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Ge=i<2?"":` + fn i2o_${e}(indices: ${l.indices}) -> u32 { + return ${le.join("+")}; + }`,Le=Ve=>(C.indicesToOffset=!0,i<2?Ve:`i2o_${e}(${Ve})`),pt=(...Ve)=>i===0?"0u":`${l.indices}(${Ve.map(M).join(",")})`,Dt=(Ve,kt)=>i<2?`${Ve}`:`${jt(Ve,kt,i)}`,Vt=(Ve,kt,tr)=>i<2?`${Ve}=${tr};`:`${jt(Ve,kt,i)}=${tr};`,lr={},fr=(Ve,kt)=>{C.broadcastedIndicesToOffset=!0;let tr=`${kt.name}broadcastedIndicesTo${e}Offset`;if(tr in lr)return`${tr}(${Ve})`;let zr=[];for(let nn=i-1;nn>=0;nn--){let un=kt.indicesGet("outputIndices",nn+kt.rank-i);zr.push(`${Dt(G,nn)} * (${un} % ${Dt(B,nn)})`)}return lr[tr]=`fn ${tr}(outputIndices: ${kt.type.indices}) -> u32 { + return ${zr.length>0?zr.join("+"):"0u"}; + }`,`${tr}(${Ve})`},er=(Ve,kt)=>(()=>{if(l.storage===l.value)return`${e}[${Ve}]=${kt};`;if(l.storage==="vec2"&&l.value==="i32")return`${e}[${Ve}]=vec2(u32(${kt}), select(0u, 0xFFFFFFFFu, ${kt} < 0));`;if(l.storage==="vec2"&&l.value==="u32")return`${e}[${Ve}]=vec2(u32(${kt}), 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}`})(),jr=(...Ve)=>{if(Ve.length!==i)throw new Error(`indices length must be ${i}`);let kt=Ve.map(M).join(",");return i===0?Nr("0u"):i===1?Nr(kt[0]):(C.get=!0,C.getByIndices=!0,C.indicesToOffset=!0,`get_${e}(${kt})`)},Jt=Ve=>i<2?Nr(Ve):(C.getByIndices=!0,C.indicesToOffset=!0,`get_${e}ByIndices(${Ve})`),dr=i<2?"":` + fn set_${e}ByIndices(indices: ${l.indices}, value: ${g}) { + ${er(`i2o_${e}(indices)`,"value")} + }`,Pr=i<2?"":(()=>{let Ve=u.map(tr=>`d${tr}: u32`).join(", "),kt=u.map(tr=>`d${tr}`).join(", ");return` + fn set_${e}(${Ve}, value: ${g}) { + set_${e}ByIndices(${pt(kt)}, value); + }`})();return{impl:()=>{let Ve=[],kt=!1;return C.offsetToIndices&&(Ve.push(ae),kt=!0),C.indicesToOffset&&(Ve.push(Ge),kt=!0),C.broadcastedIndicesToOffset&&(Object.values(lr).forEach(tr=>Ve.push(tr)),kt=!0),C.set&&(Ve.push(Pr),kt=!0),C.setByIndices&&(Ve.push(dr),kt=!0),C.get&&(Ve.push(br),kt=!0),C.getByIndices&&(Ve.push(Hr),kt=!0),!a&&kt&&Ve.unshift(`const ${B} = ${l.indices}(${r.join(",")});`,`const 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= workgroup_index / stride; + let input_col = workgroup_id_y * ${E}u + local_id.x; + let input_row = workgroup_id_x * ${E}u + local_id.y; + if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) { + tile[local_id.y][local_id.x] = ${M.getByIndices(`${M.type.indices}(input_row, input_col)`)}; + } + workgroupBarrier(); + + let output_col = workgroup_id_x * ${E}u + local_id.x; + let output_row = workgroup_id_y * ${E}u + local_id.y; + if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) { + ${C.setByIndices(`${C.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")} + } + }`:B=G=>` + ${G.registerUniform("output_size","u32").declareVariables(M,C)} + + ${ao(s,n,M,C)} + + ${G.mainStart()} + ${G.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${C.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${C.setByOffset("global_idx",M.getByIndices("aIndices"))} + }`,{name:g?"TransposeShared":"Transpose",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:()=>{let G=Se.size(a);return{outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:g?{x:Math.ceil(l[1]/E),y:Math.ceil(l[0]/E)}:{x:Math.ceil(G/64)},programUniforms:[{type:12,data:G},...Ct(w,l)]}},getShaderSource:B}},$i=(e,t)=>{ss(e.inputs),e.compute(Pn(e.inputs[0],t.perm))},oo=e=>ur({perm:e.perm})}),lo,uo,co,Si,po,ho,ki,fo,mo,Pi,An,_o,Ai,go,wo,Ii,yo,bo,Fi,Mo,vo,xo=R(()=>{Xt(),Ht(),or(),Js(),is(),lo={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},uo={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, 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${C.registerUniform("reduceSize","u32").declareVariables(g,w)} + ${M} + fn DIV_CEIL(a : u32, b : u32) -> u32 { + return ((a - 1u) / b + 1u); + } + ${C.mainStart(l)} + + let outputIndex = global_idx / ${l}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${co[n]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${l}) { + let candidate = f32(${g.getByOffset("offset + k")}); + bestValue = ${lo[n]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${l}u); + for (var currentSize = reduceSize / 2u; reduceSize > 1u; + currentSize = reduceSize / 2u) { + let interval = DIV_CEIL(reduceSize, 2u); + if (local_idx < currentSize) { + let candidate = aBestValues[local_idx + interval]; + bestValue = ${uo[n]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${w.setByOffset("outputIndex",`${n==="mean"?`${w.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${w.type.storage}(${Si[n]})`}`)}; + } + }`,getRunData:()=>({outputs:[{dims:a,dataType:s}],dispatchGroup:{x:d},programUniforms:[{type:12,data:p}]})}},An=(e,t,r,n)=>{let s=e.inputs.length===1?r:zi(e.inputs,r),a=s.axes;a.length===0&&!s.noopWithEmptyAxes&&(a=e.inputs[0].dims.map((M,C)=>C));let i=Se.normalizeAxes(a,e.inputs[0].dims.length),u=i,d=e.inputs[0],p=mo(u,e.inputs[0].dims.length);p.length>0&&(d=e.compute(Pn(e.inputs[0],p),{inputs:[0],outputs:[-1]})[0],u=po(u.length,d.dims.length));let[g,w]=ho(d.dims,u),l=g;s.keepDims&&(l=ki(g,i)),e.compute(Pi(t,{hint:s.cacheKey,inputDependencies:["type"]},[d],n,e.inputs[0].dataType,l,w),{inputs:[d]})},_o=(e,t)=>{An(e,"ReduceMeanShared",t,"mean")},Ai=(e,t)=>{An(e,"ReduceL1Shared",t,"l1")},go=(e,t)=>{An(e,"ReduceL2Shared",t,"l2")},wo=(e,t)=>{An(e,"ReduceLogSumExpShared",t,"logSumExp")},Ii=(e,t)=>{An(e,"ReduceMaxShared",t,"max")},yo=(e,t)=>{An(e,"ReduceMinShared",t,"min")},bo=(e,t)=>{An(e,"ReduceProdShared",t,"prod")},Fi=(e,t)=>{An(e,"ReduceSumShared",t,"sum")},Mo=(e,t)=>{An(e,"ReduceSumSquareShared",t,"sumSquare")},vo=(e,t)=>{An(e,"ReduceLogSumShared",t,"logSum")}}),In,To,zs,zi,Fn,Oi,Co,Eo,Di,$o,So,Li,ko,Po,Bi,zn,Ao,Ri,Io,Fo,Ni,zo,Oo,ji,Do,Lo,Js=R(()=>{Xt(),Ht(),Sr(),or(),xo(),In=e=>{if(!e||e.length===0||e.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},To=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],zs=(e,t,r,n,s,a,i=!1,u=!1)=>{let d=[],p=r[0].dims,g=p.length,w=Se.normalizeAxes(s,g),l=!u&&w.length===0;p.forEach((E,B)=>{l||w.indexOf(B)>=0?i&&d.push(1):d.push(E)});let M=d.length,C=Se.size(d);return{name:e,shaderCache:t,getShaderSource:E=>{let B=[],G=rt("_A",r[0].dataType,g),O=Ut("output",a,M),ae=n(G,O,w),Z=ae[2];for(let le=0,Ge=0;le=0?(i&&Ge++,Z=`for(var j${le}: u32 = 0; j${le} < ${p[le]}; j${le}++) { + ${ae[2].includes("last_index")?`let last_index = j${le};`:""} + ${G.indicesSet("input_indices",le,`j${le}`)} + ${Z} + }`):(B.push(`${G.indicesSet("input_indices",le,O.indicesGet("output_indices",Ge))};`),Ge++);return` + + ${E.registerUniform("output_size","u32").declareVariables(G,O)} + + ${E.mainStart()} + ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var input_indices: ${G.type.indices}; + let output_indices = ${O.offsetToIndices("global_idx")}; + + ${B.join(` +`)} + ${ae[0]} // init ops for reduce max/min + ${ae[1]} + ${Z} + ${ae[3]} + ${ae.length===4?O.setByOffset("global_idx","value"):ae.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:d,dataType:a}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:[{type:12,data:C},...Ct(p,d)]})}},zi=(e,t)=>{let r=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),ur({axes:r,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},Fn=(e,t,r,n)=>{let s=e.inputs,a=s.length===1?r:zi(s,r);e.compute(zs(t,{hint:a.cacheKey,inputDependencies:["rank"]},[s[0]],a.noopWithEmptyAxes&&a.axes.length===0?To:n,a.axes,s[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},Oi=(e,t)=>{In(e.inputs),Fn(e,"ReduceLogSum",t,(r,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${r.getByIndices("input_indices")};`,"value = 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r=(n,s,a)=>{let i=[];for(let u=0;u=0||a.length===0)&&i.push(`input_indices[${u}] = 0;`);return[`${i.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",s.setByOffset("global_idx","best_index")]};e.compute(zs("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Ui=(e,t)=>{Vi(e.inputs);let r=(n,s,a)=>{let i=[];for(let u=0;u=0||a.length===0)&&i.push(`input_indices[${u}] = 0;`);return[`${i.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",s.setByOffset("global_idx","best_index")]};e.compute(zs("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Wi=e=>ur(e)}),Gi,Ro,No,qi,Os,jo,Hi,Ki=R(()=>{Xt(),Ht(),se(),or(),Gi=(e,t)=>{let r=e[0],n=e[1],s=e[2],a=e[3],i=e[4],u=e[5];if(i&&u)throw new Error("Attention cannot have both past and attention_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let d=r.dims[0],p=r.dims[1],g=r.dims[2];if(s.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==g)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(s.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let w=s.dims[0]/3,l=w,M=l;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let ae of t.qkvHiddenSizes)if(ae%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");w=t.qkvHiddenSizes[0],l=t.qkvHiddenSizes[1],M=t.qkvHiddenSizes[2]}let C=p;if(w!==l)throw new Error("qkv_hidden_sizes first element should be same as the second");if(s.dims[0]!==w+l+M)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let E=0;if(i){if(l!==M)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==d)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==l/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(E=i.dims[3])}let B=C+E,G=-1,O=0;if(a)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");if(u){if(u.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(u.dims[0]!==d||u.dims[1]!==t.numHeads||u.dims[2]!==p||u.dims[3]!==B)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:d,sequenceLength:p,pastSequenceLength:E,kvSequenceLength:C,totalSequenceLength:B,maxSequenceLength:G,inputHiddenSize:g,hiddenSize:w,vHiddenSize:M,headSize:Math.floor(w/t.numHeads),vHeadSize:Math.floor(M/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:O,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Ro=(e,t,r)=>{let n=mr(r),s=64,a=r/n;a{let M=Ut("x",e.dataType,e.dims,n),C=Ar(e.dataType),E=[{name:"d_inv",type:"f32"},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return` + var thread_max: array; + var thread_sum: array; + ${l.registerUniforms(E).declareVariables(M)} + ${l.mainStart([s,1,1])} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = (global_idx / ${s}) * uniforms.d_comp + local_offset; + + var thread_max_vector = ${p}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + thread_max_vector = max(${p}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(n){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: ${n}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${s}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${p}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + sum_vector += exp(${p}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(n){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: ${n}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${s}; i++) { + sum += thread_sum[i]; + } + + if (sum == 0) { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + x[offset + i] = ${M.type.value}(${C}(uniforms.d_inv)); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + var f32input = ${p}(x[offset + i]); + x[offset + i] = ${M.type.value}(exp(f32input - max_value) / sum); + } + } + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${s};${d};${n}`,inputDependencies:g},getShaderSource:w,getRunData:()=>({outputs:[],dispatchGroup:{x:t},programUniforms:u})}},No=(e,t,r,n,s,a,i,u)=>{let d=u+a.kvSequenceLength,p=[a.batchSize,a.numHeads,a.sequenceLength,d],g=a.kvNumHeads===void 0&&e>1&&n,w=g?[a.batchSize,a.numHeads,d,a.headSize]:void 0,l=i.scale===0?1/Math.sqrt(a.headSize):i.scale,M=mr(a.headSize),C=a.headSize/M,E=12,B={x:Math.ceil(d/E),y:Math.ceil(a.sequenceLength/E),z:a.batchSize*a.numHeads},G=[{type:12,data:a.sequenceLength},{type:12,data:C},{type:12,data:d},{type:12,data:a.numHeads},{type:1,data:l},{type:12,data:u},{type:12,data:a.kvSequenceLength}],O=g&&n&&Se.size(n.dims)>0,ae=["type","type"];O&&ae.push("type"),s&&ae.push("type");let Z=[{dims:p,dataType:t.dataType,gpuDataType:0}];g&&Z.push({dims:w,dataType:t.dataType,gpuDataType:0});let le=Ge=>{let Le=rt("q",t.dataType,t.dims,M),pt=rt("key",r.dataType,r.dims,M),Dt=[Le,pt];if(O){let Nr=rt("past_key",n.dataType,n.dims,M);Dt.push(Nr)}s&&Dt.push(rt("attention_bias",s.dataType,s.dims));let Vt=Ut("output",t.dataType,p),lr=[Vt];g&&lr.push(Ut("present_key",t.dataType,w,M));let fr=Ar(1,M),er=[{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 = ${E}u; + + var tileQ: array<${Le.type.storage}, ${E*E}>; + var tileK: array<${Le.type.storage}, ${E*E}>; + ${Ge.registerUniforms(er).declareVariables(...Dt,...lr)} + ${Ge.mainStart([E,E,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; + ${O&&g?` + 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;`} + ${g?"let presentKeyOffset = headIdx * uniforms.N * uniforms.K;":""} + var value = ${fr}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) { + tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x]; + } + if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) { + var idx = TILE_SIZE * local_id.y + local_id.x; + ${O&&g?` + 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];"} + ${g?"present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];":""} + } + workgroupBarrier(); + + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += ${fr}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]); + } + + workgroupBarrier(); + } + + 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(M){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: ${M}`)}})()}; + output[outputIdx] = ${Vt.type.value} (sum * uniforms.alpha) + ${s?"attention_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${M};${s!==void 0};${n!==void 0};${e}`,inputDependencies:ae},getRunData:()=>({outputs:Z,dispatchGroup:B,programUniforms:G}),getShaderSource:le}},qi=(e,t,r,n,s,a)=>{let i=a+s.kvSequenceLength,u=s.nReps?s.nReps:1,d=s.vHiddenSize*u,p=s.kvNumHeads==null&&e>1&&n,g=p?[s.batchSize,s.numHeads,i,s.headSize]:void 0,w=[s.batchSize,s.sequenceLength,d],l=12,M={x:Math.ceil(s.vHeadSize/l),y:Math.ceil(s.sequenceLength/l),z:s.batchSize*s.numHeads},C=[{type:12,data:s.sequenceLength},{type:12,data:i},{type:12,data:s.vHeadSize},{type:12,data:s.numHeads},{type:12,data:d},{type:12,data:a},{type:12,data:s.kvSequenceLength}],E=p&&n&&Se.size(n.dims)>0,B=["type","type"];E&&B.push("type");let G=[{dims:w,dataType:t.dataType,gpuDataType:0}];p&&G.push({dims:g,dataType:t.dataType,gpuDataType:0});let O=ae=>{let Z=rt("probs",t.dataType,t.dims),le=rt("v",r.dataType,r.dims),Ge=[Z,le];E&&Ge.push(rt("past_value",n.dataType,n.dims));let Le=[Ut("output",t.dataType,w)];p&&Le.push(Ut("present_value",t.dataType,g));let pt=[{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 = ${l}u; + var tileQ: array<${Z.type.value}, ${l*l}>; + var tileK: array<${Z.type.value}, ${l*l}>; + ${ae.registerUniforms(pt).declareVariables(...Ge,...Le)} + ${ae.mainStart([l,l,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; + ${E&&p?` + 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; + `} + ${p?"let presentValueOffset = headIdx * uniforms.N * uniforms.K + n;":""} + var value = ${Z.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; + ${E&&p?` + 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]; + `} + ${p?"present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileK[idx];":""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + let batchIdx = workgroup_id.z / uniforms.num_heads; + let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads; + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + currentBatchHeadNumber * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:B},getRunData:()=>({outputs:G,dispatchGroup:M,programUniforms:C}),getShaderSource:O}},Os=(e,t,r,n,s,a,i,u,d,p,g)=>{let w=Math.min(e.outputCount,1+(i?1:0)+(u?1:0)),l=p.kvNumHeads!==void 0||w>1?p.pastSequenceLength:0,M=l+p.kvSequenceLength,C=d&&Se.size(d.dims)>0?d:void 0,E=[t,r];p.kvNumHeads===void 0&&w>1&&i&&Se.size(i.dims)>0&&E.push(i),C&&E.push(C);let B=e.compute(No(w,t,r,i,C,p,g,l),{inputs:E,outputs:p.kvNumHeads===void 0&&w>1?[-1,1]:[-1]})[0];e.compute(Ro(B,p.batchSize*p.numHeads*p.sequenceLength,M),{inputs:[B],outputs:[]});let G=[B,n];p.kvNumHeads===void 0&&w>1&&u&&Se.size(u.dims)>0&&G.push(u),e.compute(qi(w,B,n,u,p,l),{inputs:G,outputs:p.kvNumHeads===void 0&&w>1?[0,2]:[0]})},jo=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,s=t.inputHiddenSize,a=t.headSize,i=12,u={x:Math.ceil(t.headSize/i),y:Math.ceil(t.sequenceLength/i),z:t.batchSize*t.numHeads},d=[e.inputs[0],e.inputs[1],e.inputs[2]],p=[{type:12,data:n},{type:12,data:s},{type:12,data:a},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],g=w=>{let l=Ut("output_q",d[0].dataType,r),M=Ut("output_k",d[0].dataType,r),C=Ut("output_v",d[0].dataType,r),E=rt("input",d[0].dataType,d[0].dims),B=rt("weight",d[1].dataType,d[1].dims),G=rt("bias",d[2].dataType,d[2].dims),O=E.type.storage,ae=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return` + const TILE_SIZE = ${i}u; + var tileInput: array<${O}, ${i*i}>; + var tileWeightQ: array<${O}, ${i*i}>; + var tileWeightK: array<${O}, ${i*i}>; + var tileWeightV: array<${O}, ${i*i}>; + ${w.registerUniforms(ae).declareVariables(E,B,G,l,M,C)} + ${w.mainStart([i,i,1])} + let batchIndex = workgroup_id.z / uniforms.num_heads; + let headNumber = workgroup_id.z % uniforms.num_heads; + let m = global_id.y; + let n = global_id.x; + + let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K; + let biasOffsetQ = headNumber * uniforms.head_size; + let biasOffsetK = uniforms.hidden_size + biasOffsetQ; + let biasOffsetV = uniforms.hidden_size + biasOffsetK; + + var valueQ = ${O}(0); + var valueK = ${O}(0); + var valueV = ${O}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:p}),getShaderSource:g},{inputs:d,outputs:[-1,-1,-1]})},Hi=(e,t)=>{let r=Gi(e.inputs,t),[n,s,a]=jo(e,r);return Os(e,n,s,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}}),Vo,Xi,Uo,Wo,vd=R(()=>{Pt(),Xt(),Ht(),Sr(),or(),Vo=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,s,a)=>{let i=s.length;if(i!==n.length)throw new Error(`${a}: num dimensions != ${i}`);s.forEach((u,d)=>{if(u!==n[d])throw new Error(`${a}: dim[${d}] do not match`)})};if(e[0].dims.length>1){let n=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,n,"Invalid input scale"),r(e[2].dims,n,"Invalid input B"),r(e[3].dims,n,"Invalid input mean"),r(e[4].dims,n,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input var")},Xi=(e,t)=>{let{epsilon:r,spatial:n,format:s}=t,a=e[0].dims,i=n?mr(a[a.length-1]):1,u=s==="NHWC"&&a.length>1?i:1,d=Se.size(a)/i,p=n,g=p?a.length:a,w=rt("x",e[0].dataType,e[0].dims,i),l=rt("scale",e[1].dataType,e[1].dims,u),M=rt("bias",e[2].dataType,e[2].dims,u),C=rt("inputMean",e[3].dataType,e[3].dims,u),E=rt("inputVar",e[4].dataType,e[4].dims,u),B=Ut("y",e[0].dataType,g,i),G=()=>{let ae="";if(n)ae=`let cOffset = ${a.length===1?"0u":s==="NHWC"?`outputIndices[${a.length-1}] / ${i}`:"outputIndices[1]"};`;else if(s==="NCHW")ae=` + ${B.indicesSet("outputIndices","0","0")} + let cOffset = ${B.indicesToOffset("outputIndices")};`;else{ae=`var cIndices = ${l.type.indices}(0); + cIndices[0] = outputIndices[${a.length-1}];`;for(let Z=1;Z` + const epsilon = ${r}; + ${ae.registerUniform("outputSize","u32").declareVariables(w,l,M,C,E,B)} + ${ae.mainStart()} + ${ae.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${B.offsetToIndices(`global_idx * ${i}`)}; + ${G()} + let scale = ${l.getByOffset("cOffset")}; + let bias = ${M.getByOffset("cOffset")}; + let inputMean = ${C.getByOffset("cOffset")}; + let inputVar = ${E.getByOffset("cOffset")}; + let x = ${w.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${B.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${i}`,inputDependencies:p?["rank","type","type","type","type"]:void 0},getShaderSource:O,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p?[{type:12,data:d},...Ct(a)]:[{type:12,data:d}]})}},Uo=e=>ur(e),Wo=(e,t)=>{let{inputs:r,outputCount:n}=e,s=Uo({...t,outputCount:n});if(k.webgpu.validateInputContent&&Vo(r,s),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Xi(r,s))}}),Go,Qi,qo,xd=R(()=>{Ht(),or(),Go=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Qi=e=>{let t=e[0].dims,r=e[0].dims[2],n=Se.size(t)/4,s=e[0].dataType,a=rt("input",s,t,4),i=rt("bias",s,[r],4),u=rt("residual",s,t,4),d=Ut("output",s,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:p=>` + const channels = ${r}u / 4; + ${p.declareVariables(a,i,u,d)} + + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes(n)} + let value = ${a.getByOffset("global_idx")} + + ${i.getByOffset("global_idx % channels")} + ${u.getByOffset("global_idx")}; + ${d.setByOffset("global_idx","value")} + }`}},qo=e=>{Go(e.inputs),e.compute(Qi(e.inputs))}}),Ho,vr,Yi,Ko,Xo,Qo,Yo,Ji,Jo,Zo,Zi,el,tl,ea,rl,nl,ys,sl,Zs,ta,il,al,ei,ol,ll,ra,na,ul,sa,dl,cl,pl,ia,hl,fl,aa,oa,la,ua,da,ml,_l,ca,gl,wl,ti=R(()=>{Xt(),Ht(),Sr(),or(),Ho=(e,t,r,n,s,a,i)=>{let u=Math.ceil(t/4),d="";typeof s=="string"?d=`${s}(a)`:d=s("a");let p=rt("inputData",r,[u],4),g=Ut("outputData",n,[u],4),w=[{name:"vec_size",type:"u32"}];return i&&w.push(...i),` + ${e.registerUniforms(w).declareVariables(p,g)} + + ${a??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${p.getByOffset("global_idx")}; + ${g.setByOffset("global_idx",d)} + }`},vr=(e,t,r,n,s,a=e.dataType,i,u)=>{let d=[{type:12,data:Math.ceil(Se.size(e.dims)/4)}];return i&&d.push(...i),{name:t,shaderCache:{hint:s,inputDependencies:["type"]},getShaderSource:p=>Ho(p,Se.size(e.dims),e.dataType,a,r,n,u),getRunData:p=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil(Se.size(p[0].dims)/64/4)},programUniforms:d})}},Yi=e=>{e.compute(vr(e.inputs[0],"Abs","abs"))},Ko=e=>{e.compute(vr(e.inputs[0],"Acos","acos"))},Xo=e=>{e.compute(vr(e.inputs[0],"Acosh","acosh"))},Qo=e=>{e.compute(vr(e.inputs[0],"Asin","asin"))},Yo=e=>{e.compute(vr(e.inputs[0],"Asinh","asinh"))},Ji=e=>{e.compute(vr(e.inputs[0],"Atan","atan"))},Jo=e=>{e.compute(vr(e.inputs[0],"Atanh","atanh"))},Zo=e=>ur(e),Zi=(e,t)=>{let r;switch(t.to){case 10:r="vec4";break;case 1:r="vec4";break;case 12:r="vec4";break;case 6:r="vec4";break;case 9:r="vec4";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute(vr(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},el=e=>{let t,r,n=e.length>=2&&e[1].data!==0,s=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=n?e[1].getFloat32Array()[0]:-34028234663852886e22,r=s?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=n?e[1].getUint16Array()[0]:64511,r=s?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return ur({min:t,max:r})},tl=(e,t)=>{let r=t||el(e.inputs),n=Ar(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"Clip",s=>`clamp(${s}, vec4<${n}>(uniforms.min), vec4<${n}>(uniforms.max))`,void 0,r.cacheKey,void 0,[{type:e.inputs[0].dataType,data:r.min},{type:e.inputs[0].dataType,data:r.max}],[{name:"min",type:n},{name:"max",type:n}]),{inputs:[0]})},ea=e=>{e.compute(vr(e.inputs[0],"Ceil","ceil"))},rl=e=>{e.compute(vr(e.inputs[0],"Cos","cos"))},nl=e=>{e.compute(vr(e.inputs[0],"Cosh","cosh"))},ys=e=>ur(e),sl=(e,t)=>{let r=Ar(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"Elu",n=>`elu_vf32(${n})`,` + const elu_alpha_ = ${r}(${t.alpha}); + + fn elu_f32(a: ${r}) -> ${r} { + return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,t.cacheKey))},Zs=(e="f32")=>` +const r0: ${e} = 0.3275911; +const r1: ${e} = 0.254829592; +const r2: ${e} = -0.284496736; +const r3: ${e} = 1.421413741; +const r4: ${e} = -1.453152027; +const r5: ${e} = 1.061405429; + +fn erf_vf32(v: vec4<${e}>) -> vec4<${e}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,ta=e=>{let t=Ar(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,Zs(t)))},il=e=>{e.compute(vr(e.inputs[0],"Exp","exp"))},al=e=>{e.compute(vr(e.inputs[0],"Floor","floor"))},ei=e=>{let t=Ar(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,Zs(t)))},ol=(e,t)=>{let r=Ar(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},ll=e=>{e.compute(vr(e.inputs[0],"Not",t=>`!${t}`))},ra=e=>{e.compute(vr(e.inputs[0],"Neg",t=>`-${t}`))},na=e=>{e.compute(vr(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},ul=e=>{let t=Ar(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},sa=e=>{e.compute(vr(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},dl=e=>ur(e),cl=(e,t)=>{let r=Ar(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${n} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},pl=e=>{e.compute(vr(e.inputs[0],"Sin","sin"))},ia=e=>{e.compute(vr(e.inputs[0],"Sinh","sinh"))},hl=e=>{e.compute(vr(e.inputs[0],"Sqrt","sqrt"))},fl=e=>{e.compute(vr(e.inputs[0],"Tan","tan"))},aa=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,oa=e=>{e.compute(vr(e.inputs[0],"Tanh",aa))},la=(e="f32")=>` +const fast_gelu_a: ${e} = 0.5; +const fast_gelu_b: ${e} = 0.7978845608028654; +const fast_gelu_c: ${e} = 0.035677408136300125; + +fn tanh_v(v: vec4<${e}>) -> vec4<${e}> { + return ${aa("v")}; +} +`,ua=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,da=e=>{let t=Ar(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"FastGelu",ua,la(t),void 0,e.inputs[0].dataType))},ml=(e,t)=>{let r=Ar(e.inputs[0].dataType);return e.compute(vr(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},_l=e=>{e.compute(vr(e.inputs[0],"Log","log"))},ca=(e,t)=>` +const alpha = vec4<${e}>(${t}); +const one = ${e}(1.0); +const zero = ${e}(0.0); + +fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> { + let v = x *alpha; + var x1 : vec4<${e}>; + for (var i = 0; i < 4; i = i + 1) { + if (v[i] >= zero) { + x1[i] = one / (one + exp(-v[i])); + } else { + x1[i] = one - one / (one + exp(v[i])); + } + } + return x * x1; +} +`,gl=e=>`quick_gelu_impl(${e})`,wl=(e,t)=>{let r=Ar(e.inputs[0].dataType);e.compute(vr(e.inputs[0],"QuickGelu",gl,ca(r,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),yl,bl,pa,Td=R(()=>{Ht(),or(),ti(),yl=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},bl=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=rt("input",e[0].dataType,e[0].dims,4),n=rt("bias",e[0].dataType,[e[0].dims[2]],4),s=Ut("output",e[0].dataType,t,4),a=Se.size(t)/4,i=yr(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:u=>` + const M_SQRT2 = sqrt(2.0); + const halfChannels = ${e[0].dims[2]/4/2}u; + + ${u.declareVariables(r,n,s)} + + ${Zs(i)} + + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes(a)} + let biasIdx = global_idx % halfChannels; + let batchIndex = global_idx / halfChannels; + let inputOffset = biasIdx + batchIndex * halfChannels * 2; + let valueLeft = input[inputOffset] + bias[biasIdx]; + let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${s.setByOffset("global_idx","valueLeft * geluRight")} + }`}},pa=e=>{yl(e.inputs),e.compute(bl(e.inputs))}}),Ml,ha,On,vl,xl,Tl,ri,Cl,El,$l,fa,Sl,kl,Pl=R(()=>{Xt(),Ht(),or(),Ml=(e,t,r,n,s,a,i,u,d,p,g,w)=>{let l,M;typeof u=="string"?l=M=(O,ae)=>`${u}((${O}),(${ae}))`:typeof u=="function"?l=M=u:(l=u.scalar,M=u.vector);let C=Ut("outputData",g,n.length,4),E=rt("aData",d,t.length,4),B=rt("bData",p,r.length,4),G;if(s)if(a){let O=Se.size(t)===1,ae=Se.size(r)===1,Z=t.length>0&&t[t.length-1]%4===0,le=r.length>0&&r[r.length-1]%4===0;O||ae?G=C.setByOffset("global_idx",M(O?`${E.type.value}(${E.getByOffset("0")}.x)`:E.getByOffset("global_idx"),ae?`${B.type.value}(${B.getByOffset("0")}.x)`:B.getByOffset("global_idx"))):G=` + let outputIndices = ${C.offsetToIndices("global_idx * 4u")}; + let offsetA = ${E.broadcastedIndicesToOffset("outputIndices",C)}; + let offsetB = ${B.broadcastedIndicesToOffset("outputIndices",C)}; + ${C.setByOffset("global_idx",M(i||Z?E.getByOffset("offsetA / 4u"):`${E.type.value}(${E.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||le?B.getByOffset("offsetB / 4u"):`${B.type.value}(${B.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else G=C.setByOffset("global_idx",M(E.getByOffset("global_idx"),B.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let O=(ae,Z,le="")=>{let Ge=`aData[indexA${Z}][componentA${Z}]`,Le=`bData[indexB${Z}][componentB${Z}]`;return` + let outputIndices${Z} = ${C.offsetToIndices(`global_idx * 4u + ${Z}u`)}; + let offsetA${Z} = ${E.broadcastedIndicesToOffset(`outputIndices${Z}`,C)}; + let offsetB${Z} = ${B.broadcastedIndicesToOffset(`outputIndices${Z}`,C)}; + let indexA${Z} = offsetA${Z} / 4u; + let indexB${Z} = offsetB${Z} / 4u; + let componentA${Z} = offsetA${Z} % 4u; + let componentB${Z} = offsetB${Z} % 4u; + ${ae}[${Z}] = ${le}(${l(Ge,Le)}); + `};g===9?G=` + var data = vec4(0); + ${O("data",0,"u32")} + ${O("data",1,"u32")} + ${O("data",2,"u32")} + ${O("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:G=` + ${O("outputData[global_idx]",0)} + ${O("outputData[global_idx]",1)} + ${O("outputData[global_idx]",2)} + ${O("outputData[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(E,B,C)} + + ${w??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${G} + }`},ha=(e,t,r,n,s,a,i=r.dataType)=>{let u=!Se.areEqual(r.dims,n.dims),d=r.dims,p=Se.size(r.dims),g=!1,w=!1,l=[u];if(u){let M=wn.calcShape(r.dims,n.dims,!1);if(!M)throw new Error("Can't perform binary op on the given tensors");d=M,p=Se.size(d);let C=Se.size(r.dims)===1,E=Se.size(n.dims)===1,B=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,G=n.dims.length>0&&n.dims[n.dims.length-1]%4===0;l.push(C),l.push(E),l.push(B),l.push(G);let O=1;for(let ae=1;aeM.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:M=>Ml(M,r.dims,n.dims,d,g,u,w,s,r.dataType,n.dataType,i,a),getRunData:()=>({outputs:[{dims:d,dataType:i}],dispatchGroup:{x:Math.ceil(p/64/4)},programUniforms:[{type:12,data:Math.ceil(Se.size(d)/4)},...Ct(r.dims,n.dims,d)]})}},On=(e,t,r,n,s,a)=>{e.compute(ha(t,s??"",e.inputs[0],e.inputs[1],r,n,a))},vl=e=>{On(e,"Add",(t,r)=>`${t}+${r}`)},xl=e=>{On(e,"Div",(t,r)=>`${t}/${r}`)},Tl=e=>{On(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4(${t}==${r})`},void 0,void 0,9)},ri=e=>{On(e,"Mul",(t,r)=>`${t}*${r}`)},Cl=e=>{let t=rt("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;On(e,"Pow",{scalar:(r,n)=>`pow_custom(${r},${n})`,vector:(r,n)=>`pow_vector_custom(${r},${n})`},` + fn pow_custom(a : ${t}, b : ${t}) -> ${t} { + if (b == ${t}(0.0)) { + return ${t}(1.0); + } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) { + return ${t}(pow(f32(a), f32(b))); // NaN + } + return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); + } + fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> { + // TODO: implement vectorized pow + return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); + } + `)},El=e=>{On(e,"Sub",(t,r)=>`${t}-${r}`)},$l=e=>{On(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4(${t}>${r})`},void 0,void 0,9)},fa=e=>{On(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4(${t}<${r})`},void 0,void 0,9)},Sl=e=>{On(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4(${t}>=${r})`},void 0,void 0,9)},kl=e=>{On(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4(${t}<=${r})`},void 0,void 0,9)}}),Al,Il,Fl,zl,ma,Ol,Cd=R(()=>{Xt(),Ht(),Sr(),or(),Al=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],s=n.dataType,a=n.dims.length;e.forEach((i,u)=>{if(u!==r){if(i.dataType!==s)throw new Error("input tensors should be one type");if(i.dims.length!==a)throw new Error("input tensors should have the same shape");i.dims.forEach((d,p)=>{if(p!==t&&d!==n.dims[p])throw new Error("non concat dimensions must match")})}})},Il=(e,t)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${t}); + for (var i: u32 = 0u; i < ${e}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${e}u; + }`,Fl=(e,t)=>{let r=e.length,n=[];for(let s=0;s{let s=Se.size(r),a=new Array(e.length),i=new Array(e.length),u=0,d=[],p=[],g=[{type:12,data:s}];for(let E=0;E`uniforms.sizeInConcatAxis${E}`).join(","),C=E=>` + + ${(()=>{E.registerUniform("outputSize","u32");for(let B=0;B(${M}); + ${l} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${Fl(i,w)} + }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:g}),getShaderSource:C}},ma=(e,t)=>{let r=e.inputs,n=r[0].dims,s=Se.normalizeAxis(t.axis,n.length);Al(r,s);let a=n.slice();a[s]=r.reduce((u,d)=>u+(d.dims.length>s?d.dims[s]:0),0);let i=r.filter(u=>Se.size(u.dims)>0);e.compute(zl(i,s,a,r[0].dataType),{inputs:i})},Ol=e=>ur({axis:e.axis})}),jn,Qn,Yn,ni,Jn=R(()=>{Xt(),Ht(),jn=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value)); + value = sign(value) * (1.0 - e2x) / (1.0 + e2x); + `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},Qn=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Yn=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},ni=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[r,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=(e==null?void 0:e.activation_params)||[kn,Nn];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:r}}return{activation:t}}}),fn,si,ii=R(()=>{fn=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},si=e=>` + ${e?"value = value + getBiasByOutputCoords(coords);":""} + `}),_a,Dl=R(()=>{_a=e=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); +} +`}),Ll,Bl,ai,oi,Rl,li,ga,wa,ui=R(()=>{Xt(),Ht(),or(),Jn(),ii(),Ll=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart / innerElementSize + inputCol${t?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRow + innerRow, + kStart / innerElementSize + inputCol${t?", batchIndices":""}); + `,Bl=(e,t)=>e?` + let ACached0 = mm_Asub[k * innerElementSize][localRow]; + let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; + let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; + ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"} + for (var i = 0; i < rowPerThread; i = i + 1) { + acc[i] = BCached0 * ACached0[i] + acc[i]; + acc[i] = BCached1 * ACached1[i] + acc[i]; + acc[i] = BCached2 * ACached2[i] + acc[i]; + ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"} + }`:` + for (var i = 0; i < rowPerThread; i = i + 1) { + let ACached = mm_Asub[tileRow + i][k]; + acc[i] = BCached0 * ACached.x + acc[i]; + acc[i] = BCached1 * ACached.y + acc[i]; + acc[i] = BCached2 * ACached.z + acc[i]; + ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} + }`,ai=(e,t,r="f32",n,s=!1,a=32,i=!1,u=32)=>{let d=t[1]*e[1],p=t[0]*e[0],g=s?d:a,w=s?a:d,l=g/t[0],M=a/t[1];if(!((s&&l===4&&e[1]===4||!s&&(l===3||l===4))&&g%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${s} is true, innerElementSize ${l} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${l} must be 3 or 4. + tileAWidth ${g} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return` +var mm_Asub: array, ${g/l}>, ${w}>; +var mm_Bsub: array, ${p/e[0]}>, ${a}>; + +const rowPerThread = ${e[1]}; +const colPerThread = ${e[0]}; +const innerElementSize = ${l}; +const tileInner = ${a}; + +@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let localRow = i32(localId.y); + let tileRow = localRow * rowPerThread; + let tileCol = i32(localId.x); + + let globalRow =i32(globalId.y) * rowPerThread; + let globalCol = i32(globalId.x); + let batch = ${i?