r)=>{let n="";for(let i=t-1;i>=0;--i)n+=` + k = i32(${e.indicesGet("indices",i)}) - ${Wt("uniforms.pads",i,r)}; + if (k < 0) { + k += i32(${Wt("uniforms.x_shape",i,t)}]); + } + if (k >= i32(${Wt("uniforms.x_shape",i,t)})) { + k -= i32(${Wt("uniforms.x_shape",i,t)}); + } + offset += k * i32(${Wt("uniforms.x_strides",i,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},cd=(e,t,r)=>{switch(r.mode){case 0:return od(e,t,r.pads.length);case 1:return ld(e,t,r.pads.length);case 2:return ud(e,t,r.pads.length);case 3:return dd(e,t,r.pads.length);default:throw new Error("Invalid mode")}},pd=(e,t)=>{let r=Ee.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,i=Ee.size(r),a=[{type:12,data:i},{type:6,data:t.pads}],s=e.length>=3&&e[2].data;t.mode===0&&a.push({type:s?e[2].dataType:1,data:t.value}),a.push(...kt(e[0].dims,r));let u=["rank"],d=c=>{let g=qt("output",e[0].dataType,r.length),m=Qe("x",e[0].dataType,n.length),l=m.type.value,T=cd(g,n.length,t),x=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&x.push({name:"constant_value",type:s?l:"f32"}),` + ${c.registerUniforms(x).declareVariables(m,g)} + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${g.offsetToIndices("global_idx")}; + + var value = ${l}(0); + ${T} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${s}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Ee.size(r)/64)},programUniforms:a}),getShaderSource:d}},hd=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,i=e[0].dims.length,a=new Int32Array(2*i).fill(0);if(e.length>=4){let u=e[3].getBigInt64Array();for(let d=0;da[Number(d)]=Number(u));let s=[];return a.forEach(u=>s.push(u)),{mode:t.mode,value:n,pads:s}}else return t},fd=(e,t)=>{ad(e.inputs);let r=hd(e.inputs,t);e.compute(pd(e.inputs,r),{inputs:[0]})}}),qs,Qa,Ya,Za,Ja,md,_d,eo,to,gd,wd,ro,yd,bd,Md,cr,vd,sn,un,_n=U(()=>{At(),Yt(),Kt(),pr(),qs=e=>{if(E.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Qa=(e,t,r)=>{let n=t.format==="NHWC",i=e.dims.slice();n&&i.splice(1,0,i.pop());let a=Object.hasOwnProperty.call(t,"dilations"),s=t.kernelShape.slice(),u=t.strides.slice(),d=a?t.dilations.slice():[],c=t.pads.slice();tn.adjustPoolAttributes(r,i,s,u,d,c);let g=tn.computePoolOutputShape(r,i,u,d,s,c,t.autoPad),m=Object.assign({},t);a?Object.assign(m,{kernelShape:s,strides:u,pads:c,dilations:d,cacheKey:t.cacheKey}):Object.assign(m,{kernelShape:s,strides:u,pads:c,cacheKey:t.cacheKey});let l=g.slice();return l.push(l.splice(1,1)[0]),[m,n?l:g]},Ya=(e,t)=>{let r=t.format==="NHWC",n=Ee.size(e),i=Ee.size(t.kernelShape),a=[{type:12,data:n},{type:12,data:i}],s=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let u=t.kernelShape[t.kernelShape.length-1],d=t.strides[t.strides.length-1],c=t.pads[t.pads.length/2-1],g=t.pads[t.pads.length-1],m=!!(c+g);a.push({type:12,data:u},{type:12,data:d},{type:12,data:c},{type:12,data:g}),s.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let l=!1;if(t.kernelShape.length===2){let T=t.kernelShape[t.kernelShape.length-2],x=t.strides[t.strides.length-2],C=t.pads[t.pads.length/2-2],D=t.pads[t.pads.length-2];l=!!(C+D),a.push({type:12,data:T},{type:12,data:x},{type:12,data:C},{type:12,data:D}),s.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,s,!0,m,l]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let u=Ee.computeStrides(t.kernelShape);a.push({type:12,data:u},{type:12,data:t.pads},{type:12,data:t.strides}),s.push({name:"kernelStrides",type:"u32",length:u.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let d=t.pads.reduce((c,g)=>c+g);return[a,s,!!d,!1,!1]}},Za=(e,t,r,n,i,a,s,u,d,c,g,m)=>{let l=i.format==="NHWC",T=t.type.value,x=qt("output",t.type.tensor,n);if(i.kernelShape.length<=2){let C="",D="",V="",A=r-(l?2:1);if(g?C=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${A}] = indices[${A}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${A}] < 0 || xIndices[${A}] + >= uniforms.x_shape[${A}]) { + pad++; + continue; + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`:C=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${A}] = indices[${A}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`,i.kernelShape.length===2){let ee=r-(l?3:2);m?D=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${ee}] = indices[${ee}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${ee}] < 0 || xIndices[${ee}] >= uniforms.x_shape[${ee}]) { + pad += i32(uniforms.kw); + continue; + } + `:D=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${ee}] = indices[${ee}] * uniforms.sh - uniforms.phStart + j; + `,V=` + } + `}return` + ${e.registerUniforms(d).declareVariables(t,x)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${x.offsetToIndices("global_idx")}; + var xIndices = ${x.offsetToIndices("global_idx")}; + + var value = ${T}(${u}); + var pad = 0; + ${D} + ${C} + ${V} + ${s} + + output[global_idx] = value; + }`}else{if(l)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let C=i.kernelShape.length,D=i.pads.length,V="";return c?V=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`:V=` + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + `,` + ${e.registerUniforms(d).declareVariables(t,x)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${x.offsetToIndices("global_idx")}; + var xIndices = ${x.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${T}(${u}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${C-1}u; j++) { + offsets[j] = offset / ${Wt("uniforms.kernelStrides","j",C)}; + offset -= offsets[j] * ${Wt("uniforms.kernelStrides","j",C)}; + } + offsets[${C-1}] = offset; + + isPad = false; + for (var j = ${r-C}u; j < ${r}u; j++) { + xIndices[j] = indices[j] * ${Wt("uniforms.strides",`j - ${r-C}u`,C)} + + offsets[j - ${r-C}u] - ${Wt("uniforms.pads","j - 2u",D)}; + ${V} + } + ${s} + + output[global_idx] = value; + }`}},Ja=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,md=e=>`${Ja(e)};${e.countIncludePad}`,_d=e=>`${Ja(e)};${e.storageOrder};${e.dilations}`,eo=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}),to=(e,t,r,n)=>{let[i,a]=Qa(t,n,r),s=Qe("x",t.dataType,t.dims.length),u=s.type.value,d="value += x_val;",c="";i.countIncludePad?c+=`value /= ${u}(uniforms.kernelSize);`:c+=`value /= ${u}(i32(uniforms.kernelSize) - pad);`;let[g,m,l,T,x]=Ya(a,i);g.push(...kt(t.dims,a));let C=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${l};${T};${x}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Ee.size(a)/64)},programUniforms:g}),getShaderSource:D=>Za(D,s,t.dims.length,a.length,i,d,c,0,m,l,T,x)}},gd=e=>{let t=e.count_include_pad!==0,r=eo(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...r,cacheKey:""};return{...n,cacheKey:md(n)}},wd=(e,t)=>{qs(e.inputs),e.compute(to("AveragePool",e.inputs[0],!1,t))},ro={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},yd=e=>{let t=e.format;return{format:t,...ro,cacheKey:t}},bd=(e,t)=>{qs(e.inputs),e.compute(to("GlobalAveragePool",e.inputs[0],!0,t))},Md=(e,t,r,n)=>{let[i,a]=Qa(t,n,r),s=` + value = max(x_val, value); + `,u="",d=Qe("x",t.dataType,t.dims.length),c=["rank"],[g,m,l,T,x]=Ya(a,i);return g.push(...kt(t.dims,a)),{name:e,shaderCache:{hint:`${n.cacheKey};${l};${T};${x}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Ee.size(a)/64)},programUniforms:g}),getShaderSource:C=>Za(C,d,t.dims.length,a.length,i,s,u,t.dataType===10?-65504:-1e5,m,l,T,x)}},cr=(e,t)=>{qs(e.inputs),e.compute(Md("MaxPool",e.inputs[0],!1,t))},vd=e=>{let t=e.storage_order,r=e.dilations,n=eo(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let i={storageOrder:t,dilations:r,...n,cacheKey:""};return{...i,cacheKey:_d(i)}},sn=e=>{let t=e.format;return{format:t,...ro,cacheKey:t}},un=(e,t)=>{qs(e.inputs),e.compute(Md("GlobalMaxPool",e.inputs[0],!0,t))}}),as,oc,xd,Td,f=U(()=>{Yt(),Kt(),Pr(),pr(),as=(e,t)=>{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((r,n)=>r===e[2].dims[n]).reduce((r,n)=>r&&n,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(t.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,a)=>a===t.axis||i===e[0].dims[a]).reduce((i,a)=>i&&a,!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 r=e[0].dims[t.axis],n=e[1].dims[t.axis];if(t.blockSizeMath.ceil(r/(n-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},oc=(e,t)=>{let r=Ee.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,i=n===3,a=e[0].dims,s=e[1].dataType,u=Ee.size(a),d=n===3||n===2,c=d?[Math.ceil(Ee.size(e[0].dims)/4)]:e[0].dims,g=e[1].dims,m=e.length>2?e[2]:void 0,l=m?d?[Math.ceil(Ee.size(m.dims)/4)]:m.dims:void 0,T=g.length===0||g.length===1&&g[0]===1,x=T===!1&&g.length===1,C=_r(u),D=T&&(!d||C===4),V=D?C:1,A=D&&!d?C:1,ee=Qe("input",d?12:n,c.length,A),te=Qe("scale",s,g.length),ie=m?Qe("zero_point",d?12:n,l.length):void 0,ke=qt("output",s,a.length,V),Pe=[ee,te];ie&&Pe.push(ie);let Ye=[c,g];m&&Ye.push(l);let Ft=[{type:12,data:u/V},{type:12,data:r},{type:12,data:t.blockSize},...kt(...Ye,a)],Bt=ar=>{let nr=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${ar.registerUniforms(nr).declareVariables(...Pe,ke)} + ${ar.mainStart()} + ${ar.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${ke.offsetToIndices("global_idx")}; + + // Set input x + ${d?` + let input = ${ee.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 = ${ee.getByOffset("global_idx")};`}; + + // Set scale input + ${T?`let scale_value= ${te.getByOffset("0")}`:x?` + let scale_index = ${ke.indicesGet("output_indices","uniforms.axis")}; + let scale_value= ${te.getByOffset("scale_index")};`:` + var scale_indices: ${te.type.indices} = output_indices; + let index = ${te.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; + ${te.indicesSet("scale_indices","uniforms.axis","index")}; + let scale_value= ${te.getByIndices("scale_indices")};`}; + + // Set zero-point input + ${ie?T?d?` + let zero_point_input = ${ie.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 = ${ie.getByOffset("0")}`:x?d?` + let zero_point_index = ${ke.indicesGet("output_indices","uniforms.axis")}; + let zero_point_input = ${ie.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 = ${ke.indicesGet("output_indices","uniforms.axis")}; + let zero_point_value = ${ie.getByOffset("zero_point_index")};`:d?` + let zero_point_offset = ${te.indicesToOffset("scale_indices")}; + let zero_point_input = ${ie.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 = ${ie.getByIndices("scale_indices")};`:`let zero_point_value = ${d?i?"i32":"u32":ee.type.value}(0);`}; + // Compute and write output + ${ke.setByOffset("global_idx",`${ke.type.value}(x_value - zero_point_value) * scale_value`)}; + }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:ie?["rank","rank","rank"]:["rank","rank"]},getShaderSource:Bt,getRunData:()=>({outputs:[{dims:a,dataType:s}],dispatchGroup:{x:Math.ceil(u/V/64),y:1,z:1},programUniforms:Ft})}},xd=(e,t)=>{as(e.inputs,t),e.compute(oc(e.inputs,t))},Td=e=>or({axis:e.axis,blockSize:e.blockSize})}),b,R,ve,Ue=U(()=>{At(),Yt(),pr(),b=(e,t,r)=>{let n=e===t,i=et&&r>0;if(n||i||a)throw new Error("Range these inputs' contents are invalid.")},R=(e,t,r,n)=>{let i=Math.abs(Math.ceil((t-e)/r)),a=[i],s=i,u=[{type:12,data:s},{type:n,data:e},{type:n,data:r},...kt(a)],d=c=>{let g=qt("output",n,a.length),m=g.type.value,l=[{name:"outputSize",type:"u32"},{name:"start",type:m},{name:"delta",type:m}];return` + ${c.registerUniforms(l).declareVariables(g)} + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${m}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:d,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:u})}},ve=e=>{let t=0,r=0,n=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],n=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],n=e.inputs[2].getFloat32Array()[0]),E.webgpu.validateInputContent&&b(t,r,n),e.compute(R(t,r,n,e.inputs[0].dataType),{inputs:[]})}}),Le,pt,St,Vt,tr,Ar,Dr,yr,ir,Ir,$r,gr,Lr,Tn,dn,Gr,Xr,Qr,gn,os=U(()=>{Yt(),Kt(),Pr(),pr(),Le=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and + one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},pt=(e,t,r)=>{t.every(i=>i>=0&&i{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return t.forEach((i,a)=>n[i]=e[a]),n},St=(e,t,r,n,i,a)=>{let[s,u,d]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],c=e[0].dims.length;if(s>0&&e.length>s&&e[s].dims.length>0)e[s].getFloat32Array().forEach(g=>a.push(g));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(u>0&&e.length>u&&e[u].dims.length===1&&e[u].dims[0]>0){if(e[u].getFloat32Array().forEach(g=>n.push(g)),n.length!==0&&n.length!==c&&r>=18&&n.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");Le(n,t),t.axes.length>0&&pt(n,t.axes,c).forEach((g,m)=>n[m]=g)}if(d>0&&e.length>d&&e[d].dims.length===1&&e[d].dims[0]>0&&(e[d].getBigInt64Array().forEach(g=>i.push(Number(g))),i.length!==0&&i.length!==c&&r>=18&&i.length!==t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(n.length!==0&&n.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(i.length!==0&&i.length!==t.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof i<"u"&&n.length>0&&i.length>c)throw new Error("Resize requires only of scales or sizes to be specified")},Vt=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, + lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) { + return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5; + } else { + return 0.0; + }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) { + return 0.0; + } else { + // The whole part and the fractional part are calculated separately due to inaccuracy of floating + // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an + // offset-by-one error later in floor(). + let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1)); + let fract = + ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1); + return whole + fract; + }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { + return ${t}(roiStart) * ${t}(lengthOriginal - 1) + + (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) / + ${t}(lengthResized - 1); + } else { + return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1); + }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized); + const adjustment = ${t}(lengthResized) / outputWidth; + const center = ${t}(lengthOriginal) / 2; + const offset = center * (1 - adjustment); + return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",tr=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(t<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Ar=(e,t,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),i=e.length===0?n:e.slice();return t.length>0?(t.forEach((a,s)=>{n[a]=i[s],n[s+r]=i[t.length+s]}),n):i},Dr=(e,t,r,n)=>{let i=[];if(r.length>0)if(n.length>0){if(e.forEach(a=>i.push(a)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((a,s)=>i[a]=r[s])}else r.forEach(a=>i.push(a));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((a,s)=>Math.round(a*t[s]))}return i},yr=(e,t,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(a=>t[a]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(a=>t[a]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let i=e.slice();return r.axes.length>0?(r.axes.forEach(a=>t[a]=n),r.axes.forEach(a=>i[a]=Math.round(e[a]*t[a]))):(t.fill(n,0,t.length),i.forEach((a,s)=>i[s]=Math.round(a*t[s]))),i},ir=(e,t,r,n,i)=>` + fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> { + var original_indices: array<${e.type.value}, ${r.length}>; + for (var i:u32 = 0; i < ${r.length}; i++) { + var output_index = ${e.indicesGet("output_indices","i")}; + var scale = ${Wt("uniforms.scales","i",n)}; + var roi_low = ${Wt("uniforms.roi","i",i)}; + var roi_hi = ${Wt("uniforms.roi",`i + ${t.length}`,i)}; + if (scale == 1.0) { + original_indices[i] = ${e.type.value}(output_index); + } else { + var input_shape_i = ${Wt("uniforms.input_shape","i",t.length)}; + var output_shape_i = ${Wt("uniforms.output_shape","i",r.length)}; + original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + } + } + return original_indices; + }`,Ir=(e,t,r,n,i,a,s)=>` + fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + for (var i:u32 = 0; i < ${n.length}; i++) { + var output_index = ${t.indicesGet("output_indices","i")}; + var input_index: u32; + var scale = ${Wt("uniforms.scales","i",i)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${Wt("uniforms.roi","i",a)}; + var roi_hi = ${Wt("uniforms.roi",`i + ${r.length}`,a)}; + var input_shape_i = ${Wt("uniforms.input_shape","i",r.length)}; + var output_shape_i = ${Wt("uniforms.output_shape","i",n.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${s} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) { + if (original_idx < 0) { + input_index = 0; + } else if (original_idx > ${t.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); + } + } + ${e.indicesSet("input_indices","i"," input_index")} + } + return input_indices; + }`,$r=(e,t)=>` + fn checkInputIndices(input_indices: ${e.type.indices}) -> bool { + for (var i:u32 = 0; i < ${t.length}; i++) { + var input_index = ${e.indicesGet("input_indices","i")}; + if (input_index < 0 || input_index >= ${Wt("uniforms.input_shape","i",t.length)}) { + return false; + } + } + return true; + }`,gr=(e,t,r,n)=>e.rank>n?` + ${e.indicesSet("input_indices",t,"channel")}; + ${e.indicesSet("input_indices",r,"batch")}; +`:"",Lr=(e,t,r,n,i)=>{let[a,s,u,d]=r.length===2?[-1,0,1,-1]:[0,2,3,1],c=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${c} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",s,`max(0, min(row, ${r[s]} - 1))`)}; + ${e.indicesSet("input_indices",u,`max(0, min(col, ${r[u]} - 1))`)}; + ${gr(e,d,a,2)} + return ${e.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${c} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${c} = originalIndices[${s}]; + var col:${c} = originalIndices[${u}]; + ${n?`if (row < 0 || row > (${r[s]} - 1) || col < 0 || col > (${r[u]} - 1)) { + return ${i}; + }`:""}; + row = max(0, min(row, ${r[s]} - 1)); + col = max(0, min(col, ${r[u]} - 1)); + var row1: u32 = u32(row); + var col1: u32 = u32(col); + var row2: u32 = u32(row + 1); + var col2: u32 = u32(col + 1); + var channel: u32 = ${r.length>2?`u32(originalIndices[${d}])`:"0"}; + var batch: u32 = ${r.length>2?`u32(originalIndices[${a}])`:"0"}; + var x11: ${c} = getInputValue(batch, channel, row1, col1); + var x12: ${c} = getInputValue(batch, channel, row1, col2); + var x21: ${c} = getInputValue(batch, channel, row2, col1); + var x22: ${c} = getInputValue(batch, channel, row2, col2); + var dx1: ${c} = abs(row - ${c}(row1)); + var dx2: ${c} = abs(${c}(row2) - row); + var dy1: ${c} = abs(col - ${c}(col1)); + var dy2: ${c} = abs(${c}(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); + }`},Tn=(e,t,r,n,i,a,s,u,d,c)=>{let g=r.length===2,[m,l]=g?[0,1]:[2,3],T=e.type.value,x=C=>{let D=C===m?"row":"col";return` + fn ${D}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${T} { + var output_index = ${t.indicesGet("output_indices",C)}; + var originalIdx: ${T} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[C]}, + ${n[C]}, ${r[C]}, ${a[C]}, ${a[C]} + ${r.length}); + var fractOriginalIdx: ${T} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${u} && (originalIdx < 0 || originalIdx > (${r[C]} - 1))) { + return ${d}; + } + var data: array<${T}, 4> = array<${T}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${D}: ${T} = originalIdx + ${T}(i); + if (${D} < 0 || ${D} >= ${r[C]}) { + ${c?`coefs[i + 1] = 0.0; + continue;`:u?`return ${d};`:`${D} = max(0, min(${D}, ${r[C]} - 1));`}; + } + var input_indices_copy: ${e.type.indices} = input_indices; + ${e.indicesSet("input_indices_copy",C,`u32(${D})`)}; + data[i + 1] = ${C===m?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${x(m)}; + ${x(l)}; + fn getCubicInterpolationCoefs(s: ${T}) -> array<${T}, 4> { + var absS = abs(s); + var coeffs: array<${T}, 4> = array<${T}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${T} = 1.0 - absS; + var twoMinusAbsS: ${T} = 2.0 - absS; + var onePlusAbsS: ${T} = 1.0 + absS; + coeffs[0] = ((${s} * onePlusAbsS - 5 * ${s}) * onePlusAbsS + 8 * ${s}) * onePlusAbsS - 4 * ${s}; + coeffs[1] = ((${s} + 2) * absS - (${s} + 3)) * absS * absS + 1; + coeffs[2] = ((${s} + 2) * oneMinusAbsS - (${s} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${s} * twoMinusAbsS - 5 * ${s}) * twoMinusAbsS + 8 * ${s}) * twoMinusAbsS - 4 * ${s}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${T}, 4>, coefs: array<${T}, 4>) -> ${T} { + var coefsSum: ${T} = 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: ${t.type.indices}) -> ${T} { + var input_indices: ${e.