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workgroup_index * ${t*s*n}u + local_idx;`;return`@compute @workgroup_size(${t}, ${s}, ${n}) + fn main(${o}) { + ${a} + `}appendVariableUniforms(e){e.rank!==0&&(e.shape.startsWith("uniforms.")&&this.uniforms.push({name:e.shape.replace("uniforms.",""),type:"u32",length:e.rank}),e.strides.startsWith("uniforms.")&&this.uniforms.push({name:e.strides.replace("uniforms.",""),type:"u32",length:e.rank}))}declareVariable(e,t){if(e.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(e),this.appendVariableUniforms(e);let s=e.usage==="input"?"read":"read_write",n=e.usage==="atomicOutput"?"atomic":e.type.storage;return`@group(0) @binding(${t}) var ${e.name}: array<${n}>;`}declareVariables(...e){return e.map(t=>this.declareVariable(t,this.variableIndex++)).join(` +`)}registerInternalVariable(e){if(e.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). 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output[global_idx] = input[global_idx]; + }`},{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let R=De.size(o);return{outputs:[{dims:o,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(R/64/4)},programUniforms:[{type:12,data:Math.ceil(R/4)}]}},getShaderSource:h};let{newShape:C,newPerm:u}=Aa(e.dims,i),k=De.areEqual(u,[2,3,1]),B=De.areEqual(u,[3,1,2]);if(C.length===2||k||B){a=k?[C[0],C[1]*C[2]]:B?[C[0]*C[1],C[2]]:C,c=[a[1],a[0]];let R=16;return h=z=>{let ne=He("a",s,a.length),J=Ct("output",s,c.length);return` + ${z.registerUniform("output_size","u32").declareVariables(ne,J)} + var tile : array, ${R}>; + ${z.mainStart([R,R,1])} + let stride = (uniforms.output_shape[1] - 1) / ${R} + 1; + let workgroup_id_x = workgroup_index % stride; + let workgroup_id_y = workgroup_index / stride; + let input_col = workgroup_id_y * ${R}u + local_id.x; + let input_row = workgroup_id_x * ${R}u + local_id.y; + if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) { + tile[local_id.y][local_id.x] = ${ne.getByIndices(`${ne.type.indices}(input_row, input_col)`)}; + } + workgroupBarrier(); + + let output_col = workgroup_id_x * ${R}u + local_id.x; + let output_row = workgroup_id_y * ${R}u + local_id.y; + if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) { + ${J.setByIndices(`${J.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")} + } + }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let z=De.size(o);return{outputs:[{dims:o,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(c[1]/R),y:Math.ceil(c[0]/R)},programUniforms:[{type:12,data:z},...Mt(a,c)]}},getShaderSource:h}}return h=R=>{let z=He("a",s,a.length),ne=Ct("output",s,c.length);return` + ${R.registerUniform("output_size","u32").declareVariables(z,ne)} + + ${$a(i,n,z,ne)} + + ${R.mainStart()} + ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = 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: array; + `,R=z=>` + ${z.registerUniform("reduceSize","u32").declareVariables(C,u)} + ${B} + fn DIV_CEIL(a : u32, b : u32) -> u32 { + return ((a - 1u) / b + 1u); + } + ${z.mainStart(k)} + + let outputIndex = global_idx / ${k}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${Oa[n]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${k}) { + let candidate = f32(${C.getByOffset("offset + k")}); + bestValue = ${ei[n]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${k}u); + for (var currentSize = reduceSize / 2u; reduceSize > 1u; + currentSize = reduceSize / 2u) { + let interval = DIV_CEIL(reduceSize, 2u); + if (local_idx < currentSize) { + let candidate = aBestValues[local_idx + interval]; + bestValue = ${Fa[n]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${u.setByOffset("outputIndex",`${n==="mean"?`${u.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${u.type.storage}(${Da[n]})`}`)}; + } + }`;return{name:e,shaderCache:{hint:`${t};${k}`,inputDependencies:["type"]},getShaderSource:R,getRunData:()=>({outputs:[{dims:o,dataType:i}],dispatchGroup:{x:p},programUniforms:[{type:12,data:h}]})}},pr=(e,t,s,n)=>{let i=e.inputs.length===1?s:Ri(e.inputs,s),o=i.axes;o.length===0&&!i.noopWithEmptyAxes&&(o=e.inputs[0].dims.map((B,R)=>R));let 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output_indices = ${W.offsetToIndices("global_idx")}; + + ${ne.join(` +`)} + ${ue[0]} // init ops for reduce max/min + ${ue[1]} + ${he} + ${ue[3]} + ${ue.length===4?W.setByOffset("global_idx","value"):ue.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:p,dataType:o}],dispatchGroup:{x:Math.ceil(R/64)},programUniforms:[{type:12,data:R},...Mt(h,p)]})}},Ri=(e,t)=>{let s=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>s.push(Number(n))),jt({axes:s,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},wr=(e,t,s,n)=>{let i=e.inputs,o=i.length===1?s:Ri(i,s);e.compute(si(t,{hint:o.cacheKey,inputDependencies:["rank"]},[i[0]],o.noopWithEmptyAxes&&o.axes.length===0?ti:n,o.axes,i[0].dataType,o.keepDims,o.noopWithEmptyAxes),{inputs:[0]})},Ni=(e,t)=>{gr(e.inputs),wr(e,"ReduceLogSum",t,(s,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${s.getByIndices("input_indices")};`,"value = log(value);"])},Ha=(e,t)=>{gr(e.inputs),wr(e,"ReduceL1",t,(s,n)=>[`var value = 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s=(n,i,o)=>{let a=[];for(let c=0;c=0||o.length===0)&&a.push(`input_indices[${c}] = 0;`);return[`${a.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",i.setByOffset("global_idx","best_index")]};e.compute(si("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],s,[t.axis],7,t.keepDims),{inputs:[0]})},qi=(e,t)=>{Hi(e.inputs);let s=(n,i,o)=>{let a=[];for(let c=0;c=0||o.length===0)&&a.push(`input_indices[${c}] = 0;`);return[`${a.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",i.setByOffset("global_idx","best_index")]};e.compute(si("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],s,[t.axis],7,t.keepDims),{inputs:[0]})},Qi=e=>jt(e)}),Xi,ni,al,Yi,ll,zn,Ji,ul,Zi=y(()=>{Ot(),$t(),ce(),qt(),Xi=(e,t)=>{let s=e[0],n=e[1],i=e[2],o=e[3],a=e[4],c=e[5];if(a&&c)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],C=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]!==C)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 u=i.dims[0]/3,k=u,B=k;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let ue of t.qkvHiddenSizes)if(ue%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");u=t.qkvHiddenSizes[0],k=t.qkvHiddenSizes[1],B=t.qkvHiddenSizes[2]}let R=h;if(u!==k)throw new Error("qkv_hidden_sizes first element should be same as the second");if(i.dims[0]!==u+k+B)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let z=0;if(a){if(k!==B)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(a.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(a.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(a.dims[1]!==p)throw new Error('Input "past" second dimension must be batch_size');if(a.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(a.dims[4]!==k/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(z=a.dims[3])}let ne=R+z,J=-1,W=0;if(o)throw new Error("Mask not supported");if(a)throw new Error("past is not supported");if(c){if(c.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(c.dims[0]!==p||c.dims[1]!==t.numHeads||c.dims[2]!==h||c.dims[3]!==ne)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:h,pastSequenceLength:z,kvSequenceLength:R,totalSequenceLength:ne,maxSequenceLength:J,inputHiddenSize:C,hiddenSize:u,vHiddenSize:B,headSize:Math.floor(u/t.numHeads),vHeadSize:Math.floor(B/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:W,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},ni=(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?.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; + `,al=(e,t,s,n,i,o,a,c)=>{let p=Gt(a?1:o),h=64,C=o/p;C{let W=Ct("x",e.dataType,e.dims,p),ue=[W],he=a?He("seq_lens",a.dataType,a.dims):void 0;he&&ue.push(he);let be=c?He("total_sequence_length_input",c.dataType,c.dims):void 0;be&&ue.push(be);let Be=Cs(e.dataType),Ie=[{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; + ${J.registerUniforms(Ie).declareVariables(...ue)} + ${J.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; + ${ni(he,be,!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 = ${a?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"}; + var thread_max_vector = ${R}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + thread_max_vector = max(${R}(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 = ${R}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + sum_vector += exp(${R}(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] = ${W.type.value}(${Be}(1.0) / ${Be}(seq_causal_length)); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + var f32input = ${R}(x[offset + i]); + x[offset + i] = ${W.type.value}(exp(f32input - max_value) / sum); + } + } + ${a?` + 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] = ${W.type.value}(${Be}(0)); + }`:""}; + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${h};${B};${p}`,inputDependencies:z},getShaderSource:ne,getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(o/h),y:i,z:t*s},programUniforms:k})}},Yi=(e,t,s,n,i,o,a,c,p)=>{let h=a+o.kvSequenceLength,C=[o.batchSize,o.numHeads,o.sequenceLength,h],u=e>1&&n,k=o.kvNumHeads?o.kvNumHeads:o.numHeads,B=u?[o.batchSize,k,h,o.headSize]:void 0,R=o.nReps?o.nReps:1,z=o.scale===0?1/Math.sqrt(o.headSize):o.scale,ne=Gt(o.headSize),J=o.headSize/ne,W=12,ue={x:Math.ceil(h/W),y:Math.ceil(o.sequenceLength/W),z:o.batchSize*o.numHeads},he=[{type:12,data:o.sequenceLength},{type:12,data:J},{type:12,data:h},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:1,data:z},{type:12,data:a},{type:12,data:o.kvSequenceLength},{type:12,data:R}],be=u&&n&&De.size(n.dims)>0,Be=["type","type"];be&&Be.push("type"),i&&Be.push("type"),c&&Be.push("type"),p&&Be.push("type");let Ie=[{dims:C,dataType:t.dataType,gpuDataType:0}];u&&Ie.push({dims:B,dataType:t.dataType,gpuDataType:0});let nt=dt=>{let Et=He("q",t.dataType,t.dims,ne),zt=He("key",s.dataType,s.dims,ne),It=[Et,zt];if(be){let Ut=He("past_key",n.dataType,n.dims,ne);It.push(Ut)}i&&It.push(He("attention_bias",i.dataType,i.dims));let ht=c?He("seq_lens",c.dataType,c.dims):void 0;ht&&It.push(ht);let Jt=p?He("total_sequence_length_input",p.dataType,p.dims):void 0;Jt&&It.push(Jt);let Vt=Ct("output",t.dataType,C),St=[Vt];u&&St.push(Ct("present_key",t.dataType,B,ne));let ts=Cs(1,ne),Xt=[{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 = ${W}u; + + var tileQ: array<${Et.type.storage}, ${W*W}>; + var tileK: array<${Et.type.storage}, ${W*W}>; + ${dt.registerUniforms(Xt).declareVariables(...It,...St)} + ${dt.mainStart([W,W,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z % uniforms.num_heads; + let kvHeadIdx = ${R===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${R===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; + ${ni(ht,Jt,!0)} + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; + let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + ${be&&u?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""}; + let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K; + ${u?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""} + var value = ${ts}(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; + ${be&&u?` + 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]; + }`} + ${u?`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 += ${ts}(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(ne){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: ${ne}`)}})()}; + output[outputIdx] = ${Vt.type.value} (sum * uniforms.alpha) + ${i?"attention_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${ne};${i!==void 0};${n!==void 0};${e}`,inputDependencies:Be},getRunData:()=>({outputs:Ie,dispatchGroup:ue,programUniforms:he}),getShaderSource:nt}},ll=(e,t,s,n,i,o,a=void 0,c=void 0)=>{let p=o+i.kvSequenceLength,h=i.nReps?i.nReps:1,C=i.vHiddenSize*h,u=e>1&&n,k=i.kvNumHeads?i.kvNumHeads:i.numHeads,B=u?[i.batchSize,k,p,i.headSize]:void 0,R=[i.batchSize,i.sequenceLength,C],z=12,ne={x:Math.ceil(i.vHeadSize/z),y:Math.ceil(i.sequenceLength/z),z:i.batchSize*i.numHeads},J=[{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:C},{type:12,data:o},{type:12,data:i.kvSequenceLength},{type:12,data:h}],W=u&&n&&De.size(n.dims)>0,ue=["type","type"];W&&ue.push("type"),a&&ue.push("type"),c&&ue.push("type");let he=[{dims:R,dataType:t.dataType,gpuDataType:0}];u&&he.push({dims:B,dataType:t.dataType,gpuDataType:0});let be=Be=>{let Ie=He("probs",t.dataType,t.dims),nt=He("v",s.dataType,s.dims),dt=[Ie,nt];W&&dt.push(He("past_value",n.dataType,n.dims));let Et=a?He("seq_lens",a.dataType,a.dims):void 0;a&&dt.push(Et);let zt=c?He("total_sequence_length_input",c.dataType,c.dims):void 0;c&&dt.push(zt);let It=[Ct("output",t.dataType,R)];u&&It.push(Ct("present_value",t.dataType,B));let ht=[{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 = ${z}u; + var tileQ: array<${Ie.type.value}, ${z*z}>; + var tileV: array<${Ie.type.value}, ${z*z}>; + ${Be.registerUniforms(ht).declareVariables(...dt,...It)} + ${Be.mainStart([z,z,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; + ${ni(Et,zt,!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 + ${W&&u?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""}; + let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n; + ${u?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""} + var value = ${Ie.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; + ${W&&u?` + 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]; + }`} + ${u?` + if (w + local_id.y < present_sequence_length) { + present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx]; + }`:""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + headIdx * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:ue},getRunData:()=>({outputs:he,dispatchGroup:ne,programUniforms:J}),getShaderSource:be}},zn=(e,t,s,n,i,o,a,c,p,h,C=void 0,u=void 0)=>{let k=Math.min(e.outputCount,1+(a?1:0)+(c?1:0)),B=k>1?h.pastSequenceLength:0,R=B+h.kvSequenceLength,z=p&&De.size(p.dims)>0?p:void 0,ne=[t,s];k>1&&a&&De.size(a.dims)>0&&ne.push(a),z&&ne.push(z),C&&ne.push(C),u&&ne.push(u);let J=e.compute(Yi(k,t,s,a,z,h,B,C,u),{inputs:ne,outputs:k>1?[-1,1]:[-1]})[0];e.compute(al(J,h.batchSize,h.numHeads,B,h.sequenceLength,R,C,u),{inputs:C&&u?[J,C,u]:[J],outputs:[]});let W=[J,n];k>1&&c&&De.size(c.dims)>0&&W.push(c),C&&W.push(C),u&&W.push(u),e.compute(ll(k,J,n,c,h,B,C,u),{inputs:W,outputs:k>1?[0,2]:[0]})},Ji=(e,t)=>{let s=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,i=t.inputHiddenSize,o=t.headSize,a=12,c={x:Math.ceil(t.headSize/a),y:Math.ceil(t.sequenceLength/a),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:o},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],C=u=>{let k=Ct("output_q",p[0].dataType,s),B=Ct("output_k",p[0].dataType,s),R=Ct("output_v",p[0].dataType,s),z=He("input",p[0].dataType,p[0].dims),ne=He("weight",p[1].dataType,p[1].dims),J=He("bias",p[2].dataType,p[2].dims),W=z.type.storage,ue=[{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 = ${a}u; + var tileInput: array<${W}, ${a*a}>; + var tileWeightQ: array<${W}, ${a*a}>; + var tileWeightK: array<${W}, ${a*a}>; + var tileWeightV: array<${W}, ${a*a}>; + ${u.registerUniforms(ue).declareVariables(z,ne,J,k,B,R)} + ${u.mainStart([a,a,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 = ${W}(0); + var valueK = ${W}(0); + var valueV = ${W}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:s,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:s,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:s,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:c,programUniforms:h}),getShaderSource:C},{inputs:p,outputs:[-1,-1,-1]})},ul=(e,t)=>{let s=Xi(e.inputs,t),[n,i,o]=Ji(e,s);return zn(e,n,i,o,e.inputs[4],void 0,void 0,void 0,e.inputs[5],s)}}),eo,dl,cl,to,hc=y(()=>{We(),Ot(),$t(),is(),qt(),eo=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let s=(n,i,o)=>{let a=i.length;if(a!==n.length)throw new Error(`${o}: num dimensions != ${a}`);i.forEach((c,p)=>{if(c!==n[p])throw new Error(`${o}: dim[${p}] do not match`)})};if(e[0].dims.length>1){let n=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);s(e[1].dims,n,"Invalid input scale"),s(e[2].dims,n,"Invalid input B"),s(e[3].dims,n,"Invalid input mean"),s(e[4].dims,n,"Invalid 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${ue.registerUniform("outputSize","u32").declareVariables(u,k,B,R,z,ne)} + ${ue.mainStart()} + ${ue.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${ne.offsetToIndices(`global_idx * ${a}`)}; + ${J()} + let scale = ${k.getByOffset("cOffset")}; + let bias = ${B.getByOffset("cOffset")}; + let inputMean = ${R.getByOffset("cOffset")}; + let inputVar = ${z.getByOffset("cOffset")}; + let x = ${u.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${ne.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${a}`,inputDependencies:h?["rank","type","type","type","type"]:void 0},getShaderSource:W,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h?[{type:12,data:p},...Mt(o)]:[{type:12,data:p}]})}},cl=e=>jt(e),to=(e,t)=>{let{inputs:s,outputCount:n}=e,i=cl({...t,outputCount:n});if(F.webgpu.validateInputContent&&eo(s,i),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(dl(s,i))}}),pl,so,hl,mc=y(()=>{$t(),qt(),pl=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},so=e=>{let t=e[0].dims,s=e[0].dims[2],n=De.size(t)/4,i=e[0].dataType,o=He("input",i,t,4),a=He("bias",i,[s],4),c=He("residual",i,t,4),p=Ct("output",i,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:h=>` + const channels = ${s}u / 4; + ${h.declareVariables(o,a,c,p)} + + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes(n)} + let value = ${o.getByOffset("global_idx")} + + ${a.getByOffset("global_idx % channels")} + ${c.getByOffset("global_idx")}; + ${p.setByOffset("global_idx","value")} + }`}},hl=e=>{pl(e.inputs),e.compute(so(e.inputs))}}),ro,ls,ml,no,fl,_l,io,gl,wl,oo,yl,Ml,bl,vl,ao,xl,Bn,lo,ii,Tl,uo,Pl,El,co,Cl,kl,po,Sl,$l,ho,Al,Il,mo,Fl,Ol,fo,Dl,oi,_o,Ll,zl,Bl,go,Rl,Nl,wo=y(()=>{Ot(),$t(),is(),qt(),ro=(e,t,s,n,i,o,a)=>{let c=Math.ceil(t/4),p="";typeof i=="string"?p=`${i}(a)`:p=i("a");let h=He("inputData",s,[c],4),C=Ct("outputData",n,[c],4),u=[{name:"vec_size",type:"u32"}];return a&&u.push(...a),` + ${e.registerUniforms(u).declareVariables(h,C)} + + ${o??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${h.getByOffset("global_idx")}; + ${C.setByOffset("global_idx",p)} + }`},ls=(e,t,s,n,i,o=e.dataType,a,c)=>{let p=[{type:12,data:Math.ceil(De.size(e.dims)/4)}];return a&&p.push(...a),{name:t,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:h=>ro(h,De.size(e.dims),e.dataType,o,s,n,c),getRunData:h=>({outputs:[{dims:e.dims,dataType:o}],dispatchGroup:{x:Math.ceil(De.size(h[0].dims)/64/4)},programUniforms:p})}},ml=e=>{e.compute(ls(e.inputs[0],"Abs","abs"))},no=e=>{e.compute(ls(e.inputs[0],"Acos","acos"))},fl=e=>{e.compute(ls(e.inputs[0],"Acosh","acosh"))},_l=e=>{e.compute(ls(e.inputs[0],"Asin","asin"))},io=e=>{e.compute(ls(e.inputs[0],"Asinh","asinh"))},gl=e=>{e.compute(ls(e.inputs[0],"Atan","atan"))},wl=e=>{e.compute(ls(e.inputs[0],"Atanh","atanh"))},oo=e=>jt(e),yl=(e,t)=>{let s;switch(t.to){case 10:s="vec4";break;case 1:s="vec4";break;case 12:s="vec4";break;case 6:s="vec4";break;case 9:s="vec4";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute(ls(e.inputs[0],"Cast",s,void 0,t.cacheKey,t.to))},Ml=e=>{let t,s,n=e.length>=2&&e[1].data!==0,i=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=n?e[1].getFloat32Array()[0]:-34028234663852886e22,s=i?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=n?e[1].getUint16Array()[0]:64511,s=i?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return jt({min:t,max:s})},bl=(e,t)=>{let s=t||Ml(e.inputs),n=Cs(e.inputs[0].dataType);e.compute(ls(e.inputs[0],"Clip",i=>`clamp(${i}, vec4<${n}>(uniforms.min), vec4<${n}>(uniforms.max))`,void 0,s.cacheKey,void 0,[{type:e.inputs[0].dataType,data:s.min},{type:e.inputs[0].dataType,data:s.max}],[{name:"min",type:n},{name:"max",type:n}]),{inputs:[0]})},vl=e=>{e.compute(ls(e.inputs[0],"Ceil","ceil"))},ao=e=>{e.compute(ls(e.inputs[0],"Cos","cos"))},xl=e=>{e.compute(ls(e.inputs[0],"Cosh","cosh"))},Bn=e=>jt(e),lo=(e,t)=>{let s=Cs(e.inputs[0].dataType);e.compute(ls(e.inputs[0],"Elu",n=>`elu_vf32(${n})`,` + const elu_alpha_ = ${s}(${t.alpha}); + + fn elu_f32(a: ${s}) -> ${s} { + return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${s}>) -> vec4<${s}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,t.cacheKey))},ii=(e="f32")=>` +const r0: ${e} = 0.3275911; +const r1: ${e} = 0.254829592; +const r2: ${e} = -0.284496736; +const r3: ${e} = 1.421413741; +const r4: ${e} = -1.453152027; +const r5: ${e} = 1.061405429; + +fn erf_vf32(v: vec4<${e}>) -> vec4<${e}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,Tl=e=>{let t=Cs(e.inputs[0].dataType);e.compute(ls(e.inputs[0],"Erf",s=>`erf_vf32(${s})`,ii(t)))},uo=e=>{e.compute(ls(e.inputs[0],"Exp","exp"))},Pl=e=>{e.compute(ls(e.inputs[0],"Floor","floor"))},El=e=>{let t=Cs(e.inputs[0].dataType);e.compute(ls(e.inputs[0],"Gelu",s=>`0.5 * ${s} * (1.0 + erf_vf32(${s} * 0.7071067811865475))`,ii(t)))},co=(e,t)=>{let s=Cs(e.inputs[0].dataType);e.compute(ls(e.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${s}>(0.0))`,`const leaky_relu_alpha_ = ${s}(${t.alpha});`,t.cacheKey))},Cl=e=>{e.compute(ls(e.inputs[0],"Not",t=>`!${t}`))},kl=e=>{e.compute(ls(e.inputs[0],"Neg",t=>`-${t}`))},po=e=>{e.compute(ls(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Sl=e=>{let t=Cs(e.inputs[0].dataType);e.compute(ls(e.inputs[0],"Relu",s=>`select(vec4<${t}>(0.0), ${s}, ${s} > vec4<${t}>(0.0))`))},$l=e=>{e.compute(ls(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},ho=e=>jt(e),Al=(e,t)=>{let s=Cs(e.inputs[0].dataType);e.compute(ls(e.inputs[0],"HardSigmoid",n=>`max(vec4<${s}>(0.0), min(vec4<${s}>(1.0), ${t.alpha} * ${n} + vec4<${s}>(${t.beta})))`,void 0,t.cacheKey))},Il=e=>{e.compute(ls(e.inputs[0],"Sin","sin"))},mo=e=>{e.compute(ls(e.inputs[0],"Sinh","sinh"))},Fl=e=>{e.compute(ls(e.inputs[0],"Sqrt","sqrt"))},Ol=e=>{e.compute(ls(e.inputs[0],"Tan","tan"))},fo=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,Dl=e=>{e.compute(ls(e.inputs[0],"Tanh",fo))},oi=(e="f32")=>` +const fast_gelu_a: ${e} = 0.5; +const fast_gelu_b: ${e} = 0.7978845608028654; +const fast_gelu_c: ${e} = 0.035677408136300125; + +fn tanh_v(v: vec4<${e}>) -> vec4<${e}> { + return ${fo("v")}; +} +`,_o=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,Ll=e=>{let t=Cs(e.inputs[0].dataType);e.compute(ls(e.inputs[0],"FastGelu",_o,oi(t),void 0,e.inputs[0].dataType))},zl=(e,t)=>{let s=Cs(e.inputs[0].dataType);return e.compute(ls(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${s}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${s}>(${t.alpha});`,t.cacheKey)),0},Bl=e=>{e.compute(ls(e.inputs[0],"Log","log"))},go=(e,t)=>` +const alpha = vec4<${e}>(${t}); +const one = ${e}(1.0); +const zero = ${e}(0.0); + +fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> { + let v = x *alpha; + var x1 : vec4<${e}>; + for (var i = 0; i < 4; i = i + 1) { + if (v[i] >= zero) { + x1[i] = one / (one + exp(-v[i])); + } else { + x1[i] = one - one / (one + exp(v[i])); + } + } + return x * x1; +} +`,Rl=e=>`quick_gelu_impl(${e})`,Nl=(e,t)=>{let s=Cs(e.inputs[0].dataType);e.compute(ls(e.inputs[0],"QuickGelu",Rl,go(s,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),jl,yo,Vl,fc=y(()=>{$t(),qt(),wo(),jl=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},yo=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let s=He("input",e[0].dataType,e[0].dims,4),n=He("bias",e[0].dataType,[e[0].dims[2]],4),i=Ct("output",e[0].dataType,t,4),o=De.size(t)/4,a=ds(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:c=>` + const M_SQRT2 = sqrt(2.0); + const halfChannels = ${e[0].dims[2]/4/2}u; + + ${c.declareVariables(s,n,i)} + + ${ii(a)} + + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes(o)} + let biasIdx = global_idx % halfChannels; + let batchIndex = global_idx / halfChannels; + let inputOffset = biasIdx + batchIndex * halfChannels * 2; + let valueLeft = input[inputOffset] + bias[biasIdx]; + let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${i.setByOffset("global_idx","valueLeft * geluRight")} + }`}},Vl=e=>{jl(e.inputs),e.compute(yo(e.inputs))}}),Ul,Wl,hr,Gl,Kl,Mo,Hl,ql,bo,Ql,Xl,vo,Yl,_c=y(()=>{Ot(),$t(),qt(),Ul=(e,t,s,n,i,o,a,c,p,h,C,u)=>{let k,B;typeof c=="string"?k=B=(W,ue)=>`${c}((${W}),(${ue}))`:typeof c=="function"?k=B=c:(k=c.scalar,B=c.vector);let R=Ct("outputData",C,n.length,4),z=He("aData",p,t.length,4),ne=He("bData",h,s.length,4),J;if(i)if(o){let W=De.size(t)===1,ue=De.size(s)===1,he=t.length>0&&t[t.length-1]%4===0,be=s.length>0&&s[s.length-1]%4===0;W||ue?J=R.setByOffset("global_idx",B(W?`${z.type.value}(${z.getByOffset("0")}.x)`:z.getByOffset("global_idx"),ue?`${ne.type.value}(${ne.getByOffset("0")}.x)`:ne.getByOffset("global_idx"))):J=` + let outputIndices = ${R.offsetToIndices("global_idx * 4u")}; + let offsetA = ${z.broadcastedIndicesToOffset("outputIndices",R)}; + let offsetB = ${ne.broadcastedIndicesToOffset("outputIndices",R)}; + ${R.setByOffset("global_idx",B(a||he?z.getByOffset("offsetA / 4u"):`${z.type.value}(${z.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||be?ne.getByOffset("offsetB / 4u"):`${ne.type.value}(${ne.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else J=R.setByOffset("global_idx",B(z.getByOffset("global_idx"),ne.getByOffset("global_idx")));else{if(!o)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let W=(ue,he,be="")=>{let Be=`aData[indexA${he}][componentA${he}]`,Ie=`bData[indexB${he}][componentB${he}]`;return` + let outputIndices${he} = ${R.offsetToIndices(`global_idx * 4u + ${he}u`)}; + let offsetA${he} = ${z.broadcastedIndicesToOffset(`outputIndices${he}`,R)}; + let offsetB${he} = ${ne.broadcastedIndicesToOffset(`outputIndices${he}`,R)}; + let indexA${he} = offsetA${he} / 4u; + let indexB${he} = offsetB${he} / 4u; + let componentA${he} = offsetA${he} % 4u; + let componentB${he} = offsetB${he} % 4u; + ${ue}[${he}] = ${be}(${k(Be,Ie)}); + `};C===9?J=` + var data = vec4(0); + ${W("data",0,"u32")} + ${W("data",1,"u32")} + ${W("data",2,"u32")} + ${W("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:J=` + ${W("outputData[global_idx]",0)} + ${W("outputData[global_idx]",1)} + ${W("outputData[global_idx]",2)} + ${W("outputData[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(z,ne,R)} + + ${u??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${J} + }`},Wl=(e,t,s,n,i,o,a=s.dataType)=>{let c=s.dims.map(z=>Number(z)??1),p=n.dims.map(z=>Number(z)??1),h=!De.areEqual(c,p),C=c,u=De.size(c),k=!1,B=!1,R=[h];if(h){let z=Js.calcShape(c,p,!1);if(!z)throw new Error("Can't perform binary op on the given tensors");C=z.slice(),u=De.size(C);let ne=De.size(c)===1,J=De.size(p)===1,W=c.length>0&&c[c.length-1]%4===0,ue=p.length>0&&p[p.length-1]%4===0;R.push(ne),R.push(J),R.push(W),R.push(ue);let he=1;for(let be=1;bez.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:z=>Ul(z,c,p,C,k,h,B,i,s.dataType,n.dataType,a,o),getRunData:()=>({outputs:[{dims:C,dataType:a}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:Math.ceil(De.size(C)/4)},...Mt(c,p,C)]})}},hr=(e,t,s,n,i,o)=>{e.compute(Wl(t,i??"",e.inputs[0],e.inputs[1],s,n,o))},Gl=e=>{hr(e,"Add",(t,s)=>`${t}+${s}`)},Kl=e=>{hr(e,"Div",(t,s)=>`${t}/${s}`)},Mo=e=>{hr(e,"Equal",{scalar:(t,s)=>`u32(${t}==${s})`,vector:(t,s)=>`vec4(${t}==${s})`},void 0,void 0,9)},Hl=e=>{hr(e,"Mul",(t,s)=>`${t}*${s}`)},ql=e=>{let t=He("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;hr(e,"Pow",{scalar:(s,n)=>`pow_custom(${s},${n})`,vector:(s,n)=>`pow_vector_custom(${s},${n})`},` + fn pow_custom(a : ${t}, b : ${t}) -> ${t} { + if (b == ${t}(0.0)) { + return ${t}(1.0); + } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) { + return ${t}(pow(f32(a), f32(b))); // NaN + } + return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); + } + fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> { + // TODO: implement vectorized pow + return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); + } + `)},bo=e=>{hr(e,"Sub",(t,s)=>`${t}-${s}`)},Ql=e=>{hr(e,"Greater",{scalar:(t,s)=>`u32(${t}>${s})`,vector:(t,s)=>`vec4(${t}>${s})`},void 0,void 0,9)},Xl=e=>{hr(e,"Less",{scalar:(t,s)=>`u32(${t}<${s})`,vector:(t,s)=>`vec4(${t}<${s})`},void 0,void 0,9)},vo=e=>{hr(e,"GreaterOrEqual",{scalar:(t,s)=>`u32(${t}>=${s})`,vector:(t,s)=>`vec4(${t}>=${s})`},void 0,void 0,9)},Yl=e=>{hr(e,"LessOrEqual",{scalar:(t,s)=>`u32(${t}<=${s})`,vector:(t,s)=>`vec4(${t}<=${s})`},void 0,void 0,9)}}),xo,Jl,Zl,ai,eu,tu,su=y(()=>{Ot(),$t(),is(),qt(),xo=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let s=0,n=e[s],i=n.dataType,o=n.dims.length;e.forEach((a,c)=>{if(c!==s){if(a.dataType!==i)throw new Error("input tensors should be one type");if(a.dims.length!==o)throw new Error("input tensors should have the same shape");a.dims.forEach((p,h)=>{if(h!==t&&p!==n.dims[h])throw new Error("non concat dimensions must match")})}})},Jl=(e,t)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${t}); + for (var i: u32 = 0u; i < ${e}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${e}u; + }`,Zl=(e,t)=>{let s=e.length,n=[];for(let i=0;i{let i=De.size(s),o=new Array(e.length),a=new Array(e.length),c=0,p=[],h=[],C=[{type:12,data:i}];for(let z=0;z`uniforms.sizeInConcatAxis${z}`).join(","),R=z=>` + + ${(()=>{z.registerUniform("outputSize","u32");for(let ne=0;ne(${B}); + ${k} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${Zl(a,u)} + }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:s,dataType:n}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:C}),getShaderSource:R}},eu=(e,t)=>{let s=e.inputs,n=s[0].dims,i=De.normalizeAxis(t.axis,n.length);xo(s,i);let o=n.slice();o[i]=s.reduce((c,p)=>c+(p.dims.length>i?p.dims[i]:0),0);let a=s.filter(c=>De.size(c.dims)>0);e.compute(ai(a,i,o,s[0].dataType),{inputs:a})},tu=e=>jt({axis:e.axis})}),Vr,Zr,Ur,To,en=y(()=>{Ot(),$t(),Vr=(e,t,s="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${s}(uniforms.clip_min)), ${t}(${s}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${s}(uniforms.alpha) * value + ${s}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${s}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value)); + value = sign(value) * (1.0 - e2x) / (1.0 + e2x); + `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},Zr=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Ur=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},To=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[s,n]=e?.activation_params||[.2,.5];return{activation:t,alpha:s,beta:n}}else if(t==="Clip"){let[s,n]=e?.activation_params||[Es,Hs];return{activation:t,clipMax:n,clipMin:s}}else if(t==="LeakyRelu"){let[s]=e?.activation_params||[.01];return{activation:t,alpha:s}}return{activation:t}}}),qs,Po,Eo=y(()=>{qs=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Po=e=>` + ${e?"value = value + getBiasByOutputCoords(coords);":""} + `}),ru,gc=y(()=>{ru=e=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); +} +`}),wn,Co,ko=y(()=>{Ot(),$t(),qt(),en(),wn=(e,t,s,n,i)=>{let o=n-s;return` + ${Array.from({length:s}).map((a,c)=>` + if (${Tt(t.shape,c,t.rank)} != 1) { + ${t.indicesSet(e,c,Tt(i,c+o,n))} + } else { + ${t.indicesSet(e,c,0)} + }`).join("")} +`},Co=(e,t,s,n,i=!1,o)=>{let a=e[0].dims,c=e[1].dims,p=a[a.length-2],h=c[c.length-1],C=a[a.length-1],u=Gt(h),k=Gt(C),B=Gt(p),R=De.size(s)/u/B,z=e.length>2,ne=n?n.slice(0,-2):s.slice(0,-2),J=[De.size(ne),p,h],W=[{type:12,data:R},{type:12,data:p},{type:12,data:h},{type:12,data:C}];Zr(t,W),W.push(...Mt(ne,a,c)),z&&W.push(...Mt(e[2].dims)),W.push(...Mt(J));let ue=he=>{let be=Ii("batch_dims",e[0].dataType,ne.length),Be=He("a",e[0].dataType,a.length,k),Ie=He("b",e[1].dataType,c.length,u),nt=Ct("output",e[0].dataType,J.length,u),dt=ds(nt.type.tensor),Et=Vr(t,nt.type.value,dt),zt=[Be,Ie],It="";if(z){let Vt=i?u:1;zt.push(He("bias",e[2].dataType,e[2].dims.length,Vt)),It=`${i?`value += bias[col / ${Vt}];`:`value += ${nt.type.value}(bias[row + i]);`}`}let ht=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Ur(t,ht);let Jt=()=>{let Vt=`var a_data: ${Be.type.value};`;for(let St=0;St; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${k}) { + ${Jt()} + } + for (var i = 0u; i < ${B}u; i++) { + var value = values[i]; + ${It} + ${Et} + let cur_indices = ${nt.type.indices}(batch, row + i, col); + let offset = ${nt.indicesToOffset("cur_indices")}; + ${nt.setByOffset(`offset / ${u}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${u};${k};${B};${i}`,inputDependencies:z?