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uniforms.a_shape[1]) { + tile[local_id.y][local_id.x] = ${Z.getByIndices(`${Z.type.indices}(input_row, input_col)`)}; + } + workgroupBarrier(); + + let output_col = workgroup_id_x * ${B}u + local_id.x; + let output_row = workgroup_id_y * ${B}u + local_id.y; + if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) { + ${ee.setByIndices(`${ee.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")} + } + }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let V=ze.size(a);return{outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(u[1]/B),y:Math.ceil(u[0]/B)},programUniforms:[{type:12,data:V},...yt(i,u)]}},getShaderSource:h}}return h=B=>{let V=qe("a",s,i.length),Z=It("output",s,u.length);return` + ${B.registerUniform("output_size","u32").declareVariables(V,Z)} + + ${Na(o,n,V,Z)} + + ${B.mainStart()} + ${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = 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: array; + `,B=V=>` + ${V.registerUniform("reduceSize","u32").declareVariables(k,C)} + ${z} + fn DIV_CEIL(a : u32, b : u32) -> u32 { + return ((a - 1u) / b + 1u); + } + ${V.mainStart(d)} + + let outputIndex = global_idx / ${d}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${Wa[n]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${d}) { + let candidate = f32(${k.getByOffset("offset + k")}); + bestValue = ${ao[n]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${d}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 = ${Va[n]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${C.setByOffset("outputIndex",`${n==="mean"?`${C.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${C.type.storage}(${Ga[n]})`}`)}; + } + }`;return{name:e,shaderCache:{hint:`${t};${d}`,inputDependencies:["type"]},getShaderSource:B,getRunData:()=>({outputs:[{dims:a,dataType:o}],dispatchGroup:{x:p},programUniforms:[{type:12,data:h}]})}},hr=(e,t,s,n)=>{let o=e.inputs.length===1?s:Qo(e.inputs,s),a=o.axes;a.length===0&&!o.noopWithEmptyAxes&&(a=e.inputs[0].dims.map((z,B)=>B));let 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output_indices = ${X.offsetToIndices("global_idx")}; + + ${Z.join(` +`)} + ${he[0]} // init ops for reduce max/min + ${he[1]} + ${pe} + ${he[3]} + ${he.length===4?X.setByOffset("global_idx","value"):he.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(B/64)},programUniforms:[{type:12,data:B},...yt(h,p)]})}},Qo=(e,t)=>{let s=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>s.push(Number(n))),Bt({axes:s,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},wr=(e,t,s,n)=>{let o=e.inputs,a=o.length===1?s:Qo(o,s);e.compute(uo(t,{hint:a.cacheKey,inputDependencies:["rank"]},[o[0]],a.noopWithEmptyAxes&&a.axes.length===0?lo:n,a.axes,o[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},Xo=(e,t)=>{gr(e.inputs),wr(e,"ReduceLogSum",t,(s,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${s.getByIndices("input_indices")};`,"value = log(value);"])},rl=(e,t)=>{gr(e.inputs),wr(e,"ReduceL1",t,(s,n)=>[`var value = 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s=(n,o,a)=>{let i=[];for(let u=0;u=0||a.length===0)&&i.push(`input_indices[${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 = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",o.setByOffset("global_idx","best_index")]};e.compute(uo("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],s,[t.axis],7,t.keepDims),{inputs:[0]})},ni=(e,t)=>{ri(e.inputs);let s=(n,o,a)=>{let i=[];for(let u=0;u=0||a.length===0)&&i.push(`input_indices[${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); + }`,"",o.setByOffset("global_idx","best_index")]};e.compute(uo("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],s,[t.axis],7,t.keepDims),{inputs:[0]})},oi=e=>Bt(e)}),ii,po,gl,ai,wl,Nn,li,yl,ui=g(()=>{zt(),Ot(),ue(),Yt(),ii=(e,t)=>{let s=e[0],n=e[1],o=e[2],a=e[3],i=e[4],u=e[5];if(i&&u)throw new Error("Attention cannot have both past and attention_bias");if(s.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let p=s.dims[0],h=s.dims[1],k=s.dims[2];if(o.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==k)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(o.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let C=o.dims[0]/3,d=C,z=d;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let he of t.qkvHiddenSizes)if(he%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");C=t.qkvHiddenSizes[0],d=t.qkvHiddenSizes[1],z=t.qkvHiddenSizes[2]}let B=h;if(C!==d)throw new Error("qkv_hidden_sizes first element should be same as the second");if(o.dims[0]!==C+d+z)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let V=0;if(i){if(d!==z)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(i.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(i.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(i.dims[1]!==p)throw new Error('Input "past" second dimension must be batch_size');if(i.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(i.dims[4]!==d/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(V=i.dims[3])}let Z=B+V,ee=-1,X=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]!==p||u.dims[1]!==t.numHeads||u.dims[2]!==h||u.dims[3]!==Z)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:h,pastSequenceLength:V,kvSequenceLength:B,totalSequenceLength:Z,maxSequenceLength:ee,inputHiddenSize:k,hiddenSize:C,vHiddenSize:z,headSize:Math.floor(C/t.numHeads),vHeadSize:Math.floor(z/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:X,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},po=(e,t,s)=>t&&e?` + let total_sequence_length_input = u32(${t.getByOffset("0")}); + let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length); + let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input; + let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input; + total_sequence_length = u32(${e==null?void 0:e.getByOffset("batchIdx")}) + 1; + var past_sequence_length: u32 = 0; + if (is_first_prompt == false) { + past_sequence_length = total_sequence_length - sequence_length; + } + `:` + ${s?"let past_sequence_length = uniforms.past_sequence_length":""}; + let present_sequence_length = total_sequence_length; + `,gl=(e,t,s,n,o,a,i,u)=>{let p=qt(i?1:a),h=64,k=a/p;k{let X=It("x",e.dataType,e.dims,p),he=[X],pe=i?qe("seq_lens",i.dataType,i.dims):void 0;pe&&he.push(pe);let Me=u?qe("total_sequence_length_input",u.dataType,u.dims):void 0;Me&&he.push(Me);let Oe=Ss(e.dataType),Le=[{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; + ${ee.registerUniforms(Le).declareVariables(...he)} + ${ee.mainStart([h,1,1])} + let batchIdx = workgroup_id.z / uniforms.num_heads; + let headIdx = workgroup_id.z % uniforms.num_heads; + let sequence_length = uniforms.sequence_length; + var total_sequence_length = uniforms.total_sequence_length; + ${po(pe,Me,!1)} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = (global_idx / ${h}) * uniforms.total_sequence_length + local_offset; + let seq_causal_length = ${i?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"}; + var thread_max_vector = ${B}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + thread_max_vector = max(${B}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(p){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${p}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${h}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${B}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + sum_vector += exp(${B}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(p){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${p}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${h}; i++) { + sum += thread_sum[i]; + } + + if (sum == 0) { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + x[offset + i] = ${X.type.value}(${Oe}(1.0) / ${Oe}(seq_causal_length)); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + var f32input = ${B}(x[offset + i]); + x[offset + i] = ${X.type.value}(exp(f32input - max_value) / sum); + } + } + ${i?` + for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) { + x[offset + total_seq_id] = ${X.type.value}(${Oe}(0)); + }`:""}; + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${h};${z};${p}`,inputDependencies:V},getShaderSource:Z,getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(a/h),y:o,z:t*s},programUniforms:d})}},ai=(e,t,s,n,o,a,i,u,p)=>{let h=i+a.kvSequenceLength,k=[a.batchSize,a.numHeads,a.sequenceLength,h],C=e>1&&n,d=a.kvNumHeads?a.kvNumHeads:a.numHeads,z=C?[a.batchSize,d,h,a.headSize]:void 0,B=a.nReps?a.nReps:1,V=a.scale===0?1/Math.sqrt(a.headSize):a.scale,Z=qt(a.headSize),ee=a.headSize/Z,X=12,he={x:Math.ceil(h/X),y:Math.ceil(a.sequenceLength/X),z:a.batchSize*a.numHeads},pe=[{type:12,data:a.sequenceLength},{type:12,data:ee},{type:12,data:h},{type:12,data:a.numHeads},{type:12,data:a.headSize},{type:1,data:V},{type:12,data:i},{type:12,data:a.kvSequenceLength},{type:12,data:B}],Me=C&&n&&ze.size(n.dims)>0,Oe=["type","type"];Me&&Oe.push("type"),o&&Oe.push("type"),u&&Oe.push("type"),p&&Oe.push("type");let Le=[{dims:k,dataType:t.dataType,gpuDataType:0}];C&&Le.push({dims:z,dataType:t.dataType,gpuDataType:0});let Ye=at=>{let Pt=qe("q",t.dataType,t.dims,Z),Xt=qe("key",s.dataType,s.dims,Z),Zt=[Pt,Xt];if(Me){let Rt=qe("past_key",n.dataType,n.dims,Z);Zt.push(Rt)}o&&Zt.push(qe("attention_bias",o.dataType,o.dims));let bt=u?qe("seq_lens",u.dataType,u.dims):void 0;bt&&Zt.push(bt);let ss=p?qe("total_sequence_length_input",p.dataType,p.dims):void 0;ss&&Zt.push(ss);let St=It("output",t.dataType,k),Ft=[St];C&&Ft.push(It("present_key",t.dataType,z,Z));let bs=Ss(1,Z),Ht=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return` + const TILE_SIZE = ${X}u; + + var tileQ: array<${Pt.type.storage}, ${X*X}>; + var tileK: array<${Pt.type.storage}, ${X*X}>; + ${at.registerUniforms(Ht).declareVariables(...Zt,...Ft)} + ${at.mainStart([X,X,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z % uniforms.num_heads; + let kvHeadIdx = ${B===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${B===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; + ${po(bt,ss,!0)} + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; + let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + ${Me&&C?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""}; + let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K; + ${C?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""} + var value = ${bs}(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; + ${Me&&C?` + 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]; + }`} + ${C?`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 += ${bs}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]); + } + + workgroupBarrier(); + } + + if (global_id.y < uniforms.M && global_id.x < total_sequence_length) { + let headOffset = workgroup_id.z * uniforms.M * uniforms.N; + let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x; + var sum: f32 = ${(()=>{switch(Z){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${Z}`)}})()}; + output[outputIdx] = ${St.type.value} (sum * uniforms.alpha) + ${o?"attention_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${Z};${o!==void 0};${n!==void 0};${e}`,inputDependencies:Oe},getRunData:()=>({outputs:Le,dispatchGroup:he,programUniforms:pe}),getShaderSource:Ye}},wl=(e,t,s,n,o,a,i=void 0,u=void 0)=>{let p=a+o.kvSequenceLength,h=o.nReps?o.nReps:1,k=o.vHiddenSize*h,C=e>1&&n,d=o.kvNumHeads?o.kvNumHeads:o.numHeads,z=C?[o.batchSize,d,p,o.headSize]:void 0,B=[o.batchSize,o.sequenceLength,k],V=12,Z={x:Math.ceil(o.vHeadSize/V),y:Math.ceil(o.sequenceLength/V),z:o.batchSize*o.numHeads},ee=[{type:12,data:o.sequenceLength},{type:12,data:p},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:12,data:k},{type:12,data:a},{type:12,data:o.kvSequenceLength},{type:12,data:h}],X=C&&n&&ze.size(n.dims)>0,he=["type","type"];X&&he.push("type"),i&&he.push("type"),u&&he.push("type");let pe=[{dims:B,dataType:t.dataType,gpuDataType:0}];C&&pe.push({dims:z,dataType:t.dataType,gpuDataType:0});let Me=Oe=>{let Le=qe("probs",t.dataType,t.dims),Ye=qe("v",s.dataType,s.dims),at=[Le,Ye];X&&at.push(qe("past_value",n.dataType,n.dims));let Pt=i?qe("seq_lens",i.dataType,i.dims):void 0;i&&at.push(Pt);let Xt=u?qe("total_sequence_length_input",u.dataType,u.dims):void 0;u&&at.push(Xt);let Zt=[It("output",t.dataType,B)];C&&Zt.push(It("present_value",t.dataType,z));let bt=[{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 = ${V}u; + var tileQ: array<${Le.type.value}, ${V*V}>; + var tileV: array<${Le.type.value}, ${V*V}>; + ${Oe.registerUniforms(bt).declareVariables(...at,...Zt)} + ${Oe.mainStart([V,V,1])} + let headIdx = workgroup_id.z % uniforms.num_heads; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let kvHeadIdx = ${h===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${h===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; + let m = global_id.y; + let n = global_id.x; + let sequence_length = uniforms.M; + var total_sequence_length = uniforms.K; + ${po(Pt,Xt,!0)} + let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch + ${X&&C?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""}; + let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n; + ${C?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""} + var value = ${Le.type.storage}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + var idx = TILE_SIZE * local_id.y + local_id.x; + ${X&&C?` + 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]; + }`} + ${C?` + if (w + local_id.y < present_sequence_length) { + present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx]; + }`:""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + headIdx * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:he},getRunData:()=>({outputs:pe,dispatchGroup:Z,programUniforms:ee}),getShaderSource:Me}},Nn=(e,t,s,n,o,a,i,u,p,h,k=void 0,C=void 0)=>{let d=Math.min(e.outputCount,1+(i?1:0)+(u?1:0)),z=d>1?h.pastSequenceLength:0,B=z+h.kvSequenceLength,V=p&&ze.size(p.dims)>0?p:void 0,Z=[t,s];d>1&&i&&ze.size(i.dims)>0&&Z.push(i),V&&Z.push(V),k&&Z.push(k),C&&Z.push(C);let ee=e.compute(ai(d,t,s,i,V,h,z,k,C),{inputs:Z,outputs:d>1?[-1,1]:[-1]})[0];e.compute(gl(ee,h.batchSize,h.numHeads,z,h.sequenceLength,B,k,C),{inputs:k&&C?[ee,k,C]:[ee],outputs:[]});let X=[ee,n];d>1&&u&&ze.size(u.dims)>0&&X.push(u),k&&X.push(k),C&&X.push(C),e.compute(wl(d,ee,n,u,h,z,k,C),{inputs:X,outputs:d>1?[0,2]:[0]})},li=(e,t)=>{let s=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,o=t.inputHiddenSize,a=t.headSize,i=12,u={x:Math.ceil(t.headSize/i),y:Math.ceil(t.sequenceLength/i),z:t.batchSize*t.numHeads},p=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:n},{type:12,data:o},{type:12,data:a},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],k=C=>{let d=It("output_q",p[0].dataType,s),z=It("output_k",p[0].dataType,s),B=It("output_v",p[0].dataType,s),V=qe("input",p[0].dataType,p[0].dims),Z=qe("weight",p[1].dataType,p[1].dims),ee=qe("bias",p[2].dataType,p[2].dims),X=V.type.storage,he=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return` + const TILE_SIZE = ${i}u; + var tileInput: array<${X}, ${i*i}>; + var tileWeightQ: array<${X}, ${i*i}>; + var tileWeightK: array<${X}, ${i*i}>; + var tileWeightV: array<${X}, ${i*i}>; + ${C.registerUniforms(he).declareVariables(V,Z,ee,d,z,B)} + ${C.mainStart([i,i,1])} + let batchIndex = workgroup_id.z / uniforms.num_heads; + let headNumber = workgroup_id.z % uniforms.num_heads; + let m = global_id.y; + let n = global_id.x; + + let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K; + let biasOffsetQ = headNumber * uniforms.head_size; + let biasOffsetK = uniforms.hidden_size + biasOffsetQ; + let biasOffsetV = uniforms.hidden_size + biasOffsetK; + + var valueQ = ${X}(0); + var valueK = ${X}(0); + var valueV = ${X}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:s,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:s,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:s,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:h}),getShaderSource:k},{inputs:p,outputs:[-1,-1,-1]})},yl=(e,t)=>{let s=ii(e.inputs,t),[n,o,a]=li(e,s);return Nn(e,n,o,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],s)}}),di,Ml,bl,ci,Vc=g(()=>{We(),zt(),Ot(),rs(),Yt(),di=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let s=(n,o,a)=>{let i=o.length;if(i!==n.length)throw new Error(`${a}: num dimensions != ${i}`);o.forEach((u,p)=>{if(u!==n[p])throw new Error(`${a}: dim[${p}] do not match`)})};if(e[0].dims.length>1){let n=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);s(e[1].dims,n,"Invalid input scale"),s(e[2].dims,n,"Invalid input B"),s(e[3].dims,n,"Invalid input mean"),s(e[4].dims,n,"Invalid 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${he.registerUniform("outputSize","u32").declareVariables(C,d,z,B,V,Z)} + ${he.mainStart()} + ${he.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${Z.offsetToIndices(`global_idx * ${i}`)}; + ${ee()} + let scale = ${d.getByOffset("cOffset")}; + let bias = ${z.getByOffset("cOffset")}; + let inputMean = ${B.getByOffset("cOffset")}; + let inputVar = ${V.getByOffset("cOffset")}; + let x = ${C.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${Z.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${i}`,inputDependencies:h?["rank","type","type","type","type"]:void 0},getShaderSource:X,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h?[{type:12,data:p},...yt(a)]:[{type:12,data:p}]})}},bl=e=>Bt(e),ci=(e,t)=>{let{inputs:s,outputCount:n}=e,o=bl({...t,outputCount:n});if(O.webgpu.validateInputContent&&di(s,o),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Ml(s,o))}}),vl,pi,xl,Wc=g(()=>{Ot(),Yt(),vl=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},pi=e=>{let t=e[0].dims,s=e[0].dims[2],n=ze.size(t)/4,o=e[0].dataType,a=qe("input",o,t,4),i=qe("bias",o,[s],4),u=qe("residual",o,t,4),p=It("output",o,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:h=>` + const channels = ${s}u / 4; + ${h.declareVariables(a,i,u,p)} + + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes(n)} + let value = ${a.getByOffset("global_idx")} + + ${i.getByOffset("global_idx % channels")} + ${u.getByOffset("global_idx")}; + ${p.setByOffset("global_idx","value")} + }`}},xl=e=>{vl(e.inputs),e.compute(pi(e.inputs))}}),hi,ds,Tl,mi,Pl,El,_i,Cl,kl,fi,Sl,$l,gi,Al,Il,wi,jn,Ol,ho,Fl,yi,Dl,Ll,Mi,zl,Bl,bi,Rl,Nl,vi,jl,Ul,xi,Vl,Wl,mo,Gl,Ti,_o,Kl,Hl,ql,Ql,Pi,Xl,Ei=g(()=>{zt(),Ot(),rs(),Yt(),hi=(e,t,s,n,o,a,i)=>{let u=Math.ceil(t/4),p="";typeof o=="string"?p=`${o}(a)`:p=o("a");let h=qe("inputData",s,[u],4),k=It("outputData",n,[u],4),C=[{name:"vec_size",type:"u32"}];return i&&C.push(...i),` + ${e.registerUniforms(C).declareVariables(h,k)} + + ${a??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${h.getByOffset("global_idx")}; + ${k.setByOffset("global_idx",p)} + }`},ds=(e,t,s,n,o,a=e.dataType,i,u)=>{let p=[{type:12,data:Math.ceil(ze.size(e.dims)/4)}];return i&&p.push(...i),{name:t,shaderCache:{hint:o,inputDependencies:["type"]},getShaderSource:h=>hi(h,ze.size(e.dims),e.dataType,a,s,n,u),getRunData:h=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil(ze.size(h[0].dims)/64/4)},programUniforms:p})}},Tl=e=>{e.compute(ds(e.inputs[0],"Abs","abs"))},mi=e=>{e.compute(ds(e.inputs[0],"Acos","acos"))},Pl=e=>{e.compute(ds(e.inputs[0],"Acosh","acosh"))},El=e=>{e.compute(ds(e.inputs[0],"Asin","asin"))},_i=e=>{e.compute(ds(e.inputs[0],"Asinh","asinh"))},Cl=e=>{e.compute(ds(e.inputs[0],"Atan","atan"))},kl=e=>{e.compute(ds(e.inputs[0],"Atanh","atanh"))},fi=e=>Bt(e),Sl=(e,t)=>{let s;switch(t.to){case 10:s="vec4";break;case 1:s="vec4";break;case 12:s="vec4";break;case 6:s="vec4";break;case 9:s="vec4";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute(ds(e.inputs[0],"Cast",s,void 0,t.cacheKey,t.to))},$l=e=>{let t,s,n=e.length>=2&&e[1].data!==0,o=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=n?e[1].getFloat32Array()[0]:-34028234663852886e22,s=o?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=n?e[1].getUint16Array()[0]:64511,s=o?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return Bt({min:t,max:s})},gi=(e,t)=>{let s=t||$l(e.inputs),n=Ss(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"Clip",o=>`clamp(${o}, vec4<${n}>(uniforms.min), vec4<${n}>(uniforms.max))`,void 0,s.cacheKey,void 0,[{type:e.inputs[0].dataType,data:s.min},{type:e.inputs[0].dataType,data:s.max}],[{name:"min",type:n},{name:"max",type:n}]),{inputs:[0]})},Al=e=>{e.compute(ds(e.inputs[0],"Ceil","ceil"))},Il=e=>{e.compute(ds(e.inputs[0],"Cos","cos"))},wi=e=>{e.compute(ds(e.inputs[0],"Cosh","cosh"))},jn=e=>Bt(e),Ol=(e,t)=>{let s=Ss(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"Elu",n=>`elu_vf32(${n})`,` + const elu_alpha_ = ${s}(${t.alpha}); + + fn elu_f32(a: ${s}) -> ${s} { + return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${s}>) -> vec4<${s}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,t.cacheKey))},ho=(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<${e}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,Fl=e=>{let t=Ss(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"Erf",s=>`erf_vf32(${s})`,ho(t)))},yi=e=>{e.compute(ds(e.inputs[0],"Exp","exp"))},Dl=e=>{e.compute(ds(e.inputs[0],"Floor","floor"))},Ll=e=>{let t=Ss(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"Gelu",s=>`0.5 * ${s} * (1.0 + erf_vf32(${s} * 0.7071067811865475))`,ho(t)))},Mi=(e,t)=>{let s=Ss(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${s}>(0.0))`,`const leaky_relu_alpha_ = ${s}(${t.alpha});`,t.cacheKey))},zl=e=>{e.compute(ds(e.inputs[0],"Not",t=>`!${t}`))},Bl=e=>{e.compute(ds(e.inputs[0],"Neg",t=>`-${t}`))},bi=e=>{e.compute(ds(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Rl=e=>{let t=Ss(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"Relu",s=>`select(vec4<${t}>(0.0), ${s}, ${s} > vec4<${t}>(0.0))`))},Nl=e=>{e.compute(ds(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},vi=e=>Bt(e),jl=(e,t)=>{let s=Ss(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"HardSigmoid",n=>`max(vec4<${s}>(0.0), min(vec4<${s}>(1.0), ${t.alpha} * ${n} + vec4<${s}>(${t.beta})))`,void 0,t.cacheKey))},Ul=e=>{e.compute(ds(e.inputs[0],"Sin","sin"))},xi=e=>{e.compute(ds(e.inputs[0],"Sinh","sinh"))},Vl=e=>{e.compute(ds(e.inputs[0],"Sqrt","sqrt"))},Wl=e=>{e.compute(ds(e.inputs[0],"Tan","tan"))},mo=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Gl=e=>{e.compute(ds(e.inputs[0],"Tanh",mo))},Ti=(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 ${mo("v")}; +} +`,_o=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Kl=e=>{let t=Ss(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"FastGelu",_o,Ti(t),void 0,e.inputs[0].dataType))},Hl=(e,t)=>{let s=Ss(e.inputs[0].dataType);return e.compute(ds(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${s}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${s}>(${t.alpha});`,t.cacheKey)),0},ql=e=>{e.compute(ds(e.inputs[0],"Log","log"))},Ql=(e,t)=>` +const alpha = vec4<${e}>(${t}); +const one = ${e}(1.0); +const zero = ${e}(0.0); + +fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> { + let v = x *alpha; + var x1 : vec4<${e}>; + for (var i = 0; i < 4; i = i + 1) { + if (v[i] >= zero) { + x1[i] = one / (one + exp(-v[i])); + } else { + x1[i] = one - one / (one + exp(v[i])); + } + } + return x * x1; +} +`,Pi=e=>`quick_gelu_impl(${e})`,Xl=(e,t)=>{let s=Ss(e.inputs[0].dataType);e.compute(ds(e.inputs[0],"QuickGelu",Pi,Ql(s,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),Yl,Jl,Ci,Gc=g(()=>{Ot(),Yt(),Ei(),Yl=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 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bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${o.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Ci=e=>{Yl(e.inputs),e.compute(Jl(e.inputs))}}),Zl,eu,Mr,ki,tu,su,ru,nu,Si,ou,iu,$i,au,Kc=g(()=>{zt(),Ot(),Yt(),Zl=(e,t,s,n,o,a,i,u,p,h,k,C)=>{let d,z;typeof u=="string"?d=z=(X,he)=>`${u}((${X}),(${he}))`:typeof u=="function"?d=z=u:(d=u.scalar,z=u.vector);let B=It("outputData",k,n.length,4),V=qe("aData",p,t.length,4),Z=qe("bData",h,s.length,4),ee;if(o)if(a){let X=ze.size(t)===1,he=ze.size(s)===1,pe=t.length>0&&t[t.length-1]%4===0,Me=s.length>0&&s[s.length-1]%4===0;X||he?ee=B.setByOffset("global_idx",z(X?`${V.type.value}(${V.getByOffset("0")}.x)`:V.getByOffset("global_idx"),he?`${Z.type.value}(${Z.getByOffset("0")}.x)`:Z.getByOffset("global_idx"))):ee=` + let outputIndices = ${B.offsetToIndices("global_idx * 4u")}; + let offsetA = ${V.broadcastedIndicesToOffset("outputIndices",B)}; + let offsetB = ${Z.broadcastedIndicesToOffset("outputIndices",B)}; + ${B.setByOffset("global_idx",z(i||pe?V.getByOffset("offsetA / 4u"):`${V.type.value}(${V.getByOffset("offsetA / 4u")}[offsetA % 4u])`,i||Me?Z.getByOffset("offsetB / 4u"):`${Z.type.value}(${Z.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else ee=B.setByOffset("global_idx",z(V.getByOffset("global_idx"),Z.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let X=(he,pe,Me="")=>{let Oe=`aData[indexA${pe}][componentA${pe}]`,Le=`bData[indexB${pe}][componentB${pe}]`;return` + let outputIndices${pe} = ${B.offsetToIndices(`global_idx * 4u + ${pe}u`)}; + let offsetA${pe} = ${V.broadcastedIndicesToOffset(`outputIndices${pe}`,B)}; + let offsetB${pe} = ${Z.broadcastedIndicesToOffset(`outputIndices${pe}`,B)}; + let indexA${pe} = offsetA${pe} / 4u; + let indexB${pe} = offsetB${pe} / 4u; + let componentA${pe} = offsetA${pe} % 4u; + let componentB${pe} = offsetB${pe} % 4u; + ${he}[${pe}] = ${Me}(${d(Oe,Le)}); + `};k===9?ee=` + var data = vec4(0); + ${X("data",0,"u32")} + ${X("data",1,"u32")} + ${X("data",2,"u32")} + ${X("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:ee=` + ${X("outputData[global_idx]",0)} + ${X("outputData[global_idx]",1)} + ${X("outputData[global_idx]",2)} + ${X("outputData[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(V,Z,B)} + + ${C??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${ee} + }`},eu=(e,t,s,n,o,a,i=s.dataType)=>{let u=s.dims.map(V=>Number(V)??1),p=n.dims.map(V=>Number(V)??1),h=!ze.areEqual(u,p),k=u,C=ze.size(u),d=!1,z=!1,B=[h];if(h){let V=Ws.calcShape(u,p,!1);if(!V)throw new Error("Can't perform binary op on the given tensors");k=V.slice(),C=ze.size(k);let Z=ze.size(u)===1,ee=ze.size(p)===1,X=u.length>0&&u[u.length-1]%4===0,he=p.length>0&&p[p.length-1]%4===0;B.push(Z),B.push(ee),B.push(X),B.push(he);let pe=1;for(let Me=1;MeV.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:V=>Zl(V,u,p,k,d,h,z,o,s.dataType,n.dataType,i,a),getRunData:()=>({outputs:[{dims:k,dataType:i}],dispatchGroup:{x:Math.ceil(C/64/4)},programUniforms:[{type:12,data:Math.ceil(ze.size(k)/4)},...yt(u,p,k)]})}},Mr=(e,t,s,n,o,a)=>{e.compute(eu(t,o??"",e.inputs[0],e.inputs[1],s,n,a))},ki=e=>{Mr(e,"Add",(t,s)=>`${t}+${s}`)},tu=e=>{Mr(e,"Div",(t,s)=>`${t}/${s}`)},su=e=>{Mr(e,"Equal",{scalar:(t,s)=>`u32(${t}==${s})`,vector:(t,s)=>`vec4(${t}==${s})`},void 0,void 0,9)},ru=e=>{Mr(e,"Mul",(t,s)=>`${t}*${s}`)},nu=e=>{let t=qe("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;Mr(e,"Pow",{scalar:(s,n)=>`pow_custom(${s},${n})`,vector:(s,n)=>`pow_vector_custom(${s},${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)); + } + `)},Si=e=>{Mr(e,"Sub",(t,s)=>`${t}-${s}`)},ou=e=>{Mr(e,"Greater",{scalar:(t,s)=>`u32(${t}>${s})`,vector:(t,s)=>`vec4(${t}>${s})`},void 0,void 0,9)},iu=e=>{Mr(e,"Less",{scalar:(t,s)=>`u32(${t}<${s})`,vector:(t,s)=>`vec4(${t}<${s})`},void 0,void 0,9)},$i=e=>{Mr(e,"GreaterOrEqual",{scalar:(t,s)=>`u32(${t}>=${s})`,vector:(t,s)=>`vec4(${t}>=${s})`},void 0,void 0,9)},au=e=>{Mr(e,"LessOrEqual",{scalar:(t,s)=>`u32(${t}<=${s})`,vector:(t,s)=>`vec4(${t}<=${s})`},void 0,void 0,9)}}),Ai,lu,uu,Ii,du,cu,pu=g(()=>{zt(),Ot(),rs(),Yt(),Ai=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let s=0,n=e[s],o=n.dataType,a=n.dims.length;e.forEach((i,u)=>{if(u!==s){if(i.dataType!==o)throw new Error("input tensors should be one type");if(i.dims.length!==a)throw new Error("input tensors should have the same shape");i.dims.forEach((p,h)=>{if(h!==t&&p!==n.dims[h])throw new Error("non concat dimensions must match")})}})},lu=(e,t)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${t}); + for (var i: u32 = 0u; i < ${e}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${e}u; + }`,uu=(e,t)=>{let s=e.length,n=[];for(let o=0;o{let o=ze.size(s),a=new Array(e.length),i=new Array(e.length),u=0,p=[],h=[],k=[{type:12,data:o}];for(let V=0;V`uniforms.sizeInConcatAxis${V}`).join(","),B=V=>` + + ${(()=>{V.registerUniform("outputSize","u32");for(let Z=0;Z(${z}); + ${d} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${uu(i,C)} + }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:s,dataType:n}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:k}),getShaderSource:B}},du=(e,t)=>{let s=e.inputs,n=s[0].dims,o=ze.normalizeAxis(t.axis,n.length);Ai(s,o);let a=n.slice();a[o]=s.reduce((u,p)=>u+(p.dims.length>o?p.dims[o]:0),0);let i=s.filter(u=>ze.size(u.dims)>0);e.compute(Ii(i,o,a,s[0].dataType),{inputs:i})},cu=e=>Bt({axis:e.axis})}),nn,on,Dr,Oi,an=g(()=>{zt(),Ot(),nn=(e,t,s="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, ${t}(${s}(uniforms.clip_min)), ${t}(${s}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${s}(uniforms.alpha) * value + ${s}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${s}(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 ${e.activation}`)}},on=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Dr=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Oi=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[s,n]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:s,beta:n}}else if(t==="Clip"){let[s,n]=(e==null?void 0:e.activation_params)||[ks,Xs];return{activation:t,clipMax:n,clipMin:s}}else if(t==="LeakyRelu"){let[s]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:s}}return{activation:t}}}),Ks,Fi,Di=g(()=>{Ks=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Fi=e=>` + ${e?"value = value + getBiasByOutputCoords(coords);":""} + `}),Li,Hc=g(()=>{Li=e=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); +} +`}),Un,zi,fo=g(()=>{zt(),Ot(),Yt(),an(),Un=(e,t,s,n,o)=>{let a=n-s;return` + ${Array.from({length:s}).map((i,u)=>` + if (${$t(t.shape,u,t.rank)} != 1) { + ${t.indicesSet(e,u,$t(o,u+a,n))} + } else { + ${t.indicesSet(e,u,0)} + }`).join("")} +`},zi=(e,t,s,n,o=!1,a)=>{let i=e[0].dims,u=e[1].dims,p=i[i.length-2],h=u[u.length-1],k=i[i.length-1],C=qt(h),d=qt(k),z=qt(p),B=ze.size(s)/C/z,V=e.length>2,Z=n?n.slice(0,-2):s.slice(0,-2),ee=[ze.size(Z),p,h],X=[{type:12,data:B},{type:12,data:p},{type:12,data:h},{type:12,data:k}];on(t,X),X.push(...yt(Z,i,u)),V&&X.push(...yt(e[2].dims)),X.push(...yt(ee));let he=pe=>{let Me=Uo("batch_dims",e[0].dataType,Z.length),Oe=qe("a",e[0].dataType,i.length,d),Le=qe("b",e[1].dataType,u.length,C),Ye=It("output",e[0].dataType,ee.length,C),at=_s(Ye.type.tensor),Pt=nn(t,Ye.type.value,at),Xt=[Oe,Le],Zt="";if(V){let St=o?C:1;Xt.push(qe("bias",e[2].dataType,e[2].dims.length,St)),Zt=`${o?`value += bias[col / ${St}];`:`value += ${Ye.type.value}(bias[row + i]);`}`}let bt=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Dr(t,bt);let ss=()=>{let St=`var a_data: ${Oe.type.value};`;for(let Ft=0;Ft; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${d}) { + ${ss()} + } + for (var i = 0u; i < ${z}u; i++) { + var value = values[i]; + ${Zt} + ${Pt} + let cur_indices = ${Ye.type.indices}(batch, row + i, col); + let offset = ${Ye.indicesToOffset("cur_indices")}; + ${Ye.setByOffset(`offset / ${C}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${C};${d};${z};${o}`,inputDependencies:V?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:a?a(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(B/64)},programUniforms:X}),getShaderSource:he}}}),hu,mu,Bi,go,_u,Ri,Ni,wo,ji=g(()=>{zt(),Ot(),Yt(),an(),fo(),Di(),hu=(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":""}); + `,mu=(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];"} + }`,Bi=(e,t,s="f32",n,o=!1,a=32,i=!1,u=32)=>{let p=t[1]*e[1],h=t[0]*e[0],k=o?p:a,C=o?a:p,d=k/t[0],z=a/t[1];if(!((o&&d===4&&e[1]===4||!o&&(d===3||d===4))&&k%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${d} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${d} must be 3 or 4. + tileAWidth ${k} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return` +var mm_Asub: array, ${k/d}>, ${C}>; +var mm_Bsub: array, ${h/e[0]}>, ${a}>; + +const rowPerThread = ${e[1]}; +const colPerThread = ${e[0]}; +const innerElementSize = ${d}; +const tileInner = ${a}; + +@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let localRow = i32(localId.y); + let tileRow = localRow * rowPerThread; + let tileCol = i32(localId.x); + + let globalRow =i32(globalId.y) * rowPerThread; + let globalCol = i32(globalId.x); + let batch = ${i?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${p}; + + let num_tiles = ${i?`${Math.ceil(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 * ${z}; + 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; + ${hu(o,n)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${z}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${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]; + ${d===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${mu(o,d)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},go=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${t?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${t?", batchIndices":""}); + `,_u=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Ri=(e,t,s="f32",n,o=!1,a=32,i=!1,u=32,p=!1)=>{let h=e[1]*t[1],k=e[0]*t[0],C=o?h:a,d=o?a:h;if(!(d%t[1]===0&&C%t[0]===0&&a%t[1]===0))throw new Error(`tileAHight ${d} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${C} must be divisible by workgroupSize[0]${t[0]}, tileInner ${a} must be divisible by workgroupSize[1]${t[1]}`);let z=d/t[1],B=C/t[0],V=a/t[1],Z=p?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${h}; + let globalColStart = i32(workgroupId.x) * ${k}; + + // Loop over shared dimension. + for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var inputRow = localRow; inputRow < ${d}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${C}; inputCol = inputCol + ${t[0]}) { + ${go(o,n)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${k}; inputCol = inputCol + ${t[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalColStart + inputCol${n?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${s}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}]; + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let ACached = ${o?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + + ACached * BCached[innerCol]; + } + } + } + workgroupBarrier(); + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let gRow = globalRowStart + localRow + innerRow * ${t[1]}; + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let gCol = globalColStart + localCol + innerCol * ${t[0]}; + mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); + } + } + `:` +let tileRow = i32(localId.y) * rowPerThread; +let tileCol = i32(localId.x) * colPerThread; + +let globalRow = i32(globalId.y) * rowPerThread; +let globalCol = i32(globalId.x) * colPerThread; +let globalRowStart = i32(workgroupId.y) * ${h}; + +let tileRowA = i32(localId.y) * ${z}; +let tileColA = i32(localId.x) * ${B}; +let tileRowB = i32(localId.y) * ${V}; +// 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 < ${z}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${B}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${go(o,n)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${V}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${n?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${s}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][tileCol + inner]; + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + ${_u(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, ${d}>; + 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>; + ${Z} + } +`},Ni=(e,t,s,n,o=!1)=>{let[a,i,u,p]=n,h=_s(n[0].type.tensor);return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${a.type.indices}) -> ${Ks(e,h)} { + var value = ${Ks(e,h)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + var aIndices: ${i.type.indices}; + ${Un("aIndices",i,i.rank-2,a.rank,"batchIndices")} + ${i.indicesSet("aIndices",i.rank-2,"u32(row)")} + ${i.indicesSet("aIndices",i.rank-1,"u32(colIn)")} + value = ${i.