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},dn=(e,t,r,n,i)=>{let[a,s,u,d,c]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],g=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${g} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",s,`max(0, min(depth, ${r[s]} - 1))`)}; + ${e.indicesSet("input_indices",u,`max(0, min(height, ${r[u]} - 1))`)}; + ${e.indicesSet("input_indices",d,`max(0, min(width, ${r[d]} - 1))`)}; + ${gr(e,c,a,3)} + return ${e.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${g} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${g} = originalIndices[${s}]; + var height:${g} = originalIndices[${u}]; + var width:${g} = originalIndices[${d}]; + ${n?`if (depth < 0 || depth > (${r[s]} - 1) || height < 0 || height > (${r[u]} - 1) || width < 0 || (width > ${r[d]} - 1)) { + return ${i}; + }`:""}; + + depth = max(0, min(depth, ${r[s]} - 1)); + height = max(0, min(height, ${r[u]} - 1)); + width = max(0, min(width, ${r[d]} - 1)); + var depth1: u32 = u32(depth); + var height1: u32 = u32(height); + var width1: u32 = u32(width); + var depth2: u32 = u32(depth + 1); + var height2: u32 = u32(height + 1); + var width2: u32 = u32(width + 1); + var channel: u32 = ${r.length>3?`u32(originalIndices[${c}])`:"0"}; + var batch: u32 = ${r.length>3?`u32(originalIndices[${a}])`:"0"}; + + var x111: ${g} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${g} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${g} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${g} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${g} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${g} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${g} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${g} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${g} = abs(depth - ${g}(depth1)); + var dx2: ${g} = abs(${g}(depth2) - depth); + var dy1: ${g} = abs(height - ${g}(height1)); + var dy2: ${g} = abs(${g}(height2) - height); + var dz1: ${g} = abs(width - ${g}(width1)); + var dz2: ${g} = abs(${g}(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); + }`},Gr=(e,t,r,n,i,a)=>{let s=e.dims,u=Ar(a,t.axes,s.length),d=Dr(s,n,i,t.axes),c=n.slice();n.length===0&&(c=s.map((A,ee)=>A===0?1:d[ee]/A),t.keepAspectRatioPolicy!=="stretch"&&(d=yr(s,c,t)));let g=qt("output",e.dataType,d.length),m=Qe("input",e.dataType,s.length),l=Ee.size(d),T=s.length===d.length&&s.every((A,ee)=>A===d[ee]),x=t.coordinateTransformMode==="tf_crop_and_resize",C=t.extrapolationValue,D=m.type.value,V=A=>` + ${T?"":` + ${Vt(t.coordinateTransformMode,D)}; + ${(()=>{switch(t.mode){case"nearest":return` + ${$r(m,s)}; + ${tr(t.nearestMode,r,D)}; + ${Ir(m,g,s,d,c.length,u.length,x)}; + `;case"linear":return` + ${ir(g,s,d,c.length,u.length)}; + ${(()=>{if(s.length===2||s.length===4)return`${Lr(m,g,s,x,C)}`;if(s.length===3||s.length===5)return`${dn(m,g,s,x,C)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(s.length===2||s.length===4)return`${Tn(m,g,s,d,c,u,t.cubicCoeffA,x,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; + `;default:throw Error("Invalid resize mode")}})()}; + `} + ${A.registerUniform("output_size","u32").registerUniform("scales","f32",c.length).registerUniform("roi","f32",u.length).declareVariables(m,g)} + ${A.mainStart()} + ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${T?"output[global_idx] = input[global_idx];":` + let output_indices = ${g.offsetToIndices("global_idx")}; + var input_indices: ${m.type.indices}; + ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${m.getByIndices("input_indices")}; + } else { + output[global_idx] = ${t.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${s.length===2||s.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()}; +`} + }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${c.length>0?c:""}|${i.length>0?i:""}|${u.length>0?u:""}|${T}|${s}`,inputDependencies:["rank"]},getShaderSource:V,getRunData:()=>({outputs:[{dims:d,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:[{type:12,data:l},{type:1,data:c},{type:1,data:u},...kt(s,d)]})}},Xr=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Qr=(e,t)=>{let r=[],n=[],i=[],a=Xr(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");St(e.inputs,t,a,r,n,i),e.compute(Gr(e.inputs[0],t,a,r,n,i),{inputs:[0]})},gn=e=>{let t=e.antialias,r=e.axes,n=e.coordinateTransformMode,i=e.cubicCoeffA,a=e.excludeOutside!==0,s=e.extrapolationValue,u=e.keepAspectRatioPolicy,d=e.mode,c=e.nearestMode===""?"simple":e.nearestMode;return or({antialias:t,axes:r,coordinateTransformMode:n,cubicCoeffA:i,excludeOutside:a,extrapolationValue:s,keepAspectRatioPolicy:u,mode:d,nearestMode:c})}}),_i,no,Fc,Wn=U(()=>{Yt(),Kt(),Pr(),pr(),_i=(e,t)=>{let[r,n,i,a]=e,{numHeads:s,rotaryEmbeddingDim:u}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!Ee.areEqual(n.dims,[])&&!Ee.areEqual(n.dims,[1])&&n.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${n.dims.length}`);if(i.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(a.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(!Ee.areEqual(i.dims,a.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(u>0&&s===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let d=r.dims[0],c=r.dims[r.dims.length-2],g=i.dims[0],m=Ee.sizeFromDimension(r.dims,1)/c,l=u===0?i.dims[1]*2:m/s;if(u>l)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(d!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(c!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(l/2!==i.dims[1]&&u/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(c>g)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},no=(e,t)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:i,scale:a}=t,s=e[0].dims[0],u=Ee.sizeFromDimension(e[0].dims,1),d=e[0].dims[e[0].dims.length-2],c=u/d,g=e[2].dims[1],m=i===0?g*2:c/n,l=new Array(s,d,c/m,m-g),T=Ee.computeStrides(l),x=[{type:1,data:a},{type:12,data:l},{type:12,data:T},...e[0].dims.length===3?new Array({type:12,data:[u,c,m,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[u,m,d*m,1]}):[],...kt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],C=D=>{let V=Qe("input",e[0].dataType,e[0].dims.length),A=Qe("position_ids",e[1].dataType,e[1].dims.length),ee=Qe("cos_cache",e[2].dataType,e[2].dims.length),te=Qe("sin_cache",e[3].dataType,e[3].dims.length),ie=qt("output",e[0].dataType,e[0].dims.length);return D.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:l.length},{name:"global_strides",type:"u32",length:T.length},{name:"input_output_strides",type:"u32",length:T.length}]),` + ${D.declareVariables(V,A,ee,te,ie)} + + ${D.mainStart(pn)} + let half_rotary_emb_dim = uniforms.${ee.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${D.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${A.broadcastedIndicesToOffset("bsnh.xy",qt("",A.type.tensor,2))}; + let position_id = + u32(${A.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r}); + let j = i + select(half_rotary_emb_dim, 1, ${r}); + let re = ${V.getByOffset("i")} * ${ee.get("position_id","bsnh[3]")} - + ${V.getByOffset("j")} * ${te.get("position_id","bsnh[3]")}; + ${ie.setByOffset("i","re")} + let im = ${V.getByOffset("i")} * ${te.get("position_id","bsnh[3]")} + + ${V.getByOffset("j")} * ${ee.get("position_id","bsnh[3]")}; + ${ie.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${ie.setByOffset("k",V.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:or({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:C,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Ee.size(l)/pn)},programUniforms:x})}},Fc=(e,t)=>{_i(e.inputs,t),e.compute(no(e.inputs,t))}}),xs,lc,uc,Oc=U(()=>{Yt(),Kt(),pr(),xs=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],n=e[2];if(t.dataType!==r.dataType||t.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let i=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==a)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let s=e[3];if(s.dims.length!==1)throw new Error("Beta must be 1D");if(s.dims[s.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let s=e[4];if(s.dims.length!==1)throw new Error("Bias must be 1D");if(s.dims[s.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},lc=(e,t,r,n)=>{let i=t.simplified,a=e[0].dims,s=Ee.size(a),u=a,d=s,c=a.slice(-1)[0],g=n?a.slice(0,-1).concat(1):[],m=!i&&e.length>3,l=e.length>4,T=n&&r>1,x=n&&r>2,C=r>3,D=64,V=_r(c),A=[{type:12,data:d},{type:12,data:V},{type:12,data:c},{type:1,data:t.epsilon}],ee=ie=>{let ke=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Pe=[Qe("x",e[0].dataType,e[0].dims,V),Qe("skip",e[1].dataType,e[1].dims,V),Qe("gamma",e[2].dataType,e[2].dims,V)];m&&Pe.push(Qe("beta",e[3].dataType,e[3].dims,V)),l&&Pe.push(Qe("bias",e[4].dataType,e[4].dims,V)),Pe.push(qt("output",e[0].dataType,u,V)),T&&Pe.push(qt("mean_output",1,g)),x&&Pe.push(qt("inv_std_output",1,g)),C&&Pe.push(qt("input_skip_bias_sum",e[0].dataType,u,V));let Ye=fr(e[0].dataType),Ft=fr(1,V);return` + + ${ie.registerUniforms(ke).declareVariables(...Pe)} + var sum_shared : array<${Ft}, ${D}>; + var sum_squared_shared : array<${Ft}, ${D}>; + + ${ie.mainStart([D,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${D}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${D}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${D-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${l?"bias[offset1d + i]":Ye+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${C?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${Qn(Ye,V,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${D}; + 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 = ${Nn("sum",V)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${Nn("square_sum",V)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon); + ${T?"mean_output[global_idx] = mean;":""} + ${x?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${i?"":`- ${Ye}(mean)`}) * + ${Ye}(inv_std_dev) * gamma[offset1d + i] + ${m?"+ beta[offset1d + i]":""}; + } + }`},te=[{dims:u,dataType:e[0].dataType}];return r>1&&te.push({dims:g,dataType:1}),r>2&&te.push({dims:g,dataType:1}),r>3&&te.push({dims:a,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${V};${T};${x};${C}`,inputDependencies:e.map((ie,ke)=>"type")},getShaderSource:ee,getRunData:()=>({outputs:te,dispatchGroup:{x:Math.ceil(d/c)},programUniforms:A})}},uc=(e,t)=>{xs(e.inputs);let r=[0];e.outputCount>1&&r.push(-3),e.outputCount>2&&r.push(-3),e.outputCount>3&&r.push(3),e.compute(lc(e.inputs,t,e.outputCount,!1),{outputs:r})}}),Cd,$d,Cp,zc,$p,Ep,kp,Sp,Hf=U(()=>{Yt(),Kt(),Pr(),pr(),Cd=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,n)=>{if(e[n+1].dataType!==6&&e[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},$d=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},Cp=(e,t)=>{if(e.length>1){let r=$d(e,1),n=$d(e,2),i=$d(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),or({starts:r,ends:n,axes:i})}else return t},zc=(e,t,r,n,i)=>{let a=e;return e<0&&(a+=r[n[t]]),i[t]<0?Math.max(0,Math.min(a,r[n[t]]-1)):Math.max(0,Math.min(a,r[n[t]]))},$p=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + var carry = 0u; + for (var i = ${r.length}; i >= 0; i--) { + let input_shape_i = ${Wt("uniforms.input_shape","i",r.length)}; + let steps_i = ${Wt("uniforms.steps","i",r.length)}; + let signs_i = ${Wt("uniforms.signs","i",r.length)}; + let starts_i = ${Wt("uniforms.starts","i",r.length)}; + var output_index = ${t.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; + } + ${e.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,Ep=(e,t)=>{let r=e[0].dims,n=Ee.size(r),i=t.axes.length>0?Ee.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],a=$d(e,4);a.forEach(V=>V!==0||(()=>{throw new Error("step cannot be 0")})),a.length===0&&(a=Array(i.length).fill(1));let s=t.starts.map((V,A)=>zc(V,A,r,i,a)),u=t.ends.map((V,A)=>zc(V,A,r,i,a));if(i.length!==s.length||i.length!==u.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==r.length)for(let V=0;VMath.sign(V));a.forEach((V,A,ee)=>{if(V<0){let te=(u[A]-s[A])/V,ie=s[A],ke=ie+te*a[A];s[A]=ke,u[A]=ie,ee[A]=-V}});let c=r.slice(0);i.forEach((V,A)=>{c[V]=Math.ceil((u[V]-s[V])/a[V])});let g={dims:c,dataType:e[0].dataType},m=qt("output",e[0].dataType,c.length),l=Qe("input",e[0].dataType,e[0].dims.length),T=Ee.size(c),x=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:s.length},{name:"signs",type:"i32",length:d.length},{name:"steps",type:"u32",length:a.length}],C=[{type:12,data:T},{type:12,data:s},{type:6,data:d},{type:12,data:a},...kt(e[0].dims,c)],D=V=>` + ${V.registerUniforms(x).declareVariables(l,m)} + ${$p(l,m,r)} + ${V.mainStart()} + ${V.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${m.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${m.setByOffset("global_idx",l.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${d.length}_${s.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:D,getRunData:()=>({outputs:[g],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:C})}},kp=(e,t)=>{Cd(e.inputs,t);let r=Cp(e.inputs,t);e.compute(Ep(e.inputs,r),{inputs:[0]})},Sp=e=>{let t=e.starts,r=e.ends,n=e.axes;return or({starts:t,ends:r,axes:n})}}),Pp,Ap,Ip,Fp,Kf=U(()=>{Yt(),Kt(),Pr(),jn(),pr(),Pp=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Ap=(e,t)=>{let r=e.inputs[0],n=r.dims,i=Ee.size(n),a=64,s=n.length,u=Ee.normalizeAxis(t.axis,s),d=uYe),g[u]=s-1,g[s-1]=u,c=e.compute(xn(r,g),{inputs:[r],outputs:[-1]})[0]):c=r;let m=c.dims,l=m[s-1],T=i/l,x=_r(l),C=l/x,D=(Pe,Ye)=>Ye===4?`max(max(${Pe}.x, ${Pe}.y), max(${Pe}.z, ${Pe}.w))`:Ye===2?`max(${Pe}.x, ${Pe}.y)`:Ye===3?`max(max(${Pe}.x, ${Pe}.y), ${Pe}.z)`:Pe,V=Qe("x",c.dataType,c.dims,x),A=qt("result",c.dataType,c.dims,x),ee=V.type.value,te=fr(c.dataType)==="f32"?`var threadMax = ${ee}(-3.402823e+38f);`:`var threadMax = ${ee}(-65504.0h);`,ie=Pe=>` + var rowMaxShared : ${ee}; + var rowSumShared : ${ee}; + var threadShared : array<${ee}, ${a}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${ee} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${ee}) { + let index = row * row_stride + col; + result[index] = value; + } + ${Pe.registerUniform("packedCols","i32").declareVariables(V,A)} + ${Pe.mainStart()} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${a}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${te} + 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 = ${ee}(${D("threadShared[0]",x)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${ee}(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 = ${ee}(${Nn("threadShared[0]",x)}); + } + 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); + } + }`,ke=e.compute({name:"Softmax",shaderCache:{hint:`${x}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:m,dataType:c.dataType}],dispatchGroup:{x:T},programUniforms:[{type:6,data:C}]}),getShaderSource:ie},{inputs:[c],outputs:[d?-1:0]})[0];d&&e.compute(xn(ke,g),{inputs:[ke]})},Ip=(e,t)=>{Pp(e.inputs),Ap(e,t)},Fp=e=>or({axis:e.axis})}),Dc,Op,zp,Dp,Lp,Xf=U(()=>{Yt(),Kt(),pr(),Dc=e=>Array.from(e.getBigInt64Array(),Number),Op=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(Dc(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")},zp=(e,t)=>{let r=[];for(let n=0;n{let r=e[0].dims,n=t??Dc(e[1]),i=zp(r,n),a=Ee.size(i),s=e[0].dataType,u=Qe("input",s,r.length),d=qt("output",s,i.length),c=g=>` + const inputShape = ${u.indices(...r)}; + ${g.registerUniform("output_size","u32").declareVariables(u,d)} + ${g.mainStart()} + ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${d.offsetToIndices("global_idx")}; + var input_indices: ${u.type.indices}; + for (var i = 0; i < ${r.length}; i++) { + let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${d.indicesGet("output_indices","i")} % input_dim_i; + + ${u.indicesSet("input_indices","i","input_dim_value")} + } + ${d.setByOffset("global_idx",u.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...kt(e[0].dims,i)]}),getShaderSource:c}},Lp=e=>{Op(e.inputs),e.compute(Dp(e.inputs),{inputs:[0]})}}),Bp,Rp,Np,Qf=U(()=>{Yt(),Kt(),pr(),Bp=(e,t,r,n,i)=>{let a=qt("output_data",i,r.length,4),s=Qe("a_data",t[1].dataType,t[1].dims.length,4),u=Qe("b_data",t[2].dataType,t[2].dims.length,4),d=Qe("c_data",t[0].dataType,t[0].dims.length,4),c,g=(m,l,T)=>`select(${l}, ${m}, ${T})`;if(!n)c=a.setByOffset("global_idx",g(s.getByOffset("global_idx"),u.getByOffset("global_idx"),d.getByOffset("global_idx")));else{let m=(l,T,x="")=>{let C=`a_data[index_a${T}][component_a${T}]`,D=`b_data[index_b${T}][component_b${T}]`,V=`bool(c_data[index_c${T}] & (0xffu << (component_c${T} * 8)))`;return` + let output_indices${T} = ${a.offsetToIndices(`global_idx * 4u + ${T}u`)}; + let offset_a${T} = ${s.broadcastedIndicesToOffset(`output_indices${T}`,a)}; + let offset_b${T} = ${u.broadcastedIndicesToOffset(`output_indices${T}`,a)}; + let offset_c${T} = ${d.broadcastedIndicesToOffset(`output_indices${T}`,a)}; + let index_a${T} = offset_a${T} / 4u; + let index_b${T} = offset_b${T} / 4u; + let index_c${T} = offset_c${T} / 4u; + let component_a${T} = offset_a${T} % 4u; + let component_b${T} = offset_b${T} % 4u; + let component_c${T} = offset_c${T} % 4u; + ${l}[${T}] = ${x}(${g(C,D,V)}); + `};i===9?c=` + var data = vec4(0); + ${m("data",0,"u32")} + ${m("data",1,"u32")} + ${m("data",2,"u32")} + ${m("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:c=` + ${m("output_data[global_idx]",0)} + ${m("output_data[global_idx]",1)} + ${m("output_data[global_idx]",2)} + ${m("output_data[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(d,s,u,a)} + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${c} + }`},Rp=e=>{let t=e[1].dims,r=e[2].dims,n=e[0].dims,i=e[1].dataType,a=!(Ee.areEqual(t,r)&&Ee.areEqual(r,n)),s=t,u=Ee.size(t);if(a){let c=bn.calcShape(bn.calcShape(t,r,!1),n,!1);if(!c)throw new Error("Can't perform where op on the given tensors");s=c,u=Ee.size(s)}let d=Math.ceil(u/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:c=>Bp(c,e,s,a,i),getRunData:()=>({outputs:[{dims:s,dataType:i}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:d},...kt(n,t,r,s)]})}},Np=e=>{e.compute(Rp(e.inputs))}}),jp,Yf=U(()=>{Uo(),ii(),zd(),el(),Al(),Dd(),Ld(),jd(),Sa(),Gd(),xu(),Fa(),Hd(),Kd(),Iu(),Yd(),Zd(),Ic(),tc(),rc(),nc(),nu(),ic(),Wa(),ac(),_n(),f(),Ue(),Vi(),os(),Wn(),Oc(),Hf(),Kf(),Hu(),Xf(),jn(),aa(),Qf(),jp=new 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(should not happen)");let e=this.kernelCustomData.get(this.currentKernelId);return e||(e={},this.kernelCustomData.set(this.currentKernelId,e)),e}async initialize(e,t){this.env=e;let r=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:t.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:t.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:t.limits.maxStorageBufferBindingSize,maxBufferSize:t.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:t.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:t.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:t.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:t.limits.maxComputeWorkgroupSizeZ},requiredFeatures:r};t.features.has("chromium-experimental-timestamp-query-inside-passes")?r.push("chromium-experimental-timestamp-query-inside-passes"):t.features.has("timestamp-query")&&r.push("timestamp-query"),t.features.has("shader-f16")&&r.push("shader-f16"),this.device=await t.requestDevice(n),this.adapterInfo=new Gp(t.info||await t.requestAdapterInfo()),this.gpuDataManager=ur(this),this.programManager=new Vp(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,ns(e.logLevel,!!e.debug),this.device.onuncapturederror=i=>{i.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${i.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:t,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(),t={};this.queryType==="at-passes"&&(t.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(t)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;je(),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 n;let t=new BigUint64Array(e.getMappedRange()),r=this.pendingQueries.get(e);for(let i=0;i"u"&&(this.queryTimeBase=T);let C=Number(T-this.queryTimeBase),D=Number(x-this.queryTimeBase);if(!Number.isSafeInteger(C)||!Number.isSafeInteger(D))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:m.map(V=>({dims:V.dims,dataType:An(V.dataType)})),outputsMetadata:l.map(V=>({dims:V.dims,dataType:An(V.dataType)})),kernelId:s,kernelType:d,kernelName:c,programName:g,startTime:C,endTime:D});else{let V="";m.forEach((ee,te)=>{V+=`input[${te}]: [${ee.dims}] | ${An(ee.dataType)}, `});let A="";l.forEach((ee,te)=>{A+=`output[${te}]: [${ee.dims}] | ${An(ee.dataType)}, `}),console.log(`[profiling] kernel "${s}|${d}|${c}|${g}" ${V}${A}execution time: ${D-C} ns`)}Ke("GPU",`${g}::${T}::${x}`)}e.unmap(),this.pendingQueries.delete(e)}),Ve()}run(e,t,r,n,i,a){je(e.name);let s=[];for(let A=0;Aee):r;if(g.length!