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${d}; + + let num_tiles = ${i?`${Math.ceil(u/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${i?`i32(globalId.z) * ${u}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${M}; + 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; + ${Ll(s,n)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${M}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${n?", batchIndices":""}); + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { + let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; + let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; + let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; + ${l===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${Bl(s,l)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},oi=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${t?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${t?", batchIndices":""}); + `,Rl=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",li=(e,t,r="f32",n,s=!1,a=32,i=!1,u=32,d=!1)=>{let p=e[1]*t[1],g=e[0]*t[0],w=s?p:a,l=s?a:p;if(!(l%t[1]===0&&w%t[0]===0&&a%t[1]===0))throw new Error(`tileAHight ${l} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${w} must be divisible by workgroupSize[0]${t[0]}, tileInner ${a} must be divisible by workgroupSize[1]${t[1]}`);let M=l/t[1],C=w/t[0],E=a/t[1],B=d?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${p}; + let globalColStart = i32(workgroupId.x) * ${g}; + + // Loop over shared dimension. + for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${w}; inputCol = inputCol + ${t[0]}) { + ${oi(s,n)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${g}; inputCol = inputCol + ${t[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalColStart + inputCol${n?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${r}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}]; + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let ACached = ${s?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + + ACached * BCached[innerCol]; + } + } + } + workgroupBarrier(); + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let gRow = globalRowStart + localRow + innerRow * ${t[1]}; + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let gCol = globalColStart + localCol + innerCol * ${t[0]}; + mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); + } + } + `:` +let tileRow = i32(localId.y) * rowPerThread; +let tileCol = i32(localId.x) * colPerThread; + +let globalRow = i32(globalId.y) * rowPerThread; +let globalCol = i32(globalId.x) * colPerThread; +let globalRowStart = i32(workgroupId.y) * ${p}; + +let tileRowA = i32(localId.y) * ${M}; +let tileColA = i32(localId.x) * ${C}; +let tileRowB = i32(localId.y) * ${E}; +// 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 < ${M}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${C}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${oi(s,n)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${E}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${n?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${r}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][tileCol + inner]; + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + ${Rl(s)} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; + } + } + } + + workgroupBarrier(); +} + +for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + mm_write(batch, globalRow + innerRow, globalCol + innerCol, + acc[innerRow][innerCol]); + } +} +`;return` + var mm_Asub : array, ${l}>; + var mm_Bsub : array, ${a}>; + const rowPerThread = ${e[1]}; + const colPerThread = ${e[0]}; + const tileInner = ${a}; + +@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let batch = ${i?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${i?`${Math.ceil(u/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${i?`i32(globalId.z) * ${u}`:"0"}; + + var acc : array, rowPerThread>; + ${B} + } +`},ga=(e,t,r,n,s,a=!1)=>{let[i,u,d]=s,[p,g,w,l]=n,M=Fs(i,d),C=Fs(u,d),E=yr(n[0].type.tensor),B=()=>{let O=g.rank,ae=p.rank,Z=`var aIndices: ${g.type.indices};`;for(let le=O-2-1,Ge=ae-1;le>=0;le--,Ge--)Z+=` +aIndices[${le}] = ${ae>1?`batchIndices[${Ge}]`:"batchIndices"};`;return M.forEach(le=>{Z+=` +aIndices[${le}] = 0;`}),Z+=` +aIndices[${O-2}] = u32(row); + aIndices[${O-1}] = u32(colIn);`,Z},G=()=>{let O=w.rank,ae=p.rank,Z=`var bIndices: ${w.type.indices};`;for(let le=O-2-1,Ge=ae-1;le>=0;le--,Ge--)Z+=` +bIndices[${le}] = ${ae>1?`batchIndices[${Ge}]`:"batchIndices"};`;return C.forEach(le=>{Z+=` +bIndices[${le}] = 0;`}),Z+=` +bIndices[${O-2}] = u32(row); + bIndices[${O-1}] = u32(colIn);`,Z};return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${fn(e,E)} { + var value = ${fn(e,E)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + ${B()} + value = ${g.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${fn(e,E)} { + var value = ${fn(e,E)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + ${G()} + value = ${w.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${fn(e,E)}) { + let col = colIn * ${e}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueIn; + let coords = vec3(batch, row, colIn); + ${t?`value = value + ${a?"bias[colIn]":`${fn(e,E)}(bias[row])`};`:""} + ${r} + ${l.setByIndices("vec3(coords)","value")} + } + } + `},wa=(e,t,r,n,s=!1,a)=>{let i=e[0].dims,u=e[1].dims,d=i.slice(0,-2),p=u.slice(0,-2),g=n?n.slice(0,-2):r.slice(0,-2),w=Se.size(g),l=i[i.length-2],M=i[i.length-1],C=u[u.length-1],E=M%4===0&&C%4===0,B=l<=8?[4,1,1]:[4,4,1],G=[8,8,1],O=[Math.ceil(C/G[0]/B[0]),Math.ceil(l/G[1]/B[1]),Math.ceil(w/G[2]/B[2])],ae=E?4:1,Z=[...d,l,M/ae],le=Z.length,Ge=[...p,M,C/ae],Le=Ge.length,pt=[w,l,C/ae],Dt=[{type:6,data:l},{type:6,data:C},{type:6,data:M}];Qn(t,Dt),Dt.push(...Ct(g,Z,Ge));let Vt=["rank","rank"],lr=e.length>2;lr&&(Dt.push(...Ct(e[2].dims)),Vt.push("rank")),Dt.push(...Ct(pt));let fr=er=>{let Nr=g.length,Hr=Ti("batchDims",e[0].dataType,Nr,1),br=yr(e[0].dataType),jr=rt("a",e[0].dataType,le,ae),Jt=rt("b",e[1].dataType,Le,ae),dr=Ut("result",e[0].dataType,pt.length,ae),Pr=[jr,Jt];if(lr){let nn=s?ae:1;Pr.push(rt("bias",e[2].dataType,e[2].dims.length,nn))}let Ve=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Yn(t,Ve);let kt=yr(dr.type.tensor),tr=jn(t,dr.type.value,kt),zr=ga(ae,lr,tr,[Hr,jr,Jt,dr],[d,p,g],s);return` + ${er.registerUniforms(Ve).registerInternalVariables(Hr).declareVariables(...Pr,dr)} + ${zr} + ${E?ai(B,G,br,Hr):li(B,G,br,Hr)} + `};return{name:"MatMul",shaderCache:{hint:`${B};${t.activation};${E};${s}`,inputDependencies:Vt},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:O[0],y:O[1],z:O[2]},programUniforms:Dt}),getShaderSource:fr}}}),Zn,Nl,Ed=R(()=>{Xt(),m(),or(),Jn(),ii(),Dl(),ui(),Zn=(e,t,r,n,s=!1,a,i=4,u=4,d=4,p="f32")=>{let g=Dt=>{switch(Dt){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${p}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Dt} is not supported.`)}},w=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.`)}},l=e?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,M=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,C=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",E=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",B=e?"row":"col",G=e?"col":"row",O=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${B} / outWidth; + let outCol = ${B} % outWidth; + + let WRow = ${G} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${G} / 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 = ${G} % inChannels; + var resData = ${fn(i,p)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${C} && xCol >= 0 && xCol < ${E}) { + ${l} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${g(i)} + } + return resData;`,ae=e?t&&n?` + let col = colIn * ${i}; + ${O}`:` + let col = colIn * ${i}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${O} + } + return ${fn(i,p)}(0.0);`:n&&r?` + let col = colIn * ${i}; + ${O}`:` + let col = colIn * ${i}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${O} + } + return ${fn(i,p)}(0.0);`,Z=`${w(u)}`,le=fn(d,p),Ge=fn(e?i:u,p),Le=fn(e?u:i,p),pt=jn(a,le,p);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Ge} { + ${e?ae:Z} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Le} { + ${e?Z:ae} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${le}) { + let col = colIn * ${d}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) + { + var value = valueIn; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${M} + ${si(s)} + ${pt} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},Nl=(e,t,r,n,s,a,i,u,d)=>{let p=t.format==="NHWC",g=p?e[0].dims[3]:e[0].dims[1],w=r[0],l=p?r[2]:r[3],M=p?r[1]:r[2],C=p?r[3]:r[1],E=p&&(g%4===0||g%3===0)&&C%4===0,B=p?C:l*M,G=p?l*M:C,O=[8,8,1],ae=n<=8?[4,1,1]:[4,4,1],Z=[Math.ceil(B/O[0]/ae[0]),Math.ceil(G/O[1]/ae[1]),Math.ceil(w/O[2]/ae[2])];ue("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${Z}`);let le=E?p&&g%4!==0?3:4:1,Ge=O[1]*ae[1],Le=O[0]*ae[0],pt=Math.max(O[0]*le,O[1]),Dt=n%Ge===0,Vt=s%Le===0,lr=a%pt===0,fr=E?[le,4,4]:[1,1,1],er=[{type:6,data:n},{type:6,data:s},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Qn(t,er),er.push(...Ct(e[0].dims,e[1].dims));let Nr=["rank","rank"];i&&(er.push(...Ct(e[2].dims)),Nr.push("rank")),er.push(...Ct(r));let Hr=br=>{let jr=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Yn(t,jr);let Jt=E?4:1,dr=yr(e[0].dataType),Pr=` + fn setOutputAtIndex(flatIndex : i32, value : ${E?`vec4<${dr}>`:dr}) { + result[flatIndex] = ${E?`vec4<${dr}>`:dr}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${E?`vec4<${dr}>`:dr}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${E?"/ 4":""}, value); + }`,Ve=rt("x",e[0].dataType,e[0].dims.length,le===3?1:le),kt=rt("w",e[1].dataType,e[1].dims.length,Jt),tr=[Ve,kt],zr=Ut("result",e[0].dataType,r.length,Jt);if(i){let nn=rt("bias",e[2].dataType,e[2].dims.length,Jt);tr.push(nn),Pr+=` + fn getBiasByOutputCoords(coords : vec4) -> ${E?`vec4<${dr}>`:dr} { + return bias[coords.${p?"w":"y"}${E?"/ 4":""}]; + }`}return` + ${_a("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 }; + ${br.registerUniforms(jr).declareVariables(...tr,zr)} + ${Pr} + ${Zn(p,Dt,Vt,lr,i,t,fr[0],fr[1],fr[2],dr)} + ${E?ai(ae,O,dr,void 0,!p,pt):li(ae,O,dr,void 0,!p,pt,!1,void 0,u)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${le};${E};${Dt};${Vt};${lr};${Ge};${Le};${pt}`,inputDependencies:Nr},getRunData:()=>({outputs:[{dims:d?d(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Z[0],y:Z[1],z:Z[2]},programUniforms:er}),getShaderSource:Hr}}}),jl,ya,bs,Vl,ba,Ul,Wl,di,$d=R(()=>{Xt(),m(),Ht(),or(),Jn(),ii(),jl=e=>{let t=1;for(let r=0;rtypeof e=="number"?[e,e,e]:e,bs=(e,t)=>t<=1?e:e+(e-1)*(t-1),Vl=(e,t,r,n=1)=>{let s=bs(t,n);return Math.floor((e[0]*(r-1)-r+s)/2)},ba=(e,t,r,n,s)=>{s==null&&(s=Vl(e,t[0],n[0]));let a=[0,0,0,r];for(let i=0;i<3;i++)e[i]+2*s>=t[i]&&(a[i]=Math.trunc((e[i]-t[i]+2*s)/n[i]+1));return a},Ul=(e,t,r,n,s,a,i,u,d,p)=>{let g,w,l,M;if(e==="VALID"&&(e=0),typeof e=="number"){g={top:e,bottom:e,left:e,right:e,front:e,back:e};let C=ba([t,r,n,1],[u,d,p],1,[s,a,i],e);w=C[0],l=C[1],M=C[2]}else if(Array.isArray(e)){if(!e.every((E,B,G)=>E===G[0]))throw Error(`Unsupported padding parameter: ${e}`);g={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let C=ba([t,r,n,1],[u,d,p],1,[s,a,i],e[0]);w=C[0],l=C[1],M=C[2]}else if(e==="SAME_UPPER"){w=Math.ceil(t/s),l=Math.ceil(r/a),M=Math.ceil(n/i);let C=(w-1)*s+u-t,E=(l-1)*a+d-r,B=(M-1)*i+p-n,G=Math.floor(C/2),O=C-G,ae=Math.floor(E/2),Z=E-ae,le=Math.floor(B/2),Ge=B-le;g={top:ae,bottom:Z,left:le,right:Ge,front:G,back:O}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:g,outDepth:w,outHeight:l,outWidth:M}},Wl=(e,t,r,n,s,a=!1,i="channelsLast")=>{let u,d,p,g,w;if(i==="channelsLast")[u,d,p,g,w]=e;else if(i==="channelsFirst")[u,w,d,p,g]=e;else throw new Error(`Unknown dataFormat ${i}`);let[l,,M,C,E]=t,[B,G,O]=ya(r),[ae,Z,le]=ya(n),Ge=bs(M,ae),Le=bs(C,Z),pt=bs(E,le),{padInfo:Dt,outDepth:Vt,outHeight:lr,outWidth:fr}=Ul(s,d,p,g,B,G,O,Ge,Le,pt),er=a?l*w:l,Nr=[0,0,0,0,0];return i==="channelsFirst"?Nr=[u,er,Vt,lr,fr]:i==="channelsLast"&&(Nr=[u,Vt,lr,fr,er]),{batchSize:u,dataFormat:i,inDepth:d,inHeight:p,inWidth:g,inChannels:w,outDepth:Vt,outHeight:lr,outWidth:fr,outChannels:er,padInfo:Dt,strideDepth:B,strideHeight:G,strideWidth:O,filterDepth:M,filterHeight:C,filterWidth:E,effectiveFilterDepth:Ge,effectiveFilterHeight:Le,effectiveFilterWidth:pt,dilationDepth:ae,dilationHeight:Z,dilationWidth:le,inShape:e,outShape:Nr,filterShape:t}},di=(e,t,r,n,s,a)=>{let i=a==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let u=[64,1,1],d={x:r.map((B,G)=>G)},p=[Math.ceil(jl(d.x.map(B=>r[B]))/u[0]),1,1];ue("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${p}`);let g=1,w=Se.size(r),l=[{type:12,data:w},{type:12,data:n},{type:12,data:s},{type:12,data:t.strides},{type:12,data:t.dilations}];Qn(t,l),l.push(...Ct(e[0].dims,e[1].dims));let M=["rank","rank"],C=e.length===3;C&&(l.push(...Ct(e[2].dims)),M.push("rank")),l.push(...Ct(r));let E=B=>{let G=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:n.length},{name:"pads",type:"u32",length:s.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];Yn(t,G);let O=1,ae=yr(e[0].dataType),Z=rt("x",e[0].dataType,e[0].dims.length,g),le=rt("W",e[1].dataType,e[1].dims.length,O),Ge=[Z,le],Le=Ut("result",e[0].dataType,r.length,O),pt="";if(C){let lr=rt("bias",e[2].dataType,e[2].dims.length,O);Ge.push(lr),pt+=` + fn getBiasByOutputCoords(coords : array) -> ${ae} { + return bias[${i?jt("coords",4,5):jt("coords",1,5)}]; + }`}let Dt=fn(g,ae),Vt=jn(t,Dt,ae);return` + ${pt} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${Z.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${le.getByIndices("aIndices")}; + } + ${B.registerUniforms(G).declareVariables(...Ge,Le)} + ${B.mainStart()} + ${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${Le.offsetToIndices("global_idx")}; + let batch = ${jt("coords",0,Z.rank)}; + let d2 = ${i?jt("coords",Z.rank-1,Z.rank):jt("coords",1,Z.rank)}; + let xFRCCorner = vec3(${i?jt("coords",1,Z.rank):jt("coords",2,Z.rank)}, + ${i?jt("coords",2,Z.rank):jt("coords",3,Z.rank)}, + ${i?jt("coords",3,Z.rank):jt("coords",4,Z.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${i?jt("uniforms.x_shape",1,Z.rank):jt("uniforms.x_shape",2,Z.rank)}; + let xShapeZ = ${i?jt("uniforms.x_shape",2,Z.rank):jt("uniforms.x_shape",3,Z.rank)}; + let xShapeW = ${i?jt("uniforms.x_shape",3,Z.rank):jt("uniforms.x_shape",4,Z.rank)}; + let xShapeU = ${i?jt("uniforms.x_shape",4,Z.rank):jt("uniforms.x_shape",1,Z.rank)}; + let inputDepthNearestVec4 = (xShapeU / 4) * 4; + let inputDepthVec4Remainder = xShapeU % 4; + + var value = 0.0; + for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) { + let xF = xFCorner + wF * uniforms.dilations[0]; + if (xF < 0 || xF >= xShapeY) { + continue; + } + + for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) { + let xR = xRCorner + wR * uniforms.dilations[1]; + if (xR < 0 || xR >= xShapeZ) { + continue; + } + + for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) { + let xC = xCCorner + wC * uniforms.dilations[2]; + if (xC < 0 || xC >= xShapeW) { + continue; + } + + for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) { + ${i?`let xValues = vec4( + getX(batch, xF, xR, xC, d1), + getX(batch, xF, xR, xC, d1 + 1), + getX(batch, xF, xR, xC, d1 + 2), + getX(batch, xF, xR, xC, d1 + 3)); + `:`let xValues = vec4( + getX(batch, d1, xF, xR, xC), + getX(batch, d1 + 1, xF, xR, xC), + getX(batch, d1 + 2, xF, xR, xC), + getX(batch, d1 + 3, xF, xR, xC)); + `} + let wValues = vec4( + getW(d2, d1, wF, wR, wC), + getW(d2, d1 + 1, wF, wR, wC), + getW(d2, d1 + 2, wF, wR, wC), + getW(d2, d1 + 3, wF, wR, wC)); + value += dot(xValues, wValues); + } + if (inputDepthVec4Remainder == 1) { + ${i?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4) + * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC) + * getW(d2, inputDepthNearestVec4, wF, wR, wC);`} + } else if (inputDepthVec4Remainder == 2) { + ${i?`let xValues = vec2( + getX(batch, xF, xR, xC, inputDepthNearestVec4), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)); + `:`let xValues = vec2( + getX(batch, inputDepthNearestVec4, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC)); + `} + let wValues = vec2( + getW(d2, inputDepthNearestVec4, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC)); + value += dot(xValues, wValues); + } else if (inputDepthVec4Remainder == 3) { + ${i?`let xValues = vec3( + getX(batch, xF, xR, xC, inputDepthNearestVec4), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)); + `:`let xValues = vec3( + getX(batch, inputDepthNearestVec4, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC)); + `} + let wValues = vec3( + getW(d2, inputDepthNearestVec4, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC)); + value += dot(xValues, wValues); + } + } + } + } + ${C?"value = value + getBiasByOutputCoords(coords)":""}; + ${Vt} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${i};${g};${C}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:p[0],y:p[1],z:p[2]},programUniforms:l}),getShaderSource:E}}}),Gl,ql,ci=R(()=>{Xt(),Ht(),or(),Jn(),Gl=(e,t,r,n)=>{let s=e.length>2,a=s?"value += b[output_channel];":"",i=e[0].dims,u=e[1].dims,d=t.format==="NHWC",p=d?r[3]:r[1],g=p/t.group,w=d&&g>=4?mr(p):1,l=Se.size(r)/w,M=[{type:12,data:l},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:g}];Qn(t,M),M.push(...Ct(i,[u[0],u[1],u[2],u[3]/w]));let C=s?["rank","rank","rank"]:["rank","rank"];M.push(...Ct([r[0],r[1],r[2],r[3]/w]));let E=B=>{let G=Ut("output",e[0].dataType,r.length,w),O=yr(G.type.tensor),ae=jn(t,G.type.value,O),Z=rt("x",e[0].dataType,i.length),le=rt("w",e[1].dataType,u.length,w),Ge=[Z,le];s&&Ge.push(rt("b",e[2].dataType,e[2].dims,w));let Le=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];Yn(t,Le);let pt=d?` + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) { + continue; + } + + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) { + let input_channel = in_channel_offset + wInChannel; + let xVal = ${Z.get("batch","xHeight","xWidth","input_channel")}; + let wVal = ${le.get("wHeight","wWidth","wInChannel","output_channel")}; + value += xVal * wVal; + } + } + } + `:` + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { + let input_channel = in_channel_offset + wInChannel; + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) { + continue; + } + + let xVal = ${Z.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${le.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal * wVal; + } + } + } + `;return` + ${B.registerUniforms(Le).declareVariables(...Ge,G)} + + ${B.mainStart()} + ${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${G.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${d?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${d?1:2}], outputIndices[${d?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel * ${w} / uniforms.output_channels_per_group; + var in_channel_offset = group_id * uniforms.w_shape[${d?2:1}]; + + var value: ${G.type.value} = ${G.type.value}(0); + ${pt} + ${a} + ${ae} + ${G.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${w}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:M}),getShaderSource:E}},ql=(e,t,r,n)=>{let s=e.length>2,a=mr(r[3]),i=mr(r[2]),u=Se.size(r)/a/i,d=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/a],p=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/a],g=[r[0],r[1],r[2],r[3]/a],w=[{type:12,data:u},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];Qn(t,w),w.push(...Ct(d,p,g));let l=(i-1)*t.strides[1]+p[1],M=C=>{let E=Ut("output",e[0].dataType,g.length,a),B=yr(E.type.tensor),G=jn(t,E.type.value,B),O=rt("x",e[0].dataType,d.length,a),ae=rt("w",e[1].dataType,p.length,a),Z=[O,ae];s&&Z.push(rt("b",e[2].dataType,e[2].dims,a));let le=s?"value += b[output_channel];":"",Ge=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Yn(t,Ge),` + ${C.registerUniforms(Ge).declareVariables(...Z,E)} + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let width0 = uniforms.output_shape[3]; + let output_channel = global_idx % width0; + var index1 = global_idx / width0; + let width1 = uniforms.output_shape[2] / ${i}u; + let col = (index1 % width1) * ${i}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${O.type.value}, ${l}>; + var values: array<${E.type.value}, ${i}>; + let input_channel = output_channel; + // Use constant instead of uniform can give better performance for w's height/width. + for (var w_height: u32 = 0u; w_height < ${p[0]}; w_height++) { + let x_height = x_corner.x + i32(w_height); + if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) { + for (var i = 0; i < ${l}; i++) { + let x_width = x_corner.y + i; + if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { + x_vals[i] = ${O.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${O.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${p[1]}; w_width++) { + let w_val = ${ae.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${i}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${i}u; i++) { + var value = values[i]; + ${le} + ${G} + ${E.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${a};${i};${l};${p[0]};${p[1]}`,inputDependencies:s?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:w}),getShaderSource:M}}}),Ma,Hl,Kl,Xl=R(()=>{Xt(),Ht(),ui(),or(),Jn(),Ma=(e,t,r,n,s=!1,a)=>{let i=e[0].dims,u=e[1].dims,d=i[i.length-2],p=u[u.length-1],g=i[i.length-1],w=mr(p),l=mr(g),M=mr(d),C=Se.size(r)/w/M,E=e.length>2,B=n?n.slice(0,-2):r.slice(0,-2),G=[Se.size(B),d,p],O=[{type:12,data:C},{type:12,data:d},{type:12,data:p},{type:12,data:g}];Qn(t,O),O.push(...Ct(B,i,u)),E&&O.push(...Ct(e[2].dims)),O.push(...Ct(G));let ae=Z=>{let le=Ti("batch_dims",e[0].dataType,B.length),Ge=rt("a",e[0].dataType,i.length,l),Le=rt("b",e[1].dataType,u.length,w),pt=Ut("output",e[0].dataType,G.length,w),Dt=yr(pt.type.tensor),Vt=jn(t,pt.type.value,Dt),lr=[Ge,Le],fr="";if(E){let Pr=s?w:1;lr.push(rt("bias",e[2].dataType,e[2].dims.length,Pr)),fr=`${s?`value += bias[col / ${Pr}];`:`value += ${pt.type.value}(bias[row + i]);`}`}let er=i.slice(0,-2),Nr=u.slice(0,-2),Hr=Fs(er,B),br=Fs(Nr,B),jr=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Yn(t,jr);let Jt=(Pr,Ve)=>{let kt=Pr.rank,tr=Pr.name;if(kt===2)return`var ${tr}_indices = ${Pr.type.indices}(0u, 0u);`;let zr=le.rank,nn=`var ${tr}_indices: ${Pr.type.indices};`;for(let un=kt-2-1,Vs=zr-1;un>=0;un--,Vs--)nn+=` +${tr}_indices[${un}] = ${zr>1?`batch_indices[${Vs}]`:"batch_indices"};`;return Ve.forEach(un=>{nn+=` +${tr}_indices[${un}] = 0;`}),nn+=`${tr}_indices[${kt-2}] = 0u; + ${tr}_indices[${kt-1}] = 0u;`,nn},dr=()=>{let Pr=`var a_data: ${Ge.type.value};`;for(let Ve=0;Ve; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${l}) { + ${dr()} + } + for (var i = 0u; i < ${M}u; i++) { + var value = values[i]; + ${fr} + ${Vt} + let cur_indices = ${pt.type.indices}(batch, row + i, col); + let offset = ${pt.indicesToOffset("cur_indices")}; + ${pt.setByOffset(`offset / ${w}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${w};${l};${M};${s}`,inputDependencies:E?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:O}),getShaderSource:ae}},Hl=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},Kl=e=>{Hl(e.inputs);let t=wn.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let r=t[t.length-1],n=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&n<8?e.compute(Ma(e.inputs,{activation:""},t)):e.compute(wa(e.inputs,{activation:""},t))}}),pi,hi,Ql,fi,Yl,va,Jl,Zl,as,Sd=R(()=>{Ht(),Ed(),$d(),ui(),ci(),Jn(),Xl(),is(),pi=(e,t,r,n,s,a)=>{let i=e[0],u=e.slice(a?1:2,a?3:4),d=u.length,p=t[0],g=t.slice(2).map((l,M)=>l+(l-1)*(r[M]-1)),w=u.map((l,M)=>l+n[M]+n[M+d]).map((l,M)=>Math.floor((l-g[M]+s[M])/s[M]));return w.splice(0,0,i),w.splice(a?3:1,0,p),w},hi=[2,3,1,0],Ql=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[1]*t.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let s=e[0].dims.length-2;if(t.dilations.length!==s)throw new Error(`dilations should be ${s}D`);if(t.strides.length!==s)throw new Error(`strides should be ${s}D`);if(t.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},fi=(e,t)=>{let r=e.kernelShape.slice();r.length{let t=ni(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],s=e.dilations,a=e.group,i=e.kernel_shape,u=e.pads,d=e.strides,p=e.w_is_const();return{autoPad:n,format:r,dilations:s,group:a,kernelShape:i,pads:u,strides:d,wIsConst:p,...t,cacheKey:`${e.format};${t.activation};`}},va=(e,t,r,n)=>{let s=r.format==="NHWC",a=pi(t[0].dims,t[1].dims,r.dilations,r.pads,r.strides,s);if(r.group!==1){let Ge=[t[0]];if(s){let Le=e.kernelCustomData.wT??e.compute(Pn(t[1],hi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Le),Ge.push(Le)}else Ge.push(t[1]);t.length===3&&Ge.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&s&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1?e.compute(ql(Ge,r,a,n),{inputs:Ge}):e.compute(Gl(Ge,r,a,n),{inputs:Ge});return}let i=t.length===3,u=t[0].dims[s?1:2],d=t[0].dims[s?2:3],p=t[0].dims[s?3:1],g=t[1].dims[2],w=t[1].dims[3],l=a[s?1:2],M=a[s?2:3],C=a[s?3:1],E=s&&g===u&&w===d&&r.pads[0]===0&&r.pads[1]===0;if(E||g===1&&w===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let Ge=a[0],Le,pt,Dt,Vt=[];if(s){let er=e.kernelCustomData.wT??e.compute(Pn(t[1],hi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=er),E){let Nr=u*d*p;Le=t[0].reshape([1,Ge,Nr]),pt=er.reshape([1,Nr,C]),Dt=[1,Ge,C]}else Le=t[0].reshape([Ge,u*d,p]),pt=er.reshape([1,p,C]),Dt=[Ge,l*M,C];Vt.push(Le),Vt.push(pt)}else Le=t[0].reshape([Ge,p,u*d]),pt=t[1].reshape([1,C,p]),Dt=[Ge,C,l*M],Vt.push(pt),Vt.push(Le);i&&Vt.push(t[2]);let lr=Dt[2],fr=Vt[0].dims[Vt[0].dims.length-1];lr<8&&fr<8?e.compute(Ma(Vt,r,a,Dt,s,n),{inputs:Vt}):e.compute(wa(Vt,r,a,Dt,s,n),{inputs:Vt});return}let B=!0,G=e.kernelCustomData.wT??e.compute(Pn(t[1],hi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=G);let O=[t[0],G];i&&O.push(t[2]);let ae=s?l*M:C,Z=s?C:l*M,le=g*w*p;e.compute(Nl(O,r,a,ae,Z,le,i,B,n),{inputs:O})},Jl=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let s=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),i=[1].concat(t.dilations),u=[1].concat(t.kernelShape),d=fi({...t,pads:s,strides:a,dilations:i,kernelShape:u},n);va(e,n,d,p=>r?[p[0],p[2],p[3]]:[p[0],p[1],p[3]])},Zl=(e,t,r)=>{let n=r.format==="NHWC"?"channelsLast":"channelsFirst",s=fi(r,t),a=r.autoPad==="NOTSET"?r.pads:r.autoPad,i=Wl(t[0].dims,t[1].dims,r.strides,r.dilations,a,!1,n);e.compute(di(t,s,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],n))},as=(e,t)=>{if(Ql(e.inputs,t),e.inputs[0].dims.length===3)Jl(e,t);else if(e.inputs[0].dims.length===5)Zl(e,e.inputs,t);else{let r=fi(t,e.inputs);va(e,e.inputs,r)}}}),eu,tu,kd=R(()=>{Xt(),m(),or(),Jn(),ii(),Dl(),ui(),eu=(e,t=!1,r,n,s=4)=>{let a=B=>{switch(B){case 1:return"return w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];";case 4:return` + let coord1 = vec4(coordX, coordY, col + 1, rowInner); + let coord2 = vec4(coordX, coordY, col + 2, rowInner); + let coord3 = vec4(coordX, coordY, col + 3, rowInner); + let v0 = w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))]; + let v1 = w[getIndexFromCoords4D(coord1, vec4(uniforms.w_shape))]; + let v2 = w[getIndexFromCoords4D(coord2, vec4(uniforms.w_shape))]; + let v3 = w[getIndexFromCoords4D(coord3, vec4(uniforms.w_shape))]; + return ${n}(v0, v1, v2, v3); + `;default:throw new Error(`innerElementSize ${B} is not supported.`)}},i=e?` + let coord = vec4(batch, iXR, iXC, xCh); + `:` + let coord = vec4(batch, xCh, iXR, iXC); + `,u=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,d=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",p=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",g=e?"row":"col",w=e?"col":"row",l=` + let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${g} / outWidth; + let outCol = ${g} % outWidth; + + let WRow = ${w} / (uniforms.filter_dims[1] * inChannels); + let WCol = ${w} / inChannels % uniforms.filter_dims[1]; + let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]); + let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]); + if (xR < 0.0 || xR >= f32(${d}) || fract(xR) > 0.0) { + return ${n}(0.0); + } + if (xC < 0.0 || xC >= f32(${p}) || fract(xC) > 0.0) { + return ${n}(0.0); + } + let iXR = i32(xR); + let iXC = i32(xC); + let xCh = ${w} % inChannels; + ${i} + return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${s}];`,M=e?` + let col = colIn * ${s}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${l} + } + return ${n}(0.0);`:` + let col = colIn * ${s}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${l} + } + return ${n}(0.0);`,C=` + let col = colIn * ${s}; + let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels); + let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1]; + if (${e?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"} && coordX >= 0 && coordY >= 0) { + let rowInner = row % inChannels; + let coord = vec4(coordX, coordY, col, rowInner); + ${a(s)} + } + return ${n}(0.0); + `,E=jn(r,n);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} { + ${e?M:C} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} { + ${e?C:M} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) { + let col = colIn * ${s}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueInput; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${u} + ${si(t)} + ${E} + result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${s}] = value; + } + }`},tu=(e,t,r,n,s,a,i,u)=>{let d=t.format==="NHWC",p=d?e[0].dims[3]:e[0].dims[1],g=r[0],w=d?r[2]:r[3],l=d?r[1]:r[2],M=d?r[3]:r[1],C=d&&p%4===0&&p%3&&M%4===0,E=d?M:w*l,B=d?w*l:M,G=[8,8,1],O=n<=8?[4,1,1]:[4,4,1],ae=[Math.ceil(E/G[0]/O[0]),Math.ceil(B/G[1]/O[1]),Math.ceil(g/G[2]/O[2])];ue("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${ae}`);let Z=C?4:1,le=Math.max(G[0]*Z,G[1]),Ge=C?4:1,Le=[t.kernelShape[d?1:2],t.kernelShape[d?2:3]],pt=[Le[0]+(t.dilations[0]<=1?0:(Le[0]-1)*(t.dilations[0]-1)),Le[1]+(t.dilations[1]<=1?0:(Le[1]-1)*(t.dilations[1]-1))],Dt=[pt[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),pt[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Vt=[{type:6,data:n},{type:6,data:s},{type:6,data:a},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:Le},{type:6,data:Dt}];Qn(t,Vt),Vt.push(...Ct(e[0].dims,e[1].dims));let lr=["rank","rank"];i&&(Vt.push(...Ct(e[2].dims)),lr.push("rank")),Vt.push(...Ct(r));let fr=er=>{let Nr=rt("x",e[0].dataType,e[0].dims.length,Ge),Hr=rt("w",e[1].dataType,e[1].dims.length,1),br=Ut("result",e[0].dataType,r.length,Ge),jr=[Nr,Hr],Jt="";if(i){let Ve=rt("bias",e[2].dataType,e[2].dims.length,Ge);jr.push(Ve),Jt+=` + fn getBiasByOutputCoords(coords : vec4) -> ${Ve.type.value} { + return bias[coords.${d?"w":"y"}${C?"/ 4":""}]; + }`}let dr=[{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:Le.length},{name:"pads",type:"i32",length:Dt.length}];Yn(t,dr);let Pr=yr(e[0].dataType,1);if(Pr!=="f16"&&Pr!=="f32")throw new Error(`elemType ${Pr} is not supported.`);return` + ${_a("uniforms.result_strides")} + ${er.registerUniforms(dr).declareVariables(...jr,br)}; + ${Jt} + ${eu(d,i,t,Nr.type.value,Z)} + ${C?ai(O,G,Pr,void 0,!d,le):li(O,G,Pr,void 0,!d,le,!1,void 0,u)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${O};${G};${C}`,inputDependencies:lr},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:ae[0],y:ae[1],z:ae[2]},programUniforms:Vt}),getShaderSource:fr}}}),ru,Ds,gc=R(()=>{Xt(),m(),Ht(),or(),ru=(e,t,r,n,s,a=!1,i,u,d=!1)=>{let p=d?1:2,g=d?2:3,w=d?3:1,l=a?2:1,M=` + fn setOutputAtIndex(flatIndex : u32, value : ${a?`vec4<${i}>`:i}) { + result[flatIndex] = ${a?`vec4<${i}>`:i}(value); + }`;n&&(M+=` + fn getBiasByOutputCoords(coords : vec4) -> ${a?`vec4<${i}>`:i} { + return bias[coords.${d?"w":"y"}${a?"/ 4":""}]; + }`);let C=a?4:1,E=rt("W",t[1].dataType,t[1].dims.length,C),B=rt("Dy",t[0].dataType,t[0].dims.length,C),G=[B,E];n&&G.push(rt("bias",t[2].dataType,[r[w]].length,C));let O=Ut("result",t[0].dataType,r.length,C),ae=`{ + let batch: u32 = ${s?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1]; + let r = ${s?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1]; + let c = ${s?"global_id.y":"workgroup_id.y"} * ${l}; + let d1: u32 = ${s?"global_id.x":"workgroup_id.x"} * 4; + + let dyCorner = vec2(i32(r), i32(c)) - vec2(uniforms.pads); + + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd: array, ${l}>; + for (var i = 0; i < ${l}; i++) { + dotProd[i] = vec4<${i}>(0.0); + } + for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) { + var dyR = (${i}(dyCorner.x) + ${i}(wR)) / ${i}(uniforms.strides.x); + let wRPerm = uniforms.filter_dims[0] - 1 - wR; + if (dyR < 0.0 || dyR >= ${i}(uniforms.Dy_shape[1]) || + fract(dyR) > 0.0 || wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) { + let dyC = (${i}(dyCorner.y) + ${i}(wC)) / ${i}(uniforms.strides.y); + let dyC2 = (${i}(dyCorner.y) + 1.0 + ${i}(wC)) / ${i}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims[1] - 1 - wC; + if (wCPerm < 0) { + continue; + } + var bDyCVal = true; + var bDyCVal2 = true; + if (dyC < 0.0 || dyC >= ${i}(uniforms.Dy_shape[2]) || + fract(dyC) > 0.0) { + bDyCVal = false; + } + if (dyC2 < 0.0 || dyC2 >= ${i}(uniforms.Dy_shape[2]) || + fract(dyC2) > 0.0) { + bDyCVal2 = false; + } + + let idyC: u32 = u32(dyC); + let idyC2: u32 = u32(dyC2); + if (bDyCVal && bDyCVal2) { + let d2Length = uniforms.Dy_shape[3]; + for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${B.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${i}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + + xValue = ${B.get("batch","idyR","idyC2","d2")}; + + dotProd[1] = dotProd[1] + vec4<${i}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + } + } else if (bDyCVal) { + let d2Length = uniforms.Dy_shape[${w}]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${B.