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:o?o(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(R/64)},programUniforms:W}),getShaderSource:ue}}}),So,nu,$o,li,iu,Ao,Io,ui,Fo=y(()=>{Ot(),$t(),qt(),en(),ko(),Eo(),So=(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":""}); + `,nu=(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]; + ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} + }`,$o=(e,t,s="f32",n,i=!1,o=32,a=!1,c=32)=>{let p=t[1]*e[1],h=t[0]*e[0],C=i?p:o,u=i?o:p,k=C/t[0],B=o/t[1];if(!((i&&k===4&&e[1]===4||!i&&(k===3||k===4))&&C%t[0]===0&&o%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${k} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${k} must be 3 or 4. + tileAWidth ${C} must be divisible by workgroupSize[0]${t[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return` +var mm_Asub: array, ${C/k}>, ${u}>; +var mm_Bsub: array, ${h/e[0]}>, ${o}>; + +const rowPerThread = ${e[1]}; +const colPerThread = ${e[0]}; +const innerElementSize = ${k}; +const tileInner = ${o}; + +@compute @workgroup_size(${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 = ${a?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${p}; + + let num_tiles = ${a?`${Math.ceil(c/o)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${a?`i32(globalId.z) * ${c}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${B}; + 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; + ${So(i,n)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${B}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${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]; + ${k===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${nu(i,k)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},li=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${t?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${t?", batchIndices":""}); + `,iu=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Ao=(e,t,s="f32",n,i=!1,o=32,a=!1,c=32,p=!1)=>{let h=e[1]*t[1],C=e[0]*t[0],u=i?h:o,k=i?o:h;if(!(k%t[1]===0&&u%t[0]===0&&o%t[1]===0))throw new Error(`tileAHight ${k} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}, tileInner ${o} must be divisible by workgroupSize[1]${t[1]}`);let B=k/t[1],R=u/t[0],z=o/t[1],ne=p?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${h}; + let globalColStart = i32(workgroupId.x) * ${C}; + + // 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 < ${k}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) { + ${li(i,n)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${C}; 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) * ${B}; +let tileColA = i32(localId.x) * ${R}; +let tileRowB = i32(localId.y) * ${z}; +// 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 < ${B}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${R}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${li(i,n)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${z}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${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) { + ${iu(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, ${k}>; + var mm_Bsub : array, ${o}>; + const rowPerThread = ${e[1]}; + const colPerThread = ${e[0]}; + const tileInner = ${o}; + +@compute @workgroup_size(${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 = ${a?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${a?`${Math.ceil(c/o)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${a?`i32(globalId.z) * ${c}`:"0"}; + + var acc : array, rowPerThread>; + ${ne} + } +`},Io=(e,t,s,n,i=!1)=>{let[o,a,c,p]=n,h=ds(n[0].type.tensor);return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${o.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: ${a.type.indices}; + ${wn("aIndices",a,a.rank-2,o.rank,"batchIndices")} + ${a.indicesSet("aIndices",a.rank-2,"u32(row)")} + ${a.indicesSet("aIndices",a.rank-1,"u32(colIn)")} + value = ${a.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${o.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: ${c.type.indices}; + ${wn("bIndices",c,c.rank-2,o.rank,"batchIndices")} + ${c.indicesSet("bIndices",c.rank-2,"u32(row)")} + ${c.indicesSet("bIndices",c.rank-1,"u32(colIn)")} + value = ${c.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} + ${p.setByIndices("vec3(coords)","value")} + } + } + `},ui=(e,t,s,n,i=!1,o)=>{let a=e[0].dims,c=e[1].dims,p=a.slice(0,-2),h=c.slice(0,-2),C=n?n.slice(0,-2):s.slice(0,-2),u=De.size(C),k=a[a.length-2],B=a[a.length-1],R=c[c.length-1],z=B%4===0&&R%4===0,ne=k<=8?[4,1,1]:[4,4,1],J=[8,8,1],W=[Math.ceil(R/J[0]/ne[0]),Math.ceil(k/J[1]/ne[1]),Math.ceil(u/J[2]/ne[2])],ue=z?4:1,he=[...p,k,B/ue],be=he.length,Be=[...h,B,R/ue],Ie=Be.length,nt=[u,k,R/ue],dt=[{type:6,data:k},{type:6,data:R},{type:6,data:B}];Zr(t,dt),dt.push(...Mt(C,he,Be));let Et=["rank","rank"],zt=e.length>2;zt&&(dt.push(...Mt(e[2].dims)),Et.push("rank")),dt.push(...Mt(nt));let It=ht=>{let Jt=C.length,Vt=Ii("batchDims",e[0].dataType,Jt,1),St=ds(e[0].dataType),ts=He("a",e[0].dataType,be,ue),Xt=He("b",e[1].dataType,Ie,ue),Ut=Ct("result",e[0].dataType,nt.length,ue),$s=[ts,Xt];if(zt){let xs=i?ue:1;$s.push(He("bias",e[2].dataType,e[2].dims.length,xs))}let ut=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Ur(t,ut);let xt=ds(Ut.type.tensor),hs=Vr(t,Ut.type.value,xt),vs=Io(ue,zt,hs,[Vt,ts,Xt,Ut],i);return` + ${ht.registerUniforms(ut).registerInternalVariables(Vt).declareVariables(...$s,Ut)} + ${vs} + ${z?$o(ne,J,St,Vt):Ao(ne,J,St,Vt)} + `};return{name:"MatMul",shaderCache:{hint:`${ne};${t.activation};${z};${i}`,inputDependencies:Et},getRunData:()=>({outputs:[{dims:o?o(s):s,dataType:e[0].dataType}],dispatchGroup:{x:W[0],y:W[1],z:W[2]},programUniforms:dt}),getShaderSource:It}}}),Oo,ou,wc=y(()=>{Ot(),Te(),qt(),en(),Eo(),gc(),Fo(),Oo=(e,t,s,n,i=!1,o,a=4,c=4,p=4,h="f32")=>{let C=dt=>{switch(dt){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 ${dt} is not supported.`)}},u=dt=>{switch(dt){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${dt} is not supported.`)}},k=e?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,B=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,R=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",z=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",ne=e?"row":"col",J=e?"col":"row",W=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${ne} / outWidth; + let outCol = ${ne} % outWidth; + + let WRow = ${J} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${J} / 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 = ${J} % inChannels; + var resData = ${qs(a,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 < ${R} && xCol >= 0 && xCol < ${z}) { + ${k} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${C(a)} + } + return resData;`,ue=e?t&&n?` + let col = colIn * ${a}; + ${W}`:` + let col = colIn * ${a}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${W} + } + return ${qs(a,h)}(0.0);`:n&&s?` + let col = colIn * ${a}; + ${W}`:` + let col = colIn * ${a}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${W} + } + return ${qs(a,h)}(0.0);`,he=e?n&&s?u(c):` + let col = colIn * ${c}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${u(c)} + } + return ${qs(c,h)}(0.0);`:` + let col = colIn * ${c}; + if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) { + ${u(c)} + } + return ${qs(c,h)}(0.0);`,be=qs(p,h),Be=qs(e?a:c,h),Ie=qs(e?c:a,h),nt=Vr(o,be,h);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Be} { + ${e?ue:he} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Ie} { + ${e?he:ue} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${be}) { + 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])"}; + ${B} + ${Po(i)} + ${nt} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},ou=(e,t,s,n,i,o,a,c,p)=>{let h=t.format==="NHWC",C=h?e[0].dims[3]:e[0].dims[1],u=s[0],k=h?s[2]:s[3],B=h?s[1]:s[2],R=h?s[3]:s[1],z=h&&(C%4===0||C%3===0)&&R%4===0,ne=h?R:k*B,J=h?k*B:R,W=[8,8,1],ue=n<=8?[4,1,1]:[4,4,1],he=[Math.ceil(ne/W[0]/ue[0]),Math.ceil(J/W[1]/ue[1]),Math.ceil(u/W[2]/ue[2])];as("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${he}`);let be=z?h&&C%4!==0?3:4:1,Be=W[1]*ue[1],Ie=W[0]*ue[0],nt=Math.max(W[0]*be,W[1]),dt=n%Be===0,Et=i%Ie===0,zt=o%nt===0,It=z?[be,4,4]:[1,1,1],ht=[{type:6,data:n},{type:6,data:i},{type:6,data:o},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Zr(t,ht),ht.push(...Mt(e[0].dims,e[1].dims));let Jt=["rank","rank"];a&&(ht.push(...Mt(e[2].dims)),Jt.push("rank")),ht.push(...Mt(s));let Vt=St=>{let ts=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Ur(t,ts);let Xt=z?4:1,Ut=ds(e[0].dataType),$s=` + fn setOutputAtIndex(flatIndex : i32, value : ${z?`vec4<${Ut}>`:Ut}) { + result[flatIndex] = ${z?`vec4<${Ut}>`:Ut}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${z?`vec4<${Ut}>`:Ut}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${z?"/ 4":""}, value); + }`,ut=He("x",e[0].dataType,e[0].dims.length,be===3?1:be),xt=He("w",e[1].dataType,e[1].dims.length,Xt),hs=[ut,xt],vs=Ct("result",e[0].dataType,s.length,Xt);if(a){let xs=He("bias",e[2].dataType,e[2].dims.length,Xt);hs.push(xs),$s+=` + fn getBiasByOutputCoords(coords : vec4) -> ${z?`vec4<${Ut}>`:Ut} { + return bias[coords.${h?"w":"y"}${z?"/ 4":""}]; + }`}return` + ${ru("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 }; + ${St.registerUniforms(ts).declareVariables(...hs,vs)} + ${$s} + ${Oo(h,dt,Et,zt,a,t,It[0],It[1],It[2],Ut)} + ${z?$o(ue,W,Ut,void 0,!h,nt):Ao(ue,W,Ut,void 0,!h,nt,!1,void 0,c)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${be};${z};${dt};${Et};${zt};${Be};${Ie};${nt}`,inputDependencies:Jt},getRunData:()=>({outputs:[{dims:p?p(s):s,dataType:e[0].dataType}],dispatchGroup:{x:he[0],y:he[1],z:he[2]},programUniforms:ht}),getShaderSource:Vt}}}),Do,Lo,Rn,au,zo,di,lu,uu,yc=y(()=>{Ot(),Te(),$t(),qt(),en(),Eo(),Do=e=>{let t=1;for(let s=0;stypeof e=="number"?[e,e,e]:e,Rn=(e,t)=>t<=1?e:e+(e-1)*(t-1),au=(e,t,s,n=1)=>{let i=Rn(t,n);return Math.floor((e[0]*(s-1)-s+i)/2)},zo=(e,t,s,n,i)=>{i==null&&(i=au(e,t[0],n[0]));let o=[0,0,0,s];for(let a=0;a<3;a++)e[a]+2*i>=t[a]&&(o[a]=Math.trunc((e[a]-t[a]+2*i)/n[a]+1));return o},di=(e,t,s,n,i,o,a,c,p,h)=>{let C,u,k,B;if(e==="VALID"&&(e=0),typeof e=="number"){C={top:e,bottom:e,left:e,right:e,front:e,back:e};let R=zo([t,s,n,1],[c,p,h],1,[i,o,a],e);u=R[0],k=R[1],B=R[2]}else if(Array.isArray(e)){if(!e.every((z,ne,J)=>z===J[0]))throw Error(`Unsupported padding parameter: ${e}`);C={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let R=zo([t,s,n,1],[c,p,h],1,[i,o,a],e[0]);u=R[0],k=R[1],B=R[2]}else if(e==="SAME_UPPER"){u=Math.ceil(t/i),k=Math.ceil(s/o),B=Math.ceil(n/a);let R=(u-1)*i+c-t,z=(k-1)*o+p-s,ne=(B-1)*a+h-n,J=Math.floor(R/2),W=R-J,ue=Math.floor(z/2),he=z-ue,be=Math.floor(ne/2),Be=ne-be;C={top:ue,bottom:he,left:be,right:Be,front:J,back:W}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:C,outDepth:u,outHeight:k,outWidth:B}},lu=(e,t,s,n,i,o=!1,a="channelsLast")=>{let c,p,h,C,u;if(a==="channelsLast")[c,p,h,C,u]=e;else if(a==="channelsFirst")[c,u,p,h,C]=e;else throw new Error(`Unknown dataFormat ${a}`);let[k,,B,R,z]=t,[ne,J,W]=Lo(s),[ue,he,be]=Lo(n),Be=Rn(B,ue),Ie=Rn(R,he),nt=Rn(z,be),{padInfo:dt,outDepth:Et,outHeight:zt,outWidth:It}=di(i,p,h,C,ne,J,W,Be,Ie,nt),ht=o?k*u:k,Jt=[0,0,0,0,0];return a==="channelsFirst"?Jt=[c,ht,Et,zt,It]:a==="channelsLast"&&(Jt=[c,Et,zt,It,ht]),{batchSize:c,dataFormat:a,inDepth:p,inHeight:h,inWidth:C,inChannels:u,outDepth:Et,outHeight:zt,outWidth:It,outChannels:ht,padInfo:dt,strideDepth:ne,strideHeight:J,strideWidth:W,filterDepth:B,filterHeight:R,filterWidth:z,effectiveFilterDepth:Be,effectiveFilterHeight:Ie,effectiveFilterWidth:nt,dilationDepth:ue,dilationHeight:he,dilationWidth:be,inShape:e,outShape:Jt,filterShape:t}},uu=(e,t,s,n,i,o)=>{let a=o==="channelsLast";a?e[0].dims[3]:e[0].dims[1];let c=[64,1,1],p={x:s.map((ne,J)=>J)},h=[Math.ceil(Do(p.x.map(ne=>s[ne]))/c[0]),1,1];as("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${h}`);let C=1,u=De.size(s),k=[{type:12,data:u},{type:12,data:n},{type:12,data:i},{type:12,data:t.strides},{type:12,data:t.dilations}];Zr(t,k),k.push(...Mt(e[0].dims,e[1].dims));let B=["rank","rank"],R=e.length===3;R&&(k.push(...Mt(e[2].dims)),B.push("rank")),k.push(...Mt(s));let z=ne=>{let J=[{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}];Ur(t,J);let W=1,ue=ds(e[0].dataType),he=He("x",e[0].dataType,e[0].dims.length,C),be=He("W",e[1].dataType,e[1].dims.length,W),Be=[he,be],Ie=Ct("result",e[0].dataType,s.length,W),nt="";if(R){let zt=He("bias",e[2].dataType,e[2].dims.length,W);Be.push(zt),nt+=` + fn getBiasByOutputCoords(coords : array) -> ${ue} { + return bias[${a?Tt("coords",4,5):Tt("coords",1,5)}]; + }`}let dt=qs(C,ue),Et=Vr(t,dt,ue);return` + ${nt} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${he.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${be.getByIndices("aIndices")}; + } + ${ne.registerUniforms(J).declareVariables(...Be,Ie)} + ${ne.mainStart()} + ${ne.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${Ie.offsetToIndices("global_idx")}; + let batch = ${Tt("coords",0,he.rank)}; + let d2 = ${a?Tt("coords",he.rank-1,he.rank):Tt("coords",1,he.rank)}; + let xFRCCorner = vec3(${a?Tt("coords",1,he.rank):Tt("coords",2,he.rank)}, + ${a?Tt("coords",2,he.rank):Tt("coords",3,he.rank)}, + ${a?Tt("coords",3,he.rank):Tt("coords",4,he.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${a?Tt("uniforms.x_shape",1,he.rank):Tt("uniforms.x_shape",2,he.rank)}; + let xShapeZ = ${a?Tt("uniforms.x_shape",2,he.rank):Tt("uniforms.x_shape",3,he.rank)}; + let xShapeW = ${a?Tt("uniforms.x_shape",3,he.rank):Tt("uniforms.x_shape",4,he.rank)}; + let xShapeU = ${a?Tt("uniforms.x_shape",4,he.rank):Tt("uniforms.x_shape",1,he.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) { + ${a?`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) { + ${a?`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) { + ${a?`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) { + ${a?`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); + } + } + } + } + ${R?"value = value + getBiasByOutputCoords(coords)":""}; + ${Et} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${a};${C};${R}`,inputDependencies:B},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:h[0],y:h[1],z:h[2]},programUniforms:k}),getShaderSource:z}}}),du,cu,pu=y(()=>{Ot(),$t(),qt(),en(),du=(e,t,s,n)=>{let i=e.length>2,o=i?"value += b[output_channel];":"",a=e[0].dims,c=e[1].dims,p=t.format==="NHWC",h=p?s[3]:s[1],C=h/t.group,u=p&&C>=4?Gt(h):1,k=De.size(s)/u,B=[{type:12,data:k},{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:C}];Zr(t,B),B.push(...Mt(a,[c[0],c[1],c[2],c[3]/u]));let R=i?["rank","rank","rank"]:["rank","rank"];B.push(...Mt([s[0],s[1],s[2],s[3]/u]));let z=ne=>{let J=Ct("output",e[0].dataType,s.length,u),W=ds(J.type.tensor),ue=Vr(t,J.type.value,W),he=He("x",e[0].dataType,a.length),be=He("w",e[1].dataType,c.length,u),Be=[he,be];i&&Be.push(He("b",e[2].dataType,e[2].dims,u));let Ie=[{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"}];Ur(t,Ie);let nt=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 = ${he.get("batch","xHeight","xWidth","input_channel")}; + let wVal = ${be.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 = ${he.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${be.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal * wVal; + } + } + } + `;return` + ${ne.registerUniforms(Ie).declareVariables(...Be,J)} + + ${ne.mainStart()} + ${ne.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${J.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${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 * ${u} / uniforms.output_channels_per_group; + var in_channel_offset = group_id * uniforms.w_shape[${p?2:1}]; + + var value: ${J.type.value} = ${J.type.value}(0); + ${nt} + ${o} + ${ue} + ${J.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${u}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:n?n(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:B}),getShaderSource:z}},cu=(e,t,s,n)=>{let i=e.length>2,o=Gt(s[3]),a=Gt(s[2]),c=De.size(s)/o/a,p=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/o],h=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/o],C=[s[0],s[1],s[2],s[3]/o],u=[{type:12,data:c},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];Zr(t,u),u.push(...Mt(p,h,C));let k=(a-1)*t.strides[1]+h[1],B=R=>{let z=Ct("output",e[0].dataType,C.length,o),ne=ds(z.type.tensor),J=Vr(t,z.type.value,ne),W=He("x",e[0].dataType,p.length,o),ue=He("w",e[1].dataType,h.length,o),he=[W,ue];i&&he.push(He("b",e[2].dataType,e[2].dims,o));let be=i?"value += b[output_channel];":"",Be=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Ur(t,Be),` + ${R.registerUniforms(Be).declareVariables(...he,z)} + ${R.mainStart()} + ${R.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] / ${a}u; + let col = (index1 % width1) * ${a}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${W.type.value}, ${k}>; + var values: array<${z.type.value}, ${a}>; + 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 < ${k}; i++) { + let x_width = x_corner.y + i; + if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { + x_vals[i] = ${W.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${W.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${h[1]}; w_width++) { + let w_val = ${ue.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${a}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${a}u; i++) { + var value = values[i]; + ${be} + ${J} + ${z.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${o};${a};${k};${h[0]};${h[1]}`,inputDependencies:i?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(s):s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:u}),getShaderSource:B}}}),hu,ci,Bo,pi,Ro,No,mu,jo,Vo,Mc=y(()=>{$t(),wc(),yc(),Fo(),pu(),en(),ko(),jr(),hu=(e,t,s,n,i,o)=>{let a=e[0],c=e.slice(o?1:2,o?3:4),p=c.length,h=t[0],C=t.slice(2).map((k,B)=>k+(k-1)*(s[B]-1)),u=c.map((k,B)=>k+n[B]+n[B+p]).map((k,B)=>Math.floor((k-C[B]+i[B])/i[B]));return u.splice(0,0,a),u.splice(o?3:1,0,h),u},ci=[2,3,1,0],Bo=(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 ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},pi=(e,t)=>{let s=e.kernelShape.slice();s.length{let t=To(e),s=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,o=e.group,a=e.kernel_shape,c=e.pads,p=e.strides,h=e.w_is_const();return{autoPad:n,format:s,dilations:i,group:o,kernelShape:a,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},No=(e,t,s,n)=>{let i=s.format==="NHWC",o=hu(t[0].dims,t[1].dims,s.dilations,s.pads,s.strides,i);if(s.group!==1){let Be=[t[0]];if(i){let Ie=e.kernelCustomData.wT??e.compute(cr(t[1],ci),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ie),Be.push(Ie)}else Be.push(t[1]);t.length===3&&Be.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(cu(Be,s,o,n),{inputs:Be}):e.compute(du(Be,s,o,n),{inputs:Be});return}let a=t.length===3,c=t[0].dims[i?1:2],p=t[0].dims[i?2:3],h=t[0].dims[i?3:1],C=t[1].dims[2],u=t[1].dims[3],k=o[i?1:2],B=o[i?2:3],R=o[i?3:1],z=i&&C===c&&u===p&&s.pads[0]===0&&s.pads[1]===0;if(z||C===1&&u===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 Be=o[0],Ie,nt,dt,Et=[];if(i){let ht=e.kernelCustomData.wT??e.compute(cr(t[1],ci),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];if(s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=ht),z){let Jt=c*p*h;Ie=t[0].reshape([1,Be,Jt]),nt=ht.reshape([1,Jt,R]),dt=[1,Be,R]}else Ie=t[0].reshape([Be,c*p,h]),nt=ht.reshape([1,h,R]),dt=[Be,k*B,R];Et.push(Ie),Et.push(nt)}else Ie=t[0].reshape([Be,h,c*p]),nt=t[1].reshape([1,R,h]),dt=[Be,R,k*B],Et.push(nt),Et.push(Ie);a&&Et.push(t[2]);let zt=dt[2],It=Et[0].dims[Et[0].dims.length-1];zt<8&&It<8?e.compute(Co(Et,s,o,dt,i,n),{inputs:Et}):e.compute(ui(Et,s,o,dt,i,n),{inputs:Et});return}let ne=!0,J=e.kernelCustomData.wT??e.compute(cr(t[1],ci),{inputs:[1],outputs:[s.wIsConst?-2:-1]})[0];s.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=J);let W=[t[0],J];a&&W.push(t[2]);let ue=i?k*B:R,he=i?R:k*B,be=C*u*h;e.compute(ou(W,s,o,ue,he,be,a,ne,n),{inputs:W})},mu=(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]],o=[1].concat(t.strides),a=[1].concat(t.dilations),c=[1].concat(t.kernelShape),p=pi({...t,pads:i,strides:o,dilations:a,kernelShape:c},n);No(e,n,p,h=>s?[h[0],h[2],h[3]]:[h[0],h[1],h[3]])},jo=(e,t,s)=>{let n=s.format==="NHWC"?"channelsLast":"channelsFirst",i=pi(s,t),o=s.autoPad==="NOTSET"?s.pads:s.autoPad,a=lu(t[0].dims,t[1].dims,s.strides,s.dilations,o,!1,n);e.compute(uu(t,i,a.outShape,[a.filterDepth,a.filterHeight,a.filterWidth],[a.padInfo.front,a.padInfo.top,a.padInfo.left],n))},Vo=(e,t)=>{if(Bo(e.inputs,t),e.inputs[0].dims.length===3)mu(e,t);else if(e.inputs[0].dims.length===5)jo(e,e.inputs,t);else{let s=pi(t,e.inputs);No(e,e.inputs,s)}}}),Uo,bc=y(()=>{Ot(),Te(),$t(),qt(),Uo=(e,t,s)=>{let n=e.length>2,i=t.outputShape,o=t.format==="NHWC",a=t.group,c=e[1].dims,p=c[2]/a,h=c[3],C=o?Gt(p):1,u=o?Gt(h):1,k=o?h===1?C:u:1,B=De.size(i)/u,R=[Math.ceil(B/64),1,1];as("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${R}`);let z=["rank","rank"],ne=[t.strides[0],t.strides[1]],J=[t.kernelShape[o?1:2],t.kernelShape[o?2:3]],W=[t.dilations[0],t.dilations[1]],ue=[J[0]+(t.dilations[0]<=1?0:(t.kernelShape[o?1:2]-1)*(t.dilations[0]-1)),J[1]+(t.dilations[1]<=1?0:(t.kernelShape[o?2:3]-1)*(t.dilations[1]-1))],he=[ue[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),ue[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],be=[{type:12,data:B},{type:12,data:ne},{type:12,data:J},{type:12,data:W},{type:12,data:ue},{type:6,data:he},{type:12,data:p},{type:12,data:h},...Mt(e[0].dims,e[1].dims)];n&&(be.push(...Mt(e[2].dims)),z.push("rank")),be.push(...Mt(i));let Be=Ie=>{let nt=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:ne.length},{name:"filter_dims",type:"u32",length:J.length},{name:"dilations",type:"u32",length:J.length},{name:"effective_filter_dims",type:"u32",length:ue.length},{name:"pads",type:"i32",length:he.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],dt=ds(e[0].dataType),Et=o?1:2,zt=o?2:3,It=o?3:1,ht=He("W",e[1].dataType,e[1].dims.length,k),Jt=He("Dy",e[0].dataType,e[0].dims.length,C),Vt=[Jt,ht];n&&Vt.push(He("bias",e[2].dataType,[i[It]].length,u));let St=Ct("result",e[0].dataType,i.length,u),ts=()=>{let Ut="";if(C===1)Ut+=` + let w_offset = ${ht.indicesToOffset(`${ht.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)}; + let wValue = ${ht.getByOffset(`w_offset / ${k}`)}; + dotProd = dotProd + xValue * wValue;`;else if(h===1)Ut+=` + let wValue = ${ht.getByOffset(`${ht.indicesToOffset(`${ht.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)} / ${k}`)}; + 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 = ${St.type.value}(0.0); + for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${dt}(dyRCorner) + ${dt}(wR)) / ${dt}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${dt}(uniforms.Dy_shape[${Et}]) || 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 = (${dt}(dyCCorner) + ${dt}(wC)) / ${dt}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${dt}(uniforms.Dy_shape[${zt}]) || + 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 + ${C}) { + let xValue = ${o?Jt.getByOffset(`${Jt.indicesToOffset(`${Jt.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${C}`):Jt.get("batch","inputChannel","idyR","idyC")}; + ${ts()} + inputChannel = inputChannel + ${C}; + } + } + } + let value = dotProd${n?` + bias[d1 / ${u}]`:""}; + ${St.setByOffset("global_idx","value")}; + `;return` + ${Ie.registerUniforms(nt).declareVariables(...Vt,St)} + ${Ie.mainStart()} + ${Ie.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${Xt}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};${C}${k}${u}${h===1}`,inputDependencies:z},getRunData:()=>({dispatchGroup:{x:R[0],y:R[1],z:R[2]},outputs:[{dims:s?s(i):i,dataType:e[0].dataType}],programUniforms:be}),getShaderSource:Be}}}),fu,Wo,_u,Go,Ko,gu,Ho,wu,yu,Mu=y(()=>{bc(),en(),jr(),fu=(e,t,s,n,i,o)=>(e-1)*t+s+(n-1)*i+1-o,Wo=(e,t,s,n,i)=>{let o=Math.floor(e/2);t==="SAME_UPPER"?(s[n]=o,s[i]=e-o):t==="SAME_LOWER"&&(s[n]=e-o,s[i]=o)},_u=(e,t,s,n,i,o,a,c,p,h)=>{let C=e.length-2,u=h.length===0;p.length{let s=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((u,k)=>u*k,1)===0){s.length=0;for(let u=2;uu+k,0)===0){let u=t[0].dims.length-2;p=new Array(u).fill(1)}let h=e.strides.slice();if(h.reduce((u,k)=>u+k,0)===0){let u=t[0].dims.length-2;h=new Array(u).fill(1)}_u(c,s,p,e.autoPad,e.group,i,h,n,a,o);let C=Object.assign({},e);return Object.assign(C,{kernelShape:s,pads:i,outputPadding:a,outputShape:o,dilations:p,strides:h}),C},Ko=e=>{let t=To(e),s=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,o=e.group,a=e.kernelShape,c=e.pads,p=e.strides,h=e.wIsConst(),C=e.outputPadding,u=e.outputShape;return{autoPad:n,format:s,dilations:i,group:o,kernelShape:a,outputPadding:C,outputShape:u,pads:c,strides:p,wIsConst:h,...t,cacheKey:`${e.format};${t.activation};`}},gu=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let s=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[0];if(s!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let 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i=De.size(t),o=t.length,a=He("input",e,o),c=Ct("output",e,o),p=s.dataType===6?s.getInt32Array()[0]:Number(s.getBigInt64Array()[0]),h=De.normalizeAxis(p,o),C=u=>{let k=` i32(${a.indicesGet("inputIndices","uniforms.axis")}) `,B=Tt("uniforms.input_shape","uniforms.axis",o),R=n.reverse?k+(n.exclusive?" + 1":""):"0",z=n.reverse?B:k+(n.exclusive?"":" + 1");return` + ${u.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(a,c)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${c.offsetToIndices("global_idx")}; + var sum = ${c.type.value}(0); + let first : i32 = ${R}; + let last : i32 = ${z}; + for (var i : i32 = first; i < last; i++) { + ${a.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${a.getByIndices("inputIndices")}; + } + ${c.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},{type:12,data:h},...Mt(t,t)]}),getShaderSource:C}},vu=(e,t)=>{let s=e.inputs[0].dims,n=e.inputs[0].dataType,i=e.inputs[1];e.compute(bu(n,s,i,t),{inputs:[0]})},xu=e=>{let t=e.exclusive===1,s=e.reverse===1;return jt({exclusive:t,reverse:s})}}),Tu,Pu,qo,Eu,Cu,ku=y(()=>{Ot(),$t(),is(),qt(),Tu=e=>{if(!e||e.length!==1)throw new Error("DepthToSpace requires 1 input.");if(e[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},Pu=(e,t,s,n)=>{let i=[];i.push(`fn perm(i: ${n.type.indices}) -> ${s.type.indices} { + var a: ${s.type.indices};`);for(let o=0;o{let 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p?.forEach((C,u)=>{if(C==="..."){if(o)throw new Error("Only one ellipsis is allowed per input term");o=!0;let k=i-p.length+1;if(k<0)throw new Error("Ellipsis out of bounds");if(a=s.slice(c,c+k),this.hasEllipsis){if(this.ellipsisDims.length!==a.length||this.ellipsisDims.toString()!==a.toString())throw new Error("Ellipsis dimensions mismatch")}else if(t)this.hasEllipsis=!0,this.ellipsisDims=a;else throw new Error("Ellipsis must be specified in the LHS");for(let B=0;Be+"_max",Fu=(e,t,s,n)=>{let i=e.map(h=>h.length).map((h,C)=>He(`input${C}`,t,h)),o=De.size(n),a=Ct("output",t,n.length),c=[...s.symbolToInfo.keys()].filter(h=>!s.rhs.symbolToIndices.has(h)),p=h=>{let C=[],u="var prod = 1.0;",k="var sum = 0.0;",B="sum += prod;",R=[],z=[],ne=[],J=[],W=s.symbolToInfo.size===s.rhs.symbolToIndices.size;s.symbolToInfo.forEach((he,be)=>{if(s.rhs.symbolToIndices.has(be)){let Be=s.rhs.symbolToIndices.get(be)?.[0];Be!==void 0&&s.lhs.forEach((Ie,nt)=>{if(he.inputIndices.includes(nt)){let dt=Ie.symbolToIndices.get(be);if(dt===void 0)throw new Error("Invalid symbol error");dt.forEach(Et=>{C.push(`${i[nt].indicesSet(`input${nt}Indices`,Et,a.indicesGet("outputIndices",Be))}`)})}})}else s.lhs.forEach((Be,Ie)=>{if(he.inputIndices.includes(Ie)){let nt=Be.symbolToIndices.get(be);if(nt===void 0)throw new Error("Invalid symbol error");nt.forEach(dt=>{R.push(`${i[Ie].indicesSet(`input${Ie}Indices`,dt,`${be}`)}`)}),J.push(`prod *= ${i[Ie].getByIndices(`input${Ie}Indices`)};`)}}),z.push(`for(var ${be}: u32 = 0; ${be} < uniforms.${mi(be)}; ${be}++) {`),ne.push("}")});let ue=W?[...C,`let sum = ${i.map((he,be)=>he.getByIndices(`input${be}Indices`)).join(" * ")};`]:[...C,k,...z,...R,u,...J,B,...ne];return` + ${h.registerUniforms(c.map(he=>({name:`${mi(he)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...i,a)} + + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${a.offsetToIndices("global_idx")}; + ${i.map((he,be)=>`var input${be}Indices: ${i[be].type.indices};`).join(` +`)} + ${ue.join(` +`)}; + ${a.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:s.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let h=c.filter(u=>s.symbolToInfo.has(u)).map(u=>({type:12,data:s.symbolToInfo.get(u)?.dimValue||0}));h.push({type:12,data:o});let C=e.map((u,k)=>[...Mt(u)]).reduce((u,k)=>u.concat(k),h);return C.push(...Mt(n)),{outputs:[{dims:n,dataType:t}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:C}},getShaderSource:p}},Ou=(e,t)=>{let s=new Iu(e.inputs,t.equation),n=s.outputDims,i=e.inputs.map((o,a)=>o.dims);e.compute(Fu(i,e.inputs[0].dataType,s,n))},Du=e=>{let t=e.equation.replace(/\s+/g,"");return jt({equation:t})}}),fi,Qo,Lu,zu,jn,Tc=y(()=>{Ot(),$t(),qt(),fi=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let t=e[0].dims,s=Array.from(e[1].getBigInt64Array(),Number),n=s.length{let s=e.length-t.length,n=[];for(let i=0;ie.length>t.length?Qo(e,t):Qo(t,e),zu=e=>{let t=e[0].dims,s=Array.from(e[1].getBigInt64Array(),Number),n=Lu(t,s),i=e[0].dataType,o=i===9||De.size(t)===1,a=i===9||t.length>0&&t[t.length-1]%4===0?4:1,c=o||n.length>0&&n[n.length-1]%4===0?4:1,p=Math.ceil(De.size(n)/c),h=u=>{let k=He("input",i,t.length,a),B=Ct("output",i,n.length,c),R;if(i===9){let z=(ne,J,W="")=>` + let outputIndices${J} = ${B.offsetToIndices(`outputOffset + ${J}u`)}; + let offset${J} = ${k.broadcastedIndicesToOffset(`outputIndices${J}`,B)}; + let index${J} = offset${J} / 4u; + let component${J} = offset${J} % 4u; + ${ne}[${J}] = ${W}(${k.getByOffset(`index${J}`)}[component${J}]); + `;R=` + let outputOffset = global_idx * ${c}; + var data = vec4(0); + ${z("data",0,"u32")} + ${z("data",1,"u32")} + ${z("data",2,"u32")} + ${z("data",3,"u32")} + ${B.setByOffset("global_idx","data")} + }`}else R=` + let outputIndices = ${B.offsetToIndices(`global_idx * ${c}`)}; + let inputOffset = ${k.broadcastedIndicesToOffset("outputIndices",B)}; + let data = ${B.type.value}(${k.getByOffset(`inputOffset / ${a}`)}); + ${B.setByOffset("global_idx","data")} + }`;return` + ${u.registerUniform("vec_size","u32").declareVariables(k,B)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${R}`},C=[{type:12,data:p},...Mt(t,n)];return{name:"Expand",shaderCache:{hint:`${n.length};${a}${c}`,inputDependencies:["rank"]},getShaderSource:h,getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:C})}},jn=e=>{fi(e.inputs),e.compute(zu(e.inputs),{inputs:[0]})}}),Bu,Ru,Pc=y(()=>{Ot(),$t(),qt(),wo(),Bu=e=>{let t=e[0].dataType,s=De.size(e[0].dims),n=De.size(e[1].dims),i=n%4===0,o=a=>{let c=He("x",t,[1],4),p=He("bias",t,[1],4),h=Ct("y",t,[1],4),C=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],u=B=>` + let bias${B}_offset: u32 = (global_idx * 4 + ${B}) % uniforms.bias_size; + let bias${B} = ${p.getByOffset(`bias${B}_offset / 4`)}[bias${B}_offset % 4];`,k=i?` + let bias = ${p.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${u(0)}${u(1)}${u(2)}${u(3)} + let bias = ${c.type.value}(bias0, bias1, bias2, bias3);`;return`${a.registerUniforms(C).declareVariables(c,p,h)} + + ${oi(Cs(t))} + + ${a.mainStart(sr)} + ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${c.getByOffset("global_idx")}; + ${k} + let x_in = x + bias; + ${h.setByOffset("global_idx",_o("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${i}`,inputDependencies:["type","type"]},getShaderSource:o,getRunData:a=>({outputs:[{dims:a[0].dims,dataType:a[0].dataType}],programUniforms:[{type:12,data:Math.ceil(s/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(s/sr/4)}})}},Ru=e=>{e.inputs.length<2||De.size(e.inputs[1].dims)===0?Ll(e):e.compute(Bu(e.inputs))}}),_i,Nu,ju,Vu,Bp=y(()=>{Ot(),$t(),is(),qt(),_i=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 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${a.length>1?