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${a.type.indices}) -> ${Ks(e,h)} { + var value = ${Ks(e,h)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + var bIndices: ${u.type.indices}; + ${Un("bIndices",u,u.rank-2,a.rank,"batchIndices")} + ${u.indicesSet("bIndices",u.rank-2,"u32(row)")} + ${u.indicesSet("bIndices",u.rank-1,"u32(colIn)")} + value = ${u.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ks(e,h)}) { + let col = colIn * ${e}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueIn; + let coords = vec3(batch, row, colIn); + ${t?`value = value + ${o?"bias[colIn]":`${Ks(e,h)}(bias[row])`};`:""} + ${s} + ${p.setByIndices("vec3(coords)","value")} + } + } + `},wo=(e,t,s,n,o=!1,a)=>{let i=e[0].dims,u=e[1].dims,p=i.slice(0,-2),h=u.slice(0,-2),k=n?n.slice(0,-2):s.slice(0,-2),C=ze.size(k),d=i[i.length-2],z=i[i.length-1],B=u[u.length-1],V=z%4===0&&B%4===0,Z=d<=8?[4,1,1]:[4,4,1],ee=[8,8,1],X=[Math.ceil(B/ee[0]/Z[0]),Math.ceil(d/ee[1]/Z[1]),Math.ceil(C/ee[2]/Z[2])],he=V?4:1,pe=[...p,d,z/he],Me=pe.length,Oe=[...h,z,B/he],Le=Oe.length,Ye=[C,d,B/he],at=[{type:6,data:d},{type:6,data:B},{type:6,data:z}];on(t,at),at.push(...yt(k,pe,Oe));let Pt=["rank","rank"],Xt=e.length>2;Xt&&(at.push(...yt(e[2].dims)),Pt.push("rank")),at.push(...yt(Ye));let Zt=bt=>{let ss=k.length,St=Uo("batchDims",e[0].dataType,ss,1),Ft=_s(e[0].dataType),bs=qe("a",e[0].dataType,Me,he),Ht=qe("b",e[1].dataType,Le,he),Rt=It("result",e[0].dataType,Ye.length,he),fs=[bs,Ht];if(Xt){let Tr=o?he:1;fs.push(qe("bias",e[2].dataType,e[2].dims.length,Tr))}let it=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Dr(t,it);let Et=_s(Rt.type.tensor),ps=nn(t,Rt.type.value,Et),Ns=Ni(he,Xt,ps,[St,bs,Ht,Rt],o);return` + ${bt.registerUniforms(it).registerInternalVariables(St).declareVariables(...fs,Rt)} + ${Ns} + ${V?Bi(Z,ee,Ft,St):Ri(Z,ee,Ft,St)} + `};return{name:"MatMul",shaderCache:{hint:`${Z};${t.activation};${V};${o}`,inputDependencies:Pt},getRunData:()=>({outputs:[{dims:a?a(s):s,dataType:e[0].dataType}],dispatchGroup:{x:X[0],y:X[1],z:X[2]},programUniforms:at}),getShaderSource:Zt}}}),Ui,fu,qc=g(()=>{zt(),Pe(),Yt(),an(),Di(),Hc(),ji(),Ui=(e,t,s,n,o=!1,a,i=4,u=4,p=4,h="f32")=>{let k=at=>{switch(at){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${h}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${at} is not supported.`)}},C=at=>{switch(at){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 ${at} is not supported.`)}},d=e?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,z=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,B=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",V=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",Z=e?"row":"col",ee=e?"col":"row",X=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${Z} / outWidth; + let outCol = ${Z} % outWidth; + + let WRow = ${ee} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${ee} / 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 = ${ee} % inChannels; + var resData = ${Ks(i,h)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${B} && xCol >= 0 && xCol < ${V}) { + ${d} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${k(i)} + } + return resData;`,he=e?t&&n?` + let col = colIn * ${i}; + ${X}`:` + let col = colIn * ${i}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${X} + } + return ${Ks(i,h)}(0.0);`:n&&s?` + let col = colIn * ${i}; + ${X}`:` + let col = colIn * ${i}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${X} + } + return ${Ks(i,h)}(0.0);`,pe=e?n&&s?C(u):` + let col = colIn * ${u}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${C(u)} + } + return ${Ks(u,h)}(0.0);`:` + let col = colIn * ${u}; + if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) { + ${C(u)} + } + return ${Ks(u,h)}(0.0);`,Me=Ks(p,h),Oe=Ks(e?i:u,h),Le=Ks(e?u:i,h),Ye=nn(a,Me,h);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Oe} { + ${e?he:pe} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Le} { + ${e?pe:he} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${Me}) { + let col = colIn * ${p}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) + { + var value = valueIn; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${z} + ${Fi(o)} + ${Ye} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},fu=(e,t,s,n,o,a,i,u,p)=>{let h=t.format==="NHWC",k=h?e[0].dims[3]:e[0].dims[1],C=s[0],d=h?s[2]:s[3],z=h?s[1]:s[2],B=h?s[3]:s[1],V=h&&(k%4===0||k%3===0)&&B%4===0,Z=h?B:d*z,ee=h?d*z:B,X=[8,8,1],he=n<=8?[4,1,1]:[4,4,1],pe=[Math.ceil(Z/X[0]/he[0]),Math.ceil(ee/X[1]/he[1]),Math.ceil(C/X[2]/he[2])];as("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${pe}`);let Me=V?h&&k%4!==0?3:4:1,Oe=X[1]*he[1],Le=X[0]*he[0],Ye=Math.max(X[0]*Me,X[1]),at=n%Oe===0,Pt=o%Le===0,Xt=a%Ye===0,Zt=V?[Me,4,4]:[1,1,1],bt=[{type:6,data:n},{type:6,data:o},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];on(t,bt),bt.push(...yt(e[0].dims,e[1].dims));let ss=["rank","rank"];i&&(bt.push(...yt(e[2].dims)),ss.push("rank")),bt.push(...yt(s));let St=Ft=>{let bs=[{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}];Dr(t,bs);let Ht=V?4:1,Rt=_s(e[0].dataType),fs=` + fn setOutputAtIndex(flatIndex : i32, value : ${V?`vec4<${Rt}>`:Rt}) { + result[flatIndex] = ${V?`vec4<${Rt}>`:Rt}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${V?`vec4<${Rt}>`:Rt}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${V?"/ 4":""}, value); + }`,it=qe("x",e[0].dataType,e[0].dims.length,Me===3?1:Me),Et=qe("w",e[1].dataType,e[1].dims.length,Ht),ps=[it,Et],Ns=It("result",e[0].dataType,s.length,Ht);if(i){let Tr=qe("bias",e[2].dataType,e[2].dims.length,Ht);ps.push(Tr),fs+=` + fn getBiasByOutputCoords(coords : vec4) -> ${V?`vec4<${Rt}>`:Rt} { + return bias[coords.${h?"w":"y"}${V?"/ 4":""}]; + }`}return` + ${Li("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 }; + ${Ft.registerUniforms(bs).declareVariables(...ps,Ns)} + ${fs} + ${Ui(h,at,Pt,Xt,i,t,Zt[0],Zt[1],Zt[2],Rt)} + ${V?Bi(he,X,Rt,void 0,!h,Ye):Ri(he,X,Rt,void 0,!h,Ye,!1,void 0,u)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${Me};${V};${at};${Pt};${Xt};${Oe};${Le};${Ye}`,inputDependencies:ss},getRunData:()=>({outputs:[{dims:p?p(s):s,dataType:e[0].dataType}],dispatchGroup:{x:pe[0],y:pe[1],z:pe[2]},programUniforms:bt}),getShaderSource:St}}}),Vi,Wi,Vn,Gi,Ki,gu,Hi,wu,Qc=g(()=>{zt(),Pe(),Ot(),Yt(),an(),Di(),Vi=e=>{let t=1;for(let s=0;stypeof e=="number"?[e,e,e]:e,Vn=(e,t)=>t<=1?e:e+(e-1)*(t-1),Gi=(e,t,s,n=1)=>{let o=Vn(t,n);return Math.floor((e[0]*(s-1)-s+o)/2)},Ki=(e,t,s,n,o)=>{o==null&&(o=Gi(e,t[0],n[0]));let a=[0,0,0,s];for(let i=0;i<3;i++)e[i]+2*o>=t[i]&&(a[i]=Math.trunc((e[i]-t[i]+2*o)/n[i]+1));return a},gu=(e,t,s,n,o,a,i,u,p,h)=>{let k,C,d,z;if(e==="VALID"&&(e=0),typeof e=="number"){k={top:e,bottom:e,left:e,right:e,front:e,back:e};let B=Ki([t,s,n,1],[u,p,h],1,[o,a,i],e);C=B[0],d=B[1],z=B[2]}else if(Array.isArray(e)){if(!e.every((V,Z,ee)=>V===ee[0]))throw Error(`Unsupported padding parameter: ${e}`);k={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let B=Ki([t,s,n,1],[u,p,h],1,[o,a,i],e[0]);C=B[0],d=B[1],z=B[2]}else if(e==="SAME_UPPER"){C=Math.ceil(t/o),d=Math.ceil(s/a),z=Math.ceil(n/i);let B=(C-1)*o+u-t,V=(d-1)*a+p-s,Z=(z-1)*i+h-n,ee=Math.floor(B/2),X=B-ee,he=Math.floor(V/2),pe=V-he,Me=Math.floor(Z/2),Oe=Z-Me;k={top:he,bottom:pe,left:Me,right:Oe,front:ee,back:X}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:k,outDepth:C,outHeight:d,outWidth:z}},Hi=(e,t,s,n,o,a=!1,i="channelsLast")=>{let u,p,h,k,C;if(i==="channelsLast")[u,p,h,k,C]=e;else if(i==="channelsFirst")[u,C,p,h,k]=e;else throw new Error(`Unknown dataFormat ${i}`);let[d,,z,B,V]=t,[Z,ee,X]=Wi(s),[he,pe,Me]=Wi(n),Oe=Vn(z,he),Le=Vn(B,pe),Ye=Vn(V,Me),{padInfo:at,outDepth:Pt,outHeight:Xt,outWidth:Zt}=gu(o,p,h,k,Z,ee,X,Oe,Le,Ye),bt=a?d*C:d,ss=[0,0,0,0,0];return i==="channelsFirst"?ss=[u,bt,Pt,Xt,Zt]:i==="channelsLast"&&(ss=[u,Pt,Xt,Zt,bt]),{batchSize:u,dataFormat:i,inDepth:p,inHeight:h,inWidth:k,inChannels:C,outDepth:Pt,outHeight:Xt,outWidth:Zt,outChannels:bt,padInfo:at,strideDepth:Z,strideHeight:ee,strideWidth:X,filterDepth:z,filterHeight:B,filterWidth:V,effectiveFilterDepth:Oe,effectiveFilterHeight:Le,effectiveFilterWidth:Ye,dilationDepth:he,dilationHeight:pe,dilationWidth:Me,inShape:e,outShape:ss,filterShape:t}},wu=(e,t,s,n,o,a)=>{let i=a==="channelsLast";i?e[0].dims[3]:e[0].dims[1];let u=[64,1,1],p={x:s.map((Z,ee)=>ee)},h=[Math.ceil(Vi(p.x.map(Z=>s[Z]))/u[0]),1,1];as("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${h}`);let k=1,C=ze.size(s),d=[{type:12,data:C},{type:12,data:n},{type:12,data:o},{type:12,data:t.strides},{type:12,data:t.dilations}];on(t,d),d.push(...yt(e[0].dims,e[1].dims));let z=["rank","rank"],B=e.length===3;B&&(d.push(...yt(e[2].dims)),z.push("rank")),d.push(...yt(s));let V=Z=>{let ee=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:n.length},{name:"pads",type:"u32",length:o.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];Dr(t,ee);let X=1,he=_s(e[0].dataType),pe=qe("x",e[0].dataType,e[0].dims.length,k),Me=qe("W",e[1].dataType,e[1].dims.length,X),Oe=[pe,Me],Le=It("result",e[0].dataType,s.length,X),Ye="";if(B){let Xt=qe("bias",e[2].dataType,e[2].dims.length,X);Oe.push(Xt),Ye+=` + fn getBiasByOutputCoords(coords : array) -> ${he} { + return bias[${i?$t("coords",4,5):$t("coords",1,5)}]; + }`}let at=Ks(k,he),Pt=nn(t,at,he);return` + ${Ye} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${pe.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${Me.getByIndices("aIndices")}; + } + ${Z.registerUniforms(ee).declareVariables(...Oe,Le)} + ${Z.mainStart()} + ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${Le.offsetToIndices("global_idx")}; + let batch = ${$t("coords",0,pe.rank)}; + let d2 = ${i?$t("coords",pe.rank-1,pe.rank):$t("coords",1,pe.rank)}; + let xFRCCorner = vec3(${i?$t("coords",1,pe.rank):$t("coords",2,pe.rank)}, + ${i?$t("coords",2,pe.rank):$t("coords",3,pe.rank)}, + ${i?$t("coords",3,pe.rank):$t("coords",4,pe.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${i?$t("uniforms.x_shape",1,pe.rank):$t("uniforms.x_shape",2,pe.rank)}; + let xShapeZ = ${i?$t("uniforms.x_shape",2,pe.rank):$t("uniforms.x_shape",3,pe.rank)}; + let xShapeW = ${i?$t("uniforms.x_shape",3,pe.rank):$t("uniforms.x_shape",4,pe.rank)}; + let xShapeU = ${i?$t("uniforms.x_shape",4,pe.rank):$t("uniforms.x_shape",1,pe.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); + } + } + } + } + ${B?"value = value + getBiasByOutputCoords(coords)":""}; + ${Pt} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${i};${k};${B}`,inputDependencies:z},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:h[0],y:h[1],z:h[2]},programUniforms:d}),getShaderSource:V}}}),yu,Mu,qi=g(()=>{zt(),Ot(),Yt(),an(),yu=(e,t,s,n)=>{let o=e.length>2,a=o?"value += b[output_channel];":"",i=e[0].dims,u=e[1].dims,p=t.format==="NHWC",h=p?s[3]:s[1],k=h/t.group,C=p&&k>=4?qt(h):1,d=ze.size(s)/C,z=[{type:12,data:d},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:k}];on(t,z),z.push(...yt(i,[u[0],u[1],u[2],u[3]/C]));let B=o?["rank","rank","rank"]:["rank","rank"];z.push(...yt([s[0],s[1],s[2],s[3]/C]));let V=Z=>{let ee=It("output",e[0].dataType,s.length,C),X=_s(ee.type.tensor),he=nn(t,ee.type.value,X),pe=qe("x",e[0].dataType,i.length),Me=qe("w",e[1].dataType,u.length,C),Oe=[pe,Me];o&&Oe.push(qe("b",e[2].dataType,e[2].dims,C));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"}];Dr(t,Le);let Ye=p?` + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) { + continue; + } + + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) { + let input_channel = in_channel_offset + wInChannel; + let xVal = ${pe.get("batch","xHeight","xWidth","input_channel")}; + let wVal = ${Me.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 = ${pe.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${Me.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal * wVal; + } + } + } + `;return` + ${Z.registerUniforms(Le).declareVariables(...Oe,ee)} + + ${Z.mainStart()} + ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${ee.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${p?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${p?1:2}], outputIndices[${p?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel * ${C} / uniforms.output_channels_per_group; + var in_channel_offset = group_id * uniforms.w_shape[${p?2:1}]; + + var value: ${ee.type.value} = ${ee.type.value}(0); + ${Ye} + ${a} + ${he} + ${ee.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${C}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:n?n(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:z}),getShaderSource:V}},Mu=(e,t,s,n)=>{let o=e.length>2,a=qt(s[3]),i=qt(s[2]),u=ze.size(s)/a/i,p=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/a],h=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/a],k=[s[0],s[1],s[2],s[3]/a],C=[{type:12,data:u},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];on(t,C),C.push(...yt(p,h,k));let d=(i-1)*t.strides[1]+h[1],z=B=>{let V=It("output",e[0].dataType,k.length,a),Z=_s(V.type.tensor),ee=nn(t,V.type.value,Z),X=qe("x",e[0].dataType,p.length,a),he=qe("w",e[1].dataType,h.length,a),pe=[X,he];o&&pe.push(qe("b",e[2].dataType,e[2].dims,a));let Me=o?"value += b[output_channel];":"",Oe=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Dr(t,Oe),` + ${B.registerUniforms(Oe).declareVariables(...pe,V)} + ${B.mainStart()} + ${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let width0 = uniforms.output_shape[3]; + let output_channel = global_idx % width0; + var index1 = global_idx / width0; + let width1 = uniforms.output_shape[2] / ${i}u; + let col = (index1 % width1) * ${i}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${X.type.value}, ${d}>; + var values: array<${V.type.value}, ${i}>; + let input_channel = output_channel; + // Use constant instead of uniform can give better performance for w's height/width. + for (var w_height: u32 = 0u; w_height < ${h[0]}; w_height++) { + let x_height = x_corner.x + i32(w_height); + if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) { + for (var i = 0; i < ${d}; i++) { + let x_width = x_corner.y + i; + if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { + x_vals[i] = ${X.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${X.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${h[1]}; w_width++) { + let w_val = ${he.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]; + ${Me} + ${ee} + ${V.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${a};${i};${d};${h[0]};${h[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:C}),getShaderSource:z}}}),bu,yo,vu,Mo,Qi,bo,xu,Tu,vo,Xc=g(()=>{Ot(),qc(),Qc(),ji(),qi(),an(),fo(),Kr(),bu=(e,t,s,n,o,a)=>{let i=e[0],u=e.slice(a?1:2,a?3:4),p=u.length,h=t[0],k=t.slice(2).map((d,z)=>d+(d-1)*(s[z]-1)),C=u.map((d,z)=>d+n[z]+n[z+p]).map((d,z)=>Math.floor((d-k[z]+o[z])/o[z]));return C.splice(0,0,i),C.splice(a?3:1,0,h),C},yo=[2,3,1,0],vu=(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 s=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[1]*t.group;if(s!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let o=e[0].dims.length-2;if(t.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(t.strides.length!==o)throw new Error(`strides should be ${o}D`);if(t.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},Mo=(e,t)=>{let s=e.kernelShape.slice();s.length{let t=Oi(e),s=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],o=e.dilations,a=e.group,i=e.kernel_shape,u=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:n,format:s,dilations:o,group:a,kernelShape:i,pads:u,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},bo=(e,t,s,n)=>{let o=s.format==="NHWC",a=bu(t[0].dims,t[1].dims,s.dilations,s.pads,s.strides,o);if(s.group!==1){let Oe=[t[0]];if(o){let Le=e.kernelCustomData.wT??e.compute(pr(t[1],yo),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Le),Oe.push(Le)}else Oe.push(t[1]);t.length===3&&Oe.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&o&&t[1].dims[0]===s.group&&t[1].dims[1]===1&&s.dilations[0]===1&&s.dilations[1]===1?e.compute(Mu(Oe,s,a,n),{inputs:Oe}):e.compute(yu(Oe,s,a,n),{inputs:Oe});return}let i=t.length===3,u=t[0].dims[o?1:2],p=t[0].dims[o?2:3],h=t[0].dims[o?3:1],k=t[1].dims[2],C=t[1].dims[3],d=a[o?1:2],z=a[o?2:3],B=a[o?3:1],V=o&&k===u&&C===p&&s.pads[0]===0&&s.pads[1]===0;if(V||k===1&&C===1&&s.dilations[0]===1&&s.dilations[1]===1&&s.strides[0]===1&&s.strides[1]===1&&s.pads[0]===0&&s.pads[1]===0){let Oe=a[0],Le,Ye,at,Pt=[];if(o){let bt=e.kernelCustomData.wT??e.compute(pr(t[1],yo),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];if(s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=bt),V){let ss=u*p*h;Le=t[0].reshape([1,Oe,ss]),Ye=bt.reshape([1,ss,B]),at=[1,Oe,B]}else Le=t[0].reshape([Oe,u*p,h]),Ye=bt.reshape([1,h,B]),at=[Oe,d*z,B];Pt.push(Le),Pt.push(Ye)}else Le=t[0].reshape([Oe,h,u*p]),Ye=t[1].reshape([1,B,h]),at=[Oe,B,d*z],Pt.push(Ye),Pt.push(Le);i&&Pt.push(t[2]);let Xt=at[2],Zt=Pt[0].dims[Pt[0].dims.length-1];Xt<8&&Zt<8?e.compute(zi(Pt,s,a,at,o,n),{inputs:Pt}):e.compute(wo(Pt,s,a,at,o,n),{inputs:Pt});return}let Z=!0,ee=e.kernelCustomData.wT??e.compute(pr(t[1],yo),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=ee);let X=[t[0],ee];i&&X.push(t[2]);let he=o?d*z:B,pe=o?B:d*z,Me=k*C*h;e.compute(fu(X,s,a,he,pe,Me,i,Z,n),{inputs:X})},xu=(e,t)=>{let s=t.format==="NHWC",n=[e.inputs[0].reshape(s?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let o=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),i=[1].concat(t.dilations),u=[1].concat(t.kernelShape),p=Mo({...t,pads:o,strides:a,dilations:i,kernelShape:u},n);bo(e,n,p,h=>s?[h[0],h[2],h[3]]:[h[0],h[1],h[3]])},Tu=(e,t,s)=>{let n=s.format==="NHWC"?"channelsLast":"channelsFirst",o=Mo(s,t),a=s.autoPad==="NOTSET"?s.pads:s.autoPad,i=Hi(t[0].dims,t[1].dims,s.strides,s.dilations,a,!1,n);e.compute(wu(t,o,i.outShape,[i.filterDepth,i.filterHeight,i.filterWidth],[i.padInfo.front,i.padInfo.top,i.padInfo.left],n))},vo=(e,t)=>{if(vu(e.inputs,t),e.inputs[0].dims.length===3)xu(e,t);else if(e.inputs[0].dims.length===5)Tu(e,e.inputs,t);else{let s=Mo(t,e.inputs);bo(e,e.inputs,s)}}}),Pu,Yc=g(()=>{zt(),Pe(),Ot(),Yt(),Pu=(e,t,s)=>{let n=e.length>2,o=t.outputShape,a=t.format==="NHWC",i=t.group,u=e[1].dims,p=u[2]/i,h=u[3],k=a?qt(p):1,C=a?qt(h):1,d=a?h===1?k:C:1,z=ze.size(o)/C,B=[Math.ceil(z/64),1,1];as("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${B}`);let V=["rank","rank"],Z=[t.strides[0],t.strides[1]],ee=[t.kernelShape[a?1:2],t.kernelShape[a?2:3]],X=[t.dilations[0],t.dilations[1]],he=[ee[0]+(t.dilations[0]<=1?0:(t.kernelShape[a?1:2]-1)*(t.dilations[0]-1)),ee[1]+(t.dilations[1]<=1?0:(t.kernelShape[a?2:3]-1)*(t.dilations[1]-1))],pe=[he[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),he[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Me=[{type:12,data:z},{type:12,data:Z},{type:12,data:ee},{type:12,data:X},{type:12,data:he},{type:6,data:pe},{type:12,data:p},{type:12,data:h},...yt(e[0].dims,e[1].dims)];n&&(Me.push(...yt(e[2].dims)),V.push("rank")),Me.push(...yt(o));let Oe=Le=>{let Ye=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:Z.length},{name:"filter_dims",type:"u32",length:ee.length},{name:"dilations",type:"u32",length:ee.length},{name:"effective_filter_dims",type:"u32",length:he.length},{name:"pads",type:"i32",length:pe.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],at=_s(e[0].dataType),Pt=a?1:2,Xt=a?2:3,Zt=a?3:1,bt=qe("W",e[1].dataType,e[1].dims.length,d),ss=qe("Dy",e[0].dataType,e[0].dims.length,k),St=[ss,bt];n&&St.push(qe("bias",e[2].dataType,[o[Zt]].length,C));let Ft=It("result",e[0].dataType,o.length,C),bs=()=>{let Rt="";if(k===1)Rt+=` + let w_offset = ${bt.indicesToOffset(`${bt.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)}; + let wValue = ${bt.getByOffset(`w_offset / ${d}`)}; + dotProd = dotProd + xValue * wValue;`;else if(h===1)Rt+=` + let wValue = ${bt.getByOffset(`${bt.indicesToOffset(`${bt.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)} / ${d}`)}; + dotProd = dotProd + dot(xValue, wValue);`;else for(let fs=0;fs(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 = ${Ft.type.value}(0.0); + for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${at}(dyRCorner) + ${at}(wR)) / ${at}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${at}(uniforms.Dy_shape[${Pt}]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + wR = wR + uniforms.strides[0] - 1; + 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 = (${at}(dyCCorner) + ${at}(wC)) / ${at}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${at}(uniforms.Dy_shape[${Xt}]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + wC = wC + uniforms.strides.y - 1; + 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 + ${k}) { + let xValue = ${a?ss.getByOffset(`${ss.indicesToOffset(`${ss.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${k}`):ss.get("batch","inputChannel","idyR","idyC")}; + ${bs()} + inputChannel = inputChannel + ${k}; + } + } + } + let value = dotProd${n?` + bias[d1 / ${C}]`:""}; + ${Ft.setByOffset("global_idx","value")}; + `;return` + ${Le.registerUniforms(Ye).declareVariables(...St,Ft)} + ${Le.mainStart()} + ${Le.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${Ht}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};${k}${d}${C}${h===1}`,inputDependencies:V},getRunData:()=>({dispatchGroup:{x:B[0],y:B[1],z:B[2]},outputs:[{dims:s?s(o):o,dataType:e[0].dataType}],programUniforms:Me}),getShaderSource:Oe}}}),Eu,Xi,Cu,Yi,Ji,ku,Zi,ea,Su,Jc=g(()=>{Yc(),an(),Kr(),Eu=(e,t,s,n,o,a)=>(e-1)*t+s+(n-1)*o+1-a,Xi=(e,t,s,n,o)=>{let a=Math.floor(e/2);t==="SAME_UPPER"?(s[n]=a,s[o]=e-a):t==="SAME_LOWER"&&(s[n]=e-a,s[o]=a)},Cu=(e,t,s,n,o,a,i,u,p,h)=>{let k=e.length-2,C=h.length===0;p.length{let s=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((C,d)=>C*d,1)===0){s.length=0;for(let C=2;CC+d,0)===0){let C=t[0].dims.length-2;p=new Array(C).fill(1)}let h=e.strides.slice();if(h.reduce((C,d)=>C+d,0)===0){let C=t[0].dims.length-2;h=new Array(C).fill(1)}Cu(u,s,p,e.autoPad,e.group,o,h,n,i,a);let k=Object.assign({},e);return 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p==null||p.forEach((k,C)=>{if(k==="..."){if(a)throw new Error("Only one ellipsis is allowed per input term");a=!0;let d=o-p.length+1;if(d<0)throw new Error("Ellipsis out of bounds");if(i=s.slice(u,u+d),this.hasEllipsis){if(this.ellipsisDims.length!==i.length||this.ellipsisDims.toString()!==i.toString())throw new Error("Ellipsis dimensions mismatch")}else if(t)this.hasEllipsis=!0,this.ellipsisDims=i;else throw new Error("Ellipsis must be specified in the LHS");for(let z=0;ze+"_max",Hr=(e,t,s,n)=>{let o=e.map(h=>h.length).map((h,k)=>qe(`input${k}`,t,h)),a=ze.size(n),i=It("output",t,n.length),u=[...s.symbolToInfo.keys()].filter(h=>!s.rhs.symbolToIndices.has(h)),p=h=>{let k=[],C="var prod = 1.0;",d="var sum = 0.0;",z="sum += prod;",B=[],V=[],Z=[],ee=[],X=s.symbolToInfo.size===s.rhs.symbolToIndices.size;s.symbolToInfo.forEach((pe,Me)=>{var Oe;if(s.rhs.symbolToIndices.has(Me)){let Le=(Oe=s.rhs.symbolToIndices.get(Me))==null?void 0:Oe[0];Le!==void 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var outputIndices = ${i.offsetToIndices("global_idx")}; + ${o.map((pe,Me)=>`var input${Me}Indices: ${o[Me].type.indices};`).join(` +`)} + ${he.join(` +`)}; + ${i.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:s.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let h=u.filter(C=>s.symbolToInfo.has(C)).map(C=>{var d;return{type:12,data:((d=s.symbolToInfo.get(C))==null?void 0:d.dimValue)||0}});h.push({type:12,data:a});let k=e.map((C,d)=>[...yt(C)]).reduce((C,d)=>C.concat(d),h);return k.push(...yt(n)),{outputs:[{dims:n,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:k}},getShaderSource:p}},Ru=(e,t)=>{let s=new Bu(e.inputs,t.equation),n=s.outputDims,o=e.inputs.map((a,i)=>a.dims);e.compute(Hr(o,e.inputs[0].dataType,s,n))},Nu=e=>{let t=e.equation.replace(/\s+/g,"");return Bt({equation:t})}}),ju,Po,Uu,Vu,Wu,sp=g(()=>{zt(),Ot(),Yt(),ju=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let t=e[0].dims,s=Array.from(e[1].getBigInt64Array(),Number),n=s.length{let s=e.length-t.length,n=[];for(let o=0;oe.length>t.length?Po(e,t):Po(t,e),Vu=e=>{let t=e[0].dims,s=Array.from(e[1].getBigInt64Array(),Number),n=Uu(t,s),o=e[0].dataType,a=o===9||ze.size(t)===1,i=o===9||t.length>0&&t[t.length-1]%4===0?4:1,u=a||n.length>0&&n[n.length-1]%4===0?4:1,p=Math.ceil(ze.size(n)/u),h=C=>{let d=qe("input",o,t.length,i),z=It("output",o,n.length,u),B;if(o===9){let V=(Z,ee,X="")=>` + let outputIndices${ee} = ${z.offsetToIndices(`outputOffset + ${ee}u`)}; + let offset${ee} = ${d.broadcastedIndicesToOffset(`outputIndices${ee}`,z)}; + let index${ee} = offset${ee} / 4u; + let component${ee} = offset${ee} % 4u; + ${Z}[${ee}] = ${X}(${d.getByOffset(`index${ee}`)}[component${ee}]); + `;B=` + let outputOffset = global_idx * ${u}; + var data = vec4(0); + ${V("data",0,"u32")} + ${V("data",1,"u32")} + ${V("data",2,"u32")} + ${V("data",3,"u32")} + ${z.setByOffset("global_idx","data")} + }`}else B=` + let outputIndices = ${z.offsetToIndices(`global_idx * ${u}`)}; + let inputOffset = ${d.broadcastedIndicesToOffset("outputIndices",z)}; + let data = ${z.type.value}(${d.getByOffset(`inputOffset / ${i}`)}); + ${z.setByOffset("global_idx","data")} + }`;return` + ${C.registerUniform("vec_size","u32").declareVariables(d,z)} + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${B}`},k=[{type:12,data:p},...yt(t,n)];return{name:"Expand",shaderCache:{hint:`${n.length};${i}${u}`,inputDependencies:["rank"]},getShaderSource:h,getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:k})}},Wu=e=>{ju(e.inputs),e.compute(Vu(e.inputs),{inputs:[0]})}}),Eo,Gu,rp=g(()=>{zt(),Ot(),Yt(),Ei(),Eo=e=>{let t=e[0].dataType,s=ze.size(e[0].dims),n=ze.size(e[1].dims),o=n%4===0,a=i=>{let u=qe("x",t,[1],4),p=qe("bias",t,[1],4),h=It("y",t,[1],4),k=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],C=z=>` + let bias${z}_offset: u32 = (global_idx * 4 + ${z}) % uniforms.bias_size; + let bias${z} = ${p.getByOffset(`bias${z}_offset / 4`)}[bias${z}_offset % 4];`,d=o?` + let bias = ${p.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${C(0)}${C(1)}${C(2)}${C(3)} + let bias = ${u.type.value}(bias0, bias1, bias2, bias3);`;return`${i.registerUniforms(k).declareVariables(u,p,h)} + + ${Ti(Ss(t))} + + ${i.mainStart(or)} + ${i.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${u.getByOffset("global_idx")}; + ${d} + let x_in = x + bias; + ${h.setByOffset("global_idx",_o("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${o}`,inputDependencies:["type","type"]},getShaderSource:a,getRunData:i=>({outputs:[{dims:i[0].dims,dataType:i[0].dataType}],programUniforms:[{type:12,data:Math.ceil(s/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(s/or/4)}})}},Gu=e=>{e.inputs.length<2||ze.size(e.inputs[1].dims)===0?Kl(e):e.compute(Eo(e.inputs))}}),Ku,Gn,Hu,qu,np=g(()=>{zt(),Ot(),rs(),Yt(),Ku=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},Gn=(e,t)=>{let s=e[0].dims,n=e[1].dims,o=s.length,a=ze.normalizeAxis(t.axis,o),i=s.slice(0);i.splice(a,1,...n);let u=s[a],p=e[0].dataType===9?4:1,h=Math.ceil(ze.size(i)/p),k=[{type:12,data:h},{type:6,data:u},{type:12,data:a},...yt(e[0].dims,e[1].dims,i)],C=d=>{let z=qe("data",e[0].dataType,e[0].dims.length,p),B=qe("inputIndices",e[1].dataType,e[1].dims.length),V=It("output",e[0].dataType,i.length,p),Z=X=>{let he=n.length,pe=`var indicesIndices${X} = ${B.type.indices}(0);`;for(let Me=0;Me1?`indicesIndices${X}[${Me}]`:`indicesIndices${X}`} = ${i.length>1?`outputIndices${X}[uniforms.axis + ${Me}]`:`outputIndices${X}`};`;pe+=` + var idx${X} = ${B.getByIndices(`indicesIndices${X}`)}; + if (idx${X} < 0) { + idx${X} = idx${X} + uniforms.axisDimLimit; + } + var dataIndices${X} : ${z.type.indices}; + `;for(let Me=0,Oe=0;Me1?`dataIndices${X}[${Me}]`:`dataIndices${X}`} = u32(idx${X});`,Oe+=he):(pe+=`${o>1?`dataIndices${X}[${Me}]`:`dataIndices${X}`} = ${i.length>1?`outputIndices${X}[${Oe}]`:`outputIndices${X}`};`,Oe++);return pe},ee;if(e[0].dataType===9){let X=(he,pe,Me="")=>` + let outputIndices${pe} = ${V.offsetToIndices(`outputOffset + ${pe}u`)}; + ${Z(pe)}; + let offset${pe} = ${z.indicesToOffset(`dataIndices${pe}`)}; + let index${pe} = offset${pe} / 4u; + let component${pe} = offset${pe} % 4u; + ${he}[${pe}] = ${Me}(${z.getByOffset(`index${pe}`)}[component${pe}]); + `;ee=` + let outputOffset = global_idx * ${p}; + var value = vec4(0); + ${X("value",0,"u32")} + ${X("value",1,"u32")} + ${X("value",2,"u32")} + ${X("value",3,"u32")} + ${V.setByOffset("global_idx","value")} + `}else ee=` + let outputIndices = ${V.offsetToIndices("global_idx")}; + ${Z("")}; + let value = ${z.getByIndices("dataIndices")}; + ${V.setByOffset("global_idx","value")}; + `;return` + ${d.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(z,B,V)} + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${ee} + }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:k}),getShaderSource:C}},Hu=e=>Bt({axis:e.axis}),qu=(e,t)=>{let s=e.inputs;Ku(s),e.compute(Gn(e.inputs,t))}}),Qu,Co,Xu,op=g(()=>{zt(),Ot(),Yt(),Qu=(e,t,s,n,o,a,i,u,p)=>{let h=[{type:12,data:a},{type:12,data:n},{type:12,data:o},{type:12,data:s},{type:12,data:i},{type:12,data:u},{type:12,data:p}],k=[a];h.push(...yt(t.dims,k));let C=d=>{let z=qe("indices_data",t.dataType,t.dims.length),B=It("input_slice_offsets_data",12,1,1),V=[z,B],Z=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:o.length},{name:"sizes_from_slice_dims_data",type:"u32",length:s.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return` + ${d.registerUniforms(Z).declareVariables(...V)} + ${d.mainStart()} + ${d.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]);"} + } + ${s.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"} + } + + input_slice_offsets_data[global_idx] = base_offset + u32(relative_slice_offset); + }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${o.length}_${s.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:k,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h}),getShaderSource:C},{inputs:[t],outputs:[-1]})[0]},Co=(e,t)=>{let s=e.inputs,n=s[0].dims,o=s[0].dataType,a=s[1].dims,i=a[a.length-1],u=ze.sizeToDimension(a,a.length-1),p=ze.sizeFromDimension(n,t.batchDims+i),h=ze.sizeToDimension(n,t.batchDims),k=ze.sizeFromDimension(n,t.batchDims),C=u/h,d=new Array(i),z=p;for(let pe=0;pen.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let Z=a.slice(0,-1).concat(n.slice(V)),ee=ze.size(Z),X=[{type:12,data:ee},{type:12,data:p},...yt(s[0].dims,B.dims,Z)],he=pe=>{let Me=qe("data",s[0].dataType,s[0].dims.length),Oe=qe("slice_offsets",12,B.dims.length),Le=It("output",s[0].dataType,Z.length);return` + ${pe.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(Me,Oe,Le)} + ${pe.mainStart()} + ${pe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let slice_offset = slice_offsets[global_idx / uniforms.slice_size]; + output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size]; + }`};e.compute({name:"GatherND",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:Z,dataType:o}],dispatchGroup:{x:Math.ceil(ee/64)},programUniforms:X}),getShaderSource:he},{inputs:[s[0],B]})},Xu=e=>({batchDims:e.batch_dims,cacheKey:""})}),Yu,ip,Ju,Zu,ap=g(()=>{zt(),Ot(),rs(),Yt(),Yu=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let s=ze.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,o=e[0],a=e[2],i=e.length===4?e[3]:void 0;if(a.dims.length!==o.dims.length||!o.dims.map((u,p)=>p===s?Math.ceil(u/n)===a.dims[p]:u===a.dims[p]).reduce((u,p)=>u&&p,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(i){if(i.dataType!==o.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(i.dims.length!==a.dims.length||!i.dims.map((u,p)=>u===a.dims[p]).reduce((u,p)=>u&&p,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},ip=(e,t)=>{let s=e[0].dims,n=e[1].dims,o=s.length,a=ze.normalizeAxis(t.gatherAxis,o),i=ze.normalizeAxis(t.quantizeAxis,o),u=s.slice(0);u.splice(a,1,...n);let p=ze.size(u),h=e[2].dataType,k=e[0].dataType===22,C=[{type:12,data:p},{type:12,data:i},{type:12,data:a},{type:12,data:t.blockSize},...yt(...e.map((z,B)=>z.dims),u)],d=z=>{let B=qe("data",e[0].dataType,e[0].dims.length),V=qe("inputIndices",e[1].dataType,e[1].dims.length),Z=qe("scales",e[2].dataType,e[2].dims.length),ee=e.length>3?qe("zeroPoint",e[3].dataType,e[3].dims.length):void 0,X=It("output",h,u.length),he=[B,V,Z];ee&&he.push(ee);let pe=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${z.registerUniforms(pe).declareVariables(...he,X)} + ${z.mainStart()} + let output_indices = ${X.offsetToIndices("global_idx")}; + var indices_indices = ${V.type.indices}(0); + ${n.length>1?` + for (var i: u32 = 0; i < ${n.length}; i++) { + let index = ${X.indicesGet("output_indices","uniforms.gather_axis + i")}; + ${V.indicesSet("indices_indices","i","index")}; + }`:`indices_indices = ${X.indicesGet("output_indices","uniforms.gather_axis")};`}; + var data_indices = ${B.type.indices}(0); + for (var i: u32 = 0; i < uniforms.gather_axis; i++) { + let index = ${X.indicesGet("output_indices","i")}; + ${B.indicesSet("data_indices","i","index")}; + } + var index_from_indices = ${V.getByIndices("indices_indices")}; + if (index_from_indices < 0) { + index_from_indices += ${s[a]}; + } + ${B.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")}; + for (var i = uniforms.gather_axis + 1; i < ${u.length}; i++) { + let index = ${X.indicesGet("output_indices",`i + ${n.length} - 1`)}; + ${B.indicesSet("data_indices","i","index")}; + } + let data_offset = ${B.indicesToOffset("data_indices")}; + let data_index = data_offset % 8; + // Convert 4-bit packed data to 8-bit packed data. + let packed_4bit_quantized_data = ${B.getByOffset("data_offset / 8")}; + let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f; + let quantized_data_vec = ${k?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data)); + let quantized_data = quantized_data_vec[data_index / 2]; + var scale_indices = data_indices; + let quantize_axis_index = ${Z.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size; + ${Z.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")}; + var scale = ${Z.getByIndices("scale_indices")}; + ${ee?` + let zero_point_indices = scale_indices; + let zero_point_offset = ${ee.indicesToOffset("zero_point_indices")}; + let zero_point_index = zero_point_offset % 8; + let packed_4bit_zero_points = ${ee.getByOffset("zero_point_offset / 8")}; + let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f; + let zero_point_vec = ${k?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points)); + let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"}; + let dequantized_data = ${Ss(h)}(quantized_data - zero_point) * scale; + ${X.setByOffset("global_idx","dequantized_data")}; + }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((z,B)=>B!==1).map(z=>z.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(z,B)=>"rank")},getRunData:()=>({outputs:[{dims:u,dataType:h}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:C}),getShaderSource:d}},Ju=(e,t)=>{let s=e.inputs;Yu(s,t),e.compute(ip(e.inputs,t))},Zu=e=>Bt({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),Pn,ed,td,sd,lp=g(()=>{zt(),Ot(),rs(),Yt(),Pn=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.`)},ed=(e,t)=>{let s=e[0].dims,n=e[0].dataType,o=s.length,a=e[1].dims,i=e[1].dataType,u=ze.normalizeAxis(t.axis,o),p=s[u],h=a.slice(0),k=ze.size(h),C=qe("input",n,o),d=qe("indicesInput",i,a.length),z=It("output",n,h.length),B=[{type:12,data:k},{type:6,data:p},{type:12,data:u}];return B.push(...yt(s,a,h)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:B}),getShaderSource:V=>` + ${V.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(C,d,z)} + ${V.mainStart()} + ${V.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${z.offsetToIndices("global_idx")}; + + var idx = ${d.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${C.type.indices}(outputIndices); + ${C.