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l=e[1]*t[1],d=e[0]*t[0],c=a?l:i,u=a?i:l,h=c/e[0],f=i/e[1];if(!((a&&h===4&&t[1]===4||!a&&(h===3||h===4))&&c%e[0]===0&&i%e[1]===0&&t[0]===4))throw new Error(`If transposeA ${a} is true, innerElementSize ${h} and workPerThread[1] ${t[1]} must be 4. - Otherwise, innerElementSize ${h} must be 3 or 4. - tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return` -var mm_Asub: array, ${c/h}>, ${u}>; -var mm_Bsub: array, ${d/t[0]}>, ${i}>; - -const rowPerThread = ${t[1]}; -const colPerThread = ${t[0]}; -const innerElementSize = ${h}; -const tileInner = ${i}; - -@compute @workgroup_size(${e[0]}, ${e[1]}, ${e[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 = ${s?"0":"i32(globalId.z)"}; - ${r?`let batchIndices = ${r.offsetToIndices("u32(batch)")};`:""} - let globalRowStart = i32(workgroupId.y) * ${l}; - - let numTiles = ${s?`${Math.ceil(o/i)}`:"(uniforms.dimInner - 1) / tileInner + 1"}; - var kStart = ${s?`i32(globalId.z) * ${o}`:"0"}; - - var acc: array, rowPerThread>; - - // Loop over shared dimension. - let tileRowB = localRow * ${f}; - for (var t = 0; t < numTiles; 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; - ${su(a,r)} - } - - // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${f}; innerRow = innerRow + 1) { - let inputRow = tileRowB + innerRow; - let inputCol = tileCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${r?", 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]; - ${h===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} - - ${ou(a,h)} - } - - workgroupBarrier(); - } - - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); - } -}`},Wi=(t,e)=>t?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - kStart + inputRow, - globalRowStart + inputCol${e?", batchIndices":""}); - `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - globalRowStart + inputRow, - kStart + inputCol${e?", batchIndices":""}); - `,lu=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",ti=(t,e,n="f32",r,a=!1,i=32,s=!1,o=32,l=!1)=>{let d=t[1]*e[1],c=t[0]*e[0],u=a?d:i,h=a?i:d;if(!(h%e[1]===0&&u%e[0]===0&&i%e[1]===0))throw new Error(`tileAHight ${h} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}, tileInner ${i} must be divisible by workgroupSize[1]${e[1]}`);let f=h/e[1],g=u/e[0],w=i/e[1],b=l?` - let localRow = i32(localId.y); - let localCol = i32(localId.x); - let globalRowStart = i32(workgroupId.y) * ${d}; - let globalColStart = i32(workgroupId.x) * ${c}; - - // Loop over shared dimension. - for (var t = 0; t < numTiles; t = t + 1) { - // Load one tile of A into local memory. - for (var inputRow = localRow; inputRow < ${h}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) { - ${Wi(a,r)} - } - } - // Load one tile of B into local memory. - for (var inputRow = localRow; inputRow < ${i}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) { - mm_Bsub[inputRow][inputCol] = mm_readB(batch, - kStart + inputRow, - globalColStart + inputCol${r?", batchIndices":""}); - } - } - kStart = kStart + tileInner; - workgroupBarrier(); - - // Compute acc values for a single thread. - var BCached : array<${n}, 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 * ${e[0]}]; - } - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - let ACached = ${a?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[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 * ${e[1]}; - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { - let gCol = globalColStart + localCol + innerCol * ${e[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) * ${d}; - -let tileRowA = i32(localId.y) * ${f}; -let tileColA = i32(localId.x) * ${g}; -let tileRowB = i32(localId.y) * ${w}; -// Loop over shared dimension. -for (var t = 0; t < numTiles; 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 < ${g}; innerCol = innerCol + 1) { - let inputRow = tileRowA + innerRow; - let inputCol = tileColA + innerCol; - ${Wi(a,r)} - } - } - - // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { - let inputRow = tileRowB + innerRow; - let inputCol = tileCol + innerCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, - kStart + inputRow, - globalCol + innerCol${r?", batchIndices":""}); - } - } - kStart = kStart + tileInner; - workgroupBarrier(); - - // Compute acc values for a single thread. - var BCached : array<${n}, 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) { - ${lu(a)} - 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, ${h}>; - var mm_Bsub : array, ${i}>; - const rowPerThread = ${t[1]}; - const colPerThread = ${t[0]}; - const tileInner = ${i}; - -@compute @workgroup_size(${e[0]}, ${e[1]}, ${e[2]}) -fn main(@builtin(local_invocation_id) localId : vec3, - @builtin(global_invocation_id) globalId : vec3, - @builtin(workgroup_id) workgroupId : vec3) { - let batch = ${s?"0":"i32(globalId.z)"}; - ${r?`let batchIndices = ${r.offsetToIndices("u32(batch)")};`:""} - let numTiles = ${s?`${Math.ceil(o/i)}`:"(uniforms.dimInner - 1) / tileInner + 1"}; - var kStart = ${s?`i32(globalId.z) * ${o}`:"0"}; - - var acc : array, rowPerThread>; - - // Without this initialization strange values show up in acc. - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { - acc[innerRow][innerCol] = 0.0; - } - } - ${b} - } -`},uu=(t,e,n,r,a,i=!1)=>{let[s,o,l]=a,[d,c,u,h]=r,f=Nr(s,l),g=Nr(o,l),w=At(r[0].type.tensor),b=()=>{let $=c.rank,k=d.rank,S=`var aIndices: ${c.type.indices};`;for(let T=$-2-1,A=k-1;T>=0;T--,A--)S+=` -aIndices[${T}] = ${k>1?`batchIndices[${A}]`:"batchIndices"};`;return f.forEach(T=>{S+=` -aIndices[${T}] = 0;`}),S+=` -aIndices[${$-2}] = u32(row); - aIndices[${$-1}] = u32(colIn);`,S},y=()=>{let $=u.rank,k=d.rank,S=`var bIndices: ${u.type.indices};`;for(let T=$-2-1,A=k-1;T>=0;T--,A--)S+=` -bIndices[${T}] = ${k>1?`batchIndices[${A}]`:"batchIndices"};`;return g.forEach(T=>{S+=` -bIndices[${T}] = 0;`}),S+=` -bIndices[${$-2}] = u32(row); - bIndices[${$-1}] = u32(colIn);`,S};return` - fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${ct(t,w)} { - var value = ${ct(t,w)}(0.0); - let col = colIn * ${t}; - if(row < uniforms.dimAOuter && col < uniforms.dimInner) - { - ${b()} - value = ${c.getByIndices("aIndices")}; - } - return value; - } - - fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${ct(t,w)} { - var value = ${ct(t,w)}(0.0); - let col = colIn * ${t}; - if(row < uniforms.dimInner && col < uniforms.dimBOuter) - { - ${y()} - value = ${u.getByIndices("bIndices")}; - } - return value; - } - - fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${ct(t,w)}) { - let col = colIn * ${t}; - if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { - var value = valueIn; - let coords = vec3(batch, row, colIn); - ${e?`value = value + ${i?"bias[colIn]":`${ct(t,w)}(bias[row])`};`:""} - ${n} - ${h.setByIndices("vec3(coords)","value")} - } - } - `},oo=(t,e,n,r,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i.slice(0,-2),l=s.slice(0,-2),d=r?r.slice(0,-2):n.slice(0,-2),c=st(d.length),u=c?d.length:d,h=to("batchDims",t[0].dataType,u,1),f=J.size(d),g=i[i.length-2],w=i[i.length-1],b=s[s.length-1],y=w%4===0&&b%4===0,$=g<=8?[4,1,1]:[4,4,1],k=[8,8,1],S=[Math.ceil(b/k[0]/$[0]),Math.ceil(g/k[1]/$[1]),Math.ceil(f/k[2]/$[2])],T=At(t[0].dataType),A=y?4:1,P=[...o,g,w/A],N=st(P.length),V=N?P.length:P,j=[...l,w,b/A],M=st(j.length),G=M?j.length:j,O=[f,g,b/A],R=Q("a",t[0].dataType,V,A),H=Q("b",t[1].dataType,G,A),te=ve("result",t[0].dataType,O.length,A),ae=[R,H],re=[{type:"int32",data:g},{type:"int32",data:b},{type:"int32",data:w}];c&&re.push(...ie(d)),N&&re.push(...ie(P)),M&&re.push(...ie(j));let Y=[];Y.push(N?"rank":"dims"),Y.push(M?"rank":"dims");let ue=t.length>2,{activationFunction:W,applyActivation:oe}=tr(e,te.type.value),$e=uu(A,ue,oe,[h,R,H,te],[o,l,d],a);if(ue){let Pe=a?A:1;ae.push(Q("bias",t[2].dataType,t[2].dims.length,Pe)),re.push(...ie(t[2].dims)),Y.push("rank")}re.push(...ie(O));let Fe=Pe=>` - ${Pe.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").registerInternalVariables(h).declareVariables(...ae,te)} - ${W} - ${$e} - ${y?ei($,k,T,h):ti($,k,T,h)} - `;return{name:"MatMul",shaderCache:{hint:e.activationCacheKey+`${$}${y}${a}`,inputDependencies:Y},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:S[0],y:S[1],z:S[2]},programUniforms:re}),getShaderSource:Fe}}}),du,yh,z_=X(()=>{In(),Ce(),An(),io(),wh(),ui(),du=(t,e,n,r,a=!1,i,s=4,o=4,l=4,d="f32")=>{let c=j=>{switch(j){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${d}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${j} is not supported.`)}},u=j=>{switch(j){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 ${j} is not supported.`)}},h=t?` - let coord = vec4(batch, xRow, xCol, xCh); - `:` - let coord = vec4(batch, xCh, xRow, xCol); - `,f=t?` - let coords = vec4( - batch, - row / outWidth, - row % outWidth, - col); - `:` - let coords = vec4( - batch, - row, - col / outWidth, - col % outWidth); - `,g=t?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",w=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",b=t?"row":"col",y=t?"col":"row",$=` - let inChannels = i32(uniforms.w_shape[2]); - let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; - let outRow = ${b} / outWidth; - let outCol = ${b} % outWidth; - - let WRow = ${y} / (filterDims[1] * inChannels); - let WCol = ${y} / inChannels % filterDims[1]; - let xRow = outRow * stride[0] + dilation[0] * WRow - pad[0]; - let xCol = outCol * stride[1] + dilation[1] * WCol - pad[1]; - let xCh = ${y} % inChannels; - var resData = ${ct(s,d)}(0.0); - // The bounds checking is always needed since we use it to pad zero for - // the 'same' padding type. - if (xRow >= 0 && xRow < ${g} && xCol >= 0 && xCol < ${w}) { - ${h} - let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); - ${c(s)} - } - return resData;`,k=t?e&&r?` - let col = colIn * ${s}; - ${$}`:` - let col = colIn * ${s}; - if (row < uniforms.dimAOuter && col < uniforms.dimInner) { - ${$} - } - return ${ct(s,d)}(0.0);`:r&&n?` - let col = colIn * ${s}; - ${$}`:` - let col = colIn * ${s}; - if (row < uniforms.dimInner && col < uniforms.dimBOuter) { - ${$} - } - return ${ct(s,d)}(0.0);`,S=`${u(o)}`,T=ct(l,d),A=ct(t?s:o,d),P=ct(t?o:s,d),{activationFunction:N,applyActivation:V}=tr(i,T);return` - ${N} - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${A} { - ${t?k:S} - } - - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${P} { - ${t?S:k} - } - - fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${T}) { - let col = colIn * ${l}; - if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) - { - var value = valueIn; - let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; - ${f} - ${ao(a)} - ${V} - setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); - } - }`},yh=(t,e,n,r,a,i,s,o)=>{let l=e.format==="NHWC",d=l?t[0].dims[3]:t[0].dims[1],c=n[0],u=l?n[2]:n[3],h=l?n[1]:n[2],f=l?n[3]:n[1],g=l&&(d%4===0||d%3===0)&&f%4===0,w=l?f:u*h,b=l?u*h:f,y=[8,8,1],$=r<=8?[4,1,1]:[4,4,1],k=[Math.ceil(w/y[0]/$[0]),Math.ceil(b/y[1]/$[1]),Math.ceil(c/y[2]/$[2])];it("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${k}`);let S=g?l&&d%4!==0?3:4:1,T=y[1]*$[1],A=y[0]*$[0],P=Math.max(y[0]*S,y[1]),N=r%T===0,V=a%A===0,j=i%P===0,M=g?[S,4,4]:[1,1,1],G=At(t[0].dataType),O=g?4:1,R=[{type:"int32",data:r},{type:"int32",data:a},{type:"int32",data:i}],H=Q("x",t[0].dataType,t[0].dims.length,S===3?1:S),te=Q("w",t[1].dataType,t[1].dims.length,O),ae=[H,te];R.push(...ie(t[0].dims)),R.push(...ie(t[1].dims));let re=` - fn setOutputAtIndex(flatIndex : i32, value : ${g?`vec4<${G}>`:G}) { - result[flatIndex] = ${g?`vec4<${G}>`:G}(value); - } - fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${g?`vec4<${G}>`:G}) { - let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); - setOutputAtIndex(flatIndex ${g?"/ 4":""}, value); - }`;if(s){let ue=Q("bias",t[2].dataType,t[2].dims.length,O);ae.push(ue),R.push(...ie(t[2].dims)),re+=` - fn getBiasByOutputCoords(coords : vec4) -> ${g?`vec4<${G}>`:G} { - return bias[coords.${l?"w":"y"}${g?"/ 4":""}]; - }`}let Y=ve("result",t[0].dataType,n.length,O);return R.push(...ie(n)),{name:"Conv2DMatMul",shaderCache:{hint:e.cacheKey},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:k[0],y:k[1],z:k[2]},programUniforms:R}),getShaderSource:ue=>` - ${so("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 }; - ${ue.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...ae,Y)} - const filterDims : vec2 = vec2(${e.kernelShape[0]}, ${e.kernelShape[1]}); - const pad : vec2 = vec2(${e.pads[0]}, ${e.pads[1]}); - const stride : vec2 = vec2(${e.strides[0]}, ${e.strides[1]}); - const dilation : vec2 = vec2(${e.dilations[0]}, ${e.dilations[1]}); - ${re} - ${du(l,N,V,j,s,e,M[0],M[1],M[2],G)} - ${g?ei($,y,G,void 0,!l,P):ti($,y,G,void 0,!l,P,!1,void 0,o)}`}}}),Es,R_=X(()=>{Ie(),Ce(),$h(),An(),Es=(t,e,n)=>{let r=t.length>2,a=r?"value += b[output_channel];":"",i=t[0].dims,s=t[1].dims,o=s[0]/e.group,l=e.format==="NHWC",d=Cs(i,s,e.dilations,e.pads,e.strides,l),c=J.size(d),u=ve("output",t[0].dataType,d),{activationFunction:h,applyActivation:f}=tr(e,u.type.value),g=Q("x",t[0].dataType,i),w=Q("w",t[1].dataType,s),b=[g,w];r&&b.push(Q("b",t[2].dataType,t[2].dims));let y=$=>` - const strides: vec2 = vec2(${e.strides[0]}u, ${e.strides[1]}u); - const pads: vec2 = vec2(${e.pads[0]}u, ${e.pads[1]}u); - - ${$.declareVariables(...b,u)} - - ${h} - - ${$.mainStart()} - ${$.guardAgainstOutOfBoundsWorkgroupSizes(c)} - - let outputIndices = ${u.offsetToIndices("global_idx")}; - let batch: u32 = outputIndices[0]; - let output_channel: u32 = outputIndices[${l?3:1}]; - let xRCCorner: vec2 = vec2(outputIndices[${l?1:2}], outputIndices[${l?2:3}]) * strides - pads; - let group_id: u32 = output_channel / ${o}u; - - var value: ${u.type.value} = ${u.type.value}(0); - for (var wInChannel: u32 = 0u; wInChannel < ${s[1]}u; wInChannel++) { - let input_channel = group_id * ${s[1]}u + wInChannel; - for (var wHeight: u32 = 0u; wHeight < ${s[2]}u; wHeight++) { - let xHeight = xRCCorner.x + wHeight * ${e.dilations[0]}u; - - if (xHeight < 0u || xHeight >= ${i[l?1:2]}u) { - continue; - } - - for (var wWidth: u32 = 0u; wWidth < ${s[3]}u; wWidth++) { - let xWidth = xRCCorner.y + wWidth * ${e.dilations[1]}u; - if (xWidth < 0u || xWidth >= ${i[l?2:3]}u) { - continue; - } - - let xVal = ${l?g.get("batch","xHeight","xWidth","input_channel"):g.get("batch","input_channel","xHeight","xWidth")}; - let wVal = ${w.get("output_channel","wInChannel","wHeight","wWidth")}; - value += xVal*wVal; - } - } - } - ${a} - ${f} - ${u.setByOffset("global_idx","value")} - }`;return{name:"GroupedConv",shaderCache:{hint:e.cacheKey},getRunData:()=>({outputs:[{dims:n?n(d):d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)}}),getShaderSource:y}}}),ks,cu,bh,vh=X(()=>{Ie(),ui(),Ce(),An(),ks=(t,e,n,r,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i[i.length-2],l=s[s.length-1],d=i[i.length-1],c=$t(l),u=$t(d),h=$t(o),f=J.size(n)/c/h,g=t.length>2,w=r?r.slice(0,-2):n.slice(0,-2),b=[J.size(w),o,l],y=[{type:"uint32",data:f},{type:"uint32",data:o},{type:"uint32",data:l},{type:"uint32",data:d},...ie(w),...ie(i),...ie(s)];g&&y.push(...ie(t[2].dims)),y.push(...ie(b));let $=k=>{let S=to("batch_dims",t[0].dataType,w.length),T=Q("a",t[0].dataType,i.length,u),A=Q("b",t[1].dataType,s.length,c),P=ve("output",t[0].dataType,b.length,c),{activationFunction:N,applyActivation:V}=tr(e,P.type.value),j=[T,A],M="";if(g){let re=a?c:1;j.push(Q("bias",t[2].dataType,t[2].dims.length,re)),M=`${a?