==u.length)throw new Error(`Output size ${g.length} must be equal to ${u.length}.`);let m=[],l=[];for(let A=0;A=a)throw new Error(`Invalid output index: ${g[A]}`);if(g[A]===-3)continue;let ee=g[A]===-1,te=g[A]===-2,ie=ee||te?i(u[A].dataType,u[A].dims):n(g[A],u[A].dataType,u[A].dims);if(m.push(ie),ie.data===0)continue;let ke=this.gpuDataManager.get(ie.data);if(!ke)throw new Error(`no GPU data for output: ${ie.data}`);if(ee&&this.temporaryData.push(ke),te){let Pe=this.kernelPersistentData.get(this.currentKernelId);Pe||(Pe=[],this.kernelPersistentData.set(this.currentKernelId,Pe)),Pe.push(ke)}l.push(ke)}if(s.length!==t.length||l.length!==m.length){if(l.length===0)return Ve(e.name),m;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let T;if(c){let A=0,ee=[];c.forEach(Pe=>{let Ye=typeof Pe.data=="number"?[Pe.data]:Pe.data;if(Ye.length===0)return;let Ft=Pe.type===10?2:4,Bt,ar;Pe.type===10?(ar=Ye.length>4?16:Ye.length>2?8:Ye.length*Ft,Bt=Ye.length>4?16:Ft*Ye.length):(ar=Ye.length<=2?Ye.length*Ft:16,Bt=16),A=Math.ceil(A/ar)*ar,ee.push(A);let nr=Pe.type===10?8:4;A+=Ye.length>4?Math.ceil(Ye.length/nr)*Bt:Ye.length*Ft});let te=16;A=Math.ceil(A/te)*te;let ie=new ArrayBuffer(A);c.forEach((Pe,Ye)=>{let Ft=ee[Ye],Bt=typeof Pe.data=="number"?[Pe.data]:Pe.data;if(Pe.type===6)new Int32Array(ie,Ft,Bt.length).set(Bt);else if(Pe.type===12)new Uint32Array(ie,Ft,Bt.length).set(Bt);else if(Pe.type===10)new Uint16Array(ie,Ft,Bt.length).set(Bt);else if(Pe.type===1)new Float32Array(ie,Ft,Bt.length).set(Bt);else throw new Error(`Unsupported uniform type: ${An(Pe.type)}`)});let ke=this.gpuDataManager.create(A,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(ke.buffer,0,ie,0,A),this.gpuDataManager.release(ke.id),T={offset:0,size:A,buffer:ke.buffer}}let x=this.programManager.normalizeDispatchGroupSize(d),C=x[1]===1&&x[2]===1,D=Wp(e,t,C),V=this.programManager.getArtifact(D);if(V||(V=this.programManager.build(e,x),this.programManager.setArtifact(D,V),ae("info",()=>`[artifact] key: ${D}, programName: ${e.name}`)),c&&V.uniformVariablesInfo){if(c.length!==V.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${V.uniformVariablesInfo.length}, got ${c.length} in program "${V.programInfo.name}".`);for(let A=0;A`[ProgramManager] run "${e.name}" (key=${D}) with ${x[0]}x${x[1]}x${x[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let A={kernelId:this.currentKernelId,programName:V.programInfo.name,inputTensorViews:t,outputTensorViews:m};this.pendingKernels.push(A),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(A)}return this.programManager.run(V,s,l,x,T),Ve(e.name),m}upload(e,t){this.gpuDataManager.upload(e,t)}memcpy(e,t){this.gpuDataManager.memcpy(e,t)}async download(e,t){await this.gpuDataManager.download(e,t)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,t,r,n){let i=jp.get(e);if(!i)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:n,kernelEntry:i[0],attributes:[i[1],r]};this.kernels.set(t,a)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let r of t)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,r){let n=this.kernels.get(e);if(!n)throw new Error(`kernel not created: ${e}`);let i=n.kernelType,a=n.kernelName,s=n.kernelEntry,u=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${i}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,u[0]&&(u[1]=u[0](u[1]),u[0]=void 0),ae("info",()=>`[WebGPU] Start to run kernel "[${i}] ${a}"...`);let d=this.env.debug;this.temporaryData=[];try{return d&&this.device.pushErrorScope("validation"),s(t,u[1]),0}catch(c){return r.push(Promise.resolve(`[WebGPU] Kernel "[${i}] ${a}" failed. ${c}`)),1}finally{d&&r.push(this.device.popErrorScope().then(c=>c?`GPU validation error for kernel "[${i}] ${a}": ${c.message}`:null));for(let c of this.temporaryData)this.gpuDataManager.release(c.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,r,n){let i=this.sessionExternalDataMapping.get(e);i||(i=new Map,this.sessionExternalDataMapping.set(e,i));let a=i.get(t),s=this.gpuDataManager.registerExternalBuffer(r,n,a);return i.set(t,[s,r]),s}unregisterBuffers(e){let t=this.sessionExternalDataMapping.get(e);t&&(t.forEach(r=>this.gpuDataManager.unregisterExternalBuffer(r[0])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let t=this.gpuDataManager.get(e);if(!t)throw new Error(`no GPU data for buffer: ${e}`);return t.buffer}createDownloader(e,t,r){return async()=>{let n=await er(this,e,t);return F(n.buffer,r)}}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(){ae("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(){ae("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){ae("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),r=e.length;this.pendingKernels=[];for(let n=0;n=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}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()}}}),Hp,Lc,Bc,Rc,Kp,Xp,em=U(()=>{_(),Hp=1,Lc=()=>Hp++,Bc=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}destroy(){ae("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)}sameTypeAndShape(e,t){return this.dataType===e&&this.tensorShape.every((r,n)=>r===t[n])}},Rc=class{constructor(e,t){this.tensorManager=e,this.wrapper=t}get tensorWrapper(){return this.wrapper}releaseTensor(){this.tensorWrapper&&this.tensorManager.releaseTensor(this.tensorWrapper)}async ensureTensor(e,t,r){if(this.wrapper){if(this.wrapper.sameTypeAndShape(e,t))return this.wrapper.tensor;r&&(this.activeUpload=new Uint8Array(await this.wrapper.read())),this.tensorManager.releaseTensor(this.wrapper)}let n=MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(e,t,n,!0,!0),r&&this.activeUpload&&(this.wrapper.write(this.activeUpload),this.activeUpload=void 0),this.wrapper.tensor}upload(e){if(this.wrapper){this.wrapper.write(e);return}this.activeUpload?this.activeUpload.set(e):this.activeUpload=new Uint8Array(e)}async download(e){if(this.activeUpload)if(e){e instanceof ArrayBuffer?new Uint8Array(e).set(this.activeUpload):new Uint8Array(e.buffer,e.byteOffset,e.byteLength).set(this.activeUpload);return}else return this.activeUpload.buffer;if(!this.wrapper)throw new Error("Tensor has not been created.");return e?this.wrapper.read(e):this.wrapper.read()}},Kp=class{constructor(e){this.backend=e,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}reserveTensorId(){let e=Lc();return this.tensorTrackersById.set(e,new Rc(this)),e}releaseTensorId(e){let t=this.tensorTrackersById.get(e);t&&(this.tensorTrackersById.delete(e),t.tensorWrapper&&this.releaseTensor(t.tensorWrapper))}async ensureTensor(e,t,r,n){ae("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${e}, dataType: ${t}, shape: ${r}, copyOld: ${n}}`);let i=this.tensorTrackersById.get(e);if(!i)throw new Error("Tensor not found.");return i.ensureTensor(t,r,n)}upload(e,t){let r=this.tensorTrackersById.get(e);if(!r)throw new Error("Tensor not found.");r.upload(t)}async download(e,t){ae("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${e}, dstBuffer: ${t==null?void 0:t.byteLength}}`);let r=this.tensorTrackersById.get(e);if(!r)throw new Error("Tensor not found.");return r.download(t)}releaseTensorsForSession(e){for(let t of this.freeTensors)t.sessionId===e&&t.destroy();this.freeTensors=this.freeTensors.filter(t=>t.sessionId!==e)}registerTensor(e,t,r,n){let i=Lc(),a=new Bc({sessionId:this.backend.currentSessionId,context:e,tensor:t,dataType:r,shape:n});return this.tensorTrackersById.set(i,new Rc(this,a)),this.externalTensors.add(a),i}async getCachedTensor(e,t,r,n,i){let a=this.backend.currentSessionId;for(let[d,c]of this.freeTensors.entries())if(c.sameTypeAndShape(e,t)){let g=this.freeTensors.splice(d,1)[0];return g.sessionId=a,g}let s=this.backend.currentContext;ae("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${e}, shape: ${t}}`);let u=await s.createTensor({dataType:e,shape:t,dimensions:t,usage:r,writable:n,readable:i});return new Bc({sessionId:a,context:s,tensor:u,dataType:e,shape:t})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},Xp=(...e)=>new Kp(...e)}),Nc,Qp,tm=U(()=>{Yt(),kr(),Q(),em(),_(),Nc=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),Qp=class{constructor(e){this.tensorManager=Xp(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,ns(e.logLevel,!!e.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw new Error("No active session");return this.activeSessionId}onRunStart(e){this.activeSessionId=e}get currentContext(){let e=this.getMLContext(this.currentSessionId);if(!e)throw new Error(`No MLContext found for session ${this.currentSessionId}`);return e}registerMLContext(e,t){this.mlContextBySessionId.set(e,t);let r=this.sessionIdsByMLContext.get(t);r||(r=new Set,this.sessionIdsByMLContext.set(t,r)),r.add(e)}onReleaseSession(e){let t=this.mlContextBySessionId.get(e);if(!t)return;this.tensorManager.releaseTensorsForSession(e),this.mlContextBySessionId.delete(e);let r=this.sessionIdsByMLContext.get(t);r.delete(e),r.size===0&&this.sessionIdsByMLContext.delete(t)}getMLContext(e){return this.mlContextBySessionId.get(e)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(e){ae("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,t,r,n){let i=Nc.get(t);if(!i)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e,i,r,n)}uploadTensor(e,t){if(!mr().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");ae("verbose",()=>`[WebNN] uploadTensor {tensorId: ${e}, data: ${t.byteLength}}`),this.tensorManager.upload(e,t)}async downloadTensor(e,t){return this.tensorManager.download(e,t)}createMLTensorDownloader(e,t){return async()=>{let r=await this.tensorManager.download(e);return F(r,t)}}registerMLTensor(e,t,r){let n=Nc.get(t);if(!n)throw new Error(`Unsupported ONNX data type: ${t}`);let i=this.tensorManager.registerTensor(this.currentContext,e,n,r);return ae("verbose",()=>`[WebNN] registerMLTensor {tensor: ${e}, dataType: ${n}, dimensions: ${r}} -> {tensorId: ${i}}`),i}registerMLConstant(e,t,r,n,i,a){if(!a)throw new Error("External mounted files are not available.");let s=e;e.startsWith("./")&&(s=e.substring(2));let u=a.get(s);if(!u)throw new Error(`File with name ${s} not found in preloaded files.`);if(t+r>u.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let d=u.slice(t,t+r).buffer,c;switch(i.dataType){case"float32":c=new Float32Array(d);break;case"float16":c=new Uint16Array(d);break;case"int32":c=new Int32Array(d);break;case"uint32":c=new Uint32Array(d);break;case"int64":c=new BigInt64Array(d);break;case"uint64":c=new BigUint64Array(d);break;case"int8":c=new Int8Array(d);break;case"uint8":c=new Uint8Array(d);break;default:throw new Error(`Unsupported data type: ${i.dataType} in creating WebNN Constant from external data.`)}return ae("verbose",()=>`[WebNN] registerMLConstant {dataType: ${i.dataType}, shape: ${i.shape}}}`),n.constant(i,c)}flush(){}}}),Yp={};I(Yp,{init:()=>Jp});var dc,Zp,Jp,rm=U(()=>{Yt(),Jf(),_(),Kt(),tm(),dc=class Lf{constructor(t,r,n,i){this.module=t,this.dataType=r,this.data=n,this.dims=i}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let t=Ee.size(this.dims);return t===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,t)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let t=Ee.size(this.dims);return t===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,t)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let t=Ee.size(this.dims);return t===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,t)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let t=Ee.size(this.dims);return t===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if(Ee.size(t)!==Ee.size(this.dims))throw new Error("Invalid new shape");return new Lf(this.module,this.dataType,this.data,t)}},Zp=class{constructor(e,t,r){this.module=e,this.backend=t,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=t.adapterInfo;let n=e.HEAPU32,i=r>>>2;this.opKernelContext=n[i++];let a=n[i++];this.outputCount=n[i++],this.customDataOffset=n[i++],this.customDataSize=n[i++];let s=[];for(let u=0;utypeof u=="number"?this.inputs[u]:u))??this.inputs,n=(t==null?void 0:t.outputs)??[],i=(u,d,c)=>new dc(this.module,d,this.output(u,c),c),a=(u,d)=>{let c=Bn(u,d);if(!c)throw new Error(`Unsupported data type: ${u}`);let g=c>0?this.backend.gpuDataManager.create(c).id:0;return new dc(this.module,u,g,d)};return this.backend.run(e,r,n,i,a,this.outputCount)}output(e,t){let r=this.module.stackSave();try{let n=this.module.stackAlloc((1+t.length)*4),i=n>>2;this.module.HEAPU32[i++]=t.length;for(let a=0;a{let i=t.jsepInit;if(!i)throw new Error("Failed to initialize JSEP. 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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. + * ============================================================================= + */},"./src/backends/onnx.js":(xt,ye,O)=>{var P;O.r(ye),O.d(ye,{Tensor:()=>Ce.Tensor,createInferenceSession:()=>z,deviceToExecutionProviders:()=>fe,isONNXProxy:()=>X,isONNXTensor:()=>J});var le=O("./src/env.js"),we=O("?2ce3"),xe=O("./node_modules/onnxruntime-web/dist/ort.webgpu.bundle.min.mjs"),Ce=O("./node_modules/onnxruntime-common/dist/esm/index.js");const U=Object.freeze({auto:null,gpu:null,cpu:"cpu",wasm:"wasm",webgpu:"webgpu",cuda:"cuda",dml:"dml",webnn:{name:"webnn",deviceType:"cpu"},"webnn-npu":{name:"webnn",deviceType:"npu"},"webnn-gpu":{name:"webnn",deviceType:"gpu"},"webnn-cpu":{name:"webnn",deviceType:"cpu"}}),I=[];let L,B;const q=Symbol.for("onnxruntime");if(q in globalThis)B=globalThis[q];else if(le.apis.IS_NODE_ENV){switch(B=we??(P||(P=O.t(we,2))),process.platform){case"win32":I.push("dml");break;case"linux":process.arch==="x64"&&I.push("cuda");break}I.push("cpu"),L=["cpu"]}else B=xe,le.apis.IS_WEBNN_AVAILABLE&&I.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),le.apis.IS_WEBGPU_AVAILABLE&&I.push("webgpu"),I.push("wasm"),L=["wasm"];const re=B.InferenceSession;function fe(K=null){if(!K)return L;switch(K){case"auto":return I;case"gpu":return I.filter(j=>["webgpu","cuda","dml","webnn-gpu"].includes(j))}if(I.includes(K))return[U[K]??K];throw new Error(`Unsupported device: "${K}". 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le{constructor(L){super(),Array.isArray(L)||(L=[L]),this.eos_token_id=L}_call(L,B){return L.map(q=>{const re=q.at(-1);return this.eos_token_id.some(fe=>re==fe)})}}class U extends le{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(L,B){return new Array(L.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(xt,ye,O)=>{O.r(ye),O.d(ye,{BaseStreamer:()=>xe,TextStreamer:()=>U,WhisperTextStreamer:()=>I});var P=O("./src/utils/core.js"),le=O("./src/tokenizers.js"),we=O("./src/env.js");class xe{put(B){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const Ce=we.apis.IS_PROCESS_AVAILABLE?L=>process.stdout.write(L):L=>console.log(L);class U extends xe{constructor(B,{skip_prompt:q=!1,callback_function:re=null,token_callback_function:fe=null,decode_kwargs:de={},...z}={}){super(),this.tokenizer=B,this.skip_prompt=q,this.callback_function=re??Ce,this.token_callback_function=fe,this.decode_kwargs={...de,...z},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(B){var de;if(B.length>1)throw Error("TextStreamer only supports batch size of 1");if(this.skip_prompt&&this.next_tokens_are_prompt){this.next_tokens_are_prompt=!1;return}const q=B[0];(de=this.token_callback_function)==null||de.call(this,q),this.token_cache=(0,P.mergeArrays)(this.token_cache,q);const re=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let fe;re.endsWith(` +`)?(fe=re.slice(this.print_len),this.token_cache=[],this.print_len=0):re.length>0&&(0,le.is_chinese_char)(re.charCodeAt(re.length-1))?(fe=re.slice(this.print_len),this.print_len+=fe.length):(fe=re.slice(this.print_len,re.lastIndexOf(" 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q.Tensor("int64",BigInt64Array.from(f.map(b=>BigInt(b))),[1,f.length])}function it(f){return new q.Tensor("bool",[f],[1])}async function rt(f,b){let{encoder_outputs:R,input_ids:ve,decoder_input_ids:Ue,...Le}=b;if(!R){const St=(0,Ce.pick)(b,f.sessions.model.inputNames);R=(await lt(f,St)).last_hidden_state}return Le.input_ids=Ue,Le.encoder_hidden_states=R,f.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(Le.encoder_attention_mask=b.attention_mask),await me(f,Le,!0)}async function lt(f,b){const R=f.sessions.model,ve=(0,Ce.pick)(b,R.inputNames);if(R.inputNames.includes("inputs_embeds")&&!ve.inputs_embeds){if(!b.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");ve.inputs_embeds=await f.encode_text({input_ids:b.input_ids})}return R.inputNames.includes("token_type_ids")&&!ve.token_type_ids&&(ve.token_type_ids=new q.Tensor("int64",new BigInt64Array(ve.input_ids.data.length),ve.input_ids.dims)),await $e(R,ve)}async function me(f,b,R=!1){const ve=f.sessions[R?"decoder_model_merged":"model"],{past_key_values:Ue,...Le}=b;ve.inputNames.includes("use_cache_branch")&&(Le.use_cache_branch=it(!!Ue)),ve.inputNames.includes("position_ids")&&Le.attention_mask&&!Le.position_ids&&(Le.position_ids=ce(Le,Ue)),f.addPastKeyValues(Le,Ue);const pt=(0,Ce.pick)(Le,ve.inputNames);return await $e(ve,pt)}async function W(f,{input_ids:b=null,attention_mask:R=null,pixel_values:ve=null,position_ids:Ue=null,inputs_embeds:Le=null,past_key_values:pt=null,generation_config:St=null,logits_processor:Vt=null,...tr}){if(!Le){if(Le=await f.encode_text({input_ids:b}),ve&&b.dims[1]!==1){const Dr=await f.encode_image({pixel_values:ve});({inputs_embeds:Le,attention_mask:R}=f._merge_input_ids_with_image_features({image_features:Dr,inputs_embeds:Le,input_ids:b,attention_mask:R}))}else if(pt&&ve&&b.dims[1]===1){const Dr=b.dims[1],yr=Object.values(pt)[0].dims.at(-2);R=(0,q.cat)([(0,q.ones)([b.dims[0],yr]),R.slice(null,[R.dims[1]-Dr,R.dims[1]])],1)}}return await me(f,{inputs_embeds:Le,past_key_values:pt,attention_mask:R,position_ids:Ue,generation_config:St,logits_processor:Vt},!0)}function ce(f,b=null){const{input_ids:R,inputs_embeds:ve,attention_mask:Ue}=f,[Le,pt]=Ue.dims,St=new BigInt64Array(Ue.data.length);for(let tr=0;trLe.dims[1])){if(UeSt==f.config.image_token_index)){const St=f.config.num_image_tokens;if(!St)throw new Error("`num_image_tokens` is missing in the model configuration.");const Vt=Le.dims[1]-(Ue-St);R.input_ids=Le.slice(null,[-Vt,null]),R.attention_mask=(0,q.ones)([1,Ue+Vt])}}}return R}function We(f,b,R,ve){return R.past_key_values&&(b=b.map(Ue=>[Ue.at(-1)])),{...R,decoder_input_ids:ze(b)}}function ot(f,...b){return f.config.is_encoder_decoder?We(f,...b):Te(f,...b)}class ne extends xe.Callable{constructor(R,ve,Ue){super();be(this,"main_input_name","input_ids");be(this,"forward_params",["input_ids","attention_mask"]);this.config=R,this.sessions=ve,this.configs=Ue;const Le=k.get(this.constructor),pt=K.get(Le);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,pt){case X.DecoderOnly:this.can_generate=!0,this._forward=me,this._prepare_inputs_for_generation=Te;break;case X.Seq2Seq:case X.Vision2Seq:case X.Musicgen:this.can_generate=!0,this._forward=rt,this._prepare_inputs_for_generation=We;break;case X.EncoderDecoder:this._forward=rt;break;case X.ImageTextToText:this.can_generate=!0,this._forward=W,this._prepare_inputs_for_generation=ot;break;default:this._forward=lt;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var ve;const R=[];for(const Ue of Object.values(this.sessions))(ve=Ue==null?void 0:Ue.handler)!=null&&ve.dispose&&R.push(Ue.handler.dispose());return await Promise.all(R)}static async from_pretrained(R,{progress_callback:ve=null,config:Ue=null,cache_dir:Le=null,local_files_only:pt=!1,revision:St="main",model_file_name:Vt=null,subfolder:tr="onnx",device:Ar=null,dtype:Dr=null,use_external_data_format:yr=null,session_options:ir={}}={}){let Ir={progress_callback:ve,config:Ue,cache_dir:Le,local_files_only:pt,revision:St,model_file_name:Vt,subfolder:tr,device:Ar,dtype:Dr,use_external_data_format:yr,session_options:ir};const $r=k.get(this),gr=K.get($r);Ue=Ir.config=await P.AutoConfig.from_pretrained(R,Ir);let Lr;if(gr===X.DecoderOnly)Lr=await Promise.all([E(R,{model:Ir.model_file_name??"model"},Ir),ue(R,{generation_config:"generation_config.json"},Ir)]);else if(gr===X.Seq2Seq||gr===X.Vision2Seq)Lr=await Promise.all([E(R,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Ir),ue(R,{generation_config:"generation_config.json"},Ir)]);else if(gr===X.MaskGeneration)Lr=await Promise.all([E(R,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},Ir)]);else if(gr===X.EncoderDecoder)Lr=await Promise.all([E(R,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Ir)]);else if(gr===X.ImageTextToText){const Tn={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Ue.is_encoder_decoder&&(Tn.model="encoder_model"),Lr=await Promise.all([E(R,Tn,Ir),ue(R,{generation_config:"generation_config.json"},Ir)])}else gr===X.Musicgen?Lr=await Promise.all([E(R,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},Ir),ue(R,{generation_config:"generation_config.json"},Ir)]):(gr!