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e),jm=l.dynCall_vjjjjjjjjfffiiifiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or,Er,Gr)=>(jm=l.dynCall_vjjjjjjjjfffiiifiiiii=oe.Fg)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or,Er,Gr),Lm=l.dynCall_vjjjjjjfffifijiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or)=>(Lm=l.dynCall_vjjjjjjfffifijiiiii=oe.Gg)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or),Rm=l.dynCall_vjjjjjjfffifiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt)=>(Rm=l.dynCall_vjjjjjjfffifiiiiii=oe.Hg)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt),Nm=l.dynCall_vjjjjjjjjfffjifiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or,Er,Gr)=>(Nm=l.dynCall_vjjjjjjjjfffjifiiiiii=oe.Ig)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or,Er,Gr),Vm=l.dynCall_vijiiiiiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J)=>(Vm=l.dynCall_vijiiiiiiiiii=oe.Jg)(r,n,s,o,c,h,f,w,v,$,E,G,J),Um=l.dynCall_vijjfffiii=(r,n,s,o,c,h,f,w,v,$)=>(Um=l.dynCall_vijjfffiii=oe.Kg)(r,n,s,o,c,h,f,w,v,$),Wm=l.dynCall_viiiiiiijiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J)=>(Wm=l.dynCall_viiiiiiijiiii=oe.Lg)(r,n,s,o,c,h,f,w,v,$,E,G,J),Gm=l.dynCall_vijjjjjjifiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le)=>(Gm=l.dynCall_vijjjjjjifiiiii=oe.Mg)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le),qm=l.dynCall_viifi=(r,n,s,o,c)=>(qm=l.dynCall_viifi=oe.Ng)(r,n,s,o,c),Km=l.dynCall_vjjjjjiiii=(r,n,s,o,c,h,f,w,v,$)=>(Km=l.dynCall_vjjjjjiiii=oe.Og)(r,n,s,o,c,h,f,w,v,$),Hm=l.dynCall_vjjjjfiii=(r,n,s,o,c,h,f,w,v)=>(Hm=l.dynCall_vjjjjfiii=oe.Pg)(r,n,s,o,c,h,f,w,v),Xm=l.dynCall_viiiiiijiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e)=>(Xm=l.dynCall_viiiiiijiiiiii=oe.Qg)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e),Qm=l.dynCall_vijjii=(r,n,s,o,c,h)=>(Qm=l.dynCall_vijjii=oe.Rg)(r,n,s,o,c,h),Ym=l.dynCall_viiiiijjiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J)=>(Ym=l.dynCall_viiiiijjiiiii=oe.Sg)(r,n,s,o,c,h,f,w,v,$,E,G,J),Zm=l.dynCall_iiiiiji=(r,n,s,o,c,h,f)=>(Zm=l.dynCall_iiiiiji=oe.Tg)(r,n,s,o,c,h,f),Jm=l.dynCall_iiiiji=(r,n,s,o,c,h)=>(Jm=l.dynCall_iiiiji=oe.Ug)(r,n,s,o,c,h),e_=l.dynCall_viiiiijiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J)=>(e_=l.dynCall_viiiiijiiiiii=oe.Vg)(r,n,s,o,c,h,f,w,v,$,E,G,J),t_=l.dynCall_viiijiiiiii=(r,n,s,o,c,h,f,w,v,$,E)=>(t_=l.dynCall_viiijiiiiii=oe.Wg)(r,n,s,o,c,h,f,w,v,$,E),r_=l.dynCall_viiiijii=(r,n,s,o,c,h,f,w)=>(r_=l.dynCall_viiiijii=oe.Xg)(r,n,s,o,c,h,f,w),i_=l.dynCall_viijjiii=(r,n,s,o,c,h,f,w)=>(i_=l.dynCall_viijjiii=oe.Yg)(r,n,s,o,c,h,f,w),n_=l.dynCall_viiiiijjji=(r,n,s,o,c,h,f,w,v,$)=>(n_=l.dynCall_viiiiijjji=oe.Zg)(r,n,s,o,c,h,f,w,v,$),s_=l.dynCall_vijjjjiij=(r,n,s,o,c,h,f,w,v)=>(s_=l.dynCall_vijjjjiij=oe._g)(r,n,s,o,c,h,f,w,v),a_=l.dynCall_viiiiijij=(r,n,s,o,c,h,f,w,v)=>(a_=l.dynCall_viiiiijij=oe.$g)(r,n,s,o,c,h,f,w,v),o_=l.dynCall_viiiiiijij=(r,n,s,o,c,h,f,w,v,$)=>(o_=l.dynCall_viiiiiijij=oe.ah)(r,n,s,o,c,h,f,w,v,$),l_=l.dynCall_vijiii=(r,n,s,o,c,h)=>(l_=l.dynCall_vijiii=oe.bh)(r,n,s,o,c,h),u_=l.dynCall_viiiiiiiiifi=(r,n,s,o,c,h,f,w,v,$,E,G)=>(u_=l.dynCall_viiiiiiiiifi=oe.ch)(r,n,s,o,c,h,f,w,v,$,E,G),d_=l.dynCall_viiiiiiiiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e)=>(d_=l.dynCall_viiiiiiiiiiiii=oe.dh)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e),c_=l.dynCall_iiijiiii=(r,n,s,o,c,h,f,w)=>(c_=l.dynCall_iiijiiii=oe.eh)(r,n,s,o,c,h,f,w),p_=l.dynCall_viiiiiijjiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e)=>(p_=l.dynCall_viiiiiijjiiiii=oe.fh)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e),h_=l.dynCall_viiiiiiijiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le)=>(h_=l.dynCall_viiiiiiijiiiiii=oe.gh)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le),f_=l.dynCall_vif=(r,n,s)=>(f_=l.dynCall_vif=oe.hh)(r,n,s),m_=l.dynCall_viif=(r,n,s,o)=>(m_=l.dynCall_viif=oe.ih)(r,n,s,o),__=l.dynCall_fiii=(r,n,s,o)=>(__=l.dynCall_fiii=oe.jh)(r,n,s,o),g_=l.dynCall_diii=(r,n,s,o)=>(g_=l.dynCall_diii=oe.kh)(r,n,s,o),w_=l.dynCall_viijjiiii=(r,n,s,o,c,h,f,w,v)=>(w_=l.dynCall_viijjiiii=oe.lh)(r,n,s,o,c,h,f,w,v),y_=l.dynCall_viiiiiifiii=(r,n,s,o,c,h,f,w,v,$,E)=>(y_=l.dynCall_viiiiiifiii=oe.mh)(r,n,s,o,c,h,f,w,v,$,E),b_=l.dynCall_viiiiijiiiiiiiiiiiiiiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or,Er,Gr,li,Bi,gc,wc,yc)=>(b_=l.dynCall_viiiiijiiiiiiiiiiiiiiiiiii=oe.nh)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or,Er,Gr,li,Bi,gc,wc,yc),v_=l.dynCall_viijji=(r,n,s,o,c,h)=>(v_=l.dynCall_viijji=oe.oh)(r,n,s,o,c,h),M_=l.dynCall_iiiiiiiiiiiji=(r,n,s,o,c,h,f,w,v,$,E,G,J)=>(M_=l.dynCall_iiiiiiiiiiiji=oe.ph)(r,n,s,o,c,h,f,w,v,$,E,G,J),x_=l.dynCall_viifiifijjjii=(r,n,s,o,c,h,f,w,v,$,E,G,J)=>(x_=l.dynCall_viifiifijjjii=oe.qh)(r,n,s,o,c,h,f,w,v,$,E,G,J),T_=l.dynCall_viiiiiiiiiiiiiiiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or,Er,Gr)=>(T_=l.dynCall_viiiiiiiiiiiiiiiiiiii=oe.rh)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or,Er,Gr),C_=l.dynCall_viiiiifiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J)=>(C_=l.dynCall_viiiiifiiiiii=oe.sh)(r,n,s,o,c,h,f,w,v,$,E,G,J),k_=l.dynCall_vijiiiiiiijjii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e)=>(k_=l.dynCall_vijiiiiiiijjii=oe.th)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e),$_=l.dynCall_viiiiiiiiiiiiiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or)=>($_=l.dynCall_viiiiiiiiiiiiiiiiii=oe.uh)(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,It,Xt,or),S_=l.dynCall_viiiiiiiiiiiiiiiiiii=(r,n,s,o,c,h,f,w,v,$,E,G,J,_e,Le,ht,I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workgroupBarrier(); + x = workgroup_id.y * ${C[0]}u + local_id.x; + y = workgroup_id.x * ${C[0]}u + local_id.y; + if (x < height && y < width) { + ${d.setByOffset("y * height + x","tile[local_id.x][local_id.y]")} + } + }`}else g=y=>` + ${y.registerUniform("output_size","u32").declareVariables(_,d)} + + ${ja(u,a,_,d)} + + ${y.mainStart()} + ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${d.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${d.setByOffset("global_idx",_.getByIndices("aIndices"))} + }`;return{name:"Transpose",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:y=>{let 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${I.mainStart(l)} + + let outputIndex = global_idx / ${l}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${Va[a]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${l}) { + let candidate = f32(${C.getByOffset("offset + k")}); + bestValue = ${Ra[a]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${l}u); + for (var currentSize = reduceSize / 2u; reduceSize > 1u; + currentSize = reduceSize / 2u) { + let interval = DIV_CEIL(reduceSize, 2u); + if (local_idx < currentSize) { + let candidate = aBestValues[local_idx + interval]; + bestValue = ${Na[a]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${k.setByOffset("outputIndex",`${a==="mean"?`${k.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${k.type.storage}(${Ua[a]})`}`)}; + } + }`,getRunData:()=>({outputs:[{dims:p,dataType:u}],dispatchGroup:{x:g},programUniforms:[{type:12,data:y}]})}},Ai=(e,t,i,a)=>{let u=e.inputs.length===1?i:Qn(e.inputs,i),p=u.axes;p.length===0&&!u.noopWithEmptyAxes&&(p=e.inputs[0].dims.map((F,I)=>I));let 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Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},to=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],Xn=(e,t,i,a,u,p,d=!1,_=!1)=>{let g=[],y=i[0].dims,C=y.length,k=tt.normalizeAxes(u,C),l=!_&&k.length===0;y.forEach((L,Q)=>{l||k.indexOf(Q)>=0?d&&g.push(1):g.push(L)});let F=g.length,I=tt.size(g);return{name:e,shaderCache:t,getShaderSource:L=>{let Q=[],Z=mt("_A",i[0].dataType,C),U=Jt("output",p,F),we=a(Z,U,k),te=we[2];for(let me=0,it=0;me=0?(d&&it++,te=`for(var j${me}: u32 = 0; j${me} < ${y[me]}; j${me}++) { + ${we[2].includes("last_index")?`let last_index = j${me};`:""} + ${Z.indicesSet("input_indices",me,`j${me}`)} + ${te} + }`):(Q.push(`${Z.indicesSet("input_indices",me,U.indicesGet("output_indices",it))};`),it++);return` + + ${L.registerUniform("output_size","u32").declareVariables(Z,U)} + + ${L.mainStart()} + ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var input_indices: ${Z.type.indices}; + let output_indices = ${U.offsetToIndices("global_idx")}; + + ${Q.join(` +`)} + ${we[0]} // init ops for reduce max/min + ${we[1]} + ${te} + ${we[3]} + ${we.length===4?U.setByOffset("global_idx","value"):we.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:g,dataType:p}],dispatchGroup:{x:Math.ceil(I/64)},programUniforms:[{type:12,data:I},...Rt(y,g)]})}},Qn=(e,t)=>{let i=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(a=>i.push(Number(a))),tr({axes:i,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},ji=(e,t,i,a)=>{let u=e.inputs,p=u.length===1?i:Qn(u,i);e.compute(Xn(t,{hint:p.cacheKey,inputDependencies:["rank"]},[u[0]],p.noopWithEmptyAxes&&p.axes.length===0?to:a,p.axes,u[0].dataType,p.keepDims,p.noopWithEmptyAxes),{inputs:[0]})},ro=(e,t)=>{Ii(e.inputs),ji(e,"ReduceLogSum",t,(i,a)=>[`var value = ${a.type.storage}(0);`,"",`value += ${i.getByIndices("input_indices")};`,"value = 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p=0;p1024},uo=(e,t)=>{Fi(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?so(e,t):Ha(e,t)},co=(e,t)=>{Fi(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Es(e,t):Xa(e,t)},Is=(e,t)=>{Fi(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?io(e,t):ks(e,t)},po=(e,t)=>{Fi(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?no(e,t):Qa(e,t)},ho=(e,t)=>{Fi(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ps(e,t):Ya(e,t)},Fs=(e,t)=>{Fi(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?ao(e,t):$s(e,t)},fo=(e,t)=>{Fi(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?As(e,t):Za(e,t)},mo=(e,t)=>{Fi(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?oo(e,t):Ja(e,t)},zs=(e,t)=>{Fi(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?lo(e,t):Ss(e,t)},_o=(e,t)=>{Fi(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?ro(e,t):eo(e,t)}}),Yn,go,wo,Zn,Uu=V(()=>{sr(),Tr(),Os(),Yn=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},go=(e,t)=>{Yn(e.inputs);let i=(a,u,p)=>{let d=[];for(let _=0;_=0||p.length===0)&&d.push(`input_indices[${_}] = 0;`);return[`${d.join(` +`)}`,`var value = ${a.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${a.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) { + value = ${a.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",u.setByOffset("global_idx","best_index")]};e.compute(Xn("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],i,[t.axis],7,t.keepDims),{inputs:[0]})},wo=(e,t)=>{Yn(e.inputs);let i=(a,u,p)=>{let d=[];for(let _=0;_=0||p.length===0)&&d.push(`input_indices[${_}] = 0;`);return[`${d.join(` +`)}`,`var value = ${a.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${a.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) { + value = ${a.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",u.setByOffset("global_idx","best_index")]};e.compute(Xn("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],i,[t.axis],7,t.keepDims),{inputs:[0]})},Zn=e=>tr(e)}),yo,Ds,bo,vo,yn,Mo,xo,Jn=V(()=>{sr(),N(),mr(),yo=(e,t)=>{let i=e[0],a=e[1],u=e[2],p=e[3],d=e[4],_=e[5];if(d&&_)throw new Error("Attention cannot have both past and relative_position_bias");if(i.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let g=i.dims[0],y=i.dims[1],C=i.dims[2];if(u.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(a.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(a.dims[0]!==C)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(u.dims[0]!==a.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let k=u.dims[0]/3,l=k,F=l;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let we of t.qkvHiddenSizes)if(we%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");k=t.qkvHiddenSizes[0],l=t.qkvHiddenSizes[1],F=t.qkvHiddenSizes[2]}let I=y;if(k!==l)throw new Error("qkv_hidden_sizes first element should be same as the second");if(u.dims[0]!==k+l+F)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let L=0;if(d){if(l!==F)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(d.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(d.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(d.dims[1]!==g)throw new Error('Input "past" second dimension must be batch_size');if(d.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(d.dims[4]!==l/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(L=d.dims[3])}let Q=I+L,Z=-1,U=0;if(p)throw new Error("Mask not supported");if(d)throw new Error("past is not supported");return{batchSize:g,sequenceLength:y,pastSequenceLength:L,kvSequenceLength:I,totalSequenceLength:Q,maxSequenceLength:Z,inputHiddenSize:C,hiddenSize:k,vHiddenSize:F,headSize:Math.floor(k/t.numHeads),vHeadSize:Math.floor(F/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:U,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Ds=(e,t,i,a)=>{let u=$r(a),p=64,d=a/u;d{let F=Jt("x",t.dataType,t.dims,u),I=Or(t.dataType),L=[{name:"d_inv",type:"f32"},{name:"d_comp",type:"u32"},{name:"elements_per_thread",type:"u32"}];return` + var thread_max: array; + var thread_sum: array; + ${l.registerUniforms(L).declareVariables(F)} + ${l.mainStart([p,1,1])} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = (global_idx / ${p}) * uniforms.d_comp + local_offset; + + var thread_max_vector = ${C}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + thread_max_vector = max(${C}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(u){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: ${u}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${p}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${C}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + sum_vector += exp(${C}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(u){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: ${u}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${p}; i++) { + sum += thread_sum[i]; + } + + if (sum == 0) { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + x[offset + i] = ${F.type.value}(${I}(uniforms.d_inv)); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < uniforms.d_comp; i++) { + var f32input = ${C}(x[offset + i]); + x[offset + i] = ${F.type.value}(exp(f32input - max_value) / sum); + } + } + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${p};${y};${u}`},getShaderSource:k,getRunData:()=>({outputs:[],dispatchGroup:{x:i},programUniforms:g})}},bo=(e,t,i,a,u,p,d,_)=>{let g=_+p.kvSequenceLength,y=[p.batchSize,p.numHeads,p.sequenceLength,g],C=p.kvNumHeads===void 0&&e.outputCount>1,k=C?[p.batchSize,p.numHeads,g,p.headSize]:void 0,l=d.scale===0?1/Math.sqrt(p.headSize):d.scale,F=$r(p.headSize),I=p.headSize/F,L=12,Q={x:Math.ceil(g/L),y:Math.ceil(p.sequenceLength/L),z:p.batchSize*p.numHeads},Z=[{type:12,data:p.sequenceLength},{type:12,data:I},{type:12,data:g},{type:12,data:p.numHeads},{type:1,data:l},{type:12,data:_},{type:12,data:p.kvSequenceLength}],U=["type","type"];a&&U.push("type"),u&&U.push("type");let we=[{dims:y,dataType:t.dataType,gpuDataType:0}];C&&we.push({dims:k,dataType:t.dataType,gpuDataType:0});let te=me=>{let it=mt("q",t.dataType,t.dims,F),Ye=mt("key",i.dataType,i.dims,F),Mt=[it,Ye];if(a){let Ur=mt("past_key",a.dataType,a.dims,F);Mt.push(Ur)}u&&Mt.push(mt("relative_position_bias",u.dataType,u.dims));let Gt=Jt("output",t.dataType,y),Bt=[Gt];C&&Bt.push(Jt("present_key",t.dataType,k,F));let gr=Or(1,F),Mr=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"}];return` + const TILE_SIZE = ${L}u; + + var tileQ: array<${it.type.storage}, ${L*L}>; + var tileK: array<${it.type.storage}, ${L*L}>; + ${me.registerUniforms(Mr).declareVariables(...Mt,...Bt)} + ${me.mainStart([L,L,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z; + let m = workgroup_id.y * TILE_SIZE; + let n = workgroup_id.x * TILE_SIZE; + let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K; + ${a&&C?` + let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx; + let pastKeyOffset = uniforms.past_sequence_length * uniforms.K * headIdx;`:` + let kOffset = uniforms.N * uniforms.K * headIdx + n * uniforms.K;`} + ${C?"let presentKeyOffset = headIdx * uniforms.N * uniforms.K;":""} + var value = ${gr}(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; + ${a&&C?` + if (n + local_id.y < uniforms.past_sequence_length) { + tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; + } else { + tileK[idx] = + key[kOffset + (n + local_id.y - uniforms.past_sequence_length) * uniforms.K + w + local_id.x]; + }`:"tileK[idx] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];"} + ${C?"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 += ${gr}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]); + } + + workgroupBarrier(); + } + + let headOffset = headIdx * uniforms.M * uniforms.N; + if (global_id.y < uniforms.M && global_id.x < uniforms.N) { + let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x; + var sum: f32 = ${(()=>{switch(F){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: ${F}`)}})()}; + output[outputIdx] = ${Gt.type.value} (sum * uniforms.alpha) + ${u?"relative_position_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${F};${u!==void 0};${a!==void 0};${e.outputCount}`,inputDependencies:U},getRunData:()=>({outputs:we,dispatchGroup:Q,programUniforms:Z}),getShaderSource:te}},vo=(e,t,i,a,u,p)=>{let d=p+u.kvSequenceLength,_=u.nReps?u.nReps:1,g=u.vHiddenSize*_,y=u.kvNumHeads==null&&e.outputCount>1,C=y?[u.batchSize,u.numHeads,d,u.headSize]:void 0,k=[u.batchSize,u.sequenceLength,g],l=12,F={x:Math.ceil(u.vHeadSize/l),y:Math.ceil(u.sequenceLength/l),z:u.batchSize*u.numHeads},I=[{type:12,data:u.sequenceLength},{type:12,data:d},{type:12,data:u.vHeadSize},{type:12,data:u.numHeads},{type:12,data:g},{type:12,data:p},{type:12,data:u.kvSequenceLength}],L=a?["type","type","type"]:["type","type"],Q=[{dims:k,dataType:t.dataType,gpuDataType:0}];y&&Q.push({dims:C,dataType:t.dataType,gpuDataType:0});let Z=U=>{let we=mt("probs",t.dataType,t.dims),te=mt("v",i.dataType,i.dims),me=[we,te];a&&me.push(mt("past_value",a.dataType,a.dims));let it=[Jt("output",t.dataType,k)];y&&it.push(Jt("present_value",t.dataType,C));let Ye=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"}];return` + const TILE_SIZE = ${l}u; + var tileQ: array<${we.type.value}, ${l*l}>; + var tileK: array<${we.type.value}, ${l*l}>; + ${U.registerUniforms(Ye).declareVariables(...me,...it)} + ${U.mainStart([l,l,1])} + let headIdx = workgroup_id.z; + let m = global_id.y; + let n = global_id.x; + + let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K; + ${a&&y?` + let pastValueOffset = headIdx * uniforms.N * uniforms.past_sequence_length + n; + let vOffset = headIdx * uniforms.N * uniforms.kv_sequence_length + n; + `:` + let offsetB = headIdx * uniforms.N * uniforms.K + n; + `} + ${y?"let presentValueOffset = headIdx * uniforms.N * uniforms.K + n;":""} + var value = ${we.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; + ${a&&y?` + if (w + local_id.y < uniforms.past_sequence_length) { + tileK[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N]; + } else { + tileK[idx] = v[vOffset + (w + local_id.y - uniforms.past_sequence_length) * uniforms.N]; + } + `:` + tileK[idx] = v[offsetB + (w + local_id.y) * uniforms.N]; + `} + ${y?"present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileK[idx];":""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + let batchIdx = workgroup_id.z / uniforms.num_heads; + let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads; + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + currentBatchHeadNumber * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${a!==void 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i=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],a=t.sequenceLength,u=t.inputHiddenSize,p=t.headSize,d=12,_={x:Math.ceil(t.headSize/d),y:Math.ceil(t.sequenceLength/d),z:t.batchSize*t.numHeads},g=[e.inputs[0],e.inputs[1],e.inputs[2]],y=[{type:12,data:a},{type:12,data:u},{type:12,data:p},{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=k=>{let l=Jt("output_q",g[0].dataType,i),F=Jt("output_k",g[0].dataType,i),I=Jt("output_v",g[0].dataType,i),L=mt("input",g[0].dataType,g[0].dims),Q=mt("weight",g[1].dataType,g[1].dims),Z=mt("bias",g[2].dataType,g[2].dims),U=L.type.storage,we=[{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 = ${d}u; + var tileInput: array<${U}, ${d*d}>; + var tileWeightQ: array<${U}, ${d*d}>; + var tileWeightK: array<${U}, 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${e.registerUniform("vec_size","u32").declareVariables(g,y)} + + ${p??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${g.getByOffset("global_idx")}; + ${y.setByOffset("global_idx",_)} + }`},Pr=(e,t,i,a,u,p=e.dataType)=>({name:t,shaderCache:{hint:u,inputDependencies:["type"]},getShaderSource:d=>Ao(d,tt.size(e.dims),e.dataType,p,i,a),getRunData:d=>({outputs:[{dims:e.dims,dataType:p}],dispatchGroup:{x:Math.ceil(tt.size(d[0].dims)/64/4)},programUniforms:[{type:12,data:Math.ceil(tt.size(e.dims)/4)}]})}),Io=e=>{e.compute(Pr(e.inputs[0],"Abs","abs"))},Fo=e=>{e.compute(Pr(e.inputs[0],"Acos","acos"))},js=e=>{e.compute(Pr(e.inputs[0],"Acosh","acosh"))},zo=e=>{e.compute(Pr(e.inputs[0],"Asin","asin"))},Oo=e=>{e.compute(Pr(e.inputs[0],"Asinh","asinh"))},Ls=e=>{e.compute(Pr(e.inputs[0],"Atan","atan"))},Do=e=>{e.compute(Pr(e.inputs[0],"Atanh","atanh"))},Bo=e=>tr(e),es=(e,t)=>{let i;switch(t.to){case 10:i="vec4";break;case 1:i="vec4";break;case 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+ return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${i}>) -> vec4<${i}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,t.cacheKey))},ts=(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<${e}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,Uo=e=>{let t=Or(e.inputs[0].dataType);e.compute(Pr(e.inputs[0],"Erf",i=>`erf_vf32(${i})`,ts(t)))},Ns=e=>{e.compute(Pr(e.inputs[0],"Exp","exp"))},Wo=e=>{e.compute(Pr(e.inputs[0],"Floor","floor"))},Go=e=>{let t=Or(e.inputs[0].dataType);e.compute(Pr(e.inputs[0],"Gelu",i=>`0.5 * ${i} * (1.0 + erf_vf32(${i} * 0.7071067811865475))`,ts(t)))},qo=(e,t)=>{let 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= 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; +} +`,il=e=>`quick_gelu_impl(${e})`,nl=(e,t)=>{let i=Or(e.inputs[0].dataType);e.compute(Pr(e.inputs[0],"QuickGelu",il,rl(i,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),Zs,sl,al,ol=V(()=>{lr(),mr(),Ys(),Zs=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},sl=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let 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}`}},al=e=>{Zs(e.inputs),e.compute(sl(e.inputs))}}),ll,ul,zi,dl,cl,Js,pl,hl,fl,ml,_l,gl,ea,Gu=V(()=>{sr(),lr(),mr(),ll=(e,t,i,a,u,p,d,_,g,y,C,k)=>{let l,F;typeof _=="string"?l=F=(U,we)=>`${_}((${U}),(${we}))`:typeof _=="function"?l=F=_:(l=_.scalar,F=_.vector);let I=Jt("outputData",C,a.length,4),L=mt("aData",g,t.length,4),Q=mt("bData",y,i.length,4),Z;if(u)if(p){let U=tt.size(t)===1,we=tt.size(i)===1,te=t.length>0&&t[t.length-1]%4===0,me=i.length>0&&i[i.length-1]%4===0;U||we?Z=I.setByOffset("global_idx",F(U?`${L.type.value}(${L.getByOffset("0")}.x)`:L.getByOffset("global_idx"),we?`${Q.type.value}(${Q.getByOffset("0")}.x)`:Q.getByOffset("global_idx"))):Z=` + let outputIndices = ${I.offsetToIndices("global_idx * 4u")}; + let offsetA = ${L.broadcastedIndicesToOffset("outputIndices",I)}; + let offsetB = ${Q.broadcastedIndicesToOffset("outputIndices",I)}; + ${I.setByOffset("global_idx",F(d||te?L.getByOffset("offsetA / 4u"):`${L.type.value}(${L.getByOffset("offsetA / 4u")}[offsetA % 4u])`,d||me?Q.getByOffset("offsetB / 4u"):`${Q.type.value}(${Q.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else Z=I.setByOffset("global_idx",F(L.getByOffset("global_idx"),Q.getByOffset("global_idx")));else{if(!p)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let U=(we,te,me="")=>{let it=`aData[indexA${te}][componentA${te}]`,Ye=`bData[indexB${te}][componentB${te}]`;return` + let outputIndices${te} = ${I.offsetToIndices(`global_idx * 4u + ${te}u`)}; + let offsetA${te} = ${L.broadcastedIndicesToOffset(`outputIndices${te}`,I)}; + let offsetB${te} = ${Q.broadcastedIndicesToOffset(`outputIndices${te}`,I)}; + let indexA${te} = offsetA${te} / 4u; + let indexB${te} = offsetB${te} / 4u; + let componentA${te} = offsetA${te} % 4u; + let componentB${te} = offsetB${te} % 4u; + ${we}[${te}] = ${me}(${l(it,Ye)}); + `};C===9?Z=` + var data = vec4(0); + ${U("data",0,"u32")} + ${U("data",1,"u32")} + ${U("data",2,"u32")} + ${U("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:Z=` + ${U("outputData[global_idx]",0)} + ${U("outputData[global_idx]",1)} + ${U("outputData[global_idx]",2)} + ${U("outputData[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(L,Q,I)} + + ${k??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${Z} + }`},ul=(e,t,i,a,u,p,d=i.dataType)=>{let _=!tt.areEqual(i.dims,a.dims),g=i.dims,y=tt.size(i.dims),C=!1,k=!1,l=[_];if(_){let F=ui.calcShape(i.dims,a.dims,!1);if(!F)throw new Error("Can't perform binary op on the given tensors");g=F,y=tt.size(g);let I=tt.size(i.dims)===1,L=tt.size(a.dims)===1,Q=i.dims.length>0&&i.dims[i.dims.length-1]%4===0,Z=a.dims.length>0&&a.dims[a.dims.length-1]%4===0;l.push(I),l.push(L),l.push(Q),l.push(Z);let U=1;for(let we=1;weF.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:F=>ll(F,i.dims,a.dims,g,C,_,k,u,i.dataType,a.dataType,d,p),getRunData:()=>({outputs:[{dims:g,dataType:d}],dispatchGroup:{x:Math.ceil(y/64/4)},programUniforms:[{type:12,data:Math.ceil(tt.size(g)/4)},...Rt(i.dims,a.dims,g)]})}},zi=(e,t,i,a,u,p)=>{e.compute(ul(t,u??"",e.inputs[0],e.inputs[1],i,a,p))},dl=e=>{zi(e,"Add",(t,i)=>`${t}+${i}`)},cl=e=>{zi(e,"Div",(t,i)=>`${t}/${i}`)},Js=e=>{zi(e,"Equal",{scalar:(t,i)=>`u32(${t}==${i})`,vector:(t,i)=>`vec4(${t}==${i})`},void 0,void 0,9)},pl=e=>{zi(e,"Mul",(t,i)=>`${t}*${i}`)},hl=e=>{let t=mt("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;zi(e,"Pow",{scalar:(i,a)=>`pow_custom(${i},${a})`,vector:(i,a)=>`pow_vector_custom(${i},${a})`},` + 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)); + } + `)},fl=e=>{zi(e,"Sub",(t,i)=>`${t}-${i}`)},ml=e=>{zi(e,"Greater",{scalar:(t,i)=>`u32(${t}>${i})`,vector:(t,i)=>`vec4(${t}>${i})`},void 0,void 0,9)},_l=e=>{zi(e,"Less",{scalar:(t,i)=>`u32(${t}<${i})`,vector:(t,i)=>`vec4(${t}<${i})`},void 0,void 0,9)},gl=e=>{zi(e,"GreaterOrEqual",{scalar:(t,i)=>`u32(${t}>=${i})`,vector:(t,i)=>`vec4(${t}>=${i})`},void 0,void 0,9)},ea=e=>{zi(e,"LessOrEqual",{scalar:(t,i)=>`u32(${t}<=${i})`,vector:(t,i)=>`vec4(${t}<=${i})`},void 0,void 0,9)}}),wl,ta,yl,bl,tn,vl,qu=V(()=>{sr(),lr(),Tr(),mr(),wl=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let i=0,a=e[i],u=a.dataType,p=a.dims.length;e.forEach((d,_)=>{if(_!==i){if(d.dataType!==u)throw new Error("input tensors should be one type");if(d.dims.length!==p)throw new Error("input tensors should have the same shape");d.dims.forEach((g,y)=>{if(y!==t&&g!==a.dims[y])throw new Error("non concat dimensions must match")})}})},ta=(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 ${e}u; + }`,yl=(e,t)=>{let i=e.length,a=[];for(let u=0;u{let u=tt.size(i),p=new Array(e.length),d=new Array(e.length),_=0,g=[],y=[],C=[{type:12,data:u}];for(let L=0;L`uniforms.sizeInConcatAxis${L}`).join(","),I=L=>` + + ${(()=>{L.registerUniform("outputSize","u32");for(let Q=0;Q(${F}); + ${l} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${yl(d,k)} + }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:C}),getShaderSource:I}},tn=(e,t)=>{let i=e.inputs,a=i[0].dims,u=tt.normalizeAxis(t.axis,a.length);wl(i,u);let p=a.slice();p[u]=i.reduce((_,g)=>_+(g.dims.length>u?g.dims[u]:0),0);let d=i.filter(_=>tt.size(_.dims)>0);e.compute(bl(d,u,p,i[0].dataType),{inputs:d})},vl=e=>tr({axis:e.axis})}),rn,nn,Xi,ra,sn=V(()=>{sr(),lr(),rn=(e,t,i="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, ${t}(${i}(uniforms.clip_min)), ${t}(${i}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${i}(uniforms.alpha) * value + ${i}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${i}(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 ${e.activation}`)}},nn=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Xi=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},ra=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[i,a]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:i,beta:a}}else if(t==="Clip"){let[i,a]=(e==null?void 0:e.activation_params)||[ni,vi];return{activation:t,clipMax:a,clipMin:i}}else if(t==="LeakyRelu"){let[i]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:i}}return{activation:t}}}),pi,ia,vn=V(()=>{pi=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},ia=e=>` + ${e?"value = value + getBiasByOutputCoords(coords);":""} + `}),na,Ml=V(()=>{na=e=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); +} +`}),xl,On,rs,sa,Tl,is,ns,aa,ss=V(()=>{sr(),lr(),mr(),sn(),vn(),xl=(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":""}); + `,On=(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];"} + }`,rs=(e,t,i="f32",a,u=!1,p=32,d=!1,_=32)=>{let g=t[1]*e[1],y=t[0]*e[0],C=u?g:p,k=u?p:g,l=C/t[0],F=p/t[1];if(!((u&&l===4&&e[1]===4||!u&&(l===3||l===4))&&C%t[0]===0&&p%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${u} is true, innerElementSize ${l} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${l} must be 3 or 4. + tileAWidth ${C} must be divisible by workgroupSize[0]${t[0]}. tileInner ${p} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return` +var mm_Asub: array, ${C/l}>, ${k}>; +var mm_Bsub: array, ${y/e[0]}>, ${p}>; + +const rowPerThread = ${e[1]}; +const colPerThread = ${e[0]}; +const innerElementSize = ${l}; +const tileInner = ${p}; + +@compute @workgroup_size(${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 = ${d?"0":"i32(globalId.z)"}; + ${a?`let batchIndices = ${a.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${g}; + + let num_tiles = ${d?`${Math.ceil(_/p)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${d?`i32(globalId.z) * ${_}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${F}; + for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let inputRow = tileRow + innerRow; + let inputCol = tileCol; + ${xl(u,a)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${F}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${a?", batchIndices":""}); + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { + let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; + let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; + let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; + ${l===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${On(u,l)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},sa=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${t?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${t?", batchIndices":""}); + `,Tl=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",is=(e,t,i="f32",a,u=!1,p=32,d=!1,_=32,g=!1)=>{let y=e[1]*t[1],C=e[0]*t[0],k=u?y:p,l=u?p:y;if(!(l%t[1]===0&&k%t[0]===0&&p%t[1]===0))throw new Error(`tileAHight ${l} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${k} must be divisible by workgroupSize[0]${t[0]}, tileInner ${p} must be divisible by workgroupSize[1]${t[1]}`);let F=l/t[1],I=k/t[0],L=p/t[1],Q=g?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${y}; + 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 < ${l}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${k}; inputCol = inputCol + ${t[0]}) { + ${sa(u,a)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${p}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${C}; inputCol = inputCol + ${t[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalColStart + inputCol${a?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${i}, 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 = ${u?`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) * ${y}; + +let tileRowA = i32(localId.y) * ${F}; +let tileColA = i32(localId.x) * ${I}; +let tileRowB = i32(localId.y) * ${L}; +// Loop over shared dimension. +for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var innerRow = 0; innerRow < ${F}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${I}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${sa(u,a)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${L}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${a?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${i}, 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) { + ${Tl(u)} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; + } + } + } + + workgroupBarrier(); +} + +for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + mm_write(batch, globalRow + innerRow, globalCol + innerCol, + acc[innerRow][innerCol]); + } +} +`;return` + var mm_Asub : array, ${l}>; + var mm_Bsub : array, ${p}>; + const rowPerThread = ${e[1]}; + const colPerThread = ${e[0]}; + const tileInner = ${p}; + +@compute @workgroup_size(${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 = ${d?"0":"i32(globalId.z)"}; + ${a?`let batchIndices = ${a.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${d?`${Math.ceil(_/p)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${d?`i32(globalId.z) * ${_}`:"0"}; + + var acc : array, rowPerThread>; + ${Q} + } +`},ns=(e,t,i,a,u,p=!1)=>{let[d,_,g]=u,[y,C,k,l]=a,F=gn(d,g),I=gn(_,g),L=zr(a[0].type.tensor),Q=()=>{let U=C.rank,we=y.rank,te=`var aIndices: ${C.type.indices};`;for(let me=U-2-1,it=we-1;me>=0;me--,it--)te+=` +aIndices[${me}] = ${we>1?`batchIndices[${it}]`:"batchIndices"};`;return F.forEach(me=>{te+=` +aIndices[${me}] = 0;`}),te+=` +aIndices[${U-2}] = u32(row); + aIndices[${U-1}] = u32(colIn);`,te},Z=()=>{let U=k.rank,we=y.rank,te=`var bIndices: ${k.type.indices};`;for(let me=U-2-1,it=we-1;me>=0;me--,it--)te+=` +bIndices[${me}] = ${we>1?`batchIndices[${it}]`:"batchIndices"};`;return I.forEach(me=>{te+=` +bIndices[${me}] = 0;`}),te+=` +bIndices[${U-2}] = u32(row); + bIndices[${U-1}] = u32(colIn);`,te};return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${y.type.indices}) -> ${pi(e,L)} { + var value = ${pi(e,L)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + ${Q()} + value = ${C.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${y.type.indices}) -> ${pi(e,L)} { + var value = ${pi(e,L)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + ${Z()} + value = ${k.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${pi(e,L)}) { + 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 + ${p?"bias[colIn]":`${pi(e,L)}(bias[row])`};`:""} + ${i} + ${l.setByIndices("vec3(coords)","value")} + } + } + `},aa=(e,t,i,a,u=!1)=>{let p=e[0].dims,d=e[1].dims,_=p.slice(0,-2),g=d.slice(0,-2),y=a?a.slice(0,-2):i.slice(0,-2),C=tt.size(y),k=p[p.length-2],l=p[p.length-1],F=d[d.length-1],I=l%4===0&&F%4===0,L=k<=8?