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s=(n,i,a)=>{let o=[];for(let u=0;u=0||a.length===0)&&o.push(`input_indices[${u}] = 0;`);return[`${o.join(` -`)}`,`var value = ${n.getByIndices("input_indices")}; -var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) { - value = ${n.getByIndices("input_indices")}; - best_index = i32(last_index); - }`,"",i.setByOffset("global_idx","best_index")]};e.compute(oi("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],s,[t.axis],7,t.keepDims),{inputs:[0]})},Zi=(e,t)=>{Ji(e.inputs);let s=(n,i,a)=>{let o=[];for(let u=0;u=0||a.length===0)&&o.push(`input_indices[${u}] = 0;`);return[`${o.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); - }`,"",i.setByOffset("global_idx","best_index")]};e.compute(oi("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],s,[t.axis],7,t.keepDims),{inputs:[0]})},eo=e=>Bt(e)}),to,li,ml,so,fl,Rn,ro,_l,no=g(()=>{zt(),Ot(),ue(),Yt(),to=(e,t)=>{let s=e[0],n=e[1],i=e[2],a=e[3],o=e[4],u=e[5];if(o&&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(i.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(i.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let E=i.dims[0]/3,d=E,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");E=t.qkvHiddenSizes[0],d=t.qkvHiddenSizes[1],z=t.qkvHiddenSizes[2]}let B=h;if(E!==d)throw new Error("qkv_hidden_sizes first element should be same as the second");if(i.dims[0]!==E+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(o){if(d!==z)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(o.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(o.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(o.dims[1]!==p)throw new Error('Input "past" second dimension must be batch_size');if(o.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(o.dims[4]!==d/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(V=o.dims[3])}let Z=B+V,ee=-1,Q=0;if(a)throw new Error("Mask not supported");if(o)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:E,vHiddenSize:z,headSize:Math.floor(E/t.numHeads),vHeadSize:Math.floor(z/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:Q,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},li=(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; - } - `:` - ${s?"let past_sequence_length = uniforms.past_sequence_length":""}; - let present_sequence_length = total_sequence_length; - `,ml=(e,t,s,n,i,a,o,u)=>{let p=qt(o?1:a),h=64,k=a/p;k{let Q=It("x",e.dataType,e.dims,p),he=[Q],pe=o?qe("seq_lens",o.dataType,o.dims):void 0;pe&&he.push(pe);let Me=u?qe("total_sequence_length_input",u.dataType,u.dims):void 0;Me&&he.push(Me);let Fe=$s(e.dataType),De=[{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(De).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; - ${li(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 = ${o?"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] = ${Q.type.value}(${Fe}(1.0) / ${Fe}(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] = ${Q.type.value}(exp(f32input - max_value) / sum); - } - } - ${o?` - 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] = ${Q.type.value}(${Fe}(0)); - }`:""}; - }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${h};${z};${p}`,inputDependencies:V},getShaderSource:Z,getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(a/h),y:i,z:t*s},programUniforms:d})}},so=(e,t,s,n,i,a,o,u,p)=>{let h=o+a.kvSequenceLength,k=[a.batchSize,a.numHeads,a.sequenceLength,h],E=e>1&&n,d=a.kvNumHeads?a.kvNumHeads:a.numHeads,z=E?[a.batchSize,d,h,a.headSize]:void 0,B=a.nReps?a.nReps:1,V=a.scale===0?1/Math.sqrt(a.headSize):a.scale,Z=qt(a.headSize),ee=a.headSize/Z,Q=12,he={x:Math.ceil(h/Q),y:Math.ceil(a.sequenceLength/Q),z:a.batchSize*a.numHeads},pe=[{type:12,data:a.sequenceLength},{type:12,data:ee},{type:12,data:h},{type:12,data:a.numHeads},{type:12,data:a.headSize},{type:1,data:V},{type:12,data:o},{type:12,data:a.kvSequenceLength},{type:12,data:B}],Me=E&&n&&Le.size(n.dims)>0,Fe=["type","type"];Me&&Fe.push("type"),i&&Fe.push("type"),u&&Fe.push("type"),p&&Fe.push("type");let De=[{dims:k,dataType:t.dataType,gpuDataType:0}];E&&De.push({dims:z,dataType:t.dataType,gpuDataType:0});let Ye=at=>{let Pt=qe("q",t.dataType,t.dims,Z),Xt=qe("key",s.dataType,s.dims,Z),Zt=[Pt,Xt];if(Me){let Rt=qe("past_key",n.dataType,n.dims,Z);Zt.push(Rt)}i&&Zt.push(qe("attention_bias",i.dataType,i.dims));let bt=u?qe("seq_lens",u.dataType,u.dims):void 0;bt&&Zt.push(bt);let ss=p?qe("total_sequence_length_input",p.dataType,p.dims):void 0;ss&&Zt.push(ss);let St=It("output",t.dataType,k),Ft=[St];E&&Ft.push(It("present_key",t.dataType,z,Z));let bs=$s(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 = ${Q}u; - - var tileQ: array<${Pt.type.storage}, ${Q*Q}>; - var tileK: array<${Pt.type.storage}, ${Q*Q}>; - ${at.registerUniforms(Ht).declareVariables(...Zt,...Ft)} - ${at.mainStart([Q,Q,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; - ${li(bt,ss,!0)} - let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; - let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; - ${Me&&E?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""}; - let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K; - ${E?"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&&E?` - 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]; - }`} - ${E?`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) + ${i?"attention_bias[outputIdx]":"0.0"}; - } - }`};return{name:"AttentionProbs",shaderCache:{hint:`${Z};${i!==void 0};${n!==void 0};${e}`,inputDependencies:Fe},getRunData:()=>({outputs:De,dispatchGroup:he,programUniforms:pe}),getShaderSource:Ye}},fl=(e,t,s,n,i,a,o=void 0,u=void 0)=>{let p=a+i.kvSequenceLength,h=i.nReps?i.nReps:1,k=i.vHiddenSize*h,E=e>1&&n,d=i.kvNumHeads?i.kvNumHeads:i.numHeads,z=E?[i.batchSize,d,p,i.headSize]:void 0,B=[i.batchSize,i.sequenceLength,k],V=12,Z={x:Math.ceil(i.vHeadSize/V),y:Math.ceil(i.sequenceLength/V),z:i.batchSize*i.numHeads},ee=[{type:12,data:i.sequenceLength},{type:12,data:p},{type:12,data:i.vHeadSize},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:12,data:k},{type:12,data:a},{type:12,data:i.kvSequenceLength},{type:12,data:h}],Q=E&&n&&Le.size(n.dims)>0,he=["type","type"];Q&&he.push("type"),o&&he.push("type"),u&&he.push("type");let pe=[{dims:B,dataType:t.dataType,gpuDataType:0}];E&&pe.push({dims:z,dataType:t.dataType,gpuDataType:0});let Me=Fe=>{let De=qe("probs",t.dataType,t.dims),Ye=qe("v",s.dataType,s.dims),at=[De,Ye];Q&&at.push(qe("past_value",n.dataType,n.dims));let Pt=o?qe("seq_lens",o.dataType,o.dims):void 0;o&&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)];E&&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 = ${V}u; - var tileQ: array<${De.type.value}, ${V*V}>; - var tileV: array<${De.type.value}, ${V*V}>; - ${Fe.registerUniforms(bt).declareVariables(...at,...Zt)} - ${Fe.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; - ${li(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 - ${Q&&E?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""}; - let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n; - ${E?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""} - var value = ${De.type.storage}(0); - for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { - if (m < uniforms.M && w + local_id.x < uniforms.K) { - tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x]; - } - if (n < uniforms.N && w + local_id.y < uniforms.K) { - var idx = TILE_SIZE * local_id.y + local_id.x; - ${Q&&E?` - 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]; - }`} - ${E?` - if (w + local_id.y < present_sequence_length) { - present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx]; - }`:""} - } - workgroupBarrier(); - for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) { - value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x]; - } - workgroupBarrier(); - } - - // we need to transpose output from BNSH_v to BSND_v - if (m < uniforms.M && n < uniforms.N) { - let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size - + headIdx * uniforms.N + n; - output[outputIdx] = value; - } - }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:he},getRunData:()=>({outputs:pe,dispatchGroup:Z,programUniforms:ee}),getShaderSource:Me}},Rn=(e,t,s,n,i,a,o,u,p,h,k=void 0,E=void 0)=>{let d=Math.min(e.outputCount,1+(o?1:0)+(u?1:0)),z=d>1?h.pastSequenceLength:0,B=z+h.kvSequenceLength,V=p&&Le.size(p.dims)>0?p:void 0,Z=[t,s];d>1&&o&&Le.size(o.dims)>0&&Z.push(o),V&&Z.push(V),k&&Z.push(k),E&&Z.push(E);let ee=e.compute(so(d,t,s,o,V,h,z,k,E),{inputs:Z,outputs:d>1?[-1,1]:[-1]})[0];e.compute(ml(ee,h.batchSize,h.numHeads,z,h.sequenceLength,B,k,E),{inputs:k&&E?[ee,k,E]:[ee],outputs:[]});let Q=[ee,n];d>1&&u&&Le.size(u.dims)>0&&Q.push(u),k&&Q.push(k),E&&Q.push(E),e.compute(fl(d,ee,n,u,h,z,k,E),{inputs:Q,outputs:d>1?[0,2]:[0]})},ro=(e,t)=>{let s=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,i=t.inputHiddenSize,a=t.headSize,o=12,u={x:Math.ceil(t.headSize/o),y:Math.ceil(t.sequenceLength/o),z:t.batchSize*t.numHeads},p=[e.inputs[0],e.inputs[1],e.inputs[2]],h=[{type:12,data:n},{type:12,data:i},{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=E=>{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),Q=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 = ${o}u; - var tileInput: array<${Q}, ${o*o}>; - var tileWeightQ: array<${Q}, ${o*o}>; - var tileWeightK: array<${Q}, ${o*o}>; - var tileWeightV: array<${Q}, ${o*o}>; - ${E.registerUniforms(he).declareVariables(V,Z,ee,d,z,B)} - ${E.mainStart([o,o,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 = ${Q}(0); - var valueK = ${Q}(0); - var valueV = ${Q}(0); - for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { - if (m < uniforms.M && w + local_id.x < uniforms.K) { - tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; - } - if (n < uniforms.N && w + local_id.y < uniforms.K) { - let offset = n + (w + local_id.y) * uniforms.ldb; - tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; - tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; - tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; - } - workgroupBarrier(); - for (var k: u32 = 0u; 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${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)); - } - `)},Eo=e=>{fr(e,"Sub",(t,s)=>`${t}-${s}`)},su=e=>{fr(e,"Greater",{scalar:(t,s)=>`u32(${t}>${s})`,vector:(t,s)=>`vec4(${t}>${s})`},void 0,void 0,9)},ru=e=>{fr(e,"Less",{scalar:(t,s)=>`u32(${t}<${s})`,vector:(t,s)=>`vec4(${t}<${s})`},void 0,void 0,9)},Co=e=>{fr(e,"GreaterOrEqual",{scalar:(t,s)=>`u32(${t}>=${s})`,vector:(t,s)=>`vec4(${t}>=${s})`},void 0,void 0,9)},nu=e=>{fr(e,"LessOrEqual",{scalar:(t,s)=>`u32(${t}<=${s})`,vector:(t,s)=>`vec4(${t}<=${s})`},void 0,void 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0:e.activation_params)||[.01];return{activation:t,alpha:s}}return{activation:t}}}),Qs,$o,Ao=g(()=>{Qs=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},$o=e=>` - ${e?"value = value + getBiasByOutputCoords(coords);":""} - `}),du,Hc=g(()=>{du=e=>` -fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { - return dot(coords, vec4( - shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); -} -fn getOutputIndexFromCoords(coords : vec4) -> i32 { - return dot(coords, vec4( - i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); -} -`}),xn,Io,Oo=g(()=>{zt(),Ot(),Yt(),on(),xn=(e,t,s,n,i)=>{let a=n-s;return` - ${Array.from({length:s}).map((o,u)=>` - if (${$t(t.shape,u,t.rank)} != 1) { - ${t.indicesSet(e,u,$t(i,u+a,n))} - } else { - ${t.indicesSet(e,u,0)} - }`).join("")} -`},Io=(e,t,s,n,i=!1,a)=>{let o=e[0].dims,u=e[1].dims,p=o[o.length-2],h=u[u.length-1],k=o[o.length-1],E=qt(h),d=qt(k),z=qt(p),B=Le.size(s)/E/z,V=e.length>2,Z=n?n.slice(0,-2):s.slice(0,-2),ee=[Le.size(Z),p,h],Q=[{type:12,data:B},{type:12,data:p},{type:12,data:h},{type:12,data:k}];nn(t,Q),Q.push(...yt(Z,o,u)),V&&Q.push(...yt(e[2].dims)),Q.push(...yt(ee));let he=pe=>{let Me=zi("batch_dims",e[0].dataType,Z.length),Fe=qe("a",e[0].dataType,o.length,d),De=qe("b",e[1].dataType,u.length,E),Ye=It("output",e[0].dataType,ee.length,E),at=ms(Ye.type.tensor),Pt=Hr(t,Ye.type.value,at),Xt=[Fe,De],Zt="";if(V){let St=i?E:1;Xt.push(qe("bias",e[2].dataType,e[2].dims.length,St)),Zt=`${i?`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"}];qr(t,bt);let ss=()=>{let St=`var a_data: ${Fe.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 / ${E}`,"value")}; - } - } - `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${E};${d};${z};${i}`,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:Q}),getShaderSource:he}}}),Fo,cu,Do,pi,pu,Lo,zo,hi,Bo=g(()=>{zt(),Ot(),Yt(),on(),Oo(),Ao(),Fo=(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":""}); - `,cu=(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];"} - }`,Do=(e,t,s="f32",n,i=!1,a=32,o=!1,u=32)=>{let p=t[1]*e[1],h=t[0]*e[0],k=i?p:a,E=i?a:p,d=k/t[0],z=a/t[1];if(!((i&&d===4&&e[1]===4||!i&&(d===3||d===4))&&k%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} 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}>, ${E}>; -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 = ${o?"0":"i32(globalId.z)"}; - ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} - let globalRowStart = i32(workgroupId.y) * ${p}; - - let num_tiles = ${o?`${Math.ceil(u/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; - var kStart = ${o?`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; - ${Fo(i,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];"} - - ${cu(i,d)} - } - - workgroupBarrier(); - } - - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); - } -}`},pi=(e,t)=>e?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - kStart + inputRow, - globalRowStart + inputCol${t?", batchIndices":""}); - `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - globalRowStart + inputRow, - kStart + inputCol${t?", batchIndices":""}); - `,pu=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Lo=(e,t,s="f32",n,i=!1,a=32,o=!1,u=32,p=!1)=>{let h=e[1]*t[1],k=e[0]*t[0],E=i?h:a,d=i?a:h;if(!(d%t[1]===0&&E%t[0]===0&&a%t[1]===0))throw new Error(`tileAHight ${d} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${E} must be divisible by workgroupSize[0]${t[0]}, tileInner ${a} must be divisible by workgroupSize[1]${t[1]}`);let z=d/t[1],B=E/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 < ${E}; inputCol = inputCol + ${t[0]}) { - ${pi(i,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 = ${i?`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; - ${pi(i,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) { - ${pu(i)} - 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 = ${o?"0":"i32(globalId.z)"}; - ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} - let num_tiles = ${o?`${Math.ceil(u/a)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; - var kStart = ${o?`i32(globalId.z) * ${u}`:"0"}; - - var acc : array, rowPerThread>; - ${Z} - } -`},zo=(e,t,s,n,i=!1)=>{let[a,o,u,p]=n,h=ms(n[0].type.tensor);return` - fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${a.type.indices}) -> ${Qs(e,h)} { - var value = ${Qs(e,h)}(0.0); - let col = colIn * ${e}; - if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) - { - var aIndices: ${o.type.indices}; - ${xn("aIndices",o,o.rank-2,a.rank,"batchIndices")} - ${o.indicesSet("aIndices",o.rank-2,"u32(row)")} - ${o.indicesSet("aIndices",o.rank-1,"u32(colIn)")} - value = ${o.getByIndices("aIndices")}; - } - return value; - } - - fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${a.type.indices}) -> ${Qs(e,h)} { - var value = ${Qs(e,h)}(0.0); - let col = colIn * ${e}; - if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) - { - var bIndices: ${u.type.indices}; - ${xn("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: ${Qs(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 + ${i?"bias[colIn]":`${Qs(e,h)}(bias[row])`};`:""} - ${s} - ${p.setByIndices("vec3(coords)","value")} - } - } - `},hi=(e,t,s,n,i=!1,a)=>{let o=e[0].dims,u=e[1].dims,p=o.slice(0,-2),h=u.slice(0,-2),k=n?n.slice(0,-2):s.slice(0,-2),E=Le.size(k),d=o[o.length-2],z=o[o.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],Q=[Math.ceil(B/ee[0]/Z[0]),Math.ceil(d/ee[1]/Z[1]),Math.ceil(E/ee[2]/Z[2])],he=V?4:1,pe=[...p,d,z/he],Me=pe.length,Fe=[...h,z,B/he],De=Fe.length,Ye=[E,d,B/he],at=[{type:6,data:d},{type:6,data:B},{type:6,data:z}];nn(t,at),at.push(...yt(k,pe,Fe));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=zi("batchDims",e[0].dataType,ss,1),Ft=ms(e[0].dataType),bs=qe("a",e[0].dataType,Me,he),Ht=qe("b",e[1].dataType,De,he),Rt=It("result",e[0].dataType,Ye.length,he),_s=[bs,Ht];if(Xt){let xr=i?he:1;_s.push(qe("bias",e[2].dataType,e[2].dims.length,xr))}let ot=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];qr(t,ot);let Et=ms(Rt.type.tensor),cs=Hr(t,Rt.type.value,Et),Ns=zo(he,Xt,cs,[St,bs,Ht,Rt],i);return` - ${bt.registerUniforms(ot).registerInternalVariables(St).declareVariables(..._s,Rt)} - ${Ns} - ${V?Do(Z,ee,Ft,St):Lo(Z,ee,Ft,St)} - `};return{name:"MatMul",shaderCache:{hint:`${Z};${t.activation};${V};${i}`,inputDependencies:Pt},getRunData:()=>({outputs:[{dims:a?a(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Q[0],y:Q[1],z:Q[2]},programUniforms:at}),getShaderSource:Zt}}}),Ro,hu,qc=g(()=>{zt(),Pe(),Yt(),on(),Ao(),Hc(),Bo(),Ro=(e,t,s,n,i=!1,a,o=4,u=4,p=4,h="f32")=>{let k=at=>{switch(at){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${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.`)}},E=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",Q=` - 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 = ${Qs(o,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(o)} - } - return resData;`,he=e?t&&n?` - let col = colIn * ${o}; - ${Q}`:` - let col = colIn * ${o}; - if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { - ${Q} - } - return ${Qs(o,h)}(0.0);`:n&&s?` - let col = colIn * ${o}; - ${Q}`:` - let col = colIn * ${o}; - if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { - ${Q} - } - return ${Qs(o,h)}(0.0);`,pe=e?n&&s?E(u):` - let col = colIn * ${u}; - if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { - ${E(u)} - } - return ${Qs(u,h)}(0.0);`:` - let col = colIn * ${u}; - if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) { - ${E(u)} - } - return ${Qs(u,h)}(0.0);`,Me=Qs(p,h),Fe=Qs(e?o:u,h),De=Qs(e?u:o,h),Ye=Hr(a,Me,h);return` - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Fe} { - ${e?he:pe} - } - - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${De} { - ${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} - ${$o(i)} - ${Ye} - setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); - } - }`},hu=(e,t,s,n,i,a,o,u,p)=>{let h=t.format==="NHWC",k=h?e[0].dims[3]:e[0].dims[1],E=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,Q=[8,8,1],he=n<=8?[4,1,1]:[4,4,1],pe=[Math.ceil(Z/Q[0]/he[0]),Math.ceil(ee/Q[1]/he[1]),Math.ceil(E/Q[2]/he[2])];as("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${pe}`);let Me=V?h&&k%4!==0?3:4:1,Fe=Q[1]*he[1],De=Q[0]*he[0],Ye=Math.max(Q[0]*Me,Q[1]),at=n%Fe===0,Pt=i%De===0,Xt=a%Ye===0,Zt=V?[Me,4,4]:[1,1,1],bt=[{type:6,data:n},{type:6,data:i},{type:6,data:a},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];nn(t,bt),bt.push(...yt(e[0].dims,e[1].dims));let ss=["rank","rank"];o&&(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}];qr(t,bs);let Ht=V?4:1,Rt=ms(e[0].dataType),_s=` - 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); - }`,ot=qe("x",e[0].dataType,e[0].dims.length,Me===3?1:Me),Et=qe("w",e[1].dataType,e[1].dims.length,Ht),cs=[ot,Et],Ns=It("result",e[0].dataType,s.length,Ht);if(o){let xr=qe("bias",e[2].dataType,e[2].dims.length,Ht);cs.push(xr),_s+=` - fn getBiasByOutputCoords(coords : vec4) -> ${V?`vec4<${Rt}>`:Rt} { - return bias[coords.${h?"w":"y"}${V?"/ 4":""}]; - }`}return` - ${du("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(...cs,Ns)} - ${_s} - ${Ro(h,at,Pt,Xt,o,t,Zt[0],Zt[1],Zt[2],Rt)} - ${V?Do(he,Q,Rt,void 0,!h,Ye):Lo(he,Q,Rt,void 0,!h,Ye,!1,void 0,u)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${Me};${V};${at};${Pt};${Xt};${Fe};${De};${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}}}),No,jo,jn,mu,Uo,mi,fu,_u,Qc=g(()=>{zt(),Pe(),Ot(),Yt(),on(),Ao(),No=e=>{let t=1;for(let s=0;stypeof e=="number"?