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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); - 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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};${e}`,inputDependencies:B},getRunData:()=>({outputs:G,dispatchGroup:M,programUniforms:C}),getShaderSource:O}},Os=(e,t,r,n,s,a,i,u,d,p,g)=>{let w=Math.min(e.outputCount,1+(i?1:0)+(u?1:0)),l=p.kvNumHeads!==void 0||w>1?p.pastSequenceLength:0,M=l+p.kvSequenceLength,C=d&&Se.size(d.dims)>0?d:void 0,E=[t,r];p.kvNumHeads===void 0&&w>1&&i&&Se.size(i.dims)>0&&E.push(i),C&&E.push(C);let B=e.compute(No(w,t,r,i,C,p,g,l),{inputs:E,outputs:p.kvNumHeads===void 0&&w>1?[-1,1]:[-1]})[0];e.compute(Ro(B,p.batchSize*p.numHeads*p.sequenceLength,M),{inputs:[B],outputs:[]});let G=[B,n];p.kvNumHeads===void 0&&w>1&&u&&Se.size(u.dims)>0&&G.push(u),e.compute(qi(w,B,n,u,p,l),{inputs:G,outputs:p.kvNumHeads===void 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${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 = ${O}(0); - for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { - if (m < uniforms.M && w + local_id.x < uniforms.K) { - tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; - } - if (n < uniforms.N && w + local_id.y < uniforms.K) { - let offset = n + (w + local_id.y) * uniforms.ldb; - tileWeightQ[TILE_SIZE * local_id.y + 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${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}<=${r})`},void 0,void 0,9)}}),Al,Il,Fl,zl,ma,Ol,Cd=j(()=>{Xt(),Ht(),Sr(),or(),Al=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],s=n.dataType,a=n.dims.length;e.forEach((i,u)=>{if(u!==r){if(i.dataType!==s)throw new Error("input tensors should be one type");if(i.dims.length!==a)throw new Error("input tensors should have the same shape");i.dims.forEach((d,p)=>{if(p!==t&&d!==n.dims[p])throw new Error("non concat dimensions must match")})}})},Il=(e,t)=>` - fn calculateInputIndex(index: u32) -> u32 { - let sizeInConcatAxis = array(${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=j(()=>{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 ${e.activation}`)}},Qn=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Yn=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},ni=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[r,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=(e==null?void 0:e.activation_params)||[kn,Nn];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:r}}return{activation:t}}}),fn,si,ii=j(()=>{fn=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},si=e=>` - ${e?"value = value + getBiasByOutputCoords(coords);":""} - `}),_a,Dl=j(()=>{_a=e=>` -fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { - return dot(coords, vec4( - shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); -} -fn getOutputIndexFromCoords(coords : vec4) -> i32 { - return dot(coords, vec4( - i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); -} -`}),Ll,Bl,ai,oi,Rl,li,ga,wa,ui=j(()=>{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};${s}`,inputDependencies:Vt},getRunData:()=>({outputs:[{dims:a?a(r):r,dataType:e[0].dataType}],dispatchGroup:{x:O[0],y:O[1],z:O[2]},programUniforms:Dt}),getShaderSource:fr}}}),Zn,Nl,Ed=j(()=>{Xt(),m(),or(),Jn(),ii(),Dl(),ui(),Zn=(e,t,r,n,s=!1,a,i=4,u=4,d=4,p="f32")=>{let g=Dt=>{switch(Dt){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${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=j(()=>{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=j(()=>{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]};${p[1]}`,inputDependencies:s?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:w}),getShaderSource:M}}}),Ma,Hl,Kl,Xl=j(()=>{Xt(),Ht(),ui(),or(),Jn(),Ma=(e,t,r,n,s=!1,a)=>{let i=e[0].dims,u=e[1].dims,d=i[i.length-2],p=u[u.length-1],g=i[i.length-1],w=mr(p),l=mr(g),M=mr(d),C=Se.size(r)/w/M,E=e.length>2,B=n?n.slice(0,-2):r.slice(0,-2),G=[Se.size(B),d,p],O=[{type:12,data:C},{type:12,data:d},{type:12,data:p},{type:12,data:g}];Qn(t,O),O.push(...Ct(B,i,u)),E&&O.push(...Ct(e[2].dims)),O.push(...Ct(G));let ae=Z=>{let le=Ti("batch_dims",e[0].dataType,B.length),Ge=rt("a",e[0].dataType,i.length,l),Le=rt("b",e[1].dataType,u.length,w),pt=Ut("output",e[0].dataType,G.length,w),Dt=yr(pt.type.tensor),Vt=jn(t,pt.type.value,Dt),lr=[Ge,Le],fr="";if(E){let Pr=s?w:1;lr.push(rt("bias",e[2].dataType,e[2].dims.length,Pr)),fr=`${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=j(()=>{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=j(()=>{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=j(()=>{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:`${t.cacheKey};`,inputDependencies:d},getRunData:()=>({dispatchGroup:{x:i[0],y:i[1],z:i[2]},outputs:[{dims:r?r(s):s,dataType:e[0].dataType}],programUniforms:ae}),getShaderSource:le}}}),nu,su,iu,xa,au,Ta,ou,lu,Ca,uu,Pd=j(()=>{kd(),gc(),Jn(),is(),nu=(e,t,r,n,s,a)=>(e-1)*t+r+(n-1)*s+1-a,su=(e,t,r,n,s)=>{let a=Math.floor(e/2);t==="SAME_UPPER"?(r[n]=a,r[s]=e-a):t==="SAME_LOWER"&&(r[n]=e-a,r[s]=a)},iu=(e,t,r,n,s,a,i,u,d,p)=>{let g=e.length-2,w=p.length===0;d.length{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((w,l)=>w*l,1)===0){r.length=0;for(let w=2;ww+l,0)===0){let w=t[0].dims.length-2;d=new Array(w).fill(1)}let p=e.strides.slice();if(p.reduce((w,l)=>w+l,0)===0){let w=t[0].dims.length-2;p=new Array(w).fill(1)}iu(u,r,d,e.autoPad,e.group,s,p,n,i,a);let g=Object.assign({},e);return Object.assign(g,{kernelShape:r,pads:s,outputPadding:i,outputShape:a,dilations:d,strides:p}),g},au=e=>{let t=ni(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],s=e.dilations,a=e.group,i=e.kernelShape,u=e.pads,d=e.strides,p=e.wIsConst(),g=e.outputPadding,w=e.outputShape;return{autoPad:n,format:r,dilations:s,group:a,kernelShape:i,outputPadding:g,outputShape:w,pads:u,strides:d,wIsConst:p,...t,cacheKey:`${e.format};${t.activation};`}},Ta=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[0];if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let s=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==s))throw new Error("invalid bias");let a=e[0].dims.length-2;if(t.dilations.reduce((i,u)=>i+u,0)>0&&t.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(t.strides.reduce((i,u)=>i+u,0)>0&&t.strides.length!==a)throw new Error(`strides should be ${a}D`);if(t.pads.reduce((i,u)=>i+u,0)>0&&t.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(t.outputPadding.length!==a&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${a}D`);if(t.kernelShape.reduce((i,u)=>i+u,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},ou=[2,3,1,0],lu=(e,t,r)=>{let n=xa(r,t),s=r.format==="NHWC",a=n.outputShape,i=a[s?3:1],u=t[0].dims[s?3:1];if(n.group!==1||i===1&&u===1){e.compute(Ds(t,n));return}let d=a[s?1:2],p=a[s?2:3],g=t[1].dims[2],w=t[1].dims[3],l=s?d*p:i,M=s?i:d*p,C=g*w*u,E=!0,B=e.kernelCustomData.wT??e.compute(Pn(t[1],ou),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=B);let G=[t[0],B],O=t.length===3;O&&(!s&&t[2].dims.length===1?G.push(t[2].reshape([t[2].dims[0],1,1])):G.push(t[2])),e.compute(tu(G,n,a,l,M,C,O,E),{inputs:G})},Ca=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let s=t.kernelShape;(s.length===0||s[0]===0)&&(s=[e.inputs[1].dims[2]]);let a=t.dilations;(a.length===0||a[0]===0)&&(a=[1]);let i=t.strides;(i.length===0||i[0]===0)&&(i=[1]);let u=t.pads;u.length===0&&(u=[0,0]),u=[0,u[0],0,u[1]],i=[1].concat(i),a=[1].concat(a),s=[1].concat(s);let d=xa({...t,pads:u,strides:i,dilations:a,kernelShape:s},n);e.compute(Ds(n,d,p=>r?[p[0],p[2],p[3]]:[p[0],p[1],p[3]]))},uu=(e,t)=>{Ta(e.inputs,t),e.inputs[0].dims.length===3?Ca(e,t):lu(e,e.inputs,t)}}),Ea,du,cu,$a=j(()=>{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=j(()=>{Xt(),Ht(),Sr(),or(),pu=e=>{if(!e||e.length!==1)throw new Error("DepthToSpace requires 1 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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=j(()=>{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=j(()=>{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=j(()=>{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=j(()=>{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=j(()=>{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=j(()=>{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=j(()=>{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=j(()=>{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};${G}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:C,dataType:g}],dispatchGroup:{x:B},programUniforms:O}),getShaderSource:Ge}},Ju=(e,t)=>{Qu(e.inputs,t),e.compute(Yu(e.inputs,t))},Zu=e=>ur(e)}),ed,td,rd,nd,sd,id,ad,od,ld,Ud=j(()=>{Xt(),Ht(),or(),ed=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},td=(e,t,r)=>{let n="";for(let s=t-1;s>=0;--s)n+=` - k = i32(${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=j(()=>{At(),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)}; 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- - // 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=j(()=>{At(),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,Bf=j(()=>{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,Rf=j(()=>{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,Nf=j(()=>{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,jf=j(()=>{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,Vf=j(()=>{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,Uf=j(()=>{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 ${e}u; -}`,Cp=e=>{let t=e.length,r=[];for(let n=0;n{let r=e[0].dims,n=Se.size(r),s=e[0].dataType,a=Se.normalizeAxis(t.axis,r.length),i=new Array(t.numOutputs),u=rt("input",s,r.length),d=new 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t=e.axis,r=e.splitSizes,n=e.numOutputs<0?r.length:e.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return ur({axis:t,numOutputs:n,splitSizes:r})}}),kp,Pp,Ap,Wf=j(()=>{Xt(),Ht(),or(),kp=(e,t,r,n,s)=>{let a=Ut("output_data",s,r.length,4),i=rt("a_data",t[1].dataType,t[1].dims.length,4),u=rt("b_data",t[2].dataType,t[2].dims.length,4),d=rt("c_data",t[0].dataType,t[0].dims.length,4),p,g=(w,l,M)=>`select(${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")} - ${p} - }`},Pp=e=>{let 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Map([["Abs",[Yi]],["Acos",[Ko]],["Acosh",[Xo]],["Add",[vl]],["ArgMax",[Ui,Wi]],["ArgMin",[Bo,Wi]],["Asin",[Qo]],["Asinh",[Yo]],["Atan",[Ji]],["Atanh",[Jo]],["Attention",[Hi]],["AveragePool",[Wd,os]],["BatchNormalization",[Wo]],["BiasAdd",[qo]],["BiasSplitGelu",[pa]],["Cast",[Zi,Zo]],["Ceil",[ea]],["Clip",[tl]],["Concat",[ma,Ol]],["Conv",[as,Yl]],["ConvTranspose",[uu,au]],["Cos",[rl]],["Cosh",[nl]],["CumSum",[du,cu]],["DepthToSpace",[mu,Sa]],["DequantizeLinear",[nr,kr]],["Div",[xl]],["Einsum",[Oa,Ad]],["Elu",[sl,ys]],["Equal",[Tl]],["Erf",[ta]],["Exp",[il]],["Expand",[La]],["FastGelu",[Ba]],["Floor",[al]],["FusedConv",[as,Yl]],["Gather",[Tu,xu]],["GatherElements",[Su,$u]],["GatherBlockQuantized",[Od,Dd]],["Gelu",[ei]],["Gemm",[Pu,ku]],["GlobalAveragePool",[f,cd]],["GlobalMaxPool",[Oe,Fe]],["Greater",[$l]],["GreaterOrEqual",[Sl]],["GroupQueryAttention",[Rd,ju]],["HardSigmoid",[cl,dl]],["InstanceNormalization",[qu]],["LayerNormalization",[Xu]],["LeakyRelu",[ol,ys]],["Less",[fa]],["LessOrEqual",[kl]],["Log",[_l]],["MatMul",[Kl]],["MatMulNBits",[Ju,Zu]],["MaxPool",[T,J]],["Mul",[ri]],["MultiHeadAttention",[zu,Iu]],["Neg",[ra]],["Not",[ll]],["Pad",[ld]],["Pow",[Cl]],["QuickGelu",[wl,ys]],["Range",[xr]],["Reciprocal",[na]],["ReduceMin",[zo]],["ReduceMean",[Ao]],["ReduceMax",[Ni]],["ReduceSum",[ji]],["ReduceProd",[Oo]],["ReduceL1",[Ri]],["ReduceL2",[Io]],["ReduceLogSum",[Lo]],["ReduceLogSumExp",[Fo]],["ReduceSumSquare",[Do]],["Relu",[ul]],["Resize",[pd,ip]],["RotaryEmbedding",[lp]],["Sigmoid",[sa]],["Sin",[pl]],["Sinh",[ia]],["Slice",[_p,gp]],["SkipLayerNormalization",[cp]],["Split",[$p,Sp]],["Sqrt",[hl]],["Softmax",[bp,Mp]],["Sub",[El]],["Tan",[fl]],["Tanh",[oa]],["ThresholdedRelu",[ml,ys]],["Tile",[Bu]],["Transpose",[$i,oo]],["Where",[Ap]]])}),Fp,qf=j(()=>{At(),m(),or(),Fp=class{constructor(e){this.backend=e,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,t){this.repo.set(e,t)}run(e,t,r,n,s){Ue(e.programInfo.name);let a=this.backend.device,i=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let u=[];for(let p of t)u.push({binding:u.length,resource:{buffer:p.buffer}});for(let p of r)u.push({binding:u.length,resource:{buffer:p.buffer}});s&&u.push({binding:u.length,resource:s});let d=a.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:u,label:e.programInfo.name});if(this.backend.sessionStatus==="capturing"){let p={kernelId:this.backend.currentKernelId,computePipeline:e.computePipeline,bindGroup:d,dispatchGroup:n};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(p)}i.setPipeline(e.computePipeline),i.setBindGroup(0,d),i.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),je(e.programInfo.name)}dispose(){}build(e,t){Ue(e.name);let r=this.backend.device,n=[];r.features.has("shader-f16")&&n.push("enable f16;");let s=io(t,this.backend.device.limits),a=e.getShaderSource(s),i=`${n.join(` -`)} -${s.additionalImplementations} -${a}`,u=r.createShaderModule({code:i,label:e.name});ue("verbose",()=>`[WebGPU] ${e.name} shader code: ${i}`);let 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,Hf=j(()=>{At(),Xt(),m(),H(),Dr(),Gf(),qf(),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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(should not happen)");let e=this.kernelCustomData.get(this.currentKernelId);return e||(e={},this.kernelCustomData.set(this.currentKernelId,e)),e}async initialize(e,t){this.env=e;let r=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:t.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:t.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:t.limits.maxStorageBufferBindingSize,maxBufferSize:t.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:t.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:t.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:t.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:t.limits.maxComputeWorkgroupSizeZ},requiredFeatures:r};t.features.has("chromium-experimental-timestamp-query-inside-passes")?r.push("chromium-experimental-timestamp-query-inside-passes"):t.features.has("timestamp-query")&&r.push("timestamp-query"),t.features.has("shader-f16")&&r.push("shader-f16"),this.device=await t.requestDevice(n),this.adapterInfo=new Dp(t.info||await t.requestAdapterInfo()),this.gpuDataManager=ar(this),this.programManager=new Fp(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,ws(e.logLevel,!!e.debug),this.device.onuncapturederror=s=>{s.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${s.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:t,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e=this.getCommandEncoder(),t={};this.queryType==="at-passes"&&(t.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(t)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Ue(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{var n;let t=new BigUint64Array(e.getMappedRange()),r=this.pendingQueries.get(e);for(let s=0;s"u"&&(this.queryTimeBase=M);let E=Number(M-this.queryTimeBase),B=Number(C-this.queryTimeBase);if(!Number.isSafeInteger(E)||!Number.isSafeInteger(B))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:w.map(G=>({dims:G.dims,dataType:Sn(G.dataType)})),outputsMetadata:l.map(G=>({dims:G.dims,dataType:Sn(G.dataType)})),kernelId:i,kernelType:d,kernelName:p,programName:g,startTime:E,endTime:B});else{let G="";w.forEach((ae,Z)=>{G+=`input[${Z}]: [${ae.dims}] | ${Sn(ae.dataType)}, `});let O="";l.forEach((ae,Z)=>{O+=`output[${Z}]: [${ae.dims}] | ${Sn(ae.dataType)}, `}),console.log(`[profiling] kernel "${i}|${d}|${p}|${g}" ${G}${O}execution time: ${B-E} ns`)}Ne("GPU",`${g}::${M}::${C}`)}e.unmap(),this.pendingQueries.delete(e)}),je()}run(e,t,r,n,s,a){Ue(e.name);let i=[];for(let O=0;Oae):r;if(g.length!==u.length)throw new Error(`Output size ${g.length} must be equal to ${u.length}.`);let w=[],l=[];for(let O=0;O=a)throw new Error(`Invalid output index: ${g[O]}`);if(g[O]===-3)continue;let ae=g[O]===-1,Z=g[O]===-2,le=ae||Z?s(u[O].dataType,u[O].dims):n(g[O],u[O].dataType,u[O].dims);if(w.push(le),le.data===0)continue;let Ge=this.gpuDataManager.get(le.data);if(!Ge)throw new Error(`no GPU data for output: ${le.data}`);if(ae&&this.temporaryData.push(Ge),Z){let Le=this.kernelPersistentData.get(this.currentKernelId);Le||(Le=[],this.kernelPersistentData.set(this.currentKernelId,Le)),Le.push(Ge)}l.push(Ge)}if(i.length!==t.length||l.length!==w.length){if(l.length===0)return je(e.name),w;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. 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 O={kernelId:this.currentKernelId,programName:G.programInfo.name,inputTensorViews:t,outputTensorViews:w};this.pendingKernels.push(O),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(O)}return this.programManager.run(G,i,l,C,M),je(e.name),w}upload(e,t){this.gpuDataManager.upload(e,t)}memcpy(e,t){this.gpuDataManager.memcpy(e,t)}async download(e,t){await this.gpuDataManager.download(e,t)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,t,r,n){let s=Ip.get(e);if(!s)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:n,kernelEntry:s[0],attributes:[s[1],r]};this.kernels.set(t,a)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let r of t)this.gpuDataManager.release(r.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,r){let n=this.kernels.get(e);if(!n)throw new Error(`kernel not created: ${e}`);let s=n.kernelType,a=n.kernelName,i=n.kernelEntry,u=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${s}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,u[0]&&(u[1]=u[0](u[1]),u[0]=void 0),ue("info",()=>`[WebGPU] Start to run kernel "[${s}] ${a}"...`);let d=this.env.debug;this.temporaryData=[];try{return d&&this.device.pushErrorScope("validation"),i(t,u[1]),0}catch(p){return r.push(Promise.resolve(`[WebGPU] Kernel "[${s}] ${a}" failed. ${p}`)),1}finally{d&&r.push(this.device.popErrorScope().then(p=>p?`GPU validation error for kernel "[${s}] ${a}": ${p.message}`:null));for(let p of this.temporaryData)this.gpuDataManager.release(p.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,r,n){let s=this.sessionExternalDataMapping.get(e);s||(s=new Map,this.sessionExternalDataMapping.set(e,s));let a=s.get(t),i=this.gpuDataManager.registerExternalBuffer(r,n,a);return s.set(t,[i,r]),i}unregisterBuffers(e){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":(Pt,Ce,N)=>{var z;N.r(Ce),N.d(Ce,{Tensor:()=>Ee.Tensor,createInferenceSession:()=>ce,deviceToExecutionProviders:()=>ne,isONNXProxy:()=>re,isONNXTensor:()=>D});var me=N("./src/env.js"),ye=N("?2ce3"),Te=N("./node_modules/onnxruntime-web/dist/ort.webgpu.bundle.min.mjs"),Ee=N("./node_modules/onnxruntime-common/dist/esm/index.js");const j=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=ye??(z||(z=N.t(ye,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 te=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[j[ie]??ie];throw new Error(`Unsupported device: "${ie}". 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me{constructor(Te){xe(this,"max_length",20);xe(this,"max_new_tokens",null);xe(this,"min_length",0);xe(this,"min_new_tokens",null);xe(this,"early_stopping",!1);xe(this,"max_time",null);xe(this,"do_sample",!1);xe(this,"num_beams",1);xe(this,"num_beam_groups",1);xe(this,"penalty_alpha",null);xe(this,"use_cache",!0);xe(this,"temperature",1);xe(this,"top_k",50);xe(this,"top_p",1);xe(this,"typical_p",1);xe(this,"epsilon_cutoff",0);xe(this,"eta_cutoff",0);xe(this,"diversity_penalty",0);xe(this,"repetition_penalty",1);xe(this,"encoder_repetition_penalty",1);xe(this,"length_penalty",1);xe(this,"no_repeat_ngram_size",0);xe(this,"bad_words_ids",null);xe(this,"force_words_ids",null);xe(this,"renormalize_logits",!1);xe(this,"constraints",null);xe(this,"forced_bos_token_id",null);xe(this,"forced_eos_token_id",null);xe(this,"remove_invalid_values",!1);xe(this,"exponential_decay_length_penalty",null);xe(this,"suppress_tokens",null);xe(this,"begin_suppress_tokens",null);xe(this,"forced_decoder_ids",null);xe(this,"guidance_scale",null);xe(this,"num_return_sequences",1);xe(this,"output_attentions",!1);xe(this,"output_hidden_states",!1);xe(this,"output_scores",!1);xe(this,"return_dict_in_generate",!1);xe(this,"pad_token_id",null);xe(this,"bos_token_id",null);xe(this,"eos_token_id",null);xe(this,"encoder_no_repeat_ngram_size",0);xe(this,"decoder_start_token_id",null);xe(this,"generation_kwargs",{});Object.assign(this,(0,z.pick)(Te,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(Pt,Ce,N)=>{N.r(Ce),N.d(Ce,{ClassifierFreeGuidanceLogitsProcessor:()=>K,ForcedBOSTokenLogitsProcessor:()=>j,ForcedEOSTokenLogitsProcessor:()=>S,LogitsProcessor:()=>ye,LogitsProcessorList:()=>Ee,LogitsWarper:()=>Te,MinLengthLogitsProcessor:()=>_e,MinNewTokensLengthLogitsProcessor:()=>ce,NoBadWordsLogitsProcessor:()=>D,NoRepeatNGramLogitsProcessor:()=>te,RepetitionPenaltyLogitsProcessor:()=>ne,SuppressTokensAtBeginLogitsProcessor:()=>V,TemperatureLogitsWarper:()=>re,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 ye 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 Ee 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 j extends ye{constructor(I){super(),this.bos_token_id=I}_call(I,L){for(let k=0;k=1&&Me[Me.length-1]>=this.timestamp_begin,ze=Me.length<2||Me[Me.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 ut=this.timestamp_begin+this.max_initial_timestamp_index;de.subarray(ut+1).fill(-1/0)}const Ie=(0,me.log_softmax)(de),Ze=Math.log(Ie.subarray(this.timestamp_begin).map(Math.exp).reduce((ut,we)=>ut+we)),Ke=(0,me.max)(Ie.subarray(0,this.timestamp_begin))[0];Ze>Ke&&de.subarray(0,this.timestamp_begin).fill(-1/0)}return L}}class te extends ye{constructor(I){super(),this.no_repeat_ngram_size=I}getNgrams(I){const L=I.length,k=[];for(let Me=0;Me1 to use the classifier free guidance processor, got guidance scale ${I}.`);this.guidance_scale=I}_call(I,L){if(L.dims[0]!==2*I.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. 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sample(P){throw Error("sample should be implemented in subclasses.")}getLogits(P,te){let ne=P.dims.at(-1),_e=P.data;if(te===-1)_e=_e.slice(-ne);else{let ce=te*ne;_e=_e.slice(ce,ce+ne)}return _e}randomSelect(P){let te=0;for(let _e=0;_e1)return new S(P);if(P.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${P.num_return_sequences}.`);return new Ee(P)}}class Ee extends Te{async sample(P){const te=(0,ye.max)(P.data)[1];return[[BigInt(te),0]]}}class j extends Te{async sample(P){let te=P.dims.at(-1);this.generation_config.top_k>0&&(te=Math.min(this.generation_config.top_k,te));const[ne,_e]=await(0,me.topk)(P,te),ce=(0,ye.softmax)(ne.data);return Array.from({length:this.generation_config.num_beams},()=>{const D=this.randomSelect(ce);return[_e.data[D],Math.log(ce[D])]})}}class S extends Te{async sample(P){let te=P.dims.at(-1);this.generation_config.top_k>0&&(te=Math.min(this.generation_config.top_k,te));const[ne,_e]=await(0,me.topk)(P,te),ce=(0,ye.softmax)(ne.data);return Array.from({length:this.generation_config.num_beams},(D,K)=>[_e.data[K],Math.log(ce[K])])}}},"./src/generation/stopping_criteria.js":(Pt,Ce,N)=>{N.r(Ce),N.d(Ce,{EosTokenCriteria:()=>Ee,InterruptableStoppingCriteria:()=>j,MaxLengthCriteria:()=>Te,StoppingCriteria:()=>me,StoppingCriteriaList:()=>ye});var z=N("./src/utils/generic.js");class me extends z.Callable{_call(V,P){throw Error("StoppingCriteria needs to be subclassed")}}class ye extends z.Callable{constructor(){super(),this.criteria=[]}push(V){this.criteria.push(V)}extend(V){V instanceof ye?V=V.criteria:V instanceof me&&(V=[V]),this.criteria.push(...V)}_call(V,P){const te=new Array(V.length).fill(!1);for(const ne of this.criteria){const _e=ne(V,P);for(let ce=0;ceP.length>=this.max_length)}}class Ee extends 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")+1),this.print_len+=_e.length),this.on_finalized_text(_e,!1)}end(){let P;this.token_cache.length>0?(P=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):P="",this.next_tokens_are_prompt=!0,this.on_finalized_text(P,!0)}on_finalized_text(P,te){var ne,_e;P.length>0&&((ne=this.callback_function)==null||ne.call(this,P)),te&&this.callback_function===Ee&&ye.apis.IS_PROCESS_AVAILABLE&&((_e=this.callback_function)==null||_e.call(this,` -`))}}class S extends 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Using the default device.`),Fe=null));const Oe=Fe??(ce.apis.IS_NODE_ENV?"cpu":"wasm"),_t=(0,me.deviceToExecutionProviders)(Oe);let St=T.dtype??J.dtype;typeof St!="string"&&(St&&St.hasOwnProperty(_)?St=St[_]:(St=ye.DEFAULT_DEVICE_DTYPE_MAPPING[Oe]??ye.DATA_TYPES.fp32,console.warn(`dtype not specified for "${_}". Using the default dtype (${St}) for this device (${Oe}).`)));const Wt=St;if(ye.DEFAULT_DTYPE_SUFFIX_MAPPING.hasOwnProperty(Wt)){if(Wt===ye.DATA_TYPES.fp16&&Oe==="webgpu"&&!await(0,ye.isWebGpuFp16Supported)())throw new Error(`The device (${Oe}) does not support fp16.`)}else throw new Error(`Invalid dtype: ${Wt}. 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When `free_dimension_overrides` is not set, you may experience significant performance degradation.');const _r=(0,j.getModelFile)(f,kr,!0,T);let xr=[];if(T.use_external_data_format&&(T.use_external_data_format===!0||typeof T.use_external_data_format=="object"&&T.use_external_data_format.hasOwnProperty(_)&&T.use_external_data_format[_]===!0)){if(ce.apis.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const gr=`${_}${nr}.onnx_data`,rn=`${T.subfolder??""}/${gr}`;xr.push(new Promise(async(on,bn)=>{const pn=await(0,j.getModelFile)(f,rn,!0,T);on({path:gr,data:pn})}))}else pr.externalData!==void 0&&(xr=pr.externalData.map(async gr=>{if(typeof gr.data=="string"){const rn=await(0,j.getModelFile)(f,gr.data,!0,T);return{...gr,data:rn}}return gr}));if(xr.length>0&&(pr.externalData=await Promise.all(xr)),Oe==="webgpu"){const gr=(0,z.getKeyValueShapes)(T.config,{prefix:"present"});if(Object.keys(gr).length>0&&!(0,me.isONNXProxy)()){const rn={};for(const on in gr)rn[on]="gpu-buffer";pr.preferredOutputLocation=rn}}return{buffer:await _r,session_options:pr}}async function L(f,_,T){return Object.fromEntries(await Promise.all(Object.keys(_).map(async J=>{const{buffer:Fe,session_options:Oe}=await I(f,_[J],T),_t=await(0,me.createInferenceSession)(Fe,Oe);return[J,_t]})))}function k(f,_){const T=Object.create(null),J=[];for(const _t of f.inputNames){const St=_[_t];if(!(St instanceof P.Tensor)){J.push(_t);continue}T[_t]=(0,me.isONNXProxy)()?St.clone():St}if(J.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${J.join(", ")}.`);const Fe=Object.keys(_).length,Oe=f.inputNames.length;if(Fe>Oe){let _t=Object.keys(_).filter(St=>!f.inputNames.includes(St));console.warn(`WARNING: Too many inputs were provided (${Fe} > ${Oe}). The following inputs will be ignored: "${_t.join(", ")}".`)}return T}async function de(f,_){const T=k(f,_);try{const J=Object.fromEntries(Object.entries(T).map(([Oe,_t])=>[Oe,_t.ort_tensor]));let Fe=await f.run(J);return Fe=Me(Fe),Fe}catch(J){throw console.error(`An error occurred during model execution: "${J}".`),console.error("Inputs given to model:",T),J}}function Me(f){for(let _ in f)(0,me.isONNXTensor)(f[_])?f[_]=new P.Tensor(f[_]):typeof f[_]=="object"&&Me(f[_]);return f}function ke(f){if(f instanceof P.Tensor)return f;if(f.length===0)throw Error("items must be non-empty");if(Array.isArray(f[0])){if(f.some(_=>_.length!==f[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new P.Tensor("int64",BigInt64Array.from(f.flat().map(_=>BigInt(_))),[f.length,f[0].length])}else return new 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,Ee.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,Ee.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=we(Oe,Fe)),f.addPastKeyValues(Oe,Fe);const _t=(0,Ee.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 we(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 $e(f,..._){return f.config.is_encoder_decoder?fe(f,..._):W(f,..._)}class Y extends Te.Callable{constructor(T,J){super();xe(this,"main_input_name","input_ids");xe(this,"forward_params",["input_ids","attention_mask"]);this.config=T,this.sessions=J;const Fe=R.get(this.constructor),Oe=ie.get(Fe);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Oe){case re.DecoderOnly:this.can_generate=!0,this._forward=Ke,this._prepare_inputs_for_generation=W;break;case re.Seq2Seq:case re.Vision2Seq:case re.Musicgen:this.can_generate=!0,this._forward=Ie,this._prepare_inputs_for_generation=fe;break;case re.EncoderDecoder:this._forward=Ie;break;case re.ImageTextToText:this.can_generate=!0,this._forward=ut,this._prepare_inputs_for_generation=$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=R.get(this),Tr=ie.get(Fr);Fe=xr.config=await z.AutoConfig.from_pretrained(T,xr);let gr;if(Tr===re.DecoderOnly)gr=await Promise.all([L(T,{model:xr.model_file_name??"model"},xr),(0,j.getModelJSON)(T,"generation_config.json",!1,xr)]);else if(Tr===re.Seq2Seq||Tr===re.Vision2Seq)gr=await Promise.all([L(T,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},xr),(0,j.getModelJSON)(T,"generation_config.json",!1,xr)]);else if(Tr===re.MaskGeneration)gr=await Promise.all([L(T,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},xr)]);else if(Tr===re.EncoderDecoder)gr=await Promise.all([L(T,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},xr)]);else if(Tr===re.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,j.getModelJSON)(T,"generation_config.json",!1,xr)])}else Tr===re.Musicgen?gr=await Promise.all([L(T,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},xr),(0,j.getModelJSON)(T,"generation_config.json",!1,xr)]):(Tr!==re.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,Ee.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=R.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: ${[...Fe].join(", ")}`),Error(_t)}}prepare_inputs_for_generation(...T){return this._prepare_inputs_for_generation(this,...T)}_update_model_kwargs_for_generation({generated_input_ids:T,outputs:J,model_inputs:Fe,is_encoder_decoder:Oe}){return Fe.past_key_values=this.getPastKeyValues(J,Fe.past_key_values),Fe.input_ids=new P.Tensor("int64",T.flat(),[T.length,1]),Oe||(Fe.attention_mask=(0,P.cat)([Fe.attention_mask,(0,P.ones)([Fe.attention_mask.dims[0],1])],1)),Fe.position_ids=null,Fe}_prepare_model_inputs({inputs:T,bos_token_id:J,model_kwargs:Fe}){const Oe=(0,Ee.pick)(Fe,this.forward_params),_t=this.main_input_name;if(_t in Oe){if(T)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Oe[_t]=T;return{inputs_tensor:Oe[_t],model_inputs:Oe,model_input_name:_t}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:T,model_inputs:J,model_input_name:Fe,generation_config:Oe}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!J.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:St,pixel_values:Wt,attention_mask:nr,...kr}=J,pr=await this._prepare_inputs_embeds(J);J={...kr,...(0,Ee.pick)(pr,["inputs_embeds","attention_mask"])}}let{last_hidden_state:_t}=await Ze(this,J);if(Oe.guidance_scale!==null&&Oe.guidance_scale>1)_t=(0,P.cat)([_t,(0,P.full_like)(_t,0)],0),"attention_mask"in J&&(J.attention_mask=(0,P.cat)([J.attention_mask,(0,P.zeros_like)(J.attention_mask)],0));else if(J.decoder_input_ids){const St=ke(J.decoder_input_ids).dims[0];if(St!==_t.dims[0]){if(_t.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${_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 q 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 It extends Y{}class At extends It{}class cr extends It{async _call(_){return new an(await super._call(_))}}class Er extends It{async _call(_){return new ir(await super._call(_))}}class Qr extends It{async _call(_){return new sn(await super._call(_))}}class Or extends It{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 ve extends Y{}class Xe extends ve{}class gt extends ve{async _call(_){return new an(await super._call(_))}}class mt extends ve{async _call(_){return new ir(await super._call(_))}}class $t extends ve{async _call(_){return new sn(await super._call(_))}}class ft extends ve{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);xe(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 H extends Y{constructor(_,T,J){super(_,T),this.generation_config=J}}class se extends H{}class pe extends H{}class De extends H{async _call(_){return new ir(await super._call(_))}}class wt extends H{}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 $r extends Yt{}class ar extends Yt{}class Dr extends Y{}class Jr extends Dr{}class ur extends Dr{async _call(_){return new an(await super._call(_))}}class Sr extends Dr{async _call(_){return new ir(await super._call(_))}}class _n extends Dr{async _call(_){return new sn(await super._call(_))}}class wn extends Dr{async _call(_){return new cn(await super._call(_))}}class Se extends Y{}class Zr extends Se{}class xn extends Se{async _call(_){return new an(await super._call(_))}}class kn extends Se{async _call(_){return new ir(await super._call(_))}}class Nn extends Se{async _call(_){return new sn(await super._call(_))}}class Ht extends Se{async _call(_){return new cn(await super._call(_))}}class en extends Y{}class Xr extends en{}class yr extends en{async _call(_){return new an(await super._call(_))}}class Ar extends en{async _call(_){return new ir(await super._call(_))}}class Ct extends en{async _call(_){return new sn(await super._call(_))}}class mr extends en{async _call(_){return new cn(await super._call(_))}}class Hn extends Y{}class Kn extends Hn{}class Xn extends Hn{}class jt extends Y{constructor(T,J,Fe){super(T,J);xe(this,"requires_attention_mask",!1);xe(this,"main_input_name","input_features");xe(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Fe}}class Ys extends jt{}class rt extends jt{_prepare_generation_config(_,T){return super._prepare_generation_config(_,T,D.WhisperGenerationConfig)}_retrieve_init_tokens(_){const T=[_.decoder_start_token_id];let J=_.language;const Fe=_.task;if(_.is_multilingual){J||(console.warn("No language specified - defaulting to English (en)."),J="en");const _t=`<|${(0,K.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,Ee.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);xe(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);xe(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: ${_r.config.model_type}`)}}xe(Ir,"MODEL_CLASS_MAPPINGS",null),xe(Ir,"BASE_IF_FAIL",!1);const Od=new Map([["bert",["BertModel",ot]],["nomic_bert",["NomicBertModel",Pe]],["roformer",["RoFormerModel",Qe]],["electra",["ElectraModel",Ye]],["esm",["EsmModel",qt]],["convbert",["ConvBertModel",Et]],["camembert",["CamembertModel",At]],["deberta",["DebertaModel",at]],["deberta-v2",["DebertaV2Model",Xe]],["mpnet",["MPNetModel",Cs]],["albert",["AlbertModel",ps]],["distilbert",["DistilBertModel",Nt]],["roberta",["RobertaModel",Jr]],["xlm",["XLMModel",Zr]],["xlm-roberta",["XLMRobertaModel",Xr]],["clap",["ClapModel",pu]],["clip",["CLIPModel",Ei]],["clipseg",["CLIPSegModel",po]],["chinese_clip",["ChineseCLIPModel",co]],["siglip",["SiglipModel",oo]],["mobilebert",["MobileBertModel",Wr]],["squeezebert",["SqueezeBertModel",ks]],["wav2vec2",["Wav2Vec2Model",Nl]],["wav2vec2-bert",["Wav2Vec2BertModel",hi]],["unispeech",["UniSpeechModel",$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]]]),$u=new Map([["vits",["VitsModel",Sa]],["musicgen",["MusicgenForConditionalGeneration",Da]]]),Su=new Map([["bert",["BertForSequenceClassification",ht]],["roformer",["RoFormerForSequenceClassification",je]],["electra",["ElectraForSequenceClassification",Bt]],["esm",["EsmForSequenceClassification",Br]],["convbert",["ConvBertForSequenceClassification",q]],["camembert",["CamembertForSequenceClassification",Er]],["deberta",["DebertaForSequenceClassification",ge]],["deberta-v2",["DebertaV2ForSequenceClassification",mt]],["mpnet",["MPNetForSequenceClassification",Es]],["albert",["AlbertForSequenceClassification",ts]],["distilbert",["DistilBertForSequenceClassification",Rt]],["roberta",["RobertaForSequenceClassification",Sr]],["xlm",["XLMForSequenceClassification",kn]],["xlm-roberta",["XLMRobertaForSequenceClassification",Ar]],["bart",["BartForSequenceClassification",A]],["mbart",["MBartForSequenceClassification",De]],["mobilebert",["MobileBertForSequenceClassification",Ln]],["squeezebert",["SqueezeBertForSequenceClassification",qn]]]),Ld=new Map([["bert",["BertForTokenClassification",bt]],["roformer",["RoFormerForTokenClassification",lt]],["electra",["ElectraForTokenClassification",ct]],["esm",["EsmForTokenClassification",Kr]],["convbert",["ConvBertForTokenClassification",$]],["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",$l]]]),Vu=new Map([["clip",["CLIPVisionModelWithProjection",Pn]],["siglip",["SiglipVisionModel",lo]]]),Ha=[[Od,re.EncoderOnly],[Dd,re.EncoderDecoder],[yc,re.DecoderOnly],[Su,re.EncoderOnly],[Ld,re.EncoderOnly],[ja,re.Seq2Seq],[Na,re.Seq2Seq],[Va,re.DecoderOnly],[ku,re.EncoderOnly],[Pu,re.EncoderOnly],[Au,re.Vision2Seq],[yn,re.ImageTextToText],[Iu,re.EncoderOnly],[Bs,re.EncoderOnly],[Ou,re.EncoderOnly],[zu,re.EncoderOnly],[Ru,re.EncoderOnly],[Nu,re.EncoderOnly],[qa,re.EncoderOnly],[ju,re.EncoderOnly],[Ua,re.EncoderOnly],[Fu,re.EncoderOnly],[Wa,re.MaskGeneration],[Du,re.EncoderOnly],[Lu,re.EncoderOnly],[Eu,re.Seq2Seq],[$u,re.EncoderOnly],[Ga,re.EncoderOnly],[Bu,re.EncoderOnly],[Vu,re.EncoderOnly]];for(const[f,_]of Ha)for(const[T,J]of f.values())ie.set(T,_),R.set(J,T),X.set(T,J);const Rd=[["MusicgenForConditionalGeneration",Da,re.Musicgen],["CLIPTextModelWithProjection",ao,re.EncoderOnly],["SiglipTextModel",is,re.EncoderOnly],["ClapTextModelWithProjection",hu,re.EncoderOnly],["ClapAudioModelWithProjection",fu,re.EncoderOnly]];for(const[f,_,T]of Rd)ie.set(f,T),R.set(_,f),X.set(f,_);class Uu extends Ir{}xe(Uu,"MODEL_CLASS_MAPPINGS",Ha.map(_=>_[0])),xe(Uu,"BASE_IF_FAIL",!0);class Ka extends Ir{}xe(Ka,"MODEL_CLASS_MAPPINGS",[Su]);class Wu extends Ir{}xe(Wu,"MODEL_CLASS_MAPPINGS",[Ld]);class Gu extends Ir{}xe(Gu,"MODEL_CLASS_MAPPINGS",[ja]);class qu extends Ir{}xe(qu,"MODEL_CLASS_MAPPINGS",[Na]);class Nd extends Ir{}xe(Nd,"MODEL_CLASS_MAPPINGS",[Eu]);class Hu extends Ir{}xe(Hu,"MODEL_CLASS_MAPPINGS",[$u]);class Ku extends Ir{}xe(Ku,"MODEL_CLASS_MAPPINGS",[Va]);class Xu extends Ir{}xe(Xu,"MODEL_CLASS_MAPPINGS",[ku]);class jd extends Ir{}xe(jd,"MODEL_CLASS_MAPPINGS",[Pu]);class Qu extends Ir{}xe(Qu,"MODEL_CLASS_MAPPINGS",[Au]);class Yu extends Ir{}xe(Yu,"MODEL_CLASS_MAPPINGS",[Iu]);class Ju extends Ir{}xe(Ju,"MODEL_CLASS_MAPPINGS",[Bs]);class Zu extends Ir{}xe(Zu,"MODEL_CLASS_MAPPINGS",[zu]);class Vd extends Ir{}xe(Vd,"MODEL_CLASS_MAPPINGS",[Ou]);class ed extends Ir{}xe(ed,"MODEL_CLASS_MAPPINGS",[Ua]);class td extends Ir{}xe(td,"MODEL_CLASS_MAPPINGS",[Fu]);class rd extends Ir{}xe(rd,"MODEL_CLASS_MAPPINGS",[Wa]);class nd extends Ir{}xe(nd,"MODEL_CLASS_MAPPINGS",[Du]);class sd extends Ir{}xe(sd,"MODEL_CLASS_MAPPINGS",[Lu]);class id extends Ir{}xe(id,"MODEL_CLASS_MAPPINGS",[Ga]);class ad extends Ir{}xe(ad,"MODEL_CLASS_MAPPINGS",[Bu]);class od extends Ir{}xe(od,"MODEL_CLASS_MAPPINGS",[Bd]);class ld extends Ir{}xe(ld,"MODEL_CLASS_MAPPINGS",[Ru]);class Ud extends Ir{}xe(Ud,"MODEL_CLASS_MAPPINGS",[Nu]);class Rs extends Ir{}xe(Rs,"MODEL_CLASS_MAPPINGS",[qa]);class Xa extends Ir{}xe(Xa,"MODEL_CLASS_MAPPINGS",[ju]);class Qa extends Ir{}xe(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":(Pt,Ce,N)=>{N.r(Ce),N.d(Ce,{WHISPER_LANGUAGE_MAPPING:()=>me,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>ye,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),ye=new Map([...z.map(([Ee,j])=>[j,Ee]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function Te(Ee){Ee=Ee.toLowerCase();let j=ye.get(Ee);if(j===void 0)if(me.has(Ee))j=Ee;else{const V=Ee.length===2?me.keys():me.values();throw new Error(`Language "${Ee}" is not supported. Must be one of: ${JSON.stringify(V)}`)}return j}},"./src/models/whisper/generation_whisper.js":(Pt,Ce,N)=>{N.r(Ce),N.d(Ce,{WhisperGenerationConfig:()=>me});var z=N("./src/generation/configuration_utils.js");class me extends z.GenerationConfig{constructor(){super(...arguments);xe(this,"return_timestamps",null);xe(this,"return_token_timestamps",null);xe(this,"num_frames",null);xe(this,"alignment_heads",null);xe(this,"task",null);xe(this,"language",null);xe(this,"no_timestamps_token_id",null);xe(this,"prompt_ids",null);xe(this,"is_multilingual",null);xe(this,"lang_to_id",null);xe(this,"task_to_id",null);xe(this,"max_initial_timestamp_index",1)}}},"./src/ops/registry.js":(Pt,Ce,N)=>{N.r(Ce),N.d(Ce,{TensorOpRegistry:()=>Te});var z=N("./src/backends/onnx.js"),me=N("./src/utils/tensor.js");const ye=async(Ee,j,S)=>{const V=await(0,z.createInferenceSession)(new Uint8Array(Ee),j);return async P=>{const te=Object.fromEntries(Object.entries(P).map(([_e,ce])=>[_e,ce.ort_tensor])),ne=await V.run(te);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=ye([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=ye([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=ye([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=ye([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=ye([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=ye([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}}xe(Te,"session_options",{})},"./src/pipelines.js":(Pt,Ce,N)=>{N.r(Ce),N.d(Ce,{AudioClassificationPipeline:()=>ze,AutomaticSpeechRecognitionPipeline:()=>Ze,DepthEstimationPipeline:()=>nt,DocumentQuestionAnsweringPipeline:()=>Y,FeatureExtractionPipeline:()=>Me,FillMaskPipeline:()=>ie,ImageClassificationPipeline:()=>ut,ImageFeatureExtractionPipeline:()=>ke,ImageSegmentationPipeline:()=>we,ImageToImagePipeline:()=>dt,ImageToTextPipeline:()=>Ke,ObjectDetectionPipeline:()=>fe,Pipeline:()=>ce,QuestionAnsweringPipeline:()=>re,SummarizationPipeline:()=>R,Text2TextGenerationPipeline:()=>X,TextClassificationPipeline:()=>D,TextGenerationPipeline:()=>k,TextToAudioPipeline:()=>He,TokenClassificationPipeline:()=>K,TranslationPipeline:()=>I,ZeroShotAudioClassificationPipeline:()=>Ie,ZeroShotClassificationPipeline:()=>de,ZeroShotImageClassificationPipeline:()=>W,ZeroShotObjectDetectionPipeline:()=>$e,pipeline:()=>ht});var z=N("./src/tokenizers.js"),me=N("./src/models.js"),ye=N("./src/processors.js"),Te=N("./src/utils/generic.js"),Ee=N("./src/utils/core.js"),j=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 te(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,j.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 K 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>$&&vt.findIndex(It=>It==q[Tt])===-1));const Q=Ue[x].tolist(),he=je[x].tolist();for(let ct=1;ctTt==q[ct])!==-1)&&(Q[ct]=-1/0,he[ct]=-1/0);const Ye=(0,j.softmax)(Q).map((ct,Tt)=>[ct,Tt]),Je=(0,j.softmax)(he).map((ct,Tt)=>[ct,Tt]);Ye[0][0]=0,Je[0][0]=0;const Bt=(0,Ee.product)(Ye,Je).filter(ct=>ct[0][1]<=ct[1][1]).map(ct=>[ct[0][1],ct[1][1],ct[0][0]*ct[1][0]]).sort((ct,Tt)=>Tt[2]-ct[2]);for(let ct=0;ctQ==this.tokenizer.mask_token_id);if(vt===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const Et=Qe[lt][vt],x=await(0,V.topk)(new V.Tensor("float32",(0,j.softmax)(Et.data),Et.dims),Pe),q=x[0].tolist(),$=x[1].tolist();Ue.push($.map((Q,he)=>{const Ye=yt.slice();return Ye[vt]=Q,{score:q[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);xe(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 R extends X{constructor(Pe){super(Pe);xe(this,"_key","summary_text")}}class I extends X{constructor(Pe){super(Pe);xe(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($,{tokenize:!1,add_generation_prompt:!0}))}const je=Pe.add_special_tokens??!1,lt=Qe?!1:Pe.return_full_text??!0;this.tokenizer.padding_side="left";const yt=this.tokenizer(Ue,{add_special_tokens:je,padding:!0,truncation:!0}),vt=await this.model.generate({...yt,...Pe}),Et=this.tokenizer.batch_decode(vt,{skip_special_tokens:!0});let x;!lt&&yt.input_ids.dims.at(-1)>0&&(x=this.tokenizer.batch_decode(yt.input_ids,{skip_special_tokens:!0}).map($=>$.length));const q=Array.from({length:ee.length},$=>[]);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:$,padding:!0,truncation:!0}),he=await this.model(Q);lt?Et.push([he.logits.data[this.contradiction_id],he.logits.data[this.entailment_id]]):Et.push(he.logits.data[this.entailment_id])}const q=(lt?Et.map($=>(0,j.softmax)($)[1]):(0,j.softmax)(Et)).map(($,Q)=>[$,Q]).sort(($,Q)=>Q[0]-$[0]);yt.push({sequence:vt,labels:q.map($=>Pe[$[1]]),scores:q.map($=>$[0])})}return Ue?yt:yt[0]}}class Me extends ce{constructor(ee){super(ee)}async _call(ee,{pooling:Pe="none",normalize:Ne=!1,quantize:Qe=!1,precision:Ue="binary"}={}){const je=this.tokenizer(ee,{padding:!0,truncation:!0}),lt=await this.model(je);let yt=lt.last_hidden_state??lt.logits??lt.token_embeddings;if(Pe!=="none")if(Pe==="mean")yt=(0,V.mean_pooling)(yt,je.attention_mask);else if(Pe==="cls")yt=yt.slice(null,0);else throw Error(`Pooling method '${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 te(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,j.softmax)(Et.data),Et.dims),Pe),q=x[0].tolist(),Q=x[1].tolist().map((he,Ye)=>({label:Ue?Ue[he]:`LABEL_${he}`,score:q[Ye]}));je.push(Q)}return Array.isArray(ee)?je:je[0]}}class Ie extends ce{constructor(ee){super(ee)}async _call(ee,Pe,{hypothesis_template:Ne="This is a sound of {}."}={}){const Qe=!Array.isArray(ee);Qe&&(ee=[ee]);const Ue=Pe.map(Et=>Ne.replace("{}",Et)),je=this.tokenizer(Ue,{padding:!0,truncation:!0}),lt=this.processor.feature_extractor.config.sampling_rate,yt=await ne(ee,lt),vt=[];for(const Et of yt){const x=await this.processor(Et),q=await this.model({...je,...x}),$=(0,j.softmax)(q.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,j.max)($.data)[1]);const q=this.tokenizer.decode(x);je.push({text:q})}return Ne?je[0]:je}async _call_whisper(ee,Pe){const Ne=Pe.return_timestamps??!1,Qe=Pe.chunk_length_s??0,Ue=Pe.force_full_sequences??!1;let je=Pe.stride_length_s??null;const lt={...Pe};Ne==="word"&&(lt.return_token_timestamps=!0,lt.return_timestamps=!1);const yt=!Array.isArray(ee);yt&&(ee=[ee]);const vt=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,Et=this.processor.feature_extractor.config.hop_length,x=this.processor.feature_extractor.config.sampling_rate,q=await ne(ee,x),$=[];for(const Q of q){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 It=0;for(;;){const At=It+Bt,cr=Q.subarray(It,At),Er=await this.processor(cr),Qr=It===0,Or=At>=Q.length;if(he.push({stride:[cr.length,Qr?0:ct,Or?0:ct],input_features:Er.input_features,is_last:Or}),Or)break;It+=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,j.