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(indices[${ot}])`);let gt=d<2?"":` + fn i2o_${n}(indices: ${v.indices}) -> u32 { + return ${Ie.join("+")}; + }`,ut=ot=>(re.indicesToOffset=!0,d<2?ot:`i2o_${n}(${ot})`),yt=(...ot)=>d===0?"0u":`${v.indices}(${ot.map(oe).join(",")})`,Dt=(ot,Wt)=>d<2?`${ot}`:`${Ot(ot,Wt,d)}`,Bt=(ot,Wt,ir)=>d<2?`${ot}=${ir};`:`${Ot(ot,Wt,d)}=${ir};`,mr={},vr=(ot,Wt)=>{re.broadcastedIndicesToOffset=!0;let ir=`${Wt.name}broadcastedIndicesTo${n}Offset`;if(ir in mr)return`${ir}(${ot})`;let Vr=[];for(let fn=d-1;fn>=0;fn--){let kn=Wt.indicesGet("outputIndices",fn+Wt.rank-d);Vr.push(`${Dt(be,fn)} * (${kn} % ${Dt(we,fn)})`)}return mr[ir]=`fn ${ir}(outputIndices: ${Wt.type.indices}) -> u32 { + return ${Vr.length>0?Vr.join("+"):"0u"}; + }`,`${ir}(${ot})`},or=(ot,Wt)=>(()=>{if(v.storage===v.value)return`${n}[${ot}]=${Wt};`;if(v.storage==="vec2"&&v.value==="i32")return`${n}[${ot}]=vec2(u32(${Wt}), select(0u, 0xFFFFFFFFu, ${Wt} < 0));`;if(v.storage==="vec2"&&v.value==="u32")return`${n}[${ot}]=vec2(u32(${Wt}), 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}`})(),Gr=(...ot)=>{if(ot.length!==d)throw new Error(`indices length must be ${d}`);let Wt=ot.map(oe).join(",");return d===0?Ir("0u"):d===1?Ir(Wt[0]):(re.get=!0,re.getByIndices=!0,re.indicesToOffset=!0,`get_${n}(${Wt})`)},jt=ot=>d<2?Ir(ot):(re.getByIndices=!0,re.indicesToOffset=!0,`get_${n}ByIndices(${ot})`),er=d<2?"":` + fn set_${n}ByIndices(indices: ${v.indices}, value: ${H}) { + ${or(`i2o_${n}(indices)`,"value")} + }`,_r=d<2?"":(()=>{let ot=x.map(ir=>`d${ir}: u32`).join(", "),Wt=x.map(ir=>`d${ir}`).join(", ");return` + fn set_${n}(${ot}, value: ${H}) { + set_${n}ByIndices(${yt(Wt)}, value); + }`})();return{impl:()=>{let ot=[],Wt=!1;return re.offsetToIndices&&(ot.push(De),Wt=!0),re.indicesToOffset&&(ot.push(gt),Wt=!0),re.broadcastedIndicesToOffset&&(Object.values(mr).forEach(ir=>ot.push(ir)),Wt=!0),re.set&&(ot.push(_r),Wt=!0),re.setByIndices&&(ot.push(er),Wt=!0),re.get&&(ot.push(fr),Wt=!0),re.getByIndices&&(ot.push(Zr),Wt=!0),!h&&Wt&&ot.unshift(`const ${we} = 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[${i}, ${s}, ${u}] exceeds the maximum workgroup invocations ${this.limits.maxComputeInvocationsPerWorkgroup}.`);let c=this.normalizedDispatchGroup[1]===1&&this.normalizedDispatchGroup[2]===1,h=c?`@builtin(global_invocation_id) global_id : vec3, + @builtin(workgroup_id) workgroup_id : vec3, + @builtin(local_invocation_id) local_id : vec3`:`@builtin(global_invocation_id) global_id : vec3, + @builtin(local_invocation_id) local_id : vec3, + @builtin(local_invocation_index) local_idx : u32, + @builtin(workgroup_id) workgroup_id : vec3, + @builtin(num_workgroups) num_workgroups : vec3`,d=c?"let global_idx = global_id.x; let local_idx = local_id.x;":`let global_idx = (workgroup_id.z * num_workgroups[0] * num_workgroups[1] + + workgroup_id.y * num_workgroups[0] + workgroup_id.x) * ${i*s*u}u + local_idx;`;return`@compute @workgroup_size(${i}, ${s}, ${u}) + fn main(${h}) { + ${d} + `}appendVariableUniforms(n){n.rank!==0&&(n.shape.startsWith("uniforms.")&&this.uniforms.push({name:n.shape.replace("uniforms.",""),type:"u32",length:n.rank}),n.strides.startsWith("uniforms.")&&this.uniforms.push({name:n.strides.replace("uniforms.",""),type:"u32",length:n.rank}))}declareVariable(n,i){if(n.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(n),this.appendVariableUniforms(n);let s=n.usage==="input"?"read":"read_write",u=n.type.storage;return`@group(0) @binding(${i}) var ${n.name}: array<${u}>;`}declareVariables(...n){return n.map(i=>this.declareVariable(i,this.variableIndex++)).join(` +`)}registerInternalVariable(n){if(n.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() instead.");this.internalVariables.push(n),this.appendVariableUniforms(n)}registerInternalVariables(...n){return 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c=0;c1&&d===1&&u.unshift(h)}return u}}),du,_a,Ho,fu,hi,pu,wa,yo=p(()=>{Zt(),Jt(),gr(),ur(),du=n=>{if(!n||n.length!==1)throw new Error("Transpose requires 1 input.")},_a=(n,i)=>i&&i.length!==n?[...new Array(n).keys()].reverse():i,Ho=(n,i)=>ct.sortBasedOnPerm(n,_a(n.length,i)),fu=(n,i,s,u)=>{let c=[];c.push(`fn perm(i: ${u.type.indices}) -> ${s.type.indices} { + var a: ${s.type.indices};`);for(let h=0;h{let s=n.dataType,u=n.dims.length,c=_a(u,i),h=Ho(n.dims,c),d=Gt("output",s,h.length),x=_t("a",s,u),k;if(c.length===2&&c[0]===1&&c[1]===0){let $=d.type.value,H=[16,16,1];k=q=>` + ${q.registerUniform("output_size","u32").declareVariables(x,d)} + var tile : array, ${H[0]}>; + ${q.mainStart(H)} + var x = workgroup_id.x * ${H[0]}u + local_id.x; + var y = workgroup_id.y * ${H[0]}u + local_id.y; + let width = uniforms.output_shape[0]; + let height = uniforms.output_shape[1]; + if (x < width && y < height) { + tile[local_id.y][local_id.x] = ${x.getByOffset("y * width + x")}; + } + workgroupBarrier(); + x = workgroup_id.y * ${H[0]}u + local_id.x; + y = workgroup_id.x * ${H[0]}u + local_id.y; + if (x < height && y < width) { + ${d.setByOffset("y * height + x","tile[local_id.x][local_id.y]")} + } + }`}else k=$=>` + ${$.registerUniform("output_size","u32").declareVariables(x,d)} + + ${fu(c,u,x,d)} + + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${d.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${d.setByOffset("global_idx",x.getByIndices("aIndices"))} + }`;return{name:"Transpose",shaderCache:{hint:`${i}`,inputDependencies:["rank"]},getRunData:$=>{let H=ct.size(h);return{outputs:[{dims:h,dataType:$[0].dataType}],dispatchGroup:{x:Math.ceil(H/64)},programUniforms:[{type:12,data:H},...$t($[0].dims,h)]}},getShaderSource:k}},pu=(n,i)=>{du(n.inputs),n.compute(hi(n.inputs[0],i.perm))},wa=n=>qt({perm:n.perm})}),hu,mu,gu,_u,ya,wu,yu,va,vu,bu,Xn,xu,Mu,ba,Tu,ku,xa,Su,Eu,Ma,Cu,np=p(()=>{Zt(),Jt(),ur(),$a(),yo(),hu={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},mu={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + 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${re.mainStart(v)} + + let outputIndex = global_idx / ${v}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${gu[u]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${v}) { + let candidate = f32(${H.getByOffset("offset + k")}); + bestValue = ${hu[u]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${v}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 = ${mu[u]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${q.setByOffset("outputIndex",`${u==="mean"?`${q.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${q.type.storage}(${_u[u]})`}`)}; + } + }`,getRunData:()=>({outputs:[{dims:h,dataType:c}],dispatchGroup:{x:k},programUniforms:[{type:12,data:$}]})}},Xn=(n,i,s,u)=>{let c=n.inputs.length===1?s:Is(n.inputs,s),h=c.axes;h.length===0&&!c.noopWithEmptyAxes&&(h=n.inputs[0].dims.map((oe,re)=>re));let d=ct.normalizeAxes(h,n.inputs[0].dims.length),x=d,k=n.inputs[0],$=vu(x,n.inputs[0].dims.length);$.length>0&&(k=n.compute(hi(n.inputs[0],$),{inputs:[0],outputs:[-1]})[0],x=ya(x.length,k.dims.length));let[H,q]=wu(k.dims,x),v=H;c.keepDims&&(v=yu(H,d)),n.compute(bu(i,{hint:c.cacheKey,inputDependencies:["type"]},[k],u,n.inputs[0].dataType,v,q),{inputs:[k]})},xu=(n,i)=>{Xn(n,"ReduceMeanShared",i,"mean")},Mu=(n,i)=>{Xn(n,"ReduceL1Shared",i,"l1")},ba=(n,i)=>{Xn(n,"ReduceL2Shared",i,"l2")},Tu=(n,i)=>{Xn(n,"ReduceLogSumExpShared",i,"logSumExp")},ku=(n,i)=>{Xn(n,"ReduceMaxShared",i,"max")},xa=(n,i)=>{Xn(n,"ReduceMinShared",i,"min")},Su=(n,i)=>{Xn(n,"ReduceProdShared",i,"prod")},Eu=(n,i)=>{Xn(n,"ReduceSumShared",i,"sum")},Ma=(n,i)=>{Xn(n,"ReduceSumSquareShared",i,"sumSquare")},Cu=(n,i)=>{Xn(n,"ReduceLogSumShared",i,"logSum")}}),Qn,Pu,As,Is,ii,$u,Ta,Au,Iu,ka,Fu,zu,Sa,Ou,Du,Yn,Lu,Ru,Ea,Bu,Nu,Ca,ju,Uu,Pa,Vu,$a=p(()=>{Zt(),Jt(),gr(),ur(),np(),Qn=n=>{if(!n||n.length===0||n.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(n.length===2&&n[1].dims.length!==1)throw new Error("Invalid axes input dims.")},Pu=n=>["","",`var value = ${n.getByIndices("input_indices")};`,""],As=(n,i,s,u,c,h,d=!1,x=!1)=>{let k=[],$=s[0].dims,H=$.length,q=ct.normalizeAxes(c,H),v=!x&&q.length===0;$.forEach((ce,we)=>{v||q.indexOf(we)>=0?d&&k.push(1):k.push(ce)});let oe=k.length,re=ct.size(k);return{name:n,shaderCache:i,getShaderSource:ce=>{let we=[],be=_t("_A",s[0].dataType,H),he=Gt("output",h,oe),De=u(be,he,q),Ae=De[2];for(let Ie=0,gt=0;Ie=0?(d&>++,Ae=`for(var j${Ie}: u32 = 0; j${Ie} < ${$[Ie]}; j${Ie}++) { + ${De[2].includes("last_index")?`let last_index = j${Ie};`:""} + ${be.indicesSet("input_indices",Ie,`j${Ie}`)} + ${Ae} + }`):(we.push(`${be.indicesSet("input_indices",Ie,he.indicesGet("output_indices",gt))};`),gt++);return` + + ${ce.registerUniform("output_size","u32").declareVariables(be,he)} + + ${ce.mainStart()} + ${ce.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var input_indices: ${be.type.indices}; + let output_indices = ${he.offsetToIndices("global_idx")}; + + ${we.join(` +`)} + ${De[0]} // init ops for reduce max/min + ${De[1]} + ${Ae} + ${De[3]} + ${De.length===4?he.setByOffset("global_idx","value"):De.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:k,dataType:h}],dispatchGroup:{x:Math.ceil(re/64)},programUniforms:[{type:12,data:re},...$t($,k)]})}},Is=(n,i)=>{let s=[];return n[1].dims[0]>0&&n[1].getBigInt64Array().forEach(u=>s.push(Number(u))),qt({axes:s,keepDims:i.keepDims,noopWithEmptyAxes:i.noopWithEmptyAxes})},ii=(n,i,s,u)=>{let c=n.inputs,h=c.length===1?s:Is(c,s);n.compute(As(i,{hint:h.cacheKey,inputDependencies:["rank"]},[c[0]],h.noopWithEmptyAxes&&h.axes.length===0?Pu:u,h.axes,c[0].dataType,h.keepDims,h.noopWithEmptyAxes),{inputs:[0]})},$u=(n,i)=>{Qn(n.inputs),ii(n,"ReduceLogSum",i,(s,u)=>[`var value = ${u.type.storage}(0);`,"",`value += 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f32(0);","",`sum += f32(${s.getByIndices("input_indices")});`,`let value = ${u.type.value}(sum / ${h});`]})},zu=(n,i)=>{Qn(n.inputs),ii(n,"ReduceMin",i,(s,u,c)=>{let h=[];for(let d=0;d=0||c.length===0)&&h.push(`input_indices[${d}] = 0;`);return[`${h.join(` +`)}`,`var value = ${s.getByIndices("input_indices")};`,`value = min(value, ${s.getByIndices("input_indices")});`,""]})},Sa=(n,i)=>{Qn(n.inputs),ii(n,"ReduceProd",i,(s,u)=>[`var value = ${u.type.storage}(1);`,"",`value *= ${s.getByIndices("input_indices")};`,""])},Ou=(n,i)=>{Qn(n.inputs),ii(n,"ReduceSum",i,(s,u)=>[`var value = ${u.type.storage}(0);`,"",`value += ${s.getByIndices("input_indices")};`,""])},Du=(n,i)=>{Qn(n.inputs),ii(n,"ReduceSumSquare",i,(s,u)=>[`var t = ${u.type.value}(0); var value = ${u.type.value}(0);`,"",`t = ${s.getByIndices("input_indices")}; value += t * t;`,""])},Yn=(n,i,s)=>{if(i.length===0)return s;let u=1,c=1;for(let h=0;h1024},Lu=(n,i)=>{Yn(n.inputs[0].dims,i.axes,i.noopWithEmptyAxes)?Fu(n,i):xu(n,i)},Ru=(n,i)=>{Yn(n.inputs[0].dims,i.axes,i.noopWithEmptyAxes)?Ta(n,i):Mu(n,i)},Ea=(n,i)=>{Yn(n.inputs[0].dims,i.axes,i.noopWithEmptyAxes)?Au(n,i):ba(n,i)},Bu=(n,i)=>{Yn(n.inputs[0].dims,i.axes,i.noopWithEmptyAxes)?Iu(n,i):Tu(n,i)},Nu=(n,i)=>{Yn(n.inputs[0].dims,i.axes,i.noopWithEmptyAxes)?ka(n,i):ku(n,i)},Ca=(n,i)=>{Yn(n.inputs[0].dims,i.axes,i.noopWithEmptyAxes)?zu(n,i):xa(n,i)},ju=(n,i)=>{Yn(n.inputs[0].dims,i.axes,i.noopWithEmptyAxes)?Sa(n,i):Su(n,i)},Uu=(n,i)=>{Yn(n.inputs[0].dims,i.axes,i.noopWithEmptyAxes)?Ou(n,i):Eu(n,i)},Pa=(n,i)=>{Yn(n.inputs[0].dims,i.axes,i.noopWithEmptyAxes)?Du(n,i):Ma(n,i)},Vu=(n,i)=>{Yn(n.inputs[0].dims,i.axes,i.noopWithEmptyAxes)?$u(n,i):Cu(n,i)}}),Fs,Wu,Gu,zs,ip=p(()=>{Zt(),gr(),$a(),Fs=n=>{if(!n||n.length===0||n.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(n[0].dataType!==1)throw new Error("Invalid input type.")},Wu=(n,i)=>{Fs(n.inputs);let s=(u,c,h)=>{let d=[];for(let x=0;x=0||h.length===0)&&d.push(`input_indices[${x}] = 0;`);return[`${d.join(` +`)}`,`var value = ${u.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${u.getByIndices("input_indices")} ${i.selectLastIndex>0?"<=":"<"} value) { + value = ${u.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",c.setByOffset("global_idx","best_index")]};n.compute(As("ArgMin",{hint:i.cacheKey,inputDependencies:["rank"]},[n.inputs[0]],s,[i.axis],7,i.keepDims),{inputs:[0]})},Gu=(n,i)=>{Fs(n.inputs);let s=(u,c,h)=>{let d=[];for(let x=0;x=0||h.length===0)&&d.push(`input_indices[${x}] = 0;`);return[`${d.join(` +`)}`,`var value = ${u.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${u.getByIndices("input_indices")} ${i.selectLastIndex>0?">=":">"} value) { + value = ${u.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",c.setByOffset("global_idx","best_index")]};n.compute(As("argMax",{hint:i.cacheKey,inputDependencies:["rank"]},[n.inputs[0]],s,[i.axis],7,i.keepDims),{inputs:[0]})},zs=n=>qt(n)}),Hu,Aa,qu,Ku,vo,Xu,Qu,Os=p(()=>{Zt(),fe(),ur(),Hu=(n,i)=>{let s=n[0],u=n[1],c=n[2],h=n[3],d=n[4],x=n[5];if(d&&x)throw new Error("Attention cannot have both past and relative_position_bias");if(s.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let k=s.dims[0],$=s.dims[1],H=s.dims[2];if(c.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(u.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(u.dims[0]!==H)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(c.dims[0]!==u.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let q=c.dims[0]/3,v=q,oe=v;if(i.qkvHiddenSizes.length>0){if(i.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let De of i.qkvHiddenSizes)if(De%i.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");q=i.qkvHiddenSizes[0],v=i.qkvHiddenSizes[1],oe=i.qkvHiddenSizes[2]}let re=$;if(q!==v)throw new Error("qkv_hidden_sizes first element should be same as the second");if(c.dims[0]!==q+v+oe)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let ce=0;if(d){if(v!==oe)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(d.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(d.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(d.dims[1]!==k)throw new Error('Input "past" second dimension must be batch_size');if(d.dims[2]!==i.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(d.dims[4]!==v/i.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');i.pastPresentShareBuffer||(ce=d.dims[3])}let we=re+ce,be=-1,he=0;if(h)throw new Error("Mask not supported");if(d)throw new Error("past is not supported");return{batchSize:k,sequenceLength:$,pastSequenceLength:ce,kvSequenceLength:re,totalSequenceLength:we,maxSequenceLength:be,inputHiddenSize:H,hiddenSize:q,vHiddenSize:oe,headSize:Math.floor(q/i.numHeads),vHeadSize:Math.floor(oe/i.numHeads),numHeads:i.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:i.maskFilterValue,maskType:he,scale:i.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Aa=(n,i,s,u)=>{let c=wr(u),h=64,d=u/c;d{let oe=Gt("x",i.dataType,i.dims,c),re=[{name:"d_inv",type:kr(i.dataType)},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return` + var thread_max: array; + var thread_sum: array; + ${v.registerUniforms(re).declareVariables(oe)} + ${v.mainStart([h,1,1])} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = workgroup_id.x * uniforms.d_comp + local_offset; + + var thread_max_vector = ${H}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + thread_max_vector = max(${H}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(c){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: ${c}`)}})()}; + 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 = ${H}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + sum_vector += exp(${H}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(c){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: ${c}`)}})()}; + 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 < uniforms.d_comp; i++) { + x[offset + i] = ${oe.type.value}(uniforms.d_inv); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + var f32input = ${H}(x[offset + i]); + x[offset + i] = ${oe.type.value}(exp(f32input - max_value) / sum); + } + } + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${h};${$};${c}`},getShaderSource:q,getRunData:()=>({outputs:[],dispatchGroup:{x:s},programUniforms:k})}},qu=(n,i,s,u,c,h,d,x)=>{let k=x+h.kvSequenceLength,$=[h.batchSize,h.numHeads,h.sequenceLength,k],H=h.kvNumHeads===void 0&&n.outputCount>1,q=H?[h.batchSize,h.numHeads,k,h.headSize]:void 0,v=d.scale===0?1/Math.sqrt(h.headSize):d.scale,oe=wr(h.headSize),re=h.headSize/oe,ce=12,we={x:Math.ceil(k/ce),y:Math.ceil(h.sequenceLength/ce),z:h.batchSize*h.numHeads},be=[{type:12,data:h.sequenceLength},{type:12,data:re},{type:12,data:k},{type:12,data:h.numHeads},{type:1,data:v},{type:12,data:x},{type:12,data:h.kvSequenceLength}],he=["type","type"];u&&he.push("type"),c&&he.push("type");let De=[{dims:$,dataType:i.dataType,gpuDataType:0}];H&&De.push({dims:q,dataType:i.dataType,gpuDataType:0});let Ae=Ie=>{let gt=_t("q",i.dataType,i.dims,oe),ut=_t("key",s.dataType,s.dims,oe),yt=[gt,ut];if(u){let or=_t("past_key",u.dataType,u.dims,oe);yt.push(or)}c&&yt.push(_t("relative_position_bias",c.dataType,c.dims));let Dt=Gt("output",i.dataType,$),Bt=[Dt];H&&Bt.push(Gt("present_key",i.dataType,q,oe));let mr=kr(1,oe),vr=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"}];return` + const TILE_SIZE = ${ce}u; + + var tileQ: array<${gt.type.storage}, ${ce*ce}>; + var tileK: array<${gt.type.storage}, ${ce*ce}>; + ${Ie.registerUniforms(vr).declareVariables(...yt,...Bt)} + ${Ie.mainStart([ce,ce,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z; + let m = workgroup_id.y * TILE_SIZE; + let n = workgroup_id.x * TILE_SIZE; + let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K; + ${u&&H?` + let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx; + let pastKeyOffset = uniforms.past_sequence_length * uniforms.K * headIdx;`:` + let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;`} + ${H?"let presentKeyOffset = headIdx * uniforms.N * uniforms.K;":""} + var value = ${mr}(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; + ${u&&H?` + if (n + local_id.y < uniforms.past_sequence_length) { + tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; + } else { + tileK[idx] = + key[kOffset + (n + local_id.y - uniforms.past_sequence_length) * uniforms.K + w + local_id.x]; + }`:"tileK[idx] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];"} + ${H?"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 += ${mr}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]); + } + + workgroupBarrier(); + } + + let headOffset = headIdx * uniforms.M * uniforms.N; + if (global_id.y < uniforms.M && global_id.x < uniforms.N) { + let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x; + var sum: f32 = ${(()=>{switch(oe){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: ${oe}`)}})()}; + output[outputIdx] = ${Dt.type.value} (sum * uniforms.alpha) + ${c?"relative_position_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${oe};${c!==void 0};${u!==void 0};${n.outputCount}`,inputDependencies:he},getRunData:()=>({outputs:De,dispatchGroup:we,programUniforms:be}),getShaderSource:Ae}},Ku=(n,i,s,u,c,h)=>{let d=h+c.kvSequenceLength,x=c.nReps?c.nReps:1,k=c.vHiddenSize*x,$=c.kvNumHeads==null&&n.outputCount>1,H=$?[c.batchSize,c.numHeads,d,c.headSize]:void 0,q=[c.batchSize,c.sequenceLength,k],v=12,oe={x:Math.ceil(c.vHeadSize/v),y:Math.ceil(c.sequenceLength/v),z:c.batchSize*c.numHeads},re=[{type:12,data:c.sequenceLength},{type:12,data:d},{type:12,data:c.vHeadSize},{type:12,data:c.numHeads},{type:12,data:k},{type:12,data:h},{type:12,data:c.kvSequenceLength}],ce=u?["type","type","type"]:["type","type"],we=[{dims:q,dataType:i.dataType,gpuDataType:0}];$&&we.push({dims:H,dataType:i.dataType,gpuDataType:0});let be=he=>{let De=_t("probs",i.dataType,i.dims),Ae=_t("v",s.dataType,s.dims),Ie=[De,Ae];u&&Ie.push(_t("past_value",u.dataType,u.dims));let gt=[Gt("output",i.dataType,q)];$&>.push(Gt("present_value",i.dataType,H));let ut=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"}];return` + const TILE_SIZE = ${v}u; + var tileQ: array<${De.type.value}, ${v*v}>; + var tileK: array<${De.type.value}, ${v*v}>; + ${he.registerUniforms(ut).declareVariables(...Ie,...gt)} + ${he.mainStart([v,v,1])} + let headIdx = workgroup_id.z; + let m = global_id.y; + let n = global_id.x; + + let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K; + ${u&&$?` + let pastValueOffset = headIdx * uniforms.N * uniforms.past_sequence_length + n; + let vOffset = headIdx * uniforms.N * uniforms.kv_sequence_length + n; + `:` + let offsetB = headIdx * uniforms.N * uniforms.K + n; + `} + ${$?"let presentValueOffset = headIdx * uniforms.N * uniforms.K + n;":""} + var value = ${De.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; + ${u&&$?` + if (w + local_id.y < uniforms.past_sequence_length) { + tileK[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N]; + } else { + tileK[idx] = v[vOffset + (w + local_id.y - uniforms.past_sequence_length) * uniforms.N]; + } + `:` + tileK[idx] = v[offsetB + (w + local_id.y) * uniforms.N]; + `} + ${$?"present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileK[idx];":""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + let batchIdx = workgroup_id.z / uniforms.num_heads; + let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads; + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + currentBatchHeadNumber * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${u!==void 0};${n.outputCount}`,inputDependencies:ce},getRunData:()=>({outputs:we,dispatchGroup:oe,programUniforms:re}),getShaderSource:be}},vo=(n,i,s,u,c,h,d,x,k,$,H)=>{let q=n.outputCount,v=$.kvNumHeads!==void 0||q>1?$.pastSequenceLength:0,oe=v+$.kvSequenceLength,re=$.kvNumHeads===void 0&&q>1&&d?[i,s,d]:[i,s];k&&re.push(k);let ce=n.compute(qu(n,i,s,q>1?d:void 0,k,$,H,v),{inputs:re,outputs:$.kvNumHeads===void 0&&q>1?[-1,1]:[-1]})[0];n.compute(Aa(n,ce,$.batchSize*$.numHeads*$.sequenceLength,oe),{inputs:[ce],outputs:[]});let we=$.kvNumHeads===void 0&&q>1&&x?[ce,u,x]:[ce,u];n.compute(Ku(n,ce,u,q>1&&x?x:void 0,$,v),{inputs:we,outputs:$.kvNumHeads===void 0&&q>1?[0,2]:[0]})},Xu=(n,i)=>{let s=[i.batchSize,i.numHeads,i.sequenceLength,i.headSize],u=i.sequenceLength,c=i.inputHiddenSize,h=i.headSize,d=12,x={x:Math.ceil(i.headSize/d),y:Math.ceil(i.sequenceLength/d),z:i.batchSize*i.numHeads},k=[n.inputs[0],n.inputs[1],n.inputs[2]],$=[{type:12,data:u},{type:12,data:c},{type:12,data:h},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:12,data:i.hiddenSize},{type:12,data:i.hiddenSize+i.hiddenSize+i.vHiddenSize}],H=q=>{let v=Gt("output_q",k[0].dataType,s),oe=Gt("output_k",k[0].dataType,s),re=Gt("output_v",k[0].dataType,s),ce=_t("input",k[0].dataType,k[0].dims),we=_t("weight",k[1].dataType,k[1].dims),be=_t("bias",k[2].dataType,k[2].dims),he=ce.type.storage,De=[{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 = ${d}u; + var tileInput: array<${he}, ${d*d}>; + var tileWeightQ: array<${he}, ${d*d}>; + var tileWeightK: array<${he}, ${d*d}>; + var tileWeightV: array<${he}, ${d*d}>; + ${q.registerUniforms(De).declareVariables(ce,we,be,v,oe,re)} + ${q.mainStart([d,d,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 = ${he}(0); + var valueK = ${he}(0); + var valueV = ${he}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:s,dataType:n.inputs[0].dataType,gpuDataType:0},{dims:s,dataType:n.inputs[0].dataType,gpuDataType:0},{dims:s,dataType:n.inputs[0].dataType,gpuDataType:0}],dispatchGroup:x,programUniforms:$}),getShaderSource:H},{inputs:k,outputs:[-1,-1,-1]})},Qu=(n,i)=>{let s=Hu(n.inputs,i),[u,c,h]=Xu(n,s);return vo(n,u,c,h,n.inputs[4],void 0,void 0,void 0,n.inputs[5],s,i)}}),Yu,Zu,Ju,ec,tc=p(()=>{Y(),Zt(),Jt(),gr(),ur(),Yu=(n,i)=>{if(!n||n.length!==5)throw new Error("BatchNormalization requires 5 inputs");let s=(u,c,h)=>{let d=c.length;if(d!==u.length)throw new Error(`${h}: num dimensions != ${d}`);c.forEach((x,k)=>{if(x!==u[k])throw new Error(`${h}: dim[${k}] do not match`)})};if(n[0].dims.length>1){let u=i.format==="NHWC"?i.spatial?n[0].dims.slice(-1):n[0].dims.slice(-1).concat(n[0].dims.slice(1,n[0].dims.length-1)):n[0].dims.slice(1,i.spatial?2:void 0);s(n[1].dims,u,"Invalid input scale"),s(n[2].dims,u,"Invalid input B"),s(n[3].dims,u,"Invalid input mean"),s(n[4].dims,u,"Invalid input var")}else s(n[1].dims,[1],"Invalid input scale"),s(n[2].dims,[1],"Invalid input B"),s(n[3].dims,[1],"Invalid input mean"),s(n[4].dims,[1],"Invalid input var")},Zu=(n,i)=>{let{epsilon:s,spatial:u,format:c}=i,h=n[0].dims,d=u?wr(h[h.length-1]):1,x=c==="NHWC"&&h.length>1?d:1,k=ct.size(h)/d,$=u,H=$?h.length:h,q=_t("x",n[0].dataType,n[0].dims,d),v=_t("scale",n[1].dataType,n[1].dims,x),oe=_t("bias",n[2].dataType,n[2].dims,x),re=_t("inputMean",n[3].dataType,n[3].dims,x),ce=_t("inputVar",n[4].dataType,n[4].dims,x),we=Gt("y",n[0].dataType,H,d),be=()=>{let De="";if(u)De=`let cOffset = ${h.length===1?"0u":c==="NHWC"?`outputIndices[${h.length-1}] / ${d}`:"outputIndices[1]"};`;else if(c==="NCHW")De=` + ${we.indicesSet("outputIndices","0","0")} + let cOffset = ${we.indicesToOffset("outputIndices")};`;else{De=`var cIndices = ${v.type.indices}(0); + cIndices[0] = outputIndices[${h.length-1}];`;for(let Ae=1;Ae` + const epsilon = ${s}; + ${De.registerUniform("outputSize","u32").declareVariables(q,v,oe,re,ce,we)} + ${De.mainStart()} + ${De.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${we.offsetToIndices(`global_idx * ${d}`)}; + ${be()} + let scale = ${v.getByOffset("cOffset")}; + let bias = ${oe.getByOffset("cOffset")}; + let inputMean = ${re.getByOffset("cOffset")}; + let inputVar = ${ce.getByOffset("cOffset")}; + let x = ${q.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${we.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${i.epsilon}_${i.format}_${u}_${d}`,inputDependencies:$?["rank","type","type","type","type"]:void 0},getShaderSource:he,getRunData:()=>({outputs:[{dims:n[0].dims,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:$?[{type:12,data:k},...$t(h)]:[{type:12,data:k}]})}},Ju=n=>qt(n),ec=(n,i)=>{let{inputs:s,outputCount:u}=n,c=Ju({...i,outputCount:u});if(D.webgpu.validateInputContent&&Yu(s,c),i.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");n.compute(Zu(s,c))}}),rc,nc,Ia,op=p(()=>{Jt(),ur(),rc=n=>{if(n[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(n[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(n[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(n[0].dims[2]!==n[1].dims[0])throw new Error("last dimension of input and bias are not the same")},nc=n=>{let i=n[0].dims,s=n[0].dims[2],u=ct.size(i)/4,c=n[0].dataType,h=_t("input",c,i,4),d=_t("bias",c,[s],4),x=_t("residual",c,i,4),k=Gt("output",c,i,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:i,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)}}),getShaderSource:$=>` + const channels = ${s}u / 4; + ${$.declareVariables(h,d,x,k)} + + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes(u)} + let value = ${h.getByOffset("global_idx")} + + ${d.getByOffset("global_idx % channels")} + ${x.getByOffset("global_idx")}; + ${k.setByOffset("global_idx","value")} + }`}},Ia=n=>{rc(n.inputs),n.compute(nc(n.inputs))}}),ic,yr,oc,sc,Fa,ac,lc,za,uc,cc,Ds,dc,fc,pc,Oa,hc,bo,mc,Ls,gc,Da,_c,wc,yc,La,vc,bc,Ra,xc,Mc,Ba,Tc,kc,Na,Sc,ja,Ua,Va,Wa,Ec,Cc,Ga,Pc,$c,Ac,Ha=p(()=>{Zt(),Jt(),gr(),ur(),ic=(n,i,s,u,c,h)=>{let d=Math.ceil(i/4),x="";typeof c=="string"?x=`${c}(a)`:x=c("a");let k=_t("inputData",s,[d],4),$=Gt("outputData",u,[d],4);return` + ${n.registerUniform("vec_size","u32").declareVariables(k,$)} + + ${h??""} + + ${n.mainStart()} + ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${k.getByOffset("global_idx")}; + ${$.setByOffset("global_idx",x)} + }`},yr=(n,i,s,u,c,h=n.dataType)=>({name:i,shaderCache:{hint:c,inputDependencies:["type"]},getShaderSource:d=>ic(d,ct.size(n.dims),n.dataType,h,s,u),getRunData:d=>({outputs:[{dims:n.dims,dataType:h}],dispatchGroup:{x:Math.ceil(ct.size(d[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(ct.size(n.dims)/4)}]})}),oc=n=>{n.compute(yr(n.inputs[0],"Abs","abs"))},sc=n=>{n.compute(yr(n.inputs[0],"Acos","acos"))},Fa=n=>{n.compute(yr(n.inputs[0],"Acosh","acosh"))},ac=n=>{n.compute(yr(n.inputs[0],"Asin","asin"))},lc=n=>{n.compute(yr(n.inputs[0],"Asinh","asinh"))},za=n=>{n.compute(yr(n.inputs[0],"Atan","atan"))},uc=n=>{n.compute(yr(n.inputs[0],"Atanh","atanh"))},cc=n=>qt(n),Ds=(n,i)=>{let s;switch(i.to){case 10:s="vec4";break;case 1:s="vec4";break;case 12:s="vec4";break;case 6:s="vec4";break;case 9:s="vec4";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${i.to}`)}n.compute(yr(n.inputs[0],"Cast",s,void 0,i.cacheKey,i.to))},dc=n=>{let i=n.length>=2&&n[1].data!==0?n[1].getFloat32Array()[0]:cn,s=n.length>=3&&n[2].data!==0?n[2].getFloat32Array()[0]:Fn;return qt({min:i,max:s})},fc=(n,i)=>{let s=n.inputs.length===1?i:dc(n.inputs),u=kr(n.inputs[0].dataType);n.compute(yr(n.inputs[0],"Clip",c=>`clamp(${c}, clip_min_, clip_max_)`,` + const clip_min_: vec4<${u}> = vec4(${u}(${s.min})); + const clip_max_: vec4<${u}> = vec4(${u}(${s.max})); +`,s.cacheKey),{inputs:[0]})},pc=n=>{n.compute(yr(n.inputs[0],"Ceil","ceil"))},Oa=n=>{n.compute(yr(n.inputs[0],"Cos","cos"))},hc=n=>{n.compute(yr(n.inputs[0],"Cosh","cosh"))},bo=n=>qt(n),mc=(n,i)=>{let s=kr(n.inputs[0].dataType);n.compute(yr(n.inputs[0],"Elu",u=>`elu_vf32(${u})`,` + const elu_alpha_ = ${s}(${i.alpha}); + + fn elu_f32(a: ${s}) -> ${s} { + return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${s}>) -> vec4<${s}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,i.cacheKey))},Ls=(n="f32")=>` +const r0: ${n} = 0.3275911; +const r1: ${n} = 0.254829592; +const r2: ${n} = -0.284496736; +const r3: ${n} = 1.421413741; +const r4: ${n} = -1.453152027; +const r5: ${n} = 1.061405429; + +fn erf_vf32(v: vec4<${n}>) -> vec4<${n}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,gc=n=>{let i=kr(n.inputs[0].dataType);n.compute(yr(n.inputs[0],"Erf",s=>`erf_vf32(${s})`,Ls(i)))},Da=n=>{n.compute(yr(n.inputs[0],"Exp","exp"))},_c=n=>{n.compute(yr(n.inputs[0],"Floor","floor"))},wc=n=>{let i=kr(n.inputs[0].dataType);n.compute(yr(n.inputs[0],"Gelu",s=>`0.5 * ${s} * (1.0 + erf_vf32(${s} * 0.7071067811865475))`,Ls(i)))},yc=(n,i)=>{let s=kr(n.inputs[0].dataType);n.compute(yr(n.inputs[0],"LeakyRelu",u=>`select(leaky_relu_alpha_ * ${u}, ${u}, ${u} >= vec4<${s}>(0.0))`,`const leaky_relu_alpha_ = ${s}(${i.alpha});`,i.cacheKey))},La=n=>{n.compute(yr(n.inputs[0],"Not",i=>`!${i}`))},vc=n=>{n.compute(yr(n.inputs[0],"Neg",i=>`-${i}`))},bc=n=>{n.compute(yr(n.inputs[0],"Reciprocal",i=>`1.0/${i}`))},Ra=n=>{let i=kr(n.inputs[0].dataType);n.compute(yr(n.inputs[0],"Relu",s=>`select(vec4<${i}>(0.0), ${s}, ${s} > vec4<${i}>(0.0))`))},xc=n=>{n.compute(yr(n.inputs[0],"Sigmoid",i=>`(1.0 / (1.0 + exp(-${i})))`))},Mc=n=>qt(n),Ba=(n,i)=>{let s=kr(n.inputs[0].dataType);n.compute(yr(n.inputs[0],"HardSigmoid",u=>`max(vec4<${s}>(0.0), min(vec4<${s}>(1.0), ${i.alpha} * ${u} + vec4<${s}>(${i.beta})))`,void 0,i.cacheKey))},Tc=n=>{n.compute(yr(n.inputs[0],"Sin","sin"))},kc=n=>{n.compute(yr(n.inputs[0],"Sinh","sinh"))},Na=n=>{n.compute(yr(n.inputs[0],"Sqrt","sqrt"))},Sc=n=>{n.compute(yr(n.inputs[0],"Tan","tan"))},ja=n=>`sign(${n}) * (1 - exp(-2 * abs(${n}))) / (1 + exp(-2 * abs(${n})))`,Ua=n=>{n.compute(yr(n.inputs[0],"Tanh",ja))},Va=(n="f32")=>` +const fast_gelu_a: ${n} = 0.5; +const fast_gelu_b: ${n} = 0.7978845608028654; +const fast_gelu_c: ${n} = 0.035677408136300125; + +fn tanh_v(v: vec4<${n}>) -> vec4<${n}> { + return ${ja("v")}; +} +`,Wa=n=>`(fast_gelu_a + fast_gelu_a * tanh_v(${n} * (fast_gelu_c * ${n} * ${n} + fast_gelu_b))) * ${n}`,Ec=n=>{let i=kr(n.inputs[0].dataType);n.compute(yr(n.inputs[0],"FastGelu",Wa,Va(i),void 0,n.inputs[0].dataType))},Cc=(n,i)=>{let s=kr(n.inputs[0].dataType);return n.compute(yr(n.inputs[0],"ThresholdedRelu",u=>`select(vec4<${s}>(0.0), ${u}, ${u} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${s}>(${i.alpha});`,i.cacheKey)),0},Ga=n=>{n.compute(yr(n.inputs[0],"Log","log"))},Pc=(n,i)=>` +const alpha = vec4<${n}>(${i}); +const one = ${n}(1.0); +const zero = ${n}(0.0); + +fn quick_gelu_impl(x: vec4<${n}>) -> vec4<${n}> { + let v = x *alpha; + var x1 : vec4<${n}>; + for (var i = 0; i < 4; i = i + 1) { + if (v[i] >= zero) { + x1[i] = one / (one + exp(-v[i])); + } else { + x1[i] = one - one / (one + exp(v[i])); + } + } + return x * x1; +} +`,$c=n=>`quick_gelu_impl(${n})`,Ac=(n,i)=>{let s=kr(n.inputs[0].dataType);n.compute(yr(n.inputs[0],"QuickGelu",$c,Pc(s,i.alpha),i.cacheKey,n.inputs[0].dataType))}}),qa,Ic,Fc,zc=p(()=>{Jt(),ur(),Ha(),qa=n=>{if(n[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(n[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(n[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(n[0].dims[2]!==n[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Ic=n=>{let i=n[0].dims.slice();i[2]=i[2]/2;let s=_t("input",n[0].dataType,n[0].dims,4),u=_t("bias",n[0].dataType,[n[0].dims[2]],4),c=Gt("output",n[0].dataType,i,4),h=ct.size(i)/4,d=Tr(n[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:i,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)}}),getShaderSource:x=>` + const M_SQRT2 = sqrt(2.0); + const halfChannels = ${n[0].dims[2]/4/2}u; + + ${x.declareVariables(s,u,c)} + + ${Ls(d)} + + ${x.mainStart()} + ${x.guardAgainstOutOfBoundsWorkgroupSizes(h)} + let biasIdx = global_idx % halfChannels; + let batchIndex = global_idx / halfChannels; + let inputOffset = biasIdx + batchIndex * halfChannels * 2; + let valueLeft = input[inputOffset] + bias[biasIdx]; + let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${c.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Fc=n=>{qa(n.inputs),n.compute(Ic(n.inputs))}}),Oc,Dc,Zn,Lc,Rc,Ka,Bc,Nc,jc,Uc,Vc,Wc,Xa,sp=p(()=>{Zt(),Jt(),ur(),Oc=(n,i,s,u,c,h,d,x,k,$,H,q)=>{let v,oe;typeof x=="string"?v=oe=(he,De)=>`${x}((${he}),(${De}))`:typeof x=="function"?v=oe=x:(v=x.scalar,oe=x.vector);let re=Gt("outputData",H,u.length,4),ce=_t("aData",k,i.length,4),we=_t("bData",$,s.length,4),be;if(c)if(h){let he=ct.size(i)===1,De=ct.size(s)===1,Ae=i.length>0&&i[i.length-1]%4===0,Ie=s.length>0&&s[s.length-1]%4===0;he||De?be=re.setByOffset("global_idx",oe(he?`${ce.type.value}(${ce.getByOffset("0")}.x)`:ce.getByOffset("global_idx"),De?`${we.type.value}(${we.getByOffset("0")}.x)`:we.getByOffset("global_idx"))):be=` + let outputIndices = ${re.offsetToIndices("global_idx * 4u")}; + let offsetA = ${ce.broadcastedIndicesToOffset("outputIndices",re)}; + let offsetB = ${we.broadcastedIndicesToOffset("outputIndices",re)}; + ${re.setByOffset("global_idx",oe(d||Ae?ce.getByOffset("offsetA / 4u"):`${ce.type.value}(${ce.getByOffset("offsetA / 4u")}[offsetA % 4u])`,d||Ie?we.getByOffset("offsetB / 4u"):`${we.type.value}(${we.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else be=re.setByOffset("global_idx",oe(ce.getByOffset("global_idx"),we.getByOffset("global_idx")));else{if(!h)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let he=(De,Ae,Ie="")=>{let gt=`aData[indexA${Ae}][componentA${Ae}]`,ut=`bData[indexB${Ae}][componentB${Ae}]`;return` + let outputIndices${Ae} = ${re.offsetToIndices(`global_idx * 4u + ${Ae}u`)}; + let offsetA${Ae} = ${ce.broadcastedIndicesToOffset(`outputIndices${Ae}`,re)}; + let offsetB${Ae} = ${we.broadcastedIndicesToOffset(`outputIndices${Ae}`,re)}; + let indexA${Ae} = offsetA${Ae} / 4u; + let indexB${Ae} = offsetB${Ae} / 4u; + let componentA${Ae} = offsetA${Ae} % 4u; + let componentB${Ae} = offsetB${Ae} % 4u; + ${De}[${Ae}] = ${Ie}(${v(gt,ut)}); + `};H===9?be=` + var data = vec4(0); + ${he("data",0,"u32")} + ${he("data",1,"u32")} + ${he("data",2,"u32")} + ${he("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:be=` + ${he("outputData[global_idx]",0)} + ${he("outputData[global_idx]",1)} + ${he("outputData[global_idx]",2)} + ${he("outputData[global_idx]",3)} + `}return` + ${n.registerUniform("vec_size","u32").declareVariables(ce,we,re)} + + ${q??""} + + ${n.mainStart()} + ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${be} + }`},Dc=(n,i,s,u,c,h,d=s.dataType)=>{let x=!ct.areEqual(s.dims,u.dims),k=s.dims,$=ct.size(s.dims),H=!1,q=!1,v=[x];if(x){let oe=gn.calcShape(s.dims,u.dims,!1);if(!oe)throw new Error("Can't perform binary op on the given tensors");k=oe,$=ct.size(k);let re=ct.size(s.dims)===1,ce=ct.size(u.dims)===1,we=s.dims.length>0&&s.dims[s.dims.length-1]%4===0,be=u.dims.length>0&&u.dims[u.dims.length-1]%4===0;v.push(re),v.push(ce),v.push(we),v.push(be);let he=1;for(let De=1;Deoe.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:oe=>Oc(oe,s.dims,u.dims,k,H,x,q,c,s.dataType,u.dataType,d,h),getRunData:()=>({outputs:[{dims:k,dataType:d}],dispatchGroup:{x:Math.ceil($/64/4)},programUniforms:[{type:12,data:Math.ceil(ct.size(k)/4)},...$t(s.dims,u.dims,k)]})}},Zn=(n,i,s,u,c,h)=>{n.compute(Dc(i,c??"",n.inputs[0],n.inputs[1],s,u,h))},Lc=n=>{Zn(n,"Add",(i,s)=>`${i}+${s}`)},Rc=n=>{Zn(n,"Div",(i,s)=>`${i}/${s}`)},Ka=n=>{Zn(n,"Equal",{scalar:(i,s)=>`u32(${i}==${s})`,vector:(i,s)=>`vec4(${i}==${s})`},void 0,void 0,9)},Bc=n=>{Zn(n,"Mul",(i,s)=>`${i}*${s}`)},Nc=n=>{let i=_t("input",n.inputs[0].dataType,n.inputs[0].dims).type.value;Zn(n,"Pow",{scalar:(s,u)=>`pow_custom(${s},${u})`,vector:(s,u)=>`pow_vector_custom(${s},${u})`},` + fn pow_custom(a : ${i}, b : ${i}) -> ${i} { + if (b == ${i}(0.0)) { + return ${i}(1.0); + } else if (a < ${i}(0.0) && f32(b) != floor(f32(b))) { + return ${i}(pow(f32(a), f32(b))); // NaN + } + return select(sign(a), ${i}(1.0), round(f32(abs(b) % ${i}(2.0))) != 1.0) * ${i}(${i==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); + } + fn pow_vector_custom(a : vec4<${i}>, b : vec4<${i}>) -> vec4<${i}> { + // TODO: implement vectorized pow + return vec4<${i}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); + } + `)},jc=n=>{Zn(n,"Sub",(i,s)=>`${i}-${s}`)},Uc=n=>{Zn(n,"Greater",{scalar:(i,s)=>`u32(${i}>${s})`,vector:(i,s)=>`vec4(${i}>${s})`},void 0,void 0,9)},Vc=n=>{Zn(n,"Less",{scalar:(i,s)=>`u32(${i}<${s})`,vector:(i,s)=>`vec4(${i}<${s})`},void 0,void 0,9)},Wc=n=>{Zn(n,"GreaterOrEqual",{scalar:(i,s)=>`u32(${i}>=${s})`,vector:(i,s)=>`vec4(${i}>=${s})`},void 0,void 0,9)},Xa=n=>{Zn(n,"LessOrEqual",{scalar:(i,s)=>`u32(${i}<=${s})`,vector:(i,s)=>`vec4(${i}<=${s})`},void 0,void 0,9)}}),Gc,Qa,Hc,qc,Ui,Kc,ap=p(()=>{Zt(),Jt(),gr(),ur(),Gc=(n,i)=>{if(!n||n.length<1)throw new Error("too few inputs");let s=0,u=n[s],c=u.dataType,h=u.dims.length;n.forEach((d,x)=>{if(x!==s){if(d.dataType!==c)throw new Error("input tensors should be one type");if(d.dims.length!==h)throw new Error("input tensors should have the same shape");d.dims.forEach((k,$)=>{if($!==i&&k!==u.dims[$])throw new Error("non concat dimensions must match")})}})},Qa=(n,i)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${i}); + for (var i: u32 = 0u; i < ${n}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${n}u; + }`,Hc=(n,i)=>{let s=n.length,u=[];for(let c=0;c{let c=ct.size(s),h=new Array(n.length),d=new Array(n.length),x=0,k=[],$=[],H=[{type:12,data:c}];for(let ce=0;ce`uniforms.sizeInConcatAxis${ce}`).join(","),re=ce=>` + + ${(()=>{ce.registerUniform("outputSize","u32");for(let we=0;we(${oe}); + ${v} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${Hc(d,q)} + }`;return{name:"Concat",shaderCache:{hint:`${i}`,inputDependencies:k},getRunData:()=>({outputs:[{dims:s,dataType:u}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:H}),getShaderSource:re}},Ui=(n,i)=>{let s=n.inputs,u=s[0].dims,c=ct.normalizeAxis(i.axis,u.length);Gc(s,c);let h=u.slice();h[c]=s.reduce((x,k)=>x+(k.dims.length>c?k.dims[c]:0),0);let d=s.filter(x=>ct.size(x.dims)>0);n.compute(qc(d,c,h,s[0].dataType),{inputs:d})},Kc=n=>qt({axis:n.axis})}),Vi,Wi,Ei,Ya,Gi=p(()=>{Zt(),Jt(),Vi=(n,i,s="f32")=>{switch(n.activation){case"Relu":return`value = max(value, ${i}(0.0));`;case"Sigmoid":return`value = (${i}(1.0) / (${i}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${i}(${s}(uniforms.clip_min)), ${i}(${s}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${i}(0.0), min(${i}(1.0), ${s}(uniforms.alpha) * value + ${s}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${s}(uniforms.alpha) * value, value, value >= ${i}(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 ${n.activation}`)}},Wi=(n,i)=>{n.activation==="Clip"?i.push({type:1,data:n.clipMax},{type:1,data:n.clipMin}):n.activation==="HardSigmoid"?i.push({type:1,data:n.alpha},{type:1,data:n.beta}):n.activation==="LeakyRelu"&&i.push({type:1,data:n.alpha})},Ei=(n,i)=>{n.activation==="Clip"?i.