==X.EncoderOnly&&console.warn(`Model type for '${$r??(Ue==null?void 0:Ue.model_type)}' not found, assuming encoder-only architecture. Please report this at ${I.GITHUB_ISSUE_URL}.`),Lr=await Promise.all([E(R,{model:Ir.model_file_name??"model"},Ir)]));return new this(Ue,...Lr)}async _call(R){return await this.forward(R)}async forward(R){return await this._forward(this,R)}get generation_config(){var R;return((R=this.configs)==null?void 0:R.generation_config)??null}_get_logits_warper(R){const ve=new L.LogitsProcessorList;return R.temperature!==null&&R.temperature!==1&&ve.push(new L.TemperatureLogitsWarper(R.temperature)),R.top_k!==null&&R.top_k!==0&&ve.push(new L.TopKLogitsWarper(R.top_k)),R.top_p!==null&&R.top_p<1&&ve.push(new L.TopPLogitsWarper(R.top_p)),ve}_get_logits_processor(R,ve,Ue=null){const Le=new L.LogitsProcessorList;if(R.repetition_penalty!==null&&R.repetition_penalty!==1&&Le.push(new L.RepetitionPenaltyLogitsProcessor(R.repetition_penalty)),R.no_repeat_ngram_size!==null&&R.no_repeat_ngram_size>0&&Le.push(new L.NoRepeatNGramLogitsProcessor(R.no_repeat_ngram_size)),R.bad_words_ids!==null&&Le.push(new L.NoBadWordsLogitsProcessor(R.bad_words_ids,R.eos_token_id)),R.min_length!==null&&R.eos_token_id!==null&&R.min_length>0&&Le.push(new L.MinLengthLogitsProcessor(R.min_length,R.eos_token_id)),R.min_new_tokens!==null&&R.eos_token_id!==null&&R.min_new_tokens>0&&Le.push(new L.MinNewTokensLengthLogitsProcessor(ve,R.min_new_tokens,R.eos_token_id)),R.forced_bos_token_id!==null&&Le.push(new L.ForcedBOSTokenLogitsProcessor(R.forced_bos_token_id)),R.forced_eos_token_id!==null&&Le.push(new L.ForcedEOSTokenLogitsProcessor(R.max_length,R.forced_eos_token_id)),R.begin_suppress_tokens!==null){const pt=ve>1||R.forced_bos_token_id===null?ve:ve+1;Le.push(new L.SuppressTokensAtBeginLogitsProcessor(R.begin_suppress_tokens,pt))}return R.guidance_scale!==null&&R.guidance_scale>1&&Le.push(new L.ClassifierFreeGuidanceLogitsProcessor(R.guidance_scale)),Ue!==null&&Le.extend(Ue),Le}_prepare_generation_config(R,ve,Ue=B.GenerationConfig){const Le={...this.config};for(const St of["decoder","generator","text_config"])St in Le&&Object.assign(Le,Le[St]);const pt=new Ue(Le);return Object.assign(pt,this.generation_config??{}),R&&Object.assign(pt,R),ve&&Object.assign(pt,(0,Ce.pick)(ve,Object.getOwnPropertyNames(pt))),pt}_get_stopping_criteria(R,ve=null){const Ue=new fe.StoppingCriteriaList;return R.max_length!==null&&Ue.push(new fe.MaxLengthCriteria(R.max_length,this.config.max_position_embeddings??null)),R.eos_token_id!==null&&Ue.push(new fe.EosTokenCriteria(R.eos_token_id)),ve&&Ue.extend(ve),Ue}_validate_model_class(){if(!this.can_generate){const R=[Ga,qa,Wa,Va],ve=k.get(this.constructor),Ue=new Set,Le=this.config.model_type;for(const St of R){const Vt=St.get(Le);Vt&&Ue.add(Vt[0])}let pt=`The current model class (${ve}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Ue.size>0&&(pt+=` Please use the following class instead: ${[...Ue].join(", ")}`),Error(pt)}}prepare_inputs_for_generation(...R){return this._prepare_inputs_for_generation(this,...R)}_update_model_kwargs_for_generation({generated_input_ids:R,outputs:ve,model_inputs:Ue,is_encoder_decoder:Le}){return Ue.past_key_values=this.getPastKeyValues(ve,Ue.past_key_values),Ue.input_ids=new q.Tensor("int64",R.flat(),[R.length,1]),Le||(Ue.attention_mask=(0,q.cat)([Ue.attention_mask,(0,q.ones)([Ue.attention_mask.dims[0],1])],1)),Ue.position_ids=null,Ue}_prepare_model_inputs({inputs:R,bos_token_id:ve,model_kwargs:Ue}){const Le=(0,Ce.pick)(Ue,this.forward_params),pt=this.main_input_name;if(pt in Le){if(R)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Le[pt]=R;return{inputs_tensor:Le[pt],model_inputs:Le,model_input_name:pt}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:R,model_inputs:ve,model_input_name:Ue,generation_config:Le}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!ve.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:St,pixel_values:Vt,attention_mask:tr,...Ar}=ve,Dr=await this._prepare_inputs_embeds(ve);ve={...Ar,...(0,Ce.pick)(Dr,["inputs_embeds","attention_mask"])}}let{last_hidden_state:pt}=await lt(this,ve);if(Le.guidance_scale!==null&&Le.guidance_scale>1)pt=(0,q.cat)([pt,(0,q.full_like)(pt,0)],0),"attention_mask"in ve&&(ve.attention_mask=(0,q.cat)([ve.attention_mask,(0,q.zeros_like)(ve.attention_mask)],0));else if(ve.decoder_input_ids){const St=ze(ve.decoder_input_ids).dims[0];if(St!==pt.dims[0]){if(pt.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${pt.dims[0]}) than the decoder inputs (${St}).`);pt=(0,q.cat)(Array.from({length:St},()=>pt),0)}}return ve.encoder_outputs=pt,ve}_prepare_decoder_input_ids_for_generation({batch_size:R,model_input_name:ve,model_kwargs:Ue,decoder_start_token_id:Le,bos_token_id:pt,generation_config:St}){let{decoder_input_ids:Vt,...tr}=Ue;if(!(Vt instanceof q.Tensor)){if(Vt)Array.isArray(Vt[0])||(Vt=Array.from({length:R},()=>Vt));else if(Le??(Le=pt),this.config.model_type==="musicgen")Vt=Array.from({length:R*this.config.decoder.num_codebooks},()=>[Le]);else if(Array.isArray(Le)){if(Le.length!==R)throw new Error(`\`decoder_start_token_id\` expcted to have length ${R} but got ${Le.length}`);Vt=Le}else Vt=Array.from({length:R},()=>[Le]);Vt=ze(Vt)}return Ue.decoder_attention_mask=(0,q.ones_like)(Vt),{input_ids:Vt,model_inputs:tr}}async generate({inputs:R=null,generation_config:ve=null,logits_processor:Ue=null,stopping_criteria:Le=null,streamer:pt=null,...St}){this._validate_model_class(),ve=this._prepare_generation_config(ve,St);let{inputs_tensor:Vt,model_inputs:tr,model_input_name:Ar}=this._prepare_model_inputs({inputs:R,model_kwargs:St});const Dr=this.config.is_encoder_decoder;Dr&&("encoder_outputs"in tr||(tr=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Vt,model_inputs:tr,model_input_name:Ar,generation_config:ve})));let yr;Dr?{input_ids:yr,model_inputs:tr}=this._prepare_decoder_input_ids_for_generation({batch_size:tr[Ar].dims.at(0),model_input_name:Ar,model_kwargs:tr,decoder_start_token_id:ve.decoder_start_token_id,bos_token_id:ve.bos_token_id,generation_config:ve}):yr=tr[Ar];let ir=yr.dims.at(-1);ve.max_new_tokens!==null&&(ve.max_length=ir+ve.max_new_tokens);const Ir=this._get_logits_processor(ve,ir,Ue),$r=this._get_stopping_criteria(ve,Le),gr=tr[Ar].dims.at(0),Lr=de.LogitsSampler.getSampler(ve),Tn=new Array(gr).fill(0),dn=yr.tolist();pt&&pt.put(dn);let Gr,Xr={};for(;;){if(tr=this.prepare_inputs_for_generation(dn,tr,ve),Gr=await this.forward(tr),ve.output_attentions&&ve.return_dict_in_generate){const Wn=this.getAttentions(Gr);for(const xs in Wn)xs in Xr||(Xr[xs]=[]),Xr[xs].push(Wn[xs])}const os=Gr.logits.slice(null,-1,null),_i=Ir(dn,os),no=[];for(let Wn=0;Wn<_i.dims.at(0);++Wn){const xs=_i[Wn],lc=await Lr(xs);for(const[uc,Oc]of lc){const Cd=BigInt(uc);Tn[Wn]+=Oc,dn[Wn].push(Cd),no.push([Cd]);break}}if(pt&&pt.put(no),$r(dn).every(Wn=>Wn))break;tr=this._update_model_kwargs_for_generation({generated_input_ids:no,outputs:Gr,model_inputs:tr,is_encoder_decoder:Dr})}pt&&pt.end();const Qr=this.getPastKeyValues(Gr,tr.past_key_values,!0),gn=new q.Tensor("int64",dn.flat(),[dn.length,dn[0].length]);if(ve.return_dict_in_generate)return{sequences:gn,past_key_values:Qr,...Xr};for(const os of Object.values(Gr))os.location==="gpu-buffer"&&os.dispose();return gn}getPastKeyValues(R,ve,Ue=!1){const Le=Object.create(null);for(const pt in R)if(pt.startsWith("present")){const St=pt.replace("present","past_key_values"),Vt=pt.includes("encoder");if(Vt&&ve?Le[St]=ve[St]:Le[St]=R[pt],ve&&(!Vt||Ue)){const tr=ve[St];tr.location==="gpu-buffer"&&tr.dispose()}}return Le}getAttentions(R){const ve={};for(const Ue of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Le in R)Le.startsWith(Ue)&&(Ue in ve||(ve[Ue]=[]),ve[Ue].push(R[Le]));return ve}addPastKeyValues(R,ve){var Ue;if(ve)Object.assign(R,ve);else{const Le=this.sessions.decoder_model_merged??this.sessions.model,pt=((Ue=Le==null?void 0:Le.config)==null?void 0:Ue.kv_cache_dtype)??"float32",St=pt==="float16"?new Uint16Array:[],Vt=(0,P.getKeyValueShapes)(this.config);for(const tr in Vt)R[tr]=new q.Tensor(pt,St,Vt[tr])}}async encode_image({pixel_values:R}){const ve=(await $e(this.sessions.vision_encoder,{pixel_values:R})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${ve.dims[1]}).`),this.config.num_image_tokens=ve.dims[1]),ve}async encode_text({input_ids:R}){return(await $e(this.sessions.embed_tokens,{input_ids:R})).inputs_embeds}}class Ze{}class dt extends Ze{constructor({last_hidden_state:b,hidden_states:R=null,attentions:ve=null}){super(),this.last_hidden_state=b,this.hidden_states=R,this.attentions=ve}}class Re extends ne{}class ht extends Re{}class Mt extends Re{async _call(b){return new un(await super._call(b))}}class Xe extends Re{async _call(b){return new cr(await super._call(b))}}class Z extends Re{async _call(b){return new sn(await super._call(b))}}class Ae extends Re{async _call(b){return new _n(await super._call(b))}}class Ke extends ne{}class et extends Ke{}class je extends ne{}class Ve extends je{}class ut extends je{async _call(b){return new un(await super._call(b))}}class _t extends je{async _call(b){return new cr(await super._call(b))}}class Pt extends je{async _call(b){return new sn(await super._call(b))}}class Tt extends je{async _call(b){return new _n(await super._call(b))}}class v extends ne{}class H extends v{}class $ extends v{async _call(b){return new un(await super._call(b))}}class Y extends v{async _call(b){return new cr(await super._call(b))}}class he extends v{async _call(b){return new sn(await super._call(b))}}class nt extends v{async _call(b){return new _n(await super._call(b))}}class Je extends ne{}class Nt extends Je{}class yt extends Je{async _call(b){return new un(await super._call(b))}}class bt extends Je{async _call(b){return new cr(await super._call(b))}}class Dt extends Je{async _call(b){return new sn(await super._call(b))}}class At extends Je{async _call(b){return new _n(await super._call(b))}}class dr extends ne{}class Cr extends dr{}class Yr extends dr{async _call(b){return new un(await super._call(b))}}class Rr extends dr{async _call(b){return new cr(await super._call(b))}}class Jr extends dr{async _call(b){return new sn(await super._call(b))}}class yn extends dr{async _call(b){return new _n(await super._call(b))}}class at extends ne{}class G extends at{}class ge extends at{async _call(b){return new un(await super._call(b))}}class Ie extends at{async _call(b){return new cr(await super._call(b))}}class Se extends at{async _call(b){return new sn(await super._call(b))}}class Ne extends at{async _call(b){return new _n(await super._call(b))}}class tt extends ne{}class wt extends tt{}class mt extends tt{async _call(b){return new un(await super._call(b))}}class $t extends tt{async _call(b){return new cr(await super._call(b))}}class ft extends tt{async _call(b){return new sn(await super._call(b))}}class Lt extends tt{async _call(b){return new _n(await super._call(b))}}class jt extends ne{}class Ot extends jt{}class Fe extends jt{async _call(b){return new cr(await super._call(b))}}class Oe extends jt{async _call(b){return new sn(await super._call(b))}}class ct extends jt{async _call(b){return new _n(await super._call(b))}}class Ut extends jt{async _call(b){return new un(await super._call(b))}}class sr extends ne{}class br extends sr{}class Nr extends sr{async _call(b){return new un(await super._call(b))}}class mr extends sr{async _call(b){return new cr(await super._call(b))}}class kr extends sr{async _call(b){return new sn(await super._call(b))}}class wr extends ne{}class $n extends wr{}class Ur extends wr{async _call(b){return new un(await super._call(b))}}class hs extends wr{async _call(b){return new cr(await super._call(b))}}class Es extends wr{async _call(b){return new _n(await super._call(b))}}class Kn extends ne{}class ks extends Kn{}class Ss extends Kn{async _call(b){return new un(await super._call(b))}}class Ps extends Kn{async _call(b){return new cr(await super._call(b))}}class As extends Kn{async _call(b){return new sn(await super._call(b))}}class Is extends Kn{async _call(b){return new _n(await super._call(b))}}class ts extends ne{}class Xn extends ts{}class An extends ts{async _call(b){return new un(await super._call(b))}}class Bn extends ts{async _call(b){return new cr(await super._call(b))}}class fs extends ts{async _call(b){return new _n(await super._call(b))}}class Ln extends ne{}class ms extends Ln{}class _s extends Ln{async _call(b){return new cr(await super._call(b))}}class gs extends Ln{async _call(b){return new _n(await super._call(b))}}class Yt extends Ln{async _call(b){return new un(await super._call(b))}}class rs extends ne{constructor(){super(...arguments);be(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Fs extends rs{}class Os extends rs{}class ws extends ne{}class zs extends ws{}class Ds extends ws{}class ns extends ne{}class Ls extends ns{}class ae extends ns{}class _ extends ne{}class F extends _{}class Q extends _{}class oe extends _{async _call(b){return new cr(await super._call(b))}}class _e extends ne{}class Ge extends _e{}class gt extends _e{}class Et extends _e{async _call(b){return new cr(await super._call(b))}}class Ct extends _e{}class zt extends ne{}class er extends zt{}class Sr extends zt{}class ur extends ne{}class Wr extends ur{}class en extends ur{}class or extends ne{}class Pr extends or{}class mn extends or{async _call(b){return new un(await super._call(b))}}class bn extends or{async _call(b){return new cr(await super._call(b))}}class Ee extends or{async _call(b){return new sn(await super._call(b))}}class tn extends or{async _call(b){return new _n(await super._call(b))}}class ln extends ne{}class In extends ln{}class Rn extends ln{async _call(b){return new un(await super._call(b))}}class Kt extends ln{async _call(b){return new cr(await super._call(b))}}class pn extends ln{async _call(b){return new sn(await super._call(b))}}class Kr extends ln{async _call(b){return new _n(await super._call(b))}}class fr extends ne{}class Or extends fr{}class kt extends fr{async _call(b){return new un(await super._call(b))}}class _r extends fr{async _call(b){return new cr(await super._call(b))}}class ss extends fr{async _call(b){return new sn(await super._call(b))}}class Qn extends fr{async _call(b){return new _n(await super._call(b))}}class Nn extends ne{}class Wt extends Nn{}class ri extends Nn{}class Qe extends ne{constructor(){super(...arguments);be(this,"requires_attention_mask",!1);be(this,"main_input_name","input_features");be(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class qt extends Qe{}class Ti extends Qe{_prepare_generation_config(b,R){return super._prepare_generation_config(b,R,J.WhisperGenerationConfig)}_retrieve_init_tokens(b){const R=[b.decoder_start_token_id];let ve=b.language;const Ue=b.task;if(b.is_multilingual){ve||(console.warn("No language specified - defaulting to English (en)."),ve="en");const pt=`<|${(0,pe.whisper_language_to_code)(ve)}|>`;R.push(b.lang_to_id[pt]),R.push(b.task_to_id[Ue??"transcribe"])}else if(ve||Ue)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!b.return_timestamps&&b.no_timestamps_token_id&&R.at(-1)!==b.no_timestamps_token_id?R.push(b.no_timestamps_token_id):b.return_timestamps&&R.at(-1)===b.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),R.pop()),R.filter(Le=>Le!=null)}async generate({inputs:b=null,generation_config:R=null,logits_processor:ve=null,stopping_criteria:Ue=null,...Le}){R=this._prepare_generation_config(R,Le);const pt=Le.decoder_input_ids??this._retrieve_init_tokens(R);if(R.return_timestamps&&(ve??(ve=new L.LogitsProcessorList),ve.push(new L.WhisperTimeStampLogitsProcessor(R,pt))),R.begin_suppress_tokens&&(ve??(ve=new L.LogitsProcessorList),ve.push(new L.SuppressTokensAtBeginLogitsProcessor(R.begin_suppress_tokens,pt.length))),R.return_token_timestamps){if(!R.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");R.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),R.output_attentions=!0,R.return_dict_in_generate=!0}const St=await super.generate({inputs:b,generation_config:R,logits_processor:ve,decoder_input_ids:pt,...Le});return R.return_token_timestamps&&(St.token_timestamps=this._extract_token_timestamps(St,R.alignment_heads,R.num_frames)),St}_extract_token_timestamps(b,R,ve=null,Ue=.02){if(!b.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`.");ve==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 Le=this.config.median_filter_width;Le===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Le=7);const pt=b.cross_attentions,St=Array.from({length:this.config.decoder_layers},($r,gr)=>(0,q.cat)(pt.map(Lr=>Lr[gr]),2)),Vt=(0,q.stack)(R.map(([$r,gr])=>{if($r>=St.length)throw new Error(`Layer index ${$r} is out of bounds for cross attentions (length ${St.length}).`);return ve?St[$r].slice(null,gr,null,[0,ve]):St[$r].slice(null,gr)})).transpose(1,0,2,3),[tr,Ar]=(0,q.std_mean)(Vt,-2,0,!0),Dr=Vt.clone();for(let $r=0;$rLr[gn+1]-Lr[gn]),Gr=(0,Ce.mergeArrays)([1],dn).map(Qr=>!!Qr),Xr=[];for(let Qr=0;Qryr.findIndex(ir=>ir==Le)),Vt=St.every(yr=>yr===-1),tr=St.every(yr=>yr!==-1);if(!Vt&&!tr)throw new Error("Every input should contain either 0 or 1 image token.");if(Vt)return{inputs_embeds:b,attention_mask:Ue};const Ar=[],Dr=[];for(let yr=0;yrLe*pt,1);b.input_labels=new q.Tensor("int64",new BigInt64Array(Ue).fill(1n),ve)}const R={image_embeddings:b.image_embeddings,image_positional_embeddings:b.image_positional_embeddings};return b.input_points&&(R.input_points=b.input_points),b.input_labels&&(R.input_labels=b.input_labels),b.input_boxes&&(R.input_boxes=b.input_boxes),await $e(this.sessions.prompt_encoder_mask_decoder,R)}async _call(b){return new Vs(await super._call(b))}}class Vs extends Ze{constructor({iou_scores:b,pred_masks:R}){super(),this.iou_scores=b,this.pred_masks=R}}class wa extends ne{}class ya extends wa{}class Jl extends wa{}class ba extends ne{}class eu extends ba{}class Rd extends ba{}class Jn extends ne{}class tu extends Jn{}class Nd extends Jn{async _call(b){return new as(await super._call(b))}}class Ma extends Jn{async _call(b){return new cr(await super._call(b))}}class ru extends Jn{async _call(b){return new sn(await super._call(b))}}class va extends ne{}class nu extends va{}class su extends va{async _call(b){return new sn(await super._call(b))}}class pi extends ne{}class iu extends pi{}class Ms extends ne{}class xa extends Ms{}class Ta extends Ms{async _call(b){return new as(await super._call(b))}}class au extends Ms{async _call(b){return new cr(await super._call(b))}}class Us extends ne{}class Ca extends Us{}class jd extends Us{async _call(b){return new as(await super._call(b))}}class ou extends Us{async _call(b){return new cr(await super._call(b))}}class lu extends Us{async _call(b){return new sn(await super._call(b))}}class $a extends ne{}class uu extends $a{}class Ea extends $a{async _call(b){return new as(await super._call(b))}}class Vd extends $a{async _call(b){return new cr(await super._call(b))}}class Ud extends ne{}class du extends Jn{}class cu extends Jn{async _call(b){return new as(await super._call(b))}}class ka extends Jn{async _call(b){return new cr(await super._call(b))}}class vs extends ne{}class pu extends vs{}class hu extends vs{async _call(b){return new as(await super._call(b))}}class fu extends vs{async _call(b){return new cr(await super._call(b))}}class mu extends vs{async _call(b){return new vd(await super._call(b))}}class _u extends vs{async _call(b){return new sn(await super._call(b))}}class Sa extends ne{}class Wd extends Sa{}class gu extends Sa{}class wu extends Sa{async generate_speech(b,R,{threshold:ve=.5,minlenratio:Ue=0,maxlenratio:Le=20,vocoder:pt=null}={}){const St={input_ids:b},{encoder_outputs:Vt,encoder_attention_mask:tr}=await lt(this,St),Ar=Vt.dims[1]/this.config.reduction_factor,Dr=Math.floor(Ar*Le),yr=Math.floor(Ar*Ue),ir=this.config.num_mel_bins;let Ir=[],$r=null,gr=null,Lr=0;for(;;){++Lr;const Gr=it(!!gr);let Xr;gr?Xr=gr.output_sequence_out:Xr=new q.Tensor("float32",new Float32Array(ir),[1,1,ir]);let Qr={use_cache_branch:Gr,output_sequence:Xr,encoder_attention_mask:tr,speaker_embeddings:R,encoder_hidden_states:Vt};this.addPastKeyValues(Qr,$r),gr=await $e(this.sessions.decoder_model_merged,Qr),$r=this.getPastKeyValues(gr,$r);const{prob:gn,spectrum:os}=gr;if(Ir.push(os),Lr>=yr&&(Array.from(gn.data).filter(_i=>_i>=ve).length>0||Lr>=Dr))break}const Tn=(0,q.cat)(Ir),{waveform:dn}=await $e(pt.sessions.model,{spectrogram:Tn});return{spectrogram:Tn,waveform:dn}}}class Gd extends ne{constructor(){super(...arguments);be(this,"main_input_name","spectrogram")}}class yu extends ne{}class bu extends yu{}class Pa extends ne{}class Mu extends Pa{}class vu extends Pa{}class xu extends ne{}class hi extends xu{}class Ws extends xu{}class fi extends ne{}class Tu extends fi{}class Cu extends fi{}class mi extends ne{}class $u extends mi{}class Aa extends mi{static async from_pretrained(b,R={}){return R.model_file_name??