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${C.getByIndices("inputIndices")}; + + ${z.setByOffset("global_idx","value")}; + }`}},td=e=>Bt({axis:e.axis}),sd=(e,t)=>{let s=e.inputs;Pn(s),e.compute(ed(e.inputs,t))}}),rd,nd,od,ko,Hp=g(()=>{zt(),Ot(),Yt(),rd=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")},nd=(e,t)=>{let s=e[0].dims.slice(),n=e[1].dims.slice(),[o,a,i]=Fr.getShapeOfGemmResult(s,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),u=[o,a];if(!u)throw new Error("Can't use gemm on the given tensors");let p=16,h=Math.ceil(a/p),k=Math.ceil(o/p),C=!0,d=ze.size(u),z=[{type:12,data:C?h:d},{type:12,data:o},{type:12,data:a},{type:12,data:i},{type:1,data:t.alpha},{type:1,data:t.beta}],B=["type","type"];e.length===3&&(z.push(...yt(e[2].dims)),B.push("rank")),z.push(...yt(u));let V=ee=>{let X="";t.transA&&t.transB?X="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?X="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?X="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(X="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let he=t.alpha===1?"":"value *= uniforms.alpha;",pe=qe("a",e[0].dataType,e[0].dims),Me=qe("b",e[1].dataType,e[1].dims),Oe=pe.type.value,Le=null,Ye=[pe,Me];e.length===3&&(Le=qe("c",e[2].dataType,e[2].dims.length),Ye.push(Le));let at=It("output",e[0].dataType,u.length);Ye.push(at);let Pt=[{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` + ${ee.registerUniforms(Pt).declareVariables(...Ye)} + + ${ee.mainStart()} + ${ee.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${Oe}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${X} + } + + ${he} + ${Le!=null?`let cOffset = ${Le.broadcastedIndicesToOffset("vec2(m, n)",at)}; value += ${Oe}(uniforms.beta) * ${Le.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`},Z=ee=>{let X=qe("a",e[0].dataType,e[0].dims),he=qe("b",e[1].dataType,e[1].dims),pe=null,Me=[X,he];e.length===3&&(pe=qe("c",e[2].dataType,e[2].dims.length),Me.push(pe));let Oe=It("output",e[0].dataType,u.length);Me.push(Oe);let Le=[{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"}],Ye="",at="";t.transA&&t.transB?(at=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${X.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${he.type.value}(0); + } + `,Ye="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):t.transA&&!t.transB?(at=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${X.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${he.type.value}(0); + } + `,Ye="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!t.transA&&t.transB?(at=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${X.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${he.type.value}(0); + } + `,Ye="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!t.transA&&!t.transB&&(at=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${X.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${he.type.value}(0); + } + `,Ye="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let Pt=t.alpha===1?"":"value *= uniforms.alpha;";return` + ${ee.registerUniforms(Le).declareVariables(...Me)} + var tile_a: array, ${p}>; + var tile_b: array, ${p}>; + ${ee.mainStart([p,p,1])} + let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${p}; + let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${p}; + let num_tiles = (uniforms.K - 1) / ${p} + 1; + var k_start = 0u; + var value = ${Oe.type.value}(0); + for (var t: u32 = 0u; t < num_tiles; t++) { + ${at} + k_start = k_start + ${p}; + workgroupBarrier(); + + for (var k: u32 = 0u; k < ${p}; k++) { + ${Ye} + } + workgroupBarrier(); + } + + ${Pt} + let m = tile_row_start + local_id.y; + let n = tile_col_start + local_id.x; + ${pe!=null?`let cOffset = ${pe.broadcastedIndicesToOffset("vec2(m, n)",Oe)}; value += ${Oe.type.value}(uniforms.beta) * ${pe.getByOffset("cOffset")};`:""} + if (m < uniforms.M && n < uniforms.N) { + output[m * uniforms.N + n] = value; + } + }`};return C?{name:"GemmShared",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:h*k},programUniforms:z}),getShaderSource:Z}:{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:z}),getShaderSource:V}},od=e=>{let t=e.transA,s=e.transB,n=e.alpha,o=e.beta;return{transA:t,transB:s,alpha:n,beta:o,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},ko=(e,t)=>{rd(e.inputs),e.compute(nd(e.inputs,t))}}),Cr,Lr,ln,un,id,oa,ad,ld,ia,ud,dd,aa,cd,pd,la=g(()=>{zt(),Ot(),rs(),Yt(),[Cr,Lr,ln,un]=[0,1,2,3],id=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 ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},oa=` + fn gs_get_cubic_coeffs(x: f32) -> vec4 { + let cubic_alpha = -0.75f; + let x_abs = abs(x); + var coeffs: vec4; + coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha); + coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1); + coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1); + coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha); + return coeffs; + } +`,ad=e=>` + fn gs_bicubic_interpolate(p: mat4x4<${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; + } +`,ld=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)); + `} + } +`,ia=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); + }`:""} +`,ud=(e,t,s)=>` + fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4) -> ${t} { + var pixel = ${t}(0); + var indices = vec4(0); + indices[${Cr}] = batch; + indices[${Lr}] = channel;`+(()=>{switch(s.paddingMode){case"zeros":return` + if (r >= 0 && r < H && c >=0 && c < W) { + indices[${ln}] = u32(r); + indices[${un}] = u32(c); + } + `;case"border":return` + indices[${ln}] = u32(clamp(r, 0, H - 1)); + indices[${un}] = u32(clamp(c, 0, W - 1)); + `;case"reflection":return` + indices[${ln}] = gs_reflect(r, border[1], border[3]); + indices[${un}] = gs_reflect(c, border[0], border[2]); + `;default:throw new Error(`padding mode ${s.paddingMode} is not supported`)}})()+` + return ${e.getByIndices("indices")}; + } +`,dd=(e,t,s)=>(()=>{switch(s.mode){case"nearest":return` + let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${Cr}], indices[${Lr}], border); + `;case"bilinear":return` + let x1 = i32(floor(x)); + let y1 = i32(floor(y)); + let x2 = x1 + 1; + let y2 = y1 + 1; + + let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${Cr}], indices[${Lr}], border); + let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${Cr}], indices[${Lr}], border); + let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${Cr}], indices[${Lr}], border); + let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${Cr}], indices[${Lr}], border); + + let dx2 = ${t}(f32(x2) - x); + let dx1 = ${t}(x - f32(x1)); + let dy2 = ${t}(f32(y2) - y); + let dy1 = ${t}(y - f32(y1)); + let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22); + `;case"bicubic":return` + let x0 = i32(floor(x)) - 1; + let y0 = i32(floor(y)) - 1; + var p: mat4x4<${t}>; + for (var h = 0; h < 4; h++) { + for (var w = 0; w < 4; w++) { + p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${Cr}], indices[${Lr}], 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 ${s.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,aa=(e,t)=>{let s=qe("x",e[0].dataType,e[0].dims.length),n=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],o=qe("grid",e[1].dataType,n.length,2),a=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];t.format==="NHWC"&&(a=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[Cr,Lr,ln,un]=[0,3,1,2]);let i=It("output",e[0].dataType,a.length),u=s.type.value,p=ze.size(a),h=[{type:12,data:p},...yt(e[0].dims,n,a)],k=C=>` + ${C.registerUniform("output_size","u32").declareVariables(s,o,i)} + ${oa} + ${ad(u)} + ${ld(t)} + ${ia(t)} + ${ud(s,u,t)} + + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let H_in = i32(uniforms.x_shape[${ln}]); + let W_in = i32(uniforms.x_shape[${un}]); + + ${t.alignCorners===0?` + let x_min = -0.5; + let x_max = f32(W_in) - 0.5; + let y_min = -0.5; + let y_max = f32(H_in) - 0.5; + `:` + let x_min = 0.0; + let x_max = f32(W_in) - 1.0; + let y_min = 0.0; + let y_max = f32(H_in) - 1.0; + `}; + let border = vec4(x_min, y_min, x_max, y_max); + + let indices = ${i.offsetToIndices("global_idx")}; + var grid_indices = vec3(indices[${Cr}], indices[${ln}], indices[${un}]); + let nxy = ${o.getByIndices("grid_indices")}; + var x = gs_denormalize(f32(nxy[0]), W_in); + var y = gs_denormalize(f32(nxy[1]), H_in); + + ${dd(i,u,t)} + }`;return{name:"GridSample",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:["type","type"]},getRunData:C=>{let d=ze.size(a);return{outputs:[{dims:a,dataType:C[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:h}},getShaderSource:k}},cd=(e,t)=>{id(e.inputs),e.compute(aa(e.inputs,t))},pd=e=>Bt({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),nr,hd,md,ua,da,dn,up,_d=g(()=>{zt(),Ot(),rs(),ue(),ui(),Yt(),Kr(),nr=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,hd=(e,t)=>{let s=e[0],n=nr(e,1),o=nr(e,2),a=nr(e,3),i=nr(e,4),u=nr(e,5),p=nr(e,6),h=nr(e,7);if(s.dims.length!==3&&s.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let k=s.dims[0],C=s.dims[1],d=s.dims.length===3?s.dims[2]:t.numHeads*s.dims[4],z=C,B=0,V=0,Z=Math.floor(d/t.numHeads);if(p&&h&&ze.size(p.dims)&&ze.size(h.dims)){if(p.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(p.dims[0]!==k||p.dims[1]!==t.numHeads||p.dims[3]!==Z)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(h.dims[0]!==k||h.dims[1]!==t.numHeads||h.dims[3]!==Z)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(p.dims[2]!==h.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(h.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');B=p.dims[2],V=p.dims[2]}else if(p&&ze.size(p.dims)||h&&ze.size(h.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let ee;if(n&&ze.size(n.dims)>0){if(s.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(s.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]!==s.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');ee=2,z=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==Z)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');ee=5,z=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==Z)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');ee=0,z=n.dims[2]}}else{if(s.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(s.dims[2]!==t.numHeads||s.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');ee=3}if(a&&ze.size(a.dims)>0){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(n&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let X=B+z,he=0;if(i&&ze.size(i.dims)>0){he=8;let Le=i.dims;throw Le.length===1?Le[0]===k?he=1:Le[0]===3*k+2&&(he=3):Le.length===2&&Le[0]===k&&Le[1]===X&&(he=5),he===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let pe=!1,Me=d;if(o&&ze.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(s.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(z!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');Me=o.dims[2]}else{if(z!==o.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');Me=o.dims[1]*o.dims[3],pe=!0}}let Oe=!1;if(i&&ze.size(i.dims)>0)throw new Error("Key padding mask is not supported");if(u&&ze.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]!==k||u.dims[1]!==t.numHeads||u.dims[2]!==C||u.dims[3]!==X)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:k,sequenceLength:C,pastSequenceLength:B,kvSequenceLength:z,totalSequenceLength:X,maxSequenceLength:V,inputHiddenSize:0,hiddenSize:d,vHiddenSize:Me,headSize:Z,vHeadSize:Math.floor(Me/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:he,scale:t.scale,broadcastResPosBias:Oe,passPastInKv:pe,qkvFormat:ee}},md=e=>Bt({...e}),ua=Bt({perm:[0,2,1,3]}),da=(e,t,s,n,o,a,i)=>{let u=[n,o,a],p=ze.size(u),h=[{type:12,data:p},{type:12,data:i},{type:12,data:a}],k=C=>{let d=It("qkv_with_bias",t.dataType,u),z=qe("qkv",t.dataType,u),B=qe("bias",s.dataType,u),V=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${C.registerUniforms(V).declareVariables(z,B,d)} + ${C.mainStart()} + ${C.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(p/64)},programUniforms:h}),getShaderSource:k},{inputs:[t,s],outputs:[-1]})[0]},dn=(e,t,s,n,o,a,i,u)=>{let p=a;if(i&&ze.size(i.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return p=da(e,a,i,t,n,s*o,u),p=p.reshape([t,n,s,o]),s===1||n===1?p:e.compute(pr(p,ua.perm),{inputs:[p],outputs:[-1]})[0]}else return a.dims.length===3&&(p=a.reshape([t,n,s,o])),s===1||n===1?p:e.compute(pr(p,ua.perm),{inputs:[p],outputs:[-1]})[0]},up=(e,t)=>{let s=hd(e.inputs,t),n=e.inputs[0],o=nr(e.inputs,1),a=nr(e.inputs,2),i=nr(e.inputs,3),u=nr(e.inputs,4),p=nr(e.inputs,5),h=nr(e.inputs,6),k=nr(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((o==null?void 0:o.dims.length)===5)throw new Error("Packed KV is not implemented");let C=o&&a&&o.dims.length===4&&a.dims.length===4,d=dn(e,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,n,i,0);if(C)return Nn(e,d,o,a,u,void 0,h,k,p,s);if(!o||!a)throw new Error("key and value must be provided");let z=dn(e,s.batchSize,s.numHeads,s.kvSequenceLength,s.headSize,o,i,s.hiddenSize),B=dn(e,s.batchSize,s.numHeads,s.kvSequenceLength,s.vHeadSize,a,i,2*s.hiddenSize);Nn(e,d,z,B,u,void 0,h,k,p,s)}}),fd,ca,gd,wd,So,yd,Md,pa=g(()=>{zt(),Ot(),rs(),Yt(),fd=e=>{if(!e||e.length<1)throw new Error("too few inputs")},ca=(e,t)=>{let s=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(o=>s.push(Number(o))),n=s.length),Bt({numOutputs:n,axis:t.axis,splitSizes:s})},gd=e=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${e}u; i += 1u ) { + if (index < ${$t("uniforms.size_in_split_axis","i",e)}) { + return i; + } + } + return ${e}u; +}`,wd=e=>{let t=e.length,s=[];for(let n=0;n{let s=e[0].dims,n=ze.size(s),o=e[0].dataType,a=ze.normalizeAxis(t.axis,s.length),i=new Array(t.numOutputs),u=qe("input",o,s.length),p=new Array(t.numOutputs),h=[],k=[],C=0,d=[{type:12,data:n}];for(let B=0;B` + ${B.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",p.length).declareVariables(u,...i)} + ${gd(p.length)} + ${wd(i)} + + ${B.mainStart()} + ${B.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${u.offsetToIndices("global_idx")}; + var index = ${u.indicesGet("indices",a)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${$t("uniforms.size_in_split_axis","output_number - 1u",p.length)}; + ${u.indicesSet("indices",a,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:z,getRunData:()=>({outputs:h,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:d})}},yd=(e,t)=>{fd(e.inputs);let s=e.inputs.length===1?t:ca(e.inputs,t);e.compute(So(e.inputs,s),{inputs:[0]})},Md=e=>{let t=e.axis,s=e.splitSizes,n=e.numOutputs<0?s.length:e.numOutputs;if(n!==s.length)throw new Error("numOutputs and splitSizes lengh must be equal");return Bt({axis:t,numOutputs:n,splitSizes:s})}}),dp,cp,$o,ha,pp=g(()=>{rs(),ui(),_d(),pa(),Kr(),dp=(e,t)=>{if(t.doRotary)throw new Error("GroupQuerryAttention do_rotary attribute is not supported");if(t.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let s=e[0],n=e[1],o=e[2],a=e[3],i=e[4];if(t.localWindowSize!==-1)throw new Error("Local attention is not supported");if(t.softcap!==0)throw new Error("Softcap is not supported");if(t.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(t.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(s.dims.length!==3&&s.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let u=!1,p=s.dims[0],h=s.dims[1],k=s.dims.length===3?u?s.dims[2]/3:s.dims[2]:t.numHeads*s.dims[4],C=h,d=0,z=!n||n.dims.length===0,B=Math.floor(z?k/(t.numHeads+2*t.kvNumHeads):k/t.numHeads);z&&(k=B*t.numHeads);let V=a&&a.dims.length!==0,Z=i&&i.dims.length!==0;if(V&&a.dims.length===4&&a.dims[0]===p&&a.dims[1]!==t.kvNumHeads&&a.dims[2]===t.kvNumHeads&&a.dims[3]===B)throw new Error("BSNH pastKey/pastValue is not supported");if(V&&Z){if(a.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(i.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');d=a.dims[2]}else if(V||Z)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let ee=1;if(n&&n.dims.length>0){if(s.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(s.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(s.dims[2]%n.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');C=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(o)throw new Error('Expect "value" be none when "key" has packed kv format.');C=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');C=n.dims[2]}}else{if(s.dims.length!==3&&s.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(s.dims.length===5&&(s.dims[2]!==t.numHeads||s.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');ee=3}let X=0,he=!1,pe=t.kvNumHeads?B*t.kvNumHeads:k;if(o&&o.dims.length>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(s.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(C!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');pe=o.dims[2]}else{if(C!==o.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');pe=o.dims[1]*o.dims[3],he=!0}}let Me=e.length>4?e[5]:void 0;if(Me&&Me.dims.length!==1&&Me.dims[0]!==p)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:p,sequenceLength:h,pastSequenceLength:d,kvSequenceLength:C,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:k,vHiddenSize:pe,headSize:B,vHeadSize:Math.floor(pe/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:X,scale:t.scale,broadcastResPosBias:!1,passPastInKv:he,qkvFormat:ee}},cp=Bt({perm:[0,2,1,3]}),$o=(e,t,s)=>{let n=t,o=s.kvNumHeads;return t.dims.length===3&&s.kvSequenceLength!==0&&(n=t.reshape([s.batchSize,s.kvSequenceLength,o,s.headSize]),n=e.compute(pr(n,cp.perm),{inputs:[n],outputs:[-1]})[0]),n},ha=(e,t)=>{var Z;let s=dp(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((Z=e.inputs[1])==null?void 0:Z.dims.length)===5)throw new Error("Packed KV is not implemented");let n=e.inputs[0],o=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,a=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,i=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,u=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,p=e.inputs.length>4?e.inputs[5]:void 0,h=e.inputs.length>5?e.inputs[6]:void 0,k=s.kvNumHeads?s.kvNumHeads:s.numHeads,C=Bt({axis:2,numOutputs:3,splitSizes:[s.numHeads*s.headSize,k*s.headSize,k*s.headSize]}),[d,z,B]=!o&&!a?e.compute(So([n],C),{inputs:[n],outputs:[-1,-1,-1]}):[n,o,a],V=dn(e,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,d,void 0,0);Nn(e,V,$o(e,z,s),$o(e,B,s),void 0,void 0,i,u,void 0,s,p,h)}}),ma,_a,bd,vd,xd=g(()=>{zt(),Ot(),Kr(),Yt(),ma=(e,t,s,n,o,a,i,u)=>{let p=qt(a),h=p===1?"f32":`vec${p}f`,k=p===1?"vec2f":`mat2x${p}f`,C=o*i,d=64;C===1&&(d=256);let z=[o,i,a/p],B=[o,i,2],V=["rank","type","type"],Z=[];Z.push(...yt(z,B));let ee=X=>{let he=qe("x",t.dataType,3,p),pe=qe("scale",s.dataType,s.dims),Me=qe("bias",n.dataType,n.dims),Oe=It("output",1,3,2),Le=[he,pe,Me,Oe];return` + var workgroup_shared : array<${k}, ${d}>; + const workgroup_size = ${d}u; + ${X.declareVariables(...Le)} + ${X.mainStart(d)} + let batch = workgroup_index / uniforms.x_shape[1]; + let channel = workgroup_index % uniforms.x_shape[1]; + let hight = uniforms.x_shape[2]; + // initialize workgroup memory + var sum = ${h}(0); + var squared_sum = ${h}(0); + for (var h = local_idx; h < hight; h += workgroup_size) { + let value = ${h}(${he.get("batch","channel","h")}); + sum += value; + squared_sum += value * value; + } + workgroup_shared[local_idx] = ${k}(sum, squared_sum); + workgroupBarrier(); + + for (var currSize = workgroup_size >> 1; currSize > 0; currSize = currSize >> 1) { + if (local_idx < currSize) { + workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize]; + } + workgroupBarrier(); + } + if (local_idx == 0) { + let sum_final = ${Gs("workgroup_shared[0][0]",p)} / f32(hight * ${p}); + let squared_sum_final = ${Gs("workgroup_shared[0][1]",p)} / f32(hight * ${p}); + + let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${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:`${p};${u};${d}`,inputDependencies:V},getRunData:()=>({outputs:[{dims:B,dataType:1}],dispatchGroup:{x:C},programUniforms:Z}),getShaderSource:ee},{inputs:[t,s,n],outputs:[-1]})[0]},_a=(e,t,s)=>{let n=t[0].dims,o=n,a=2,i=n[0],u=n[1],p=ze.sizeFromDimension(n,a),h=qt(p),k=ze.size(o)/h,C=ma(e,t[0],t[1],t[2],i,p,u,s.epsilon),d=[i,u,p/h],z=[i,u],B=["type","none"],V=Z=>{let ee=qe("x",t[0].dataType,d.length,h),X=qe("scale_shift",1,z.length,2),he=It("output",t[0].dataType,d.length,h),pe=[ee,X,he];return` + ${Z.registerUniform("output_size","u32").declareVariables(...pe)} + ${Z.mainStart()} + ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let outputIndices = ${he.offsetToIndices("global_idx")}; + let batch = outputIndices[0]; + let channel = outputIndices[1]; + let scale_shift = ${X.getByIndices("vec2(batch, channel)")}; + let value = ${ee.getByOffset("global_idx")} * ${he.type.value}(scale_shift.x) + ${he.type.value}(scale_shift.y); + ${he.setByOffset("global_idx","value")}; + }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${h}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:[{type:12,data:k},...yt(d,z,d)]}),getShaderSource:V},{inputs:[t[0],C]})},bd=(e,t,s)=>{let n=t[0].dims,o=n,a=n[0],i=n[n.length-1],u=ze.sizeFromDimension(n,1)/i,p=qt(i),h=ze.size(o)/p,k=[{type:12,data:u},{type:12,data:Math.floor(i/p)}],C=["type","type"],d=!1,z=[0,n.length-1];for(let ee=0;een[z[X]])),V=ma(e,B,t[1],t[2],a,u,i,s.epsilon),Z=ee=>{let X=_s(t[0].dataType),he=p===1?"vec2f":`mat${p}x2f`,pe=Le=>{let Ye=Le===0?"x":"y",at=p===1?"f32":`vec${p}f`;switch(p){case 1:return`${X}(${at}(scale.${Ye}))`;case 2:return`vec2<${X}>(${at}(scale[0].${Ye}, scale[1].${Ye}))`;case 4:return`vec4<${X}>(${at}(scale[0].${Ye}, scale[1].${Ye}, scale[2].${Ye}, scale[3].${Ye}))`;default:throw new Error(`Not supported compoents ${p}`)}},Me=qe("input",t[0].dataType,t[0].dims,p),Oe=It("output",t[0].dataType,o,p);return` + @group(0) @binding(0) var input : array<${Me.type.storage}>; + @group(0) @binding(1) var scale_input : array<${he}>; + @group(0) @binding(2) var output : array<${Oe.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${ee.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], ${pe(0)}, ${pe(1)}); + }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:k}),getShaderSource:Z},{inputs:[t[0],V]})},vd=(e,t)=>{t.format==="NHWC"?bd(e,e.inputs,t):_a(e,e.inputs,t)}}),Td,Pd,fa,hp=g(()=>{zt(),Ot(),Yt(),Td=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Pd=(e,t,s)=>{let n=t.simplified,o=e[0].dims,a=e[1],i=!n&&e[2],u=o,p=ze.normalizeAxis(t.axis,o.length),h=ze.sizeToDimension(o,p),k=ze.sizeFromDimension(o,p),C=ze.size(a.dims),d=i?ze.size(i.dims):0;if(C!==k||i&&d!==k)throw new Error(`Size of X.shape()[axis:] == ${k}. + Size of scale and bias (if provided) must match this. + Got scale size of ${C} and bias size of ${d}`);let z=[];for(let Me=0;Me1,X=s>2,he=Me=>{let Oe=_s(e[0].dataType),Le=[qe("x",e[0].dataType,e[0].dims,B),qe("scale",a.dataType,a.dims,B)];i&&Le.push(qe("bias",i.dataType,i.dims,B)),Le.push(It("output",e[0].dataType,u,B)),ee&&Le.push(It("mean_data_output",1,z)),X&&Le.push(It("inv_std_output",1,z));let Ye=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${Me.registerUniforms(Ye).declareVariables(...Le)} + ${Me.mainStart()} + ${Me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${Ls("f32",B)}; + var mean_square_vector = ${Ls("f32",B)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${$s(Oe,B,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${Gs("mean_vector",B)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${Gs("mean_square_vector",B)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${$s(Oe,B,"x[j + offset]")}; + let f32scale = ${$s(Oe,B,"scale[j]")}; + output[j + offset] = ${Le[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale + ${i?`+ ${$s(Oe,B,"bias[j]")}`:""} + ); + } + + ${ee?"mean_data_output[global_idx] = mean":""}; + ${X?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},pe=[{dims:u,dataType:e[0].dataType}];return ee&&pe.push({dims:z,dataType:1}),X&&pe.push({dims:z,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${B};${s};${n}`,inputDependencies:V},getRunData:()=>({outputs:pe,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:Z}),getShaderSource:he}},fa=(e,t)=>{Td(e.inputs),e.compute(Pd(e.inputs,t,e.outputCount))}}),Ed,Cd,mp=g(()=>{Ot(),fo(),ji(),Ed=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.")},Cd=e=>{Ed(e.inputs);let t=Ws.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let s=t[t.length-1],n=e.inputs[0].dims[e.inputs[0].dims.length-1];if(s<8&&n<8)e.compute(zi(e.inputs,{activation:""},t));else{let o=t[t.length-2],a=ze.size(e.inputs[0].dims.slice(0,-2)),i=ze.size(e.inputs[1].dims.slice(0,-2));if(a!==1&&o===1&&i===1){let u=e.inputs[0].reshape([1,a,n]),p=e.inputs[1].reshape([1,n,s]),h=[1,a,s],k=[u,p];e.compute(wo(k,{activation:""},t,h),{inputs:k})}else e.compute(wo(e.inputs,{activation:""},t))}}}),kd,Sd,$d,Ad,Id,Od=g(()=>{zt(),Ot(),rs(),Yt(),kd=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let s=e[0],n=s.dims.length;if(s.dims[n-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let o=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,i=e[1];if(!ze.areEqual(i.dims,[t.n,o,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let u=e[2].dims;if(ze.size(u)!==t.n*o)throw new Error("scales input size error.");if(e.length===4){let p=e[3].dims,h=t.bits>4?t.n*o:t.n*Math.floor((o+1)/2);if(ze.size(p)!==h)throw new Error("zeroPoints input size error.")}},Sd=(e,t)=>{let s=e[0].dims,n=s.length,o=s[n-2],a=t.k,i=t.n,u=s.slice(0,n-2),p=ze.size(u),h=e[1].dims[2]/4,k=e[0].dataType,C=qt(t.k),d=qt(h),z=qt(i),B=u.concat([o,i]),V=o>1&&i/z%2===0?2:1,Z=ze.size(B)/z/V,ee=64,X=[],he=[p,o,a/C],pe=ze.convertShape(e[1].dims).slice();pe.splice(-1,1,h/d),X.push(...yt(he)),X.push(...yt(pe)),X.push(...yt(e[2].dims)),e.length===4&&X.push(...yt(ze.convertShape(e[3].dims)));let Me=[p,o,i/z];X.push(...yt(Me));let Oe=Le=>{let Ye=he.length,at=qe("a",e[0].dataType,Ye,C),Pt=qe("b",12,pe.length,d),Xt=qe("scales",e[2].dataType,e[2].dims.length),Zt=[at,Pt,Xt],bt=e.length===4?qe("zero_points",12,e[3].dims.length):void 0;bt&&Zt.push(bt);let ss=Me.length,St=It("output",e[0].dataType,ss,z),Ft=_s(e[0].dataType),bs=(()=>{switch(C){case 1:return`array<${Ft}, 8>`;case 2:return`mat4x2<${Ft}>`;case 4:return`mat2x4<${Ft}>`;default:throw new Error(`${C}-component is not supported.`)}})(),Ht=()=>{let it=` + // reuse a data + var input_offset = ${at.indicesToOffset(`${at.type.indices}(batch, row, word_offset)`)}; + var a_data: ${bs}; + for (var j: u32 = 0; j < ${8/C}; j++) { + a_data[j] = ${at.getByOffset("input_offset")}; + input_offset++; + } + `;for(let Et=0;Et> 4) & b_mask); + b_quantized_values = ${bs}(${Array.from({length:4},(ps,Ns)=>`${Ft}(b_value_lower[${Ns}]), ${Ft}(b_value_upper[${Ns}])`).join(", ")}); + b_dequantized_values = ${C===1?`${bs}(${Array.from({length:8},(ps,Ns)=>`(b_quantized_values[${Ns}] - ${bt?`zero_point${Et}`:"zero_point"}) * scale${Et}`).join(", ")});`:`(b_quantized_values - ${bs}(${Array(8).fill(`${bt?`zero_point${Et}`:"zero_point"}`).join(",")})) * scale${Et};`}; + workgroup_shared[local_id.x * ${V} + ${Math.floor(Et/z)}]${z>1?`[${Et%z}]`:""} += ${Array.from({length:8/C},(ps,Ns)=>`${C===1?`a_data[${Ns}] * b_dequantized_values[${Ns}]`:`dot(a_data[${Ns}], b_dequantized_values[${Ns}])`}`).join(" + ")}; + `;return it},Rt=()=>{let it=` + var col_index = col * ${z}; + ${bt?` + 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 = ${Ft}(8);`} + `;for(let Et=0;Et> 0x1u); + zero_point_word_index = zero_point_byte_count >> 0x2u; + zero_point_byte_offset = zero_point_byte_count & 0x3u; + zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + zero_point_word = ${bt.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point${Et} = ${Ft}((zero_point_word) & 0xFu);`:""} + col_index += 1;`;return it},fs=()=>{let it=`col_index = col * ${z};`;for(let Et=0;Et; + var b_value_upper: vec4; + var b_quantized_values: ${bs}; + var b_dequantized_values: ${bs};`,it};return` + var workgroup_shared: array<${St.type.value}, ${V*ee}>; + ${Le.declareVariables(...Zt,St)} + ${Le.mainStart([ee,1,1])} + let output_indices = ${St.offsetToIndices(`(global_idx / ${ee}) * ${V}`)}; + 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 += ${ee}) { + //process one block + var word_offset: u32 = block * ${t.blockSize/C}; + ${Rt()} + for (var word: u32 = 0; word < ${h}; word += ${d}) { + ${fs()} + for (var i: u32 = 0; i < ${d}; i++) { + ${Ht()} + word_offset += ${8/C}; + } + } + } + workgroupBarrier(); + + if (local_id.x < ${V}) { + var output_value: ${St.type.value} = ${St.type.value}(0); + var workgroup_shared_offset: u32 = local_id.x; + for (var b: u32 = 0u; b < ${ee}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${V}; + } + ${St.setByIndices(`${St.type.indices}(batch, row, col + local_id.x)`,"output_value")}; + } + }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${C};${d};${z};${V};${ee}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:B,dataType:k}],dispatchGroup:{x:Z},programUniforms:X}),getShaderSource:Oe}},$d=(e,t)=>{let s=e[0].dims,n=s.length,o=s[n-2],a=t.k,i=t.n,u=s.slice(0,n-2),p=ze.size(u),h=e[1].dims[2]/4,k=e[0].dataType,C=qt(t.k),d=qt(h),z=u.concat([o,i]),B=128,V=i%8===0?8:i%4===0?4:1,Z=B/V,ee=Z*d*8,X=ee/C,he=ee/t.blockSize,pe=ze.size(z)/V,Me=[],Oe=[p,o,a/C],Le=ze.convertShape(e[1].dims).slice();Le.splice(-1,1,h/d),Me.push(...yt(Oe)),Me.push(...yt(Le)),Me.push(...yt(e[2].dims)),e.length===4&&Me.push(...yt(ze.convertShape(e[3].dims)));let Ye=[p,o,i];Me.push(...yt(Ye));let at=Pt=>{let Xt=Oe.length,Zt=qe("a",e[0].dataType,Xt,C),bt=qe("b",12,Le.length,d),ss=qe("scales",e[2].dataType,e[2].dims.length),St=[Zt,bt,ss],Ft=e.length===4?qe("zero_points",12,e[3].dims.length):void 0;Ft&&St.push(Ft);let bs=Ye.length,Ht=It("output",e[0].dataType,bs),Rt=_s(e[0].dataType),fs=()=>{switch(C){case 1:return` + let a_data0 = vec4<${Rt}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]); + let a_data1 = vec4<${Rt}>(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<${Rt}>(sub_a[word_offset], sub_a[word_offset + 1]); + let a_data1 = vec4<${Rt}>(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(`${C}-component is not supported.`)}};return` + var sub_a: array<${Zt.type.value}, ${X}>; + var inter_results: array, ${V}>; + ${Pt.declareVariables(...St,Ht)} + ${Pt.mainStart([Z,V,1])} + let output_indices = ${Ht.offsetToIndices(`workgroup_index * ${V}`)}; + 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) / ${he} + 1; + + // Loop over shared dimension. + for (var tile: u32 = 0; tile < num_tiles; tile += 1) { + let a_col_start = tile * ${X}; + // load one tile A data into shared memory. + for (var a_offset = local_idx; a_offset < ${X}; a_offset += ${B}) + { + let a_col = a_col_start + a_offset; + if (a_col < uniforms.a_shape[2]) + { + sub_a[a_offset] = ${Zt.getByIndices(`${Zt.type.indices}(batch, row, a_col)`)}; + } else { + sub_a[a_offset] = ${Zt.type.value}(0); + } + } + workgroupBarrier(); + + // each thread process one block + let b_row = col + local_id.y; + let block = tile * ${he} + local_id.x; + ${Ft?` + 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 = ${Ft.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point = ${Rt}((zero_point_word) & 0xFu);`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${Rt}(8);`} + let scale = ${ss.getByOffset("b_row * n_blocks_per_col + block")}; + let b_data = ${bt.getByIndices(`${bt.type.indices}(b_row, block, 0)`)}; + var word_offset = local_id.x * ${t.blockSize/C}; + for (var i: u32 = 0; i < ${d}; i++) { + ${fs()} + let b_value = ${d===1?"b_data":"b_data[i]"}; + let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu); + let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu); + let b_quantized_values = mat2x4<${Rt}>(${Array.from({length:4},(it,Et)=>`${Rt}(b_value_lower[${Et}]), ${Rt}(b_value_upper[${Et}])`).join(", ")}); + let b_dequantized_values = (b_quantized_values - mat2x4<${Rt}>(${Array(8).fill("zero_point").join(",")})) * scale; + inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(it,Et)=>`${`dot(a_data${Et}, b_dequantized_values[${Et}])`}`).join(" + ")}; + word_offset += ${8/C}; + } + workgroupBarrier(); + } + + if (local_idx < ${V}) { + var output_value: ${Ht.type.value} = ${Ht.type.value}(0); + for (var b = 0u; b < ${Z}; b++) { + output_value += inter_results[local_idx][b]; + } + if (col + local_idx < uniforms.output_shape[2]) + { + ${Ht.setByIndices(`${Ht.type.indices}(batch, row, col + local_idx)`,"output_value")} + } + } + }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${t.blockSize};${C};${d};${Z};${V}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:z,dataType:k}],dispatchGroup:{x:pe},programUniforms:Me}),getShaderSource:at}},Ad=(e,t)=>{kd(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute($d(e.inputs,t)):e.compute(Sd(e.inputs,t))},Id=e=>Bt(e)}),Fd,Dd,ga,Ld,zd,ws,_p,fp,gp,Bd=g(()=>{zt(),Ot(),Yt(),Fd=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].")}},Dd=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${$t("uniforms.pads",o,s)}; + if (k < 0) { + break; + } + if (k >= i32(${$t("uniforms.x_shape",o,t)})) { + break; + } + offset += k * i32(${$t("uniforms.x_strides",o,t)}); + `;return` + value = ${e.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + } + `},ga=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${$t("uniforms.pads",o,s)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${$t("uniforms.x_shape",o,t)}) - 1); + k = k % _2n_1; + if(k >= i32(${$t("uniforms.x_shape",o,t)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${$t("uniforms.x_strides",o,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},Ld=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${$t("uniforms.pads",o,s)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${$t("uniforms.x_shape",o,t)})) { + k = i32(${$t("uniforms.x_shape",o,t)}) - 1; + } + offset += k * i32(${$t("uniforms.x_strides",o,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},zd=(e,t,s)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${$t("uniforms.pads",o,s)}; + if (k < 0) { + k += i32(${$t("uniforms.x_shape",o,t)}]); + } + if (k >= i32(${$t("uniforms.x_shape",o,t)})) { + k -= i32(${$t("uniforms.x_shape",o,t)}); + } + offset += k * i32(${$t("uniforms.x_strides",o,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},ws=(e,t,s)=>{switch(s.mode){case 0:return Dd(e,t,s.pads.length);case 1:return ga(e,t,s.pads.length);case 2:return Ld(e,t,s.pads.length);case 3:return zd(e,t,s.pads.length);default:throw new Error("Invalid mode")}},_p=(e,t)=>{let s=ze.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,o=ze.size(s),a=[{type:12,data:o},{type:6,data:t.pads}],i=e.length>=3&&e[2].data;t.mode===0&&a.push({type:i?e[2].dataType:1,data:t.value}),a.push(...yt(e[0].dims,s));let u=["rank"],p=h=>{let k=It("output",e[0].dataType,s.length),C=qe("x",e[0].dataType,n.length),d=C.type.value,z=ws(k,n.length,t),B=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&B.push({name:"constant_value",type:i?d:"f32"}),` + ${h.registerUniforms(B).declareVariables(C,k)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${k.offsetToIndices("global_idx")}; + + var value = ${d}(0); + ${z} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${i}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(ze.size(s)/64)},programUniforms:a}),getShaderSource:p}},fp=(e,t)=>{if(e.length>1){let s=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,o=e[0].dims.length,a=new Int32Array(2*o).fill(0);if(e.length>=4){let u=e[3].getBigInt64Array();for(let p=0;pa[Number(p)]=Number(u));let i=[];return a.forEach(u=>i.push(u)),{mode:t.mode,value:n,pads:i}}else return t},gp=(e,t)=>{Fd(e.inputs);let s=fp(e.inputs,t);e.compute(_p(e.inputs,s),{inputs:[0]})}}),Kn,wa,ya,Ma,Ao,ba,wp,va,xa,Ta,yp,Rd,Nd,jd,Pa,Ud,Vd,Wd,Gd,Mp=g(()=>{We(),zt(),Ot(),Yt(),Kn=e=>{if(O.