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):n.activation==="HardSigmoid"?i.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):n.activation==="LeakyRelu"&&i.push({name:"alpha",type:"f32"})},Ya=n=>{let i=(n==null?void 0:n.activation)||"";if(i==="HardSigmoid"){let[s,u]=(n==null?void 0:n.activation_params)||[.2,.5];return{activation:i,alpha:s,beta:u}}else if(i==="Clip"){let[s,u]=(n==null?void 0:n.activation_params)||[cn,Fn];return{activation:i,clipMax:u,clipMin:s}}else if(i==="LeakyRelu"){let[s]=(n==null?void 0:n.activation_params)||[.01];return{activation:i,alpha:s}}return{activation:i}}}),vn,Za,xo=p(()=>{vn=(n,i)=>{switch(n){case 1:return i;case 2:return`vec2<${i}>`;case 3:return`vec3<${i}>`;case 4:return`vec4<${i}>`;default:throw new Error(`${n}-component is not supported.`)}},Za=n=>` + ${n?"value = value + getBiasByOutputCoords(coords);":""} + `}),Ja,Xc=p(()=>{Ja=n=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${n}.x), i32(${n}.y), i32(${n}.z), 1)); +} +`}),Qc,qo,Rs,el,Yc,Bs,Ns,tl,js=p(()=>{Zt(),Jt(),ur(),Gi(),xo(),Qc=(n,i)=>n?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart / innerElementSize + inputCol${i?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRow + innerRow, + kStart / innerElementSize + inputCol${i?", batchIndices":""}); + `,qo=(n,i)=>n?` + let ACached0 = mm_Asub[k * innerElementSize][localRow]; + let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; + let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; + ${i===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]; + ${i===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]; + ${i===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} + }`,Rs=(n,i,s="f32",u,c=!1,h=32,d=!1,x=32)=>{let k=i[1]*n[1],$=i[0]*n[0],H=c?k:h,q=c?h:k,v=H/i[0],oe=h/i[1];if(!((c&&v===4&&n[1]===4||!c&&(v===3||v===4))&&H%i[0]===0&&h%i[1]===0&&n[0]===4))throw new Error(`If transposeA ${c} is true, innerElementSize ${v} and workPerThread[1] ${n[1]} must be 4. + Otherwise, innerElementSize ${v} must be 3 or 4. + tileAWidth ${H} must be divisible by workgroupSize[0]${i[0]}. tileInner ${h} must be divisible by workgroupSize[1] ${i[1]}. colPerThread ${n[0]} must be 4.`);return` +var mm_Asub: array, ${H/v}>, ${q}>; +var mm_Bsub: array, ${$/n[0]}>, ${h}>; + +const rowPerThread = ${n[1]}; +const colPerThread = ${n[0]}; +const innerElementSize = ${v}; +const tileInner = ${h}; + +@compute @workgroup_size(${i[0]}, ${i[1]}, ${i[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 = ${d?"0":"i32(globalId.z)"}; + ${u?`let batchIndices = ${u.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${k}; + + let num_tiles = ${d?`${Math.ceil(x/h)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${d?`i32(globalId.z) * ${x}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${oe}; + for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let inputRow = tileRow + innerRow; + let inputCol = tileCol; + ${Qc(c,u)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${oe}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${u?", 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]; + ${v===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${qo(c,v)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},el=(n,i)=>n?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${i?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${i?", batchIndices":""}); + `,Yc=n=>n?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Bs=(n,i,s="f32",u,c=!1,h=32,d=!1,x=32,k=!1)=>{let $=n[1]*i[1],H=n[0]*i[0],q=c?$:h,v=c?h:$;if(!(v%i[1]===0&&q%i[0]===0&&h%i[1]===0))throw new Error(`tileAHight ${v} must be divisible by workgroupSize[1]${i[1]}, tileAWidth ${q} must be divisible by workgroupSize[0]${i[0]}, tileInner ${h} must be divisible by workgroupSize[1]${i[1]}`);let oe=v/i[1],re=q/i[0],ce=h/i[1],we=k?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${$}; + let globalColStart = i32(workgroupId.x) * ${H}; + + // 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 < ${v}; inputRow = inputRow + ${i[1]}) { + for (var inputCol = localCol; inputCol < ${q}; inputCol = inputCol + ${i[0]}) { + ${el(c,u)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${h}; inputRow = inputRow + ${i[1]}) { + for (var inputCol = localCol; inputCol < ${H}; inputCol = inputCol + ${i[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalColStart + inputCol${u?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${s}, 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 * ${i[0]}]; + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let ACached = ${c?`mm_Asub[k][localRow + innerRow * ${i[1]}];`:`mm_Asub[localRow + innerRow * ${i[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 * ${i[1]}; + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let gCol = globalColStart + localCol + innerCol * ${i[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) * ${$}; + +let tileRowA = i32(localId.y) * ${oe}; +let tileColA = i32(localId.x) * ${re}; +let tileRowB = i32(localId.y) * ${ce}; +// 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 < ${oe}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${re}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${el(c,u)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${ce}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${u?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${s}, 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) { + ${Yc(c)} + 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, ${v}>; + var mm_Bsub : array, ${h}>; + const rowPerThread = ${n[1]}; + const colPerThread = ${n[0]}; + const tileInner = ${h}; + +@compute @workgroup_size(${i[0]}, ${i[1]}, ${i[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let batch = ${d?"0":"i32(globalId.z)"}; + ${u?`let batchIndices = ${u.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${d?`${Math.ceil(x/h)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${d?`i32(globalId.z) * ${x}`:"0"}; + + var acc : array, rowPerThread>; + ${we} + } +`},Ns=(n,i,s,u,c,h=!1)=>{let[d,x,k]=c,[$,H,q,v]=u,oe=wo(d,k),re=wo(x,k),ce=Tr(u[0].type.tensor),we=()=>{let he=H.rank,De=$.rank,Ae=`var aIndices: ${H.type.indices};`;for(let Ie=he-2-1,gt=De-1;Ie>=0;Ie--,gt--)Ae+=` +aIndices[${Ie}] = ${De>1?`batchIndices[${gt}]`:"batchIndices"};`;return oe.forEach(Ie=>{Ae+=` +aIndices[${Ie}] = 0;`}),Ae+=` +aIndices[${he-2}] = u32(row); + aIndices[${he-1}] = u32(colIn);`,Ae},be=()=>{let he=q.rank,De=$.rank,Ae=`var bIndices: ${q.type.indices};`;for(let Ie=he-2-1,gt=De-1;Ie>=0;Ie--,gt--)Ae+=` +bIndices[${Ie}] = ${De>1?`batchIndices[${gt}]`:"batchIndices"};`;return re.forEach(Ie=>{Ae+=` +bIndices[${Ie}] = 0;`}),Ae+=` +bIndices[${he-2}] = u32(row); + bIndices[${he-1}] = u32(colIn);`,Ae};return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${$.type.indices}) -> ${vn(n,ce)} { + var value = ${vn(n,ce)}(0.0); + let col = colIn * ${n}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + ${we()} + value = ${H.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${$.type.indices}) -> ${vn(n,ce)} { + var value = ${vn(n,ce)}(0.0); + let col = colIn * ${n}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + ${be()} + value = ${q.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${vn(n,ce)}) { + let col = colIn * ${n}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueIn; + let coords = vec3(batch, row, colIn); + ${i?`value = value + ${h?"bias[colIn]":`${vn(n,ce)}(bias[row])`};`:""} + ${s} + ${v.setByIndices("vec3(coords)","value")} + } + } + `},tl=(n,i,s,u,c=!1)=>{let h=n[0].dims,d=n[1].dims,x=h.slice(0,-2),k=d.slice(0,-2),$=u?u.slice(0,-2):s.slice(0,-2),H=ct.size($),q=h[h.length-2],v=h[h.length-1],oe=d[d.length-1],re=v%4===0&&oe%4===0,ce=q<=8?[4,1,1]:[4,4,1],we=[8,8,1],be=[Math.ceil(oe/we[0]/ce[0]),Math.ceil(q/we[1]/ce[1]),Math.ceil(H/we[2]/ce[2])],he=re?4:1,De=[...x,q,v/he],Ae=De.length,Ie=[...k,v,oe/he],gt=Ie.length,ut=[H,q,oe/he],yt=[{type:6,data:q},{type:6,data:oe},{type:6,data:v}];Wi(i,yt),yt.push(...$t($,De,Ie));let Dt=["rank","rank"],Bt=n.length>2;Bt&&(yt.push(...$t(n[2].dims)),Dt.push("rank")),yt.push(...$t(ut));let mr=vr=>{let or=$.length,Ir=ma("batchDims",n[0].dataType,or,1),Zr=Tr(n[0].dataType),fr=_t("a",n[0].dataType,Ae,he),Gr=_t("b",n[1].dataType,gt,he),jt=Gt("result",n[0].dataType,ut.length,he),er=[fr,Gr];if(Bt){let Vr=c?he:1;er.push(_t("bias",n[2].dataType,n[2].dims.length,Vr))}let _r=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Ei(i,_r);let ot=Tr(jt.type.tensor),Wt=Vi(i,jt.type.value,ot),ir=Ns(he,Bt,Wt,[Ir,fr,Gr,jt],[x,k,$],c);return` + ${vr.registerUniforms(_r).registerInternalVariables(Ir).declareVariables(...er,jt)} + ${ir} + ${re?Rs(ce,we,Zr,Ir):Bs(ce,we,Zr,Ir)} + `};return{name:"MatMul",shaderCache:{hint:`${ce};${i.activation};${re};${c}`,inputDependencies:Dt},getRunData:()=>({outputs:[{dims:s,dataType:n[0].dataType}],dispatchGroup:{x:be[0],y:be[1],z:be[2]},programUniforms:yt}),getShaderSource:mr}}}),Zc,lp,up=p(()=>{Zt(),Nn(),ur(),Gi(),xo(),Xc(),js(),Zc=(n,i,s,u,c=!1,h,d=4,x=4,k=4,$="f32")=>{let H=Dt=>{switch(Dt){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${$}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Dt} is not supported.`)}},q=Dt=>{switch(Dt){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 ${Dt} is not supported.`)}},v=n?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,oe=n?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,re=n?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",ce=n?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",we=n?"row":"col",be=n?"col":"row",he=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${n?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${we} / outWidth; + let outCol = ${we} % outWidth; + + let WRow = ${be} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${be} / 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 = ${be} % inChannels; + var resData = ${vn(d,$)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${re} && xCol >= 0 && xCol < ${ce}) { + ${v} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${H(d)} + } + return resData;`,De=n?i&&u?` + let col = colIn * ${d}; + ${he}`:` + let col = colIn * ${d}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${he} + } + return ${vn(d,$)}(0.0);`:u&&s?` + let col = colIn * ${d}; + ${he}`:` + let col = colIn * ${d}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${he} + } + return ${vn(d,$)}(0.0);`,Ae=`${q(x)}`,Ie=vn(k,$),gt=vn(n?d:x,$),ut=vn(n?x:d,$),yt=Vi(h,Ie,$);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${gt} { + ${n?De:Ae} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${ut} { + ${n?Ae:De} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${Ie}) { + let col = colIn * ${k}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) + { + var value = valueIn; + let outWidth = ${n?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${oe} + ${Za(c)} + ${yt} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},lp=(n,i,s,u,c,h,d,x)=>{let k=i.format==="NHWC",$=k?n[0].dims[3]:n[0].dims[1],H=s[0],q=k?s[2]:s[3],v=k?s[1]:s[2],oe=k?s[3]:s[1],re=k&&($%4===0||$%3===0)&&oe%4===0,ce=k?oe:q*v,we=k?q*v:oe,be=[8,8,1],he=u<=8?[4,1,1]:[4,4,1],De=[Math.ceil(ce/be[0]/he[0]),Math.ceil(we/be[1]/he[1]),Math.ceil(H/be[2]/he[2])];Ur("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${De}`);let Ae=re?k&&$%4!==0?3:4:1,Ie=be[1]*he[1],gt=be[0]*he[0],ut=Math.max(be[0]*Ae,be[1]),yt=u%Ie===0,Dt=c%gt===0,Bt=h%ut===0,mr=re?[Ae,4,4]:[1,1,1],vr=[{type:6,data:u},{type:6,data:c},{type:6,data:h},{type:6,data:[i.pads[0],i.pads[1]]},{type:6,data:i.strides},{type:6,data:i.dilations}];Wi(i,vr),vr.push(...$t(n[0].dims,n[1].dims));let or=["rank","rank"];d&&(vr.push(...$t(n[2].dims)),or.push("rank")),vr.push(...$t(s));let Ir=Zr=>{let fr=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Ei(i,fr);let Gr=re?4:1,jt=Tr(n[0].dataType),er=` + fn setOutputAtIndex(flatIndex : i32, value : ${re?`vec4<${jt}>`:jt}) { + result[flatIndex] = ${re?`vec4<${jt}>`:jt}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${re?`vec4<${jt}>`:jt}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${re?"/ 4":""}, value); + }`,_r=_t("x",n[0].dataType,n[0].dims.length,Ae===3?1:Ae),ot=_t("w",n[1].dataType,n[1].dims.length,Gr),Wt=[_r,ot],ir=Gt("result",n[0].dataType,s.length,Gr);if(d){let Vr=_t("bias",n[2].dataType,n[2].dims.length,Gr);Wt.push(Vr),er+=` + fn getBiasByOutputCoords(coords : vec4) -> ${re?`vec4<${jt}>`:jt} { + return bias[coords.${k?"w":"y"}${re?"/ 4":""}]; + }`}return` + ${Ja("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 }; + ${Zr.registerUniforms(fr).declareVariables(...Wt,ir)} + ${er} + ${Zc(k,yt,Dt,Bt,d,i,mr[0],mr[1],mr[2],jt)} + ${re?Rs(he,be,jt,void 0,!k,ut):Bs(he,be,jt,void 0,!k,ut,!1,void 0,x)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${i.cacheKey};${Ae};${re};${yt};${Dt};${Bt};${Ie};${gt};${ut}`,inputDependencies:or},getRunData:()=>({outputs:[{dims:s,dataType:n[0].dataType}],dispatchGroup:{x:De[0],y:De[1],z:De[2]},programUniforms:vr}),getShaderSource:Ir}}}),Jc,rl,Ci,ed,nl,td,rd,nd,il=p(()=>{Zt(),Nn(),Jt(),ur(),Gi(),xo(),Jc=n=>{let i=1;for(let s=0;stypeof n=="number"?[n,n,n]:n,Ci=(n,i)=>i<=1?n:n+(n-1)*(i-1),ed=(n,i,s,u=1)=>{let c=Ci(i,u);return Math.floor((n[0]*(s-1)-s+c)/2)},nl=(n,i,s,u,c)=>{c==null&&(c=ed(n,i[0],u[0]));let h=[0,0,0,s];for(let d=0;d<3;d++)n[d]+2*c>=i[d]&&(h[d]=Math.trunc((n[d]-i[d]+2*c)/u[d]+1));return h},td=(n,i,s,u,c,h,d,x,k,$)=>{let H,q,v,oe;if(n==="VALID"&&(n=0),typeof n=="number"){H={top:n,bottom:n,left:n,right:n,front:n,back:n};let re=nl([i,s,u,1],[x,k,$],1,[c,h,d],n);q=re[0],v=re[1],oe=re[2]}else if(Array.isArray(n)){if(!n.every((ce,we,be)=>ce===be[0]))throw Error(`Unsupported padding parameter: ${n}`);H={top:n[0],bottom:n[1],left:n[2],right:n[3],front:n[4],back:n[5]};let re=nl([i,s,u,1],[x,k,$],1,[c,h,d],n[0]);q=re[0],v=re[1],oe=re[2]}else if(n==="SAME_UPPER"){q=Math.ceil(i/c),v=Math.ceil(s/h),oe=Math.ceil(u/d);let re=(q-1)*c+x-i,ce=(v-1)*h+k-s,we=(oe-1)*d+$-u,be=Math.floor(re/2),he=re-be,De=Math.floor(ce/2),Ae=ce-De,Ie=Math.floor(we/2),gt=we-Ie;H={top:De,bottom:Ae,left:Ie,right:gt,front:be,back:he}}else throw Error(`Unknown padding parameter: ${n}`);return{padInfo:H,outDepth:q,outHeight:v,outWidth:oe}},rd=(n,i,s,u,c,h=!1,d="channelsLast")=>{let x,k,$,H,q;if(d==="channelsLast")[x,k,$,H,q]=n;else if(d==="channelsFirst")[x,q,k,$,H]=n;else throw new Error(`Unknown dataFormat ${d}`);let[v,,oe,re,ce]=i,[we,be,he]=rl(s),[De,Ae,Ie]=rl(u),gt=Ci(oe,De),ut=Ci(re,Ae),yt=Ci(ce,Ie),{padInfo:Dt,outDepth:Bt,outHeight:mr,outWidth:vr}=td(c,k,$,H,we,be,he,gt,ut,yt),or=h?v*q:v,Ir=[0,0,0,0,0];return d==="channelsFirst"?Ir=[x,or,Bt,mr,vr]:d==="channelsLast"&&(Ir=[x,Bt,mr,vr,or]),{batchSize:x,dataFormat:d,inDepth:k,inHeight:$,inWidth:H,inChannels:q,outDepth:Bt,outHeight:mr,outWidth:vr,outChannels:or,padInfo:Dt,strideDepth:we,strideHeight:be,strideWidth:he,filterDepth:oe,filterHeight:re,filterWidth:ce,effectiveFilterDepth:gt,effectiveFilterHeight:ut,effectiveFilterWidth:yt,dilationDepth:De,dilationHeight:Ae,dilationWidth:Ie,inShape:n,outShape:Ir,filterShape:i}},nd=(n,i,s,u,c,h)=>{let d=h==="channelsLast";d?n[0].dims[3]:n[0].dims[1];let x=[64,1,1],k={x:s.map((we,be)=>be)},$=[Math.ceil(Jc(k.x.map(we=>s[we]))/x[0]),1,1];Ur("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${$}`);let H=1,q=ct.size(s),v=[{type:12,data:q},{type:12,data:u},{type:12,data:c},{type:12,data:i.strides},{type:12,data:i.dilations}];Wi(i,v),v.push(...$t(n[0].dims,n[1].dims));let oe=["rank","rank"],re=n.length===3;re&&(v.push(...$t(n[2].dims)),oe.push("rank")),v.push(...$t(s));let ce=we=>{let be=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:u.length},{name:"pads",type:"u32",length:c.length},{name:"strides",type:"u32",length:i.strides.length},{name:"dilations",type:"u32",length:i.dilations.length}];Ei(i,be);let he=1,De=Tr(n[0].dataType),Ae=_t("x",n[0].dataType,n[0].dims.length,H),Ie=_t("W",n[1].dataType,n[1].dims.length,he),gt=[Ae,Ie],ut=Gt("result",n[0].dataType,s.length,he),yt="";if(re){let mr=_t("bias",n[2].dataType,n[2].dims.length,he);gt.push(mr),yt+=` + fn getBiasByOutputCoords(coords : array) -> ${De} { + return bias[${d?Ot("coords",4,5):Ot("coords",1,5)}]; + }`}let Dt=vn(H,De),Bt=Vi(i,Dt,De);return` + ${yt} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${Ae.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${Ie.getByIndices("aIndices")}; + } + ${we.registerUniforms(be).declareVariables(...gt,ut)} + ${we.mainStart()} + ${we.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${ut.offsetToIndices("global_idx")}; + let batch = ${Ot("coords",0,Ae.rank)}; + let d2 = ${d?Ot("coords",Ae.rank-1,Ae.rank):Ot("coords",1,Ae.rank)}; + let xFRCCorner = vec3(${d?Ot("coords",1,Ae.rank):Ot("coords",2,Ae.rank)}, + ${d?Ot("coords",2,Ae.rank):Ot("coords",3,Ae.rank)}, + ${d?Ot("coords",3,Ae.rank):Ot("coords",4,Ae.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${d?Ot("uniforms.x_shape",1,Ae.rank):Ot("uniforms.x_shape",2,Ae.rank)}; + let xShapeZ = ${d?Ot("uniforms.x_shape",2,Ae.rank):Ot("uniforms.x_shape",3,Ae.rank)}; + let xShapeW = ${d?Ot("uniforms.x_shape",3,Ae.rank):Ot("uniforms.x_shape",4,Ae.rank)}; + let xShapeU = ${d?Ot("uniforms.x_shape",4,Ae.rank):Ot("uniforms.x_shape",1,Ae.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) { + ${d?`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) { + ${d?`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) { + ${d?`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) { + ${d?`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); + } + } + } + } + ${re?"value = value + getBiasByOutputCoords(coords)":""}; + ${Bt} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${i.cacheKey};${d};${H};${re}`,inputDependencies:oe},getRunData:()=>({outputs:[{dims:s,dataType:n[0].dataType}],dispatchGroup:{x:$[0],y:$[1],z:$[2]},programUniforms:v}),getShaderSource:ce}}}),id,od,cp=p(()=>{Zt(),Jt(),ur(),cd(),Gi(),id=(n,i,s)=>{let u=n.length>2,c=u?"value += b[output_channel];":"",h=n[0].dims,d=n[1].dims,x=d[0]/i.group,k=i.format==="NHWC",$=Us(h,d,i.dilations,i.pads,i.strides,k),H=ct.size($),q=[{type:12,data:H},{type:12,data:i.dilations},{type:12,data:[i.strides[0],i.strides[1]]},{type:12,data:[i.pads[0],i.pads[1]]},{type:12,data:x}];Wi(i,q),q.push(...$t(h,d));let v=["rank","rank"];u&&(q.push(...$t(n[2].dims)),v.push("rank")),q.push(...$t($));let oe=re=>{let ce=Gt("output",n[0].dataType,$.length),we=Tr(ce.type.tensor),be=Vi(i,ce.type.value,we),he=_t("x",n[0].dataType,h.length),De=_t("w",n[1].dataType,d.length),Ae=[he,De];u&&Ae.push(_t("b",n[2].dataType,n[2].dims.length));let Ie=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:i.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];return Ei(i,Ie),` + ${re.registerUniforms(Ie).declareVariables(...Ae,ce)} + + ${re.mainStart()} + ${re.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${ce.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${k?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${k?1:2}], outputIndices[${k?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel / uniforms.output_channels_per_group; + + var value: ${ce.type.value} = ${ce.type.value}(0); + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { + let input_channel = group_id * uniforms.w_shape[1] + wInChannel; + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[${k?1:2}]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[${k?2:3}]) { + continue; + } + + let xVal = ${k?he.get("batch","xHeight","xWidth","input_channel"):he.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${De.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal*wVal; + } + } + } + ${c} + ${be} + ${ce.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:i.cacheKey,inputDependencies:v},getRunData:()=>({outputs:[{dims:s?s($):$,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(H/64)},programUniforms:q}),getShaderSource:oe}},od=(n,i,s)=>{let u=n.length>2,c=wr(s[3]),h=wr(s[2]),d=ct.size(s)/c/h,x=[n[0].dims[0],n[0].dims[1],n[0].dims[2],n[0].dims[3]/c],k=[n[1].dims[0],n[1].dims[1],n[1].dims[2],n[1].dims[3]/c],$=[s[0],s[1],s[2],s[3]/c],H=[{type:12,data:d},{type:6,data:[i.strides[0],i.strides[1]]},{type:6,data:[i.pads[0],i.pads[1]]}];Wi(i,H),H.push(...$t(x,k,$));let q=(h-1)*i.strides[1]+k[1],v=oe=>{let re=Gt("output",n[0].dataType,$.length,c),ce=Tr(re.type.tensor),we=Vi(i,re.type.value,ce),be=_t("x",n[0].dataType,x.length,c),he=_t("w",n[1].dataType,k.length,c),De=[be,he];u&&De.push(_t("b",n[2].dataType,n[2].dims,c));let Ae=u?"value += b[output_channel];":"",Ie=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Ei(i,Ie),` + ${oe.registerUniforms(Ie).declareVariables(...De,re)} + ${oe.mainStart()} + ${oe.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] / ${h}u; + let col = (index1 % width1) * ${h}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${be.type.value}, ${q}>; + var values: array<${re.type.value}, ${h}>; + 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 < ${k[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 < ${q}; i++) { + let x_width = x_corner.y + i; + if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { + x_vals[i] = ${be.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${be.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${k[1]}; w_width++) { + let w_val = ${he.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${h}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${h}u; i++) { + var value = values[i]; + ${Ae} + ${we} + ${re.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${i.cacheKey};${c};${h};${q};${k[0]};${k[1]}`,inputDependencies:u?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:s,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:H}),getShaderSource:v}}}),ol,sd,ad,sl=p(()=>{Zt(),Jt(),js(),ur(),Gi(),ol=(n,i,s,u,c=!1)=>{let h=n[0].dims,d=n[1].dims,x=h[h.length-2],k=d[d.length-1],$=h[h.length-1],H=wr(k),q=wr($),v=wr(x),oe=ct.size(s)/H/v,re=n.length>2,ce=u?u.slice(0,-2):s.slice(0,-2),we=[ct.size(ce),x,k],be=[{type:12,data:oe},{type:12,data:x},{type:12,data:k},{type:12,data:$}];Wi(i,be),be.push(...$t(ce,h,d)),re&&be.push(...$t(n[2].dims)),be.push(...$t(we));let he=De=>{let Ae=ma("batch_dims",n[0].dataType,ce.length),Ie=_t("a",n[0].dataType,h.length,q),gt=_t("b",n[1].dataType,d.length,H),ut=Gt("output",n[0].dataType,we.length,H),yt=Tr(ut.type.tensor),Dt=Vi(i,ut.type.value,yt),Bt=[Ie,gt],mr="";if(re){let er=c?H:1;Bt.push(_t("bias",n[2].dataType,n[2].dims.length,er)),mr=`${c?`value += bias[col / ${er}];`:`value += ${ut.type.value}(bias[row + i]);`}`}let vr=h.slice(0,-2),or=d.slice(0,-2),Ir=wo(vr,ce),Zr=wo(or,ce),fr=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Ei(i,fr);let Gr=(er,_r)=>{let ot=er.rank,Wt=er.name;if(ot===2)return`var ${Wt}_indices = ${er.type.indices}(0u, 0u);`;let ir=Ae.rank,Vr=`var ${Wt}_indices: ${er.type.indices};`;for(let fn=ot-2-1,kn=ir-1;fn>=0;fn--,kn--)Vr+=` +${Wt}_indices[${fn}] = ${ir>1?`batch_indices[${kn}]`:"batch_indices"};`;return _r.forEach(fn=>{Vr+=` +${Wt}_indices[${fn}] = 0;`}),Vr+=`${Wt}_indices[${ot-2}] = 0u; + ${Wt}_indices[${ot-1}] = 0u;`,Vr},jt=()=>{let er=`var a_data: ${Ie.type.value};`;for(let _r=0;_r; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${q}) { + ${jt()} + } + for (var i = 0u; i < ${v}u; i++) { + var value = values[i]; + ${mr} + ${Dt} + let cur_indices = ${ut.type.indices}(batch, row + i, col); + let offset = ${ut.indicesToOffset("cur_indices")}; + ${ut.setByOffset(`offset / ${H}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${i.activation};${H};${q};${v};${c}`,inputDependencies:re?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:s,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(oe/64)},programUniforms:be}),getShaderSource:he}},sd=n=>{if(!n||n.length!==2)throw new Error("MatMul requires 2 inputs.");if(n[0].dims[n[0].dims.length-1]!==n[1].dims[n[1].dims.length-2])throw new Error("shared dimension does not match.")},ad=n=>{sd(n.inputs);let i=gn.calcShape(n.inputs[0].dims,n.inputs[1].dims,!0);if(!i)throw new Error("Can't use matmul on the given tensors");let s=i[i.length-1],u=n.inputs[0].dims[n.inputs[0].dims.length-1];s<8&&u<8?n.compute(ol(n.inputs,{activation:""},i)):n.compute(tl(n.inputs,{activation:""},i))}}),Us,Vs,al,Ws,ll,ul,ld,ud,Ko,cd=p(()=>{Jt(),up(),il(),js(),cp(),Gi(),sl(),yo(),Us=(n,i,s,u,c,h)=>{let d=n[0],x=n.slice(h?1:2,h?3:4),k=x.length,$=i[0],H=i.slice(2).map((v,oe)=>v+(v-1)*(s[oe]-1)),q=x.map((v,oe)=>v+u[oe]+u[oe+k]).map((v,oe)=>Math.floor((v-H[oe]+c[oe])/c[oe]));return q.splice(0,0,d),q.splice(h?3:1,0,$),q},Vs=[2,3,1,0],al=(n,i)=>{if(!n||n.length!==2&&n.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(n[0].dims.length>5)throw new Error("greater than 5D is not supported");if(n[0].dims.length!==n[1].dims.length)throw new Error("filter does not have same dimension as input");let s=n[0].dims[i.format==="NHWC"?n[0].dims.length-1:1],u=n[1].dims[1]*i.group;if(s!==u)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(n.length===3&&(n[2].dims.length!==1||n[1].dims[0]!==n[2].dims[0]))throw new Error("invalid bias");let c=n[0].dims.length-2;if(i.dilations.length!==c)throw new Error(`dilations should be ${c}D`);if(i.strides.length!==c)throw new Error(`strides should be ${c}D`);if(i.pads.length!==c*2)throw new Error(`pads should be ${c*2}D`);if(i.kernelShape.length!==0&&i.kernelShape.length!==n[1].dims.length-2)throw new Error("invalid kernel shape")},Ws=(n,i)=>{let s=n.kernelShape.slice();for(let h=2;h{let i=Ya(n),s=n.format,u=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][n.auto_pad],c=n.dilations,h=n.group,d=n.kernel_shape,x=n.pads,k=n.strides,$=n.w_is_const();return{autoPad:u,format:s,dilations:c,group:h,kernelShape:d,pads:x,strides:k,wIsConst:$,...i,cacheKey:`${n.format};${i.activation};`}},ul=(n,i,s)=>{let u=Ws(s,i),c=s.format==="NHWC";if(s.group!==1){if(!n.adapterInfo.isArchitecture("ampere")&&c&&i[1].dims[0]===s.group&&i[1].dims[1]===1&&s.dilations[0]===1&&s.dilations[1]===1){let gt=Us(i[0].dims,i[1].dims,s.dilations,u.pads,s.strides,c),ut=n.kernelCustomData.wT??n.compute(hi(i[1],Vs),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!n.kernelCustomData.wT&&(n.kernelCustomData.wT=ut);let yt=[i[0],ut];i.length===3&&yt.push(i[2]),n.compute(od(yt,u,gt),{inputs:yt})}else n.compute(id(i,u));return}let h=i.length===3,d=i[0].dims[c?1:2],x=i[0].dims[c?2:3],k=i[0].dims[c?3:1],$=i[1].dims[2],H=i[1].dims[3],q=Us(i[0].dims,i[1].dims,s.dilations,u.pads,s.strides,c),v=q[c?1:2],oe=q[c?2:3],re=q[c?3:1],ce=c&&$===d&&H===x&&s.pads[0]===0&&s.pads[1]===0;if(ce||$===1&&H===1&&s.dilations[0]===1&&s.dilations[1]===1&&s.strides[0]===1&&s.strides[1]===1&&s.pads[0]===0&&s.pads[1]===0){let gt=q[0],ut,yt,Dt,Bt=[];if(c){let or=n.kernelCustomData.wT??n.compute(hi(i[1],Vs),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];if(s.wIsConst&&!n.kernelCustomData.wT&&(n.kernelCustomData.wT=or),ce){let Ir=d*x*k;ut=i[0].reshape([1,gt,Ir]),yt=or.reshape([1,Ir,re]),Dt=[1,gt,re]}else ut=i[0].reshape([gt,d*x,k]),yt=or.reshape([1,k,re]),Dt=[gt,v*oe,re];Bt.push(ut),Bt.push(yt)}else ut=i[0].reshape([gt,k,d*x]),yt=i[1].reshape([1,re,k]),Dt=[gt,re,v*oe],Bt.push(yt),Bt.push(ut);h&&Bt.push(i[2]);let mr=Dt[2],vr=Bt[0].dims[Bt[0].dims.length-1];mr<8&&vr<8?n.compute(ol(Bt,u,q,Dt,c),{inputs:Bt}):n.compute(tl(Bt,u,q,Dt,c),{inputs:Bt});return}let we=!0,be=n.kernelCustomData.wT??n.compute(hi(i[1],Vs),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!n.kernelCustomData.wT&&(n.kernelCustomData.wT=be);let he=[i[0],be];h&&he.push(i[2]);let De=c?v*oe:re,Ae=c?re:v*oe,Ie=$*H*k;n.compute(lp(he,u,q,De,Ae,Ie,h,we),{inputs:he})},ld=(n,i)=>{let s=i.format==="NHWC",u=[n.inputs[0].reshape(s?[n.inputs[0].dims[0],1,n.inputs[0].dims[1],n.inputs[0].dims[2]]:[n.inputs[0].dims[0],n.inputs[0].dims[1],1,n.inputs[0].dims[2]]),n.inputs[1].reshape([n.inputs[1].dims[0],n.inputs[1].dims[1],1,n.inputs[1].dims[2]])];n.inputs.length===3&&u.push(n.inputs[2]);let c=[0,i.pads[0],0,i.pads[1]],h=[1].concat(i.strides),d=[1].concat(i.dilations),x=[1].concat(i.kernelShape),k=Ws({...i,pads:c,strides:h,dilations:d,kernelShape:x},u);n.compute(id(u,k,$=>s?[$[0],$[2],$[3]]:[]))},ud=(n,i,s)=>{let u=s.format==="NHWC"?"channelsLast":"channelsFirst",c=Ws(s,i),h=s.autoPad==="NOTSET"?s.pads:s.autoPad,d=rd(i[0].dims,i[1].dims,s.strides,s.dilations,h,!1,u);n.compute(nd(i,c,d.outShape,[d.filterDepth,d.filterHeight,d.filterWidth],[d.padInfo.front,d.padInfo.top,d.padInfo.left],u))},Ko=(n,i)=>{al(n.inputs,i),n.inputs[0].dims.length===3?ld(n,i):n.inputs[0].dims.length===5?ud(n,n.inputs,i):ul(n,n.inputs,i)}}),dd,fd,dp=p(()=>{Zt(),Nn(),ur(),Gi(),xo(),Xc(),js(),dd=(n,i=!1,s,u,c=4)=>{let h=we=>{switch(we){case 1:return"return w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];";case 4:return` + let coord1 = vec4(coordX, coordY, col + 1, rowInner); + let coord2 = vec4(coordX, coordY, col + 2, rowInner); + let coord3 = vec4(coordX, coordY, col + 3, rowInner); + let v0 = w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))]; + let v1 = w[getIndexFromCoords4D(coord1, vec4(uniforms.w_shape))]; + let v2 = w[getIndexFromCoords4D(coord2, vec4(uniforms.w_shape))]; + let v3 = w[getIndexFromCoords4D(coord3, vec4(uniforms.w_shape))]; + return ${u}(v0, v1, v2, v3); + `;default:throw new Error(`innerElementSize ${we} is not supported.`)}},d=n?` + let coord = vec4(batch, iXR, iXC, xCh); + `:` + let coord = vec4(batch, xCh, iXR, iXC); + `,x=n?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,k=n?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",$=n?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",H=n?"row":"col",q=n?"col":"row",v=` + let inChannels = ${n?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let outWidth = ${n?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${H} / outWidth; + let outCol = ${H} % outWidth; + + let WRow = ${q} / (uniforms.filter_dims[1] * inChannels); + let WCol = ${q} / inChannels % uniforms.filter_dims[1]; + let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]); + let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]); + if (xR < 0.0 || xR >= f32(${k}) || fract(xR) > 0.0) { + return ${u}(0.0); + } + if (xC < 0.0 || xC >= f32(${$}) || fract(xC) > 0.0) { + return ${u}(0.0); + } + let iXR = i32(xR); + let iXC = i32(xC); + let xCh = ${q} % inChannels; + ${d} + return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${c}];`,oe=n?` + let col = colIn * ${c}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${v} + } + return ${u}(0.0);`:` + let col = colIn * ${c}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${v} + } + return ${u}(0.0);`,re=` + let col = colIn * ${c}; + let inChannels = ${n?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels); + let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1]; + if (${n?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"} && coordX >= 0 && coordY >= 0) { + let rowInner = row % inChannels; + let coord = vec4(coordX, coordY, col, rowInner); + ${h(c)} + } + return ${u}(0.0); + `,ce=Vi(s,u);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${u} { + ${n?oe:re} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${u} { + ${n?re:oe} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${u}) { + let col = colIn * ${c}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueInput; + let outWidth = ${n?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${x} + ${Za(i)} + ${ce} + result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${c}] = value; + } + }`},fd=(n,i,s,u,c,h,d,x)=>{let k=i.format==="NHWC",$=k?n[0].dims[3]:n[0].dims[1],H=s[0],q=k?s[2]:s[3],v=k?s[1]:s[2],oe=k?s[3]:s[1],re=k&&$%4===0&&$%3&&oe%4===0,ce=k?oe:q*v,we=k?q*v:oe,be=[8,8,1],he=u<=8?[4,1,1]:[4,4,1],De=[Math.ceil(ce/be[0]/he[0]),Math.ceil(we/be[1]/he[1]),Math.ceil(H/be[2]/he[2])];Ur("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${De}`);let Ae=re?4:1,Ie=Math.max(be[0]*Ae,be[1]),gt=re?4:1,ut=[i.kernelShape[k?1:2],i.kernelShape[k?2:3]],yt=[ut[0]+(i.dilations[0]<=1?0:(ut[0]-1)*(i.dilations[0]-1)),ut[1]+(i.dilations[1]<=1?0:(ut[1]-1)*(i.dilations[1]-1))],Dt=[yt[0]-1-Math.floor((i.pads[0]+i.pads[2])/2),yt[1]-1-Math.floor((i.pads[1]+i.pads[3])/2)],Bt=[{type:6,data:u},{type:6,data:c},{type:6,data:h},{type:6,data:i.strides},{type:6,data:i.dilations},{type:6,data:ut},{type:6,data:Dt}];Wi(i,Bt),Bt.push(...$t(n[0].dims,n[1].dims));let mr=["rank","rank"];d&&(Bt.push(...$t(n[2].dims)),mr.push("rank")),Bt.push(...$t(s));let vr=or=>{let Ir=_t("x",n[0].dataType,n[0].dims.length,gt),Zr=_t("w",n[1].dataType,n[1].dims.length,1),fr=Gt("result",n[0].dataType,s.length,gt),Gr=[Ir,Zr],jt="";if(d){let ot=_t("bias",n[2].dataType,n[2].dims.length,gt);Gr.push(ot),jt+=` + fn getBiasByOutputCoords(coords : vec4) -> ${ot.type.value} { + return bias[coords.${k?"w":"y"}${re?"/ 4":""}]; + }`}let er=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:ut.length},{name:"pads",type:"i32",length:Dt.length}];Ei(i,er);let _r=Tr(n[0].dataType,1);if(_r!=="f16"&&_r!=="f32")throw new Error(`elemType ${_r} is not supported.`);return` + ${Ja("uniforms.result_strides")} + ${or.registerUniforms(er).declareVariables(...Gr,fr)}; + ${jt} + ${dd(k,d,i,Ir.type.value,Ae)} + ${re?Rs(he,be,_r,void 0,!k,Ie):Bs(he,be,_r,void 0,!k,Ie,!1,void 0,x)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${i.cacheKey};${he};${be};${re}`,inputDependencies:mr},getRunData:()=>({outputs:[{dims:s,dataType:n[0].dataType}],dispatchGroup:{x:De[0],y:De[1],z:De[2]},programUniforms:Bt}),getShaderSource:vr}}}),cl,Xo,mm=p(()=>{Zt(),Nn(),Jt(),ur(),cl=(n,i,s,u,c,h=!1,d,x,k=!1)=>{let $=k?1:2,H=k?2:3,q=k?3:1,v=h?2:1,oe=` + fn setOutputAtIndex(flatIndex : u32, value : ${h?`vec4<${d}>`:d}) { + result[flatIndex] = ${h?`vec4<${d}>`:d}(value); + }`;u&&(oe+=` + fn getBiasByOutputCoords(coords : vec4) -> ${h?`vec4<${d}>`:d} { + return bias[coords.${k?"w":"y"}${h?"/ 4":""}]; + }`);let re=h?4:1,ce=_t("W",i[1].dataType,i[1].dims.length,re),we=_t("Dy",i[0].dataType,i[0].dims.length,re),be=[we,ce];u&&be.push(_t("bias",i[2].dataType,[s[q]].length,re));let he=Gt("result",i[0].dataType,s.length,re),De=`{ + let batch: u32 = ${c?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1]; + let r = ${c?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1]; + let c = ${c?"global_id.y":"workgroup_id.y"} * ${v}; + let d1: u32 = ${c?"global_id.x":"workgroup_id.x"} * 4; + + let dyCorner = vec2(i32(r), i32(c)) - vec2(uniforms.pads); + + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd: array, ${v}>; + for (var i = 0; i < ${v}; i++) { + dotProd[i] = vec4<${d}>(0.0); + } + for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) { + var dyR = (${d}(dyCorner.x) + ${d}(wR)) / ${d}(uniforms.strides.x); + let wRPerm = uniforms.filter_dims[0] - 1 - wR; + if (dyR < 0.0 || dyR >= ${d}(uniforms.Dy_shape[1]) || + fract(dyR) > 0.0 || wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) { + let dyC = (${d}(dyCorner.y) + ${d}(wC)) / ${d}(uniforms.strides.y); + let dyC2 = (${d}(dyCorner.y) + 1.0 + ${d}(wC)) / ${d}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims[1] - 1 - wC; + if (wCPerm < 0) { + continue; + } + var bDyCVal = true; + var bDyCVal2 = true; + if (dyC < 0.0 || dyC >= ${d}(uniforms.Dy_shape[2]) || + fract(dyC) > 0.0) { + bDyCVal = false; + } + if (dyC2 < 0.0 || dyC2 >= ${d}(uniforms.Dy_shape[2]) || + fract(dyC2) > 0.0) { + bDyCVal2 = false; + } + + let idyC: u32 = u32(dyC); + let idyC2: u32 = u32(dyC2); + if (bDyCVal && bDyCVal2) { + let d2Length = uniforms.Dy_shape[3]; + for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${we.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${d}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + + xValue = ${we.get("batch","idyR","idyC2","d2")}; + + dotProd[1] = dotProd[1] + vec4<${d}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + } + } else if (bDyCVal) { + let d2Length = uniforms.Dy_shape[${q}]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${we.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${d}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + } + } else if (bDyCVal2) { + let d2Length = uniforms.Dy_shape[3]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${ce.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${we.get("batch","idyR","idyC2","d2")}; + let tmpval = vec4<${d}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[1] = dotProd[1] + tmpval; + } + } + } + } + + for (var i: u32 = 0; i < ${v}; i = i + 1) { + let value = dotProd[i] + ${u?"bias[c+i]":`vec4<${d}>(0.0)`}; + ${he.set("batch","r","c + i","d1","value")}; + } + }`,Ae=` + let outputIndices = ${he.offsetToIndices("global_idx")}; + let batch = ${he.indicesGet("outputIndices",0)}; + let d1 = ${he.indicesGet("outputIndices",q)}; + let r = ${he.indicesGet("outputIndices",$)}; + let c = ${he.indicesGet("outputIndices",H)}; + 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 = ${d}(0.0); + for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${d}(dyRCorner) + ${d}(wR)) / ${d}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${d}(uniforms.Dy_shape[${$}]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { + if (wC % uniforms.dilations.y != 0) { + continue; + } + let dyC = (${d}(dyCCorner) + ${d}(wC)) / ${d}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${d}(uniforms.Dy_shape[${H}]) || + 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 = ${k?we.get("batch","idyR","idyC","inputChannel"):we.get("batch","inputChannel","idyR","idyC")}; + let wValue = ${ce.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")}; + dotProd = dotProd + xValue * wValue; + inputChannel = inputChannel + 1; + } + } + } + let value = dotProd + ${u?"bias[d1]":`${d}(0.0)`}; + ${he.setByOffset("global_idx","value")}; + `;return` + ${n.registerUniforms(x).declareVariables(...be,he)} + ${oe} + + ${n.mainStart()} + ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${h?De:Ae}}`},Xo=(n,i,s)=>{let u=n.length>2,c=i.outputShape,h=ct.size(c),d=[Math.ceil(h/64),1,1];Ur("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${d}`);let x=i.format==="NHWC",k=["rank","rank"],$=[i.strides[0],i.strides[1]],H=[i.kernelShape[x?1:2],i.kernelShape[x?2:3]],q=[i.dilations[0],i.dilations[1]],v=[H[0]+(i.dilations[0]<=1?0:(i.kernelShape[x?1:2]-1)*(i.dilations[0]-1)),H[1]+(i.dilations[1]<=1?0:(i.kernelShape[x?2:3]-1)*(i.dilations[1]-1))],oe=[v[0]-1-Math.floor((i.pads[0]+i.pads[2])/2),v[1]-1-Math.floor(i.pads[1]+i.pads[3])/2],re=!1,ce=i.group,we=n[1].dims,be=we[0]/ce,he=we[1],De=[{type:12,data:h},{type:12,data:$},{type:12,data:H},{type:12,data:q},{type:12,data:v},{type:6,data:oe},{type:12,data:be},{type:12,data:he},...$t(n[0].dims,n[1].dims)];u&&(De.push(...$t(n[2].dims)),k.push("rank")),De.push(...$t(c));let Ae=d[1]===1&&d[2]===1,Ie=gt=>{let ut=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:$.length},{name:"filter_dims",type:"u32",length:H.length},{name:"dilations",type:"u32",length:H.length},{name:"effective_filter_dims",type:"u32",length:v.length},{name:"pads",type:"i32",length:oe.