`value += bias[col / ${re}];`:`value += ${P.type.value}(bias[row + i]);`}`}let G=i.slice(0,-2),O=s.slice(0,-2),R=Nr(G,w),H=Nr(O,w),te=(re,Y)=>{let ue=re.rank,W=re.name;if(ue===2)return`var ${W}_indices = ${re.type.indices}(0u, 0u);`;let oe=S.rank,$e=`var ${W}_indices: ${re.type.indices};`;for(let Fe=ue-2-1,Pe=oe-1;Fe>=0;Fe--,Pe--)$e+=` -${W}_indices[${Fe}] = ${oe>1?`batch_indices[${Pe}]`:"batch_indices"};`;return Y.forEach(Fe=>{$e+=` -${W}_indices[${Fe}] = 0;`}),$e+=`${W}_indices[${ue-2}] = 0u; - ${W}_indices[${ue-1}] = 0u;`,$e},ae=()=>{let re=`var a_data: ${T.type.value};`;for(let Y=0;Y; - for (var k: u32 = 0u; k < uniforms.K; k = k + ${u}) { - ${ae()} - } - for (var i = 0u; i < ${h}u; i++) { - var value = values[i]; - ${M} - ${V} - let cur_indices = ${P.type.indices}(batch, row + i, col); - let offset = ${P.indicesToOffset("cur_indices")}; - ${P.setByOffset(`offset / ${c}`,"value")}; - } - } - `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activationCacheKey}_${c}_${u}_${h}_${a}`,inputDependencies:g?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:y}),getShaderSource:$}},cu=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.")},bh=t=>{cu(t.inputs);let e=Xn.calcShape(t.inputs[0].dims,t.inputs[1].dims,!0);if(!e)throw new Error("Can't use matmul on the given tensors");let n=e[e.length-1],r=t.inputs[0].dims[t.inputs[0].dims.length-1];n<8&&r<8?t.compute(ks(t.inputs,{activation:"",activationCacheKey:""},e)):t.compute(oo(t.inputs,{activation:"",activationCacheKey:""},e))}}),Cs,Vi,pu,Gi,Ts,hu,fu,Is,$h=X(()=>{Ie(),ot(),z_(),ui(),R_(),An(),vh(),Hr(),Cs=(t,e,n,r,a,i)=>{let s=t[0],o=t.slice(i?1:2,i?3:4),l=o.length,d=e[0],c=e.slice(2).map((h,f)=>h+(h-1)*(n[f]-1)),u=o.map((h,f)=>h+r[f]+r[f+l]).map((h,f)=>Math.floor((h-c[f]+a[f])/a[f]));return u.splice(0,0,s),u.splice(i?3:1,0,d),u},Vi=[2,3,1,0],pu=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4&&t[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(t[0].dims.length!==t[1].dims.length)throw new Error("filter does not have same dimension as input");let n=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],r=t[1].dims[1]*e.group;if(n!==r)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(t.length===3&&(t[2].dims.length!==1||t[1].dims[0]!==t[2].dims[0]))throw new Error("invalid bias");let a=t[0].dims.length-2;if(e.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(e.strides.length!==a)throw new Error(`strides should be ${a}D`);if(e.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape")},Gi=(t,e)=>{let n=t.kernelShape.slice();for(let i=2;i{let e=ro(t),n=t.format,r=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],a=t.dilations,i=t.group,s=t.kernel_shape,o=t.pads,l=t.strides,d=t.w_is_const();return qe({autoPad:r,format:n,dilations:a,group:i,kernelShape:s,pads:o,strides:l,wIsConst:d,...e})},hu=(t,e,n)=>{let r=Gi(n,e),a=n.format==="NHWC";if(n.group!==1){t.compute(Es(e,r));return}let i=e.length===3,s=e[0].dims[a?1:2],o=e[0].dims[a?2:3],l=e[0].dims[a?3:1],d=e[1].dims[2],c=e[1].dims[3],u=Cs(e[0].dims,e[1].dims,n.dilations,r.pads,n.strides,a),h=u[a?1:2],f=u[a?2:3],g=u[a?3:1],w=a&&d===s&&c===o&&n.pads[0]===0&&n.pads[1]===0;if(w||d===1&&c===1&&n.dilations[0]===1&&n.dilations[1]===1&&n.strides[0]===1&&n.strides[1]===1&&n.pads[0]===0&&n.pads[1]===0){let A=u[0],P,N,V,j=[];if(a){let O=t.kernelCustomData.wT??t.compute(_n(e[1],Vi),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];if(n.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=O),w){let R=s*o*l;P=e[0].reshape([1,A,R]),N=O.reshape([1,R,g]),V=[1,A,g]}else P=e[0].reshape([A,s*o,l]),N=O.reshape([1,l,g]),V=[A,h*f,g];j.push(P),j.push(N)}else P=e[0].reshape([A,l,s*o]),N=e[1].reshape([1,g,l]),V=[A,g,h*f],j.push(N),j.push(P);i&&j.push(e[2]);let M=V[2],G=j[0].dims[j[0].dims.length-1];M<8&&G<8?t.compute(ks(j,r,u,V,a),{inputs:j}):t.compute(oo(j,r,u,V,a),{inputs:j});return}let b=!0,y=t.kernelCustomData.wT??t.compute(_n(e[1],Vi),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=y);let $=[e[0],y];i&&$.push(e[2]);let k=a?h*f:g,S=a?g:h*f,T=d*c*l;t.compute(yh($,r,u,k,S,T,i,b),{inputs:$})},fu=(t,e)=>{let n=e.format==="NHWC",r=[t.inputs[0].reshape(n?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];t.inputs.length===3&&r.push(t.inputs[2]);let a=[0,e.pads[0],0,e.pads[1]],i=[1].concat(e.strides),s=[1].concat(e.dilations),o=[1].concat(e.kernelShape),l=Gi({...e,pads:a,strides:i,dilations:s,kernelShape:o},r);t.compute(Es(r,l,d=>n?[d[0],d[2],d[3]]:[]))},Is=(t,e)=>{pu(t.inputs,e),t.inputs[0].dims.length===3?fu(t,e):hu(t,t.inputs,e)}}),mu,xh,B_=X(()=>{In(),Ce(),An(),io(),wh(),ui(),mu=(t,e=!1,n,r=4)=>{let a=ct(r,"f32"),i=y=>{switch(y){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 vec4(v0, v1, v2, v3); - `;default:throw new Error(`innerElementSize ${y} is not supported.`)}},s=t?` - let coord = vec4(batch, iXR, iXC, xCh); - `:` - let coord = vec4(batch, xCh, iXR, iXC); - `,o=t?` - let coords = vec4( - batch, - row / outWidth, - row % outWidth, - col); - `:` - let coords = vec4( - batch, - row, - col / outWidth, - col % outWidth); - `,l=t?"outBackprop[1]":"outBackprop[2]",d=t?"outBackprop[2]":"outBackprop[3]",c=t?"row":"col",u=t?"col":"row",h=` - let inChannels = ${t?"outBackprop[3]":"outBackprop[1]"}; - let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; - let outRow = ${c} / outWidth; - let outCol = ${c} % outWidth; - - let WRow = ${u} / (filterDims[1] * inChannels); - let WCol = ${u} / inChannels % filterDims[1]; - let xR = f32(outRow - pads[0] + dilation[0] * WRow) / f32(strides[0]); - let xC = f32(outCol - pads[1] + dilation[1] * WCol) / f32(strides[1]); - if (xR < 0.0 || xR >= f32(${l}) || fract(xR) > 0.0) { - return ${a}(0.0); - } - if (xC < 0.0 || xC >= f32(${d}) || fract(xC) > 0.0) { - return ${a}(0.0); - } - let iXR = i32(xR); - let iXC = i32(xC); - let xCh = ${u} % inChannels; - ${s} - return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${r}];`,f=t?` - let col = colIn * ${r}; - if (row < uniforms.dimAOuter && col < uniforms.dimInner) { - ${h} - } - return ${a}(0.0);`:` - let col = colIn * ${r}; - if (row < uniforms.dimInner && col < uniforms.dimBOuter) { - ${h} - } - return ${a}(0.0);`,g=` - let col = colIn * ${r}; - let inChannels = ${t?"outBackprop[3]":"outBackprop[1]"}; - let coordX = filterDims.x - 1 - row / (filterDims[1] * inChannels); - let coordY = filterDims.y - 1 - (row / inChannels) % filterDims[1]; - if (${t?"row < uniforms.dimInner && col < uniforms.dimBOuter":"row < uniforms.dimInner && col < uniforms.dimAOuter"} && coordX >= 0 && coordY >= 0) { - let rowInner = row % inChannels; - let coord = vec4(coordX, coordY, col, rowInner); - ${i(r)} - } - return ${a}(0.0); - `,{activationFunction:w,applyActivation:b}=tr(n,a);return` - ${w} - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${a} { - ${t?f:g} - } - - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${a} { - ${t?g:f} - } - - fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${a}) { - let col = colIn * ${r}; - if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { - var value = valueInput; - let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; - ${o} - ${ao(e)} - ${b} - result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${r}] = value; - } - }`},xh=(t,e,n,r,a,i,s,o)=>{let l=e.format==="NHWC",d=l?t[0].dims[3]:t[0].dims[1],c=n[0],u=l?n[2]:n[3],h=l?n[1]:n[2],f=l?n[3]:n[1],g=l?d%4===0&&f%4===0:u%4===0&&f%4===0,w=l?f:u*h,b=l?u*h:f,y=g?[8,8,1]:[w<=4||b<=4?4:16,w>4&&b<=4?4:16,1],$=g?[4,4,1]:[w<=4?1:4,w>4&&b<=4?1:4,1],k=[Math.ceil(w/y[0]/$[0]),Math.ceil(b/y[1]/$[1]),Math.ceil(c/y[2]/$[2])];it("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${k}`);let S=g?4:1,T=Math.max(y[0]*S,y[1]),A=g?4:1,P=[{type:"int32",data:r},{type:"int32",data:a},{type:"int32",data:i}],N=Q("x",t[0].dataType,t[0].dims.length,A),V=Q("w",t[1].dataType,t[1].dims.length,1),j=ve("result",t[0].dataType,n.length,A),M=[N,V];P.push(...ie(t[0].dims)),P.push(...ie(t[1].dims));let G="";if(s){let O=Q("bias",t[2].dataType,t[2].dims.length,A);M.push(O),P.push(...ie(t[2].dims)),G+=` - fn getBiasByOutputCoords(coords : vec4) -> ${g?"vec4":"f32"} { - return bias[coords.${l?"w":"y"}${g?"/ 4":""}]; - }`}return P.push(...ie(n)),{name:"Conv2DTransposeMatMul",shaderCache:{hint:e.cacheKey},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:k[0],y:k[1],z:k[2]},programUniforms:P}),getShaderSource:O=>` - ${so("uniforms.result_strides")} - ${O.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...M,j)}; - const outBackprop : vec4 = vec4(${t[0].dims.join(",")}); - const filterDims : vec2 = vec2(${e.kernelShape[l?1:2]}, ${e.kernelShape[l?2:3]}); - const effectiveFilterDims : vec2 = filterDims + vec2( - ${e.dilations[0]<=1?0:(e.kernelShape[l?1:2]-1)*(e.dilations[0]-1)}, - ${e.dilations[1]<=1?0:(e.kernelShape[l?2:3]-1)*(e.dilations[1]-1)}); - const pads : vec2 = vec2(i32(effectiveFilterDims[0]) - 1 - (${e.pads[0]+e.pads[2]})/2, - i32(effectiveFilterDims[1]) - 1 - (${e.pads[1]+e.pads[3]})/2); - const strides : vec2 = vec2(${e.strides[0]}, ${e.strides[1]}); - const dilation : vec2 = vec2(${e.dilations[0]}, ${e.dilations[1]}); - const dimAOuter : i32 = ${r}; - const dimBOuter : i32 = ${a}; - const dimInner : i32 = ${i}; - ${G} - ${mu(l,s,e,S)} - ${g?ei($,y,"f32",void 0,!l,T):ti($,y,"f32",void 0,!l,T,!1,void 0,o)}`}}}),gu,As,P_=X(()=>{In(),Ie(),Ce(),gu=(t,e,n,r,a,i,s=!1,o)=>{let l=n.format==="NHWC",d=l?1:2,c=l?2:3,u=l?3:1,h=J.size(r),f=s?2:1,g=n.group,w=e[1].dims,b=w[0]/g,y=w[1],$=` - fn setOutputAtIndex(flatIndex : u32, value : ${s?`vec4<${o}>`:o}) { - result[flatIndex] = ${s?`vec4<${o}>`:o}(value); - }`;a&&($+=` - fn getBiasByOutputCoords(coords : vec4) -> ${s?`vec4<${o}>`:o} { - return bias[coords.${l?"w":"y"}${s?"/ 4":""}]; - }`);let k=s?4:1,S=Q("W",e[1].dataType,e[1].dims,k),T=Q("Dy",e[0].dataType,e[0].dims,k),A=[T,S];a&&A.push(Q("bias",e[2].dataType,[r[u]],k));let P=ve("result",e[0].dataType,r,k),N=`{ - let batch: u32 = ${i?"global_id.z":"workgroup_id.z"} / outShape[1]; - let r = ${i?"global_id.z":"workgroup_id.z"} % outShape[1]; - let c = ${i?"global_id.y":"workgroup_id.y"} * ${f}; - let d1: u32 = ${i?"global_id.x":"workgroup_id.x"} * 4; - - let dyCorner = vec2(i32(r), i32(c)) - vec2(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, ${f}>; - for (var i = 0; i < ${f}; i++) { - dotProd[i] = vec4<${o}>(0.0); - } - for (var wR: u32 = 0; wR < filterDims[0]; wR = wR + 1) { - var dyR = (${o}(dyCorner.x) + ${o}(wR)) / ${o}(strides.x); - let wRPerm = filterDims[0] - 1 - wR; - if (dyR < 0.0 || dyR >= ${o}(outBackprop[1]) || - fract(dyR) > 0.0 || wRPerm < 0) { - continue; - } - let idyR: u32 = u32(dyR); - - for (var wC: u32 = 0; wC < filterDims[1]; wC = wC + 1) { - let dyC = (${o}(dyCorner.y) + ${o}(wC)) / ${o}(strides.y); - let dyC2 = (${o}(dyCorner.y) + 1.0 + ${o}(wC)) / ${o}(strides.y); - let wCPerm = filterDims[1] - 1 - wC; - if (wCPerm < 0) { - continue; - } - var bDyCVal = true; - var bDyCVal2 = true; - if (dyC < 0.0 || dyC >= ${o}(outBackprop[2]) || - fract(dyC) > 0.0) { - bDyCVal = false; - } - if (dyC2 < 0.0 || dyC2 >= ${o}(outBackprop[2]) || - fract(dyC2) > 0.0) { - bDyCVal2 = false; - } - - let idyC: u32 = u32(dyC); - let idyC2: u32 = u32(dyC2); - if (bDyCVal && bDyCVal2) { - let d2Length = outBackprop[3]; - for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) { - let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${T.get("batch","idyR","idyC","d2")}; - let tmpval = vec4<${o}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - dotProd[0] = dotProd[0] + tmpval; - - xValue = ${T.get("batch","idyR","idyC2","d2")}; - - dotProd[1] = dotProd[1] + vec4<${o}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - } - } else if (bDyCVal) { - let d2Length = outBackprop[${u}]; - for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { - let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${T.get("batch","idyR","idyC","d2")}; - let tmpval = vec4<${o}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - dotProd[0] = dotProd[0] + tmpval; - } - } else if (bDyCVal2) { - let d2Length = outBackprop[3]; - for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) { - let wValue0 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${S.