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]:$}}class Ke extends ce{constructor(ee){super(ee)}async _call(ee,Pe={}){const Ne=Array.isArray(ee),Qe=await te(ee),{pixel_values:Ue}=await this.processor(Qe),je=[];for(const lt of Ue){lt.dims=[1,...lt.dims];const yt=await this.model.generate({inputs:lt,...Pe}),vt=this.tokenizer.batch_decode(yt,{skip_special_tokens:!0}).map(Et=>({generated_text:Et.trim()}));je.push(vt)}return Ne?je:je[0]}}class ut extends ce{constructor(ee){super(ee)}async _call(ee,{top_k:Pe=5}={}){const Ne=await te(ee),{pixel_values:Qe}=await this.processor(Ne),Ue=await this.model({pixel_values:Qe}),je=this.model.config.id2label,lt=[];for(const yt of Ue.logits){const vt=await(0,V.topk)(new V.Tensor("float32",(0,j.softmax)(yt.data),yt.dims),Pe),Et=vt[0].tolist(),q=vt[1].tolist().map(($,Q)=>({label:je?je[$]:`LABEL_${$}`,score:Et[Q]}));lt.push(q)}return Array.isArray(ee)?lt:lt[0]}}class we extends ce{constructor(ee){super(ee),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(ee,{threshold:Pe=.5,mask_threshold:Ne=.5,overlap_mask_area_threshold:Qe=.8,label_ids_to_fuse:Ue=null,target_sizes:je=null,subtask:lt=null}={}){if(Array.isArray(ee)&&ee.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const vt=await te(ee),Et=vt.map(Je=>[Je.height,Je.width]),{pixel_values:x,pixel_mask:q}=await this.processor(vt),$=await this.model({pixel_values:x,pixel_mask:q});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($,Pe,Ne,Qe,Ue,je??Et)[0],Bt=Je.segmentation;for(const ct of Je.segments_info){const Tt=new Uint8ClampedArray(Bt.data.length);for(let At=0;AtNe.replace("{}",q)),lt=this.tokenizer(je,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:yt}=await this.processor(Ue),vt=await this.model({...lt,pixel_values:yt}),Et=this.model.config.model_type==="siglip"?q=>q.sigmoid().data:q=>(0,j.softmax)(q.data),x=[];for(const q of vt.logits_per_image){const Q=[...Et(q)].map((he,Ye)=>({score:he,label:Pe[Ye]}));Q.sort((he,Ye)=>Ye.score-he.score),x.push(Q)}return Qe?x:x[0]}}class fe extends ce{constructor(ee){super(ee)}async _call(ee,{threshold:Pe=.9,percentage:Ne=!1}={}){const Qe=Array.isArray(ee);if(Qe&&ee.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ue=await te(ee),je=Ne?null:Ue.map($=>[$.height,$.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,q=Et.map($=>$.boxes.map((Q,he)=>({score:$.scores[he],label:x[$.classes[he]],box:_e(Q,!Ne)})));return Qe?q:q[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 te(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);xe(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 te(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 te(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:K,model:me.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:z.AutoTokenizer,pipeline:re,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:R,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:ye.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:z.AutoTokenizer,pipeline:Ie,model:me.AutoModel,processor:ye.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:z.AutoTokenizer,pipeline:Ze,model:[me.AutoModelForSpeechSeq2Seq,me.AutoModelForCTC],processor:ye.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:z.AutoTokenizer,pipeline:He,model:[me.AutoModelForTextToWaveform,me.AutoModelForTextToSpectrogram],processor:[ye.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:z.AutoTokenizer,pipeline:Ke,model:me.AutoModelForVision2Seq,processor:ye.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:ut,model:me.AutoModelForImageClassification,processor:ye.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:we,model:[me.AutoModelForImageSegmentation,me.AutoModelForSemanticSegmentation,me.AutoModelForUniversalSegmentation],processor:ye.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:z.AutoTokenizer,pipeline:W,model:me.AutoModel,processor:ye.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:fe,model:me.AutoModelForObjectDetection,processor:ye.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:z.AutoTokenizer,pipeline:$e,model:me.AutoModelForZeroShotObjectDetection,processor:ye.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:z.AutoTokenizer,pipeline:Y,model:me.AutoModelForDocumentQuestionAnswering,processor:ye.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:dt,model:me.AutoModelForImageToImage,processor:ye.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:nt,model:me.AutoModelForDepthEstimation,processor:ye.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:z.AutoTokenizer,pipeline:Me,model:me.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:ye.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: ${qe}. Must be one of [${Object.keys(ot)}]`);ee||(ee=x.default.model,console.log(`No model specified. Using default model: "${ee}".`));const q={progress_callback:Pe,config:Ne,cache_dir:Qe,local_files_only:Ue,revision:je,device:lt,dtype:yt,model_file_name:vt,session_options:Et},$=new Map([["tokenizer",x.tokenizer],["model",x.model],["processor",x.processor]]),Q=await bt($,ee,q);Q.task=qe,(0,Ee.dispatchCallback)(Pe,{status:"ready",task:qe,model:ee});const he=x.pipeline;return new he(Q)}async function bt(qe,ee,Pe){const Ne=Object.create(null),Qe=[];for(const[Ue,je]of qe.entries()){if(!je)continue;let lt;Array.isArray(je)?lt=new Promise(async(yt,vt)=>{var x,q;let Et;for(const $ of je){if($===null){yt(null);return}try{yt(await $.from_pretrained(ee,Pe));return}catch(Q){if((x=Q.message)!=null&&x.includes("Unsupported model type"))Et=Q;else if((q=Q.message)!=null&&q.includes("Could not locate file"))Et=Q;else{vt(Q);return}}}vt(Et)}):lt=je.from_pretrained(ee,Pe),Ne[Ue]=lt,Qe.push(lt)}await Promise.all(Qe);for(const[Ue,je]of Object.entries(Ne))Ne[Ue]=await je;return Ne}},"./src/processors.js":(Pt,Ce,N)=>{N.r(Ce),N.d(Ce,{ASTFeatureExtractor:()=>Q,AutoProcessor:()=>Ur,BeitFeatureExtractor:()=>Pe,BitImageProcessor:()=>Me,CLIPFeatureExtractor:()=>ze,CLIPImageProcessor:()=>Ie,ChineseCLIPFeatureExtractor:()=>Ze,ClapFeatureExtractor:()=>he,ConvNextFeatureExtractor:()=>ut,ConvNextImageProcessor:()=>we,DPTFeatureExtractor:()=>k,DPTImageProcessor:()=>de,DeiTFeatureExtractor:()=>ee,DetrFeatureExtractor:()=>Ue,DonutFeatureExtractor:()=>Ne,EfficientNetImageProcessor:()=>$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:()=>R,SeamlessM4TFeatureExtractor:()=>$,SegformerFeatureExtractor:()=>I,SiglipImageProcessor:()=>Ke,SpeechT5FeatureExtractor:()=>Bt,SpeechT5Processor:()=>Er,Swin2SRImageProcessor:()=>vt,ViTFeatureExtractor:()=>W,ViTImageProcessor:()=>fe,VitMatteImageProcessor:()=>Et,Wav2Vec2FeatureExtractor:()=>q,Wav2Vec2ProcessorWithLM:()=>At,WeSpeakerFeatureExtractor:()=>Je,WhisperFeatureExtractor:()=>x,WhisperProcessor:()=>It,YolosFeatureExtractor:()=>lt});var z=N("./src/utils/generic.js"),me=N("./src/utils/core.js"),ye=N("./src/utils/hub.js"),Te=N("./src/utils/maths.js"),Ee=N("./src/utils/tensor.js");N("./src/utils/image.js");var j=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,ve=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 $t}function P(at,U=null){const ge=at.logits,Ae=ge.dims[0];if(U!==null&&U.length!==Ae)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const et=[];for(let ve=0;vezt[st]&&(zt[st]=tt[st],Nt[st]=be)}const Rt=new Array(gt.dims[0]);for(let be=0;bebe!==void 0);et.push({segmentation:ft,labels:Gt})}return et}function te(at,U,ge,Ae){const et=[],ve=[],Xe=[];for(let gt=0;gtge&&(et.push($t),ve.push(Nt),Xe.push(ft))}return[et,ve,Xe]}function ne(at,U,ge,Ae=.5,et=.8){const ve=[];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),[$t,ve]}function _e(at,U,ge,Ae,et,ve=null,Xe=null){const[gt,mt]=Xe??at[0].dims,$t=new Ee.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=$t.data;for(let be=0;beAe&&(ve=Math.floor(et)*U),veve?$t=Math.floor(ve*mt/et):ve>et&&(mt=Math.floor(et*$t/ve)),await U.resize($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(ve)));return{pixel_values:(0,Ee.stack)(Ae.map(ve=>ve.pixel_values),0),original_sizes:Ae.map(ve=>ve.original_size),reshaped_input_sizes:Ae.map(ve=>ve.reshaped_input_size)}}}class R 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 Me 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),[ve,Xe]=this.get_resize_output_image_size(U,{shortest_edge:et});U=await U.resize(ve,Xe,{resample:this.resample}),U=await U.center_crop(ge,ge)}else U=await U.resize(ge,ge,{resample:this.resample});return U}}class we 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[ve,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,Ee.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 ve=(0,me.calculateDimensions)(U);if(ve.length===3)et||(ve=[1,...ve]),U=[U];else if(ve.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[ve]))throw Error(`The first ${Ae.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new Ee.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 ve=await super._call(U);if(ge&&(ve.input_points=this.reshape_input_points(ge,ve.original_sizes,ve.reshaped_input_sizes)),Ae){if(!ve.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");ve.input_labels=this.add_input_labels(Ae,ve.input_points)}return et&&(ve.input_boxes=this.reshape_input_points(et,ve.original_sizes,ve.reshaped_input_sizes,!0)),ve}async post_process_masks(U,ge,Ae,{mask_threshold:et=0,binarize:ve=!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 Ee.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:ve=32,crop_n_points_downscale_factor:Xe=1}={}){}}class vt extends X{pad_image(U,ge,Ae,et={}){const[ve,Xe,gt]=ge;return super.pad_image(U,ge,{width:Xe+(Ae-Xe%Ae)%Ae,height:ve+(Ae-ve%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,Ee.stack)(Ae.map((Xe,gt)=>(0,Ee.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,j.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,j.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(U){const ge=await(0,j.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 ve=0;vethis.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 q extends ie{_zero_mean_unit_var_norm(U){const Ae=U.reduce((ve,Xe)=>ve+Xe,0)/U.length,et=U.reduce((ve,Xe)=>ve+(Xe-Ae)**2,0)/U.length;return U.map(ve=>(ve-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 Ee.Tensor("float32",ge,Ae),attention_mask:new Ee.Tensor("int64",new BigInt64Array(ge.length).fill(1n),Ae)}}}class $ extends ie{constructor(U){super(U);const ge=this.config.sampling_rate,Ae=(0,j.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,j.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:ve=!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 Ee.Tensor(Xe.type,qt,[wr,be]),ve&&(gt=new Ee.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(ve){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),ve=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-$t)})))}return et}}class Je extends ie{constructor(U){super(U);const ge=this.config.sampling_rate,Ae=(0,j.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ge/2),ge,null,"kaldi",!0);for(let et=0;etge*32768),(0,j.spectrogram)(U,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(U){D(U,"WeSpeakerFeatureExtractor");const ge=(await this._extract_fbank_features(U)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const Ae=ge.mean(1).data,et=ge.data,[ve,Xe,gt]=ge.dims;for(let mt=0;mt/gm,bboxes:/([^<]+)?/gm},this.size_per_bin=1e3}construct_prompts(U){typeof U=="string"&&(U=[U]);const ge=[];for(const Ae of U)if(this.task_prompts_without_inputs.has(Ae))ge.push(this.task_prompts_without_inputs.get(Ae));else{for(const[et,ve]of this.task_prompts_with_input)if(Ae.includes(et)){ge.push(ve.replaceAll("{input}",Ae).replaceAll(et,""));break}ge.length!==U.length&&ge.push(Ae)}return ge}post_process_generation(U,ge,Ae){const et=this.tasks_answer_post_processing_type.get(ge)??"pure_text";U=U.replaceAll("","").replaceAll("","");let ve;switch(et){case"pure_text":ve=U;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const Xe=et==="ocr"?"quad_boxes":"bboxes",gt=U.matchAll(this.regexes[Xe]),mt=[],$t=[];for(const[ft,zt,...Nt]of gt)mt.push(zt?zt.trim():mt.at(-1)??""),$t.push(Nt.map((Rt,Gt)=>(Number(Rt)+.5)/this.size_per_bin*Ae[Gt%2]));ve={labels:mt,[Xe]:$t};break;default:throw new Error(`Task "${ge}" (of type "${et}") not yet implemented.`)}return{[ge]:ve}}}class Ur{static async from_pretrained(U,{progress_callback:ge=null,config:Ae=null,cache_dir:et=null,local_files_only:ve=!1,revision:Xe="main"}={}){let gt=Ae??await(0,ye.getModelJSON)(U,"preprocessor_config.json",!0,{progress_callback:ge,config:Ae,cache_dir:et,local_files_only:ve,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 $t(gt);return new ft(zt)}}xe(Ur,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:X,WhisperFeatureExtractor:x,ViTFeatureExtractor:W,MobileViTFeatureExtractor:ot,MobileViTImageProcessor:Re,MobileNetV1FeatureExtractor:Y,MobileNetV2FeatureExtractor:He,MobileNetV3FeatureExtractor:dt,MobileNetV4FeatureExtractor:nt,OwlViTFeatureExtractor:ht,Owlv2ImageProcessor:bt,CLIPFeatureExtractor:ze,CLIPImageProcessor:Ie,Florence2Processor:Or,ChineseCLIPFeatureExtractor:Ze,SiglipImageProcessor:Ke,ConvNextFeatureExtractor:ut,ConvNextImageProcessor:we,SegformerFeatureExtractor:I,SapiensFeatureExtractor:R,BitImageProcessor:Me,DPTImageProcessor:de,DPTFeatureExtractor:k,PvtImageProcessor:L,GLPNFeatureExtractor:ke,BeitFeatureExtractor:Pe,DeiTFeatureExtractor:ee,DetrFeatureExtractor:Ue,RTDetrImageProcessor:qe,MaskFormerFeatureExtractor:je,YolosFeatureExtractor:lt,DonutFeatureExtractor:Ne,NougatImageProcessor:Qe,EfficientNetImageProcessor:$e,ViTImageProcessor:fe,VitMatteImageProcessor:Et,SamImageProcessor:yt,Swin2SRImageProcessor:vt,Wav2Vec2FeatureExtractor:q,SeamlessM4TFeatureExtractor:$,SpeechT5FeatureExtractor:Bt,ASTFeatureExtractor:Q,ClapFeatureExtractor:he,PyAnnoteFeatureExtractor:Ye,WeSpeakerFeatureExtractor:Je}),xe(Ur,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:It,Wav2Vec2ProcessorWithLM:At,PyAnnoteProcessor:cr,SamProcessor:Tt,SpeechT5Processor:Er,OwlViTProcessor:Qr,Florence2Processor:Or})},"./src/tokenizers.js":(Pt,Ce,N)=>{N.r(Ce),N.d(Ce,{AlbertTokenizer:()=>gt,AutoTokenizer:()=>ns,BartTokenizer:()=>Kr,BertTokenizer:()=>Xe,BlenderbotSmallTokenizer:()=>Ps,BlenderbotTokenizer:()=>_s,BloomTokenizer:()=>Ln,CLIPTokenizer:()=>fs,CamembertTokenizer:()=>tt,CodeGenTokenizer:()=>hs,CodeLlamaTokenizer:()=>Cs,CohereTokenizer:()=>ws,ConvBertTokenizer:()=>Rt,DebertaTokenizer:()=>ft,DebertaV2Tokenizer:()=>zt,DistilBertTokenizer:()=>be,ElectraTokenizer:()=>qt,EsmTokenizer:()=>Gn,FalconTokenizer:()=>$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:()=>ve,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"),ye=N("./src/utils/hub.js"),Te=N("./src/utils/maths.js"),Ee=N("./src/utils/tensor.js"),j=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 te(ue,m){const A=await Promise.all([(0,ye.getModelJSON)(ue,"tokenizer.json",!0,m),(0,ye.getModelJSON)(ue,"tokenizer_config.json",!0,m)]);return m.legacy!==null&&(A[1].legacy=m.legacy),A}function ne(ue,m){const A=[];let H=0;for(const se of ue.matchAll(m)){const pe=se[0];H0&&A.push(pe),H=se.index+pe.length}return H=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 R(ue,m,A){const H=[];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,H]of this.tokens_to_ids)this.vocab[H]=A}encode(m){const A=[];for(const H of m){const se=[...H];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 H=m.vocab.length;this.vocab=new Array(H),this.scores=new Array(H);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 j.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(m){const A=m.chars,H=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 H=m.map(se=>String.fromCharCode(se));return Object.fromEntries(ue.map((se,pe)=>[se,H[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[H,se]of this.tokens_to_ids)this.vocab[se]=H;const A=Array.isArray(m.merges[0]);this.merges=A?m.merges:m.merges.map(H=>H.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((H,se)=>[JSON.stringify(H),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 H=Array.from(m);this.end_of_word_suffix&&(H[H.length-1]+=this.end_of_word_suffix);let se=[];if(H.length>1){const pe=new j.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 we 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[H,se]of this.tokens_to_ids)this.vocab[se]=H}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 $e extends W{normalize(m){return m=m.normalize("NFC"),m}}class Y extends W{normalize(m){return m=m.normalize("NFKC"),m}}class He extends W{normalize(m){return m=m.normalize("NFKD"),m}}class dt extends W{normalize(m){return this.config.strip_left&&this.config.strip_right?m=m.trim():(this.config.strip_left&&(m=m.trimStart()),this.config.strip_right&&(m=m.trimEnd())),m}}class nt extends W{normalize(m){return m=re(m),m}}class ot extends W{normalize(m){return m=m.toLowerCase(),m}}class Re extends W{normalize(m){return m=this.config.prepend+m,m}}class ht extends W{constructor(m){super(m),this.normalizers=m.normalizers.map(A=>W.fromConfig(A))}normalize(m){return this.normalizers.reduce((A,H)=>H.normalize(A),m)}}class bt extends W{_tokenize_chinese_chars(m){const A=[];for(let H=0;Hthis.pre_tokenize_text(H,A)):this.pre_tokenize_text(m,A)).flat()}_call(m,A){return this.pre_tokenize(m,A)}}class ee extends qe{constructor(m){super(),this.pattern=new RegExp(`[^\\s${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:H=!0}={}){H&&(m=(0,me.mergeArrays)([this.cls],m,[this.sep]));let se=new Array(m.length).fill(0);if(A!==null){const pe=H&&this instanceof yt?[this.sep]:[],De=H?[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:H=!0}={}){const se=A===null?this.single:this.pair;let pe=[],De=[];for(const wt of se)"SpecialToken"in wt?H&&(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,H={}){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,H);m=De.tokens,se=De.token_type_ids}return{tokens:m,token_type_ids:se}}}class q 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 It(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 q{decode_chain(m){const A=_e(this.config.pattern);return A===null?m:m.map(H=>H.replaceAll(A,this.config.content))}}class Q extends q{constructor(m){super(m),this.text_decoder=new TextDecoder}decode_chain(m){const A=[];let H=[];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)H.push(pe);else{if(H.length>0){const De=this.text_decoder.decode(Uint8Array.from(H));A.push(De),H=[]}A.push(se)}}if(H.length>0){const se=this.text_decoder.decode(Uint8Array.from(H));A.push(se),H=[]}return A}}class he extends q{decode_chain(m){return[m.join("")]}}class Ye extends q{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 H=0;for(let pe=0;pe(H!==0&&(A.startsWith(this.config.prefix)?A=A.replace(this.config.prefix,""):A=" "+A),this.cleanup&&(A=K(A)),A))}}class Bt extends q{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(""),H=new Uint8Array([...A].map(pe=>this.byte_decoder[pe]));return this.text_decoder.decode(H)}decode_chain(m){const A=[];let H=[];for(const se of m)this.added_tokens.find(pe=>pe.content===se)!==void 0?(H.length>0&&(A.push(this.convert_tokens_to_string(H)),H=[]),A.push(se)):H.push(se);return H.length>0&&A.push(this.convert_tokens_to_string(H)),A}}class ct extends q{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=K(se).replaceAll(this.word_delimiter_token," ").trim()),se}decode_chain(m){return[this.convert_tokens_to_string(m)]}}class Tt extends q{constructor(m){super(m),this.decoders=m.decoders.map(A=>q.fromConfig(A))}decode_chain(m){return this.decoders.reduce((A,H)=>H.decode_chain(A),m)}}class It extends q{constructor(m){super(m),this.suffix=this.config.suffix}decode_chain(m){return m.map((A,H)=>A.replaceAll(this.suffix,H===m.length-1?"":" "))}}class At extends q{decode_chain(m){let A="";for(let H=1;HH.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((H,se)=>se.pre_tokenize(H,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,H){for(const se of Object.keys(ue)){const pe=m-ue[se].length,De=A(se),wt=new Array(pe).fill(De);ue[se]=H==="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 ve extends z.Callable{constructor(A,H){super();xe(this,"return_token_type_ids",!1);xe(this,"padding_side","right");this._tokenizer_config=H,this.normalizer=W.fromConfig(A.normalizer),this.pre_tokenizer=qe.fromConfig(A.pre_tokenizer),this.model=ke.fromConfig(A.model,H),this.post_processor=je.fromConfig(A.post_processor),this.decoder=q.fromConfig(A.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const se of A.added_tokens){const pe=new Me(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=H.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=H.model_max_length,this.remove_space=H.remove_space,this.clean_up_tokenization_spaces=H.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=H.do_lowercase_and_remove_accent??!1,H.padding_side&&(this.padding_side=H.padding_side),this.legacy=!1,this.chat_template=H.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 H of A){const se=this._tokenizer_config[H];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:H=null,config:se=null,cache_dir:pe=null,local_files_only:De=!1,revision:wt="main",legacy:xt=null}={}){const Mt=await te(A,{progress_callback:H,config:se,cache_dir:pe,local_files_only:De,revision:wt,legacy:xt});return new this(...Mt)}_call(A,{text_pair:H=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(H!==null){if(Array.isArray(H)){if(A.length!==H.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:H[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(H))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:H,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 Ee.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:H=null,add_special_tokens:se=!0,return_token_type_ids:pe=null}={}){const{tokens:De,token_type_ids:wt}=this._tokenize_helper(A,{pair:H,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:H=null,add_special_tokens:se=!1}={}){const pe=this._encode_text(A),De=this._encode_text(H);return this.post_processor?this.post_processor(pe,De,{add_special_tokens:se}):{tokens:(0,me.mergeArrays)(pe??[],De??[])}}tokenize(A,{pair:H=null,add_special_tokens:se=!1}={}){return this._tokenize_helper(A,{pair:H,add_special_tokens:se}).tokens}encode(A,{text_pair:H=null,add_special_tokens:se=!0,return_token_type_ids:pe=null}={}){return this._encode_plus(A,{text_pair:H,add_special_tokens:se,return_token_type_ids:pe}).input_ids}batch_decode(A,H={}){return A instanceof Ee.Tensor&&(A=A.tolist()),A.map(se=>this.decode(se,H))}decode(A,H={}){if(A instanceof Ee.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,H)}decode_single(A,{skip_special_tokens:H=!1,clean_up_tokenization_spaces:se=null}){let pe=this.model.convert_ids_to_tokens(A);H&&(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," "),H&&(De=De.trim())),(se??this.clean_up_tokenization_spaces)&&(De=K(De)),De}get_chat_template({chat_template:A=null,tools:H=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(H!==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 ${Object.keys(se).sort()}.`)}else if(A===null)if(this.chat_template)A=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! 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Must be one of: {${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 H.forced_bos_token_id=ue.model.convert_tokens_to_ids([ue.lang_to_token(pe)])[0],ue._call(m,A)}class Cn extends ve{constructor(m,A){super(m,A),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(H=>this.languageRegex.test(H)),this.lang_to_token=H=>H}_build_translation_inputs(m,A,H){return Sn(this,m,A,H)}}class ps extends ve{constructor(m,A){super(m,A),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(H=>this.languageRegex.test(H)).map(H=>H.slice(2,-2)),this.lang_to_token=H=>`__${H}__`}_build_translation_inputs(m,A,H){return Sn(this,m,A,H)}}class ts extends ve{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(m,{return_timestamps:A=!1,return_language:H=!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-$r)*se+Yt,Ct=(0,Te.round)(Ar,2);if(kn!==null&&yr>=kn)Jr=!0;else if(Jr||ar.length>0&&yr0?(ar.push(Ht),wt&&Dr.push(en)):ar.every(Xr=>Xr.length===0)&&(Ot=xt(),ar=[],Ht=[],Dr=[],en=[])}if(ar.length>0){if(pe&&A)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. 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instead.");this.variables.push(e),this.appendVariableUniforms(e);let t=e.usage==="input"?"read":"read_write",s=e.usage==="atomicOutput"?"atomic":e.type.storage;return`@group(0) @binding(${r}) var ${e.name}: array<${s}>;`}declareVariables(...e){return e.map(r=>this.declareVariable(r,this.variableIndex++)).join(` +`)}registerInternalVariable(e){if(e.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() instead.");this.internalVariables.push(e),this.appendVariableUniforms(e)}registerInternalVariables(...e){return e.forEach(r=>this.registerInternalVariable(r)),this}registerUniform(e,r,t=1){return this.uniforms.push({name:e,type:r,length:t}),this}registerUniforms(e){return this.uniforms=this.uniforms.concat(e),this}uniformDeclaration(){if(this.uniforms.length===0)return"";let e=[];for(let{name:r,type:t,length:s}of this.uniforms)if(s&&s>4)t==="f16"?e.push(`@align(16) ${r}:array, ${Math.ceil(s/8)}>`):e.push(`${r}:array, ${Math.ceil(s/4)}>`);else{let o=s==null||s===1?t:`vec${s}<${t}>`;e.push(`${r}:${o}`)}return` + struct Uniforms { ${e.join(", ")} }; + @group(0) @binding(${this.variableIndex}) var uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(e=>e.impl()).join(` +`)+this.internalVariables.map(e=>e.impl()).join(` +`)}get variablesInfo(){if(this.uniforms.length===0)return;let e=r=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(r)];return this.uniforms.map(r=>[e(r.type),r.length??1])}},zc=(e,r)=>new Lc(e,r)}),Bc,il,Rc,jc,Nc,Vc,Lr,Uc,Wc,zs=Be(()=>{ut(),mt(),Xt(),ft(),Bc=(e,r)=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(r.length!==0&&r.length!==e[0].dims.length)throw new Error(`perm size ${r.length} does not match input rank ${e[0].dims.length}`)},il=(e,r)=>r.length!==0?r:[...new Array(e).keys()].reverse(),Rc=(e,r)=>Me.sortBasedOnPerm(e,il(e.length,r)),jc=(e,r,t,s)=>{let o=`fn perm(i: ${s.type.indices}) -> 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${T.registerUniform("output_size","u32").declareVariables(k,w)} + var tile : array, ${_}>; + ${T.mainStart([_,_,1])} + let stride = (uniforms.output_shape[1] - 1) / ${_} + 1; + let workgroup_id_x = workgroup_index % stride; + let workgroup_id_y = workgroup_index / stride; + let input_col = workgroup_id_y * ${_}u + local_id.x; + let input_row = workgroup_id_x * ${_}u + local_id.y; + if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) { + tile[local_id.y][local_id.x] = ${k.getByIndices(`${k.type.indices}(input_row, input_col)`)}; + } + workgroupBarrier(); + + let output_col = workgroup_id_x * ${_}u + local_id.x; + let output_row = workgroup_id_y * ${_}u + local_id.y; + if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) { + ${w.setByIndices(`${w.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")} + } + }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let T=Me.size(n);return{outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(a[1]/_),y:Math.ceil(a[0]/_)},programUniforms:[{type:12,data:T},...st(i,a)]}},getShaderSource:u}}return u=_=>{let T=Pe("a",t,i.length),k=Ye("output",t,a.length);return` + ${_.registerUniform("output_size","u32").declareVariables(T,k)} + + ${jc(o,s,T,k)} + + ${_.mainStart()} + ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${k.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${k.setByOffset("global_idx",T.getByIndices("aIndices"))} + }`},{name:"Transpose",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>{let _=Me.size(n);return{outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...st(i,a)]}},getShaderSource:u}},Uc=(e,r)=>{Bc(e.inputs,r.perm),e.compute(Lr(e.inputs[0],r.perm))},Wc=e=>St({perm:e.perm})}),Gc,Kc,Hc,qc,Xc,Qc,Jc,Yc,Zc,ep,ss,tp,rp,sp,np,op,ip,ap,lp,up,dp,Tv=Be(()=>{ut(),mt(),ft(),ll(),zs(),Gc={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"},Kc={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + 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outputIndex = global_idx / ${c}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${Hc[s]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${c}) { + let candidate = f32(${p.getByOffset("offset + k")}); + bestValue = ${Gc[s]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${c}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 = ${Kc[s]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${d.setByOffset("outputIndex",`${s==="mean"?`${d.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${d.type.storage}(${qc[s]})`}`)}; + } + }`;return{name:e,shaderCache:{hint:`${r};${c}`,inputDependencies:["type"]},getShaderSource:_,getRunData:()=>({outputs:[{dims:n,dataType:o}],dispatchGroup:{x:l},programUniforms:[{type:12,data:u}]})}},ss=(e,r,t,s)=>{let o=e.inputs.length===1?t:al(e.inputs,t),n=o.axes;n.length===0&&!o.noopWithEmptyAxes&&(n=e.inputs[0].dims.map((f,_)=>_));let i=Me.normalizeAxes(n,e.inputs[0].dims.length),a=i,l=e.inputs[0],u=Zc(a,e.inputs[0].dims.length);u.length>0&&(l=e.compute(Lr(e.inputs[0],u),{inputs:[0],outputs:[-1]})[0],a=Xc(a.length,l.dims.length));let[p,d]=Qc(l.dims,a),c=p;o.keepDims&&(c=Jc(p,i)),e.compute(ep(r,o.cacheKey,[l],s,e.inputs[0].dataType,c,d),{inputs:[l]})},tp=(e,r)=>{ss(e,"ReduceMeanShared",r,"mean")},rp=(e,r)=>{ss(e,"ReduceL1Shared",r,"l1")},sp=(e,r)=>{ss(e,"ReduceL2Shared",r,"l2")},np=(e,r)=>{ss(e,"ReduceLogSumExpShared",r,"logSumExp")},op=(e,r)=>{ss(e,"ReduceMaxShared",r,"max")},ip=(e,r)=>{ss(e,"ReduceMinShared",r,"min")},ap=(e,r)=>{ss(e,"ReduceProdShared",r,"prod")},lp=(e,r)=>{ss(e,"ReduceSumShared",r,"sum")},up=(e,r)=>{ss(e,"ReduceSumSquareShared",r,"sumSquare")},dp=(e,r)=>{ss(e,"ReduceLogSumShared",r,"logSum")}}),ns,cp,ui,al,os,pp,hp,mp,_p,fp,gp,wp,Mp,bp,yp,is,vp,xp,Tp,Ep,Pp,Cp,Sp,$p,kp,Ip,ll=Be(()=>{ut(),mt(),Xt(),ft(),Tv(),ns=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.")},cp=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],ui=(e,r,t,s,o,n,i=!1,a=!1)=>{let l=[],u=t[0].dims,p=u.length,d=Me.normalizeAxes(o,p),c=!a&&d.length===0;u.forEach((T,k)=>{c||d.indexOf(k)>=0?i&&l.push(1):l.push(T)});let f=l.length,_=Me.size(l);return{name:e,shaderCache:r,getShaderSource:T=>{let k=[],w=Pe("_A",t[0].dataType,p),g=Ye("output",n,f),S=s(w,g,d),E=S[2];for(let y=0,M=0;y=0?(i&&M++,E=`for(var j${y}: u32 = 0; j${y} < ${u[y]}; j${y}++) { + ${S[2].includes("last_index")?`let last_index = j${y};`:""} + ${w.indicesSet("input_indices",y,`j${y}`)} + ${E} + }`):(k.push(`${w.indicesSet("input_indices",y,g.indicesGet("output_indices",M))};`),M++);return` + + ${T.registerUniform("output_size","u32").declareVariables(w,g)} + + ${T.mainStart()} + ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var input_indices: ${w.type.indices}; + let output_indices = ${g.offsetToIndices("global_idx")}; + + ${k.join(` +`)} + ${S[0]} // init ops for reduce max/min + ${S[1]} + ${E} + ${S[3]} + ${S.length===4?g.setByOffset("global_idx","value"):S.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:l,dataType:n}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...st(u,l)]})}},al=(e,r)=>{let t=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(s=>t.push(Number(s))),St({axes:t,keepDims:r.keepDims,noopWithEmptyAxes:r.noopWithEmptyAxes})},os=(e,r,t,s)=>{let o=e.inputs,n=o.length===1?t:al(o,t);e.compute(ui(r,{hint:n.cacheKey,inputDependencies:["rank"]},[o[0]],n.noopWithEmptyAxes&&n.axes.length===0?cp:s,n.axes,o[0].dataType,n.keepDims,n.noopWithEmptyAxes),{inputs:[0]})},pp=(e,r)=>{ns(e.inputs),os(e,"ReduceLogSum",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,"value = log(value);"])},hp=(e,r)=>{ns(e.inputs),os(e,"ReduceL1",r,(t,s)=>[`var value = ${s.type.storage}(0);`,"",`value 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n=0;n1024},vp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?gp(e,r):tp(e,r)},xp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?hp(e,r):rp(e,r)},Tp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?mp(e,r):sp(e,r)},Ep=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?_p(e,r):np(e,r)},Pp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?fp(e,r):op(e,r)},Cp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?wp(e,r):ip(e,r)},Sp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?Mp(e,r):ap(e,r)},$p=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?bp(e,r):lp(e,r)},kp=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?yp(e,r):up(e,r)},Ip=(e,r)=>{is(e.inputs[0].dims,r.axes,r.noopWithEmptyAxes)?pp(e,r):dp(e,r)}}),ul,Ap,Fp,dl,Ev=Be(()=>{ut(),Xt(),ll(),ul=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},Ap=(e,r)=>{ul(e.inputs);let t=(s,o,n)=>{let i=[];for(let a=0;a=0||n.length===0)&&i.push(`input_indices[${a}] = 0;`);return[`${i.join(` +`)}`,`var value = ${s.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${s.getByIndices("input_indices")} ${r.selectLastIndex>0?"<=":"<"} value) { + value = ${s.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",o.setByOffset("global_idx","best_index")]};e.compute(ui("ArgMin",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},Fp=(e,r)=>{ul(e.inputs);let t=(s,o,n)=>{let i=[];for(let a=0;a=0||n.length===0)&&i.push(`input_indices[${a}] = 0;`);return[`${i.join(` +`)}`,`var value = ${s.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${s.getByIndices("input_indices")} ${r.selectLastIndex>0?">=":">"} value) { + value = ${s.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",o.setByOffset("global_idx","best_index")]};e.compute(ui("argMax",{hint:r.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],t,[r.axis],7,r.keepDims),{inputs:[0]})},dl=e=>St(e)}),Op,di,Dp,Lp,zp,mo,Bp,Rp,cl=Be(()=>{ut(),mt(),el(),ft(),Op=(e,r)=>{let t=e[0],s=e[1],o=e[2],n=e[3],i=e[4],a=e[5];if(i&&a)throw new Error("Attention cannot have both past and attention_bias");if(t.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let l=t.dims[0],u=t.dims[1],p=t.dims[2];if(o.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(s.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(s.dims[0]!==p)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(o.dims[0]!==s.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let d=o.dims[0]/3,c=d,f=c;if(r.qkvHiddenSizes.length>0){if(r.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let S of r.qkvHiddenSizes)if(S%r.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");d=r.qkvHiddenSizes[0],c=r.qkvHiddenSizes[1],f=r.qkvHiddenSizes[2]}let _=u;if(d!==c)throw new Error("qkv_hidden_sizes first element should be same as the second");if(o.dims[0]!==d+c+f)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let T=0;if(i){if(c!==f)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]!==l)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==r.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==c/r.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');r.pastPresentShareBuffer||(T=i.dims[3])}let k=_+T,w=-1,g=0;if(n)throw new Error("Mask not supported");if(i)throw new Error("past is not supported");if(a){if(a.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(a.dims[0]!==l||a.dims[1]!==r.numHeads||a.dims[2]!==u||a.dims[3]!==k)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:l,sequenceLength:u,pastSequenceLength:T,kvSequenceLength:_,totalSequenceLength:k,maxSequenceLength:w,inputHiddenSize:p,hiddenSize:d,vHiddenSize:f,headSize:Math.floor(d/r.numHeads),vHeadSize:Math.floor(f/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:g,scale:r.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},di=(e,r,t)=>r&&e?` + let total_sequence_length_input = u32(${r.getByOffset("0")}); + let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length); + let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input; + let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input; + total_sequence_length = u32(${e==null?void 0:e.getByOffset("batchIdx")}) + 1; + var past_sequence_length: u32 = 0; + if (is_first_prompt == false) { + past_sequence_length = total_sequence_length - sequence_length; + } + `:` + ${t?"let past_sequence_length = uniforms.past_sequence_length":""}; + let present_sequence_length = total_sequence_length; + `,Dp=(e,r,t,s,o,n,i,a)=>{let l=Kt(i?1:n),u=64,p=n/l;p{let g=Ye("x",e.dataType,e.dims,l),S=[g],E=i?Pe("seq_lens",i.dataType,i.dims):void 0;E&&S.push(E);let y=a?Pe("total_sequence_length_input",a.dataType,a.dims):void 0;y&&S.push(y);let M=yr(e.dataType),v=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return` + var thread_max: array; + var thread_sum: array; + ${w.registerUniforms(v).declareVariables(...S)} + ${w.mainStart([u,1,1])} + let batchIdx = workgroup_id.z / uniforms.num_heads; + let headIdx = workgroup_id.z % uniforms.num_heads; + let sequence_length = uniforms.sequence_length; + var total_sequence_length = uniforms.total_sequence_length; + ${di(E,y,!1)} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = (global_idx / ${u}) * uniforms.total_sequence_length + local_offset; + let seq_causal_length = ${i?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"}; + var thread_max_vector = ${_}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + thread_max_vector = max(${_}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(l){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${l}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${u}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${_}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + sum_vector += exp(${_}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(l){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${l}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${u}; i++) { + sum += thread_sum[i]; + } + + if (sum == 0) { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + x[offset + i] = ${g.type.value}(${M}(1.0) / ${M}(seq_causal_length)); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + var f32input = ${_}(x[offset + i]); + x[offset + i] = ${g.type.value}(exp(f32input - max_value) / sum); + } + } + ${i?` + for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) { + x[offset + total_seq_id] = ${g.type.value}(${M}(0)); + }`:""}; + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${u};${f};${l}`,inputDependencies:T},getShaderSource:k,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:o,z:r*t},programUniforms:c})}},Lp=(e,r,t,s,o,n,i,a,l)=>{let u=i+n.kvSequenceLength,p=[n.batchSize,n.numHeads,n.sequenceLength,u],d=e>1&&s,c=n.kvNumHeads?n.kvNumHeads:n.numHeads,f=d?[n.batchSize,c,u,n.headSize]:void 0,_=n.nReps?n.nReps:1,T=n.scale===0?1/Math.sqrt(n.headSize):n.scale,k=Kt(n.headSize),w=n.headSize/k,g=12,S={x:Math.ceil(u/g),y:Math.ceil(n.sequenceLength/g),z:n.batchSize*n.numHeads},E=[{type:12,data:n.sequenceLength},{type:12,data:w},{type:12,data:u},{type:12,data:n.numHeads},{type:12,data:n.headSize},{type:1,data:T},{type:12,data:i},{type:12,data:n.kvSequenceLength},{type:12,data:_}],y=d&&s&&Me.size(s.dims)>0,M=["type","type"];y&&M.push("type"),o&&M.push("type"),a&&M.push("type"),l&&M.push("type");let v=[{dims:p,dataType:r.dataType,gpuDataType:0}];d&&v.push({dims:f,dataType:r.dataType,gpuDataType:0});let C=A=>{let B=Pe("q",r.dataType,r.dims,k),K=Pe("key",t.dataType,t.dims,k),G=[B,K];if(y){let me=Pe("past_key",s.dataType,s.dims,k);G.push(me)}o&&G.push(Pe("attention_bias",o.dataType,o.dims));let j=a?Pe("seq_lens",a.dataType,a.dims):void 0;j&&G.push(j);let ee=l?Pe("total_sequence_length_input",l.dataType,l.dims):void 0;ee&&G.push(ee);let H=Ye("output",r.dataType,p),Z=[H];d&&Z.push(Ye("present_key",r.dataType,f,k));let X=yr(1,k),oe=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return` + const TILE_SIZE = ${g}u; + + var tileQ: array<${B.type.storage}, ${g*g}>; + var tileK: array<${B.type.storage}, ${g*g}>; + ${A.registerUniforms(oe).declareVariables(...G,...Z)} + ${A.mainStart([g,g,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z % uniforms.num_heads; + let kvHeadIdx = ${_===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${_===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let m = workgroup_id.y * TILE_SIZE; + let n = workgroup_id.x * TILE_SIZE; + let sequence_length = uniforms.M; + var total_sequence_length = uniforms.N; + ${di(j,ee,!0)} + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; + let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + ${y&&d?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""}; + let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K; + ${d?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""} + var value = ${X}(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; + ${y&&d?` + if (n + local_id.y < past_sequence_length) { + tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; + } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) { + tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x]; + }`:` + if (n + local_id.y < uniforms.kv_sequence_length) { + tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; + }`} + ${d?`if (n + local_id.y < present_sequence_length) { + present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx]; + }`:""} + } + workgroupBarrier(); + + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += ${X}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]); + } + + workgroupBarrier(); + } + + if (global_id.y < uniforms.M && global_id.x < total_sequence_length) { + let headOffset = workgroup_id.z * uniforms.M * uniforms.N; + let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x; + var sum: f32 = ${(()=>{switch(k){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: ${k}`)}})()}; + output[outputIdx] = ${H.type.value} (sum * uniforms.alpha) + ${o?"attention_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${k};${o!==void 0};${s!==void 0};${e}`,inputDependencies:M},getRunData:()=>({outputs:v,dispatchGroup:S,programUniforms:E}),getShaderSource:C}},zp=(e,r,t,s,o,n,i=void 0,a=void 0)=>{let l=n+o.kvSequenceLength,u=o.nReps?o.nReps:1,p=o.vHiddenSize*u,d=e>1&&s,c=o.kvNumHeads?o.kvNumHeads:o.numHeads,f=d?[o.batchSize,c,l,o.headSize]:void 0,_=[o.batchSize,o.sequenceLength,p],T=12,k={x:Math.ceil(o.vHeadSize/T),y:Math.ceil(o.sequenceLength/T),z:o.batchSize*o.numHeads},w=[{type:12,data:o.sequenceLength},{type:12,data:l},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:12,data:p},{type:12,data:n},{type:12,data:o.kvSequenceLength},{type:12,data:u}],g=d&&s&&Me.size(s.dims)>0,S=["type","type"];g&&S.push("type"),i&&S.push("type"),a&&S.push("type");let E=[{dims:_,dataType:r.dataType,gpuDataType:0}];d&&E.push({dims:f,dataType:r.dataType,gpuDataType:0});let y=M=>{let v=Pe("probs",r.dataType,r.dims),C=Pe("v",t.dataType,t.dims),A=[v,C];g&&A.push(Pe("past_value",s.dataType,s.dims));let B=i?Pe("seq_lens",i.dataType,i.dims):void 0;i&&A.push(B);let K=a?Pe("total_sequence_length_input",a.dataType,a.dims):void 0;a&&A.push(K);let G=[Ye("output",r.dataType,_)];d&&G.push(Ye("present_value",r.dataType,f));let j=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return` + const TILE_SIZE = ${T}u; + var tileQ: array<${v.type.value}, ${T*T}>; + var tileV: array<${v.type.value}, ${T*T}>; + ${M.registerUniforms(j).declareVariables(...A,...G)} + ${M.mainStart([T,T,1])} + let headIdx = workgroup_id.z % uniforms.num_heads; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let kvHeadIdx = ${u===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${u===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; + let m = global_id.y; + let n = global_id.x; + let sequence_length = uniforms.M; + var total_sequence_length = uniforms.K; + ${di(B,K,!0)} + let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch + ${g&&d?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""}; + let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n; + ${d?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""} + var value = ${v.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; + ${g&&d?` + if (w + local_id.y < past_sequence_length) { + tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N]; + } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) { + tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N]; + } + `:` + if (w + local_id.y < uniforms.kv_sequence_length) { + tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N]; + }`} + ${d?` + if (w + local_id.y < present_sequence_length) { + present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx]; + }`:""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + headIdx * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${s!==void 0};${e}`,inputDependencies:S},getRunData:()=>({outputs:E,dispatchGroup:k,programUniforms:w}),getShaderSource:y}},mo=(e,r,t,s,o,n,i,a,l,u,p=void 0,d=void 0)=>{let c=Math.min(e.outputCount,1+(i?1:0)+(a?1:0)),f=c>1?u.pastSequenceLength:0,_=f+u.kvSequenceLength,T=l&&Me.size(l.dims)>0?l:void 0,k=[r,t];c>1&&i&&Me.size(i.dims)>0&&k.push(i),T&&k.push(T),p&&k.push(p),d&&k.push(d);let w=e.compute(Lp(c,r,t,i,T,u,f,p,d),{inputs:k,outputs:c>1?[-1,1]:[-1]})[0];e.compute(Dp(w,u.batchSize,u.numHeads,f,u.sequenceLength,_,p,d),{inputs:p&&d?[w,p,d]:[w],outputs:[]});let g=[w,s];c>1&&a&&Me.size(a.dims)>0&&g.push(a),p&&g.push(p),d&&g.push(d),e.compute(zp(c,w,s,a,u,f,p,d),{inputs:g,outputs:c>1?[0,2]:[0]})},Bp=(e,r)=>{let t=[r.batchSize,r.numHeads,r.sequenceLength,r.headSize],s=r.sequenceLength,o=r.inputHiddenSize,n=r.headSize,i=12,a={x:Math.ceil(r.headSize/i),y:Math.ceil(r.sequenceLength/i),z:r.batchSize*r.numHeads},l=[e.inputs[0],e.inputs[1],e.inputs[2]],u=[{type:12,data:s},{type:12,data:o},{type:12,data:n},{type:12,data:r.numHeads},{type:12,data:r.headSize},{type:12,data:r.hiddenSize},{type:12,data:r.hiddenSize+r.hiddenSize+r.vHiddenSize}],p=d=>{let c=Ye("output_q",l[0].dataType,t),f=Ye("output_k",l[0].dataType,t),_=Ye("output_v",l[0].dataType,t),T=Pe("input",l[0].dataType,l[0].dims),k=Pe("weight",l[1].dataType,l[1].dims),w=Pe("bias",l[2].dataType,l[2].dims),g=T.type.storage,S=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return` + const TILE_SIZE = ${i}u; + var tileInput: array<${g}, ${i*i}>; + var tileWeightQ: array<${g}, ${i*i}>; + var tileWeightK: array<${g}, ${i*i}>; + var tileWeightV: array<${g}, ${i*i}>; + ${d.registerUniforms(S).declareVariables(T,k,w,c,f,_)} + ${d.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 = ${g}(0); + var valueK = ${g}(0); + var valueV = ${g}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:t,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:a,programUniforms:u}),getShaderSource:p},{inputs:l,outputs:[-1,-1,-1]})},Rp=(e,r)=>{let t=Op(e.inputs,r),[s,o,n]=Bp(e,t);return mo(e,s,o,n,e.inputs[4],void 0,void 0,void 0,e.inputs[5],t)}}),jp,Np,Vp,Up,Pv=Be(()=>{ts(),ut(),mt(),Xt(),ft(),jp=(e,r)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let t=(s,o,n)=>{let i=o.length;if(i!==s.length)throw new Error(`${n}: num dimensions != ${i}`);o.forEach((a,l)=>{if(a!