(R.model_file_name="text_model"),super.from_pretrained(b,R)}}class Eu extends mi{static async from_pretrained(b,R={}){return R.model_file_name??(R.model_file_name="audio_model"),super.from_pretrained(b,R)}}class ku extends ne{}class Ia extends ku{async _call(b){return new Td(await super._call(b))}}class Fa extends ne{}class qd extends Fa{}class Oa extends Fa{}class Su extends Fa{}class za extends ne{}class Pu extends za{}class Hd extends za{}class Da extends ne{}class Au extends Da{}class Kd extends Da{async _call(b){return new cr(await super._call(b))}}class La extends ne{}class Xd extends La{}class Qd extends La{}class Ba extends ne{constructor(){super(...arguments);be(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(R){const[ve,Ue]=R.dims,Le=this.config.decoder.num_codebooks,pt=Ue-Le;let St=0;for(let Ar=0;Ar0&&ir<=pt&&(R.data[St++]=R.data[Ar])}const Vt=Math.floor(ve/Le),tr=St/(Vt*Le);return new q.Tensor(R.type,R.data.slice(0,St),[Vt,Le,tr])}prepare_inputs_for_generation(R,ve,Ue){let Le=structuredClone(R);for(let St=0;St=Vt&&(Le[St][Vt]=BigInt(this.config.decoder.pad_token_id));return Ue.guidance_scale!==null&&Ue.guidance_scale>1&&(Le=Le.concat(Le)),super.prepare_inputs_for_generation(Le,ve,Ue)}async generate(R){const ve=await super.generate(R),Ue=this._apply_and_filter_by_delay_pattern_mask(ve).unsqueeze_(0),{audio_values:Le}=await $e(this.sessions.encodec_decode,{audio_codes:Ue});return Le}}class Iu extends ne{}class Fu extends Iu{}class Ou extends Iu{async _call(b){return new cr(await super._call(b))}}class Ra extends ne{}class zu extends Ra{}class Yd extends Ra{async _call(b){return new cr(await super._call(b))}}class Na extends ne{}class Du extends Na{}class Lu extends Na{async _call(b){return new cr(await super._call(b))}}class ja extends ne{}class Zd extends ja{}class Bu extends ja{async _call(b){return new cr(await super._call(b))}}class Ru extends ne{}class Nu extends Ru{}class zr{static async from_pretrained(b,{progress_callback:R=null,config:ve=null,cache_dir:Ue=null,local_files_only:Le=!1,revision:pt="main",model_file_name:St=null,subfolder:Vt="onnx",device:tr=null,dtype:Ar=null,use_external_data_format:Dr=null,session_options:yr={}}={}){const ir={progress_callback:R,config:ve,cache_dir:Ue,local_files_only:Le,revision:pt,model_file_name:St,subfolder:Vt,device:tr,dtype:Ar,use_external_data_format:Dr,session_options:yr};if(ir.config=await P.AutoConfig.from_pretrained(b,ir),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const Ir of this.MODEL_CLASS_MAPPINGS){const $r=Ir.get(ir.config.model_type);if($r)return await $r[1].from_pretrained(b,ir)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${ir.config.model_type}", attempting to construct from base class.`),await ne.from_pretrained(b,ir);throw Error(`Unsupported model type: ${ir.config.model_type}`)}}be(zr,"MODEL_CLASS_MAPPINGS",null),be(zr,"BASE_IF_FAIL",!1);const Ic=new Map([["bert",["BertModel",ht]],["nomic_bert",["NomicBertModel",et]],["roformer",["RoFormerModel",Ve]],["electra",["ElectraModel",Nt]],["esm",["EsmModel",br]],["convbert",["ConvBertModel",H]],["camembert",["CamembertModel",Cr]],["deberta",["DebertaModel",G]],["deberta-v2",["DebertaV2Model",wt]],["mpnet",["MPNetModel",ks]],["albert",["AlbertModel",ms]],["distilbert",["DistilBertModel",Ot]],["roberta",["RobertaModel",Pr]],["xlm",["XLMModel",In]],["xlm-roberta",["XLMRobertaModel",Or]],["clap",["ClapModel",$u]],["clip",["CLIPModel",ho]],["clipseg",["CLIPSegModel",bo]],["chinese_clip",["ChineseCLIPModel",yo]],["siglip",["SiglipModel",mo]],["mobilebert",["MobileBertModel",$n]],["squeezebert",["SqueezeBertModel",Xn]],["wav2vec2",["Wav2Vec2Model",tu]],["wav2vec2-bert",["Wav2Vec2BertModel",uu]],["unispeech",["UniSpeechModel",xa]],["unispeech-sat",["UniSpeechSatModel",Ca]],["hubert",["HubertModel",du]],["wavlm",["WavLMModel",pu]],["audio-spectrogram-transformer",["ASTModel",Wt]],["vits",["VitsModel",Ia]],["pyannote",["PyAnnoteModel",nu]],["wespeaker-resnet",["WeSpeakerResNetModel",iu]],["detr",["DetrModel",vl]],["rt_detr",["RTDetrModel",Tl]],["table-transformer",["TableTransformerModel",El]],["vit",["ViTModel",tl]],["pvt",["PvtModel",rl]],["vit_msn",["ViTMSNModel",al]],["vit_mae",["ViTMAEModel",il]],["groupvit",["GroupViTModel",ul]],["fastvit",["FastViTModel",dl]],["mobilevit",["MobileViTModel",ai]],["mobilevitv2",["MobileViTV2Model",fl]],["owlvit",["OwlViTModel",_l]],["owlv2",["Owlv2Model",wl]],["beit",["BeitModel",bl]],["deit",["DeiTModel",Sl]],["hiera",["HieraModel",Il]],["convnext",["ConvNextModel",ql]],["convnextv2",["ConvNextV2Model",ma]],["dinov2",["Dinov2Model",Xl]],["resnet",["ResNetModel",Ol]],["swin",["SwinModel",Dl]],["swin2sr",["Swin2SRModel",Bl]],["donut-swin",["DonutSwinModel",ha]],["yolos",["YolosModel",Ql]],["dpt",["DPTModel",Nl]],["glpn",["GLPNModel",hn]],["hifigan",["SpeechT5HifiGan",Gd]],["efficientnet",["EfficientNetModel",Au]],["decision_transformer",["DecisionTransformerModel",Nu]],["mobilenet_v1",["MobileNetV1Model",Fu]],["mobilenet_v2",["MobileNetV2Model",zu]],["mobilenet_v3",["MobileNetV3Model",Du]],["mobilenet_v4",["MobileNetV4Model",Zd]],["maskformer",["MaskFormerModel",Zn]]]),Mn=new Map([["t5",["T5Model",Fs]],["longt5",["LongT5Model",zs]],["mt5",["MT5Model",Ls]],["bart",["BartModel",F]],["mbart",["MBartModel",Ge]],["marian",["MarianModel",ya]],["whisper",["WhisperModel",qt]],["m2m_100",["M2M100Model",eu]],["blenderbot",["BlenderbotModel",er]],["blenderbot-small",["BlenderbotSmallModel",Wr]]]),Jd=new Map([["bloom",["BloomModel",Xo]],["jais",["JAISModel",xo]],["gpt2",["GPT2Model",vo]],["gptj",["GPTJModel",Od]],["gpt_bigcode",["GPTBigCodeModel",ni]],["gpt_neo",["GPTNeoModel",Co]],["gpt_neox",["GPTNeoXModel",Eo]],["codegen",["CodeGenModel",So]],["llama",["LlamaModel",Ao]],["olmo",["OlmoModel",zo]],["mobilellm",["MobileLLMModel",Fo]],["granite",["GraniteModel",Do]],["cohere",["CohereModel",Bo]],["gemma",["GemmaModel",No]],["gemma2",["Gemma2Model",Vi]],["openelm",["OpenELMModel",Vo]],["qwen2",["Qwen2Model",Wo]],["phi",["PhiModel",Go]],["phi3",["Phi3Model",Ho]],["mpt",["MptModel",Yo]],["opt",["OPTModel",Zo]],["mistral",["MistralModel",Mu]],["starcoder2",["Starcoder2Model",hi]],["falcon",["FalconModel",Tu]],["stablelm",["StableLmModel",Pu]]]),Va=new Map([["speecht5",["SpeechT5ForSpeechToText",gu]],["whisper",["WhisperForConditionalGeneration",Ti]]]),Ua=new Map([["speecht5",["SpeechT5ForTextToSpeech",wu]]]),ju=new Map([["vits",["VitsModel",Ia]],["musicgen",["MusicgenForConditionalGeneration",Ba]]]),Gs=new Map([["bert",["BertForSequenceClassification",Xe]],["roformer",["RoFormerForSequenceClassification",_t]],["electra",["ElectraForSequenceClassification",bt]],["esm",["EsmForSequenceClassification",mr]],["convbert",["ConvBertForSequenceClassification",Y]],["camembert",["CamembertForSequenceClassification",Rr]],["deberta",["DebertaForSequenceClassification",Ie]],["deberta-v2",["DebertaV2ForSequenceClassification",$t]],["mpnet",["MPNetForSequenceClassification",Ps]],["albert",["AlbertForSequenceClassification",_s]],["distilbert",["DistilBertForSequenceClassification",Fe]],["roberta",["RobertaForSequenceClassification",bn]],["xlm",["XLMForSequenceClassification",Kt]],["xlm-roberta",["XLMRobertaForSequenceClassification",_r]],["bart",["BartForSequenceClassification",oe]],["mbart",["MBartForSequenceClassification",Et]],["mobilebert",["MobileBertForSequenceClassification",hs]],["squeezebert",["SqueezeBertForSequenceClassification",Bn]]]),Vu=new Map([["bert",["BertForTokenClassification",Z]],["roformer",["RoFormerForTokenClassification",Pt]],["electra",["ElectraForTokenClassification",Dt]],["esm",["EsmForTokenClassification",kr]],["convbert",["ConvBertForTokenClassification",he]],["camembert",["CamembertForTokenClassification",Jr]],["deberta",["DebertaForTokenClassification",Se]],["deberta-v2",["DebertaV2ForTokenClassification",ft]],["mpnet",["MPNetForTokenClassification",As]],["distilbert",["DistilBertForTokenClassification",Oe]],["roberta",["RobertaForTokenClassification",Ee]],["xlm",["XLMForTokenClassification",pn]],["xlm-roberta",["XLMRobertaForTokenClassification",ss]]]),Wa=new Map([["t5",["T5ForConditionalGeneration",Os]],["longt5",["LongT5ForConditionalGeneration",Ds]],["mt5",["MT5ForConditionalGeneration",ae]],["bart",["BartForConditionalGeneration",Q]],["mbart",["MBartForConditionalGeneration",gt]],["marian",["MarianMTModel",Jl]],["m2m_100",["M2M100ForConditionalGeneration",Rd]],["blenderbot",["BlenderbotForConditionalGeneration",Sr]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",en]]]),Ga=new Map([["bloom",["BloomForCausalLM",Qo]],["gpt2",["GPT2LMHeadModel",Fn]],["jais",["JAISLMHeadModel",To]],["gptj",["GPTJForCausalLM",On]],["gpt_bigcode",["GPTBigCodeForCausalLM",Oi]],["gpt_neo",["GPTNeoForCausalLM",$o]],["gpt_neox",["GPTNeoXForCausalLM",ko]],["codegen",["CodeGenForCausalLM",Po]],["llama",["LlamaForCausalLM",Io]],["olmo",["OlmoForCausalLM",zn]],["mobilellm",["MobileLLMForCausalLM",Oo]],["granite",["GraniteForCausalLM",Lo]],["cohere",["CohereForCausalLM",Ro]],["gemma",["GemmaForCausalLM",jo]],["gemma2",["Gemma2ForCausalLM",Ui]],["openelm",["OpenELMForCausalLM",Gi]],["qwen2",["Qwen2ForCausalLM",si]],["phi",["PhiForCausalLM",qo]],["phi3",["Phi3ForCausalLM",Ko]],["mpt",["MptForCausalLM",zd]],["opt",["OPTForCausalLM",Jo]],["mbart",["MBartForCausalLM",Ct]],["mistral",["MistralForCausalLM",vu]],["starcoder2",["Starcoder2ForCausalLM",Ws]],["falcon",["FalconForCausalLM",Cu]],["trocr",["TrOCRForCausalLM",bu]],["stablelm",["StableLmForCausalLM",Hd]]]),Uu=new Map([["bert",["BertForMaskedLM",Mt]],["roformer",["RoFormerForMaskedLM",ut]],["electra",["ElectraForMaskedLM",yt]],["esm",["EsmForMaskedLM",Nr]],["convbert",["ConvBertForMaskedLM",$]],["camembert",["CamembertForMaskedLM",Yr]],["deberta",["DebertaForMaskedLM",ge]],["deberta-v2",["DebertaV2ForMaskedLM",mt]],["mpnet",["MPNetForMaskedLM",Ss]],["albert",["AlbertForMaskedLM",Yt]],["distilbert",["DistilBertForMaskedLM",Ut]],["roberta",["RobertaForMaskedLM",mn]],["xlm",["XLMWithLMHeadModel",Rn]],["xlm-roberta",["XLMRobertaForMaskedLM",kt]],["mobilebert",["MobileBertForMaskedLM",Ur]],["squeezebert",["SqueezeBertForMaskedLM",An]]]),Wu=new Map([["bert",["BertForQuestionAnswering",Ae]],["roformer",["RoFormerForQuestionAnswering",Tt]],["electra",["ElectraForQuestionAnswering",At]],["convbert",["ConvBertForQuestionAnswering",nt]],["camembert",["CamembertForQuestionAnswering",yn]],["deberta",["DebertaForQuestionAnswering",Ne]],["deberta-v2",["DebertaV2ForQuestionAnswering",Lt]],["mpnet",["MPNetForQuestionAnswering",Is]],["albert",["AlbertForQuestionAnswering",gs]],["distilbert",["DistilBertForQuestionAnswering",ct]],["roberta",["RobertaForQuestionAnswering",tn]],["xlm",["XLMForQuestionAnswering",Kr]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Qn]],["mobilebert",["MobileBertForQuestionAnswering",Es]],["squeezebert",["SqueezeBertForQuestionAnswering",fs]]]),qa=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ci]]]),Gu=new Map([["llava",["LlavaForConditionalGeneration",ys]],["moondream1",["Moondream1ForConditionalGeneration",pr]],["florence2",["Florence2ForConditionalGeneration",$i]]]),ec=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ci]]]),qu=new Map([["vit",["ViTForImageClassification",vr]],["pvt",["PvtForImageClassification",nl]],["vit_msn",["ViTMSNForImageClassification",ol]],["fastvit",["FastViTForImageClassification",cl]],["mobilevit",["MobileViTForImageClassification",hl]],["mobilevitv2",["MobileViTV2ForImageClassification",ml]],["beit",["BeitForImageClassification",Ml]],["deit",["DeiTForImageClassification",Pl]],["hiera",["HieraForImageClassification",Fl]],["convnext",["ConvNextForImageClassification",Hl]],["convnextv2",["ConvNextV2ForImageClassification",Kl]],["dinov2",["Dinov2ForImageClassification",_a]],["resnet",["ResNetForImageClassification",zl]],["swin",["SwinForImageClassification",Ll]],["segformer",["SegformerForImageClassification",Oa]],["efficientnet",["EfficientNetForImageClassification",Kd]],["mobilenet_v1",["MobileNetV1ForImageClassification",Ou]],["mobilenet_v2",["MobileNetV2ForImageClassification",Yd]],["mobilenet_v3",["MobileNetV3ForImageClassification",Lu]],["mobilenet_v4",["MobileNetV4ForImageClassification",Bu]]]),Hu=new Map([["detr",["DetrForObjectDetection",xl]],["rt_detr",["RTDetrForObjectDetection",Cl]],["table-transformer",["TableTransformerForObjectDetection",kl]],["yolos",["YolosForObjectDetection",Yl]]]),Ku=new Map([["owlvit",["OwlViTForObjectDetection",gl]],["owlv2",["Owlv2ForObjectDetection",yl]]]),Xu=new Map([["detr",["DetrForSegmentation",li]],["clipseg",["CLIPSegForImageSegmentation",Mo]]]),Ha=new Map([["segformer",["SegformerForSemanticSegmentation",Su]],["sapiens",["SapiensForSemanticSegmentation",Ul]]]),Qu=new Map([["detr",["DetrForSegmentation",li]],["maskformer",["MaskFormerForInstanceSegmentation",ca]]]),tc=new Map([["sam",["SamModel",ga]]]),Ka=new Map([["wav2vec2",["Wav2Vec2ForCTC",Nd]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Ea]],["unispeech",["UniSpeechForCTC",Ta]],["unispeech-sat",["UniSpeechSatForCTC",jd]],["wavlm",["WavLMForCTC",hu]],["hubert",["HubertForCTC",cu]]]),Yu=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Ma]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Vd]],["unispeech",["UniSpeechForSequenceClassification",au]],["unispeech-sat",["UniSpeechSatForSequenceClassification",ou]],["wavlm",["WavLMForSequenceClassification",fu]],["hubert",["HubertForSequenceClassification",ka]],["audio-spectrogram-transformer",["ASTForAudioClassification",ri]]]),Zu=new Map([["wavlm",["WavLMForXVector",mu]]]),Ju=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",lu]],["wavlm",["WavLMForAudioFrameClassification",_u]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",ru]],["pyannote",["PyAnnoteForAudioFrameClassification",su]]]),rc=new Map([["vitmatte",["VitMatteForImageMatting",Bs]]]),ed=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Rl]]]),td=new Map([["dpt",["DPTForDepthEstimation",Dd]],["depth_anything",["DepthAnythingForDepthEstimation",Vl]],["glpn",["GLPNForDepthEstimation",pa]],["sapiens",["SapiensForDepthEstimation",Wl]],["depth_pro",["DepthProForDepthEstimation",Yn]]]),rd=new Map([["sapiens",["SapiensForNormalEstimation",Gl]]]),nc=new Map([["clip",["CLIPVisionModelWithProjection",fo]],["siglip",["SiglipVisionModel",go]]]),nd=[[Ic,X.EncoderOnly],[Mn,X.EncoderDecoder],[Jd,X.DecoderOnly],[Gs,X.EncoderOnly],[Vu,X.EncoderOnly],[Wa,X.Seq2Seq],[Va,X.Seq2Seq],[Ga,X.DecoderOnly],[Uu,X.EncoderOnly],[Wu,X.EncoderOnly],[qa,X.Vision2Seq],[Gu,X.ImageTextToText],[qu,X.EncoderOnly],[Xu,X.EncoderOnly],[Qu,X.EncoderOnly],[Ha,X.EncoderOnly],[rc,X.EncoderOnly],[ed,X.EncoderOnly],[td,X.EncoderOnly],[rd,X.EncoderOnly],[Hu,X.EncoderOnly],[Ku,X.EncoderOnly],[tc,X.MaskGeneration],[Ka,X.EncoderOnly],[Yu,X.EncoderOnly],[Ua,X.Seq2Seq],[ju,X.EncoderOnly],[Zu,X.EncoderOnly],[Ju,X.EncoderOnly],[nc,X.EncoderOnly]];for(const[f,b]of nd)for(const[R,ve]of f.values())K.set(R,b),k.set(ve,R),j.set(R,ve);const sc=[["MusicgenForConditionalGeneration",Ba,X.Musicgen],["CLIPTextModelWithProjection",xn,X.EncoderOnly],["SiglipTextModel",_o,X.EncoderOnly],["ClapTextModelWithProjection",Aa,X.EncoderOnly],["ClapAudioModelWithProjection",Eu,X.EncoderOnly]];for(const[f,b,R]of sc)K.set(f,R),k.set(b,f),j.set(f,b);class Xa extends zr{}be(Xa,"MODEL_CLASS_MAPPINGS",nd.map(b=>b[0])),be(Xa,"BASE_IF_FAIL",!0);class sd extends zr{}be(sd,"MODEL_CLASS_MAPPINGS",[Gs]);class id extends zr{}be(id,"MODEL_CLASS_MAPPINGS",[Vu]);class ic extends zr{}be(ic,"MODEL_CLASS_MAPPINGS",[Wa]);class ad extends zr{}be(ad,"MODEL_CLASS_MAPPINGS",[Va]);class od extends zr{}be(od,"MODEL_CLASS_MAPPINGS",[Ua]);class ld extends zr{}be(ld,"MODEL_CLASS_MAPPINGS",[ju]);class ud extends zr{}be(ud,"MODEL_CLASS_MAPPINGS",[Ga]);class dd extends zr{}be(dd,"MODEL_CLASS_MAPPINGS",[Uu]);class cd extends zr{}be(cd,"MODEL_CLASS_MAPPINGS",[Wu]);class pd extends zr{}be(pd,"MODEL_CLASS_MAPPINGS",[qa]);class hd extends zr{}be(hd,"MODEL_CLASS_MAPPINGS",[qu]);class fd extends zr{}be(fd,"MODEL_CLASS_MAPPINGS",[Xu]);class ac extends zr{}be(ac,"MODEL_CLASS_MAPPINGS",[Ha]);class qs extends zr{}be(qs,"MODEL_CLASS_MAPPINGS",[Qu]);class Qa extends zr{}be(Qa,"MODEL_CLASS_MAPPINGS",[Hu]);class Ya extends zr{}be(Ya,"MODEL_CLASS_MAPPINGS",[Ku]);class Za extends zr{}be(Za,"MODEL_CLASS_MAPPINGS",[tc]);class Ja extends zr{}be(Ja,"MODEL_CLASS_MAPPINGS",[Ka]);class md extends zr{}be(md,"MODEL_CLASS_MAPPINGS",[Yu]);class _d extends zr{}be(_d,"MODEL_CLASS_MAPPINGS",[Zu]);class eo extends zr{}be(eo,"MODEL_CLASS_MAPPINGS",[Ju]);class to extends zr{}be(to,"MODEL_CLASS_MAPPINGS",[ec]);class gd extends zr{}be(gd,"MODEL_CLASS_MAPPINGS",[rc]);class wd extends zr{}be(wd,"MODEL_CLASS_MAPPINGS",[ed]);class ro extends zr{}be(ro,"MODEL_CLASS_MAPPINGS",[td]);class yd extends zr{}be(yd,"MODEL_CLASS_MAPPINGS",[rd]);class bd extends zr{}be(bd,"MODEL_CLASS_MAPPINGS",[nc]);class Md extends Ze{constructor({logits:b,past_key_values:R,encoder_outputs:ve,decoder_attentions:Ue=null,cross_attentions:Le=null}){super(),this.logits=b,this.past_key_values=R,this.encoder_outputs=ve,this.decoder_attentions=Ue,this.cross_attentions=Le}}class cr extends Ze{constructor({logits:b}){super(),this.logits=b}}class vd extends Ze{constructor({logits:b,embeddings:R}){super(),this.logits=b,this.embeddings=R}}class sn extends Ze{constructor({logits:b}){super(),this.logits=b}}class un extends Ze{constructor({logits:b}){super(),this.logits=b}}class _n extends Ze{constructor({start_logits:b,end_logits:R}){super(),this.start_logits=b,this.end_logits=R}}class as extends Ze{constructor({logits:b}){super(),this.logits=b}}class oc extends Ze{constructor({logits:b,past_key_values:R}){super(),this.logits=b,this.past_key_values=R}}class xd extends Ze{constructor({alphas:b}){super(),this.alphas=b}}class Td extends Ze{constructor({waveform:b,spectrogram:R}){super(),this.waveform=b,this.spectrogram=R}}},"./src/models/whisper/common_whisper.js":(xt,ye,O)=>{O.r(ye),O.d(ye,{WHISPER_LANGUAGE_MAPPING:()=>le,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>we,whisper_language_to_code:()=>xe});const P=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],le=new Map(P),we=new Map([...P.map(([Ce,U])=>[U,Ce]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function xe(Ce){Ce=Ce.toLowerCase();let U=we.get(Ce);if(U===void 0)if(le.has(Ce))U=Ce;else{const L=Ce.length===2?le.keys():le.values();throw new Error(`Language "${Ce}" is not supported. Must be one of: ${JSON.stringify(L)}`)}return U}},"./src/models/whisper/generation_whisper.js":(xt,ye,O)=>{O.r(ye),O.d(ye,{WhisperGenerationConfig:()=>le});var P=O("./src/generation/configuration_utils.js");class le extends P.GenerationConfig{constructor(){super(...arguments);be(this,"return_timestamps",null);be(this,"return_token_timestamps",null);be(this,"num_frames",null);be(this,"alignment_heads",null);be(this,"task",null);be(this,"language",null);be(this,"no_timestamps_token_id",null);be(this,"prompt_ids",null);be(this,"is_multilingual",null);be(this,"lang_to_id",null);be(this,"task_to_id",null);be(this,"max_initial_timestamp_index",1)}}},"./src/ops/registry.js":(xt,ye,O)=>{O.r(ye),O.d(ye,{TensorOpRegistry:()=>xe});var P=O("./src/backends/onnx.js"),le=O("./src/utils/tensor.js");const we=async(Ce,U,I)=>{const L=await(0,P.createInferenceSession)(new Uint8Array(Ce),U);return async B=>{const q=Object.fromEntries(Object.entries(B).map(([fe,de])=>[fe,de.ort_tensor])),re=await L.run(q);return Array.isArray(I)?I.map(fe=>new le.Tensor(re[fe])):new le.Tensor(re[I])}};class xe{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=we([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=we([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=we([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=we([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=we([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=we([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}}be(xe,"session_options",{})},"./src/pipelines.js":(xt,ye,O)=>{O.r(ye),O.d(ye,{AudioClassificationPipeline:()=>De,AutomaticSpeechRecognitionPipeline:()=>it,DepthEstimationPipeline:()=>Ze,DocumentQuestionAnsweringPipeline:()=>We,FeatureExtractionPipeline:()=>Me,FillMaskPipeline:()=>X,ImageClassificationPipeline:()=>lt,ImageFeatureExtractionPipeline:()=>$e,ImageSegmentationPipeline:()=>me,ImageToImagePipeline:()=>ne,ImageToTextPipeline:()=>rt,ObjectDetectionPipeline:()=>ce,Pipeline:()=>de,QuestionAnsweringPipeline:()=>pe,SummarizationPipeline:()=>j,Text2TextGenerationPipeline:()=>K,TextClassificationPipeline:()=>z,TextGenerationPipeline:()=>E,TextToAudioPipeline:()=>ot,TokenClassificationPipeline:()=>J,TranslationPipeline:()=>k,ZeroShotAudioClassificationPipeline:()=>ze,ZeroShotClassificationPipeline:()=>ue,ZeroShotImageClassificationPipeline:()=>W,ZeroShotObjectDetectionPipeline:()=>Te,pipeline:()=>ht});var P=O("./src/tokenizers.js"),le=O("./src/models.js"),we=O("./src/processors.js"),xe=O("./src/utils/generic.js"),Ce=O("./src/utils/core.js"),U=O("./src/utils/maths.js"),I=O("./src/utils/audio.js"),L=O("./src/utils/tensor.js"),B=O("./src/utils/image.js");async function q(Xe){return Array.isArray(Xe)||(Xe=[Xe]),await Promise.all(Xe.map(Z=>B.RawImage.read(Z)))}async function re(Xe,Z){return Array.isArray(Xe)||(Xe=[Xe]),await Promise.all(Xe.map(Ae=>typeof Ae=="string"||Ae instanceof URL?