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},wa=(e,t,s)=>{let n=t.format==="NHWC",o=e.dims.slice();n&&o.splice(1,0,o.pop());let a=Object.hasOwnProperty.call(t,"dilations"),i=t.kernelShape.slice(),u=t.strides.slice(),p=a?t.dilations.slice():[],h=t.pads.slice();Js.adjustPoolAttributes(s,o,i,u,p,h);let k=Js.computePoolOutputShape(s,o,u,p,i,h,t.autoPad),C=Object.assign({},t);a?Object.assign(C,{kernelShape:i,strides:u,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(C,{kernelShape:i,strides:u,pads:h,cacheKey:t.cacheKey});let d=k.slice();return d.push(d.splice(1,1)[0]),[C,n?d:k]},ya=(e,t)=>{let s=t.format==="NHWC",n=ze.size(e),o=ze.size(t.kernelShape),a=[{type:12,data:n},{type:12,data:o}],i=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let u=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],k=t.pads[t.pads.length-1],C=!!(h+k);a.push({type:12,data:u},{type:12,data:p},{type:12,data:h},{type:12,data:k}),i.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let d=!1;if(t.kernelShape.length===2){let z=t.kernelShape[t.kernelShape.length-2],B=t.strides[t.strides.length-2],V=t.pads[t.pads.length/2-2],Z=t.pads[t.pads.length-2];d=!!(V+Z),a.push({type:12,data:z},{type:12,data:B},{type:12,data:V},{type:12,data:Z}),i.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,i,!0,C,d]}else{if(s)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let u=ze.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 p=t.pads.reduce((h,k)=>h+k);return[a,i,!!p,!1,!1]}},Ma=(e,t,s,n,o,a,i,u,p,h,k,C)=>{let d=o.format==="NHWC",z=t.type.value,B=It("output",t.type.tensor,n);if(o.kernelShape.length<=2){let V="",Z="",ee="",X=s-(d?2:1);if(k?V=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${X}] = indices[${X}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${X}] < 0 || xIndices[${X}] + >= uniforms.x_shape[${X}]) { + pad++; + continue; + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`:V=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${X}] = indices[${X}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`,o.kernelShape.length===2){let he=s-(d?3:2);C?Z=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${he}] = indices[${he}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${he}] < 0 || xIndices[${he}] >= uniforms.x_shape[${he}]) { + pad += i32(uniforms.kw); + continue; + } + `:Z=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${he}] = indices[${he}] * uniforms.sh - uniforms.phStart + j; + `,ee=` + } + `}return` + ${e.registerUniforms(p).declareVariables(t,B)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${B.offsetToIndices("global_idx")}; + var xIndices = ${B.offsetToIndices("global_idx")}; + + var value = ${z}(${u}); + var pad = 0; + ${Z} + ${V} + ${ee} + ${i} + + output[global_idx] = value; + }`}else{if(d)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let V=o.kernelShape.length,Z=o.pads.length,ee="";return h?ee=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + }`:ee=` + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${a} + `,` + ${e.registerUniforms(p).declareVariables(t,B)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${B.offsetToIndices("global_idx")}; + var xIndices = ${B.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${z}(${u}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${V-1}u; j++) { + offsets[j] = offset / ${$t("uniforms.kernelStrides","j",V)}; + offset -= offsets[j] * ${$t("uniforms.kernelStrides","j",V)}; + } + offsets[${V-1}] = offset; + + isPad = false; + for (var j = ${s-V}u; j < ${s}u; j++) { + xIndices[j] = indices[j] * ${$t("uniforms.strides",`j - ${s-V}u`,V)} + + offsets[j - ${s-V}u] - ${$t("uniforms.pads","j - 2u",Z)}; + ${ee} + } + ${i} + + output[global_idx] = value; + }`}},Ao=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,ba=e=>`${Ao(e)};${e.countIncludePad}`,wp=e=>`${Ao(e)};${e.storageOrder};${e.dilations}`,va=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}),xa=(e,t,s,n)=>{let[o,a]=wa(t,n,s),i=qe("x",t.dataType,t.dims.length),u=i.type.value,p="value += x_val;",h="";o.countIncludePad?h+=`value /= ${u}(uniforms.kernelSize);`:h+=`value /= ${u}(i32(uniforms.kernelSize) - pad);`;let[k,C,d,z,B]=ya(a,o);k.push(...yt(t.dims,a));let V=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${d};${z};${B}`,inputDependencies:V},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(ze.size(a)/64)},programUniforms:k}),getShaderSource:Z=>Ma(Z,i,t.dims.length,a.length,o,p,h,0,C,d,z,B)}},Ta=e=>{let t=e.count_include_pad!==0,s=va(e);if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...s,cacheKey:""};return{...n,cacheKey:ba(n)}},yp=(e,t)=>{Kn(e.inputs),e.compute(xa("AveragePool",e.inputs[0],!1,t))},Rd={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Nd=e=>{let t=e.format;return{format:t,...Rd,cacheKey:t}},jd=(e,t)=>{Kn(e.inputs),e.compute(xa("GlobalAveragePool",e.inputs[0],!0,t))},Pa=(e,t,s,n)=>{let[o,a]=wa(t,n,s),i=` + value = max(x_val, value); + `,u="",p=qe("x",t.dataType,t.dims.length),h=["rank"],[k,C,d,z,B]=ya(a,o);return k.push(...yt(t.dims,a)),{name:e,shaderCache:{hint:`${n.cacheKey};${d};${z};${B}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(ze.size(a)/64)},programUniforms:k}),getShaderSource:V=>Ma(V,p,t.dims.length,a.length,o,i,u,t.dataType===10?-65504:-1e5,C,d,z,B)}},Ud=(e,t)=>{Kn(e.inputs),e.compute(Pa("MaxPool",e.inputs[0],!1,t))},Vd=e=>{let t=e.storage_order,s=e.dilations,n=va(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let o={storageOrder:t,dilations:s,...n,cacheKey:""};return{...o,cacheKey:wp(o)}},Wd=e=>{let t=e.format;return{format:t,...Rd,cacheKey:t}},Gd=(e,t)=>{Kn(e.inputs),e.compute(Pa("GlobalMaxPool",e.inputs[0],!0,t))}}),Kd,Hd,qd,Qd,qp=g(()=>{zt(),Ot(),rs(),Yt(),Kd=(e,t)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((s,n)=>s===e[2].dims[n]).reduce((s,n)=>s&&n,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(t.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((o,a)=>a===t.axis||o===e[0].dims[a]).reduce((o,a)=>o&&a,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let s=e[0].dims[t.axis],n=e[1].dims[t.axis];if(t.blockSizeMath.ceil(s/(n-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},Hd=(e,t)=>{let s=ze.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,o=n===3,a=e[0].dims,i=e[1].dataType,u=ze.size(a),p=n===3||n===2,h=p?[Math.ceil(ze.size(e[0].dims)/4)]:e[0].dims,k=e[1].dims,C=e.length>2?e[2]:void 0,d=C?p?[Math.ceil(ze.size(C.dims)/4)]:C.dims:void 0,z=k.length===0||k.length===1&&k[0]===1,B=z===!1&&k.length===1,V=qt(u),Z=z&&(!p||V===4),ee=Z?V:1,X=Z&&!p?V:1,he=qe("input",p?12:n,h.length,X),pe=qe("scale",i,k.length),Me=C?qe("zero_point",p?12:n,d.length):void 0,Oe=It("output",i,a.length,ee),Le=[he,pe];Me&&Le.push(Me);let Ye=[h,k];C&&Ye.push(d);let at=[{type:12,data:u/ee},{type:12,data:s},{type:12,data:t.blockSize},...yt(...Ye,a)],Pt=Xt=>{let Zt=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${Xt.registerUniforms(Zt).declareVariables(...Le,Oe)} + ${Xt.mainStart()} + ${Xt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${Oe.offsetToIndices("global_idx")}; + + // Set input x + ${p?` + let input = ${he.getByOffset("global_idx / 4")}; + let x_vec = ${o?"unpack4xI8(input)":"unpack4xU8(input)"}; + let x_value = ${ee===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${he.getByOffset("global_idx")};`}; + + // Set scale input + ${z?`let scale_value= ${pe.getByOffset("0")}`:B?` + let scale_index = ${Oe.indicesGet("output_indices","uniforms.axis")}; + let scale_value= ${pe.getByOffset("scale_index")};`:` + var scale_indices: ${pe.type.indices} = output_indices; + let index = ${pe.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; + ${pe.indicesSet("scale_indices","uniforms.axis","index")}; + let scale_value= ${pe.getByIndices("scale_indices")};`}; + + // Set zero-point input + ${Me?z?p?` + let zero_point_input = ${Me.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 = ${Me.getByOffset("0")}`:B?p?` + let zero_point_index = ${Oe.indicesGet("output_indices","uniforms.axis")}; + let zero_point_input = ${Me.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 = ${Oe.indicesGet("output_indices","uniforms.axis")}; + let zero_point_value = ${Me.getByOffset("zero_point_index")};`:p?` + let zero_point_offset = ${pe.indicesToOffset("scale_indices")}; + let zero_point_input = ${Me.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 = ${Me.getByIndices("scale_indices")};`:`let zero_point_value = ${p?o?"i32":"u32":he.type.value}(0);`}; + // Compute and write output + ${Oe.setByOffset("global_idx",`${Oe.type.value}(x_value - zero_point_value) * scale_value`)}; + }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:Me?["rank","rank","rank"]:["rank","rank"]},getShaderSource:Pt,getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(u/ee/64),y:1,z:1},programUniforms:at})}},qd=(e,t)=>{Kd(e.inputs,t),e.compute(Hd(e.inputs,t))},Qd=e=>Bt({axis:e.axis,blockSize:e.blockSize})}),Xd,Yd,Jd,bp=g(()=>{We(),zt(),Yt(),Xd=(e,t,s)=>{let n=e===t,o=et&&s>0;if(n||o||a)throw new Error("Range these inputs' contents are invalid.")},Yd=(e,t,s,n)=>{let o=Math.abs(Math.ceil((t-e)/s)),a=[o],i=o,u=[{type:12,data:i},{type:n,data:e},{type:n,data:s},...yt(a)],p=h=>{let k=It("output",n,a.length),C=k.type.value,d=[{name:"outputSize",type:"u32"},{name:"start",type:C},{name:"delta",type:C}];return` + ${h.registerUniforms(d).declareVariables(k)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${C}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:u})}},Jd=e=>{let t=0,s=0,n=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],s=e.inputs[1].getInt32Array()[0],n=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],s=e.inputs[1].getFloat32Array()[0],n=e.inputs[2].getFloat32Array()[0]),O.webgpu.validateInputContent&&Xd(t,s,n),e.compute(Yd(t,s,n,e.inputs[0].dataType),{inputs:[]})}}),Zd,ec,vp,Ea,xp=g(()=>{zt(),Ot(),rs(),Yt(),Zd=(e,t,s,n)=>{if(e!=="none"&&n!=="i32"&&n!=="u32"&&n!=="f32")throw new Error(`Input ${n} is not supported with reduction ${e}.`);let o=`{ + var oldValue = 0; + loop { + let newValueF32 =`,a=`; + let newValue = bitcast(newValueF32); + let res = atomicCompareExchangeWeak(&${t}, oldValue, newValue); + if res.exchanged { + break; + } + oldValue = res.old_value; + } + }`;switch(e){case"none":return`${t}=${s};`;case"add":return n==="i32"||n==="u32"?`atomicAdd(&${t}, bitcast<${n}>(${s}));`:` + ${o}bitcast<${n}>(oldValue) + (${s})${a}`;case"max":return n==="i32"||n==="u32"?`atomicMax(&${t}, bitcast<${n}>(${s}));`:` + ${o}max(bitcast(oldValue), (${s}))${a}`;case"min":return n==="i32"||n==="u32"?`atomicMin(&${t}, bitcast<${n}>(${s}));`:`${o}min(bitcast<${n}>(oldValue), (${s}))${a}`;case"mul":return`${o}(bitcast<${n}>(oldValue) * (${s}))${a}`;default:throw new Error(`Reduction ${e} is not supported.`)}},ec=(e,t)=>{let s=e[0].dims,n=e[1].dims,o=s,a=1,i=Math.ceil(ze.size(n)/a),u=n[n.length-1],p=ze.sizeFromDimension(s,u),h=[{type:12,data:i},{type:12,data:u},{type:12,data:p},...yt(e[1].dims,e[2].dims,o)],k=C=>{let d=qe("indices",e[1].dataType,e[1].dims.length),z=qe("updates",e[2].dataType,e[2].dims.length,a),B=t.reduction!=="none"&&t.reduction!==""?La("output",e[0].dataType,o.length):It("output",e[0].dataType,o.length,a);return` + ${C.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(d,z,B)} + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var data_offset = 0u; + let indices_start = uniforms.last_index_dimension * global_idx; + let indices_end = indices_start + uniforms.last_index_dimension; + for (var i = indices_start; i < indices_end; i++) { + var index = i32(indices[i].x); + ${e[0].dims.length===1?` + let element_count_dim = uniforms.output_strides; + let dim_value = uniforms.output_shape;`:` + let element_count_dim = uniforms.output_strides[i - indices_start]; + let dim_value = uniforms.output_shape[i - indices_start + uniforms.last_index_dimension];`} + if (index >= 0) { + if (index >= i32(dim_value)) { + index = i32(dim_value - 1); + } + } else { + if (index < -i32(dim_value)) { + index = 0; + } else { + index += i32(dim_value); + } + } + data_offset += u32((u32(index) * element_count_dim)); + } + + for (var i = 0u; i < uniforms.num_updates_elements; i++) { + let value = updates[uniforms.num_updates_elements * global_idx + i]; + ${Zd(t.reduction,"output[data_offset + i]","value",B.type.value)} + } + + }`};return{name:"ScatterND",shaderCache:{hint:`${t.cacheKey}_${t.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:h}),getShaderSource:k}},vp=e=>Bt({reduction:e.reduction}),Ea=(e,t)=>{e.compute(ec(e.inputs,t),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),tc,sc,rc,nc,oc,ic,ac,lc,uc,dc,cc,Ca,pc,hc,mc,_c,fc,gc,wc,Tp=g(()=>{zt(),Ot(),rs(),Yt(),tc=(e,t)=>{if(e.every(s=>s>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")}},sc=(e,t,s)=>{t.every(o=>o>=0&&o{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(s).fill(1);return t.forEach((o,a)=>n[o]=e[a]),n},rc=(e,t,s,n,o,a)=>{let[i,u,p]=s>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(i>0&&e.length>i&&e[i].dims.length>0)e[i].getFloat32Array().forEach(k=>a.push(k));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(u>0&&e.length>u&&e[u].dims.length===1&&e[u].dims[0]>0){if(e[u].getFloat32Array().forEach(k=>n.push(k)),n.length!==0&&n.length!==h&&s>=18&&n.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");tc(n,t),t.axes.length>0&&sc(n,t.axes,h).forEach((k,C)=>n[C]=k)}if(p>0&&e.length>p&&e[p].dims.length===1&&e[p].dims[0]>0&&(e[p].getBigInt64Array().forEach(k=>o.push(Number(k))),o.length!==0&&o.length!==h&&s>=18&&o.length!==t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(n.length!==0&&n.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(o.length!==0&&o.length!==t.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof o<"u"&&n.length>0&&o.length>h)throw new Error("Resize requires only of scales or sizes to be specified")},nc=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, + lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) { + return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5; + } else { + return 0.0; + }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) { + return 0.0; + } else { + // The whole part and the fractional part are calculated separately due to inaccuracy of floating + // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an + // offset-by-one error later in floor(). + let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1)); + let fract = + ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1); + return whole + fract; + }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { + return ${t}(roiStart) * ${t}(lengthOriginal - 1) + + (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) / + ${t}(lengthResized - 1); + } else { + return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1); + }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized); + const adjustment = ${t}(lengthResized) / outputWidth; + const center = ${t}(lengthOriginal) / 2; + const offset = center * (1 - adjustment); + return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",oc=(e,t,s)=>`fn getNearestPixelFromOriginal(xOriginal: ${s}, isDownSample: bool) -> ${s} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(t<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",ic=(e,t,s)=>{let n=new Array(s).fill(0).concat(new Array(s).fill(1)),o=e.length===0?n:e.slice();return t.length>0?(t.forEach((a,i)=>{n[a]=o[i],n[i+s]=o[t.length+i]}),n):o},ac=(e,t,s,n)=>{let o=[];if(s.length>0)if(n.length>0){if(e.forEach(a=>o.push(a)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((a,i)=>o[a]=s[i])}else s.forEach(a=>o.push(a));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");o=e.map((a,i)=>Math.round(a*t[i]))}return o},lc=(e,t,s)=>{let n=(()=>{switch(s.keepAspectRatioPolicy){case"not_larger":return s.axes.length>0?Math.min(...s.axes.map(a=>t[a]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return s.axes.length>0?Math.max(...s.axes.map(a=>t[a]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${s.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let o=e.slice();return s.axes.length>0?(s.axes.forEach(a=>t[a]=n),s.axes.forEach(a=>o[a]=Math.round(e[a]*t[a]))):(t.fill(n,0,t.length),o.forEach((a,i)=>o[i]=Math.round(a*t[i]))),o},uc=(e,t,s,n,o)=>` + fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${s.length}> { + var original_indices: array<${e.type.value}, ${s.length}>; + for (var i:u32 = 0; i < ${s.length}; i++) { + var output_index = ${e.indicesGet("output_indices","i")}; + var scale = ${$t("uniforms.scales","i",n)}; + var roi_low = ${$t("uniforms.roi","i",o)}; + var roi_hi = ${$t("uniforms.roi",`i + ${t.length}`,o)}; + if (scale == 1.0) { + original_indices[i] = ${e.type.value}(output_index); + } else { + var input_shape_i = ${$t("uniforms.input_shape","i",t.length)}; + var output_shape_i = ${$t("uniforms.output_shape","i",s.length)}; + original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + } + } + return original_indices; + }`,dc=(e,t,s,n,o,a,i)=>` + fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + for (var i:u32 = 0; i < ${n.length}; i++) { + var output_index = ${t.indicesGet("output_indices","i")}; + var input_index: u32; + var scale = ${$t("uniforms.scales","i",o)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${$t("uniforms.roi","i",a)}; + var roi_hi = ${$t("uniforms.roi",`i + ${s.length}`,a)}; + var input_shape_i = ${$t("uniforms.input_shape","i",s.length)}; + var output_shape_i = ${$t("uniforms.output_shape","i",n.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${i} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) { + if (original_idx < 0) { + input_index = 0; + } else if (original_idx > ${t.type.value}(input_shape_i - 1)) { + input_index = input_shape_i - 1; + } else { + input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1)); + } + } else { + input_index = u32(original_idx); + } + } + ${e.indicesSet("input_indices","i"," input_index")} + } + return input_indices; + }`,cc=(e,t)=>` + fn checkInputIndices(input_indices: ${e.type.indices}) -> bool { + for (var i:u32 = 0; i < ${t.length}; i++) { + var input_index = ${e.indicesGet("input_indices","i")}; + if (input_index < 0 || input_index >= ${$t("uniforms.input_shape","i",t.length)}) { + return false; + } + } + return true; + }`,Ca=(e,t,s,n)=>e.rank>n?` + ${e.indicesSet("input_indices",t,"channel")}; + ${e.indicesSet("input_indices",s,"batch")}; +`:"",pc=(e,t,s,n,o)=>{let[a,i,u,p]=s.length===2?[-1,0,1,-1]:[0,2,3,1],h=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${h} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",i,`max(0, min(row, ${s[i]} - 1))`)}; + ${e.indicesSet("input_indices",u,`max(0, min(col, ${s[u]} - 1))`)}; + ${Ca(e,p,a,2)} + return ${e.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${h} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${h} = originalIndices[${i}]; + var col:${h} = originalIndices[${u}]; + ${n?`if (row < 0 || row > (${s[i]} - 1) || col < 0 || col > (${s[u]} - 1)) { + return ${o}; + }`:""}; + row = max(0, min(row, ${s[i]} - 1)); + col = max(0, min(col, ${s[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 = ${s.length>2?`u32(originalIndices[${p}])`:"0"}; + var batch: u32 = ${s.length>2?`u32(originalIndices[${a}])`:"0"}; + var x11: ${h} = getInputValue(batch, channel, row1, col1); + var x12: ${h} = getInputValue(batch, channel, row1, col2); + var x21: ${h} = getInputValue(batch, channel, row2, col1); + var x22: ${h} = getInputValue(batch, channel, row2, col2); + var dx1: ${h} = abs(row - ${h}(row1)); + var dx2: ${h} = abs(${h}(row2) - row); + var dy1: ${h} = abs(col - ${h}(col1)); + var dy2: ${h} = abs(${h}(col2) - col); + if (row1 == row2) { + dx1 = 0.5; + dx2 = 0.5; + } + if (col1 == col2) { + dy1 = 0.5; + dy2 = 0.5; + } + return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1); + }`},hc=(e,t,s,n,o,a,i,u,p,h)=>{let k=s.length===2,[C,d]=k?[0,1]:[2,3],z=e.type.value,B=V=>{let Z=V===C?"row":"col";return` + fn ${Z}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${z} { + var output_index = ${t.indicesGet("output_indices",V)}; + var originalIdx: ${z} = getOriginalCoordinateFromResizedCoordinate(output_index, ${o[V]}, + ${n[V]}, ${s[V]}, ${a[V]}, ${a[V]} + ${s.length}); + var fractOriginalIdx: ${z} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${u} && (originalIdx < 0 || originalIdx > (${s[V]} - 1))) { + return ${p}; + } + var data: array<${z}, 4> = array<${z}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${Z}: ${z} = originalIdx + ${z}(i); + if (${Z} < 0 || ${Z} >= ${s[V]}) { + ${h?`coefs[i + 1] = 0.0; + continue;`:u?`return ${p};`:`${Z} = max(0, min(${Z}, ${s[V]} - 1));`}; + } + var input_indices_copy: ${e.type.indices} = input_indices; + ${e.indicesSet("input_indices_copy",V,`u32(${Z})`)}; + data[i + 1] = ${V===C?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${B(C)}; + ${B(d)}; + fn getCubicInterpolationCoefs(s: ${z}) -> array<${z}, 4> { + var absS = abs(s); + var coeffs: array<${z}, 4> = array<${z}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${z} = 1.0 - absS; + var twoMinusAbsS: ${z} = 2.0 - absS; + var onePlusAbsS: ${z} = 1.0 + absS; + coeffs[0] = ((${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 * ${i}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${z}, 4>, coefs: array<${z}, 4>) -> ${z} { + var coefsSum: ${z} = 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}) -> ${z} { + var input_indices: ${e.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},mc=(e,t,s,n,o)=>{let[a,i,u,p,h]=s.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],k=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${k} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",i,`max(0, min(depth, ${s[i]} - 1))`)}; + ${e.indicesSet("input_indices",u,`max(0, min(height, ${s[u]} - 1))`)}; + ${e.indicesSet("input_indices",p,`max(0, min(width, ${s[p]} - 1))`)}; + ${Ca(e,h,a,3)} + return ${e.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${k} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${k} = originalIndices[${i}]; + var height:${k} = originalIndices[${u}]; + var width:${k} = originalIndices[${p}]; + ${n?`if (depth < 0 || depth > (${s[i]} - 1) || height < 0 || height > (${s[u]} - 1) || width < 0 || (width > ${s[p]} - 1)) { + return ${o}; + }`:""}; + + depth = max(0, min(depth, ${s[i]} - 1)); + height = max(0, min(height, ${s[u]} - 1)); + width = max(0, min(width, ${s[p]} - 1)); + var depth1: u32 = u32(depth); + var height1: u32 = u32(height); + var width1: u32 = u32(width); + var depth2: u32 = u32(depth + 1); + var height2: u32 = u32(height + 1); + var width2: u32 = u32(width + 1); + var channel: u32 = ${s.length>3?`u32(originalIndices[${h}])`:"0"}; + var batch: u32 = ${s.length>3?`u32(originalIndices[${a}])`:"0"}; + + var x111: ${k} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${k} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${k} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${k} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${k} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${k} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${k} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${k} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${k} = abs(depth - ${k}(depth1)); + var dx2: ${k} = abs(${k}(depth2) - depth); + var dy1: ${k} = abs(height - ${k}(height1)); + var dy2: ${k} = abs(${k}(height2) - height); + var dz1: ${k} = abs(width - ${k}(width1)); + var dz2: ${k} = abs(${k}(width2) - width); + if (depth1 == depth2) { + dx1 = 0.5; + dx2 = 0.5; + } + if (height1 == height2) { + dy1 = 0.5; + dy2 = 0.5; + } + if (width1 == width2) { + dz1 = 0.5; + dz2 = 0.5; + } + return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 + + x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1); + }`},_c=(e,t,s,n,o,a)=>{let i=e.dims,u=ic(a,t.axes,i.length),p=ac(i,n,o,t.axes),h=n.slice();n.length===0&&(h=i.map((X,he)=>X===0?1:p[he]/X),t.keepAspectRatioPolicy!=="stretch"&&(p=lc(i,h,t)));let k=It("output",e.dataType,p.length),C=qe("input",e.dataType,i.length),d=ze.size(p),z=i.length===p.length&&i.every((X,he)=>X===p[he]),B=t.coordinateTransformMode==="tf_crop_and_resize",V=t.extrapolationValue,Z=C.type.value,ee=X=>` + ${z?"":` + ${nc(t.coordinateTransformMode,Z)}; + ${(()=>{switch(t.mode){case"nearest":return` + ${cc(C,i)}; + ${oc(t.nearestMode,s,Z)}; + ${dc(C,k,i,p,h.length,u.length,B)}; + `;case"linear":return` + ${uc(k,i,p,h.length,u.length)}; + ${(()=>{if(i.length===2||i.length===4)return`${pc(C,k,i,B,V)}`;if(i.length===3||i.length===5)return`${mc(C,k,i,B,V)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(i.length===2||i.length===4)return`${hc(C,k,i,p,h,u,t.cubicCoeffA,B,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; + `;default:throw Error("Invalid resize mode")}})()}; + `} + ${X.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",u.length).declareVariables(C,k)} + ${X.mainStart()} + ${X.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${z?"output[global_idx] = input[global_idx];":` + let output_indices = ${k.offsetToIndices("global_idx")}; + var input_indices: ${C.type.indices}; + ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${C.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}|${s}|${h.length>0?h:""}|${o.length>0?o:""}|${u.length>0?u:""}|${z}|${i}`,inputDependencies:["rank"]},getShaderSource:ee,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:[{type:12,data:d},{type:1,data:h},{type:1,data:u},...yt(i,p)]})}},fc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},gc=(e,t)=>{let s=[],n=[],o=[],a=fc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");rc(e.inputs,t,a,s,n,o),e.compute(_c(e.inputs[0],t,a,s,n,o),{inputs:[0]})},wc=e=>{let t=e.antialias,s=e.axes,n=e.coordinateTransformMode,o=e.cubicCoeffA,a=e.excludeOutside!==0,i=e.extrapolationValue,u=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return Bt({antialias:t,axes:s,coordinateTransformMode:n,cubicCoeffA:o,excludeOutside:a,extrapolationValue:i,keepAspectRatioPolicy:u,mode:p,nearestMode:h})}}),yc,Mc,bc,Pp=g(()=>{zt(),Ot(),rs(),Yt(),yc=(e,t)=>{let[s,n,o,a]=e,{numHeads:i,rotaryEmbeddingDim:u}=t;if(s.dims.length!==3&&s.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${s.dims.length}`);if(!ze.areEqual(n.dims,[])&&!ze.areEqual(n.dims,[1])&&n.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${n.dims.length}`);if(o.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(a.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${a.dims.length}`);if(!ze.areEqual(o.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 p=s.dims[0],h=s.dims[s.dims.length-2],k=o.dims[0],C=ze.sizeFromDimension(s.dims,1)/h,d=u===0?o.dims[1]*2:C/i;if(u>d)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(p!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(h!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(d/2!==o.dims[1]&&u/2!==o.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${o.dims[1]}`);if(h>k)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Mc=(e,t)=>{let{interleaved:s,numHeads:n,rotaryEmbeddingDim:o,scale:a}=t,i=e[0].dims[0],u=ze.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=u/p,k=e[2].dims[1],C=o===0?k*2:h/n,d=new Array(i,p,h/C,C-k),z=ze.computeStrides(d),B=[{type:1,data:a},{type:12,data:d},{type:12,data:z},...e[0].dims.length===3?new Array({type:12,data:[u,h,C,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[u,C,p*C,1]}):[],...yt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],V=Z=>{let ee=qe("input",e[0].dataType,e[0].dims.length),X=qe("position_ids",e[1].dataType,e[1].dims.length),he=qe("cos_cache",e[2].dataType,e[2].dims.length),pe=qe("sin_cache",e[3].dataType,e[3].dims.length),Me=It("output",e[0].dataType,e[0].dims.length);return Z.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:d.length},{name:"global_strides",type:"u32",length:z.length},{name:"input_output_strides",type:"u32",length:z.length}]),` + ${Z.declareVariables(ee,X,he,pe,Me)} + + ${Z.mainStart(or)} + let half_rotary_emb_dim = uniforms.${he.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${Z.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${X.broadcastedIndicesToOffset("bsnh.xy",It("",X.type.tensor,2))}; + let position_id = + u32(${X.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${s}); + let j = i + select(half_rotary_emb_dim, 1, ${s}); + let re = ${ee.getByOffset("i")} * ${he.get("position_id","bsnh[3]")} - + ${ee.getByOffset("j")} * ${pe.get("position_id","bsnh[3]")}; + ${Me.setByOffset("i","re")} + let im = ${ee.getByOffset("i")} * ${pe.get("position_id","bsnh[3]")} + + ${ee.getByOffset("j")} * ${he.get("position_id","bsnh[3]")}; + ${Me.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${Me.setByOffset("k",ee.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:Bt({interleaved:s}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:V,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(ze.size(d)/or)},programUniforms:B})}},bc=(e,t)=>{yc(e.inputs,t),e.compute(Mc(e.inputs,t))}}),vc,xc,Tc,Qp=g(()=>{zt(),Ot(),Yt(),vc=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],s=e[1],n=e[2];if(t.dataType!==s.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(s.dims.length!==3&&s.dims.length!==2)throw new Error("Skip must be 2D or 3D");let o=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(s.dims[s.dims.length-1]!==o)throw new Error("Skip must have the same hidden size as input");if(s.dims[s.dims.length-2]!==a)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==o)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let i=e[3];if(i.dims.length!==1)throw new Error("Beta must be 1D");if(i.dims[i.dims.length-1]!==o)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let i=e[4];if(i.dims.length!==1)throw new Error("Bias must be 1D");if(i.dims[i.dims.length-1]!==o)throw new Error("Bias must have the same hidden size as input")}},xc=(e,t,s,n)=>{let o=t.simplified,a=e[0].dims,i=ze.size(a),u=a,p=i,h=a.slice(-1)[0],k=n?a.slice(0,-1).concat(1):[],C=!o&&e.length>3,d=e.length>4,z=n&&s>1,B=n&&s>2,V=s>3,Z=64,ee=qt(h),X=[{type:12,data:p},{type:12,data:ee},{type:12,data:h},{type:1,data:t.epsilon}],he=Me=>{let Oe=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Le=[qe("x",e[0].dataType,e[0].dims,ee),qe("skip",e[1].dataType,e[1].dims,ee),qe("gamma",e[2].dataType,e[2].dims,ee)];C&&Le.push(qe("beta",e[3].dataType,e[3].dims,ee)),d&&Le.push(qe("bias",e[4].dataType,e[4].dims,ee)),Le.push(It("output",e[0].dataType,u,ee)),z&&Le.push(It("mean_output",1,k)),B&&Le.push(It("inv_std_output",1,k)),V&&Le.push(It("input_skip_bias_sum",e[0].dataType,u,ee));let Ye=_s(e[0].dataType),at=_s(1,ee);return` + + ${Me.registerUniforms(Oe).declareVariables(...Le)} + var sum_shared : array<${at}, ${Z}>; + var sum_squared_shared : array<${at}, ${Z}>; + + ${Me.mainStart([Z,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${Z}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${Z}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${Z-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${d?"bias[offset1d + i]":Ye+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${V?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${$s(Ye,ee,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${Z}; + for (var curr_size = reduce_size >> 1; curr_size > 0; curr_size = reduce_size >> 1) { + reduce_size = curr_size + (reduce_size & 1); + if (ix < curr_size) { + sum_shared[ix] += sum_shared[ix + reduce_size]; + sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size]; + } + workgroupBarrier(); + } + + let sum = sum_shared[0]; + let square_sum = sum_squared_shared[0]; + let mean = ${Gs("sum",ee)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${Gs("square_sum",ee)} / f32(uniforms.hidden_size) ${o?"":"- mean * mean"} + uniforms.epsilon); + ${z?"mean_output[global_idx] = mean;":""} + ${B?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${o?"":`- ${Ye}(mean)`}) * + ${Ye}(inv_std_dev) * gamma[offset1d + i] + ${C?"+ beta[offset1d + i]":""}; + } + }`},pe=[{dims:u,dataType:e[0].dataType}];return s>1&&pe.push({dims:k,dataType:1}),s>2&&pe.push({dims:k,dataType:1}),s>3&&pe.push({dims:a,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${ee};${z};${B};${V}`,inputDependencies:e.map((Me,Oe)=>"type")},getShaderSource:he,getRunData:()=>({outputs:pe,dispatchGroup:{x:Math.ceil(p/h)},programUniforms:X})}},Tc=(e,t)=>{vc(e.inputs);let s=[0];e.outputCount>1&&s.push(-3),e.outputCount>2&&s.push(-3),e.outputCount>3&&s.push(3),e.compute(xc(e.inputs,t,e.outputCount,!1),{outputs:s})}}),Qt,Hn,Hs,qs,tr,cn,Ep,Pc,Cp=g(()=>{zt(),Ot(),rs(),Yt(),Qt=(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((s,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`)})},Hn=(e,t)=>{let s=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(n=>s.push(Number(n)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(n=>s.push(Number(n)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return s},Hs=(e,t)=>{if(e.length>1){let s=Hn(e,1),n=Hn(e,2),o=Hn(e,3);return o.length===0&&(o=[...Array(e[0].dims.length).keys()]),Bt({starts:s,ends:n,axes:o})}else return t},qs=(e,t,s,n,o)=>{let a=e;return e<0&&(a+=s[n[t]]),o[t]<0?Math.max(0,Math.min(a,s[n[t]]-1)):Math.max(0,Math.min(a,s[n[t]]))},tr=(e,t,s)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + var carry = 0u; + for (var i = ${s.length}; i >= 0; i--) { + let input_shape_i = ${$t("uniforms.input_shape","i",s.length)}; + let steps_i = ${$t("uniforms.steps","i",s.length)}; + let signs_i = ${$t("uniforms.signs","i",s.length)}; + let starts_i = ${$t("uniforms.starts","i",s.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; + }`,cn=(e,t)=>{let s=e[0].dims,n=ze.size(s),o=t.axes.length>0?ze.normalizeAxes(t.axes,s.length):[...Array(s.length).keys()],a=Hn(e,4);a.forEach(ee=>ee!==0||(()=>{throw new Error("step cannot be 0")})),a.length===0&&(a=Array(o.length).fill(1));let i=t.starts.map((ee,X)=>qs(ee,X,s,o,a)),u=t.ends.map((ee,X)=>qs(ee,X,s,o,a));if(o.length!==i.length||o.length!==u.length)throw new Error("start, ends and axes should have the same number of elements");if(o.length!==s.length)for(let ee=0;eeMath.sign(ee));a.forEach((ee,X,he)=>{if(ee<0){let pe=(u[X]-i[X])/ee,Me=i[X],Oe=Me+pe*a[X];i[X]=Oe,u[X]=Me,he[X]=-ee}});let h=s.slice(0);o.forEach((ee,X)=>{h[ee]=Math.ceil((u[ee]-i[ee])/a[ee])});let k={dims:h,dataType:e[0].dataType},C=It("output",e[0].dataType,h.length),d=qe("input",e[0].dataType,e[0].dims.length),z=ze.size(h),B=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:i.length},{name:"signs",type:"i32",length:p.length},{name:"steps",type:"u32",length:a.length}],V=[{type:12,data:z},{type:12,data:i},{type:6,data:p},{type:12,data:a},...yt(e[0].dims,h)],Z=ee=>` + ${ee.registerUniforms(B).declareVariables(d,C)} + ${tr(d,C,s)} + ${ee.mainStart()} + ${ee.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${C.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${C.setByOffset("global_idx",d.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${p.length}_${i.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:Z,getRunData:()=>({outputs:[k],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:V})}},Ep=(e,t)=>{Qt(e.inputs,t);let s=Hs(e.inputs,t);e.compute(cn(e.inputs,s),{inputs:[0]})},Pc=e=>{let t=e.starts,s=e.ends,n=e.axes;return Bt({starts:t,ends:s,axes:n})}}),_,T,N,fe,Fe=g(()=>{zt(),Ot(),rs(),Kr(),Yt(),_=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},T=(e,t)=>{let s=e.inputs[0],n=s.dims,o=ze.size(n),a=n.length,i=ze.normalizeAxis(t.axis,a),u=iYe),h[i]=a-1,h[a-1]=i,p=e.compute(pr(s,h),{inputs:[s],outputs:[-1]})[0]):p=s;let k=p.dims,C=k[a-1],d=o/C,z=qt(C),B=C/z,V=64;d===1&&(V=256);let Z=(Le,Ye)=>Ye===4?`max(max(${Le}.x, ${Le}.y), max(${Le}.z, ${Le}.w))`:Ye===2?`max(${Le}.x, ${Le}.y)`:Ye===3?`max(max(${Le}.x, ${Le}.y), ${Le}.z)`:Le,ee=qe("x",p.dataType,p.dims,z),X=It("result",p.dataType,p.dims,z),he=ee.type.value,pe=_s(p.dataType)==="f32"?`var threadMax = ${he}(-3.402823e+38f);`:`var threadMax = ${he}(-65504.0h);`,Me=Le=>` + var rowMaxShared : ${he}; + var rowSumShared : ${he}; + var threadShared : array<${he}, ${V}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${he} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${he}) { + let index = row * row_stride + col; + result[index] = value; + } + ${Le.registerUniform("packedCols","i32").declareVariables(ee,X)} + ${Le.mainStart(V)} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${V}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${pe} + 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 = ${he}(${Z("threadShared[0]",z)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${he}(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 = ${he}(${Gs("threadShared[0]",z)}); + } + 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); + } + }`,Oe=e.compute({name:"Softmax",shaderCache:{hint:`${z};${V}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:k,dataType:p.dataType}],dispatchGroup:{x:d},programUniforms:[{type:6,data:B}]}),getShaderSource:Me},{inputs:[p],outputs:[u?