==s[l])throw new Error(`${n}: dim[${l}] do not match`)})};if(e[0].dims.length>1){let s=r.format==="NHWC"?r.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,r.spatial?2:void 0);t(e[1].dims,s,"Invalid input scale"),t(e[2].dims,s,"Invalid input B"),t(e[3].dims,s,"Invalid input mean"),t(e[4].dims,s,"Invalid 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${S.mainStart()} + ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${k.offsetToIndices(`global_idx * ${i}`)}; + ${w()} + let scale = ${c.getByOffset("cOffset")}; + let bias = ${f.getByOffset("cOffset")}; + let inputMean = ${_.getByOffset("cOffset")}; + let inputVar = ${T.getByOffset("cOffset")}; + let x = ${d.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${k.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${r.epsilon}_${r.format}_${s}_${i}`,inputDependencies:u?["rank","type","type","type","type"]:void 0},getShaderSource:g,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u?[{type:12,data:l},...st(n)]:[{type:12,data:l}]})}},Vp=e=>St(e),Up=(e,r)=>{let{inputs:t,outputCount:s}=e,o=Vp({...r,outputCount:s});if(Bt.webgpu.validateInputContent&&jp(t,o),r.trainingMode)throw new 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${t} { + return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${t}>) -> vec4<${t}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,r.cacheKey))},ci=(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<${e}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,uh=e=>{let r=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"Erf",t=>`erf_vf32(${t})`,ci(r)))},dh=e=>{e.compute(Et(e.inputs[0],"Exp","exp"))},ch=e=>{e.compute(Et(e.inputs[0],"Floor","floor"))},ph=e=>{let r=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"Gelu",t=>`0.5 * ${t} * (1.0 + erf_vf32(${t} * 0.7071067811865475))`,ci(r)))},hh=(e,r)=>{let 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abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Eh=e=>{e.compute(Et(e.inputs[0],"Tanh",pl))},hl=(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 ${pl("v")}; +} +`,ml=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Ph=e=>{let r=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"FastGelu",ml,hl(r),void 0,e.inputs[0].dataType))},Ch=(e,r)=>{let t=yr(e.inputs[0].dataType);return e.compute(Et(e.inputs[0],"ThresholdedRelu",s=>`select(vec4<${t}>(0.0), ${s}, ${s} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${t}>(${r.alpha});`,r.cacheKey)),0},Sh=e=>{e.compute(Et(e.inputs[0],"Log","log"))},$h=(e,r)=>` +const alpha = vec4<${e}>(${r}); +const one = ${e}(1.0); +const zero = ${e}(0.0); + +fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> { + let v = x *alpha; + var x1 : vec4<${e}>; + for (var i = 0; i < 4; i = i + 1) { + if (v[i] >= zero) { + x1[i] = one / (one + exp(-v[i])); + } else { + x1[i] = one - one / (one + exp(v[i])); + } + } + return x * x1; +} +`,kh=e=>`quick_gelu_impl(${e})`,Ih=(e,r)=>{let t=yr(e.inputs[0].dataType);e.compute(Et(e.inputs[0],"QuickGelu",kh,$h(t,r.alpha),r.cacheKey,e.inputs[0].dataType))}}),Ah,Fh,Oh,Sv=Be(()=>{mt(),ft(),_l(),Ah=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")},Fh=e=>{let r=e[0].dims.slice();r[2]=r[2]/2;let t=Pe("input",e[0].dataType,e[0].dims,4),s=Pe("bias",e[0].dataType,[e[0].dims[2]],4),o=Ye("output",e[0].dataType,r,4),n=Me.size(r)/4,i=lr(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:a=>` + const M_SQRT2 = sqrt(2.0); + const halfChannels = ${e[0].dims[2]/4/2}u; + + ${a.declareVariables(t,s,o)} + + ${ci(i)} + + ${a.mainStart()} + ${a.guardAgainstOutOfBoundsWorkgroupSizes(n)} + let biasIdx = global_idx % halfChannels; + let batchIndex = global_idx / halfChannels; + let inputOffset = biasIdx + batchIndex * halfChannels * 2; + let valueLeft = input[inputOffset] + bias[biasIdx]; + let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${o.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Oh=e=>{Ah(e.inputs),e.compute(Fh(e.inputs))}}),Dh,Lh,as,zh,Bh,Rh,jh,Nh,Vh,Uh,Wh,Gh,Kh,$v=Be(()=>{ut(),mt(),ft(),Dh=(e,r,t,s,o,n,i,a,l,u,p,d)=>{let c,f;typeof a=="string"?c=f=(g,S)=>`${a}((${g}),(${S}))`:typeof a=="function"?c=f=a:(c=a.scalar,f=a.vector);let _=Ye("outputData",p,s.length,4),T=Pe("aData",l,r.length,4),k=Pe("bData",u,t.length,4),w;if(o)if(n){let g=Me.size(r)===1,S=Me.size(t)===1,E=r.length>0&&r[r.length-1]%4===0,y=t.length>0&&t[t.length-1]%4===0;g||S?w=_.setByOffset("global_idx",f(g?`${T.type.value}(${T.getByOffset("0")}.x)`:T.getByOffset("global_idx"),S?`${k.type.value}(${k.getByOffset("0")}.x)`:k.getByOffset("global_idx"))):w=` + let outputIndices = ${_.offsetToIndices("global_idx * 4u")}; + let offsetA = ${T.broadcastedIndicesToOffset("outputIndices",_)}; + let offsetB = ${k.broadcastedIndicesToOffset("outputIndices",_)}; + ${_.setByOffset("global_idx",f(i||E?T.getByOffset("offsetA / 4u"):`${T.type.value}(${T.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||y?k.getByOffset("offsetB / 4u"):`${k.type.value}(${k.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else w=_.setByOffset("global_idx",f(T.getByOffset("global_idx"),k.getByOffset("global_idx")));else{if(!n)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let g=(S,E,y="")=>{let M=`aData[indexA${E}][componentA${E}]`,v=`bData[indexB${E}][componentB${E}]`;return` + let outputIndices${E} = ${_.offsetToIndices(`global_idx * 4u + ${E}u`)}; + let offsetA${E} = ${T.broadcastedIndicesToOffset(`outputIndices${E}`,_)}; + let offsetB${E} = ${k.broadcastedIndicesToOffset(`outputIndices${E}`,_)}; + let indexA${E} = offsetA${E} / 4u; + let indexB${E} = offsetB${E} / 4u; + let componentA${E} = offsetA${E} % 4u; + let componentB${E} = offsetB${E} % 4u; + ${S}[${E}] = ${y}(${c(M,v)}); + `};p===9?w=` + var data = vec4(0); + ${g("data",0,"u32")} + ${g("data",1,"u32")} + ${g("data",2,"u32")} + ${g("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:w=` + ${g("outputData[global_idx]",0)} + ${g("outputData[global_idx]",1)} + ${g("outputData[global_idx]",2)} + ${g("outputData[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(T,k,_)} + + ${d??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${w} + }`},Lh=(e,r,t,s,o,n,i=t.dataType)=>{let a=t.dims.map(T=>Number(T)??1),l=s.dims.map(T=>Number(T)??1),u=!Me.areEqual(a,l),p=a,d=Me.size(a),c=!1,f=!1,_=[u];if(u){let T=Nn.calcShape(a,l,!1);if(!T)throw new Error("Can't perform binary op on the given tensors");p=T.slice(),d=Me.size(p);let k=Me.size(a)===1,w=Me.size(l)===1,g=a.length>0&&a[a.length-1]%4===0,S=l.length>0&&l[l.length-1]%4===0;_.push(k),_.push(w),_.push(g),_.push(S);let E=1;for(let y=1;yT.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:T=>Dh(T,a,l,p,c,u,f,o,t.dataType,s.dataType,i,n),getRunData:()=>({outputs:[{dims:p,dataType:i}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:[{type:12,data:Math.ceil(Me.size(p)/4)},...st(a,l,p)]})}},as=(e,r,t,s,o,n)=>{e.compute(Lh(r,o??"",e.inputs[0],e.inputs[1],t,s,n))},zh=e=>{as(e,"Add",(r,t)=>`${r}+${t}`)},Bh=e=>{as(e,"Div",(r,t)=>`${r}/${t}`)},Rh=e=>{as(e,"Equal",{scalar:(r,t)=>`u32(${r}==${t})`,vector:(r,t)=>`vec4(${r}==${t})`},void 0,void 0,9)},jh=e=>{as(e,"Mul",(r,t)=>`${r}*${t}`)},Nh=e=>{let r=Pe("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;as(e,"Pow",{scalar:(t,s)=>`pow_custom(${t},${s})`,vector:(t,s)=>`pow_vector_custom(${t},${s})`},` + fn pow_custom(a : ${r}, b : ${r}) -> ${r} { + if (b == ${r}(0.0)) { + return ${r}(1.0); + } else if (a < ${r}(0.0) && f32(b) != floor(f32(b))) { + return ${r}(pow(f32(a), f32(b))); // NaN + } + return select(sign(a), ${r}(1.0), round(f32(abs(b) % ${r}(2.0))) != 1.0) * ${r}(${r==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); + } + fn pow_vector_custom(a : vec4<${r}>, b : vec4<${r}>) -> vec4<${r}> { + // TODO: implement vectorized pow + return vec4<${r}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); + } + `)},Vh=e=>{as(e,"Sub",(r,t)=>`${r}-${t}`)},Uh=e=>{as(e,"Greater",{scalar:(r,t)=>`u32(${r}>${t})`,vector:(r,t)=>`vec4(${r}>${t})`},void 0,void 0,9)},Wh=e=>{as(e,"Less",{scalar:(r,t)=>`u32(${r}<${t})`,vector:(r,t)=>`vec4(${r}<${t})`},void 0,void 0,9)},Gh=e=>{as(e,"GreaterOrEqual",{scalar:(r,t)=>`u32(${r}>=${t})`,vector:(r,t)=>`vec4(${r}>=${t})`},void 0,void 0,9)},Kh=e=>{as(e,"LessOrEqual",{scalar:(r,t)=>`u32(${r}<=${t})`,vector:(r,t)=>`vec4(${r}<=${t})`},void 0,void 0,9)}}),Hh,qh,Xh,Qh,Jh,Yh,kv=Be(()=>{ut(),mt(),Xt(),ft(),Hh=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");let t=0,s=e[t],o=s.dataType,n=s.dims.length;e.forEach((i,a)=>{if(a!==t){if(i.dataType!==o)throw new Error("input tensors should be one type");if(i.dims.length!==n)throw new Error("input tensors should have the same shape");i.dims.forEach((l,u)=>{if(u!==r&&l!==s.dims[u])throw new Error("non concat dimensions must match")})}})},qh=(e,r)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${r}); + for (var i: u32 = 0u; i < ${e}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${e}u; + }`,Xh=(e,r)=>{let t=e.length,s=[];for(let o=0;o{let o=Me.size(t),n=new Array(e.length),i=new Array(e.length),a=0,l=[],u=[],p=[{type:12,data:o}];for(let T=0;T`uniforms.sizeInConcatAxis${T}`).join(","),_=T=>` + + ${(()=>{T.registerUniform("outputSize","u32");for(let k=0;k(${f}); + ${c} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${Xh(i,d)} + }`;return{name:"Concat",shaderCache:{hint:`${r}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:t,dataType:s}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:p}),getShaderSource:_}},Jh=(e,r)=>{let t=e.inputs,s=t[0].dims,o=Me.normalizeAxis(r.axis,s.length);Hh(t,o);let n=s.slice();n[o]=t.reduce((a,l)=>a+(l.dims.length>o?l.dims[o]:0),0);let i=t.filter(a=>Me.size(a.dims)>0);e.compute(Qh(i,o,n,t[0].dataType),{inputs:i})},Yh=e=>St({axis:e.axis})}),rn,sn,nn,fl,on=Be(()=>{ut(),mt(),rn=(e,r,t="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${r}(0.0));`;case"Sigmoid":return`value = (${r}(1.0) / (${r}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${r}(${t}(uniforms.clip_min)), ${r}(${t}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${r}(0.0), min(${r}(1.0), ${t}(uniforms.alpha) * value + ${t}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${t}(uniforms.alpha) * value, value, value >= ${r}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value)); + value = sign(value) * (1.0 - e2x) / (1.0 + e2x); + `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},sn=(e,r)=>{e.activation==="Clip"?r.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?r.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&r.push({type:1,data:e.alpha})},nn=(e,r)=>{e.activation==="Clip"?r.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?r.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&r.push({name:"alpha",type:"f32"})},fl=e=>{let r=(e==null?void 0:e.activation)||"";if(r==="HardSigmoid"){let[t,s]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:r,alpha:t,beta:s}}else if(r==="Clip"){let[t,s]=(e==null?void 0:e.activation_params)||[yc,vc];return{activation:r,clipMax:s,clipMin:t}}else if(r==="LeakyRelu"){let[t]=(e==null?void 0:e.activation_params)||[.01];return{activation:r,alpha:t}}return{activation:r}}}),_r,Zh,gl=Be(()=>{_r=(e,r)=>{switch(e){case 1:return r;case 2:return`vec2<${r}>`;case 3:return`vec3<${r}>`;case 4:return`vec4<${r}>`;default:throw new Error(`${e}-component is not supported.`)}},Zh=e=>` + ${e?"value = value + getBiasByOutputCoords(coords);":""} + `}),em,Iv=Be(()=>{em=e=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); +} +`}),fo,wl,Ml=Be(()=>{ut(),mt(),ft(),on(),fo=(e,r,t,s,o)=>{let n=s-t;return` + ${Array.from({length:t}).map((i,a)=>` + if (${et(r.shape,a,r.rank)} != 1) { + ${r.indicesSet(e,a,et(o,a+n,s))} + } else { + ${r.indicesSet(e,a,0)} + }`).join("")} +`},wl=(e,r,t,s,o=!1,n)=>{let i=e[0].dims,a=e[1].dims,l=i[i.length-2],u=a[a.length-1],p=i[i.length-1],d=Kt(u),c=Kt(p),f=Kt(l),_=Me.size(t)/d/f,T=e.length>2,k=s?s.slice(0,-2):t.slice(0,-2),w=[Me.size(k),l,u],g=[{type:12,data:_},{type:12,data:l},{type:12,data:u},{type:12,data:p}];sn(r,g),g.push(...st(k,i,a)),T&&g.push(...st(e[2].dims)),g.push(...st(w));let S=E=>{let y=ol("batch_dims",e[0].dataType,k.length),M=Pe("a",e[0].dataType,i.length,c),v=Pe("b",e[1].dataType,a.length,d),C=Ye("output",e[0].dataType,w.length,d),A=lr(C.type.tensor),B=rn(r,C.type.value,A),K=[M,v],G="";if(T){let H=o?d:1;K.push(Pe("bias",e[2].dataType,e[2].dims.length,H)),G=`${o?`value += bias[col / ${H}];`:`value += ${C.type.value}(bias[row + i]);`}`}let j=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];nn(r,j);let ee=()=>{let H=`var a_data: ${M.type.value};`;for(let Z=0;Z; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${c}) { + ${ee()} + } + for (var i = 0u; i < ${f}u; i++) { + var value = values[i]; + ${G} + ${B} + let cur_indices = ${C.type.indices}(batch, row + i, col); + let offset = ${C.indicesToOffset("cur_indices")}; + ${C.setByOffset(`offset / ${d}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${r.activation};${d};${c};${f};${o}`,inputDependencies:T?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:g}),getShaderSource:S}}}),tm,rm,bl,yl,sm,vl,nm,pi,xl=Be(()=>{ut(),mt(),ft(),on(),Ml(),gl(),tm=(e,r)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart / innerElementSize + inputCol${r?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRow + innerRow, + kStart / innerElementSize + inputCol${r?", batchIndices":""}); + `,rm=(e,r)=>e?` + let ACached0 = mm_Asub[k * innerElementSize][localRow]; + let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; + let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; + ${r===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"} + for (var i = 0; i < rowPerThread; i = i + 1) { + acc[i] = BCached0 * ACached0[i] + acc[i]; + acc[i] = BCached1 * ACached1[i] + acc[i]; + acc[i] = BCached2 * ACached2[i] + acc[i]; + ${r===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"} + }`:` + for (var i = 0; i < rowPerThread; i = i + 1) { + let ACached = mm_Asub[tileRow + i][k]; + acc[i] = BCached0 * ACached.x + acc[i]; + acc[i] = BCached1 * ACached.y + acc[i]; + acc[i] = BCached2 * ACached.z + acc[i]; + ${r===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} + }`,bl=(e,r,t="f32",s,o=!1,n=32,i=!1,a=32)=>{let l=r[1]*e[1],u=r[0]*e[0],p=o?l:n,d=o?n:l,c=p/r[0],f=n/r[1];if(!((o&&c===4&&e[1]===4||!o&&(c===3||c===4))&&p%r[0]===0&&n%r[1]===0&&e[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${c} must be 3 or 4. + tileAWidth ${p} must be divisible by workgroupSize[0]${r[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${r[1]}. colPerThread ${e[0]} must be 4.`);return` +var mm_Asub: array, ${p/c}>, ${d}>; +var mm_Bsub: array, ${u/e[0]}>, ${n}>; + +const rowPerThread = ${e[1]}; +const colPerThread = ${e[0]}; +const innerElementSize = ${c}; +const tileInner = ${n}; + +@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let localRow = i32(localId.y); + let tileRow = localRow * rowPerThread; + let tileCol = i32(localId.x); + + let globalRow =i32(globalId.y) * rowPerThread; + let globalCol = i32(globalId.x); + let batch = ${i?"0":"i32(globalId.z)"}; + ${s?`let batchIndices = ${s.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${l}; + + let num_tiles = ${i?`${Math.ceil(a/n)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${i?`i32(globalId.z) * ${a}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${f}; + 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; + ${tm(o,s)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${f}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${s?", batchIndices":""}); + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { + let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; + let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; + let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; + ${c===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${rm(o,c)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},yl=(e,r)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${r?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${r?", batchIndices":""}); + `,sm=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",vl=(e,r,t="f32",s,o=!1,n=32,i=!1,a=32,l=!1)=>{let u=e[1]*r[1],p=e[0]*r[0],d=o?u:n,c=o?n:u;if(!(c%r[1]===0&&d%r[0]===0&&n%r[1]===0))throw new Error(`tileAHight ${c} must be divisible by workgroupSize[1]${r[1]}, tileAWidth ${d} must be divisible by workgroupSize[0]${r[0]}, tileInner ${n} must be divisible by workgroupSize[1]${r[1]}`);let f=c/r[1],_=d/r[0],T=n/r[1],k=l?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${u}; + let globalColStart = i32(workgroupId.x) * ${p}; + + // Loop over shared dimension. + for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${r[1]}) { + for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${r[0]}) { + ${yl(o,s)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${n}; inputRow = inputRow + ${r[1]}) { + for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${r[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalColStart + inputCol${s?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${t}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][localCol + inner * ${r[0]}]; + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let ACached = ${o?`mm_Asub[k][localRow + innerRow * ${r[1]}];`:`mm_Asub[localRow + innerRow * ${r[1]}][k];`} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + + ACached * BCached[innerCol]; + } + } + } + workgroupBarrier(); + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let gRow = globalRowStart + localRow + innerRow * ${r[1]}; + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let gCol = globalColStart + localCol + innerCol * ${r[0]}; + mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); + } + } + `:` +let tileRow = i32(localId.y) * rowPerThread; +let tileCol = i32(localId.x) * colPerThread; + +let globalRow = i32(globalId.y) * rowPerThread; +let globalCol = i32(globalId.x) * colPerThread; +let globalRowStart = i32(workgroupId.y) * ${u}; + +let tileRowA = i32(localId.y) * ${f}; +let tileColA = i32(localId.x) * ${_}; +let tileRowB = i32(localId.y) * ${T}; +// 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 < ${f}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${_}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${yl(o,s)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${T}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${s?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${t}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][tileCol + inner]; + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + ${sm(o)} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; + } + } + } + + workgroupBarrier(); +} + +for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + mm_write(batch, globalRow + innerRow, globalCol + innerCol, + acc[innerRow][innerCol]); + } +} +`;return` + var mm_Asub : array, ${c}>; + var mm_Bsub : array, ${n}>; + const rowPerThread = ${e[1]}; + const colPerThread = ${e[0]}; + const tileInner = ${n}; + +@compute @workgroup_size(${r[0]}, ${r[1]}, ${r[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let batch = ${i?"0":"i32(globalId.z)"}; + ${s?`let batchIndices = ${s.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${i?`${Math.ceil(a/n)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${i?`i32(globalId.z) * ${a}`:"0"}; + + var acc : array, rowPerThread>; + ${k} + } +`},nm=(e,r,t,s,o=!1)=>{let[n,i,a,l]=s,u=lr(s[0].type.tensor);return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${n.type.indices}) -> ${_r(e,u)} { + var value = ${_r(e,u)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + var aIndices: ${i.type.indices}; + ${fo("aIndices",i,i.rank-2,n.rank,"batchIndices")} + ${i.indicesSet("aIndices",i.rank-2,"u32(row)")} + ${i.indicesSet("aIndices",i.rank-1,"u32(colIn)")} + value = ${i.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${n.type.indices}) -> ${_r(e,u)} { + var value = ${_r(e,u)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + var bIndices: ${a.type.indices}; + ${fo("bIndices",a,a.rank-2,n.rank,"batchIndices")} + ${a.indicesSet("bIndices",a.rank-2,"u32(row)")} + ${a.indicesSet("bIndices",a.rank-1,"u32(colIn)")} + value = ${a.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${_r(e,u)}) { + let col = colIn * ${e}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueIn; + let coords = vec3(batch, row, colIn); + ${r?`value = value + ${o?"bias[colIn]":`${_r(e,u)}(bias[row])`};`:""} + ${t} + ${l.setByIndices("vec3(coords)","value")} + } + } + `},pi=(e,r,t,s,o=!1,n)=>{let i=e[0].dims,a=e[1].dims,l=i.slice(0,-2),u=a.slice(0,-2),p=s?s.slice(0,-2):t.slice(0,-2),d=Me.size(p),c=i[i.length-2],f=i[i.length-1],_=a[a.length-1],T=f%4===0&&_%4===0,k=c<=8?[4,1,1]:[4,4,1],w=[8,8,1],g=[Math.ceil(_/w[0]/k[0]),Math.ceil(c/w[1]/k[1]),Math.ceil(d/w[2]/k[2])],S=T?4:1,E=[...l,c,f/S],y=E.length,M=[...u,f,_/S],v=M.length,C=[d,c,_/S],A=[{type:6,data:c},{type:6,data:_},{type:6,data:f}];sn(r,A),A.push(...st(p,E,M));let B=["rank","rank"],K=e.length>2;K&&(A.push(...st(e[2].dims)),B.push("rank")),A.push(...st(C));let G=j=>{let ee=p.length,H=ol("batchDims",e[0].dataType,ee,1),Z=lr(e[0].dataType),X=Pe("a",e[0].dataType,y,S),oe=Pe("b",e[1].dataType,v,S),me=Ye("result",e[0].dataType,C.length,S),ae=[X,oe];if(K){let _e=o?S:1;ae.push(Pe("bias",e[2].dataType,e[2].dims.length,_e))}let V=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];nn(r,V);let F=lr(me.type.tensor),W=rn(r,me.type.value,F),re=nm(S,K,W,[H,X,oe,me],o);return` + ${j.registerUniforms(V).registerInternalVariables(H).declareVariables(...ae,me)} + ${re} + ${T?bl(k,w,Z,H):vl(k,w,Z,H)} + `};return{name:"MatMul",shaderCache:{hint:`${k};${r.activation};${T};${o}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:n?n(t):t,dataType:e[0].dataType}],dispatchGroup:{x:g[0],y:g[1],z:g[2]},programUniforms:A}),getShaderSource:G}}}),om,im,Av=Be(()=>{ut(),Es(),ft(),on(),gl(),Iv(),xl(),om=(e,r,t,s,o=!1,n,i=4,a=4,l=4,u="f32")=>{let p=A=>{switch(A){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${u}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${A} is not supported.`)}},d=A=>{switch(A){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 ${A} is not supported.`)}},c=e?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,f=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,_=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",T=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",k=e?"row":"col",w=e?"col":"row",g=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${k} / outWidth; + let outCol = ${k} % outWidth; + + let WRow = ${w} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${w} / 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 = ${w} % inChannels; + var resData = ${_r(i,u)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${_} && xCol >= 0 && xCol < ${T}) { + ${c} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${p(i)} + } + return resData;`,S=e?r&&s?` + let col = colIn * ${i}; + ${g}`:` + let col = colIn * ${i}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${g} + } + return ${_r(i,u)}(0.0);`:s&&t?` + let col = colIn * ${i}; + ${g}`:` + let col = colIn * ${i}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${g} + } + return ${_r(i,u)}(0.0);`,E=e?s&&t?d(a):` + let col = colIn * ${a}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${d(a)} + } + return ${_r(a,u)}(0.0);`:` + let col = colIn * ${a}; + if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) { + ${d(a)} + } + return ${_r(a,u)}(0.0);`,y=_r(l,u),M=_r(e?i:a,u),v=_r(e?a:i,u),C=rn(n,y,u);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${M} { + ${e?S:E} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${v} { + ${e?E:S} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${y}) { + let col = colIn * ${l}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) + { + var value = valueIn; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${f} + ${Zh(o)} + ${C} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},im=(e,r,t,s,o,n,i,a,l)=>{let u=r.format==="NHWC",p=u?e[0].dims[3]:e[0].dims[1],d=t[0],c=u?t[2]:t[3],f=u?t[1]:t[2],_=u?t[3]:t[1],T=u&&(p%4===0||p%3===0)&&_%4===0,k=u?_:c*f,w=u?c*f:_,g=[8,8,1],S=s<=8?[4,1,1]:[4,4,1],E=[Math.ceil(k/g[0]/S[0]),Math.ceil(w/g[1]/S[1]),Math.ceil(d/g[2]/S[2])];vt("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let y=T?u&&p%4!==0?3:4:1,M=g[1]*S[1],v=g[0]*S[0],C=Math.max(g[0]*y,g[1]),A=s%M===0,B=o%v===0,K=n%C===0,G=T?[y,4,4]:[1,1,1],j=[{type:6,data:s},{type:6,data:o},{type:6,data:n},{type:6,data:[r.pads[0],r.pads[1]]},{type:6,data:r.strides},{type:6,data:r.dilations}];sn(r,j),j.push(...st(e[0].dims,e[1].dims));let ee=["rank","rank"];i&&(j.push(...st(e[2].dims)),ee.push("rank")),j.push(...st(t));let H=Z=>{let X=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];nn(r,X);let oe=T?4:1,me=lr(e[0].dataType),ae=` + fn setOutputAtIndex(flatIndex : i32, value : ${T?`vec4<${me}>`:me}) { + result[flatIndex] = ${T?`vec4<${me}>`:me}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${T?`vec4<${me}>`:me}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${T?"/ 4":""}, value); + }`,V=Pe("x",e[0].dataType,e[0].dims.length,y===3?1:y),F=Pe("w",e[1].dataType,e[1].dims.length,oe),W=[V,F],re=Ye("result",e[0].dataType,t.length,oe);if(i){let _e=Pe("bias",e[2].dataType,e[2].dims.length,oe);W.push(_e),ae+=` + fn getBiasByOutputCoords(coords : vec4) -> ${T?`vec4<${me}>`:me} { + return bias[coords.${u?"w":"y"}${T?"/ 4":""}]; + }`}return` + ${em("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 }; + ${Z.registerUniforms(X).declareVariables(...W,re)} + ${ae} + ${om(u,A,B,K,i,r,G[0],G[1],G[2],me)} + ${T?bl(S,g,me,void 0,!u,C):vl(S,g,me,void 0,!u,C,!1,void 0,a)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${r.cacheKey};${y};${T};${A};${B};${K};${M};${v};${C}`,inputDependencies:ee},getRunData:()=>({outputs:[{dims:l?l(t):t,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:j}),getShaderSource:H}}}),am,Tl,go,lm,El,um,dm,cm,Fv=Be(()=>{ut(),Es(),mt(),ft(),on(),gl(),am=e=>{let r=1;for(let t=0;ttypeof e=="number"?[e,e,e]:e,go=(e,r)=>r<=1?e:e+(e-1)*(r-1),lm=(e,r,t,s=1)=>{let o=go(r,s);return Math.floor((e[0]*(t-1)-t+o)/2)},El=(e,r,t,s,o)=>{o==null&&(o=lm(e,r[0],s[0]));let n=[0,0,0,t];for(let i=0;i<3;i++)e[i]+2*o>=r[i]&&(n[i]=Math.trunc((e[i]-r[i]+2*o)/s[i]+1));return n},um=(e,r,t,s,o,n,i,a,l,u)=>{let p,d,c,f;if(e==="VALID"&&(e=0),typeof e=="number"){p={top:e,bottom:e,left:e,right:e,front:e,back:e};let _=El([r,t,s,1],[a,l,u],1,[o,n,i],e);d=_[0],c=_[1],f=_[2]}else if(Array.isArray(e)){if(!e.every((T,k,w)=>T===w[0]))throw Error(`Unsupported padding parameter: ${e}`);p={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let _=El([r,t,s,1],[a,l,u],1,[o,n,i],e[0]);d=_[0],c=_[1],f=_[2]}else if(e==="SAME_UPPER"){d=Math.ceil(r/o),c=Math.ceil(t/n),f=Math.ceil(s/i);let _=(d-1)*o+a-r,T=(c-1)*n+l-t,k=(f-1)*i+u-s,w=Math.floor(_/2),g=_-w,S=Math.floor(T/2),E=T-S,y=Math.floor(k/2),M=k-y;p={top:S,bottom:E,left:y,right:M,front:w,back:g}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:d,outHeight:c,outWidth:f}},dm=(e,r,t,s,o,n=!1,i="channelsLast")=>{let a,l,u,p,d;if(i==="channelsLast")[a,l,u,p,d]=e;else if(i==="channelsFirst")[a,d,l,u,p]=e;else throw new Error(`Unknown dataFormat ${i}`);let[c,,f,_,T]=r,[k,w,g]=Tl(t),[S,E,y]=Tl(s),M=go(f,S),v=go(_,E),C=go(T,y),{padInfo:A,outDepth:B,outHeight:K,outWidth:G}=um(o,l,u,p,k,w,g,M,v,C),j=n?c*d:c,ee=[0,0,0,0,0];return i==="channelsFirst"?ee=[a,j,B,K,G]:i==="channelsLast"&&(ee=[a,B,K,G,j]),{batchSize:a,dataFormat:i,inDepth:l,inHeight:u,inWidth:p,inChannels:d,outDepth:B,outHeight:K,outWidth:G,outChannels:j,padInfo:A,strideDepth:k,strideHeight:w,strideWidth:g,filterDepth:f,filterHeight:_,filterWidth:T,effectiveFilterDepth:M,effectiveFilterHeight:v,effectiveFilterWidth:C,dilationDepth:S,dilationHeight:E,dilationWidth:y,inShape:e,outShape:ee,filterShape:r}},cm=(e,r,t,s,o,n)=>{let i=n==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let a=[64,1,1],l={x:t.map((k,w)=>w)},u=[Math.ceil(am(l.x.map(k=>t[k]))/a[0]),1,1];vt("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${u}`);let p=1,d=Me.size(t),c=[{type:12,data:d},{type:12,data:s},{type:12,data:o},{type:12,data:r.strides},{type:12,data:r.dilations}];sn(r,c),c.push(...st(e[0].dims,e[1].dims));let f=["rank","rank"],_=e.length===3;_&&(c.push(...st(e[2].dims)),f.push("rank")),c.push(...st(t));let T=k=>{let w=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:s.length},{name:"pads",type:"u32",length:o.length},{name:"strides",type:"u32",length:r.strides.length},{name:"dilations",type:"u32",length:r.dilations.length}];nn(r,w);let g=1,S=lr(e[0].dataType),E=Pe("x",e[0].dataType,e[0].dims.length,p),y=Pe("W",e[1].dataType,e[1].dims.length,g),M=[E,y],v=Ye("result",e[0].dataType,t.length,g),C="";if(_){let K=Pe("bias",e[2].dataType,e[2].dims.length,g);M.push(K),C+=` + fn getBiasByOutputCoords(coords : array) -> ${S} { + return bias[${i?et("coords",4,5):et("coords",1,5)}]; + }`}let A=_r(p,S),B=rn(r,A,S);return` + ${C} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${E.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${y.getByIndices("aIndices")}; + } + ${k.registerUniforms(w).declareVariables(...M,v)} + ${k.mainStart()} + ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${v.offsetToIndices("global_idx")}; + let batch = ${et("coords",0,E.rank)}; + let d2 = ${i?et("coords",E.rank-1,E.rank):et("coords",1,E.rank)}; + let xFRCCorner = vec3(${i?et("coords",1,E.rank):et("coords",2,E.rank)}, + ${i?et("coords",2,E.rank):et("coords",3,E.rank)}, + ${i?et("coords",3,E.rank):et("coords",4,E.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${i?et("uniforms.x_shape",1,E.rank):et("uniforms.x_shape",2,E.rank)}; + let xShapeZ = ${i?et("uniforms.x_shape",2,E.rank):et("uniforms.x_shape",3,E.rank)}; + let xShapeW = ${i?et("uniforms.x_shape",3,E.rank):et("uniforms.x_shape",4,E.rank)}; + let xShapeU = ${i?et("uniforms.x_shape",4,E.rank):et("uniforms.x_shape",1,E.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); + } + } + } + } + ${_?"value = value + getBiasByOutputCoords(coords)":""}; + ${B} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${r.cacheKey};${i};${p};${_}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:u[0],y:u[1],z:u[2]},programUniforms:c}),getShaderSource:T}}}),pm,hm,Ov=Be(()=>{ut(),mt(),ft(),on(),pm=(e,r,t,s)=>{let o=e.length>2,n=o?"value += b[output_channel];":"",i=e[0].dims,a=e[1].dims,l=r.format==="NHWC",u=l?t[3]:t[1],p=u/r.group,d=l&&p>=4?Kt(u):1,c=Me.size(t)/d,f=[{type:12,data:c},{type:12,data:r.dilations},{type:12,data:[r.strides[0],r.strides[1]]},{type:12,data:[r.pads[0],r.pads[1]]},{type:12,data:p}];sn(r,f),f.push(...st(i,[a[0],a[1],a[2],a[3]/d]));let _=o?["rank","rank","rank"]:["rank","rank"];f.push(...st([t[0],t[1],t[2],t[3]/d]));let T=k=>{let w=Ye("output",e[0].dataType,t.length,d),g=lr(w.type.tensor),S=rn(r,w.type.value,g),E=Pe("x",e[0].dataType,i.length),y=Pe("w",e[1].dataType,a.length,d),M=[E,y];o&&M.push(Pe("b",e[2].dataType,e[2].dims,d));let v=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:r.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];nn(r,v);let C=l?` + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) { + continue; + } + + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) { + let input_channel = in_channel_offset + wInChannel; + let xVal = ${E.get("batch","xHeight","xWidth","input_channel")}; + let wVal = ${y.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 = ${E.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${y.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal * wVal; + } + } + } + `;return` + ${k.registerUniforms(v).declareVariables(...M,w)} + + ${k.mainStart()} + ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${w.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${l?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${l?1:2}], outputIndices[${l?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel * ${d} / uniforms.output_channels_per_group; + var in_channel_offset = group_id * uniforms.w_shape[${l?2:1}]; + + var value: ${w.type.value} = ${w.type.value}(0); + ${C} + ${n} + ${S} + ${w.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:`${r.cacheKey}_${d}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:s?s(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:f}),getShaderSource:T}},hm=(e,r,t,s)=>{let o=e.length>2,n=Kt(t[3]),i=Kt(t[2]),a=Me.size(t)/n/i,l=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/n],u=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/n],p=[t[0],t[1],t[2],t[3]/n],d=[{type:12,data:a},{type:6,data:[r.strides[0],r.strides[1]]},{type:6,data:[r.pads[0],r.pads[1]]}];sn(r,d),d.push(...st(l,u,p));let c=(i-1)*r.strides[1]+u[1],f=_=>{let T=Ye("output",e[0].dataType,p.length,n),k=lr(T.type.tensor),w=rn(r,T.type.value,k),g=Pe("x",e[0].dataType,l.length,n),S=Pe("w",e[1].dataType,u.length,n),E=[g,S];o&&E.push(Pe("b",e[2].dataType,e[2].dims,n));let y=o?"value += b[output_channel];":"",M=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return nn(r,M),` + ${_.registerUniforms(M).declareVariables(...E,T)} + ${_.mainStart()} + ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let width0 = uniforms.output_shape[3]; + let output_channel = global_idx % width0; + var index1 = global_idx / width0; + let width1 = uniforms.output_shape[2] / ${i}u; + let col = (index1 % width1) * ${i}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${g.type.value}, ${c}>; + var values: array<${T.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 < ${u[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 < ${c}; i++) { + let x_width = x_corner.y + i; + if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { + x_vals[i] = ${g.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${g.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${u[1]}; w_width++) { + let w_val = ${S.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]; + ${y} + ${w} + ${T.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${r.cacheKey};${n};${i};${c};${u[0]};${u[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:s?s(t):t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:f}}}),mm,hi,_m,mi,Pl,Cl,fm,gm,Sl,Dv=Be(()=>{mt(),Av(),Fv(),xl(),Ov(),on(),Ml(),zs(),mm=(e,r,t,s,o,n)=>{let i=e[0],a=e.slice(n?1:2,n?3:4),l=a.length,u=r[0],p=r.slice(2).map((c,f)=>c+(c-1)*(t[f]-1)),d=a.map((c,f)=>c+s[f]+s[f+l]).map((c,f)=>Math.floor((c-p[f]+o[f])/o[f]));return d.splice(0,0,i),d.splice(n?3:1,0,u),d},hi=[2,3,1,0],_m=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let t=e[0].dims[r.format==="NHWC"?e[0].dims.length-1:1],s=e[1].dims[1]*r.group;if(t!==s)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let o=e[0].dims.length-2;if(r.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(r.strides.length!==o)throw new Error(`strides should be ${o}D`);if(r.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},mi=(e,r)=>{let t=e.kernelShape.slice();t.length{let r=fl(e),t=e.format,s=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],o=e.dilations,n=e.group,i=e.kernel_shape,a=e.pads,l=e.strides,u=e.w_is_const();return{autoPad:s,format:t,dilations:o,group:n,kernelShape:i,pads:a,strides:l,wIsConst:u,...r,cacheKey:`${e.format};${r.activation};`}},Cl=(e,r,t,s)=>{let o=t.format==="NHWC",n=mm(r[0].dims,r[1].dims,t.dilations,t.pads,t.strides,o);if(t.group!==1){let M=[r[0]];if(o){let v=e.kernelCustomData.wT??e.compute(Lr(r[1],hi),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=v),M.push(v)}else M.push(r[1]);r.length===3&&M.push(r[2]),!e.adapterInfo.isArchitecture("ampere")&&o&&r[1].dims[0]===t.group&&r[1].dims[1]===1&&t.dilations[0]===1&&t.dilations[1]===1?e.compute(hm(M,t,n,s),{inputs:M}):e.compute(pm(M,t,n,s),{inputs:M});return}let i=r.length===3,a=r[0].dims[o?1:2],l=r[0].dims[o?2:3],u=r[0].dims[o?3:1],p=r[1].dims[2],d=r[1].dims[3],c=n[o?1:2],f=n[o?2:3],_=n[o?3:1],T=o&&p===a&&d===l&&t.pads[0]===0&&t.pads[1]===0;if(T||p===1&&d===1&&t.dilations[0]===1&&t.dilations[1]===1&&t.strides[0]===1&&t.strides[1]===1&&t.pads[0]===0&&t.pads[1]===0){let M=n[0],v,C,A,B=[];if(o){let j=e.kernelCustomData.wT??e.compute(Lr(r[1],hi),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];if(t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=j),T){let ee=a*l*u;v=r[0].reshape([1,M,ee]),C=j.reshape([1,ee,_]),A=[1,M,_]}else v=r[0].reshape([M,a*l,u]),C=j.reshape([1,u,_]),A=[M,c*f,_];B.push(v),B.push(C)}else v=r[0].reshape([M,u,a*l]),C=r[1].reshape([1,_,u]),A=[M,_,c*f],B.push(C),B.push(v);i&&B.push(r[2]);let K=A[2],G=B[0].dims[B[0].dims.length-1];K<8&&G<8?e.compute(wl(B,t,n,A,o,s),{inputs:B}):e.compute(pi(B,t,n,A,o,s),{inputs:B});return}let k=!0,w=e.kernelCustomData.wT??e.compute(Lr(r[1],hi),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=w);let g=[r[0],w];i&&g.push(r[2]);let S=o?c*f:_,E=o?_:c*f,y=p*d*u;e.compute(im(g,t,n,S,E,y,i,k,s),{inputs:g})},fm=(e,r)=>{let t=r.format==="NHWC",s=[e.inputs[0].reshape(t?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&s.push(e.inputs[2]);let o=[0,r.pads[0],0,r.pads[1]],n=[1].concat(r.strides),i=[1].concat(r.dilations),a=[1].concat(r.kernelShape),l=mi({...r,pads:o,strides:n,dilations:i,kernelShape:a},s);Cl(e,s,l,u=>t?[u[0],u[2],u[3]]:[u[0],u[1],u[3]])},gm=(e,r,t)=>{let s=t.format==="NHWC"?"channelsLast":"channelsFirst",o=mi(t,r),n=t.autoPad==="NOTSET"?t.pads:t.autoPad,i=dm(r[0].dims,r[1].dims,t.strides,t.dilations,n,!1,s);e.compute(cm(r,o,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],s))},Sl=(e,r)=>{if(_m(e.inputs,r),e.inputs[0].dims.length===3)fm(e,r);else if(e.inputs[0].dims.length===5)gm(e,e.inputs,r);else{let t=mi(r,e.inputs);Cl(e,e.inputs,t)}}}),wm,Lv=Be(()=>{ut(),Es(),mt(),ft(),wm=(e,r,t)=>{let s=e.length>2,o=r.outputShape,n=r.format==="NHWC",i=r.group,a=e[1].dims,l=a[2]/i,u=a[3],p=n?Kt(l):1,d=n&&u===1&&l>=4,c=d?Math.floor(l/4)*4:Math.floor(l/p)*p,f=l-c,_=n?Kt(u):1,T=n?u===1?p:_:1,k=Me.size(o)/_,w=[Math.ceil(k/64),1,1];vt("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${w}`);let g=["rank","rank"],S=[r.strides[0],r.strides[1]],E=[r.kernelShape[n?1:2],r.kernelShape[n?2:3]],y=[r.dilations[0],r.dilations[1]],M=[E[0]+(r.dilations[0]<=1?0:(r.kernelShape[n?1:2]-1)*(r.dilations[0]-1)),E[1]+(r.dilations[1]<=1?0:(r.kernelShape[n?2:3]-1)*(r.dilations[1]-1))],v=[M[0]-1-Math.floor((r.pads[0]+r.pads[2])/2),M[1]-1-Math.floor((r.pads[1]+r.pads[3])/2)],C=[{type:12,data:k},{type:12,data:S},{type:12,data:E},{type:12,data:y},{type:12,data:M},{type:6,data:v},{type:12,data:c},{type:12,data:l},{type:12,data:u},...st(e[0].dims,e[1].dims)];s&&(C.push(...st(e[2].dims)),g.push("rank")),C.push(...st(o));let A=B=>{let K=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:S.length},{name:"filter_dims",type:"u32",length:E.length},{name:"dilations",type:"u32",length:E.length},{name:"effective_filter_dims",type:"u32",length:M.length},{name:"pads",type:"i32",length:v.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],G=lr(e[0].dataType),j=n?1:2,ee=n?2:3,H=n?3:1,Z=Pe("W",e[1].dataType,e[1].dims.length,T),X=Pe("Dy",e[0].dataType,e[0].dims.length,p),oe=[X,Z];s&&oe.push(Pe("bias",e[2].dataType,[o[H]].length,_));let me=Ye("result",e[0].dataType,o.length,_),ae=()=>{let W="";if(d)p===4?W+=` + let xValue = ${X.getByOffset("x_offset")}; + let wValue = ${Z.getByOffset("w_offset")}; + dotProd = dotProd + dot(xValue, wValue); + x_offset += 1u; + w_offset += 1u;`:p===2?W+=` + dotProd = dotProd + dot(vec4<${G}>(${X.getByOffset("x_offset")}, ${X.getByOffset("x_offset + 1u")}), vec4<${G}>(${Z.getByOffset("w_offset")}, ${Z.getByOffset("w_offset + 1u")})); + x_offset += 2u; + w_offset += 2u;`:p===1&&(W+=` + dotProd = dotProd + dot(vec4<${G}>(${X.getByOffset("x_offset")}, ${X.getByOffset("x_offset + 1u")}, ${X.getByOffset("x_offset + 2u")}, ${X.getByOffset("x_offset + 3u")}), vec4<${G}>(${Z.getByOffset("w_offset")}, ${Z.getByOffset("w_offset + 1u")}, ${Z.getByOffset("w_offset + 2u")}, ${Z.getByOffset("w_offset + 3u")})); + x_offset += 4u; + w_offset += 4u;`);else if(W+=` + let xValue = ${n?X.getByOffset(`${X.indicesToOffset(`${X.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}`):X.get("batch","inputChannel","idyR","idyC")}; + `,p===1)W+=` + let w_offset = ${Z.indicesToOffset(`${Z.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)}; + let wValue = ${Z.getByOffset(`w_offset / ${T}`)}; + dotProd = dotProd + xValue * wValue;`;else for(let re=0;re{if(f===0)return"";if(!d)throw new Error(`packInputAs4 ${d} is not true.`);let W="";if(p===1){W+="dotProd = dotProd";for(let re=0;re(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 = ${me.type.value}(0.0); + var wR: u32 = 0; + if (uniforms.dilations.x == 1) { + // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0 + wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner); + } + for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${G}(dyRCorner) + ${G}(wR)) / ${G}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${G}(uniforms.Dy_shape[${j}]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + var wC: u32 = 0; + if (uniforms.dilations.y == 1) { + // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0 + wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner); + } + for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { + if (wC % uniforms.dilations.y != 0) { + continue; + } + let dyC = (${G}(dyCCorner) + ${G}(wC)) / ${G}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${G}(uniforms.Dy_shape[${ee}]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC: u32 = u32(dyC); + var inputChannel = groupId * uniforms.input_channels_per_group; + ${d?` + var x_offset = ${X.indicesToOffset(`${X.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}; + var w_offset = ${Z.indicesToOffset(`${Z.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${T}; + `:""} + for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${d?4:p}) { + ${ae()} + inputChannel = inputChannel + ${d?4:p}; + } + ${V()} + wC = wC + uniforms.strides.y - 1; + } + wR = wR + uniforms.strides[0] - 1; + } + let value = dotProd${s?` + bias[d1 / ${_}]`:""}; + ${me.setByOffset("global_idx","value")}; + `;return` + ${B.registerUniforms(K).declareVariables(...oe,me)} + ${B.mainStart()} + ${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${F}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${r.cacheKey};${p}${T}${_}${d}${f}`,inputDependencies:g},getRunData:()=>({dispatchGroup:{x:w[0],y:w[1],z:w[2]},outputs:[{dims:t?t(o):o,dataType:e[0].dataType}],programUniforms:C}),getShaderSource:A}}}),Mm,bm,ym,$l,vm,xm,kl,Tm,Em,zv=Be(()=>{Lv(),on(),zs(),Mm=(e,r,t,s,o,n)=>(e-1)*r+t+(s-1)*o+1-n,bm=(e,r,t,s,o)=>{let n=Math.floor(e/2);r==="SAME_UPPER"?(t[s]=n,t[o]=e-n):r==="SAME_LOWER"&&(t[s]=e-n,t[o]=n)},ym=(e,r,t,s,o,n,i,a,l,u)=>{let p=e.length-2,d=u.length===0;l.length{let t=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((d,c)=>d*c,1)===0){t.length=0;for(let d=2;dd+c,0)===0){let d=r[0].dims.length-2;l=new Array(d).fill(1)}let u=e.strides.slice();if(u.reduce((d,c)=>d+c,0)===0){let d=r[0].dims.length-2;u=new Array(d).fill(1)}ym(a,t,l,e.autoPad,e.group,o,u,s,i,n);let p=Object.assign({},e);return Object.assign(p,{kernelShape:t,pads:o,outputPadding:i,outputShape:n,dilations:l,strides:u}),p},vm=e=>{let r=fl(e),t=e.format,s=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],o=e.dilations,n=e.group,i=e.kernelShape,a=e.pads,l=e.strides,u=e.wIsConst(),p=e.outputPadding,d=e.outputShape;return{autoPad:s,format:t,dilations:o,group:n,kernelShape:i,outputPadding:p,outputShape:d,pads:a,strides:l,wIsConst:u,...r,cacheKey:`${e.format};${r.activation};`}},xm=(e,r)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let t=e[0].dims[r.format==="NHWC"?e[0].dims.length-1:1],s=e[1].dims[0];if(t!==s)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let o=e[1].dims[1]*r.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==o))throw new Error("invalid bias");let n=e[0].dims.length-2;if(r.dilations.reduce((i,a)=>i+a,0)>0&&r.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(r.strides.reduce((i,a)=>i+a,0)>0&&r.strides.length!==n)throw new Error(`strides should be ${n}D`);if(r.pads.reduce((i,a)=>i+a,0)>0&&r.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(r.outputPadding.length!==n&&r.outputPadding.length!==0)throw new Error(`output_padding should be ${n}D`);if(r.kernelShape.reduce((i,a)=>i+a,0)>0&&r.kernelShape.length!==0&&r.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(r.outputShape.length!==0&&r.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},kl=(e,r,t,s)=>{let o=e.kernelCustomData.wT??e.compute(Lr(r[1],[2,3,0,1]),{inputs:[1],outputs:[t.wIsConst?-2:-1]})[0];t.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=o);let n=[r[0],o];r.length===3&&n.push(r[2]),e.compute(wm(n,t,s),{inputs:n})},Tm=(e,r)=>{let t=r.format==="NHWC",s=[e.inputs[0].reshape(t?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&s.push(e.inputs[2]);let o=r.kernelShape;(o.length===0||o[0]===0)&&(o=[e.inputs[1].dims[2]]);let n=r.dilations;(n.length===0||n[0]===0)&&(n=[1]);let i=r.strides;(i.length===0||i[0]===0)&&(i=[1]);let a=r.pads;a.length===0&&(a=[0,0]),a=[0,a[0],0,a[1]],i=[1].concat(i),n=[1].concat(n),o=[1].concat(o);let l=r.outputPadding;l=[0].concat(l);let u=$l({...r,pads:a,strides:i,dilations:n,kernelShape:o,outputPadding:l},s);kl(e,s,u,p=>t?[p[0],p[2],p[3]]:[p[0],p[1],p[3]])},Em=(e,r)=>{if(xm(e.inputs,r),e.inputs[0].dims.length===3)Tm(e,r);else{let t=$l(r,e.inputs);kl(e,e.inputs,t)}}}),Pm,Cm,Sm,Bv=Be(()=>{ut(),mt(),Xt(),ft(),Pm=(e,r,t,s)=>{let o=Me.size(r),n=r.length,i=Pe("input",e,n),a=Ye("output",e,n),l=t.dataType===6?t.getInt32Array()[0]:Number(t.getBigInt64Array()[0]),u=Me.normalizeAxis(l,n),p=d=>{let c=` i32(${i.indicesGet("inputIndices","uniforms.axis")}) `,f=et("uniforms.input_shape","uniforms.axis",n),_=s.reverse?c+(s.exclusive?" + 1":""):"0",T=s.reverse?f:c+(s.exclusive?"":" + 1");return` + ${d.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(i,a)} + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${a.offsetToIndices("global_idx")}; + var sum = ${a.type.value}(0); + let first : i32 = ${_}; + let last : i32 = ${T}; + for (var i : i32 = first; i < last; i++) { + ${i.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${i.getByIndices("inputIndices")}; + } + ${a.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:s.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:[{type:12,data:o},{type:12,data:u},...st(r,r)]}),getShaderSource:p}},Cm=(e,r)=>{let t=e.inputs[0].dims,s=e.inputs[0].dataType,o=e.inputs[1];e.compute(Pm(s,t,o,r),{inputs:[0]})},Sm=e=>{let r=e.exclusive===1,t=e.reverse===1;return St({exclusive:r,reverse:t})}}),$m,km,Im,Am,Fm,Rv=Be(()=>{ut(),mt(),Xt(),ft(),$m=e=>{if(!e||e.length!