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workgroup_index * ${t*r*n}u + local_idx;`;return`@compute @workgroup_size(${t}, ${r}, ${n}) + fn main(${a}) { + ${i} + `}appendVariableUniforms(e){e.rank!==0&&(e.shape.startsWith("uniforms.")&&this.uniforms.push({name:e.shape.replace("uniforms.",""),type:"u32",length:e.rank}),e.strides.startsWith("uniforms.")&&this.uniforms.push({name:e.strides.replace("uniforms.",""),type:"u32",length:e.rank}))}declareVariable(e,t){if(e.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(e),this.appendVariableUniforms(e);let r=e.usage==="input"?"read":"read_write",n=e.usage==="atomicOutput"?"atomic":e.type.storage;return`@group(0) @binding(${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() instead.");this.internalVariables.push(e),this.appendVariableUniforms(e)}registerInternalVariables(...e){return e.forEach(t=>this.registerInternalVariable(t)),this}registerUniform(e,t,r=1){return this.uniforms.push({name:e,type:t,length:r}),this}registerUniforms(e){return this.uniforms=this.uniforms.concat(e),this}uniformDeclaration(){if(this.uniforms.length===0)return"";let e=[];for(let{name:t,type:r,length:n}of this.uniforms)if(n&&n>4)r==="f16"?e.push(`@align(16) ${t}:array, ${Math.ceil(n/8)}>`):e.push(`${t}:array, ${Math.ceil(n/4)}>`);else{let o=n==null||n===1?r:`vec${n}<${r}>`;e.push(`${t}:${o}`)}return` + struct Uniforms { ${e.join(", ")} }; + @group(0) @binding(${this.variableIndex}) var uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(e=>e.impl()).join(` +`)+this.internalVariables.map(e=>e.impl()).join(` +`)}get variablesInfo(){if(this.uniforms.length===0)return;let 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R=$e.size(a);return{outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(R/64/4)},programUniforms:[{type:12,data:Math.ceil(R/4)}]}},getShaderSource:h};let{newShape:k,newPerm:S}=jo(e.dims,o),u=$e.areEqual(S,[2,3,1]),B=$e.areEqual(S,[3,1,2]);if(k.length===2||u||B){i=u?[k[0],k[1]*k[2]]:B?[k[0]*k[1],k[2]]:k,d=[i[1],i[0]];let R=16;return h=j=>{let Z=ze("a",r,i.length),te=wt("output",r,d.length);return` + ${j.registerUniform("output_size","u32").declareVariables(Z,te)} + var tile : array, ${R}>; + ${j.mainStart([R,R,1])} + let stride = (uniforms.output_shape[1] - 1) / ${R} + 1; + let workgroup_id_x = workgroup_index % stride; + let workgroup_id_y = workgroup_index / stride; + let input_col = workgroup_id_y * ${R}u + local_id.x; + let input_row = workgroup_id_x * ${R}u + local_id.y; + if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) { + tile[local_id.y][local_id.x] = ${Z.getByIndices(`${Z.type.indices}(input_row, input_col)`)}; + } + workgroupBarrier(); + + let output_col = workgroup_id_x * ${R}u + local_id.x; + let output_row = workgroup_id_y * ${R}u + local_id.y; + if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) { + ${te.setByIndices(`${te.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")} + } + }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let j=$e.size(a);return{outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(d[1]/R),y:Math.ceil(d[0]/R)},programUniforms:[{type:12,data:j},...vt(i,d)]}},getShaderSource:h}}return h=R=>{let j=ze("a",r,i.length),Z=wt("output",r,d.length);return` + ${R.registerUniform("output_size","u32").declareVariables(j,Z)} + + ${Ea(o,n,j,Z)} + + ${R.mainStart()} + ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${Z.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${Z.setByOffset("global_idx",j.getByIndices("aIndices"))} + }`},{name:"Transpose",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:()=>{let R=$e.size(a);return{outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(R/64)},programUniforms:[{type:12,data:R},...vt(i,d)]}},getShaderSource:h}},Uo=(e,t)=>{Ta(e.inputs),e.compute(ss(e.inputs[0],t.perm))},Ca=e=>it({perm:e.perm})}),Vo,Oc,ka,Wo,Sa,eo,$a,Aa,Ia,Oa,hs,Fa,Da,Go,La,Ko,rn,za,Fc,Ba,Dc,Lc=w(()=>{zt(),Bt(),Qt(),ei(),Ws(),Vo={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},Oc={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + 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return ((a - 1u) / b + 1u); + } + ${j.mainStart(u)} + + let outputIndex = global_idx / ${u}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${ka[n]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${u}) { + let candidate = f32(${k.getByOffset("offset + k")}); + bestValue = ${Vo[n]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${u}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 = ${Oc[n]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${S.setByOffset("outputIndex",`${n==="mean"?`${S.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${S.type.storage}(${Wo[n]})`}`)}; + } + }`;return{name:e,shaderCache:{hint:`${t};${u}`,inputDependencies:["type"]},getShaderSource:R,getRunData:()=>({outputs:[{dims:a,dataType:o}],dispatchGroup:{x:p},programUniforms:[{type:12,data:h}]})}},hs=(e,t,r,n)=>{let o=e.inputs.length===1?r:Ho(e.inputs,r),a=o.axes;a.length===0&&!o.noopWithEmptyAxes&&(a=e.inputs[0].dims.map((B,R)=>R));let 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output_indices = ${X.offsetToIndices("global_idx")}; + + ${Z.join(` +`)} + ${_e[0]} // init ops for reduce max/min + ${_e[1]} + ${me} + ${_e[3]} + ${_e.length===4?X.setByOffset("global_idx","value"):_e.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(R/64)},programUniforms:[{type:12,data:R},...vt(h,p)]})}},Ho=(e,t)=>{let r=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),it({axes:r,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},ms=(e,t,r,n)=>{let o=e.inputs,a=o.length===1?r:Ho(o,r);e.compute(to(t,{hint:a.cacheKey,inputDependencies:["rank"]},[o[0]],a.noopWithEmptyAxes&&a.axes.length===0?Ra:n,a.axes,o[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},Na=(e,t)=>{us(e.inputs),ms(e,"ReduceLogSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = log(value);"])},ro=(e,t)=>{us(e.inputs),ms(e,"ReduceL1",t,(r,n)=>[`var value = 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r=(n,o,a)=>{let i=[];for(let d=0;d=0||a.length===0)&&i.push(`input_indices[${d}] = 0;`);return[`${i.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",o.setByOffset("global_idx","best_index")]};e.compute(to("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Za=(e,t)=>{ti(e.inputs);let r=(n,o,a)=>{let i=[];for(let d=0;d=0||a.length===0)&&i.push(`input_indices[${d}] = 0;`);return[`${i.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); + }`,"",o.setByOffset("global_idx","best_index")]};e.compute(to("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},si=e=>it(e)}),tl,so,ni,rl,sl,vn,nl,ol,no=w(()=>{zt(),Bt(),yn(),Qt(),tl=(e,t)=>{let r=e[0],n=e[1],o=e[2],a=e[3],i=e[4],d=e[5];if(i&&d)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 p=r.dims[0],h=r.dims[1],k=r.dims[2];if(o.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]!==k)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(o.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let S=o.dims[0]/3,u=S,B=u;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let _e of t.qkvHiddenSizes)if(_e%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");S=t.qkvHiddenSizes[0],u=t.qkvHiddenSizes[1],B=t.qkvHiddenSizes[2]}let R=h;if(S!==u)throw new Error("qkv_hidden_sizes first element should be same as the second");if(o.dims[0]!==S+u+B)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let j=0;if(i){if(u!==B)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==p)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==u/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(j=i.dims[3])}let Z=R+j,te=-1,X=0;if(a)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");if(d){if(d.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(d.dims[0]!==p||d.dims[1]!==t.numHeads||d.dims[2]!==h||d.dims[3]!==Z)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:h,pastSequenceLength:j,kvSequenceLength:R,totalSequenceLength:Z,maxSequenceLength:te,inputHiddenSize:k,hiddenSize:S,vHiddenSize:B,headSize:Math.floor(S/t.numHeads),vHeadSize:Math.floor(B/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:X,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},so=(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; + `,ni=(e,t,r,n,o,a,i,d)=>{let p=yr(i?1:a),h=64,k=a/p;k{let X=wt("x",e.dataType,e.dims,p),_e=[X],me=i?ze("seq_lens",i.dataType,i.dims):void 0;me&&_e.push(me);let ye=d?ze("total_sequence_length_input",d.dataType,d.dims):void 0;ye&&_e.push(ye);let Ae=mr(e.dataType),Ie=[{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; + ${te.registerUniforms(Ie).declareVariables(..._e)} + ${te.mainStart([h,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; + ${so(me,ye,!1)} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = (global_idx / ${h}) * uniforms.total_sequence_length + local_offset; + let seq_causal_length = ${i?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"}; + var thread_max_vector = ${R}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + thread_max_vector = max(${R}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(p){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: ${p}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${h}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${R}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + sum_vector += exp(${R}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(p){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: ${p}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${h}; 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] = ${X.type.value}(${Ae}(1.0) / ${Ae}(seq_causal_length)); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + var f32input = ${R}(x[offset + i]); + x[offset + i] = ${X.type.value}(exp(f32input - max_value) / sum); + } + } + ${i?` + 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] = ${X.type.value}(${Ae}(0)); + }`:""}; + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${h};${B};${p}`,inputDependencies:j},getShaderSource:Z,getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(a/h),y:o,z:t*r},programUniforms:u})}},rl=(e,t,r,n,o,a,i,d,p)=>{let h=i+a.kvSequenceLength,k=[a.batchSize,a.numHeads,a.sequenceLength,h],S=e>1&&n,u=a.kvNumHeads?a.kvNumHeads:a.numHeads,B=S?[a.batchSize,u,h,a.headSize]:void 0,R=a.nReps?a.nReps:1,j=a.scale===0?1/Math.sqrt(a.headSize):a.scale,Z=yr(a.headSize),te=a.headSize/Z,X=12,_e={x:Math.ceil(h/X),y:Math.ceil(a.sequenceLength/X),z:a.batchSize*a.numHeads},me=[{type:12,data:a.sequenceLength},{type:12,data:te},{type:12,data:h},{type:12,data:a.numHeads},{type:12,data:a.headSize},{type:1,data:j},{type:12,data:i},{type:12,data:a.kvSequenceLength},{type:12,data:R}],ye=S&&n&&$e.size(n.dims)>0,Ae=["type","type"];ye&&Ae.push("type"),o&&Ae.push("type"),d&&Ae.push("type"),p&&Ae.push("type");let Ie=[{dims:k,dataType:t.dataType,gpuDataType:0}];S&&Ie.push({dims:B,dataType:t.dataType,gpuDataType:0});let Ge=lt=>{let xt=ze("q",t.dataType,t.dims,Z),Kt=ze("key",r.dataType,r.dims,Z),Yt=[xt,Kt];if(ye){let Gt=ze("past_key",n.dataType,n.dims,Z);Yt.push(Gt)}o&&Yt.push(ze("attention_bias",o.dataType,o.dims));let Ct=d?ze("seq_lens",d.dataType,d.dims):void 0;Ct&&Yt.push(Ct);let Jt=p?ze("total_sequence_length_input",p.dataType,p.dims):void 0;Jt&&Yt.push(Jt);let $t=wt("output",t.dataType,k),jt=[$t];S&&jt.push(wt("present_key",t.dataType,B,Z));let vr=mr(1,Z),Ht=[{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 = ${X}u; + + var tileQ: array<${xt.type.storage}, ${X*X}>; + var tileK: array<${xt.type.storage}, ${X*X}>; + ${lt.registerUniforms(Ht).declareVariables(...Yt,...jt)} + ${lt.mainStart([X,X,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z % uniforms.num_heads; + let kvHeadIdx = ${R===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${R===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; + ${so(Ct,Jt,!0)} + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; + let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + ${ye&&S?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""}; + let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K; + ${S?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""} + var value = ${vr}(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; + ${ye&&S?` + 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]; + }`} + ${S?`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 += ${vr}(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(Z){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: ${Z}`)}})()}; + output[outputIdx] = ${$t.type.value} (sum * uniforms.alpha) + ${o?"attention_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${Z};${o!==void 0};${n!==void 0};${e}`,inputDependencies:Ae},getRunData:()=>({outputs:Ie,dispatchGroup:_e,programUniforms:me}),getShaderSource:Ge}},sl=(e,t,r,n,o,a,i=void 0,d=void 0)=>{let p=a+o.kvSequenceLength,h=o.nReps?o.nReps:1,k=o.vHiddenSize*h,S=e>1&&n,u=o.kvNumHeads?o.kvNumHeads:o.numHeads,B=S?[o.batchSize,u,p,o.headSize]:void 0,R=[o.batchSize,o.sequenceLength,k],j=12,Z={x:Math.ceil(o.vHeadSize/j),y:Math.ceil(o.sequenceLength/j),z:o.batchSize*o.numHeads},te=[{type:12,data:o.sequenceLength},{type:12,data:p},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:12,data:k},{type:12,data:a},{type:12,data:o.kvSequenceLength},{type:12,data:h}],X=S&&n&&$e.size(n.dims)>0,_e=["type","type"];X&&_e.push("type"),i&&_e.push("type"),d&&_e.push("type");let me=[{dims:R,dataType:t.dataType,gpuDataType:0}];S&&me.push({dims:B,dataType:t.dataType,gpuDataType:0});let ye=Ae=>{let Ie=ze("probs",t.dataType,t.dims),Ge=ze("v",r.dataType,r.dims),lt=[Ie,Ge];X&<.push(ze("past_value",n.dataType,n.dims));let xt=i?ze("seq_lens",i.dataType,i.dims):void 0;i&<.push(xt);let Kt=d?ze("total_sequence_length_input",d.dataType,d.dims):void 0;d&<.push(Kt);let Yt=[wt("output",t.dataType,R)];S&&Yt.push(wt("present_value",t.dataType,B));let Ct=[{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 = ${j}u; + var tileQ: array<${Ie.type.value}, ${j*j}>; + var tileV: array<${Ie.type.value}, ${j*j}>; + ${Ae.registerUniforms(Ct).declareVariables(...lt,...Yt)} + ${Ae.mainStart([j,j,1])} + let headIdx = workgroup_id.z % uniforms.num_heads; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let kvHeadIdx = ${h===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${h===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; + ${so(xt,Kt,!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 + ${X&&S?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""}; + let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n; + ${S?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""} + var value = ${Ie.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; + ${X&&S?` + 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]; + }`} + ${S?` + if (w + local_id.y < present_sequence_length) { + present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx]; + }`:""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + headIdx * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:_e},getRunData:()=>({outputs:me,dispatchGroup:Z,programUniforms:te}),getShaderSource:ye}},vn=(e,t,r,n,o,a,i,d,p,h,k=void 0,S=void 0)=>{let u=Math.min(e.outputCount,1+(i?1:0)+(d?1:0)),B=u>1?h.pastSequenceLength:0,R=B+h.kvSequenceLength,j=p&&$e.size(p.dims)>0?p:void 0,Z=[t,r];u>1&&i&&$e.size(i.dims)>0&&Z.push(i),j&&Z.push(j),k&&Z.push(k),S&&Z.push(S);let te=e.compute(rl(u,t,r,i,j,h,B,k,S),{inputs:Z,outputs:u>1?[-1,1]:[-1]})[0];e.compute(ni(te,h.batchSize,h.numHeads,B,h.sequenceLength,R,k,S),{inputs:k&&S?[te,k,S]:[te],outputs:[]});let X=[te,n];u>1&&d&&$e.size(d.dims)>0&&X.push(d),k&&X.push(k),S&&X.push(S),e.compute(sl(u,te,n,d,h,B,k,S),{inputs:X,outputs:u>1?[0,2]:[0]})},nl=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,o=t.inputHiddenSize,a=t.headSize,i=12,d={x:Math.ceil(t.headSize/i),y:Math.ceil(t.sequenceLength/i),z:t.batchSize*t.numHeads},p=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:n},{type:12,data:o},{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}],k=S=>{let u=wt("output_q",p[0].dataType,r),B=wt("output_k",p[0].dataType,r),R=wt("output_v",p[0].dataType,r),j=ze("input",p[0].dataType,p[0].dims),Z=ze("weight",p[1].dataType,p[1].dims),te=ze("bias",p[2].dataType,p[2].dims),X=j.type.storage,_e=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return` + const TILE_SIZE = ${i}u; + var tileInput: array<${X}, ${i*i}>; + var tileWeightQ: array<${X}, ${i*i}>; + var tileWeightK: array<${X}, ${i*i}>; + var tileWeightV: array<${X}, ${i*i}>; + ${S.registerUniforms(_e).declareVariables(j,Z,te,u,B,R)} + ${S.mainStart([i,i,1])} + let batchIndex = workgroup_id.z / uniforms.num_heads; + let headNumber = workgroup_id.z % uniforms.num_heads; + let m = global_id.y; + let n = global_id.x; + + let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K; + let biasOffsetQ = headNumber * uniforms.head_size; + let biasOffsetK = uniforms.hidden_size + biasOffsetQ; + let biasOffsetV = uniforms.hidden_size + biasOffsetK; + + var valueQ = ${X}(0); + var valueK = ${X}(0); + var valueV = ${X}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:d,programUniforms:h}),getShaderSource:k},{inputs:p,outputs:[-1,-1,-1]})},ol=(e,t)=>{let r=tl(e.inputs,t),[n,o,a]=nl(e,r);return vn(e,n,o,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r)}}),il,al,oi,ll,zc=w(()=>{Qe(),zt(),Bt(),Pt(),Qt(),il=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,o,a)=>{let i=o.length;if(i!==n.length)throw new Error(`${a}: num dimensions != ${i}`);o.forEach((d,p)=>{if(d!==n[p])throw new Error(`${a}: dim[${p}] do not match`)})};if(e[0].dims.length>1){let n=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,n,"Invalid input scale"),r(e[2].dims,n,"Invalid input B"),r(e[3].dims,n,"Invalid input mean"),r(e[4].dims,n,"Invalid 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${_e.registerUniform("outputSize","u32").declareVariables(S,u,B,R,j,Z)} + ${_e.mainStart()} + ${_e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${Z.offsetToIndices(`global_idx * ${i}`)}; + ${te()} + let scale = ${u.getByOffset("cOffset")}; + let bias = ${B.getByOffset("cOffset")}; + let inputMean = ${R.getByOffset("cOffset")}; + let inputVar = ${j.getByOffset("cOffset")}; + let x = ${S.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${Z.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${i}`,inputDependencies:h?["rank","type","type","type","type"]:void 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+ ${e.registerUniforms(S).declareVariables(h,k)} + + ${a??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${h.getByOffset("global_idx")}; + ${k.setByOffset("global_idx",p)} + }`},ur=(e,t,r,n,o,a=e.dataType,i,d)=>{let p=[{type:12,data:Math.ceil($e.size(e.dims)/4)}];return i&&p.push(...i),{name:t,shaderCache:{hint:o,inputDependencies:["type"]},getShaderSource:h=>pl(h,$e.size(e.dims),e.dataType,a,r,n,d),getRunData:h=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil($e.size(h[0].dims)/64/4)},programUniforms:p})}},ai=e=>{e.compute(ur(e.inputs[0],"Abs","abs"))},hl=e=>{e.compute(ur(e.inputs[0],"Acos","acos"))},ml=e=>{e.compute(ur(e.inputs[0],"Acosh","acosh"))},li=e=>{e.compute(ur(e.inputs[0],"Asin","asin"))},_l=e=>{e.compute(ur(e.inputs[0],"Asinh","asinh"))},fl=e=>{e.compute(ur(e.inputs[0],"Atan","atan"))},ui=e=>{e.compute(ur(e.inputs[0],"Atanh","atanh"))},gl=e=>it(e),wl=(e,t)=>{let r;switch(t.to){case 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((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,pi=e=>{let t=mr(e.inputs[0].dataType);e.compute(ur(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,oo(t)))},Tl=e=>{e.compute(ur(e.inputs[0],"Exp","exp"))},xl=e=>{e.compute(ur(e.inputs[0],"Floor","floor"))},hi=e=>{let t=mr(e.inputs[0].dataType);e.compute(ur(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,oo(t)))},El=(e,t)=>{let r=mr(e.inputs[0].dataType);e.compute(ur(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))},Pl=e=>{e.compute(ur(e.inputs[0],"Not",t=>`!${t}`))},mi=e=>{e.compute(ur(e.inputs[0],"Neg",t=>`-${t}`))},Cl=e=>{e.compute(ur(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},kl=e=>{let t=mr(e.inputs[0].dataType);e.compute(ur(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},_i=e=>{e.compute(ur(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + 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bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${o.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Nl=e=>{Rl(e.inputs),e.compute(vi(e.inputs))}}),jl,Ul,ds,Vl,Wl,Gl,Kl,Ti,Hl,ql,xi,Xl,Ql,Yl=w(()=>{zt(),Bt(),Qt(),jl=(e,t,r,n,o,a,i,d,p,h,k,S)=>{let u,B;typeof d=="string"?u=B=(X,_e)=>`${d}((${X}),(${_e}))`:typeof d=="function"?u=B=d:(u=d.scalar,B=d.vector);let R=wt("outputData",k,n.length,4),j=ze("aData",p,t.length,4),Z=ze("bData",h,r.length,4),te;if(o)if(a){let X=$e.size(t)===1,_e=$e.size(r)===1,me=t.length>0&&t[t.length-1]%4===0,ye=r.length>0&&r[r.length-1]%4===0;X||_e?te=R.setByOffset("global_idx",B(X?`${j.type.value}(${j.getByOffset("0")}.x)`:j.getByOffset("global_idx"),_e?`${Z.type.value}(${Z.getByOffset("0")}.x)`:Z.getByOffset("global_idx"))):te=` + let outputIndices = ${R.offsetToIndices("global_idx * 4u")}; + let offsetA = ${j.broadcastedIndicesToOffset("outputIndices",R)}; + let offsetB = ${Z.broadcastedIndicesToOffset("outputIndices",R)}; + ${R.setByOffset("global_idx",B(i||me?j.getByOffset("offsetA / 4u"):`${j.type.value}(${j.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||ye?Z.getByOffset("offsetB / 4u"):`${Z.type.value}(${Z.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else te=R.setByOffset("global_idx",B(j.getByOffset("global_idx"),Z.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let X=(_e,me,ye="")=>{let Ae=`aData[indexA${me}][componentA${me}]`,Ie=`bData[indexB${me}][componentB${me}]`;return` + let outputIndices${me} = ${R.offsetToIndices(`global_idx * 4u + ${me}u`)}; + let offsetA${me} = ${j.broadcastedIndicesToOffset(`outputIndices${me}`,R)}; + let offsetB${me} = ${Z.broadcastedIndicesToOffset(`outputIndices${me}`,R)}; + let indexA${me} = offsetA${me} / 4u; + let indexB${me} = offsetB${me} / 4u; + let componentA${me} = offsetA${me} % 4u; + let componentB${me} = offsetB${me} % 4u; + ${_e}[${me}] = ${ye}(${u(Ae,Ie)}); + `};k===9?te=` + var data = vec4(0); + ${X("data",0,"u32")} + ${X("data",1,"u32")} + ${X("data",2,"u32")} + ${X("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:te=` + ${X("outputData[global_idx]",0)} + ${X("outputData[global_idx]",1)} + ${X("outputData[global_idx]",2)} + ${X("outputData[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(j,Z,R)} + + ${S??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${te} + }`},Ul=(e,t,r,n,o,a,i=r.dataType)=>{let d=r.dims.map(j=>Number(j)??1),p=n.dims.map(j=>Number(j)??1),h=!$e.areEqual(d,p),k=d,S=$e.size(d),u=!1,B=!1,R=[h];if(h){let j=rr.calcShape(d,p,!1);if(!j)throw new Error("Can't perform binary op on the given tensors");k=j.slice(),S=$e.size(k);let Z=$e.size(d)===1,te=$e.size(p)===1,X=d.length>0&&d[d.length-1]%4===0,_e=p.length>0&&p[p.length-1]%4===0;R.push(Z),R.push(te),R.push(X),R.push(_e);let me=1;for(let ye=1;yej.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:j=>jl(j,d,p,k,u,h,B,o,r.dataType,n.dataType,i,a),getRunData:()=>({outputs:[{dims:k,dataType:i}],dispatchGroup:{x:Math.ceil(S/64/4)},programUniforms:[{type:12,data:Math.ceil($e.size(k)/4)},...vt(d,p,k)]})}},ds=(e,t,r,n,o,a)=>{e.compute(Ul(t,o??"",e.inputs[0],e.inputs[1],r,n,a))},Vl=e=>{ds(e,"Add",(t,r)=>`${t}+${r}`)},Wl=e=>{ds(e,"Div",(t,r)=>`${t}/${r}`)},Gl=e=>{ds(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4(${t}==${r})`},void 0,void 0,9)},Kl=e=>{ds(e,"Mul",(t,r)=>`${t}*${r}`)},Ti=e=>{let t=ze("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;ds(e,"Pow",{scalar:(r,n)=>`pow_custom(${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)); + } + `)},Hl=e=>{ds(e,"Sub",(t,r)=>`${t}-${r}`)},ql=e=>{ds(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4(${t}>${r})`},void 0,void 0,9)},xi=e=>{ds(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4(${t}<${r})`},void 0,void 0,9)},Xl=e=>{ds(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4(${t}>=${r})`},void 0,void 0,9)},Ql=e=>{ds(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4(${t}<=${r})`},void 0,void 0,9)}}),Jl,Zl,Ei,eu,tu,Pi,Rc=w(()=>{zt(),Bt(),Pt(),Qt(),Jl=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],o=n.dataType,a=n.dims.length;e.forEach((i,d)=>{if(d!==r){if(i.dataType!==o)throw new Error("input tensors should be one type");if(i.dims.length!==a)throw new Error("input tensors should have the same shape");i.dims.forEach((p,h)=>{if(h!==t&&p!==n.dims[h])throw new Error("non concat dimensions must match")})}})},Zl=(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 ${e}u; + }`,Ei=(e,t)=>{let r=e.length,n=[];for(let o=0;o{let o=$e.size(r),a=new Array(e.length),i=new Array(e.length),d=0,p=[],h=[],k=[{type:12,data:o}];for(let j=0;j`uniforms.sizeInConcatAxis${j}`).join(","),R=j=>` + + ${(()=>{j.registerUniform("outputSize","u32");for(let Z=0;Z(${B}); + ${u} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${Ei(i,S)} + }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:k}),getShaderSource:R}},tu=(e,t)=>{let r=e.inputs,n=r[0].dims,o=$e.normalizeAxis(t.axis,n.length);Jl(r,o);let a=n.slice();a[o]=r.reduce((d,p)=>d+(p.dims.length>o?p.dims[o]:0),0);let i=r.filter(d=>$e.size(d.dims)>0);e.compute(eu(i,o,a,r[0].dataType),{inputs:i})},Pi=e=>it({axis:e.axis})}),sn,Is,nn,Ci,Gs=w(()=>{zt(),Bt(),sn=(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 ${e.activation}`)}},Is=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},nn=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Ci=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[r,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=(e==null?void 0:e.activation_params)||[Jr,Zr];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:r}}return{activation:t}}}),Vr,ru,ao=w(()=>{Vr=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},ru=e=>` + ${e?"value = value + getBiasByOutputCoords(coords);":""} + `}),su,Nc=w(()=>{su=e=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); +} +`}),Bn,lo,ki=w(()=>{zt(),Bt(),Qt(),Gs(),Bn=(e,t,r,n,o)=>{let a=n-r;return` + ${Array.from({length:r}).map((i,d)=>` + if (${Mt(t.shape,d,t.rank)} != 1) { + ${t.indicesSet(e,d,Mt(o,d+a,n))} + } else { + ${t.indicesSet(e,d,0)} + }`).join("")} +`},lo=(e,t,r,n,o=!1,a)=>{let i=e[0].dims,d=e[1].dims,p=i[i.length-2],h=d[d.length-1],k=i[i.length-1],S=yr(h),u=yr(k),B=yr(p),R=$e.size(r)/S/B,j=e.length>2,Z=n?n.slice(0,-2):r.slice(0,-2),te=[$e.size(Z),p,h],X=[{type:12,data:R},{type:12,data:p},{type:12,data:h},{type:12,data:k}];Is(t,X),X.push(...vt(Z,i,d)),j&&X.push(...vt(e[2].dims)),X.push(...vt(te));let _e=me=>{let ye=tn("batch_dims",e[0].dataType,Z.length),Ae=ze("a",e[0].dataType,i.length,u),Ie=ze("b",e[1].dataType,d.length,S),Ge=wt("output",e[0].dataType,te.length,S),lt=er(Ge.type.tensor),xt=sn(t,Ge.type.value,lt),Kt=[Ae,Ie],Yt="";if(j){let $t=o?S:1;Kt.push(ze("bias",e[2].dataType,e[2].dims.length,$t)),Yt=`${o?`value += bias[col / ${$t}];`:`value += ${Ge.type.value}(bias[row + i]);`}`}let Ct=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];nn(t,Ct);let Jt=()=>{let $t=`var a_data: ${Ae.type.value};`;for(let jt=0;jt; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${u}) { + ${Jt()} + } + for (var i = 0u; i < ${B}u; i++) { + var value = values[i]; + ${Yt} + ${xt} + let cur_indices = ${Ge.type.indices}(batch, row + i, col); + let offset = ${Ge.indicesToOffset("cur_indices")}; + ${Ge.setByOffset(`offset / ${S}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${S};${u};${B};${o}`,inputDependencies:j?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(R/64)},programUniforms:X}),getShaderSource:_e}}}),nu,ou,uo,Si,iu,co,au,po,ho=w(()=>{zt(),Bt(),Qt(),Gs(),ki(),ao(),nu=(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":""}); + `,ou=(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];"} + }`,uo=(e,t,r="f32",n,o=!1,a=32,i=!1,d=32)=>{let p=t[1]*e[1],h=t[0]*e[0],k=o?p:a,S=o?a:p,u=k/t[0],B=a/t[1];if(!((o&&u===4&&e[1]===4||!o&&(u===3||u===4))&&k%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${u} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${u} must be 3 or 4. + tileAWidth ${k} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return` +var mm_Asub: array, ${k/u}>, ${S}>; +var mm_Bsub: array, ${h/e[0]}>, ${a}>; + +const rowPerThread = ${e[1]}; +const colPerThread = ${e[0]}; +const innerElementSize = ${u}; +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 = ${i?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${p}; + + let num_tiles = ${i?`${Math.ceil(d/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${i?`i32(globalId.z) * ${d}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${B}; + 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; + ${nu(o,n)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${B}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${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]; + ${u===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${ou(o,u)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},Si=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${t?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${t?", batchIndices":""}); + `,iu=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",co=(e,t,r="f32",n,o=!1,a=32,i=!1,d=32,p=!1)=>{let h=e[1]*t[1],k=e[0]*t[0],S=o?h:a,u=o?a:h;if(!(u%t[1]===0&&S%t[0]===0&&a%t[1]===0))throw new Error(`tileAHight ${u} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${S} must be divisible by workgroupSize[0]${t[0]}, tileInner ${a} must be divisible by workgroupSize[1]${t[1]}`);let B=u/t[1],R=S/t[0],j=a/t[1],Z=p?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${h}; + let globalColStart = i32(workgroupId.x) * ${k}; + + // 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 < ${u}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${S}; inputCol = inputCol + ${t[0]}) { + ${Si(o,n)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${k}; 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 = ${o?`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) * ${h}; + +let tileRowA = i32(localId.y) * ${B}; +let tileColA = i32(localId.x) * ${R}; +let tileRowB = i32(localId.y) * ${j}; +// 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 < ${B}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${R}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${Si(o,n)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${j}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${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) { + ${iu(o)} + 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, ${u}>; + 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 = ${i?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${i?`${Math.ceil(d/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${i?`i32(globalId.z) * ${d}`:"0"}; + + var acc : array, rowPerThread>; + ${Z} + } +`},au=(e,t,r,n,o=!1)=>{let[a,i,d,p]=n,h=er(n[0].type.tensor);return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${a.type.indices}) -> ${Vr(e,h)} { + var value = ${Vr(e,h)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + var aIndices: ${i.type.indices}; + ${Bn("aIndices",i,i.rank-2,a.rank,"batchIndices")} + ${i.indicesSet("aIndices",i.rank-2,"u32(row)")} + ${i.indicesSet("aIndices",i.rank-1,"u32(colIn)")} + value = ${i.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${a.type.indices}) -> ${Vr(e,h)} { + var value = ${Vr(e,h)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + var bIndices: ${d.type.indices}; + ${Bn("bIndices",d,d.rank-2,a.rank,"batchIndices")} + ${d.indicesSet("bIndices",d.rank-2,"u32(row)")} + ${d.indicesSet("bIndices",d.rank-1,"u32(colIn)")} + value = ${d.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Vr(e,h)}) { + 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 + ${o?"bias[colIn]":`${Vr(e,h)}(bias[row])`};`:""} + ${r} + ${p.setByIndices("vec3(coords)","value")} + } + } + `},po=(e,t,r,n,o=!1,a)=>{let i=e[0].dims,d=e[1].dims,p=i.slice(0,-2),h=d.slice(0,-2),k=n?n.slice(0,-2):r.slice(0,-2),S=$e.size(k),u=i[i.length-2],B=i[i.length-1],R=d[d.length-1],j=B%4===0&&R%4===0,Z=u<=8?[4,1,1]:[4,4,1],te=[8,8,1],X=[Math.ceil(R/te[0]/Z[0]),Math.ceil(u/te[1]/Z[1]),Math.ceil(S/te[2]/Z[2])],_e=j?4:1,me=[...p,u,B/_e],ye=me.length,Ae=[...h,B,R/_e],Ie=Ae.length,Ge=[S,u,R/_e],lt=[{type:6,data:u},{type:6,data:R},{type:6,data:B}];Is(t,lt),lt.push(...vt(k,me,Ae));let xt=["rank","rank"],Kt=e.length>2;Kt&&(lt.push(...vt(e[2].dims)),xt.push("rank")),lt.push(...vt(Ge));let Yt=Ct=>{let Jt=k.length,$t=tn("batchDims",e[0].dataType,Jt,1),jt=er(e[0].dataType),vr=ze("a",e[0].dataType,ye,_e),Ht=ze("b",e[1].dataType,Ie,_e),Gt=wt("result",e[0].dataType,Ge.length,_e),Cr=[vr,Ht];if(Kt){let ys=o?_e:1;Cr.push(ze("bias",e[2].dataType,e[2].dims.length,ys))}let ot=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];nn(t,ot);let Et=er(Gt.type.tensor),cr=sn(t,Gt.type.value,Et),Lr=au(_e,Kt,cr,[$t,vr,Ht,Gt],o);return` + ${Ct.registerUniforms(ot).registerInternalVariables($t).declareVariables(...Cr,Gt)} + ${Lr} + ${j?uo(Z,te,jt,$t):co(Z,te,jt,$t)} + `};return{name:"MatMul",shaderCache:{hint:`${Z};${t.activation};${j};${o}`,inputDependencies:xt},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:X[0],y:X[1],z:X[2]},programUniforms:lt}),getShaderSource:Yt}}}),lu,uu,du=w(()=>{zt(),Yr(),Qt(),Gs(),ao(),Nc(),ho(),lu=(e,t,r,n,o=!1,a,i=4,d=4,p=4,h="f32")=>{let k=lt=>{switch(lt){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${lt} is not supported.`)}},S=lt=>{switch(lt){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 ${lt} is not supported.`)}},u=e?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,B=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,R=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",j=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",Z=e?"row":"col",te=e?"col":"row",X=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${Z} / outWidth; + let outCol = ${Z} % outWidth; + + let WRow = ${te} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${te} / 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 = ${te} % inChannels; + var resData = ${Vr(i,h)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${R} && xCol >= 0 && xCol < ${j}) { + ${u} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${k(i)} + } + return resData;`,_e=e?t&&n?` + let col = colIn * ${i}; + ${X}`:` + let col = colIn * ${i}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${X} + } + return ${Vr(i,h)}(0.0);`:n&&r?` + let col = colIn * ${i}; + ${X}`:` + let col = colIn * ${i}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${X} + } + return ${Vr(i,h)}(0.0);`,me=`${S(d)}`,ye=Vr(p,h),Ae=Vr(e?i:d,h),Ie=Vr(e?d:i,h),Ge=sn(a,ye,h);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Ae} { + ${e?_e:me} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Ie} { + ${e?me:_e} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${ye}) { + let col = colIn * ${p}; + 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])"}; + ${B} + ${ru(o)} + ${Ge} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},uu=(e,t,r,n,o,a,i,d,p)=>{let h=t.format==="NHWC",k=h?e[0].dims[3]:e[0].dims[1],S=r[0],u=h?r[2]:r[3],B=h?r[1]:r[2],R=h?r[3]:r[1],j=h&&(k%4===0||k%3===0)&&R%4===0,Z=h?R:u*B,te=h?u*B:R,X=[8,8,1],_e=n<=8?[4,1,1]:[4,4,1],me=[Math.ceil(Z/X[0]/_e[0]),Math.ceil(te/X[1]/_e[1]),Math.ceil(S/X[2]/_e[2])];ir("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${me}`);let ye=j?h&&k%4!==0?3:4:1,Ae=X[1]*_e[1],Ie=X[0]*_e[0],Ge=Math.max(X[0]*ye,X[1]),lt=n%Ae===0,xt=o%Ie===0,Kt=a%Ge===0,Yt=j?[ye,4,4]:[1,1,1],Ct=[{type:6,data:n},{type:6,data:o},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Is(t,Ct),Ct.push(...vt(e[0].dims,e[1].dims));let Jt=["rank","rank"];i&&(Ct.push(...vt(e[2].dims)),Jt.push("rank")),Ct.push(...vt(r));let $t=jt=>{let vr=[{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}];nn(t,vr);let Ht=j?4:1,Gt=er(e[0].dataType),Cr=` + fn setOutputAtIndex(flatIndex : i32, value : ${j?`vec4<${Gt}>`:Gt}) { + result[flatIndex] = ${j?`vec4<${Gt}>`:Gt}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${j?`vec4<${Gt}>`:Gt}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${j?"