[4,1,1]:[4,4,1],Q=[8,8,1],Z=[Math.ceil(F/Q[0]/L[0]),Math.ceil(k/Q[1]/L[1]),Math.ceil(C/Q[2]/L[2])],U=I?4:1,we=[..._,k,l/U],te=we.length,me=[...g,l,F/U],it=me.length,Ye=[C,k,F/U],Mt=[{type:6,data:k},{type:6,data:F},{type:6,data:l}];nn(t,Mt),Mt.push(...Rt(y,we,me));let Gt=["rank","rank"],Bt=e.length>2;Bt&&(Mt.push(...Rt(e[2].dims)),Gt.push("rank")),Mt.push(...Rt(Ye));let gr=Mr=>{let Ur=y.length,Ir=bs("batchDims",e[0].dataType,Ur,1),Sr=zr(e[0].dataType),ri=mt("a",e[0].dataType,te,U),Kr=mt("b",e[1].dataType,it,U),Wt=Jt("result",e[0].dataType,Ye.length,U),pr=[ri,Kr];if(Bt){let Yr=u?U:1;pr.push(mt("bias",e[2].dataType,e[2].dims.length,Yr))}let cr=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Xi(t,cr);let lt=zr(Wt.type.tensor),qt=rn(t,Wt.type.value,lt),fr=ns(U,Bt,qt,[Ir,ri,Kr,Wt],[_,g,y],u);return` + ${Mr.registerUniforms(cr).registerInternalVariables(Ir).declareVariables(...pr,Wt)} + ${fr} + ${I?rs(L,Q,Sr,Ir):is(L,Q,Sr,Ir)} + `};return{name:"MatMul",shaderCache:{hint:`${L};${t.activation};${I};${u}`,inputDependencies:Gt},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Z[0],y:Z[1],z:Z[2]},programUniforms:Mt}),getShaderSource:gr}}}),Cl,Ku,Hu=V(()=>{sr(),Ci(),mr(),sn(),vn(),Ml(),ss(),Cl=(e,t,i,a,u=!1,p,d=4,_=4,g=4,y="f32")=>{let C=Gt=>{switch(Gt){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${y}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Gt} is not supported.`)}},k=Gt=>{switch(Gt){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 ${Gt} is not supported.`)}},l=e?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,F=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,I=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",L=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",Q=e?"row":"col",Z=e?"col":"row",U=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${Q} / outWidth; + let outCol = ${Q} % outWidth; + + let WRow = ${Z} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${Z} / 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 = ${Z} % inChannels; + var resData = ${pi(d,y)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${I} && xCol >= 0 && xCol < ${L}) { + ${l} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${C(d)} + } + return resData;`,we=e?t&&a?` + let col = colIn * ${d}; + ${U}`:` + let col = colIn * ${d}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${U} + } + return ${pi(d,y)}(0.0);`:a&&i?` + let col = colIn * ${d}; + ${U}`:` + let col = colIn * ${d}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${U} + } + return ${pi(d,y)}(0.0);`,te=`${k(_)}`,me=pi(g,y),it=pi(e?d:_,y),Ye=pi(e?_:d,y),Mt=rn(p,me,y);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${it} { + ${e?we:te} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Ye} { + ${e?te:we} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${me}) { + let col = colIn * ${g}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) + { + var value = valueIn; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${F} + ${ia(u)} + ${Mt} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},Ku=(e,t,i,a,u,p,d,_)=>{let g=t.format==="NHWC",y=g?e[0].dims[3]:e[0].dims[1],C=i[0],k=g?i[2]:i[3],l=g?i[1]:i[2],F=g?i[3]:i[1],I=g&&(y%4===0||y%3===0)&&F%4===0,L=g?F:k*l,Q=g?k*l:F,Z=[8,8,1],U=a<=8?[4,1,1]:[4,4,1],we=[Math.ceil(L/Z[0]/U[0]),Math.ceil(Q/Z[1]/U[1]),Math.ceil(C/Z[2]/U[2])];qr("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${we}`);let te=I?g&&y%4!==0?3:4:1,me=Z[1]*U[1],it=Z[0]*U[0],Ye=Math.max(Z[0]*te,Z[1]),Mt=a%me===0,Gt=u%it===0,Bt=p%Ye===0,gr=I?[te,4,4]:[1,1,1],Mr=[{type:6,data:a},{type:6,data:u},{type:6,data:p},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];nn(t,Mr),Mr.push(...Rt(e[0].dims,e[1].dims));let Ur=["rank","rank"];d&&(Mr.push(...Rt(e[2].dims)),Ur.push("rank")),Mr.push(...Rt(i));let Ir=Sr=>{let ri=[{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}];Xi(t,ri);let Kr=I?4:1,Wt=zr(e[0].dataType),pr=` + fn setOutputAtIndex(flatIndex : i32, value : ${I?`vec4<${Wt}>`:Wt}) { + result[flatIndex] = ${I?`vec4<${Wt}>`:Wt}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${I?`vec4<${Wt}>`:Wt}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${I?"/ 4":""}, value); + }`,cr=mt("x",e[0].dataType,e[0].dims.length,te===3?1:te),lt=mt("w",e[1].dataType,e[1].dims.length,Kr),qt=[cr,lt],fr=Jt("result",e[0].dataType,i.length,Kr);if(d){let Yr=mt("bias",e[2].dataType,e[2].dims.length,Kr);qt.push(Yr),pr+=` + fn getBiasByOutputCoords(coords : vec4) -> ${I?`vec4<${Wt}>`:Wt} { + return bias[coords.${g?"w":"y"}${I?"/ 4":""}]; + }`}return` + ${na("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 }; + ${Sr.registerUniforms(ri).declareVariables(...qt,fr)} + ${pr} + ${Cl(g,Mt,Gt,Bt,d,t,gr[0],gr[1],gr[2],Wt)} + ${I?rs(U,Z,Wt,void 0,!g,Ye):is(U,Z,Wt,void 0,!g,Ye,!1,void 0,_)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${te};${I};${Mt};${Gt};${Bt};${me};${it};${Ye}`,inputDependencies:Ur},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:we[0],y:we[1],z:we[2]},programUniforms:Mr}),getShaderSource:Ir}}}),kl,oa,Qi,$l,la,Sl,El,Pl,ua=V(()=>{sr(),Ci(),lr(),mr(),sn(),vn(),kl=e=>{let t=1;for(let i=0;itypeof e=="number"?[e,e,e]:e,Qi=(e,t)=>t<=1?e:e+(e-1)*(t-1),$l=(e,t,i,a=1)=>{let u=Qi(t,a);return Math.floor((e[0]*(i-1)-i+u)/2)},la=(e,t,i,a,u)=>{u==null&&(u=$l(e,t[0],a[0]));let p=[0,0,0,i];for(let d=0;d<3;d++)e[d]+2*u>=t[d]&&(p[d]=Math.trunc((e[d]-t[d]+2*u)/a[d]+1));return p},Sl=(e,t,i,a,u,p,d,_,g,y)=>{let C,k,l,F;if(e==="VALID"&&(e=0),typeof e=="number"){C={top:e,bottom:e,left:e,right:e,front:e,back:e};let I=la([t,i,a,1],[_,g,y],1,[u,p,d],e);k=I[0],l=I[1],F=I[2]}else if(Array.isArray(e)){if(!e.every((L,Q,Z)=>L===Z[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 I=la([t,i,a,1],[_,g,y],1,[u,p,d],e[0]);k=I[0],l=I[1],F=I[2]}else if(e==="SAME_UPPER"){k=Math.ceil(t/u),l=Math.ceil(i/p),F=Math.ceil(a/d);let I=(k-1)*u+_-t,L=(l-1)*p+g-i,Q=(F-1)*d+y-a,Z=Math.floor(I/2),U=I-Z,we=Math.floor(L/2),te=L-we,me=Math.floor(Q/2),it=Q-me;C={top:we,bottom:te,left:me,right:it,front:Z,back:U}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:C,outDepth:k,outHeight:l,outWidth:F}},El=(e,t,i,a,u,p=!1,d="channelsLast")=>{let _,g,y,C,k;if(d==="channelsLast")[_,g,y,C,k]=e;else if(d==="channelsFirst")[_,k,g,y,C]=e;else throw new Error(`Unknown dataFormat ${d}`);let[l,,F,I,L]=t,[Q,Z,U]=oa(i),[we,te,me]=oa(a),it=Qi(F,we),Ye=Qi(I,te),Mt=Qi(L,me),{padInfo:Gt,outDepth:Bt,outHeight:gr,outWidth:Mr}=Sl(u,g,y,C,Q,Z,U,it,Ye,Mt),Ur=p?l*k:l,Ir=[0,0,0,0,0];return d==="channelsFirst"?Ir=[_,Ur,Bt,gr,Mr]:d==="channelsLast"&&(Ir=[_,Bt,gr,Mr,Ur]),{batchSize:_,dataFormat:d,inDepth:g,inHeight:y,inWidth:C,inChannels:k,outDepth:Bt,outHeight:gr,outWidth:Mr,outChannels:Ur,padInfo:Gt,strideDepth:Q,strideHeight:Z,strideWidth:U,filterDepth:F,filterHeight:I,filterWidth:L,effectiveFilterDepth:it,effectiveFilterHeight:Ye,effectiveFilterWidth:Mt,dilationDepth:we,dilationHeight:te,dilationWidth:me,inShape:e,outShape:Ir,filterShape:t}},Pl=(e,t,i,a,u,p)=>{let d=p==="channelsLast";d?e[0].dims[3]:e[0].dims[1];let _=[64,1,1],g={x:i.map((Q,Z)=>Z)},y=[Math.ceil(kl(g.x.map(Q=>i[Q]))/_[0]),1,1];qr("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${y}`);let C=1,k=tt.size(i),l=[{type:12,data:k},{type:12,data:a},{type:12,data:u},{type:12,data:t.strides},{type:12,data:t.dilations}];nn(t,l),l.push(...Rt(e[0].dims,e[1].dims));let F=["rank","rank"],I=e.length===3;I&&(l.push(...Rt(e[2].dims)),F.push("rank")),l.push(...Rt(i));let L=Q=>{let Z=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:a.length},{name:"pads",type:"u32",length:u.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];Xi(t,Z);let U=1,we=zr(e[0].dataType),te=mt("x",e[0].dataType,e[0].dims.length,C),me=mt("W",e[1].dataType,e[1].dims.length,U),it=[te,me],Ye=Jt("result",e[0].dataType,i.length,U),Mt="";if(I){let gr=mt("bias",e[2].dataType,e[2].dims.length,U);it.push(gr),Mt+=` + fn getBiasByOutputCoords(coords : array) -> ${we} { + return bias[${d?Kt("coords",4,5):Kt("coords",1,5)}]; + }`}let Gt=pi(C,we),Bt=rn(t,Gt,we);return` + ${Mt} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${te.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${me.getByIndices("aIndices")}; + } + ${Q.registerUniforms(Z).declareVariables(...it,Ye)} + ${Q.mainStart()} + ${Q.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${Ye.offsetToIndices("global_idx")}; + let batch = ${Kt("coords",0,te.rank)}; + let d2 = ${d?Kt("coords",te.rank-1,te.rank):Kt("coords",1,te.rank)}; + let xFRCCorner = vec3(${d?Kt("coords",1,te.rank):Kt("coords",2,te.rank)}, + ${d?Kt("coords",2,te.rank):Kt("coords",3,te.rank)}, + ${d?Kt("coords",3,te.rank):Kt("coords",4,te.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${d?Kt("uniforms.x_shape",1,te.rank):Kt("uniforms.x_shape",2,te.rank)}; + let xShapeZ = ${d?Kt("uniforms.x_shape",2,te.rank):Kt("uniforms.x_shape",3,te.rank)}; + let xShapeW = ${d?Kt("uniforms.x_shape",3,te.rank):Kt("uniforms.x_shape",4,te.rank)}; + let xShapeU = ${d?Kt("uniforms.x_shape",4,te.rank):Kt("uniforms.x_shape",1,te.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) { + ${d?`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) { + ${d?`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) { + ${d?`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) { + ${d?`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); + } + } + } + } + ${I?"value = value + getBiasByOutputCoords(coords)":""}; + ${Bt} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${d};${C};${I}`,inputDependencies:F},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:y[0],y:y[1],z:y[2]},programUniforms:l}),getShaderSource:L}}}),Al,Il,Xu=V(()=>{sr(),lr(),mr(),Bl(),sn(),Al=(e,t,i)=>{let a=e.length>2,u=a?"value += b[output_channel];":"",p=e[0].dims,d=e[1].dims,_=d[0]/t.group,g=t.format==="NHWC",y=as(p,d,t.dilations,t.pads,t.strides,g),C=tt.size(y),k=[{type:12,data:C},{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:_}];nn(t,k),k.push(...Rt(p,d));let l=["rank","rank"];a&&(k.push(...Rt(e[2].dims)),l.push("rank")),k.push(...Rt(y));let F=I=>{let L=Jt("output",e[0].dataType,y.length),Q=zr(L.type.tensor),Z=rn(t,L.type.value,Q),U=mt("x",e[0].dataType,p.length),we=mt("w",e[1].dataType,d.length),te=[U,we];a&&te.push(mt("b",e[2].dataType,e[2].dims.length));let me=[{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"}];return Xi(t,me),` + ${I.registerUniforms(me).declareVariables(...te,L)} + + ${I.mainStart()} + ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${L.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${g?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${g?1:2}], outputIndices[${g?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel / uniforms.output_channels_per_group; + + var value: ${L.type.value} = ${L.type.value}(0); + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { + let input_channel = group_id * uniforms.w_shape[1] + wInChannel; + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[${g?1:2}]) { + 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[${g?2:3}]) { + continue; + } + + let xVal = ${g?U.get("batch","xHeight","xWidth","input_channel"):U.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${we.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal*wVal; + } + } + } + ${u} + ${Z} + ${L.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:t.cacheKey,inputDependencies:l},getRunData:()=>({outputs:[{dims:i?i(y):y,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:k}),getShaderSource:F}},Il=(e,t,i)=>{let a=e.length>2,u=$r(i[3]),p=$r(i[2]),d=tt.size(i)/u/p,_=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/u],g=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/u],y=[i[0],i[1],i[2],i[3]/u],C=[{type:12,data:d},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];nn(t,C),C.push(...Rt(_,g,y));let k=(p-1)*t.strides[1]+g[1],l=F=>{let I=Jt("output",e[0].dataType,y.length,u),L=zr(I.type.tensor),Q=rn(t,I.type.value,L),Z=mt("x",e[0].dataType,_.length,u),U=mt("w",e[1].dataType,g.length,u),we=[Z,U];a&&we.push(mt("b",e[2].dataType,e[2].dims,u));let te=a?"value += b[output_channel];":"",me=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Xi(t,me),` + ${F.registerUniforms(me).declareVariables(...we,I)} + ${F.mainStart()} + ${F.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] / ${p}u; + let col = (index1 % width1) * ${p}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${Z.type.value}, ${k}>; + var values: array<${I.type.value}, ${p}>; + 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 < ${g[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] = ${Z.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${Z.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${g[1]}; w_width++) { + let w_val = ${U.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${p}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${p}u; i++) { + var value = values[i]; + ${te} + ${Q} + ${I.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${u};${p};${k};${g[0]};${g[1]}`,inputDependencies:a?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:C}),getShaderSource:l}}}),da,Fl,zl,ca=V(()=>{sr(),lr(),ss(),mr(),sn(),da=(e,t,i,a,u=!1)=>{let p=e[0].dims,d=e[1].dims,_=p[p.length-2],g=d[d.length-1],y=p[p.length-1],C=$r(g),k=$r(y),l=$r(_),F=tt.size(i)/C/l,I=e.length>2,L=a?a.slice(0,-2):i.slice(0,-2),Q=[tt.size(L),_,g],Z=[{type:12,data:F},{type:12,data:_},{type:12,data:g},{type:12,data:y}];nn(t,Z),Z.push(...Rt(L,p,d)),I&&Z.push(...Rt(e[2].dims)),Z.push(...Rt(Q));let U=we=>{let te=bs("batch_dims",e[0].dataType,L.length),me=mt("a",e[0].dataType,p.length,k),it=mt("b",e[1].dataType,d.length,C),Ye=Jt("output",e[0].dataType,Q.length,C),Mt=zr(Ye.type.tensor),Gt=rn(t,Ye.type.value,Mt),Bt=[me,it],gr="";if(I){let pr=u?C:1;Bt.push(mt("bias",e[2].dataType,e[2].dims.length,pr)),gr=`${u?`value += bias[col / ${pr}];`:`value += ${Ye.type.value}(bias[row + i]);`}`}let Mr=p.slice(0,-2),Ur=d.slice(0,-2),Ir=gn(Mr,L),Sr=gn(Ur,L),ri=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Xi(t,ri);let Kr=(pr,cr)=>{let lt=pr.rank,qt=pr.name;if(lt===2)return`var ${qt}_indices = ${pr.type.indices}(0u, 0u);`;let fr=te.rank,Yr=`var ${qt}_indices: ${pr.type.indices};`;for(let ai=lt-2-1,gi=fr-1;ai>=0;ai--,gi--)Yr+=` +${qt}_indices[${ai}] = ${fr>1?`batch_indices[${gi}]`:"batch_indices"};`;return cr.forEach(ai=>{Yr+=` +${qt}_indices[${ai}] = 0;`}),Yr+=`${qt}_indices[${lt-2}] = 0u; + ${qt}_indices[${lt-1}] = 0u;`,Yr},Wt=()=>{let pr=`var a_data: ${me.type.value};`;for(let cr=0;cr; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${k}) { + ${Wt()} + } + for (var i = 0u; i < ${l}u; i++) { + var value = values[i]; + ${gr} + ${Gt} + let cur_indices = ${Ye.type.indices}(batch, row + i, col); + let offset = ${Ye.indicesToOffset("cur_indices")}; + ${Ye.setByOffset(`offset / ${C}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${C};${k};${l};${u}`,inputDependencies:I?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(F/64)},programUniforms:Z}),getShaderSource:U}},Fl=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.")},zl=e=>{Fl(e.inputs);let t=ui.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let i=t[t.length-1],a=e.inputs[0].dims[e.inputs[0].dims.length-1];i<8&&a<8?e.compute(da(e.inputs,{activation:""},t)):e.compute(aa(e.inputs,{activation:""},t))}}),as,os,pa,ls,ha,fa,Ol,Dl,Dn,Bl=V(()=>{lr(),Hu(),ua(),ss(),Xu(),sn(),ca(),wn(),as=(e,t,i,a,u,p)=>{let d=e[0],_=e.slice(p?1:2,p?3:4),g=_.length,y=t[0],C=t.slice(2).map((l,F)=>l+(l-1)*(i[F]-1)),k=_.map((l,F)=>l+a[F]+a[F+g]).map((l,F)=>Math.floor((l-C[F]+u[F])/u[F]));return k.splice(0,0,d),k.splice(p?3:1,0,y),k},os=[2,3,1,0],pa=(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 i=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],a=e[1].dims[1]*t.group;if(i!==a)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 u=e[0].dims.length-2;if(t.dilations.length!==u)throw new Error(`dilations should be ${u}D`);if(t.strides.length!==u)throw new Error(`strides should be ${u}D`);if(t.pads.length!==u*2)throw new Error(`pads should be ${u*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},ls=(e,t)=>{let i=e.kernelShape.slice();for(let p=2;p{let t=ra(e),i=e.format,a=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],u=e.dilations,p=e.group,d=e.kernel_shape,_=e.pads,g=e.strides,y=e.w_is_const();return{autoPad:a,format:i,dilations:u,group:p,kernelShape:d,pads:_,strides:g,wIsConst:y,...t,cacheKey:`${e.format};${t.activation};`}},fa=(e,t,i)=>{let a=ls(i,t),u=i.format==="NHWC";if(i.group!==1){if(!e.adapterInfo.isArchitecture("ampere")&&u&&t[1].dims[0]===i.group&&t[1].dims[1]===1&&i.dilations[0]===1&&i.dilations[1]===1){let it=as(t[0].dims,t[1].dims,i.dilations,a.pads,i.strides,u),Ye=e.kernelCustomData.wT??e.compute(Vi(t[1],os),{inputs:[1],outputs:[i.wIsConst?-2:-1]})[0];i.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ye);let Mt=[t[0],Ye];t.length===3&&Mt.push(t[2]),e.compute(Il(Mt,a,it),{inputs:Mt})}else e.compute(Al(t,a));return}let p=t.length===3,d=t[0].dims[u?1:2],_=t[0].dims[u?2:3],g=t[0].dims[u?3:1],y=t[1].dims[2],C=t[1].dims[3],k=as(t[0].dims,t[1].dims,i.dilations,a.pads,i.strides,u),l=k[u?1:2],F=k[u?2:3],I=k[u?3:1],L=u&&y===d&&C===_&&i.pads[0]===0&&i.pads[1]===0;if(L||y===1&&C===1&&i.dilations[0]===1&&i.dilations[1]===1&&i.strides[0]===1&&i.strides[1]===1&&i.pads[0]===0&&i.pads[1]===0){let it=k[0],Ye,Mt,Gt,Bt=[];if(u){let Ur=e.kernelCustomData.wT??e.compute(Vi(t[1],os),{inputs:[1],outputs:[i.wIsConst?-2:-1]})[0];if(i.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Ur),L){let Ir=d*_*g;Ye=t[0].reshape([1,it,Ir]),Mt=Ur.reshape([1,Ir,I]),Gt=[1,it,I]}else Ye=t[0].reshape([it,d*_,g]),Mt=Ur.reshape([1,g,I]),Gt=[it,l*F,I];Bt.push(Ye),Bt.push(Mt)}else Ye=t[0].reshape([it,g,d*_]),Mt=t[1].reshape([1,I,g]),Gt=[it,I,l*F],Bt.push(Mt),Bt.push(Ye);p&&Bt.push(t[2]);let gr=Gt[2],Mr=Bt[0].dims[Bt[0].dims.length-1];gr<8&&Mr<8?e.compute(da(Bt,a,k,Gt,u),{inputs:Bt}):e.compute(aa(Bt,a,k,Gt,u),{inputs:Bt});return}let Q=!0,Z=e.kernelCustomData.wT??e.compute(Vi(t[1],os),{inputs:[1],outputs:[i.wIsConst?-2:-1]})[0];i.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Z);let U=[t[0],Z];p&&U.push(t[2]);let we=u?l*F:I,te=u?I:l*F,me=y*C*g;e.compute(Ku(U,a,k,we,te,me,p,Q),{inputs:U})},Ol=(e,t)=>{let i=t.format==="NHWC",a=[e.inputs[0].reshape(i?[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&&a.push(e.inputs[2]);let u=[0,t.pads[0],0,t.pads[1]],p=[1].concat(t.strides),d=[1].concat(t.dilations),_=[1].concat(t.kernelShape),g=ls({...t,pads:u,strides:p,dilations:d,kernelShape:_},a);e.compute(Al(a,g,y=>i?[y[0],y[2],y[3]]:[]))},Dl=(e,t,i)=>{let a=i.format==="NHWC"?"channelsLast":"channelsFirst",u=ls(i,t),p=i.autoPad==="NOTSET"?i.pads:i.autoPad,d=El(t[0].dims,t[1].dims,i.strides,i.dilations,p,!1,a);e.compute(Pl(t,u,d.outShape,[d.filterDepth,d.filterHeight,d.filterWidth],[d.padInfo.front,d.padInfo.top,d.padInfo.left],a))},Dn=(e,t)=>{pa(e.inputs,t),e.inputs[0].dims.length===3?Ol(e,t):e.inputs[0].dims.length===5?Dl(e,e.inputs,t):fa(e,e.inputs,t)}}),jl,Ll,Qu=V(()=>{sr(),Ci(),mr(),sn(),vn(),Ml(),ss(),jl=(e,t=!1,i,a,u=4)=>{let p=Q=>{switch(Q){case 1:return"return w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];";case 4:return` + let coord1 = vec4(coordX, coordY, col + 1, rowInner); + let coord2 = vec4(coordX, coordY, col + 2, rowInner); + let coord3 = vec4(coordX, coordY, col + 3, rowInner); + let v0 = w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))]; + let v1 = w[getIndexFromCoords4D(coord1, vec4(uniforms.w_shape))]; + let v2 = w[getIndexFromCoords4D(coord2, vec4(uniforms.w_shape))]; + let v3 = w[getIndexFromCoords4D(coord3, vec4(uniforms.w_shape))]; + return ${a}(v0, v1, v2, v3); + `;default:throw new Error(`innerElementSize ${Q} is not supported.`)}},d=e?` + let coord = vec4(batch, iXR, iXC, xCh); + `:` + let coord = vec4(batch, xCh, iXR, iXC); + `,_=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,g=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",y=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",C=e?"row":"col",k=e?"col":"row",l=` + let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${C} / outWidth; + let outCol = ${C} % outWidth; + + let WRow = ${k} / (uniforms.filter_dims[1] * inChannels); + let WCol = ${k} / inChannels % uniforms.filter_dims[1]; + let xR = f32(outRow - uniforms.pads[0] + uniforms.dilations[0] * WRow) / f32(uniforms.strides[0]); + let xC = f32(outCol - uniforms.pads[1] + uniforms.dilations[1] * WCol) / f32(uniforms.strides[1]); + if (xR < 0.0 || xR >= f32(${g}) || fract(xR) > 0.0) { + return ${a}(0.0); + } + if (xC < 0.0 || xC >= f32(${y}) || fract(xC) > 0.0) { + return ${a}(0.0); + } + let iXR = i32(xR); + let iXC = i32(xC); + let xCh = ${k} % inChannels; + ${d} + return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${u}];`,F=e?` + let col = colIn * ${u}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${l} + } + return ${a}(0.0);`:` + let col = colIn * ${u}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${l} + } + return ${a}(0.0);`,I=` + let col = colIn * ${u}; + let inChannels = ${e?"i32(uniforms.x_shape[3])":"i32(uniforms.x_shape[1])"}; + let coordX = uniforms.filter_dims[0] - 1 - row / (uniforms.filter_dims[1] * inChannels); + let coordY = uniforms.filter_dims[1] - 1 - (row / inChannels) % uniforms.filter_dims[1]; + if (${e?"row < uniforms.dim_inner && col < uniforms.dim_b_outer":"row < uniforms.dim_inner && col < uniforms.dim_a_outer"} && coordX >= 0 && coordY >= 0) { + let rowInner = row % inChannels; + let coord = vec4(coordX, coordY, col, rowInner); + ${p(u)} + } + return ${a}(0.0); + `,L=rn(i,a);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${a} { + ${e?F:I} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${a} { + ${e?I:F} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${a}) { + let col = colIn * ${u}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueInput; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${_} + ${ia(t)} + ${L} + result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${u}] = value; + } + }`},Ll=(e,t,i,a,u,p,d,_)=>{let g=t.format==="NHWC",y=g?e[0].dims[3]:e[0].dims[1],C=i[0],k=g?i[2]:i[3],l=g?i[1]:i[2],F=g?i[3]:i[1],I=g&&y%4===0&&y%3&&F%4===0,L=g?F:k*l,Q=g?k*l:F,Z=[8,8,1],U=a<=8?[4,1,1]:[4,4,1],we=[Math.ceil(L/Z[0]/U[0]),Math.ceil(Q/Z[1]/U[1]),Math.ceil(C/Z[2]/U[2])];qr("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${we}`);let te=I?4:1,me=Math.max(Z[0]*te,Z[1]),it=I?4:1,Ye=[t.kernelShape[g?1:2],t.kernelShape[g?2:3]],Mt=[Ye[0]+(t.dilations[0]<=1?0:(Ye[0]-1)*(t.dilations[0]-1)),Ye[1]+(t.dilations[1]<=1?0:(Ye[1]-1)*(t.dilations[1]-1))],Gt=[Mt[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),Mt[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],Bt=[{type:6,data:a},{type:6,data:u},{type:6,data:p},{type:6,data:t.strides},{type:6,data:t.dilations},{type:6,data:Ye},{type:6,data:Gt}];nn(t,Bt),Bt.push(...Rt(e[0].dims,e[1].dims));let gr=["rank","rank"];d&&(Bt.push(...Rt(e[2].dims)),gr.push("rank")),Bt.push(...Rt(i));let Mr=Ur=>{let Ir=mt("x",e[0].dataType,e[0].dims.length,it),Sr=mt("w",e[1].dataType,e[1].dims.length,1),ri=Jt("result",e[0].dataType,i.length,it),Kr=[Ir,Sr],Wt="";if(d){let lt=mt("bias",e[2].dataType,e[2].dims.length,it);Kr.push(lt),Wt+=` + fn getBiasByOutputCoords(coords : vec4) -> ${lt.type.value} { + return bias[coords.${g?"w":"y"}${I?"/ 4":""}]; + }`}let pr=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"strides",type:"i32",length:2},{name:"dilations",type:"i32",length:2},{name:"filter_dims",type:"i32",length:Ye.length},{name:"pads",type:"i32",length:Gt.length}];Xi(t,pr);let cr=zr(e[0].dataType,1);if(cr!=="f16"&&cr!=="f32")throw new Error(`elemType ${cr} is not supported.`);return` + ${na("uniforms.result_strides")} + ${Ur.registerUniforms(pr).declareVariables(...Kr,ri)}; + ${Wt} + ${jl(g,d,t,Ir.type.value,te)} + ${I?rs(U,Z,cr,void 0,!g,me):is(U,Z,cr,void 0,!g,me,!1,void 0,_)}`};return{name:"Conv2DTransposeMatMul",shaderCache:{hint:`${t.cacheKey};${U};${Z};${I}`,inputDependencies:gr},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:we[0],y:we[1],z:we[2]},programUniforms:Bt}),getShaderSource:Mr}}}),ma,Bn,Dd=V(()=>{sr(),Ci(),lr(),mr(),ma=(e,t,i,a,u,p=!1,d,_,g=!1)=>{let y=g?1:2,C=g?2:3,k=g?3:1,l=p?2:1,F=` + fn setOutputAtIndex(flatIndex : u32, value : ${p?`vec4<${d}>`:d}) { + result[flatIndex] = ${p?`vec4<${d}>`:d}(value); + }`;a&&(F+=` + fn getBiasByOutputCoords(coords : vec4) -> ${p?`vec4<${d}>`:d} { + return bias[coords.${g?"w":"y"}${p?"/ 4":""}]; + }`);let I=p?4:1,L=mt("W",t[1].dataType,t[1].dims.length,I),Q=mt("Dy",t[0].dataType,t[0].dims.length,I),Z=[Q,L];a&&Z.push(mt("bias",t[2].dataType,[i[k]].length,I));let U=Jt("result",t[0].dataType,i.length,I),we=`{ + let batch: u32 = ${u?"global_id.z":"workgroup_id.z"} / uniforms.result_shape[1]; + let r = ${u?"global_id.z":"workgroup_id.z"} % uniforms.result_shape[1]; + let c = ${u?"global_id.y":"workgroup_id.y"} * ${l}; + let d1: u32 = ${u?"global_id.x":"workgroup_id.x"} * 4; + + let dyCorner = vec2(i32(r), i32(c)) - vec2(uniforms.pads); + + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd: array, ${l}>; + for (var i = 0; i < ${l}; i++) { + dotProd[i] = vec4<${d}>(0.0); + } + for (var wR: u32 = 0; wR < uniforms.filter_dims[0]; wR = wR + 1) { + var dyR = (${d}(dyCorner.x) + ${d}(wR)) / ${d}(uniforms.strides.x); + let wRPerm = uniforms.filter_dims[0] - 1 - wR; + if (dyR < 0.0 || dyR >= ${d}(uniforms.Dy_shape[1]) || + fract(dyR) > 0.0 || wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.filter_dims[1]; wC = wC + 1) { + let dyC = (${d}(dyCorner.y) + ${d}(wC)) / ${d}(uniforms.strides.y); + let dyC2 = (${d}(dyCorner.y) + 1.0 + ${d}(wC)) / ${d}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims[1] - 1 - wC; + if (wCPerm < 0) { + continue; + } + var bDyCVal = true; + var bDyCVal2 = true; + if (dyC < 0.0 || dyC >= ${d}(uniforms.Dy_shape[2]) || + fract(dyC) > 0.0) { + bDyCVal = false; + } + if (dyC2 < 0.0 || dyC2 >= ${d}(uniforms.Dy_shape[2]) || + fract(dyC2) > 0.0) { + bDyCVal2 = false; + } + + let idyC: u32 = u32(dyC); + let idyC2: u32 = u32(dyC2); + if (bDyCVal && bDyCVal2) { + let d2Length = uniforms.Dy_shape[3]; + for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${Q.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${d}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + + xValue = ${Q.get("batch","idyR","idyC2","d2")}; + + dotProd[1] = dotProd[1] + vec4<${d}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + } + } else if (bDyCVal) { + let d2Length = uniforms.Dy_shape[${k}]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${Q.get("batch","idyR","idyC","d2")}; + let tmpval = vec4<${d}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[0] = dotProd[0] + tmpval; + } + } else if (bDyCVal2) { + let d2Length = uniforms.Dy_shape[3]; + for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { + let wValue0 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; + let wValue1 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; + let wValue2 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; + let wValue3 = ${L.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; + + var xValue = ${Q.get("batch","idyR","idyC2","d2")}; + let tmpval = vec4<${d}>(dot(xValue, wValue0), + dot(xValue, wValue1), + dot(xValue, wValue2), + dot(xValue, wValue3)); + dotProd[1] = dotProd[1] + tmpval; + } + } + } + } + + for (var i: u32 = 0; i < ${l}; i = i + 1) { + let value = dotProd[i] + ${a?"bias[c+i]":`vec4<${d}>(0.0)`}; + ${U.set("batch","r","c + i","d1","value")}; + } + }`,te=` + let outputIndices = ${U.offsetToIndices("global_idx")}; + let batch = ${U.indicesGet("outputIndices",0)}; + let d1 = ${U.indicesGet("outputIndices",k)}; + let r = ${U.indicesGet("outputIndices",y)}; + let c = ${U.indicesGet("outputIndices",C)}; + let dyCorner = vec2(i32(r), i32(c)) - uniforms.pads; + let dyRCorner = dyCorner.x; + let dyCCorner = dyCorner.y; + let groupId = d1 / uniforms.output_channels_per_group; + let wOutChannel = d1 - groupId * uniforms.output_channels_per_group; + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd = ${d}(0.0); + for (var wR: u32 = 0; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${d}(dyRCorner) + ${d}(wR)) / ${d}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${d}(uniforms.Dy_shape[${y}]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + + for (var wC: u32 = 0; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { + if (wC % uniforms.dilations.y != 0) { + continue; + } + let dyC = (${d}(dyCCorner) + ${d}(wC)) / ${d}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${d}(uniforms.Dy_shape[${C}]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC: u32 = u32(dyC); + var inputChannel = groupId * uniforms.input_channels_per_group; + for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + 1) { + let xValue = ${g?Q.get("batch","idyR","idyC","inputChannel"):Q.get("batch","inputChannel","idyR","idyC")}; + let wValue = ${L.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")}; + dotProd = dotProd + xValue * wValue; + inputChannel = inputChannel + 1; + } + } + } + let value = dotProd + ${a?"bias[d1]":`${d}(0.0)`}; + ${U.setByOffset("global_idx","value")}; + `;return` + ${e.registerUniforms(_).declareVariables(...Z,U)} + ${F} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${p?we:te}}`},Bn=(e,t,i)=>{let a=e.length>2,u=t.outputShape,p=tt.size(u),d=[Math.ceil(p/64),1,1];qr("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${d}`);let _=t.format==="NHWC",g=["rank","rank"],y=[t.strides[0],t.strides[1]],C=[t.kernelShape[_?1:2],t.kernelShape[_?2:3]],k=[t.dilations[0],t.dilations[1]],l=[C[0]+(t.dilations[0]<=1?0:(t.kernelShape[_?1:2]-1)*(t.dilations[0]-1)),C[1]+(t.dilations[1]<=1?0:(t.kernelShape[_?2:3]-1)*(t.dilations[1]-1))],F=[l[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),l[1]-1-Math.floor(t.pads[1]+t.pads[3])/2],I=!1,L=t.group,Q=e[1].dims,Z=Q[0]/L,U=Q[1],we=[{type:12,data:p},{type:12,data:y},{type:12,data:C},{type:12,data:k},{type:12,data:l},{type:6,data:F},{type:12,data:Z},{type:12,data:U},...Rt(e[0].dims,e[1].dims)];a&&(we.push(...Rt(e[2].dims)),g.push("rank")),we.push(...Rt(u));let te=d[1]===1&&d[2]===1,me=it=>{let Ye=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:y.length},{name:"filter_dims",type:"u32",length:C.length},{name:"dilations",type:"u32",length:C.length},{name:"effective_filter_dims",type:"u32",length:l.length},{name:"pads",type:"i32",length:F.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],Mt=zr(e[0].dataType);return`${ma(it,e,u,a,te,I,Mt,Ye,_)}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};`,inputDependencies:g},getRunData:()=>({dispatchGroup:{x:d[0],y:d[1],z:d[2]},outputs:[{dims:i?i(u):u,dataType:e[0].dataType}],programUniforms:we}),getShaderSource:me}}}),Rl,Nl,_a,ga,Vl,wa,Ul,Wl,ya,Yu,Bd=V(()=>{Qu(),Dd(),sn(),wn(),Rl=(e,t,i,a,u,p)=>(e-1)*t+i+(a-1)*u+1-p,Nl=(e,t,i,a,u)=>{let p=Math.floor(e/2);t==="SAME_UPPER"?(i[a]=p,i[u]=e-p):t==="SAME_LOWER"&&(i[a]=e-p,i[u]=p)},_a=(e,t,i,a,u,p,d,_,g,y)=>{let C=e.length-2,k=y.length===0;if(g.length===0)for(let I=0;I{let i=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((k,l)=>k*l,1)===0){i.length=0;for(let k=2;kk+l,0)===0){let k=t[0].dims.length-2;g=new Array(k).fill(1)}let y=e.strides.slice();if(y.reduce((k,l)=>k+l,0)===0){let k=t[0].dims.length-2;y=new Array(k).fill(1)}_a(_,i,g,e.autoPad,e.group,u,y,a,d,p);let C=Object.assign({},e);return Object.assign(C,{kernelShape:i,pads:u,outputPadding:d,outputShape:p,dilations:g,strides:y}),C},Vl=e=>{let t=ra(e),i=e.format,a=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],u=e.dilations,p=e.group,d=e.kernelShape,_=e.pads,g=e.strides,y=e.wIsConst(),C=e.outputPadding,k=e.outputShape;return{autoPad:a,format:i,dilations:u,group:p,kernelShape:d,outputPadding:C,outputShape:k,pads:_,strides:g,wIsConst:y,...t,cacheKey:`${e.format};${t.activation};`}},wa=(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 i=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],a=e[1].dims[0];if(i!==a)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let u=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==u))throw new Error("invalid bias");let p=e[0].dims.length-2;if(t.dilations.reduce((d,_)=>d+_,0)>0&&t.dilations.length!==p)throw new Error(`dilations should be ${p}D`);if(t.strides.reduce((d,_)=>d+_,0)>0&&t.strides.length!==p)throw new Error(`strides should be ${p}D`);if(t.pads.reduce((d,_)=>d+_,0)>0&&t.pads.length!==p*2)throw new Error(`pads should be ${p*2}D`);if(t.outputPadding.length!==p&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${p}D`);if(t.kernelShape.reduce((d,_)=>d+_,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Ul=[2,3,1,0],Wl=(e,t,i)=>{let a=ga(i,t),u=i.format==="NHWC",p=a.outputShape,d=p[u?3:1],_=t[0].dims[u?3:1];if(a.group!==1||d===1&&_===1){e.compute(Bn(t,a));return}let g=p[u?1:2],y=p[u?2:3],C=t[1].dims[2],k=t[1].dims[3],l=u?g*y:d,F=u?d:g*y,I=C*k*_,L=!0,Q=e.kernelCustomData.wT??e.compute(Vi(t[1],Ul),{inputs:[1],outputs:[i.wIsConst?-2:-1]})[0];i.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Q);let Z=[t[0],Q],U=t.length===3;U&&(!u&&t[2].dims.length===1?Z.push(t[2].reshape([t[2].dims[0],1,1])):Z.push(t[2])),e.compute(Ll(Z,a,p,l,F,I,U,L),{inputs:Z})},ya=(e,t)=>{let i=t.format==="NHWC",a=[e.inputs[0].reshape(i?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&a.push(e.inputs[2]);let u=t.kernelShape;(u.length===0||u[0]===0)&&(u=[e.inputs[1].dims[2]]);let p=t.dilations;(p.length===0||p[0]===0)&&(p=[1]);let d=t.strides;(d.length===0||d[0]===0)&&(d=[1]);let _=t.pads;_.length===0&&(_=[0,0]),_=[0,_[0],0,_[1]],d=[1].concat(d),p=[1].concat(p),u=[1].concat(u);let g=ga({...t,pads:_,strides:d,dilations:p,kernelShape:u},a);e.compute(Bn(a,g,y=>i?[y[0],y[2],y[3]]:[y[0],y[1],y[3]]))},Yu=(e,t)=>{wa(e.inputs,t),e.inputs[0].dims.length===3?ya(e,t):Wl(e,e.inputs,t)}}),ba,va,Gl,Zu=V(()=>{sr(),lr(),Tr(),mr(),ba=(e,t,i,a)=>{let u=tt.size(t),p=t.length,d=mt("input",e,p),_=Jt("output",e,p),g=i.dataType===6?i.getInt32Array()[0]:Number(i.getBigInt64Array()[0]),y=tt.normalizeAxis(g,p),C=k=>{let l=` i32(${d.indicesGet("inputIndices","uniforms.axis")}) `,F=Kt("uniforms.input_shape","uniforms.axis",p),I=a.reverse?l+(a.exclusive?" + 1":""):"0",L=a.reverse?F:l+(a.exclusive?"":" + 1");return` + ${k.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(d,_)} + ${k.mainStart()} + ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${_.offsetToIndices("global_idx")}; + var sum = ${_.type.value}(0); + let first : i32 = ${I}; + let last : i32 = ${L}; + for (var i : i32 = first; i < last; i++) { + ${d.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${d.getByIndices("inputIndices")}; + } + ${_.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:a.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:[{type:12,data:u},{type:12,data:y},...Rt(t,t)]}),getShaderSource:C}},va=(e,t)=>{let i=e.inputs[0].dims,a=e.inputs[0].dataType,u=e.inputs[1];e.compute(ba(a,i,u,t),{inputs:[0]})},Gl=e=>{let t=e.exclusive===1,i=e.reverse===1;return tr({exclusive:t,reverse:i})}}),Ma,ql,Kl,xa,Hl,Ju=V(()=>{sr(),lr(),Tr(),mr(),Ma=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.")