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${i}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + } + } else if (bDyCVal2) { + let d2Length = uniforms.Dy_shape[3]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${B.get("batch","idyR","idyC2","d2")}; + let tmpval = vec4<${i}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[1] = dotProd[1] + tmpval; + } + } + } + } + + for (var i: u32 = 0; i < ${l}; i = i + 1) { + let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${i}>(0.0)`}; + ${O.set("batch","r","c + i","d1","value")}; + } + }`,Z=` + let outputIndices = ${O.offsetToIndices("global_idx")}; + let batch = ${O.indicesGet("outputIndices",0)}; + let d1 = ${O.indicesGet("outputIndices",w)}; + let r = ${O.indicesGet("outputIndices",p)}; + let c = ${O.indicesGet("outputIndices",g)}; + let dyCorner = vec2(i32(r), i32(c)) - uniforms.pads; + let dyRCorner = dyCorner.x; + let dyCCorner = dyCorner.y; + let groupId = d1 / uniforms.output_channels_per_group; + let wOutChannel = d1 - groupId * uniforms.output_channels_per_group; + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd = ${i}(0.0); + for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${i}(dyRCorner) + ${i}(wR)) / ${i}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${i}(uniforms.Dy_shape[${p}]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { + if (wC % uniforms.dilations.y != 0) { + continue; + } + let dyC = (${i}(dyCCorner) + ${i}(wC)) / ${i}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${i}(uniforms.Dy_shape[${g}]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC: u32 = u32(dyC); + var inputChannel = groupId * uniforms.input_channels_per_group; + for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) { + let xValue = ${d?B.get("batch","idyR","idyC","inputChannel"):B.get("batch","inputChannel","idyR","idyC")}; + let wValue = ${E.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")}; + dotProd = dotProd + xValue * wValue; + inputChannel = inputChannel + 1; + } + } + } + let value = dotProd + ${n?"bias[d1]":`${i}(0.0)`}; + ${O.setByOffset("global_idx","value")}; + `;return` + ${e.registerUniforms(u).declareVariables(...G,O)} + ${M} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${a?ae:Z}}`},Ds=(e,t,r)=>{let n=e.length>2,s=t.outputShape,a=Se.size(s),i=[Math.ceil(a/64),1,1];ue("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${i}`);let u=t.format==="NHWC",d=["rank","rank"],p=[t.strides[0],t.strides[1]],g=[t.kernelShape[u?1:2],t.kernelShape[u?2:3]],w=[t.dilations[0],t.dilations[1]],l=[g[0]+(t.dilations[0]<=1?0:(t.kernelShape[u?1:2]-1)*(t.dilations[0]-1)),g[1]+(t.dilations[1]<=1?0:(t.kernelShape[u?2:3]-1)*(t.dilations[1]-1))],M=[l[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),l[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],C=!1,E=t.group,B=e[1].dims,G=B[0]/E,O=B[1],ae=[{type:12,data:a},{type:12,data:p},{type:12,data:g},{type:12,data:w},{type:12,data:l},{type:6,data:M},{type:12,data:G},{type:12,data:O},...Ct(e[0].dims,e[1].dims)];n&&(ae.push(...Ct(e[2].dims)),d.push("rank")),ae.push(...Ct(s));let Z=i[1]===1&&i[2]===1,le=Ge=>{let Le=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:p.length},{name:"filter_dims",type:"u32",length:g.length},{name:"dilations",type:"u32",length:g.length},{name:"effective_filter_dims",type:"u32",length:l.length},{name:"pads",type:"i32",length:M.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],pt=yr(e[0].dataType);return`${ru(Ge,e,s,n,Z,C,pt,Le,u)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:d},getRunData:()=>({dispatchGroup:{x:i[0],y:i[1],z:i[2]},outputs:[{dims:r?r(s):s,dataType:e[0].dataType}],programUniforms:ae}),getShaderSource:le}}}),nu,su,iu,xa,au,Ta,ou,lu,Ca,uu,Pd=R(()=>{kd(),gc(),Jn(),is(),nu=(e,t,r,n,s,a)=>(e-1)*t+r+(n-1)*s+1-a,su=(e,t,r,n,s)=>{let a=Math.floor(e/2);t==="SAME_UPPER"?(r[n]=a,r[s]=e-a):t==="SAME_LOWER"&&(r[n]=e-a,r[s]=a)},iu=(e,t,r,n,s,a,i,u,d,p)=>{let g=e.length-2,w=p.length===0;d.length{let 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shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},ou=[2,3,1,0],lu=(e,t,r)=>{let n=xa(r,t),s=r.format==="NHWC",a=n.outputShape,i=a[s?3:1],u=t[0].dims[s?3:1];if(n.group!==1||i===1&&u===1){e.compute(Ds(t,n));return}let d=a[s?1:2],p=a[s?2:3],g=t[1].dims[2],w=t[1].dims[3],l=s?d*p:i,M=s?i:d*p,C=g*w*u,E=!0,B=e.kernelCustomData.wT??e.compute(Pn(t[1],ou),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=B);let G=[t[0],B],O=t.length===3;O&&(!s&&t[2].dims.length===1?G.push(t[2].reshape([t[2].dims[0],1,1])):G.push(t[2])),e.compute(tu(G,n,a,l,M,C,O,E),{inputs:G})},Ca=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let s=t.kernelShape;(s.length===0||s[0]===0)&&(s=[e.inputs[1].dims[2]]);let a=t.dilations;(a.length===0||a[0]===0)&&(a=[1]);let i=t.strides;(i.length===0||i[0]===0)&&(i=[1]);let u=t.pads;u.length===0&&(u=[0,0]),u=[0,u[0],0,u[1]],i=[1].concat(i),a=[1].concat(a),s=[1].concat(s);let d=xa({...t,pads:u,strides:i,dilations:a,kernelShape:s},n);e.compute(Ds(n,d,p=>r?[p[0],p[2],p[3]]:[p[0],p[1],p[3]]))},uu=(e,t)=>{Ta(e.inputs,t),e.inputs[0].dims.length===3?Ca(e,t):lu(e,e.inputs,t)}}),Ea,du,cu,$a=R(()=>{Xt(),Ht(),Sr(),or(),Ea=(e,t,r,n)=>{let s=Se.size(t),a=t.length,i=rt("input",e,a),u=Ut("output",e,a),d=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),p=Se.normalizeAxis(d,a),g=w=>{let l=` i32(${i.indicesGet("inputIndices","uniforms.axis")}) `,M=jt("uniforms.input_shape","uniforms.axis",a),C=n.reverse?l+(n.exclusive?" + 1":""):"0",E=n.reverse?M:l+(n.exclusive?"":" + 1");return` + ${w.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(i,u)} + ${w.mainStart()} + ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${u.offsetToIndices("global_idx")}; + var sum = ${u.type.value}(0); + let first : i32 = ${C}; + let last : i32 = ${E}; + for (var i : i32 = first; i < last; i++) { + ${i.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${i.getByIndices("inputIndices")}; + } + ${u.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:[{type:12,data:s},{type:12,data:p},...Ct(t,t)]}),getShaderSource:g}},du=(e,t)=>{let r=e.inputs[0].dims,n=e.inputs[0].dataType,s=e.inputs[1];e.compute(Ea(n,r,s,t),{inputs:[0]})},cu=e=>{let t=e.exclusive===1,r=e.reverse===1;return ur({exclusive:t,reverse:r})}}),pu,hu,fu,mu,Sa,ka=R(()=>{Xt(),Ht(),Sr(),or(),pu=e=>{if(!e||e.length!==1)throw new Error("DepthToSpace requires 1 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}`;return{name:"DepthToSpace",shaderCache:{hint:`${e.dims};${t.blocksize};${t.mode}`,inputDependencies:["rank"]},getRunData:G=>{let O=d?[r,n*p,s*p,a/p**2]:[r,a/p**2,n*p,s*p],ae=Se.size(O),Z=w.dims,le=Se.sortBasedOnPerm(Z,u);return{outputs:[{dims:O,dataType:G[0].dataType}],dispatchGroup:{x:Math.ceil(ae/64)},programUniforms:[{type:12,data:ae},...Ct(Z,le)]}},getShaderSource:B}},mu=(e,t)=>{pu(e.inputs),e.compute(fu(e.inputs[0],t))},Sa=e=>ur({blocksize:e.blocksize,mode:e.mode,format:e.format})}),Pa,Ls,Aa,Ia,_u,gu,Fa,za,wu,Oa,Ad,wc=R(()=>{Xt(),Ht(),Sr(),or(),Pa="[a-zA-Z]|\\.\\.\\.",Ls="("+Pa+")+",Aa="^"+Ls+"$",Ia="("+Ls+",)*"+Ls,_u="^"+Ia+"$",gu=class{constructor(e=-1){this.symbolToIndices=new Map,this.inputIndex=e}addSymbol(e,t){let r=this.symbolToIndices.get(e);r===void 0?r=[t]:r.push(t),this.symbolToIndices.set(e,r)}},Fa=class{constructor(e,t){var s;this.equation=t,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new 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${p.registerUniforms(u.map(Z=>({name:`${za(Z)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...s,i)} + + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${i.offsetToIndices("global_idx")}; + ${s.map((Z,le)=>`var input${le}Indices: ${s[le].type.indices};`).join(` +`)} + ${ae.join(` +`)}; + ${i.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let p=u.filter(w=>r.symbolToInfo.has(w)).map(w=>{var l;return{type:12,data:((l=r.symbolToInfo.get(w))==null?void 0:l.dimValue)||0}});p.push({type:12,data:a});let g=e.map((w,l)=>[...Ct(w)]).reduce((w,l)=>w.concat(l),p);return g.push(...Ct(n)),{outputs:[{dims:n,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:g}},getShaderSource:d}},Oa=(e,t)=>{let r=new Fa(e.inputs,t.equation),n=r.outputDims,s=e.inputs.map((a,i)=>a.dims);e.compute(wu(s,e.inputs[0].dataType,r,n))},Ad=e=>{let t=e.equation.replace(/\s+/g,"");return ur({equation:t})}}),Da,mi,yu,bu,La,Id=R(()=>{Xt(),Ht(),or(),Da=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let t=e[0].dims,r=Array.from(e[1].getBigInt64Array(),Number),n=r.length{let r=e.length-t.length,n=[];for(let s=0;se.length>t.length?mi(e,t):mi(t,e),bu=e=>{let t=e[0].dims,r=Array.from(e[1].getBigInt64Array(),Number),n=yu(t,r),s=e[0].dataType,a=s===9?4:1,i=Math.ceil(Se.size(n)/a),u=p=>{let g=rt("input",s,t.length,a),w=Ut("output",s,n.length,a),l;if(s===9){let M=(C,E,B="")=>` + let outputIndices${E} = ${w.offsetToIndices(`outputOffset + ${E}u`)}; + let offset${E} = ${g.broadcastedIndicesToOffset(`outputIndices${E}`,w)}; + let index${E} = offset${E} / 4u; + let component${E} = offset${E} % 4u; + ${C}[${E}] = ${B}(${g.getByOffset(`index${E}`)}[component${E}]); + `;l=` + let outputOffset = global_idx * ${a}; + var data = vec4(0); + ${M("data",0,"u32")} + ${M("data",1,"u32")} + ${M("data",2,"u32")} + ${M("data",3,"u32")} + ${w.setByOffset("global_idx","data")} + }`}else l=` + let outputIndices = ${w.offsetToIndices("global_idx")}; + let inputOffset = ${g.broadcastedIndicesToOffset("outputIndices",w)}; + ${w.setByOffset("global_idx",g.getByOffset("inputOffset"))} + }`;return` + ${p.registerUniform("vec_size","u32").declareVariables(g,w)} + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${l}`},d=[{type:12,data:i},...Ct(t,n)];return{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:["rank"]},getShaderSource:u,getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:d})}},La=e=>{Da(e.inputs),e.compute(bu(e.inputs),{inputs:[0]})}}),Mu,Ba,Fd=R(()=>{Xt(),Ht(),or(),ti(),Mu=e=>{let t=e[0].dataType,r=Se.size(e[0].dims),n=Se.size(e[1].dims),s=n%4===0,a=i=>{let u=rt("x",t,[1],4),d=rt("bias",t,[1],4),p=Ut("y",t,[1],4),g=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],w=M=>` + let bias${M}_offset: u32 = (global_idx * 4 + ${M}) % uniforms.bias_size; + let bias${M} = ${d.getByOffset(`bias${M}_offset / 4`)}[bias${M}_offset % 4];`,l=s?` + let bias = ${d.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${w(0)}${w(1)}${w(2)}${w(3)} + let bias = ${u.type.value}(bias0, bias1, bias2, bias3);`;return`${i.registerUniforms(g).declareVariables(u,d,p)} + + ${la(Ar(t))} + + ${i.mainStart(en)} + ${i.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${u.getByOffset("global_idx")}; + ${l} + let x_in = x + bias; + ${p.setByOffset("global_idx",ua("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${s}`,inputDependencies:["type","type"]},getShaderSource:a,getRunData:i=>({outputs:[{dims:i[0].dims,dataType:i[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/en/4)}})}},Ba=e=>{e.inputs.length<2||Se.size(e.inputs[1].dims)===0?da(e):e.compute(Mu(e.inputs))}}),vu,Ra,xu,Tu,zd=R(()=>{Xt(),Ht(),Sr(),or(),vu=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},Ra=(e,t)=>{let r=e[0].dims,n=e[1].dims,s=r.length,a=Se.normalizeAxis(t.axis,s),i=r.slice(0);i.splice(a,1,...n);let u=r[a],d=e[0].dataType===9?4:1,p=Math.ceil(Se.size(i)/d),g=[{type:12,data:p},{type:6,data:u},{type:12,data:a},...Ct(e[0].dims,e[1].dims,i)],w=l=>{let M=rt("data",e[0].dataType,e[0].dims.length,d),C=rt("inputIndices",e[1].dataType,e[1].dims.length),E=Ut("output",e[0].dataType,i.length,d),B=O=>{let ae=n.length,Z=`var indicesIndices${O} = ${C.type.indices}(0);`;for(let le=0;le1?`indicesIndices${O}[${le}]`:`indicesIndices${O}`} = ${i.length>1?`outputIndices${O}[uniforms.axis + ${le}]`:`outputIndices${O}`};`;Z+=` + var idx${O} = ${C.getByIndices(`indicesIndices${O}`)}; + if (idx${O} < 0) { + idx${O} = idx${O} + uniforms.axisDimLimit; + } + var dataIndices${O} : ${M.type.indices}; + `;for(let le=0,Ge=0;le1?`dataIndices${O}[${le}]`:`dataIndices${O}`} = u32(idx${O});`,Ge+=ae):(Z+=`${s>1?`dataIndices${O}[${le}]`:`dataIndices${O}`} = ${i.length>1?`outputIndices${O}[${Ge}]`:`outputIndices${O}`};`,Ge++);return Z},G;if(e[0].dataType===9){let O=(ae,Z,le="")=>` + let outputIndices${Z} = ${E.offsetToIndices(`outputOffset + ${Z}u`)}; + ${B(Z)}; + let offset${Z} = ${M.indicesToOffset(`dataIndices${Z}`)}; + let index${Z} = offset${Z} / 4u; + let component${Z} = offset${Z} % 4u; + ${ae}[${Z}] = ${le}(${M.getByOffset(`index${Z}`)}[component${Z}]); + `;G=` + let outputOffset = global_idx * ${d}; + var value = vec4(0); + ${O("value",0,"u32")} + ${O("value",1,"u32")} + ${O("value",2,"u32")} + ${O("value",3,"u32")} + ${E.setByOffset("global_idx","value")} + `}else G=` + let outputIndices = ${E.offsetToIndices("global_idx")}; + ${B("")}; + let value = ${M.getByIndices("dataIndices")}; + ${E.setByOffset("global_idx","value")}; + `;return` + ${l.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(M,C,E)} + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${G} + }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:g}),getShaderSource:w}},xu=e=>ur({axis:e.axis}),Tu=(e,t)=>{let r=e.inputs;vu(r),e.compute(Ra(e.inputs,t))}}),Cu,Ir,Od,Dd,yc=R(()=>{Xt(),Ht(),Sr(),or(),Cu=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let r=Se.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,s=e[0],a=e[2],i=e.length===4?e[3]:void 0;if(a.dims.length!==s.dims.length||!s.dims.map((u,d)=>d===r?Math.ceil(u/n)===a.dims[d]:u===a.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(i){if(i.dataType!==s.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(i.dims.length!==a.dims.length||!i.dims.map((u,d)=>u===a.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},Ir=(e,t)=>{let r=e[0].dims,n=e[1].dims,s=r.length,a=Se.normalizeAxis(t.gatherAxis,s),i=Se.normalizeAxis(t.quantizeAxis,s),u=r.slice(0);u.splice(a,1,...n);let d=Se.size(u),p=e[2].dataType,g=e[0].dataType===22,w=[{type:12,data:d},{type:12,data:i},{type:12,data:a},{type:12,data:t.blockSize},...Ct(...e.map((M,C)=>M.dims),u)],l=M=>{let C=rt("data",e[0].dataType,e[0].dims.length),E=rt("inputIndices",e[1].dataType,e[1].dims.length),B=rt("scales",e[2].dataType,e[2].dims.length),G=e.length>3?rt("zeroPoint",e[3].dataType,e[3].dims.length):void 0,O=Ut("output",p,u.length),ae=[C,E,B];G&&ae.push(G);let Z=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${M.registerUniforms(Z).declareVariables(...ae,O)} + ${M.mainStart()} + let output_indices = ${O.offsetToIndices("global_idx")}; + var indices_indices = ${E.type.indices}(0); + ${n.length>1?` + for (var i: u32 = 0; i < ${n.length}; i++) { + let index = ${O.indicesGet("output_indices","uniforms.gather_axis + i")}; + ${E.indicesSet("indices_indices","i","index")}; + }`:`indices_indices = ${O.indicesGet("output_indices","uniforms.gather_axis")};`}; + var data_indices = ${C.type.indices}(0); + for (var i: u32 = 0; i < uniforms.gather_axis; i++) { + let index = ${O.indicesGet("output_indices","i")}; + ${C.indicesSet("data_indices","i","index")}; + } + var index_from_indices = ${E.getByIndices("indices_indices")}; + if (index_from_indices < 0) { + index_from_indices += ${r[a]}; + } + ${C.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")}; + for (var i = uniforms.gather_axis + 1; i < ${u.length}; i++) { + let index = ${O.indicesGet("output_indices",`i + ${n.length} - 1`)}; + ${C.indicesSet("data_indices","i","index")}; + } + let data_offset = ${C.indicesToOffset("data_indices")}; + let data_index = data_offset % 8; + // Convert 4-bit packed data to 8-bit packed data. + let packed_4bit_quantized_data = ${C.getByOffset("data_offset / 8")}; + let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f; + let quantized_data_vec = ${g?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data)); + let quantized_data = quantized_data_vec[data_index / 2]; + var scale_indices = data_indices; + let quantize_axis_index = ${B.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size; + ${B.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")}; + var scale = ${B.getByIndices("scale_indices")}; + ${G?` + let zero_point_indices = scale_indices; + let zero_point_offset = ${G.indicesToOffset("zero_point_indices")}; + let zero_point_index = zero_point_offset % 8; + let packed_4bit_zero_points = ${G.getByOffset("zero_point_offset / 8")}; + let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f; + let zero_point_vec = ${g?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points)); + let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"}; + let dequantized_data = ${Ar(p)}(quantized_data - zero_point) * scale; + ${O.setByOffset("global_idx","dequantized_data")}; + }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((M,C)=>C!==1).map(M=>M.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(M,C)=>"rank")},getRunData:()=>({outputs:[{dims:u,dataType:p}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:w}),getShaderSource:l}},Od=(e,t)=>{let r=e.inputs;Cu(r,t),e.compute(Ir(e.inputs,t))},Dd=e=>ur({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),Na,Eu,$u,Su,Ld=R(()=>{Xt(),Ht(),Sr(),or(),Na=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and + indices input tensors be of same rank.`)},Eu=(e,t)=>{let r=e[0].dims,n=e[0].dataType,s=r.length,a=e[1].dims,i=e[1].dataType,u=Se.normalizeAxis(t.axis,s),d=r[u],p=a.slice(0),g=Se.size(p),w=rt("input",n,s),l=rt("indicesInput",i,a.length),M=Ut("output",n,p.length),C=[{type:12,data:g},{type:6,data:d},{type:12,data:u}];return C.push(...Ct(r,a,p)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:p,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:C}),getShaderSource:E=>` + ${E.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(w,l,M)} + ${E.mainStart()} + ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${M.offsetToIndices("global_idx")}; + + var idx = ${l.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${w.type.indices}(outputIndices); + ${w.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${w.getByIndices("inputIndices")}; + + ${M.setByOffset("global_idx","value")}; + }`}},$u=e=>ur({axis:e.axis}),Su=(e,t)=>{let r=e.inputs;Na(r),e.compute(Eu(e.inputs,t))}}),ja,Va,ku,Pu,Au=R(()=>{Xt(),Ht(),or(),ja=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},Va=(e,t)=>{let r=e[0].dims.slice(),n=e[1].dims.slice(),[s,a,i]=xn.getShapeOfGemmResult(r,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),u=[s,a];if(!u)throw new Error("Can't use gemm on the given tensors");let d=Se.size(u),p=[{type:12,data:d},{type:12,data:s},{type:12,data:a},{type:12,data:i},{type:1,data:t.alpha},{type:1,data:t.beta}],g=["type","type"];e.length===3&&(p.push(...Ct(e[2].dims)),g.push("rank")),p.push(...Ct(u));let w=l=>{let M="";t.transA&&t.transB?M="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?M="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?M="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(M="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let C=t.alpha===1?"":"value *= uniforms.alpha;",E=rt("a",e[0].dataType,e[0].dims),B=rt("b",e[1].dataType,e[1].dims),G=E.type.value,O=null,ae=[E,B];e.length===3&&(O=rt("c",e[2].dataType,e[2].dims.length),ae.push(O));let Z=Ut("output",e[0].dataType,u.length);ae.push(Z);let le=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return` + ${l.registerUniforms(le).declareVariables(...ae)} + + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${G}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${M} + } + + ${C} + ${O!=null?`let cOffset = ${O.broadcastedIndicesToOffset("vec2(m, n)",Z)}; value += ${G}(uniforms.beta) * ${O.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:w}},ku=e=>{let t=e.transA,r=e.transB,n=e.alpha,s=e.beta;return{transA:t,transB:r,alpha:n,beta:s,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},Pu=(e,t)=>{ja(e.inputs),e.compute(Va(e.inputs,t))}}),yn,Bd,Iu,Ua,Fu,Bs,zu,Ou=R(()=>{Xt(),Ht(),Sr(),se(),Ki(),or(),is(),yn=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,Bd=(e,t)=>{let r=e[0],n=yn(e,1),s=yn(e,2),a=yn(e,3),i=yn(e,4),u=yn(e,5),d=yn(e,6),p=yn(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let g=r.dims[0],w=r.dims[1],l=r.dims.length===3?r.dims[2]:t.numHeads*r.dims[4],M=w,C=0,E=0,B=Math.floor(l/t.numHeads);if(d&&p&&Se.size(d.dims)&&Se.size(p.dims)){if(d.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(d.dims[0]!==g||d.dims[1]!==t.numHeads||d.dims[3]!==B)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(p.dims[0]!==g||p.dims[1]!==t.numHeads||p.dims[3]!==B)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(d.dims[2]!==p.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(p.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');C=d.dims[2],E=d.dims[2]}else if(d&&Se.size(d.dims)||p&&Se.size(p.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let G;if(n&&Se.size(n.dims)>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');G=2,M=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==B)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(s)throw new Error('Expect "value" be none when "key" has packed kv format.');G=5,M=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==B)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');G=0,M=n.dims[2]}}else{if(r.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(r.dims[2]!==t.numHeads||r.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');G=3}if(a&&Se.size(a.dims)>0){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(n&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let O=C+M,ae=0;if(i&&Se.size(i.dims)>0){ae=8;let Le=i.dims;throw Le.length===1?Le[0]===g?ae=1:Le[0]===3*g+2&&(ae=3):Le.length===2&&Le[0]===g&&Le[1]===O&&(ae=5),ae===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let Z=!1,le=l;if(s&&Se.size(s.dims)>0){if(s.dims.length!==3&&s.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==s.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(s.dims.length===3){if(M!==s.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');le=s.dims[2]}else{if(M!==s.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');le=s.dims[1]*s.dims[3],Z=!0}}let Ge=!1;if(i&&Se.size(i.dims)>0)throw new Error("Key padding mask is not supported");if(u&&Se.size(u.dims)>0){if(u.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(u.dims[0]!==g||u.dims[1]!==t.numHeads||u.dims[2]!==w||u.dims[3]!==O)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:g,sequenceLength:w,pastSequenceLength:C,kvSequenceLength:M,totalSequenceLength:O,maxSequenceLength:E,inputHiddenSize:0,hiddenSize:l,vHiddenSize:le,headSize:B,vHeadSize:Math.floor(le/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:ae,scale:t.scale,broadcastResPosBias:Ge,passPastInKv:Z,qkvFormat:G}},Iu=e=>ur({...e}),Ua=ur({perm:[0,2,1,3]}),Fu=(e,t,r,n,s,a,i)=>{let u=[n,s,a],d=Se.size(u),p=[{type:12,data:d},{type:12,data:i},{type:12,data:a}],g=w=>{let l=Ut("qkv_with_bias",t.dataType,u),M=rt("qkv",t.dataType,u),C=rt("bias",r.dataType,u),E=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${w.registerUniforms(E).declareVariables(M,C,l)} + ${w.mainStart()} + ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:g},{inputs:[t,r],outputs:[-1]})[0]},Bs=(e,t,r,n,s,a,i,u)=>{let d=a;if(i&&Se.size(i.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return d=Fu(e,a,i,t,n,r*s,u),d=d.reshape([t,n,r,s]),e.compute(Pn(d,Ua.perm),{inputs:[d],outputs:[-1]})[0]}else return a.dims.length===3&&(d=a.reshape([t,n,r,s])),e.compute(Pn(d,Ua.perm),{inputs:[d],outputs:[-1]})[0]},zu=(e,t)=>{let r=Bd(e.inputs,t),n=e.inputs[0],s=yn(e.inputs,1),a=yn(e.inputs,2),i=yn(e.inputs,3),u=yn(e.inputs,4),d=yn(e.inputs,5),p=yn(e.inputs,6),g=yn(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((s==null?void 0:s.dims.length)===5)throw new Error("Packed KV is not implemented");let w=s&&a&&s.dims.length===4&&a.dims.length===4,l=Bs(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,i,0);if(w)return Os(e,l,s,a,u,void 0,p,g,d,r,t);if(!s||!a)throw new Error("key and value must be provided");let M=Bs(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,s,i,r.hiddenSize),C=Bs(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,a,i,2*r.hiddenSize);Os(e,l,M,C,u,void 0,p,g,d,r,t)}}),Wa,Du,Lu,Ga,Bu,Ru=R(()=>{Xt(),Ht(),or(),Wa=e=>Array.from(e.getBigInt64Array(),Number),Du=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==10&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Wa(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Lu=(e,t)=>{let r=[];for(let n=0;n{let r=e[0].dims,n=t??Wa(e[1]),s=Lu(r,n),a=Se.size(s),i=e[0].dataType,u=rt("input",i,r.length),d=Ut("output",i,s.length),p=g=>` + const inputShape = ${u.indices(...r)}; + ${g.registerUniform("output_size","u32").declareVariables(u,d)} + ${g.mainStart()} + ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${d.offsetToIndices("global_idx")}; + var input_indices: ${u.type.indices}; + for (var i = 0; i < ${r.length}; i++) { + let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${d.indicesGet("output_indices","i")} % input_dim_i; + + ${u.indicesSet("input_indices","i","input_dim_value")} + } + ${d.setByOffset("global_idx",u.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...Ct(e[0].dims,s)]}),getShaderSource:p}},Bu=e=>{Du(e.inputs),e.compute(Ga(e.inputs),{inputs:[0]})}}),Nu,qa,ju,Vu,Ha,Rd,Uu=R(()=>{Xt(),Ht(),Sr(),Ki(),or(),Ou(),Ru(),is(),Nu=(e,t)=>{let r=e[0],n=e[1],s=e[2],a=e[3],i=e[4];if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let u=!1,d=r.dims[0],p=r.dims[1],g=r.dims.length===3?u?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],w=p,l=0,M=0,C=Math.floor(g/t.numHeads),E=a&&a.dims.length!==0,B=i&&i.dims.length!==0,G=!0;if(E&&B){if(a.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(i.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');l=a.dims[1],M=a.dims[1]}else if(E||B)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let O;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(r.dims[2]%n.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');O=2,w=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==C)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(s)throw new Error('Expect "value" be none when "key" has packed kv format.');O=5,w=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==C)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');O=0,w=n.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');O=3}let ae=0,Z=!1,le=g;if(s){if(s.dims.length!==3&&s.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==s.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(s.dims.length===3){if(w!==s.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');le=s.dims[2]}else{if(w!==s.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');le=s.dims[1]*s.dims[3],Z=!0}}let Ge=l+w;return{batchSize:d,sequenceLength:p,pastSequenceLength:l,kvSequenceLength:w,totalSequenceLength:Ge,maxSequenceLength:M,inputHiddenSize:0,hiddenSize:g,vHiddenSize:le,headSize:C,vHeadSize:Math.floor(le/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:ae,scale:t.scale,broadcastResPosBias:!1,passPastInKv:Z,qkvFormat:O,isPastkvBSNH:G}},qa=(e,t,r,n)=>{let s=[n.batchSize,n.totalSequenceLength,n.kvNumHeads,n.headSize],a=4,i=Se.size(s)/a,u=n.totalSequenceLength,d=Ut("present_kv",r,s.length,a),p=rt("new_kv",e.dataType,e.dims.length,a),g=t?rt("past_kv",t.dataType,t.dims.length,a):void 0,w=Math.ceil(n.headSize/a),l={x:u,y:e.dims[0],z:1},M=t?["rank","rank"]:["rank"],C=[{type:12,data:i},{type:12,data:n.pastSequenceLength},{type:12,data:n.kvSequenceLength},{type:12,data:n.totalSequenceLength}],E=[p];g?(C.push(...Ct(e.dims),...Ct(t.dims),...Ct(s)),E.push(g)):C.push(...Ct(e.dims),...Ct(s));let B=[{name:"output_size",type:"u32"},{name:"past_seqlen",type:"u32"},{name:"new_seqlen",type:"u32"},{name:"present_seqlen",type:"u32"}],G=` 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];`,O=` 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];`,ae=t?`if (s < past_seqlen) { + ${G} + } else if (s < past_seqlen + uniforms.new_seqlen) { + ${O} + }`:`if (s < past_seqlen + uniforms.new_seqlen) { + ${O} + }`,Z=le=>` + + ${le.registerUniforms(B).declareVariables(...E,d)} + ${le.mainStart([w,n.kvNumHeads,1])} + ${le.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var indices = ${d.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 = ${n.kvNumHeads}u; + let H = ${w}u; + + let present_seqlen = uniforms.present_seqlen; + let present_batch_stride = present_seqlen * num_heads * H; + var row_stride = H; + let is_bsnh = ${n.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; + ${ae} + }`;return{name:"ConcatPastNew",shaderCache:{hint:`${n.kvNumHeads}${w}${!!t}`,inputDependencies:M},getRunData:()=>({outputs:[{dims:s,dataType:r}],dispatchGroup:l,programUniforms:C}),getShaderSource:Z}},ju=e=>ur({...e}),Vu=ur({perm:[0,2,1,3]}),Ha=(e,t,r,n,s)=>{let a=t,i=n.kvNumHeads,u=n.nReps;return t.dims.length===3&&n.kvSequenceLength!==0&&(a=t.reshape([n.batchSize,n.kvSequenceLength,i,n.headSize])),r?a=e.compute(qa(a,r,a.dataType,n),{inputs:[a,r],outputs:[n.isPastkvBSNH?s:-1]})[0]:a=e.compute(qa(a,void 0,a.dataType,n),{inputs:[a],outputs:[n.isPastkvBSNH?s:-1]})[0],u!==1&&(a=e.compute(Ga([a],[1,1,1,u]),{inputs:[a],outputs:[-1]})[0],a=a.reshape([n.batchSize,n.totalSequenceLength,i*u,n.headSize])),e.compute(Pn(a,Vu.perm),{inputs:[a],outputs:[-1]})[0]},Rd=(e,t)=>{var d;let r=Nu(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((d=e.inputs[1])==null?void 0:d.dims.length)===5)throw new Error("Packed KV is not implemented");let n=Bs(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,e.inputs[0],void 0,0),s=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,a=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,i=Ha(e,e.inputs[1],s,r,1),u=Ha(e,e.inputs[2],a,r,2);Os(e,n,i,u,void 0,void 0,void 0,void 0,void 0,r,t)}}),Ka,Wu,Gu,qu,Nd=R(()=>{Xt(),Ht(),is(),or(),Ka=(e,t,r,n,s,a,i,u)=>{let d=mr(a),p=d===1?"f32":`vec${d}f`,g=d===1?"vec2f":`mat2x${d}f`,w=s*i,l=[s,i,a/d],M=[s,i,2],C=["rank","type","type"],E=[];E.push(...Ct(l,M));let B=G=>{let O=rt("x",t.dataType,3,d),ae=rt("scale",r.dataType,r.dims),Z=rt("bias",n.dataType,n.dims),le=Ut("output",1,3,2),Ge=[O,ae,Z,le],Le=64;return` + var workgroup_shared : array<${g}, ${Le}>; + const workgroup_size = ${Le}u; + ${G.declareVariables(...Ge)} + ${G.mainStart(Le)} + let batch = workgroup_index / uniforms.x_shape[1]; + let channel = workgroup_index % uniforms.x_shape[1]; + let hight = uniforms.x_shape[2]; + // initialize workgroup memory + var sum = ${p}(0); + var squared_sum = ${p}(0); + for (var h = local_idx; h < hight; h += workgroup_size) { + let value = ${p}(${O.get("batch","channel","h")}); + sum += value; + squared_sum += value * value; + } + workgroup_shared[local_idx] = ${g}(sum, squared_sum); + workgroupBarrier(); + + for (var currSize = workgroup_size >> 1; currSize > 0; currSize = currSize >> 1) { + if (local_idx < currSize) { + workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize]; + } + workgroupBarrier(); + } + if (local_idx == 0) { + let sum_final = ${Xn("workgroup_shared[0][0]",d)} / f32(hight * ${d}); + let squared_sum_final = ${Xn("workgroup_shared[0][1]",d)} / f32(hight * ${d}); + + let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${u})); + let channel_scale = inv_std_dev * f32(scale[channel]); + let channel_shift = f32(bias[channel]) - sum_final * channel_scale; + output[workgroup_index] = vec2f(channel_scale, channel_shift); + } + }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${d};${u}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:M,dataType:1}],dispatchGroup:{x:w},programUniforms:E}),getShaderSource:B},{inputs:[t,r,n],outputs:[-1]})[0]},Wu=(e,t,r)=>{let n=t[0].dims,s=n,a=2,i=n[0],u=n[1],d=Se.sizeFromDimension(n,a),p=mr(d),g=Se.size(s)/p,w=Ka(e,t[0],t[1],t[2],i,d,u,r.epsilon),l=[i,u,d/p],M=[i,u],C=["type","none"],E=B=>{let G=rt("x",t[0].dataType,l.length,p),O=rt("scale_shift",1,M.length,2),ae=Ut("output",t[0].dataType,l.length,p),Z=[G,O,ae];return` + ${B.registerUniform("output_size","u32").declareVariables(...Z)} + ${B.mainStart()} + ${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let outputIndices = ${ae.offsetToIndices("global_idx")}; + let batch = outputIndices[0]; + let channel = outputIndices[1]; + let scale_shift = ${O.getByIndices("vec2(batch, channel)")}; + let value = ${G.getByOffset("global_idx")} * ${ae.type.value}(scale_shift.x) + ${ae.type.value}(scale_shift.y); + ${ae.setByOffset("global_idx","value")}; + }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${p}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:[{type:12,data:g},...Ct(l,M,l)]}),getShaderSource:E},{inputs:[t[0],w]})},Gu=(e,t,r)=>{let n=t[0].dims,s=n,a=n[0],i=n[n.length-1],u=Se.sizeFromDimension(n,1)/i,d=mr(i),p=Se.size(s)/d,g=[{type:12,data:u},{type:12,data:Math.floor(i/d)}],w=["type","type"],l=[0,n.length-1];for(let B=0;B{let G=yr(t[0].dataType),O=d===1?"vec2f":`mat${d}x2f`,ae=Ge=>{let Le=Ge===0?"x":"y",pt=d===1?"f32":`vec${d}f`;switch(d){case 1:return`${G}(${pt}(scale.${Le}))`;case 2:return`vec2<${G}>(${pt}(scale[0].${Le}, scale[1].${Le}))`;case 4:return`vec4<${G}>(${pt}(scale[0].${Le}, scale[1].${Le}, scale[2].${Le}, scale[3].${Le}))`;default:throw new Error(`Not supported compoents ${d}`)}},Z=rt("input",t[0].dataType,t[0].dims,d),le=Ut("output",t[0].dataType,s,d);return` + @group(0) @binding(0) var input : array<${Z.type.storage}>; + @group(0) @binding(1) var scale_input : array<${O}>; + @group(0) @binding(2) var output : array<${le.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${B.mainStart()} + let current_image_number = global_idx / (uniforms.C * uniforms.H); + let current_channel_number = global_idx % uniforms.C; + + let scale_offset = current_image_number * uniforms.C + current_channel_number; + let scale = scale_input[scale_offset]; + output[global_idx] = fma(input[global_idx], ${ae(0)}, ${ae(1)}); + }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${d}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:g}),getShaderSource:E},{inputs:[t[0],C]})},qu=(e,t)=>{t.format==="NHWC"?Gu(e,e.inputs,t):Wu(e,e.inputs,t)}}),Hu,Ku,Xu,jd=R(()=>{Xt(),Ht(),or(),Hu=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Ku=(e,t,r)=>{let n=t.simplified,s=e[0].dims,a=e[1],i=!n&&e[2],u=s,d=Se.normalizeAxis(t.axis,s.length),p=Se.sizeToDimension(s,d),g=Se.sizeFromDimension(s,d),w=Se.size(a.dims),l=i?Se.size(i.dims):0;if(w!==g||i&&l!==g)throw new Error(`Size of X.shape()[axis:] == ${g}. + Size of scale and bias (if provided) must match this. + Got scale size of ${w} and bias size of ${l}`);let M=[];for(let le=0;le1,O=r>2,ae=le=>{let Ge=yr(e[0].dataType),Le=[rt("x",e[0].dataType,e[0].dims,C),rt("scale",a.dataType,a.dims,C)];i&&Le.push(rt("bias",i.dataType,i.dims,C)),Le.push(Ut("output",e[0].dataType,u,C)),G&&Le.push(Ut("mean_data_output",1,M)),O&&Le.push(Ut("inv_std_output",1,M));let pt=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${le.registerUniforms(pt).declareVariables(...Le)} + ${le.mainStart()} + ${le.