==1)throw new Error("DepthToSpace requires 1 input.");if(e[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},km=(e,r,t,s)=>{let o=[];o.push(`fn perm(i: ${s.type.indices}) -> ${t.type.indices} { + var a: ${t.type.indices};`);for(let n=0;n{let t,s,o,n,i,a,l=r.format==="NHWC",u=r.blocksize,p=r.mode==="DCR";l?([t,s,o,n]=e.dims,i=p?[t,s,o,u,u,n/u**2]:[t,s,o,n/u**2,u,u],a=p?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([t,s,o,n]=[e.dims[0],e.dims[2],e.dims[3],e.dims[1]],i=p?[t,u,u,n/u**2,s,o]:[t,n/u**2,u,u,s,o],a=p?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let d=e.reshape(i),c=d.dims.length,f=e.dataType,_=Pe("a",f,c),T=Ye("output",f,c),k=w=>` + ${w.registerUniform("output_size","u32").declareVariables(_,T)} + + ${km(a,c,_,T)} + + ${w.mainStart()} + ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${T.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${T.setByOffset("global_idx",_.getByIndices("aIndices"))} + }`;return{name:"DepthToSpace",shaderCache:{hint:`${e.dims};${r.blocksize};${r.mode}`,inputDependencies:["rank"]},getRunData:w=>{let g=l?[t,s*u,o*u,n/u**2]:[t,n/u**2,s*u,o*u],S=Me.size(g),E=d.dims,y=Me.sortBasedOnPerm(E,a);return{outputs:[{dims:g,dataType:w[0].dataType}],dispatchGroup:{x:Math.ceil(S/64)},programUniforms:[{type:12,data:S},...st(E,y)]}},getShaderSource:k}},Am=(e,r)=>{$m(e.inputs),e.compute(Im(e.inputs[0],r))},Fm=e=>St({blocksize:e.blocksize,mode:e.mode,format:e.format})}),_i,wo,Il,Om,Dm,Lm,zm,Al,Bm,Rm,jm,jv=Be(()=>{ut(),mt(),Xt(),ft(),_i="[a-zA-Z]|\\.\\.\\.",wo="("+_i+")+",Il="^"+wo+"$",Om="("+wo+",)*"+wo,Dm="^"+Om+"$",Lm=class{constructor(e=-1){this.symbolToIndices=new Map,this.inputIndex=e}addSymbol(e,r){let t=this.symbolToIndices.get(e);t===void 0?t=[r]:t.push(r),this.symbolToIndices.set(e,t)}},zm=class{constructor(e,r){var o;this.equation=r,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[t,s]=r.includes("->")?r.split("->",2):[r,""];if(!t.match(RegExp(Dm)))throw new Error("Invalid LHS term");if(t.split(",").forEach((n,i)=>{let a=e[i].dims.slice();if(!n.match(RegExp(Il)))throw new Error("Invalid LHS term");let l=this.processTerm(n,!0,a,i);this.lhs.push(l)}),s==="")s+=[...this.symbolToInfo.entries()].filter(([n,i])=>i.count===1||n==="...").map(([n])=>n).join("");else if(!s.match(RegExp(wo)))throw new Error("Invalid RHS");(o=s.match(RegExp(_i,"g")))==null||o.forEach(n=>{if(n==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let i=this.symbolToInfo.get(n);if(i===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(i.dimValue)}}),this.rhs=this.processTerm(s,!1,this.outputDims)}addSymbol(e,r,t){let s=this.symbolToInfo.get(e);if(s!==void 0){if(s.dimValue!==r&&s.count!==1)throw new Error("Dimension mismatch");s.count++,s.inputIndices.push(t)}else s={count:1,dimValue:r,inputIndices:[t]};this.symbolToInfo.set(e,s)}processTerm(e,r,t,s=-1){let o=t.length,n=!1,i=[],a=0;if(!e.match(RegExp(Il))&&!r&&e!=="")throw new Error("Invalid LHS term");let l=e.match(RegExp(_i,"g")),u=new Lm(s);return l==null||l.forEach((p,d)=>{if(p==="..."){if(n)throw new Error("Only one ellipsis is allowed per input term");n=!0;let c=o-l.length+1;if(c<0)throw new Error("Ellipsis out of bounds");if(i=t.slice(a,a+c),this.hasEllipsis){if(this.ellipsisDims.length!==i.length||this.ellipsisDims.toString()!==i.toString())throw new Error("Ellipsis dimensions mismatch")}else if(r)this.hasEllipsis=!0,this.ellipsisDims=i;else throw new Error("Ellipsis must be specified in the LHS");for(let f=0;fe+"_max",Bm=(e,r,t,s)=>{let o=e.map(u=>u.length).map((u,p)=>Pe(`input${p}`,r,u)),n=Me.size(s),i=Ye("output",r,s.length),a=[...t.symbolToInfo.keys()].filter(u=>!t.rhs.symbolToIndices.has(u)),l=u=>{let p=[],d="var prod = 1.0;",c="var sum = 0.0;",f="sum += prod;",_=[],T=[],k=[],w=[],g=t.symbolToInfo.size===t.rhs.symbolToIndices.size;t.symbolToInfo.forEach((E,y)=>{var M;if(t.rhs.symbolToIndices.has(y)){let v=(M=t.rhs.symbolToIndices.get(y))==null?void 0:M[0];v!==void 0&&t.lhs.forEach((C,A)=>{if(E.inputIndices.includes(A)){let B=C.symbolToIndices.get(y);if(B===void 0)throw new Error("Invalid symbol error");B.forEach(K=>{p.push(`${o[A].indicesSet(`input${A}Indices`,K,i.indicesGet("outputIndices",v))}`)})}})}else t.lhs.forEach((v,C)=>{if(E.inputIndices.includes(C)){let A=v.symbolToIndices.get(y);if(A===void 0)throw new Error("Invalid symbol error");A.forEach(B=>{_.push(`${o[C].indicesSet(`input${C}Indices`,B,`${y}`)}`)}),w.push(`prod *= ${o[C].getByIndices(`input${C}Indices`)};`)}}),T.push(`for(var ${y}: u32 = 0; ${y} < uniforms.${Al(y)}; ${y}++) {`),k.push("}")});let S=g?[...p,`let sum = ${o.map((E,y)=>E.getByIndices(`input${y}Indices`)).join(" * ")};`]:[...p,c,...T,..._,d,...w,f,...k];return` + ${u.registerUniforms(a.map(E=>({name:`${Al(E)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...o,i)} + + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${i.offsetToIndices("global_idx")}; + ${o.map((E,y)=>`var input${y}Indices: ${o[y].type.indices};`).join(` +`)} + ${S.join(` +`)}; + ${i.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:t.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let u=a.filter(d=>t.symbolToInfo.has(d)).map(d=>{var c;return{type:12,data:((c=t.symbolToInfo.get(d))==null?void 0:c.dimValue)||0}});u.push({type:12,data:n});let p=e.map((d,c)=>[...st(d)]).reduce((d,c)=>d.concat(c),u);return p.push(...st(s)),{outputs:[{dims:s,dataType:r}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:p}},getShaderSource:l}},Rm=(e,r)=>{let t=new zm(e.inputs,r.equation),s=t.outputDims,o=e.inputs.map((n,i)=>n.dims);e.compute(Bm(o,e.inputs[0].dataType,t,s))},jm=e=>{let r=e.equation.replace(/\s+/g,"");return St({equation:r})}}),Nm,Fl,Vm,Um,Wm,Nv=Be(()=>{ut(),mt(),ft(),Nm=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let r=e[0].dims,t=Array.from(e[1].getBigInt64Array(),Number),s=t.length{let t=e.length-r.length,s=[];for(let o=0;oe.length>r.length?Fl(e,r):Fl(r,e),Um=e=>{let r=e[0].dims,t=Array.from(e[1].getBigInt64Array(),Number),s=Vm(r,t),o=e[0].dataType,n=o===9||Me.size(r)===1,i=o===9||r.length>0&&r[r.length-1]%4===0?4:1,a=n||s.length>0&&s[s.length-1]%4===0?4:1,l=Math.ceil(Me.size(s)/a),u=d=>{let c=Pe("input",o,r.length,i),f=Ye("output",o,s.length,a),_;if(o===9){let T=(k,w,g="")=>` + let outputIndices${w} = ${f.offsetToIndices(`outputOffset + ${w}u`)}; + let offset${w} = ${c.broadcastedIndicesToOffset(`outputIndices${w}`,f)}; + let index${w} = offset${w} / 4u; + let component${w} = offset${w} % 4u; + ${k}[${w}] = ${g}(${c.getByOffset(`index${w}`)}[component${w}]); + `;_=` + let outputOffset = global_idx * ${a}; + var data = vec4(0); + ${T("data",0,"u32")} + ${T("data",1,"u32")} + ${T("data",2,"u32")} + ${T("data",3,"u32")} + ${f.setByOffset("global_idx","data")} + }`}else _=` + let outputIndices = ${f.offsetToIndices(`global_idx * ${a}`)}; + let inputOffset = ${c.broadcastedIndicesToOffset("outputIndices",f)}; + let data = ${f.type.value}(${c.getByOffset(`inputOffset / ${i}`)}); + ${f.setByOffset("global_idx","data")} + }`;return` + ${d.registerUniform("vec_size","u32").declareVariables(c,f)} + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${_}`},p=[{type:12,data:l},...st(r,s)];return{name:"Expand",shaderCache:{hint:`${s.length};${i}${a}`,inputDependencies:["rank"]},getShaderSource:u,getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:p})}},Wm=e=>{Nm(e.inputs),e.compute(Um(e.inputs),{inputs:[0]})}}),Gm,Km,Vv=Be(()=>{ut(),mt(),ft(),_l(),Gm=e=>{let r=e[0].dataType,t=Me.size(e[0].dims),s=Me.size(e[1].dims),o=s%4===0,n=i=>{let a=Pe("x",r,[1],4),l=Pe("bias",r,[1],4),u=Ye("y",r,[1],4),p=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],d=f=>` + let bias${f}_offset: u32 = (global_idx * 4 + ${f}) % uniforms.bias_size; + let bias${f} = ${l.getByOffset(`bias${f}_offset / 4`)}[bias${f}_offset % 4];`,c=o?` + let bias = ${l.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${d(0)}${d(1)}${d(2)}${d(3)} + let bias = ${a.type.value}(bias0, bias1, bias2, bias3);`;return`${i.registerUniforms(p).declareVariables(a,l,u)} + + ${hl(yr(r))} + + ${i.mainStart(Vn)} + ${i.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${a.getByOffset("global_idx")}; + ${c} + let x_in = x + bias; + ${u.setByOffset("global_idx",ml("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${o}`,inputDependencies:["type","type"]},getShaderSource:n,getRunData:i=>({outputs:[{dims:i[0].dims,dataType:i[0].dataType}],programUniforms:[{type:12,data:Math.ceil(t/4)},{type:12,data:s}],dispatchGroup:{x:Math.ceil(t/Vn/4)}})}},Km=e=>{e.inputs.length<2||Me.size(e.inputs[1].dims)===0?Ph(e):e.compute(Gm(e.inputs))}}),Hm,qm,Xm,Qm,Uv=Be(()=>{ut(),mt(),Xt(),ft(),Hm=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},qm=(e,r)=>{let t=e[0].dims,s=e[1].dims,o=t.length,n=Me.normalizeAxis(r.axis,o),i=t.slice(0);i.splice(n,1,...s);let a=t[n],l=e[0].dataType===9?4:1,u=Math.ceil(Me.size(i)/l),p=[{type:12,data:u},{type:6,data:a},{type:12,data:n},...st(e[0].dims,e[1].dims,i)],d=c=>{let f=Pe("data",e[0].dataType,e[0].dims.length,l),_=Pe("inputIndices",e[1].dataType,e[1].dims.length),T=Ye("output",e[0].dataType,i.length,l),k=g=>{let S=s.length,E=`var indicesIndices${g} = ${_.type.indices}(0);`;for(let y=0;y1?`indicesIndices${g}[${y}]`:`indicesIndices${g}`} = ${i.length>1?`outputIndices${g}[uniforms.axis + ${y}]`:`outputIndices${g}`};`;E+=` + var idx${g} = ${_.getByIndices(`indicesIndices${g}`)}; + if (idx${g} < 0) { + idx${g} = idx${g} + uniforms.axisDimLimit; + } + var dataIndices${g} : ${f.type.indices}; + `;for(let y=0,M=0;y1?`dataIndices${g}[${y}]`:`dataIndices${g}`} = u32(idx${g});`,M+=S):(E+=`${o>1?`dataIndices${g}[${y}]`:`dataIndices${g}`} = ${i.length>1?`outputIndices${g}[${M}]`:`outputIndices${g}`};`,M++);return E},w;if(e[0].dataType===9){let g=(S,E,y="")=>` + let outputIndices${E} = ${T.offsetToIndices(`outputOffset + ${E}u`)}; + ${k(E)}; + let offset${E} = ${f.indicesToOffset(`dataIndices${E}`)}; + let index${E} = offset${E} / 4u; + let component${E} = offset${E} % 4u; + ${S}[${E}] = ${y}(${f.getByOffset(`index${E}`)}[component${E}]); + `;w=` + let outputOffset = global_idx * ${l}; + var value = vec4(0); + ${g("value",0,"u32")} + ${g("value",1,"u32")} + ${g("value",2,"u32")} + ${g("value",3,"u32")} + ${T.setByOffset("global_idx","value")} + `}else w=` + let outputIndices = ${T.offsetToIndices("global_idx")}; + ${k("")}; + let value = ${f.getByIndices("dataIndices")}; + ${T.setByOffset("global_idx","value")}; + `;return` + ${c.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(f,_,T)} + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${w} + }`};return{name:"Gather",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:p}),getShaderSource:d}},Xm=e=>St({axis:e.axis}),Qm=(e,r)=>{let t=e.inputs;Hm(t),e.compute(qm(e.inputs,r))}}),Jm,Ym,Zm,Wv=Be(()=>{ut(),mt(),ft(),Jm=(e,r,t,s,o,n,i,a,l)=>{let u=[{type:12,data:n},{type:12,data:s},{type:12,data:o},{type:12,data:t},{type:12,data:i},{type:12,data:a},{type:12,data:l}],p=[n];u.push(...st(r.dims,p));let d=c=>{let f=Pe("indices_data",r.dataType,r.dims.length),_=Ye("input_slice_offsets_data",12,1,1),T=[f,_],k=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:o.length},{name:"sizes_from_slice_dims_data",type:"u32",length:t.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return` + ${c.registerUniforms(k).declareVariables(...T)} + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let batch_idx = global_idx / uniforms.num_slices_per_batch; + let base_offset = batch_idx * uniforms.input_batch_stride; + + let slice_indices_base_offset = global_idx * uniforms.num_slice_dims; + var relative_slice_offset = 0; + for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) { + var index = i32(indices_data[dim_idx + slice_indices_base_offset].x); + let input_dim_idx = uniforms.batch_dims + dim_idx; + if (index < 0) { + ${o.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"} + } + ${t.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"} + } + + input_slice_offsets_data[global_idx] = base_offset + u32(relative_slice_offset); + }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${o.length}_${t.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:p,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:u}),getShaderSource:d},{inputs:[r],outputs:[-1]})[0]},Ym=(e,r)=>{let t=e.inputs,s=t[0].dims,o=t[0].dataType,n=t[1].dims,i=n[n.length-1],a=Me.sizeToDimension(n,n.length-1),l=Me.sizeFromDimension(s,r.batchDims+i),u=Me.sizeToDimension(s,r.batchDims),p=Me.sizeFromDimension(s,r.batchDims),d=a/u,c=new Array(i),f=l;for(let E=0;Es.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let k=n.slice(0,-1).concat(s.slice(T)),w=Me.size(k),g=[{type:12,data:w},{type:12,data:l},...st(t[0].dims,_.dims,k)],S=E=>{let y=Pe("data",t[0].dataType,t[0].dims.length),M=Pe("slice_offsets",12,_.dims.length),v=Ye("output",t[0].dataType,k.length);return` + ${E.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(y,M,v)} + ${E.mainStart()} + ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let slice_offset = slice_offsets[global_idx / uniforms.slice_size]; + output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size]; + }`};e.compute({name:"GatherND",shaderCache:{hint:r.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:k,dataType:o}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:g}),getShaderSource:S},{inputs:[t[0],_]})},Zm=e=>({batchDims:e.batch_dims,cacheKey:""})}),e_,t_,r_,s_,Gv=Be(()=>{ut(),mt(),Xt(),ft(),e_=(e,r)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let t=Me.normalizeAxis(r.quantizeAxis,e[0].dims.length),s=r.blockSize,o=e[0],n=e[2],i=e.length===4?e[3]:void 0;if(n.dims.length!==o.dims.length||!o.dims.map((a,l)=>l===t?Math.ceil(a/s)===n.dims[l]:a===n.dims[l]).reduce((a,l)=>a&&l,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(i){if(i.dataType!==o.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(i.dims.length!==n.dims.length||!i.dims.map((a,l)=>a===n.dims[l]).reduce((a,l)=>a&&l,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},t_=(e,r)=>{let t=e[0].dims,s=e[1].dims,o=t.length,n=Me.normalizeAxis(r.gatherAxis,o),i=Me.normalizeAxis(r.quantizeAxis,o),a=t.slice(0);a.splice(n,1,...s);let l=Me.size(a),u=e[2].dataType,p=e[0].dataType===22,d=[{type:12,data:l},{type:12,data:i},{type:12,data:n},{type:12,data:r.blockSize},...st(...e.map((f,_)=>f.dims),a)],c=f=>{let _=Pe("data",e[0].dataType,e[0].dims.length),T=Pe("inputIndices",e[1].dataType,e[1].dims.length),k=Pe("scales",e[2].dataType,e[2].dims.length),w=e.length>3?Pe("zeroPoint",e[3].dataType,e[3].dims.length):void 0,g=Ye("output",u,a.length),S=[_,T,k];w&&S.push(w);let E=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${f.registerUniforms(E).declareVariables(...S,g)} + ${f.mainStart()} + let output_indices = ${g.offsetToIndices("global_idx")}; + var indices_indices = ${T.type.indices}(0); + ${s.length>1?` + for (var i: u32 = 0; i < ${s.length}; i++) { + let index = ${g.indicesGet("output_indices","uniforms.gather_axis + i")}; + ${T.indicesSet("indices_indices","i","index")}; + }`:`indices_indices = ${g.indicesGet("output_indices","uniforms.gather_axis")};`}; + var data_indices = ${_.type.indices}(0); + for (var i: u32 = 0; i < uniforms.gather_axis; i++) { + let index = ${g.indicesGet("output_indices","i")}; + ${_.indicesSet("data_indices","i","index")}; + } + var index_from_indices = ${T.getByIndices("indices_indices")}; + if (index_from_indices < 0) { + index_from_indices += ${t[n]}; + } + ${_.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")}; + for (var i = uniforms.gather_axis + 1; i < ${a.length}; i++) { + let index = ${g.indicesGet("output_indices",`i + ${s.length} - 1`)}; + ${_.indicesSet("data_indices","i","index")}; + } + let data_offset = ${_.indicesToOffset("data_indices")}; + let data_index = data_offset % 8; + // Convert 4-bit packed data to 8-bit packed data. + let packed_4bit_quantized_data = ${_.getByOffset("data_offset / 8")}; + let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f; + let quantized_data_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data)); + let quantized_data = quantized_data_vec[data_index / 2]; + var scale_indices = data_indices; + let quantize_axis_index = ${k.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size; + ${k.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")}; + var scale = ${k.getByIndices("scale_indices")}; + ${w?` + let zero_point_indices = scale_indices; + let zero_point_offset = ${w.indicesToOffset("zero_point_indices")}; + let zero_point_index = zero_point_offset % 8; + let packed_4bit_zero_points = ${w.getByOffset("zero_point_offset / 8")}; + let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f; + let zero_point_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points)); + let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"}; + let dequantized_data = ${yr(u)}(quantized_data - zero_point) * scale; + ${g.setByOffset("global_idx","dequantized_data")}; + }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${r.cacheKey};${e.filter((f,_)=>_!==1).map(f=>f.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(f,_)=>"rank")},getRunData:()=>({outputs:[{dims:a,dataType:u}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:c}},r_=(e,r)=>{let t=e.inputs;e_(t,r),e.compute(t_(e.inputs,r))},s_=e=>St({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),n_,o_,i_,a_,Kv=Be(()=>{ut(),mt(),Xt(),ft(),n_=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.`)},o_=(e,r)=>{let t=e[0].dims,s=e[0].dataType,o=t.length,n=e[1].dims,i=e[1].dataType,a=Me.normalizeAxis(r.axis,o),l=t[a],u=n.slice(0),p=Me.size(u),d=Pe("input",s,o),c=Pe("indicesInput",i,n.length),f=Ye("output",s,u.length),_=[{type:12,data:p},{type:6,data:l},{type:12,data:a}];return _.push(...st(t,n,u)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:_}),getShaderSource:T=>` + ${T.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(d,c,f)} + ${T.mainStart()} + ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${f.offsetToIndices("global_idx")}; + + var idx = ${c.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${d.type.indices}(outputIndices); + ${d.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${d.getByIndices("inputIndices")}; + + ${f.setByOffset("global_idx","value")}; + }`}},i_=e=>St({axis:e.axis}),a_=(e,r)=>{let t=e.inputs;n_(t),e.compute(o_(e.inputs,r))}}),l_,u_,d_,c_,Hv=Be(()=>{ut(),mt(),ft(),l_=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")},u_=(e,r)=>{let t=e[0].dims.slice(),s=e[1].dims.slice(),[o,n,i]=bc.getShapeOfGemmResult(t,r.transA,s,r.transB,e.length===3?e[2].dims:void 0),a=[o,n];if(!a)throw new Error("Can't use gemm on the given tensors");let l=16,u=Math.ceil(n/l),p=Math.ceil(o/l),d=!0,c=Me.size(a),f=[{type:12,data:d?u:c},{type:12,data:o},{type:12,data:n},{type:12,data:i},{type:1,data:r.alpha},{type:1,data:r.beta}],_=["type","type"];e.length===3&&(f.push(...st(e[2].dims)),_.push("rank")),f.push(...st(a));let T=w=>{let g="";r.transA&&r.transB?g="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":r.transA&&!r.transB?g="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!r.transA&&r.transB?g="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!r.transA&&!r.transB&&(g="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let S=r.alpha===1?"":"value *= uniforms.alpha;",E=Pe("a",e[0].dataType,e[0].dims),y=Pe("b",e[1].dataType,e[1].dims),M=E.type.value,v=null,C=[E,y];e.length===3&&(v=Pe("c",e[2].dataType,e[2].dims.length),C.push(v));let A=Ye("output",e[0].dataType,a.length);C.push(A);let B=[{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` + ${w.registerUniforms(B).declareVariables(...C)} + + ${w.mainStart()} + ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${M}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${g} + } + + ${S} + ${v!=null?`let cOffset = ${v.broadcastedIndicesToOffset("vec2(m, n)",A)}; value += ${M}(uniforms.beta) * ${v.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`},k=w=>{let g=Pe("a",e[0].dataType,e[0].dims),S=Pe("b",e[1].dataType,e[1].dims),E=null,y=[g,S];e.length===3&&(E=Pe("c",e[2].dataType,e[2].dims.length),y.push(E));let M=Ye("output",e[0].dataType,a.length);y.push(M);let v=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],C="",A="";r.transA&&r.transB?(A=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${g.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${S.type.value}(0); + } + `,C="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):r.transA&&!r.transB?(A=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${g.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${S.type.value}(0); + } + `,C="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!r.transA&&r.transB?(A=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${g.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${S.type.value}(0); + } + `,C="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!r.transA&&!r.transB&&(A=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${g.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${S.type.value}(0); + } + `,C="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let B=r.alpha===1?"":"value *= uniforms.alpha;";return` + ${w.registerUniforms(v).declareVariables(...y)} + var tile_a: array, ${l}>; + var tile_b: array, ${l}>; + ${w.mainStart([l,l,1])} + let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${l}; + let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${l}; + let num_tiles = (uniforms.K - 1) / ${l} + 1; + var k_start = 0u; + var value = ${M.type.value}(0); + for (var t: u32 = 0u; t < num_tiles; t++) { + ${A} + k_start = k_start + ${l}; + workgroupBarrier(); + + for (var k: u32 = 0u; k < ${l}; k++) { + ${C} + } + workgroupBarrier(); + } + + ${B} + let m = tile_row_start + local_id.y; + let n = tile_col_start + local_id.x; + ${E!=null?`let cOffset = ${E.broadcastedIndicesToOffset("vec2(m, n)",M)}; value += ${M.type.value}(uniforms.beta) * ${E.getByOffset("cOffset")};`:""} + if (m < uniforms.M && n < uniforms.N) { + output[m * uniforms.N + n] = value; + } + }`};return d?{name:"GemmShared",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:u*p},programUniforms:f}),getShaderSource:k}:{name:"Gemm",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:f}),getShaderSource:T}},d_=e=>{let r=e.transA,t=e.transB,s=e.alpha,o=e.beta;return{transA:r,transB:t,alpha:s,beta:o,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},c_=(e,r)=>{l_(e.inputs),e.compute(u_(e.inputs,r))}}),ws,Ps,an,ln,p_,h_,m_,__,f_,g_,w_,M_,b_,y_,qv=Be(()=>{ut(),mt(),Xt(),ft(),[ws,Ps,an,ln]=[0,1,2,3],p_=e=>{if(e[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(e[0].dims.length!==e[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(e[0].dims.length-2!==e[1].dims[e[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},h_=` + fn gs_get_cubic_coeffs(x: f32) -> vec4 { + let cubic_alpha = -0.75f; + let x_abs = abs(x); + var coeffs: vec4; + coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha); + coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1); + coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1); + coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha); + return coeffs; + } +`,m_=e=>` + fn gs_bicubic_interpolate(p: mat4x4<${e}>, x: f32, y: f32) -> ${e} { + var v: vec4; + var coeffs = gs_get_cubic_coeffs(x); + for (var i = 0; i < 4; i++) { + v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3]; + } + coeffs = gs_get_cubic_coeffs(y); + let pixel = ${e}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]); + return pixel; + } +`,__=e=>` + fn gs_denormalize(n: f32, length: i32) -> f32 { + ${e.alignCorners===0?` + // alignCorners: false => [-1, 1] to [-0.5, length - 0.5] + return ((n + 1.0) * f32(length) - 1.0) / 2.0; + `:` + // alignCorners: true => [-1, 1] to [0, length - 1] + return (n + 1.0) / 2.0 * (f32(length - 1)); + `} + } +`,f_=e=>` + ${e.paddingMode==="reflection"?` + fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 { + var dx = 0.0; + var fx = f32(x); + let range = x_max - x_min; + if (fx < x_min) { + dx = x_min - fx; + let n = u32(dx / range); + let r = dx - f32(n) * range; + if (n % 2 == 0) { + fx = x_min + r; + } else { + fx = x_max - r; + } + } else if (fx > x_max) { + dx = fx - x_max; + let n = u32(dx / range); + let r = dx - f32(n) * range; + if (n % 2 == 0) { + fx = x_max - r; + } else { + fx = x_min + r; + } + } + return u32(fx); + }`:""} +`,g_=(e,r,t)=>` + fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4) -> ${r} { + var pixel = ${r}(0); + var indices = vec4(0); + indices[${ws}] = batch; + indices[${Ps}] = channel;`+(()=>{switch(t.paddingMode){case"zeros":return` + if (r >= 0 && r < H && c >=0 && c < W) { + indices[${an}] = u32(r); + indices[${ln}] = u32(c); + } else { + return ${r}(0); + } + `;case"border":return` + indices[${an}] = u32(clamp(r, 0, H - 1)); + indices[${ln}] = u32(clamp(c, 0, W - 1)); + `;case"reflection":return` + indices[${an}] = gs_reflect(r, border[1], border[3]); + indices[${ln}] = gs_reflect(c, border[0], border[2]); + `;default:throw new Error(`padding mode ${t.paddingMode} is not supported`)}})()+` + return ${e.getByIndices("indices")}; + } +`,w_=(e,r,t)=>(()=>{switch(t.mode){case"nearest":return` + let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${ws}], indices[${Ps}], border); + `;case"bilinear":return` + let x1 = i32(floor(x)); + let y1 = i32(floor(y)); + let x2 = x1 + 1; + let y2 = y1 + 1; + + let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${ws}], indices[${Ps}], border); + let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${ws}], indices[${Ps}], border); + let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${ws}], indices[${Ps}], border); + let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${ws}], indices[${Ps}], border); + + let dx2 = ${r}(f32(x2) - x); + let dx1 = ${r}(x - f32(x1)); + let dy2 = ${r}(f32(y2) - y); + let dy1 = ${r}(y - f32(y1)); + let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22); + `;case"bicubic":return` + let x0 = i32(floor(x)) - 1; + let y0 = i32(floor(y)) - 1; + var p: mat4x4<${r}>; + for (var h = 0; h < 4; h++) { + for (var w = 0; w < 4; w++) { + p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${ws}], indices[${Ps}], border); + } + } + + let dx = x - f32(x0 + 1); + let dy = y - f32(y0 + 1); + let result = gs_bicubic_interpolate(p, dx, dy); + `;default:throw new Error(`mode ${t.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,M_=(e,r)=>{let t=Pe("x",e[0].dataType,e[0].dims.length),s=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],o=Pe("grid",e[1].dataType,s.length,2),n=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];r.format==="NHWC"&&(n=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[ws,Ps,an,ln]=[0,3,1,2]);let i=Ye("output",e[0].dataType,n.length),a=t.type.value,l=Me.size(n),u=[{type:12,data:l},...st(e[0].dims,s,n)],p=d=>` + ${d.registerUniform("output_size","u32").declareVariables(t,o,i)} + ${h_} + ${m_(a)} + ${__(r)} + ${f_(r)} + ${g_(t,a,r)} + + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let H_in = i32(uniforms.x_shape[${an}]); + let W_in = i32(uniforms.x_shape[${ln}]); + + ${r.alignCorners===0?` + let x_min = -0.5; + let x_max = f32(W_in) - 0.5; + let y_min = -0.5; + let y_max = f32(H_in) - 0.5; + `:` + let x_min = 0.0; + let x_max = f32(W_in) - 1.0; + let y_min = 0.0; + let y_max = f32(H_in) - 1.0; + `}; + let border = vec4(x_min, y_min, x_max, y_max); + + let indices = ${i.offsetToIndices("global_idx")}; + var grid_indices = vec3(indices[${ws}], indices[${an}], indices[${ln}]); + let nxy = ${o.getByIndices("grid_indices")}; + var x = gs_denormalize(f32(nxy[0]), W_in); + var y = gs_denormalize(f32(nxy[1]), H_in); + + ${w_(i,a,r)} + }`;return{name:"GridSample",shaderCache:{hint:`${r.cacheKey}`,inputDependencies:["type","type"]},getRunData:d=>{let c=Me.size(n);return{outputs:[{dims:n,dataType:d[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:u}},getShaderSource:p}},b_=(e,r)=>{p_(e.inputs),e.compute(M_(e.inputs,r))},y_=e=>St({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),Cr,v_,x_,Ol,T_,Mo,E_,P_=Be(()=>{ut(),mt(),Xt(),el(),cl(),ft(),zs(),Cr=(e,r)=>e.length>r&&e[r].dims.length>0?e[r]:void 0,v_=(e,r)=>{let t=e[0],s=Cr(e,1),o=Cr(e,2),n=Cr(e,3),i=Cr(e,4),a=Cr(e,5),l=Cr(e,6),u=Cr(e,7);if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let p=t.dims[0],d=t.dims[1],c=t.dims.length===3?t.dims[2]:r.numHeads*t.dims[4],f=d,_=0,T=0,k=Math.floor(c/r.numHeads);if(l&&u&&Me.size(l.dims)&&Me.size(u.dims)){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(l.dims[0]!==p||l.dims[1]!==r.numHeads||l.dims[3]!==k)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[0]!==p||u.dims[1]!==r.numHeads||u.dims[3]!==k)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[2]!==u.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(u.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');_=l.dims[2],T=l.dims[2]}else if(l&&Me.size(l.dims)||u&&Me.size(u.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let w;if(s&&Me.size(s.dims)>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(s.dims.length<3||s.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==s.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(s.dims.length===3){if(s.dims[2]!==t.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');w=2,f=s.dims[1]}else if(s.dims.length===5){if(s.dims[2]!==r.numHeads||s.dims[3]!==2||s.dims[4]!==k)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');w=5,f=s.dims[1]}else{if(s.dims[1]!==r.numHeads||s.dims[3]!==k)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');w=0,f=s.dims[2]}}else{if(t.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(t.dims[2]!==r.numHeads||t.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');w=3}if(n&&Me.size(n.dims)>0){if(n.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(s&&s.dims.length===5&&s.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let g=_+f,S=0;if(i&&Me.size(i.dims)>0){S=8;let v=i.dims;throw v.length===1?v[0]===p?S=1:v[0]===3*p+2&&(S=3):v.length===2&&v[0]===p&&v[1]===g&&(S=5),S===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let E=!1,y=c;if(o&&Me.size(o.dims)>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(f!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');y=o.dims[2]}else{if(f!==o.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');y=o.dims[1]*o.dims[3],E=!0}}let M=!1;if(i&&Me.size(i.dims)>0)throw new Error("Key padding mask is not supported");if(a&&Me.size(a.dims)>0){if(a.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(a.dims[0]!==p||a.dims[1]!==r.numHeads||a.dims[2]!==d||a.dims[3]!==g)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:d,pastSequenceLength:_,kvSequenceLength:f,totalSequenceLength:g,maxSequenceLength:T,inputHiddenSize:0,hiddenSize:c,vHiddenSize:y,headSize:k,vHeadSize:Math.floor(y/r.numHeads),numHeads:r.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:r.maskFilterValue,maskType:S,scale:r.scale,broadcastResPosBias:M,passPastInKv:E,qkvFormat:w}},x_=e=>St({...e}),Ol=St({perm:[0,2,1,3]}),T_=(e,r,t,s,o,n,i)=>{let a=[s,o,n],l=Me.size(a),u=[{type:12,data:l},{type:12,data:i},{type:12,data:n}],p=d=>{let c=Ye("qkv_with_bias",r.dataType,a),f=Pe("qkv",r.dataType,a),_=Pe("bias",t.dataType,a),T=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${d.registerUniforms(T).declareVariables(f,_,c)} + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:a,dataType:r.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:u}),getShaderSource:p},{inputs:[r,t],outputs:[-1]})[0]},Mo=(e,r,t,s,o,n,i,a)=>{let l=n;if(i&&Me.size(i.dims)>0){if(s===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=T_(e,n,i,r,s,t*o,a),l=l.reshape([r,s,t,o]),t===1||s===1?l:e.compute(Lr(l,Ol.perm),{inputs:[l],outputs:[-1]})[0]}else return n.dims.length===3&&(l=n.reshape([r,s,t,o])),t===1||s===1?l:e.compute(Lr(l,Ol.perm),{inputs:[l],outputs:[-1]})[0]},E_=(e,r)=>{let t=v_(e.inputs,r),s=e.inputs[0],o=Cr(e.inputs,1),n=Cr(e.inputs,2),i=Cr(e.inputs,3),a=Cr(e.inputs,4),l=Cr(e.inputs,5),u=Cr(e.inputs,6),p=Cr(e.inputs,7);if(s.dims.length===5)throw new Error("Packed QKV is not implemented");if((o==null?void 0:o.dims.length)===5)throw new Error("Packed KV is not implemented");let d=o&&n&&o.dims.length===4&&n.dims.length===4,c=Mo(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,s,i,0);if(d)return mo(e,c,o,n,a,void 0,u,p,l,t);if(!o||!n)throw new Error("key and value must be provided");let f=Mo(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.headSize,o,i,t.hiddenSize),_=Mo(e,t.batchSize,t.numHeads,t.kvSequenceLength,t.vHeadSize,n,i,2*t.hiddenSize);mo(e,c,f,_,a,void 0,u,p,l,t)}}),C_,S_,$_,k_,Dl,I_,A_,F_=Be(()=>{ut(),mt(),Xt(),ft(),C_=e=>{if(!e||e.length<1)throw new Error("too few inputs")},S_=(e,r)=>{let t=[],s=r.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(o=>t.push(Number(o))),s=t.length),St({numOutputs:s,axis:r.axis,splitSizes:t})},$_=e=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${e}u; i += 1u ) { + if (index < ${et("uniforms.size_in_split_axis","i",e)}) { + return i; + } + } + return ${e}u; +}`,k_=e=>{let r=e.length,t=[];for(let s=0;s{let t=e[0].dims,s=Me.size(t),o=e[0].dataType,n=Me.normalizeAxis(r.axis,t.length),i=new Array(r.numOutputs),a=Pe("input",o,t.length),l=new Array(r.numOutputs),u=[],p=[],d=0,c=[{type:12,data:s}];for(let _=0;_` + ${_.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(a,...i)} + ${$_(l.length)} + ${k_(i)} + + ${_.mainStart()} + ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${a.offsetToIndices("global_idx")}; + var index = ${a.indicesGet("indices",n)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${et("uniforms.size_in_split_axis","output_number - 1u",l.length)}; + ${a.indicesSet("indices",n,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getShaderSource:f,getRunData:()=>({outputs:u,dispatchGroup:{x:Math.ceil(s/64)},programUniforms:c})}},I_=(e,r)=>{C_(e.inputs);let t=e.inputs.length===1?r:S_(e.inputs,r);e.compute(Dl(e.inputs,t),{inputs:[0]})},A_=e=>{let r=e.axis,t=e.splitSizes,s=e.numOutputs<0?t.length:e.numOutputs;if(s!==t.length)throw new Error("numOutputs and splitSizes lengh must be equal");return St({axis:r,numOutputs:s,splitSizes:t})}}),O_,fi,D_,L_=Be(()=>{ut(),mt(),Xt(),ft(),O_=(e,r)=>{let[t,s,o,n]=e,{numHeads:i,rotaryEmbeddingDim:a}=r;if(t.dims.length!==3&&t.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${t.dims.length}`);if(!Me.areEqual(s.dims,[])&&!Me.areEqual(s.dims,[1])&&s.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${s.dims.length}`);if(o.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(n.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${n.dims.length}`);if(!Me.areEqual(o.dims,n.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(a>0&&i===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let l=t.dims[0],u=t.dims[t.dims.length-2],p=o.dims[0],d=Me.sizeFromDimension(t.dims,1)/u,c=a===0?o.dims[1]*2:d/i;if(a>c)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(s.dims.length===2){if(l!==s.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${s.dims[0]}`);if(u!==s.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${s.dims[1]}`)}if(c/2!==o.dims[1]&&a/2!==o.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${o.dims[1]}`);if(u>p)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},fi=(e,r)=>{let{interleaved:t,numHeads:s,rotaryEmbeddingDim:o,scale:n}=r,i=e[0].dims[0],a=Me.sizeFromDimension(e[0].dims,1),l=e[0].dims[e[0].dims.length-2],u=a/l,p=e[2].dims[1],d=o===0?p*2:u/s,c=new Array(i,l,u/d,d-p),f=Me.computeStrides(c),_=[{type:1,data:n},{type:12,data:c},{type:12,data:f},...e[0].dims.length===3?new Array({type:12,data:[a,u,d,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[a,d,l*d,1]}):[],...st(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],T=k=>{let w=Pe("input",e[0].dataType,e[0].dims.length),g=Pe("position_ids",e[1].dataType,e[1].dims.length),S=Pe("cos_cache",e[2].dataType,e[2].dims.length),E=Pe("sin_cache",e[3].dataType,e[3].dims.length),y=Ye("output",e[0].dataType,e[0].dims.length);return k.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:c.length},{name:"global_strides",type:"u32",length:f.length},{name:"input_output_strides",type:"u32",length:f.length}]),` + ${k.declareVariables(w,g,S,E,y)} + + ${k.mainStart(Vn)} + let half_rotary_emb_dim = uniforms.${S.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${k.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${g.broadcastedIndicesToOffset("bsnh.xy",Ye("",g.type.tensor,2))}; + let position_id = + u32(${g.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${t}); + let j = i + select(half_rotary_emb_dim, 1, ${t}); + let re = ${w.getByOffset("i")} * ${S.get("position_id","bsnh[3]")} - + ${w.getByOffset("j")} * ${E.get("position_id","bsnh[3]")}; + ${y.setByOffset("i","re")} + let im = ${w.getByOffset("i")} * ${E.get("position_id","bsnh[3]")} + + ${w.getByOffset("j")} * ${S.get("position_id","bsnh[3]")}; + ${y.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${y.setByOffset("k",w.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:St({interleaved:t}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:T,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Me.size(c)/Vn)},programUniforms:_})}},D_=(e,r)=>{O_(e.inputs,r),e.compute(fi(e.inputs,r))}}),z_,B_,Ll,R_,j_,Xv=Be(()=>{Xt(),ut(),cl(),P_(),F_(),zs(),L_(),ft(),z_=(e,r)=>{if(r.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let t=e[0],s=e[1],o=e[2],n=e[3],i=e[4];if(r.doRotary!==0&&e.length<=7)throw new Error("cos_cast and sin_cache are expected if do_rotary attribute is non-zero");if(r.localWindowSize!==-1)throw new Error("Local attention is not supported");if(r.softcap!==0)throw new Error("Softcap is not supported");if(r.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(r.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(t.dims.length!==3&&t.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let a=!1,l=t.dims[0],u=t.dims[1],p=t.dims.length===3?a?t.dims[2]/3:t.dims[2]:r.numHeads*t.dims[4],d=u,c=0,f=!s||s.dims.length===0,_=Math.floor(f?p/(r.numHeads+2*r.kvNumHeads):p/r.numHeads);f&&(p=_*r.numHeads);let T=n&&n.dims.length!==0,k=i&&i.dims.length!==0;if(T&&n.dims.length===4&&n.dims[0]===l&&n.dims[1]!==r.kvNumHeads&&n.dims[2]===r.kvNumHeads&&n.dims[3]===_)throw new Error("BSNH pastKey/pastValue is not supported");if(T&&k){if(n.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');c=n.dims[2]}else if(T||k)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let w=1;if(s&&s.dims.length>0){if(t.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(s.dims.length<3||s.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(t.dims[0]!==s.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(s.dims.length===3){if(t.dims[2]%s.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');d=s.dims[1]}else if(s.dims.length===5){if(s.dims[2]!==r.numHeads||s.dims[3]!==2||s.dims[4]!==_)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');d=s.dims[1]}else{if(s.dims[1]!==r.numHeads||s.dims[3]!==_)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');d=s.dims[2]}}else{if(t.dims.length!==3&&t.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(t.dims.length===5&&(t.dims[2]!==r.numHeads||t.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');w=3}let g=0,S=!1,E=r.kvNumHeads?_*r.kvNumHeads:p;if(o&&o.dims.length>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(t.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(d!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');E=o.dims[2]}else{if(d!==o.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');E=o.dims[1]*o.dims[3],S=!0}}let y=e.length>4?e[5]:void 0;if(y&&y.dims.length!==1&&y.dims[0]!==l)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:l,sequenceLength:u,pastSequenceLength:c,kvSequenceLength:d,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:p,vHiddenSize:E,headSize:_,vHeadSize:Math.floor(E/r.kvNumHeads),numHeads:r.numHeads,kvNumHeads:r.kvNumHeads,nReps:r.numHeads/r.kvNumHeads,pastPresentShareBuffer:!1,maskType:g,scale:r.scale,broadcastResPosBias:!1,passPastInKv:S,qkvFormat:w}},B_=St({perm:[0,2,1,3]}),Ll=(e,r,t)=>{let s=r,o=t.kvNumHeads;return r.dims.length===3&&t.kvSequenceLength!==0&&(s=r.reshape([t.batchSize,t.kvSequenceLength,o,t.headSize]),s=e.compute(Lr(s,B_.perm),{inputs:[s],outputs:[-1]})[0]),s},R_=(e,r,t,s)=>{let o=7,n=["type","type"],i=[e*r],a=e*r,l=[{type:12,data:a},{type:12,data:r},{type:12,data:e}],u=p=>{let d=Pe("seq_lens",t.dataType,t.dims),c=Pe("total_seq_lens",s.dataType,s.dims),f=Ye("pos_ids",o,i),_=[{name:"output_size",type:"u32"},{name:"sequence_length",type:"u32"},{name:"batch_size",type:"u32"}];return` + ${p.registerUniforms(_).declareVariables(d,c,f)} + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let total_sequence_length = u32(${c.getByOffset("0")}); + let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length; + let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length; + let batch_idx = global_idx / uniforms.sequence_length; + let sequence_idx = i32(global_idx % uniforms.sequence_length); + var pos_id: i32 = 0; + let seqlen = ${d.getByOffset("batch_idx")}; + let total_seqlen = seqlen + 1; + if (is_first_prompt) { + if (sequence_idx < total_seqlen) { + pos_id = sequence_idx; + } else { + pos_id = 1; + } + ${f.setByOffset("global_idx","pos_id")} + } else if (is_subsequent_prompt) { + let past_seqlen = total_seqlen - i32(uniforms.sequence_length); + if (past_seqlen + sequence_idx < total_seqlen) { + pos_id = past_seqlen + sequence_idx; + } else { + pos_id = 1; + } + ${f.setByOffset("global_idx","pos_id")} + } else if (global_idx < uniforms.batch_size) { + ${f.setByOffset("global_idx","seqlen")} + }; + } + `};return{name:"GeneratePositionIds",shaderCache:{hint:`${e};${r}`,inputDependencies:n},getRunData:()=>({outputs:[{dims:i,dataType:o}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:l}),getShaderSource:u}},j_=(e,r)=>{var E;let t=z_(e.inputs,r);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((E=e.inputs[1])==null?void 0:E.dims.length)===5)throw new Error("Packed KV is not implemented");let s=e.inputs[0],o=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,n=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,i=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,a=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,l=e.inputs.length>4?e.inputs[5]:void 0,u=e.inputs.length>5?e.inputs[6]:void 0,p=t.kvNumHeads?t.kvNumHeads:t.numHeads,d=St({axis:2,numOutputs:3,splitSizes:[t.numHeads*t.headSize,p*t.headSize,p*t.headSize]}),[c,f,_]=!o&&!n?e.compute(Dl([s],d),{inputs:[s],outputs:[-1,-1,-1]}):[s,o,n],T,k;if(r.doRotary){let y=e.compute(R_(t.batchSize,t.sequenceLength,l,u),{inputs:[l,u],outputs:[-1]})[0],M=e.inputs[7],v=e.inputs[8],C=St({interleaved:r.rotaryInterleaved!==0,numHeads:t.numHeads,rotaryEmbeddingDim:0,scale:r.scale}),A=[c,y,M,v],B=[-1];T=e.compute(fi(A,C),{inputs:A,outputs:B})[0],A.splice(0,1,f);let K=St({interleaved:r.rotaryInterleaved!==0,numHeads:t.kvNumHeads,rotaryEmbeddingDim:0,scale:r.scale});k=e.compute(fi(A,K),{inputs:A,outputs:B})[0]}let w=Mo(e,t.batchSize,t.numHeads,t.sequenceLength,t.headSize,r.doRotary?T:c,void 0,0),g=Ll(e,r.doRotary?k:f,t),S=Ll(e,_,t);mo(e,w,g,S,void 0,void 0,i,a,void 0,t,l,u)}}),zl,N_,V_,U_,Qv=Be(()=>{ut(),mt(),zs(),ft(),zl=(e,r,t,s,o,n,i,a)=>{let l=Kt(n),u=l===1?"f32":`vec${l}f`,p=l===1?"vec2f":`mat2x${l}f`,d=o*i,c=64;d===1&&(c=256);let f=[o,i,n/l],_=[o,i,2],T=["rank","type","type"],k=[];k.push(...st(f,_));let w=g=>{let S=Pe("x",r.dataType,3,l),E=Pe("scale",t.dataType,t.dims),y=Pe("bias",s.dataType,s.dims),M=Ye("output",1,3,2),v=[S,E,y,M];return` + var workgroup_shared : array<${p}, ${c}>; + const workgroup_size = ${c}u; + ${g.declareVariables(...v)} + ${g.mainStart(c)} + 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 = ${u}(0); + var squared_sum = ${u}(0); + for (var h = local_idx; h < hight; h += workgroup_size) { + let value = ${u}(${S.get("batch","channel","h")}); + sum += value; + squared_sum += value * value; + } + workgroup_shared[local_idx] = ${p}(sum, squared_sum); + workgroupBarrier(); + + for (var currSize = workgroup_size >> 1; currSize > 0; currSize = currSize >> 1) { + if (local_idx < currSize) { + workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize]; + } + workgroupBarrier(); + } + if (local_idx == 0) { + let sum_final = ${Ls("workgroup_shared[0][0]",l)} / f32(hight * ${l}); + let squared_sum_final = ${Ls("workgroup_shared[0][1]",l)} / f32(hight * ${l}); + + let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${a})); + let channel_scale = inv_std_dev * f32(scale[channel]); + let channel_shift = f32(bias[channel]) - sum_final * channel_scale; + output[workgroup_index] = vec2f(channel_scale, channel_shift); + } + }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${a};${c}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:_,dataType:1}],dispatchGroup:{x:d},programUniforms:k}),getShaderSource:w},{inputs:[r,t,s],outputs:[-1]})[0]},N_=(e,r,t)=>{let s=r[0].dims,o=s,n=2,i=s[0],a=s[1],l=Me.sizeFromDimension(s,n),u=Kt(l),p=Me.size(o)/u,d=zl(e,r[0],r[1],r[2],i,l,a,t.epsilon),c=[i,a,l/u],f=[i,a],_=["type","none"],T=k=>{let w=Pe("x",r[0].dataType,c.length,u),g=Pe("scale_shift",1,f.length,2),S=Ye("output",r[0].dataType,c.length,u),E=[w,g,S];return` + ${k.registerUniform("output_size","u32").declareVariables(...E)} + ${k.mainStart()} + ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let outputIndices = ${S.offsetToIndices("global_idx")}; + let batch = outputIndices[0]; + let channel = outputIndices[1]; + let scale_shift = ${g.getByIndices("vec2(batch, channel)")}; + let value = ${w.getByOffset("global_idx")} * ${S.type.value}(scale_shift.x) + ${S.type.value}(scale_shift.y); + ${S.setByOffset("global_idx","value")}; + }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${u}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:o,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...st(c,f,c)]}),getShaderSource:T},{inputs:[r[0],d]})},V_=(e,r,t)=>{let s=r[0].dims,o=s,n=s[0],i=s[s.length-1],a=Me.sizeFromDimension(s,1)/i,l=Kt(i),u=Me.size(o)/l,p=[{type:12,data:a},{type:12,data:Math.floor(i/l)}],d=["type","type"],c=!1,f=[0,s.length-1];for(let w=0;ws[f[g]])),T=zl(e,_,r[1],r[2],n,a,i,t.epsilon),k=w=>{let g=lr(r[0].dataType),S=l===1?"vec2f":`mat${l}x2f`,E=v=>{let C=v===0?"x":"y",A=l===1?"f32":`vec${l}f`;switch(l){case 1:return`${g}(${A}(scale.${C}))`;case 2:return`vec2<${g}>(${A}(scale[0].${C}, scale[1].${C}))`;case 4:return`vec4<${g}>(${A}(scale[0].${C}, scale[1].${C}, scale[2].${C}, scale[3].${C}))`;default:throw new Error(`Not supported compoents ${l}`)}},y=Pe("input",r[0].dataType,r[0].dims,l),M=Ye("output",r[0].dataType,o,l);return` + @group(0) @binding(0) var input : array<${y.type.storage}>; + @group(0) @binding(1) var scale_input : array<${S}>; + @group(0) @binding(2) var output : array<${M.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${w.