-1:0]})[0];u&&e.compute(pr(Oe,h),{inputs:[Oe]})},N=(e,t)=>{_(e.inputs),T(e,t)},fe=e=>Bt({axis:e.axis})}),Ae,et,rt,ft,Mt,jt=g(()=>{zt(),Ot(),Yt(),Ae=e=>Array.from(e.getBigInt64Array(),Number),et=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(Ae(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")},rt=(e,t)=>{let s=[];for(let n=0;n{let s=e[0].dims,n=t??Ae(e[1]),o=rt(s,n),a=ze.size(o),i=e[0].dataType,u=qe("input",i,s.length),p=It("output",i,o.length),h=k=>` + const inputShape = ${u.indices(...s)}; + ${k.registerUniform("output_size","u32").declareVariables(u,p)} + ${k.mainStart()} + ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${p.offsetToIndices("global_idx")}; + var input_indices: ${u.type.indices}; + for (var i = 0; i < ${s.length}; i++) { + let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${p.indicesGet("output_indices","i")} % input_dim_i; + + ${u.indicesSet("input_indices","i","input_dim_value")} + } + ${p.setByOffset("global_idx",u.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:[{type:12,data:a},...yt(e[0].dims,o)]}),getShaderSource:h}},Mt=e=>{et(e.inputs),e.compute(ft(e.inputs),{inputs:[0]})}}),Vt,Lt,Gt,ts=g(()=>{zt(),Ot(),Yt(),Vt=(e,t,s,n,o)=>{let a=It("output_data",o,s.length,4),i=qe("a_data",t[1].dataType,t[1].dims.length,4),u=qe("b_data",t[2].dataType,t[2].dims.length,4),p=qe("c_data",t[0].dataType,t[0].dims.length,4),h,k=(C,d,z)=>`select(${d}, ${C}, ${z})`;if(!n)h=a.setByOffset("global_idx",k(i.getByOffset("global_idx"),u.getByOffset("global_idx"),p.getByOffset("global_idx")));else{let C=(d,z,B="")=>{let V=`a_data[index_a${z}][component_a${z}]`,Z=`b_data[index_b${z}][component_b${z}]`,ee=`bool(c_data[index_c${z}] & (0xffu << (component_c${z} * 8)))`;return` + let output_indices${z} = ${a.offsetToIndices(`global_idx * 4u + ${z}u`)}; + let offset_a${z} = ${i.broadcastedIndicesToOffset(`output_indices${z}`,a)}; + let offset_b${z} = ${u.broadcastedIndicesToOffset(`output_indices${z}`,a)}; + let offset_c${z} = ${p.broadcastedIndicesToOffset(`output_indices${z}`,a)}; + let index_a${z} = offset_a${z} / 4u; + let index_b${z} = offset_b${z} / 4u; + let index_c${z} = offset_c${z} / 4u; + let component_a${z} = offset_a${z} % 4u; + let component_b${z} = offset_b${z} % 4u; + let component_c${z} = offset_c${z} % 4u; + ${d}[${z}] = ${B}(${k(V,Z,ee)}); + `};o===9?h=` + var data = vec4(0); + ${C("data",0,"u32")} + ${C("data",1,"u32")} + ${C("data",2,"u32")} + ${C("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:h=` + ${C("output_data[global_idx]",0)} + ${C("output_data[global_idx]",1)} + ${C("output_data[global_idx]",2)} + ${C("output_data[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(p,i,u,a)} + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${h} + }`},Lt=e=>{let t=e[1].dims,s=e[2].dims,n=e[0].dims,o=e[1].dataType,a=!(ze.areEqual(t,s)&&ze.areEqual(s,n)),i=t,u=ze.size(t);if(a){let h=Ws.calcShape(Ws.calcShape(t,s,!1),n,!1);if(!h)throw new Error("Can't perform where op on the given tensors");i=h,u=ze.size(i)}let p=Math.ceil(u/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:h=>Vt(h,e,i,a,o),getRunData:()=>({outputs:[{dims:i,dataType:o}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:p},...yt(n,t,s,i)]})}},Gt=e=>{e.compute(Lt(e.inputs))}}),ns,Jt=g(()=>{Uc(),ui(),Vc(),Wc(),Gc(),Kc(),pu(),Xc(),Jc(),Zc(),ep(),tp(),sp(),rp(),np(),op(),ap(),lp(),Hp(),la(),pp(),xd(),hp(),mp(),Od(),_d(),Bd(),Mp(),qp(),bp(),xp(),co(),Tp(),Pp(),Qp(),Cp(),Fe(),pa(),jt(),Kr(),Ei(),ts(),ns=new 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n=e.name;return(o=e.shaderCache)!=null&&o.hint&&(n+="["+e.shaderCache.hint+"]"),n+=":"+s+`:${xs(t,((a=e.shaderCache)==null?void 0:a.inputDependencies)??new Array(t.length).fill("dims"))}`,n},Es=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},Is=class{constructor(e){this.subgroupsSupported=e.features.has("subgroups"),this.subgroupsF16Supported=e.features.has("subgroups");let t=e.limits;!this.subgroupsSupported||!t.minSubgroupSize||!t.maxSubgroupSize?this.subgroupSizeRange=void 0:this.subgroupSizeRange=[t.minSubgroupSize,t.maxSubgroupSize]}},Zs=class{constructor(){this.currentSessionId=null,this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.maxDispatchNumber=16,this.pendingDispatchNumber=0,this.pendingKernels=[],this.pendingQueries=new Map,this.sessionStatus="default",this.capturedCommandList=new Map,this.capturedPendingKernels=new 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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 s=[],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:s},o=a=>t.features.has(a)&&s.push(a)&&!0;o("chromium-experimental-timestamp-query-inside-passes")||o("timestamp-query"),o("shader-f16"),o("subgroups")&&o("subgroups-f16"),this.device=await t.requestDevice(n),this.deviceInfo=new Is(this.device),this.adapterInfo=new Es(t.info||await t.requestAdapterInfo()),this.gpuDataManager=ms(this),this.programManager=new is(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Tn(e.logLevel,!!e.debug),this.device.onuncapturederror=a=>{a.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${a.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:t,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e=this.getCommandEncoder(),t={};this.queryType==="at-passes"&&(t.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(t)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Ne(),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()),s=this.pendingQueries.get(e);for(let o=0;o"u"&&(this.queryTimeBase=z);let V=Number(z-this.queryTimeBase),Z=Number(B-this.queryTimeBase);if(!Number.isSafeInteger(V)||!Number.isSafeInteger(Z))throw new RangeError("incorrect timestamp range");if((n=this.env.webgpu.profiling)!=null&&n.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:C.map(ee=>({dims:ee.dims,dataType:_r(ee.dataType)})),outputsMetadata:d.map(ee=>({dims:ee.dims,dataType:_r(ee.dataType)})),kernelId:i,kernelType:p,kernelName:h,programName:k,startTime:V,endTime:Z});else{let ee="";C.forEach((he,pe)=>{ee+=`input[${pe}]: [${he.dims}] | ${_r(he.dataType)}, `});let X="";d.forEach((he,pe)=>{X+=`output[${pe}]: [${he.dims}] | ${_r(he.dataType)}, `}),console.log(`[profiling] kernel "${i}|${p}|${h}|${k}" ${ee}${X}execution time: ${Z-V} ns`)}Ue("GPU",`${k}::${z}::${B}`)}e.unmap(),this.pendingQueries.delete(e)}),Re()}run(e,t,s,n,o,a){Ne(e.name);let i=[];for(let X=0;Xhe):s;if(k.length!==u.length)throw new Error(`Output size ${k.length} must be equal to ${u.length}.`);let C=[],d=[];for(let X=0;X=a)throw new Error(`Invalid output index: ${k[X]}`);if(k[X]===-3)continue;let he=k[X]===-1,pe=k[X]===-2,Me=he||pe?o(u[X].dataType,u[X].dims):n(k[X],u[X].dataType,u[X].dims);if(C.push(Me),Me.data===0)continue;let Oe=this.gpuDataManager.get(Me.data);if(!Oe)throw new Error(`no GPU data for output: ${Me.data}`);if(he&&this.temporaryData.push(Oe),pe){let Le=this.kernelPersistentData.get(this.currentKernelId);Le||(Le=[],this.kernelPersistentData.set(this.currentKernelId,Le)),Le.push(Oe)}d.push(Oe)}if(i.length!==t.length||d.length!==C.length){if(d.length===0)return Re(e.name),C;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let z;if(h){let X=0,he=[];h.forEach(Le=>{let Ye=typeof Le.data=="number"?[Le.data]:Le.data;if(Ye.length===0)return;let at=Le.type===10?2:4,Pt,Xt;Le.type===10?(Xt=Ye.length>4?16:Ye.length>2?8:Ye.length*at,Pt=Ye.length>4?16:at*Ye.length):(Xt=Ye.length<=2?Ye.length*at:16,Pt=16),X=Math.ceil(X/Xt)*Xt,he.push(X);let Zt=Le.type===10?8:4;X+=Ye.length>4?Math.ceil(Ye.length/Zt)*Pt:Ye.length*at});let pe=16;X=Math.ceil(X/pe)*pe;let Me=new ArrayBuffer(X);h.forEach((Le,Ye)=>{let at=he[Ye],Pt=typeof Le.data=="number"?[Le.data]:Le.data;if(Le.type===6)new Int32Array(Me,at,Pt.length).set(Pt);else if(Le.type===12)new Uint32Array(Me,at,Pt.length).set(Pt);else if(Le.type===10)new Uint16Array(Me,at,Pt.length).set(Pt);else if(Le.type===1)new Float32Array(Me,at,Pt.length).set(Pt);else throw new Error(`Unsupported uniform type: ${_r(Le.type)}`)});let Oe=this.gpuDataManager.create(X,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Oe.buffer,0,Me,0,X),this.gpuDataManager.release(Oe.id),z={offset:0,size:X,buffer:Oe.buffer}}let B=this.programManager.normalizeDispatchGroupSize(p),V=B[1]===1&&B[2]===1,Z=cs(e,t,V),ee=this.programManager.getArtifact(Z);if(ee||(ee=this.programManager.build(e,B),this.programManager.setArtifact(Z,ee),as("info",()=>`[artifact] key: ${Z}, programName: ${e.name}`)),h&&ee.uniformVariablesInfo){if(h.length!==ee.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${ee.uniformVariablesInfo.length}, got ${h.length} in program "${ee.programInfo.name}".`);for(let X=0;X`[ProgramManager] run "${e.name}" (key=${Z}) with ${B[0]}x${B[1]}x${B[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let X={kernelId:this.currentKernelId,programName:ee.programInfo.name,inputTensorViews:t,outputTensorViews:C};this.pendingKernels.push(X),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(X)}return this.programManager.run(ee,i,d,B,z),Re(e.name),C}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,s,n){let o=ns.get(e);if(!o)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:n,kernelEntry:o[0],attributes:[o[1],s]};this.kernels.set(t,a)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let s of t)this.gpuDataManager.release(s.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,s){let n=this.kernels.get(e);if(!n)throw new Error(`kernel not created: ${e}`);let o=n.kernelType,a=n.kernelName,i=n.kernelEntry,u=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${o}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,u[0]&&(u[1]=u[0](u[1]),u[0]=void 0),as("info",()=>`[WebGPU] Start to run kernel "[${o}] ${a}"...`);let p=this.env.debug;this.temporaryData=[];try{return p&&this.device.pushErrorScope("validation"),i(t,u[1]),0}catch(h){return s.push(Promise.resolve(`[WebGPU] Kernel "[${o}] ${a}" failed. ${h}`)),1}finally{p&&s.push(this.device.popErrorScope().then(h=>h?`GPU validation error for kernel "[${o}] ${a}": ${h.message}`:null));for(let h of this.temporaryData)this.gpuDataManager.release(h.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,s,n){let o=this.sessionExternalDataMapping.get(e);o||(o=new Map,this.sessionExternalDataMapping.set(e,o));let a=o.get(t),i=this.gpuDataManager.registerExternalBuffer(s,n,a);return o.set(t,[i,s]),i}unregisterBuffers(e){let t=this.sessionExternalDataMapping.get(e);t&&(t.forEach(s=>this.gpuDataManager.unregisterExternalBuffer(s[0])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let t=this.gpuDataManager.get(e);if(!t)throw new Error(`no GPU data for buffer: ${e}`);return t.buffer}createDownloader(e,t,s){return async()=>{let n=await xt(this,e,t);return P(n.buffer,s)}}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(){as("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(){as("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){as("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),s=e.length;this.pendingKernels=[];for(let n=0;n=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),br,pn,ka,ir,vr,Io,Oo,Fo,Sa=g(()=>{Pe(),br=1,pn=()=>br++,ka=new Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),ir=(e,t)=>{let s=ka.get(e);if(!s)throw new Error("Unsupported data type.");return t.length>0?Math.ceil(t.reduce((n,o)=>n*o)*s/8):0},vr=class{constructor(e){this.sessionId=e.sessionId,this.mlContext=e.context,this.mlTensor=e.tensor,this.dataType=e.dataType,this.tensorShape=e.shape}get tensor(){return this.mlTensor}get type(){return this.dataType}get shape(){return this.tensorShape}get byteLength(){return ir(this.dataType,this.tensorShape)}destroy(){as("verbose",()=>"[WebNN] TensorWrapper.destroy"),this.mlTensor.destroy()}write(e){this.mlContext.writeTensor(this.mlTensor,e)}async read(e){return e?this.mlContext.readTensor(this.mlTensor,e):this.mlContext.readTensor(this.mlTensor)}canReuseTensor(e,t,s){return this.mlContext===e&&this.dataType===t&&this.tensorShape.length===s.length&&this.tensorShape.every((n,o)=>n===s[o])}},Io=class{constructor(e,t){this.tensorManager=e,this.wrapper=t}get tensorWrapper(){return this.wrapper}releaseTensor(){this.tensorWrapper&&(this.tensorManager.releaseTensor(this.tensorWrapper),this.wrapper=void 0)}async ensureTensor(e,t,s,n){if(this.wrapper){if(this.wrapper.canReuseTensor(e,t,s))return this.wrapper.tensor;if(n){if(this.wrapper.byteLength!==ir(t,s))throw new Error("Unable to copy data to tensor with different size.");this.activeUpload=new Uint8Array(await this.wrapper.read())}this.tensorManager.releaseTensor(this.wrapper)}let o=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(t,s,o,!0,!0),n&&this.activeUpload&&(this.wrapper.write(this.activeUpload),this.activeUpload=void 0),this.wrapper.tensor}upload(e){if(this.wrapper)if(e.byteLength===this.wrapper.byteLength){this.wrapper.write(e);return}else as("verbose",()=>"Data size does not match tensor size. Releasing tensor."),this.releaseTensor();this.activeUpload?this.activeUpload.set(e):this.activeUpload=new Uint8Array(e)}async download(e){if(this.activeUpload)if(e){e instanceof ArrayBuffer?new Uint8Array(e).set(this.activeUpload):new Uint8Array(e.buffer,e.byteOffset,e.byteLength).set(this.activeUpload);return}else return this.activeUpload.buffer;if(!this.wrapper)throw new Error("Tensor has not been created.");return e?this.wrapper.read(e):this.wrapper.read()}},Oo=class{constructor(e){this.backend=e,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}reserveTensorId(){let e=pn();return this.tensorTrackersById.set(e,new Io(this)),e}releaseTensorId(e){let t=this.tensorTrackersById.get(e);t&&(this.tensorTrackersById.delete(e),t.tensorWrapper&&this.releaseTensor(t.tensorWrapper))}async ensureTensor(e,t,s,n){as("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${e}, dataType: ${t}, shape: ${s}, copyOld: ${n}}`);let o=this.tensorTrackersById.get(e);if(!o)throw new Error("Tensor not found.");return o.ensureTensor(this.backend.currentContext,t,s,n)}upload(e,t){let s=this.tensorTrackersById.get(e);if(!s)throw new Error("Tensor not found.");s.upload(t)}async download(e,t){as("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${e}, dstBuffer: ${t==null?void 0:t.byteLength}}`);let s=this.tensorTrackersById.get(e);if(!s)throw new Error("Tensor not found.");return s.download(t)}releaseTensorsForSession(e){for(let t of this.freeTensors)t.sessionId===e&&t.destroy();this.freeTensors=this.freeTensors.filter(t=>t.sessionId!==e)}registerTensor(e,t,s,n){let o=pn(),a=new vr({sessionId:this.backend.currentSessionId,context:e,tensor:t,dataType:s,shape:n});return this.tensorTrackersById.set(o,new Io(this,a)),this.externalTensors.add(a),o}async getCachedTensor(e,t,s,n,o){let a=this.backend.currentSessionId,i=this.backend.currentContext;for(let[p,h]of this.freeTensors.entries())if(h.canReuseTensor(i,e,t)){as("verbose",()=>`[WebNN] Reusing tensor {dataType: ${e}, shape: ${t}}`);let k=this.freeTensors.splice(p,1)[0];return k.sessionId=a,k}as("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${e}, shape: ${t}}`);let u=await i.createTensor({dataType:e,shape:t,dimensions:t,usage:s,writable:n,readable:o});return new vr({sessionId:a,context:i,tensor:u,dataType:e,shape:t})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},Fo=(...e)=>new Oo(...e)}),Ts,Rs,qr,En=g(()=>{zt(),lr(),Q(),Sa(),Pe(),Ts=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),Rs=(e,t)=>{if(e===t)return!0;if(e===void 0||t===void 0)return!1;let s=Object.keys(e).sort(),n=Object.keys(t).sort();return s.length===n.length&&s.every((o,a)=>o===n[a]&&e[o]===t[o])},qr=class{constructor(e){this.tensorManager=Fo(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],Tn(e.logLevel,!!e.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw new Error("No active session");return this.activeSessionId}onRunStart(e){this.activeSessionId=e}async createMLContext(e){if(e instanceof GPUDevice){let s=this.mlContextCache.findIndex(n=>n.gpuDevice===e);if(s!==-1)return this.mlContextCache[s].mlContext;{let n=await navigator.ml.createContext(e);return this.mlContextCache.push({gpuDevice:e,mlContext:n}),n}}else if(e===void 0){let s=this.mlContextCache.findIndex(n=>n.options===void 0&&n.gpuDevice===void 0);if(s!==-1)return this.mlContextCache[s].mlContext;{let n=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:n}),n}}let t=this.mlContextCache.findIndex(s=>Rs(s.options,e));if(t!==-1)return this.mlContextCache[t].mlContext;{let s=await navigator.ml.createContext(e);return this.mlContextCache.push({options:e,mlContext:s}),s}}get currentContext(){let e=this.getMLContext(this.currentSessionId);if(!e)throw new Error(`No MLContext found for session ${this.currentSessionId}`);return e}registerMLContext(e,t){this.mlContextBySessionId.set(e,t);let s=this.sessionIdsByMLContext.get(t);s||(s=new Set,this.sessionIdsByMLContext.set(t,s)),s.add(e)}onReleaseSession(e){let t=this.mlContextBySessionId.get(e);if(!t)return;this.tensorManager.releaseTensorsForSession(e),this.mlContextBySessionId.delete(e);let s=this.sessionIdsByMLContext.get(t);if(s.delete(e),s.size===0){this.sessionIdsByMLContext.delete(t);let n=this.mlContextCache.findIndex(o=>o.mlContext===t);n!==-1&&this.mlContextCache.splice(n,1)}}getMLContext(e){return this.mlContextBySessionId.get(e)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(e){as("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,t,s,n){let o=Ts.get(t);if(!o)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e,o,s,n)}uploadTensor(e,t){if(!Ms().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");as("verbose",()=>`[WebNN] uploadTensor {tensorId: ${e}, data: ${t.byteLength}}`),this.tensorManager.upload(e,t)}async downloadTensor(e,t){return this.tensorManager.download(e,t)}createMLTensorDownloader(e,t){return async()=>{let s=await this.tensorManager.download(e);return P(s,t)}}registerMLTensor(e,t,s){let n=Ts.get(t);if(!n)throw new Error(`Unsupported ONNX data type: ${t}`);let o=this.tensorManager.registerTensor(this.currentContext,e,n,s);return as("verbose",()=>`[WebNN] registerMLTensor {tensor: ${e}, dataType: ${n}, dimensions: ${s}} -> {tensorId: ${o}}`),o}registerMLConstant(e,t,s,n,o,a){if(!a)throw new Error("External mounted files are not 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__="1.21.0-dev.20250114-228dd16893",f_=$e;{let e=(m_(),y(Dh)).wasmBackend;K("webgpu",e,5),K("webnn",e,5),K("cpu",e,10),K("wasm",e,10)}Object.defineProperty(O.versions,"web",{value:__,enumerable:!0});/** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */},"./src/backends/onnx.js":(De,A,r)=>{var f;r.r(A),r.d(A,{Tensor:()=>R.Tensor,createInferenceSession:()=>ne,deviceToExecutionProviders:()=>K,isONNXProxy:()=>q,isONNXTensor:()=>W});var D=r("./src/env.js"),U=r("?2ce3"),Y=r("./node_modules/onnxruntime-web/dist/ort.bundle.min.mjs?3a96"),R=r("./node_modules/onnxruntime-common/dist/esm/index.js");const g=Object.freeze({auto:null,gpu:null,cpu:"cpu",wasm:"wasm",webgpu:"webgpu",cuda:"cuda",dml:"dml",webnn:{name:"webnn",deviceType:"cpu"},"webnn-npu":{name:"webnn",deviceType:"npu"},"webnn-gpu":{name:"webnn",deviceType:"gpu"},"webnn-cpu":{name:"webnn",deviceType:"cpu"}}),v=[];let M,y;const b=Symbol.for("onnxruntime");if(b in globalThis)y=globalThis[b];else if(D.apis.IS_NODE_ENV){switch(y=U??(f||(f=r.t(U,2))),process.platform){case"win32":v.push("dml");break;case"linux":process.arch==="x64"&&v.push("cuda");break}v.push("cpu"),M=["cpu"]}else y=Y,D.apis.IS_WEBNN_AVAILABLE&&v.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),D.apis.IS_WEBGPU_AVAILABLE&&v.push("webgpu"),v.push("wasm"),M=["wasm"];const I=y.InferenceSession;function K($=null){if(!$)return M;switch($){case"auto":return v;case"gpu":return v.filter(S=>["webgpu","cuda","dml","webnn-gpu"].includes(S))}if(v.includes($))return[g[$]??$];throw new Error(`Unsupported device: "${$}". 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D{constructor(Y){_e(this,"max_length",20);_e(this,"max_new_tokens",null);_e(this,"min_length",0);_e(this,"min_new_tokens",null);_e(this,"early_stopping",!1);_e(this,"max_time",null);_e(this,"do_sample",!1);_e(this,"num_beams",1);_e(this,"num_beam_groups",1);_e(this,"penalty_alpha",null);_e(this,"use_cache",!0);_e(this,"temperature",1);_e(this,"top_k",50);_e(this,"top_p",1);_e(this,"typical_p",1);_e(this,"epsilon_cutoff",0);_e(this,"eta_cutoff",0);_e(this,"diversity_penalty",0);_e(this,"repetition_penalty",1);_e(this,"encoder_repetition_penalty",1);_e(this,"length_penalty",1);_e(this,"no_repeat_ngram_size",0);_e(this,"bad_words_ids",null);_e(this,"force_words_ids",null);_e(this,"renormalize_logits",!1);_e(this,"constraints",null);_e(this,"forced_bos_token_id",null);_e(this,"forced_eos_token_id",null);_e(this,"remove_invalid_values",!1);_e(this,"exponential_decay_length_penalty",null);_e(this,"suppress_tokens",null);_e(this,"streamer",null);_e(this,"begin_suppress_tokens",null);_e(this,"forced_decoder_ids",null);_e(this,"guidance_scale",null);_e(this,"num_return_sequences",1);_e(this,"output_attentions",!1);_e(this,"output_hidden_states",!1);_e(this,"output_scores",!1);_e(this,"return_dict_in_generate",!1);_e(this,"pad_token_id",null);_e(this,"bos_token_id",null);_e(this,"eos_token_id",null);_e(this,"encoder_no_repeat_ngram_size",0);_e(this,"decoder_start_token_id",null);_e(this,"generation_kwargs",{});Object.assign(this,(0,f.pick)(Y,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(De,A,r)=>{r.r(A),r.d(A,{ClassifierFreeGuidanceLogitsProcessor:()=>W,ForcedBOSTokenLogitsProcessor:()=>g,ForcedEOSTokenLogitsProcessor:()=>v,LogitsProcessor:()=>U,LogitsProcessorList:()=>R,LogitsWarper:()=>Y,MinLengthLogitsProcessor:()=>K,MinNewTokensLengthLogitsProcessor:()=>te,NoBadWordsLogitsProcessor:()=>ne,NoRepeatNGramLogitsProcessor:()=>b,RepetitionPenaltyLogitsProcessor:()=>I,SuppressTokensAtBeginLogitsProcessor:()=>M,TemperatureLogitsWarper:()=>j,TopKLogitsWarper:()=>$,TopPLogitsWarper:()=>q,WhisperTimeStampLogitsProcessor:()=>y});var f=r("./src/utils/generic.js");r("./src/utils/tensor.js");var D=r("./src/utils/maths.js");class U extends f.Callable{_call(w,x){throw Error("`_call` should be implemented in a subclass")}}class Y extends f.Callable{_call(w,x){throw Error("`_call` should be implemented in a subclass")}}class R extends f.Callable{constructor(){super(),this.processors=[]}push(w){this.processors.push(w)}extend(w){this.processors.push(...w)}_call(w,x){let O=x;for(const ae of this.processors)O=ae(w,O);return O}[Symbol.iterator](){return this.processors.values()}}class g extends U{constructor(w){super(),this.bos_token_id=w}_call(w,x){for(let O=0;O=1&&oe[oe.length-1]>=this.timestamp_begin,we=oe.length<2||oe[oe.length-2]>=this.timestamp_begin;if(ve&&(we?ae.subarray(this.timestamp_begin).fill(-1/0):ae.subarray(0,this.eos_token_id).fill(-1/0)),w[O].length===this.begin_index&&this.max_initial_timestamp_index!==null){const ke=this.timestamp_begin+this.max_initial_timestamp_index;ae.subarray(ke+1).fill(-1/0)}const re=(0,D.log_softmax)(ae),xe=Math.log(re.subarray(this.timestamp_begin).map(Math.exp).reduce((ke,Ie)=>ke+Ie)),ce=(0,D.max)(re.subarray(0,this.timestamp_begin))[0];xe>ce&&ae.subarray(0,this.timestamp_begin).fill(-1/0)}return x}}class b extends U{constructor(w){super(),this.no_repeat_ngram_size=w}getNgrams(w){const x=w.length,O=[];for(let oe=0;oe1 to use the classifier free guidance processor, got guidance scale ${w}.`);this.guidance_scale=w}_call(w,x){if(x.dims[0]!==2*w.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${x.dims[0]} for the logits and ${w.length} for the input ids.`);const O=w.length,ae=x.slice([0,O],null),oe=x.slice([O,x.dims[0]],null);for(let ve=0;ve1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${w}`);if(!Number.isInteger(O)||O<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${O}`);this.top_p=w,this.filter_value=x,this.min_tokens_to_keep=O}}class $ extends Y{constructor(w,{filter_value:x=-1/0,min_tokens_to_keep:O=1}={}){if(super(),!Number.isInteger(w)||w<0)throw new Error(`\`top_k\` must be a positive integer, but is ${w}`);this.top_k=Math.max(w,O),this.filter_value=x}}},"./src/generation/logits_sampler.js":(De,A,r)=>{r.r(A),r.d(A,{LogitsSampler:()=>Y});var f=r("./src/utils/generic.js"),D=r("./src/utils/tensor.js"),U=r("./src/utils/maths.js");r("./src/generation/configuration_utils.js");class Y extends f.Callable{constructor(y){super(),this.generation_config=y}async _call(y){return this.sample(y)}async sample(y){throw Error("sample should be implemented in subclasses.")}getLogits(y,b){let I=y.dims.at(-1),K=y.data;if(b===-1)K=K.slice(-I);else{let te=b*I;K=K.slice(te,te+I)}return K}randomSelect(y){let b=0;for(let K=0;K1)return new v(y);if(y.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${y.num_return_sequences}.`);return new R(y)}}class R extends Y{async sample(y){const b=(0,U.max)(y.data)[1];return[[BigInt(b),0]]}}class g extends Y{async sample(y){let b=y.dims.at(-1);this.generation_config.top_k>0&&(b=Math.min(this.generation_config.top_k,b));const[I,K]=await(0,D.topk)(y,b),te=(0,U.softmax)(I.data);return Array.from({length:this.generation_config.num_beams},()=>{const ne=this.randomSelect(te);return[K.data[ne],Math.log(te[ne])]})}}class v extends Y{async sample(y){let b=y.dims.at(-1);this.generation_config.top_k>0&&(b=Math.min(this.generation_config.top_k,b));const[I,K]=await(0,D.topk)(y,b),te=(0,U.softmax)(I.data);return 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Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");fe.token_type_ids=(0,b.zeros_like)(fe.input_ids)}if(N.inputNames.includes("pixel_mask")&&!fe.pixel_mask){if(!fe.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const Fe=fe.pixel_values.dims;fe.pixel_mask=(0,b.ones)([Fe[0],Fe[2],Fe[3]])}return await we(N,fe)}async function Ee(_,T,N=!1){const fe=_.sessions[N?"decoder_model_merged":"model"],{past_key_values:Fe,...Ae}=T;if(fe.inputNames.includes("use_cache_branch")&&(Ae.use_cache_branch=ce(!!Fe)),fe.inputNames.includes("position_ids")&&Ae.attention_mask&&!Ae.position_ids){const rt=_.config.model_type==="paligemma"?1:0;Ae.position_ids=J(Ae,Fe,rt)}_.addPastKeyValues(Ae,Fe);const et=(0,R.pick)(Ae,fe.inputNames);return await we(fe,et)}function tt({image_token_id:_,inputs_embeds:T,image_features:N,input_ids:fe,attention_mask:Fe}){const 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Promise.all([ae(N,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},ts)]);else if(Jt===$.EncoderDecoder)is=await Promise.all([ae(N,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},ts)]);else if(Jt===$.ImageTextToText){const As={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Fe.is_encoder_decoder&&(As.model="encoder_model"),is=await Promise.all([ae(N,As,ts),oe(N,{generation_config:"generation_config.json"},ts)])}else if(Jt===$.Musicgen)is=await Promise.all([ae(N,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},ts),oe(N,{generation_config:"generation_config.json"},ts)]);else if(Jt===$.MultiModality)is=await Promise.all([ae(N,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},ts),oe(N,{generation_config:"generation_config.json"},ts)]);else if(Jt===$.Phi3V)is=await Promise.all([ae(N,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},ts),oe(N,{generation_config:"generation_config.json"},ts)]);else{if(Jt!==$.EncoderOnly){const As=ns??(Fe==null?void 0:Fe.model_type);As!=="custom"&&console.warn(`Model type for '${As}' not found, assuming encoder-only architecture. Please report this at ${v.GITHUB_ISSUE_URL}.`)}is=await Promise.all([ae(N,{model:ts.model_file_name??"model"},ts)])}return new this(Fe,...is)}async _call(N){return await this.forward(N)}async forward(N){return await this._forward(this,N)}get generation_config(){var N;return((N=this.configs)==null?void 0:N.generation_config)??null}_get_logits_warper(N){const fe=new M.LogitsProcessorList;return N.temperature!==null&&N.temperature!==1&&fe.push(new M.TemperatureLogitsWarper(N.temperature)),N.top_k!==null&&N.top_k!==0&&fe.push(new M.TopKLogitsWarper(N.top_k)),N.top_p!==null&&N.top_p<1&&fe.push(new M.TopPLogitsWarper(N.top_p)),fe}_get_logits_processor(N,fe,Fe=null){const Ae=new M.LogitsProcessorList;if(N.repetition_penalty!==null&&N.repetition_penalty!==1&&Ae.push(new M.RepetitionPenaltyLogitsProcessor(N.repetition_penalty)),N.no_repeat_ngram_size!==null&&N.no_repeat_ngram_size>0&&Ae.push(new M.NoRepeatNGramLogitsProcessor(N.no_repeat_ngram_size)),N.bad_words_ids!==null&&Ae.push(new M.NoBadWordsLogitsProcessor(N.bad_words_ids,N.eos_token_id)),N.min_length!==null&&N.eos_token_id!==null&&N.min_length>0&&Ae.push(new M.MinLengthLogitsProcessor(N.min_length,N.eos_token_id)),N.min_new_tokens!==null&&N.eos_token_id!==null&&N.min_new_tokens>0&&Ae.push(new M.MinNewTokensLengthLogitsProcessor(fe,N.min_new_tokens,N.eos_token_id)),N.forced_bos_token_id!==null&&Ae.push(new M.ForcedBOSTokenLogitsProcessor(N.forced_bos_token_id)),N.forced_eos_token_id!==null&&Ae.push(new M.ForcedEOSTokenLogitsProcessor(N.max_length,N.forced_eos_token_id)),N.begin_suppress_tokens!==null){const et=fe>1||N.forced_bos_token_id===null?fe:fe+1;Ae.push(new M.SuppressTokensAtBeginLogitsProcessor(N.begin_suppress_tokens,et))}return N.guidance_scale!==null&&N.guidance_scale>1&&Ae.push(new M.ClassifierFreeGuidanceLogitsProcessor(N.guidance_scale)),Fe!==null&&Ae.extend(Fe),Ae}_prepare_generation_config(N,fe,Fe=y.GenerationConfig){const Ae={...this.config};for(const rt of["decoder","generator","text_config"])rt in Ae&&Object.assign(Ae,Ae[rt]);const et=new Fe(Ae);return Object.assign(et,this.generation_config??{}),N&&Object.assign(et,N),fe&&Object.assign(et,(0,R.pick)(fe,Object.getOwnPropertyNames(et))),et}_get_stopping_criteria(N,fe=null){const Fe=new te.StoppingCriteriaList;return N.max_length!==null&&Fe.push(new te.MaxLengthCriteria(N.max_length,this.config.max_position_embeddings??null)),N.eos_token_id!==null&&Fe.push(new te.EosTokenCriteria(N.eos_token_id)),fe&&Fe.extend(fe),Fe}_validate_model_class(){if(!this.can_generate){const N=[ba,Ta,Ao,Bd],fe=x.get(this.constructor),Fe=new Set,Ae=this.config.model_type;for(const rt of N){const ft=rt.get(Ae);ft&&Fe.add(ft[0])}let et=`The current model class (${fe}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Fe.size>0&&(et+=` Please use the following class instead: ${[...Fe].join(", ")}`),Error(et)}}prepare_inputs_for_generation(...N){return this._prepare_inputs_for_generation(this,...N)}_update_model_kwargs_for_generation({generated_input_ids:N,outputs:fe,model_inputs:Fe,is_encoder_decoder:Ae}){return Fe.past_key_values=this.getPastKeyValues(fe,Fe.past_key_values),Fe.input_ids=new b.Tensor("int64",N.flat(),[N.length,1]),Ae||(Fe.attention_mask=(0,b.cat)([Fe.attention_mask,(0,b.ones)([Fe.attention_mask.dims[0],1])],1)),Fe.position_ids=null,Fe}_prepare_model_inputs({inputs:N,bos_token_id:fe,model_kwargs:Fe}){const Ae=(0,R.pick)(Fe,this.forward_params),et=this.main_input_name;if(et in Ae){if(N)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Ae[et]=N;return{inputs_tensor:Ae[et],model_inputs:Ae,model_input_name:et}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:N,model_inputs:fe,model_input_name:Fe,generation_config:Ae}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!fe.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:rt,pixel_values:ft,attention_mask:Mt,...jt}=fe,Vt=await this._prepare_inputs_embeds(fe);fe={...jt,...(0,R.pick)(Vt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:et}=await Ie(this,fe);if(Ae.guidance_scale!==null&&Ae.guidance_scale>1)et=(0,b.cat)([et,(0,b.full_like)(et,0)],0),"attention_mask"in fe&&(fe.attention_mask=(0,b.cat)([fe.attention_mask,(0,b.zeros_like)(fe.attention_mask)],0));else if(fe.decoder_input_ids){const rt=xe(fe.decoder_input_ids).dims[0];if(rt!==et.dims[0]){if(et.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${et.dims[0]}) than the decoder inputs (${rt}).`);et=(0,b.cat)(Array.from({length:rt},()=>et),0)}}return fe.encoder_outputs=et,fe}_prepare_decoder_input_ids_for_generation({batch_size:N,model_input_name:fe,model_kwargs:Fe,decoder_start_token_id:Ae,bos_token_id:et,generation_config:rt}){let{decoder_input_ids:ft,...Mt}=Fe;if(!(ft instanceof b.Tensor)){if(ft)Array.isArray(ft[0])||(ft=Array.from({length:N},()=>ft));else if(Ae??(Ae=et),this.config.model_type==="musicgen")ft=Array.from({length:N*this.config.decoder.num_codebooks},()=>[Ae]);else if(Array.isArray(Ae)){if(Ae.length!==N)throw new Error(`\`decoder_start_token_id\` expcted to have length ${N} but got ${Ae.length}`);ft=Ae}else ft=Array.from({length:N},()=>[Ae]);ft=xe(ft)}return Fe.decoder_attention_mask=(0,b.ones_like)(ft),{input_ids:ft,model_inputs:Mt}}async generate({inputs:N=null,generation_config:fe=null,logits_processor:Fe=null,stopping_criteria:Ae=null,streamer:et=null,...rt}){this._validate_model_class(),fe=this._prepare_generation_config(fe,rt);let{inputs_tensor:ft,model_inputs:Mt,model_input_name:jt}=this._prepare_model_inputs({inputs:N,model_kwargs:rt});const Vt=this.config.is_encoder_decoder;Vt&&("encoder_outputs"in Mt||(Mt=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:ft,model_inputs:Mt,model_input_name:jt,generation_config:fe})));let Lt;Vt?{input_ids:Lt,model_inputs:Mt}=this._prepare_decoder_input_ids_for_generation({batch_size:Mt[jt].dims.at(0),model_input_name:jt,model_kwargs:Mt,decoder_start_token_id:fe.decoder_start_token_id,bos_token_id:fe.bos_token_id,generation_config:fe}):Lt=Mt[jt];let Gt=Lt.dims.at(-1);fe.max_new_tokens!==null&&(fe.max_length=Gt+fe.max_new_tokens);const ts=this._get_logits_processor(fe,Gt,Fe),ns=this._get_stopping_criteria(fe,Ae),Jt=Mt[jt].dims.at(0),is=ne.LogitsSampler.getSampler(fe),As=new Array(Jt).fill(0),xs=Lt.tolist();et&&et.put(xs);let cs,Es={};for(;;){if(Mt=this.prepare_inputs_for_generation(xs,Mt,fe),cs=await this.forward(Mt),fe.output_attentions&&fe.return_dict_in_generate){const ir=this.getAttentions(cs);for(const vr in ir)vr in Es||(Es[vr]=[]),Es[vr].push(ir[vr])}const Ys=cs.logits.slice(null,-1,null),br=ts(xs,Ys),pn=[];for(let ir=0;irir))break;Mt=this._update_model_kwargs_for_generation({generated_input_ids:pn,outputs:cs,model_inputs:Mt,is_encoder_decoder:Vt})}et&&et.end();const Is=this.getPastKeyValues(cs,Mt.past_key_values,!0),Zs=new b.Tensor("int64",xs.flat(),[xs.length,xs[0].length]);if(fe.return_dict_in_generate)return{sequences:Zs,past_key_values:Is,...Es};for(const Ys of Object.values(cs))Ys.location==="gpu-buffer"&&Ys.dispose();return Zs}getPastKeyValues(N,fe,Fe=!1){const Ae=Object.create(null);for(const et in N)if(et.startsWith("present")){const rt=et.replace("present","past_key_values"),ft=et.includes("encoder");if(ft&&fe?Ae[rt]=fe[rt]:Ae[rt]=N[et],fe&&(!ft||Fe)){const Mt=fe[rt];Mt.location==="gpu-buffer"&&Mt.dispose()}}return Ae}getAttentions(N){const fe={};for(const Fe of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Ae in N)Ae.startsWith(Fe)&&(Fe in fe||(fe[Fe]=[]),fe[Fe].push(N[Ae]));return fe}addPastKeyValues(N,fe){var Fe,Ae,et;if(fe)Object.assign(N,fe);else{const rt=this.sessions.decoder_model_merged??this.sessions.model,ft=((Fe=rt==null?void 0:rt.config)==null?void 0:Fe.kv_cache_dtype)??"float32",Mt=ft==="float16"?new Uint16Array:[],jt=((et=(Ae=N[this.main_input_name]??N.attention_mask)==null?void 0:Ae.dims)==null?void 0:et[0])??1,Vt=(0,f.getKeyValueShapes)(this.config,{batch_size:jt});for(const Lt in Vt)N[Lt]=new b.Tensor(ft,Mt,Vt[Lt])}}async encode_image({pixel_values:N}){const fe=(await we(this.sessions.vision_encoder,{pixel_values:N})).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 (${fe.dims[1]}).