`outputIndices${W}[${Be}]`:`outputIndices${W}`};`,Be++);return he},J;if(e[0].dataType===9){let W=(ue,he,be="")=>` + let outputIndices${he} = ${z.offsetToIndices(`outputOffset + ${he}u`)}; + ${ne(he)}; + let offset${he} = ${B.indicesToOffset(`dataIndices${he}`)}; + let index${he} = offset${he} / 4u; + let component${he} = offset${he} % 4u; + ${ue}[${he}] = ${be}(${B.getByOffset(`index${he}`)}[component${he}]); + `;J=` + let outputOffset = global_idx * ${p}; + var value = vec4(0); + ${W("value",0,"u32")} + ${W("value",1,"u32")} + ${W("value",2,"u32")} + ${W("value",3,"u32")} + ${z.setByOffset("global_idx","value")} + `}else J=` + let outputIndices = ${z.offsetToIndices("global_idx")}; + ${ne("")}; + let value = ${B.getByIndices("dataIndices")}; + ${z.setByOffset("global_idx","value")}; + `;return` + ${k.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(B,R,z)} + ${k.mainStart()} + ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${J} + }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:C}),getShaderSource:u}},ju=e=>jt({axis:e.axis}),Vu=(e,t)=>{let s=e.inputs;_i(s),e.compute(Nu(e.inputs,t))}}),Uu,Wu,Gu,Vn=y(()=>{Ot(),$t(),qt(),Uu=(e,t,s,n,i,o,a,c,p)=>{let h=[{type:12,data:o},{type:12,data:n},{type:12,data:i},{type:12,data:s},{type:12,data:a},{type:12,data:c},{type:12,data:p}],C=[o];h.push(...Mt(t.dims,C));let u=k=>{let B=He("indices_data",t.dataType,t.dims.length),R=Ct("input_slice_offsets_data",12,1,1),z=[B,R],ne=[{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` + ${k.registerUniforms(ne).declareVariables(...z)} + ${k.mainStart()} + ${k.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:C,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:h}),getShaderSource:u},{inputs:[t],outputs:[-1]})[0]},Wu=(e,t)=>{let s=e.inputs,n=s[0].dims,i=s[0].dataType,o=s[1].dims,a=o[o.length-1],c=De.sizeToDimension(o,o.length-1),p=De.sizeFromDimension(n,t.batchDims+a),h=De.sizeToDimension(n,t.batchDims),C=De.sizeFromDimension(n,t.batchDims),u=c/h,k=new Array(a),B=p;for(let he=0;hen.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let ne=o.slice(0,-1).concat(n.slice(z)),J=De.size(ne),W=[{type:12,data:J},{type:12,data:p},...Mt(s[0].dims,R.dims,ne)],ue=he=>{let be=He("data",s[0].dataType,s[0].dims.length),Be=He("slice_offsets",12,R.dims.length),Ie=Ct("output",s[0].dataType,ne.length);return` + ${he.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(be,Be,Ie)} + ${he.mainStart()} + ${he.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let slice_offset = slice_offsets[global_idx / uniforms.slice_size]; + output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size]; + }`};e.compute({name:"GatherND",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:ne,dataType:i}],dispatchGroup:{x:Math.ceil(J/64)},programUniforms:W}),getShaderSource:ue},{inputs:[s[0],R]})},Gu=e=>({batchDims:e.batch_dims,cacheKey:""})}),Ku,Hu,qu,Qu,Ec=y(()=>{Ot(),$t(),is(),qt(),Ku=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let s=De.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,i=e[0],o=e[2],a=e.length===4?e[3]:void 0;if(o.dims.length!==i.dims.length||!i.dims.map((c,p)=>p===s?Math.ceil(c/n)===o.dims[p]:c===o.dims[p]).reduce((c,p)=>c&&p,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(a){if(a.dataType!==i.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(a.dims.length!==o.dims.length||!a.dims.map((c,p)=>c===o.dims[p]).reduce((c,p)=>c&&p,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},Hu=(e,t)=>{let s=e[0].dims,n=e[1].dims,i=s.length,o=De.normalizeAxis(t.gatherAxis,i),a=De.normalizeAxis(t.quantizeAxis,i),c=s.slice(0);c.splice(o,1,...n);let p=De.size(c),h=e[2].dataType,C=e[0].dataType===22,u=[{type:12,data:p},{type:12,data:a},{type:12,data:o},{type:12,data:t.blockSize},...Mt(...e.map((B,R)=>B.dims),c)],k=B=>{let R=He("data",e[0].dataType,e[0].dims.length),z=He("inputIndices",e[1].dataType,e[1].dims.length),ne=He("scales",e[2].dataType,e[2].dims.length),J=e.length>3?He("zeroPoint",e[3].dataType,e[3].dims.length):void 0,W=Ct("output",h,c.length),ue=[R,z,ne];J&&ue.push(J);let he=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${B.registerUniforms(he).declareVariables(...ue,W)} + ${B.mainStart()} + let output_indices = ${W.offsetToIndices("global_idx")}; + var indices_indices = ${z.type.indices}(0); + ${n.length>1?` + for (var i: u32 = 0; i < ${n.length}; i++) { + let index = ${W.indicesGet("output_indices","uniforms.gather_axis + i")}; + ${z.indicesSet("indices_indices","i","index")}; + }`:`indices_indices = ${W.indicesGet("output_indices","uniforms.gather_axis")};`}; + var data_indices = ${R.type.indices}(0); + for (var i: u32 = 0; i < uniforms.gather_axis; i++) { + let index = ${W.indicesGet("output_indices","i")}; + ${R.indicesSet("data_indices","i","index")}; + } + var index_from_indices = ${z.getByIndices("indices_indices")}; + if (index_from_indices < 0) { + index_from_indices += ${s[o]}; + } + ${R.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")}; + for (var i = uniforms.gather_axis + 1; i < ${c.length}; i++) { + let index = ${W.indicesGet("output_indices",`i + ${n.length} - 1`)}; + ${R.indicesSet("data_indices","i","index")}; + } + let data_offset = ${R.indicesToOffset("data_indices")}; + let data_index = data_offset % 8; + // Convert 4-bit packed data to 8-bit packed data. + let packed_4bit_quantized_data = ${R.getByOffset("data_offset / 8")}; + let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f; + let quantized_data_vec = ${C?"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 = ${ne.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size; + ${ne.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")}; + var scale = ${ne.getByIndices("scale_indices")}; + ${J?` + let zero_point_indices = scale_indices; + let zero_point_offset = ${J.indicesToOffset("zero_point_indices")}; + let zero_point_index = zero_point_offset % 8; + let packed_4bit_zero_points = ${J.getByOffset("zero_point_offset / 8")}; + let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f; + let zero_point_vec = ${C?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points)); + let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"}; + let dequantized_data = ${Cs(h)}(quantized_data - zero_point) * scale; + ${W.setByOffset("global_idx","dequantized_data")}; + }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((B,R)=>R!==1).map(B=>B.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(B,R)=>"rank")},getRunData:()=>({outputs:[{dims:c,dataType:h}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:u}),getShaderSource:k}},qu=(e,t)=>{let s=e.inputs;Ku(s,t),e.compute(Hu(e.inputs,t))},Qu=e=>jt({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),Xu,Yu,gi,Cc,kc=y(()=>{Ot(),$t(),is(),qt(),Xu=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.`)},Yu=(e,t)=>{let s=e[0].dims,n=e[0].dataType,i=s.length,o=e[1].dims,a=e[1].dataType,c=De.normalizeAxis(t.axis,i),p=s[c],h=o.slice(0),C=De.size(h),u=He("input",n,i),k=He("indicesInput",a,o.length),B=Ct("output",n,h.length),R=[{type:12,data:C},{type:6,data:p},{type:12,data:c}];return R.push(...Mt(s,o,h)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:h,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:R}),getShaderSource:z=>` + ${z.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(u,k,B)} + ${z.mainStart()} + ${z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${B.offsetToIndices("global_idx")}; + + var idx = ${k.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${u.type.indices}(outputIndices); + ${u.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${u.getByIndices("inputIndices")}; + + ${B.setByOffset("global_idx","value")}; + }`}},gi=e=>jt({axis:e.axis}),Cc=(e,t)=>{let s=e.inputs;Xu(s),e.compute(Yu(e.inputs,t))}}),Ju,Zu,ed,td,sd=y(()=>{Ot(),$t(),qt(),Ju=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")},Zu=(e,t)=>{let s=e[0].dims.slice(),n=e[1].dims.slice(),[i,o,a]=kr.getShapeOfGemmResult(s,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),c=[i,o];if(!c)throw new Error("Can't use gemm on the given tensors");let p=16,h=Math.ceil(o/p),C=Math.ceil(i/p),u=!0,k=De.size(c),B=[{type:12,data:u?h:k},{type:12,data:i},{type:12,data:o},{type:12,data:a},{type:1,data:t.alpha},{type:1,data:t.beta}],R=["type","type"];e.length===3&&(B.push(...Mt(e[2].dims)),R.push("rank")),B.push(...Mt(c));let z=J=>{let W="";t.transA&&t.transB?W="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?W="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?W="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(W="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let ue=t.alpha===1?"":"value *= uniforms.alpha;",he=He("a",e[0].dataType,e[0].dims),be=He("b",e[1].dataType,e[1].dims),Be=he.type.value,Ie=null,nt=[he,be];e.length===3&&(Ie=He("c",e[2].dataType,e[2].dims.length),nt.push(Ie));let dt=Ct("output",e[0].dataType,c.length);nt.push(dt);let Et=[{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` + ${J.registerUniforms(Et).declareVariables(...nt)} + + ${J.mainStart()} + ${J.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${Be}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${W} + } + + ${ue} + ${Ie!=null?`let cOffset = ${Ie.broadcastedIndicesToOffset("vec2(m, n)",dt)}; value += ${Be}(uniforms.beta) * ${Ie.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`},ne=J=>{let W=He("a",e[0].dataType,e[0].dims),ue=He("b",e[1].dataType,e[1].dims),he=null,be=[W,ue];e.length===3&&(he=He("c",e[2].dataType,e[2].dims.length),be.push(he));let Be=Ct("output",e[0].dataType,c.length);be.push(Be);let Ie=[{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"}],nt="",dt="";t.transA&&t.transB?(dt=` + 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] = ${W.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] = ${ue.type.value}(0); + } + `,nt="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):t.transA&&!t.transB?(dt=` + 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] = ${W.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] = ${ue.type.value}(0); + } + `,nt="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!t.transA&&t.transB?(dt=` + 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] = ${W.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] = ${ue.type.value}(0); + } + `,nt="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!t.transA&&!t.transB&&(dt=` + 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] = ${W.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] = ${ue.type.value}(0); + } + `,nt="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let Et=t.alpha===1?"":"value *= uniforms.alpha;";return` + ${J.registerUniforms(Ie).declareVariables(...be)} + var tile_a: array, ${p}>; + var tile_b: array, ${p}>; + ${J.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 = ${Be.type.value}(0); + for (var t: u32 = 0u; t < num_tiles; t++) { + ${dt} + k_start = k_start + ${p}; + workgroupBarrier(); + + for (var k: u32 = 0u; k < ${p}; k++) { + ${nt} + } + workgroupBarrier(); + } + + ${Et} + let m = tile_row_start + local_id.y; + let n = tile_col_start + local_id.x; + ${he!=null?`let cOffset = ${he.broadcastedIndicesToOffset("vec2(m, n)",Be)}; value += ${Be.type.value}(uniforms.beta) * ${he.getByOffset("cOffset")};`:""} + if (m < uniforms.M && n < uniforms.N) { + output[m * uniforms.N + n] = value; + } + }`};return u?{name:"GemmShared",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:h*C},programUniforms:B}),getShaderSource:ne}:{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:B}),getShaderSource:z}},ed=e=>{let t=e.transA,s=e.transB,n=e.alpha,i=e.beta;return{transA:t,transB:s,alpha:n,beta:i,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},td=(e,t)=>{Ju(e.inputs),e.compute(Zu(e.inputs,t))}}),vr,$r,Wr,tn,rd,Xo,nd,id,wi,od,ad,ld,ud,Yo,Jo=y(()=>{Ot(),$t(),is(),qt(),[vr,$r,Wr,tn]=[0,1,2,3],rd=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 ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},Xo=` + fn gs_get_cubic_coeffs(x: f32) -> vec4 { + let cubic_alpha = -0.75f; + let x_abs = abs(x); + var coeffs: vec4; + coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha); + coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1); + coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1); + coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha); + return coeffs; + } +`,nd=e=>` + fn gs_bicubic_interpolate(p: mat4x4<${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; + } +`,id=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)); + `} + } +`,wi=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); + }`:""} +`,od=(e,t,s)=>` + fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4) -> ${t} { + var pixel = ${t}(0); + var indices = vec4(0); + indices[${vr}] = batch; + indices[${$r}] = channel;`+(()=>{switch(s.paddingMode){case"zeros":return` + if (r >= 0 && r < H && c >=0 && c < W) { + indices[${Wr}] = u32(r); + indices[${tn}] = u32(c); + } + `;case"border":return` + indices[${Wr}] = u32(clamp(r, 0, H - 1)); + indices[${tn}] = u32(clamp(c, 0, W - 1)); + `;case"reflection":return` + indices[${Wr}] = gs_reflect(r, border[1], border[3]); + indices[${tn}] = gs_reflect(c, border[0], border[2]); + `;default:throw new Error(`padding mode ${s.paddingMode} is not supported`)}})()+` + return ${e.getByIndices("indices")}; + } +`,ad=(e,t,s)=>(()=>{switch(s.mode){case"nearest":return` + let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${vr}], indices[${$r}], 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[${vr}], indices[${$r}], border); + let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${vr}], indices[${$r}], border); + let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${vr}], indices[${$r}], border); + let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${vr}], indices[${$r}], 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[${vr}], indices[${$r}], 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")}`,ld=(e,t)=>{let s=He("x",e[0].dataType,e[0].dims.length),n=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],i=He("grid",e[1].dataType,n.length,2),o=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];t.format==="NHWC"&&(o=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[vr,$r,Wr,tn]=[0,3,1,2]);let a=Ct("output",e[0].dataType,o.length),c=s.type.value,p=De.size(o),h=[{type:12,data:p},...Mt(e[0].dims,n,o)],C=u=>` + ${u.registerUniform("output_size","u32").declareVariables(s,i,a)} + ${Xo} + ${nd(c)} + ${id(t)} + ${wi(t)} + ${od(s,c,t)} + + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let H_in = i32(uniforms.x_shape[${Wr}]); + let W_in = i32(uniforms.x_shape[${tn}]); + + ${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 = ${a.offsetToIndices("global_idx")}; + var grid_indices = vec3(indices[${vr}], indices[${Wr}], indices[${tn}]); + let nxy = ${i.getByIndices("grid_indices")}; + var x = gs_denormalize(f32(nxy[0]), W_in); + var y = gs_denormalize(f32(nxy[1]), H_in); + + ${ad(a,c,t)} + }`;return{name:"GridSample",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:["type","type"]},getRunData:u=>{let k=De.size(o);return{outputs:[{dims:o,dataType:u[0].dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:h}},getShaderSource:C}},ud=(e,t)=>{rd(e.inputs),e.compute(ld(e.inputs,t))},Yo=e=>jt({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),rr,dd,cd,yi,pd,Un,Zo,hd=y(()=>{Ot(),$t(),is(),ce(),Zi(),qt(),jr(),rr=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,dd=(e,t)=>{let s=e[0],n=rr(e,1),i=rr(e,2),o=rr(e,3),a=rr(e,4),c=rr(e,5),p=rr(e,6),h=rr(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 C=s.dims[0],u=s.dims[1],k=s.dims.length===3?s.dims[2]:t.numHeads*s.dims[4],B=u,R=0,z=0,ne=Math.floor(k/t.numHeads);if(p&&h&&De.size(p.dims)&&De.size(h.dims)){if(p.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(p.dims[0]!==C||p.dims[1]!==t.numHeads||p.dims[3]!==ne)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(h.dims[0]!==C||h.dims[1]!==t.numHeads||h.dims[3]!==ne)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');R=p.dims[2],z=p.dims[2]}else if(p&&De.size(p.dims)||h&&De.size(h.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let J;if(n&&De.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)');J=2,B=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==ne)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.');J=5,B=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==ne)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');J=0,B=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');J=3}if(o&&De.size(o.dims)>0){if(o.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 W=R+B,ue=0;if(a&&De.size(a.dims)>0){ue=8;let Ie=a.dims;throw Ie.length===1?Ie[0]===C?ue=1:Ie[0]===3*C+2&&(ue=3):Ie.length===2&&Ie[0]===C&&Ie[1]===W&&(ue=5),ue===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let he=!1,be=k;if(i&&De.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(B!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');be=i.dims[2]}else{if(B!==i.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');be=i.dims[1]*i.dims[3],he=!0}}let Be=!1;if(a&&De.size(a.dims)>0)throw new Error("Key padding mask is not supported");if(c&&De.size(c.dims)>0){if(c.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(c.dims[0]!==C||c.dims[1]!==t.numHeads||c.dims[2]!==u||c.dims[3]!==W)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:C,sequenceLength:u,pastSequenceLength:R,kvSequenceLength:B,totalSequenceLength:W,maxSequenceLength:z,inputHiddenSize:0,hiddenSize:k,vHiddenSize:be,headSize:ne,vHeadSize:Math.floor(be/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:ue,scale:t.scale,broadcastResPosBias:Be,passPastInKv:he,qkvFormat:J}},cd=e=>jt({...e}),yi=jt({perm:[0,2,1,3]}),pd=(e,t,s,n,i,o,a)=>{let c=[n,i,o],p=De.size(c),h=[{type:12,data:p},{type:12,data:a},{type:12,data:o}],C=u=>{let k=Ct("qkv_with_bias",t.dataType,c),B=He("qkv",t.dataType,c),R=He("bias",s.dataType,c),z=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${u.registerUniforms(z).declareVariables(B,R,k)} + ${u.mainStart()} + ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:c,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:C},{inputs:[t,s],outputs:[-1]})[0]},Un=(e,t,s,n,i,o,a,c)=>{let p=o;if(a&&De.size(a.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return p=pd(e,o,a,t,n,s*i,c),p=p.reshape([t,n,s,i]),s===1||n===1?p:e.compute(cr(p,yi.perm),{inputs:[p],outputs:[-1]})[0]}else return o.dims.length===3&&(p=o.reshape([t,n,s,i])),s===1||n===1?p:e.compute(cr(p,yi.perm),{inputs:[p],outputs:[-1]})[0]},Zo=(e,t)=>{let s=dd(e.inputs,t),n=e.inputs[0],i=rr(e.inputs,1),o=rr(e.inputs,2),a=rr(e.inputs,3),c=rr(e.inputs,4),p=rr(e.inputs,5),h=rr(e.inputs,6),C=rr(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if(i?.dims.length===5)throw new Error("Packed KV is not implemented");let u=i&&o&&i.dims.length===4&&o.dims.length===4,k=Un(e,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,n,a,0);if(u)return zn(e,k,i,o,c,void 0,h,C,p,s);if(!i||!o)throw new Error("key and value must be provided");let B=Un(e,s.batchSize,s.numHeads,s.kvSequenceLength,s.headSize,i,a,s.hiddenSize),R=Un(e,s.batchSize,s.numHeads,s.kvSequenceLength,s.vHeadSize,o,a,2*s.hiddenSize);zn(e,k,B,R,c,void 0,h,C,p,s)}}),md,ea,Sc,$c,Mi,ta,fd,_d=y(()=>{Ot(),$t(),is(),qt(),md=e=>{if(!e||e.length<1)throw new Error("too few inputs")},ea=(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),jt({numOutputs:n,axis:t.axis,splitSizes:s})},Sc=e=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${e}u; i += 1u ) { + if (index < ${Tt("uniforms.size_in_split_axis","i",e)}) { + return i; + } + } + return ${e}u; +}`,$c=e=>{let t=e.length,s=[];for(let n=0;n{let s=e[0].dims,n=De.size(s),i=e[0].dataType,o=De.normalizeAxis(t.axis,s.length),a=new Array(t.numOutputs),c=He("input",i,s.length),p=new Array(t.numOutputs),h=[],C=[],u=0,k=[{type:12,data:n}];for(let R=0;R` + ${R.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",p.length).declareVariables(c,...a)} + ${Sc(p.length)} + ${$c(a)} + + ${R.mainStart()} + ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${c.offsetToIndices("global_idx")}; + var index = ${c.indicesGet("indices",o)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${Tt("uniforms.size_in_split_axis","output_number - 1u",p.length)}; + ${c.indicesSet("indices",o,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:B,getRunData:()=>({outputs:h,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:k})}},ta=(e,t)=>{md(e.inputs);let s=e.inputs.length===1?t:ea(e.inputs,t);e.compute(Mi(e.inputs,s),{inputs:[0]})},fd=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 jt({axis:t,numOutputs:n,splitSizes:s})}}),sa,gd,ra,na,Ac=y(()=>{is(),Zi(),hd(),_d(),jr(),sa=(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],o=e[3],a=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 c=!1,p=s.dims[0],h=s.dims[1],C=s.dims.length===3?c?s.dims[2]/3:s.dims[2]:t.numHeads*s.dims[4],u=h,k=0,B=!n||n.dims.length===0,R=Math.floor(B?C/(t.numHeads+2*t.kvNumHeads):C/t.numHeads);B&&(C=R*t.numHeads);let z=o&&o.dims.length!==0,ne=a&&a.dims.length!==0;if(z&&o.dims.length===4&&o.dims[0]===p&&o.dims[1]!==t.kvNumHeads&&o.dims[2]===t.kvNumHeads&&o.dims[3]===R)throw new Error("BSNH pastKey/pastValue is not supported");if(z&&ne){if(o.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(a.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');k=o.dims[2]}else if(z||ne)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let J=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"');u=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==R)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.');u=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==R)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');u=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');J=3}let W=0,ue=!1,he=t.kvNumHeads?R*t.kvNumHeads:C;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(u!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');he=i.dims[2]}else{if(u!==i.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');he=i.dims[1]*i.dims[3],ue=!0}}let be=e.length>4?e[5]:void 0;if(be&&be.dims.length!==1&&be.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:k,kvSequenceLength:u,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:C,vHiddenSize:he,headSize:R,vHeadSize:Math.floor(he/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:W,scale:t.scale,broadcastResPosBias:!1,passPastInKv:ue,qkvFormat:J}},gd=jt({perm:[0,2,1,3]}),ra=(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(cr(n,gd.perm),{inputs:[n],outputs:[-1]})[0]),n},na=(e,t)=>{let s=sa(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.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,o=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,a=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,c=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,C=s.kvNumHeads?s.kvNumHeads:s.numHeads,u=jt({axis:2,numOutputs:3,splitSizes:[s.numHeads*s.headSize,C*s.headSize,C*s.headSize]}),[k,B,R]=!i&&!o?e.compute(Mi([n],u),{inputs:[n],outputs:[-1,-1,-1]}):[n,i,o],z=Un(e,s.batchSize,s.numHeads,s.sequenceLength,s.headSize,k,void 0,0);zn(e,z,ra(e,B,s),ra(e,R,s),void 0,void 0,a,c,void 0,s,p,h)}}),ia,oa,wd,yd,Ic=y(()=>{Ot(),$t(),jr(),qt(),ia=(e,t,s,n,i,o,a,c)=>{let p=Gt(o),h=p===1?"f32":`vec${p}f`,C=p===1?"vec2f":`mat2x${p}f`,u=i*a,k=64;u===1&&(k=256);let B=[i,a,o/p],R=[i,a,2],z=["rank","type","type"],ne=[];ne.push(...Mt(B,R));let J=W=>{let ue=He("x",t.dataType,3,p),he=He("scale",s.dataType,s.dims),be=He("bias",n.dataType,n.dims),Be=Ct("output",1,3,2),Ie=[ue,he,be,Be];return` + var workgroup_shared : array<${C}, ${k}>; + const workgroup_size = ${k}u; + ${W.declareVariables(...Ie)} + ${W.mainStart(k)} + 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 = ${h}(${ue.get("batch","channel","h")}); + sum += value; + squared_sum += value * value; + } + workgroup_shared[local_idx] = ${C}(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 = ${js("workgroup_shared[0][0]",p)} / f32(hight * ${p}); + let squared_sum_final = ${js("workgroup_shared[0][1]",p)} / f32(hight * ${p}); + + let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${c})); + 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:`${p};${c};${k}`,inputDependencies:z},getRunData:()=>({outputs:[{dims:R,dataType:1}],dispatchGroup:{x:u},programUniforms:ne}),getShaderSource:J},{inputs:[t,s,n],outputs:[-1]})[0]},oa=(e,t,s)=>{let n=t[0].dims,i=n,o=2,a=n[0],c=n[1],p=De.sizeFromDimension(n,o),h=Gt(p),C=De.size(i)/h,u=ia(e,t[0],t[1],t[2],a,p,c,s.epsilon),k=[a,c,p/h],B=[a,c],R=["type","none"],z=ne=>{let J=He("x",t[0].dataType,k.length,h),W=He("scale_shift",1,B.length,2),ue=Ct("output",t[0].dataType,k.length,h),he=[J,W,ue];return` + ${ne.registerUniform("output_size","u32").declareVariables(...he)} + ${ne.mainStart()} + ${ne.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let outputIndices = ${ue.offsetToIndices("global_idx")}; + let batch = outputIndices[0]; + let channel = outputIndices[1]; + let scale_shift = ${W.getByIndices("vec2(batch, channel)")}; + let value = ${J.getByOffset("global_idx")} * ${ue.type.value}(scale_shift.x) + ${ue.type.value}(scale_shift.y); + ${ue.setByOffset("global_idx","value")}; + }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${h}`,inputDependencies:R},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:[{type:12,data:C},...Mt(k,B,k)]}),getShaderSource:z},{inputs:[t[0],u]})},wd=(e,t,s)=>{let n=t[0].dims,i=n,o=n[0],a=n[n.length-1],c=De.sizeFromDimension(n,1)/a,p=Gt(a),h=De.size(i)/p,C=[{type:12,data:c},{type:12,data:Math.floor(a/p)}],u=["type","type"],k=!1,B=[0,n.length-1];for(let J=0;Jn[B[W]])),z=ia(e,R,t[1],t[2],o,c,a,s.epsilon),ne=J=>{let W=ds(t[0].dataType),ue=p===1?"vec2f":`mat${p}x2f`,he=Ie=>{let nt=Ie===0?"x":"y",dt=p===1?"f32":`vec${p}f`;switch(p){case 1:return`${W}(${dt}(scale.${nt}))`;case 2:return`vec2<${W}>(${dt}(scale[0].${nt}, scale[1].${nt}))`;case 4:return`vec4<${W}>(${dt}(scale[0].${nt}, scale[1].${nt}, scale[2].${nt}, scale[3].${nt}))`;default:throw new Error(`Not supported compoents ${p}`)}},be=He("input",t[0].dataType,t[0].dims,p),Be=Ct("output",t[0].dataType,i,p);return` + @group(0) @binding(0) var input : array<${be.type.storage}>; + @group(0) @binding(1) var scale_input : array<${ue}>; + @group(0) @binding(2) var output : array<${Be.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${J.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], ${he(0)}, ${he(1)}); + }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${p}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:C}),getShaderSource:ne},{inputs:[t[0],z]})},yd=(e,t)=>{t.format==="NHWC"?wd(e,e.inputs,t):oa(e,e.inputs,t)}}),Md,bd,vd,Fc=y(()=>{Ot(),$t(),qt(),Md=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},bd=(e,t,s)=>{let n=t.simplified,i=e[0].dims,o=e[1],a=!n&&e[2],c=i,p=De.normalizeAxis(t.axis,i.length),h=De.sizeToDimension(i,p),C=De.sizeFromDimension(i,p),u=De.size(o.dims),k=a?De.size(a.dims):0;if(u!==C||a&&k!==C)throw new Error(`Size of X.shape()[axis:] == ${C}. + Size of scale and bias (if provided) must match this. + Got scale size of ${u} and bias size of ${k}`);let B=[];for(let be=0;be1,W=s>2,ue=be=>{let Be=ds(e[0].dataType),Ie=[He("x",e[0].dataType,e[0].dims,R),He("scale",o.dataType,o.dims,R)];a&&Ie.push(He("bias",a.dataType,a.dims,R)),Ie.push(Ct("output",e[0].dataType,c,R)),J&&Ie.push(Ct("mean_data_output",1,B)),W&&Ie.push(Ct("inv_std_output",1,B));let nt=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${be.registerUniforms(nt).declareVariables(...Ie)} + ${be.mainStart()} + ${be.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${Is("f32",R)}; + var mean_square_vector = ${Is("f32",R)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${ks(Be,R,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${js("mean_vector",R)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${js("mean_square_vector",R)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${ks(Be,R,"x[j + offset]")}; + let f32scale = ${ks(Be,R,"scale[j]")}; + output[j + offset] = ${Ie[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale + ${a?`+ ${ks(Be,R,"bias[j]")}`:""} + ); + } + + ${J?"mean_data_output[global_idx] = mean":""}; + ${W?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},he=[{dims:c,dataType:e[0].dataType}];return J&&he.push({dims:B,dataType:1}),W&&he.push({dims:B,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${R};${s};${n}`,inputDependencies:z},getRunData:()=>({outputs:he,dispatchGroup:{x:Math.ceil(h/64)},programUniforms:ne}),getShaderSource:ue}},vd=(e,t)=>{Md(e.inputs),e.compute(bd(e.inputs,t,e.outputCount))}}),xd,Td,Pd=y(()=>{$t(),ko(),Fo(),xd=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},Td=e=>{xd(e.inputs);let t=Js.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let s=t[t.length-1],n=e.inputs[0].dims[e.inputs[0].dims.length-1];if(s<8&&n<8)e.compute(Co(e.inputs,{activation:""},t));else{let i=t[t.length-2],o=De.size(e.inputs[0].dims.slice(0,-2)),a=De.size(e.inputs[1].dims.slice(0,-2));if(o!==1&&i===1&&a===1){let c=e.inputs[0].reshape([1,o,n]),p=e.inputs[1].reshape([1,n,s]),h=[1,o,s],C=[c,p];e.compute(ui(C,{activation:""},t,h),{inputs:C})}else e.compute(ui(e.inputs,{activation:""},t))}}}),Ed,Cd,aa,kd,Sd,fs=y(()=>{Ot(),$t(),is(),qt(),Ed=(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),o=t.blockSize/8*t.bits,a=e[1];if(!De.areEqual(a.dims,[t.n,i,o]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let c=e[2].dims;if(De.size(c)!==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(De.size(p)!==h)throw new Error("zeroPoints input size error.")}},Cd=(e,t)=>{let s=e[0].dims,n=s.length,i=s[n-2],o=t.k,a=t.n,c=s.slice(0,n-2),p=De.size(c),h=e[1].dims[2]/4,C=e[0].dataType,u=Gt(t.k),k=Gt(h),B=Gt(a),R=c.concat([i,a]),z=i>1&&a/B%2===0?2:1,ne=De.size(R)/B/z,J=64,W=[],ue=[p,i,o/u],he=De.convertShape(e[1].dims).slice();he.splice(-1,1,h/k),W.push(...Mt(ue)),W.push(...Mt(he)),W.push(...Mt(e[2].dims)),e.length===4&&W.push(...Mt(De.convertShape(e[3].dims)));let be=[p,i,a/B];W.push(...Mt(be));let Be=Ie=>{let nt=ue.length,dt=He("a",e[0].dataType,nt,u),Et=He("b",12,he.length,k),zt=He("scales",e[2].dataType,e[2].dims.length),It=[dt,Et,zt],ht=e.length===4?He("zero_points",12,e[3].dims.length):void 0;ht&&It.push(ht);let Jt=be.length,Vt=Ct("output",e[0].dataType,Jt,B),St=ds(e[0].dataType),ts=(()=>{switch(u){case 1:return`array<${St}, 8>`;case 2:return`mat4x2<${St}>`;case 4:return`mat2x4<${St}>`;default:throw new Error(`${u}-component is not supported.`)}})(),Xt=()=>{let ut=` + // reuse a data + var input_offset = ${dt.indicesToOffset(`${dt.type.indices}(batch, row, word_offset)`)}; + var a_data: ${ts}; + for (var j: u32 = 0; j < ${8/u}; j++) { + a_data[j] = ${dt.getByOffset("input_offset")}; + input_offset++; + } + `;for(let xt=0;xt> 4) & b_mask); + b_quantized_values = ${ts}(${Array.from({length:4},(hs,vs)=>`${St}(b_value_lower[${vs}]), ${St}(b_value_upper[${vs}])`).join(", ")}); + b_dequantized_values = ${u===1?`${ts}(${Array.from({length:8},(hs,vs)=>`(b_quantized_values[${vs}] - ${ht?`zero_point${xt}`:"zero_point"}) * scale${xt}`).join(", ")});`:`(b_quantized_values - ${ts}(${Array(8).fill(`${ht?`zero_point${xt}`:"zero_point"}`).join(",")})) * scale${xt};`}; + workgroup_shared[local_id.x * ${z} + ${Math.floor(xt/B)}]${B>1?`[${xt%B}]`:""} += ${Array.from({length:8/u},(hs,vs)=>`${u===1?`a_data[${vs}] * b_dequantized_values[${vs}]`:`dot(a_data[${vs}], b_dequantized_values[${vs}])`}`).join(" + ")}; + `;return ut},Ut=()=>{let ut=` + var col_index = col * ${B}; + ${ht?` + 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 = ${St}(8);`} + `;for(let xt=0;xt> 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 = ${ht.