[e,e,e]:e,jn=(e,t)=>t<=1?e:e+(e-1)*(t-1),mu=(e,t,s,n=1)=>{let i=jn(t,n);return Math.floor((e[0]*(s-1)-s+i)/2)},Uo=(e,t,s,n,i)=>{i==null&&(i=mu(e,t[0],n[0]));let a=[0,0,0,s];for(let o=0;o<3;o++)e[o]+2*i>=t[o]&&(a[o]=Math.trunc((e[o]-t[o]+2*i)/n[o]+1));return a},mi=(e,t,s,n,i,a,o,u,p,h)=>{let k,E,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=Uo([t,s,n,1],[u,p,h],1,[i,a,o],e);E=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=Uo([t,s,n,1],[u,p,h],1,[i,a,o],e[0]);E=B[0],d=B[1],z=B[2]}else if(e==="SAME_UPPER"){E=Math.ceil(t/i),d=Math.ceil(s/a),z=Math.ceil(n/o);let B=(E-1)*i+u-t,V=(d-1)*a+p-s,Z=(z-1)*o+h-n,ee=Math.floor(B/2),Q=B-ee,he=Math.floor(V/2),pe=V-he,Me=Math.floor(Z/2),Fe=Z-Me;k={top:he,bottom:pe,left:Me,right:Fe,front:ee,back:Q}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:k,outDepth:E,outHeight:d,outWidth:z}},fu=(e,t,s,n,i,a=!1,o="channelsLast")=>{let u,p,h,k,E;if(o==="channelsLast")[u,p,h,k,E]=e;else if(o==="channelsFirst")[u,E,p,h,k]=e;else throw new Error(`Unknown dataFormat ${o}`);let[d,,z,B,V]=t,[Z,ee,Q]=jo(s),[he,pe,Me]=jo(n),Fe=jn(z,he),De=jn(B,pe),Ye=jn(V,Me),{padInfo:at,outDepth:Pt,outHeight:Xt,outWidth:Zt}=mi(i,p,h,k,Z,ee,Q,Fe,De,Ye),bt=a?d*E:d,ss=[0,0,0,0,0];return o==="channelsFirst"?ss=[u,bt,Pt,Xt,Zt]:o==="channelsLast"&&(ss=[u,Pt,Xt,Zt,bt]),{batchSize:u,dataFormat:o,inDepth:p,inHeight:h,inWidth:k,inChannels:E,outDepth:Pt,outHeight:Xt,outWidth:Zt,outChannels:bt,padInfo:at,strideDepth:Z,strideHeight:ee,strideWidth:Q,filterDepth:z,filterHeight:B,filterWidth:V,effectiveFilterDepth:Fe,effectiveFilterHeight:De,effectiveFilterWidth:Ye,dilationDepth:he,dilationHeight:pe,dilationWidth:Me,inShape:e,outShape:ss,filterShape:t}},_u=(e,t,s,n,i,a)=>{let o=a==="channelsLast";o?e[0].dims[3]:e[0].dims[1];let u=[64,1,1],p={x:s.map((Z,ee)=>ee)},h=[Math.ceil(No(p.x.map(Z=>s[Z]))/u[0]),1,1];as("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${h}`);let k=1,E=Le.size(s),d=[{type:12,data:E},{type:12,data:n},{type:12,data:i},{type:12,data:t.strides},{type:12,data:t.dilations}];nn(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:i.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];qr(t,ee);let Q=1,he=ms(e[0].dataType),pe=qe("x",e[0].dataType,e[0].dims.length,k),Me=qe("W",e[1].dataType,e[1].dims.length,Q),Fe=[pe,Me],De=It("result",e[0].dataType,s.length,Q),Ye="";if(B){let Xt=qe("bias",e[2].dataType,e[2].dims.length,Q);Fe.push(Xt),Ye+=` - fn getBiasByOutputCoords(coords : array) -> ${he} { - return bias[${o?$t("coords",4,5):$t("coords",1,5)}]; - }`}let at=Qs(k,he),Pt=Hr(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(...Fe,De)} - ${Z.mainStart()} - ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - let coords = ${De.offsetToIndices("global_idx")}; - let batch = ${$t("coords",0,pe.rank)}; - let d2 = ${o?$t("coords",pe.rank-1,pe.rank):$t("coords",1,pe.rank)}; - let xFRCCorner = vec3(${o?$t("coords",1,pe.rank):$t("coords",2,pe.rank)}, - ${o?$t("coords",2,pe.rank):$t("coords",3,pe.rank)}, - ${o?$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 = ${o?$t("uniforms.x_shape",1,pe.rank):$t("uniforms.x_shape",2,pe.rank)}; - let xShapeZ = ${o?$t("uniforms.x_shape",2,pe.rank):$t("uniforms.x_shape",3,pe.rank)}; - let xShapeW = ${o?$t("uniforms.x_shape",3,pe.rank):$t("uniforms.x_shape",4,pe.rank)}; - let xShapeU = ${o?$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) { - ${o?`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) { - ${o?`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) { - ${o?`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) { - ${o?`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};${o};${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}}}),gu,wu,yu=g(()=>{zt(),Ot(),Yt(),on(),gu=(e,t,s,n)=>{let i=e.length>2,a=i?"value += b[output_channel];":"",o=e[0].dims,u=e[1].dims,p=t.format==="NHWC",h=p?s[3]:s[1],k=h/t.group,E=p&&k>=4?qt(h):1,d=Le.size(s)/E,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}];nn(t,z),z.push(...yt(o,[u[0],u[1],u[2],u[3]/E]));let B=i?["rank","rank","rank"]:["rank","rank"];z.push(...yt([s[0],s[1],s[2],s[3]/E]));let V=Z=>{let ee=It("output",e[0].dataType,s.length,E),Q=ms(ee.type.tensor),he=Hr(t,ee.type.value,Q),pe=qe("x",e[0].dataType,o.length),Me=qe("w",e[1].dataType,u.length,E),Fe=[pe,Me];i&&Fe.push(qe("b",e[2].dataType,e[2].dims,E));let De=[{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"}];qr(t,De);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(De).declareVariables(...Fe,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 * ${E} / uniforms.output_channels_per_group; - var in_channel_offset = group_id * uniforms.w_shape[${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}_${E}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:n?n(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:z}),getShaderSource:V}},wu=(e,t,s,n)=>{let i=e.length>2,a=qt(s[3]),o=qt(s[2]),u=Le.size(s)/a/o,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],E=[{type:12,data:u},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];nn(t,E),E.push(...yt(p,h,k));let d=(o-1)*t.strides[1]+h[1],z=B=>{let V=It("output",e[0].dataType,k.length,a),Z=ms(V.type.tensor),ee=Hr(t,V.type.value,Z),Q=qe("x",e[0].dataType,p.length,a),he=qe("w",e[1].dataType,h.length,a),pe=[Q,he];i&&pe.push(qe("b",e[2].dataType,e[2].dims,a));let Me=i?"value += b[output_channel];":"",Fe=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return qr(t,Fe),` - ${B.registerUniforms(Fe).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] / ${o}u; - let col = (index1 % width1) * ${o}u; - index1 = index1 / width1; - let row = index1 % uniforms.output_shape[1]; - let batch = index1 / uniforms.output_shape[1]; - - let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; - - var x_vals: array<${Q.type.value}, ${d}>; - var values: array<${V.type.value}, ${o}>; - 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] = ${Q.get("batch","u32(x_height)","u32(x_width)","input_channel")}; - } else { - x_vals[i] = ${Q.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 < ${o}u; i++) { - values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); - } - } - } - } - - for (var i = 0u; i < ${o}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};${a};${o};${d};${h[0]};${h[1]}`,inputDependencies:i?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:E}),getShaderSource:z}}}),Mu,fi,Vo,_i,Wo,Go,bu,Ko,Ho,Xc=g(()=>{Ot(),qc(),Qc(),Bo(),yu(),on(),Oo(),Kr(),Mu=(e,t,s,n,i,a)=>{let o=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)),E=u.map((d,z)=>d+n[z]+n[z+p]).map((d,z)=>Math.floor((d-k[z]+i[z])/i[z]));return E.splice(0,0,o),E.splice(a?3:1,0,h),E},fi=[2,3,1,0],Vo=(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 i=e[0].dims.length-2;if(t.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(t.strides.length!==i)throw new Error(`strides should be ${i}D`);if(t.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},_i=(e,t)=>{let s=e.kernelShape.slice();s.length{let t=So(e),s=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,a=e.group,o=e.kernel_shape,u=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:n,format:s,dilations:i,group:a,kernelShape:o,pads:u,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},Go=(e,t,s,n)=>{let i=s.format==="NHWC",a=Mu(t[0].dims,t[1].dims,s.dilations,s.pads,s.strides,i);if(s.group!==1){let Fe=[t[0]];if(i){let De=e.kernelCustomData.wT??e.compute(hr(t[1],fi),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=De),Fe.push(De)}else Fe.push(t[1]);t.length===3&&Fe.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&i&&t[1].dims[0]===s.group&&t[1].dims[1]===1&&s.dilations[0]===1&&s.dilations[1]===1?e.compute(wu(Fe,s,a,n),{inputs:Fe}):e.compute(gu(Fe,s,a,n),{inputs:Fe});return}let o=t.length===3,u=t[0].dims[i?1:2],p=t[0].dims[i?2:3],h=t[0].dims[i?3:1],k=t[1].dims[2],E=t[1].dims[3],d=a[i?1:2],z=a[i?2:3],B=a[i?3:1],V=i&&k===u&&E===p&&s.pads[0]===0&&s.pads[1]===0;if(V||k===1&&E===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 Fe=a[0],De,Ye,at,Pt=[];if(i){let bt=e.kernelCustomData.wT??e.compute(hr(t[1],fi),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];if(s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=bt),V){let ss=u*p*h;De=t[0].reshape([1,Fe,ss]),Ye=bt.reshape([1,ss,B]),at=[1,Fe,B]}else De=t[0].reshape([Fe,u*p,h]),Ye=bt.reshape([1,h,B]),at=[Fe,d*z,B];Pt.push(De),Pt.push(Ye)}else De=t[0].reshape([Fe,h,u*p]),Ye=t[1].reshape([1,B,h]),at=[Fe,B,d*z],Pt.push(Ye),Pt.push(De);o&&Pt.push(t[2]);let Xt=at[2],Zt=Pt[0].dims[Pt[0].dims.length-1];Xt<8&&Zt<8?e.compute(Io(Pt,s,a,at,i,n),{inputs:Pt}):e.compute(hi(Pt,s,a,at,i,n),{inputs:Pt});return}let Z=!0,ee=e.kernelCustomData.wT??e.compute(hr(t[1],fi),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=ee);let Q=[t[0],ee];o&&Q.push(t[2]);let he=i?d*z:B,pe=i?B:d*z,Me=k*E*h;e.compute(hu(Q,s,a,he,pe,Me,o,Z,n),{inputs:Q})},bu=(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 i=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),o=[1].concat(t.dilations),u=[1].concat(t.kernelShape),p=_i({...t,pads:i,strides:a,dilations:o,kernelShape:u},n);Go(e,n,p,h=>s?[h[0],h[2],h[3]]:[h[0],h[1],h[3]])},Ko=(e,t,s)=>{let n=s.format==="NHWC"?"channelsLast":"channelsFirst",i=_i(s,t),a=s.autoPad==="NOTSET"?s.pads:s.autoPad,o=fu(t[0].dims,t[1].dims,s.strides,s.dilations,a,!1,n);e.compute(_u(t,i,o.outShape,[o.filterDepth,o.filterHeight,o.filterWidth],[o.padInfo.front,o.padInfo.top,o.padInfo.left],n))},Ho=(e,t)=>{if(Vo(e.inputs,t),e.inputs[0].dims.length===3)bu(e,t);else if(e.inputs[0].dims.length===5)Ko(e,e.inputs,t);else{let s=_i(t,e.inputs);Go(e,e.inputs,s)}}}),qo,Yc=g(()=>{zt(),Pe(),Ot(),Yt(),qo=(e,t,s)=>{let n=e.length>2,i=t.outputShape,a=t.format==="NHWC",o=t.group,u=e[1].dims,p=u[2]/o,h=u[3],k=a?qt(p):1,E=a?qt(h):1,d=a?h===1?k:E:1,z=Le.size(i)/E,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]],Q=[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:Q},{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(i));let Fe=De=>{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=ms(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,[i[Zt]].length,E));let Ft=It("result",e[0].dataType,i.length,E),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 _s=0;_s(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 / ${E}]`:""}; - ${Ft.setByOffset("global_idx","value")}; - `;return` - ${De.registerUniforms(Ye).declareVariables(...St,Ft)} - ${De.mainStart()} - 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Me=0;Me1?`indicesIndices${Q}[${Me}]`:`indicesIndices${Q}`} = ${o.length>1?`outputIndices${Q}[uniforms.axis + ${Me}]`:`outputIndices${Q}`};`;pe+=` - var idx${Q} = ${B.getByIndices(`indicesIndices${Q}`)}; - if (idx${Q} < 0) { - idx${Q} = idx${Q} + uniforms.axisDimLimit; - } - var dataIndices${Q} : ${z.type.indices}; - `;for(let Me=0,Fe=0;Me1?`dataIndices${Q}[${Me}]`:`dataIndices${Q}`} = u32(idx${Q});`,Fe+=he):(pe+=`${i>1?`dataIndices${Q}[${Me}]`:`dataIndices${Q}`} = ${o.length>1?`outputIndices${Q}[${Fe}]`:`outputIndices${Q}`};`,Fe++);return pe},ee;if(e[0].dataType===9){let Q=(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); - ${Q("value",0,"u32")} - 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h=[{type:12,data:a},{type:12,data:n},{type:12,data:i},{type:12,data:s},{type:12,data:o},{type:12,data:u},{type:12,data:p}],k=[a];h.push(...yt(t.dims,k));let E=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:i.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) { - ${i.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"} - } - ${s.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"} - } - - input_slice_offsets_data[global_idx] = base_offset + u32(relative_slice_offset); - }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${i.length}_${s.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:k,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h}),getShaderSource:E},{inputs:[t],outputs:[-1]})[0]},Yu=(e,t)=>{let s=e.inputs,n=s[0].dims,i=s[0].dataType,a=s[1].dims,o=a[a.length-1],u=Le.sizeToDimension(a,a.length-1),p=Le.sizeFromDimension(n,t.batchDims+o),h=Le.sizeToDimension(n,t.batchDims),k=Le.sizeFromDimension(n,t.batchDims),E=u/h,d=new Array(o),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=Le.size(Z),Q=[{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),Fe=qe("slice_offsets",12,B.dims.length),De=It("output",s[0].dataType,Z.length);return` - ${pe.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(Me,Fe,De)} - ${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:i}],dispatchGroup:{x:Math.ceil(ee/64)},programUniforms:Q}),getShaderSource:he},{inputs:[s[0],B]})},Ju=e=>({batchDims:e.batch_dims,cacheKey:""})}),Zu,ed,td,sd,sp=g(()=>{zt(),Ot(),rs(),Yt(),Zu=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let s=Le.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,i=e[0],a=e[2],o=e.length===4?e[3]:void 0;if(a.dims.length!==i.dims.length||!i.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(o){if(o.dataType!==i.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(o.dims.length!==a.dims.length||!o.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.")}},ed=(e,t)=>{let s=e[0].dims,n=e[1].dims,i=s.length,a=Le.normalizeAxis(t.gatherAxis,i),o=Le.normalizeAxis(t.quantizeAxis,i),u=s.slice(0);u.splice(a,1,...n);let p=Le.size(u),h=e[2].dataType,k=e[0].dataType===22,E=[{type:12,data:p},{type:12,data:o},{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,Q=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,Q)} - ${z.mainStart()} - let output_indices = ${Q.offsetToIndices("global_idx")}; - var indices_indices = ${V.type.indices}(0); - ${n.length>1?` - for (var i: u32 = 0; i < ${n.length}; i++) { - let index = ${Q.indicesGet("output_indices","uniforms.gather_axis + i")}; - ${V.indicesSet("indices_indices","i","index")}; - }`:`indices_indices = ${Q.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 = ${Q.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 = ${Q.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 = ${$s(h)}(quantized_data - zero_point) * scale; - ${Q.setByOffset("global_idx","dequantized_data")}; - }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${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:E}),getShaderSource:d}},td=(e,t)=>{let s=e.inputs;Zu(s,t),e.compute(ed(e.inputs,t))},sd=e=>Bt({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),rd,nd,bi,rp,np=g(()=>{zt(),Ot(),rs(),Yt(),rd=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.`)},nd=(e,t)=>{let s=e[0].dims,n=e[0].dataType,i=s.length,a=e[1].dims,o=e[1].dataType,u=Le.normalizeAxis(t.axis,i),p=s[u],h=a.slice(0),k=Le.size(h),E=qe("input",n,i),d=qe("indicesInput",o,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(E,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 = ${E.type.indices}(outputIndices); - ${E.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; - let value = ${E.getByIndices("inputIndices")}; - - ${z.setByOffset("global_idx","value")}; - }`}},bi=e=>Bt({axis:e.axis}),rp=(e,t)=>{let s=e.inputs;rd(s),e.compute(nd(e.inputs,t))}}),id,od,ad,ld,ud=g(()=>{zt(),Ot(),Yt(),id=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},od=(e,t)=>{let s=e[0].dims.slice(),n=e[1].dims.slice(),[i,a,o]=Fr.getShapeOfGemmResult(s,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),u=[i,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(i/p),E=!0,d=Le.size(u),z=[{type:12,data:E?h:d},{type:12,data:i},{type:12,data:a},{type:12,data:o},{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 Q="";t.transA&&t.transB?Q="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?Q="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?Q="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(Q="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),Fe=pe.type.value,De=null,Ye=[pe,Me];e.length===3&&(De=qe("c",e[2].dataType,e[2].dims.length),Ye.push(De));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 = ${Fe}(0); - for (var k: u32 = 0u; k < uniforms.K; k++) { - ${Q} - } - - ${he} - ${De!=null?`let cOffset = ${De.broadcastedIndicesToOffset("vec2(m, n)",at)}; value += ${Fe}(uniforms.beta) * ${De.getByOffset("cOffset")};`:""} - output[global_idx] = value; - }`},Z=ee=>{let Q=qe("a",e[0].dataType,e[0].dims),he=qe("b",e[1].dataType,e[1].dims),pe=null,Me=[Q,he];e.length===3&&(pe=qe("c",e[2].dataType,e[2].dims.length),Me.push(pe));let Fe=It("output",e[0].dataType,u.length);Me.push(Fe);let De=[{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] = ${Q.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] = ${Q.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] = ${Q.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] = ${Q.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(De).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 = ${Fe.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)",Fe)}; value += ${Fe.type.value}(uniforms.beta) * ${pe.getByOffset("cOffset")};`:""} - if (m < uniforms.M && n < uniforms.N) { - output[m * uniforms.N + n] = value; - } - }`};return E?{name:"GemmShared",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:h*k},programUniforms:z}),getShaderSource:Z}:{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:z}),getShaderSource:V}},ad=e=>{let t=e.transA,s=e.transB,n=e.alpha,i=e.beta;return{transA:t,transB:s,alpha:n,beta:i,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},ld=(e,t)=>{id(e.inputs),e.compute(od(e.inputs,t))}}),Er,Lr,Qr,an,dd,ta,cd,pd,vi,hd,md,fd,_d,sa,ra=g(()=>{zt(),Ot(),rs(),Yt(),[Er,Lr,Qr,an]=[0,1,2,3],dd=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 ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},ta=` - fn gs_get_cubic_coeffs(x: f32) -> vec4 { - let cubic_alpha = -0.75f; - let x_abs = abs(x); - var coeffs: vec4; - coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha); - coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1); - coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1); - coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha); - return coeffs; - } -`,cd=e=>` - fn gs_bicubic_interpolate(p: mat4x4<${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; - } -`,pd=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)); - `} - } -`,vi=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); - }`:""} -`,hd=(e,t,s)=>` - fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4) -> ${t} { - var pixel = ${t}(0); - var indices = vec4(0); - indices[${Er}] = batch; - indices[${Lr}] = channel;`+(()=>{switch(s.paddingMode){case"zeros":return` - if (r >= 0 && r < H && c >=0 && c < W) { - indices[${Qr}] = u32(r); - indices[${an}] = u32(c); - } - `;case"border":return` - indices[${Qr}] = u32(clamp(r, 0, H - 1)); - indices[${an}] = u32(clamp(c, 0, W - 1)); - `;case"reflection":return` - indices[${Qr}] = gs_reflect(r, border[1], border[3]); - indices[${an}] = gs_reflect(c, border[0], border[2]); - `;default:throw new Error(`padding mode ${s.paddingMode} is not supported`)}})()+` - return ${e.getByIndices("indices")}; - } -`,md=(e,t,s)=>(()=>{switch(s.mode){case"nearest":return` - let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${Er}], indices[${Lr}], border); - `;case"bilinear":return` - let x1 = i32(floor(x)); - let y1 = i32(floor(y)); - let x2 = x1 + 1; - let y2 = y1 + 1; - - let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${Er}], indices[${Lr}], border); - let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${Er}], indices[${Lr}], border); - let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${Er}], indices[${Lr}], border); - let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${Er}], 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[${Er}], indices[${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")}`,fd=(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]],i=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]],[Er,Lr,Qr,an]=[0,3,1,2]);let o=It("output",e[0].dataType,a.length),u=s.type.value,p=Le.size(a),h=[{type:12,data:p},...yt(e[0].dims,n,a)],k=E=>` - ${E.registerUniform("output_size","u32").declareVariables(s,i,o)} - ${ta} - ${cd(u)} - ${pd(t)} - ${vi(t)} - ${hd(s,u,t)} - - ${E.mainStart()} - ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - let H_in = i32(uniforms.x_shape[${Qr}]); - let W_in = i32(uniforms.x_shape[${an}]); - - ${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 = ${o.offsetToIndices("global_idx")}; - var grid_indices = vec3(indices[${Er}], indices[${Qr}], indices[${an}]); - let nxy = ${i.getByIndices("grid_indices")}; - var x = gs_denormalize(f32(nxy[0]), W_in); - var y = gs_denormalize(f32(nxy[1]), H_in); - - ${md(o,u,t)} - }`;return{name:"GridSample",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:["type","type"]},getRunData:E=>{let d=Le.size(a);return{outputs:[{dims:a,dataType:E[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:h}},getShaderSource:k}},_d=(e,t)=>{dd(e.inputs),e.compute(fd(e.inputs,t))},sa=e=>Bt({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),ir,gd,wd,xi,yd,Gn,na,Md=g(()=>{zt(),Ot(),rs(),ue(),no(),Yt(),Kr(),ir=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,gd=(e,t)=>{let s=e[0],n=ir(e,1),i=ir(e,2),a=ir(e,3),o=ir(e,4),u=ir(e,5),p=ir(e,6),h=ir(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],E=s.dims[1],d=s.dims.length===3?s.dims[2]:t.numHeads*s.dims[4],z=E,B=0,V=0,Z=Math.floor(d/t.numHeads);if(p&&h&&Le.size(p.dims)&&Le.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&&Le.size(p.dims)||h&&Le.size(h.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let ee;if(n&&Le.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(i)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&&Le.