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${Hn("f32",C)}; + var mean_square_vector = ${Hn("f32",C)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${Kn(Ge,C,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${Xn("mean_vector",C)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${Xn("mean_square_vector",C)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${Kn(Ge,C,"x[j + offset]")}; + let f32scale = ${Kn(Ge,C,"scale[j]")}; + output[j + offset] = ${Le[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale + ${i?`+ ${Kn(Ge,C,"bias[j]")}`:""} + ); + } + + ${G?"mean_data_output[global_idx] = mean":""}; + ${O?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},Z=[{dims:u,dataType:e[0].dataType}];return G&&Z.push({dims:M,dataType:1}),O&&Z.push({dims:M,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${C};${r};${n}`,inputDependencies:E},getRunData:()=>({outputs:Z,dispatchGroup:{x:Math.ceil(p/64)},programUniforms:B}),getShaderSource:ae}},Xu=(e,t)=>{Hu(e.inputs),e.compute(Ku(e.inputs,t,e.outputCount))}}),Qu,Yu,Ju,Zu,Vd=R(()=>{Xt(),Ht(),Sr(),or(),Qu=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],n=r.dims.length;if(r.dims[n-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let s=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,i=e[1];if(!Se.areEqual(i.dims,[t.n,s,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let u=e[2].dims;if(Se.size(u)!==t.n*s)throw new Error("scales input size error.");if(e.length===4){let d=e[3].dims,p=t.bits>4?t.n*s:t.n*Math.floor((s+1)/2);if(Se.size(d)!==p)throw new Error("zeroPoints input size error.")}},Yu=(e,t)=>{let r=e[0].dims,n=r.length,s=r[n-2],a=t.k,i=t.n,u=r.slice(0,n-2),d=Se.size(u),p=e[1].dims[2]/4,g=e[0].dataType,w=mr(t.k),l=mr(p),M=mr(i),C=u.concat([s,i]),E=s>1&&i/M%2===0?2:1,B=Se.size(C)/M/E,G=64,O=[],ae=[d,s,a/w],Z=Se.convertShape(e[1].dims).slice();Z.splice(-1,1,p/l),O.push(...Ct(ae)),O.push(...Ct(Z)),O.push(...Ct(e[2].dims)),e.length===4&&O.push(...Ct(Se.convertShape(e[3].dims)));let le=[d,s,i/M];O.push(...Ct(le));let Ge=Le=>{let pt=ae.length,Dt=rt("a",e[0].dataType,pt,w),Vt=rt("b",12,Z.length,l),lr=rt("scales",e[2].dataType,e[2].dims.length),fr=[Dt,Vt,lr],er=e.length===4?rt("zero_points",12,e[3].dims.length):void 0;er&&fr.push(er);let Nr=le.length,Hr=Ut("output",e[0].dataType,Nr,M),br=yr(e[0].dataType),jr=(()=>{switch(w){case 1:return`array<${br}, 8>`;case 2:return`mat4x2<${br}>`;case 4:return`mat2x4<${br}>`;default:throw new Error(`${w}-component is not supported.`)}})(),Jt=()=>{let Ve=` + // reuse a data + var input_offset = ${Dt.indicesToOffset(`${Dt.type.indices}(batch, row, word_offset)`)}; + var a_data: ${jr}; + for (var j: u32 = 0; j < ${8/w}; j++) { + a_data[j] = ${Dt.getByOffset("input_offset")}; + input_offset++; + } + `;for(let kt=0;kt> 4) & b_mask); + b_quantized_values = ${jr}(${Array.from({length:4},(tr,zr)=>`${br}(b_value_lower[${zr}]), ${br}(b_value_upper[${zr}])`).join(", ")}); + b_dequantized_values = ${w===1?`${jr}(${Array.from({length:8},(tr,zr)=>`(b_quantized_values[${zr}] - ${er?`zero_point${kt}`:"zero_point"}) * scale${kt}`).join(", ")});`:`(b_quantized_values - ${jr}(${Array(8).fill(`${er?`zero_point${kt}`:"zero_point"}`).join(",")})) * scale${kt};`}; + workgroup_shared[local_id.x * ${E} + ${Math.floor(kt/M)}]${M>1?`[${kt%M}]`:""} += ${Array.from({length:8/w},(tr,zr)=>`${w===1?`a_data[${zr}] * b_dequantized_values[${zr}]`:`dot(a_data[${zr}], b_dequantized_values[${zr}])`}`).join(" + ")}; + `;return Ve},dr=()=>{let Ve=` + var col_index = col * ${M}; + ${er?` + let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2; + var zero_point_byte_count: u32; + var zero_point_word_index: u32; + var zero_point_byte_offset: u32; + let zero_point_nibble_offset: u32 = block & 0x1u; + var zero_point_bits_offset: u32; + var zero_point_word: u32;`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${br}(8);`} + `;for(let kt=0;kt> 0x1u); + zero_point_word_index = zero_point_byte_count >> 0x2u; + zero_point_byte_offset = zero_point_byte_count & 0x3u; + zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + zero_point_word = ${er.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point${kt} = ${br}((zero_point_word) & 0xFu);`:""} + col_index += 1;`;return Ve},Pr=()=>{let Ve=`col_index = col * ${M};`;for(let kt=0;kt; + var b_value_upper: vec4; + var b_quantized_values: ${jr}; + var b_dequantized_values: ${jr};`,Ve};return` + var workgroup_shared: array<${Hr.type.value}, ${E*G}>; + ${Le.declareVariables(...fr,Hr)} + ${Le.mainStart([G,1,1])} + let output_indices = ${Hr.offsetToIndices(`(global_idx / ${G}) * ${E}`)}; + let col = output_indices[2]; + let row = output_indices[1]; + let batch = output_indices[0]; + let nBlocksPerCol = uniforms.b_shape[1]; + + for (var block = local_id.x; block < nBlocksPerCol; block += ${G}) { + //process one block + var word_offset: u32 = block * ${t.blockSize/w}; + ${dr()} + for (var word: u32 = 0; word < ${p}; word += ${l}) { + ${Pr()} + for (var i: u32 = 0; i < ${l}; i++) { + ${Jt()} + word_offset += ${8/w}; + } + } + } + workgroupBarrier(); + + if (local_id.x < ${E}) { + var output_value: ${Hr.type.value} = ${Hr.type.value}(0); + var workgroup_shared_offset: u32 = local_id.x; + for (var b: u32 = 0u; b < ${G}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${E}; + } + ${Hr.setByIndices(`${Hr.type.indices}(batch, row, col + local_id.x)`,"output_value")}; + } + }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${w};${l};${M};${E};${G}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:C,dataType:g}],dispatchGroup:{x:B},programUniforms:O}),getShaderSource:Ge}},Ju=(e,t)=>{Qu(e.inputs,t),e.compute(Yu(e.inputs,t))},Zu=e=>ur(e)}),ed,td,rd,nd,sd,id,ad,od,ld,Ud=R(()=>{Xt(),Ht(),or(),ed=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},td=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=` + k = i32(${e.indicesGet("indices",s)}) - ${jt("uniforms.pads",s,r)}; + if (k < 0) { + break; + } + if (k >= i32(${jt("uniforms.x_shape",s,t)})) { + break; + } + offset += k * i32(${jt("uniforms.x_strides",s,t)}); + `;return` + value = ${e.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + } + `},rd=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=` + k = i32(${e.indicesGet("indices",s)}) - ${jt("uniforms.pads",s,r)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${jt("uniforms.x_shape",s,t)}) - 1); + k = k % _2n_1; + if(k >= i32(${jt("uniforms.x_shape",s,t)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${jt("uniforms.x_strides",s,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},nd=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=` + k = i32(${e.indicesGet("indices",s)}) - ${jt("uniforms.pads",s,r)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${jt("uniforms.x_shape",s,t)})) { + k = i32(${jt("uniforms.x_shape",s,t)}) - 1; + } + offset += k * i32(${jt("uniforms.x_strides",s,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},sd=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=` + k = i32(${e.indicesGet("indices",s)}) - ${jt("uniforms.pads",s,r)}; + if (k < 0) { + k += i32(${jt("uniforms.x_shape",s,t)}]); + } + if (k >= i32(${jt("uniforms.x_shape",s,t)})) { + k -= i32(${jt("uniforms.x_shape",s,t)}); + } + offset += k * i32(${jt("uniforms.x_strides",s,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},id=(e,t,r)=>{switch(r.mode){case 0:return td(e,t,r.pads.length);case 1:return rd(e,t,r.pads.length);case 2:return nd(e,t,r.pads.length);case 3:return sd(e,t,r.pads.length);default:throw new Error("Invalid mode")}},ad=(e,t)=>{let r=Se.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,s=Se.size(r),a=[{type:12,data:s},{type:6,data:t.pads}],i=e.length>=3&&e[2].data;t.mode===0&&a.push({type:i?e[2].dataType:1,data:t.value}),a.push(...Ct(e[0].dims,r));let u=["rank"],d=p=>{let g=Ut("output",e[0].dataType,r.length),w=rt("x",e[0].dataType,n.length),l=w.type.value,M=id(g,n.length,t),C=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&C.push({name:"constant_value",type:i?l:"f32"}),` + ${p.registerUniforms(C).declareVariables(w,g)} + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${g.offsetToIndices("global_idx")}; + + var value = ${l}(0); + ${M} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${i}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Se.size(r)/64)},programUniforms:a}),getShaderSource:d}},od=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,s=e[0].dims.length,a=new Int32Array(2*s).fill(0);if(e.length>=4){let u=e[3].getBigInt64Array();for(let d=0;da[Number(d)]=Number(u));let i=[];return a.forEach(u=>i.push(u)),{mode:t.mode,value:n,pads:i}}else return t},ld=(e,t)=>{ed(e.inputs);let r=od(e.inputs,t);e.compute(ad(e.inputs,r),{inputs:[0]})}}),Rs,Xa,Qa,ud,ir,dd,sn,an,cn,os,Wd,Ya,cd,f,_,T,J,Fe,Oe,_t=R(()=>{Pt(),Xt(),Ht(),or(),Rs=e=>{if(k.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Xa=(e,t,r)=>{let n=t.format==="NHWC",s=e.dims.slice();n&&s.splice(1,0,s.pop());let a=Object.hasOwnProperty.call(t,"dilations"),i=t.kernelShape.slice(),u=t.strides.slice(),d=a?t.dilations.slice():[],p=t.pads.slice();Zr.adjustPoolAttributes(r,s,i,u,d,p);let g=Zr.computePoolOutputShape(r,s,u,d,i,p,t.autoPad),w=Object.assign({},t);a?Object.assign(w,{kernelShape:i,strides:u,pads:p,dilations:d,cacheKey:t.cacheKey}):Object.assign(w,{kernelShape:i,strides:u,pads:p,cacheKey:t.cacheKey});let l=g.slice();return l.push(l.splice(1,1)[0]),[w,n?l:g]},Qa=(e,t)=>{let r=t.format==="NHWC",n=Se.size(e),s=Se.size(t.kernelShape),a=[{type:12,data:n},{type:12,data:s}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let u=t.kernelShape[t.kernelShape.length-1],d=t.strides[t.strides.length-1],p=t.pads[t.pads.length/2-1],g=t.pads[t.pads.length-1],w=!!(p+g);a.push({type:12,data:u},{type:12,data:d},{type:12,data:p},{type:12,data:g}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let l=!1;if(t.kernelShape.length===2){let M=t.kernelShape[t.kernelShape.length-2],C=t.strides[t.strides.length-2],E=t.pads[t.pads.length/2-2],B=t.pads[t.pads.length-2];l=!!(E+B),a.push({type:12,data:M},{type:12,data:C},{type:12,data:E},{type:12,data:B}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,i,!0,w,l]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let u=Se.computeStrides(t.kernelShape);a.push({type:12,data:u},{type:12,data:t.pads},{type:12,data:t.strides}),i.push({name:"kernelStrides",type:"u32",length:u.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let d=t.pads.reduce((p,g)=>p+g);return[a,i,!!d,!1,!1]}},ud=(e,t,r,n,s,a,i,u,d,p,g,w)=>{let l=s.format==="NHWC",M=t.type.value,C=Ut("output",t.type.tensor,n);if(s.kernelShape.length<=2){let E="",B="",G="",O=r-(l?2:1);if(g?E=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${O}] = indices[${O}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${O}] < 0 || xIndices[${O}] + >= uniforms.x_shape[${O}]) { + pad++; + continue; + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`:E=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${O}] = indices[${O}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`,s.kernelShape.length===2){let ae=r-(l?3:2);w?B=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${ae}] = indices[${ae}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${ae}] < 0 || xIndices[${ae}] >= uniforms.x_shape[${ae}]) { + pad += i32(uniforms.kw); + continue; + } + `:B=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${ae}] = indices[${ae}] * uniforms.sh - uniforms.phStart + j; + `,G=` + } + `}return` + ${e.registerUniforms(d).declareVariables(t,C)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${C.offsetToIndices("global_idx")}; + var xIndices = ${C.offsetToIndices("global_idx")}; + + var value = ${M}(${u}); + var pad = 0; + ${B} + ${E} + ${G} + ${i} + + output[global_idx] = value; + }`}else{if(l)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let E=s.kernelShape.length,B=s.pads.length,G="";return p?G=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`:G=` + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + `,` + ${e.registerUniforms(d).declareVariables(t,C)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${C.offsetToIndices("global_idx")}; + var xIndices = ${C.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${M}(${u}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${E-1}u; j++) { + offsets[j] = offset / ${jt("uniforms.kernelStrides","j",E)}; + offset -= offsets[j] * ${jt("uniforms.kernelStrides","j",E)}; + } + offsets[${E-1}] = offset; + + isPad = false; + for (var j = ${r-E}u; j < ${r}u; j++) { + xIndices[j] = indices[j] * ${jt("uniforms.strides",`j - ${r-E}u`,E)} + + offsets[j - ${r-E}u] - ${jt("uniforms.pads","j - 2u",B)}; + ${G} + } + ${i} + + output[global_idx] = value; + }`}},ir=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,dd=e=>`${ir(e)};${e.countIncludePad}`,sn=e=>`${ir(e)};${e.storageOrder};${e.dilations}`,an=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),cn=(e,t,r,n)=>{let[s,a]=Xa(t,n,r),i=rt("x",t.dataType,t.dims.length),u=i.type.value,d="value += x_val;",p="";s.countIncludePad?p+=`value /= ${u}(uniforms.kernelSize);`:p+=`value /= ${u}(i32(uniforms.kernelSize) - pad);`;let[g,w,l,M,C]=Qa(a,s);g.push(...Ct(t.dims,a));let E=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${l};${M};${C}`,inputDependencies:E},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Se.size(a)/64)},programUniforms:g}),getShaderSource:B=>ud(B,i,t.dims.length,a.length,s,d,p,0,w,l,M,C)}},os=e=>{let t=e.count_include_pad!==0,r=an(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...r,cacheKey:""};return{...n,cacheKey:dd(n)}},Wd=(e,t)=>{Rs(e.inputs),e.compute(cn("AveragePool",e.inputs[0],!1,t))},Ya={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},cd=e=>{let t=e.format;return{format:t,...Ya,cacheKey:t}},f=(e,t)=>{Rs(e.inputs),e.compute(cn("GlobalAveragePool",e.inputs[0],!0,t))},_=(e,t,r,n)=>{let[s,a]=Xa(t,n,r),i=` + value = max(x_val, value); + `,u="",d=rt("x",t.dataType,t.dims.length),p=["rank"],[g,w,l,M,C]=Qa(a,s);return g.push(...Ct(t.dims,a)),{name:e,shaderCache:{hint:`${n.cacheKey};${l};${M};${C}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Se.size(a)/64)},programUniforms:g}),getShaderSource:E=>ud(E,d,t.dims.length,a.length,s,i,u,t.dataType===10?-65504:-1e5,w,l,M,C)}},T=(e,t)=>{Rs(e.inputs),e.compute(_("MaxPool",e.inputs[0],!1,t))},J=e=>{let t=e.storage_order,r=e.dilations,n=an(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let s={storageOrder:t,dilations:r,...n,cacheKey:""};return{...s,cacheKey:sn(s)}},Fe=e=>{let t=e.format;return{format:t,...Ya,cacheKey:t}},Oe=(e,t)=>{Rs(e.inputs),e.compute(_("GlobalMaxPool",e.inputs[0],!0,t))}}),St,Wt,nr,kr,pr=R(()=>{Xt(),Ht(),Sr(),or(),St=(e,t)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((r,n)=>r===e[2].dims[n]).reduce((r,n)=>r&&n,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(t.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((s,a)=>a===t.axis||s===e[0].dims[a]).reduce((s,a)=>s&&a,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let r=e[0].dims[t.axis],n=e[1].dims[t.axis];if(t.blockSizeMath.ceil(r/(n-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},Wt=(e,t)=>{let r=Se.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,s=n===3,a=e[0].dims,i=e[1].dataType,u=Se.size(a),d=n===3||n===2,p=d?[Math.ceil(Se.size(e[0].dims)/4)]:e[0].dims,g=e[1].dims,w=e.length>2?e[2]:void 0,l=w?d?[Math.ceil(Se.size(w.dims)/4)]:w.dims:void 0,M=g.length===0||g.length===1&&g[0]===1,C=M===!1&&g.length===1,E=mr(u),B=M&&(!d||E===4),G=B?E:1,O=B&&!d?E:1,ae=rt("input",d?12:n,p.length,O),Z=rt("scale",i,g.length),le=w?rt("zero_point",d?12:n,l.length):void 0,Ge=Ut("output",i,a.length,G),Le=[ae,Z];le&&Le.push(le);let pt=[p,g];w&&pt.push(l);let Dt=[{type:12,data:u/G},{type:12,data:r},{type:12,data:t.blockSize},...Ct(...pt,a)],Vt=lr=>{let fr=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${lr.registerUniforms(fr).declareVariables(...Le,Ge)} + ${lr.mainStart()} + ${lr.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${Ge.offsetToIndices("global_idx")}; + + // Set input x + ${d?` + let input = ${ae.getByOffset("global_idx / 4")}; + let x_vec = ${s?"unpack4xI8(input)":"unpack4xU8(input)"}; + let x_value = ${G===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${ae.getByOffset("global_idx")};`}; + + // Set scale input + ${M?`let scale_value= ${Z.getByOffset("0")}`:C?` + let scale_index = ${Ge.indicesGet("output_indices","uniforms.axis")}; + let scale_value= ${Z.getByOffset("scale_index")};`:` + var scale_indices: ${Z.type.indices} = output_indices; + let index = ${Z.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; + ${Z.indicesSet("scale_indices","uniforms.axis","index")}; + let scale_value= ${Z.getByIndices("scale_indices")};`}; + + // Set zero-point input + ${le?M?d?` + let zero_point_input = ${le.getByOffset("0")}; + let zero_point_vec = ${s?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${le.getByOffset("0")}`:C?d?` + let zero_point_index = ${Ge.indicesGet("output_indices","uniforms.axis")}; + let zero_point_input = ${le.getByOffset("zero_point_index / 4")}; + let zero_point_vec = ${s?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_index % 4]`:` + let zero_point_index = ${Ge.indicesGet("output_indices","uniforms.axis")}; + let zero_point_value = ${le.getByOffset("zero_point_index")};`:d?` + let zero_point_offset = ${Z.indicesToOffset("scale_indices")}; + let zero_point_input = ${le.getByOffset("zero_point_offset / 4")}; + let zero_point_vec = ${s?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${le.getByIndices("scale_indices")};`:`let zero_point_value = ${d?s?"i32":"u32":ae.type.value}(0);`}; + // Compute and write output + ${Ge.setByOffset("global_idx",`${Ge.type.value}(x_value - zero_point_value) * scale_value`)}; + }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:le?["rank","rank","rank"]:["rank","rank"]},getShaderSource:Vt,getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(u/G/64),y:1,z:1},programUniforms:Dt})}},nr=(e,t)=>{St(e.inputs,t),e.compute(Wt(e.inputs,t))},kr=e=>ur({axis:e.axis,blockSize:e.blockSize})}),hr,_r,xr,Fr=R(()=>{Pt(),Xt(),or(),hr=(e,t,r)=>{let n=e===t,s=et&&r>0;if(n||s||a)throw new Error("Range these inputs' contents are invalid.")},_r=(e,t,r,n)=>{let s=Math.abs(Math.ceil((t-e)/r)),a=[s],i=s,u=[{type:12,data:i},{type:n,data:e},{type:n,data:r},...Ct(a)],d=p=>{let g=Ut("output",n,a.length),w=g.type.value,l=[{name:"outputSize",type:"u32"},{name:"start",type:w},{name:"delta",type:w}];return` + ${p.registerUniforms(l).declareVariables(g)} + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${w}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:d,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:u})}},xr=e=>{let t=0,r=0,n=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],n=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],n=e.inputs[2].getFloat32Array()[0]),k.webgpu.validateInputContent&&hr(t,r,n),e.compute(_r(t,r,n,e.inputs[0].dataType),{inputs:[]})}}),Tr,gr,rn,on,bn,pn,ln,Mn,Ms,_i,Ja,Gd,Rn,vs,qd,Hd,Kd,pd,ip,zf=R(()=>{Xt(),Ht(),Sr(),or(),Tr=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and + one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},gr=(e,t,r)=>{t.every(s=>s>=0&&s{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return t.forEach((s,a)=>n[s]=e[a]),n},rn=(e,t,r,n,s,a)=>{let[i,u,d]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],p=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(g=>a.push(g));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(u>0&&e.length>u&&e[u].dims.length>0){if(e[u].getFloat32Array().forEach(g=>n.push(g)),n.length!==0&&n.length!==p&&r>=18&&n.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");Tr(n,t),t.axes.length>0&&gr(n,t.axes,p).forEach((g,w)=>n[w]=g)}if(d>0&&e.length>d&&(e[d].getBigInt64Array().forEach(g=>s.push(Number(g))),s.length!==p||r>=18&&s.length===t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(n.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(s.length!==t.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof s<"u"&&n.length>0&&s.length>p)throw new Error("Resize requires only of scales or sizes to be specified")},on=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, + lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) { + return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5; + } else { + return 0.0; + }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) { + return 0.0; + } else { + // The whole part and the fractional part are calculated separately due to inaccuracy of floating + // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an + // offset-by-one error later in floor(). + let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1)); + let fract = + ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1); + return whole + fract; + }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { + return ${t}(roiStart) * ${t}(lengthOriginal - 1) + + (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) / + ${t}(lengthResized - 1); + } else { + return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1); + }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized); + const adjustment = ${t}(lengthResized) / outputWidth; + const center = ${t}(lengthOriginal) / 2; + const offset = center * (1 - adjustment); + return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",bn=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(t<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",pn=(e,t,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),s=e.length===0?n:e.slice();return t.length>0?(t.forEach((a,i)=>{n[a]=s[i],n[i+r]=s[t.length+i]}),n):s},ln=(e,t,r,n)=>{let s=[];if(r.length>0)if(n.length>0){if(e.forEach(a=>s.push(a)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((a,i)=>s[a]=r[i])}else r.forEach(a=>s.push(a));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");s=e.map((a,i)=>Math.round(a*t[i]))}return s},Mn=(e,t,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(a=>t[a]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(a=>t[a]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let s=e.slice();return r.axes.length>0?(r.axes.forEach(a=>t[a]=n),r.axes.forEach(a=>s[a]=Math.round(e[a]*t[a]))):(t.fill(n,0,t.length),s.forEach((a,i)=>s[i]=Math.round(a*t[i]))),s},Ms=(e,t,r,n,s)=>` + fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> { + var original_indices: array<${e.type.value}, ${r.length}>; + for (var i:u32 = 0; i < ${r.length}; i++) { + var output_index = ${e.indicesGet("output_indices","i")}; + var scale = ${jt("uniforms.scales","i",n)}; + var roi_low = ${jt("uniforms.roi","i",s)}; + var roi_hi = ${jt("uniforms.roi",`i + ${t.length}`,s)}; + if (scale == 1.0) { + original_indices[i] = ${e.type.value}(output_index); + } else { + var input_shape_i = ${jt("uniforms.input_shape","i",t.length)}; + var output_shape_i = ${jt("uniforms.output_shape","i",r.length)}; + original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + } + } + return original_indices; + }`,_i=(e,t,r,n,s,a,i)=>` + fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + for (var i:u32 = 0; i < ${n.length}; i++) { + var output_index = ${t.indicesGet("output_indices","i")}; + var input_index: u32; + var scale = ${jt("uniforms.scales","i",s)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${jt("uniforms.roi","i",a)}; + var roi_hi = ${jt("uniforms.roi",`i + ${r.length}`,a)}; + var input_shape_i = ${jt("uniforms.input_shape","i",r.length)}; + var output_shape_i = ${jt("uniforms.output_shape","i",n.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${i} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) { + if (original_idx < 0) { + input_index = 0; + } else if (original_idx > ${t.type.value}(input_shape_i - 1)) { + input_index = input_shape_i - 1; + } else { + input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1)); + } + } else { + input_index = u32(original_idx); + } + } + ${e.indicesSet("input_indices","i"," input_index")} + } + return input_indices; + }`,Ja=(e,t)=>` + fn checkInputIndices(input_indices: ${e.type.indices}) -> bool { + for (var i:u32 = 0; i < ${t.length}; i++) { + var input_index = ${e.indicesGet("input_indices","i")}; + if (input_index < 0 || input_index >= ${jt("uniforms.input_shape","i",t.length)}) { + return false; + } + } + return true; + }`,Gd=(e,t,r,n)=>e.rank>n?` + ${e.indicesSet("input_indices",t,"channel")}; + ${e.indicesSet("input_indices",r,"batch")}; +`:"",Rn=(e,t,r,n,s)=>{let[a,i,u,d]=r.length===2?[-1,0,1,-1]:[0,2,3,1],p=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${p} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",i,`max(0, min(row, ${r[i]} - 1))`)}; + ${e.indicesSet("input_indices",u,`max(0, min(col, ${r[u]} - 1))`)}; + ${Gd(e,d,a,2)} + return ${e.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${p} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${p} = originalIndices[${i}]; + var col:${p} = originalIndices[${u}]; + ${n?`if (row < 0 || row > (${r[i]} - 1) || col < 0 || col > (${r[u]} - 1)) { + return ${s}; + }`:""}; + row = max(0, min(row, ${r[i]} - 1)); + col = max(0, min(col, ${r[u]} - 1)); + var row1: u32 = u32(row); + var col1: u32 = u32(col); + var row2: u32 = u32(row + 1); + var col2: u32 = u32(col + 1); + var channel: u32 = ${r.length>2?`u32(originalIndices[${d}])`:"0"}; + var batch: u32 = ${r.length>2?`u32(originalIndices[${a}])`:"0"}; + var x11: ${p} = getInputValue(batch, channel, row1, col1); + var x12: ${p} = getInputValue(batch, channel, row1, col2); + var x21: ${p} = getInputValue(batch, channel, row2, col1); + var x22: ${p} = getInputValue(batch, channel, row2, col2); + var dx1: ${p} = abs(row - ${p}(row1)); + var dx2: ${p} = abs(${p}(row2) - row); + var dy1: ${p} = abs(col - ${p}(col1)); + var dy2: ${p} = abs(${p}(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); + }`},vs=(e,t,r,n,s,a,i,u,d,p)=>{let g=r.length===2,[w,l]=g?[0,1]:[2,3],M=e.type.value,C=E=>{let B=E===w?"row":"col";return` + fn ${B}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${M} { + var output_index = ${t.indicesGet("output_indices",E)}; + var originalIdx: ${M} = getOriginalCoordinateFromResizedCoordinate(output_index, ${s[E]}, + ${n[E]}, ${r[E]}, ${a[E]}, ${a[E]} + ${r.length}); + var fractOriginalIdx: ${M} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${u} && (originalIdx < 0 || originalIdx > (${r[E]} - 1))) { + return ${d}; + } + var data: array<${M}, 4> = array<${M}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${B}: ${M} = originalIdx + ${M}(i); + if (${B} < 0 || ${B} >= ${r[E]}) { + ${p?`coefs[i + 1] = 0.0; + continue;`:u?`return ${d};`:`${B} = max(0, min(${B}, ${r[E]} - 1));`}; + } + var input_indices_copy: ${e.type.indices} = input_indices; + ${e.indicesSet("input_indices_copy",E,`u32(${B})`)}; + data[i + 1] = ${E===w?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${C(w)}; + ${C(l)}; + fn getCubicInterpolationCoefs(s: ${M}) -> array<${M}, 4> { + var absS = abs(s); + var coeffs: array<${M}, 4> = array<${M}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${M} = 1.0 - absS; + var twoMinusAbsS: ${M} = 2.0 - absS; + var onePlusAbsS: ${M} = 1.0 + absS; + coeffs[0] = ((${i} * onePlusAbsS - 5 * ${i}) * onePlusAbsS + 8 * ${i}) * onePlusAbsS - 4 * ${i}; + coeffs[1] = ((${i} + 2) * absS - (${i} + 3)) * absS * absS + 1; + coeffs[2] = ((${i} + 2) * oneMinusAbsS - (${i} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${i} * twoMinusAbsS - 5 * ${i}) * twoMinusAbsS + 8 * ${i}) * twoMinusAbsS - 4 * ${i}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${M}, 4>, coefs: array<${M}, 4>) -> ${M} { + var coefsSum: ${M} = coefs[0] + coefs[1] + coefs[2] + coefs[3]; + return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum; + } + + fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${M} { + var input_indices: ${e.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},qd=(e,t,r,n,s)=>{let[a,i,u,d,p]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],g=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${g} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",i,`max(0, min(depth, ${r[i]} - 1))`)}; + ${e.indicesSet("input_indices",u,`max(0, min(height, ${r[u]} - 1))`)}; + ${e.indicesSet("input_indices",d,`max(0, min(width, ${r[d]} - 1))`)}; + ${Gd(e,p,a,3)} + return ${e.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${g} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${g} = originalIndices[${i}]; + var height:${g} = originalIndices[${u}]; + var width:${g} = originalIndices[${d}]; + ${n?`if (depth < 0 || depth > (${r[i]} - 1) || height < 0 || height > (${r[u]} - 1) || width < 0 || (width > ${r[d]} - 1)) { + return ${s}; + }`:""}; + + depth = max(0, min(depth, ${r[i]} - 1)); + height = max(0, min(height, ${r[u]} - 1)); + width = max(0, min(width, ${r[d]} - 1)); + var depth1: u32 = u32(depth); + var height1: u32 = u32(height); + var width1: u32 = u32(width); + var depth2: u32 = u32(depth + 1); + var height2: u32 = u32(height + 1); + var width2: u32 = u32(width + 1); + var channel: u32 = ${r.length>3?`u32(originalIndices[${p}])`:"0"}; + var batch: u32 = ${r.length>3?`u32(originalIndices[${a}])`:"0"}; + + var x111: ${g} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${g} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${g} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${g} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${g} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${g} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${g} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${g} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${g} = abs(depth - ${g}(depth1)); + var dx2: ${g} = abs(${g}(depth2) - depth); + var dy1: ${g} = abs(height - ${g}(height1)); + var dy2: ${g} = abs(${g}(height2) - height); + var dz1: ${g} = abs(width - ${g}(width1)); + var dz2: ${g} = abs(${g}(width2) - width); + if (depth1 == depth2) { + dx1 = 0.5; + dx2 = 0.5; + } + if (height1 == height2) { + dy1 = 0.5; + dy2 = 0.5; + } + if (width1 == width2) { + dz1 = 0.5; + dz2 = 0.5; + } + return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 + + x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1); + }`},Hd=(e,t,r,n,s,a)=>{let i=e.dims,u=pn(a,t.axes,i.length),d=ln(i,n,s,t.axes),p=n.slice();n.length===0&&(p=i.map((O,ae)=>O===0?1:d[ae]/O),t.keepAspectRatioPolicy!=="stretch"&&(d=Mn(i,p,t)));let g=Ut("output",e.dataType,d.length),w=rt("input",e.dataType,i.length),l=Se.size(d),M=i.length===d.length&&i.every((O,ae)=>O===d[ae]),C=t.coordinateTransformMode==="tf_crop_and_resize",E=t.extrapolationValue,B=w.type.value,G=O=>` + ${M?"":` + ${on(t.coordinateTransformMode,B)}; + ${(()=>{switch(t.mode){case"nearest":return` + ${Ja(w,i)}; + ${bn(t.nearestMode,r,B)}; + ${_i(w,g,i,d,p.length,u.length,C)}; + `;case"linear":return` + ${Ms(g,i,d,p.length,u.length)}; + ${(()=>{if(i.length===2||i.length===4)return`${Rn(w,g,i,C,E)}`;if(i.length===3||i.length===5)return`${qd(w,g,i,C,E)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(i.length===2||i.length===4)return`${vs(w,g,i,d,p,u,t.cubicCoeffA,C,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; + `;default:throw Error("Invalid resize mode")}})()}; + `} + ${O.registerUniform("output_size","u32").registerUniform("scales","f32",p.length).registerUniform("roi","f32",u.length).declareVariables(w,g)} + ${O.mainStart()} + ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${M?"output[global_idx] = input[global_idx];":` + let output_indices = ${g.offsetToIndices("global_idx")}; + var input_indices: ${w.type.indices}; + ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${w.getByIndices("input_indices")}; + } else { + output[global_idx] = ${t.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${i.length===2||i.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()}; +`} + }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${p.length>0?p:""}|${s.length>0?s:""}|${u.length>0?u:""}|${M}|${i}`,inputDependencies:["rank"]},getShaderSource:G,getRunData:()=>({outputs:[{dims:d,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:[{type:12,data:l},{type:1,data:p},{type:1,data:u},...Ct(i,d)]})}},Kd=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},pd=(e,t)=>{let r=[],n=[],s=[],a=Kd(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");rn(e.inputs,t,a,r,n,s),e.compute(Hd(e.inputs[0],t,a,r,n,s),{inputs:[0]})},ip=e=>{let t=e.antialias,r=e.axes,n=e.coordinateTransformMode,s=e.cubicCoeffA,a=e.excludeOutside!==0,i=e.extrapolationValue,u=e.keepAspectRatioPolicy,d=e.mode,p=e.nearestMode===""?"simple":e.nearestMode;return ur({antialias:t,axes:r,coordinateTransformMode:n,cubicCoeffA:s,excludeOutside:a,extrapolationValue:i,keepAspectRatioPolicy:u,mode:d,nearestMode:p})}}),ap,op,lp,Of=R(()=>{Xt(),Ht(),Sr(),or(),ap=(e,t)=>{let[r,n,s,a]=e,{numHeads:i,rotaryEmbeddingDim:u}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!Se.areEqual(n.dims,[])&&!Se.areEqual(n.dims,[1])&&n.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${n.dims.length}`);if(s.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${s.dims.length}`);if(a.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(!Se.areEqual(s.dims,a.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(u>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let d=r.dims[0],p=r.dims[r.dims.length-2],g=s.dims[0],w=Se.sizeFromDimension(r.dims,1)/p,l=u===0?s.dims[1]*2:w/i;if(u>l)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(d!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(p!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(l/2!==s.dims[1]&&u/2!==s.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${s.dims[1]}`);if(p>g)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},op=(e,t)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:s,scale:a}=t,i=e[0].dims[0],u=Se.sizeFromDimension(e[0].dims,1),d=e[0].dims[e[0].dims.length-2],p=u/d,g=e[2].dims[1],w=s===0?g*2:p/n,l=new Array(i,d,p/w,w-g),M=Se.computeStrides(l),C=[{type:1,data:a},{type:12,data:l},{type:12,data:M},...e[0].dims.length===3?new Array({type:12,data:[u,p,w,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[u,w,d*w,1]}):[],...Ct(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],E=B=>{let G=rt("input",e[0].dataType,e[0].dims.length),O=rt("position_ids",e[1].dataType,e[1].dims.length),ae=rt("cos_cache",e[2].dataType,e[2].dims.length),Z=rt("sin_cache",e[3].dataType,e[3].dims.length),le=Ut("output",e[0].dataType,e[0].dims.length);return B.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:l.length},{name:"global_strides",type:"u32",length:M.length},{name:"input_output_strides",type:"u32",length:M.length}]),` + ${B.declareVariables(G,O,ae,Z,le)} + + ${B.mainStart(en)} + let half_rotary_emb_dim = uniforms.