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],yt=Tr(n[0].dataType);return`${cl(gt,n,c,u,Ae,re,yt,ut,x)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${i.cacheKey};`,inputDependencies:k},getRunData:()=>({dispatchGroup:{x:d[0],y:d[1],z:d[2]},outputs:[{dims:s?s(c):c,dataType:n[0].dataType}],programUniforms:De}),getShaderSource:Ie}}}),pd,hd,dl,fl,md,pl,gd,_d,hl,fp,gm=p(()=>{dp(),mm(),Gi(),yo(),pd=(n,i,s,u,c,h)=>(n-1)*i+s+(u-1)*c+1-h,hd=(n,i,s,u,c)=>{let h=Math.floor(n/2);i==="SAME_UPPER"?(s[u]=h,s[c]=n-h):i==="SAME_LOWER"&&(s[u]=n-h,s[c]=h)},dl=(n,i,s,u,c,h,d,x,k,$)=>{let H=n.length-2,q=$.length===0;if(k.length===0)for(let re=0;re{let s=n.kernelShape.slice();if(n.kernelShape.length===0||n.kernelShape.reduce((q,v)=>q*v,1)===0){s.length=0;for(let q=2;qq+v,0)===0){let q=i[0].dims.length-2;k=new Array(q).fill(1)}let $=n.strides.slice();if($.reduce((q,v)=>q+v,0)===0){let q=i[0].dims.length-2;$=new Array(q).fill(1)}dl(x,s,k,n.autoPad,n.group,c,$,u,d,h);let H=Object.assign({},n);return Object.assign(H,{kernelShape:s,pads:c,outputPadding:d,outputShape:h,dilations:k,strides:$}),H},md=n=>{let i=Ya(n),s=n.format,u=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof n.autoPad>"u"?0:n.autoPad],c=n.dilations,h=n.group,d=n.kernelShape,x=n.pads,k=n.strides,$=n.wIsConst(),H=n.outputPadding,q=n.outputShape;return{autoPad:u,format:s,dilations:c,group:h,kernelShape:d,outputPadding:H,outputShape:q,pads:x,strides:k,wIsConst:$,...i,cacheKey:`${n.format};${i.activation};`}},pl=(n,i)=>{if(!n||n.length!==2&&n.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(n[0].dims.length!==4&&n[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(n[0].dims.length!==n[1].dims.length)throw new Error("filter does not have same dimension as input");let s=n[0].dims[i.format==="NHWC"?n[0].dims.length-1:1],u=n[1].dims[0];if(s!==u)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let c=n[1].dims[1]*i.group;if(n.length===3&&(n[2].dims.length!==1||n[2].dims[0]!==c))throw new Error("invalid bias");let h=n[0].dims.length-2;if(i.dilations.reduce((d,x)=>d+x,0)>0&&i.dilations.length!==h)throw new Error(`dilations should be ${h}D`);if(i.strides.reduce((d,x)=>d+x,0)>0&&i.strides.length!==h)throw new Error(`strides should be ${h}D`);if(i.pads.reduce((d,x)=>d+x,0)>0&&i.pads.length!==h*2)throw new Error(`pads should be ${h*2}D`);if(i.outputPadding.length!==h&&i.outputPadding.length!==0)throw new Error(`output_padding should be ${h}D`);if(i.kernelShape.reduce((d,x)=>d+x,0)>0&&i.kernelShape.length!==0&&i.kernelShape.length!==n[1].dims.length-2)throw new Error("invalid kernel shape");if(i.outputShape.length!==0&&i.outputShape.length!==n[0].dims.length-2)throw new Error("invalid output shape")},gd=[2,3,1,0],_d=(n,i,s)=>{let u=fl(s,i),c=s.format==="NHWC",h=u.outputShape,d=h[c?3:1],x=i[0].dims[c?3:1];if(u.group!==1||d===1&&x===1){n.compute(Xo(i,u));return}let k=h[c?1:2],$=h[c?2:3],H=i[1].dims[2],q=i[1].dims[3],v=c?k*$:d,oe=c?d:k*$,re=H*q*x,ce=!0,we=n.kernelCustomData.wT??n.compute(hi(i[1],gd),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!n.kernelCustomData.wT&&(n.kernelCustomData.wT=we);let be=[i[0],we],he=i.length===3;he&&(!c&&i[2].dims.length===1?be.push(i[2].reshape([i[2].dims[0],1,1])):be.push(i[2])),n.compute(fd(be,u,h,v,oe,re,he,ce),{inputs:be})},hl=(n,i)=>{let s=i.format==="NHWC",u=[n.inputs[0].reshape(s?[n.inputs[0].dims[0],1,n.inputs[0].dims[1],n.inputs[0].dims[2]]:[n.inputs[0].dims[0],n.inputs[0].dims[1],1,n.inputs[0].dims[2]]),n.inputs[1].reshape([n.inputs[1].dims[0],n.inputs[1].dims[1],1,n.inputs[1].dims[2]])];n.inputs.length===3&&u.push(n.inputs[2]);let c=i.kernelShape;(c.length===0||c[0]===0)&&(c=[n.inputs[1].dims[2]]);let h=i.dilations;(h.length===0||h[0]===0)&&(h=[1]);let d=i.strides;(d.length===0||d[0]===0)&&(d=[1]);let x=i.pads;x.length===0&&(x=[0,0]),x=[0,x[0],0,x[1]],d=[1].concat(d),h=[1].concat(h),c=[1].concat(c);let k=fl({...i,pads:x,strides:d,dilations:h,kernelShape:c},u);n.compute(Xo(u,k,$=>s?[$[0],$[2],$[3]]:[$[0],$[1],$[3]]))},fp=(n,i)=>{pl(n.inputs,i),n.inputs[0].dims.length===3?hl(n,i):_d(n,n.inputs,i)}}),ml,gl,wd,pp=p(()=>{Zt(),Jt(),gr(),ur(),ml=(n,i,s,u)=>{let c=ct.size(i),h=i.length,d=_t("input",n,h),x=Gt("output",n,h),k=s.dataType===6?s.getInt32Array()[0]:Number(s.getBigInt64Array()[0]),$=ct.normalizeAxis(k,h),H=q=>{let v=` i32(${d.indicesGet("inputIndices","uniforms.axis")}) `,oe=Ot("uniforms.input_shape","uniforms.axis",h),re=u.reverse?v+(u.exclusive?" + 1":""):"0",ce=u.reverse?oe:v+(u.exclusive?"":" + 1");return` + ${q.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(d,x)} + ${q.mainStart()} + ${q.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${x.offsetToIndices("global_idx")}; + var sum = ${x.type.value}(0); + let first : i32 = ${re}; + let last : i32 = ${ce}; + for (var i : i32 = first; i < last; i++) { + ${d.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${d.getByIndices("inputIndices")}; + } + ${x.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:u.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:[{type:12,data:c},{type:12,data:$},...$t(i,i)]}),getShaderSource:H}},gl=(n,i)=>{let s=n.inputs[0].dims,u=n.inputs[0].dataType,c=n.inputs[1];n.compute(ml(u,s,c,i),{inputs:[0]})},wd=n=>{let i=n.exclusive===1,s=n.reverse===1;return qt({exclusive:i,reverse:s})}}),_l,yd,vd,wl,bd,hp=p(()=>{Zt(),Jt(),gr(),ur(),_l=n=>{if(!n||n.length!==1)throw new Error("DepthToSpace requires 1 input.");if(n[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},yd=(n,i,s,u)=>{let c=[];c.push(`fn perm(i: ${u.type.indices}) -> ${s.type.indices} { + var a: ${s.type.indices};`);for(let h=0;h{let s,u,c,h,d,x,k=i.format==="NHWC",$=i.blocksize,H=i.mode==="DCR";k?([s,u,c,h]=n.dims,d=H?[s,u,c,$,$,h/$**2]:[s,u,c,h/$**2,$,$],x=H?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([s,u,c,h]=[n.dims[0],n.dims[2],n.dims[3],n.dims[1]],d=H?[s,$,$,h/$**2,u,c]:[s,h/$**2,$,$,u,c],x=H?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let q=n.reshape(d),v=q.dims.length,oe=n.dataType,re=_t("a",oe,v),ce=Gt("output",oe,v),we=be=>` + ${be.registerUniform("output_size","u32").declareVariables(re,ce)} + + ${yd(x,v,re,ce)} + + ${be.mainStart()} + ${be.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${ce.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${ce.setByOffset("global_idx",re.getByIndices("aIndices"))} + }`;return{name:"DepthToSpace",shaderCache:{hint:`${n.dims};${i.blocksize};${i.mode}`,inputDependencies:["rank"]},getRunData:be=>{let he=k?[s,u*$,c*$,h/$**2]:[s,h/$**2,u*$,c*$],De=ct.size(he),Ae=q.dims,Ie=ct.sortBasedOnPerm(Ae,x);return{outputs:[{dims:he,dataType:be[0].dataType}],dispatchGroup:{x:Math.ceil(De/64)},programUniforms:[{type:12,data:De},...$t(Ae,Ie)]}},getShaderSource:we}},wl=(n,i)=>{_l(n.inputs),n.compute(vd(n.inputs[0],i))},bd=n=>qt({blocksize:n.blocksize,mode:n.mode,format:n.format})}),Qo,Yo,yl,Lr,mp,gp,_p,Gs,xd,Md,Td,wp=p(()=>{Zt(),Jt(),gr(),ur(),Qo="[a-zA-Z]|\\.\\.\\.",Yo="("+Qo+")+",yl="^"+Yo+"$",Lr="("+Yo+",)*"+Yo,mp="^"+Lr+"$",gp=class{constructor(n=-1){this.symbolToIndices=new Map,this.inputIndex=n}addSymbol(n,i){let s=this.symbolToIndices.get(n);s===void 0?s=[i]:s.push(i),this.symbolToIndices.set(n,s)}},_p=class{constructor(n,i){var c;this.equation=i,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[s,u]=i.includes("->")?i.split("->",2):[i,""];if(!s.match(RegExp(mp)))throw new Error("Invalid LHS term");if(s.split(",").forEach((h,d)=>{let x=n[d].dims.slice();if(!h.match(RegExp(yl)))throw new Error("Invalid LHS term");let k=this.processTerm(h,!0,x,d);this.lhs.push(k)}),u==="")u+=[...this.symbolToInfo.entries()].filter(([h,d])=>d.count===1||h==="...").map(([h])=>h).join("");else if(!u.match(RegExp(Yo)))throw new Error("Invalid RHS");(c=u.match(RegExp(Qo,"g")))==null||c.forEach(h=>{if(h==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let d=this.symbolToInfo.get(h);if(d===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(d.dimValue)}}),this.rhs=this.processTerm(u,!1,this.outputDims)}addSymbol(n,i,s){let u=this.symbolToInfo.get(n);if(u!==void 0){if(u.dimValue!==i&&u.count!==1)throw new Error("Dimension mismatch");u.count++,u.inputIndices.push(s)}else u={count:1,dimValue:i,inputIndices:[s]};this.symbolToInfo.set(n,u)}processTerm(n,i,s,u=-1){let c=s.length,h=!1,d=[],x=0;if(!n.match(RegExp(yl))&&!i&&n!=="")throw new Error("Invalid LHS term");let k=n.match(RegExp(Qo,"g")),$=new gp(u);return k==null||k.forEach((H,q)=>{if(H==="..."){if(h)throw new Error("Only one ellipsis is allowed per input term");h=!0;let v=c-k.length+1;if(v<0)throw new Error("Ellipsis out of bounds");if(d=s.slice(x,x+v),this.hasEllipsis){if(this.ellipsisDims.length!==d.length||this.ellipsisDims.toString()!==d.toString())throw new Error("Ellipsis dimensions mismatch")}else if(i)this.hasEllipsis=!0,this.ellipsisDims=d;else throw new Error("Ellipsis must be specified in the LHS");for(let oe=0;oen+"_max",xd=(n,i,s,u)=>{let c=n.map($=>$.length).map(($,H)=>_t(`input${H}`,i,$)),h=ct.size(u),d=Gt("output",i,u.length),x=[...s.symbolToInfo.keys()].filter($=>!s.rhs.symbolToIndices.has($)),k=$=>{let H=[],q="var prod = 1.0;",v="var sum = 0.0;",oe="sum += prod;",re=[],ce=[],we=[],be=[],he=s.symbolToInfo.size===s.rhs.symbolToIndices.size;s.symbolToInfo.forEach((Ae,Ie)=>{var gt;if(s.rhs.symbolToIndices.has(Ie)){let ut=(gt=s.rhs.symbolToIndices.get(Ie))==null?void 0:gt[0];ut!==void 0&&s.lhs.forEach((yt,Dt)=>{if(Ae.inputIndices.includes(Dt)){let Bt=yt.symbolToIndices.get(Ie);if(Bt===void 0)throw new Error("Invalid symbol error");Bt.forEach(mr=>{H.push(`${c[Dt].indicesSet(`input${Dt}Indices`,mr,d.indicesGet("outputIndices",ut))}`)})}})}else s.lhs.forEach((ut,yt)=>{if(Ae.inputIndices.includes(yt)){let Dt=ut.symbolToIndices.get(Ie);if(Dt===void 0)throw new Error("Invalid symbol error");Dt.forEach(Bt=>{re.push(`${c[yt].indicesSet(`input${yt}Indices`,Bt,`${Ie}`)}`)}),be.push(`prod *= ${c[yt].getByIndices(`input${yt}Indices`)};`)}}),ce.push(`for(var ${Ie}: u32 = 0; ${Ie} < uniforms.${Gs(Ie)}; ${Ie}++) {`),we.push("}")});let De=he?[...H,`let sum = ${c.map((Ae,Ie)=>Ae.getByIndices(`input${Ie}Indices`)).join(" * ")};`]:[...H,v,...ce,...re,q,...be,oe,...we];return` + ${$.registerUniforms(x.map(Ae=>({name:`${Gs(Ae)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...c,d)} + + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${d.offsetToIndices("global_idx")}; + ${c.map((Ae,Ie)=>`var input${Ie}Indices: ${c[Ie].type.indices};`).join(` +`)} + ${De.join(` +`)}; + ${d.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:s.equation,inputDependencies:n.map(()=>"rank")},getRunData:()=>{let $=x.filter(q=>s.symbolToInfo.has(q)).map(q=>{var v;return{type:12,data:((v=s.symbolToInfo.get(q))==null?void 0:v.dimValue)||0}});$.push({type:12,data:h});let H=n.map((q,v)=>[...$t(q)]).reduce((q,v)=>q.concat(v),$);return H.push(...$t(u)),{outputs:[{dims:u,dataType:i}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:H}},getShaderSource:k}},Md=(n,i)=>{let s=new _p(n.inputs,i.equation),u=s.outputDims,c=n.inputs.map((h,d)=>h.dims);n.compute(xd(c,n.inputs[0].dataType,s,u))},Td=n=>{let i=n.equation.replace(/\s+/g,"");return qt({equation:i})}}),vl,Hs,kd,Sd,bl,_m=p(()=>{Zt(),Jt(),ur(),vl=n=>{if(!n||n.length!==2)throw new Error("Expand requires 2 input.");let i=n[0].dims,s=Array.from(n[1].getBigInt64Array(),Number),u=s.length{let s=n.length-i.length,u=[];for(let c=0;cn.length>i.length?Hs(n,i):Hs(i,n),Sd=n=>{let i=n[0].dims,s=Array.from(n[1].getBigInt64Array(),Number),u=kd(i,s),c=n[0].dataType,h=c===9?4:1,d=Math.ceil(ct.size(u)/h),x=$=>{let H=_t("input",c,i.length,h),q=Gt("output",c,u.length,h),v;if(c===9){let oe=(re,ce,we="")=>` + let outputIndices${ce} = ${q.offsetToIndices(`outputOffset + ${ce}u`)}; + let offset${ce} = ${H.broadcastedIndicesToOffset(`outputIndices${ce}`,q)}; + let index${ce} = offset${ce} / 4u; + let component${ce} = offset${ce} % 4u; + ${re}[${ce}] = ${we}(${H.getByOffset(`index${ce}`)}[component${ce}]); + `;v=` + let outputOffset = global_idx * ${h}; + var data = vec4(0); + ${oe("data",0,"u32")} + ${oe("data",1,"u32")} + ${oe("data",2,"u32")} + ${oe("data",3,"u32")} + ${q.setByOffset("global_idx","data")} + }`}else v=` + let outputIndices = ${q.offsetToIndices("global_idx")}; + let inputOffset = ${H.broadcastedIndicesToOffset("outputIndices",q)}; + ${q.setByOffset("global_idx",H.getByOffset("inputOffset"))} + }`;return` + ${$.registerUniform("vec_size","u32").declareVariables(H,q)} + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${v}`},k=[{type:12,data:d},...$t(i,u)];return{name:"Expand",shaderCache:{hint:`${u.length}`,inputDependencies:["rank"]},getShaderSource:x,getRunData:()=>({outputs:[{dims:u,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:k})}},bl=n=>{vl(n.inputs),n.compute(Sd(n.inputs),{inputs:[0]})}}),yp,Ed,vp=p(()=>{Zt(),Jt(),ur(),Ha(),yp=n=>{let i=n[0].dataType,s=ct.size(n[0].dims),u=ct.size(n[1].dims),c=u%4===0,h=d=>{let x=_t("x",i,[1],4),k=_t("bias",i,[1],4),$=Gt("y",i,[1],4),H=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],q=oe=>` + let bias${oe}_offset: u32 = (global_idx * 4 + ${oe}) % uniforms.bias_size; + let bias${oe} = ${k.getByOffset(`bias${oe}_offset / 4`)}[bias${oe}_offset % 4];`,v=c?` + let bias = ${k.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${q(0)}${q(1)}${q(2)}${q(3)} + let bias = ${x.type.value}(bias0, bias1, bias2, bias3);`;return`${d.registerUniforms(H).declareVariables(x,k,$)} + + ${Va(kr(i))} + + ${d.mainStart(jn)} + ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${x.getByOffset("global_idx")}; + ${v} + let x_in = x + bias; + ${$.setByOffset("global_idx",Wa("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${c}`,inputDependencies:["type","type"]},getShaderSource:h,getRunData:d=>({outputs:[{dims:d[0].dims,dataType:d[0].dataType}],programUniforms:[{type:12,data:Math.ceil(s/4)},{type:12,data:u}],dispatchGroup:{x:Math.ceil(s/jn/4)}})}},Ed=n=>{n.inputs.length<2||ct.size(n.inputs[1].dims)===0?Ec(n):n.compute(yp(n.inputs))}}),Cd,Pd,$d,Ad,bp=p(()=>{Zt(),Jt(),gr(),ur(),Cd=n=>{if(!n||n.length!==2)throw new Error("Gather requires 2 inputs.")},Pd=(n,i)=>{let s=n[0].dims,u=n[1].dims,c=s.length,h=ct.normalizeAxis(i.axis,c),d=s.slice(0);d.splice(h,1,...u);let x=s[h],k=n[0].dataType===9?4:1,$=Math.ceil(ct.size(d)/k),H=[{type:12,data:$},{type:6,data:x},{type:12,data:h},...$t(n[0].dims,n[1].dims,d)],q=v=>{let oe=_t("data",n[0].dataType,n[0].dims.length,k),re=_t("inputIndices",n[1].dataType,n[1].dims.length),ce=Gt("output",n[0].dataType,d.length,k),we=he=>{let De=u.length,Ae=`var indicesIndices${he} = ${re.type.indices}(0);`;for(let Ie=0;Ie1?`indicesIndices${he}[${Ie}]`:`indicesIndices${he}`} = ${d.length>1?`outputIndices${he}[uniforms.axis + ${Ie}]`:`outputIndices${he}`};`;Ae+=` + var idx${he} = ${re.getByIndices(`indicesIndices${he}`)}; + if (idx${he} < 0) { + idx${he} = idx${he} + uniforms.axisDimLimit; + } + var dataIndices${he} : ${oe.type.indices}; + `;for(let Ie=0,gt=0;Ie1?`dataIndices${he}[${Ie}]`:`dataIndices${he}`} = u32(idx${he});`,gt+=De):(Ae+=`${c>1?`dataIndices${he}[${Ie}]`:`dataIndices${he}`} = ${d.length>1?`outputIndices${he}[${gt}]`:`outputIndices${he}`};`,gt++);return Ae},be;if(n[0].dataType===9){let he=(De,Ae,Ie="")=>` + let outputIndices${Ae} = ${ce.offsetToIndices(`outputOffset + ${Ae}u`)}; + ${we(Ae)}; + let offset${Ae} = ${oe.indicesToOffset(`dataIndices${Ae}`)}; + let index${Ae} = offset${Ae} / 4u; + let component${Ae} = offset${Ae} % 4u; + ${De}[${Ae}] = ${Ie}(${oe.getByOffset(`index${Ae}`)}[component${Ae}]); + `;be=` + let outputOffset = global_idx * ${k}; + var value = vec4(0); + ${he("value",0,"u32")} + ${he("value",1,"u32")} + ${he("value",2,"u32")} + ${he("value",3,"u32")} + ${ce.setByOffset("global_idx","value")} + `}else be=` + let outputIndices = ${ce.offsetToIndices("global_idx")}; + ${we("")}; + let value = ${oe.getByIndices("dataIndices")}; + ${ce.setByOffset("global_idx","value")}; + `;return` + ${v.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(oe,re,ce)} + ${v.mainStart()} + ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${be} + }`};return{name:"Gather",shaderCache:{hint:i.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil($/64)},programUniforms:H}),getShaderSource:q}},$d=n=>qt({axis:n.axis}),Ad=(n,i)=>{let s=n.inputs;Cd(s),n.compute(Pd(n.inputs,i))}}),Id,Fd,zd,Od,xp=p(()=>{Zt(),Jt(),gr(),ur(),Id=n=>{if(!n||n.length!==2)throw new Error("GatherElements requires 2 inputs.");if(n[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(n[0].dims.length!==n[1].dims.length)throw new Error(`GatherElements requires that the data input and + indices input tensors be of same rank.`)},Fd=(n,i)=>{let s=n[0].dims,u=n[0].dataType,c=s.length,h=n[1].dims,d=n[1].dataType,x=ct.normalizeAxis(i.axis,c),k=s[x],$=h.slice(0),H=ct.size($),q=_t("input",u,c),v=_t("indicesInput",d,h.length),oe=Gt("output",u,$.length),re=[{type:12,data:H},{type:6,data:k},{type:12,data:x}];return re.push(...$t(s,h,$)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:$,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(H/64)},programUniforms:re}),getShaderSource:ce=>` + ${ce.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(q,v,oe)} + ${ce.mainStart()} + ${ce.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${oe.offsetToIndices("global_idx")}; + + var idx = ${v.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${q.type.indices}(outputIndices); + ${q.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${q.getByIndices("inputIndices")}; + + ${oe.setByOffset("global_idx","value")}; + }`}},zd=n=>qt({axis:n.axis}),Od=(n,i)=>{let s=n.inputs;Id(s),n.compute(Fd(n.inputs,i))}}),Dd,Ld,Rd,Mp,Bd=p(()=>{Zt(),Jt(),ur(),Dd=n=>{if(!n)throw new Error("Input is missing");if(n.length<2||n.length>3)throw new Error("Invaid input number.");if(n.length===3&&n[2].dims.length>2)throw new Error("Invalid input shape of C");if(n[0].dataType!==n[1].dataType||n.length===3&&n[0].dataType!==n[2].dataType)throw new Error("Input types are mismatched")},Ld=(n,i)=>{let s=n[0].dims.slice(),u=n[1].dims.slice(),[c,h,d]=br.getShapeOfGemmResult(s,i.transA,u,i.transB,n.length===3?n[2].dims:void 0),x=[c,h];if(!x)throw new Error("Can't use gemm on the given tensors");let k=ct.size(x),$=[{type:12,data:k},{type:12,data:c},{type:12,data:h},{type:12,data:d},{type:1,data:i.alpha},{type:1,data:i.beta}],H=["type","type"];n.length===3&&($.push(...$t(n[2].dims)),H.push("rank")),$.push(...$t(x));let q=v=>{let oe="";i.transA&&i.transB?oe="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":i.transA&&!i.transB?oe="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!i.transA&&i.transB?oe="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!i.transA&&!i.transB&&(oe="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let re=i.alpha===1?"":"value *= uniforms.alpha;",ce=_t("a",n[0].dataType,n[0].dims),we=_t("b",n[1].dataType,n[1].dims),be=ce.type.value,he=null,De=[ce,we];n.length===3&&(he=_t("c",n[2].dataType,n[2].dims.length),De.push(he));let Ae=Gt("output",n[0].dataType,x.length);De.push(Ae);let Ie=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return` + ${v.registerUniforms(Ie).declareVariables(...De)} + + ${v.mainStart()} + ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${be}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${oe} + } + + ${re} + ${he!=null?`let cOffset = ${he.broadcastedIndicesToOffset("vec2(m, n)",Ae)}; value += ${be}(uniforms.beta) * ${he.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`};return{name:"Gemm",shaderCache:{hint:`${i.cacheKey}`,inputDependencies:H},getRunData:()=>({outputs:[{dims:x,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:$}),getShaderSource:q}},Rd=n=>{let i=n.transA,s=n.transB,u=n.alpha,c=n.beta;return{transA:i,transB:s,alpha:u,beta:c,cacheKey:`${n.transA};${n.transB};${n.alpha===1}`}},Mp=(n,i)=>{Dd(n.inputs),n.compute(Ld(n.inputs,i))}}),Pn,Nd,jd,xl,Ud,Zo,Vd,Wd=p(()=>{Zt(),Jt(),gr(),fe(),Os(),ur(),yo(),Pn=(n,i)=>n.length>i&&n[i].dims.length>0&&ct.size(n[i].dims)>0?n[i]:void 0,Nd=(n,i)=>{let s=n[0],u=Pn(n,1),c=Pn(n,2),h=Pn(n,3),d=Pn(n,4),x=Pn(n,5),k=Pn(n,6),$=Pn(n,7);if(s.dims.length!==3&&s.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let H=!1,q=s.dims[0],v=s.dims[1],oe=s.dims.length===3?H?s.dims[2]/3:s.dims[2]:i.numHeads*s.dims[4],re=v,ce=0,we=0,be=Math.floor(oe/i.numHeads);if(k&&$){if(k.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(k.dims[0]!==q||k.dims[1]!==i.numHeads||k.dims[3]!==be)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if($.dims[0]!==q||$.dims[1]!==i.numHeads||$.dims[3]!==be)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(k.dims[2]!==$.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if($.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');ce=k.dims[2],we=k.dims[2]}else if(k||$)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let he;if(u){if(s.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(u.dims.length<3||u.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(s.dims[0]!==u.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(u.dims.length===3){if(u.dims[2]!==s.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');he=2,re=u.dims[1]}else if(u.dims.length===5){if(u.dims[2]!==i.numHeads||u.dims[3]!==2||u.dims[4]!==be)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(c)throw new Error('Expect "value" be none when "key" has packed kv format.');he=5,re=u.dims[1]}else{if(u.dims[1]!==i.numHeads||u.dims[3]!==be)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');he=0,re=u.dims[2]}}else{if(s.dims.length!==3&&s.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(s.dims.length===5&&(s.dims[2]!==i.numHeads||s.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');he=3}if(h){if(h.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(c&&s.dims.length===5&&s.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let De=0;if(d){De=8;let yt=d.dims;throw yt.length===1?yt[0]===q?De=1:yt[0]===3*q+2&&(De=3):yt.length===2&&yt[0]===q&&yt[1]===re&&(De=5),De===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let Ae=!1,Ie=oe;if(c){if(c.dims.length!==3&&c.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(s.dims[0]!==c.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(c.dims.length===3){if(re!==c.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');Ie=c.dims[2]}else{if(re!==c.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');Ie=c.dims[1]*c.dims[3],Ae=!0}}let gt=ce+re,ut=!1;if(d)throw new Error("Key padding mask is not supported");if(x){if(x.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(x.dims[0]!==q&&x.dims[0]!==1||x.dims[1]!==i.numHeads||x.dims[2]!==v||x.dims[3]!==gt)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:q,sequenceLength:v,pastSequenceLength:ce,kvSequenceLength:re,totalSequenceLength:gt,maxSequenceLength:we,inputHiddenSize:0,hiddenSize:oe,vHiddenSize:Ie,headSize:be,vHeadSize:Math.floor(Ie/i.numHeads),numHeads:i.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:i.maskFilterValue,maskType:De,scale:i.scale,broadcastResPosBias:ut,passPastInKv:Ae,qkvFormat:he}},jd=n=>qt({...n}),xl=qt({perm:[0,2,1,3]}),Ud=(n,i,s,u,c,h,d)=>{let x=[u,c,h],k=ct.size(x),$=[{type:12,data:k},{type:12,data:d},{type:12,data:h}],H=q=>{let v=Gt("qkv_with_bias",i.dataType,x),oe=_t("qkv",i.dataType,x),re=_t("bias",s.dataType,x),ce=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${q.registerUniforms(ce).declareVariables(oe,re,v)} + ${q.mainStart()} + ${q.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return n.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:x,dataType:i.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:$}),getShaderSource:H},{inputs:[i,s],outputs:[-1]})[0]},Zo=(n,i,s,u,c,h,d,x)=>{let k=h;if(d){if(u===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return k=Ud(n,h,d,i,u,s*c,x),k=k.reshape([i,u,s,c]),n.compute(hi(k,xl.perm),{inputs:[k],outputs:[-1]})[0]}else return h.dims.length===3&&(k=h.reshape([i,u,s,c])),n.compute(hi(k,xl.perm),{inputs:[k],outputs:[-1]})[0]},Vd=(n,i)=>{let s=Nd(n.inputs,i),u=n.inputs[0],c=Pn(n.inputs,1),h=Pn(n.inputs,2),d=Pn(n.inputs,3),x=Pn(n.inputs,4),k=Pn(n.inputs,5),$=Pn(n.inputs,6),H=Pn(n.inputs,7);if(u.dims.length===5)throw new Error("Packed QKV is not implemented");if((c==null?void 0:c.dims.length)===5)throw new Error("Packed KV is not implemented");let q=c&&h&&c.dims.length===4&&h.dims.length===4,v=Zo(n,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,u,d,0);if(q)return vo(n,v,c,h,x,void 0,$,H,k,s,i);if(!c||!h)throw new Error("key and value must be provided");let oe=Zo(n,s.batchSize,s.numHeads,s.kvSequenceLength,s.headSize,c,d,s.hiddenSize),re=Zo(n,s.batchSize,s.numHeads,s.kvSequenceLength,s.vHeadSize,h,d,2*s.hiddenSize);vo(n,v,oe,re,x,void 0,$,H,k,s,i)}}),Ml,Gd,Hd,Tl,qd,Kd=p(()=>{Zt(),Jt(),ur(),Ml=n=>Array.from(n.getBigInt64Array(),Number),Gd=n=>{if(!n||n.length!==2)throw new Error("Tile requires 2 inputs.");if(n[0].dataType!==1&&n[0].dataType!==10&&n[0].dataType!==6&&n[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(n[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(n[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Ml(n[1]).length!==n[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Hd=(n,i)=>{let s=[];for(let u=0;u{let s=n[0].dims,u=i??Ml(n[1]),c=Hd(s,u),h=ct.size(c),d=n[0].dataType,x=_t("input",d,s.length),k=Gt("output",d,c.length),$=H=>` + const inputShape = ${x.indices(...s)}; + ${H.registerUniform("output_size","u32").declareVariables(x,k)} + ${H.mainStart()} + ${H.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${k.offsetToIndices("global_idx")}; + var input_indices: ${x.type.indices}; + for (var i = 0; i < ${s.length}; i++) { + let input_dim_i = ${x.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${k.indicesGet("output_indices","i")} % input_dim_i; + + ${x.indicesSet("input_indices","i","input_dim_value")} + } + ${k.setByOffset("global_idx",x.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${u}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:c,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:[{type:12,data:h},...$t(n[0].dims,c)]}),getShaderSource:$}},qd=n=>{Gd(n.inputs),n.compute(Tl(n.inputs),{inputs:[0]})}}),Xd,kl,Qd,Yd,Sl,Zd,Tp=p(()=>{Zt(),Jt(),gr(),Os(),ur(),Wd(),Kd(),yo(),Xd=(n,i)=>{let s=n[0],u=n[1],c=n[2],h=n[3],d=n[4];if(s.dims.length!==3&&s.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let x=!1,k=s.dims[0],$=s.dims[1],H=s.dims.length===3?x?s.dims[2]/3:s.dims[2]:i.numHeads*s.dims[4],q=$,v=0,oe=0,re=Math.floor(H/i.numHeads),ce=h&&h.dims.length!==0,we=d&&d.dims.length!==0,be=!0;if(ce&&we){if(h.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(d.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');v=h.dims[1],oe=h.dims[1]}else if(ce||we)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let he;if(u){if(s.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(u.dims.length<3||u.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(s.dims[0]!==u.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(u.dims.length===3){if(s.dims[2]%u.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');he=2,q=u.dims[1]}else if(u.dims.length===5){if(u.dims[2]!==i.numHeads||u.dims[3]!==2||u.dims[4]!==re)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(c)throw new Error('Expect "value" be none when "key" has packed kv format.');he=5,q=u.dims[1]}else{if(u.dims[1]!==i.numHeads||u.dims[3]!==re)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');he=0,q=u.dims[2]}}else{if(s.dims.length!==3&&s.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(s.dims.length===5&&(s.dims[2]!==i.numHeads||s.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');he=3}let De=0,Ae=!1,Ie=H;if(c){if(c.dims.length!==3&&c.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(s.dims[0]!==c.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(c.dims.length===3){if(q!==c.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');Ie=c.dims[2]}else{if(q!==c.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');Ie=c.dims[1]*c.dims[3],Ae=!0}}let gt=v+q;return{batchSize:k,sequenceLength:$,pastSequenceLength:v,kvSequenceLength:q,totalSequenceLength:gt,maxSequenceLength:oe,inputHiddenSize:0,hiddenSize:H,vHiddenSize:Ie,headSize:re,vHeadSize:Math.floor(Ie/i.kvNumHeads),numHeads:i.numHeads,kvNumHeads:i.kvNumHeads,nReps:i.numHeads/i.kvNumHeads,pastPresentShareBuffer:!1,maskType:De,scale:i.scale,broadcastResPosBias:!1,passPastInKv:Ae,qkvFormat:he,isPastkvBSNH:be}},kl=(n,i,s,u)=>{let c=[u.batchSize,u.totalSequenceLength,u.kvNumHeads,u.headSize],h=4,d=ct.size(c)/h,x=u.totalSequenceLength,k=Gt("present_kv",s,c.length,h),$=_t("new_kv",n.dataType,n.dims.length,h),H=i?_t("past_kv",i.dataType,i.dims.length,h):void 0,q=Math.ceil(u.headSize/h),v={x,y:n.dims[0],z:1},oe=i?["rank","rank"]:["rank"],re=[{type:12,data:d},{type:12,data:u.pastSequenceLength},{type:12,data:u.kvSequenceLength},{type:12,data:u.totalSequenceLength}],ce=[$];H?(re.push(...$t(n.dims),...$t(i.dims),...$t(c)),ce.push(H)):re.push(...$t(n.dims),...$t(c));let we=[{name:"output_size",type:"u32"},{name:"past_seqlen",type:"u32"},{name:"new_seqlen",type:"u32"},{name:"present_seqlen",type:"u32"}],be=` let past_batch_stride = uniforms.past_seqlen * num_heads * H; + var past_head_stride = uniforms.past_seqlen * H; + if (is_bsnh) { + past_head_stride = H; + } + let in_offset = b * past_batch_stride + s * row_stride + n * past_head_stride + h; + present_kv[out_offset] = past_kv[in_offset];`,he=` let new_batch_stride = uniforms.new_seqlen * num_heads * H; + let new_row_stride = num_heads * H; + let new_head_stride = H; + let in_offset = b * new_batch_stride + (s - past_seqlen) * new_row_stride + n * new_head_stride + h; + present_kv[out_offset] = new_kv[in_offset];`,De=i?`if (s < past_seqlen) { + ${be} + } else if (s < past_seqlen + uniforms.new_seqlen) { + ${he} + }`:`if (s < past_seqlen + uniforms.new_seqlen) { + ${he} + }`,Ae=Ie=>` + + ${Ie.registerUniforms(we).declareVariables(...ce,k)} + ${Ie.mainStart([q,u.kvNumHeads,1])} + ${Ie.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var indices = ${k.offsetToIndices("global_idx")}; + let h = local_id.x; + let n = local_id.y; + let s = workgroup_id.x; + let b = workgroup_id.y; + let num_heads = ${u.kvNumHeads}u; + let H = ${q}u; + + let present_seqlen = uniforms.present_seqlen; + let present_batch_stride = present_seqlen * num_heads * H; + var row_stride = H; + let is_bsnh = ${u.isPastkvBSNH}; + + if (is_bsnh) { + row_stride = num_heads * H; + } + var present_head_stride = present_seqlen * H; + if (is_bsnh) { + present_head_stride = H; + } + + let past_seqlen = uniforms.past_seqlen; + + let out_offset = b * present_batch_stride + s * row_stride + n * present_head_stride + h; + ${De} + }`;return{name:"ConcatPastNew",shaderCache:{hint:`${u.kvNumHeads}${q}${!!i}`,inputDependencies:oe},getRunData:()=>({outputs:[{dims:c,dataType:s}],dispatchGroup:v,programUniforms:re}),getShaderSource:Ae}},Qd=n=>qt({...n}),Yd=qt({perm:[0,2,1,3]}),Sl=(n,i,s,u,c)=>{let h=i,d=u.kvNumHeads,x=u.nReps;return i.dims.length===3&&u.kvSequenceLength!==0&&(h=i.reshape([u.batchSize,u.kvSequenceLength,d,u.headSize])),s?h=n.compute(kl(h,s,h.dataType,u),{inputs:[h,s],outputs:[u.isPastkvBSNH?c:-1]})[0]:h=n.compute(kl(h,void 0,h.dataType,u),{inputs:[h],outputs:[u.isPastkvBSNH?c:-1]})[0],x!==1&&(h=n.compute(Tl([h],[1,1,1,x]),{inputs:[h],outputs:[-1]})[0],h=h.reshape([u.batchSize,u.totalSequenceLength,d*x,u.headSize])),n.compute(hi(h,Yd.perm),{inputs:[h],outputs:[-1]})[0]},Zd=(n,i)=>{var k;let s=Xd(n.inputs,i);if(n.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((k=n.inputs[1])==null?void 0:k.dims.length)===5)throw new Error("Packed KV is not implemented");let u=Zo(n,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,n.inputs[0],void 0,0),c=n.inputs[3]&&n.inputs[3].dims.length!==0?n.inputs[3]:void 0,h=n.inputs[4]&&n.inputs[4].dims.length!==0?n.inputs[4]:void 0,d=Sl(n,n.inputs[1],c,s,1),x=Sl(n,n.inputs[2],h,s,2);vo(n,u,d,x,void 0,void 0,void 0,void 0,void 0,s,i)}}),Jd,ef,tf,rf,wm=p(()=>{Zt(),Jt(),ur(),Jd=(n,i)=>{let s=n[0].dims,u=s,c=2,h=ct.sizeToDimension(s,c),d=ct.sizeFromDimension(s,c),x=wr(d),k=d/x,$=[s[0],s[1],k],H=["rank","type","type"],q=[{type:12,data:d},{type:12,data:k}];q.push(...$t($,$));let v=oe=>{let re=_t("x",n[0].dataType,$.length,x),ce=_t("scale",n[1].dataType,n[1].dims),we=_t("bias",n[2].dataType,n[2].dims),be=Gt("output",n[0].dataType,$.length,x),he=[re,ce,we,be],De=re.type.value,Ae=x===1?"f32":`vec${x}`,Ie=64,gt=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return` + var meanShared : f32; + var squaredNormShared : f32; + var workgroupShared : array<${Ae}, ${Ie}>; + const workgroupSize = ${Ie}u; + ${oe.registerUniforms(gt).declareVariables(...he)} + ${oe.mainStart(Ie)} + let norm = global_idx / workgroupSize; + let batch = norm / uniforms.x_shape[1]; + let channel = norm % uniforms.x_shape[1]; + let localIndex = local_id.x; + + // initialize workgroup memory + var initial = ${Ae}(0); + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + initial = initial + ${Ae}(${re.get("batch","channel","h")}); + } + workgroupShared[localIndex] = initial; + workgroupBarrier(); + + // Calculate the mean of current channel data. + for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) { + if (localIndex < currSize) { + workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize]; + } + workgroupBarrier(); + } + if (localIndex == 0) { + meanShared = ${Un("workgroupShared[0]",x)} / f32(uniforms.normSize); + } + workgroupBarrier(); + + // reinitialize workgroup memory. + initial = ${Ae}(0); + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + let deviation = ${Ae}(${re.get("batch","channel","h")}) - ${Ae}(meanShared); + initial = initial + deviation * deviation; + } + workgroupShared[localIndex] = initial; + workgroupBarrier(); + + // Calculate the sum of square of deviation of current channel data. + for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) { + if (localIndex < currSize) { + workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize]; + } + workgroupBarrier(); + } + if (localIndex == 0) { + squaredNormShared = ${Un("workgroupShared[0]",x)}; + } + workgroupBarrier(); + + let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${i.epsilon})); + let channelScale = invStdDev * f32(${ce.getByOffset("channel")}); + let channelShift = f32(${we.getByOffset("channel")}) - meanShared * channelScale; + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + let value = ${re.get("batch","channel","h")} * ${De}(${Ae}(channelScale)) + ${De}(${Ae}(channelShift)); + ${be.set("batch","channel","h","value")}; + } + }`};return{name:"InstanceNormalization",shaderCache:{hint:`${i.epsilon};${x}`,inputDependencies:H},getRunData:()=>({outputs:[{dims:u,dataType:n[0].dataType}],dispatchGroup:{x:h},programUniforms:q}),getShaderSource:v}},ef=(n,i,s,u,c,h,d,x)=>{let k=wr(d),$=64,H=k===1?"vec2f":`mat2x${k}f`,q=k===1?"f32":`vec${k}f`,v=(gt,ut)=>`${H}(${gt}, ${ut})`,oe=c*d/k,re=Math.ceil(h/$),ce=["type"],we=[{type:12,data:re},{type:12,data:h},{type:12,data:Math.floor(d/k)},{type:12,data:Math.floor(h*d/k)}],be=gt=>{let ut=_t("input",i.dataType,i.dims,k);return` + ${gt.declareVariables(ut)} + @group(0) @binding(1) var output : array<${H}>; + struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32}; + @group(0) @binding(2) var uniforms: Uniforms; + + ${gt.mainStart($)} + let currentImageNumber = global_idx / ${$} / uniforms.C; + let currentChannelNumber = (global_idx / ${$}) % uniforms.C; + let wgOffset = local_id.x * uniforms.wg_size; + if (wgOffset >= uniforms.H) { + return; + } + let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H); + + let offset = currentImageNumber * uniforms.image_size + currentChannelNumber; + var sum = ${Dr("f32",k)}; + var squaredSum = ${Dr("f32",k)}; + for (var i: u32 = wgOffset; i < wgMax; i++) { + let value = ${q}(input[offset + i * uniforms.C]); + sum += value; + squaredSum += value * value; + } + output[global_idx] = ${v("sum","squaredSum")}; + }`},he=n.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${k}`,inputDependencies:ce},getRunData:()=>({outputs:[{dims:[c,d,$,2],dataType:1}],dispatchGroup:{x:c*d/k},programUniforms:we}),getShaderSource:be},{inputs:[i],outputs:[-1]})[0],De=[{type:12,data:oe},{type:12,data:h},{type:12,data:Math.floor(d/k)},{type:12,data:Math.floor($*d/k)}],Ae=["type","type","type"],Ie=gt=>{let ut=_t("scale",s.dataType,s.dims,k),yt=_t("bias",u.dataType,u.dims,k);return` + @group(0) @binding(0) var input : array<${H}>; + @group(0) @binding(1) var scale : array<${ut.type.storage}>; + @group(0) @binding(2) var bias : array<${yt.type.storage}>; + @group(0) @binding(3) var output : array<${H}>; + struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32}; + @group(0) @binding(4) var uniforms: Uniforms; + + ${gt.mainStart()} + ${gt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")} + let currentImageNumber = global_idx / uniforms.C; + let currentChannelNumber = global_idx % uniforms.C; + + let offset = currentImageNumber * uniforms.image_size; + var sum = ${Dr("f32",k)}; + var squaredSum = ${Dr("f32",k)}; + for (var i: u32 = 0; i < min(${$}, uniforms.H); i++) { + let value = input[offset + i + currentChannelNumber * ${$}]; + sum += value[0]; + squaredSum += value[1]; + } + sum = sum / f32(uniforms.H); + squaredSum = squaredSum / f32(uniforms.H); + let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${x})); + let channelScale = invStdDev * ${q}(scale[currentChannelNumber]); + let channelShift = ${q}(bias[currentChannelNumber]) - sum * channelScale; + + output[global_idx] = ${v("channelScale","channelShift")}; + }`};return n.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${k};${x}`,inputDependencies:Ae},getRunData:()=>({outputs:[{dims:[c,d,2],dataType:1}],dispatchGroup:{x:Math.ceil(oe/64)},programUniforms:De}),getShaderSource:Ie},{inputs:[he,s,u],outputs:[-1]})[0]},tf=(n,i,s)=>{let u=i[0].dims,c=u,h=u[0],d=u[u.length-1],x=ct.sizeFromDimension(u,1)/d,k=wr(d),$=ct.size(c)/k,H=[{type:12,data:x},{type:12,data:Math.floor(d/k)}],q=["type","type"],v=ef(n,i[0],i[1],i[2],h,x,d,s.epsilon),oe=re=>{let ce=Tr(i[0].dataType),we=k===1?"vec2f":`mat2x${k}f`,be=k===1?ce:`vec${k}<${ce}>`,he=_t("input",i[0].dataType,i[0].dims,k),De=Gt("output",i[0].dataType,c,k);return` + @group(0) @binding(0) var input : array<${he.type.storage}>; + @group(0) @binding(1) var scaleInput : array<${we}>; + @group(0) @binding(2) var output : array<${De.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${re.mainStart()} + let currentImageNumber = global_idx / (uniforms.C * uniforms.H); + let currentChannelNumber = global_idx % uniforms.C; + + let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber; + let scale = scaleInput[scaleOffset]; + output[global_idx] = fma(input[global_idx], ${be}(scale[0]), ${be}(scale[1])); + }`};n.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${k}`,inputDependencies:q},getRunData:()=>({outputs:[{dims:c,dataType:i[0].dataType}],dispatchGroup:{x:Math.ceil($/64)},programUniforms:H}),getShaderSource:oe},{inputs:[i[0],v]})},rf=(n,i)=>{i.format==="NHWC"?tf(n,n.inputs,i):n.compute(Jd(n.inputs,i))}}),cr,nf,hn,bn=p(()=>{Zt(),Jt(),ur(),cr=n=>{if(!n||n.length<2)throw new Error("layerNorm requires at least 2 inputs.")},nf=(n,i,s)=>{let u=i.simplified,c=n[0].dims,h=n[1],d=!u&&n[2],x=c,k=ct.normalizeAxis(i.axis,c.length),$=ct.sizeToDimension(c,k),H=ct.sizeFromDimension(c,k),q=ct.size(h.dims),v=d?ct.size(d.dims):0;if(q!==H||d&&v!