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${T.get("batch","idyR","idyC2","d2")}; - let tmpval = vec4<${o}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - dotProd[1] = dotProd[1] + tmpval; - } - } - } - } - - for (var i: u32 = 0; i < ${f}; i = i + 1) { - let value = dotProd[i] + ${a?"bias[c+i]":`vec4<${o}>(0.0)`}; - ${P.set("batch","r","c + i","d1","value")}; - } - }`,V=` - let outputIndices = ${P.offsetToIndices("global_idx")}; - let batch = ${P.indicesGet("outputIndices",0)}; - let d1 = ${P.indicesGet("outputIndices",u)}; - let r = ${P.indicesGet("outputIndices",d)}; - let c = ${P.indicesGet("outputIndices",c)}; - let dyCorner = vec2(i32(r), i32(c)) - pads; - let dyRCorner = dyCorner.x; - let dyCCorner = dyCorner.y; - let groupId = d1 / ${y}; - let wOutChannel = d1 - groupId * ${y}; - // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). - // ? = to be determined. : = across all values in that axis. - var dotProd = ${o}(0.0); - for (var wR: u32 = 0; wR < effectiveFilterDims.x; wR = wR + 1) { - if (wR % dilations.x != 0) { - continue; - } - let dyR = (${o}(dyRCorner) + ${o}(wR)) / ${o}(strides[0]); - let wRPerm = filterDims.x - 1 - wR / dilations.x; - if (dyR < 0.0 || dyR >= ${o}(outBackprop[${d}]) || fract(dyR) > 0.0 || - wRPerm < 0) { - continue; - } - let idyR: u32 = u32(dyR); - - for (var wC: u32 = 0; wC < effectiveFilterDims.y; wC = wC + 1) { - if (wC % dilations.y != 0) { - continue; - } - let dyC = (${o}(dyCCorner) + ${o}(wC)) / ${o}(strides.y); - let wCPerm = filterDims.y - 1 - wC / dilations.y; - if (dyC < 0.0 || dyC >= ${o}(outBackprop[${c}]) || - fract(dyC) > 0.0 || wCPerm < 0) { - continue; - } - let idyC: u32 = u32(dyC); - var inputChannel = groupId * ${b}; - for (var d2: u32 = 0; d2 < ${b}; d2 = d2 + 1) { - let xValue = ${l?T.get("batch","idyR","idyC","inputChannel"):T.get("batch","inputChannel","idyR","idyC")}; - let wValue = ${S.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")}; - dotProd = dotProd + xValue * wValue; - inputChannel = inputChannel + 1; - } - } - } - let value = dotProd + ${a?"bias[d1]":`${o}(0.0)`}; - ${P.setByOffset("global_idx","value")}; - `;return` - ${t.declareVariables(...A,P)} - ${$} - const outShape : vec4 = vec4(${r.join(",")}); - const outBackprop : vec4 = vec4(${e[0].dims.join(",")}); - const strides : vec2 = vec2(${n.strides[0]}, ${n.strides[1]}); - const filterDims : vec2 = vec2(${n.kernelShape[l?1:2]}, ${n.kernelShape[l?2:3]}); - const dilations : vec2 = vec2(${n.dilations[0]}, ${n.dilations[1]}); - const effectiveFilterDims : vec2 = filterDims + vec2( - ${n.dilations[0]<=1?0:(n.kernelShape[l?1:2]-1)*(n.dilations[0]-1)}, - ${n.dilations[1]<=1?0:(n.kernelShape[l?2:3]-1)*(n.dilations[1]-1)}); - const pads : vec2 = vec2(i32(effectiveFilterDims[0]) - 1 - (${n.pads[0]+n.pads[2]})/2, - i32(effectiveFilterDims[1]) - 1 - (${n.pads[1]+n.pads[3]})/2); - ${t.mainStart()} - ${t.guardAgainstOutOfBoundsWorkgroupSizes(h)}; - ${s?N:V}}`},As=(t,e,n)=>{let r=t.length>2,a=e.outputShape,i=J.size(a),s=[Math.ceil(i/64),1,1];it("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${s}`);let o=At(t[0].dataType);return{name:"ConvTranspose2D",shaderCache:{hint:e.cacheKey},getRunData:()=>({dispatchGroup:{x:s[0],y:s[1],z:s[2]},outputs:[{dims:n?n(a):a,dataType:t[0].dataType}]}),getShaderSource:l=>gu(l,t,e,a,r,s[1]===1&&s[2]===1,!1,o)}}}),_u,wu,yu,Hi,Sh,bu,vu,$u,xu,Eh,D_=X(()=>{ot(),B_(),P_(),An(),Hr(),_u=(t,e,n,r,a,i)=>(t-1)*e+n+(r-1)*a+1-i,wu=(t,e,n,r,a)=>{let i=Math.floor(t/2);e==="SAME_UPPER"?(n[r]=i,n[a]=t-i):e==="SAME_LOWER"&&(n[r]=t-i,n[a]=i)},yu=(t,e,n,r,a,i,s,o,l,d)=>{let c=t.length-2,u=d.length===0;if(l.length===0)for(let g=0;g{let n=t.kernelShape.slice();if(t.kernelShape.length===0||t.kernelShape.reduce((h,f)=>h*f,1)===0){n.length=0;for(let h=2;hh+f,0)===0){let h=e[0].dims.length-2;l=new Array(h).fill(1)}let d=t.strides.slice();if(d.reduce((h,f)=>h+f,0)===0){let h=e[0].dims.length-2;d=new 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n=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],r=t[1].dims[0];if(n!==r)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let a=t[1].dims[1]*e.group;if(t.length===3&&(t[2].dims.length!==1||t[2].dims[0]!==a))throw new Error("invalid bias");let i=t[0].dims.length-2;if(e.dilations.reduce((s,o)=>s+o,0)>0&&e.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(e.strides.reduce((s,o)=>s+o,0)>0&&e.strides.length!==i)throw new Error(`strides should be ${i}D`);if(e.pads.reduce((s,o)=>s+o,0)>0&&e.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(e.outputPadding.length!==i&&e.outputPadding.length!==0)throw new Error(`output_padding should be ${i}D`);if(e.kernelShape.reduce((s,o)=>s+o,0)>0&&e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if(e.outputShape.length!==0&&e.outputShape.length!==t[0].dims.length-2)throw new Error("invalid output shape")},vu=[2,3,1,0],$u=(t,e,n)=>{let r=Hi(n,e),a=n.format==="NHWC",i=r.outputShape,s=i[a?3:1],o=e[0].dims[a?3:1];if(r.group!==1||s===1&&o===1){t.compute(As(e,r));return}let l=i[a?1:2],d=i[a?2:3],c=e[1].dims[2],u=e[1].dims[3],h=a?l*d:s,f=a?s:l*d,g=c*u*o,w=!0,b=t.kernelCustomData.wT??t.compute(_n(e[1],vu),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=b);let y=[e[0],b],$=e.length===3;$&&(!a&&e[2].dims.length===1?y.push(e[2].reshape([e[2].dims[0],1,1])):y.push(e[2])),t.compute(xh(y,r,i,h,f,g,$,w),{inputs:y})},xu=(t,e)=>{let n=e.format==="NHWC",r=[t.inputs[0].reshape(n?[t.inputs[0].dims[0],1,t.inputs[0].dims[1],t.inputs[0].dims[2]]:[t.inputs[0].dims[0],t.inputs[0].dims[1],1,t.inputs[0].dims[2]]),t.inputs[1].reshape([t.inputs[1].dims[0],t.inputs[1].dims[1],1,t.inputs[1].dims[2]])];r.length===3&&r.push(t.inputs[2]);let a=e.kernelShape;(a.length===0||a[0]===0)&&(a=[t.inputs[1].dims[2]]);let i=e.dilations;(i.length===0||i[0]===0)&&(i=[1]);let s=e.strides;(s.length===0||s[0]===0)&&(s=[1]);let o=e.pads;o.length===0&&(o=[0,0]),o=[0,o[0],0,o[1]],s=[1].concat(s),i=[1].concat(i),a=[1].concat(a);let l=Hi({...e,pads:o,strides:s,dilations:i,kernelShape:a},r);t.compute(As(r,l,d=>n?[d[0],d[2],d[3]]:[d[0],d[1],d[3]]))},Eh=(t,e)=>{bu(t.inputs,e),t.inputs[0].dims.length===3?xu(t,e):$u(t,t.inputs,e)}}),Su,kh,Ch,N_=X(()=>{Ye(),Ie(),ot(),Ce(),Su=(t,e,n,r)=>{let a=J.size(e),i=e.length,s=Q("input",t,i),o=ve("output",t,i),l=n.dataType===6?n.getInt32Array()[0]:Number(n.getBigInt64Array()[0]),d=J.normalizeAxis(l,i),c=u=>{let h=` i32(${s.indicesGet("inputIndices","uniforms.axis")}) `,f=xe("uniforms.input_shape","uniforms.axis",i),g=r.reverse?h+(r.exclusive?" + 1":""):"0",w=r.reverse?f:h+(r.exclusive?"":" + 1");return` - ${u.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(s,o)} - ${u.mainStart()} - ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - var inputIndices = 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r=this.symbolToInfo.get(t);if(r!==void 0){if(r.dimValue!==e&&r.count!==1)throw new Error("Dimension mismatch");r.count++,r.inputIndices.push(n)}else r={count:1,dimValue:e,inputIndices:[n]};this.symbolToInfo.set(t,r)}processTerm(t,e,n,r=-1){let a=n.length,i=!1,s=[],o=0;if(!t.match(RegExp(qi))&&!e&&t!=="")throw new Error("Invalid LHS term");let l=t.match(RegExp(Oa,"g")),d=new Cu(r);return l?.forEach((c,u)=>{if(c==="..."){if(i)throw new Error("Only one ellipsis is allowed per input term");i=!0;let h=a-l.length+1;if(h<0)throw new Error("Ellipsis out of bounds");if(s=n.slice(o,o+h),this.hasEllipsis){if(this.ellipsisDims.length!==s.length||this.ellipsisDims.toString()!==s.toString())throw new Error("Ellipsis dimensions mismatch")}else if(e)this.hasEllipsis=!0,this.ellipsisDims=s;else throw new Error("Ellipsis must be specified in the LHS");for(let f=0;ft+"_max",Iu=(t,e,n,r,a)=>{let 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o&&c.push(...ie(e)),l&&c.push(...ie(r)),{name:"Expand",shaderCache:{hint:`${r.length}`,inputDependencies:[o?"rank":"dims"]},getShaderSource:d,getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:c})}},Ah=t=>{Au(t.inputs),t.compute(Ou(t.inputs),{inputs:[0]})}}),zu,Ru,Mh,Oh,U_=X(()=>{Ye(),Ie(),ot(),Ce(),zu=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},Ru=(t,e)=>{let n=t[0].dims,r=t[1].dims,a=n.length,i=J.normalizeAxis(e.axis,a),s=n.slice(0);s.splice(i,1,...r);let o=n[i],l=t[0].dataType===9?4:1,d=Math.ceil(J.size(s)/l),c=st(t[0].dims.length),u=c?t[0].dims.length:t[0].dims,h=st(t[1].dims.length),f=h?t[1].dims.length:t[1].dims,g=st(s.length),w=g?s.length:s,b=[{type:"uint32",data:d},{type:"int32",data:o},{type:"uint32",data:i}];c&&b.push(...ie(t[0].dims)),h&&b.push(...ie(t[1].dims)),g&&b.push(...ie(s));let y=[];y.push(c?"rank":"dims"),y.push(h?"rank":"dims");let $=k=>{let S=Q("data",t[0].dataType,u,l),T=Q("inputIndices",t[1].dataType,f),A=ve("output",t[0].dataType,w,l),P=V=>{let j=r.length,M=`var indicesIndices${V} = ${T.type.indices}(0);`;for(let G=0;G1?`indicesIndices${V}[${G}]`:`indicesIndices${V}`} = ${s.length>1?`outputIndices${V}[uniforms.axis + ${G}]`:`outputIndices${V}`};`;M+=` - var idx${V} = ${T.getByIndices(`indicesIndices${V}`)}; - if (idx${V} < 0) { - idx${V} = idx${V} + uniforms.axisDimLimit; - } - var dataIndices${V} = ${S.type.indices}(0); - `;for(let G=0,O=0;G1?`dataIndices${V}[${G}]`:`dataIndices${V}`} = u32(idx${V});`,O+=j):(M+=`${a>1?`dataIndices${V}[${G}]`:`dataIndices${V}`} = ${s.length>1?`outputIndices${V}[${O}]`:`outputIndices${V}`};`,O++);return M},N;if(t[0].dataType===9){let V=(j,M,G="")=>` - let outputIndices${M} = ${A.offsetToIndices(`outputOffset + ${M}u`)}; - ${P(M)}; - let offset${M} = ${S.indicesToOffset(`dataIndices${M}`)}; - let index${M} = offset${M} / 4u; - let component${M} = offset${M} % 4u; - ${j}[${M}] = ${G}(${S.getByOffset(`index${M}`)}[component${M}]); - `;N=` - let outputOffset = global_idx * ${l}; - var value = vec4(0); - ${V("value",0,"u32")} - ${V("value",1,"u32")} - ${V("value",2,"u32")} - ${V("value",3,"u32")} - ${A.setByOffset("global_idx","value")} - `}else N=` - let outputIndices = ${A.offsetToIndices("global_idx")}; - ${P("")}; - let value = ${S.getByIndices("dataIndices")}; - ${A.setByOffset("global_idx","value")}; - `;return` - ${k.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(S,T,A)} - ${k.mainStart()} - ${k.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - ${N} - }`};return{name:"Gather",shaderCache:{hint:e.cacheKey,inputDependencies:y},getRunData:()=>({outputs:[{dims:s,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:b}),getShaderSource:$}},Mh=t=>qe({axis:t.axis}),Oh=(t,e)=>{let n=t.inputs;zu(n),t.compute(Ru(t.inputs,e))}}),Bu,Pu,zh,Rh,W_=X(()=>{Ie(),ot(),Ce(),Bu=t=>{if(!t||t.length!==2)throw new Error("GatherElements requires 2 inputs.");if(t[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(t[0].dims.length!==t[1].dims.length)throw new Error(`GatherElements requires that the data input and - indices input tensors be of same rank.`)},Pu=(t,e)=>{let n=t[0].dims,r=t[0].dataType,a=n.length,i=t[1].dims,s=t[1].dataType,o=J.normalizeAxis(e.axis,a),l=n[o],d=i.slice(0),c=J.size(d),u=Q("input",r,a),h=Q("indicesInput",s,i.length),f=ve("output",r,d.length),g=[{type:"uint32",data:c},{type:"int32",data:l},{type:"uint32",data:o}];return g.push(...ie(n)),g.push(...ie(i)),g.push(...ie(d)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:g}),getShaderSource:w=>` - ${w.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(u,h,f)} - ${w.mainStart()} - ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - - let outputIndices = ${f.offsetToIndices("global_idx")}; - - var idx = ${h.getByOffset("global_idx")}; - if (idx < 0) { - idx = idx + uniforms.axisDimLimit; - } - var inputIndices = ${u.type.indices}(outputIndices); - ${u.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; - let value = ${u.getByIndices("inputIndices")}; - - ${f.setByOffset("global_idx","value")}; - }`}},zh=t=>qe({axis:t.axis}),Rh=(t,e)=>{let n=t.inputs;Bu(n),t.compute(Pu(t.inputs,e))}}),Du,Nu,Bh,Ph,V_=X(()=>{Ie(),Ce(),Du=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},Nu=(t,e)=>{let n=t[0].dims.slice(),r=t[1].dims.slice(),[a,i,s]=Qc.getShapeOfGemmResult(n,e.transA,r,e.transB,t.length===3?t[2].dims:void 0),o=[a,i];if(!o)throw new Error("Can't use gemm on the given tensors");let l=J.size(o),d=[{type:"uint32",data:l},{type:"uint32",data:a},{type:"uint32",data:i},{type:"uint32",data:s},{type:"float32",data:e.alpha},{type:"float32",data:e.beta}],c=["type","type"];t.length===3&&(d.push(...ie(t[2].dims)),c.push("rank")),d.push(...ie(o));let u=h=>{let f="";e.transA&&e.transB?f="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?f="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?f="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(f="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let g=e.alpha===1?"":"value *= uniforms.alpha;",w=Q("a",t[0].dataType,t[0].dims),b=Q("b",t[1].dataType,t[1].dims),y=w.type.value,$=null,k=[w,b];t.length===3&&($=Q("c",t[2].dataType,t[2].dims.length),k.push($));let S=ve("output",t[0].dataType,o.length);k.push(S);let T=[{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` - ${h.registerUniforms(T).declareVariables(...k)} - - ${h.mainStart()} - ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - - let m = global_idx / uniforms.N; - let n = global_idx % uniforms.N; - - var value = ${y}(0); - for (var k: u32 = 0u; k < uniforms.K; k++) { - ${f} - } - - ${g} - ${$!=null?`let cOffset = ${$.broadcastedIndicesToOffset("vec2(m, n)",S)}; value += ${y}(uniforms.beta) * ${$.getByOffset("cOffset")};`:""} - output[global_idx] = value; - }`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:u}},Bh=t=>{let e=t.transA,n=t.transB,r=t.alpha,a=t.beta;return{transA:e,transB:n,alpha:r,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},Ph=(t,e)=>{Du(t.inputs),t.compute(Nu(t.inputs,e))}}),Fu,Lu,Uu,Dh,G_=X(()=>{Ye(),Ie(),Ce(),Fu=(t,e)=>{let n=t[0].dims,r=n,a=2,i=J.sizeToDimension(n,a),s=J.sizeFromDimension(n,a),o=$t(s),l=s/o,d=[n[0],n[1],l],c=["rank","type","type"],u=[{type:"uint32",data:s},{type:"uint32",data:l}];u.push(...ie(d),...ie(d));let h=f=>{let g=Q("x",t[0].dataType,d.length,o),w=Q("scale",t[1].dataType,t[1].dims),b=Q("bias",t[2].dataType,t[2].dims),y=ve("output",t[0].dataType,d.length,o),$=[g,w,b,y],k=g.type.value,S=o===1?"f32":`vec${o}`,T=64,A=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return` - var meanShared : f32; - var squaredNormShared : f32; - var workgroupShared : array<${S}, ${T}>; - const workgroupSize = ${T}u; - ${f.registerUniforms(A).declareVariables(...