`),this.config.num_image_tokens=fe.dims[1]),fe}async encode_text({input_ids:N}){return(await we(this.sessions.embed_tokens,{input_ids:N})).inputs_embeds}}class Ke{}class je extends Ke{constructor({last_hidden_state:T,hidden_states:N=null,attentions:fe=null}){super(),this.last_hidden_state=T,this.hidden_states=N,this.attentions=fe}}class le extends se{}class Te extends le{}class Ue extends le{async _call(T){return new qs(await super._call(T))}}class Ve extends le{async _call(T){return new Qt(await super._call(T))}}class Ne extends le{async _call(T){return new Hs(await super._call(T))}}class Re extends le{async _call(T){return new tr(await super._call(T))}}class st extends se{}class dt extends st{}class ct extends st{async _call(T){return new qs(await super._call(T))}}class lt extends st{async _call(T){return new Qt(await super._call(T))}}class ht extends st{async _call(T){return new Hs(await super._call(T))}}class L extends se{}class ie extends L{}class H extends se{}class me extends H{}class $e extends H{async _call(T){return new qs(await super._call(T))}}class We extends H{async _call(T){return new Qt(await super._call(T))}}class Je extends H{async _call(T){return new Hs(await super._call(T))}}class ut extends H{async _call(T){return new tr(await super._call(T))}}class mt extends se{}class vt extends mt{}class kt extends mt{async _call(T){return new qs(await super._call(T))}}class At extends mt{async _call(T){return new Qt(await super._call(T))}}class os extends mt{async _call(T){return new Hs(await super._call(T))}}class ys extends mt{async _call(T){return new tr(await super._call(T))}}class Cs extends se{}class Ds extends Cs{}class sr extends Cs{async _call(T){return new qs(await super._call(T))}}class Sr extends Cs{async _call(T){return new Qt(await super._call(T))}}class Yr extends Cs{async _call(T){return new Hs(await super._call(T))}}class Us extends Cs{async _call(T){return new tr(await super._call(T))}}class Pr extends se{}class Nt extends Pr{}class Jr extends Pr{async _call(T){return new qs(await super._call(T))}}class $r extends Pr{async _call(T){return new Qt(await super._call(T))}}class Ar extends Pr{async _call(T){return new Hs(await super._call(T))}}class Zr extends Pr{async _call(T){return new tr(await super._call(T))}}class cr extends se{}class en extends cr{}class Ir extends cr{async _call(T){return new qs(await super._call(T))}}class Rr extends cr{async _call(T){return new Qt(await super._call(T))}}class Nr extends cr{async _call(T){return new Hs(await super._call(T))}}class ar extends cr{async _call(T){return new tr(await super._call(T))}}class ot extends se{}class Tt extends ot{}class Dt extends ot{async _call(T){return new qs(await super._call(T))}}class Vs extends ot{async _call(T){return new Qt(await super._call(T))}}class jr extends ot{async _call(T){return new Hs(await super._call(T))}}class Or extends ot{async _call(T){return new tr(await super._call(T))}}class Ms extends se{}class lr extends Ms{}class Os extends Ms{async _call(T){return new Qt(await super._call(T))}}class Er extends Ms{async _call(T){return new Hs(await super._call(T))}}class es extends Ms{async _call(T){return new tr(await super._call(T))}}class wn extends Ms{async _call(T){return new qs(await super._call(T))}}class Ur extends se{}class oo extends Ur{}class An extends Ur{async _call(T){return new qs(await super._call(T))}}class In extends Ur{async _call(T){return new Qt(await super._call(T))}}class On extends Ur{async _call(T){return new Hs(await super._call(T))}}class Vr extends se{}class Fn extends Vr{}class io extends Vr{async _call(T){return new qs(await super._call(T))}}class Wr extends Vr{async _call(T){return new Qt(await super._call(T))}}class _r extends Vr{async _call(T){return new tr(await super._call(T))}}class ur extends se{}class yn extends ur{}class tn extends ur{async _call(T){return new qs(await super._call(T))}}class Mn extends ur{async _call(T){return new Qt(await super._call(T))}}class bn extends ur{async _call(T){return new Hs(await super._call(T))}}class vn extends ur{async _call(T){return new tr(await super._call(T))}}class zt extends se{}class xn extends zt{}class Dn extends zt{async _call(T){return new qs(await super._call(T))}}class Ln extends zt{async _call(T){return new Qt(await super._call(T))}}class zn extends zt{async _call(T){return new tr(await super._call(T))}}class Gr extends se{}class Bn extends Gr{}class Tn extends Gr{async _call(T){return new Qt(await super._call(T))}}class Rn extends Gr{async _call(T){return new tr(await super._call(T))}}class as extends Gr{async _call(T){return new qs(await super._call(T))}}class Pe extends se{constructor(){super(...arguments);_e(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class P extends Pe{}class Q extends Pe{}class ue extends se{}class be extends ue{}class Se extends ue{}class Qe extends se{}class pt extends Qe{}class gt extends Qe{}class _t extends se{}class xt extends _t{}class Kt extends _t{}class ms extends _t{async _call(T){return new Qt(await super._call(T))}}class us extends se{}class Fs extends us{}class Bt extends us{}class rs extends us{async _call(T){return new Qt(await super._call(T))}}class rr extends us{}class Ws extends se{}class ze extends Ws{}class Js extends Ws{}class Fr extends se{}class ks extends Fr{}class Xs extends Fr{}class Ot extends se{}class or extends Ot{}class fr extends Ot{async _call(T){return new qs(await super._call(T))}}class _s extends Ot{async _call(T){return new Qt(await super._call(T))}}class Ss extends Ot{async _call(T){return new Hs(await super._call(T))}}class yt extends Ot{async _call(T){return new tr(await super._call(T))}}class qt extends se{}class Ls extends qt{}class $s extends qt{async _call(T){return new qs(await super._call(T))}}class Gs extends qt{async _call(T){return new Qt(await super._call(T))}}class $t extends qt{async _call(T){return new Hs(await super._call(T))}}class sn extends qt{async _call(T){return new tr(await super._call(T))}}class qe extends se{}class It extends qe{}class La extends qe{async _call(T){return new qs(await super._call(T))}}class Uo extends qe{async _call(T){return new Qt(await super._call(T))}}class za extends qe{async _call(T){return new Hs(await super._call(T))}}class Ba extends qe{async _call(T){return new tr(await super._call(T))}}class Yt extends se{}class Ra extends Yt{}class Vo extends Yt{}class Wo extends se{constructor(){super(...arguments);_e(this,"requires_attention_mask",!1);_e(this,"main_input_name","input_features");_e(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Na extends Wo{}class ja extends Wo{_prepare_generation_config(T,N){return super._prepare_generation_config(T,N,j.WhisperGenerationConfig)}_retrieve_init_tokens(T){const N=[T.decoder_start_token_id];let fe=T.language;const Fe=T.task;if(T.is_multilingual){fe||(console.warn("No language specified - defaulting to English (en)."),fe="en");const et=`<|${(0,q.whisper_language_to_code)(fe)}|>`;N.push(T.lang_to_id[et]),N.push(T.task_to_id[Fe??"transcribe"])}else if(fe||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!T.return_timestamps&&T.no_timestamps_token_id&&N.at(-1)!==T.no_timestamps_token_id?N.push(T.no_timestamps_token_id):T.return_timestamps&&N.at(-1)===T.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),N.pop()),N.filter(Ae=>Ae!=null)}async generate({inputs:T=null,generation_config:N=null,logits_processor:fe=null,stopping_criteria:Fe=null,...Ae}){N=this._prepare_generation_config(N,Ae);const et=Ae.decoder_input_ids??this._retrieve_init_tokens(N);if(N.return_timestamps&&(fe??(fe=new M.LogitsProcessorList),fe.push(new M.WhisperTimeStampLogitsProcessor(N,et))),N.begin_suppress_tokens&&(fe??(fe=new M.LogitsProcessorList),fe.push(new M.SuppressTokensAtBeginLogitsProcessor(N.begin_suppress_tokens,et.length))),N.return_token_timestamps){if(!N.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.");N.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),N.output_attentions=!0,N.return_dict_in_generate=!0}const rt=await super.generate({inputs:T,generation_config:N,logits_processor:fe,decoder_input_ids:et,...Ae});return N.return_token_timestamps&&(rt.token_timestamps=this._extract_token_timestamps(rt,N.alignment_heads,N.num_frames)),rt}_extract_token_timestamps(T,N,fe=null,Fe=.02){if(!T.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`.");fe==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 Ae=this.config.median_filter_width;Ae===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Ae=7);const et=T.cross_attentions,rt=Array.from({length:this.config.decoder_layers},(ns,Jt)=>(0,b.cat)(et.map(is=>is[Jt]),2)),ft=(0,b.stack)(N.map(([ns,Jt])=>{if(ns>=rt.length)throw new Error(`Layer index ${ns} is out of bounds for cross attentions (length ${rt.length}).`);return fe?rt[ns].slice(null,Jt,null,[0,fe]):rt[ns].slice(null,Jt)})).transpose(1,0,2,3),[Mt,jt]=(0,b.std_mean)(ft,-2,0,!0),Vt=ft.clone();for(let ns=0;nsis[Zs+1]-is[Zs]),cs=(0,R.mergeArrays)([1],xs).map(Is=>!!Is),Es=[];for(let Is=0;IsLt.findIndex(Gt=>Gt==Ae)),ft=rt.every(Lt=>Lt===-1),Mt=rt.every(Lt=>Lt!==-1);if(!ft&&!Mt)throw new Error("Every input should contain either 0 or 1 image token.");if(ft)return{inputs_embeds:T,attention_mask:Fe};const jt=[],Vt=[];for(let Lt=0;LtArray.from({length:T.dims[0]},xs=>Array.from({length:T.dims[1]},cs=>1))),ts=N?N.tolist():[],ns=fe?fe.tolist():[];let Jt=0,is=0;for(let As=0;AsLt[As][Rs]==1),Es=xs.reduce((Ts,Rs,qr)=>(Rs==ft&&Ts.push(qr),Ts),[]).map(Ts=>xs[Ts+1]),Is=Es.filter(Ts=>Ts==et).length,Zs=Es.filter(Ts=>Ts==rt).length;let Ys=[],br=0,pn=Is,ka=Zs;for(let Ts=0;Tsmr>br&&Qr==et),qr=xs.findIndex((Qr,mr)=>mr>br&&Qr==rt),En=pn>0&&Rs!==-1?Rs:xs.length+1,Cn=ka>0&&qr!==-1?qr:xs.length+1;let kn,$a,Aa,Ec;En0?(0,K.max)(Ys.at(-1))[0]+1:0;Ys.push(Array.from({length:3*zr},(Qr,mr)=>Cc+mr%zr));const Qn=zr+Cc,Xn=kp*Do*qn,kc=Array.from({length:Xn},(Qr,mr)=>Qn+Math.floor(mr/(Do*qn))),Sc=Array.from({length:Xn},(Qr,mr)=>Qn+Math.floor(mr/qn)%Do),$c=Array.from({length:Xn},(Qr,mr)=>Qn+mr%qn);Ys.push([kc,Sc,$c].flat()),br=kn+Xn}if(br0?(0,K.max)(Ys.at(-1))[0]+1:0,Rs=xs.length-br;Ys.push(Array.from({length:3*Rs},(qr,En)=>Ts+En%Rs))}const ir=Ys.reduce((Ts,Rs)=>Ts+Rs.length,0),vr=new Array(ir);let Io=0;for(let Ts=0;Ts<3;++Ts)for(let Rs=0;RsVt[Jt%Vt.length]),ts=Array.from({length:Lt[0]},(ns,Jt)=>(0,K.max)(Vt.subarray(Lt[1]*Jt,Lt[1]*(Jt+1)))[0]+1n+BigInt(Lt[1]));return[new b.Tensor("int64",Gt,[3,...Lt]),new b.Tensor("int64",ts,[ts.length,1])]}else{const[Vt,Lt]=T.dims,Gt=BigInt64Array.from({length:3*Vt*Lt},(ts,ns)=>BigInt(Math.floor(ns%Lt/Vt)));return[new b.Tensor("int64",Gt,[3,...T.dims]),(0,b.zeros)([Vt,1])]}}async encode_image({pixel_values:T,image_grid_thw:N}){return(await we(this.sessions.vision_encoder,{pixel_values:T,grid_thw:N})).image_features}_merge_input_ids_with_image_features(T){return tt({image_token_id:this.config.image_token_id,...T})}prepare_inputs_for_generation(T,N,fe){if(N.attention_mask&&!N.position_ids)if(!N.past_key_values)[N.position_ids,N.rope_deltas]=this.get_rope_index(N.input_ids,N.image_grid_thw,N.video_grid_thw,N.attention_mask);else{N.pixel_values=null;const Fe=BigInt(Object.values(N.past_key_values)[0].dims.at(-2)),Ae=N.rope_deltas.map(et=>Fe+et);N.position_ids=(0,b.stack)([Ae,Ae,Ae],0)}return N}}class yi extends se{}class Dl extends yi{}class Ll extends yi{}class Mi extends se{}class zl extends Mi{}class Bl extends Mi{}class bi extends se{}class Rl extends bi{}class Nl extends bi{}class vi extends se{}class jl extends vi{}class Ul extends vi{}class xi extends se{}class Vl extends xi{}class Wl extends xi{}class mo extends se{}class Gl extends mo{}class Ti extends mo{async _call(T){return new Qt(await super._call(T))}}class _o extends se{}class Kl extends _o{}class Hl extends _o{async _call(T){return new Qt(await super._call(T))}}class ql extends se{}class Ql extends ql{}class Pi extends se{}class Xl extends Pi{}class Ei extends Pi{async _call(T){return new Qt(await super._call(T))}}class Yl extends se{}class Jl extends Yl{}class Ci extends se{}class Gc extends Ci{}class Zl extends Ci{async _call(T){return new Qt(await super._call(T))}}class eu extends se{}class Mr extends eu{}class ki extends se{}class tu extends ki{}class su extends ki{async _call(T){return new Qt(await super._call(T))}}class ru extends se{}class nu extends ru{async _call(T){return new Pc(await super._call(T))}}class Si extends se{}class ou extends Si{}class iu extends Si{async _call(T){return new Qt(await super._call(T))}}class $i extends se{}class au extends $i{}class Kc extends $i{async _call(T){return new Qt(await super._call(T))}}class Ai extends se{}class lu extends Ai{}class uu extends Ai{}class Ii extends se{}class du extends Ii{}class cu extends Ii{}class pu extends se{}class nn extends pu{}class on extends pu{async _call(T){return new Qt(await super._call(T))}}class Dr extends se{}class Oi extends Dr{}class an extends Dr{async _call(T){return new Fi(await super._call(T))}}class Ks extends Dr{async _call(T){return new Di(await super._call(T))}}class Fi extends Ke{constructor({logits:T,pred_boxes:N}){super(),this.logits=T,this.pred_boxes=N}}class Di extends Ke{constructor({logits:T,pred_boxes:N,pred_masks:fe}){super(),this.logits=T,this.pred_boxes=N,this.pred_masks=fe}}class Li extends se{}class Hc extends Li{}class Un extends Li{async _call(T){return new zi(await super._call(T))}}class zi extends Ke{constructor({logits:T,pred_boxes:N}){super(),this.logits=T,this.pred_boxes=N}}class fo extends se{}class hu extends fo{}class mu extends fo{async _call(T){return new Bi(await super._call(T))}}class Bi extends Fi{}class go extends se{}class _u extends go{}class Ri extends go{async _call(T){return new Qt(await super._call(T))}}class Ni extends se{}class wo extends Ni{}class ji extends Ni{async _call(T){return new Qt(await super._call(T))}}class Ui extends se{}class fu extends Ui{}class qc extends Ui{async _call(T){return new Qt(await super._call(T))}}class Vi extends se{}class Wi extends Vi{}class Vn extends Vi{async _call(T){return new Qt(await super._call(T))}}class Gi extends se{}class Ki extends Gi{}class gu extends Gi{}class Hi extends se{}class wu extends Hi{}class Qc extends Hi{}class yu extends se{}class Mu extends yu{}class qi extends se{}class bu extends qi{}class yo extends qi{}class vu extends qi{}class Mo extends se{}class Qi extends Mo{}class bo extends se{}class xu extends bo{}class Tu extends bo{}class vo extends se{}class Xc extends vo{}class Pu extends vo{}class Yc extends se{}class Eu extends Yc{}class Xi extends se{}class Cu extends Xi{}class Yi extends Xi{async _call(T){return new Qt(await super._call(T))}}class Ji extends se{}class ku extends Ji{}class Zi extends Ji{async _call(T){return new Qt(await super._call(T))}}class ea extends se{}class Su extends ea{}class Jc extends ea{async _call(T){return new Qt(await super._call(T))}}class ta extends se{}class $u extends ta{}class Au extends ta{async _call(T){return new Qt(await super._call(T))}}class Zc extends se{}class Iu extends Zc{}class sa extends se{}class Ou extends sa{}class Fu extends sa{async _call(T){return new Du(await super._call(T))}}class Du extends Ke{constructor({logits:T,pred_boxes:N}){super(),this.logits=T,this.pred_boxes=N}}class ep extends se{}class xo extends ep{async get_image_embeddings({pixel_values:T}){return await Ie(this,{pixel_values:T})}async forward(T){if((!T.image_embeddings||!T.image_positional_embeddings)&&(T={...T,...await this.get_image_embeddings(T)}),!T.input_labels&&T.input_points){const fe=T.input_points.dims.slice(0,-1),Fe=fe.reduce((Ae,et)=>Ae*et,1);T.input_labels=new b.Tensor("int64",new BigInt64Array(Fe).fill(1n),fe)}const N={image_embeddings:T.image_embeddings,image_positional_embeddings:T.image_positional_embeddings};return T.input_points&&(N.input_points=T.input_points),T.input_labels&&(N.input_labels=T.input_labels),T.input_boxes&&(N.input_boxes=T.input_boxes),await we(this.sessions.prompt_encoder_mask_decoder,N)}async _call(T){return new Wn(await super._call(T))}}class Wn extends Ke{constructor({iou_scores:T,pred_masks:N}){super(),this.iou_scores=T,this.pred_masks=N}}class To extends se{}class Lu extends To{}class zu extends To{}class ra extends se{}class Bu extends ra{}class na extends ra{}class Hr extends se{}class Ru extends Hr{}class Nu extends Hr{async _call(T){return new cn(await super._call(T))}}class tp extends Hr{async _call(T){return new Qt(await super._call(T))}}class ju extends Hr{async _call(T){return new Hs(await super._call(T))}}class Po extends se{}class Uu extends Po{}class Vu extends Po{async _call(T){return new Hs(await super._call(T))}}class Wu extends se{}class sp extends Wu{}class Eo extends se{}class Gu extends Eo{}class rp extends Eo{async _call(T){return new cn(await super._call(T))}}class Ku extends Eo{async _call(T){return new Qt(await super._call(T))}}class Gn extends se{}class Hu extends Gn{}class qu extends Gn{async _call(T){return new cn(await super._call(T))}}class np extends Gn{async _call(T){return new Qt(await super._call(T))}}class Qu extends Gn{async _call(T){return new Hs(await super._call(T))}}class Co extends se{}class Xu extends Co{}class op extends Co{async _call(T){return new cn(await super._call(T))}}class Yu extends Co{async _call(T){return new Qt(await super._call(T))}}class ip extends se{}class Ju extends Hr{}class Zu extends Hr{async _call(T){return new cn(await super._call(T))}}class ap extends Hr{async _call(T){return new Qt(await super._call(T))}}class Pn extends se{}class ed extends Pn{}class td extends Pn{async _call(T){return new cn(await super._call(T))}}class sd extends Pn{async _call(T){return new Qt(await super._call(T))}}class lp extends Pn{async _call(T){return new Hn(await super._call(T))}}class rd extends Pn{async _call(T){return new Hs(await super._call(T))}}class nd extends se{}class od extends nd{}class ko extends se{}class Hp extends ko{}class Cr extends ko{}class Lr extends ko{async generate_speech(T,N,{threshold:fe=.5,minlenratio:Fe=0,maxlenratio:Ae=20,vocoder:et=null}={}){const rt={input_ids:T},{encoder_outputs:ft,encoder_attention_mask:Mt}=await Ie(this,rt),jt=ft.dims[1]/this.config.reduction_factor,Vt=Math.floor(jt*Ae),Lt=Math.floor(jt*Fe),Gt=this.config.num_mel_bins;let ts=[],ns=null,Jt=null,is=0;for(;;){++is;const cs=ce(!!Jt);let Es;Jt?Es=Jt.output_sequence_out:Es=new b.Tensor("float32",new Float32Array(Gt),[1,1,Gt]);let Is={use_cache_branch:cs,output_sequence:Es,encoder_attention_mask:Mt,speaker_embeddings:N,encoder_hidden_states:ft};this.addPastKeyValues(Is,ns),Jt=await we(this.sessions.decoder_model_merged,Is),ns=this.getPastKeyValues(Jt,ns);const{prob:Zs,spectrum:Ys}=Jt;if(ts.push(Ys),is>=Lt&&(Array.from(Zs.data).filter(br=>br>=fe).length>0||is>=Vt))break}const As=(0,b.cat)(ts),{waveform:xs}=await we(et.sessions.model,{spectrogram:As});return{spectrogram:As,waveform:xs}}}class ln extends se{constructor(){super(...arguments);_e(this,"main_input_name","spectrogram")}}class un extends se{}class id extends un{}class oa extends se{}class ad extends oa{}class ld extends oa{}class ia extends se{}class ud extends ia{}class dd extends ia{}class aa extends se{}class cd extends aa{}class pd extends aa{}class la extends se{}class nr extends la{}class hd extends la{static async from_pretrained(T,N={}){return super.from_pretrained(T,{...N,model_file_name:N.model_file_name??"text_model"})}}class md extends la{static async from_pretrained(T,N={}){return super.from_pretrained(T,{...N,model_file_name:N.model_file_name??"audio_model"})}}class ua extends se{}class da extends ua{async _call(T){return new Cp(await super._call(T))}}class dn extends se{}class up extends dn{}class _d extends dn{}class fd extends dn{}class ca extends se{}class gd extends ca{}class wd extends ca{}class So extends se{}class yd extends So{}class Md extends So{async _call(T){return new Qt(await super._call(T))}}class pa extends se{}class dp extends pa{}class cp extends pa{}class $o extends se{constructor(){super(...arguments);_e(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(N){const[fe,Fe]=N.dims,Ae=this.config.decoder.num_codebooks,et=Fe-Ae;let rt=0;for(let jt=0;jt0&&Gt<=et&&(N.data[rt++]=N.data[jt])}const ft=Math.floor(fe/Ae),Mt=rt/(ft*Ae);return new b.Tensor(N.type,N.data.slice(0,rt),[ft,Ae,Mt])}prepare_inputs_for_generation(N,fe,Fe){let Ae=structuredClone(N);for(let rt=0;rt=ft&&(Ae[rt][ft]=BigInt(this.config.decoder.pad_token_id));return Fe.guidance_scale!==null&&Fe.guidance_scale>1&&(Ae=Ae.concat(Ae)),super.prepare_inputs_for_generation(Ae,fe,Fe)}async generate(N){const fe=await super.generate(N),Fe=this._apply_and_filter_by_delay_pattern_mask(fe).unsqueeze_(0),{audio_values:Ae}=await we(this.sessions.encodec_decode,{audio_codes:Fe});return Ae}}class ha extends se{}class pp extends ha{}class ma extends ha{async _call(T){return new Qt(await super._call(T))}}class _a extends se{}class bd extends _a{}class vd extends _a{async _call(T){return new Qt(await super._call(T))}}class xd extends se{}class Td extends xd{}class Pd extends xd{async _call(T){return new Qt(await super._call(T))}}class fa extends se{}class hp extends fa{}class Ed extends fa{async _call(T){return new Qt(await super._call(T))}}class Cd extends se{}class mp extends Cd{}class kd extends se{}class Sd extends kd{constructor(...N){super(...N);_e(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(N){const fe=this._generation_mode??"text";let Fe;if(fe==="text"||!N.past_key_values){const Mt=this.sessions.prepare_inputs_embeds,jt=(0,R.pick)(N,Mt.inputNames);Fe=await we(Mt,jt)}else{const Mt=this.sessions.gen_img_embeds,jt=(0,R.pick)({image_ids:N.input_ids},Mt.inputNames);Fe=await we(Mt,jt)}const Ae={...N,...Fe},et=await Ee(this,Ae),rt=this.sessions[fe==="text"?"lm_head":"gen_head"];if(!rt)throw new Error(`Unable to find "${rt}" generation head`);const ft=await we(rt,(0,R.pick)(et,rt.inputNames));return{...Fe,...et,...ft}}async generate(N){return this._generation_mode="text",super.generate(N)}async generate_images(N){this._generation_mode="image";const fe=(N.inputs??N[this.main_input_name]).dims[1],Ae=(await super.generate(N)).slice(null,[fe,null]),et=this.sessions.image_decode,{decoded_image:rt}=await we(et,{generated_tokens:Ae}),ft=rt.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),Mt=[];for(const jt of ft){const Vt=I.RawImage.fromTensor(jt);Mt.push(Vt)}return Mt}}class $d extends Ke{constructor({char_logits:T,bpe_logits:N,wp_logits:fe}){super(),this.char_logits=T,this.bpe_logits=N,this.wp_logits=fe}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class Ad extends se{}class Id extends Ad{async _call(T){return new $d(await super._call(T))}}class Od extends se{}class Fd extends Od{}class Dd extends Od{}class ga extends se{}class Ld extends ga{}class zd extends ga{}class ws{static async from_pretrained(T,{progress_callback:N=null,config:fe=null,cache_dir:Fe=null,local_files_only:Ae=!1,revision:et="main",model_file_name:rt=null,subfolder:ft="onnx",device:Mt=null,dtype:jt=null,use_external_data_format:Vt=null,session_options:Lt={}}={}){const Gt={progress_callback:N,config:fe,cache_dir:Fe,local_files_only:Ae,revision:et,model_file_name:rt,subfolder:ft,device:Mt,dtype:jt,use_external_data_format:Vt,session_options:Lt};if(Gt.config=await f.AutoConfig.from_pretrained(T,Gt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const ts of this.MODEL_CLASS_MAPPINGS){const ns=ts.get(Gt.config.model_type);if(ns)return await ns[1].from_pretrained(T,Gt)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${Gt.config.model_type}", attempting to construct from base class.`),await se.from_pretrained(T,Gt);throw Error(`Unsupported model type: ${Gt.config.model_type}`)}}_e(ws,"MODEL_CLASS_MAPPINGS",null),_e(ws,"BASE_IF_FAIL",!1);const _p=new Map([["bert",["BertModel",Te]],["modernbert",["ModernBertModel",dt]],["nomic_bert",["NomicBertModel",ie]],["roformer",["RoFormerModel",me]],["electra",["ElectraModel",Ds]],["esm",["EsmModel",oo]],["convbert",["ConvBertModel",vt]],["camembert",["CamembertModel",Nt]],["deberta",["DebertaModel",en]],["deberta-v2",["DebertaV2Model",Tt]],["mpnet",["MPNetModel",yn]],["albert",["AlbertModel",Bn]],["distilbert",["DistilBertModel",lr]],["roberta",["RobertaModel",or]],["xlm",["XLMModel",Ls]],["xlm-roberta",["XLMRobertaModel",It]],["clap",["ClapModel",nr]],["clip",["CLIPModel",Ya]],["clipseg",["CLIPSegModel",rl]],["chinese_clip",["ChineseCLIPModel",gr]],["siglip",["SiglipModel",el]],["jina_clip",["JinaCLIPModel",uo]],["mobilebert",["MobileBertModel",Fn]],["squeezebert",["SqueezeBertModel",xn]],["wav2vec2",["Wav2Vec2Model",Ru]],["wav2vec2-bert",["Wav2Vec2BertModel",Xu]],["unispeech",["UniSpeechModel",Gu]],["unispeech-sat",["UniSpeechSatModel",Hu]],["hubert",["HubertModel",Ju]],["wavlm",["WavLMModel",ed]],["audio-spectrogram-transformer",["ASTModel",Ra]],["vits",["VitsModel",da]],["pyannote",["PyAnnoteModel",Uu]],["wespeaker-resnet",["WeSpeakerResNetModel",sp]],["detr",["DetrModel",Oi]],["rt_detr",["RTDetrModel",Hc]],["table-transformer",["TableTransformerModel",hu]],["vit",["ViTModel",Gl]],["ijepa",["IJepaModel",Kl]],["pvt",["PvtModel",Xl]],["vit_msn",["ViTMSNModel",Gc]],["vit_mae",["ViTMAEModel",Jl]],["groupvit",["GroupViTModel",Mr]],["fastvit",["FastViTModel",tu]],["mobilevit",["MobileViTModel",ou]],["mobilevitv2",["MobileViTV2Model",au]],["owlvit",["OwlViTModel",lu]],["owlv2",["Owlv2Model",du]],["beit",["BeitModel",nn]],["deit",["DeiTModel",_u]],["hiera",["HieraModel",wo]],["convnext",["ConvNextModel",Cu]],["convnextv2",["ConvNextV2Model",ku]],["dinov2",["Dinov2Model",Su]],["dinov2_with_registers",["Dinov2WithRegistersModel",$u]],["resnet",["ResNetModel",fu]],["swin",["SwinModel",Wi]],["swin2sr",["Swin2SRModel",Ki]],["donut-swin",["DonutSwinModel",Eu]],["yolos",["YolosModel",Ou]],["dpt",["DPTModel",wu]],["glpn",["GLPNModel",Xc]],["hifigan",["SpeechT5HifiGan",ln]],["efficientnet",["EfficientNetModel",yd]],["decision_transformer",["DecisionTransformerModel",mp]],["patchtst",["PatchTSTForPrediction",Fd]],["patchtsmixer",["PatchTSMixerForPrediction",Ld]],["mobilenet_v1",["MobileNetV1Model",pp]],["mobilenet_v2",["MobileNetV2Model",bd]],["mobilenet_v3",["MobileNetV3Model",Td]],["mobilenet_v4",["MobileNetV4Model",hp]],["maskformer",["MaskFormerModel",xu]],["mgp-str",["MgpstrForSceneTextRecognition",Id]],["style_text_to_speech_2",["StyleTextToSpeech2Model",od]]]),fp=new Map([["t5",["T5Model",P]],["longt5",["LongT5Model",be]],["mt5",["MT5Model",pt]],["bart",["BartModel",xt]],["mbart",["MBartModel",Fs]],["marian",["MarianModel",Lu]],["whisper",["WhisperModel",Na]],["m2m_100",["M2M100Model",Bu]],["blenderbot",["BlenderbotModel",ze]],["blenderbot-small",["BlenderbotSmallModel",ks]]]),gp=new Map([["bloom",["BloomModel",Rl]],["jais",["JAISModel",al]],["gpt2",["GPT2Model",ol]],["gptj",["GPTJModel",pl]],["gpt_bigcode",["GPTBigCodeModel",ml]],["gpt_neo",["GPTNeoModel",yr]],["gpt_neox",["GPTNeoXModel",dl]],["codegen",["CodeGenModel",ri]],["llama",["LlamaModel",oi]],["exaone",["ExaoneModel",yl]],["olmo",["OlmoModel",Vc]],["olmo2",["Olmo2Model",xl]],["mobilellm",["MobileLLMModel",Ml]],["granite",["GraniteModel",ds]],["cohere",["CohereModel",Pl]],["gemma",["GemmaModel",Cl]],["gemma2",["Gemma2Model",Sl]],["helium",["HeliumModel",po]],["glm",["GlmModel",wl]],["openelm",["OpenELMModel",Al]],["qwen2",["Qwen2Model",jn]],["phi",["PhiModel",Dl]],["phi3",["Phi3Model",zl]],["mpt",["MptModel",jl]],["opt",["OPTModel",Vl]],["mistral",["MistralModel",ad]],["starcoder2",["Starcoder2Model",ud]],["falcon",["FalconModel",cd]],["stablelm",["StableLmModel",gd]]]),Bd=new Map([["speecht5",["SpeechT5ForSpeechToText",Cr]],["whisper",["WhisperForConditionalGeneration",ja]],["moonshine",["MoonshineForConditionalGeneration",Ua]]]),Kn=new Map([["speecht5",["SpeechT5ForTextToSpeech",Lr]]]),wa=new Map([["vits",["VitsModel",da]],["musicgen",["MusicgenForConditionalGeneration",$o]]]),ya=new Map([["bert",["BertForSequenceClassification",Ve]],["modernbert",["ModernBertForSequenceClassification",lt]],["roformer",["RoFormerForSequenceClassification",We]],["electra",["ElectraForSequenceClassification",Sr]],["esm",["EsmForSequenceClassification",In]],["convbert",["ConvBertForSequenceClassification",At]],["camembert",["CamembertForSequenceClassification",$r]],["deberta",["DebertaForSequenceClassification",Rr]],["deberta-v2",["DebertaV2ForSequenceClassification",Vs]],["mpnet",["MPNetForSequenceClassification",Mn]],["albert",["AlbertForSequenceClassification",Tn]],["distilbert",["DistilBertForSequenceClassification",Os]],["roberta",["RobertaForSequenceClassification",_s]],["xlm",["XLMForSequenceClassification",Gs]],["xlm-roberta",["XLMRobertaForSequenceClassification",Uo]],["bart",["BartForSequenceClassification",ms]],["mbart",["MBartForSequenceClassification",rs]],["mobilebert",["MobileBertForSequenceClassification",Wr]],["squeezebert",["SqueezeBertForSequenceClassification",Ln]]]),Ma=new Map([["bert",["BertForTokenClassification",Ne]],["modernbert",["ModernBertForTokenClassification",ht]],["roformer",["RoFormerForTokenClassification",Je]],["electra",["ElectraForTokenClassification",Yr]],["esm",["EsmForTokenClassification",On]],["convbert",["ConvBertForTokenClassification",os]],["camembert",["CamembertForTokenClassification",Ar]],["deberta",["DebertaForTokenClassification",Nr]],["deberta-v2",["DebertaV2ForTokenClassification",jr]],["mpnet",["MPNetForTokenClassification",bn]],["distilbert",["DistilBertForTokenClassification",Er]],["roberta",["RobertaForTokenClassification",Ss]],["xlm",["XLMForTokenClassification",$t]],["xlm-roberta",["XLMRobertaForTokenClassification",za]]]),Ao=new Map([["t5",["T5ForConditionalGeneration",Q]],["longt5",["LongT5ForConditionalGeneration",Se]],["mt5",["MT5ForConditionalGeneration",gt]],["bart",["BartForConditionalGeneration",Kt]],["mbart",["MBartForConditionalGeneration",Bt]],["marian",["MarianMTModel",zu]],["m2m_100",["M2M100ForConditionalGeneration",na]],["blenderbot",["BlenderbotForConditionalGeneration",Js]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Xs]]]),ba=new Map([["bloom",["BloomForCausalLM",Nl]],["gpt2",["GPT2LMHeadModel",il]],["jais",["JAISLMHeadModel",ll]],["gptj",["GPTJForCausalLM",hl]],["gpt_bigcode",["GPTBigCodeForCausalLM",_l]],["gpt_neo",["GPTNeoForCausalLM",ul]],["gpt_neox",["GPTNeoXForCausalLM",cl]],["codegen",["CodeGenForCausalLM",fl]],["llama",["LlamaForCausalLM",Uc]],["exaone",["ExaoneForCausalLM",ui]],["olmo",["OlmoForCausalLM",vl]],["olmo2",["Olmo2ForCausalLM",Wc]],["mobilellm",["MobileLLMForCausalLM",bl]],["granite",["GraniteForCausalLM",Tl]],["cohere",["CohereForCausalLM",El]],["gemma",["GemmaForCausalLM",kl]],["gemma2",["Gemma2ForCausalLM",$l]],["helium",["HeliumForCausalLM",gl]],["glm",["GlmForCausalLM",Nn]],["openelm",["OpenELMForCausalLM",Il]],["qwen2",["Qwen2ForCausalLM",Ol]],["phi",["PhiForCausalLM",Ll]],["phi3",["Phi3ForCausalLM",Bl]],["mpt",["MptForCausalLM",Ul]],["opt",["OPTForCausalLM",Wl]],["mbart",["MBartForCausalLM",rr]],["mistral",["MistralForCausalLM",ld]],["starcoder2",["Starcoder2ForCausalLM",dd]],["falcon",["FalconForCausalLM",pd]],["trocr",["TrOCRForCausalLM",id]],["stablelm",["StableLmForCausalLM",wd]],["phi3_v",["Phi3VForCausalLM",hr]]]),wp=new Map([["multi_modality",["MultiModalityCausalLM",Sd]]]),va=new Map([["bert",["BertForMaskedLM",Ue]],["modernbert",["ModernBertForMaskedLM",ct]],["roformer",["RoFormerForMaskedLM",$e]],["electra",["ElectraForMaskedLM",sr]],["esm",["EsmForMaskedLM",An]],["convbert",["ConvBertForMaskedLM",kt]],["camembert",["CamembertForMaskedLM",Jr]],["deberta",["DebertaForMaskedLM",Ir]],["deberta-v2",["DebertaV2ForMaskedLM",Dt]],["mpnet",["MPNetForMaskedLM",tn]],["albert",["AlbertForMaskedLM",as]],["distilbert",["DistilBertForMaskedLM",wn]],["roberta",["RobertaForMaskedLM",fr]],["xlm",["XLMWithLMHeadModel",$s]],["xlm-roberta",["XLMRobertaForMaskedLM",La]],["mobilebert",["MobileBertForMaskedLM",io]],["squeezebert",["SqueezeBertForMaskedLM",Dn]]]),xa=new Map([["bert",["BertForQuestionAnswering",Re]],["roformer",["RoFormerForQuestionAnswering",ut]],["electra",["ElectraForQuestionAnswering",Us]],["convbert",["ConvBertForQuestionAnswering",ys]],["camembert",["CamembertForQuestionAnswering",Zr]],["deberta",["DebertaForQuestionAnswering",ar]],["deberta-v2",["DebertaV2ForQuestionAnswering",Or]],["mpnet",["MPNetForQuestionAnswering",vn]],["albert",["AlbertForQuestionAnswering",Rn]],["distilbert",["DistilBertForQuestionAnswering",es]],["roberta",["RobertaForQuestionAnswering",yt]],["xlm",["XLMForQuestionAnswering",sn]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Ba]],["mobilebert",["MobileBertForQuestionAnswering",_r]],["squeezebert",["SqueezeBertForQuestionAnswering",zn]]]),Ta=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ko]],["idefics3",["Idefics3ForConditionalGeneration",Ho]]]),yp=new Map([["llava",["LlavaForConditionalGeneration",ao]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",Va]],["moondream1",["Moondream1ForConditionalGeneration",Wa]],["florence2",["Florence2ForConditionalGeneration",Ka]],["qwen2-vl",["Qwen2VLForConditionalGeneration",Fl]],["idefics3",["Idefics3ForConditionalGeneration",Ho]],["paligemma",["PaliGemmaForConditionalGeneration",qa]]]),Rd=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Ko]]]),Nd=new Map([["vit",["ViTForImageClassification",Ti]],["ijepa",["IJepaForImageClassification",Hl]],["pvt",["PvtForImageClassification",Ei]],["vit_msn",["ViTMSNForImageClassification",Zl]],["fastvit",["FastViTForImageClassification",su]],["mobilevit",["MobileViTForImageClassification",iu]],["mobilevitv2",["MobileViTV2ForImageClassification",Kc]],["beit",["BeitForImageClassification",on]],["deit",["DeiTForImageClassification",Ri]],["hiera",["HieraForImageClassification",ji]],["convnext",["ConvNextForImageClassification",Yi]],["convnextv2",["ConvNextV2ForImageClassification",Zi]],["dinov2",["Dinov2ForImageClassification",Jc]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",Au]],["resnet",["ResNetForImageClassification",qc]],["swin",["SwinForImageClassification",Vn]],["segformer",["SegformerForImageClassification",_d]],["efficientnet",["EfficientNetForImageClassification",Md]],["mobilenet_v1",["MobileNetV1ForImageClassification",ma]],["mobilenet_v2",["MobileNetV2ForImageClassification",vd]],["mobilenet_v3",["MobileNetV3ForImageClassification",Pd]],["mobilenet_v4",["MobileNetV4ForImageClassification",Ed]]]),jd=new Map([["detr",["DetrForObjectDetection",an]],["rt_detr",["RTDetrForObjectDetection",Un]],["table-transformer",["TableTransformerForObjectDetection",mu]],["yolos",["YolosForObjectDetection",Fu]]]),Pa=new Map([["owlvit",["OwlViTForObjectDetection",uu]],["owlv2",["Owlv2ForObjectDetection",cu]],["grounding-dino",["GroundingDinoForObjectDetection",Iu]]]),Ud=new Map([["detr",["DetrForSegmentation",Ks]],["clipseg",["CLIPSegForImageSegmentation",nl]]]),Vd=new Map([["segformer",["SegformerForSemanticSegmentation",fd]],["sapiens",["SapiensForSemanticSegmentation",bu]]]),Wd=new Map([["detr",["DetrForSegmentation",Ks]],["maskformer",["MaskFormerForInstanceSegmentation",Tu]]]),Gd=new Map([["sam",["SamModel",xo]]]),Mp=new Map([["wav2vec2",["Wav2Vec2ForCTC",Nu]],["wav2vec2-bert",["Wav2Vec2BertForCTC",op]],["unispeech",["UniSpeechForCTC",rp]],["unispeech-sat",["UniSpeechSatForCTC",qu]],["wavlm",["WavLMForCTC",td]],["hubert",["HubertForCTC",Zu]]]),Kd=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",tp]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Yu]],["unispeech",["UniSpeechForSequenceClassification",Ku]],["unispeech-sat",["UniSpeechSatForSequenceClassification",np]],["wavlm",["WavLMForSequenceClassification",sd]],["hubert",["HubertForSequenceClassification",ap]],["audio-spectrogram-transformer",["ASTForAudioClassification",Vo]]]),Hd=new Map([["wavlm",["WavLMForXVector",lp]]]),qd=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Qu]],["wavlm",["WavLMForAudioFrameClassification",rd]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",ju]],["pyannote",["PyAnnoteForAudioFrameClassification",Vu]]]),Qd=new Map([["vitmatte",["VitMatteForImageMatting",nu]]]),qp=new Map([["patchtst",["PatchTSTForPrediction",Dd]],["patchtsmixer",["PatchTSMixerForPrediction",zd]]]),Xd=new Map([["swin2sr",["Swin2SRForImageSuperResolution",gu]]]),Yd=new Map([["dpt",["DPTForDepthEstimation",Qc]],["depth_anything",["DepthAnythingForDepthEstimation",Mu]],["glpn",["GLPNForDepthEstimation",Pu]],["sapiens",["SapiensForDepthEstimation",yo]],["depth_pro",["DepthProForDepthEstimation",Qi]]]),Jd=new Map([["sapiens",["SapiensForNormalEstimation",vu]]]),bp=new Map([["vitpose",["VitPoseForPoseEstimation",Ql]]]),Zd=new Map([["clip",["CLIPVisionModelWithProjection",Za]],["siglip",["SiglipVisionModel",sl]],["jina_clip",["JinaCLIPVisionModel",wr]]]),ec=[[_p,$.EncoderOnly],[fp,$.EncoderDecoder],[gp,$.DecoderOnly],[ya,$.EncoderOnly],[Ma,$.EncoderOnly],[Ao,$.Seq2Seq],[Bd,$.Seq2Seq],[ba,$.DecoderOnly],[wp,$.MultiModality],[va,$.EncoderOnly],[xa,$.EncoderOnly],[Ta,$.Vision2Seq],[yp,$.ImageTextToText],[Nd,$.EncoderOnly],[Ud,$.EncoderOnly],[Wd,$.EncoderOnly],[Vd,$.EncoderOnly],[Qd,$.EncoderOnly],[qp,$.EncoderOnly],[Xd,$.EncoderOnly],[Yd,$.EncoderOnly],[Jd,$.EncoderOnly],[bp,$.EncoderOnly],[jd,$.EncoderOnly],[Pa,$.EncoderOnly],[Gd,$.MaskGeneration],[Mp,$.EncoderOnly],[Kd,$.EncoderOnly],[Kn,$.Seq2Seq],[wa,$.EncoderOnly],[Hd,$.EncoderOnly],[qd,$.EncoderOnly],[Zd,$.EncoderOnly]];for(const[_,T]of ec)for(const[N,fe]of _.values())S.set(N,T),x.set(fe,N),w.set(N,fe);const vp=[["MusicgenForConditionalGeneration",$o,$.Musicgen],["Phi3VForCausalLM",hr,$.Phi3V],["CLIPTextModelWithProjection",Ja,$.EncoderOnly],["SiglipTextModel",tl,$.EncoderOnly],["JinaCLIPTextModel",Qo,$.EncoderOnly],["ClapTextModelWithProjection",hd,$.EncoderOnly],["ClapAudioModelWithProjection",md,$.EncoderOnly]];for(const[_,T,N]of vp)S.set(_,N),x.set(T,_),w.set(_,T);class Ea extends ws{}_e(Ea,"MODEL_CLASS_MAPPINGS",ec.map(T=>T[0])),_e(Ea,"BASE_IF_FAIL",!0);class xp extends ws{}_e(xp,"MODEL_CLASS_MAPPINGS",[ya]);class tc extends ws{}_e(tc,"MODEL_CLASS_MAPPINGS",[Ma]);class sc extends ws{}_e(sc,"MODEL_CLASS_MAPPINGS",[Ao]);class rc extends ws{}_e(rc,"MODEL_CLASS_MAPPINGS",[Bd]);class nc extends ws{}_e(nc,"MODEL_CLASS_MAPPINGS",[Kn]);class oc extends ws{}_e(oc,"MODEL_CLASS_MAPPINGS",[wa]);class ic extends ws{}_e(ic,"MODEL_CLASS_MAPPINGS",[ba]);class ac extends ws{}_e(ac,"MODEL_CLASS_MAPPINGS",[va]);class lc extends ws{}_e(lc,"MODEL_CLASS_MAPPINGS",[xa]);class uc extends ws{}_e(uc,"MODEL_CLASS_MAPPINGS",[Ta]);class dc extends ws{}_e(dc,"MODEL_CLASS_MAPPINGS",[Nd]);class cc extends ws{}_e(cc,"MODEL_CLASS_MAPPINGS",[Ud]);class Ca extends ws{}_e(Ca,"MODEL_CLASS_MAPPINGS",[Vd]);class pc extends ws{}_e(pc,"MODEL_CLASS_MAPPINGS",[Wd]);class hc extends ws{}_e(hc,"MODEL_CLASS_MAPPINGS",[jd]);class mc extends ws{}_e(mc,"MODEL_CLASS_MAPPINGS",[Pa]);class _c extends ws{}_e(_c,"MODEL_CLASS_MAPPINGS",[Gd]);class fc extends ws{}_e(fc,"MODEL_CLASS_MAPPINGS",[Mp]);class gc extends ws{}_e(gc,"MODEL_CLASS_MAPPINGS",[Kd]);class wc extends ws{}_e(wc,"MODEL_CLASS_MAPPINGS",[Hd]);class Tp extends ws{}_e(Tp,"MODEL_CLASS_MAPPINGS",[qd]);class yc extends ws{}_e(yc,"MODEL_CLASS_MAPPINGS",[Rd]);class Mc extends ws{}_e(Mc,"MODEL_CLASS_MAPPINGS",[Qd]);class bc extends ws{}_e(bc,"MODEL_CLASS_MAPPINGS",[Xd]);class Pp extends ws{}_e(Pp,"MODEL_CLASS_MAPPINGS",[Yd]);class vc extends ws{}_e(vc,"MODEL_CLASS_MAPPINGS",[Jd]);class xc extends ws{}_e(xc,"MODEL_CLASS_MAPPINGS",[bp]);class Tc extends ws{}_e(Tc,"MODEL_CLASS_MAPPINGS",[Zd]);class Qp extends Ke{constructor({logits:T,past_key_values:N,encoder_outputs:fe,decoder_attentions:Fe=null,cross_attentions:Ae=null}){super(),this.logits=T,this.past_key_values=N,this.encoder_outputs=fe,this.decoder_attentions=Fe,this.cross_attentions=Ae}}class Qt extends Ke{constructor({logits:T,...N}){super(),this.logits=T;const fe=Object.values(N);fe.length>0&&(this.attentions=fe)}}class Hn extends Ke{constructor({logits:T,embeddings:N}){super(),this.logits=T,this.embeddings=N}}class Hs extends Ke{constructor({logits:T}){super(),this.logits=T}}class qs extends Ke{constructor({logits:T}){super(),this.logits=T}}class tr extends Ke{constructor({start_logits:T,end_logits:N}){super(),this.start_logits=T,this.end_logits=N}}class cn extends Ke{constructor({logits:T}){super(),this.logits=T}}class Ep extends Ke{constructor({logits:T,past_key_values:N}){super(),this.logits=T,this.past_key_values=N}}class Pc extends 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f.FeatureExtractor{constructor(R){super(R),this.mel_filters=(0,D.