},ql=(e,t,i,a)=>{let u=[];u.push(`fn perm(i: ${a.type.indices}) -> ${i.type.indices} { + var a: ${i.type.indices};`);for(let p=0;p{let i,a,u,p,d,_,g=t.format==="NHWC",y=t.blocksize,C=t.mode==="DCR";g?([i,a,u,p]=e.dims,d=C?[i,a,u,y,y,p/y**2]:[i,a,u,p/y**2,y,y],_=C?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([i,a,u,p]=[e.dims[0],e.dims[2],e.dims[3],e.dims[1]],d=C?[i,y,y,p/y**2,a,u]:[i,p/y**2,y,y,a,u],_=C?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let k=e.reshape(d),l=k.dims.length,F=e.dataType,I=mt("a",F,l),L=Jt("output",F,l),Q=Z=>` + ${Z.registerUniform("output_size","u32").declareVariables(I,L)} + + ${ql(_,l,I,L)} + + ${Z.mainStart()} + ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${L.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${L.setByOffset("global_idx",I.getByIndices("aIndices"))} + }`;return{name:"DepthToSpace",shaderCache:{hint:`${e.dims};${t.blocksize};${t.mode}`,inputDependencies:["rank"]},getRunData:Z=>{let U=g?[i,a*y,u*y,p/y**2]:[i,p/y**2,a*y,u*y],we=tt.size(U),te=k.dims,me=tt.sortBasedOnPerm(te,_);return{outputs:[{dims:U,dataType:Z[0].dataType}],dispatchGroup:{x:Math.ceil(we/64)},programUniforms:[{type:12,data:we},...Rt(te,me)]}},getShaderSource:Q}},xa=(e,t)=>{Ma(e.inputs),e.compute(Kl(e.inputs[0],t))},Hl=e=>tr({blocksize:e.blocksize,mode:e.mode,format:e.format})}),jn,Ln,Ta,Vr,ed,td,rd,us,Xl,Ql,Yl,id=V(()=>{sr(),lr(),Tr(),mr(),jn="[a-zA-Z]|\\.\\.\\.",Ln="("+jn+")+",Ta="^"+Ln+"$",Vr="("+Ln+",)*"+Ln,ed="^"+Vr+"$",td=class{constructor(e=-1){this.symbolToIndices=new Map,this.inputIndex=e}addSymbol(e,t){let i=this.symbolToIndices.get(e);i===void 0?i=[t]:i.push(t),this.symbolToIndices.set(e,i)}},rd=class{constructor(e,t){var u;this.equation=t,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[i,a]=t.includes("->")?t.split("->",2):[t,""];if(!i.match(RegExp(ed)))throw new Error("Invalid LHS term");if(i.split(",").forEach((p,d)=>{let _=e[d].dims.slice();if(!p.match(RegExp(Ta)))throw new Error("Invalid LHS term");let g=this.processTerm(p,!0,_,d);this.lhs.push(g)}),a==="")a+=[...this.symbolToInfo.entries()].filter(([p,d])=>d.count===1||p==="...").map(([p])=>p).join("");else if(!a.match(RegExp(Ln)))throw new Error("Invalid RHS");(u=a.match(RegExp(jn,"g")))==null||u.forEach(p=>{if(p==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let d=this.symbolToInfo.get(p);if(d===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(d.dimValue)}}),this.rhs=this.processTerm(a,!1,this.outputDims)}addSymbol(e,t,i){let a=this.symbolToInfo.get(e);if(a!==void 0){if(a.dimValue!==t&&a.count!==1)throw new Error("Dimension mismatch");a.count++,a.inputIndices.push(i)}else a={count:1,dimValue:t,inputIndices:[i]};this.symbolToInfo.set(e,a)}processTerm(e,t,i,a=-1){let u=i.length,p=!1,d=[],_=0;if(!e.match(RegExp(Ta))&&!t&&e!=="")throw new Error("Invalid LHS term");let g=e.match(RegExp(jn,"g")),y=new td(a);return g==null||g.forEach((C,k)=>{if(C==="..."){if(p)throw new Error("Only one ellipsis is allowed per input term");p=!0;let l=u-g.length+1;if(l<0)throw new Error("Ellipsis out of bounds");if(d=i.slice(_,_+l),this.hasEllipsis){if(this.ellipsisDims.length!==d.length||this.ellipsisDims.toString()!==d.toString())throw new Error("Ellipsis dimensions mismatch")}else if(t)this.hasEllipsis=!0,this.ellipsisDims=d;else throw new Error("Ellipsis must be specified in the LHS");for(let F=0;Fe+"_max",Xl=(e,t,i,a)=>{let u=e.map(y=>y.length).map((y,C)=>mt(`input${C}`,t,y)),p=tt.size(a),d=Jt("output",t,a.length),_=[...i.symbolToInfo.keys()].filter(y=>!i.rhs.symbolToIndices.has(y)),g=y=>{let C=[],k="var prod = 1.0;",l="var sum = 0.0;",F="sum += prod;",I=[],L=[],Q=[],Z=[],U=i.symbolToInfo.size===i.rhs.symbolToIndices.size;i.symbolToInfo.forEach((te,me)=>{var it;if(i.rhs.symbolToIndices.has(me)){let Ye=(it=i.rhs.symbolToIndices.get(me))==null?void 0:it[0];Ye!==void 0&&i.lhs.forEach((Mt,Gt)=>{if(te.inputIndices.includes(Gt)){let Bt=Mt.symbolToIndices.get(me);if(Bt===void 0)throw new Error("Invalid symbol error");Bt.forEach(gr=>{C.push(`${u[Gt].indicesSet(`input${Gt}Indices`,gr,d.indicesGet("outputIndices",Ye))}`)})}})}else i.lhs.forEach((Ye,Mt)=>{if(te.inputIndices.includes(Mt)){let Gt=Ye.symbolToIndices.get(me);if(Gt===void 0)throw new Error("Invalid symbol error");Gt.forEach(Bt=>{I.push(`${u[Mt].indicesSet(`input${Mt}Indices`,Bt,`${me}`)}`)}),Z.push(`prod *= ${u[Mt].getByIndices(`input${Mt}Indices`)};`)}}),L.push(`for(var ${me}: u32 = 0; ${me} < uniforms.${us(me)}; ${me}++) {`),Q.push("}")});let we=U?[...C,`let sum = ${u.map((te,me)=>te.getByIndices(`input${me}Indices`)).join(" * ")};`]:[...C,l,...L,...I,k,...Z,F,...Q];return` + ${y.registerUniforms(_.map(te=>({name:`${us(te)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...u,d)} + + ${y.mainStart()} + ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${d.offsetToIndices("global_idx")}; + ${u.map((te,me)=>`var input${me}Indices: ${u[me].type.indices};`).join(` +`)} + ${we.join(` +`)}; + ${d.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:i.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let y=_.filter(k=>i.symbolToInfo.has(k)).map(k=>{var l;return{type:12,data:((l=i.symbolToInfo.get(k))==null?void 0:l.dimValue)||0}});y.push({type:12,data:p});let C=e.map((k,l)=>[...Rt(k)]).reduce((k,l)=>k.concat(l),y);return C.push(...Rt(a)),{outputs:[{dims:a,dataType:t}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:C}},getShaderSource:g}},Ql=(e,t)=>{let i=new rd(e.inputs,t.equation),a=i.outputDims,u=e.inputs.map((p,d)=>p.dims);e.compute(Xl(u,e.inputs[0].dataType,i,a))},Yl=e=>{let t=e.equation.replace(/\s+/g,"");return tr({equation:t})}}),Ca,ds,Zl,Jl,ka,jd=V(()=>{sr(),lr(),mr(),Ca=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let t=e[0].dims,i=Array.from(e[1].getBigInt64Array(),Number),a=i.length{let i=e.length-t.length,a=[];for(let u=0;ue.length>t.length?ds(e,t):ds(t,e),Jl=e=>{let t=e[0].dims,i=Array.from(e[1].getBigInt64Array(),Number),a=Zl(t,i),u=e[0].dataType,p=u===9?4:1,d=Math.ceil(tt.size(a)/p),_=y=>{let C=mt("input",u,t.length,p),k=Jt("output",u,a.length,p),l;if(u===9){let F=(I,L,Q="")=>` + let outputIndices${L} = ${k.offsetToIndices(`outputOffset + ${L}u`)}; + let offset${L} = ${C.broadcastedIndicesToOffset(`outputIndices${L}`,k)}; + let index${L} = offset${L} / 4u; + let component${L} = offset${L} % 4u; + ${I}[${L}] = ${Q}(${C.getByOffset(`index${L}`)}[component${L}]); + `;l=` + let outputOffset = global_idx * ${p}; + var data = vec4(0); + ${F("data",0,"u32")} + ${F("data",1,"u32")} + ${F("data",2,"u32")} + ${F("data",3,"u32")} + ${k.setByOffset("global_idx","data")} + }`}else l=` + let outputIndices = ${k.offsetToIndices("global_idx")}; + let inputOffset = ${C.broadcastedIndicesToOffset("outputIndices",k)}; + ${k.setByOffset("global_idx",C.getByOffset("inputOffset"))} + }`;return` + ${y.registerUniform("vec_size","u32").declareVariables(C,k)} + ${y.mainStart()} + ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${l}`},g=[{type:12,data:d},...Rt(t,a)];return{name:"Expand",shaderCache:{hint:`${a.length}`,inputDependencies:["rank"]},getShaderSource:_,getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:g})}},ka=e=>{Ca(e.inputs),e.compute(Jl(e.inputs),{inputs:[0]})}}),nd,eu,sd=V(()=>{sr(),lr(),mr(),Ys(),nd=e=>{let t=e[0].dataType,i=tt.size(e[0].dims),a=tt.size(e[1].dims),u=a%4===0,p=d=>{let _=mt("x",t,[1],4),g=mt("bias",t,[1],4),y=Jt("y",t,[1],4),C=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],k=F=>` + let bias${F}_offset: u32 = (global_idx * 4 + ${F}) % uniforms.bias_size; + let bias${F} = ${g.getByOffset(`bias${F}_offset / 4`)}[bias${F}_offset % 4];`,l=u?` + let bias = ${g.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${k(0)}${k(1)}${k(2)}${k(3)} + let bias = ${_.type.value}(bias0, bias1, bias2, bias3);`;return`${d.registerUniforms(C).declareVariables(_,g,y)} + + ${Hs(Or(t))} + + ${d.mainStart(ki)} + ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${_.getByOffset("global_idx")}; + ${l} + let x_in = x + bias; + ${y.setByOffset("global_idx",Xs("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${u}`,inputDependencies:["type","type"]},getShaderSource:p,getRunData:d=>({outputs:[{dims:d[0].dims,dataType:d[0].dataType}],programUniforms:[{type:12,data:Math.ceil(i/4)},{type:12,data:a}],dispatchGroup:{x:Math.ceil(i/ki/4)}})}},eu=e=>{e.inputs.length<2||tt.size(e.inputs[1].dims)===0?el(e):e.compute(nd(e.inputs))}}),tu,ru,iu,nu,ad=V(()=>{sr(),lr(),Tr(),mr(),tu=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},ru=(e,t)=>{let i=e[0].dims,a=e[1].dims,u=i.length,p=tt.normalizeAxis(t.axis,u),d=i.slice(0);d.splice(p,1,...a);let _=i[p],g=e[0].dataType===9?4:1,y=Math.ceil(tt.size(d)/g),C=[{type:12,data:y},{type:6,data:_},{type:12,data:p},...Rt(e[0].dims,e[1].dims,d)],k=l=>{let F=mt("data",e[0].dataType,e[0].dims.length,g),I=mt("inputIndices",e[1].dataType,e[1].dims.length),L=Jt("output",e[0].dataType,d.length,g),Q=U=>{let we=a.length,te=`var indicesIndices${U} = ${I.type.indices}(0);`;for(let me=0;me1?`indicesIndices${U}[${me}]`:`indicesIndices${U}`} = ${d.length>1?`outputIndices${U}[uniforms.axis + ${me}]`:`outputIndices${U}`};`;te+=` + var idx${U} = ${I.getByIndices(`indicesIndices${U}`)}; + if (idx${U} < 0) { + idx${U} = idx${U} + uniforms.axisDimLimit; + } + var dataIndices${U} : ${F.type.indices}; + `;for(let me=0,it=0;me1?`dataIndices${U}[${me}]`:`dataIndices${U}`} = u32(idx${U});`,it+=we):(te+=`${u>1?`dataIndices${U}[${me}]`:`dataIndices${U}`} = ${d.length>1?`outputIndices${U}[${it}]`:`outputIndices${U}`};`,it++);return te},Z;if(e[0].dataType===9){let U=(we,te,me="")=>` + let outputIndices${te} = ${L.offsetToIndices(`outputOffset + ${te}u`)}; + ${Q(te)}; + let offset${te} = ${F.indicesToOffset(`dataIndices${te}`)}; + let index${te} = offset${te} / 4u; + let component${te} = offset${te} % 4u; + ${we}[${te}] = ${me}(${F.getByOffset(`index${te}`)}[component${te}]); + `;Z=` + let outputOffset = global_idx * ${g}; + var value = vec4(0); + ${U("value",0,"u32")} + ${U("value",1,"u32")} + ${U("value",2,"u32")} + ${U("value",3,"u32")} + ${L.setByOffset("global_idx","value")} + `}else Z=` + let outputIndices = ${L.offsetToIndices("global_idx")}; + ${Q("")}; + let value = ${F.getByIndices("dataIndices")}; + ${L.setByOffset("global_idx","value")}; + `;return` + ${l.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(F,I,L)} + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${Z} + }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:C}),getShaderSource:k}},iu=e=>tr({axis:e.axis}),nu=(e,t)=>{let i=e.inputs;tu(i),e.compute(ru(e.inputs,t))}}),su,au,ou,lu,od=V(()=>{sr(),lr(),Tr(),mr(),su=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.`)},au=(e,t)=>{let i=e[0].dims,a=e[0].dataType,u=i.length,p=e[1].dims,d=e[1].dataType,_=tt.normalizeAxis(t.axis,u),g=i[_],y=p.slice(0),C=tt.size(y),k=mt("input",a,u),l=mt("indicesInput",d,p.length),F=Jt("output",a,y.length),I=[{type:12,data:C},{type:6,data:g},{type:12,data:_}];return I.push(...Rt(i,p,y)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:y,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(C/64)},programUniforms:I}),getShaderSource:L=>` + ${L.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(k,l,F)} + ${L.mainStart()} + ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${F.offsetToIndices("global_idx")}; + + var idx = ${l.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${k.type.indices}(outputIndices); + ${k.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${k.getByIndices("inputIndices")}; + + ${F.setByOffset("global_idx","value")}; + }`}},ou=e=>tr({axis:e.axis}),lu=(e,t)=>{let i=e.inputs;su(i),e.compute(au(e.inputs,t))}}),uu,du,cu,ld,pu=V(()=>{sr(),lr(),mr(),uu=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")},du=(e,t)=>{let i=e[0].dims.slice(),a=e[1].dims.slice(),[u,p,d]=Fr.getShapeOfGemmResult(i,t.transA,a,t.transB,e.length===3?e[2].dims:void 0),_=[u,p];if(!_)throw new Error("Can't use gemm on the given tensors");let g=tt.size(_),y=[{type:12,data:g},{type:12,data:u},{type:12,data:p},{type:12,data:d},{type:1,data:t.alpha},{type:1,data:t.beta}],C=["type","type"];e.length===3&&(y.push(...Rt(e[2].dims)),C.push("rank")),y.push(...Rt(_));let k=l=>{let F="";t.transA&&t.transB?F="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?F="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?F="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(F="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let I=t.alpha===1?"":"value *= uniforms.alpha;",L=mt("a",e[0].dataType,e[0].dims),Q=mt("b",e[1].dataType,e[1].dims),Z=L.type.value,U=null,we=[L,Q];e.length===3&&(U=mt("c",e[2].dataType,e[2].dims.length),we.push(U));let te=Jt("output",e[0].dataType,_.length);we.push(te);let me=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return` + ${l.registerUniforms(me).declareVariables(...we)} + + ${l.mainStart()} + ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${Z}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${F} + } + + ${I} + ${U!=null?`let cOffset = ${U.broadcastedIndicesToOffset("vec2(m, n)",te)}; value += ${Z}(uniforms.beta) * ${U.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:_,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:y}),getShaderSource:k}},cu=e=>{let t=e.transA,i=e.transB,a=e.alpha,u=e.beta;return{transA:t,transB:i,alpha:a,beta:u,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},ld=(e,t)=>{uu(e.inputs),e.compute(du(e.inputs,t))}}),yi,hu,fu,$a,mu,Rn,_u,gu=V(()=>{sr(),lr(),Tr(),N(),Jn(),mr(),wn(),yi=(e,t)=>e.length>t&&e[t].dims.length>0&&tt.size(e[t].dims)>0?e[t]:void 0,hu=(e,t)=>{let i=e[0],a=yi(e,1),u=yi(e,2),p=yi(e,3),d=yi(e,4),_=yi(e,5),g=yi(e,6),y=yi(e,7);if(i.dims.length!==3&&i.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let C=!1,k=i.dims[0],l=i.dims[1],F=i.dims.length===3?C?i.dims[2]/3:i.dims[2]:t.numHeads*i.dims[4],I=l,L=0,Q=0,Z=Math.floor(F/t.numHeads);if(g&&y){if(g.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(g.dims[0]!==k||g.dims[1]!==t.numHeads||g.dims[3]!==Z)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(y.dims[0]!==k||y.dims[1]!==t.numHeads||y.dims[3]!==Z)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(g.dims[2]!==y.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(y.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');L=g.dims[2],Q=g.dims[2]}else if(g||y)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let U;if(a){if(i.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(a.dims.length<3||a.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(i.dims[0]!==a.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(a.dims.length===3){if(a.dims[2]!==i.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');U=2,I=a.dims[1]}else if(a.dims.length===5){if(a.dims[2]!==t.numHeads||a.dims[3]!==2||a.dims[4]!==Z)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(u)throw new Error('Expect "value" be none when "key" has packed kv format.');U=5,I=a.dims[1]}else{if(a.dims[1]!==t.numHeads||a.dims[3]!==Z)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');U=0,I=a.dims[2]}}else{if(i.dims.length!==3&&i.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(i.dims.length===5&&(i.dims[2]!==t.numHeads||i.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');U=3}if(p){if(p.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(u&&i.dims.length===5&&i.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let we=0;if(d){we=8;let Mt=d.dims;throw Mt.length===1?Mt[0]===k?we=1:Mt[0]===3*k+2&&(we=3):Mt.length===2&&Mt[0]===k&&Mt[1]===I&&(we=5),we===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let te=!1,me=F;if(u){if(u.dims.length!==3&&u.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(i.dims[0]!==u.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(u.dims.length===3){if(I!==u.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');me=u.dims[2]}else{if(I!==u.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');me=u.dims[1]*u.dims[3],te=!0}}let it=L+I,Ye=!1;if(d)throw new Error("Key padding mask is not supported");if(_){if(_.dims.length!==4)throw new Error('Input "relative_position_bias" is expected to have 4 dimensions');if(_.dims[0]!==k&&_.dims[0]!==1||_.dims[1]!==t.numHeads||_.dims[2]!==l||_.dims[3]!==it)throw new Error('Input "relative_position_bias" shape (batch_size, 1, sequence_length, kv_sequence_length)')}return{batchSize:k,sequenceLength:l,pastSequenceLength:L,kvSequenceLength:I,totalSequenceLength:it,maxSequenceLength:Q,inputHiddenSize:0,hiddenSize:F,vHiddenSize:me,headSize:Z,vHeadSize:Math.floor(me/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:we,scale:t.scale,broadcastResPosBias:Ye,passPastInKv:te,qkvFormat:U}},fu=e=>tr({...e}),$a=tr({perm:[0,2,1,3]}),mu=(e,t,i,a,u,p,d)=>{let _=[a,u,p],g=tt.size(_),y=[{type:12,data:g},{type:12,data:d},{type:12,data:p}],C=k=>{let l=Jt("qkv_with_bias",t.dataType,_),F=mt("qkv",t.dataType,_),I=mt("bias",i.dataType,_),L=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${k.registerUniforms(L).declareVariables(F,I,l)} + ${k.mainStart()} + ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:_,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:y}),getShaderSource:C},{inputs:[t,i],outputs:[-1]})[0]},Rn=(e,t,i,a,u,p,d,_)=>{let g=p;if(d){if(a===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return g=mu(e,p,d,t,a,i*u,_),g=g.reshape([t,a,i,u]),e.compute(Vi(g,$a.perm),{inputs:[g],outputs:[-1]})[0]}else return p.dims.length===3&&(g=p.reshape([t,a,i,u])),e.compute(Vi(g,$a.perm),{inputs:[g],outputs:[-1]})[0]},_u=(e,t)=>{let i=hu(e.inputs,t),a=e.inputs[0],u=yi(e.inputs,1),p=yi(e.inputs,2),d=yi(e.inputs,3),_=yi(e.inputs,4),g=yi(e.inputs,5),y=yi(e.inputs,6),C=yi(e.inputs,7);if(a.dims.length===5)throw new Error("Packed QKV is not implemented");if((u==null?void 0:u.dims.length)===5)throw new Error("Packed KV is not implemented");let k=u&&p&&u.dims.length===4&&p.dims.length===4,l=Rn(e,i.batchSize,i.numHeads,i.sequenceLength,i.headSize,a,d,0);if(k)return yn(e,l,u,p,_,void 0,y,C,g,i,t);if(!u||!p)throw new Error("key and value must be provided");let F=Rn(e,i.batchSize,i.numHeads,i.kvSequenceLength,i.headSize,u,d,i.hiddenSize),I=Rn(e,i.batchSize,i.numHeads,i.kvSequenceLength,i.vHeadSize,p,d,2*i.hiddenSize);yn(e,l,F,I,_,void 0,y,C,g,i,t)}}),Sa,wu,yu,Ea,bu,vu=V(()=>{sr(),lr(),mr(),Sa=e=>Array.from(e.getBigInt64Array(),Number),wu=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(Sa(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")},yu=(e,t)=>{let i=[];for(let a=0;a{let i=e[0].dims,a=t??Sa(e[1]),u=yu(i,a),p=tt.size(u),d=e[0].dataType,_=mt("input",d,i.length),g=Jt("output",d,u.length),y=C=>` + const inputShape = ${_.indices(...i)}; + ${C.registerUniform("output_size","u32").declareVariables(_,g)} + ${C.mainStart()} + ${C.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${g.offsetToIndices("global_idx")}; + var input_indices: ${_.type.indices}; + for (var i = 0; i < ${i.length}; i++) { + let input_dim_i = ${_.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${g.indicesGet("output_indices","i")} % input_dim_i; + + ${_.indicesSet("input_indices","i","input_dim_value")} + } + ${g.setByOffset("global_idx",_.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${a}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...Rt(e[0].dims,u)]}),getShaderSource:y}},bu=e=>{wu(e.inputs),e.compute(Ea(e.inputs),{inputs:[0]})}}),Mu,Pa,xu,Tu,Aa,Cu,ud=V(()=>{sr(),lr(),Tr(),Jn(),mr(),gu(),vu(),wn(),Mu=(e,t)=>{let i=e[0],a=e[1],u=e[2],p=e[3],d=e[4];if(i.dims.length!==3&&i.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let _=!1,g=i.dims[0],y=i.dims[1],C=i.dims.length===3?_?i.dims[2]/3:i.dims[2]:t.numHeads*i.dims[4],k=y,l=0,F=0,I=Math.floor(C/t.numHeads),L=p&&p.dims.length!==0,Q=d&&d.dims.length!==0,Z=!0;if(L&&Q){if(p.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(d.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');l=p.dims[1],F=p.dims[1]}else if(L||Q)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let U;if(a){if(i.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(a.dims.length<3||a.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(i.dims[0]!==a.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(a.dims.length===3){if(i.dims[2]%a.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');U=2,k=a.dims[1]}else if(a.dims.length===5){if(a.dims[2]!==t.numHeads||a.dims[3]!==2||a.dims[4]!==I)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(u)throw new Error('Expect "value" be none when "key" has packed kv format.');U=5,k=a.dims[1]}else{if(a.dims[1]!==t.numHeads||a.dims[3]!==I)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');U=0,k=a.dims[2]}}else{if(i.dims.length!==3&&i.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(i.dims.length===5&&(i.dims[2]!==t.numHeads||i.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');U=3}let we=0,te=!1,me=C;if(u){if(u.dims.length!==3&&u.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(i.dims[0]!==u.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(u.dims.length===3){if(k!==u.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');me=u.dims[2]}else{if(k!==u.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');me=u.dims[1]*u.dims[3],te=!0}}let it=l+k;return{batchSize:g,sequenceLength:y,pastSequenceLength:l,kvSequenceLength:k,totalSequenceLength:it,maxSequenceLength:F,inputHiddenSize:0,hiddenSize:C,vHiddenSize:me,headSize:I,vHeadSize:Math.floor(me/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:we,scale:t.scale,broadcastResPosBias:!1,passPastInKv:te,qkvFormat:U,isPastkvBSNH:Z}},Pa=(e,t,i,a)=>{let u=[a.batchSize,a.totalSequenceLength,a.kvNumHeads,a.headSize],p=4,d=tt.size(u)/p,_=a.totalSequenceLength,g=Jt("present_kv",i,u.length,p),y=mt("new_kv",e.dataType,e.dims.length,p),C=t?mt("past_kv",t.dataType,t.dims.length,p):void 0,k=Math.ceil(a.headSize/p),l={x:_,y:e.dims[0],z:1},F=t?["rank","rank"]:["rank"],I=[{type:12,data:d},{type:12,data:a.pastSequenceLength},{type:12,data:a.kvSequenceLength},{type:12,data:a.totalSequenceLength}],L=[y];C?(I.push(...Rt(e.dims),...Rt(t.dims),...Rt(u)),L.push(C)):I.push(...Rt(e.dims),...Rt(u));let Q=[{name:"output_size",type:"u32"},{name:"past_seqlen",type:"u32"},{name:"new_seqlen",type:"u32"},{name:"present_seqlen",type:"u32"}],Z=` let past_batch_stride = uniforms.past_seqlen * num_heads * H; + var past_head_stride = uniforms.past_seqlen * H; + if (is_bsnh) { + past_head_stride = H; + } + let in_offset = b * past_batch_stride + s * row_stride + n * past_head_stride + h; + present_kv[out_offset] = past_kv[in_offset];`,U=` let new_batch_stride = uniforms.new_seqlen * num_heads * H; + let new_row_stride = num_heads * H; + let new_head_stride = H; + let in_offset = b * new_batch_stride + (s - past_seqlen) * new_row_stride + n * new_head_stride + h; + present_kv[out_offset] = new_kv[in_offset];`,we=t?`if (s < past_seqlen) { + ${Z} + } else if (s < past_seqlen + uniforms.new_seqlen) { + ${U} + }`:`if (s < past_seqlen + uniforms.new_seqlen) { + ${U} + }`,te=me=>` + + ${me.registerUniforms(Q).declareVariables(...L,g)} + ${me.mainStart([k,a.kvNumHeads,1])} + ${me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var indices = ${g.offsetToIndices("global_idx")}; + let h = local_id.x; + let n = local_id.y; + let s = workgroup_id.x; + let b = workgroup_id.y; + let num_heads = ${a.kvNumHeads}u; + let H = ${k}u; + + let present_seqlen = uniforms.present_seqlen; + let present_batch_stride = present_seqlen * num_heads * H; + var row_stride = H; + let is_bsnh = ${a.isPastkvBSNH}; + + if (is_bsnh) { + row_stride = num_heads * H; + } + var present_head_stride = present_seqlen * H; + if (is_bsnh) { + present_head_stride = H; + } + + let past_seqlen = uniforms.past_seqlen; + + let out_offset = b * present_batch_stride + s * row_stride + n * present_head_stride + h; + ${we} + }`;return{name:"ConcatPastNew",shaderCache:{hint:`${a.kvNumHeads}${k}${!!t}`,inputDependencies:F},getRunData:()=>({outputs:[{dims:u,dataType:i}],dispatchGroup:l,programUniforms:I}),getShaderSource:te}},xu=e=>tr({...e}),Tu=tr({perm:[0,2,1,3]}),Aa=(e,t,i,a,u)=>{let p=t,d=a.kvNumHeads,_=a.nReps;return t.dims.length===3&&a.kvSequenceLength!==0&&(p=t.reshape([a.batchSize,a.kvSequenceLength,d,a.headSize])),i?p=e.compute(Pa(p,i,p.dataType,a),{inputs:[p,i],outputs:[a.isPastkvBSNH?u:-1]})[0]:p=e.compute(Pa(p,void 0,p.dataType,a),{inputs:[p],outputs:[a.isPastkvBSNH?u:-1]})[0],_!==1&&(p=e.compute(Ea([p],[1,1,1,_]),{inputs:[p],outputs:[-1]})[0],p=p.reshape([a.batchSize,a.totalSequenceLength,d*_,a.headSize])),e.compute(Vi(p,Tu.perm),{inputs:[p],outputs:[-1]})[0]},Cu=(e,t)=>{var g;let i=Mu(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((g=e.inputs[1])==null?void 0:g.dims.length)===5)throw new Error("Packed KV is not implemented");let a=Rn(e,i.batchSize,i.numHeads,i.sequenceLength,i.headSize,e.inputs[0],void 0,0),u=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,p=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,d=Aa(e,e.inputs[1],u,i,1),_=Aa(e,e.inputs[2],p,i,2);yn(e,a,d,_,void 0,void 0,void 0,void 0,void 0,i,t)}}),ku,$u,Su,Eu,Ld=V(()=>{sr(),lr(),mr(),ku=(e,t)=>{let i=e[0].dims,a=i,u=2,p=tt.sizeToDimension(i,u),d=tt.sizeFromDimension(i,u),_=$r(d),g=d/_,y=[i[0],i[1],g],C=["rank","type","type"],k=[{type:12,data:d},{type:12,data:g}];k.push(...Rt(y,y));let l=F=>{let I=mt("x",e[0].dataType,y.length,_),L=mt("scale",e[1].dataType,e[1].dims),Q=mt("bias",e[2].dataType,e[2].dims),Z=Jt("output",e[0].dataType,y.length,_),U=[I,L,Q,Z],we=I.type.value,te=_===1?"f32":`vec${_}`,me=64,it=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return` + var meanShared : f32; + var squaredNormShared : f32; + var workgroupShared : array<${te}, ${me}>; + const workgroupSize = ${me}u; + ${F.registerUniforms(it).declareVariables(...U)} + ${F.mainStart(me)} + let norm = global_idx / workgroupSize; + let batch = norm / uniforms.x_shape[1]; + let channel = norm % uniforms.x_shape[1]; + let localIndex = local_id.x; + + // initialize workgroup memory + var initial = ${te}(0); + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + initial = initial + ${te}(${I.get("batch","channel","h")}); + } + workgroupShared[localIndex] = initial; + workgroupBarrier(); + + // Calculate the mean of current channel data. + for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) { + if (localIndex < currSize) { + workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize]; + } + workgroupBarrier(); + } + if (localIndex == 0) { + meanShared = ${$i("workgroupShared[0]",_)} / f32(uniforms.normSize); + } + workgroupBarrier(); + + // reinitialize workgroup memory. + initial = ${te}(0); + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + let deviation = ${te}(${I.get("batch","channel","h")}) - ${te}(meanShared); + initial = initial + deviation * deviation; + } + workgroupShared[localIndex] = initial; + workgroupBarrier(); + + // Calculate the sum of square of deviation of current channel data. + for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) { + if (localIndex < currSize) { + workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize]; + } + workgroupBarrier(); + } + if (localIndex == 0) { + squaredNormShared = ${$i("workgroupShared[0]",_)}; + } + workgroupBarrier(); + + let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon})); + let channelScale = invStdDev * f32(${L.getByOffset("channel")}); + let channelShift = f32(${Q.getByOffset("channel")}) - meanShared * channelScale; + for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { + let value = ${I.get("batch","channel","h")} * ${we}(${te}(channelScale)) + ${we}(${te}(channelShift)); + ${Z.set("batch","channel","h","value")}; + } + }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${_}`,inputDependencies:C},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:p},programUniforms:k}),getShaderSource:l}},$u=(e,t,i,a,u,p,d,_)=>{let g=$r(d),y=64,C=g===1?"vec2f":`mat2x${g}f`,k=g===1?"f32":`vec${g}f`,l=(it,Ye)=>`${C}(${it}, ${Ye})`,F=u*d/g,I=Math.ceil(p/y),L=["type"],Q=[{type:12,data:I},{type:12,data:p},{type:12,data:Math.floor(d/g)},{type:12,data:Math.floor(p*d/g)}],Z=it=>{let Ye=mt("input",t.dataType,t.dims,g);return` + ${it.declareVariables(Ye)} + @group(0) @binding(1) var output : array<${C}>; + struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32}; + @group(0) @binding(2) var uniforms: Uniforms; + + ${it.mainStart(y)} + let currentImageNumber = global_idx / ${y} / uniforms.C; + let currentChannelNumber = (global_idx / ${y}) % uniforms.C; + let wgOffset = local_id.x * uniforms.wg_size; + if (wgOffset >= uniforms.H) { + return; + } + let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H); + + let offset = currentImageNumber * uniforms.image_size + currentChannelNumber; + var sum = ${Nr("f32",g)}; + var squaredSum = ${Nr("f32",g)}; + for (var i: u32 = wgOffset; i < wgMax; i++) { + let value = ${k}(input[offset + i * uniforms.C]); + sum += value; + squaredSum += value * value; + } + output[global_idx] = ${l("sum","squaredSum")}; + }`},U=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${g}`,inputDependencies:L},getRunData:()=>({outputs:[{dims:[u,d,y,2],dataType:1}],dispatchGroup:{x:u*d/g},programUniforms:Q}),getShaderSource:Z},{inputs:[t],outputs:[-1]})[0],we=[{type:12,data:F},{type:12,data:p},{type:12,data:Math.floor(d/g)},{type:12,data:Math.floor(y*d/g)}],te=["type","type","type"],me=it=>{let Ye=mt("scale",i.dataType,i.dims,g),Mt=mt("bias",a.dataType,a.dims,g);return` + @group(0) @binding(0) var input : array<${C}>; + @group(0) @binding(1) var scale : array<${Ye.type.storage}>; + @group(0) @binding(2) var bias : array<${Mt.type.storage}>; + @group(0) @binding(3) var output : array<${C}>; + struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32}; + @group(0) @binding(4) var uniforms: Uniforms; + + ${it.mainStart()} + ${it.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")} + let currentImageNumber = global_idx / uniforms.C; + let currentChannelNumber = global_idx % uniforms.C; + + let offset = currentImageNumber * uniforms.image_size; + var sum = ${Nr("f32",g)}; + var squaredSum = ${Nr("f32",g)}; + for (var i: u32 = 0; i < min(${y}, uniforms.H); i++) { + let value = input[offset + i + currentChannelNumber * ${y}]; + sum += value[0]; + squaredSum += value[1]; + } + sum = sum / f32(uniforms.H); + squaredSum = squaredSum / f32(uniforms.H); + let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${_})); + let channelScale = invStdDev * ${k}(scale[currentChannelNumber]); + let channelShift = ${k}(bias[currentChannelNumber]) - sum * channelScale; + + output[global_idx] = ${l("channelScale","channelShift")}; + }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${g};${_}`,inputDependencies:te},getRunData:()=>({outputs:[{dims:[u,d,2],dataType:1}],dispatchGroup:{x:Math.ceil(F/64)},programUniforms:we}),getShaderSource:me},{inputs:[U,i,a],outputs:[-1]})[0]},Su=(e,t,i)=>{let a=t[0].dims,u=a,p=a[0],d=a[a.length-1],_=tt.sizeFromDimension(a,1)/d,g=$r(d),y=tt.size(u)/g,C=[{type:12,data:_},{type:12,data:Math.floor(d/g)}],k=["type","type"],l=$u(e,t[0],t[1],t[2],p,_,d,i.epsilon),F=I=>{let L=zr(t[0].dataType),Q=g===1?"vec2f":`mat2x${g}f`,Z=g===1?L:`vec${g}<${L}>`,U=mt("input",t[0].dataType,t[0].dims,g),we=Jt("output",t[0].dataType,u,g);return` + @group(0) @binding(0) var input : array<${U.type.storage}>; + @group(0) @binding(1) var scaleInput : array<${Q}>; + @group(0) @binding(2) var output : array<${we.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${I.mainStart()} + let currentImageNumber = global_idx / (uniforms.C * uniforms.H); + let currentChannelNumber = global_idx % uniforms.C; + + let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber; + let scale = scaleInput[scaleOffset]; + output[global_idx] = fma(input[global_idx], ${Z}(scale[0]), ${Z}(scale[1])); + }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${g}`,inputDependencies:k},getRunData:()=>({outputs:[{dims:u,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:C}),getShaderSource:F},{inputs:[t[0],l]})},Eu=(e,t)=>{t.format==="NHWC"?Su(e,e.inputs,t):e.compute(ku(e.inputs,t))}}),wr,Pu,oi,hi=V(()=>{sr(),lr(),mr(),wr=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Pu=(e,t,i)=>{let a=t.simplified,u=e[0].dims,p=e[1],d=!a&&e[2],_=u,g=tt.normalizeAxis(t.axis,u.length),y=tt.sizeToDimension(u,g),C=tt.sizeFromDimension(u,g),k=tt.size(p.dims),l=d?tt.size(d.dims):0;if(k!==C||d&&l!==C)throw new Error(`Size of X.shape()[axis:] == ${C}. + Size of scale and bias (if provided) must match this. + Got scale size of ${k} and bias size of ${l}`);let F=[];for(let me=0;me1,U=i>2,we=me=>{let it=zr(e[0].dataType),Ye=[mt("x",e[0].dataType,e[0].dims,I),mt("scale",p.dataType,p.dims,I)];d&&Ye.push(mt("bias",d.dataType,d.dims,I)),Ye.push(Jt("output",e[0].dataType,_,I)),Z&&Ye.push(Jt("mean_data_output",1,F)),U&&Ye.push(Jt("inv_std_output",1,F));let Mt=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${me.registerUniforms(Mt).declareVariables(...Ye)} + ${me.mainStart()} + ${me.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${Nr("f32",I)}; + var mean_square_vector = ${Nr("f32",I)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${Qr(it,I,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${$i("mean_vector",I)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${$i("mean_square_vector",I)} / uniforms.norm_size ${a?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${Qr(it,I,"x[j + offset]")}; + let f32scale = ${Qr(it,I,"scale[j]")}; + output[j + offset] = ${Ye[0].type.value}((f32input ${a?"":"- mean"}) * inv_std_dev * f32scale + ${d?`+ ${Qr(it,I,"bias[j]")}`:""} + ); + } + + ${Z?"mean_data_output[global_idx] = mean":""}; + ${U?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},te=[{dims:_,dataType:e[0].dataType}];return Z&&te.push({dims:F,dataType:1}),U&&te.push({dims:F,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${I};${i};${a}`,inputDependencies:L},getRunData:()=>({outputs:te,dispatchGroup:{x:Math.ceil(y/64)},programUniforms:Q}),getShaderSource:we}},oi=(e,t)=>{wr(e.inputs),e.compute(Pu(e.inputs,t,e.outputCount))}}),fi,ln,dd,Au,cd=V(()=>{sr(),lr(),Tr(),mr(),fi=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let i=e[0],a=i.dims.length;if(i.dims[a-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let u=Math.floor((t.k+t.blockSize-1)/t.blockSize),p=t.blockSize/8*t.bits,d=e[1];if(!tt.areEqual(d.dims,[t.n,u,p]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let _=e[2].dims;if(tt.size(_)!==t.n*u)throw new Error("scales input size error.");if(e.length===4){let g=e[3].dims,y=t.bits>4?t.n*u:t.n*Math.floor((u+1)/2);if(tt.size(g)!==y)throw new Error("zeroPoints input size error.")}},ln=(e,t,i,a)=>{let u=e[0].dims,p=u.length,d=Math.floor((t.k+t.blockSize-1)/t.blockSize),_=u[p-2],g=t.k,y=t.n,C=u.slice(0,p-2),k=tt.size(C),l=t.blockSize/8*t.bits/4,F=e[0].dataType,I=$r(_),L=$r(t.k),Q=$r(l),Z=Ki(F,_*d),U=Math.floor(a/Z),we=d<=i[0]&&U>0,te=!we||U>=4?