(0,I.read_audio)(Ae,Z):Ae instanceof Float64Array?new Float32Array(Ae):Ae))}function fe(Xe,Z){Z&&(Xe=Xe.map(Ve=>Ve|0));const[Ae,Ke,et,je]=Xe;return{xmin:Ae,ymin:Ke,xmax:et,ymax:je}}class de extends xe.Callable{constructor({task:Z,model:Ae,tokenizer:Ke=null,processor:et=null}){super(),this.task=Z,this.model=Ae,this.tokenizer=Ke,this.processor=et}async dispose(){await this.model.dispose()}}class z extends de{constructor(Z){super(Z)}async _call(Z,{top_k:Ae=1}={}){const Ke=this.tokenizer(Z,{padding:!0,truncation:!0}),et=await this.model(Ke),je=this.model.config.problem_type==="multi_label_classification"?_t=>_t.sigmoid():_t=>new L.Tensor("float32",(0,U.softmax)(_t.data),_t.dims),Ve=this.model.config.id2label,ut=[];for(const _t of et.logits){const Pt=je(_t),Tt=await(0,L.topk)(Pt,Ae),v=Tt[0].tolist(),$=Tt[1].tolist().map((Y,he)=>({label:Ve?Ve[Y]:`LABEL_${Y}`,score:v[he]}));Ae===1?ut.push(...$):ut.push($)}return Array.isArray(Z)||Ae===1?ut:ut[0]}}class J extends de{constructor(Z){super(Z)}async _call(Z,{ignore_labels:Ae=["O"]}={}){const Ke=Array.isArray(Z),et=this.tokenizer(Ke?Z:[Z],{padding:!0,truncation:!0}),Ve=(await this.model(et)).logits,ut=this.model.config.id2label,_t=[];for(let Pt=0;Ptyt==this.tokenizer.sep_token_id);_t[v].map((yt,bt)=>yt==1&&(bt===0||bt>$&&Pt.findIndex(Dt=>Dt==H[bt])===-1));const Y=je[v].tolist(),he=Ve[v].tolist();for(let yt=1;ytbt==H[yt])!==-1)&&(Y[yt]=-1/0,he[yt]=-1/0);const nt=(0,U.softmax)(Y).map((yt,bt)=>[yt,bt]),Je=(0,U.softmax)(he).map((yt,bt)=>[yt,bt]);nt[0][0]=0,Je[0][0]=0;const Nt=(0,Ce.product)(nt,Je).filter(yt=>yt[0][1]<=yt[1][1]).map(yt=>[yt[0][1],yt[1][1],yt[0][0]*yt[1][0]]).sort((yt,bt)=>bt[2]-yt[2]);for(let yt=0;ytY==this.tokenizer.mask_token_id);if(Pt===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const Tt=et[ut][Pt],v=await(0,L.topk)(new L.Tensor("float32",(0,U.softmax)(Tt.data),Tt.dims),Ae),H=v[0].tolist(),$=v[1].tolist();je.push($.map((Y,he)=>{const nt=_t.slice();return nt[Pt]=Y,{score:H[he],token:Number(Y),token_str:this.tokenizer.model.vocab[Y],sequence:this.tokenizer.decode(nt,{skip_special_tokens:!0})}}))}return Array.isArray(Z)?je:je[0]}}class K extends de{constructor(Ae){super(Ae);be(this,"_key","generated_text")}async _call(Ae,Ke={}){Array.isArray(Ae)||(Ae=[Ae]),this.model.config.prefix&&(Ae=Ae.map(Pt=>this.model.config.prefix+Pt));const et=this.model.config.task_specific_params;et&&et[this.task]&&et[this.task].prefix&&(Ae=Ae.map(Pt=>et[this.task].prefix+Pt));const je=this.tokenizer,Ve={padding:!0,truncation:!0};let ut;this instanceof k&&"_build_translation_inputs"in je?ut=je._build_translation_inputs(Ae,Ve,Ke):ut=je(Ae,Ve);const _t=await this.model.generate({...ut,...Ke});return je.batch_decode(_t,{skip_special_tokens:!0}).map(Pt=>({[this._key]:Pt}))}}class j extends K{constructor(Ae){super(Ae);be(this,"_key","summary_text")}}class k extends K{constructor(Ae){super(Ae);be(this,"_key","translation_text")}}function N(Xe){return Array.isArray(Xe)&&Xe.every(Z=>"role"in Z&&"content"in Z)}class E extends de{constructor(Z){super(Z)}async _call(Z,Ae={}){let Ke=!1,et=!1,je;if(typeof Z=="string")je=Z=[Z];else if(Array.isArray(Z)&&Z.every($=>typeof $=="string"))Ke=!0,je=Z;else{if(N(Z))Z=[Z];else if(Array.isArray(Z)&&Z.every(N))Ke=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");et=!0,je=Z.map($=>this.tokenizer.apply_chat_template($,{tokenize:!1,add_generation_prompt:!0}))}const Ve=Ae.add_special_tokens??!1,ut=et?!1:Ae.return_full_text??!0;this.tokenizer.padding_side="left";const _t=this.tokenizer(je,{add_special_tokens:Ve,padding:!0,truncation:!0}),Pt=await this.model.generate({..._t,...Ae}),Tt=this.tokenizer.batch_decode(Pt,{skip_special_tokens:!0});let v;!ut&&_t.input_ids.dims.at(-1)>0&&(v=this.tokenizer.batch_decode(_t.input_ids,{skip_special_tokens:!0}).map($=>$.length));const H=Array.from({length:Z.length},$=>[]);for(let $=0;$[Ae.toLowerCase(),Ke])),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(Z,Ae,{hypothesis_template:Ke="This example is {}.",multi_label:et=!1}={}){const je=Array.isArray(Z);je||(Z=[Z]),Array.isArray(Ae)||(Ae=[Ae]);const Ve=Ae.map(Pt=>Ke.replace("{}",Pt)),ut=et||Ae.length===1,_t=[];for(const Pt of Z){const Tt=[];for(const $ of Ve){const Y=this.tokenizer(Pt,{text_pair:$,padding:!0,truncation:!0}),he=await this.model(Y);ut?Tt.push([he.logits.data[this.contradiction_id],he.logits.data[this.entailment_id]]):Tt.push(he.logits.data[this.entailment_id])}const H=(ut?Tt.map($=>(0,U.softmax)($)[1]):(0,U.softmax)(Tt)).map(($,Y)=>[$,Y]).sort(($,Y)=>Y[0]-$[0]);_t.push({sequence:Pt,labels:H.map($=>Ae[$[1]]),scores:H.map($=>$[0])})}return je?_t:_t[0]}}class Me extends de{constructor(Z){super(Z)}async _call(Z,{pooling:Ae="none",normalize:Ke=!1,quantize:et=!1,precision:je="binary"}={}){const Ve=this.tokenizer(Z,{padding:!0,truncation:!0}),ut=await this.model(Ve);let _t=ut.last_hidden_state??ut.logits??ut.token_embeddings;if(Ae!=="none")if(Ae==="mean")_t=(0,L.mean_pooling)(_t,Ve.attention_mask);else if(Ae==="cls")_t=_t.slice(null,0);else throw Error(`Pooling method '${Ae}' not supported.`);return Ke&&(_t=_t.normalize(2,-1)),et&&(_t=(0,L.quantize_embeddings)(_t,je)),_t}}class $e extends de{constructor(Z){super(Z)}async _call(Z,{pool:Ae=null}={}){const Ke=await q(Z),{pixel_values:et}=await this.processor(Ke),je=await this.model({pixel_values:et});let Ve;if(Ae){if(!("pooler_output"in je))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Ve=je.pooler_output}else Ve=je.last_hidden_state??je.logits??je.image_embeds;return Ve}}class De extends de{constructor(Z){super(Z)}async _call(Z,{top_k:Ae=5}={}){const Ke=this.processor.feature_extractor.config.sampling_rate,et=await re(Z,Ke),je=this.model.config.id2label,Ve=[];for(const ut of et){const _t=await this.processor(ut),Tt=(await this.model(_t)).logits[0],v=await(0,L.topk)(new L.Tensor("float32",(0,U.softmax)(Tt.data),Tt.dims),Ae),H=v[0].tolist(),Y=v[1].tolist().map((he,nt)=>({label:je?je[he]:`LABEL_${he}`,score:H[nt]}));Ve.push(Y)}return Array.isArray(Z)?Ve:Ve[0]}}class ze extends de{constructor(Z){super(Z)}async _call(Z,Ae,{hypothesis_template:Ke="This is a sound of {}."}={}){const et=!Array.isArray(Z);et&&(Z=[Z]);const je=Ae.map(Tt=>Ke.replace("{}",Tt)),Ve=this.tokenizer(je,{padding:!0,truncation:!0}),ut=this.processor.feature_extractor.config.sampling_rate,_t=await re(Z,ut),Pt=[];for(const Tt of _t){const v=await this.processor(Tt),H=await this.model({...Ve,...v}),$=(0,U.softmax)(H.logits_per_audio.data);Pt.push([...$].map((Y,he)=>({score:Y,label:Ae[he]})))}return et?Pt[0]:Pt}}class it extends de{constructor(Z){super(Z)}async _call(Z,Ae={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(Z,Ae);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(Z,Ae);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(Z,Ae){Ae.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),Ae.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const Ke=!Array.isArray(Z);Ke&&(Z=[Z]);const et=this.processor.feature_extractor.config.sampling_rate,je=await re(Z,et),Ve=[];for(const ut of je){const _t=await this.processor(ut),Tt=(await this.model(_t)).logits[0],v=[];for(const $ of Tt)v.push((0,U.max)($.data)[1]);const H=this.tokenizer.decode(v);Ve.push({text:H})}return Ke?Ve[0]:Ve}async _call_whisper(Z,Ae){const Ke=Ae.return_timestamps??!1,et=Ae.chunk_length_s??0,je=Ae.force_full_sequences??!1;let Ve=Ae.stride_length_s??null;const ut={...Ae};Ke==="word"&&(ut.return_token_timestamps=!0,ut.return_timestamps=!1);const _t=!Array.isArray(Z);_t&&(Z=[Z]);const Pt=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,Tt=this.processor.feature_extractor.config.hop_length,v=this.processor.feature_extractor.config.sampling_rate,H=await re(Z,v),$=[];for(const Y of H){let he=[];if(et>0){if(Ve===null)Ve=et/6;else if(et<=Ve)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const Nt=v*et,yt=v*Ve,bt=Nt-2*yt;let Dt=0;for(;;){const At=Dt+Nt,dr=Y.subarray(Dt,At),Cr=await this.processor(dr),Yr=Dt===0,Rr=At>=Y.length;if(he.push({stride:[dr.length,Yr?0:yt,Rr?0:yt],input_features:Cr.input_features,is_last:Rr}),Rr)break;Dt+=bt}}else he=[{stride:[Y.length,0,0],input_features:(await this.processor(Y)).input_features,is_last:!0}];for(const Nt of he){ut.num_frames=Math.floor(Nt.stride[0]/Tt);const yt=await this.model.generate({inputs:Nt.input_features,...ut});Ke==="word"?(Nt.tokens=yt.sequences.tolist()[0],Nt.token_timestamps=yt.token_timestamps.tolist()[0].map(bt=>(0,U.round)(bt,2))):Nt.tokens=yt[0].tolist(),Nt.stride=Nt.stride.map(bt=>bt/v)}const[nt,Je]=this.tokenizer._decode_asr(he,{time_precision:Pt,return_timestamps:Ke,force_full_sequences:je});$.push({text:nt,...Je})}return _t?$[0]:$}}class rt extends de{constructor(Z){super(Z)}async _call(Z,Ae={}){const Ke=Array.isArray(Z),et=await q(Z),{pixel_values:je}=await this.processor(et),Ve=[];for(const ut of je){ut.dims=[1,...ut.dims];const _t=await this.model.generate({inputs:ut,...Ae}),Pt=this.tokenizer.batch_decode(_t,{skip_special_tokens:!0}).map(Tt=>({generated_text:Tt.trim()}));Ve.push(Pt)}return Ke?Ve:Ve[0]}}class lt extends de{constructor(Z){super(Z)}async _call(Z,{top_k:Ae=5}={}){const Ke=await q(Z),{pixel_values:et}=await this.processor(Ke),je=await this.model({pixel_values:et}),Ve=this.model.config.id2label,ut=[];for(const _t of je.logits){const Pt=await(0,L.topk)(new L.Tensor("float32",(0,U.softmax)(_t.data),_t.dims),Ae),Tt=Pt[0].tolist(),H=Pt[1].tolist().map(($,Y)=>({label:Ve?Ve[$]:`LABEL_${$}`,score:Tt[Y]}));ut.push(H)}return Array.isArray(Z)?ut:ut[0]}}class me extends de{constructor(Z){super(Z),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(Z,{threshold:Ae=.5,mask_threshold:Ke=.5,overlap_mask_area_threshold:et=.8,label_ids_to_fuse:je=null,target_sizes:Ve=null,subtask:ut=null}={}){if(Array.isArray(Z)&&Z.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const Pt=await q(Z),Tt=Pt.map(Je=>[Je.height,Je.width]),{pixel_values:v,pixel_mask:H}=await this.processor(Pt),$=await this.model({pixel_values:v,pixel_mask:H});let Y=null;if(ut!==null)Y=this.subtasks_mapping[ut];else for(let[Je,Nt]of Object.entries(this.subtasks_mapping))if(Nt in this.processor.feature_extractor){Y=this.processor.feature_extractor[Nt].bind(this.processor.feature_extractor),ut=Je;break}const he=this.model.config.id2label,nt=[];if(ut==="panoptic"||ut==="instance"){const Je=Y($,Ae,Ke,et,je,Ve??Tt)[0],Nt=Je.segmentation;for(const yt of Je.segments_info){const bt=new Uint8ClampedArray(Nt.data.length);for(let At=0;AtKe.replace("{}",H)),ut=this.tokenizer(Ve,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:_t}=await this.processor(je),Pt=await this.model({...ut,pixel_values:_t}),Tt=this.model.config.model_type==="siglip"?H=>H.sigmoid().data:H=>(0,U.softmax)(H.data),v=[];for(const H of Pt.logits_per_image){const Y=[...Tt(H)].map((he,nt)=>({score:he,label:Ae[nt]}));Y.sort((he,nt)=>nt.score-he.score),v.push(Y)}return et?v:v[0]}}class ce extends de{constructor(Z){super(Z)}async _call(Z,{threshold:Ae=.9,percentage:Ke=!1}={}){const et=Array.isArray(Z);if(et&&Z.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const je=await q(Z),Ve=Ke?null:je.map($=>[$.height,$.width]),{pixel_values:ut,pixel_mask:_t}=await this.processor(je),Pt=await this.model({pixel_values:ut,pixel_mask:_t}),Tt=this.processor.feature_extractor.post_process_object_detection(Pt,Ae,Ve),v=this.model.config.id2label,H=Tt.map($=>$.boxes.map((Y,he)=>({score:$.scores[he],label:v[$.classes[he]],box:fe(Y,!Ke)})));return et?H:H[0]}}class Te extends de{constructor(Z){super(Z)}async _call(Z,Ae,{threshold:Ke=.1,top_k:et=null,percentage:je=!1}={}){const Ve=Array.isArray(Z),ut=await q(Z),_t=this.tokenizer(Ae,{padding:!0,truncation:!0}),Pt=await this.processor(ut),Tt=[];for(let v=0;v({score:nt.scores[yt],label:Ae[nt.classes[yt]],box:fe(Nt,!je)})).sort((Nt,yt)=>yt.score-Nt.score);et!==null&&(Je=Je.slice(0,et)),Tt.push(Je)}return Ve?Tt:Tt[0]}}class We extends de{constructor(Z){super(Z)}async _call(Z,Ae,Ke={}){const et=(await q(Z))[0],{pixel_values:je}=await this.processor(et),Ve=`${Ae}`,ut=this.tokenizer(Ve,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,_t=await this.model.generate({inputs:je,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:ut,...Ke}),Tt=this.tokenizer.batch_decode(_t)[0].match(/(.*?)<\/s_answer>/);let v=null;return Tt&&Tt.length>=2&&(v=Tt[1].trim()),[{answer:v}]}}class ot extends de{constructor(Ae){super(Ae);be(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=Ae.vocoder??null}async _call(Ae,{speaker_embeddings:Ke=null}={}){return this.processor?this._call_text_to_spectrogram(Ae,{speaker_embeddings:Ke}):this._call_text_to_waveform(Ae)}async _call_text_to_waveform(Ae){const Ke=this.tokenizer(Ae,{padding:!0,truncation:!0}),{waveform:et}=await this.model(Ke),je=this.model.config.sampling_rate;return{audio:et.data,sampling_rate:je}}async _call_text_to_spectrogram(Ae,{speaker_embeddings:Ke}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await le.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Ke=="string"||Ke instanceof URL)&&(Ke=new Float32Array(await(await fetch(Ke)).arrayBuffer())),Ke instanceof Float32Array)Ke=new L.Tensor("float32",Ke,[1,Ke.length]);else if(!(Ke instanceof L.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:et}=this.tokenizer(Ae,{padding:!0,truncation:!0}),{waveform:je}=await this.model.generate_speech(et,Ke,{vocoder:this.vocoder}),Ve=this.processor.feature_extractor.config.sampling_rate;return{audio:je.data,sampling_rate:Ve}}}class ne extends de{constructor(Z){super(Z)}async _call(Z){const Ae=await q(Z),Ke=await this.processor(Ae),et=await this.model(Ke),je=[];for(const Ve of et.reconstruction){const ut=Ve.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");je.push(B.RawImage.fromTensor(ut))}return je.length>1?je:je[0]}}class Ze extends de{constructor(Z){super(Z)}async _call(Z){const Ae=await q(Z),Ke=await this.processor(Ae),{predicted_depth:et}=await this.model(Ke),je=[];for(let Ve=0;Ve1?je:je[0]}}const 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Ke}},"./src/processors.js":(xt,ye,O)=>{O.r(ye),O.d(ye,{ASTFeatureExtractor:()=>he,AutoProcessor:()=>yn,BeitFeatureExtractor:()=>Ae,BitImageProcessor:()=>Me,CLIPFeatureExtractor:()=>De,CLIPImageProcessor:()=>ze,ChineseCLIPFeatureExtractor:()=>it,ClapFeatureExtractor:()=>nt,ConvNextFeatureExtractor:()=>lt,ConvNextImageProcessor:()=>me,DPTFeatureExtractor:()=>E,DPTImageProcessor:()=>ue,DeiTFeatureExtractor:()=>Z,DetrFeatureExtractor:()=>Ve,DonutFeatureExtractor:()=>Ke,DonutImageProcessor:()=>et,EfficientNetImageProcessor:()=>Te,FeatureExtractor:()=>X,Florence2Processor:()=>Jr,GLPNFeatureExtractor:()=>$e,ImageFeatureExtractor:()=>K,MaskFormerFeatureExtractor:()=>ut,MobileNetV1FeatureExtractor:()=>We,MobileNetV2FeatureExtractor:()=>ot,MobileNetV3FeatureExtractor:()=>ne,MobileNetV4FeatureExtractor:()=>Ze,MobileViTFeatureExtractor:()=>dt,MobileViTImageProcessor:()=>Re,NougatImageProcessor:()=>je,OwlViTFeatureExtractor:()=>ht,OwlViTProcessor:()=>Rr,Owlv2ImageProcessor:()=>Mt,Processor:()=>bt,PvtImageProcessor:()=>N,PyAnnoteFeatureExtractor:()=>Je,PyAnnoteProcessor:()=>Cr,RTDetrImageProcessor:()=>Xe,SamImageProcessor:()=>Pt,SamProcessor:()=>Dt,SapiensFeatureExtractor:()=>j,SeamlessM4TFeatureExtractor:()=>Y,SegformerFeatureExtractor:()=>k,SiglipImageProcessor:()=>rt,SpeechT5FeatureExtractor:()=>yt,SpeechT5Processor:()=>Yr,Swin2SRImageProcessor:()=>Tt,ViTFeatureExtractor:()=>W,ViTImageProcessor:()=>ce,VitMatteImageProcessor:()=>v,Wav2Vec2FeatureExtractor:()=>$,Wav2Vec2ProcessorWithLM:()=>dr,WeSpeakerFeatureExtractor:()=>Nt,WhisperFeatureExtractor:()=>H,WhisperProcessor:()=>At,YolosFeatureExtractor:()=>_t});var P=O("./src/utils/generic.js"),le=O("./src/utils/core.js"),we=O("./src/utils/hub.js"),xe=O("./src/utils/maths.js"),Ce=O("./src/utils/tensor.js");O("./src/utils/image.js");var U=O("./src/utils/audio.js");function I([at,G,ge,Ie]){return[at-ge/2,G-Ie/2,at+ge/2,G+Ie/2]}function L(at,G=.5,ge=null,Ie=!1){const Se=at.logits,Ne=at.pred_boxes,[tt,wt,mt]=Se.dims;if(ge!==null&&ge.length!==tt)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let $t=[];for(let ft=0;ftG&&Ut.push(br)}else{let br=(0,xe.max)(ct.data)[1];if(br===mt-1||(sr=(0,xe.softmax)(ct.data),sr[br]mr*Lt[(kr+1)%2])),jt.boxes.push(Nr),jt.classes.push(br),jt.scores.push(sr[br])}}$t.push(jt)}return $t}function B(at,G=null){const ge=at.logits,Ie=ge.dims[0];if(G!==null&&G.length!==Ie)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const Se=[];for(let Ne=0;NeLt[Ut]&&(Lt[Ut]=ct[Ut],jt[Ut]=Oe)}const Ot=new Array(wt.dims[0]);for(let Oe=0;OeOe!==void 0);Se.push({segmentation:ft,labels:Fe})}return Se}function q(at,G,ge,Ie){const Se=[],Ne=[],tt=[];for(let wt=0;wtge&&(Se.push($t),Ne.push(jt),tt.push(ft))}return[Se,Ne,tt]}function re(at,G,ge,Ie=.5,Se=.8){const Ne=[];let tt=0,wt=0;const mt=G[ge].data;for(let ft=0;ft=Ie&&++wt;let $t=tt>0&&wt>0;return $t&&($t=tt/wt>Se),[$t,Ne]}function fe(at,G,ge,Ie,Se,Ne=null,tt=null){const[wt,mt]=tt??at[0].dims,$t=new Ce.Tensor("int32",new Int32Array(wt*mt),[wt,mt]),ft=[];if(tt!==null)for(let Oe=0;Oejt[sr]&&(Lt[sr]=Oe,jt[sr]=Ut[sr])}let Ot=0;const Fe=$t.data;for(let Oe=0;OeIe&&(Ne=Math.floor(Se)*G),NeNe?$t=Math.floor(Ne*mt/Se):Ne>Se&&(mt=Math.floor(Se*$t/Ne)),await G.resize($t,mt,{resample:Ie}))}async crop_margin(G,ge=200){const Ie=G.clone().grayscale(),Se=(0,xe.min)(Ie.data)[0],tt=(0,xe.max)(Ie.data)[0]-Se;if(tt===0)return G;const wt=ge/255;let mt=Ie.width,$t=Ie.height,ft=0,Lt=0;const jt=Ie.data;for(let Ot=0;Otthis.preprocess(Ne)));return{pixel_values:(0,Ce.stack)(Ie.map(Ne=>Ne.pixel_values),0),original_sizes:Ie.map(Ne=>Ne.original_size),reshaped_input_sizes:Ie.map(Ne=>Ne.reshaped_input_size)}}}class j extends K{post_process_semantic_segmentation(...G){return B(...G)}}class k extends K{post_process_semantic_segmentation(...G){return B(...G)}}class N extends K{}class E extends K{}class ue extends E{}class Me extends K{}class $e extends K{}class De extends K{}class ze extends De{}class it extends K{}class rt extends K{}class lt extends K{constructor(G){super(G),this.crop_pct=this.config.crop_pct??.875}async resize(G){var Ie;const ge=(Ie=this.size)==null?void 0:Ie.shortest_edge;if(ge===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(ge<384){const Se=Math.floor(ge/this.crop_pct),[Ne,tt]=this.get_resize_output_image_size(G,{shortest_edge:Se});G=await G.resize(Ne,tt,{resample:this.resample}),G=await G.center_crop(ge,ge)}else G=await G.resize(ge,ge,{resample:this.resample});return G}}class me extends lt{}class W extends K{}class ce extends K{}class Te extends K{constructor(G){super(G),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(ge=>ge*ge))}}class We extends K{}class ot extends K{}class ne extends K{}class Ze extends K{}class dt extends K{}class Re extends dt{}class ht extends K{post_process_object_detection(...G){return L(...G)}}class Mt extends ht{}class Xe extends K{post_process_object_detection(...G){return L(...G)}}class Z extends K{}class Ae extends K{}class Ke extends K{pad_image(G,ge,Ie,Se={}){const[Ne,tt,wt]=ge;let mt=this.image_mean;Array.isArray(this.image_mean)||(mt=new Array(wt).fill(mt));let $t=this.image_std;Array.isArray($t)||($t=new Array(wt).fill(mt));const ft=mt.map((Lt,jt)=>-Lt/$t[jt]);return super.pad_image(G,ge,Ie,{center:!0,constant_values:ft,...Se})}}class et extends Ke{}class je extends Ke{}class Ve extends K{async _call(G){const ge=await super._call(G),Ie=[ge.pixel_values.dims[0],64,64],Se=(0,Ce.full)(Ie,1n);return{...ge,pixel_mask:Se}}post_process_object_detection(...G){return L(...G)}post_process_panoptic_segmentation(...G){return de(...G)}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class ut extends K{post_process_panoptic_segmentation(...G){return de(...G)}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class _t extends K{post_process_object_detection(...G){return L(...G)}}class Pt extends K{reshape_input_points(G,ge,Ie,Se=!1){G=structuredClone(G);let Ne=(0,le.calculateDimensions)(G);if(Ne.length===3)Se||(Ne=[1,...Ne]),G=[G];else if(Ne.