${ae.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${B.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${O.broadcastedIndicesToOffset("bsnh.xy",Ut("",O.type.tensor,2))}; + let position_id = + u32(${O.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r}); + let j = i + select(half_rotary_emb_dim, 1, ${r}); + let re = ${G.getByOffset("i")} * ${ae.get("position_id","bsnh[3]")} - + ${G.getByOffset("j")} * ${Z.get("position_id","bsnh[3]")}; + ${le.setByOffset("i","re")} + let im = ${G.getByOffset("i")} * ${Z.get("position_id","bsnh[3]")} + + ${G.getByOffset("j")} * ${ae.get("position_id","bsnh[3]")}; + ${le.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${le.setByOffset("k",G.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:ur({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:E,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Se.size(l)/en)},programUniforms:C})}},lp=(e,t)=>{ap(e.inputs,t),e.compute(op(e.inputs,t))}}),up,dp,cp,Df=R(()=>{Xt(),Ht(),or(),up=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],n=e[2];if(t.dataType!==r.dataType||t.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let s=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==s)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==a)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==s)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let i=e[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==s)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let i=e[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==s)throw new Error("Bias must have the same hidden size as input")}},dp=(e,t,r,n)=>{let s=t.simplified,a=e[0].dims,i=Se.size(a),u=a,d=i,p=a.slice(-1)[0],g=n?a.slice(0,-1).concat(1):[],w=!s&&e.length>3,l=e.length>4,M=n&&r>1,C=n&&r>2,E=r>3,B=64,G=mr(p),O=[{type:12,data:d},{type:12,data:G},{type:12,data:p},{type:1,data:t.epsilon}],ae=le=>{let Ge=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Le=[rt("x",e[0].dataType,e[0].dims,G),rt("skip",e[1].dataType,e[1].dims,G),rt("gamma",e[2].dataType,e[2].dims,G)];w&&Le.push(rt("beta",e[3].dataType,e[3].dims,G)),l&&Le.push(rt("bias",e[4].dataType,e[4].dims,G)),Le.push(Ut("output",e[0].dataType,u,G)),M&&Le.push(Ut("mean_output",1,g)),C&&Le.push(Ut("inv_std_output",1,g)),E&&Le.push(Ut("input_skip_bias_sum",e[0].dataType,u,G));let pt=yr(e[0].dataType),Dt=yr(1,G);return` + + ${le.registerUniforms(Ge).declareVariables(...Le)} + var sum_shared : array<${Dt}, ${B}>; + var sum_squared_shared : array<${Dt}, ${B}>; + + ${le.mainStart([B,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${B}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${B}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${B-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${l?"bias[offset1d + i]":pt+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${E?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${Kn(pt,G,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${B}; + 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 = ${Xn("sum",G)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${Xn("square_sum",G)} / f32(uniforms.hidden_size) ${s?"":"- mean * mean"} + uniforms.epsilon); + ${M?"mean_output[global_idx] = mean;":""} + ${C?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${s?"":`- ${pt}(mean)`}) * + ${pt}(inv_std_dev) * gamma[offset1d + i] + ${w?"+ beta[offset1d + i]":""}; + } + }`},Z=[{dims:u,dataType:e[0].dataType}];return r>1&&Z.push({dims:g,dataType:1}),r>2&&Z.push({dims:g,dataType:1}),r>3&&Z.push({dims:a,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${G};${M};${C};${E}`,inputDependencies:e.map((le,Ge)=>"type")},getShaderSource:ae,getRunData:()=>({outputs:Z,dispatchGroup:{x:Math.ceil(d/p)},programUniforms:O})}},cp=(e,t)=>{up(e.inputs);let r=[0];e.outputCount>1&&r.push(-3),e.outputCount>2&&r.push(-3),e.outputCount>3&&r.push(3),e.compute(dp(e.inputs,t,e.outputCount,!1),{outputs:r})}}),pp,hd,hp,bc,fp,mp,_p,gp,Lf=R(()=>{Xt(),Ht(),Sr(),or(),pp=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,n)=>{if(e[n+1].dataType!==6&&e[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},hd=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},hp=(e,t)=>{if(e.length>1){let r=hd(e,1),n=hd(e,2),s=hd(e,3);return s.length===0&&(s=[...Array(e[0].dims.length).keys()]),ur({starts:r,ends:n,axes:s})}else return t},bc=(e,t,r,n,s)=>{let a=e;return e<0&&(a+=r[n[t]]),s[t]<0?Math.max(0,Math.min(a,r[n[t]]-1)):Math.max(0,Math.min(a,r[n[t]]))},fp=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + var carry = 0u; + for (var i = ${r.length}; i >= 0; i--) { + let input_shape_i = ${jt("uniforms.input_shape","i",r.length)}; + let steps_i = ${jt("uniforms.steps","i",r.length)}; + let signs_i = ${jt("uniforms.signs","i",r.length)}; + let starts_i = ${jt("uniforms.starts","i",r.length)}; + var output_index = ${t.indicesGet("output_indices","i")}; + var input_index = output_index * steps_i + starts_i + carry; + carry = input_index / input_shape_i; + input_index = input_index % input_shape_i; + if (signs_i < 0) { + input_index = input_shape_i - input_index - 1u + starts_i; + } + ${e.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,mp=(e,t)=>{let r=e[0].dims,n=Se.size(r),s=t.axes.length>0?Se.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],a=hd(e,4);a.forEach(G=>G!==0||(()=>{throw new Error("step cannot be 0")})),a.length===0&&(a=Array(s.length).fill(1));let i=t.starts.map((G,O)=>bc(G,O,r,s,a)),u=t.ends.map((G,O)=>bc(G,O,r,s,a));if(s.length!==i.length||s.length!==u.length)throw new Error("start, ends and axes should have the same number of elements");if(s.length!==r.length)for(let G=0;GMath.sign(G));a.forEach((G,O,ae)=>{if(G<0){let Z=(u[O]-i[O])/G,le=i[O],Ge=le+Z*a[O];i[O]=Ge,u[O]=le,ae[O]=-G}});let p=r.slice(0);s.forEach((G,O)=>{p[G]=Math.ceil((u[G]-i[G])/a[G])});let g={dims:p,dataType:e[0].dataType},w=Ut("output",e[0].dataType,p.length),l=rt("input",e[0].dataType,e[0].dims.length),M=Se.size(p),C=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:d.length},{name:"steps",type:"u32",length:a.length}],E=[{type:12,data:M},{type:12,data:i},{type:6,data:d},{type:12,data:a},...Ct(e[0].dims,p)],B=G=>` + ${G.registerUniforms(C).declareVariables(l,w)} + ${fp(l,w,r)} + ${G.mainStart()} + ${G.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${w.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${w.setByOffset("global_idx",l.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${d.length}_${i.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:B,getRunData:()=>({outputs:[g],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:E})}},_p=(e,t)=>{pp(e.inputs,t);let r=hp(e.inputs,t);e.compute(mp(e.inputs,r),{inputs:[0]})},gp=e=>{let t=e.starts,r=e.ends,n=e.axes;return ur({starts:t,ends:r,axes:n})}}),wp,yp,bp,Mp,Bf=R(()=>{Xt(),Ht(),Sr(),or(),wp=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},yp=(e,t)=>{let r=e.dims,n=Se.size(r),s=64,a=t.axis;if(a<0&&(a=r.length+a),aG===4?`max(max(${B}.x, ${B}.y), max(${B}.z, ${B}.w))`:G===2?`max(${B}.x, ${B}.y)`:G===3?`max(max(${B}.x, ${B}.y), ${B}.z)`:B,w=rt("x",e.dataType,e.dims,d),l=Ut("result",e.dataType,e.dims,d),M=w.type.value,C=yr(e.dataType)==="f32"?`var threadMax = ${M}(-3.402823e+38f);`:`var threadMax = ${M}(-65504.0h);`,E=B=>` + var rowMaxShared : ${M}; + var rowSumShared : ${M}; + var threadShared : array<${M}, ${s}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${M} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${M}) { + let index = row * row_stride + col; + result[index] = value; + } + ${B.registerUniform("packedCols","i32").declareVariables(w,l)} + ${B.mainStart()} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${s}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${C} + 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 = ${M}(${g("threadShared[0]",d)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${M}(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 = ${M}(${Xn("threadShared[0]",d)}); + } + 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:`${d}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:u},programUniforms:[{type:6,data:p}]}),getShaderSource:E}},bp=(e,t)=>{wp(e.inputs),e.compute(yp(e.inputs[0],t))},Mp=e=>ur({axis:e.axis})}),vp,xp,Tp,Cp,Ep,$p,Sp,Rf=R(()=>{Xt(),Ht(),Sr(),or(),vp=e=>{if(!e||e.length<1)throw new Error("too few inputs")},xp=(e,t)=>{let r=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(s=>r.push(Number(s))),n=r.length),ur({numOutputs:n,axis:t.axis,splitSizes:r})},Tp=e=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${e}u; i += 1u ) { + if (index < ${jt("uniforms.size_in_split_axis","i",e)}) { + return i; + } + } + return ${e}u; +}`,Cp=e=>{let t=e.length,r=[];for(let n=0;n{let r=e[0].dims,n=Se.size(r),s=e[0].dataType,a=Se.normalizeAxis(t.axis,r.length),i=new Array(t.numOutputs),u=rt("input",s,r.length),d=new Array(t.numOutputs),p=[],g=[],w=0,l=[{type:12,data:n}];for(let C=0;C` + ${C.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",d.length).declareVariables(u,...i)} + ${Tp(d.length)} + ${Cp(i)} + + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${u.offsetToIndices("global_idx")}; + var index = ${u.indicesGet("indices",a)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${jt("uniforms.size_in_split_axis","output_number - 1u",d.length)}; + ${u.indicesSet("indices",a,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:M,getRunData:()=>({outputs:p,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:l})}},$p=(e,t)=>{vp(e.inputs);let r=e.inputs.length===1?t:xp(e.inputs,t);e.compute(Ep(e.inputs,r),{inputs:[0]})},Sp=e=>{let t=e.axis,r=e.splitSizes,n=e.numOutputs<0?r.length:e.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return ur({axis:t,numOutputs:n,splitSizes:r})}}),kp,Pp,Ap,Nf=R(()=>{Xt(),Ht(),or(),kp=(e,t,r,n,s)=>{let a=Ut("output_data",s,r.length,4),i=rt("a_data",t[1].dataType,t[1].dims.length,4),u=rt("b_data",t[2].dataType,t[2].dims.length,4),d=rt("c_data",t[0].dataType,t[0].dims.length,4),p,g=(w,l,M)=>`select(${l}, ${w}, ${M})`;if(!n)p=a.setByOffset("global_idx",g(i.getByOffset("global_idx"),u.getByOffset("global_idx"),d.getByOffset("global_idx")));else{let w=(l,M,C="")=>{let E=`a_data[index_a${M}][component_a${M}]`,B=`b_data[index_b${M}][component_b${M}]`,G=`bool(c_data[index_c${M}] & (0xffu << (component_c${M} * 8)))`;return` + let output_indices${M} = ${a.offsetToIndices(`global_idx * 4u + ${M}u`)}; + let offset_a${M} = ${i.broadcastedIndicesToOffset(`output_indices${M}`,a)}; + let offset_b${M} = ${u.broadcastedIndicesToOffset(`output_indices${M}`,a)}; + let offset_c${M} = ${d.broadcastedIndicesToOffset(`output_indices${M}`,a)}; + let index_a${M} = offset_a${M} / 4u; + let index_b${M} = offset_b${M} / 4u; + let index_c${M} = offset_c${M} / 4u; + let component_a${M} = offset_a${M} % 4u; + let component_b${M} = offset_b${M} % 4u; + let component_c${M} = offset_c${M} % 4u; + ${l}[${M}] = ${C}(${g(E,B,G)}); + `};s===9?p=` + var data = vec4(0); + ${w("data",0,"u32")} + ${w("data",1,"u32")} + ${w("data",2,"u32")} + ${w("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:p=` + ${w("output_data[global_idx]",0)} + ${w("output_data[global_idx]",1)} + ${w("output_data[global_idx]",2)} + ${w("output_data[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(d,i,u,a)} + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${p} + }`},Pp=e=>{let 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d=r.createComputePipeline({compute:{module:u,entryPoint:"main"},layout:"auto",label:e.name});return je(e.name),{programInfo:e,computePipeline:d,uniformVariablesInfo:s.variablesInfo}}normalizeDispatchGroupSize(e){let t=typeof e=="number"?e:e.x,r=typeof e=="number"?1:e.y||1,n=typeof e=="number"?1:e.z||1,s=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(t<=s&&r<=s&&n<=s)return[t,r,n];let a=t*r*n,i=Math.ceil(Math.sqrt(a));if(i>s){if(i=Math.ceil(Math.cbrt(a)),i>s)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[i,i,i]}else return[i,i,1]}}}),zp,Op,Dp,Lp,Uf=R(()=>{Pt(),Xt(),m(),K(),Dr(),jf(),Vf(),zp=(e,t)=>{if(t.length!==e.length)throw new Error(`inputDependencies length ${t.length} is not equal to inputTensors length ${e.length}.`);let r=[];for(let n=0;n{var s,a;let n=e.name;return(s=e.shaderCache)!=null&&s.hint&&(n+="["+e.shaderCache.hint+"]"),n+=":"+r+`:${zp(t,((a=e.shaderCache)==null?void 0:a.inputDependencies)??new 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This is not supported now.`)}let M;if(p){let O=0,ae=[];p.forEach(Le=>{let pt=typeof Le.data=="number"?[Le.data]:Le.data;if(pt.length===0)return;let Dt=Le.type===10?2:4,Vt,lr;Le.type===10?(lr=pt.length>4?16:pt.length>2?8:pt.length*Dt,Vt=pt.length>4?16:Dt*pt.length):(lr=pt.length<=2?pt.length*Dt:16,Vt=16),O=Math.ceil(O/lr)*lr,ae.push(O);let fr=Le.type===10?8:4;O+=pt.length>4?Math.ceil(pt.length/fr)*Vt:pt.length*Dt});let Z=16;O=Math.ceil(O/Z)*Z;let le=new ArrayBuffer(O);p.forEach((Le,pt)=>{let Dt=ae[pt],Vt=typeof Le.data=="number"?[Le.data]:Le.data;if(Le.type===6)new Int32Array(le,Dt,Vt.length).set(Vt);else if(Le.type===12)new Uint32Array(le,Dt,Vt.length).set(Vt);else if(Le.type===10)new Uint16Array(le,Dt,Vt.length).set(Vt);else if(Le.type===1)new Float32Array(le,Dt,Vt.length).set(Vt);else throw new Error(`Unsupported uniform type: ${Sn(Le.type)}`)});let Ge=this.gpuDataManager.create(O,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Ge.buffer,0,le,0,O),this.gpuDataManager.release(Ge.id),M={offset:0,size:O,buffer:Ge.buffer}}let C=this.programManager.normalizeDispatchGroupSize(d),E=C[1]===1&&C[2]===1,B=Op(e,t,E),G=this.programManager.getArtifact(B);if(G||(G=this.programManager.build(e,C),this.programManager.setArtifact(B,G),ue("info",()=>`[artifact] key: ${B}, programName: ${e.name}`)),p&&G.uniformVariablesInfo){if(p.length!==G.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${G.uniformVariablesInfo.length}, got ${p.length} in program "${G.programInfo.name}".`);for(let O=0;O`[ProgramManager] run "${e.name}" (key=${B}) with ${C[0]}x${C[1]}x${C[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let 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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(){ue("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(){ue("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){ue("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),r=e.length;this.pendingKernels=[];for(let 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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":(Ft,Ee,N)=>{var z;N.r(Ee),N.d(Ee,{Tensor:()=>Ce.Tensor,createInferenceSession:()=>ce,deviceToExecutionProviders:()=>ne,isONNXProxy:()=>te,isONNXTensor:()=>D});var me=N("./src/env.js"),we=N("?2ce3"),Te=N("./node_modules/onnxruntime-web/dist/ort.webgpu.bundle.min.mjs"),Ce=N("./node_modules/onnxruntime-common/dist/esm/index.js");const R=Object.freeze({auto:null,gpu:null,cpu:"cpu",wasm:"wasm",webgpu:"webgpu",cuda:"cuda",dml:"dml",webnn:{name:"webnn",deviceType:"cpu"},"webnn-npu":{name:"webnn",deviceType:"npu"},"webnn-gpu":{name:"webnn",deviceType:"gpu"},"webnn-cpu":{name:"webnn",deviceType:"cpu"}}),S=[];let V,P;if(me.apis.IS_NODE_ENV){switch(P=we??(z||(z=N.t(we,2))),process.platform){case"win32":S.push("dml");break;case"linux":process.arch==="x64"&&S.push("cuda");break}S.push("cpu"),V=["cpu"]}else P=Te,me.apis.IS_WEBNN_AVAILABLE&&S.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),me.apis.IS_WEBGPU_AVAILABLE&&S.push("webgpu"),S.push("wasm"),V=["wasm"];const re=P.InferenceSession;function ne(ie=null){if(!ie)return V;switch(ie){case"auto":return S;case"gpu":return S.filter(X=>["webgpu","cuda","dml","webnn-gpu"].includes(X))}if(S.includes(ie))return[R[ie]??ie];throw new Error(`Unsupported device: "${ie}". 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me{constructor(Te){Me(this,"max_length",20);Me(this,"max_new_tokens",null);Me(this,"min_length",0);Me(this,"min_new_tokens",null);Me(this,"early_stopping",!1);Me(this,"max_time",null);Me(this,"do_sample",!1);Me(this,"num_beams",1);Me(this,"num_beam_groups",1);Me(this,"penalty_alpha",null);Me(this,"use_cache",!0);Me(this,"temperature",1);Me(this,"top_k",50);Me(this,"top_p",1);Me(this,"typical_p",1);Me(this,"epsilon_cutoff",0);Me(this,"eta_cutoff",0);Me(this,"diversity_penalty",0);Me(this,"repetition_penalty",1);Me(this,"encoder_repetition_penalty",1);Me(this,"length_penalty",1);Me(this,"no_repeat_ngram_size",0);Me(this,"bad_words_ids",null);Me(this,"force_words_ids",null);Me(this,"renormalize_logits",!1);Me(this,"constraints",null);Me(this,"forced_bos_token_id",null);Me(this,"forced_eos_token_id",null);Me(this,"remove_invalid_values",!1);Me(this,"exponential_decay_length_penalty",null);Me(this,"suppress_tokens",null);Me(this,"begin_suppress_tokens",null);Me(this,"forced_decoder_ids",null);Me(this,"guidance_scale",null);Me(this,"num_return_sequences",1);Me(this,"output_attentions",!1);Me(this,"output_hidden_states",!1);Me(this,"output_scores",!1);Me(this,"return_dict_in_generate",!1);Me(this,"pad_token_id",null);Me(this,"bos_token_id",null);Me(this,"eos_token_id",null);Me(this,"encoder_no_repeat_ngram_size",0);Me(this,"decoder_start_token_id",null);Me(this,"generation_kwargs",{});Object.assign(this,(0,z.pick)(Te,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(Ft,Ee,N)=>{N.r(Ee),N.d(Ee,{ClassifierFreeGuidanceLogitsProcessor:()=>q,ForcedBOSTokenLogitsProcessor:()=>R,ForcedEOSTokenLogitsProcessor:()=>S,LogitsProcessor:()=>we,LogitsProcessorList:()=>Ce,LogitsWarper:()=>Te,MinLengthLogitsProcessor:()=>_e,MinNewTokensLengthLogitsProcessor:()=>ce,NoBadWordsLogitsProcessor:()=>D,NoRepeatNGramLogitsProcessor:()=>re,RepetitionPenaltyLogitsProcessor:()=>ne,SuppressTokensAtBeginLogitsProcessor:()=>V,TemperatureLogitsWarper:()=>te,TopKLogitsWarper:()=>X,TopPLogitsWarper:()=>ie,WhisperTimeStampLogitsProcessor:()=>P});var z=N("./src/utils/generic.js");N("./src/utils/tensor.js");var me=N("./src/utils/maths.js");class we extends z.Callable{_call(I,L){throw Error("`_call` should be implemented in a subclass")}}class Te extends z.Callable{_call(I,L){throw Error("`_call` should be implemented in a subclass")}}class Ce extends z.Callable{constructor(){super(),this.processors=[]}push(I){this.processors.push(I)}extend(I){this.processors.push(...I)}_call(I,L){let k=L;for(const de of this.processors)k=de(I,k);return k}[Symbol.iterator](){return this.processors.values()}}class R extends we{constructor(I){super(),this.bos_token_id=I}_call(I,L){for(let k=0;k=1&&ve[ve.length-1]>=this.timestamp_begin,ze=ve.length<2||ve[ve.length-2]>=this.timestamp_begin;if(ke&&(ze?de.subarray(this.timestamp_begin).fill(-1/0):de.subarray(0,this.eos_token_id).fill(-1/0)),I[k].length===this.begin_index&&this.max_initial_timestamp_index!==null){const 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Te{constructor(P,{skip_prompt:re=!1,callback_function:ne=null,token_callback_function:_e=null,decode_kwargs:ce={},...D}={}){super(),this.tokenizer=P,this.skip_prompt=re,this.callback_function=ne??Ce,this.token_callback_function=_e,this.decode_kwargs={...ce,...D},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(P){var ce;if(P.length>1)throw Error("TextStreamer only supports batch size of 1");if(this.skip_prompt&&this.next_tokens_are_prompt){this.next_tokens_are_prompt=!1;return}const re=P[0];(ce=this.token_callback_function)==null||ce.call(this,re),this.token_cache=(0,z.mergeArrays)(this.token_cache,re);const ne=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let _e;ne.endsWith(` +`)?(_e=ne.slice(this.print_len),this.token_cache=[],this.print_len=0):ne.length>0&&(0,me.is_chinese_char)(ne.charCodeAt(ne.length-1))?(_e=ne.slice(this.print_len),this.print_len+=_e.length):(_e=ne.slice(this.print_len,ne.lastIndexOf(" 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R{constructor(P,{skip_prompt:re=!1,callback_function:ne=null,token_callback_function:_e=null,on_chunk_start:ce=null,on_chunk_end:D=null,on_finalize:q=null,time_precision:te=.02,skip_special_tokens:ie=!0,decode_kwargs:X={}}={}){super(P,{skip_prompt:re,callback_function:ne,token_callback_function:_e,decode_kwargs:{skip_special_tokens:ie,...X}}),this.timestamp_begin=P.timestamp_begin,this.on_chunk_start=ce,this.on_chunk_end=D,this.on_finalize=q,this.time_precision=te,this.waiting_for_timestamp=!1}put(P){var ne,_e;if(P.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const re=P[0];if(re.length===1){const ce=Number(re[0])-this.timestamp_begin;if(ce>=0){const D=ce*this.time_precision;this.waiting_for_timestamp?(ne=this.on_chunk_end)==null||ne.call(this,D):(_e=this.on_chunk_start)==null||_e.call(this,D),this.waiting_for_timestamp=!this.waiting_for_timestamp,P=[[]]}}return super.put(P)}end(){var 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P.Tensor("int64",BigInt64Array.from(f.map(_=>BigInt(_))),[1,f.length])}function ze(f){return new P.Tensor("bool",[f],[1])}async function Ie(f,_){let{encoder_outputs:T,input_ids:J,decoder_input_ids:Fe,...Oe}=_;if(!T){const St=(0,Ce.pick)(_,f.sessions.model.inputNames);T=(await Ze(f,St)).last_hidden_state}return Oe.input_ids=Fe,Oe.encoder_hidden_states=T,f.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(Oe.encoder_attention_mask=_.attention_mask),await Ke(f,Oe,!0)}async function Ze(f,_){const T=f.sessions.model,J=(0,Ce.pick)(_,T.inputNames);if(T.inputNames.includes("inputs_embeds")&&!J.inputs_embeds){if(!_.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");J.inputs_embeds=await f.encode_text({input_ids:_.input_ids})}return T.inputNames.includes("token_type_ids")&&!J.token_type_ids&&(J.token_type_ids=new P.Tensor("int64",new BigInt64Array(J.input_ids.data.length),J.input_ids.dims)),await de(T,J)}async function Ke(f,_,T=!1){const J=f.sessions[T?"decoder_model_merged":"model"],{past_key_values:Fe,...Oe}=_;J.inputNames.includes("use_cache_branch")&&(Oe.use_cache_branch=ze(!!Fe)),J.inputNames.includes("position_ids")&&Oe.attention_mask&&!Oe.position_ids&&(Oe.position_ids=ye(Oe,Fe)),f.addPastKeyValues(Oe,Fe);const _t=(0,Ce.pick)(Oe,J.inputNames);return await de(J,_t)}async function ut(f,{input_ids:_=null,attention_mask:T=null,pixel_values:J=null,position_ids:Fe=null,inputs_embeds:Oe=null,past_key_values:_t=null,generation_config:St=null,logits_processor:Wt=null,...nr}){if(!Oe){if(Oe=await f.encode_text({input_ids:_}),J&&_.dims[1]!==1){const pr=await f.encode_image({pixel_values:J});({inputs_embeds:Oe,attention_mask:T}=f._merge_input_ids_with_image_features({image_features:pr,inputs_embeds:Oe,input_ids:_,attention_mask:T}))}else if(_t&&J&&_.dims[1]===1){const pr=_.dims[1],hr=Object.values(_t)[0].dims.at(-2);T=(0,P.cat)([(0,P.ones)([_.dims[0],hr]),T.slice(null,[T.dims[1]-pr,T.dims[1]])],1)}}return await Ke(f,{inputs_embeds:Oe,past_key_values:_t,attention_mask:T,position_ids:Fe,generation_config:St,logits_processor:Wt},!0)}function ye(f,_=null){const{input_ids:T,inputs_embeds:J,attention_mask:Fe}=f,[Oe,_t]=Fe.dims,St=new BigInt64Array(Fe.data.length);for(let nr=0;nrOe.dims[1])){if(FeSt==f.config.image_token_index)){const St=f.config.num_image_tokens;if(!St)throw new Error("`num_image_tokens` is missing in the model configuration.");const Wt=Oe.dims[1]-(Fe-St);T.input_ids=Oe.slice(null,[-Wt,null]),T.attention_mask=(0,P.ones)([1,Fe+Wt])}}}return T}function fe(f,_,T,J){return T.past_key_values&&(_=_.map(Fe=>[Fe.at(-1)])),{...T,decoder_input_ids:ke(_)}}function $e(f,..._){return f.config.is_encoder_decoder?fe(f,..._):W(f,..._)}class Y extends Te.Callable{constructor(T,J){super();Me(this,"main_input_name","input_ids");Me(this,"forward_params",["input_ids","attention_mask"]);this.config=T,this.sessions=J;const Fe=j.get(this.constructor),Oe=ie.get(Fe);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Oe){case te.DecoderOnly:this.can_generate=!0,this._forward=Ke,this._prepare_inputs_for_generation=W;break;case te.Seq2Seq:case te.Vision2Seq:case te.Musicgen:this.can_generate=!0,this._forward=Ie,this._prepare_inputs_for_generation=fe;break;case te.EncoderDecoder:this._forward=Ie;break;case te.ImageTextToText:this.can_generate=!0,this._forward=ut,this._prepare_inputs_for_generation=$e;break;default:this._forward=Ze;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var J;const T=[];for(const Fe of Object.values(this.sessions))(J=Fe==null?void 0:Fe.handler)!=null&&J.dispose&&T.push(Fe.handler.dispose());return await Promise.all(T)}static async from_pretrained(T,{progress_callback:J=null,config:Fe=null,cache_dir:Oe=null,local_files_only:_t=!1,revision:St="main",model_file_name:Wt=null,subfolder:nr="onnx",device:kr=null,dtype:pr=null,use_external_data_format:hr=null,session_options:_r={}}={}){let xr={progress_callback:J,config:Fe,cache_dir:Oe,local_files_only:_t,revision:St,model_file_name:Wt,subfolder:nr,device:kr,dtype:pr,use_external_data_format:hr,session_options:_r};const Fr=j.get(this),Tr=ie.get(Fr);Fe=xr.config=await z.AutoConfig.from_pretrained(T,xr);let gr;if(Tr===te.DecoderOnly)gr=await Promise.all([L(T,{model:xr.model_file_name??"model"},xr),(0,R.getModelJSON)(T,"generation_config.json",!1,xr)]);else if(Tr===te.Seq2Seq||Tr===te.Vision2Seq)gr=await Promise.all([L(T,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},xr),(0,R.getModelJSON)(T,"generation_config.json",!1,xr)]);else if(Tr===te.MaskGeneration)gr=await Promise.all([L(T,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},xr)]);else if(Tr===te.EncoderDecoder)gr=await Promise.all([L(T,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},xr)]);else if(Tr===te.ImageTextToText){const rn={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Fe.is_encoder_decoder&&(rn.model="encoder_model"),gr=await Promise.all([L(T,rn,xr),(0,R.getModelJSON)(T,"generation_config.json",!1,xr)])}else Tr===te.Musicgen?gr=await Promise.all([L(T,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},xr),(0,R.getModelJSON)(T,"generation_config.json",!1,xr)]):(Tr!==te.EncoderOnly&&console.warn(`Model type for '${Fr??(Fe==null?void 0:Fe.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),gr=await Promise.all([L(T,{model:xr.model_file_name??"model"},xr)]));return new this(Fe,...gr)}async _call(T){return await this.forward(T)}async forward(T){return await this._forward(this,T)}_get_logits_warper(T){const J=new S.LogitsProcessorList;return T.temperature!==null&&T.temperature!==1&&J.push(new S.TemperatureLogitsWarper(T.temperature)),T.top_k!==null&&T.top_k!==0&&J.push(new S.TopKLogitsWarper(T.top_k)),T.top_p!==null&&T.top_p<1&&J.push(new S.TopPLogitsWarper(T.top_p)),J}_get_logits_processor(T,J,Fe=null){const Oe=new S.LogitsProcessorList;if(T.repetition_penalty!==null&&T.repetition_penalty!==1&&Oe.push(new S.RepetitionPenaltyLogitsProcessor(T.repetition_penalty)),T.no_repeat_ngram_size!==null&&T.no_repeat_ngram_size>0&&Oe.push(new S.NoRepeatNGramLogitsProcessor(T.no_repeat_ngram_size)),T.bad_words_ids!==null&&Oe.push(new S.NoBadWordsLogitsProcessor(T.bad_words_ids,T.eos_token_id)),T.min_length!==null&&T.eos_token_id!==null&&T.min_length>0&&Oe.push(new S.MinLengthLogitsProcessor(T.min_length,T.eos_token_id)),T.min_new_tokens!==null&&T.eos_token_id!==null&&T.min_new_tokens>0&&Oe.push(new S.MinNewTokensLengthLogitsProcessor(J,T.min_new_tokens,T.eos_token_id)),T.forced_bos_token_id!==null&&Oe.push(new S.ForcedBOSTokenLogitsProcessor(T.forced_bos_token_id)),T.forced_eos_token_id!==null&&Oe.push(new S.ForcedEOSTokenLogitsProcessor(T.max_length,T.forced_eos_token_id)),T.begin_suppress_tokens!==null){const _t=J>1||T.forced_bos_token_id===null?J:J+1;Oe.push(new S.SuppressTokensAtBeginLogitsProcessor(T.begin_suppress_tokens,_t))}return T.guidance_scale!==null&&T.guidance_scale>1&&Oe.push(new S.ClassifierFreeGuidanceLogitsProcessor(T.guidance_scale)),Fe!==null&&Oe.extend(Fe),Oe}_prepare_generation_config(T,J,Fe=V.GenerationConfig){const Oe={...this.config};for(const St of["decoder","generator","text_config"])St in Oe&&Object.assign(Oe,Oe[St]);const _t=new Fe(Oe);return"generation_config"in this&&Object.assign(_t,this.generation_config),T&&Object.assign(_t,T),J&&Object.assign(_t,(0,Ce.pick)(J,Object.getOwnPropertyNames(_t))),_t}_get_stopping_criteria(T,J=null){const Fe=new ne.StoppingCriteriaList;return T.max_length!==null&&Fe.push(new ne.MaxLengthCriteria(T.max_length,this.config.max_position_embeddings??null)),T.eos_token_id!==null&&Fe.push(new ne.EosTokenCriteria(T.eos_token_id)),J&&Fe.extend(J),Fe}_validate_model_class(){if(!this.can_generate){const T=[Va,Au,ja,Na],J=j.get(this.constructor),Fe=new Set,Oe=this.config.model_type;for(const St of T){const Wt=St.get(Oe);Wt&&Fe.add(Wt[0])}let _t=`The current model class (${J}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Fe.size>0&&(_t+=` Please use the following class instead: ${[...Fe].join(", ")}`),Error(_t)}}prepare_inputs_for_generation(...T){return this._prepare_inputs_for_generation(this,...T)}_update_model_kwargs_for_generation({generated_input_ids:T,outputs:J,model_inputs:Fe,is_encoder_decoder:Oe}){return Fe.past_key_values=this.getPastKeyValues(J,Fe.past_key_values),Fe.input_ids=new P.Tensor("int64",T.flat(),[T.length,1]),Oe||(Fe.attention_mask=(0,P.cat)([Fe.attention_mask,(0,P.ones)([Fe.attention_mask.dims[0],1])],1)),Fe.position_ids=null,Fe}_prepare_model_inputs({inputs:T,bos_token_id:J,model_kwargs:Fe}){const Oe=(0,Ce.pick)(Fe,this.forward_params),_t=this.main_input_name;if(_t in Oe){if(T)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Oe[_t]=T;return{inputs_tensor:Oe[_t],model_inputs:Oe,model_input_name:_t}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:T,model_inputs:J,model_input_name:Fe,generation_config:Oe}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!J.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:St,pixel_values:Wt,attention_mask:nr,...kr}=J,pr=await this._prepare_inputs_embeds(J);J={...kr,...(0,Ce.pick)(pr,["inputs_embeds","attention_mask"])}}let{last_hidden_state:_t}=await Ze(this,J);if(Oe.guidance_scale!==null&&Oe.guidance_scale>1)_t=(0,P.cat)([_t,(0,P.full_like)(_t,0)],0),"attention_mask"in J&&(J.attention_mask=(0,P.cat)([J.attention_mask,(0,P.zeros_like)(J.attention_mask)],0));else if(J.decoder_input_ids){const St=ke(J.decoder_input_ids).dims[0];if(St!==_t.dims[0]){if(_t.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${_t.dims[0]}) than the decoder inputs (${St}).`);_t=(0,P.cat)(Array.from({length:St},()=>_t),0)}}return J.encoder_outputs=_t,J}_prepare_decoder_input_ids_for_generation({batch_size:T,model_input_name:J,model_kwargs:Fe,decoder_start_token_id:Oe,bos_token_id:_t,generation_config:St}){let{decoder_input_ids:Wt,...nr}=Fe;if(Wt)Array.isArray(Wt[0])||(Wt=Array.from({length:T},()=>Wt));else if(Oe??(Oe=_t),this.config.model_type==="musicgen")Wt=Array.from({length:T*this.config.decoder.num_codebooks},()=>[Oe]);else if(Array.isArray(Oe)){if(Oe.length!==T)throw new Error(`\`decoder_start_token_id\` expcted to have length ${T} but got ${Oe.length}`);Wt=Oe}else Wt=Array.from({length:T},()=>[Oe]);return Wt=ke(Wt),Fe.decoder_attention_mask=(0,P.ones_like)(Wt),{input_ids:Wt,model_inputs:nr}}async generate({inputs:T=null,generation_config:J=null,logits_processor:Fe=null,stopping_criteria:Oe=null,streamer:_t=null,...St}){this._validate_model_class(),J=this._prepare_generation_config(J,St);let{inputs_tensor:Wt,model_inputs:nr,model_input_name:kr}=this._prepare_model_inputs({inputs:T,model_kwargs:St});const pr=this.config.is_encoder_decoder;pr&&("encoder_outputs"in nr||(nr=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Wt,model_inputs:nr,model_input_name:kr,generation_config:J})));let hr;pr?{input_ids:hr,model_inputs:nr}=this._prepare_decoder_input_ids_for_generation({batch_size:nr[kr].dims.at(0),model_input_name:kr,model_kwargs:nr,decoder_start_token_id:J.decoder_start_token_id,bos_token_id:J.bos_token_id,generation_config:J}):hr=nr[kr];let _r=hr.dims.at(-1);J.max_new_tokens!==null&&(J.max_length=_r+J.max_new_tokens);const xr=this._get_logits_processor(J,_r,Fe),Fr=this._get_stopping_criteria(J,Oe),Tr=nr[kr].dims.at(0),gr=_e.LogitsSampler.getSampler(J),rn=new Array(Tr).fill(0),on=hr.tolist();_t&&_t.put(on);let bn,pn={};for(;;){if(nr=this.prepare_inputs_for_generation(on,nr,J),bn=await this.forward(nr),J.output_attentions&&J.return_dict_in_generate){const Rn=this.getAttentions(bn);for(const vs in Rn)vs in pn||(pn[vs]=[]),pn[vs].push(Rn[vs])}const Ms=bn.logits.slice(null,-1,null),_i=xr(on,Ms),Ja=[];for(let Rn=0;Rn<_i.dims.at(0);++Rn){const vs=_i[Rn],qd=await gr(vs);for(const[Hd,Kd]of qd){const pd=BigInt(Hd);rn[Rn]+=Kd,on[Rn].push(pd),Ja.push([pd]);break}}if(_t&&_t.put(Ja),Fr(on).every(Rn=>Rn))break;nr=this._update_model_kwargs_for_generation({generated_input_ids:Ja,outputs:bn,model_inputs:nr,is_encoder_decoder:pr})}_t&&_t.end();const ln=this.getPastKeyValues(bn,nr.past_key_values,!0),Mn=new P.Tensor("int64",on.flat(),[on.length,on[0].length]);if(J.return_dict_in_generate)return{sequences:Mn,past_key_values:ln,...pn};for(const Ms of Object.values(bn))Ms.location==="gpu-buffer"&&Ms.dispose();return Mn}getPastKeyValues(T,J,Fe=!1){const Oe=Object.create(null);for(const _t in T)if(_t.startsWith("present")){const St=_t.replace("present","past_key_values"),Wt=_t.includes("encoder");if(Wt&&J?Oe[St]=J[St]:Oe[St]=T[_t],J&&(!Wt||Fe)){const nr=J[St];nr.location==="gpu-buffer"&&nr.dispose()}}return Oe}getAttentions(T){const J={};for(const Fe of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Oe in T)Oe.startsWith(Fe)&&(Fe in J||(J[Fe]=[]),J[Fe].push(T[Oe]));return J}addPastKeyValues(T,J){if(J)Object.assign(T,J);else{const Fe=this.custom_config.kv_cache_dtype??"float32",Oe=Fe==="float16"?new Uint16Array:[],_t=(0,z.getKeyValueShapes)(this.config);for(const St in _t)T[St]=new P.Tensor(Fe,Oe,_t[St])}}async encode_image({pixel_values:T}){const J=(await de(this.sessions.vision_encoder,{pixel_values:T})).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 (${J.dims[1]}).