$r(y):U>=2&&$r(y)>=2?2:1,me=C.concat([_,y]),it=tt.size(me)/te/I,Ye=we?[]:[{type:12,data:it},{type:12,data:t.blockSize}],Mt=[k,_,g/L],Gt=tt.convertShape(e[1].dims).slice();Gt.splice(-1,1,l/Q),Ye.push(...Rt(Mt)),Ye.push(...Rt(Gt)),Ye.push(...Rt(e[2].dims)),e.length===4&&Ye.push(...Rt(tt.convertShape(e[3].dims)));let Bt=[k,_,y/te];Ye.push(...Rt(Bt));let gr=Mr=>{let Ur=Mt.length,Ir=mt("a",e[0].dataType,Ur,L),Sr=mt("b",12,Gt.length,Q),ri=mt("scales",e[2].dataType,e[2].dims.length),Kr=[Ir,Sr,ri],Wt=e.length===4?mt("zero_points",12,e[3].dims.length):void 0;Wt&&Kr.push(Wt);let pr=Bt.length,cr=Jt("output",e[0].dataType,pr,te),lt=[{name:"output_size",type:"u32"},{name:"block_size",type:"u32"}],qt=zr(e[0].dataType),fr=(()=>{switch(L){case 1:return`array<${qt}, 8>`;case 2:return`mat4x2<${qt}>`;case 4:return`mat2x4<${qt}>`;default:throw new Error(`${L}-component is not supported.`)}})(),Yr=` + for (var word: u32 = 0; word < ${l}; word += ${Q}) { + ${Sr.indicesSet("b_indices","2","word")}; + let b_data = ${Sr.getByIndices("b_indices")}; + for (var i: u32 = 0; i < ${Q}; i++) { + let b_value: u32 = ${Q===1?"b_data":"b_data[word + i]"}; + let b_mask: u32 = 0x0F0F0F0Fu; + let b_value_lower: vec4 = unpack4xU8(b_value & b_mask); + let b_value_upper: vec4 = unpack4xU8((b_value >> 4) & b_mask); + let b_quantized_values = ${fr}(${Array.from({length:4},(gi,Oi)=>`${qt}(b_value_lower[${Oi}]), ${qt}(b_value_upper[${Oi}])`).join(", ")}); + let b_dequantized_values = ${L===1?`${fr}(${Array.from({length:8},(gi,Oi)=>`(b_quantized_values[${Oi}] - zero_point) * scale`).join(", ")});`:`(b_quantized_values - ${fr}(${Array(8).fill("zero_point").join(",")})) * scale;`}; + // Number of B elements per 32-bit word is 32/bits = 32/4 = 8 + for (var m: u32 = 0; m < ${we?_:I}u; m++) { + ${Ir.indicesSet("a_indices",Ur-2,we?"m":`row * ${I} + m`)}; + ${Ir.indicesSet("a_indices",Ur-1,"word_offset")}; + var input_offset = ${Ir.indicesToOffset("a_indices")}; + var a_data: ${fr}; + for (var j: u32 = 0; j < ${8/L}; j++) { + a_data[j] = ${Ir.getByOffset("input_offset")}; + input_offset++; + } + ${we?"workgroup_shared[workgroup_shared_offset + m]":"output_values[m]"}${te>1?"[c]":""} += ${Array.from({length:8/L},(gi,Oi)=>`${L===1?`a_data[${Oi}] * b_dequantized_values[${Oi}]`:`dot(a_data[${Oi}], b_dequantized_values[${Oi}])`}`).join(" + ")}; + } + word_offset += ${8/L}; + } + }`,ai=Wt?` + zero_point_offset += 4; + if (zero_point_offset == 32) { + zero_point_offset = 0; + zero_point_index++; + zero_point_word = ${Wt.getByOffset("zero_point_index")}; + }`:"";return we?` + var workgroup_shared: array<${cr.type.value}, ${_*d}>; + ${Mr.declareVariables(...Kr,cr)} + ${Mr.mainStart([d,1,1])} + var a_indices: ${Ir.type.indices}; + var block = local_id.x; + var col = workgroup_id.y; + var batch = workgroup_id.z; + ${Ir.indicesSet("a_indices","0","batch")}; + // Two zero points are packed into one byte when uniforms.bits is 4. + for (var c: u32 = 0; c < ${te}; c++) { + let col_times_components_plus_c = col * ${te} + c; + ${Wt?` + var zero_point_bytes_per_col: u32 = (${d} + 1) / 2; + var zero_point_byte_count: u32 = col_times_components_plus_c * zero_point_bytes_per_col + (block >> 0x1u); + var zero_point_word_index: u32 = zero_point_byte_count >> 0x2u; + var zero_point_byte_offset: u32 = zero_point_byte_count & 0x3u; + var zero_point_nibble_offset: u32 = block & 0x1u; + var zero_point_bits_offset: u32 = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + var zero_point_word: u32 = ${Wt.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;`:""} + var b_indices: ${Sr.type.indices}; + ${Sr.indicesSet("b_indices","0","col_times_components_plus_c")}; + // The scale and zero points are computed per block. + var scales_index = col_times_components_plus_c * ${d} + block; + let scale = ${ri.getByOffset("scales_index")}; + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${qt}(${Wt?"(zero_point_word) & 0xFu":8}); + ${Sr.indicesSet("b_indices","1","block")}; + var word_offset: u32 = block * ${t.blockSize/L}; + var workgroup_shared_offset: u32 = block * ${_}; + ${Yr} + } + workgroupBarrier(); + var output_indices: ${cr.type.indices}; + var elements_per_thread: u32 = ${Math.ceil(_/d)}; + ${cr.indicesSet("output_indices","0","batch")}; + ${cr.indicesSet("output_indices",pr-1,"col")}; + ${cr.indicesSet("output_indices",pr-2,"local_id.x * elements_per_thread")}; + var output_offset = ${cr.indicesToOffset("output_indices")}; + for (var m: u32 = 0u; m < elements_per_thread; m++) { + var row = m + local_id.x * elements_per_thread; + if (row < ${_}) { + var output_value: ${cr.type.value} = ${cr.type.value}(0); + var workgroup_shared_offset: u32 = row; + for (var b: u32 = 0u; b < ${d}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${_}; + } + ${cr.setByOffset("output_offset","output_value")}; + output_offset += ${y/te}; + } + } + }`:` + ${Mr.registerUniforms(lt).declareVariables(...Kr,cr)} + ${Mr.mainStart()} + ${Mr.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var output_values: array<${cr.type.value}, ${I}>; + var output_indices = ${cr.offsetToIndices("global_idx")}; + var col = ${cr.indicesGet("output_indices",pr-1)}; + var row = ${cr.indicesGet("output_indices",pr-2)}; + var a_indices: ${Ir.type.indices} = output_indices; + // Two zero points are packed into one byte because uniforms.bits <= 4. + // zero_point_offset is either 0 or 4. It is bit offset within one byte. + // TODO support zero_point_offset for bits > 4 + ${Wt?` + var zero_point_abs_offset = col * ${te} * ((${d} + 1) / 2); + var zero_point_index: u32 = zero_point_abs_offset / 4; + var zero_point_word: u32 = ${Wt.getByOffset("zero_point_index")}; + var zero_point_offset: u32 = (zero_point_abs_offset % 4) * 8;`:""} + var scale_index = col * ${d*te}; + var b_indices: ${Sr.type.indices}; + for (var c: u32 = 0; c < ${te}; c++) { + ${Sr.indicesSet("b_indices","0",`col * ${te} + c`)}; + var block_offset: u32 = 0; + for (var block: u32 = 0; block < ${d}; block++) { + // The scale and zero points are computed per block. + let scale = ${ri.getByOffset("scale_index")}; + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${qt}(${Wt?"extractBits(zero_point_word, zero_point_offset, 4)":8}); + ${Sr.indicesSet("b_indices","1","block")}; + var word_offset: u32 = block_offset; + ${Yr} + scale_index++; + ${ai} + block_offset += uniforms.block_size / ${L}; + } + // Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte. + ${Wt?`if (zero_point_offset % 8 > 0) { + ${ai} + }`:""} + } + for (var k: u32 = 0u; k < ${I}u; k++) { + ${cr.indicesSet("output_indices",pr-2,`${I} * row + k`)}; + ${cr.setByIndices("output_indices","output_values[k]")} + } + }`};return{name:we?"BlockwiseMatMulNBits":"MatMulNBits",shaderCache:{hint:`${t.cacheKey};${_};${F};${e.length}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:me,dataType:F}],name:we?"BlockwiseMatMulNBits":"MatMulNBits",dispatchGroup:we?{x:1,y:Math.ceil(y/te),z:k}:{x:Math.ceil(it/64)},programUniforms:Ye}),getShaderSource:gr}},dd=(e,t)=>{fi(e.inputs,t);let i=e.getMaxComputeWorkgroupSizes(),a=e.getMaxComputeWorkgroupStoragesize();e.compute(ln(e.inputs,t,i,a))},Au=e=>tr(e)}),M,T,O,ie,Ue,He,wt,Lt,er,yr=V(()=>{sr(),lr(),mr(),M=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},T=(e,t,i)=>{let a="";for(let u=t-1;u>=0;--u)a+=` + k = i32(${e.indicesGet("indices",u)}) - ${Kt("uniforms.pads",u,i)}; + if (k < 0) { + break; + } + if (k >= i32(${Kt("uniforms.x_shape",u,t)})) { + break; + } + offset += k * i32(${Kt("uniforms.x_strides",u,t)}); + `;return` + value = ${e.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${a} + value = x[offset]; + } + `},O=(e,t,i)=>{let a="";for(let u=t-1;u>=0;--u)a+=` + k = i32(${e.indicesGet("indices",u)}) - ${Kt("uniforms.pads",u,i)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${Kt("uniforms.x_shape",u,t)}) - 1); + k = k % _2n_1; + if(k >= i32(${Kt("uniforms.x_shape",u,t)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${Kt("uniforms.x_strides",u,t)}); + `;return` + var offset = 0; + var k = 0; + ${a} + value = x[offset]; + `},ie=(e,t,i)=>{let a="";for(let u=t-1;u>=0;--u)a+=` + k = i32(${e.indicesGet("indices",u)}) - ${Kt("uniforms.pads",u,i)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${Kt("uniforms.x_shape",u,t)})) { + k = i32(${Kt("uniforms.x_shape",u,t)}) - 1; + } + offset += k * i32(${Kt("uniforms.x_strides",u,t)}); + `;return` + var offset = 0; + var k = 0; + ${a} + value = x[offset]; + `},Ue=(e,t,i)=>{let a="";for(let u=t-1;u>=0;--u)a+=` + k = i32(${e.indicesGet("indices",u)}) - ${Kt("uniforms.pads",u,i)}; + if (k < 0) { + k += i32(${Kt("uniforms.x_shape",u,t)}]); + } + if (k >= i32(${Kt("uniforms.x_shape",u,t)})) { + k -= i32(${Kt("uniforms.x_shape",u,t)}); + } + offset += k * i32(${Kt("uniforms.x_strides",u,t)}); + `;return` + var offset = 0; + var k = 0; + ${a} + value = x[offset]; + `},He=(e,t,i)=>{switch(i.mode){case 0:return T(e,t,i.pads.length);case 1:return O(e,t,i.pads.length);case 2:return ie(e,t,i.pads.length);case 3:return Ue(e,t,i.pads.length);default:throw new Error("Invalid mode")}},wt=(e,t)=>{let i=tt.padShape(e[0].dims.slice(),t.pads),a=e[0].dims,u=tt.size(i),p=[{type:12,data:u},{type:6,data:t.pads}];t.mode===0&&p.push({type:e[0].dataType,data:t.value}),p.push(...Rt(e[0].dims,i));let d=["rank"],_=g=>{let y=Jt("output",e[0].dataType,i.length),C=mt("x",e[0].dataType,a.length),k=C.type.value,l=He(y,a.length,t),F=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&F.push({name:"constant_value",type:k}),` + ${g.registerUniforms(F).declareVariables(C,y)} + ${g.mainStart()} + ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${y.offsetToIndices("global_idx")}; + + var value = ${k}(0); + ${l} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(tt.size(i)/64)},programUniforms:p}),getShaderSource:_}},Lt=(e,t)=>{if(e.length>1){let i=e[1].getBigInt64Array(),a=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,u=e[0].dims.length,p=new Int32Array(2*u).fill(0);if(e.length>=4){let _=e[3].getBigInt64Array();for(let g=0;g<_.length;g++)p[Number(_[g])]=Number(i[g]),p[Number(_[g])+u]=Number(i[g+_.length])}else i.forEach((_,g)=>p[Number(g)]=Number(_));let d=[];return p.forEach(_=>d.push(_)),{mode:t.mode,value:a,pads:d}}else return t},er=(e,t)=>{M(e.inputs);let i=Lt(e.inputs,t);e.compute(wt(e.inputs,i),{inputs:[0]})}}),_r,Lr,br,kr,vr,Cr,Ar,Wr,bi,Mi,Yi,mi,si,_i,cs,ps,Ia,Rd,Ui,Nn=V(()=>{z(),sr(),lr(),mr(),_r=e=>{if(j.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Lr=(e,t,i)=>{let a=t.format==="NHWC",u=e.dims.slice();a&&u.splice(1,0,u.pop());let p=Object.hasOwnProperty.call(t,"dilations"),d=t.kernelShape.slice(),_=t.strides.slice(),g=p?t.dilations.slice():[],y=t.pads.slice();Pi.adjustPoolAttributes(i,u,d,_,g,y);let C=Pi.computePoolOutputShape(i,u,_,g,d,y,t.autoPad),k=Object.assign({},t);p?Object.assign(k,{kernelShape:d,strides:_,pads:y,dilations:g,cacheKey:t.cacheKey}):Object.assign(k,{kernelShape:d,strides:_,pads:y,cacheKey:t.cacheKey});let l=C.slice();return l.push(l.splice(1,1)[0]),[k,a?l:C]},br=(e,t)=>{let i=t.format==="NHWC",a=tt.size(e),u=tt.size(t.kernelShape),p=[{type:12,data:a},{type:12,data:u}],d=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let _=t.kernelShape[t.kernelShape.length-1],g=t.strides[t.strides.length-1],y=t.pads[t.pads.length/2-1],C=t.pads[t.pads.length-1],k=!!(y+C);p.push({type:12,data:_},{type:12,data:g},{type:12,data:y},{type:12,data:C}),d.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let l=!1;if(t.kernelShape.length===2){let F=t.kernelShape[t.kernelShape.length-2],I=t.strides[t.strides.length-2],L=t.pads[t.pads.length/2-2],Q=t.pads[t.pads.length-2];l=!!(L+Q),p.push({type:12,data:F},{type:12,data:I},{type:12,data:L},{type:12,data:Q}),d.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[p,d,!0,k,l]}else{if(i)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let _=tt.computeStrides(t.kernelShape);p.push({type:12,data:_},{type:12,data:t.pads},{type:12,data:t.strides}),d.push({name:"kernelStrides",type:"u32",length:_.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let g=t.pads.reduce((y,C)=>y+C);return[p,d,!!g,!1,!1]}},kr=(e,t,i,a,u,p,d,_,g,y,C,k)=>{let l=u.format==="NHWC",F=t.type.value,I=Jt("output",t.type.tensor,a);if(u.kernelShape.length<=2){let L="",Q="",Z="",U=i-(l?2:1);if(C?L=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${U}] = indices[${U}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${U}] < 0 || xIndices[${U}] + >= uniforms.x_shape[${U}]) { + pad++; + continue; + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${p} + }`:L=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${U}] = indices[${U}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${p} + }`,u.kernelShape.length===2){let we=i-(l?3:2);k?Q=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${we}] = indices[${we}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${we}] < 0 || xIndices[${we}] >= uniforms.x_shape[${we}]) { + pad += i32(uniforms.kw); + continue; + } + `:Q=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${we}] = indices[${we}] * uniforms.sh - uniforms.phStart + j; + `,Z=` + } + `}return` + ${e.registerUniforms(g).declareVariables(t,I)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${I.offsetToIndices("global_idx")}; + var xIndices = ${I.offsetToIndices("global_idx")}; + + var value = ${F}(${_}); + var pad = 0; + ${Q} + ${L} + ${Z} + ${d} + + output[global_idx] = value; + }`}else{if(l)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let L=u.kernelShape.length,Q=u.pads.length,Z="";return y?Z=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${p} + }`:Z=` + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${p} + `,` + ${e.registerUniforms(g).declareVariables(t,I)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${I.offsetToIndices("global_idx")}; + var xIndices = ${I.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${F}(${_}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${L-1}u; j++) { + offsets[j] = offset / ${Kt("uniforms.kernelStrides","j",L)}; + offset -= offsets[j] * ${Kt("uniforms.kernelStrides","j",L)}; + } + offsets[${L-1}] = offset; + + isPad = false; + for (var j = ${i-L}u; j < ${i}u; j++) { + xIndices[j] = indices[j] * ${Kt("uniforms.strides",`j - ${i-L}u`,L)} + + offsets[j - ${i-L}u] - ${Kt("uniforms.pads","j - 2u",Q)}; + ${Z} + } + ${d} + + output[global_idx] = value; + }`}},vr=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Cr=e=>`${vr(e)};${e.countIncludePad}`,Ar=e=>`${vr(e)};${e.storageOrder};${e.dilations}`,Wr=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}),bi=(e,t,i,a)=>{let[u,p]=Lr(t,a,i),d=mt("x",t.dataType,t.dims.length),_=d.type.value,g="value += x_val;",y="";u.countIncludePad?y+=`value /= ${_}(uniforms.kernelSize);`:y+=`value /= ${_}(i32(uniforms.kernelSize) - pad);`;let[C,k,l,F,I]=br(p,u);C.push(...Rt(t.dims,p));let L=["rank"];return{name:e,shaderCache:{hint:`${a.cacheKey};${l};${F};${I}`,inputDependencies:L},getRunData:()=>({outputs:[{dims:p,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(tt.size(p)/64)},programUniforms:C}),getShaderSource:Q=>kr(Q,d,t.dims.length,p.length,u,g,y,0,k,l,F,I)}},Mi=e=>{let t=e.count_include_pad!==0,i=Wr(e);if(i.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let a={countIncludePad:t,...i,cacheKey:""};return{...a,cacheKey:Cr(a)}},Yi=(e,t)=>{_r(e.inputs),e.compute(bi("AveragePool",e.inputs[0],!1,t))},mi={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},si=e=>{let t=e.format;return{format:t,...mi,cacheKey:t}},_i=(e,t)=>{_r(e.inputs),e.compute(bi("GlobalAveragePool",e.inputs[0],!0,t))},cs=(e,t,i,a)=>{let[u,p]=Lr(t,a,i),d=` + value = max(x_val, value); + `,_="",g=mt("x",t.dataType,t.dims.length),y=["rank"],[C,k,l,F,I]=br(p,u);return C.push(...Rt(t.dims,p)),{name:e,shaderCache:{hint:`${a.cacheKey};${l};${F};${I}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:p,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(tt.size(p)/64)},programUniforms:C}),getShaderSource:L=>kr(L,g,t.dims.length,p.length,u,d,_,t.dataType===10?-65504:-1e5,k,l,F,I)}},ps=(e,t)=>{_r(e.inputs),e.compute(cs("MaxPool",e.inputs[0],!1,t))},Ia=e=>{let t=e.storage_order,i=e.dilations,a=Wr(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(a.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let u={storageOrder:t,dilations:i,...a,cacheKey:""};return{...u,cacheKey:Ar(u)}},Rd=e=>{let t=e.format;return{format:t,...mi,cacheKey:t}},Ui=(e,t)=>{_r(e.inputs),e.compute(cs("GlobalMaxPool",e.inputs[0],!0,t))}}),pd,hd,fd,Iu,Dg=V(()=>{sr(),lr(),Tr(),mr(),pd=(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((i,a)=>i===e[2].dims[a]).reduce((i,a)=>i&&a,!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((u,p)=>p===t.axis||u===e[0].dims[p]).reduce((u,p)=>u&&p,!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 i=e[0].dims[t.axis],a=e[1].dims[t.axis];if(t.blockSizeMath.ceil(i/(a-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},hd=(e,t)=>{let i=tt.normalizeAxis(t.axis,e[0].dims.length),a=e[0].dataType,u=a===3,p=e[0].dims,d=e[1].dataType,_=tt.size(p),g=a===3||a===2,y=g?[Math.ceil(tt.size(e[0].dims)/4)]:e[0].dims,C=e[1].dims,k=e.length>2?e[2]:void 0,l=k?g?[Math.ceil(tt.size(k.dims)/4)]:k.dims:void 0,F=C.length===0||C.length===1&&C[0]===1,I=F===!1&&C.length===1,L=$r(_),Q=F&&(!g||L===4),Z=Q?L:1,U=Q&&!g?L:1,we=mt("input",g?12:a,y.length,U),te=mt("scale",d,C.length),me=k?mt("zero_point",g?12:a,l.length):void 0,it=Jt("output",d,p.length,Z),Ye=[we,te];me&&Ye.push(me);let Mt=[y,C];k&&Mt.push(l);let Gt=[{type:12,data:_/Z},{type:12,data:i},{type:12,data:t.blockSize},...Rt(...Mt,p)],Bt=gr=>{let Mr=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${gr.registerUniforms(Mr).declareVariables(...Ye,it)} + ${gr.mainStart()} + ${gr.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${it.offsetToIndices("global_idx")}; + + // Set input x + ${g?` + let input = ${we.getByOffset("global_idx / 4")}; + let x_vec = ${u?"unpack4xI8(input)":"unpack4xU8(input)"}; + let x_value = ${Z===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${we.getByOffset("global_idx")};`}; + + // Set scale input + ${F?`let scale_value= ${te.getByOffset("0")}`:I?` + let scale_index = ${it.indicesGet("output_indices","uniforms.axis")}; + let scale_value= ${te.getByOffset("scale_index")};`:` + var scale_indices: ${te.type.indices} = output_indices; + let index = ${te.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; + ${te.indicesSet("scale_indices","uniforms.axis","index")}; + let scale_value= ${te.getByIndices("scale_indices")};`}; + + // Set zero-point input + ${me?F?g?` + let zero_point_input = ${me.getByOffset("0")}; + let zero_point_vec = ${u?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${me.getByOffset("0")}`:I?g?` + let zero_point_index = ${it.indicesGet("output_indices","uniforms.axis")}; + let zero_point_input = ${me.getByOffset("zero_point_index / 4")}; + let zero_point_vec = ${u?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_index % 4]`:` + let zero_point_index = ${it.indicesGet("output_indices","uniforms.axis")}; + let zero_point_value = ${me.getByOffset("zero_point_index")};`:g?` + let zero_point_offset = ${te.indicesToOffset("scale_indices")}; + let zero_point_input = ${me.getByOffset("zero_point_offset / 4")}; + let zero_point_vec = ${u?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${me.getByIndices("scale_indices")};`:`let zero_point_value = ${g?u?"i32":"u32":we.type.value}(0);`}; + // Compute and write output + ${it.setByOffset("global_idx",`${it.type.value}(x_value - zero_point_value) * scale_value`)}; + }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:me?["rank","rank","rank"]:["rank","rank"]},getShaderSource:Bt,getRunData:()=>({outputs:[{dims:p,dataType:d}],dispatchGroup:{x:Math.ceil(_/Z/64),y:1,z:1},programUniforms:Gt})}},fd=(e,t)=>{pd(e.inputs,t),e.compute(hd(e.inputs,t))},Iu=e=>tr({axis:e.axis,blockSize:e.blockSize})}),Mc,xc,Tc,Bg=V(()=>{z(),sr(),mr(),Mc=(e,t,i)=>{let a=e===t,u=et&&i>0;if(a||u||p)throw new Error("Range these inputs' contents are invalid.")},xc=(e,t,i,a)=>{let u=Math.abs(Math.ceil((t-e)/i)),p=[u],d=u,_=[{type:12,data:d},{type:a,data:e},{type:a,data:i},...Rt(p)],g=y=>{let C=Jt("output",a,p.length),k=C.type.value,l=[{name:"outputSize",type:"u32"},{name:"start",type:k},{name:"delta",type:k}];return` + ${y.registerUniforms(l).declareVariables(C)} + ${y.mainStart()} + ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${k}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${a}`},getShaderSource:g,getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:_})}},Tc=e=>{let t=0,i=0,a=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],i=e.inputs[1].getInt32Array()[0],a=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],i=e.inputs[1].getFloat32Array()[0],a=e.inputs[2].getFloat32Array()[0]),j.webgpu.validateInputContent&&Mc(t,i,a),e.compute(xc(t,i,a,e.inputs[0].dataType),{inputs:[]})}}),Cc,kc,$c,Sc,Ec,Pc,Ac,Ic,Fc,zc,Oc,Nd,Dc,Bc,jc,Lc,Rc,Nc,Vc,jg=V(()=>{sr(),lr(),Tr(),mr(),Cc=(e,t)=>{if(e.every(i=>i>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and + one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},kc=(e,t,i)=>{t.every(u=>u>=0&&u{throw new Error("Resize requires axes input values to be positive and less than rank")}));let a=new Array(i).fill(1);return t.forEach((u,p)=>a[u]=e[p]),a},$c=(e,t,i,a,u,p)=>{let[d,_,g]=i>10?[1,2,3]:[-1,e.length>1?1:-1,-1],y=e[0].dims.length;if(d>0&&e.length>d&&e[d].dims.length>0)e[d].getFloat32Array().forEach(C=>p.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(_>0&&e.length>_&&e[_].dims.length>0){if(e[_].getFloat32Array().forEach(C=>a.push(C)),a.length!==0&&a.length!==y&&i>=18&&a.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");Cc(a,t),t.axes.length>0&&kc(a,t.axes,y).forEach((C,k)=>a[k]=C)}if(g>0&&e.length>g&&(e[g].getBigInt64Array().forEach(C=>u.push(Number(C))),u.length!==y||i>=18&&u.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(a.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(u.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 a<"u"&&typeof u<"u"&&a.length>0&&u.length>y)throw new Error("Resize requires only of scales or sizes to be specified")},Sc=(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`)}})()+"}",Ec=(e,t,i)=>`fn getNearestPixelFromOriginal(xOriginal: ${i}, isDownSample: bool) -> ${i} {`+(()=>{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`)}})()+"}",Pc=(e,t,i)=>{let a=new Array(i).fill(0).concat(new Array(i).fill(1)),u=e.length===0?a:e.slice();return t.length>0?(t.forEach((p,d)=>{a[p]=u[d],a[d+i]=u[t.length+d]}),a):u},Ac=(e,t,i,a)=>{let u=[];if(i.length>0)if(a.length>0){if(e.forEach(p=>u.push(p)),Math.max(...a)>e.length)throw new Error("axes is out of bound");a.forEach((p,d)=>u[p]=i[d])}else i.forEach(p=>u.push(p));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");u=e.map((p,d)=>Math.round(p*t[d]))}return u},Ic=(e,t,i)=>{let a=(()=>{switch(i.keepAspectRatioPolicy){case"not_larger":return i.axes.length>0?Math.min(...i.axes.map(p=>t[p]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return i.axes.length>0?Math.max(...i.axes.map(p=>t[p]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${i.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let u=e.slice();return i.axes.length>0?(i.axes.forEach(p=>t[p]=a),i.axes.forEach(p=>u[p]=Math.round(e[p]*t[p]))):(t.fill(a,0,t.length),u.forEach((p,d)=>u[d]=Math.round(p*t[d]))),u},Fc=(e,t,i,a,u)=>` + fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${i.length}> { + var original_indices: array<${e.type.value}, ${i.length}>; + for (var i:u32 = 0; i < ${i.length}; i++) { + var output_index = ${e.indicesGet("output_indices","i")}; + var scale = ${Kt("uniforms.scales","i",a)}; + var roi_low = ${Kt("uniforms.roi","i",u)}; + var roi_hi = ${Kt("uniforms.roi",`i + ${t.length}`,u)}; + if (scale == 1.0) { + original_indices[i] = ${e.type.value}(output_index); + } else { + var input_shape_i = ${Kt("uniforms.input_shape","i",t.length)}; + var output_shape_i = ${Kt("uniforms.output_shape","i",i.length)}; + original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + } + } + return original_indices; + }`,zc=(e,t,i,a,u,p,d)=>` + fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + for (var i:u32 = 0; i < ${a.length}; i++) { + var output_index = ${t.indicesGet("output_indices","i")}; + var input_index: u32; + var scale = ${Kt("uniforms.scales","i",u)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${Kt("uniforms.roi","i",p)}; + var roi_hi = ${Kt("uniforms.roi",`i + ${i.length}`,p)}; + var input_shape_i = ${Kt("uniforms.input_shape","i",i.length)}; + var output_shape_i = ${Kt("uniforms.output_shape","i",a.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${d} || (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; + }`,Oc=(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 >= ${Kt("uniforms.input_shape","i",t.length)}) { + return false; + } + } + return true; + }`,Nd=(e,t,i,a)=>e.rank>a?` + ${e.indicesSet("input_indices",t,"channel")}; + ${e.indicesSet("input_indices",i,"batch")}; +`:"",Dc=(e,t,i,a,u)=>{let[p,d,_,g]=i.length===2?[-1,0,1,-1]:[0,2,3,1],y=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${y} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",d,`max(0, min(row, ${i[d]} - 1))`)}; + ${e.indicesSet("input_indices",_,`max(0, min(col, ${i[_]} - 1))`)}; + ${Nd(e,g,p,2)} + return ${e.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${y} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${y} = originalIndices[${d}]; + var col:${y} = originalIndices[${_}]; + ${a?`if (row < 0 || row > (${i[d]} - 1) || col < 0 || col > (${i[_]} - 1)) { + return ${u}; + }`:""}; + row = max(0, min(row, ${i[d]} - 1)); + col = max(0, min(col, ${i[_]} - 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 = ${i.length>2?`u32(originalIndices[${g}])`:"0"}; + var batch: u32 = ${i.length>2?`u32(originalIndices[${p}])`:"0"}; + var x11: ${y} = getInputValue(batch, channel, row1, col1); + var x12: ${y} = getInputValue(batch, channel, row1, col2); + var x21: ${y} = getInputValue(batch, channel, row2, col1); + var x22: ${y} = getInputValue(batch, channel, row2, col2); + var dx1: ${y} = abs(row - ${y}(row1)); + var dx2: ${y} = abs(${y}(row2) - row); + var dy1: ${y} = abs(col - ${y}(col1)); + var dy2: ${y} = abs(${y}(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); + }`},Bc=(e,t,i,a,u,p,d,_,g,y)=>{let C=i.length===2,[k,l]=C?[0,1]:[2,3],F=e.type.value,I=L=>{let Q=L===k?"row":"col";return` + fn ${Q}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${F} { + var output_index = ${t.indicesGet("output_indices",L)}; + var originalIdx: ${F} = getOriginalCoordinateFromResizedCoordinate(output_index, ${u[L]}, + ${a[L]}, ${i[L]}, ${p[L]}, ${p[L]} + ${i.length}); + var fractOriginalIdx: ${F} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${_} && (originalIdx < 0 || originalIdx > (${i[L]} - 1))) { + return ${g}; + } + var data: array<${F}, 4> = array<${F}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${Q}: ${F} = originalIdx + ${F}(i); + if (${Q} < 0 || ${Q} >= ${i[L]}) { + ${y?`coefs[i + 1] = 0.0; + continue;`:_?`return ${g};`:`${Q} = max(0, min(${Q}, ${i[L]} - 1));`}; + } + var input_indices_copy: ${e.type.indices} = input_indices; + ${e.indicesSet("input_indices_copy",L,`u32(${Q})`)}; + data[i + 1] = ${L===k?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${I(k)}; + ${I(l)}; + fn getCubicInterpolationCoefs(s: ${F}) -> array<${F}, 4> { + var absS = abs(s); + var coeffs: array<${F}, 4> = array<${F}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${F} = 1.0 - absS; + var twoMinusAbsS: ${F} = 2.0 - absS; + var onePlusAbsS: ${F} = 1.0 + absS; + coeffs[0] = ((${d} * onePlusAbsS - 5 * ${d}) * onePlusAbsS + 8 * ${d}) * onePlusAbsS - 4 * ${d}; + coeffs[1] = ((${d} + 2) * absS - (${d} + 3)) * absS * absS + 1; + coeffs[2] = ((${d} + 2) * oneMinusAbsS - (${d} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${d} * twoMinusAbsS - 5 * ${d}) * twoMinusAbsS + 8 * ${d}) * twoMinusAbsS - 4 * ${d}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${F}, 4>, coefs: array<${F}, 4>) -> ${F} { + var coefsSum: ${F} = coefs[0] + coefs[1] + coefs[2] + coefs[3]; + return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum; + } + + fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${F} { + var input_indices: ${e.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},jc=(e,t,i,a,u)=>{let[p,d,_,g,y]=i.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",d,`max(0, min(depth, ${i[d]} - 1))`)}; + ${e.indicesSet("input_indices",_,`max(0, min(height, ${i[_]} - 1))`)}; + ${e.indicesSet("input_indices",g,`max(0, min(width, ${i[g]} - 1))`)}; + ${Nd(e,y,p,3)} + return ${e.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${C} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${C} = originalIndices[${d}]; + var height:${C} = originalIndices[${_}]; + var width:${C} = originalIndices[${g}]; + ${a?`if (depth < 0 || depth > (${i[d]} - 1) || height < 0 || height > (${i[_]} - 1) || width < 0 || (width > ${i[g]} - 1)) { + return ${u}; + }`:""}; + + depth = max(0, min(depth, ${i[d]} - 1)); + height = max(0, min(height, ${i[_]} - 1)); + width = max(0, min(width, ${i[g]} - 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 = ${i.length>3?`u32(originalIndices[${y}])`:"0"}; + var batch: u32 = ${i.length>3?`u32(originalIndices[${p}])`:"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); + }`},Lc=(e,t,i,a,u,p)=>{let d=e.dims,_=Pc(p,t.axes,d.length),g=Ac(d,a,u,t.axes),y=a.slice();a.length===0&&(y=d.map((U,we)=>U===0?1:g[we]/U),t.keepAspectRatioPolicy!=="stretch"&&(g=Ic(d,y,t)));let C=Jt("output",e.dataType,g.length),k=mt("input",e.dataType,d.length),l=tt.size(g),F=d.length===g.length&&d.every((U,we)=>U===g[we]),I=t.coordinateTransformMode==="tf_crop_and_resize",L=t.extrapolationValue,Q=k.type.value,Z=U=>` + ${F?"":` + ${Sc(t.coordinateTransformMode,Q)}; + ${(()=>{switch(t.mode){case"nearest":return` + ${Oc(k,d)}; + ${Ec(t.nearestMode,i,Q)}; + ${zc(k,C,d,g,y.length,_.length,I)}; + `;case"linear":return` + ${Fc(C,d,g,y.length,_.length)}; + ${(()=>{if(d.length===2||d.length===4)return`${Dc(k,C,d,I,L)}`;if(d.length===3||d.length===5)return`${jc(k,C,d,I,L)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(d.length===2||d.length===4)return`${Bc(k,C,d,g,y,_,t.cubicCoeffA,I,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")}})()}; + `} + ${U.registerUniform("output_size","u32").registerUniform("scales","f32",y.length).registerUniform("roi","f32",_.length).declareVariables(k,C)} + ${U.mainStart()} + ${U.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${F?"output[global_idx] = input[global_idx];":` + let output_indices = ${C.offsetToIndices("global_idx")}; + var input_indices: ${k.type.indices}; + ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${k.getByIndices("input_indices")}; + } else { + output[global_idx] = ${t.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${d.length===2||d.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}|${i}|${y.length>0?y:""}|${u.length>0?u:""}|${_.length>0?_:""}|${F}|${d}`,inputDependencies:["rank"]},getShaderSource:Z,getRunData:()=>({outputs:[{dims:g,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:[{type:12,data:l},{type:1,data:y},{type:1,data:_},...Rt(d,g)]})}},Rc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Nc=(e,t)=>{let i=[],a=[],u=[],p=Rc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");$c(e.inputs,t,p,i,a,u),e.compute(Lc(e.inputs[0],t,p,i,a,u),{inputs:[0]})},Vc=e=>{let t=e.antialias,i=e.axes,a=e.coordinateTransformMode,u=e.cubicCoeffA,p=e.excludeOutside!==0,d=e.extrapolationValue,_=e.keepAspectRatioPolicy,g=e.mode,y=e.nearestMode===""?"simple":e.nearestMode;return tr({antialias:t,axes:i,coordinateTransformMode:a,cubicCoeffA:u,excludeOutside:p,extrapolationValue:d,keepAspectRatioPolicy:_,mode:g,nearestMode:y})}}),Uc,Wc,Gc,Lg=V(()=>{sr(),lr(),Tr(),mr(),Uc=(e,t)=>{let[i,a,u,p]=e,{numHeads:d,rotaryEmbeddingDim:_}=t;if(i.dims.length!==3&&i.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${i.dims.length}`);if(!tt.areEqual(a.dims,[])&&!tt.areEqual(a.dims,[1])&&a.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${a.dims.length}`);if(u.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${u.dims.length}`);if(p.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${p.dims.length}`);if(!tt.areEqual(u.dims,p.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(_>0&&d===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let g=i.dims[0],y=i.dims[i.dims.length-2],C=u.dims[0],k=tt.sizeFromDimension(i.dims,1)/y,l=_===0?u.dims[1]*2:k/d;if(_>l)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(a.dims.length===2){if(g!==a.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${a.dims[0]}`);if(y!==a.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${a.dims[1]}`)}if(l/2!==u.dims[1]&&_/2!==u.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${u.dims[1]}`);if(y>C)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Wc=(e,t)=>{let{interleaved:i,numHeads:a,rotaryEmbeddingDim:u,scale:p}=t,d=e[0].dims[0],_=tt.sizeFromDimension(e[0].dims,1),g=e[0].dims[e[0].dims.length-2],y=_/g,C=e[2].dims[1],k=u===0?C*2:y/a,l=new Array(d,g,y/k,k-C),F=tt.computeStrides(l),I=[{type:1,data:p},{type:12,data:l},{type:12,data:F},...e[0].dims.length===3?new Array({type:12,data:[_,y,k,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[_,k,g*k,1]}):[],...Rt(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],L=Q=>{let Z=mt("input",e[0].dataType,e[0].dims.length),U=mt("position_ids",e[1].dataType,e[1].dims.length),we=mt("cos_cache",e[2].dataType,e[2].dims.length),te=mt("sin_cache",e[3].dataType,e[3].dims.length),me=Jt("output",e[0].dataType,e[0].dims.length);return Q.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:l.length},{name:"global_strides",type:"u32",length:F.length},{name:"input_output_strides",type:"u32",length:F.length}]),` + ${Q.declareVariables(Z,U,we,te,me)} + + ${Q.mainStart(ki)} + let half_rotary_emb_dim = uniforms.${we.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${Q.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${U.broadcastedIndicesToOffset("bsnh.xy",Jt("",U.type.tensor,2))}; + let position_id = + u32(${U.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${i}); + let j = i + select(half_rotary_emb_dim, 1, ${i}); + let re = ${Z.getByOffset("i")} * ${we.get("position_id","bsnh[3]")} - + ${Z.getByOffset("j")} * ${te.get("position_id","bsnh[3]")}; + ${me.setByOffset("i","re")} + let im = ${Z.getByOffset("i")} * ${te.get("position_id","bsnh[3]")} + + ${Z.getByOffset("j")} * ${we.get("position_id","bsnh[3]")}; + ${me.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${me.setByOffset("k",Z.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:tr({interleaved:i}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:L,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(tt.size(l)/ki)},programUniforms:I})}},Gc=(e,t)=>{Uc(e.inputs,t),e.compute(Wc(e.inputs,t))}}),qc,Kc,Hc,Rg=V(()=>{sr(),lr(),mr(),qc=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],i=e[1],a=e[2];if(t.dataType!