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let tt=0;ttSe!==ge.dims[Ne]))throw Error(`The first ${Ie.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new Ce.Tensor("int64",G.flat(1/0).map(BigInt),Ie)}async _call(G,{input_points:ge=null,input_labels:Ie=null,input_boxes:Se=null}={}){const Ne=await super._call(G);if(ge&&(Ne.input_points=this.reshape_input_points(ge,Ne.original_sizes,Ne.reshaped_input_sizes)),Ie){if(!Ne.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");Ne.input_labels=this.add_input_labels(Ie,Ne.input_points)}return Se&&(Ne.input_boxes=this.reshape_input_points(Se,Ne.original_sizes,Ne.reshaped_input_sizes,!0)),Ne}async post_process_masks(G,ge,Ie,{mask_threshold:Se=0,binarize:Ne=!0,pad_size:tt=null}={}){const wt=[];tt=tt??this.pad_size;const mt=[tt.height,tt.width];for(let $t=0;$tSe&&(Fe[Oe]=1);jt=new Ce.Tensor("bool",Fe,jt.dims)}wt.push(jt)}return wt}generate_crop_boxes(G,ge,{crop_n_layers:Ie=0,overlap_ratio:Se=.3413333333333333,points_per_crop:Ne=32,crop_n_points_downscale_factor:tt=1}={}){}}class Tt extends K{pad_image(G,ge,Ie,Se={}){const[Ne,tt,wt]=ge;return super.pad_image(G,ge,{width:tt+(Ie-tt%Ie)%Ie,height:Ne+(Ie-Ne%Ie)%Ie},{mode:"symmetric",center:!1,constant_values:-1,...Se})}}class v extends K{async _call(G,ge){Array.isArray(G)||(G=[G]),Array.isArray(ge)||(ge=[ge]);const Ie=await Promise.all(G.map(tt=>this.preprocess(tt))),Se=await Promise.all(ge.map(tt=>this.preprocess(tt,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,Ce.stack)(Ie.map((tt,wt)=>(0,Ce.cat)([tt.pixel_values,Se[wt].pixel_values],0)),0),original_sizes:Ie.map(tt=>tt.original_size),reshaped_input_sizes:Ie.map(tt=>tt.reshaped_input_size)}}}class H extends X{constructor(G){var ge;super(G),(ge=this.config).mel_filters??(ge.mel_filters=(0,U.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney")),this.window=(0,U.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(G){const ge=await(0,U.spectrogram)(G,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),Ie=ge.data,Se=(0,xe.max)(Ie)[0];for(let Ne=0;Nethis.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),ge=G.slice(0,this.config.n_samples)):(ge=new Float32Array(this.config.n_samples),ge.set(G)),{input_features:(await this._extract_fbank_features(ge)).unsqueeze_(0)}}}class $ extends X{_zero_mean_unit_var_norm(G){const Ie=G.reduce((Ne,tt)=>Ne+tt,0)/G.length,Se=G.reduce((Ne,tt)=>Ne+(tt-Ie)**2,0)/G.length;return G.map(Ne=>(Ne-Ie)/Math.sqrt(Se+1e-7))}async _call(G){z(G,"Wav2Vec2FeatureExtractor"),G instanceof Float64Array&&(G=new Float32Array(G));let ge=G;this.config.do_normalize&&(ge=this._zero_mean_unit_var_norm(ge));const Ie=[1,ge.length];return{input_values:new Ce.Tensor("float32",ge,Ie),attention_mask:new Ce.Tensor("int64",new BigInt64Array(ge.length).fill(1n),Ie)}}}class Y extends X{constructor(G){super(G);const ge=this.config.sampling_rate,Ie=(0,U.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ge/2),ge,null,"kaldi",!0);for(let Se=0;SeIe*32768),(0,U.spectrogram)(G,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:ge,transpose:!0})}async _call(G,{padding:ge=!0,pad_to_multiple_of:Ie=2,do_normalize_per_mel_bins:Se=!0,return_attention_mask:Ne=!0}={}){z(G,"SeamlessM4TFeatureExtractor");let tt=await this._extract_fbank_features(G,this.config.max_length);if(Se){const[Fe,Oe]=tt.dims,ct=tt.data;for(let Ut=0;Ut0){const sr=new Float32Array(Oe*(Fe+Ut));sr.set(ct),sr.fill(this.config.padding_value,ct.length);const br=Fe+Ut;tt=new Ce.Tensor(tt.type,sr,[br,Oe]),Ne&&(wt=new Ce.Tensor("int64",new BigInt64Array(br),[1,br]),wt.data.fill(1n,0,Fe))}}const[mt,$t]=tt.dims,ft=this.config.stride;if(mt%ft!==0)throw new Error(`The number of frames (${mt}) must be a multiple of the stride (${ft}).`);const jt=tt.view(1,Math.floor(mt/ft),$t*ft),Ot={input_features:jt};if(Ne){const Fe=jt.dims[1],Oe=new BigInt64Array(Fe);if(wt){const ct=wt.data;for(let Ut=1,sr=0;Ut0)if(Ie==="rand_trunc"){const wt=Math.floor(Math.random()*(tt+1));G=G.subarray(wt,wt+ge),Ne=await this._extract_fbank_features(G,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${Ie}" not implemented`);else{if(tt<0){let wt=new Float64Array(ge);if(wt.set(G),Se==="repeat")for(let mt=G.length;mt({id:mt,start:$t*Ie,end:ft*Ie,confidence:Lt/(ft-$t)})))}return Se}}class Nt extends X{constructor(G){super(G);const ge=this.config.sampling_rate,Ie=(0,U.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ge/2),ge,null,"kaldi",!0);for(let Se=0;Sege*32768),(0,U.spectrogram)(G,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(G){z(G,"WeSpeakerFeatureExtractor");const ge=(await this._extract_fbank_features(G)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const Ie=ge.mean(1).data,Se=ge.data,[Ne,tt,wt]=ge.dims;for(let mt=0;mt/gm,bboxes:/([^<]+)?/gm},this.size_per_bin=1e3}construct_prompts(G){typeof G=="string"&&(G=[G]);const ge=[];for(const Ie of G)if(this.task_prompts_without_inputs.has(Ie))ge.push(this.task_prompts_without_inputs.get(Ie));else{for(const[Se,Ne]of this.task_prompts_with_input)if(Ie.includes(Se)){ge.push(Ne.replaceAll("{input}",Ie).replaceAll(Se,""));break}ge.length!==G.length&&ge.push(Ie)}return ge}post_process_generation(G,ge,Ie){const Se=this.tasks_answer_post_processing_type.get(ge)??"pure_text";G=G.replaceAll("","").replaceAll("","");let Ne;switch(Se){case"pure_text":Ne=G;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const tt=Se==="ocr"?"quad_boxes":"bboxes",wt=G.matchAll(this.regexes[tt]),mt=[],$t=[];for(const[ft,Lt,...jt]of wt)mt.push(Lt?Lt.trim():mt.at(-1)??""),$t.push(jt.map((Ot,Fe)=>(Number(Ot)+.5)/this.size_per_bin*Ie[Fe%2]));Ne={labels:mt,[tt]:$t};break;default:throw new Error(`Task "${ge}" (of type "${Se}") not yet implemented.`)}return{[ge]:Ne}}}class yn{static async from_pretrained(G,{progress_callback:ge=null,config:Ie=null,cache_dir:Se=null,local_files_only:Ne=!1,revision:tt="main"}={}){let wt=Ie??await(0,we.getModelJSON)(G,"preprocessor_config.json",!0,{progress_callback:ge,config:Ie,cache_dir:Se,local_files_only:Ne,revision:tt}),mt=wt.feature_extractor_type??wt.image_processor_type,$t=this.FEATURE_EXTRACTOR_CLASS_MAPPING[mt];if(!$t)if(wt.size!==void 0)console.warn(`Feature extractor type "${mt}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),$t=K;else throw new Error(`Unknown Feature Extractor type: ${mt}`);let ft=this.PROCESSOR_CLASS_MAPPING[wt.processor_class]??bt,Lt=new $t(wt);return new ft(Lt)}}be(yn,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:K,WhisperFeatureExtractor:H,ViTFeatureExtractor:W,MobileViTFeatureExtractor:dt,MobileViTImageProcessor:Re,MobileNetV1FeatureExtractor:We,MobileNetV2FeatureExtractor:ot,MobileNetV3FeatureExtractor:ne,MobileNetV4FeatureExtractor:Ze,OwlViTFeatureExtractor:ht,Owlv2ImageProcessor:Mt,CLIPFeatureExtractor:De,CLIPImageProcessor:ze,Florence2Processor:Jr,ChineseCLIPFeatureExtractor:it,SiglipImageProcessor:rt,ConvNextFeatureExtractor:lt,ConvNextImageProcessor:me,SegformerFeatureExtractor:k,SapiensFeatureExtractor:j,BitImageProcessor:Me,DPTImageProcessor:ue,DPTFeatureExtractor:E,PvtImageProcessor:N,GLPNFeatureExtractor:$e,BeitFeatureExtractor:Ae,DeiTFeatureExtractor:Z,DetrFeatureExtractor:Ve,RTDetrImageProcessor:Xe,MaskFormerFeatureExtractor:ut,YolosFeatureExtractor:_t,DonutFeatureExtractor:Ke,DonutImageProcessor:et,NougatImageProcessor:je,EfficientNetImageProcessor:Te,ViTImageProcessor:ce,VitMatteImageProcessor:v,SamImageProcessor:Pt,Swin2SRImageProcessor:Tt,Wav2Vec2FeatureExtractor:$,SeamlessM4TFeatureExtractor:Y,SpeechT5FeatureExtractor:yt,ASTFeatureExtractor:he,ClapFeatureExtractor:nt,PyAnnoteFeatureExtractor:Je,WeSpeakerFeatureExtractor:Nt}),be(yn,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:At,Wav2Vec2ProcessorWithLM:dr,PyAnnoteProcessor:Cr,SamProcessor:Dt,SpeechT5Processor:Yr,OwlViTProcessor:Rr,Florence2Processor:Jr})},"./src/tokenizers.js":(xt,ye,O)=>{O.r(ye),O.d(ye,{AlbertTokenizer:()=>tt,AutoTokenizer:()=>Ls,BartTokenizer:()=>Nr,BertTokenizer:()=>Ne,BlenderbotSmallTokenizer:()=>Os,BlenderbotTokenizer:()=>Fs,BloomTokenizer:()=>$n,CLIPTokenizer:()=>_s,CamembertTokenizer:()=>Oe,CodeGenTokenizer:()=>ms,CodeLlamaTokenizer:()=>Es,CohereTokenizer:()=>ns,ConvBertTokenizer:()=>jt,DebertaTokenizer:()=>$t,DebertaV2Tokenizer:()=>ft,DistilBertTokenizer:()=>Fe,ElectraTokenizer:()=>Ut,EsmTokenizer:()=>As,FalconTokenizer:()=>Ss,GPT2Tokenizer:()=>br,GPTNeoXTokenizer:()=>Ps,GemmaTokenizer:()=>ts,Grok1Tokenizer:()=>Xn,HerbertTokenizer:()=>Lt,LlamaTokenizer:()=>hs,M2M100Tokenizer:()=>fs,MBart50Tokenizer:()=>kr,MBartTokenizer:()=>mr,MPNetTokenizer:()=>ks,MarianTokenizer:()=>Yt,MobileBertTokenizer:()=>wt,NllbTokenizer:()=>Bn,NougatTokenizer:()=>zs,PreTrainedTokenizer:()=>Se,Qwen2Tokenizer:()=>Is,RoFormerTokenizer:()=>Ot,RobertaTokenizer:()=>wr,SiglipTokenizer:()=>gs,SpeechT5Tokenizer:()=>ws,SqueezeBertTokenizer:()=>mt,T5Tokenizer:()=>sr,TokenizerModel:()=>$e,VitsTokenizer:()=>Ds,Wav2Vec2CTCTokenizer:()=>rs,WhisperTokenizer:()=>Ln,XLMRobertaTokenizer:()=>Kn,XLMTokenizer:()=>ct,is_chinese_char:()=>X});var P=O("./src/utils/generic.js"),le=O("./src/utils/core.js"),we=O("./src/utils/hub.js"),xe=O("./src/utils/maths.js"),Ce=O("./src/utils/tensor.js"),U=O("./src/utils/data-structures.js"),I=O("./node_modules/@huggingface/jinja/dist/index.js"),L=O("./src/models/whisper/common_whisper.js");O("./src/utils/constants.js");async function B(ae,_){const F=await Promise.all([(0,we.getModelJSON)(ae,"tokenizer.json",!0,_),(0,we.getModelJSON)(ae,"tokenizer_config.json",!0,_)]);return _.legacy!==null&&(F[1].legacy=_.legacy),F}function q(ae,_){const F=[];let Q=0;for(const oe of ae.matchAll(_)){const _e=oe[0];Q0&&F.push(_e),Q=oe.index+_e.length}return Q=19968&&ae<=40959||ae>=13312&&ae<=19903||ae>=131072&&ae<=173791||ae>=173824&&ae<=177983||ae>=177984&&ae<=178207||ae>=178208&&ae<=183983||ae>=63744&&ae<=64255||ae>=194560&&ae<=195103}function K(ae,_,F){const Q=[];let oe=0;for(;oethis.tokens_to_ids.get(F)??this.unk_token_id)}convert_ids_to_tokens(_){return _.map(F=>this.vocab[F]??this.unk_token)}}class De extends $e{constructor(_){super(_),this.tokens_to_ids=fe(_.vocab),this.unk_token_id=this.tokens_to_ids.get(_.unk_token),this.unk_token=_.unk_token,this.max_input_chars_per_word=_.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[F,Q]of this.tokens_to_ids)this.vocab[Q]=F}encode(_){const F=[];for(const Q of _){const oe=[...Q];if(oe.length>this.max_input_chars_per_word){F.push(this.unk_token);continue}let _e=!1,Ge=0;const gt=[];for(;Ge0&&(zt=this.config.continuing_subword_prefix+zt),this.tokens_to_ids.has(zt)){Ct=zt;break}--Et}if(Ct===null){_e=!0;break}gt.push(Ct),Ge=Et}_e?F.push(this.unk_token):F.push(...gt)}return F}}class ze extends $e{constructor(_,F){super(_);const Q=_.vocab.length;this.vocab=new Array(Q),this.scores=new Array(Q);for(let oe=0;oe[oe,_e])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=F.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,xe.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new U.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(_){const F=_.chars,Q=1;let oe=0;for(;oe{const ae=[...Array.from({length:94},(oe,_e)=>_e+33),...Array.from({length:12},(oe,_e)=>_e+161),...Array.from({length:82},(oe,_e)=>_e+174)],_=ae.slice();let F=0;for(let oe=0;oe<256;++oe)ae.includes(oe)||(ae.push(oe),_.push(256+F),F+=1);const Q=_.map(oe=>String.fromCharCode(oe));return Object.fromEntries(ae.map((oe,_e)=>[oe,Q[_e]]))})(),rt=(0,le.reverseDictionary)(it);class lt extends $e{constructor(_){super(_),this.tokens_to_ids=fe(_.vocab),this.unk_token_id=this.tokens_to_ids.get(_.unk_token),this.unk_token=_.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[Q,oe]of this.tokens_to_ids)this.vocab[oe]=Q;const F=Array.isArray(_.merges[0]);this.merges=F?_.merges:_.merges.map(Q=>Q.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((Q,oe)=>[JSON.stringify(Q),oe])),this.end_of_word_suffix=_.end_of_word_suffix,this.continuing_subword_suffix=_.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(_){if(_.length===0)return[];const F=this.cache.get(_);if(F!==void 0)return F;const Q=Array.from(_);this.end_of_word_suffix&&(Q[Q.length-1]+=this.end_of_word_suffix);let oe=[];if(Q.length>1){const _e=new U.PriorityQueue((Et,Ct)=>Et.score`<0x${gt.toString(16).toUpperCase().padStart(2,"0")}>`);Ge.every(gt=>this.tokens_to_ids.has(gt))?F.push(...Ge):F.push(this.unk_token)}else F.push(this.unk_token)}return F}}class me extends $e{constructor(_,F){super(_),this.tokens_to_ids=fe(F.target_lang?_.vocab[F.target_lang]:_.vocab),this.bos_token=F.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=F.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=F.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=F.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[Q,oe]of this.tokens_to_ids)this.vocab[oe]=Q}encode(_){return _}}class W extends P.Callable{constructor(_){super(),this.config=_}static fromConfig(_){if(_===null)return null;switch(_.type){case"BertNormalizer":return new Mt(_);case"Precompiled":return new Yr(_);case"Sequence":return new ht(_);case"Replace":return new ce(_);case"NFC":return new Te(_);case"NFKC":return new We(_);case"NFKD":return new ot(_);case"Strip":return new ne(_);case"StripAccents":return new Ze(_);case"Lowercase":return new dt(_);case"Prepend":return new Re(_);default:throw new Error(`Unknown Normalizer type: ${_.type}`)}}normalize(_){throw Error("normalize should be implemented in subclass.")}_call(_){return this.normalize(_)}}class ce extends W{normalize(_){const F=re(this.config.pattern);return F===null?_:_.replaceAll(F,this.config.content)}}class Te extends W{normalize(_){return _=_.normalize("NFC"),_}}class We extends W{normalize(_){return _=_.normalize("NFKC"),_}}class ot extends W{normalize(_){return _=_.normalize("NFKD"),_}}class ne extends W{normalize(_){return this.config.strip_left&&this.config.strip_right?_=_.trim():(this.config.strip_left&&(_=_.trimStart()),this.config.strip_right&&(_=_.trimEnd())),_}}class Ze extends W{normalize(_){return _=J(_),_}}class dt extends W{normalize(_){return _=_.toLowerCase(),_}}class Re extends W{normalize(_){return _=this.config.prepend+_,_}}class ht extends W{constructor(_){super(_),this.normalizers=_.normalizers.map(F=>W.fromConfig(F))}normalize(_){return this.normalizers.reduce((F,Q)=>Q.normalize(F),_)}}class Mt extends W{_tokenize_chinese_chars(_){const F=[];for(let Q=0;Q<_.length;++Q){const oe=_[Q],_e=oe.charCodeAt(0);X(_e)?(F.push(" "),F.push(oe),F.push(" ")):F.push(oe)}return F.join("")}stripAccents(_){return _.normalize("NFD").replace(new RegExp("\\p{Mn}","gu"),"")}_is_control(_){switch(_){case" ":case` +`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(_)}}_clean_text(_){const F=[];for(const Q of _){const oe=Q.charCodeAt(0);oe===0||oe===65533||this._is_control(Q)||(/^\s$/.test(Q)?F.push(" "):F.push(Q))}return F.join("")}normalize(_){return this.config.clean_text&&(_=this._clean_text(_)),this.config.handle_chinese_chars&&(_=this._tokenize_chinese_chars(_)),this.config.lowercase?(_=_.toLowerCase(),this.config.strip_accents!==!1&&(_=this.stripAccents(_))):this.config.strip_accents&&(_=this.stripAccents(_)),_}}class Xe extends P.Callable{static fromConfig(_){if(_===null)return null;switch(_.type){case"BertPreTokenizer":return new Z(_);case"Sequence":return new Rr(_);case"Whitespace":return new Jr(_);case"WhitespaceSplit":return new yn(_);case"Metaspace":return new dr(_);case"ByteLevel":return new Ae(_);case"Split":return new Ke(_);case"Punctuation":return new et(_);case"Digits":return new je(_);case"Replace":return new at(_);default:throw new Error(`Unknown PreTokenizer type: ${_.type}`)}}pre_tokenize_text(_,F){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(_,F){return(Array.isArray(_)?_.map(Q=>this.pre_tokenize_text(Q,F)):this.pre_tokenize_text(_,F)).flat()}_call(_,F){return this.pre_tokenize(_,F)}}class Z extends Xe{constructor(_){super(),this.pattern=new RegExp(`[^\\s${k}]+|[${k}]`,"gu")}pre_tokenize_text(_,F){return _.trim().match(this.pattern)||[]}}class Ae extends Xe{constructor(_){super(),this.config=_,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=it,this.text_encoder=new TextEncoder}pre_tokenize_text(_,F){return this.add_prefix_space&&!_.startsWith(" ")&&(_=" "+_),(this.use_regex?_.match(this.pattern)||[]:[_]).map(oe=>Array.from(this.text_encoder.encode(oe),_e=>this.byte_encoder[_e]).join(""))}}class Ke extends Xe{constructor(_){super(),this.config=_,this.pattern=re(this.config.pattern,this.config.invert)}pre_tokenize_text(_,F){return this.pattern===null?[]:this.config.invert?_.match(this.pattern)||[]:q(_,this.pattern)}}class et extends Xe{constructor(_){super(),this.config=_,this.pattern=new RegExp(`[^${k}]+|[${k}]+`,"gu")}pre_tokenize_text(_,F){return _.match(this.pattern)||[]}}class je extends Xe{constructor(_){super(),this.config=_;const F=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(F,"gu")}pre_tokenize_text(_,F){return _.match(this.pattern)||[]}}class Ve extends P.Callable{constructor(_){super(),this.config=_}static fromConfig(_){if(_===null)return null;switch(_.type){case"TemplateProcessing":return new Pt(_);case"ByteLevel":return new Tt(_);case"RobertaProcessing":return new _t(_);case"BertProcessing":return new ut(_);case"Sequence":return new v(_);default:throw new Error(`Unknown PostProcessor type: ${_.type}`)}}post_process(_,...F){throw Error("post_process should be implemented in subclass.")}_call(_,...F){return this.post_process(_,...F)}}class ut extends Ve{constructor(_){super(_),this.cls=_.cls[0],this.sep=_.sep[0]}post_process(_,F=null,{add_special_tokens:Q=!0}={}){Q&&(_=(0,le.mergeArrays)([this.cls],_,[this.sep]));let oe=new Array(_.length).fill(0);if(F!==null){const _e=Q&&this instanceof _t?[this.sep]:[],Ge=Q?[this.sep]:[];_=(0,le.mergeArrays)(_,_e,F,Ge),oe=(0,le.mergeArrays)(oe,new Array(F.length+_e.length+Ge.length).fill(1))}return{tokens:_,token_type_ids:oe}}}class _t extends ut{}class Pt extends Ve{constructor(_){super(_),this.single=_.single,this.pair=_.pair}post_process(_,F=null,{add_special_tokens:Q=!0}={}){const oe=F===null?this.single:this.pair;let _e=[],Ge=[];for(const gt of oe)"SpecialToken"in gt?Q&&(_e.push(gt.SpecialToken.id),Ge.push(gt.SpecialToken.type_id)):"Sequence"in gt&&(gt.Sequence.id==="A"?(_e=(0,le.mergeArrays)(_e,_),Ge=(0,le.mergeArrays)(Ge,new Array(_.length).fill(gt.Sequence.type_id))):gt.Sequence.id==="B"&&(_e=(0,le.mergeArrays)(_e,F),Ge=(0,le.mergeArrays)(Ge,new Array(F.length).fill(gt.Sequence.type_id))));return{tokens:_e,token_type_ids:Ge}}}class Tt extends Ve{post_process(_,F=null){return F&&(_=(0,le.mergeArrays)(_,F)),{tokens:_}}}class v extends Ve{constructor(_){super(_),this.processors=_.processors.map(F=>Ve.fromConfig(F))}post_process(_,F=null,Q={}){let oe;for(const _e of this.processors)if(_e instanceof Tt)_=_e.post_process(_).tokens,F&&(F=_e.post_process(F).tokens);else{const Ge=_e.post_process(_,F,Q);_=Ge.tokens,oe=Ge.token_type_ids}return{tokens:_,token_type_ids:oe}}}class H extends P.Callable{constructor(_){super(),this.config=_,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=_.trim_offsets}static fromConfig(_){if(_===null)return null;switch(_.type){case"WordPiece":return new Je(_);case"Metaspace":return new Cr(_);case"ByteLevel":return new Nt(_);case"Replace":return new $(_);case"ByteFallback":return new Y(_);case"Fuse":return new he(_);case"Strip":return new nt(_);case"Sequence":return new bt(_);case"CTC":return new yt(_);case"BPEDecoder":return new Dt(_);default:throw new Error(`Unknown Decoder type: ${_.type}`)}}_call(_){return this.decode(_)}decode(_){return this.decode_chain(_).join("")}decode_chain(_){throw Error("`decode_chain` should be implemented in subclass.")}}class $ extends H{decode_chain(_){const F=re(this.config.pattern);return F===null?