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,D.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,D.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(R,g,v,M){let y;const b=R.length-g;if(b>0)if(v==="rand_trunc"){const I=Math.floor(Math.random()*(b+1));R=R.subarray(I,I+g),y=await this._extract_fbank_features(R,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${v}" not implemented`);else{if(b<0){let I=new Float64Array(g);if(I.set(R),M==="repeat")for(let K=R.length;K{r.r(A),r.d(A,{CLIPFeatureExtractor:()=>U,CLIPImageProcessor:()=>D});var 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f.Processor{constructor(g,v){super(g,v);const{tasks_answer_post_processing_type:M,task_prompts_without_inputs:y,task_prompts_with_input:b}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(M??{})),this.task_prompts_without_inputs=new Map(Object.entries(y??{})),this.task_prompts_with_input=new Map(Object.entries(b??{})),this.regexes={quad_boxes:/(.+?)/gm,bboxes:/([^<]+)?/gm},this.size_per_bin=1e3}construct_prompts(g){typeof g=="string"&&(g=[g]);const v=[];for(const M of g)if(this.task_prompts_without_inputs.has(M))v.push(this.task_prompts_without_inputs.get(M));else{for(const[y,b]of this.task_prompts_with_input)if(M.includes(y)){v.push(b.replaceAll("{input}",M).replaceAll(y,""));break}v.length!==g.length&&v.push(M)}return v}post_process_generation(g,v,M){const y=this.tasks_answer_post_processing_type.get(v)??"pure_text";g=g.replaceAll("","").replaceAll("","");let 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f=r("./src/models/beit/image_processing_beit.js"),D=r("./src/models/bit/image_processing_bit.js"),U=r("./src/models/chinese_clip/image_processing_chinese_clip.js"),Y=r("./src/models/clip/image_processing_clip.js"),R=r("./src/models/convnext/image_processing_convnext.js"),g=r("./src/models/deit/image_processing_deit.js"),v=r("./src/models/detr/image_processing_detr.js"),M=r("./src/models/donut/image_processing_donut.js"),y=r("./src/models/dpt/image_processing_dpt.js"),b=r("./src/models/efficientnet/image_processing_efficientnet.js"),I=r("./src/models/glpn/image_processing_glpn.js"),K=r("./src/models/grounding_dino/image_processing_grounding_dino.js"),te=r("./src/models/idefics3/image_processing_idefics3.js"),ne=r("./src/models/janus/image_processing_janus.js"),W=r("./src/models/jina_clip/image_processing_jina_clip.js"),j=r("./src/models/llava_onevision/image_processing_llava_onevision.js"),q=r("./src/models/mask2former/image_processing_mask2former.js"),$=r("./src/models/maskformer/image_processing_maskformer.js"),S=r("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),w=r("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),x=r("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),O=r("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),ae=r("./src/models/mobilevit/image_processing_mobilevit.js"),oe=r("./src/models/nougat/image_processing_nougat.js"),ve=r("./src/models/owlv2/image_processing_owlv2.js"),we=r("./src/models/owlvit/image_processing_owlvit.js"),re=r("./src/models/phi3_v/image_processing_phi3_v.js"),xe=r("./src/models/pvt/image_processing_pvt.js"),ce=r("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),ke=r("./src/models/rt_detr/image_processing_rt_detr.js"),Ie=r("./src/models/sam/image_processing_sam.js"),Ee=r("./src/models/segformer/image_processing_segformer.js"),tt=r("./src/models/siglip/image_processing_siglip.js"),Ge=r("./src/models/swin2sr/image_processing_swin2sr.js"),ye=r("./src/models/vit/image_processing_vit.js"),J=r("./src/models/vitmatte/image_processing_vitmatte.js"),de=r("./src/models/vitpose/image_processing_vitpose.js"),Ce=r("./src/models/yolos/image_processing_yolos.js")},"./src/models/janus/image_processing_janus.js":(De,A,r)=>{r.r(A),r.d(A,{VLMImageProcessor:()=>D});var f=r("./src/base/image_processors_utils.js");class D extends f.ImageProcessor{constructor(Y){super({do_pad:!0,pad_size:{width:Y.image_size,height:Y.image_size},...Y}),this.constant_values=this.config.background_color.map(R=>R*this.rescale_factor)}pad_image(Y,R,g,v){return super.pad_image(Y,R,g,{constant_values:this.constant_values,center:!0,...v})}}},"./src/models/janus/processing_janus.js":(De,A,r)=>{r.r(A),r.d(A,{VLChatProcessor:()=>v});var f=r("./src/base/processing_utils.js"),D=r("./src/models/auto/image_processing_auto.js"),U=r("./src/tokenizers.js"),Y=r("./src/utils/core.js"),R=r("./src/utils/tensor.js"),g=r("./src/utils/image.js");class v extends f.Processor{constructor(y,b){super(y,b),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(y,{images:b=null,chat_template:I="default"}={}){b?Array.isArray(b)||(b=[b]):b=await 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f.ImageProcessor{constructor(Y){const{resize_mode:R,fill_color:g,interpolation:v,size:M,...y}=Y,b=R==="squash"?{width:M,height:M}:R==="shortest"?{shortest_edge:M}:{longest_edge:M},I=v==="bicubic"?3:2;super({...y,size:b,resample:I,do_center_crop:!0,crop_size:M,do_normalize:!0})}}},"./src/models/jina_clip/processing_jina_clip.js":(De,A,r)=>{r.r(A),r.d(A,{JinaCLIPProcessor:()=>Y});var f=r("./src/base/processing_utils.js"),D=r("./src/models/auto/image_processing_auto.js"),U=r("./src/tokenizers.js");class Y extends f.Processor{async _call(g=null,v=null,M={}){if(!g&&!v)throw new Error("Either text or images must be provided");const y=g?this.tokenizer(g,M):{},b=v?await this.image_processor(v,M):{};return{...y,...b}}}_e(Y,"tokenizer_class",U.AutoTokenizer),_e(Y,"image_processor_class",D.AutoImageProcessor)},"./src/models/llava_onevision/image_processing_llava_onevision.js":(De,A,r)=>{r.r(A),r.d(A,{LlavaOnevisionImageProcessor:()=>D});var f=r("./src/base/image_processors_utils.js");class D 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R={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class g extends f.Processor{get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(M,y){if(!R.hasOwnProperty(y))throw new Error(`Format ${y} is not supported.`);const[b,I]=R[y],K=this[b].bind(this),[te,ne]=M.dims,W=[],j=[],q=M.tolist();for(let S=0;S0?O.reduce((oe,ve)=>oe*ve,1):0;j.push(x),W.push(ae)}return[K(j),W]}char_decode(M){return this.char_tokenizer.batch_decode(M).map(y=>y.replaceAll(" ",""))}bpe_decode(M){return this.bpe_tokenizer.batch_decode(M)}wp_decode(M){return this.wp_tokenizer.batch_decode(M).map(y=>y.replaceAll(" ",""))}batch_decode([M,y,b]){const[I,K]=this._decode_helper(M,"char"),[te,ne]=this._decode_helper(y,"bpe"),[W,j]=this._decode_helper(b,"wp"),q=[],$=[];for(let S=0;S{r.r(A),r.d(A,{MobileNetV1FeatureExtractor:()=>U,MobileNetV1ImageProcessor:()=>D});var 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M=0;Mg*32768),(0,D.spectrogram)(R,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(R){(0,f.validate_audio_inputs)(R,"WeSpeakerFeatureExtractor");const g=(await this._extract_fbank_features(R)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const v=g.mean(1).data,M=g.data,[y,b,I]=g.dims;for(let K=0;K{r.r(A),r.d(A,{WHISPER_LANGUAGE_MAPPING:()=>D,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>U,whisper_language_to_code:()=>Y});const f=[["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"]],D=new Map(f),U=new Map([...f.map(([R,g])=>[g,R]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function Y(R){R=R.toLowerCase();let g=U.get(R);if(g===void 0)if(D.has(R))g=R;else{const M=R.length===2?D.keys():D.values();throw new Error(`Language "${R}" is not supported. Must be one of: ${JSON.stringify(M)}`)}return g}},"./src/models/whisper/feature_extraction_whisper.js":(De,A,r)=>{r.r(A),r.d(A,{WhisperFeatureExtractor:()=>Y});var f=r("./src/base/feature_extraction_utils.js");r("./src/utils/tensor.js");var D=r("./src/utils/audio.js"),U=r("./src/utils/maths.js");class Y extends f.FeatureExtractor{constructor(g){var v;super(g),(v=this.config).mel_filters??(v.mel_filters=(0,D.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,D.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(g){const v=await(0,D.spectrogram)(g,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),M=v.data,y=(0,U.max)(M)[0];for(let b=0;bthis.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),v=g.slice(0,this.config.n_samples)):(v=new Float32Array(this.config.n_samples),v.set(g)),{input_features:(await this._extract_fbank_features(v)).unsqueeze_(0)}}}},"./src/models/whisper/generation_whisper.js":(De,A,r)=>{r.r(A),r.d(A,{WhisperGenerationConfig:()=>D});var f=r("./src/generation/configuration_utils.js");class D extends f.GenerationConfig{constructor(){super(...arguments);_e(this,"return_timestamps",null);_e(this,"return_token_timestamps",null);_e(this,"num_frames",null);_e(this,"alignment_heads",null);_e(this,"task",null);_e(this,"language",null);_e(this,"no_timestamps_token_id",null);_e(this,"prompt_ids",null);_e(this,"is_multilingual",null);_e(this,"lang_to_id",null);_e(this,"task_to_id",null);_e(this,"max_initial_timestamp_index",1)}}},"./src/models/whisper/processing_whisper.js":(De,A,r)=>{r.r(A),r.d(A,{WhisperProcessor:()=>Y});var f=r("./src/models/auto/feature_extraction_auto.js"),D=r("./src/tokenizers.js"),U=r("./src/base/processing_utils.js");class Y extends U.Processor{async _call(g){return await this.feature_extractor(g)}}_e(Y,"tokenizer_class",D.AutoTokenizer),_e(Y,"feature_extractor_class",f.AutoFeatureExtractor)},"./src/models/yolos/image_processing_yolos.js":(De,A,r)=>{r.r(A),r.d(A,{YolosFeatureExtractor:()=>U,YolosImageProcessor:()=>D});var f=r("./src/base/image_processors_utils.js");class D extends f.ImageProcessor{post_process_object_detection(...R){return(0,f.post_process_object_detection)(...R)}}class U extends D{}},"./src/ops/registry.js":(De,A,r)=>{r.r(A),r.d(A,{TensorOpRegistry:()=>g});var f=r("./src/backends/onnx.js"),D=r("./src/utils/tensor.js"),U=r("./src/env.js");const Y=U.apis.IS_BROWSER_ENV||U.apis.IS_WEBWORKER_ENV,R=async(v,M,y)=>{const b=await(0,f.createInferenceSession)(new Uint8Array(v),M);let I=Promise.resolve();return async K=>{const te=(0,f.isONNXProxy)(),ne=Object.fromEntries(Object.entries(K).map(([j,q])=>[j,(te?q.clone():q).ort_tensor])),W=await(I=Y?I.then(()=>b.run(ne)):b.run(ne));return Array.isArray(y)?y.map(j=>new D.Tensor(W[j])):new D.Tensor(W[y])}};class g{static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=R([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=R([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=R([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=R([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=R([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=R([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=R([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=R([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}}_e(g,"session_options",{})},"./src/pipelines.js":(De,A,r)=>{r.r(A),r.d(A,{AudioClassificationPipeline:()=>we,AutomaticSpeechRecognitionPipeline:()=>xe,DepthEstimationPipeline:()=>Ce,DocumentQuestionAnsweringPipeline:()=>ye,FeatureExtractionPipeline:()=>oe,FillMaskPipeline:()=>q,ImageClassificationPipeline:()=>ke,ImageFeatureExtractionPipeline:()=>ve,ImageSegmentationPipeline:()=>Ie,ImageToImagePipeline:()=>de,ImageToTextPipeline:()=>ce,ObjectDetectionPipeline:()=>tt,Pipeline:()=>te,QuestionAnsweringPipeline:()=>j,SummarizationPipeline:()=>S,Text2TextGenerationPipeline:()=>$,TextClassificationPipeline:()=>ne,TextGenerationPipeline:()=>O,TextToAudioPipeline:()=>J,TokenClassificationPipeline:()=>W,TranslationPipeline:()=>w,ZeroShotAudioClassificationPipeline:()=>re,ZeroShotClassificationPipeline:()=>ae,ZeroShotImageClassificationPipeline:()=>Ee,ZeroShotObjectDetectionPipeline:()=>Ge,pipeline:()=>se});var f=r("./src/tokenizers.js"),D=r("./src/models.js"),U=r("./src/models/auto/processing_auto.js");r("./src/base/processing_utils.js");var Y=r("./src/utils/generic.js"),R=r("./src/utils/core.js"),g=r("./src/utils/maths.js"),v=r("./src/utils/audio.js"),M=r("./src/utils/tensor.js"),y=r("./src/utils/image.js");async function b(je){return Array.isArray(je)||(je=[je]),await Promise.all(je.map(le=>y.RawImage.read(le)))}async function I(je,le){return Array.isArray(je)||(je=[je]),await Promise.all(je.map(Te=>typeof Te=="string"||Te instanceof URL?(0,v.read_audio)(Te,le):Te instanceof Float64Array?new Float32Array(Te):Te))}function K(je,le){le&&(je=je.map(Re=>Re|0));const[Te,Ue,Ve,Ne]=je;return{xmin:Te,ymin:Ue,xmax:Ve,ymax:Ne}}class te extends Y.Callable{constructor({task:le,model:Te,tokenizer:Ue=null,processor:Ve=null}){super(),this.task=le,this.model=Te,this.tokenizer=Ue,this.processor=Ve}async dispose(){await this.model.dispose()}}class ne extends te{constructor(le){super(le)}async _call(le,{top_k:Te=1}={}){const Ue=this.tokenizer(le,{padding:!0,truncation:!0}),Ve=await this.model(Ue),Ne=this.model.config.problem_type==="multi_label_classification"?dt=>dt.sigmoid():dt=>new M.Tensor("float32",(0,g.softmax)(dt.data),dt.dims),Re=this.model.config.id2label,st=[];for(const dt of Ve.logits){const ct=Ne(dt),lt=await(0,M.topk)(ct,Te),ht=lt[0].tolist(),ie=lt[1].tolist().map((H,me)=>({label:Re?Re[H]:`LABEL_${H}`,score:ht[me]}));Te===1?st.push(...ie):st.push(ie)}return Array.isArray(le)||Te===1?st:st[0]}}class W extends te{constructor(le){super(le)}async _call(le,{ignore_labels:Te=["O"]}={}){const Ue=Array.isArray(le),Ve=this.tokenizer(Ue?le:[le],{padding:!0,truncation:!0}),Re=(await this.model(Ve)).logits,st=this.model.config.id2label,dt=[];for(let ct=0;ctut==this.tokenizer.sep_token_id);dt[ht].map((ut,mt)=>ut==1&&(mt===0||mt>ie&&ct.findIndex(vt=>vt==L[mt])===-1));const H=Ne[ht].tolist(),me=Re[ht].tolist();for(let ut=1;utmt==L[ut])!==-1)&&(H[ut]=-1/0,me[ut]=-1/0);const $e=(0,g.softmax)(H).map((ut,mt)=>[ut,mt]),We=(0,g.softmax)(me).map((ut,mt)=>[ut,mt]);$e[0][0]=0,We[0][0]=0;const Je=(0,R.product)($e,We).filter(ut=>ut[0][1]<=ut[1][1]).map(ut=>[ut[0][1],ut[1][1],ut[0][0]*ut[1][0]]).sort((ut,mt)=>mt[2]-ut[2]);for(let ut=0;utH==this.tokenizer.mask_token_id);if(ct===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const lt=Ve[st][ct],ht=await(0,M.topk)(new M.Tensor("float32",(0,g.softmax)(lt.data),lt.dims),Te),L=ht[0].tolist(),ie=ht[1].tolist();Ne.push(ie.map((H,me)=>{const $e=dt.slice();return $e[ct]=H,{score:L[me],token:Number(H),token_str:this.tokenizer.decode([H]),sequence:this.tokenizer.decode($e,{skip_special_tokens:!0})}}))}return Array.isArray(le)?Ne:Ne[0]}}class $ extends te{constructor(Te){super(Te);_e(this,"_key","generated_text")}async _call(Te,Ue={}){Array.isArray(Te)||(Te=[Te]),this.model.config.prefix&&(Te=Te.map(ct=>this.model.config.prefix+ct));const Ve=this.model.config.task_specific_params;Ve&&Ve[this.task]&&Ve[this.task].prefix&&(Te=Te.map(ct=>Ve[this.task].prefix+ct));const Ne=this.tokenizer,Re={padding:!0,truncation:!0};let st;this instanceof w&&"_build_translation_inputs"in Ne?st=Ne._build_translation_inputs(Te,Re,Ue):st=Ne(Te,Re);const dt=await this.model.generate({...st,...Ue});return Ne.batch_decode(dt,{skip_special_tokens:!0}).map(ct=>({[this._key]:ct}))}}class S extends ${constructor(Te){super(Te);_e(this,"_key","summary_text")}}class w extends ${constructor(Te){super(Te);_e(this,"_key","translation_text")}}function x(je){return Array.isArray(je)&&je.every(le=>"role"in le&&"content"in le)}class O extends te{constructor(le){super(le)}async _call(le,Te={}){let Ue=!1,Ve=!1,Ne;if(typeof le=="string")Ne=le=[le];else if(Array.isArray(le)&&le.every(ie=>typeof ie=="string"))Ue=!0,Ne=le;else{if(x(le))le=[le];else if(Array.isArray(le)&&le.every(x))Ue=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");Ve=!0,Ne=le.map(ie=>this.tokenizer.apply_chat_template(ie,{tokenize:!1,add_generation_prompt:!0}))}const Re=Te.add_special_tokens??!1,st=Ve?!1:Te.return_full_text??!0;this.tokenizer.padding_side="left";const dt=this.tokenizer(Ne,{add_special_tokens:Re,padding:!0,truncation:!0}),ct=await this.model.generate({...dt,...Te}),lt=this.tokenizer.batch_decode(ct,{skip_special_tokens:!0});let ht;!st&&dt.input_ids.dims.at(-1)>0&&(ht=this.tokenizer.batch_decode(dt.input_ids,{skip_special_tokens:!0}).map(ie=>ie.length));const L=Array.from({length:le.length},ie=>[]);for(let ie=0;ie[Te.toLowerCase(),Ue])),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(le,Te,{hypothesis_template:Ue="This example is {}.",multi_label:Ve=!1}={}){const Ne=Array.isArray(le);Ne||(le=[le]),Array.isArray(Te)||(Te=[Te]);const Re=Te.map(ct=>Ue.replace("{}",ct)),st=Ve||Te.length===1,dt=[];for(const ct of le){const lt=[];for(const ie of Re){const H=this.tokenizer(ct,{text_pair:ie,padding:!0,truncation:!0}),me=await this.model(H);st?lt.push([me.logits.data[this.contradiction_id],me.logits.data[this.entailment_id]]):lt.push(me.logits.data[this.entailment_id])}const L=(st?lt.map(ie=>(0,g.softmax)(ie)[1]):(0,g.softmax)(lt)).map((ie,H)=>[ie,H]).sort((ie,H)=>H[0]-ie[0]);dt.push({sequence:ct,labels:L.map(ie=>Te[ie[1]]),scores:L.map(ie=>ie[0])})}return Ne?dt:dt[0]}}class oe extends te{constructor(le){super(le)}async _call(le,{pooling:Te="none",normalize:Ue=!1,quantize:Ve=!1,precision:Ne="binary"}={}){const Re=this.tokenizer(le,{padding:!0,truncation:!0}),st=await this.model(Re);let dt=st.last_hidden_state??st.logits??st.token_embeddings;if(Te!=="none")if(Te==="mean")dt=(0,M.mean_pooling)(dt,Re.attention_mask);else if(Te==="cls")dt=dt.slice(null,0);else throw Error(`Pooling method '${Te}' not supported.`);return Ue&&(dt=dt.normalize(2,-1)),Ve&&(dt=(0,M.quantize_embeddings)(dt,Ne)),dt}}class ve extends te{constructor(le){super(le)}async _call(le,{pool:Te=null}={}){const Ue=await b(le),{pixel_values:Ve}=await this.processor(Ue),Ne=await this.model({pixel_values:Ve});let Re;if(Te){if(!("pooler_output"in Ne))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");Re=Ne.pooler_output}else Re=Ne.last_hidden_state??Ne.logits??Ne.image_embeds;return Re}}class we extends te{constructor(le){super(le)}async _call(le,{top_k:Te=5}={}){const Ue=this.processor.feature_extractor.config.sampling_rate,Ve=await I(le,Ue),Ne=this.model.config.id2label,Re=[];for(const st of Ve){const dt=await this.processor(st),lt=(await this.model(dt)).logits[0],ht=await(0,M.topk)(new M.Tensor("float32",(0,g.softmax)(lt.data),lt.dims),Te),L=ht[0].tolist(),H=ht[1].tolist().map((me,$e)=>({label:Ne?Ne[me]:`LABEL_${me}`,score:L[$e]}));Re.push(H)}return Array.isArray(le)?Re:Re[0]}}class re extends te{constructor(le){super(le)}async _call(le,Te,{hypothesis_template:Ue="This is a sound of {}."}={}){const Ve=!Array.isArray(le);Ve&&(le=[le]);const Ne=Te.map(lt=>Ue.replace("{}",lt)),Re=this.tokenizer(Ne,{padding:!0,truncation:!0}),st=this.processor.feature_extractor.config.sampling_rate,dt=await I(le,st),ct=[];for(const lt of dt){const ht=await this.processor(lt),L=await this.model({...Re,...ht}),ie=(0,g.softmax)(L.logits_per_audio.data);ct.push([...ie].map((H,me)=>({score:H,label:Te[me]})))}return Ve?ct[0]:ct}}class xe extends te{constructor(le){super(le)}async _call(le,Te={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(le,Te);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(le,Te);case"moonshine":return this._call_moonshine(le,Te);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(le,Te){Te.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),Te.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const Ue=!Array.isArray(le);Ue&&(le=[le]);const Ve=this.processor.feature_extractor.config.sampling_rate,Ne=await I(le,Ve),Re=[];for(const st of Ne){const dt=await this.processor(st),lt=(await this.model(dt)).logits[0],ht=[];for(const ie of lt)ht.push((0,g.max)(ie.data)[1]);const L=this.tokenizer.decode(ht);Re.push({text:L})}return Ue?Re[0]:Re}async _call_whisper(le,Te){const Ue=Te.return_timestamps??!1,Ve=Te.chunk_length_s??0,Ne=Te.force_full_sequences??!1;let Re=Te.stride_length_s??null;const st={...Te};Ue==="word"&&(st.return_token_timestamps=!0,st.return_timestamps=!1);const dt=!Array.isArray(le);dt&&(le=[le]);const ct=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,lt=this.processor.feature_extractor.config.hop_length,ht=this.processor.feature_extractor.config.sampling_rate,L=await I(le,ht),ie=[];for(const H of L){let me=[];if(Ve>0){if(Re===null)Re=Ve/6;else if(Ve<=Re)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const Je=ht*Ve,ut=ht*Re,mt=Je-2*ut;let vt=0;for(;;){const kt=vt+Je,At=H.subarray(vt,kt),os=await this.processor(At),ys=vt===0,Cs=kt>=H.length;if(me.push({stride:[At.length,ys?0:ut,Cs?0:ut],input_features:os.input_features,is_last:Cs}),Cs)break;vt+=mt}}else me=[{stride:[H.length,0,0],input_features:(await this.processor(H)).input_features,is_last:!0}];for(const Je of me){st.num_frames=Math.floor(Je.stride[0]/lt);const ut=await this.model.generate({inputs:Je.input_features,...st});Ue==="word"?(Je.tokens=ut.sequences.tolist()[0],Je.token_timestamps=ut.token_timestamps.tolist()[0].map(mt=>(0,g.round)(mt,2))):Je.tokens=ut[0].tolist(),Je.stride=Je.stride.map(mt=>mt/ht)}const[$e,We]=this.tokenizer._decode_asr(me,{time_precision:ct,return_timestamps:Ue,force_full_sequences:Ne});ie.push({text:$e,...We})}return dt?ie[0]:ie}async _call_moonshine(le,Te){const Ue=!Array.isArray(le);Ue&&(le=[le]);const Ve=this.processor.feature_extractor.config.sampling_rate,Ne=await I(le,Ve),Re=[];for(const st of Ne){const dt=await this.processor(st),ct=Math.floor(st.length/Ve)*6,lt=await this.model.generate({max_new_tokens:ct,...Te,...dt}),ht=this.processor.batch_decode(lt,{skip_special_tokens:!0})[0];Re.push({text:ht})}return Ue?Re[0]:Re}}class ce extends te{constructor(le){super(le)}async _call(le,Te={}){const Ue=Array.isArray(le),Ve=await b(le),{pixel_values:Ne}=await this.processor(Ve),Re=[];for(const st of Ne){st.dims=[1,...st.dims];const dt=await this.model.generate({inputs:st,...Te}),ct=this.tokenizer.batch_decode(dt,{skip_special_tokens:!0}).map(lt=>({generated_text:lt.trim()}));Re.push(ct)}return Ue?Re:Re[0]}}class ke extends te{constructor(le){super(le)}async _call(le,{top_k:Te=5}={}){const Ue=await b(le),{pixel_values:Ve}=await this.processor(Ue),Ne=await this.model({pixel_values:Ve}),Re=this.model.config.id2label,st=[];for(const dt of Ne.logits){const ct=await(0,M.topk)(new M.Tensor("float32",(0,g.softmax)(dt.data),dt.dims),Te),lt=ct[0].tolist(),L=ct[1].tolist().map((ie,H)=>({label:Re?Re[ie]:`LABEL_${ie}`,score:lt[H]}));st.push(L)}return Array.isArray(le)?st:st[0]}}class Ie extends te{constructor(le){super(le),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(le,{threshold:Te=.5,mask_threshold:Ue=.5,overlap_mask_area_threshold:Ve=.8,label_ids_to_fuse:Ne=null,target_sizes:Re=null,subtask:st=null}={}){if(Array.isArray(le)&&le.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const ct=await b(le),lt=ct.map(We=>[We.height,We.width]),{pixel_values:ht,pixel_mask:L}=await this.processor(ct),ie=await this.model({pixel_values:ht,pixel_mask:L});let H=null;if(st!==null)H=this.subtasks_mapping[st];else for(let[We,Je]of Object.entries(this.subtasks_mapping))if(Je in this.processor.image_processor){H=this.processor.image_processor[Je].bind(this.processor.image_processor),st=We;break}const me=this.model.config.id2label,$e=[];if(st==="panoptic"||st==="instance"){const We=H(ie,Te,Ue,Ve,Ne,Re??lt)[0],Je=We.segmentation;for(const ut of We.segments_info){const mt=new Uint8ClampedArray(Je.data.length);for(let kt=0;ktUe.replace("{}",L)),st=this.tokenizer(Re,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:dt}=await this.processor(Ne),ct=await this.model({...st,pixel_values:dt}),lt=this.model.config.model_type==="siglip"?L=>L.sigmoid().data:L=>(0,g.softmax)(L.data),ht=[];for(const L of ct.logits_per_image){const H=[...lt(L)].map((me,$e)=>({score:me,label:Te[$e]}));H.sort((me,$e)=>$e.score-me.score),ht.push(H)}return Ve?ht:ht[0]}}class tt extends te{constructor(le){super(le)}async _call(le,{threshold:Te=.9,percentage:Ue=!1}={}){const Ve=Array.isArray(le);if(Ve&&le.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ne=await b(le),Re=Ue?null:Ne.map(ie=>[ie.height,ie.width]),{pixel_values:st,pixel_mask:dt}=await this.processor(Ne),ct=await this.model({pixel_values:st,pixel_mask:dt}),lt=this.processor.image_processor.post_process_object_detection(ct,Te,Re),ht=this.model.config.id2label,L=lt.map(ie=>ie.boxes.map((H,me)=>({score:ie.scores[me],label:ht[ie.classes[me]],box:K(H,!Ue)})));return Ve?L:L[0]}}class Ge extends te{constructor(le){super(le)}async _call(le,Te,{threshold:Ue=.1,top_k:Ve=null,percentage:Ne=!1}={}){const Re=Array.isArray(le),st=await b(le),dt=this.tokenizer(Te,{padding:!0,truncation:!0}),ct=await this.processor(st),lt=[];for(let ht=0;ht({score:We.scores[ut],label:We.labels[ut],box:K(Je,!Ne)}))}else{const We=this.processor.image_processor.post_process_object_detection(me,Ue,ie,!0)[0];$e=We.boxes.map((Je,ut)=>({score:We.scores[ut],label:Te[We.classes[ut]],box:K(Je,!Ne)}))}$e.sort((We,Je)=>Je.score-We.score),Ve!==null&&($e=$e.slice(0,Ve)),lt.push($e)}return Re?lt:lt[0]}}class ye extends te{constructor(le){super(le)}async _call(le,Te,Ue={}){const Ve=(await b(le))[0],{pixel_values:Ne}=await this.processor(Ve),Re=`${Te}`,st=this.tokenizer(Re,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,dt=await this.model.generate({inputs:Ne,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:st,...Ue}),lt=this.tokenizer.batch_decode(dt)[0].match(/(.*?)<\/s_answer>/);let ht=null;return lt&<.length>=2&&(ht=lt[1].trim()),[{answer:ht}]}}class J extends te{constructor(Te){super(Te);_e(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=Te.vocoder??null}async _call(Te,{speaker_embeddings:Ue=null}={}){return this.processor?this._call_text_to_spectrogram(Te,{speaker_embeddings:Ue}):this._call_text_to_waveform(Te)}async _call_text_to_waveform(Te){const Ue=this.tokenizer(Te,{padding:!0,truncation:!0}),{waveform:Ve}=await this.model(Ue),Ne=this.model.config.sampling_rate;return new v.RawAudio(Ve.data,Ne)}async _call_text_to_spectrogram(Te,{speaker_embeddings:Ue}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await D.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Ue=="string"||Ue instanceof URL)&&(Ue=new Float32Array(await(await fetch(Ue)).arrayBuffer())),Ue instanceof Float32Array)Ue=new M.Tensor("float32",Ue,[1,Ue.length]);else if(!(Ue instanceof M.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:Ve}=this.tokenizer(Te,{padding:!0,truncation:!0}),{waveform:Ne}=await this.model.generate_speech(Ve,Ue,{vocoder:this.vocoder}),Re=this.processor.feature_extractor.config.sampling_rate;return new v.RawAudio(Ne.data,Re)}}class de extends te{constructor(le){super(le)}async _call(le){const Te=await b(le),Ue=await this.processor(Te),Ve=await this.model(Ue),Ne=[];for(const Re of Ve.reconstruction){const st=Re.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Ne.push(y.RawImage.fromTensor(st))}return Ne.length>1?Ne:Ne[0]}}class Ce extends te{constructor(le){super(le)}async _call(le){const Te=await b(le),Ue=await this.processor(Te),{predicted_depth:Ve}=await this.model(Ue),Ne=[];for(let Re=0;Re1?Ne:Ne[0]}}const Be=Object.freeze({"text-classification":{tokenizer:f.AutoTokenizer,pipeline:ne,model:D.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:f.AutoTokenizer,pipeline:W,model:D.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:f.AutoTokenizer,pipeline:j,model:D.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:f.AutoTokenizer,pipeline:q,model:D.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:f.AutoTokenizer,pipeline:S,model:D.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:f.AutoTokenizer,pipeline:w,model:D.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:f.AutoTokenizer,pipeline:$,model:D.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:f.AutoTokenizer,pipeline:O,model:D.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:f.AutoTokenizer,pipeline:ae,model:D.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:we,model:D.AutoModelForAudioClassification,processor:U.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:f.AutoTokenizer,pipeline:re,model:D.AutoModel,processor:U.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:f.AutoTokenizer,pipeline:xe,model:[D.AutoModelForSpeechSeq2Seq,D.AutoModelForCTC],processor:U.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:f.AutoTokenizer,pipeline:J,model:[D.AutoModelForTextToWaveform,D.AutoModelForTextToSpectrogram],processor:[U.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:f.AutoTokenizer,pipeline:ce,model:D.AutoModelForVision2Seq,processor:U.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:ke,model:D.AutoModelForImageClassification,processor:U.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:Ie,model:[D.AutoModelForImageSegmentation,D.AutoModelForSemanticSegmentation,D.AutoModelForUniversalSegmentation],processor:U.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:f.AutoTokenizer,pipeline:Ee,model:D.AutoModel,processor:U.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:tt,model:D.AutoModelForObjectDetection,processor:U.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:f.AutoTokenizer,pipeline:Ge,model:D.AutoModelForZeroShotObjectDetection,processor:U.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:f.AutoTokenizer,pipeline:ye,model:D.AutoModelForDocumentQuestionAnswering,processor:U.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:de,model:D.AutoModelForImageToImage,processor:U.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Ce,model:D.AutoModelForDepthEstimation,processor:U.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:f.AutoTokenizer,pipeline:oe,model:D.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:U.AutoProcessor,pipeline:ve,model:[D.AutoModelForImageFeatureExtraction,D.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Ze=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function se(je,le=null,{progress_callback:Te=null,config:Ue=null,cache_dir:Ve=null,local_files_only:Ne=!1,revision:Re="main",device:st=null,dtype:dt=null,model_file_name:ct=null,session_options:lt={}}={}){je=Ze[je]??je;const ht=Be[je.split("_",1)[0]];if(!ht)throw Error(`Unsupported pipeline: ${je}. Must be one of [${Object.keys(Be)}]`);le||(le=ht.default.model,console.log(`No model specified. Using default model: "${le}".`));const L={progress_callback:Te,config:Ue,cache_dir:Ve,local_files_only:Ne,revision:Re,device:st,dtype:dt,model_file_name:ct,session_options:lt},ie=new Map([["tokenizer",ht.tokenizer],["model",ht.model],["processor",ht.processor]]),H=await Ke(ie,le,L);H.task=je,(0,R.dispatchCallback)(Te,{status:"ready",task:je,model:le});const me=ht.pipeline;return new me(H)}async function Ke(je,le,Te){const Ue=Object.create(null),Ve=[];for(const[Ne,Re]of je.entries()){if(!Re)continue;let st;Array.isArray(Re)?st=new Promise(async(dt,ct)=>{var ht,L;let lt;for(const ie of Re){if(ie===null){dt(null);return}try{dt(await ie.from_pretrained(le,Te));return}catch(H){if((ht=H.message)!=null&&ht.includes("Unsupported model type"))lt=H;else if((L=H.message)!=null&&L.includes("Could not locate file"))lt=H;else{ct(H);return}}}ct(lt)}):st=Re.from_pretrained(le,Te),Ue[Ne]=st,Ve.push(st)}await Promise.all(Ve);for(const[Ne,Re]of Object.entries(Ue))Ue[Ne]=await Re;return Ue}},"./src/tokenizers.js":(De,A,r)=>{r.r(A),r.d(A,{AlbertTokenizer:()=>$r,AutoTokenizer:()=>as,BartTokenizer:()=>Or,BertTokenizer:()=>Jr,BlenderbotSmallTokenizer:()=>Ln,BlenderbotTokenizer:()=>Dn,BloomTokenizer:()=>Er,CLIPTokenizer:()=>bn,CamembertTokenizer:()=>ot,CodeGenTokenizer:()=>Mn,CodeLlamaTokenizer:()=>Ur,CohereTokenizer:()=>Tn,ConvBertTokenizer:()=>Rr,DebertaTokenizer:()=>cr,DebertaV2Tokenizer:()=>en,DistilBertTokenizer:()=>ar,ElectraTokenizer:()=>Dt,EsmTokenizer:()=>Vr,FalconTokenizer:()=>In,GPT2Tokenizer:()=>jr,GPTNeoXTokenizer:()=>On,GemmaTokenizer:()=>io,Grok1Tokenizer:()=>Wr,HerbertTokenizer:()=>Ir,LlamaTokenizer:()=>wn,M2M100Tokenizer:()=>yn,MBart50Tokenizer:()=>lr,MBartTokenizer:()=>Ms,MPNetTokenizer:()=>An,MarianTokenizer:()=>zt,MgpstrTokenizer:()=>Rn,MobileBertTokenizer:()=>Ar,NllbTokenizer:()=>ur,NougatTokenizer:()=>Gr,PreTrainedTokenizer:()=>Nt,Qwen2Tokenizer:()=>Fn,RoFormerTokenizer:()=>Nr,RobertaTokenizer:()=>Os,SiglipTokenizer:()=>vn,SpeechT5Tokenizer:()=>zn,SqueezeBertTokenizer:()=>Zr,T5Tokenizer:()=>Vs,TokenizerModel:()=>ve,VitsTokenizer:()=>Bn,Wav2Vec2CTCTokenizer:()=>xn,WhisperTokenizer:()=>tn,XLMRobertaTokenizer:()=>oo,XLMTokenizer:()=>Tt,is_chinese_char:()=>q});var f=r("./src/utils/generic.js"),D=r("./src/utils/core.js"),U=r("./src/utils/hub.js"),Y=r("./src/utils/maths.js"),R=r("./src/utils/tensor.js"),g=r("./src/utils/data-structures.js"),v=r("./node_modules/@huggingface/jinja/dist/index.js"),M=r("./src/models/whisper/common_whisper.js");async function y(Pe,P){const Q=await Promise.all([(0,U.getModelJSON)(Pe,"tokenizer.json",!0,P),(0,U.getModelJSON)(Pe,"tokenizer_config.json",!0,P)]);return P.legacy!