==i.dataType||t.dataType!==a.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(i.dims.length!==3&&i.dims.length!==2)throw new Error("Skip must be 2D or 3D");let u=t.dims[t.dims.length-1],p=t.dims[t.dims.length-2];if(i.dims[i.dims.length-1]!==u)throw new Error("Skip must have the same hidden size as input");if(i.dims[i.dims.length-2]!==p)throw new Error("Skip must have the same sequence length as input");if(a.dims.length!==1)throw new Error("Gamma must be 1D");if(a.dims[a.dims.length-1]!==u)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let d=e[3];if(d.dims.length!==1)throw new Error("Beta must be 1D");if(d.dims[d.dims.length-1]!==u)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let d=e[4];if(d.dims.length!==1)throw new Error("Bias must be 1D");if(d.dims[d.dims.length-1]!==u)throw new Error("Bias must have the same hidden size as input")}},Kc=(e,t,i,a)=>{let u=t.simplified,p=e[0].dims,d=tt.size(p),_=p,g=d,y=p.slice(-1)[0],C=a?p.slice(0,-1).concat(1):[],k=!u&&e.length>3,l=e.length>4,F=a&&i>1,I=a&&i>2,L=i>3,Q=64,Z=$r(y),U=[{type:12,data:g},{type:12,data:Z},{type:12,data:y},{type:1,data:t.epsilon}],we=me=>{let it=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],Ye=[mt("x",e[0].dataType,e[0].dims,Z),mt("skip",e[1].dataType,e[1].dims,Z),mt("gamma",e[2].dataType,e[2].dims,Z)];k&&Ye.push(mt("beta",e[3].dataType,e[3].dims,Z)),l&&Ye.push(mt("bias",e[4].dataType,e[4].dims,Z)),Ye.push(Jt("output",e[0].dataType,_,Z)),F&&Ye.push(Jt("mean_output",1,C)),I&&Ye.push(Jt("inv_std_output",1,C)),L&&Ye.push(Jt("input_skip_bias_sum",e[0].dataType,_,Z));let Mt=zr(e[0].dataType),Gt=zr(1,Z);return` + + ${me.registerUniforms(it).declareVariables(...Ye)} + var sum_shared : array<${Gt}, ${Q}>; + var sum_squared_shared : array<${Gt}, ${Q}>; + + ${me.mainStart([Q,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${Q}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${Q}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${Q-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${l?"bias[offset1d + i]":Mt+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${L?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${Qr(Mt,Z,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${Q}; + 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 = ${$i("sum",Z)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${$i("square_sum",Z)} / f32(uniforms.hidden_size) ${u?"":"- mean * mean"} + uniforms.epsilon); + ${F?"mean_output[global_idx] = mean;":""} + ${I?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${u?"":`- ${Mt}(mean)`}) * + ${Mt}(inv_std_dev) * gamma[offset1d + i] + ${k?"+ beta[offset1d + i]":""}; + } + }`},te=[{dims:_,dataType:e[0].dataType}];return i>1&&te.push({dims:C,dataType:1}),i>2&&te.push({dims:C,dataType:1}),i>3&&te.push({dims:p,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${Z};${F};${I};${L}`,inputDependencies:e.map((me,it)=>"type")},getShaderSource:we,getRunData:()=>({outputs:te,dispatchGroup:{x:Math.ceil(g/y)},programUniforms:U})}},Hc=(e,t)=>{qc(e.inputs);let i=[0];e.outputCount>1&&i.push(-3),e.outputCount>2&&i.push(-3),e.outputCount>3&&i.push(3),e.compute(Kc(e.inputs,t,e.outputCount,!1),{outputs:i})}}),Xc,Fu,Qc,Vd,Yc,Zc,Jc,ep,Ng=V(()=>{sr(),lr(),Tr(),mr(),Xc=(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((i,a)=>{if(e[a+1].dataType!==6&&e[a+1].dataType!==7)throw new Error(`Input ${a} must be an array of int32 or int64`)})},Fu=(e,t)=>{let i=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(a=>i.push(Number(a)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(a=>i.push(Number(a)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return i},Qc=(e,t)=>{if(e.length>1){let i=Fu(e,1),a=Fu(e,2),u=Fu(e,3);return u.length===0&&(u=[...Array(e[0].dims.length).keys()]),tr({starts:i,ends:a,axes:u})}else return t},Vd=(e,t,i,a,u)=>{let p=e;return e<0&&(p+=i[a[t]]),u[t]<0?Math.max(0,Math.min(p,i[a[t]]-1)):Math.max(0,Math.min(p,i[a[t]]))},Yc=(e,t,i)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + var carry = 0u; + for (var i = ${i.length}; i >= 0; i--) { + let input_shape_i = ${Kt("uniforms.input_shape","i",i.length)}; + let steps_i = ${Kt("uniforms.steps","i",i.length)}; + let signs_i = ${Kt("uniforms.signs","i",i.length)}; + let starts_i = ${Kt("uniforms.starts","i",i.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; + }`,Zc=(e,t)=>{let i=e[0].dims,a=tt.size(i),u=t.axes.length>0?tt.normalizeAxes(t.axes,i.length):[...Array(i.length).keys()],p=Fu(e,4);p.forEach(Z=>Z!==0||(()=>{throw new Error("step cannot be 0")})),p.length===0&&(p=Array(u.length).fill(1));let d=t.starts.map((Z,U)=>Vd(Z,U,i,u,p)),_=t.ends.map((Z,U)=>Vd(Z,U,i,u,p));if(u.length!==d.length||u.length!==_.length)throw new Error("start, ends and axes should have the same number of elements");if(u.length!==i.length)for(let Z=0;ZMath.sign(Z));p.forEach((Z,U,we)=>{if(Z<0){let te=(_[U]-d[U])/Z,me=d[U],it=me+te*p[U];d[U]=it,_[U]=me,we[U]=-Z}});let y=i.slice(0);u.forEach((Z,U)=>{y[Z]=Math.ceil((_[Z]-d[Z])/p[Z])});let C={dims:y,dataType:e[0].dataType},k=Jt("output",e[0].dataType,y.length),l=mt("input",e[0].dataType,e[0].dims.length),F=tt.size(y),I=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:d.length},{name:"signs",type:"i32",length:g.length},{name:"steps",type:"u32",length:p.length}],L=[{type:12,data:F},{type:12,data:d},{type:6,data:g},{type:12,data:p},...Rt(e[0].dims,y)],Q=Z=>` + ${Z.registerUniforms(I).declareVariables(l,k)} + ${Yc(l,k,i)} + ${Z.mainStart()} + ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${k.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${k.setByOffset("global_idx",l.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${g.length}_${d.length}_${p.length}`,inputDependencies:["rank"]},getShaderSource:Q,getRunData:()=>({outputs:[C],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:L})}},Jc=(e,t)=>{Xc(e.inputs,t);let i=Qc(e.inputs,t);e.compute(Zc(e.inputs,i),{inputs:[0]})},ep=e=>{let t=e.starts,i=e.ends,a=e.axes;return tr({starts:t,ends:i,axes:a})}}),tp,rp,ip,np,Vg=V(()=>{sr(),lr(),Tr(),mr(),tp=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},rp=(e,t)=>{let i=e.dims,a=tt.size(i),u=64,p=t.axis;if(p<0&&(p=i.length+p),pZ===4?`max(max(${Q}.x, ${Q}.y), max(${Q}.z, ${Q}.w))`:Z===2?`max(${Q}.x, ${Q}.y)`:Z===3?`max(max(${Q}.x, ${Q}.y), ${Q}.z)`:Q,k=mt("x",e.dataType,e.dims,g),l=Jt("result",e.dataType,e.dims,g),F=k.type.value,I=zr(e.dataType)==="f32"?`var threadMax = ${F}(-3.402823e+38f);`:`var threadMax = ${F}(-65504.0h);`,L=Q=>` + var rowMaxShared : ${F}; + var rowSumShared : ${F}; + var threadShared : array<${F}, ${u}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${F} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${F}) { + let index = row * row_stride + col; + result[index] = value; + } + ${Q.registerUniform("packedCols","i32").declareVariables(k,l)} + ${Q.mainStart()} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${u}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${I} + 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 = ${F}(${C("threadShared[0]",g)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${F}(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 = ${F}(${$i("threadShared[0]",g)}); + } + workgroupBarrier(); + + // calculate final value for each element in the row + for (var col = lindex; col < cols; col += wg) { + let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared; + setValue(row, col, row_stride, value); + } + }`;return{name:"Softmax",shaderCache:{hint:`${g}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:_},programUniforms:[{type:6,data:y}]}),getShaderSource:L}},ip=(e,t)=>{tp(e.inputs),e.compute(rp(e.inputs[0],t))},np=e=>tr({axis:e.axis})}),sp,ap,op,lp,up,dp,cp,Ug=V(()=>{sr(),lr(),Tr(),mr(),sp=e=>{if(!e||e.length<1)throw new Error("too few inputs")},ap=(e,t)=>{let i=[],a=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(u=>i.push(Number(u))),a=i.length),tr({numOutputs:a,axis:t.axis,splitSizes:i})},op=e=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${e}u; i += 1u ) { + if (index < ${Kt("uniforms.size_in_split_axis","i",e)}) { + return i; + } + } + return ${e}u; +}`,lp=e=>{let t=e.length,i=[];for(let a=0;a{let i=e[0].dims,a=tt.size(i),u=e[0].dataType,p=tt.normalizeAxis(t.axis,i.length),d=new Array(t.numOutputs),_=mt("input",u,i.length),g=new Array(t.numOutputs),y=[],C=[],k=0,l=[{type:12,data:a}];for(let I=0;I` + ${I.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",g.length).declareVariables(_,...d)} + ${op(g.length)} + ${lp(d)} + + ${I.mainStart()} + ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${_.offsetToIndices("global_idx")}; + var index = ${_.indicesGet("indices",p)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${Kt("uniforms.size_in_split_axis","output_number - 1u",g.length)}; + ${_.indicesSet("indices",p,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:F,getRunData:()=>({outputs:y,dispatchGroup:{x:Math.ceil(a/64)},programUniforms:l})}},dp=(e,t)=>{sp(e.inputs);let i=e.inputs.length===1?t:ap(e.inputs,t);e.compute(up(e.inputs,i),{inputs:[0]})},cp=e=>{let t=e.axis,i=e.splitSizes,a=e.numOutputs<0?i.length:e.numOutputs;if(a!==i.length)throw new Error("numOutputs and splitSizes lengh must be equal");return tr({axis:t,numOutputs:a,splitSizes:i})}}),pp,hp,fp,Wg=V(()=>{sr(),lr(),mr(),pp=(e,t,i,a,u)=>{let p=Jt("output_data",u,i.length,4),d=mt("a_data",t[1].dataType,t[1].dims.length,4),_=mt("b_data",t[2].dataType,t[2].dims.length,4),g=mt("c_data",t[0].dataType,t[0].dims.length,4),y,C=(k,l,F)=>`select(${l}, ${k}, ${F})`;if(!a)y=p.setByOffset("global_idx",C(d.getByOffset("global_idx"),_.getByOffset("global_idx"),g.getByOffset("global_idx")));else{let k=(l,F,I="")=>{let L=`a_data[index_a${F}][component_a${F}]`,Q=`b_data[index_b${F}][component_b${F}]`,Z=`bool(c_data[index_c${F}] & (0xffu << (component_c${F} * 8)))`;return` + let output_indices${F} = ${p.offsetToIndices(`global_idx * 4u + ${F}u`)}; + let offset_a${F} = ${d.broadcastedIndicesToOffset(`output_indices${F}`,p)}; + let offset_b${F} = ${_.broadcastedIndicesToOffset(`output_indices${F}`,p)}; + let offset_c${F} = ${g.broadcastedIndicesToOffset(`output_indices${F}`,p)}; + let index_a${F} = offset_a${F} / 4u; + let index_b${F} = offset_b${F} / 4u; + let index_c${F} = offset_c${F} / 4u; + let component_a${F} = offset_a${F} % 4u; + let component_b${F} = offset_b${F} % 4u; + let component_c${F} = offset_c${F} % 4u; + ${l}[${F}] = ${I}(${C(L,Q,Z)}); + `};u===9?y=` + var data = vec4(0); + ${k("data",0,"u32")} + ${k("data",1,"u32")} + ${k("data",2,"u32")} + ${k("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:y=` + ${k("output_data[global_idx]",0)} + ${k("output_data[global_idx]",1)} + ${k("output_data[global_idx]",2)} + ${k("output_data[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(g,d,_,p)} + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${y} + }`},hp=e=>{let t=e[1].dims,i=e[2].dims,a=e[0].dims,u=e[1].dataType,p=!(tt.areEqual(t,i)&&tt.areEqual(i,a)),d=t,_=tt.size(t);if(p){let y=ui.calcShape(ui.calcShape(t,i,!1),a,!1);if(!y)throw new Error("Can't perform where op on the given tensors");d=y,_=tt.size(d)}let g=Math.ceil(_/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:y=>pp(y,e,d,p,u),getRunData:()=>({outputs:[{dims:d,dataType:u}],dispatchGroup:{x:Math.ceil(_/64/4)},programUniforms:[{type:12,data:g},...Rt(a,t,i,d)]})}},fp=e=>{e.compute(hp(e.inputs))}}),mp,Gg=V(()=>{Uu(),Jn(),So(),Wu(),ol(),Gu(),qu(),Bl(),Bd(),Zu(),Ju(),id(),jd(),sd(),ad(),od(),pu(),ud(),Ld(),hi(),ca(),cd(),gu(),yr(),Nn(),Dg(),Bg(),Os(),jg(),Lg(),Rg(),Ng(),Vg(),Ug(),vu(),wn(),Ys(),Wg(),mp=new Map([["Abs",[Io]],["Acos",[Fo]],["Acosh",[js]],["Add",[dl]],["ArgMax",[wo,Zn]],["ArgMin",[go,Zn]],["Asin",[zo]],["Asinh",[Oo]],["Atan",[Ls]],["Atanh",[Do]],["Attention",[xo]],["AveragePool",[Yi,Mi]],["BatchNormalization",[$o]],["BiasAdd",[Bs]],["BiasSplitGelu",[al]],["Cast",[es,Bo]],["Ceil",[Ro]],["Clip",[Lo]],["Concat",[tn,vl]],["Conv",[Dn,ha]],["ConvTranspose",[Yu,Vl]],["Cos",[Rs]],["Cosh",[No]],["CumSum",[va,Gl]],["DepthToSpace",[xa,Hl]],["DequantizeLinear",[fd,Iu]],["Div",[cl]],["Einsum",[Ql,Yl]],["Elu",[Vo,bn]],["Equal",[Js]],["Erf",[Uo]],["Exp",[Ns]],["Expand",[ka]],["FastGelu",[eu]],["Floor",[Wo]],["FusedConv",[Dn,ha]],["Gather",[nu,iu]],["GatherElements",[lu,ou]],["Gelu",[Go]],["Gemm",[ld,cu]],["GlobalAveragePool",[_i,si]],["GlobalMaxPool",[Ui,Rd]],["Greater",[ml]],["GreaterOrEqual",[gl]],["GroupQueryAttention",[Cu,xu]],["HardSigmoid",[Ws,Qo]],["InstanceNormalization",[Eu]],["LayerNormalization",[oi]],["LeakyRelu",[qo,bn]],["Less",[_l]],["LessOrEqual",[ea]],["Log",[Qs]],["MatMul",[zl]],["MatMulNBits",[dd,Au]],["MaxPool",[ps,Ia]],["Mul",[pl]],["MultiHeadAttention",[_u,fu]],["Neg",[Ko]],["Not",[Vs]],["Pad",[er]],["Pow",[hl]],["QuickGelu",[nl,bn]],["Range",[Tc]],["Reciprocal",[Ho]],["ReduceMin",[Fs]],["ReduceMean",[uo]],["ReduceMax",[ho]],["ReduceSum",[mo]],["ReduceProd",[fo]],["ReduceL1",[co]],["ReduceL2",[Is]],["ReduceLogSum",[_o]],["ReduceLogSumExp",[po]],["ReduceSumSquare",[zs]],["Relu",[Us]],["Resize",[Nc,Vc]],["RotaryEmbedding",[Gc]],["Sigmoid",[Xo]],["Sin",[Yo]],["Sinh",[Zo]],["Slice",[Jc,ep]],["SkipLayerNormalization",[Hc]],["Split",[dp,cp]],["Sqrt",[Gs]],["Softmax",[ip,np]],["Sub",[fl]],["Tan",[Jo]],["Tanh",[Ks]],["ThresholdedRelu",[tl,bn]],["Tile",[bu]],["Transpose",[La,xs]],["Where",[fp]]])}),_p,qg=V(()=>{z(),Ci(),mr(),_p=class{constructor(e){this.backend=e,this.repo=new 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g=i.createComputePipeline({compute:{module:_,entryPoint:"main"},layout:"auto",label:e.name});return et(e.name),{programInfo:e,computePipeline:g,uniformVariablesInfo:u.variablesInfo}}normalizeDispatchGroupSize(e){let t=typeof e=="number"?e:e.x,i=typeof e=="number"?1:e.y||1,a=typeof e=="number"?1:e.z||1,u=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(t<=u&&i<=u&&a<=u)return[t,i,a];let p=t*i*a,d=Math.ceil(Math.sqrt(p));if(d>u){if(d=Math.ceil(Math.cbrt(p)),d>u)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[d,d,d]}else return[d,d,1]}}}),gp,wp,yp,bp,Kg=V(()=>{z(),sr(),Ci(),x(),jr(),Gg(),qg(),gp=(e,t)=>{if(t.length!==e.length)throw new Error(`inputDependencies length ${t.length} is not equal to inputTensors length ${e.length}.`);let i=[];for(let a=0;a{var u,p;let a=e.name;return(u=e.shaderCache)!=null&&u.hint&&(a+="["+e.shaderCache.hint+"]"),a+=":"+i+`:${gp(t,((p=e.shaderCache)==null?void 0:p.inputDependencies)??new 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U={kernelId:this.currentKernelId,programName:Z.programInfo.name,inputTensorViews:t,outputTensorViews:k};this.pendingKernels.push(U),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(U)}return this.programManager.run(Z,d,l,I,F),et(e.name),k}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,i,a){let u=mp.get(e);if(!u)throw new Error(`kernel not implemented: ${e}`);let p={kernelType:e,kernelName:a,kernelEntry:u[0],attributes:[u[1],i]};this.kernels.set(t,p)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let i of t)this.gpuDataManager.release(i.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,i){let a=this.kernels.get(e);if(!a)throw new Error(`kernel not 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self<"u"&&!self.crossOriginIsolated)j.wasm.numThreads=1;else{let e=typeof navigator>"u"?Oe("node:os").cpus().length:navigator.hardwareConcurrency;j.wasm.numThreads=Math.min(4,Math.ceil((e||1)/2))}},Lp=class{async init(e){jp(),await Sp(),await Ep(e)}async createInferenceSessionHandler(e,t){let i=new Bp;return await i.loadModel(e,t),Promise.resolve(i)}}}),Rp={};A(Rp,{wasmBackend:()=>Np});var Np,Yg=V(()=>{Qg(),Np=new Lp});z(),z(),z();var Zg="1.20.0",Jg=Ne;{let e=(Yg(),B(Rp)).wasmBackend;pe("webgpu",e,5),pe("webnn",e,5),pe("cpu",e,10),pe("wasm",e,10)}Object.defineProperty(j.versions,"web",{value:Zg,enumerable:!0});/** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + *//** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */},"./src/backends/onnx.js":(Ft,Te,m)=>{var P;m.r(Te),m.d(Te,{Tensor:()=>Oe.Tensor,createInferenceSession:()=>fe,deviceToExecutionProviders:()=>de,isONNXProxy:()=>he,isONNXTensor:()=>q});var re=m("./src/env.js"),ke=m("?2ce3"),ze=m("./node_modules/onnxruntime-web/dist/ort.webgpu.bundle.min.mjs"),Oe=m("./node_modules/onnxruntime-common/dist/esm/index.js");const V=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"}}),A=[];let H,B;if(re.apis.IS_NODE_ENV){switch(B=ke??(P||(P=m.t(ke,2))),process.platform){case"win32":A.push("dml");break;case"linux":process.arch==="x64"&&A.push("cuda");break}A.push("cpu"),H=["cpu"]}else B=ze,re.apis.IS_WEBNN_AVAILABLE&&A.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),re.apis.IS_WEBGPU_AVAILABLE&&A.push("webgpu"),A.push("wasm"),H=["wasm"];const le=B.InferenceSession;function de(ye=null){if(!ye)return H;switch(ye){case"auto":return A;case"gpu":return A.filter(ge=>["webgpu","cuda","dml","webnn-gpu"].includes(ge))}if(A.includes(ye))return[V[ye]??ye];throw new Error(`Unsupported device: "${ye}". Should be one of: ${A.join(", ")}.`)}let pe=null;async function fe(ye,ge){pe&&await pe;const K=le.create(ye,ge);return pe??(pe=K),await K}function q(ye){return ye instanceof B.Tensor}const ae=B==null?void 0:B.env;ae!=null&&ae.wasm&&(ae.wasm.wasmPaths=`https://cdn.jsdelivr.net/npm/@huggingface/transformers@${re.env.version}/dist/`,ae.wasm.proxy=!re.apis.IS_WEBWORKER_ENV,(typeof crossOriginIsolated>"u"||!crossOriginIsolated)&&(ae.wasm.numThreads=1),typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(ae.wasm.simd=!1)),ae!=null&&ae.webgpu&&(ae.webgpu.powerPreference="high-performance");function he(){var ye;return(ye=ae==null?void 0:ae.wasm)==null?void 0:ye.proxy}re.env.backends.onnx=ae},"./src/configs.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{AutoConfig:()=>A,PretrainedConfig:()=>V,getKeyValueShapes:()=>Oe});var P=m("./src/utils/core.js"),re=m("./src/utils/hub.js");async function ke(H,B){return await(0,re.getModelJSON)(H,"config.json",!0,B)}function ze(H){const B={};let le={};switch(H.model_type){case"llava":case"paligemma":case"florence2":le=ze(H.text_config);break;case"moondream1":le=ze(H.phi_config);break;case"musicgen":le=ze(H.decoder);break;case"gpt2":case"gptj":case"codegen":case"gpt_bigcode":B.num_heads="n_head",B.num_layers="n_layer",B.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"phi":case"phi3":case"falcon":B.num_heads="num_attention_heads",B.num_layers="num_hidden_layers",B.hidden_size="hidden_size";break;case"llama":case"cohere":case"mistral":case"starcoder2":case"qwen2":B.num_heads="num_key_value_heads",B.num_layers="num_hidden_layers",B.hidden_size="hidden_size",B.num_attention_heads="num_attention_heads";break;case"gemma":case"gemma2":B.num_heads="num_key_value_heads",B.num_layers="num_hidden_layers",B.dim_kv="head_dim";break;case"openelm":B.num_heads="num_kv_heads",B.num_layers="num_transformer_layers",B.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":B.num_heads="num_heads",B.num_layers="num_layers",B.hidden_size="hidden_size";break;case"bloom":B.num_heads="n_head",B.num_layers="n_layer",B.hidden_size="hidden_size";break;case"mpt":B.num_heads="n_heads",B.num_layers="n_layers",B.hidden_size="d_model";break;case"t5":case"mt5":case"longt5":B.num_decoder_layers="num_decoder_layers",B.num_decoder_heads="num_heads",B.decoder_dim_kv="d_kv",B.num_encoder_layers="num_layers",B.num_encoder_heads="num_heads",B.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":B.num_decoder_layers="decoder_layers",B.num_decoder_heads="decoder_attention_heads",B.decoder_hidden_size="d_model",B.num_encoder_layers="encoder_layers",B.num_encoder_heads="encoder_attention_heads",B.encoder_hidden_size="d_model";break;case"speecht5":B.num_decoder_layers="decoder_layers",B.num_decoder_heads="decoder_attention_heads",B.decoder_hidden_size="hidden_size",B.num_encoder_layers="encoder_layers",B.num_encoder_heads="encoder_attention_heads",B.encoder_hidden_size="hidden_size";break;case"trocr":B.num_encoder_layers=B.num_decoder_layers="decoder_layers",B.num_encoder_heads=B.num_decoder_heads="decoder_attention_heads",B.encoder_hidden_size=B.decoder_hidden_size="d_model";break;case"musicgen_decoder":B.num_encoder_layers=B.num_decoder_layers="num_hidden_layers",B.num_encoder_heads=B.num_decoder_heads="num_attention_heads",B.encoder_hidden_size=B.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const pe=ze(H.decoder),fe="num_decoder_layers"in pe,q=(0,P.pick)(H,["model_type","is_encoder_decoder"]);return fe?(q.num_decoder_layers=pe.num_decoder_layers,q.num_decoder_heads=pe.num_decoder_heads,q.decoder_hidden_size=pe.decoder_hidden_size,q.num_encoder_layers=pe.num_encoder_layers,q.num_encoder_heads=pe.num_encoder_heads,q.encoder_hidden_size=pe.encoder_hidden_size):(q.num_layers=pe.num_layers,q.num_heads=pe.num_heads,q.hidden_size=pe.hidden_size),q}const de={...le,...(0,P.pick)(H,["model_type","multi_query","is_encoder_decoder"])};for(const pe in B)de[pe]=H[B[pe]];return de}function Oe(H,{prefix:B="past_key_values"}={}){const le={},de=H.normalized_config,pe=1;if(de.is_encoder_decoder&&"num_encoder_heads"in de&&"num_decoder_heads"in de){const fe=de.encoder_dim_kv??de.encoder_hidden_size/de.num_encoder_heads,q=de.decoder_dim_kv??de.decoder_hidden_size/de.num_decoder_heads,ae=[pe,de.num_encoder_heads,0,fe],he=[pe,de.num_decoder_heads,0,q];for(let ye=0;ye{var j;m.r(Te),m.d(Te,{apis:()=>q,env:()=>R});var P=m("?569f"),re=m("?3f59"),ke=m("?154a");const ze="3.0.0-alpha.6",Oe=typeof self<"u",V=Oe&&self.constructor.name==="DedicatedWorkerGlobalScope",A=Oe&&"caches"in self,H=typeof navigator<"u"&&"gpu"in navigator,B=typeof navigator<"u"&&"ml"in navigator,le=typeof process<"u",de=le&&((j=process==null?void 0:process.release)==null?void 0:j.name)==="node",pe=!W(P),fe=!W(re),q=Object.freeze({IS_BROWSER_ENV:Oe,IS_WEBWORKER_ENV:V,IS_WEB_CACHE_AVAILABLE:A,IS_WEBGPU_AVAILABLE:H,IS_WEBNN_AVAILABLE:B,IS_PROCESS_AVAILABLE:le,IS_NODE_ENV:de,IS_FS_AVAILABLE:pe,IS_PATH_AVAILABLE:fe}),ae=pe&&fe,he=ae?re.dirname(re.dirname(ke.fileURLToPath(self.location.href))):"./",ye=ae?re.join(he,"/.cache/"):null,ge="/models/",K=ae?re.join(he,ge):ge,R={version:ze,backends:{onnx:{},tfjs:{}},allowRemoteModels:!0,remoteHost:"https://huggingface.co./",remotePathTemplate:"{model}/resolve/{revision}/",allowLocalModels:!Oe,localModelPath:K,useFS:pe,useBrowserCache:A,useFSCache:pe,cacheDir:ye,useCustomCache:!1,customCache:null};function W(Ce){return Object.keys(Ce).length===0}},"./src/generation/configuration_utils.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{GenerationConfig:()=>re});var P=m("./src/utils/core.js");class re{constructor(ze){De(this,"max_length",20);De(this,"max_new_tokens",null);De(this,"min_length",0);De(this,"min_new_tokens",null);De(this,"early_stopping",!1);De(this,"max_time",null);De(this,"do_sample",!1);De(this,"num_beams",1);De(this,"num_beam_groups",1);De(this,"penalty_alpha",null);De(this,"use_cache",!0);De(this,"temperature",1);De(this,"top_k",50);De(this,"top_p",1);De(this,"typical_p",1);De(this,"epsilon_cutoff",0);De(this,"eta_cutoff",0);De(this,"diversity_penalty",0);De(this,"repetition_penalty",1);De(this,"encoder_repetition_penalty",1);De(this,"length_penalty",1);De(this,"no_repeat_ngram_size",0);De(this,"bad_words_ids",null);De(this,"force_words_ids",null);De(this,"renormalize_logits",!1);De(this,"constraints",null);De(this,"forced_bos_token_id",null);De(this,"forced_eos_token_id",null);De(this,"remove_invalid_values",!1);De(this,"exponential_decay_length_penalty",null);De(this,"suppress_tokens",null);De(this,"begin_suppress_tokens",null);De(this,"forced_decoder_ids",null);De(this,"guidance_scale",null);De(this,"num_return_sequences",1);De(this,"output_attentions",!1);De(this,"output_hidden_states",!1);De(this,"output_scores",!1);De(this,"return_dict_in_generate",!1);De(this,"pad_token_id",null);De(this,"bos_token_id",null);De(this,"eos_token_id",null);De(this,"encoder_no_repeat_ngram_size",0);De(this,"decoder_start_token_id",null);De(this,"generation_kwargs",{});Object.assign(this,(0,P.pick)(ze,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{ClassifierFreeGuidanceLogitsProcessor:()=>ae,ForcedBOSTokenLogitsProcessor:()=>V,ForcedEOSTokenLogitsProcessor:()=>A,LogitsProcessor:()=>ke,LogitsProcessorList:()=>Oe,LogitsWarper:()=>ze,MinLengthLogitsProcessor:()=>pe,MinNewTokensLengthLogitsProcessor:()=>fe,NoBadWordsLogitsProcessor:()=>q,NoRepeatNGramLogitsProcessor:()=>le,RepetitionPenaltyLogitsProcessor:()=>de,SuppressTokensAtBeginLogitsProcessor:()=>H,TemperatureLogitsWarper:()=>he,TopKLogitsWarper:()=>ge,TopPLogitsWarper:()=>ye,WhisperTimeStampLogitsProcessor:()=>B});var P=m("./src/utils/generic.js");m("./src/utils/tensor.js");var re=m("./src/utils/maths.js");class ke extends P.Callable{_call(R,W){throw Error("`_call` should be implemented in a subclass")}}class ze extends P.Callable{_call(R,W){throw Error("`_call` should be implemented in a subclass")}}class Oe extends P.Callable{constructor(){super(),this.processors=[]}push(R){this.processors.push(R)}extend(R){this.processors.push(...R)}_call(R,W){let j=W;for(const Ce of this.processors)j=Ce(R,j);return j}[Symbol.iterator](){return this.processors.values()}}class V extends ke{constructor(R){super(),this.bos_token_id=R}_call(R,W){for(let j=0;j=1&&Be[Be.length-1]>=this.timestamp_begin,Ge=Be.length<2||Be[Be.length-2]>=this.timestamp_begin;if(Ve&&(Ge?Se.subarray(this.timestamp_begin).fill(-1/0):Se.subarray(0,this.eos_token_id).fill(-1/0)),R[j].length===this.begin_index&&this.max_initial_timestamp_index!==null){const $e=this.timestamp_begin+this.max_initial_timestamp_index;Se.subarray($e+1).fill(-1/0)}const pt=(0,re.log_softmax)(Se),ot=Math.log(pt.subarray(this.timestamp_begin).map(Math.exp).reduce(($e,X)=>$e+X)),Tt=(0,re.max)(pt.subarray(0,this.timestamp_begin))[0];ot>Tt&&Se.subarray(0,this.timestamp_begin).fill(-1/0)}return W}}class le extends ke{constructor(R){super(),this.no_repeat_ngram_size=R}getNgrams(R){const W=R.length,j=[];for(let Se=0;Se1 to use the classifier free guidance processor, got guidance scale ${R}.`);this.guidance_scale=R}_call(R,W){if(W.dims[0]!==2*R.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${W.dims[0]} for the logits and ${R.length} for the input ids.`);const j=R.length,Ce=W.slice([0,j],null),Se=W.slice([j,W.dims[0]],null);for(let Be=0;Be1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${R}`);if(!Number.isInteger(j)||j<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${j}`);this.top_p=R,this.filter_value=W,this.min_tokens_to_keep=j}}class ge extends ze{constructor(R,{filter_value:W=-1/0,min_tokens_to_keep:j=1}={}){if(super(),!Number.isInteger(R)||R<0)throw new Error(`\`top_k\` must be a positive integer, but is ${R}`);this.top_k=Math.max(R,j),this.filter_value=W}}},"./src/generation/logits_sampler.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{LogitsSampler:()=>ze});var P=m("./src/utils/generic.js"),re=m("./src/utils/tensor.js"),ke=m("./src/utils/maths.js");m("./src/generation/configuration_utils.js");class ze extends P.Callable{constructor(B){super(),this.generation_config=B}async _call(B){return this.sample(B)}async sample(B){throw Error("sample should be implemented in subclasses.")}getLogits(B,le){let de=B.dims.at(-1),pe=B.data;if(le===-1)pe=pe.slice(-de);else{let fe=le*de;pe=pe.slice(fe,fe+de)}return pe}randomSelect(B){let le=0;for(let pe=0;pe1)return new A(B);if(B.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${B.num_return_sequences}.`);return new Oe(B)}}class Oe extends ze{async sample(B){const le=(0,ke.max)(B.data)[1];return[[BigInt(le),0]]}}class V extends ze{async sample(B){let le=B.dims.at(-1);this.generation_config.top_k>0&&(le=Math.min(this.generation_config.top_k,le));const[de,pe]=await(0,re.topk)(B,le),fe=(0,ke.softmax)(de.data);return Array.from({length:this.generation_config.num_beams},()=>{const q=this.randomSelect(fe);return[pe.data[q],Math.log(fe[q])]})}}class A extends ze{async sample(B){let le=B.dims.at(-1);this.generation_config.top_k>0&&(le=Math.min(this.generation_config.top_k,le));const[de,pe]=await(0,re.topk)(B,le),fe=(0,ke.softmax)(de.data);return Array.from({length:this.generation_config.num_beams},(q,ae)=>[pe.data[ae],Math.log(fe[ae])])}}},"./src/generation/stopping_criteria.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{EosTokenCriteria:()=>Oe,InterruptableStoppingCriteria:()=>V,MaxLengthCriteria:()=>ze,StoppingCriteria:()=>re,StoppingCriteriaList:()=>ke});var P=m("./src/utils/generic.js");class re extends P.Callable{_call(H,B){throw Error("StoppingCriteria needs to be subclassed")}}class ke extends P.Callable{constructor(){super(),this.criteria=[]}push(H){this.criteria.push(H)}extend(H){H instanceof ke?H=H.criteria:H instanceof re&&(H=[H]),this.criteria.push(...H)}_call(H,B){const le=new Array(H.length).fill(!1);for(const de of this.criteria){const pe=de(H,B);for(let fe=0;feB.length>=this.max_length)}}class Oe extends re{constructor(H){super(),Array.isArray(H)||(H=[H]),this.eos_token_id=H}_call(H,B){return H.map(le=>{const de=le.at(-1);return this.eos_token_id.some(pe=>de==pe)})}}class V extends re{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(H,B){return new Array(H.length).fill(this.interrupted)}}},"./src/generation/streamers.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{BaseStreamer:()=>ze,TextStreamer:()=>V,WhisperTextStreamer:()=>A});var P=m("./src/utils/core.js"),re=m("./src/tokenizers.js"),ke=m("./src/env.js");class ze{put(B){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const Oe=ke.apis.IS_PROCESS_AVAILABLE?H=>process.stdout.write(H):H=>console.log(H);class V extends ze{constructor(B,{skip_prompt:le=!1,callback_function:de=null,token_callback_function:pe=null,decode_kwargs:fe={},...q}={}){super(),this.tokenizer=B,this.skip_prompt=le,this.callback_function=de??Oe,this.token_callback_function=pe,this.decode_kwargs={...fe,...q},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(B){var fe;if(B.length>1)throw Error("TextStreamer only supports batch size of 1");if(this.skip_prompt&&this.next_tokens_are_prompt){this.next_tokens_are_prompt=!1;return}const le=B[0];(fe=this.token_callback_function)==null||fe.call(this,le),this.token_cache=(0,P.mergeArrays)(this.token_cache,le);const de=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let pe;de.endsWith(` +`)?(pe=de.slice(this.print_len),this.token_cache=[],this.print_len=0):de.length>0&&(0,re.is_chinese_char)(de.charCodeAt(de.length-1))?(pe=de.slice(this.print_len),this.print_len+=pe.length):(pe=de.slice(this.print_len,de.lastIndexOf(" ")+1),this.print_len+=pe.length),this.on_finalized_text(pe,!1)}end(){let B;this.token_cache.length>0?(B=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):B="",this.next_tokens_are_prompt=!0,this.on_finalized_text(B,!0)}on_finalized_text(B,le){var de,pe;B.length>0&&((de=this.callback_function)==null||de.call(this,B)),le&&this.callback_function===Oe&&ke.apis.IS_PROCESS_AVAILABLE&&((pe=this.callback_function)==null||pe.call(this,` +`))}}class A extends V{constructor(B,{skip_prompt:le=!1,callback_function:de=null,token_callback_function:pe=null,on_chunk_start:fe=null,on_chunk_end:q=null,on_finalize:ae=null,time_precision:he=.02,skip_special_tokens:ye=!0,decode_kwargs:ge={}}={}){super(B,{skip_prompt:le,callback_function:de,token_callback_function:pe,decode_kwargs:{skip_special_tokens:ye,...ge}}),this.timestamp_begin=B.timestamp_begin,this.on_chunk_start=fe,this.on_chunk_end=q,this.on_finalize=ae,this.time_precision=he,this.waiting_for_timestamp=!1}put(B){var de,pe;if(B.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const le=B[0];if(le.length===1){const fe=Number(le[0])-this.timestamp_begin;if(fe>=0){const q=fe*this.time_precision;this.waiting_for_timestamp?(de=this.on_chunk_end)==null||de.call(this,q):(pe=this.on_chunk_start)==null||pe.call(this,q),this.waiting_for_timestamp=!this.waiting_for_timestamp,B=[[]]}}return super.put(B)}end(){var 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OCRPreTrainedModel:()=>Fl,UniSpeechForCTC:()=>Ml,UniSpeechForSequenceClassification:()=>xl,UniSpeechModel:()=>na,UniSpeechPreTrainedModel:()=>vn,UniSpeechSatForAudioFrameClassification:()=>is,UniSpeechSatForCTC:()=>sa,UniSpeechSatForSequenceClassification:()=>Tl,UniSpeechSatModel:()=>rs,UniSpeechSatPreTrainedModel:()=>On,ViTForImageClassification:()=>xo,ViTModel:()=>Mo,ViTPreTrainedModel:()=>yn,VisionEncoderDecoderModel:()=>vs,VitMatteForImageMatting:()=>$o,VitMattePreTrainedModel:()=>ko,VitsModel:()=>ma,VitsModelOutput:()=>cd,VitsPreTrainedModel:()=>Qu,Wav2Vec2BertForCTC:()=>ss,Wav2Vec2BertForSequenceClassification:()=>Cl,Wav2Vec2BertModel:()=>aa,Wav2Vec2BertPreTrainedModel:()=>ns,Wav2Vec2ForAudioFrameClassification:()=>nn,Wav2Vec2ForCTC:()=>qu,Wav2Vec2ForSequenceClassification:()=>rn,Wav2Vec2Model:()=>vl,Wav2Vec2PreTrainedModel:()=>tn,WavLMForAudioFrameClassification:()=>Pl,WavLMForCTC:()=>la,WavLMForSequenceClassification:()=>Sl,WavLMForXVector:()=>El,WavLMModel:()=>$l,WavLMPreTrainedModel:()=>Qi,WeSpeakerResNetModel:()=>ia,WeSpeakerResNetPreTrainedModel:()=>pi,WhisperForConditionalGeneration:()=>bs,WhisperModel:()=>Jt,WhisperPreTrainedModel:()=>mt,XLMForQuestionAnswering:()=>ci,XLMForSequenceClassification:()=>lr,XLMForTokenClassification:()=>ki,XLMModel:()=>ni,XLMPreTrainedModel:()=>Fr,XLMRobertaForMaskedLM:()=>Rt,XLMRobertaForQuestionAnswering:()=>Qr,XLMRobertaForSequenceClassification:()=>$r,XLMRobertaForTokenClassification:()=>Nr,XLMRobertaModel:()=>Or,XLMRobertaPreTrainedModel:()=>zr,XLMWithLMHeadModel:()=>vi,XVectorOutput:()=>Pu,YolosForObjectDetection:()=>hl,YolosModel:()=>pl,YolosObjectDetectionOutput:()=>fl,YolosPreTrainedModel:()=>Js});var 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B.Tensor("int64",BigInt64Array.from(M.map(T=>BigInt(T))),[1,M.length])}function Ve(M){return new B.Tensor("bool",[M],[1])}async function Ge(M,T){let{encoder_outputs:O,input_ids:ie,decoder_input_ids:Ue,...He}=T;if(!O){const Lt=(0,Oe.pick)(T,M.sessions.model.inputNames);O=(await pt(M,Lt)).last_hidden_state}return He.input_ids=Ue,He.encoder_hidden_states=O,M.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(He.encoder_attention_mask=T.attention_mask),await ot(M,He,!0)}async function pt(M,T){const O=M.sessions.model,ie=(0,Oe.pick)(T,O.inputNames);if(O.inputNames.includes("inputs_embeds")&&!ie.inputs_embeds){if(!T.