$)} - ${f.mainStart(T)} - 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 = ${S}(0); - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - initial = initial + ${S}(${g.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 = ${Qt("workgroupShared[0]",o)} / f32(uniforms.normSize); - } - workgroupBarrier(); - - // reinitialize workgroup memory. - initial = ${S}(0); - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - let deviation = ${S}(${g.get("batch","channel","h")}) - ${S}(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 = ${Qt("workgroupShared[0]",o)}; - } - workgroupBarrier(); - - let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${e.epsilon})); - let channelScale = invStdDev * f32(${w.getByOffset("channel")}); - let channelShift = f32(${b.getByOffset("channel")}) - meanShared * channelScale; - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - let value = ${g.get("batch","channel","h")} * ${k}(${S}(channelScale)) + ${k}(${S}(channelShift)); - ${y.set("batch","channel","h","value")}; - } - }`};return{name:"InstanceNormalization",shaderCache:{hint:`${e.epsilon};${o}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:i},programUniforms:u}),getShaderSource:h}},Lu=(t,e,n,r,a,i,s,o)=>{let l=$t(s),d=64,c=l===1?"vec2f":`mat2x${l}f`,u=l===1?"f32":`vec${l}f`,h=(A,P)=>`${c}(${A}, ${P})`,f=a*s/l,g=Math.ceil(i/d),w=["type"],b=[{type:"uint32",data:g},{type:"uint32",data:i},{type:"uint32",data:Math.floor(s/l)},{type:"uint32",data:Math.floor(i*s/l)}],y=A=>{let P=Q("input",e.dataType,e.dims,l);return` - ${A.declareVariables(P)} - @group(0) @binding(1) var output : array<${c}>; - struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32}; - @group(0) @binding(2) var uniforms: Uniforms; - - ${A.mainStart(d)} - let currentImageNumber = global_idx / ${d} / uniforms.C; - let currentChannelNumber = (global_idx / ${d}) % uniforms.C; - let wgId = global_idx % ${d}; - let wgOffset = wgId * 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 = ${vt("f32",l)}; - var squaredSum = ${vt("f32",l)}; - for (var i: u32 = wgOffset; i < wgMax; i++) { - let value = ${u}(input[offset + i * uniforms.C]); - sum += value; - squaredSum += value * value; - } - output[global_idx] = ${h("sum","squaredSum")}; - }`},$=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${l}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:[a,s,d,2],dataType:1}],dispatchGroup:{x:a*s/l},programUniforms:b}),getShaderSource:y},{inputs:[e],outputs:[-1]})[0],k=[{type:"uint32",data:f},{type:"uint32",data:i},{type:"uint32",data:Math.floor(s/l)},{type:"uint32",data:Math.floor(d*s/l)}],S=["type","type","type"],T=A=>{let P=Q("scale",n.dataType,n.dims,l),N=Q("bias",r.dataType,r.dims,l);return` - @group(0) @binding(0) var input : array<${c}>; - @group(0) @binding(1) var scale : array<${P.type.storage}>; - @group(0) @binding(2) var bias : array<${N.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; - - ${A.mainStart()} - ${A.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 = ${vt("f32",l)}; - var squaredSum = ${vt("f32",l)}; - for (var i: u32 = 0; i < ${d}; i++) { - let value = input[offset + i + currentChannelNumber * ${d}]; - sum += value[0]; - squaredSum += value[1]; - } - sum = sum / f32(uniforms.H); - squaredSum = squaredSum / f32(uniforms.H); - let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${o})); - let channelScale = invStdDev * ${u}(scale[currentChannelNumber]); - let channelShift = ${u}(bias[currentChannelNumber]) - sum * channelScale; - - output[global_idx] = ${h("channelScale","channelShift")}; - }`};return t.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${o}`,inputDependencies:S},getRunData:()=>({outputs:[{dims:[a,s,2],dataType:1}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:k}),getShaderSource:T},{inputs:[$,n,r],outputs:[-1]})[0]},Uu=(t,e,n)=>{let r=e[0].dims,a=r,i=r[0],s=r[r.length-1],o=J.sizeFromDimension(r,1)/s,l=$t(s),d=J.size(a)/l,c=[{type:"uint32",data:o},{type:"uint32",data:Math.floor(s/l)}],u=["type","type"],h=Lu(t,e[0],e[1],e[2],i,o,s,n.epsilon),f=g=>{let w=At(e[0].dataType),b=l===1?"vec2f":`mat2x${l}f`,y=l===1?w:`vec${l}<${w}>`,$=Q("input",e[0].dataType,e[0].dims,l),k=ve("output",e[0].dataType,a,l);return` - @group(0) @binding(0) var input : array<${$.type.storage}>; - @group(0) @binding(1) var scaleInput : array<${b}>; - @group(0) @binding(2) var output : array<${k.type.storage}>; - struct Uniforms {H: u32, C : u32}; - @group(0) @binding(3) var uniforms: Uniforms; - - ${g.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], ${y}(scale[0]), ${y}(scale[1])); - }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:f},{inputs:[e[0],h]})},Dh=(t,e)=>{e.format==="NHWC"?Uu(t,t.inputs,e):t.compute(Fu(t.inputs,e))}}),Wu,Vu,Nh,H_=X(()=>{Ye(),Ie(),Ce(),Wu=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Vu=(t,e,n)=>{let r=t[0].dims,a=t[1],i=t[2],s=r,o=J.normalizeAxis(e.axis,r.length),l=J.sizeToDimension(r,o),d=J.sizeFromDimension(r,o),c=J.size(a.dims),u=i?J.size(i.dims):0;if(c!==d||i&&u!==d)throw new Error(`Size of X.shape()[axis:] == ${d}. - Size of scale and bias (if provided) must match this. - Got scale size of ${c} and bias size of ${u}`);let h=[];for(let S=0;S1,y=n>2,$=S=>{let T=At(t[0].dataType),A=[Q("x",t[0].dataType,t[0].dims,f),Q("scale",a.dataType,a.dims,f)];i&&A.push(Q("bias",i.dataType,i.dims,f)),A.push(ve("output",t[0].dataType,s,f)),b&&A.push(ve("mean_data_output",1,h)),y&&A.push(ve("inv_std_output",1,h));let P=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` - ${S.registerUniforms(P).declareVariables(...A)} - ${S.mainStart()} - ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} - let offset = global_idx * uniforms.norm_size_vectorized; - var meanVector = ${vt("f32",f)}; - var meanSquareVector = ${vt("f32",f)}; - - for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { - let value = ${rn(T,f,"x[h + offset]")}; - meanVector += value; - meanSquareVector += value * value; - } - let mean = ${Qt("meanVector",f)} / uniforms.norm_size; - let invStdDev = - inverseSqrt(${Qt("meanSquareVector",f)} / uniforms.norm_size - mean * mean + uniforms.epsilon); - - for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { - let f32input = ${rn(T,f,"x[j + offset]")}; - let f32scale = ${rn(T,f,"scale[j]")}; - output[j + offset] = ${A[0].type.value}((f32input - mean) * invStdDev * f32scale - ${i?`+ ${rn(T,f,"bias[j]")}`:""} - ); - } - - ${b?"mean_data_output[global_idx] = mean":""}; - ${y?"inv_std_output[global_idx] = invStdDev":""}; - }`},k=[{dims:s,dataType:t[0].dataType}];return b&&k.push({dims:h,dataType:1}),y&&k.push({dims:h,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${f};${n}`,inputDependencies:g},getRunData:()=>({outputs:k,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:w}),getShaderSource:$}},Nh=(t,e)=>{Wu(t.inputs),t.compute(Vu(t.inputs,e,t.outputCount))}}),Gu,Fh,Yi,Hu,za,Lh,q_=X(()=>{Ie(),ot(),Zs(),Sp(),Ce(),Hr(),Gu=(t,e)=>{let n=t[0],r=t[1],a=t[2],i=t[3],s=t[4],o=t[5],l=t[6],d=t[7];if(n.dims.length!==3&&n.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let c=!1,u=n.dims[0],h=n.dims[1],f=n.dims.length===3?c?n.dims[2]/3:n.dims[2]:e.numHeads*n.dims[4],g=h,w=0,b=0,y=Math.floor(f/e.numHeads);if(l&&d){if(l.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');w=l.dims[2],b=l.dims[2]}else if(l||d)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let $;if(r){if(n.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(r.dims.length<3||r.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==r.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(r.dims.length===3){if(r.dims[2]!==n.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');$=2,g=r.dims[1]}else if(r.dims.length===5){if(r.dims[2]!==e.numHeads||r.dims[3]!==2||r.dims[4]!==y)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(a)throw new Error('Expect "value" be none when "key" has packed kv format.');$=5,g=r.dims[1]}else{if(r.dims[1]!==e.numHeads||r.dims[3]!==y)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');$=0,g=r.dims[2]}}else{if(n.dims.length!==3&&n.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(n.dims.length===5&&(n.dims[2]!==e.numHeads||n.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');$=3}if(i){if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(a&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let k=0;if(s){k=8;let N=s.dims;throw N.length===1?N[0]===u?k=1:N[0]===3*u+2&&(k=3):N.length===2&&N[0]===u&&N[1]===g&&(k=5),k===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let S=!1,T=f;if(a){if(a.dims.length!==3&&a.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==a.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(a.dims.length===3){if(g!==a.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');T=a.dims[2]}else{if(g!==a.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');T=a.dims[1]*a.dims[3],S=!0}}let A=w+g,P=!1;if(s)throw new Error("Key padding mask is not supported");if(o)throw new Error("extraAddQk is not supported");if(l)throw new Error("pastKey is not supported");if(d)throw new Error("pastValue is not supported");return{batchSize:u,sequenceLength:h,pastSequenceLength:w,kvSequenceLength:g,totalSequenceLength:A,maxSequenceLength:b,inputHiddenSize:0,hiddenSize:f,vHiddenSize:T,headSize:y,vHeadSize:Math.floor(T/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:k,scale:e.scale,broadcastResPosBias:P,passPastInKv:S,qkvFormat:$}},Fh=t=>qe({...t}),Yi=qe({perm:[0,2,1,3]}),Hu=(t,e,n,r,a,i,s)=>{let o=[r,a,i],l=J.size(o),d=[{type:"uint32",data:l},{type:"uint32",data:s},{type:"uint32",data:i}],c=u=>{let h=ve("qkv_with_bias",e.dataType,o),f=Q("qkv",e.dataType,o),g=Q("bias",n.dataType,o),w=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` - ${u.registerUniforms(w).declareVariables(f,g,h)} - ${u.mainStart()} - ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; - - qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; - }`};return t.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:e.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:c},{inputs:[e,n],outputs:[-1]})[0]},za=(t,e,n,r,a,i,s,o)=>{let l=i;if(s){if(r===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=Hu(t,i,s,e,r,n*a,o),l=l.reshape([e,r,n,a]),t.compute(_n(l,Yi.perm),{inputs:[l],outputs:[-1]})[0]}else return i.dims.length===3&&(l=i.reshape([e,r,n,a])),t.compute(_n(l,Yi.perm),{inputs:[l],outputs:[-1]})[0]},Lh=(t,e)=>{let n=Gu(t.inputs,e);if(t.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(t.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let r=t.inputs[1]&&t.inputs[2]&&t.inputs[1].dims.length===4&&t.inputs[2].dims.length===4,a=za(t,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,t.inputs[0],t.inputs[3],0);if(r)return Ja(t,a,t.inputs[1],t.inputs[2],t.inputs[4],void 0,void 0,void 0,t.inputs[5],n,e);let i=za(t,n.batchSize,n.numHeads,n.kvSequenceLength,n.headSize,t.inputs[1],t.inputs[3],n.hiddenSize),s=za(t,n.batchSize,n.numHeads,n.kvSequenceLength,n.vHeadSize,t.inputs[2],t.inputs[3],2*n.hiddenSize);Ja(t,a,i,s,t.inputs[4],void 0,t.inputs[6],t.inputs[7],t.inputs[5],n,e)}}),qu,ju,Ku,Yu,Xu,Qu,Zu,Ju,Uh,j_=X(()=>{Ye(),Ie(),Ce(),qu=t=>{if(!t||t.length<1)throw new Error("Too few inputs");if(t[0].dataType!==1)throw new Error("Input type must be float.");if(t.length>=2){let e=t[0].dims.length*2===t[1].dims[0];if(t.length===4&&(e=t[3].dims[0]*2===t[1].dims[0]),!e)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},ju=(t,e,n)=>{let r="";for(let a=e-1;a>=0;--a)r+=` - k = i32(${t.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,n)}; - if (k < 0) { - break; - } - if (k >= i32(${xe("uniforms.x_shape",a,e)})) { - break; - } - offset += k * i32(${xe("uniforms.x_strides",a,e)}); - `;return` - value = ${t.type.value}(uniforms.constant_value); - for (var i = 0; i < 1; i++) { - var offset = 0; - var k = 0; - ${r} - value = x[offset]; - } - `},Ku=(t,e,n)=>{let r="";for(let a=e-1;a>=0;--a)r+=` - k = i32(${t.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,n)}; - if (k < 0) { - k = -k; - } - { - let _2n_1 = 2 * (i32(${xe("uniforms.x_shape",a,e)}) - 1); - k = k % _2n_1; - if(k >= i32(${xe("uniforms.x_shape",a,e)})) { - k = _2n_1 - k; - } - } - offset += k * i32(${xe("uniforms.x_strides",a,e)}); - `;return` - var offset = 0; - var k = 0; - ${r} - value = x[offset]; - `},Yu=(t,e,n)=>{let r="";for(let a=e-1;a>=0;--a)r+=` - k = i32(${t.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,n)}; - if (k < 0) { - k = 0; - } - if (k >= i32(${xe("uniforms.x_shape",a,e)})) { - k = i32(${xe("uniforms.x_shape",a,e)}) - 1; - } - offset += k * i32(${xe("uniforms.x_strides",a,e)}); - `;return` - var offset = 0; - var k = 0; - ${r} - value = x[offset]; - `},Xu=(t,e,n)=>{let r="";for(let a=e-1;a>=0;--a)r+=` - k = i32(${t.indicesGet("indices",a)}) - ${xe("uniforms.pads",a,n)}; - if (k < 0) { - k += i32(${xe("uniforms.x_shape",a,e)}]); - } - if (k >= i32(${xe("uniforms.x_shape",a,e)})) { - k -= i32(${xe("uniforms.x_shape",a,e)}); - } - offset += k * i32(${xe("uniforms.x_strides",a,e)}); - `;return` - var offset = 0; - var k = 0; - ${r} - value = x[offset]; - `},Qu=(t,e,n)=>{switch(n.mode){case 0:return ju(t,e,n.pads.length);case 1:return Ku(t,e,n.pads.length);case 2:return Yu(t,e,n.pads.length);case 3:return Xu(t,e,n.pads.length);default:throw new Error("Invalid mode")}},Zu=(t,e)=>{let n=J.padShape(t[0].dims.slice(),e.pads),r=t[0].dims,a=[{type:"uint32",data:J.size(n)},{type:"uint32",data:e.pads}];if(e.mode===0){let o=Yt(t[0].dataType);a.push({type:o,data:e.value})}a.push(...ie(t[0].dims),...ie(n));let i=["rank"],s=o=>{let l=ve("output",t[0].dataType,n.length),d=Q("x",t[0].dataType,r.length),c=d.type.value,u=Qu(l,r.length,e),h=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.mode===0&&h.push({name:"constant_value",type:c}),` - ${o.registerUniforms(h).declareVariables(d,l)} - ${o.mainStart()} - ${o.