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point${xt} = ${St}((zero_point_word) & 0xFu);`:""} + col_index += 1;`;return ut},$s=()=>{let ut=`col_index = col * ${B};`;for(let xt=0;xt; + var b_value_upper: vec4; + var b_quantized_values: ${ts}; + var b_dequantized_values: ${ts};`,ut};return` + var workgroup_shared: array<${Vt.type.value}, ${z*J}>; + ${Ie.declareVariables(...It,Vt)} + ${Ie.mainStart([J,1,1])} + let output_indices = ${Vt.offsetToIndices(`(global_idx / ${J}) * ${z}`)}; + 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 += ${J}) { + //process one block + var word_offset: u32 = block * ${t.blockSize/u}; + ${Ut()} + for (var word: u32 = 0; word < ${h}; word += ${k}) { + ${$s()} + for (var i: u32 = 0; i < ${k}; i++) { + ${Xt()} + word_offset += ${8/u}; + } + } + } + workgroupBarrier(); + + if (local_id.x < ${z}) { + var output_value: ${Vt.type.value} = ${Vt.type.value}(0); + var workgroup_shared_offset: u32 = local_id.x; + for (var b: u32 = 0u; b < ${J}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${z}; + } + ${Vt.setByIndices(`${Vt.type.indices}(batch, row, col + local_id.x)`,"output_value")}; + } + }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${u};${k};${B};${z};${J}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:R,dataType:C}],dispatchGroup:{x:ne},programUniforms:W}),getShaderSource:Be}},aa=(e,t)=>{let s=e[0].dims,n=s.length,i=s[n-2],o=t.k,a=t.n,c=s.slice(0,n-2),p=De.size(c),h=e[1].dims[2]/4,C=e[0].dataType,u=Gt(t.k),k=Gt(h),B=c.concat([i,a]),R=128,z=a%8===0?8:a%4===0?4:1,ne=R/z,J=ne*k*8,W=J/u,ue=J/t.blockSize,he=De.size(B)/z,be=[],Be=[p,i,o/u],Ie=De.convertShape(e[1].dims).slice();Ie.splice(-1,1,h/k),be.push(...Mt(Be)),be.push(...Mt(Ie)),be.push(...Mt(e[2].dims)),e.length===4&&be.push(...Mt(De.convertShape(e[3].dims)));let nt=[p,i,a];be.push(...Mt(nt));let dt=Et=>{let zt=Be.length,It=He("a",e[0].dataType,zt,u),ht=He("b",12,Ie.length,k),Jt=He("scales",e[2].dataType,e[2].dims.length),Vt=[It,ht,Jt],St=e.length===4?He("zero_points",12,e[3].dims.length):void 0;St&&Vt.push(St);let ts=nt.length,Xt=Ct("output",e[0].dataType,ts),Ut=ds(e[0].dataType),$s=()=>{switch(u){case 1:return` + let a_data0 = vec4<${Ut}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]); + let a_data1 = vec4<${Ut}>(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<${Ut}>(sub_a[word_offset], sub_a[word_offset + 1]); + let a_data1 = vec4<${Ut}>(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(`${u}-component is not supported.`)}};return` + var sub_a: array<${It.type.value}, ${W}>; + var inter_results: array, ${z}>; + ${Et.declareVariables(...Vt,Xt)} + ${Et.mainStart([ne,z,1])} + let output_indices = ${Xt.offsetToIndices(`workgroup_index * ${z}`)}; + 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) / ${ue} + 1; + + // Loop over shared dimension. + for (var tile: u32 = 0; tile < num_tiles; tile += 1) { + let a_col_start = tile * ${W}; + // load one tile A data into shared memory. + for (var a_offset = local_idx; a_offset < ${W}; a_offset += ${R}) + { + let a_col = a_col_start + a_offset; + if (a_col < uniforms.a_shape[2]) + { + sub_a[a_offset] = ${It.getByIndices(`${It.type.indices}(batch, row, a_col)`)}; + } else { + sub_a[a_offset] = ${It.type.value}(0); + } + } + workgroupBarrier(); + + // each thread process one block + let b_row = col + local_id.y; + let block = tile * ${ue} + local_id.x; + ${St?` + 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 = ${St.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point = ${Ut}((zero_point_word) & 0xFu);`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${Ut}(8);`} + let scale = ${Jt.getByOffset("b_row * n_blocks_per_col + block")}; + let b_data = ${ht.getByIndices(`${ht.type.indices}(b_row, block, 0)`)}; + var word_offset = local_id.x * ${t.blockSize/u}; + for (var i: u32 = 0; i < ${k}; i++) { + ${$s()} + let b_value = ${k===1?"b_data":"b_data[i]"}; + let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu); + let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu); + let b_quantized_values = mat2x4<${Ut}>(${Array.from({length:4},(ut,xt)=>`${Ut}(b_value_lower[${xt}]), ${Ut}(b_value_upper[${xt}])`).join(", ")}); + let b_dequantized_values = (b_quantized_values - mat2x4<${Ut}>(${Array(8).fill("zero_point").join(",")})) * scale; + inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(ut,xt)=>`${`dot(a_data${xt}, b_dequantized_values[${xt}])`}`).join(" + ")}; + word_offset += ${8/u}; + } + workgroupBarrier(); + } + + if (local_idx < ${z}) { + var output_value: ${Xt.type.value} = ${Xt.type.value}(0); + for (var b = 0u; b < ${ne}; b++) { + output_value += inter_results[local_idx][b]; + } + if (col + local_idx < uniforms.output_shape[2]) + { + ${Xt.setByIndices(`${Xt.type.indices}(batch, row, col + local_idx)`,"output_value")} + } + } + }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${t.blockSize};${u};${k};${ne};${z}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:B,dataType:C}],dispatchGroup:{x:he},programUniforms:be}),getShaderSource:dt}},kd=(e,t)=>{Ed(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(aa(e.inputs,t)):e.compute(Cd(e.inputs,t))},Sd=e=>jt(e)}),Oc,Dc,Lc,la,$d,Ad,Id,Fd,ua,Od=y(()=>{Ot(),$t(),qt(),Oc=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Dc=(e,t,s)=>{let n="";for(let i=t-1;i>=0;--i)n+=` + k = i32(${e.indicesGet("indices",i)}) - ${Tt("uniforms.pads",i,s)}; + if (k < 0) { + break; + } + if (k >= i32(${Tt("uniforms.x_shape",i,t)})) { + break; + } + offset += k * i32(${Tt("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; + ${n} + value = x[offset]; + } + `},Lc=(e,t,s)=>{let n="";for(let i=t-1;i>=0;--i)n+=` + k = i32(${e.indicesGet("indices",i)}) - ${Tt("uniforms.pads",i,s)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${Tt("uniforms.x_shape",i,t)}) - 1); + k = k % _2n_1; + if(k >= i32(${Tt("uniforms.x_shape",i,t)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${Tt("uniforms.x_strides",i,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},la=(e,t,s)=>{let n="";for(let i=t-1;i>=0;--i)n+=` + k = i32(${e.indicesGet("indices",i)}) - ${Tt("uniforms.pads",i,s)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${Tt("uniforms.x_shape",i,t)})) { + k = i32(${Tt("uniforms.x_shape",i,t)}) - 1; + } + offset += k * i32(${Tt("uniforms.x_strides",i,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},$d=(e,t,s)=>{let n="";for(let i=t-1;i>=0;--i)n+=` + k = i32(${e.indicesGet("indices",i)}) - ${Tt("uniforms.pads",i,s)}; + if (k < 0) { + k += i32(${Tt("uniforms.x_shape",i,t)}]); + } + if (k >= i32(${Tt("uniforms.x_shape",i,t)})) { + k -= i32(${Tt("uniforms.x_shape",i,t)}); + } + offset += k * i32(${Tt("uniforms.x_strides",i,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},Ad=(e,t,s)=>{switch(s.mode){case 0:return Dc(e,t,s.pads.length);case 1:return Lc(e,t,s.pads.length);case 2:return la(e,t,s.pads.length);case 3:return $d(e,t,s.pads.length);default:throw new Error("Invalid mode")}},Id=(e,t)=>{let s=De.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,i=De.size(s),o=[{type:12,data:i},{type:6,data:t.pads}],a=e.length>=3&&e[2].data;t.mode===0&&o.push({type:a?e[2].dataType:1,data:t.value}),o.push(...Mt(e[0].dims,s));let c=["rank"],p=h=>{let C=Ct("output",e[0].dataType,s.length),u=He("x",e[0].dataType,n.length),k=u.type.value,B=Ad(C,n.length,t),R=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&R.push({name:"constant_value",type:a?k:"f32"}),` + ${h.registerUniforms(R).declareVariables(u,C)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${C.offsetToIndices("global_idx")}; + + var value = ${k}(0); + ${B} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${a}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(De.size(s)/64)},programUniforms:o}),getShaderSource:p}},Fd=(e,t)=>{if(e.length>1){let s=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,i=e[0].dims.length,o=new Int32Array(2*i).fill(0);if(e.length>=4){let c=e[3].getBigInt64Array();for(let p=0;po[Number(p)]=Number(c));let a=[];return o.forEach(c=>a.push(c)),{mode:t.mode,value:n,pads:a}}else return t},ua=(e,t)=>{Oc(e.inputs);let s=Fd(e.inputs,t);e.compute(Id(e.inputs,s),{inputs:[0]})}}),bi,da,ca,vi,Dd,zc,Ld,pa,ha,zd,Bd,ma,Rd,Nd,fa,jd,Vd,Ud,Bc,Rc=y(()=>{We(),Ot(),$t(),qt(),bi=e=>{if(F.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},da=(e,t,s)=>{let n=t.format==="NHWC",i=e.dims.slice();n&&i.splice(1,0,i.pop());let o=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),c=t.strides.slice(),p=o?t.dilations.slice():[],h=t.pads.slice();Xs.adjustPoolAttributes(s,i,a,c,p,h);let C=Xs.computePoolOutputShape(s,i,c,p,a,h,t.autoPad),u=Object.assign({},t);o?Object.assign(u,{kernelShape:a,strides:c,pads:h,dilations:p,cacheKey:t.cacheKey}):Object.assign(u,{kernelShape:a,strides:c,pads:h,cacheKey:t.cacheKey});let k=C.slice();return k.push(k.splice(1,1)[0]),[u,n?k:C]},ca=(e,t)=>{let s=t.format==="NHWC",n=De.size(e),i=De.size(t.kernelShape),o=[{type:12,data:n},{type:12,data:i}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let c=t.kernelShape[t.kernelShape.length-1],p=t.strides[t.strides.length-1],h=t.pads[t.pads.length/2-1],C=t.pads[t.pads.length-1],u=!!(h+C);o.push({type:12,data:c},{type:12,data:p},{type:12,data:h},{type:12,data:C}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let k=!1;if(t.kernelShape.length===2){let B=t.kernelShape[t.kernelShape.length-2],R=t.strides[t.strides.length-2],z=t.pads[t.pads.length/2-2],ne=t.pads[t.pads.length-2];k=!!(z+ne),o.push({type:12,data:B},{type:12,data:R},{type:12,data:z},{type:12,data:ne}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[o,a,!0,u,k]}else{if(s)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let c=De.computeStrides(t.kernelShape);o.push({type:12,data:c},{type:12,data:t.pads},{type:12,data:t.strides}),a.push({name:"kernelStrides",type:"u32",length:c.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let p=t.pads.reduce((h,C)=>h+C);return[o,a,!!p,!1,!1]}},vi=(e,t,s,n,i,o,a,c,p,h,C,u)=>{let k=i.format==="NHWC",B=t.type.value,R=Ct("output",t.type.tensor,n);if(i.kernelShape.length<=2){let z="",ne="",J="",W=s-(k?2:1);if(C?z=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${W}] = indices[${W}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${W}] < 0 || xIndices[${W}] + >= uniforms.x_shape[${W}]) { + pad++; + continue; + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${o} + }`:z=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${W}] = indices[${W}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${o} + }`,i.kernelShape.length===2){let ue=s-(k?3:2);u?ne=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${ue}] = indices[${ue}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${ue}] < 0 || xIndices[${ue}] >= uniforms.x_shape[${ue}]) { + pad += i32(uniforms.kw); + continue; + } + `:ne=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${ue}] = indices[${ue}] * uniforms.sh - uniforms.phStart + j; + `,J=` + } + `}return` + ${e.registerUniforms(p).declareVariables(t,R)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${R.offsetToIndices("global_idx")}; + var xIndices = ${R.offsetToIndices("global_idx")}; + + var value = ${B}(${c}); + var pad = 0; + ${ne} + ${z} + ${J} + ${a} + + output[global_idx] = value; + }`}else{if(k)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let z=i.kernelShape.length,ne=i.pads.length,J="";return h?J=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${o} + }`:J=` + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${o} + `,` + ${e.registerUniforms(p).declareVariables(t,R)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${R.offsetToIndices("global_idx")}; + var xIndices = ${R.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${B}(${c}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${z-1}u; j++) { + offsets[j] = offset / ${Tt("uniforms.kernelStrides","j",z)}; + offset -= offsets[j] * ${Tt("uniforms.kernelStrides","j",z)}; + } + offsets[${z-1}] = offset; + + isPad = false; + for (var j = ${s-z}u; j < ${s}u; j++) { + xIndices[j] = indices[j] * ${Tt("uniforms.strides",`j - ${s-z}u`,z)} + + offsets[j - ${s-z}u] - ${Tt("uniforms.pads","j - 2u",ne)}; + ${J} + } + ${a} + + output[global_idx] = value; + }`}},Dd=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,zc=e=>`${Dd(e)};${e.countIncludePad}`,Ld=e=>`${Dd(e)};${e.storageOrder};${e.dilations}`,pa=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}),ha=(e,t,s,n)=>{let[i,o]=da(t,n,s),a=He("x",t.dataType,t.dims.length),c=a.type.value,p="value += x_val;",h="";i.countIncludePad?h+=`value /= ${c}(uniforms.kernelSize);`:h+=`value /= ${c}(i32(uniforms.kernelSize) - pad);`;let[C,u,k,B,R]=ca(o,i);C.push(...Mt(t.dims,o));let z=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${k};${B};${R}`,inputDependencies:z},getRunData:()=>({outputs:[{dims:o,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(De.size(o)/64)},programUniforms:C}),getShaderSource:ne=>vi(ne,a,t.dims.length,o.length,i,p,h,0,u,k,B,R)}},zd=e=>{let t=e.count_include_pad!==0,s=pa(e);if(s.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...s,cacheKey:""};return{...n,cacheKey:zc(n)}},Bd=(e,t)=>{bi(e.inputs),e.compute(ha("AveragePool",e.inputs[0],!1,t))},ma={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Rd=e=>{let t=e.format;return{format:t,...ma,cacheKey:t}},Nd=(e,t)=>{bi(e.inputs),e.compute(ha("GlobalAveragePool",e.inputs[0],!0,t))},fa=(e,t,s,n)=>{let[i,o]=da(t,n,s),a=` + value = max(x_val, value); + `,c="",p=He("x",t.dataType,t.dims.length),h=["rank"],[C,u,k,B,R]=ca(o,i);return C.push(...Mt(t.dims,o)),{name:e,shaderCache:{hint:`${n.cacheKey};${k};${B};${R}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:o,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(De.size(o)/64)},programUniforms:C}),getShaderSource:z=>vi(z,p,t.dims.length,o.length,i,a,c,t.dataType===10?-65504:-1e5,u,k,B,R)}},jd=(e,t)=>{bi(e.inputs),e.compute(fa("MaxPool",e.inputs[0],!1,t))},Vd=e=>{let t=e.storage_order,s=e.dilations,n=pa(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let i={storageOrder:t,dilations:s,...n,cacheKey:""};return{...i,cacheKey:Ld(i)}},Ud=e=>{let t=e.format;return{format:t,...ma,cacheKey:t}},Bc=(e,t)=>{bi(e.inputs),e.compute(fa("GlobalMaxPool",e.inputs[0],!0,t))}}),Wd,Gd,Kd,Hd,Nc=y(()=>{Ot(),$t(),is(),qt(),Wd=(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,o)=>o===t.axis||i===e[0].dims[o]).reduce((i,o)=>i&&o,!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)].")}},Gd=(e,t)=>{let s=De.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,i=n===3,o=e[0].dims,a=e[1].dataType,c=De.size(o),p=n===3||n===2,h=p?[Math.ceil(De.size(e[0].dims)/4)]:e[0].dims,C=e[1].dims,u=e.length>2?e[2]:void 0,k=u?p?[Math.ceil(De.size(u.dims)/4)]:u.dims:void 0,B=C.length===0||C.length===1&&C[0]===1,R=B===!1&&C.length===1,z=Gt(c),ne=B&&(!p||z===4),J=ne?z:1,W=ne&&!p?z:1,ue=He("input",p?12:n,h.length,W),he=He("scale",a,C.length),be=u?He("zero_point",p?12:n,k.length):void 0,Be=Ct("output",a,o.length,J),Ie=[ue,he];be&&Ie.push(be);let nt=[h,C];u&&nt.push(k);let dt=[{type:12,data:c/J},{type:12,data:s},{type:12,data:t.blockSize},...Mt(...nt,o)],Et=zt=>{let It=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${zt.registerUniforms(It).declareVariables(...Ie,Be)} + ${zt.mainStart()} + ${zt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${Be.offsetToIndices("global_idx")}; + + // Set input x + ${p?` + let input = ${ue.getByOffset("global_idx / 4")}; + let x_vec = ${i?"unpack4xI8(input)":"unpack4xU8(input)"}; + let x_value = ${J===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${ue.getByOffset("global_idx")};`}; + + // Set scale input + ${B?`let scale_value= ${he.getByOffset("0")}`:R?` + let scale_index = ${Be.indicesGet("output_indices","uniforms.axis")}; + let scale_value= ${he.getByOffset("scale_index")};`:` + var scale_indices: ${he.type.indices} = output_indices; + let index = ${he.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; + ${he.indicesSet("scale_indices","uniforms.axis","index")}; + let scale_value= ${he.getByIndices("scale_indices")};`}; + + // Set zero-point input + ${be?B?p?` + let zero_point_input = ${be.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 = ${be.getByOffset("0")}`:R?p?` + let zero_point_index = ${Be.indicesGet("output_indices","uniforms.axis")}; + let zero_point_input = ${be.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 = ${Be.indicesGet("output_indices","uniforms.axis")}; + let zero_point_value = ${be.getByOffset("zero_point_index")};`:p?` + let zero_point_offset = ${he.indicesToOffset("scale_indices")}; + let zero_point_input = ${be.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 = ${be.getByIndices("scale_indices")};`:`let zero_point_value = ${p?i?"i32":"u32":ue.type.value}(0);`}; + // Compute and write output + ${Be.setByOffset("global_idx",`${Be.type.value}(x_value - zero_point_value) * scale_value`)}; + }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:be?["rank","rank","rank"]:["rank","rank"]},getShaderSource:Et,getRunData:()=>({outputs:[{dims:o,dataType:a}],dispatchGroup:{x:Math.ceil(c/J/64),y:1,z:1},programUniforms:dt})}},Kd=(e,t)=>{Wd(e.inputs,t),e.compute(Gd(e.inputs,t))},Hd=e=>jt({axis:e.axis,blockSize:e.blockSize})}),jc,Vc,Uc,Rp=y(()=>{We(),Ot(),qt(),jc=(e,t,s)=>{let n=e===t,i=et&&s>0;if(n||i||o)throw new Error("Range these inputs' contents are invalid.")},Vc=(e,t,s,n)=>{let i=Math.abs(Math.ceil((t-e)/s)),o=[i],a=i,c=[{type:12,data:a},{type:n,data:e},{type:n,data:s},...Mt(o)],p=h=>{let C=Ct("output",n,o.length),u=C.type.value,k=[{name:"outputSize",type:"u32"},{name:"start",type:u},{name:"delta",type:u}];return` + ${h.registerUniforms(k).declareVariables(C)} + ${h.mainStart()} + ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${u}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:o,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:c})}},Uc=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]),F.webgpu.validateInputContent&&jc(t,s,n),e.compute(Vc(t,s,n,e.inputs[0].dataType),{inputs:[]})}}),Wc,Gc,Kc,Hc,Np=y(()=>{Ot(),$t(),is(),qt(),Wc=(e,t,s,n)=>{if(e!=="none"&&n!=="i32"&&n!=="u32"&&n!=="f32")throw new Error(`Input ${n} is not supported with reduction ${e}.`);let i=`{ + var oldValue = 0; + loop { + let newValueF32 =`,o=`; + let newValue = bitcast(newValueF32); + let res = atomicCompareExchangeWeak(&${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) + (${s})${o}`;case"max":return n==="i32"||n==="u32"?`atomicMax(&${t}, bitcast<${n}>(${s}));`:` + ${i}max(bitcast(oldValue), (${s}))${o}`;case"min":return n==="i32"||n==="u32"?`atomicMin(&${t}, bitcast<${n}>(${s}));`:`${i}min(bitcast<${n}>(oldValue), (${s}))${o}`;case"mul":return`${i}(bitcast<${n}>(oldValue) * (${s}))${o}`;default:throw new Error(`Reduction ${e} is not supported.`)}},Gc=(e,t)=>{let s=e[0].dims,n=e[1].dims,i=s,o=1,a=Math.ceil(De.size(n)/o),c=n[n.length-1],p=De.sizeFromDimension(s,c),h=[{type:12,data:a},{type:12,data:c},{type:12,data:p},...Mt(e[1].dims,e[2].dims,i)],C=u=>{let k=He("indices",e[1].dataType,e[1].dims.length),B=He("updates",e[2].dataType,e[2].dims.length,o),R=t.reduction!=="none"&&t.reduction!==""?Ea("output",e[0].dataType,i.length):Ct("output",e[0].dataType,i.length,o);return` + ${u.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(k,B,R)} + ${u.mainStart()} + ${u.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]; + ${Wc(t.reduction,"output[data_offset + i]","value",R.type.value)} + } + + }`};return{name:"ScatterND",shaderCache:{hint:`${t.cacheKey}_${t.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h}),getShaderSource:C}},Kc=e=>jt({reduction:e.reduction}),Hc=(e,t)=>{e.compute(Gc(e.inputs,t),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),qc,Qc,Xc,Yc,Jc,Zc,ep,tp,sp,rp,np,qd,ip,op,ap,lp,up,dp,cp,jp=y(()=>{Ot(),$t(),is(),qt(),qc=(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")}},Qc=(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,o)=>n[i]=e[o]),n},Xc=(e,t,s,n,i,o)=>{let[a,c,p]=s>10?[1,2,3]:[-1,e.length>1?1:-1,-1],h=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach(C=>o.push(C));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(c>0&&e.length>c&&e[c].dims.length===1&&e[c].dims[0]>0){if(e[c].getFloat32Array().forEach(C=>n.push(C)),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");qc(n,t),t.axes.length>0&&Qc(n,t.axes,h).forEach((C,u)=>n[u]=C)}if(p>0&&e.length>p&&e[p].dims.length===1&&e[p].dims[0]>0&&(e[p].getBigInt64Array().forEach(C=>i.push(Number(C))),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")},Yc=(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`)}})()+"}",Jc=(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`)}})()+"}",Zc=(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((o,a)=>{n[o]=i[a],n[a+s]=i[t.length+a]}),n):i},ep=(e,t,s,n)=>{let i=[];if(s.length>0)if(n.length>0){if(e.forEach(o=>i.push(o)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((o,a)=>i[o]=s[a])}else s.forEach(o=>i.push(o));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((o,a)=>Math.round(o*t[a]))}return i},tp=(e,t,s)=>{let n=(()=>{switch(s.keepAspectRatioPolicy){case"not_larger":return s.axes.length>0?Math.min(...s.axes.map(o=>t[o]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return s.axes.length>0?Math.max(...s.axes.map(o=>t[o]),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(o=>t[o]=n),s.axes.forEach(o=>i[o]=Math.round(e[o]*t[o]))):(t.fill(n,0,t.length),i.forEach((o,a)=>i[a]=Math.round(o*t[a]))),i},sp=(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 = ${Tt("uniforms.scales","i",n)}; + var roi_low = ${Tt("uniforms.roi","i",i)}; + var roi_hi = ${Tt("uniforms.roi",`i + ${t.length}`,i)}; + if (scale == 1.0) { + original_indices[i] = ${e.type.value}(output_index); + } else { + var input_shape_i = ${Tt("uniforms.input_shape","i",t.length)}; + var output_shape_i = ${Tt("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; + }`,rp=(e,t,s,n,i,o,a)=>` + 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 = ${Tt("uniforms.scales","i",i)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${Tt("uniforms.roi","i",o)}; + var roi_hi = ${Tt("uniforms.roi",`i + ${s.length}`,o)}; + var input_shape_i = ${Tt("uniforms.input_shape","i",s.length)}; + var output_shape_i = ${Tt("uniforms.output_shape","i",n.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${a} || (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; + }`,np=(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 >= ${Tt("uniforms.input_shape","i",t.length)}) { + return false; + } + } + return true; + }`,qd=(e,t,s,n)=>e.rank>n?` + ${e.indicesSet("input_indices",t,"channel")}; + ${e.indicesSet("input_indices",s,"batch")}; +`:"",ip=(e,t,s,n,i)=>{let[o,a,c,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",a,`max(0, min(row, ${s[a]} - 1))`)}; + ${e.indicesSet("input_indices",c,`max(0, min(col, ${s[c]} - 1))`)}; + ${qd(e,p,o,2)} + return ${e.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${h} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${h} = originalIndices[${a}]; + var col:${h} = originalIndices[${c}]; + ${n?`if (row < 0 || row > (${s[a]} - 1) || col < 0 || col > (${s[c]} - 1)) { + return ${i}; + }`:""}; + row = max(0, min(row, ${s[a]} - 1)); + col = max(0, min(col, ${s[c]} - 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 = ${s.length>2?`u32(originalIndices[${o}])`:"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); + }`},op=(e,t,s,n,i,o,a,c,p,h)=>{let C=s.length===2,[u,k]=C?[0,1]:[2,3],B=e.type.value,R=z=>{let ne=z===u?"row":"col";return` + fn ${ne}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${B} { + var output_index = ${t.indicesGet("output_indices",z)}; + var originalIdx: ${B} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[z]}, + ${n[z]}, ${s[z]}, ${o[z]}, ${o[z]} + ${s.length}); + var fractOriginalIdx: ${B} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${c} && (originalIdx < 0 || originalIdx > (${s[z]} - 1))) { + return ${p}; + } + var data: array<${B}, 4> = array<${B}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${ne}: ${B} = originalIdx + ${B}(i); + if (${ne} < 0 || ${ne} >= ${s[z]}) { + ${h?`coefs[i + 1] = 0.0; + continue;`:c?`return ${p};`:`${ne} = max(0, min(${ne}, ${s[z]} - 1));`}; + } + var input_indices_copy: ${e.type.indices} = input_indices; + ${e.indicesSet("input_indices_copy",z,`u32(${ne})`)}; + data[i + 1] = ${z===u?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${R(u)}; + ${R(k)}; + fn getCubicInterpolationCoefs(s: ${B}) -> array<${B}, 4> { + var absS = abs(s); + var coeffs: array<${B}, 4> = array<${B}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${B} = 1.0 - absS; + var twoMinusAbsS: ${B} = 2.0 - absS; + var onePlusAbsS: ${B} = 1.0 + absS; + coeffs[0] = ((${a} * onePlusAbsS - 5 * ${a}) * onePlusAbsS + 8 * ${a}) * onePlusAbsS - 4 * ${a}; + coeffs[1] = ((${a} + 2) * absS - (${a} + 3)) * absS * absS + 1; + coeffs[2] = ((${a} + 2) * oneMinusAbsS - (${a} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${a} * twoMinusAbsS - 5 * ${a}) * twoMinusAbsS + 8 * ${a}) * twoMinusAbsS - 4 * ${a}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${B}, 4>, coefs: array<${B}, 4>) -> ${B} { + var coefsSum: ${B} = 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}) -> ${B} { + var input_indices: ${e.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},ap=(e,t,s,n,i)=>{let[o,a,c,p,h]=s.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],C=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${C} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",a,`max(0, min(depth, ${s[a]} - 1))`)}; + ${e.indicesSet("input_indices",c,`max(0, min(height, ${s[c]} - 1))`)}; + ${e.indicesSet("input_indices",p,`max(0, min(width, ${s[p]} - 1))`)}; + ${qd(e,h,o,3)} + return ${e.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${C} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${C} = originalIndices[${a}]; + var height:${C} = originalIndices[${c}]; + var width:${C} = originalIndices[${p}]; + ${n?`if (depth < 0 || depth > (${s[a]} - 1) || height < 0 || height > (${s[c]} - 1) || width < 0 || (width > ${s[p]} - 1)) { + return ${i}; + }`:""}; + + depth = max(0, min(depth, ${s[a]} - 1)); + height = max(0, min(height, ${s[c]} - 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 = ${s.length>3?`u32(originalIndices[${o}])`:"0"}; + + var x111: ${C} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${C} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${C} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${C} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${C} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${C} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${C} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${C} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${C} = abs(depth - ${C}(depth1)); + var dx2: ${C} = abs(${C}(depth2) - depth); + var dy1: ${C} = abs(height - ${C}(height1)); + var dy2: ${C} = abs(${C}(height2) - height); + var dz1: ${C} = abs(width - ${C}(width1)); + var dz2: ${C} = abs(${C}(width2) - width); + if (depth1 == depth2) { + dx1 = 0.5; + dx2 = 0.5; + } + if (height1 == height2) { + dy1 = 0.5; + dy2 = 0.5; + } + if (width1 == width2) { + dz1 = 0.5; + dz2 = 0.5; + } + return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 + + x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1); + }`},lp=(e,t,s,n,i,o)=>{let a=e.dims,c=Zc(o,t.axes,a.length),p=ep(a,n,i,t.axes),h=n.slice();n.length===0&&(h=a.map((W,ue)=>W===0?1:p[ue]/W),t.keepAspectRatioPolicy!=="stretch"&&(p=tp(a,h,t)));let C=Ct("output",e.dataType,p.length),u=He("input",e.dataType,a.length),k=De.size(p),B=a.length===p.length&&a.every((W,ue)=>W===p[ue]),R=t.coordinateTransformMode==="tf_crop_and_resize",z=t.extrapolationValue,ne=u.type.value,J=W=>` + ${B?"":` + ${Yc(t.coordinateTransformMode,ne)}; + ${(()=>{switch(t.mode){case"nearest":return` + ${np(u,a)}; + ${Jc(t.nearestMode,s,ne)}; + ${rp(u,C,a,p,h.length,c.length,R)}; + `;case"linear":return` + ${sp(C,a,p,h.length,c.length)}; + ${(()=>{if(a.length===2||a.length===4)return`${ip(u,C,a,R,z)}`;if(a.length===3||a.length===5)return`${ap(u,C,a,R,z)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(a.length===2||a.length===4)return`${op(u,C,a,p,h,c,t.cubicCoeffA,R,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")}})()}; + `} + ${W.registerUniform("output_size","u32").registerUniform("scales","f32",h.length).registerUniform("roi","f32",c.length).declareVariables(u,C)} + ${W.mainStart()} + ${W.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${B?"output[global_idx] = input[global_idx];":` + let output_indices = ${C.offsetToIndices("global_idx")}; + var input_indices: ${u.type.indices}; + ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${u.getByIndices("input_indices")}; + } else { + output[global_idx] = ${t.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${a.length===2||a.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()}; +`} + }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${s}|${h.length>0?h:""}|${i.length>0?i:""}|${c.length>0?c:""}|${B}|${a}`,inputDependencies:["rank"]},getShaderSource:J,getRunData:()=>({outputs:[{dims:p,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(k/64)},programUniforms:[{type:12,data:k},{type:1,data:h},{type:1,data:c},...Mt(a,p)]})}},up=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},dp=(e,t)=>{let s=[],n=[],i=[],o=up(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Xc(e.inputs,t,o,s,n,i),e.compute(lp(e.inputs[0],t,o,s,n,i),{inputs:[0]})},cp=e=>{let t=e.antialias,s=e.axes,n=e.coordinateTransformMode,i=e.cubicCoeffA,o=e.excludeOutside!==0,a=e.extrapolationValue,c=e.keepAspectRatioPolicy,p=e.mode,h=e.nearestMode===""?"simple":e.nearestMode;return jt({antialias:t,axes:s,coordinateTransformMode:n,cubicCoeffA:i,excludeOutside:o,extrapolationValue:a,keepAspectRatioPolicy:c,mode:p,nearestMode:h})}}),pp,hp,Kt,mp=y(()=>{Ot(),$t(),is(),qt(),pp=(e,t)=>{let[s,n,i,o]=e,{numHeads:a,rotaryEmbeddingDim:c}=t;if(s.dims.length!==3&&s.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${s.dims.length}`);if(!De.areEqual(n.dims,[])&&!De.areEqual(n.dims,[1])&&n.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${n.dims.length}`);if(i.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(o.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(!De.areEqual(i.dims,o.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(c>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let p=s.dims[0],h=s.dims[s.dims.length-2],C=i.dims[0],u=De.sizeFromDimension(s.dims,1)/h,k=c===0?i.dims[1]*2:u/a;if(c>k)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(p!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(h!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(k/2!==i.dims[1]&&c/2!==i.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${i.dims[1]}`);if(h>C)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},hp=(e,t)=>{let{interleaved:s,numHeads:n,rotaryEmbeddingDim:i,scale:o}=t,a=e[0].dims[0],c=De.sizeFromDimension(e[0].dims,1),p=e[0].dims[e[0].dims.length-2],h=c/p,C=e[2].dims[1],u=i===0?C*2:h/n,k=new Array(a,p,h/u,u-C),B=De.computeStrides(k),R=[{type:1,data:o},{type:12,data:k},{type:12,data:B},...e[0].dims.length===3?new Array({type:12,data:[c,h,u,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[c,u,p*u,1]}):[],...Mt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],z=ne=>{let J=He("input",e[0].dataType,e[0].dims.length),W=He("position_ids",e[1].dataType,e[1].dims.length),ue=He("cos_cache",e[2].dataType,e[2].dims.length),he=He("sin_cache",e[3].dataType,e[3].dims.length),be=Ct("output",e[0].dataType,e[0].dims.length);return ne.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:k.length},{name:"global_strides",type:"u32",length:B.length},{name:"input_output_strides",type:"u32",length:B.length}]),` + ${ne.declareVariables(J,W,ue,he,be)} + + ${ne.mainStart(sr)} + let half_rotary_emb_dim = uniforms.${ue.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${ne.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${W.broadcastedIndicesToOffset("bsnh.xy",Ct("",W.type.tensor,2))}; + let position_id = + u32(${W.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 = ${J.getByOffset("i")} * ${ue.get("position_id","bsnh[3]")} - + ${J.getByOffset("j")} * ${he.get("position_id","bsnh[3]")}; + ${be.setByOffset("i","re")} + let im = ${J.getByOffset("i")} * ${he.get("position_id","bsnh[3]")} + + ${J.getByOffset("j")} * ${ue.get("position_id","bsnh[3]")}; + ${be.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${be.setByOffset("k",J.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:jt({interleaved:s}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:z,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(De.size(k)/sr)},programUniforms:R})}},Kt=(e,t)=>{pp(e.inputs,t),e.compute(hp(e.inputs,t))}}),Vs,Qs,Zs,yn=y(()=>{Ot(),$t(),qt(),Vs=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],o=t.dims[t.dims.length-2];if(s.dims[s.