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");ie.inputs_embeds=await M.encode_text({input_ids:T.input_ids})}return O.inputNames.includes("token_type_ids")&&!ie.token_type_ids&&(ie.token_type_ids=new B.Tensor("int64",new BigInt64Array(ie.input_ids.data.length),ie.input_ids.dims)),await Ce(O,ie)}async function ot(M,T,O=!1){const ie=M.sessions[O?"decoder_model_merged":"model"],{past_key_values:Ue,...He}=T;ie.inputNames.includes("use_cache_branch")&&(He.use_cache_branch=Ve(!!Ue)),ie.inputNames.includes("position_ids")&&He.attention_mask&&!He.position_ids&&(He.position_ids=$e(He,Ue)),M.addPastKeyValues(He,Ue);const wt=(0,Oe.pick)(He,ie.inputNames);return await Ce(ie,wt)}async function Tt(M,{input_ids:T=null,attention_mask:O=null,pixel_values:ie=null,position_ids:Ue=null,inputs_embeds:He=null,past_key_values:wt=null,generation_config:Lt=null,logits_processor:er=null,...yr}){if(!He){if(He=await M.encode_text({input_ids:T}),ie&&T.dims[1]!==1){const Lr=await M.encode_image({pixel_values:ie});({inputs_embeds:He,attention_mask:O}=M._merge_input_ids_with_image_features({image_features:Lr,inputs_embeds:He,input_ids:T,attention_mask:O}))}else if(wt&&ie&&T.dims[1]===1){const Lr=T.dims[1],br=Object.values(wt)[0].dims.at(-2);O=(0,B.cat)([(0,B.ones)([T.dims[0],br]),O.slice(null,[O.dims[1]-Lr,O.dims[1]])],1)}}return await ot(M,{inputs_embeds:He,past_key_values:wt,attention_mask:O,position_ids:Ue,generation_config:Lt,logits_processor:er},!0)}function $e(M,T=null){const{input_ids:O,inputs_embeds:ie,attention_mask:Ue}=M,[He,wt]=Ue.dims,Lt=new BigInt64Array(Ue.data.length);for(let yr=0;yrHe.dims[1])){if(UeLt==M.config.image_token_index)){const Lt=M.config.num_image_tokens;if(!Lt)throw new Error("`num_image_tokens` is missing in the model configuration.");const er=He.dims[1]-(Ue-Lt);O.input_ids=He.slice(null,[-er,null]),O.attention_mask=(0,B.ones)([1,Ue+er])}}}return O}function Me(M,T,O,ie){return O.past_key_values&&(T=T.map(Ue=>[Ue.at(-1)])),{...O,decoder_input_ids:Be(T)}}function je(M,...T){return M.config.is_encoder_decoder?Me(M,...T):X(M,...T)}class ue extends ze.Callable{constructor(O,ie){super();De(this,"main_input_name","input_ids");De(this,"forward_params",["input_ids","attention_mask"]);this.config=O,this.sessions=ie;const Ue=K.get(this.constructor),He=ye.get(Ue);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,He){case he.DecoderOnly:this.can_generate=!0,this._forward=ot,this._prepare_inputs_for_generation=X;break;case he.Seq2Seq:case he.Vision2Seq:case he.Musicgen:this.can_generate=!0,this._forward=Ge,this._prepare_inputs_for_generation=Me;break;case he.EncoderDecoder:this._forward=Ge;break;case he.ImageTextToText:this.can_generate=!0,this._forward=Tt,this._prepare_inputs_for_generation=je;break;default:this._forward=pt;break}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var ie;const O=[];for(const Ue of Object.values(this.sessions))(ie=Ue==null?void 0:Ue.handler)!=null&&ie.dispose&&O.push(Ue.handler.dispose());return await Promise.all(O)}static async from_pretrained(O,{progress_callback:ie=null,config:Ue=null,cache_dir:He=null,local_files_only:wt=!1,revision:Lt="main",model_file_name:er=null,subfolder:yr="onnx",device:_r=null,dtype:Lr=null,use_external_data_format:br=null,session_options:kr={}}={}){let vr={progress_callback:ie,config:Ue,cache_dir:He,local_files_only:wt,revision:Lt,model_file_name:er,subfolder:yr,device:_r,dtype:Lr,use_external_data_format:br,session_options:kr};const Cr=K.get(this),Ar=ye.get(Cr);Ue=vr.config=await P.AutoConfig.from_pretrained(O,vr);let Wr;if(Ar===he.DecoderOnly)Wr=await Promise.all([W(O,{model:vr.model_file_name??"model"},vr),(0,V.getModelJSON)(O,"generation_config.json",!1,vr)]);else if(Ar===he.Seq2Seq||Ar===he.Vision2Seq)Wr=await Promise.all([W(O,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},vr),(0,V.getModelJSON)(O,"generation_config.json",!1,vr)]);else if(Ar===he.MaskGeneration)Wr=await Promise.all([W(O,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},vr)]);else if(Ar===he.EncoderDecoder)Wr=await Promise.all([W(O,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},vr)]);else if(Ar===he.ImageTextToText){const bi={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Ue.is_encoder_decoder&&(bi.model="encoder_model"),Wr=await Promise.all([W(O,bi,vr),(0,V.getModelJSON)(O,"generation_config.json",!1,vr)])}else Ar===he.Musicgen?Wr=await Promise.all([W(O,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},vr),(0,V.getModelJSON)(O,"generation_config.json",!1,vr)]):(Ar!==he.EncoderOnly&&console.warn(`Model type for '${Cr??(Ue==null?void 0:Ue.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),Wr=await Promise.all([W(O,{model:vr.model_file_name??"model"},vr)]));return new this(Ue,...Wr)}async _call(O){return await this.forward(O)}async forward(O){return await this._forward(this,O)}_get_logits_warper(O){const ie=new A.LogitsProcessorList;return O.temperature!==null&&O.temperature!==1&&ie.push(new A.TemperatureLogitsWarper(O.temperature)),O.top_k!==null&&O.top_k!==0&&ie.push(new A.TopKLogitsWarper(O.top_k)),O.top_p!==null&&O.top_p<1&&ie.push(new A.TopPLogitsWarper(O.top_p)),ie}_get_logits_processor(O,ie,Ue=null){const He=new A.LogitsProcessorList;if(O.repetition_penalty!==null&&O.repetition_penalty!==1&&He.push(new A.RepetitionPenaltyLogitsProcessor(O.repetition_penalty)),O.no_repeat_ngram_size!==null&&O.no_repeat_ngram_size>0&&He.push(new A.NoRepeatNGramLogitsProcessor(O.no_repeat_ngram_size)),O.bad_words_ids!==null&&He.push(new A.NoBadWordsLogitsProcessor(O.bad_words_ids,O.eos_token_id)),O.min_length!==null&&O.eos_token_id!==null&&O.min_length>0&&He.push(new A.MinLengthLogitsProcessor(O.min_length,O.eos_token_id)),O.min_new_tokens!==null&&O.eos_token_id!==null&&O.min_new_tokens>0&&He.push(new A.MinNewTokensLengthLogitsProcessor(ie,O.min_new_tokens,O.eos_token_id)),O.forced_bos_token_id!==null&&He.push(new A.ForcedBOSTokenLogitsProcessor(O.forced_bos_token_id)),O.forced_eos_token_id!==null&&He.push(new A.ForcedEOSTokenLogitsProcessor(O.max_length,O.forced_eos_token_id)),O.begin_suppress_tokens!==null){const wt=ie>1||O.forced_bos_token_id===null?ie:ie+1;He.push(new A.SuppressTokensAtBeginLogitsProcessor(O.begin_suppress_tokens,wt))}return O.guidance_scale!==null&&O.guidance_scale>1&&He.push(new A.ClassifierFreeGuidanceLogitsProcessor(O.guidance_scale)),Ue!==null&&He.extend(Ue),He}_prepare_generation_config(O,ie,Ue=H.GenerationConfig){const He={...this.config};for(const Lt of["decoder","generator","text_config"])Lt in He&&Object.assign(He,He[Lt]);const wt=new Ue(He);return"generation_config"in this&&Object.assign(wt,this.generation_config),O&&Object.assign(wt,O),ie&&Object.assign(wt,(0,Oe.pick)(ie,Object.getOwnPropertyNames(wt))),wt}_get_stopping_criteria(O,ie=null){const Ue=new de.StoppingCriteriaList;return O.max_length!==null&&Ue.push(new de.MaxLengthCriteria(O.max_length,this.config.max_position_embeddings??null)),O.eos_token_id!==null&&Ue.push(new de.EosTokenCriteria(O.eos_token_id)),ie&&Ue.extend(ie),Ue}_validate_model_class(){if(!this.can_generate){const O=[ds,ka,Ca,us],ie=K.get(this.constructor),Ue=new Set,He=this.config.model_type;for(const Lt of O){const er=Lt.get(He);er&&Ue.add(er[0])}let wt=`The current model class (${ie}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Ue.size>0&&(wt+=` Please use the following class instead: ${[...Ue].join(", ")}`),Error(wt)}}prepare_inputs_for_generation(...O){return this._prepare_inputs_for_generation(this,...O)}_update_model_kwargs_for_generation({generated_input_ids:O,outputs:ie,model_inputs:Ue,is_encoder_decoder:He}){return Ue.past_key_values=this.getPastKeyValues(ie,Ue.past_key_values),Ue.input_ids=new B.Tensor("int64",O.flat(),[O.length,1]),He||(Ue.attention_mask=(0,B.cat)([Ue.attention_mask,(0,B.ones)([Ue.attention_mask.dims[0],1])],1)),Ue.position_ids=null,Ue}_prepare_model_inputs({inputs:O,bos_token_id:ie,model_kwargs:Ue}){const He=(0,Oe.pick)(Ue,this.forward_params),wt=this.main_input_name;if(wt in He){if(O)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else He[wt]=O;return{inputs_tensor:He[wt],model_inputs:He,model_input_name:wt}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:O,model_inputs:ie,model_input_name:Ue,generation_config:He}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!ie.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:Lt,pixel_values:er,attention_mask:yr,..._r}=ie,Lr=await this._prepare_inputs_embeds(ie);ie={..._r,...(0,Oe.pick)(Lr,["inputs_embeds","attention_mask"])}}let{last_hidden_state:wt}=await pt(this,ie);if(He.guidance_scale!==null&&He.guidance_scale>1)wt=(0,B.cat)([wt,(0,B.full_like)(wt,0)],0),"attention_mask"in ie&&(ie.attention_mask=(0,B.cat)([ie.attention_mask,(0,B.zeros_like)(ie.attention_mask)],0));else if(ie.decoder_input_ids){const Lt=Be(ie.decoder_input_ids).dims[0];if(Lt!==wt.dims[0]){if(wt.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${wt.dims[0]}) than the decoder inputs (${Lt}).`);wt=(0,B.cat)(Array.from({length:Lt},()=>wt),0)}}return ie.encoder_outputs=wt,ie}_prepare_decoder_input_ids_for_generation({batch_size:O,model_input_name:ie,model_kwargs:Ue,decoder_start_token_id:He,bos_token_id:wt,generation_config:Lt}){let{decoder_input_ids:er,...yr}=Ue;if(er)Array.isArray(er[0])||(er=Array.from({length:O},()=>er));else if(He??(He=wt),this.config.model_type==="musicgen")er=Array.from({length:O*this.config.decoder.num_codebooks},()=>[He]);else if(Array.isArray(He)){if(He.length!==O)throw new Error(`\`decoder_start_token_id\` expcted to have length ${O} but got ${He.length}`);er=He}else er=Array.from({length:O},()=>[He]);return er=Be(er),Ue.decoder_attention_mask=(0,B.ones_like)(er),{input_ids:er,model_inputs:yr}}async generate({inputs:O=null,generation_config:ie=null,logits_processor:Ue=null,stopping_criteria:He=null,streamer:wt=null,...Lt}){this._validate_model_class(),ie=this._prepare_generation_config(ie,Lt);let{inputs_tensor:er,model_inputs:yr,model_input_name:_r}=this._prepare_model_inputs({inputs:O,model_kwargs:Lt});const Lr=this.config.is_encoder_decoder;Lr&&("encoder_outputs"in yr||(yr=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:er,model_inputs:yr,model_input_name:_r,generation_config:ie})));let br;Lr?{input_ids:br,model_inputs:yr}=this._prepare_decoder_input_ids_for_generation({batch_size:yr[_r].dims.at(0),model_input_name:_r,model_kwargs:yr,decoder_start_token_id:ie.decoder_start_token_id,bos_token_id:ie.bos_token_id,generation_config:ie}):br=yr[_r];let kr=br.dims.at(-1);ie.max_new_tokens!==null&&(ie.max_length=kr+ie.max_new_tokens);const vr=this._get_logits_processor(ie,kr,Ue),Cr=this._get_stopping_criteria(ie,He),Ar=yr[_r].dims.at(0),Wr=pe.LogitsSampler.getSampler(ie),bi=new Array(Ar).fill(0),Mi=br.tolist();wt&&wt.put(Mi);let Yi=null,mi={};for(;;){yr=this.prepare_inputs_for_generation(Mi,yr,ie);const _i=await this.forward(yr);if(ie.output_attentions&&ie.return_dict_in_generate){const Ui=this.getAttentions(_i);for(const Nn in Ui)Nn in mi||(mi[Nn]=[]),mi[Nn].push(Ui[Nn])}const cs=_i.logits.slice(null,-1,null),ps=vr(Mi,cs),Ia=[];for(let Ui=0;UiUi)){ie.return_dict_in_generate&&(Yi=this.getPastKeyValues(_i,yr.past_key_values,!1));break}yr=this._update_model_kwargs_for_generation({generated_input_ids:Ia,outputs:_i,model_inputs:yr,is_encoder_decoder:Lr})}wt&&wt.end();const si=new B.Tensor("int64",Mi.flat(),[Mi.length,Mi[0].length]);return ie.return_dict_in_generate?{sequences:si,past_key_values:Yi,...mi}:si}getPastKeyValues(O,ie,Ue=!0){const He=Object.create(null);for(const wt in O)if(wt.startsWith("present")){const Lt=wt.replace("present","past_key_values");if(ie&&wt.includes("encoder"))He[Lt]=ie[Lt];else{if(Ue&&ie){const er=ie[Lt];er.location==="gpu-buffer"&&er.dispose()}He[Lt]=O[wt]}}return He}getAttentions(O){const ie={};for(const Ue of["cross_attentions","encoder_attentions","decoder_attentions"])for(const He in O)He.startsWith(Ue)&&(Ue in ie||(ie[Ue]=[]),ie[Ue].push(O[He]));return ie}addPastKeyValues(O,ie){if(ie)Object.assign(O,ie);else{const Ue=this.custom_config.kv_cache_dtype??"float32",He=Ue==="float16"?new Uint16Array:[],wt=(0,P.getKeyValueShapes)(this.config);for(const Lt in wt)O[Lt]=new B.Tensor(Ue,He,wt[Lt])}}async encode_image({pixel_values:O}){const ie=(await Ce(this.sessions.vision_encoder,{pixel_values:O})).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 (${ie.dims[1]}).`),this.config.num_image_tokens=ie.dims[1]),ie}async encode_text({input_ids:O}){return(await Ce(this.sessions.embed_tokens,{input_ids:O})).inputs_embeds}}class nt{}class xt extends nt{constructor({last_hidden_state:T,hidden_states:O=null,attentions:ie=null}){super(),this.last_hidden_state=T,this.hidden_states=O,this.attentions=ie}}class ft extends ue{}class yt extends ft{}class Qe extends ft{async _call(T){return new hi(await super._call(T))}}class gt extends ft{async _call(T){return new wr(await super._call(T))}}class Dt extends ft{async _call(T){return new oi(await super._call(T))}}class Ke extends ft{async _call(T){return new fi(await super._call(T))}}class ce extends ue{}class Re extends ce{}class Je extends ue{}class rt extends Je{}class et extends Je{async _call(T){return new hi(await super._call(T))}}class st extends Je{async _call(T){return new wr(await super._call(T))}}class bt extends Je{async _call(T){return new oi(await super._call(T))}}class kt extends Je{async _call(T){return new fi(await super._call(T))}}class Et extends ue{}class Ot extends Et{}class S extends Et{async _call(T){return new hi(await super._call(T))}}class Y extends Et{async _call(T){return new wr(await super._call(T))}}class D extends Et{async _call(T){return new oi(await super._call(T))}}class ne extends Et{async _call(T){return new fi(await super._call(T))}}class xe extends ue{}class ut extends xe{}class ct extends xe{async _call(T){return new hi(await super._call(T))}}class Ut extends xe{async _call(T){return new wr(await super._call(T))}}class $t extends xe{async _call(T){return new oi(await super._call(T))}}class Ne extends xe{async _call(T){return new fi(await super._call(T))}}class z extends ue{}class ee extends z{}class Ee extends z{async _call(T){return new hi(await super._call(T))}}class Xe extends z{async _call(T){return new wr(await super._call(T))}}class We extends z{async _call(T){return new oi(await super._call(T))}}class Ze extends z{async _call(T){return new fi(await super._call(T))}}class vt extends ue{}class _t extends vt{}class zt extends vt{async _call(T){return new hi(await super._call(T))}}class Ct extends vt{async _call(T){return new wr(await super._call(T))}}class jt extends vt{async _call(T){return new oi(await super._call(T))}}class Qt extends vt{async _call(T){return new fi(await super._call(T))}}class at extends ue{}class Zt extends at{}class Yt extends at{async _call(T){return new hi(await super._call(T))}}class ir extends at{async _call(T){return new wr(await super._call(T))}}class nr extends at{async _call(T){return new oi(await super._call(T))}}class dr extends at{async _call(T){return new fi(await super._call(T))}}class rr extends ue{}class Dr extends rr{}class Jr extends rr{async _call(T){return new wr(await super._call(T))}}class Br extends rr{async _call(T){return new oi(await super._call(T))}}class dt extends rr{async _call(T){return new fi(await super._call(T))}}class Nt extends rr{async _call(T){return new hi(await super._call(T))}}class Ht extends ue{}class ii extends Ht{}class Zi extends Ht{async _call(T){return new hi(await super._call(T))}}class Gi extends Ht{async _call(T){return new wr(await super._call(T))}}class Hr extends Ht{async _call(T){return new oi(await super._call(T))}}class di extends ue{}class Xr extends di{}class Ri extends di{async _call(T){return new hi(await super._call(T))}}class Rr extends di{async _call(T){return new wr(await super._call(T))}}class Ji extends di{async _call(T){return new fi(await super._call(T))}}class qi extends ue{}class qn extends qi{}class Cn extends qi{async _call(T){return new hi(await super._call(T))}}class kn extends qi{async _call(T){return new wr(await super._call(T))}}class $n extends qi{async _call(T){return new oi(await super._call(T))}}class Sn extends qi{async _call(T){return new fi(await super._call(T))}}class en extends ue{}class Kn extends en{}class pn extends en{async _call(T){return new hi(await super._call(T))}}class Ni extends en{async _call(T){return new wr(await super._call(T))}}class Ki extends en{async _call(T){return new fi(await super._call(T))}}class Hi extends ue{}class an extends Hi{}class hn extends Hi{async _call(T){return new wr(await super._call(T))}}class fn extends Hi{async _call(T){return new fi(await super._call(T))}}class sr extends Hi{async _call(T){return new hi(await super._call(T))}}class on extends ue{constructor(O,ie,Ue){super(O,ie);De(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Ue}}class En extends on{}class Pn extends on{}class mn extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class An extends mn{}class In extends mn{}class _n extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class Fn extends _n{}class qr extends _n{}class Ci extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class Ae extends Ci{}class x extends Ci{}class N extends Ci{async _call(T){return new wr(await super._call(T))}}class se extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class be extends se{}class ve extends se{}class qe extends se{async _call(T){return new wr(await super._call(T))}}class St extends se{}class At extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class Pt extends At{}class Vt extends At{}class ur extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class jr extends ur{}class hr extends ur{}class tr extends ue{}class Tr extends tr{}class wi extends tr{async _call(T){return new hi(await super._call(T))}}class ui extends tr{async _call(T){return new wr(await super._call(T))}}class tt extends tr{async _call(T){return new oi(await super._call(T))}}class Pi extends tr{async _call(T){return new fi(await super._call(T))}}class Fr extends ue{}class ni extends Fr{}class vi extends Fr{async _call(T){return new hi(await super._call(T))}}class lr extends Fr{async _call(T){return new wr(await super._call(T))}}class ki extends Fr{async _call(T){return new oi(await super._call(T))}}class ci extends Fr{async _call(T){return new fi(await super._call(T))}}class zr extends ue{}class Or extends zr{}class Rt extends zr{async _call(T){return new hi(await super._call(T))}}class $r extends zr{async _call(T){return new wr(await super._call(T))}}class Nr extends zr{async _call(T){return new oi(await super._call(T))}}class Qr extends zr{async _call(T){return new fi(await super._call(T))}}class $i extends ue{}class Kt extends $i{}class Hn extends $i{}class mt extends ue{constructor(O,ie,Ue){super(O,ie);De(this,"requires_attention_mask",!1);De(this,"main_input_name","input_features");De(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Ue}}class Jt extends mt{}class bs extends mt{_prepare_generation_config(T,O){return super._prepare_generation_config(T,O,q.WhisperGenerationConfig)}_retrieve_init_tokens(T){const O=[T.decoder_start_token_id];let ie=T.language;const Ue=T.task;if(T.is_multilingual){ie||(console.warn("No language specified - defaulting to English (en)."),ie="en");const wt=`<|${(0,ae.whisper_language_to_code)(ie)}|>`;O.push(T.lang_to_id[wt]),O.push(T.task_to_id[Ue??"transcribe"])}else if(ie||Ue)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!T.return_timestamps&&T.no_timestamps_token_id&&O.at(-1)!==T.no_timestamps_token_id?O.push(T.no_timestamps_token_id):T.return_timestamps&&O.at(-1)===T.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),O.pop()),O.filter(He=>He!=null)}async generate({inputs:T=null,generation_config:O=null,logits_processor:ie=null,stopping_criteria:Ue=null,...He}){O=this._prepare_generation_config(O,He);const wt=He.decoder_input_ids??this._retrieve_init_tokens(O);if(O.return_timestamps&&(ie??(ie=new A.LogitsProcessorList),ie.push(new A.WhisperTimeStampLogitsProcessor(O,wt))),O.begin_suppress_tokens&&(ie??(ie=new A.LogitsProcessorList),ie.push(new A.SuppressTokensAtBeginLogitsProcessor(O.begin_suppress_tokens,wt.length))),O.return_token_timestamps){if(!O.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.");O.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),O.output_attentions=!0,O.return_dict_in_generate=!0}const Lt=await super.generate({inputs:T,generation_config:O,logits_processor:ie,decoder_input_ids:wt,...He});return O.return_token_timestamps&&(Lt.token_timestamps=this._extract_token_timestamps(Lt,O.alignment_heads,O.num_frames)),Lt}_extract_token_timestamps(T,O,ie=null,Ue=.02){if(!T.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");ie==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 He=this.config.median_filter_width;He===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),He=7);const wt=T.cross_attentions,Lt=Array.from({length:this.config.decoder_layers},(Cr,Ar)=>(0,B.cat)(wt.map(Wr=>Wr[Ar]),2)),er=(0,B.stack)(O.map(([Cr,Ar])=>{if(Cr>=Lt.length)throw new Error(`Layer index ${Cr} is out of bounds for cross attentions (length ${Lt.length}).`);return ie?Lt[Cr].slice(null,Ar,null,[0,ie]):Lt[Cr].slice(null,Ar)})).transpose(1,0,2,3),[yr,_r]=(0,B.std_mean)(er,-2,0,!0),Lr=er.clone();for(let Cr=0;CrWr[_i+1]-Wr[_i]),Yi=(0,Oe.mergeArrays)([1],Mi).map(si=>!!si),mi=[];for(let si=0;sibr.findIndex(kr=>kr==He)),er=Lt.every(br=>br===-1),yr=Lt.every(br=>br!==-1);if(!er&&!yr)throw new Error("Every input should contain either 0 or 1 image token.");if(er)return{inputs_embeds:T,attention_mask:Ue};const _r=[],Lr=[];for(let br=0;brHe*wt,1);T.input_labels=new B.Tensor("int64",new BigInt64Array(Ue).fill(1n),ie)}const O={image_embeddings:T.image_embeddings,image_positional_embeddings:T.image_positional_embeddings};return T.input_points&&(O.input_points=T.input_points),T.input_labels&&(O.input_labels=T.input_labels),T.input_boxes&&(O.input_boxes=T.input_boxes),await Ce(this.sessions.prompt_encoder_mask_decoder,O)}async _call(T){return new gl(await super._call(T))}}class gl extends nt{constructor({iou_scores:T,pred_masks:O}){super(),this.iou_scores=T,this.pred_masks=O}}class ea extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class Gu extends ea{}class wl extends ea{}class ta extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class yl extends ta{}class bl extends ta{}class tn extends ue{}class vl extends tn{}class qu extends tn{async _call(T){return new ln(await super._call(T))}}class rn extends tn{async _call(T){return new wr(await super._call(T))}}class nn extends tn{async _call(T){return new oi(await super._call(T))}}class Xi extends ue{}class ra extends Xi{}class sn extends Xi{async _call(T){return new oi(await super._call(T))}}class pi extends ue{}class ia extends pi{}class vn extends ue{}class na extends vn{}class Ml extends vn{async _call(T){return new ln(await super._call(T))}}class xl extends vn{async _call(T){return new wr(await super._call(T))}}class On extends ue{}class rs extends On{}class sa extends On{async _call(T){return new ln(await super._call(T))}}class Tl extends On{async _call(T){return new wr(await super._call(T))}}class is extends On{async _call(T){return new oi(await super._call(T))}}class ns extends ue{}class aa extends ns{}class ss extends ns{async _call(T){return new ln(await super._call(T))}}class Cl extends ns{async _call(T){return new wr(await super._call(T))}}class Ku extends ue{}class Hu extends tn{}class kl extends tn{async _call(T){return new ln(await super._call(T))}}class oa extends tn{async _call(T){return new wr(await super._call(T))}}class Qi extends ue{}class $l extends Qi{}class la extends Qi{async _call(T){return new ln(await super._call(T))}}class Sl extends Qi{async _call(T){return new wr(await super._call(T))}}class El extends Qi{async _call(T){return new Pu(await super._call(T))}}class Pl extends Qi{async _call(T){return new oi(await super._call(T))}}class ua extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class Al extends ua{}class Il extends ua{}class Xu extends ua{async generate_speech(T,O,{threshold:ie=.5,minlenratio:Ue=0,maxlenratio:He=20,vocoder:wt=null}={}){const Lt={input_ids:T},{encoder_outputs:er,encoder_attention_mask:yr}=await pt(this,Lt),_r=er.dims[1]/this.config.reduction_factor,Lr=Math.floor(_r*He),br=Math.floor(_r*Ue),kr=this.config.num_mel_bins;let vr=[],Cr=null,Ar=null,Wr=0;for(;;){++Wr;const Yi=Ve(!!Ar);let mi;Ar?mi=Ar.output_sequence_out:mi=new B.Tensor("float32",new Float32Array(kr),[1,1,kr]);let si={use_cache_branch:Yi,output_sequence:mi,encoder_attention_mask:yr,speaker_embeddings:O,encoder_hidden_states:er};this.addPastKeyValues(si,Cr),Ar=await Ce(this.sessions.decoder_model_merged,si),Cr=this.getPastKeyValues(Ar,Cr);const{prob:_i,spectrum:cs}=Ar;if(vr.push(cs),Wr>=br&&(Array.from(_i.data).filter(ps=>ps>=ie).length>0||Wr>=Lr))break}const bi=(0,B.cat)(vr),{waveform:Mi}=await Ce(wt.sessions.model,{spectrogram:bi});return{spectrogram:bi,waveform:Mi}}}class da extends ue{constructor(){super(...arguments);De(this,"main_input_name","spectrogram")}}class Fl extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class zl extends Fl{}class ca extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class as extends ca{}class os extends ca{}class pa extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class ls extends pa{}class ha extends pa{}class fa extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class Ol extends fa{}class Dl extends fa{}class Dn extends ue{}class Bl extends Dn{}class jl extends Dn{static async from_pretrained(T,O={}){return O.model_file_name??(O.model_file_name="text_model"),super.from_pretrained(T,O)}}class Ll extends Dn{static async from_pretrained(T,O={}){return O.model_file_name??(O.model_file_name="audio_model"),super.from_pretrained(T,O)}}class Qu extends ue{}class ma extends Qu{async _call(T){return new cd(await super._call(T))}}class Bn extends ue{}class Dd extends Bn{}class Rl extends Bn{}class Nl extends Bn{}class _a extends ue{constructor(T,O,ie){super(T,O),this.generation_config=ie}}class ga extends _a{}class Vl extends _a{}class wa extends ue{}class Ul extends wa{}class Wl extends wa{async _call(T){return new wr(await super._call(T))}}class ya extends ue{}class Yu extends ya{}class Bd extends ya{}class ba extends ue{constructor(O,ie,Ue){super(O,ie);De(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=Ue}_apply_and_filter_by_delay_pattern_mask(O){const[ie,Ue]=O.dims,He=this.config.decoder.num_codebooks,wt=Ue-He;let Lt=0;for(let _r=0;_r0&&kr<=wt&&(O.data[Lt++]=O.data[_r])}const er=Math.floor(ie/He),yr=Lt/(er*He);return new B.Tensor(O.type,O.data.slice(0,Lt),[er,He,yr])}prepare_inputs_for_generation(O,ie,Ue){let He=structuredClone(O);for(let Lt=0;Lt=er&&(He[Lt][er]=BigInt(this.config.decoder.pad_token_id));return Ue.guidance_scale!==null&&Ue.guidance_scale>1&&(He=He.concat(He)),super.prepare_inputs_for_generation(He,ie,Ue)}async generate(O){const ie=await super.generate(O),Ue=this._apply_and_filter_by_delay_pattern_mask(ie).unsqueeze_(0),{audio_values:He}=await Ce(this.sessions.encodec_decode,{audio_codes:Ue});return He}}class va extends ue{}class Gl extends va{}class Zu extends va{async _call(T){return new wr(await super._call(T))}}class Ma extends ue{}class ql extends Ma{}class Kl extends Ma{async _call(T){return new wr(await super._call(T))}}class xa extends ue{}class Hl extends xa{}class Ju extends xa{async _call(T){return new wr(await super._call(T))}}class jn extends ue{}class Ln extends jn{}class Ta extends jn{async _call(T){return new wr(await super._call(T))}}class Vr{static async from_pretrained(T,{progress_callback:O=null,config:ie=null,cache_dir:Ue=null,local_files_only:He=!1,revision:wt="main",model_file_name:Lt=null,subfolder:er="onnx",device:yr=null,dtype:_r=null,use_external_data_format:Lr=null,session_options:br={}}={}){let kr={progress_callback:O,config:ie,cache_dir:Ue,local_files_only:He,revision:wt,model_file_name:Lt,subfolder:er,device:yr,dtype:_r,use_external_data_format:Lr,session_options:br};if(kr.config=await P.AutoConfig.from_pretrained(T,kr),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let vr of this.MODEL_CLASS_MAPPINGS){const Cr=vr.get(kr.config.model_type);if(Cr)return await Cr[1].from_pretrained(T,kr)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${kr.config.model_type}", attempting to construct from base class.`),await ue.from_pretrained(T,kr);throw Error(`Unsupported model type: ${kr.config.model_type}`)}}De(Vr,"MODEL_CLASS_MAPPINGS",null),De(Vr,"BASE_IF_FAIL",!1);const ed=new Map([["bert",["BertModel",yt]],["nomic_bert",["NomicBertModel",Re]],["roformer",["RoFormerModel",rt]],["electra",["ElectraModel",ut]],["esm",["EsmModel",ii]],["convbert",["ConvBertModel",Ot]],["camembert",["CamembertModel",ee]],["deberta",["DebertaModel",_t]],["deberta-v2",["DebertaV2Model",Zt]],["mpnet",["MPNetModel",qn]],["albert",["AlbertModel",an]],["distilbert",["DistilBertModel",Dr]],["roberta",["RobertaModel",Tr]],["xlm",["XLMModel",ni]],["xlm-roberta",["XLMRobertaModel",Or]],["clap",["ClapModel",Bl]],["clip",["CLIPModel",ja]],["clipseg",["CLIPSegModel",Wa]],["chinese_clip",["ChineseCLIPModel",Ua]],["siglip",["SiglipModel",wn]],["mobilebert",["MobileBertModel",Xr]],["squeezebert",["SqueezeBertModel",Kn]],["wav2vec2",["Wav2Vec2Model",vl]],["wav2vec2-bert",["Wav2Vec2BertModel",aa]],["unispeech",["UniSpeechModel",na]],["unispeech-sat",["UniSpeechSatModel",rs]],["hubert",["HubertModel",Hu]],["wavlm",["WavLMModel",$l]],["audio-spectrogram-transformer",["ASTModel",Kt]],["vits",["VitsModel",ma]],["pyannote",["PyAnnoteModel",ra]],["wespeaker-resnet",["WeSpeakerResNetModel",ia]],["detr",["DetrModel",jo]],["rt_detr",["RTDetrModel",Vo]],["table-transformer",["TableTransformerModel",Wo]],["vit",["ViTModel",Mo]],["fastvit",["FastViTModel",To]],["mobilevit",["MobileViTModel",Eo]],["mobilevitv2",["MobileViTV2Model",Wu]],["owlvit",["OwlViTModel",Io]],["owlv2",["Owlv2Model",zo]],["beit",["BeitModel",Do]],["deit",["DeiTModel",Ko]],["convnext",["ConvNextModel",sl]],["convnextv2",["ConvNextV2Model",ll]],["dinov2",["Dinov2Model",dl]],["resnet",["ResNetModel",Xo]],["swin",["SwinModel",Yo]],["swin2sr",["Swin2SRModel",Jo]],["donut-swin",["DonutSwinModel",Ys]],["yolos",["YolosModel",pl]],["dpt",["DPTModel",Hs]],["glpn",["GLPNModel",rl]],["hifigan",["SpeechT5HifiGan",da]],["efficientnet",["EfficientNetModel",Ul]],["mobilenet_v1",["MobileNetV1Model",Gl]],["mobilenet_v2",["MobileNetV2Model",ql]],["mobilenet_v3",["MobileNetV3Model",Hl]],["mobilenet_v4",["MobileNetV4Model",Ln]]]),td=new Map([["t5",["T5Model",En]],["longt5",["LongT5Model",An]],["mt5",["MT5Model",Fn]],["bart",["BartModel",Ae]],["mbart",["MBartModel",be]],["marian",["MarianModel",Gu]],["whisper",["WhisperModel",Jt]],["m2m_100",["M2M100Model",yl]],["blenderbot",["BlenderbotModel",Pt]],["blenderbot-small",["BlenderbotSmallModel",jr]]]),rd=new Map([["bloom",["BloomModel",go]],["gpt2",["GPT2Model",qa]],["gptj",["GPTJModel",Za]],["gpt_bigcode",["GPTBigCodeModel",eo]],["gpt_neo",["GPTNeoModel",Ha]],["gpt_neox",["GPTNeoXModel",Qa]],["codegen",["CodeGenModel",to]],["llama",["LlamaModel",ji]],["cohere",["CohereModel",io]],["gemma",["GemmaModel",so]],["gemma2",["Gemma2Model",oo]],["openelm",["OpenELMModel",uo]],["qwen2",["Qwen2Model",po]],["phi",["PhiModel",fo]],["phi3",["Phi3Model",_o]],["mpt",["MptModel",Uu]],["opt",["OPTModel",bo]],["mistral",["MistralModel",as]],["starcoder2",["Starcoder2Model",ls]],["falcon",["FalconModel",Ol]],["stablelm",["StableLmModel",ga]]]),us=new Map([["speecht5",["SpeechT5ForSpeechToText",Il]],["whisper",["WhisperForConditionalGeneration",bs]]]),Xl=new Map([["speecht5",["SpeechT5ForTextToSpeech",Xu]]]),Ql=new Map([["vits",["VitsModel",ma]],["musicgen",["MusicgenForConditionalGeneration",ba]]]),Yl=new Map([["bert",["BertForSequenceClassification",gt]],["roformer",["RoFormerForSequenceClassification",st]],["electra",["ElectraForSequenceClassification",Ut]],["esm",["EsmForSequenceClassification",Gi]],["convbert",["ConvBertForSequenceClassification",Y]],["camembert",["CamembertForSequenceClassification",Xe]],["deberta",["DebertaForSequenceClassification",Ct]],["deberta-v2",["DebertaV2ForSequenceClassification",ir]],["mpnet",["MPNetForSequenceClassification",kn]],["albert",["AlbertForSequenceClassification",hn]],["distilbert",["DistilBertForSequenceClassification",Jr]],["roberta",["RobertaForSequenceClassification",ui]],["xlm",["XLMForSequenceClassification",lr]],["xlm-roberta",["XLMRobertaForSequenceClassification",$r]],["bart",["BartForSequenceClassification",N]],["mbart",["MBartForSequenceClassification",qe]],["mobilebert",["MobileBertForSequenceClassification",Rr]],["squeezebert",["SqueezeBertForSequenceClassification",Ni]]]),id=new Map([["bert",["BertForTokenClassification",Dt]],["roformer",["RoFormerForTokenClassification",bt]],["electra",["ElectraForTokenClassification",$t]],["esm",["EsmForTokenClassification",Hr]],["convbert",["ConvBertForTokenClassification",D]],["camembert",["CamembertForTokenClassification",We]],["deberta",["DebertaForTokenClassification",jt]],["deberta-v2",["DebertaV2ForTokenClassification",nr]],["mpnet",["MPNetForTokenClassification",$n]],["distilbert",["DistilBertForTokenClassification",Br]],["roberta",["RobertaForTokenClassification",tt]],["xlm",["XLMForTokenClassification",ki]],["xlm-roberta",["XLMRobertaForTokenClassification",Nr]]]),Ca=new Map([["t5",["T5ForConditionalGeneration",Pn]],["longt5",["LongT5ForConditionalGeneration",In]],["mt5",["MT5ForConditionalGeneration",qr]],["bart",["BartForConditionalGeneration",x]],["mbart",["MBartForConditionalGeneration",ve]],["marian",["MarianMTModel",wl]],["m2m_100",["M2M100ForConditionalGeneration",bl]],["blenderbot",["BlenderbotForConditionalGeneration",Vt]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",hr]]]),ds=new Map([["bloom",["BloomForCausalLM",wo]],["gpt2",["GPT2LMHeadModel",Ka]],["gptj",["GPTJForCausalLM",Ja]],["gpt_bigcode",["GPTBigCodeForCausalLM",Vu]],["gpt_neo",["GPTNeoForCausalLM",Xa]],["gpt_neox",["GPTNeoXForCausalLM",Ya]],["codegen",["CodeGenForCausalLM",Xn]],["llama",["LlamaForCausalLM",ro]],["cohere",["CohereForCausalLM",no]],["gemma",["GemmaForCausalLM",ao]],["gemma2",["Gemma2ForCausalLM",lo]],["openelm",["OpenELMForCausalLM",co]],["qwen2",["Qwen2ForCausalLM",ho]],["phi",["PhiForCausalLM",mo]],["phi3",["Phi3ForCausalLM",Os]],["mpt",["MptForCausalLM",yo]],["opt",["OPTForCausalLM",vo]],["mbart",["MBartForCausalLM",St]],["mistral",["MistralForCausalLM",os]],["starcoder2",["Starcoder2ForCausalLM",ha]],["falcon",["FalconForCausalLM",Dl]],["trocr",["TrOCRForCausalLM",zl]],["stablelm",["StableLmForCausalLM",Vl]]]),Zl=new Map([["bert",["BertForMaskedLM",Qe]],["roformer",["RoFormerForMaskedLM",et]],["electra",["ElectraForMaskedLM",ct]],["esm",["EsmForMaskedLM",Zi]],["convbert",["ConvBertForMaskedLM",S]],["camembert",["CamembertForMaskedLM",Ee]],["deberta",["DebertaForMaskedLM",zt]],["deberta-v2",["DebertaV2ForMaskedLM",Yt]],["mpnet",["MPNetForMaskedLM",Cn]],["albert",["AlbertForMaskedLM",sr]],["distilbert",["DistilBertForMaskedLM",Nt]],["roberta",["RobertaForMaskedLM",wi]],["xlm",["XLMWithLMHeadModel",vi]],["xlm-roberta",["XLMRobertaForMaskedLM",Rt]],["mobilebert",["MobileBertForMaskedLM",Ri]],["squeezebert",["SqueezeBertForMaskedLM",pn]]]),Jl=new