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], ${E(0)}, ${E(1)}); + }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:o,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:p}),getShaderSource:k},{inputs:[r[0],T]})},U_=(e,r)=>{r.format==="NHWC"?V_(e,e.inputs,r):N_(e,e.inputs,r)}}),W_,G_,K_,Jv=Be(()=>{ut(),mt(),ft(),W_=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},G_=(e,r,t)=>{let s=r.simplified,o=e[0].dims,n=e[1],i=!s&&e[2],a=o,l=Me.normalizeAxis(r.axis,o.length),u=Me.sizeToDimension(o,l),p=Me.sizeFromDimension(o,l),d=Me.size(n.dims),c=i?Me.size(i.dims):0;if(d!==p||i&&c!==p)throw new Error(`Size of X.shape()[axis:] == ${p}. + Size of scale and bias (if provided) must match this. + Got scale size of ${d} and bias size of ${c}`);let f=[];for(let y=0;y1,g=t>2,S=y=>{let M=lr(e[0].dataType),v=[Pe("x",e[0].dataType,e[0].dims,_),Pe("scale",n.dataType,n.dims,_)];i&&v.push(Pe("bias",i.dataType,i.dims,_)),v.push(Ye("output",e[0].dataType,a,_)),w&&v.push(Ye("mean_data_output",1,f)),g&&v.push(Ye("inv_std_output",1,f));let C=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${y.registerUniforms(C).declareVariables(...v)} + ${y.mainStart()} + ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${nl("f32",_)}; + var mean_square_vector = ${nl("f32",_)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${Un(M,_,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${Ls("mean_vector",_)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${Ls("mean_square_vector",_)} / uniforms.norm_size ${s?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${Un(M,_,"x[j + offset]")}; + let f32scale = ${Un(M,_,"scale[j]")}; + output[j + offset] = ${v[0].type.value}((f32input ${s?"":"- mean"}) * inv_std_dev * f32scale + ${i?`+ ${Un(M,_,"bias[j]")}`:""} + ); + } + + ${w?"mean_data_output[global_idx] = mean":""}; + ${g?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},E=[{dims:a,dataType:e[0].dataType}];return w&&E.push({dims:f,dataType:1}),g&&E.push({dims:f,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${_};${t};${s}`,inputDependencies:T},getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(u/64)},programUniforms:k}),getShaderSource:S}},K_=(e,r)=>{W_(e.inputs),e.compute(G_(e.inputs,r,e.outputCount))}}),H_,q_,Yv=Be(()=>{mt(),Ml(),xl(),H_=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.")},q_=e=>{H_(e.inputs);let r=Nn.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!r)throw new Error("Can't use matmul on the given tensors");let t=r[r.length-1],s=e.inputs[0].dims[e.inputs[0].dims.length-1];if(t<8&&s<8)e.compute(wl(e.inputs,{activation:""},r));else{let o=r[r.length-2],n=Me.size(e.inputs[0].dims.slice(0,-2)),i=Me.size(e.inputs[1].dims.slice(0,-2));if(n!==1&&o===1&&i===1){let a=e.inputs[0].reshape([1,n,s]),l=e.inputs[1].reshape([1,s,t]),u=[1,n,t],p=[a,l];e.compute(pi(p,{activation:""},r,u),{inputs:p})}else e.compute(pi(e.inputs,{activation:""},r))}}}),X_,Q_,J_,Y_,Z_,Zv=Be(()=>{ut(),mt(),Xt(),ft(),X_=(e,r)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let t=e[0],s=t.dims.length;if(t.dims[s-1]!==r.k)throw new Error("The last dim of input shape does not match the k value");let o=Math.floor((r.k+r.blockSize-1)/r.blockSize),n=r.blockSize/8*r.bits,i=e[1];if(!Me.areEqual(i.dims,[r.n,o,n]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let a=e[2].dims;if(Me.size(a)!==r.n*o)throw new Error("scales input size error.");if(e.length===4){let l=e[3].dims,u=r.bits>4?r.n*o:r.n*Math.floor((o+1)/2);if(Me.size(l)!==u)throw new Error("zeroPoints input size error.")}},Q_=(e,r)=>{let t=e[0].dims,s=t.length,o=t[s-2],n=r.k,i=r.n,a=t.slice(0,s-2),l=Me.size(a),u=e[1].dims[2]/4,p=e[0].dataType,d=Kt(r.k),c=Kt(u),f=Kt(i),_=a.concat([o,i]),T=o>1&&i/f%2===0?2:1,k=Me.size(_)/f/T,w=64,g=[],S=[l,o,n/d],E=Me.convertShape(e[1].dims).slice();E.splice(-1,1,u/c),g.push(...st(S)),g.push(...st(E)),g.push(...st(e[2].dims)),e.length===4&&g.push(...st(Me.convertShape(e[3].dims)));let y=[l,o,i/f];g.push(...st(y));let M=v=>{let C=S.length,A=Pe("a",e[0].dataType,C,d),B=Pe("b",12,E.length,c),K=Pe("scales",e[2].dataType,e[2].dims.length),G=[A,B,K],j=e.length===4?Pe("zero_points",12,e[3].dims.length):void 0;j&&G.push(j);let ee=y.length,H=Ye("output",e[0].dataType,ee,f),Z=lr(e[0].dataType),X=(()=>{switch(d){case 1:return`array<${Z}, 8>`;case 2:return`mat4x2<${Z}>`;case 4:return`mat2x4<${Z}>`;default:throw new Error(`${d}-component is not supported.`)}})(),oe=()=>{let V=` + // reuse a data + var input_offset = ${A.indicesToOffset(`${A.type.indices}(batch, row, word_offset)`)}; + var a_data: ${X}; + for (var j: u32 = 0; j < ${8/d}; j++) { + a_data[j] = ${A.getByOffset("input_offset")}; + input_offset++; + } + `;for(let F=0;F> 4) & b_mask); + b_quantized_values = ${X}(${Array.from({length:4},(W,re)=>`${Z}(b_value_lower[${re}]), ${Z}(b_value_upper[${re}])`).join(", ")}); + b_dequantized_values = ${d===1?`${X}(${Array.from({length:8},(W,re)=>`(b_quantized_values[${re}] - ${j?`zero_point${F}`:"zero_point"}) * scale${F}`).join(", ")});`:`(b_quantized_values - ${X}(${Array(8).fill(`${j?`zero_point${F}`:"zero_point"}`).join(",")})) * scale${F};`}; + workgroup_shared[local_id.x * ${T} + ${Math.floor(F/f)}]${f>1?`[${F%f}]`:""} += ${Array.from({length:8/d},(W,re)=>`${d===1?`a_data[${re}] * b_dequantized_values[${re}]`:`dot(a_data[${re}], b_dequantized_values[${re}])`}`).join(" + ")}; + `;return V},me=()=>{let V=` + var col_index = col * ${f}; + ${j?` + 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 = ${Z}(8);`} + `;for(let F=0;F> 0x1u); + zero_point_word_index = zero_point_byte_count >> 0x2u; + zero_point_byte_offset = zero_point_byte_count & 0x3u; + zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + zero_point_word = ${j.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point${F} = ${Z}((zero_point_word) & 0xFu);`:""} + col_index += 1;`;return V},ae=()=>{let V=`col_index = col * ${f};`;for(let F=0;F; + var b_value_upper: vec4; + var b_quantized_values: ${X}; + var b_dequantized_values: ${X};`,V};return` + var workgroup_shared: array<${H.type.value}, ${T*w}>; + ${v.declareVariables(...G,H)} + ${v.mainStart([w,1,1])} + let output_indices = ${H.offsetToIndices(`(global_idx / ${w}) * ${T}`)}; + 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 += ${w}) { + //process one block + var word_offset: u32 = block * ${r.blockSize/d}; + ${me()} + for (var word: u32 = 0; word < ${u}; word += ${c}) { + ${ae()} + for (var i: u32 = 0; i < ${c}; i++) { + ${oe()} + word_offset += ${8/d}; + } + } + } + workgroupBarrier(); + + if (local_id.x < ${T}) { + var output_value: ${H.type.value} = ${H.type.value}(0); + var workgroup_shared_offset: u32 = local_id.x; + for (var b: u32 = 0u; b < ${w}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${T}; + } + ${H.setByIndices(`${H.type.indices}(batch, row, col + local_id.x)`,"output_value")}; + } + }`};return{name:"MatMulNBits",shaderCache:{hint:`${r.blockSize};${r.bits};${d};${c};${f};${T};${w}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:_,dataType:p}],dispatchGroup:{x:k},programUniforms:g}),getShaderSource:M}},J_=(e,r)=>{let t=e[0].dims,s=t.length,o=t[s-2],n=r.k,i=r.n,a=t.slice(0,s-2),l=Me.size(a),u=e[1].dims[2]/4,p=e[0].dataType,d=Kt(r.k),c=Kt(u),f=a.concat([o,i]),_=128,T=i%8===0?8:i%4===0?4:1,k=_/T,w=k*c*8,g=w/d,S=w/r.blockSize,E=Me.size(f)/T,y=[],M=[l,o,n/d],v=Me.convertShape(e[1].dims).slice();v.splice(-1,1,u/c),y.push(...st(M)),y.push(...st(v)),y.push(...st(e[2].dims)),e.length===4&&y.push(...st(Me.convertShape(e[3].dims)));let C=[l,o,i];y.push(...st(C));let A=B=>{let K=M.length,G=Pe("a",e[0].dataType,K,d),j=Pe("b",12,v.length,c),ee=Pe("scales",e[2].dataType,e[2].dims.length),H=[G,j,ee],Z=e.length===4?Pe("zero_points",12,e[3].dims.length):void 0;Z&&H.push(Z);let X=C.length,oe=Ye("output",e[0].dataType,X),me=lr(e[0].dataType),ae=()=>{switch(d){case 1:return` + let a_data0 = vec4<${me}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]); + let a_data1 = vec4<${me}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return` + let a_data0 = vec4<${me}>(sub_a[word_offset], sub_a[word_offset + 1]); + let a_data1 = vec4<${me}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return` + let a_data0 = sub_a[word_offset]; + let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${d}-component is not supported.`)}};return` + var sub_a: array<${G.type.value}, ${g}>; + var inter_results: array, ${T}>; + ${B.declareVariables(...H,oe)} + ${B.mainStart([k,T,1])} + let output_indices = ${oe.offsetToIndices(`workgroup_index * ${T}`)}; + let col = output_indices[2]; + let row = output_indices[1]; + let batch = output_indices[0]; + let n_blocks_per_col = uniforms.b_shape[1]; + let num_tiles = (n_blocks_per_col - 1) / ${S} + 1; + + // Loop over shared dimension. + for (var tile: u32 = 0; tile < num_tiles; tile += 1) { + let a_col_start = tile * ${g}; + // load one tile A data into shared memory. + for (var a_offset = local_idx; a_offset < ${g}; a_offset += ${_}) + { + let a_col = a_col_start + a_offset; + if (a_col < uniforms.a_shape[2]) + { + sub_a[a_offset] = ${G.getByIndices(`${G.type.indices}(batch, row, a_col)`)}; + } else { + sub_a[a_offset] = ${G.type.value}(0); + } + } + workgroupBarrier(); + + // each thread process one block + let b_row = col + local_id.y; + let block = tile * ${S} + local_id.x; + ${Z?` + let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2; + let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u); + let zero_point_word_index = zero_point_byte_count >> 0x2u; + let zero_point_byte_offset = zero_point_byte_count & 0x3u; + let zero_point_nibble_offset: u32 = block & 0x1u; + let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + let zero_point_word = ${Z.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point = ${me}((zero_point_word) & 0xFu);`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${me}(8);`} + let scale = ${ee.getByOffset("b_row * n_blocks_per_col + block")}; + let b_data = ${j.getByIndices(`${j.type.indices}(b_row, block, 0)`)}; + var word_offset = local_id.x * ${r.blockSize/d}; + for (var i: u32 = 0; i < ${c}; i++) { + ${ae()} + let b_value = ${c===1?"b_data":"b_data[i]"}; + let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu); + let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu); + let b_quantized_values = mat2x4<${me}>(${Array.from({length:4},(V,F)=>`${me}(b_value_lower[${F}]), ${me}(b_value_upper[${F}])`).join(", ")}); + let b_dequantized_values = (b_quantized_values - mat2x4<${me}>(${Array(8).fill("zero_point").join(",")})) * scale; + inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(V,F)=>`${`dot(a_data${F}, b_dequantized_values[${F}])`}`).join(" + ")}; + word_offset += ${8/d}; + } + workgroupBarrier(); + } + + if (local_idx < ${T}) { + var output_value: ${oe.type.value} = ${oe.type.value}(0); + for (var b = 0u; b < ${k}; b++) { + output_value += inter_results[local_idx][b]; + } + if (col + local_idx < uniforms.output_shape[2]) + { + ${oe.setByIndices(`${oe.type.indices}(batch, row, col + local_idx)`,"output_value")} + } + } + }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${r.blockSize};${d};${c};${k};${T}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:f,dataType:p}],dispatchGroup:{x:E},programUniforms:y}),getShaderSource:A}},Y_=(e,r)=>{X_(e.inputs,r),r.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(J_(e.inputs,r)):e.compute(Q_(e.inputs,r))},Z_=e=>St(e)}),ef,tf,rf,sf,nf,of,af,lf,uf,ex=Be(()=>{ut(),mt(),ft(),ef=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let r=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(r=e[3].dims[0]*2===e[1].dims[0]),!r)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},tf=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=` + k = i32(${e.indicesGet("indices",o)}) - ${et("uniforms.pads",o,t)}; + if (k < 0) { + break; + } + if (k >= i32(${et("uniforms.x_shape",o,r)})) { + break; + } + offset += k * i32(${et("uniforms.x_strides",o,r)}); + `;return` + value = ${e.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${s} + value = x[offset]; + } + `},rf=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=` + k = i32(${e.indicesGet("indices",o)}) - ${et("uniforms.pads",o,t)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${et("uniforms.x_shape",o,r)}) - 1); + k = k % _2n_1; + if(k >= i32(${et("uniforms.x_shape",o,r)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${et("uniforms.x_strides",o,r)}); + `;return` + var offset = 0; + var k = 0; + ${s} + value = x[offset]; + `},sf=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=` + k = i32(${e.indicesGet("indices",o)}) - ${et("uniforms.pads",o,t)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${et("uniforms.x_shape",o,r)})) { + k = i32(${et("uniforms.x_shape",o,r)}) - 1; + } + offset += k * i32(${et("uniforms.x_strides",o,r)}); + `;return` + var offset = 0; + var k = 0; + ${s} + value = x[offset]; + `},nf=(e,r,t)=>{let s="";for(let o=r-1;o>=0;--o)s+=` + k = i32(${e.indicesGet("indices",o)}) - ${et("uniforms.pads",o,t)}; + if (k < 0) { + k += i32(${et("uniforms.x_shape",o,r)}]); + } + if (k >= i32(${et("uniforms.x_shape",o,r)})) { + k -= i32(${et("uniforms.x_shape",o,r)}); + } + offset += k * i32(${et("uniforms.x_strides",o,r)}); + `;return` + var offset = 0; + var k = 0; + ${s} + value = x[offset]; + `},of=(e,r,t)=>{switch(t.mode){case 0:return tf(e,r,t.pads.length);case 1:return rf(e,r,t.pads.length);case 2:return sf(e,r,t.pads.length);case 3:return nf(e,r,t.pads.length);default:throw new Error("Invalid mode")}},af=(e,r)=>{let t=Me.padShape(e[0].dims.slice(),r.pads),s=e[0].dims,o=Me.size(t),n=[{type:12,data:o},{type:6,data:r.pads}],i=e.length>=3&&e[2].data;r.mode===0&&n.push({type:i?e[2].dataType:1,data:r.value}),n.push(...st(e[0].dims,t));let a=["rank"],l=u=>{let p=Ye("output",e[0].dataType,t.length),d=Pe("x",e[0].dataType,s.length),c=d.type.value,f=of(p,s.length,r),_=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:r.pads.length}];return r.mode===0&&_.push({name:"constant_value",type:i?c:"f32"}),` + ${u.registerUniforms(_).declareVariables(d,p)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${p.offsetToIndices("global_idx")}; + + var value = ${c}(0); + ${f} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${r.mode}${i}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Me.size(t)/64)},programUniforms:n}),getShaderSource:l}},lf=(e,r)=>{if(e.length>1){let t=e[1].getBigInt64Array(),s=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,o=e[0].dims.length,n=new Int32Array(2*o).fill(0);if(e.length>=4){let a=e[3].getBigInt64Array();for(let l=0;ln[Number(l)]=Number(a));let i=[];return n.forEach(a=>i.push(a)),{mode:r.mode,value:s,pads:i}}else return r},uf=(e,r)=>{ef(e.inputs);let t=lf(e.inputs,r);e.compute(af(e.inputs,t),{inputs:[0]})}}),bo,Bl,Rl,jl,Nl,df,cf,Vl,Ul,pf,hf,Wl,mf,_f,Gl,ff,gf,wf,Mf,tx=Be(()=>{ts(),ut(),mt(),ft(),bo=e=>{if(Bt.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Bl=(e,r,t)=>{let s=r.format==="NHWC",o=e.dims.slice();s&&o.splice(1,0,o.pop());let n=Object.hasOwnProperty.call(r,"dilations"),i=r.kernelShape.slice(),a=r.strides.slice(),l=n?r.dilations.slice():[],u=r.pads.slice();ni.adjustPoolAttributes(t,o,i,a,l,u);let p=ni.computePoolOutputShape(t,o,a,l,i,u,r.autoPad),d=Object.assign({},r);n?Object.assign(d,{kernelShape:i,strides:a,pads:u,dilations:l,cacheKey:r.cacheKey}):Object.assign(d,{kernelShape:i,strides:a,pads:u,cacheKey:r.cacheKey});let c=p.slice();return c.push(c.splice(1,1)[0]),[d,s?c:p]},Rl=(e,r)=>{let t=r.format==="NHWC",s=Me.size(e),o=Me.size(r.kernelShape),n=[{type:12,data:s},{type:12,data:o}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(r.kernelShape.length<=2){let a=r.kernelShape[r.kernelShape.length-1],l=r.strides[r.strides.length-1],u=r.pads[r.pads.length/2-1],p=r.pads[r.pads.length-1],d=!!(u+p);n.push({type:12,data:a},{type:12,data:l},{type:12,data:u},{type:12,data:p}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let c=!1;if(r.kernelShape.length===2){let f=r.kernelShape[r.kernelShape.length-2],_=r.strides[r.strides.length-2],T=r.pads[r.pads.length/2-2],k=r.pads[r.pads.length-2];c=!!(T+k),n.push({type:12,data:f},{type:12,data:_},{type:12,data:T},{type:12,data:k}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[n,i,!0,d,c]}else{if(t)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let a=Me.computeStrides(r.kernelShape);n.push({type:12,data:a},{type:12,data:r.pads},{type:12,data:r.strides}),i.push({name:"kernelStrides",type:"u32",length:a.length},{name:"pads",type:"u32",length:r.pads.length},{name:"strides",type:"u32",length:r.strides.length});let l=r.pads.reduce((u,p)=>u+p);return[n,i,!!l,!1,!1]}},jl=(e,r,t,s,o,n,i,a,l,u,p,d)=>{let c=o.format==="NHWC",f=r.type.value,_=Ye("output",r.type.tensor,s);if(o.kernelShape.length<=2){let T="",k="",w="",g=t-(c?2:1);if(p?T=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${g}] < 0 || xIndices[${g}] + >= uniforms.x_shape[${g}]) { + pad++; + continue; + } + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + }`:T=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${g}] = indices[${g}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + }`,o.kernelShape.length===2){let S=t-(c?3:2);d?k=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${S}] = indices[${S}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${S}] < 0 || xIndices[${S}] >= uniforms.x_shape[${S}]) { + pad += i32(uniforms.kw); + continue; + } + `:k=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${S}] = indices[${S}] * uniforms.sh - uniforms.phStart + j; + `,w=` + } + `}return` + ${e.registerUniforms(l).declareVariables(r,_)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${_.offsetToIndices("global_idx")}; + var xIndices = ${_.offsetToIndices("global_idx")}; + + var value = ${f}(${a}); + var pad = 0; + ${k} + ${T} + ${w} + ${i} + + output[global_idx] = value; + }`}else{if(c)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let T=o.kernelShape.length,k=o.pads.length,w="";return u?w=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + }`:w=` + } + let x_val = x[${r.indicesToOffset("xIndices")}]; + ${n} + `,` + ${e.registerUniforms(l).declareVariables(r,_)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${_.offsetToIndices("global_idx")}; + var xIndices = ${_.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${f}(${a}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${T-1}u; j++) { + offsets[j] = offset / ${et("uniforms.kernelStrides","j",T)}; + offset -= offsets[j] * ${et("uniforms.kernelStrides","j",T)}; + } + offsets[${T-1}] = offset; + + isPad = false; + for (var j = ${t-T}u; j < ${t}u; j++) { + xIndices[j] = indices[j] * ${et("uniforms.strides",`j - ${t-T}u`,T)} + + offsets[j - ${t-T}u] - ${et("uniforms.pads","j - 2u",k)}; + ${w} + } + ${i} + + output[global_idx] = value; + }`}},Nl=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,df=e=>`${Nl(e)};${e.countIncludePad}`,cf=e=>`${Nl(e)};${e.storageOrder};${e.dilations}`,Vl=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}),Ul=(e,r,t,s)=>{let[o,n]=Bl(r,s,t),i=Pe("x",r.dataType,r.dims.length),a=i.type.value,l="value += x_val;",u="";o.countIncludePad?u+=`value /= ${a}(uniforms.kernelSize);`:u+=`value /= ${a}(i32(uniforms.kernelSize) - pad);`;let[p,d,c,f,_]=Rl(n,o);p.push(...st(r.dims,n));let T=["rank"];return{name:e,shaderCache:{hint:`${s.cacheKey};${c};${f};${_}`,inputDependencies:T},getRunData:()=>({outputs:[{dims:n,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(Me.size(n)/64)},programUniforms:p}),getShaderSource:k=>jl(k,i,r.dims.length,n.length,o,l,u,0,d,c,f,_)}},pf=e=>{let r=e.count_include_pad!==0,t=Vl(e);if(t.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let s={countIncludePad:r,...t,cacheKey:""};return{...s,cacheKey:df(s)}},hf=(e,r)=>{bo(e.inputs),e.compute(Ul("AveragePool",e.inputs[0],!1,r))},Wl={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},mf=e=>{let r=e.format;return{format:r,...Wl,cacheKey:r}},_f=(e,r)=>{bo(e.inputs),e.compute(Ul("GlobalAveragePool",e.inputs[0],!0,r))},Gl=(e,r,t,s)=>{let[o,n]=Bl(r,s,t),i=` + value = max(x_val, value); + `,a="",l=Pe("x",r.dataType,r.dims.length),u=["rank"],[p,d,c,f,_]=Rl(n,o);return p.push(...st(r.dims,n)),{name:e,shaderCache:{hint:`${s.cacheKey};${c};${f};${_}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:n,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(Me.size(n)/64)},programUniforms:p}),getShaderSource:T=>jl(T,l,r.dims.length,n.length,o,i,a,r.dataType===10?-65504:-1e5,d,c,f,_)}},ff=(e,r)=>{bo(e.inputs),e.compute(Gl("MaxPool",e.inputs[0],!1,r))},gf=e=>{let r=e.storage_order,t=e.dilations,s=Vl(e);if(r!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let o={storageOrder:r,dilations:t,...s,cacheKey:""};return{...o,cacheKey:cf(o)}},wf=e=>{let r=e.format;return{format:r,...Wl,cacheKey:r}},Mf=(e,r)=>{bo(e.inputs),e.compute(Gl("GlobalMaxPool",e.inputs[0],!0,r))}}),bf,yf,vf,xf,rx=Be(()=>{ut(),mt(),Xt(),ft(),bf=(e,r)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((t,s)=>t===e[2].dims[s]).reduce((t,s)=>t&&s,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(r.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((o,n)=>n===r.axis||o===e[0].dims[n]).reduce((o,n)=>o&&n,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let t=e[0].dims[r.axis],s=e[1].dims[r.axis];if(r.blockSizeMath.ceil(t/(s-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},yf=(e,r)=>{let t=Me.normalizeAxis(r.axis,e[0].dims.length),s=e[0].dataType,o=s===3,n=e[0].dims,i=e[1].dataType,a=Me.size(n),l=s===3||s===2,u=l?[Math.ceil(Me.size(e[0].dims)/4)]:e[0].dims,p=e[1].dims,d=e.length>2?e[2]:void 0,c=d?l?[Math.ceil(Me.size(d.dims)/4)]:d.dims:void 0,f=p.length===0||p.length===1&&p[0]===1,_=f===!1&&p.length===1,T=Kt(a),k=f&&(!l||T===4),w=k?T:1,g=k&&!l?T:1,S=Pe("input",l?12:s,u.length,g),E=Pe("scale",i,p.length),y=d?Pe("zero_point",l?12:s,c.length):void 0,M=Ye("output",i,n.length,w),v=[S,E];y&&v.push(y);let C=[u,p];d&&C.push(c);let A=[{type:12,data:a/w},{type:12,data:t},{type:12,data:r.blockSize},...st(...C,n)],B=K=>{let G=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${K.registerUniforms(G).declareVariables(...v,M)} + ${K.mainStart()} + ${K.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${M.offsetToIndices("global_idx")}; + + // Set input x + ${l?` + let input = ${S.getByOffset("global_idx / 4")}; + let x_vec = ${o?"unpack4xI8(input)":"unpack4xU8(input)"}; + let x_value = ${w===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${S.getByOffset("global_idx")};`}; + + // Set scale input + ${f?`let scale_value= ${E.getByOffset("0")}`:_?` + let scale_index = ${M.indicesGet("output_indices","uniforms.axis")}; + let scale_value= ${E.getByOffset("scale_index")};`:` + var scale_indices: ${E.type.indices} = output_indices; + let index = ${E.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; + ${E.indicesSet("scale_indices","uniforms.axis","index")}; + let scale_value= ${E.getByIndices("scale_indices")};`}; + + // Set zero-point input + ${y?f?l?` + let zero_point_input = ${y.getByOffset("0")}; + let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${y.getByOffset("0")}`:_?l?` + let zero_point_index = ${M.indicesGet("output_indices","uniforms.axis")}; + let zero_point_input = ${y.getByOffset("zero_point_index / 4")}; + let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_index % 4]`:` + let zero_point_index = ${M.indicesGet("output_indices","uniforms.axis")}; + let zero_point_value = ${y.getByOffset("zero_point_index")};`:l?` + let zero_point_offset = ${E.indicesToOffset("scale_indices")}; + let zero_point_input = ${y.getByOffset("zero_point_offset / 4")}; + let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${y.getByIndices("scale_indices")};`:`let zero_point_value = ${l?o?"i32":"u32":S.type.value}(0);`}; + // Compute and write output + ${M.setByOffset("global_idx",`${M.type.value}(x_value - zero_point_value) * scale_value`)}; + }`};return{name:"DequantizeLinear",shaderCache:{hint:r.cacheKey,inputDependencies:y?["rank","rank","rank"]:["rank","rank"]},getShaderSource:B,getRunData:()=>({outputs:[{dims:n,dataType:i}],dispatchGroup:{x:Math.ceil(a/w/64),y:1,z:1},programUniforms:A})}},vf=(e,r)=>{bf(e.inputs,r),e.compute(yf(e.inputs,r))},xf=e=>St({axis:e.axis,blockSize:e.blockSize})}),Tf,Ef,Pf,sx=Be(()=>{ts(),ut(),ft(),Tf=(e,r,t)=>{let s=e===r,o=er&&t>0;if(s||o||n)throw new Error("Range these inputs' contents are invalid.")},Ef=(e,r,t,s)=>{let o=Math.abs(Math.ceil((r-e)/t)),n=[o],i=o,a=[{type:12,data:i},{type:s,data:e},{type:s,data:t},...st(n)],l=u=>{let p=Ye("output",s,n.length),d=p.type.value,c=[{name:"outputSize",type:"u32"},{name:"start",type:d},{name:"delta",type:d}];return` + ${u.registerUniforms(c).declareVariables(p)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${d}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${s}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:n,dataType:s}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:a})}},Pf=e=>{let r=0,t=0,s=0;e.inputs[0].dataType===6?(r=e.inputs[0].getInt32Array()[0],t=e.inputs[1].getInt32Array()[0],s=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(r=e.inputs[0].getFloat32Array()[0],t=e.inputs[1].getFloat32Array()[0],s=e.inputs[2].getFloat32Array()[0]),Bt.webgpu.validateInputContent&&Tf(r,t,s),e.compute(Ef(r,t,s,e.inputs[0].dataType),{inputs:[]})}}),Cf,Kl,Hl,Sf,$f,kf,nx=Be(()=>{ut(),mt(),Xt(),ft(),Cf=(e,r,t,s)=>{if(e!=="none"&&s!=="i32"&&s!=="u32"&&s!=="f32")throw new Error(`Input ${s} is not supported with reduction ${e}.`);let o=`{ + var oldValue = 0; + loop { + let newValueF32 =`,n=`; + let newValue = bitcast(newValueF32); + let res = atomicCompareExchangeWeak(&${r}, oldValue, newValue); + if res.exchanged { + break; + } + oldValue = res.old_value; + } + }`;switch(e){case"none":return`${r}=${t};`;case"add":return s==="i32"||s==="u32"?`atomicAdd(&${r}, bitcast<${s}>(${t}));`:` + ${o}bitcast<${s}>(oldValue) + (${t})${n}`;case"max":return s==="i32"||s==="u32"?`atomicMax(&${r}, bitcast<${s}>(${t}));`:` + ${o}max(bitcast(oldValue), (${t}))${n}`;case"min":return s==="i32"||s==="u32"?`atomicMin(&${r}, bitcast<${s}>(${t}));`:`${o}min(bitcast<${s}>(oldValue), (${t}))${n}`;case"mul":return`${o}(bitcast<${s}>(oldValue) * (${t}))${n}`;default:throw new Error(`Reduction ${e} is not supported.`)}},Kl=(e,r)=>`${e===1?` + let element_count_dim = uniforms.output_strides; + let dim_value = uniforms.output_shape;`:` + let element_count_dim = uniforms.output_strides[${r?"i - indices_start":"i"}]; + let dim_value = uniforms.output_shape[${r?"i - indices_start":"i"} + uniforms.last_index_dimension];`} + + if (index >= 0) { + if (index >= i32(dim_value)) { + index = i32(dim_value - 1); + } + } else { + if (index < -i32(dim_value)) { + index = 0; + } else { + index += i32(dim_value); + } + } + data_offset += u32((u32(index) * element_count_dim));`,Hl=(e,r,t)=>`for (var i = 0u; i < uniforms.num_updates_elements; i++) { + let value = updates[uniforms.num_updates_elements * ${t?"global_idx":"idx"} + i]; + ${Cf(e.reduction,"output[data_offset + i]","value",r)} + }`,Sf=(e,r)=>{let t=e[0].dims,s=e[1].dims,o=t,n=1,i=Math.ceil(Me.size(s)/n),a=s[s.length-1],l=Me.sizeFromDimension(t,a),u=Me.sizeFromDimension(s,0)/a,p=[{type:12,data:i},{type:12,data:a},{type:12,data:l},...st(e[1].dims,e[2].dims,o)],d=c=>{let f=Pe("indices",e[1].dataType,e[1].dims.length),_=Pe("updates",e[2].dataType,e[2].dims.length,n),T=r.reduction!=="none"&&r.reduction!==""?Dc("output",e[0].dataType,o.length):Ye("output",e[0].dataType,o.length,n);return` + ${c.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(f,_,T)} + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var hasDuplicates = false; + if (${r.reduction==="none"}) { + for (var i = 0; i < ${u}; i = i + 1) { + for (var j = i + 1; j < ${u}; j = j + 1) { + var index_i = i32(indices[i].x); + var index_j = i32(indices[j].x); + if (index_i == index_j) { + hasDuplicates = true; + break; + } + } + if (hasDuplicates) { + break; + } + } + } + + if (${r.reduction==="none"} && hasDuplicates) { + if (global_idx != 0u) { + return; + } + // Process each index-update pair individually when duplicates exist + for (var idx = 0u; idx < ${u}u; idx++) { + var data_offset = 0u; + for (var i = 0u; i < uniforms.last_index_dimension; i++) { + var index = i32(indices[idx * uniforms.last_index_dimension + i].x); + ${Kl(t.length,!1)} + } + ${Hl(r,T.type.value,!1)} + } + return; + } + + var data_offset = 0u; + var indices_start = uniforms.last_index_dimension * global_idx; + var indices_end = indices_start + uniforms.last_index_dimension; + for (var i = indices_start; i < indices_end; i++) { + var index = i32(indices[i].x); + ${Kl(t.length,!0)} + } + ${Hl(r,T.type.value,!0)} + }`};return{name:"ScatterND",shaderCache:{hint:`${r.cacheKey}_${r.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:p}),getShaderSource:d}},$f=e=>St({reduction:e.reduction}),kf=(e,r)=>{e.compute(Sf(e.inputs,r),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),If,Af,Ff,ql,Of,Df,Lf,zf,Bf,Rf,jf,Nf,Xl,Vf,Uf,Wf,Gf,Kf,Hf,qf,ox=Be(()=>{ut(),mt(),Xt(),ft(),If=(e,r)=>{if(e.every(t=>t>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(r.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and + one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(r.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},Af=(e,r,t)=>{r.every(o=>o>=0&&o{throw new Error("Resize requires axes input values to be positive and less than rank")}));let s=new Array(t).fill(1);return r.forEach((o,n)=>s[o]=e[n]),s},Ff=(e,r,t,s,o,n)=>{let[i,a,l]=t>10?[1,2,3]:[-1,e.length>1?1:-1,-1],u=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(p=>n.push(p));else if(r.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(a>0&&e.length>a&&e[a].dims.length===1&&e[a].dims[0]>0){if(e[a].getFloat32Array().forEach(p=>s.push(p)),s.length!==0&&s.length!==u&&t>=18&&s.length!==r.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");If(s,r),r.axes.length>0&&Af(s,r.axes,u).forEach((p,d)=>s[d]=p)}if(l>0&&e.length>l&&e[l].dims.length===1&&e[l].dims[0]>0&&(e[l].getBigInt64Array().forEach(p=>o.push(Number(p))),o.length!==0&&o.length!==u&&t>=18&&o.length!==r.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(r.axes.length>0){if(s.length!==0&&s.length!==r.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(o.length!==0&&o.length!==r.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof s<"u"&&typeof o<"u"&&s.length>0&&o.length>u)throw new Error("Resize requires only of scales or sizes to be specified")},ql=(e,r,t,s)=>` + // The whole part and the fractional part are calculated separately due to inaccuracy of floating + // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an + // offset-by-one error later in floor(). + let big = (${e}) * (${r}); + let whole = ${s}(big / (${t})); + let fract = ${s}(big % (${t})) / ${s}(${t}); + return whole + fract; +`,Of=(e,r)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, + lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${r} { `+(()=>{switch(e){case"asymmetric":return` + if (xScale < 1.0 || floor(xScale) != xScale) { + return ${r}(xResized) / ${r}(xScale); + } else { + ${ql("xResized","lengthOriginal","lengthResized",r)} + } + `;case"pytorch_half_pixel":return`if (lengthResized > 1) { + return (${r}(xResized) + 0.5) / ${r}(xScale) - 0.5; + } else { + return 0.0; + }`;case"tf_half_pixel_for_nn":return`return (${r}(xResized) + 0.5) / ${r}(xScale);`;case"align_corners":return`if (lengthResized == 1) { + return 0.0; + } else { + ${ql("xResized","lengthOriginal - 1","lengthResized - 1",r)} + }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { + return ${r}(roiStart) * ${r}(lengthOriginal - 1) + + (${r}(xResized) * ${r}(roiEnd - roiStart) * ${r}(lengthOriginal - 1)) / + ${r}(lengthResized - 1); + } else { + return 0.5 * ${r}(roiStart + roiEnd) * ${r}(lengthOriginal - 1); + }`;case"half_pixel_symmetric":return`const outputWidth = ${r}xScale * ${r}(lengthResized); + const adjustment = ${r}(lengthResized) / outputWidth; + const center = ${r}(lengthOriginal) / 2; + const offset = center * (1 - adjustment); + return offset + ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;case"half_pixel":return`return ((${r}(xResized) + 0.5) / ${r}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",Df=(e,r,t)=>`fn getNearestPixelFromOriginal(xOriginal: ${t}, isDownSample: bool) -> ${t} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(r<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Lf=(e,r,t)=>{let s=new Array(t).fill(0).concat(new Array(t).fill(1)),o=e.length===0?s:e.slice();return r.length>0?(r.forEach((n,i)=>{s[n]=o[i],s[i+t]=o[r.length+i]}),s):o},zf=(e,r,t,s)=>{let o=[];if(t.length>0)if(s.length>0){if(e.forEach(n=>o.push(n)),Math.max(...s)>e.length)throw new Error("axes is out of bound");s.forEach((n,i)=>o[n]=t[i])}else t.forEach(n=>o.push(n));else{if(r.length===0)throw new Error("Resize requires either scales or sizes.");o=e.map((n,i)=>Math.round(n*r[i]))}return o},Bf=(e,r,t)=>{let s=(()=>{switch(t.keepAspectRatioPolicy){case"not_larger":return t.axes.length>0?Math.min(...t.axes.map(n=>r[n]),Number.MAX_VALUE):Math.min(...r,Number.MAX_VALUE);case"not_smaller":return t.axes.length>0?Math.max(...t.axes.map(n=>r[n]),Number.MIN_VALUE):Math.max(...r,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${t.keepAspectRatioPolicy} is not supported`)}})();r.fill(1,0,r.length);let o=e.slice();return t.axes.length>0?(t.axes.forEach(n=>r[n]=s),t.axes.forEach(n=>o[n]=Math.round(e[n]*r[n]))):(r.fill(s,0,r.length),o.forEach((n,i)=>o[i]=Math.round(n*r[i]))),o},Rf=(e,r,t,s,o)=>` + fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${t.length}> { + var original_indices: array<${e.type.value}, ${t.length}>; + for (var i:u32 = 0; i < ${t.length}; i++) { + var output_index = ${e.indicesGet("output_indices","i")}; + var scale = ${et("uniforms.scales","i",s)}; + var roi_low = ${et("uniforms.roi","i",o)}; + var roi_hi = ${et("uniforms.roi",`i + ${r.length}`,o)}; + if (scale == 1.0) { + original_indices[i] = ${e.type.value}(output_index); + } else { + var input_shape_i = ${et("uniforms.input_shape","i",r.length)}; + var output_shape_i = ${et("uniforms.output_shape","i",t.length)}; + original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + } + } + return original_indices; + }`,jf=(e,r,t,s,o,n,i)=>` + fn calculateInputIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + for (var i:u32 = 0; i < ${s.length}; i++) { + var output_index = ${r.indicesGet("output_indices","i")}; + var input_index: u32; + var scale = ${et("uniforms.scales","i",o)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${et("uniforms.roi","i",n)}; + var roi_hi = ${et("uniforms.roi",`i + ${t.length}`,n)}; + var input_shape_i = ${et("uniforms.input_shape","i",t.length)}; + var output_shape_i = ${et("uniforms.output_shape","i",s.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${i} || (original_idx >= 0 && original_idx < ${r.type.value}(input_shape_i))) { + if (original_idx < 0) { + input_index = 0; + } else if (original_idx > ${r.type.value}(input_shape_i - 1)) { + input_index = input_shape_i - 1; + } else { + input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1)); + } + } else { + input_index = u32(original_idx); + } + } + ${e.indicesSet("input_indices","i","input_index")} + } + return input_indices; + }`,Nf=(e,r)=>` + fn checkInputIndices(input_indices: ${e.type.indices}) -> bool { + for (var i:u32 = 0; i < ${r.length}; i++) { + var input_index = ${e.indicesGet("input_indices","i")}; + if (input_index < 0 || input_index >= ${et("uniforms.input_shape","i",r.length)}) { + return false; + } + } + return true; + }`,Xl=(e,r,t,s)=>e.rank>s?` + ${e.indicesSet("input_indices",r,"channel")}; + ${e.indicesSet("input_indices",t,"batch")}; +`:"",Vf=(e,r,t,s,o)=>{let[n,i,a,l]=t.length===2?[-1,0,1,-1]:[0,2,3,1],u=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${u} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",i,`max(0, min(row, ${t[i]} - 1))`)}; + ${e.indicesSet("input_indices",a,`max(0, min(col, ${t[a]} - 1))`)}; + ${Xl(e,l,n,2)} + return ${e.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${r.type.indices}) -> ${u} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${u} = originalIndices[${i}]; + var col:${u} = originalIndices[${a}]; + ${s?`if (row < 0 || row > (${t[i]} - 1) || col < 0 || col > (${t[a]} - 1)) { + return ${o}; + }`:""}; + row = max(0, min(row, ${t[i]} - 1)); + col = max(0, min(col, ${t[a]} - 1)); + var row1: u32 = u32(row); + var col1: u32 = u32(col); + var row2: u32 = u32(row + 1); + var col2: u32 = u32(col + 1); + var channel: u32 = ${t.length>2?`u32(originalIndices[${l}])`:"0"}; + var batch: u32 = ${t.length>2?`u32(originalIndices[${n}])`:"0"}; + var x11: ${u} = getInputValue(batch, channel, row1, col1); + var x12: ${u} = getInputValue(batch, channel, row1, col2); + var x21: ${u} = getInputValue(batch, channel, row2, col1); + var x22: ${u} = getInputValue(batch, channel, row2, col2); + var dx1: ${u} = abs(row - ${u}(row1)); + var dx2: ${u} = abs(${u}(row2) - row); + var dy1: ${u} = abs(col - ${u}(col1)); + var dy2: ${u} = abs(${u}(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); + }`},Uf=(e,r,t,s,o,n,i,a,l,u)=>{let p=t.length===2,[d,c]=p?[0,1]:[2,3],f=e.type.value,_=T=>{let k=T===d?"row":"col";return` + fn ${k}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${r.type.indices}) -> ${f} { + var output_index = ${r.indicesGet("output_indices",T)}; + var originalIdx: ${f} = getOriginalCoordinateFromResizedCoordinate(output_index, ${o[T]}, + ${s[T]}, ${t[T]}, ${n[T]}, ${n[T]} + ${t.length}); + var fractOriginalIdx: ${f} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${a} && (originalIdx < 0 || originalIdx > (${t[T]} - 1))) { + return ${l}; + } + var data: array<${f}, 4> = array<${f}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${k}: ${f} = originalIdx + ${f}(i); + if (${k} < 0 || ${k} >= ${t[T]}) { + ${u?`coefs[i + 1] = 0.0; + continue;`:a?`return ${l};`:`${k} = max(0, min(${k}, ${t[T]} - 1));`}; + } + var input_indices_copy: ${e.type.indices} = input_indices; + ${e.indicesSet("input_indices_copy",T,`u32(${k})`)}; + data[i + 1] = ${T===d?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${_(d)}; + ${_(c)}; + fn getCubicInterpolationCoefs(s: ${f}) -> array<${f}, 4> { + var absS = abs(s); + var coeffs: array<${f}, 4> = array<${f}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${f} = 1.0 - absS; + var twoMinusAbsS: ${f} = 2.0 - absS; + var onePlusAbsS: ${f} = 1.0 + absS; + coeffs[0] = ((${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 * ${i}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${f}, 4>, coefs: array<${f}, 4>) -> ${f} { + var coefsSum: ${f} = coefs[0] + coefs[1] + coefs[2] + coefs[3]; + return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum; + } + + fn bicubicInterpolation(output_indices: ${r.type.indices}) -> ${f} { + var input_indices: ${e.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},Wf=(e,r,t,s,o)=>{let[n,i,a,l,u]=t.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],p=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${p} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",i,`max(0, min(depth, ${t[i]} - 1))`)}; + ${e.indicesSet("input_indices",a,`max(0, min(height, ${t[a]} - 1))`)}; + ${e.indicesSet("input_indices",l,`max(0, min(width, ${t[l]} - 1))`)}; + ${Xl(e,u,n,3)} + return ${e.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${r.type.indices}) -> ${p} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${p} = originalIndices[${i}]; + var height:${p} = originalIndices[${a}]; + var width:${p} = originalIndices[${l}]; + ${s?`if (depth < 0 || depth > (${t[i]} - 1) || height < 0 || height > (${t[a]} - 1) || width < 0 || (width > ${t[l]} - 1)) { + return ${o}; + }`:""}; + + depth = max(0, min(depth, ${t[i]} - 1)); + height = max(0, min(height, ${t[a]} - 1)); + width = max(0, min(width, ${t[l]} - 1)); + var depth1: u32 = u32(depth); + var height1: u32 = u32(height); + var width1: u32 = u32(width); + var depth2: u32 = u32(depth + 1); + var height2: u32 = u32(height + 1); + var width2: u32 = u32(width + 1); + var channel: u32 = ${t.length>3?`u32(originalIndices[${u}])`:"0"}; + var batch: u32 = ${t.length>3?`u32(originalIndices[${n}])`:"0"}; + + var x111: ${p} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${p} = abs(depth - ${p}(depth1)); + var dx2: ${p} = abs(${p}(depth2) - depth); + var dy1: ${p} = abs(height - ${p}(height1)); + var dy2: ${p} = abs(${p}(height2) - height); + var dz1: ${p} = abs(width - ${p}(width1)); + var dz2: ${p} = abs(${p}(width2) - width); + if (depth1 == depth2) { + dx1 = 0.5; + dx2 = 0.5; + } + if (height1 == height2) { + dy1 = 0.5; + dy2 = 0.5; + } + if (width1 == width2) { + dz1 = 0.5; + dz2 = 0.5; + } + return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 + + x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1); + }`},Gf=(e,r,t,s,o,n)=>{let i=e.dims,a=Lf(n,r.axes,i.length),l=zf(i,s,o,r.axes),u=s.slice();s.length===0&&(u=i.map((g,S)=>g===0?1:l[S]/g),r.keepAspectRatioPolicy!=="stretch"&&(l=Bf(i,u,r)));let p=Ye("output",e.dataType,l.length),d=Pe("input",e.dataType,i.length),c=Me.size(l),f=i.length===l.length&&i.every((g,S)=>g===l[S]),_=r.coordinateTransformMode==="tf_crop_and_resize",T=r.extrapolationValue,k=d.type.value,w=g=>` + ${f?"":` + ${Of(r.coordinateTransformMode,k)}; + ${(()=>{switch(r.mode){case"nearest":return` + ${Nf(d,i)}; + ${Df(r.nearestMode,t,k)}; + ${jf(d,p,i,l,u.length,a.length,_)}; + `;case"linear":return` + ${Rf(p,i,l,u.length,a.length)}; + ${(()=>{if(i.length===2||i.length===4)return`${Vf(d,p,i,_,T)}`;if(i.length===3||i.length===5)return`${Wf(d,p,i,_,T)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(i.length===2||i.length===4)return`${Uf(d,p,i,l,u,a,r.cubicCoeffA,_,r.extrapolationValue,r.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; + `;default:throw Error("Invalid resize mode")}})()}; + `} + ${g.registerUniform("output_size","u32").registerUniform("scales","f32",u.length).registerUniform("roi","f32",a.length).declareVariables(d,p)} + ${g.mainStart()} + ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${f?"output[global_idx] = input[global_idx];":` + let output_indices = ${p.offsetToIndices("global_idx")}; + var input_indices: ${d.type.indices}; + ${(()=>{switch(r.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${d.getByIndices("input_indices")}; + } else { + output[global_idx] = ${r.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: ${r.mode}`)}})()}; +`} + }`;return{name:"Resize",shaderCache:{hint:`${r.cacheKey}|${t}|${u.length>0?r.mode==="cubic"?u:u.length:""}|${o.length>0?o:""}|${a.length>0?a:""}|${f}|${r.mode==="nearest"?i.length:i}`,inputDependencies:["rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:l,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:[{type:12,data:c},{type:1,data:u},{type:1,data:a},...st(i,l)]})}},Kf=e=>{let r=e.customDataBuffer;return new Uint32Array(r,r.byteOffset,1)[0]},Hf=(e,r)=>{let t=[],s=[],o=[],n=Kf(e);if(r.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Ff(e.inputs,r,n,t,s,o),e.compute(Gf(e.inputs[0],r,n,t,s,o),{inputs:[0]})},qf=e=>{let r=e.antialias,t=e.axes,s=e.coordinateTransformMode,o=e.cubicCoeffA,n=e.excludeOutside!==0,i=e.extrapolationValue,a=e.keepAspectRatioPolicy,l=e.mode,u=e.nearestMode===""?"simple":e.nearestMode;return St({antialias:r,axes:t,coordinateTransformMode:s,cubicCoeffA:o,excludeOutside:n,extrapolationValue:i,keepAspectRatioPolicy:a,mode:l,nearestMode:u})}}),Xf,Qf,Jf,ix=Be(()=>{ut(),mt(),ft(),Xf=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let r=e[0],t=e[1],s=e[2];if(r.dataType!==t.dataType||r.dataType!==s.dataType)throw new Error("All inputs must have the same data type");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Input must be 2D or 3D");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Skip must be 2D or 3D");let o=r.dims[r.dims.length-1],n=r.dims[r.dims.length-2];if(t.dims[t.dims.length-1]!==o)throw new Error("Skip must have the same hidden size as input");if(t.dims[t.dims.length-2]!==n)throw new Error("Skip must have the same sequence length as input");if(s.dims.length!==1)throw new Error("Gamma must be 1D");if(s.dims[s.dims.length-1]!==o)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let i=e[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==o)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let i=e[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==o)throw new Error("Bias must have the same hidden size as input")}},Qf=(e,r,t,s)=>{let o=r.simplified,n=e[0].dims,i=Me.size(n),a=n,l=i,u=n.slice(-1)[0],p=s?n.slice(0,-1).concat(1):[],d=!o&&e.length>3,c=e.length>4,f=s&&t>1,_=s&&t>2,T=t>3,k=64,w=Kt(u),g=[{type:12,data:l},{type:12,data:w},{type:12,data:u},{type:1,data:r.epsilon}],S=y=>{let M=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],v=[Pe("x",e[0].dataType,e[0].dims,w),Pe("skip",e[1].dataType,e[1].dims,w),Pe("gamma",e[2].dataType,e[2].dims,w)];d&&v.push(Pe("beta",e[3].dataType,e[3].dims,w)),c&&v.push(Pe("bias",e[4].dataType,e[4].dims,w)),v.push(Ye("output",e[0].dataType,a,w)),f&&v.push(Ye("mean_output",1,p)),_&&v.push(Ye("inv_std_output",1,p)),T&&v.push(Ye("input_skip_bias_sum",e[0].dataType,a,w));let C=lr(e[0].dataType),A=lr(1,w);return` + + ${y.registerUniforms(M).declareVariables(...v)} + var sum_shared : array<${A}, ${k}>; + var sum_squared_shared : array<${A}, ${k}>; + + ${y.mainStart([k,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${k}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${k}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${k-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${c?"bias[offset1d + i]":C+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${T?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${Un(C,w,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${k}; + 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 = ${Ls("sum",w)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${Ls("square_sum",w)} / f32(uniforms.hidden_size) ${o?"":"- mean * mean"} + uniforms.epsilon); + ${f?"mean_output[global_idx] = mean;":""} + ${_?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${o?"":`- ${C}(mean)`}) * + ${C}(inv_std_dev) * gamma[offset1d + i] + ${d?"+ beta[offset1d + i]":""}; + } + }`},E=[{dims:a,dataType:e[0].dataType}];return t>1&&E.push({dims:p,dataType:1}),t>2&&E.push({dims:p,dataType:1}),t>3&&E.push({dims:n,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${w};${f};${_};${T}`,inputDependencies:e.map((y,M)=>"type")},getShaderSource:S,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/u)},programUniforms:g})}},Jf=(e,r)=>{Xf(e.inputs);let t=[0];e.outputCount>1&&t.push(-3),e.outputCount>2&&t.push(-3),e.outputCount>3&&t.push(3),e.compute(Qf(e.inputs,r,e.outputCount,!1),{outputs:t})}}),Yf,yo,Zf,Ql,eg,tg,rg,sg,ax=Be(()=>{ut(),mt(),Xt(),ft(),Yf=(e,r)=>{if(!e||e.length<1)throw new Error("too few inputs");if(r.axes.length!