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(s.dims[s.dims.length-2]!==o)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let a=e[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let a=e[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},Qs=(e,t,s,n)=>{let i=t.simplified,o=e[0].dims,a=De.size(o),c=o,p=a,h=o.slice(-1)[0],C=n?o.slice(0,-1).concat(1):[],u=!i&&e.length>3,k=e.length>4,B=n&&s>1,R=n&&s>2,z=s>3,ne=64,J=Gt(h),W=[{type:12,data:p},{type:12,data:J},{type:12,data:h},{type:1,data:t.epsilon}],ue=be=>{let Be=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Ie=[He("x",e[0].dataType,e[0].dims,J),He("skip",e[1].dataType,e[1].dims,J),He("gamma",e[2].dataType,e[2].dims,J)];u&&Ie.push(He("beta",e[3].dataType,e[3].dims,J)),k&&Ie.push(He("bias",e[4].dataType,e[4].dims,J)),Ie.push(Ct("output",e[0].dataType,c,J)),B&&Ie.push(Ct("mean_output",1,C)),R&&Ie.push(Ct("inv_std_output",1,C)),z&&Ie.push(Ct("input_skip_bias_sum",e[0].dataType,c,J));let nt=ds(e[0].dataType),dt=ds(1,J);return` + + ${be.registerUniforms(Be).declareVariables(...Ie)} + var sum_shared : array<${dt}, ${ne}>; + var sum_squared_shared : array<${dt}, ${ne}>; + + ${be.mainStart([ne,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${ne}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${ne}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${ne-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${k?"bias[offset1d + i]":nt+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${z?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${ks(nt,J,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${ne}; + 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 = ${js("sum",J)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${js("square_sum",J)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon); + ${B?"mean_output[global_idx] = mean;":""} + ${R?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${i?"":`- ${nt}(mean)`}) * + ${nt}(inv_std_dev) * gamma[offset1d + i] + ${u?"+ beta[offset1d + i]":""}; + } + }`},he=[{dims:c,dataType:e[0].dataType}];return s>1&&he.push({dims:C,dataType:1}),s>2&&he.push({dims:C,dataType:1}),s>3&&he.push({dims:o,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${J};${B};${R};${z}`,inputDependencies:e.map((be,Be)=>"type")},getShaderSource:ue,getRunData:()=>({outputs:he,dispatchGroup:{x:Math.ceil(p/h)},programUniforms:W})}},Zs=(e,t)=>{Vs(e.inputs);let s=[0];e.outputCount>1&&s.push(-3),e.outputCount>2&&s.push(-3),e.outputCount>3&&s.push(3),e.compute(Qs(e.inputs,t,e.outputCount,!1),{outputs:s})}}),fp,Wn,Qd,_,f,q,xe,$e,Le=y(()=>{Ot(),$t(),is(),qt(),fp=(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`)})},Wn=(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},Qd=(e,t)=>{if(e.length>1){let s=Wn(e,1),n=Wn(e,2),i=Wn(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),jt({starts:s,ends:n,axes:i})}else return t},_=(e,t,s,n,i)=>{let o=e;return e<0&&(o+=s[n[t]]),i[t]<0?Math.max(0,Math.min(o,s[n[t]]-1)):Math.max(0,Math.min(o,s[n[t]]))},f=(e,t,s)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + var carry = 0u; + for (var i = ${s.length}; i >= 0; i--) { + let input_shape_i = ${Tt("uniforms.input_shape","i",s.length)}; + let steps_i = ${Tt("uniforms.steps","i",s.length)}; + let signs_i = ${Tt("uniforms.signs","i",s.length)}; + let starts_i = ${Tt("uniforms.starts","i",s.length)}; + var output_index = ${t.indicesGet("output_indices","i")}; + var input_index = output_index * steps_i + starts_i + carry; + carry = input_index / input_shape_i; + input_index = input_index % input_shape_i; + if (signs_i < 0) { + input_index = input_shape_i - input_index - 1u + starts_i; + } + ${e.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,q=(e,t)=>{let s=e[0].dims,n=De.size(s),i=t.axes.length>0?De.normalizeAxes(t.axes,s.length):[...Array(s.length).keys()],o=Wn(e,4);o.forEach(J=>J!==0||(()=>{throw new Error("step cannot be 0")})),o.length===0&&(o=Array(i.length).fill(1));let a=t.starts.map((J,W)=>_(J,W,s,i,o)),c=t.ends.map((J,W)=>_(J,W,s,i,o));if(i.length!==a.length||i.length!==c.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==s.length)for(let J=0;JMath.sign(J));o.forEach((J,W,ue)=>{if(J<0){let he=(c[W]-a[W])/J,be=a[W],Be=be+he*o[W];a[W]=Be,c[W]=be,ue[W]=-J}});let h=s.slice(0);i.forEach((J,W)=>{h[J]=Math.ceil((c[J]-a[J])/o[J])});let C={dims:h,dataType:e[0].dataType},u=Ct("output",e[0].dataType,h.length),k=He("input",e[0].dataType,e[0].dims.length),B=De.size(h),R=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:a.length},{name:"signs",type:"i32",length:p.length},{name:"steps",type:"u32",length:o.length}],z=[{type:12,data:B},{type:12,data:a},{type:6,data:p},{type:12,data:o},...Mt(e[0].dims,h)],ne=J=>` + ${J.registerUniforms(R).declareVariables(k,u)} + ${f(k,u,s)} + ${J.mainStart()} + ${J.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${u.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${u.setByOffset("global_idx",k.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${p.length}_${a.length}_${o.length}`,inputDependencies:["rank"]},getShaderSource:ne,getRunData:()=>({outputs:[C],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:z})}},xe=(e,t)=>{fp(e.inputs,t);let s=Qd(e.inputs,t);e.compute(q(e.inputs,s),{inputs:[0]})},$e=e=>{let t=e.starts,s=e.ends,n=e.axes;return jt({starts:t,ends:s,axes:n})}}),rt,ot,_t,kt,Zt=y(()=>{Ot(),$t(),is(),jr(),qt(),rt=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},ot=(e,t)=>{let s=e.inputs[0],n=s.dims,i=De.size(n),o=n.length,a=De.normalizeAxis(t.axis,o),c=ant),h[a]=o-1,h[o-1]=a,p=e.compute(cr(s,h),{inputs:[s],outputs:[-1]})[0]):p=s;let C=p.dims,u=C[o-1],k=i/u,B=Gt(u),R=u/B,z=64;k===1&&(z=256);let ne=(Ie,nt)=>nt===4?`max(max(${Ie}.x, ${Ie}.y), max(${Ie}.z, ${Ie}.w))`:nt===2?`max(${Ie}.x, ${Ie}.y)`:nt===3?`max(max(${Ie}.x, ${Ie}.y), ${Ie}.z)`:Ie,J=He("x",p.dataType,p.dims,B),W=Ct("result",p.dataType,p.dims,B),ue=J.type.value,he=ds(p.dataType)==="f32"?`var threadMax = ${ue}(-3.402823e+38f);`:`var threadMax = ${ue}(-65504.0h);`,be=Ie=>` + var rowMaxShared : ${ue}; + var rowSumShared : ${ue}; + var threadShared : array<${ue}, ${z}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${ue} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${ue}) { + let index = row * row_stride + col; + result[index] = value; + } + ${Ie.registerUniform("packedCols","i32").declareVariables(J,W)} + ${Ie.mainStart(z)} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${z}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${he} + for (var col = lindex; col < cols; col += wg) { + let value = getValue(row, col, row_stride); + threadMax = max(threadMax, value); + } + if (lindex < cols) { + threadShared[lindex] = threadMax; + } + workgroupBarrier(); + + var reduceSize = min(cols, wg); + for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) { + reduceSize = currSize + (reduceSize & 1); + if (lindex < currSize) { + threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]); + } + workgroupBarrier(); + } + if (lindex == 0) { + rowMaxShared = ${ue}(${ne("threadShared[0]",B)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${ue}(0.0); + for (var col = lindex; col < cols; col += wg) { + let subExp = exp(getValue(row, col, row_stride) - rowMaxShared); + threadSum += subExp; + } + threadShared[lindex] = threadSum; + workgroupBarrier(); + + for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) { + if (lindex < currSize) { + threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize]; + } + workgroupBarrier(); + } + if (lindex == 0) { + rowSumShared = ${ue}(${js("threadShared[0]",B)}); + } + workgroupBarrier(); + + // calculate final value for each element in the row + for (var col = lindex; col < cols; col += wg) { + let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared; + setValue(row, col, row_stride, value); + } + }`,Be=e.compute({name:"Softmax",shaderCache:{hint:`${B};${z}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:C,dataType:p.dataType}],dispatchGroup:{x:k},programUniforms:[{type:6,data:R}]}),getShaderSource:be},{inputs:[p],outputs:[c?-1:0]})[0];c&&e.compute(cr(Be,h),{inputs:[Be]})},_t=(e,t)=>{rt(e.inputs),ot(e,t)},kt=e=>jt({axis:e.axis})}),Wt,Dt,At,bs,Ht,Qt=y(()=>{Ot(),$t(),qt(),Wt=e=>Array.from(e.getBigInt64Array(),Number),Dt=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==10&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Wt(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},At=(e,t)=>{let s=[];for(let n=0;n{let s=e[0].dims,n=t??Wt(e[1]),i=At(s,n),o=De.size(i),a=e[0].dataType,c=He("input",a,s.length),p=Ct("output",a,i.length),h=C=>` + const inputShape = ${c.indices(...s)}; + ${C.registerUniform("output_size","u32").declareVariables(c,p)} + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${p.offsetToIndices("global_idx")}; + var input_indices: ${c.type.indices}; + for (var i = 0; i < ${s.length}; i++) { + let input_dim_i = ${c.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${p.indicesGet("output_indices","i")} % input_dim_i; + + ${c.indicesSet("input_indices","i","input_dim_value")} + } + ${p.setByOffset("global_idx",c.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:[{type:12,data:o},...Mt(e[0].dims,i)]}),getShaderSource:h}},Ht=e=>{Dt(e.inputs),e.compute(bs(e.inputs),{inputs:[0]})}}),ps,ys,es,Us=y(()=>{Ot(),$t(),qt(),ps=(e,t,s,n,i)=>{let o=Ct("output_data",i,s.length,4),a=He("a_data",t[1].dataType,t[1].dims.length,4),c=He("b_data",t[2].dataType,t[2].dims.length,4),p=He("c_data",t[0].dataType,t[0].dims.length,4),h,C=(u,k,B)=>`select(${k}, ${u}, ${B})`;if(!n)h=o.setByOffset("global_idx",C(a.getByOffset("global_idx"),c.getByOffset("global_idx"),p.getByOffset("global_idx")));else{let u=(k,B,R="")=>{let z=`a_data[index_a${B}][component_a${B}]`,ne=`b_data[index_b${B}][component_b${B}]`,J=`bool(c_data[index_c${B}] & (0xffu << (component_c${B} * 8)))`;return` + let output_indices${B} = ${o.offsetToIndices(`global_idx * 4u + ${B}u`)}; + let offset_a${B} = ${a.broadcastedIndicesToOffset(`output_indices${B}`,o)}; + let offset_b${B} = ${c.broadcastedIndicesToOffset(`output_indices${B}`,o)}; + let offset_c${B} = ${p.broadcastedIndicesToOffset(`output_indices${B}`,o)}; + let index_a${B} = offset_a${B} / 4u; + let index_b${B} = offset_b${B} / 4u; + let index_c${B} = offset_c${B} / 4u; + let component_a${B} = offset_a${B} % 4u; + let component_b${B} = offset_b${B} % 4u; + let component_c${B} = offset_c${B} % 4u; + ${k}[${B}] = ${R}(${C(z,ne,J)}); + `};i===9?h=` + var data = vec4(0); + ${u("data",0,"u32")} + ${u("data",1,"u32")} + ${u("data",2,"u32")} + ${u("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:h=` + ${u("output_data[global_idx]",0)} + ${u("output_data[global_idx]",1)} + ${u("output_data[global_idx]",2)} + ${u("output_data[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(p,a,c,o)} + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${h} + }`},ys=e=>{let t=e[1].dims,s=e[2].dims,n=e[0].dims,i=e[1].dataType,o=!(De.areEqual(t,s)&&De.areEqual(s,n)),a=t,c=De.size(t);if(o){let h=Js.calcShape(Js.calcShape(t,s,!1),n,!1);if(!h)throw new Error("Can't perform where op on the given tensors");a=h,c=De.size(a)}let p=Math.ceil(c/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:h=>ps(h,e,a,o,i),getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(c/64/4)},programUniforms:[{type:12,data:p},...Mt(n,t,s,a)]})}},es=e=>{e.compute(ys(e.inputs))}}),zs,Fs=y(()=>{pc(),Zi(),hc(),mc(),fc(),_c(),su(),Mc(),Mu(),vc(),ku(),xc(),Tc(),Pc(),Bp(),Vn(),Ec(),kc(),sd(),Jo(),Ac(),Ic(),Fc(),Pd(),fs(),hd(),Od(),Rc(),Nc(),Rp(),Np(),ri(),jp(),mp(),yn(),Le(),Zt(),_d(),Qt(),jr(),wo(),Us(),zs=new 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e.shaderCache?.hint&&(n+="["+e.shaderCache.hint+"]"),n+=":"+s+`:${mr(t,e.shaderCache?.inputDependencies??new Array(t.length).fill("dims"))}`,n},lr=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},Ar=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]}},Gn=class{constructor(){this.currentSessionId=null,this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.maxDispatchNumber=16,this.pendingDispatchNumber=0,this.pendingKernels=[],this.pendingQueries=new Map,this.sessionStatus="default",this.capturedCommandList=new Map,this.capturedPendingKernels=new Map,this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. (should not happen)");let 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=o=>t.features.has(o)&&s.push(o)&&!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 Ar(this.device),this.adapterInfo=new lr(t.info||await t.requestAdapterInfo()),this.gpuDataManager=Yt(this),this.programManager=new Bs(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,gn(e.logLevel,!!e.debug),this.device.onuncapturederror=o=>{o.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${o.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:t,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e=this.getCommandEncoder(),t={};this.queryType==="at-passes"&&(t.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(t)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Ue(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{let t=new BigUint64Array(e.getMappedRange()),s=this.pendingQueries.get(e);for(let n=0;n"u"&&(this.queryTimeBase=k);let R=Number(k-this.queryTimeBase),z=Number(B-this.queryTimeBase);if(!Number.isSafeInteger(R)||!Number.isSafeInteger(z))throw new RangeError("incorrect timestamp range");if(this.env.webgpu.profiling?.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:C.map(ne=>({dims:ne.dims,dataType:fr(ne.dataType)})),outputsMetadata:u.map(ne=>({dims:ne.dims,dataType:fr(ne.dataType)})),kernelId:o,kernelType:c,kernelName:p,programName:h,startTime:R,endTime:z});else{let ne="";C.forEach((W,ue)=>{ne+=`input[${ue}]: [${W.dims}] | ${fr(W.dataType)}, `});let J="";u.forEach((W,ue)=>{J+=`output[${ue}]: [${W.dims}] | ${fr(W.dataType)}, `}),console.log(`[profiling] kernel "${o}|${c}|${p}|${h}" ${ne}${J}execution time: ${z-R} ns`)}Ke("GPU",`${h}::${k}::${B}`)}e.unmap(),this.pendingQueries.delete(e)}),ze()}run(e,t,s,n,i,o){Ue(e.name);let a=[];for(let W=0;Wue):s;if(C.length!==c.length)throw new Error(`Output size ${C.length} must be equal to ${c.length}.`);let u=[],k=[];for(let W=0;W=o)throw new Error(`Invalid output index: ${C[W]}`);if(C[W]===-3)continue;let ue=C[W]===-1,he=C[W]===-2,be=ue||he?i(c[W].dataType,c[W].dims):n(C[W],c[W].dataType,c[W].dims);if(u.push(be),be.data===0)continue;let Be=this.gpuDataManager.get(be.data);if(!Be)throw new Error(`no GPU data for output: ${be.data}`);if(ue&&this.temporaryData.push(Be),he){let Ie=this.kernelPersistentData.get(this.currentKernelId);Ie||(Ie=[],this.kernelPersistentData.set(this.currentKernelId,Ie)),Ie.push(Be)}k.push(Be)}if(a.length!==t.length||k.length!==u.length){if(k.length===0)return ze(e.name),u;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let B;if(h){let W=0,ue=[];h.forEach(Ie=>{let nt=typeof Ie.data=="number"?[Ie.data]:Ie.data;if(nt.length===0)return;let dt=Ie.type===10?2:4,Et,zt;Ie.type===10?(zt=nt.length>4?16:nt.length>2?8:nt.length*dt,Et=nt.length>4?16:dt*nt.length):(zt=nt.length<=2?nt.length*dt:16,Et=16),W=Math.ceil(W/zt)*zt,ue.push(W);let It=Ie.type===10?8:4;W+=nt.length>4?Math.ceil(nt.length/It)*Et:nt.length*dt});let he=16;W=Math.ceil(W/he)*he;let be=new ArrayBuffer(W);h.forEach((Ie,nt)=>{let dt=ue[nt],Et=typeof Ie.data=="number"?[Ie.data]:Ie.data;if(Ie.type===6)new Int32Array(be,dt,Et.length).set(Et);else if(Ie.type===12)new Uint32Array(be,dt,Et.length).set(Et);else if(Ie.type===10)new Uint16Array(be,dt,Et.length).set(Et);else if(Ie.type===1)new Float32Array(be,dt,Et.length).set(Et);else throw new Error(`Unsupported uniform type: ${fr(Ie.type)}`)});let Be=this.gpuDataManager.create(W,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(Be.buffer,0,be,0,W),this.gpuDataManager.release(Be.id),B={offset:0,size:W,buffer:Be.buffer}}let R=this.programManager.normalizeDispatchGroupSize(p),z=R[1]===1&&R[2]===1,ne=_a(e,t,z),J=this.programManager.getArtifact(ne);if(J||(J=this.programManager.build(e,R),this.programManager.setArtifact(ne,J),as("info",()=>`[artifact] key: ${ne}, programName: ${e.name}`)),h&&J.uniformVariablesInfo){if(h.length!==J.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${J.uniformVariablesInfo.length}, got ${h.length} in program "${J.programInfo.name}".`);for(let W=0;W`[ProgramManager] run "${e.name}" (key=${ne}) with ${R[0]}x${R[1]}x${R[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let W={kernelId:this.currentKernelId,programName:J.programInfo.name,inputTensorViews:t,outputTensorViews:u};this.pendingKernels.push(W),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(W)}return this.programManager.run(J,a,k,R,B),ze(e.name),u}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=zs.get(e);if(!i)throw new Error(`kernel not implemented: ${e}`);let o={kernelType:e,kernelName:n,kernelEntry:i[0],attributes:[i[1],s]};this.kernels.set(t,o)}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: ${e}`);let i=n.kernelType,o=n.kernelName,a=n.kernelEntry,c=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${i}] ${o}" is not allowed to be called recursively`);this.currentKernelId=e,c[0]&&(c[1]=c[0](c[1]),c[0]=void 0),as("info",()=>`[WebGPU] Start to run kernel "[${i}] ${o}"...`);let p=this.env.debug;this.temporaryData=[];try{return p&&this.device.pushErrorScope("validation"),a(t,c[1]),0}catch(h){return s.push(Promise.resolve(`[WebGPU] Kernel "[${i}] ${o}" failed. ${h}`)),1}finally{p&&s.push(this.device.popErrorScope().then(h=>h?`GPU validation error for kernel "[${i}] ${o}": ${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 o=i.get(t),a=this.gpuDataManager.registerExternalBuffer(s,n,o);return i.set(t,[a,s]),a}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 pt(this,e,t);return M(n.buffer,s)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){this.queryType="none",(this.env.webgpu.profiling?.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()}}}),xi,Mn,_p,ws,Os,Ir,sn,bn,ga=y(()=>{Te(),xi=1,Mn=()=>xi++,_p=new Map([["float32",32],["float16",16],["int32",32],["uint32",32],["int64",64],["uint64",64],["int8",8],["uint8",8],["int4",4],["uint4",4]]),ws=(e,t)=>{let s=_p.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},Os=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 ws(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])}},Ir=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!==ws(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. 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this.freeTensors.entries())if(h.canReuseTensor(a,e,t)){as("verbose",()=>`[WebNN] Reusing tensor {dataType: ${e}, shape: ${t}}`);let C=this.freeTensors.splice(p,1)[0];return C.sessionId=o,C}as("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${e}, shape: ${t}}`);let c=await a.createTensor({dataType:e,shape:t,dimensions:t,usage:s,writable:n,readable:i});return new Os({sessionId:o,context:a,tensor:c,dataType:e,shape:t})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},bn=(...e)=>new sn(...e)}),Ti,wa,ya,Vp=y(()=>{Ot(),br(),Y(),ga(),Te(),Ti=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),wa=(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 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All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */},"./src/backends/onnx.js":(ke,A,r)=>{var g;r.r(A),r.d(A,{Tensor:()=>j.Tensor,createInferenceSession:()=>oe,deviceToExecutionProviders:()=>K,isONNXProxy:()=>H,isONNXTensor:()=>V});var $=r("./src/env.js"),N=r("?2ce3"),Z=r("./node_modules/onnxruntime-web/dist/ort.bundle.min.mjs?3a96"),j=r("./node_modules/onnxruntime-common/dist/esm/index.js");const y=Object.freeze({auto:null,gpu:null,cpu:"cpu",wasm:"wasm",webgpu:"webgpu",cuda:"cuda",dml:"dml",webnn:{name:"webnn",deviceType:"cpu"},"webnn-npu":{name:"webnn",deviceType:"npu"},"webnn-gpu":{name:"webnn",deviceType:"gpu"},"webnn-cpu":{name:"webnn",deviceType:"cpu"}}),T=[];let b,x;const v=Symbol.for("onnxruntime");if(v in globalThis)x=globalThis[v];else if($.apis.IS_NODE_ENV){switch(x=N??(g||(g=r.t(N,2))),process.platform){case"win32":T.push("dml");break;case"linux":process.arch==="x64"&&T.push("cuda");break}T.push("cpu"),b=["cpu"]}else x=Z,$.apis.IS_WEBNN_AVAILABLE&&T.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),$.apis.IS_WEBGPU_AVAILABLE&&T.push("webgpu"),T.push("wasm"),b=["wasm"];const L=x.InferenceSession;function K(I=null){if(!I)return b;switch(I){case"auto":return T;case"gpu":return T.filter(S=>["webgpu","cuda","dml","webnn-gpu"].includes(S))}if(T.includes(I))return[y[I]??I];throw new Error(`Unsupported device: "${I}". 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When `free_dimension_overrides` is not set, you may experience significant performance degradation.');const Ht=(0,y.getModelFile)(_,Dt,!0,q),Qt=q.use_external_data_format??xe.use_external_data_format;let ps=[];if(Qt&&(Qt===!0||typeof Qt=="object"&&Qt.hasOwnProperty(f)&&Qt[f]===!0)){if(V.apis.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const es=`${f}${Wt}.onnx_data`,Us=`${q.subfolder??""}/${es}`;ps.push(new Promise(async(zs,Fs)=>{const Bs=await(0,y.getModelFile)(_,Us,!0,q);zs({path:es,data:Bs})}))}else At.externalData!==void 0&&(ps=At.externalData.map(async es=>{if(typeof es.data=="string"){const Us=await(0,y.getModelFile)(_,es.data,!0,q);return{...es,data:Us}}return es}));if(ps.length>0&&(At.externalData=await Promise.all(ps)),Le==="webgpu"){const es=(0,g.getKeyValueShapes)(q.config,{prefix:"present"});if(Object.keys(es).length>0&&!(0,$.isONNXProxy)()){const Us={};for(const zs in es)Us[zs]="gpu-buffer";At.preferredOutputLocation=Us}}return{buffer:await Ht,session_options:At,session_config:Zt}}async function ae(_,f,q){return Object.fromEntries(await Promise.all(Object.keys(f).map(async xe=>{const{buffer:$e,session_options:Le,session_config:rt}=await F(_,f[xe],q),ot=await(0,$.createInferenceSession)($e,Le,rt);return[xe,ot]})))}async function ie(_,f,q){return Object.fromEntries(await Promise.all(Object.keys(f).map(async xe=>{const $e=await(0,y.getModelJSON)(_,f[xe],!1,q);return[xe,$e]})))}function ye(_,f){const q=Object.create(null),xe=[];for(const rt of _.inputNames){const ot=f[rt];if(!(ot instanceof v.Tensor)){xe.push(rt);continue}q[rt]=(0,$.isONNXProxy)()?ot.clone():ot}if(xe.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${xe.join(", ")}.`);const $e=Object.keys(f).length,Le=_.inputNames.length;if($e>Le){let rt=Object.keys(f).filter(ot=>!_.inputNames.includes(ot));console.warn(`WARNING: Too many inputs were provided (${$e} > ${Le}). The following inputs will be ignored: "${rt.join(", ")}".`)}return q}async function ge(_,f){const q=ye(_,f);try{const xe=Object.fromEntries(Object.entries(q).map(([Le,rt])=>[Le,rt.ort_tensor]));let $e=await _.run(xe);return $e=re($e),$e}catch(xe){const $e=Object.fromEntries(Object.entries(q).map(([Le,{type:rt,dims:ot,data:_t}])=>[Le,{type:rt,dims:ot,data:_t}]));throw console.error(`An error occurred during model execution: "${xe}".`),console.error("Inputs given to model:",$e),xe}}function re(_){for(let f in _)(0,$.isONNXTensor)(_[f])?_[f]=new v.Tensor(_[f]):typeof _[f]=="object"&&re(_[f]);return _}function Me(_){if(_ instanceof v.Tensor)return _;if(_.length===0)throw Error("items must be non-empty");if(Array.isArray(_[0])){if(_.some(f=>f.length!==_[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new v.Tensor("int64",BigInt64Array.from(_.flat().map(f=>BigInt(f))),[_.length,_[0].length])}else return new v.Tensor("int64",BigInt64Array.from(_.map(f=>BigInt(f))),[1,_.length])}function pe(_){return new v.Tensor("bool",[_],[1])}async function Ce(_,f){let{encoder_outputs:q,input_ids:xe,decoder_input_ids:$e,...Le}=f;if(!q){const ot=(0,j.pick)(f,_.sessions.model.inputNames);q=(await Ae(_,ot)).last_hidden_state}return Le.input_ids=$e,Le.encoder_hidden_states=q,_.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(Le.encoder_attention_mask=f.attention_mask),await Pe(_,Le,!0)}async function Ae(_,f){const q=_.sessions.model,xe=(0,j.pick)(f,q.inputNames);if(q.inputNames.includes("inputs_embeds")&&!xe.inputs_embeds){if(!f.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");xe.inputs_embeds=await _.encode_text({input_ids:f.input_ids})}if(q.inputNames.includes("token_type_ids")&&!xe.token_type_ids){if(!xe.input_ids)throw new Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");xe.token_type_ids=(0,v.zeros_like)(xe.input_ids)}if(q.inputNames.includes("pixel_mask")&&!xe.pixel_mask){if(!xe.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const $e=xe.pixel_values.dims;xe.pixel_mask=(0,v.ones)([$e[0],$e[2],$e[3]])}return await ge(q,xe)}async function Pe(_,f,q=!1){const xe=_.sessions[q?"decoder_model_merged":"model"],{past_key_values:$e,...Le}=f;if(xe.inputNames.includes("use_cache_branch")&&(Le.use_cache_branch=pe(!!$e)),xe.inputNames.includes("position_ids")&&Le.attention_mask&&!Le.position_ids){const ot=_.config.model_type==="paligemma"?1:0;Le.position_ids=X(Le,$e,ot)}_.addPastKeyValues(Le,$e);const rt=(0,j.pick)(Le,xe.inputNames);return await ge(xe,rt)}function Je({image_token_id:_,inputs_embeds:f,image_features:q,input_ids:xe,attention_mask:$e}){const Le=xe.tolist().map(kt=>kt.reduce((Zt,Wt,Dt)=>(Wt==_&&Zt.push(Dt),Zt),[])),rt=Le.reduce((kt,Zt)=>kt+Zt.length,0),ot=q.dims[0];if(rt!==ot)throw new Error(`Image features and image tokens do not match: tokens: ${rt}, features ${ot}`);let _t=0;for(let kt=0;ktLe.dims[1])){if($eot==_.config.image_token_index)){const ot=_.config.num_image_tokens;if(!ot)throw new Error("`num_image_tokens` is missing in the model configuration.");const _t=Le.dims[1]-($e-ot);q.input_ids=Le.slice(null,[-_t,null]),q.attention_mask=(0,v.ones)([1,$e+_t])}}}return q}function Ee(_,f,q,xe){return q.past_key_values&&(f=f.map($e=>[$e.at(-1)])),{...q,decoder_input_ids:Me(f)}}function Oe(_,...f){return _.config.is_encoder_decoder?Ee(_,...f):de(_,...f)}function Xe(_,f,q,xe){const $e=!!q.past_key_values;return xe.guidance_scale!==null&&xe.guidance_scale>1&&($e?q.input_ids=(0,v.cat)([q.input_ids,q.input_ids],0):(q.input_ids=(0,v.cat)([q.input_ids,(0,v.full_like)(q.input_ids,BigInt(xe.pad_token_id))],0),q.attention_mask=(0,v.cat)([q.attention_mask,(0,v.full_like)(q.attention_mask,0n)],0))),($e||!q.pixel_values)&&(q.pixel_values=(0,v.full)([0,0,3,384,384],1)),$e&&(q.images_seq_mask=new v.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),q.images_emb_mask=new v.Tensor("bool",new Array(0).fill(!1),[1,1,0])),q}class ee extends Z.Callable{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(f,q,xe){super(),this.config=f,this.sessions=q,this.configs=xe;const $e=P.get(this.constructor),Le=S.get($e);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,Le){case I.DecoderOnly:this.can_generate=!0,this._forward=Pe,this._prepare_inputs_for_generation=de;break;case I.Seq2Seq:case I.Vision2Seq:case I.Musicgen:this.can_generate=!0,this._forward=Ce,this._prepare_inputs_for_generation=Ee;break;case I.EncoderDecoder:this._forward=Ce;break;case I.ImageTextToText:this.can_generate=!0,this._forward=je,this._prepare_inputs_for_generation=Oe;break;case I.Phi3V:this.can_generate=!0,this._prepare_inputs_for_generation=Oe;break;case I.MultiModality:this.can_generate=!0,this._prepare_inputs_for_generation=Xe;break;default:this._forward=Ae;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const f=[];for(const q of Object.values(this.sessions))q?.handler?.dispose&&f.push(q.handler.dispose());return await Promise.all(f)}static async from_pretrained(f,{progress_callback:q=null,config:xe=null,cache_dir:$e=null,local_files_only:Le=!1,revision:rt="main",model_file_name:ot=null,subfolder:_t="onnx",device:kt=null,dtype:Zt=null,use_external_data_format:Wt=null,session_options:Dt={}}={}){let At={progress_callback:q,config:xe,cache_dir:$e,local_files_only:Le,revision:rt,model_file_name:ot,subfolder:_t,device:kt,dtype:Zt,use_external_data_format:Wt,session_options:Dt};const bs=P.get(this),Ht=S.get(bs);xe=At.config=await g.AutoConfig.from_pretrained(f,At);let Qt;if(Ht===I.DecoderOnly)Qt=await Promise.all([ae(f,{model:At.model_file_name??"model"},At),ie(f,{generation_config:"generation_config.json"},At)]);else if(Ht===I.Seq2Seq||Ht===I.Vision2Seq)Qt=await Promise.all([ae(f,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},At),ie(f,{generation_config:"generation_config.json"},At)]);else if(Ht===I.MaskGeneration)Qt=await Promise.all([ae(f,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},At)]);else if(Ht===I.EncoderDecoder)Qt=await Promise.all([ae(f,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},At)]);else if(Ht===I.ImageTextToText){const ps={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};xe.is_encoder_decoder&&(ps.model="encoder_model"),Qt=await Promise.all([ae(f,ps,At),ie(f,{generation_config:"generation_config.json"},At)])}else if(Ht===I.Musicgen)Qt=await Promise.all([ae(f,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},At),ie(f,{generation_config:"generation_config.json"},At)]);else if(Ht===I.MultiModality)Qt=await Promise.all([ae(f,{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"},At),ie(f,{generation_config:"generation_config.json"},At)]);else if(Ht===I.Phi3V)Qt=await Promise.all([ae(f,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},At),ie(f,{generation_config:"generation_config.json"},At)]);else{if(Ht!==I.EncoderOnly){const ps=bs??xe?.model_type;ps!=="custom"&&console.warn(`Model type for '${ps}' not found, assuming encoder-only architecture. Please report this at ${T.GITHUB_ISSUE_URL}.`)}Qt=await Promise.all([ae(f,{model:At.model_file_name??"model"},At)])}return new this(xe,...Qt)}async _call(f){return await this.forward(f)}async forward(f){return await this._forward(this,f)}get generation_config(){return this.configs?.generation_config??null}_get_logits_warper(f){const q=new b.LogitsProcessorList;return f.temperature!==null&&f.temperature!==1&&q.push(new b.TemperatureLogitsWarper(f.temperature)),f.top_k!==null&&f.top_k!==0&&q.push(new b.TopKLogitsWarper(f.top_k)),f.top_p!==null&&f.top_p<1&&q.push(new b.TopPLogitsWarper(f.top_p)),q}_get_logits_processor(f,q,xe=null){const $e=new b.LogitsProcessorList;if(f.repetition_penalty!==null&&f.repetition_penalty!==1&&$e.push(new b.RepetitionPenaltyLogitsProcessor(f.repetition_penalty)),f.no_repeat_ngram_size!==null&&f.no_repeat_ngram_size>0&&$e.push(new b.NoRepeatNGramLogitsProcessor(f.no_repeat_ngram_size)),f.bad_words_ids!==null&&$e.push(new b.NoBadWordsLogitsProcessor(f.bad_words_ids,f.eos_token_id)),f.min_length!==null&&f.eos_token_id!==null&&f.min_length>0&&$e.push(new b.MinLengthLogitsProcessor(f.min_length,f.eos_token_id)),f.min_new_tokens!==null&&f.eos_token_id!==null&&f.min_new_tokens>0&&$e.push(new b.MinNewTokensLengthLogitsProcessor(q,f.min_new_tokens,f.eos_token_id)),f.forced_bos_token_id!==null&&$e.push(new b.ForcedBOSTokenLogitsProcessor(f.forced_bos_token_id)),f.forced_eos_token_id!==null&&$e.push(new b.ForcedEOSTokenLogitsProcessor(f.max_length,f.forced_eos_token_id)),f.begin_suppress_tokens!==null){const Le=q>1||f.forced_bos_token_id===null?q:q+1;$e.push(new b.SuppressTokensAtBeginLogitsProcessor(f.begin_suppress_tokens,Le))}return f.guidance_scale!==null&&f.guidance_scale>1&&$e.push(new b.ClassifierFreeGuidanceLogitsProcessor(f.guidance_scale)),xe!==null&&$e.extend(xe),$e}_prepare_generation_config(f,q,xe=x.GenerationConfig){const $e={...this.config};for(const rt of["decoder","generator","text_config"])rt in $e&&Object.assign($e,$e[rt]);const Le=new xe($e);return Object.assign(Le,this.generation_config??{}),f&&Object.assign(Le,f),q&&Object.assign(Le,(0,j.pick)(q,Object.getOwnPropertyNames(Le))),Le}_get_stopping_criteria(f,q=null){const xe=new se.StoppingCriteriaList;return f.max_length!==null&&xe.push(new se.MaxLengthCriteria(f.max_length,this.config.max_position_embeddings??null)),f.eos_token_id!