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 Q=B+z,he=0;if(o&&Le.size(o.dims)>0){he=8;let De=o.dims;throw De.length===1?De[0]===k?he=1:De[0]===3*k+2&&(he=3):De.length===2&&De[0]===k&&De[1]===Q&&(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(i&&Le.size(i.dims)>0){if(i.dims.length!==3&&i.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(s.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(i.dims.length===3){if(z!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');Me=i.dims[2]}else{if(z!==i.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');Me=i.dims[1]*i.dims[3],pe=!0}}let Fe=!1;if(o&&Le.size(o.dims)>0)throw new Error("Key padding mask is not supported");if(u&&Le.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]!==E||u.dims[3]!==Q)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:k,sequenceLength:E,pastSequenceLength:B,kvSequenceLength:z,totalSequenceLength:Q,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:Fe,passPastInKv:pe,qkvFormat:ee}},wd=e=>Bt({...e}),xi=Bt({perm:[0,2,1,3]}),yd=(e,t,s,n,i,a,o)=>{let u=[n,i,a],p=Le.size(u),h=[{type:12,data:p},{type:12,data:o},{type:12,data:a}],k=E=>{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` - ${E.registerUniforms(V).declareVariables(z,B,d)} - ${E.mainStart()} - ${E.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]},Gn=(e,t,s,n,i,a,o,u)=>{let p=a;if(o&&Le.size(o.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return p=yd(e,a,o,t,n,s*i,u),p=p.reshape([t,n,s,i]),s===1||n===1?p:e.compute(hr(p,xi.perm),{inputs:[p],outputs:[-1]})[0]}else return a.dims.length===3&&(p=a.reshape([t,n,s,i])),s===1||n===1?p:e.compute(hr(p,xi.perm),{inputs:[p],outputs:[-1]})[0]},na=(e,t)=>{let s=gd(e.inputs,t),n=e.inputs[0],i=ir(e.inputs,1),a=ir(e.inputs,2),o=ir(e.inputs,3),u=ir(e.inputs,4),p=ir(e.inputs,5),h=ir(e.inputs,6),k=ir(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if((i==null?void 0:i.dims.length)===5)throw new Error("Packed KV is not implemented");let E=i&&a&&i.dims.length===4&&a.dims.length===4,d=Gn(e,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,n,o,0);if(E)return Rn(e,d,i,a,u,void 0,h,k,p,s);if(!i||!a)throw new Error("key and value must be provided");let z=Gn(e,s.batchSize,s.numHeads,s.kvSequenceLength,s.headSize,i,o,s.hiddenSize),B=Gn(e,s.batchSize,s.numHeads,s.kvSequenceLength,s.vHeadSize,a,o,2*s.hiddenSize);Rn(e,d,z,B,u,void 0,h,k,p,s)}}),bd,ia,ip,op,Ti,oa,vd,xd=g(()=>{zt(),Ot(),rs(),Yt(),bd=e=>{if(!e||e.length<1)throw new Error("too few inputs")},ia=(e,t)=>{let s=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(i=>s.push(Number(i))),n=s.length),Bt({numOutputs:n,axis:t.axis,splitSizes:s})},ip=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; -}`,op=e=>{let t=e.length,s=[];for(let n=0;n{let s=e[0].dims,n=Le.size(s),i=e[0].dataType,a=Le.normalizeAxis(t.axis,s.length),o=new Array(t.numOutputs),u=qe("input",i,s.length),p=new Array(t.numOutputs),h=[],k=[],E=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,...o)} - ${ip(p.length)} - ${op(o)} - - ${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})}},oa=(e,t)=>{bd(e.inputs);let s=e.inputs.length===1?t:ia(e.inputs,t);e.compute(Ti(e.inputs,s),{inputs:[0]})},vd=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})}}),aa,Td,la,ua,ap=g(()=>{rs(),no(),Md(),xd(),Kr(),aa=(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],i=e[2],a=e[3],o=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],E=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=o&&o.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(o.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"');E=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(i)throw new Error('Expect "value" be none when "key" has packed kv format.');E=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');E=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 Q=0,he=!1,pe=t.kvNumHeads?B*t.kvNumHeads:k;if(i&&i.dims.length>0){if(i.dims.length!==3&&i.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(s.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(i.dims.length===3){if(E!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');pe=i.dims[2]}else{if(E!==i.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');pe=i.dims[1]*i.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:E,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:Q,scale:t.scale,broadcastResPosBias:!1,passPastInKv:he,qkvFormat:ee}},Td=Bt({perm:[0,2,1,3]}),la=(e,t,s)=>{let n=t,i=s.kvNumHeads;return t.dims.length===3&&s.kvSequenceLength!==0&&(n=t.reshape([s.batchSize,s.kvSequenceLength,i,s.headSize]),n=e.compute(hr(n,Td.perm),{inputs:[n],outputs:[-1]})[0]),n},ua=(e,t)=>{var Z;let s=aa(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],i=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,o=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,E=Bt({axis:2,numOutputs:3,splitSizes:[s.numHeads*s.headSize,k*s.headSize,k*s.headSize]}),[d,z,B]=!i&&!a?e.compute(Ti([n],E),{inputs:[n],outputs:[-1,-1,-1]}):[n,i,a],V=Gn(e,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,d,void 0,0);Rn(e,V,la(e,z,s),la(e,B,s),void 0,void 0,o,u,void 0,s,p,h)}}),da,ca,Pd,Ed,lp=g(()=>{zt(),Ot(),Kr(),Yt(),da=(e,t,s,n,i,a,o,u)=>{let p=qt(a),h=p===1?"f32":`vec${p}f`,k=p===1?"vec2f":`mat2x${p}f`,E=i*o,d=64;E===1&&(d=256);let z=[i,o,a/p],B=[i,o,2],V=["rank","type","type"],Z=[];Z.push(...yt(z,B));let ee=Q=>{let he=qe("x",t.dataType,3,p),pe=qe("scale",s.dataType,s.dims),Me=qe("bias",n.dataType,n.dims),Fe=It("output",1,3,2),De=[he,pe,Me,Fe];return` - var workgroup_shared : array<${k}, ${d}>; - const workgroup_size = ${d}u; - ${Q.declareVariables(...De)} - ${Q.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:E},programUniforms:Z}),getShaderSource:ee},{inputs:[t,s,n],outputs:[-1]})[0]},ca=(e,t,s)=>{let n=t[0].dims,i=n,a=2,o=n[0],u=n[1],p=Le.sizeFromDimension(n,a),h=qt(p),k=Le.size(i)/h,E=da(e,t[0],t[1],t[2],o,p,u,s.epsilon),d=[o,u,p/h],z=[o,u],B=["type","none"],V=Z=>{let ee=qe("x",t[0].dataType,d.length,h),Q=qe("scale_shift",1,z.length,2),he=It("output",t[0].dataType,d.length,h),pe=[ee,Q,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 = ${Q.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:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:[{type:12,data:k},...yt(d,z,d)]}),getShaderSource:V},{inputs:[t[0],E]})},Pd=(e,t,s)=>{let n=t[0].dims,i=n,a=n[0],o=n[n.length-1],u=Le.sizeFromDimension(n,1)/o,p=qt(o),h=Le.size(i)/p,k=[{type:12,data:u},{type:12,data:Math.floor(o/p)}],E=["type","type"],d=!1,z=[0,n.length-1];for(let ee=0;een[z[Q]])),V=da(e,B,t[1],t[2],a,u,o,s.epsilon),Z=ee=>{let Q=ms(t[0].dataType),he=p===1?"vec2f":`mat${p}x2f`,pe=De=>{let Ye=De===0?"x":"y",at=p===1?"f32":`vec${p}f`;switch(p){case 1:return`${Q}(${at}(scale.${Ye}))`;case 2:return`vec2<${Q}>(${at}(scale[0].${Ye}, scale[1].${Ye}))`;case 4:return`vec4<${Q}>(${at}(scale[0].${Ye}, scale[1].${Ye}, scale[2].${Ye}, scale[3].${Ye}))`;default:throw new Error(`Not supported compoents ${p}`)}},Me=qe("input",t[0].dataType,t[0].dims,p),Fe=It("output",t[0].dataType,i,p);return` - @group(0) @binding(0) var input : array<${Me.type.storage}>; - @group(0) @binding(1) var scale_input : array<${he}>; - @group(0) @binding(2) var output : array<${Fe.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:E},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:k}),getShaderSource:Z},{inputs:[t[0],V]})},Ed=(e,t)=>{t.format==="NHWC"?Pd(e,e.inputs,t):ca(e,e.inputs,t)}}),Cd,kd,Sd,up=g(()=>{zt(),Ot(),Yt(),Cd=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},kd=(e,t,s)=>{let n=t.simplified,i=e[0].dims,a=e[1],o=!n&&e[2],u=i,p=Le.normalizeAxis(t.axis,i.length),h=Le.sizeToDimension(i,p),k=Le.sizeFromDimension(i,p),E=Le.size(a.dims),d=o?Le.size(o.dims):0;if(E!==k||o&&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 ${E} and bias size of ${d}`);let z=[];for(let Me=0;Me1,Q=s>2,he=Me=>{let Fe=ms(e[0].dataType),De=[qe("x",e[0].dataType,e[0].dims,B),qe("scale",a.dataType,a.dims,B)];o&&De.push(qe("bias",o.dataType,o.dims,B)),De.push(It("output",e[0].dataType,u,B)),ee&&De.push(It("mean_data_output",1,z)),Q&&De.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(...De)} - ${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 = ${As(Fe,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 = ${As(Fe,B,"x[j + offset]")}; - let f32scale = ${As(Fe,B,"scale[j]")}; - output[j + offset] = ${De[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale - ${o?`+ ${As(Fe,B,"bias[j]")}`:""} - ); - } - - ${ee?"mean_data_output[global_idx] = mean":""}; - ${Q?"inv_std_output[global_idx] = inv_std_dev":""}; - }`},pe=[{dims:u,dataType:e[0].dataType}];return ee&&pe.push({dims:z,dataType:1}),Q&&pe.push({dims:z,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${B};${s};${n}`,inputDependencies:V},getRunData:()=>({outputs:pe,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:Z}),getShaderSource:he}},Sd=(e,t)=>{Cd(e.inputs),e.compute(kd(e.inputs,t,e.outputCount))}}),$d,Ad,Id=g(()=>{Ot(),Oo(),Bo(),$d=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.")},Ad=e=>{$d(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(Io(e.inputs,{activation:""},t));else{let i=t[t.length-2],a=Le.size(e.inputs[0].dims.slice(0,-2)),o=Le.size(e.inputs[1].dims.slice(0,-2));if(a!==1&&i===1&&o===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(hi(k,{activation:""},t,h),{inputs:k})}else e.compute(hi(e.inputs,{activation:""},t))}}}),Od,Fd,pa,Dd,Ld,ys=g(()=>{zt(),Ot(),rs(),Yt(),Od=(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 i=Math.floor((t.k+t.blockSize-1)/t.blockSize),a=t.blockSize/8*t.bits,o=e[1];if(!Le.areEqual(o.dims,[t.n,i,a]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let u=e[2].dims;if(Le.size(u)!==t.n*i)throw new Error("scales input size error.");if(e.length===4){let p=e[3].dims,h=t.bits>4?t.n*i:t.n*Math.floor((i+1)/2);if(Le.size(p)!==h)throw new Error("zeroPoints input size error.")}},Fd=(e,t)=>{let s=e[0].dims,n=s.length,i=s[n-2],a=t.k,o=t.n,u=s.slice(0,n-2),p=Le.size(u),h=e[1].dims[2]/4,k=e[0].dataType,E=qt(t.k),d=qt(h),z=qt(o),B=u.concat([i,o]),V=i>1&&o/z%2===0?2:1,Z=Le.size(B)/z/V,ee=64,Q=[],he=[p,i,a/E],pe=Le.convertShape(e[1].dims).slice();pe.splice(-1,1,h/d),Q.push(...yt(he)),Q.push(...yt(pe)),Q.push(...yt(e[2].dims)),e.length===4&&Q.push(...yt(Le.convertShape(e[3].dims)));let Me=[p,i,o/z];Q.push(...yt(Me));let Fe=De=>{let Ye=he.length,at=qe("a",e[0].dataType,Ye,E),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=ms(e[0].dataType),bs=(()=>{switch(E){case 1:return`array<${Ft}, 8>`;case 2:return`mat4x2<${Ft}>`;case 4:return`mat2x4<${Ft}>`;default:throw new Error(`${E}-component is not supported.`)}})(),Ht=()=>{let ot=` - // 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/E}; 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},(cs,Ns)=>`${Ft}(b_value_lower[${Ns}]), ${Ft}(b_value_upper[${Ns}])`).join(", ")}); - b_dequantized_values = ${E===1?`${bs}(${Array.from({length:8},(cs,Ns)=>`(b_quantized_values[${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}]`:""} += ${Array.from({length:8/E},(cs,Ns)=>`${E===1?`a_data[${Ns}] * b_dequantized_values[${Ns}]`:`dot(a_data[${Ns}], b_dequantized_values[${Ns}])`}`).join(" + ")}; - `;return ot},Rt=()=>{let ot=` - 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 ot},_s=()=>{let ot=`col_index = col * ${z};`;for(let Et=0;Et; - var b_value_upper: vec4; - var b_quantized_values: ${bs}; - var b_dequantized_values: ${bs};`,ot};return` - var workgroup_shared: array<${St.type.value}, ${V*ee}>; - ${De.declareVariables(...Zt,St)} - ${De.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/E}; - ${Rt()} - for (var word: u32 = 0; word < ${h}; word += ${d}) { - ${_s()} - for (var i: u32 = 0; i < ${d}; i++) { - ${Ht()} - word_offset += ${8/E}; - } - } - } - 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};${E};${d};${z};${V};${ee}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:B,dataType:k}],dispatchGroup:{x:Z},programUniforms:Q}),getShaderSource:Fe}},pa=(e,t)=>{let s=e[0].dims,n=s.length,i=s[n-2],a=t.k,o=t.n,u=s.slice(0,n-2),p=Le.size(u),h=e[1].dims[2]/4,k=e[0].dataType,E=qt(t.k),d=qt(h),z=u.concat([i,o]),B=128,V=o%8===0?8:o%4===0?4:1,Z=B/V,ee=Z*d*8,Q=ee/E,he=ee/t.blockSize,pe=Le.size(z)/V,Me=[],Fe=[p,i,a/E],De=Le.convertShape(e[1].dims).slice();De.splice(-1,1,h/d),Me.push(...yt(Fe)),Me.push(...yt(De)),Me.push(...yt(e[2].dims)),e.length===4&&Me.push(...yt(Le.convertShape(e[3].dims)));let Ye=[p,i,o];Me.push(...yt(Ye));let at=Pt=>{let Xt=Fe.length,Zt=qe("a",e[0].dataType,Xt,E),bt=qe("b",12,De.length,d),ss=qe("scales",e[2].dataType,e[2].dims.length),St=[Zt,bt,ss],Ft=e.length===4?qe("zero_points",12,e[3].dims.length):void 0;Ft&&St.push(Ft);let bs=Ye.length,Ht=It("output",e[0].dataType,bs),Rt=ms(e[0].dataType),_s=()=>{switch(E){case 1:return` - let a_data0 = vec4<${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<${Rt}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return` - let a_data0 = sub_a[word_offset]; - let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${E}-component is not supported.`)}};return` - var sub_a: array<${Zt.type.value}, ${Q}>; - 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 * ${Q}; - // load one tile A data into shared memory. - for (var a_offset = local_idx; a_offset < ${Q}; 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/E}; - for (var i: u32 = 0; i < ${d}; i++) { - ${_s()} - 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}>(${Array.from({length:4},(ot,Et)=>`${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] += ${Array.from({length:2},(ot,Et)=>`${`dot(a_data${Et}, b_dequantized_values[${Et}])`}`).join(" + ")}; - word_offset += ${8/E}; - } - 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:`${t.blockSize};${E};${d};${Z};${V}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:z,dataType:k}],dispatchGroup:{x:pe},programUniforms:Me}),getShaderSource:at}},Dd=(e,t)=>{Od(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(pa(e.inputs,t)):e.compute(Fd(e.inputs,t))},Ld=e=>Bt(e)}),dp,cp,pp,ha,zd,Bd,Rd,Nd,ma,jd=g(()=>{zt(),Ot(),Yt(),dp=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},cp=(e,t,s)=>{let n="";for(let i=t-1;i>=0;--i)n+=` - k = i32(${e.indicesGet("indices",i)}) - ${$t("uniforms.pads",i,s)}; - if (k < 0) { - break; - } - if (k >= i32(${$t("uniforms.x_shape",i,t)})) { - break; - } - offset += k * i32(${$t("uniforms.x_strides",i,t)}); - `;return` - value = ${e.type.value}(uniforms.constant_value); - for (var i = 0; i < 1; i++) { - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - } - `},pp=(e,t,s)=>{let n="";for(let i=t-1;i>=0;--i)n+=` - k = i32(${e.indicesGet("indices",i)}) - ${$t("uniforms.pads",i,s)}; - if (k < 0) { - k = -k; - } - { - let _2n_1 = 2 * (i32(${$t("uniforms.x_shape",i,t)}) - 1); - k = k % _2n_1; - if(k >= i32(${$t("uniforms.x_shape",i,t)})) { - k = _2n_1 - k; - } - } - offset += k * i32(${$t("uniforms.x_strides",i,t)}); - `;return` - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - `},ha=(e,t,s)=>{let n="";for(let i=t-1;i>=0;--i)n+=` - k = i32(${e.indicesGet("indices",i)}) - ${$t("uniforms.pads",i,s)}; - if (k < 0) { - k = 0; - } - if (k >= i32(${$t("uniforms.x_shape",i,t)})) { - k = i32(${$t("uniforms.x_shape",i,t)}) - 1; - } - offset += k * i32(${$t("uniforms.x_strides",i,t)}); - `;return` - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - `},zd=(e,t,s)=>{let n="";for(let i=t-1;i>=0;--i)n+=` - k = i32(${e.indicesGet("indices",i)}) - ${$t("uniforms.pads",i,s)}; - if (k < 0) { - k += i32(${$t("uniforms.x_shape",i,t)}]); - } - if (k >= i32(${$t("uniforms.x_shape",i,t)})) { - k -= i32(${$t("uniforms.x_shape",i,t)}); - } - offset += k * i32(${$t("uniforms.x_strides",i,t)}); - `;return` - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - `},Bd=(e,t,s)=>{switch(s.mode){case 0:return cp(e,t,s.pads.length);case 1:return pp(e,t,s.pads.length);case 2:return ha(e,t,s.pads.length);case 3:return zd(e,t,s.pads.length);default:throw new Error("Invalid mode")}},Rd=(e,t)=>{let s=Le.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,i=Le.size(s),a=[{type:12,data:i},{type:6,data:t.pads}],o=e.length>=3&&e[2].data;t.mode===0&&a.push({type:o?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),E=qe("x",e[0].dataType,n.length),d=E.type.value,z=Bd(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:o?d:"f32"}),` - ${h.registerUniforms(B).declareVariables(E,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}${o}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(Le.size(s)/64)},programUniforms:a}),getShaderSource:p}},Nd=(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,i=e[0].dims.length,a=new Int32Array(2*i).fill(0);if(e.length>=4){let u=e[3].getBigInt64Array();for(let p=0;pa[Number(p)]=Number(u));let o=[];return a.forEach(u=>o.push(u)),{mode:t.mode,value:n,pads:o}}else return t},ma=(e,t)=>{dp(e.inputs);let s=Nd(e.inputs,t);e.compute(Rd(e.inputs,s),{inputs:[0]})}}),Pi,fa,_a,Ei,Ud,hp,Vd,ga,wa,Wd,Gd,ya,Kd,Hd,Ma,qd,Qd,Xd,mp,fp=g(()=>{We(),zt(),Ot(),Yt(),Pi=e=>{if(O.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},fa=(e,t,s)=>{let n=t.format==="NHWC",i=e.dims.slice();n&&i.splice(1,0,i.pop());let a=Object.hasOwnProperty.call(t,"dilations"),o=t.kernelShape.slice(),u=t.strides.slice(),p=a?t.dilations.slice():[],h=t.pads.slice();Js.adjustPoolAttributes(s,i,o,u,p,h);let k=Js.computePoolOutputShape(s,i,u,p,o,h,t.autoPad),E=Object.assign({},t);a?Object.assign(E,{kernelShape:o,strides:u,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(E,{kernelShape:o,strides:u,pads:h,cacheKey:t.cacheKey});let d=k.slice();return d.push(d.splice(1,1)[0]),[E,n?d:k]},_a=(e,t)=>{let s=t.format==="NHWC",n=Le.size(e),i=Le.size(t.kernelShape),a=[{type:12,data:n},{type:12,data:i}],o=[{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],E=!!(h+k);a.push({type:12,data:u},{type:12,data:p},{type:12,data:h},{type:12,data:k}),o.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}),o.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,o,!0,E,d]}else{if(s)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let u=Le.computeStrides(t.kernelShape);a.push({type:12,data:u},{type:12,data:t.pads},{type:12,data:t.strides}),o.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,o,!!p,!1,!1]}},Ei=(e,t,s,n,i,a,o,u,p,h,k,E)=>{let d=i.format==="NHWC",z=t.type.value,B=It("output",t.type.tensor,n);if(i.kernelShape.length<=2){let V="",Z="",ee="",Q=s-(d?2:1);if(k?V=` - for (var i: u32 = 0u; i < uniforms.kw; i++) { - xIndices[${Q}] = indices[${Q}] * uniforms.sw - uniforms.pwStart + i; - if (xIndices[${Q}] < 0 || xIndices[${Q}] - >= uniforms.x_shape[${Q}]) { - pad++; - continue; - } - let x_val = x[${t.indicesToOffset("xIndices")}]; - ${a} - }`:V=` - for (var i: u32 = 0u; i < uniforms.kw; i++) { - xIndices[${Q}] = indices[${Q}] * uniforms.sw - uniforms.pwStart + i; - let x_val = x[${t.indicesToOffset("xIndices")}]; - ${a} - }`,i.kernelShape.length===2){let he=s-(d?3:2);E?Z=` - for (var j: u32 = 0u; j < uniforms.kh; j++) { - xIndices[${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} - ${o} - - output[global_idx] = value; - }`}else{if(d)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let V=i.kernelShape.length,Z=i.pads.length,ee="";return h?ee=` - if (xIndices[j] >= uniforms.x_shape[j]) { - pad++; - isPad = true; - break; - } - } - if (!isPad) { - let x_val = x[${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} - } - ${o} - - output[global_idx] = value; - }`}},Ud=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,hp=e=>`${Ud(e)};${e.countIncludePad}`,Vd=e=>`${Ud(e)};${e.storageOrder};${e.dilations}`,ga=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}),wa=(e,t,s,n)=>{let[i,a]=fa(t,n,s),o=qe("x",t.dataType,t.dims.length),u=o.type.value,p="value += x_val;",h="";i.countIncludePad?h+=`value /= ${u}(uniforms.kernelSize);`:h+=`value /= ${u}(i32(uniforms.kernelSize) - pad);`;let[k,E,d,z,B]=_a(a,i);k.push(...yt(t.dims,a));let V=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${d};${z};${B}`,inputDependencies:V},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Le.size(a)/64)},programUniforms:k}),getShaderSource:Z=>Ei(Z,o,t.dims.length,a.length,i,p,h,0,E,d,z,B)}},Wd=e=>{let t=e.count_include_pad!==0,s=ga(e);if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...s,cacheKey:""};return{...n,cacheKey:hp(n)}},Gd=(e,t)=>{Pi(e.inputs),e.compute(wa("AveragePool",e.inputs[0],!1,t))},ya={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Kd=e=>{let t=e.format;return{format:t,...ya,cacheKey:t}},Hd=(e,t)=>{Pi(e.inputs),e.compute(wa("GlobalAveragePool",e.inputs[0],!0,t))},Ma=(e,t,s,n)=>{let[i,a]=fa(t,n,s),o=` - value = max(x_val, value); - `,u="",p=qe("x",t.dataType,t.dims.length),h=["rank"],[k,E,d,z,B]=_a(a,i);return k.push(...yt(t.dims,a)),{name:e,shaderCache:{hint:`${n.cacheKey};${d};${z};${B}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(Le.size(a)/64)},programUniforms:k}),getShaderSource:V=>Ei(V,p,t.dims.length,a.length,i,o,u,t.dataType===10?