_:_.map(Q=>Q.replaceAll(F,this.config.content))}}class Y extends H{constructor(_){super(_),this.text_decoder=new TextDecoder}decode_chain(_){const F=[];let Q=[];for(const oe of _){let _e=null;if(oe.length===6&&oe.startsWith("<0x")&&oe.endsWith(">")){const Ge=parseInt(oe.slice(3,5),16);isNaN(Ge)||(_e=Ge)}if(_e!==null)Q.push(_e);else{if(Q.length>0){const Ge=this.text_decoder.decode(Uint8Array.from(Q));F.push(Ge),Q=[]}F.push(oe)}}if(Q.length>0){const oe=this.text_decoder.decode(Uint8Array.from(Q));F.push(oe),Q=[]}return F}}class he extends H{decode_chain(_){return[_.join("")]}}class nt extends H{constructor(_){super(_),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(_){return _.map(F=>{let Q=0;for(let _e=0;_e(Q!==0&&(F.startsWith(this.config.prefix)?F=F.replace(this.config.prefix,""):F=" "+F),this.cleanup&&(F=z(F)),F))}}class Nt extends H{constructor(_){super(_),this.byte_decoder=rt,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(_){const F=_.join(""),Q=new Uint8Array([...F].map(_e=>this.byte_decoder[_e]));return this.text_decoder.decode(Q)}decode_chain(_){const F=[];let Q=[];for(const oe of _)this.added_tokens.find(_e=>_e.content===oe)!==void 0?(Q.length>0&&(F.push(this.convert_tokens_to_string(Q)),Q=[]),F.push(oe)):Q.push(oe);return Q.length>0&&F.push(this.convert_tokens_to_string(Q)),F}}class yt extends H{constructor(_){super(_),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(_){if(_.length===0)return"";const F=[_[0]];for(let _e=1;_e<_.length;++_e)_[_e]!==F.at(-1)&&F.push(_[_e]);let oe=F.filter(_e=>_e!==this.pad_token).join("");return this.cleanup&&(oe=z(oe).replaceAll(this.word_delimiter_token," ").trim()),oe}decode_chain(_){return[this.convert_tokens_to_string(_)]}}class bt extends H{constructor(_){super(_),this.decoders=_.decoders.map(F=>H.fromConfig(F))}decode_chain(_){return this.decoders.reduce((F,Q)=>Q.decode_chain(F),_)}}class Dt extends H{constructor(_){super(_),this.suffix=this.config.suffix}decode_chain(_){return _.map((F,Q)=>F.replaceAll(this.suffix,Q===_.length-1?"":" "))}}class At extends H{decode_chain(_){let F="";for(let Q=1;Q<_.length;Q+=2)F+=_[Q];return[F]}}class dr extends Xe{constructor(_){super(),this.addPrefixSpace=_.add_prefix_space,this.replacement=_.replacement,this.strRep=_.str_rep||this.replacement,this.prepend_scheme=_.prepend_scheme??"always"}pre_tokenize_text(_,{section_index:F=void 0}={}){let Q=_.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!Q.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&F===0)&&(Q=this.strRep+Q),[Q]}}class Cr extends H{constructor(_){super(_),this.addPrefixSpace=_.add_prefix_space,this.replacement=_.replacement}decode_chain(_){const F=[];for(let Q=0;Q<_.length;++Q){let oe=_[Q].replaceAll(this.replacement," ");this.addPrefixSpace&&Q==0&&oe.startsWith(" ")&&(oe=oe.substring(1)),F.push(oe)}return F}}class Yr extends W{constructor(_){super(_),this.charsmap=_.precompiled_charsmap}normalize(_){return _=_.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),_=_.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),_.includes("～")?_=_.split("～").map(Q=>Q.normalize("NFKC")).join("～"):_=_.normalize("NFKC"),_}}class Rr extends Xe{constructor(_){super(),this.tokenizers=_.pretokenizers.map(F=>Xe.fromConfig(F))}pre_tokenize_text(_,F){return this.tokenizers.reduce((Q,oe)=>oe.pre_tokenize(Q,F),[_])}}class Jr extends Xe{constructor(_){super()}pre_tokenize_text(_,F){return _.match(/\w+|[^\w\s]+/g)||[]}}class yn extends Xe{constructor(_){super()}pre_tokenize_text(_,F){return j(_)}}class at extends Xe{constructor(_){super(),this.config=_,this.pattern=re(this.config.pattern),this.content=this.config.content}pre_tokenize_text(_,F){return this.pattern===null?[_]:[_.replaceAll(this.pattern,this.config.content)]}}const G=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function ge(ae,_,F,Q){for(const oe of Object.keys(ae)){const _e=_-ae[oe].length,Ge=F(oe),gt=new Array(_e).fill(Ge);ae[oe]=Q==="right"?(0,le.mergeArrays)(ae[oe],gt):(0,le.mergeArrays)(gt,ae[oe])}}function Ie(ae,_){for(const F of Object.keys(ae))ae[F].length=_}class Se extends P.Callable{constructor(F,Q){super();be(this,"return_token_type_ids",!1);be(this,"padding_side","right");this._tokenizer_config=Q,this.normalizer=W.fromConfig(F.normalizer),this.pre_tokenizer=Xe.fromConfig(F.pre_tokenizer),this.model=$e.fromConfig(F.model,Q),this.post_processor=Ve.fromConfig(F.post_processor),this.decoder=H.fromConfig(F.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const oe of F.added_tokens){const _e=new Me(oe);this.added_tokens.push(_e),this.model.tokens_to_ids.set(_e.content,_e.id),this.model.vocab[_e.id]=_e.content,_e.special&&(this.special_tokens.push(_e.content),this.all_special_ids.push(_e.id))}if(this.additional_special_tokens=Q.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort((oe,_e)=>_e.content.length-oe.content.length).map(oe=>`${oe.lstrip?"\\s*":""}(${(0,le.escapeRegExp)(oe.content)})${oe.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.model_max_length=Q.model_max_length,this.remove_space=Q.remove_space,this.clean_up_tokenization_spaces=Q.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=Q.do_lowercase_and_remove_accent??!1,Q.padding_side&&(this.padding_side=Q.padding_side),this.legacy=!1,this.chat_template=Q.chat_template??null,Array.isArray(this.chat_template)){const oe=Object.create(null);for(const{name:_e,template:Ge}of this.chat_template){if(typeof _e!="string"||typeof Ge!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');oe[_e]=Ge}this.chat_template=oe}this._compiled_template_cache=new Map}getToken(...F){for(const Q of F){const oe=this._tokenizer_config[Q];if(oe)if(typeof oe=="object"){if(oe.__type==="AddedToken")return oe.content;throw Error(`Unknown token: ${oe}`)}else return oe}return null}static async from_pretrained(F,{progress_callback:Q=null,config:oe=null,cache_dir:_e=null,local_files_only:Ge=!1,revision:gt="main",legacy:Et=null}={}){const Ct=await B(F,{progress_callback:Q,config:oe,cache_dir:_e,local_files_only:Ge,revision:gt,legacy:Et});return new this(...Ct)}_call(F,{text_pair:Q=null,add_special_tokens:oe=!0,padding:_e=!1,truncation:Ge=null,max_length:gt=null,return_tensor:Et=!0,return_token_type_ids:Ct=null}={}){const zt=Array.isArray(F);let er;if(zt){if(F.length===0)throw Error("text array must be non-empty");if(Q!==null){if(Array.isArray(Q)){if(F.length!==Q.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");er=F.map((ur,Wr)=>this._encode_plus(ur,{text_pair:Q[Wr],add_special_tokens:oe,return_token_type_ids:Ct}))}else er=F.map(ur=>this._encode_plus(ur,{add_special_tokens:oe,return_token_type_ids:Ct}))}else{if(F==null)throw Error("text may not be null or undefined");if(Array.isArray(Q))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");er=[this._encode_plus(F,{text_pair:Q,add_special_tokens:oe,return_token_type_ids:Ct})]}if(gt===null?_e==="max_length"?gt=this.model_max_length:gt=(0,xe.max)(er.map(ur=>ur.input_ids.length))[0]:Ge||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."),gt=Math.min(gt,this.model_max_length??1/0),_e||Ge)for(let ur=0;urgt?Ge&&Ie(er[ur],gt):_e&&ge(er[ur],gt,Wr=>Wr==="input_ids"?this.pad_token_id:0,this.padding_side));const Sr={};if(Et){if(!(_e&&Ge)&&er.some(Wr=>{var en;for(const or of Object.keys(Wr))if(Wr[or].length!==((en=er[0][or])==null?void 0:en.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 ur=[er.length,er[0].input_ids.length];for(const Wr of Object.keys(er[0]))Sr[Wr]=new Ce.Tensor("int64",BigInt64Array.from(er.flatMap(en=>en[Wr]).map(BigInt)),ur)}else{for(const ur of Object.keys(er[0]))Sr[ur]=er.map(Wr=>Wr[ur]);if(!zt)for(const ur of Object.keys(Sr))Sr[ur]=Sr[ur][0]}return Sr}_encode_text(F){return F===null?null:(this.added_tokens_regex?F.split(this.added_tokens_regex).filter(_e=>_e):[F]).map((_e,Ge)=>{if(this.added_tokens.find(Et=>Et.content===_e)!==void 0)return _e;{if(this.remove_space===!0&&(_e=_e.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(_e=pe(_e)),this.normalizer!==null&&(_e=this.normalizer(_e)),_e.length===0)return[];const Et=this.pre_tokenizer!==null?this.pre_tokenizer(_e,{section_index:Ge}):[_e];return this.model(Et)}}).flat()}_encode_plus(F,{text_pair:Q=null,add_special_tokens:oe=!0,return_token_type_ids:_e=null}={}){const{tokens:Ge,token_type_ids:gt}=this._tokenize_helper(F,{pair:Q,add_special_tokens:oe}),Et=this.model.convert_tokens_to_ids(Ge),Ct={input_ids:Et,attention_mask:new Array(Et.length).fill(1)};return(_e??this.return_token_type_ids)&>&&(Ct.token_type_ids=gt),Ct}_tokenize_helper(F,{pair:Q=null,add_special_tokens:oe=!1}={}){const _e=this._encode_text(F),Ge=this._encode_text(Q);return this.post_processor?this.post_processor(_e,Ge,{add_special_tokens:oe}):{tokens:(0,le.mergeArrays)(_e??[],Ge??[])}}tokenize(F,{pair:Q=null,add_special_tokens:oe=!1}={}){return this._tokenize_helper(F,{pair:Q,add_special_tokens:oe}).tokens}encode(F,{text_pair:Q=null,add_special_tokens:oe=!0,return_token_type_ids:_e=null}={}){return this._encode_plus(F,{text_pair:Q,add_special_tokens:oe,return_token_type_ids:_e}).input_ids}batch_decode(F,Q={}){return F instanceof Ce.Tensor&&(F=F.tolist()),F.map(oe=>this.decode(oe,Q))}decode(F,Q={}){if(F instanceof Ce.Tensor&&(F=de(F)),!Array.isArray(F)||F.length===0||!(0,le.isIntegralNumber)(F[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(F,Q)}decode_single(F,{skip_special_tokens:Q=!1,clean_up_tokenization_spaces:oe=null}){let _e=this.model.convert_ids_to_tokens(F);Q&&(_e=_e.filter(gt=>!this.special_tokens.includes(gt)));let Ge=this.decoder?this.decoder(_e):_e.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Ge=Ge.replaceAll(this.decoder.end_of_word_suffix," "),Q&&(Ge=Ge.trim())),(oe??this.clean_up_tokenization_spaces)&&(Ge=z(Ge)),Ge}get_chat_template({chat_template:F=null,tools:Q=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const oe=this.chat_template;if(F!==null&&Object.hasOwn(oe,F))F=oe[F];else if(F===null)if(Q!==null&&"tool_use"in oe)F=oe.tool_use;else if("default"in oe)F=oe.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(oe).sort()}.`)}else if(F===null)if(this.chat_template)F=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 F}apply_chat_template(F,{tools:Q=null,documents:oe=null,chat_template:_e=null,add_generation_prompt:Ge=!1,tokenize:gt=!0,padding:Et=!1,truncation:Ct=!1,max_length:zt=null,return_tensor:er=!0,return_dict:Sr=!1,tokenizer_kwargs:ur={},...Wr}={}){if(_e=this.get_chat_template({chat_template:_e,tools:Q}),typeof _e!="string")throw Error(`chat_template must be a string, but got ${typeof _e}`);let en=this._compiled_template_cache.get(_e);en===void 0&&(en=new I.Template(_e),this._compiled_template_cache.set(_e,en));const or=Object.create(null);for(const mn of G){const bn=this.getToken(mn);bn&&(or[mn]=bn)}const Pr=en.render({messages:F,add_generation_prompt:Ge,tools:Q,documents:oe,...or,...Wr});if(gt){const mn=this._call(Pr,{add_special_tokens:!1,padding:Et,truncation:Ct,max_length:zt,return_tensor:er,...ur});return Sr?mn:mn.input_ids}return Pr}}class Ne extends Se{constructor(){super(...arguments);be(this,"return_token_type_ids",!0)}}class tt extends Se{constructor(){super(...arguments);be(this,"return_token_type_ids",!0)}}class wt extends Se{constructor(){super(...arguments);be(this,"return_token_type_ids",!0)}}class mt extends Se{constructor(){super(...arguments);be(this,"return_token_type_ids",!0)}}class $t extends Se{constructor(){super(...arguments);be(this,"return_token_type_ids",!0)}}class ft extends Se{constructor(){super(...arguments);be(this,"return_token_type_ids",!0)}}class Lt extends Se{constructor(){super(...arguments);be(this,"return_token_type_ids",!0)}}class jt extends Se{constructor(){super(...arguments);be(this,"return_token_type_ids",!0)}}class Ot extends Se{constructor(){super(...arguments);be(this,"return_token_type_ids",!0)}}class Fe extends Se{}class Oe extends Se{}class ct extends Se{constructor(F,Q){super(F,Q);be(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Ut extends Se{constructor(){super(...arguments);be(this,"return_token_type_ids",!0)}}class sr extends Se{}class br extends Se{}class Nr extends Se{}class mr extends Se{constructor(_,F){super(_,F),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)),this.lang_to_token=Q=>Q}_build_translation_inputs(_,F,Q){return An(this,_,F,Q)}}class kr extends mr{}class wr extends Se{}class $n extends Se{}const Ur="▁";class hs extends Se{constructor(F,Q){super(F,Q);be(this,"padding_side","left");this.legacy=Q.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new dr({replacement:Ur,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(F){if(F===null)return null;if(this.legacy||F.length===0)return super._encode_text(F);let Q=super._encode_text(Ur+F.replaceAll(Ur," "));return Q.length>1&&Q[0]===Ur&&this.special_tokens.includes(Q[1])&&(Q=Q.slice(1)),Q}}class Es extends Se{}class Kn extends Se{}class ks extends Se{}class Ss extends Se{}class Ps extends Se{}class As extends Se{}class Is extends Se{}class ts extends Se{}class Xn extends Se{}function An(ae,_,F,Q){if(!("language_codes"in ae)||!Array.isArray(ae.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in ae)||!(ae.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in ae)||typeof ae.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const oe=Q.src_lang,_e=Q.tgt_lang;if(!ae.language_codes.includes(_e))throw new Error(`Target language code "${_e}" is not valid. Must be one of: {${ae.language_codes.join(", ")}}`);if(oe!==void 0){if(!ae.language_codes.includes(oe))throw new Error(`Source language code "${oe}" is not valid. Must be one of: {${ae.language_codes.join(", ")}}`);for(const Ge of ae.post_processor.config.single)if("SpecialToken"in Ge&&ae.languageRegex.test(Ge.SpecialToken.id)){Ge.SpecialToken.id=ae.lang_to_token(oe);break}}return Q.forced_bos_token_id=ae.model.convert_tokens_to_ids([ae.lang_to_token(_e)])[0],ae._call(_,F)}class Bn extends Se{constructor(_,F){super(_,F),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)),this.lang_to_token=Q=>Q}_build_translation_inputs(_,F,Q){return An(this,_,F,Q)}}class fs extends Se{constructor(_,F){super(_,F),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)).map(Q=>Q.slice(2,-2)),this.lang_to_token=Q=>`__${Q}__`}_build_translation_inputs(_,F,Q){return An(this,_,F,Q)}}class Ln extends Se{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(_,{return_timestamps:F=!1,return_language:Q=!1,time_precision:oe=null,force_full_sequences:_e=!0}={}){if(oe===null)throw Error("Must specify time_precision");let Ge=null;const gt=F==="word";function Et(){return{language:Ge,timestamp:[null,null],text:""}}const Ct=[];let zt=Et(),er=0;const Sr=this.timestamp_begin;let ur=[],Wr=[],en=!1,or=null;const Pr=new Set(this.all_special_ids);for(const Ee of _){const tn=Ee.tokens,ln=gt?Ee.token_timestamps:null;let In=null,Rn=Sr;if("stride"in Ee){const[Kr,fr,Or]=Ee.stride;if(er-=fr,or=Kr-Or,fr&&(Rn=fr/oe+Sr),Or)for(let kt=tn.length-1;kt>=0;--kt){const _r=Number(tn[kt]);if(_r>=Sr){if(In!==null&&(_r-Sr)*oe=Sr){const Or=(fr-Sr)*oe+er,kt=(0,xe.round)(Or,2);if(In!==null&&fr>=In)en=!0;else if(en||ur.length>0&&fr0?(ur.push(Kt),gt&&Wr.push(pn)):ur.every(Kr=>Kr.length===0)&&(zt=Et(),ur=[],Kt=[],Wr=[],pn=[])}if(ur.length>0){if(_e&&F)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[Ee,tn]=this.findLongestCommonSequence(ur,Wr),ln=this.decode(Ee);zt.text=ln,gt&&(zt.words=this.collateWordTimestamps(Ee,tn,Ge)),Ct.push(zt)}let mn=Object.create(null);const bn=Ct.map(Ee=>Ee.text).join("");if(F||Q){for(let Ee=0;Ee0;let gt=Ge?[]:null,Et=Ge?F[0]:null;for(let Ct=1;Ct<_.length;++Ct){const zt=_[Ct];let er=0,Sr=[oe,oe,0,0];const ur=zt.length;for(let Ee=1;Eekt===pn[_r]&&Et[tn+_r]<=F[Ct][Rn+_r]).length:Kr=In.filter((kt,_r)=>kt===pn[_r]).length;const fr=Ee/1e4,Or=Kr/Ee+fr;Kr>1&&Or>er&&(er=Or,Sr=[tn,ln,Rn,Kt])}const[Wr,en,or,Pr]=Sr,mn=Math.floor((en+Wr)/2),bn=Math.floor((Pr+or)/2);_e.push(...Q.slice(0,mn)),Q=zt.slice(bn),oe=Q.length,Ge&&(gt.push(...Et.slice(0,mn)),Et=F[Ct].slice(bn))}return _e.push(...Q),Ge?(gt.push(...Et),[_e,gt]):[_e,[]]}collateWordTimestamps(_,F,Q){const[oe,_e,Ge]=this.combineTokensIntoWords(_,Q),gt=[];for(let Et=0;Et=oe){const gt=((Ge-oe)*Q).toFixed(2);_e.push(`<|${gt}|>`),_e.push([])}else _e[_e.length-1].push(Ge);return _e=_e.map(Ge=>typeof Ge=="string"?Ge:super.decode(Ge,F)),_e.join("")}splitTokensOnUnicode(_){const F=this.decode(_,{decode_with_timestamps:!0}),Q="�",oe=[],_e=[],Ge=[];let gt=[],Et=[],Ct=0;for(let zt=0;zt<_.length;++zt){const er=_[zt];gt.push(er),Et.push(zt);const Sr=this.decode(gt,{decode_with_timestamps:!0});(!Sr.includes(Q)||F[Ct+Sr.indexOf(Q)]===Q)&&(oe.push(Sr),_e.push(gt),Ge.push(Et),gt=[],Et=[],Ct+=Sr.length)}return[oe,_e,Ge]}splitTokensOnSpaces(_){const[F,Q,oe]=this.splitTokensOnUnicode(_),_e=[],Ge=[],gt=[],Et=new RegExp(`^[${k}]$`,"gu");for(let Ct=0;Ct=this.model.tokens_to_ids.get("<|endoftext|>"),Wr=zt.startsWith(" "),en=zt.trim(),or=Et.test(en);if(ur||Wr||or||_e.length===0)_e.push(zt),Ge.push(er),gt.push(Sr);else{const Pr=_e.length-1;_e[Pr]+=zt,Ge[Pr].push(...er),gt[Pr].push(...Sr)}}return[_e,Ge,gt]}mergePunctuations(_,F,Q,oe,_e){const Ge=structuredClone(_),gt=structuredClone(F),Et=structuredClone(Q);let Ct=Ge.length-2,zt=Ge.length-1;for(;Ct>=0;)Ge[Ct].startsWith(" ")&&oe.includes(Ge[Ct].trim())?(Ge[zt]=Ge[Ct]+Ge[zt],gt[zt]=(0,le.mergeArrays)(gt[Ct],gt[zt]),Et[zt]=(0,le.mergeArrays)(Et[Ct],Et[zt]),Ge[Ct]="",gt[Ct]=[],Et[Ct]=[]):zt=Ct,--Ct;for(Ct=0,zt=1;zter),gt.filter(er=>er.length>0),Et.filter(er=>er.length>0)]}}class ms extends Se{}class _s extends Se{}class gs extends Se{}class Yt extends Se{constructor(_,F){super(_,F),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(Q=>this.languageRegex.test(Q)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(_){if(_===null)return null;const[F,...Q]=_.trim().split(this.languageRegex);if(Q.length===0)return super._encode_text(F);if(Q.length===2){const[oe,_e]=Q;return this.supported_language_codes.includes(oe)||console.warn(`Unsupported language code "${oe}" detected, which may lead to unexpected behavior. 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Vf.from_pretrained(ye,{local:!1,device:"webgpu",dtype:"fp32",quantized:!1,progress_callback:P=>{O==null||O({status:"initiate",file:"model",...P})}}),this.isInitialized=!0,[this.tokenizer,this.processor,this.model]}catch(P){throw console.error("Pipeline initialization error:",P),P}}}be(ps,"model_id",null),be(ps,"tokenizer",null),be(ps,"processor",null),be(ps,"model",null),be(ps,"isInitialized",!1);let xp=!1,Ad=null,$s=new Map,Ac=!1;async function Gf(xt,ye){try{const[O,P,le]=await Ad,xe={...await P(xt),max_new_tokens:Wf,num_beams:1,temperature:0,return_dict_in_generate:!1,output_scores:!1};ps.model_id.endsWith(".en")||(xe.language=ye.language,xe.task="transcribe");const Ce=await le.generate(xe);return O.batch_decode(Ce,{skip_special_tokens:!0})[0]}catch(O){throw console.error("Transcription error:",O),O}}async function Tp(){if(!(Ac||$s.size===0)){Ac=!0;try{const[xt]=$s.keys(),ye=$s.get(xt);console.log("Processing task:",ye.data.segmentId);try{const O=await Gf(ye.data.audioData,{language:ye.data.language});self.postMessage({status:"complete",segmentId:ye.data.segmentId,output:O,isMerged:ye.data.isMerged})}catch(O){console.error("Error transcribing segment:",ye.data.segmentId,O),self.postMessage({status:"error",segmentId:ye.data.segmentId,error:O.message})}$s.delete(xt)}catch(xt){console.error("Queue processing error:",xt)}finally{Ac=!1,$s.size>0&&setTimeout(Tp,100)}}}async function qf(xt){if(xp){console.log("Model already loaded"),self.postMessage({status:"ready"});return}console.log("Loading Whisper model:",xt),self.postMessage({status:"loading",data:`Loading ${xt}...`});try{Ad=ps.getInstance(xt,we=>{self.postMessage(we)}),await Ad,console.log("Warming up model..."),self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const[ye,O,P]=await Ad,le={input_features:Uf([1,80,3e3],0),max_new_tokens:1,num_beams:1,temperature:0};xt.endsWith(".en")||(le.language="en",le.task="transcribe"),await P.generate(le),xp=!0,console.log("Model ready"),self.postMessage({status:"ready"})}catch(ye){throw console.error("Error loading model:",ye),ye}}self.addEventListener("message",async xt=>{var P;const{type:ye,data:O}=xt.data;switch(ye){case"load":await qf(O.modelId);break;case"transcribe":if($s.has(O.segmentId))console.log("Segment already in queue:",O.segmentId);else{const le={...O,isEnglishOnly:(P=ps.model_id)==null?void 0:P.endsWith(".en"),language:O.language||"en"};$s.set(O.segmentId,{type:ye,data:le}),console.log("Added to queue:",O.segmentId,"Queue size:",$s.size,"Language:",le.isEnglishOnly?"en (English-only model)":le.language,"Model:",ps.model_id),Tp()}break}})})();