==H)throw new Error(`Size of X.shape()[axis:] == ${H}. + Size of scale and bias (if provided) must match this. + Got scale size of ${q} and bias size of ${v}`);let oe=[];for(let Ie=0;Ie1,he=s>2,De=Ie=>{let gt=Tr(n[0].dataType),ut=[_t("x",n[0].dataType,n[0].dims,re),_t("scale",h.dataType,h.dims,re)];d&&ut.push(_t("bias",d.dataType,d.dims,re)),ut.push(Gt("output",n[0].dataType,x,re)),be&&ut.push(Gt("mean_data_output",1,oe)),he&&ut.push(Gt("inv_std_output",1,oe));let yt=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${Ie.registerUniforms(yt).declareVariables(...ut)} + ${Ie.mainStart()} + ${Ie.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${Dr("f32",re)}; + var mean_square_vector = ${Dr("f32",re)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${Xr(gt,re,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${Un("mean_vector",re)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${Un("mean_square_vector",re)} / uniforms.norm_size ${u?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${Xr(gt,re,"x[j + offset]")}; + let f32scale = ${Xr(gt,re,"scale[j]")}; + output[j + offset] = ${ut[0].type.value}((f32input ${u?"":"- mean"}) * inv_std_dev * f32scale + ${d?`+ ${Xr(gt,re,"bias[j]")}`:""} + ); + } + + ${be?"mean_data_output[global_idx] = mean":""}; + ${he?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},Ae=[{dims:x,dataType:n[0].dataType}];return be&&Ae.push({dims:oe,dataType:1}),he&&Ae.push({dims:oe,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${re};${s};${u}`,inputDependencies:ce},getRunData:()=>({outputs:Ae,dispatchGroup:{x:Math.ceil($/64)},programUniforms:we}),getShaderSource:De}},hn=(n,i)=>{cr(n.inputs),n.compute(nf(n.inputs,i,n.outputCount))}}),xn,to,kp,of,Sp=p(()=>{Zt(),Jt(),gr(),ur(),xn=(n,i)=>{if(n.length<3||n.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let s=n[0],u=s.dims.length;if(s.dims[u-1]!==i.k)throw new Error("The last dim of input shape does not match the k value");let c=Math.floor((i.k+i.blockSize-1)/i.blockSize),h=i.blockSize/8*i.bits,d=n[1];if(!ct.areEqual(d.dims,[i.n,c,h]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let x=n[2].dims;if(ct.size(x)!==i.n*c)throw new Error("scales input size error.");if(n.length===4){let k=n[3].dims,$=i.bits>4?i.n*c:i.n*Math.floor((c+1)/2);if(ct.size(k)!==$)throw new Error("zeroPoints input size error.")}},to=(n,i,s,u)=>{let c=n[0].dims,h=c.length,d=Math.floor((i.k+i.blockSize-1)/i.blockSize),x=c[h-2],k=i.k,$=i.n,H=c.slice(0,h-2),q=ct.size(H),v=i.blockSize/8*i.bits/4,oe=n[0].dataType,re=wr(x),ce=wr(i.k),we=wr(v),be=ki(oe),he=x*d*be,De=Math.floor(u/he),Ae=d<=s[0]&&De>0,Ie=!Ae||De>=4?wr($):De>=2&&wr($)>=2?2:1,gt=H.concat([x,$]),ut=ct.size(gt)/Ie/re,yt=Ae?[]:[{type:12,data:ut},{type:12,data:i.blockSize}],Dt=[q,x,k/ce],Bt=ct.convertShape(n[1].dims).slice();Bt.splice(-1,1,v/we),yt.push(...$t(Dt)),yt.push(...$t(Bt)),yt.push(...$t(n[2].dims)),n.length===4&&yt.push(...$t(ct.convertShape(n[3].dims)));let mr=[q,x,$/Ie];yt.push(...$t(mr));let vr=or=>{let Ir=Dt.length,Zr=_t("a",n[0].dataType,Ir,ce),fr=_t("b",12,Bt.length,we),Gr=_t("scales",n[2].dataType,n[2].dims.length),jt=[Zr,fr,Gr],er=n.length===4?_t("zero_points",12,n[3].dims.length):void 0;er&&jt.push(er);let _r=mr.length,ot=Gt("output",n[0].dataType,_r,Ie),Wt=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],ir=Tr(n[0].dataType),Vr=(()=>{switch(ce){case 1:return`array<${ir}, 8>`;case 2:return`mat4x2<${ir}>`;case 4:return`mat2x4<${ir}>`;default:throw new Error(`${ce}-component is not supported.`)}})(),fn=` + for (var word: u32 = 0; word < ${v}; word += ${we}) { + ${fr.indicesSet("b_indices","2","word")}; + let b_data = ${fr.getByIndices("b_indices")}; + for (var i: u32 = 0; i < ${we}; i++) { + let b_value: u32 = ${we===1?"b_data":"b_data[word + i]"}; + let b_mask: u32 = 0x0F0F0F0Fu; + let b_value_lower: vec4 = unpack4xU8(b_value & b_mask); + let b_value_upper: vec4 = unpack4xU8((b_value >> 4) & b_mask); + let b_quantized_values = ${Vr}(${Array.from({length:4},(rs,si)=>`${ir}(b_value_lower[${si}]), ${ir}(b_value_upper[${si}])`).join(", ")}); + let b_dequantized_values = ${ce===1?`${Vr}(${Array.from({length:8},(rs,si)=>`(b_quantized_values[${si}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${Vr}(${Array(8).fill("zero_point").join(",")})) * scale;`}; + // Number of B elements per 32-bit word is 32/bits = 32/4 = 8 + for (var m: u32 = 0; m < ${Ae?x:re}u; m++) { + ${Zr.indicesSet("a_indices",Ir-2,Ae?"m":`row * ${re} + m`)}; + ${Zr.indicesSet("a_indices",Ir-1,"word_offset")}; + var input_offset = ${Zr.indicesToOffset("a_indices")}; + var a_data: ${Vr}; + for (var j: u32 = 0; j < ${8/ce}; j++) { + a_data[j] = ${Zr.getByOffset("input_offset")}; + input_offset++; + } + ${Ae?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${Ie>1?"[c]":""} += ${Array.from({length:8/ce},(rs,si)=>`${ce===1?`a_data[${si}] * b_dequantized_values[${si}]`:`dot(a_data[${si}], b_dequantized_values[${si}])`}`).join(" + ")}; + } + word_offset += ${8/ce}; + } + }`,kn=er?` + zero_point_offset += 4; + if (zero_point_offset == 32) { + zero_point_offset = 0; + zero_point_index++; + zero_point_word = ${er.getByOffset("zero_point_index")}; + }`:"";return Ae?` + var workgroup_shared: array<${ot.type.value}, ${x*d}>; + ${or.declareVariables(...jt,ot)} + ${or.mainStart([d,1,1])} + var a_indices: ${Zr.type.indices}; + var block = local_id.x; + var col = workgroup_id.y; + var batch = workgroup_id.z; + ${Zr.indicesSet("a_indices","0","batch")}; + // Two zero points are packed into one byte when uniforms.bits is 4. + for (var c: u32 = 0; c < ${Ie}; c++) { + let col_times_components_plus_c = col * ${Ie} + c; + ${er?` + var zero_point_bytes_per_col: u32 = (${d} + 1) / 2; + var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u); + var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u; + var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u; + var zero_point_nibble_offset: u32 = block & 0x1u; + var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + var zero_point_word: u32 = ${er.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""} + var b_indices: ${fr.type.indices}; + ${fr.indicesSet("b_indices","0","col_times_components_plus_c")}; + // The scale and zero points are computed per block. + var scales_index = col_times_components_plus_c * ${d} + block; + let scale = ${Gr.getByOffset("scales_index")}; + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${ir}(${er?"(zero_point_word) & 0xFu":8}); + ${fr.indicesSet("b_indices","1","block")}; + var word_offset: u32 = block * ${i.blockSize/ce}; + var workgroup_shared_offset: u32 = block * ${x}; + ${fn} + } + workgroupBarrier(); + var output_indices: ${ot.type.indices}; + var elements_per_thread: u32 = ${Math.ceil(x/d)}; + ${ot.indicesSet("output_indices","0","batch")}; + ${ot.indicesSet("output_indices",_r-1,"col")}; + ${ot.indicesSet("output_indices",_r-2,"local_id.x * elements_per_thread")}; + var output_offset = ${ot.indicesToOffset("output_indices")}; + for (var m: u32 = 0u; m < elements_per_thread; m++) { + var row = m + local_id.x * elements_per_thread; + if (row < ${x}) { + var output_value: ${ot.type.value} = ${ot.type.value}(0); + var workgroup_shared_offset: u32 = row; + for (var b: u32 = 0u; b < ${d}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${x}; + } + ${ot.setByOffset("output_offset","output_value")}; + output_offset += ${$/Ie}; + } + } + }`:` + ${or.registerUniforms(Wt).declareVariables(...jt,ot)} + ${or.mainStart()} + ${or.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var output_values: array<${ot.type.value}, ${re}>; + var output_indices = ${ot.offsetToIndices("global_idx")}; + var col = ${ot.indicesGet("output_indices",_r-1)}; + var row = ${ot.indicesGet("output_indices",_r-2)}; + var a_indices: ${Zr.type.indices} = output_indices; + // Two zero points are packed into one byte because uniforms.bits <= 4. + // zero_point_offset is either 0 or 4. It is bit offset within one byte. + // TODO support zero_point_offset for bits > 4 + ${er?` + var zero_point_abs_offset = col * ${Ie} * ((${d} + 1) / 2); + var zero_point_index: u32 = zero_point_abs_offset / 4; + var zero_point_word: u32 = ${er.getByOffset("zero_point_index")}; + var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""} + var scale_index = col * ${d*Ie}; + var b_indices: ${fr.type.indices}; + for (var c: u32 = 0; c < ${Ie}; c++) { + ${fr.indicesSet("b_indices","0",`col * ${Ie} + c`)}; + var block_offset: u32 = 0; + for (var block: u32 = 0; block < ${d}; block++) { + // The scale and zero points are computed per block. + let scale = ${Gr.getByOffset("scale_index")}; + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${ir}(${er?"extractBits(zero_point_word, zero_point_offset, 4)":8}); + ${fr.indicesSet("b_indices","1","block")}; + var word_offset: u32 = block_offset; + ${fn} + scale_index++; + ${kn} + block_offset += uniforms.block_size / ${ce}; + } + // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte. + ${er?`if (zero_point_offset % 8 > 0) { + ${kn} + }`:""} + } + for (var k: u32 = 0u; k < ${re}u; k++) { + ${ot.indicesSet("output_indices",_r-2,`${re} * row + k`)}; + ${ot.setByIndices("output_indices","output_values[k]")} + } + }`};return{name:Ae?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${i.cacheKey};${x};${oe};${n.length}`,inputDependencies:Array(n.length).fill("rank")},getRunData:()=>({outputs:[{dims:gt,dataType:oe}],name:Ae?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:Ae?{x:1,y:Math.ceil($/Ie),z:q}:{x:Math.ceil(ut/64)},programUniforms:yt}),getShaderSource:vr}},kp=(n,i)=>{xn(n.inputs,i);let s=n.getMaxComputeWorkgroupSizes(),u=n.getMaxComputeWorkgroupStoragesize();n.compute(to(n.inputs,i,s,u))},of=n=>qt(n)}),U,G,ie,ke,nt,Ze,bt,At,Rt,dr=p(()=>{Zt(),Jt(),ur(),U=n=>{if(!n||n.length<1)throw new Error("Too few inputs");if(n[0].dataType!==1&&n[0].dataType!==10)throw new Error("Input type must be float or float16.");if(n.length>=2){let i=n[0].dims.length*2===n[1].dims[0];if(n.length===4&&(i=n[3].dims[0]*2===n[1].dims[0]),!i)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},G=(n,i,s)=>{let u="";for(let c=i-1;c>=0;--c)u+=` + k = i32(${n.indicesGet("indices",c)}) - ${Ot("uniforms.pads",c,s)}; + if (k < 0) { + break; + } + if (k >= i32(${Ot("uniforms.x_shape",c,i)})) { + break; + } + offset += k * i32(${Ot("uniforms.x_strides",c,i)}); + `;return` + value = ${n.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${u} + value = x[offset]; + } + `},ie=(n,i,s)=>{let u="";for(let c=i-1;c>=0;--c)u+=` + k = i32(${n.indicesGet("indices",c)}) - ${Ot("uniforms.pads",c,s)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${Ot("uniforms.x_shape",c,i)}) - 1); + k = k % _2n_1; + if(k >= i32(${Ot("uniforms.x_shape",c,i)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${Ot("uniforms.x_strides",c,i)}); + `;return` + var offset = 0; + var k = 0; + ${u} + value = x[offset]; + `},ke=(n,i,s)=>{let u="";for(let c=i-1;c>=0;--c)u+=` + k = i32(${n.indicesGet("indices",c)}) - ${Ot("uniforms.pads",c,s)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${Ot("uniforms.x_shape",c,i)})) { + k = i32(${Ot("uniforms.x_shape",c,i)}) - 1; + } + offset += k * i32(${Ot("uniforms.x_strides",c,i)}); + `;return` + var offset = 0; + var k = 0; + ${u} + value = x[offset]; + `},nt=(n,i,s)=>{let u="";for(let c=i-1;c>=0;--c)u+=` + k = i32(${n.indicesGet("indices",c)}) - ${Ot("uniforms.pads",c,s)}; + if (k < 0) { + k += i32(${Ot("uniforms.x_shape",c,i)}]); + } + if (k >= i32(${Ot("uniforms.x_shape",c,i)})) { + k -= i32(${Ot("uniforms.x_shape",c,i)}); + } + offset += k * i32(${Ot("uniforms.x_strides",c,i)}); + `;return` + var offset = 0; + var k = 0; + ${u} + value = x[offset]; + `},Ze=(n,i,s)=>{switch(s.mode){case 0:return G(n,i,s.pads.length);case 1:return ie(n,i,s.pads.length);case 2:return ke(n,i,s.pads.length);case 3:return nt(n,i,s.pads.length);default:throw new Error("Invalid mode")}},bt=(n,i)=>{let s=ct.padShape(n[0].dims.slice(),i.pads),u=n[0].dims,c=ct.size(s),h=[{type:12,data:c},{type:6,data:i.pads}];i.mode===0&&h.push({type:n[0].dataType,data:i.value}),h.push(...$t(n[0].dims,s));let d=["rank"],x=k=>{let $=Gt("output",n[0].dataType,s.length),H=_t("x",n[0].dataType,u.length),q=H.type.value,v=Ze($,u.length,i),oe=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:i.pads.length}];return i.mode===0&&oe.push({name:"constant_value",type:q}),` + ${k.registerUniforms(oe).declareVariables(H,$)} + ${k.mainStart()} + ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${$.offsetToIndices("global_idx")}; + + var value = ${q}(0); + ${v} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${i.mode}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:s,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(ct.size(s)/64)},programUniforms:h}),getShaderSource:x}},At=(n,i)=>{if(n.length>1){let s=n[1].getBigInt64Array(),u=n.length>=3&&n[2].data?n[2].getFloat32Array()[0]:0,c=n[0].dims.length,h=new Int32Array(2*c).fill(0);if(n.length>=4){let x=n[3].getBigInt64Array();for(let k=0;kh[Number(k)]=Number(x));let d=[];return h.forEach(x=>d.push(x)),{mode:i.mode,value:u,pads:d}}else return i},Rt=(n,i)=>{U(n.inputs);let s=At(n.inputs,i);n.compute(bt(n.inputs,s),{inputs:[0]})}}),hr,Ar,nr,ar,xr,Er,Sr,Nr,Vn,zn,Pi,Mn,dn,Tn,qs,Ks,El,ym,mi,Jo=p(()=>{Y(),Zt(),Jt(),ur(),hr=n=>{if(D.webgpu.validateInputContent&&(!n||n.length!==1))throw new Error("Pool ops requires 1 input.")},Ar=(n,i,s)=>{let u=i.format==="NHWC",c=n.dims.slice();u&&c.splice(1,0,c.pop());let h=Object.hasOwnProperty.call(i,"dilations"),d=i.kernelShape.slice(),x=i.strides.slice(),k=h?i.dilations.slice():[],$=i.pads.slice();Kn.adjustPoolAttributes(s,c,d,x,k,$);let H=Kn.computePoolOutputShape(s,c,x,k,d,$,i.autoPad),q=Object.assign({},i);h?Object.assign(q,{kernelShape:d,strides:x,pads:$,dilations:k,cacheKey:i.cacheKey}):Object.assign(q,{kernelShape:d,strides:x,pads:$,cacheKey:i.cacheKey});let v=H.slice();return v.push(v.splice(1,1)[0]),[q,u?v:H]},nr=(n,i)=>{let s=i.format==="NHWC",u=ct.size(n),c=ct.size(i.kernelShape),h=[{type:12,data:u},{type:12,data:c}],d=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(i.kernelShape.length<=2){let x=i.kernelShape[i.kernelShape.length-1],k=i.strides[i.strides.length-1],$=i.pads[i.pads.length/2-1],H=i.pads[i.pads.length-1],q=!!($+H);h.push({type:12,data:x},{type:12,data:k},{type:12,data:$},{type:12,data:H}),d.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let v=!1;if(i.kernelShape.length===2){let oe=i.kernelShape[i.kernelShape.length-2],re=i.strides[i.strides.length-2],ce=i.pads[i.pads.length/2-2],we=i.pads[i.pads.length-2];v=!!(ce+we),h.push({type:12,data:oe},{type:12,data:re},{type:12,data:ce},{type:12,data:we}),d.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[h,d,!0,q,v]}else{if(s)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let x=ct.computeStrides(i.kernelShape);h.push({type:12,data:x},{type:12,data:i.pads},{type:12,data:i.strides}),d.push({name:"kernelStrides",type:"u32",length:x.length},{name:"pads",type:"u32",length:i.pads.length},{name:"strides",type:"u32",length:i.strides.length});let k=i.pads.reduce(($,H)=>$+H);return[h,d,!!k,!1,!1]}},ar=(n,i,s,u,c,h,d,x,k,$,H,q)=>{let v=c.format==="NHWC",oe=i.type.value,re=Gt("output",i.type.tensor,u);if(c.kernelShape.length<=2){let ce="",we="",be="",he=s-(v?2:1);if(H?ce=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${he}] = indices[${he}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${he}] < 0 || xIndices[${he}] + >= uniforms.x_shape[${he}]) { + pad++; + continue; + } + let x_val = x[${i.indicesToOffset("xIndices")}]; + ${h} + }`:ce=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${he}] = indices[${he}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${i.indicesToOffset("xIndices")}]; + ${h} + }`,c.kernelShape.length===2){let De=s-(v?3:2);q?we=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${De}] = indices[${De}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${De}] < 0 || xIndices[${De}] >= uniforms.x_shape[${De}]) { + pad += i32(uniforms.kw); + continue; + } + `:we=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${De}] = indices[${De}] * uniforms.sh - uniforms.phStart + j; + `,be=` + } + `}return` + ${n.registerUniforms(k).declareVariables(i,re)} + + ${n.mainStart()} + ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${re.offsetToIndices("global_idx")}; + var xIndices = ${re.offsetToIndices("global_idx")}; + + var value = ${oe}(${x}); + var pad = 0; + ${we} + ${ce} + ${be} + ${d} + + output[global_idx] = value; + }`}else{if(v)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let ce=c.kernelShape.length,we=c.pads.length,be="";return $?be=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${i.indicesToOffset("xIndices")}]; + ${h} + }`:be=` + } + let x_val = x[${i.indicesToOffset("xIndices")}]; + ${h} + `,` + ${n.registerUniforms(k).declareVariables(i,re)} + + ${n.mainStart()} + ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${re.offsetToIndices("global_idx")}; + var xIndices = ${re.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${oe}(${x}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${ce-1}u; j++) { + offsets[j] = offset / ${Ot("uniforms.kernelStrides","j",ce)}; + offset -= offsets[j] * ${Ot("uniforms.kernelStrides","j",ce)}; + } + offsets[${ce-1}] = offset; + + isPad = false; + for (var j = ${s-ce}u; j < ${s}u; j++) { + xIndices[j] = indices[j] * ${Ot("uniforms.strides",`j - ${s-ce}u`,ce)} + + offsets[j - ${s-ce}u] - ${Ot("uniforms.pads","j - 2u",we)}; + ${be} + } + ${d} + + output[global_idx] = value; + }`}},xr=n=>`${n.format};${n.ceilMode};${n.autoPad};${n.kernelShape.length}`,Er=n=>`${xr(n)};${n.countIncludePad}`,Sr=n=>`${xr(n)};${n.storageOrder};${n.dilations}`,Nr=n=>({format:n.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][n.auto_pad],ceilMode:n.ceil_mode,kernelShape:n.kernel_shape,strides:n.strides,pads:n.pads}),Vn=(n,i,s,u)=>{let[c,h]=Ar(i,u,s),d=_t("x",i.dataType,i.dims.length),x=d.type.value,k="value += x_val;",$="";c.countIncludePad?$+=`value /= ${x}(uniforms.kernelSize);`:$+=`value /= ${x}(i32(uniforms.kernelSize) - pad);`;let[H,q,v,oe,re]=nr(h,c);H.push(...$t(i.dims,h));let ce=["rank"];return{name:n,shaderCache:{hint:`${u.cacheKey};${v};${oe};${re}`,inputDependencies:ce},getRunData:()=>({outputs:[{dims:h,dataType:i.dataType}],dispatchGroup:{x:Math.ceil(ct.size(h)/64)},programUniforms:H}),getShaderSource:we=>ar(we,d,i.dims.length,h.length,c,k,$,0,q,v,oe,re)}},zn=n=>{let i=n.count_include_pad!==0,s=Nr(n);if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let u={countIncludePad:i,...s,cacheKey:""};return{...u,cacheKey:Er(u)}},Pi=(n,i)=>{hr(n.inputs),n.compute(Vn("AveragePool",n.inputs[0],!1,i))},Mn={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},dn=n=>{let i=n.format;return{format:i,...Mn,cacheKey:i}},Tn=(n,i)=>{hr(n.inputs),n.compute(Vn("GlobalAveragePool",n.inputs[0],!0,i))},qs=(n,i,s,u)=>{let[c,h]=Ar(i,u,s),d=` + value = max(x_val, value); + `,x="",k=_t("x",i.dataType,i.dims.length),$=["rank"],[H,q,v,oe,re]=nr(h,c);return H.push(...$t(i.dims,h)),{name:n,shaderCache:{hint:`${u.cacheKey};${v};${oe};${re}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:h,dataType:i.dataType}],dispatchGroup:{x:Math.ceil(ct.size(h)/64)},programUniforms:H}),getShaderSource:ce=>ar(ce,k,i.dims.length,h.length,c,d,x,i.dataType===10?-65504:-1e5,q,v,oe,re)}},Ks=(n,i)=>{hr(n.inputs),n.compute(qs("MaxPool",n.inputs[0],!1,i))},El=n=>{let i=n.storage_order,s=n.dilations,u=Nr(n);if(i!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(u.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let c={storageOrder:i,dilations:s,...u,cacheKey:""};return{...c,cacheKey:Sr(c)}},ym=n=>{let i=n.format;return{format:i,...Mn,cacheKey:i}},mi=(n,i)=>{hr(n.inputs),n.compute(qs("GlobalMaxPool",n.inputs[0],!0,i))}}),Ep,Cp,Pp,$p=p(()=>{Y(),Zt(),ur(),Ep=(n,i,s)=>{let u=n===i,c=ni&&s>0;if(u||c||h)throw new Error("Range these inputs' contents are invalid.")},Cp=(n,i,s,u)=>{let c=Math.abs(Math.ceil((i-n)/s)),h=[c],d=c,x=[{type:12,data:d},{type:u,data:n},{type:u,data:s},...$t(h)],k=$=>{let H=Gt("output",u,h.length),q=H.type.value,v=[{name:"outputSize",type:"u32"},{name:"start",type:q},{name:"delta",type:q}];return` + ${$.registerUniforms(v).declareVariables(H)} + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${q}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${u}`},getShaderSource:k,getRunData:()=>({outputs:[{dims:h,dataType:u}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:x})}},Pp=n=>{let i=0,s=0,u=0;n.inputs[0].dataType===6?(i=n.inputs[0].getInt32Array()[0],s=n.inputs[1].getInt32Array()[0],u=n.inputs[2].getInt32Array()[0]):n.inputs[0].dataType===1&&(i=n.inputs[0].getFloat32Array()[0],s=n.inputs[1].getFloat32Array()[0],u=n.inputs[2].getFloat32Array()[0]),D.webgpu.validateInputContent&&Ep(i,s,u),n.compute(Cp(i,s,u,n.inputs[0].dataType),{inputs:[]})}}),b0,x0,M0,T0,k0,S0,E0,C0,P0,$0,A0,vm,I0,F0,z0,O0,D0,L0,R0,nx=p(()=>{Zt(),Jt(),gr(),ur(),b0=(n,i)=>{if(n.every(s=>s>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),n.length>0){if(i.mode==="linear"){if(!(n.length===2||n.length===3||n.length===4&&n[0]===1&&n[1]===1||n.length===4&&n[0]===1&&n[3]===1||n.length===5&&n[0]===1&&n[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and + one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(i.mode==="cubic"&&!(n.length===2||n.length===4&&n[0]===1&&n[1]===1||n.length===4&&n[0]===1&&n[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},x0=(n,i,s)=>{i.every(c=>c>=0&&c{throw new Error("Resize requires axes input values to be positive and less than rank")}));let u=new Array(s).fill(1);return i.forEach((c,h)=>u[c]=n[h]),u},M0=(n,i,s,u,c,h)=>{let[d,x,k]=s>10?[1,2,3]:[-1,n.length>1?1:-1,-1],$=n[0].dims.length;if(d>0&&n.length>d&&n[d].dims.length>0)n[d].getFloat32Array().forEach(H=>h.push(H));else if(i.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(x>0&&n.length>x&&n[x].dims.length>0){if(n[x].getFloat32Array().forEach(H=>u.push(H)),u.length!==0&&u.length!==$&&s>=18&&u.length!==i.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");b0(u,i),i.axes.length>0&&x0(u,i.axes,$).forEach((H,q)=>u[q]=H)}if(k>0&&n.length>k&&(n[k].getBigInt64Array().forEach(H=>c.push(Number(H))),c.length!==$||s>=18&&c.length===i.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(i.axes.length>0){if(u.length!==i.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(c.length!==i.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof u<"u"&&typeof c<"u"&&u.length>0&&c.length>$)throw new Error("Resize requires only of scales or sizes to be specified")},T0=(n,i)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, + lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${i} { `+(()=>{switch(n){case"asymmetric":return`return ${i}(xResized) / ${i}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) { + return (${i}(xResized) + 0.5) / ${i}(xScale) - 0.5; + } else { + return 0.0; + }`;case"tf_half_pixel_for_nn":return`return (${i}(xResized) + 0.5) / ${i}(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 = ${i}(xResized * (lengthOriginal - 1) / (lengthResized - 1)); + let fract = + ${i}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${i}(lengthResized - 1); + return whole + fract; + }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { + return ${i}(roiStart) * ${i}(lengthOriginal - 1) + + (${i}(xResized) * ${i}(roiEnd - roiStart) * ${i}(lengthOriginal - 1)) / + ${i}(lengthResized - 1); + } else { + return 0.5 * ${i}(roiStart + roiEnd) * ${i}(lengthOriginal - 1); + }`;case"half_pixel_symmetric":return`const outputWidth = ${i}xScale * ${i}(lengthResized); + const adjustment = ${i}(lengthResized) / outputWidth; + const center = ${i}(lengthOriginal) / 2; + const offset = center * (1 - adjustment); + return offset + ((${i}(xResized) + 0.5) / ${i}(xScale)) - 0.5;`;case"half_pixel":return`return ((${i}(xResized) + 0.5) / ${i}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${n} is not supported`)}})()+"}",k0=(n,i,s)=>`fn getNearestPixelFromOriginal(xOriginal: ${s}, isDownSample: bool) -> ${s} {`+(()=>{switch(n){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(i<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${n} is not supported`)}})()+"}",S0=(n,i,s)=>{let u=new Array(s).fill(0).concat(new Array(s).fill(1)),c=n.length===0?u:n.slice();return i.length>0?(i.forEach((h,d)=>{u[h]=c[d],u[d+s]=c[i.length+d]}),u):c},E0=(n,i,s,u)=>{let c=[];if(s.length>0)if(u.length>0){if(n.forEach(h=>c.push(h)),Math.max(...u)>n.length)throw new Error("axes is out of bound");u.forEach((h,d)=>c[h]=s[d])}else s.forEach(h=>c.push(h));else{if(i.length===0)throw new Error("Resize requires either scales or sizes.");c=n.map((h,d)=>Math.round(h*i[d]))}return c},C0=(n,i,s)=>{let u=(()=>{switch(s.keepAspectRatioPolicy){case"not_larger":return s.axes.length>0?Math.min(...s.axes.map(h=>i[h]),Number.MAX_VALUE):Math.min(...i,Number.MAX_VALUE);case"not_smaller":return s.axes.length>0?Math.max(...s.axes.map(h=>i[h]),Number.MIN_VALUE):Math.max(...i,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${s.keepAspectRatioPolicy} is not supported`)}})();i.fill(1,0,i.length);let c=n.slice();return s.axes.length>0?(s.axes.forEach(h=>i[h]=u),s.axes.forEach(h=>c[h]=Math.round(n[h]*i[h]))):(i.fill(u,0,i.length),c.forEach((h,d)=>c[d]=Math.round(h*i[d]))),c},P0=(n,i,s,u,c)=>` + fn calculateOriginalIndicesFromOutputIndices(output_indices: ${n.type.indices}) -> array<${n.type.value}, ${s.length}> { + var original_indices: array<${n.type.value}, ${s.length}>; + for (var i:u32 = 0; i < ${s.length}; i++) { + var output_index = ${n.indicesGet("output_indices","i")}; + var scale = ${Ot("uniforms.scales","i",u)}; + var roi_low = ${Ot("uniforms.roi","i",c)}; + var roi_hi = ${Ot("uniforms.roi",`i + ${i.length}`,c)}; + if (scale == 1.0) { + original_indices[i] = ${n.type.value}(output_index); + } else { + var input_shape_i = ${Ot("uniforms.input_shape","i",i.length)}; + var output_shape_i = ${Ot("uniforms.output_shape","i",s.length)}; + original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + } + } + return original_indices; + }`,$0=(n,i,s,u,c,h,d)=>` + fn calculateInputIndicesFromOutputIndices(output_indices: ${i.type.indices}) -> ${n.type.indices} { + var input_indices: ${n.type.indices}; + for (var i:u32 = 0; i < ${u.length}; i++) { + var output_index = ${i.indicesGet("output_indices","i")}; + var input_index: u32; + var scale = ${Ot("uniforms.scales","i",c)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${Ot("uniforms.roi","i",h)}; + var roi_hi = ${Ot("uniforms.roi",`i + ${s.length}`,h)}; + var input_shape_i = ${Ot("uniforms.input_shape","i",s.length)}; + var output_shape_i = ${Ot("uniforms.output_shape","i",u.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${d} || (original_idx >= 0 && original_idx < ${i.type.value}(input_shape_i))) { + if (original_idx < 0) { + input_index = 0; + } else if (original_idx > ${i.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); + } + } + ${n.indicesSet("input_indices","i"," input_index")} + } + return input_indices; + }`,A0=(n,i)=>` + fn checkInputIndices(input_indices: ${n.type.indices}) -> bool { + for (var i:u32 = 0; i < ${i.length}; i++) { + var input_index = ${n.indicesGet("input_indices","i")}; + if (input_index < 0 || input_index >= ${Ot("uniforms.input_shape","i",i.length)}) { + return false; + } + } + return true; + }`,vm=(n,i,s,u)=>n.rank>u?` + ${n.indicesSet("input_indices",i,"channel")}; + ${n.indicesSet("input_indices",s,"batch")}; +`:"",I0=(n,i,s,u,c)=>{let[h,d,x,k]=s.length===2?[-1,0,1,-1]:[0,2,3,1],$=n.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${$} { + var input_indices: ${n.type.indices}; + ${n.indicesSet("input_indices",d,`max(0, min(row, ${s[d]} - 1))`)}; + ${n.indicesSet("input_indices",x,`max(0, min(col, ${s[x]} - 1))`)}; + ${vm(n,k,h,2)} + return ${n.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${i.type.indices}) -> ${$} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${$} = originalIndices[${d}]; + var col:${$} = originalIndices[${x}]; + ${u?`if (row < 0 || row > (${s[d]} - 1) || col < 0 || col > (${s[x]} - 1)) { + return ${c}; + }`:""}; + row = max(0, min(row, ${s[d]} - 1)); + col = max(0, min(col, ${s[x]} - 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 = ${s.length>2?`u32(originalIndices[${k}])`:"0"}; + var batch: u32 = ${s.length>2?`u32(originalIndices[${h}])`:"0"}; + var x11: ${$} = getInputValue(batch, channel, row1, col1); + var x12: ${$} = getInputValue(batch, channel, row1, col2); + var x21: ${$} = getInputValue(batch, channel, row2, col1); + var x22: ${$} = getInputValue(batch, channel, row2, col2); + var dx1: ${$} = abs(row - ${$}(row1)); + var dx2: ${$} = abs(${$}(row2) - row); + var dy1: ${$} = abs(col - ${$}(col1)); + var dy2: ${$} = abs(${$}(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); + }`},F0=(n,i,s,u,c,h,d,x,k,$)=>{let H=s.length===2,[q,v]=H?[0,1]:[2,3],oe=n.type.value,re=ce=>{let we=ce===q?"row":"col";return` + fn ${we}CubicInterpolation(input_indices: ${n.type.indices}, output_indices: ${i.type.indices}) -> ${oe} { + var output_index = ${i.indicesGet("output_indices",ce)}; + var originalIdx: ${oe} = getOriginalCoordinateFromResizedCoordinate(output_index, ${c[ce]}, + ${u[ce]}, ${s[ce]}, ${h[ce]}, ${h[ce]} + ${s.length}); + var fractOriginalIdx: ${oe} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${x} && (originalIdx < 0 || originalIdx > (${s[ce]} - 1))) { + return ${k}; + } + var data: array<${oe}, 4> = array<${oe}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${we}: ${oe} = originalIdx + ${oe}(i); + if (${we} < 0 || ${we} >= ${s[ce]}) { + ${$?`coefs[i + 1] = 0.0; + continue;`:x?`return ${k};`:`${we} = max(0, min(${we}, ${s[ce]} - 1));`}; + } + var input_indices_copy: ${n.type.indices} = input_indices; + ${n.indicesSet("input_indices_copy",ce,`u32(${we})`)}; + data[i + 1] = ${ce===q?n.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${re(q)}; + ${re(v)}; + fn getCubicInterpolationCoefs(s: ${oe}) -> array<${oe}, 4> { + var absS = abs(s); + var coeffs: array<${oe}, 4> = array<${oe}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${oe} = 1.0 - absS; + var twoMinusAbsS: ${oe} = 2.0 - absS; + var onePlusAbsS: ${oe} = 1.0 + absS; + coeffs[0] = ((${d} * onePlusAbsS - 5 * ${d}) * onePlusAbsS + 8 * ${d}) * onePlusAbsS - 4 * ${d}; + coeffs[1] = ((${d} + 2) * absS - (${d} + 3)) * absS * absS + 1; + coeffs[2] = ((${d} + 2) * oneMinusAbsS - (${d} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${d} * twoMinusAbsS - 5 * ${d}) * twoMinusAbsS + 8 * ${d}) * twoMinusAbsS - 4 * ${d}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${oe}, 4>, coefs: array<${oe}, 4>) -> ${oe} { + var coefsSum: ${oe} = 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: ${i.type.indices}) -> ${oe} { + var input_indices: ${n.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},z0=(n,i,s,u,c)=>{let[h,d,x,k,$]=s.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],H=n.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${H} { + var input_indices: ${n.type.indices}; + ${n.indicesSet("input_indices",d,`max(0, min(depth, ${s[d]} - 1))`)}; + ${n.indicesSet("input_indices",x,`max(0, min(height, ${s[x]} - 1))`)}; + ${n.indicesSet("input_indices",k,`max(0, min(width, ${s[k]} - 1))`)}; + ${vm(n,$,h,3)} + return ${n.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${i.type.indices}) -> ${H} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${H} = originalIndices[${d}]; + var height:${H} = originalIndices[${x}]; + var width:${H} = originalIndices[${k}]; + ${u?`if (depth < 0 || depth > (${s[d]} - 1) || height < 0 || height > (${s[x]} - 1) || width < 0 || (width > ${s[k]} - 1)) { + return ${c}; + }`:""}; + + depth = max(0, min(depth, ${s[d]} - 1)); + height = max(0, min(height, ${s[x]} - 1)); + width = max(0, min(width, ${s[k]} - 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 = ${s.length>3?`u32(originalIndices[${$}])`:"0"}; + var batch: u32 = ${s.length>3?`u32(originalIndices[${h}])`:"0"}; + + var x111: ${H} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${H} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${H} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${H} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${H} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${H} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${H} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${H} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${H} = abs(depth - ${H}(depth1)); + var dx2: ${H} = abs(${H}(depth2) - depth); + var dy1: ${H} = abs(height - ${H}(height1)); + var dy2: ${H} = abs(${H}(height2) - height); + var dz1: ${H} = abs(width - ${H}(width1)); + var dz2: ${H} = abs(${H}(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); + }`},O0=(n,i,s,u,c,h)=>{let d=n.dims,x=S0(h,i.axes,d.length),k=E0(d,u,c,i.axes),$=u.slice();u.length===0&&($=d.map((he,De)=>he===0?1:k[De]/he),i.keepAspectRatioPolicy!=="stretch"&&(k=C0(d,$,i)));let H=Gt("output",n.dataType,k.length),q=_t("input",n.dataType,d.length),v=ct.size(k),oe=d.length===k.length&&d.every((he,De)=>he===k[De]),re=i.coordinateTransformMode==="tf_crop_and_resize",ce=i.extrapolationValue,we=q.type.value,be=he=>` + ${oe?"":` + ${T0(i.coordinateTransformMode,we)}; + ${(()=>{switch(i.mode){case"nearest":return` + ${A0(q,d)}; + ${k0(i.nearestMode,s,we)}; + ${$0(q,H,d,k,$.length,x.length,re)}; + `;case"linear":return` + ${P0(H,d,k,$.length,x.length)}; + ${(()=>{if(d.length===2||d.length===4)return`${I0(q,H,d,re,ce)}`;if(d.length===3||d.length===5)return`${z0(q,H,d,re,ce)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(d.length===2||d.length===4)return`${F0(q,H,d,k,$,x,i.cubicCoeffA,re,i.extrapolationValue,i.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; + `;default:throw Error("Invalid resize mode")}})()}; + `} + ${he.registerUniform("output_size","u32").registerUniform("scales","f32",$.length).registerUniform("roi","f32",x.length).declareVariables(q,H)} + ${he.mainStart()} + ${he.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${oe?"output[global_idx] = input[global_idx];":` + let output_indices = ${H.offsetToIndices("global_idx")}; + var input_indices: ${q.type.indices}; + ${(()=>{switch(i.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${q.getByIndices("input_indices")}; + } else { + output[global_idx] = ${i.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${d.length===2||d.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${i.mode}`)}})()}; +`} + }`;return{name:"Resize",shaderCache:{hint:`${i.cacheKey}|${s}|${$.length>0?$:""}|${c.length>0?c:""}|${x.length>0?x:""}|${oe}|${d}`,inputDependencies:["rank"]},getShaderSource:be,getRunData:()=>({outputs:[{dims:k,dataType:n.dataType}],dispatchGroup:{x:Math.ceil(v/64)},programUniforms:[{type:12,data:v},{type:1,data:$},{type:1,data:x},...$t(d,k)]})}},D0=n=>{let i=n.customDataBuffer;return new Uint32Array(i,i.byteOffset,1)[0]},L0=(n,i)=>{let s=[],u=[],c=[],h=D0(n);if(i.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");M0(n.inputs,i,h,s,u,c),n.compute(O0(n.inputs[0],i,h,s,u,c),{inputs:[0]})},R0=n=>{let i=n.antialias,s=n.axes,u=n.coordinateTransformMode,c=n.cubicCoeffA,h=n.excludeOutside!==0,d=n.extrapolationValue,x=n.keepAspectRatioPolicy,k=n.mode,$=n.nearestMode===""?"simple":n.nearestMode;return qt({antialias:i,axes:s,coordinateTransformMode:u,cubicCoeffA:c,excludeOutside:h,extrapolationValue:d,keepAspectRatioPolicy:x,mode:k,nearestMode:$})}}),B0,N0,j0,ix=p(()=>{Zt(),Jt(),gr(),ur(),B0=(n,i)=>{let[s,u,c,h]=n,{numHeads:d,rotaryEmbeddingDim:x}=i;if(s.dims.length!==3&&s.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${s.dims.length}`);if(!ct.areEqual(u.dims,[])&&!ct.areEqual(u.dims,[1])&&u.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${u.dims.length}`);if(c.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${c.dims.length}`);if(h.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${h.dims.length}`);if(!ct.areEqual(c.dims,h.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(x>0&&d===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let k=s.dims[0],$=s.dims[s.dims.length-2],H=c.dims[0],q=ct.sizeFromDimension(s.dims,1)/$,v=x===0?c.dims[1]*2:q/d;if(x>v)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(u.dims.length===2){if(k!==u.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${u.dims[0]}`);if($!==u.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${u.dims[1]}`)}if(v/2!==c.dims[1]&&x/2!==c.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${c.dims[1]}`);if($>H)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},N0=(n,i)=>{let{interleaved:s,numHeads:u,rotaryEmbeddingDim:c,scale:h}=i,d=n[0].dims[0],x=ct.sizeFromDimension(n[0].dims,1),k=n[0].dims[n[0].dims.length-2],$=x/k,H=n[2].dims[1],q=c===0?H*2:$/u,v=new Array(d,k,$/q,q-H),oe=ct.computeStrides(v),re=[{type:1,data:h},{type:12,data:v},{type:12,data:oe},...n[0].dims.length===3?new Array({type:12,data:[x,$,q,1]}):[],...n[0].dims.length===4?new Array({type:12,data:[x,q,k*q,1]}):[],...$t(n[0].dims,n[1].dims,n[2].dims,n[3].dims,n[0].dims)],ce=we=>{let be=_t("input",n[0].dataType,n[0].dims.length),he=_t("position_ids",n[1].dataType,n[1].dims.length),De=_t("cos_cache",n[2].dataType,n[2].dims.length),Ae=_t("sin_cache",n[3].dataType,n[3].dims.length),Ie=Gt("output",n[0].dataType,n[0].dims.length);return we.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:v.length},{name:"global_strides",type:"u32",length:oe.length},{name:"input_output_strides",type:"u32",length:oe.length}]),` + ${we.declareVariables(be,he,De,Ae,Ie)} + + ${we.mainStart(jn)} + let half_rotary_emb_dim = uniforms.${De.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${we.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${he.broadcastedIndicesToOffset("bsnh.xy",Gt("",he.type.tensor,2))}; + let position_id = + u32(${he.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${s}); + let j = i + select(half_rotary_emb_dim, 1, ${s}); + let re = ${be.getByOffset("i")} * ${De.get("position_id","bsnh[3]")} - + ${be.getByOffset("j")} * ${Ae.get("position_id","bsnh[3]")}; + ${Ie.setByOffset("i","re")} + let im = ${be.getByOffset("i")} * ${Ae.get("position_id","bsnh[3]")} + + ${be.getByOffset("j")} * ${De.get("position_id","bsnh[3]")}; + ${Ie.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${Ie.setByOffset("k",be.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:qt({interleaved:s}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:ce,getRunData:()=>({outputs:[{dims:n[0].dims,dataType:n[0].dataType}],dispatchGroup:{x:Math.ceil(ct.size(v)/jn)},programUniforms:re})}},j0=(n,i)=>{B0(n.inputs,i),n.compute(N0(n.inputs,i))}}),U0,V0,W0,ox=p(()=>{Zt(),Jt(),ur(),U0=n=>{if(!n||n.length<3)throw new Error("layerNorm requires at least 3 inputs.");let i=n[0],s=n[1],u=n[2];if(i.dataType!==s.dataType||i.dataType!==u.dataType)throw new Error("All inputs must have the same data type");if(i.dims.length!==3&&i.dims.length!==2)throw new Error("Input must be 2D or 3D");if(s.dims.length!==3&&s.dims.length!==2)throw new Error("Skip must be 2D or 3D");let c=i.dims[i.dims.length-1],h=i.dims[i.dims.length-2];if(s.dims[s.dims.length-1]!==c)throw new Error("Skip must have the same hidden size as input");if(s.dims[s.dims.length-2]!==h)throw new Error("Skip must have the same sequence length as input");if(u.dims.length!==1)throw new Error("Gamma must be 1D");if(u.dims[u.dims.length-1]!==c)throw new Error("Gamma must have the same hidden size as input");if(n.length>3){let d=n[3];if(d.dims.length!==1)throw new Error("Beta must be 1D");if(d.dims[d.dims.length-1]!==c)throw new Error("Beta must have the same hidden size as input")}if(n.length>4){let d=n[4];if(d.dims.length!==1)throw new Error("Bias must be 1D");if(d.dims[d.dims.length-1]!