-65504:-1e5,E,d,z,B)}},qd=(e,t)=>{Pi(e.inputs),e.compute(Ma("MaxPool",e.inputs[0],!1,t))},Qd=e=>{let t=e.storage_order,s=e.dilations,n=ga(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 i={storageOrder:t,dilations:s,...n,cacheKey:""};return{...i,cacheKey:Vd(i)}},Xd=e=>{let t=e.format;return{format:t,...ya,cacheKey:t}},mp=(e,t)=>{Pi(e.inputs),e.compute(Ma("GlobalMaxPool",e.inputs[0],!0,t))}}),Yd,Jd,Zd,ec,_p=g(()=>{zt(),Ot(),rs(),Yt(),Yd=(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((i,a)=>a===t.axis||i===e[0].dims[a]).reduce((i,a)=>i&&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)].")}},Jd=(e,t)=>{let s=Le.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,i=n===3,a=e[0].dims,o=e[1].dataType,u=Le.size(a),p=n===3||n===2,h=p?[Math.ceil(Le.size(e[0].dims)/4)]:e[0].dims,k=e[1].dims,E=e.length>2?e[2]:void 0,d=E?p?[Math.ceil(Le.size(E.dims)/4)]:E.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,Q=Z&&!p?V:1,he=qe("input",p?12:n,h.length,Q),pe=qe("scale",o,k.length),Me=E?qe("zero_point",p?12:n,d.length):void 0,Fe=It("output",o,a.length,ee),De=[he,pe];Me&&De.push(Me);let Ye=[h,k];E&&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(...De,Fe)} - ${Xt.mainStart()} - ${Xt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - let output_indices = ${Fe.offsetToIndices("global_idx")}; - - // Set input x - ${p?` - let input = ${he.getByOffset("global_idx / 4")}; - let x_vec = ${i?"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 = ${Fe.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 = ${i?"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 = ${Fe.indicesGet("output_indices","uniforms.axis")}; - let zero_point_input = ${Me.getByOffset("zero_point_index / 4")}; - let zero_point_vec = ${i?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; - let zero_point_value = zero_point_vec[zero_point_index % 4]`:` - let zero_point_index = ${Fe.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 = ${i?"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?i?"i32":"u32":he.type.value}(0);`}; - // Compute and write output - ${Fe.setByOffset("global_idx",`${Fe.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:o}],dispatchGroup:{x:Math.ceil(u/ee/64),y:1,z:1},programUniforms:at})}},Zd=(e,t)=>{Yd(e.inputs,t),e.compute(Jd(e.inputs,t))},ec=e=>Bt({axis:e.axis,blockSize:e.blockSize})}),gp,ba,tc,wp=g(()=>{We(),zt(),Yt(),gp=(e,t,s)=>{let n=e===t,i=et&&s>0;if(n||i||a)throw new Error("Range these inputs' contents are invalid.")},ba=(e,t,s,n)=>{let i=Math.abs(Math.ceil((t-e)/s)),a=[i],o=i,u=[{type:12,data:o},{type:n,data:e},{type:n,data:s},...yt(a)],p=h=>{let k=It("output",n,a.length),E=k.type.value,d=[{name:"outputSize",type:"u32"},{name:"start",type:E},{name:"delta",type:E}];return` - ${h.registerUniforms(d).declareVariables(k)} - ${h.mainStart()} - ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - output[global_idx] = uniforms.start + ${E}(global_idx) * uniforms.delta; - }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:a,dataType:n}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:u})}},tc=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&&gp(t,s,n),e.compute(ba(t,s,n,e.inputs[0].dataType),{inputs:[]})}}),sc,rc,nc,ic,yp=g(()=>{zt(),Ot(),rs(),Yt(),sc=(e,t,s,n)=>{if(e!=="none"&&n!=="i32"&&n!=="u32"&&n!=="f32")throw new Error(`Input ${n} is not supported with reduction ${e}.`);let i=`{ - var oldValue = 0; - loop { - let newValueF32 =`,a=`; - let newValue = bitcast(newValueF32); - let res = atomicCompareExchangeWeak(&${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}));`:` - ${i}bitcast<${n}>(oldValue) + (${s})${a}`;case"max":return n==="i32"||n==="u32"?`atomicMax(&${t}, bitcast<${n}>(${s}));`:` - ${i}max(bitcast(oldValue), (${s}))${a}`;case"min":return n==="i32"||n==="u32"?`atomicMin(&${t}, bitcast<${n}>(${s}));`:`${i}min(bitcast<${n}>(oldValue), (${s}))${a}`;case"mul":return`${i}(bitcast<${n}>(oldValue) * (${s}))${a}`;default:throw new Error(`Reduction ${e} is not supported.`)}},rc=(e,t)=>{let s=e[0].dims,n=e[1].dims,i=s,a=1,o=Math.ceil(Le.size(n)/a),u=n[n.length-1],p=Le.sizeFromDimension(s,u),h=[{type:12,data:o},{type:12,data:u},{type:12,data:p},...yt(e[1].dims,e[2].dims,i)],k=E=>{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!==""?Oa("output",e[0].dataType,i.length):It("output",e[0].dataType,i.length,a);return` - ${E.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(d,z,B)} - ${E.mainStart()} - ${E.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]; - ${sc(t.reduction,"output[data_offset + i]","value",B.type.value)} - } - - }`};return{name:"ScatterND",shaderCache:{hint:`${t.cacheKey}_${t.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:h}),getShaderSource:k}},nc=e=>Bt({reduction:e.reduction}),ic=(e,t)=>{e.compute(rc(e.inputs,t),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),oc,ac,lc,uc,dc,cc,pc,hc,mc,fc,_c,va,gc,wc,yc,Mc,bc,vc,xc,Mp=g(()=>{zt(),Ot(),rs(),Yt(),oc=(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")}},ac=(e,t,s)=>{t.every(i=>i>=0&&i{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((i,a)=>n[i]=e[a]),n},lc=(e,t,s,n,i,a)=>{let[o,u,p]=s>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(o>0&&e.length>o&&e[o].dims.length>0)e[o].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");oc(n,t),t.axes.length>0&&ac(n,t.axes,h).forEach((k,E)=>n[E]=k)}if(p>0&&e.length>p&&e[p].dims.length===1&&e[p].dims[0]>0&&(e[p].getBigInt64Array().forEach(k=>i.push(Number(k))),i.length!==0&&i.length!==h&&s>=18&&i.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(i.length!==0&&i.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 i<"u"&&n.length>0&&i.length>h)throw new Error("Resize requires only of scales or sizes to be specified")},uc=(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`)}})()+"}",dc=(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`)}})()+"}",cc=(e,t,s)=>{let n=new Array(s).fill(0).concat(new Array(s).fill(1)),i=e.length===0?n:e.slice();return t.length>0?(t.forEach((a,o)=>{n[a]=i[o],n[o+s]=i[t.length+o]}),n):i},pc=(e,t,s,n)=>{let i=[];if(s.length>0)if(n.length>0){if(e.forEach(a=>i.push(a)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((a,o)=>i[a]=s[o])}else s.forEach(a=>i.push(a));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((a,o)=>Math.round(a*t[o]))}return i},hc=(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 i=e.slice();return s.axes.length>0?(s.axes.forEach(a=>t[a]=n),s.axes.forEach(a=>i[a]=Math.round(e[a]*t[a]))):(t.fill(n,0,t.length),i.forEach((a,o)=>i[o]=Math.round(a*t[o]))),i},mc=(e,t,s,n,i)=>` - 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",i)}; - var roi_hi = ${$t("uniforms.roi",`i + ${t.length}`,i)}; - 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; - }`,fc=(e,t,s,n,i,a,o)=>` - 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",i)}; - 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 (!${o} || (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; - }`,_c=(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; - }`,va=(e,t,s,n)=>e.rank>n?` - ${e.indicesSet("input_indices",t,"channel")}; - ${e.indicesSet("input_indices",s,"batch")}; -`:"",gc=(e,t,s,n,i)=>{let[a,o,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",o,`max(0, min(row, ${s[o]} - 1))`)}; - ${e.indicesSet("input_indices",u,`max(0, min(col, ${s[u]} - 1))`)}; - ${va(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[${o}]; - var col:${h} = originalIndices[${u}]; - ${n?`if (row < 0 || row > (${s[o]} - 1) || col < 0 || col > (${s[u]} - 1)) { - return ${i}; - }`:""}; - row = max(0, min(row, ${s[o]} - 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); - }`},wc=(e,t,s,n,i,a,o,u,p,h)=>{let k=s.length===2,[E,d]=k?[0,1]:[2,3],z=e.type.value,B=V=>{let Z=V===E?"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, ${i[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===E?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; - } - return cubicInterpolation1D(data, coefs); - }`};return` - ${B(E)}; - ${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: ${z} = 1.0 + absS; - coeffs[0] = ((${o} * onePlusAbsS - 5 * ${o}) * onePlusAbsS + 8 * ${o}) * onePlusAbsS - 4 * ${o}; - coeffs[1] = ((${o} + 2) * absS - (${o} + 3)) * absS * absS + 1; - coeffs[2] = ((${o} + 2) * oneMinusAbsS - (${o} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; - coeffs[3] = ((${o} * twoMinusAbsS - 5 * ${o}) * twoMinusAbsS + 8 * ${o}) * twoMinusAbsS - 4 * ${o}; - return coeffs; - } - - fn cubicInterpolation1D(x: array<${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); - } - `},yc=(e,t,s,n,i)=>{let[a,o,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",o,`max(0, min(depth, ${s[o]} - 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))`)}; - ${va(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[${o}]; - var height:${k} = originalIndices[${u}]; - var width:${k} = originalIndices[${p}]; - ${n?`if (depth < 0 || depth > (${s[o]} - 1) || height < 0 || height > (${s[u]} - 1) || width < 0 || (width > ${s[p]} - 1)) { - return ${i}; - }`:""}; - - depth = max(0, min(depth, ${s[o]} - 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 * 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${(()=>{if(o.length===2||o.length===4)return`${wc(E,k,o,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")}})()}; - `} - ${Q.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",u.length).declareVariables(E,k)} - ${Q.mainStart()} - ${Q.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - ${z?"output[global_idx] = input[global_idx];":` - let output_indices = ${k.offsetToIndices("global_idx")}; - var input_indices: ${E.type.indices}; - ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); - if (checkInputIndices(input_indices)) { - output[global_idx] = ${E.getByIndices("input_indices")}; - } else { - output[global_idx] = ${t.extrapolationValue}; - }`;case"linear":return`output[global_idx] = 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- - ${Z.mainStart(nr)} - let half_rotary_emb_dim = uniforms.${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 = - ${Q.broadcastedIndicesToOffset("bsnh.xy",It("",Q.type.tensor,2))}; - let position_id = - u32(${Q.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(Le.size(d)/nr)},programUniforms:B})}},Qt=(e,t)=>{Tc(e.inputs,t),e.compute(bp(e.inputs,t))}}),Ks,Xs,tr,Tn=g(()=>{zt(),Ot(),Yt(),Ks=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 i=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(s.dims[s.dims.length-1]!==i)throw new Error("Skip must have the same 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Fe=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],De=[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)];E&&De.push(qe("beta",e[3].dataType,e[3].dims,ee)),d&&De.push(qe("bias",e[4].dataType,e[4].dims,ee)),De.push(It("output",e[0].dataType,u,ee)),z&&De.push(It("mean_output",1,k)),B&&De.push(It("inv_std_output",1,k)),V&&De.push(It("input_skip_bias_sum",e[0].dataType,u,ee));let Ye=ms(e[0].dataType),at=ms(1,ee);return` - - ${Me.registerUniforms(Fe).declareVariables(...De)} - var sum_shared : array<${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 = ${As(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) ${i?"":"- 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] ${i?"":`- ${Ye}(mean)`}) * - ${Ye}(inv_std_dev) * gamma[offset1d + i] - ${E?"+ beta[offset1d + i]":""}; - } - }`},pe=[{dims:u,dataType:e[0].dataType}];return s>1&&pe.push({dims:k,dataType:1}),s>2&&pe.push({dims:k,dataType:1}),s>3&&pe.push({dims:a,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${ee};${z};${B};${V}`,inputDependencies:e.map((Me,Fe)=>"type")},getShaderSource:he,getRunData:()=>({outputs:pe,dispatchGroup:{x:Math.ceil(p/h)},programUniforms:Q})}},tr=(e,t)=>{Ks(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(Xs(e.inputs,t,e.outputCount,!1),{outputs:s})}}),xp,Kn,Pc,f,T,R,_e,Ie,Oe=g(()=>{zt(),Ot(),rs(),Yt(),xp=(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`)})},Kn=(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 ${t} must be an array of int32 or int64`);return s},Pc=(e,t)=>{if(e.length>1){let s=Kn(e,1),n=Kn(e,2),i=Kn(e,3);return 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n=e.name;return(i=e.shaderCache)!=null&&i.hint&&(n+="["+e.shaderCache.hint+"]"),n+=":"+s+`:${br(t,((a=e.shaderCache)==null?void 0:a.inputDependencies)??new Array(t.length).fill("dims"))}`,n},xa=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},ur=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]}},Cr=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},i=a=>t.features.has(a)&&s.push(a)&&!0;i("chromium-experimental-timestamp-query-inside-passes")||i("timestamp-query"),i("shader-f16"),i("subgroups")&&i("subgroups-f16"),this.device=await t.requestDevice(n),this.deviceInfo=new ur(this.device),this.adapterInfo=new xa(t.info||await t.requestAdapterInfo()),this.gpuDataManager=hs(this),this.programManager=new Zs(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,vn(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 i=0;i"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:E.map(ee=>({dims:ee.dims,dataType:_r(ee.dataType)})),outputsMetadata:d.map(ee=>({dims:ee.dims,dataType:_r(ee.dataType)})),kernelId:o,kernelType:p,kernelName:h,programName:k,startTime:V,endTime:Z});else{let ee="";E.forEach((he,pe)=>{ee+=`input[${pe}]: [${he.dims}] | ${_r(he.dataType)}, `});let Q="";d.forEach((he,pe)=>{Q+=`output[${pe}]: [${he.dims}] | ${_r(he.dataType)}, `}),console.log(`[profiling] kernel "${o}|${p}|${h}|${k}" ${ee}${Q}execution time: ${Z-V} ns`)}Ue("GPU",`${k}::${z}::${B}`)}e.unmap(),this.pendingQueries.delete(e)}),Re()}run(e,t,s,n,i,a){Ne(e.name);let o=[];for(let Q=0;Qhe):s;if(k.length!==u.length)throw new Error(`Output size ${k.length} must be equal to ${u.length}.`);let E=[],d=[];for(let Q=0;Q=a)throw new Error(`Invalid output index: ${k[Q]}`);if(k[Q]===-3)continue;let he=k[Q]===-1,pe=k[Q]===-2,Me=he||pe?i(u[Q].dataType,u[Q].dims):n(k[Q],u[Q].dataType,u[Q].dims);if(E.push(Me),Me.data===0)continue;let Fe=this.gpuDataManager.get(Me.data);if(!Fe)throw new Error(`no GPU data for output: ${Me.data}`);if(he&&this.temporaryData.push(Fe),pe){let De=this.kernelPersistentData.get(this.currentKernelId);De||(De=[],this.kernelPersistentData.set(this.currentKernelId,De)),De.push(Fe)}d.push(Fe)}if(o.length!==t.length||d.length!==E.length){if(d.length===0)return Re(e.name),E;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 Q=0,he=[];h.forEach(De=>{let Ye=typeof De.data=="number"?[De.data]:De.data;if(Ye.length===0)return;let at=De.type===10?2:4,Pt,Xt;De.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),Q=Math.ceil(Q/Xt)*Xt,he.push(Q);let Zt=De.type===10?8:4;Q+=Ye.length>4?Math.ceil(Ye.length/Zt)*Pt:Ye.length*at});let pe=16;Q=Math.ceil(Q/pe)*pe;let Me=new ArrayBuffer(Q);h.forEach((De,Ye)=>{let at=he[Ye],Pt=typeof De.data=="number"?[De.data]:De.data;if(De.type===6)new Int32Array(Me,at,Pt.length).set(Pt);else if(De.type===12)new Uint32Array(Me,at,Pt.length).set(Pt);else if(De.type===10)new Uint16Array(Me,at,Pt.length).set(Pt);else if(De.type===1)new Float32Array(Me,at,Pt.length).set(Pt);else throw new Error(`Unsupported uniform type: ${_r(De.type)}`)});let Fe=this.gpuDataManager.create(Q,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Fe.buffer,0,Me,0,Q),this.gpuDataManager.release(Fe.id),z={offset:0,size:Q,buffer:Fe.buffer}}let B=this.programManager.normalizeDispatchGroupSize(p),V=B[1]===1&&B[2]===1,Z=Pn(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 Q=0;Q`[ProgramManager] run "${e.name}" (key=${Z}) with ${B[0]}x${B[1]}x${B[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let Q={kernelId:this.currentKernelId,programName:ee.programInfo.name,inputTensorViews:t,outputTensorViews:E};this.pendingKernels.push(Q),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(Q)}return this.programManager.run(ee,o,d,B,z),Re(e.name),E}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 i=Es.get(e);if(!i)throw new Error(`kernel not implemented: ${e}`);let a={kernelType:e,kernelName:n,kernelEntry:i[0],attributes:[i[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: ${e}`);let i=n.kernelType,a=n.kernelName,o=n.kernelEntry,u=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${i}] ${a}" is not allowed to be called recursively`);this.currentKernelId=e,u[0]&&(u[1]=u[0](u[1]),u[0]=void 0),as("info",()=>`[WebGPU] Start to run kernel "[${i}] ${a}"...`);let p=this.env.debug;this.temporaryData=[];try{return p&&this.device.pushErrorScope("validation"),o(t,u[1]),0}catch(h){return s.push(Promise.resolve(`[WebGPU] Kernel "[${i}] ${a}" failed. ${h}`)),1}finally{p&&s.push(this.device.popErrorScope().then(h=>h?`GPU validation error for kernel "[${i}] ${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 i=this.sessionExternalDataMapping.get(e);i||(i=new Map,this.sessionExternalDataMapping.set(e,i));let a=i.get(t),o=this.gpuDataManager.registerExternalBuffer(s,n,a);return i.set(t,[o,s]),o}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 C(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()}}}),Ci,Hn,ki,Ts,zs,zr,ln,En,Ta=g(()=>{Pe(),Ci=1,Hn=()=>Ci++,ki=new Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),Ts=(e,t)=>{let s=ki.get(e);if(!s)throw new Error("Unsupported data type.");return t.length>0?Math.ceil(t.reduce((n,i)=>n*i)*s/8):0},zs=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 Ts(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,i)=>n===s[i])}},zr=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!==Ts(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 i=typeof MLTensorUsage>"u"?void 0:MLTensorUsage.READ|MLTensorUsage.WRITE;return this.wrapper=await this.tensorManager.getCachedTensor(t,s,i,!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()}},ln=class{constructor(e){this.backend=e,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}reserveTensorId(){let e=Hn();return this.tensorTrackersById.set(e,new zr(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 i=this.tensorTrackersById.get(e);if(!i)throw new Error("Tensor not found.");return i.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 i=Hn(),a=new zs({sessionId:this.backend.currentSessionId,context:e,tensor:t,dataType:s,shape:n});return this.tensorTrackersById.set(i,new zr(this,a)),this.externalTensors.add(a),i}async getCachedTensor(e,t,s,n,i){let a=this.backend.currentSessionId,o=this.backend.currentContext;for(let[p,h]of this.freeTensors.entries())if(h.canReuseTensor(o,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 o.createTensor({dataType:e,shape:t,dimensions:t,usage:s,writable:n,readable:i});return new zs({sessionId:a,context:o,tensor:u,dataType:e,shape:t})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},En=(...e)=>new ln(...e)}),Si,Pa,Ea,jp=g(()=>{zt(),ar(),q(),Ta(),Pe(),Si=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),Pa=(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((i,a)=>i===n[a]&&e[i]===t[i])},Ea=class{constructor(e){this.tensorManager=En(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],vn(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=>Pa(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(i=>i.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 i=Si.get(t);if(!i)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e,i,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 C(s,t)}}registerMLTensor(e,t,s){let n=Si.get(t);if(!n)throw new Error(`Unsupported ONNX data type: ${t}`);let i=this.tensorManager.registerTensor(this.currentContext,e,n,s);return as("verbose",()=>`[WebNN] registerMLTensor {tensor: ${e}, dataType: ${n}, dimensions: ${s}} -> {tensorId: ${i}}`),i}registerMLConstant(e,t,s,n,i,a){if(!a)throw new Error("External mounted files are not 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af="1.21.0-dev.20250114-228dd16893",lf=$e;{let e=(of(),M(Oh)).wasmBackend;K("webgpu",e,5),K("webnn",e,5),K("cpu",e,10),K("wasm",e,10)}Object.defineProperty(O.versions,"web",{value:af,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":(ze,A,r)=>{var _;r.r(A),r.d(A,{Tensor:()=>N.Tensor,createInferenceSession:()=>ne,deviceToExecutionProviders:()=>K,isONNXProxy:()=>X,isONNXTensor:()=>W});var D=r("./src/env.js"),j=r("?2ce3"),Y=r("./node_modules/onnxruntime-web/dist/ort.bundle.min.mjs?3a96"),N=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 y,M;const b=Symbol.for("onnxruntime");if(b in globalThis)M=globalThis[b];else if(D.apis.IS_NODE_ENV){switch(M=j??(_||(_=r.t(j,2))),process.platform){case"win32":v.push("dml");break;case"linux":process.arch==="x64"&&v.push("cuda");break}v.push("cpu"),y=["cpu"]}else M=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"),y=["wasm"];const I=M.InferenceSession;function K($=null){if(!