`),this.config.num_image_tokens=J.dims[1]),J}async encode_text({input_ids:T}){return(await de(this.sessions.embed_tokens,{input_ids:T})).inputs_embeds}}class He{}class dt extends He{constructor({last_hidden_state:_,hidden_states:T=null,attentions:J=null}){super(),this.last_hidden_state=_,this.hidden_states=T,this.attentions=J}}class nt extends Y{}class ot extends nt{}class Re extends nt{async _call(_){return new an(await super._call(_))}}class ht extends nt{async _call(_){return new ir(await super._call(_))}}class bt extends nt{async _call(_){return new sn(await super._call(_))}}class qe extends nt{async _call(_){return new cn(await super._call(_))}}class ee extends Y{}class Pe extends ee{}class Ne extends Y{}class Qe extends Ne{}class Ue extends Ne{async _call(_){return new an(await super._call(_))}}class je extends Ne{async _call(_){return new ir(await super._call(_))}}class lt extends Ne{async _call(_){return new sn(await super._call(_))}}class yt extends Ne{async _call(_){return new cn(await super._call(_))}}class vt extends Y{}class Et extends vt{}class x extends vt{async _call(_){return new an(await super._call(_))}}class H extends vt{async _call(_){return new ir(await super._call(_))}}class $ extends vt{async _call(_){return new sn(await super._call(_))}}class Q extends vt{async _call(_){return new cn(await super._call(_))}}class he extends Y{}class Ye extends he{}class Je extends he{async _call(_){return new an(await super._call(_))}}class Bt extends he{async _call(_){return new ir(await super._call(_))}}class ct extends he{async _call(_){return new sn(await super._call(_))}}class Tt extends he{async _call(_){return new cn(await super._call(_))}}class At extends Y{}class Pt extends At{}class cr extends At{async _call(_){return new an(await super._call(_))}}class Er extends At{async _call(_){return new ir(await super._call(_))}}class Qr extends At{async _call(_){return new sn(await super._call(_))}}class Or extends At{async _call(_){return new cn(await super._call(_))}}class Ur extends Y{}class at extends Ur{}class U extends Ur{async _call(_){return new an(await super._call(_))}}class ge extends Ur{async _call(_){return new ir(await super._call(_))}}class Ae extends Ur{async _call(_){return new sn(await super._call(_))}}class et extends Ur{async _call(_){return new cn(await super._call(_))}}class xe extends Y{}class Xe extends xe{}class gt extends xe{async _call(_){return new an(await super._call(_))}}class mt extends xe{async _call(_){return new ir(await super._call(_))}}class $t extends xe{async _call(_){return new sn(await super._call(_))}}class ft extends xe{async _call(_){return new cn(await super._call(_))}}class zt extends Y{}class Nt extends zt{}class Rt extends zt{async _call(_){return new ir(await super._call(_))}}class Gt extends zt{async _call(_){return new sn(await super._call(_))}}class be extends zt{async _call(_){return new cn(await super._call(_))}}class tt extends zt{async _call(_){return new an(await super._call(_))}}class st extends Y{}class qt extends st{}class wr extends st{async _call(_){return new an(await super._call(_))}}class Br extends st{async _call(_){return new ir(await super._call(_))}}class Kr extends st{async _call(_){return new sn(await super._call(_))}}class rr extends Y{}class Wr extends rr{}class Vr extends rr{async _call(_){return new an(await super._call(_))}}class Ln extends rr{async _call(_){return new ir(await super._call(_))}}class Rr extends rr{async _call(_){return new cn(await super._call(_))}}class Bn extends Y{}class Cs extends Bn{}class Xs extends Bn{async _call(_){return new an(await super._call(_))}}class Es extends Bn{async _call(_){return new ir(await super._call(_))}}class $s extends Bn{async _call(_){return new sn(await super._call(_))}}class Ss extends Bn{async _call(_){return new cn(await super._call(_))}}class Gn extends Y{}class ks extends Gn{}class Qs extends Gn{async _call(_){return new an(await super._call(_))}}class qn extends Gn{async _call(_){return new ir(await super._call(_))}}class Sn extends Gn{async _call(_){return new cn(await super._call(_))}}class Cn extends Y{}class ps extends Cn{}class ts extends Cn{async _call(_){return new ir(await super._call(_))}}class hs extends Cn{async _call(_){return new cn(await super._call(_))}}class fs extends Cn{async _call(_){return new an(await super._call(_))}}class rs extends Y{constructor(T,J,Fe){super(T,J);Me(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Fe}}class Xt extends rs{}class ms extends rs{}class _s extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class Ps extends _s{}class As extends _s{}class gs extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class Is extends gs{}class ws extends gs{}class ns extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class ue extends ns{}class m extends ns{}class A extends ns{async _call(_){return new ir(await super._call(_))}}class K extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class se extends K{}class pe extends K{}class De extends K{async _call(_){return new ir(await super._call(_))}}class wt extends K{}class xt extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class Mt extends xt{}class Ot extends xt{}class Yt extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class $r extends Yt{}class ar extends Yt{}class Dr extends Y{}class Jr extends Dr{}class ur extends Dr{async _call(_){return new an(await super._call(_))}}class Sr extends Dr{async _call(_){return new ir(await super._call(_))}}class _n extends Dr{async _call(_){return new sn(await super._call(_))}}class wn extends Dr{async _call(_){return new cn(await super._call(_))}}class Se extends Y{}class Zr extends Se{}class xn extends Se{async _call(_){return new an(await super._call(_))}}class kn extends Se{async _call(_){return new ir(await super._call(_))}}class Nn extends Se{async _call(_){return new sn(await super._call(_))}}class Ht extends Se{async _call(_){return new cn(await super._call(_))}}class en extends Y{}class Xr extends en{}class yr extends en{async _call(_){return new an(await super._call(_))}}class Ar extends en{async _call(_){return new ir(await super._call(_))}}class Ct extends en{async _call(_){return new sn(await super._call(_))}}class mr extends en{async _call(_){return new cn(await super._call(_))}}class Hn extends Y{}class Kn extends Hn{}class Xn extends Hn{}class jt extends Y{constructor(T,J,Fe){super(T,J);Me(this,"requires_attention_mask",!1);Me(this,"main_input_name","input_features");Me(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Fe}}class Ys extends jt{}class rt extends jt{_prepare_generation_config(_,T){return super._prepare_generation_config(_,T,D.WhisperGenerationConfig)}_retrieve_init_tokens(_){const T=[_.decoder_start_token_id];let J=_.language;const Fe=_.task;if(_.is_multilingual){J||(console.warn("No language specified - defaulting to English (en)."),J="en");const _t=`<|${(0,q.whisper_language_to_code)(J)}|>`;T.push(_.lang_to_id[_t]),T.push(_.task_to_id[Fe??"transcribe"])}else if(J||Fe)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!_.return_timestamps&&_.no_timestamps_token_id&&T.at(-1)!==_.no_timestamps_token_id?T.push(_.no_timestamps_token_id):_.return_timestamps&&T.at(-1)===_.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),T.pop()),T.filter(Oe=>Oe!=null)}async generate({inputs:_=null,generation_config:T=null,logits_processor:J=null,stopping_criteria:Fe=null,...Oe}){T=this._prepare_generation_config(T,Oe);const _t=Oe.decoder_input_ids??this._retrieve_init_tokens(T);if(T.return_timestamps&&(J??(J=new S.LogitsProcessorList),J.push(new S.WhisperTimeStampLogitsProcessor(T,_t))),T.begin_suppress_tokens&&(J??(J=new S.LogitsProcessorList),J.push(new S.SuppressTokensAtBeginLogitsProcessor(T.begin_suppress_tokens,_t.length))),T.return_token_timestamps){if(!T.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.");T.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),T.output_attentions=!0,T.return_dict_in_generate=!0}const St=await super.generate({inputs:_,generation_config:T,logits_processor:J,decoder_input_ids:_t,...Oe});return T.return_token_timestamps&&(St.token_timestamps=this._extract_token_timestamps(St,T.alignment_heads,T.num_frames)),St}_extract_token_timestamps(_,T,J=null,Fe=.02){if(!_.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");J==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 Oe=this.config.median_filter_width;Oe===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Oe=7);const _t=_.cross_attentions,St=Array.from({length:this.config.decoder_layers},(Fr,Tr)=>(0,P.cat)(_t.map(gr=>gr[Tr]),2)),Wt=(0,P.stack)(T.map(([Fr,Tr])=>{if(Fr>=St.length)throw new Error(`Layer index ${Fr} is out of bounds for cross attentions (length ${St.length}).`);return J?St[Fr].slice(null,Tr,null,[0,J]):St[Fr].slice(null,Tr)})).transpose(1,0,2,3),[nr,kr]=(0,P.std_mean)(Wt,-2,0,!0),pr=Wt.clone();for(let Fr=0;Frgr[Mn+1]-gr[Mn]),bn=(0,Ce.mergeArrays)([1],on).map(ln=>!!ln),pn=[];for(let ln=0;lnhr.findIndex(_r=>_r==Oe)),Wt=St.every(hr=>hr===-1),nr=St.every(hr=>hr!==-1);if(!Wt&&!nr)throw new Error("Every input should contain either 0 or 1 image token.");if(Wt)return{inputs_embeds:_,attention_mask:Fe};const kr=[],pr=[];for(let hr=0;hrOe*_t,1);_.input_labels=new P.Tensor("int64",new BigInt64Array(Fe).fill(1n),J)}const T={image_embeddings:_.image_embeddings,image_positional_embeddings:_.image_positional_embeddings};return _.input_points&&(T.input_points=_.input_points),_.input_labels&&(T.input_labels=_.input_labels),_.input_boxes&&(T.input_boxes=_.input_boxes),await de(this.sessions.prompt_encoder_mask_decoder,T)}async _call(_){return new ai(await super._call(_))}}class ai extends He{constructor({iou_scores:_,pred_masks:T}){super(),this.iou_scores=_,this.pred_masks=T}}class oi extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class Rl extends oi{}class li extends oi{}class ga extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class wa extends ga{}class ui extends ga{}class Zn extends Y{}class Nl extends Zn{}class Ed extends Zn{async _call(_){return new os(await super._call(_))}}class jl extends Zn{async _call(_){return new ir(await super._call(_))}}class ya extends Zn{async _call(_){return new sn(await super._call(_))}}class bs extends Y{}class Vl extends bs{}class ba extends bs{async _call(_){return new sn(await super._call(_))}}class Ul extends Y{}class Wl extends Ul{}class di extends Y{}class $d extends di{}class Gl extends di{async _call(_){return new os(await super._call(_))}}class ql extends di{async _call(_){return new ir(await super._call(_))}}class ci extends Y{}class Ma extends ci{}class Hl extends ci{async _call(_){return new os(await super._call(_))}}class Kl extends ci{async _call(_){return new ir(await super._call(_))}}class Xl extends ci{async _call(_){return new sn(await super._call(_))}}class pi extends Y{}class hi extends pi{}class Ql extends pi{async _call(_){return new os(await super._call(_))}}class fi extends pi{async _call(_){return new ir(await super._call(_))}}class Yl extends Y{}class va extends Zn{}class Jl extends Zn{async _call(_){return new os(await super._call(_))}}class Zl extends Zn{async _call(_){return new ir(await super._call(_))}}class as extends Y{}class Sd extends as{}class eu extends as{async _call(_){return new os(await super._call(_))}}class tu extends as{async _call(_){return new ir(await super._call(_))}}class kd extends as{async _call(_){return new dd(await super._call(_))}}class ru extends as{async _call(_){return new sn(await super._call(_))}}class Ds extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class gc extends Ds{}class nu extends Ds{}class su extends Ds{async generate_speech(_,T,{threshold:J=.5,minlenratio:Fe=0,maxlenratio:Oe=20,vocoder:_t=null}={}){const St={input_ids:_},{encoder_outputs:Wt,encoder_attention_mask:nr}=await Ze(this,St),kr=Wt.dims[1]/this.config.reduction_factor,pr=Math.floor(kr*Oe),hr=Math.floor(kr*Fe),_r=this.config.num_mel_bins;let xr=[],Fr=null,Tr=null,gr=0;for(;;){++gr;const bn=ze(!!Tr);let pn;Tr?pn=Tr.output_sequence_out:pn=new P.Tensor("float32",new Float32Array(_r),[1,1,_r]);let ln={use_cache_branch:bn,output_sequence:pn,encoder_attention_mask:nr,speaker_embeddings:T,encoder_hidden_states:Wt};this.addPastKeyValues(ln,Fr),Tr=await de(this.sessions.decoder_model_merged,ln),Fr=this.getPastKeyValues(Tr,Fr);const{prob:Mn,spectrum:Ms}=Tr;if(xr.push(Ms),gr>=hr&&(Array.from(Mn.data).filter(_i=>_i>=J).length>0||gr>=pr))break}const rn=(0,P.cat)(xr),{waveform:on}=await de(_t.sessions.model,{spectrogram:rn});return{spectrogram:rn,waveform:on}}}class iu extends Y{constructor(){super(...arguments);Me(this,"main_input_name","spectrogram")}}class xa extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class au extends xa{}class Ta extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class ou extends Ta{}class lu extends Ta{}class Ca extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class uu extends Ca{}class Pd extends Ca{}class Ea extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class du extends Ea{}class cu extends Ea{}class $a extends Y{}class pu extends $a{}class hu extends $a{static async from_pretrained(_,T={}){return T.model_file_name??(T.model_file_name="text_model"),super.from_pretrained(_,T)}}class fu extends $a{static async from_pretrained(_,T={}){return T.model_file_name??(T.model_file_name="audio_model"),super.from_pretrained(_,T)}}class mu extends Y{}class Sa extends mu{async _call(_){return new cd(await super._call(_))}}class ka extends Y{}class Pa extends ka{}class Ls extends ka{}class Aa extends ka{}class Ia extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class _u extends Ia{}class gu extends Ia{}class Fa extends Y{}class za extends Fa{}class wu extends Fa{async _call(_){return new ir(await super._call(_))}}class Oa extends Y{}class Ad extends Oa{}class wc extends Oa{}class Da extends Y{constructor(T,J,Fe){super(T,J);Me(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Fe}_apply_and_filter_by_delay_pattern_mask(T){const[J,Fe]=T.dims,Oe=this.config.decoder.num_codebooks,_t=Fe-Oe;let St=0;for(let kr=0;kr0&&_r<=_t&&(T.data[St++]=T.data[kr])}const Wt=Math.floor(J/Oe),nr=St/(Wt*Oe);return new P.Tensor(T.type,T.data.slice(0,St),[Wt,Oe,nr])}prepare_inputs_for_generation(T,J,Fe){let Oe=structuredClone(T);for(let St=0;St=Wt&&(Oe[St][Wt]=BigInt(this.config.decoder.pad_token_id));return Fe.guidance_scale!==null&&Fe.guidance_scale>1&&(Oe=Oe.concat(Oe)),super.prepare_inputs_for_generation(Oe,J,Fe)}async generate(T){const J=await super.generate(T),Fe=this._apply_and_filter_by_delay_pattern_mask(J).unsqueeze_(0),{audio_values:Oe}=await de(this.sessions.encodec_decode,{audio_codes:Fe});return Oe}}class mi extends Y{}class yu extends mi{}class bu extends mi{async _call(_){return new ir(await super._call(_))}}class La extends Y{}class Id extends La{}class Mu extends La{async _call(_){return new ir(await super._call(_))}}class Ba extends Y{}class Fd extends Ba{}class vu extends Ba{async _call(_){return new ir(await super._call(_))}}class Ra extends Y{}class xu extends Ra{}class Tu extends Ra{async _call(_){return new ir(await super._call(_))}}class zd extends Y{}class Cu extends zd{}class Ir{static async from_pretrained(_,{progress_callback:T=null,config:J=null,cache_dir:Fe=null,local_files_only:Oe=!1,revision:_t="main",model_file_name:St=null,subfolder:Wt="onnx",device:nr=null,dtype:kr=null,use_external_data_format:pr=null,session_options:hr={}}={}){const _r={progress_callback:T,config:J,cache_dir:Fe,local_files_only:Oe,revision:_t,model_file_name:St,subfolder:Wt,device:nr,dtype:kr,use_external_data_format:pr,session_options:hr};if(_r.config=await z.AutoConfig.from_pretrained(_,_r),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const xr of this.MODEL_CLASS_MAPPINGS){const Fr=xr.get(_r.config.model_type);if(Fr)return await Fr[1].from_pretrained(_,_r)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${_r.config.model_type}", attempting to construct from base class.`),await Y.from_pretrained(_,_r);throw Error(`Unsupported model type: ${_r.config.model_type}`)}}Me(Ir,"MODEL_CLASS_MAPPINGS",null),Me(Ir,"BASE_IF_FAIL",!1);const Od=new Map([["bert",["BertModel",ot]],["nomic_bert",["NomicBertModel",Pe]],["roformer",["RoFormerModel",Qe]],["electra",["ElectraModel",Ye]],["esm",["EsmModel",qt]],["convbert",["ConvBertModel",Et]],["camembert",["CamembertModel",Pt]],["deberta",["DebertaModel",at]],["deberta-v2",["DebertaV2Model",Xe]],["mpnet",["MPNetModel",Cs]],["albert",["AlbertModel",ps]],["distilbert",["DistilBertModel",Nt]],["roberta",["RobertaModel",Jr]],["xlm",["XLMModel",Zr]],["xlm-roberta",["XLMRobertaModel",Xr]],["clap",["ClapModel",pu]],["clip",["CLIPModel",Ei]],["clipseg",["CLIPSegModel",po]],["chinese_clip",["ChineseCLIPModel",co]],["siglip",["SiglipModel",oo]],["mobilebert",["MobileBertModel",Wr]],["squeezebert",["SqueezeBertModel",ks]],["wav2vec2",["Wav2Vec2Model",Nl]],["wav2vec2-bert",["Wav2Vec2BertModel",hi]],["unispeech",["UniSpeechModel",$d]],["unispeech-sat",["UniSpeechSatModel",Ma]],["hubert",["HubertModel",va]],["wavlm",["WavLMModel",Sd]],["audio-spectrogram-transformer",["ASTModel",Kn]],["vits",["VitsModel",Sa]],["pyannote",["PyAnnoteModel",Vl]],["wespeaker-resnet",["WeSpeakerResNetModel",Wl]],["detr",["DetrModel",ol]],["rt_detr",["RTDetrModel",dl]],["table-transformer",["TableTransformerModel",hl]],["vit",["ViTModel",Ki]],["pvt",["PvtModel",Uo]],["vit_msn",["ViTMSNModel",qo]],["vit_mae",["ViTMAEModel",Go]],["groupvit",["GroupViTModel",vr]],["fastvit",["FastViTModel",Ko]],["mobilevit",["MobileViTModel",Jo]],["mobilevitv2",["MobileViTV2Model",el]],["owlvit",["OwlViTModel",rl]],["owlv2",["Owlv2Model",sl]],["beit",["BeitModel",il]],["deit",["DeiTModel",la]],["hiera",["HieraModel",ml]],["convnext",["ConvNextModel",Ol]],["convnextv2",["ConvNextV2Model",Qn]],["dinov2",["Dinov2Model",Jn]],["resnet",["ResNetModel",gl]],["swin",["SwinModel",yl]],["swin2sr",["Swin2SRModel",Td]],["donut-swin",["DonutSwinModel",zl]],["yolos",["YolosModel",ii]],["dpt",["DPTModel",On]],["glpn",["GLPNModel",Al]],["hifigan",["SpeechT5HifiGan",iu]],["efficientnet",["EfficientNetModel",za]],["decision_transformer",["DecisionTransformerModel",Cu]],["mobilenet_v1",["MobileNetV1Model",yu]],["mobilenet_v2",["MobileNetV2Model",Id]],["mobilenet_v3",["MobileNetV3Model",Fd]],["mobilenet_v4",["MobileNetV4Model",xu]],["maskformer",["MaskFormerModel",Sl]]]),Dd=new Map([["t5",["T5Model",Xt]],["longt5",["LongT5Model",Ps]],["mt5",["MT5Model",Is]],["bart",["BartModel",ue]],["mbart",["MBartModel",se]],["marian",["MarianModel",Rl]],["whisper",["WhisperModel",Ys]],["m2m_100",["M2M100Model",wa]],["blenderbot",["BlenderbotModel",Mt]],["blenderbot-small",["BlenderbotSmallModel",$r]]]),yc=new Map([["bloom",["BloomModel",Wi]],["jais",["JAISModel",An]],["gpt2",["GPT2Model",fo]],["gptj",["GPTJModel",Mo]],["gpt_bigcode",["GPTBigCodeModel",In]],["gpt_neo",["GPTNeoModel",go]],["gpt_neox",["GPTNeoXModel",yo]],["codegen",["CodeGenModel",zi]],["llama",["LlamaModel",Co]],["cohere",["CohereModel",$o]],["gemma",["GemmaModel",ko]],["gemma2",["Gemma2Model",zn]],["openelm",["OpenELMModel",Io]],["qwen2",["Qwen2Model",zo]],["phi",["PhiModel",Do]],["phi3",["Phi3Model",Vi]],["mpt",["MptModel",Ro]],["opt",["OPTModel",Os]],["mistral",["MistralModel",ou]],["starcoder2",["Starcoder2Model",uu]],["falcon",["FalconModel",du]],["stablelm",["StableLmModel",_u]]]),Na=new Map([["speecht5",["SpeechT5ForSpeechToText",nu]],["whisper",["WhisperForConditionalGeneration",rt]]]),Eu=new Map([["speecht5",["SpeechT5ForTextToSpeech",su]]]),$u=new Map([["vits",["VitsModel",Sa]],["musicgen",["MusicgenForConditionalGeneration",Da]]]),Su=new Map([["bert",["BertForSequenceClassification",ht]],["roformer",["RoFormerForSequenceClassification",je]],["electra",["ElectraForSequenceClassification",Bt]],["esm",["EsmForSequenceClassification",Br]],["convbert",["ConvBertForSequenceClassification",H]],["camembert",["CamembertForSequenceClassification",Er]],["deberta",["DebertaForSequenceClassification",ge]],["deberta-v2",["DebertaV2ForSequenceClassification",mt]],["mpnet",["MPNetForSequenceClassification",Es]],["albert",["AlbertForSequenceClassification",ts]],["distilbert",["DistilBertForSequenceClassification",Rt]],["roberta",["RobertaForSequenceClassification",Sr]],["xlm",["XLMForSequenceClassification",kn]],["xlm-roberta",["XLMRobertaForSequenceClassification",Ar]],["bart",["BartForSequenceClassification",A]],["mbart",["MBartForSequenceClassification",De]],["mobilebert",["MobileBertForSequenceClassification",Ln]],["squeezebert",["SqueezeBertForSequenceClassification",qn]]]),Ld=new Map([["bert",["BertForTokenClassification",bt]],["roformer",["RoFormerForTokenClassification",lt]],["electra",["ElectraForTokenClassification",ct]],["esm",["EsmForTokenClassification",Kr]],["convbert",["ConvBertForTokenClassification",$]],["camembert",["CamembertForTokenClassification",Qr]],["deberta",["DebertaForTokenClassification",Ae]],["deberta-v2",["DebertaV2ForTokenClassification",$t]],["mpnet",["MPNetForTokenClassification",$s]],["distilbert",["DistilBertForTokenClassification",Gt]],["roberta",["RobertaForTokenClassification",_n]],["xlm",["XLMForTokenClassification",Nn]],["xlm-roberta",["XLMRobertaForTokenClassification",Ct]]]),ja=new Map([["t5",["T5ForConditionalGeneration",ms]],["longt5",["LongT5ForConditionalGeneration",As]],["mt5",["MT5ForConditionalGeneration",ws]],["bart",["BartForConditionalGeneration",m]],["mbart",["MBartForConditionalGeneration",pe]],["marian",["MarianMTModel",li]],["m2m_100",["M2M100ForConditionalGeneration",ui]],["blenderbot",["BlenderbotForConditionalGeneration",Ot]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",ar]]]),Va=new Map([["bloom",["BloomForCausalLM",Md]],["gpt2",["GPT2LMHeadModel",mo]],["jais",["JAISLMHeadModel",_o]],["gptj",["GPTJForCausalLM",vo]],["gpt_bigcode",["GPTBigCodeForCausalLM",To]],["gpt_neo",["GPTNeoForCausalLM",wo]],["gpt_neox",["GPTNeoXForCausalLM",bo]],["codegen",["CodeGenForCausalLM",Fn]],["llama",["LlamaForCausalLM",Eo]],["cohere",["CohereForCausalLM",So]],["gemma",["GemmaForCausalLM",Po]],["gemma2",["Gemma2ForCausalLM",Ao]],["openelm",["OpenELMForCausalLM",Fo]],["qwen2",["Qwen2ForCausalLM",Oo]],["phi",["PhiForCausalLM",Lo]],["phi3",["Phi3ForCausalLM",Bo]],["mpt",["MptForCausalLM",No]],["opt",["OPTForCausalLM",jo]],["mbart",["MBartForCausalLM",wt]],["mistral",["MistralForCausalLM",lu]],["starcoder2",["Starcoder2ForCausalLM",Pd]],["falcon",["FalconForCausalLM",cu]],["trocr",["TrOCRForCausalLM",au]],["stablelm",["StableLmForCausalLM",gu]]]),ku=new Map([["bert",["BertForMaskedLM",Re]],["roformer",["RoFormerForMaskedLM",Ue]],["electra",["ElectraForMaskedLM",Je]],["esm",["EsmForMaskedLM",wr]],["convbert",["ConvBertForMaskedLM",x]],["camembert",["CamembertForMaskedLM",cr]],["deberta",["DebertaForMaskedLM",U]],["deberta-v2",["DebertaV2ForMaskedLM",gt]],["mpnet",["MPNetForMaskedLM",Xs]],["albert",["AlbertForMaskedLM",fs]],["distilbert",["DistilBertForMaskedLM",tt]],["roberta",["RobertaForMaskedLM",ur]],["xlm",["XLMWithLMHeadModel",xn]],["xlm-roberta",["XLMRobertaForMaskedLM",yr]],["mobilebert",["MobileBertForMaskedLM",Vr]],["squeezebert",["SqueezeBertForMaskedLM",Qs]]]),Pu=new Map([["bert",["BertForQuestionAnswering",qe]],["roformer",["RoFormerForQuestionAnswering",yt]],["electra",["ElectraForQuestionAnswering",Tt]],["convbert",["ConvBertForQuestionAnswering",Q]],["camembert",["CamembertForQuestionAnswering",Or]],["deberta",["DebertaForQuestionAnswering",et]],["deberta-v2",["DebertaV2ForQuestionAnswering",ft]],["mpnet",["MPNetForQuestionAnswering",Ss]],["albert",["AlbertForQuestionAnswering",hs]],["distilbert",["DistilBertForQuestionAnswering",be]],["roberta",["RobertaForQuestionAnswering",wn]],["xlm",["XLMForQuestionAnswering",Ht]],["xlm-roberta",["XLMRobertaForQuestionAnswering",mr]],["mobilebert",["MobileBertForQuestionAnswering",Rr]],["squeezebert",["SqueezeBertForQuestionAnswering",Sn]]]),Au=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ut]]]),yn=new Map([["llava",["LlavaForConditionalGeneration",Ci]],["moondream1",["Moondream1ForConditionalGeneration",io]],["florence2",["Florence2ForConditionalGeneration",or]]]),Bd=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ut]]]),Iu=new Map([["vit",["ViTForImageClassification",Vo]],["pvt",["PvtForImageClassification",Wo]],["vit_msn",["ViTMSNForImageClassification",xd]],["fastvit",["FastViTForImageClassification",Xo]],["mobilevit",["MobileViTForImageClassification",Zo]],["mobilevitv2",["MobileViTV2ForImageClassification",tl]],["beit",["BeitForImageClassification",al]],["deit",["DeiTForImageClassification",ua]],["hiera",["HieraForImageClassification",_l]],["convnext",["ConvNextForImageClassification",Cd]],["convnextv2",["ConvNextV2ForImageClassification",Yn]],["dinov2",["Dinov2ForImageClassification",fn]],["resnet",["ResNetForImageClassification",wl]],["swin",["SwinForImageClassification",bl]],["segformer",["SegformerForImageClassification",Ls]],["efficientnet",["EfficientNetForImageClassification",wu]],["mobilenet_v1",["MobileNetV1ForImageClassification",bu]],["mobilenet_v2",["MobileNetV2ForImageClassification",Mu]],["mobilenet_v3",["MobileNetV3ForImageClassification",vu]],["mobilenet_v4",["MobileNetV4ForImageClassification",Tu]]]),Ua=new Map([["detr",["DetrForObjectDetection",ll]],["rt_detr",["RTDetrForObjectDetection",cl]],["table-transformer",["TableTransformerForObjectDetection",fl]],["yolos",["YolosForObjectDetection",_a]]]),Fu=new Map([["owlvit",["OwlViTForObjectDetection",nl]],["owlv2",["Owlv2ForObjectDetection",Zs]]]),Bs=new Map([["detr",["DetrForSegmentation",ra]],["clipseg",["CLIPSegForImageSegmentation",ho]]]),zu=new Map([["segformer",["SegformerForSemanticSegmentation",Aa]],["sapiens",["SapiensForSemanticSegmentation",Cl]]]),Ou=new Map([["detr",["DetrForSegmentation",ra]],["maskformer",["MaskFormerForInstanceSegmentation",kl]]]),Wa=new Map([["sam",["SamModel",Bl]]]),Du=new Map([["wav2vec2",["Wav2Vec2ForCTC",Ed]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Ql]],["unispeech",["UniSpeechForCTC",Gl]],["unispeech-sat",["UniSpeechSatForCTC",Hl]],["wavlm",["WavLMForCTC",eu]],["hubert",["HubertForCTC",Jl]]]),Lu=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",jl]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",fi]],["unispeech",["UniSpeechForSequenceClassification",ql]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Kl]],["wavlm",["WavLMForSequenceClassification",tu]],["hubert",["HubertForSequenceClassification",Zl]],["audio-spectrogram-transformer",["ASTForAudioClassification",Xn]]]),Ga=new Map([["wavlm",["WavLMForXVector",kd]]]),Bu=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Xl]],["wavlm",["WavLMForAudioFrameClassification",ru]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",ya]],["pyannote",["PyAnnoteForAudioFrameClassification",ba]]]),Ru=new Map([["vitmatte",["VitMatteForImageMatting",Yo]]]),Nu=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Ml]]]),qa=new Map([["dpt",["DPTForDepthEstimation",vl]],["depth_anything",["DepthAnythingForDepthEstimation",Tl]],["glpn",["GLPNForDepthEstimation",Il]],["sapiens",["SapiensForDepthEstimation",El]]]),ju=new Map([["sapiens",["SapiensForNormalEstimation",$l]]]),Vu=new Map([["clip",["CLIPVisionModelWithProjection",Pn]],["siglip",["SiglipVisionModel",lo]]]),Ha=[[Od,te.EncoderOnly],[Dd,te.EncoderDecoder],[yc,te.DecoderOnly],[Su,te.EncoderOnly],[Ld,te.EncoderOnly],[ja,te.Seq2Seq],[Na,te.Seq2Seq],[Va,te.DecoderOnly],[ku,te.EncoderOnly],[Pu,te.EncoderOnly],[Au,te.Vision2Seq],[yn,te.ImageTextToText],[Iu,te.EncoderOnly],[Bs,te.EncoderOnly],[Ou,te.EncoderOnly],[zu,te.EncoderOnly],[Ru,te.EncoderOnly],[Nu,te.EncoderOnly],[qa,te.EncoderOnly],[ju,te.EncoderOnly],[Ua,te.EncoderOnly],[Fu,te.EncoderOnly],[Wa,te.MaskGeneration],[Du,te.EncoderOnly],[Lu,te.EncoderOnly],[Eu,te.Seq2Seq],[$u,te.EncoderOnly],[Ga,te.EncoderOnly],[Bu,te.EncoderOnly],[Vu,te.EncoderOnly]];for(const[f,_]of Ha)for(const[T,J]of f.values())ie.set(T,_),j.set(J,T),X.set(T,J);const Rd=[["MusicgenForConditionalGeneration",Da,te.Musicgen],["CLIPTextModelWithProjection",ao,te.EncoderOnly],["SiglipTextModel",is,te.EncoderOnly],["ClapTextModelWithProjection",hu,te.EncoderOnly],["ClapAudioModelWithProjection",fu,te.EncoderOnly]];for(const[f,_,T]of Rd)ie.set(f,T),j.set(_,f),X.set(f,_);class Uu extends Ir{}Me(Uu,"MODEL_CLASS_MAPPINGS",Ha.map(_=>_[0])),Me(Uu,"BASE_IF_FAIL",!0);class Ka extends Ir{}Me(Ka,"MODEL_CLASS_MAPPINGS",[Su]);class Wu extends Ir{}Me(Wu,"MODEL_CLASS_MAPPINGS",[Ld]);class Gu extends Ir{}Me(Gu,"MODEL_CLASS_MAPPINGS",[ja]);class qu extends Ir{}Me(qu,"MODEL_CLASS_MAPPINGS",[Na]);class Nd extends Ir{}Me(Nd,"MODEL_CLASS_MAPPINGS",[Eu]);class Hu extends Ir{}Me(Hu,"MODEL_CLASS_MAPPINGS",[$u]);class Ku extends Ir{}Me(Ku,"MODEL_CLASS_MAPPINGS",[Va]);class Xu extends Ir{}Me(Xu,"MODEL_CLASS_MAPPINGS",[ku]);class jd extends Ir{}Me(jd,"MODEL_CLASS_MAPPINGS",[Pu]);class Qu extends Ir{}Me(Qu,"MODEL_CLASS_MAPPINGS",[Au]);class Yu extends Ir{}Me(Yu,"MODEL_CLASS_MAPPINGS",[Iu]);class Ju extends Ir{}Me(Ju,"MODEL_CLASS_MAPPINGS",[Bs]);class Zu extends Ir{}Me(Zu,"MODEL_CLASS_MAPPINGS",[zu]);class Vd extends Ir{}Me(Vd,"MODEL_CLASS_MAPPINGS",[Ou]);class ed extends Ir{}Me(ed,"MODEL_CLASS_MAPPINGS",[Ua]);class td extends Ir{}Me(td,"MODEL_CLASS_MAPPINGS",[Fu]);class rd extends Ir{}Me(rd,"MODEL_CLASS_MAPPINGS",[Wa]);class nd extends Ir{}Me(nd,"MODEL_CLASS_MAPPINGS",[Du]);class sd extends Ir{}Me(sd,"MODEL_CLASS_MAPPINGS",[Lu]);class id extends Ir{}Me(id,"MODEL_CLASS_MAPPINGS",[Ga]);class ad extends Ir{}Me(ad,"MODEL_CLASS_MAPPINGS",[Bu]);class od extends Ir{}Me(od,"MODEL_CLASS_MAPPINGS",[Bd]);class ld extends Ir{}Me(ld,"MODEL_CLASS_MAPPINGS",[Ru]);class Ud extends Ir{}Me(Ud,"MODEL_CLASS_MAPPINGS",[Nu]);class Rs extends Ir{}Me(Rs,"MODEL_CLASS_MAPPINGS",[qa]);class Xa extends Ir{}Me(Xa,"MODEL_CLASS_MAPPINGS",[ju]);class Qa extends Ir{}Me(Qa,"MODEL_CLASS_MAPPINGS",[Vu]);class ud extends He{constructor({logits:_,past_key_values:T,encoder_outputs:J,decoder_attentions:Fe=null,cross_attentions:Oe=null}){super(),this.logits=_,this.past_key_values=T,this.encoder_outputs=J,this.decoder_attentions=Fe,this.cross_attentions=Oe}}class ir extends He{constructor({logits:_}){super(),this.logits=_}}class dd extends He{constructor({logits:_,embeddings:T}){super(),this.logits=_,this.embeddings=T}}class sn extends He{constructor({logits:_}){super(),this.logits=_}}class an extends He{constructor({logits:_}){super(),this.logits=_}}class cn extends He{constructor({start_logits:_,end_logits:T}){super(),this.start_logits=_,this.end_logits=T}}class os extends He{constructor({logits:_}){super(),this.logits=_}}class Wd extends He{constructor({logits:_,past_key_values:T}){super(),this.logits=_,this.past_key_values=T}}class Ya extends He{constructor({alphas:_}){super(),this.alphas=_}}class cd extends He{constructor({waveform:_,spectrogram:T}){super(),this.waveform=_,this.spectrogram=T}}},"./src/models/whisper/common_whisper.js":(Ft,Ee,N)=>{N.r(Ee),N.d(Ee,{WHISPER_LANGUAGE_MAPPING:()=>me,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>we,whisper_language_to_code:()=>Te});const z=[["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"]],me=new Map(z),we=new Map([...z.map(([Ce,R])=>[R,Ce]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function Te(Ce){Ce=Ce.toLowerCase();let R=we.get(Ce);if(R===void 0)if(me.has(Ce))R=Ce;else{const V=Ce.length===2?me.keys():me.values();throw new Error(`Language "${Ce}" is not supported. Must be one of: ${JSON.stringify(V)}`)}return R}},"./src/models/whisper/generation_whisper.js":(Ft,Ee,N)=>{N.r(Ee),N.d(Ee,{WhisperGenerationConfig:()=>me});var z=N("./src/generation/configuration_utils.js");class me extends z.GenerationConfig{constructor(){super(...arguments);Me(this,"return_timestamps",null);Me(this,"return_token_timestamps",null);Me(this,"num_frames",null);Me(this,"alignment_heads",null);Me(this,"task",null);Me(this,"language",null);Me(this,"no_timestamps_token_id",null);Me(this,"prompt_ids",null);Me(this,"is_multilingual",null);Me(this,"lang_to_id",null);Me(this,"task_to_id",null);Me(this,"max_initial_timestamp_index",1)}}},"./src/ops/registry.js":(Ft,Ee,N)=>{N.r(Ee),N.d(Ee,{TensorOpRegistry:()=>Te});var z=N("./src/backends/onnx.js"),me=N("./src/utils/tensor.js");const we=async(Ce,R,S)=>{const V=await(0,z.createInferenceSession)(new Uint8Array(Ce),R);return async P=>{const re=Object.fromEntries(Object.entries(P).map(([_e,ce])=>[_e,ce.ort_tensor])),ne=await V.run(re);return Array.isArray(S)?S.map(_e=>new me.Tensor(ne[_e])):new me.Tensor(ne[S])}};class Te{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=we([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=we([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=we([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=we([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=we([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=we([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}}Me(Te,"session_options",{})},"./src/pipelines.js":(Ft,Ee,N)=>{N.r(Ee),N.d(Ee,{AudioClassificationPipeline:()=>ze,AutomaticSpeechRecognitionPipeline:()=>Ze,DepthEstimationPipeline:()=>nt,DocumentQuestionAnsweringPipeline:()=>Y,FeatureExtractionPipeline:()=>ve,FillMaskPipeline:()=>ie,ImageClassificationPipeline:()=>ut,ImageFeatureExtractionPipeline:()=>ke,ImageSegmentationPipeline:()=>ye,ImageToImagePipeline:()=>dt,ImageToTextPipeline:()=>Ke,ObjectDetectionPipeline:()=>fe,Pipeline:()=>ce,QuestionAnsweringPipeline:()=>te,SummarizationPipeline:()=>j,Text2TextGenerationPipeline:()=>X,TextClassificationPipeline:()=>D,TextGenerationPipeline:()=>k,TextToAudioPipeline:()=>He,TokenClassificationPipeline:()=>q,TranslationPipeline:()=>I,ZeroShotAudioClassificationPipeline:()=>Ie,ZeroShotClassificationPipeline:()=>de,ZeroShotImageClassificationPipeline:()=>W,ZeroShotObjectDetectionPipeline:()=>$e,pipeline:()=>ht});var z=N("./src/tokenizers.js"),me=N("./src/models.js"),we=N("./src/processors.js"),Te=N("./src/utils/generic.js"),Ce=N("./src/utils/core.js"),R=N("./src/utils/maths.js"),S=N("./src/utils/audio.js"),V=N("./src/utils/tensor.js"),P=N("./src/utils/image.js");async function re(qe){return Array.isArray(qe)||(qe=[qe]),await Promise.all(qe.map(ee=>P.RawImage.read(ee)))}async function ne(qe,ee){return Array.isArray(qe)||(qe=[qe]),await Promise.all(qe.map(Pe=>typeof Pe=="string"||Pe instanceof URL?(0,S.read_audio)(Pe,ee):Pe instanceof Float64Array?new Float32Array(Pe):Pe))}function _e(qe,ee){ee&&(qe=qe.map(je=>je|0));const[Pe,Ne,Qe,Ue]=qe;return{xmin:Pe,ymin:Ne,xmax:Qe,ymax:Ue}}class ce extends Te.Callable{constructor({task:ee,model:Pe,tokenizer:Ne=null,processor:Qe=null}){super(),this.task=ee,this.model=Pe,this.tokenizer=Ne,this.processor=Qe}async dispose(){await this.model.dispose()}}class D extends ce{constructor(ee){super(ee)}async _call(ee,{top_k:Pe=1}={}){const Ne=this.tokenizer(ee,{padding:!0,truncation:!0}),Qe=await this.model(Ne),Ue=this.model.config.problem_type==="multi_label_classification"?yt=>yt.sigmoid():yt=>new V.Tensor("float32",(0,R.softmax)(yt.data),yt.dims),je=this.model.config.id2label,lt=[];for(const yt of Qe.logits){const vt=Ue(yt),Et=await(0,V.topk)(vt,Pe),x=Et[0].tolist(),$=Et[1].tolist().map((Q,he)=>({label:je?je[Q]:`LABEL_${Q}`,score:x[he]}));Pe===1?lt.push(...