/ 4":""}, value); + }`,ot=ze("x",e[0].dataType,e[0].dims.length,ye===3?1:ye),Et=ze("w",e[1].dataType,e[1].dims.length,Ht),cr=[ot,Et],Lr=wt("result",e[0].dataType,r.length,Ht);if(i){let ys=ze("bias",e[2].dataType,e[2].dims.length,Ht);cr.push(ys),Cr+=` + fn getBiasByOutputCoords(coords : vec4) -> ${j?`vec4<${Gt}>`:Gt} { + return bias[coords.${h?"w":"y"}${j?"/ 4":""}]; + }`}return` + ${su("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 }; + ${jt.registerUniforms(vr).declareVariables(...cr,Lr)} + ${Cr} + ${lu(h,lt,xt,Kt,i,t,Yt[0],Yt[1],Yt[2],Gt)} + ${j?uo(_e,X,Gt,void 0,!h,Ge):co(_e,X,Gt,void 0,!h,Ge,!1,void 0,d)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${ye};${j};${lt};${xt};${Kt};${Ae};${Ie};${Ge}`,inputDependencies:Jt},getRunData:()=>({outputs:[{dims:p?p(r):r,dataType:e[0].dataType}],dispatchGroup:{x:me[0],y:me[1],z:me[2]},programUniforms:Ct}),getShaderSource:$t}}}),cu,$i,Tn,pu,Ai,Ii,hu,mu,jc=w(()=>{zt(),Yr(),Bt(),Qt(),Gs(),ao(),cu=e=>{let t=1;for(let r=0;rtypeof e=="number"?[e,e,e]:e,Tn=(e,t)=>t<=1?e:e+(e-1)*(t-1),pu=(e,t,r,n=1)=>{let o=Tn(t,n);return Math.floor((e[0]*(r-1)-r+o)/2)},Ai=(e,t,r,n,o)=>{o==null&&(o=pu(e,t[0],n[0]));let a=[0,0,0,r];for(let i=0;i<3;i++)e[i]+2*o>=t[i]&&(a[i]=Math.trunc((e[i]-t[i]+2*o)/n[i]+1));return a},Ii=(e,t,r,n,o,a,i,d,p,h)=>{let k,S,u,B;if(e==="VALID"&&(e=0),typeof e=="number"){k={top:e,bottom:e,left:e,right:e,front:e,back:e};let R=Ai([t,r,n,1],[d,p,h],1,[o,a,i],e);S=R[0],u=R[1],B=R[2]}else if(Array.isArray(e)){if(!e.every((j,Z,te)=>j===te[0]))throw Error(`Unsupported padding parameter: ${e}`);k={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let R=Ai([t,r,n,1],[d,p,h],1,[o,a,i],e[0]);S=R[0],u=R[1],B=R[2]}else if(e==="SAME_UPPER"){S=Math.ceil(t/o),u=Math.ceil(r/a),B=Math.ceil(n/i);let R=(S-1)*o+d-t,j=(u-1)*a+p-r,Z=(B-1)*i+h-n,te=Math.floor(R/2),X=R-te,_e=Math.floor(j/2),me=j-_e,ye=Math.floor(Z/2),Ae=Z-ye;k={top:_e,bottom:me,left:ye,right:Ae,front:te,back:X}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:k,outDepth:S,outHeight:u,outWidth:B}},hu=(e,t,r,n,o,a=!1,i="channelsLast")=>{let d,p,h,k,S;if(i==="channelsLast")[d,p,h,k,S]=e;else if(i==="channelsFirst")[d,S,p,h,k]=e;else throw new Error(`Unknown dataFormat ${i}`);let[u,,B,R,j]=t,[Z,te,X]=$i(r),[_e,me,ye]=$i(n),Ae=Tn(B,_e),Ie=Tn(R,me),Ge=Tn(j,ye),{padInfo:lt,outDepth:xt,outHeight:Kt,outWidth:Yt}=Ii(o,p,h,k,Z,te,X,Ae,Ie,Ge),Ct=a?u*S:u,Jt=[0,0,0,0,0];return i==="channelsFirst"?Jt=[d,Ct,xt,Kt,Yt]:i==="channelsLast"&&(Jt=[d,xt,Kt,Yt,Ct]),{batchSize:d,dataFormat:i,inDepth:p,inHeight:h,inWidth:k,inChannels:S,outDepth:xt,outHeight:Kt,outWidth:Yt,outChannels:Ct,padInfo:lt,strideDepth:Z,strideHeight:te,strideWidth:X,filterDepth:B,filterHeight:R,filterWidth:j,effectiveFilterDepth:Ae,effectiveFilterHeight:Ie,effectiveFilterWidth:Ge,dilationDepth:_e,dilationHeight:me,dilationWidth:ye,inShape:e,outShape:Jt,filterShape:t}},mu=(e,t,r,n,o,a)=>{let i=a==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let d=[64,1,1],p={x:r.map((Z,te)=>te)},h=[Math.ceil(cu(p.x.map(Z=>r[Z]))/d[0]),1,1];ir("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${h}`);let k=1,S=$e.size(r),u=[{type:12,data:S},{type:12,data:n},{type:12,data:o},{type:12,data:t.strides},{type:12,data:t.dilations}];Is(t,u),u.push(...vt(e[0].dims,e[1].dims));let B=["rank","rank"],R=e.length===3;R&&(u.push(...vt(e[2].dims)),B.push("rank")),u.push(...vt(r));let j=Z=>{let te=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:n.length},{name:"pads",type:"u32",length:o.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];nn(t,te);let X=1,_e=er(e[0].dataType),me=ze("x",e[0].dataType,e[0].dims.length,k),ye=ze("W",e[1].dataType,e[1].dims.length,X),Ae=[me,ye],Ie=wt("result",e[0].dataType,r.length,X),Ge="";if(R){let Kt=ze("bias",e[2].dataType,e[2].dims.length,X);Ae.push(Kt),Ge+=` + fn getBiasByOutputCoords(coords : array) -> ${_e} { + return bias[${i?Mt("coords",4,5):Mt("coords",1,5)}]; + }`}let lt=Vr(k,_e),xt=sn(t,lt,_e);return` + ${Ge} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${me.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${ye.getByIndices("aIndices")}; + } + ${Z.registerUniforms(te).declareVariables(...Ae,Ie)} + ${Z.mainStart()} + ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${Ie.offsetToIndices("global_idx")}; + let batch = ${Mt("coords",0,me.rank)}; + let d2 = ${i?Mt("coords",me.rank-1,me.rank):Mt("coords",1,me.rank)}; + let xFRCCorner = vec3(${i?Mt("coords",1,me.rank):Mt("coords",2,me.rank)}, + ${i?Mt("coords",2,me.rank):Mt("coords",3,me.rank)}, + ${i?Mt("coords",3,me.rank):Mt("coords",4,me.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${i?Mt("uniforms.x_shape",1,me.rank):Mt("uniforms.x_shape",2,me.rank)}; + let xShapeZ = ${i?Mt("uniforms.x_shape",2,me.rank):Mt("uniforms.x_shape",3,me.rank)}; + let xShapeW = ${i?Mt("uniforms.x_shape",3,me.rank):Mt("uniforms.x_shape",4,me.rank)}; + let xShapeU = ${i?Mt("uniforms.x_shape",4,me.rank):Mt("uniforms.x_shape",1,me.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) { + ${i?`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) { + ${i?`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) { + ${i?`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) { + ${i?`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); + } + } + } + } + ${R?"value = value + getBiasByOutputCoords(coords)":""}; + ${xt} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${i};${k};${R}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:h[0],y:h[1],z:h[2]},programUniforms:u}),getShaderSource:j}}}),_u,mo,Uc=w(()=>{zt(),Bt(),Qt(),Gs(),_u=(e,t,r,n)=>{let o=e.length>2,a=o?"value += b[output_channel];":"",i=e[0].dims,d=e[1].dims,p=t.format==="NHWC",h=p?r[3]:r[1],k=h/t.group,S=p&&k>=4?yr(h):1,u=$e.size(r)/S,B=[{type:12,data:u},{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:k}];Is(t,B),B.push(...vt(i,[d[0],d[1],d[2],d[3]/S]));let R=o?["rank","rank","rank"]:["rank","rank"];B.push(...vt([r[0],r[1],r[2],r[3]/S]));let j=Z=>{let te=wt("output",e[0].dataType,r.length,S),X=er(te.type.tensor),_e=sn(t,te.type.value,X),me=ze("x",e[0].dataType,i.length),ye=ze("w",e[1].dataType,d.length,S),Ae=[me,ye];o&&Ae.push(ze("b",e[2].dataType,e[2].dims,S));let Ie=[{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"}];nn(t,Ie);let Ge=p?` + 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 = ${me.get("batch","xHeight","xWidth","input_channel")}; + let wVal = ${ye.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 = ${me.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${ye.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal * wVal; + } + } + } + `;return` + ${Z.registerUniforms(Ie).declareVariables(...Ae,te)} + + ${Z.mainStart()} + ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${te.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${p?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${p?1:2}], outputIndices[${p?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel * ${S} / uniforms.output_channels_per_group; + var in_channel_offset = group_id * uniforms.w_shape[${p?2:1}]; + + var value: ${te.type.value} = ${te.type.value}(0); + ${Ge} + ${a} + ${_e} + ${te.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${S}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:B}),getShaderSource:j}},mo=(e,t,r,n)=>{let o=e.length>2,a=yr(r[3]),i=yr(r[2]),d=$e.size(r)/a/i,p=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/a],h=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/a],k=[r[0],r[1],r[2],r[3]/a],S=[{type:12,data:d},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];Is(t,S),S.push(...vt(p,h,k));let u=(i-1)*t.strides[1]+h[1],B=R=>{let j=wt("output",e[0].dataType,k.length,a),Z=er(j.type.tensor),te=sn(t,j.type.value,Z),X=ze("x",e[0].dataType,p.length,a),_e=ze("w",e[1].dataType,h.length,a),me=[X,_e];o&&me.push(ze("b",e[2].dataType,e[2].dims,a));let ye=o?"value += b[output_channel];":"",Ae=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return nn(t,Ae),` + ${R.registerUniforms(Ae).declareVariables(...me,j)} + ${R.mainStart()} + ${R.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] / ${i}u; + let col = (index1 % width1) * ${i}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${X.type.value}, ${u}>; + var values: array<${j.type.value}, ${i}>; + 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 < ${h[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 < ${u}; i++) { + let x_width = x_corner.y + i; + if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { + x_vals[i] = ${X.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${X.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${h[1]}; w_width++) { + let w_val = ${_e.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${i}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${i}u; i++) { + var value = values[i]; + ${ye} + ${te} + ${j.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${a};${i};${u};${h[0]};${h[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:S}),getShaderSource:B}}}),fu,_o,gu,fo,go,Oi,wu,Fi,Di,Vc=w(()=>{Bt(),du(),jc(),ho(),Uc(),Gs(),ki(),Ws(),fu=(e,t,r,n,o,a)=>{let i=e[0],d=e.slice(a?1:2,a?3:4),p=d.length,h=t[0],k=t.slice(2).map((u,B)=>u+(u-1)*(r[B]-1)),S=d.map((u,B)=>u+n[B]+n[B+p]).map((u,B)=>Math.floor((u-k[B]+o[B])/o[B]));return S.splice(0,0,i),S.splice(a?3:1,0,h),S},_o=[2,3,1,0],gu=(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 o=e[0].dims.length-2;if(t.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(t.strides.length!==o)throw new Error(`strides should be ${o}D`);if(t.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},fo=(e,t)=>{let r=e.kernelShape.slice();r.length{let t=Ci(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],o=e.dilations,a=e.group,i=e.kernel_shape,d=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:n,format:r,dilations:o,group:a,kernelShape:i,pads:d,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},Oi=(e,t,r,n)=>{let o=r.format==="NHWC",a=fu(t[0].dims,t[1].dims,r.dilations,r.pads,r.strides,o);if(r.group!==1){let Ae=[t[0]];if(o){let Ie=e.kernelCustomData.wT??e.compute(ss(t[1],_o),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ie),Ae.push(Ie)}else Ae.push(t[1]);t.length===3&&Ae.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&o&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1?e.compute(mo(Ae,r,a,n),{inputs:Ae}):e.compute(_u(Ae,r,a,n),{inputs:Ae});return}let i=t.length===3,d=t[0].dims[o?1:2],p=t[0].dims[o?2:3],h=t[0].dims[o?3:1],k=t[1].dims[2],S=t[1].dims[3],u=a[o?1:2],B=a[o?2:3],R=a[o?3:1],j=o&&k===d&&S===p&&r.pads[0]===0&&r.pads[1]===0;if(j||k===1&&S===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 Ae=a[0],Ie,Ge,lt,xt=[];if(o){let Ct=e.kernelCustomData.wT??e.compute(ss(t[1],_o),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ct),j){let Jt=d*p*h;Ie=t[0].reshape([1,Ae,Jt]),Ge=Ct.reshape([1,Jt,R]),lt=[1,Ae,R]}else Ie=t[0].reshape([Ae,d*p,h]),Ge=Ct.reshape([1,h,R]),lt=[Ae,u*B,R];xt.push(Ie),xt.push(Ge)}else Ie=t[0].reshape([Ae,h,d*p]),Ge=t[1].reshape([1,R,h]),lt=[Ae,R,u*B],xt.push(Ge),xt.push(Ie);i&&xt.push(t[2]);let Kt=lt[2],Yt=xt[0].dims[xt[0].dims.length-1];Kt<8&&Yt<8?e.compute(lo(xt,r,a,lt,o,n),{inputs:xt}):e.compute(po(xt,r,a,lt,o,n),{inputs:xt});return}let Z=!0,te=e.kernelCustomData.wT??e.compute(ss(t[1],_o),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=te);let X=[t[0],te];i&&X.push(t[2]);let _e=o?u*B:R,me=o?R:u*B,ye=k*S*h;e.compute(uu(X,r,a,_e,me,ye,i,Z,n),{inputs:X})},wu=(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 o=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),i=[1].concat(t.dilations),d=[1].concat(t.kernelShape),p=fo({...t,pads:o,strides:a,dilations:i,kernelShape:d},n);Oi(e,n,p,h=>r?[h[0],h[2],h[3]]:[h[0],h[1],h[3]])},Fi=(e,t,r)=>{let n=r.format==="NHWC"?"channelsLast":"channelsFirst",o=fo(r,t),a=r.autoPad==="NOTSET"?r.pads:r.autoPad,i=hu(t[0].dims,t[1].dims,r.strides,r.dilations,a,!1,n);e.compute(mu(t,o,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],n))},Di=(e,t)=>{if(gu(e.inputs,t),e.inputs[0].dims.length===3)wu(e,t);else if(e.inputs[0].dims.length===5)Fi(e,e.inputs,t);else{let r=fo(t,e.inputs);Oi(e,e.inputs,r)}}}),yu,Wc=w(()=>{zt(),Yr(),Bt(),Qt(),yu=(e,t,r)=>{let n=e.length>2,o=t.outputShape,a=t.format==="NHWC",i=t.group,d=e[1].dims,p=d[2]/i,h=d[3],k=a?yr(h):1,S=$e.size(o)/k,u=[Math.ceil(S/64),1,1];ir("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${u}`);let B=["rank","rank"],R=[t.strides[0],t.strides[1]],j=[t.kernelShape[a?1:2],t.kernelShape[a?2:3]],Z=[t.dilations[0],t.dilations[1]],te=[j[0]+(t.dilations[0]<=1?0:(t.kernelShape[a?1:2]-1)*(t.dilations[0]-1)),j[1]+(t.dilations[1]<=1?0:(t.kernelShape[a?2:3]-1)*(t.dilations[1]-1))],X=[te[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),te[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],_e=[{type:12,data:S},{type:12,data:R},{type:12,data:j},{type:12,data:Z},{type:12,data:te},{type:6,data:X},{type:12,data:p},{type:12,data:h},...vt(e[0].dims,e[1].dims)];n&&(_e.push(...vt(e[2].dims)),B.push("rank")),_e.push(...vt(o));let me=ye=>{let Ae=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:R.length},{name:"filter_dims",type:"u32",length:j.length},{name:"dilations",type:"u32",length:j.length},{name:"effective_filter_dims",type:"u32",length:te.length},{name:"pads",type:"i32",length:X.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],Ie=er(e[0].dataType),Ge=a?1:2,lt=a?2:3,xt=a?3:1,Kt=ze("W",e[1].dataType,e[1].dims.length,k),Yt=ze("Dy",e[0].dataType,e[0].dims.length),Ct=[Yt,Kt];n&&Ct.push(ze("bias",e[2].dataType,[o[xt]].length,k));let Jt=wt("result",e[0].dataType,o.length,k),$t=` + let outputIndices = ${Jt.offsetToIndices(`global_idx * ${k}`)}; + let batch = ${Jt.indicesGet("outputIndices",0)}; + let d1 = ${Jt.indicesGet("outputIndices",xt)}; + let r = ${Jt.indicesGet("outputIndices",Ge)}; + let c = ${Jt.indicesGet("outputIndices",lt)}; + 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 = ${Jt.type.value}(0.0); + for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${Ie}(dyRCorner) + ${Ie}(wR)) / ${Ie}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${Ie}(uniforms.Dy_shape[${Ge}]) || 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 = (${Ie}(dyCCorner) + ${Ie}(wC)) / ${Ie}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${Ie}(uniforms.Dy_shape[${lt}]) || + 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 = ${a?Yt.get("batch","idyR","idyC","inputChannel"):Yt.get("batch","inputChannel","idyR","idyC")}; + let w_offset = ${Kt.indicesToOffset(`${Kt.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)}; + let wValue = ${Kt.getByOffset(`w_offset / ${k}`)}; + dotProd = dotProd + xValue * wValue; + inputChannel = inputChannel + 1; + } + } + } + let value = dotProd${n?` + bias[d1 / ${k}]`:""}; + ${Jt.setByOffset("global_idx","value")}; + `;return` + ${ye.registerUniforms(Ae).declareVariables(...Ct,Jt)} + ${ye.mainStart()} + ${ye.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${$t}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};${k}`,inputDependencies:B},getRunData:()=>({dispatchGroup:{x:u[0],y:u[1],z:u[2]},outputs:[{dims:r?r(o):o,dataType:e[0].dataType}],programUniforms:_e}),getShaderSource:me}}}),Li,Mu,bu,wo,vu,Tu,yo,xu,Eu,Pu=w(()=>{Wc(),Gs(),Ws(),Li=(e,t,r,n,o,a)=>(e-1)*t+r+(n-1)*o+1-a,Mu=(e,t,r,n,o)=>{let a=Math.floor(e/2);t==="SAME_UPPER"?(r[n]=a,r[o]=e-a):t==="SAME_LOWER"&&(r[n]=e-a,r[o]=a)},bu=(e,t,r,n,o,a,i,d,p,h)=>{let k=e.length-2,S=h.length===0;p.length{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((S,u)=>S*u,1)===0){r.length=0;for(let S=2;SS+u,0)===0){let S=t[0].dims.length-2;p=new Array(S).fill(1)}let h=e.strides.slice();if(h.reduce((S,u)=>S+u,0)===0){let S=t[0].dims.length-2;h=new Array(S).fill(1)}bu(d,r,p,e.autoPad,e.group,o,h,n,i,a);let k=Object.assign({},e);return Object.assign(k,{kernelShape:r,pads:o,outputPadding:i,outputShape:a,dilations:p,strides:h}),k},vu=e=>{let t=Ci(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],o=e.dilations,a=e.group,i=e.kernelShape,d=e.pads,p=e.strides,h=e.wIsConst(),k=e.outputPadding,S=e.outputShape;return{autoPad:n,format:r,dilations:o,group:a,kernelShape:i,outputPadding:k,outputShape:S,pads:d,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},Tu=(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 o=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==o))throw new Error("invalid bias");let 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var outputIndices = ${i.offsetToIndices("global_idx")}; + ${o.map((me,ye)=>`var input${ye}Indices: ${o[ye].type.indices};`).join(` +`)} + ${_e.join(` +`)}; + ${i.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let h=d.filter(S=>r.symbolToInfo.has(S)).map(S=>{var u;return{type:12,data:((u=r.symbolToInfo.get(S))==null?void 0:u.dimValue)||0}});h.push({type:12,data:a});let k=e.map((S,u)=>[...vt(S)]).reduce((S,u)=>S.concat(u),h);return k.push(...vt(n)),{outputs:[{dims:n,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:k}},getShaderSource:p}},Nu=(e,t)=>{let r=new Bu(e.inputs,t.equation),n=r.outputDims,o=e.inputs.map((a,i)=>a.dims);e.compute(Ru(o,e.inputs[0].dataType,r,n))},ju=e=>{let t=e.equation.replace(/\s+/g,"");return it({equation:t})}}),vo,Bi,Uu,Vu,Nn,Hc=w(()=>{zt(),Bt(),Qt(),vo=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let t=e[0].dims,r=Array.from(e[1].getBigInt64Array(),Number),n=r.length{let r=e.length-t.length,n=[];for(let o=0;oe.length>t.length?Bi(e,t):Bi(t,e),Vu=e=>{let t=e[0].dims,r=Array.from(e[1].getBigInt64Array(),Number),n=Uu(t,r),o=e[0].dataType,a=o===9||$e.size(t)===1,i=o===9||t.length>0&&t[t.length-1]%4===0?4:1,d=a||n.length>0&&n[n.length-1]%4===0?4:1,p=Math.ceil($e.size(n)/d),h=S=>{let u=ze("input",o,t.length,i),B=wt("output",o,n.length,d),R;if(o===9){let j=(Z,te,X="")=>` + let outputIndices${te} = ${B.offsetToIndices(`outputOffset + ${te}u`)}; + let offset${te} = ${u.broadcastedIndicesToOffset(`outputIndices${te}`,B)}; + let index${te} = offset${te} / 4u; + let component${te} = offset${te} % 4u; + ${Z}[${te}] = ${X}(${u.getByOffset(`index${te}`)}[component${te}]); + `;R=` + let outputOffset = global_idx * ${d}; + var data = vec4(0); + ${j("data",0,"u32")} + ${j("data",1,"u32")} + ${j("data",2,"u32")} + ${j("data",3,"u32")} + ${B.setByOffset("global_idx","data")} + }`}else R=` + let outputIndices = ${B.offsetToIndices(`global_idx * ${d}`)}; + let inputOffset = ${u.broadcastedIndicesToOffset("outputIndices",B)}; + let data = ${B.type.value}(${u.getByOffset(`inputOffset / ${i}`)}); + ${B.setByOffset("global_idx","data")} + }`;return` + ${S.registerUniform("vec_size","u32").declareVariables(u,B)} + ${S.mainStart()} + ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${R}`},k=[{type:12,data:p},...vt(t,n)];return{name:"Expand",shaderCache:{hint:`${n.length};${i}${d}`,inputDependencies:["rank"]},getShaderSource:h,getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:k})}},Nn=e=>{vo(e.inputs),e.compute(Vu(e.inputs),{inputs:[0]})}}),Wu,Gu,qc=w(()=>{zt(),Bt(),Qt(),bi(),Wu=e=>{let t=e[0].dataType,r=$e.size(e[0].dims),n=$e.size(e[1].dims),o=n%4===0,a=i=>{let d=ze("x",t,[1],4),p=ze("bias",t,[1],4),h=wt("y",t,[1],4),k=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],S=B=>` + let bias${B}_offset: u32 = (global_idx * 4 + ${B}) % uniforms.bias_size; + let bias${B} = ${p.getByOffset(`bias${B}_offset / 4`)}[bias${B}_offset % 4];`,u=o?` + let bias = ${p.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${S(0)}${S(1)}${S(2)}${S(3)} + let bias = ${d.type.value}(bias0, bias1, bias2, bias3);`;return`${i.registerUniforms(k).declareVariables(d,p,h)} + + ${io(mr(t))} + + ${i.mainStart(Nr)} + ${i.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${d.getByOffset("global_idx")}; + ${u} + let x_in = x + bias; + ${h.setByOffset("global_idx",yi("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${o}`,inputDependencies:["type","type"]},getShaderSource:a,getRunData:i=>({outputs:[{dims:i[0].dims,dataType:i[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/Nr/4)}})}},Gu=e=>{e.inputs.length<2||$e.size(e.inputs[1].dims)===0?Fl(e):e.compute(Wu(e.inputs))}}),To,Ku,Hu,qu,Rp=w(()=>{zt(),Bt(),Pt(),Qt(),To=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},Ku=(e,t)=>{let r=e[0].dims,n=e[1].dims,o=r.length,a=$e.normalizeAxis(t.axis,o),i=r.slice(0);i.splice(a,1,...n);let d=r[a],p=e[0].dataType===9?4:1,h=Math.ceil($e.size(i)/p),k=[{type:12,data:h},{type:6,data:d},{type:12,data:a},...vt(e[0].dims,e[1].dims,i)],S=u=>{let B=ze("data",e[0].dataType,e[0].dims.length,p),R=ze("inputIndices",e[1].dataType,e[1].dims.length),j=wt("output",e[0].dataType,i.length,p),Z=X=>{let _e=n.length,me=`var indicesIndices${X} = ${R.type.indices}(0);`;for(let 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${X("value",0,"u32")} + ${X("value",1,"u32")} + ${X("value",2,"u32")} + ${X("value",3,"u32")} + ${j.setByOffset("global_idx","value")} + `}else te=` + let outputIndices = ${j.offsetToIndices("global_idx")}; + ${Z("")}; + let value = ${B.getByIndices("dataIndices")}; + ${j.setByOffset("global_idx","value")}; + `;return` + ${u.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(B,R,j)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${te} + }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:k}),getShaderSource:S}},Hu=e=>it({axis:e.axis}),qu=(e,t)=>{let r=e.inputs;To(r),e.compute(Ku(e.inputs,t))}}),Xu,Qu,Yu,jn=w(()=>{zt(),Bt(),Qt(),Xu=(e,t,r,n,o,a,i,d,p)=>{let h=[{type:12,data:a},{type:12,data:n},{type:12,data:o},{type:12,data:r},{type:12,data:i},{type:12,data:d},{type:12,data:p}],k=[a];h.push(...vt(t.dims,k));let S=u=>{let B=ze("indices_data",t.dataType,t.dims.length),R=wt("input_slice_offsets_data",12,1,1),j=[B,R],Z=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:o.length},{name:"sizes_from_slice_dims_data",type:"u32",length:r.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return` + ${u.registerUniforms(Z).declareVariables(...j)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let batch_idx = global_idx / uniforms.num_slices_per_batch; + let base_offset = batch_idx * uniforms.input_batch_stride; + + let slice_indices_base_offset = global_idx * uniforms.num_slice_dims; + var relative_slice_offset = 0; + for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) { + var index = i32(indices_data[dim_idx + slice_indices_base_offset].x); + let input_dim_idx = uniforms.batch_dims + dim_idx; + if (index < 0) { + ${o.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"} + } + ${r.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"} + } + + input_slice_offsets_data[global_idx] = base_offset + u32(relative_slice_offset); + }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${o.length}_${r.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:k,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h}),getShaderSource:S},{inputs:[t],outputs:[-1]})[0]},Qu=(e,t)=>{let r=e.inputs,n=r[0].dims,o=r[0].dataType,a=r[1].dims,i=a[a.length-1],d=$e.sizeToDimension(a,a.length-1),p=$e.sizeFromDimension(n,t.batchDims+i),h=$e.sizeToDimension(n,t.batchDims),k=$e.sizeFromDimension(n,t.batchDims),S=d/h,u=new Array(i),B=p;for(let me=0;men.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let Z=a.slice(0,-1).concat(n.slice(j)),te=$e.size(Z),X=[{type:12,data:te},{type:12,data:p},...vt(r[0].dims,R.dims,Z)],_e=me=>{let ye=ze("data",r[0].dataType,r[0].dims.length),Ae=ze("slice_offsets",12,R.dims.length),Ie=wt("output",r[0].dataType,Z.length);return` + ${me.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(ye,Ae,Ie)} + ${me.mainStart()} + ${me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let slice_offset = slice_offsets[global_idx / uniforms.slice_size]; + output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size]; + }`};e.compute({name:"GatherND",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:Z,dataType:o}],dispatchGroup:{x:Math.ceil(te/64)},programUniforms:X}),getShaderSource:_e},{inputs:[r[0],R]})},Yu=e=>({batchDims:e.batch_dims,cacheKey:""})}),Ju,Zu,ed,td,Xc=w(()=>{zt(),Bt(),Pt(),Qt(),Ju=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let r=$e.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,o=e[0],a=e[2],i=e.length===4?e[3]:void 0;if(a.dims.length!==o.dims.length||!o.dims.map((d,p)=>p===r?Math.ceil(d/n)===a.dims[p]:d===a.dims[p]).reduce((d,p)=>d&&p,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(i){if(i.dataType!==o.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(i.dims.length!==a.dims.length||!i.dims.map((d,p)=>d===a.dims[p]).reduce((d,p)=>d&&p,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},Zu=(e,t)=>{let r=e[0].dims,n=e[1].dims,o=r.length,a=$e.normalizeAxis(t.gatherAxis,o),i=$e.normalizeAxis(t.quantizeAxis,o),d=r.slice(0);d.splice(a,1,...n);let p=$e.size(d),h=e[2].dataType,k=e[0].dataType===22,S=[{type:12,data:p},{type:12,data:i},{type:12,data:a},{type:12,data:t.blockSize},...vt(...e.map((B,R)=>B.dims),d)],u=B=>{let R=ze("data",e[0].dataType,e[0].dims.length),j=ze("inputIndices",e[1].dataType,e[1].dims.length),Z=ze("scales",e[2].dataType,e[2].dims.length),te=e.length>3?ze("zeroPoint",e[3].dataType,e[3].dims.length):void 0,X=wt("output",h,d.length),_e=[R,j,Z];te&&_e.push(te);let me=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${B.registerUniforms(me).declareVariables(..._e,X)} + ${B.mainStart()} + let output_indices = ${X.offsetToIndices("global_idx")}; + var indices_indices = ${j.type.indices}(0); + ${n.length>1?` + for (var i: u32 = 0; i < ${n.length}; i++) { + let index = ${X.indicesGet("output_indices","uniforms.gather_axis + i")}; + ${j.indicesSet("indices_indices","i","index")}; + }`:`indices_indices = ${X.indicesGet("output_indices","uniforms.gather_axis")};`}; + var data_indices = ${R.type.indices}(0); + for (var i: u32 = 0; i < uniforms.gather_axis; i++) { + let index = ${X.indicesGet("output_indices","i")}; + ${R.indicesSet("data_indices","i","index")}; + } + var index_from_indices = ${j.getByIndices("indices_indices")}; + if (index_from_indices < 0) { + index_from_indices += ${r[a]}; + } + ${R.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")}; + for (var i = uniforms.gather_axis + 1; i < ${d.length}; i++) { + let index = ${X.indicesGet("output_indices",`i + ${n.length} - 1`)}; + ${R.indicesSet("data_indices","i","index")}; + } + let data_offset = ${R.indicesToOffset("data_indices")}; + let data_index = data_offset % 8; + // Convert 4-bit packed data to 8-bit packed data. + let packed_4bit_quantized_data = ${R.getByOffset("data_offset / 8")}; + let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f; + let quantized_data_vec = ${k?"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 = ${Z.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size; + ${Z.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")}; + var scale = ${Z.getByIndices("scale_indices")}; + ${te?` + let zero_point_indices = scale_indices; + let zero_point_offset = ${te.indicesToOffset("zero_point_indices")}; + let zero_point_index = zero_point_offset % 8; + let packed_4bit_zero_points = ${te.getByOffset("zero_point_offset / 8")}; + let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f; + let zero_point_vec = ${k?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points)); + let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"}; + let dequantized_data = ${mr(h)}(quantized_data - zero_point) * scale; + ${X.setByOffset("global_idx","dequantized_data")}; + }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((B,R)=>R!==1).map(B=>B.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(B,R)=>"rank")},getRunData:()=>({outputs:[{dims:d,dataType:h}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:S}),getShaderSource:u}},ed=(e,t)=>{let r=e.inputs;Ju(r,t),e.compute(Zu(e.inputs,t))},td=e=>it({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),xo,Qc,rd,sd,Yc=w(()=>{zt(),Bt(),Pt(),Qt(),xo=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and + indices input tensors be of same rank.`)},Qc=(e,t)=>{let r=e[0].dims,n=e[0].dataType,o=r.length,a=e[1].dims,i=e[1].dataType,d=$e.normalizeAxis(t.axis,o),p=r[d],h=a.slice(0),k=$e.size(h),S=ze("input",n,o),u=ze("indicesInput",i,a.length),B=wt("output",n,h.length),R=[{type:12,data:k},{type:6,data:p},{type:12,data:d}];return R.push(...vt(r,a,h)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:R}),getShaderSource:j=>` + ${j.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(S,u,B)} + ${j.mainStart()} + ${j.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${B.offsetToIndices("global_idx")}; + + var idx = ${u.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${S.type.indices}(outputIndices); + ${S.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${S.getByIndices("inputIndices")}; + + ${B.setByOffset("global_idx","value")}; + }`}},rd=e=>it({axis:e.axis}),sd=(e,t)=>{let r=e.inputs;xo(r),e.compute(Qc(e.inputs,t))}}),nd,od,Ri,id,Jc=w(()=>{zt(),Bt(),Qt(),nd=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},od=(e,t)=>{let r=e[0].dims.slice(),n=e[1].dims.slice(),[o,a,i]=Rr.getShapeOfGemmResult(r,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),d=[o,a];if(!d)throw new Error("Can't use gemm on the given tensors");let p=16,h=Math.ceil(a/p),k=Math.ceil(o/p),S=!0,u=$e.size(d),B=[{type:12,data:S?h:u},{type:12,data:o},{type:12,data:a},{type:12,data:i},{type:1,data:t.alpha},{type:1,data:t.beta}],R=["type","type"];e.length===3&&(B.push(...vt(e[2].dims)),R.push("rank")),B.push(...vt(d));let j=te=>{let X="";t.transA&&t.transB?X="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?X="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?X="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(X="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let _e=t.alpha===1?"":"value *= uniforms.alpha;",me=ze("a",e[0].dataType,e[0].dims),ye=ze("b",e[1].dataType,e[1].dims),Ae=me.type.value,Ie=null,Ge=[me,ye];e.length===3&&(Ie=ze("c",e[2].dataType,e[2].dims.length),Ge.push(Ie));let lt=wt("output",e[0].dataType,d.length);Ge.push(lt);let xt=[{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` + ${te.registerUniforms(xt).declareVariables(...Ge)} + + ${te.mainStart()} + ${te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${Ae}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${X} + } + + ${_e} + ${Ie!=null?`let cOffset = ${Ie.broadcastedIndicesToOffset("vec2(m, n)",lt)}; value += ${Ae}(uniforms.beta) * ${Ie.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`},Z=te=>{let X=ze("a",e[0].dataType,e[0].dims),_e=ze("b",e[1].dataType,e[1].dims),me=null,ye=[X,_e];e.length===3&&(me=ze("c",e[2].dataType,e[2].dims.length),ye.push(me));let Ae=wt("output",e[0].dataType,d.length);ye.push(Ae);let Ie=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],Ge="",lt="";t.transA&&t.transB?(lt=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${X.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${_e.type.value}(0); + } + `,Ge="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):t.transA&&!t.transB?(lt=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${X.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${_e.type.value}(0); + } + `,Ge="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!t.transA&&t.transB?(lt=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${X.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${_e.type.value}(0); + } + `,Ge="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!t.transA&&!t.transB&&(lt=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${X.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${_e.type.value}(0); + } + `,Ge="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let xt=t.alpha===1?"":"value *= uniforms.alpha;";return` + ${te.registerUniforms(Ie).declareVariables(...ye)} + var tile_a: array, ${p}>; + var tile_b: array, ${p}>; + ${te.mainStart([p,p,1])} + let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${p}; + let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${p}; + let num_tiles = (uniforms.K - 1) / ${p} + 1; + var k_start = 0u; + var value = ${Ae.type.value}(0); + for (var t: u32 = 0u; t < num_tiles; t++) { + ${lt} + k_start = k_start + ${p}; + workgroupBarrier(); + + for (var k: u32 = 0u; k < ${p}; k++) { + ${Ge} + } + workgroupBarrier(); + } + + ${xt} + let m = tile_row_start + local_id.y; + let n = tile_col_start + local_id.x; + ${me!=null?`let cOffset = ${me.broadcastedIndicesToOffset("vec2(m, n)",Ae)}; value += ${Ae.type.value}(uniforms.beta) * ${me.getByOffset("cOffset")};`:""} + if (m < uniforms.M && n < uniforms.N) { + output[m * uniforms.N + n] = value; + } + }`};return S?{name:"GemmShared",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:h*k},programUniforms:B}),getShaderSource:Z}:{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:B}),getShaderSource:j}},Ri=e=>{let t=e.transA,r=e.transB,n=e.alpha,o=e.beta;return{transA:t,transB:r,alpha:n,beta:o,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},id=(e,t)=>{nd(e.inputs),e.compute(od(e.inputs,t))}}),fs,Fs,on,Ks,ad,ld,Eo,ud,dd,cd,pd,Ni,Po,Zc,ep=w(()=>{zt(),Bt(),Pt(),Qt(),[fs,Fs,on,Ks]=[0,1,2,3],ad=e=>{if(e[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(e[0].dims.length!==e[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(e[0].dims.length-2!==e[1].dims[e[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},ld=` + fn gs_get_cubic_coeffs(x: f32) -> vec4 { + let cubic_alpha = -0.75f; + let x_abs = abs(x); + var coeffs: vec4; + coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha); + coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1); + coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1); + coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha); + return coeffs; + } +`,Eo=e=>` + fn gs_bicubic_interpolate(p: mat4x4<${e}>, x: f32, y: f32) -> ${e} { + var v: vec4; + var coeffs = gs_get_cubic_coeffs(x); + for (var i = 0; i < 4; i++) { + v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3]; + } + coeffs = gs_get_cubic_coeffs(y); + let pixel = ${e}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]); + return pixel; + } +`,ud=e=>` + fn gs_denormalize(n: f32, length: i32) -> f32 { + ${e.alignCorners===0?` + // alignCorners: false => [-1, 1] to [-0.5, length - 0.5] + return ((n + 1.0) * f32(length) - 1.0) / 2.0; + `:` + // alignCorners: true => [-1, 1] to [0, length - 1] + return (n + 1.0) / 2.0 * (f32(length - 1)); + `} + } +`,dd=e=>` + ${e.paddingMode==="reflection"?` + fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 { + var dx = 0.0; + var fx = f32(x); + let range = x_max - x_min; + if (fx < x_min) { + dx = x_min - fx; + let n = u32(dx / range); + let r = dx - f32(n) * range; + if (n % 2 == 0) { + fx = x_min + r; + } else { + fx = x_max - r; + } + } else if (fx > x_max) { + dx = fx - x_max; + let n = u32(dx / range); + let r = dx - f32(n) * range; + if (n % 2 == 0) { + fx = x_max - r; + } else { + fx = x_min + r; + } + } + return u32(fx); + }`:""} +`,cd=(e,t,r)=>` + fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4) -> ${t} { + var pixel = ${t}(0); + var indices = vec4(0); + indices[${fs}] = batch; + indices[${Fs}] = channel;`+(()=>{switch(r.paddingMode){case"zeros":return` + if (r >= 0 && r < H && c >=0 && c < W) { + indices[${on}] = u32(r); + indices[${Ks}] = u32(c); + } + `;case"border":return` + indices[${on}] = u32(clamp(r, 0, H - 1)); + indices[${Ks}] = u32(clamp(c, 0, W - 1)); + `;case"reflection":return` + indices[${on}] = gs_reflect(r, border[1], border[3]); + indices[${Ks}] = gs_reflect(c, border[0], border[2]); + `;default:throw new Error(`padding mode ${r.paddingMode} is not supported`)}})()+` + return ${e.getByIndices("indices")}; + } +`,pd=(e,t,r)=>(()=>{switch(r.mode){case"nearest":return` + let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${fs}], indices[${Fs}], border); + `;case"bilinear":return` + let x1 = i32(floor(x)); + let y1 = i32(floor(y)); + let x2 = x1 + 1; + let y2 = y1 + 1; + + let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${fs}], indices[${Fs}], border); + let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${fs}], indices[${Fs}], border); + let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${fs}], indices[${Fs}], border); + let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${fs}], indices[${Fs}], border); + + let dx2 = ${t}(f32(x2) - x); + let dx1 = ${t}(x - f32(x1)); + let dy2 = ${t}(f32(y2) - y); + let dy1 = ${t}(y - f32(y1)); + let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22); + `;case"bicubic":return` + let x0 = i32(floor(x)) - 1; + let y0 = i32(floor(y)) - 1; + var p: mat4x4<${t}>; + for (var h = 0; h < 4; h++) { + for (var w = 0; w < 4; w++) { + p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${fs}], indices[${Fs}], border); + } + } + + let dx = x - f32(x0 + 1); + let dy = y - f32(y0 + 1); + let result = gs_bicubic_interpolate(p, dx, dy); + `;default:throw new Error(`mode ${r.