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - - let indices = ${l.offsetToIndices("global_idx")}; - - var value = ${c}(0); - ${u} - output[global_idx] = value; - }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(J.size(n)/64)},programUniforms:a}),getShaderSource:s}},Ju=(t,e)=>{if(t.length>1){let n=t[1].getBigInt64Array(),r=t.length>=3&&t[2].data?t[2].getFloat32Array()[0]:0,a=t[0].dims.length,i=new Int32Array(2*a).fill(0);if(t.length>=4){let o=t[3].getBigInt64Array();for(let l=0;li[Number(l)]=Number(o));let s=[];return i.forEach(o=>s.push(o)),{mode:e.mode,value:r,pads:s}}else return e},Uh=(t,e)=>{qu(t.inputs);let n=Ju(t.inputs,e);t.compute(Zu(t.inputs,n),{inputs:[0]})}}),Sr,Xi,Qi,Zi,Ji,ed,td,es,ts,Wh,Vh,ns,Gh,Hh,rs,qh,jh,Kh,Yh,K_=X(()=>{Vt(),Ie(),Ce(),Sr=t=>{if(Ne.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},Xi=(t,e,n)=>{let r=e.format==="NHWC",a=t.dims.slice();r&&a.splice(1,0,a.pop());let i=Object.hasOwnProperty.call(e,"dilations"),s=e.kernelShape.slice(),o=e.strides.slice(),l=i?e.dilations.slice():[],d=e.pads.slice();Qa.adjustPoolAttributes(n,a,s,o,l,d);let c=Qa.computePoolOutputShape(n,a,o,l,s,d,e.autoPad),u=Object.assign({},e);i?Object.assign(u,{kernelShape:s,strides:o,pads:d,dilations:l,cacheKey:e.cacheKey}):Object.assign(u,{kernelShape:s,strides:o,pads:d,cacheKey:e.cacheKey});let h=c.slice();return h.push(h.splice(1,1)[0]),[u,r?h:c]},Qi=(t,e)=>{let n=e.format==="NHWC",r=J.size(t),a=J.size(e.kernelShape),i=[{type:"uint32",data:r},{type:"uint32",data:a}],s=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(e.kernelShape.length<=2){let o=e.kernelShape[e.kernelShape.length-1],l=e.strides[e.strides.length-1],d=e.pads[e.pads.length/2-1],c=e.pads[e.pads.length-1],u=!!(d+c);i.push({type:"uint32",data:o},{type:"uint32",data:l},{type:"uint32",data:d},{type:"uint32",data:c}),s.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let h=!1;if(e.kernelShape.length===2){let f=e.kernelShape[e.kernelShape.length-2],g=e.strides[e.strides.length-2],w=e.pads[e.pads.length/2-2],b=e.pads[e.pads.length-2];h=!!(w+b),i.push({type:"uint32",data:f},{type:"uint32",data:g},{type:"uint32",data:w},{type:"uint32",data:b}),s.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[i,s,!0,u,h]}else{if(n)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=J.computeStrides(e.kernelShape);i.push({type:"uint32",data:o},{type:"uint32",data:e.pads},{type:"uint32",data:e.strides}),s.push({name:"kernelStrides",type:"u32",length:o.length},{name:"pads",type:"u32",length:e.pads.length},{name:"strides",type:"u32",length:e.strides.length});let l=e.pads.reduce((d,c)=>d+c);return[i,s,!!l,!1,!1]}},Zi=(t,e,n,r,a,i,s,o,l,d,c,u)=>{let h=a.format==="NHWC",f=e.type.value,g=ve("output",e.type.tensor,r);if(a.kernelShape.length<=2){let w="",b="",y="",$=n-(h?2:1);if(c?w=` - for (var i: u32 = 0u; i < uniforms.kw; i++) { - xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i; - if (xIndices[${$}] < 0 || xIndices[${$}] - >= uniforms.x_shape[${$}]) { - pad++; - continue; - } - let x_val = x[${e.indicesToOffset("xIndices")}]; - ${i} - }`:w=` - for (var i: u32 = 0u; i < uniforms.kw; i++) { - xIndices[${$}] = indices[${$}] * uniforms.sw - uniforms.pwStart + i; - let x_val = x[${e.indicesToOffset("xIndices")}]; - ${i} - }`,a.kernelShape.length===2){let k=n-(h?3:2);u?b=` - for (var j: u32 = 0u; j < uniforms.kh; j++) { - xIndices[${k}] = indices[${k}] * uniforms.sh - uniforms.phStart + j; - if (xIndices[${k}] < 0 || xIndices[${k}] >= uniforms.x_shape[${k}]) { - pad += i32(uniforms.kw); - continue; - } - `:b=` - for (var j: u32 = 0u; j < uniforms.kh; j++) { - xIndices[${k}] = indices[${k}] * uniforms.sh - uniforms.phStart + j; - `,y=` - } - `}return` - ${t.registerUniforms(l).declareVariables(e,g)} - - ${t.mainStart()} - ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - - let indices = ${g.offsetToIndices("global_idx")}; - var xIndices = ${g.offsetToIndices("global_idx")}; - - var value = ${f}(${o}); - var pad = 0; - ${b} - ${w} - ${y} - ${s} - - output[global_idx] = value; - }`}else{if(h)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let w=a.kernelShape.length,b=a.pads.length,y="";return d?y=` - if (xIndices[j] >= uniforms.x_shape[j]) { - pad++; - isPad = true; - break; - } - } - if (!isPad) { - let x_val = x[${e.indicesToOffset("xIndices")}]; - ${i} - }`:y=` - } - let x_val = x[${e.indicesToOffset("xIndices")}]; - ${i} - `,` - ${t.registerUniforms(l).declareVariables(e,g)} - - ${t.mainStart()} - ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - let indices = ${g.offsetToIndices("global_idx")}; - var xIndices = ${g.offsetToIndices("global_idx")}; - - var offsets: array; - - var value = ${f}(${o}); - var pad = 0; - var isPad = false; - - for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { - var offset = i; - for (var j = 0u; j < ${w-1}u; j++) { - offsets[j] = offset / ${xe("uniforms.kernelStrides","j",w)}; - offset -= offsets[j] * ${xe("uniforms.kernelStrides","j",w)}; - } - offsets[${w-1}] = offset; - - isPad = false; - for (var j = ${n-w}u; j < ${n}u; j++) { - xIndices[j] = indices[j] * ${xe("uniforms.strides",`j - ${n-w}u`,w)} - + offsets[j - ${n-w}u] - ${xe("uniforms.pads","j - 2u",b)}; - ${y} - } - ${s} - - output[global_idx] = value; - }`}},Ji=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,ed=t=>`${Ji(t)};${t.countIncludePad}`,td=t=>`${Ji(t)};${t.storageOrder};${t.dilations}`,es=t=>({format:t.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][t.auto_pad],ceilMode:t.ceil_mode,kernelShape:t.kernel_shape,strides:t.strides,pads:t.pads}),ts=(t,e,n,r)=>{let[a,i]=Xi(e,r,n),s=Q("x",e.dataType,e.dims.length),o=s.type.value,l="value += x_val;",d="";a.countIncludePad?d+=`value /= ${o}(uniforms.kernelSize);`:d+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[c,u,h,f,g]=Qi(i,a);c.push(...ie(e.dims),...ie(i));let w=["rank"];return{name:t,shaderCache:{hint:`${r.cacheKey};${h};${f};${g}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(J.size(i)/64)},programUniforms:c}),getShaderSource:b=>Zi(b,s,e.dims.length,i.length,a,l,d,0,u,h,f,g)}},Wh=t=>{let e=t.count_include_pad!==0,n=es(t);if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let r={countIncludePad:e,...n,cacheKey:""};return{...r,cacheKey:ed(r)}},Vh=(t,e)=>{Sr(t.inputs),t.compute(ts("AveragePool",t.inputs[0],!1,e))},ns={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Gh=t=>{let e=t.format;return{format:e,...ns,cacheKey:e}},Hh=(t,e)=>{Sr(t.inputs),t.compute(ts("GlobalAveragePool",t.inputs[0],!0,e))},rs=(t,e,n,r)=>{let[a,i]=Xi(e,r,n),s=` - value = max(x_val, value); - `,o="",l=Q("x",e.dataType,e.dims.length),d=["rank"],[c,u,h,f,g]=Qi(i,a);return c.push(...ie(e.dims),...ie(i)),{name:t,shaderCache:{hint:`${r.cacheKey};${h};${f};${g}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(J.size(i)/64)},programUniforms:c}),getShaderSource:w=>Zi(w,l,e.dims.length,i.length,a,s,o,-1e5,u,h,f,g)}},qh=(t,e)=>{Sr(t.inputs),t.compute(rs("MaxPool",t.inputs[0],!1,e))},jh=t=>{let e=t.storage_order,n=t.dilations,r=es(t);if(e!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let a={storageOrder:e,dilations:n,...r,cacheKey:""};return{...a,cacheKey:td(a)}},Kh=t=>{let e=t.format;return{format:e,...ns,cacheKey:e}},Yh=(t,e)=>{Sr(t.inputs),t.compute(rs("GlobalMaxPool",t.inputs[0],!0,e))}}),nd,rd,Xh,Y_=X(()=>{Vt(),Ye(),Ce(),nd=(t,e,n)=>{let r=t===e,a=te&&n>0;if(r||a||i)throw new Error("Range these inputs' contents are invalid.")},rd=(t,e,n,r)=>{let a=Math.abs(Math.ceil((e-t)/n)),i=[a],s=a,o=Yt(r),l=[{type:"uint32",data:s},{type:o,data:t},{type:o,data:n},...ie(i)],d=c=>{let u=ve("output",r,i.length),h=u.type.value,f=[{name:"outputSize",type:"u32"},{name:"start",type:h},{name:"delta",type:h}];return` - ${c.registerUniforms(f).declareVariables(u)} - ${c.mainStart()} - ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - output[global_idx] = uniforms.start + ${h}(global_idx) * uniforms.delta; - }`};return{name:"Range",shaderCache:{hint:`${r}`},getShaderSource:d,getRunData:()=>({outputs:[{dims:i,dataType:r}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:l})}},Xh=t=>{let e=0,n=0,r=0;t.inputs[0].dataType===6?(e=t.inputs[0].getInt32Array()[0],n=t.inputs[1].getInt32Array()[0],r=t.inputs[2].getInt32Array()[0]):t.inputs[0].dataType===1&&(e=t.inputs[0].getFloat32Array()[0],n=t.inputs[1].getFloat32Array()[0],r=t.inputs[2].getFloat32Array()[0]),Ne.webgpu.validateInputContent&&nd(e,n,r),t.compute(rd(e,n,r,t.inputs[0].dataType),{inputs:[]})}}),ad,id,sd,od,ld,ud,dd,cd,pd,hd,fd,as,md,gd,_d,wd,yd,Qh,Zh,X_=X(()=>{Ie(),ot(),Ce(),ad=(t,e)=>{if(t.every(n=>n>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),t.length>0){if(e.mode==="linear"){if(!(t.length===2||t.length===3||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1||t.length===5&&t[0]===1&&t[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(e.mode==="cubic"&&!(t.length===2||t.length===4&&t[0]===1&&t[1]===1||t.length===4&&t[0]===1&&t[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},id=(t,e,n)=>{e.every(a=>a>=0&&a{throw new Error("Resize requires axes input values to be positive and less than rank")}));let r=new Array(n).fill(1);return e.forEach((a,i)=>r[a]=t[i]),r},sd=(t,e,n,r,a,i)=>{let[s,o,l]=n>10?[1,2,3]:[-1,t.length>1?1:-1,-1],d=t[0].dims.length;if(s>0&&t.length>s&&t[s].dims.length>0)t[s].getFloat32Array().forEach(c=>i.push(c));else if(e.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(o>0&&t.length>o&&t[o].dims.length>0){if(t[o].getFloat32Array().forEach(c=>r.push(c)),r.length!==0&&r.length!==d&&n>=18&&r.length!==e.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");ad(r,e),e.axes.length>0&&id(r,e.axes,d).forEach((c,u)=>r[u]=c)}if(l>0&&t.length>l&&(t[l].getBigInt64Array().forEach(c=>a.push(Number(c))),a.length!==d||n>=18&&a.length===e.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(e.axes.length>0){if(r.length!==e.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(a.length!==e.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof r<"u"&&typeof a<"u"&&r.length>0&&a.length>d)throw new Error("Resize requires only of scales or sizes to be specified")},od=(t,e)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, - lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${e} { `+(()=>{switch(t){case"asymmetric":return`return ${e}(xResized) / ${e}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) { - return (${e}(xResized) + 0.5) / ${e}(xScale) - 0.5; - } else { - return 0.0; - }`;case"tf_half_pixel_for_nn":return`return (${e}(xResized) + 0.5) / ${e}(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 = ${e}(xResized * (lengthOriginal - 1) / (lengthResized - 1)); - let fract = - ${e}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${e}(lengthResized - 1); - return whole + fract; - }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { - return ${e}(roiStart) * ${e}(lengthOriginal - 1) + - (${e}(xResized) * ${e}(roiEnd - roiStart) * ${e}(lengthOriginal - 1)) / - ${e}(lengthResized - 1); - } else { - return 0.5 * ${e}(roiStart + roiEnd) * ${e}(lengthOriginal - 1); - }`;case"half_pixel_symmetric":return`const outputWidth = ${e}xScale * ${e}(lengthResized); - const adjustment = ${e}(lengthResized) / outputWidth; - const center = ${e}(lengthOriginal) / 2; - const offset = center * (1 - adjustment); - return offset + ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;case"half_pixel":return`return ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${t} is not supported`)}})()+"}",ld=(t,e,n)=>`fn getNearestPixelFromOriginal(xOriginal: ${n}, isDownSample: bool) -> ${n} {`+(()=>{switch(t){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(e<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${t} is not supported`)}})()+"}",ud=(t,e,n)=>{let r=new Array(n).fill(0).concat(new Array(n).fill(1)),a=t.length===0?r:t.slice();return e.length>0?(e.forEach((i,s)=>{r[i]=a[s],r[s+n]=a[e.length+s]}),r):a},dd=(t,e,n,r)=>{let a=[];if(n.length>0)if(r.length>0){if(t.forEach(i=>a.push(i)),Math.max(...r)>t.length)throw new Error("axes is out of bound");r.forEach((i,s)=>a[i]=n[s])}else n.forEach(i=>a.push(i));else{if(e.length===0)throw new Error("Resize requires either scales or sizes.");a=t.map((i,s)=>Math.round(i*e[s]))}return a},cd=(t,e,n)=>{let r=(()=>{switch(n.keepAspectRatioPolicy){case"not_larger":return n.axes.length>0?Math.min(...n.axes.map(i=>e[i]),Number.MAX_VALUE):Math.min(...e,Number.MAX_VALUE);case"not_smaller":return n.axes.length>0?Math.max(...n.axes.map(i=>e[i]),Number.MIN_VALUE):Math.max(...e,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${n.keepAspectRatioPolicy} is not supported`)}})();e.fill(1,0,e.length);let a=t.slice();return n.axes.length>0?(n.axes.forEach(i=>e[i]=r),n.axes.forEach(i=>a[i]=Math.round(t[i]*e[i]))):(e.fill(r,0,e.length),a.forEach((i,s)=>a[s]=Math.round(i*e[s]))),a},pd=(t,e,n,r,a)=>` - fn calculateOriginalIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> array<${t.type.value}, ${n.length}> { - var original_indices: array<${t.type.value}, ${n.length}>; - for (var i:u32 = 0; i < ${n.length}; i++) { - var output_index = ${t.indicesGet("output_indices","i")}; - var scale = ${xe("uniforms.scales","i",r)}; - var roi_low = ${xe("uniforms.roi","i",a)}; - var roi_hi = ${xe("uniforms.roi",`i + ${e.length}`,a)}; - if (scale == 1.0) { - original_indices[i] = ${t.type.value}(output_index); - } else { - var input_shape_i = ${xe("uniforms.input_shape","i",e.length)}; - var output_shape_i = ${xe("uniforms.output_shape","i",n.length)}; - original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, - input_shape_i, roi_low, roi_hi); - } - } - return original_indices; - }`,hd=(t,e,n,r,a,i,s)=>` - fn calculateInputIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} { - var input_indices: ${t.type.indices}; - for (var i:u32 = 0; i < ${r.length}; i++) { - var output_index = ${e.indicesGet("output_indices","i")}; - var input_index: u32; - var scale = ${xe("uniforms.scales","i",a)}; - if (scale == 1.0) { - input_index = output_index; - } else { - var roi_low = ${xe("uniforms.roi","i",i)}; - var roi_hi = ${xe("uniforms.roi",`i + ${n.length}`,i)}; - var input_shape_i = ${xe("uniforms.input_shape","i",n.length)}; - var output_shape_i = ${xe("uniforms.output_shape","i",r.length)}; - var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, - input_shape_i, roi_low, roi_hi); - if (!