==null&&xe.push(new se.EosTokenCriteria(f.eos_token_id)),q&&xe.extend(q),xe}_validate_model_class(){if(!this.can_generate){const f=[Od,vi,ua,la],q=P.get(this.constructor),xe=new Set,$e=this.config.model_type;for(const rt of f){const ot=rt.get($e);ot&&xe.add(ot[0])}let Le=`The current model class (${q}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw xe.size>0&&(Le+=` Please use the following class instead: ${[...xe].join(", ")}`),Error(Le)}}prepare_inputs_for_generation(...f){return this._prepare_inputs_for_generation(this,...f)}_update_model_kwargs_for_generation({generated_input_ids:f,outputs:q,model_inputs:xe,is_encoder_decoder:$e}){return xe.past_key_values=this.getPastKeyValues(q,xe.past_key_values),xe.input_ids=new v.Tensor("int64",f.flat(),[f.length,1]),$e||(xe.attention_mask=(0,v.cat)([xe.attention_mask,(0,v.ones)([xe.attention_mask.dims[0],1])],1)),xe.position_ids=null,xe}_prepare_model_inputs({inputs:f,bos_token_id:q,model_kwargs:xe}){const $e=(0,j.pick)(xe,this.forward_params),Le=this.main_input_name;if(Le in $e){if(f)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else $e[Le]=f;return{inputs_tensor:$e[Le],model_inputs:$e,model_input_name:Le}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:f,model_inputs:q,model_input_name:xe,generation_config:$e}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!q.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:rt,pixel_values:ot,attention_mask:_t,...kt}=q,Zt=await this._prepare_inputs_embeds(q);q={...kt,...(0,j.pick)(Zt,["inputs_embeds","attention_mask"])}}let{last_hidden_state:Le}=await Ae(this,q);if($e.guidance_scale!==null&&$e.guidance_scale>1)Le=(0,v.cat)([Le,(0,v.full_like)(Le,0)],0),"attention_mask"in q&&(q.attention_mask=(0,v.cat)([q.attention_mask,(0,v.zeros_like)(q.attention_mask)],0));else if(q.decoder_input_ids){const rt=Me(q.decoder_input_ids).dims[0];if(rt!==Le.dims[0]){if(Le.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${Le.dims[0]}) than the decoder inputs (${rt}).`);Le=(0,v.cat)(Array.from({length:rt},()=>Le),0)}}return q.encoder_outputs=Le,q}_prepare_decoder_input_ids_for_generation({batch_size:f,model_input_name:q,model_kwargs:xe,decoder_start_token_id:$e,bos_token_id:Le,generation_config:rt}){let{decoder_input_ids:ot,..._t}=xe;if(!(ot instanceof v.Tensor)){if(ot)Array.isArray(ot[0])||(ot=Array.from({length:f},()=>ot));else if($e??=Le,this.config.model_type==="musicgen")ot=Array.from({length:f*this.config.decoder.num_codebooks},()=>[$e]);else if(Array.isArray($e)){if($e.length!==f)throw new Error(`\`decoder_start_token_id\` expcted to have length ${f} but got ${$e.length}`);ot=$e}else ot=Array.from({length:f},()=>[$e]);ot=Me(ot)}return xe.decoder_attention_mask=(0,v.ones_like)(ot),{input_ids:ot,model_inputs:_t}}async generate({inputs:f=null,generation_config:q=null,logits_processor:xe=null,stopping_criteria:$e=null,streamer:Le=null,...rt}){this._validate_model_class(),q=this._prepare_generation_config(q,rt);let{inputs_tensor:ot,model_inputs:_t,model_input_name:kt}=this._prepare_model_inputs({inputs:f,model_kwargs:rt});const Zt=this.config.is_encoder_decoder;Zt&&("encoder_outputs"in _t||(_t=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:ot,model_inputs:_t,model_input_name:kt,generation_config:q})));let Wt;Zt?{input_ids:Wt,model_inputs:_t}=this._prepare_decoder_input_ids_for_generation({batch_size:_t[kt].dims.at(0),model_input_name:kt,model_kwargs:_t,decoder_start_token_id:q.decoder_start_token_id,bos_token_id:q.bos_token_id,generation_config:q}):Wt=_t[kt];let Dt=Wt.dims.at(-1);q.max_new_tokens!==null&&(q.max_length=Dt+q.max_new_tokens);const At=this._get_logits_processor(q,Dt,xe),bs=this._get_stopping_criteria(q,$e),Ht=_t[kt].dims.at(0),Qt=oe.LogitsSampler.getSampler(q),ps=new Array(Ht).fill(0),ys=Wt.tolist();Le&&Le.put(ys);let es,Us={};for(;;){if(_t=this.prepare_inputs_for_generation(ys,_t,q),es=await this.forward(_t),q.output_attentions&&q.return_dict_in_generate){const lr=this.getAttentions(es);for(const Ar in lr)Ar in Us||(Us[Ar]=[]),Us[Ar].push(lr[Ar])}const Bs=es.logits.slice(null,-1,null),nr=At(ys,Bs),mr=[];for(let lr=0;lrlr))break;_t=this._update_model_kwargs_for_generation({generated_input_ids:mr,outputs:es,model_inputs:_t,is_encoder_decoder:Zt})}Le&&Le.end();const zs=this.getPastKeyValues(es,_t.past_key_values,!0),Fs=new v.Tensor("int64",ys.flat(),[ys.length,ys[0].length]);if(q.return_dict_in_generate)return{sequences:Fs,past_key_values:zs,...Us};for(const Bs of Object.values(es))Bs.location==="gpu-buffer"&&Bs.dispose();return Fs}getPastKeyValues(f,q,xe=!1){const $e=Object.create(null);for(const Le in f)if(Le.startsWith("present")){const rt=Le.replace("present","past_key_values"),ot=Le.includes("encoder");if(ot&&q?$e[rt]=q[rt]:$e[rt]=f[Le],q&&(!ot||xe)){const _t=q[rt];_t.location==="gpu-buffer"&&_t.dispose()}}return $e}getAttentions(f){const q={};for(const xe of["cross_attentions","encoder_attentions","decoder_attentions"])for(const $e in f)$e.startsWith(xe)&&(xe in q||(q[xe]=[]),q[xe].push(f[$e]));return q}addPastKeyValues(f,q){if(q)Object.assign(f,q);else{const $e=(this.sessions.decoder_model_merged??this.sessions.model)?.config?.kv_cache_dtype??"float32",Le=$e==="float16"?new Uint16Array:[],rt=(f[this.main_input_name]??f.attention_mask)?.dims?.[0]??1,ot=(0,g.getKeyValueShapes)(this.config,{batch_size:rt});for(const _t in ot)f[_t]=new v.Tensor($e,Le,ot[_t])}}async encode_image({pixel_values:f}){const q=(await ge(this.sessions.vision_encoder,{pixel_values:f})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${q.dims[1]}).`),this.config.num_image_tokens=q.dims[1]),q}async encode_text({input_ids:f}){return(await ge(this.sessions.embed_tokens,{input_ids:f})).inputs_embeds}}class Ve{}class Re extends Ve{constructor({last_hidden_state:f,hidden_states:q=null,attentions:xe=null}){super(),this.last_hidden_state=f,this.hidden_states=q,this.attentions=xe}}class te extends ee{}class ve extends te{}class Ke extends te{async _call(f){return new Qs(await super._call(f))}}class Ne extends te{async _call(f){return new Kt(await super._call(f))}}class Ue extends te{async _call(f){return new Vs(await super._call(f))}}class ze extends te{async _call(f){return new Zs(await super._call(f))}}class Ze extends ee{}class at extends Ze{}class mt extends Ze{async _call(f){return new Qs(await super._call(f))}}class lt extends Ze{async _call(f){return new Kt(await super._call(f))}}class ct extends Ze{async _call(f){return new Vs(await super._call(f))}}class O extends ee{}class le extends O{}class Q extends ee{}class we extends Q{}class Se extends Q{async _call(f){return new Qs(await super._call(f))}}class We extends Q{async _call(f){return new Kt(await super._call(f))}}class qe extends Q{async _call(f){return new Vs(await super._call(f))}}class tt extends Q{async _call(f){return new Zs(await super._call(f))}}class st extends ee{}class ft extends st{}class Nt extends st{async _call(f){return new Qs(await super._call(f))}}class ss extends st{async _call(f){return new Kt(await super._call(f))}}class Ts extends st{async _call(f){return new Vs(await super._call(f))}}class ms extends st{async _call(f){return new Zs(await super._call(f))}}class Ps extends ee{}class As extends Ps{}class tr extends Ps{async _call(f){return new Qs(await super._call(f))}}class Tr extends Ps{async _call(f){return new Kt(await super._call(f))}}class Gr extends Ps{async _call(f){return new Vs(await super._call(f))}}class Ns extends Ps{async _call(f){return new Zs(await super._call(f))}}class Mr extends ee{}class Lt extends Mr{}class Kr extends Mr{async _call(f){return new Qs(await super._call(f))}}class Pr extends Mr{async _call(f){return new Kt(await super._call(f))}}class Er extends Mr{async _call(f){return new Vs(await super._call(f))}}class Hr extends Mr{async _call(f){return new Zs(await super._call(f))}}class dr extends ee{}class qr extends dr{}class Cr extends dr{async _call(f){return new Qs(await super._call(f))}}class Dr extends dr{async _call(f){return new Kt(await super._call(f))}}class Lr extends dr{async _call(f){return new Vs(await super._call(f))}}class or extends dr{async _call(f){return new Zs(await super._call(f))}}class it extends ee{}class vt extends it{}class Ft extends it{async _call(f){return new Qs(await super._call(f))}}class Ys extends it{async _call(f){return new Kt(await super._call(f))}}class Cn extends it{async _call(f){return new Vs(await super._call(f))}}class dn extends it{async _call(f){return new Zs(await super._call(f))}}class gs extends ee{}class br extends gs{}class Ls extends gs{async _call(f){return new Kt(await super._call(f))}}class Qr extends gs{async _call(f){return new Vs(await super._call(f))}}class ns extends gs{async _call(f){return new Zs(await super._call(f))}}class cn extends gs{async _call(f){return new Qs(await super._call(f))}}class zr extends ee{}class Jn extends zr{}class kn extends zr{async _call(f){return new Qs(await super._call(f))}}class Sn extends zr{async _call(f){return new Kt(await super._call(f))}}class $n extends zr{async _call(f){return new Vs(await super._call(f))}}class Br extends ee{}class An extends Br{}class Zn extends Br{async _call(f){return new Qs(await super._call(f))}}class Rr extends Br{async _call(f){return new Kt(await super._call(f))}}class fr extends Br{async _call(f){return new Zs(await super._call(f))}}class ar extends ee{}class pn extends ar{}class Xr extends ar{async _call(f){return new Qs(await super._call(f))}}class hn extends ar{async _call(f){return new Kt(await super._call(f))}}class mn extends ar{async _call(f){return new Vs(await super._call(f))}}class fn extends ar{async _call(f){return new Zs(await super._call(f))}}class Ot extends ee{}class _n extends Ot{}class In extends Ot{async _call(f){return new Qs(await super._call(f))}}class Fn extends Ot{async _call(f){return new Kt(await super._call(f))}}class On extends Ot{async _call(f){return new Zs(await super._call(f))}}class Nr extends ee{}class Dn extends Nr{}class gn extends Nr{async _call(f){return new Kt(await super._call(f))}}class Ln extends Nr{async _call(f){return new Zs(await super._call(f))}}class as extends Nr{async _call(f){return new Qs(await super._call(f))}}class Te extends ee{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]}class M extends Te{}class Y extends Te{}class ce extends ee{}class me extends ce{}class Fe extends ce{}class Ye extends ee{}class gt extends Ye{}class wt extends Ye{}class yt extends ee{}class pt extends yt{}class rs extends yt{}class Yt extends yt{async _call(f){return new Kt(await super._call(f))}}class Ms extends ee{}class Gs extends Ms{}class jt extends Ms{}class is extends Ms{async _call(f){return new Kt(await super._call(f))}}class Ks extends Ms{}class Js extends ee{}class De extends Js{}class Xs extends Js{}class kr extends ee{}class Es extends kr{}class Hs extends kr{}class $t extends ee{}class sr extends $t{}class _r extends $t{async _call(f){return new Qs(await super._call(f))}}class ds extends $t{async _call(f){return new Kt(await super._call(f))}}class Cs extends $t{async _call(f){return new Vs(await super._call(f))}}class Mt extends $t{async _call(f){return new Zs(await super._call(f))}}class Gt extends ee{}class Is extends Gt{}class ks extends Gt{async _call(f){return new Qs(await super._call(f))}}class js extends Gt{async _call(f){return new Kt(await super._call(f))}}class Tt extends Gt{async _call(f){return new Vs(await super._call(f))}}class Yr extends Gt{async _call(f){return new Zs(await super._call(f))}}class He extends ee{}class Ct extends He{}class Ea extends He{async _call(f){return new Qs(await super._call(f))}}class Ii extends He{async _call(f){return new Kt(await super._call(f))}}class Ca extends He{async _call(f){return new Vs(await super._call(f))}}class ka extends He{async _call(f){return new Zs(await super._call(f))}}class qt extends ee{}class Sa extends qt{}class Fi extends qt{}class Oi extends ee{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]}class $a extends Oi{}class Aa extends Oi{_prepare_generation_config(f,q){return super._prepare_generation_config(f,q,U.WhisperGenerationConfig)}_retrieve_init_tokens(f){const q=[f.decoder_start_token_id];let xe=f.language;const $e=f.task;if(f.is_multilingual){xe||(console.warn("No language specified - defaulting to English (en)."),xe="en");const rt=`<|${(0,H.whisper_language_to_code)(xe)}|>`;q.push(f.lang_to_id[rt]),q.push(f.task_to_id[$e??"transcribe"])}else if(xe||$e)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!f.return_timestamps&&f.no_timestamps_token_id&&q.at(-1)!==f.no_timestamps_token_id?q.push(f.no_timestamps_token_id):f.return_timestamps&&q.at(-1)===f.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),q.pop()),q.filter(Le=>Le!=null)}async generate({inputs:f=null,generation_config:q=null,logits_processor:xe=null,stopping_criteria:$e=null,...Le}){q=this._prepare_generation_config(q,Le);const rt=Le.decoder_input_ids??this._retrieve_init_tokens(q);if(q.return_timestamps&&(xe??=new b.LogitsProcessorList,xe.push(new b.WhisperTimeStampLogitsProcessor(q,rt))),q.begin_suppress_tokens&&(xe??=new b.LogitsProcessorList,xe.push(new b.SuppressTokensAtBeginLogitsProcessor(q.begin_suppress_tokens,rt.length))),q.return_token_timestamps){if(!q.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");q.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),q.output_attentions=!0,q.return_dict_in_generate=!0}const ot=await super.generate({inputs:f,generation_config:q,logits_processor:xe,decoder_input_ids:rt,...Le});return q.return_token_timestamps&&(ot.token_timestamps=this._extract_token_timestamps(ot,q.alignment_heads,q.num_frames)),ot}_extract_token_timestamps(f,q,xe=null,$e=.02){if(!f.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`.");xe==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 Le=this.config.median_filter_width;Le===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Le=7);const rt=f.cross_attentions,ot=Array.from({length:this.config.decoder_layers},(Ht,Qt)=>(0,v.cat)(rt.map(ps=>ps[Qt]),2)),_t=(0,v.stack)(q.map(([Ht,Qt])=>{if(Ht>=ot.length)throw new Error(`Layer index ${Ht} is out of bounds for cross attentions (length ${ot.length}).`);return xe?ot[Ht].slice(null,Qt,null,[0,xe]):ot[Ht].slice(null,Qt)})).transpose(1,0,2,3),[kt,Zt]=(0,v.std_mean)(_t,-2,0,!0),Wt=_t.clone();for(let Ht=0;Htps[Bs+1]-ps[Bs]),Us=(0,j.mergeArrays)([1],es).map(Fs=>!!Fs),zs=[];for(let Fs=0;FsDt.findIndex(At=>At==Le)),_t=ot.every(Dt=>Dt===-1),kt=ot.every(Dt=>Dt!==-1);if(!_t&&!kt)throw new Error("Every input should contain either 0 or 1 image token.");if(_t)return{inputs_embeds:f,attention_mask:$e};const Zt=[],Wt=[];for(let Dt=0;DtArray.from({length:f.dims[0]},es=>Array.from({length:f.dims[1]},Us=>1))),bs=q?q.tolist():[],Ht=xe?xe.tolist():[];let Qt=0,ps=0;for(let ys=0;ysDt[ys][Os]==1),zs=es.reduce((ws,Os,Ir)=>(Os==_t&&ws.push(Ir),ws),[]).map(ws=>es[ws+1]),Fs=zs.filter(ws=>ws==rt).length,Bs=zs.filter(ws=>ws==ot).length;let nr=[],mr=0,_a=Fs,lr=Bs;for(let ws=0;wsur>mr&&nn==rt),Ir=es.findIndex((nn,ur)=>ur>mr&&nn==ot),sn=_a>0&&Os!==-1?Os:es.length+1,bn=lr>0&&Ir!==-1?Ir:es.length+1;let ga,Ti,wa,ya;sn0?(0,K.max)(nr.at(-1))[0]+1:0;nr.push(Array.from({length:3*ba},(nn,ur)=>Yd+ur%ba));const Jd=ba+Yd,Pi=Vp*Ma*vn,Zd=Array.from({length:Pi},(nn,ur)=>Jd+Math.floor(ur/(Ma*vn))),ec=Array.from({length:Pi},(nn,ur)=>Jd+Math.floor(ur/vn)%Ma),rn=Array.from({length:Pi},(nn,ur)=>Jd+ur%vn);nr.push([Zd,ec,rn].flat()),mr=ga+Pi}if(mr0?(0,K.max)(nr.at(-1))[0]+1:0,Os=es.length-mr;nr.push(Array.from({length:3*Os},(Ir,sn)=>ws+sn%Os))}const Ar=nr.reduce((ws,Os)=>ws+Os.length,0),Gn=new Array(Ar);let Xd=0;for(let ws=0;ws<3;++ws)for(let Os=0;OsWt[Qt%Wt.length]),bs=Array.from({length:Dt[0]},(Ht,Qt)=>(0,K.max)(Wt.subarray(Dt[1]*Qt,Dt[1]*(Qt+1)))[0]+1n+BigInt(Dt[1]));return[new v.Tensor("int64",At,[3,...Dt]),new v.Tensor("int64",bs,[bs.length,1])]}else{const[Wt,Dt]=f.dims,At=BigInt64Array.from({length:3*Wt*Dt},(bs,Ht)=>BigInt(Math.floor(Ht%Dt/Wt)));return[new v.Tensor("int64",At,[3,...f.dims]),(0,v.zeros)([Wt,1])]}}async encode_image({pixel_values:f,image_grid_thw:q}){return(await ge(this.sessions.vision_encoder,{pixel_values:f,grid_thw:q})).image_features}_merge_input_ids_with_image_features(f){return Je({image_token_id:this.config.image_token_id,...f})}prepare_inputs_for_generation(f,q,xe){if(q.attention_mask&&!q.position_ids)if(!q.past_key_values)[q.position_ids,q.rope_deltas]=this.get_rope_index(q.input_ids,q.image_grid_thw,q.video_grid_thw,q.attention_mask);else{q.pixel_values=null;const $e=BigInt(Object.values(q.past_key_values)[0].dims.at(-2)),Le=q.rope_deltas.map(rt=>$e+rt);q.position_ids=(0,v.stack)([Le,Le,Le],0)}return q}}class ao extends ee{}class xl extends ao{}class Bn extends ao{}class lo extends ee{}class ii extends lo{}class Tl extends lo{}class uo extends ee{}class Pl extends uo{}class El extends uo{}class co extends ee{}class Cl extends co{}class kl extends co{}class po extends ee{}class Sl extends po{}class $l extends po{}class ho extends ee{}class Al extends ho{}class Il extends ho{async _call(f){return new Kt(await super._call(f))}}class mo extends ee{}class Fl extends mo{}class Ol extends mo{async _call(f){return new Kt(await super._call(f))}}class fo extends ee{}class Dl extends fo{}class oi extends ee{}class _o extends oi{}class Ll extends oi{async _call(f){return new Kt(await super._call(f))}}class zl extends ee{}class Bl extends zl{}class go extends ee{}class Rl extends go{}class Nl extends go{async _call(f){return new Kt(await super._call(f))}}class wo extends ee{}class jl extends wo{}class yo extends ee{}class Vl extends yo{}class fc extends yo{async _call(f){return new Kt(await super._call(f))}}class Ul extends ee{}class Wl extends Ul{async _call(f){return new Wn(await super._call(f))}}class hr extends ee{}class Gl extends hr{}class Kl extends hr{async _call(f){return new Kt(await super._call(f))}}class Mo extends ee{}class Hl extends Mo{}class ql extends Mo{async _call(f){return new Kt(await super._call(f))}}class bo extends ee{}class Ql extends bo{}class Xl extends bo{}class vo extends ee{}class Yl extends vo{}class _c extends vo{}class xo extends ee{}class Jl extends xo{}class Zl extends xo{async _call(f){return new Kt(await super._call(f))}}class ai extends ee{}class eu extends ai{}class tu extends ai{async _call(f){return new Vr(await super._call(f))}}class su extends ai{async _call(f){return new Zr(await super._call(f))}}class Vr extends Ve{constructor({logits:f,pred_boxes:q}){super(),this.logits=f,this.pred_boxes=q}}class Zr extends Ve{constructor({logits:f,pred_boxes:q,pred_masks:xe}){super(),this.logits=f,this.pred_boxes=q,this.pred_masks=xe}}class Ur extends ee{}class To extends Ur{}class en extends Ur{async _call(f){return new qs(await super._call(f))}}class qs extends Ve{constructor({logits:f,pred_boxes:q}){super(),this.logits=f,this.pred_boxes=q}}class Po extends ee{}class Eo extends Po{}class ru extends Po{async _call(f){return new gc(await super._call(f))}}class gc extends Vr{}class wn extends ee{}class Co extends wn{}class ko extends wn{async _call(f){return new Kt(await super._call(f))}}class So extends ee{}class nu extends So{}class $o extends So{async _call(f){return new Kt(await super._call(f))}}class li extends ee{}class iu extends li{}class Ao extends li{async _call(f){return new Kt(await super._call(f))}}class Io extends ee{}class ui extends Io{}class Fo extends Io{async _call(f){return new Kt(await super._call(f))}}class Oo extends ee{}class ou extends Oo{}class wc extends Oo{}class Do extends ee{}class Lo extends Do{}class Rn extends Do{}class au extends ee{}class zo extends au{}class di extends ee{}class lu extends di{}class uu extends di{}class yc extends di{}class du extends ee{}class cu extends du{}class pu extends ee{}class hu extends pu{}class ci extends pu{}class Bo extends ee{}class pi extends Bo{}class Ro extends Bo{}class No extends ee{}class mu extends No{}class jo extends ee{}class Vo extends jo{}class Mc extends jo{async _call(f){return new Kt(await super._call(f))}}class Uo extends ee{}class bc extends Uo{}class fu extends Uo{async _call(f){return new Kt(await super._call(f))}}class Wo extends ee{}class _u extends Wo{}class Go extends Wo{async _call(f){return new Kt(await super._call(f))}}class Ko extends ee{}class gu extends Ko{}class Ho extends Ko{async _call(f){return new Kt(await super._call(f))}}class wu extends ee{}class yu extends wu{}class Mu extends ee{}class bu extends Mu{}class vu extends Mu{async _call(f){return new xu(await super._call(f))}}class xu extends Ve{constructor({logits:f,pred_boxes:q}){super(),this.logits=f,this.pred_boxes=q}}class vc extends ee{}class Tu extends vc{async get_image_embeddings({pixel_values:f}){return await Ae(this,{pixel_values:f})}async forward(f){if((!f.image_embeddings||!f.image_positional_embeddings)&&(f={...f,...await this.get_image_embeddings(f)}),!f.input_labels&&f.input_points){const xe=f.input_points.dims.slice(0,-1),$e=xe.reduce((Le,rt)=>Le*rt,1);f.input_labels=new v.Tensor("int64",new BigInt64Array($e).fill(1n),xe)}const q={image_embeddings:f.image_embeddings,image_positional_embeddings:f.image_positional_embeddings};return f.input_points&&(q.input_points=f.input_points),f.input_labels&&(q.input_labels=f.input_labels),f.input_boxes&&(q.input_boxes=f.input_boxes),await ge(this.sessions.prompt_encoder_mask_decoder,q)}async _call(f){return new Pu(await super._call(f))}}class Pu extends Ve{constructor({iou_scores:f,pred_masks:q}){super(),this.iou_scores=f,this.pred_masks=q}}class qo extends ee{}class Eu extends qo{}class Cu extends qo{}class ku extends ee{}class hi extends ku{}class Nn extends ku{}class Sr extends ee{}class Su extends Sr{}class $u extends Sr{async _call(f){return new yn(await super._call(f))}}class Au extends Sr{async _call(f){return new Kt(await super._call(f))}}class Iu extends Sr{async _call(f){return new Vs(await super._call(f))}}class mi extends ee{}class Fu extends mi{}class Ou extends mi{async _call(f){return new Vs(await super._call(f))}}class Du extends ee{}class xc extends Du{}class fi extends ee{}class Qo extends fi{}class Lu extends fi{async _call(f){return new yn(await super._call(f))}}class zu extends fi{async _call(f){return new Kt(await super._call(f))}}class jn extends ee{}class Tc extends jn{}class Bu extends jn{async _call(f){return new yn(await super._call(f))}}class Ru extends jn{async _call(f){return new Kt(await super._call(f))}}class Pc extends jn{async _call(f){return new Vs(await super._call(f))}}class _i extends ee{}class Nu extends _i{}class ju extends _i{async _call(f){return new yn(await super._call(f))}}class Vu extends _i{async _call(f){return new Kt(await super._call(f))}}class Bp extends ee{}class Uu extends Sr{}class Wu extends Sr{async _call(f){return new yn(await super._call(f))}}class Gu extends Sr{async _call(f){return new Kt(await super._call(f))}}class Vn extends ee{}class Ku extends Vn{}class Hu extends Vn{async _call(f){return new yn(await super._call(f))}}class qu extends Vn{async _call(f){return new Kt(await super._call(f))}}class Qu extends Vn{async _call(f){return new mp(await super._call(f))}}class Ec extends Vn{async _call(f){return new Vs(await super._call(f))}}class Xu extends ee{}class Yu extends Xu{}class gi extends ee{}class Cc extends gi{}class kc extends gi{}class Ju extends gi{async generate_speech(f,q,{threshold:xe=.5,minlenratio:$e=0,maxlenratio:Le=20,vocoder:rt=null}={}){const ot={input_ids:f},{encoder_outputs:_t,encoder_attention_mask:kt}=await Ae(this,ot),Zt=_t.dims[1]/this.config.reduction_factor,Wt=Math.floor(Zt*Le),Dt=Math.floor(Zt*$e),At=this.config.num_mel_bins;let bs=[],Ht=null,Qt=null,ps=0;for(;;){++ps;const Us=pe(!!Qt);let zs;Qt?zs=Qt.output_sequence_out:zs=new v.Tensor("float32",new Float32Array(At),[1,1,At]);let Fs={use_cache_branch:Us,output_sequence:zs,encoder_attention_mask:kt,speaker_embeddings:q,encoder_hidden_states:_t};this.addPastKeyValues(Fs,Ht),Qt=await ge(this.sessions.decoder_model_merged,Fs),Ht=this.getPastKeyValues(Qt,Ht);const{prob:Bs,spectrum:nr}=Qt;if(bs.push(nr),ps>=Dt&&(Array.from(Bs.data).filter(mr=>mr>=xe).length>0||ps>=Wt))break}const ys=(0,v.cat)(bs),{waveform:es}=await ge(rt.sessions.model,{spectrogram:ys});return{spectrogram:ys,waveform:es}}}class Zu extends ee{main_input_name="spectrogram"}class ed extends ee{}class td extends ed{}class sd extends ee{}class vr extends sd{}class $r extends sd{}class Wr extends ee{}class tn extends Wr{}class rd extends Wr{}class Xo extends ee{}class nd extends Xo{}class id extends Xo{}class wi extends ee{}class od extends wi{}class ad extends wi{static async from_pretrained(f,q={}){return super.from_pretrained(f,{...q,model_file_name:q.model_file_name??"text_model"})}}class ld extends wi{static async from_pretrained(f,q={}){return super.from_pretrained(f,{...q,model_file_name:q.model_file_name??"audio_model"})}}class ud extends ee{}class Yo extends ud{async _call(f){return new Qd(await super._call(f))}}class Jo extends ee{}class rr extends Jo{}class dd extends Jo{}class cd extends Jo{}class yi extends ee{}class pd extends yi{}class Un extends yi{}class Zo extends ee{}class hd extends Zo{}class md extends Zo{async _call(f){return new Kt(await super._call(f))}}class ea extends ee{}class Sc extends ea{}class $c extends ea{}class Mi extends ee{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];_apply_and_filter_by_delay_pattern_mask(f){const[q,xe]=f.dims,$e=this.config.decoder.num_codebooks,Le=xe-$e;let rt=0;for(let kt=0;kt0&&Dt<=Le&&(f.data[rt++]=f.data[kt])}const ot=Math.floor(q/$e),_t=rt/(ot*$e);return new v.Tensor(f.type,f.data.slice(0,rt),[ot,$e,_t])}prepare_inputs_for_generation(f,q,xe){let $e=structuredClone(f);for(let rt=0;rt<$e.length;++rt)for(let ot=0;ot<$e[rt].length;++ot)rt%this.config.decoder.num_codebooks>=ot&&($e[rt][ot]=BigInt(this.config.decoder.pad_token_id));return xe.guidance_scale!==null&&xe.guidance_scale>1&&($e=$e.concat($e)),super.prepare_inputs_for_generation($e,q,xe)}async generate(f){const q=await super.generate(f),xe=this._apply_and_filter_by_delay_pattern_mask(q).unsqueeze_(0),{audio_values:$e}=await ge(this.sessions.encodec_decode,{audio_codes:xe});return $e}}class ta extends ee{}class fd extends ta{}class _d extends ta{async _call(f){return new Kt(await super._call(f))}}class sa extends ee{}class gd extends sa{}class ra extends sa{async _call(f){return new Kt(await super._call(f))}}class na extends ee{}class Ac extends na{}class ia extends na{async _call(f){return new Kt(await super._call(f))}}class oa extends ee{}class wd extends oa{}class yd extends oa{async _call(f){return new Kt(await super._call(f))}}class Ic extends ee{}class Md extends Ic{}class bd extends ee{}class vd extends bd{forward_params=["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"];constructor(...f){super(...f),this._generation_mode="text"}async forward(f){const q=this._generation_mode??"text";let xe;if(q==="text"||!f.past_key_values){const _t=this.sessions.prepare_inputs_embeds,kt=(0,j.pick)(f,_t.inputNames);xe=await ge(_t,kt)}else{const _t=this.sessions.gen_img_embeds,kt=(0,j.pick)({image_ids:f.input_ids},_t.inputNames);xe=await ge(_t,kt)}const $e={...f,...xe},Le=await Pe(this,$e),rt=this.sessions[q==="text"?"lm_head":"gen_head"];if(!rt)throw new Error(`Unable to find "${rt}" generation head`);const ot=await ge(rt,(0,j.pick)(Le,rt.inputNames));return{...xe,...Le,...ot}}async generate(f){return this._generation_mode="text",super.generate(f)}async generate_images(f){this._generation_mode="image";const q=(f.inputs??f[this.main_input_name]).dims[1],$e=(await super.generate(f)).slice(null,[q,null]),Le=this.sessions.image_decode,{decoded_image:rt}=await ge(Le,{generated_tokens:$e}),ot=rt.add_(1).mul_(255/2).clamp_(0,255).to("uint8"),_t=[];for(const kt of ot){const Zt=L.RawImage.fromTensor(kt);_t.push(Zt)}return _t}}class Fc extends Ve{constructor({char_logits:f,bpe_logits:q,wp_logits:xe}){super(),this.char_logits=f,this.bpe_logits=q,this.wp_logits=xe}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class xd extends ee{}class Td extends xd{async _call(f){return new Fc(await super._call(f))}}class Pd extends ee{}class Ed extends Pd{}class Cd extends Pd{}class aa extends ee{}class kd extends aa{}class Sd extends aa{}class fs{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(f,{progress_callback:q=null,config:xe=null,cache_dir:$e=null,local_files_only:Le=!1,revision:rt="main",model_file_name:ot=null,subfolder:_t="onnx",device:kt=null,dtype:Zt=null,use_external_data_format:Wt=null,session_options:Dt={}}={}){const At={progress_callback:q,config:xe,cache_dir:$e,local_files_only:Le,revision:rt,model_file_name:ot,subfolder:_t,device:kt,dtype:Zt,use_external_data_format:Wt,session_options:Dt};if(At.config=await g.AutoConfig.from_pretrained(f,At),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const bs of this.MODEL_CLASS_MAPPINGS){const Ht=bs.get(At.config.model_type);if(Ht)return await Ht[1].from_pretrained(f,At)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${At.config.model_type}", attempting to construct from base class.