==c)throw new Error("Bias must have the same hidden size as input")}},V0=(n,i,s,u)=>{let c=i.simplified,h=n[0].dims,d=ct.size(h),x=h,k=d,$=h.slice(-1)[0],H=u?h.slice(0,-1).concat(1):[],q=!c&&n.length>3,v=n.length>4,oe=u&&s>1,re=u&&s>2,ce=s>3,we=64,be=wr($),he=[{type:12,data:k},{type:12,data:be},{type:12,data:$},{type:1,data:i.epsilon}],De=Ie=>{let gt=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],ut=[_t("x",n[0].dataType,n[0].dims,be),_t("skip",n[1].dataType,n[1].dims,be),_t("gamma",n[2].dataType,n[2].dims,be)];q&&ut.push(_t("beta",n[3].dataType,n[3].dims,be)),v&&ut.push(_t("bias",n[4].dataType,n[4].dims,be)),ut.push(Gt("output",n[0].dataType,x,be)),oe&&ut.push(Gt("mean_output",1,H)),re&&ut.push(Gt("inv_std_output",1,H)),ce&&ut.push(Gt("input_skip_bias_sum",n[0].dataType,x,be));let yt=Tr(n[0].dataType),Dt=Tr(1,be);return` + + ${Ie.registerUniforms(gt).declareVariables(...ut)} + var sum_shared : array<${Dt}, ${we}>; + var sum_squared_shared : array<${Dt}, ${we}>; + + ${Ie.mainStart([we,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${we}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${we}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${we-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${v?"bias[offset1d + i]":yt+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${ce?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${Xr(yt,be,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${we}; + 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 = ${Un("sum",be)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${Un("square_sum",be)} / f32(uniforms.hidden_size) ${c?"":"- mean * mean"} + uniforms.epsilon); + ${oe?"mean_output[global_idx] = mean;":""} + ${re?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${c?"":`- ${yt}(mean)`}) * + ${yt}(inv_std_dev) * gamma[offset1d + i] + ${q?"+ beta[offset1d + i]":""}; + } + }`},Ae=[{dims:x,dataType:n[0].dataType}];return s>1&&Ae.push({dims:H,dataType:1}),s>2&&Ae.push({dims:H,dataType:1}),s>3&&Ae.push({dims:h,dataType:n[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${be};${oe};${re};${ce}`,inputDependencies:n.map((Ie,gt)=>"type")},getShaderSource:De,getRunData:()=>({outputs:Ae,dispatchGroup:{x:Math.ceil(k/$)},programUniforms:he})}},W0=(n,i)=>{U0(n.inputs);let s=[0];n.outputCount>1&&s.push(-3),n.outputCount>2&&s.push(-3),n.outputCount>3&&s.push(3),n.compute(V0(n.inputs,i,n.outputCount,!1),{outputs:s})}}),G0,sf,H0,bm,q0,K0,X0,Q0,sx=p(()=>{Zt(),Jt(),gr(),ur(),G0=(n,i)=>{if(!n||n.length<1)throw new Error("too few inputs");if(i.axes.length!==0){if(i.axes.length!==i.starts.length||i.axes.length!==i.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(i.starts.length!==i.ends.length)throw new Error("starts and ends must have the same length");n.slice(1).forEach((s,u)=>{if(n[u+1].dataType!==6&&n[u+1].dataType!==7)throw new Error(`Input ${u} must be an array of int32 or int64`)})},sf=(n,i)=>{let s=[];if(n.length>i)if(n[i].dataType===7)n[i].getBigInt64Array().forEach(u=>s.push(Number(u)));else if(n[i].dataType===6)n[i].getInt32Array().forEach(u=>s.push(Number(u)));else throw new Error(`Input ${i} must be an array of int32 or int64`);return s},H0=(n,i)=>{if(n.length>1){let s=sf(n,1),u=sf(n,2),c=sf(n,3);return c.length===0&&(c=[...Array(n[0].dims.length).keys()]),qt({starts:s,ends:u,axes:c})}else return i},bm=(n,i,s,u,c)=>{let h=n;return n<0&&(h+=s[u[i]]),c[i]<0?Math.max(0,Math.min(h,s[u[i]]-1)):Math.max(0,Math.min(h,s[u[i]]))},q0=(n,i,s)=>`fn calculateInputIndices(output_indices: ${i.type.indices}) -> ${n.type.indices} { + var input_indices: ${n.type.indices}; + var carry = 0u; + for (var i = ${s.length}; i >= 0; i--) { + let input_shape_i = ${Ot("uniforms.input_shape","i",s.length)}; + let steps_i = ${Ot("uniforms.steps","i",s.length)}; + let signs_i = ${Ot("uniforms.signs","i",s.length)}; + let starts_i = ${Ot("uniforms.starts","i",s.length)}; + var output_index = ${i.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; + } + ${n.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,K0=(n,i)=>{let s=n[0].dims,u=ct.size(s),c=i.axes.length>0?ct.normalizeAxes(i.axes,s.length):[...Array(s.length).keys()],h=sf(n,4);h.forEach(be=>be!==0||(()=>{throw new Error("step cannot be 0")})),h.length===0&&(h=Array(c.length).fill(1));let d=i.starts.map((be,he)=>bm(be,he,s,c,h)),x=i.ends.map((be,he)=>bm(be,he,s,c,h));if(c.length!==d.length||c.length!==x.length)throw new Error("start, ends and axes should have the same number of elements");if(c.length!==s.length)for(let be=0;beMath.sign(be));h.forEach((be,he,De)=>{if(be<0){let Ae=(x[he]-d[he])/be,Ie=d[he],gt=Ie+Ae*h[he];d[he]=gt,x[he]=Ie,De[he]=-be}});let $=s.slice(0);c.forEach((be,he)=>{$[be]=Math.ceil((x[be]-d[be])/h[be])});let H={dims:$,dataType:n[0].dataType},q=Gt("output",n[0].dataType,$.length),v=_t("input",n[0].dataType,n[0].dims.length),oe=ct.size($),re=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:d.length},{name:"signs",type:"i32",length:k.length},{name:"steps",type:"u32",length:h.length}],ce=[{type:12,data:oe},{type:12,data:d},{type:6,data:k},{type:12,data:h},...$t(n[0].dims,$)],we=be=>` + ${be.registerUniforms(re).declareVariables(v,q)} + ${q0(v,q,s)} + ${be.mainStart()} + ${be.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${q.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${q.setByOffset("global_idx",v.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${k.length}_${d.length}_${h.length}`,inputDependencies:["rank"]},getShaderSource:we,getRunData:()=>({outputs:[H],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:ce})}},X0=(n,i)=>{G0(n.inputs,i);let s=H0(n.inputs,i);n.compute(K0(n.inputs,s),{inputs:[0]})},Q0=n=>{let i=n.starts,s=n.ends,u=n.axes;return qt({starts:i,ends:s,axes:u})}}),Y0,Z0,J0,ew,ax=p(()=>{Zt(),Jt(),gr(),ur(),Y0=n=>{if(!n||n.length!==1)throw new Error("Softmax op requires 1 input.")},Z0=(n,i)=>{let s=n.dims,u=ct.size(s),c=64,h=i.axis;if(h<0&&(h=s.length+h),hbe===4?`max(max(${we}.x, ${we}.y), max(${we}.z, ${we}.w))`:be===2?`max(${we}.x, ${we}.y)`:be===3?`max(max(${we}.x, ${we}.y), ${we}.z)`:we,q=_t("x",n.dataType,n.dims,k),v=Gt("result",n.dataType,n.dims,k),oe=q.type.value,re=Tr(n.dataType)==="f32"?`var threadMax = ${oe}(-3.402823e+38f);`:`var threadMax = ${oe}(-65504.0h);`,ce=we=>` + var rowMaxShared : ${oe}; + var rowSumShared : ${oe}; + var threadShared : array<${oe}, ${c}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${oe} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${oe}) { + let index = row * row_stride + col; + result[index] = value; + } + ${we.registerUniform("packedCols","i32").declareVariables(q,v)} + ${we.mainStart()} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${c}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${re} + 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 = ${oe}(${H("threadShared[0]",k)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${oe}(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 = ${oe}(${Un("threadShared[0]",k)}); + } + workgroupBarrier(); + + // calculate final value for each element in the row + for (var col = lindex; col < cols; col += wg) { + let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared; + setValue(row, col, row_stride, value); + } + }`;return{name:"Softmax",shaderCache:{hint:`${k}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:s,dataType:n.dataType}],dispatchGroup:{x},programUniforms:[{type:6,data:$}]}),getShaderSource:ce}},J0=(n,i)=>{Y0(n.inputs),n.compute(Z0(n.inputs[0],i))},ew=n=>qt({axis:n.axis})}),tw,rw,nw,iw,ow,sw,aw,lx=p(()=>{Zt(),Jt(),gr(),ur(),tw=n=>{if(!n||n.length<1)throw new Error("too few inputs")},rw=(n,i)=>{let s=[],u=i.numOutputs;return n[1].dims[0]>0&&(n[1].getBigInt64Array().forEach(c=>s.push(Number(c))),u=s.length),qt({numOutputs:u,axis:i.axis,splitSizes:s})},nw=n=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${n}u; i += 1u ) { + if (index < ${Ot("uniforms.size_in_split_axis","i",n)}) { + return i; + } + } + return ${n}u; +}`,iw=n=>{let i=n.length,s=[];for(let u=0;u{let s=n[0].dims,u=ct.size(s),c=n[0].dataType,h=ct.normalizeAxis(i.axis,s.length),d=new Array(i.numOutputs),x=_t("input",c,s.length),k=new Array(i.numOutputs),$=[],H=[],q=0,v=[{type:12,data:u}];for(let re=0;re` + ${re.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",k.length).declareVariables(x,...d)} + ${nw(k.length)} + ${iw(d)} + + ${re.mainStart()} + ${re.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${x.offsetToIndices("global_idx")}; + var index = ${x.indicesGet("indices",h)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${Ot("uniforms.size_in_split_axis","output_number - 1u",k.length)}; + ${x.indicesSet("indices",h,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:i.cacheKey,inputDependencies:["rank"]},getShaderSource:oe,getRunData:()=>({outputs:$,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:v})}},sw=(n,i)=>{tw(n.inputs);let s=n.inputs.length===1?i:rw(n.inputs,i);n.compute(ow(n.inputs,s),{inputs:[0]})},aw=n=>{let i=n.axis,s=n.splitSizes,u=n.numOutputs<0?s.length:n.numOutputs;if(u!==s.length)throw new Error("numOutputs and splitSizes lengh must be equal");return qt({axis:i,numOutputs:u,splitSizes:s})}}),lw,uw,cw,ux=p(()=>{Zt(),Jt(),ur(),lw=(n,i,s,u,c)=>{let h=Gt("output_data",c,s.length,4),d=_t("a_data",i[1].dataType,i[1].dims.length,4),x=_t("b_data",i[2].dataType,i[2].dims.length,4),k=_t("c_data",i[0].dataType,i[0].dims.length,4),$,H=(q,v,oe)=>`select(${v}, ${q}, ${oe})`;if(!u)$=h.setByOffset("global_idx",H(d.getByOffset("global_idx"),x.getByOffset("global_idx"),k.getByOffset("global_idx")));else{let q=(v,oe,re="")=>{let ce=`a_data[index_a${oe}][component_a${oe}]`,we=`b_data[index_b${oe}][component_b${oe}]`,be=`bool(c_data[index_c${oe}] & (0xffu << (component_c${oe} * 8)))`;return` + let output_indices${oe} = ${h.offsetToIndices(`global_idx * 4u + ${oe}u`)}; + let offset_a${oe} = ${d.broadcastedIndicesToOffset(`output_indices${oe}`,h)}; + let offset_b${oe} = ${x.broadcastedIndicesToOffset(`output_indices${oe}`,h)}; + let offset_c${oe} = ${k.broadcastedIndicesToOffset(`output_indices${oe}`,h)}; + let index_a${oe} = offset_a${oe} / 4u; + let index_b${oe} = offset_b${oe} / 4u; + let index_c${oe} = offset_c${oe} / 4u; + let component_a${oe} = offset_a${oe} % 4u; + let component_b${oe} = offset_b${oe} % 4u; + let component_c${oe} = offset_c${oe} % 4u; + ${v}[${oe}] = ${re}(${H(ce,we,be)}); + `};c===9?$=` + var data = vec4(0); + ${q("data",0,"u32")} + ${q("data",1,"u32")} + ${q("data",2,"u32")} + ${q("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:$=` + ${q("output_data[global_idx]",0)} + ${q("output_data[global_idx]",1)} + ${q("output_data[global_idx]",2)} + ${q("output_data[global_idx]",3)} + `}return` + ${n.registerUniform("vec_size","u32").declareVariables(k,d,x,h)} + ${n.mainStart()} + ${n.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${$} + }`},uw=n=>{let i=n[1].dims,s=n[2].dims,u=n[0].dims,c=n[1].dataType,h=!(ct.areEqual(i,s)&&ct.areEqual(s,u)),d=i,x=ct.size(i);if(h){let $=gn.calcShape(gn.calcShape(i,s,!1),u,!1);if(!$)throw new Error("Can't perform where op on the given tensors");d=$,x=ct.size(d)}let k=Math.ceil(x/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:$=>lw($,n,d,h,c),getRunData:()=>({outputs:[{dims:d,dataType:c}],dispatchGroup:{x:Math.ceil(x/64/4)},programUniforms:[{type:12,data:k},...$t(u,i,s,d)]})}},cw=n=>{n.compute(uw(n.inputs))}}),dw,cx=p(()=>{ip(),Os(),tc(),op(),zc(),sp(),ap(),cd(),gm(),pp(),hp(),wp(),_m(),vp(),bp(),xp(),Bd(),Tp(),wm(),bn(),sl(),Sp(),Wd(),dr(),Jo(),$p(),$a(),nx(),ix(),ox(),sx(),ax(),lx(),Kd(),yo(),Ha(),ux(),dw=new 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Map,this.attributesBound=!1}getArtifact(n){return this.repo.get(n)}setArtifact(n,i){this.repo.set(n,i)}run(n,i,s,u,c){et(n.programInfo.name);let h=this.backend.device,d=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let x=[];for(let $ of i)x.push({binding:x.length,resource:{buffer:$.buffer}});for(let $ of s)x.push({binding:x.length,resource:{buffer:$.buffer}});c&&x.push({binding:x.length,resource:c});let k=h.createBindGroup({layout:n.computePipeline.getBindGroupLayout(0),entries:x,label:n.programInfo.name});if(this.backend.sessionStatus==="capturing"){let $={kernelId:this.backend.currentKernelId,computePipeline:n.computePipeline,bindGroup:k,dispatchGroup:u};this.backend.capturedCommandList.get(this.backend.currentSessionId).push($)}d.setPipeline(n.computePipeline),d.setBindGroup(0,k),d.dispatchWorkgroups(...u),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),A(n.programInfo.name)}dispose(){}build(n,i){et(n.name);let s=this.backend.device,u=[];s.features.has("shader-f16")&&u.push("enable f16;");let c=cu(i,this.backend.device.limits),h=n.getShaderSource(c),d=`${u.join(` +`)} +${c.additionalImplementations} +${h}`,x=s.createShaderModule({code:d,label:n.name});Ur("verbose",()=>`[WebGPU] ${n.name} shader code: ${d}`);let k=s.createComputePipeline({compute:{module:x,entryPoint:"main"},layout:"auto",label:n.name});return A(n.name),{programInfo:n,computePipeline:k,uniformVariablesInfo:c.variablesInfo}}normalizeDispatchGroupSize(n){let i=typeof n=="number"?n:n.x,s=typeof n=="number"?1:n.y||1,u=typeof n=="number"?1:n.z||1,c=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(i<=c&&s<=c&&u<=c)return[i,s,u];let h=i*s*u,d=Math.ceil(Math.sqrt(h));if(d>c){if(d=Math.ceil(Math.cbrt(h)),d>c)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[d,d,d]}else return[d,d,1]}}}),pw,hw,mw,gw,fx=p(()=>{Y(),Zt(),Nn(),W(),$r(),cx(),dx(),pw=(n,i)=>{if(i.length!==n.length)throw new Error(`inputDependencies length ${i.length} is not equal to inputTensors length ${n.length}.`);let s=[];for(let u=0;u{var c,h;let u=n.name;return(c=n.shaderCache)!=null&&c.hint&&(u+="["+n.shaderCache.hint+"]"),u+=":"+s+`:${pw(i,((h=n.shaderCache)==null?void 0:h.inputDependencies)??new Array(i.length).fill("dims"))}`,u},mw=class{constructor(n){n&&(this.architecture=n.architecture,this.vendor=n.vendor)}isArchitecture(n){return this.architecture===n}isVendor(n){return this.vendor===n}},gw=class{constructor(){this.currentSessionId=null,this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.maxDispatchNumber=16,this.pendingDispatchNumber=0,this.pendingKernels=[],this.pendingQueries=new Map,this.sessionStatus="default",this.capturedCommandList=new Map,this.capturedPendingKernels=new Map,this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. (should not happen)");let n=this.kernelCustomData.get(this.currentKernelId);return n||(n={},this.kernelCustomData.set(this.currentKernelId,n)),n}async initialize(n,i){this.env=n;let s=[],u={requiredLimits:{maxComputeWorkgroupStorageSize:i.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:i.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:i.limits.maxStorageBufferBindingSize,maxBufferSize:i.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:i.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:i.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:i.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:i.limits.maxComputeWorkgroupSizeZ},requiredFeatures:s};i.features.has("chromium-experimental-timestamp-query-inside-passes")?s.push("chromium-experimental-timestamp-query-inside-passes"):i.features.has("timestamp-query")&&s.push("timestamp-query"),i.features.has("shader-f16")&&s.push("shader-f16"),this.device=await i.requestDevice(u),this.adapterInfo=new mw(i.info||await i.requestAdapterInfo()),this.gpuDataManager=tr(this),this.programManager=new fw(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,_o(n.logLevel,!!n.debug),this.device.onuncapturederror=c=>{c.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${c.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:i,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 n=this.getCommandEncoder(),i={};this.queryType==="at-passes"&&(i.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=n.beginComputePass(i)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;et(),this.endComputePass();let n;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),n=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(n,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,n,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&n.mapAsync(GPUMapMode.READ).then(()=>{var u;let i=new BigUint64Array(n.getMappedRange()),s=this.pendingQueries.get(n);for(let c=0;c"u"&&(this.queryTimeBase=oe);let ce=Number(oe-this.queryTimeBase),we=Number(re-this.queryTimeBase);if(!Number.isSafeInteger(ce)||!Number.isSafeInteger(we))throw new RangeError("incorrect timestamp range");if((u=this.env.webgpu.profiling)!=null&&u.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:q.map(be=>({dims:be.dims,dataType:pi(be.dataType)})),outputsMetadata:v.map(be=>({dims:be.dims,dataType:pi(be.dataType)})),kernelId:d,kernelType:k,kernelName:$,programName:H,startTime:ce,endTime:we});else{let be="";q.forEach((De,Ae)=>{be+=`input[${Ae}]: [${De.dims}] | ${pi(De.dataType)}, `});let he="";v.forEach((De,Ae)=>{he+=`output[${Ae}]: [${De.dims}] | ${pi(De.dataType)}, `}),console.log(`[profiling] kernel "${d}|${k}|${$}|${H}" ${be}${he}execution time: ${we-ce} ns`)}je("GPU",`${H}::${oe}::${re}`)}n.unmap(),this.pendingQueries.delete(n)}),A()}run(n,i,s,u,c,h){et(n.name);let d=[];for(let he=0;heDe):s;if(H.length!==x.length)throw new Error(`Output size ${H.length} must be equal to ${x.length}.`);let q=[],v=[];for(let he=0;he=h)throw new Error(`Invalid output index: ${H[he]}`);if(H[he]===-3)continue;let De=H[he]===-1,Ae=H[he]===-2,Ie=De||Ae?c(x[he].dataType,x[he].dims):u(H[he],x[he].dataType,x[he].dims);if(q.push(Ie),Ie.data===0)continue;let gt=this.gpuDataManager.get(Ie.data);if(!gt)throw new Error(`no GPU data for output: ${Ie.data}`);if(De&&this.temporaryData.push(gt),Ae){let ut=this.kernelPersistentData.get(this.currentKernelId);ut||(ut=[],this.kernelPersistentData.set(this.currentKernelId,ut)),ut.push(gt)}v.push(gt)}if(d.length!==i.length||v.length!==q.length){if(v.length===0)return A(n.name),q;throw new Error(`Program ${n.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let oe;if($){let he=0,De=[];$.forEach(ut=>{let yt=typeof ut.data=="number"?[ut.data]:ut.data;if(yt.length===0)return;let Dt=ut.type===10?2:4,Bt,mr;ut.type===10?(mr=yt.length>4?16:yt.length>2?8:yt.length*Dt,Bt=yt.length>4?16:Dt*yt.length):(mr=yt.length<=2?yt.length*Dt:16,Bt=16),he=Math.ceil(he/mr)*mr,De.push(he);let vr=ut.type===10?8:4;he+=yt.length>4?Math.ceil(yt.length/vr)*Bt:yt.length*Dt});let Ae=16;he=Math.ceil(he/Ae)*Ae;let Ie=new ArrayBuffer(he);$.forEach((ut,yt)=>{let Dt=De[yt],Bt=typeof ut.data=="number"?[ut.data]:ut.data;if(ut.type===6)new Int32Array(Ie,Dt,Bt.length).set(Bt);else if(ut.type===12)new Uint32Array(Ie,Dt,Bt.length).set(Bt);else if(ut.type===10)new Uint16Array(Ie,Dt,Bt.length).set(Bt);else if(ut.type===1)new Float32Array(Ie,Dt,Bt.length).set(Bt);else throw new Error(`Unsupported uniform type: ${pi(ut.type)}`)});let gt=this.gpuDataManager.create(he,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(gt.buffer,0,Ie,0,he),this.gpuDataManager.release(gt.id),oe={offset:0,size:he,buffer:gt.buffer}}let re=this.programManager.normalizeDispatchGroupSize(k),ce=re[1]===1&&re[2]===1,we=hw(n,i,ce),be=this.programManager.getArtifact(we);if(be||(be=this.programManager.build(n,re),this.programManager.setArtifact(we,be),Ur("info",()=>`[artifact] key: ${we}, programName: ${n.name}`)),$&&be.uniformVariablesInfo){if($.length!==be.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${be.uniformVariablesInfo.length}, got ${$.length} in program "${be.programInfo.name}".`);for(let he=0;he<$.length;he++){let De=$[he],Ae=De.type,Ie=typeof De.data=="number"?1:De.data.length,[gt,ut]=be.uniformVariablesInfo[he];if(Ae!==gt||Ie!==ut)throw new Error(`Uniform variable ${he} mismatch: expect type ${gt} with size ${ut}, got type ${Ae} with size ${Ie} in program "${be.programInfo.name}".`)}}if(Ur("info",()=>`[ProgramManager] run "${n.name}" (key=${we}) with ${re[0]}x${re[1]}x${re[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let he={kernelId:this.currentKernelId,programName:be.programInfo.name,inputTensorViews:i,outputTensorViews:q};this.pendingKernels.push(he),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(he)}return this.programManager.run(be,d,v,re,oe),A(n.name),q}upload(n,i){this.gpuDataManager.upload(n,i)}memcpy(n,i){this.gpuDataManager.memcpy(n,i)}async download(n,i){await this.gpuDataManager.download(n,i)}alloc(n){return this.gpuDataManager.create(n).id}free(n){return this.gpuDataManager.release(n)}createKernel(n,i,s,u){let c=dw.get(n);if(!c)throw new Error(`kernel not implemented: ${n}`);let h={kernelType:n,kernelName:u,kernelEntry:c[0],attributes:[c[1],s]};this.kernels.set(i,h)}releaseKernel(n){let i=this.kernelPersistentData.get(n);if(i){for(let s of i)this.gpuDataManager.release(s.id);this.kernelPersistentData.delete(n)}this.kernelCustomData.delete(n),this.kernels.delete(n)}computeKernel(n,i,s){let u=this.kernels.get(n);if(!u)throw new Error(`kernel not created: ${n}`);let c=u.kernelType,h=u.kernelName,d=u.kernelEntry,x=u.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${c}] ${h}" is not allowed to be called recursively`);this.currentKernelId=n,x[0]&&(x[1]=x[0](x[1]),x[0]=void 0),Ur("info",()=>`[WebGPU] Start to run kernel "[${c}] ${h}"...`);let k=this.env.debug;this.temporaryData=[];try{return k&&this.device.pushErrorScope("validation"),d(i,x[1]),0}catch($){return s.push(Promise.resolve(`[WebGPU] Kernel "[${c}] ${h}" failed. ${$}`)),1}finally{k&&s.push(this.device.popErrorScope().then($=>$?`GPU validation error for kernel "[${c}] ${h}": ${$.message}`:null));for(let $ of this.temporaryData)this.gpuDataManager.release($.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(n,i,s,u){let c=this.sessionExternalDataMapping.get(n);c||(c=new Map,this.sessionExternalDataMapping.set(n,c));let h=c.get(i),d=this.gpuDataManager.registerExternalBuffer(s,u,h==null?void 0:h[1]);return c.set(i,[d,s]),d}unregisterBuffers(n){let i=this.sessionExternalDataMapping.get(n);i&&(i.forEach(s=>this.gpuDataManager.unregisterExternalBuffer(s[1])),this.sessionExternalDataMapping.delete(n))}getBuffer(n){let i=this.gpuDataManager.get(n);if(!i)throw new Error(`no GPU data for buffer: ${n}`);return i.buffer}createDownloader(n,i,s){return async()=>{let u=await kt(this,n,i);return Ue(u.buffer,s)}}writeTimestamp(n){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,n)}setQueryType(){var n;this.queryType="none",(((n=this.env.webgpu.profiling)==null?void 0:n.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(){Ur("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(){Ur("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){Ur("info","replay"),this.sessionStatus="replaying";let n=this.capturedCommandList.get(this.currentSessionId),i=this.capturedPendingKernels.get(this.currentSessionId),s=n.length;this.pendingKernels=[];for(let u=0;u=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onReleaseSession(n){this.unregisterBuffers(n),this.capturedCommandList.has(n)&&this.capturedCommandList.delete(n),this.capturedPendingKernels.has(n)&&this.capturedPendingKernels.delete(n),this.gpuDataManager.onReleaseSession(n)}onRunStart(n){this.currentSessionId=n,this.setQueryType()}}}),_w={};g(_w,{init:()=>yw});var Ap,ww,yw,px=p(()=>{Zt(),fx(),Nn(),Jt(),Ap=class ex{constructor(i,s,u,c){this.module=i,this.dataType=s,this.data=u,this.dims=c}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let i=ct.size(this.dims);return i===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,i)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let i=ct.size(this.dims);return i===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,i)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let i=ct.size(this.dims);return i===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,i)}reshape(i){if(ct.size(i)!==ct.size(this.dims))throw new Error("Invalid new shape");return new ex(this.module,this.dataType,this.data,i)}},ww=class{constructor(n,i,s){this.module=n,this.backend=i,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=i.adapterInfo;let u=n.HEAPU32,c=s>>>2;this.opKernelContext=u[c++];let h=u[c++];this.outputCount=u[c++],this.customDataOffset=u[c++],this.customDataSize=u[c++];let d=[];for(let x=0;xtypeof x=="number"?this.inputs[x]:x))??this.inputs,u=(i==null?void 0:i.outputs)??[],c=(x,k,$)=>new Ap(this.module,k,this.output(x,$),$),h=(x,k)=>{let $=ki(x);if(!$)throw new Error(`Unsupported data type: ${x}`);let H=$*ct.size(k),q=H>0?this.backend.gpuDataManager.create(H).id:0;return new Ap(this.module,x,q,k)};return this.backend.run(n,s,u,c,h,this.outputCount)}output(n,i){let s=this.module.stackSave();try{let u=this.module.stackAlloc((1+i.length)*4),c=u>>2;this.module.HEAPU32[c++]=i.length;for(let h=0;h{let c=i.jsepInit;if(!c)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(n==="webgpu"){let h=new gw;await h.initialize(s,u),c("webgpu",[h,d=>h.alloc(d),d=>h.free(d),(d,x,k,$=!1)=>{if($)Ur("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${d}, dst=${x}, size=${k}`),h.memcpy(d,x);else{Ur("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${d}, gpuDataId=${x}, size=${k}`);let H=i.HEAPU8.subarray(d>>>0,(d>>>0)+k);h.upload(x,H)}},async(d,x,k)=>{Ur("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${d}, dataOffset=${x}, size=${k}`),await h.download(d,()=>i.HEAPU8.subarray(x>>>0,(x>>>0)+k))},(d,x,k)=>h.createKernel(d,x,k,i.UTF8ToString(i._JsepGetNodeName(x))),d=>h.releaseKernel(d),(d,x,k,$)=>{Ur("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${k}, kernel=${d}, contextDataOffset=${x}`);let H=new ww(i,h,x);return h.computeKernel(d,H,$)},()=>h.captureBegin(),()=>h.captureEnd(),()=>h.replay()])}else c("webnn")}}),vw,xm,Mm,es,bw,Ip,Tm,km,Sm,Em,Cm,Pm,xw=p(()=>{Cs(),Ps(),Zt(),wn(),Ni(),jo(),vw=(n,i)=>{Wr()._OrtInit(n,i)!==0&&Or("Can't initialize onnxruntime.")},xm=async n=>{vw(n.wasm.numThreads,Ji(n.logLevel))},Mm=async(n,i)=>{{let s=(px(),w(_w)).init;if(i==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");let u=n.webgpu.adapter;if(u){if(typeof u.limits!="object"||typeof u.features!="object"||typeof u.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let c=n.webgpu.powerPreference;if(c!==void 0&&c!=="low-power"&&c!=="high-performance")throw new Error(`Invalid powerPreference setting: "${c}"`);let h=n.webgpu.forceFallbackAdapter;if(h!==void 0&&typeof h!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${h}"`);if(u=await navigator.gpu.requestAdapter({powerPreference:c,forceFallbackAdapter:h}),!u)throw new Error('Failed to get GPU adapter. 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All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */},"./src/backends/onnx.js":(e,t,r)=>{var o;r.r(t),r.d(t,{Tensor:()=>_.Tensor,createInferenceSession:()=>z,deviceToExecutionProviders:()=>C,isONNXProxy:()=>y,isONNXTensor:()=>S});var a=r("./src/env.js"),l=r("?2ce3"),f=r("./node_modules/onnxruntime-web/dist/ort.webgpu.bundle.min.mjs"),_=r("./node_modules/onnxruntime-common/dist/esm/index.js");const p=[];let g,M;if(a.apis.IS_NODE_ENV){switch(M=l??(o||(o=r.t(l,2))),process.platform){case"win32":p.push("dml");break;case"linux":process.arch==="x64"&&p.push("cuda");break}p.push("cpu"),g=["cpu"]}else M=f,a.apis.IS_WEBGPU_AVAILABLE&&p.push("webgpu"),p.push("wasm"),g=["wasm"];const w=M.InferenceSession;function C(b){let I=g;if(b){if(!p.includes(b))throw new Error(`Unsupported device: "${b}". 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o=r("./src/utils/generic.js");r("./src/utils/tensor.js");var a=r("./src/utils/maths.js");class l extends o.Callable{_call(N,j){throw Error("`_call` should be implemented in a subclass")}}class f extends o.Callable{_call(N,j){throw Error("`_call` should be implemented in a subclass")}}class _ extends o.Callable{constructor(){super(),this.processors=[]}push(N){this.processors.push(N)}extend(N){this.processors.push(...N)}_call(N,j){let D=j;for(const ee of this.processors)D=ee(N,D);return D}[Symbol.iterator](){return this.processors.values()}}class p extends l{constructor(N){super(),this.bos_token_id=N}_call(N,j){for(let D=0;D=1&&Ce[Ce.length-1]>=this.timestamp_begin,_e=Ce.length<2||Ce[Ce.length-2]>=this.timestamp_begin;if(te&&(_e?de.subarray(this.timestamp_begin).fill(-1/0):de.subarray(0,this.eos_token_id).fill(-1/0)),N[D].length===this.begin_index&&this.max_initial_timestamp_index!==null){const ge=this.timestamp_begin+this.max_initial_timestamp_index;de.subarray(ge+1).fill(-1/0)}const R=(0,a.log_softmax)(de),xe=Math.log(R.subarray(this.timestamp_begin).map(Math.exp).reduce((ge,X)=>ge+X)),Xe=(0,a.max)(R.subarray(0,this.timestamp_begin))[0];xe>Xe&&de.subarray(0,this.timestamp_begin).fill(-1/0)}return j}}class C extends l{constructor(N){super(),this.no_repeat_ngram_size=N}getNgrams(N){const j=N.length,D=[];for(let de=0;de1 to use the classifier free guidance processor, got guidance scale ${N}.`);this.guidance_scale=N}_call(N,j){if(j.dims[0]!==2*N.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 ${j.dims[0]} for the logits and ${N.length} for the input ids.`);const D=N.length,ee=j.slice([0,D],null),de=j.slice([D,j.dims[0]],null);for(let Ce=0;Ce1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${N}`);if(!Number.isInteger(D)||D<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${D}`);this.top_p=N,this.filter_value=j,this.min_tokens_to_keep=D}}class V extends f{constructor(N,{filter_value:j=-1/0,min_tokens_to_keep:D=1}={}){if(super(),!Number.isInteger(N)||N<0)throw new Error(`\`top_k\` must be a positive integer, but is ${N}`);this.top_k=Math.max(N,D),this.filter_value=j}}},"./src/generation/logits_sampler.js":(e,t,r)=>{r.r(t),r.d(t,{LogitsSampler:()=>f});var o=r("./src/utils/generic.js"),a=r("./src/utils/tensor.js"),l=r("./src/utils/maths.js");r("./src/generation/configuration_utils.js");class f extends o.Callable{constructor(w){super(),this.generation_config=w}async _call(w){return this.sample(w)}async sample(w){throw Error("sample should be implemented in subclasses.")}getLogits(w,C){let T=w.dims.at(-1),z=w.data;if(C===-1)z=z.slice(-T);else{let S=C*T;z=z.slice(S,S+T)}return z}randomSelect(w){let C=0;for(let z=0;z1)return new g(w);if(w.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${w.num_return_sequences}.`);return new _(w)}}class _ extends f{async sample(w){const C=(0,l.max)(w.data)[1];return[[BigInt(C),0]]}}class p extends f{async sample(w){let C=w.dims.at(-1);this.generation_config.top_k>0&&(C=Math.min(this.generation_config.top_k,C));const[T,z]=await(0,a.topk)(w,C),S=(0,l.softmax)(T.data);return Array.from({length:this.generation_config.num_beams},()=>{const E=this.randomSelect(S);return[z.data[E],Math.log(S[E])]})}}class g extends f{async sample(w){let C=w.dims.at(-1);this.generation_config.top_k>0&&(C=Math.min(this.generation_config.top_k,C));const[T,z]=await(0,a.topk)(w,C),S=(0,l.softmax)(T.data);return Array.from({length:this.generation_config.num_beams},(E,y)=>[z.data[y],Math.log(S[y])])}}},"./src/generation/stopping_criteria.js":(e,t,r)=>{r.r(t),r.d(t,{EosTokenCriteria:()=>_,InterruptableStoppingCriteria:()=>p,MaxLengthCriteria:()=>f,StoppingCriteria:()=>a,StoppingCriteriaList:()=>l});var o=r("./src/utils/generic.js");class a extends o.Callable{_call(M,w){throw Error("StoppingCriteria needs to be subclassed")}}class l extends o.Callable{constructor(){super(),this.criteria=[]}push(M){this.criteria.push(M)}extend(M){M instanceof l?M=M.criteria:M instanceof a&&(M=[M]),this.criteria.push(...M)}_call(M,w){const C=new Array(M.length).fill(!1);for(const T of this.criteria){const z=T(M,w);for(let S=0;Sw.length>=this.max_length)}}class _ extends a{constructor(M){super(),Array.isArray(M)||(M=[M]),this.eos_token_id=M}_call(M,w){return M.map(C=>{const T=C.at(-1);return this.eos_token_id.some(z=>T==z)})}}class p extends 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o=r("./src/configs.js"),a=r("./src/backends/onnx.js"),l=r("./src/utils/dtypes.js"),f=r("./src/utils/generic.js"),_=r("./src/utils/core.js"),p=r("./src/utils/hub.js"),g=r("./src/generation/logits_process.js"),M=r("./src/generation/configuration_utils.js"),w=r("./src/utils/tensor.js"),C=r("./src/utils/maths.js"),T=r("./src/generation/stopping_criteria.js"),z=r("./src/generation/logits_sampler.js"),S=r("./src/env.js"),E=r("./src/models/whisper/generation_whisper.js"),y=r("./src/models/whisper/common_whisper.js");const b={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},I=new Map,V=new Map,O=new Map;async function N(U,G,ie){let ke=ie.device;ke&&typeof ke!="string"&&(ke.hasOwnProperty(G)?ke=ke[G]:(console.warn(`device not specified for "${G}". Using the default device.`),ke=null));const nt=(0,a.deviceToExecutionProviders)(ke);let Ze=ie.dtype;if(typeof Ze!="string"&&(Ze&&Ze.hasOwnProperty(G)?Ze=Ze[G]:(Ze=l.DEFAULT_DEVICE_DTYPE_MAPPING[nt[0]],console.warn(`dtype not specified for "${G}". Using the default dtype for this device (${Ze}).`))),l.DEFAULT_DTYPE_SUFFIX_MAPPING.hasOwnProperty(Ze)){if(Ze===l.DATA_TYPES.fp16&&ke==="webgpu"&&!await(0,l.isWebGpuFp16Supported)())throw new Error(`The device (${ke}) does not support fp16.`)}else throw new Error(`Invalid dtype: ${Ze}. 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w.Tensor("int64",BigInt64Array.from(U.map(G=>BigInt(G))),[1,U.length])}function te(U){return new w.Tensor("bool",[U],[1])}async function _e(U,G){let{encoder_outputs:ie,input_ids:ke,decoder_input_ids:nt,...Ze}=G;if(!ie){const At=(0,_.pick)(G,U.sessions.model.inputNames);ie=(await R(U,At)).last_hidden_state}return Ze.input_ids=nt,Ze.encoder_hidden_states=ie,U.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(Ze.encoder_attention_mask=G.attention_mask),await xe(U,Ze,!0)}async function R(U,G){const ie=U.sessions.model,ke=(0,_.pick)(G,ie.inputNames);if(ie.inputNames.includes("inputs_embeds")&&!ke.inputs_embeds){if(!G.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");ke.inputs_embeds=await U.encode_text({input_ids:G.input_ids})}return ie.inputNames.includes("token_type_ids")&&!ke.token_type_ids&&(ke.token_type_ids=new w.Tensor("int64",new BigInt64Array(ke.input_ids.data.length),ke.input_ids.dims)),await ee(ie,ke)}async function xe(U,G,ie=!1){const ke=U.sessions[ie?"decoder_model_merged":"model"],{past_key_values:nt,...Ze}=G;ke.inputNames.includes("use_cache_branch")&&(Ze.use_cache_branch=te(!!nt)),ke.inputNames.includes("position_ids")&&Ze.attention_mask&&!Ze.position_ids&&(Ze.position_ids=ge(Ze,nt)),U.addPastKeyValues(Ze,nt);const bt=(0,_.pick)(Ze,ke.inputNames);return await ee(ke,bt)}async function Xe(U,{input_ids:G=null,attention_mask:ie=null,pixel_values:ke=null,position_ids:nt=null,inputs_embeds:Ze=null,past_key_values:bt=null,generation_config:At=null,logits_processor:Rt=null,...dr}){if(!Ze){if(Ze=await U.encode_text({input_ids:G}),ke&&G.dims[1]!==1){const Ar=await U.encode_image({pixel_values:ke});({inputs_embeds:Ze,attention_mask:ie}=U._merge_input_ids_with_image_features({image_features:Ar,inputs_embeds:Ze,input_ids:G,attention_mask:ie}))}else if(bt&&ke&&G.dims[1]===1){const Ar=G.dims[1],nr=Object.values(bt)[0].dims.at(-2);ie=(0,w.cat)([(0,w.ones)([G.dims[0],nr]),ie.slice(null,[ie.dims[1]-Ar,ie.dims[1]])],1)}}return await xe(U,{inputs_embeds:Ze,past_key_values:bt,attention_mask:ie,position_ids:nt,generation_config:At,logits_processor:Rt},!0)}function ge(U,G=null){const{input_ids:ie,inputs_embeds:ke,attention_mask:nt}=U,[Ze,bt]=nt.dims,At=new BigInt64Array(nt.data.length);for(let dr=0;drZe.dims[1])){if(ntAt==U.config.image_token_index)){const At=U.config.num_image_tokens;if(!At)throw new Error("`num_image_tokens` is missing in the model configuration.");const Rt=Ze.dims[1]-(nt-At);ie.input_ids=Ze.slice(null,[-Rt,null]),ie.attention_mask=(0,w.ones)([1,nt+Rt])}}}return ie}function ae(U,G,ie,ke){return ie.past_key_values&&(G=G.map(nt=>[nt.at(-1)])),{...ie,decoder_input_ids:Ce(G)}}function Se(U,...G){return U.config.is_encoder_decoder?ae(U,...G):X(U,...G)}class Q extends f.Callable{constructor(ie,ke){super();He(this,"main_input_name","input_ids");He(this,"forward_params",["input_ids","attention_mask"]);this.config=ie,this.sessions=ke;const nt=O.get(this.constructor),Ze=I.get(nt);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Ze){case b.DecoderOnly:this.can_generate=!0,this._forward=xe,this._prepare_inputs_for_generation=X;break;case b.Seq2Seq:case b.Vision2Seq:case b.Musicgen:this.can_generate=!0,this._forward=_e,this._prepare_inputs_for_generation=ae;break;case b.EncoderDecoder:this._forward=_e;break;case b.ImageTextToText:this.can_generate=!0,this._forward=Xe,this._prepare_inputs_for_generation=Se;break;default:this._forward=R;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var ke;const ie=[];for(const nt of Object.values(this.sessions))(ke=nt==null?void 0:nt.handler)!=null&&ke.dispose&&ie.push(nt.handler.dispose());return await Promise.all(ie)}static async from_pretrained(ie,{progress_callback:ke=null,config:nt=null,cache_dir:Ze=null,local_files_only:bt=!1,revision:At="main",model_file_name:Rt=null,subfolder:dr="onnx",device:hr=null,dtype:Ar=null,use_external_data_format:nr=null,session_options:ar={}}={}){let xr={progress_callback:ke,config:nt,cache_dir:Ze,local_files_only:bt,revision:At,model_file_name:Rt,subfolder:dr,device:hr,dtype:Ar,use_external_data_format:nr,session_options:ar};const Er=O.get(this),Sr=I.get(Er);nt=xr.config=await o.AutoConfig.from_pretrained(ie,xr);let Nr;if(Sr===b.DecoderOnly)Nr=await Promise.all([j(ie,{model:xr.model_file_name??"model"},xr),(0,p.getModelJSON)(ie,"generation_config.json",!1,xr)]);else if(Sr===b.Seq2Seq||Sr===b.Vision2Seq)Nr=await Promise.all([j(ie,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},xr),(0,p.getModelJSON)(ie,"generation_config.json",!1,xr)]);else if(Sr===b.MaskGeneration)Nr=await Promise.all([j(ie,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},xr)]);else if(Sr===b.EncoderDecoder)Nr=await Promise.all([j(ie,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},xr)]);else if(Sr===b.ImageTextToText){const Vn={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};nt.is_encoder_decoder&&(Vn.model="encoder_model"),Nr=await Promise.all([j(ie,Vn,xr),(0,p.getModelJSON)(ie,"generation_config.json",!1,xr)])}else Sr===b.Musicgen?Nr=await Promise.all([j(ie,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},xr),(0,p.getModelJSON)(ie,"generation_config.json",!1,xr)]):(Sr!==b.EncoderOnly&&console.warn(`Model type for '${Er??(nt==null?void 0:nt.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),Nr=await Promise.all([j(ie,{model:xr.model_file_name??"model"},xr)]));return new this(nt,...Nr)}async _call(ie){return await this.forward(ie)}async forward(ie){return await this._forward(this,ie)}_get_logits_warper(ie){const ke=new g.LogitsProcessorList;return ie.temperature!==null&&ie.temperature!==1&&ke.push(new g.TemperatureLogitsWarper(ie.temperature)),ie.top_k!==null&&ie.top_k!==0&&ke.push(new g.TopKLogitsWarper(ie.top_k)),ie.top_p!==null&&ie.top_p<1&&ke.push(new g.TopPLogitsWarper(ie.top_p)),ke}_get_logits_processor(ie,ke,nt=null){const Ze=new g.LogitsProcessorList;if(ie.repetition_penalty!==null&&ie.repetition_penalty!==1&&Ze.push(new g.RepetitionPenaltyLogitsProcessor(ie.repetition_penalty)),ie.no_repeat_ngram_size!==null&&ie.no_repeat_ngram_size>0&&Ze.push(new g.NoRepeatNGramLogitsProcessor(ie.no_repeat_ngram_size)),ie.bad_words_ids!==null&&Ze.push(new g.NoBadWordsLogitsProcessor(ie.bad_words_ids,ie.eos_token_id)),ie.min_length!==null&&ie.eos_token_id!==null&&ie.min_length>0&&Ze.push(new g.MinLengthLogitsProcessor(ie.min_length,ie.eos_token_id)),ie.min_new_tokens!==null&&ie.eos_token_id!==null&&ie.min_new_tokens>0&&Ze.push(new g.MinNewTokensLengthLogitsProcessor(ke,ie.min_new_tokens,ie.eos_token_id)),ie.forced_bos_token_id!