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,Ni=(e,t)=>{let r=ze("x",e[0].dataType,e[0].dims.length),n=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],o=ze("grid",e[1].dataType,n.length,2),a=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];t.format==="NHWC"&&(a=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[fs,Fs,on,Ks]=[0,3,1,2]);let i=wt("output",e[0].dataType,a.length),d=r.type.value,p=$e.size(a),h=[{type:12,data:p},...vt(e[0].dims,n,a)],k=S=>` + ${S.registerUniform("output_size","u32").declareVariables(r,o,i)} + ${ld} + ${Eo(d)} + ${ud(t)} + ${dd(t)} + ${cd(r,d,t)} + + ${S.mainStart()} + ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let H_in = i32(uniforms.x_shape[${on}]); + let W_in = i32(uniforms.x_shape[${Ks}]); + + ${t.alignCorners===0?` + let x_min = -0.5; + let x_max = f32(W_in) - 0.5; + let y_min = -0.5; + let y_max = f32(H_in) - 0.5; + `:` + let x_min = 0.0; + let x_max = f32(W_in) - 1.0; + let y_min = 0.0; + let y_max = f32(H_in) - 1.0; + `}; + let border = vec4(x_min, y_min, x_max, y_max); + + let indices = ${i.offsetToIndices("global_idx")}; + var grid_indices = vec3(indices[${fs}], indices[${on}], indices[${Ks}]); + let nxy = ${o.getByIndices("grid_indices")}; + var x = gs_denormalize(f32(nxy[0]), W_in); + var y = gs_denormalize(f32(nxy[1]), H_in); + + ${pd(i,d,t)} + }`;return{name:"GridSample",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:["type","type"]},getRunData:S=>{let u=$e.size(a);return{outputs:[{dims:a,dataType:S[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:h}},getShaderSource:k}},Po=(e,t)=>{ad(e.inputs),e.compute(Ni(e.inputs,t))},Zc=e=>it({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),es,ji,hd,Ui,Vi,Un,md,Wi=w(()=>{zt(),Bt(),Pt(),yn(),no(),Qt(),Ws(),es=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,ji=(e,t)=>{let r=e[0],n=es(e,1),o=es(e,2),a=es(e,3),i=es(e,4),d=es(e,5),p=es(e,6),h=es(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let k=r.dims[0],S=r.dims[1],u=r.dims.length===3?r.dims[2]:t.numHeads*r.dims[4],B=S,R=0,j=0,Z=Math.floor(u/t.numHeads);if(p&&h&&$e.size(p.dims)&&$e.size(h.dims)){if(p.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(p.dims[0]!==k||p.dims[1]!==t.numHeads||p.dims[3]!==Z)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(h.dims[0]!==k||h.dims[1]!==t.numHeads||h.dims[3]!==Z)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(p.dims[2]!==h.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(h.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');R=p.dims[2],j=p.dims[2]}else if(p&&$e.size(p.dims)||h&&$e.size(h.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let te;if(n&&$e.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)');te=2,B=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==Z)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');te=5,B=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==Z)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');te=0,B=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');te=3}if(a&&$e.size(a.dims)>0){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(n&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let X=R+B,_e=0;if(i&&$e.size(i.dims)>0){_e=8;let Ie=i.dims;throw Ie.length===1?Ie[0]===k?_e=1:Ie[0]===3*k+2&&(_e=3):Ie.length===2&&Ie[0]===k&&Ie[1]===X&&(_e=5),_e===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let me=!1,ye=u;if(o&&$e.size(o.dims)>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(B!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');ye=o.dims[2]}else{if(B!==o.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');ye=o.dims[1]*o.dims[3],me=!0}}let Ae=!1;if(i&&$e.size(i.dims)>0)throw new Error("Key padding mask is not supported");if(d&&$e.size(d.dims)>0){if(d.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(d.dims[0]!==k||d.dims[1]!==t.numHeads||d.dims[2]!==S||d.dims[3]!==X)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:k,sequenceLength:S,pastSequenceLength:R,kvSequenceLength:B,totalSequenceLength:X,maxSequenceLength:j,inputHiddenSize:0,hiddenSize:u,vHiddenSize:ye,headSize:Z,vHeadSize:Math.floor(ye/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:_e,scale:t.scale,broadcastResPosBias:Ae,passPastInKv:me,qkvFormat:te}},hd=e=>it({...e}),Ui=it({perm:[0,2,1,3]}),Vi=(e,t,r,n,o,a,i)=>{let d=[n,o,a],p=$e.size(d),h=[{type:12,data:p},{type:12,data:i},{type:12,data:a}],k=S=>{let u=wt("qkv_with_bias",t.dataType,d),B=ze("qkv",t.dataType,d),R=ze("bias",r.dataType,d),j=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${S.registerUniforms(j).declareVariables(B,R,u)} + ${S.mainStart()} + ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:d,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:k},{inputs:[t,r],outputs:[-1]})[0]},Un=(e,t,r,n,o,a,i,d)=>{let p=a;if(i&&$e.size(i.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return p=Vi(e,a,i,t,n,r*o,d),p=p.reshape([t,n,r,o]),r===1||n===1?p:e.compute(ss(p,Ui.perm),{inputs:[p],outputs:[-1]})[0]}else return a.dims.length===3&&(p=a.reshape([t,n,r,o])),r===1||n===1?p:e.compute(ss(p,Ui.perm),{inputs:[p],outputs:[-1]})[0]},md=(e,t)=>{let r=ji(e.inputs,t),n=e.inputs[0],o=es(e.inputs,1),a=es(e.inputs,2),i=es(e.inputs,3),d=es(e.inputs,4),p=es(e.inputs,5),h=es(e.inputs,6),k=es(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((o==null?void 0:o.dims.length)===5)throw new Error("Packed KV is not implemented");let S=o&&a&&o.dims.length===4&&a.dims.length===4,u=Un(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(S)return vn(e,u,o,a,d,void 0,h,k,p,r);if(!o||!a)throw new Error("key and value must be provided");let B=Un(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,o,i,r.hiddenSize),R=Un(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,a,i,2*r.hiddenSize);vn(e,u,B,R,d,void 0,h,k,p,r)}}),tp,rp,Gi,Ki,Hi,_d,qi,fd=w(()=>{zt(),Bt(),Pt(),Qt(),tp=e=>{if(!e||e.length<1)throw new Error("too few inputs")},rp=(e,t)=>{let r=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(o=>r.push(Number(o))),n=r.length),it({numOutputs:n,axis:t.axis,splitSizes:r})},Gi=e=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${e}u; i += 1u ) { + if (index < ${Mt("uniforms.size_in_split_axis","i",e)}) { + return i; + } + } + return ${e}u; +}`,Ki=e=>{let t=e.length,r=[];for(let n=0;n{let r=e[0].dims,n=$e.size(r),o=e[0].dataType,a=$e.normalizeAxis(t.axis,r.length),i=new Array(t.numOutputs),d=ze("input",o,r.length),p=new Array(t.numOutputs),h=[],k=[],S=0,u=[{type:12,data:n}];for(let R=0;R` + ${R.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",p.length).declareVariables(d,...i)} + ${Gi(p.length)} + ${Ki(i)} + + ${R.mainStart()} + ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${d.offsetToIndices("global_idx")}; + var index = ${d.indicesGet("indices",a)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${Mt("uniforms.size_in_split_axis","output_number - 1u",p.length)}; + ${d.indicesSet("indices",a,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:B,getRunData:()=>({outputs:h,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:u})}},_d=(e,t)=>{tp(e.inputs);let r=e.inputs.length===1?t:rp(e.inputs,t);e.compute(Hi(e.inputs,r),{inputs:[0]})},qi=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 it({axis:t,numOutputs:n,splitSizes:r})}}),gd,Xi,Qi,wd,yd=w(()=>{Pt(),no(),Wi(),fd(),Ws(),gd=(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],o=e[2],a=e[3],i=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 d=!1,p=r.dims[0],h=r.dims[1],k=r.dims.length===3?d?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],S=h,u=0,B=!n||n.dims.length===0,R=Math.floor(B?k/(t.numHeads+2*t.kvNumHeads):k/t.numHeads);B&&(k=R*t.numHeads);let j=a&&a.dims.length!==0,Z=i&&i.dims.length!==0;if(j&&a.dims.length===4&&a.dims[0]===p&&a.dims[1]!==t.kvNumHeads&&a.dims[2]===t.kvNumHeads&&a.dims[3]===R)throw new Error("BSNH pastKey/pastValue is not supported");if(j&&Z){if(a.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(i.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');u=a.dims[2]}else if(j||Z)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let te=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"');S=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==R)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');S=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==R)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');S=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');te=3}let X=0,_e=!1,me=t.kvNumHeads?R*t.kvNumHeads:k;if(o&&o.dims.length>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(S!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');me=o.dims[2]}else{if(S!==o.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');me=o.dims[1]*o.dims[3],_e=!0}}let ye=e.length>4?e[5]:void 0;if(ye&&ye.dims.length!==1&&ye.dims[0]!==p)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:p,sequenceLength:h,pastSequenceLength:u,kvSequenceLength:S,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:k,vHiddenSize:me,headSize:R,vHeadSize:Math.floor(me/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:X,scale:t.scale,broadcastResPosBias:!1,passPastInKv:_e,qkvFormat:te}},Xi=it({perm:[0,2,1,3]}),Qi=(e,t,r)=>{let n=t,o=r.kvNumHeads;return t.dims.length===3&&r.kvSequenceLength!==0&&(n=t.reshape([r.batchSize,r.kvSequenceLength,o,r.headSize]),n=e.compute(ss(n,Xi.perm),{inputs:[n],outputs:[-1]})[0]),n},wd=(e,t)=>{var Z;let r=gd(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((Z=e.inputs[1])==null?void 0:Z.dims.length)===5)throw new Error("Packed KV is not implemented");let n=e.inputs[0],o=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,i=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,d=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,p=e.inputs.length>4?e.inputs[5]:void 0,h=e.inputs.length>5?e.inputs[6]:void 0,k=r.kvNumHeads?r.kvNumHeads:r.numHeads,S=it({axis:2,numOutputs:3,splitSizes:[r.numHeads*r.headSize,k*r.headSize,k*r.headSize]}),[u,B,R]=!o&&!a?e.compute(Hi([n],S),{inputs:[n],outputs:[-1,-1,-1]}):[n,o,a],j=Un(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,u,void 0,0);vn(e,j,Qi(e,B,r),Qi(e,R,r),void 0,void 0,i,d,void 0,r,p,h)}}),Yi,Md,bd,vd,sp=w(()=>{zt(),Bt(),Ws(),Qt(),Yi=(e,t,r,n,o,a,i,d)=>{let p=yr(a),h=p===1?"f32":`vec${p}f`,k=p===1?"vec2f":`mat2x${p}f`,S=o*i,u=64;S===1&&(u=256);let B=[o,i,a/p],R=[o,i,2],j=["rank","type","type"],Z=[];Z.push(...vt(B,R));let te=X=>{let _e=ze("x",t.dataType,3,p),me=ze("scale",r.dataType,r.dims),ye=ze("bias",n.dataType,n.dims),Ae=wt("output",1,3,2),Ie=[_e,me,ye,Ae];return` + var workgroup_shared : array<${k}, ${u}>; + const workgroup_size = ${u}u; + ${X.declareVariables(...Ie)} + ${X.mainStart(u)} + 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 = ${h}(0); + var squared_sum = ${h}(0); + for (var h = local_idx; h < hight; h += workgroup_size) { + let value = ${h}(${_e.get("batch","channel","h")}); + sum += value; + squared_sum += value * value; + } + workgroup_shared[local_idx] = ${k}(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 = ${qr("workgroup_shared[0][0]",p)} / f32(hight * ${p}); + let squared_sum_final = ${qr("workgroup_shared[0][1]",p)} / f32(hight * ${p}); + + let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${d})); + 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:`${p};${d};${u}`,inputDependencies:j},getRunData:()=>({outputs:[{dims:R,dataType:1}],dispatchGroup:{x:S},programUniforms:Z}),getShaderSource:te},{inputs:[t,r,n],outputs:[-1]})[0]},Md=(e,t,r)=>{let n=t[0].dims,o=n,a=2,i=n[0],d=n[1],p=$e.sizeFromDimension(n,a),h=yr(p),k=$e.size(o)/h,S=Yi(e,t[0],t[1],t[2],i,p,d,r.epsilon),u=[i,d,p/h],B=[i,d],R=["type","none"],j=Z=>{let te=ze("x",t[0].dataType,u.length,h),X=ze("scale_shift",1,B.length,2),_e=wt("output",t[0].dataType,u.length,h),me=[te,X,_e];return` + ${Z.registerUniform("output_size","u32").declareVariables(...me)} + ${Z.mainStart()} + ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let outputIndices = ${_e.offsetToIndices("global_idx")}; + let batch = outputIndices[0]; + let channel = outputIndices[1]; + let scale_shift = ${X.getByIndices("vec2(batch, channel)")}; + let value = ${te.getByOffset("global_idx")} * ${_e.type.value}(scale_shift.x) + ${_e.type.value}(scale_shift.y); + ${_e.setByOffset("global_idx","value")}; + }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${h}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:[{type:12,data:k},...vt(u,B,u)]}),getShaderSource:j},{inputs:[t[0],S]})},bd=(e,t,r)=>{let n=t[0].dims,o=n,a=n[0],i=n[n.length-1],d=$e.sizeFromDimension(n,1)/i,p=yr(i),h=$e.size(o)/p,k=[{type:12,data:d},{type:12,data:Math.floor(i/p)}],S=["type","type"],u=!1,B=[0,n.length-1];for(let te=0;ten[B[X]])),j=Yi(e,R,t[1],t[2],a,d,i,r.epsilon),Z=te=>{let X=er(t[0].dataType),_e=p===1?"vec2f":`mat${p}x2f`,me=Ie=>{let Ge=Ie===0?"x":"y",lt=p===1?"f32":`vec${p}f`;switch(p){case 1:return`${X}(${lt}(scale.${Ge}))`;case 2:return`vec2<${X}>(${lt}(scale[0].${Ge}, scale[1].${Ge}))`;case 4:return`vec4<${X}>(${lt}(scale[0].${Ge}, scale[1].${Ge}, scale[2].${Ge}, scale[3].${Ge}))`;default:throw new Error(`Not supported compoents ${p}`)}},ye=ze("input",t[0].dataType,t[0].dims,p),Ae=wt("output",t[0].dataType,o,p);return` + @group(0) @binding(0) var input : array<${ye.type.storage}>; + @group(0) @binding(1) var scale_input : array<${_e}>; + @group(0) @binding(2) var output : array<${Ae.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${te.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], ${me(0)}, ${me(1)}); + }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:k}),getShaderSource:Z},{inputs:[t[0],j]})},vd=(e,t)=>{t.format==="NHWC"?bd(e,e.inputs,t):Md(e,e.inputs,t)}}),Td,xd,Ed,np=w(()=>{zt(),Bt(),Qt(),Td=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},xd=(e,t,r)=>{let n=t.simplified,o=e[0].dims,a=e[1],i=!n&&e[2],d=o,p=$e.normalizeAxis(t.axis,o.length),h=$e.sizeToDimension(o,p),k=$e.sizeFromDimension(o,p),S=$e.size(a.dims),u=i?$e.size(i.dims):0;if(S!==k||i&&u!==k)throw new Error(`Size of X.shape()[axis:] == ${k}. + Size of scale and bias (if provided) must match this. + Got scale size of ${S} and bias size of ${u}`);let B=[];for(let ye=0;ye1,X=r>2,_e=ye=>{let Ae=er(e[0].dataType),Ie=[ze("x",e[0].dataType,e[0].dims,R),ze("scale",a.dataType,a.dims,R)];i&&Ie.push(ze("bias",i.dataType,i.dims,R)),Ie.push(wt("output",e[0].dataType,d,R)),te&&Ie.push(wt("mean_data_output",1,B)),X&&Ie.push(wt("inv_std_output",1,B));let Ge=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${ye.registerUniforms(Ge).declareVariables(...Ie)} + ${ye.mainStart()} + ${ye.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${Es("f32",R)}; + var mean_square_vector = ${Es("f32",R)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${Fr(Ae,R,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${qr("mean_vector",R)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${qr("mean_square_vector",R)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${Fr(Ae,R,"x[j + offset]")}; + let f32scale = ${Fr(Ae,R,"scale[j]")}; + output[j + offset] = ${Ie[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale + ${i?`+ ${Fr(Ae,R,"bias[j]")}`:""} + ); + } + + ${te?"mean_data_output[global_idx] = mean":""}; + ${X?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},me=[{dims:d,dataType:e[0].dataType}];return te&&me.push({dims:B,dataType:1}),X&&me.push({dims:B,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${R};${r};${n}`,inputDependencies:j},getRunData:()=>({outputs:me,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:Z}),getShaderSource:_e}},Ed=(e,t)=>{Td(e.inputs),e.compute(xd(e.inputs,t,e.outputCount))}}),Ji,Pd,op=w(()=>{Bt(),ki(),ho(),Ji=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.")},Pd=e=>{Ji(e.inputs);let t=rr.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];if(r<8&&n<8)e.compute(lo(e.inputs,{activation:""},t));else{let o=t[t.length-2],a=$e.size(e.inputs[0].dims.slice(0,-2)),i=$e.size(e.inputs[1].dims.slice(0,-2));if(a!==1&&o===1&&i===1){let d=e.inputs[0].reshape([1,a,n]),p=e.inputs[1].reshape([1,n,r]),h=[1,a,r],k=[d,p];e.compute(po(k,{activation:""},t,h),{inputs:k})}else e.compute(po(e.inputs,{activation:""},t))}}}),Zi,Cd,kd,_r,ip,Np=w(()=>{zt(),Bt(),Pt(),Qt(),Zi=(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 o=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,i=e[1];if(!$e.areEqual(i.dims,[t.n,o,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let d=e[2].dims;if($e.size(d)!==t.n*o)throw new Error("scales input size error.");if(e.length===4){let p=e[3].dims,h=t.bits>4?t.n*o:t.n*Math.floor((o+1)/2);if($e.size(p)!==h)throw new Error("zeroPoints input size error.")}},Cd=(e,t)=>{let r=e[0].dims,n=r.length,o=r[n-2],a=t.k,i=t.n,d=r.slice(0,n-2),p=$e.size(d),h=e[1].dims[2]/4,k=e[0].dataType,S=yr(t.k),u=yr(h),B=yr(i),R=d.concat([o,i]),j=o>1&&i/B%2===0?2:1,Z=$e.size(R)/B/j,te=64,X=[],_e=[p,o,a/S],me=$e.convertShape(e[1].dims).slice();me.splice(-1,1,h/u),X.push(...vt(_e)),X.push(...vt(me)),X.push(...vt(e[2].dims)),e.length===4&&X.push(...vt($e.convertShape(e[3].dims)));let ye=[p,o,i/B];X.push(...vt(ye));let Ae=Ie=>{let Ge=_e.length,lt=ze("a",e[0].dataType,Ge,S),xt=ze("b",12,me.length,u),Kt=ze("scales",e[2].dataType,e[2].dims.length),Yt=[lt,xt,Kt],Ct=e.length===4?ze("zero_points",12,e[3].dims.length):void 0;Ct&&Yt.push(Ct);let Jt=ye.length,$t=wt("output",e[0].dataType,Jt,B),jt=er(e[0].dataType),vr=(()=>{switch(S){case 1:return`array<${jt}, 8>`;case 2:return`mat4x2<${jt}>`;case 4:return`mat2x4<${jt}>`;default:throw new Error(`${S}-component is not supported.`)}})(),Ht=()=>{let ot=` + // reuse a data + var input_offset = ${lt.indicesToOffset(`${lt.type.indices}(batch, row, word_offset)`)}; + var a_data: ${vr}; + for (var j: u32 = 0; j < ${8/S}; j++) { + a_data[j] = ${lt.getByOffset("input_offset")}; + input_offset++; + } + `;for(let Et=0;Et> 4) & b_mask); + b_quantized_values = ${vr}(${Array.from({length:4},(cr,Lr)=>`${jt}(b_value_lower[${Lr}]), ${jt}(b_value_upper[${Lr}])`).join(", ")}); + b_dequantized_values = ${S===1?`${vr}(${Array.from({length:8},(cr,Lr)=>`(b_quantized_values[${Lr}] - ${Ct?`zero_point${Et}`:"zero_point"}) * scale${Et}`).join(", ")});`:`(b_quantized_values - ${vr}(${Array(8).fill(`${Ct?`zero_point${Et}`:"zero_point"}`).join(",")})) * scale${Et};`}; + workgroup_shared[local_id.x * ${j} + ${Math.floor(Et/B)}]${B>1?`[${Et%B}]`:""} += ${Array.from({length:8/S},(cr,Lr)=>`${S===1?`a_data[${Lr}] * b_dequantized_values[${Lr}]`:`dot(a_data[${Lr}], b_dequantized_values[${Lr}])`}`).join(" + ")}; + `;return ot},Gt=()=>{let ot=` + var col_index = col * ${B}; + ${Ct?` + 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 = ${jt}(8);`} + `;for(let Et=0;Et> 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 = ${Ct.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point${Et} = ${jt}((zero_point_word) & 0xFu);`:""} + col_index += 1;`;return ot},Cr=()=>{let ot=`col_index = col * ${B};`;for(let Et=0;Et; + var b_value_upper: vec4; + var b_quantized_values: ${vr}; + var b_dequantized_values: ${vr};`,ot};return` + var workgroup_shared: array<${$t.type.value}, ${j*te}>; + ${Ie.declareVariables(...Yt,$t)} + ${Ie.mainStart([te,1,1])} + let output_indices = ${$t.offsetToIndices(`(global_idx / ${te}) * ${j}`)}; + 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 += ${te}) { + //process one block + var word_offset: u32 = block * ${t.blockSize/S}; + ${Gt()} + for (var word: u32 = 0; word < ${h}; word += ${u}) { + ${Cr()} + for (var i: u32 = 0; i < ${u}; i++) { + ${Ht()} + word_offset += ${8/S}; + } + } + } + workgroupBarrier(); + + if (local_id.x < ${j}) { + var output_value: ${$t.type.value} = ${$t.type.value}(0); + var workgroup_shared_offset: u32 = local_id.x; + for (var b: u32 = 0u; b < ${te}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${j}; + } + ${$t.setByIndices(`${$t.type.indices}(batch, row, col + local_id.x)`,"output_value")}; + } + }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${S};${u};${B};${j};${te}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:R,dataType:k}],dispatchGroup:{x:Z},programUniforms:X}),getShaderSource:Ae}},kd=(e,t)=>{let r=e[0].dims,n=r.length,o=r[n-2],a=t.k,i=t.n,d=r.slice(0,n-2),p=$e.size(d),h=e[1].dims[2]/4,k=e[0].dataType,S=yr(t.k),u=yr(h),B=d.concat([o,i]),R=128,j=i%8===0?8:i%4===0?4:1,Z=R/j,te=Z*u*8,X=te/S,_e=te/t.blockSize,me=$e.size(B)/j,ye=[],Ae=[p,o,a/S],Ie=$e.convertShape(e[1].dims).slice();Ie.splice(-1,1,h/u),ye.push(...vt(Ae)),ye.push(...vt(Ie)),ye.push(...vt(e[2].dims)),e.length===4&&ye.push(...vt($e.convertShape(e[3].dims)));let Ge=[p,o,i];ye.push(...vt(Ge));let lt=xt=>{let Kt=Ae.length,Yt=ze("a",e[0].dataType,Kt,S),Ct=ze("b",12,Ie.length,u),Jt=ze("scales",e[2].dataType,e[2].dims.length),$t=[Yt,Ct,Jt],jt=e.length===4?ze("zero_points",12,e[3].dims.length):void 0;jt&&$t.push(jt);let vr=Ge.length,Ht=wt("output",e[0].dataType,vr),Gt=er(e[0].dataType),Cr=()=>{switch(S){case 1:return` + let a_data0 = vec4<${Gt}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]); + let a_data1 = vec4<${Gt}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return` + let a_data0 = vec4<${Gt}>(sub_a[word_offset], sub_a[word_offset + 1]); + let a_data1 = vec4<${Gt}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return` + let a_data0 = sub_a[word_offset]; + let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${S}-component is not supported.`)}};return` + var sub_a: array<${Yt.type.value}, ${X}>; + var inter_results: array, ${j}>; + ${xt.declareVariables(...$t,Ht)} + ${xt.mainStart([Z,j,1])} + let output_indices = ${Ht.offsetToIndices(`workgroup_index * ${j}`)}; + 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) / ${_e} + 1; + + // Loop over shared dimension. + for (var tile: u32 = 0; tile < num_tiles; tile += 1) { + let a_col_start = tile * ${X}; + // load one tile A data into shared memory. + for (var a_offset = local_idx; a_offset < ${X}; a_offset += ${R}) + { + let a_col = a_col_start + a_offset; + if (a_col < uniforms.a_shape[2]) + { + sub_a[a_offset] = ${Yt.getByIndices(`${Yt.type.indices}(batch, row, a_col)`)}; + } else { + sub_a[a_offset] = ${Yt.type.value}(0); + } + } + workgroupBarrier(); + + // each thread process one block + let b_row = col + local_id.y; + let block = tile * ${_e} + local_id.x; + ${jt?` + 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 = ${jt.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point = ${Gt}((zero_point_word) & 0xFu);`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${Gt}(8);`} + let scale = ${Jt.getByOffset("b_row * n_blocks_per_col + block")}; + let b_data = ${Ct.getByIndices(`${Ct.type.indices}(b_row, block, 0)`)}; + var word_offset = local_id.x * ${t.blockSize/S}; + for (var i: u32 = 0; i < ${u}; i++) { + ${Cr()} + let b_value = ${u===1?"b_data":"b_data[i]"}; + let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu); + let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu); + let b_quantized_values = mat2x4<${Gt}>(${Array.from({length:4},(ot,Et)=>`${Gt}(b_value_lower[${Et}]), ${Gt}(b_value_upper[${Et}])`).join(", ")}); + let b_dequantized_values = (b_quantized_values - mat2x4<${Gt}>(${Array(8).fill("zero_point").join(",")})) * scale; + inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(ot,Et)=>`${`dot(a_data${Et}, b_dequantized_values[${Et}])`}`).join(" + ")}; + word_offset += ${8/S}; + } + workgroupBarrier(); + } + + if (local_idx < ${j}) { + var output_value: ${Ht.type.value} = ${Ht.type.value}(0); + for (var b = 0u; b < ${Z}; b++) { + output_value += inter_results[local_idx][b]; + } + if (col + local_idx < uniforms.output_shape[2]) + { + ${Ht.setByIndices(`${Ht.type.indices}(batch, row, col + local_idx)`,"output_value")} + } + } + }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${t.blockSize};${S};${u};${Z};${j}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:B,dataType:k}],dispatchGroup:{x:me},programUniforms:ye}),getShaderSource:lt}},_r=(e,t)=>{Zi(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(kd(e.inputs,t)):e.compute(Cd(e.inputs,t))},ip=e=>it(e)}),ap,ea,Sd,$d,Ad,Id,ta,ra,lp,up=w(()=>{zt(),Bt(),Qt(),ap=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].")}},ea=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,r)}; + if (k < 0) { + break; + } + if (k >= i32(${Mt("uniforms.x_shape",o,t)})) { + break; + } + offset += k * i32(${Mt("uniforms.x_strides",o,t)}); + `;return` + value = ${e.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + } + `},Sd=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,r)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${Mt("uniforms.x_shape",o,t)}) - 1); + k = k % _2n_1; + if(k >= i32(${Mt("uniforms.x_shape",o,t)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${Mt("uniforms.x_strides",o,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},$d=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,r)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${Mt("uniforms.x_shape",o,t)})) { + k = i32(${Mt("uniforms.x_shape",o,t)}) - 1; + } + offset += k * i32(${Mt("uniforms.x_strides",o,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},Ad=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${Mt("uniforms.pads",o,r)}; + if (k < 0) { + k += i32(${Mt("uniforms.x_shape",o,t)}]); + } + if (k >= i32(${Mt("uniforms.x_shape",o,t)})) { + k -= i32(${Mt("uniforms.x_shape",o,t)}); + } + offset += k * i32(${Mt("uniforms.x_strides",o,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},Id=(e,t,r)=>{switch(r.mode){case 0:return ea(e,t,r.pads.length);case 1:return Sd(e,t,r.pads.length);case 2:return $d(e,t,r.pads.length);case 3:return Ad(e,t,r.pads.length);default:throw new Error("Invalid mode")}},ta=(e,t)=>{let r=$e.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,o=$e.size(r),a=[{type:12,data:o},{type:6,data:t.pads}],i=e.length>=3&&e[2].data;t.mode===0&&a.push({type:i?e[2].dataType:1,data:t.value}),a.push(...vt(e[0].dims,r));let d=["rank"],p=h=>{let k=wt("output",e[0].dataType,r.length),S=ze("x",e[0].dataType,n.length),u=S.type.value,B=Id(k,n.length,t),R=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&R.push({name:"constant_value",type:i?u:"f32"}),` + ${h.registerUniforms(R).declareVariables(S,k)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${k.offsetToIndices("global_idx")}; + + var value = ${u}(0); + ${B} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${i}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil($e.size(r)/64)},programUniforms:a}),getShaderSource:p}},ra=(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,o=e[0].dims.length,a=new Int32Array(2*o).fill(0);if(e.length>=4){let d=e[3].getBigInt64Array();for(let p=0;pa[Number(p)]=Number(d));let i=[];return a.forEach(d=>i.push(d)),{mode:t.mode,value:n,pads:i}}else return t},lp=(e,t)=>{ap(e.inputs);let r=ra(e.inputs,t);e.compute(ta(e.inputs,r),{inputs:[0]})}}),Vn,Co,Od,Fd,sa,Dd,Ld,na,oa,zd,Bd,ia,Rd,Nd,aa,jd,dp,Ud,Vd,cp=w(()=>{Qe(),zt(),Bt(),Qt(),Vn=e=>{if(T.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Co=(e,t,r)=>{let n=t.format==="NHWC",o=e.dims.slice();n&&o.splice(1,0,o.pop());let a=Object.hasOwnProperty.call(t,"dilations"),i=t.kernelShape.slice(),d=t.strides.slice(),p=a?t.dilations.slice():[],h=t.pads.slice();wr.adjustPoolAttributes(r,o,i,d,p,h);let k=wr.computePoolOutputShape(r,o,d,p,i,h,t.autoPad),S=Object.assign({},t);a?Object.assign(S,{kernelShape:i,strides:d,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(S,{kernelShape:i,strides:d,pads:h,cacheKey:t.cacheKey});let u=k.slice();return u.push(u.splice(1,1)[0]),[S,n?u:k]},Od=(e,t)=>{let r=t.format==="NHWC",n=$e.size(e),o=$e.size(t.kernelShape),a=[{type:12,data:n},{type:12,data:o}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let d=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],k=t.pads[t.pads.length-1],S=!!(h+k);a.push({type:12,data:d},{type:12,data:p},{type:12,data:h},{type:12,data:k}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let u=!1;if(t.kernelShape.length===2){let B=t.kernelShape[t.kernelShape.length-2],R=t.strides[t.strides.length-2],j=t.pads[t.pads.length/2-2],Z=t.pads[t.pads.length-2];u=!!(j+Z),a.push({type:12,data:B},{type:12,data:R},{type:12,data:j},{type:12,data:Z}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,i,!0,S,u]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let d=$e.computeStrides(t.kernelShape);a.push({type:12,data:d},{type:12,data:t.pads},{type:12,data:t.strides}),i.push({name:"kernelStrides",type:"u32",length:d.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let p=t.pads.reduce((h,k)=>h+k);return[a,i,!!p,!1,!1]}},Fd=(e,t,r,n,o,a,i,d,p,h,k,S)=>{let u=o.format==="NHWC",B=t.type.value,R=wt("output",t.type.tensor,n);if(o.kernelShape.length<=2){let j="",Z="",te="",X=r-(u?2:1);if(k?j=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${X}] = indices[${X}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${X}] < 0 || xIndices[${X}] + >= uniforms.x_shape[${X}]) { + pad++; + continue; + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`:j=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${X}] = indices[${X}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`,o.kernelShape.length===2){let _e=r-(u?3:2);S?Z=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${_e}] = indices[${_e}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${_e}] < 0 || xIndices[${_e}] >= uniforms.x_shape[${_e}]) { + pad += i32(uniforms.kw); + continue; + } + `:Z=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${_e}] = indices[${_e}] * uniforms.sh - uniforms.phStart + j; + `,te=` + } + `}return` + ${e.registerUniforms(p).declareVariables(t,R)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${R.offsetToIndices("global_idx")}; + var xIndices = ${R.offsetToIndices("global_idx")}; + + var value = ${B}(${d}); + var pad = 0; + ${Z} + ${j} + ${te} + ${i} + + output[global_idx] = value; + }`}else{if(u)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let j=o.kernelShape.length,Z=o.pads.length,te="";return h?te=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`:te=` + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + `,` + ${e.registerUniforms(p).declareVariables(t,R)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${R.offsetToIndices("global_idx")}; + var xIndices = ${R.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${B}(${d}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${j-1}u; j++) { + offsets[j] = offset / ${Mt("uniforms.kernelStrides","j",j)}; + offset -= offsets[j] * ${Mt("uniforms.kernelStrides","j",j)}; + } + offsets[${j-1}] = offset; + + isPad = false; + for (var j = ${r-j}u; j < ${r}u; j++) { + xIndices[j] = indices[j] * ${Mt("uniforms.strides",`j - ${r-j}u`,j)} + + offsets[j - ${r-j}u] - ${Mt("uniforms.pads","j - 2u",Z)}; + ${te} + } + ${i} + + output[global_idx] = value; + }`}},sa=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Dd=e=>`${sa(e)};${e.countIncludePad}`,Ld=e=>`${sa(e)};${e.storageOrder};${e.dilations}`,na=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}),oa=(e,t,r,n)=>{let[o,a]=Co(t,n,r),i=ze("x",t.dataType,t.dims.length),d=i.type.value,p="value += x_val;",h="";o.countIncludePad?h+=`value /= ${d}(uniforms.kernelSize);`:h+=`value /= ${d}(i32(uniforms.kernelSize) - pad);`;let[k,S,u,B,R]=Od(a,o);k.push(...vt(t.dims,a));let j=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${u};${B};${R}`,inputDependencies:j},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil($e.size(a)/64)},programUniforms:k}),getShaderSource:Z=>Fd(Z,i,t.dims.length,a.length,o,p,h,0,S,u,B,R)}},zd=e=>{let t=e.count_include_pad!==0,r=na(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:Dd(n)}},Bd=(e,t)=>{Vn(e.inputs),e.compute(oa("AveragePool",e.inputs[0],!1,t))},ia={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Rd=e=>{let t=e.format;return{format:t,...ia,cacheKey:t}},Nd=(e,t)=>{Vn(e.inputs),e.compute(oa("GlobalAveragePool",e.inputs[0],!0,t))},aa=(e,t,r,n)=>{let[o,a]=Co(t,n,r),i=` + value = max(x_val, value); + `,d="",p=ze("x",t.dataType,t.dims.length),h=["rank"],[k,S,u,B,R]=Od(a,o);return k.push(...vt(t.dims,a)),{name:e,shaderCache:{hint:`${n.cacheKey};${u};${B};${R}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil($e.size(a)/64)},programUniforms:k}),getShaderSource:j=>Fd(j,p,t.dims.length,a.length,o,i,d,t.dataType===10?-65504:-1e5,S,u,B,R)}},jd=(e,t)=>{Vn(e.inputs),e.compute(aa("MaxPool",e.inputs[0],!1,t))},dp=e=>{let t=e.storage_order,r=e.dilations,n=na(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 o={storageOrder:t,dilations:r,...n,cacheKey:""};return{...o,cacheKey:Ld(o)}},Ud=e=>{let t=e.format;return{format:t,...ia,cacheKey:t}},Vd=(e,t)=>{Vn(e.inputs),e.compute(aa("GlobalMaxPool",e.inputs[0],!0,t))}}),Wd,Gd,Kd,pp,Hd=w(()=>{zt(),Bt(),Pt(),Qt(),Wd=(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((o,a)=>a===t.axis||o===e[0].dims[a]).reduce((o,a)=>o&&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)].")}},Gd=(e,t)=>{let r=$e.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,o=n===3,a=e[0].dims,i=e[1].dataType,d=$e.size(a),p=n===3||n===2,h=p?[Math.ceil($e.size(e[0].dims)/4)]:e[0].dims,k=e[1].dims,S=e.length>2?e[2]:void 0,u=S?p?[Math.ceil($e.size(S.dims)/4)]:S.dims:void 0,B=k.length===0||k.length===1&&k[0]===1,R=B===!1&&k.length===1,j=yr(d),Z=B&&(!p||j===4),te=Z?j:1,X=Z&&!p?j:1,_e=ze("input",p?12:n,h.length,X),me=ze("scale",i,k.length),ye=S?ze("zero_point",p?12:n,u.length):void 0,Ae=wt("output",i,a.length,te),Ie=[_e,me];ye&&Ie.push(ye);let Ge=[h,k];S&&Ge.push(u);let lt=[{type:12,data:d/te},{type:12,data:r},{type:12,data:t.blockSize},...vt(...Ge,a)],xt=Kt=>{let Yt=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${Kt.registerUniforms(Yt).declareVariables(...Ie,Ae)} + ${Kt.mainStart()} + ${Kt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${Ae.offsetToIndices("global_idx")}; + + // Set input x + ${p?` + let input = ${_e.getByOffset("global_idx / 4")}; + let x_vec = ${o?"unpack4xI8(input)":"unpack4xU8(input)"}; + let x_value = ${te===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${_e.getByOffset("global_idx")};`}; + + // Set scale input + ${B?`let scale_value= ${me.getByOffset("0")}`:R?` + let scale_index = ${Ae.indicesGet("output_indices","uniforms.axis")}; + let scale_value= ${me.getByOffset("scale_index")};`:` + var scale_indices: ${me.type.indices} = output_indices; + let index = ${me.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; + ${me.indicesSet("scale_indices","uniforms.axis","index")}; + let scale_value= ${me.getByIndices("scale_indices")};`}; + + // Set zero-point input + ${ye?B?p?` + let zero_point_input = ${ye.getByOffset("0")}; + let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${ye.getByOffset("0")}`:R?p?` + let zero_point_index = ${Ae.indicesGet("output_indices","uniforms.axis")}; + let zero_point_input = ${ye.getByOffset("zero_point_index / 4")}; + let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_index % 4]`:` + let zero_point_index = ${Ae.indicesGet("output_indices","uniforms.axis")}; + let zero_point_value = ${ye.getByOffset("zero_point_index")};`:p?` + let zero_point_offset = ${me.indicesToOffset("scale_indices")}; + let zero_point_input = ${ye.getByOffset("zero_point_offset / 4")}; + let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${ye.getByIndices("scale_indices")};`:`let zero_point_value = ${p?o?"i32":"u32":_e.type.value}(0);`}; + // Compute and write output + ${Ae.setByOffset("global_idx",`${Ae.type.value}(x_value - zero_point_value) * scale_value`)}; + }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:ye?["rank","rank","rank"]:["rank","rank"]},getShaderSource:xt,getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(d/te/64),y:1,z:1},programUniforms:lt})}},Kd=(e,t)=>{Wd(e.inputs,t),e.compute(Gd(e.inputs,t))},pp=e=>it({axis:e.axis,blockSize:e.blockSize})}),qd,Xd,Qd,hp=w(()=>{Qe(),zt(),Qt(),qd=(e,t,r)=>{let n=e===t,o=et&&r>0;if(n||o||a)throw new Error("Range these inputs' contents are invalid.")