${s} || (original_idx >= 0 && original_idx < ${e.type.value}(input_shape_i))) { - if (original_idx < 0) { - input_index = 0; - } else if (original_idx > ${e.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); - } - } - ${t.indicesSet("input_indices","i"," input_index")} - } - return input_indices; - }`,fd=(t,e)=>` - fn checkInputIndices(input_indices: ${t.type.indices}) -> bool { - for (var i:u32 = 0; i < ${e.length}; i++) { - var input_index = ${t.indicesGet("input_indices","i")}; - if (input_index < 0 || input_index >= ${xe("uniforms.input_shape","i",e.length)}) { - return false; - } - } - return true; - }`,as=(t,e,n,r)=>t.rank>r?` - ${t.indicesSet("input_indices",e,"channel")}; - ${t.indicesSet("input_indices",n,"batch")}; -`:"",md=(t,e,n,r,a)=>{let[i,s,o,l]=n.length===2?[-1,0,1,-1]:[0,2,3,1],d=t.type.value;return` - fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} { - var input_indices: ${t.type.indices}; - ${t.indicesSet("input_indices",s,`max(0, min(row, ${n[s]} - 1))`)}; - ${t.indicesSet("input_indices",o,`max(0, min(col, ${n[o]} - 1))`)}; - ${as(t,l,i,2)} - return ${t.getByIndices("input_indices")}; - } - - fn bilinearInterpolation(output_indices: ${e.type.indices}) -> ${d} { - var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); - var row:${d} = originalIndices[${s}]; - var col:${d} = originalIndices[${o}]; - ${r?`if (row < 0 || row > (${n[s]} - 1) || col < 0 || col > (${n[o]} - 1)) { - return ${a}; - }`:""}; - row = max(0, min(row, ${n[s]} - 1)); - col = max(0, min(col, ${n[o]} - 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 = ${n.length>2?`u32(originalIndices[${l}])`:"0"}; - var batch: u32 = ${n.length>2?`u32(originalIndices[${i}])`:"0"}; - var x11: ${d} = getInputValue(batch, channel, row1, col1); - var x12: ${d} = getInputValue(batch, channel, row1, col2); - var x21: ${d} = getInputValue(batch, channel, row2, col1); - var x22: ${d} = getInputValue(batch, channel, row2, col2); - var dx1: ${d} = abs(row - ${d}(row1)); - var dx2: ${d} = abs(${d}(row2) - row); - var dy1: ${d} = abs(col - ${d}(col1)); - var dy2: ${d} = abs(${d}(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); - }`},gd=(t,e,n,r,a,i,s,o,l,d)=>{let c=n.length===2,[u,h]=c?[0,1]:[2,3],f=t.type.value,g=w=>{let b=w===u?"row":"col";return` - fn ${b}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${e.type.indices}) -> ${f} { - var output_index = ${e.indicesGet("output_indices",w)}; - var originalIdx: ${f} = getOriginalCoordinateFromResizedCoordinate(output_index, ${a[w]}, - ${r[w]}, ${n[w]}, ${i[w]}, ${i[w]} + ${n.length}); - var fractOriginalIdx: ${f} = originalIdx - floor(originalIdx); - var coefs = getCubicInterpolationCoefs(fractOriginalIdx); - - if (${o} && (originalIdx < 0 || originalIdx > (${n[w]} - 1))) { - return ${l}; - } - var data: array<${f}, 4> = array<${f}, 4>(0.0, 0.0, 0.0, 0.0); - for (var i: i32 = -1; i < 3; i++) { - var ${b}: ${f} = originalIdx + ${f}(i); - if (${b} < 0 || ${b} >= ${n[w]}) { - ${d?`coefs[i + 1] = 0.0; - continue;`:o?`return ${l};`:`${b} = max(0, min(${b}, ${n[w]} - 1));`}; - } - var input_indices_copy: ${t.type.indices} = input_indices; - ${t.indicesSet("input_indices_copy",w,`u32(${b})`)}; - data[i + 1] = ${w===u?t.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; - } - return cubicInterpolation1D(data, coefs); - }`};return` - ${g(u)}; - ${g(h)}; - 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: ${f} = 1.0 + absS; - coeffs[0] = ((${s} * onePlusAbsS - 5 * ${s}) * onePlusAbsS + 8 * ${s}) * onePlusAbsS - 4 * ${s}; - coeffs[1] = ((${s} + 2) * absS - (${s} + 3)) * absS * absS + 1; - coeffs[2] = ((${s} + 2) * oneMinusAbsS - (${s} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; - coeffs[3] = ((${s} * twoMinusAbsS - 5 * ${s}) * twoMinusAbsS + 8 * ${s}) * twoMinusAbsS - 4 * ${s}; - return coeffs; - } - - fn cubicInterpolation1D(x: array<${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: ${e.type.indices}) -> ${f} { - var input_indices: ${t.type.indices} = output_indices; - return colCubicInterpolation(input_indices, output_indices); - } - `},_d=(t,e,n,r,a)=>{let[i,s,o,l,d]=n.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],c=t.type.value;return` - fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${c} { - var input_indices: ${t.type.indices}; - ${t.indicesSet("input_indices",s,`max(0, min(depth, ${n[s]} - 1))`)}; - ${t.indicesSet("input_indices",o,`max(0, min(height, ${n[o]} - 1))`)}; - ${t.indicesSet("input_indices",l,`max(0, min(width, ${n[l]} - 1))`)}; - ${as(t,d,i,3)} - return ${t.getByIndices("input_indices")}; - } - - fn trilinearInterpolation(output_indices: ${e.type.indices}) -> ${c} { - var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); - var depth:${c} = originalIndices[${s}]; - var height:${c} = originalIndices[${o}]; - var width:${c} = originalIndices[${l}]; - ${r?`if (depth < 0 || depth > (${n[s]} - 1) || height < 0 || height > (${n[o]} - 1) || width < 0 || (width > ${n[l]} - 1)) { - return ${a}; - }`:""}; - - depth = max(0, min(depth, ${n[s]} - 1)); - height = max(0, min(height, ${n[o]} - 1)); - width = max(0, min(width, ${n[l]} - 1)); - var depth1: u32 = u32(depth); - var height1: u32 = u32(height); - var width1: u32 = u32(width); - var depth2: u32 = u32(depth + 1); - var height2: u32 = u32(height + 1); - var width2: u32 = u32(width + 1); - var channel: u32 = ${n.length>3?`u32(originalIndices[${d}])`:"0"}; - var batch: u32 = ${n.length>3?`u32(originalIndices[${i}])`:"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); - }`},wd=(t,e,n,r,a,i)=>{let s=t.dims,o=ud(i,e.axes,s.length),l=dd(s,r,a,e.axes),d=r.slice();r.length===0&&(d=s.map(($,k)=>$===0?1:l[k]/$),e.keepAspectRatioPolicy!=="stretch"&&(l=cd(s,d,e)));let c=ve("output",t.dataType,l.length),u=Q("input",t.dataType,s.length),h=J.size(l),f=s.length===l.length&&s.every(($,k)=>$===l[k]),g=e.coordinateTransformMode==="tf_crop_and_resize",w=e.extrapolationValue,b=u.type.value,y=$=>` - ${f?"":` - ${od(e.coordinateTransformMode,b)}; - ${(()=>{switch(e.mode){case"nearest":return` - ${fd(u,s)}; - ${ld(e.nearestMode,n,b)}; - ${hd(u,c,s,l,d.length,o.length,g)}; - `;case"linear":return` - ${pd(c,s,l,d.length,o.length)}; - ${(()=>{if(s.length===2||s.length===4)return`${md(u,c,s,g,w)}`;if(s.length===3||s.length===5)return`${_d(u,c,s,g,w)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; - `;case"cubic":return` - ${(()=>{if(s.length===2||s.length===4)return`${gd(u,c,s,l,d,o,e.cubicCoeffA,g,e.extrapolationValue,e.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; - `;default:throw Error("Invalid resize mode")}})()}; - `} - ${$.registerUniform("output_size","u32").registerUniform("scales","f32",d.length).registerUniform("roi","f32",o.length).declareVariables(u,c)} - ${$.mainStart()} - ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - ${f?"output[global_idx] = input[global_idx];":` - let output_indices = ${c.offsetToIndices("global_idx")}; - var input_indices: ${u.type.indices}; - ${(()=>{switch(e.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); - if (checkInputIndices(input_indices)) { - output[global_idx] = ${u.getByIndices("input_indices")}; - } else { - output[global_idx] = ${e.extrapolationValue}; - }`;case"linear":return`output[global_idx] = ${s.length===2||s.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${e.mode}`)}})()}; -`} - }`;return{name:"Resize",shaderCache:{hint:`${e.cacheKey}|${n}|${d.length>0?d:""}|${a.length>0?a:""}|${o.length>0?o:""}|${f}|${s}`,inputDependencies:["rank"]},getShaderSource:y,getRunData:()=>({outputs:[{dims:l,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:[{type:"uint32",data:h},{type:"float32",data:d},{type:"float32",data:o},...ie(s),...ie(l)]})}},yd=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},Qh=(t,e)=>{let n=[],r=[],a=[],i=yd(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");sd(t.inputs,e,i,n,r,a),t.compute(wd(t.inputs[0],e,i,n,r,a),{inputs:[0]})},Zh=t=>{let e=t.antialias,n=t.axes,r=t.coordinateTransformMode,a=t.cubicCoeffA,i=t.excludeOutside!==0,s=t.extrapolationValue,o=t.keepAspectRatioPolicy,l=t.mode,d=t.nearestMode===""?"simple":t.nearestMode;return qe({antialias:e,axes:n,coordinateTransformMode:r,cubicCoeffA:a,excludeOutside:i,extrapolationValue:s,keepAspectRatioPolicy:o,mode:l,nearestMode:d})}}),bd,vd,Jh,ef,Q_=X(()=>{Ye(),Ie(),ot(),Ce(),bd=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],n=t[1],r=t[2];if(e.dataType!==n.dataType||e.dataType!==r.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(n.dims.length!==3&&n.dims.length!==2)throw new Error("Skip must be 2D or 3D");let a=e.dims[e.dims.length-1],i=e.dims[e.dims.length-2];if(n.dims[n.dims.length-1]!==a)throw new Error("Skip must have the same hidden size as input");if(n.dims[n.dims.length-2]!==i)throw new Error("Skip must have the same sequence length as input");if(r.dims.length!==1)throw new Error("Gamma must be 1D");if(r.dims[r.dims.length-1]!==a)throw new Error("Gamma must have the same hidden size as input");if(t.length>3){let s=t[3];if(s.dims.length!==1)throw new Error("Beta must be 1D");if(s.dims[s.dims.length-1]!==a)throw new Error("Beta must have the same hidden size as input")}if(t.length>4){let s=t[4];if(s.dims.length!==1)throw new Error("Bias must be 1D");if(s.dims[s.dims.length-1]!==a)throw new Error("Bias must have the same hidden size as input")}},vd=(t,e,n,r)=>{let a=t[0].dims,i=J.size(a),s=a,o=i,l=a.slice(-1)[0],d=r?a.slice(0,-1).concat(1):[],c=t.length>3,u=t.length>4,h=r&&n>1,f=r&&n>2,g=n>3,w=$t(l),b=[Q("x",t[0].dataType,t[0].dims,w),Q("skip",t[1].dataType,t[1].dims,w),Q("gamma",t[2].dataType,t[2].dims,w)];c&&b.push(Q("beta",t[3].dataType,t[3].dims,w)),u&&b.push(Q("bias",t[4].dataType,t[4].dims,w)),b.push(ve("output",t[0].dataType,s,w)),h&&b.push(ve("meanOutput",1,d)),f&&b.push(ve("invStdOutput",1,d)),g&&b.push(ve("inputSkipBiasSum",t[0].dataType,s,w));let y=At(t[0].dataType),$=S=>` - const hiddenSize: f32 = ${l}; - const hiddenSizeVectorized: u32 = ${l/w}; - const epsilon: f32 = ${e.epsilon}; - - ${S.declareVariables(...b)} - - ${S.mainStart()} - ${S.guardAgainstOutOfBoundsWorkgroupSizes(o/l)} - let offset = global_idx * hiddenSizeVectorized; - var sum = ${vt("f32",w)}; - var squareSum = ${vt("f32",w)}; - for (var i: u32 = 0; i < hiddenSizeVectorized; i++) { - let skipValue = skip[offset + i]; - let biasValue = ${u?"bias[i]":"0.0"}; - let inputValue = x[offset + i]; - let value = inputValue + skipValue + biasValue; - ${g?"inputSkipBiasSum[offset + i] = value;":""} - output[offset + i] = value; - let f32Value = ${rn(y,w,"value")}; - sum += f32Value; - squareSum += f32Value * f32Value; - } - let mean = ${Qt("sum",w)} / hiddenSize; - let invStdDev = inverseSqrt(${Qt("squareSum",w)} / hiddenSize - mean * mean + epsilon); - ${h?"meanOutput[global_idx] = mean;":""} - ${f?"invStdOutput[global_idx] = invStdDev;":""} - for (var i: u32 = 0; i < hiddenSizeVectorized; i++) { - output[offset + i] = (output[offset + i] - ${y}(mean)) * ${y}(invStdDev) * gamma[i] - + ${c?"beta[i]":"0.0"}; - } - }`,k=[{dims:s,dataType:t[0].dataType}];return n>1&&k.push({dims:d,dataType:1}),n>2&&k.push({dims:d,dataType:1}),n>3&&k.push({dims:a,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:e.cacheKey},getShaderSource:$,getRunData:()=>({outputs:k,dispatchGroup:{x:Math.ceil(o/l/64)}})}},Jh=(t,e)=>{bd(t.inputs);let n=[0];t.outputCount>1&&n.push(-3),t.outputCount>2&&n.push(-3),t.outputCount>3&&n.push(3),t.compute(vd(t.inputs,e,t.outputCount,!1),{outputs:n})},ef=t=>{let e=t.epsilon;return qe({epsilon:e})}}),$d,Er,xd,is,Sd,Ed,tf,nf,Z_=X(()=>{Ye(),Ie(),ot(),Ce(),$d=(t,e)=>{if(!t||t.length<1)throw new Error("too few inputs");if(e.axes.length!==0){if(e.axes.length!==e.starts.length||e.axes.length!==e.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(e.starts.length!==e.ends.length)throw new Error("starts and ends must have the same length");t.slice(1).forEach((n,r)=>{if(t[r+1].dataType!==6&&t[r+1].dataType!==7)throw new Error(`Input ${r} must be an array of int32 or int64`)})},Er=(t,e)=>{let n=[];if(t.length>e)if(t[e].dataType===7)t[e].getBigInt64Array().forEach(r=>n.push(Number(r)));else if(t[e].dataType===6)t[e].getInt32Array().forEach(r=>n.push(Number(r)));else throw new Error(`Input ${e} must be an array of int32 or int64`);return n},xd=(t,e)=>{if(t.length>1){let n=Er(t,1),r=Er(t,2),a=Er(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),qe({starts:n,ends:r,axes:a})}else return e},is=(t,e,n,r,a)=>{let i=t;return t<0&&(i+=n[r[e]]),a[e]<0?Math.max(0,Math.min(i,n[r[e]]-1)):Math.max(0,Math.min(i,n[r[e]]))},Sd=(t,e,n)=>`fn calculateInputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} { - var input_indices: ${t.type.indices}; - var carry = 0u; - for (var i = ${n.length}; i >= 0; i--) { - let input_shape_i = ${xe("uniforms.input_shape","i",n.length)}; - let steps_i = ${xe("uniforms.steps","i",n.length)}; - let signs_i = ${xe("uniforms.signs","i",n.length)}; - let starts_i = ${xe("uniforms.starts","i",n.length)}; - var output_index = ${e.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; - } - ${t.indicesSet("input_indices","i","input_index")}; - } - return input_indices; - }`,Ed=(t,e)=>{let n=t[0].dims,r=J.size(n),a=e.axes.length>0?J.normalizeAxes(e.axes,n.length):[...Array(n.length).keys()],i=Er(t,4);i.forEach(y=>y!==0||(()=>{throw new Error("step cannot be 0")})),i.length===0&&(i=Array(a.length).fill(1));let s=e.starts.map((y,$)=>is(y,$,n,a,i)),o=e.ends.map((y,$)=>is(y,$,n,a,i));if(a.length!