$):lt.push($)}return Array.isArray(ee)||Pe===1?lt:lt[0]}}class q extends ce{constructor(ee){super(ee)}async _call(ee,{ignore_labels:Pe=["O"]}={}){const Ne=Array.isArray(ee),Qe=this.tokenizer(Ne?ee:[ee],{padding:!0,truncation:!0}),je=(await this.model(Qe)).logits,lt=this.model.config.id2label,yt=[];for(let vt=0;vtct==this.tokenizer.sep_token_id);yt[x].map((ct,Tt)=>ct==1&&(Tt===0||Tt>$&&vt.findIndex(At=>At==H[Tt])===-1));const Q=Ue[x].tolist(),he=je[x].tolist();for(let ct=1;ctTt==H[ct])!==-1)&&(Q[ct]=-1/0,he[ct]=-1/0);const Ye=(0,R.softmax)(Q).map((ct,Tt)=>[ct,Tt]),Je=(0,R.softmax)(he).map((ct,Tt)=>[ct,Tt]);Ye[0][0]=0,Je[0][0]=0;const Bt=(0,Ce.product)(Ye,Je).filter(ct=>ct[0][1]<=ct[1][1]).map(ct=>[ct[0][1],ct[1][1],ct[0][0]*ct[1][0]]).sort((ct,Tt)=>Tt[2]-ct[2]);for(let ct=0;ctQ==this.tokenizer.mask_token_id);if(vt===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const Et=Qe[lt][vt],x=await(0,V.topk)(new V.Tensor("float32",(0,R.softmax)(Et.data),Et.dims),Pe),H=x[0].tolist(),$=x[1].tolist();Ue.push($.map((Q,he)=>{const Ye=yt.slice();return Ye[vt]=Q,{score:H[he],token:Number(Q),token_str:this.tokenizer.model.vocab[Q],sequence:this.tokenizer.decode(Ye,{skip_special_tokens:!0})}}))}return Array.isArray(ee)?Ue:Ue[0]}}class X extends ce{constructor(Pe){super(Pe);Me(this,"_key","generated_text")}async _call(Pe,Ne={}){Array.isArray(Pe)||(Pe=[Pe]),this.model.config.prefix&&(Pe=Pe.map(vt=>this.model.config.prefix+vt));const Qe=this.model.config.task_specific_params;Qe&&Qe[this.task]&&Qe[this.task].prefix&&(Pe=Pe.map(vt=>Qe[this.task].prefix+vt));const Ue=this.tokenizer,je={padding:!0,truncation:!0};let lt;this instanceof I&&"_build_translation_inputs"in Ue?lt=Ue._build_translation_inputs(Pe,je,Ne):lt=Ue(Pe,je);const yt=await this.model.generate({...lt,...Ne});return Ue.batch_decode(yt,{skip_special_tokens:!0}).map(vt=>({[this._key]:vt}))}}class j extends X{constructor(Pe){super(Pe);Me(this,"_key","summary_text")}}class I extends X{constructor(Pe){super(Pe);Me(this,"_key","translation_text")}}function L(qe){return Array.isArray(qe)&&qe.every(ee=>"role"in ee&&"content"in ee)}class k extends ce{constructor(ee){super(ee)}async _call(ee,Pe={}){let Ne=!1,Qe=!1,Ue;if(typeof ee=="string")Ue=ee=[ee];else if(Array.isArray(ee)&&ee.every($=>typeof $=="string"))Ne=!0,Ue=ee;else{if(L(ee))ee=[ee];else if(Array.isArray(ee)&&ee.every(L))Ne=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");Qe=!0,Ue=ee.map($=>this.tokenizer.apply_chat_template($,{tokenize:!1,add_generation_prompt:!0}))}const je=Pe.add_special_tokens??!1,lt=Qe?!1:Pe.return_full_text??!0;this.tokenizer.padding_side="left";const yt=this.tokenizer(Ue,{add_special_tokens:je,padding:!0,truncation:!0}),vt=await this.model.generate({...yt,...Pe}),Et=this.tokenizer.batch_decode(vt,{skip_special_tokens:!0});let x;!lt&&yt.input_ids.dims.at(-1)>0&&(x=this.tokenizer.batch_decode(yt.input_ids,{skip_special_tokens:!0}).map($=>$.length));const H=Array.from({length:ee.length},$=>[]);for(let $=0;$[Pe.toLowerCase(),Ne])),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(ee,Pe,{hypothesis_template:Ne="This example is {}.",multi_label:Qe=!1}={}){const Ue=Array.isArray(ee);Ue||(ee=[ee]),Array.isArray(Pe)||(Pe=[Pe]);const je=Pe.map(vt=>Ne.replace("{}",vt)),lt=Qe||Pe.length===1,yt=[];for(const vt of ee){const Et=[];for(const $ of je){const Q=this.tokenizer(vt,{text_pair:$,padding:!0,truncation:!0}),he=await this.model(Q);lt?Et.push([he.logits.data[this.contradiction_id],he.logits.data[this.entailment_id]]):Et.push(he.logits.data[this.entailment_id])}const H=(lt?Et.map($=>(0,R.softmax)($)[1]):(0,R.softmax)(Et)).map(($,Q)=>[$,Q]).sort(($,Q)=>Q[0]-$[0]);yt.push({sequence:vt,labels:H.map($=>Pe[$[1]]),scores:H.map($=>$[0])})}return Ue?yt:yt[0]}}class ve extends ce{constructor(ee){super(ee)}async _call(ee,{pooling:Pe="none",normalize:Ne=!1,quantize:Qe=!1,precision:Ue="binary"}={}){const je=this.tokenizer(ee,{padding:!0,truncation:!0}),lt=await this.model(je);let yt=lt.last_hidden_state??lt.logits??lt.token_embeddings;if(Pe!=="none")if(Pe==="mean")yt=(0,V.mean_pooling)(yt,je.attention_mask);else if(Pe==="cls")yt=yt.slice(null,0);else throw Error(`Pooling method '${Pe}' not supported.`);return Ne&&(yt=yt.normalize(2,-1)),Qe&&(yt=(0,V.quantize_embeddings)(yt,Ue)),yt}}class ke extends ce{constructor(ee){super(ee)}async _call(ee,{pool:Pe=null}={}){const Ne=await re(ee),{pixel_values:Qe}=await this.processor(Ne),Ue=await this.model({pixel_values:Qe});let je;if(Pe){if(!("pooler_output"in Ue))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");je=Ue.pooler_output}else je=Ue.last_hidden_state??Ue.logits??Ue.image_embeds;return je}}class ze extends ce{constructor(ee){super(ee)}async _call(ee,{top_k:Pe=5}={}){const Ne=this.processor.feature_extractor.config.sampling_rate,Qe=await ne(ee,Ne),Ue=this.model.config.id2label,je=[];for(const lt of Qe){const yt=await this.processor(lt),Et=(await this.model(yt)).logits[0],x=await(0,V.topk)(new V.Tensor("float32",(0,R.softmax)(Et.data),Et.dims),Pe),H=x[0].tolist(),Q=x[1].tolist().map((he,Ye)=>({label:Ue?Ue[he]:`LABEL_${he}`,score:H[Ye]}));je.push(Q)}return Array.isArray(ee)?je:je[0]}}class Ie extends ce{constructor(ee){super(ee)}async _call(ee,Pe,{hypothesis_template:Ne="This is a sound of {}."}={}){const Qe=!Array.isArray(ee);Qe&&(ee=[ee]);const Ue=Pe.map(Et=>Ne.replace("{}",Et)),je=this.tokenizer(Ue,{padding:!0,truncation:!0}),lt=this.processor.feature_extractor.config.sampling_rate,yt=await ne(ee,lt),vt=[];for(const Et of yt){const x=await this.processor(Et),H=await this.model({...je,...x}),$=(0,R.softmax)(H.logits_per_audio.data);vt.push([...$].map((Q,he)=>({score:Q,label:Pe[he]})))}return Qe?vt[0]:vt}}class Ze extends ce{constructor(ee){super(ee)}async _call(ee,Pe={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(ee,Pe);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(ee,Pe);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(ee,Pe){Pe.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),Pe.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const Ne=!Array.isArray(ee);Ne&&(ee=[ee]);const Qe=this.processor.feature_extractor.config.sampling_rate,Ue=await ne(ee,Qe),je=[];for(const lt of Ue){const yt=await this.processor(lt),Et=(await this.model(yt)).logits[0],x=[];for(const $ of Et)x.push((0,R.max)($.data)[1]);const H=this.tokenizer.decode(x);je.push({text:H})}return Ne?je[0]:je}async _call_whisper(ee,Pe){const Ne=Pe.return_timestamps??!1,Qe=Pe.chunk_length_s??0,Ue=Pe.force_full_sequences??!1;let je=Pe.stride_length_s??null;const lt={...Pe};Ne==="word"&&(lt.return_token_timestamps=!0,lt.return_timestamps=!1);const yt=!Array.isArray(ee);yt&&(ee=[ee]);const vt=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,Et=this.processor.feature_extractor.config.hop_length,x=this.processor.feature_extractor.config.sampling_rate,H=await ne(ee,x),$=[];for(const Q of H){let he=[];if(Qe>0){if(je===null)je=Qe/6;else if(Qe<=je)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const Bt=x*Qe,ct=x*je,Tt=Bt-2*ct;let At=0;for(;;){const Pt=At+Bt,cr=Q.subarray(At,Pt),Er=await this.processor(cr),Qr=At===0,Or=Pt>=Q.length;if(he.push({stride:[cr.length,Qr?0:ct,Or?0:ct],input_features:Er.input_features,is_last:Or}),Or)break;At+=Tt}}else he=[{stride:[Q.length,0,0],input_features:(await this.processor(Q)).input_features,is_last:!0}];for(const Bt of he){lt.num_frames=Math.floor(Bt.stride[0]/Et);const ct=await this.model.generate({inputs:Bt.input_features,...lt});Ne==="word"?(Bt.tokens=ct.sequences.tolist()[0],Bt.token_timestamps=ct.token_timestamps.tolist()[0].map(Tt=>(0,R.round)(Tt,2))):Bt.tokens=ct[0].tolist(),Bt.stride=Bt.stride.map(Tt=>Tt/x)}const[Ye,Je]=this.tokenizer._decode_asr(he,{time_precision:vt,return_timestamps:Ne,force_full_sequences:Ue});$.push({text:Ye,...Je})}return yt?$[0]:$}}class Ke extends ce{constructor(ee){super(ee)}async _call(ee,Pe={}){const Ne=Array.isArray(ee),Qe=await re(ee),{pixel_values:Ue}=await this.processor(Qe),je=[];for(const lt of Ue){lt.dims=[1,...lt.dims];const yt=await this.model.generate({inputs:lt,...Pe}),vt=this.tokenizer.batch_decode(yt,{skip_special_tokens:!0}).map(Et=>({generated_text:Et.trim()}));je.push(vt)}return Ne?je:je[0]}}class ut extends ce{constructor(ee){super(ee)}async _call(ee,{top_k:Pe=5}={}){const Ne=await re(ee),{pixel_values:Qe}=await this.processor(Ne),Ue=await this.model({pixel_values:Qe}),je=this.model.config.id2label,lt=[];for(const yt of Ue.logits){const vt=await(0,V.topk)(new V.Tensor("float32",(0,R.softmax)(yt.data),yt.dims),Pe),Et=vt[0].tolist(),H=vt[1].tolist().map(($,Q)=>({label:je?je[$]:`LABEL_${$}`,score:Et[Q]}));lt.push(H)}return Array.isArray(ee)?lt:lt[0]}}class ye extends ce{constructor(ee){super(ee),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(ee,{threshold:Pe=.5,mask_threshold:Ne=.5,overlap_mask_area_threshold:Qe=.8,label_ids_to_fuse:Ue=null,target_sizes:je=null,subtask:lt=null}={}){if(Array.isArray(ee)&&ee.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const vt=await re(ee),Et=vt.map(Je=>[Je.height,Je.width]),{pixel_values:x,pixel_mask:H}=await this.processor(vt),$=await this.model({pixel_values:x,pixel_mask:H});let Q=null;if(lt!==null)Q=this.subtasks_mapping[lt];else for(let[Je,Bt]of Object.entries(this.subtasks_mapping))if(Bt in this.processor.feature_extractor){Q=this.processor.feature_extractor[Bt].bind(this.processor.feature_extractor),lt=Je;break}const he=this.model.config.id2label,Ye=[];if(lt==="panoptic"||lt==="instance"){const Je=Q($,Pe,Ne,Qe,Ue,je??Et)[0],Bt=Je.segmentation;for(const ct of Je.segments_info){const Tt=new Uint8ClampedArray(Bt.data.length);for(let Pt=0;PtNe.replace("{}",H)),lt=this.tokenizer(je,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:yt}=await this.processor(Ue),vt=await this.model({...lt,pixel_values:yt}),Et=this.model.config.model_type==="siglip"?H=>H.sigmoid().data:H=>(0,R.softmax)(H.data),x=[];for(const H of vt.logits_per_image){const Q=[...Et(H)].map((he,Ye)=>({score:he,label:Pe[Ye]}));Q.sort((he,Ye)=>Ye.score-he.score),x.push(Q)}return Qe?x:x[0]}}class fe extends ce{constructor(ee){super(ee)}async _call(ee,{threshold:Pe=.9,percentage:Ne=!1}={}){const Qe=Array.isArray(ee);if(Qe&&ee.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ue=await re(ee),je=Ne?null:Ue.map($=>[$.height,$.width]),{pixel_values:lt,pixel_mask:yt}=await this.processor(Ue),vt=await this.model({pixel_values:lt,pixel_mask:yt}),Et=this.processor.feature_extractor.post_process_object_detection(vt,Pe,je),x=this.model.config.id2label,H=Et.map($=>$.boxes.map((Q,he)=>({score:$.scores[he],label:x[$.classes[he]],box:_e(Q,!Ne)})));return Qe?H:H[0]}}class $e extends ce{constructor(ee){super(ee)}async _call(ee,Pe,{threshold:Ne=.1,top_k:Qe=null,percentage:Ue=!1}={}){const je=Array.isArray(ee),lt=await re(ee),yt=this.tokenizer(Pe,{padding:!0,truncation:!0}),vt=await this.processor(lt),Et=[];for(let x=0;x({score:Ye.scores[ct],label:Pe[Ye.classes[ct]],box:_e(Bt,!Ue)})).sort((Bt,ct)=>ct.score-Bt.score);Qe!==null&&(Je=Je.slice(0,Qe)),Et.push(Je)}return je?Et:Et[0]}}class Y extends ce{constructor(ee){super(ee)}async _call(ee,Pe,Ne={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class He extends ce{constructor(Pe){super(Pe);Me(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=Pe.vocoder??null}async _call(Pe,{speaker_embeddings:Ne=null}={}){return this.processor?this._call_text_to_spectrogram(Pe,{speaker_embeddings:Ne}):this._call_text_to_waveform(Pe)}async _call_text_to_waveform(Pe){const Ne=this.tokenizer(Pe,{padding:!0,truncation:!0}),{waveform:Qe}=await this.model(Ne),Ue=this.model.config.sampling_rate;return{audio:Qe.data,sampling_rate:Ue}}async _call_text_to_spectrogram(Pe,{speaker_embeddings:Ne}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await me.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Ne=="string"||Ne instanceof URL)&&(Ne=new Float32Array(await(await fetch(Ne)).arrayBuffer())),Ne instanceof Float32Array)Ne=new V.Tensor("float32",Ne,[1,Ne.length]);else if(!(Ne instanceof V.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:Qe}=this.tokenizer(Pe,{padding:!0,truncation:!0}),{waveform:Ue}=await this.model.generate_speech(Qe,Ne,{vocoder:this.vocoder}),je=this.processor.feature_extractor.config.sampling_rate;return{audio:Ue.data,sampling_rate:je}}}class dt extends ce{constructor(ee){super(ee)}async _call(ee){const Pe=await re(ee),Ne=await this.processor(Pe),Qe=await this.model(Ne),Ue=[];for(const je of Qe.reconstruction){const lt=je.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Ue.push(P.RawImage.fromTensor(lt))}return Ue.length>1?Ue:Ue[0]}}class nt extends ce{constructor(ee){super(ee)}async _call(ee){const Pe=await re(ee),Ne=await this.processor(Pe),{predicted_depth:Qe}=await this.model(Ne),Ue=[];for(let je=0;je1?Ue:Ue[0]}}const ot=Object.freeze({"text-classification":{tokenizer:z.AutoTokenizer,pipeline:D,model:me.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:z.AutoTokenizer,pipeline:q,model:me.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:z.AutoTokenizer,pipeline:te,model:me.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:z.AutoTokenizer,pipeline:ie,model:me.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:z.AutoTokenizer,pipeline:j,model:me.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:z.AutoTokenizer,pipeline:I,model:me.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:z.AutoTokenizer,pipeline:X,model:me.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:z.AutoTokenizer,pipeline:k,model:me.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:z.AutoTokenizer,pipeline:de,model:me.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:ze,model:me.AutoModelForAudioClassification,processor:we.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:z.AutoTokenizer,pipeline:Ie,model:me.AutoModel,processor:we.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:z.AutoTokenizer,pipeline:Ze,model:[me.AutoModelForSpeechSeq2Seq,me.AutoModelForCTC],processor:we.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:z.AutoTokenizer,pipeline:He,model:[me.AutoModelForTextToWaveform,me.AutoModelForTextToSpectrogram],processor:[we.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:z.AutoTokenizer,pipeline:Ke,model:me.AutoModelForVision2Seq,processor:we.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:ut,model:me.AutoModelForImageClassification,processor:we.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ye,model:[me.AutoModelForImageSegmentation,me.AutoModelForSemanticSegmentation,me.AutoModelForUniversalSegmentation],processor:we.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:z.AutoTokenizer,pipeline:W,model:me.AutoModel,processor:we.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:fe,model:me.AutoModelForObjectDetection,processor:we.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:z.AutoTokenizer,pipeline:$e,model:me.AutoModelForZeroShotObjectDetection,processor:we.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:z.AutoTokenizer,pipeline:Y,model:me.AutoModelForDocumentQuestionAnswering,processor:we.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:dt,model:me.AutoModelForImageToImage,processor:we.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:nt,model:me.AutoModelForDepthEstimation,processor:we.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:z.AutoTokenizer,pipeline:ve,model:me.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:we.AutoProcessor,pipeline:ke,model:[me.AutoModelForImageFeatureExtraction,me.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Re=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function ht(qe,ee=null,{progress_callback:Pe=null,config:Ne=null,cache_dir:Qe=null,local_files_only:Ue=!1,revision:je="main",device:lt=null,dtype:yt=null,model_file_name:vt=null,session_options:Et={}}={}){qe=Re[qe]??qe;const x=ot[qe.split("_",1)[0]];if(!x)throw Error(`Unsupported pipeline: ${qe}. 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Ne}},"./src/processors.js":(Ft,Ee,N)=>{N.r(Ee),N.d(Ee,{ASTFeatureExtractor:()=>Q,AutoProcessor:()=>Ur,BeitFeatureExtractor:()=>Pe,BitImageProcessor:()=>ve,CLIPFeatureExtractor:()=>ze,CLIPImageProcessor:()=>Ie,ChineseCLIPFeatureExtractor:()=>Ze,ClapFeatureExtractor:()=>he,ConvNextFeatureExtractor:()=>ut,ConvNextImageProcessor:()=>ye,DPTFeatureExtractor:()=>k,DPTImageProcessor:()=>de,DeiTFeatureExtractor:()=>ee,DetrFeatureExtractor:()=>Ue,DonutFeatureExtractor:()=>Ne,EfficientNetImageProcessor:()=>$e,FeatureExtractor:()=>ie,Florence2Processor:()=>Or,GLPNFeatureExtractor:()=>ke,ImageFeatureExtractor:()=>X,MaskFormerFeatureExtractor:()=>je,MobileNetV1FeatureExtractor:()=>Y,MobileNetV2FeatureExtractor:()=>He,MobileNetV3FeatureExtractor:()=>dt,MobileNetV4FeatureExtractor:()=>nt,MobileViTFeatureExtractor:()=>ot,MobileViTImageProcessor:()=>Re,NougatImageProcessor:()=>Qe,OwlViTFeatureExtractor:()=>ht,OwlViTProcessor:()=>Qr,Owlv2ImageProcessor:()=>bt,Processor:()=>ct,PvtImageProcessor:()=>L,PyAnnoteFeatureExtractor:()=>Ye,PyAnnoteProcessor:()=>cr,RTDetrImageProcessor:()=>qe,SamImageProcessor:()=>yt,SamProcessor:()=>Tt,SapiensFeatureExtractor:()=>j,SeamlessM4TFeatureExtractor:()=>$,SegformerFeatureExtractor:()=>I,SiglipImageProcessor:()=>Ke,SpeechT5FeatureExtractor:()=>Bt,SpeechT5Processor:()=>Er,Swin2SRImageProcessor:()=>vt,ViTFeatureExtractor:()=>W,ViTImageProcessor:()=>fe,VitMatteImageProcessor:()=>Et,Wav2Vec2FeatureExtractor:()=>H,Wav2Vec2ProcessorWithLM:()=>Pt,WeSpeakerFeatureExtractor:()=>Je,WhisperFeatureExtractor:()=>x,WhisperProcessor:()=>At,YolosFeatureExtractor:()=>lt});var z=N("./src/utils/generic.js"),me=N("./src/utils/core.js"),we=N("./src/utils/hub.js"),Te=N("./src/utils/maths.js"),Ce=N("./src/utils/tensor.js");N("./src/utils/image.js");var R=N("./src/utils/audio.js");function S([at,U,ge,Ae]){return[at-ge/2,U-Ae/2,at+ge/2,U+Ae/2]}function V(at,U=.5,ge=null,Ae=!1){const et=at.logits,xe=at.pred_boxes,[Xe,gt,mt]=et.dims;if(ge!==null&&ge.length!==Xe)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let $t=[];for(let ft=0;ftU&&st.push(wr)}else{let wr=(0,Te.max)(tt.data)[1];if(wr===mt-1||(qt=(0,Te.softmax)(tt.data),qt[wr]Kr*zt[(rr+1)%2])),Nt.boxes.push(Br),Nt.classes.push(wr),Nt.scores.push(qt[wr])}}$t.push(Nt)}return $t}function P(at,U=null){const ge=at.logits,Ae=ge.dims[0];if(U!==null&&U.length!==Ae)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const et=[];for(let xe=0;xezt[st]&&(zt[st]=tt[st],Nt[st]=be)}const Rt=new Array(gt.dims[0]);for(let be=0;bebe!==void 0);et.push({segmentation:ft,labels:Gt})}return et}function re(at,U,ge,Ae){const et=[],xe=[],Xe=[];for(let gt=0;gtge&&(et.push($t),xe.push(Nt),Xe.push(ft))}return[et,xe,Xe]}function ne(at,U,ge,Ae=.5,et=.8){const xe=[];let Xe=0,gt=0;const mt=U[ge].data;for(let ft=0;ft=Ae&&++gt;let $t=Xe>0&>>0;return $t&&($t=Xe/gt>et),[$t,xe]}function _e(at,U,ge,Ae,et,xe=null,Xe=null){const[gt,mt]=Xe??at[0].dims,$t=new Ce.Tensor("int32",new Int32Array(gt*mt),[gt,mt]),ft=[];if(Xe!==null)for(let be=0;beNt[qt]&&(zt[qt]=be,Nt[qt]=st[qt])}let Rt=0;const Gt=$t.data;for(let be=0;beAe&&(xe=Math.floor(et)*U),xexe?$t=Math.floor(xe*mt/et):xe>et&&(mt=Math.floor(et*$t/xe)),await U.resize($t,mt,{resample:Ae}))}async crop_margin(U,ge=200){const Ae=U.clone().grayscale(),et=(0,Te.min)(Ae.data)[0],Xe=(0,Te.max)(Ae.data)[0]-et;if(Xe===0)return U;const gt=ge/255;let mt=Ae.width,$t=Ae.height,ft=0,zt=0;const Nt=Ae.data;for(let Rt=0;Rtthis.preprocess(xe)));return{pixel_values:(0,Ce.stack)(Ae.map(xe=>xe.pixel_values),0),original_sizes:Ae.map(xe=>xe.original_size),reshaped_input_sizes:Ae.map(xe=>xe.reshaped_input_size)}}}class j extends X{post_process_semantic_segmentation(...U){return P(...U)}}class I extends X{post_process_semantic_segmentation(...U){return P(...U)}}class L extends X{}class k extends X{}class de extends k{}class ve extends X{}class ke extends X{}class ze extends X{}class Ie extends ze{}class Ze extends X{}class Ke extends X{}class ut extends X{constructor(U){super(U),this.crop_pct=this.config.crop_pct??.875}async resize(U){var Ae;const ge=(Ae=this.size)==null?void 0:Ae.shortest_edge;if(ge===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(ge<384){const et=Math.floor(ge/this.crop_pct),[xe,Xe]=this.get_resize_output_image_size(U,{shortest_edge:et});U=await U.resize(xe,Xe,{resample:this.resample}),U=await U.center_crop(ge,ge)}else U=await U.resize(ge,ge,{resample:this.resample});return U}}class ye extends ut{}class W extends X{}class fe extends X{}class $e extends X{constructor(U){super(U),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(ge=>ge*ge))}}class Y extends X{}class He extends X{}class dt extends X{}class nt extends X{}class ot extends X{}class Re extends ot{}class ht extends X{post_process_object_detection(...U){return V(...U)}}class bt extends ht{}class qe extends X{post_process_object_detection(...U){return V(...U)}}class ee extends X{}class Pe extends X{}class Ne extends X{pad_image(U,ge,Ae,et={}){const[xe,Xe,gt]=ge;let mt=this.image_mean;Array.isArray(this.image_mean)||(mt=new Array(gt).fill(mt));let $t=this.image_std;Array.isArray($t)||($t=new Array(gt).fill(mt));const ft=mt.map((zt,Nt)=>-zt/$t[Nt]);return super.pad_image(U,ge,Ae,{center:!0,constant_values:ft,...et})}}class Qe extends Ne{}class Ue extends X{async _call(U){const ge=await super._call(U),Ae=[ge.pixel_values.dims[0],64,64],et=(0,Ce.full)(Ae,1n);return{...ge,pixel_mask:et}}post_process_object_detection(...U){return V(...U)}post_process_panoptic_segmentation(...U){return ce(...U)}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class je extends X{post_process_panoptic_segmentation(...U){return ce(...U)}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class lt extends X{post_process_object_detection(...U){return V(...U)}}class yt extends X{reshape_input_points(U,ge,Ae,et=!1){U=structuredClone(U);let xe=(0,me.calculateDimensions)(U);if(xe.length===3)et||(xe=[1,...xe]),U=[U];else if(xe.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let Xe=0;Xeet!==ge.dims[xe]))throw Error(`The first ${Ae.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new Ce.Tensor("int64",U.flat(1/0).map(BigInt),Ae)}async _call(U,{input_points:ge=null,input_labels:Ae=null,input_boxes:et=null}={}){const xe=await super._call(U);if(ge&&(xe.input_points=this.reshape_input_points(ge,xe.original_sizes,xe.reshaped_input_sizes)),Ae){if(!xe.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");xe.input_labels=this.add_input_labels(Ae,xe.input_points)}return et&&(xe.input_boxes=this.reshape_input_points(et,xe.original_sizes,xe.reshaped_input_sizes,!0)),xe}async post_process_masks(U,ge,Ae,{mask_threshold:et=0,binarize:xe=!0,pad_size:Xe=null}={}){const gt=[];Xe=Xe??this.pad_size;const mt=[Xe.height,Xe.width];for(let $t=0;$tet&&(Gt[be]=1);Nt=new Ce.Tensor("bool",Gt,Nt.dims)}gt.push(Nt)}return gt}generate_crop_boxes(U,ge,{crop_n_layers:Ae=0,overlap_ratio:et=.3413333333333333,points_per_crop:xe=32,crop_n_points_downscale_factor:Xe=1}={}){}}class vt extends X{pad_image(U,ge,Ae,et={}){const[xe,Xe,gt]=ge;return super.pad_image(U,ge,{width:Xe+(Ae-Xe%Ae)%Ae,height:xe+(Ae-xe%Ae)%Ae},{mode:"symmetric",center:!1,constant_values:-1,...et})}}class Et extends X{async _call(U,ge){Array.isArray(U)||(U=[U]),Array.isArray(ge)||(ge=[ge]);const Ae=await Promise.all(U.map(Xe=>this.preprocess(Xe))),et=await Promise.all(ge.map(Xe=>this.preprocess(Xe,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,Ce.stack)(Ae.map((Xe,gt)=>(0,Ce.cat)([Xe.pixel_values,et[gt].pixel_values],0)),0),original_sizes:Ae.map(Xe=>Xe.original_size),reshaped_input_sizes:Ae.map(Xe=>Xe.reshaped_input_size)}}}class x extends ie{constructor(U){var ge;super(U),(ge=this.config).mel_filters??(ge.mel_filters=(0,R.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,R.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(U){const ge=await(0,R.spectrogram)(U,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}),Ae=ge.data,et=(0,Te.max)(Ae)[0];for(let xe=0;xethis.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),ge=U.slice(0,this.config.n_samples)):(ge=new Float32Array(this.config.n_samples),ge.set(U)),{input_features:(await this._extract_fbank_features(ge)).unsqueeze_(0)}}}class H extends ie{_zero_mean_unit_var_norm(U){const Ae=U.reduce((xe,Xe)=>xe+Xe,0)/U.length,et=U.reduce((xe,Xe)=>xe+(Xe-Ae)**2,0)/U.length;return U.map(xe=>(xe-Ae)/Math.sqrt(et+1e-7))}async _call(U){D(U,"Wav2Vec2FeatureExtractor"),U instanceof Float64Array&&(U=new Float32Array(U));let ge=U;this.config.do_normalize&&(ge=this._zero_mean_unit_var_norm(ge));const Ae=[1,ge.length];return{input_values:new Ce.Tensor("float32",ge,Ae),attention_mask:new Ce.Tensor("int64",new BigInt64Array(ge.length).fill(1n),Ae)}}}class $ extends ie{constructor(U){super(U);const ge=this.config.sampling_rate,Ae=(0,R.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ge/2),ge,null,"kaldi",!0);for(let et=0;etAe*32768),(0,R.spectrogram)(U,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:ge,transpose:!0})}async _call(U,{padding:ge=!0,pad_to_multiple_of:Ae=2,do_normalize_per_mel_bins:et=!0,return_attention_mask:xe=!0}={}){D(U,"SeamlessM4TFeatureExtractor");let Xe=await this._extract_fbank_features(U,this.config.max_length);if(et){const[Gt,be]=Xe.dims,tt=Xe.data;for(let st=0;st0){const qt=new Float32Array(be*(Gt+st));qt.set(tt),qt.fill(this.config.padding_value,tt.length);const wr=Gt+st;Xe=new Ce.Tensor(Xe.type,qt,[wr,be]),xe&&(gt=new Ce.Tensor("int64",new BigInt64Array(wr),[1,wr]),gt.data.fill(1n,0,Gt))}}const[mt,$t]=Xe.dims,ft=this.config.stride;if(mt%ft!==0)throw new Error(`The number of frames (${mt}) must be a multiple of the stride (${ft}).`);const Nt=Xe.view(1,Math.floor(mt/ft),$t*ft),Rt={input_features:Nt};if(xe){const Gt=Nt.dims[1],be=new BigInt64Array(Gt);if(gt){const tt=gt.data;for(let st=1,qt=0;st0)if(Ae==="rand_trunc"){const gt=Math.floor(Math.random()*(Xe+1));U=U.subarray(gt,gt+ge),xe=await this._extract_fbank_features(U,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${Ae}" not implemented`);else{if(Xe<0){let gt=new Float64Array(ge);if(gt.set(U),et==="repeat")for(let mt=U.length;mt({id:mt,start:$t*Ae,end:ft*Ae,confidence:zt/(ft-$t)})))}return et}}class Je extends ie{constructor(U){super(U);const ge=this.config.sampling_rate,Ae=(0,R.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ge/2),ge,null,"kaldi",!0);for(let et=0;etge*32768),(0,R.spectrogram)(U,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(U){D(U,"WeSpeakerFeatureExtractor");const ge=(await this._extract_fbank_features(U)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const Ae=ge.mean(1).data,et=ge.data,[xe,Xe,gt]=ge.dims;for(let mt=0;mt/gm,bboxes:/([^<]+)?/gm},this.size_per_bin=1e3}construct_prompts(U){typeof U=="string"&&(U=[U]);const ge=[];for(const Ae of U)if(this.task_prompts_without_inputs.has(Ae))ge.push(this.task_prompts_without_inputs.get(Ae));else{for(const[et,xe]of this.task_prompts_with_input)if(Ae.includes(et)){ge.push(xe.replaceAll("{input}",Ae).replaceAll(et,""));break}ge.length!==U.length&&ge.push(Ae)}return ge}post_process_generation(U,ge,Ae){const et=this.tasks_answer_post_processing_type.get(ge)??"pure_text";U=U.replaceAll("","").replaceAll("","");let xe;switch(et){case"pure_text":xe=U;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const Xe=et==="ocr"?"quad_boxes":"bboxes",gt=U.matchAll(this.regexes[Xe]),mt=[],$t=[];for(const[ft,zt,...Nt]of gt)mt.push(zt?zt.trim():mt.at(-1)??""),$t.push(Nt.map((Rt,Gt)=>(Number(Rt)+.5)/this.size_per_bin*Ae[Gt%2]));xe={labels:mt,[Xe]:$t};break;default:throw new Error(`Task "${ge}" (of type "${et}") not yet implemented.`)}return{[ge]:xe}}}class Ur{static async from_pretrained(U,{progress_callback:ge=null,config:Ae=null,cache_dir:et=null,local_files_only:xe=!1,revision:Xe="main"}={}){let gt=Ae??await(0,we.getModelJSON)(U,"preprocessor_config.json",!0,{progress_callback:ge,config:Ae,cache_dir:et,local_files_only:xe,revision:Xe}),mt=gt.feature_extractor_type??gt.image_processor_type,$t=this.FEATURE_EXTRACTOR_CLASS_MAPPING[mt];if(!$t)if(gt.size!==void 0)console.warn(`Feature extractor type "${mt}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),$t=X;else throw new Error(`Unknown Feature Extractor type: ${mt}`);let ft=this.PROCESSOR_CLASS_MAPPING[gt.processor_class]??ct,zt=new $t(gt);return new ft(zt)}}Me(Ur,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:X,WhisperFeatureExtractor:x,ViTFeatureExtractor:W,MobileViTFeatureExtractor:ot,MobileViTImageProcessor:Re,MobileNetV1FeatureExtractor:Y,MobileNetV2FeatureExtractor:He,MobileNetV3FeatureExtractor:dt,MobileNetV4FeatureExtractor:nt,OwlViTFeatureExtractor:ht,Owlv2ImageProcessor:bt,CLIPFeatureExtractor:ze,CLIPImageProcessor:Ie,Florence2Processor:Or,ChineseCLIPFeatureExtractor:Ze,SiglipImageProcessor:Ke,ConvNextFeatureExtractor:ut,ConvNextImageProcessor:ye,SegformerFeatureExtractor:I,SapiensFeatureExtractor:j,BitImageProcessor:ve,DPTImageProcessor:de,DPTFeatureExtractor:k,PvtImageProcessor:L,GLPNFeatureExtractor:ke,BeitFeatureExtractor:Pe,DeiTFeatureExtractor:ee,DetrFeatureExtractor:Ue,RTDetrImageProcessor:qe,MaskFormerFeatureExtractor:je,YolosFeatureExtractor:lt,DonutFeatureExtractor:Ne,NougatImageProcessor:Qe,EfficientNetImageProcessor:$e,ViTImageProcessor:fe,VitMatteImageProcessor:Et,SamImageProcessor:yt,Swin2SRImageProcessor:vt,Wav2Vec2FeatureExtractor:H,SeamlessM4TFeatureExtractor:$,SpeechT5FeatureExtractor:Bt,ASTFeatureExtractor:Q,ClapFeatureExtractor:he,PyAnnoteFeatureExtractor:Ye,WeSpeakerFeatureExtractor:Je}),Me(Ur,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:At,Wav2Vec2ProcessorWithLM:Pt,PyAnnoteProcessor:cr,SamProcessor:Tt,SpeechT5Processor:Er,OwlViTProcessor:Qr,Florence2Processor:Or})},"./src/tokenizers.js":(Ft,Ee,N)=>{N.r(Ee),N.d(Ee,{AlbertTokenizer:()=>gt,AutoTokenizer:()=>ns,BartTokenizer:()=>Kr,BertTokenizer:()=>Xe,BlenderbotSmallTokenizer:()=>Ps,BlenderbotTokenizer:()=>_s,BloomTokenizer:()=>Ln,CLIPTokenizer:()=>fs,CamembertTokenizer:()=>tt,CodeGenTokenizer:()=>hs,CodeLlamaTokenizer:()=>Cs,CohereTokenizer:()=>ws,ConvBertTokenizer:()=>Rt,DebertaTokenizer:()=>ft,DebertaV2Tokenizer:()=>zt,DistilBertTokenizer:()=>be,ElectraTokenizer:()=>qt,EsmTokenizer:()=>Gn,FalconTokenizer:()=>$s,GPT2Tokenizer:()=>Br,GPTNeoXTokenizer:()=>Ss,GemmaTokenizer:()=>Qs,Grok1Tokenizer:()=>qn,HerbertTokenizer:()=>Nt,LlamaTokenizer:()=>Bn,M2M100Tokenizer:()=>ps,MBart50Tokenizer:()=>Wr,MBartTokenizer:()=>rr,MPNetTokenizer:()=>Es,MarianTokenizer:()=>Xt,MobileBertTokenizer:()=>mt,NllbTokenizer:()=>Cn,NougatTokenizer:()=>gs,PreTrainedTokenizer:()=>xe,Qwen2Tokenizer:()=>ks,RoFormerTokenizer:()=>Gt,RobertaTokenizer:()=>Vr,SiglipTokenizer:()=>rs,SpeechT5Tokenizer:()=>As,SqueezeBertTokenizer:()=>$t,T5Tokenizer:()=>wr,TokenizerModel:()=>ke,VitsTokenizer:()=>Is,Wav2Vec2CTCTokenizer:()=>ms,WhisperTokenizer:()=>ts,XLMRobertaTokenizer:()=>Xs,XLMTokenizer:()=>st,is_chinese_char:()=>X});var z=N("./src/utils/generic.js"),me=N("./src/utils/core.js"),we=N("./src/utils/hub.js"),Te=N("./src/utils/maths.js"),Ce=N("./src/utils/tensor.js"),R=N("./src/utils/data-structures.js"),S=N("./node_modules/@huggingface/jinja/dist/index.js"),V=N("./src/models/whisper/common_whisper.js"),P=N("./src/utils/constants.js");async function re(ue,m){const A=await Promise.all([(0,we.getModelJSON)(ue,"tokenizer.json",!0,m),(0,we.getModelJSON)(ue,"tokenizer_config.json",!0,m)]);return m.legacy!==null&&(A[1].legacy=m.legacy),A}function ne(ue,m){const A=[];let K=0;for(const se of ue.matchAll(m)){const pe=se[0];K0&&A.push(pe),K=se.index+pe.length}return K=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 j(ue,m,A){const K=[];let se=0;for(;sethis.tokens_to_ids.get(A)??this.unk_token_id)}convert_ids_to_tokens(m){return m.map(A=>this.vocab[A]??this.unk_token)}}class ze extends ke{constructor(m){super(m),this.tokens_to_ids=ce(m.vocab),this.unk_token_id=this.tokens_to_ids.get(m.unk_token),this.unk_token=m.unk_token,this.max_input_chars_per_word=m.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[A,K]of this.tokens_to_ids)this.vocab[K]=A}encode(m){const A=[];for(const K of m){const se=[...K];if(se.length>this.max_input_chars_per_word){A.push(this.unk_token);continue}let pe=!1,De=0;const wt=[];for(;De0&&(Ot=this.config.continuing_subword_prefix+Ot),this.tokens_to_ids.has(Ot)){Mt=Ot;break}--xt}if(Mt===null){pe=!0;break}wt.push(Mt),De=xt}pe?A.push(this.unk_token):A.push(...wt)}return A}}class Ie extends ke{constructor(m,A){super(m);const K=m.vocab.length;this.vocab=new Array(K),this.scores=new Array(K);for(let se=0;se[se,pe])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=A.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,Te.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new R.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(m){const A=m.chars,K=1;let se=0;for(;se{const ue=[...Array.from({length:94},(se,pe)=>pe+33),...Array.from({length:12},(se,pe)=>pe+161),...Array.from({length:82},(se,pe)=>pe+174)],m=ue.slice();let A=0;for(let se=0;se<256;++se)ue.includes(se)||(ue.push(se),m.push(256+A),A+=1);const K=m.map(se=>String.fromCharCode(se));return Object.fromEntries(ue.map((se,pe)=>[se,K[pe]]))})(),Ke=(0,me.reverseDictionary)(Ze);class ut extends ke{constructor(m){super(m),this.tokens_to_ids=ce(m.vocab),this.unk_token_id=this.tokens_to_ids.get(m.unk_token),this.unk_token=m.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[K,se]of this.tokens_to_ids)this.vocab[se]=K;const A=Array.isArray(m.merges[0]);this.merges=A?m.merges:m.merges.map(K=>K.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((K,se)=>[JSON.stringify(K),se])),this.end_of_word_suffix=m.end_of_word_suffix,this.continuing_subword_suffix=m.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(m){if(m.length===0)return[];const A=this.cache.get(m);if(A!==void 0)return A;const K=Array.from(m);this.end_of_word_suffix&&(K[K.length-1]+=this.end_of_word_suffix);let se=[];if(K.length>1){const pe=new R.PriorityQueue((xt,Mt)=>xt.score`<0x${wt.toString(16).toUpperCase().padStart(2,"0")}>`);De.every(wt=>this.tokens_to_ids.has(wt))?A.push(...De):A.push(this.unk_token)}else A.push(this.unk_token)}return A}}class ye extends ke{constructor(m,A){super(m),this.tokens_to_ids=ce(A.target_lang?m.vocab[A.target_lang]:m.vocab),this.bos_token=A.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=A.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=A.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=A.