==0){if(r.axes.length!==r.starts.length||r.axes.length!==r.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(r.starts.length!==r.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((t,s)=>{if(e[s+1].dataType!==6&&e[s+1].dataType!==7)throw new Error(`Input ${s} must be an array of int32 or int64`)})},yo=(e,r)=>{let t=[];if(e.length>r)if(e[r].dataType===7)e[r].getBigInt64Array().forEach(s=>t.push(Number(s)));else if(e[r].dataType===6)e[r].getInt32Array().forEach(s=>t.push(Number(s)));else throw new Error(`Input ${r} must be an array of int32 or int64`);return t},Zf=(e,r)=>{if(e.length>1){let t=yo(e,1),s=yo(e,2),o=yo(e,3);return 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+ return input_indices; + }`,tg=(e,r)=>{let t=e[0].dims,s=Me.size(t),o=r.axes.length>0?Me.normalizeAxes(r.axes,t.length):[...Array(t.length).keys()],n=yo(e,4);n.forEach(w=>w!==0||(()=>{throw new Error("step cannot be 0")})),n.length===0&&(n=Array(o.length).fill(1));let i=r.starts.map((w,g)=>Ql(w,g,t,o,n)),a=r.ends.map((w,g)=>Ql(w,g,t,o,n));if(o.length!==i.length||o.length!==a.length)throw new Error("start, ends and axes should have the same number of elements");if(o.length!==t.length)for(let w=0;wMath.sign(w));n.forEach((w,g,S)=>{if(w<0){let E=(a[g]-i[g])/w,y=i[g],M=y+E*n[g];i[g]=M,a[g]=y,S[g]=-w}});let u=t.slice(0);o.forEach((w,g)=>{u[w]=Math.ceil((a[w]-i[w])/n[w])});let p={dims:u,dataType:e[0].dataType},d=Ye("output",e[0].dataType,u.length),c=Pe("input",e[0].dataType,e[0].dims.length),f=Me.size(u),_=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:n.length}],T=[{type:12,data:f},{type:12,data:i},{type:6,data:l},{type:12,data:n},...st(e[0].dims,u)],k=w=>` + ${w.registerUniforms(_).declareVariables(c,d)} + ${eg(c,d,t)} + ${w.mainStart()} + ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${d.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${d.setByOffset("global_idx",c.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${i.length}_${n.length}`,inputDependencies:["rank"]},getShaderSource:k,getRunData:()=>({outputs:[p],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:T})}},rg=(e,r)=>{Yf(e.inputs,r);let t=Zf(e.inputs,r);e.compute(tg(e.inputs,t),{inputs:[0]})},sg=e=>{let r=e.starts,t=e.ends,s=e.axes;return St({starts:r,ends:t,axes:s})}}),ng,og,ig,ag,lx=Be(()=>{ut(),mt(),Xt(),zs(),ft(),ng=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},og=(e,r)=>{let t=e.inputs[0],s=t.dims,o=Me.size(s),n=s.length,i=Me.normalizeAxis(r.axis,n),a=iC),u[i]=n-1,u[n-1]=i,l=e.compute(Lr(t,u),{inputs:[t],outputs:[-1]})[0]):l=t;let p=l.dims,d=p[n-1],c=o/d,f=Kt(d),_=d/f,T=64;c===1&&(T=256);let k=(v,C)=>C===4?`max(max(${v}.x, ${v}.y), max(${v}.z, ${v}.w))`:C===2?`max(${v}.x, ${v}.y)`:C===3?`max(max(${v}.x, ${v}.y), ${v}.z)`:v,w=Pe("x",l.dataType,l.dims,f),g=Ye("result",l.dataType,l.dims,f),S=w.type.value,E=lr(l.dataType)==="f32"?`var threadMax = ${S}(-3.402823e+38f);`:`var threadMax = ${S}(-65504.0h);`,y=v=>` + var rowMaxShared : ${S}; + var rowSumShared : ${S}; + var threadShared : array<${S}, ${T}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${S} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${S}) { + let index = row * row_stride + col; + result[index] = value; + } + ${v.registerUniform("packedCols","i32").declareVariables(w,g)} + ${v.mainStart(T)} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${T}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${E} + for (var col = lindex; col < cols; col += wg) { + let value = getValue(row, col, row_stride); + threadMax = max(threadMax, value); + } + if (lindex < cols) { + threadShared[lindex] = threadMax; + } + workgroupBarrier(); + + var reduceSize = min(cols, wg); + for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) { + reduceSize = currSize + (reduceSize & 1); + if (lindex < currSize) { + threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]); + } + workgroupBarrier(); + } + if (lindex == 0) { + rowMaxShared = ${S}(${k("threadShared[0]",f)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${S}(0.0); + for (var col = lindex; col < cols; col += wg) { + let subExp = exp(getValue(row, col, row_stride) - rowMaxShared); + threadSum += subExp; + } + threadShared[lindex] = threadSum; + workgroupBarrier(); + + for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) { + if (lindex < currSize) { + threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize]; + } + workgroupBarrier(); + } + if (lindex == 0) { + rowSumShared = ${S}(${Ls("threadShared[0]",f)}); + } + workgroupBarrier(); + + // calculate final value for each element in the row + for (var col = lindex; col < cols; col += wg) { + let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared; + setValue(row, col, row_stride, value); + } + }`,M=e.compute({name:"Softmax",shaderCache:{hint:`${f};${T}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:p,dataType:l.dataType}],dispatchGroup:{x:c},programUniforms:[{type:6,data:_}]}),getShaderSource:y},{inputs:[l],outputs:[a?-1:0]})[0];a&&e.compute(Lr(M,u),{inputs:[M]})},ig=(e,r)=>{ng(e.inputs),og(e,r)},ag=e=>St({axis:e.axis})}),Jl,lg,ug,dg,cg,ux=Be(()=>{ut(),mt(),ft(),Jl=e=>Array.from(e.getBigInt64Array(),Number),lg=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(Jl(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")},ug=(e,r)=>{let t=[];for(let s=0;s{let t=e[0].dims,s=r??Jl(e[1]),o=ug(t,s),n=Me.size(o),i=e[0].dataType,a=Pe("input",i,t.length),l=Ye("output",i,o.length),u=p=>` + const inputShape = ${a.indices(...t)}; + ${p.registerUniform("output_size","u32").declareVariables(a,l)} + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${l.offsetToIndices("global_idx")}; + var input_indices: ${a.type.indices}; + for (var i = 0; i < ${t.length}; i++) { + let input_dim_i = ${a.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${l.indicesGet("output_indices","i")} % input_dim_i; + + ${a.indicesSet("input_indices","i","input_dim_value")} + } + ${l.setByOffset("global_idx",a.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${s}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:[{type:12,data:n},...st(e[0].dims,o)]}),getShaderSource:u}},cg=e=>{lg(e.inputs),e.compute(dg(e.inputs),{inputs:[0]})}}),pg,hg,mg,dx=Be(()=>{ut(),mt(),ft(),pg=(e,r,t,s,o)=>{let n=Ye("output_data",o,t.length,4),i=Pe("a_data",r[1].dataType,r[1].dims.length,4),a=Pe("b_data",r[2].dataType,r[2].dims.length,4),l=Pe("c_data",r[0].dataType,r[0].dims.length,4),u,p=(d,c,f)=>`select(${c}, ${d}, ${f})`;if(!s)u=n.setByOffset("global_idx",p(i.getByOffset("global_idx"),a.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let d=(c,f,_="")=>{let T=`a_data[index_a${f}][component_a${f}]`,k=`b_data[index_b${f}][component_b${f}]`,w=`bool(c_data[index_c${f}] & (0xffu << (component_c${f} * 8)))`;return` + let output_indices${f} = ${n.offsetToIndices(`global_idx * 4u + ${f}u`)}; + let offset_a${f} = ${i.broadcastedIndicesToOffset(`output_indices${f}`,n)}; + let offset_b${f} = ${a.broadcastedIndicesToOffset(`output_indices${f}`,n)}; + let offset_c${f} = ${l.broadcastedIndicesToOffset(`output_indices${f}`,n)}; + let index_a${f} = offset_a${f} / 4u; + let index_b${f} = offset_b${f} / 4u; + let index_c${f} = offset_c${f} / 4u; + let component_a${f} = offset_a${f} % 4u; + let component_b${f} = offset_b${f} % 4u; + let component_c${f} = offset_c${f} % 4u; + ${c}[${f}] = ${_}(${p(T,k,w)}); + `};o===9?u=` + var data = vec4(0); + ${d("data",0,"u32")} + ${d("data",1,"u32")} + ${d("data",2,"u32")} + ${d("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:u=` + ${d("output_data[global_idx]",0)} + ${d("output_data[global_idx]",1)} + ${d("output_data[global_idx]",2)} + ${d("output_data[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(l,i,a,n)} + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${u} + }`},hg=e=>{let r=e[1].dims,t=e[2].dims,s=e[0].dims,o=e[1].dataType,n=!(Me.areEqual(r,t)&&Me.areEqual(t,s)),i=r,a=Me.size(r);if(n){let u=Nn.calcShape(Nn.calcShape(r,t,!1),s,!1);if(!u)throw new Error("Can't perform where op on the given tensors");i=u,a=Me.size(i)}let l=Math.ceil(a/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:u=>pg(u,e,i,n,o),getRunData:()=>({outputs:[{dims:i,dataType:o}],dispatchGroup:{x:Math.ceil(a/64/4)},programUniforms:[{type:12,data:l},...st(s,r,t,i)]})}},mg=e=>{e.compute(hg(e.inputs))}}),_g,cx=Be(()=>{Ev(),cl(),Pv(),Cv(),Sv(),$v(),kv(),Dv(),zv(),Bv(),Rv(),jv(),Nv(),Vv(),Uv(),Wv(),Gv(),Kv(),Hv(),qv(),Xv(),Qv(),Jv(),Yv(),Zv(),P_(),ex(),tx(),rx(),sx(),nx(),ll(),ox(),L_(),ix(),ax(),lx(),F_(),ux(),zs(),_l(),dx(),_g=new 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(should not happen)");let e=this.kernelCustomData.get(this.currentKernelId);return e||(e={},this.kernelCustomData.set(this.currentKernelId,e)),e}async initialize(e,r){this.env=e;let t=[],s={requiredLimits:{maxComputeWorkgroupStorageSize:r.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.limits.maxStorageBufferBindingSize,maxBufferSize:r.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:r.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:r.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:r.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:r.limits.maxComputeWorkgroupSizeZ},requiredFeatures:t},o=n=>r.features.has(n)&&t.push(n)&&!0;o("chromium-experimental-timestamp-query-inside-passes")||o("timestamp-query"),o("shader-f16"),o("subgroups"),this.device=await r.requestDevice(s),this.adapterInfo=new bg(r.info||await r.requestAdapterInfo()),this.gpuDataManager=Fc(this),this.programManager=new fg(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Ka(e.logLevel,!!e.debug),this.device.onuncapturederror=n=>{n.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${n.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:r,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e=this.getCommandEncoder(),r={};this.queryType==="at-passes"&&(r.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(r)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;es(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{var s;let r=new BigUint64Array(e.getMappedRange()),t=this.pendingQueries.get(e);for(let o=0;o"u"&&(this.queryTimeBase=f);let T=Number(f-this.queryTimeBase),k=Number(_-this.queryTimeBase);if(!Number.isSafeInteger(T)||!Number.isSafeInteger(k))throw new RangeError("incorrect timestamp range");if((s=this.env.webgpu.profiling)!=null&&s.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:d.map(w=>({dims:w.dims,dataType:Ts(w.dataType)})),outputsMetadata:c.map(w=>({dims:w.dims,dataType:Ts(w.dataType)})),kernelId:i,kernelType:l,kernelName:u,programName:p,startTime:T,endTime:k});else{let w="";d.forEach((S,E)=>{w+=`input[${E}]: [${S.dims}] | ${Ts(S.dataType)}, `});let g="";c.forEach((S,E)=>{g+=`output[${E}]: [${S.dims}] | ${Ts(S.dataType)}, `}),console.log(`[profiling] kernel "${i}|${l}|${u}|${p}" ${w}${g}execution time: ${k-T} ns`)}uo("GPU",`${p}::${f}::${_}`)}e.unmap(),this.pendingQueries.delete(e)}),Gr()}run(e,r,t,s,o,n){es(e.name);let i=[];for(let g=0;gS):t;if(p.length!==a.length)throw new Error(`Output size ${p.length} must be equal to ${a.length}.`);let d=[],c=[];for(let g=0;g=n)throw new Error(`Invalid output index: ${p[g]}`);if(p[g]===-3)continue;let S=p[g]===-1,E=p[g]===-2,y=S||E?o(a[g].dataType,a[g].dims):s(p[g],a[g].dataType,a[g].dims);if(d.push(y),y.data===0)continue;let M=this.gpuDataManager.get(y.data);if(!M)throw new Error(`no GPU data for output: ${y.data}`);if(S&&this.temporaryData.push(M),E){let v=this.kernelPersistentData.get(this.currentKernelId);v||(v=[],this.kernelPersistentData.set(this.currentKernelId,v)),v.push(M)}c.push(M)}if(i.length!==r.length||c.length!==d.length){if(c.length===0)return Gr(e.name),d;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let f;if(u){let g=0,S=[];u.forEach(v=>{let C=typeof v.data=="number"?[v.data]:v.data;if(C.length===0)return;let A=v.type===10?2:4,B,K;v.type===10?(K=C.length>4?16:C.length>2?8:C.length*A,B=C.length>4?16:A*C.length):(K=C.length<=2?C.length*A:16,B=16),g=Math.ceil(g/K)*K,S.push(g);let G=v.type===10?8:4;g+=C.length>4?Math.ceil(C.length/G)*B:C.length*A});let E=16;g=Math.ceil(g/E)*E;let y=new ArrayBuffer(g);u.forEach((v,C)=>{let A=S[C],B=typeof v.data=="number"?[v.data]:v.data;if(v.type===6)new Int32Array(y,A,B.length).set(B);else if(v.type===12)new Uint32Array(y,A,B.length).set(B);else if(v.type===10)new Uint16Array(y,A,B.length).set(B);else if(v.type===1)new Float32Array(y,A,B.length).set(B);else throw new Error(`Unsupported uniform type: ${Ts(v.type)}`)});let M=this.gpuDataManager.create(g,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(M.buffer,0,y,0,g),this.gpuDataManager.release(M.id),f={offset:0,size:g,buffer:M.buffer}}let _=this.programManager.normalizeDispatchGroupSize(l),T=_[1]===1&&_[2]===1,k=Mg(e,r,T),w=this.programManager.getArtifact(k);if(w||(w=this.programManager.build(e,_),this.programManager.setArtifact(k,w),vt("info",()=>`[artifact] key: ${k}, programName: ${e.name}`)),u&&w.uniformVariablesInfo){if(u.length!==w.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${w.uniformVariablesInfo.length}, got ${u.length} in program "${w.programInfo.name}".`);for(let g=0;g`[ProgramManager] run "${e.name}" (key=${k}) with ${_[0]}x${_[1]}x${_[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let g={kernelId:this.currentKernelId,programName:w.programInfo.name,inputTensorViews:r,outputTensorViews:d};this.pendingKernels.push(g),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(g)}return this.programManager.run(w,i,c,_,f),Gr(e.name),d}upload(e,r){this.gpuDataManager.upload(e,r)}memcpy(e,r){this.gpuDataManager.memcpy(e,r)}async download(e,r){await this.gpuDataManager.download(e,r)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,r,t,s){let o=_g.get(e);if(!o)throw new Error(`kernel not implemented: ${e}`);let n={kernelType:e,kernelName:s,kernelEntry:o[0],attributes:[o[1],t]};this.kernels.set(r,n)}releaseKernel(e){let r=this.kernelPersistentData.get(e);if(r){for(let t of r)this.gpuDataManager.release(t.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,r,t){let s=this.kernels.get(e);if(!s)throw new Error(`kernel not created: ${e}`);let o=s.kernelType,n=s.kernelName,i=s.kernelEntry,a=s.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${o}] ${n}" is not allowed to be called 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r=this.gpuDataManager.get(e);if(!r)throw new Error(`no GPU data for buffer: ${e}`);return r.buffer}createDownloader(e,r,t){return async()=>{let s=await sl(this,e,r);return Ha(s.buffer,t)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){var e;this.queryType="none",(((e=this.env.webgpu.profiling)==null?void 0:e.mode)==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){vt("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(){vt("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){vt("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),r=this.capturedPendingKernels.get(this.currentSessionId),t=e.length;this.pendingKernels=[];for(let s=0;s=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),vg={};Rn(vg,{init:()=>Tg});var gi,xg,Tg,mx=Be(()=>{ut(),Es(),mt(),vv(),gi=class z0{constructor(r,t,s,o){this.module=r,this.dataType=t,this.data=s,this.dims=o}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,r)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,r)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,r)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let r=Me.size(this.dims);return r===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,r)}reshape(r){if(Me.size(r)!==Me.size(this.dims))throw new Error("Invalid new shape");return new z0(this.module,this.dataType,this.data,r)}},xg=class{constructor(e,r,t){this.module=e,this.backend=r,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=r.adapterInfo;let s=e.PTR_SIZE,o=t/e.PTR_SIZE,n=s===4?"i32":"i64";this.opKernelContext=Number(e.getValue(s*o++,n));let i=Number(e.getValue(s*o++,n));this.outputCount=Number(e.getValue(s*o++,n)),this.customDataOffset=Number(e.getValue(s*o++,"*")),this.customDataSize=Number(e.getValue(s*o++,n));let a=[];for(let l=0;ltypeof a=="number"?this.inputs[a]:a))??this.inputs,s=(r==null?void 0:r.outputs)??[],o=(a,l,u)=>new gi(this.module,l,this.output(a,u),u),n=(a,l)=>{let u=tn(a,l);if(!u)throw new Error(`Unsupported data type: ${a}`);let p=u>0?this.backend.gpuDataManager.create(u).id:0;return new gi(this.module,a,p,l)};return this.backend.run(e,t,s,o,n,this.outputCount)}output(e,r){let t=this.module.stackSave();try{let s=this.module.PTR_SIZE,o=s===4?"i32":"i64",n=this.module.stackAlloc((1+r.length)*s);this.module.setValue(n,r.length,o);for(let i=0;i{let o=r.jsepInit;if(!o)throw new Error("Failed to initialize JSEP. 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All Rights Reserved. +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================================= +*//** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */var Mx=Object.freeze({__proto__:null,get InferenceSession(){return Ea},get TRACE(){return uo},get TRACE_FUNC_BEGIN(){return es},get TRACE_FUNC_END(){return Gr},get Tensor(){return Zr},default:wx,get env(){return Bt},get registerBackend(){return 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Should be one of: ${Object.keys(n.DATA_TYPES).join(", ")}`);const it=ne.kv_cache_dtype,pt=it?typeof it=="string"?it:it[qe]??"float32":void 0;if(pt&&!["float32","float16"].includes(pt))throw new Error(`Invalid kv_cache_dtype: ${pt}. Should be one of: float32, float16`);const ot={dtype:qe,kv_cache_dtype:pt},Mt=n.DEFAULT_DTYPE_SUFFIX_MAPPING[qe],ct=`${P}${Mt}.onnx`,gt=`${D.subfolder??""}/${ct}`,rt={...D.session_options};rt.executionProviders??(rt.executionProviders=Ce);const yt=ne.free_dimension_overrides;yt?rt.freeDimensionOverrides??(rt.freeDimensionOverrides=yt):fe.startsWith("webnn")&&!rt.freeDimensionOverrides&&console.warn(`WebNN does not currently support dynamic shapes and requires 'free_dimension_overrides' to be set in config.json, preferably as a field within config["transformers.js_config"]["device_config"]["${fe}"]. When 'free_dimension_overrides' is not set, you may experience significant performance degradation.`);const Lt=w.apis.IS_NODE_ENV&&w.env.useFSCache,Ut=(0,l.getModelFile)(b,gt,!0,D,Lt),Jt=D.use_external_data_format??ne.use_external_data_format;let qt=[];if(Jt){let bt;typeof Jt=="object"?Jt.hasOwnProperty(ct)?bt=Jt[ct]:Jt.hasOwnProperty(P)?bt=Jt[P]:bt=!1:bt=Jt;const ir=+bt;if(ir>l.MAX_EXTERNAL_DATA_CHUNKS)throw new Error(`The number of external data chunks (${ir}) exceeds the maximum allowed value (${l.MAX_EXTERNAL_DATA_CHUNKS}).`);for(let Vr=0;Vr{const Fn=await(0,l.getModelFile)(b,$r,!0,D,Lt);Jr(Fn instanceof Uint8Array?{path:An,data:Fn}:An)}))}}else rt.externalData!==void 0&&(qt=rt.externalData.map(async bt=>{if(typeof bt.data=="string"){const ir=await(0,l.getModelFile)(b,bt.data,!0,D);return{...bt,data:ir}}return bt}));if(qt.length>0){const bt=await Promise.all(qt);w.apis.IS_NODE_ENV||(rt.externalData=bt)}if(fe==="webgpu"){const bt=(0,s.getKeyValueShapes)(D.config,{prefix:"present"});if(Object.keys(bt).length>0&&!(0,o.isONNXProxy)()){const ir={};for(const Vr in bt)ir[Vr]="gpu-buffer";rt.preferredOutputLocation=ir}}return{buffer_or_path:await Ut,session_options:rt,session_config:ot}}async function A(b,P,D){return Object.fromEntries(await Promise.all(Object.keys(P).map(async ne=>{const{buffer_or_path:ge,session_options:fe,session_config:Ce}=await C(b,P[ne],D),Le=await(0,o.createInferenceSession)(ge,fe,Ce);return[ne,Le]})))}async function B(b,P,D){return Object.fromEntries(await Promise.all(Object.keys(P).map(async ne=>{const ge=await(0,l.getModelJSON)(b,P[ne],!1,D);return[ne,ge]})))}function K(b,P){const D=Object.create(null),ne=[];for(const Ce of b.inputNames){const Le=P[Ce];if(!(Le instanceof c.Tensor)){ne.push(Ce);continue}D[Ce]=(0,o.isONNXProxy)()?Le.clone():Le}if(ne.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${ne.join(", ")}.`);const ge=Object.keys(P).length,fe=b.inputNames.length;if(ge>fe){let Ce=Object.keys(P).filter(Le=>!b.inputNames.includes(Le));console.warn(`WARNING: Too many inputs were provided (${ge} > ${fe}). The following inputs will be ignored: "${Ce.join(", ")}".`)}return D}async function G(b,P){const D=K(b,P);try{const ne=Object.fromEntries(Object.entries(D).map(([fe,Ce])=>[fe,Ce.ort_tensor]));let ge=await b.run(ne);return ge=j(ge),ge}catch(ne){const ge=Object.fromEntries(Object.entries(D).map(([fe,{type:Ce,dims:Le,data:Ne}])=>[fe,{type:Ce,dims:Le,data:Ne}]));throw console.error(`An error occurred during model execution: "${ne}".`),console.error("Inputs given to model:",ge),ne}}function j(b){for(let P in b)(0,o.isONNXTensor)(b[P])?b[P]=new c.Tensor(b[P]):typeof b[P]=="object"&&j(b[P]);return b}function ee(b){if(b instanceof c.Tensor)return b;if(b.length===0)throw Error("items must be non-empty");if(Array.isArray(b[0])){if(b.some(P=>P.length!==b[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new c.Tensor("int64",BigInt64Array.from(b.flat().map(P=>BigInt(P))),[b.length,b[0].length])}else return new c.Tensor("int64",BigInt64Array.from(b.map(P=>BigInt(P))),[1,b.length])}function H(b){return new c.Tensor("bool",[b],[1])}async function Z(b,P){let{encoder_outputs:D,input_ids:ne,decoder_input_ids:ge,...fe}=P;if(!D){const Le=(0,a.pick)(P,b.sessions.model.inputNames);D=(await X(b,Le)).last_hidden_state}return fe.input_ids=ge,fe.encoder_hidden_states=D,b.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(fe.encoder_attention_mask=P.attention_mask),await me(b,fe,!0)}async function X(b,P){const D=b.sessions.model,ne=(0,a.pick)(P,D.inputNames);if(D.inputNames.includes("inputs_embeds")&&!ne.inputs_embeds){if(!P.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");ne.inputs_embeds=await b.encode_text({input_ids:P.input_ids})}if(D.inputNames.includes("token_type_ids")&&!ne.token_type_ids){if(!ne.input_ids)throw new Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");ne.token_type_ids=(0,c.zeros_like)(ne.input_ids)}if(D.inputNames.includes("pixel_mask")&&!ne.pixel_mask){if(!ne.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const ge=ne.pixel_values.dims;ne.pixel_mask=(0,c.ones)([ge[0],ge[2],ge[3]])}return await G(D,ne)}async function oe(b,P){const D=await b.encode(P);return await b.decode(D)}async function me(b,P,D=!1){const ne=b.sessions[D?"decoder_model_merged":"model"],{past_key_values:ge,...fe}=P;if(ne.inputNames.includes("use_cache_branch")&&(fe.use_cache_branch=H(!!ge)),ne.inputNames.includes("position_ids")&&fe.attention_mask&&!fe.position_ids){const Le=["paligemma","gemma3_text","gemma3"].includes(b.config.model_type)?1:0;fe.position_ids=ce(fe,ge,Le)}b.addPastKeyValues(fe,ge);const Ce=(0,a.pick)(fe,ne.inputNames);return await G(ne,Ce)}function ae({modality_token_id:b,inputs_embeds:P,modality_features:D,input_ids:ne,attention_mask:ge}){const fe=ne.tolist().map(qe=>qe.reduce((it,pt,ot)=>(pt==b&&it.push(ot),it),[])),Ce=fe.reduce((qe,it)=>qe+it.length,0),Le=D.dims[0];if(Ce!==Le)throw new Error(`Number of tokens and features do not match: tokens: ${Ce}, features ${Le}`);let Ne=0;for(let qe=0;qefe.dims[1])){if(geLe==b.config.image_token_index)){const Le=b.config.num_image_tokens;if(!Le)throw new Error("`num_image_tokens` is missing in the model configuration.");const Ne=fe.dims[1]-(ge-Le);D.input_ids=fe.slice(null,[-Ne,null]),D.attention_mask=(0,c.ones)([1,ge+Ne])}}}return D}function Ue(b,P,D,ne){return D.past_key_values&&(P=P.map(ge=>[ge.at(-1)])),{...D,decoder_input_ids:ee(P)}}function we(b,...P){return b.config.is_encoder_decoder?Ue(b,...P):$e(b,...P)}function q(b,P,D,ne){const ge=!!D.past_key_values;return ne.guidance_scale!==null&&ne.guidance_scale>1&&(ge?D.input_ids=(0,c.cat)([D.input_ids,D.input_ids],0):(D.input_ids=(0,c.cat)([D.input_ids,(0,c.full_like)(D.input_ids,BigInt(ne.pad_token_id))],0),D.attention_mask=(0,c.cat)([D.attention_mask,(0,c.full_like)(D.attention_mask,0n)],0))),(ge||!D.pixel_values)&&(D.pixel_values=(0,c.full)([0,0,3,384,384],1)),ge&&(D.images_seq_mask=new c.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),D.images_emb_mask=new c.Tensor("bool",new Array(0).fill(!1),[1,1,0])),D}class R extends i.Callable{constructor(D,ne,ge){super();te(this,"main_input_name","input_ids");te(this,"forward_params",["input_ids","attention_mask"]);this.config=D,this.sessions=ne,this.configs=ge;const fe=v.get(this.constructor),Ce=y.get(fe);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Ce){case E.DecoderOnly:this.can_generate=!0,this._forward=me,this._prepare_inputs_for_generation=$e;break;case E.Seq2Seq:case E.Vision2Seq:case E.Musicgen:this.can_generate=!0,this._forward=Z,this._prepare_inputs_for_generation=Ue;break;case E.EncoderDecoder:this._forward=Z;break;case E.ImageTextToText:this.can_generate=!0,this._forward=_e,this._prepare_inputs_for_generation=we;break;case E.AudioTextToText:this.can_generate=!0,this._forward=re,this._prepare_inputs_for_generation=we;break;case E.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=we;break;case E.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=q;break;case E.AutoEncoder:this._forward=oe;break;default:this._forward=X;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var ne;const D=[];for(const ge of Object.values(this.sessions))(ne=ge==null?void 0:ge.handler)!=null&&ne.dispose&&D.push(ge.handler.dispose());return await Promise.all(D)}static async from_pretrained(D,{progress_callback:ne=null,config:ge=null,cache_dir:fe=null,local_files_only:Ce=!1,revision:Le="main",model_file_name:Ne=null,subfolder:qe="onnx",device:it=null,dtype:pt=null,use_external_data_format:ot=null,session_options:Mt={}}={}){let ct={progress_callback:ne,config:ge,cache_dir:fe,local_files_only:Ce,revision:Le,model_file_name:Ne,subfolder:qe,device:it,dtype:pt,use_external_data_format:ot,session_options:Mt};const gt=v.get(this),rt=y.get(gt);ge=ct.config=await s.AutoConfig.from_pretrained(D,ct);let yt;if(rt===E.DecoderOnly)yt=await Promise.all([A(D,{model:ct.model_file_name??"model"},ct),B(D,{generation_config:"generation_config.json"},ct)]);else if(rt===E.Seq2Seq||rt===E.Vision2Seq)yt=await Promise.all([A(D,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},ct),B(D,{generation_config:"generation_config.json"},ct)]);else if(rt===E.MaskGeneration)yt=await Promise.all([A(D,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},ct)]);else if(rt===E.EncoderDecoder)yt=await Promise.all([A(D,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},ct)]);else if(rt===E.ImageTextToText){const Lt={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};ge.is_encoder_decoder&&(Lt.model="encoder_model"),yt=await Promise.all([A(D,Lt,ct),B(D,{generation_config:"generation_config.json"},ct)])}else if(rt===E.AudioTextToText){const Lt={embed_tokens:"embed_tokens",audio_encoder:"audio_encoder",decoder_model_merged:"decoder_model_merged"};yt=await Promise.all([A(D,Lt,ct),B(D,{generation_config:"generation_config.json"},ct)])}else if(rt===E.Musicgen)yt=await Promise.all([A(D,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},ct),B(D,{generation_config:"generation_config.json"},ct)]);else if(rt===E.MultiModality)yt=await Promise.all([A(D,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},ct),B(D,{generation_config:"generation_config.json"},ct)]);else if(rt===E.Phi3V)yt=await Promise.all([A(D,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},ct),B(D,{generation_config:"generation_config.json"},ct)]);else if(rt===E.AutoEncoder)yt=await Promise.all([A(D,{encoder_model:"encoder_model",decoder_model:"decoder_model"},ct)]);else{if(rt!==E.EncoderOnly){const Lt=gt??(ge==null?void 0:ge.model_type);Lt!=="custom"&&console.warn(`Model type for '${Lt}' not found, assuming encoder-only architecture. Please report this at ${u.GITHUB_ISSUE_URL}.`)}yt=await Promise.all([A(D,{model:ct.model_file_name??"model"},ct)])}return new this(ge,...yt)}async _call(D){return await this.forward(D)}async forward(D){return await this._forward(this,D)}get generation_config(){var D;return((D=this.configs)==null?void 0:D.generation_config)??null}_get_logits_warper(D){const ne=new p.LogitsProcessorList;return D.temperature!==null&&D.temperature!==1&&ne.push(new p.TemperatureLogitsWarper(D.temperature)),D.top_k!==null&&D.top_k!==0&&ne.push(new p.TopKLogitsWarper(D.top_k)),D.top_p!==null&&D.top_p<1&&ne.push(new p.TopPLogitsWarper(D.top_p)),ne}_get_logits_processor(D,ne,ge=null){const fe=new p.LogitsProcessorList;if(D.repetition_penalty!==null&&D.repetition_penalty!==1&&fe.push(new p.RepetitionPenaltyLogitsProcessor(D.repetition_penalty)),D.no_repeat_ngram_size!==null&&D.no_repeat_ngram_size>0&&fe.push(new p.NoRepeatNGramLogitsProcessor(D.no_repeat_ngram_size)),D.bad_words_ids!==null&&fe.push(new p.NoBadWordsLogitsProcessor(D.bad_words_ids,D.eos_token_id)),D.min_length!==null&&D.eos_token_id!==null&&D.min_length>0&&fe.push(new p.MinLengthLogitsProcessor(D.min_length,D.eos_token_id)),D.min_new_tokens!==null&&D.eos_token_id!==null&&D.min_new_tokens>0&&fe.push(new p.MinNewTokensLengthLogitsProcessor(ne,D.min_new_tokens,D.eos_token_id)),D.forced_bos_token_id!==null&&fe.push(new p.ForcedBOSTokenLogitsProcessor(D.forced_bos_token_id)),D.forced_eos_token_id!==null&&fe.push(new p.ForcedEOSTokenLogitsProcessor(D.max_length,D.forced_eos_token_id)),D.begin_suppress_tokens!==null){const Ce=ne>1||D.forced_bos_token_id===null?ne:ne+1;fe.push(new p.SuppressTokensAtBeginLogitsProcessor(D.begin_suppress_tokens,Ce))}return D.guidance_scale!==null&&D.guidance_scale>1&&fe.push(new p.ClassifierFreeGuidanceLogitsProcessor(D.guidance_scale)),ge!==null&&fe.extend(ge),fe}_prepare_generation_config(D,ne,ge=d.GenerationConfig){const fe={...this.config};for(const Le of["decoder","generator","text_config"])Le in fe&&Object.assign(fe,fe[Le]);const Ce=new ge(fe);return Object.assign(Ce,this.generation_config??{}),D&&Object.assign(Ce,D),ne&&Object.assign(Ce,(0,a.pick)(ne,Object.getOwnPropertyNames(Ce))),Ce}_get_stopping_criteria(D,ne=null){const ge=new T.StoppingCriteriaList;return D.max_length!==null&&ge.push(new T.MaxLengthCriteria(D.max_length,this.config.max_position_embeddings??null)),D.eos_token_id!==null&&ge.push(new T.EosTokenCriteria(D.eos_token_id)),ne&&ge.extend(ne),ge}_validate_model_class(){if(!this.can_generate){const D=[md,_d,hd,pd],ne=v.get(this.constructor),ge=new Set,fe=this.config.model_type;for(const Le of D){const Ne=Le.get(fe);Ne&&ge.add(Ne[0])}let Ce=`The current model class (${ne}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw ge.size>0&&(Ce+=` Please use the following class instead: ${[...ge].join(", ")}`),Error(Ce)}}prepare_inputs_for_generation(...D){return this._prepare_inputs_for_generation(this,...D)}_update_model_kwargs_for_generation({generated_input_ids:D,outputs:ne,model_inputs:ge,is_encoder_decoder:fe}){return ge.past_key_values=this.getPastKeyValues(ne,ge.past_key_values),ge.input_ids=new c.Tensor("int64",D.flat(),[D.length,1]),fe||(ge.attention_mask=(0,c.cat)([ge.attention_mask,(0,c.ones)([ge.attention_mask.dims[0],1])],1)),ge.position_ids=null,ge}_prepare_model_inputs({inputs:D,bos_token_id:ne,model_kwargs:ge}){const fe=(0,a.pick)(ge,this.forward_params),Ce=this.main_input_name;if(Ce in fe){if(D)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else fe[Ce]=D;return{inputs_tensor:fe[Ce],model_inputs:fe,model_input_name:Ce}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:D,model_inputs:ne,model_input_name:ge,generation_config:fe}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!ne.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:Le,pixel_values:Ne,attention_mask:qe,...it}=ne,pt=await this._prepare_inputs_embeds(ne);ne={...it,...(0,a.pick)(pt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Ce}=await X(this,ne);if(fe.guidance_scale!==null&&fe.guidance_scale>1)Ce=(0,c.cat)([Ce,(0,c.full_like)(Ce,0)],0),"attention_mask"in ne&&(ne.attention_mask=(0,c.cat)([ne.attention_mask,(0,c.zeros_like)(ne.attention_mask)],0));else if(ne.decoder_input_ids){const Le=ee(ne.decoder_input_ids).dims[0];if(Le!==Ce.dims[0]){if(Ce.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Ce.dims[0]}) than the decoder inputs (${Le}).`);Ce=(0,c.cat)(Array.from({length:Le},()=>Ce),0)}}return ne.encoder_outputs=Ce,ne}_prepare_decoder_input_ids_for_generation({batch_size:D,model_input_name:ne,model_kwargs:ge,decoder_start_token_id:fe,bos_token_id:Ce,generation_config:Le}){let{decoder_input_ids:Ne,...qe}=ge;if(!(Ne instanceof c.Tensor)){if(Ne)Array.isArray(Ne[0])||(Ne=Array.from({length:D},()=>Ne));else if(fe??(fe=Ce),this.config.model_type==="musicgen")Ne=Array.from({length:D*this.config.decoder.num_codebooks},()=>[fe]);else if(Array.isArray(fe)){if(fe.length!==D)throw new Error(`\`decoder_start_token_id\` expcted to have length ${D} but got ${fe.length}`);Ne=fe}else Ne=Array.from({length:D},()=>[fe]);Ne=ee(Ne)}return ge.decoder_attention_mask=(0,c.ones_like)(Ne),{input_ids:Ne,model_inputs:qe}}async generate({inputs:D=null,generation_config:ne=null,logits_processor:ge=null,stopping_criteria:fe=null,streamer:Ce=null,...Le}){this._validate_model_class(),ne=this._prepare_generation_config(ne,Le);let{inputs_tensor:Ne,model_inputs:qe,model_input_name:it}=this._prepare_model_inputs({inputs:D,model_kwargs:Le});const pt=this.config.is_encoder_decoder;pt&&("encoder_outputs"in qe||(qe=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Ne,model_inputs:qe,model_input_name:it,generation_config:ne})));let ot;pt?{input_ids:ot,model_inputs:qe}=this._prepare_decoder_input_ids_for_generation({batch_size:qe[it].dims.at(0),model_input_name:it,model_kwargs:qe,decoder_start_token_id:ne.decoder_start_token_id,bos_token_id:ne.bos_token_id,generation_config:ne}):ot=qe[it];let Mt=ot.dims.at(-1);ne.max_new_tokens!==null&&(ne.max_length=Mt+ne.max_new_tokens);const ct=this._get_logits_processor(ne,Mt,ge),gt=this._get_stopping_criteria(ne,fe),rt=qe[it].dims.at(0),yt=k.LogitsSampler.getSampler(ne),Lt=new Array(rt).fill(0),Ut=ot.tolist();Ce&&Ce.put(Ut);let Jt,qt={};for(;;){if(qe=this.prepare_inputs_for_generation(Ut,qe,ne),Jt=await this.forward(qe),ne.output_attentions&&ne.return_dict_in_generate){const $r=this.getAttentions(Jt);for(const Jr in $r)Jr in qt||(qt[Jr]=[]),qt[Jr].push($r[Jr])}const bt=Jt.logits.slice(null,-1,null),ir=ct(Ut,bt),Vr=[];for(let $r=0;$r$r))break;qe=this._update_model_kwargs_for_generation({generated_input_ids:Vr,outputs:Jt,model_inputs:qe,is_encoder_decoder:pt})}Ce&&Ce.end();const nr=this.getPastKeyValues(Jt,qe.past_key_values,!0),mr=new c.Tensor("int64",Ut.flat(),[Ut.length,Ut[0].length]);if(ne.return_dict_in_generate)return{sequences:mr,past_key_values:nr,...qt};for(const bt of Object.values(Jt))bt.location==="gpu-buffer"&&bt.dispose();return mr}getPastKeyValues(D,ne,ge=!1){const fe=Object.create(null);for(const Ce in D)if(Ce.startsWith("present")){const Le=Ce.replace("present","past_key_values"),Ne=Ce.includes("encoder");if(Ne&&ne?fe[Le]=ne[Le]:fe[Le]=D[Ce],ne&&(!Ne||ge)){const qe=ne[Le];qe.location==="gpu-buffer"&&qe.dispose()}}return fe}getAttentions(D){const ne={};for(const ge of["cross_attentions","encoder_attentions","decoder_attentions"])for(const fe in D)fe.startsWith(ge)&&(ge in ne||(ne[ge]=[]),ne[ge].push(D[fe]));return ne}addPastKeyValues(D,ne){var ge,fe,Ce;if(ne)Object.assign(D,ne);else{const Le=this.sessions.decoder_model_merged??this.sessions.model,Ne=((ge=Le==null?void 0:Le.config)==null?void 0:ge.kv_cache_dtype)??"float32",qe=Ne==="float16"?new c.DataTypeMap.float16:[],it=((Ce=(fe=D[this.main_input_name]??D.attention_mask)==null?void 0:fe.dims)==null?void 0:Ce[0])??1,pt=(0,s.getKeyValueShapes)(this.config,{batch_size:it});for(const ot in pt)D[ot]=new c.Tensor(Ne,qe,pt[ot])}}async encode_image({pixel_values:D}){const ne=(await G(this.sessions.vision_encoder,{pixel_values:D})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${ne.dims[1]}).`),this.config.num_image_tokens=ne.dims[1]),ne}async encode_text({input_ids:D}){return(await G(this.sessions.embed_tokens,{input_ids:D})).inputs_embeds}async encode_audio({audio_values:D}){return(await G(this.sessions.audio_encoder,{audio_values:D})).audio_features}}class pe{}class xe extends pe{constructor({last_hidden_state:P,hidden_states:D=null,attentions:ne=null}){super(),this.last_hidden_state=P,this.hidden_states=D,this.attentions=ne}}class be extends R{}class Se extends be{}class Ae extends be{async _call(P){return new Mr(await super._call(P))}}class Fe extends be{async _call(P){return new _t(await super._call(P))}}class ze extends be{async _call(P){return new hr(await super._call(P))}}class Ve extends be{async _call(P){return new Er(await super._call(P))}}class O extends R{}class Y extends O{}class z extends O{async _call(P){return new Mr(await super._call(P))}}class J extends O{async _call(P){return new _t(await super._call(P))}}class le extends O{async _call(P){return new hr(await super._call(P))}}class ye extends R{}class Ee extends ye{}class ke extends R{}class Ie extends ke{}class Re extends ke{async _call(P){return new Mr(await super._call(P))}}class Xe extends ke{async _call(P){return new _t(await super._call(P))}}class Ge extends ke{async _call(P){return new hr(await super._call(P))}}class lt extends ke{async _call(P){return new Er(await super._call(P))}}class wt extends R{}class Gt extends wt{}class Ot extends wt{async _call(P){return new Mr(await super._call(P))}}class ur extends wt{async _call(P){return new _t(await super._call(P))}}class ls extends wt{async _call(P){return new hr(await super._call(P))}}class Ms extends wt{async _call(P){return new Er(await super._call(P))}}class Ir extends R{}class js extends Ir{}class Ss extends Ir{async _call(P){return new Mr(await super._call(P))}}class Ns extends Ir{async _call(P){return new _t(await super._call(P))}}class at extends Ir{async _call(P){return new hr(await super._call(P))}}class us extends Ir{async _call(P){return new Er(await super._call(P))}}class Ar extends R{}class bs extends Ar{}class Ft extends Ar{async _call(P){return new Mr(await super._call(P))}}class ds extends Ar{async _call(P){return new _t(await super._call(P))}}class cs extends Ar{async _call(P){return new hr(await super._call(P))}}class ys extends Ar{async _call(P){return new Er(await super._call(P))}}class Qt extends R{}class De extends Qt{}class Qe extends Qt{async _call(P){return new Mr(await super._call(P))}}class tt extends Qt{async _call(P){return new _t(await super._call(P))}}class Rt extends Qt{async _call(P){return new hr(await super._call(P))}}class zr extends Qt{async _call(P){return new Er(await super._call(P))}}class Sr extends R{}class ps extends Sr{}class hs extends Sr{async _call(P){return new Mr(await super._call(P))}}class Kr extends Sr{async _call(P){return new _t(await super._call(P))}}class ms extends Sr{async _call(P){return new hr(await super._call(P))}}class _s extends Sr{async _call(P){return new Er(await super._call(P))}}class Hr extends R{}class vr extends Hr{}class cn extends Hr{async _call(P){return new _t(await super._call(P))}}class xr extends Hr{async _call(P){return new hr(await super._call(P))}}class $s extends Hr{async _call(P){return new Er(await super._call(P))}}class ks extends Hr{async _call(P){return new Mr(await super._call(P))}}class fr extends R{}class Vs extends fr{}class pn extends fr{async _call(P){return new Mr(await super._call(P))}}class Fr extends fr{async _call(P){return new _t(await super._call(P))}}class hn extends fr{async _call(P){return new hr(await super._call(P))}}class qr extends R{}class Tr extends qr{}class Is extends qr{async _call(P){return new Mr(await super._call(P))}}class dr extends qr{async _call(P){return new _t(await super._call(P))}}class gr extends qr{async _call(P){return new Er(await super._call(P))}}class Br extends R{}class mn extends Br{}class _n extends Br{async _call(P){return new Mr(await super._call(P))}}class fn extends Br{async _call(P){return new _t(await super._call(P))}}class gn extends Br{async _call(P){return new hr(await super._call(P))}}class wn extends Br{async _call(P){return new Er(await super._call(P))}}class vs extends R{}class Mn extends vs{}class Us extends vs{async _call(P){return new Mr(await super._call(P))}}class bn extends vs{async _call(P){return new _t(await super._call(P))}}class yn extends vs{async _call(P){return new Er(await super._call(P))}}class xs extends R{}class vn extends xs{}class he extends xs{async _call(P){return new _t(await super._call(P))}}class $ extends xs{async _call(P){return new Er(await super._call(P))}}class N extends xs{async _call(P){return new Mr(await super._call(P))}}class Q extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class ie extends Q{}class de extends Q{}class ve extends R{}class je extends ve{}class He extends ve{}class We extends R{}class Je extends We{}class dt extends We{}class xt extends R{}class Pt extends xt{}class jt extends xt{}class kt extends xt{async _call(P){return new _t(await super._call(P))}}class Ht extends R{}class br extends Ht{}class cr extends Ht{}class pr extends Ht{async _call(P){return new _t(await super._call(P))}}class Nt extends Ht{}class Xr extends R{}class Dt extends Xr{}class rr extends Xr{}class wr extends R{}class Qr extends wr{}class Rr extends wr{}class Yt extends R{}class jr extends Yt{}class or extends Yt{async _call(P){return new Mr(await super._call(P))}}class Vt extends Yt{async _call(P){return new _t(await super._call(P))}}class Zt extends Yt{async _call(P){return new hr(await super._call(P))}}class er extends Yt{async _call(P){return new Er(await super._call(P))}}class tr extends R{}class Ws extends tr{}class xn extends tr{async _call(P){return new Mr(await super._call(P))}}class vi extends tr{async _call(P){return new _t(await super._call(P))}}class xi extends tr{async _call(P){return new hr(await super._call(P))}}class Ti extends tr{async _call(P){return new Er(await super._call(P))}}class As extends R{}class Ei extends As{}class Pi extends As{async _call(P){return new Mr(await super._call(P))}}class Ci extends As{async _call(P){return new _t(await super._call(P))}}class Si extends As{async _call(P){return new hr(await super._call(P))}}class $i extends As{async _call(P){return new Er(await super._call(P))}}class To extends R{}class ki extends To{}class Ii extends To{}class Eo extends R{constructor(){super(...arguments);te(this,"requires_attention_mask",!1);te(this,"main_input_name","input_features");te(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Ai extends Eo{}class Po extends Eo{_prepare_generation_config(P,D){return super._prepare_generation_config(P,D,g.WhisperGenerationConfig)}_retrieve_init_tokens(P){const D=[P.decoder_start_token_id];let ne=P.language;const ge=P.task;if(P.is_multilingual){ne||(console.warn("No language specified - defaulting to English (en)."),ne="en");const Ce=`<|${(0,S.whisper_language_to_code)(ne)}|>`;D.push(P.lang_to_id[Ce]),D.push(P.task_to_id[ge??"transcribe"])}else if(ne||ge)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!P.return_timestamps&&P.no_timestamps_token_id&&D.at(-1)!==P.no_timestamps_token_id?D.push(P.no_timestamps_token_id):P.return_timestamps&&D.at(-1)===P.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),D.pop()),D.filter(fe=>fe!=null)}async generate({inputs:P=null,generation_config:D=null,logits_processor:ne=null,stopping_criteria:ge=null,...fe}){D=this._prepare_generation_config(D,fe);const Ce=fe.decoder_input_ids??this._retrieve_init_tokens(D);if(D.return_timestamps&&(ne??(ne=new p.LogitsProcessorList),ne.push(new p.WhisperTimeStampLogitsProcessor(D,Ce))),D.begin_suppress_tokens&&(ne??(ne=new p.LogitsProcessorList),ne.push(new p.SuppressTokensAtBeginLogitsProcessor(D.begin_suppress_tokens,Ce.length))),D.return_token_timestamps){if(!D.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.");D.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),D.output_attentions=!0,D.return_dict_in_generate=!0}const Le=await super.generate({inputs:P,generation_config:D,logits_processor:ne,decoder_input_ids:Ce,...fe});return D.return_token_timestamps&&(Le.token_timestamps=this._extract_token_timestamps(Le,D.alignment_heads,D.num_frames)),Le}_extract_token_timestamps(P,D,ne=null,ge=.02){if(!P.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`.");ne==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 fe=this.config.median_filter_width;fe===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),fe=7);const Ce=P.cross_attentions,Le=Array.from({length:this.config.decoder_layers},(gt,rt)=>(0,c.cat)(Ce.map(yt=>yt[rt]),2)),Ne=(0,c.stack)(D.map(([gt,rt])=>{if(gt>=Le.length)throw new Error(`Layer index ${gt} is out of bounds for cross attentions (length ${Le.length}).`);return ne?Le[gt].slice(null,rt,null,[0,ne]):Le[gt].slice(null,rt)})).transpose(1,0,2,3),[qe,it]=(0,c.std_mean)(Ne,-2,0,!0),pt=Ne.clone();for(let gt=0;gtyt[mr+1]-yt[mr]),Jt=(0,a.mergeArrays)([1],Ut).map(nr=>!!nr),qt=[];for(let nr=0;nrot.findIndex(Mt=>Mt==fe)),Ne=Le.every(ot=>ot===-1),qe=Le.every(ot=>ot!==-1);if(!Ne&&!qe)throw new Error("Every input should contain either 0 or 1 image token.");if(Ne)return{inputs_embeds:P,attention_mask:ge};const it=[],pt=[];for(let ot=0;otArray.from({length:P.dims[0]},Ut=>Array.from({length:P.dims[1]},Jt=>1))),ct=D?D.tolist():[],gt=ne?ne.tolist():[];let rt=0,yt=0;for(let Lt=0;Ltot[Lt][ar]==1),qt=Ut.reduce((Wt,ar,Js)=>(ar==Ne&&Wt.push(Js),Wt),[]).map(Wt=>Ut[Wt+1]),nr=qt.filter(Wt=>Wt==Ce).length,mr=qt.filter(Wt=>Wt==Le).length;let bt=[],ir=0,Vr=nr,An=mr;for(let Wt=0;Wtfs>ir&&Dn==Ce),Js=Ut.findIndex((Dn,fs)=>fs>ir&&Dn==Le),On=Vr>0&&ar!==-1?ar:Ut.length+1,oo=An>0&&Js!==-1?Js:Ut.length+1;let ha,gd,wd,Md;On0?(0,_.max)(bt.at(-1))[0]+1:0;bt.push(Array.from({length:3*yd},(Dn,fs)=>O0+fs%yd));const vd=yd+O0,_a=Ux*bd*ma,Wx=Array.from({length:_a},(Dn,fs)=>vd+Math.floor(fs/(bd*ma))),Gx=Array.from({length:_a},(Dn,fs)=>vd+Math.floor(fs/ma)%bd),Kx=Array.from({length:_a},(Dn,fs)=>vd+fs%ma);bt.push([Wx,Gx,Kx].flat()),ir=ha+_a}if(ir0?