==s.length||a.length!==o.length)throw new Error("start, ends and axes should have the same number of elements");if(a.length!==n.length)for(let y=0;yMath.sign(y));i.forEach((y,$,k)=>{if(y<0){let S=(o[$]-s[$])/y,T=s[$],A=T+S*i[$];s[$]=A,o[$]=T,k[$]=-y}});let d=n.slice(0);a.forEach((y,$)=>{d[y]=Math.ceil((o[y]-s[y])/i[y])});let c={dims:d,dataType:t[0].dataType},u=ve("output",t[0].dataType,d.length),h=Q("input",t[0].dataType,t[0].dims.length),f=J.size(d),g=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:s.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:i.length}],w=[{type:"uint32",data:f},{type:"uint32",data:s},{type:"int32",data:l},{type:"uint32",data:i},...ie(t[0].dims),...ie(d)],b=y=>` - ${y.registerUniforms(g).declareVariables(h,u)} - ${Sd(h,u,n)} - ${y.mainStart()} - ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - let output_indices = ${u.offsetToIndices("global_idx")}; - let input_indices = calculateInputIndices(output_indices); - ${u.setByOffset("global_idx",h.getByIndices("input_indices"))} - }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${s.length}_${i.length}`,inputDependencies:["rank"]},getShaderSource:b,getRunData:()=>({outputs:[c],dispatchGroup:{x:Math.ceil(r/64)},programUniforms:w})}},tf=(t,e)=>{$d(t.inputs,e);let n=xd(t.inputs,e);t.compute(Ed(t.inputs,n),{inputs:[0]})},nf=t=>{let e=t.starts,n=t.ends,r=t.axes;return qe({starts:e,ends:n,axes:r})}}),kd,Cd,rf,af,J_=X(()=>{Ie(),ot(),Ce(),kd=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},Cd=(t,e)=>{let n=t.dims,r=J.size(n),a=64,i=e.axis;if(i<0&&(i=n.length+i),iy===4?`max(max(${b}.x, ${b}.y), max(${b}.z, ${b}.w))`:y===2?`max(${b}.x, ${b}.y)`:y===3?`max(max(${b}.x, ${b}.y), ${b}.z)`:b,u=Q("x",t.dataType,t.dims,l),h=ve("result",t.dataType,t.dims,l),f=u.type.value,g=At(t.dataType)==="f32"?`var threadMax = ${f}(-3.402823e+38f);`:`var threadMax = ${f}(-65504.0h);`,w=b=>` - var rowMaxShared : ${f}; - var rowSumShared : ${f}; - var threadShared : array<${f}, ${a}>; - - 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; - } - ${b.registerUniform("packedCols","i32").declareVariables(u,h)} - ${b.mainStart()} - let gindex = i32(global_idx); - let lindex = i32(local_idx); - const wg = ${a}; - let row = gindex / wg; - let cols = uniforms.packedCols; - let row_stride : i32 = uniforms.packedCols; - - // find the rows max - ${g} - 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]",l)}); - } - 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 = ${f}(${Qt("threadShared[0]",l)}); - } - 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:`${l}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:n,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:"uint32",data:d}]}),getShaderSource:w}},rf=(t,e)=>{kd(t.inputs),t.compute(Cd(t.inputs[0],e))},af=t=>qe({axis:t.axis})}),Td,Id,Ad,Md,Od,sf,of,e0=X(()=>{Ie(),ot(),Ce(),Td=t=>{if(!t||t.length<1)throw new Error("too few inputs")},Id=(t,e)=>{let n=[],r=e.numOutputs;return t[1].dims[0]>0&&(t[1].getBigInt64Array().forEach(a=>n.push(Number(a))),r=n.length),qe({numOutputs:r,axis:e.axis,splitSizes:n})},Ad=t=>` -fn calculateOutputIndex(index: u32) -> u32 { - for (var i: u32 = 0u; i < ${t}u; i += 1u ) { - if (index < ${xe("uniforms.size_in_split_axis","i",t)}) { - return i; - } - } - return ${t}u; -}`,Md=t=>{let e=t.length,n=[];for(let r=0;r{let n=t[0].dims,r=J.size(n),a=t[0].dataType,i=J.normalizeAxis(e.axis,n.length),s=new Array(e.numOutputs),o=Q("input",a,n),l=new Array(e.numOutputs),d=[],c=[],u=0,h=[{type:"uint32",data:r}];for(let g=0;gh.push(...ie(g)));let f=g=>` - ${g.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(o,...s)} - ${Ad(l.length)} - ${Md(s)} - - ${g.mainStart()} - ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} - - var indices = ${o.offsetToIndices("global_idx")}; - var index = ${o.indicesGet("indices",i)}; - let output_number = calculateOutputIndex(index); - if (output_number != 0) { - index -= ${xe("uniforms.size_in_split_axis","output_number - 1u",l.length)}; - ${o.indicesSet("indices",i,"index")}; - } - writeBufferData(output_number, indices, global_idx); - }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:f,getRunData:()=>({outputs:d,dispatchGroup:{x:Math.ceil(r/64)},programUniforms:h})}},sf=(t,e)=>{Td(t.inputs);let n=t.inputs.length===1?e:Id(t.inputs,e);t.compute(Od(t.inputs,n),{inputs:[0]})},of=t=>{let e=t.axis,n=t.splitSizes,r=t.numOutputs<0?n.length:t.numOutputs;if(r!==n.length)throw new Error("numOutputs and splitSizes lengh must be equal");return qe({axis:e,numOutputs:r,splitSizes:n})}}),ss,zd,Rd,Bd,lf,t0=X(()=>{Ye(),Ie(),Ce(),ss=t=>Array.from(t.getBigInt64Array(),Number),zd=t=>{if(!t||t.length!==2)throw new Error("Tile requires 2 inputs.");if(t[0].dataType!==1&&t[0].dataType!==6&&t[0].dataType!==12)throw new Error("Tile only support float, int32, and uint32 data types");if(t[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(t[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(ss(t[1]).length!==t[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Rd=(t,e)=>{let n=[];for(let r=0;r{let e=t[0].dims,n=ss(t[1]),r=Rd(e,n),a=J.size(r),i=t[0].dataType,s=Q("input",i,e.length),o=ve("output",i,r.length),l=d=>` - const inputShape = ${s.indices(...e)}; - 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$=d[y]===-1,k=d[y]===-2,S=$||k?a(s[y].dataType,s[y].dims):r(d[y],s[y].dataType,s[y].dims),T=this.gpuDataManager.get(S.data);if(!T)throw new Error(`no GPU data for output: ${S.data}`);if($&&this.temporaryData.push(T),k){let A=this.kernelPersistentData.get(this.currentKernelId);A||(A=[],this.kernelPersistentData.set(this.currentKernelId,A)),A.push(T)}c.push(S),u.push(T)}let h;if(l){let y=0,$=[];l.forEach(A=>{let P=typeof A.data=="number"?[A.data]:A.data;if(P.length===0)return;let N=P.length<=2?P.length*4:16;y=Math.ceil(y/N)*N,$.push(y),y+=P.length>4?Math.ceil(P.length/4)*16:P.length*4});let k=16;y=Math.ceil(y/k)*k;let S=new ArrayBuffer(y);l.forEach((A,P)=>{let N=$[P],V=typeof A.data=="number"?[A.data]:A.data;A.type==="int32"?new Int32Array(S,N,V.length).set(V):A.type==="uint32"?new Uint32Array(S,N,V.length).set(V):new Float32Array(S,N,V.length).set(V)});let 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u=[n.batchSize,n.numHeads,n.sequenceLength,n.kvSequenceLength+n.pastSequenceLength],l=s.scale===0?1/Math.sqrt(n.headSize):s.scale,a=Fe(n.headSize),p=n.headSize/a,h=12,g={x:Math.ceil(n.totalSequenceLength/h),y:Math.ceil(n.sequenceLength/h),z:n.batchSize*n.numHeads},b=Xe(t.dataType),w=[{type:"uint32",data:n.sequenceLength},{type:"uint32",data:p},{type:"uint32",data:n.totalSequenceLength},{type:"uint32",data:n.kvSequenceLength},{type:b,data:l}],y=[t,r],_=$=>{let x=M("q",t.dataType,t.dims,a),E=M("key",r.dataType,r.dims,a),A=F("output",t.dataType,u),z=Le(t.dataType),R=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"alpha",type:z}];return`\n const beta: ${z} = 1.0;\n const TILE_SIZE = ${h}u;\n\n var tileQ: array<${x.type.storage}, ${h*h}>;\n var tileK: array<${x.type.storage}, ${h*h}>;\n ${$.registerUniforms(R).declareVariables(x,E,A)}\n ${$.mainStart([h,h,1])}\n // x holds the N and y holds the M\n let headIdx = workgroup_id.z;\n let m = workgroup_id.y * TILE_SIZE;\n let n = workgroup_id.x * TILE_SIZE;\n let lm = m + local_id.y;\n let ln = n + local_id.x;\n\n let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;\n let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx + n * uniforms.K;\n\n var value = ${Ze(z,a)};\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m + local_id.y < uniforms.M && w + local_id.x < uniforms.K) {\n tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];\n }\n if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {\n tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];\n }\n workgroupBarrier();\n\n for (var k: u32 = 0u; k({outputs:[{dims:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:g,programUniforms:w}),getShaderSource:_},{inputs:y,outputs:[-1]})[0];return Vd(e,I,n.batchSize*n.numHeads*n.sequenceLength,n.totalSequenceLength),I},Wd=(e,t,r,o)=>{let n=[o.batchSize,o.sequenceLength,o.vHiddenSize],s=12,u={x:Math.ceil(o.vHeadSize/s),y:Math.ceil(o.sequenceLength/s),z:o.batchSize*o.numHeads},l=[{type:"uint32",data:o.sequenceLength},{type:"uint32",data:o.totalSequenceLength},{type:"uint32",data:o.vHeadSize},{type:"uint32",data:o.numHeads},{type:"uint32",data:o.vHiddenSize}],a=p=>{let h=M("probs",t.dataType,t.dims),g=M("v",r.dataType,r.dims),b=F("output",t.dataType,n),w=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`\n const TILE_SIZE = ${s}u;\n var tileQ: array<${h.type.value}, ${s*s}>;\n var tileK: array<${h.type.value}, ${s*s}>;\n ${p.registerUniforms(w).declareVariables(h,g,b)}\n ${p.mainStart([s,s,1])}\n let headIdx = workgroup_id.z;\n let m = workgroup_id.y * TILE_SIZE + local_id.y;\n let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n let offsetB = headIdx * (uniforms.N * uniforms.K) + n;\n\n var value = ${h.type.storage}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m < uniforms.M && w + local_id.x < uniforms.K) {\n tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n }\n if (n < uniforms.N && w + local_id.y < uniforms.K) {\n tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n }\n workgroupBarrier();\n for (var k: u32 = 0u; k({outputs:[{dims:n,dataType:t.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:l}),getShaderSource:a},{inputs:[t,r],outputs:[0]})[0]},Kr=(e,t,r,o,n,s,u,l,a,p,h)=>{let g=Nd(e,t,r,a,p,h);Wd(e,g,o,p)},Hd=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],o=t.sequenceLength,n=t.inputHiddenSize,s=t.headSize,u=12,l={x:Math.ceil(t.headSize/u),y:Math.ceil(t.sequenceLength/u),z:t.batchSize*t.numHeads},a=[e.inputs[0],e.inputs[1],e.inputs[2]],p=[{type:"uint32",data:o},{type:"uint32",data:n},{type:"uint32",data:s},{type:"uint32",data:t.numHeads},{type:"uint32",data:t.headSize},{type:"uint32",data:t.hiddenSize},{type:"uint32",data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],h=g=>{let b=F("output_q",a[0].dataType,r),w=F("output_k",a[0].dataType,r),y=F("output_v",a[0].dataType,r),_=M("input",a[0].dataType,a[0].dims),I=M("weight",a[1].dataType,a[1].dims),$=M("bias",a[2].dataType,a[2].dims),x=_.type.storage,E=[{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`\n const TILE_SIZE = ${u}u;\n var tileInput: array<${x}, ${u*u}>;\n var tileWeightQ: array<${x}, ${u*u}>;\n var tileWeightK: array<${x}, ${u*u}>;\n var tileWeightV: array<${x}, ${u*u}>;\n ${g.registerUniforms(E).declareVariables(_,I,$,b,w,y)}\n ${g.mainStart([u,u,1])}\n let batchIndex = workgroup_id.z / uniforms.num_heads;\n let headNumber = workgroup_id.z % uniforms.num_heads;\n let m = workgroup_id.y * TILE_SIZE + local_id.y;\n let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;\n let biasOffsetQ = headNumber * uniforms.head_size;\n let biasOffsetK = uniforms.hidden_size + biasOffsetQ;\n let biasOffsetV = uniforms.hidden_size + biasOffsetK;\n\n var valueQ = ${x}(0);\n var valueK = ${x}(0);\n var valueV = ${x}(0);\n for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n if (m < uniforms.M && w + local_id.x < uniforms.K) {\n tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];\n }\n if (n < uniforms.N && w + local_id.y < uniforms.K) {\n let offset = n + (w + local_id.y) * uniforms.ldb;\n tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n }\n workgroupBarrier();\n for (var k: u32 = 0u; k({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:l,programUniforms:p}),getShaderSource:h},{inputs:a,outputs:[-1,-1,-1]})},Ha=(e,t)=>{let r=Ud(e.inputs,t),[o,n,s]=Hd(e,r);return Kr(e,o,n,s,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var Gd,Ld,Fd,Ga,La=j(()=>{"use strict";Lt();$e();je();ve();Gd=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(o,n,s)=>{let u=n.length;if(u!==o.length)throw new Error(`${s}: num dimensions != ${u}`);n.forEach((l,a)=>{if(l!==o[a])throw new Error(`${s}: dim[${a}] do not match`)})};if(e[0].dims.length>1){let o=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,o,"Invalid input scale"),r(e[2].dims,o,"Invalid input B"),r(e[3].dims,o,"Invalid input mean"),r(e[4].dims,o,"Invalid input var")}else r(e[1].dims,[1],"Invalid input scale"),r(e[2].dims,[1],"Invalid input B"),r(e[3].dims,[1],"Invalid input mean"),r(e[4].dims,[1],"Invalid input var")},Ld=(e,t)=>{let{epsilon:r,spatial:o,format:n}=t,s=e[0].dims,u=o?Fe(s[s.length-1]):1,l=n==="NHWC"&&s.length>1?u:1,a=U.size(s)/u,p=Re(s.length)&&o,h=p?s.length:s,g=M("x",e[0].dataType,e[0].dims,u),b=M("scale",e[1].dataType,e[1].dims,l),w=M("bias",e[2].dataType,e[2].dims,l),y=M("inputMean",e[3].dataType,e[3].dims,l),_=M("inputVar",e[4].dataType,e[4].dims,l),I=F("y",e[0].dataType,h,u),$=()=>{let E="";if(o)E=`let cOffset = ${s.length===1?"0u":n==="NHWC"?`outputIndices[${s.length-1}] / ${u}`:"outputIndices[1]"};`;else if(n==="NCHW")E=`\n ${I.indicesSet("outputIndices","0","0")}\n let cOffset = ${I.indicesToOffset("outputIndices")};`;else{E=`var cIndices = ${b.type.indices}(0);\n cIndices[0] = outputIndices[${s.length-1}];`;for(let A=1;A`\n const epsilon = ${r};\n ${E.registerUniform("outputSize","u32").declareVariables(g,b,w,y,_,I)}\n ${E.mainStart()}\n ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n var outputIndices = ${I.offsetToIndices(`global_idx * ${u}`)};\n ${$()}\n let scale = ${b.getByOffset("cOffset")};\n let bias = ${w.getByOffset("cOffset")};\n let inputMean = ${y.getByOffset("cOffset")};\n let inputVar = ${_.getByOffset("cOffset")};\n let x = ${g.getByOffset("global_idx")};\n let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;\n ${I.setByOffset("global_idx","value")}\n }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${o}_${u}`,inputDependencies:p?["rank","type","type","type","type"]:void 0},getShaderSource:x,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p?