$)return y;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){fe(this,"max_length",20);fe(this,"max_new_tokens",null);fe(this,"min_length",0);fe(this,"min_new_tokens",null);fe(this,"early_stopping",!1);fe(this,"max_time",null);fe(this,"do_sample",!1);fe(this,"num_beams",1);fe(this,"num_beam_groups",1);fe(this,"penalty_alpha",null);fe(this,"use_cache",!0);fe(this,"temperature",1);fe(this,"top_k",50);fe(this,"top_p",1);fe(this,"typical_p",1);fe(this,"epsilon_cutoff",0);fe(this,"eta_cutoff",0);fe(this,"diversity_penalty",0);fe(this,"repetition_penalty",1);fe(this,"encoder_repetition_penalty",1);fe(this,"length_penalty",1);fe(this,"no_repeat_ngram_size",0);fe(this,"bad_words_ids",null);fe(this,"force_words_ids",null);fe(this,"renormalize_logits",!1);fe(this,"constraints",null);fe(this,"forced_bos_token_id",null);fe(this,"forced_eos_token_id",null);fe(this,"remove_invalid_values",!1);fe(this,"exponential_decay_length_penalty",null);fe(this,"suppress_tokens",null);fe(this,"streamer",null);fe(this,"begin_suppress_tokens",null);fe(this,"forced_decoder_ids",null);fe(this,"guidance_scale",null);fe(this,"num_return_sequences",1);fe(this,"output_attentions",!1);fe(this,"output_hidden_states",!1);fe(this,"output_scores",!1);fe(this,"return_dict_in_generate",!1);fe(this,"pad_token_id",null);fe(this,"bos_token_id",null);fe(this,"eos_token_id",null);fe(this,"encoder_no_repeat_ngram_size",0);fe(this,"decoder_start_token_id",null);fe(this,"generation_kwargs",{});Object.assign(this,(0,_.pick)(Y,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(ze,A,r)=>{r.r(A),r.d(A,{ClassifierFreeGuidanceLogitsProcessor:()=>W,ForcedBOSTokenLogitsProcessor:()=>g,ForcedEOSTokenLogitsProcessor:()=>v,LogitsProcessor:()=>j,LogitsProcessorList:()=>N,LogitsWarper:()=>Y,MinLengthLogitsProcessor:()=>K,MinNewTokensLengthLogitsProcessor:()=>te,NoBadWordsLogitsProcessor:()=>ne,NoRepeatNGramLogitsProcessor:()=>b,RepetitionPenaltyLogitsProcessor:()=>I,SuppressTokensAtBeginLogitsProcessor:()=>y,TemperatureLogitsWarper:()=>U,TopKLogitsWarper:()=>$,TopPLogitsWarper:()=>X,WhisperTimeStampLogitsProcessor:()=>M});var _=r("./src/utils/generic.js");r("./src/utils/tensor.js");var D=r("./src/utils/maths.js");class j extends _.Callable{_call(w,x){throw Error("`_call` should be implemented in a subclass")}}class Y extends _.Callable{_call(w,x){throw Error("`_call` should be implemented in a subclass")}}class N extends _.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 j{constructor(w){super(),this.bos_token_id=w}_call(w,x){for(let O=0;O=1&&ie[ie.length-1]>=this.timestamp_begin,we=ie.length<2||ie[ie.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,Ae)=>ke+Ae)),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 j{constructor(w){super(),this.no_repeat_ngram_size=w}getNgrams(w){const x=w.length,O=[];for(let ie=0;ie1 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. 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Error("sample should be implemented in subclasses.")}getLogits(M,b){let I=M.dims.at(-1),K=M.data;if(b===-1)K=K.slice(-I);else{let te=b*I;K=K.slice(te,te+I)}return K}randomSelect(M){let b=0;for(let K=0;K1)return new v(M);if(M.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${M.num_return_sequences}.`);return new N(M)}}class N extends Y{async sample(M){const b=(0,j.max)(M.data)[1];return[[BigInt(b),0]]}}class g extends Y{async sample(M){let b=M.dims.at(-1);this.generation_config.top_k>0&&(b=Math.min(this.generation_config.top_k,b));const[I,K]=await(0,D.topk)(M,b),te=(0,j.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(M){let b=M.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)(M,b),te=(0,j.softmax)(I.data);return 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Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");_e.token_type_ids=(0,b.zeros_like)(_e.input_ids)}if(R.inputNames.includes("pixel_mask")&&!_e.pixel_mask){if(!_e.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const Ie=_e.pixel_values.dims;_e.pixel_mask=(0,b.ones)([Ie[0],Ie[2],Ie[3]])}return await we(R,_e)}async function Ee(f,T,R=!1){const _e=f.sessions[R?"decoder_model_merged":"model"],{past_key_values:Ie,...Oe}=T;if(_e.inputNames.includes("use_cache_branch")&&(Oe.use_cache_branch=ce(!!Ie)),_e.inputNames.includes("position_ids")&&Oe.attention_mask&&!Oe.position_ids){const rt=f.config.model_type==="paligemma"?1:0;Oe.position_ids=J(Oe,Ie,rt)}f.addPastKeyValues(Oe,Ie);const et=(0,N.pick)(Oe,_e.inputNames);return await we(_e,et)}function tt({image_token_id:f,inputs_embeds:T,image_features:R,input_ids:_e,attention_mask:Ie}){const 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from_pretrained(R,{progress_callback:_e=null,config:Ie=null,cache_dir:Oe=null,local_files_only:et=!1,revision:rt="main",model_file_name:_t=null,subfolder:Mt="onnx",device:jt=null,dtype:Vt=null,use_external_data_format:Lt=null,session_options:Gt={}}={}){let es={progress_callback:_e,config:Ie,cache_dir:Oe,local_files_only:et,revision:rt,model_file_name:_t,subfolder:Mt,device:jt,dtype:Vt,use_external_data_format:Lt,session_options:Gt};const ns=x.get(this),Jt=S.get(ns);Ie=es.config=await _.AutoConfig.from_pretrained(R,es);let os;if(Jt===$.DecoderOnly)os=await Promise.all([ae(R,{model:es.model_file_name??"model"},es),ie(R,{generation_config:"generation_config.json"},es)]);else if(Jt===$.Seq2Seq||Jt===$.Vision2Seq)os=await Promise.all([ae(R,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},es),ie(R,{generation_config:"generation_config.json"},es)]);else if(Jt===$.MaskGeneration)os=await Promise.all([ae(R,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},es)]);else if(Jt===$.EncoderDecoder)os=await Promise.all([ae(R,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},es)]);else if(Jt===$.ImageTextToText){const Cs={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Ie.is_encoder_decoder&&(Cs.model="encoder_model"),os=await Promise.all([ae(R,Cs,es),ie(R,{generation_config:"generation_config.json"},es)])}else if(Jt===$.Musicgen)os=await Promise.all([ae(R,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},es),ie(R,{generation_config:"generation_config.json"},es)]);else if(Jt===$.MultiModality)os=await Promise.all([ae(R,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},es),ie(R,{generation_config:"generation_config.json"},es)]);else if(Jt===$.Phi3V)os=await Promise.all([ae(R,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},es),ie(R,{generation_config:"generation_config.json"},es)]);else{if(Jt!==$.EncoderOnly){const Cs=ns??(Ie==null?void 0:Ie.model_type);Cs!=="custom"&&console.warn(`Model type for '${Cs}' not found, assuming encoder-only architecture. Please report this at ${v.GITHUB_ISSUE_URL}.`)}os=await Promise.all([ae(R,{model:es.model_file_name??"model"},es)])}return new this(Ie,...os)}async _call(R){return await this.forward(R)}async forward(R){return await this._forward(this,R)}get generation_config(){var R;return((R=this.configs)==null?void 0:R.generation_config)??null}_get_logits_warper(R){const _e=new y.LogitsProcessorList;return R.temperature!==null&&R.temperature!==1&&_e.push(new y.TemperatureLogitsWarper(R.temperature)),R.top_k!==null&&R.top_k!==0&&_e.push(new y.TopKLogitsWarper(R.top_k)),R.top_p!==null&&R.top_p<1&&_e.push(new y.TopPLogitsWarper(R.top_p)),_e}_get_logits_processor(R,_e,Ie=null){const Oe=new y.LogitsProcessorList;if(R.repetition_penalty!==null&&R.repetition_penalty!==1&&Oe.push(new y.RepetitionPenaltyLogitsProcessor(R.repetition_penalty)),R.no_repeat_ngram_size!==null&&R.no_repeat_ngram_size>0&&Oe.push(new y.NoRepeatNGramLogitsProcessor(R.no_repeat_ngram_size)),R.bad_words_ids!==null&&Oe.push(new y.NoBadWordsLogitsProcessor(R.bad_words_ids,R.eos_token_id)),R.min_length!==null&&R.eos_token_id!==null&&R.min_length>0&&Oe.push(new y.MinLengthLogitsProcessor(R.min_length,R.eos_token_id)),R.min_new_tokens!==null&&R.eos_token_id!==null&&R.min_new_tokens>0&&Oe.push(new y.MinNewTokensLengthLogitsProcessor(_e,R.min_new_tokens,R.eos_token_id)),R.forced_bos_token_id!==null&&Oe.push(new y.ForcedBOSTokenLogitsProcessor(R.forced_bos_token_id)),R.forced_eos_token_id!==null&&Oe.push(new y.ForcedEOSTokenLogitsProcessor(R.max_length,R.forced_eos_token_id)),R.begin_suppress_tokens!==null){const et=_e>1||R.forced_bos_token_id===null?_e:_e+1;Oe.push(new y.SuppressTokensAtBeginLogitsProcessor(R.begin_suppress_tokens,et))}return R.guidance_scale!==null&&R.guidance_scale>1&&Oe.push(new y.ClassifierFreeGuidanceLogitsProcessor(R.guidance_scale)),Ie!==null&&Oe.extend(Ie),Oe}_prepare_generation_config(R,_e,Ie=M.GenerationConfig){const Oe={...this.config};for(const rt of["decoder","generator","text_config"])rt in Oe&&Object.assign(Oe,Oe[rt]);const et=new Ie(Oe);return Object.assign(et,this.generation_config??{}),R&&Object.assign(et,R),_e&&Object.assign(et,(0,N.pick)(_e,Object.getOwnPropertyNames(et))),et}_get_stopping_criteria(R,_e=null){const Ie=new te.StoppingCriteriaList;return R.max_length!==null&&Ie.push(new te.MaxLengthCriteria(R.max_length,this.config.max_position_embeddings??null)),R.eos_token_id!==null&&Ie.push(new te.EosTokenCriteria(R.eos_token_id)),_e&&Ie.extend(_e),Ie}_validate_model_class(){if(!this.can_generate){const R=[jd,Ei,ma,ha],_e=x.get(this.constructor),Ie=new Set,Oe=this.config.model_type;for(const rt of R){const _t=rt.get(Oe);_t&&Ie.add(_t[0])}let et=`The current model class (${_e}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Ie.size>0&&(et+=` Please use the following class instead: ${[...Ie].join(", ")}`),Error(et)}}prepare_inputs_for_generation(...R){return this._prepare_inputs_for_generation(this,...R)}_update_model_kwargs_for_generation({generated_input_ids:R,outputs:_e,model_inputs:Ie,is_encoder_decoder:Oe}){return Ie.past_key_values=this.getPastKeyValues(_e,Ie.past_key_values),Ie.input_ids=new b.Tensor("int64",R.flat(),[R.length,1]),Oe||(Ie.attention_mask=(0,b.cat)([Ie.attention_mask,(0,b.ones)([Ie.attention_mask.dims[0],1])],1)),Ie.position_ids=null,Ie}_prepare_model_inputs({inputs:R,bos_token_id:_e,model_kwargs:Ie}){const Oe=(0,N.pick)(Ie,this.forward_params),et=this.main_input_name;if(et in Oe){if(R)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Oe[et]=R;return{inputs_tensor:Oe[et],model_inputs:Oe,model_input_name:et}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:R,model_inputs:_e,model_input_name:Ie,generation_config:Oe}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!_e.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:rt,pixel_values:_t,attention_mask:Mt,...jt}=_e,Vt=await this._prepare_inputs_embeds(_e);_e={...jt,...(0,N.pick)(Vt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:et}=await Ae(this,_e);if(Oe.guidance_scale!==null&&Oe.guidance_scale>1)et=(0,b.cat)([et,(0,b.full_like)(et,0)],0),"attention_mask"in _e&&(_e.attention_mask=(0,b.cat)([_e.attention_mask,(0,b.zeros_like)(_e.attention_mask)],0));else if(_e.decoder_input_ids){const rt=xe(_e.decoder_input_ids).dims[0];if(rt!==et.dims[0]){if(et.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${et.dims[0]}) than the decoder inputs (${rt}).`);et=(0,b.cat)(Array.from({length:rt},()=>et),0)}}return _e.encoder_outputs=et,_e}_prepare_decoder_input_ids_for_generation({batch_size:R,model_input_name:_e,model_kwargs:Ie,decoder_start_token_id:Oe,bos_token_id:et,generation_config:rt}){let{decoder_input_ids:_t,...Mt}=Ie;if(!(_t instanceof b.Tensor)){if(_t)Array.isArray(_t[0])||(_t=Array.from({length:R},()=>_t));else if(Oe??(Oe=et),this.config.model_type==="musicgen")_t=Array.from({length:R*this.config.decoder.num_codebooks},()=>[Oe]);else if(Array.isArray(Oe)){if(Oe.length!==R)throw new Error(`\`decoder_start_token_id\` expcted to have length ${R} but got ${Oe.length}`);_t=Oe}else _t=Array.from({length:R},()=>[Oe]);_t=xe(_t)}return Ie.decoder_attention_mask=(0,b.ones_like)(_t),{input_ids:_t,model_inputs:Mt}}async generate({inputs:R=null,generation_config:_e=null,logits_processor:Ie=null,stopping_criteria:Oe=null,streamer:et=null,...rt}){this._validate_model_class(),_e=this._prepare_generation_config(_e,rt);let{inputs_tensor:_t,model_inputs:Mt,model_input_name:jt}=this._prepare_model_inputs({inputs:R,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:_t,model_inputs:Mt,model_input_name:jt,generation_config:_e})));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:_e.decoder_start_token_id,bos_token_id:_e.bos_token_id,generation_config:_e}):Lt=Mt[jt];let Gt=Lt.dims.at(-1);_e.max_new_tokens!==null&&(_e.max_length=Gt+_e.max_new_tokens);const es=this._get_logits_processor(_e,Gt,Ie),ns=this._get_stopping_criteria(_e,Oe),Jt=Mt[jt].dims.at(0),os=ne.LogitsSampler.getSampler(_e),Cs=new Array(Jt).fill(0),xs=Lt.tolist();et&&et.put(xs);let fs,Es={};for(;;){if(Mt=this.prepare_inputs_for_generation(xs,Mt,_e),fs=await this.forward(Mt),_e.output_attentions&&_e.return_dict_in_generate){const ur=this.getAttentions(fs);for(const Cr in ur)Cr in Es||(Es[Cr]=[]),Es[Cr].push(ur[Cr])}const Ys=fs.logits.slice(null,-1,null),br=es(xs,Ys),Pn=[];for(let ur=0;urur))break;Mt=this._update_model_kwargs_for_generation({generated_input_ids:Pn,outputs:fs,model_inputs:Mt,is_encoder_decoder:Vt})}et&&et.end();const Os=this.getPastKeyValues(fs,Mt.past_key_values,!0),Zs=new b.Tensor("int64",xs.flat(),[xs.length,xs[0].length]);if(_e.return_dict_in_generate)return{sequences:Zs,past_key_values:Os,...Es};for(const Ys of Object.values(fs))Ys.location==="gpu-buffer"&&Ys.dispose();return Zs}getPastKeyValues(R,_e,Ie=!1){const Oe=Object.create(null);for(const et in R)if(et.startsWith("present")){const rt=et.replace("present","past_key_values"),_t=et.includes("encoder");if(_t&&_e?Oe[rt]=_e[rt]:Oe[rt]=R[et],_e&&(!_t||Ie)){const Mt=_e[rt];Mt.location==="gpu-buffer"&&Mt.dispose()}}return Oe}getAttentions(R){const _e={};for(const Ie of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Oe in R)Oe.startsWith(Ie)&&(Ie in _e||(_e[Ie]=[]),_e[Ie].push(R[Oe]));return _e}addPastKeyValues(R,_e){var Ie,Oe,et;if(_e)Object.assign(R,_e);else{const rt=this.sessions.decoder_model_merged??this.sessions.model,_t=((Ie=rt==null?void 0:rt.config)==null?void 0:Ie.kv_cache_dtype)??"float32",Mt=_t==="float16"?new Uint16Array:[],jt=((et=(Oe=R[this.main_input_name]??R.attention_mask)==null?void 0:Oe.dims)==null?void 0:et[0])??1,Vt=(0,_.getKeyValueShapes)(this.config,{batch_size:jt});for(const Lt in Vt)R[Lt]=new b.Tensor(_t,Mt,Vt[Lt])}}async encode_image({pixel_values:R}){const _e=(await we(this.sessions.vision_encoder,{pixel_values:R})).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 (${_e.dims[1]}).`),this.config.num_image_tokens=_e.dims[1]),_e}async encode_text({input_ids:R}){return(await we(this.sessions.embed_tokens,{input_ids:R})).inputs_embeds}}class Ke{}class je extends Ke{constructor({last_hidden_state:T,hidden_states:R=null,attentions:_e=null}){super(),this.last_hidden_state=T,this.hidden_states=R,this.attentions=_e}}class le extends se{}class Te extends le{}class Ue extends le{async _call(T){return new Xs(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 Ks(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 Xs(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 Ks(await super._call(T))}}class L extends se{}class oe extends L{}class H extends se{}class me extends H{}class $e extends H{async _call(T){return new Xs(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 Ks(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 Xs(await super._call(T))}}class At extends mt{async _call(T){return new Qt(await super._call(T))}}class is extends mt{async _call(T){return new Ks(await super._call(T))}}class ws extends mt{async _call(T){return new tr(await super._call(T))}}class ks extends se{}class Ds extends ks{}class sr extends ks{async _call(T){return new Xs(await super._call(T))}}class Sr extends ks{async _call(T){return new Qt(await super._call(T))}}class Yr extends ks{async _call(T){return new Ks(await super._call(T))}}class Us extends ks{async _call(T){return new tr(await super._call(T))}}class Tr extends se{}class Nt extends Tr{}class Jr extends Tr{async _call(T){return new Xs(await super._call(T))}}class $r extends Tr{async _call(T){return new Qt(await super._call(T))}}class Ar extends Tr{async _call(T){return new Ks(await super._call(T))}}class Zr extends Tr{async _call(T){return new tr(await super._call(T))}}class pr extends se{}class en extends pr{}class Ir extends pr{async _call(T){return new Xs(await super._call(T))}}class Rr extends pr{async _call(T){return new Qt(await super._call(T))}}class Nr extends pr{async _call(T){return new Ks(await super._call(T))}}class or extends pr{async _call(T){return new tr(await super._call(T))}}class it extends se{}class Tt extends it{}class Dt extends it{async _call(T){return new Xs(await super._call(T))}}class Vs extends it{async _call(T){return new Qt(await super._call(T))}}class jr extends it{async _call(T){return new Ks(await super._call(T))}}class Or extends it{async _call(T){return new tr(await super._call(T))}}class Ms extends se{}class ar extends Ms{}class Is extends Ms{async _call(T){return new Qt(await super._call(T))}}class Pr extends Ms{async _call(T){return new Ks(await super._call(T))}}class ts extends Ms{async _call(T){return new tr(await super._call(T))}}class _n extends Ms{async _call(T){return new Xs(await super._call(T))}}class Ur extends se{}class si extends Ur{}class $n extends Ur{async _call(T){return new Xs(await super._call(T))}}class An extends Ur{async _call(T){return new Qt(await super._call(T))}}class In extends Ur{async _call(T){return new Ks(await super._call(T))}}class Vr extends se{}class On extends Vr{}class ri extends Vr{async _call(T){return new Xs(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 lr extends se{}class gn extends lr{}class tn extends lr{async _call(T){return new Xs(await super._call(T))}}class wn extends lr{async _call(T){return new Qt(await super._call(T))}}class yn extends lr{async _call(T){return new Ks(await super._call(T))}}class Mn extends lr{async _call(T){return new tr(await super._call(T))}}class zt extends se{}class bn extends zt{}class Fn extends zt{async _call(T){return new Xs(await super._call(T))}}class Dn extends zt{async _call(T){return new Qt(await super._call(T))}}class Ln extends zt{async _call(T){return new tr(await super._call(T))}}class Gr extends se{}class zn extends Gr{}class vn extends Gr{async _call(T){return new Qt(await super._call(T))}}class Bn extends Gr{async _call(T){return new tr(await super._call(T))}}class as extends Gr{async _call(T){return new Xs(await super._call(T))}}class Pe extends se{constructor(){super(...arguments);fe(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class C 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 ft extends se{}class xt extends ft{}class Kt extends ft{}class hs extends ft{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 Le extends Ws{}class Js extends Ws{}class Fr extends se{}class Ss extends Fr{}class qs extends Fr{}class Ot extends se{}class nr extends Ot{}class gr extends Ot{async _call(T){return new Xs(await super._call(T))}}class ms extends Ot{async _call(T){return new Qt(await super._call(T))}}class $s extends Ot{async _call(T){return new Ks(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 As extends qt{async _call(T){return new Xs(await super._call(T))}}class Gs extends qt{async _call(T){return new Qt(await super._call(T))}}class $t extends qt{async _call(T){return new Ks(await super._call(T))}}class sn extends qt{async _call(T){return new tr(await super._call(T))}}class qe extends se{}class It extends qe{}class Oa extends qe{async _call(T){return new Xs(await super._call(T))}}class zi extends qe{async _call(T){return new Qt(await super._call(T))}}class Fa extends qe{async _call(T){return new Ks(await super._call(T))}}class Da extends qe{async _call(T){return new tr(await super._call(T))}}class Yt extends se{}class La extends Yt{}class Bi extends Yt{}class Ri extends se{constructor(){super(...arguments);fe(this,"requires_attention_mask",!1);fe(this,"main_input_name","input_features");fe(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class za extends Ri{}class Ba extends Ri{_prepare_generation_config(T,R){return super._prepare_generation_config(T,R,U.WhisperGenerationConfig)}_retrieve_init_tokens(T){const R=[T.decoder_start_token_id];let _e=T.language;const Ie=T.task;if(T.is_multilingual){_e||(console.warn("No language specified - defaulting to English (en)."),_e="en");const et=`<|${(0,X.whisper_language_to_code)(_e)}|>`;R.push(T.lang_to_id[et]),R.push(T.task_to_id[Ie??"transcribe"])}else if(_e||Ie)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&&R.at(-1)!==T.no_timestamps_token_id?R.push(T.no_timestamps_token_id):T.return_timestamps&&R.at(-1)===T.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),R.pop()),R.filter(Oe=>Oe!=null)}async generate({inputs:T=null,generation_config:R=null,logits_processor:_e=null,stopping_criteria:Ie=null,...Oe}){R=this._prepare_generation_config(R,Oe);const et=Oe.decoder_input_ids??this._retrieve_init_tokens(R);if(R.return_timestamps&&(_e??