},Xd=(e,t,r,n)=>{let o=Math.abs(Math.ceil((t-e)/r)),a=[o],i=o,d=[{type:12,data:i},{type:n,data:e},{type:n,data:r},...vt(a)],p=h=>{let k=wt("output",n,a.length),S=k.type.value,u=[{name:"outputSize",type:"u32"},{name:"start",type:S},{name:"delta",type:S}];return` + ${h.registerUniforms(u).declareVariables(k)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${S}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:d})}},Qd=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]),T.webgpu.validateInputContent&&qd(t,r,n),e.compute(Xd(t,r,n,e.inputs[0].dataType),{inputs:[]})}}),Yd,Jd,Zd,ec,mp=w(()=>{zt(),Bt(),Pt(),Qt(),Yd=(e,t,r,n)=>{if(e!=="none"&&n!=="i32"&&n!=="u32"&&n!=="f32")throw new Error(`Input ${n} is not supported with reduction ${e}.`);let o=`{ + var oldValue = 0; + loop { + let newValueF32 =`,a=`; + let newValue = bitcast(newValueF32); + let res = atomicCompareExchangeWeak(&${t}, oldValue, newValue); + if res.exchanged { + break; + } + oldValue = res.old_value; + } + }`;switch(e){case"none":return`${t}=${r};`;case"add":return n==="i32"||n==="u32"?`atomicAdd(&${t}, bitcast<${n}>(${r}));`:` + ${o}bitcast<${n}>(oldValue) + (${r})${a}`;case"max":return n==="i32"||n==="u32"?`atomicMax(&${t}, bitcast<${n}>(${r}));`:` + ${o}max(bitcast(oldValue), (${r}))${a}`;case"min":return n==="i32"||n==="u32"?`atomicMin(&${t}, bitcast<${n}>(${r}));`:`${o}min(bitcast<${n}>(oldValue), (${r}))${a}`;case"mul":return`${o}(bitcast<${n}>(oldValue) * (${r}))${a}`;default:throw new Error(`Reduction ${e} is not supported.`)}},Jd=(e,t)=>{let r=e[0].dims,n=e[1].dims,o=r,a=1,i=Math.ceil($e.size(n)/a),d=n[n.length-1],p=$e.sizeFromDimension(r,d),h=[{type:12,data:i},{type:12,data:d},{type:12,data:p},...vt(e[1].dims,e[2].dims,o)],k=S=>{let u=ze("indices",e[1].dataType,e[1].dims.length),B=ze("updates",e[2].dataType,e[2].dims.length,a),R=t.reduction!=="none"&&t.reduction!==""?rs("output",e[0].dataType,o.length):wt("output",e[0].dataType,o.length,a);return` + ${S.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(u,B,R)} + ${S.mainStart()} + ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var data_offset = 0u; + let indices_start = uniforms.last_index_dimension * global_idx; + let indices_end = indices_start + uniforms.last_index_dimension; + for (var i = indices_start; i < indices_end; i++) { + var index = i32(indices[i].x); + ${e[0].dims.length===1?` + let element_count_dim = uniforms.output_strides; + let dim_value = uniforms.output_shape;`:` + let element_count_dim = uniforms.output_strides[i - indices_start]; + let dim_value = uniforms.output_shape[i - indices_start + uniforms.last_index_dimension];`} + if (index >= 0) { + if (index >= i32(dim_value)) { + index = i32(dim_value - 1); + } + } else { + if (index < -i32(dim_value)) { + index = 0; + } else { + index += i32(dim_value); + } + } + data_offset += u32((u32(index) * element_count_dim)); + } + + for (var i = 0u; i < uniforms.num_updates_elements; i++) { + let value = updates[uniforms.num_updates_elements * global_idx + i]; + ${Yd(t.reduction,"output[data_offset + i]","value",R.type.value)} + } + + }`};return{name:"ScatterND",shaderCache:{hint:`${t.cacheKey}_${t.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:h}),getShaderSource:k}},Zd=e=>it({reduction:e.reduction}),ec=(e,t)=>{e.compute(Jd(e.inputs,t),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),tc,rc,sc,nc,oc,ic,ac,lc,uc,dc,cc,la,pc,hc,mc,_c,fc,gc,wc,jp=w(()=>{zt(),Bt(),Pt(),Qt(),tc=(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")}},rc=(e,t,r)=>{t.every(o=>o>=0&&o{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((o,a)=>n[o]=e[a]),n},sc=(e,t,r,n,o,a)=>{let[i,d,p]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(k=>a.push(k));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(d>0&&e.length>d&&e[d].dims.length===1&&e[d].dims[0]>0){if(e[d].getFloat32Array().forEach(k=>n.push(k)),n.length!==0&&n.length!==h&&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");tc(n,t),t.axes.length>0&&rc(n,t.axes,h).forEach((k,S)=>n[S]=k)}if(p>0&&e.length>p&&e[p].dims.length===1&&e[p].dims[0]>0&&(e[p].getBigInt64Array().forEach(k=>o.push(Number(k))),o.length!==0&&o.length!==h&&r>=18&&o.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(o.length!==0&&o.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 o<"u"&&n.length>0&&o.length>h)throw new Error("Resize requires only of scales or sizes to be specified")},nc=(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`)}})()+"}",oc=(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`)}})()+"}",ic=(e,t,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),o=e.length===0?n:e.slice();return t.length>0?(t.forEach((a,i)=>{n[a]=o[i],n[i+r]=o[t.length+i]}),n):o},ac=(e,t,r,n)=>{let o=[];if(r.length>0)if(n.length>0){if(e.forEach(a=>o.push(a)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((a,i)=>o[a]=r[i])}else r.forEach(a=>o.push(a));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");o=e.map((a,i)=>Math.round(a*t[i]))}return o},lc=(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 o=e.slice();return r.axes.length>0?(r.axes.forEach(a=>t[a]=n),r.axes.forEach(a=>o[a]=Math.round(e[a]*t[a]))):(t.fill(n,0,t.length),o.forEach((a,i)=>o[i]=Math.round(a*t[i]))),o},uc=(e,t,r,n,o)=>` + 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 = ${Mt("uniforms.scales","i",n)}; + var roi_low = ${Mt("uniforms.roi","i",o)}; + var roi_hi = ${Mt("uniforms.roi",`i + ${t.length}`,o)}; + if (scale == 1.0) { + original_indices[i] = ${e.type.value}(output_index); + } else { + var input_shape_i = ${Mt("uniforms.input_shape","i",t.length)}; + var output_shape_i = ${Mt("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; + }`,dc=(e,t,r,n,o,a,i)=>` + 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 = ${Mt("uniforms.scales","i",o)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${Mt("uniforms.roi","i",a)}; + var roi_hi = ${Mt("uniforms.roi",`i + ${r.length}`,a)}; + var input_shape_i = ${Mt("uniforms.input_shape","i",r.length)}; + var output_shape_i = ${Mt("uniforms.output_shape","i",n.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${i} || (original_idx >= 0 && original_idx < ${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; + }`,cc=(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 >= ${Mt("uniforms.input_shape","i",t.length)}) { + return false; + } + } + return true; + }`,la=(e,t,r,n)=>e.rank>n?` + ${e.indicesSet("input_indices",t,"channel")}; + ${e.indicesSet("input_indices",r,"batch")}; +`:"",pc=(e,t,r,n,o)=>{let[a,i,d,p]=r.length===2?[-1,0,1,-1]:[0,2,3,1],h=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${h} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",i,`max(0, min(row, ${r[i]} - 1))`)}; + ${e.indicesSet("input_indices",d,`max(0, min(col, ${r[d]} - 1))`)}; + ${la(e,p,a,2)} + return ${e.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${h} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${h} = originalIndices[${i}]; + var col:${h} = originalIndices[${d}]; + ${n?`if (row < 0 || row > (${r[i]} - 1) || col < 0 || col > (${r[d]} - 1)) { + return ${o}; + }`:""}; + row = max(0, min(row, ${r[i]} - 1)); + col = max(0, min(col, ${r[d]} - 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 = ${r.length>2?`u32(originalIndices[${p}])`:"0"}; + var batch: u32 = ${r.length>2?`u32(originalIndices[${a}])`:"0"}; + var x11: ${h} = getInputValue(batch, channel, row1, col1); + var x12: ${h} = getInputValue(batch, channel, row1, col2); + var x21: ${h} = getInputValue(batch, channel, row2, col1); + var x22: ${h} = getInputValue(batch, channel, row2, col2); + var dx1: ${h} = abs(row - ${h}(row1)); + var dx2: ${h} = abs(${h}(row2) - row); + var dy1: ${h} = abs(col - ${h}(col1)); + var dy2: ${h} = abs(${h}(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); + }`},hc=(e,t,r,n,o,a,i,d,p,h)=>{let k=r.length===2,[S,u]=k?[0,1]:[2,3],B=e.type.value,R=j=>{let Z=j===S?"row":"col";return` + fn ${Z}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${B} { + var output_index = ${t.indicesGet("output_indices",j)}; + var originalIdx: ${B} = getOriginalCoordinateFromResizedCoordinate(output_index, ${o[j]}, + ${n[j]}, ${r[j]}, ${a[j]}, ${a[j]} + ${r.length}); + var fractOriginalIdx: ${B} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${d} && (originalIdx < 0 || originalIdx > (${r[j]} - 1))) { + return ${p}; + } + var data: array<${B}, 4> = array<${B}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${Z}: ${B} = originalIdx + ${B}(i); + if (${Z} < 0 || ${Z} >= ${r[j]}) { + ${h?`coefs[i + 1] = 0.0; + continue;`:d?`return ${p};`:`${Z} = max(0, min(${Z}, ${r[j]} - 1));`}; + } + var input_indices_copy: ${e.type.indices} = input_indices; + ${e.indicesSet("input_indices_copy",j,`u32(${Z})`)}; + data[i + 1] = ${j===S?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${R(S)}; + ${R(u)}; + fn getCubicInterpolationCoefs(s: ${B}) -> array<${B}, 4> { + var absS = abs(s); + var coeffs: array<${B}, 4> = array<${B}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${B} = 1.0 - absS; + var twoMinusAbsS: ${B} = 2.0 - absS; + var onePlusAbsS: ${B} = 1.0 + absS; + coeffs[0] = ((${i} * onePlusAbsS - 5 * ${i}) * onePlusAbsS + 8 * ${i}) * onePlusAbsS - 4 * ${i}; + coeffs[1] = ((${i} + 2) * absS - (${i} + 3)) * absS * absS + 1; + coeffs[2] = ((${i} + 2) * oneMinusAbsS - (${i} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${i} * twoMinusAbsS - 5 * ${i}) * twoMinusAbsS + 8 * ${i}) * twoMinusAbsS - 4 * ${i}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${B}, 4>, coefs: array<${B}, 4>) -> ${B} { + var coefsSum: ${B} = 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}) -> ${B} { + var input_indices: ${e.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},mc=(e,t,r,n,o)=>{let[a,i,d,p,h]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],k=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${k} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",i,`max(0, min(depth, ${r[i]} - 1))`)}; + ${e.indicesSet("input_indices",d,`max(0, min(height, ${r[d]} - 1))`)}; + ${e.indicesSet("input_indices",p,`max(0, min(width, ${r[p]} - 1))`)}; + ${la(e,h,a,3)} + return ${e.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${k} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${k} = originalIndices[${i}]; + var height:${k} = originalIndices[${d}]; + var width:${k} = originalIndices[${p}]; + ${n?`if (depth < 0 || depth > (${r[i]} - 1) || height < 0 || height > (${r[d]} - 1) || width < 0 || (width > ${r[p]} - 1)) { + return ${o}; + }`:""}; + + depth = max(0, min(depth, ${r[i]} - 1)); + height = max(0, min(height, ${r[d]} - 1)); + width = max(0, min(width, ${r[p]} - 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 = ${r.length>3?`u32(originalIndices[${h}])`:"0"}; + var batch: u32 = ${r.length>3?`u32(originalIndices[${a}])`:"0"}; + + var x111: ${k} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${k} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${k} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${k} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${k} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${k} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${k} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${k} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${k} = abs(depth - ${k}(depth1)); + var dx2: ${k} = abs(${k}(depth2) - depth); + var dy1: ${k} = abs(height - ${k}(height1)); + var dy2: ${k} = abs(${k}(height2) - height); + var dz1: ${k} = abs(width - ${k}(width1)); + var dz2: ${k} = abs(${k}(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); + }`},_c=(e,t,r,n,o,a)=>{let i=e.dims,d=ic(a,t.axes,i.length),p=ac(i,n,o,t.axes),h=n.slice();n.length===0&&(h=i.map((X,_e)=>X===0?1:p[_e]/X),t.keepAspectRatioPolicy!=="stretch"&&(p=lc(i,h,t)));let k=wt("output",e.dataType,p.length),S=ze("input",e.dataType,i.length),u=$e.size(p),B=i.length===p.length&&i.every((X,_e)=>X===p[_e]),R=t.coordinateTransformMode==="tf_crop_and_resize",j=t.extrapolationValue,Z=S.type.value,te=X=>` + ${B?"":` + ${nc(t.coordinateTransformMode,Z)}; + ${(()=>{switch(t.mode){case"nearest":return` + ${cc(S,i)}; + ${oc(t.nearestMode,r,Z)}; + ${dc(S,k,i,p,h.length,d.length,R)}; + `;case"linear":return` + ${uc(k,i,p,h.length,d.length)}; + ${(()=>{if(i.length===2||i.length===4)return`${pc(S,k,i,R,j)}`;if(i.length===3||i.length===5)return`${mc(S,k,i,R,j)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(i.length===2||i.length===4)return`${hc(S,k,i,p,h,d,t.cubicCoeffA,R,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")}})()}; + `} + ${X.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",d.length).declareVariables(S,k)} + ${X.mainStart()} + ${X.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${B?"output[global_idx] = input[global_idx];":` + let output_indices = ${k.offsetToIndices("global_idx")}; + var input_indices: ${S.type.indices}; + ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${S.getByIndices("input_indices")}; + } else { + output[global_idx] = ${t.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()}; +`} + }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${h.length>0?h:""}|${o.length>0?o:""}|${d.length>0?d:""}|${B}|${i}`,inputDependencies:["rank"]},getShaderSource:te,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:[{type:12,data:u},{type:1,data:h},{type:1,data:d},...vt(i,p)]})}},fc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},gc=(e,t)=>{let r=[],n=[],o=[],a=fc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");sc(e.inputs,t,a,r,n,o),e.compute(_c(e.inputs[0],t,a,r,n,o),{inputs:[0]})},wc=e=>{let t=e.antialias,r=e.axes,n=e.coordinateTransformMode,o=e.cubicCoeffA,a=e.excludeOutside!==0,i=e.extrapolationValue,d=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return it({antialias:t,axes:r,coordinateTransformMode:n,cubicCoeffA:o,excludeOutside:a,extrapolationValue:i,keepAspectRatioPolicy:d,mode:p,nearestMode:h})}}),Xt,yc,jr,Wr=w(()=>{zt(),Bt(),Pt(),Qt(),Xt=(e,t)=>{let[r,n,o,a]=e,{numHeads:i,rotaryEmbeddingDim:d}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!$e.areEqual(n.dims,[])&&!$e.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(o.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(a.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(!$e.areEqual(o.dims,a.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(d>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let p=r.dims[0],h=r.dims[r.dims.length-2],k=o.dims[0],S=$e.sizeFromDimension(r.dims,1)/h,u=d===0?o.dims[1]*2:S/i;if(d>u)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(p!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(h!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(u/2!==o.dims[1]&&d/2!==o.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${o.dims[1]}`);if(h>k)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},yc=(e,t)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:o,scale:a}=t,i=e[0].dims[0],d=$e.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=d/p,k=e[2].dims[1],S=o===0?k*2:h/n,u=new Array(i,p,h/S,S-k),B=$e.computeStrides(u),R=[{type:1,data:a},{type:12,data:u},{type:12,data:B},...e[0].dims.length===3?new Array({type:12,data:[d,h,S,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[d,S,p*S,1]}):[],...vt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],j=Z=>{let te=ze("input",e[0].dataType,e[0].dims.length),X=ze("position_ids",e[1].dataType,e[1].dims.length),_e=ze("cos_cache",e[2].dataType,e[2].dims.length),me=ze("sin_cache",e[3].dataType,e[3].dims.length),ye=wt("output",e[0].dataType,e[0].dims.length);return Z.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:u.length},{name:"global_strides",type:"u32",length:B.length},{name:"input_output_strides",type:"u32",length:B.length}]),` + ${Z.declareVariables(te,X,_e,me,ye)} + + ${Z.mainStart(Nr)} + let half_rotary_emb_dim = uniforms.${_e.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${Z.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${X.broadcastedIndicesToOffset("bsnh.xy",wt("",X.type.tensor,2))}; + let position_id = + u32(${X.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r}); + let j = i + select(half_rotary_emb_dim, 1, ${r}); + let re = ${te.getByOffset("i")} * ${_e.get("position_id","bsnh[3]")} - + ${te.getByOffset("j")} * ${me.get("position_id","bsnh[3]")}; + ${ye.setByOffset("i","re")} + let im = ${te.getByOffset("i")} * ${me.get("position_id","bsnh[3]")} + + ${te.getByOffset("j")} * ${_e.get("position_id","bsnh[3]")}; + ${ye.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${ye.setByOffset("k",te.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:it({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:j,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil($e.size(u)/Nr)},programUniforms:R})}},jr=(e,t)=>{Xt(e.inputs,t),e.compute(yc(e.inputs,t))}}),Xr,an,_p,fp=w(()=>{zt(),Bt(),Qt(),Xr=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 o=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==o)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]!==o)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let i=e[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==o)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let i=e[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==o)throw new Error("Bias must have the same hidden size as input")}},an=(e,t,r,n)=>{let o=t.simplified,a=e[0].dims,i=$e.size(a),d=a,p=i,h=a.slice(-1)[0],k=n?a.slice(0,-1).concat(1):[],S=!o&&e.length>3,u=e.length>4,B=n&&r>1,R=n&&r>2,j=r>3,Z=64,te=yr(h),X=[{type:12,data:p},{type:12,data:te},{type:12,data:h},{type:1,data:t.epsilon}],_e=ye=>{let Ae=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Ie=[ze("x",e[0].dataType,e[0].dims,te),ze("skip",e[1].dataType,e[1].dims,te),ze("gamma",e[2].dataType,e[2].dims,te)];S&&Ie.push(ze("beta",e[3].dataType,e[3].dims,te)),u&&Ie.push(ze("bias",e[4].dataType,e[4].dims,te)),Ie.push(wt("output",e[0].dataType,d,te)),B&&Ie.push(wt("mean_output",1,k)),R&&Ie.push(wt("inv_std_output",1,k)),j&&Ie.push(wt("input_skip_bias_sum",e[0].dataType,d,te));let Ge=er(e[0].dataType),lt=er(1,te);return` + + ${ye.registerUniforms(Ae).declareVariables(...Ie)} + var sum_shared : array<${lt}, ${Z}>; + var sum_squared_shared : array<${lt}, ${Z}>; + + ${ye.mainStart([Z,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${Z}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${Z}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${Z-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${u?"bias[offset1d + i]":Ge+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${j?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${Fr(Ge,te,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${Z}; + 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 = ${qr("sum",te)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${qr("square_sum",te)} / f32(uniforms.hidden_size) ${o?"":"- mean * mean"} + uniforms.epsilon); + ${B?"mean_output[global_idx] = mean;":""} + ${R?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${o?"":`- ${Ge}(mean)`}) * + ${Ge}(inv_std_dev) * gamma[offset1d + i] + ${S?"+ beta[offset1d + i]":""}; + } + }`},me=[{dims:d,dataType:e[0].dataType}];return r>1&&me.push({dims:k,dataType:1}),r>2&&me.push({dims:k,dataType:1}),r>3&&me.push({dims:a,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${te};${B};${R};${j}`,inputDependencies:e.map((ye,Ae)=>"type")},getShaderSource:_e,getRunData:()=>({outputs:me,dispatchGroup:{x:Math.ceil(p/h)},programUniforms:X})}},_p=(e,t)=>{Xr(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(an(e.inputs,t,e.outputCount,!1),{outputs:r})}}),Mc,_,x,V,be,Oe,Se,Ye,rt=w(()=>{zt(),Bt(),Pt(),Qt(),Mc=(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`)})},_=(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},x=(e,t)=>{if(e.length>1){let r=_(e,1),n=_(e,2),o=_(e,3);return o.length===0&&(o=[...Array(e[0].dims.length).keys()]),it({starts:r,ends:n,axes:o})}else return t},V=(e,t,r,n,o)=>{let a=e;return e<0&&(a+=r[n[t]]),o[t]<0?Math.max(0,Math.min(a,r[n[t]]-1)):Math.max(0,Math.min(a,r[n[t]]))},be=(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 = ${Mt("uniforms.input_shape","i",r.length)}; + let steps_i = ${Mt("uniforms.steps","i",r.length)}; + let signs_i = ${Mt("uniforms.signs","i",r.length)}; + let starts_i = ${Mt("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; + } + ${e.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,Oe=(e,t)=>{let r=e[0].dims,n=$e.size(r),o=t.axes.length>0?$e.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],a=_(e,4);a.forEach(te=>te!==0||(()=>{throw new Error("step cannot be 0")})),a.length===0&&(a=Array(o.length).fill(1));let i=t.starts.map((te,X)=>V(te,X,r,o,a)),d=t.ends.map((te,X)=>V(te,X,r,o,a));if(o.length!==i.length||o.length!==d.length)throw new Error("start, ends and axes should have the same number of elements");if(o.length!==r.length)for(let te=0;teMath.sign(te));a.forEach((te,X,_e)=>{if(te<0){let me=(d[X]-i[X])/te,ye=i[X],Ae=ye+me*a[X];i[X]=Ae,d[X]=ye,_e[X]=-te}});let h=r.slice(0);o.forEach((te,X)=>{h[te]=Math.ceil((d[te]-i[te])/a[te])});let k={dims:h,dataType:e[0].dataType},S=wt("output",e[0].dataType,h.length),u=ze("input",e[0].dataType,e[0].dims.length),B=$e.size(h),R=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:p.length},{name:"steps",type:"u32",length:a.length}],j=[{type:12,data:B},{type:12,data:i},{type:6,data:p},{type:12,data:a},...vt(e[0].dims,h)],Z=te=>` + ${te.registerUniforms(R).declareVariables(u,S)} + ${be(u,S,r)} + ${te.mainStart()} + ${te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${S.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${S.setByOffset("global_idx",u.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${p.length}_${i.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:Z,getRunData:()=>({outputs:[k],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:j})}},Se=(e,t)=>{Mc(e.inputs,t);let 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+ let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${_e}) { + let index = row * row_stride + col; + result[index] = value; + } + ${Ie.registerUniform("packedCols","i32").declareVariables(te,X)} + ${Ie.mainStart(j)} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${j}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${me} + 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 = ${_e}(${Z("threadShared[0]",B)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${_e}(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 = ${_e}(${qr("threadShared[0]",B)}); + } + 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); + } + 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}`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...vt(e[0].dims,o)]}),getShaderSource:h}},or=e=>{Zt(e.inputs),e.compute(qt(e.inputs),{inputs:[0]})}}),Tr,dr,Pr,$r=w(()=>{zt(),Bt(),Qt(),Tr=(e,t,r,n,o)=>{let a=wt("output_data",o,r.length,4),i=ze("a_data",t[1].dataType,t[1].dims.length,4),d=ze("b_data",t[2].dataType,t[2].dims.length,4),p=ze("c_data",t[0].dataType,t[0].dims.length,4),h,k=(S,u,B)=>`select(${u}, ${S}, ${B})`;if(!n)h=a.setByOffset("global_idx",k(i.getByOffset("global_idx"),d.getByOffset("global_idx"),p.getByOffset("global_idx")));else{let S=(u,B,R="")=>{let j=`a_data[index_a${B}][component_a${B}]`,Z=`b_data[index_b${B}][component_b${B}]`,te=`bool(c_data[index_c${B}] & (0xffu << (component_c${B} * 8)))`;return` + let output_indices${B} = ${a.offsetToIndices(`global_idx * 4u + ${B}u`)}; + let offset_a${B} = ${i.broadcastedIndicesToOffset(`output_indices${B}`,a)}; + let offset_b${B} = ${d.broadcastedIndicesToOffset(`output_indices${B}`,a)}; + let offset_c${B} = ${p.broadcastedIndicesToOffset(`output_indices${B}`,a)}; + let index_a${B} = offset_a${B} / 4u; + let index_b${B} = offset_b${B} / 4u; + let index_c${B} = offset_c${B} / 4u; + let component_a${B} = offset_a${B} % 4u; + let component_b${B} = offset_b${B} % 4u; + let component_c${B} = offset_c${B} % 4u; + ${u}[${B}] = ${R}(${k(j,Z,te)}); + `};o===9?h=` + var data = vec4(0); + ${S("data",0,"u32")} + ${S("data",1,"u32")} + ${S("data",2,"u32")} + ${S("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:h=` + ${S("output_data[global_idx]",0)} + ${S("output_data[global_idx]",1)} + ${S("output_data[global_idx]",2)} + ${S("output_data[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(p,i,d,a)} + ${e.mainStart()} + 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Map,this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. (should not happen)");let e=this.kernelCustomData.get(this.currentKernelId);return e||(e={},this.kernelCustomData.set(this.currentKernelId,e)),e}async initialize(e,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},o=a=>t.features.has(a)&&r.push(a)&&!0;o("chromium-experimental-timestamp-query-inside-passes")||o("timestamp-query"),o("shader-f16"),o("subgroups")&&o("subgroups-f16"),this.device=await t.requestDevice(n),this.deviceInfo=new da(this.device),this.adapterInfo=new Ps(t.info||await t.requestAdapterInfo()),this.gpuDataManager=ct(this),this.programManager=new gs(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,gn(e.logLevel,!!e.debug),this.device.onuncapturederror=a=>{a.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${a.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value: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;We(),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 o=0;o"u"&&(this.queryTimeBase=B);let j=Number(B-this.queryTimeBase),Z=Number(R-this.queryTimeBase);if(!Number.isSafeInteger(j)||!Number.isSafeInteger(Z))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:S.map(te=>({dims:te.dims,dataType:Ts(te.dataType)})),outputsMetadata:u.map(te=>({dims:te.dims,dataType:Ts(te.dataType)})),kernelId:i,kernelType:p,kernelName:h,programName:k,startTime:j,endTime:Z});else{let te="";S.forEach((_e,me)=>{te+=`input[${me}]: [${_e.dims}] | ${Ts(_e.dataType)}, `});let X="";u.forEach((_e,me)=>{X+=`output[${me}]: [${_e.dims}] | ${Ts(_e.dataType)}, `}),console.log(`[profiling] kernel "${i}|${p}|${h}|${k}" ${te}${X}execution time: ${Z-j} ns`)}Re("GPU",`${k}::${B}::${R}`)}e.unmap(),this.pendingQueries.delete(e)}),Ne()}run(e,t,r,n,o,a){We(e.name);let i=[];for(let X=0;X_e):r;if(k.length!==d.length)throw new Error(`Output size ${k.length} must be equal to ${d.length}.`);let S=[],u=[];for(let X=0;X=a)throw new Error(`Invalid output index: ${k[X]}`);if(k[X]===-3)continue;let _e=k[X]===-1,me=k[X]===-2,ye=_e||me?o(d[X].dataType,d[X].dims):n(k[X],d[X].dataType,d[X].dims);if(S.push(ye),ye.data===0)continue;let Ae=this.gpuDataManager.get(ye.data);if(!Ae)throw new Error(`no GPU data for output: ${ye.data}`);if(_e&&this.temporaryData.push(Ae),me){let Ie=this.kernelPersistentData.get(this.currentKernelId);Ie||(Ie=[],this.kernelPersistentData.set(this.currentKernelId,Ie)),Ie.push(Ae)}u.push(Ae)}if(i.length!==t.length||u.length!==S.length){if(u.length===0)return Ne(e.name),S;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let B;if(h){let X=0,_e=[];h.forEach(Ie=>{let Ge=typeof Ie.data=="number"?[Ie.data]:Ie.data;if(Ge.length===0)return;let lt=Ie.type===10?2:4,xt,Kt;Ie.type===10?(Kt=Ge.length>4?16:Ge.length>2?8:Ge.length*lt,xt=Ge.length>4?16:lt*Ge.length):(Kt=Ge.length<=2?Ge.length*lt:16,xt=16),X=Math.ceil(X/Kt)*Kt,_e.push(X);let Yt=Ie.type===10?8:4;X+=Ge.length>4?Math.ceil(Ge.length/Yt)*xt:Ge.length*lt});let me=16;X=Math.ceil(X/me)*me;let ye=new ArrayBuffer(X);h.forEach((Ie,Ge)=>{let lt=_e[Ge],xt=typeof Ie.data=="number"?[Ie.data]:Ie.data;if(Ie.type===6)new Int32Array(ye,lt,xt.length).set(xt);else if(Ie.type===12)new Uint32Array(ye,lt,xt.length).set(xt);else if(Ie.type===10)new Uint16Array(ye,lt,xt.length).set(xt);else if(Ie.type===1)new Float32Array(ye,lt,xt.length).set(xt);else throw new Error(`Unsupported uniform type: ${Ts(Ie.type)}`)});let Ae=this.gpuDataManager.create(X,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Ae.buffer,0,ye,0,X),this.gpuDataManager.release(Ae.id),B={offset:0,size:X,buffer:Ae.buffer}}let R=this.programManager.normalizeDispatchGroupSize(p),j=R[1]===1&&R[2]===1,Z=ns(e,t,j),te=this.programManager.getArtifact(Z);if(te||(te=this.programManager.build(e,R),this.programManager.setArtifact(Z,te),ir("info",()=>`[artifact] key: ${Z}, programName: ${e.name}`)),h&&te.uniformVariablesInfo){if(h.length!==te.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${te.uniformVariablesInfo.length}, got ${h.length} in program "${te.programInfo.name}".`);for(let X=0;X`[ProgramManager] run "${e.name}" (key=${Z}) with ${R[0]}x${R[1]}x${R[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let X={kernelId:this.currentKernelId,programName:te.programInfo.name,inputTensorViews:t,outputTensorViews:S};this.pendingKernels.push(X),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(X)}return this.programManager.run(te,i,u,R,B),Ne(e.name),S}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 o=Gr.get(e);if(!o)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:n,kernelEntry:o[0],attributes:[o[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: ${e}`);let o=n.kernelType,a=n.kernelName,i=n.kernelEntry,d=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${o}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,d[0]&&(d[1]=d[0](d[1]),d[0]=void 0),ir("info",()=>`[WebGPU] Start to run kernel "[${o}] ${a}"...`);let p=this.env.debug;this.temporaryData=[];try{return p&&this.device.pushErrorScope("validation"),i(t,d[1]),0}catch(h){return r.push(Promise.resolve(`[WebGPU] Kernel "[${o}] ${a}" failed. ${h}`)),1}finally{p&&r.push(this.device.popErrorScope().then(h=>h?`GPU validation error for kernel "[${o}] ${a}": ${h.message}`:null));for(let h of this.temporaryData)this.gpuDataManager.release(h.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,r,n){let o=this.sessionExternalDataMapping.get(e);o||(o=new Map,this.sessionExternalDataMapping.set(e,o));let a=o.get(t),i=this.gpuDataManager.registerExternalBuffer(r,n,a);return o.set(t,[i,r]),i}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 Ce(this,e,t);return wn(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(){ir("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(){ir("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){ir("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()}}}),So,xr,Dr,Ds,Hs,ln,$o,pa,bc=w(()=>{Yr(),So=1,xr=()=>So++,Dr=new Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),Ds=(e,t)=>{let r=Dr.get(e);if(!r)throw new Error("Unsupported data type.");return t.length>0?Math.ceil(t.reduce((n,o)=>n*o)*r/8):0},Hs=class{constructor(e){this.sessionId=e.sessionId,this.mlContext=e.context,this.mlTensor=e.tensor,this.dataType=e.dataType,this.tensorShape=e.shape}get tensor(){return this.mlTensor}get type(){return this.dataType}get shape(){return this.tensorShape}get byteLength(){return Ds(this.dataType,this.tensorShape)}destroy(){ir("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.length===t.length&&this.tensorShape.every((r,n)=>r===t[n])}},ln=class{constructor(e,t){this.tensorManager=e,this.wrapper=t}get tensorWrapper(){return this.wrapper}releaseTensor(){this.tensorWrapper&&(this.tensorManager.releaseTensor(this.tensorWrapper),this.wrapper=void 0)}async ensureTensor(e,t,r){if(this.wrapper){if(this.wrapper.sameTypeAndShape(e,t))return this.wrapper.tensor;if(r){if(this.wrapper.byteLength!==Ds(e,t))throw new Error("Unable to copy data to tensor with different size.");this.activeUpload=new Uint8Array(await this.wrapper.read())}this.tensorManager.releaseTensor(this.wrapper)}let n=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(e,t,n,!0,!0),r&&this.activeUpload&&(this.wrapper.write(this.activeUpload),this.activeUpload=void 0),this.wrapper.tensor}upload(e){if(this.wrapper)if(e.byteLength===this.wrapper.byteLength){this.wrapper.write(e);return}else ir("verbose",()=>"Data size does not match tensor size. Releasing tensor."),this.releaseTensor();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()}},$o=class{constructor(e){this.backend=e,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}reserveTensorId(){let e=xr();return this.tensorTrackersById.set(e,new ln(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){ir("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${e}, dataType: ${t}, shape: ${r}, copyOld: ${n}}`);let o=this.tensorTrackersById.get(e);if(!o)throw new Error("Tensor not found.");return o.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){ir("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 o=xr(),a=new Hs({sessionId:this.backend.currentSessionId,context:e,tensor:t,dataType:r,shape:n});return this.tensorTrackersById.set(o,new ln(this,a)),this.externalTensors.add(a),o}async getCachedTensor(e,t,r,n,o){let a=this.backend.currentSessionId;for(let[p,h]of this.freeTensors.entries())if(h.sameTypeAndShape(e,t)){ir("verbose",()=>`[WebNN] Reusing tensor {dataType: ${e}, shape: ${t}}`);let k=this.freeTensors.splice(p,1)[0];return k.sessionId=a,k}let i=this.backend.currentContext;ir("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${e}, shape: ${t}}`);let d=await i.createTensor({dataType:e,shape:t,dimensions:t,usage:r,writable:n,readable:o});return new Hs({sessionId:a,context:i,tensor:d,dataType:e,shape:t})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},pa=(...e)=>new $o(...e)}),Ao,gp,ha,ma=w(()=>{zt(),Ot(),Ln(),bc(),Yr(),Ao=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),gp=(e,t)=>{if(e===t)return!0;if(e===void 0||t===void 0)return!1;let r=Object.keys(e).sort(),n=Object.keys(t).sort();return r.length===n.length&&r.every((o,a)=>o===n[a]&&e[o]===t[o])},ha=class{constructor(e){this.tensorManager=pa(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],gn(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}async createMLContext(e){if(e instanceof GPUDevice){let r=this.mlContextCache.findIndex(n=>n.gpuDevice===e);if(r!==-1)return this.mlContextCache[r].mlContext;{let n=await navigator.ml.createContext(e);return this.mlContextCache.push({gpuDevice:e,mlContext:n}),n}}else if(e===void 0){let r=this.mlContextCache.findIndex(n=>n.options===void 0&&n.gpuDevice===void 0);if(r!==-1)return this.mlContextCache[r].mlContext;{let n=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:n}),n}}let t=this.mlContextCache.findIndex(r=>gp(r.options,e));if(t!==-1)return this.mlContextCache[t].mlContext;{let r=await navigator.ml.createContext(e);return this.mlContextCache.push({options:e,mlContext:r}),r}}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);if(r.delete(e),r.size===0){this.sessionIdsByMLContext.delete(t);let n=this.mlContextCache.findIndex(o=>o.mlContext===t);n!==-1&&this.mlContextCache.splice(n,1)}}getMLContext(e){return this.mlContextBySessionId.get(e)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(e){ir("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,t,r,n){let o=Ao.get(t);if(!o)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e,o,r,n)}uploadTensor(e,t){if(!_t().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");ir("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 wn(r,t)}}registerMLTensor(e,t,r){let n=Ao.get(t);if(!n)throw new Error(`Unsupported ONNX data type: ${t}`);let o=this.tensorManager.registerTensor(this.currentContext,e,n,r);return ir("verbose",()=>`[WebNN] registerMLTensor {tensor: ${e}, dataType: ${n}, dimensions: ${r}} -> {tensorId: ${o}}`),o}registerMLConstant(e,t,r,n,o,a){if(!a)throw new Error("External mounted files are not available.");let i=e;e.startsWith("./")&&(i=e.substring(2));let d=a.get(i);if(!d)throw new Error(`File with name ${i} not found in preloaded files.`);if(t+r>d.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let p=d.slice(t,t+r).buffer,h;switch(o.dataType){case"float32":h=new Float32Array(p);break;case"float16":h=new Uint16Array(p);break;case"int32":h=new Int32Array(p);break;case"uint32":h=new Uint32Array(p);break;case"int64":h=new BigInt64Array(p);break;case"uint64":h=new BigUint64Array(p);break;case"int8":h=new Int8Array(p);break;case"int4":case"uint4":case"uint8":h=new Uint8Array(p);break;default:throw new Error(`Unsupported data type: ${o.dataType} in creating WebNN Constant from external data.`)}return ir("verbose",()=>`[WebNN] registerMLConstant {dataType: ${o.dataType}, shape: ${o.shape}}}`),n.constant(o,h)}flush(){}}}),_a={};v(_a,{init:()=>Oo});var Io,fa,Oo,Up=w(()=>{zt(),ca(),Yr(),Bt(),ma(),Io=class p_{constructor(t,r,n,o){this.module=t,this.dataType=r,this.data=n,this.dims=o}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let 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e=(__(),M(Nh)).wasmBackend;q("webgpu",e,5),q("webnn",e,5),q("cpu",e,10),q("wasm",e,10)}Object.defineProperty(T.versions,"web",{value:f_,enumerable:!0});/** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */},"./src/backends/onnx.js":(Te,A,s)=>{var f;s.r(A),s.d(A,{Tensor:()=>U.Tensor,createInferenceSession:()=>ie,deviceToExecutionProviders:()=>q,isONNXProxy:()=>Y,isONNXTensor:()=>z});var O=s("./src/env.js"),N=s("?2ce3"),Q=s("./node_modules/onnxruntime-web/dist/ort.bundle.min.mjs"),U=s("./node_modules/onnxruntime-common/dist/esm/index.js");const w=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"}}),v=[];let y,M;const b=Symbol.for("onnxruntime");if(b in globalThis)M=globalThis[b];else if(O.apis.IS_NODE_ENV){switch(M=N??