(0,_.max)(bt.at(-1))[0]+1:0,ar=Ut.length-ir;bt.push(Array.from({length:3*ar},(Js,On)=>Wt+On%ar))}const $r=bt.reduce((Wt,ar)=>Wt+ar.length,0),Jr=new Array($r);let da=0;for(let Wt=0;Wt<3;++Wt)for(let ar=0;arpt[rt%pt.length]),ct=Array.from({length:ot[0]},(gt,rt)=>(0,_.max)(pt.subarray(ot[1]*rt,ot[1]*(rt+1)))[0]+1n+BigInt(ot[1]));return[new c.Tensor("int64",Mt,[3,...ot]),new c.Tensor("int64",ct,[ct.length,1])]}else{const[pt,ot]=P.dims,Mt=BigInt64Array.from({length:3*pt*ot},(ct,gt)=>BigInt(Math.floor(gt%ot/pt)));return[new c.Tensor("int64",Mt,[3,...P.dims]),(0,c.zeros)([pt,1])]}}async encode_image({pixel_values:P,image_grid_thw:D}){return(await G(this.sessions.vision_encoder,{pixel_values:P,grid_thw:D})).image_features}_merge_input_ids_with_image_features(P){return V({image_token_id:this.config.image_token_id,...P})}prepare_inputs_for_generation(P,D,ne){if(D.attention_mask&&!D.position_ids)if(!D.past_key_values)[D.position_ids,D.rope_deltas]=this.get_rope_index(D.input_ids,D.image_grid_thw,D.video_grid_thw,D.attention_mask);else{D.pixel_values=null;const ge=BigInt(Object.values(D.past_key_values)[0].dims.at(-2)),fe=D.rope_deltas.map(Ce=>ge+Ce);D.position_ids=(0,c.stack)([fe,fe,fe],0)}return D}}class bu extends R{}class rw extends bu{}class sw extends bu{}class yu extends R{}class nw extends yu{}class ow extends yu{}class vu extends R{}class iw extends vu{}class aw extends vu{}class xu extends R{}class lw extends xu{}class uw extends xu{}class Tu extends R{}class dw extends Tu{}class cw extends Tu{}class Eu extends R{}class pw extends Eu{}class hw extends Eu{async _call(P){return new _t(await super._call(P))}}class Pu extends R{}class mw extends Pu{}class _w extends Pu{async _call(P){return new _t(await super._call(P))}}class fw extends R{}class gw extends fw{}class Cu extends R{}class ww extends Cu{}class Mw extends Cu{async _call(P){return new _t(await super._call(P))}}class bw extends R{}class yw extends bw{}class Su extends R{}class vw extends Su{}class xw extends Su{async _call(P){return new _t(await super._call(P))}}class Tw extends R{}class Ew extends Tw{}class $u extends R{}class Pw extends $u{}class Cw extends $u{async _call(P){return new _t(await super._call(P))}}class Sw extends R{}class $w extends Sw{async _call(P){return new A0(await super._call(P))}}class ku extends R{}class kw extends ku{}class Iw extends ku{async _call(P){return new _t(await super._call(P))}}class Iu extends R{}class Aw extends Iu{}class Fw extends Iu{async _call(P){return new _t(await super._call(P))}}class Au extends R{}class Ow extends Au{}class Dw extends Au{}class Fu extends R{}class Lw extends Fu{}class zw extends Fu{}class Ou extends R{}class Bw extends Ou{}class Rw extends Ou{async _call(P){return new _t(await super._call(P))}}class qi extends R{}class jw extends qi{}class Nw extends qi{async _call(P){return new Lu(await super._call(P))}}class Du extends qi{async _call(P){return new Vw(await super._call(P))}}class Lu extends pe{constructor({logits:P,pred_boxes:D}){super(),this.logits=P,this.pred_boxes=D}}class Vw extends pe{constructor({logits:P,pred_boxes:D,pred_masks:ne}){super(),this.logits=P,this.pred_boxes=D,this.pred_masks=ne}}class zu extends R{}class Uw extends zu{}class Ww extends zu{async _call(P){return new Xi(await super._call(P))}}class Xi extends pe{constructor({logits:P,pred_boxes:D}){super(),this.logits=P,this.pred_boxes=D}}class Bu extends R{}class Gw extends Bu{}class Kw extends Bu{async _call(P){return new Hw(await super._call(P))}}class Hw extends Xi{}class Ru extends R{}class qw extends Ru{}class Xw extends Ru{async _call(P){return new Qw(await super._call(P))}}class Qw extends Xi{}class ju extends R{}class Jw extends ju{}class Yw extends ju{async _call(P){return new Zw(await super._call(P))}}class Zw extends Lu{}class Nu extends R{}class eM extends Nu{}class tM extends Nu{async _call(P){return new _t(await super._call(P))}}class Vu extends R{}class rM extends Vu{}class sM extends Vu{async _call(P){return new _t(await super._call(P))}}class Uu extends R{}class nM extends Uu{}class oM extends Uu{async _call(P){return new _t(await super._call(P))}}class Qi extends R{}class iM extends Qi{}class aM extends Qi{async _call(P){return new _t(await super._call(P))}}class lM extends Qi{}class Wu extends R{}class uM extends Wu{}class dM extends Wu{}class Gu extends R{}class cM extends Gu{}class pM extends Gu{}class hM extends R{}class mM extends hM{}class Ji extends R{}class _M extends Ji{}class fM extends Ji{}class gM extends Ji{}class wM extends R{}class MM extends wM{}class bM extends R{}class yM extends bM{}class vM extends R{}class xM extends vM{}class Ku extends R{}class TM extends Ku{}class EM extends Ku{}class Hu extends R{}class PM extends Hu{}class CM extends Hu{}class SM extends R{}class $M extends SM{}class qu extends R{}class kM extends qu{}class IM extends qu{async _call(P){return new _t(await super._call(P))}}class Xu extends R{}class AM extends Xu{}class FM extends Xu{async _call(P){return new _t(await super._call(P))}}class Qu extends R{}class OM extends Qu{}class DM extends Qu{async _call(P){return new _t(await super._call(P))}}class Ju extends R{}class LM extends Ju{}class zM extends Ju{async _call(P){return new _t(await super._call(P))}}class BM extends R{}class RM extends BM{}class Yu extends R{}class jM extends Yu{}class NM extends Yu{async _call(P){return new VM(await super._call(P))}}class VM extends pe{constructor({logits:P,pred_boxes:D}){super(),this.logits=P,this.pred_boxes=D}}class UM extends R{}class WM extends UM{async get_image_embeddings({pixel_values:P}){return await X(this,{pixel_values:P})}async forward(P){if((!P.image_embeddings||!P.image_positional_embeddings)&&(P={...P,...await this.get_image_embeddings(P)}),!P.input_labels&&P.input_points){const ne=P.input_points.dims.slice(0,-1),ge=ne.reduce((fe,Ce)=>fe*Ce,1);P.input_labels=new c.Tensor("int64",new BigInt64Array(ge).fill(1n),ne)}const D={image_embeddings:P.image_embeddings,image_positional_embeddings:P.image_positional_embeddings};return P.input_points&&(D.input_points=P.input_points),P.input_labels&&(D.input_labels=P.input_labels),P.input_boxes&&(D.input_boxes=P.input_boxes),await G(this.sessions.prompt_encoder_mask_decoder,D)}async _call(P){return new GM(await super._call(P))}}class GM extends pe{constructor({iou_scores:P,pred_masks:D}){super(),this.iou_scores=P,this.pred_masks=D}}class Zu extends R{}class KM extends Zu{}class HM extends Zu{}class ed extends R{}class qM extends ed{}class XM extends ed{}class Qs extends R{}class QM extends Qs{}class JM extends Qs{async _call(P){return new In(await super._call(P))}}class YM extends Qs{async _call(P){return new _t(await super._call(P))}}class ZM extends Qs{async _call(P){return new hr(await super._call(P))}}class td extends R{}class eb extends td{}class tb extends td{async _call(P){return new hr(await super._call(P))}}class rb extends R{}class sb extends rb{}class Yi extends R{}class nb extends Yi{}class ob extends Yi{async _call(P){return new In(await super._call(P))}}class ib extends Yi{async _call(P){return new _t(await super._call(P))}}class Qo extends R{}class ab extends Qo{}class lb extends Qo{async _call(P){return new In(await super._call(P))}}class ub extends Qo{async _call(P){return new _t(await super._call(P))}}class db extends Qo{async _call(P){return new hr(await super._call(P))}}class Zi extends R{}class cb extends Zi{}class pb extends Zi{async _call(P){return new In(await super._call(P))}}class hb extends Zi{async _call(P){return new _t(await super._call(P))}}class Sx extends R{}class mb extends Qs{}class _b extends Qs{async _call(P){return new In(await super._call(P))}}class fb extends Qs{async _call(P){return new _t(await super._call(P))}}class so extends R{}class gb extends so{}class wb extends so{async _call(P){return new In(await super._call(P))}}class Mb extends so{async _call(P){return new _t(await super._call(P))}}class bb extends so{async _call(P){return new I0(await super._call(P))}}class yb extends so{async _call(P){return new hr(await super._call(P))}}class vb extends R{}class xb extends vb{}class ea extends R{}class $x extends ea{}class Tb extends ea{}class Eb extends ea{async generate_speech(P,D,{threshold:ne=.5,minlenratio:ge=0,maxlenratio:fe=20,vocoder:Ce=null}={}){const Le={input_ids:P},{encoder_outputs:Ne,encoder_attention_mask:qe}=await X(this,Le),it=Ne.dims[1]/this.config.reduction_factor,pt=Math.floor(it*fe),ot=Math.floor(it*ge),Mt=this.config.num_mel_bins;let ct=[],gt=null,rt=null,yt=0;for(;;){++yt;const Jt=H(!!rt);let qt;rt?qt=rt.output_sequence_out:qt=new c.Tensor("float32",new Float32Array(Mt),[1,1,Mt]);let nr={use_cache_branch:Jt,output_sequence:qt,encoder_attention_mask:qe,speaker_embeddings:D,encoder_hidden_states:Ne};this.addPastKeyValues(nr,gt),rt=await G(this.sessions.decoder_model_merged,nr),gt=this.getPastKeyValues(rt,gt);const{prob:mr,spectrum:bt}=rt;if(ct.push(bt),yt>=ot&&(Array.from(mr.data).filter(ir=>ir>=ne).length>0||yt>=pt))break}const Lt=(0,c.cat)(ct),{waveform:Ut}=await G(Ce.sessions.model,{spectrogram:Lt});return{spectrogram:Lt,waveform:Ut}}}class Pb extends R{constructor(){super(...arguments);te(this,"main_input_name","spectrogram")}}class Cb extends R{}class Sb extends Cb{}class rd extends R{}class $b extends rd{}class kb extends rd{}class sd extends R{}class Ib extends sd{}class Ab extends sd{}class nd extends R{}class Fb extends nd{}class Ob extends nd{}class ta extends R{}class Db extends ta{}class Lb extends ta{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"text_model"})}}class zb extends ta{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"audio_model"})}}class Bb extends R{}class od extends Bb{async _call(P){return new F0(await super._call(P))}}class ra extends R{}class kx extends ra{}class Rb extends ra{}class jb extends ra{}class id extends R{}class Nb extends id{}class Vb extends id{}class ad extends R{}class Ub extends ad{}class Wb extends ad{async _call(P){return new _t(await super._call(P))}}class ld extends R{}class Ix extends ld{}class Ax extends ld{}class ud extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}_apply_and_filter_by_delay_pattern_mask(D){const[ne,ge]=D.dims,fe=this.config.decoder.num_codebooks,Ce=ge-fe;let Le=0;for(let it=0;it0&&Mt<=Ce&&(D.data[Le++]=D.data[it])}const Ne=Math.floor(ne/fe),qe=Le/(Ne*fe);return new c.Tensor(D.type,D.data.slice(0,Le),[Ne,fe,qe])}prepare_inputs_for_generation(D,ne,ge){let fe=structuredClone(D);for(let Le=0;Le=Ne&&(fe[Le][Ne]=BigInt(this.config.decoder.pad_token_id));return ge.guidance_scale!==null&&ge.guidance_scale>1&&(fe=fe.concat(fe)),super.prepare_inputs_for_generation(fe,ne,ge)}async generate(D){const ne=await super.generate(D),ge=this._apply_and_filter_by_delay_pattern_mask(ne).unsqueeze_(0),{audio_values:fe}=await G(this.sessions.encodec_decode,{audio_codes:ge});return fe}}class sa extends R{}class Gb extends sa{}class Kb extends sa{async _call(P){return new _t(await super._call(P))}}class Hb extends sa{}class na extends R{}class qb extends na{}class Xb extends na{async _call(P){return new _t(await super._call(P))}}class Qb extends na{}class oa extends R{}class Jb extends oa{}class Yb extends oa{async _call(P){return new _t(await super._call(P))}}class Zb extends oa{}class ia extends R{}class ey extends ia{}class ty extends ia{async _call(P){return new _t(await super._call(P))}}class ry extends ia{}class sy extends R{}class ny extends sy{}class oy extends R{}class iy extends oy{constructor(...D){super(...D);te(this,"forward_params",["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"]);this._generation_mode="text"}async forward(D){const ne=this._generation_mode??"text";let ge;if(ne==="text"||!D.past_key_values){const qe=this.sessions.prepare_inputs_embeds,it=(0,a.pick)(D,qe.inputNames);ge=await G(qe,it)}else{const qe=this.sessions.gen_img_embeds,it=(0,a.pick)({image_ids:D.input_ids},qe.inputNames);ge=await G(qe,it)}const fe={...D,...ge},Ce=await me(this,fe),Le=this.sessions[ne==="text"?"lm_head":"gen_head"];if(!Le)throw new Error(`Unable to find "${Le}" generation head`);const Ne=await G(Le,(0,a.pick)(Ce,Le.inputNames));return{...ge,...Ce,...Ne}}async generate(D){return this._generation_mode="text",super.generate(D)}async generate_images(D){this._generation_mode="image";const ne=(D.inputs??D[this.main_input_name]).dims[1],fe=(await super.generate(D)).slice(null,[ne,null]),Ce=this.sessions.image_decode,{decoded_image:Le}=await G(Ce,{generated_tokens:fe}),Ne=Le.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),qe=[];for(const it of Ne){const pt=f.RawImage.fromTensor(it);qe.push(pt)}return qe}}class ay extends pe{constructor({char_logits:P,bpe_logits:D,wp_logits:ne}){super(),this.char_logits=P,this.bpe_logits=D,this.wp_logits=ne}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class ly extends R{}class uy extends ly{async _call(P){return new ay(await super._call(P))}}class dd extends R{}class dy extends dd{}class cy extends dd{}class cd extends R{}class py extends cd{}class hy extends cd{}class my extends R{constructor(){super(...arguments);te(this,"forward_params",["input_ids","attention_mask","position_ids","audio_values","past_key_values"])}}class _y extends my{_merge_input_ids_with_audio_features(P){const D=P.audio_features.dims.at(-1),ne=P.audio_features.view(-1,D);return F({audio_token_id:this.config.ignore_index,...P,audio_features:ne})}}class aa extends R{constructor(){super(...arguments);te(this,"main_input_name","input_values");te(this,"forward_params",["input_values"])}}class fy extends pe{constructor({audio_codes:P}){super(),this.audio_codes=P}}class gy extends pe{constructor({audio_values:P}){super(),this.audio_values=P}}class wy extends aa{async encode(P){return new fy(await G(this.sessions.encoder_model,P))}async decode(P){return new gy(await G(this.sessions.decoder_model,P))}}class My extends aa{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"encoder_model"})}}class by extends aa{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"decoder_model"})}}class la extends R{constructor(){super(...arguments);te(this,"main_input_name","input_values");te(this,"forward_params",["input_values"])}}class yy extends pe{constructor({audio_codes:P}){super(),this.audio_codes=P}}class vy extends pe{constructor({audio_values:P}){super(),this.audio_values=P}}class xy extends la{async encode(P){return new yy(await G(this.sessions.encoder_model,P))}async decode(P){return new vy(await G(this.sessions.decoder_model,P))}}class Ty extends la{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"encoder_model"})}}class Ey extends la{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"decoder_model"})}}class ua extends R{constructor(){super(...arguments);te(this,"main_input_name","input_values");te(this,"forward_params",["input_values"])}}class Py extends ua{async encode(P){return await G(this.sessions.encoder_model,P)}async decode(P){return await G(this.sessions.decoder_model,P)}}class Cy extends ua{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"encoder_model"})}}class Sy extends ua{static async from_pretrained(P,D={}){return super.from_pretrained(P,{...D,model_file_name:D.model_file_name??"decoder_model"})}}class Ct{static async from_pretrained(P,{progress_callback:D=null,config:ne=null,cache_dir:ge=null,local_files_only:fe=!1,revision:Ce="main",model_file_name:Le=null,subfolder:Ne="onnx",device:qe=null,dtype:it=null,use_external_data_format:pt=null,session_options:ot={}}={}){const Mt={progress_callback:D,config:ne,cache_dir:ge,local_files_only:fe,revision:Ce,model_file_name:Le,subfolder:Ne,device:qe,dtype:it,use_external_data_format:pt,session_options:ot};if(Mt.config=await s.AutoConfig.from_pretrained(P,Mt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);const ct=Mt.config.model_type;for(const gt of this.MODEL_CLASS_MAPPINGS){let rt=gt.get(ct);if(!rt){for(const yt of gt.values())if(yt[0]===ct){rt=yt;break}if(!rt)continue}return await rt[1].from_pretrained(P,Mt)}if(this.BASE_IF_FAIL)return e0.has(ct)||console.warn(`Unknown model class "${ct}", attempting to construct from base class.`),await R.from_pretrained(P,Mt);throw Error(`Unsupported model type: ${ct}`)}}te(Ct,"MODEL_CLASS_MAPPINGS",null),te(Ct,"BASE_IF_FAIL",!1);const Fx=new Map([["bert",["BertModel",Se]],["modernbert",["ModernBertModel",Y]],["nomic_bert",["NomicBertModel",Ee]],["roformer",["RoFormerModel",Ie]],["electra",["ElectraModel",js]],["esm",["EsmModel",Vs]],["convbert",["ConvBertModel",Gt]],["camembert",["CamembertModel",bs]],["deberta",["DebertaModel",De]],["deberta-v2",["DebertaV2Model",ps]],["mpnet",["MPNetModel",mn]],["albert",["AlbertModel",vn]],["distilbert",["DistilBertModel",vr]],["roberta",["RobertaModel",jr]],["xlm",["XLMModel",Ws]],["xlm-roberta",["XLMRobertaModel",Ei]],["clap",["ClapModel",Db]],["clip",["CLIPModel",Ni]],["clipseg",["CLIPSegModel",Lo]],["chinese_clip",["ChineseCLIPModel",Gi]],["siglip",["SiglipModel",En]],["jina_clip",["JinaCLIPModel",Hn]],["mobilebert",["MobileBertModel",Tr]],["squeezebert",["SqueezeBertModel",Mn]],["wav2vec2",["Wav2Vec2Model",QM]],["wav2vec2-bert",["Wav2Vec2BertModel",cb]],["unispeech",["UniSpeechModel",nb]],["unispeech-sat",["UniSpeechSatModel",ab]],["hubert",["HubertModel",mb]],["wavlm",["WavLMModel",gb]],["audio-spectrogram-transformer",["ASTModel",ki]],["vits",["VitsModel",od]],["pyannote",["PyAnnoteModel",eb]],["wespeaker-resnet",["WeSpeakerResNetModel",sb]],["detr",["DetrModel",jw]],["rt_detr",["RTDetrModel",Uw]],["rt_detr_v2",["RTDetrV2Model",Gw]],["rf_detr",["RFDetrModel",qw]],["table-transformer",["TableTransformerModel",Jw]],["vit",["ViTModel",pw]],["ijepa",["IJepaModel",mw]],["pvt",["PvtModel",ww]],["vit_msn",["ViTMSNModel",vw]],["vit_mae",["ViTMAEModel",yw]],["groupvit",["GroupViTModel",Ew]],["fastvit",["FastViTModel",Pw]],["mobilevit",["MobileViTModel",kw]],["mobilevitv2",["MobileViTV2Model",Aw]],["owlvit",["OwlViTModel",Ow]],["owlv2",["Owlv2Model",Lw]],["beit",["BeitModel",Bw]],["deit",["DeiTModel",eM]],["hiera",["HieraModel",rM]],["convnext",["ConvNextModel",kM]],["convnextv2",["ConvNextV2Model",AM]],["dinov2",["Dinov2Model",OM]],["dinov2_with_registers",["Dinov2WithRegistersModel",LM]],["resnet",["ResNetModel",nM]],["swin",["SwinModel",iM]],["swin2sr",["Swin2SRModel",uM]],["donut-swin",["DonutSwinModel",$M]],["yolos",["YolosModel",jM]],["dpt",["DPTModel",cM]],["glpn",["GLPNModel",PM]],["hifigan",["SpeechT5HifiGan",Pb]],["efficientnet",["EfficientNetModel",Ub]],["decision_transformer",["DecisionTransformerModel",ny]],["patchtst",["PatchTSTForPrediction",dy]],["patchtsmixer",["PatchTSMixerForPrediction",py]],["mobilenet_v1",["MobileNetV1Model",Gb]],["mobilenet_v2",["MobileNetV2Model",qb]],["mobilenet_v3",["MobileNetV3Model",Jb]],["mobilenet_v4",["MobileNetV4Model",ey]],["maskformer",["MaskFormerModel",TM]],["mgp-str",["MgpstrForSceneTextRecognition",uy]],["style_text_to_speech_2",["StyleTextToSpeech2Model",xb]]]),Ox=new Map([["t5",["T5Model",ie]],["longt5",["LongT5Model",je]],["mt5",["MT5Model",Je]],["bart",["BartModel",Pt]],["mbart",["MBartModel",br]],["marian",["MarianModel",KM]],["whisper",["WhisperModel",Ai]],["m2m_100",["M2M100Model",qM]],["blenderbot",["BlenderbotModel",Dt]],["blenderbot-small",["BlenderbotSmallModel",Qr]]]),Dx=new Map([["mimi",["MimiModel",wy]],["dac",["DacModel",xy]],["snac",["SnacModel",Py]]]),Lx=new Map([["bloom",["BloomModel",iw]],["jais",["JAISModel",Pn]],["gpt2",["GPT2Model",Ki]],["gptj",["GPTJModel",Vo]],["gpt_bigcode",["GPTBigCodeModel",Yn]],["gpt_neo",["GPTNeoModel",jo]],["gpt_neox",["GPTNeoXModel",Nr]],["codegen",["CodeGenModel",Go]],["llama",["LlamaModel",eo]],["exaone",["ExaoneModel",I]],["olmo",["OlmoModel",Ke]],["olmo2",["Olmo2Model",Tt]],["mobilellm",["MobileLLMModel",ue]],["granite",["GraniteModel",Os]],["cohere",["CohereModel",Vg]],["gemma",["GemmaModel",Wg]],["gemma2",["Gemma2Model",Kg]],["gemma3_text",["Gemma3Model",qg]],["helium",["HeliumModel",Ho]],["glm",["GlmModel",Xo]],["openelm",["OpenELMModel",Qg]],["qwen2",["Qwen2Model",Yg]],["phi",["PhiModel",rw]],["phi3",["Phi3Model",nw]],["mpt",["MptModel",lw]],["opt",["OPTModel",dw]],["mistral",["MistralModel",$b]],["starcoder2",["Starcoder2Model",Ib]],["falcon",["FalconModel",Fb]],["stablelm",["StableLmModel",Nb]]]),pd=new Map([["speecht5",["SpeechT5ForSpeechToText",Tb]],["whisper",["WhisperForConditionalGeneration",Po]],["lite-whisper",["LiteWhisperForConditionalGeneration",Fi]],["moonshine",["MoonshineForConditionalGeneration",Co]]]),$y=new Map([["speecht5",["SpeechT5ForTextToSpeech",Eb]]]),ky=new Map([["vits",["VitsModel",od]],["musicgen",["MusicgenForConditionalGeneration",ud]]]),Iy=new Map([["bert",["BertForSequenceClassification",Fe]],["modernbert",["ModernBertForSequenceClassification",J]],["roformer",["RoFormerForSequenceClassification",Xe]],["electra",["ElectraForSequenceClassification",Ns]],["esm",["EsmForSequenceClassification",Fr]],["convbert",["ConvBertForSequenceClassification",ur]],["camembert",["CamembertForSequenceClassification",ds]],["deberta",["DebertaForSequenceClassification",tt]],["deberta-v2",["DebertaV2ForSequenceClassification",Kr]],["mpnet",["MPNetForSequenceClassification",fn]],["albert",["AlbertForSequenceClassification",he]],["distilbert",["DistilBertForSequenceClassification",cn]],["roberta",["RobertaForSequenceClassification",Vt]],["xlm",["XLMForSequenceClassification",vi]],["xlm-roberta",["XLMRobertaForSequenceClassification",Ci]],["bart",["BartForSequenceClassification",kt]],["mbart",["MBartForSequenceClassification",pr]],["mobilebert",["MobileBertForSequenceClassification",dr]],["squeezebert",["SqueezeBertForSequenceClassification",bn]]]),Ay=new Map([["bert",["BertForTokenClassification",ze]],["modernbert",["ModernBertForTokenClassification",le]],["roformer",["RoFormerForTokenClassification",Ge]],["electra",["ElectraForTokenClassification",at]],["esm",["EsmForTokenClassification",hn]],["convbert",["ConvBertForTokenClassification",ls]],["camembert",["CamembertForTokenClassification",cs]],["deberta",["DebertaForTokenClassification",Rt]],["deberta-v2",["DebertaV2ForTokenClassification",ms]],["mpnet",["MPNetForTokenClassification",gn]],["distilbert",["DistilBertForTokenClassification",xr]],["roberta",["RobertaForTokenClassification",Zt]],["xlm",["XLMForTokenClassification",xi]],["xlm-roberta",["XLMRobertaForTokenClassification",Si]]]),hd=new Map([["t5",["T5ForConditionalGeneration",de]],["longt5",["LongT5ForConditionalGeneration",He]],["mt5",["MT5ForConditionalGeneration",dt]],["bart",["BartForConditionalGeneration",jt]],["mbart",["MBartForConditionalGeneration",cr]],["marian",["MarianMTModel",HM]],["m2m_100",["M2M100ForConditionalGeneration",XM]],["blenderbot",["BlenderbotForConditionalGeneration",rr]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Rr]]]),md=new Map([["bloom",["BloomForCausalLM",aw]],["gpt2",["GPT2LMHeadModel",Bo]],["jais",["JAISLMHeadModel",nt]],["gptj",["GPTJForCausalLM",Uo]],["gpt_bigcode",["GPTBigCodeForCausalLM",Wo]],["gpt_neo",["GPTNeoForCausalLM",No]],["gpt_neox",["GPTNeoXForCausalLM",Cn]],["codegen",["CodeGenForCausalLM",$n]],["llama",["LlamaForCausalLM",Ko]],["exaone",["ExaoneForCausalLM",L]],["olmo",["OlmoForCausalLM",Ze]],["olmo2",["Olmo2ForCausalLM",It]],["mobilellm",["MobileLLMForCausalLM",Te]],["granite",["GraniteForCausalLM",Hi]],["cohere",["CohereForCausalLM",Ug]],["gemma",["GemmaForCausalLM",Gg]],["gemma2",["Gemma2ForCausalLM",Hg]],["gemma3_text",["Gemma3ForCausalLM",Xg]],["helium",["HeliumForCausalLM",qo]],["glm",["GlmForCausalLM",h]],["openelm",["OpenELMForCausalLM",Jg]],["qwen2",["Qwen2ForCausalLM",Zg]],["phi",["PhiForCausalLM",sw]],["phi3",["Phi3ForCausalLM",ow]],["mpt",["MptForCausalLM",uw]],["opt",["OPTForCausalLM",cw]],["mbart",["MBartForCausalLM",Nt]],["mistral",["MistralForCausalLM",kb]],["starcoder2",["Starcoder2ForCausalLM",Ab]],["falcon",["FalconForCausalLM",Ob]],["trocr",["TrOCRForCausalLM",Sb]],["stablelm",["StableLmForCausalLM",Vb]],["phi3_v",["Phi3VForCausalLM",Io]]]),zx=new Map([["multi_modality",["MultiModalityCausalLM",iy]]]),Fy=new Map([["bert",["BertForMaskedLM",Ae]],["modernbert",["ModernBertForMaskedLM",z]],["roformer",["RoFormerForMaskedLM",Re]],["electra",["ElectraForMaskedLM",Ss]],["esm",["EsmForMaskedLM",pn]],["convbert",["ConvBertForMaskedLM",Ot]],["camembert",["CamembertForMaskedLM",Ft]],["deberta",["DebertaForMaskedLM",Qe]],["deberta-v2",["DebertaV2ForMaskedLM",hs]],["mpnet",["MPNetForMaskedLM",_n]],["albert",["AlbertForMaskedLM",N]],["distilbert",["DistilBertForMaskedLM",ks]],["roberta",["RobertaForMaskedLM",or]],["xlm",["XLMWithLMHeadModel",xn]],["xlm-roberta",["XLMRobertaForMaskedLM",Pi]],["mobilebert",["MobileBertForMaskedLM",Is]],["squeezebert",["SqueezeBertForMaskedLM",Us]]]),Oy=new Map([["bert",["BertForQuestionAnswering",Ve]],["roformer",["RoFormerForQuestionAnswering",lt]],["electra",["ElectraForQuestionAnswering",us]],["convbert",["ConvBertForQuestionAnswering",Ms]],["camembert",["CamembertForQuestionAnswering",ys]],["deberta",["DebertaForQuestionAnswering",zr]],["deberta-v2",["DebertaV2ForQuestionAnswering",_s]],["mpnet",["MPNetForQuestionAnswering",wn]],["albert",["AlbertForQuestionAnswering",$]],["distilbert",["DistilBertForQuestionAnswering",$s]],["roberta",["RobertaForQuestionAnswering",er]],["xlm",["XLMForQuestionAnswering",Ti]],["xlm-roberta",["XLMRobertaForQuestionAnswering",$i]],["mobilebert",["MobileBertForQuestionAnswering",gr]],["squeezebert",["SqueezeBertForQuestionAnswering",yn]]]),_d=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",So]],["idefics3",["Idefics3ForConditionalGeneration",Ks]],["smolvlm",["SmolVLMForConditionalGeneration",Kn]]]),Dy=new Map([["llava",["LlavaForConditionalGeneration",Tn]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",$o]],["moondream1",["Moondream1ForConditionalGeneration",Gs]],["florence2",["Florence2ForConditionalGeneration",ko]],["qwen2-vl",["Qwen2VLForConditionalGeneration",tw]],["idefics3",["Idefics3ForConditionalGeneration",Ks]],["smolvlm",["SmolVLMForConditionalGeneration",Kn]],["paligemma",["PaliGemmaForConditionalGeneration",Bi]]]),Ly=new Map([["ultravox",["UltravoxModel",_y]]]),Bx=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",So]]]),zy=new Map([["vit",["ViTForImageClassification",hw]],["ijepa",["IJepaForImageClassification",_w]],["pvt",["PvtForImageClassification",Mw]],["vit_msn",["ViTMSNForImageClassification",xw]],["fastvit",["FastViTForImageClassification",Cw]],["mobilevit",["MobileViTForImageClassification",Iw]],["mobilevitv2",["MobileViTV2ForImageClassification",Fw]],["beit",["BeitForImageClassification",Rw]],["deit",["DeiTForImageClassification",tM]],["hiera",["HieraForImageClassification",sM]],["convnext",["ConvNextForImageClassification",IM]],["convnextv2",["ConvNextV2ForImageClassification",FM]],["dinov2",["Dinov2ForImageClassification",DM]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",zM]],["resnet",["ResNetForImageClassification",oM]],["swin",["SwinForImageClassification",aM]],["segformer",["SegformerForImageClassification",Rb]],["efficientnet",["EfficientNetForImageClassification",Wb]],["mobilenet_v1",["MobileNetV1ForImageClassification",Kb]],["mobilenet_v2",["MobileNetV2ForImageClassification",Xb]],["mobilenet_v3",["MobileNetV3ForImageClassification",Yb]],["mobilenet_v4",["MobileNetV4ForImageClassification",ty]]]),By=new Map([["detr",["DetrForObjectDetection",Nw]],["rt_detr",["RTDetrForObjectDetection",Ww]],["rt_detr_v2",["RTDetrV2ForObjectDetection",Kw]],["rf_detr",["RFDetrForObjectDetection",Xw]],["table-transformer",["TableTransformerForObjectDetection",Yw]],["yolos",["YolosForObjectDetection",NM]]]),Ry=new Map([["owlvit",["OwlViTForObjectDetection",Dw]],["owlv2",["Owlv2ForObjectDetection",zw]],["grounding-dino",["GroundingDinoForObjectDetection",RM]]]),no=new Map([["detr",["DetrForSegmentation",Du]],["clipseg",["CLIPSegForImageSegmentation",zo]]]),jy=new Map([["segformer",["SegformerForSemanticSegmentation",jb]],["sapiens",["SapiensForSemanticSegmentation",_M]],["swin",["SwinForSemanticSegmentation",lM]],["mobilenet_v1",["MobileNetV1ForSemanticSegmentation",Hb]],["mobilenet_v2",["MobileNetV2ForSemanticSegmentation",Qb]],["mobilenet_v3",["MobileNetV3ForSemanticSegmentation",Zb]],["mobilenet_v4",["MobileNetV4ForSemanticSegmentation",ry]]]),Ny=new Map([["detr",["DetrForSegmentation",Du]],["maskformer",["MaskFormerForInstanceSegmentation",EM]]]),Vy=new Map([["sam",["SamModel",WM]]]),Uy=new Map([["wav2vec2",["Wav2Vec2ForCTC",JM]],["wav2vec2-bert",["Wav2Vec2BertForCTC",pb]],["unispeech",["UniSpeechForCTC",ob]],["unispeech-sat",["UniSpeechSatForCTC",lb]],["wavlm",["WavLMForCTC",wb]],["hubert",["HubertForCTC",_b]]]),Wy=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",YM]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",hb]],["unispeech",["UniSpeechForSequenceClassification",ib]],["unispeech-sat",["UniSpeechSatForSequenceClassification",ub]],["wavlm",["WavLMForSequenceClassification",Mb]],["hubert",["HubertForSequenceClassification",fb]],["audio-spectrogram-transformer",["ASTForAudioClassification",Ii]]]),Gy=new Map([["wavlm",["WavLMForXVector",bb]]]),Ky=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",db]],["wavlm",["WavLMForAudioFrameClassification",yb]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",ZM]],["pyannote",["PyAnnoteForAudioFrameClassification",tb]]]),Hy=new Map([["vitmatte",["VitMatteForImageMatting",$w]]]),Rx=new Map([["patchtst",["PatchTSTForPrediction",cy]],["patchtsmixer",["PatchTSMixerForPrediction",hy]]]),qy=new Map([["swin2sr",["Swin2SRForImageSuperResolution",dM]]]),Xy=new Map([["dpt",["DPTForDepthEstimation",pM]],["depth_anything",["DepthAnythingForDepthEstimation",mM]],["glpn",["GLPNForDepthEstimation",CM]],["sapiens",["SapiensForDepthEstimation",fM]],["depth_pro",["DepthProForDepthEstimation",MM]],["metric3d",["Metric3DForDepthEstimation",yM]],["metric3dv2",["Metric3Dv2ForDepthEstimation",xM]]]),Qy=new Map([["sapiens",["SapiensForNormalEstimation",gM]]]),Jy=new Map([["vitpose",["VitPoseForPoseEstimation",gw]]]),Yy=new Map([["clip",["CLIPVisionModelWithProjection",Ui]],["siglip",["SiglipVisionModel",Fo]],["jina_clip",["JinaCLIPVisionModel",Do]]]),Zy=[[Fx,E.EncoderOnly],[Ox,E.EncoderDecoder],[Lx,E.DecoderOnly],[Dx,E.AutoEncoder],[Iy,E.EncoderOnly],[Ay,E.EncoderOnly],[hd,E.Seq2Seq],[pd,E.Seq2Seq],[md,E.DecoderOnly],[zx,E.MultiModality],[Fy,E.EncoderOnly],[Oy,E.EncoderOnly],[_d,E.Vision2Seq],[Dy,E.ImageTextToText],[Ly,E.AudioTextToText],[zy,E.EncoderOnly],[no,E.EncoderOnly],[Ny,E.EncoderOnly],[jy,E.EncoderOnly],[Hy,E.EncoderOnly],[Rx,E.EncoderOnly],[qy,E.EncoderOnly],[Xy,E.EncoderOnly],[Qy,E.EncoderOnly],[Jy,E.EncoderOnly],[By,E.EncoderOnly],[Ry,E.EncoderOnly],[Vy,E.MaskGeneration],[Uy,E.EncoderOnly],[Wy,E.EncoderOnly],[$y,E.Seq2Seq],[ky,E.EncoderOnly],[Gy,E.EncoderOnly],[Ky,E.EncoderOnly],[Yy,E.EncoderOnly]];for(const[b,P]of Zy)for(const[D,ne]of b.values())y.set(D,P),v.set(ne,D),M.set(D,ne);const jx=[["MusicgenForConditionalGeneration",ud,E.Musicgen],["Phi3VForCausalLM",Io,E.Phi3V],["CLIPTextModelWithProjection",Vi,E.EncoderOnly],["SiglipTextModel",Hs,E.EncoderOnly],["JinaCLIPTextModel",Oo,E.EncoderOnly],["ClapTextModelWithProjection",Lb,E.EncoderOnly],["ClapAudioModelWithProjection",zb,E.EncoderOnly],["DacEncoderModel",Ty,E.EncoderOnly],["DacDecoderModel",Ey,E.EncoderOnly],["MimiEncoderModel",My,E.EncoderOnly],["MimiDecoderModel",by,E.EncoderOnly],["SnacEncoderModel",Cy,E.EncoderOnly],["SnacDecoderModel",Sy,E.EncoderOnly]];for(const[b,P,D]of jx)y.set(b,D),v.set(P,b),M.set(b,P);const e0=new Map([["modnet",no],["birefnet",no],["isnet",no],["ben",no]]);for(const[b,P]of e0.entries())P.set(b,["PreTrainedModel",R]),y.set(b,E.EncoderOnly),v.set(R,b),M.set(b,R);class fd extends Ct{}te(fd,"MODEL_CLASS_MAPPINGS",Zy.map(P=>P[0])),te(fd,"BASE_IF_FAIL",!0);class t0 extends Ct{}te(t0,"MODEL_CLASS_MAPPINGS",[Iy]);class r0 extends Ct{}te(r0,"MODEL_CLASS_MAPPINGS",[Ay]);class s0 extends Ct{}te(s0,"MODEL_CLASS_MAPPINGS",[hd]);class n0 extends Ct{}te(n0,"MODEL_CLASS_MAPPINGS",[pd]);class o0 extends Ct{}te(o0,"MODEL_CLASS_MAPPINGS",[$y]);class i0 extends Ct{}te(i0,"MODEL_CLASS_MAPPINGS",[ky]);class a0 extends Ct{}te(a0,"MODEL_CLASS_MAPPINGS",[md]);class l0 extends Ct{}te(l0,"MODEL_CLASS_MAPPINGS",[Fy]);class u0 extends Ct{}te(u0,"MODEL_CLASS_MAPPINGS",[Oy]);class d0 extends Ct{}te(d0,"MODEL_CLASS_MAPPINGS",[_d]);class c0 extends Ct{}te(c0,"MODEL_CLASS_MAPPINGS",[zy]);class p0 extends Ct{}te(p0,"MODEL_CLASS_MAPPINGS",[no]);class h0 extends Ct{}te(h0,"MODEL_CLASS_MAPPINGS",[jy]);class m0 extends Ct{}te(m0,"MODEL_CLASS_MAPPINGS",[Ny]);class _0 extends Ct{}te(_0,"MODEL_CLASS_MAPPINGS",[By]);class f0 extends Ct{}te(f0,"MODEL_CLASS_MAPPINGS",[Ry]);class g0 extends Ct{}te(g0,"MODEL_CLASS_MAPPINGS",[Vy]);class w0 extends Ct{}te(w0,"MODEL_CLASS_MAPPINGS",[Uy]);class M0 extends Ct{}te(M0,"MODEL_CLASS_MAPPINGS",[Wy]);class b0 extends Ct{}te(b0,"MODEL_CLASS_MAPPINGS",[Gy]);class y0 extends Ct{}te(y0,"MODEL_CLASS_MAPPINGS",[Ky]);class v0 extends Ct{}te(v0,"MODEL_CLASS_MAPPINGS",[Bx]);class x0 extends Ct{}te(x0,"MODEL_CLASS_MAPPINGS",[Hy]);class T0 extends Ct{}te(T0,"MODEL_CLASS_MAPPINGS",[qy]);class E0 extends Ct{}te(E0,"MODEL_CLASS_MAPPINGS",[Xy]);class P0 extends Ct{}te(P0,"MODEL_CLASS_MAPPINGS",[Qy]);class C0 extends Ct{}te(C0,"MODEL_CLASS_MAPPINGS",[Jy]);class S0 extends Ct{}te(S0,"MODEL_CLASS_MAPPINGS",[Yy]);class $0 extends Ct{}te($0,"MODEL_CLASS_MAPPINGS",[Dy]);class k0 extends Ct{}te(k0,"MODEL_CLASS_MAPPINGS",[Ly]);class Nx extends pe{constructor({logits:P,past_key_values:D,encoder_outputs:ne,decoder_attentions:ge=null,cross_attentions:fe=null}){super(),this.logits=P,this.past_key_values=D,this.encoder_outputs=ne,this.decoder_attentions=ge,this.cross_attentions=fe}}class _t extends pe{constructor({logits:P,...D}){super(),this.logits=P;const ne=Object.values(D);ne.length>0&&(this.attentions=ne)}}class I0 extends pe{constructor({logits:P,embeddings:D}){super(),this.logits=P,this.embeddings=D}}class hr extends pe{constructor({logits:P}){super(),this.logits=P}}class Mr extends pe{constructor({logits:P}){super(),this.logits=P}}class Er extends pe{constructor({start_logits:P,end_logits:D}){super(),this.start_logits=P,this.end_logits=D}}class In extends pe{constructor({logits:P}){super(),this.logits=P}}class Vx extends pe{constructor({logits:P,past_key_values:D}){super(),this.logits=P,this.past_key_values=D}}class A0 extends pe{constructor({alphas:P}){super(),this.alphas=P}}class F0 extends pe{constructor({waveform:P,spectrogram:D}){super(),this.waveform=P,this.spectrogram=D}}},"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(e,r,t)=>{t.r(r),t.d(r,{ASTFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var o=t("./src/utils/audio.js");class n extends s.FeatureExtractor{constructor(a){super(a);const l=this.config.sampling_rate,u=(0,o.mel_filter_bank)(257,this.config.num_mel_bins,20,Math.floor(l/2),l,null,"kaldi",!0);this.mel_filters=u,this.window=(0,o.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(a,l){return(0,o.spectrogram)(a,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:l,transpose:!0})}async _call(a){(0,s.validate_audio_inputs)(a,"ASTFeatureExtractor");const l=await this._extract_fbank_features(a,this.config.max_length);if(this.config.do_normalize){const u=this.std*2,p=l.data;for(let d=0;d{t.r(r),t.d(r,{AutoFeatureExtractor:()=>i});var s=t("./src/utils/constants.js"),o=t("./src/utils/hub.js");t("./src/base/feature_extraction_utils.js");var n=t("./src/models/feature_extractors.js");class i{static async from_pretrained(l,u={}){const p=await(0,o.getModelJSON)(l,s.FEATURE_EXTRACTOR_NAME,!0,u),d=p.feature_extractor_type,c=n[d];if(!c)throw new Error(`Unknown feature_extractor_type: '${d}'. Please report this at ${s.GITHUB_ISSUE_URL}.`);return new c(p)}}},"./src/models/auto/image_processing_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoImageProcessor:()=>a});var s=t("./src/utils/constants.js"),o=t("./src/utils/hub.js"),n=t("./src/base/image_processors_utils.js"),i=t("./src/models/image_processors.js");class a{static async from_pretrained(u,p={}){const d=await(0,o.getModelJSON)(u,s.IMAGE_PROCESSOR_NAME,!0,p),c=d.image_processor_type??d.feature_extractor_type;let f=i[c];return f||(c!==void 0&&console.warn(`Image processor type '${c}' not found, assuming base ImageProcessor. Please report this at ${s.GITHUB_ISSUE_URL}.`),f=n.ImageProcessor),new f(d)}}},"./src/models/auto/processing_auto.js":(e,r,t)=>{t.r(r),t.d(r,{AutoProcessor:()=>u});var s=t("./src/utils/constants.js"),o=t("./src/utils/hub.js"),n=t("./src/base/processing_utils.js"),i=t("./src/models/processors.js"),a=t("./src/models/image_processors.js"),l=t("./src/models/feature_extractors.js");class u{static async from_pretrained(d,c={}){const f=await(0,o.getModelJSON)(d,s.IMAGE_PROCESSOR_NAME,!0,c),{image_processor_type:_,feature_extractor_type:T,processor_class:k}=f;if(k&&i[k])return i[k].from_pretrained(d,c);if(!_&&!T)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const w={};if(_){const S=a[_];if(!S)throw new Error(`Unknown image_processor_type: '${_}'.`);w.image_processor=new S(f)}if(T){const S=a[T];if(S)w.image_processor=new S(f);else{const E=l[T];if(!E)throw new Error(`Unknown feature_extractor_type: '${T}'.`);w.feature_extractor=new E(f)}}const g={};return new n.Processor(g,w)}}},"./src/models/beit/image_processing_beit.js":(e,r,t)=>{t.r(r),t.d(r,{BeitFeatureExtractor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/bit/image_processing_bit.js":(e,r,t)=>{t.r(r),t.d(r,{BitImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/chinese_clip/image_processing_chinese_clip.js":(e,r,t)=>{t.r(r),t.d(r,{ChineseCLIPFeatureExtractor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}},"./src/models/clap/feature_extraction_clap.js":(e,r,t)=>{t.r(r),t.d(r,{ClapFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var o=t("./src/utils/audio.js");class n extends s.FeatureExtractor{constructor(a){super(a),this.mel_filters=(0,o.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,o.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,o.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(a,l,u,p){let d;const c=a.length-l;if(c>0)if(u==="rand_trunc"){const f=Math.floor(Math.random()*(c+1));a=a.subarray(f,f+l),d=await this._extract_fbank_features(a,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${u}" not implemented`);else{if(c<0){let f=new Float64Array(l);if(f.set(a),p==="repeat")for(let _=a.length;_{t.r(r),t.d(r,{CLIPFeatureExtractor:()=>n,CLIPImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/convnext/image_processing_convnext.js":(e,r,t)=>{t.r(r),t.d(r,{ConvNextFeatureExtractor:()=>n,ConvNextImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(a){super(a),this.crop_pct=this.config.crop_pct??224/256}async resize(a){var u;const l=(u=this.size)==null?void 0:u.shortest_edge;if(l===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(l<384){const p=Math.floor(l/this.crop_pct),[d,c]=this.get_resize_output_image_size(a,{shortest_edge:p});a=await a.resize(d,c,{resample:this.resample}),a=await a.center_crop(l,l)}else a=await a.resize(l,l,{resample:this.resample});return a}}class n extends o{}},"./src/models/dac/feature_extraction_dac.js":(e,r,t)=>{t.r(r),t.d(r,{DacFeatureExtractor:()=>o});var s=t("./src/models/encodec/feature_extraction_encodec.js");class o extends s.EncodecFeatureExtractor{}},"./src/models/deit/image_processing_deit.js":(e,r,t)=>{t.r(r),t.d(r,{DeiTFeatureExtractor:()=>n,DeiTImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/detr/image_processing_detr.js":(e,r,t)=>{t.r(r),t.d(r,{DetrFeatureExtractor:()=>i,DetrImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js"),o=t("./src/utils/tensor.js");class n extends s.ImageProcessor{async _call(l){const u=await super._call(l),p=[u.pixel_values.dims[0],64,64],d=(0,o.full)(p,1n);return{...u,pixel_mask:d}}post_process_object_detection(...l){return(0,s.post_process_object_detection)(...l)}post_process_panoptic_segmentation(...l){return(0,s.post_process_panoptic_segmentation)(...l)}post_process_instance_segmentation(...l){return(0,s.post_process_instance_segmentation)(...l)}}class i extends n{}},"./src/models/donut/image_processing_donut.js":(e,r,t)=>{t.r(r),t.d(r,{DonutFeatureExtractor:()=>n,DonutImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{pad_image(a,l,u,p={}){const[d,c,f]=l;let _=this.image_mean;Array.isArray(this.image_mean)||(_=new Array(f).fill(_));let T=this.image_std;Array.isArray(T)||(T=new Array(f).fill(_));const k=_.map((w,g)=>-w/T[g]);return super.pad_image(a,l,u,{center:!0,constant_values:k,...p})}}class n extends o{}},"./src/models/dpt/image_processing_dpt.js":(e,r,t)=>{t.r(r),t.d(r,{DPTFeatureExtractor:()=>n,DPTImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{}class n extends o{}},"./src/models/efficientnet/image_processing_efficientnet.js":(e,r,t)=>{t.r(r),t.d(r,{EfficientNetImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(i){super(i),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(a=>a*a))}}},"./src/models/encodec/feature_extraction_encodec.js":(e,r,t)=>{t.r(r),t.d(r,{EncodecFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js"),o=t("./src/utils/tensor.js");class n extends s.FeatureExtractor{async _call(a){(0,s.validate_audio_inputs)(a,"EncodecFeatureExtractor"),a instanceof Float64Array&&(a=new Float32Array(a));const l=this.config.feature_size;if(a.length%l!==0)throw new Error(`The length of the audio data must be a multiple of the number of channels (${l}).`);const u=[1,l,a.length/l];return{input_values:new o.Tensor("float32",a,u)}}}},"./src/models/feature_extractors.js":(e,r,t)=>{t.r(r),t.d(r,{ASTFeatureExtractor:()=>s.ASTFeatureExtractor,ClapFeatureExtractor:()=>n.ClapFeatureExtractor,DacFeatureExtractor:()=>i.DacFeatureExtractor,EncodecFeatureExtractor:()=>o.EncodecFeatureExtractor,ImageFeatureExtractor:()=>T.ImageProcessor,MoonshineFeatureExtractor:()=>a.MoonshineFeatureExtractor,PyAnnoteFeatureExtractor:()=>l.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>u.SeamlessM4TFeatureExtractor,SnacFeatureExtractor:()=>p.SnacFeatureExtractor,SpeechT5FeatureExtractor:()=>d.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>c.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>f.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>_.WhisperFeatureExtractor});var s=t("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),o=t("./src/models/encodec/feature_extraction_encodec.js"),n=t("./src/models/clap/feature_extraction_clap.js"),i=t("./src/models/dac/feature_extraction_dac.js"),a=t("./src/models/moonshine/feature_extraction_moonshine.js"),l=t("./src/models/pyannote/feature_extraction_pyannote.js"),u=t("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),p=t("./src/models/snac/feature_extraction_snac.js"),d=t("./src/models/speecht5/feature_extraction_speecht5.js"),c=t("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),f=t("./src/models/wespeaker/feature_extraction_wespeaker.js"),_=t("./src/models/whisper/feature_extraction_whisper.js"),T=t("./src/base/image_processors_utils.js")},"./src/models/florence2/processing_florence2.js":(e,r,t)=>{t.r(r),t.d(r,{Florence2Processor:()=>i});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");class i extends s.Processor{constructor(l,u){super(l,u);const{tasks_answer_post_processing_type:p,task_prompts_without_inputs:d,task_prompts_with_input:c}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(p??{})),this.task_prompts_without_inputs=new Map(Object.entries(d??{})),this.task_prompts_with_input=new Map(Object.entries(c??{})),this.regexes={quad_boxes:/(.+?)/gm,bboxes:/([^<]+)?/gm},this.size_per_bin=1e3}construct_prompts(l){typeof l=="string"&&(l=[l]);const u=[];for(const p of l)if(this.task_prompts_without_inputs.has(p))u.push(this.task_prompts_without_inputs.get(p));else{for(const[d,c]of this.task_prompts_with_input)if(p.includes(d)){u.push(c.replaceAll("{input}",p).replaceAll(d,""));break}u.length!==l.length&&u.push(p)}return u}post_process_generation(l,u,p){const 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this.image_processor(p,c):{};return{...d?this.tokenizer(d,c):{},...f}}post_process_grounded_object_detection(p,d,{box_threshold:c=.25,text_threshold:f=.25,target_sizes:_=null}={}){const{logits:T,pred_boxes:k}=p,w=T.dims[0];if(_!==null&&_.length!==w)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const g=T.dims.at(1),S=T.sigmoid(),E=S.max(-1).tolist(),y=k.tolist().map(v=>v.map(C=>(0,i.center_to_corners_format)(C))),M=[];for(let v=0;vj.map((ee,H)=>ee*C[(H+1)%2])));const A=E[v],B=[],K=[],G=[];for(let j=0;j{t.r(r),t.d(r,{Idefics3ImageProcessor:()=>n});var s=t("./src/base/image_processors_utils.js"),o=t("./src/utils/tensor.js");class n extends s.ImageProcessor{constructor(a){super(a),this.do_image_splitting=a.do_image_splitting??!0,this.max_image_size=a.max_image_size}get_resize_for_vision_encoder(a,l){let[u,p]=a.dims.slice(-2);const d=p/u;return p>=u?(p=Math.ceil(p/l)*l,u=Math.floor(p/d),u=Math.ceil(u/l)*l):(u=Math.ceil(u/l)*l,p=Math.floor(u*d),p=Math.ceil(p/l)*l),{height:u,width:p}}async _call(a,{do_image_splitting:l=null,return_row_col_info:u=!1}={}){let p;if(!Array.isArray(a))p=[[a]];else{if(a.length===0||!a[0])throw new Error("No images provided.");Array.isArray(a[0])?p=a:p=[a]}let d=[],c=[],f=[];const _=[],T=[];for(const v of p){let C=await Promise.all(v.map(K=>this.preprocess(K)));_.push(...C.map(K=>K.original_size)),T.push(...C.map(K=>K.reshaped_input_size)),C.forEach(K=>K.pixel_values.unsqueeze_(0));const{longest_edge:A}=this.max_image_size;let B;if(l??this.do_image_splitting){let K=new Array(C.length),G=new Array(C.length);B=await Promise.all(C.map(async(j,ee)=>{const H=this.get_resize_for_vision_encoder(j.pixel_values,A),Z=await(0,o.interpolate_4d)(j.pixel_values,{size:[H.height,H.width]}),{frames:X,num_splits_h:oe,num_splits_w:me}=await this.split_image(Z,this.max_image_size);return 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ee=0;ee{t.r(r),t.d(r,{BeitFeatureExtractor:()=>s.BeitFeatureExtractor,BitImageProcessor:()=>o.BitImageProcessor,CLIPFeatureExtractor:()=>i.CLIPFeatureExtractor,CLIPImageProcessor:()=>i.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>n.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>a.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>a.ConvNextImageProcessor,DPTFeatureExtractor:()=>d.DPTFeatureExtractor,DPTImageProcessor:()=>d.DPTImageProcessor,DeiTFeatureExtractor:()=>l.DeiTFeatureExtractor,DeiTImageProcessor:()=>l.DeiTImageProcessor,DetrFeatureExtractor:()=>u.DetrFeatureExtractor,DetrImageProcessor:()=>u.DetrImageProcessor,DonutFeatureExtractor:()=>p.DonutFeatureExtractor,DonutImageProcessor:()=>p.DonutImageProcessor,EfficientNetImageProcessor:()=>c.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>f.GLPNFeatureExtractor,GroundingDinoImageProcessor:()=>_.GroundingDinoImageProcessor,Idefics3ImageProcessor:()=>T.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>w.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>g.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>S.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>E.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>E.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>y.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>y.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>M.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>M.