Map([["bert",["BertForQuestionAnswering",Ke]],["roformer",["RoFormerForQuestionAnswering",kt]],["electra",["ElectraForQuestionAnswering",Ne]],["convbert",["ConvBertForQuestionAnswering",ne]],["camembert",["CamembertForQuestionAnswering",Ze]],["deberta",["DebertaForQuestionAnswering",Qt]],["deberta-v2",["DebertaV2ForQuestionAnswering",dr]],["mpnet",["MPNetForQuestionAnswering",Sn]],["albert",["AlbertForQuestionAnswering",fn]],["distilbert",["DistilBertForQuestionAnswering",dt]],["roberta",["RobertaForQuestionAnswering",Pi]],["xlm",["XLMForQuestionAnswering",ci]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Qr]],["mobilebert",["MobileBertForQuestionAnswering",Ji]],["squeezebert",["SqueezeBertForQuestionAnswering",Ki]]]),ka=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",vs]]]),jd=new Map([["llava",["LlavaForConditionalGeneration",gn]],["moondream1",["Moondream1ForConditionalGeneration",mr]],["florence2",["Florence2ForConditionalGeneration",Ms]]]),nd=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",vs]]]),eu=new Map([["vit",["ViTForImageClassification",xo]],["fastvit",["FastViTForImageClassification",Co]],["mobilevit",["MobileViTForImageClassification",Po]],["mobilevitv2",["MobileViTV2ForImageClassification",Ao]],["beit",["BeitForImageClassification",Bo]],["deit",["DeiTForImageClassification",Ho]],["convnext",["ConvNextForImageClassification",al]],["convnextv2",["ConvNextV2ForImageClassification",ul]],["dinov2",["Dinov2ForImageClassification",cl]],["resnet",["ResNetForImageClassification",Qo]],["swin",["SwinForImageClassification",Zo]],["segformer",["SegformerForImageClassification",Rl]],["efficientnet",["EfficientNetForImageClassification",Wl]],["mobilenet_v1",["MobileNetV1ForImageClassification",Zu]],["mobilenet_v2",["MobileNetV2ForImageClassification",Kl]],["mobilenet_v3",["MobileNetV3ForImageClassification",Ju]],["mobilenet_v4",["MobileNetV4ForImageClassification",Ta]]]),sd=new Map([["detr",["DetrForObjectDetection",Lo]],["rt_detr",["RTDetrForObjectDetection",ts]],["table-transformer",["TableTransformerForObjectDetection",Go]],["yolos",["YolosForObjectDetection",hl]]]),tu=new Map([["owlvit",["OwlViTForObjectDetection",Fo]],["owlv2",["Owlv2ForObjectDetection",Oo]]]),ru=new Map([["detr",["DetrForSegmentation",Ro]],["clipseg",["CLIPSegForImageSegmentation",Ga]]]),iu=new Map([["segformer",["SegformerForSemanticSegmentation",Nl]]]),nu=new Map([["sam",["SamModel",_l]]]),ad=new Map([["wav2vec2",["Wav2Vec2ForCTC",qu]],["wav2vec2-bert",["Wav2Vec2BertForCTC",ss]],["unispeech",["UniSpeechForCTC",Ml]],["unispeech-sat",["UniSpeechSatForCTC",sa]],["wavlm",["WavLMForCTC",la]],["hubert",["HubertForCTC",kl]]]),su=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",rn]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Cl]],["unispeech",["UniSpeechForSequenceClassification",xl]],["unispeech-sat",["UniSpeechSatForSequenceClassification",Tl]],["wavlm",["WavLMForSequenceClassification",Sl]],["hubert",["HubertForSequenceClassification",oa]],["audio-spectrogram-transformer",["ASTForAudioClassification",Hn]]]),au=new Map([["wavlm",["WavLMForXVector",El]]]),ou=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",is]],["wavlm",["WavLMForAudioFrameClassification",Pl]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",nn]],["pyannote",["PyAnnoteForAudioFrameClassification",sn]]]),lu=new Map([["vitmatte",["VitMatteForImageMatting",$o]]]),od=new Map([["swin2sr",["Swin2SRForImageSuperResolution",qs]]]),uu=new Map([["dpt",["DPTForDepthEstimation",Xs]],["depth_anything",["DepthAnythingForDepthEstimation",tl]],["glpn",["GLPNForDepthEstimation",il]]]),du=new Map([["clip",["CLIPVisionModelWithProjection",La]],["siglip",["SiglipVisionModel",Na]]]),cu=[[ed,he.EncoderOnly],[td,he.EncoderDecoder],[rd,he.DecoderOnly],[Yl,he.EncoderOnly],[id,he.EncoderOnly],[Ca,he.Seq2Seq],[us,he.Seq2Seq],[ds,he.DecoderOnly],[Zl,he.EncoderOnly],[Jl,he.EncoderOnly],[ka,he.Vision2Seq],[jd,he.ImageTextToText],[eu,he.EncoderOnly],[ru,he.EncoderOnly],[iu,he.EncoderOnly],[lu,he.EncoderOnly],[od,he.EncoderOnly],[uu,he.EncoderOnly],[sd,he.EncoderOnly],[tu,he.EncoderOnly],[nu,he.MaskGeneration],[ad,he.EncoderOnly],[su,he.EncoderOnly],[Xl,he.Seq2Seq],[Ql,he.EncoderOnly],[au,he.EncoderOnly],[ou,he.EncoderOnly],[du,he.EncoderOnly]];for(const[M,T]of cu)for(const[O,ie]of M.values())ye.set(O,T),K.set(ie,O),ge.set(O,ie);const ld=[["MusicgenForConditionalGeneration",ba,he.Musicgen],["CLIPTextModelWithProjection",Vi,he.EncoderOnly],["SiglipTextModel",Ra,he.EncoderOnly],["ClapTextModelWithProjection",jl,he.EncoderOnly],["ClapAudioModelWithProjection",Ll,he.EncoderOnly]];for(const[M,T,O]of ld)ye.set(M,O),K.set(T,M),ge.set(M,T);class pu extends Vr{}De(pu,"MODEL_CLASS_MAPPINGS",cu.map(T=>T[0])),De(pu,"BASE_IF_FAIL",!0);class yi extends Vr{}De(yi,"MODEL_CLASS_MAPPINGS",[Yl]);class hu extends Vr{}De(hu,"MODEL_CLASS_MAPPINGS",[id]);class fu extends Vr{}De(fu,"MODEL_CLASS_MAPPINGS",[Ca]);class $a extends Vr{}De($a,"MODEL_CLASS_MAPPINGS",[us]);class mu extends Vr{}De(mu,"MODEL_CLASS_MAPPINGS",[Xl]);class Rn extends Vr{}De(Rn,"MODEL_CLASS_MAPPINGS",[Ql]);class _u extends Vr{}De(_u,"MODEL_CLASS_MAPPINGS",[ds]);class gu extends Vr{}De(gu,"MODEL_CLASS_MAPPINGS",[Zl]);class Sa extends Vr{}De(Sa,"MODEL_CLASS_MAPPINGS",[Jl]);class wu extends Vr{}De(wu,"MODEL_CLASS_MAPPINGS",[ka]);class yu extends Vr{}De(yu,"MODEL_CLASS_MAPPINGS",[eu]);class Ea extends Vr{}De(Ea,"MODEL_CLASS_MAPPINGS",[ru]);class bu extends Vr{}De(bu,"MODEL_CLASS_MAPPINGS",[iu]);class vu extends Vr{}De(vu,"MODEL_CLASS_MAPPINGS",[sd]);class Mu extends Vr{}De(Mu,"MODEL_CLASS_MAPPINGS",[tu]);class Pa extends Vr{}De(Pa,"MODEL_CLASS_MAPPINGS",[nu]);class xu extends Vr{}De(xu,"MODEL_CLASS_MAPPINGS",[ad]);class Tu extends Vr{}De(Tu,"MODEL_CLASS_MAPPINGS",[su]);class Aa extends Vr{}De(Aa,"MODEL_CLASS_MAPPINGS",[au]);class Cu extends Vr{}De(Cu,"MODEL_CLASS_MAPPINGS",[ou]);class ud extends Vr{}De(ud,"MODEL_CLASS_MAPPINGS",[nd]);class ku extends Vr{}De(ku,"MODEL_CLASS_MAPPINGS",[lu]);class $u extends Vr{}De($u,"MODEL_CLASS_MAPPINGS",[od]);class Su extends Vr{}De(Su,"MODEL_CLASS_MAPPINGS",[uu]);class Eu extends Vr{}De(Eu,"MODEL_CLASS_MAPPINGS",[du]);class Ld extends nt{constructor({logits:T,past_key_values:O,encoder_outputs:ie,decoder_attentions:Ue=null,cross_attentions:He=null}){super(),this.logits=T,this.past_key_values=O,this.encoder_outputs=ie,this.decoder_attentions=Ue,this.cross_attentions=He}}class wr extends nt{constructor({logits:T}){super(),this.logits=T}}class Pu extends nt{constructor({logits:T,embeddings:O}){super(),this.logits=T,this.embeddings=O}}class oi extends nt{constructor({logits:T}){super(),this.logits=T}}class hi extends nt{constructor({logits:T}){super(),this.logits=T}}class fi extends nt{constructor({start_logits:T,end_logits:O}){super(),this.start_logits=T,this.end_logits=O}}class ln extends nt{constructor({logits:T}){super(),this.logits=T}}class dd extends nt{constructor({logits:T,past_key_values:O}){super(),this.logits=T,this.past_key_values=O}}class Au extends nt{constructor({alphas:T}){super(),this.alphas=T}}class cd extends nt{constructor({waveform:T,spectrogram:O}){super(),this.waveform=T,this.spectrogram=O}}},"./src/models/whisper/common_whisper.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{WHISPER_LANGUAGE_MAPPING:()=>re,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>ke,whisper_language_to_code:()=>ze});const P=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],re=new Map(P),ke=new Map([...P.map(([Oe,V])=>[V,Oe]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function ze(Oe){Oe=Oe.toLowerCase();let V=ke.get(Oe);if(V===void 0)if(re.has(Oe))V=Oe;else{const H=Oe.length===2?re.keys():re.values();throw new Error(`Language "${Oe}" is not supported. Must be one of: ${JSON.stringify(H)}`)}return V}},"./src/models/whisper/generation_whisper.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{WhisperGenerationConfig:()=>re});var P=m("./src/generation/configuration_utils.js");class re extends P.GenerationConfig{constructor(){super(...arguments);De(this,"return_timestamps",null);De(this,"return_token_timestamps",null);De(this,"num_frames",null);De(this,"alignment_heads",null);De(this,"task",null);De(this,"language",null);De(this,"no_timestamps_token_id",null);De(this,"prompt_ids",null);De(this,"is_multilingual",null);De(this,"lang_to_id",null);De(this,"task_to_id",null);De(this,"max_initial_timestamp_index",1)}}},"./src/ops/registry.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{TensorOpRegistry:()=>ze});var P=m("./src/backends/onnx.js"),re=m("./src/utils/tensor.js");const ke=async(Oe,V,A)=>{const H=await(0,P.createInferenceSession)(new Uint8Array(Oe),V);return async B=>{const le=Object.fromEntries(Object.entries(B).map(([pe,fe])=>[pe,fe.ort_tensor])),de=await H.run(le);return Array.isArray(A)?A.map(pe=>new re.Tensor(de[pe])):new re.Tensor(de[A])}};class ze{static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=ke([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=ke([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=ke([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=ke([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=ke([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=ke([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}}De(ze,"session_options",{})},"./src/pipelines.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{AudioClassificationPipeline:()=>Ve,AutomaticSpeechRecognitionPipeline:()=>pt,DepthEstimationPipeline:()=>ft,DocumentQuestionAnsweringPipeline:()=>ue,FeatureExtractionPipeline:()=>Se,FillMaskPipeline:()=>ye,ImageClassificationPipeline:()=>Tt,ImageFeatureExtractionPipeline:()=>Be,ImageSegmentationPipeline:()=>$e,ImageToImagePipeline:()=>xt,ImageToTextPipeline:()=>ot,ObjectDetectionPipeline:()=>Me,Pipeline:()=>fe,QuestionAnsweringPipeline:()=>he,SummarizationPipeline:()=>K,Text2TextGenerationPipeline:()=>ge,TextClassificationPipeline:()=>q,TextGenerationPipeline:()=>j,TextToAudioPipeline:()=>nt,TokenClassificationPipeline:()=>ae,TranslationPipeline:()=>R,ZeroShotAudioClassificationPipeline:()=>Ge,ZeroShotClassificationPipeline:()=>Ce,ZeroShotImageClassificationPipeline:()=>X,ZeroShotObjectDetectionPipeline:()=>je,pipeline:()=>gt});var P=m("./src/tokenizers.js"),re=m("./src/models.js"),ke=m("./src/processors.js"),ze=m("./src/utils/generic.js"),Oe=m("./src/utils/core.js"),V=m("./src/utils/maths.js"),A=m("./src/utils/audio.js"),H=m("./src/utils/tensor.js"),B=m("./src/utils/image.js");async function le(Ke){return Array.isArray(Ke)||(Ke=[Ke]),await Promise.all(Ke.map(ce=>B.RawImage.read(ce)))}async function de(Ke,ce){return Array.isArray(Ke)||(Ke=[Ke]),await Promise.all(Ke.map(Re=>typeof Re=="string"||Re instanceof URL?(0,A.read_audio)(Re,ce):Re instanceof Float64Array?new Float32Array(Re):Re))}function pe(Ke,ce){ce&&(Ke=Ke.map(st=>st|0));const[Re,Je,rt,et]=Ke;return{xmin:Re,ymin:Je,xmax:rt,ymax:et}}class fe extends ze.Callable{constructor({task:ce,model:Re,tokenizer:Je=null,processor:rt=null}){super(),this.task=ce,this.model=Re,this.tokenizer=Je,this.processor=rt}async dispose(){await this.model.dispose()}}class q extends fe{constructor(ce){super(ce)}async _call(ce,{top_k:Re=1}={}){const Je=this.tokenizer(ce,{padding:!0,truncation:!0}),rt=await this.model(Je),et=this.model.config.problem_type==="multi_label_classification"?kt=>kt.sigmoid():kt=>new H.Tensor("float32",(0,V.softmax)(kt.data),kt.dims),st=this.model.config.id2label,bt=[];for(const kt of rt.logits){const Et=et(kt),Ot=await(0,H.topk)(Et,Re),S=Ot[0].tolist(),D=Ot[1].tolist().map((ne,xe)=>({label:st?st[ne]:`LABEL_${ne}`,score:S[xe]}));Re===1?bt.push(...D):bt.push(D)}return Array.isArray(ce)||Re===1?bt:bt[0]}}class ae extends fe{constructor(ce){super(ce)}async _call(ce,{ignore_labels:Re=["O"]}={}){const Je=Array.isArray(ce),rt=this.tokenizer(Je?ce:[ce],{padding:!0,truncation:!0}),st=(await this.model(rt)).logits,bt=this.model.config.id2label,kt=[];for(let Et=0;Et$t==this.tokenizer.sep_token_id);kt[S].map(($t,Ne)=>$t==1&&(Ne===0||Ne>D&&Et.findIndex(z=>z==Y[Ne])===-1));const ne=et[S].tolist(),xe=st[S].tolist();for(let $t=1;$tNe==Y[$t])!==-1)&&(ne[$t]=-1/0,xe[$t]=-1/0);const ut=(0,V.softmax)(ne).map(($t,Ne)=>[$t,Ne]),ct=(0,V.softmax)(xe).map(($t,Ne)=>[$t,Ne]);ut[0][0]=0,ct[0][0]=0;const Ut=(0,Oe.product)(ut,ct).filter($t=>$t[0][1]<=$t[1][1]).map($t=>[$t[0][1],$t[1][1],$t[0][0]*$t[1][0]]).sort(($t,Ne)=>Ne[2]-$t[2]);for(let $t=0;$tne==this.tokenizer.mask_token_id);if(Et===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const Ot=rt[bt][Et],S=await(0,H.topk)(new H.Tensor("float32",(0,V.softmax)(Ot.data),Ot.dims),Re),Y=S[0].tolist(),D=S[1].tolist();et.push(D.map((ne,xe)=>{const ut=kt.slice();return ut[Et]=ne,{score:Y[xe],token:Number(ne),token_str:this.tokenizer.model.vocab[ne],sequence:this.tokenizer.decode(ut,{skip_special_tokens:!0})}}))}return Array.isArray(ce)?et:et[0]}}class ge extends fe{constructor(Re){super(Re);De(this,"_key","generated_text")}async _call(Re,Je={}){Array.isArray(Re)||(Re=[Re]),this.model.config.prefix&&(Re=Re.map(Et=>this.model.config.prefix+Et));const rt=this.model.config.task_specific_params;rt&&rt[this.task]&&rt[this.task].prefix&&(Re=Re.map(Et=>rt[this.task].prefix+Et));const et=this.tokenizer,st={padding:!0,truncation:!0};let bt;this instanceof R&&"_build_translation_inputs"in et?bt=et._build_translation_inputs(Re,st,Je):bt=et(Re,st);const kt=await this.model.generate({...bt,...Je});return et.batch_decode(kt,{skip_special_tokens:!0}).map(Et=>({[this._key]:Et}))}}class K extends ge{constructor(Re){super(Re);De(this,"_key","summary_text")}}class R extends ge{constructor(Re){super(Re);De(this,"_key","translation_text")}}function W(Ke){return Array.isArray(Ke)&&Ke.every(ce=>"role"in ce&&"content"in ce)}class j extends fe{constructor(ce){super(ce)}async _call(ce,Re={}){let Je=!1,rt=!1,et;if(typeof ce=="string")et=ce=[ce];else if(Array.isArray(ce)&&ce.every(D=>typeof D=="string"))Je=!0,et=ce;else{if(W(ce))ce=[ce];else if(Array.isArray(ce)&&ce.every(W))Je=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");rt=!0,et=ce.map(D=>this.tokenizer.apply_chat_template(D,{tokenize:!1,add_generation_prompt:!0}))}const st=Re.add_special_tokens??!1,bt=rt?!1:Re.return_full_text??!0;this.tokenizer.padding_side="left";const kt=this.tokenizer(et,{add_special_tokens:st,padding:!0,truncation:!0}),Et=await this.model.generate({...kt,...Re}),Ot=this.tokenizer.batch_decode(Et,{skip_special_tokens:!0});let S;!bt&&kt.input_ids.dims.at(-1)>0&&(S=this.tokenizer.batch_decode(kt.input_ids,{skip_special_tokens:!0}).map(D=>D.length));const Y=Array.from({length:ce.length},D=>[]);for(let D=0;D[Re.toLowerCase(),Je])),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(ce,Re,{hypothesis_template:Je="This example is {}.",multi_label:rt=!1}={}){const et=Array.isArray(ce);et||(ce=[ce]),Array.isArray(Re)||(Re=[Re]);const st=Re.map(Et=>Je.replace("{}",Et)),bt=rt||Re.length===1,kt=[];for(const Et of ce){const Ot=[];for(const D of st){const ne=this.tokenizer(Et,{text_pair:D,padding:!0,truncation:!0}),xe=await this.model(ne);bt?Ot.push([xe.logits.data[this.contradiction_id],xe.logits.data[this.entailment_id]]):Ot.push(xe.logits.data[this.entailment_id])}const Y=(bt?Ot.map(D=>(0,V.softmax)(D)[1]):(0,V.softmax)(Ot)).map((D,ne)=>[D,ne]).sort((D,ne)=>ne[0]-D[0]);kt.push({sequence:Et,labels:Y.map(D=>Re[D[1]]),scores:Y.map(D=>D[0])})}return et?kt:kt[0]}}class Se extends fe{constructor(ce){super(ce)}async _call(ce,{pooling:Re="none",normalize:Je=!1,quantize:rt=!1,precision:et="binary"}={}){const st=this.tokenizer(ce,{padding:!0,truncation:!0}),bt=await this.model(st);let kt=bt.last_hidden_state??bt.logits??bt.token_embeddings;if(Re!=="none")if(Re==="mean")kt=(0,H.mean_pooling)(kt,st.attention_mask);else if(Re==="cls")kt=kt.slice(null,0);else throw Error(`Pooling method '${Re}' not supported.`);return Je&&(kt=kt.normalize(2,-1)),rt&&(kt=(0,H.quantize_embeddings)(kt,et)),kt}}class Be extends fe{constructor(ce){super(ce)}async _call(ce,{pool:Re=null}={}){const Je=await le(ce),{pixel_values:rt}=await this.processor(Je),et=await this.model({pixel_values:rt});let st;if(Re){if(!("pooler_output"in et))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");st=et.pooler_output}else st=et.last_hidden_state??et.logits??et.image_embeds;return st}}class Ve extends fe{constructor(ce){super(ce)}async _call(ce,{top_k:Re=5}={}){const Je=this.processor.feature_extractor.config.sampling_rate,rt=await de(ce,Je),et=this.model.config.id2label,st=[];for(const bt of rt){const kt=await this.processor(bt),Ot=(await this.model(kt)).logits[0],S=await(0,H.topk)(new H.Tensor("float32",(0,V.softmax)(Ot.data),Ot.dims),Re),Y=S[0].tolist(),ne=S[1].tolist().map((xe,ut)=>({label:et?et[xe]:`LABEL_${xe}`,score:Y[ut]}));st.push(ne)}return Array.isArray(ce)?st:st[0]}}class Ge extends fe{constructor(ce){super(ce)}async _call(ce,Re,{hypothesis_template:Je="This is a sound of {}."}={}){const rt=!Array.isArray(ce);rt&&(ce=[ce]);const et=Re.map(Ot=>Je.replace("{}",Ot)),st=this.tokenizer(et,{padding:!0,truncation:!0}),bt=this.processor.feature_extractor.config.sampling_rate,kt=await de(ce,bt),Et=[];for(const Ot of kt){const S=await this.processor(Ot),Y=await this.model({...st,...S}),D=(0,V.softmax)(Y.logits_per_audio.data);Et.push([...D].map((ne,xe)=>({score:ne,label:Re[xe]})))}return rt?Et[0]:Et}}class pt extends fe{constructor(ce){super(ce)}async _call(ce,Re={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(ce,Re);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(ce,Re);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(ce,Re){Re.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),Re.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const Je=!Array.isArray(ce);Je&&(ce=[ce]);const rt=this.processor.feature_extractor.config.sampling_rate,et=await de(ce,rt),st=[];for(const bt of et){const kt=await this.processor(bt),Ot=(await this.model(kt)).logits[0],S=[];for(const D of Ot)S.push((0,V.max)(D.data)[1]);const Y=this.tokenizer.decode(S);st.push({text:Y})}return Je?st[0]:st}async _call_whisper(ce,Re){const Je=Re.return_timestamps??!1,rt=Re.chunk_length_s??0,et=Re.force_full_sequences??!1;let st=Re.stride_length_s??null;const bt={...Re};Je==="word"&&(bt.return_token_timestamps=!0,bt.return_timestamps=!1);const kt=!Array.isArray(ce);kt&&(ce=[ce]);const Et=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,Ot=this.processor.feature_extractor.config.hop_length,S=this.processor.feature_extractor.config.sampling_rate,Y=await de(ce,S),D=[];for(const ne of Y){let xe=[];if(rt>0){if(st===null)st=rt/6;else if(rt<=st)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const Ut=S*rt,$t=S*st,Ne=Ut-2*$t;let z=0;for(;;){const ee=z+Ut,Ee=ne.subarray(z,ee),Xe=await this.processor(Ee),We=z===0,Ze=ee>=ne.length;if(xe.push({stride:[Ee.length,We?0:$t,Ze?0:$t],input_features:Xe.input_features,is_last:Ze}),Ze)break;z+=Ne}}else xe=[{stride:[ne.length,0,0],input_features:(await this.processor(ne)).input_features,is_last:!0}];for(const Ut of xe){bt.num_frames=Math.floor(Ut.stride[0]/Ot);const $t=await this.model.generate({inputs:Ut.input_features,...bt});Je==="word"?(Ut.tokens=$t.sequences.tolist()[0],Ut.token_timestamps=$t.token_timestamps.tolist()[0].map(Ne=>(0,V.round)(Ne,2))):Ut.tokens=$t[0].tolist(),Ut.stride=Ut.stride.map(Ne=>Ne/S)}const[ut,ct]=this.tokenizer._decode_asr(xe,{time_precision:Et,return_timestamps:Je,force_full_sequences:et});D.push({text:ut,...ct})}return kt?D[0]:D}}class ot extends fe{constructor(ce){super(ce)}async _call(ce,Re={}){const Je=Array.isArray(ce),rt=await le(ce),{pixel_values:et}=await this.processor(rt),st=[];for(const bt of et){bt.dims=[1,...bt.dims];const kt=await this.model.generate({inputs:bt,...Re}),Et=this.tokenizer.batch_decode(kt,{skip_special_tokens:!0}).map(Ot=>({generated_text:Ot.trim()}));st.push(Et)}return Je?st:st[0]}}class Tt extends fe{constructor(ce){super(ce)}async _call(ce,{top_k:Re=5}={}){const Je=await le(ce),{pixel_values:rt}=await this.processor(Je),et=await this.model({pixel_values:rt}),st=this.model.config.id2label,bt=[];for(const kt of et.logits){const Et=await(0,H.topk)(new H.Tensor("float32",(0,V.softmax)(kt.data),kt.dims),Re),Ot=Et[0].tolist(),Y=Et[1].tolist().map((D,ne)=>({label:st?st[D]:`LABEL_${D}`,score:Ot[ne]}));bt.push(Y)}return Array.isArray(ce)?bt:bt[0]}}class $e extends fe{constructor(ce){super(ce),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(ce,{threshold:Re=.5,mask_threshold:Je=.5,overlap_mask_area_threshold:rt=.8,label_ids_to_fuse:et=null,target_sizes:st=null,subtask:bt=null}={}){if(Array.isArray(ce)&&ce.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const Et=await le(ce),Ot=Et.map(ct=>[ct.height,ct.width]),{pixel_values:S,pixel_mask:Y}=await this.processor(Et),D=await this.model({pixel_values:S,pixel_mask:Y});let ne=null;if(bt!==null)ne=this.subtasks_mapping[bt];else for(let[ct,Ut]of Object.entries(this.subtasks_mapping))if(Ut in this.processor.feature_extractor){ne=this.processor.feature_extractor[Ut].bind(this.processor.feature_extractor),bt=ct;break}const xe=this.model.config.id2label,ut=[];if(bt==="panoptic"||bt==="instance"){const ct=ne(D,Re,Je,rt,et,st??Ot)[0],Ut=ct.segmentation;for(const $t of ct.segments_info){const Ne=new Uint8ClampedArray(Ut.data.length);for(let ee=0;eeJe.replace("{}",Y)),bt=this.tokenizer(st,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:kt}=await this.processor(et),Et=await this.model({...bt,pixel_values:kt}),Ot=this.model.config.model_type==="siglip"?Y=>Y.sigmoid().data:Y=>(0,V.softmax)(Y.data),S=[];for(const Y of Et.logits_per_image){const ne=[...Ot(Y)].map((xe,ut)=>({score:xe,label:Re[ut]}));ne.sort((xe,ut)=>ut.score-xe.score),S.push(ne)}return rt?S:S[0]}}class Me extends fe{constructor(ce){super(ce)}async _call(ce,{threshold:Re=.9,percentage:Je=!1}={}){const rt=Array.isArray(ce);if(rt&&ce.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const et=await le(ce),st=Je?null:et.map(D=>[D.height,D.width]),{pixel_values:bt,pixel_mask:kt}=await this.processor(et),Et=await this.model({pixel_values:bt,pixel_mask:kt}),Ot=this.processor.feature_extractor.post_process_object_detection(Et,Re,st),S=this.model.config.id2label,Y=Ot.map(D=>D.boxes.map((ne,xe)=>({score:D.scores[xe],label:S[D.classes[xe]],box:pe(ne,!Je)})));return rt?Y:Y[0]}}class je extends fe{constructor(ce){super(ce)}async _call(ce,Re,{threshold:Je=.1,top_k:rt=null,percentage:et=!1}={}){const st=Array.isArray(ce),bt=await le(ce),kt=this.tokenizer(Re,{padding:!0,truncation:!0}),Et=await this.processor(bt),Ot=[];for(let S=0;S({score:ut.scores[$t],label:Re[ut.classes[$t]],box:pe(Ut,!et)})).sort((Ut,$t)=>$t.score-Ut.score);rt!==null&&(ct=ct.slice(0,rt)),Ot.push(ct)}return st?Ot:Ot[0]}}class ue extends fe{constructor(ce){super(ce)}async _call(ce,Re,Je={}){throw new Error("This pipeline is not yet supported in Transformers.js v3.")}}class nt extends fe{constructor(Re){super(Re);De(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=Re.vocoder??null}async _call(Re,{speaker_embeddings:Je=null}={}){return this.processor?this._call_text_to_spectrogram(Re,{speaker_embeddings:Je}):this._call_text_to_waveform(Re)}async _call_text_to_waveform(Re){const Je=this.tokenizer(Re,{padding:!0,truncation:!0}),{waveform:rt}=await this.model(Je),et=this.model.config.sampling_rate;return{audio:rt.data,sampling_rate:et}}async _call_text_to_spectrogram(Re,{speaker_embeddings:Je}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await re.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof Je=="string"||Je instanceof URL)&&(Je=new Float32Array(await(await fetch(Je)).arrayBuffer())),Je instanceof Float32Array)Je=new H.Tensor("float32",Je,[1,Je.length]);else if(!(Je instanceof H.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:rt}=this.tokenizer(Re,{padding:!0,truncation:!0}),{waveform:et}=await this.model.generate_speech(rt,Je,{vocoder:this.vocoder}),st=this.processor.feature_extractor.config.sampling_rate;return{audio:et.data,sampling_rate:st}}}class xt extends fe{constructor(ce){super(ce)}async _call(ce){const Re=await le(ce),Je=await this.processor(Re),rt=await this.model(Je),et=[];for(const st of rt.reconstruction){const bt=st.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");et.push(B.RawImage.fromTensor(bt))}return et.length>1?et:et[0]}}class ft extends fe{constructor(ce){super(ce)}async _call(ce){const Re=await le(ce),Je=await this.processor(Re),{predicted_depth:rt}=await this.model(Je),et=[];for(let st=0;st1?et:et[0]}}const yt=Object.freeze({"text-classification":{tokenizer:P.AutoTokenizer,pipeline:q,model:re.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:P.AutoTokenizer,pipeline:ae,model:re.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:P.AutoTokenizer,pipeline:he,model:re.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:P.AutoTokenizer,pipeline:ye,model:re.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:P.AutoTokenizer,pipeline:K,model:re.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:P.AutoTokenizer,pipeline:R,model:re.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:P.AutoTokenizer,pipeline:ge,model:re.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:P.AutoTokenizer,pipeline:j,model:re.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:P.AutoTokenizer,pipeline:Ce,model:re.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:Ve,model:re.AutoModelForAudioClassification,processor:ke.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:P.AutoTokenizer,pipeline:Ge,model:re.AutoModel,processor:ke.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:P.AutoTokenizer,pipeline:pt,model:[re.AutoModelForSpeechSeq2Seq,re.AutoModelForCTC],processor:ke.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:P.AutoTokenizer,pipeline:nt,model:[re.AutoModelForTextToWaveform,re.AutoModelForTextToSpectrogram],processor:[ke.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:P.AutoTokenizer,pipeline:ot,model:re.AutoModelForVision2Seq,processor:ke.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Tt,model:re.AutoModelForImageClassification,processor:ke.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:$e,model:[re.AutoModelForImageSegmentation,re.AutoModelForSemanticSegmentation],processor:ke.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:P.AutoTokenizer,pipeline:X,model:re.AutoModel,processor:ke.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:Me,model:re.AutoModelForObjectDetection,processor:ke.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:P.AutoTokenizer,pipeline:je,model:re.AutoModelForZeroShotObjectDetection,processor:ke.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:P.AutoTokenizer,pipeline:ue,model:re.AutoModelForDocumentQuestionAnswering,processor:ke.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:xt,model:re.AutoModelForImageToImage,processor:ke.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:ft,model:re.AutoModelForDepthEstimation,processor:ke.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:P.AutoTokenizer,pipeline:Se,model:re.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:ke.AutoProcessor,pipeline:Be,model:[re.AutoModelForImageFeatureExtraction,re.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),Qe=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function gt(Ke,ce=null,{progress_callback:Re=null,config:Je=null,cache_dir:rt=null,local_files_only:et=!1,revision:st="main",device:bt=null,dtype:kt=null,model_file_name:Et=null,session_options:Ot={}}={}){Ke=Qe[Ke]??Ke;const S=yt[Ke.split("_",1)[0]];if(!S)throw Error(`Unsupported pipeline: ${Ke}. Must be one of [${Object.keys(yt)}]`);ce||(ce=S.default.model,console.log(`No model specified. Using default model: "${ce}".`));const Y={progress_callback:Re,config:Je,cache_dir:rt,local_files_only:et,revision:st,device:bt,dtype:kt,model_file_name:Et,session_options:Ot},D=new Map([["tokenizer",S.tokenizer],["model",S.model],["processor",S.processor]]),ne=await Dt(D,ce,Y);ne.task=Ke,(0,Oe.dispatchCallback)(Re,{status:"ready",task:Ke,model:ce});const xe=S.pipeline;return new xe(ne)}async function Dt(Ke,ce,Re){const Je=Object.create(null),rt=[];for(let[et,st]of Ke.entries()){if(!st)continue;let bt;Array.isArray(st)?bt=new Promise(async(kt,Et)=>{var S,Y;let Ot;for(let D of st){if(D===null){kt(null);return}try{kt(await D.from_pretrained(ce,Re));return}catch(ne){if((S=ne.message)!=null&&S.includes("Unsupported model type"))Ot=ne;else if((Y=ne.message)!=null&&Y.includes("Could not locate file"))Ot=ne;else{Et(ne);return}}}Et(Ot)}):bt=st.from_pretrained(ce,Re),Je[et]=bt,rt.push(bt)}await Promise.all(rt);for(let[et,st]of Object.entries(Je))Je[et]=await st;return Je}},"./src/processors.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{ASTFeatureExtractor:()=>st,AutoProcessor:()=>$t,BeitFeatureExtractor:()=>ft,BitImageProcessor:()=>ye,CLIPFeatureExtractor:()=>K,CLIPImageProcessor:()=>R,ChineseCLIPFeatureExtractor:()=>W,ClapFeatureExtractor:()=>bt,ConvNextFeatureExtractor:()=>Ce,ConvNextImageProcessor:()=>Se,DPTFeatureExtractor:()=>ae,DPTImageProcessor:()=>he,DeiTFeatureExtractor:()=>xt,DetrFeatureExtractor:()=>gt,DonutFeatureExtractor:()=>yt,EfficientNetImageProcessor:()=>Ge,FeatureExtractor:()=>pe,Florence2Processor:()=>Ut,GLPNFeatureExtractor:()=>ge,ImageFeatureExtractor:()=>fe,MobileNetV1FeatureExtractor:()=>pt,MobileNetV2FeatureExtractor:()=>ot,MobileNetV3FeatureExtractor:()=>Tt,MobileNetV4FeatureExtractor:()=>$e,MobileViTFeatureExtractor:()=>X,MobileViTImageProcessor:()=>Me,NougatImageProcessor:()=>Qe,OwlViTFeatureExtractor:()=>je,OwlViTProcessor:()=>ct,Owlv2ImageProcessor:()=>ue,Processor:()=>S,PyAnnoteFeatureExtractor:()=>kt,PyAnnoteProcessor:()=>xe,RTDetrImageProcessor:()=>nt,SamImageProcessor:()=>Ke,SamProcessor:()=>Y,SeamlessM4TFeatureExtractor:()=>et,SegformerFeatureExtractor:()=>q,SiglipImageProcessor:()=>j,SpeechT5FeatureExtractor:()=>Ot,SpeechT5Processor:()=>ut,Swin2SRImageProcessor:()=>ce,ViTFeatureExtractor:()=>Be,ViTImageProcessor:()=>Ve,VitMatteImageProcessor:()=>Re,Wav2Vec2FeatureExtractor:()=>rt,Wav2Vec2ProcessorWithLM:()=>ne,WeSpeakerFeatureExtractor:()=>Et,WhisperFeatureExtractor:()=>Je,WhisperProcessor:()=>D,YolosFeatureExtractor:()=>Dt});var P=m("./src/utils/generic.js"),re=m("./src/utils/core.js"),ke=m("./src/utils/hub.js"),ze=m("./src/utils/maths.js"),Oe=m("./src/utils/tensor.js");m("./src/utils/image.js");var V=m("./src/utils/audio.js");function A([Ne,z,ee,Ee]){return[Ne-ee/2,z-Ee/2,Ne+ee/2,z+Ee/2]}function H(Ne,z=.5,ee=null,Ee=!1){const Xe=Ne.logits,We=Ne.pred_boxes,[Ze,vt,_t]=Xe.dims;if(ee!==null&&ee.length!==Ze)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let zt=[];for(let Ct=0;Ctz&&nr.push(rr)}else{let rr=(0,ze.max)(ir.data)[1];if(rr===_t-1||(dr=(0,ze.softmax)(ir.data),dr[rr]Jr*jt[(Br+1)%2])),Qt.boxes.push(Dr),Qt.classes.push(rr),Qt.scores.push(dr[rr])}}zt.push(Qt)}return zt}function B(Ne,z){var ee;if(!(Ne instanceof Float32Array||Ne instanceof Float64Array))throw new Error(`${z} expects input to be a Float32Array or a Float64Array, but got ${((ee=Ne==null?void 0:Ne.constructor)==null?void 0:ee.name)??typeof Ne} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function le(Ne,z,ee=0,Ee=null){const Xe=Ne/z;let We=(0,ze.bankers_round)(Xe)*z;return Ee!==null&&We>Ee&&(We=Math.floor(Xe)*z),WeWe?zt=Math.floor(We*_t/Xe):We>Xe&&(_t=Math.floor(Xe*zt/We)),await z.resize(zt,_t,{resample:Ee}))}async crop_margin(z,ee=200){const Ee=z.clone().grayscale(),Xe=(0,ze.min)(Ee.data)[0],Ze=(0,ze.max)(Ee.data)[0]-Xe;if(Ze===0)return z;const vt=ee/255;let _t=Ee.width,zt=Ee.height,Ct=0,jt=0;const Qt=Ee.data;for(let at=0;atthis.preprocess(We)));return{pixel_values:(0,Oe.stack)(Ee.map(We=>We.pixel_values),0),original_sizes:Ee.map(We=>We.original_size),reshaped_input_sizes:Ee.map(We=>We.reshaped_input_size)}}}class q extends fe{post_process_semantic_segmentation(z,ee=null){const Ee=z.logits,Xe=Ee.dims[0];if(ee!==null&&ee.length!==Xe)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const We=[];for(let Ze=0;ZeQt[rr]&&(Qt[rr]=dr[rr],at[rr]=nr)}const Zt=new Array(_t.dims[0]),Yt=jt.data;for(let nr=0;nrnr!==void 0);We.push({segmentation:jt,labels:ir})}return We}}class ae extends fe{}class he extends ae{}class ye extends fe{}class ge extends fe{}class K extends fe{}class R extends K{}class W extends fe{}class j extends fe{}class Ce extends fe{constructor(z){super(z),this.crop_pct=this.config.crop_pct??.875}async resize(z){var Ee;const ee=(Ee=this.size)==null?void 0:Ee.shortest_edge;if(ee===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(ee<384){const Xe=Math.floor(ee/this.crop_pct),[We,Ze]=this.get_resize_output_image_size(z,{shortest_edge:Xe});z=await z.resize(We,Ze,{resample:this.resample}),z=await z.center_crop(ee,ee)}else z=await z.resize(ee,ee,{resample:this.resample});return z}}class Se extends Ce{}class Be extends fe{}class Ve extends fe{}class Ge extends fe{constructor(z){super(z),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(ee=>ee*ee))}}class pt extends fe{}class ot extends fe{}class Tt extends fe{}class $e extends fe{}class X extends fe{}class Me extends X{}class je extends fe{post_process_object_detection(...z){return H(...z)}}class ue extends je{}class nt extends fe{post_process_object_detection(...z){return H(...z)}}class xt extends fe{}class ft extends fe{}class yt extends fe{pad_image(z,ee,Ee,Xe={}){const[We,Ze,vt]=ee;let _t=this.image_mean;Array.isArray(this.image_mean)||(_t=new Array(vt).fill(_t));let zt=this.image_std;Array.isArray(zt)||(zt=new Array(vt).fill(_t));const Ct=_t.map((jt,Qt)=>-jt/zt[Qt]);return super.pad_image(z,ee,Ee,{center:!0,constant_values:Ct,...Xe})}}class Qe extends yt{}class gt extends fe{async _call(z){const ee=await super._call(z),Ee=[ee.pixel_values.dims[0],64,64],Xe=new Oe.Tensor("int64",new BigInt64Array(Ee.reduce((We,Ze)=>We*Ze)).fill(1n),Ee);return{...ee,pixel_mask:Xe}}post_process_object_detection(...z){return H(...z)}remove_low_and_no_objects(z,ee,Ee,Xe){let We=[],Ze=[],vt=[];for(let _t=0;_tEe&&(We.push(Ct),Ze.push(at),vt.push(jt))}return[We,Ze,vt]}check_segment_validity(z,ee,Ee,Xe=.5,We=.8){let Ze=[],vt=0,_t=0;const zt=ee[Ee].data;for(let jt=0;jt=Xe&&++_t;let Ct=vt>0&&_t>0;return Ct&&(Ct=vt/_t>We),[Ct,Ze]}compute_segments(z,ee,Ee,Xe,We,Ze=null,vt=null){let[_t,zt]=vt??z[0].dims,Ct=new Oe.Tensor("int32",new Int32Array(_t*zt),[_t,zt]),jt=[];if(vt!==null)for(let ir=0;irat[rr]&&(Qt[rr]=ir,at[rr]=dr[rr])}let Zt=0;const Yt=Ct.data;for(let ir=0;irXe!==ee.dims[We]))throw Error(`The first ${Ee.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new Oe.Tensor("int64",z.flat(1/0).map(BigInt),Ee)}async _call(z,{input_points:ee=null,input_labels:Ee=null,input_boxes:Xe=null}={}){const We=await super._call(z);if(ee&&(We.input_points=this.reshape_input_points(ee,We.original_sizes,We.reshaped_input_sizes)),Ee){if(!We.