(f||(f=s.t(N,2))),process.platform){case"win32":v.push("dml");break;case"linux":process.arch==="x64"&&v.push("cuda");break}v.push("cpu"),y=["cpu"]}else M=Q,O.apis.IS_WEBNN_AVAILABLE&&v.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),O.apis.IS_WEBGPU_AVAILABLE&&v.push("webgpu"),v.push("wasm"),y=["wasm"];const L=M.InferenceSession;function q(D=null){if(!D)return y;switch(D){case"auto":return v;case"gpu":return v.filter($=>["webgpu","cuda","dml","webnn-gpu"].includes($))}if(v.includes(D))return[w[D]??D];throw new Error(`Unsupported device: "${D}". 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Float32Array($[0].data.length);for(let ue=0;ue<$.length;++ue){let re=g[ue];const he=$[ue].data;for(let Pe=0;Peqe[Pe]&&(Le[Pe]=ue,qe[Pe]=he[Pe])}let Ue=0;const ut=ke.data;for(let ue=0;ue200)throw new Error(`absolute aspect ratio must be smaller than 200, got ${Math.max($,g)/Math.min($,g)}`);let J=Math.round($/C)*C,le=Math.round(g/C)*C;if(J*le>ee){const de=Math.sqrt($*g/ee);J=Math.floor($/de/C)*C,le=Math.floor(g/de/C)*C}else if(J*leJ?ke=Math.floor(J*fe/ee):J>ee&&(fe=Math.floor(ee*ke/J)),await g.resize(ke,fe,{resample:T}))}async crop_margin(g,C=200){const T=g.clone().grayscale(),ee=(0,N.min)(T.data)[0],le=(0,N.max)(T.data)[0]-ee;if(le===0)return g;const de=C/255;let fe=T.width,ke=T.height,xe=0,Le=0;const qe=T.data;for(let Ue=0;Uethis.preprocess(J)));return{pixel_values:(0,O.stack)(T.map(J=>J.pixel_values),0),original_sizes:T.map(J=>J.original_size),reshaped_input_sizes:T.map(J=>J.reshaped_input_size)}}static async from_pretrained(g,C){const T=await(0,U.getModelJSON)(g,w.IMAGE_PROCESSOR_NAME,!0,C);return new this(T)}}},"./src/base/processing_utils.js":(Te,A,s)=>{s.r(A),s.d(A,{Processor:()=>Q});var f=s("./src/utils/constants.js"),O=s("./src/utils/generic.js"),N=s("./src/utils/hub.js");class Q extends O.Callable{constructor(w,v){super(),this.config=w,this.components=v}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(w,v={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(w,{tokenize:!1,...v})}batch_decode(...w){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...w)}async _call(w,...v){for(const y of[this.image_processor,this.feature_extractor,this.tokenizer])if(y)return y(w,...v);throw new Error("No image processor, feature extractor, or 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O{constructor(Q){ge(this,"max_length",20);ge(this,"max_new_tokens",null);ge(this,"min_length",0);ge(this,"min_new_tokens",null);ge(this,"early_stopping",!1);ge(this,"max_time",null);ge(this,"do_sample",!1);ge(this,"num_beams",1);ge(this,"num_beam_groups",1);ge(this,"penalty_alpha",null);ge(this,"use_cache",!0);ge(this,"temperature",1);ge(this,"top_k",50);ge(this,"top_p",1);ge(this,"typical_p",1);ge(this,"epsilon_cutoff",0);ge(this,"eta_cutoff",0);ge(this,"diversity_penalty",0);ge(this,"repetition_penalty",1);ge(this,"encoder_repetition_penalty",1);ge(this,"length_penalty",1);ge(this,"no_repeat_ngram_size",0);ge(this,"bad_words_ids",null);ge(this,"force_words_ids",null);ge(this,"renormalize_logits",!1);ge(this,"constraints",null);ge(this,"forced_bos_token_id",null);ge(this,"forced_eos_token_id",null);ge(this,"remove_invalid_values",!1);ge(this,"exponential_decay_length_penalty",null);ge(this,"suppress_tokens",null);ge(this,"streamer",null);ge(this,"begin_suppress_tokens",null);ge(this,"forced_decoder_ids",null);ge(this,"guidance_scale",null);ge(this,"num_return_sequences",1);ge(this,"output_attentions",!1);ge(this,"output_hidden_states",!1);ge(this,"output_scores",!1);ge(this,"return_dict_in_generate",!1);ge(this,"pad_token_id",null);ge(this,"bos_token_id",null);ge(this,"eos_token_id",null);ge(this,"encoder_no_repeat_ngram_size",0);ge(this,"decoder_start_token_id",null);ge(this,"generation_kwargs",{});Object.assign(this,(0,f.pick)(Q,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(Te,A,s)=>{s.r(A),s.d(A,{ClassifierFreeGuidanceLogitsProcessor:()=>z,ForcedBOSTokenLogitsProcessor:()=>w,ForcedEOSTokenLogitsProcessor:()=>v,LogitsProcessor:()=>N,LogitsProcessorList:()=>U,LogitsWarper:()=>Q,MinLengthLogitsProcessor:()=>q,MinNewTokensLengthLogitsProcessor:()=>se,NoBadWordsLogitsProcessor:()=>ie,NoRepeatNGramLogitsProcessor:()=>b,RepetitionPenaltyLogitsProcessor:()=>L,SuppressTokensAtBeginLogitsProcessor:()=>y,TemperatureLogitsWarper:()=>W,TopKLogitsWarper:()=>D,TopPLogitsWarper:()=>Y,WhisperTimeStampLogitsProcessor:()=>M});var f=s("./src/utils/generic.js");s("./src/utils/tensor.js");var O=s("./src/utils/maths.js");class N extends f.Callable{_call(g,C){throw Error("`_call` should be implemented in a subclass")}}class Q extends f.Callable{_call(g,C){throw Error("`_call` should be implemented in a subclass")}}class U extends f.Callable{constructor(){super(),this.processors=[]}push(g){this.processors.push(g)}extend(g){this.processors.push(...g)}_call(g,C){let T=C;for(const ee of this.processors)T=ee(g,T);return T}[Symbol.iterator](){return this.processors.values()}}class w extends N{constructor(g){super(),this.bos_token_id=g}_call(g,C){for(let T=0;T=1&&J[J.length-1]>=this.timestamp_begin,de=J.length<2||J[J.length-2]>=this.timestamp_begin;if(le&&(de?ee.subarray(this.timestamp_begin).fill(-1/0):ee.subarray(0,this.eos_token_id).fill(-1/0)),g[T].length===this.begin_index&&this.max_initial_timestamp_index!==null){const Le=this.timestamp_begin+this.max_initial_timestamp_index;ee.subarray(Le+1).fill(-1/0)}const fe=(0,O.log_softmax)(ee),ke=Math.log(fe.subarray(this.timestamp_begin).map(Math.exp).reduce((Le,qe)=>Le+qe)),xe=(0,O.max)(fe.subarray(0,this.timestamp_begin))[0];ke>xe&&ee.subarray(0,this.timestamp_begin).fill(-1/0)}return C}}class b extends N{constructor(g){super(),this.no_repeat_ngram_size=g}getNgrams(g){const C=g.length,T=[];for(let J=0;J1 to use the classifier free guidance processor, got guidance scale ${g}.`);this.guidance_scale=g}_call(g,C){if(C.dims[0]!==2*g.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${C.dims[0]} for the logits and ${g.length} for the input ids.`);const T=g.length,ee=C.slice([0,T],null),J=C.slice([T,C.dims[0]],null);for(let le=0;le1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${g}`);if(!Number.isInteger(T)||T<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${T}`);this.top_p=g,this.filter_value=C,this.min_tokens_to_keep=T}}class D extends Q{constructor(g,{filter_value:C=-1/0,min_tokens_to_keep:T=1}={}){if(super(),!Number.isInteger(g)||g<0)throw new Error(`\`top_k\` must be a positive integer, but is ${g}`);this.top_k=Math.max(g,T),this.filter_value=C}}},"./src/generation/logits_sampler.js":(Te,A,s)=>{s.r(A),s.d(A,{LogitsSampler:()=>Q});var f=s("./src/utils/generic.js"),O=s("./src/utils/tensor.js"),N=s("./src/utils/maths.js");s("./src/generation/configuration_utils.js");class Q extends f.Callable{constructor(M){super(),this.generation_config=M}async _call(M){return this.sample(M)}async sample(M){throw Error("sample should be implemented in subclasses.")}getLogits(M,b){let L=M.dims.at(-1),q=M.data;if(b===-1)q=q.slice(-L);else{let se=b*L;q=q.slice(se,se+L)}return q}randomSelect(M){let b=0;for(let q=0;q1)return new v(M);if(M.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${M.num_return_sequences}.`);return new U(M)}}class U extends Q{async sample(M){const b=(0,N.max)(M.data)[1];return[[BigInt(b),0]]}}class w extends Q{async sample(M){let b=M.dims.at(-1);this.generation_config.top_k>0&&(b=Math.min(this.generation_config.top_k,b));const[L,q]=await(0,O.topk)(M,b),se=(0,N.softmax)(L.data);return Array.from({length:this.generation_config.num_beams},()=>{const ie=this.randomSelect(se);return[q.data[ie],Math.log(se[ie])]})}}class v extends Q{async sample(M){let b=M.dims.at(-1);this.generation_config.top_k>0&&(b=Math.min(this.generation_config.top_k,b));const[L,q]=await(0,O.topk)(M,b),se=(0,N.softmax)(L.data);return Array.from({length:this.generation_config.num_beams},(ie,z)=>[q.data[z],Math.log(se[z])])}}},"./src/generation/stopping_criteria.js":(Te,A,s)=>{s.r(A),s.d(A,{EosTokenCriteria:()=>U,InterruptableStoppingCriteria:()=>w,MaxLengthCriteria:()=>Q,StoppingCriteria:()=>O,StoppingCriteriaList:()=>N});var f=s("./src/utils/generic.js");class O extends f.Callable{_call(y,M){throw Error("StoppingCriteria needs to be subclassed")}}class N extends f.Callable{constructor(){super(),this.criteria=[]}push(y){this.criteria.push(y)}extend(y){y instanceof N?y=y.criteria:y instanceof O&&(y=[y]),this.criteria.push(...y)}_call(y,M){const b=new Array(y.length).fill(!1);for(const L of this.criteria){const q=L(y,M);for(let se=0;seM.length>=this.max_length)}}class U extends O{constructor(y){super(),Array.isArray(y)||(y=[y]),this.eos_token_id=y}_call(y,M){return y.map(b=>{const L=b.at(-1);return this.eos_token_id.some(q=>L==q)})}}class w extends O{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(y,M){return new Array(y.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(Te,A,s)=>{s.r(A),s.d(A,{BaseStreamer:()=>Q,TextStreamer:()=>w,WhisperTextStreamer:()=>v});var f=s("./src/utils/core.js"),O=s("./src/tokenizers.js"),N=s("./src/env.js");class Q{put(M){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const U=N.apis.IS_PROCESS_AVAILABLE?y=>process.stdout.write(y):y=>console.log(y);class w extends Q{constructor(M,{skip_prompt:b=!1,callback_function:L=null,token_callback_function:q=null,decode_kwargs:se={},...ie}={}){super(),this.tokenizer=M,this.skip_prompt=b,this.callback_function=L??U,this.token_callback_function=q,this.decode_kwargs={...se,...ie},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(M){var se;if(M.length>1)throw Error("TextStreamer only supports batch size of 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L,q;M.length>0&&((L=this.callback_function)==null||L.call(this,M)),b&&this.callback_function===U&&N.apis.IS_PROCESS_AVAILABLE&&((q=this.callback_function)==null||q.call(this,` +`))}}class v extends w{constructor(M,{skip_prompt:b=!1,callback_function:L=null,token_callback_function:q=null,on_chunk_start:se=null,on_chunk_end:ie=null,on_finalize:z=null,time_precision:W=.02,skip_special_tokens:Y=!0,decode_kwargs:D={}}={}){super(M,{skip_prompt:b,callback_function:L,token_callback_function:q,decode_kwargs:{skip_special_tokens:Y,...D}}),this.timestamp_begin=M.timestamp_begin,this.on_chunk_start=se,this.on_chunk_end=ie,this.on_finalize=z,this.time_precision=W,this.waiting_for_timestamp=!1}put(M){var L,q;if(M.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const b=M[0];if(b.length===1){const se=Number(b[0])-this.timestamp_begin;if(se>=0){const 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f=s("./src/configs.js"),O=s("./src/backends/onnx.js"),N=s("./src/utils/dtypes.js"),Q=s("./src/utils/generic.js"),U=s("./src/utils/core.js"),w=s("./src/utils/hub.js"),v=s("./src/utils/constants.js"),y=s("./src/generation/logits_process.js"),M=s("./src/generation/configuration_utils.js"),b=s("./src/utils/tensor.js"),L=s("./src/utils/image.js"),q=s("./src/utils/maths.js"),se=s("./src/generation/stopping_criteria.js"),ie=s("./src/generation/logits_sampler.js"),z=s("./src/env.js"),W=s("./src/models/whisper/generation_whisper.js"),Y=s("./src/models/whisper/common_whisper.js");const D={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7,MultiModality:8,Phi3V:9},$=new Map,g=new Map,C=new Map;async function T(_,x,V){var Tr;const be=((Tr=V.config)==null?void 0:Tr["transformers.js_config"])??{};let Oe=V.device??be.device;Oe&&typeof Oe!="string"&&(Oe.hasOwnProperty(x)?Oe=Oe[x]:(console.warn(`device not specified for "${x}". Using the default device.`),Oe=null));const Se=Oe??(z.apis.IS_NODE_ENV?"cpu":"wasm"),Ye=(0,O.deviceToExecutionProviders)(Se);let rt=V.dtype??be.dtype;if(typeof rt!="string"&&(rt&&rt.hasOwnProperty(x)?rt=rt[x]:(rt=N.DEFAULT_DEVICE_DTYPE_MAPPING[Se]??N.DATA_TYPES.fp32,console.warn(`dtype not specified for "${x}". Using the default dtype (${rt}) for this device (${Se}).`))),rt===N.DATA_TYPES.auto){let dr=be.dtype;typeof dr!="string"&&(dr=dr[x]),dr&&dr!==N.DATA_TYPES.auto&&N.DATA_TYPES.hasOwnProperty(dr)?rt=dr:rt=N.DEFAULT_DEVICE_DTYPE_MAPPING[Se]??N.DATA_TYPES.fp32}const mt=rt;if(N.DEFAULT_DTYPE_SUFFIX_MAPPING.hasOwnProperty(mt)){if(mt===N.DATA_TYPES.fp16&&Se==="webgpu"&&!await(0,N.isWebGpuFp16Supported)())throw new Error(`The device (${Se}) does not support fp16.`)}else throw new Error(`Invalid dtype: ${mt}. Should be one of: ${Object.keys(N.DATA_TYPES).join(", ")}`);const Tt=be.kv_cache_dtype?typeof be.kv_cache_dtype=="string"?be.kv_cache_dtype:be.kv_cache_dtype[mt]??"float32":void 0;if(Tt&&!["float32","float16"].includes(Tt))throw new Error(`Invalid kv_cache_dtype: ${Tt}. Should be one of: float32, float16`);const Lt={dtype:mt,kv_cache_dtype:Tt},Ut=N.DEFAULT_DTYPE_SUFFIX_MAPPING[mt],Dt=`${V.subfolder??""}/${x}${Ut}.onnx`,Vt={...V.session_options};Vt.executionProviders??(Vt.executionProviders=Ye);const Zt=be.free_dimension_overrides;Zt?Vt.freeDimensionOverrides??(Vt.freeDimensionOverrides=Zt):Se.startsWith("webnn")&&!Vt.freeDimensionOverrides&&console.warn('WebNN does not currently support dynamic shapes and requires `free_dimension_overrides` to be set in config.json as a field within "transformers.js_config". When `free_dimension_overrides` is not set, you may experience significant performance degradation.');const sr=(0,w.getModelFile)(_,Dt,!0,V),qt=V.use_external_data_format??be.use_external_data_format;let or=[];if(qt&&(qt===!0||typeof qt=="object"&&qt.hasOwnProperty(x)&&qt[x]===!0)){if(z.apis.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const dr=`${x}${Ut}.onnx_data`,Pr=`${V.subfolder??""}/${dr}`;or.push(new Promise(async($r,Gr)=>{const Ur=await(0,w.getModelFile)(_,Pr,!0,V);$r({path:dr,data:Ur})}))}else Vt.externalData!==void 0&&(or=Vt.externalData.map(async dr=>{if(typeof dr.data=="string"){const Pr=await(0,w.getModelFile)(_,dr.data,!0,V);return{...dr,data:Pr}}return dr}));if(or.length>0&&(Vt.externalData=await Promise.all(or)),Se==="webgpu"){const dr=(0,f.getKeyValueShapes)(V.config,{prefix:"present"});if(Object.keys(dr).length>0&&!(0,O.isONNXProxy)()){const Pr={};for(const $r in dr)Pr[$r]="gpu-buffer";Vt.preferredOutputLocation=Pr}}return{buffer:await sr,session_options:Vt,session_config:Lt}}async function ee(_,x,V){return Object.fromEntries(await Promise.all(Object.keys(x).map(async be=>{const{buffer:Oe,session_options:Se,session_config:Ye}=await T(_,x[be],V),rt=await(0,O.createInferenceSession)(Oe,Se,Ye);return[be,rt]})))}async function J(_,x,V){return Object.fromEntries(await Promise.all(Object.keys(x).map(async be=>{const Oe=await(0,w.getModelJSON)(_,x[be],!1,V);return[be,Oe]})))}function le(_,x){const V=Object.create(null),be=[];for(const Ye of _.inputNames){const rt=x[Ye];if(!(rt instanceof b.Tensor)){be.push(Ye);continue}V[Ye]=(0,O.isONNXProxy)()?rt.clone():rt}if(be.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${be.join(", ")}.`);const Oe=Object.keys(x).length,Se=_.inputNames.length;if(Oe>Se){let Ye=Object.keys(x).filter(rt=>!_.inputNames.includes(rt));console.warn(`WARNING: Too many inputs were provided (${Oe} > ${Se}). The following inputs will be ignored: "${Ye.join(", ")}".`)}return V}async function de(_,x){const V=le(_,x);try{const be=Object.fromEntries(Object.entries(V).map(([Se,Ye])=>[Se,Ye.ort_tensor]));let Oe=await _.run(be);return Oe=fe(Oe),Oe}catch(be){const Oe=Object.fromEntries(Object.entries(V).map(([Se,{type:Ye,dims:rt,data:mt}])=>[Se,{type:Ye,dims:rt,data:mt}]));throw console.error(`An error occurred during model execution: "${be}".`),console.error("Inputs given to model:",Oe),be}}function fe(_){for(let x in _)(0,O.isONNXTensor)(_[x])?_[x]=new b.Tensor(_[x]):typeof _[x]=="object"&&fe(_[x]);return _}function ke(_){if(_ instanceof b.Tensor)return _;if(_.length===0)throw Error("items must be non-empty");if(Array.isArray(_[0])){if(_.some(x=>x.length!==_[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new b.Tensor("int64",BigInt64Array.from(_.flat().map(x=>BigInt(x))),[_.length,_[0].length])}else return new b.Tensor("int64",BigInt64Array.from(_.map(x=>BigInt(x))),[1,_.length])}function xe(_){return new b.Tensor("bool",[_],[1])}async function Le(_,x){let{encoder_outputs:V,input_ids:be,decoder_input_ids:Oe,...Se}=x;if(!V){const rt=(0,U.pick)(x,_.sessions.model.inputNames);V=(await qe(_,rt)).last_hidden_state}return Se.input_ids=Oe,Se.encoder_hidden_states=V,_.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(Se.encoder_attention_mask=x.attention_mask),await Ue(_,Se,!0)}async function qe(_,x){const V=_.sessions.model,be=(0,U.pick)(x,V.inputNames);if(V.inputNames.includes("inputs_embeds")&&!be.inputs_embeds){if(!x.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");be.inputs_embeds=await _.encode_text({input_ids:x.input_ids})}return V.inputNames.includes("token_type_ids")&&!be.token_type_ids&&(be.token_type_ids=new b.Tensor("int64",new BigInt64Array(be.input_ids.data.length),be.input_ids.dims)),await de(V,be)}async function Ue(_,x,V=!1){const be=_.sessions[V?"decoder_model_merged":"model"],{past_key_values:Oe,...Se}=x;if(be.inputNames.includes("use_cache_branch")&&(Se.use_cache_branch=xe(!!Oe)),be.inputNames.includes("position_ids")&&Se.attention_mask&&!Se.position_ids){const rt=_.config.model_type==="paligemma"?1:0;Se.position_ids=he(Se,Oe,rt)}_.addPastKeyValues(Se,Oe);const Ye=(0,U.pick)(Se,be.inputNames);return await de(be,Ye)}function ut({image_token_id:_,inputs_embeds:x,image_features:V,input_ids:be,attention_mask:Oe}){const Se=be.tolist().map(Tt=>Tt.reduce((Lt,Ut,Dt)=>(Ut==_&&Lt.push(Dt),Lt),[])),Ye=Se.reduce((Tt,Lt)=>Tt+Lt.length,0),rt=V.dims[0];if(Ye!==rt)throw new Error(`Image features and image tokens do not match: tokens: ${Ye}, features ${rt}`);let mt=0;for(let Tt=0;TtSe.dims[1])){if(Oert==_.config.image_token_index)){const rt=_.config.num_image_tokens;if(!rt)throw new Error("`num_image_tokens` is missing in the model configuration.");const mt=Se.dims[1]-(Oe-rt);V.input_ids=Se.slice(null,[-mt,null]),V.attention_mask=(0,b.ones)([1,Oe+mt])}}}return V}function Be(_,x,V,be){return V.past_key_values&&(x=x.map(Oe=>[Oe.at(-1)])),{...V,decoder_input_ids:ke(x)}}function et(_,...x){return _.config.is_encoder_decoder?Be(_,...x):Pe(_,...x)}function Xe(_,x,V,be){const Oe=!!V.past_key_values;return be.guidance_scale!==null&&be.guidance_scale>1&&(Oe?V.input_ids=(0,b.cat)([V.input_ids,V.input_ids],0):(V.input_ids=(0,b.cat)([V.input_ids,(0,b.full_like)(V.input_ids,BigInt(be.pad_token_id))],0),V.attention_mask=(0,b.cat)([V.attention_mask,(0,b.full_like)(V.attention_mask,0n)],0))),(Oe||!V.pixel_values)&&(V.pixel_values=(0,b.full)([0,0,3,384,384],1)),Oe&&(V.images_seq_mask=new b.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),V.images_emb_mask=new b.Tensor("bool",new Array(0).fill(!1),[1,1,0])),V}class oe extends Q.Callable{constructor(V,be,Oe){super();ge(this,"main_input_name","input_ids");ge(this,"forward_params",["input_ids","attention_mask"]);this.config=V,this.sessions=be,this.configs=Oe;const Se=C.get(this.constructor),Ye=$.get(Se);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Ye){case D.DecoderOnly:this.can_generate=!0,this._forward=Ue,this._prepare_inputs_for_generation=Pe;break;case D.Seq2Seq:case D.Vision2Seq:case D.Musicgen:this.can_generate=!0,this._forward=Le,this._prepare_inputs_for_generation=Be;break;case D.EncoderDecoder:this._forward=Le;break;case D.ImageTextToText:this.can_generate=!0,this._forward=ue,this._prepare_inputs_for_generation=et;break;case D.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=et;break;case D.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=Xe;break;default:this._forward=qe;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var be;const V=[];for(const Oe of Object.values(this.sessions))(be=Oe==null?void 0:Oe.handler)!=null&&be.dispose&&V.push(Oe.handler.dispose());return await Promise.all(V)}static async from_pretrained(V,{progress_callback:be=null,config:Oe=null,cache_dir:Se=null,local_files_only:Ye=!1,revision:rt="main",model_file_name:mt=null,subfolder:Tt="onnx",device:Lt=null,dtype:Ut=null,use_external_data_format:Dt=null,session_options:Vt={}}={}){let Zt={progress_callback:be,config:Oe,cache_dir:Se,local_files_only:Ye,revision:rt,model_file_name:mt,subfolder:Tt,device:Lt,dtype:Ut,use_external_data_format:Dt,session_options:Vt};const sr=C.get(this),qt=$.get(sr);Oe=Zt.config=await f.AutoConfig.from_pretrained(V,Zt);let or;if(qt===D.DecoderOnly)or=await Promise.all([ee(V,{model:Zt.model_file_name??"model"},Zt),J(V,{generation_config:"generation_config.json"},Zt)]);else if(qt===D.Seq2Seq||qt===D.Vision2Seq)or=await Promise.all([ee(V,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Zt),J(V,{generation_config:"generation_config.json"},Zt)]);else if(qt===D.MaskGeneration)or=await Promise.all([ee(V,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},Zt)]);else if(qt===D.EncoderDecoder)or=await Promise.all([ee(V,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},Zt)]);else if(qt===D.ImageTextToText){const Sr={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Oe.is_encoder_decoder&&(Sr.model="encoder_model"),or=await Promise.all([ee(V,Sr,Zt),J(V,{generation_config:"generation_config.json"},Zt)])}else if(qt===D.Musicgen)or=await Promise.all([ee(V,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},Zt),J(V,{generation_config:"generation_config.json"},Zt)]);else if(qt===D.MultiModality)or=await Promise.all([ee(V,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},Zt),J(V,{generation_config:"generation_config.json"},Zt)]);else if(qt===D.Phi3V)or=await Promise.all([ee(V,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},Zt),J(V,{generation_config:"generation_config.json"},Zt)]);else{if(qt!==D.EncoderOnly){const Sr=sr??(Oe==null?void 0:Oe.model_type);Sr!=="custom"&&console.warn(`Model type for '${Sr}' not found, assuming encoder-only architecture. Please report this at ${v.GITHUB_ISSUE_URL}.`)}or=await Promise.all([ee(V,{model:Zt.model_file_name??"model"},Zt)])}return new this(Oe,...or)}async _call(V){return await this.forward(V)}async forward(V){return await this._forward(this,V)}get generation_config(){var V;return((V=this.configs)==null?void 0:V.generation_config)??null}_get_logits_warper(V){const be=new y.LogitsProcessorList;return V.temperature!==null&&V.temperature!==1&&be.push(new y.TemperatureLogitsWarper(V.temperature)),V.top_k!==null&&V.top_k!==0&&be.push(new y.TopKLogitsWarper(V.top_k)),V.top_p!==null&&V.top_p<1&&be.push(new y.TopPLogitsWarper(V.top_p)),be}_get_logits_processor(V,be,Oe=null){const Se=new y.LogitsProcessorList;if(V.repetition_penalty!==null&&V.repetition_penalty!==1&&Se.push(new y.RepetitionPenaltyLogitsProcessor(V.repetition_penalty)),V.no_repeat_ngram_size!==null&&V.no_repeat_ngram_size>0&&Se.push(new y.NoRepeatNGramLogitsProcessor(V.no_repeat_ngram_size)),V.bad_words_ids!==null&&Se.push(new y.NoBadWordsLogitsProcessor(V.bad_words_ids,V.eos_token_id)),V.min_length!==null&&V.eos_token_id!==null&&V.min_length>0&&Se.push(new y.MinLengthLogitsProcessor(V.min_length,V.eos_token_id)),V.min_new_tokens!==null&&V.eos_token_id!==null&&V.min_new_tokens>0&&Se.push(new y.MinNewTokensLengthLogitsProcessor(be,V.min_new_tokens,V.eos_token_id)),V.forced_bos_token_id!==null&&Se.push(new y.ForcedBOSTokenLogitsProcessor(V.forced_bos_token_id)),V.forced_eos_token_id!==null&&Se.push(new y.ForcedEOSTokenLogitsProcessor(V.max_length,V.forced_eos_token_id)),V.begin_suppress_tokens!==null){const Ye=be>1||V.forced_bos_token_id===null?be:be+1;Se.push(new y.SuppressTokensAtBeginLogitsProcessor(V.begin_suppress_tokens,Ye))}return V.guidance_scale!==null&&V.guidance_scale>1&&Se.push(new y.ClassifierFreeGuidanceLogitsProcessor(V.guidance_scale)),Oe!==null&&Se.extend(Oe),Se}_prepare_generation_config(V,be,Oe=M.GenerationConfig){const Se={...this.config};for(const rt of["decoder","generator","text_config"])rt in Se&&Object.assign(Se,Se[rt]);const Ye=new Oe(Se);return Object.assign(Ye,this.generation_config??{}),V&&Object.assign(Ye,V),be&&Object.assign(Ye,(0,U.pick)(be,Object.getOwnPropertyNames(Ye))),Ye}_get_stopping_criteria(V,be=null){const Oe=new se.StoppingCriteriaList;return V.max_length!==null&&Oe.push(new se.MaxLengthCriteria(V.max_length,this.config.max_position_embeddings??null)),V.eos_token_id!==null&&Oe.push(new se.EosTokenCriteria(V.eos_token_id)),be&&Oe.extend(be),Oe}_validate_model_class(){if(!this.can_generate){const V=[ra,Co,ta,ea],be=C.get(this.constructor),Oe=new Set,Se=this.config.model_type;for(const rt of V){const mt=rt.get(Se);mt&&Oe.add(mt[0])}let Ye=`The current model class (${be}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Oe.size>0&&(Ye+=` Please use the following class instead: ${[...Oe].join(", ")}`),Error(Ye)}}prepare_inputs_for_generation(...V){return this._prepare_inputs_for_generation(this,...V)}_update_model_kwargs_for_generation({generated_input_ids:V,outputs:be,model_inputs:Oe,is_encoder_decoder:Se}){return Oe.past_key_values=this.getPastKeyValues(be,Oe.past_key_values),Oe.input_ids=new b.Tensor("int64",V.flat(),[V.length,1]),Se||(Oe.attention_mask=(0,b.cat)([Oe.attention_mask,(0,b.ones)([Oe.attention_mask.dims[0],1])],1)),Oe.position_ids=null,Oe}_prepare_model_inputs({inputs:V,bos_token_id:be,model_kwargs:Oe}){const Se=(0,U.pick)(Oe,this.forward_params),Ye=this.main_input_name;if(Ye in Se){if(V)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Se[Ye]=V;return{inputs_tensor:Se[Ye],model_inputs:Se,model_input_name:Ye}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:V,model_inputs:be,model_input_name:Oe,generation_config:Se}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!be.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:rt,pixel_values:mt,attention_mask:Tt,...Lt}=be,Ut=await this._prepare_inputs_embeds(be);be={...Lt,...(0,U.pick)(Ut,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Ye}=await qe(this,be);if(Se.guidance_scale!==null&&Se.guidance_scale>1)Ye=(0,b.cat)([Ye,(0,b.full_like)(Ye,0)],0),"attention_mask"in be&&(be.attention_mask=(0,b.cat)([be.attention_mask,(0,b.zeros_like)(be.attention_mask)],0));else if(be.decoder_input_ids){const rt=ke(be.decoder_input_ids).dims[0];if(rt!==Ye.dims[0]){if(Ye.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Ye.dims[0]}) than the decoder inputs (${rt}).`);Ye=(0,b.cat)(Array.from({length:rt},()=>Ye),0)}}return be.encoder_outputs=Ye,be}_prepare_decoder_input_ids_for_generation({batch_size:V,model_input_name:be,model_kwargs:Oe,decoder_start_token_id:Se,bos_token_id:Ye,generation_config:rt}){let{decoder_input_ids:mt,...Tt}=Oe;if(!(mt instanceof b.Tensor)){if(mt)Array.isArray(mt[0])||(mt=Array.from({length:V},()=>mt));else if(Se??(Se=Ye),this.config.model_type==="musicgen")mt=Array.from({length:V*this.config.decoder.num_codebooks},()=>[Se]);else if(Array.isArray(Se)){if(Se.length!==V)throw new Error(`\`decoder_start_token_id\` expcted to have length ${V} but got ${Se.length}`);mt=Se}else mt=Array.from({length:V},()=>[Se]);mt=ke(mt)}return Oe.decoder_attention_mask=(0,b.ones_like)(mt),{input_ids:mt,model_inputs:Tt}}async generate({inputs:V=null,generation_config:be=null,logits_processor:Oe=null,stopping_criteria:Se=null,streamer:Ye=null,...rt}){this._validate_model_class(),be=this._prepare_generation_config(be,rt);let{inputs_tensor:mt,model_inputs:Tt,model_input_name:Lt}=this._prepare_model_inputs({inputs:V,model_kwargs:rt});const Ut=this.config.is_encoder_decoder;Ut&&("encoder_outputs"in Tt||(Tt=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:mt,model_inputs:Tt,model_input_name:Lt,generation_config:be})));let Dt;Ut?{input_ids:Dt,model_inputs:Tt}=this._prepare_decoder_input_ids_for_generation({batch_size:Tt[Lt].dims.at(0),model_input_name:Lt,model_kwargs:Tt,decoder_start_token_id:be.decoder_start_token_id,bos_token_id:be.bos_token_id,generation_config:be}):Dt=Tt[Lt];let Vt=Dt.dims.at(-1);be.max_new_tokens!==null&&(be.max_length=Vt+be.max_new_tokens);const Zt=this._get_logits_processor(be,Vt,Oe),sr=this._get_stopping_criteria(be,Se),qt=Tt[Lt].dims.at(0),or=ie.LogitsSampler.getSampler(be),Sr=new Array(qt).fill(0),Tr=Dt.tolist();Ye&&Ye.put(Tr);let dr,Pr={};for(;;){if(Tt=this.prepare_inputs_for_generation(Tr,Tt,be),dr=await this.forward(Tt),be.output_attentions&&be.return_dict_in_generate){const ns=this.getAttentions(dr);for(const Ps in ns)Ps in Pr||(Pr[Ps]=[]),Pr[Ps].push(ns[Ps])}const Ur=dr.logits.slice(null,-1,null),gs=Zt(Tr,Ur),Wn=[];for(let ns=0;nsns))break;Tt=this._update_model_kwargs_for_generation({generated_input_ids:Wn,outputs:dr,model_inputs:Tt,is_encoder_decoder:Ut})}Ye&&Ye.end();const $r=this.getPastKeyValues(dr,Tt.past_key_values,!0),Gr=new b.Tensor("int64",Tr.flat(),[Tr.length,Tr[0].length]);if(be.return_dict_in_generate)return{sequences:Gr,past_key_values:$r,...Pr};for(const Ur of Object.values(dr))Ur.location==="gpu-buffer"&&Ur.dispose();return Gr}getPastKeyValues(V,be,Oe=!1){const Se=Object.create(null);for(const Ye in V)if(Ye.startsWith("present")){const rt=Ye.replace("present","past_key_values"),mt=Ye.includes("encoder");if(mt&&be?Se[rt]=be[rt]:Se[rt]=V[Ye],be&&(!mt||Oe)){const Tt=be[rt];Tt.location==="gpu-buffer"&&Tt.dispose()}}return Se}getAttentions(V){const be={};for(const Oe of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Se in V)Se.startsWith(Oe)&&(Oe in be||(be[Oe]=[]),be[Oe].push(V[Se]));return be}addPastKeyValues(V,be){var Oe,Se,Ye;if(be)Object.assign(V,be);else{const rt=this.sessions.decoder_model_merged??this.sessions.model,mt=((Oe=rt==null?void 0:rt.config)==null?void 0:Oe.kv_cache_dtype)??"float32",Tt=mt==="float16"?new Uint16Array:[],Lt=((Ye=(Se=V[this.main_input_name]??V.attention_mask)==null?void 0:Se.dims)==null?void 0:Ye[0])??1,Ut=(0,f.getKeyValueShapes)(this.config,{batch_size:Lt});for(const Dt in Ut)V[Dt]=new b.Tensor(mt,Tt,Ut[Dt])}}async encode_image({pixel_values:V}){const be=(await de(this.sessions.vision_encoder,{pixel_values:V})).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 (${be.dims[1]}).`),this.config.num_image_tokens=be.dims[1]),be}async encode_text({input_ids:V}){return(await de(this.sessions.embed_tokens,{input_ids:V})).inputs_embeds}}class Je{}class De extends Je{constructor({last_hidden_state:x,hidden_states:V=null,attentions:be=null}){super(),this.last_hidden_state=x,this.hidden_states=V,this.attentions=be}}class ce extends oe{}class ve extends ce{}class Re extends ce{async _call(x){return new Wr(await super._call(x))}}class je extends ce{async _call(x){return new Xt(await super._call(x))}}class We extends ce{async _call(x){return new jr(await super._call(x))}}class Ne extends ce{async _call(x){return new Xr(await super._call(x))}}class Ze extends oe{}class at extends Ze{}class ft extends Ze{async _call(x){return new Wr(await super._call(x))}}class dt extends Ze{async _call(x){return new Xt(await super._call(x))}}class gt extends Ze{async _call(x){return new jr(await super._call(x))}}class F extends oe{}class ne extends F{}class K extends oe{}class pe extends K{}class Fe extends K{async _call(x){return new Wr(await super._call(x))}}class Qe extends K{async _call(x){return new Xt(await super._call(x))}}class st extends K{async _call(x){return new jr(await super._call(x))}}class pt extends K{async _call(x){return new Xr(await super._call(x))}}class It extends oe{}class St extends It{}class Ft extends It{async _call(x){return new Wr(await super._call(x))}}class At extends It{async _call(x){return new Xt(await super._call(x))}}class nr extends It{async _call(x){return new jr(await super._call(x))}}class gr extends It{async _call(x){return new Xr(await super._call(x))}}class kr extends oe{}class Ar extends kr{}class Qr extends kr{async _call(x){return new Wr(await super._call(x))}}class os extends kr{async _call(x){return new Xt(await super._call(x))}}class Xs extends kr{async _call(x){return new jr(await super._call(x))}}class zs extends kr{async _call(x){return new Xr(await super._call(x))}}class Ms extends oe{}class Nt extends Ms{}class Qs extends Ms{async _call(x){return new Wr(await super._call(x))}}class ks extends Ms{async _call(x){return new Xt(await super._call(x))}}class Bs extends Ms{async _call(x){return new jr(await super._call(x))}}class Ss extends Ms{async _call(x){return new Xr(await super._call(x))}}class is extends oe{}class $s extends is{}class cs extends is{async _call(x){return new Wr(await super._call(x))}}class As extends is{async _call(x){return new Xt(await super._call(x))}}class Ys extends is{async _call(x){return new jr(await super._call(x))}}class as extends is{async _call(x){return new Xr(await super._call(x))}}class nt extends oe{}class _t extends nt{}class Ot extends nt{async _call(x){return new Wr(await super._call(x))}}class lr extends nt{async _call(x){return new Xt(await super._call(x))}}class bs extends nt{async _call(x){return new jr(await super._call(x))}}class tr extends nt{async _call(x){return new Xr(await super._call(x))}}class ts extends oe{}class Rs extends ts{}class Js extends ts{async _call(x){return new Xt(await super._call(x))}}class Ns extends ts{async _call(x){return new jr(await super._call(x))}}class vs extends ts{async _call(x){return new Xr(await super._call(x))}}class Sn extends ts{async _call(x){return new Wr(await super._call(x))}}class js extends oe{}class $n extends js{}class Jn extends js{async _call(x){return new Wr(await super._call(x))}}class Us extends js{async _call(x){return new Xt(await super._call(x))}}class Ts extends js{async _call(x){return new jr(await super._call(x))}}class ls extends oe{}class mn extends ls{}class Zs extends ls{async _call(x){return new Wr(await super._call(x))}}class _n extends ls{async _call(x){return new Xt(await super._call(x))}}class en extends ls{async _call(x){return new Xr(await super._call(x))}}class xs extends oe{}class zt extends xs{}class fn extends xs{async _call(x){return new Wr(await super._call(x))}}class An extends xs{async _call(x){return new Xt(await super._call(x))}}class In extends xs{async _call(x){return new jr(await super._call(x))}}class On extends xs{async _call(x){return new Xr(await super._call(x))}}class Vs extends oe{}class Fn extends Vs{}class gn extends Vs{async _call(x){return new Wr(await super._call(x))}}class Dn extends Vs{async _call(x){return new Xt(await super._call(x))}}class ir extends Vs{async _call(x){return new Xr(await super._call(x))}}class Yr extends oe{}class wn extends Yr{}class Ln extends Yr{async _call(x){return new Xt(await super._call(x))}}class yn extends Yr{async _call(x){return new Xr(await super._call(x))}}class Mn extends Yr{async _call(x){return new Wr(await super._call(x))}}class Ee extends oe{constructor(){super(...arguments);ge(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class P extends Ee{}class H extends Ee{}class ae extends oe{}class Me extends ae{}class Ce extends ae{}class He extends oe{}class ct extends He{}class yt extends He{}class ht extends oe{}class it extends ht{}class Pt extends ht{}class hr extends ht{async _call(x){return new Xt(await super._call(x))}}class rr extends oe{}class $e extends rr{}class wr extends rr{}class Rr extends rr{async _call(x){return new Xt(await super._call(x))}}class Jr extends rr{}class Zr extends oe{}class Bt extends Zr{}class Nr extends Zr{}class ps extends oe{}class er extends ps{}class mr extends ps{}class vt extends oe{}class yr extends vt{}class Es extends vt{async _call(x){return new Wr(await super._call(x))}}class Fr extends vt{async _call(x){return new Xt(await super._call(x))}}class qr extends vt{async _call(x){return new jr(await super._call(x))}}class Mt extends vt{async _call(x){return new Xr(await super._call(x))}}class br extends oe{}class ze extends br{}class wt extends br{async _call(x){return new Wr(await super._call(x))}}class rs extends br{async _call(x){return new Xt(await super._call(x))}}class tn extends br{async _call(x){return new jr(await super._call(x))}}class Zn extends br{async _call(x){return new Xr(await super._call(x))}}class bn extends oe{}class Qt extends bn{}class Ta extends bn{async _call(x){return new Wr(await super._call(x))}}class No extends bn{async _call(x){return new Xt(await super._call(x))}}class xa extends bn{async _call(x){return new jr(await super._call(x))}}class Ea extends bn{async _call(x){return new Xr(await super._call(x))}}class jo extends oe{}class Pa extends jo{}class ss extends jo{}class Uo extends oe{constructor(){super(...arguments);ge(this,"requires_attention_mask",!1);ge(this,"main_input_name","input_features");ge(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Ca extends Uo{}class Ws extends Uo{_prepare_generation_config(x,V){return super._prepare_generation_config(x,V,W.WhisperGenerationConfig)}_retrieve_init_tokens(x){const V=[x.decoder_start_token_id];let be=x.language;const Oe=x.task;if(x.is_multilingual){be||(console.warn("No language specified - defaulting to English (en)."),be="en");const Ye=`<|${(0,Y.whisper_language_to_code)(be)}|>`;V.push(x.lang_to_id[Ye]),V.push(x.task_to_id[Oe??"transcribe"])}else if(be||Oe)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!x.return_timestamps&&x.no_timestamps_token_id&&V.at(-1)!