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>v.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>v.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>C.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>C.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>A.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>A.MobileViTImageProcessor,NougatImageProcessor:()=>B.NougatImageProcessor,OwlViTFeatureExtractor:()=>G.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>G.OwlViTImageProcessor,Owlv2ImageProcessor:()=>K.Owlv2ImageProcessor,Phi3VImageProcessor:()=>j.Phi3VImageProcessor,PvtImageProcessor:()=>ee.PvtImageProcessor,Qwen2VLImageProcessor:()=>H.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>Z.RTDetrImageProcessor,SamImageProcessor:()=>X.SamImageProcessor,SegformerFeatureExtractor:()=>oe.SegformerFeatureExtractor,SegformerImageProcessor:()=>oe.SegformerImageProcessor,SiglipImageProcessor:()=>me.SiglipImageProcessor,SmolVLMImageProcessor:()=>ae.SmolVLMImageProcessor,Swin2SRImageProcessor:()=>V.Swin2SRImageProcessor,VLMImageProcessor:()=>k.VLMImageProcessor,ViTFeatureExtractor:()=>F.ViTFeatureExtractor,ViTImageProcessor:()=>F.ViTImageProcessor,VitMatteImageProcessor:()=>W.VitMatteImageProcessor,VitPoseImageProcessor:()=>re.VitPoseImageProcessor,YolosFeatureExtractor:()=>_e.YolosFeatureExtractor,YolosImageProcessor:()=>_e.YolosImageProcessor});var 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s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(i){super({do_pad:!0,pad_size:{width:i.image_size,height:i.image_size},...i}),this.constant_values=this.config.background_color.map(a=>a*this.rescale_factor)}pad_image(i,a,l,u){return super.pad_image(i,a,l,{constant_values:this.constant_values,center:!0,...u})}}},"./src/models/janus/processing_janus.js":(e,r,t)=>{t.r(r),t.d(r,{VLChatProcessor:()=>u});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js"),i=t("./src/utils/core.js"),a=t("./src/utils/tensor.js"),l=t("./src/utils/image.js");class u extends s.Processor{constructor(d,c){super(d,c),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(d,{images:c=null,chat_template:f="default"}={}){c?Array.isArray(c)||(c=[c]):c=await Promise.all(d.filter(B=>B.images).flatMap(B=>B.images).map(B=>l.RawImage.read(B)));const _=this.tokenizer,T=_.apply_chat_template(d,{tokenize:!1,add_generation_prompt:!0,chat_template:f}),k=B=>_.encode(B,{add_special_tokens:!1}),w=T.split(this.image_tag),g=w.length-1;if(c.length!==g)throw new Error(`Number of images provided (${c.length}) does not match number of "${this.image_tag}" image tags (${g})`);const[S,E,y]=_.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let M=k(w[0]),v=new Array(M.length).fill(!1);for(let B=1;B0){const B=await this.image_processor(c);return B.pixel_values.unsqueeze_(0),{...A,...B}}return A}}te(u,"image_processor_class",o.AutoImageProcessor),te(u,"tokenizer_class",n.AutoTokenizer),te(u,"uses_processor_config",!0)},"./src/models/jina_clip/image_processing_jina_clip.js":(e,r,t)=>{t.r(r),t.d(r,{JinaCLIPImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{constructor(i){const{resize_mode:a,fill_color:l,interpolation:u,size:p,...d}=i,c=a==="squash"?{width:p,height:p}:a==="shortest"?{shortest_edge:p}:{longest_edge:p},f=u==="bicubic"?3:2;super({...d,size:c,resample:f,do_center_crop:!0,crop_size:p,do_normalize:!0})}}},"./src/models/jina_clip/processing_jina_clip.js":(e,r,t)=>{t.r(r),t.d(r,{JinaCLIPProcessor:()=>i});var s=t("./src/base/processing_utils.js"),o=t("./src/models/auto/image_processing_auto.js"),n=t("./src/tokenizers.js");class i extends s.Processor{async _call(l=null,u=null,p={}){if(!l&&!u)throw new Error("Either text or images must be provided");const d=l?this.tokenizer(l,p):{},c=u?await this.image_processor(u,p):{};return{...d,...c}}}te(i,"tokenizer_class",n.AutoTokenizer),te(i,"image_processor_class",o.AutoImageProcessor)},"./src/models/llava_onevision/image_processing_llava_onevision.js":(e,r,t)=>{t.r(r),t.d(r,{LlavaOnevisionImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o 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a={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class l extends s.Processor{get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(p,d){if(!a.hasOwnProperty(d))throw new Error(`Format ${d} is not supported.`);const[c,f]=a[d],_=this[c].bind(this),[T,k]=p.dims,w=[],g=[],S=p.tolist();for(let y=0;y0?C.reduce((B,K)=>B*K,1):0;g.push(v),w.push(A)}return[_(g),w]}char_decode(p){return this.char_tokenizer.batch_decode(p).map(d=>d.replaceAll(" ",""))}bpe_decode(p){return this.bpe_tokenizer.batch_decode(p)}wp_decode(p){return this.wp_tokenizer.batch_decode(p).map(d=>d.replaceAll(" ",""))}batch_decode([p,d,c]){const[f,_]=this._decode_helper(p,"char"),[T,k]=this._decode_helper(d,"bpe"),[w,g]=this._decode_helper(c,"wp"),S=[],E=[];for(let y=0;y{t.r(r),t.d(r,{MobileNetV1FeatureExtractor:()=>n,MobileNetV1ImageProcessor:()=>o});var 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_call(a,l){Array.isArray(a)||(a=[a]),Array.isArray(l)||(l=[l]);const u=await Promise.all(a.map(c=>this.preprocess(c))),p=await Promise.all(l.map(c=>this.preprocess(c,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,o.stack)(u.map((c,f)=>(0,o.cat)([c.pixel_values,p[f].pixel_values],0)),0),original_sizes:u.map(c=>c.original_size),reshaped_input_sizes:u.map(c=>c.reshaped_input_size)}}}},"./src/models/vitpose/image_processing_vitpose.js":(e,r,t)=>{t.r(r),t.d(r,{VitPoseImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{post_process_pose_estimation(i,a,{threshold:l=null}={}){const u=i.tolist(),[p,d,c,f]=i.dims,_=[];for(let T=0;T{t.r(r),t.d(r,{Wav2Vec2FeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js"),o=t("./src/utils/tensor.js");class n extends s.FeatureExtractor{_zero_mean_unit_var_norm(a){const u=a.reduce((d,c)=>d+c,0)/a.length,p=a.reduce((d,c)=>d+(c-u)**2,0)/a.length;return a.map(d=>(d-u)/Math.sqrt(p+1e-7))}async _call(a){(0,s.validate_audio_inputs)(a,"Wav2Vec2FeatureExtractor"),a instanceof Float64Array&&(a=new Float32Array(a));let l=a;this.config.do_normalize&&(l=this._zero_mean_unit_var_norm(l));const u=[1,l.length];return{input_values:new o.Tensor("float32",l,u),attention_mask:new o.Tensor("int64",new BigInt64Array(l.length).fill(1n),u)}}}},"./src/models/wav2vec2/processing_wav2vec2.js":(e,r,t)=>{t.r(r),t.d(r,{Wav2Vec2Processor:()=>i});var s=t("./src/tokenizers.js"),o=t("./src/models/auto/feature_extraction_auto.js"),n=t("./src/base/processing_utils.js");class i extends n.Processor{async _call(l){return await this.feature_extractor(l)}}te(i,"tokenizer_class",s.AutoTokenizer),te(i,"feature_extractor_class",o.AutoFeatureExtractor)},"./src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js":(e,r,t)=>{t.r(r),t.d(r,{Wav2Vec2ProcessorWithLM:()=>i});var s=t("./src/tokenizers.js"),o=t("./src/models/auto/feature_extraction_auto.js"),n=t("./src/base/processing_utils.js");class i extends n.Processor{async _call(l){return await this.feature_extractor(l)}}te(i,"tokenizer_class",s.AutoTokenizer),te(i,"feature_extractor_class",o.AutoFeatureExtractor)},"./src/models/wespeaker/feature_extraction_wespeaker.js":(e,r,t)=>{t.r(r),t.d(r,{WeSpeakerFeatureExtractor:()=>n});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var o=t("./src/utils/audio.js");class n extends s.FeatureExtractor{constructor(a){super(a);const l=this.config.sampling_rate,u=(0,o.mel_filter_bank)(257,this.config.num_mel_bins,20,Math.floor(l/2),l,null,"kaldi",!0);this.mel_filters=u,this.window=(0,o.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(a){return a=a.map(l=>l*32768),(0,o.spectrogram)(a,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(a){(0,s.validate_audio_inputs)(a,"WeSpeakerFeatureExtractor");const l=(await this._extract_fbank_features(a)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const u=l.mean(1).data,p=l.data,[d,c,f]=l.dims;for(let _=0;_{t.r(r),t.d(r,{WHISPER_LANGUAGE_MAPPING:()=>o,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>n,whisper_language_to_code:()=>i});const s=[["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"]],o=new Map(s),n=new Map([...s.map(([a,l])=>[l,a]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function i(a){a=a.toLowerCase();let l=n.get(a);if(l===void 0){const u=a.match(/^<\|([a-z]{2})\|>$/);if(u&&(a=u[1]),o.has(a))l=a;else{const d=a.length===2?o.keys():o.values();throw new Error(`Language "${a}" is not supported. Must be one of: ${JSON.stringify(Array.from(d))}`)}}return l}},"./src/models/whisper/feature_extraction_whisper.js":(e,r,t)=>{t.r(r),t.d(r,{WhisperFeatureExtractor:()=>i});var s=t("./src/base/feature_extraction_utils.js");t("./src/utils/tensor.js");var o=t("./src/utils/audio.js"),n=t("./src/utils/maths.js");class i extends s.FeatureExtractor{constructor(l){var u;super(l),(u=this.config).mel_filters??(u.mel_filters=(0,o.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney")),this.window=(0,o.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(l){const u=await(0,o.spectrogram)(l,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:Math.min(Math.floor(l.length/this.config.hop_length),this.config.nb_max_frames)}),p=u.data,d=(0,n.max)(p)[0];for(let c=0;cd?(l.length>this.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`."),p=l.slice(0,d)):(p=new Float32Array(d),p.set(l)),{input_features:(await this._extract_fbank_features(p)).unsqueeze_(0)}}}},"./src/models/whisper/generation_whisper.js":(e,r,t)=>{t.r(r),t.d(r,{WhisperGenerationConfig:()=>o});var s=t("./src/generation/configuration_utils.js");class o extends s.GenerationConfig{constructor(){super(...arguments);te(this,"return_timestamps",null);te(this,"return_token_timestamps",null);te(this,"num_frames",null);te(this,"alignment_heads",null);te(this,"task",null);te(this,"language",null);te(this,"no_timestamps_token_id",null);te(this,"prompt_ids",null);te(this,"is_multilingual",null);te(this,"lang_to_id",null);te(this,"task_to_id",null);te(this,"max_initial_timestamp_index",1)}}},"./src/models/whisper/processing_whisper.js":(e,r,t)=>{t.r(r),t.d(r,{WhisperProcessor:()=>i});var s=t("./src/models/auto/feature_extraction_auto.js"),o=t("./src/tokenizers.js"),n=t("./src/base/processing_utils.js");class i extends n.Processor{async _call(l){return await this.feature_extractor(l)}}te(i,"tokenizer_class",o.AutoTokenizer),te(i,"feature_extractor_class",s.AutoFeatureExtractor)},"./src/models/yolos/image_processing_yolos.js":(e,r,t)=>{t.r(r),t.d(r,{YolosFeatureExtractor:()=>n,YolosImageProcessor:()=>o});var s=t("./src/base/image_processors_utils.js");class o extends s.ImageProcessor{post_process_object_detection(...a){return(0,s.post_process_object_detection)(...a)}}class n extends o{}},"./src/ops/registry.js":(e,r,t)=>{t.r(r),t.d(r,{TensorOpRegistry:()=>l});var s=t("./src/backends/onnx.js"),o=t("./src/utils/tensor.js"),n=t("./src/env.js");const i=n.apis.IS_BROWSER_ENV||n.apis.IS_WEBWORKER_ENV,a=async(u,p,d)=>{const c=await(0,s.createInferenceSession)(new Uint8Array(u),p);let f=Promise.resolve();return async _=>{const T=(0,s.isONNXProxy)(),k=Object.fromEntries(Object.entries(_).map(([g,S])=>[g,(T?S.clone():S).ort_tensor])),w=await(f=i?f.then(()=>c.run(k)):c.run(k));return Array.isArray(d)?d.map(g=>new o.Tensor(w[g])):new o.Tensor(w[d])}};class l{static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=a([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,"y")),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=a([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=a([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=a([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=a([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=a([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=a([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=a([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}te(l,"session_options",{})},"./src/pipelines.js":(e,r,t)=>{t.r(r),t.d(r,{AudioClassificationPipeline:()=>G,AutomaticSpeechRecognitionPipeline:()=>ee,BackgroundRemovalPipeline:()=>oe,DepthEstimationPipeline:()=>_e,DocumentQuestionAnsweringPipeline:()=>F,FeatureExtractionPipeline:()=>B,FillMaskPipeline:()=>S,ImageClassificationPipeline:()=>Z,ImageFeatureExtractionPipeline:()=>K,ImageSegmentationPipeline:()=>X,ImageToImagePipeline:()=>re,ImageToTextPipeline:()=>H,ObjectDetectionPipeline:()=>ae,Pipeline:()=>T,QuestionAnsweringPipeline:()=>g,SummarizationPipeline:()=>y,Text2TextGenerationPipeline:()=>E,TextClassificationPipeline:()=>k,TextGenerationPipeline:()=>C,TextToAudioPipeline:()=>W,TokenClassificationPipeline:()=>w,TranslationPipeline:()=>M,ZeroShotAudioClassificationPipeline:()=>j,ZeroShotClassificationPipeline:()=>A,ZeroShotImageClassificationPipeline:()=>me,ZeroShotObjectDetectionPipeline:()=>V,pipeline:()=>$e});var s=t("./src/tokenizers.js"),o=t("./src/models.js"),n=t("./src/models/auto/processing_auto.js");t("./src/base/processing_utils.js");var i=t("./src/utils/generic.js"),a=t("./src/utils/core.js"),l=t("./src/utils/maths.js"),u=t("./src/utils/audio.js"),p=t("./src/utils/tensor.js"),d=t("./src/utils/image.js");async function c(we){return Array.isArray(we)||(we=[we]),await Promise.all(we.map(q=>d.RawImage.read(q)))}async function f(we,q){return Array.isArray(we)||(we=[we]),await Promise.all(we.map(R=>typeof R=="string"||R instanceof URL?(0,u.read_audio)(R,q):R instanceof Float64Array?new Float32Array(R):R))}function _(we,q){q&&(we=we.map(Se=>Se|0));const[R,pe,xe,be]=we;return{xmin:R,ymin:pe,xmax:xe,ymax:be}}class T extends i.Callable{constructor({task:q,model:R,tokenizer:pe=null,processor:xe=null}){super(),this.task=q,this.model=R,this.tokenizer=pe,this.processor=xe}async dispose(){await this.model.dispose()}}class k extends T{constructor(q){super(q)}async _call(q,{top_k:R=1}={}){const pe=this.tokenizer(q,{padding:!0,truncation:!0}),xe=await this.model(pe),be=this.model.config.problem_type==="multi_label_classification"?Fe=>Fe.sigmoid():Fe=>new p.Tensor("float32",(0,l.softmax)(Fe.data),Fe.dims),Se=this.model.config.id2label,Ae=[];for(const Fe of xe.logits){const ze=be(Fe),Ve=await(0,p.topk)(ze,R),O=Ve[0].tolist(),z=Ve[1].tolist().map((J,le)=>({label:Se?Se[J]:`LABEL_${J}`,score:O[le]}));R===1?Ae.push(...z):Ae.push(z)}return Array.isArray(q)||R===1?Ae:Ae[0]}}class w extends T{constructor(q){super(q)}async _call(q,{ignore_labels:R=["O"]}={}){const pe=Array.isArray(q),xe=this.tokenizer(pe?q:[q],{padding:!0,truncation:!0}),Se=(await this.model(xe)).logits,Ae=this.model.config.id2label,Fe=[];for(let ze=0;zeIe==this.tokenizer.sep_token_id);Fe[O].map((Ie,Re)=>Ie==1&&(Re===0||Re>z&&ze.findIndex(Xe=>Xe==Y[Re])===-1));const J=be[O].tolist(),le=Se[O].tolist();for(let Ie=1;IeRe==Y[Ie])!==-1)&&(J[Ie]=-1/0,le[Ie]=-1/0);const ye=(0,l.softmax)(J).map((Ie,Re)=>[Ie,Re]),Ee=(0,l.softmax)(le).map((Ie,Re)=>[Ie,Re]);ye[0][0]=0,Ee[0][0]=0;const ke=(0,a.product)(ye,Ee).filter(Ie=>Ie[0][1]<=Ie[1][1]).map(Ie=>[Ie[0][1],Ie[1][1],Ie[0][0]*Ie[1][0]]).sort((Ie,Re)=>Re[2]-Ie[2]);for(let Ie=0;IeJ==this.tokenizer.mask_token_id);if(ze===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const Ve=xe[Ae][ze],O=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Ve.data),Ve.dims),R),Y=O[0].tolist(),z=O[1].tolist();be.push(z.map((J,le)=>{const ye=Fe.slice();return ye[ze]=J,{score:Y[le],token:Number(J),token_str:this.tokenizer.decode([J]),sequence:this.tokenizer.decode(ye,{skip_special_tokens:!0})}}))}return Array.isArray(q)?be:be[0]}}class E extends T{constructor(R){super(R);te(this,"_key","generated_text")}async _call(R,pe={}){Array.isArray(R)||(R=[R]),this.model.config.prefix&&(R=R.map(ze=>this.model.config.prefix+ze));const xe=this.model.config.task_specific_params;xe&&xe[this.task]&&xe[this.task].prefix&&(R=R.map(ze=>xe[this.task].prefix+ze));const be=this.tokenizer,Se={padding:!0,truncation:!0};let Ae;this instanceof M&&"_build_translation_inputs"in be?Ae=be._build_translation_inputs(R,Se,pe):Ae=be(R,Se);const Fe=await this.model.generate({...Ae,...pe});return be.batch_decode(Fe,{skip_special_tokens:!0}).map(ze=>({[this._key]:ze}))}}class y extends E{constructor(R){super(R);te(this,"_key","summary_text")}}class M extends E{constructor(R){super(R);te(this,"_key","translation_text")}}function v(we){return Array.isArray(we)&&we.every(q=>"role"in q&&"content"in q)}class C extends T{constructor(q){super(q)}async _call(q,R={}){let pe=!1,xe=!1,be;if(typeof q=="string")be=q=[q];else if(Array.isArray(q)&&q.every(z=>typeof z=="string"))pe=!0,be=q;else{if(v(q))q=[q];else if(Array.isArray(q)&&q.every(v))pe=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");xe=!0,be=q.map(z=>this.tokenizer.apply_chat_template(z,{tokenize:!1,add_generation_prompt:!0}))}const Se=R.add_special_tokens??!1,Ae=xe?!1:R.return_full_text??!0;this.tokenizer.padding_side="left";const Fe=this.tokenizer(be,{add_special_tokens:Se,padding:!0,truncation:!0}),ze=await this.model.generate({...Fe,...R}),Ve=this.tokenizer.batch_decode(ze,{skip_special_tokens:!0});let O;!Ae&&Fe.input_ids.dims.at(-1)>0&&(O=this.tokenizer.batch_decode(Fe.input_ids,{skip_special_tokens:!0}).map(z=>z.length));const Y=Array.from({length:q.length},z=>[]);for(let z=0;z[R.toLowerCase(),pe])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(q,R,{hypothesis_template:pe="This example is {}.",multi_label:xe=!1}={}){const be=Array.isArray(q);be||(q=[q]),Array.isArray(R)||(R=[R]);const Se=R.map(ze=>pe.replace("{}",ze)),Ae=xe||R.length===1,Fe=[];for(const ze of q){const Ve=[];for(const z of Se){const J=this.tokenizer(ze,{text_pair:z,padding:!0,truncation:!0}),le=await this.model(J);Ae?Ve.push([le.logits.data[this.contradiction_id],le.logits.data[this.entailment_id]]):Ve.push(le.logits.data[this.entailment_id])}const Y=(Ae?Ve.map(z=>(0,l.softmax)(z)[1]):(0,l.softmax)(Ve)).map((z,J)=>[z,J]).sort((z,J)=>J[0]-z[0]);Fe.push({sequence:ze,labels:Y.map(z=>R[z[1]]),scores:Y.map(z=>z[0])})}return be?Fe:Fe[0]}}class B extends T{constructor(q){super(q)}async _call(q,{pooling:R="none",normalize:pe=!1,quantize:xe=!1,precision:be="binary"}={}){const Se=this.tokenizer(q,{padding:!0,truncation:!0}),Ae=await this.model(Se);let Fe=Ae.last_hidden_state??Ae.logits??Ae.token_embeddings;if(R!=="none")if(R==="mean")Fe=(0,p.mean_pooling)(Fe,Se.attention_mask);else if(R==="cls")Fe=Fe.slice(null,0);else throw Error(`Pooling method '${R}' not supported.`);return pe&&(Fe=Fe.normalize(2,-1)),xe&&(Fe=(0,p.quantize_embeddings)(Fe,be)),Fe}}class K extends T{constructor(q){super(q)}async _call(q,{pool:R=null}={}){const pe=await c(q),{pixel_values:xe}=await this.processor(pe),be=await this.model({pixel_values:xe});let Se;if(R){if(!("pooler_output"in be))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Se=be.pooler_output}else Se=be.last_hidden_state??be.logits??be.image_embeds;return Se}}class G extends T{constructor(q){super(q)}async _call(q,{top_k:R=5}={}){const pe=this.processor.feature_extractor.config.sampling_rate,xe=await f(q,pe),be=this.model.config.id2label,Se=[];for(const Ae of xe){const Fe=await this.processor(Ae),Ve=(await this.model(Fe)).logits[0],O=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Ve.data),Ve.dims),R),Y=O[0].tolist(),J=O[1].tolist().map((le,ye)=>({label:be?be[le]:`LABEL_${le}`,score:Y[ye]}));Se.push(J)}return Array.isArray(q)?Se:Se[0]}}class j extends T{constructor(q){super(q)}async _call(q,R,{hypothesis_template:pe="This is a sound of {}."}={}){const xe=!Array.isArray(q);xe&&(q=[q]);const be=R.map(Ve=>pe.replace("{}",Ve)),Se=this.tokenizer(be,{padding:!0,truncation:!0}),Ae=this.processor.feature_extractor.config.sampling_rate,Fe=await f(q,Ae),ze=[];for(const Ve of Fe){const O=await this.processor(Ve),Y=await this.model({...Se,...O}),z=(0,l.softmax)(Y.logits_per_audio.data);ze.push([...z].map((J,le)=>({score:J,label:R[le]})))}return xe?ze[0]:ze}}class ee extends T{constructor(q){super(q)}async _call(q,R={}){switch(this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(q,R);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(q,R);case"moonshine":return this._call_moonshine(q,R);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(q,R){R.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),R.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const pe=!Array.isArray(q);pe&&(q=[q]);const xe=this.processor.feature_extractor.config.sampling_rate,be=await f(q,xe),Se=[];for(const Ae of be){const Fe=await this.processor(Ae),Ve=(await this.model(Fe)).logits[0],O=[];for(const z of Ve)O.push((0,l.max)(z.data)[1]);const Y=this.tokenizer.decode(O);Se.push({text:Y})}return pe?Se[0]:Se}async _call_whisper(q,R){const pe=R.return_timestamps??!1,xe=R.chunk_length_s??0,be=R.force_full_sequences??!1;let Se=R.stride_length_s??null;const Ae={...R};pe==="word"&&(Ae.return_token_timestamps=!0,Ae.return_timestamps=!1);const Fe=!Array.isArray(q);Fe&&(q=[q]);const ze=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,Ve=this.processor.feature_extractor.config.hop_length,O=this.processor.feature_extractor.config.sampling_rate,Y=await f(q,O),z=[];for(const J of Y){let le=[];if(xe>0){if(Se===null)Se=xe/6;else if(xe<=Se)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const ke=O*xe,Ie=O*Se,Re=ke-2*Ie;let Xe=0;for(;;){const Ge=Xe+ke,lt=J.subarray(Xe,Ge),wt=await this.processor(lt),Gt=Xe===0,Ot=Ge>=J.length;if(le.push({stride:[lt.length,Gt?0:Ie,Ot?0:Ie],input_features:wt.input_features,is_last:Ot}),Ot)break;Xe+=Re}}else le=[{stride:[J.length,0,0],input_features:(await this.processor(J)).input_features,is_last:!0}];for(const ke of le){Ae.num_frames=Math.floor(ke.stride[0]/Ve);const Ie=await this.model.generate({inputs:ke.input_features,...Ae});pe==="word"?(ke.tokens=Ie.sequences.tolist()[0],ke.token_timestamps=Ie.token_timestamps.tolist()[0].map(Re=>(0,l.round)(Re,2))):ke.tokens=Ie[0].tolist(),ke.stride=ke.stride.map(Re=>Re/O)}const[ye,Ee]=this.tokenizer._decode_asr(le,{time_precision:ze,return_timestamps:pe,force_full_sequences:be});z.push({text:ye,...Ee})}return Fe?z[0]:z}async _call_moonshine(q,R){const pe=!Array.isArray(q);pe&&(q=[q]);const xe=this.processor.feature_extractor.config.sampling_rate,be=await f(q,xe),Se=[];for(const Ae of be){const Fe=await this.processor(Ae),ze=Math.floor(Ae.length/xe)*6,Ve=await this.model.generate({max_new_tokens:ze,...R,...Fe}),O=this.processor.batch_decode(Ve,{skip_special_tokens:!0})[0];Se.push({text:O})}return pe?Se[0]:Se}}class H extends T{constructor(q){super(q)}async _call(q,R={}){const pe=Array.isArray(q),xe=await c(q),{pixel_values:be}=await this.processor(xe),Se=[];for(const Ae of be){Ae.dims=[1,...Ae.dims];const Fe=await this.model.generate({inputs:Ae,...R}),ze=this.tokenizer.batch_decode(Fe,{skip_special_tokens:!0}).map(Ve=>({generated_text:Ve.trim()}));Se.push(ze)}return pe?Se:Se[0]}}class Z extends T{constructor(q){super(q)}async _call(q,{top_k:R=5}={}){const pe=await c(q),{pixel_values:xe}=await this.processor(pe),be=await this.model({pixel_values:xe}),Se=this.model.config.id2label,Ae=[];for(const Fe of be.logits){const ze=await(0,p.topk)(new p.Tensor("float32",(0,l.softmax)(Fe.data),Fe.dims),R),Ve=ze[0].tolist(),Y=ze[1].tolist().map((z,J)=>({label:Se?Se[z]:`LABEL_${z}`,score:Ve[J]}));Ae.push(Y)}return Array.isArray(q)?Ae:Ae[0]}}class X extends T{constructor(q){super(q),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(q,{threshold:R=.5,mask_threshold:pe=.5,overlap_mask_area_threshold:xe=.8,label_ids_to_fuse:be=null,target_sizes:Se=null,subtask:Ae=null}={}){if(Array.isArray(q)&&q.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const ze=await c(q),Ve=ze.map(ke=>[ke.height,ke.width]),O=await this.processor(ze),{inputNames:Y,outputNames:z}=this.model.sessions.model;if(!Y.includes("pixel_values")){if(Y.length!==1)throw Error(`Expected a single input name, but got ${Y.length} inputs: ${Y}.`);const ke=Y[0];if(ke in O)throw Error(`Input name ${ke} already exists in the inputs.`);O[ke]=O.pixel_values}const J=await this.model(O);let le=null;if(Ae!==null)le=this.subtasks_mapping[Ae];else if(this.processor.image_processor){for(const[ke,Ie]of Object.entries(this.subtasks_mapping))if(Ie in this.processor.image_processor){le=this.processor.image_processor[Ie].bind(this.processor.image_processor),Ae=ke;break}}const ye=this.model.config.id2label,Ee=[];if(Ae)if(Ae==="panoptic"||Ae==="instance"){const ke=le(J,R,pe,xe,be,Se??Ve)[0],Ie=ke.segmentation;for(const Re of ke.segments_info){const Xe=new Uint8ClampedArray(Ie.data.length);for(let lt=0;ltwt<-1e-5||wt>1+1e-5)&&Ge.sigmoid_();const lt=await d.RawImage.fromTensor(Ge.mul_(255).to("uint8")).resize(Xe[1],Xe[0]);Ee.push({label:null,score:null,mask:lt})}}return Ee}}class oe extends X{constructor(q){super(q)}async _call(q,R={}){if(Array.isArray(q)&&q.length!==1)throw Error("Background removal pipeline currently only supports a batch size of 1.");const xe=await c(q),be=await super._call(q,R);return xe.map((Ae,Fe)=>{const ze=Ae.clone();return ze.putAlpha(be[Fe].mask),ze})}}class me extends T{constructor(q){super(q)}async _call(q,R,{hypothesis_template:pe="This is a photo of {}"}={}){const xe=Array.isArray(q),be=await c(q),Se=R.map(Y=>pe.replace("{}",Y)),Ae=this.tokenizer(Se,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:Fe}=await this.processor(be),ze=await this.model({...Ae,pixel_values:Fe}),Ve=this.model.config.model_type==="siglip"?Y=>Y.sigmoid().data:Y=>(0,l.softmax)(Y.data),O=[];for(const Y of ze.logits_per_image){const J=[...Ve(Y)].map((le,ye)=>({score:le,label:R[ye]}));J.sort((le,ye)=>ye.score-le.score),O.push(J)}return xe?O:O[0]}}class ae extends T{constructor(q){super(q)}async _call(q,{threshold:R=.9,percentage:pe=!1}={}){const xe=Array.isArray(q);if(xe&&q.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const be=await c(q),Se=pe?null:be.map(z=>[z.height,z.width]),{pixel_values:Ae,pixel_mask:Fe}=await this.processor(be),ze=await this.model({pixel_values:Ae,pixel_mask:Fe}),Ve=this.processor.image_processor.post_process_object_detection(ze,R,Se),O=this.model.config.id2label,Y=Ve.map(z=>z.boxes.map((J,le)=>({score:z.scores[le],label:O[z.classes[le]],box:_(J,!pe)})));return xe?Y:Y[0]}}class V extends T{constructor(q){super(q)}async _call(q,R,{threshold:pe=.1,top_k:xe=null,percentage:be=!1}={}){const Se=Array.isArray(q),Ae=await c(q),Fe=this.tokenizer(R,{padding:!0,truncation:!0}),ze=await this.processor(Ae),Ve=[];for(let O=0;O({score:Ee.scores[Ie],label:Ee.labels[Ie],box:_(ke,!be)}))}else{const Ee=this.processor.image_processor.post_process_object_detection(le,pe,z,!0)[0];ye=Ee.boxes.map((ke,Ie)=>({score:Ee.scores[Ie],label:R[Ee.classes[Ie]],box:_(ke,!be)}))}ye.sort((Ee,ke)=>ke.score-Ee.score),xe!==null&&(ye=ye.slice(0,xe)),Ve.push(ye)}return Se?Ve:Ve[0]}}class F extends T{constructor(q){super(q)}async _call(q,R,pe={}){const xe=(await c(q))[0],{pixel_values:be}=await this.processor(xe),Se=`${R}`,Ae=this.tokenizer(Se,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,Fe=await this.model.generate({inputs:be,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Ae,...pe}),Ve=this.tokenizer.batch_decode(Fe)[0].match(/(.*?)<\/s_answer>/);let O=null;return Ve&&Ve.length>=2&&(O=Ve[1].trim()),[{answer:O}]}}class W extends T{constructor(R){super(R);te(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=R.vocoder??null}async _call(R,{speaker_embeddings:pe=null}={}){return this.processor?this._call_text_to_spectrogram(R,{speaker_embeddings:pe}):this._call_text_to_waveform(R)}async _call_text_to_waveform(R){const pe=this.tokenizer(R,{padding:!0,truncation:!0}),{waveform:xe}=await this.model(pe),be=this.model.config.sampling_rate;return new u.RawAudio(xe.data,be)}async _call_text_to_spectrogram(R,{speaker_embeddings:pe}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await o.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof pe=="string"||pe instanceof URL)&&(pe=new Float32Array(await(await fetch(pe)).arrayBuffer())),pe instanceof Float32Array)pe=new p.Tensor("float32",pe,[1,pe.length]);else if(!(pe instanceof p.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:xe}=this.tokenizer(R,{padding:!0,truncation:!0}),{waveform:be}=await this.model.generate_speech(xe,pe,{vocoder:this.vocoder}),Se=this.processor.feature_extractor.config.sampling_rate;return new u.RawAudio(be.data,Se)}}class re extends T{constructor(q){super(q)}async _call(q){const R=await c(q),pe=await this.processor(R),xe=await this.model(pe),be=[];for(const Se of xe.reconstruction){const Ae=Se.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");be.push(d.RawImage.fromTensor(Ae))}return be.length>1?be:be[0]}}class _e extends T{constructor(q){super(q)}async _call(q){const R=await c(q),pe=await this.processor(R),{predicted_depth:xe}=await this.model(pe),be=[];for(let Se=0;Se1?be:be[0]}}const se=Object.freeze({"text-classification":{tokenizer:s.AutoTokenizer,pipeline:k,model:o.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:s.AutoTokenizer,pipeline:w,model:o.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:s.AutoTokenizer,pipeline:g,model:o.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:s.AutoTokenizer,pipeline:S,model:o.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:s.AutoTokenizer,pipeline:y,model:o.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:s.AutoTokenizer,pipeline:M,model:o.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:s.AutoTokenizer,pipeline:E,model:o.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:s.AutoTokenizer,pipeline:C,model:o.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:s.AutoTokenizer,pipeline:A,model:o.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:G,model:o.AutoModelForAudioClassification,processor:n.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:s.AutoTokenizer,pipeline:j,model:o.AutoModel,processor:n.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:s.AutoTokenizer,pipeline:ee,model:[o.AutoModelForSpeechSeq2Seq,o.AutoModelForCTC],processor:n.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:s.AutoTokenizer,pipeline:W,model:[o.AutoModelForTextToWaveform,o.AutoModelForTextToSpectrogram],processor:[n.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:s.AutoTokenizer,pipeline:H,model:o.AutoModelForVision2Seq,processor:n.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Z,model:o.AutoModelForImageClassification,processor:n.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:X,model:[o.AutoModelForImageSegmentation,o.AutoModelForSemanticSegmentation,o.AutoModelForUniversalSegmentation],processor:n.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"background-removal":{pipeline:oe,model:[o.AutoModelForImageSegmentation,o.AutoModelForSemanticSegmentation,o.AutoModelForUniversalSegmentation],processor:n.AutoProcessor,default:{model:"Xenova/modnet"},type:"image"},"zero-shot-image-classification":{tokenizer:s.AutoTokenizer,pipeline:me,model:o.AutoModel,processor:n.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:ae,model:o.AutoModelForObjectDetection,processor:n.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:s.AutoTokenizer,pipeline:V,model:o.AutoModelForZeroShotObjectDetection,processor:n.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:s.AutoTokenizer,pipeline:F,model:o.AutoModelForDocumentQuestionAnswering,processor:n.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:re,model:o.AutoModelForImageToImage,processor:n.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:_e,model:o.AutoModelForDepthEstimation,processor:n.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:s.AutoTokenizer,pipeline:B,model:o.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:n.AutoProcessor,pipeline:K,model:[o.AutoModelForImageFeatureExtraction,o.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),ce=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async 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z{decode_chain($){let N="";for(let Q=1;Q<$.length;Q+=2)N+=$[Q];return[N]}}class wt extends q{constructor($){super(),this.addPrefixSpace=$.add_prefix_space,this.replacement=$.replacement,this.strRep=$.str_rep||this.replacement,this.prepend_scheme=$.prepend_scheme??"always"}pre_tokenize_text($,{section_index:N=void 0}={}){let Q=$.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!Q.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&N===0)&&(Q=this.strRep+Q),[Q]}}class Gt extends z{constructor($){super($),this.addPrefixSpace=$.add_prefix_space,this.replacement=$.replacement}decode_chain($){const N=[];for(let Q=0;Q<$.length;++Q){let ie=$[Q].replaceAll(this.replacement," ");this.addPrefixSpace&&Q==0&&ie.startsWith(" ")&&(ie=ie.substring(1)),N.push(ie)}return N}}class Ot extends oe{constructor($){super($),this.charsmap=$.precompiled_charsmap}normalize($){return $=$.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),$=$.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),$.includes("～")?$=$.split("～").map(Q=>Q.normalize("NFKC")).join("～"):$=$.normalize("NFKC"),$}}class ur extends q{constructor($){super(),this.tokenizers=$.pretokenizers.map(N=>q.fromConfig(N))}pre_tokenize_text($,N){return this.tokenizers.reduce((Q,ie)=>ie.pre_tokenize(Q,N),[$])}}class ls extends q{constructor($){super()}pre_tokenize_text($,N){return $.match(/\w+|[^\w\s]+/g)||[]}}class Ms extends q{constructor($){super()}pre_tokenize_text($,N){return y($)}}class Ir extends q{constructor($){super(),this.config=$,this.pattern=f(this.config.pattern),this.content=this.config.content}pre_tokenize_text($,N){return this.pattern===null?[$]:[$.replaceAll(this.pattern,this.config.content)]}}const js=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Ss(he,$,N,Q){for(const ie of Object.keys(he)){const de=$-he[ie].length,ve=N(ie),je=new Array(de).fill(ve);he[ie]=Q==="right"?(0,o.mergeArrays)(he[ie],je):(0,o.mergeArrays)(je,he[ie])}}function Ns(he,$){for(const N of Object.keys(he))he[N].length=$}class at extends s.Callable{constructor(N,Q){super();te(this,"return_token_type_ids",!1);te(this,"padding_side","right");this._tokenizer_config=Q,this.normalizer=oe.fromConfig(N.normalizer),this.pre_tokenizer=q.fromConfig(N.pre_tokenizer),this.model=K.fromConfig(N.model,Q),this.post_processor=Ae.fromConfig(N.post_processor),this.decoder=z.fromConfig(N.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const ie of N.added_tokens){const de=new B(ie);this.added_tokens.push(de),this.model.tokens_to_ids.set(de.content,de.id),this.model.vocab[de.id]=de.content,de.special&&(this.special_tokens.push(de.content),this.all_special_ids.push(de.id))}if(this.additional_special_tokens=Q.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_splitter=new l.DictionarySplitter(this.added_tokens.map(ie=>ie.content)),this.added_tokens_map=new Map(this.added_tokens.map(ie=>[ie.content,ie])),this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=Q.model_max_length,this.remove_space=Q.remove_space,this.clean_up_tokenization_spaces=Q.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=Q.do_lowercase_and_remove_accent??!1,Q.padding_side&&(this.padding_side=Q.padding_side),this.legacy=!1,this.chat_template=Q.chat_template??null,Array.isArray(this.chat_template)){const ie=Object.create(null);for(const{name:de,template:ve}of this.chat_template){if(typeof de!="string"||typeof ve!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');ie[de]=ve}this.chat_template=ie}this._compiled_template_cache=new Map}getToken(...N){for(const Q of N){const ie=this._tokenizer_config[Q];if(ie)if(typeof ie=="object"){if(ie.__type==="AddedToken")return ie.content;throw Error(`Unknown token: ${ie}`)}else return ie}return null}static async from_pretrained(N,{progress_callback:Q=null,config:ie=null,cache_dir:de=null,local_files_only:ve=!1,revision:je="main",legacy:He=null}={}){const We=await d(N,{progress_callback:Q,config:ie,cache_dir:de,local_files_only:ve,revision:je,legacy:He});return new this(...We)}_call(N,{text_pair:Q=null,add_special_tokens:ie=!0,padding:de=!1,truncation:ve=null,max_length:je=null,return_tensor:He=!0,return_token_type_ids:We=null}={}){const Je=Array.isArray(N);let dt;if(Je){if(N.length===0)throw Error("text array must be non-empty");if(Q!==null){if(Array.isArray(Q)){if(N.length!==Q.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");dt=N.map((Pt,jt)=>this._encode_plus(Pt,{text_pair:Q[jt],add_special_tokens:ie,return_token_type_ids:We}))}else dt=N.map(Pt=>this._encode_plus(Pt,{add_special_tokens:ie,return_token_type_ids:We}))}else{if(N==null)throw Error("text may not be null or undefined");if(Array.isArray(Q))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");dt=[this._encode_plus(N,{text_pair:Q,add_special_tokens:ie,return_token_type_ids:We})]}if(je===null?de==="max_length"?je=this.model_max_length:je=(0,i.max)(dt.map(Pt=>Pt.input_ids.length))[0]:ve||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."),je=Math.min(je,this.model_max_length??1/0),de||ve)for(let Pt=0;Ptje?ve&&Ns(dt[Pt],je):de&&Ss(dt[Pt],je,jt=>jt==="input_ids"?this.pad_token_id:0,this.padding_side));const xt={};if(He){if(!(de&&ve)&&dt.some(jt=>{var kt;for(const Ht of Object.keys(jt))if(jt[Ht].length!==((kt=dt[0][Ht])==null?void 0:kt.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 Pt=[dt.length,dt[0].input_ids.length];for(const jt of Object.keys(dt[0]))xt[jt]=new a.Tensor("int64",BigInt64Array.from(dt.flatMap(kt=>kt[jt]).map(BigInt)),Pt)}else{for(const Pt of Object.keys(dt[0]))xt[Pt]=dt.map(jt=>jt[Pt]);if(!Je)for(const Pt of Object.keys(xt))xt[Pt]=xt[Pt][0]}return xt}_encode_text(N){if(N===null)return null;const Q=this.added_tokens_splitter.split(N);for(let de=0;de0&&(Q[de-1]=Q[de-1].trimEnd()),ve.rstrip&&de{if(de.length===0)return[];if(this.added_tokens_map.has(de))return[de];if(this.remove_space===!0&&(de=de.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(de=g(de)),this.normalizer!==null&&(de=this.normalizer(de)),de.length===0)return[];const je=this.pre_tokenizer!==null?this.pre_tokenizer(de,{section_index:ve}):[de];return this.model(je)})}_encode_plus(N,{text_pair:Q=null,add_special_tokens:ie=!0,return_token_type_ids:de=null}={}){const{tokens:ve,token_type_ids:je}=this._tokenize_helper(N,{pair:Q,add_special_tokens:ie}),He=this.model.convert_tokens_to_ids(ve),We={input_ids:He,attention_mask:new Array(He.length).fill(1)};return(de??this.return_token_type_ids)&&je&&(We.token_type_ids=je),We}_tokenize_helper(N,{pair:Q=null,add_special_tokens:ie=!1}={}){const de=this._encode_text(N),ve=this._encode_text(Q);return this.post_processor?this.post_processor(de,ve,{add_special_tokens:ie}):{tokens:(0,o.mergeArrays)(de??[],ve??[])}}tokenize(N,{pair:Q=null,add_special_tokens:ie=!1}={}){return this._tokenize_helper(N,{pair:Q,add_special_tokens:ie}).tokens}encode(N,{text_pair:Q=null,add_special_tokens:ie=!0,return_token_type_ids:de=null}={}){return this._encode_plus(N,{text_pair:Q,add_special_tokens:ie,return_token_type_ids:de}).input_ids}batch_decode(N,Q={}){return N instanceof a.Tensor&&(N=N.tolist()),N.map(ie=>this.decode(ie,Q))}decode(N,Q={}){if(N instanceof a.Tensor&&(N=T(N)),!Array.isArray(N)||N.length===0||!(0,o.isIntegralNumber)(N[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(N,Q)}decode_single(N,{skip_special_tokens:Q=!1,clean_up_tokenization_spaces:ie=null}){let de=this.model.convert_ids_to_tokens(N);Q&&(de=de.filter(je=>!this.special_tokens.includes(je)));let ve=this.decoder?this.decoder(de):de.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(ve=ve.replaceAll(this.decoder.end_of_word_suffix," "),Q&&(ve=ve.trim())),(ie??this.clean_up_tokenization_spaces)&&(ve=k(ve)),ve}get_chat_template({chat_template:N=null,tools:Q=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const ie=this.chat_template;if(N!==null&&Object.hasOwn(ie,N))N=ie[N];else if(N===null)if(Q!==null&&"tool_use"in ie)N=ie.tool_use;else if("default"in ie)N=ie.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(ie).sort()}.`)}else if(N===null)if(this.chat_template)N=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 N}apply_chat_template(N,{tools:Q=null,documents:ie=null,chat_template:de=null,add_generation_prompt:ve=!1,tokenize:je=!0,padding:He=!1,truncation:We=!1,max_length:Je=null,return_tensor:dt=!0,return_dict:xt=!1,tokenizer_kwargs:Pt={},...jt}={}){if(de=this.get_chat_template({chat_template:de,tools:Q}),typeof de!="string")throw Error(`chat_template must be a string, but got ${typeof de}`);let kt=this._compiled_template_cache.get(de);kt===void 0&&(kt=new u.Template(de),this._compiled_template_cache.set(de,kt));const Ht=Object.create(null);for(const cr of js){const pr=this.getToken(cr);pr&&(Ht[cr]=pr)}const br=kt.render({messages:N,add_generation_prompt:ve,tools:Q,documents:ie,...Ht,...jt});if(je){const cr=this._call(br,{add_special_tokens:!1,padding:He,truncation:We,max_length:Je,return_tensor:dt,...Pt});return xt?cr:cr.input_ids}return br}}class us extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Ar extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class bs extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Ft extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class ds extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class cs extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class ys extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Qt extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class De extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Qe extends at{}class tt extends at{}class Rt extends at{constructor(N,Q){super(N,Q);te(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class zr extends at{constructor(){super(...arguments);te(this,"return_token_type_ids",!0)}}class Sr extends at{}class ps extends at{}class hs extends at{}class Kr extends at{constructor($,N){super($,N),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)),this.lang_to_token=Q=>Q}_build_translation_inputs($,N,Q){return Tr(this,$,N,Q)}}class ms extends Kr{}class _s extends at{}class Hr extends at{}const vr="▁";class cn extends at{constructor(N,Q){super(N,Q);te(this,"padding_side","left");this.legacy=Q.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new wt({replacement:vr,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(N){if(N===null)return null;if(this.legacy||N.length===0)return super._encode_text(N);let Q=super._encode_text(vr+N.replaceAll(vr," "));return Q.length>1&&Q[0]===vr&&this.special_tokens.includes(Q[1])&&(Q=Q.slice(1)),Q}}class xr extends at{}class $s extends at{}class ks extends at{}class fr extends at{}class Vs extends at{}class pn extends at{}class Fr extends at{}class hn extends at{}class qr extends at{}function Tr(he,$,N,Q){if(!("language_codes"in he)||!Array.isArray(he.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in he)||!(he.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in he)||typeof he.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const ie=Q.src_lang,de=Q.tgt_lang;if(!he.language_codes.includes(de))throw new Error(`Target language code "${de}" is not valid. Must be one of: {${he.language_codes.join(", ")}}`);if(ie!==void 0){if(!he.language_codes.includes(ie))throw new Error(`Source language code "${ie}" is not valid. Must be one of: {${he.language_codes.join(", ")}}`);for(const ve of he.post_processor.config.single)if("SpecialToken"in ve&&he.languageRegex.test(ve.SpecialToken.id)){ve.SpecialToken.id=he.lang_to_token(ie);break}}return Q.forced_bos_token_id=he.model.convert_tokens_to_ids([he.lang_to_token(de)])[0],he._call($,N)}class Is extends at{constructor($,N){super($,N),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)),this.lang_to_token=Q=>Q}_build_translation_inputs($,N,Q){return Tr(this,$,N,Q)}}class dr extends at{constructor($,N){super($,N),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(Q=>this.languageRegex.test(Q)).map(Q=>Q.slice(2,-2)),this.lang_to_token=Q=>`__${Q}__`}_build_translation_inputs($,N,Q){return Tr(this,$,N,Q)}}class gr extends at{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr($,{return_timestamps:N=!1,return_language:Q=!1,time_precision:ie=null,force_full_sequences:de=!0}={}){if(ie===null)throw Error("Must specify time_precision");let ve=null;const je=N==="word";function He(){return{language:ve,timestamp:[null,null],text:""}}const We=[];let Je=He(),dt=0;const xt=this.timestamp_begin,jt=xt+1500;let kt=[],Ht=[],br=!1,cr=null;const pr=new Set(this.all_special_ids);for(const Dt of $){const rr=Dt.tokens,wr=je?Dt.token_timestamps:null;let Qr=null,Rr=xt;if("stride"in Dt){const[or,Vt,Zt]=Dt.stride;if(dt-=Vt,cr=or-Zt,Vt&&(Rr=Vt/ie+xt),Zt)for(let er=rr.length-1;er>=0;--er){const tr=Number(rr[er]);if(tr>=xt){if(Qr!==null&&(tr-xt)*ie=xt&&Vt<=jt){const Zt=(Vt-xt)*ie+dt,er=(0,i.round)(Zt,2);if(Qr!==null&&Vt>=Qr)br=!0;else if(br||kt.length>0&&Vt0?(kt.push(Yt),je&&Ht.push(jr)):kt.every(or=>or.length===0)&&(Je=He(),kt=[],Yt=[],Ht=[],jr=[])}if(kt.length>0){if(de&&N)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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Error(`Internal error in dynamic time warping. 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cu{static async getInstance(r=null){return this.tokenizer??(this.tokenizer=vx.from_pretrained(this.model_id,{progress_callback:r})),this.model??(this.model=yx.from_pretrained(this.model_id,{dtype:"q4f16",device:"webgpu",progress_callback:r})),Promise.all([this.tokenizer,this.model])}}te(cu,"model_id","onnx-community/Llama-3.2-1B-Instruct-q4f16");const yi=new xx;async function Ex(e){const[r,t]=await cu.getInstance(),s=r.apply_chat_template(e,{add_generation_prompt:!0,return_dict:!0});let o,n=0,i;const a=()=>{o??(o=performance.now()),n++>0&&(i=n/(performance.now()-o)*1e3)},l=f=>{self.postMessage({status:"update",output:f,tps:i,numTokens:n})},u=new Tx(r,{skip_prompt:!0,skip_special_tokens:!0,callback_function:l,token_callback_function:a});self.postMessage({status:"start"});const{past_key_values:p,sequences:d}=await t.generate({...s,do_sample:!1,max_new_tokens:1024,streamer:u,stopping_criteria:yi,return_dict_in_generate:!0}),c=r.batch_decode(d,{skip_special_tokens:!0});self.postMessage({status:"complete",output:c})}async function Px(){try{if(!await navigator.gpu.requestAdapter())throw new Error("WebGPU is not supported (no adapter found)")}catch(e){self.postMessage({status:"error",data:e.toString()})}}async function Cx(){self.postMessage({status:"loading",data:"Loading model..."});const[e,r]=await cu.getInstance(s=>{self.postMessage(s)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const t=e("a");await r.generate({...t,max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async e=>{const{type:r,data:t}=e.data;switch(r){case"check":Px();break;case"load":Cx();break;case"generate":yi.reset(),Ex(t);break;case"interrupt":yi.interrupt();break;case"reset":yi.reset();break}})})(); diff --git a/banner.png b/banner.png index 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