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[K,se]of this.tokens_to_ids)this.vocab[se]=K}encode(m){return m}}class W extends z.Callable{constructor(m){super(),this.config=m}static fromConfig(m){if(m===null)return null;switch(m.type){case"BertNormalizer":return new bt(m);case"Precompiled":return new Qr(m);case"Sequence":return new ht(m);case"Replace":return new fe(m);case"NFC":return new $e(m);case"NFKC":return new Y(m);case"NFKD":return new He(m);case"Strip":return new dt(m);case"StripAccents":return new nt(m);case"Lowercase":return new ot(m);case"Prepend":return new Re(m);default:throw new Error(`Unknown Normalizer type: ${m.type}`)}}normalize(m){throw Error("normalize should be implemented in subclass.")}_call(m){return this.normalize(m)}}class fe extends W{normalize(m){const A=_e(this.config.pattern);return A===null?m:m.replaceAll(A,this.config.content)}}class $e extends W{normalize(m){return m=m.normalize("NFC"),m}}class Y extends W{normalize(m){return m=m.normalize("NFKC"),m}}class He extends W{normalize(m){return m=m.normalize("NFKD"),m}}class dt extends W{normalize(m){return this.config.strip_left&&this.config.strip_right?m=m.trim():(this.config.strip_left&&(m=m.trimStart()),this.config.strip_right&&(m=m.trimEnd())),m}}class nt extends W{normalize(m){return m=te(m),m}}class ot extends W{normalize(m){return m=m.toLowerCase(),m}}class Re extends W{normalize(m){return m=this.config.prepend+m,m}}class ht extends W{constructor(m){super(m),this.normalizers=m.normalizers.map(A=>W.fromConfig(A))}normalize(m){return this.normalizers.reduce((A,K)=>K.normalize(A),m)}}class bt extends W{_tokenize_chinese_chars(m){const A=[];for(let K=0;Kthis.pre_tokenize_text(K,A)):this.pre_tokenize_text(m,A)).flat()}_call(m,A){return this.pre_tokenize(m,A)}}class ee extends qe{constructor(m){super(),this.pattern=new RegExp(`[^\\s${L}]+|[${L}]`,"gu")}pre_tokenize_text(m,A){return m.trim().match(this.pattern)||[]}}class Pe extends qe{constructor(m){super(),this.config=m,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=Ze,this.text_encoder=new TextEncoder}pre_tokenize_text(m,A){return this.add_prefix_space&&!m.startsWith(" ")&&(m=" "+m),(this.use_regex?m.match(this.pattern)||[]:[m]).map(se=>Array.from(this.text_encoder.encode(se),pe=>this.byte_encoder[pe]).join(""))}}class Ne extends qe{constructor(m){super(),this.config=m,this.pattern=_e(this.config.pattern,this.config.invert)}pre_tokenize_text(m,A){return this.pattern===null?[]:this.config.invert?m.match(this.pattern)||[]:ne(m,this.pattern)}}class Qe extends qe{constructor(m){super(),this.config=m,this.pattern=new RegExp(`[^${L}]+|[${L}]+`,"gu")}pre_tokenize_text(m,A){return m.match(this.pattern)||[]}}class Ue extends qe{constructor(m){super(),this.config=m;const A=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(A,"gu")}pre_tokenize_text(m,A){return m.match(this.pattern)||[]}}class je extends z.Callable{constructor(m){super(),this.config=m}static fromConfig(m){if(m===null)return null;switch(m.type){case"TemplateProcessing":return new vt(m);case"ByteLevel":return new Et(m);case"RobertaProcessing":return new yt(m);case"BertProcessing":return new lt(m);case"Sequence":return new x(m);default:throw new Error(`Unknown PostProcessor type: ${m.type}`)}}post_process(m,...A){throw Error("post_process should be implemented in subclass.")}_call(m,...A){return this.post_process(m,...A)}}class lt extends je{constructor(m){super(m),this.cls=m.cls[0],this.sep=m.sep[0]}post_process(m,A=null,{add_special_tokens:K=!0}={}){K&&(m=(0,me.mergeArrays)([this.cls],m,[this.sep]));let se=new Array(m.length).fill(0);if(A!==null){const pe=K&&this instanceof yt?[this.sep]:[],De=K?[this.sep]:[];m=(0,me.mergeArrays)(m,pe,A,De),se=(0,me.mergeArrays)(se,new Array(A.length+pe.length+De.length).fill(1))}return{tokens:m,token_type_ids:se}}}class yt extends lt{}class vt extends je{constructor(m){super(m),this.single=m.single,this.pair=m.pair}post_process(m,A=null,{add_special_tokens:K=!0}={}){const se=A===null?this.single:this.pair;let pe=[],De=[];for(const wt of se)"SpecialToken"in wt?K&&(pe.push(wt.SpecialToken.id),De.push(wt.SpecialToken.type_id)):"Sequence"in wt&&(wt.Sequence.id==="A"?(pe=(0,me.mergeArrays)(pe,m),De=(0,me.mergeArrays)(De,new Array(m.length).fill(wt.Sequence.type_id))):wt.Sequence.id==="B"&&(pe=(0,me.mergeArrays)(pe,A),De=(0,me.mergeArrays)(De,new Array(A.length).fill(wt.Sequence.type_id))));return{tokens:pe,token_type_ids:De}}}class Et extends je{post_process(m,A=null){return A&&(m=(0,me.mergeArrays)(m,A)),{tokens:m}}}class x extends je{constructor(m){super(m),this.processors=m.processors.map(A=>je.fromConfig(A))}post_process(m,A=null,K={}){let se;for(const pe of this.processors)if(pe instanceof Et)m=pe.post_process(m).tokens,A&&(A=pe.post_process(A).tokens);else{const De=pe.post_process(m,A,K);m=De.tokens,se=De.token_type_ids}return{tokens:m,token_type_ids:se}}}class H extends z.Callable{constructor(m){super(),this.config=m,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=m.trim_offsets}static fromConfig(m){if(m===null)return null;switch(m.type){case"WordPiece":return new Je(m);case"Metaspace":return new Er(m);case"ByteLevel":return new Bt(m);case"Replace":return new $(m);case"ByteFallback":return new Q(m);case"Fuse":return new he(m);case"Strip":return new Ye(m);case"Sequence":return new Tt(m);case"CTC":return new ct(m);case"BPEDecoder":return new At(m);default:throw new Error(`Unknown Decoder type: ${m.type}`)}}_call(m){return this.decode(m)}decode(m){return this.decode_chain(m).join("")}decode_chain(m){throw Error("`decode_chain` should be implemented in subclass.")}}class $ extends H{decode_chain(m){const A=_e(this.config.pattern);return A===null?m:m.map(K=>K.replaceAll(A,this.config.content))}}class Q extends H{constructor(m){super(m),this.text_decoder=new TextDecoder}decode_chain(m){const A=[];let K=[];for(const se of m){let pe=null;if(se.length===6&&se.startsWith("<0x")&&se.endsWith(">")){const De=parseInt(se.slice(3,5),16);isNaN(De)||(pe=De)}if(pe!==null)K.push(pe);else{if(K.length>0){const De=this.text_decoder.decode(Uint8Array.from(K));A.push(De),K=[]}A.push(se)}}if(K.length>0){const se=this.text_decoder.decode(Uint8Array.from(K));A.push(se),K=[]}return A}}class he extends H{decode_chain(m){return[m.join("")]}}class Ye extends H{constructor(m){super(m),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(m){return m.map(A=>{let K=0;for(let pe=0;pe(K!==0&&(A.startsWith(this.config.prefix)?A=A.replace(this.config.prefix,""):A=" "+A),this.cleanup&&(A=q(A)),A))}}class Bt extends H{constructor(m){super(m),this.byte_decoder=Ke,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(m){const A=m.join(""),K=new Uint8Array([...A].map(pe=>this.byte_decoder[pe]));return this.text_decoder.decode(K)}decode_chain(m){const A=[];let K=[];for(const se of m)this.added_tokens.find(pe=>pe.content===se)!==void 0?(K.length>0&&(A.push(this.convert_tokens_to_string(K)),K=[]),A.push(se)):K.push(se);return K.length>0&&A.push(this.convert_tokens_to_string(K)),A}}class ct extends H{constructor(m){super(m),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(m){if(m.length===0)return"";const A=[m[0]];for(let pe=1;pepe!==this.pad_token).join("");return this.cleanup&&(se=q(se).replaceAll(this.word_delimiter_token," ").trim()),se}decode_chain(m){return[this.convert_tokens_to_string(m)]}}class Tt extends H{constructor(m){super(m),this.decoders=m.decoders.map(A=>H.fromConfig(A))}decode_chain(m){return this.decoders.reduce((A,K)=>K.decode_chain(A),m)}}class At extends H{constructor(m){super(m),this.suffix=this.config.suffix}decode_chain(m){return m.map((A,K)=>A.replaceAll(this.suffix,K===m.length-1?"":" "))}}class Pt extends H{decode_chain(m){let A="";for(let K=1;KK.normalize("NFKC")).join("～"):m=m.normalize("NFKC"),m}}class Or extends qe{constructor(m){super(),this.tokenizers=m.pretokenizers.map(A=>qe.fromConfig(A))}pre_tokenize_text(m,A){return this.tokenizers.reduce((K,se)=>se.pre_tokenize(K,A),[m])}}class Ur extends qe{constructor(m){super()}pre_tokenize_text(m,A){return m.match(/\w+|[^\w\s]+/g)||[]}}class at extends qe{constructor(m){super()}pre_tokenize_text(m,A){return I(m)}}class U extends qe{constructor(m){super(),this.config=m,this.pattern=_e(this.config.pattern),this.content=this.config.content}pre_tokenize_text(m,A){return this.pattern===null?[m]:[m.replaceAll(this.pattern,this.config.content)]}}const ge=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Ae(ue,m,A,K){for(const se of Object.keys(ue)){const pe=m-ue[se].length,De=A(se),wt=new Array(pe).fill(De);ue[se]=K==="right"?(0,me.mergeArrays)(ue[se],wt):(0,me.mergeArrays)(wt,ue[se])}}function et(ue,m){for(const A of Object.keys(ue))ue[A].length=m}class xe extends z.Callable{constructor(A,K){super();Me(this,"return_token_type_ids",!1);Me(this,"padding_side","right");this._tokenizer_config=K,this.normalizer=W.fromConfig(A.normalizer),this.pre_tokenizer=qe.fromConfig(A.pre_tokenizer),this.model=ke.fromConfig(A.model,K),this.post_processor=je.fromConfig(A.post_processor),this.decoder=H.fromConfig(A.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const se of A.added_tokens){const pe=new ve(se);this.added_tokens.push(pe),this.model.tokens_to_ids.set(pe.content,pe.id),this.model.vocab[pe.id]=pe.content,pe.special&&(this.special_tokens.push(pe.content),this.all_special_ids.push(pe.id))}if(this.additional_special_tokens=K.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort((se,pe)=>pe.content.length-se.content.length).map(se=>`${se.lstrip?"\\s*":""}(${(0,me.escapeRegExp)(se.content)})${se.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=K.model_max_length,this.remove_space=K.remove_space,this.clean_up_tokenization_spaces=K.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=K.do_lowercase_and_remove_accent??!1,K.padding_side&&(this.padding_side=K.padding_side),this.legacy=!1,this.chat_template=K.chat_template??null,Array.isArray(this.chat_template)){const se=Object.create(null);for(const{name:pe,template:De}of this.chat_template){if(typeof pe!="string"||typeof De!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');se[pe]=De}this.chat_template=se}this._compiled_template_cache=new Map}getToken(...A){for(const K of A){const se=this._tokenizer_config[K];if(se)if(typeof se=="object"){if(se.__type==="AddedToken")return se.content;throw Error(`Unknown token: ${se}`)}else return se}return null}static async from_pretrained(A,{progress_callback:K=null,config:se=null,cache_dir:pe=null,local_files_only:De=!1,revision:wt="main",legacy:xt=null}={}){const Mt=await re(A,{progress_callback:K,config:se,cache_dir:pe,local_files_only:De,revision:wt,legacy:xt});return new this(...Mt)}_call(A,{text_pair:K=null,add_special_tokens:se=!0,padding:pe=!1,truncation:De=null,max_length:wt=null,return_tensor:xt=!0,return_token_type_ids:Mt=null}={}){const Ot=Array.isArray(A);let Yt;if(Ot){if(A.length===0)throw Error("text array must be non-empty");if(K!==null){if(Array.isArray(K)){if(A.length!==K.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");Yt=A.map((ar,Dr)=>this._encode_plus(ar,{text_pair:K[Dr],add_special_tokens:se,return_token_type_ids:Mt}))}else Yt=A.map(ar=>this._encode_plus(ar,{add_special_tokens:se,return_token_type_ids:Mt}))}else{if(A==null)throw Error("text may not be null or undefined");if(Array.isArray(K))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");Yt=[this._encode_plus(A,{text_pair:K,add_special_tokens:se,return_token_type_ids:Mt})]}if(wt===null?pe==="max_length"?wt=this.model_max_length:wt=(0,Te.max)(Yt.map(ar=>ar.input_ids.length))[0]:De||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."),wt=Math.min(wt,this.model_max_length??1/0),pe||De)for(let ar=0;arwt?De&&et(Yt[ar],wt):pe&&Ae(Yt[ar],wt,Dr=>Dr==="input_ids"?this.pad_token_id:0,this.padding_side));const $r={};if(xt){if(!(pe&&De)&&Yt.some(Dr=>{var Jr;for(const ur of Object.keys(Dr))if(Dr[ur].length!==((Jr=Yt[0][ur])==null?void 0:Jr.length))return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const ar=[Yt.length,Yt[0].input_ids.length];for(const Dr of Object.keys(Yt[0]))$r[Dr]=new Ce.Tensor("int64",BigInt64Array.from(Yt.flatMap(Jr=>Jr[Dr]).map(BigInt)),ar)}else{for(const ar of Object.keys(Yt[0]))$r[ar]=Yt.map(Dr=>Dr[ar]);if(!Ot)for(const ar of Object.keys($r))$r[ar]=$r[ar][0]}return $r}_encode_text(A){return A===null?null:(this.added_tokens_regex?A.split(this.added_tokens_regex).filter(pe=>pe):[A]).map((pe,De)=>{if(this.added_tokens.find(xt=>xt.content===pe)!==void 0)return pe;{if(this.remove_space===!0&&(pe=pe.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(pe=ie(pe)),this.normalizer!==null&&(pe=this.normalizer(pe)),pe.length===0)return[];const xt=this.pre_tokenizer!==null?this.pre_tokenizer(pe,{section_index:De}):[pe];return this.model(xt)}}).flat()}_encode_plus(A,{text_pair:K=null,add_special_tokens:se=!0,return_token_type_ids:pe=null}={}){const{tokens:De,token_type_ids:wt}=this._tokenize_helper(A,{pair:K,add_special_tokens:se}),xt=this.model.convert_tokens_to_ids(De),Mt={input_ids:xt,attention_mask:new Array(xt.length).fill(1)};return(pe??this.return_token_type_ids)&&wt&&(Mt.token_type_ids=wt),Mt}_tokenize_helper(A,{pair:K=null,add_special_tokens:se=!1}={}){const pe=this._encode_text(A),De=this._encode_text(K);return this.post_processor?this.post_processor(pe,De,{add_special_tokens:se}):{tokens:(0,me.mergeArrays)(pe??[],De??[])}}tokenize(A,{pair:K=null,add_special_tokens:se=!1}={}){return this._tokenize_helper(A,{pair:K,add_special_tokens:se}).tokens}encode(A,{text_pair:K=null,add_special_tokens:se=!0,return_token_type_ids:pe=null}={}){return this._encode_plus(A,{text_pair:K,add_special_tokens:se,return_token_type_ids:pe}).input_ids}batch_decode(A,K={}){return A instanceof Ce.Tensor&&(A=A.tolist()),A.map(se=>this.decode(se,K))}decode(A,K={}){if(A instanceof Ce.Tensor&&(A=D(A)),!Array.isArray(A)||A.length===0||!(0,me.isIntegralNumber)(A[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(A,K)}decode_single(A,{skip_special_tokens:K=!1,clean_up_tokenization_spaces:se=null}){let pe=this.model.convert_ids_to_tokens(A);K&&(pe=pe.filter(wt=>!this.special_tokens.includes(wt)));let De=this.decoder?this.decoder(pe):pe.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(De=De.replaceAll(this.decoder.end_of_word_suffix," "),K&&(De=De.trim())),(se??this.clean_up_tokenization_spaces)&&(De=q(De)),De}get_chat_template({chat_template:A=null,tools:K=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const se=this.chat_template;if(A!==null&&Object.hasOwn(se,A))A=se[A];else if(A===null)if(K!==null&&"tool_use"in se)A=se.tool_use;else if("default"in se)A=se.default;else throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(se).sort()}.`)}else if(A===null)if(this.chat_template)A=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return A}apply_chat_template(A,{tools:K=null,documents:se=null,chat_template:pe=null,add_generation_prompt:De=!1,tokenize:wt=!0,padding:xt=!1,truncation:Mt=!1,max_length:Ot=null,return_tensor:Yt=!0,return_dict:$r=!1,tokenizer_kwargs:ar={},...Dr}={}){if(pe=this.get_chat_template({chat_template:pe,tools:K}),typeof pe!="string")throw Error(`chat_template must be a string, but got ${typeof pe}`);let Jr=this._compiled_template_cache.get(pe);Jr===void 0&&(Jr=new S.Template(pe),this._compiled_template_cache.set(pe,Jr));const ur=Object.create(null);for(const _n of ge){const wn=this.getToken(_n);wn&&(ur[_n]=wn)}const Sr=Jr.render({messages:A,add_generation_prompt:De,tools:K,documents:se,...ur,...Dr});if(wt){const _n=this._call(Sr,{add_special_tokens:!1,padding:xt,truncation:Mt,max_length:Ot,return_tensor:Yt,...ar});return $r?_n:_n.input_ids}return Sr}}class Xe extends xe{constructor(){super(...arguments);Me(this,"return_token_type_ids",!0)}}class gt extends xe{constructor(){super(...arguments);Me(this,"return_token_type_ids",!0)}}class mt extends xe{constructor(){super(...arguments);Me(this,"return_token_type_ids",!0)}}class $t extends xe{constructor(){super(...arguments);Me(this,"return_token_type_ids",!0)}}class ft extends xe{constructor(){super(...arguments);Me(this,"return_token_type_ids",!0)}}class zt extends xe{constructor(){super(...arguments);Me(this,"return_token_type_ids",!0)}}class Nt extends xe{constructor(){super(...arguments);Me(this,"return_token_type_ids",!0)}}class Rt extends xe{constructor(){super(...arguments);Me(this,"return_token_type_ids",!0)}}class Gt extends xe{constructor(){super(...arguments);Me(this,"return_token_type_ids",!0)}}class be extends xe{}class tt extends xe{}class st extends xe{constructor(A,K){super(A,K);Me(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. 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Must be one of: {${ue.language_codes.join(", ")}}`);if(se!==void 0){if(!ue.language_codes.includes(se))throw new Error(`Source language code "${se}" is not valid. Must be one of: {${ue.language_codes.join(", ")}}`);for(const De of ue.post_processor.config.single)if("SpecialToken"in De&&ue.languageRegex.test(De.SpecialToken.id)){De.SpecialToken.id=ue.lang_to_token(se);break}}return K.forced_bos_token_id=ue.model.convert_tokens_to_ids([ue.lang_to_token(pe)])[0],ue._call(m,A)}class Cn extends xe{constructor(m,A){super(m,A),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(K=>this.languageRegex.test(K)),this.lang_to_token=K=>K}_build_translation_inputs(m,A,K){return Sn(this,m,A,K)}}class ps extends xe{constructor(m,A){super(m,A),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(K=>this.languageRegex.test(K)).map(K=>K.slice(2,-2)),this.lang_to_token=K=>`__${K}__`}_build_translation_inputs(m,A,K){return Sn(this,m,A,K)}}class ts extends xe{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(m,{return_timestamps:A=!1,return_language:K=!1,time_precision:se=null,force_full_sequences:pe=!0}={}){if(se===null)throw Error("Must specify time_precision");let De=null;const wt=A==="word";function xt(){return{language:De,timestamp:[null,null],text:""}}const Mt=[];let Ot=xt(),Yt=0;const $r=this.timestamp_begin;let ar=[],Dr=[],Jr=!1,ur=null;const Sr=new Set(this.all_special_ids);for(const Se of m){const Zr=Se.tokens,xn=wt?Se.token_timestamps:null;let kn=null,Nn=$r;if("stride"in Se){const[Xr,yr,Ar]=Se.stride;if(Yt-=yr,ur=Xr-Ar,yr&&(Nn=yr/se+$r),Ar)for(let Ct=Zr.length-1;Ct>=0;--Ct){const mr=Number(Zr[Ct]);if(mr>=$r){if(kn!==null&&(mr-$r)*se=$r){const Ar=(yr-$r)*se+Yt,Ct=(0,Te.round)(Ar,2);if(kn!==null&&yr>=kn)Jr=!0;else if(Jr||ar.length>0&&yr0?(ar.push(Ht),wt&&Dr.push(en)):ar.every(Xr=>Xr.length===0)&&(Ot=xt(),ar=[],Ht=[],Dr=[],en=[])}if(ar.length>0){if(pe&&A)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. 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kf=Gr("./src/env.js"),qr=Gr("./src/pipelines.js"),b=Gr("./src/models.js"),sr=Gr("./src/tokenizers.js"),Zt=Gr("./src/processors.js"),fc=Gr("./src/configs.js"),xi=Gr("./src/utils/audio.js"),Pf=Gr("./src/utils/image.js"),dn=Gr("./src/utils/tensor.js"),$n=Gr("./src/utils/maths.js"),mc=Gr("./src/generation/streamers.js"),so=Gr("./src/generation/stopping_criteria.js");c.ASTFeatureExtractor,c.ASTForAudioClassification,c.ASTModel,c.ASTPreTrainedModel,c.AlbertForMaskedLM,c.AlbertForQuestionAnswering,c.AlbertForSequenceClassification,c.AlbertModel,c.AlbertPreTrainedModel,c.AlbertTokenizer,c.AudioClassificationPipeline,c.AutoConfig,c.AutoModel,c.AutoModelForAudioClassification,c.AutoModelForAudioFrameClassification,c.AutoModelForCTC,c.AutoModelForCausalLM,c.AutoModelForDepthEstimation,c.AutoModelForDocumentQuestionAnswering,c.AutoModelForImageClassification,c.AutoModelForImageFeatureExtraction,c.AutoModelForImageMatting,c.AutoModelForImageSegmentation,c.AutoModelForImageToImage,c.AutoModelForMaskGeneration,c.AutoModelForMaskedLM,c.AutoModelForNormalEstimation,c.AutoModelForObjectDetection,c.AutoModelForQuestionAnswering,c.AutoModelForSemanticSegmentation,c.AutoModelForSeq2SeqLM,c.AutoModelForSequenceClassification,c.AutoModelForSpeechSeq2Seq,c.AutoModelForTextToSpectrogram,c.AutoModelForTextToWaveform,c.AutoModelForTokenClassification,c.AutoModelForUniversalSegmentation,c.AutoModelForVision2Seq,c.AutoModelForXVector,c.AutoModelForZeroShotObjectDetection,c.AutoProcessor,c.AutoTokenizer,c.AutomaticSpeechRecognitionPipeline,c.BartForConditionalGeneration,c.BartForSequenceClassification,c.BartModel,c.BartPretrainedModel,c.BartTokenizer,c.BaseModelOutput,c.BaseStreamer,c.BeitFeatureExtractor,c.BeitForImageClassification,c.BeitModel,c.BeitPreTrainedModel,c.BertForMaskedLM,c.BertForQuestionAnswering,c.BertForSequenceClassification,c.BertForTokenClassification,c.BertModel,c.BertPreTrainedModel,c.BertTokenizer,c.BitImageProcessor,c.BlenderbotForConditionalGeneration,c.BlenderbotModel,c.BlenderbotPreTrainedModel,c.BlenderbotSmallForConditionalGeneration,c.BlenderbotSmallModel,c.BlenderbotSmallPreTrainedModel,c.BlenderbotSmallTokenizer,c.BlenderbotTokenizer,c.BloomForCausalLM,c.BloomModel,c.BloomPreTrainedModel,c.BloomTokenizer,c.CLIPFeatureExtractor,c.CLIPImageProcessor,c.CLIPModel,c.CLIPPreTrainedModel,c.CLIPSegForImageSegmentation,c.CLIPSegModel,c.CLIPSegPreTrainedModel,c.CLIPTextModel,c.CLIPTextModelWithProjection,c.CLIPTokenizer,c.CLIPVisionModel,c.CLIPVisionModelWithProjection,c.CamembertForMaskedLM,c.CamembertForQuestionAnswering,c.CamembertForSequenceClassification,c.CamembertForTokenClassification,c.CamembertModel,c.CamembertPreTrainedModel,c.CamembertTokenizer,c.CausalLMOutput,c.CausalLMOutputWithPast,c.ChineseCLIPFeatureExtractor,c.ChineseCLIPModel,c.ChineseCLIPPreTrainedModel,c.ClapAudioModelWithProjection,c.ClapFeatureExtractor,c.ClapModel,c.ClapPreTrainedModel,c.ClapTextModelWithProjection,c.CodeGenForCausalLM,c.CodeGenModel,c.CodeGenPreTrainedModel,c.CodeGenTokenizer,c.CodeLlamaTokenizer,c.CohereForCausalLM,c.CohereModel,c.CoherePreTrainedModel,c.CohereTokenizer,c.ConvBertForMaskedLM,c.ConvBertForQuestionAnswering,c.ConvBertForSequenceClassification,c.ConvBertForTokenClassification,c.ConvBertModel,c.ConvBertPreTrainedModel,c.ConvBertTokenizer,c.ConvNextFeatureExtractor,c.ConvNextForImageClassification,c.ConvNextImageProcessor,c.ConvNextModel,c.ConvNextPreTrainedModel,c.ConvNextV2ForImageClassification,c.ConvNextV2Model,c.ConvNextV2PreTrainedModel,c.DPTFeatureExtractor,c.DPTForDepthEstimation,c.DPTImageProcessor,c.DPTModel,c.DPTPreTrainedModel,c.DebertaForMaskedLM,c.DebertaForQuestionAnswering,c.DebertaForSequenceClassification,c.DebertaForTokenClassification,c.DebertaModel,c.DebertaPreTrainedModel,c.DebertaTokenizer,c.DebertaV2ForMaskedLM,c.DebertaV2ForQuestionAnswering,c.DebertaV2ForSequenceClassification,c.DebertaV2ForTokenClassification,c.DebertaV2Model,c.DebertaV2PreTrainedModel,c.DebertaV2Tokenizer,c.DecisionTransformerModel,c.DecisionTransformerPreTrainedModel,c.DeiTFeatureExtractor,c.DeiTForImageClassification,c.DeiTModel,c.DeiTPreTrainedModel,c.DepthAnythingForDepthEstimation,c.DepthAnythingPreTrainedModel,c.DepthEstimationPipeline,c.DetrFeatureExtractor,c.DetrForObjectDetection,c.DetrForSegmentation,c.DetrModel,c.DetrObjectDetectionOutput,c.DetrPreTrainedModel,c.DetrSegmentationOutput,c.Dinov2ForImageClassification,c.Dinov2Model,c.Dinov2PreTrainedModel,c.DistilBertForMaskedLM,c.DistilBertForQuestionAnswering,c.DistilBertForSequenceClassification,c.DistilBertForTokenClassification,c.DistilBertModel,c.DistilBertPreTrainedModel,c.DistilBertTokenizer,c.DocumentQuestionAnsweringPipeline,c.DonutFeatureExtractor,c.DonutSwinModel,c.DonutSwinPreTrainedModel,c.EfficientNetForImageClassification,c.EfficientNetImageProcessor,c.EfficientNetModel,c.EfficientNetPreTrainedModel,c.ElectraForMaskedLM,c.ElectraForQuestionAnswering,c.ElectraForSequenceClassification,c.ElectraForTokenClassification,c.ElectraModel,c.ElectraPreTrainedModel,c.ElectraTokenizer,c.EosTokenCriteria,c.EsmForMaskedLM,c.EsmForSequenceClassification,c.EsmForTokenClassification,c.EsmModel,c.EsmPreTrainedModel,c.EsmTokenizer,c.FFT,c.FalconForCausalLM,c.FalconModel,c.FalconPreTrainedModel,c.FalconTokenizer,c.FastViTForImageClassification,c.FastViTModel,c.FastViTPreTrainedModel,c.FeatureExtractionPipeline,c.FeatureExtractor,c.FillMaskPipeline,c.Florence2ForConditionalGeneration,c.Florence2PreTrainedModel,c.Florence2Processor,c.GLPNFeatureExtractor,c.GLPNForDepthEstimation,c.GLPNModel,c.GLPNPreTrainedModel,c.GPT2LMHeadModel,c.GPT2Model,c.GPT2PreTrainedModel,c.GPT2Tokenizer,c.GPTBigCodeForCausalLM,c.GPTBigCodeModel,c.GPTBigCodePreTrainedModel,c.GPTJForCausalLM,c.GPTJModel,c.GPTJPreTrainedModel,c.GPTNeoForCausalLM,c.GPTNeoModel,c.GPTNeoPreTrainedModel,c.GPTNeoXForCausalLM,c.GPTNeoXModel,c.GPTNeoXPreTrainedModel,c.GPTNeoXTokenizer,c.Gemma2ForCausalLM,c.Gemma2Model,c.Gemma2PreTrainedModel,c.GemmaForCausalLM,c.GemmaModel,c.GemmaPreTrainedModel,c.GemmaTokenizer,c.Grok1Tokenizer,c.GroupViTModel,c.GroupViTPreTrainedModel,c.HerbertTokenizer,c.HieraForImageClassification,c.HieraModel,c.HieraPreTrainedModel,c.HubertForCTC,c.HubertForSequenceClassification,c.HubertModel,c.HubertPreTrainedModel,c.ImageClassificationPipeline,c.ImageFeatureExtractionPipeline,c.ImageFeatureExtractor,c.ImageMattingOutput,c.ImageSegmentationPipeline,c.ImageToImagePipeline,c.ImageToTextPipeline,c.InterruptableStoppingCriteria,c.JAISLMHeadModel,c.JAISModel,c.JAISPreTrainedModel,c.LlamaForCausalLM,c.LlamaModel,c.LlamaPreTrainedModel,c.LlamaTokenizer,c.LlavaForConditionalGeneration,c.LlavaPreTrainedModel,c.LongT5ForConditionalGeneration,c.LongT5Model,c.LongT5PreTrainedModel,c.M2M100ForConditionalGeneration,c.M2M100Model,c.M2M100PreTrainedModel,c.M2M100Tokenizer,c.MBart50Tokenizer,c.MBartForCausalLM,c.MBartForConditionalGeneration,c.MBartForSequenceClassification,c.MBartModel,c.MBartPreTrainedModel,c.MBartTokenizer,c.MPNetForMaskedLM,c.MPNetForQuestionAnswering,c.MPNetForSequenceClassification,c.MPNetForTokenClassification,c.MPNetModel,c.MPNetPreTrainedModel,c.MPNetTokenizer,c.MT5ForConditionalGeneration,c.MT5Model,c.MT5PreTrainedModel,c.MarianMTModel,c.MarianModel,c.MarianPreTrainedModel,c.MarianTokenizer,c.MaskFormerFeatureExtractor,c.MaskFormerForInstanceSegmentation,c.MaskFormerModel,c.MaskFormerPreTrainedModel,c.MaskedLMOutput,c.MaxLengthCriteria,c.MistralForCausalLM,c.MistralModel,c.MistralPreTrainedModel,c.MobileBertForMaskedLM,c.MobileBertForQuestionAnswering,c.MobileBertForSequenceClassification,c.MobileBertModel,c.MobileBertPreTrainedModel,c.MobileBertTokenizer,c.MobileNetV1FeatureExtractor,c.MobileNetV1ForImageClassification,c.MobileNetV1Model,c.MobileNetV1PreTrainedModel,c.MobileNetV2FeatureExtractor,c.MobileNetV2ForImageClassification,c.MobileNetV2Model,c.MobileNetV2PreTrainedModel,c.MobileNetV3FeatureExtractor,c.MobileNetV3ForImageClassification,c.MobileNetV3Model,c.MobileNetV3PreTrainedModel,c.MobileNetV4FeatureExtractor,c.MobileNetV4ForImageClassification,c.MobileNetV4Model,c.MobileNetV4PreTrainedModel,c.MobileViTFeatureExtractor,c.MobileViTForImageClassification,c.MobileViTImageProcessor,c.MobileViTModel,c.MobileViTPreTrainedModel,c.MobileViTV2ForImageClassification,c.MobileViTV2Model,c.MobileViTV2PreTrainedModel,c.ModelOutput,c.Moondream1ForConditionalGeneration,c.MptForCausalLM,c.MptModel,c.MptPreTrainedModel,c.MusicgenForCausalLM,c.MusicgenForConditionalGeneration,c.MusicgenModel,c.MusicgenPreTrainedModel,c.NllbTokenizer,c.NomicBertModel,c.NomicBertPreTrainedModel,c.NougatImageProcessor,c.NougatTokenizer,c.OPTForCausalLM,c.OPTModel,c.OPTPreTrainedModel,c.ObjectDetectionPipeline,c.OpenELMForCausalLM,c.OpenELMModel,c.OpenELMPreTrainedModel,c.OwlViTFeatureExtractor,c.OwlViTForObjectDetection,c.OwlViTModel,c.OwlViTPreTrainedModel,c.OwlViTProcessor,c.Owlv2ForObjectDetection,c.Owlv2ImageProcessor,c.Owlv2Model,c.Owlv2PreTrainedModel,c.Phi3ForCausalLM,c.Phi3Model,c.Phi3PreTrainedModel,c.PhiForCausalLM,c.PhiModel,c.PhiPreTrainedModel,c.Pipeline,c.PreTrainedModel,c.PreTrainedTokenizer,c.PretrainedConfig,c.PretrainedMixin,c.Processor,c.PvtForImageClassification,c.PvtImageProcessor,c.PvtModel,c.PvtPreTrainedModel,c.PyAnnoteFeatureExtractor,c.PyAnnoteForAudioFrameClassification,c.PyAnnoteModel,c.PyAnnotePreTrainedModel,c.PyAnnoteProcessor,c.QuestionAnsweringModelOutput,c.QuestionAnsweringPipeline,c.Qwen2ForCausalLM,c.Qwen2Model,c.Qwen2PreTrainedModel,c.Qwen2Tokenizer,c.RTDetrForObjectDetection,c.RTDetrImageProcessor,c.RTDetrModel,c.RTDetrObjectDetectionOutput,c.RTDetrPreTrainedModel,c.RawImage,c.ResNetForImageClassification,c.ResNetModel,c.ResNetPreTrainedModel,c.RoFormerForMaskedLM,c.RoFormerForQuestionAnswering,c.RoFormerForSequenceClassification,c.RoFormerForTokenClassification,c.RoFormerModel,c.RoFormerPreTrainedModel,c.RoFormerTokenizer,c.RobertaForMaskedLM,c.RobertaForQuestionAnswering,c.RobertaForSequenceClassification,c.RobertaForTokenClassification,c.RobertaModel,c.RobertaPreTrainedModel,c.RobertaTokenizer,c.SamImageProcessor,c.SamImageSegmentationOutput,c.SamModel,c.SamPreTrainedModel,c.SamProcessor,c.SapiensFeatureExtractor,c.SapiensForDepthEstimation,c.SapiensForNormalEstimation,c.SapiensForSemanticSegmentation,c.SapiensPreTrainedModel,c.SeamlessM4TFeatureExtractor,c.SegformerFeatureExtractor,c.SegformerForImageClassification,c.SegformerForSemanticSegmentation,c.SegformerModel,c.SegformerPreTrainedModel,c.Seq2SeqLMOutput,c.SequenceClassifierOutput,c.SiglipImageProcessor,c.SiglipModel,c.SiglipPreTrainedModel,c.SiglipTextModel,c.SiglipTokenizer,c.SiglipVisionModel,c.SpeechT5FeatureExtractor,c.SpeechT5ForSpeechToText,c.SpeechT5ForTextToSpeech,c.SpeechT5HifiGan,c.SpeechT5Model,c.SpeechT5PreTrainedModel,c.SpeechT5Processor,c.SpeechT5Tokenizer,c.SqueezeBertForMaskedLM,c.SqueezeBertForQuestionAnswering,c.SqueezeBertForSequenceClassification,c.SqueezeBertModel,c.SqueezeBertPreTrainedModel,c.SqueezeBertTokenizer,c.StableLmForCausalLM,c.StableLmModel,c.StableLmPreTrainedModel,c.Starcoder2ForCausalLM,c.Starcoder2Model,c.Starcoder2PreTrainedModel,c.StoppingCriteria,c.StoppingCriteriaList,c.SummarizationPipeline,c.Swin2SRForImageSuperResolution,c.Swin2SRImageProcessor,c.Swin2SRModel,c.Swin2SRPreTrainedModel,c.SwinForImageClassification,c.SwinModel,c.SwinPreTrainedModel,c.T5ForConditionalGeneration,c.T5Model,c.T5PreTrainedModel,c.T5Tokenizer,c.TableTransformerForObjectDetection,c.TableTransformerModel,c.TableTransformerObjectDetectionOutput,c.TableTransformerPreTrainedModel,c.Tensor,c.Text2TextGenerationPipeline,c.TextClassificationPipeline,c.TextGenerationPipeline,c.TextStreamer,c.TextToAudioPipeline,c.TokenClassificationPipeline,c.TokenClassifierOutput,c.TokenizerModel,c.TrOCRForCausalLM,c.TrOCRPreTrainedModel,c.TranslationPipeline,c.UniSpeechForCTC,c.UniSpeechForSequenceClassification,c.UniSpeechModel,c.UniSpeechPreTrainedModel,c.UniSpeechSatForAudioFrameClassification,c.UniSpeechSatForCTC,c.UniSpeechSatForSequenceClassification,c.UniSpeechSatModel,c.UniSpeechSatPreTrainedModel,c.ViTFeatureExtractor,c.ViTForImageClassification,c.ViTImageProcessor,c.ViTMAEModel,c.ViTMAEPreTrainedModel,c.ViTMSNForImageClassification,c.ViTMSNModel,c.ViTMSNPreTrainedModel,c.ViTModel,c.ViTPreTrainedModel,c.VisionEncoderDecoderModel,c.VitMatteForImageMatting,c.VitMatteImageProcessor,c.VitMattePreTrainedModel,c.VitsModel,c.VitsModelOutput,c.VitsPreTrainedModel,c.VitsTokenizer,c.Wav2Vec2BertForCTC,c.Wav2Vec2BertForSequenceClassification,c.Wav2Vec2BertModel,c.Wav2Vec2BertPreTrainedModel,c.Wav2Vec2CTCTokenizer,c.Wav2Vec2FeatureExtractor,c.Wav2Vec2ForAudioFrameClassification,c.Wav2Vec2ForCTC,c.Wav2Vec2ForSequenceClassification,c.Wav2Vec2Model,c.Wav2Vec2PreTrainedModel,c.Wav2Vec2ProcessorWithLM,c.WavLMForAudioFrameClassification,c.WavLMForCTC,c.WavLMForSequenceClassification,c.WavLMForXVector,c.WavLMModel,c.WavLMPreTrainedModel,c.WeSpeakerFeatureExtractor,c.WeSpeakerResNetModel,c.WeSpeakerResNetPreTrainedModel,c.WhisperFeatureExtractor,c.WhisperForConditionalGeneration,c.WhisperModel,c.WhisperPreTrainedModel,c.WhisperProcessor;var Af=c.WhisperTextStreamer;c.WhisperTokenizer,c.XLMForQuestionAnswering,c.XLMForSequenceClassification,c.XLMForTokenClassification,c.XLMModel,c.XLMPreTrainedModel,c.XLMRobertaForMaskedLM,c.XLMRobertaForQuestionAnswering,c.XLMRobertaForSequenceClassification,c.XLMRobertaForTokenClassification,c.XLMRobertaModel,c.XLMRobertaPreTrainedModel,c.XLMRobertaTokenizer,c.XLMTokenizer,c.XLMWithLMHeadModel,c.XVectorOutput,c.YolosFeatureExtractor,c.YolosForObjectDetection,c.YolosModel,c.YolosObjectDetectionOutput,c.YolosPreTrainedModel,c.ZeroShotAudioClassificationPipeline,c.ZeroShotClassificationPipeline,c.ZeroShotImageClassificationPipeline,c.ZeroShotObjectDetectionPipeline,c.bankers_round,c.cat,c.cos_sim,c.dot,c.dynamic_time_warping,c.env,c.full,c.full_like,c.getKeyValueShapes,c.hamming,c.hanning,c.interpolate,c.interpolate_4d,c.interpolate_data,c.is_chinese_char,c.layer_norm,c.log_softmax,c.magnitude,c.matmul,c.max,c.mean,c.mean_pooling,c.medianFilter,c.mel_filter_bank,c.min,c.ones,c.ones_like,c.permute,c.permute_data;var If=c.pipeline;c.quantize_embeddings,c.read_audio,c.rfft,c.round,c.softmax,c.spectrogram,c.stack,c.std_mean,c.topk,c.window_function,c.zeros,c.zeros_like;class wd{constructor(Ee,N){this.tokenizer=Ee,this.model=N}static async getInstance(Ee=null){return this.instance===null&&(this.instance=If(this.task,this.model,{dtype:{encoder_model:this.model==="onnx-community/whisper-large-v3-turbo"?"fp16":"fp32",decoder_model_merged:"q4"},device:"webgpu",progress_callback:Ee})),this.instance}}Me(wd,"task",null),Me(wd,"model",null),Me(wd,"instance",null),self.addEventListener("message",async Ft=>{const Ee=Ft.data;let N=await Ff(Ee);N!==null&&self.postMessage({status:"complete",data:N})});class _c extends wd{}Me(_c,"task","automatic-speech-recognition"),Me(_c,"model",null);const Ff=async({audio:Ft,model:Ee,subtask:N,language:z})=>{const me=Ee.startsWith("distil-whisper/"),we=_c;we.model!==Ee&&(we.model=Ee,we.instance!==null&&((await we.getInstance()).dispose(),we.instance=null));const Te=await we.getInstance(q=>{self.postMessage(q)}),Ce=Te.processor.feature_extractor.config.chunk_length/Te.model.config.max_source_positions,R=[],S=me?20:30,V=me?3:5;let P=0,re,ne=0,_e;const ce=new Af(Te.tokenizer,{time_precision:Ce,on_chunk_start:q=>{const te=(S-V)*P;R.push({text:"",timestamp:[te+q,null],finalised:!1,offset:te})},token_callback_function:q=>{re??(re=performance.now()),ne++>0&&(_e=ne/(performance.now()-re)*1e3)},callback_function:q=>{R.length!==0&&(R.at(-1).text+=q,self.postMessage({status:"update",data:{text:"",chunks:R,tps:_e}}))},on_chunk_end:q=>{const te=R.at(-1);te.timestamp[1]=q+te.offset,te.finalised=!0},on_finalize:()=>{re=null,ne=0,++P}}),D=await Te(Ft,{top_k:0,do_sample:!1,chunk_length_s:S,stride_length_s:V,language:z,task:N,return_timestamps:!0,force_full_sequences:!1,streamer:ce}).catch(q=>(console.error(q),self.postMessage({status:"error",data:q}),null));return{tps:_e,...D}}})();