==x.no_timestamps_token_id?V.push(x.no_timestamps_token_id):x.return_timestamps&&V.at(-1)===x.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),V.pop()),V.filter(Se=>Se!=null)}async generate({inputs:x=null,generation_config:V=null,logits_processor:be=null,stopping_criteria:Oe=null,...Se}){V=this._prepare_generation_config(V,Se);const Ye=Se.decoder_input_ids??this._retrieve_init_tokens(V);if(V.return_timestamps&&(be??(be=new y.LogitsProcessorList),be.push(new y.WhisperTimeStampLogitsProcessor(V,Ye))),V.begin_suppress_tokens&&(be??(be=new y.LogitsProcessorList),be.push(new y.SuppressTokensAtBeginLogitsProcessor(V.begin_suppress_tokens,Ye.length))),V.return_token_timestamps){if(!V.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.");V.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),V.output_attentions=!0,V.return_dict_in_generate=!0}const rt=await super.generate({inputs:x,generation_config:V,logits_processor:be,decoder_input_ids:Ye,...Se});return V.return_token_timestamps&&(rt.token_timestamps=this._extract_token_timestamps(rt,V.alignment_heads,V.num_frames)),rt}_extract_token_timestamps(x,V,be=null,Oe=.02){if(!x.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`.");be==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 Se=this.config.median_filter_width;Se===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Se=7);const Ye=x.cross_attentions,rt=Array.from({length:this.config.decoder_layers},(sr,qt)=>(0,b.cat)(Ye.map(or=>or[qt]),2)),mt=(0,b.stack)(V.map(([sr,qt])=>{if(sr>=rt.length)throw new Error(`Layer index ${sr} is out of bounds for cross attentions (length ${rt.length}).`);return be?rt[sr].slice(null,qt,null,[0,be]):rt[sr].slice(null,qt)})).transpose(1,0,2,3),[Tt,Lt]=(0,b.std_mean)(mt,-2,0,!0),Ut=mt.clone();for(let sr=0;sror[Gr+1]-or[Gr]),dr=(0,U.mergeArrays)([1],Tr).map($r=>!!$r),Pr=[];for(let $r=0;$rDt.findIndex(Vt=>Vt==Se)),mt=rt.every(Dt=>Dt===-1),Tt=rt.every(Dt=>Dt!==-1);if(!mt&&!Tt)throw new Error("Every input should contain either 0 or 1 image token.");if(mt)return{inputs_embeds:x,attention_mask:Oe};const Lt=[],Ut=[];for(let Dt=0;DtArray.from({length:x.dims[0]},Tr=>Array.from({length:x.dims[1]},dr=>1))),Zt=V?V.tolist():[],sr=be?be.tolist():[];let qt=0,or=0;for(let Sr=0;SrDt[Sr][Dr]==1),Pr=Tr.reduce((xr,Dr,Ds)=>(Dr==mt&&xr.push(Ds),xr),[]).map(xr=>Tr[xr+1]),$r=Pr.filter(xr=>xr==Ye).length,Gr=Pr.filter(xr=>xr==rt).length;let Ur=[],gs=0,Wn=$r,ua=Gr;for(let xr=0;xrKr>gs&&qs==Ye),Ds=Tr.findIndex((qs,Kr)=>Kr>gs&&qs==rt),Hs=Wn>0&&Dr!==-1?Dr:Tr.length+1,ln=ua>0&&Ds!==-1?Ds:Tr.length+1;let $o,pa,bc,Ao;Hs0?(0,q.max)(Ur.at(-1))[0]+1:0;Ur.push(Array.from({length:3*_a},(qs,Kr)=>Io+Kr%_a));const fa=_a+Io,Oo=gp*ha*ma,Up=Array.from({length:Oo},(qs,Kr)=>fa+Math.floor(Kr/(ha*ma))),wp=Array.from({length:Oo},(qs,Kr)=>fa+Math.floor(Kr/ma)%ha),vc=Array.from({length:Oo},(qs,Kr)=>fa+Kr%ma);Ur.push([Up,wp,vc].flat()),gs=$o+Oo}if(gs0?(0,q.max)(Ur.at(-1))[0]+1:0,Dr=Tr.length-gs;Ur.push(Array.from({length:3*Dr},(Ds,Hs)=>xr+Hs%Dr))}const ns=Ur.reduce((xr,Dr)=>xr+Dr.length,0),Ps=new Array(ns);let da=0;for(let xr=0;xr<3;++xr)for(let Dr=0;DrUt[qt%Ut.length]),Zt=Array.from({length:Dt[0]},(sr,qt)=>(0,q.max)(Ut.subarray(Dt[1]*qt,Dt[1]*(qt+1)))[0]+1+Dt[1]);return[new b.Tensor("int64",Vt,[3,...Dt]),new b.Tensor("int64",Zt,[Zt.length,1])]}else{const[Ut,Dt]=x.dims,Vt=BigInt64Array.from({length:3*Ut*Dt},(Zt,sr)=>BigInt(Math.floor(sr%Dt/Ut)));return[new b.Tensor("int64",Vt,[3,...x.dims]),(0,b.zeros)([Ut,1])]}}async encode_image({pixel_values:x,image_grid_thw:V}){return(await de(this.sessions.vision_encoder,{pixel_values:x,grid_thw:V})).image_features}_merge_input_ids_with_image_features(x){return ut({image_token_id:this.config.image_token_id,...x})}prepare_inputs_for_generation(x,V,be){if(V.attention_mask&&!V.position_ids)if(!V.past_key_values)[V.position_ids,V.rope_deltas]=this.get_rope_index(V.input_ids,V.image_grid_thw,V.video_grid_thw,V.attention_mask);else{V.pixel_values=null;const Oe=BigInt(Object.values(V.past_key_values)[0].dims.at(-2)),Se=V.rope_deltas.map(Ye=>Oe+Ye);V.position_ids=(0,b.stack)([Se,Se,Se],0)}return V}}class pi extends oe{}class Tl extends pi{}class xl extends pi{}class hi extends oe{}class El extends hi{}class Pl extends hi{}class mi extends oe{}class Cl extends mi{}class kl extends mi{}class _i extends oe{}class Sl extends _i{}class $l extends _i{}class fi extends oe{}class Al extends fi{}class Il extends fi{}class gi extends oe{}class wi extends gi{}class Ol extends gi{async _call(x){return new Xt(await super._call(x))}}class io extends oe{}class yi extends io{}class Fl extends io{async _call(x){return new Xt(await super._call(x))}}class Dl extends oe{}class Ll extends Dl{}class Mi extends oe{}class zl extends Mi{}class Bl extends Mi{async _call(x){return new Xt(await super._call(x))}}class bi extends oe{}class Rl extends bi{}class vi extends oe{}class Nl extends vi{}class Bc extends vi{async _call(x){return new Xt(await super._call(x))}}class jl extends oe{}class Ul extends jl{}class ds extends oe{}class Vl extends ds{}class Wl extends ds{async _call(x){return new Xt(await super._call(x))}}class Gl extends oe{}class Kl extends Gl{async _call(x){return new fp(await super._call(x))}}class Ti extends oe{}class Hl extends Ti{}class ql extends Ti{async _call(x){return new Xt(await super._call(x))}}class xi extends oe{}class Xl extends xi{}class Ql extends xi{async _call(x){return new Xt(await super._call(x))}}class Yl extends oe{}class Jl extends Yl{}class Zl extends Yl{}class Ei extends oe{}class eu extends Ei{}class tu extends Ei{}class Pi extends oe{}class Rc extends Pi{}class sn extends Pi{async _call(x){return new Xt(await super._call(x))}}class Is extends oe{}class nn extends Is{}class Ci extends Is{async _call(x){return new Vr(await super._call(x))}}class Gs extends Is{async _call(x){return new ru(await super._call(x))}}class Vr extends Je{constructor({logits:x,pred_boxes:V}){super(),this.logits=x,this.pred_boxes=V}}class ru extends Je{constructor({logits:x,pred_boxes:V,pred_masks:be}){super(),this.logits=x,this.pred_boxes=V,this.pred_masks=be}}class ao extends oe{}class su extends ao{}class Nc extends ao{async _call(x){return new Bn(await super._call(x))}}class Bn extends Je{constructor({logits:x,pred_boxes:V}){super(),this.logits=x,this.pred_boxes=V}}class lo extends oe{}class ki extends lo{}class nu extends lo{async _call(x){return new ou(await super._call(x))}}class ou extends Vr{}class uo extends oe{}class Si extends uo{}class iu extends uo{async _call(x){return new Xt(await super._call(x))}}class co extends oe{}class au extends co{}class po extends co{async _call(x){return new Xt(await super._call(x))}}class ho extends oe{}class lu extends ho{}class uu extends ho{async _call(x){return new Xt(await super._call(x))}}class du extends oe{}class cu extends du{}class $i extends du{async _call(x){return new Xt(await super._call(x))}}class Tn extends oe{}class pu extends Tn{}class Ai extends Tn{}class Ii extends oe{}class hu extends Ii{}class mu extends Ii{}class jc extends oe{}class _u extends jc{}class mo extends oe{}class Uc extends mo{}class fu extends mo{}class _o extends mo{}class gu extends oe{}class fo extends gu{}class go extends oe{}class Oi extends go{}class wu extends go{}class Fi extends oe{}class Di extends Fi{}class Vc extends Fi{}class yu extends oe{}class Wc extends yu{}class Li extends oe{}class Mu extends Li{}class bu extends Li{async _call(x){return new Xt(await super._call(x))}}class wo extends oe{}class vu extends wo{}class Tu extends wo{async _call(x){return new Xt(await super._call(x))}}class yo extends oe{}class xu extends yo{}class Eu extends yo{async _call(x){return new Xt(await super._call(x))}}class Pu extends oe{}class Cu extends Pu{}class ku extends Pu{async _call(x){return new Su(await super._call(x))}}class Su extends Je{constructor({logits:x,pred_boxes:V}){super(),this.logits=x,this.pred_boxes=V}}class Gc extends oe{}class $u extends Gc{async get_image_embeddings({pixel_values:x}){return await qe(this,{pixel_values:x})}async forward(x){if((!x.image_embeddings||!x.image_positional_embeddings)&&(x={...x,...await this.get_image_embeddings(x)}),!x.input_labels&&x.input_points){const be=x.input_points.dims.slice(0,-1),Oe=be.reduce((Se,Ye)=>Se*Ye,1);x.input_labels=new b.Tensor("int64",new BigInt64Array(Oe).fill(1n),be)}const V={image_embeddings:x.image_embeddings,image_positional_embeddings:x.image_positional_embeddings};return x.input_points&&(V.input_points=x.input_points),x.input_labels&&(V.input_labels=x.input_labels),x.input_boxes&&(V.input_boxes=x.input_boxes),await de(this.sessions.prompt_encoder_mask_decoder,V)}async _call(x){return new Au(await super._call(x))}}class Au extends Je{constructor({iou_scores:x,pred_masks:V}){super(),this.iou_scores=x,this.pred_masks=V}}class zi extends oe{}class Iu extends zi{}class Ou extends zi{}class Fu extends oe{}class Mo extends Fu{}class Rn extends Fu{}class Os extends oe{}class Du extends Os{}class Lu extends Os{async _call(x){return new an(await super._call(x))}}class zu extends Os{async _call(x){return new Xt(await super._call(x))}}class Bu extends Os{async _call(x){return new jr(await super._call(x))}}class bo extends oe{}class Ru extends bo{}class Nu extends bo{async _call(x){return new jr(await super._call(x))}}class ju extends oe{}class Kc extends ju{}class vo extends oe{}class Bi extends vo{}class Uu extends vo{async _call(x){return new an(await super._call(x))}}class Vu extends vo{async _call(x){return new Xt(await super._call(x))}}class Nn extends oe{}class Hc extends Nn{}class Wu extends Nn{async _call(x){return new an(await super._call(x))}}class Gu extends Nn{async _call(x){return new Xt(await super._call(x))}}class qc extends Nn{async _call(x){return new jr(await super._call(x))}}class To extends oe{}class Ku extends To{}class Hu extends To{async _call(x){return new an(await super._call(x))}}class qu extends To{async _call(x){return new Xt(await super._call(x))}}class Rp extends oe{}class Xu extends Os{}class Qu extends Os{async _call(x){return new an(await super._call(x))}}class Yu extends Os{async _call(x){return new Xt(await super._call(x))}}class jn extends oe{}class Ju extends jn{}class Zu extends jn{async _call(x){return new an(await super._call(x))}}class ed extends jn{async _call(x){return new Xt(await super._call(x))}}class td extends jn{async _call(x){return new yc(await super._call(x))}}class Xc extends jn{async _call(x){return new jr(await super._call(x))}}class xo extends oe{}class Qc extends xo{}class rd extends xo{}class sd extends xo{async generate_speech(x,V,{threshold:be=.5,minlenratio:Oe=0,maxlenratio:Se=20,vocoder:Ye=null}={}){const rt={input_ids:x},{encoder_outputs:mt,encoder_attention_mask:Tt}=await qe(this,rt),Lt=mt.dims[1]/this.config.reduction_factor,Ut=Math.floor(Lt*Se),Dt=Math.floor(Lt*Oe),Vt=this.config.num_mel_bins;let Zt=[],sr=null,qt=null,or=0;for(;;){++or;const dr=xe(!!qt);let Pr;qt?Pr=qt.output_sequence_out:Pr=new b.Tensor("float32",new Float32Array(Vt),[1,1,Vt]);let $r={use_cache_branch:dr,output_sequence:Pr,encoder_attention_mask:Tt,speaker_embeddings:V,encoder_hidden_states:mt};this.addPastKeyValues($r,sr),qt=await de(this.sessions.decoder_model_merged,$r),sr=this.getPastKeyValues(qt,sr);const{prob:Gr,spectrum:Ur}=qt;if(Zt.push(Ur),or>=Dt&&(Array.from(Gr.data).filter(gs=>gs>=be).length>0||or>=Ut))break}const Sr=(0,b.cat)(Zt),{waveform:Tr}=await de(Ye.sessions.model,{spectrogram:Sr});return{spectrogram:Sr,waveform:Tr}}}class Yc extends oe{constructor(){super(...arguments);ge(this,"main_input_name","spectrogram")}}class nd extends oe{}class od extends nd{}class Ri extends oe{}class id extends Ri{}class Jc extends Ri{}class fs extends oe{}class Fs extends fs{}class on extends fs{}class Ks extends oe{}class ad extends Ks{}class ld extends Ks{}class Eo extends oe{}class ud extends Eo{}class dd extends Eo{static async from_pretrained(x,V={}){return super.from_pretrained(x,{model_file_name:"text_model",...V})}}class cd extends Eo{static async from_pretrained(x,V={}){return super.from_pretrained(x,{model_file_name:"audio_model",...V})}}class pd extends oe{}class Ni extends pd{async _call(x){return new Mc(await super._call(x))}}class Po extends oe{}class Zc extends Po{}class ep extends Po{}class es extends Po{}class ji extends oe{}class hd extends ji{}class Ui extends ji{}class Vi extends oe{}class Un extends Vi{}class md extends Vi{async _call(x){return new Xt(await super._call(x))}}class Wi extends oe{}class tp extends Wi{}class rp extends Wi{}class Gi extends oe{constructor(){super(...arguments);ge(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(V){const[be,Oe]=V.dims,Se=this.config.decoder.num_codebooks,Ye=Oe-Se;let rt=0;for(let Lt=0;Lt0&&Vt<=Ye&&(V.data[rt++]=V.data[Lt])}const mt=Math.floor(be/Se),Tt=rt/(mt*Se);return new b.Tensor(V.type,V.data.slice(0,rt),[mt,Se,Tt])}prepare_inputs_for_generation(V,be,Oe){let Se=structuredClone(V);for(let rt=0;rt=mt&&(Se[rt][mt]=BigInt(this.config.decoder.pad_token_id));return Oe.guidance_scale!==null&&Oe.guidance_scale>1&&(Se=Se.concat(Se)),super.prepare_inputs_for_generation(Se,be,Oe)}async generate(V){const be=await super.generate(V),Oe=this._apply_and_filter_by_delay_pattern_mask(be).unsqueeze_(0),{audio_values:Se}=await de(this.sessions.encodec_decode,{audio_codes:Oe});return Se}}class Ki extends oe{}class Hi extends Ki{}class _d extends Ki{async _call(x){return new Xt(await super._call(x))}}class qi extends oe{}class fd extends qi{}class gd extends qi{async _call(x){return new Xt(await super._call(x))}}class Xi extends oe{}class Qi extends Xi{}class wd extends Xi{async _call(x){return new Xt(await super._call(x))}}class yd extends oe{}class Yi extends yd{}class Md extends yd{async _call(x){return new Xt(await super._call(x))}}class bd extends oe{}class vd extends bd{}class sp extends oe{}class Td extends sp{constructor(...V){super(...V);ge(this,"forward_params",["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"]);this._generation_mode="text"}async forward(V){const be=this._generation_mode??"text";let Oe;if(be==="text"||!V.past_key_values){const Tt=this.sessions.prepare_inputs_embeds,Lt=(0,U.pick)(V,Tt.inputNames);Oe=await de(Tt,Lt)}else{const Tt=this.sessions.gen_img_embeds,Lt=(0,U.pick)({image_ids:V.input_ids},Tt.inputNames);Oe=await de(Tt,Lt)}const Se={...V,...Oe},Ye=await Ue(this,Se),rt=this.sessions[be==="text"?"lm_head":"gen_head"];if(!rt)throw new Error(`Unable to find "${rt}" generation head`);const mt=await de(rt,(0,U.pick)(Ye,rt.inputNames));return{...Oe,...Ye,...mt}}async generate(V){return this._generation_mode="text",super.generate(V)}async generate_images(V){this._generation_mode="image";const be=(V.inputs??V[this.main_input_name]).dims[1],Se=(await super.generate(V)).slice(null,[be,null]),Ye=this.sessions.image_decode,{decoded_image:rt}=await de(Ye,{generated_tokens:Se}),mt=rt.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),Tt=[];for(const Lt of mt){const Ut=L.RawImage.fromTensor(Lt);Tt.push(Ut)}return Tt}}class xd extends Je{constructor({char_logits:x,bpe_logits:V,wp_logits:be}){super(),this.char_logits=x,this.bpe_logits=V,this.wp_logits=be}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class Ed extends oe{}class np extends Ed{async _call(x){return new xd(await super._call(x))}}class Ji extends oe{}class Pd extends Ji{}class op extends Ji{}class Zi extends oe{}class Cd extends Zi{}class kd extends Zi{}class _r{static async from_pretrained(x,{progress_callback:V=null,config:be=null,cache_dir:Oe=null,local_files_only:Se=!1,revision:Ye="main",model_file_name:rt=null,subfolder:mt="onnx",device:Tt=null,dtype:Lt=null,use_external_data_format:Ut=null,session_options:Dt={}}={}){const Vt={progress_callback:V,config:be,cache_dir:Oe,local_files_only:Se,revision:Ye,model_file_name:rt,subfolder:mt,device:Tt,dtype:Lt,use_external_data_format:Ut,session_options:Dt};if(Vt.config=await f.AutoConfig.from_pretrained(x,Vt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const Zt of this.MODEL_CLASS_MAPPINGS){const sr=Zt.get(Vt.config.model_type);if(sr)return await sr[1].from_pretrained(x,Vt)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${Vt.config.model_type}", attempting to construct from base class.`),await oe.from_pretrained(x,Vt);throw Error(`Unsupported model type: ${Vt.config.model_type}`)}}ge(_r,"MODEL_CLASS_MAPPINGS",null),ge(_r,"BASE_IF_FAIL",!1);const ip=new Map([["bert",["BertModel",ve]],["modernbert",["ModernBertModel",at]],["nomic_bert",["NomicBertModel",ne]],["roformer",["RoFormerModel",pe]],["electra",["ElectraModel",Ar]],["esm",["EsmModel",$n]],["convbert",["ConvBertModel",St]],["camembert",["CamembertModel",Nt]],["deberta",["DebertaModel",$s]],["deberta-v2",["DebertaV2Model",_t]],["mpnet",["MPNetModel",zt]],["albert",["AlbertModel",wn]],["distilbert",["DistilBertModel",Rs]],["roberta",["RobertaModel",yr]],["xlm",["XLMModel",ze]],["xlm-roberta",["XLMRobertaModel",Qt]],["clap",["ClapModel",ud]],["clip",["CLIPModel",za]],["clipseg",["CLIPSegModel",Wa]],["chinese_clip",["ChineseCLIPModel",Na]],["siglip",["SiglipModel",Ra]],["jina_clip",["JinaCLIPModel",ja]],["mobilebert",["MobileBertModel",mn]],["squeezebert",["SqueezeBertModel",Fn]],["wav2vec2",["Wav2Vec2Model",Du]],["wav2vec2-bert",["Wav2Vec2BertModel",Ku]],["unispeech",["UniSpeechModel",Bi]],["unispeech-sat",["UniSpeechSatModel",Hc]],["hubert",["HubertModel",Xu]],["wavlm",["WavLMModel",Ju]],["audio-spectrogram-transformer",["ASTModel",Pa]],["vits",["VitsModel",Ni]],["pyannote",["PyAnnoteModel",Ru]],["wespeaker-resnet",["WeSpeakerResNetModel",Kc]],["detr",["DetrModel",nn]],["rt_detr",["RTDetrModel",su]],["table-transformer",["TableTransformerModel",ki]],["vit",["ViTModel",wi]],["ijepa",["IJepaModel",yi]],["pvt",["PvtModel",zl]],["vit_msn",["ViTMSNModel",Nl]],["vit_mae",["ViTMAEModel",Rl]],["groupvit",["GroupViTModel",Ul]],["fastvit",["FastViTModel",Vl]],["mobilevit",["MobileViTModel",Hl]],["mobilevitv2",["MobileViTV2Model",Xl]],["owlvit",["OwlViTModel",Jl]],["owlv2",["Owlv2Model",eu]],["beit",["BeitModel",Rc]],["deit",["DeiTModel",Si]],["hiera",["HieraModel",au]],["convnext",["ConvNextModel",Mu]],["convnextv2",["ConvNextV2Model",vu]],["dinov2",["Dinov2Model",xu]],["resnet",["ResNetModel",lu]],["swin",["SwinModel",cu]],["swin2sr",["Swin2SRModel",pu]],["donut-swin",["DonutSwinModel",Wc]],["yolos",["YolosModel",Cu]],["dpt",["DPTModel",hu]],["glpn",["GLPNModel",Di]],["hifigan",["SpeechT5HifiGan",Yc]],["efficientnet",["EfficientNetModel",Un]],["decision_transformer",["DecisionTransformerModel",vd]],["patchtst",["PatchTSTForPrediction",Pd]],["patchtsmixer",["PatchTSMixerForPrediction",Cd]],["mobilenet_v1",["MobileNetV1Model",Hi]],["mobilenet_v2",["MobileNetV2Model",fd]],["mobilenet_v3",["MobileNetV3Model",Qi]],["mobilenet_v4",["MobileNetV4Model",Yi]],["maskformer",["MaskFormerModel",Oi]],["mgp-str",["MgpstrForSceneTextRecognition",np]]]),Np=new Map([["t5",["T5Model",P]],["longt5",["LongT5Model",Me]],["mt5",["MT5Model",ct]],["bart",["BartModel",it]],["mbart",["MBartModel",$e]],["marian",["MarianModel",Iu]],["whisper",["WhisperModel",Ca]],["m2m_100",["M2M100Model",Mo]],["blenderbot",["BlenderbotModel",Bt]],["blenderbot-small",["BlenderbotSmallModel",er]]]),ap=new Map([["bloom",["BloomModel",Cl]],["jais",["JAISModel",Ha]],["gpt2",["GPT2Model",Ka]],["gptj",["GPTJModel",ei]],["gpt_bigcode",["GPTBigCodeModel",Za]],["gpt_neo",["GPTNeoModel",Xa]],["gpt_neox",["GPTNeoXModel",Ya]],["codegen",["CodeGenModel",tl]],["llama",["LlamaModel",rl]],["exaone",["ExaoneModel",nl]],["olmo",["OlmoModel",ll]],["olmo2",["Olmo2Model",ul]],["mobilellm",["MobileLLMModel",il]],["granite",["GraniteModel",pl]],["cohere",["CohereModel",hl]],["gemma",["GemmaModel",_l]],["gemma2",["Gemma2Model",gl]],["openelm",["OpenELMModel",yl]],["qwen2",["Qwen2Model",bl]],["phi",["PhiModel",Tl]],["phi3",["Phi3Model",El]],["mpt",["MptModel",Sl]],["opt",["OPTModel",Al]],["mistral",["MistralModel",id]],["starcoder2",["Starcoder2Model",Fs]],["falcon",["FalconModel",ad]],["stablelm",["StableLmModel",hd]]]),ea=new Map([["speecht5",["SpeechT5ForSpeechToText",rd]],["whisper",["WhisperForConditionalGeneration",Ws]],["moonshine",["MoonshineForConditionalGeneration",ka]]]),Sd=new Map([["speecht5",["SpeechT5ForTextToSpeech",sd]]]),$d=new Map([["vits",["VitsModel",Ni]],["musicgen",["MusicgenForConditionalGeneration",Gi]]]),Ad=new Map([["bert",["BertForSequenceClassification",je]],["modernbert",["ModernBertForSequenceClassification",dt]],["roformer",["RoFormerForSequenceClassification",Qe]],["electra",["ElectraForSequenceClassification",os]],["esm",["EsmForSequenceClassification",Us]],["convbert",["ConvBertForSequenceClassification",At]],["camembert",["CamembertForSequenceClassification",ks]],["deberta",["DebertaForSequenceClassification",As]],["deberta-v2",["DebertaV2ForSequenceClassification",lr]],["mpnet",["MPNetForSequenceClassification",An]],["albert",["AlbertForSequenceClassification",Ln]],["distilbert",["DistilBertForSequenceClassification",Js]],["roberta",["RobertaForSequenceClassification",Fr]],["xlm",["XLMForSequenceClassification",rs]],["xlm-roberta",["XLMRobertaForSequenceClassification",No]],["bart",["BartForSequenceClassification",hr]],["mbart",["MBartForSequenceClassification",Rr]],["mobilebert",["MobileBertForSequenceClassification",_n]],["squeezebert",["SqueezeBertForSequenceClassification",Dn]]]),Id=new Map([["bert",["BertForTokenClassification",We]],["modernbert",["ModernBertForTokenClassification",gt]],["roformer",["RoFormerForTokenClassification",st]],["electra",["ElectraForTokenClassification",Xs]],["esm",["EsmForTokenClassification",Ts]],["convbert",["ConvBertForTokenClassification",nr]],["camembert",["CamembertForTokenClassification",Bs]],["deberta",["DebertaForTokenClassification",Ys]],["deberta-v2",["DebertaV2ForTokenClassification",bs]],["mpnet",["MPNetForTokenClassification",In]],["distilbert",["DistilBertForTokenClassification",Ns]],["roberta",["RobertaForTokenClassification",qr]],["xlm",["XLMForTokenClassification",tn]],["xlm-roberta",["XLMRobertaForTokenClassification",xa]]]),ta=new Map([["t5",["T5ForConditionalGeneration",H]],["longt5",["LongT5ForConditionalGeneration",Ce]],["mt5",["MT5ForConditionalGeneration",yt]],["bart",["BartForConditionalGeneration",Pt]],["mbart",["MBartForConditionalGeneration",wr]],["marian",["MarianMTModel",Ou]],["m2m_100",["M2M100ForConditionalGeneration",Rn]],["blenderbot",["BlenderbotForConditionalGeneration",Nr]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",mr]]]),ra=new Map([["bloom",["BloomForCausalLM",kl]],["gpt2",["GPT2LMHeadModel",_s]],["jais",["JAISLMHeadModel",qa]],["gptj",["GPTJForCausalLM",ti]],["gpt_bigcode",["GPTBigCodeForCausalLM",si]],["gpt_neo",["GPTNeoForCausalLM",Qa]],["gpt_neox",["GPTNeoXForCausalLM",Ja]],["codegen",["CodeGenForCausalLM",so]],["llama",["LlamaForCausalLM",sl]],["exaone",["ExaoneForCausalLM",ol]],["olmo",["OlmoForCausalLM",zc]],["olmo2",["Olmo2ForCausalLM",dl]],["mobilellm",["MobileLLMForCausalLM",al]],["granite",["GraniteForCausalLM",ur]],["cohere",["CohereForCausalLM",ml]],["gemma",["GemmaForCausalLM",fl]],["gemma2",["Gemma2ForCausalLM",wl]],["openelm",["OpenELMForCausalLM",Ml]],["qwen2",["Qwen2ForCausalLM",zn]],["phi",["PhiForCausalLM",xl]],["phi3",["Phi3ForCausalLM",Pl]],["mpt",["MptForCausalLM",$l]],["opt",["OPTForCausalLM",Il]],["mbart",["MBartForCausalLM",Jr]],["mistral",["MistralForCausalLM",Jc]],["starcoder2",["Starcoder2ForCausalLM",on]],["falcon",["FalconForCausalLM",ld]],["trocr",["TrOCRForCausalLM",od]],["stablelm",["StableLmForCausalLM",Ui]],["phi3_v",["Phi3VForCausalLM",Ko]]]),lp=new Map([["multi_modality",["MultiModalityCausalLM",Td]]]),up=new Map([["bert",["BertForMaskedLM",Re]],["modernbert",["ModernBertForMaskedLM",ft]],["roformer",["RoFormerForMaskedLM",Fe]],["electra",["ElectraForMaskedLM",Qr]],["esm",["EsmForMaskedLM",Jn]],["convbert",["ConvBertForMaskedLM",Ft]],["camembert",["CamembertForMaskedLM",Qs]],["deberta",["DebertaForMaskedLM",cs]],["deberta-v2",["DebertaV2ForMaskedLM",Ot]],["mpnet",["MPNetForMaskedLM",fn]],["albert",["AlbertForMaskedLM",Mn]],["distilbert",["DistilBertForMaskedLM",Sn]],["roberta",["RobertaForMaskedLM",Es]],["xlm",["XLMWithLMHeadModel",wt]],["xlm-roberta",["XLMRobertaForMaskedLM",Ta]],["mobilebert",["MobileBertForMaskedLM",Zs]],["squeezebert",["SqueezeBertForMaskedLM",gn]]]),Vn=new Map([["bert",["BertForQuestionAnswering",Ne]],["roformer",["RoFormerForQuestionAnswering",pt]],["electra",["ElectraForQuestionAnswering",zs]],["convbert",["ConvBertForQuestionAnswering",gr]],["camembert",["CamembertForQuestionAnswering",Ss]],["deberta",["DebertaForQuestionAnswering",as]],["deberta-v2",["DebertaV2ForQuestionAnswering",tr]],["mpnet",["MPNetForQuestionAnswering",On]],["albert",["AlbertForQuestionAnswering",yn]],["distilbert",["DistilBertForQuestionAnswering",vs]],["roberta",["RobertaForQuestionAnswering",Mt]],["xlm",["XLMForQuestionAnswering",Zn]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Ea]],["mobilebert",["MobileBertForQuestionAnswering",en]],["squeezebert",["SqueezeBertForQuestionAnswering",ir]]]),Co=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Wo]],["idefics3",["Idefics3ForConditionalGeneration",Go]]]),Od=new Map([["llava",["LlavaForConditionalGeneration",eo]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",$a]],["moondream1",["Moondream1ForConditionalGeneration",Aa]],["florence2",["Florence2ForConditionalGeneration",Oa]],["qwen2-vl",["Qwen2VLForConditionalGeneration",oo]],["idefics3",["Idefics3ForConditionalGeneration",Go]],["paligemma",["PaliGemmaForConditionalGeneration",Fa]]]),Fd=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Wo]]]),sa=new Map([["vit",["ViTForImageClassification",Ol]],["ijepa",["IJepaForImageClassification",Fl]],["pvt",["PvtForImageClassification",Bl]],["vit_msn",["ViTMSNForImageClassification",Bc]],["fastvit",["FastViTForImageClassification",Wl]],["mobilevit",["MobileViTForImageClassification",ql]],["mobilevitv2",["MobileViTV2ForImageClassification",Ql]],["beit",["BeitForImageClassification",sn]],["deit",["DeiTForImageClassification",iu]],["hiera",["HieraForImageClassification",po]],["convnext",["ConvNextForImageClassification",bu]],["convnextv2",["ConvNextV2ForImageClassification",Tu]],["dinov2",["Dinov2ForImageClassification",Eu]],["resnet",["ResNetForImageClassification",uu]],["swin",["SwinForImageClassification",$i]],["segformer",["SegformerForImageClassification",ep]],["efficientnet",["EfficientNetForImageClassification",md]],["mobilenet_v1",["MobileNetV1ForImageClassification",_d]],["mobilenet_v2",["MobileNetV2ForImageClassification",gd]],["mobilenet_v3",["MobileNetV3ForImageClassification",wd]],["mobilenet_v4",["MobileNetV4ForImageClassification",Md]]]),Dd=new Map([["detr",["DetrForObjectDetection",Ci]],["rt_detr",["RTDetrForObjectDetection",Nc]],["table-transformer",["TableTransformerForObjectDetection",nu]],["yolos",["YolosForObjectDetection",ku]]]),Ld=new Map([["owlvit",["OwlViTForObjectDetection",Zl]],["owlv2",["Owlv2ForObjectDetection",tu]]]),na=new Map([["detr",["DetrForSegmentation",Gs]],["clipseg",["CLIPSegForImageSegmentation",Ga]]]),oa=new Map([["segformer",["SegformerForSemanticSegmentation",es]],["sapiens",["SapiensForSemanticSegmentation",Uc]]]),zd=new Map([["detr",["DetrForSegmentation",Gs]],["maskformer",["MaskFormerForInstanceSegmentation",wu]]]),Bd=new Map([["sam",["SamModel",$u]]]),ia=new Map([["wav2vec2",["Wav2Vec2ForCTC",Lu]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Hu]],["unispeech",["UniSpeechForCTC",Uu]],["unispeech-sat",["UniSpeechSatForCTC",Wu]],["wavlm",["WavLMForCTC",Zu]],["hubert",["HubertForCTC",Qu]]]),Rd=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",zu]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",qu]],["unispeech",["UniSpeechForSequenceClassification",Vu]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Gu]],["wavlm",["WavLMForSequenceClassification",ed]],["hubert",["HubertForSequenceClassification",Yu]],["audio-spectrogram-transformer",["ASTForAudioClassification",ss]]]),Nd=new Map([["wavlm",["WavLMForXVector",td]]]),aa=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",qc]],["wavlm",["WavLMForAudioFrameClassification",Xc]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Bu]],["pyannote",["PyAnnoteForAudioFrameClassification",Nu]]]),jd=new Map([["vitmatte",["VitMatteForImageMatting",Kl]]]),dp=new Map([["patchtst",["PatchTSTForPrediction",op]],["patchtsmixer",["PatchTSMixerForPrediction",kd]]]),Ud=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Ai]]]),Vd=new Map([["dpt",["DPTForDepthEstimation",mu]],["depth_anything",["DepthAnythingForDepthEstimation",_u]],["glpn",["GLPNForDepthEstimation",Vc]],["sapiens",["SapiensForDepthEstimation",fu]],["depth_pro",["DepthProForDepthEstimation",fo]]]),cp=new Map([["sapiens",["SapiensForNormalEstimation",_o]]]),Wd=new Map([["vitpose",["VitPoseForPoseEstimation",Ll]]]),Gd=new Map([["clip",["CLIPVisionModelWithProjection",Lc]],["siglip",["SiglipVisionModel",Ho]],["jina_clip",["JinaCLIPVisionModel",Va]]]),Kd=[[ip,D.EncoderOnly],[Np,D.EncoderDecoder],[ap,D.DecoderOnly],[Ad,D.EncoderOnly],[Id,D.EncoderOnly],[ta,D.Seq2Seq],[ea,D.Seq2Seq],[ra,D.DecoderOnly],[lp,D.MultiModality],[up,D.EncoderOnly],[Vn,D.EncoderOnly],[Co,D.Vision2Seq],[Od,D.ImageTextToText],[sa,D.EncoderOnly],[na,D.EncoderOnly],[zd,D.EncoderOnly],[oa,D.EncoderOnly],[jd,D.EncoderOnly],[dp,D.EncoderOnly],[Ud,D.EncoderOnly],[Vd,D.EncoderOnly],[cp,D.EncoderOnly],[Wd,D.EncoderOnly],[Dd,D.EncoderOnly],[Ld,D.EncoderOnly],[Bd,D.MaskGeneration],[ia,D.EncoderOnly],[Rd,D.EncoderOnly],[Sd,D.Seq2Seq],[$d,D.EncoderOnly],[Nd,D.EncoderOnly],[aa,D.EncoderOnly],[Gd,D.EncoderOnly]];for(const[_,x]of Kd)for(const[V,be]of _.values())$.set(V,x),C.set(be,V),g.set(V,be);const pp=[["MusicgenForConditionalGeneration",Gi,D.Musicgen],["Phi3VForCausalLM",Ko,D.Phi3V],["CLIPTextModelWithProjection",Ba,D.EncoderOnly],["SiglipTextModel",to,D.EncoderOnly],["JinaCLIPTextModel",Ua,D.EncoderOnly],["ClapTextModelWithProjection",dd,D.EncoderOnly],["ClapAudioModelWithProjection",cd,D.EncoderOnly]];for(const[_,x,V]of pp)$.set(_,V),C.set(x,_),g.set(_,x);class Hd extends _r{}ge(Hd,"MODEL_CLASS_MAPPINGS",Kd.map(x=>x[0])),ge(Hd,"BASE_IF_FAIL",!0);class qd extends _r{}ge(qd,"MODEL_CLASS_MAPPINGS",[Ad]);class Xd extends _r{}ge(Xd,"MODEL_CLASS_MAPPINGS",[Id]);class Qd extends _r{}ge(Qd,"MODEL_CLASS_MAPPINGS",[ta]);class hp extends _r{}ge(hp,"MODEL_CLASS_MAPPINGS",[ea]);class Yd extends _r{}ge(Yd,"MODEL_CLASS_MAPPINGS",[Sd]);class Jd extends _r{}ge(Jd,"MODEL_CLASS_MAPPINGS",[$d]);class Zd extends _r{}ge(Zd,"MODEL_CLASS_MAPPINGS",[ra]);class ec extends _r{}ge(ec,"MODEL_CLASS_MAPPINGS",[up]);class mp extends _r{}ge(mp,"MODEL_CLASS_MAPPINGS",[Vn]);class tc extends _r{}ge(tc,"MODEL_CLASS_MAPPINGS",[Co]);class rc extends _r{}ge(rc,"MODEL_CLASS_MAPPINGS",[sa]);class sc extends _r{}ge(sc,"MODEL_CLASS_MAPPINGS",[na]);class nc extends _r{}ge(nc,"MODEL_CLASS_MAPPINGS",[oa]);class oc extends _r{}ge(oc,"MODEL_CLASS_MAPPINGS",[zd]);class ic extends _r{}ge(ic,"MODEL_CLASS_MAPPINGS",[Dd]);class ac extends _r{}ge(ac,"MODEL_CLASS_MAPPINGS",[Ld]);class lc extends _r{}ge(lc,"MODEL_CLASS_MAPPINGS",[Bd]);class uc extends _r{}ge(uc,"MODEL_CLASS_MAPPINGS",[ia]);class dc extends _r{}ge(dc,"MODEL_CLASS_MAPPINGS",[Rd]);class cc extends _r{}ge(cc,"MODEL_CLASS_MAPPINGS",[Nd]);class la extends _r{}ge(la,"MODEL_CLASS_MAPPINGS",[aa]);class pc extends _r{}ge(pc,"MODEL_CLASS_MAPPINGS",[Fd]);class hc extends _r{}ge(hc,"MODEL_CLASS_MAPPINGS",[jd]);class mc extends _r{}ge(mc,"MODEL_CLASS_MAPPINGS",[Ud]);class _c extends _r{}ge(_c,"MODEL_CLASS_MAPPINGS",[Vd]);class fc extends _r{}ge(fc,"MODEL_CLASS_MAPPINGS",[cp]);class gc extends _r{}ge(gc,"MODEL_CLASS_MAPPINGS",[Wd]);class wc extends _r{}ge(wc,"MODEL_CLASS_MAPPINGS",[Gd]);class jp extends Je{constructor({logits:x,past_key_values:V,encoder_outputs:be,decoder_attentions:Oe=null,cross_attentions:Se=null}){super(),this.logits=x,this.past_key_values=V,this.encoder_outputs=be,this.decoder_attentions=Oe,this.cross_attentions=Se}}class Xt extends Je{constructor({logits:x}){super(),this.logits=x}}class yc extends Je{constructor({logits:x,embeddings:V}){super(),this.logits=x,this.embeddings=V}}class jr extends Je{constructor({logits:x}){super(),this.logits=x}}class Wr extends Je{constructor({logits:x}){super(),this.logits=x}}class Xr extends Je{constructor({start_logits:x,end_logits:V}){super(),this.start_logits=x,this.end_logits=V}}class an extends Je{constructor({logits:x}){super(),this.logits=x}}class _p extends Je{constructor({logits:x,past_key_values:V}){super(),this.logits=x,this.past_key_values=V}}class fp extends Je{constructor({alphas:x}){super(),this.alphas=x}}class Mc extends Je{constructor({waveform:x,spectrogram:V}){super(),this.waveform=x,this.spectrogram=V}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(Te,A,s)=>{s.r(A),s.d(A,{ASTFeatureExtractor:()=>N});var f=s("./src/base/feature_extraction_utils.js");s("./src/utils/tensor.js");var O=s("./src/utils/audio.js");class N extends f.FeatureExtractor{constructor(U){super(U);const w=this.config.sampling_rate,v=(0,O.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(w/2),w,null,"kaldi",!0);for(let y=0;y{s.r(A),s.d(A,{AutoFeatureExtractor:()=>Q});var f=s("./src/utils/constants.js"),O=s("./src/utils/hub.js");s("./src/base/feature_extraction_utils.js");var N=s("./src/models/feature_extractors.js");class Q{static async from_pretrained(w,v={}){const y=await(0,O.getModelJSON)(w,f.FEATURE_EXTRACTOR_NAME,!0,v),M=y.feature_extractor_type,b=N[M];if(!b)throw new Error(`Unknown feature_extractor_type: '${M}'. 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D(L)}}const W={};return new N.Processor(W,z)}}},"./src/models/beit/image_processing_beit.js":(Te,A,s)=>{s.r(A),s.d(A,{BeitFeatureExtractor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}},"./src/models/bit/image_processing_bit.js":(Te,A,s)=>{s.r(A),s.d(A,{BitImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}},"./src/models/chinese_clip/image_processing_chinese_clip.js":(Te,A,s)=>{s.r(A),s.d(A,{ChineseCLIPFeatureExtractor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}},"./src/models/clap/feature_extraction_clap.js":(Te,A,s)=>{s.r(A),s.d(A,{ClapFeatureExtractor:()=>N});var f=s("./src/base/feature_extraction_utils.js");s("./src/utils/tensor.js");var O=s("./src/utils/audio.js");class N extends f.FeatureExtractor{constructor(U){super(U),this.mel_filters=(0,O.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,O.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,O.