`),await ee.from_pretrained(f,At);throw Error(`Unsupported model type: ${At.config.model_type}`)}}const Oc=new Map([["bert",["BertModel",ve]],["modernbert",["ModernBertModel",at]],["nomic_bert",["NomicBertModel",le]],["roformer",["RoFormerModel",we]],["electra",["ElectraModel",As]],["esm",["EsmModel",Jn]],["convbert",["ConvBertModel",ft]],["camembert",["CamembertModel",Lt]],["deberta",["DebertaModel",qr]],["deberta-v2",["DebertaV2Model",vt]],["mpnet",["MPNetModel",pn]],["albert",["AlbertModel",Dn]],["distilbert",["DistilBertModel",br]],["roberta",["RobertaModel",sr]],["xlm",["XLMModel",Is]],["xlm-roberta",["XLMRobertaModel",Ct]],["clap",["ClapModel",od]],["clip",["CLIPModel",ja]],["clipseg",["CLIPSegModel",Ha]],["chinese_clip",["ChineseCLIPModel",gr]],["siglip",["SiglipModel",Wa]],["jina_clip",["JinaCLIPModel",si]],["mobilebert",["MobileBertModel",An]],["squeezebert",["SqueezeBertModel",_n]],["wav2vec2",["Wav2Vec2Model",Su]],["wav2vec2-bert",["Wav2Vec2BertModel",Nu]],["unispeech",["UniSpeechModel",Qo]],["unispeech-sat",["UniSpeechSatModel",Tc]],["hubert",["HubertModel",Uu]],["wavlm",["WavLMModel",Ku]],["audio-spectrogram-transformer",["ASTModel",Sa]],["vits",["VitsModel",Yo]],["pyannote",["PyAnnoteModel",Fu]],["wespeaker-resnet",["WeSpeakerResNetModel",xc]],["detr",["DetrModel",eu]],["rt_detr",["RTDetrModel",To]],["table-transformer",["TableTransformerModel",Eo]],["vit",["ViTModel",Al]],["ijepa",["IJepaModel",Fl]],["pvt",["PvtModel",_o]],["vit_msn",["ViTMSNModel",Rl]],["vit_mae",["ViTMAEModel",Bl]],["groupvit",["GroupViTModel",jl]],["fastvit",["FastViTModel",Vl]],["mobilevit",["MobileViTModel",Gl]],["mobilevitv2",["MobileViTV2Model",Hl]],["owlvit",["OwlViTModel",Ql]],["owlv2",["Owlv2Model",Yl]],["beit",["BeitModel",Jl]],["deit",["DeiTModel",Co]],["hiera",["HieraModel",nu]],["convnext",["ConvNextModel",Vo]],["convnextv2",["ConvNextV2Model",bc]],["dinov2",["Dinov2Model",_u]],["dinov2_with_registers",["Dinov2WithRegistersModel",gu]],["resnet",["ResNetModel",iu]],["swin",["SwinModel",ui]],["swin2sr",["Swin2SRModel",ou]],["donut-swin",["DonutSwinModel",mu]],["yolos",["YolosModel",bu]],["dpt",["DPTModel",Lo]],["glpn",["GLPNModel",pi]],["hifigan",["SpeechT5HifiGan",Zu]],["efficientnet",["EfficientNetModel",hd]],["decision_transformer",["DecisionTransformerModel",Md]],["patchtst",["PatchTSTForPrediction",Ed]],["patchtsmixer",["PatchTSMixerForPrediction",kd]],["mobilenet_v1",["MobileNetV1Model",fd]],["mobilenet_v2",["MobileNetV2Model",gd]],["mobilenet_v3",["MobileNetV3Model",Ac]],["mobilenet_v4",["MobileNetV4Model",wd]],["maskformer",["MaskFormerModel",hu]],["mgp-str",["MgpstrForSceneTextRecognition",Td]],["style_text_to_speech_2",["StyleTextToSpeech2Model",Yu]]]),Dc=new Map([["t5",["T5Model",M]],["longt5",["LongT5Model",me]],["mt5",["MT5Model",gt]],["bart",["BartModel",pt]],["mbart",["MBartModel",Gs]],["marian",["MarianModel",Eu]],["whisper",["WhisperModel",$a]],["m2m_100",["M2M100Model",hi]],["blenderbot",["BlenderbotModel",De]],["blenderbot-small",["BlenderbotSmallModel",Es]]]),Lc=new Map([["bloom",["BloomModel",Pl]],["jais",["JAISModel",Ya]],["gpt2",["GPT2Model",Qa]],["gptj",["GPTJModel",sl]],["gpt_bigcode",["GPTBigCodeModel",nl]],["gpt_neo",["GPTNeoModel",yr]],["gpt_neox",["GPTNeoXModel",el]],["codegen",["CodeGenModel",Hi]],["llama",["LlamaModel",Qi]],["exaone",["ExaoneModel",ni]],["olmo",["OlmoModel",ul]],["olmo2",["Olmo2Model",dl]],["mobilellm",["MobileLLMModel",ll]],["granite",["GraniteModel",hc]],["cohere",["CohereModel",hl]],["gemma",["GemmaModel",ls]],["gemma2",["Gemma2Model",fl]],["openelm",["OpenELMModel",gl]],["qwen2",["Qwen2Model",yl]],["phi",["PhiModel",xl]],["phi3",["Phi3Model",ii]],["mpt",["MptModel",Cl]],["opt",["OPTModel",Sl]],["mistral",["MistralModel",vr]],["starcoder2",["Starcoder2Model",tn]],["falcon",["FalconModel",nd]],["stablelm",["StableLmModel",pd]]]),la=new Map([["speecht5",["SpeechT5ForSpeechToText",kc]],["whisper",["WhisperForConditionalGeneration",Aa]],["moonshine",["MoonshineForConditionalGeneration",Ia]]]),$d=new Map([["speecht5",["SpeechT5ForTextToSpeech",Ju]]]),Ad=new Map([["vits",["VitsModel",Yo]],["musicgen",["MusicgenForConditionalGeneration",Mi]]]),Id=new Map([["bert",["BertForSequenceClassification",Ne]],["modernbert",["ModernBertForSequenceClassification",lt]],["roformer",["RoFormerForSequenceClassification",We]],["electra",["ElectraForSequenceClassification",Tr]],["esm",["EsmForSequenceClassification",Sn]],["convbert",["ConvBertForSequenceClassification",ss]],["camembert",["CamembertForSequenceClassification",Pr]],["deberta",["DebertaForSequenceClassification",Dr]],["deberta-v2",["DebertaV2ForSequenceClassification",Ys]],["mpnet",["MPNetForSequenceClassification",hn]],["albert",["AlbertForSequenceClassification",gn]],["distilbert",["DistilBertForSequenceClassification",Ls]],["roberta",["RobertaForSequenceClassification",ds]],["xlm",["XLMForSequenceClassification",js]],["xlm-roberta",["XLMRobertaForSequenceClassification",Ii]],["bart",["BartForSequenceClassification",Yt]],["mbart",["MBartForSequenceClassification",is]],["mobilebert",["MobileBertForSequenceClassification",Rr]],["squeezebert",["SqueezeBertForSequenceClassification",Fn]]]),Fd=new Map([["bert",["BertForTokenClassification",Ue]],["modernbert",["ModernBertForTokenClassification",ct]],["roformer",["RoFormerForTokenClassification",qe]],["electra",["ElectraForTokenClassification",Gr]],["esm",["EsmForTokenClassification",$n]],["convbert",["ConvBertForTokenClassification",Ts]],["camembert",["CamembertForTokenClassification",Er]],["deberta",["DebertaForTokenClassification",Lr]],["deberta-v2",["DebertaV2ForTokenClassification",Cn]],["mpnet",["MPNetForTokenClassification",mn]],["distilbert",["DistilBertForTokenClassification",Qr]],["roberta",["RobertaForTokenClassification",Cs]],["xlm",["XLMForTokenClassification",Tt]],["xlm-roberta",["XLMRobertaForTokenClassification",Ca]]]),ua=new Map([["t5",["T5ForConditionalGeneration",Y]],["longt5",["LongT5ForConditionalGeneration",Fe]],["mt5",["MT5ForConditionalGeneration",wt]],["bart",["BartForConditionalGeneration",rs]],["mbart",["MBartForConditionalGeneration",jt]],["marian",["MarianMTModel",Cu]],["m2m_100",["M2M100ForConditionalGeneration",Nn]],["blenderbot",["BlenderbotForConditionalGeneration",Xs]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Hs]]]),Od=new Map([["bloom",["BloomForCausalLM",El]],["gpt2",["GPT2LMHeadModel",Xa]],["jais",["JAISLMHeadModel",Ja]],["gptj",["GPTJForCausalLM",rl]],["gpt_bigcode",["GPTBigCodeForCausalLM",il]],["gpt_neo",["GPTNeoForCausalLM",Za]],["gpt_neox",["GPTNeoXForCausalLM",tl]],["codegen",["CodeGenForCausalLM",ol]],["llama",["LlamaForCausalLM",pc]],["exaone",["ExaoneForCausalLM",al]],["olmo",["OlmoForCausalLM",Zi]],["olmo2",["Olmo2ForCausalLM",cl]],["mobilellm",["MobileLLMForCausalLM",zn]],["granite",["GraniteForCausalLM",pl]],["cohere",["CohereForCausalLM",mc]],["gemma",["GemmaForCausalLM",ml]],["gemma2",["Gemma2ForCausalLM",_l]],["openelm",["OpenELMForCausalLM",wl]],["qwen2",["Qwen2ForCausalLM",Ml]],["phi",["PhiForCausalLM",Bn]],["phi3",["Phi3ForCausalLM",Tl]],["mpt",["MptForCausalLM",kl]],["opt",["OPTForCausalLM",$l]],["mbart",["MBartForCausalLM",Ks]],["mistral",["MistralForCausalLM",$r]],["starcoder2",["Starcoder2ForCausalLM",rd]],["falcon",["FalconForCausalLM",id]],["trocr",["TrOCRForCausalLM",td]],["stablelm",["StableLmForCausalLM",Un]],["phi3_v",["Phi3VForCausalLM",pr]]]),bi=new Map([["multi_modality",["MultiModalityCausalLM",vd]]]),da=new Map([["bert",["BertForMaskedLM",Ke]],["modernbert",["ModernBertForMaskedLM",mt]],["roformer",["RoFormerForMaskedLM",Se]],["electra",["ElectraForMaskedLM",tr]],["esm",["EsmForMaskedLM",kn]],["convbert",["ConvBertForMaskedLM",Nt]],["camembert",["CamembertForMaskedLM",Kr]],["deberta",["DebertaForMaskedLM",Cr]],["deberta-v2",["DebertaV2ForMaskedLM",Ft]],["mpnet",["MPNetForMaskedLM",Xr]],["albert",["AlbertForMaskedLM",as]],["distilbert",["DistilBertForMaskedLM",cn]],["roberta",["RobertaForMaskedLM",_r]],["xlm",["XLMWithLMHeadModel",ks]],["xlm-roberta",["XLMRobertaForMaskedLM",Ea]],["mobilebert",["MobileBertForMaskedLM",Zn]],["squeezebert",["SqueezeBertForMaskedLM",In]]]),ca=new Map([["bert",["BertForQuestionAnswering",ze]],["roformer",["RoFormerForQuestionAnswering",tt]],["electra",["ElectraForQuestionAnswering",Ns]],["convbert",["ConvBertForQuestionAnswering",ms]],["camembert",["CamembertForQuestionAnswering",Hr]],["deberta",["DebertaForQuestionAnswering",or]],["deberta-v2",["DebertaV2ForQuestionAnswering",dn]],["mpnet",["MPNetForQuestionAnswering",fn]],["albert",["AlbertForQuestionAnswering",Ln]],["distilbert",["DistilBertForQuestionAnswering",ns]],["roberta",["RobertaForQuestionAnswering",Mt]],["xlm",["XLMForQuestionAnswering",Yr]],["xlm-roberta",["XLMRobertaForQuestionAnswering",ka]],["mobilebert",["MobileBertForQuestionAnswering",fr]],["squeezebert",["SqueezeBertForQuestionAnswering",On]]]),vi=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Li]],["idefics3",["Idefics3ForConditionalGeneration",zi]]]),Dd=new Map([["llava",["LlavaForConditionalGeneration",ei]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",Fa]],["moondream1",["Moondream1ForConditionalGeneration",Oa]],["florence2",["Florence2ForConditionalGeneration",La]],["qwen2-vl",["Qwen2VLForConditionalGeneration",vl]],["idefics3",["Idefics3ForConditionalGeneration",zi]],["paligemma",["PaliGemmaForConditionalGeneration",Ba]]]),zc=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Li]]]),Ld=new Map([["vit",["ViTForImageClassification",Il]],["ijepa",["IJepaForImageClassification",Ol]],["pvt",["PvtForImageClassification",Ll]],["vit_msn",["ViTMSNForImageClassification",Nl]],["fastvit",["FastViTForImageClassification",fc]],["mobilevit",["MobileViTForImageClassification",Kl]],["mobilevitv2",["MobileViTV2ForImageClassification",ql]],["beit",["BeitForImageClassification",Zl]],["deit",["DeiTForImageClassification",ko]],["hiera",["HieraForImageClassification",$o]],["convnext",["ConvNextForImageClassification",Mc]],["convnextv2",["ConvNextV2ForImageClassification",fu]],["dinov2",["Dinov2ForImageClassification",Go]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",Ho]],["resnet",["ResNetForImageClassification",Ao]],["swin",["SwinForImageClassification",Fo]],["segformer",["SegformerForImageClassification",dd]],["efficientnet",["EfficientNetForImageClassification",md]],["mobilenet_v1",["MobileNetV1ForImageClassification",_d]],["mobilenet_v2",["MobileNetV2ForImageClassification",ra]],["mobilenet_v3",["MobileNetV3ForImageClassification",ia]],["mobilenet_v4",["MobileNetV4ForImageClassification",yd]]]),pa=new Map([["detr",["DetrForObjectDetection",tu]],["rt_detr",["RTDetrForObjectDetection",en]],["table-transformer",["TableTransformerForObjectDetection",ru]],["yolos",["YolosForObjectDetection",vu]]]),ha=new Map([["owlvit",["OwlViTForObjectDetection",Xl]],["owlv2",["Owlv2ForObjectDetection",_c]],["grounding-dino",["GroundingDinoForObjectDetection",yu]]]),zd=new Map([["detr",["DetrForSegmentation",su]],["clipseg",["CLIPSegForImageSegmentation",qa]]]),Bd=new Map([["segformer",["SegformerForSemanticSegmentation",cd]],["sapiens",["SapiensForSemanticSegmentation",lu]]]),ma=new Map([["detr",["DetrForSegmentation",su]],["maskformer",["MaskFormerForInstanceSegmentation",ci]]]),Rd=new Map([["sam",["SamModel",Tu]]]),Nd=new Map([["wav2vec2",["Wav2Vec2ForCTC",$u]],["wav2vec2-bert",["Wav2Vec2BertForCTC",ju]],["unispeech",["UniSpeechForCTC",Lu]],["unispeech-sat",["UniSpeechSatForCTC",Bu]],["wavlm",["WavLMForCTC",Hu]],["hubert",["HubertForCTC",Wu]]]),fa=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Au]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Vu]],["unispeech",["UniSpeechForSequenceClassification",zu]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Ru]],["wavlm",["WavLMForSequenceClassification",qu]],["hubert",["HubertForSequenceClassification",Gu]],["audio-spectrogram-transformer",["ASTForAudioClassification",Fi]]]),jd=new Map([["wavlm",["WavLMForXVector",Qu]]]),Vd=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Pc]],["wavlm",["WavLMForAudioFrameClassification",Ec]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Iu]],["pyannote",["PyAnnoteForAudioFrameClassification",Ou]]]),Ud=new Map([["vitmatte",["VitMatteForImageMatting",Wl]]]),Bc=new Map([["patchtst",["PatchTSTForPrediction",Cd]],["patchtsmixer",["PatchTSMixerForPrediction",Sd]]]),Rc=new Map([["swin2sr",["Swin2SRForImageSuperResolution",wc]]]),Wd=new Map([["dpt",["DPTForDepthEstimation",Rn]],["depth_anything",["DepthAnythingForDepthEstimation",zo]],["glpn",["GLPNForDepthEstimation",Ro]],["sapiens",["SapiensForDepthEstimation",uu]],["depth_pro",["DepthProForDepthEstimation",cu]]]),Gd=new Map([["sapiens",["SapiensForNormalEstimation",yc]]]),Kd=new Map([["vitpose",["VitPoseForPoseEstimation",Dl]]]),Hd=new Map([["clip",["CLIPVisionModelWithProjection",Ua]],["siglip",["SiglipVisionModel",Ka]],["jina_clip",["JinaCLIPVisionModel",wr]]]),Nc=[[Oc,I.EncoderOnly],[Dc,I.EncoderDecoder],[Lc,I.DecoderOnly],[Id,I.EncoderOnly],[Fd,I.EncoderOnly],[ua,I.Seq2Seq],[la,I.Seq2Seq],[Od,I.DecoderOnly],[bi,I.MultiModality],[da,I.EncoderOnly],[ca,I.EncoderOnly],[vi,I.Vision2Seq],[Dd,I.ImageTextToText],[Ld,I.EncoderOnly],[zd,I.EncoderOnly],[ma,I.EncoderOnly],[Bd,I.EncoderOnly],[Ud,I.EncoderOnly],[Bc,I.EncoderOnly],[Rc,I.EncoderOnly],[Wd,I.EncoderOnly],[Gd,I.EncoderOnly],[Kd,I.EncoderOnly],[pa,I.EncoderOnly],[ha,I.EncoderOnly],[Rd,I.MaskGeneration],[Nd,I.EncoderOnly],[fa,I.EncoderOnly],[$d,I.Seq2Seq],[Ad,I.EncoderOnly],[jd,I.EncoderOnly],[Vd,I.EncoderOnly],[Hd,I.EncoderOnly]];for(const[_,f]of Nc)for(const[q,xe]of _.values())S.set(q,f),P.set(xe,q),w.set(q,xe);const jc=[["MusicgenForConditionalGeneration",Mi,I.Musicgen],["Phi3VForCausalLM",pr,I.Phi3V],["CLIPTextModelWithProjection",Va,I.EncoderOnly],["SiglipTextModel",Ga,I.EncoderOnly],["JinaCLIPTextModel",Ri,I.EncoderOnly],["ClapTextModelWithProjection",ad,I.EncoderOnly],["ClapAudioModelWithProjection",ld,I.EncoderOnly]];for(const[_,f,q]of jc)S.set(_,q),P.set(f,_),w.set(_,f);class Vc extends fs{static MODEL_CLASS_MAPPINGS=Nc.map(f=>f[0]);static BASE_IF_FAIL=!0}class Uc extends fs{static MODEL_CLASS_MAPPINGS=[Id]}class Rp extends fs{static MODEL_CLASS_MAPPINGS=[Fd]}class Wc extends fs{static MODEL_CLASS_MAPPINGS=[ua]}class Gc extends fs{static MODEL_CLASS_MAPPINGS=[la]}class Kc extends fs{static MODEL_CLASS_MAPPINGS=[$d]}class Hc extends fs{static MODEL_CLASS_MAPPINGS=[Ad]}class Np extends fs{static MODEL_CLASS_MAPPINGS=[Od]}class qc extends fs{static MODEL_CLASS_MAPPINGS=[da]}class Qc extends fs{static MODEL_CLASS_MAPPINGS=[ca]}class Xc extends fs{static MODEL_CLASS_MAPPINGS=[vi]}class Yc extends fs{static MODEL_CLASS_MAPPINGS=[Ld]}class Jc extends fs{static MODEL_CLASS_MAPPINGS=[zd]}class Zc extends fs{static MODEL_CLASS_MAPPINGS=[Bd]}class ep extends fs{static MODEL_CLASS_MAPPINGS=[ma]}class tp extends fs{static MODEL_CLASS_MAPPINGS=[pa]}class sp extends fs{static MODEL_CLASS_MAPPINGS=[ha]}class rp extends fs{static MODEL_CLASS_MAPPINGS=[Rd]}class np extends fs{static MODEL_CLASS_MAPPINGS=[Nd]}class qd extends fs{static MODEL_CLASS_MAPPINGS=[fa]}class ip extends fs{static MODEL_CLASS_MAPPINGS=[jd]}class op extends fs{static MODEL_CLASS_MAPPINGS=[Vd]}class ap extends fs{static MODEL_CLASS_MAPPINGS=[zc]}class lp extends fs{static MODEL_CLASS_MAPPINGS=[Ud]}class up extends fs{static MODEL_CLASS_MAPPINGS=[Rc]}class dp extends fs{static MODEL_CLASS_MAPPINGS=[Wd]}class cp extends fs{static MODEL_CLASS_MAPPINGS=[Gd]}class jp extends fs{static MODEL_CLASS_MAPPINGS=[Kd]}class pp extends fs{static MODEL_CLASS_MAPPINGS=[Hd]}class hp extends Ve{constructor({logits:f,past_key_values:q,encoder_outputs:xe,decoder_attentions:$e=null,cross_attentions:Le=null}){super(),this.logits=f,this.past_key_values=q,this.encoder_outputs=xe,this.decoder_attentions=$e,this.cross_attentions=Le}}class Kt extends Ve{constructor({logits:f,...q}){super(),this.logits=f;const xe=Object.values(q);xe.length>0&&(this.attentions=xe)}}class mp extends Ve{constructor({logits:f,embeddings:q}){super(),this.logits=f,this.embeddings=q}}class Vs extends Ve{constructor({logits:f}){super(),this.logits=f}}class Qs extends Ve{constructor({logits:f}){super(),this.logits=f}}class Zs extends Ve{constructor({start_logits:f,end_logits:q}){super(),this.start_logits=f,this.end_logits=q}}class yn extends Ve{constructor({logits:f}){super(),this.logits=f}}class fp extends Ve{constructor({logits:f,past_key_values:q}){super(),this.logits=f,this.past_key_values=q}}class Wn extends Ve{constructor({alphas:f}){super(),this.alphas=f}}class Qd extends 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g.FeatureExtractor{constructor(j){super(j),this.mel_filters=(0,$.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,$.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,$.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(j,y,T,b){let x;const v=j.length-y;if(v>0)if(T==="rand_trunc"){const L=Math.floor(Math.random()*(v+1));j=j.subarray(L,L+y),x=await this._extract_fbank_features(j,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${T}" not implemented`);else{if(v<0){let L=new Float64Array(y);if(L.set(j),b==="repeat")for(let K=j.length;K{r.r(A),r.d(A,{CLIPFeatureExtractor:()=>N,CLIPImageProcessor:()=>$});var 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this._nearest_interpolate_4d||(this._nearest_interpolate_4d=N([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=N([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=N([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=N([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=N([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=N([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=N([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=N([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}}},"./src/pipelines.js":(ke,A,r)=>{r.r(A),r.d(A,{AudioClassificationPipeline:()=>ge,AutomaticSpeechRecognitionPipeline:()=>Me,DepthEstimationPipeline:()=>Ee,DocumentQuestionAnsweringPipeline:()=>_e,FeatureExtractionPipeline:()=>ie,FillMaskPipeline:()=>H,ImageClassificationPipeline:()=>Ce,ImageFeatureExtractionPipeline:()=>ye,ImageSegmentationPipeline:()=>Ae,ImageToImagePipeline:()=>de,ImageToTextPipeline:()=>pe,ObjectDetectionPipeline:()=>Je,Pipeline:()=>se,QuestionAnsweringPipeline:()=>U,SummarizationPipeline:()=>S,Text2TextGenerationPipeline:()=>I,TextClassificationPipeline:()=>oe,TextGenerationPipeline:()=>F,TextToAudioPipeline:()=>X,TokenClassificationPipeline:()=>V,TranslationPipeline:()=>w,ZeroShotAudioClassificationPipeline:()=>re,ZeroShotClassificationPipeline:()=>ae,ZeroShotImageClassificationPipeline:()=>Pe,ZeroShotObjectDetectionPipeline:()=>je,pipeline:()=>ee});var g=r("./src/tokenizers.js"),$=r("./src/models.js"),N=r("./src/models/auto/processing_auto.js");r("./src/base/processing_utils.js");var Z=r("./src/utils/generic.js"),j=r("./src/utils/core.js"),y=r("./src/utils/maths.js"),T=r("./src/utils/audio.js"),b=r("./src/utils/tensor.js"),x=r("./src/utils/image.js");async function v(Re){return Array.isArray(Re)||(Re=[Re]),await Promise.all(Re.map(te=>x.RawImage.read(te)))}async function L(Re,te){return Array.isArray(Re)||(Re=[Re]),await Promise.all(Re.map(ve=>typeof ve=="string"||ve instanceof URL?(0,T.read_audio)(ve,te):ve instanceof Float64Array?new Float32Array(ve):ve))}function K(Re,te){te&&(Re=Re.map(ze=>ze|0));const[ve,Ke,Ne,Ue]=Re;return{xmin:ve,ymin:Ke,xmax:Ne,ymax:Ue}}class se extends Z.Callable{constructor({task:te,model:ve,tokenizer:Ke=null,processor:Ne=null}){super(),this.task=te,this.model=ve,this.tokenizer=Ke,this.processor=Ne}async dispose(){await this.model.dispose()}}class oe extends se{constructor(te){super(te)}async _call(te,{top_k:ve=1}={}){const Ke=this.tokenizer(te,{padding:!0,truncation:!0}),Ne=await this.model(Ke),Ue=this.model.config.problem_type==="multi_label_classification"?at=>at.sigmoid():at=>new b.Tensor("float32",(0,y.softmax)(at.data),at.dims),ze=this.model.config.id2label,Ze=[];for(const at of Ne.logits){const mt=Ue(at),lt=await(0,b.topk)(mt,ve),ct=lt[0].tolist(),le=lt[1].tolist().map((Q,we)=>({label:ze?ze[Q]:`LABEL_${Q}`,score:ct[we]}));ve===1?Ze.push(...le):Ze.push(le)}return Array.isArray(te)||ve===1?Ze:Ze[0]}}class V extends se{constructor(te){super(te)}async _call(te,{ignore_labels:ve=["O"]}={}){const Ke=Array.isArray(te),Ne=this.tokenizer(Ke?te:[te],{padding:!0,truncation:!0}),ze=(await this.model(Ne)).logits,Ze=this.model.config.id2label,at=[];for(let mt=0;mttt==this.tokenizer.sep_token_id);at[ct].map((tt,st)=>tt==1&&(st===0||st>le&&mt.findIndex(ft=>ft==O[st])===-1));const Q=Ue[ct].tolist(),we=ze[ct].tolist();for(let tt=1;ttst==O[tt])!==-1)&&(Q[tt]=-1/0,we[tt]=-1/0);const Se=(0,y.softmax)(Q).map((tt,st)=>[tt,st]),We=(0,y.softmax)(we).map((tt,st)=>[tt,st]);Se[0][0]=0,We[0][0]=0;const qe=(0,j.product)(Se,We).filter(tt=>tt[0][1]<=tt[1][1]).map(tt=>[tt[0][1],tt[1][1],tt[0][0]*tt[1][0]]).sort((tt,st)=>st[2]-tt[2]);for(let tt=0;ttQ==this.tokenizer.mask_token_id);if(mt===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const lt=Ne[Ze][mt],ct=await(0,b.topk)(new b.Tensor("float32",(0,y.softmax)(lt.data),lt.dims),ve),O=ct[0].tolist(),le=ct[1].tolist();Ue.push(le.map((Q,we)=>{const Se=at.slice();return Se[mt]=Q,{score:O[we],token:Number(Q),token_str:this.tokenizer.decode([Q]),sequence:this.tokenizer.decode(Se,{skip_special_tokens:!0})}}))}return Array.isArray(te)?Ue:Ue[0]}}class I extends se{_key="generated_text";constructor(te){super(te)}async _call(te,ve={}){Array.isArray(te)||(te=[te]),this.model.config.prefix&&(te=te.map(at=>this.model.config.prefix+at));const Ke=this.model.config.task_specific_params;Ke&&Ke[this.task]&&Ke[this.task].prefix&&(te=te.map(at=>Ke[this.task].prefix+at));const Ne=this.tokenizer,Ue={padding:!0,truncation:!0};let ze;this instanceof w&&"_build_translation_inputs"in Ne?ze=Ne._build_translation_inputs(te,Ue,ve):ze=Ne(te,Ue);const Ze=await this.model.generate({...ze,...ve});return Ne.batch_decode(Ze,{skip_special_tokens:!0}).map(at=>({[this._key]:at}))}}class S extends I{_key="summary_text";constructor(te){super(te)}}class w extends I{_key="translation_text";constructor(te){super(te)}}function P(Re){return Array.isArray(Re)&&Re.every(te=>"role"in te&&"content"in te)}class F extends se{constructor(te){super(te)}async _call(te,ve={}){let Ke=!1,Ne=!1,Ue;if(typeof te=="string")Ue=te=[te];else if(Array.isArray(te)&&te.every(le=>typeof le=="string"))Ke=!0,Ue=te;else{if(P(te))te=[te];else if(Array.isArray(te)&&te.every(P))Ke=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");Ne=!0,Ue=te.map(le=>this.tokenizer.apply_chat_template(le,{tokenize:!1,add_generation_prompt:!0}))}const ze=ve.add_special_tokens??!1,Ze=Ne?!1:ve.return_full_text??!0;this.tokenizer.padding_side="left";const at=this.tokenizer(Ue,{add_special_tokens:ze,padding:!0,truncation:!0}),mt=await this.model.generate({...at,...ve}),lt=this.tokenizer.batch_decode(mt,{skip_special_tokens:!0});let ct;!Ze&&at.input_ids.dims.at(-1)>0&&(ct=this.tokenizer.batch_decode(at.input_ids,{skip_special_tokens:!0}).map(le=>le.length));const O=Array.from({length:te.length},le=>[]);for(let le=0;le[ve.toLowerCase(),Ke])),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(te,ve,{hypothesis_template:Ke="This example is {}.",multi_label:Ne=!1}={}){const Ue=Array.isArray(te);Ue||(te=[te]),Array.isArray(ve)||(ve=[ve]);const ze=ve.map(mt=>Ke.replace("{}",mt)),Ze=Ne||ve.length===1,at=[];for(const mt of te){const lt=[];for(const le of ze){const Q=this.tokenizer(mt,{text_pair:le,padding:!0,truncation:!0}),we=await this.model(Q);Ze?lt.push([we.logits.data[this.contradiction_id],we.logits.data[this.entailment_id]]):lt.push(we.logits.data[this.entailment_id])}const O=(Ze?lt.map(le=>(0,y.softmax)(le)[1]):(0,y.softmax)(lt)).map((le,Q)=>[le,Q]).sort((le,Q)=>Q[0]-le[0]);at.push({sequence:mt,labels:O.map(le=>ve[le[1]]),scores:O.map(le=>le[0])})}return Ue?at:at[0]}}class ie extends se{constructor(te){super(te)}async _call(te,{pooling:ve="none",normalize:Ke=!1,quantize:Ne=!1,precision:Ue="binary"}={}){const ze=this.tokenizer(te,{padding:!0,truncation:!0}),Ze=await this.model(ze);let at=Ze.last_hidden_state??Ze.logits??Ze.token_embeddings;if(ve!=="none")if(ve==="mean")at=(0,b.mean_pooling)(at,ze.attention_mask);else if(ve==="cls")at=at.slice(null,0);else throw Error(`Pooling method '${ve}' not supported.`);return Ke&&(at=at.normalize(2,-1)),Ne&&(at=(0,b.quantize_embeddings)(at,Ue)),at}}class ye extends se{constructor(te){super(te)}async _call(te,{pool:ve=null}={}){const Ke=await v(te),{pixel_values:Ne}=await this.processor(Ke),Ue=await this.model({pixel_values:Ne});let ze;if(ve){if(!("pooler_output"in Ue))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");ze=Ue.pooler_output}else ze=Ue.last_hidden_state??Ue.logits??Ue.image_embeds;return ze}}class ge extends se{constructor(te){super(te)}async _call(te,{top_k:ve=5}={}){const Ke=this.processor.feature_extractor.config.sampling_rate,Ne=await L(te,Ke),Ue=this.model.config.id2label,ze=[];for(const Ze of Ne){const at=await this.processor(Ze),lt=(await this.model(at)).logits[0],ct=await(0,b.topk)(new b.Tensor("float32",(0,y.softmax)(lt.data),lt.dims),ve),O=ct[0].tolist(),Q=ct[1].tolist().map((we,Se)=>({label:Ue?Ue[we]:`LABEL_${we}`,score:O[Se]}));ze.push(Q)}return Array.isArray(te)?ze:ze[0]}}class re extends se{constructor(te){super(te)}async _call(te,ve,{hypothesis_template:Ke="This is a sound of {}."}={}){const Ne=!Array.isArray(te);Ne&&(te=[te]);const Ue=ve.map(lt=>Ke.replace("{}",lt)),ze=this.tokenizer(Ue,{padding:!0,truncation:!0}),Ze=this.processor.feature_extractor.config.sampling_rate,at=await L(te,Ze),mt=[];for(const lt of at){const ct=await this.processor(lt),O=await this.model({...ze,...ct}),le=(0,y.softmax)(O.logits_per_audio.data);mt.push([...le].map((Q,we)=>({score:Q,label:ve[we]})))}return Ne?mt[0]:mt}}class Me extends se{constructor(te){super(te)}async _call(te,ve={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(te,ve);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(te,ve);case"moonshine":return this._call_moonshine(te,ve);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(te,ve){ve.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),ve.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const Ke=!Array.isArray(te);Ke&&(te=[te]);const Ne=this.processor.feature_extractor.config.sampling_rate,Ue=await L(te,Ne),ze=[];for(const Ze of Ue){const at=await this.processor(Ze),lt=(await this.model(at)).logits[0],ct=[];for(const le of lt)ct.push((0,y.max)(le.data)[1]);const O=this.tokenizer.decode(ct);ze.push({text:O})}return Ke?ze[0]:ze}async _call_whisper(te,ve){const Ke=ve.return_timestamps??!1,Ne=ve.chunk_length_s??0,Ue=ve.force_full_sequences??!1;let ze=ve.stride_length_s??null;const Ze={...ve};Ke==="word"&&(Ze.return_token_timestamps=!0,Ze.return_timestamps=!1);const at=!Array.isArray(te);at&&(te=[te]);const mt=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,lt=this.processor.feature_extractor.config.hop_length,ct=this.processor.feature_extractor.config.sampling_rate,O=await L(te,ct),le=[];for(const Q of O){let we=[];if(Ne>0){if(ze===null)ze=Ne/6;else if(Ne<=ze)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const qe=ct*Ne,tt=ct*ze,st=qe-2*tt;let ft=0;for(;;){const Nt=ft+qe,ss=Q.subarray(ft,Nt),Ts=await this.processor(ss),ms=ft===0,Ps=Nt>=Q.length;if(we.push({stride:[ss.length,ms?0:tt,Ps?0:tt],input_features:Ts.input_features,is_last:Ps}),Ps)break;ft+=st}}else we=[{stride:[Q.length,0,0],input_features:(await this.processor(Q)).input_features,is_last:!0}];for(const qe of we){Ze.num_frames=Math.floor(qe.stride[0]/lt);const tt=await this.model.generate({inputs:qe.input_features,...Ze});Ke==="word"?(qe.tokens=tt.sequences.tolist()[0],qe.token_timestamps=tt.token_timestamps.tolist()[0].map(st=>(0,y.round)(st,2))):qe.tokens=tt[0].tolist(),qe.stride=qe.stride.map(st=>st/ct)}const[Se,We]=this.tokenizer._decode_asr(we,{time_precision:mt,return_timestamps:Ke,force_full_sequences:Ue});le.push({text:Se,...We})}return at?le[0]:le}async _call_moonshine(te,ve){const Ke=!Array.isArray(te);Ke&&(te=[te]);const Ne=this.processor.feature_extractor.config.sampling_rate,Ue=await L(te,Ne),ze=[];for(const Ze of Ue){const at=await this.processor(Ze),mt=Math.floor(Ze.length/Ne)*6,lt=await this.model.generate({max_new_tokens:mt,...ve,...at}),ct=this.processor.batch_decode(lt,{skip_special_tokens:!0})[0];ze.push({text:ct})}return Ke?ze[0]:ze}}class pe extends se{constructor(te){super(te)}async _call(te,ve={}){const Ke=Array.isArray(te),Ne=await v(te),{pixel_values:Ue}=await this.processor(Ne),ze=[];for(const Ze of Ue){Ze.dims=[1,...Ze.dims];const at=await this.model.generate({inputs:Ze,...ve}),mt=this.tokenizer.batch_decode(at,{skip_special_tokens:!0}).map(lt=>({generated_text:lt.trim()}));ze.push(mt)}return Ke?ze:ze[0]}}class Ce extends se{constructor(te){super(te)}async _call(te,{top_k:ve=5}={}){const Ke=await v(te),{pixel_values:Ne}=await this.processor(Ke),Ue=await this.model({pixel_values:Ne}),ze=this.model.config.id2label,Ze=[];for(const at of Ue.logits){const mt=await(0,b.topk)(new b.Tensor("float32",(0,y.softmax)(at.data),at.dims),ve),lt=mt[0].tolist(),O=mt[1].tolist().map((le,Q)=>({label:ze?ze[le]:`LABEL_${le}`,score:lt[Q]}));Ze.push(O)}return Array.isArray(te)?Ze:Ze[0]}}class Ae extends se{constructor(te){super(te),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(te,{threshold:ve=.5,mask_threshold:Ke=.5,overlap_mask_area_threshold:Ne=.8,label_ids_to_fuse:Ue=null,target_sizes:ze=null,subtask:Ze=null}={}){if(Array.isArray(te)&&te.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const mt=await v(te),lt=mt.map(We=>[We.height,We.width]),{pixel_values:ct,pixel_mask:O}=await this.processor(mt),le=await this.model({pixel_values:ct,pixel_mask:O});let Q=null;if(Ze!==null)Q=this.subtasks_mapping[Ze];else for(let[We,qe]of Object.entries(this.subtasks_mapping))if(qe in this.processor.image_processor){Q=this.processor.image_processor[qe].bind(this.processor.image_processor),Ze=We;break}const we=this.model.config.id2label,Se=[];if(Ze==="panoptic"||Ze==="instance"){const We=Q(le,ve,Ke,Ne,Ue,ze??lt)[0],qe=We.segmentation;for(const tt of We.segments_info){const st=new Uint8ClampedArray(qe.data.length);for(let Nt=0;NtKe.replace("{}",O)),Ze=this.tokenizer(ze,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:at}=await this.processor(Ue),mt=await this.model({...Ze,pixel_values:at}),lt=this.model.config.model_type==="siglip"?O=>O.sigmoid().data:O=>(0,y.softmax)(O.data),ct=[];for(const O of mt.logits_per_image){const Q=[...lt(O)].map((we,Se)=>({score:we,label:ve[Se]}));Q.sort((we,Se)=>Se.score-we.score),ct.push(Q)}return Ne?ct:ct[0]}}class Je extends se{constructor(te){super(te)}async _call(te,{threshold:ve=.9,percentage:Ke=!1}={}){const Ne=Array.isArray(te);if(Ne&&te.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const Ue=await v(te),ze=Ke?null:Ue.map(le=>[le.height,le.width]),{pixel_values:Ze,pixel_mask:at}=await this.processor(Ue),mt=await this.model({pixel_values:Ze,pixel_mask:at}),lt=this.processor.image_processor.post_process_object_detection(mt,ve,ze),ct=this.model.config.id2label,O=lt.map(le=>le.boxes.map((Q,we)=>({score:le.scores[we],label:ct[le.classes[we]],box:K(Q,!Ke)})));return Ne?O:O[0]}}class je extends se{constructor(te){super(te)}async _call(te,ve,{threshold:Ke=.1,top_k:Ne=null,percentage:Ue=!1}={}){const ze=Array.isArray(te),Ze=await v(te),at=this.tokenizer(ve,{padding:!0,truncation:!0}),mt=await this.processor(Ze),lt=[];for(let ct=0;ct({score:We.scores[tt],label:We.labels[tt],box:K(qe,!Ue)}))}else{const We=this.processor.image_processor.post_process_object_detection(we,Ke,le,!0)[0];Se=We.boxes.map((qe,tt)=>({score:We.scores[tt],label:ve[We.classes[tt]],box:K(qe,!Ue)}))}Se.sort((We,qe)=>qe.score-We.score),Ne!==null&&(Se=Se.slice(0,Ne)),lt.push(Se)}return ze?lt:lt[0]}}class _e extends se{constructor(te){super(te)}async _call(te,ve,Ke={}){const Ne=(await v(te))[0],{pixel_values:Ue}=await this.processor(Ne),ze=`${ve}`,Ze=this.tokenizer(ze,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,at=await this.model.generate({inputs:Ue,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Ze,...Ke}),lt=this.tokenizer.batch_decode(at)[0].match(/(.*?)<\/s_answer>/);let ct=null;return lt&<.length>=2&&(ct=lt[1].trim()),[{answer:ct}]}}class X extends se{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(te){super(te),this.vocoder=te.vocoder??null}async _call(te,{speaker_embeddings:ve=null}={}){return this.processor?this._call_text_to_spectrogram(te,{speaker_embeddings:ve}):this._call_text_to_waveform(te)}async _call_text_to_waveform(te){const ve=this.tokenizer(te,{padding:!0,truncation:!0}),{waveform:Ke}=await this.model(ve),Ne=this.model.config.sampling_rate;return new T.RawAudio(Ke.data,Ne)}async _call_text_to_spectrogram(te,{speaker_embeddings:ve}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await $.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof ve=="string"||ve instanceof URL)&&(ve=new Float32Array(await(await fetch(ve)).arrayBuffer())),ve instanceof Float32Array)ve=new b.Tensor("float32",ve,[1,ve.length]);else if(!(ve instanceof b.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:Ke}=this.tokenizer(te,{padding:!0,truncation:!0}),{waveform:Ne}=await this.model.generate_speech(Ke,ve,{vocoder:this.vocoder}),Ue=this.processor.feature_extractor.config.sampling_rate;return new T.RawAudio(Ne.data,Ue)}}class de extends se{constructor(te){super(te)}async _call(te){const ve=await v(te),Ke=await this.processor(ve),Ne=await this.model(Ke),Ue=[];for(const ze of Ne.reconstruction){const Ze=ze.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");Ue.push(x.RawImage.fromTensor(Ze))}return Ue.length>1?Ue:Ue[0]}}class Ee extends se{constructor(te){super(te)}async _call(te){const ve=await v(te),Ke=await this.processor(ve),{predicted_depth:Ne}=await this.model(Ke),Ue=[];for(let ze=0;ze1?Ue:Ue[0]}}const Oe=Object.freeze({"text-classification":{tokenizer:g.AutoTokenizer,pipeline:oe,model:$.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:g.AutoTokenizer,pipeline:V,model:$.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:g.AutoTokenizer,pipeline:U,model:$.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:g.AutoTokenizer,pipeline:H,model:$.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:g.AutoTokenizer,pipeline:S,model:$.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:g.AutoTokenizer,pipeline:w,model:$.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:g.AutoTokenizer,pipeline:I,model:$.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:g.AutoTokenizer,pipeline:F,model:$.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:g.AutoTokenizer,pipeline:ae,model:$.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:ge,model:$.AutoModelForAudioClassification,processor:N.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:g.AutoTokenizer,pipeline:re,model:$.AutoModel,processor:N.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:g.AutoTokenizer,pipeline:Me,model:[$.AutoModelForSpeechSeq2Seq,$.AutoModelForCTC],processor:N.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:g.AutoTokenizer,pipeline:X,model:[$.AutoModelForTextToWaveform,$.AutoModelForTextToSpectrogram],processor:[N.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:g.AutoTokenizer,pipeline:pe,model:$.AutoModelForVision2Seq,processor:N.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Ce,model:$.AutoModelForImageClassification,processor:N.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:Ae,model:[$.AutoModelForImageSegmentation,$.AutoModelForSemanticSegmentation,$.AutoModelForUniversalSegmentation],processor:N.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:g.AutoTokenizer,pipeline:Pe,model:$.AutoModel,processor:N.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:Je,model:$.AutoModelForObjectDetection,processor:N.