(_e=new y.LogitsProcessorList),_e.push(new y.WhisperTimeStampLogitsProcessor(R,et))),R.begin_suppress_tokens&&(_e??(_e=new y.LogitsProcessorList),_e.push(new y.SuppressTokensAtBeginLogitsProcessor(R.begin_suppress_tokens,et.length))),R.return_token_timestamps){if(!R.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.");R.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),R.output_attentions=!0,R.return_dict_in_generate=!0}const rt=await super.generate({inputs:T,generation_config:R,logits_processor:_e,decoder_input_ids:et,...Oe});return R.return_token_timestamps&&(rt.token_timestamps=this._extract_token_timestamps(rt,R.alignment_heads,R.num_frames)),rt}_extract_token_timestamps(T,R,_e=null,Ie=.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`.");_e==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let Oe=this.config.median_filter_width;Oe===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Oe=7);const et=T.cross_attentions,rt=Array.from({length:this.config.decoder_layers},(ns,Jt)=>(0,b.cat)(et.map(os=>os[Jt]),2)),_t=(0,b.stack)(R.map(([ns,Jt])=>{if(ns>=rt.length)throw new Error(`Layer index ${ns} is out of bounds for cross attentions (length ${rt.length}).`);return _e?rt[ns].slice(null,Jt,null,[0,_e]):rt[ns].slice(null,Jt)})).transpose(1,0,2,3),[Mt,jt]=(0,b.std_mean)(_t,-2,0,!0),Vt=_t.clone();for(let ns=0;nsos[Zs+1]-os[Zs]),fs=(0,N.mergeArrays)([1],xs).map(Os=>!!Os),Es=[];for(let Os=0;OsLt.findIndex(Gt=>Gt==Oe)),_t=rt.every(Lt=>Lt===-1),Mt=rt.every(Lt=>Lt!==-1);if(!_t&&!Mt)throw new Error("Every input should contain either 0 or 1 image token.");if(_t)return{inputs_embeds:T,attention_mask:Ie};const jt=[],Vt=[];for(let Lt=0;LtArray.from({length:T.dims[0]},xs=>Array.from({length:T.dims[1]},fs=>1))),es=R?R.tolist():[],ns=_e?_e.tolist():[];let Jt=0,os=0;for(let Cs=0;CsLt[Cs][zs]==1),Es=xs.reduce((Ts,zs,zr)=>(zs==_t&&Ts.push(zr),Ts),[]).map(Ts=>xs[Ts+1]),Os=Es.filter(Ts=>Ts==et).length,Zs=Es.filter(Ts=>Ts==rt).length;let Ys=[],br=0,Pn=Os,xa=Zs;for(let Ts=0;Tsdr>br&&dn==et),zr=xs.findIndex((dn,dr)=>dr>br&&dn==rt),ln=Pn>0&&zs!==-1?zs:xs.length+1,En=xa>0&&zr!==-1?zr:xs.length+1;let Ta,Si,Pa,Ea;ln0?(0,K.max)(Ys.at(-1))[0]+1:0;Ys.push(Array.from({length:3*ka},(dn,dr)=>Cc+dr%ka));const kc=ka+Cc,$i=jp*Ca*Cn,Sc=Array.from({length:$i},(dn,dr)=>kc+Math.floor(dr/(Ca*Cn))),$c=Array.from({length:$i},(dn,dr)=>kc+Math.floor(dr/Cn)%Ca),un=Array.from({length:$i},(dn,dr)=>kc+dr%Cn);Ys.push([Sc,$c,un].flat()),br=Ta+$i}if(br0?(0,K.max)(Ys.at(-1))[0]+1:0,zs=xs.length-br;Ys.push(Array.from({length:3*zs},(zr,ln)=>Ts+ln%zs))}const ur=Ys.reduce((Ts,zs)=>Ts+zs.length,0),Cr=new Array(ur);let Ec=0;for(let Ts=0;Ts<3;++Ts)for(let zs=0;zsVt[Jt%Vt.length]),es=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",es,[es.length,1])]}else{const[Vt,Lt]=T.dims,Gt=BigInt64Array.from({length:3*Vt*Lt},(es,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:R}){return(await we(this.sessions.vision_encoder,{pixel_values:T,grid_thw:R})).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,R,_e){if(R.attention_mask&&!R.position_ids)if(!R.past_key_values)[R.position_ids,R.rope_deltas]=this.get_rope_index(R.input_ids,R.image_grid_thw,R.video_grid_thw,R.attention_mask);else{R.pixel_values=null;const Ie=BigInt(Object.values(R.past_key_values)[0].dims.at(-2)),Oe=R.rope_deltas.map(et=>Ie+et);R.position_ids=(0,b.stack)([Oe,Oe,Oe],0)}return R}}class ho extends se{}class $l extends ho{}class Nn extends ho{}class mo extends se{}class ui extends mo{}class Al extends mo{}class fo extends se{}class Il extends fo{}class Ol extends fo{}class _o extends se{}class Fl extends _o{}class Dl extends _o{}class go extends se{}class Ll extends go{}class zl extends go{}class wo extends se{}class Bl extends wo{}class Rl extends wo{async _call(T){return new Qt(await super._call(T))}}class yo extends se{}class Nl extends yo{}class jl extends yo{async _call(T){return new Qt(await super._call(T))}}class Mo extends se{}class Ul extends Mo{}class di extends se{}class bo extends di{}class Vl extends di{async _call(T){return new Qt(await super._call(T))}}class Wl extends se{}class Gl extends Wl{}class vo extends se{}class Kl extends vo{}class Hl extends vo{async _call(T){return new Qt(await super._call(T))}}class xo extends se{}class ql extends xo{}class To extends se{}class Ql extends To{}class Gc extends To{async _call(T){return new Qt(await super._call(T))}}class Xl extends se{}class Yl extends Xl{async _call(T){return new Kn(await super._call(T))}}class fr extends se{}class Jl extends fr{}class Zl extends fr{async _call(T){return new Qt(await super._call(T))}}class Po extends se{}class eu extends Po{}class tu extends Po{async _call(T){return new Qt(await super._call(T))}}class Eo extends se{}class su extends Eo{}class ru extends Eo{}class Co extends se{}class nu extends Co{}class Kc extends Co{}class ko extends se{}class iu extends ko{}class ou extends ko{async _call(T){return new Qt(await super._call(T))}}class ci extends se{}class au extends ci{}class lu extends ci{async _call(T){return new Hr(await super._call(T))}}class uu extends ci{async _call(T){return new nn(await super._call(T))}}class Hr extends Ke{constructor({logits:T,pred_boxes:R}){super(),this.logits=T,this.pred_boxes=R}}class nn extends Ke{constructor({logits:T,pred_boxes:R,pred_masks:_e}){super(),this.logits=T,this.pred_boxes=R,this.pred_masks=_e}}class qr extends se{}class So extends qr{}class on extends qr{async _call(T){return new Qs(await super._call(T))}}class Qs extends Ke{constructor({logits:T,pred_boxes:R}){super(),this.logits=T,this.pred_boxes=R}}class $o extends se{}class Ao extends $o{}class du extends $o{async _call(T){return new Hc(await super._call(T))}}class Hc extends Hr{}class xn extends se{}class Io extends xn{}class Oo extends xn{async _call(T){return new Qt(await super._call(T))}}class Fo extends se{}class cu extends Fo{}class Do extends Fo{async _call(T){return new Qt(await super._call(T))}}class pi extends se{}class pu extends pi{}class Lo extends pi{async _call(T){return new Qt(await super._call(T))}}class zo extends se{}class hi extends zo{}class Bo extends zo{async _call(T){return new Qt(await super._call(T))}}class Ro extends se{}class hu extends Ro{}class qc extends Ro{}class No extends se{}class jo extends No{}class jn extends No{}class mu extends se{}class Uo extends mu{}class mi extends se{}class fu extends mi{}class _u extends mi{}class Qc extends mi{}class gu extends se{}class wu extends gu{}class yu extends se{}class Mu extends yu{}class fi extends yu{}class Vo extends se{}class _i extends Vo{}class Wo extends Vo{}class Go extends se{}class bu extends Go{}class Ko extends se{}class Ho extends Ko{}class Xc extends Ko{async _call(T){return new Qt(await super._call(T))}}class qo extends se{}class Yc extends qo{}class vu extends qo{async _call(T){return new Qt(await super._call(T))}}class Qo extends se{}class xu extends Qo{}class Xo extends Qo{async _call(T){return new Qt(await super._call(T))}}class Yo extends se{}class Tu extends Yo{}class Jo extends Yo{async _call(T){return new Qt(await super._call(T))}}class Pu extends se{}class Eu extends Pu{}class Cu extends se{}class ku extends Cu{}class Su extends Cu{async _call(T){return new $u(await super._call(T))}}class $u extends Ke{constructor({logits:T,pred_boxes:R}){super(),this.logits=T,this.pred_boxes=R}}class Jc extends se{}class Au extends Jc{async get_image_embeddings({pixel_values:T}){return await Ae(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 _e=T.input_points.dims.slice(0,-1),Ie=_e.reduce((Oe,et)=>Oe*et,1);T.input_labels=new b.Tensor("int64",new BigInt64Array(Ie).fill(1n),_e)}const R={image_embeddings:T.image_embeddings,image_positional_embeddings:T.image_positional_embeddings};return T.input_points&&(R.input_points=T.input_points),T.input_labels&&(R.input_labels=T.input_labels),T.input_boxes&&(R.input_boxes=T.input_boxes),await we(this.sessions.prompt_encoder_mask_decoder,R)}async _call(T){return new Iu(await super._call(T))}}class Iu extends Ke{constructor({iou_scores:T,pred_masks:R}){super(),this.iou_scores=T,this.pred_masks=R}}class Zo extends se{}class Ou extends Zo{}class Fu extends Zo{}class Du extends se{}class gi extends Du{}class Un extends Du{}class Dr extends se{}class Lu extends Dr{}class zu extends Dr{async _call(T){return new Tn(await super._call(T))}}class Bu extends Dr{async _call(T){return new Qt(await super._call(T))}}class Ru extends Dr{async _call(T){return new Ks(await super._call(T))}}class wi extends se{}class Nu extends wi{}class ju extends wi{async _call(T){return new Ks(await super._call(T))}}class Uu extends se{}class Zc extends Uu{}class yi extends se{}class ea extends yi{}class Vu extends yi{async _call(T){return new Tn(await super._call(T))}}class Wu extends yi{async _call(T){return new Qt(await super._call(T))}}class Vn extends se{}class ep extends Vn{}class Gu extends Vn{async _call(T){return new Tn(await super._call(T))}}class Ku extends Vn{async _call(T){return new Qt(await super._call(T))}}class tp extends Vn{async _call(T){return new Ks(await super._call(T))}}class Mi extends se{}class Hu extends Mi{}class qu extends Mi{async _call(T){return new Tn(await super._call(T))}}class Qu extends Mi{async _call(T){return new Qt(await super._call(T))}}class Np extends se{}class Xu extends Dr{}class Yu extends Dr{async _call(T){return new Tn(await super._call(T))}}class Ju extends Dr{async _call(T){return new Qt(await super._call(T))}}class Wn extends se{}class Zu extends Wn{}class ed extends Wn{async _call(T){return new Tn(await super._call(T))}}class td extends Wn{async _call(T){return new Qt(await super._call(T))}}class sd extends Wn{async _call(T){return new vp(await super._call(T))}}class sp extends Wn{async _call(T){return new Ks(await super._call(T))}}class rd extends se{}class nd extends rd{}class bi extends se{}class rp extends bi{}class np extends bi{}class id extends bi{async generate_speech(T,R,{threshold:_e=.5,minlenratio:Ie=0,maxlenratio:Oe=20,vocoder:et=null}={}){const rt={input_ids:T},{encoder_outputs:_t,encoder_attention_mask:Mt}=await Ae(this,rt),jt=_t.dims[1]/this.config.reduction_factor,Vt=Math.floor(jt*Oe),Lt=Math.floor(jt*Ie),Gt=this.config.num_mel_bins;let es=[],ns=null,Jt=null,os=0;for(;;){++os;const fs=ce(!!Jt);let Es;Jt?Es=Jt.output_sequence_out:Es=new b.Tensor("float32",new Float32Array(Gt),[1,1,Gt]);let Os={use_cache_branch:fs,output_sequence:Es,encoder_attention_mask:Mt,speaker_embeddings:R,encoder_hidden_states:_t};this.addPastKeyValues(Os,ns),Jt=await we(this.sessions.decoder_model_merged,Os),ns=this.getPastKeyValues(Jt,ns);const{prob:Zs,spectrum:Ys}=Jt;if(es.push(Ys),os>=Lt&&(Array.from(Zs.data).filter(br=>br>=_e).length>0||os>=Vt))break}const Cs=(0,b.cat)(es),{waveform:xs}=await we(et.sessions.model,{spectrogram:Cs});return{spectrogram:Cs,waveform:xs}}}class od extends se{constructor(){super(...arguments);fe(this,"main_input_name","spectrogram")}}class ad extends se{}class ld extends ad{}class ud extends se{}class Er extends ud{}class Lr extends ud{}class Qr extends se{}class an extends Qr{}class dd extends Qr{}class ta extends se{}class cd extends ta{}class pd extends ta{}class vi extends se{}class hd extends vi{}class md extends vi{static async from_pretrained(T,R={}){return super.from_pretrained(T,{...R,model_file_name:R.model_file_name??"text_model"})}}class fd extends vi{static async from_pretrained(T,R={}){return super.from_pretrained(T,{...R,model_file_name:R.model_file_name??"audio_model"})}}class _d extends se{}class sa extends _d{async _call(T){return new Pc(await super._call(T))}}class ra extends se{}class ir extends ra{}class gd extends ra{}class wd extends ra{}class xi extends se{}class yd extends xi{}class Gn extends xi{}class na extends se{}class Md extends na{}class bd extends na{async _call(T){return new Qt(await super._call(T))}}class ia extends se{}class ip extends ia{}class op extends ia{}class Ti extends se{constructor(){super(...arguments);fe(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(R){const[_e,Ie]=R.dims,Oe=this.config.decoder.num_codebooks,et=Ie-Oe;let rt=0;for(let jt=0;jt0&&Gt<=et&&(R.data[rt++]=R.data[jt])}const _t=Math.floor(_e/Oe),Mt=rt/(_t*Oe);return new b.Tensor(R.type,R.data.slice(0,rt),[_t,Oe,Mt])}prepare_inputs_for_generation(R,_e,Ie){let Oe=structuredClone(R);for(let rt=0;rt=_t&&(Oe[rt][_t]=BigInt(this.config.decoder.pad_token_id));return Ie.guidance_scale!==null&&Ie.guidance_scale>1&&(Oe=Oe.concat(Oe)),super.prepare_inputs_for_generation(Oe,_e,Ie)}async generate(R){const _e=await super.generate(R),Ie=this._apply_and_filter_by_delay_pattern_mask(_e).unsqueeze_(0),{audio_values:Oe}=await we(this.sessions.encodec_decode,{audio_codes:Ie});return Oe}}class oa extends se{}class vd extends oa{}class xd extends oa{async _call(T){return new Qt(await super._call(T))}}class aa extends se{}class Td extends aa{}class la extends aa{async _call(T){return new Qt(await super._call(T))}}class ua extends se{}class ap extends ua{}class da extends ua{async _call(T){return new Qt(await super._call(T))}}class ca extends se{}class Pd extends ca{}class Ed extends ca{async _call(T){return new Qt(await super._call(T))}}class lp extends se{}class Cd extends lp{}class kd extends se{}class Sd extends kd{constructor(...R){super(...R);fe(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(R){const _e=this._generation_mode??"text";let Ie;if(_e==="text"||!R.past_key_values){const Mt=this.sessions.prepare_inputs_embeds,jt=(0,N.pick)(R,Mt.inputNames);Ie=await we(Mt,jt)}else{const Mt=this.sessions.gen_img_embeds,jt=(0,N.pick)({image_ids:R.input_ids},Mt.inputNames);Ie=await we(Mt,jt)}const Oe={...R,...Ie},et=await Ee(this,Oe),rt=this.sessions[_e==="text"?"lm_head":"gen_head"];if(!rt)throw new Error(`Unable to find "${rt}" generation head`);const _t=await we(rt,(0,N.pick)(et,rt.inputNames));return{...Ie,...et,..._t}}async generate(R){return this._generation_mode="text",super.generate(R)}async generate_images(R){this._generation_mode="image";const _e=(R.inputs??R[this.main_input_name]).dims[1],Oe=(await super.generate(R)).slice(null,[_e,null]),et=this.sessions.image_decode,{decoded_image:rt}=await we(et,{generated_tokens:Oe}),_t=rt.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),Mt=[];for(const jt of _t){const Vt=I.RawImage.fromTensor(jt);Mt.push(Vt)}return Mt}}class up extends Ke{constructor({char_logits:T,bpe_logits:R,wp_logits:_e}){super(),this.char_logits=T,this.bpe_logits=R,this.wp_logits=_e}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class $d extends se{}class Ad extends $d{async _call(T){return new up(await super._call(T))}}class Id extends se{}class Od extends Id{}class Fd extends Id{}class pa extends se{}class Dd extends pa{}class Ld extends pa{}class ys{static async from_pretrained(T,{progress_callback:R=null,config:_e=null,cache_dir:Ie=null,local_files_only:Oe=!1,revision:et="main",model_file_name:rt=null,subfolder:_t="onnx",device:Mt=null,dtype:jt=null,use_external_data_format:Vt=null,session_options:Lt={}}={}){const Gt={progress_callback:R,config:_e,cache_dir:Ie,local_files_only:Oe,revision:et,model_file_name:rt,subfolder:_t,device:Mt,dtype:jt,use_external_data_format:Vt,session_options:Lt};if(Gt.config=await _.AutoConfig.from_pretrained(T,Gt),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const es of this.MODEL_CLASS_MAPPINGS){const ns=es.get(Gt.config.model_type);if(ns)return await ns[1].from_pretrained(T,Gt)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${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}`)}}fe(ys,"MODEL_CLASS_MAPPINGS",null),fe(ys,"BASE_IF_FAIL",!1);const dp=new Map([["bert",["BertModel",Te]],["modernbert",["ModernBertModel",dt]],["nomic_bert",["NomicBertModel",oe]],["roformer",["RoFormerModel",me]],["electra",["ElectraModel",Ds]],["esm",["EsmModel",si]],["convbert",["ConvBertModel",vt]],["camembert",["CamembertModel",Nt]],["deberta",["DebertaModel",en]],["deberta-v2",["DebertaV2Model",Tt]],["mpnet",["MPNetModel",gn]],["albert",["AlbertModel",zn]],["distilbert",["DistilBertModel",ar]],["roberta",["RobertaModel",nr]],["xlm",["XLMModel",Ls]],["xlm-roberta",["XLMRobertaModel",It]],["clap",["ClapModel",hd]],["clip",["CLIPModel",qa]],["clipseg",["CLIPSegModel",el]],["chinese_clip",["ChineseCLIPModel",wr]],["siglip",["SiglipModel",Ya]],["jina_clip",["JinaCLIPModel",oi]],["mobilebert",["MobileBertModel",On]],["squeezebert",["SqueezeBertModel",bn]],["wav2vec2",["Wav2Vec2Model",Lu]],["wav2vec2-bert",["Wav2Vec2BertModel",Hu]],["unispeech",["UniSpeechModel",ea]],["unispeech-sat",["UniSpeechSatModel",ep]],["hubert",["HubertModel",Xu]],["wavlm",["WavLMModel",Zu]],["audio-spectrogram-transformer",["ASTModel",La]],["vits",["VitsModel",sa]],["pyannote",["PyAnnoteModel",Nu]],["wespeaker-resnet",["WeSpeakerResNetModel",Zc]],["detr",["DetrModel",au]],["rt_detr",["RTDetrModel",So]],["table-transformer",["TableTransformerModel",Ao]],["vit",["ViTModel",Bl]],["ijepa",["IJepaModel",Nl]],["pvt",["PvtModel",bo]],["vit_msn",["ViTMSNModel",Kl]],["vit_mae",["ViTMAEModel",Gl]],["groupvit",["GroupViTModel",ql]],["fastvit",["FastViTModel",Ql]],["mobilevit",["MobileViTModel",Jl]],["mobilevitv2",["MobileViTV2Model",eu]],["owlvit",["OwlViTModel",su]],["owlv2",["Owlv2Model",nu]],["beit",["BeitModel",iu]],["deit",["DeiTModel",Io]],["hiera",["HieraModel",cu]],["convnext",["ConvNextModel",Ho]],["convnextv2",["ConvNextV2Model",Yc]],["dinov2",["Dinov2Model",xu]],["dinov2_with_registers",["Dinov2WithRegistersModel",Tu]],["resnet",["ResNetModel",pu]],["swin",["SwinModel",hi]],["swin2sr",["Swin2SRModel",hu]],["donut-swin",["DonutSwinModel",bu]],["yolos",["YolosModel",ku]],["dpt",["DPTModel",jo]],["glpn",["GLPNModel",_i]],["hifigan",["SpeechT5HifiGan",od]],["efficientnet",["EfficientNetModel",Md]],["decision_transformer",["DecisionTransformerModel",Cd]],["patchtst",["PatchTSTForPrediction",Od]],["patchtsmixer",["PatchTSMixerForPrediction",Dd]],["mobilenet_v1",["MobileNetV1Model",vd]],["mobilenet_v2",["MobileNetV2Model",Td]],["mobilenet_v3",["MobileNetV3Model",ap]],["mobilenet_v4",["MobileNetV4Model",Pd]],["maskformer",["MaskFormerModel",Mu]],["mgp-str",["MgpstrForSceneTextRecognition",Ad]],["style_text_to_speech_2",["StyleTextToSpeech2Model",nd]]]),cp=new Map([["t5",["T5Model",C]],["longt5",["LongT5Model",be]],["mt5",["MT5Model",pt]],["bart",["BartModel",xt]],["mbart",["MBartModel",Fs]],["marian",["MarianModel",Ou]],["whisper",["WhisperModel",za]],["m2m_100",["M2M100Model",gi]],["blenderbot",["BlenderbotModel",Le]],["blenderbot-small",["BlenderbotSmallModel",Ss]]]),pp=new Map([["bloom",["BloomModel",Il]],["jais",["JAISModel",nl]],["gpt2",["GPT2Model",sl]],["gptj",["GPTJModel",ul]],["gpt_bigcode",["GPTBigCodeModel",cl]],["gpt_neo",["GPTNeoModel",Mr]],["gpt_neox",["GPTNeoXModel",al]],["codegen",["CodeGenModel",Ji]],["llama",["LlamaModel",eo]],["exaone",["ExaoneModel",li]],["olmo",["OlmoModel",_l]],["olmo2",["Olmo2Model",gl]],["mobilellm",["MobileLLMModel",fl]],["granite",["GraniteModel",Vc]],["cohere",["CohereModel",Ml]],["gemma",["GemmaModel",ds]],["gemma2",["Gemma2Model",vl]],["openelm",["OpenELMModel",Tl]],["qwen2",["Qwen2Model",El]],["phi",["PhiModel",$l]],["phi3",["Phi3Model",ui]],["mpt",["MptModel",Fl]],["opt",["OPTModel",Ll]],["mistral",["MistralModel",Er]],["starcoder2",["Starcoder2Model",an]],["falcon",["FalconModel",cd]],["stablelm",["StableLmModel",yd]]]),ha=new Map([["speecht5",["SpeechT5ForSpeechToText",np]],["whisper",["WhisperForConditionalGeneration",Ba]],["moonshine",["MoonshineForConditionalGeneration",Ra]]]),zd=new Map([["speecht5",["SpeechT5ForTextToSpeech",id]]]),Bd=new Map([["vits",["VitsModel",sa]],["musicgen",["MusicgenForConditionalGeneration",Ti]]]),Rd=new Map([["bert",["BertForSequenceClassification",Ve]],["modernbert",["ModernBertForSequenceClassification",lt]],["roformer",["RoFormerForSequenceClassification",We]],["electra",["ElectraForSequenceClassification",Sr]],["esm",["EsmForSequenceClassification",An]],["convbert",["ConvBertForSequenceClassification",At]],["camembert",["CamembertForSequenceClassification",$r]],["deberta",["DebertaForSequenceClassification",Rr]],["deberta-v2",["DebertaV2ForSequenceClassification",Vs]],["mpnet",["MPNetForSequenceClassification",wn]],["albert",["AlbertForSequenceClassification",vn]],["distilbert",["DistilBertForSequenceClassification",Is]],["roberta",["RobertaForSequenceClassification",ms]],["xlm",["XLMForSequenceClassification",Gs]],["xlm-roberta",["XLMRobertaForSequenceClassification",zi]],["bart",["BartForSequenceClassification",hs]],["mbart",["MBartForSequenceClassification",rs]],["mobilebert",["MobileBertForSequenceClassification",Wr]],["squeezebert",["SqueezeBertForSequenceClassification",Dn]]]),Nd=new