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(U,w,v,y){let M;const b=U.length-w;if(b>0)if(v==="rand_trunc"){const L=Math.floor(Math.random()*(b+1));U=U.subarray(L,L+w),M=await this._extract_fbank_features(U,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${v}" not implemented`);else{if(b<0){let L=new Float64Array(w);if(L.set(U),y==="repeat")for(let q=U.length;q{s.r(A),s.d(A,{CLIPFeatureExtractor:()=>N,CLIPImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/convnext/image_processing_convnext.js":(Te,A,s)=>{s.r(A),s.d(A,{ConvNextFeatureExtractor:()=>N,ConvNextImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{constructor(U){super(U),this.crop_pct=this.config.crop_pct??.875}async resize(U){var v;const w=(v=this.size)==null?void 0:v.shortest_edge;if(w===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(w<384){const y=Math.floor(w/this.crop_pct),[M,b]=this.get_resize_output_image_size(U,{shortest_edge:y});U=await U.resize(M,b,{resample:this.resample}),U=await U.center_crop(w,w)}else U=await U.resize(w,w,{resample:this.resample});return U}}class N extends O{}},"./src/models/deit/image_processing_deit.js":(Te,A,s)=>{s.r(A),s.d(A,{DeiTFeatureExtractor:()=>N,DeiTImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/detr/image_processing_detr.js":(Te,A,s)=>{s.r(A),s.d(A,{DetrFeatureExtractor:()=>Q,DetrImageProcessor:()=>N});var f=s("./src/base/image_processors_utils.js"),O=s("./src/utils/tensor.js");class N extends f.ImageProcessor{async _call(w){const v=await super._call(w),y=[v.pixel_values.dims[0],64,64],M=(0,O.full)(y,1n);return{...v,pixel_mask:M}}post_process_object_detection(...w){return(0,f.post_process_object_detection)(...w)}post_process_panoptic_segmentation(...w){return(0,f.post_process_panoptic_segmentation)(...w)}post_process_instance_segmentation(...w){return(0,f.post_process_instance_segmentation)(...w)}}class Q extends N{}},"./src/models/donut/image_processing_donut.js":(Te,A,s)=>{s.r(A),s.d(A,{DonutFeatureExtractor:()=>N,DonutImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{pad_image(U,w,v,y={}){const[M,b,L]=w;let q=this.image_mean;Array.isArray(this.image_mean)||(q=new Array(L).fill(q));let se=this.image_std;Array.isArray(se)||(se=new Array(L).fill(q));const ie=q.map((z,W)=>-z/se[W]);return super.pad_image(U,w,v,{center:!0,constant_values:ie,...y})}}class N extends O{}},"./src/models/dpt/image_processing_dpt.js":(Te,A,s)=>{s.r(A),s.d(A,{DPTFeatureExtractor:()=>N,DPTImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/efficientnet/image_processing_efficientnet.js":(Te,A,s)=>{s.r(A),s.d(A,{EfficientNetImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{constructor(Q){super(Q),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(U=>U*U))}}},"./src/models/feature_extractors.js":(Te,A,s)=>{s.r(A),s.d(A,{ASTFeatureExtractor:()=>f.ASTFeatureExtractor,ClapFeatureExtractor:()=>O.ClapFeatureExtractor,ImageFeatureExtractor:()=>b.ImageProcessor,MoonshineFeatureExtractor:()=>N.MoonshineFeatureExtractor,PyAnnoteFeatureExtractor:()=>Q.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>U.SeamlessM4TFeatureExtractor,SpeechT5FeatureExtractor:()=>w.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>v.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>y.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>M.WhisperFeatureExtractor});var f=s("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),O=s("./src/models/clap/feature_extraction_clap.js"),N=s("./src/models/moonshine/feature_extraction_moonshine.js"),Q=s("./src/models/pyannote/feature_extraction_pyannote.js"),U=s("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),w=s("./src/models/speecht5/feature_extraction_speecht5.js"),v=s("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),y=s("./src/models/wespeaker/feature_extraction_wespeaker.js"),M=s("./src/models/whisper/feature_extraction_whisper.js"),b=s("./src/base/image_processors_utils.js")},"./src/models/florence2/processing_florence2.js":(Te,A,s)=>{s.r(A),s.d(A,{Florence2Processor:()=>Q});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js");class Q extends f.Processor{constructor(w,v){super(w,v);const{tasks_answer_post_processing_type:y,task_prompts_without_inputs:M,task_prompts_with_input:b}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(y??{})),this.task_prompts_without_inputs=new Map(Object.entries(M??{})),this.task_prompts_with_input=new Map(Object.entries(b??{})),this.regexes={quad_boxes:/(.+?)/gm,bboxes:/([^<]+)?/gm},this.size_per_bin=1e3}construct_prompts(w){typeof w=="string"&&(w=[w]);const v=[];for(const y of w)if(this.task_prompts_without_inputs.has(y))v.push(this.task_prompts_without_inputs.get(y));else{for(const[M,b]of this.task_prompts_with_input)if(y.includes(M)){v.push(b.replaceAll("{input}",y).replaceAll(M,""));break}v.length!==w.length&&v.push(y)}return v}post_process_generation(w,v,y){const M=this.tasks_answer_post_processing_type.get(v)??"pure_text";w=w.replaceAll("","").replaceAll("","");let 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f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{constructor(Q){super({do_pad:!0,pad_size:{width:Q.image_size,height:Q.image_size},...Q}),this.constant_values=this.config.background_color.map(U=>U*this.rescale_factor)}pad_image(Q,U,w,v){return super.pad_image(Q,U,w,{constant_values:this.constant_values,center:!0,...v})}}},"./src/models/janus/processing_janus.js":(Te,A,s)=>{s.r(A),s.d(A,{VLChatProcessor:()=>v});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js"),Q=s("./src/utils/core.js"),U=s("./src/utils/tensor.js"),w=s("./src/utils/image.js");class v extends f.Processor{constructor(M,b){super(M,b),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(M,{images:b=null,chat_template:L="default"}={}){b?Array.isArray(b)||(b=[b]):b=await 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f.ImageProcessor{constructor(Q){const{resize_mode:U,fill_color:w,interpolation:v,size:y,...M}=Q,b=U==="squash"?{width:y,height:y}:U==="shortest"?{shortest_edge:y}:{longest_edge:y},L=v==="bicubic"?3:2;super({...M,size:b,resample:L,do_center_crop:!0,crop_size:y,do_normalize:!0})}}},"./src/models/jina_clip/processing_jina_clip.js":(Te,A,s)=>{s.r(A),s.d(A,{JinaCLIPProcessor:()=>Q});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js");class Q extends f.Processor{async _call(w=null,v=null,y={}){if(!w&&!v)throw new Error("Either text or images must be provided");const M=w?this.tokenizer(w,y):{},b=v?await this.image_processor(v,y):{};return{...M,...b}}}ge(Q,"tokenizer_class",N.AutoTokenizer),ge(Q,"image_processor_class",O.AutoImageProcessor)},"./src/models/llava_onevision/image_processing_llava_onevision.js":(Te,A,s)=>{s.r(A),s.d(A,{LlavaOnevisionImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}},"./src/models/mask2former/image_processing_mask2former.js":(Te,A,s)=>{s.r(A),s.d(A,{Mask2FormerImageProcessor:()=>O});var f=s("./src/models/maskformer/image_processing_maskformer.js");class O extends f.MaskFormerImageProcessor{}},"./src/models/maskformer/image_processing_maskformer.js":(Te,A,s)=>{s.r(A),s.d(A,{MaskFormerFeatureExtractor:()=>N,MaskFormerImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{post_process_panoptic_segmentation(...U){return(0,f.post_process_panoptic_segmentation)(...U)}post_process_instance_segmentation(...U){return(0,f.post_process_instance_segmentation)(...U)}}class N extends O{}},"./src/models/mgp_str/processing_mgp_str.js":(Te,A,s)=>{s.r(A),s.d(A,{MgpstrProcessor:()=>w});var f=s("./src/base/processing_utils.js"),O=s("./src/models/auto/image_processing_auto.js"),N=s("./src/tokenizers.js"),Q=s("./src/utils/maths.js");const U={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class w extends f.Processor{get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(y,M){if(!U.hasOwnProperty(M))throw new Error(`Format ${M} is not supported.`);const[b,L]=U[M],q=this[b].bind(this),[se,ie]=y.dims,z=[],W=[],Y=y.tolist();for(let $=0;$0?T.reduce((J,le)=>J*le,1):0;W.push(C),z.push(ee)}return[q(W),z]}char_decode(y){return this.char_tokenizer.batch_decode(y).map(M=>M.replaceAll(" ",""))}bpe_decode(y){return this.bpe_tokenizer.batch_decode(y)}wp_decode(y){return this.wp_tokenizer.batch_decode(y).map(M=>M.replaceAll(" ",""))}batch_decode([y,M,b]){const[L,q]=this._decode_helper(y,"char"),[se,ie]=this._decode_helper(M,"bpe"),[z,W]=this._decode_helper(b,"wp"),Y=[],D=[];for(let $=0;${s.r(A),s.d(A,{MobileNetV1FeatureExtractor:()=>N,MobileNetV1ImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/mobilenet_v2/image_processing_mobilenet_v2.js":(Te,A,s)=>{s.r(A),s.d(A,{MobileNetV2FeatureExtractor:()=>N,MobileNetV2ImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/mobilenet_v3/image_processing_mobilenet_v3.js":(Te,A,s)=>{s.r(A),s.d(A,{MobileNetV3FeatureExtractor:()=>N,MobileNetV3ImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends O{}},"./src/models/mobilenet_v4/image_processing_mobilenet_v4.js":(Te,A,s)=>{s.r(A),s.d(A,{MobileNetV4FeatureExtractor:()=>N,MobileNetV4ImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{}class N extends 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Must be one of: ${JSON.stringify(y)}`)}return w}},"./src/models/whisper/feature_extraction_whisper.js":(Te,A,s)=>{s.r(A),s.d(A,{WhisperFeatureExtractor:()=>Q});var f=s("./src/base/feature_extraction_utils.js");s("./src/utils/tensor.js");var O=s("./src/utils/audio.js"),N=s("./src/utils/maths.js");class Q extends f.FeatureExtractor{constructor(w){var v;super(w),(v=this.config).mel_filters??(v.mel_filters=(0,O.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,O.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(w){const v=await(0,O.spectrogram)(w,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}),y=v.data,M=(0,N.max)(y)[0];for(let b=0;bthis.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`."),v=w.slice(0,this.config.n_samples)):(v=new Float32Array(this.config.n_samples),v.set(w)),{input_features:(await this._extract_fbank_features(v)).unsqueeze_(0)}}}},"./src/models/whisper/generation_whisper.js":(Te,A,s)=>{s.r(A),s.d(A,{WhisperGenerationConfig:()=>O});var f=s("./src/generation/configuration_utils.js");class O extends f.GenerationConfig{constructor(){super(...arguments);ge(this,"return_timestamps",null);ge(this,"return_token_timestamps",null);ge(this,"num_frames",null);ge(this,"alignment_heads",null);ge(this,"task",null);ge(this,"language",null);ge(this,"no_timestamps_token_id",null);ge(this,"prompt_ids",null);ge(this,"is_multilingual",null);ge(this,"lang_to_id",null);ge(this,"task_to_id",null);ge(this,"max_initial_timestamp_index",1)}}},"./src/models/whisper/processing_whisper.js":(Te,A,s)=>{s.r(A),s.d(A,{WhisperProcessor:()=>Q});var f=s("./src/models/auto/feature_extraction_auto.js"),O=s("./src/tokenizers.js"),N=s("./src/base/processing_utils.js");class Q extends N.Processor{async _call(w){return await this.feature_extractor(w)}}ge(Q,"tokenizer_class",O.AutoTokenizer),ge(Q,"feature_extractor_class",f.AutoFeatureExtractor)},"./src/models/yolos/image_processing_yolos.js":(Te,A,s)=>{s.r(A),s.d(A,{YolosFeatureExtractor:()=>N,YolosImageProcessor:()=>O});var f=s("./src/base/image_processors_utils.js");class O extends f.ImageProcessor{post_process_object_detection(...U){return(0,f.post_process_object_detection)(...U)}}class N extends O{}},"./src/ops/registry.js":(Te,A,s)=>{s.r(A),s.d(A,{TensorOpRegistry:()=>Q});var f=s("./src/backends/onnx.js"),O=s("./src/utils/tensor.js");const N=async(U,w,v)=>{const y=await(0,f.createInferenceSession)(new Uint8Array(U),w);return async M=>{const b=Object.fromEntries(Object.entries(M).map(([q,se])=>[q,se.ort_tensor])),L=await y.run(b);return Array.isArray(v)?v.map(q=>new O.Tensor(L[q])):new O.Tensor(L[v])}};class Q{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=N([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=N([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=N([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=N([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=N([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=N([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=N([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}ge(Q,"session_options",{})},"./src/pipelines.js":(Te,A,s)=>{s.r(A),s.d(A,{AudioClassificationPipeline:()=>de,AutomaticSpeechRecognitionPipeline:()=>ke,DepthEstimationPipeline:()=>Be,DocumentQuestionAnsweringPipeline:()=>re,FeatureExtractionPipeline:()=>J,FillMaskPipeline:()=>Y,ImageClassificationPipeline:()=>Le,ImageFeatureExtractionPipeline:()=>le,ImageSegmentationPipeline:()=>qe,ImageToImagePipeline:()=>Pe,ImageToTextPipeline:()=>xe,ObjectDetectionPipeline:()=>ut,Pipeline:()=>se,QuestionAnsweringPipeline:()=>W,SummarizationPipeline:()=>$,Text2TextGenerationPipeline:()=>D,TextClassificationPipeline:()=>ie,TextGenerationPipeline:()=>T,TextToAudioPipeline:()=>he,TokenClassificationPipeline:()=>z,TranslationPipeline:()=>g,ZeroShotAudioClassificationPipeline:()=>fe,ZeroShotClassificationPipeline:()=>ee,ZeroShotImageClassificationPipeline:()=>Ue,ZeroShotObjectDetectionPipeline:()=>ue,pipeline:()=>oe});var f=s("./src/tokenizers.js"),O=s("./src/models.js"),N=s("./src/models/auto/processing_auto.js");s("./src/base/processing_utils.js");var Q=s("./src/utils/generic.js"),U=s("./src/utils/core.js"),w=s("./src/utils/maths.js"),v=s("./src/utils/audio.js"),y=s("./src/utils/tensor.js"),M=s("./src/utils/image.js");async function b(De){return Array.isArray(De)||(De=[De]),await Promise.all(De.map(ce=>M.RawImage.read(ce)))}async function L(De,ce){return Array.isArray(De)||(De=[De]),await Promise.all(De.map(ve=>typeof ve=="string"||ve instanceof URL?(0,v.read_audio)(ve,ce):ve instanceof Float64Array?new Float32Array(ve):ve))}function q(De,ce){ce&&(De=De.map(Ne=>Ne|0));const[ve,Re,je,We]=De;return{xmin:ve,ymin:Re,xmax:je,ymax:We}}class se extends Q.Callable{constructor({task:ce,model:ve,tokenizer:Re=null,processor:je=null}){super(),this.task=ce,this.model=ve,this.tokenizer=Re,this.processor=je}async dispose(){await this.model.dispose()}}class ie extends se{constructor(ce){super(ce)}async _call(ce,{top_k:ve=1}={}){const Re=this.tokenizer(ce,{padding:!0,truncation:!0}),je=await this.model(Re),We=this.model.config.problem_type==="multi_label_classification"?at=>at.sigmoid():at=>new y.Tensor("float32",(0,w.softmax)(at.data),at.dims),Ne=this.model.config.id2label,Ze=[];for(const at of je.logits){const ft=We(at),dt=await(0,y.topk)(ft,ve),gt=dt[0].tolist(),ne=dt[1].tolist().map((K,pe)=>({label:Ne?Ne[K]:`LABEL_${K}`,score:gt[pe]}));ve===1?Ze.push(...ne):Ze.push(ne)}return Array.isArray(ce)||ve===1?Ze:Ze[0]}}class z extends se{constructor(ce){super(ce)}async _call(ce,{ignore_labels:ve=["O"]}={}){const Re=Array.isArray(ce),je=this.tokenizer(Re?ce:[ce],{padding:!0,truncation:!0}),Ne=(await this.model(je)).logits,Ze=this.model.config.id2label,at=[];for(let ft=0;ftpt==this.tokenizer.sep_token_id);at[gt].map((pt,It)=>pt==1&&(It===0||It>ne&&ft.findIndex(St=>St==F[It])===-1));const K=We[gt].tolist(),pe=Ne[gt].tolist();for(let pt=1;ptIt==F[pt])!==-1)&&(K[pt]=-1/0,pe[pt]=-1/0);const Fe=(0,w.softmax)(K).map((pt,It)=>[pt,It]),Qe=(0,w.softmax)(pe).map((pt,It)=>[pt,It]);Fe[0][0]=0,Qe[0][0]=0;const st=(0,U.product)(Fe,Qe).filter(pt=>pt[0][1]<=pt[1][1]).map(pt=>[pt[0][1],pt[1][1],pt[0][0]*pt[1][0]]).sort((pt,It)=>It[2]-pt[2]);for(let pt=0;ptK==this.tokenizer.mask_token_id);if(ft===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const dt=je[Ze][ft],gt=await(0,y.topk)(new y.Tensor("float32",(0,w.softmax)(dt.data),dt.dims),ve),F=gt[0].tolist(),ne=gt[1].tolist();We.push(ne.map((K,pe)=>{const Fe=at.slice();return Fe[ft]=K,{score:F[pe],token:Number(K),token_str:this.tokenizer.decode([K]),sequence:this.tokenizer.decode(Fe,{skip_special_tokens:!0})}}))}return Array.isArray(ce)?We:We[0]}}class D extends se{constructor(ve){super(ve);ge(this,"_key","generated_text")}async _call(ve,Re={}){Array.isArray(ve)||(ve=[ve]),this.model.config.prefix&&(ve=ve.map(ft=>this.model.config.prefix+ft));const je=this.model.config.task_specific_params;je&&je[this.task]&&je[this.task].prefix&&(ve=ve.map(ft=>je[this.task].prefix+ft));const We=this.tokenizer,Ne={padding:!0,truncation:!0};let Ze;this instanceof g&&"_build_translation_inputs"in We?Ze=We._build_translation_inputs(ve,Ne,Re):Ze=We(ve,Ne);const at=await this.model.generate({...Ze,...Re});return We.batch_decode(at,{skip_special_tokens:!0}).map(ft=>({[this._key]:ft}))}}class $ extends D{constructor(ve){super(ve);ge(this,"_key","summary_text")}}class g extends D{constructor(ve){super(ve);ge(this,"_key","translation_text")}}function C(De){return Array.isArray(De)&&De.every(ce=>"role"in ce&&"content"in ce)}class T extends se{constructor(ce){super(ce)}async _call(ce,ve={}){let Re=!1,je=!1,We;if(typeof ce=="string")We=ce=[ce];else if(Array.isArray(ce)&&ce.every(ne=>typeof ne=="string"))Re=!0,We=ce;else{if(C(ce))ce=[ce];else if(Array.isArray(ce)&&ce.every(C))Re=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");je=!0,We=ce.map(ne=>this.tokenizer.apply_chat_template(ne,{tokenize:!1,add_generation_prompt:!0}))}const Ne=ve.add_special_tokens??!1,Ze=je?!1:ve.return_full_text??!0;this.tokenizer.padding_side="left";const at=this.tokenizer(We,{add_special_tokens:Ne,padding:!0,truncation:!0}),ft=await this.model.generate({...at,...ve}),dt=this.tokenizer.batch_decode(ft,{skip_special_tokens:!0});let gt;!Ze&&at.input_ids.dims.at(-1)>0&&(gt=this.tokenizer.batch_decode(at.input_ids,{skip_special_tokens:!0}).map(ne=>ne.length));const F=Array.from({length:ce.length},ne=>[]);for(let ne=0;ne[ve.toLowerCase(),Re])),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(ce,ve,{hypothesis_template:Re="This example is {}.",multi_label:je=!1}={}){const We=Array.isArray(ce);We||(ce=[ce]),Array.isArray(ve)||(ve=[ve]);const Ne=ve.map(ft=>Re.replace("{}",ft)),Ze=je||ve.length===1,at=[];for(const ft of ce){const dt=[];for(const ne of Ne){const K=this.tokenizer(ft,{text_pair:ne,padding:!0,truncation:!0}),pe=await this.model(K);Ze?dt.push([pe.logits.data[this.contradiction_id],pe.logits.data[this.entailment_id]]):dt.push(pe.logits.data[this.entailment_id])}const F=(Ze?dt.map(ne=>(0,w.softmax)(ne)[1]):(0,w.softmax)(dt)).map((ne,K)=>[ne,K]).sort((ne,K)=>K[0]-ne[0]);at.push({sequence:ft,labels:F.map(ne=>ve[ne[1]]),scores:F.map(ne=>ne[0])})}return We?at:at[0]}}class J extends se{constructor(ce){super(ce)}async _call(ce,{pooling:ve="none",normalize:Re=!1,quantize:je=!1,precision:We="binary"}={}){const Ne=this.tokenizer(ce,{padding:!0,truncation:!0}),Ze=await this.model(Ne);let at=Ze.last_hidden_state??Ze.logits??Ze.token_embeddings;if(ve!=="none")if(ve==="mean")at=(0,y.mean_pooling)(at,Ne.attention_mask);else if(ve==="cls")at=at.slice(null,0);else throw Error(`Pooling method '${ve}' not supported.`);return Re&&(at=at.normalize(2,-1)),je&&(at=(0,y.quantize_embeddings)(at,We)),at}}class le extends se{constructor(ce){super(ce)}async _call(ce,{pool:ve=null}={}){const Re=await b(ce),{pixel_values:je}=await this.processor(Re),We=await this.model({pixel_values:je});let Ne;if(ve){if(!("pooler_output"in We))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Ne=We.pooler_output}else Ne=We.last_hidden_state??We.logits??We.image_embeds;return Ne}}class de extends se{constructor(ce){super(ce)}async _call(ce,{top_k:ve=5}={}){const Re=this.processor.feature_extractor.config.sampling_rate,je=await L(ce,Re),We=this.model.config.id2label,Ne=[];for(const Ze of je){const at=await this.processor(Ze),dt=(await this.model(at)).logits[0],gt=await(0,y.topk)(new y.Tensor("float32",(0,w.softmax)(dt.data),dt.dims),ve),F=gt[0].tolist(),K=gt[1].tolist().map((pe,Fe)=>({label:We?We[pe]:`LABEL_${pe}`,score:F[Fe]}));Ne.push(K)}return Array.isArray(ce)?Ne:Ne[0]}}class fe extends se{constructor(ce){super(ce)}async _call(ce,ve,{hypothesis_template:Re="This is a sound of {}."}={}){const je=!Array.isArray(ce);je&&(ce=[ce]);const We=ve.map(dt=>Re.replace("{}",dt)),Ne=this.tokenizer(We,{padding:!0,truncation:!0}),Ze=this.processor.feature_extractor.config.sampling_rate,at=await L(ce,Ze),ft=[];for(const dt of at){const gt=await this.processor(dt),F=await this.model({...Ne,...gt}),ne=(0,w.softmax)(F.logits_per_audio.data);ft.push([...ne].map((K,pe)=>({score:K,label:ve[pe]})))}return je?ft[0]:ft}}class ke extends se{constructor(ce){super(ce)}async _call(ce,ve={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(ce,ve);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(ce,ve);case"moonshine":return this._call_moonshine(ce,ve);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(ce,ve){ve.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),ve.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const Re=!Array.isArray(ce);Re&&(ce=[ce]);const je=this.processor.feature_extractor.config.sampling_rate,We=await L(ce,je),Ne=[];for(const Ze of We){const at=await this.processor(Ze),dt=(await this.model(at)).logits[0],gt=[];for(const ne of dt)gt.push((0,w.max)(ne.data)[1]);const F=this.tokenizer.decode(gt);Ne.push({text:F})}return Re?Ne[0]:Ne}async _call_whisper(ce,ve){const Re=ve.return_timestamps??!1,je=ve.chunk_length_s??0,We=ve.force_full_sequences??!1;let Ne=ve.stride_length_s??null;const Ze={...ve};Re==="word"&&(Ze.return_token_timestamps=!0,Ze.return_timestamps=!1);const at=!Array.isArray(ce);at&&(ce=[ce]);const ft=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,dt=this.processor.feature_extractor.config.hop_length,gt=this.processor.feature_extractor.config.sampling_rate,F=await L(ce,gt),ne=[];for(const K of F){let pe=[];if(je>0){if(Ne===null)Ne=je/6;else if(je<=Ne)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const st=gt*je,pt=gt*Ne,It=st-2*pt;let St=0;for(;;){const Ft=St+st,At=K.subarray(St,Ft),nr=await this.processor(At),gr=St===0,kr=Ft>=K.length;if(pe.push({stride:[At.length,gr?0:pt,kr?0:pt],input_features:nr.input_features,is_last:kr}),kr)break;St+=It}}else pe=[{stride:[K.length,0,0],input_features:(await this.processor(K)).input_features,is_last:!0}];for(const st of pe){Ze.num_frames=Math.floor(st.stride[0]/dt);const pt=await this.model.generate({inputs:st.input_features,...Ze});Re==="word"?(st.tokens=pt.sequences.tolist()[0],st.token_timestamps=pt.token_timestamps.tolist()[0].map(It=>(0,w.round)(It,2))):st.tokens=pt[0].tolist(),st.stride=st.stride.map(It=>It/gt)}const[Fe,Qe]=this.tokenizer._decode_asr(pe,{time_precision:ft,return_timestamps:Re,force_full_sequences:We});ne.push({text:Fe,...Qe})}return at?ne[0]:ne}async _call_moonshine(ce,ve){const Re=!Array.isArray(ce);Re&&(ce=[ce]);const je=this.processor.feature_extractor.config.sampling_rate,We=await L(ce,je),Ne=[];for(const Ze of We){const at=await this.processor(Ze),ft=Math.floor(Ze.length/je)*6,dt=await this.model.generate({max_new_tokens:ft,...ve,...at}),gt=this.processor.batch_decode(dt,{skip_special_tokens:!0})[0];Ne.push({text:gt})}return Re?Ne[0]:Ne}}class xe extends se{constructor(ce){super(ce)}async _call(ce,ve={}){const Re=Array.isArray(ce),je=await b(ce),{pixel_values:We}=await this.processor(je),Ne=[];for(const Ze of We){Ze.dims=[1,...Ze.dims];const at=await this.model.generate({inputs:Ze,...ve}),ft=this.tokenizer.batch_decode(at,{skip_special_tokens:!0}).map(dt=>({generated_text:dt.trim()}));Ne.push(ft)}return Re?Ne:Ne[0]}}class Le extends se{constructor(ce){super(ce)}async _call(ce,{top_k:ve=5}={}){const Re=await b(ce),{pixel_values:je}=await this.processor(Re),We=await this.model({pixel_values:je}),Ne=this.model.config.id2label,Ze=[];for(const at of We.logits){const ft=await(0,y.topk)(new y.Tensor("float32",(0,w.softmax)(at.data),at.dims),ve),dt=ft[0].tolist(),F=ft[1].tolist().map((ne,K)=>({label:Ne?Ne[ne]:`LABEL_${ne}`,score:dt[K]}));Ze.push(F)}return Array.isArray(ce)?Ze:Ze[0]}}class qe extends se{constructor(ce){super(ce),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(ce,{threshold:ve=.5,mask_threshold:Re=.5,overlap_mask_area_threshold:je=.8,label_ids_to_fuse:We=null,target_sizes:Ne=null,subtask:Ze=null}={}){if(Array.isArray(ce)&&ce.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const ft=await b(ce),dt=ft.map(Qe=>[Qe.height,Qe.width]),{pixel_values:gt,pixel_mask:F}=await this.processor(ft),ne=await this.model({pixel_values:gt,pixel_mask:F});let K=null;if(Ze!==null)K=this.subtasks_mapping[Ze];else for(let[Qe,st]of Object.entries(this.subtasks_mapping))if(st in this.processor.image_processor){K=this.processor.image_processor[st].bind(this.processor.image_processor),Ze=Qe;break}const pe=this.model.config.id2label,Fe=[];if(Ze==="panoptic"||Ze==="instance"){const Qe=K(ne,ve,Re,je,We,Ne??dt)[0],st=Qe.segmentation;for(const pt of Qe.segments_info){const It=new Uint8ClampedArray(st.data.length);for(let Ft=0;FtRe.replace("{}",F)),Ze=this.tokenizer(Ne,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:at}=await this.processor(We),ft=await this.model({...Ze,pixel_values:at}),dt=this.model.config.model_type==="siglip"?F=>F.sigmoid().data:F=>(0,w.softmax)(F.data),gt=[];for(const F of ft.logits_per_image){const K=[...dt(F)].map((pe,Fe)=>({score:pe,label:ve[Fe]}));K.sort((pe,Fe)=>Fe.score-pe.score),gt.push(K)}return je?gt:gt[0]}}class ut extends se{constructor(ce){super(ce)}async _call(ce,{threshold:ve=.9,percentage:Re=!1}={}){const je=Array.isArray(ce);if(je&&ce.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const We=await b(ce),Ne=Re?null:We.map(ne=>[ne.height,ne.width]),{pixel_values:Ze,pixel_mask:at}=await this.processor(We),ft=await this.model({pixel_values:Ze,pixel_mask:at}),dt=this.processor.image_processor.post_process_object_detection(ft,ve,Ne),gt=this.model.config.id2label,F=dt.map(ne=>ne.boxes.map((K,pe)=>({score:ne.scores[pe],label:gt[ne.classes[pe]],box:q(K,!Re)})));return je?F:F[0]}}class ue extends se{constructor(ce){super(ce)}async _call(ce,ve,{threshold:Re=.1,top_k:je=null,percentage:We=!1}={}){const Ne=Array.isArray(ce),Ze=await b(ce),at=this.tokenizer(ve,{padding:!0,truncation:!0}),ft=await this.processor(Ze),dt=[];for(let gt=0;gt({score:Fe.scores[pt],label:ve[Fe.classes[pt]],box:q(st,!We)})).sort((st,pt)=>pt.score-st.score);je!==null&&(Qe=Qe.slice(0,je)),dt.push(Qe)}return Ne?dt:dt[0]}}class re extends se{constructor(ce){super(ce)}async _call(ce,ve,Re={}){const je=(await b(ce))[0],{pixel_values:We}=await this.processor(je),Ne=`${ve}`,Ze=this.tokenizer(Ne,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,at=await this.model.generate({inputs:We,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Ze,...Re}),dt=this.tokenizer.batch_decode(at)[0].match(/(.*?)<\/s_answer>/);let gt=null;return dt&&dt.length>=2&&(gt=dt[1].trim()),[{answer:gt}]}}class he extends se{constructor(ve){super(ve);ge(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=ve.vocoder??null}async _call(ve,{speaker_embeddings:Re=null}={}){return this.processor?this._call_text_to_spectrogram(ve,{speaker_embeddings:Re}):this._call_text_to_waveform(ve)}async _call_text_to_waveform(ve){const Re=this.tokenizer(ve,{padding:!0,truncation:!0}),{waveform:je}=await this.model(Re),We=this.model.config.sampling_rate;return{audio:je.data,sampling_rate:We}}async _call_text_to_spectrogram(ve,{speaker_embeddings:Re}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await O.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Re=="string"||Re instanceof URL)&&(Re=new Float32Array(await(await fetch(Re)).arrayBuffer())),Re instanceof Float32Array)Re=new y.Tensor("float32",Re,[1,Re.length]);else if(!(Re instanceof y.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:je}=this.tokenizer(ve,{padding:!0,truncation:!0}),{waveform:We}=await this.model.generate_speech(je,Re,{vocoder:this.vocoder}),Ne=this.processor.feature_extractor.config.sampling_rate;return{audio:We.data,sampling_rate:Ne}}}class Pe extends se{constructor(ce){super(ce)}async _call(ce){const ve=await b(ce),Re=await this.processor(ve),je=await this.model(Re),We=[];for(const Ne of je.reconstruction){const Ze=Ne.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");We.push(M.RawImage.fromTensor(Ze))}return We.length>1?We:We[0]}}class Be extends se{constructor(ce){super(ce)}async _call(ce){const ve=await b(ce),Re=await this.processor(ve),{predicted_depth:je}=await this.model(Re),We=[];for(let Ne=0;Ne1?We:We[0]}}const et=Object.freeze({"text-classification":{tokenizer:f.AutoTokenizer,pipeline:ie,model:O.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:f.AutoTokenizer,pipeline:z,model:O.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:f.AutoTokenizer,pipeline:W,model:O.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:f.AutoTokenizer,pipeline:Y,model:O.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:f.AutoTokenizer,pipeline:$,model:O.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:f.AutoTokenizer,pipeline:g,model:O.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:f.AutoTokenizer,pipeline:D,model:O.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:f.AutoTokenizer,pipeline:T,model:O.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:f.AutoTokenizer,pipeline:ee,model:O.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:de,model:O.AutoModelForAudioClassification,processor:N.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:f.AutoTokenizer,pipeline:fe,model:O.AutoModel,processor:N.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:f.AutoTokenizer,pipeline:ke,model:[O.AutoModelForSpeechSeq2Seq,O.AutoModelForCTC],processor:N.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:f.AutoTokenizer,pipeline:he,model:[O.AutoModelForTextToWaveform,O.AutoModelForTextToSpectrogram],processor:[N.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:f.AutoTokenizer,pipeline:xe,model:O.AutoModelForVision2Seq,processor:N.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Le,model:O.AutoModelForImageClassification,processor:N.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:qe,model:[O.AutoModelForImageSegmentation,O.AutoModelForSemanticSegmentation,O.AutoModelForUniversalSegmentation],processor:N.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:f.AutoTokenizer,pipeline:Ue,model:O.AutoModel,processor:N.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:ut,model:O.AutoModelForObjectDetection,processor:N.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:f.AutoTokenizer,pipeline:ue,model:O.AutoModelForZeroShotObjectDetection,processor:N.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:f.AutoTokenizer,pipeline:re,model:O.AutoModelForDocumentQuestionAnswering,processor:N.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:Pe,model:O.AutoModelForImageToImage,processor:N.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Be,model:O.AutoModelForDepthEstimation,processor:N.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:f.AutoTokenizer,pipeline:J,model:O.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:N.AutoProcessor,pipeline:le,model:[O.AutoModelForImageFeatureExtraction,O.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Xe=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function oe(De,ce=null,{progress_callback:ve=null,config:Re=null,cache_dir:je=null,local_files_only:We=!1,revision:Ne="main",device:Ze=null,dtype:at=null,model_file_name:ft=null,session_options:dt={}}={}){De=Xe[De]??De;const gt=et[De.split("_",1)[0]];if(!gt)throw Error(`Unsupported pipeline: ${De}. Must be one of [${Object.keys(et)}]`);ce||(ce=gt.default.model,console.log(`No model specified. Using default model: "${ce}".`));const F={progress_callback:ve,config:Re,cache_dir:je,local_files_only:We,revision:Ne,device:Ze,dtype:at,model_file_name:ft,session_options:dt},ne=new Map([["tokenizer",gt.tokenizer],["model",gt.model],["processor",gt.processor]]),K=await Je(ne,ce,F);K.task=De,(0,U.dispatchCallback)(ve,{status:"ready",task:De,model:ce});const pe=gt.pipeline;return new pe(K)}async function Je(De,ce,ve){const Re=Object.create(null),je=[];for(const[We,Ne]of De.entries()){if(!Ne)continue;let Ze;Array.isArray(Ne)?Ze=new Promise(async(at,ft)=>{var gt,F;let dt;for(const ne of Ne){if(ne===null){at(null);return}try{at(await ne.from_pretrained(ce,ve));return}catch(K){if((gt=K.message)!=null&>.includes("Unsupported model type"))dt=K;else if((F=K.message)!=null&&F.includes("Could not locate file"))dt=K;else{ft(K);return}}}ft(dt)}):Ze=Ne.from_pretrained(ce,ve),Re[We]=Ze,je.push(Ze)}await Promise.all(je);for(const[We,Ne]of Object.entries(Re))Re[We]=await Ne;return Re}},"./src/tokenizers.js":(Te,A,s)=>{s.r(A),s.d(A,{AlbertTokenizer:()=>ks,AutoTokenizer:()=>Mn,BartTokenizer:()=>tr,BertTokenizer:()=>Qs,BlenderbotSmallTokenizer:()=>Dn,BlenderbotTokenizer:()=>gn,BloomTokenizer:()=>Ns,CLIPTokenizer:()=>In,CamembertTokenizer:()=>nt,CodeGenTokenizer:()=>An,CodeLlamaTokenizer:()=>js,CohereTokenizer:()=>Ln,ConvBertTokenizer:()=>As,DebertaTokenizer:()=>is,DebertaV2Tokenizer:()=>$s,DistilBertTokenizer:()=>as,ElectraTokenizer:()=>Ot,EsmTokenizer:()=>ls,FalconTokenizer:()=>Us,GPT2Tokenizer:()=>bs,GPTNeoXTokenizer:()=>Ts,GemmaTokenizer:()=>Zs,Grok1Tokenizer:()=>_n,HerbertTokenizer:()=>cs,LlamaTokenizer:()=>Sn,M2M100Tokenizer:()=>zt,MBart50Tokenizer:()=>Rs,MBartTokenizer:()=>ts,MPNetTokenizer:()=>Jn,MarianTokenizer:()=>Vs,MgpstrTokenizer:()=>yn,MobileBertTokenizer:()=>Bs,NllbTokenizer:()=>xs,NougatTokenizer:()=>Yr,PreTrainedTokenizer:()=>Nt,Qwen2Tokenizer:()=>mn,RoFormerTokenizer:()=>Ys,RobertaTokenizer:()=>Js,SiglipTokenizer:()=>On,SpeechT5Tokenizer:()=>ir,SqueezeBertTokenizer:()=>Ss,T5Tokenizer:()=>lr,TokenizerModel:()=>le,VitsTokenizer:()=>wn,Wav2Vec2CTCTokenizer:()=>Fn,WhisperTokenizer:()=>fn,XLMRobertaTokenizer:()=>$n,XLMTokenizer:()=>_t,is_chinese_char:()=>Y});var f=s("./src/utils/generic.js"),O=s("./src/utils/core.js"),N=s("./src/utils/hub.js"),Q=s("./src/utils/maths.js"),U=s("./src/utils/tensor.js"),w=s("./src/utils/data-structures.js"),v=s("./node_modules/@huggingface/jinja/dist/index.js"),y=s("./src/models/whisper/common_whisper.js");s("./src/utils/constants.js");async function M(Ee,P){const H=await Promise.all([(0,N.getModelJSON)(Ee,"tokenizer.json",!0,P),(0,N.getModelJSON)(Ee,"tokenizer_config.json",!0,P)]);return P.legacy!==null&&(H[1].legacy=P.legacy),H}function b(Ee,P){const H=[];let ae=0;for(const Me of Ee.matchAll(P)){const Ce=Me[0];ae0&&H.push(Ce),ae=Me.index+Ce.length}return ae=19968&&Ee<=40959||Ee>=13312&&Ee<=19903||Ee>=131072&&Ee<=173791||Ee>=173824&&Ee<=177983||Ee>=177984&&Ee<=178207||Ee>=178208&&Ee<=183983||Ee>=63744&&Ee<=64255||Ee>=194560&&Ee<=195103}function D(Ee,P,H){const ae=[];let Me=0;for(;Methis.tokens_to_ids.get(H)??this.unk_token_id)}convert_ids_to_tokens(P){return P.map(H=>this.vocab[H]??this.unk_token)}}class de extends le{constructor(P){super(P),this.tokens_to_ids=q(P.vocab),this.unk_token_id=this.tokens_to_ids.get(P.unk_token),this.unk_token=P.unk_token,this.max_input_chars_per_word=P.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[H,ae]of this.tokens_to_ids)this.vocab[ae]=H}encode(P){const H=[];for(const ae of P){const Me=[...ae];if(Me.length>this.max_input_chars_per_word){H.push(this.unk_token);continue}let Ce=!1,He=0;const ct=[];for(;He0&&(it=this.config.continuing_subword_prefix+it),this.tokens_to_ids.has(it)){ht=it;break}--yt}if(ht===null){Ce=!0;break}ct.push(ht),He=yt}Ce?H.push(this.unk_token):H.push(...ct)}return H}}class fe extends le{constructor(P,H){super(P);const ae=P.vocab.length;this.vocab=new Array(ae),this.scores=new Array(ae);for(let Me=0;Me[Me,Ce])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=H.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,Q.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new w.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(P){const H=P.chars,ae=1;let Me=0;for(;Me{const Ee=[...Array.from({length:94},(Me,Ce)=>Ce+33),...Array.from({length:12},(Me,Ce)=>Ce+161),...Array.from({length:82},(Me,Ce)=>Ce+174)],P=Ee.slice();let H=0;for(let Me=0;Me<256;++Me)Ee.includes(Me)||(Ee.push(Me),P.push(256+H),H+=1);const ae=P.map(Me=>String.fromCharCode(Me));return Object.fromEntries(Ee.map((Me,Ce)=>[Me,ae[Ce]]))})(),xe=(0,O.reverseDictionary)(ke);class Le extends le{constructor(P){super(P),this.tokens_to_ids=q(P.vocab),this.unk_token_id=this.tokens_to_ids.get(P.unk_token),this.unk_token=P.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[ae,Me]of this.tokens_to_ids)this.vocab[Me]=ae;const H=Array.isArray(P.merges[0]);this.merges=H?P.merges:P.merges.map(ae=>ae.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((ae,Me)=>[JSON.stringify(ae),Me])),this.end_of_word_suffix=P.end_of_word_suffix,this.continuing_subword_suffix=P.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(P){if(P.length===0)return[];const H=this.cache.get(P);if(H!==void 0)return H;const ae=Array.from(P);this.end_of_word_suffix&&(ae[ae.length-1]+=this.end_of_word_suffix);let Me=[];if(ae.length>1){const Ce=new w.PriorityQueue((yt,ht)=>yt.score`<0x${ct.toString(16).toUpperCase().padStart(2,"0")}>`);He.every(ct=>this.tokens_to_ids.has(ct))?H.push(...He):H.push(this.unk_token)}else H.push(this.unk_token)}return H}}class qe extends le{constructor(P,H){super(P),this.tokens_to_ids=q(H.target_lang?P.vocab[H.target_lang]:P.vocab),this.bos_token=H.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=H.