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");We.input_labels=this.add_input_labels(Ee,We.input_points)}return Xe&&(We.input_boxes=this.reshape_input_points(Xe,We.original_sizes,We.reshaped_input_sizes,!0)),We}async post_process_masks(z,ee,Ee,{mask_threshold:Xe=0,binarize:We=!0,pad_size:Ze=null}={}){const vt=[];Ze=Ze??this.pad_size;const _t=[Ze.height,Ze.width];for(let zt=0;ztXe&&(Zt[Yt]=1);Qt=new Oe.Tensor("bool",Zt,Qt.dims)}vt.push(Qt)}return vt}generate_crop_boxes(z,ee,{crop_n_layers:Ee=0,overlap_ratio:Xe=.3413333333333333,points_per_crop:We=32,crop_n_points_downscale_factor:Ze=1}={}){}}class ce extends fe{pad_image(z,ee,Ee,Xe={}){const[We,Ze,vt]=ee;return super.pad_image(z,ee,{width:Ze+(Ee-Ze%Ee)%Ee,height:We+(Ee-We%Ee)%Ee},{mode:"symmetric",center:!1,constant_values:-1,...Xe})}}class Re extends fe{async _call(z,ee){Array.isArray(z)||(z=[z]),Array.isArray(ee)||(ee=[ee]);const Ee=await Promise.all(z.map(Ze=>this.preprocess(Ze))),Xe=await Promise.all(ee.map(Ze=>this.preprocess(Ze,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,Oe.stack)(Ee.map((Ze,vt)=>(0,Oe.cat)([Ze.pixel_values,Xe[vt].pixel_values],0)),0),original_sizes:Ee.map(Ze=>Ze.original_size),reshaped_input_sizes:Ee.map(Ze=>Ze.reshaped_input_size)}}}class Je extends pe{constructor(z){var ee;super(z),(ee=this.config).mel_filters??(ee.mel_filters=(0,V.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney")),this.window=(0,V.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(z){const ee=await(0,V.spectrogram)(z,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),Ee=ee.data,Xe=(0,ze.max)(Ee)[0];for(let We=0;Wethis.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),ee=z.slice(0,this.config.n_samples)):(ee=new Float32Array(this.config.n_samples),ee.set(z)),{input_features:(await this._extract_fbank_features(ee)).unsqueeze_(0)}}}class rt extends pe{_zero_mean_unit_var_norm(z){const Ee=z.reduce((We,Ze)=>We+Ze,0)/z.length,Xe=z.reduce((We,Ze)=>We+(Ze-Ee)**2,0)/z.length;return z.map(We=>(We-Ee)/Math.sqrt(Xe+1e-7))}async _call(z){B(z,"Wav2Vec2FeatureExtractor"),z instanceof Float64Array&&(z=new Float32Array(z));let ee=z;this.config.do_normalize&&(ee=this._zero_mean_unit_var_norm(ee));const Ee=[1,ee.length];return{input_values:new Oe.Tensor("float32",ee,Ee),attention_mask:new Oe.Tensor("int64",new BigInt64Array(ee.length).fill(1n),Ee)}}}class et extends pe{constructor(z){super(z);const ee=this.config.sampling_rate,Ee=(0,V.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ee/2),ee,null,"kaldi",!0);for(let Xe=0;XeEe*32768),(0,V.spectrogram)(z,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:ee,transpose:!0})}async _call(z,{padding:ee=!0,pad_to_multiple_of:Ee=2,do_normalize_per_mel_bins:Xe=!0,return_attention_mask:We=!0}={}){B(z,"SeamlessM4TFeatureExtractor");let Ze=await this._extract_fbank_features(z,this.config.max_length);if(Xe){const[Zt,Yt]=Ze.dims,ir=Ze.data;for(let nr=0;nr0){const dr=new Float32Array(Yt*(Zt+nr));dr.set(ir),dr.fill(this.config.padding_value,ir.length);const rr=Zt+nr;Ze=new Oe.Tensor(Ze.type,dr,[rr,Yt]),We&&(vt=new Oe.Tensor("int64",new BigInt64Array(rr),[1,rr]),vt.data.fill(1n,0,Zt))}}const[_t,zt]=Ze.dims,Ct=this.config.stride;if(_t%Ct!==0)throw new Error(`The number of frames (${_t}) must be a multiple of the stride (${Ct}).`);const Qt=Ze.view(1,Math.floor(_t/Ct),zt*Ct),at={input_features:Qt};if(We){const Zt=Qt.dims[1],Yt=new BigInt64Array(Zt);if(vt){const ir=vt.data;for(let nr=1,dr=0;nr<_t;nr+=Ct,++dr)Yt[dr]=ir[nr]}else Yt.fill(1n);at.attention_mask=new Oe.Tensor("int64",Yt,[1,Zt])}return at}}class st extends pe{constructor(z){super(z);const ee=this.config.sampling_rate,Ee=(0,V.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ee/2),ee,null,"kaldi",!0);for(let Xe=0;Xe0)if(Ee==="rand_trunc"){const vt=Math.floor(Math.random()*(Ze+1));z=z.subarray(vt,vt+ee),We=await this._extract_fbank_features(z,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${Ee}" not implemented`);else{if(Ze<0){let vt=new Float64Array(ee);if(vt.set(z),Xe==="repeat")for(let _t=z.length;_t({id:_t,start:zt*Ee,end:Ct*Ee,confidence:jt/(Ct-zt)})))}return Xe}}class Et extends pe{constructor(z){super(z);const ee=this.config.sampling_rate,Ee=(0,V.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(ee/2),ee,null,"kaldi",!0);for(let Xe=0;Xeee*32768),(0,V.spectrogram)(z,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(z){B(z,"WeSpeakerFeatureExtractor");const ee=(await this._extract_fbank_features(z)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const Ee=ee.mean(1).data,Xe=ee.data,[We,Ze,vt]=ee.dims;for(let _t=0;_t/gm,bboxes:/([^<]+)?/gm},this.size_per_bin=1e3}construct_prompts(z){typeof z=="string"&&(z=[z]);const ee=[];for(const Ee of z)if(this.task_prompts_without_inputs.has(Ee))ee.push(this.task_prompts_without_inputs.get(Ee));else{for(const[Xe,We]of this.task_prompts_with_input)if(Ee.includes(Xe)){ee.push(We.replaceAll("{input}",Ee).replaceAll(Xe,""));break}ee.length!==z.length&&ee.push(Ee)}return ee}post_process_generation(z,ee,Ee){const Xe=this.tasks_answer_post_processing_type.get(ee)??"pure_text";z=z.replaceAll("","").replaceAll("","");let We;switch(Xe){case"pure_text":We=z;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const Ze=Xe==="ocr"?"quad_boxes":"bboxes",vt=z.matchAll(this.regexes[Ze]),_t=[],zt=[];for(const[Ct,jt,...Qt]of vt)_t.push(jt?jt.trim():_t.at(-1)??""),zt.push(Qt.map((at,Zt)=>(Number(at)+.5)/this.size_per_bin*Ee[Zt%2]));We={labels:_t,[Ze]:zt};break;default:throw new Error(`Task "${ee}" (of type "${Xe}") not yet implemented.`)}return{[ee]:We}}}class $t{static async from_pretrained(z,{progress_callback:ee=null,config:Ee=null,cache_dir:Xe=null,local_files_only:We=!1,revision:Ze="main"}={}){let vt=Ee??await(0,ke.getModelJSON)(z,"preprocessor_config.json",!0,{progress_callback:ee,config:Ee,cache_dir:Xe,local_files_only:We,revision:Ze}),_t=vt.feature_extractor_type??vt.image_processor_type,zt=this.FEATURE_EXTRACTOR_CLASS_MAPPING[_t];if(!zt)if(vt.size!==void 0)console.warn(`Feature extractor type "${_t}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),zt=fe;else throw new Error(`Unknown Feature Extractor type: ${_t}`);let Ct=this.PROCESSOR_CLASS_MAPPING[vt.processor_class]??S,jt=new zt(vt);return new Ct(jt)}}De($t,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor:fe,WhisperFeatureExtractor:Je,ViTFeatureExtractor:Be,MobileViTFeatureExtractor:X,MobileViTImageProcessor:Me,MobileNetV1FeatureExtractor:pt,MobileNetV2FeatureExtractor:ot,MobileNetV3FeatureExtractor:Tt,MobileNetV4FeatureExtractor:$e,OwlViTFeatureExtractor:je,Owlv2ImageProcessor:ue,CLIPFeatureExtractor:K,CLIPImageProcessor:R,Florence2Processor:Ut,ChineseCLIPFeatureExtractor:W,SiglipImageProcessor:j,ConvNextFeatureExtractor:Ce,ConvNextImageProcessor:Se,SegformerFeatureExtractor:q,BitImageProcessor:ye,DPTImageProcessor:he,DPTFeatureExtractor:ae,GLPNFeatureExtractor:ge,BeitFeatureExtractor:ft,DeiTFeatureExtractor:xt,DetrFeatureExtractor:gt,RTDetrImageProcessor:nt,YolosFeatureExtractor:Dt,DonutFeatureExtractor:yt,NougatImageProcessor:Qe,EfficientNetImageProcessor:Ge,ViTImageProcessor:Ve,VitMatteImageProcessor:Re,SamImageProcessor:Ke,Swin2SRImageProcessor:ce,Wav2Vec2FeatureExtractor:rt,SeamlessM4TFeatureExtractor:et,SpeechT5FeatureExtractor:Ot,ASTFeatureExtractor:st,ClapFeatureExtractor:bt,PyAnnoteFeatureExtractor:kt,WeSpeakerFeatureExtractor:Et}),De($t,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor:D,Wav2Vec2ProcessorWithLM:ne,PyAnnoteProcessor:xe,SamProcessor:Y,SpeechT5Processor:ut,OwlViTProcessor:ct,Florence2Processor:Ut})},"./src/tokenizers.js":(Ft,Te,m)=>{m.r(Te),m.d(Te,{AlbertTokenizer:()=>Yt,AutoTokenizer:()=>Ci,BartTokenizer:()=>Hr,BertTokenizer:()=>Zt,BlenderbotSmallTokenizer:()=>An,BlenderbotTokenizer:()=>mn,BloomTokenizer:()=>Rr,CLIPTokenizer:()=>sr,CamembertTokenizer:()=>Nt,CodeGenTokenizer:()=>fn,CodeLlamaTokenizer:()=>qn,CohereTokenizer:()=>qr,ConvBertTokenizer:()=>Jr,DebertaTokenizer:()=>dr,DebertaV2Tokenizer:()=>rr,DistilBertTokenizer:()=>dt,ElectraTokenizer:()=>ii,EsmTokenizer:()=>en,FalconTokenizer:()=>$n,GPT2Tokenizer:()=>Gi,GPTNeoXTokenizer:()=>Sn,GemmaTokenizer:()=>pn,Grok1Tokenizer:()=>Ni,HerbertTokenizer:()=>Dr,LlamaTokenizer:()=>qi,M2M100Tokenizer:()=>an,MBart50Tokenizer:()=>Xr,MBartTokenizer:()=>di,MPNetTokenizer:()=>kn,MarianTokenizer:()=>En,MobileBertTokenizer:()=>ir,NllbTokenizer:()=>Hi,NougatTokenizer:()=>_n,PreTrainedTokenizer:()=>at,Qwen2Tokenizer:()=>Kn,RoFormerTokenizer:()=>Br,RobertaTokenizer:()=>Ri,SiglipTokenizer:()=>on,SpeechT5Tokenizer:()=>In,SqueezeBertTokenizer:()=>nr,T5Tokenizer:()=>Zi,TokenizerModel:()=>Be,VitsTokenizer:()=>Fn,Wav2Vec2CTCTokenizer:()=>Pn,WhisperTokenizer:()=>hn,XLMRobertaTokenizer:()=>Cn,XLMTokenizer:()=>Ht,is_chinese_char:()=>ge});var P=m("./src/utils/generic.js"),re=m("./src/utils/core.js"),ke=m("./src/utils/hub.js"),ze=m("./src/utils/maths.js"),Oe=m("./src/utils/tensor.js"),V=m("./src/utils/data-structures.js"),A=m("./node_modules/@huggingface/jinja/dist/index.js"),H=m("./src/models/whisper/common_whisper.js"),B=m("./src/utils/constants.js");async function le(Ae,x){const N=await Promise.all([(0,ke.getModelJSON)(Ae,"tokenizer.json",!0,x),(0,ke.getModelJSON)(Ae,"tokenizer_config.json",!0,x)]);return x.legacy!==null&&(N[1].legacy=x.legacy),N}function de(Ae,x){const N=[];let se=0;for(const be of Ae.matchAll(x)){const ve=be[0];se0&&N.push(ve),se=be.index+ve.length}return se=19968&&Ae<=40959||Ae>=13312&&Ae<=19903||Ae>=131072&&Ae<=173791||Ae>=173824&&Ae<=177983||Ae>=177984&&Ae<=178207||Ae>=178208&&Ae<=183983||Ae>=63744&&Ae<=64255||Ae>=194560&&Ae<=195103}function K(Ae,x,N){const se=[];let be=0;for(;bethis.tokens_to_ids.get(N)??this.unk_token_id)}convert_ids_to_tokens(x){return x.map(N=>this.vocab[N]??this.unk_token)}}class Ve extends Be{constructor(x){super(x),this.tokens_to_ids=fe(x.vocab),this.unk_token_id=this.tokens_to_ids.get(x.unk_token),this.unk_token=x.unk_token,this.max_input_chars_per_word=x.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[N,se]of this.tokens_to_ids)this.vocab[se]=N}encode(x){const N=[];for(const se of x){const be=[...se];if(be.length>this.max_input_chars_per_word){N.push(this.unk_token);continue}let ve=!1,qe=0;const St=[];for(;qe0&&(Vt=this.config.continuing_subword_prefix+Vt),this.tokens_to_ids.has(Vt)){Pt=Vt;break}--At}if(Pt===null){ve=!0;break}St.push(Pt),qe=At}ve?N.push(this.unk_token):N.push(...St)}return N}}class Ge extends Be{constructor(x,N){super(x);const se=x.vocab.length;this.vocab=new Array(se),this.scores=new Array(se);for(let be=0;be[be,ve])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=N.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=(0,ze.min)(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new V.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(x){const N=x.sentence,se=N.length;let be=0;for(;be{const Ae=[...Array.from({length:94},(be,ve)=>ve+33),...Array.from({length:12},(be,ve)=>ve+161),...Array.from({length:82},(be,ve)=>ve+174)],x=Ae.slice();let N=0;for(let be=0;be<256;++be)Ae.includes(be)||(Ae.push(be),x.push(256+N),N+=1);const se=x.map(be=>String.fromCharCode(be));return Object.fromEntries(Ae.map((be,ve)=>[be,se[ve]]))})(),ot=(0,re.reverseDictionary)(pt);class Tt extends Be{constructor(x){super(x),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=fe(x.vocab),this.unk_token_id=this.tokens_to_ids.get(x.unk_token),this.unk_token=x.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[N,se]of this.tokens_to_ids)this.vocab[se]=N;this.bpe_ranks=new Map(x.merges.map((N,se)=>[N,se])),this.merges=x.merges.map(N=>N.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=x.end_of_word_suffix,this.continuing_subword_suffix=x.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(x){if(x.length===0)return[];const N=this.cache.get(x);if(N!==void 0)return N;const se=Array.from(x);this.end_of_word_suffix&&(se[se.length-1]+=this.end_of_word_suffix);let be=[];if(se.length>1){const ve=new V.PriorityQueue((At,Pt)=>At.score`<0x${qe.toString(16).toUpperCase().padStart(2,"0")}>`)):N.push(this.unk_token)}return N}}class $e extends Be{constructor(x,N){super(x),this.tokens_to_ids=fe(N.target_lang?x.vocab[N.target_lang]:x.vocab),this.bos_token=N.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=N.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=N.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=N.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[se,be]of this.tokens_to_ids)this.vocab[be]=se}encode(x){return x}}class X extends P.Callable{constructor(x){super(),this.config=x}static fromConfig(x){if(x===null)return null;switch(x.type){case"BertNormalizer":return new Dt(x);case"Precompiled":return new We(x);case"Sequence":return new gt(x);case"Replace":return new Me(x);case"NFC":return new je(x);case"NFKC":return new ue(x);case"NFKD":return new nt(x);case"Strip":return new xt(x);case"StripAccents":return new ft(x);case"Lowercase":return new yt(x);case"Prepend":return new Qe(x);default:throw new Error(`Unknown Normalizer type: ${x.type}`)}}normalize(x){throw Error("normalize should be implemented in subclass.")}_call(x){return this.normalize(x)}}class Me extends X{normalize(x){const N=pe(this.config.pattern);return N===null?x:x.replaceAll(N,this.config.content)}}class je extends X{normalize(x){return x=x.normalize("NFC"),x}}class ue extends X{normalize(x){return x=x.normalize("NFKC"),x}}class nt extends X{normalize(x){return x=x.normalize("NFKD"),x}}class xt extends X{normalize(x){return this.config.strip_left&&this.config.strip_right?x=x.trim():(this.config.strip_left&&(x=x.trimStart()),this.config.strip_right&&(x=x.trimEnd())),x}}class ft extends X{normalize(x){return x=he(x),x}}class yt extends X{normalize(x){return x=x.toLowerCase(),x}}class Qe extends X{normalize(x){return x=this.config.prepend+x,x}}class gt extends X{constructor(x){super(x),this.normalizers=x.normalizers.map(N=>X.fromConfig(N))}normalize(x){return this.normalizers.reduce((N,se)=>se.normalize(N),x)}}class Dt extends X{_tokenize_chinese_chars(x){const N=[];for(let se=0;sethis.pre_tokenize_text(se,N)):this.pre_tokenize_text(x,N)).flat()}_call(x,N){return this.pre_tokenize(x,N)}}class ce extends Ke{constructor(x){super(),this.pattern=new RegExp(`[^\\s${W}]+|[${W}]`,"gu")}pre_tokenize_text(x,N){return x.trim().match(this.pattern)||[]}}class Re extends Ke{constructor(x){super(),this.config=x,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=pt,this.text_encoder=new TextEncoder}pre_tokenize_text(x,N){return this.add_prefix_space&&!x.startsWith(" ")&&(x=" "+x),(this.use_regex?x.match(this.pattern)||[]:[x]).map(be=>Array.from(this.text_encoder.encode(be),ve=>this.byte_encoder[ve]).join(""))}}class Je extends Ke{constructor(x){super(),this.config=x,this.pattern=pe(this.config.pattern,this.config.invert)}pre_tokenize_text(x,N){return this.pattern===null?[]:this.config.invert?x.match(this.pattern)||[]:de(x,this.pattern)}}class rt extends Ke{constructor(x){super(),this.config=x,this.pattern=new RegExp(`[^${W}]+|[${W}]+`,"gu")}pre_tokenize_text(x,N){return x.match(this.pattern)||[]}}class et extends Ke{constructor(x){super(),this.config=x;const N=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(N,"gu")}pre_tokenize_text(x,N){return x.match(this.pattern)||[]}}class st extends P.Callable{constructor(x){super(),this.config=x}static fromConfig(x){if(x===null)return null;switch(x.type){case"TemplateProcessing":return new Et(x);case"ByteLevel":return new Ot(x);case"RobertaProcessing":return new kt(x);case"BertProcessing":return new bt(x);case"Sequence":return new S(x);default:throw new Error(`Unknown PostProcessor type: ${x.type}`)}}post_process(x,...N){throw Error("post_process should be implemented in subclass.")}_call(x,...N){return this.post_process(x,...N)}}class bt extends st{constructor(x){super(x),this.cls=x.cls[0],this.sep=x.sep[0]}post_process(x,N=null,{add_special_tokens:se=!0}={}){se&&(x=(0,re.mergeArrays)([this.cls],x,[this.sep]));let be=new Array(x.length).fill(0);if(N!==null){const ve=se&&this instanceof kt?[this.sep]:[],qe=se?[this.sep]:[];x=(0,re.mergeArrays)(x,ve,N,qe),be=(0,re.mergeArrays)(be,new Array(N.length+ve.length+qe.length).fill(1))}return{tokens:x,token_type_ids:be}}}class kt extends bt{}class Et extends st{constructor(x){super(x),this.single=x.single,this.pair=x.pair}post_process(x,N=null,{add_special_tokens:se=!0}={}){const be=N===null?this.single:this.pair;let ve=[],qe=[];for(const St of be)"SpecialToken"in St?se&&(ve.push(St.SpecialToken.id),qe.push(St.SpecialToken.type_id)):"Sequence"in St&&(St.Sequence.id==="A"?(ve=(0,re.mergeArrays)(ve,x),qe=(0,re.mergeArrays)(qe,new Array(x.length).fill(St.Sequence.type_id))):St.Sequence.id==="B"&&(ve=(0,re.mergeArrays)(ve,N),qe=(0,re.mergeArrays)(qe,new Array(N.length).fill(St.Sequence.type_id))));return{tokens:ve,token_type_ids:qe}}}class Ot extends st{post_process(x,N=null){return N&&(x=(0,re.mergeArrays)(x,N)),{tokens:x}}}class S extends st{constructor(x){super(x),this.processors=x.processors.map(N=>st.fromConfig(N))}post_process(x,N=null,se={}){let be;for(const ve of this.processors)if(ve instanceof Ot)x=ve.post_process(x).tokens,N&&(N=ve.post_process(N).tokens);else{const qe=ve.post_process(x,N,se);x=qe.tokens,be=qe.token_type_ids}return{tokens:x,token_type_ids:be}}}class Y extends P.Callable{constructor(x){super(),this.config=x,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=x.trim_offsets}static fromConfig(x){if(x===null)return null;switch(x.type){case"WordPiece":return new ct(x);case"Metaspace":return new Xe(x);case"ByteLevel":return new Ut(x);case"Replace":return new D(x);case"ByteFallback":return new ne(x);case"Fuse":return new xe(x);case"Strip":return new ut(x);case"Sequence":return new Ne(x);case"CTC":return new $t(x);case"BPEDecoder":return new z(x);default:throw new Error(`Unknown Decoder type: ${x.type}`)}}_call(x){return this.decode(x)}decode(x){return this.decode_chain(x).join("")}decode_chain(x){throw Error("`decode_chain` should be implemented in subclass.")}}class D extends Y{decode_chain(x){const N=pe(this.config.pattern);return N===null?x:x.map(se=>se.replaceAll(N,this.config.content))}}class ne extends Y{constructor(x){super(x),this.text_decoder=new TextDecoder}decode_chain(x){const N=[];let se=[];for(const be of x){let ve=null;if(be.length===6&&be.startsWith("<0x")&&be.endsWith(">")){const qe=parseInt(be.slice(3,5),16);isNaN(qe)||(ve=qe)}if(ve!==null)se.push(ve);else{if(se.length>0){const qe=this.text_decoder.decode(Uint8Array.from(se));N.push(qe),se=[]}N.push(be)}}if(se.length>0){const be=this.text_decoder.decode(Uint8Array.from(se));N.push(be),se=[]}return N}}class xe extends Y{decode_chain(x){return[x.join("")]}}class ut extends Y{constructor(x){super(x),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(x){return x.map(N=>{let se=0;for(let ve=0;ve(se!==0&&(N.startsWith(this.config.prefix)?N=N.replace(this.config.prefix,""):N=" "+N),this.cleanup&&(N=ae(N)),N))}}class Ut extends Y{constructor(x){super(x),this.byte_decoder=ot,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(x){const N=x.join(""),se=new Uint8Array([...N].map(ve=>this.byte_decoder[ve]));return this.text_decoder.decode(se)}decode_chain(x){const N=[];let se=[];for(const be of x)this.added_tokens.find(ve=>ve.content===be)!==void 0?(se.length>0&&(N.push(this.convert_tokens_to_string(se)),se=[]),N.push(be)):se.push(be);return se.length>0&&N.push(this.convert_tokens_to_string(se)),N}}class $t extends Y{constructor(x){super(x),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(x){if(x.length===0)return"";const N=[x[0]];for(let ve=1;veve!==this.pad_token).join("");return this.cleanup&&(be=ae(be).replaceAll(this.word_delimiter_token," ").trim()),be}decode_chain(x){return[this.convert_tokens_to_string(x)]}}class Ne extends Y{constructor(x){super(x),this.decoders=x.decoders.map(N=>Y.fromConfig(N))}decode_chain(x){return this.decoders.reduce((N,se)=>se.decode_chain(N),x)}}class z extends Y{constructor(x){super(x),this.suffix=this.config.suffix}decode_chain(x){return x.map((N,se)=>N.replaceAll(this.suffix,se===x.length-1?"":" "))}}class ee extends Y{decode_chain(x){let N="";for(let se=1;sese.normalize("NFKC")).join("～"):x=x.normalize("NFKC"),x}}class Ze extends Ke{constructor(x){super(),this.tokenizers=x.pretokenizers.map(N=>Ke.fromConfig(N))}pre_tokenize_text(x,N){return this.tokenizers.reduce((se,be)=>be.pre_tokenize(se,N),[x])}}class vt extends Ke{constructor(x){super()}pre_tokenize_text(x,N){return x.match(/\w+|[^\w\s]+/g)||[]}}class _t extends Ke{constructor(x){super()}pre_tokenize_text(x,N){return R(x)}}class zt extends Ke{constructor(x){super(),this.config=x,this.pattern=pe(this.config.pattern),this.content=this.config.content}pre_tokenize_text(x,N){return this.pattern===null?[x]:[x.replaceAll(this.pattern,this.config.content)]}}const Ct=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function jt(Ae,x,N,se){for(const be of Object.keys(Ae)){const ve=x-Ae[be].length,qe=N(be),St=new Array(ve).fill(qe);Ae[be]=se==="right"?(0,re.mergeArrays)(Ae[be],St):(0,re.mergeArrays)(St,Ae[be])}}function Qt(Ae,x){for(const N of Object.keys(Ae))Ae[N].length=x}class at extends P.Callable{constructor(N,se){super();De(this,"return_token_type_ids",!1);De(this,"padding_side","right");this._tokenizer_config=se,this.normalizer=X.fromConfig(N.normalizer),this.pre_tokenizer=Ke.fromConfig(N.pre_tokenizer),this.model=Be.fromConfig(N.model,se),this.post_processor=st.fromConfig(N.post_processor),this.decoder=Y.fromConfig(N.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const be of N.added_tokens){const ve=new Se(be);this.added_tokens.push(ve),this.model.tokens_to_ids.set(ve.content,ve.id),this.model.vocab[ve.id]=ve.content,ve.special&&(this.special_tokens.push(ve.content),this.all_special_ids.push(ve.id))}if(this.additional_special_tokens=se.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.toSorted((be,ve)=>ve.content.length-be.content.length).map(be=>`${be.lstrip?"\\s*":""}(${(0,re.escapeRegExp)(be.content)})${be.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.model_max_length=se.model_max_length,this.remove_space=se.remove_space,this.clean_up_tokenization_spaces=se.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=se.do_lowercase_and_remove_accent??!1,se.padding_side&&(this.padding_side=se.padding_side),this.legacy=!1,this.chat_template=se.chat_template??null,Array.isArray(this.chat_template)){const be=Object.create(null);for(const{name:ve,template:qe}of this.chat_template){if(typeof ve!="string"||typeof qe!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');be[ve]=qe}this.chat_template=be}this._compiled_template_cache=new Map}getToken(...N){for(const se of N){const be=this._tokenizer_config[se];if(be)if(typeof be=="object"){if(be.__type==="AddedToken")return be.content;throw Error(`Unknown token: ${be}`)}else return be}return null}static async from_pretrained(N,{progress_callback:se=null,config:be=null,cache_dir:ve=null,local_files_only:qe=!1,revision:St="main",legacy:At=null}={}){const Pt=await le(N,{progress_callback:se,config:be,cache_dir:ve,local_files_only:qe,revision:St,legacy:At});return new this(...Pt)}_call(N,{text_pair:se=null,add_special_tokens:be=!0,padding:ve=!1,truncation:qe=null,max_length:St=null,return_tensor:At=!0,return_token_type_ids:Pt=null}={}){const Vt=Array.isArray(N);let ur;if(Vt){if(N.length===0)throw Error("text array must be non-empty");if(se!==null){if(Array.isArray(se)){if(N.length!==se.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");ur=N.map((hr,tr)=>this._encode_plus(hr,{text_pair:se[tr],add_special_tokens:be,return_token_type_ids:Pt}))}else ur=N.map(hr=>this._encode_plus(hr,{add_special_tokens:be,return_token_type_ids:Pt}))}else{if(N==null)throw Error("text may not be null or undefined");if(Array.isArray(se))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");ur=[this._encode_plus(N,{text_pair:se,add_special_tokens:be,return_token_type_ids:Pt})]}if(St===null?ve==="max_length"?St=this.model_max_length:St=(0,ze.max)(ur.map(hr=>hr.input_ids.length))[0]:qe||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),St=Math.min(St,this.model_max_length??1/0),ve||qe)for(let hr=0;hrSt?qe&&Qt(ur[hr],St):ve&&jt(ur[hr],St,tr=>tr==="input_ids"?this.pad_token_id:0,this.padding_side));const jr={};if(At){if(!(ve&&qe)&&ur.some(tr=>{var Tr;for(const wi of Object.keys(tr))if(tr[wi].length!==((Tr=ur[0][wi])==null?void 0:Tr.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 hr=[ur.length,ur[0].input_ids.length];for(const tr of Object.keys(ur[0]))jr[tr]=new Oe.Tensor("int64",BigInt64Array.from(ur.flatMap(Tr=>Tr[tr]).map(BigInt)),hr)}else{for(const hr of Object.keys(ur[0]))jr[hr]=ur.map(tr=>tr[hr]);if(!Vt)for(const hr of Object.keys(jr))jr[hr]=jr[hr][0]}return jr}_encode_text(N){return N===null?null:(this.added_tokens_regex?N.split(this.added_tokens_regex).filter(ve=>ve):[N]).map((ve,qe)=>{if(this.added_tokens.find(At=>At.content===ve)!==void 0)return ve;{if(this.remove_space===!0&&(ve=ve.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(ve=ye(ve)),this.normalizer!==null&&(ve=this.normalizer(ve)),ve.length===0)return[];const At=this.pre_tokenizer!==null?this.pre_tokenizer(ve,{section_index:qe}):[ve];return this.model(At)}}).flat()}_encode_plus(N,{text_pair:se=null,add_special_tokens:be=!0,return_token_type_ids:ve=null}={}){const{tokens:qe,token_type_ids:St}=this._tokenize_helper(N,{pair:se,add_special_tokens:be}),At=this.model.convert_tokens_to_ids(qe),Pt={input_ids:At,attention_mask:new Array(At.length).fill(1)};return(ve??this.return_token_type_ids)&&St&&(Pt.token_type_ids=St),Pt}_tokenize_helper(N,{pair:se=null,add_special_tokens:be=!1}={}){const ve=this._encode_text(N),qe=this._encode_text(se);return this.post_processor?this.post_processor(ve,qe,{add_special_tokens:be}):{tokens:(0,re.mergeArrays)(ve??[],qe??[])}}tokenize(N,{pair:se=null,add_special_tokens:be=!1}={}){return this._tokenize_helper(N,{pair:se,add_special_tokens:be}).tokens}encode(N,{text_pair:se=null,add_special_tokens:be=!0,return_token_type_ids:ve=null}={}){return this._encode_plus(N,{text_pair:se,add_special_tokens:be,return_token_type_ids:ve}).input_ids}batch_decode(N,se={}){return N instanceof Oe.Tensor&&(N=N.tolist()),N.map(be=>this.decode(be,se))}decode(N,se={}){if(N instanceof Oe.Tensor&&(N=q(N)),!Array.isArray(N)||N.length===0||!(0,re.isIntegralNumber)(N[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(N,se)}decode_single(N,{skip_special_tokens:se=!1,clean_up_tokenization_spaces:be=null}){let ve=this.model.convert_ids_to_tokens(N);se&&(ve=ve.filter(St=>!this.special_tokens.includes(St)));let qe=this.decoder?this.decoder(ve):ve.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(qe=qe.replaceAll(this.decoder.end_of_word_suffix," "),se&&(qe=qe.trim())),(be??this.clean_up_tokenization_spaces)&&(qe=ae(qe)),qe}apply_chat_template(N,{tools:se=null,documents:be=null,chat_template:ve=null,add_generation_prompt:qe=!1,tokenize:St=!0,padding:At=!1,truncation:Pt=!1,max_length:Vt=null,return_tensor:ur=!0,return_dict:jr=!1,tokenizer_kwargs:hr={},...tr}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null){const tt=this.chat_template;if(ve!==null&&Object.hasOwn(tt,ve))ve=tt[ve];else if(ve===null&&"default"in tt)ve=tt.default;else if(ve===null)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(tt).sort()}.`)}else if(this.chat_template)ve=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");if(typeof ve!="string")throw Error(`chat_template must be a string, but got ${typeof ve}`);let Tr=this._compiled_template_cache.get(ve);Tr===void 0&&(Tr=new A.Template(ve),this._compiled_template_cache.set(ve,Tr));const wi=Object.create(null);for(const tt of Ct){const Pi=this.getToken(tt);Pi&&(wi[tt]=Pi)}const ui=Tr.render({messages:N,add_generation_prompt:qe,tools:se,documents:be,...wi,...tr});if(St){const tt=this._call(ui,{add_special_tokens:!1,padding:At,truncation:Pt,max_length:Vt,return_tensor:ur,...hr});return jr?tt:tt.input_ids}return ui}}class Zt extends at{constructor(){super(...arguments);De(this,"return_token_type_ids",!0)}}class Yt extends at{constructor(){super(...arguments);De(this,"return_token_type_ids",!0)}}class ir extends at{constructor(){super(...arguments);De(this,"return_token_type_ids",!0)}}class nr extends at{constructor(){super(...arguments);De(this,"return_token_type_ids",!0)}}class dr extends at{constructor(){super(...arguments);De(this,"return_token_type_ids",!0)}}class rr extends at{constructor(){super(...arguments);De(this,"return_token_type_ids",!0)}}class Dr extends at{constructor(){super(...arguments);De(this,"return_token_type_ids",!0)}}class Jr extends at{constructor(){super(...arguments);De(this,"return_token_type_ids",!0)}}class Br extends at{constructor(){super(...arguments);De(this,"return_token_type_ids",!0)}}class dt extends at{}class Nt extends at{}class Ht extends at{constructor(N,se){super(N,se);De(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class ii extends at{constructor(){super(...arguments);De(this,"return_token_type_ids",!0)}}class Zi extends at{}class Gi extends at{}class Hr extends at{}class di extends at{constructor(x,N){super(x,N),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(se=>this.languageRegex.test(se)),this.lang_to_token=se=>se}_build_translation_inputs(x,N,se){return Ki(this,x,N,se)}}class Xr extends di{}class Ri extends at{}class Rr extends at{constructor(x,N){var ve,qe;const se=".,!?…。，、।۔،",be=(qe=(ve=x.pre_tokenizer)==null?void 0:ve.pretokenizers[0])==null?void 0:qe.pattern;be&&be.Regex===` ?[^(\\s|[${se}])]+`&&(be.Regex=` ?[^\\s${se}]+`),super(x,N)}}const Ji="▁";class qi extends at{constructor(N,se){super(N,se);De(this,"padding_side","left");this.legacy=se.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new Ee({replacement:Ji,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(N){if(N===null)return null;if(this.legacy||N.length===0)return super._encode_text(N);let se=super._encode_text(Ji+N.replaceAll(Ji," "));return se.length>1&&se[0]===Ji&&this.special_tokens.includes(se[1])&&(se=se.slice(1)),se}}class qn extends at{}class Cn extends at{}class kn extends at{}class $n extends at{}class Sn extends at{}class en extends at{}class Kn extends at{}class pn extends at{}class Ni extends at{}function Ki(Ae,x,N,se){if(!("language_codes"in Ae)||!Array.isArray(Ae.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in Ae)||!(Ae.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in Ae)||typeof Ae.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const be=se.src_lang,ve=se.tgt_lang;if(!Ae.language_codes.includes(ve))throw new Error(`Target language code "${ve}" is not valid. Must be one of: {${Ae.language_codes.join(", ")}}`);if(be!==void 0){if(!Ae.language_codes.includes(be))throw new Error(`Source language code "${be}" is not valid. 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Ag=b.TextStreamer;b.TextToAudioPipeline,b.TokenClassificationPipeline,b.TokenClassifierOutput,b.TokenizerModel,b.TrOCRForCausalLM,b.TrOCRPreTrainedModel,b.TranslationPipeline,b.UniSpeechForCTC,b.UniSpeechForSequenceClassification,b.UniSpeechModel,b.UniSpeechPreTrainedModel,b.UniSpeechSatForAudioFrameClassification,b.UniSpeechSatForCTC,b.UniSpeechSatForSequenceClassification,b.UniSpeechSatModel,b.UniSpeechSatPreTrainedModel,b.ViTFeatureExtractor,b.ViTForImageClassification,b.ViTImageProcessor,b.ViTModel,b.ViTPreTrainedModel,b.VisionEncoderDecoderModel,b.VitMatteForImageMatting,b.VitMatteImageProcessor,b.VitMattePreTrainedModel,b.VitsModel,b.VitsModelOutput,b.VitsPreTrainedModel,b.VitsTokenizer,b.Wav2Vec2BertForCTC,b.Wav2Vec2BertForSequenceClassification,b.Wav2Vec2BertModel,b.Wav2Vec2BertPreTrainedModel,b.Wav2Vec2CTCTokenizer,b.Wav2Vec2FeatureExtractor,b.Wav2Vec2ForAudioFrameClassification,b.Wav2Vec2ForCTC,b.Wav2Vec2ForSequenceClassification,b.Wav2Vec2Model,b.Wav2Vec2PreTrainedModel,b.Wav2Vec2ProcessorWithLM,b.WavLMForAudioFrameClassification,b.WavLMForCTC,b.WavLMForSequenceClassification,b.WavLMForXVector,b.WavLMModel,b.WavLMPreTrainedModel,b.WeSpeakerFeatureExtractor,b.WeSpeakerResNetModel,b.WeSpeakerResNetPreTrainedModel,b.WhisperFeatureExtractor,b.WhisperForConditionalGeneration,b.WhisperModel,b.WhisperPreTrainedModel,b.WhisperProcessor,b.WhisperTextStreamer,b.WhisperTokenizer,b.XLMForQuestionAnswering,b.XLMForSequenceClassification,b.XLMForTokenClassification,b.XLMModel,b.XLMPreTrainedModel,b.XLMRobertaForMaskedLM,b.XLMRobertaForQuestionAnswering,b.XLMRobertaForSequenceClassification,b.XLMRobertaForTokenClassification,b.XLMRobertaModel,b.XLMRobertaPreTrainedModel,b.XLMRobertaTokenizer,b.XLMTokenizer,b.XLMWithLMHeadModel,b.XVectorOutput,b.YolosFeatureExtractor,b.YolosForObjectDetection,b.YolosModel,b.YolosObjectDetectionOutput,b.YolosPreTrainedModel,b.ZeroShotAudioClassificationPipeline,b.ZeroShotClassificationPipeline,b.ZeroShotImageClassificationPipeline,b.ZeroShotObjectDetectionPipeline,b.bankers_round,b.cat,b.cos_sim,b.dot,b.dynamic_time_warping;var Ig=b.env;b.full,b.full_like,b.getKeyValueShapes,b.hamming,b.hanning,b.interpolate,b.interpolate_4d,b.interpolate_data,b.is_chinese_char,b.layer_norm,b.log_softmax,b.magnitude,b.matmul,b.max,b.mean,b.mean_pooling,b.medianFilter,b.mel_filter_bank,b.min,b.ones,b.ones_like,b.permute,b.permute_data,b.pipeline,b.quantize_embeddings,b.read_audio,b.rfft,b.round,b.softmax,b.spectrogram,b.stack,b.std_mean,b.topk,b.window_function,b.zeros,b.zeros_like,Ig.backends.onnx.wasm.wasmPaths="/";async function Fg(){try{return(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{return!1}}class vc{static async getInstance(Te=null){return this.fp16??(this.fp16=await Fg()),this.model_id??(this.model_id=this.fp16?"HuggingFaceTB/smollm-360M-instruct-add-basics-onnx-fp16":"HuggingFaceTB/SmolLM-360M-Instruct"),this.tokenizer??(this.tokenizer=Eg.from_pretrained(this.model_id,{progress_callback:Te})),this.model??(this.model=Sg.from_pretrained(this.model_id,{dtype:this.fp16?"fp16":"q4",device:"webgpu",progress_callback:Te})),Promise.all([this.tokenizer,this.model])}}const Nu=new Pg;async function zg(Ft){const[Te,m]=await vc.getInstance(),P=Te.apply_chat_template(Ft,{add_generation_prompt:!0,return_dict:!0});let re,ke=0,ze;const Oe=le=>{re??(re=performance.now()),ke++>0&&(ze=ke/(performance.now()-re)*1e3)},V=le=>{self.postMessage({status:"update",output:le,tps:ze,numTokens:ke})},A=new Ag(Te,{skip_prompt:!0,skip_special_tokens:!0,callback_function:V,token_callback_function:Oe});self.postMessage({status:"start"});const H=await m.generate({...P,max_new_tokens:1024,streamer:A,stopping_criteria:Nu}),B=Te.batch_decode(H,{skip_special_tokens:!0});self.postMessage({status:"complete",output:B})}async function Og(){self.postMessage({status:"loading",data:"Loading model..."});const[Ft,Te]=await vc.getInstance(P=>{self.postMessage(P)});self.postMessage({status:"loading",data:"Compiling shaders and warming up model..."});const m=Ft("a");await Te.generate({...m,max_new_tokens:1}),self.postMessage({status:"ready"})}self.addEventListener("message",async Ft=>{const{type:Te,data:m}=Ft.data;switch(Te){case"load":Og();break;case"generate":Nu.reset(),zg(m);break;case"interrupt":Nu.interrupt();break;case"reset":Nu.reset();break}})})();