==null&&(Q[1].legacy=P.legacy),Q}function b(Pe,P){const Q=[];let ue=0;for(const be of Pe.matchAll(P)){const Se=be[0];ue0&&Q.push(Se),ue=be.index+Se.length}return ue=19968&&Pe<=40959||Pe>=13312&&Pe<=19903||Pe>=131072&&Pe<=173791||Pe>=173824&&Pe<=177983||Pe>=177984&&Pe<=178207||Pe>=178208&&Pe<=183983||Pe>=63744&&Pe<=64255||Pe>=194560&&Pe<=195103}function $(Pe,P,Q){const ue=[];let be=0;for(;bethis.tokens_to_ids.get(Q)??this.unk_token_id)}convert_ids_to_tokens(P){return P.map(Q=>this.vocab[Q]??this.unk_token)}}class we extends ve{constructor(P){super(P),this.tokens_to_ids=K(P.vocab),this.unk_token_id=this.tokens_to_ids.get(P.unk_token),this.unk_token=P.unk_token,this.max_input_chars_per_word=P.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[Q,ue]of this.tokens_to_ids)this.vocab[ue]=Q}encode(P){const Q=[];for(const ue of P){const be=[...ue];if(be.length>this.max_input_chars_per_word){Q.push(this.unk_token);continue}let Se=!1,Qe=0;const pt=[];for(;Qe0&&(xt=this.config.continuing_subword_prefix+xt),this.tokens_to_ids.has(xt)){_t=xt;break}--gt}if(_t===null){Se=!0;break}pt.push(_t),Qe=gt}Se?Q.push(this.unk_token):Q.push(...pt)}return Q}}class re extends ve{constructor(P,Q){super(P);const ue=P.vocab.length;this.vocab=new Array(ue),this.scores=new Array(ue);for(let be=0;be[be,Se])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=Q.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,Y.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new g.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(P){const Q=P.chars,ue=1;let be=0;for(;be{const Pe=[...Array.from({length:94},(be,Se)=>Se+33),...Array.from({length:12},(be,Se)=>Se+161),...Array.from({length:82},(be,Se)=>Se+174)],P=Pe.slice();let Q=0;for(let be=0;be<256;++be)Pe.includes(be)||(Pe.push(be),P.push(256+Q),Q+=1);const ue=P.map(be=>String.fromCharCode(be));return Object.fromEntries(Pe.map((be,Se)=>[be,ue[Se]]))})(),ce=(0,D.reverseDictionary)(xe);class ke extends ve{constructor(P){super(P),this.tokens_to_ids=K(P.vocab),this.unk_token_id=this.tokens_to_ids.get(P.unk_token),this.unk_token=P.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[ue,be]of this.tokens_to_ids)this.vocab[be]=ue;const Q=Array.isArray(P.merges[0]);this.merges=Q?P.merges:P.merges.map(ue=>ue.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((ue,be)=>[JSON.stringify(ue),be])),this.end_of_word_suffix=P.end_of_word_suffix,this.continuing_subword_suffix=P.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(P){if(P.length===0)return[];const Q=this.cache.get(P);if(Q!==void 0)return Q;const ue=Array.from(P);this.end_of_word_suffix&&(ue[ue.length-1]+=this.end_of_word_suffix);let be=[];if(ue.length>1){const Se=new g.PriorityQueue((gt,_t)=>gt.score<_t.score);let Qe={token:ue[0],bias:0,prev:null,next:null},pt=Qe;for(let gt=1;gt`<0x${pt.toString(16).toUpperCase().padStart(2,"0")}>`);Qe.every(pt=>this.tokens_to_ids.has(pt))?Q.push(...Qe):Q.push(this.unk_token)}else Q.push(this.unk_token)}return Q}}class Ie extends ve{constructor(P,Q){super(P),this.tokens_to_ids=K(Q.target_lang?P.vocab[Q.target_lang]:P.vocab),this.bos_token=Q.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=Q.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=Q.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=Q.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[ue,be]of this.tokens_to_ids)this.vocab[be]=ue}encode(P){return P}}class Ee extends f.Callable{constructor(P){super(),this.config=P}static fromConfig(P){if(P===null)return null;switch(P.type){case"BertNormalizer":return new Ke(P);case"Precompiled":return new ys(P);case"Sequence":return new se(P);case"Replace":return new tt(P);case"NFC":return new Ge(P);case"NFKC":return new ye(P);case"NFKD":return new J(P);case"Strip":return new de(P);case"StripAccents":return new Ce(P);case"Lowercase":return new Be(P);case"Prepend":return new Ze(P);default:throw new Error(`Unknown Normalizer type: ${P.type}`)}}normalize(P){throw Error("normalize should be implemented in subclass.")}_call(P){return this.normalize(P)}}class tt extends Ee{normalize(P){const Q=I(this.config.pattern);return Q===null?P:P.replaceAll(Q,this.config.content)}}class Ge extends Ee{normalize(P){return P=P.normalize("NFC"),P}}class ye extends Ee{normalize(P){return P=P.normalize("NFKC"),P}}class J extends Ee{normalize(P){return P=P.normalize("NFKD"),P}}class de extends Ee{normalize(P){return this.config.strip_left&&this.config.strip_right?P=P.trim():(this.config.strip_left&&(P=P.trimStart()),this.config.strip_right&&(P=P.trimEnd())),P}}class Ce extends Ee{normalize(P){return P=W(P),P}}class Be extends Ee{normalize(P){return P=P.toLowerCase(),P}}class Ze extends Ee{normalize(P){return P=this.config.prepend+P,P}}class se extends Ee{constructor(P){super(P),this.normalizers=P.normalizers.map(Q=>Ee.fromConfig(Q))}normalize(P){return this.normalizers.reduce((Q,ue)=>ue.normalize(Q),P)}}class Ke extends Ee{_tokenize_chinese_chars(P){const Q=[];for(let ue=0;uethis.pre_tokenize_text(ue,Q)):this.pre_tokenize_text(P,Q)).flat()}_call(P,Q){return this.pre_tokenize(P,Q)}}class le extends je{constructor(P){super(),this.pattern=new RegExp(`[^\\s${w}]+|[${w}]`,"gu")}pre_tokenize_text(P,Q){return P.trim().match(this.pattern)||[]}}class Te extends je{constructor(P){super(),this.config=P,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=xe,this.text_encoder=new TextEncoder}pre_tokenize_text(P,Q){return this.add_prefix_space&&!P.startsWith(" ")&&(P=" "+P),(this.use_regex?P.match(this.pattern)||[]:[P]).map(be=>Array.from(this.text_encoder.encode(be),Se=>this.byte_encoder[Se]).join(""))}}class Ue extends je{constructor(P){super(),this.config=P,this.pattern=I(this.config.pattern,this.config.invert)}pre_tokenize_text(P,Q){var ue;return this.pattern===null?[]:this.config.invert?P.match(this.pattern)||[]:((ue=this.config.behavior)==null?void 0:ue.toLowerCase())==="removed"?P.split(this.pattern).filter(be=>be):b(P,this.pattern)}}class Ve extends je{constructor(P){super(),this.config=P,this.pattern=new RegExp(`[^${w}]+|[${w}]+`,"gu")}pre_tokenize_text(P,Q){return P.match(this.pattern)||[]}}class Ne extends je{constructor(P){super(),this.config=P;const Q=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(Q,"gu")}pre_tokenize_text(P,Q){return P.match(this.pattern)||[]}}class Re extends f.Callable{constructor(P){super(),this.config=P}static fromConfig(P){if(P===null)return null;switch(P.type){case"TemplateProcessing":return new ct(P);case"ByteLevel":return new lt(P);case"RobertaProcessing":return new dt(P);case"BertProcessing":return new st(P);case"Sequence":return new ht(P);default:throw new Error(`Unknown PostProcessor type: ${P.type}`)}}post_process(P,...Q){throw Error("post_process should be implemented in subclass.")}_call(P,...Q){return this.post_process(P,...Q)}}class st extends Re{constructor(P){super(P),this.cls=P.cls[0],this.sep=P.sep[0]}post_process(P,Q=null,{add_special_tokens:ue=!0}={}){ue&&(P=(0,D.mergeArrays)([this.cls],P,[this.sep]));let be=new Array(P.length).fill(0);if(Q!==null){const Se=ue&&this instanceof dt?[this.sep]:[],Qe=ue?[this.sep]:[];P=(0,D.mergeArrays)(P,Se,Q,Qe),be=(0,D.mergeArrays)(be,new Array(Q.length+Se.length+Qe.length).fill(1))}return{tokens:P,token_type_ids:be}}}class dt extends st{}class ct extends Re{constructor(P){super(P),this.single=P.single,this.pair=P.pair}post_process(P,Q=null,{add_special_tokens:ue=!0}={}){const be=Q===null?this.single:this.pair;let Se=[],Qe=[];for(const pt of be)"SpecialToken"in pt?ue&&(Se.push(pt.SpecialToken.id),Qe.push(pt.SpecialToken.type_id)):"Sequence"in pt&&(pt.Sequence.id==="A"?(Se=(0,D.mergeArrays)(Se,P),Qe=(0,D.mergeArrays)(Qe,new Array(P.length).fill(pt.Sequence.type_id))):pt.Sequence.id==="B"&&(Se=(0,D.mergeArrays)(Se,Q),Qe=(0,D.mergeArrays)(Qe,new Array(Q.length).fill(pt.Sequence.type_id))));return{tokens:Se,token_type_ids:Qe}}}class lt extends Re{post_process(P,Q=null){return Q&&(P=(0,D.mergeArrays)(P,Q)),{tokens:P}}}class ht extends Re{constructor(P){super(P),this.processors=P.processors.map(Q=>Re.fromConfig(Q))}post_process(P,Q=null,ue={}){let be;for(const Se of this.processors)if(Se instanceof lt)P=Se.post_process(P).tokens,Q&&(Q=Se.post_process(Q).tokens);else{const Qe=Se.post_process(P,Q,ue);P=Qe.tokens,be=Qe.token_type_ids}return{tokens:P,token_type_ids:be}}}class L extends f.Callable{constructor(P){super(),this.config=P,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=P.trim_offsets}static fromConfig(P){if(P===null)return null;switch(P.type){case"WordPiece":return new We(P);case"Metaspace":return new os(P);case"ByteLevel":return new Je(P);case"Replace":return new ie(P);case"ByteFallback":return new H(P);case"Fuse":return new me(P);case"Strip":return new $e(P);case"Sequence":return new mt(P);case"CTC":return new ut(P);case"BPEDecoder":return new vt(P);default:throw new Error(`Unknown Decoder type: ${P.type}`)}}_call(P){return this.decode(P)}decode(P){return this.decode_chain(P).join("")}decode_chain(P){throw Error("`decode_chain` should be implemented in subclass.")}}class ie extends L{decode_chain(P){const Q=I(this.config.pattern);return Q===null?P:P.map(ue=>ue.replaceAll(Q,this.config.content))}}class H extends L{constructor(P){super(P),this.text_decoder=new TextDecoder}decode_chain(P){const Q=[];let ue=[];for(const be of P){let Se=null;if(be.length===6&&be.startsWith("<0x")&&be.endsWith(">")){const Qe=parseInt(be.slice(3,5),16);isNaN(Qe)||(Se=Qe)}if(Se!==null)ue.push(Se);else{if(ue.length>0){const Qe=this.text_decoder.decode(Uint8Array.from(ue));Q.push(Qe),ue=[]}Q.push(be)}}if(ue.length>0){const be=this.text_decoder.decode(Uint8Array.from(ue));Q.push(be),ue=[]}return Q}}class me extends L{decode_chain(P){return[P.join("")]}}class $e extends L{constructor(P){super(P),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(P){return P.map(Q=>{let ue=0;for(let Se=0;Se(ue!==0&&(Q.startsWith(this.config.prefix)?Q=Q.replace(this.config.prefix,""):Q=" "+Q),this.cleanup&&(Q=ne(Q)),Q))}}class Je extends L{constructor(P){super(P),this.byte_decoder=ce,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(P){const Q=P.join(""),ue=new Uint8Array([...Q].map(Se=>this.byte_decoder[Se]));return this.text_decoder.decode(ue)}decode_chain(P){const Q=[];let ue=[];for(const be of P)this.added_tokens.find(Se=>Se.content===be)!==void 0?(ue.length>0&&(Q.push(this.convert_tokens_to_string(ue)),ue=[]),Q.push(be)):ue.push(be);return ue.length>0&&Q.push(this.convert_tokens_to_string(ue)),Q}}class ut extends L{constructor(P){super(P),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(P){if(P.length===0)return"";const Q=[P[0]];for(let Se=1;SeSe!==this.pad_token).join("");return this.cleanup&&(be=ne(be).replaceAll(this.word_delimiter_token," ").trim()),be}decode_chain(P){return[this.convert_tokens_to_string(P)]}}class mt extends L{constructor(P){super(P),this.decoders=P.decoders.map(Q=>L.fromConfig(Q))}decode_chain(P){return this.decoders.reduce((Q,ue)=>ue.decode_chain(Q),P)}}class vt extends L{constructor(P){super(P),this.suffix=this.config.suffix}decode_chain(P){return P.map((Q,ue)=>Q.replaceAll(this.suffix,ue===P.length-1?"":" "))}}class kt extends L{decode_chain(P){let Q="";for(let ue=1;ueue.normalize("NFKC")).join("～"):P=P.normalize("NFKC"),P}}class Cs extends je{constructor(P){super(),this.tokenizers=P.pretokenizers.map(Q=>je.fromConfig(Q))}pre_tokenize_text(P,Q){return this.tokenizers.reduce((ue,be)=>be.pre_tokenize(ue,Q),[P])}}class Ds extends je{constructor(P){super()}pre_tokenize_text(P,Q){return P.match(/\w+|[^\w\s]+/g)||[]}}class sr extends je{constructor(P){super()}pre_tokenize_text(P,Q){return S(P)}}class Sr extends je{constructor(P){super(),this.config=P,this.pattern=I(this.config.pattern),this.content=this.config.content}pre_tokenize_text(P,Q){return this.pattern===null?[P]:[P.replaceAll(this.pattern,this.config.content)]}}const Yr=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Us(Pe,P,Q,ue){for(const be of Object.keys(Pe)){const Se=P-Pe[be].length,Qe=Q(be),pt=new Array(Se).fill(Qe);Pe[be]=ue==="right"?(0,D.mergeArrays)(Pe[be],pt):(0,D.mergeArrays)(pt,Pe[be])}}function Pr(Pe,P){for(const Q of Object.keys(Pe))Pe[Q].length=P}class Nt extends f.Callable{constructor(Q,ue){super();_e(this,"return_token_type_ids",!1);_e(this,"padding_side","right");this._tokenizer_config=ue,this.normalizer=Ee.fromConfig(Q.normalizer),this.pre_tokenizer=je.fromConfig(Q.pre_tokenizer),this.model=ve.fromConfig(Q.model,ue),this.post_processor=Re.fromConfig(Q.post_processor),this.decoder=L.fromConfig(Q.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const be of Q.added_tokens){const Se=new oe(be);this.added_tokens.push(Se),this.model.tokens_to_ids.set(Se.content,Se.id),this.model.vocab[Se.id]=Se.content,Se.special&&(this.special_tokens.push(Se.content),this.all_special_ids.push(Se.id))}if(this.additional_special_tokens=ue.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((be,Se)=>Se.content.length-be.content.length).map(be=>`${be.lstrip?"\\s*":""}(${(0,D.escapeRegExp)(be.content)})${be.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=ue.model_max_length,this.remove_space=ue.remove_space,this.clean_up_tokenization_spaces=ue.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=ue.do_lowercase_and_remove_accent??!1,ue.padding_side&&(this.padding_side=ue.padding_side),this.legacy=!1,this.chat_template=ue.chat_template??null,Array.isArray(this.chat_template)){const be=Object.create(null);for(const{name:Se,template:Qe}of this.chat_template){if(typeof Se!="string"||typeof Qe!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');be[Se]=Qe}this.chat_template=be}this._compiled_template_cache=new Map}getToken(...Q){for(const ue of Q){const be=this._tokenizer_config[ue];if(be)if(typeof be=="object"){if(be.__type==="AddedToken")return be.content;throw Error(`Unknown token: ${be}`)}else return be}return null}static async from_pretrained(Q,{progress_callback:ue=null,config:be=null,cache_dir:Se=null,local_files_only:Qe=!1,revision:pt="main",legacy:gt=null}={}){const _t=await y(Q,{progress_callback:ue,config:be,cache_dir:Se,local_files_only:Qe,revision:pt,legacy:gt});return new this(..._t)}_call(Q,{text_pair:ue=null,add_special_tokens:be=!0,padding:Se=!1,truncation:Qe=null,max_length:pt=null,return_tensor:gt=!0,return_token_type_ids:_t=null}={}){const xt=Array.isArray(Q);let Kt;if(xt){if(Q.length===0)throw Error("text array must be non-empty");if(ue!==null){if(Array.isArray(ue)){if(Q.length!==ue.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");Kt=Q.map((us,Fs)=>this._encode_plus(us,{text_pair:ue[Fs],add_special_tokens:be,return_token_type_ids:_t}))}else Kt=Q.map(us=>this._encode_plus(us,{add_special_tokens:be,return_token_type_ids:_t}))}else{if(Q==null)throw Error("text may not be null or undefined");if(Array.isArray(ue))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");Kt=[this._encode_plus(Q,{text_pair:ue,add_special_tokens:be,return_token_type_ids:_t})]}if(pt===null?Se==="max_length"?pt=this.model_max_length:pt=(0,Y.max)(Kt.map(us=>us.input_ids.length))[0]:Qe||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."),pt=Math.min(pt,this.model_max_length??1/0),Se||Qe)for(let us=0;uspt?Qe&&Pr(Kt[us],pt):Se&&Us(Kt[us],pt,Fs=>Fs==="input_ids"?this.pad_token_id:0,this.padding_side));const ms={};if(gt){if(!(Se&&Qe)&&Kt.some(Fs=>{var Bt;for(const rs of Object.keys(Fs))if(Fs[rs].length!==((Bt=Kt[0][rs])==null?void 0:Bt.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 us=[Kt.length,Kt[0].input_ids.length];for(const Fs of Object.keys(Kt[0]))ms[Fs]=new R.Tensor("int64",BigInt64Array.from(Kt.flatMap(Bt=>Bt[Fs]).map(BigInt)),us)}else{for(const us of Object.keys(Kt[0]))ms[us]=Kt.map(Fs=>Fs[us]);if(!xt)for(const us of Object.keys(ms))ms[us]=ms[us][0]}return ms}_encode_text(Q){return Q===null?null:(this.added_tokens_regex?Q.split(this.added_tokens_regex).filter(Se=>Se):[Q]).map((Se,Qe)=>{if(this.added_tokens.find(gt=>gt.content===Se)!==void 0)return Se;{if(this.remove_space===!0&&(Se=Se.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(Se=j(Se)),this.normalizer!==null&&(Se=this.normalizer(Se)),Se.length===0)return[];const gt=this.pre_tokenizer!==null?this.pre_tokenizer(Se,{section_index:Qe}):[Se];return this.model(gt)}}).flat()}_encode_plus(Q,{text_pair:ue=null,add_special_tokens:be=!0,return_token_type_ids:Se=null}={}){const{tokens:Qe,token_type_ids:pt}=this._tokenize_helper(Q,{pair:ue,add_special_tokens:be}),gt=this.model.convert_tokens_to_ids(Qe),_t={input_ids:gt,attention_mask:new Array(gt.length).fill(1)};return(Se??this.return_token_type_ids)&&pt&&(_t.token_type_ids=pt),_t}_tokenize_helper(Q,{pair:ue=null,add_special_tokens:be=!1}={}){const Se=this._encode_text(Q),Qe=this._encode_text(ue);return this.post_processor?this.post_processor(Se,Qe,{add_special_tokens:be}):{tokens:(0,D.mergeArrays)(Se??[],Qe??[])}}tokenize(Q,{pair:ue=null,add_special_tokens:be=!1}={}){return this._tokenize_helper(Q,{pair:ue,add_special_tokens:be}).tokens}encode(Q,{text_pair:ue=null,add_special_tokens:be=!0,return_token_type_ids:Se=null}={}){return this._encode_plus(Q,{text_pair:ue,add_special_tokens:be,return_token_type_ids:Se}).input_ids}batch_decode(Q,ue={}){return Q instanceof R.Tensor&&(Q=Q.tolist()),Q.map(be=>this.decode(be,ue))}decode(Q,ue={}){if(Q instanceof R.Tensor&&(Q=te(Q)),!Array.isArray(Q)||Q.length===0||!(0,D.isIntegralNumber)(Q[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(Q,ue)}decode_single(Q,{skip_special_tokens:ue=!1,clean_up_tokenization_spaces:be=null}){let Se=this.model.convert_ids_to_tokens(Q);ue&&(Se=Se.filter(pt=>!this.special_tokens.includes(pt)));let Qe=this.decoder?this.decoder(Se):Se.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Qe=Qe.replaceAll(this.decoder.end_of_word_suffix," "),ue&&(Qe=Qe.trim())),(be??this.clean_up_tokenization_spaces)&&(Qe=ne(Qe)),Qe}get_chat_template({chat_template:Q=null,tools:ue=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const be=this.chat_template;if(Q!==null&&Object.hasOwn(be,Q))Q=be[Q];else if(Q===null)if(ue!==null&&"tool_use"in be)Q=be.tool_use;else if("default"in be)Q=be.default;else throw Error(`This model has multiple chat templates with no default specified! 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Therefore, you may experience slightly inaccurate results.')}}class Dt extends Nt{constructor(){super(...arguments);_e(this,"return_token_type_ids",!0)}}class Vs extends Nt{}class jr extends Nt{}class Or extends Nt{}class Ms extends Nt{constructor(P,Q){super(P,Q),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(ue=>this.languageRegex.test(ue)),this.lang_to_token=ue=>ue}_build_translation_inputs(P,Q,ue){return _r(this,P,Q,ue)}}class lr extends Ms{}class Os extends Nt{}class Er extends Nt{}const es="▁";class wn extends Nt{constructor(Q,ue){super(Q,ue);_e(this,"padding_side","left");this.legacy=ue.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new At({replacement:es,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(Q){if(Q===null)return null;if(this.legacy||Q.length===0)return super._encode_text(Q);let ue=super._encode_text(es+Q.replaceAll(es," "));return ue.length>1&&ue[0]===es&&this.special_tokens.includes(ue[1])&&(ue=ue.slice(1)),ue}}class Ur extends Nt{}class oo extends Nt{}class An extends Nt{}class In extends Nt{}class On extends Nt{}class Vr extends Nt{}class Fn extends Nt{}class io extends Nt{}class Wr extends Nt{}function _r(Pe,P,Q,ue){if(!("language_codes"in Pe)||!Array.isArray(Pe.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Pe)||!(Pe.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Pe)||typeof Pe.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const be=ue.src_lang,Se=ue.tgt_lang;if(!Pe.language_codes.includes(Se))throw new Error(`Target language code "${Se}" is not valid. 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Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[ks,Xs]=this.findLongestCommonSequence(Bt,rs),Ot=this.decode(ks);xt.text=Ot,pt&&(xt.words=this.collateWordTimestamps(ks,Xs,Qe)),_t.push(xt)}let Js=Object.create(null);const Fr=_t.map(ks=>ks.text).join("");if(Q||ue){for(let ks=0;ks<_t.length;++ks){const Xs=_t[ks];Q||delete Xs.timestamp,ue||delete Xs.language}if(pt){const ks=[];for(const Xs of _t)for(const Ot of Xs.words)ks.push(Ot);Js={chunks:ks}}else Js={chunks:_t}}return[Fr,Js]}findLongestCommonSequence(P,Q=null){let ue=P[0],be=ue.length,Se=[];const Qe=Array.isArray(Q)&&Q.length>0;let pt=Qe?[]:null,gt=Qe?Q[0]:null;for(let _t=1;_tqt===fr[Ls]&>[Fr+Ls]<=Q[_t][Ot+Ls]).length:_s=Xs.filter((qt,Ls)=>qt===fr[Ls]).length;const Ss=Js/1e4,yt=_s/Js+Ss;_s>1&&yt>Kt&&(Kt=yt,ms=[Fr,ks,Ot,or])}const[Fs,Bt,rs,rr]=ms,Ws=Math.floor((Bt+Fs)/2),ze=Math.floor((rr+rs)/2);Se.push(...ue.slice(0,Ws)),ue=xt.slice(ze),be=ue.length,Qe&&(pt.push(...gt.slice(0,Ws)),gt=Q[_t].slice(ze))}return Se.push(...ue),Qe?(pt.push(...gt),[Se,pt]):[Se,[]]}collateWordTimestamps(P,Q,ue){const[be,Se,Qe]=this.combineTokensIntoWords(P,ue),pt=[];for(let gt=0;gt=be){const pt=((Qe-be)*ue).toFixed(2);Se.push(`<|${pt}|>`),Se.push([])}else Se[Se.length-1].push(Qe);return Se=Se.map(Qe=>typeof Qe=="string"?Qe:super.decode(Qe,Q)),Se.join("")}splitTokensOnUnicode(P){const Q=this.decode(P,{decode_with_timestamps:!0}),ue="�",be=[],Se=[],Qe=[];let pt=[],gt=[],_t=0;for(let xt=0;xt=this.model.tokens_to_ids.get("<|endoftext|>"),Fs=xt.startsWith(" "),Bt=xt.trim(),rs=gt.test(Bt);if(us||Fs||rs||Se.length===0)Se.push(xt),Qe.push(Kt),pt.push(ms);else{const rr=Se.length-1;Se[rr]+=xt,Qe[rr].push(...Kt),pt[rr].push(...ms)}}return[Se,Qe,pt]}mergePunctuations(P,Q,ue,be,Se){const Qe=structuredClone(P),pt=structuredClone(Q),gt=structuredClone(ue);let _t=Qe.length-2,xt=Qe.length-1;for(;_t>=0;)Qe[_t].startsWith(" ")&&be.includes(Qe[_t].trim())?(Qe[xt]=Qe[_t]+Qe[xt],pt[xt]=(0,D.mergeArrays)(pt[_t],pt[xt]),gt[xt]=(0,D.mergeArrays)(gt[_t],gt[xt]),Qe[_t]="",pt[_t]=[],gt[_t]=[]):xt=_t,--_t;for(_t=0,xt=1;xtKt),pt.filter(Kt=>Kt.length>0),gt.filter(Kt=>Kt.length>0)]}}class Mn extends Nt{}class bn extends Nt{}class vn extends Nt{}class zt extends Nt{constructor(P,Q){super(P,Q),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(ue=>this.languageRegex.test(ue)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(P){if(P===null)return null;const[Q,...ue]=P.trim().split(this.languageRegex);if(ue.length===0)return super._encode_text(Q);if(ue.length===2){const[be,Se]=ue;return this.supported_language_codes.includes(be)||console.warn(`Unsupported language code "${be}" detected, which may lead to unexpected behavior. 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eForCausalLM,c.GraniteModel,c.GranitePreTrainedModel,c.Grok1Tokenizer,c.GroundingDinoForObjectDetection,c.GroundingDinoImageProcessor,c.GroundingDinoPreTrainedModel,c.GroundingDinoProcessor,c.GroupViTModel,c.GroupViTPreTrainedModel,c.HeliumForCausalLM,c.HeliumModel,c.HeliumPreTrainedModel,c.HerbertTokenizer,c.HieraForImageClassification,c.HieraModel,c.HieraPreTrainedModel,c.HubertForCTC,c.HubertForSequenceClassification,c.HubertModel,c.HubertPreTrainedModel,c.IJepaForImageClassification,c.IJepaModel,c.IJepaPreTrainedModel,c.Idefics3ForConditionalGeneration,c.Idefics3ImageProcessor,c.Idefics3PreTrainedModel,c.Idefics3Processor,c.ImageClassificationPipeline,c.ImageFeatureExtractionPipeline,c.ImageFeatureExtractor,c.ImageMattingOutput,c.ImageProcessor,c.ImageSegmentationPipeline,c.ImageToImagePipeline,c.ImageToTextPipeline;var i_=c.InterruptableStoppingCriteria;c.JAISLMHeadModel,c.JAISModel,c.JAISPreTrainedModel,c.JinaCLIPImageProcessor,c.JinaCLIPModel,c.JinaCLIPPreTrainedModel,c.JinaCLIPProcessor,c.JinaCLIPTextModel,c.JinaCLIPVisionModel,c.LlamaForCausalLM,c.LlamaModel,c.LlamaPreTrainedModel,c.LlamaTokenizer,c.LlavaForConditionalGeneration,c.LlavaOnevisionForConditionalGeneration,c.LlavaOnevisionImageProcessor,c.LlavaPreTrainedModel,c.LogitsProcessor,c.LogitsProcessorList,c.LogitsWarper,c.LongT5ForConditionalGeneration,c.LongT5Model,c.LongT5PreTrainedModel,c.M2M100ForConditionalGeneration,c.M2M100Model,c.M2M100PreTrainedModel,c.M2M100Tokenizer,c.MBart50Tokenizer,c.MBartForCausalLM,c.MBartForConditionalGeneration,c.MBartForSequenceClassification,c.MBartModel,c.MBartPreTrainedModel,c.MBartTokenizer,c.MPNetForMaskedLM,c.MPNetForQuestionAnswering,c.MPNetForSequenceClassification,c.MPNetForTokenClassification,c.MPNetModel,c.MPNetPreTrainedModel,c.MPNetTokenizer,c.MT5ForConditionalGeneration,c.MT5Model,c.MT5PreTrainedModel,c.MarianMTModel,c.MarianModel,c.MarianPreTrainedModel,c.MarianTokenizer,c.Mask2FormerImageProcessor,c.MaskFormerFeatureExtractor,c.MaskFormerForInstanceSegmentation,c.MaskFormerImageProcessor,c.MaskFormerModel,c.MaskFormerPreTrainedModel,c.MaskedLMOutput,c.MaxLengthCriteria,c.MgpstrForSceneTextRecognition,c.MgpstrModelOutput,c.MgpstrPreTrainedModel,c.MgpstrProcessor,c.MgpstrTokenizer,c.MinLengthLogitsProcessor,c.MinNewTokensLengthLogitsProcessor,c.MistralForCausalLM,c.MistralModel,c.MistralPreTrainedModel,c.MobileBertForMaskedLM,c.MobileBertForQuestionAnswering,c.MobileBertForSequenceClassification,c.MobileBertModel,c.MobileBertPreTrainedModel,c.MobileBertTokenizer,c.MobileLLMForCausalLM,c.MobileLLMModel,c.MobileLLMPreTrainedModel,c.MobileNetV1FeatureExtractor,c.MobileNetV1ForImageClassification,c.MobileNetV1ImageProcessor,c.MobileNetV1Model,c.MobileNetV1PreTrainedModel,c.MobileNetV2FeatureExtractor,c.MobileNetV2ForImageClassification,c.MobileNetV2ImageProcessor,c.MobileNetV2Model,c.MobileNetV2PreTrainedModel,c.MobileNetV3FeatureExtractor,c.MobileNetV3ForImageClassification,c.MobileNetV3ImageProcessor,c.MobileNetV3Model,c.MobileNetV3PreTrainedModel,c.MobileNetV4FeatureExtractor,c.MobileNetV4ForImageClassification,c.MobileNetV4ImageProcessor,c.MobileNetV4Model,c.MobileNetV4PreTrainedModel,c.MobileViTFeatureExtractor,c.MobileViTForImageClassification,c.MobileViTImageProcessor,c.MobileViTModel,c.MobileViTPreTrainedModel,c.MobileViTV2ForImageClassification,c.MobileViTV2Model,c.MobileViTV2PreTrainedModel,c.ModelOutput,c.ModernBertForMaskedLM,c.ModernBertForSequenceClassification,c.ModernBertForTokenClassification,c.ModernBertModel,c.ModernBertPreTrainedModel,c.Moondream1ForConditionalGeneration,c.MoonshineFeatureExtractor,c.MoonshineForConditionalGeneration,c.MoonshineModel,c.MoonshinePreTrainedModel,c.MoonshineProcessor,c.MptForCausalLM,c.MptModel,c.MptPreTrainedModel,c.MultiModalityCausalLM,c.MultiModalityPreTrainedModel,c.MusicgenForCausalLM,c.MusicgenForConditionalGeneration,c.MusicgenModel,c.MusicgenPreTrainedModel,c.NllbTokenizer,c.NoBadWordsLogitsProcessor,c.NoRepeatNGramLogitsProcessor,c.NomicBertModel,c.NomicBertPreTrainedModel,c.NougatImageProcessor,c.NougatTokenizer,c.OPTForCausalLM,c.OPTModel,c.OPTPreTrainedModel,c.ObjectDetectionPipeline,c.Olmo2ForCausalLM,c.Olmo2Model,c.Olmo2PreTrainedModel,c.OlmoForCausalLM,c.OlmoModel,c.OlmoPreTrainedModel,c.OpenELMForCausalLM,c.OpenELMModel,c.OpenELMPreTrainedModel,c.OwlViTFeatureExtractor,c.OwlViTForObjectDetection,c.OwlViTImageProcessor,c.OwlViTModel,c.OwlViTPreTrainedModel,c.OwlViTProcessor,c.Owlv2ForObjectDetection,c.Owlv2ImageProcessor,c.Owlv2Model,c.Owlv2PreTrainedModel,c.PaliGemmaForConditionalGeneration,c.PaliGemmaPreTrainedModel,c.PaliGemmaProcessor,c.PatchTSMixerForPrediction,c.PatchTSMixerModel,c.PatchTSMixerPreTrainedModel,c.PatchTSTForPrediction,c.PatchTSTModel,c.PatchTSTPreTrainedModel,c.Phi3ForCausalLM,c.Phi3Model,c.Phi3PreTrainedModel,c.Phi3VForCausalLM,c.Phi3VImageProcessor,c.Phi3VPreTrainedModel,c.Phi3VProcessor,c.PhiForCausalLM,c.PhiModel,c.PhiPreTrainedModel,c.Pipeline,c.PreTrainedModel,c.PreTrainedTokenizer,c.PretrainedConfig,c.PretrainedMixin,c.Processor,c.PvtForImageClassification,c.PvtImageProcessor,c.PvtModel,c.PvtPreTrainedModel,c.PyAnnoteFeatureExtractor,c.PyAnnoteForAudioFrameClassification,c.PyAnnoteModel,c.PyAnnotePreTrainedModel,c.PyAnnoteProcessor,c.QuestionAnsweringModelOutput,c.QuestionAnsweringPipeline,c.Qwen2ForCausalLM,c.Qwen2Model,c.Qwen2PreTrainedModel,c.Qwen2Tokenizer,c.Qwen2VLForConditionalGeneration,c.Qwen2VLImageProcessor,c.Qwen2VLPreTrainedModel,c.Qwen2VLProcessor,c.RTDetrForObjectDetection,c.RTDetrImageProcessor,c.RTDetrModel,c.RTDetrObjectDetectionOutput,c.RTDetrPreTrainedModel,c.RawAudio,c.RawImage,c.RepetitionPenaltyLogitsProcessor,c.ResNetForImageClassification,c.ResNetModel,c.ResNetPreTrainedModel,c.RoFormerForMaskedLM,c.RoFormerForQuestionAnswering,c.RoFormerForSequenceClassification,c.RoFormerForTokenClassification,c.RoFormerModel,c.RoFormerPreTrainedModel,c.RoFormerTokenizer,c.RobertaForMaskedLM,c.RobertaForQuestionAnswering,c.RobertaForSequenceClassification,c.RobertaForTokenClassification,c.RobertaModel,c.RobertaPreTrainedModel,c.RobertaTokenizer,c.SamImageProcessor,c.SamImageSegmentationOutput,c.SamModel,c.SamPreTrainedModel,c.SamProcessor,c.SapiensForDepthEstimation,c.SapiensForNormalEstimation,c.SapiensForSemanticSegmentation,c.SapiensPreTrainedModel,c.SeamlessM4TFeatureExtractor,c.SegformerFeatureExtractor,c.SegformerForImageClassification,c.SegformerForSemanticSegmentation,c.SegformerImageProcessor,c.SegformerModel,c.SegformerPreTrainedModel,c.Seq2SeqLMOutput,c.SequenceClassifierOutput,c.SiglipImageProcessor,c.SiglipModel,c.SiglipPreTrainedModel,c.SiglipTextModel,c.SiglipTokenizer,c.SiglipVisionModel,c.SpeechT5FeatureExtractor,c.SpeechT5ForSpeechToText,c.SpeechT5ForTextToSpeech,c.SpeechT5HifiGan,c.SpeechT5Model,c.SpeechT5PreTrainedModel,c.SpeechT5Processor,c.SpeechT5Tokenizer,c.SqueezeBertForMaskedLM,c.SqueezeBertForQuestionAnswering,c.SqueezeBertForSequenceClassification,c.SqueezeBertModel,c.SqueezeBertPreTrainedModel,c.SqueezeBertTokenizer,c.StableLmForCausalLM,c.StableLmModel,c.StableLmPreTrainedModel,c.Starcoder2ForCausalLM,c.Starcoder2Model,c.Starcoder2PreTrainedModel,c.StoppingCriteria,c.StoppingCriteriaList,c.StyleTextToSpeech2Model,c.StyleTextToSpeech2PreTrainedModel,c.SummarizationPipeline,c.SuppressTokensAtBeginLogitsProcessor,c.Swin2SRForImageSuperResolution,c.Swin2SRImageProcessor,c.Swin2SRModel,c.Swin2SRPreTrainedModel,c.SwinForImageClassification,c.SwinModel,c.SwinPreTrainedModel,c.T5ForConditionalGeneration,c.T5Model,c.T5PreTrainedModel,c.T5Tokenizer,c.TableTransformerForObjectDetection,c.TableTransformerModel,c.TableTransformerObjectDetectionOutput,c.TableTransformerPreTrainedModel,c.TemperatureLogitsWarper,c.Tensor,c.Text2TextGenerationPipeline,c.TextClassificationPipeline,c.TextGenerationPipeline;var a_=c.TextStreamer;c.TextToAudioPipeline,c.TokenClassificationPipeline,c.TokenClassifierOutput,c.TokenizerModel,c.TopKLogitsWarper,c.TopPLogitsWarper,c.TrOCRForCausalLM,c.TrOCRPreTrainedModel,c.TranslationPipeline,c.UniSpeechForCTC,c.UniSpeechForSequenceClassification,c.UniSpeechModel,c.UniSpeechPreTrainedModel,c.UniSpeechSatForAudioFrameClassification,c.UniSpeechSatForCTC,c.UniSpeechSatForSequenceClassification,c.UniSpeechSatModel,c.UniSpeechSatPreTrainedModel,c.VLChatProcessor,c.VLMImageProcessor,c.ViTFeatureExtractor,c.ViTForImageClassification,c.ViTImageProcessor,c.ViTMAEModel,c.ViTMAEPreTrainedModel,c.ViTMSNForImageClassification,c.ViTMSNModel,c.ViTMSNPreTrainedModel,c.ViTModel,c.ViTPreTrainedModel,c.VisionEncoderDecoderModel,c.VitMatteForImageMatting,c.VitMatteImageProcessor,c.VitMattePreTrainedModel,c.VitPoseForPoseEstimation,c.VitPoseImageProcessor,c.VitPosePreTrainedModel,c.VitsModel,c.VitsModelOutput,c.VitsPreTrainedModel,c.VitsTokenizer,c.Wav2Vec2BertForCTC,c.Wav2Vec2BertForSequenceClassification,c.Wav2Vec2BertModel,c.Wav2Vec2BertPreTrainedModel,c.Wav2Vec2CTCTokenizer,c.Wav2Vec2FeatureExtractor,c.Wav2Vec2ForAudioFrameClassification,c.Wav2Vec2ForCTC,c.Wav2Vec2ForSequenceClassification,c.Wav2Vec2Model,c.Wav2Vec2PreTrainedModel,c.Wav2Vec2Processor,c.Wav2Vec2ProcessorWithLM,c.WavLMForAudioFrameClassification,c.WavLMForCTC,c.WavLMForSequenceClassification,c.WavLMForXVector,c.WavLMModel,c.WavLMPreTrainedModel,c.WeSpeakerFeatureExtractor,c.WeSpeakerResNetModel,c.WeSpeakerResNetPreTrainedModel,c.WhisperFeatureExtractor,c.WhisperForConditionalGeneration,c.WhisperModel,c.WhisperPreTrainedModel,c.WhisperProcessor,c.WhisperTextStreamer,c.WhisperTimeStampLogitsProcessor,c.WhisperTokenizer,c.XLMForQuestionAnswering,c.XLMForSequenceClassification,c.XLMForTokenClassification,c.XLMModel,c.XLMPreTrainedModel,c.XLMRobertaForMaskedLM,c.XLMRobertaForQuestionAnswering,c.XLMRobertaForSequenceClassification,c.XLMRobertaForTokenClassification,c.XLMRobertaModel,c.XLMRobertaPreTrainedModel,c.XLMRobertaTokenizer,c.XLMTokenizer,c.XLMWithLMHeadModel,c.XVectorOutput,c.YolosFeatureExtractor,c.YolosForObjectDetection,c.YolosImageProcessor,c.YolosModel,c.YolosObjectDetectionOutput,c.YolosPreTrainedModel,c.ZeroShotAudioClassificationPipeline,c.ZeroShotClassificationPipeline,c.ZeroShotImageClassificationPipeline,c.ZeroShotObjectDetectionPipeline,c.bankers_round,c.cat,c.cos_sim,c.dot,c.dynamic_time_warping,c.env,c.full,c.full_like,c.getKeyValueShapes,c.hamming,c.hanning,c.interpolate,c.interpolate_4d,c.interpolate_data,c.is_chinese_char,c.layer_norm;var l_=c.load_image;c.log_softmax,c.magnitude,c.matmul,c.max,c.mean,c.mean_pooling,c.medianFilter,c.mel_filter_bank,c.min,c.ones,c.ones_like,c.permute,c.permute_data,c.pipeline,c.quantize_embeddings,c.rand,c.read_audio,c.rfft,c.round,c.slice,c.softmax,c.spectrogram,c.stack,c.std_mean,c.topk,c.window_function,c.zeros,c.zeros_like;const u_=1024;let Gp=!1;async function d_(){try{const De=await navigator.gpu.requestAdapter();if(!De)throw new Error("WebGPU is not supported (no adapter found)");Gp=De.features.has("shader-f16")}catch(De){self.postMessage({status:"error",data:De.toString()})}}class Kp{static async getInstance(A=null){return this.processor??(this.processor=o_.from_pretrained(this.model_id,{progress_callback:A})),this.model??(this.model=n_.from_pretrained(this.model_id,{dtype:{embed_tokens:Gp?"fp16":"fp32",vision_encoder:"fp32",decoder_model_merged:Gp?"fp16":"q4"},device:"webgpu",progress_callback:A})),Promise.all([this.processor,this.model])}}_e(Kp,"model_id","HuggingFaceTB/SmolVLM-500M-Instruct");const Bc=new i_;async function c_(De){De=De.slice(-1);const[A,r]=await Kp.getInstance(),f=await Promise.all(De.map(te=>te.content).flat(1/0).filter(te=>te.image!==void 0).map(te=>l_(te.image))),D=A.apply_chat_template(De,{add_generation_prompt:!0}),U=await A(D,f,{});let Y,R=0,g;const v=te=>{Y??(Y=performance.now()),R++>0&&(g=R/(performance.now()-Y)*1e3)},M=te=>{self.postMessage({status:"update",output:te,tps:g,numTokens:R})},y=new a_(A.tokenizer,{skip_prompt:!0,skip_special_tokens:!0,callback_function:M,token_callback_function:v});self.postMessage({status:"start"});const{past_key_values:b,sequences:I}=await r.generate({...U,do_sample:!1,repetition_penalty:1.1,max_new_tokens:u_,streamer:y,stopping_criteria:Bc,return_dict_in_generate:!0}).catch(te=>{self.postMessage({status:"error",data:te.toString()})}),K=A.batch_decode(I,{skip_special_tokens:!0});self.postMessage({status:"complete",output:K})}async function p_(){self.postMessage({status:"loading",data:"Loading model..."}),await Kp.getInstance(De=>{self.postMessage(De)}),self.postMessage({status:"ready"})}self.addEventListener("message",async De=>{const{type:A,data:r}=De.data;switch(A){case"check":d_();break;case"load":p_();break;case"generate":Bc.reset(),c_(r);break;case"interrupt":Bc.interrupt();break;case"reset":Bc.reset();break}})})();