==null&&Ze.push(new g.ForcedBOSTokenLogitsProcessor(ie.forced_bos_token_id)),ie.forced_eos_token_id!==null&&Ze.push(new g.ForcedEOSTokenLogitsProcessor(ie.max_length,ie.forced_eos_token_id)),ie.begin_suppress_tokens!==null){const bt=ke>1||ie.forced_bos_token_id===null?ke:ke+1;Ze.push(new g.SuppressTokensAtBeginLogitsProcessor(ie.begin_suppress_tokens,bt))}return ie.guidance_scale!==null&&ie.guidance_scale>1&&Ze.push(new g.ClassifierFreeGuidanceLogitsProcessor(ie.guidance_scale)),nt!==null&&Ze.extend(nt),Ze}_prepare_generation_config(ie,ke,nt=M.GenerationConfig){const Ze={...this.config};for(const At of["decoder","generator","text_config"])At in Ze&&Object.assign(Ze,Ze[At]);const bt=new nt(Ze);return"generation_config"in this&&Object.assign(bt,this.generation_config),ie&&Object.assign(bt,ie),ke&&Object.assign(bt,(0,_.pick)(ke,Object.getOwnPropertyNames(bt))),bt}_get_stopping_criteria(ie,ke=null){const nt=new T.StoppingCriteriaList;return ie.max_length!==null&&nt.push(new T.MaxLengthCriteria(ie.max_length,this.config.max_position_embeddings??null)),ie.eos_token_id!==null&&nt.push(new T.EosTokenCriteria(ie.eos_token_id)),ke&&nt.extend(ke),nt}_validate_model_class(){if(!this.can_generate){const ie=[Hs,bl,vl,Gs],ke=O.get(this.constructor),nt=new Set,Ze=this.config.model_type;for(const At of ie){const Rt=At.get(Ze);Rt&&nt.add(Rt[0])}let bt=`The current model class (${ke}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw nt.size>0&&(bt+=` Please use the following class instead: ${[...nt].join(", ")}`),Error(bt)}}prepare_inputs_for_generation(...ie){return this._prepare_inputs_for_generation(this,...ie)}_update_model_kwargs_for_generation({generated_input_ids:ie,outputs:ke,model_inputs:nt,is_encoder_decoder:Ze}){return nt.past_key_values=this.getPastKeyValues(ke,nt.past_key_values),nt.input_ids=new w.Tensor("int64",ie.flat(),[ie.length,1]),Ze||(nt.attention_mask=(0,w.cat)([nt.attention_mask,(0,w.ones)([nt.attention_mask.dims[0],1])],1)),nt.position_ids=null,nt}_prepare_model_inputs({inputs:ie,bos_token_id:ke,model_kwargs:nt}){const Ze=(0,_.pick)(nt,this.forward_params),bt=this.main_input_name;if(bt in Ze){if(ie)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Ze[bt]=ie;return{inputs_tensor:Ze[bt],model_inputs:Ze,model_input_name:bt}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:ie,model_inputs:ke,model_input_name:nt,generation_config:Ze}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!ke.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:At,pixel_values:Rt,attention_mask:dr,...hr}=ke,Ar=await this._prepare_inputs_embeds(ke);ke={...hr,...(0,_.pick)(Ar,["inputs_embeds","attention_mask"])}}let{last_hidden_state:bt}=await R(this,ke);if(Ze.guidance_scale!==null&&Ze.guidance_scale>1)bt=(0,w.cat)([bt,(0,w.full_like)(bt,0)],0),"attention_mask"in ke&&(ke.attention_mask=(0,w.cat)([ke.attention_mask,(0,w.zeros_like)(ke.attention_mask)],0));else if(ke.decoder_input_ids){const At=Ce(ke.decoder_input_ids).dims[0];if(At!==bt.dims[0]){if(bt.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${bt.dims[0]}) than the decoder inputs (${At}).`);bt=(0,w.cat)(Array.from({length:At},()=>bt),0)}}return ke.encoder_outputs=bt,ke}_prepare_decoder_input_ids_for_generation({batch_size:ie,model_input_name:ke,model_kwargs:nt,decoder_start_token_id:Ze,bos_token_id:bt,generation_config:At}){let{decoder_input_ids:Rt,...dr}=nt;if(Rt)Array.isArray(Rt[0])||(Rt=Array.from({length:ie},()=>Rt));else if(Ze??(Ze=bt),this.config.model_type==="musicgen")Rt=Array.from({length:ie*this.config.decoder.num_codebooks},()=>[Ze]);else if(Array.isArray(Ze)){if(Ze.length!==ie)throw new Error(`\`decoder_start_token_id\` expcted to have length ${ie} but got ${Ze.length}`);Rt=Ze}else Rt=Array.from({length:ie},()=>[Ze]);return Rt=Ce(Rt),nt.decoder_attention_mask=(0,w.ones_like)(Rt),{input_ids:Rt,model_inputs:dr}}async generate({inputs:ie=null,generation_config:ke=null,logits_processor:nt=null,stopping_criteria:Ze=null,streamer:bt=null,...At}){this._validate_model_class(),ke=this._prepare_generation_config(ke,At);let{inputs_tensor:Rt,model_inputs:dr,model_input_name:hr}=this._prepare_model_inputs({inputs:ie,model_kwargs:At});const Ar=this.config.is_encoder_decoder;Ar&&("encoder_outputs"in dr||(dr=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Rt,model_inputs:dr,model_input_name:hr,generation_config:ke})));let nr;Ar?{input_ids:nr,model_inputs:dr}=this._prepare_decoder_input_ids_for_generation({batch_size:dr[hr].dims.at(0),model_input_name:hr,model_kwargs:dr,decoder_start_token_id:ke.decoder_start_token_id,bos_token_id:ke.bos_token_id,generation_config:ke}):nr=dr[hr];let ar=nr.dims.at(-1);ke.max_new_tokens!==null&&(ke.max_length=ar+ke.max_new_tokens);const xr=this._get_logits_processor(ke,ar,nt),Er=this._get_stopping_criteria(ke,Ze),Sr=dr[hr].dims.at(0),Nr=z.LogitsSampler.getSampler(ke),Vn=new Array(Sr).fill(0),zn=nr.tolist();bt&&bt.put(zn);let Pi=null,Mn={};for(;;){dr=this.prepare_inputs_for_generation(zn,dr,ke);const Tn=await this.forward(dr);if(ke.output_attentions&&ke.return_dict_in_generate){const mi=this.getAttentions(Tn);for(const Jo in mi)Jo in Mn||(Mn[Jo]=[]),Mn[Jo].push(mi[Jo])}const qs=Tn.logits.slice(null,-1,null),Ks=xr(zn,qs),El=[];for(let mi=0;mimi)){ke.return_dict_in_generate&&(Pi=this.getPastKeyValues(Tn,dr.past_key_values,!1));break}dr=this._update_model_kwargs_for_generation({generated_input_ids:El,outputs:Tn,model_inputs:dr,is_encoder_decoder:Ar})}bt&&bt.end();const dn=new w.Tensor("int64",zn.flat(),[zn.length,zn[0].length]);return ke.return_dict_in_generate?{sequences:dn,past_key_values:Pi,...Mn}:dn}getPastKeyValues(ie,ke,nt=!0){const Ze=Object.create(null);for(const bt in ie)if(bt.startsWith("present")){const At=bt.replace("present","past_key_values");if(ke&&bt.includes("encoder"))Ze[At]=ke[At];else{if(nt&&ke){const Rt=ke[At];Rt.location==="gpu-buffer"&&Rt.dispose()}Ze[At]=ie[bt]}}return Ze}getAttentions(ie){const ke={};for(const nt of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Ze in ie)Ze.startsWith(nt)&&(nt in ke||(ke[nt]=[]),ke[nt].push(ie[Ze]));return ke}addPastKeyValues(ie,ke){if(ke)Object.assign(ie,ke);else{const nt=this.custom_config.kv_cache_dtype??"float32",Ze=nt==="float16"?new Uint16Array:[],bt=(0,o.getKeyValueShapes)(this.config);for(const At in bt)ie[At]=new w.Tensor(nt,Ze,bt[At])}}async encode_image({pixel_values:ie}){const ke=(await ee(this.sessions.vision_encoder,{pixel_values:ie})).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 (${ke.dims[1]}).`),this.config.num_image_tokens=ke.dims[1]),ke}async encode_text({input_ids:ie}){return(await ee(this.sessions.embed_tokens,{input_ids:ie})).inputs_embeds}}class me{}class Ke extends me{constructor({last_hidden_state:G,hidden_states:ie=null,attentions:ke=null}){super(),this.last_hidden_state=G,this.hidden_states=ie,this.attentions=ke}}class Le extends Q{}class Be extends Le{}class We extends Le{async _call(G){return new bn(await super._call(G))}}class st extends Le{async _call(G){return new cr(await super._call(G))}}class ft extends Le{async _call(G){return new hn(await super._call(G))}}class Ge extends Le{async _call(G){return new xn(await super._call(G))}}class Me extends Q{}class je extends Me{}class Ye extends Q{}class et extends Ye{}class A extends Ye{async _call(G){return new bn(await super._call(G))}}class $e extends Ye{async _call(G){return new cr(await super._call(G))}}class Pe extends Ye{async _call(G){return new hn(await super._call(G))}}class se extends Ye{async _call(G){return new xn(await super._call(G))}}class ne extends Q{}class ye extends ne{}class B extends ne{async _call(G){return new bn(await super._call(G))}}class J extends ne{async _call(G){return new cr(await super._call(G))}}class L extends ne{async _call(G){return new hn(await super._call(G))}}class ue extends ne{async _call(G){return new xn(await super._call(G))}}class le extends Q{}class Te extends le{}class Ne extends le{async _call(G){return new bn(await super._call(G))}}class dt extends le{async _call(G){return new cr(await super._call(G))}}class Je extends le{async _call(G){return new hn(await super._call(G))}}class Ve extends le{async _call(G){return new xn(await super._call(G))}}class Y extends Q{}class ve extends Y{}class Re extends Y{async _call(G){return new bn(await super._call(G))}}class qe extends Y{async _call(G){return new cr(await super._call(G))}}class Qe extends Y{async _call(G){return new hn(await super._call(G))}}class tt extends Y{async _call(G){return new xn(await super._call(G))}}class pt extends Q{}class ht extends pt{}class Tt extends pt{async _call(G){return new bn(await super._call(G))}}class vt extends pt{async _call(G){return new cr(await super._call(G))}}class Et extends pt{async _call(G){return new hn(await super._call(G))}}class Lt extends pt{async _call(G){return new xn(await super._call(G))}}class at extends Q{}class zt extends at{}class Vt extends at{async _call(G){return new bn(await super._call(G))}}class Xt extends at{async _call(G){return new cr(await super._call(G))}}class Yt extends at{async _call(G){return new hn(await super._call(G))}}class rr extends at{async _call(G){return new xn(await super._call(G))}}class Ht extends Q{}class Cr extends Ht{}class Yr extends Ht{async _call(G){return new cr(await super._call(G))}}class Pr extends Ht{async _call(G){return new hn(await super._call(G))}}class mt extends Ht{async _call(G){return new xn(await super._call(G))}}class Pt extends Ht{async _call(G){return new bn(await super._call(G))}}class Nt extends Q{}class un extends Nt{}class Bi extends Nt{async _call(G){return new bn(await super._call(G))}}class Mi extends Nt{async _call(G){return new cr(await super._call(G))}}class Wr extends Nt{async _call(G){return new hn(await super._call(G))}}class wn extends Q{}class Kr extends wn{}class fi extends wn{async _call(G){return new bn(await super._call(G))}}class Or extends wn{async _call(G){return new cr(await super._call(G))}}class Ni extends wn{async _call(G){return new xn(await super._call(G))}}class Ti extends Q{}class Cs extends Ti{}class Lo extends Ti{async _call(G){return new bn(await super._call(G))}}class Ro extends Ti{async _call(G){return new cr(await super._call(G))}}class Bo extends Ti{async _call(G){return new hn(await super._call(G))}}class No extends Ti{async _call(G){return new xn(await super._call(G))}}class ji extends Q{}class Ps extends ji{}class po extends ji{async _call(G){return new bn(await super._call(G))}}class pi extends ji{async _call(G){return new cr(await super._call(G))}}class ki extends ji{async _call(G){return new xn(await super._call(G))}}class Si extends Q{}class Ji extends Si{}class ho extends Si{async _call(G){return new cr(await super._call(G))}}class mo extends Si{async _call(G){return new xn(await super._call(G))}}class Zt extends Si{async _call(G){return new bn(await super._call(G))}}class eo extends Q{constructor(ie,ke,nt){super(ie,ke);He(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=nt}}class jo extends eo{}class Uo extends eo{}class go extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class Vo extends go{}class Wo extends go{}class _o extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class Go extends _o{}class Ur extends _o{}class Nn extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class Ue extends Nn{}class W extends Nn{}class fe extends Nn{async _call(G){return new cr(await super._call(G))}}class Ee extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class Fe extends Ee{}class ze extends Ee{}class rt extends Ee{async _call(G){return new cr(await super._call(G))}}class Mt extends Ee{}class St extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class kt extends St{}class It extends St{}class tr extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class $r extends tr{}class sr extends tr{}class qt extends Q{}class gr extends qt{}class Cn extends qt{async _call(G){return new bn(await super._call(G))}}class gn extends qt{async _call(G){return new cr(await super._call(G))}}class ct extends qt{async _call(G){return new hn(await super._call(G))}}class Kn extends qt{async _call(G){return new xn(await super._call(G))}}class br extends Q{}class cn extends br{}class Fn extends br{async _call(G){return new bn(await super._call(G))}}class Jt extends br{async _call(G){return new cr(await super._call(G))}}class jn extends br{async _call(G){return new hn(await super._call(G))}}class yn extends br{async _call(G){return new xn(await super._call(G))}}class Tr extends Q{}class kr extends Tr{}class $t extends Tr{async _call(G){return new bn(await super._call(G))}}class wr extends Tr{async _call(G){return new cr(await super._call(G))}}class Dr extends Tr{async _call(G){return new hn(await super._call(G))}}class Xr extends Tr{async _call(G){return new xn(await super._call(G))}}class Un extends Q{}class Ot extends Un{}class $s extends Un{}class _t extends Q{constructor(ie,ke,nt){super(ie,ke);He(this,"requires_attention_mask",!1);He(this,"main_input_name","input_features");He(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=nt}}class Gt extends _t{}class ma extends _t{_prepare_generation_config(G,ie){return super._prepare_generation_config(G,ie,E.WhisperGenerationConfig)}_retrieve_init_tokens(G){const ie=[G.decoder_start_token_id];let ke=G.language;const nt=G.task;if(G.is_multilingual){ke||(console.warn("No language specified - defaulting to English (en)."),ke="en");const bt=`<|${(0,y.whisper_language_to_code)(ke)}|>`;ie.push(G.lang_to_id[bt]),ie.push(G.task_to_id[nt??"transcribe"])}else if(ke||nt)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!G.return_timestamps&&G.no_timestamps_token_id&&ie.at(-1)!==G.no_timestamps_token_id?ie.push(G.no_timestamps_token_id):G.return_timestamps&&ie.at(-1)===G.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),ie.pop()),ie.filter(Ze=>Ze!=null)}async generate({inputs:G=null,generation_config:ie=null,logits_processor:ke=null,stopping_criteria:nt=null,...Ze}){ie=this._prepare_generation_config(ie,Ze);const bt=Ze.decoder_input_ids??this._retrieve_init_tokens(ie);if(ie.return_timestamps&&(ke??(ke=new g.LogitsProcessorList),ke.push(new g.WhisperTimeStampLogitsProcessor(ie,bt))),ie.begin_suppress_tokens&&(ke??(ke=new g.LogitsProcessorList),ke.push(new g.SuppressTokensAtBeginLogitsProcessor(ie.begin_suppress_tokens,bt.length))),ie.return_token_timestamps){if(!ie.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.");ie.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),ie.output_attentions=!0,ie.return_dict_in_generate=!0}const At=await super.generate({inputs:G,generation_config:ie,logits_processor:ke,decoder_input_ids:bt,...Ze});return ie.return_token_timestamps&&(At.token_timestamps=this._extract_token_timestamps(At,ie.alignment_heads,ie.num_frames)),At}_extract_token_timestamps(G,ie,ke=null,nt=.02){if(!G.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`.");ke==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 Ze=this.config.median_filter_width;Ze===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Ze=7);const bt=G.cross_attentions,At=Array.from({length:this.config.decoder_layers},(Er,Sr)=>(0,w.cat)(bt.map(Nr=>Nr[Sr]),2)),Rt=(0,w.stack)(ie.map(([Er,Sr])=>{if(Er>=At.length)throw new Error(`Layer index ${Er} is out of bounds for cross attentions (length ${At.length}).`);return ke?At[Er].slice(null,Sr,null,[0,ke]):At[Er].slice(null,Sr)})).transpose(1,0,2,3),[dr,hr]=(0,w.std_mean)(Rt,-2,0,!0),Ar=Rt.clone();for(let Er=0;ErNr[Tn+1]-Nr[Tn]),Pi=(0,_.mergeArrays)([1],zn).map(dn=>!!dn),Mn=[];for(let dn=0;dnnr.findIndex(ar=>ar==Ze)),Rt=At.every(nr=>nr===-1),dr=At.every(nr=>nr!==-1);if(!Rt&&!dr)throw new Error("Every input should contain either 0 or 1 image token.");if(Rt)return{inputs_embeds:G,attention_mask:nt};const hr=[],Ar=[];for(let nr=0;nrZe*bt,1);G.input_labels=new w.Tensor("int64",new BigInt64Array(nt).fill(1n),ke)}const ie={image_embeddings:G.image_embeddings,image_positional_embeddings:G.image_positional_embeddings};return G.input_points&&(ie.input_points=G.input_points),G.input_labels&&(ie.input_labels=G.input_labels),G.input_boxes&&(ie.input_boxes=G.input_boxes),await ee(this.sessions.prompt_encoder_mask_decoder,ie)}async _call(G){return new Wc(await super._call(G))}}class Wc extends me{constructor({iou_scores:G,pred_masks:ie}){super(),this.iou_scores=G,this.pred_masks=ie}}class Xa extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class sp extends Xa{}class Gc extends Xa{}class Qa extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class Hc extends Qa{}class qc extends Qa{}class Ui extends Q{}class Kc extends Ui{}class ap extends Ui{async _call(G){return new to(await super._call(G))}}class Vi extends Ui{async _call(G){return new cr(await super._call(G))}}class Wi extends Ui{async _call(G){return new hn(await super._call(G))}}class Ei extends Q{}class Ya extends Ei{}class Gi extends Ei{async _call(G){return new hn(await super._call(G))}}class vn extends Q{}class Za extends vn{}class xo extends Q{}class Ja extends xo{}class Xc extends xo{async _call(G){return new to(await super._call(G))}}class Qc extends xo{async _call(G){return new cr(await super._call(G))}}class qo extends Q{}class Rs extends qo{}class el extends qo{async _call(G){return new to(await super._call(G))}}class Yc extends qo{async _call(G){return new cr(await super._call(G))}}class Bs extends qo{async _call(G){return new hn(await super._call(G))}}class Ns extends Q{}class tl extends Ns{}class js extends Ns{async _call(G){return new to(await super._call(G))}}class Zc extends Ns{async _call(G){return new cr(await super._call(G))}}class lp extends Q{}class up extends Ui{}class Jc extends Ui{async _call(G){return new to(await super._call(G))}}class rl extends Ui{async _call(G){return new cr(await super._call(G))}}class Ci extends Q{}class ed extends Ci{}class nl extends Ci{async _call(G){return new to(await super._call(G))}}class td extends Ci{async _call(G){return new cr(await super._call(G))}}class rd extends Ci{async _call(G){return new nf(await super._call(G))}}class nd extends Ci{async _call(G){return new hn(await super._call(G))}}class il extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class id extends il{}class od extends il{}class cp extends il{async generate_speech(G,ie,{threshold:ke=.5,minlenratio:nt=0,maxlenratio:Ze=20,vocoder:bt=null}={}){const At={input_ids:G},{encoder_outputs:Rt,encoder_attention_mask:dr}=await R(this,At),hr=Rt.dims[1]/this.config.reduction_factor,Ar=Math.floor(hr*Ze),nr=Math.floor(hr*nt),ar=this.config.num_mel_bins;let xr=[],Er=null,Sr=null,Nr=0;for(;;){++Nr;const Pi=te(!!Sr);let Mn;Sr?Mn=Sr.output_sequence_out:Mn=new w.Tensor("float32",new Float32Array(ar),[1,1,ar]);let dn={use_cache_branch:Pi,output_sequence:Mn,encoder_attention_mask:dr,speaker_embeddings:ie,encoder_hidden_states:Rt};this.addPastKeyValues(dn,Er),Sr=await ee(this.sessions.decoder_model_merged,dn),Er=this.getPastKeyValues(Sr,Er);const{prob:Tn,spectrum:qs}=Sr;if(xr.push(qs),Nr>=nr&&(Array.from(Tn.data).filter(Ks=>Ks>=ke).length>0||Nr>=Ar))break}const Vn=(0,w.cat)(xr),{waveform:zn}=await ee(bt.sessions.model,{spectrogram:Vn});return{spectrogram:Vn,waveform:zn}}}class ol extends Q{constructor(){super(...arguments);He(this,"main_input_name","spectrogram")}}class sd extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class ad extends sd{}class sl extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class Us extends sl{}class Vs extends sl{}class al extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class Ws extends al{}class ll extends al{}class ul extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class ld extends ul{}class ud extends ul{}class Ko extends Q{}class cd extends Ko{}class dd extends Ko{static async from_pretrained(G,ie={}){return ie.model_file_name??(ie.model_file_name="text_model"),super.from_pretrained(G,ie)}}class fd extends Ko{static async from_pretrained(G,ie={}){return ie.model_file_name??(ie.model_file_name="audio_model"),super.from_pretrained(G,ie)}}class dp extends Q{}class cl extends dp{async _call(G){return new Sp(await super._call(G))}}class Xo extends Q{}class mm extends Xo{}class pd extends Xo{}class hd extends Xo{}class dl extends Q{constructor(G,ie,ke){super(G,ie),this.generation_config=ke}}class fl extends dl{}class md extends dl{}class pl extends Q{}class gd extends pl{}class _d extends pl{async _call(G){return new cr(await super._call(G))}}class hl extends Q{}class fp extends hl{}class gm extends hl{}class ml extends Q{constructor(ie,ke,nt){super(ie,ke);He(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=nt}_apply_and_filter_by_delay_pattern_mask(ie){const[ke,nt]=ie.dims,Ze=this.config.decoder.num_codebooks,bt=nt-Ze;let At=0;for(let hr=0;hr0&&ar<=bt&&(ie.data[At++]=ie.data[hr])}const Rt=Math.floor(ke/Ze),dr=At/(Rt*Ze);return new w.Tensor(ie.type,ie.data.slice(0,At),[Rt,Ze,dr])}prepare_inputs_for_generation(ie,ke,nt){let Ze=structuredClone(ie);for(let At=0;At=Rt&&(Ze[At][Rt]=BigInt(this.config.decoder.pad_token_id));return nt.guidance_scale!==null&&nt.guidance_scale>1&&(Ze=Ze.concat(Ze)),super.prepare_inputs_for_generation(Ze,ke,nt)}async generate(ie){const ke=await super.generate(ie),nt=this._apply_and_filter_by_delay_pattern_mask(ke).unsqueeze_(0),{audio_values:Ze}=await ee(this.sessions.encodec_decode,{audio_codes:nt});return Ze}}class gl extends Q{}class wd extends gl{}class pp extends gl{async _call(G){return new cr(await super._call(G))}}class _l extends Q{}class yd extends _l{}class vd extends _l{async _call(G){return new cr(await super._call(G))}}class wl extends Q{}class bd extends wl{}class hp extends wl{async _call(G){return new cr(await super._call(G))}}class Qo extends Q{}class Yo extends Qo{}class yl extends Qo{async _call(G){return new cr(await super._call(G))}}class Lr{static async from_pretrained(G,{progress_callback:ie=null,config:ke=null,cache_dir:nt=null,local_files_only:Ze=!1,revision:bt="main",model_file_name:At=null,subfolder:Rt="onnx",device:dr=null,dtype:hr=null,use_external_data_format:Ar=null,session_options:nr={}}={}){let ar={progress_callback:ie,config:ke,cache_dir:nt,local_files_only:Ze,revision:bt,model_file_name:At,subfolder:Rt,device:dr,dtype:hr,use_external_data_format:Ar,session_options:nr};if(ar.config=await o.AutoConfig.from_pretrained(G,ar),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let xr of this.MODEL_CLASS_MAPPINGS){const Er=xr.get(ar.config.model_type);if(Er)return await Er[1].from_pretrained(G,ar)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${ar.config.model_type}", attempting to construct from base class.`),await Q.from_pretrained(G,ar);throw Error(`Unsupported model type: ${ar.config.model_type}`)}}He(Lr,"MODEL_CLASS_MAPPINGS",null),He(Lr,"BASE_IF_FAIL",!1);const mp=new Map([["bert",["BertModel",Be]],["nomic_bert",["NomicBertModel",je]],["roformer",["RoFormerModel",et]],["electra",["ElectraModel",Te]],["esm",["EsmModel",un]],["convbert",["ConvBertModel",ye]],["camembert",["CamembertModel",ve]],["deberta",["DebertaModel",ht]],["deberta-v2",["DebertaV2Model",zt]],["mpnet",["MPNetModel",Cs]],["albert",["AlbertModel",Ji]],["distilbert",["DistilBertModel",Cr]],["roberta",["RobertaModel",gr]],["xlm",["XLMModel",cn]],["xlm-roberta",["XLMRobertaModel",kr]],["clap",["ClapModel",cd]],["clip",["CLIPModel",fu]],["clipseg",["CLIPSegModel",wu]],["chinese_clip",["ChineseCLIPModel",_u]],["siglip",["SiglipModel",yo]],["mobilebert",["MobileBertModel",Kr]],["squeezebert",["SqueezeBertModel",Ps]],["wav2vec2",["Wav2Vec2Model",Kc]],["wav2vec2-bert",["Wav2Vec2BertModel",tl]],["unispeech",["UniSpeechModel",Ja]],["unispeech-sat",["UniSpeechSatModel",Rs]],["hubert",["HubertModel",up]],["wavlm",["WavLMModel",ed]],["audio-spectrogram-transformer",["ASTModel",Ot]],["vits",["VitsModel",cl]],["pyannote",["PyAnnoteModel",Ya]],["wespeaker-resnet",["WeSpeakerResNetModel",Za]],["detr",["DetrModel",dc]],["rt_detr",["RTDetrModel",mc]],["table-transformer",["TableTransformerModel",_c]],["vit",["ViTModel",Xu]],["fastvit",["FastViTModel",Yu]],["mobilevit",["MobileViTModel",rc]],["mobilevitv2",["MobileViTV2Model",op]],["owlvit",["OwlViTModel",oc]],["owlv2",["Owlv2Model",ac]],["beit",["BeitModel",uc]],["deit",["DeiTModel",vc]],["convnext",["ConvNextModel",Ic]],["convnextv2",["ConvNextV2Model",Oc]],["dinov2",["Dinov2Model",Lc]],["resnet",["ResNetModel",xc]],["swin",["SwinModel",Tc]],["swin2sr",["Swin2SRModel",Sc]],["donut-swin",["DonutSwinModel",Ha]],["yolos",["YolosModel",Bc]],["dpt",["DPTModel",Va]],["glpn",["GLPNModel",Pc]],["hifigan",["SpeechT5HifiGan",ol]],["efficientnet",["EfficientNetModel",gd]],["mobilenet_v1",["MobileNetV1Model",wd]],["mobilenet_v2",["MobileNetV2Model",yd]],["mobilenet_v3",["MobileNetV3Model",bd]],["mobilenet_v4",["MobileNetV4Model",Yo]]]),gp=new Map([["t5",["T5Model",jo]],["longt5",["LongT5Model",Vo]],["mt5",["MT5Model",Go]],["bart",["BartModel",Ue]],["mbart",["MBartModel",Fe]],["marian",["MarianModel",sp]],["whisper",["WhisperModel",Gt]],["m2m_100",["M2M100Model",Hc]],["blenderbot",["BlenderbotModel",kt]],["blenderbot-small",["BlenderbotSmallModel",$r]]]),_p=new Map([["bloom",["BloomModel",Wu]],["gpt2",["GPT2Model",vu]],["gptj",["GPTJModel",Su]],["gpt_bigcode",["GPTBigCodeModel",Cu]],["gpt_neo",["GPTNeoModel",xu]],["gpt_neox",["GPTNeoXModel",Tu]],["codegen",["CodeGenModel",Pu]],["llama",["LlamaModel",ii]],["cohere",["CohereModel",Au]],["gemma",["GemmaModel",Fu]],["gemma2",["Gemma2Model",Ou]],["openelm",["OpenELMModel",Lu]],["qwen2",["Qwen2Model",Bu]],["phi",["PhiModel",ju]],["phi3",["Phi3Model",Vu]],["mpt",["MptModel",ip]],["opt",["OPTModel",qu]],["mistral",["MistralModel",Us]],["starcoder2",["Starcoder2Model",Ws]],["falcon",["FalconModel",ld]],["stablelm",["StableLmModel",fl]]]),Gs=new Map([["speecht5",["SpeechT5ForSpeechToText",od]],["whisper",["WhisperForConditionalGeneration",ma]]]),xd=new Map([["speecht5",["SpeechT5ForTextToSpeech",cp]]]),Md=new Map([["vits",["VitsModel",cl]],["musicgen",["MusicgenForConditionalGeneration",ml]]]),Td=new Map([["bert",["BertForSequenceClassification",st]],["roformer",["RoFormerForSequenceClassification",$e]],["electra",["ElectraForSequenceClassification",dt]],["esm",["EsmForSequenceClassification",Mi]],["convbert",["ConvBertForSequenceClassification",J]],["camembert",["CamembertForSequenceClassification",qe]],["deberta",["DebertaForSequenceClassification",vt]],["deberta-v2",["DebertaV2ForSequenceClassification",Xt]],["mpnet",["MPNetForSequenceClassification",Ro]],["albert",["AlbertForSequenceClassification",ho]],["distilbert",["DistilBertForSequenceClassification",Yr]],["roberta",["RobertaForSequenceClassification",gn]],["xlm",["XLMForSequenceClassification",Jt]],["xlm-roberta",["XLMRobertaForSequenceClassification",wr]],["bart",["BartForSequenceClassification",fe]],["mbart",["MBartForSequenceClassification",rt]],["mobilebert",["MobileBertForSequenceClassification",Or]],["squeezebert",["SqueezeBertForSequenceClassification",pi]]]),wp=new Map([["bert",["BertForTokenClassification",ft]],["roformer",["RoFormerForTokenClassification",Pe]],["electra",["ElectraForTokenClassification",Je]],["esm",["EsmForTokenClassification",Wr]],["convbert",["ConvBertForTokenClassification",L]],["camembert",["CamembertForTokenClassification",Qe]],["deberta",["DebertaForTokenClassification",Et]],["deberta-v2",["DebertaV2ForTokenClassification",Yt]],["mpnet",["MPNetForTokenClassification",Bo]],["distilbert",["DistilBertForTokenClassification",Pr]],["roberta",["RobertaForTokenClassification",ct]],["xlm",["XLMForTokenClassification",jn]],["xlm-roberta",["XLMRobertaForTokenClassification",Dr]]]),vl=new Map([["t5",["T5ForConditionalGeneration",Uo]],["longt5",["LongT5ForConditionalGeneration",Wo]],["mt5",["MT5ForConditionalGeneration",Ur]],["bart",["BartForConditionalGeneration",W]],["mbart",["MBartForConditionalGeneration",ze]],["marian",["MarianMTModel",Gc]],["m2m_100",["M2M100ForConditionalGeneration",qc]],["blenderbot",["BlenderbotForConditionalGeneration",It]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",sr]]]),Hs=new Map([["bloom",["BloomForCausalLM",Gu]],["gpt2",["GPT2LMHeadModel",bu]],["gptj",["GPTJForCausalLM",Eu]],["gpt_bigcode",["GPTBigCodeForCausalLM",np]],["gpt_neo",["GPTNeoForCausalLM",Mu]],["gpt_neox",["GPTNeoXForCausalLM",ku]],["codegen",["CodeGenForCausalLM",As]],["llama",["LlamaForCausalLM",$u]],["cohere",["CohereForCausalLM",Iu]],["gemma",["GemmaForCausalLM",zu]],["gemma2",["Gemma2ForCausalLM",Du]],["openelm",["OpenELMForCausalLM",Ru]],["qwen2",["Qwen2ForCausalLM",Nu]],["phi",["PhiForCausalLM",Uu]],["phi3",["Phi3ForCausalLM",$a]],["mpt",["MptForCausalLM",Hu]],["opt",["OPTForCausalLM",Ku]],["mbart",["MBartForCausalLM",Mt]],["mistral",["MistralForCausalLM",Vs]],["starcoder2",["Starcoder2ForCausalLM",ll]],["falcon",["FalconForCausalLM",ud]],["trocr",["TrOCRForCausalLM",ad]],["stablelm",["StableLmForCausalLM",md]]]),kd=new Map([["bert",["BertForMaskedLM",We]],["roformer",["RoFormerForMaskedLM",A]],["electra",["ElectraForMaskedLM",Ne]],["esm",["EsmForMaskedLM",Bi]],["convbert",["ConvBertForMaskedLM",B]],["camembert",["CamembertForMaskedLM",Re]],["deberta",["DebertaForMaskedLM",Tt]],["deberta-v2",["DebertaV2ForMaskedLM",Vt]],["mpnet",["MPNetForMaskedLM",Lo]],["albert",["AlbertForMaskedLM",Zt]],["distilbert",["DistilBertForMaskedLM",Pt]],["roberta",["RobertaForMaskedLM",Cn]],["xlm",["XLMWithLMHeadModel",Fn]],["xlm-roberta",["XLMRobertaForMaskedLM",$t]],["mobilebert",["MobileBertForMaskedLM",fi]],["squeezebert",["SqueezeBertForMaskedLM",po]]]),Sd=new Map([["bert",["BertForQuestionAnswering",Ge]],["roformer",["RoFormerForQuestionAnswering",se]],["electra",["ElectraForQuestionAnswering",Ve]],["convbert",["ConvBertForQuestionAnswering",ue]],["camembert",["CamembertForQuestionAnswering",tt]],["deberta",["DebertaForQuestionAnswering",Lt]],["deberta-v2",["DebertaV2ForQuestionAnswering",rr]],["mpnet",["MPNetForQuestionAnswering",No]],["albert",["AlbertForQuestionAnswering",mo]],["distilbert",["DistilBertForQuestionAnswering",mt]],["roberta",["RobertaForQuestionAnswering",Kn]],["xlm",["XLMForQuestionAnswering",yn]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Xr]],["mobilebert",["MobileBertForQuestionAnswering",Ni]],["squeezebert",["SqueezeBertForQuestionAnswering",ki]]]),bl=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",ga]]]),_m=new Map([["llava",["LlavaForConditionalGeneration",wo]],["moondream1",["Moondream1ForConditionalGeneration",ur]],["florence2",["Florence2ForConditionalGeneration",_a]]]),yp=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",ga]]]),Ed=new Map([["vit",["ViTForImageClassification",Qu]],["fastvit",["FastViTForImageClassification",Zu]],["mobilevit",["MobileViTForImageClassification",nc]],["mobilevitv2",["MobileViTV2ForImageClassification",ic]],["beit",["BeitForImageClassification",cc]],["deit",["DeiTForImageClassification",bc]],["convnext",["ConvNextForImageClassification",Fc]],["convnextv2",["ConvNextV2ForImageClassification",Dc]],["dinov2",["Dinov2ForImageClassification",Rc]],["resnet",["ResNetForImageClassification",Mc]],["swin",["SwinForImageClassification",kc]],["segformer",["SegformerForImageClassification",pd]],["efficientnet",["EfficientNetForImageClassification",_d]],["mobilenet_v1",["MobileNetV1ForImageClassification",pp]],["mobilenet_v2",["MobileNetV2ForImageClassification",vd]],["mobilenet_v3",["MobileNetV3ForImageClassification",hp]],["mobilenet_v4",["MobileNetV4ForImageClassification",yl]]]),vp=new Map([["detr",["DetrForObjectDetection",fc]],["rt_detr",["RTDetrForObjectDetection",Ls]],["table-transformer",["TableTransformerForObjectDetection",wc]],["yolos",["YolosForObjectDetection",Nc]]]),Cd=new Map([["owlvit",["OwlViTForObjectDetection",sc]],["owlv2",["Owlv2ForObjectDetection",lc]]]),Pd=new Map([["detr",["DetrForSegmentation",pc]],["clipseg",["CLIPSegForImageSegmentation",yu]]]),$d=new Map([["segformer",["SegformerForSemanticSegmentation",hd]]]),Ad=new Map([["sam",["SamModel",Vc]]]),bp=new Map([["wav2vec2",["Wav2Vec2ForCTC",ap]],["wav2vec2-bert",["Wav2Vec2BertForCTC",js]],["unispeech",["UniSpeechForCTC",Xc]],["unispeech-sat",["UniSpeechSatForCTC",el]],["wavlm",["WavLMForCTC",nl]],["hubert",["HubertForCTC",Jc]]]),Id=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Vi]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Zc]],["unispeech",["UniSpeechForSequenceClassification",Qc]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Yc]],["wavlm",["WavLMForSequenceClassification",td]],["hubert",["HubertForSequenceClassification",rl]],["audio-spectrogram-transformer",["ASTForAudioClassification",$s]]]),Fd=new Map([["wavlm",["WavLMForXVector",rd]]]),zd=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Bs]],["wavlm",["WavLMForAudioFrameClassification",nd]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Wi]],["pyannote",["PyAnnoteForAudioFrameClassification",Gi]]]),Od=new Map([["vitmatte",["VitMatteForImageMatting",ec]]]),xp=new Map([["swin2sr",["Swin2SRForImageSuperResolution",ja]]]),Dd=new Map([["dpt",["DPTForDepthEstimation",Wa]],["depth_anything",["DepthAnythingForDepthEstimation",Cc]],["glpn",["GLPNForDepthEstimation",$c]]]),Ld=new Map([["clip",["CLIPVisionModelWithProjection",pu]],["siglip",["SiglipVisionModel",mu]]]),Rd=[[mp,b.EncoderOnly],[gp,b.EncoderDecoder],[_p,b.DecoderOnly],[Td,b.EncoderOnly],[wp,b.EncoderOnly],[vl,b.Seq2Seq],[Gs,b.Seq2Seq],[Hs,b.DecoderOnly],[kd,b.EncoderOnly],[Sd,b.EncoderOnly],[bl,b.Vision2Seq],[_m,b.ImageTextToText],[Ed,b.EncoderOnly],[Pd,b.EncoderOnly],[$d,b.EncoderOnly],[Od,b.EncoderOnly],[xp,b.EncoderOnly],[Dd,b.EncoderOnly],[vp,b.EncoderOnly],[Cd,b.EncoderOnly],[Ad,b.MaskGeneration],[bp,b.EncoderOnly],[Id,b.EncoderOnly],[xd,b.Seq2Seq],[Md,b.EncoderOnly],[Fd,b.EncoderOnly],[zd,b.EncoderOnly],[Ld,b.EncoderOnly]];for(const[U,G]of Rd)for(const[ie,ke]of U.values())I.set(ie,G),O.set(ke,ie),V.set(ie,ke);const Mp=[["MusicgenForConditionalGeneration",ml,b.Musicgen],["CLIPTextModelWithProjection",hi,b.EncoderOnly],["SiglipTextModel",hu,b.EncoderOnly],["ClapTextModelWithProjection",dd,b.EncoderOnly],["ClapAudioModelWithProjection",fd,b.EncoderOnly]];for(const[U,G,ie]of Mp)I.set(U,ie),O.set(G,U),V.set(U,G);class Bd extends Lr{}He(Bd,"MODEL_CLASS_MAPPINGS",Rd.map(G=>G[0])),He(Bd,"BASE_IF_FAIL",!0);class Pn extends Lr{}He(Pn,"MODEL_CLASS_MAPPINGS",[Td]);class Nd extends Lr{}He(Nd,"MODEL_CLASS_MAPPINGS",[wp]);class jd extends Lr{}He(jd,"MODEL_CLASS_MAPPINGS",[vl]);class xl extends Lr{}He(xl,"MODEL_CLASS_MAPPINGS",[Gs]);class Ud extends Lr{}He(Ud,"MODEL_CLASS_MAPPINGS",[xd]);class Zo extends Lr{}He(Zo,"MODEL_CLASS_MAPPINGS",[Md]);class Vd extends Lr{}He(Vd,"MODEL_CLASS_MAPPINGS",[Hs]);class Wd extends Lr{}He(Wd,"MODEL_CLASS_MAPPINGS",[kd]);class Ml extends Lr{}He(Ml,"MODEL_CLASS_MAPPINGS",[Sd]);class Gd extends Lr{}He(Gd,"MODEL_CLASS_MAPPINGS",[bl]);class Hd extends Lr{}He(Hd,"MODEL_CLASS_MAPPINGS",[Ed]);class Tl extends Lr{}He(Tl,"MODEL_CLASS_MAPPINGS",[Pd]);class qd extends Lr{}He(qd,"MODEL_CLASS_MAPPINGS",[$d]);class Kd extends Lr{}He(Kd,"MODEL_CLASS_MAPPINGS",[vp]);class Xd extends Lr{}He(Xd,"MODEL_CLASS_MAPPINGS",[Cd]);class kl extends Lr{}He(kl,"MODEL_CLASS_MAPPINGS",[Ad]);class Qd extends Lr{}He(Qd,"MODEL_CLASS_MAPPINGS",[bp]);class Yd extends Lr{}He(Yd,"MODEL_CLASS_MAPPINGS",[Id]);class Sl extends Lr{}He(Sl,"MODEL_CLASS_MAPPINGS",[Fd]);class Zd extends Lr{}He(Zd,"MODEL_CLASS_MAPPINGS",[zd]);class Tp extends Lr{}He(Tp,"MODEL_CLASS_MAPPINGS",[yp]);class Jd extends Lr{}He(Jd,"MODEL_CLASS_MAPPINGS",[Od]);class ef extends Lr{}He(ef,"MODEL_CLASS_MAPPINGS",[xp]);class tf extends Lr{}He(tf,"MODEL_CLASS_MAPPINGS",[Dd]);class rf extends Lr{}He(rf,"MODEL_CLASS_MAPPINGS",[Ld]);class wm extends me{constructor({logits:G,past_key_values:ie,encoder_outputs:ke,decoder_attentions:nt=null,cross_attentions:Ze=null}){super(),this.logits=G,this.past_key_values=ie,this.encoder_outputs=ke,this.decoder_attentions=nt,this.cross_attentions=Ze}}class cr extends me{constructor({logits:G}){super(),this.logits=G}}class nf extends me{constructor({logits:G,embeddings:ie}){super(),this.logits=G,this.embeddings=ie}}class hn extends me{constructor({logits:G}){super(),this.logits=G}}class bn extends me{constructor({logits:G}){super(),this.logits=G}}class xn extends me{constructor({start_logits:G,end_logits:ie}){super(),this.start_logits=G,this.end_logits=ie}}class to extends me{constructor({logits:G}){super(),this.logits=G}}class kp extends me{constructor({logits:G,past_key_values:ie}){super(),this.logits=G,this.past_key_values=ie}}class of extends me{constructor({alphas:G}){super(),this.alphas=G}}class Sp extends me{constructor({waveform:G,spectrogram:ie}){super(),this.waveform=G,this.spectrogram=ie}}},"./src/models/whisper/common_whisper.js":(e,t,r)=>{r.r(t),r.d(t,{WHISPER_LANGUAGE_MAPPING:()=>a,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>l,whisper_language_to_code:()=>f});const o=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],a=new Map(o),l=new Map([...o.map(([_,p])=>[p,_]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function f(_){_=_.toLowerCase();let p=l.get(_);if(p===void 0)if(a.has(_))p=_;else{const M=_.length===2?a.keys():a.values();throw new Error(`Language "${_}" is not supported. Must be one of: ${JSON.stringify(M)}`)}return p}},"./src/models/whisper/generation_whisper.js":(e,t,r)=>{r.r(t),r.d(t,{WhisperGenerationConfig:()=>a});var o=r("./src/generation/configuration_utils.js");class a extends o.GenerationConfig{constructor(){super(...arguments);He(this,"return_timestamps",null);He(this,"return_token_timestamps",null);He(this,"num_frames",null);He(this,"alignment_heads",null);He(this,"task",null);He(this,"language",null);He(this,"no_timestamps_token_id",null);He(this,"prompt_ids",null);He(this,"is_multilingual",null);He(this,"lang_to_id",null);He(this,"task_to_id",null);He(this,"max_initial_timestamp_index",1)}}},"./src/ops/registry.js":(e,t,r)=>{r.r(t),r.d(t,{TensorOpRegistry:()=>f});var o=r("./src/backends/onnx.js"),a=r("./src/utils/tensor.js");const l=async(_,p,g)=>{const M=await(0,o.createInferenceSession)(new Uint8Array(_),p);return async w=>{const C=Object.fromEntries(Object.entries(w).map(([z,S])=>[z,S.ort_tensor])),T=await M.run(C);return Array.isArray(g)?g.map(z=>new a.Tensor(T[z])):new a.Tensor(T[g])}};class f{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=l([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=l([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=l([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=l([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=l([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=l([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}}He(f,"session_options",{})},"./src/pipelines.js":(e,t,r)=>{r.r(t),r.d(t,{AudioClassificationPipeline:()=>te,AutomaticSpeechRecognitionPipeline:()=>R,DepthEstimationPipeline:()=>Le,DocumentQuestionAnsweringPipeline:()=>Q,FeatureExtractionPipeline:()=>de,FillMaskPipeline:()=>I,ImageClassificationPipeline:()=>Xe,ImageFeatureExtractionPipeline:()=>Ce,ImageSegmentationPipeline:()=>ge,ImageToImagePipeline:()=>Ke,ImageToTextPipeline:()=>xe,ObjectDetectionPipeline:()=>ae,Pipeline:()=>S,QuestionAnsweringPipeline:()=>b,SummarizationPipeline:()=>O,Text2TextGenerationPipeline:()=>V,TextClassificationPipeline:()=>E,TextGenerationPipeline:()=>D,TextToAudioPipeline:()=>me,TokenClassificationPipeline:()=>y,TranslationPipeline:()=>N,ZeroShotAudioClassificationPipeline:()=>_e,ZeroShotClassificationPipeline:()=>ee,ZeroShotImageClassificationPipeline:()=>X,ZeroShotObjectDetectionPipeline:()=>Se,pipeline:()=>st});var o=r("./src/tokenizers.js"),a=r("./src/models.js"),l=r("./src/processors.js"),f=r("./src/utils/generic.js"),_=r("./src/utils/core.js"),p=r("./src/utils/maths.js"),g=r("./src/utils/audio.js"),M=r("./src/utils/tensor.js"),w=r("./src/utils/image.js");async function C(Ge){return Array.isArray(Ge)||(Ge=[Ge]),await Promise.all(Ge.map(Me=>w.RawImage.read(Me)))}async function T(Ge,Me){return Array.isArray(Ge)||(Ge=[Ge]),await Promise.all(Ge.map(je=>typeof je=="string"||je instanceof URL?