Map([["bert",["BertForTokenClassification",Ne]],["modernbert",["ModernBertForTokenClassification",ht]],["roformer",["RoFormerForTokenClassification",Je]],["electra",["ElectraForTokenClassification",Yr]],["esm",["EsmForTokenClassification",In]],["convbert",["ConvBertForTokenClassification",is]],["camembert",["CamembertForTokenClassification",Ar]],["deberta",["DebertaForTokenClassification",Nr]],["deberta-v2",["DebertaV2ForTokenClassification",jr]],["mpnet",["MPNetForTokenClassification",yn]],["distilbert",["DistilBertForTokenClassification",Pr]],["roberta",["RobertaForTokenClassification",$s]],["xlm",["XLMForTokenClassification",$t]],["xlm-roberta",["XLMRobertaForTokenClassification",Fa]]]),ma=new Map([["t5",["T5ForConditionalGeneration",q]],["longt5",["LongT5ForConditionalGeneration",Se]],["mt5",["MT5ForConditionalGeneration",gt]],["bart",["BartForConditionalGeneration",Kt]],["mbart",["MBartForConditionalGeneration",Bt]],["marian",["MarianMTModel",Fu]],["m2m_100",["M2M100ForConditionalGeneration",Un]],["blenderbot",["BlenderbotForConditionalGeneration",Js]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",qs]]]),jd=new Map([["bloom",["BloomForCausalLM",Ol]],["gpt2",["GPT2LMHeadModel",rl]],["jais",["JAISLMHeadModel",il]],["gptj",["GPTJForCausalLM",dl]],["gpt_bigcode",["GPTBigCodeForCausalLM",pl]],["gpt_neo",["GPTNeoForCausalLM",ol]],["gpt_neox",["GPTNeoXForCausalLM",ll]],["codegen",["CodeGenForCausalLM",hl]],["llama",["LlamaForCausalLM",Uc]],["exaone",["ExaoneForCausalLM",ml]],["olmo",["OlmoForCausalLM",no]],["olmo2",["Olmo2ForCausalLM",wl]],["mobilellm",["MobileLLMForCausalLM",Rn]],["granite",["GraniteForCausalLM",yl]],["cohere",["CohereForCausalLM",Wc]],["gemma",["GemmaForCausalLM",bl]],["gemma2",["Gemma2ForCausalLM",xl]],["openelm",["OpenELMForCausalLM",Pl]],["qwen2",["Qwen2ForCausalLM",Cl]],["phi",["PhiForCausalLM",Nn]],["phi3",["Phi3ForCausalLM",Al]],["mpt",["MptForCausalLM",Dl]],["opt",["OPTForCausalLM",zl]],["mbart",["MBartForCausalLM",rr]],["mistral",["MistralForCausalLM",Lr]],["starcoder2",["Starcoder2ForCausalLM",dd]],["falcon",["FalconForCausalLM",pd]],["trocr",["TrOCRForCausalLM",ld]],["stablelm",["StableLmForCausalLM",Gn]],["phi3_v",["Phi3VForCausalLM",mr]]]),Pi=new Map([["multi_modality",["MultiModalityCausalLM",Sd]]]),fa=new Map([["bert",["BertForMaskedLM",Ue]],["modernbert",["ModernBertForMaskedLM",ct]],["roformer",["RoFormerForMaskedLM",$e]],["electra",["ElectraForMaskedLM",sr]],["esm",["EsmForMaskedLM",$n]],["convbert",["ConvBertForMaskedLM",kt]],["camembert",["CamembertForMaskedLM",Jr]],["deberta",["DebertaForMaskedLM",Ir]],["deberta-v2",["DebertaV2ForMaskedLM",Dt]],["mpnet",["MPNetForMaskedLM",tn]],["albert",["AlbertForMaskedLM",as]],["distilbert",["DistilBertForMaskedLM",_n]],["roberta",["RobertaForMaskedLM",gr]],["xlm",["XLMWithLMHeadModel",As]],["xlm-roberta",["XLMRobertaForMaskedLM",Oa]],["mobilebert",["MobileBertForMaskedLM",ri]],["squeezebert",["SqueezeBertForMaskedLM",Fn]]]),_a=new Map([["bert",["BertForQuestionAnswering",Re]],["roformer",["RoFormerForQuestionAnswering",ut]],["electra",["ElectraForQuestionAnswering",Us]],["convbert",["ConvBertForQuestionAnswering",ws]],["camembert",["CamembertForQuestionAnswering",Zr]],["deberta",["DebertaForQuestionAnswering",or]],["deberta-v2",["DebertaV2ForQuestionAnswering",Or]],["mpnet",["MPNetForQuestionAnswering",Mn]],["albert",["AlbertForQuestionAnswering",Bn]],["distilbert",["DistilBertForQuestionAnswering",ts]],["roberta",["RobertaForQuestionAnswering",yt]],["xlm",["XLMForQuestionAnswering",sn]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Da]],["mobilebert",["MobileBertForQuestionAnswering",_r]],["squeezebert",["SqueezeBertForQuestionAnswering",Ln]]]),Ei=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",ji]],["idefics3",["Idefics3ForConditionalGeneration",Ui]]]),Ud=new Map([["llava",["LlavaForConditionalGeneration",ni]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",Na]],["moondream1",["Moondream1ForConditionalGeneration",ja]],["florence2",["Florence2ForConditionalGeneration",Va]],["qwen2-vl",["Qwen2VLForConditionalGeneration",Sl]],["idefics3",["Idefics3ForConditionalGeneration",Ui]],["paligemma",["PaliGemmaForConditionalGeneration",Ga]]]),hp=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",ji]]]),Vd=new Map([["vit",["ViTForImageClassification",Rl]],["ijepa",["IJepaForImageClassification",jl]],["pvt",["PvtForImageClassification",Vl]],["vit_msn",["ViTMSNForImageClassification",Hl]],["fastvit",["FastViTForImageClassification",Gc]],["mobilevit",["MobileViTForImageClassification",Zl]],["mobilevitv2",["MobileViTV2ForImageClassification",tu]],["beit",["BeitForImageClassification",ou]],["deit",["DeiTForImageClassification",Oo]],["hiera",["HieraForImageClassification",Do]],["convnext",["ConvNextForImageClassification",Xc]],["convnextv2",["ConvNextV2ForImageClassification",vu]],["dinov2",["Dinov2ForImageClassification",Xo]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",Jo]],["resnet",["ResNetForImageClassification",Lo]],["swin",["SwinForImageClassification",Bo]],["segformer",["SegformerForImageClassification",gd]],["efficientnet",["EfficientNetForImageClassification",bd]],["mobilenet_v1",["MobileNetV1ForImageClassification",xd]],["mobilenet_v2",["MobileNetV2ForImageClassification",la]],["mobilenet_v3",["MobileNetV3ForImageClassification",da]],["mobilenet_v4",["MobileNetV4ForImageClassification",Ed]]]),ga=new Map([["detr",["DetrForObjectDetection",lu]],["rt_detr",["RTDetrForObjectDetection",on]],["table-transformer",["TableTransformerForObjectDetection",du]],["yolos",["YolosForObjectDetection",Su]]]),wa=new Map([["owlvit",["OwlViTForObjectDetection",ru]],["owlv2",["Owlv2ForObjectDetection",Kc]],["grounding-dino",["GroundingDinoForObjectDetection",Eu]]]),Wd=new Map([["detr",["DetrForSegmentation",uu]],["clipseg",["CLIPSegForImageSegmentation",tl]]]),Gd=new Map([["segformer",["SegformerForSemanticSegmentation",wd]],["sapiens",["SapiensForSemanticSegmentation",fu]]]),ya=new Map([["detr",["DetrForSegmentation",uu]],["maskformer",["MaskFormerForInstanceSegmentation",fi]]]),Kd=new Map([["sam",["SamModel",Au]]]),Hd=new Map([["wav2vec2",["Wav2Vec2ForCTC",zu]],["wav2vec2-bert",["Wav2Vec2BertForCTC",qu]],["unispeech",["UniSpeechForCTC",Vu]],["unispeech-sat",["UniSpeechSatForCTC",Gu]],["wavlm",["WavLMForCTC",ed]],["hubert",["HubertForCTC",Yu]]]),Ma=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Bu]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Qu]],["unispeech",["UniSpeechForSequenceClassification",Wu]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Ku]],["wavlm",["WavLMForSequenceClassification",td]],["hubert",["HubertForSequenceClassification",Ju]],["audio-spectrogram-transformer",["ASTForAudioClassification",Bi]]]),qd=new Map([["wavlm",["WavLMForXVector",sd]]]),Qd=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",tp]],["wavlm",["WavLMForAudioFrameClassification",sp]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Ru]],["pyannote",["PyAnnoteForAudioFrameClassification",ju]]]),Xd=new Map([["vitmatte",["VitMatteForImageMatting",Yl]]]),mp=new Map([["patchtst",["PatchTSTForPrediction",Fd]],["patchtsmixer",["PatchTSMixerForPrediction",Ld]]]),fp=new Map([["swin2sr",["Swin2SRForImageSuperResolution",qc]]]),Yd=new Map([["dpt",["DPTForDepthEstimation",jn]],["depth_anything",["DepthAnythingForDepthEstimation",Uo]],["glpn",["GLPNForDepthEstimation",Wo]],["sapiens",["SapiensForDepthEstimation",_u]],["depth_pro",["DepthProForDepthEstimation",wu]]]),Jd=new Map([["sapiens",["SapiensForNormalEstimation",Qc]]]),Zd=new Map([["vitpose",["VitPoseForPoseEstimation",Ul]]]),ec=new Map([["clip",["CLIPVisionModelWithProjection",Xa]],["siglip",["SiglipVisionModel",Za]],["jina_clip",["JinaCLIPVisionModel",yr]]]),_p=[[dp,$.EncoderOnly],[cp,$.EncoderDecoder],[pp,$.DecoderOnly],[Rd,$.EncoderOnly],[Nd,$.EncoderOnly],[ma,$.Seq2Seq],[ha,$.Seq2Seq],[jd,$.DecoderOnly],[Pi,$.MultiModality],[fa,$.EncoderOnly],[_a,$.EncoderOnly],[Ei,$.Vision2Seq],[Ud,$.ImageTextToText],[Vd,$.EncoderOnly],[Wd,$.EncoderOnly],[ya,$.EncoderOnly],[Gd,$.EncoderOnly],[Xd,$.EncoderOnly],[mp,$.EncoderOnly],[fp,$.EncoderOnly],[Yd,$.EncoderOnly],[Jd,$.EncoderOnly],[Zd,$.EncoderOnly],[ga,$.EncoderOnly],[wa,$.EncoderOnly],[Kd,$.MaskGeneration],[Hd,$.EncoderOnly],[Ma,$.EncoderOnly],[zd,$.Seq2Seq],[Bd,$.EncoderOnly],[qd,$.EncoderOnly],[Qd,$.EncoderOnly],[ec,$.EncoderOnly]];for(const[f,T]of _p)for(const[R,_e]of f.values())S.set(R,T),x.set(_e,R),w.set(R,_e);const gp=[["MusicgenForConditionalGeneration",Ti,$.Musicgen],["Phi3VForCausalLM",mr,$.Phi3V],["CLIPTextModelWithProjection",Qa,$.EncoderOnly],["SiglipTextModel",Ja,$.EncoderOnly],["JinaCLIPTextModel",Wi,$.EncoderOnly],["ClapTextModelWithProjection",md,$.EncoderOnly],["ClapAudioModelWithProjection",fd,$.EncoderOnly]];for(const[f,T,R]of gp)S.set(f,R),x.set(T,f),w.set(f,T);class ba extends ys{}fe(ba,"MODEL_CLASS_MAPPINGS",_p.map(T=>T[0])),fe(ba,"BASE_IF_FAIL",!0);class tc extends ys{}fe(tc,"MODEL_CLASS_MAPPINGS",[Rd]);class wp extends ys{}fe(wp,"MODEL_CLASS_MAPPINGS",[Nd]);class sc extends ys{}fe(sc,"MODEL_CLASS_MAPPINGS",[ma]);class rc extends ys{}fe(rc,"MODEL_CLASS_MAPPINGS",[ha]);class nc extends ys{}fe(nc,"MODEL_CLASS_MAPPINGS",[zd]);class ic extends ys{}fe(ic,"MODEL_CLASS_MAPPINGS",[Bd]);class yp extends ys{}fe(yp,"MODEL_CLASS_MAPPINGS",[jd]);class oc extends ys{}fe(oc,"MODEL_CLASS_MAPPINGS",[fa]);class ac extends ys{}fe(ac,"MODEL_CLASS_MAPPINGS",[_a]);class lc extends ys{}fe(lc,"MODEL_CLASS_MAPPINGS",[Ei]);class uc extends ys{}fe(uc,"MODEL_CLASS_MAPPINGS",[Vd]);class dc extends ys{}fe(dc,"MODEL_CLASS_MAPPINGS",[Wd]);class cc extends ys{}fe(cc,"MODEL_CLASS_MAPPINGS",[Gd]);class pc extends ys{}fe(pc,"MODEL_CLASS_MAPPINGS",[ya]);class hc extends ys{}fe(hc,"MODEL_CLASS_MAPPINGS",[ga]);class mc extends ys{}fe(mc,"MODEL_CLASS_MAPPINGS",[wa]);class fc extends ys{}fe(fc,"MODEL_CLASS_MAPPINGS",[Kd]);class _c extends ys{}fe(_c,"MODEL_CLASS_MAPPINGS",[Hd]);class va extends ys{}fe(va,"MODEL_CLASS_MAPPINGS",[Ma]);class gc extends ys{}fe(gc,"MODEL_CLASS_MAPPINGS",[qd]);class wc extends ys{}fe(wc,"MODEL_CLASS_MAPPINGS",[Qd]);class yc extends ys{}fe(yc,"MODEL_CLASS_MAPPINGS",[hp]);class Mc extends ys{}fe(Mc,"MODEL_CLASS_MAPPINGS",[Xd]);class bc extends ys{}fe(bc,"MODEL_CLASS_MAPPINGS",[fp]);class vc extends ys{}fe(vc,"MODEL_CLASS_MAPPINGS",[Yd]);class xc extends ys{}fe(xc,"MODEL_CLASS_MAPPINGS",[Jd]);class Mp extends ys{}fe(Mp,"MODEL_CLASS_MAPPINGS",[Zd]);class Tc 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_.FeatureExtractor{constructor(N){super(N),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(N,g,v,y){let M;const b=N.length-g;if(b>0)if(v==="rand_trunc"){const I=Math.floor(Math.random()*(b+1));N=N.subarray(I,I+g),M=await this._extract_fbank_features(N,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(N),y==="repeat")for(let K=N.length;K{r.r(A),r.d(A,{CLIPFeatureExtractor:()=>j,CLIPImageProcessor:()=>D});var 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_=[["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(_),j=new Map([..._.map(([N,g])=>[g,N]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function Y(N){N=N.toLowerCase();let g=j.get(N);if(g===void 0)if(D.has(N))g=N;else{const y=N.length===2?D.keys():D.values();throw new Error(`Language "${N}" is not supported. Must be one of: ${JSON.stringify(y)}`)}return g}},"./src/models/whisper/feature_extraction_whisper.js":(ze,A,r)=>{r.r(A),r.d(A,{WhisperFeatureExtractor:()=>Y});var _=r("./src/base/feature_extraction_utils.js");r("./src/utils/tensor.js");var D=r("./src/utils/audio.js"),j=r("./src/utils/maths.js");class Y extends _.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}),y=v.data,M=(0,j.max)(y)[0];for(let b=0;bthis.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),v=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":(ze,A,r)=>{r.r(A),r.d(A,{WhisperGenerationConfig:()=>D});var _=r("./src/generation/configuration_utils.js");class D extends _.GenerationConfig{constructor(){super(...arguments);fe(this,"return_timestamps",null);fe(this,"return_token_timestamps",null);fe(this,"num_frames",null);fe(this,"alignment_heads",null);fe(this,"task",null);fe(this,"language",null);fe(this,"no_timestamps_token_id",null);fe(this,"prompt_ids",null);fe(this,"is_multilingual",null);fe(this,"lang_to_id",null);fe(this,"task_to_id",null);fe(this,"max_initial_timestamp_index",1)}}},"./src/models/whisper/processing_whisper.js":(ze,A,r)=>{r.r(A),r.d(A,{WhisperProcessor:()=>Y});var _=r("./src/models/auto/feature_extraction_auto.js"),D=r("./src/tokenizers.js"),j=r("./src/base/processing_utils.js");class Y extends j.Processor{async _call(g){return await this.feature_extractor(g)}}fe(Y,"tokenizer_class",D.AutoTokenizer),fe(Y,"feature_extractor_class",_.AutoFeatureExtractor)},"./src/models/yolos/image_processing_yolos.js":(ze,A,r)=>{r.r(A),r.d(A,{YolosFeatureExtractor:()=>j,YolosImageProcessor:()=>D});var _=r("./src/base/image_processors_utils.js");class D extends _.ImageProcessor{post_process_object_detection(...N){return(0,_.post_process_object_detection)(...N)}}class j extends D{}},"./src/ops/registry.js":(ze,A,r)=>{r.r(A),r.d(A,{TensorOpRegistry:()=>Y});var _=r("./src/backends/onnx.js"),D=r("./src/utils/tensor.js");const j=async(N,g,v)=>{const y=await(0,_.createInferenceSession)(new Uint8Array(N),g);return async M=>{const b=(0,_.isONNXProxy)(),I=Object.fromEntries(Object.entries(M).map(([te,ne])=>[te,(b?ne.clone():ne).ort_tensor])),K=await y.run(I);return Array.isArray(v)?v.map(te=>new D.Tensor(K[te])):new D.Tensor(K[v])}};class Y{static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=j([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=j([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=j([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=j([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=j([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=j([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=j([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=j([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}}fe(Y,"session_options",{})},"./src/pipelines.js":(ze,A,r)=>{r.r(A),r.d(A,{AudioClassificationPipeline:()=>we,AutomaticSpeechRecognitionPipeline:()=>xe,DepthEstimationPipeline:()=>Ce,DocumentQuestionAnsweringPipeline:()=>ye,FeatureExtractionPipeline:()=>ie,FillMaskPipeline:()=>X,ImageClassificationPipeline:()=>ke,ImageFeatureExtractionPipeline:()=>ve,ImageSegmentationPipeline:()=>Ae,ImageToImagePipeline:()=>de,ImageToTextPipeline:()=>ce,ObjectDetectionPipeline:()=>tt,Pipeline:()=>te,QuestionAnsweringPipeline:()=>U,SummarizationPipeline:()=>S,Text2TextGenerationPipeline:()=>$,TextClassificationPipeline:()=>ne,TextGenerationPipeline:()=>O,TextToAudioPipeline:()=>J,TokenClassificationPipeline:()=>W,TranslationPipeline:()=>w,ZeroShotAudioClassificationPipeline:()=>re,ZeroShotClassificationPipeline:()=>ae,ZeroShotImageClassificationPipeline:()=>Ee,ZeroShotObjectDetectionPipeline:()=>Ge,pipeline:()=>se});var _=r("./src/tokenizers.js"),D=r("./src/models.js"),j=r("./src/models/auto/processing_auto.js");r("./src/base/processing_utils.js");var Y=r("./src/utils/generic.js"),N=r("./src/utils/core.js"),g=r("./src/utils/maths.js"),v=r("./src/utils/audio.js"),y=r("./src/utils/tensor.js"),M=r("./src/utils/image.js");async function b(je){return Array.isArray(je)||(je=[je]),await Promise.all(je.map(le=>M.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 y.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,y.topk)(ct,Te),ht=lt[0].tolist(),oe=lt[1].tolist().map((H,me)=>({label:Re?Re[H]:`LABEL_${H}`,score:ht[me]}));Te===1?st.push(...oe):st.push(oe)}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>oe&&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,N.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,y.topk)(new y.Tensor("float32",(0,g.softmax)(lt.data),lt.dims),Te),L=ht[0].tolist(),oe=ht[1].tolist();Ne.push(oe.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);fe(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);fe(this,"_key","summary_text")}}class w extends ${constructor(Te){super(Te);fe(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(oe=>typeof oe=="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(oe=>this.tokenizer.apply_chat_template(oe,{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(oe=>oe.length));const L=Array.from({length:le.length},oe=>[]);for(let oe=0;oe[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 oe of Re){const H=this.tokenizer(ct,{text_pair:oe,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(oe=>(0,g.softmax)(oe)[1]):(0,g.softmax)(lt)).map((oe,H)=>[oe,H]).sort((oe,H)=>H[0]-oe[0]);dt.push({sequence:ct,labels:L.map(oe=>Te[oe[1]]),scores:L.map(oe=>oe[0])})}return Ne?dt:dt[0]}}class ie 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,y.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,y.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,y.topk)(new y.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[$e]}));Re.push(H)}return Array.isArray(le)?Re:Re[0]}}class re extends te{constructor(le){super(le)}async _call(le,Te,{hypothesis_template:Ue="This is a sound of {}."}={}){const Ve=!Array.isArray(le);Ve&&(le=[le]);const Ne=Te.map(lt=>Ue.replace("{}",lt)),Re=this.tokenizer(Ne,{padding:!0,truncation:!0}),st=this.processor.feature_extractor.config.sampling_rate,dt=await I(le,st),ct=[];for(const lt of dt){const ht=await this.processor(lt),L=await this.model({...Re,...ht}),oe=(0,g.softmax)(L.logits_per_audio.data);ct.push([...oe].map((H,me)=>({score:H,label:Te[me]})))}return Ve?ct[0]:ct}}class xe extends te{constructor(le){super(le)}async _call(le,Te={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(le,Te);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(le,Te);case"moonshine":return this._call_moonshine(le,Te);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${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 oe of lt)ht.push((0,g.max)(oe.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),oe=[];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),is=await this.processor(At),ws=vt===0,ks=kt>=H.length;if(me.push({stride:[At.length,ws?0:ut,ks?0:ut],input_features:is.input_features,is_last:ks}),ks)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[$e,We]=this.tokenizer._decode_asr(me,{time_precision:ct,return_timestamps:Ue,force_full_sequences:Ne});oe.push({text:$e,...We})}return dt?oe[0]:oe}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,y.topk)(new y.Tensor("float32",(0,g.softmax)(dt.data),dt.dims),Te),lt=ct[0].tolist(),L=ct[1].tolist().map((oe,H)=>({label:Re?Re[oe]:`LABEL_${oe}`,score:lt[H]}));st.push(L)}return Array.isArray(le)?st:st[0]}}class Ae extends te{constructor(le){super(le),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(le,{threshold:Te=.5,mask_threshold:Ue=.5,overlap_mask_area_threshold:Ve=.8,label_ids_to_fuse:Ne=null,target_sizes:Re=null,subtask:st=null}={}){if(Array.isArray(le)&&le.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const ct=await b(le),lt=ct.map(We=>[We.height,We.width]),{pixel_values:ht,pixel_mask:L}=await this.processor(ct),oe=await this.model({pixel_values:ht,pixel_mask:L});let H=null;if(st!==null)H=this.subtasks_mapping[st];else for(let[We,Je]of Object.entries(this.subtasks_mapping))if(Je in this.processor.image_processor){H=this.processor.image_processor[Je].bind(this.processor.image_processor),st=We;break}const me=this.model.config.id2label,$e=[];if(st==="panoptic"||st==="instance"){const We=H(oe,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(oe=>[oe.height,oe.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(oe=>oe.boxes.map((H,me)=>({score:oe.scores[me],label:ht[oe.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,oe,!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);fe(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 y.Tensor("float32",Ue,[1,Ue.length]);else if(!(Ue instanceof y.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(M.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:_.AutoTokenizer,pipeline:ne,model:D.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:_.AutoTokenizer,pipeline:W,model:D.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:_.AutoTokenizer,pipeline:U,model:D.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:_.AutoTokenizer,pipeline:X,model:D.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:_.AutoTokenizer,pipeline:S,model:D.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:_.AutoTokenizer,pipeline:w,model:D.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:_.AutoTokenizer,pipeline:$,model:D.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:_.AutoTokenizer,pipeline:O,model:D.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:_.AutoTokenizer,pipeline:ae,model:D.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:we,model:D.AutoModelForAudioClassification,processor:j.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:_.AutoTokenizer,pipeline:re,model:D.AutoModel,processor:j.