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=H.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=H.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[ae,Me]of this.tokens_to_ids)this.vocab[Me]=ae}encode(P){return P}}class Ue extends f.Callable{constructor(P){super(),this.config=P}static fromConfig(P){if(P===null)return null;switch(P.type){case"BertNormalizer":return new Je(P);case"Precompiled":return new gr(P);case"Sequence":return new oe(P);case"Replace":return new ut(P);case"NFC":return new ue(P);case"NFKC":return new re(P);case"NFKD":return new he(P);case"Strip":return new Pe(P);case"StripAccents":return new Be(P);case"Lowercase":return new et(P);case"Prepend":return new Xe(P);default:throw new Error(`Unknown Normalizer type: ${P.type}`)}}normalize(P){throw Error("normalize should be implemented in subclass.")}_call(P){return this.normalize(P)}}class ut extends Ue{normalize(P){const H=L(this.config.pattern);return H===null?P:P.replaceAll(H,this.config.content)}}class ue extends Ue{normalize(P){return P=P.normalize("NFC"),P}}class re extends Ue{normalize(P){return P=P.normalize("NFKC"),P}}class he extends Ue{normalize(P){return P=P.normalize("NFKD"),P}}class Pe extends Ue{normalize(P){return this.config.strip_left&&this.config.strip_right?P=P.trim():(this.config.strip_left&&(P=P.trimStart()),this.config.strip_right&&(P=P.trimEnd())),P}}class Be extends Ue{normalize(P){return P=z(P),P}}class et extends Ue{normalize(P){return P=P.toLowerCase(),P}}class Xe extends Ue{normalize(P){return P=this.config.prepend+P,P}}class oe extends Ue{constructor(P){super(P),this.normalizers=P.normalizers.map(H=>Ue.fromConfig(H))}normalize(P){return this.normalizers.reduce((H,ae)=>ae.normalize(H),P)}}class Je extends Ue{_tokenize_chinese_chars(P){const H=[];for(let ae=0;aethis.pre_tokenize_text(ae,H)):this.pre_tokenize_text(P,H)).flat()}_call(P,H){return this.pre_tokenize(P,H)}}class ce extends De{constructor(P){super(),this.pattern=new RegExp(`[^\\s${g}]+|[${g}]`,"gu")}pre_tokenize_text(P,H){return P.trim().match(this.pattern)||[]}}class ve extends De{constructor(P){super(),this.config=P,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=ke,this.text_encoder=new TextEncoder}pre_tokenize_text(P,H){return this.add_prefix_space&&!P.startsWith(" ")&&(P=" "+P),(this.use_regex?P.match(this.pattern)||[]:[P]).map(Me=>Array.from(this.text_encoder.encode(Me),Ce=>this.byte_encoder[Ce]).join(""))}}class Re extends De{constructor(P){super(),this.config=P,this.pattern=L(this.config.pattern,this.config.invert)}pre_tokenize_text(P,H){var ae;return this.pattern===null?[]:this.config.invert?P.match(this.pattern)||[]:((ae=this.config.behavior)==null?void 0:ae.toLowerCase())==="removed"?P.split(this.pattern).filter(Me=>Me):b(P,this.pattern)}}class je extends De{constructor(P){super(),this.config=P,this.pattern=new RegExp(`[^${g}]+|[${g}]+`,"gu")}pre_tokenize_text(P,H){return P.match(this.pattern)||[]}}class We extends De{constructor(P){super(),this.config=P;const H=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(H,"gu")}pre_tokenize_text(P,H){return P.match(this.pattern)||[]}}class Ne extends f.Callable{constructor(P){super(),this.config=P}static fromConfig(P){if(P===null)return null;switch(P.type){case"TemplateProcessing":return new ft(P);case"ByteLevel":return new dt(P);case"RobertaProcessing":return new at(P);case"BertProcessing":return new Ze(P);case"Sequence":return new gt(P);default:throw new Error(`Unknown PostProcessor type: ${P.type}`)}}post_process(P,...H){throw Error("post_process should be implemented in subclass.")}_call(P,...H){return this.post_process(P,...H)}}class Ze extends Ne{constructor(P){super(P),this.cls=P.cls[0],this.sep=P.sep[0]}post_process(P,H=null,{add_special_tokens:ae=!0}={}){ae&&(P=(0,O.mergeArrays)([this.cls],P,[this.sep]));let Me=new Array(P.length).fill(0);if(H!==null){const Ce=ae&&this instanceof at?[this.sep]:[],He=ae?[this.sep]:[];P=(0,O.mergeArrays)(P,Ce,H,He),Me=(0,O.mergeArrays)(Me,new Array(H.length+Ce.length+He.length).fill(1))}return{tokens:P,token_type_ids:Me}}}class at extends Ze{}class ft extends Ne{constructor(P){super(P),this.single=P.single,this.pair=P.pair}post_process(P,H=null,{add_special_tokens:ae=!0}={}){const Me=H===null?this.single:this.pair;let Ce=[],He=[];for(const ct of Me)"SpecialToken"in ct?ae&&(Ce.push(ct.SpecialToken.id),He.push(ct.SpecialToken.type_id)):"Sequence"in ct&&(ct.Sequence.id==="A"?(Ce=(0,O.mergeArrays)(Ce,P),He=(0,O.mergeArrays)(He,new Array(P.length).fill(ct.Sequence.type_id))):ct.Sequence.id==="B"&&(Ce=(0,O.mergeArrays)(Ce,H),He=(0,O.mergeArrays)(He,new Array(H.length).fill(ct.Sequence.type_id))));return{tokens:Ce,token_type_ids:He}}}class dt extends Ne{post_process(P,H=null){return H&&(P=(0,O.mergeArrays)(P,H)),{tokens:P}}}class gt extends Ne{constructor(P){super(P),this.processors=P.processors.map(H=>Ne.fromConfig(H))}post_process(P,H=null,ae={}){let Me;for(const Ce of this.processors)if(Ce instanceof dt)P=Ce.post_process(P).tokens,H&&(H=Ce.post_process(H).tokens);else{const He=Ce.post_process(P,H,ae);P=He.tokens,Me=He.token_type_ids}return{tokens:P,token_type_ids:Me}}}class F extends f.Callable{constructor(P){super(),this.config=P,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=P.trim_offsets}static fromConfig(P){if(P===null)return null;switch(P.type){case"WordPiece":return new Qe(P);case"Metaspace":return new nr(P);case"ByteLevel":return new st(P);case"Replace":return new ne(P);case"ByteFallback":return new K(P);case"Fuse":return new pe(P);case"Strip":return new Fe(P);case"Sequence":return new It(P);case"CTC":return new pt(P);case"BPEDecoder":return new St(P);default:throw new Error(`Unknown Decoder type: ${P.type}`)}}_call(P){return this.decode(P)}decode(P){return this.decode_chain(P).join("")}decode_chain(P){throw Error("`decode_chain` should be implemented in subclass.")}}class ne extends F{decode_chain(P){const H=L(this.config.pattern);return H===null?P:P.map(ae=>ae.replaceAll(H,this.config.content))}}class K extends F{constructor(P){super(P),this.text_decoder=new TextDecoder}decode_chain(P){const H=[];let ae=[];for(const Me of P){let Ce=null;if(Me.length===6&&Me.startsWith("<0x")&&Me.endsWith(">")){const He=parseInt(Me.slice(3,5),16);isNaN(He)||(Ce=He)}if(Ce!==null)ae.push(Ce);else{if(ae.length>0){const He=this.text_decoder.decode(Uint8Array.from(ae));H.push(He),ae=[]}H.push(Me)}}if(ae.length>0){const Me=this.text_decoder.decode(Uint8Array.from(ae));H.push(Me),ae=[]}return H}}class pe extends F{decode_chain(P){return[P.join("")]}}class Fe extends F{constructor(P){super(P),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(P){return P.map(H=>{let ae=0;for(let Ce=0;Ce(ae!==0&&(H.startsWith(this.config.prefix)?H=H.replace(this.config.prefix,""):H=" "+H),this.cleanup&&(H=ie(H)),H))}}class st extends F{constructor(P){super(P),this.byte_decoder=xe,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(P){const H=P.join(""),ae=new Uint8Array([...H].map(Ce=>this.byte_decoder[Ce]));return this.text_decoder.decode(ae)}decode_chain(P){const H=[];let ae=[];for(const Me of P)this.added_tokens.find(Ce=>Ce.content===Me)!==void 0?(ae.length>0&&(H.push(this.convert_tokens_to_string(ae)),ae=[]),H.push(Me)):ae.push(Me);return ae.length>0&&H.push(this.convert_tokens_to_string(ae)),H}}class pt extends F{constructor(P){super(P),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(P){if(P.length===0)return"";const H=[P[0]];for(let Ce=1;CeCe!==this.pad_token).join("");return this.cleanup&&(Me=ie(Me).replaceAll(this.word_delimiter_token," ").trim()),Me}decode_chain(P){return[this.convert_tokens_to_string(P)]}}class It extends F{constructor(P){super(P),this.decoders=P.decoders.map(H=>F.fromConfig(H))}decode_chain(P){return this.decoders.reduce((H,ae)=>ae.decode_chain(H),P)}}class St extends F{constructor(P){super(P),this.suffix=this.config.suffix}decode_chain(P){return P.map((H,ae)=>H.replaceAll(this.suffix,ae===P.length-1?"":" "))}}class Ft extends F{decode_chain(P){let H="";for(let ae=1;aeae.normalize("NFKC")).join("～"):P=P.normalize("NFKC"),P}}class kr extends De{constructor(P){super(),this.tokenizers=P.pretokenizers.map(H=>De.fromConfig(H))}pre_tokenize_text(P,H){return this.tokenizers.reduce((ae,Me)=>Me.pre_tokenize(ae,H),[P])}}class Ar extends De{constructor(P){super()}pre_tokenize_text(P,H){return P.match(/\w+|[^\w\s]+/g)||[]}}class Qr extends De{constructor(P){super()}pre_tokenize_text(P,H){return $(P)}}class os extends De{constructor(P){super(),this.config=P,this.pattern=L(this.config.pattern),this.content=this.config.content}pre_tokenize_text(P,H){return this.pattern===null?[P]:[P.replaceAll(this.pattern,this.config.content)]}}const Xs=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function zs(Ee,P,H,ae){for(const Me of Object.keys(Ee)){const Ce=P-Ee[Me].length,He=H(Me),ct=new Array(Ce).fill(He);Ee[Me]=ae==="right"?(0,O.mergeArrays)(Ee[Me],ct):(0,O.mergeArrays)(ct,Ee[Me])}}function Ms(Ee,P){for(const H of Object.keys(Ee))Ee[H].length=P}class Nt extends f.Callable{constructor(H,ae){super();ge(this,"return_token_type_ids",!1);ge(this,"padding_side","right");this._tokenizer_config=ae,this.normalizer=Ue.fromConfig(H.normalizer),this.pre_tokenizer=De.fromConfig(H.pre_tokenizer),this.model=le.fromConfig(H.model,ae),this.post_processor=Ne.fromConfig(H.post_processor),this.decoder=F.fromConfig(H.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const Me of H.added_tokens){const Ce=new J(Me);this.added_tokens.push(Ce),this.model.tokens_to_ids.set(Ce.content,Ce.id),this.model.vocab[Ce.id]=Ce.content,Ce.special&&(this.special_tokens.push(Ce.content),this.all_special_ids.push(Ce.id))}if(this.additional_special_tokens=ae.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort((Me,Ce)=>Ce.content.length-Me.content.length).map(Me=>`${Me.lstrip?"\\s*":""}(${(0,O.escapeRegExp)(Me.content)})${Me.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=ae.model_max_length,this.remove_space=ae.remove_space,this.clean_up_tokenization_spaces=ae.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=ae.do_lowercase_and_remove_accent??!1,ae.padding_side&&(this.padding_side=ae.padding_side),this.legacy=!1,this.chat_template=ae.chat_template??null,Array.isArray(this.chat_template)){const Me=Object.create(null);for(const{name:Ce,template:He}of this.chat_template){if(typeof Ce!="string"||typeof He!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');Me[Ce]=He}this.chat_template=Me}this._compiled_template_cache=new Map}getToken(...H){for(const ae of H){const Me=this._tokenizer_config[ae];if(Me)if(typeof Me=="object"){if(Me.__type==="AddedToken")return Me.content;throw Error(`Unknown token: ${Me}`)}else return Me}return null}static async from_pretrained(H,{progress_callback:ae=null,config:Me=null,cache_dir:Ce=null,local_files_only:He=!1,revision:ct="main",legacy:yt=null}={}){const ht=await M(H,{progress_callback:ae,config:Me,cache_dir:Ce,local_files_only:He,revision:ct,legacy:yt});return new this(...ht)}_call(H,{text_pair:ae=null,add_special_tokens:Me=!0,padding:Ce=!1,truncation:He=null,max_length:ct=null,return_tensor:yt=!0,return_token_type_ids:ht=null}={}){const it=Array.isArray(H);let Pt;if(it){if(H.length===0)throw Error("text array must be non-empty");if(ae!==null){if(Array.isArray(ae)){if(H.length!==ae.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");Pt=H.map((rr,$e)=>this._encode_plus(rr,{text_pair:ae[$e],add_special_tokens:Me,return_token_type_ids:ht}))}else Pt=H.map(rr=>this._encode_plus(rr,{add_special_tokens:Me,return_token_type_ids:ht}))}else{if(H==null)throw Error("text may not be null or undefined");if(Array.isArray(ae))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");Pt=[this._encode_plus(H,{text_pair:ae,add_special_tokens:Me,return_token_type_ids:ht})]}if(ct===null?Ce==="max_length"?ct=this.model_max_length:ct=(0,Q.max)(Pt.map(rr=>rr.input_ids.length))[0]:He||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),ct=Math.min(ct,this.model_max_length??1/0),Ce||He)for(let rr=0;rrct?He&&Ms(Pt[rr],ct):Ce&&zs(Pt[rr],ct,$e=>$e==="input_ids"?this.pad_token_id:0,this.padding_side));const hr={};if(yt){if(!(Ce&&He)&&Pt.some($e=>{var wr;for(const Rr of Object.keys($e))if($e[Rr].length!==((wr=Pt[0][Rr])==null?void 0:wr.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 rr=[Pt.length,Pt[0].input_ids.length];for(const $e of Object.keys(Pt[0]))hr[$e]=new U.Tensor("int64",BigInt64Array.from(Pt.flatMap(wr=>wr[$e]).map(BigInt)),rr)}else{for(const rr of Object.keys(Pt[0]))hr[rr]=Pt.map($e=>$e[rr]);if(!it)for(const rr of Object.keys(hr))hr[rr]=hr[rr][0]}return hr}_encode_text(H){return H===null?null:(this.added_tokens_regex?H.split(this.added_tokens_regex).filter(Ce=>Ce):[H]).map((Ce,He)=>{if(this.added_tokens.find(yt=>yt.content===Ce)!==void 0)return Ce;{if(this.remove_space===!0&&(Ce=Ce.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(Ce=W(Ce)),this.normalizer!==null&&(Ce=this.normalizer(Ce)),Ce.length===0)return[];const yt=this.pre_tokenizer!==null?this.pre_tokenizer(Ce,{section_index:He}):[Ce];return this.model(yt)}}).flat()}_encode_plus(H,{text_pair:ae=null,add_special_tokens:Me=!0,return_token_type_ids:Ce=null}={}){const{tokens:He,token_type_ids:ct}=this._tokenize_helper(H,{pair:ae,add_special_tokens:Me}),yt=this.model.convert_tokens_to_ids(He),ht={input_ids:yt,attention_mask:new Array(yt.length).fill(1)};return(Ce??this.return_token_type_ids)&&ct&&(ht.token_type_ids=ct),ht}_tokenize_helper(H,{pair:ae=null,add_special_tokens:Me=!1}={}){const Ce=this._encode_text(H),He=this._encode_text(ae);return this.post_processor?this.post_processor(Ce,He,{add_special_tokens:Me}):{tokens:(0,O.mergeArrays)(Ce??[],He??[])}}tokenize(H,{pair:ae=null,add_special_tokens:Me=!1}={}){return this._tokenize_helper(H,{pair:ae,add_special_tokens:Me}).tokens}encode(H,{text_pair:ae=null,add_special_tokens:Me=!0,return_token_type_ids:Ce=null}={}){return this._encode_plus(H,{text_pair:ae,add_special_tokens:Me,return_token_type_ids:Ce}).input_ids}batch_decode(H,ae={}){return H instanceof U.Tensor&&(H=H.tolist()),H.map(Me=>this.decode(Me,ae))}decode(H,ae={}){if(H instanceof U.Tensor&&(H=se(H)),!Array.isArray(H)||H.length===0||!(0,O.isIntegralNumber)(H[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(H,ae)}decode_single(H,{skip_special_tokens:ae=!1,clean_up_tokenization_spaces:Me=null}){let Ce=this.model.convert_ids_to_tokens(H);ae&&(Ce=Ce.filter(ct=>!this.special_tokens.includes(ct)));let He=this.decoder?this.decoder(Ce):Ce.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(He=He.replaceAll(this.decoder.end_of_word_suffix," "),ae&&(He=He.trim())),(Me??this.clean_up_tokenization_spaces)&&(He=ie(He)),He}get_chat_template({chat_template:H=null,tools:ae=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const Me=this.chat_template;if(H!==null&&Object.hasOwn(Me,H))H=Me[H];else if(H===null)if(ae!==null&&"tool_use"in Me)H=Me.tool_use;else if("default"in Me)H=Me.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 ${Object.keys(Me).sort()}.`)}else if(H===null)if(this.chat_template)H=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co./docs/transformers/main/en/chat_templating");return H}apply_chat_template(H,{tools:ae=null,documents:Me=null,chat_template:Ce=null,add_generation_prompt:He=!1,tokenize:ct=!0,padding:yt=!1,truncation:ht=!1,max_length:it=null,return_tensor:Pt=!0,return_dict:hr=!1,tokenizer_kwargs:rr={},...$e}={}){if(Ce=this.get_chat_template({chat_template:Ce,tools:ae}),typeof Ce!="string")throw Error(`chat_template must be a string, but got ${typeof Ce}`);let wr=this._compiled_template_cache.get(Ce);wr===void 0&&(wr=new v.Template(Ce),this._compiled_template_cache.set(Ce,wr));const Rr=Object.create(null);for(const Zr of Xs){const Bt=this.getToken(Zr);Bt&&(Rr[Zr]=Bt)}const Jr=wr.render({messages:H,add_generation_prompt:He,tools:ae,documents:Me,...Rr,...$e});if(ct){const Zr=this._call(Jr,{add_special_tokens:!1,padding:yt,truncation:ht,max_length:it,return_tensor:Pt,...rr});return hr?Zr:Zr.input_ids}return Jr}}class Qs extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class ks extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class Bs extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class Ss extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class is extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class $s extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class cs extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class As extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class Ys extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class as extends Nt{}class nt extends Nt{}class _t extends Nt{constructor(H,ae){super(H,ae);ge(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Ot extends Nt{constructor(){super(...arguments);ge(this,"return_token_type_ids",!0)}}class lr extends Nt{}class bs extends Nt{}class tr extends Nt{}class ts extends Nt{constructor(P,H){super(P,H),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(ae=>this.languageRegex.test(ae)),this.lang_to_token=ae=>ae}_build_translation_inputs(P,H,ae){return en(this,P,H,ae)}}class Rs extends ts{}class Js extends Nt{}class Ns extends Nt{}const vs="▁";class Sn extends Nt{constructor(H,ae){super(H,ae);ge(this,"padding_side","left");this.legacy=ae.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new At({replacement:vs,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(H){if(H===null)return null;if(this.legacy||H.length===0)return super._encode_text(H);let ae=super._encode_text(vs+H.replaceAll(vs," "));return ae.length>1&&ae[0]===vs&&this.special_tokens.includes(ae[1])&&(ae=ae.slice(1)),ae}}class js extends Nt{}class $n extends Nt{}class Jn extends Nt{}class Us extends Nt{}class Ts extends Nt{}class ls extends Nt{}class mn extends Nt{}class Zs extends Nt{}class _n extends Nt{}function en(Ee,P,H,ae){if(!("language_codes"in Ee)||!Array.isArray(Ee.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Ee)||!(Ee.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Ee)||typeof Ee.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const Me=ae.src_lang,Ce=ae.tgt_lang;if(!Ee.language_codes.includes(Ce))throw new Error(`Target language code "${Ce}" is not valid. Must be one of: {${Ee.language_codes.join(", ")}}`);if(Me!==void 0){if(!Ee.language_codes.includes(Me))throw new Error(`Source language code "${Me}" is not valid. Must be one of: {${Ee.language_codes.join(", ")}}`);for(const He of Ee.post_processor.config.single)if("SpecialToken"in He&&Ee.languageRegex.test(He.SpecialToken.id)){He.SpecialToken.id=Ee.lang_to_token(Me);break}}return ae.forced_bos_token_id=Ee.model.convert_tokens_to_ids([Ee.lang_to_token(Ce)])[0],Ee._call(P,H)}class xs extends Nt{constructor(P,H){super(P,H),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(ae=>this.languageRegex.test(ae)),this.lang_to_token=ae=>ae}_build_translation_inputs(P,H,ae){return en(this,P,H,ae)}}class zt extends Nt{constructor(P,H){super(P,H),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(ae=>this.languageRegex.test(ae)).map(ae=>ae.slice(2,-2)),this.lang_to_token=ae=>`__${ae}__`}_build_translation_inputs(P,H,ae){return en(this,P,H,ae)}}class fn extends Nt{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(P,{return_timestamps:H=!1,return_language:ae=!1,time_precision:Me=null,force_full_sequences:Ce=!0}={}){if(Me===null)throw Error("Must specify time_precision");let He=null;const ct=H==="word";function yt(){return{language:He,timestamp:[null,null],text:""}}const ht=[];let it=yt(),Pt=0;const hr=this.timestamp_begin,$e=hr+1500;let wr=[],Rr=[],Jr=!1,Zr=null;const Bt=new Set(this.all_special_ids);for(const er of P){const mr=er.tokens,vt=ct?er.token_timestamps:null;let yr=null,Es=hr;if("stride"in er){const[Mt,br,ze]=er.stride;if(Pt-=br,Zr=Mt-ze,br&&(Es=br/Me+hr),ze)for(let wt=mr.length-1;wt>=0;--wt){const rs=Number(mr[wt]);if(rs>=hr){if(yr!==null&&(rs-hr)*Me=hr&&br<=$e){const ze=(br-hr)*Me+Pt,wt=(0,Q.round)(ze,2);if(yr!==null&&br>=yr)Jr=!0;else if(Jr||wr.length>0&&br0?(wr.push(Fr),ct&&Rr.push(qr)):wr.every(Mt=>Mt.length===0)&&(it=yt(),wr=[],Fr=[],Rr=[],qr=[])}if(wr.length>0){if(Ce&&H)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[er,mr]=this.findLongestCommonSequence(wr,Rr),vt=this.decode(er);it.text=vt,ct&&(it.words=this.collateWordTimestamps(er,mr,He)),ht.push(it)}let Nr=Object.create(null);const ps=ht.map(er=>er.text).join("");if(H||ae){for(let er=0;er0;let ct=He?[]:null,yt=He?H[0]:null;for(let ht=1;htbr===Es[ze]&&yt[ps+ze]<=H[ht][vt+ze]).length:Fr=mr.filter((br,ze)=>br===Es[ze]).length;const qr=Nr/1e4,Mt=Fr/Nr+qr;Fr>1&&Mt>Pt&&(Pt=Mt,hr=[ps,er,vt,yr])}const[$e,wr,Rr,Jr]=hr,Zr=Math.floor((wr+$e)/2),Bt=Math.floor((Jr+Rr)/2);Ce.push(...ae.slice(0,Zr)),ae=it.slice(Bt),Me=ae.length,He&&(ct.push(...yt.slice(0,Zr)),yt=H[ht].slice(Bt))}return Ce.push(...ae),He?(ct.push(...yt),[Ce,ct]):[Ce,[]]}collateWordTimestamps(P,H,ae){const[Me,Ce,He]=this.combineTokensIntoWords(P,ae),ct=[];for(let yt=0;yt=Me){const ct=((He-Me)*ae).toFixed(2);Ce.push(`<|${ct}|>`),Ce.push([])}else Ce[Ce.length-1].push(He);return Ce=Ce.map(He=>typeof He=="string"?He:super.decode(He,H)),Ce.join("")}splitTokensOnUnicode(P){const H=this.decode(P,{decode_with_timestamps:!0}),ae="�",Me=[],Ce=[],He=[];let ct=[],yt=[],ht=0;for(let it=0;it=this.model.tokens_to_ids.get("<|endoftext|>"),$e=it.startsWith(" "),wr=it.trim(),Rr=yt.test(wr);if(rr||$e||Rr||Ce.length===0)Ce.push(it),He.push(Pt),ct.push(hr);else{const Jr=Ce.length-1;Ce[Jr]+=it,He[Jr].push(...Pt),ct[Jr].push(...hr)}}return[Ce,He,ct]}mergePunctuations(P,H,ae,Me,Ce){const He=structuredClone(P),ct=structuredClone(H),yt=structuredClone(ae);let ht=He.length-2,it=He.length-1;for(;ht>=0;)He[ht].startsWith(" ")&&Me.includes(He[ht].trim())?(He[it]=He[ht]+He[it],ct[it]=(0,O.mergeArrays)(ct[ht],ct[it]),yt[it]=(0,O.mergeArrays)(yt[ht],yt[it]),He[ht]="",ct[ht]=[],yt[ht]=[]):it=ht,--ht;for(ht=0,it=1;itPt),ct.filter(Pt=>Pt.length>0),yt.filter(Pt=>Pt.length>0)]}}class An extends Nt{}class In extends Nt{}class On extends Nt{}class Vs extends Nt{constructor(P,H){super(P,H),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(ae=>this.languageRegex.test(ae)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(P){if(P===null)return null;const[H,...ae]=P.trim().split(this.languageRegex);if(ae.length===0)return super._encode_text(H);if(ae.length===2){const[Me,Ce]=ae;return this.supported_language_codes.includes(Me)||console.warn(`Unsupported language code "${Me}" detected, which may lead to unexpected behavior. 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Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co./docs/transformers.js/guides/node-audio-processing.");const J=await(await(0,f.getFile)(T)).arrayBuffer(),le=new AudioContext({sampleRate:ee});typeof ee>"u"&&console.warn(`No sampling rate provided, using default of ${le.sampleRate}Hz.`);const de=await le.decodeAudioData(J);let fe;if(de.numberOfChannels===2){const ke=Math.sqrt(2),xe=de.getChannelData(0),Le=de.getChannelData(1);fe=new Float32Array(xe.length);for(let qe=0;qe2595*Math.log10(1+T/700),kaldi:T=>1127*Math.log(1+T/700),slaney:(T,ee=1e3,J=15,le=27/Math.log(6.4))=>T>=ee?J+Math.log(T/ee)*le:3*T/200};function b(T,ee="htk"){const J=M[ee];if(!J)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof T=="number"?J(T):T.map(le=>J(le))}const L={htk:T=>700*(10**(T/2595)-1),kaldi:T=>700*(Math.exp(T/1127)-1),slaney:(T,ee=1e3,J=15,le=Math.log(6.4)/27)=>T>=J?ee*Math.exp(le*(T-J)):200*T/3};function q(T,ee="htk"){const J=L[ee];if(!J)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof T=="number"?J(T):T.map(le=>J(le))}function se(T,ee){const J=Float64Array.from({length:ee.length-1},(ke,xe)=>ee[xe+1]-ee[xe]),le=Array.from({length:T.length},()=>new Array(ee.length));for(let ke=0;kenew Array(T.length));for(let ke=0;keT+le*fe)}function z(T,ee,J,le,de,fe=null,ke="htk",xe=!1){if(fe!==null&&fe!=="slaney")throw new Error('norm must be one of null or "slaney"');const Le=b(J,ke),qe=b(le,ke),Ue=ie(Le,qe,ee+2);let ut=q(Ue,ke),ue;if(xe){const he=de/(T*2);ue=b(Float64Array.from({length:T},(Pe,Be)=>Be*he),ke),ut=Ue}else ue=ie(0,Math.floor(de/2),T);const re=se(ue,ut);if(fe!==null&&fe==="slaney")for(let he=0;hede)throw Error(`frame_length (${J}) may not be larger than fft_length (${de})`);if(De!==J)throw new Error(`Length of the window (${De}) must equal frame_length (${J})`);if(le<=0)throw new Error("hop_length must be greater than zero");if(fe===null&&Ue!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. Specify `power` to fix this issue.");if(ke){if(xe!=="reflect")throw new Error(`pad_mode="${xe}" not implemented yet.`);const F=Math.floor((de-1)/2)+1;T=W(T,F,F)}let ce=Math.floor(1+Math.floor((T.length-J)/le));et!==null&&cece?oe&&(je=Xe):je=Re=Xe);const We=new O.FFT(de),Ne=new Float64Array(de),Ze=new Float64Array(We.outputBufferSize),at=new Float32Array(ve*je);for(let F=0;F=1;--pe)Ne[pe]-=qe*Ne[pe-1];Ne[0]*=1-qe}for(let pe=0;peMath.pow(xe,.85));break;default:throw new Error(`Unknown window type ${ee}.`)}if(J&&(ke=ke.subarray(0,T)),le===null)return ke;if(T>le)throw new Error(`Length of the window (${T}) may not be larger than frame_length (${le})`);return ke}},"./src/utils/constants.js":(Te,A,s)=>{s.r(A),s.d(A,{CHAT_TEMPLATE_NAME:()=>w,CONFIG_NAME:()=>O,FEATURE_EXTRACTOR_NAME:()=>N,GENERATION_CONFIG_NAME:()=>v,GITHUB_ISSUE_URL:()=>f,IMAGE_PROCESSOR_NAME:()=>Q,PROCESSOR_NAME:()=>U});const 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lMaskPipeline;c.Florence2ForConditionalGeneration;c.Florence2PreTrainedModel;c.Florence2Processor;c.ForcedBOSTokenLogitsProcessor;c.ForcedEOSTokenLogitsProcessor;c.GLPNFeatureExtractor;c.GLPNForDepthEstimation;c.GLPNModel;c.GLPNPreTrainedModel;c.GPT2LMHeadModel;c.GPT2Model;c.GPT2PreTrainedModel;c.GPT2Tokenizer;c.GPTBigCodeForCausalLM;c.GPTBigCodeModel;c.GPTBigCodePreTrainedModel;c.GPTJForCausalLM;c.GPTJModel;c.GPTJPreTrainedModel;c.GPTNeoForCausalLM;c.GPTNeoModel;c.GPTNeoPreTrainedModel;c.GPTNeoXForCausalLM;c.GPTNeoXModel;c.GPTNeoXPreTrainedModel;c.GPTNeoXTokenizer;c.Gemma2ForCausalLM;c.Gemma2Model;c.Gemma2PreTrainedModel;c.GemmaForCausalLM;c.GemmaModel;c.GemmaPreTrainedModel;c.GemmaTokenizer;c.GraniteForCausalLM;c.GraniteModel;c.GranitePreTrainedModel;c.Grok1Tokenizer;c.GroupViTModel;c.GroupViTPreTrainedModel;c.HerbertTokenizer;c.HieraForImageClassification;c.HieraModel;c.HieraPreTrainedModel;c.HubertForCTC;c.HubertForSequenceClassification;c.HubertModel;c.HubertPreTrainedModel;c.IJepaForImageClassification;c.IJepaModel;c.IJepaPreTrainedModel;c.Idefics3ForConditionalGeneration;c.Idefics3ImageProcessor;c.Idefics3PreTrainedModel;c.Idefics3Processor;c.ImageClassificationPipeline;c.ImageFeatureExtractionPipeline;c.ImageFeatureExtractor;c.ImageMattingOutput;c.ImageProcessor;c.ImageSegmentationPipeline;c.ImageToImagePipeline;c.ImageToTextPipeline;c.InterruptableStoppingCriteria;c.JAISLMHeadModel;c.JAISModel;c.JAISPreTrainedModel;c.JinaCLIPImageProcessor;c.JinaCLIPModel;c.JinaCLIPPreTrainedModel;c.JinaCLIPProcessor;c.JinaCLIPTextModel;c.JinaCLIPVisionModel;c.LlamaForCausalLM;c.LlamaModel;c.LlamaPreTrainedModel;c.LlamaTokenizer;c.LlavaForConditionalGeneration;c.LlavaOnevisionForConditionalGeneration;c.LlavaOnevisionImageProcessor;c.LlavaPreTrainedModel;c.LogitsProcessor;c.LogitsProcessorList;c.LogitsWarper;c.LongT5ForConditionalGeneration;c.LongT5Model;c.LongT5PreTrainedModel;c.M2M100ForConditionalGeneration;c.M2M100Model;c.M2M100PreTrainedModel;c.M2M100Tokenizer;c.MBart50Tokenizer;c.MBartForCausalLM;c.MBartForConditionalGeneration;c.MBartForSequenceClassification;c.MBartModel;c.MBartPreTrainedModel;c.MBartTokenizer;c.MPNetForMaskedLM;c.MPNetForQuestionAnswering;c.MPNetForSequenceClassification;c.MPNetForTokenClassification;c.MPNetModel;c.MPNetPreTrainedModel;c.MPNetTokenizer;c.MT5ForConditionalGeneration;c.MT5Model;c.MT5PreTrainedModel;c.MarianMTModel;c.MarianModel;c.MarianPreTrainedModel;c.MarianTokenizer;c.Mask2FormerImageProcessor;c.MaskFormerFeatureExtractor;c.MaskFormerForInstanceSegmentation;c.MaskFormerImageProcessor;c.MaskFormerModel;c.MaskFormerPreTrainedModel;c.MaskedLMOutput;c.MaxLengthCriteria;c.MgpstrForSceneTextRecognition;c.MgpstrModelOutput;c.MgpstrPreTrainedModel;c.MgpstrProcessor;c.MgpstrTokenizer;c.MinLengthLogitsProcessor;c.MinNewTokensLengthLogitsProcessor;c.MistralForCausalLM;c.MistralModel;c.MistralPreTrainedModel;c.MobileBertForMaskedLM;c.MobileBertForQuestionAnswering;c.MobileBertForSequenceClassification;c.MobileBertModel;c.MobileBertPreTrainedModel;c.MobileBertTokenizer;c.MobileLLMForCausalLM;c.MobileLLMModel;c.MobileLLMPreTrainedModel;c.MobileNetV1FeatureExtractor;c.MobileNetV1ForImageClassification;c.MobileNetV1ImageProcessor;c.MobileNetV1Model;c.MobileNetV1PreTrainedModel;c.MobileNetV2FeatureExtractor;c.MobileNetV2ForImageClassification;c.MobileNetV2ImageProcessor;c.MobileNetV2Model;c.MobileNetV2PreTrainedModel;c.MobileNetV3FeatureExtractor;c.MobileNetV3ForImageClassification;c.MobileNetV3ImageProcessor;c.MobileNetV3Model;c.MobileNetV3PreTrainedModel;c.MobileNetV4FeatureExtractor;c.MobileNetV4ForImageClassification;c.MobileNetV4ImageProcessor;c.MobileNetV4Model;c.MobileNetV4PreTrainedModel;c.MobileViTFeatureExtractor;c.MobileViTForImageClassification;c.MobileViTImageProcessor;c.MobileViTModel;c.MobileViTPreTrainedModel;c.MobileViTV2ForImageClassification;c.MobileViTV2Model;c.MobileViTV2PreTrainedModel;c.ModelOutput;c.ModernBertForMaskedLM;c.ModernBertForSequenceClassification;c.ModernBertForTokenClassification;c.ModernBertModel;c.ModernBertPreTrainedModel;c.Moondream1ForConditionalGeneration;c.MoonshineFeatureExtractor;c.MoonshineForConditionalGeneration;c.MoonshineModel;c.MoonshinePreTrainedModel;c.MoonshineProcessor;c.MptForCausalLM;c.MptModel;c.MptPreTrainedModel;c.MultiModalityCausalLM;c.MultiModalityPreTrainedModel;c.MusicgenForCausalLM;c.MusicgenForConditionalGeneration;c.MusicgenModel;c.MusicgenPreTrainedModel;c.NllbTokenizer;c.NoBadWordsLogitsProcessor;c.NoRepeatNGramLogitsProcessor;c.NomicBertModel;c.NomicBertPreTrainedModel;c.NougatImageProcessor;c.NougatTokenizer;c.OPTForCausalLM;c.OPTModel;c.OPTPreTrainedModel;c.ObjectDetectionPipeline;c.Olmo2ForCausalLM;c.Olmo2Model;c.Olmo2PreTrainedModel;c.OlmoForCausalLM;c.OlmoModel;c.OlmoPreTrainedModel;c.OpenELMForCausalLM;c.OpenELMModel;c.OpenELMPreTrainedModel;c.OwlViTFeatureExtractor;c.OwlViTForObjectDetection;c.OwlViTImageProcessor;c.OwlViTModel;c.OwlViTPreTrainedModel;c.OwlViTProcessor;c.Owlv2ForObjectDetection;c.Owlv2ImageProce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xh=c.Tensor;c.Text2TextGenerationPipeline;c.TextClassificationPipeline;c.TextGenerationPipeline;c.TextStreamer;c.TextToAudioPipeline;c.TokenClassificationPipeline;c.TokenClassifierOutput;c.TokenizerModel;c.TopKLogitsWarper;c.TopPLogitsWarper;c.TrOCRForCausalLM;c.TrOCRPreTrainedModel;c.TranslationPipeline;c.UniSpeechForCTC;c.UniSpeechForSequenceClassification;c.UniSpeechModel;c.UniSpeechPreTrainedModel;c.UniSpeechSatForAudioFrameClassification;c.UniSpeechSatForCTC;c.UniSpeechSatForSequenceClassification;c.UniSpeechSatModel;c.UniSpeechSatPreTrainedModel;c.VLChatProcessor;c.VLMImageProcessor;c.ViTFeatureExtractor;c.ViTForImageClassification;c.ViTImageProcessor;c.ViTMAEModel;c.ViTMAEPreTrainedModel;c.ViTMSNForImageClassification;c.ViTMSNModel;c.ViTMSNPreTrainedModel;c.ViTModel;c.ViTPreTrainedModel;c.VisionEncoderDecoderModel;c.VitMatteForImageMatting;c.VitMatteImageProcessor;c.VitMattePreTrainedModel;c.VitPoseForPoseEstimation;c.VitPoseImageProcessor;c.VitPosePreTrainedModel;c.VitsModel;c.VitsModelOutput;c.VitsPreTrainedModel;c.VitsTokenizer;c.Wav2Vec2BertForCTC;c.Wav2Vec2BertForSequenceClassification;c.Wav2Vec2BertModel;c.Wav2Vec2BertPreTrainedModel;c.Wav2Vec2CTCTokenizer;c.Wav2Vec2FeatureExtractor;c.Wav2Vec2ForAudioFrameClassification;c.Wav2Vec2ForCTC;c.Wav2Vec2ForSequenceClassification;c.Wav2Vec2Model;c.Wav2Vec2PreTrainedModel;c.Wav2Vec2ProcessorWithLM;c.WavLMForAudioFrameClassification;c.WavLMForCTC;c.WavLMForSequenceClassification;c.WavLMForXVector;c.WavLMModel;c.WavLMPreTrainedModel;c.WeSpeakerFeatureExtractor;c.WeSpeakerResNetModel;c.WeSpeakerResNetPreTrainedModel;c.WhisperFeatureExtractor;c.WhisperForConditionalGeneration;c.WhisperModel;c.WhisperPreTrainedModel;c.WhisperProcessor;c.WhisperTextStreamer;c.WhisperTimeStampLogitsProcessor;c.WhisperTokenizer;c.XLMForQuestionAnswering;c.XLMForSequenceClassification;c.XLMForTokenClassification;c.XLMModel;c.XLMPreTrainedModel;c.XLMRobertaForMaskedLM;c.XLMRobertaForQuestionAnswering;c.XLMRobertaForSequenceClassification;c.XLMRobertaForTokenClassification;c.XLMRobertaModel;c.XLMRobertaPreTrainedModel;c.XLMRobertaTokenizer;c.XLMTokenizer;c.XLMWithLMHeadModel;c.XVectorOutput;c.YolosFeatureExtractor;c.YolosForObjectDetection;c.YolosImageProcessor;c.YolosModel;c.YolosObjectDetectionOutput;c.YolosPreTrainedModel;c.ZeroShotAudioClassificationPipeline;c.ZeroShotClassificationPipeline;c.ZeroShotImageClassificationPipeline;c.ZeroShotObjectDetectionPipeline;c.bankers_round;c.cat;c.cos_sim;c.dot;c.dynamic_time_warping;c.env;c.full;c.full_like;c.getKeyValueShapes;c.hamming;c.hanning;c.interpolate;c.interpolate_4d;c.interpolate_data;c.is_chinese_char;c.layer_norm;c.load_image;c.log_softmax;c.magnitude;c.matmul;c.max;c.mean;c.mean_pooling;c.medianFilter;c.mel_filter_bank;c.min;c.ones;c.ones_like;c.permute;c.permute_data;var Nf=c.pipeline;c.quantize_embeddings;c.rand;c.read_audio;c.rfft;c.round;c.slice;c.softmax;c.spectrogram;c.stack;c.std_mean;c.topk;c.window_function;c.zeros;c.zeros_like;const va=16e3,Eh=va/1e3,jf=.3,Uf=.1,Vf=400,Wf=Vf*Eh,Gf=80,vh=Gf*Eh,Kf=250*Eh,Hf=30,qf=512,Xf=Math.ceil(vh/qf);async function Qf(){try{return!("gpu"in navigator)||!navigator.gpu?!1:(await navigator.gpu.requestAdapter(),!0)}catch(Te){return console.error(Te),!1}}var kn=(Te=>(Te.Status="status",Te.Output="output",Te.Info="info",Te.Request="request",Te.Error="error",Te.Load="load",Te))(kn||{}),Ph=(Te=>(Te.RecordingStart="recording_start",Te.RecordingEnd="recording_end",Te.Ready="ready",Te))(Ph||{}),Lp=(Te=>(Te.UntilNext="until_next",Te))(Lp||{});let Th,zp,Bp=Promise.resolve();const ba=new Float32Array(Hf*va);let Cn=0;const Yf=new xh("int64",[va],[]);let l_=new xh("float32",new Float32Array(2*1*128),[2,1,128]),$c=!1,Ac=0;const Jf={webgpu:{encoder_model:"fp32",decoder_model_merged:"q4"},wasm:{encoder_model:"fp32",decoder_model_merged:"q8"}};async function Zf(){return await Rf.from_pretrained("onnx-community/silero-vad",{config:{model_type:"custom"},dtype:"fp32"}).catch(Te=>{throw self.postMessage({type:kn.Error,error:Te}),Te})}async function eg(Te){return await Nf("automatic-speech-recognition","onnx-community/moonshine-base-ONNX",{device:Te,dtype:Jf[Te]}).catch(A=>{throw self.postMessage({type:kn.Error,error:A}),A})}async function tg(Te){if(Th===void 0)return console.warn("VAD model not loaded yet"),!1;const A=new xh("float32",Te,[1,Te.length]),{stateN:s,output:f}=await(Bp=Bp.then(N=>Th({input:A,sr:Yf,state:l_})));l_=s;const O=f.data[0];return O>jf||$c&&O>=Uf}async function rg(Te,A){if(zp===void 0){console.warn("Transcriber model not loaded yet");return}const{text:s}=await(Bp=Bp.then(f=>zp(Te)));self.postMessage({type:kn.Output,buffer:Te,message:s,...A})}function h_(Te=0){self.postMessage({type:kn.Status,status:Ph.RecordingEnd,message:"Transcribing...",duration:Lp.UntilNext}),ba.fill(0,Te),Cn=Te,$c=!1,Ac=0}const Ic=[];function u_(Te){const s=Date.now()-(Ac+vh)/va*1e3,f=s-Cn/va*1e3,O=s-f,N=(Te==null?void 0:Te.length)??0,Q=ba.slice(0,Cn+vh),U=Ic.reduce((y,M)=>y+M.length,0),w=new Float32Array(U+Q.length);let v=0;for(const y of Ic)w.set(y,v),v+=y.length;w.set(Q,v),rg(w,{start:f,end:s,duration:O}),Te&&ba.set(Te,0),h_(N)}async function sg(){const Te=await Qf()?"webgpu":"wasm";self.postMessage({type:kn.Info,message:`Using device: "${Te}"`}),self.postMessage({type:kn.Info,message:"Loading models...",duration:Lp.UntilNext}),Th=await Zf(),zp=await eg(Te),await zp(new Float32Array(va)),self.postMessage({type:"status",status:"ready",message:"Ready!"}),self.onmessage=async A=>{const{buffer:s}=A.data,f=$c,O=await tg(s);if(!f&&!O){Ic.length>=Xf&&Ic.shift(),Ic.push(s);return}const N=ba.length-Cn;if(s.length>=N){ba.set(s.subarray(0,N),Cn),Cn+=N;const Q=s.subarray(N);u_(Q);return}else ba.set(s,Cn),Cn+=s.length;if(O){$c||self.postMessage({type:kn.Status,status:Ph.RecordingStart,message:"Listening...",duration:Lp.UntilNext}),$c=!0,Ac=0;return}if(Ac+=s.length,!(Ac{const{type:A}=Te.data;switch(A){case kn.Load:sg();break}});