(0,g.read_audio)(je,Me):je instanceof Float64Array?new Float32Array(je):je))}function z(Ge,Me){Me&&(Ge=Ge.map($e=>$e|0));const[je,Ye,et,A]=Ge;return{xmin:je,ymin:Ye,xmax:et,ymax:A}}class S extends f.Callable{constructor({task:Me,model:je,tokenizer:Ye=null,processor:et=null}){super(),this.task=Me,this.model=je,this.tokenizer=Ye,this.processor=et}async dispose(){await this.model.dispose()}}class E extends S{constructor(Me){super(Me)}async _call(Me,{top_k:je=1}={}){const Ye=this.tokenizer(Me,{padding:!0,truncation:!0}),et=await this.model(Ye),A=this.model.config.problem_type==="multi_label_classification"?se=>se.sigmoid():se=>new M.Tensor("float32",(0,p.softmax)(se.data),se.dims),$e=this.model.config.id2label,Pe=[];for(const se of et.logits){const ne=A(se),ye=await(0,M.topk)(ne,je),B=ye[0].tolist(),L=ye[1].tolist().map((ue,le)=>({label:$e?$e[ue]:`LABEL_${ue}`,score:B[le]}));je===1?Pe.push(...L):Pe.push(L)}return Array.isArray(Me)||je===1?Pe:Pe[0]}}class y extends S{constructor(Me){super(Me)}async _call(Me,{ignore_labels:je=["O"]}={}){const Ye=Array.isArray(Me),et=this.tokenizer(Ye?Me:[Me],{padding:!0,truncation:!0}),$e=(await this.model(et)).logits,Pe=this.model.config.id2label,se=[];for(let ne=0;ne<$e.dims[0];++ne){const ye=et.input_ids[ne],B=$e[ne],J=[];for(let L=0;LJe==this.tokenizer.sep_token_id);se[B].map((Je,Ve)=>Je==1&&(Ve===0||Ve>L&&ne.findIndex(Y=>Y==J[Ve])===-1));const ue=A[B].tolist(),le=$e[B].tolist();for(let Je=1;JeVe==J[Je])!==-1)&&(ue[Je]=-1/0,le[Je]=-1/0);const Te=(0,p.softmax)(ue).map((Je,Ve)=>[Je,Ve]),Ne=(0,p.softmax)(le).map((Je,Ve)=>[Je,Ve]);Te[0][0]=0,Ne[0][0]=0;const dt=(0,_.product)(Te,Ne).filter(Je=>Je[0][1]<=Je[1][1]).map(Je=>[Je[0][1],Je[1][1],Je[0][0]*Je[1][0]]).sort((Je,Ve)=>Ve[2]-Je[2]);for(let Je=0;Jeue==this.tokenizer.mask_token_id);if(ne===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const ye=et[Pe][ne],B=await(0,M.topk)(new M.Tensor("float32",(0,p.softmax)(ye.data),ye.dims),je),J=B[0].tolist(),L=B[1].tolist();A.push(L.map((ue,le)=>{const Te=se.slice();return Te[ne]=ue,{score:J[le],token:Number(ue),token_str:this.tokenizer.model.vocab[ue],sequence:this.tokenizer.decode(Te,{skip_special_tokens:!0})}}))}return Array.isArray(Me)?A:A[0]}}class V extends S{constructor(je){super(je);He(this,"_key","generated_text")}async _call(je,Ye={}){Array.isArray(je)||(je=[je]),this.model.config.prefix&&(je=je.map(ne=>this.model.config.prefix+ne));const et=this.model.config.task_specific_params;et&&et[this.task]&&et[this.task].prefix&&(je=je.map(ne=>et[this.task].prefix+ne));const A=this.tokenizer,$e={padding:!0,truncation:!0};let Pe;this instanceof N&&"_build_translation_inputs"in A?Pe=A._build_translation_inputs(je,$e,Ye):Pe=A(je,$e);const se=await this.model.generate({...Pe,...Ye});return A.batch_decode(se,{skip_special_tokens:!0}).map(ne=>({[this._key]:ne}))}}class O extends V{constructor(je){super(je);He(this,"_key","summary_text")}}class N extends V{constructor(je){super(je);He(this,"_key","translation_text")}}function j(Ge){return Array.isArray(Ge)&&Ge.every(Me=>"role"in Me&&"content"in Me)}class D extends S{constructor(Me){super(Me)}async _call(Me,je={}){let Ye=!1,et=!1,A;if(typeof Me=="string")A=Me=[Me];else if(Array.isArray(Me)&&Me.every(L=>typeof L=="string"))Ye=!0,A=Me;else{if(j(Me))Me=[Me];else if(Array.isArray(Me)&&Me.every(j))Ye=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");et=!0,A=Me.map(L=>this.tokenizer.apply_chat_template(L,{tokenize:!1,add_generation_prompt:!0}))}const $e=je.add_special_tokens??!1,Pe=et?!1:je.return_full_text??!0;this.tokenizer.padding_side="left";const se=this.tokenizer(A,{add_special_tokens:$e,padding:!0,truncation:!0}),ne=await this.model.generate({...se,...je}),ye=this.tokenizer.batch_decode(ne,{skip_special_tokens:!0});let B;!Pe&&se.input_ids.dims.at(-1)>0&&(B=this.tokenizer.batch_decode(se.input_ids,{skip_special_tokens:!0}).map(L=>L.length));const J=Array.from({length:Me.length},L=>[]);for(let L=0;L[je.toLowerCase(),Ye])),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(Me,je,{hypothesis_template:Ye="This example is {}.",multi_label:et=!1}={}){const A=Array.isArray(Me);A||(Me=[Me]),Array.isArray(je)||(je=[je]);const $e=je.map(ne=>Ye.replace("{}",ne)),Pe=et||je.length===1,se=[];for(const ne of Me){const ye=[];for(const L of $e){const ue=this.tokenizer(ne,{text_pair:L,padding:!0,truncation:!0}),le=await this.model(ue);Pe?ye.push([le.logits.data[this.contradiction_id],le.logits.data[this.entailment_id]]):ye.push(le.logits.data[this.entailment_id])}const J=(Pe?ye.map(L=>(0,p.softmax)(L)[1]):(0,p.softmax)(ye)).map((L,ue)=>[L,ue]).sort((L,ue)=>ue[0]-L[0]);se.push({sequence:ne,labels:J.map(L=>je[L[1]]),scores:J.map(L=>L[0])})}return A?se:se[0]}}class de extends S{constructor(Me){super(Me)}async _call(Me,{pooling:je="none",normalize:Ye=!1,quantize:et=!1,precision:A="binary"}={}){const $e=this.tokenizer(Me,{padding:!0,truncation:!0}),Pe=await this.model($e);let se=Pe.last_hidden_state??Pe.logits??Pe.token_embeddings;if(je!=="none")if(je==="mean")se=(0,M.mean_pooling)(se,$e.attention_mask);else if(je==="cls")se=se.slice(null,0);else throw Error(`Pooling method '${je}' not supported.`);return Ye&&(se=se.normalize(2,-1)),et&&(se=(0,M.quantize_embeddings)(se,A)),se}}class Ce extends S{constructor(Me){super(Me)}async _call(Me,{pool:je=null}={}){const Ye=await C(Me),{pixel_values:et}=await this.processor(Ye),A=await this.model({pixel_values:et});let $e;if(je){if(!("pooler_output"in A))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");$e=A.pooler_output}else $e=A.last_hidden_state??A.logits??A.image_embeds;return $e}}class te extends S{constructor(Me){super(Me)}async _call(Me,{top_k:je=5}={}){const Ye=this.processor.feature_extractor.config.sampling_rate,et=await T(Me,Ye),A=this.model.config.id2label,$e=[];for(const Pe of et){const se=await this.processor(Pe),ye=(await this.model(se)).logits[0],B=await(0,M.topk)(new M.Tensor("float32",(0,p.softmax)(ye.data),ye.dims),je),J=B[0].tolist(),ue=B[1].tolist().map((le,Te)=>({label:A?A[le]:`LABEL_${le}`,score:J[Te]}));$e.push(ue)}return Array.isArray(Me)?$e:$e[0]}}class _e extends S{constructor(Me){super(Me)}async _call(Me,je,{hypothesis_template:Ye="This is a sound of {}."}={}){const et=!Array.isArray(Me);et&&(Me=[Me]);const A=je.map(ye=>Ye.replace("{}",ye)),$e=this.tokenizer(A,{padding:!0,truncation:!0}),Pe=this.processor.feature_extractor.config.sampling_rate,se=await T(Me,Pe),ne=[];for(const ye of se){const B=await this.processor(ye),J=await this.model({...$e,...B}),L=(0,p.softmax)(J.logits_per_audio.data);ne.push([...L].map((ue,le)=>({score:ue,label:je[le]})))}return et?ne[0]:ne}}class R extends S{constructor(Me){super(Me)}async _call(Me,je={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(Me,je);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(Me,je);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(Me,je){je.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),je.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const Ye=!Array.isArray(Me);Ye&&(Me=[Me]);const et=this.processor.feature_extractor.config.sampling_rate,A=await T(Me,et),$e=[];for(const Pe of A){const se=await this.processor(Pe),ye=(await this.model(se)).logits[0],B=[];for(const L of ye)B.push((0,p.max)(L.data)[1]);const J=this.tokenizer.decode(B);$e.push({text:J})}return Ye?$e[0]:$e}async _call_whisper(Me,je){const Ye=je.return_timestamps??!1,et=je.chunk_length_s??0,A=je.force_full_sequences??!1;let $e=je.stride_length_s??null;const Pe={...je};Ye==="word"&&(Pe.return_token_timestamps=!0,Pe.return_timestamps=!1);const se=!Array.isArray(Me);se&&(Me=[Me]);const ne=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,ye=this.processor.feature_extractor.config.hop_length,B=this.processor.feature_extractor.config.sampling_rate,J=await T(Me,B),L=[];for(const ue of J){let le=[];if(et>0){if($e===null)$e=et/6;else if(et<=$e)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const dt=B*et,Je=B*$e,Ve=dt-2*Je;let Y=0;for(;;){const ve=Y+dt,Re=ue.subarray(Y,ve),qe=await this.processor(Re),Qe=Y===0,tt=ve>=ue.length;if(le.push({stride:[Re.length,Qe?0:Je,tt?0:Je],input_features:qe.input_features,is_last:tt}),tt)break;Y+=Ve}}else le=[{stride:[ue.length,0,0],input_features:(await this.processor(ue)).input_features,is_last:!0}];for(const dt of le){Pe.num_frames=Math.floor(dt.stride[0]/ye);const Je=await this.model.generate({inputs:dt.input_features,...Pe});Ye==="word"?(dt.tokens=Je.sequences.tolist()[0],dt.token_timestamps=Je.token_timestamps.tolist()[0].map(Ve=>(0,p.round)(Ve,2))):dt.tokens=Je[0].tolist(),dt.stride=dt.stride.map(Ve=>Ve/B)}const[Te,Ne]=this.tokenizer._decode_asr(le,{time_precision:ne,return_timestamps:Ye,force_full_sequences:A});L.push({text:Te,...Ne})}return se?L[0]:L}}class xe extends S{constructor(Me){super(Me)}async _call(Me,je={}){const Ye=Array.isArray(Me),et=await C(Me),{pixel_values:A}=await this.processor(et),$e=[];for(const Pe of A){Pe.dims=[1,...Pe.dims];const se=await this.model.generate({inputs:Pe,...je}),ne=this.tokenizer.batch_decode(se,{skip_special_tokens:!0}).map(ye=>({generated_text:ye.trim()}));$e.push(ne)}return Ye?$e:$e[0]}}class Xe extends S{constructor(Me){super(Me)}async _call(Me,{top_k:je=5}={}){const Ye=await C(Me),{pixel_values:et}=await this.processor(Ye),A=await this.model({pixel_values:et}),$e=this.model.config.id2label,Pe=[];for(const se of A.logits){const ne=await(0,M.topk)(new M.Tensor("float32",(0,p.softmax)(se.data),se.dims),je),ye=ne[0].tolist(),J=ne[1].tolist().map((L,ue)=>({label:$e?$e[L]:`LABEL_${L}`,score:ye[ue]}));Pe.push(J)}return Array.isArray(Me)?Pe:Pe[0]}}class ge extends S{constructor(Me){super(Me),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(Me,{threshold:je=.5,mask_threshold:Ye=.5,overlap_mask_area_threshold:et=.8,label_ids_to_fuse:A=null,target_sizes:$e=null,subtask:Pe=null}={}){if(Array.isArray(Me)&&Me.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const ne=await C(Me),ye=ne.map(Ne=>[Ne.height,Ne.width]),{pixel_values:B,pixel_mask:J}=await this.processor(ne),L=await this.model({pixel_values:B,pixel_mask:J});let ue=null;if(Pe!==null)ue=this.subtasks_mapping[Pe];else for(let[Ne,dt]of Object.entries(this.subtasks_mapping))if(dt in this.processor.feature_extractor){ue=this.processor.feature_extractor[dt].bind(this.processor.feature_extractor),Pe=Ne;break}const le=this.model.config.id2label,Te=[];if(Pe==="panoptic"||Pe==="instance"){const Ne=ue(L,je,Ye,et,A,$e??ye)[0],dt=Ne.segmentation;for(const Je of Ne.segments_info){const Ve=new Uint8ClampedArray(dt.data.length);for(let ve=0;veYe.replace("{}",J)),Pe=this.tokenizer($e,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:se}=await this.processor(A),ne=await this.model({...Pe,pixel_values:se}),ye=this.model.config.model_type==="siglip"?J=>J.sigmoid().data:J=>(0,p.softmax)(J.data),B=[];for(const J of ne.logits_per_image){const ue=[...ye(J)].map((le,Te)=>({score:le,label:je[Te]}));ue.sort((le,Te)=>Te.score-le.score),B.push(ue)}return et?B:B[0]}}class ae extends S{constructor(Me){super(Me)}async _call(Me,{threshold:je=.9,percentage:Ye=!1}={}){const et=Array.isArray(Me);if(et&&Me.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const A=await C(Me),$e=Ye?null:A.map(L=>[L.height,L.width]),{pixel_values:Pe,pixel_mask:se}=await this.processor(A),ne=await this.model({pixel_values:Pe,pixel_mask:se}),ye=this.processor.feature_extractor.post_process_object_detection(ne,je,$e),B=this.model.config.id2label,J=ye.map(L=>L.boxes.map((ue,le)=>({score:L.scores[le],label:B[L.classes[le]],box:z(ue,!Ye)})));return et?J:J[0]}}class Se extends S{constructor(Me){super(Me)}async _call(Me,je,{threshold:Ye=.1,top_k:et=null,percentage:A=!1}={}){const $e=Array.isArray(Me),Pe=await C(Me),se=this.tokenizer(je,{padding:!0,truncation:!0}),ne=await this.processor(Pe),ye=[];for(let B=0;B({score:Te.scores[Je],label:je[Te.classes[Je]],box:z(dt,!A)})).sort((dt,Je)=>Je.score-dt.score);et!==null&&(Ne=Ne.slice(0,et)),ye.push(Ne)}return $e?ye:ye[0]}}class Q extends S{constructor(Me){super(Me)}async _call(Me,je,Ye={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class me extends S{constructor(je){super(je);He(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=je.vocoder??null}async _call(je,{speaker_embeddings:Ye=null}={}){return this.processor?this._call_text_to_spectrogram(je,{speaker_embeddings:Ye}):this._call_text_to_waveform(je)}async _call_text_to_waveform(je){const Ye=this.tokenizer(je,{padding:!0,truncation:!0}),{waveform:et}=await this.model(Ye),A=this.model.config.sampling_rate;return{audio:et.data,sampling_rate:A}}async _call_text_to_spectrogram(je,{speaker_embeddings:Ye}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await a.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Ye=="string"||Ye instanceof URL)&&(Ye=new Float32Array(await(await fetch(Ye)).arrayBuffer())),Ye instanceof Float32Array)Ye=new M.Tensor("float32",Ye,[1,Ye.length]);else if(!(Ye instanceof M.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:et}=this.tokenizer(je,{padding:!0,truncation:!0}),{waveform:A}=await this.model.generate_speech(et,Ye,{vocoder:this.vocoder}),$e=this.processor.feature_extractor.config.sampling_rate;return{audio:A.data,sampling_rate:$e}}}class Ke extends S{constructor(Me){super(Me)}async _call(Me){const je=await C(Me),Ye=await this.processor(je),et=await this.model(Ye),A=[];for(const $e of et.reconstruction){const Pe=$e.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");A.push(w.RawImage.fromTensor(Pe))}return A.length>1?A:A[0]}}class Le extends S{constructor(Me){super(Me)}async _call(Me){const je=await C(Me),Ye=await this.processor(je),{predicted_depth:et}=await this.model(Ye),A=[];for(let $e=0;$e1?A:A[0]}}const Be=Object.freeze({"text-classification":{tokenizer:o.AutoTokenizer,pipeline:E,model:a.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:o.AutoTokenizer,pipeline:y,model:a.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:o.AutoTokenizer,pipeline:b,model:a.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:o.AutoTokenizer,pipeline:I,model:a.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:o.AutoTokenizer,pipeline:O,model:a.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:o.AutoTokenizer,pipeline:N,model:a.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:o.AutoTokenizer,pipeline:V,model:a.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:o.AutoTokenizer,pipeline:D,model:a.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:o.AutoTokenizer,pipeline:ee,model:a.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:te,model:a.AutoModelForAudioClassification,processor:l.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:o.AutoTokenizer,pipeline:_e,model:a.AutoModel,processor:l.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:o.AutoTokenizer,pipeline:R,model:[a.AutoModelForSpeechSeq2Seq,a.AutoModelForCTC],processor:l.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:o.AutoTokenizer,pipeline:me,model:[a.AutoModelForTextToWaveform,a.AutoModelForTextToSpectrogram],processor:[l.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:o.AutoTokenizer,pipeline:xe,model:a.AutoModelForVision2Seq,processor:l.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Xe,model:a.AutoModelForImageClassification,processor:l.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ge,model:[a.AutoModelForImageSegmentation,a.AutoModelForSemanticSegmentation],processor:l.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:o.AutoTokenizer,pipeline:X,model:a.AutoModel,processor:l.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:ae,model:a.AutoModelForObjectDetection,processor:l.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:o.AutoTokenizer,pipeline:Se,model:a.AutoModelForZeroShotObjectDetection,processor:l.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:o.AutoTokenizer,pipeline:Q,model:a.AutoModelForDocumentQuestionAnswering,processor:l.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:Ke,model:a.AutoModelForImageToImage,processor:l.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Le,model:a.AutoModelForDepthEstimation,processor:l.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:o.AutoTokenizer,pipeline:de,model:a.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:l.AutoProcessor,pipeline:Ce,model:[a.AutoModelForImageFeatureExtraction,a.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),We=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function st(Ge,Me=null,{progress_callback:je=null,config:Ye=null,cache_dir:et=null,local_files_only:A=!1,revision:$e="main",device:Pe=null,dtype:se=null,model_file_name:ne=null,session_options:ye={}}={}){Ge=We[Ge]??Ge;const B=Be[Ge.split("_",1)[0]];if(!B)throw Error(`Unsupported pipeline: ${Ge}. Must be one of [${Object.keys(Be)}]`);Me||(Me=B.default.model,console.log(`No model specified. Using default model: "${Me}".`));const J={progress_callback:je,config:Ye,cache_dir:et,local_files_only:A,revision:$e,device:Pe,dtype:se,model_file_name:ne,session_options:ye},L=new Map([["tokenizer",B.tokenizer],["model",B.model],["processor",B.processor]]),ue=await ft(L,Me,J);ue.task=Ge,(0,_.dispatchCallback)(je,{status:"ready",task:Ge,model:Me});const le=B.pipeline;return new le(ue)}async function ft(Ge,Me,je){const Ye=Object.create(null),et=[];for(let[A,$e]of Ge.entries()){if(!$e)continue;let Pe;Array.isArray($e)?Pe=new Promise(async(se,ne)=>{var B,J;let ye;for(let L of $e){if(L===null){se(null);return}try{se(await L.from_pretrained(Me,je));return}catch(ue){if((B=ue.message)!=null&&B.includes("Unsupported model type"))ye=ue;else if((J=ue.message)!=null&&J.includes("Could not locate file"))ye=ue;else{ne(ue);return}}}ne(ye)}):Pe=$e.from_pretrained(Me,je),Ye[A]=Pe,et.push(Pe)}await Promise.all(et);for(let[A,$e]of Object.entries(Ye))Ye[A]=await $e;return Ye}},"./src/processors.js":(e,t,r)=>{r.r(t),r.d(t,{ASTFeatureExtractor:()=>$e,AutoProcessor:()=>Je,BeitFeatureExtractor:()=>Le,BitImageProcessor:()=>I,CLIPFeatureExtractor:()=>O,CLIPImageProcessor:()=>N,ChineseCLIPFeatureExtractor:()=>j,ClapFeatureExtractor:()=>Pe,ConvNextFeatureExtractor:()=>ee,ConvNextImageProcessor:()=>de,DPTFeatureExtractor:()=>y,DPTImageProcessor:()=>b,DeiTFeatureExtractor:()=>Ke,DetrFeatureExtractor:()=>st,DonutFeatureExtractor:()=>Be,EfficientNetImageProcessor:()=>_e,FeatureExtractor:()=>z,Florence2Processor:()=>dt,GLPNFeatureExtractor:()=>V,ImageFeatureExtractor:()=>S,MobileNetV1FeatureExtractor:()=>R,MobileNetV2FeatureExtractor:()=>xe,MobileNetV3FeatureExtractor:()=>Xe,MobileNetV4FeatureExtractor:()=>ge,MobileViTFeatureExtractor:()=>X,MobileViTImageProcessor:()=>ae,NougatImageProcessor:()=>We,OwlViTFeatureExtractor:()=>Se,OwlViTProcessor:()=>Ne,Owlv2ImageProcessor:()=>Q,Processor:()=>B,PyAnnoteFeatureExtractor:()=>se,PyAnnoteProcessor:()=>le,RTDetrImageProcessor:()=>me,SamImageProcessor:()=>Ge,SamProcessor:()=>J,SeamlessM4TFeatureExtractor:()=>A,SegformerFeatureExtractor:()=>E,SiglipImageProcessor:()=>D,SpeechT5FeatureExtractor:()=>ye,SpeechT5Processor:()=>Te,Swin2SRImageProcessor:()=>Me,ViTFeatureExtractor:()=>Ce,ViTImageProcessor:()=>te,VitMatteImageProcessor:()=>je,Wav2Vec2FeatureExtractor:()=>et,Wav2Vec2ProcessorWithLM:()=>ue,WeSpeakerFeatureExtractor:()=>ne,WhisperFeatureExtractor:()=>Ye,WhisperProcessor:()=>L,YolosFeatureExtractor:()=>ft});var o=r("./src/utils/generic.js"),a=r("./src/utils/core.js"),l=r("./src/utils/hub.js"),f=r("./src/utils/maths.js"),_=r("./src/utils/tensor.js");r("./src/utils/image.js");var p=r("./src/utils/audio.js");function g([Ve,Y,ve,Re]){return[Ve-ve/2,Y-Re/2,Ve+ve/2,Y+Re/2]}function M(Ve,Y=.5,ve=null,Re=!1){const qe=Ve.logits,Qe=Ve.pred_boxes,[tt,pt,ht]=qe.dims;if(ve!==null&&ve.length!==tt)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let Tt=[];for(let vt=0;vtY&&Yt.push(Ht)}else{let Ht=(0,f.max)(Xt.data)[1];if(Ht===ht-1||(rr=(0,f.softmax)(Xt.data),rr[Ht]Yr*Et[(Pr+1)%2])),Lt.boxes.push(Cr),Lt.classes.push(Ht),Lt.scores.push(rr[Ht])}}Tt.push(Lt)}return Tt}function w(Ve,Y){var ve;if(!(Ve instanceof Float32Array||Ve instanceof Float64Array))throw new Error(`${Y} expects input to be a Float32Array or a Float64Array, but got ${((ve=Ve==null?void 0:Ve.constructor)==null?void 0:ve.name)??typeof Ve} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function C(Ve,Y,ve=0,Re=null){const qe=Ve/Y;let Qe=(0,f.bankers_round)(qe)*Y;return Re!==null&&Qe>Re&&(Qe=Math.floor(qe)*Y),QeQe?Tt=Math.floor(Qe*ht/qe):Qe>qe&&(ht=Math.floor(qe*Tt/Qe)),await Y.resize(Tt,ht,{resample:Re}))}async crop_margin(Y,ve=200){const Re=Y.clone().grayscale(),qe=(0,f.min)(Re.data)[0],tt=(0,f.max)(Re.data)[0]-qe;if(tt===0)return Y;const pt=ve/255;let ht=Re.width,Tt=Re.height,vt=0,Et=0;const Lt=Re.data;for(let at=0;atthis.preprocess(Qe)));return{pixel_values:(0,_.stack)(Re.map(Qe=>Qe.pixel_values),0),original_sizes:Re.map(Qe=>Qe.original_size),reshaped_input_sizes:Re.map(Qe=>Qe.reshaped_input_size)}}}class E extends S{post_process_semantic_segmentation(Y,ve=null){const Re=Y.logits,qe=Re.dims[0];if(ve!==null&&ve.length!==qe)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const Qe=[];for(let tt=0;ttLt[Ht]&&(Lt[Ht]=rr[Ht],at[Ht]=Yt)}const zt=new Array(ht.dims[0]),Vt=Et.data;for(let Yt=0;YtYt!==void 0);Qe.push({segmentation:Et,labels:Xt})}return Qe}}class y extends S{}class b extends y{}class I extends S{}class V extends S{}class O extends S{}class N extends O{}class j extends S{}class D extends S{}class ee extends S{constructor(Y){super(Y),this.crop_pct=this.config.crop_pct??224/256}async resize(Y){var Re;const ve=(Re=this.size)==null?void 0:Re.shortest_edge;if(ve===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(ve<384){const qe=Math.floor(ve/this.crop_pct),[Qe,tt]=this.get_resize_output_image_size(Y,{shortest_edge:qe});Y=await Y.resize(Qe,tt,{resample:this.resample}),Y=await Y.center_crop(ve,ve)}else Y=await Y.resize(ve,ve,{resample:this.resample});return Y}}class de extends ee{}class Ce extends S{}class te extends S{}class _e extends S{constructor(Y){super(Y),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(ve=>ve*ve))}}class R extends S{}class xe extends S{}class Xe extends S{}class ge extends S{}class X extends S{}class ae extends X{}class Se extends S{post_process_object_detection(...Y){return M(...Y)}}class Q extends Se{}class me extends S{post_process_object_detection(...Y){return M(...Y)}}class Ke extends S{}class Le extends S{}class Be extends S{pad_image(Y,ve,Re,qe={}){const[Qe,tt,pt]=ve;let ht=this.image_mean;Array.isArray(this.image_mean)||(ht=new Array(pt).fill(ht));let Tt=this.image_std;Array.isArray(Tt)||(Tt=new Array(pt).fill(ht));const vt=ht.map((Et,Lt)=>-Et/Tt[Lt]);return super.pad_image(Y,ve,Re,{center:!0,constant_values:vt,...qe})}}class We extends Be{}class st extends S{async _call(Y){const ve=await super._call(Y),Re=[ve.pixel_values.dims[0],64,64],qe=new _.Tensor("int64",new BigInt64Array(Re.reduce((Qe,tt)=>Qe*tt)).fill(1n),Re);return{...ve,pixel_mask:qe}}post_process_object_detection(...Y){return M(...Y)}remove_low_and_no_objects(Y,ve,Re,qe){let Qe=[],tt=[],pt=[];for(let ht=0;htRe&&(Qe.push(vt),tt.push(at),pt.push(Et))}return[Qe,tt,pt]}check_segment_validity(Y,ve,Re,qe=.5,Qe=.8){let tt=[],pt=0,ht=0;const Tt=ve[Re].data;for(let Et=0;Et=qe&&++ht;let vt=pt>0&&ht>0;return vt&&(vt=pt/ht>Qe),[vt,tt]}compute_segments(Y,ve,Re,qe,Qe,tt=null,pt=null){let[ht,Tt]=pt??Y[0].dims,vt=new _.Tensor("int32",new Int32Array(ht*Tt),[ht,Tt]),Et=[];if(pt!==null)for(let Xt=0;Xtat[Ht]&&(Lt[Ht]=Xt,at[Ht]=rr[Ht])}let zt=0;const Vt=vt.data;for(let Xt=0;Xtqe!==ve.dims[Qe]))throw Error(`The first ${Re.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new _.Tensor("int64",Y.flat(1/0).map(BigInt),Re)}async _call(Y,{input_points:ve=null,input_labels:Re=null,input_boxes:qe=null}={}){const Qe=await super._call(Y);if(ve&&(Qe.input_points=this.reshape_input_points(ve,Qe.original_sizes,Qe.reshaped_input_sizes)),Re){if(!Qe.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");Qe.input_labels=this.add_input_labels(Re,Qe.input_points)}return qe&&(Qe.input_boxes=this.reshape_input_points(qe,Qe.original_sizes,Qe.reshaped_input_sizes,!0)),Qe}async post_process_masks(Y,ve,Re,{mask_threshold:qe=0,binarize:Qe=!0,pad_size:tt=null}={}){const pt=[];tt=tt??this.pad_size;const ht=[tt.height,tt.width];for(let Tt=0;Ttqe&&(zt[Vt]=1);Lt=new _.Tensor("bool",zt,Lt.dims)}pt.push(Lt)}return pt}generate_crop_boxes(Y,ve,{crop_n_layers:Re=0,overlap_ratio:qe=512/1500,points_per_crop:Qe=32,crop_n_points_downscale_factor:tt=1}={}){}}class Me extends S{pad_image(Y,ve,Re,qe={}){const[Qe,tt,pt]=ve;return super.pad_image(Y,ve,{width:tt+(Re-tt%Re)%Re,height:Qe+(Re-Qe%Re)%Re},{mode:"symmetric",center:!1,constant_values:-1,...qe})}}class je extends S{async _call(Y,ve){Array.isArray(Y)||(Y=[Y]),Array.isArray(ve)||(ve=[ve]);const Re=await Promise.all(Y.map(tt=>this.preprocess(tt))),qe=await Promise.all(ve.map(tt=>this.preprocess(tt,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,_.stack)(Re.map((tt,pt)=>(0,_.cat)([tt.pixel_values,qe[pt].pixel_values],0)),0),original_sizes:Re.map(tt=>tt.original_size),reshaped_input_sizes:Re.map(tt=>tt.reshaped_input_size)}}}class Ye extends z{constructor(Y){var ve;super(Y),(ve=this.config).mel_filters??(ve.mel_filters=(0,p.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,p.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(Y){const ve=await(0,p.spectrogram)(Y,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}),Re=ve.data,qe=(0,f.max)(Re)[0];for(let Qe=0;Qethis.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`."),ve=Y.slice(0,this.config.n_samples)):(ve=new Float32Array(this.config.n_samples),ve.set(Y)),{input_features:(await this._extract_fbank_features(ve)).unsqueeze_(0)}}}class et extends z{_zero_mean_unit_var_norm(Y){const Re=Y.reduce((Qe,tt)=>Qe+tt,0)/Y.length,qe=Y.reduce((Qe,tt)=>Qe+(tt-Re)**2,0)/Y.length;return Y.map(Qe=>(Qe-Re)/Math.sqrt(qe+1e-7))}async _call(Y){w(Y,"Wav2Vec2FeatureExtractor"),Y instanceof Float64Array&&(Y=new Float32Array(Y));let ve=Y;this.config.do_normalize&&(ve=this._zero_mean_unit_var_norm(ve));const Re=[1,ve.length];return{input_values:new _.Tensor("float32",ve,Re),attention_mask:new _.Tensor("int64",new BigInt64Array(ve.length).fill(1n),Re)}}}class A extends z{constructor(Y){super(Y);const ve=this.config.sampling_rate,Re=(0,p.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ve/2),ve,null,"kaldi",!0);for(let qe=0;qeRe*32768),(0,p.spectrogram)(Y,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:ve,transpose:!0})}async _call(Y,{padding:ve=!0,pad_to_multiple_of:Re=2,do_normalize_per_mel_bins:qe=!0,return_attention_mask:Qe=!0}={}){w(Y,"SeamlessM4TFeatureExtractor");let tt=await this._extract_fbank_features(Y,this.config.max_length);if(qe){const[zt,Vt]=tt.dims,Xt=tt.data;for(let Yt=0;Yt0){const rr=new Float32Array(Vt*(zt+Yt));rr.set(Xt),rr.fill(this.config.padding_value,Xt.length);const Ht=zt+Yt;tt=new _.Tensor(tt.type,rr,[Ht,Vt]),Qe&&(pt=new _.Tensor("int64",new BigInt64Array(Ht),[1,Ht]),pt.data.fill(1n,0,zt))}}const[ht,Tt]=tt.dims,vt=this.config.stride;if(ht%vt!==0)throw new Error(`The number of frames (${ht}) must be a multiple of the stride (${vt}).`);const Lt=tt.view(1,Math.floor(ht/vt),Tt*vt),at={input_features:Lt};if(Qe){const zt=Lt.dims[1],Vt=new BigInt64Array(zt);if(pt){const Xt=pt.data;for(let Yt=1,rr=0;Yt0)if(Re==="rand_trunc"){const pt=Math.floor(Math.random()*(tt+1));Y=Y.subarray(pt,pt+ve),Qe=await this._extract_fbank_features(Y,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${Re}" not implemented`);else{if(tt<0){let pt=new Float64Array(ve);if(pt.set(Y),qe==="repeat")for(let ht=Y.length;ht({id:ht,start:Tt*Re,end:vt*Re,confidence:Et/(vt-Tt)})))}return qe}}class ne extends z{constructor(Y){super(Y);const ve=this.config.sampling_rate,Re=(0,p.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ve/2),ve,null,"kaldi",!0);for(let qe=0;qeve*32768),(0,p.spectrogram)(Y,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(Y){w(Y,"WeSpeakerFeatureExtractor");const ve=(await this._extract_fbank_features(Y)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const Re=ve.mean(1).data,qe=ve.data,[Qe,tt,pt]=ve.dims;for(let ht=0;ht/gm,bboxes:/([^<]+)?/gm},this.size_per_bin=1e3}construct_prompts(Y){typeof Y=="string"&&(Y=[Y]);const ve=[];for(const Re of Y)if(this.task_prompts_without_inputs.has(Re))ve.push(this.task_prompts_without_inputs.get(Re));else{for(const[qe,Qe]of this.task_prompts_with_input)if(Re.includes(qe)){ve.push(Qe.replaceAll("{input}",Re).replaceAll(qe,""));break}ve.length!==Y.length&&ve.push(Re)}return ve}post_process_generation(Y,ve,Re){const qe=this.tasks_answer_post_processing_type.get(ve)??"pure_text";Y=Y.replaceAll("","").replaceAll("","");let Qe;switch(qe){case"pure_text":Qe=Y;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const tt=qe==="ocr"?"quad_boxes":"bboxes",pt=Y.matchAll(this.regexes[tt]),ht=[],Tt=[];for(const[vt,Et,...Lt]of pt)ht.push(Et?Et.trim():ht.at(-1)??""),Tt.push(Lt.map((at,zt)=>(Number(at)+.5)/this.size_per_bin*Re[zt%2]));Qe={labels:ht,[tt]:Tt};break;default:throw new Error(`Task "${ve}" (of type "${qe}") not yet implemented.`)}return{[ve]:Qe}}}class Je{static async from_pretrained(Y,{progress_callback:ve=null,config:Re=null,cache_dir:qe=null,local_files_only:Qe=!1,revision:tt="main"}={}){let pt=Re??await(0,l.getModelJSON)(Y,"preprocessor_config.json",!0,{progress_callback:ve,config:Re,cache_dir:qe,local_files_only:Qe,revision:tt}),ht=pt.feature_extractor_type??pt.image_processor_type,Tt=this.FEATURE_EXTRACTOR_CLASS_MAPPING[ht];if(!Tt)if(pt.size!==void 0)console.warn(`Feature extractor type "${ht}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),Tt=S;else throw new Error(`Unknown Feature Extractor type: ${ht}`);let vt=this.PROCESSOR_CLASS_MAPPING[pt.processor_class]??B,Et=new Tt(pt);return new vt(Et)}}He(Je,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:S,WhisperFeatureExtractor:Ye,ViTFeatureExtractor:Ce,MobileViTFeatureExtractor:X,MobileViTImageProcessor:ae,MobileNetV1FeatureExtractor:R,MobileNetV2FeatureExtractor:xe,MobileNetV3FeatureExtractor:Xe,MobileNetV4FeatureExtractor:ge,OwlViTFeatureExtractor:Se,Owlv2ImageProcessor:Q,CLIPFeatureExtractor:O,CLIPImageProcessor:N,Florence2Processor:dt,ChineseCLIPFeatureExtractor:j,SiglipImageProcessor:D,ConvNextFeatureExtractor:ee,ConvNextImageProcessor:de,SegformerFeatureExtractor:E,BitImageProcessor:I,DPTImageProcessor:b,DPTFeatureExtractor:y,GLPNFeatureExtractor:V,BeitFeatureExtractor:Le,DeiTFeatureExtractor:Ke,DetrFeatureExtractor:st,RTDetrImageProcessor:me,YolosFeatureExtractor:ft,DonutFeatureExtractor:Be,NougatImageProcessor:We,EfficientNetImageProcessor:_e,ViTImageProcessor:te,VitMatteImageProcessor:je,SamImageProcessor:Ge,Swin2SRImageProcessor:Me,Wav2Vec2FeatureExtractor:et,SeamlessM4TFeatureExtractor:A,SpeechT5FeatureExtractor:ye,ASTFeatureExtractor:$e,ClapFeatureExtractor:Pe,PyAnnoteFeatureExtractor:se,WeSpeakerFeatureExtractor:ne}),He(Je,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:L,Wav2Vec2ProcessorWithLM:ue,PyAnnoteProcessor:le,SamProcessor:J,SpeechT5Processor:Te,OwlViTProcessor:Ne,Florence2Processor:dt})},"./src/tokenizers.js":(e,t,r)=>{r.r(t),r.d(t,{AlbertTokenizer:()=>Vt,AutoTokenizer:()=>Nn,BartTokenizer:()=>Wr,BertTokenizer:()=>zt,BlenderbotSmallTokenizer:()=>Vo,BlenderbotTokenizer:()=>go,BloomTokenizer:()=>Or,CLIPTokenizer:()=>Zt,CamembertTokenizer:()=>Pt,CodeGenTokenizer:()=>mo,CodeLlamaTokenizer:()=>Cs,CohereTokenizer:()=>Ur,ConvBertTokenizer:()=>Yr,DebertaTokenizer:()=>rr,DebertaV2Tokenizer:()=>Ht,DistilBertTokenizer:()=>mt,ElectraTokenizer:()=>un,EsmTokenizer:()=>ji,FalconTokenizer:()=>Bo,GPT2Tokenizer:()=>Mi,GPTNeoXTokenizer:()=>No,GemmaTokenizer:()=>po,Grok1Tokenizer:()=>pi,HerbertTokenizer:()=>Cr,LlamaTokenizer:()=>Ti,M2M100Tokenizer:()=>Ji,MBart50Tokenizer:()=>Kr,MBartTokenizer:()=>wn,MPNetTokenizer:()=>Ro,MarianTokenizer:()=>jo,MobileBertTokenizer:()=>Xt,NllbTokenizer:()=>Si,NougatTokenizer:()=>_o,PreTrainedTokenizer:()=>at,Qwen2Tokenizer:()=>Ps,RoFormerTokenizer:()=>Pr,RobertaTokenizer:()=>fi,SiglipTokenizer:()=>eo,SpeechT5Tokenizer:()=>Wo,SqueezeBertTokenizer:()=>Yt,T5Tokenizer:()=>Bi,TokenizerModel:()=>Ce,VitsTokenizer:()=>Go,Wav2Vec2CTCTokenizer:()=>Uo,WhisperTokenizer:()=>ho,XLMRobertaTokenizer:()=>Lo,XLMTokenizer:()=>Nt,is_chinese_char:()=>V});var o=r("./src/utils/generic.js"),a=r("./src/utils/core.js"),l=r("./src/utils/hub.js"),f=r("./src/utils/maths.js"),_=r("./src/utils/tensor.js"),p=r("./src/utils/data-structures.js"),g=r("./node_modules/@huggingface/jinja/dist/index.js"),M=r("./src/models/whisper/common_whisper.js"),w=r("./src/utils/constants.js");async function C(Ue,W){const fe=await Promise.all([(0,l.getModelJSON)(Ue,"tokenizer.json",!0,W),(0,l.getModelJSON)(Ue,"tokenizer_config.json",!0,W)]);return W.legacy!==null&&(fe[1].legacy=W.legacy),fe}function T(Ue,W){const fe=[];let Ee=0;for(const Fe of Ue.matchAll(W)){const ze=Fe[0];Ee0&&fe.push(ze),Ee=Fe.index+ze.length}return Ee=19968&&Ue<=40959||Ue>=13312&&Ue<=19903||Ue>=131072&&Ue<=173791||Ue>=173824&&Ue<=177983||Ue>=177984&&Ue<=178207||Ue>=178208&&Ue<=183983||Ue>=63744&&Ue<=64255||Ue>=194560&&Ue<=195103}function O(Ue,W,fe){const Ee=[];let Fe=0;for(;Fethis.tokens_to_ids.get(fe)??this.unk_token_id)}convert_ids_to_tokens(W){return W.map(fe=>this.vocab[fe]??this.unk_token)}}class te extends Ce{constructor(W){super(W),this.tokens_to_ids=S(W.vocab),this.unk_token_id=this.tokens_to_ids.get(W.unk_token),this.unk_token=W.unk_token,this.max_input_chars_per_word=W.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[fe,Ee]of this.tokens_to_ids)this.vocab[Ee]=fe}encode(W){const fe=[];for(const Ee of W){const Fe=[...Ee];if(Fe.length>this.max_input_chars_per_word){fe.push(this.unk_token);continue}let ze=!1,rt=0;const Mt=[];for(;rt0&&(It=this.config.continuing_subword_prefix+It),this.tokens_to_ids.has(It)){kt=It;break}--St}if(kt===null){ze=!0;break}Mt.push(kt),rt=St}ze?fe.push(this.unk_token):fe.push(...Mt)}return fe}}class _e extends Ce{constructor(W,fe){super(W);const Ee=W.vocab.length;this.vocab=new Array(Ee),this.scores=new Array(Ee);for(let Fe=0;Fe[Fe,ze])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=fe.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=(0,f.min)(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new p.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(W){const fe=W.sentence,Ee=fe.length;let Fe=0;for(;Fe{const Ue=[...Array.from({length:94},(Fe,ze)=>ze+33),...Array.from({length:12},(Fe,ze)=>ze+161),...Array.from({length:82},(Fe,ze)=>ze+174)],W=Ue.slice();let fe=0;for(let Fe=0;Fe<256;++Fe)Ue.includes(Fe)||(Ue.push(Fe),W.push(256+fe),fe+=1);const Ee=W.map(Fe=>String.fromCharCode(Fe));return Object.fromEntries(Ue.map((Fe,ze)=>[Fe,Ee[ze]]))})(),xe=(0,a.reverseDictionary)(R);class Xe extends Ce{constructor(W){super(W),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=S(W.vocab),this.unk_token_id=this.tokens_to_ids.get(W.unk_token),this.unk_token=W.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[fe,Ee]of this.tokens_to_ids)this.vocab[Ee]=fe;this.bpe_ranks=new Map(W.merges.map((fe,Ee)=>[fe,Ee])),this.merges=W.merges.map(fe=>fe.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=W.end_of_word_suffix,this.continuing_subword_suffix=W.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(W){if(W.length===0)return[];const fe=this.cache.get(W);if(fe!==void 0)return fe;const Ee=Array.from(W);this.end_of_word_suffix&&(Ee[Ee.length-1]+=this.end_of_word_suffix);let Fe=[];if(Ee.length>1){const ze=new p.PriorityQueue((St,kt)=>St.score`<0x${rt.toString(16).toUpperCase().padStart(2,"0")}>`)):fe.push(this.unk_token)}return fe}}class ge extends Ce{constructor(W,fe){super(W),this.tokens_to_ids=S(fe.target_lang?W.vocab[fe.target_lang]:W.vocab),this.bos_token=fe.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=fe.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=fe.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=fe.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[Ee,Fe]of this.tokens_to_ids)this.vocab[Fe]=Ee}encode(W){return W}}class X extends o.Callable{constructor(W){super(),this.config=W}static fromConfig(W){if(W===null)return null;switch(W.type){case"BertNormalizer":return new ft(W);case"Precompiled":return new Qe(W);case"Sequence":return new st(W);case"Replace":return new ae(W);case"NFC":return new Se(W);case"NFKC":return new Q(W);case"NFKD":return new me(W);case"Strip":return new Ke(W);case"StripAccents":return new Le(W);case"Lowercase":return new Be(W);case"Prepend":return new We(W);default:throw new Error(`Unknown Normalizer type: ${W.type}`)}}normalize(W){throw Error("normalize should be implemented in subclass.")}_call(W){return this.normalize(W)}}class ae extends X{normalize(W){const fe=z(this.config.pattern);return fe===null?W:W.replaceAll(fe,this.config.content)}}class Se extends X{normalize(W){return W=W.normalize("NFC"),W}}class Q extends X{normalize(W){return W=W.normalize("NFKC"),W}}class me extends X{normalize(W){return W=W.normalize("NFKD"),W}}class Ke extends X{normalize(W){return this.config.strip_left&&this.config.strip_right?W=W.trim():(this.config.strip_left&&(W=W.trimStart()),this.config.strip_right&&(W=W.trimEnd())),W}}class Le extends X{normalize(W){return W=b(W),W}}class Be extends X{normalize(W){return W=W.toLowerCase(),W}}class We extends X{normalize(W){return W=this.config.prepend+W,W}}class st extends X{constructor(W){super(W),this.normalizers=W.normalizers.map(fe=>X.fromConfig(fe))}normalize(W){return this.normalizers.reduce((fe,Ee)=>Ee.normalize(fe),W)}}class ft extends X{_tokenize_chinese_chars(W){const fe=[];for(let Ee=0;Eethis.pre_tokenize_text(Ee,fe)):this.pre_tokenize_text(W,fe)).flat()}_call(W,fe){return this.pre_tokenize(W,fe)}}class Me extends Ge{constructor(W){super(),this.pattern=new RegExp(`[^\\s${j}]+|[${j}]`,"gu")}pre_tokenize_text(W,fe){return W.trim().match(this.pattern)||[]}}class je extends Ge{constructor(W){super(),this.config=W,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=R,this.text_encoder=new TextEncoder}pre_tokenize_text(W,fe){return this.add_prefix_space&&!W.startsWith(" ")&&(W=" "+W),(this.use_regex?W.match(this.pattern)||[]:[W]).map(Fe=>Array.from(this.text_encoder.encode(Fe),ze=>this.byte_encoder[ze]).join(""))}}class Ye extends Ge{constructor(W){super(),this.config=W,this.pattern=z(this.config.pattern,this.config.invert)}pre_tokenize_text(W,fe){return this.pattern===null?[]:this.config.invert?W.match(this.pattern)||[]:T(W,this.pattern)}}class et extends Ge{constructor(W){super(),this.config=W,this.pattern=new RegExp(`[^${j}]+|[${j}]+`,"gu")}pre_tokenize_text(W,fe){return W.match(this.pattern)||[]}}class A extends Ge{constructor(W){super(),this.config=W;const fe=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(fe,"gu")}pre_tokenize_text(W,fe){return W.match(this.pattern)||[]}}class $e extends o.Callable{constructor(W){super(),this.config=W}static fromConfig(W){if(W===null)return null;switch(W.type){case"TemplateProcessing":return new ne(W);case"ByteLevel":return new ye(W);case"RobertaProcessing":return new se(W);case"BertProcessing":return new Pe(W);case"Sequence":return new B(W);default:throw new Error(`Unknown PostProcessor type: ${W.type}`)}}post_process(W,...fe){throw Error("post_process should be implemented in subclass.")