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:g.AutoTokenizer,pipeline:je,model:$.AutoModelForZeroShotObjectDetection,processor:N.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:g.AutoTokenizer,pipeline:_e,model:$.AutoModelForDocumentQuestionAnswering,processor:N.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:de,model:$.AutoModelForImageToImage,processor:N.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:Ee,model:$.AutoModelForDepthEstimation,processor:N.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:g.AutoTokenizer,pipeline:ie,model:$.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:N.AutoProcessor,pipeline:ye,model:[$.AutoModelForImageFeatureExtraction,$.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Xe=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function ee(Re,te=null,{progress_callback:ve=null,config:Ke=null,cache_dir:Ne=null,local_files_only:Ue=!1,revision:ze="main",device:Ze=null,dtype:at=null,model_file_name:mt=null,session_options:lt={}}={}){Re=Xe[Re]??Re;const ct=Oe[Re.split("_",1)[0]];if(!ct)throw Error(`Unsupported pipeline: ${Re}. Must be one of [${Object.keys(Oe)}]`);te||(te=ct.default.model,console.log(`No model specified. Using default model: "${te}".`));const O={progress_callback:ve,config:Ke,cache_dir:Ne,local_files_only:Ue,revision:ze,device:Ze,dtype:at,model_file_name:mt,session_options:lt},le=new Map([["tokenizer",ct.tokenizer],["model",ct.model],["processor",ct.processor]]),Q=await Ve(le,te,O);Q.task=Re,(0,j.dispatchCallback)(ve,{status:"ready",task:Re,model:te});const we=ct.pipeline;return new we(Q)}async function Ve(Re,te,ve){const Ke=Object.create(null),Ne=[];for(const[Ue,ze]of Re.entries()){if(!ze)continue;let Ze;Array.isArray(ze)?Ze=new Promise(async(at,mt)=>{let lt;for(const ct of ze){if(ct===null){at(null);return}try{at(await ct.from_pretrained(te,ve));return}catch(O){if(O.message?.includes("Unsupported model type"))lt=O;else if(O.message?.includes("Could not locate file"))lt=O;else{mt(O);return}}}mt(lt)}):Ze=ze.from_pretrained(te,ve),Ke[Ue]=Ze,Ne.push(Ze)}await Promise.all(Ne);for(const[Ue,ze]of Object.entries(Ke))Ke[Ue]=await ze;return Ke}},"./src/tokenizers.js":(ke,A,r)=>{r.r(A),r.d(A,{AlbertTokenizer:()=>Pr,AutoTokenizer:()=>as,BartTokenizer:()=>dn,BertTokenizer:()=>Kr,BlenderbotSmallTokenizer:()=>Fn,BlenderbotTokenizer:()=>In,BloomTokenizer:()=>Qr,CLIPTokenizer:()=>mn,CamembertTokenizer:()=>it,CodeGenTokenizer:()=>hn,CodeLlamaTokenizer:()=>zr,CohereTokenizer:()=>gn,ConvBertTokenizer:()=>Dr,DebertaTokenizer:()=>dr,DebertaV2Tokenizer:()=>qr,DistilBertTokenizer:()=>or,ElectraTokenizer:()=>Ft,EsmTokenizer:()=>Br,FalconTokenizer:()=>Sn,GPT2Tokenizer:()=>Cn,GPTNeoXTokenizer:()=>$n,GemmaTokenizer:()=>Zn,Grok1Tokenizer:()=>Rr,HerbertTokenizer:()=>Cr,LlamaTokenizer:()=>cn,M2M100Tokenizer:()=>pn,MBart50Tokenizer:()=>br,MBartTokenizer:()=>gs,MPNetTokenizer:()=>kn,MarianTokenizer:()=>Ot,MgpstrTokenizer:()=>Ln,MobileBertTokenizer:()=>Er,NllbTokenizer:()=>ar,NougatTokenizer:()=>Nr,PreTrainedTokenizer:()=>Lt,Qwen2Tokenizer:()=>An,RoFormerTokenizer:()=>Lr,RobertaTokenizer:()=>Ls,SiglipTokenizer:()=>fn,SpeechT5Tokenizer:()=>On,SqueezeBertTokenizer:()=>Hr,T5Tokenizer:()=>Ys,TokenizerModel:()=>ye,VitsTokenizer:()=>Dn,Wav2Vec2CTCTokenizer:()=>_n,WhisperTokenizer:()=>Xr,XLMRobertaTokenizer:()=>Jn,XLMTokenizer:()=>vt,is_chinese_char:()=>H});var g=r("./src/utils/generic.js"),$=r("./src/utils/core.js"),N=r("./src/utils/hub.js"),Z=r("./src/utils/maths.js"),j=r("./src/utils/tensor.js"),y=r("./src/utils/data-structures.js"),T=r("./node_modules/@huggingface/jinja/dist/index.js"),b=r("./src/models/whisper/common_whisper.js");async function x(Te,M){const Y=await Promise.all([(0,N.getModelJSON)(Te,"tokenizer.json",!0,M),(0,N.getModelJSON)(Te,"tokenizer_config.json",!0,M)]);return M.legacy!==null&&(Y[1].legacy=M.legacy),Y}function v(Te,M){const Y=[];let ce=0;for(const me of Te.matchAll(M)){const Fe=me[0];ce0&&Y.push(Fe),ce=me.index+Fe.length}return ce=19968&&Te<=40959||Te>=13312&&Te<=19903||Te>=131072&&Te<=173791||Te>=173824&&Te<=177983||Te>=177984&&Te<=178207||Te>=178208&&Te<=183983||Te>=63744&&Te<=64255||Te>=194560&&Te<=195103}function I(Te,M,Y){const ce=[];let me=0;for(;methis.tokens_to_ids.get(Y)??this.unk_token_id)}convert_ids_to_tokens(M){return M.map(Y=>this.vocab[Y]??this.unk_token)}}class ge extends ye{constructor(M){super(M),this.tokens_to_ids=K(M.vocab),this.unk_token_id=this.tokens_to_ids.get(M.unk_token),this.unk_token=M.unk_token,this.max_input_chars_per_word=M.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[Y,ce]of this.tokens_to_ids)this.vocab[ce]=Y}encode(M){const Y=[];for(const ce of M){const me=[...ce];if(me.length>this.max_input_chars_per_word){Y.push(this.unk_token);continue}let Fe=!1,Ye=0;const gt=[];for(;Ye0&&(pt=this.config.continuing_subword_prefix+pt),this.tokens_to_ids.has(pt)){yt=pt;break}--wt}if(yt===null){Fe=!0;break}gt.push(yt),Ye=wt}Fe?Y.push(this.unk_token):Y.push(...gt)}return Y}}class re extends ye{constructor(M,Y){super(M);const ce=M.vocab.length;this.vocab=new Array(ce),this.scores=new Array(ce);for(let me=0;me[me,Fe])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=Y.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,Z.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new y.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(M){const Y=M.chars,ce=1;let me=0;for(;me{const Te=[...Array.from({length:94},(me,Fe)=>Fe+33),...Array.from({length:12},(me,Fe)=>Fe+161),...Array.from({length:82},(me,Fe)=>Fe+174)],M=Te.slice();let Y=0;for(let me=0;me<256;++me)Te.includes(me)||(Te.push(me),M.push(256+Y),Y+=1);const ce=M.map(me=>String.fromCharCode(me));return Object.fromEntries(Te.map((me,Fe)=>[me,ce[Fe]]))})(),pe=(0,$.reverseDictionary)(Me);class Ce extends ye{constructor(M){super(M),this.tokens_to_ids=K(M.vocab),this.unk_token_id=this.tokens_to_ids.get(M.unk_token),this.unk_token=M.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[ce,me]of this.tokens_to_ids)this.vocab[me]=ce;const Y=Array.isArray(M.merges[0]);this.merges=Y?M.merges:M.merges.map(ce=>ce.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((ce,me)=>[JSON.stringify(ce),me])),this.end_of_word_suffix=M.end_of_word_suffix,this.continuing_subword_suffix=M.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(M){if(M.length===0)return[];const Y=this.cache.get(M);if(Y!==void 0)return Y;const ce=Array.from(M);this.end_of_word_suffix&&(ce[ce.length-1]+=this.end_of_word_suffix);let me=[];if(ce.length>1){const Fe=new y.PriorityQueue((wt,yt)=>wt.score`<0x${gt.toString(16).toUpperCase().padStart(2,"0")}>`);Ye.every(gt=>this.tokens_to_ids.has(gt))?Y.push(...Ye):Y.push(this.unk_token)}else Y.push(this.unk_token)}return Y}}class Ae extends ye{constructor(M,Y){super(M),this.tokens_to_ids=K(Y.target_lang?M.vocab[Y.target_lang]:M.vocab),this.bos_token=Y.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=Y.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=Y.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=Y.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[ce,me]of this.tokens_to_ids)this.vocab[me]=ce}encode(M){return M}}class Pe extends g.Callable{constructor(M){super(),this.config=M}static fromConfig(M){if(M===null)return null;switch(M.type){case"BertNormalizer":return new Ve(M);case"Precompiled":return new ms(M);case"Sequence":return new ee(M);case"Replace":return new Je(M);case"NFC":return new je(M);case"NFKC":return new _e(M);case"NFKD":return new X(M);case"Strip":return new de(M);case"StripAccents":return new Ee(M);case"Lowercase":return new Oe(M);case"Prepend":return new Xe(M);default:throw new Error(`Unknown Normalizer type: ${M.type}`)}}normalize(M){throw Error("normalize should be implemented in subclass.")}_call(M){return this.normalize(M)}}class Je extends Pe{normalize(M){const Y=L(this.config.pattern);return Y===null?M:M.replaceAll(Y,this.config.content)}}class je extends Pe{normalize(M){return M=M.normalize("NFC"),M}}class _e extends Pe{normalize(M){return M=M.normalize("NFKC"),M}}class X extends Pe{normalize(M){return M=M.normalize("NFKD"),M}}class de extends Pe{normalize(M){return this.config.strip_left&&this.config.strip_right?M=M.trim():(this.config.strip_left&&(M=M.trimStart()),this.config.strip_right&&(M=M.trimEnd())),M}}class Ee extends Pe{normalize(M){return M=V(M),M}}class Oe extends Pe{normalize(M){return M=M.toLowerCase(),M}}class Xe extends Pe{normalize(M){return M=this.config.prepend+M,M}}class ee extends Pe{constructor(M){super(M),this.normalizers=M.normalizers.map(Y=>Pe.fromConfig(Y))}normalize(M){return this.normalizers.reduce((Y,ce)=>ce.normalize(Y),M)}}class Ve extends Pe{_tokenize_chinese_chars(M){const Y=[];for(let ce=0;cethis.pre_tokenize_text(ce,Y)):this.pre_tokenize_text(M,Y)).flat()}_call(M,Y){return this.pre_tokenize(M,Y)}}class te extends Re{constructor(M){super(),this.pattern=new RegExp(`[^\\s${w}]+|[${w}]`,"gu")}pre_tokenize_text(M,Y){return M.trim().match(this.pattern)||[]}}class ve extends Re{constructor(M){super(),this.config=M,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=Me,this.text_encoder=new TextEncoder}pre_tokenize_text(M,Y){return this.add_prefix_space&&!M.startsWith(" ")&&(M=" "+M),(this.use_regex?M.match(this.pattern)||[]:[M]).map(me=>Array.from(this.text_encoder.encode(me),Fe=>this.byte_encoder[Fe]).join(""))}}class Ke extends Re{constructor(M){super(),this.config=M,this.pattern=L(this.config.pattern,this.config.invert)}pre_tokenize_text(M,Y){return this.pattern===null?[]:this.config.invert?M.match(this.pattern)||[]:this.config.behavior?.toLowerCase()==="removed"?M.split(this.pattern).filter(ce=>ce):v(M,this.pattern)}}class Ne extends Re{constructor(M){super(),this.config=M,this.pattern=new RegExp(`[^${w}]+|[${w}]+`,"gu")}pre_tokenize_text(M,Y){return M.match(this.pattern)||[]}}class Ue extends Re{constructor(M){super(),this.config=M;const Y=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(Y,"gu")}pre_tokenize_text(M,Y){return M.match(this.pattern)||[]}}class ze extends g.Callable{constructor(M){super(),this.config=M}static fromConfig(M){if(M===null)return null;switch(M.type){case"TemplateProcessing":return new mt(M);case"ByteLevel":return new lt(M);case"RobertaProcessing":return new at(M);case"BertProcessing":return new Ze(M);case"Sequence":return new ct(M);default:throw new Error(`Unknown PostProcessor type: ${M.type}`)}}post_process(M,...Y){throw Error("post_process should be implemented in subclass.")}_call(M,...Y){return this.post_process(M,...Y)}}class Ze extends ze{constructor(M){super(M),this.cls=M.cls[0],this.sep=M.sep[0]}post_process(M,Y=null,{add_special_tokens:ce=!0}={}){ce&&(M=(0,$.mergeArrays)([this.cls],M,[this.sep]));let me=new Array(M.length).fill(0);if(Y!==null){const Fe=ce&&this instanceof at?[this.sep]:[],Ye=ce?[this.sep]:[];M=(0,$.mergeArrays)(M,Fe,Y,Ye),me=(0,$.mergeArrays)(me,new Array(Y.length+Fe.length+Ye.length).fill(1))}return{tokens:M,token_type_ids:me}}}class at extends Ze{}class mt extends ze{constructor(M){super(M),this.single=M.single,this.pair=M.pair}post_process(M,Y=null,{add_special_tokens:ce=!0}={}){const me=Y===null?this.single:this.pair;let Fe=[],Ye=[];for(const gt of me)"SpecialToken"in gt?ce&&(Fe.push(gt.SpecialToken.id),Ye.push(gt.SpecialToken.type_id)):"Sequence"in gt&&(gt.Sequence.id==="A"?(Fe=(0,$.mergeArrays)(Fe,M),Ye=(0,$.mergeArrays)(Ye,new Array(M.length).fill(gt.Sequence.type_id))):gt.Sequence.id==="B"&&(Fe=(0,$.mergeArrays)(Fe,Y),Ye=(0,$.mergeArrays)(Ye,new Array(Y.length).fill(gt.Sequence.type_id))));return{tokens:Fe,token_type_ids:Ye}}}class lt extends ze{post_process(M,Y=null){return Y&&(M=(0,$.mergeArrays)(M,Y)),{tokens:M}}}class ct extends ze{constructor(M){super(M),this.processors=M.processors.map(Y=>ze.fromConfig(Y))}post_process(M,Y=null,ce={}){let me;for(const Fe of this.processors)if(Fe instanceof lt)M=Fe.post_process(M).tokens,Y&&(Y=Fe.post_process(Y).tokens);else{const Ye=Fe.post_process(M,Y,ce);M=Ye.tokens,me=Ye.token_type_ids}return{tokens:M,token_type_ids:me}}}class O extends g.Callable{constructor(M){super(),this.config=M,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=M.trim_offsets}static fromConfig(M){if(M===null)return null;switch(M.type){case"WordPiece":return new We(M);case"Metaspace":return new Ts(M);case"ByteLevel":return new qe(M);case"Replace":return new le(M);case"ByteFallback":return new Q(M);case"Fuse":return new we(M);case"Strip":return new Se(M);case"Sequence":return new st(M);case"CTC":return new tt(M);case"BPEDecoder":return new ft(M);default:throw new Error(`Unknown Decoder type: ${M.type}`)}}_call(M){return this.decode(M)}decode(M){return this.decode_chain(M).join("")}decode_chain(M){throw Error("`decode_chain` should be implemented in subclass.")}}class le extends O{decode_chain(M){const Y=L(this.config.pattern);return Y===null?M:M.map(ce=>ce.replaceAll(Y,this.config.content))}}class Q extends O{constructor(M){super(M),this.text_decoder=new TextDecoder}decode_chain(M){const Y=[];let ce=[];for(const me of M){let Fe=null;if(me.length===6&&me.startsWith("<0x")&&me.endsWith(">")){const Ye=parseInt(me.slice(3,5),16);isNaN(Ye)||(Fe=Ye)}if(Fe!==null)ce.push(Fe);else{if(ce.length>0){const Ye=this.text_decoder.decode(Uint8Array.from(ce));Y.push(Ye),ce=[]}Y.push(me)}}if(ce.length>0){const me=this.text_decoder.decode(Uint8Array.from(ce));Y.push(me),ce=[]}return Y}}class we extends O{decode_chain(M){return[M.join("")]}}class Se extends O{constructor(M){super(M),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(M){return M.map(Y=>{let ce=0;for(let Fe=0;Fe(ce!==0&&(Y.startsWith(this.config.prefix)?Y=Y.replace(this.config.prefix,""):Y=" "+Y),this.cleanup&&(Y=oe(Y)),Y))}}class qe extends O{constructor(M){super(M),this.byte_decoder=pe,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(M){const Y=M.join(""),ce=new Uint8Array([...Y].map(Fe=>this.byte_decoder[Fe]));return this.text_decoder.decode(ce)}decode_chain(M){const Y=[];let ce=[];for(const me of M)this.added_tokens.find(Fe=>Fe.content===me)!==void 0?(ce.length>0&&(Y.push(this.convert_tokens_to_string(ce)),ce=[]),Y.push(me)):ce.push(me);return ce.length>0&&Y.push(this.convert_tokens_to_string(ce)),Y}}class tt extends O{constructor(M){super(M),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(M){if(M.length===0)return"";const Y=[M[0]];for(let Fe=1;FeFe!==this.pad_token).join("");return this.cleanup&&(me=oe(me).replaceAll(this.word_delimiter_token," ").trim()),me}decode_chain(M){return[this.convert_tokens_to_string(M)]}}class st extends O{constructor(M){super(M),this.decoders=M.decoders.map(Y=>O.fromConfig(Y))}decode_chain(M){return this.decoders.reduce((Y,ce)=>ce.decode_chain(Y),M)}}class ft extends O{constructor(M){super(M),this.suffix=this.config.suffix}decode_chain(M){return M.map((Y,ce)=>Y.replaceAll(this.suffix,ce===M.length-1?"":" "))}}class Nt extends O{decode_chain(M){let Y="";for(let ce=1;cece.normalize("NFKC")).join("～"):M=M.normalize("NFKC"),M}}class Ps extends Re{constructor(M){super(),this.tokenizers=M.pretokenizers.map(Y=>Re.fromConfig(Y))}pre_tokenize_text(M,Y){return this.tokenizers.reduce((ce,me)=>me.pre_tokenize(ce,Y),[M])}}class As extends Re{constructor(M){super()}pre_tokenize_text(M,Y){return M.match(/\w+|[^\w\s]+/g)||[]}}class tr extends Re{constructor(M){super()}pre_tokenize_text(M,Y){return S(M)}}class Tr extends Re{constructor(M){super(),this.config=M,this.pattern=L(this.config.pattern),this.content=this.config.content}pre_tokenize_text(M,Y){return this.pattern===null?[M]:[M.replaceAll(this.pattern,this.config.content)]}}const Gr=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Ns(Te,M,Y,ce){for(const me of Object.keys(Te)){const Fe=M-Te[me].length,Ye=Y(me),gt=new Array(Fe).fill(Ye);Te[me]=ce==="right"?(0,$.mergeArrays)(Te[me],gt):(0,$.mergeArrays)(gt,Te[me])}}function Mr(Te,M){for(const Y of Object.keys(Te))Te[Y].length=M}class Lt extends g.Callable{return_token_type_ids=!1;padding_side="right";constructor(M,Y){super(),this._tokenizer_config=Y,this.normalizer=Pe.fromConfig(M.normalizer),this.pre_tokenizer=Re.fromConfig(M.pre_tokenizer),this.model=ye.fromConfig(M.model,Y),this.post_processor=ze.fromConfig(M.post_processor),this.decoder=O.fromConfig(M.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const ce of M.added_tokens){const me=new ie(ce);this.added_tokens.push(me),this.model.tokens_to_ids.set(me.content,me.id),this.model.vocab[me.id]=me.content,me.special&&(this.special_tokens.push(me.content),this.all_special_ids.push(me.id))}if(this.additional_special_tokens=Y.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort((ce,me)=>me.content.length-ce.content.length).map(ce=>`${ce.lstrip?"\\s*":""}(${(0,$.escapeRegExp)(ce.content)})${ce.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=Y.model_max_length,this.remove_space=Y.remove_space,this.clean_up_tokenization_spaces=Y.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=Y.do_lowercase_and_remove_accent??!1,Y.padding_side&&(this.padding_side=Y.padding_side),this.legacy=!1,this.chat_template=Y.chat_template??null,Array.isArray(this.chat_template)){const ce=Object.create(null);for(const{name:me,template:Fe}of this.chat_template){if(typeof me!="string"||typeof Fe!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');ce[me]=Fe}this.chat_template=ce}this._compiled_template_cache=new Map}getToken(...M){for(const Y of M){const ce=this._tokenizer_config[Y];if(ce)if(typeof ce=="object"){if(ce.__type==="AddedToken")return ce.content;throw Error(`Unknown token: ${ce}`)}else return ce}return null}static async from_pretrained(M,{progress_callback:Y=null,config:ce=null,cache_dir:me=null,local_files_only:Fe=!1,revision:Ye="main",legacy:gt=null}={}){const wt=await x(M,{progress_callback:Y,config:ce,cache_dir:me,local_files_only:Fe,revision:Ye,legacy:gt});return new this(...wt)}_call(M,{text_pair:Y=null,add_special_tokens:ce=!0,padding:me=!1,truncation:Fe=null,max_length:Ye=null,return_tensor:gt=!0,return_token_type_ids:wt=null}={}){const yt=Array.isArray(M);let pt;if(yt){if(M.length===0)throw Error("text array must be non-empty");if(Y!==null){if(Array.isArray(Y)){if(M.length!==Y.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");pt=M.map((Yt,Ms)=>this._encode_plus(Yt,{text_pair:Y[Ms],add_special_tokens:ce,return_token_type_ids:wt}))}else pt=M.map(Yt=>this._encode_plus(Yt,{add_special_tokens:ce,return_token_type_ids:wt}))}else{if(M==null)throw Error("text may not be null or undefined");if(Array.isArray(Y))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");pt=[this._encode_plus(M,{text_pair:Y,add_special_tokens:ce,return_token_type_ids:wt})]}if(Ye===null?me==="max_length"?Ye=this.model_max_length:Ye=(0,Z.max)(pt.map(Yt=>Yt.input_ids.length))[0]:Fe||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."),Ye=Math.min(Ye,this.model_max_length??1/0),me||Fe)for(let Yt=0;YtYe?Fe&&Mr(pt[Yt],Ye):me&&Ns(pt[Yt],Ye,Ms=>Ms==="input_ids"?this.pad_token_id:0,this.padding_side));const rs={};if(gt){if(!(me&&Fe)&&pt.some(Ms=>{for(const Gs of Object.keys(Ms))if(Ms[Gs].length!==pt[0][Gs]?.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 Yt=[pt.length,pt[0].input_ids.length];for(const Ms of Object.keys(pt[0]))rs[Ms]=new j.Tensor("int64",BigInt64Array.from(pt.flatMap(Gs=>Gs[Ms]).map(BigInt)),Yt)}else{for(const Yt of Object.keys(pt[0]))rs[Yt]=pt.map(Ms=>Ms[Yt]);if(!yt)for(const Yt of Object.keys(rs))rs[Yt]=rs[Yt][0]}return rs}_encode_text(M){return M===null?null:(this.added_tokens_regex?M.split(this.added_tokens_regex).filter(me=>me):[M]).map((me,Fe)=>{if(this.added_tokens.find(gt=>gt.content===me)!==void 0)return me;{if(this.remove_space===!0&&(me=me.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(me=U(me)),this.normalizer!==null&&(me=this.normalizer(me)),me.length===0)return[];const gt=this.pre_tokenizer!==null?this.pre_tokenizer(me,{section_index:Fe}):[me];return this.model(gt)}}).flat()}_encode_plus(M,{text_pair:Y=null,add_special_tokens:ce=!0,return_token_type_ids:me=null}={}){const{tokens:Fe,token_type_ids:Ye}=this._tokenize_helper(M,{pair:Y,add_special_tokens:ce}),gt=this.model.convert_tokens_to_ids(Fe),wt={input_ids:gt,attention_mask:new Array(gt.length).fill(1)};return(me??this.return_token_type_ids)&&Ye&&(wt.token_type_ids=Ye),wt}_tokenize_helper(M,{pair:Y=null,add_special_tokens:ce=!1}={}){const me=this._encode_text(M),Fe=this._encode_text(Y);return this.post_processor?this.post_processor(me,Fe,{add_special_tokens:ce}):{tokens:(0,$.mergeArrays)(me??[],Fe??[])}}tokenize(M,{pair:Y=null,add_special_tokens:ce=!1}={}){return this._tokenize_helper(M,{pair:Y,add_special_tokens:ce}).tokens}encode(M,{text_pair:Y=null,add_special_tokens:ce=!0,return_token_type_ids:me=null}={}){return this._encode_plus(M,{text_pair:Y,add_special_tokens:ce,return_token_type_ids:me}).input_ids}batch_decode(M,Y={}){return M instanceof j.Tensor&&(M=M.tolist()),M.map(ce=>this.decode(ce,Y))}decode(M,Y={}){if(M instanceof j.Tensor&&(M=se(M)),!Array.isArray(M)||M.length===0||!(0,$.isIntegralNumber)(M[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(M,Y)}decode_single(M,{skip_special_tokens:Y=!1,clean_up_tokenization_spaces:ce=null}){let me=this.model.convert_ids_to_tokens(M);Y&&(me=me.filter(Ye=>!this.special_tokens.includes(Ye)));let Fe=this.decoder?this.decoder(me):me.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Fe=Fe.replaceAll(this.decoder.end_of_word_suffix," "),Y&&(Fe=Fe.trim())),(ce??this.clean_up_tokenization_spaces)&&(Fe=oe(Fe)),Fe}get_chat_template({chat_template:M=null,tools:Y=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const ce=this.chat_template;if(M!==null&&Object.hasOwn(ce,M))M=ce[M];else if(M===null)if(Y!==null&&"tool_use"in ce)M=ce.tool_use;else if("default"in ce)M=ce.default;else throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(ce).sort()}.`)}else if(M===null)if(this.chat_template)M=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return M}apply_chat_template(M,{tools:Y=null,documents:ce=null,chat_template:me=null,add_generation_prompt:Fe=!1,tokenize:Ye=!0,padding:gt=!1,truncation:wt=!1,max_length:yt=null,return_tensor:pt=!0,return_dict:rs=!1,tokenizer_kwargs:Yt={},...Ms}={}){if(me=this.get_chat_template({chat_template:me,tools:Y}),typeof me!="string")throw Error(`chat_template must be a string, but got ${typeof me}`);let Gs=this._compiled_template_cache.get(me);Gs===void 0&&(Gs=new T.Template(me),this._compiled_template_cache.set(me,Gs));const jt=Object.create(null);for(const Ks of Gr){const Js=this.getToken(Ks);Js&&(jt[Ks]=Js)}const is=Gs.render({messages:M,add_generation_prompt:Fe,tools:Y,documents:ce,...jt,...Ms});if(Ye){const Ks=this._call(is,{add_special_tokens:!1,padding:gt,truncation:wt,max_length:yt,return_tensor:pt,...Yt});return rs?Ks:Ks.input_ids}return is}}class Kr extends Lt{return_token_type_ids=!0}class Pr extends Lt{return_token_type_ids=!0}class Er extends Lt{return_token_type_ids=!0}class Hr extends Lt{return_token_type_ids=!0}class dr extends Lt{return_token_type_ids=!0}class qr extends Lt{return_token_type_ids=!0}class Cr extends Lt{return_token_type_ids=!0}class Dr extends Lt{return_token_type_ids=!0}class Lr extends Lt{return_token_type_ids=!0}class or extends Lt{}class it extends Lt{}class vt extends Lt{return_token_type_ids=!0;constructor(M,Y){super(M,Y),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Ft extends Lt{return_token_type_ids=!0}class Ys extends Lt{}class Cn extends Lt{}class dn extends Lt{}class gs extends Lt{constructor(M,Y){super(M,Y),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(ce=>this.languageRegex.test(ce)),this.lang_to_token=ce=>ce}_build_translation_inputs(M,Y,ce){return fr(this,M,Y,ce)}}class br extends gs{}class Ls extends Lt{}class Qr extends Lt{}const ns="▁";class cn extends Lt{padding_side="left";constructor(M,Y){super(M,Y),this.legacy=Y.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new ss({replacement:ns,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(M){if(M===null)return null;if(this.legacy||M.length===0)return super._encode_text(M);let Y=super._encode_text(ns+M.replaceAll(ns," "));return Y.length>1&&Y[0]===ns&&this.special_tokens.includes(Y[1])&&(Y=Y.slice(1)),Y}}class zr extends Lt{}class Jn extends Lt{}class kn extends Lt{}class Sn extends Lt{}class $n extends Lt{}class Br extends Lt{}class An extends Lt{}class Zn extends Lt{}class Rr extends Lt{}function fr(Te,M,Y,ce){if(!("language_codes"in Te)||!Array.isArray(Te.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Te)||!(Te.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Te)||typeof Te.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const me=ce.src_lang,Fe=ce.tgt_lang;if(!Te.language_codes.includes(Fe))throw new Error(`Target language code "${Fe}" is not valid. Must be one of: {${Te.language_codes.join(", ")}}`);if(me!==void 0){if(!Te.language_codes.includes(me))throw new Error(`Source language code "${me}" is not valid. Must be one of: {${Te.language_codes.join(", ")}}`);for(const Ye of Te.post_processor.config.single)if("SpecialToken"in Ye&&Te.languageRegex.test(Ye.SpecialToken.id)){Ye.SpecialToken.id=Te.lang_to_token(me);break}}return ce.forced_bos_token_id=Te.model.convert_tokens_to_ids([Te.lang_to_token(Fe)])[0],Te._call(M,Y)}class ar extends Lt{constructor(M,Y){super(M,Y),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(ce=>this.languageRegex.test(ce)),this.lang_to_token=ce=>ce}_build_translation_inputs(M,Y,ce){return fr(this,M,Y,ce)}}class pn extends Lt{constructor(M,Y){super(M,Y),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(ce=>this.languageRegex.test(ce)).map(ce=>ce.slice(2,-2)),this.lang_to_token=ce=>`__${ce}__`}_build_translation_inputs(M,Y,ce){return fr(this,M,Y,ce)}}class Xr extends Lt{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(M,{return_timestamps:Y=!1,return_language:ce=!1,time_precision:me=null,force_full_sequences:Fe=!0}={}){if(me===null)throw Error("Must specify time_precision");let Ye=null;const gt=Y==="word";function wt(){return{language:Ye,timestamp:[null,null],text:""}}const yt=[];let pt=wt(),rs=0;const Yt=this.timestamp_begin,Gs=Yt+1500;let jt=[],is=[],Ks=!1,Js=null;const De=new Set(this.all_special_ids);for(const Es of M){const Hs=Es.tokens,$t=gt?Es.token_timestamps:null;let sr=null,_r=Yt;if("stride"in Es){const[Mt,Gt,Is]=Es.stride;if(rs-=Gt,Js=Mt-Is,Gt&&(_r=Gt/me+Yt),Is)for(let ks=Hs.length-1;ks>=0;--ks){const js=Number(Hs[ks]);if(js>=Yt){if(sr!==null&&(js-Yt)*me=Yt&&Gt<=Gs){const Is=(Gt-Yt)*me+rs,ks=(0,Z.round)(Is,2);if(sr!==null&&Gt>=sr)Ks=!0;else if(Ks||jt.length>0&&Gt<_r)Ks=!1;else if(pt.timestamp[0]===null)pt.timestamp[0]=ks;else if(ks!==pt.timestamp[0]){pt.timestamp[1]=ks,jt.push(ds),gt&&is.push(Cs);const[js,Tt]=this.findLongestCommonSequence(jt,is),Yr=this.decode(js);pt.text=Yr,gt&&(pt.words=this.collateWordTimestamps(js,Tt,Ye)),yt.push(pt),jt=[],ds=[],is=[],Cs=[],pt=wt()}}else if(ds.push(Gt),gt){let Is=(0,Z.round)($t[Mt]+rs,2),ks;if(Mt+1<$t.length){ks=(0,Z.round)($t[Mt+1]+rs,2);const js=this.decode([Gt]);P.test(js)&&(ks=(0,Z.round)(Math.min(Is+me,ks),2))}else ks=null;Cs.push([Is,ks])}}if("stride"in Es){const[Mt,Gt,Is]=Es.stride;rs+=Mt-Is}ds.length>0?(jt.push(ds),gt&&is.push(Cs)):jt.every(Mt=>Mt.length===0)&&(pt=wt(),jt=[],ds=[],is=[],Cs=[])}if(jt.length>0){if(Fe&&Y)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. 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dModel;d.Grok1Tokenizer;d.GroundingDinoForObjectDetection;d.GroundingDinoImageProcessor;d.GroundingDinoPreTrainedModel;d.GroundingDinoProcessor;d.GroupViTModel;d.GroupViTPreTrainedModel;d.HerbertTokenizer;d.HieraForImageClassification;d.HieraModel;d.HieraPreTrainedModel;d.HubertForCTC;d.HubertForSequenceClassification;d.HubertModel;d.HubertPreTrainedModel;d.IJepaForImageClassification;d.IJepaModel;d.IJepaPreTrainedModel;d.Idefics3ForConditionalGeneration;d.Idefics3ImageProcessor;d.Idefics3PreTrainedModel;d.Idefics3Processor;d.ImageClassificationPipeline;d.ImageFeatureExtractionPipeline;d.ImageFeatureExtractor;d.ImageMattingOutput;d.ImageProcessor;d.ImageSegmentationPipeline;d.ImageToImagePipeline;d.ImageToTextPipeline;d.InterruptableStoppingCriteria;d.JAISLMHeadModel;d.JAISModel;d.JAISPreTrainedModel;d.JinaCLIPImageProcessor;d.JinaCLIPModel;d.JinaCLIPPreTrainedModel;d.JinaCLIPProcessor;d.JinaCLIPTextModel;d.JinaCLIPVisionModel;d.LlamaForCausalLM;d.LlamaModel;d.LlamaPreTrainedModel;d.LlamaTokenizer;d.LlavaForConditionalGeneration;d.LlavaOnevisionForConditionalGeneration;d.LlavaOnevisionImageProcessor;d.LlavaPreTrainedModel;d.LogitsProcessor;d.LogitsProcessorList;d.LogitsWarper;d.LongT5ForConditionalGeneration;d.LongT5Model;d.LongT5PreTrainedModel;d.M2M100ForConditionalGeneration;d.M2M100Model;d.M2M100PreTrainedModel;d.M2M100Tokenizer;d.MBart50Tokenizer;d.MBartForCausalLM;d.MBartForConditionalGeneration;d.MBartForSequenceClassification;d.MBartModel;d.MBartPreTrainedModel;d.MBartTokenizer;d.MPNetForMaskedLM;d.MPNetForQuestionAnswering;d.MPNetForSequenceClassification;d.MPNetForTokenClassification;d.MPNetModel;d.MPNetPreTrainedModel;d.MPNetTokenizer;d.MT5ForConditionalGeneration;d.MT5Model;d.MT5PreTrainedModel;d.MarianMTModel;d.MarianModel;d.MarianPreTrainedModel;d.MarianTokenizer;d.Mask2FormerImageProcessor;d.MaskFormerFeatureExtractor;d.MaskFormerForInstanceSegmentation;d.MaskFormerImageProcessor;d.MaskFormerModel;d.MaskFormerPreTrainedModel;d.MaskedLMOutput;d.MaxLengthCriteria;d.MgpstrForSceneTextRecognition;d.MgpstrModelOutput;d.MgpstrPreTrainedModel;d.MgpstrProcessor;d.MgpstrTokenizer;d.MinLengthLogitsProcessor;d.MinNewTokensLengthLogitsProcessor;d.MistralForCausalLM;d.MistralModel;d.MistralPreTrainedModel;d.MobileBertForMaskedLM;d.MobileBertForQuestionAnswering;d.MobileBertForSequenceClassification;d.MobileBertModel;d.MobileBertPreTrainedModel;d.MobileBertTokenizer;d.MobileLLMForCausalLM;d.MobileLLMModel;d.MobileLLMPreTrainedModel;d.MobileNetV1FeatureExtractor;d.MobileNetV1ForImageClassification;d.MobileNetV1ImageProcessor;d.MobileNetV1Model;d.MobileNetV1PreTrainedModel;d.MobileNetV2FeatureExtractor;d.MobileNetV2ForImageClassification;d.MobileNetV2ImageProcessor;d.MobileNetV2Model;d.MobileNetV2PreTrainedModel;d.MobileNetV3FeatureExtractor;d.MobileNetV3ForImageClassification;d.MobileNetV3ImageProcessor;d.MobileNetV3Model;d.MobileNetV3PreTrainedModel;d.MobileNetV4FeatureExtractor;d.MobileNetV4ForImageClassification;d.MobileNetV4ImageProcessor;d.MobileNetV4Model;d.MobileNetV4PreTrainedModel;d.MobileViTFeatureExtractor;d.MobileViTForImageClassification;d.MobileViTImageProcessor;d.MobileViTModel;d.MobileViTPreTrainedModel;d.MobileViTV2ForImageClassification;d.MobileViTV2Model;d.MobileViTV2PreTrainedModel;d.ModelOutput;d.ModernBertForMaskedLM;d.ModernBertForSequenceClassification;d.ModernBertForTokenClassification;d.ModernBertModel;d.ModernBertPreTrainedModel;d.Moondream1ForConditionalGeneration;d.MoonshineFeatureExtractor;d.MoonshineForConditionalGeneration;d.MoonshineModel;d.MoonshinePreTrainedModel;d.MoonshineProcessor;d.MptForCausalLM;d.MptModel;d.MptPreTrainedModel;d.MultiModalityCausalLM;d.MultiModalityPreTrainedModel;d.MusicgenForCausalLM;d.MusicgenForConditionalGeneration;d.MusicgenModel;d.MusicgenPreTrainedModel;d.NllbTokenizer;d.NoBadWordsLogitsProcessor;d.NoRepeatNGramLogitsProcessor;d.NomicBertModel;d.NomicBertPreTrainedModel;d.NougatImageProcessor;d.NougatTokenizer;d.OPTForCausalLM;d.OPTModel;d.OPTPreTrainedModel;d.ObjectDetectionPipeline;d.Olmo2ForCausalLM;d.Olmo2Model;d.Olmo2PreTrainedModel;d.OlmoForCausalLM;d.OlmoModel;d.OlmoPreTrainedModel;d.OpenELMForCausalLM;d.OpenELMModel;d.OpenELMPreTrainedModel;d.OwlViTFeatureExtractor;d.OwlViTForObjectDetection;d.OwlViTImageProcessor;d.OwlViTModel;d.OwlViTPreTrainedModel;d.OwlViTProcessor;d.Owlv2ForObjectDetection;d.Owlv2ImageProcessor;d.Owlv2Model;d.Owlv2PreTrainedModel;d.PaliGemmaForConditionalGeneration;d.PaliGemmaPreTrainedModel;d.PaliGemmaProcessor;d.PatchTSMixerForPrediction;d.PatchTSMixerModel;d.PatchTSMixerPreTrainedModel;d.PatchTSTForPrediction;d.PatchTSTModel;d.PatchTSTPreTrainedModel;d.Phi3ForCausalLM;d.Phi3Model;d.Phi3PreTrainedModel;d.Phi3VForCausalLM;d.Phi3VImageProcessor;d.Phi3VPreTrainedModel;d.Phi3VProcessor;d.PhiForCausalLM;d.PhiModel;d.PhiPreTrainedModel;d.Pipeline;d.PreTrainedModel;d.PreTrainedTokenizer;d.PretrainedConfig;d.PretrainedMixin;d.Processor;d.PvtForImageClassification;d.PvtImageProcessor;d.PvtModel;d.PvtPreTrainedModel;d.PyA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