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:_.AutoTokenizer,pipeline:xe,model:[D.AutoModelForSpeechSeq2Seq,D.AutoModelForCTC],processor:j.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:_.AutoTokenizer,pipeline:J,model:[D.AutoModelForTextToWaveform,D.AutoModelForTextToSpectrogram],processor:[j.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:_.AutoTokenizer,pipeline:ce,model:D.AutoModelForVision2Seq,processor:j.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:ke,model:D.AutoModelForImageClassification,processor:j.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:Ae,model:[D.AutoModelForImageSegmentation,D.AutoModelForSemanticSegmentation,D.AutoModelForUniversalSegmentation],processor:j.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:_.AutoTokenizer,pipeline:Ee,model:D.AutoModel,processor:j.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:tt,model:D.AutoModelForObjectDetection,processor:j.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:_.AutoTokenizer,pipeline:Ge,model:D.AutoModelForZeroShotObjectDetection,processor:j.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:_.AutoTokenizer,pipeline:ye,model:D.AutoModelForDocumentQuestionAnswering,processor:j.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:de,model:D.AutoModelForImageToImage,processor:j.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Ce,model:D.AutoModelForDepthEstimation,processor:j.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:_.AutoTokenizer,pipeline:ie,model:D.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:j.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: "${le}".`));const L={progress_callback:Te,config:Ue,cache_dir:Ve,local_files_only:Ne,revision:Re,device:st,dtype:dt,model_file_name:ct,session_options:lt},oe=new Map([["tokenizer",ht.tokenizer],["model",ht.model],["processor",ht.processor]]),H=await Ke(oe,le,L);H.task=je,(0,N.dispatchCallback)(Te,{status:"ready",task:je,model:le});const me=ht.pipeline;return new me(H)}async function Ke(je,le,Te){const Ue=Object.create(null),Ve=[];for(const[Ne,Re]of je.entries()){if(!Re)continue;let st;Array.isArray(Re)?st=new Promise(async(dt,ct)=>{var ht,L;let lt;for(const oe of Re){if(oe===null){dt(null);return}try{dt(await oe.from_pretrained(le,Te));return}catch(H){if((ht=H.message)!=null&&ht.includes("Unsupported model type"))lt=H;else if((L=H.message)!=null&&L.includes("Could not locate file"))lt=H;else{ct(H);return}}}ct(lt)}):st=Re.from_pretrained(le,Te),Ue[Ne]=st,Ve.push(st)}await Promise.all(Ve);for(const[Ne,Re]of Object.entries(Ue))Ue[Ne]=await Re;return Ue}},"./src/tokenizers.js":(ze,A,r)=>{r.r(A),r.d(A,{AlbertTokenizer:()=>$r,AutoTokenizer:()=>as,BartTokenizer:()=>Or,BertTokenizer:()=>Jr,BlenderbotSmallTokenizer:()=>Dn,BlenderbotTokenizer:()=>Fn,BloomTokenizer:()=>Pr,CLIPTokenizer:()=>yn,CamembertTokenizer:()=>it,CodeGenTokenizer:()=>wn,CodeLlamaTokenizer:()=>Ur,CohereTokenizer:()=>vn,ConvBertTokenizer:()=>Rr,DebertaTokenizer:()=>pr,DebertaV2Tokenizer:()=>en,DistilBertTokenizer:()=>or,ElectraTokenizer:()=>Dt,EsmTokenizer:()=>Vr,FalconTokenizer:()=>An,GPT2Tokenizer:()=>jr,GPTNeoXTokenizer:()=>In,GemmaTokenizer:()=>ri,Grok1Tokenizer:()=>Wr,HerbertTokenizer:()=>Ir,LlamaTokenizer:()=>_n,M2M100Tokenizer:()=>gn,MBart50Tokenizer:()=>ar,MBartTokenizer:()=>Ms,MPNetTokenizer:()=>$n,MarianTokenizer:()=>zt,MgpstrTokenizer:()=>Bn,MobileBertTokenizer:()=>Ar,NllbTokenizer:()=>lr,NougatTokenizer:()=>Gr,PreTrainedTokenizer:()=>Nt,Qwen2Tokenizer:()=>On,RoFormerTokenizer:()=>Nr,RobertaTokenizer:()=>Is,SiglipTokenizer:()=>Mn,SpeechT5Tokenizer:()=>Ln,SqueezeBertTokenizer:()=>Zr,T5Tokenizer:()=>Vs,TokenizerModel:()=>ve,VitsTokenizer:()=>zn,Wav2Vec2CTCTokenizer:()=>bn,WhisperTokenizer:()=>tn,XLMRobertaTokenizer:()=>si,XLMTokenizer:()=>Tt,is_chinese_char:()=>X});var _=r("./src/utils/generic.js"),D=r("./src/utils/core.js"),j=r("./src/utils/hub.js"),Y=r("./src/utils/maths.js"),N=r("./src/utils/tensor.js"),g=r("./src/utils/data-structures.js"),v=r("./node_modules/@huggingface/jinja/dist/index.js"),y=r("./src/models/whisper/common_whisper.js");async function M(Pe,C){const q=await Promise.all([(0,j.getModelJSON)(Pe,"tokenizer.json",!0,C),(0,j.getModelJSON)(Pe,"tokenizer_config.json",!0,C)]);return C.legacy!==null&&(q[1].legacy=C.legacy),q}function b(Pe,C){const q=[];let ue=0;for(const be of Pe.matchAll(C)){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,C,q){const ue=[];let be=0;for(;bethis.tokens_to_ids.get(q)??this.unk_token_id)}convert_ids_to_tokens(C){return C.map(q=>this.vocab[q]??this.unk_token)}}class we extends ve{constructor(C){super(C),this.tokens_to_ids=K(C.vocab),this.unk_token_id=this.tokens_to_ids.get(C.unk_token),this.unk_token=C.unk_token,this.max_input_chars_per_word=C.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(C){const q=[];for(const ue of C){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)){ft=xt;break}--gt}if(ft===null){Se=!0;break}pt.push(ft),Qe=gt}Se?q.push(this.unk_token):q.push(...pt)}return q}}class re extends ve{constructor(C,q){super(C);const ue=C.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(C){const q=C.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)],C=Pe.slice();let q=0;for(let be=0;be<256;++be)Pe.includes(be)||(Pe.push(be),C.push(256+q),q+=1);const ue=C.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(C){super(C),this.tokens_to_ids=K(C.vocab),this.unk_token_id=this.tokens_to_ids.get(C.unk_token),this.unk_token=C.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(C.merges[0]);this.merges=q?C.merges:C.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=C.end_of_word_suffix,this.continuing_subword_suffix=C.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(C){if(C.length===0)return[];const q=this.cache.get(C);if(q!==void 0)return q;const ue=Array.from(C);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,ft)=>gt.score`<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 Ae extends ve{constructor(C,q){super(C),this.tokens_to_ids=K(q.target_lang?C.vocab[q.target_lang]:C.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(C){return C}}class Ee extends _.Callable{constructor(C){super(),this.config=C}static fromConfig(C){if(C===null)return null;switch(C.type){case"BertNormalizer":return new Ke(C);case"Precompiled":return new ws(C);case"Sequence":return new se(C);case"Replace":return new tt(C);case"NFC":return new Ge(C);case"NFKC":return new ye(C);case"NFKD":return new J(C);case"Strip":return new de(C);case"StripAccents":return new Ce(C);case"Lowercase":return new Be(C);case"Prepend":return new Ze(C);default:throw new Error(`Unknown Normalizer type: ${C.type}`)}}normalize(C){throw Error("normalize should be implemented in subclass.")}_call(C){return this.normalize(C)}}class tt extends Ee{normalize(C){const q=I(this.config.pattern);return q===null?C:C.replaceAll(q,this.config.content)}}class Ge extends Ee{normalize(C){return C=C.normalize("NFC"),C}}class ye extends Ee{normalize(C){return C=C.normalize("NFKC"),C}}class J extends Ee{normalize(C){return C=C.normalize("NFKD"),C}}class de extends Ee{normalize(C){return this.config.strip_left&&this.config.strip_right?C=C.trim():(this.config.strip_left&&(C=C.trimStart()),this.config.strip_right&&(C=C.trimEnd())),C}}class Ce extends Ee{normalize(C){return C=W(C),C}}class Be extends Ee{normalize(C){return C=C.toLowerCase(),C}}class Ze extends Ee{normalize(C){return C=this.config.prepend+C,C}}class se extends Ee{constructor(C){super(C),this.normalizers=C.normalizers.map(q=>Ee.fromConfig(q))}normalize(C){return this.normalizers.reduce((q,ue)=>ue.normalize(q),C)}}class Ke extends Ee{_tokenize_chinese_chars(C){const q=[];for(let ue=0;uethis.pre_tokenize_text(ue,q)):this.pre_tokenize_text(C,q)).flat()}_call(C,q){return this.pre_tokenize(C,q)}}class le extends je{constructor(C){super(),this.pattern=new RegExp(`[^\\s${w}]+|[${w}]`,"gu")}pre_tokenize_text(C,q){return C.trim().match(this.pattern)||[]}}class Te extends je{constructor(C){super(),this.config=C,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=xe,this.text_encoder=new TextEncoder}pre_tokenize_text(C,q){return this.add_prefix_space&&!C.startsWith(" ")&&(C=" "+C),(this.use_regex?C.match(this.pattern)||[]:[C]).map(be=>Array.from(this.text_encoder.encode(be),Se=>this.byte_encoder[Se]).join(""))}}class Ue extends je{constructor(C){super(),this.config=C,this.pattern=I(this.config.pattern,this.config.invert)}pre_tokenize_text(C,q){var ue;return this.pattern===null?[]:this.config.invert?C.match(this.pattern)||[]:((ue=this.config.behavior)==null?void 0:ue.toLowerCase())==="removed"?C.split(this.pattern).filter(be=>be):b(C,this.pattern)}}class Ve extends je{constructor(C){super(),this.config=C,this.pattern=new RegExp(`[^${w}]+|[${w}]+`,"gu")}pre_tokenize_text(C,q){return C.match(this.pattern)||[]}}class Ne extends je{constructor(C){super(),this.config=C;const q=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(q,"gu")}pre_tokenize_text(C,q){return C.match(this.pattern)||[]}}class Re extends _.Callable{constructor(C){super(),this.config=C}static fromConfig(C){if(C===null)return null;switch(C.type){case"TemplateProcessing":return new ct(C);case"ByteLevel":return new lt(C);case"RobertaProcessing":return new dt(C);case"BertProcessing":return new st(C);case"Sequence":return new ht(C);default:throw new Error(`Unknown PostProcessor type: ${C.type}`)}}post_process(C,...q){throw Error("post_process should be implemented in subclass.")}_call(C,...q){return this.post_process(C,...q)}}class st extends Re{constructor(C){super(C),this.cls=C.cls[0],this.sep=C.sep[0]}post_process(C,q=null,{add_special_tokens:ue=!0}={}){ue&&(C=(0,D.mergeArrays)([this.cls],C,[this.sep]));let be=new Array(C.length).fill(0);if(q!==null){const Se=ue&&this instanceof dt?[this.sep]:[],Qe=ue?[this.sep]:[];C=(0,D.mergeArrays)(C,Se,q,Qe),be=(0,D.mergeArrays)(be,new Array(q.length+Se.length+Qe.length).fill(1))}return{tokens:C,token_type_ids:be}}}class dt extends st{}class ct extends Re{constructor(C){super(C),this.single=C.single,this.pair=C.pair}post_process(C,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,C),Qe=(0,D.mergeArrays)(Qe,new Array(C.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(C,q=null){return q&&(C=(0,D.mergeArrays)(C,q)),{tokens:C}}}class ht extends Re{constructor(C){super(C),this.processors=C.processors.map(q=>Re.fromConfig(q))}post_process(C,q=null,ue={}){let be;for(const Se of this.processors)if(Se instanceof lt)C=Se.post_process(C).tokens,q&&(q=Se.post_process(q).tokens);else{const Qe=Se.post_process(C,q,ue);C=Qe.tokens,be=Qe.token_type_ids}return{tokens:C,token_type_ids:be}}}class L extends _.Callable{constructor(C){super(),this.config=C,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=C.trim_offsets}static fromConfig(C){if(C===null)return null;switch(C.type){case"WordPiece":return new We(C);case"Metaspace":return new is(C);case"ByteLevel":return new Je(C);case"Replace":return new oe(C);case"ByteFallback":return new H(C);case"Fuse":return new me(C);case"Strip":return new $e(C);case"Sequence":return new mt(C);case"CTC":return new ut(C);case"BPEDecoder":return new vt(C);default:throw new Error(`Unknown Decoder type: ${C.type}`)}}_call(C){return this.decode(C)}decode(C){return this.decode_chain(C).join("")}decode_chain(C){throw Error("`decode_chain` should be implemented in subclass.")}}class oe extends L{decode_chain(C){const q=I(this.config.pattern);return q===null?C:C.map(ue=>ue.replaceAll(q,this.config.content))}}class H extends L{constructor(C){super(C),this.text_decoder=new TextDecoder}decode_chain(C){const q=[];let ue=[];for(const be of C){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(C){return[C.join("")]}}class $e extends L{constructor(C){super(C),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(C){return C.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(C){super(C),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(C){const q=C.join(""),ue=new Uint8Array([...q].map(Se=>this.byte_decoder[Se]));return this.text_decoder.decode(ue)}decode_chain(C){const q=[];let ue=[];for(const be of C)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(C){super(C),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(C){if(C.length===0)return"";const q=[C[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(C){return[this.convert_tokens_to_string(C)]}}class mt extends L{constructor(C){super(C),this.decoders=C.decoders.map(q=>L.fromConfig(q))}decode_chain(C){return this.decoders.reduce((q,ue)=>ue.decode_chain(q),C)}}class vt extends L{constructor(C){super(C),this.suffix=this.config.suffix}decode_chain(C){return C.map((q,ue)=>q.replaceAll(this.suffix,ue===C.length-1?"":" "))}}class kt extends L{decode_chain(C){let q="";for(let ue=1;ueue.normalize("NFKC")).join("～"):C=C.normalize("NFKC"),C}}class ks extends je{constructor(C){super(),this.tokenizers=C.pretokenizers.map(q=>je.fromConfig(q))}pre_tokenize_text(C,q){return this.tokenizers.reduce((ue,be)=>be.pre_tokenize(ue,q),[C])}}class Ds extends je{constructor(C){super()}pre_tokenize_text(C,q){return C.match(/\w+|[^\w\s]+/g)||[]}}class sr extends je{constructor(C){super()}pre_tokenize_text(C,q){return S(C)}}class Sr extends je{constructor(C){super(),this.config=C,this.pattern=I(this.config.pattern),this.content=this.config.content}pre_tokenize_text(C,q){return this.pattern===null?[C]:[C.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,C,q,ue){for(const be of Object.keys(Pe)){const Se=C-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 Tr(Pe,C){for(const q of Object.keys(Pe))Pe[q].length=C}class Nt extends _.Callable{constructor(q,ue){super();fe(this,"return_token_type_ids",!1);fe(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 ie(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 ft=await M(q,{progress_callback:ue,config:be,cache_dir:Se,local_files_only:Qe,revision:pt,legacy:gt});return new this(...ft)}_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:ft=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:ft}))}else Kt=q.map(us=>this._encode_plus(us,{add_special_tokens:be,return_token_type_ids:ft}))}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:ft})]}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&&Tr(Kt[us],pt):Se&&Us(Kt[us],pt,Fs=>Fs==="input_ids"?this.pad_token_id:0,this.padding_side));const hs={};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]))hs[Fs]=new N.Tensor("int64",BigInt64Array.from(Kt.flatMap(Bt=>Bt[Fs]).map(BigInt)),us)}else{for(const us of Object.keys(Kt[0]))hs[us]=Kt.map(Fs=>Fs[us]);if(!xt)for(const us of Object.keys(hs))hs[us]=hs[us][0]}return hs}_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=U(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),ft={input_ids:gt,attention_mask:new Array(gt.length).fill(1)};return(Se??this.return_token_type_ids)&&pt&&(ft.token_type_ids=pt),ft}_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 N.Tensor&&(q=q.tolist()),q.map(be=>this.decode(be,ue))}decode(q,ue={}){if(q instanceof N.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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Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[Ss,qs]=this.findLongestCommonSequence(Bt,rs),Ot=this.decode(Ss);xt.text=Ot,pt&&(xt.words=this.collateWordTimestamps(Ss,qs,Qe)),ft.push(xt)}let Js=Object.create(null);const Fr=ft.map(Ss=>Ss.text).join("");if(q||ue){for(let Ss=0;Ss0;let pt=Qe?[]:null,gt=Qe?q[0]:null;for(let ft=1;ftqt===gr[Ls]&>[Fr+Ls]<=q[ft][Ot+Ls]).length:ms=qs.filter((qt,Ls)=>qt===gr[Ls]).length;const $s=Js/1e4,yt=ms/Js+$s;ms>1&&yt>Kt&&(Kt=yt,hs=[Fr,Ss,Ot,nr])}const[Fs,Bt,rs,rr]=hs,Ws=Math.floor((Bt+Fs)/2),Le=Math.floor((rr+rs)/2);Se.push(...ue.slice(0,Ws)),ue=xt.slice(Le),be=ue.length,Qe&&(pt.push(...gt.slice(0,Ws)),gt=q[ft].slice(Le))}return Se.push(...ue),Qe?(pt.push(...gt),[Se,pt]):[Se,[]]}collateWordTimestamps(C,q,ue){const[be,Se,Qe]=this.combineTokensIntoWords(C,ue),pt=[];for(let gt=0;gt=be){const pt=((Qe-be)*ue).toFixed(2);Se.push(`<|${pt}|>`),Se.push([])}else Se[Se.length-1].push(Qe);return Se=Se.map(Qe=>typeof Qe=="string"?Qe:super.decode(Qe,q)),Se.join("")}splitTokensOnUnicode(C){const q=this.decode(C,{decode_with_timestamps:!0}),ue="�",be=[],Se=[],Qe=[];let pt=[],gt=[],ft=0;for(let xt=0;xt=this.model.tokens_to_ids.get("<|endoftext|>"),Fs=xt.startsWith(" "),Bt=xt.trim(),rs=gt.test(Bt);if(us||Fs||rs||Se.length===0)Se.push(xt),Qe.push(Kt),pt.push(hs);else{const rr=Se.length-1;Se[rr]+=xt,Qe[rr].push(...Kt),pt[rr].push(...hs)}}return[Se,Qe,pt]}mergePunctuations(C,q,ue,be,Se){const Qe=structuredClone(C),pt=structuredClone(q),gt=structuredClone(ue);let ft=Qe.length-2,xt=Qe.length-1;for(;ft>=0;)Qe[ft].startsWith(" ")&&be.includes(Qe[ft].trim())?(Qe[xt]=Qe[ft]+Qe[xt],pt[xt]=(0,D.mergeArrays)(pt[ft],pt[xt]),gt[xt]=(0,D.mergeArrays)(gt[ft],gt[xt]),Qe[ft]="",pt[ft]=[],gt[ft]=[]):xt=ft,--ft;for(ft=0,xt=1;xtKt),pt.filter(Kt=>Kt.length>0),gt.filter(Kt=>Kt.length>0)]}}class wn extends Nt{}class yn extends Nt{}class Mn extends Nt{}class zt extends Nt{constructor(C,q){super(C,q),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(ue=>this.languageRegex.test(ue)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(C){if(C===null)return null;const[q,...ue]=C.trim().split(this.languageRegex);if(ue.length===0)return super._encode_text(q);if(ue.length===2){const[be,Se]=ue;return this.supported_language_codes.includes(be)||console.warn(`Unsupported language code "${be}" detected, which may lead to unexpected behavior. 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sf=c.TextStreamer;c.TextToAudioPipeline,c.TokenClassificationPipeline,c.TokenClassifierOutput,c.TokenizerModel,c.TopKLogitsWarper,c.TopPLogitsWarper,c.TrOCRForCausalLM,c.TrOCRPreTrainedModel,c.TranslationPipeline,c.UniSpeechForCTC,c.UniSpeechForSequenceClassification,c.UniSpeechModel,c.UniSpeechPreTrainedModel,c.UniSpeechSatForAudioFrameClassification,c.UniSpeechSatForCTC,c.UniSpeechSatForSequenceClassification,c.UniSpeechSatModel,c.UniSpeechSatPreTrainedModel,c.VLChatProcessor,c.VLMImageProcessor,c.ViTFeatureExtractor,c.ViTForImageClassification,c.ViTImageProcessor,c.ViTMAEModel,c.ViTMAEPreTrainedModel,c.ViTMSNForImageClassification,c.ViTMSNModel,c.ViTMSNPreTrainedModel,c.ViTModel,c.ViTPreTrainedModel,c.VisionEncoderDecoderModel,c.VitMatteForImageMatting,c.VitMatteImageProcessor,c.VitMattePreTrainedModel,c.VitPoseForPoseEstimation,c.VitPoseImageProcessor,c.VitPosePreTrainedModel,c.VitsModel,c.VitsModelOutput,c.VitsPreTrainedModel,c.VitsTokenizer,c.Wav2Vec2BertForCTC,c.Wav2Vec2BertForSequenceClassification,c.Wav2Vec2BertModel,c.Wav2Vec2BertPreTrainedModel,c.Wav2Vec2CTCTokenizer,c.Wav2Vec2FeatureExtractor,c.Wav2Vec2ForAudioFrameClassification,c.Wav2Vec2ForCTC,c.Wav2Vec2ForSequenceClassification,c.Wav2Vec2Model,c.Wav2Vec2PreTrainedModel,c.Wav2Vec2ProcessorWithLM,c.WavLMForAudioFrameClassification,c.WavLMForCTC,c.WavLMForSequenceClassification,c.WavLMForXVector,c.WavLMModel,c.WavLMPreTrainedModel,c.WeSpeakerFeatureExtractor,c.WeSpeakerResNetModel,c.WeSpeakerResNetPreTrainedModel,c.WhisperFeatureExtractor,c.WhisperForConditionalGeneration,c.WhisperModel,c.WhisperPreTrainedModel,c.WhisperProcessor,c.WhisperTextStreamer,c.WhisperTimeStampLogitsProcessor,c.WhisperTokenizer,c.XLMForQuestionAnswering,c.XLMForSequenceClassification,c.XLMForTokenClassification,c.XLMModel,c.XLMPreTrainedModel,c.XLMRobertaForMaskedLM,c.XLMRobertaForQuestionAnswering,c.XLMRobertaForSequenceClassification,c.XLMRobertaForTokenClassification,c.XLMRobertaModel,c.XLMRobertaPreTrainedModel,c.XLMRobertaTokenizer,c.XLMTokenizer,c.XLMWithLMHeadModel,c.XVectorOutput,c.YolosFeatureExtractor,c.YolosForObjectDetection,c.YolosImageProcessor,c.YolosModel,c.YolosObjectDetectionOutput,c.YolosPreTrainedModel,c.ZeroShotAudioClassificationPipeline,c.ZeroShotClassificationPipeline,c.ZeroShotImageClassificationPipeline,c.ZeroShotObjectDetectionPipeline,c.bankers_round,c.cat,c.cos_sim,c.dot,c.dynamic_time_warping,c.env,c.full,c.full_like,c.getKeyValueShapes,c.hamming,c.hanning,c.interpolate,c.interpolate_4d,c.interpolate_data,c.is_chinese_char,c.layer_norm,c.load_image,c.log_softmax,c.magnitude,c.matmul,c.max,c.mean,c.mean_pooling,c.medianFilter,c.mel_filter_bank,c.min,c.ones,c.ones_like,c.permute,c.permute_data;var rf=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;class Bc{static async getInstance(A=null){return this.instance??(this.instance=rf(this.task,this.model,{progress_callback:A})),this.instance}}fe(Bc,"task","translation"),fe(Bc,"model","Xenova/nllb-200-distilled-600M"),fe(Bc,"instance",null),self.addEventListener("message",async ze=>{const A=await Bc.getInstance(D=>{self.postMessage(D)}),r=new sf(A.tokenizer,{skip_prompt:!0,skip_special_tokens:!0,callback_function:function(D){self.postMessage({status:"update",output:D})}}),_=await A(ze.data.text,{tgt_lang:ze.data.tgt_lang,src_lang:ze.data.src_lang,streamer:r});self.postMessage({status:"complete",output:_})})})();