}_call(W,...fe){return this.post_process(W,...fe)}}class Pe extends $e{constructor(W){super(W),this.cls=W.cls[0],this.sep=W.sep[0]}post_process(W,fe=null,{add_special_tokens:Ee=!0}={}){Ee&&(W=(0,a.mergeArrays)([this.cls],W,[this.sep]));let Fe=new Array(W.length).fill(0);if(fe!==null){const ze=Ee&&this instanceof se?[this.sep]:[],rt=Ee?[this.sep]:[];W=(0,a.mergeArrays)(W,ze,fe,rt),Fe=(0,a.mergeArrays)(Fe,new Array(fe.length+ze.length+rt.length).fill(1))}return{tokens:W,token_type_ids:Fe}}}class se extends Pe{}class ne extends $e{constructor(W){super(W),this.single=W.single,this.pair=W.pair}post_process(W,fe=null,{add_special_tokens:Ee=!0}={}){const Fe=fe===null?this.single:this.pair;let ze=[],rt=[];for(const Mt of Fe)"SpecialToken"in Mt?Ee&&(ze.push(Mt.SpecialToken.id),rt.push(Mt.SpecialToken.type_id)):"Sequence"in Mt&&(Mt.Sequence.id==="A"?(ze=(0,a.mergeArrays)(ze,W),rt=(0,a.mergeArrays)(rt,new Array(W.length).fill(Mt.Sequence.type_id))):Mt.Sequence.id==="B"&&(ze=(0,a.mergeArrays)(ze,fe),rt=(0,a.mergeArrays)(rt,new Array(fe.length).fill(Mt.Sequence.type_id))));return{tokens:ze,token_type_ids:rt}}}class ye extends $e{post_process(W,fe=null){return fe&&(W=(0,a.mergeArrays)(W,fe)),{tokens:W}}}class B extends $e{constructor(W){super(W),this.processors=W.processors.map(fe=>$e.fromConfig(fe))}post_process(W,fe=null,Ee={}){let Fe;for(const ze of this.processors)if(ze instanceof ye)W=ze.post_process(W).tokens,fe&&(fe=ze.post_process(fe).tokens);else{const rt=ze.post_process(W,fe,Ee);W=rt.tokens,Fe=rt.token_type_ids}return{tokens:W,token_type_ids:Fe}}}class J extends o.Callable{constructor(W){super(),this.config=W,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=W.trim_offsets}static fromConfig(W){if(W===null)return null;switch(W.type){case"WordPiece":return new Ne(W);case"Metaspace":return new qe(W);case"ByteLevel":return new dt(W);case"Replace":return new L(W);case"ByteFallback":return new ue(W);case"Fuse":return new le(W);case"Strip":return new Te(W);case"Sequence":return new Ve(W);case"CTC":return new Je(W);case"BPEDecoder":return new Y(W);default:throw new Error(`Unknown Decoder type: ${W.type}`)}}_call(W){return this.decode(W)}decode(W){return this.decode_chain(W).join("")}decode_chain(W){throw Error("`decode_chain` should be implemented in subclass.")}}class L extends J{decode_chain(W){const fe=z(this.config.pattern);return fe===null?W:W.map(Ee=>Ee.replaceAll(fe,this.config.content))}}class ue extends J{constructor(W){super(W),this.text_decoder=new TextDecoder}decode_chain(W){const fe=[];let Ee=[];for(const Fe of W){let ze=null;if(Fe.length===6&&Fe.startsWith("<0x")&&Fe.endsWith(">")){const rt=parseInt(Fe.slice(3,5),16);isNaN(rt)||(ze=rt)}if(ze!==null)Ee.push(ze);else{if(Ee.length>0){const rt=this.text_decoder.decode(Uint8Array.from(Ee));fe.push(rt),Ee=[]}fe.push(Fe)}}if(Ee.length>0){const Fe=this.text_decoder.decode(Uint8Array.from(Ee));fe.push(Fe),Ee=[]}return fe}}class le extends J{decode_chain(W){return[W.join("")]}}class Te extends J{constructor(W){super(W),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(W){return W.map(fe=>{let Ee=0;for(let ze=0;ze(Ee!==0&&(fe.startsWith(this.config.prefix)?fe=fe.replace(this.config.prefix,""):fe=" "+fe),this.cleanup&&(fe=y(fe)),fe))}}class dt extends J{constructor(W){super(W),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(W){const fe=W.join(""),Ee=new Uint8Array([...fe].map(ze=>this.byte_decoder[ze]));return this.text_decoder.decode(Ee)}decode_chain(W){const fe=[];let Ee=[];for(const Fe of W)this.added_tokens.find(ze=>ze.content===Fe)!==void 0?(Ee.length>0&&(fe.push(this.convert_tokens_to_string(Ee)),Ee=[]),fe.push(Fe)):Ee.push(Fe);return Ee.length>0&&fe.push(this.convert_tokens_to_string(Ee)),fe}}class Je extends J{constructor(W){super(W),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(W){if(W.length===0)return"";const fe=[W[0]];for(let ze=1;zeze!==this.pad_token).join("");return this.cleanup&&(Fe=y(Fe).replaceAll(this.word_delimiter_token," ").trim()),Fe}decode_chain(W){return[this.convert_tokens_to_string(W)]}}class Ve extends J{constructor(W){super(W),this.decoders=W.decoders.map(fe=>J.fromConfig(fe))}decode_chain(W){return this.decoders.reduce((fe,Ee)=>Ee.decode_chain(fe),W)}}class Y extends J{constructor(W){super(W),this.suffix=this.config.suffix}decode_chain(W){return W.map((fe,Ee)=>fe.replaceAll(this.suffix,Ee===W.length-1?"":" "))}}class ve extends J{decode_chain(W){let fe="";for(let Ee=1;EeEe.normalize("NFKC")).join("～"):W=W.normalize("NFKC"),W}}class tt extends Ge{constructor(W){super(),this.tokenizers=W.pretokenizers.map(fe=>Ge.fromConfig(fe))}pre_tokenize_text(W,fe){return this.tokenizers.reduce((Ee,Fe)=>Fe.pre_tokenize(Ee,fe),[W])}}class pt extends Ge{constructor(W){super()}pre_tokenize_text(W,fe){return W.match(/\w+|[^\w\s]+/g)||[]}}class ht extends Ge{constructor(W){super()}pre_tokenize_text(W,fe){return N(W)}}class Tt extends Ge{constructor(W){super(),this.config=W,this.pattern=z(this.config.pattern),this.content=this.config.content}pre_tokenize_text(W,fe){return this.pattern===null?[W]:[W.replaceAll(this.pattern,this.config.content)]}}const vt=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Et(Ue,W,fe,Ee){for(const Fe of Object.keys(Ue)){const ze=W-Ue[Fe].length,rt=fe(Fe),Mt=new Array(ze).fill(rt);Ue[Fe]=Ee==="right"?(0,a.mergeArrays)(Ue[Fe],Mt):(0,a.mergeArrays)(Mt,Ue[Fe])}}function Lt(Ue,W){for(const fe of Object.keys(Ue))Ue[fe].length=W}class at extends o.Callable{constructor(fe,Ee){super();He(this,"return_token_type_ids",!1);He(this,"padding_side","right");this._tokenizer_config=Ee,this.normalizer=X.fromConfig(fe.normalizer),this.pre_tokenizer=Ge.fromConfig(fe.pre_tokenizer),this.model=Ce.fromConfig(fe.model,Ee),this.post_processor=$e.fromConfig(fe.post_processor),this.decoder=J.fromConfig(fe.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const Fe of fe.added_tokens){const ze=new de(Fe);this.added_tokens.push(ze),this.model.tokens_to_ids.set(ze.content,ze.id),this.model.vocab[ze.id]=ze.content,ze.special&&(this.special_tokens.push(ze.content),this.all_special_ids.push(ze.id))}if(this.additional_special_tokens=Ee.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.toSorted((Fe,ze)=>ze.content.length-Fe.content.length).map(Fe=>`${Fe.lstrip?"\\s*":""}(${(0,a.escapeRegExp)(Fe.content)})${Fe.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.model_max_length=Ee.model_max_length,this.remove_space=Ee.remove_space,this.clean_up_tokenization_spaces=Ee.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=Ee.do_lowercase_and_remove_accent??!1,Ee.padding_side&&(this.padding_side=Ee.padding_side),this.legacy=!1,this.chat_template=Ee.chat_template??null,Array.isArray(this.chat_template)){const Fe=Object.create(null);for(const{name:ze,template:rt}of this.chat_template){if(typeof ze!="string"||typeof rt!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');Fe[ze]=rt}this.chat_template=Fe}this._compiled_template_cache=new Map}getToken(...fe){for(const Ee of fe){const Fe=this._tokenizer_config[Ee];if(Fe)if(typeof Fe=="object"){if(Fe.__type==="AddedToken")return Fe.content;throw Error(`Unknown token: ${Fe}`)}else return Fe}return null}static async from_pretrained(fe,{progress_callback:Ee=null,config:Fe=null,cache_dir:ze=null,local_files_only:rt=!1,revision:Mt="main",legacy:St=null}={}){const kt=await C(fe,{progress_callback:Ee,config:Fe,cache_dir:ze,local_files_only:rt,revision:Mt,legacy:St});return new this(...kt)}_call(fe,{text_pair:Ee=null,add_special_tokens:Fe=!0,padding:ze=!1,truncation:rt=null,max_length:Mt=null,return_tensor:St=!0,return_token_type_ids:kt=null}={}){const It=Array.isArray(fe);let tr;if(It){if(fe.length===0)throw Error("text array must be non-empty");if(Ee!==null){if(Array.isArray(Ee)){if(fe.length!==Ee.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");tr=fe.map((sr,qt)=>this._encode_plus(sr,{text_pair:Ee[qt],add_special_tokens:Fe,return_token_type_ids:kt}))}else tr=fe.map(sr=>this._encode_plus(sr,{add_special_tokens:Fe,return_token_type_ids:kt}))}else{if(fe==null)throw Error("text may not be null or undefined");if(Array.isArray(Ee))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");tr=[this._encode_plus(fe,{text_pair:Ee,add_special_tokens:Fe,return_token_type_ids:kt})]}if(Mt===null?ze==="max_length"?Mt=this.model_max_length:Mt=(0,f.max)(tr.map(sr=>sr.input_ids.length))[0]:rt||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."),Mt=Math.min(Mt,this.model_max_length??1/0),ze||rt)for(let sr=0;srMt?rt&&Lt(tr[sr],Mt):ze&&Et(tr[sr],Mt,qt=>qt==="input_ids"?this.pad_token_id:0,this.padding_side));const $r={};if(St){if(!(ze&&rt)&&tr.some(qt=>{var gr;for(const Cn of Object.keys(qt))if(qt[Cn].length!==((gr=tr[0][Cn])==null?void 0:gr.length))return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const sr=[tr.length,tr[0].input_ids.length];for(const qt of Object.keys(tr[0]))$r[qt]=new _.Tensor("int64",BigInt64Array.from(tr.flatMap(gr=>gr[qt]).map(BigInt)),sr)}else{for(const sr of Object.keys(tr[0]))$r[sr]=tr.map(qt=>qt[sr]);if(!It)for(const sr of Object.keys($r))$r[sr]=$r[sr][0]}return $r}_encode_text(fe){return fe===null?null:(this.added_tokens_regex?fe.split(this.added_tokens_regex).filter(ze=>ze):[fe]).map((ze,rt)=>{if(this.added_tokens.find(St=>St.content===ze)!==void 0)return ze;{if(this.remove_space===!0&&(ze=ze.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(ze=I(ze)),this.normalizer!==null&&(ze=this.normalizer(ze)),ze.length===0)return[];const St=this.pre_tokenizer!==null?this.pre_tokenizer(ze,{section_index:rt}):[ze];return this.model(St)}}).flat()}_encode_plus(fe,{text_pair:Ee=null,add_special_tokens:Fe=!0,return_token_type_ids:ze=null}={}){const{tokens:rt,token_type_ids:Mt}=this._tokenize_helper(fe,{pair:Ee,add_special_tokens:Fe}),St=this.model.convert_tokens_to_ids(rt),kt={input_ids:St,attention_mask:new Array(St.length).fill(1)};return(ze??this.return_token_type_ids)&&Mt&&(kt.token_type_ids=Mt),kt}_tokenize_helper(fe,{pair:Ee=null,add_special_tokens:Fe=!1}={}){const ze=this._encode_text(fe),rt=this._encode_text(Ee);return this.post_processor?this.post_processor(ze,rt,{add_special_tokens:Fe}):{tokens:(0,a.mergeArrays)(ze??[],rt??[])}}tokenize(fe,{pair:Ee=null,add_special_tokens:Fe=!1}={}){return this._tokenize_helper(fe,{pair:Ee,add_special_tokens:Fe}).tokens}encode(fe,{text_pair:Ee=null,add_special_tokens:Fe=!0,return_token_type_ids:ze=null}={}){return this._encode_plus(fe,{text_pair:Ee,add_special_tokens:Fe,return_token_type_ids:ze}).input_ids}batch_decode(fe,Ee={}){return fe instanceof _.Tensor&&(fe=fe.tolist()),fe.map(Fe=>this.decode(Fe,Ee))}decode(fe,Ee={}){if(fe instanceof _.Tensor&&(fe=E(fe)),!Array.isArray(fe)||fe.length===0||!(0,a.isIntegralNumber)(fe[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(fe,Ee)}decode_single(fe,{skip_special_tokens:Ee=!1,clean_up_tokenization_spaces:Fe=null}){let ze=this.model.convert_ids_to_tokens(fe);Ee&&(ze=ze.filter(Mt=>!this.special_tokens.includes(Mt)));let rt=this.decoder?this.decoder(ze):ze.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(rt=rt.replaceAll(this.decoder.end_of_word_suffix," "),Ee&&(rt=rt.trim())),(Fe??this.clean_up_tokenization_spaces)&&(rt=y(rt)),rt}apply_chat_template(fe,{tools:Ee=null,documents:Fe=null,chat_template:ze=null,add_generation_prompt:rt=!1,tokenize:Mt=!0,padding:St=!1,truncation:kt=!1,max_length:It=null,return_tensor:tr=!0,return_dict:$r=!1,tokenizer_kwargs:sr={},...qt}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null){const ct=this.chat_template;if(ze!==null&&Object.hasOwn(ct,ze))ze=ct[ze];else if(ze===null&&"default"in ct)ze=ct.default;else if(ze===null)throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(ct).sort()}.`)}else if(this.chat_template)ze=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");if(typeof ze!="string")throw Error(`chat_template must be a string, but got ${typeof ze}`);let gr=this._compiled_template_cache.get(ze);gr===void 0&&(gr=new g.Template(ze),this._compiled_template_cache.set(ze,gr));const Cn=Object.create(null);for(const ct of vt){const Kn=this.getToken(ct);Kn&&(Cn[ct]=Kn)}const gn=gr.render({messages:fe,add_generation_prompt:rt,tools:Ee,documents:Fe,...Cn,...qt});if(Mt){const ct=this._call(gn,{add_special_tokens:!1,padding:St,truncation:kt,max_length:It,return_tensor:tr,...sr});return $r?ct:ct.input_ids}return gn}}class zt extends at{constructor(){super(...arguments);He(this,"return_token_type_ids",!0)}}class Vt extends at{constructor(){super(...arguments);He(this,"return_token_type_ids",!0)}}class Xt extends at{constructor(){super(...arguments);He(this,"return_token_type_ids",!0)}}class Yt extends at{constructor(){super(...arguments);He(this,"return_token_type_ids",!0)}}class rr extends at{constructor(){super(...arguments);He(this,"return_token_type_ids",!0)}}class Ht extends at{constructor(){super(...arguments);He(this,"return_token_type_ids",!0)}}class Cr extends at{constructor(){super(...arguments);He(this,"return_token_type_ids",!0)}}class Yr extends at{constructor(){super(...arguments);He(this,"return_token_type_ids",!0)}}class Pr extends at{constructor(){super(...arguments);He(this,"return_token_type_ids",!0)}}class mt extends at{}class Pt extends at{}class Nt extends at{constructor(fe,Ee){super(fe,Ee);He(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class un extends at{constructor(){super(...arguments);He(this,"return_token_type_ids",!0)}}class Bi extends at{}class Mi extends at{}class Wr extends at{}class wn extends at{constructor(W,fe){super(W,fe),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(Ee=>this.languageRegex.test(Ee)),this.lang_to_token=Ee=>Ee}_build_translation_inputs(W,fe,Ee){return ki(this,W,fe,Ee)}}class Kr extends wn{}class fi extends at{}class Or extends at{constructor(W,fe){var ze,rt;const Ee=".,!?…。，、।۔،",Fe=(rt=(ze=W.pre_tokenizer)==null?void 0:ze.pretokenizers[0])==null?void 0:rt.pattern;Fe&&Fe.Regex===` ?[^(\\s|[${Ee}])]+`&&(Fe.Regex=` ?[^\\s${Ee}]+`),super(W,fe)}}const Ni="▁";class Ti extends at{constructor(fe,Ee){super(fe,Ee);He(this,"padding_side","left");this.legacy=Ee.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new Re({replacement:Ni,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(fe){if(fe===null)return null;if(this.legacy||fe.length===0)return super._encode_text(fe);let Ee=super._encode_text(Ni+fe.replaceAll(Ni," "));return Ee.length>1&&Ee[0]===Ni&&this.special_tokens.includes(Ee[1])&&(Ee=Ee.slice(1)),Ee}}class Cs extends at{}class Lo extends at{}class Ro extends at{}class Bo extends at{}class No extends at{}class ji extends at{}class Ps extends at{}class po extends at{}class pi extends at{}function ki(Ue,W,fe,Ee){if(!("language_codes"in Ue)||!Array.isArray(Ue.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Ue)||!(Ue.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Ue)||typeof Ue.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const Fe=Ee.src_lang,ze=Ee.tgt_lang;if(!Ue.language_codes.includes(ze))throw new Error(`Target language code "${ze}" is not valid. Must be one of: {${Ue.language_codes.join(", ")}}`);if(Fe!==void 0){if(!Ue.language_codes.includes(Fe))throw new Error(`Source language code "${Fe}" is not valid. Must be one of: {${Ue.language_codes.join(", ")}}`);for(const rt of Ue.post_processor.config.single)if("SpecialToken"in rt&&Ue.languageRegex.test(rt.SpecialToken.id)){rt.SpecialToken.id=Ue.lang_to_token(Fe);break}}return Ee.forced_bos_token_id=Ue.model.convert_tokens_to_ids([Ue.lang_to_token(ze)])[0],Ue._call(W,fe)}class Si extends at{constructor(W,fe){super(W,fe),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(Ee=>this.languageRegex.test(Ee)),this.lang_to_token=Ee=>Ee}_build_translation_inputs(W,fe,Ee){return ki(this,W,fe,Ee)}}class Ji extends at{constructor(W,fe){super(W,fe),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(Ee=>this.languageRegex.test(Ee)).map(Ee=>Ee.slice(2,-2)),this.lang_to_token=Ee=>`__${Ee}__`}_build_translation_inputs(W,fe,Ee){return ki(this,W,fe,Ee)}}class ho extends at{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(W,{return_timestamps:fe=!1,return_language:Ee=!1,time_precision:Fe=null,force_full_sequences:ze=!0}={}){if(Fe===null)throw Error("Must specify time_precision");let rt=null;const Mt=fe==="word";function St(){return{language:rt,timestamp:[null,null],text:""}}const kt=[];let It=St(),tr=0;const $r=this.timestamp_begin;let sr=[],qt=[],gr=!1,Cn=null;const gn=new Set(this.all_special_ids);for(const br of W){const cn=br.tokens,Fn=Mt?br.token_timestamps:null;let Jt=null,jn=$r;if("stride"in br){const[kr,$t,wr]=br.stride;if(tr-=$t,Cn=kr-wr,$t&&(jn=$t/Fe+$r),wr)for(let Dr=cn.length-1;Dr>=0;--Dr){const Xr=Number(cn[Dr]);if(Xr>=$r){if(Jt!==null&&(Xr-$r)*Fe=$r){const wr=($t-$r)*Fe+tr,Dr=(0,f.round)(wr,2);if(Jt!==null&&$t>=Jt)gr=!0;else if(gr||sr.length>0&&$t0?(sr.push(yn),Mt&&qt.push(Tr)):sr.every(kr=>kr.length===0)&&(It=St(),sr=[],yn=[],qt=[],Tr=[])}if(sr.length>0){if(ze&&fe)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. 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See https://raw.github.com/Stuk/jszip/main/LICENSE.markdown. + +JSZip uses the library pako released under the MIT license : +https://github.com/nodeca/pako/blob/main/LICENSE +*/(function(e,t){(function(r){e.exports=r()})(function(){return function r(o,a,l){function f(g,M){if(!a[g]){if(!o[g]){var w=typeof fh=="function"&&fh;if(!M&&w)return w(g,!0);if(_)return _(g,!0);var C=new Error("Cannot find module '"+g+"'");throw C.code="MODULE_NOT_FOUND",C}var T=a[g]={exports:{}};o[g][0].call(T.exports,function(z){var S=o[g][1][z];return f(S||z)},T,T.exports,r,o,a,l)}return a[g].exports}for(var _=typeof fh=="function"&&fh,p=0;p>2,T=(3&g)<<4|M>>4,z=1>6:64,S=2>4,M=(15&C)<<4|(T=_.indexOf(p.charAt(S++)))>>2,w=(3&T)<<6|(z=_.indexOf(p.charAt(S++))),b[E++]=g,T!==64&&(b[E++]=M),z!==64&&(b[E++]=w);return b}},{"./support":30,"./utils":32}],2:[function(r,o,a){var l=r("./external"),f=r("./stream/DataWorker"),_=r("./stream/Crc32Probe"),p=r("./stream/DataLengthProbe");function g(M,w,C,T,z){this.compressedSize=M,this.uncompressedSize=w,this.crc32=C,this.compression=T,this.compressedContent=z}g.prototype={getContentWorker:function(){var M=new f(l.Promise.resolve(this.compressedContent)).pipe(this.compression.uncompressWorker()).pipe(new p("data_length")),w=this;return M.on("end",function(){if(this.streamInfo.data_length!==w.uncompressedSize)throw new Error("Bug : uncompressed data size mismatch")}),M},getCompressedWorker:function(){return new f(l.Promise.resolve(this.compressedContent)).withStreamInfo("compressedSize",this.compressedSize).withStreamInfo("uncompressedSize",this.uncompressedSize).withStreamInfo("crc32",this.crc32).withStreamInfo("compression",this.compression)}},g.createWorkerFrom=function(M,w,C){return M.pipe(new _).pipe(new p("uncompressedSize")).pipe(w.compressWorker(C)).pipe(new p("compressedSize")).withStreamInfo("compression",w)},o.exports=g},{"./external":6,"./stream/Crc32Probe":25,"./stream/DataLengthProbe":26,"./stream/DataWorker":27}],3:[function(r,o,a){var l=r("./stream/GenericWorker");a.STORE={magic:"\0\0",compressWorker:function(){return new l("STORE compression")},uncompressWorker:function(){return new l("STORE decompression")}},a.DEFLATE=r("./flate")},{"./flate":7,"./stream/GenericWorker":28}],4:[function(r,o,a){var l=r("./utils"),f=function(){for(var _,p=[],g=0;g<256;g++){_=g;for(var M=0;M<8;M++)_=1&_?3988292384^_>>>1:_>>>1;p[g]=_}return p}();o.exports=function(_,p){return _!==void 0&&_.length?l.getTypeOf(_)!=="string"?function(g,M,w,C){var T=f,z=C+w;g^=-1;for(var S=C;S>>8^T[255&(g^M[S])];return-1^g}(0|p,_,_.length,0):function(g,M,w,C){var T=f,z=C+w;g^=-1;for(var 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M(w,C){p.call(this,"FlateWorker/"+w),this._pako=null,this._pakoAction=w,this._pakoOptions=C,this.meta={}}a.magic="\b\0",_.inherits(M,p),M.prototype.processChunk=function(w){this.meta=w.meta,this._pako===null&&this._createPako(),this._pako.push(_.transformTo(g,w.data),!1)},M.prototype.flush=function(){p.prototype.flush.call(this),this._pako===null&&this._createPako(),this._pako.push([],!0)},M.prototype.cleanUp=function(){p.prototype.cleanUp.call(this),this._pako=null},M.prototype._createPako=function(){this._pako=new f[this._pakoAction]({raw:!0,level:this._pakoOptions.level||-1});var w=this;this._pako.onData=function(C){w.push({data:C,meta:w.meta})}},a.compressWorker=function(w){return new M("Deflate",w)},a.uncompressWorker=function(){return new M("Inflate",{})}},{"./stream/GenericWorker":28,"./utils":32,pako:38}],8:[function(r,o,a){function l(T,z){var S,E="";for(S=0;S>>=8;return E}function f(T,z,S,E,y,b){var I,V,O=T.file,N=T.compression,j=b!==g.utf8encode,D=_.transformTo("string",b(O.name)),ee=_.transformTo("string",g.utf8encode(O.name)),de=O.comment,Ce=_.transformTo("string",b(de)),te=_.transformTo("string",g.utf8encode(de)),_e=ee.length!==O.name.length,R=te.length!==de.length,xe="",Xe="",ge="",X=O.dir,ae=O.date,Se={crc32:0,compressedSize:0,uncompressedSize:0};z&&!S||(Se.crc32=T.crc32,Se.compressedSize=T.compressedSize,Se.uncompressedSize=T.uncompressedSize);var Q=0;z&&(Q|=8),j||!_e&&!R||(Q|=2048);var me=0,Ke=0;X&&(me|=16),y==="UNIX"?(Ke=798,me|=function(Be,We){var st=Be;return Be||(st=We?16893:33204),(65535&st)<<16}(O.unixPermissions,X)):(Ke=20,me|=function(Be){return 63&(Be||0)}(O.dosPermissions)),I=ae.getUTCHours(),I<<=6,I|=ae.getUTCMinutes(),I<<=5,I|=ae.getUTCSeconds()/2,V=ae.getUTCFullYear()-1980,V<<=4,V|=ae.getUTCMonth()+1,V<<=5,V|=ae.getUTCDate(),_e&&(Xe=l(1,1)+l(M(D),4)+ee,xe+="up"+l(Xe.length,2)+Xe),R&&(ge=l(1,1)+l(M(Ce),4)+te,xe+="uc"+l(ge.length,2)+ge);var Le="";return Le+=` +\0`,Le+=l(Q,2),Le+=N.magic,Le+=l(I,2),Le+=l(V,2),Le+=l(Se.crc32,4),Le+=l(Se.compressedSize,4),Le+=l(Se.uncompressedSize,4),Le+=l(D.length,2),Le+=l(xe.length,2),{fileRecord:w.LOCAL_FILE_HEADER+Le+D+xe,dirRecord:w.CENTRAL_FILE_HEADER+l(Ke,2)+Le+l(Ce.length,2)+"\0\0\0\0"+l(me,4)+l(E,4)+D+xe+Ce}}var _=r("../utils"),p=r("../stream/GenericWorker"),g=r("../utf8"),M=r("../crc32"),w=r("../signature");function C(T,z,S,E){p.call(this,"ZipFileWorker"),this.bytesWritten=0,this.zipComment=z,this.zipPlatform=S,this.encodeFileName=E,this.streamFiles=T,this.accumulate=!1,this.contentBuffer=[],this.dirRecords=[],this.currentSourceOffset=0,this.entriesCount=0,this.currentFile=null,this._sources=[]}_.inherits(C,p),C.prototype.push=function(T){var z=T.meta.percent||0,S=this.entriesCount,E=this._sources.length;this.accumulate?this.contentBuffer.push(T):(this.bytesWritten+=T.data.length,p.prototype.push.call(this,{data:T.data,meta:{currentFile:this.currentFile,percent:S?(z+100*(S-E-1))/S:100}}))},C.prototype.openedSource=function(T){this.currentSourceOffset=this.bytesWritten,this.currentFile=T.file.name;var z=this.streamFiles&&!T.file.dir;if(z){var S=f(T,z,!1,this.currentSourceOffset,this.zipPlatform,this.encodeFileName);this.push({data:S.fileRecord,meta:{percent:0}})}else this.accumulate=!0},C.prototype.closedSource=function(T){this.accumulate=!1;var z=this.streamFiles&&!T.file.dir,S=f(T,z,!0,this.currentSourceOffset,this.zipPlatform,this.encodeFileName);if(this.dirRecords.push(S.dirRecord),z)this.push({data:function(E){return w.DATA_DESCRIPTOR+l(E.crc32,4)+l(E.compressedSize,4)+l(E.uncompressedSize,4)}(T),meta:{percent:100}});else for(this.push({data:S.fileRecord,meta:{percent:0}});this.contentBuffer.length;)this.push(this.contentBuffer.shift());this.currentFile=null},C.prototype.flush=function(){for(var T=this.bytesWritten,z=0;z=this.index;p--)g=(g<<8)+this.byteAt(p);return this.index+=_,g},readString:function(_){return l.transformTo("string",this.readData(_))},readData:function(){},lastIndexOfSignature:function(){},readAndCheckSignature:function(){},readDate:function(){var _=this.readInt(4);return new Date(Date.UTC(1980+(_>>25&127),(_>>21&15)-1,_>>16&31,_>>11&31,_>>5&63,(31&_)<<1))}},o.exports=f},{"../utils":32}],19:[function(r,o,a){var l=r("./Uint8ArrayReader");function f(_){l.call(this,_)}r("../utils").inherits(f,l),f.prototype.readData=function(_){this.checkOffset(_);var p=this.data.slice(this.zero+this.index,this.zero+this.index+_);return this.index+=_,p},o.exports=f},{"../utils":32,"./Uint8ArrayReader":21}],20:[function(r,o,a){var l=r("./DataReader");function f(_){l.call(this,_)}r("../utils").inherits(f,l),f.prototype.byteAt=function(_){return this.data.charCodeAt(this.zero+_)},f.prototype.lastIndexOfSignature=function(_){return this.data.lastIndexOf(_)-this.zero},f.prototype.readAndCheckSignature=function(_){return _===this.readData(4)},f.prototype.readData=function(_){this.checkOffset(_);var p=this.data.slice(this.zero+this.index,this.zero+this.index+_);return this.index+=_,p},o.exports=f},{"../utils":32,"./DataReader":18}],21:[function(r,o,a){var l=r("./ArrayReader");function f(_){l.call(this,_)}r("../utils").inherits(f,l),f.prototype.readData=function(_){if(this.checkOffset(_),_===0)return new Uint8Array(0);var p=this.data.subarray(this.zero+this.index,this.zero+this.index+_);return this.index+=_,p},o.exports=f},{"../utils":32,"./ArrayReader":17}],22:[function(r,o,a){var l=r("../utils"),f=r("../support"),_=r("./ArrayReader"),p=r("./StringReader"),g=r("./NodeBufferReader"),M=r("./Uint8ArrayReader");o.exports=function(w){var C=l.getTypeOf(w);return l.checkSupport(C),C!=="string"||f.uint8array?C==="nodebuffer"?new g(w):f.uint8array?new M(l.transformTo("uint8array",w)):new _(l.transformTo("array",w)):new p(w)}},{"../support":30,"../utils":32,"./ArrayReader":17,"./NodeBufferReader":19,"./StringReader":20,"./Uint8ArrayReader":21}],23:[function(r,o,a){a.LOCAL_FILE_HEADER="PK",a.CENTRAL_FILE_HEADER="PK",a.CENTRAL_DIRECTORY_END="PK",a.ZIP64_CENTRAL_DIRECTORY_LOCATOR="PK\x07",a.ZIP64_CENTRAL_DIRECTORY_END="PK",a.DATA_DESCRIPTOR="PK\x07\b"},{}],24:[function(r,o,a){var l=r("./GenericWorker"),f=r("../utils");function _(p){l.call(this,"ConvertWorker to "+p),this.destType=p}f.inherits(_,l),_.prototype.processChunk=function(p){this.push({data:f.transformTo(this.destType,p.data),meta:p.meta})},o.exports=_},{"../utils":32,"./GenericWorker":28}],25:[function(r,o,a){var l=r("./GenericWorker"),f=r("../crc32");function _(){l.call(this,"Crc32Probe"),this.withStreamInfo("crc32",0)}r("../utils").inherits(_,l),_.prototype.processChunk=function(p){this.streamInfo.crc32=f(p.data,this.streamInfo.crc32||0),this.push(p)},o.exports=_},{"../crc32":4,"../utils":32,"./GenericWorker":28}],26:[function(r,o,a){var l=r("../utils"),f=r("./GenericWorker");function _(p){f.call(this,"DataLengthProbe for "+p),this.propName=p,this.withStreamInfo(p,0)}l.inherits(_,f),_.prototype.processChunk=function(p){if(p){var g=this.streamInfo[this.propName]||0;this.streamInfo[this.propName]=g+p.data.length}f.prototype.processChunk.call(this,p)},o.exports=_},{"../utils":32,"./GenericWorker":28}],27:[function(r,o,a){var l=r("../utils"),f=r("./GenericWorker");function _(p){f.call(this,"DataWorker");var g=this;this.dataIsReady=!1,this.index=0,this.max=0,this.data=null,this.type="",this._tickScheduled=!1,p.then(function(M){g.dataIsReady=!0,g.data=M,g.max=M&&M.length||0,g.type=l.getTypeOf(M),g.isPaused||g._tickAndRepeat()},function(M){g.error(M)})}l.inherits(_,f),_.prototype.cleanUp=function(){f.prototype.cleanUp.call(this),this.data=null},_.prototype.resume=function(){return!!f.prototype.resume.call(this)&&(!this._tickScheduled&&this.dataIsReady&&(this._tickScheduled=!0,l.delay(this._tickAndRepeat,[],this)),!0)},_.prototype._tickAndRepeat=function(){this._tickScheduled=!1,this.isPaused||this.isFinished||(this._tick(),this.isFinished||(l.delay(this._tickAndRepeat,[],this),this._tickScheduled=!0))},_.prototype._tick=function(){if(this.isPaused||this.isFinished)return!1;var p=null,g=Math.min(this.max,this.index+16384);if(this.index>=this.max)return this.end();switch(this.type){case"string":p=this.data.substring(this.index,g);break;case"uint8array":p=this.data.subarray(this.index,g);break;case"array":case"nodebuffer":p=this.data.slice(this.index,g)}return this.index=g,this.push({data:p,meta:{percent:this.max?this.index/this.max*100:0}})},o.exports=_},{"../utils":32,"./GenericWorker":28}],28:[function(r,o,a){function l(f){this.name=f||"default",this.streamInfo={},this.generatedError=null,this.extraStreamInfo={},this.isPaused=!0,this.isFinished=!1,this.isLocked=!1,this._listeners={data:[],end:[],error:[]},this.previous=null}l.prototype={push:function(f){this.emit("data",f)},end:function(){if(this.isFinished)return!1;this.flush();try{this.emit("end"),this.cleanUp(),this.isFinished=!0}catch(f){this.emit("error",f)}return!0},error:function(f){return!this.isFinished&&(this.isPaused?this.generatedError=f:(this.isFinished=!0,this.emit("error",f),this.previous&&this.previous.error(f),this.cleanUp()),!0)},on:function(f,_){return this._listeners[f].push(_),this},cleanUp:function(){this.streamInfo=this.generatedError=this.extraStreamInfo=null,this._listeners=[]},emit:function(f,_){if(this._listeners[f])for(var p=0;p "+f:f}},o.exports=l},{}],29:[function(r,o,a){var l=r("../utils"),f=r("./ConvertWorker"),_=r("./GenericWorker"),p=r("../base64"),g=r("../support"),M=r("../external"),w=null;if(g.nodestream)try{w=r("../nodejs/NodejsStreamOutputAdapter")}catch{}function C(z,S){return new M.Promise(function(E,y){var b=[],I=z._internalType,V=z._outputType,O=z._mimeType;z.on("data",function(N,j){b.push(N),S&&S(j)}).on("error",function(N){b=[],y(N)}).on("end",function(){try{var N=function(j,D,ee){switch(j){case"blob":return l.newBlob(l.transformTo("arraybuffer",D),ee);case"base64":return p.encode(D);default:return l.transformTo(j,D)}}(V,function(j,D){var ee,de=0,Ce=null,te=0;for(ee=0;ee"u")a.blob=!1;else{var l=new ArrayBuffer(0);try{a.blob=new Blob([l],{type:"application/zip"}).size===0}catch{try{var f=new(self.BlobBuilder||self.WebKitBlobBuilder||self.MozBlobBuilder||self.MSBlobBuilder);f.append(l),a.blob=f.getBlob("application/zip").size===0}catch{a.blob=!1}}}try{a.nodestream=!!r("readable-stream").Readable}catch{a.nodestream=!1}},{"readable-stream":16}],31:[function(r,o,a){for(var l=r("./utils"),f=r("./support"),_=r("./nodejsUtils"),p=r("./stream/GenericWorker"),g=new Array(256),M=0;M<256;M++)g[M]=252<=M?6:248<=M?5:240<=M?4:224<=M?3:192<=M?2:1;g[254]=g[254]=1;function w(){p.call(this,"utf-8 decode"),this.leftOver=null}function C(){p.call(this,"utf-8 encode")}a.utf8encode=function(T){return f.nodebuffer?_.newBufferFrom(T,"utf-8"):function(z){var S,E,y,b,I,V=z.length,O=0;for(b=0;b>>6:(E<65536?S[I++]=224|E>>>12:(S[I++]=240|E>>>18,S[I++]=128|E>>>12&63),S[I++]=128|E>>>6&63),S[I++]=128|63&E);return S}(T)},a.utf8decode=function(T){return f.nodebuffer?l.transformTo("nodebuffer",T).toString("utf-8"):function(z){var S,E,y,b,I=z.length,V=new Array(2*I);for(S=E=0;S>10&1023,V[E++]=56320|1023&y)}return V.length!==E&&(V.subarray?V=V.subarray(0,E):V.length=E),l.applyFromCharCode(V)}(T=l.transformTo(f.uint8array?"uint8array":"array",T))},l.inherits(w,p),w.prototype.processChunk=function(T){var z=l.transformTo(f.uint8array?"uint8array":"array",T.data);if(this.leftOver&&this.leftOver.length){if(f.uint8array){var S=z;(z=new Uint8Array(S.length+this.leftOver.length)).set(this.leftOver,0),z.set(S,this.leftOver.length)}else z=this.leftOver.concat(z);this.leftOver=null}var E=function(b,I){var V;for((I=I||b.length)>b.length&&(I=b.length),V=I-1;0<=V&&(192&b[V])==128;)V--;return 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y=0;y>8;this.dir=!!(16&this.externalFileAttributes),T==0&&(this.dosPermissions=63&this.externalFileAttributes),T==3&&(this.unixPermissions=this.externalFileAttributes>>16&65535),this.dir||this.fileNameStr.slice(-1)!=="/"||(this.dir=!0)},parseZIP64ExtraField:function(){if(this.extraFields[1]){var T=l(this.extraFields[1].value);this.uncompressedSize===f.MAX_VALUE_32BITS&&(this.uncompressedSize=T.readInt(8)),this.compressedSize===f.MAX_VALUE_32BITS&&(this.compressedSize=T.readInt(8)),this.localHeaderOffset===f.MAX_VALUE_32BITS&&(this.localHeaderOffset=T.readInt(8)),this.diskNumberStart===f.MAX_VALUE_32BITS&&(this.diskNumberStart=T.readInt(4))}},readExtraFields:function(T){var z,S,E,y=T.index+this.extraFieldsLength;for(this.extraFields||(this.extraFields={});T.index+4>>6:(T<65536?C[E++]=224|T>>>12:(C[E++]=240|T>>>18,C[E++]=128|T>>>12&63),C[E++]=128|T>>>6&63),C[E++]=128|63&T);return C},a.buf2binstring=function(w){return M(w,w.length)},a.binstring2buf=function(w){for(var C=new 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Pe,se,ne=A.max_chain_length,ye=A.strstart,B=A.prev_length,J=A.nice_match,L=A.strstart>A.w_size-Ce?A.strstart-(A.w_size-Ce):0,ue=A.window,le=A.w_mask,Te=A.prev,Ne=A.strstart+de,dt=ue[ye+B-1],Je=ue[ye+B];A.prev_length>=A.good_match&&(ne>>=2),J>A.lookahead&&(J=A.lookahead);do if(ue[(Pe=$e)+B]===Je&&ue[Pe+B-1]===dt&&ue[Pe]===ue[ye]&&ue[++Pe]===ue[ye+1]){ye+=2,Pe++;do;while(ue[++ye]===ue[++Pe]&&ue[++ye]===ue[++Pe]&&ue[++ye]===ue[++Pe]&&ue[++ye]===ue[++Pe]&&ue[++ye]===ue[++Pe]&&ue[++ye]===ue[++Pe]&&ue[++ye]===ue[++Pe]&&ue[++ye]===ue[++Pe]&&yeL&&--ne!=0);return B<=A.lookahead?B:A.lookahead}function We(A){var $e,Pe,se,ne,ye,B,J,L,ue,le,Te=A.w_size;do{if(ne=A.window_size-A.lookahead-A.strstart,A.strstart>=Te+(Te-Ce)){for(f.arraySet(A.window,A.window,Te,Te,0),A.match_start-=Te,A.strstart-=Te,A.block_start-=Te,$e=Pe=A.hash_size;se=A.head[--$e],A.head[$e]=Te<=se?se-Te:0,--Pe;);for($e=Pe=Te;se=A.prev[--$e],A.prev[$e]=Te<=se?se-Te:0,--Pe;);ne+=Te}if(A.strm.avail_in===0)break;if(B=A.strm,J=A.window,L=A.strstart+A.lookahead,ue=ne,le=void 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A.insert=0,$e===C?(me(A,!0),A.strm.avail_out===0?Xe:ge):(A.strstart>A.block_start&&(me(A,!1),A.strm.avail_out),R)}),new Ge(4,4,8,4,st),new Ge(4,5,16,8,st),new Ge(4,6,32,32,st),new Ge(4,4,16,16,ft),new Ge(8,16,32,32,ft),new Ge(8,16,128,128,ft),new Ge(8,32,128,256,ft),new Ge(32,128,258,1024,ft),new Ge(32,258,258,4096,ft)],a.deflateInit=function(A,$e){return et(A,$e,b,15,8,0)},a.deflateInit2=et,a.deflateReset=Ye,a.deflateResetKeep=je,a.deflateSetHeader=function(A,$e){return A&&A.state?A.state.wrap!==2?z:(A.state.gzhead=$e,T):z},a.deflate=function(A,$e){var Pe,se,ne,ye;if(!A||!A.state||5<$e||$e<0)return A?X(A,z):z;if(se=A.state,!A.output||!A.input&&A.avail_in!==0||se.status===666&&$e!==C)return 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B=b+(se.w_bits-8<<4)<<8;B|=(2<=se.strategy||se.level<2?0:se.level<6?1:se.level===6?2:3)<<6,se.strstart!==0&&(B|=32),B+=31-B%31,se.status=_e,Le(se,B),se.strstart!==0&&(Le(se,A.adler>>>16),Le(se,65535&A.adler)),A.adler=1}if(se.status===69)if(se.gzhead.extra){for(ne=se.pending;se.gzindex<(65535&se.gzhead.extra.length)&&(se.pending!==se.pending_buf_size||(se.gzhead.hcrc&&se.pending>ne&&(A.adler=g(A.adler,se.pending_buf,se.pending-ne,ne)),Q(A),ne=se.pending,se.pending!==se.pending_buf_size));)Ke(se,255&se.gzhead.extra[se.gzindex]),se.gzindex++;se.gzhead.hcrc&&se.pending>ne&&(A.adler=g(A.adler,se.pending_buf,se.pending-ne,ne)),se.gzindex===se.gzhead.extra.length&&(se.gzindex=0,se.status=73)}else se.status=73;if(se.status===73)if(se.gzhead.name){ne=se.pending;do{if(se.pending===se.pending_buf_size&&(se.gzhead.hcrc&&se.pending>ne&&(A.adler=g(A.adler,se.pending_buf,se.pending-ne,ne)),Q(A),ne=se.pending,se.pending===se.pending_buf_size)){ye=1;break}ye=se.gzindexne&&(A.adler=g(A.adler,se.pending_buf,se.pending-ne,ne)),ye===0&&(se.gzindex=0,se.status=91)}else se.status=91;if(se.status===91)if(se.gzhead.comment){ne=se.pending;do{if(se.pending===se.pending_buf_size&&(se.gzhead.hcrc&&se.pending>ne&&(A.adler=g(A.adler,se.pending_buf,se.pending-ne,ne)),Q(A),ne=se.pending,se.pending===se.pending_buf_size)){ye=1;break}ye=se.gzindexne&&(A.adler=g(A.adler,se.pending_buf,se.pending-ne,ne)),ye===0&&(se.status=103)}else se.status=103;if(se.status===103&&(se.gzhead.hcrc?(se.pending+2>se.pending_buf_size&&Q(A),se.pending+2<=se.pending_buf_size&&(Ke(se,255&A.adler),Ke(se,A.adler>>8&255),A.adler=0,se.status=_e)):se.status=_e),se.pending!==0){if(Q(A),A.avail_out===0)return se.last_flush=-1,T}else if(A.avail_in===0&&ae($e)<=ae(Pe)&&$e!==C)return X(A,-5);if(se.status===666&&A.avail_in!==0)return X(A,-5);if(A.avail_in!==0||se.lookahead!==0||$e!==w&&se.status!==666){var J=se.strategy===2?function(L,ue){for(var le;;){if(L.lookahead===0&&(We(L),L.lookahead===0)){if(ue===w)return R;break}if(L.match_length=0,le=_._tr_tally(L,0,L.window[L.strstart]),L.lookahead--,L.strstart++,le&&(me(L,!1),L.strm.avail_out===0))return R}return L.insert=0,ue===C?(me(L,!0),L.strm.avail_out===0?Xe:ge):L.last_lit&&(me(L,!1),L.strm.avail_out===0)?R:xe}(se,$e):se.strategy===3?function(L,ue){for(var le,Te,Ne,dt,Je=L.window;;){if(L.lookahead<=de){if(We(L),L.lookahead<=de&&ue===w)return 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$e!==C?T:se.wrap<=0?1:(se.wrap===2?(Ke(se,255&A.adler),Ke(se,A.adler>>8&255),Ke(se,A.adler>>16&255),Ke(se,A.adler>>24&255),Ke(se,255&A.total_in),Ke(se,A.total_in>>8&255),Ke(se,A.total_in>>16&255),Ke(se,A.total_in>>24&255)):(Le(se,A.adler>>>16),Le(se,65535&A.adler)),Q(A),0=Pe.w_size&&(ye===0&&(Se(Pe.head),Pe.strstart=0,Pe.block_start=0,Pe.insert=0),ue=new f.Buf8(Pe.w_size),f.arraySet(ue,$e,le-Pe.w_size,Pe.w_size,0),$e=ue,le=Pe.w_size),B=A.avail_in,J=A.next_in,L=A.input,A.avail_in=le,A.next_in=0,A.input=$e,We(Pe);Pe.lookahead>=ee;){for(se=Pe.strstart,ne=Pe.lookahead-(ee-1);Pe.ins_h=(Pe.ins_h<>>=ee=D>>>24,I-=ee,(ee=D>>>16&255)===0)xe[M++]=65535&D;else{if(!(16&ee)){if(!(64&ee)){D=V[(65535&D)+(b&(1<>>=ee,I-=ee),I<15&&(b+=R[p++]<>>=ee=D>>>24,I-=ee,!(16&(ee=D>>>16&255))){if(!(64&ee)){D=O[(65535&D)+(b&(1<>>=ee,I-=ee,(ee=M-w)>3,b&=(1<<(I-=de<<3))-1,l.next_in=p,l.next_out=M,l.avail_in=p>>24&255)+(te>>>8&65280)+((65280&te)<<8)+((255&te)<<24)}function 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te&&te.state?(xe=te.state,_e<0?(R=0,_e=-_e):(R=1+(_e>>4),_e<48&&(_e&=15)),_e&&(_e<8||15<_e)?T:(xe.window!==null&&xe.wbits!==_e&&(xe.window=null),xe.wrap=R,xe.wbits=_e,V(te))):T}function N(te,_e){var R,xe;return te?(xe=new b,(te.state=xe).window=null,(R=O(te,_e))!==C&&(te.state=null),R):T}var j,D,ee=!0;function de(te){if(ee){var _e;for(j=new l.Buf32(512),D=new l.Buf32(32),_e=0;_e<144;)te.lens[_e++]=8;for(;_e<256;)te.lens[_e++]=9;for(;_e<280;)te.lens[_e++]=7;for(;_e<288;)te.lens[_e++]=8;for(g(M,te.lens,0,288,j,0,te.work,{bits:9}),_e=0;_e<32;)te.lens[_e++]=5;g(w,te.lens,0,32,D,0,te.work,{bits:5}),ee=!1}te.lencode=j,te.lenbits=9,te.distcode=D,te.distbits=5}function Ce(te,_e,R,xe){var Xe,ge=te.state;return ge.window===null&&(ge.wsize=1<=ge.wsize?(l.arraySet(ge.window,_e,R-ge.wsize,ge.wsize,0),ge.wnext=0,ge.whave=ge.wsize):(xe<(Xe=ge.wsize-ge.wnext)&&(Xe=xe),l.arraySet(ge.window,_e,R-xe,Xe,ge.wnext),(xe-=Xe)?(l.arraySet(ge.window,_e,R-xe,xe,0),ge.wnext=xe,ge.whave=ge.wsize):(ge.wnext+=Xe,ge.wnext===ge.wsize&&(ge.wnext=0),ge.whave>>8&255,R.check=_(R.check,ye,2,0),me=Q=0,R.mode=2;break}if(R.flags=0,R.head&&(R.head.done=!1),!(1&R.wrap)||(((255&Q)<<8)+(Q>>8))%31){te.msg="incorrect header check",R.mode=30;break}if((15&Q)!=8){te.msg="unknown compression method",R.mode=30;break}if(me-=4,A=8+(15&(Q>>>=4)),R.wbits===0)R.wbits=A;else if(A>R.wbits){te.msg="invalid window size",R.mode=30;break}R.dmax=1<>8&1),512&R.flags&&(ye[0]=255&Q,ye[1]=Q>>>8&255,R.check=_(R.check,ye,2,0)),me=Q=0,R.mode=3;case 3:for(;me<32;){if(ae===0)break e;ae--,Q+=xe[ge++]<>>8&255,ye[2]=Q>>>16&255,ye[3]=Q>>>24&255,R.check=_(R.check,ye,4,0)),me=Q=0,R.mode=4;case 4:for(;me<16;){if(ae===0)break 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