[{type:"uint32",data:a},...L(s)]:[{type:"uint32",data:a}]})}},Fd=e=>ge(e),Ga=(e,t)=>{let{inputs:r,outputCount:o}=e,n=Fd({...t,outputCount:o});if(Gt.webgpu.validateInputContent&&Gd(r,n),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Ld(r,n))}});var jd,qd,Fa,ja=j(()=>{"use strict";$e();ve();jd=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},qd=e=>{let t=e[0].dims,r=e[0].dims[2],o=U.size(t)/4,n=e[0].dataType,s=M("input",n,t,4),u=M("bias",n,[r],4),l=M("residual",n,t,4),a=F("output",n,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:h=>`\n const channels = ${r}u / 4;\n ${h.declareVariables(s,u,l,a)}\n\n ${h.mainStart()}\n ${h.guardAgainstOutOfBoundsWorkgroupSizes(o)}\n let value = ${s.getByOffset("global_idx")}\n + ${u.getByOffset("global_idx % channels")} + ${l.getByOffset("global_idx")};\n ${a.setByOffset("global_idx","value")}\n }`}},Fa=e=>{jd(e.inputs),e.compute(qd(e.inputs))}});var Kd,Ae,qa,Ka,Ya,Za,Qa,Xa,Ja,ei,ti,Yd,ri,ni,oi,ai,Yr,ii,Zr,si,ui,di,li,ci,pi,mi,fi,hi,gi,yi,bi,wi,vi,$i,Si,xi,Vn=j(()=>{"use strict";Ne();$e();je();ve();Kd=(e,t,r,o,n,s)=>{let u=Math.ceil(t/4),l="";typeof n=="string"?l=`${n}(a)`:l=n("a");let a=M("inputData",r,[u],4),p=F("outputData",o,[u],4);return`\n ${e.registerUniform("vec_size","u32").declareVariables(a,p)}\n\n ${s??""}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n\n let a = ${a.getByOffset("global_idx")};\n ${p.setByOffset("global_idx",l)}\n }`},Ae=(e,t,r,o,n,s=e.dataType)=>({name:t,shaderCache:{hint:n,inputDependencies:["type"]},getShaderSource:u=>Kd(u,U.size(e.dims),e.dataType,s,r,o),getRunData:u=>({outputs:[{dims:e.dims,dataType:s}],dispatchGroup:{x:Math.ceil(U.size(u[0].dims)/64/4)},programUniforms:[{type:"uint32",data:Math.ceil(U.size(e.dims)/4)}]})}),qa=e=>{e.compute(Ae(e.inputs[0],"Abs","abs"))},Ka=e=>{e.compute(Ae(e.inputs[0],"Acos","acos"))},Ya=e=>{e.compute(Ae(e.inputs[0],"Acosh","acosh"))},Za=e=>{e.compute(Ae(e.inputs[0],"Asin","asin"))},Qa=e=>{e.compute(Ae(e.inputs[0],"Asinh","asinh"))},Xa=e=>{e.compute(Ae(e.inputs[0],"Atan","atan"))},Ja=e=>{e.compute(Ae(e.inputs[0],"Atanh","atanh"))},ei=e=>ge(e),ti=(e,t)=>{let r;switch(t.to){case 10:r="vec4";break;case 1:r="vec4";break;case 12:r="vec4";break;case 6:r="vec4";break;case 9:r="vec4";break;default:throw new RangeError(`not supported type (specified in attribute \'to\' from \'Cast\' operator): ${t.to}`)}e.compute(Ae(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},Yd=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:Gr,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:Lr;return ge({min:t,max:r})},ri=(e,t)=>{let r=e.inputs.length===1?t:Yd(e.inputs),o=lt(e.inputs[0].dataType);e.compute(Ae(e.inputs[0],"Clip",n=>`clamp(${n}, clip_min_, clip_max_)`,`\n const clip_min_: vec4<${o}> = vec4(${o}(${r.min}));\n const clip_max_: vec4<${o}> = vec4(${o}(${r.max}));\n`,r.cacheKey),{inputs:[0]})},ni=e=>{e.compute(Ae(e.inputs[0],"Ceil","ceil"))},oi=e=>{e.compute(Ae(e.inputs[0],"Cos","cos"))},ai=e=>{e.compute(Ae(e.inputs[0],"Cosh","cosh"))},Yr=e=>ge(e),ii=(e,t)=>{let r=lt(e.inputs[0].dataType);e.compute(Ae(e.inputs[0],"Elu",o=>`elu_vf32(${o})`,`\n const elu_alpha_ = ${r}(${t.alpha});\n\n fn elu_f32(a: ${r}) -> ${r} {\n return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);\n }\n\n fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> {\n return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));\n }`,t.cacheKey))},Zr=(e,t="f32")=>`\nconst r0: ${t} = 0.3275911;\nconst r1: ${t} = 0.254829592;\nconst r2: ${t} = -0.284496736;\nconst r3: ${t} = 1.421413741;\nconst r4: ${t} = -1.453152027;\nconst r5: ${t} = 1.061405429;\n\nfn erf_vf32(v: ${e}) -> ${e} {\n let absv = abs(v);\n let x = 1.0 / (1.0 + r0 * absv);\n return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));\n}`,si=e=>{let t=lt(e.inputs[0].dataType);e.compute(Ae(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,Zr(`vec4<${t}>`,t)))},ui=e=>{e.compute(Ae(e.inputs[0],"Exp","exp"))},di=e=>{e.compute(Ae(e.inputs[0],"Floor","floor"))},li=e=>{let t=lt(e.inputs[0].dataType);e.compute(Ae(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,Zr(`vec4<${t}>`,t)))},ci=(e,t)=>{let r=lt(e.inputs[0].dataType);e.compute(Ae(e.inputs[0],"LeakyRelu",o=>`select(leaky_relu_alpha_ * ${o}, ${o}, ${o} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},pi=e=>{e.compute(Ae(e.inputs[0],"Not",t=>`!${t}`))},mi=e=>{e.compute(Ae(e.inputs[0],"Neg",t=>`-${t}`))},fi=e=>{e.compute(Ae(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},hi=e=>{let t=lt(e.inputs[0].dataType);e.compute(Ae(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},gi=e=>{e.compute(Ae(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},yi=e=>{e.compute(Ae(e.inputs[0],"Sin","sin"))},bi=e=>{e.compute(Ae(e.inputs[0],"Sinh","sinh"))},wi=e=>{e.compute(Ae(e.inputs[0],"Sqrt","sqrt"))},vi=e=>{e.compute(Ae(e.inputs[0],"Tan","tan"))},$i=e=>{e.compute(Ae(e.inputs[0],"Tanh","tanh"))},Si=(e,t)=>{let r=lt(e.inputs[0].dataType);return e.compute(Ae(e.inputs[0],"ThresholdedRelu",o=>`select(vec4<${r}>(0.0), ${o}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},xi=e=>{e.compute(Ae(e.inputs[0],"Log","log"))}});var Qd,Xd,_i,Ci=j(()=>{"use strict";$e();ve();Vn();Qd=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")},Xd=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=M("input",e[0].dataType,e[0].dims,4),o=M("bias",e[0].dataType,[e[0].dims[2]],4),n=F("output",e[0].dataType,t,4),s=U.size(t)/4,u=Le(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:a=>`\n const M_SQRT2 = sqrt(2.0);\n const halfChannels = ${e[0].dims[2]/4/2}u;\n\n ${a.declareVariables(r,o,n)}\n\n ${Zr(`vec4<${u}>`,u)}\n\n ${a.mainStart()}\n ${a.guardAgainstOutOfBoundsWorkgroupSizes(s)}\n let biasIdx = global_idx % halfChannels;\n let batchIndex = global_idx / halfChannels;\n let inputOffset = biasIdx + batchIndex * halfChannels * 2;\n let valueLeft = input[inputOffset] + bias[biasIdx];\n let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];\n let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);\n\n ${n.setByOffset("global_idx","valueLeft * geluRight")}\n }`}},_i=e=>{Qd(e.inputs),e.compute(Xd(e.inputs))}});var Jd,el,ht,Ii,Ai,Ti,Ei,Oi,Pi,ki,Ri,Bi,Di,Mi=j(()=>{"use strict";Ne();$e();ve();Jd=(e,t,r,o,n,s,u,l,a,p,h,g,b)=>{let w,y;typeof l=="string"?w=y=(R,V)=>`${l}((${R}),(${V}))`:typeof l=="function"?w=y=l:(w=l.scalar,y=l.vector);let _=g?t.length:t,I=g?r.length:r,$=g?o.length:o,x=F("outputData",h,$,4),E=M("aData",a,_,4),A=M("bData",p,I,4),z;if(n)if(s){let R=U.size(t)===1,V=U.size(r)===1,T=t.length>0&&t[t.length-1]%4===0,N=r.length>0&&r[r.length-1]%4===0;R||V?z=x.setByOffset("global_idx",y(R?`${E.type.value}(${E.getByOffset("0")}.x)`:E.getByOffset("global_idx"),V?`${A.type.value}(${A.getByOffset("0")}.x)`:A.getByOffset("global_idx"))):z=`\n let outputIndices = ${x.offsetToIndices("global_idx * 4u")};\n let offsetA = ${E.broadcastedIndicesToOffset("outputIndices",x)};\n let offsetB = ${A.broadcastedIndicesToOffset("outputIndices",x)};\n ${x.setByOffset("global_idx",y(u||T?E.getByOffset("offsetA / 4u"):`${E.type.value}(${E.getByOffset("offsetA / 4u")}[offsetA % 4u])`,u||N?A.getByOffset("offsetB / 4u"):`${A.type.value}(${A.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}\n `}else z=x.setByOffset("global_idx",y(E.getByOffset("global_idx"),A.getByOffset("global_idx")));else{if(!s)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let R=(V,T,N="")=>{let te=`aData[indexA${T}][componentA${T}]`,Y=`bData[indexB${T}][componentB${T}]`;return`\n let outputIndices${T} = ${x.offsetToIndices(`global_idx * 4u + ${T}u`)};\n let offsetA${T} = ${E.broadcastedIndicesToOffset(`outputIndices${T}`,x)};\n let offsetB${T} = ${A.broadcastedIndicesToOffset(`outputIndices${T}`,x)};\n let indexA${T} = offsetA${T} / 4u;\n let indexB${T} = offsetB${T} / 4u;\n let componentA${T} = offsetA${T} % 4u;\n let componentB${T} = offsetB${T} % 4u;\n ${V}[${T}] = ${N}(${w(te,Y)});\n `};h===9?z=`\n var data = vec4(0);\n ${R("data",0,"u32")}\n ${R("data",1,"u32")}\n ${R("data",2,"u32")}\n ${R("data",3,"u32")}\n outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:z=`\n ${R("outputData[global_idx]",0)}\n ${R("outputData[global_idx]",1)}\n ${R("outputData[global_idx]",2)}\n ${R("outputData[global_idx]",3)}\n `}return`\n ${e.registerUniform("vec_size","u32").declareVariables(E,A,x)}\n\n ${b??""}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${z}\n }`},el=(e,t,r,o,n,s,u=r.dataType)=>{let l=!U.areEqual(r.dims,o.dims),a=r.dims,p=U.size(r.dims),h=!1,g=!1,b=[l];if(l){let y=dt.calcShape(r.dims,o.dims,!1);if(!y)throw new Error("Can\'t perform binary op on the given tensors");a=y,p=U.size(a);let _=U.size(r.dims)===1,I=U.size(o.dims)===1,$=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,x=o.dims.length>0&&o.dims[o.dims.length-1]%4===0;b.push(_),b.push(I),b.push($),b.push(x);let E=1;for(let A=1;Ay.toString()).join("_"),inputDependencies:w?["rank","rank"]:["dims","dims"]},getShaderSource:y=>Jd(y,r.dims,o.dims,a,h,l,g,n,r.dataType,o.dataType,u,w,s),getRunData:()=>({outputs:[{dims:a,dataType:u}],dispatchGroup:{x:Math.ceil(p/64/4)},programUniforms:w?[{type:"uint32",data:Math.ceil(U.size(a)/4)},...L(r.dims),...L(o.dims),...L(a)]:[{type:"uint32",data:Math.ceil(U.size(a)/4)}]})}},ht=(e,t,r,o,n,s)=>{e.compute(el(t,n??"",e.inputs[0],e.inputs[1],r,o,s))},Ii=e=>{ht(e,"Add",(t,r)=>`${t}+${r}`)},Ai=e=>{ht(e,"Div",(t,r)=>`${t}/${r}`)},Ti=e=>{ht(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4(${t}==${r})`},void 0,void 0,9)},Ei=e=>{ht(e,"Mul",(t,r)=>`${t}*${r}`)},Oi=e=>{let t=M("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;ht(e,"Pow",{scalar:(o,n)=>`pow_custom(${o},${n})`,vector:(o,n)=>`pow_vector_custom(${o},${n})`},`\n fn pow_custom(a : ${t}, b : ${t}) -> ${t} {\n if (b == ${t}(0.0)) {\n return ${t}(1.0);\n } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {\n return ${t}(pow(f32(a), f32(b))); // NaN\n }\n 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))));\n }\n fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {\n // TODO: implement vectorized pow\n return vec4<${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));\n }\n `)},Pi=e=>{ht(e,"Sub",(t,r)=>`${t}-${r}`)},ki=e=>{ht(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4(${t}>${r})`},void 0,void 0,9)},Ri=e=>{ht(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4(${t}<${r})`},void 0,void 0,9)},Bi=e=>{ht(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4(${t}>=${r})`},void 0,void 0,9)},Di=e=>{ht(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4(${t}<=${r})`},void 0,void 0,9)}});var rl,nl,ol,al,zi,Ui,Vi=j(()=>{"use strict";$e();je();ve();rl=e=>{if(!e||e.length<1)throw new Error("too few inputs");let t=e[0].dataType,r=e[0].dims.length;for(let o of e){if(o.dataType!==t)throw new Error("input tensors should be one type");if(o.dims.length!==r)throw new Error("input tensors should have the same shape")}},nl=(e,t)=>`\n fn calculateInputIndex(index: u32) -> u32 {\n let sizeInConcatAxis = array(${t});\n for (var i: u32 = 0u; i < ${e}; i += 1u ) {\n if (index < sizeInConcatAxis[i]) {\n return i;\n }\n }\n return ${e}u;\n }`,ol=(e,t)=>{let r=e.length,o=[];for(let n=0;n{let r=e[0].dims.slice();if(t>=r.length||t<-1*r.length)throw new Error("axis specified for concat doesn\'t match input dimensionality");let o=t<0?r.length+t:t,n=r.slice(0);for(let A=1;A`uniforms.sizeInConcatAxis${A}`).join(","),E=A=>`\n\n ${(()=>{A.registerUniform("outputSize","u32");for(let z=0;z(${x});\n ${$} -= sizeInConcatAxis[inputIndex - 1u];\n }\n\n ${ol(l,I)}\n }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:w}),getShaderSource:E}},zi=(e,t)=>{rl(e.inputs),e.compute(al(e.inputs,t.axis))},Ui=e=>ge({axis:e.axis})});var gt,Qr,It=j(()=>{"use strict";$e();gt=(e,t)=>{switch(e.activation){case"Relu":return{activationFunction:"",applyActivation:`value = max(value, ${t}(0.0));`};case"Sigmoid":return{activationFunction:"",applyActivation:`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`};case"Clip":return{activationFunction:`const clip_min_=${t}(${e.clipMin});const clip_max_=${t}(${e.clipMax});`,applyActivation:"value = clamp(value, clip_min_, clip_max_);"};default:return{activationFunction:"",applyActivation:""}}},Qr=e=>{let t=e?.activation||"";if(t==="Clip"){let[r,o]=e?.activation_params||[Gr,Lr];return{activation:t,clipMax:o,clipMin:r,activationCacheKey:`${t}:${r},${o}`}}return{activation:t,activationCacheKey:t}}});var Ke,Xr,Jr=j(()=>{"use strict";Ke=(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.`)}},Xr=e=>`\n ${e?"value = value + getBiasByOutputCoords(coords);":""}\n `});var en,Nn=j(()=>{"use strict";en=e=>`\nfn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`});var il,sl,mr,Ni,ul,fr,dl,tn,hr=j(()=>{"use strict";$e();ve();It();Jr();il=(e,t)=>e?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRow + innerRow,\n kStart / innerElementSize + inputCol${t?", batchIndices":""});\n `,sl=(e,t)=>e?`\n let ACached0 = mm_Asub[k * innerElementSize][localRow];\n let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n for (var i = 0; i < rowPerThread; i = i + 1) {\n acc[i] = BCached0 * ACached0[i] + acc[i];\n acc[i] = BCached1 * ACached1[i] + acc[i];\n acc[i] = BCached2 * ACached2[i] + acc[i];\n ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n }`:`\n for (var i = 0; i < rowPerThread; i = i + 1) {\n let ACached = mm_Asub[tileRow + i][k];\n acc[i] = BCached0 * ACached.x + acc[i];\n acc[i] = BCached1 * ACached.y + acc[i];\n acc[i] = BCached2 * ACached.z + acc[i];\n ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n }`,mr=(e,t,r="f32",o,n=!1,s=32,u=!1,l=32)=>{let a=t[1]*e[1],p=t[0]*e[0],h=n?a:s,g=n?s:a,b=h/t[0],w=s/t[1];if(!((n&&b===4&&e[1]===4||!n&&(b===3||b===4))&&h%t[0]===0&&s%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${n} is true, innerElementSize ${b} and workPerThread[1] ${e[1]} must be 4.\n Otherwise, innerElementSize ${b} must be 3 or 4.\n tileAWidth ${h} must be divisible by workgroupSize[0]${t[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar mm_Asub: array, ${h/b}>, ${g}>;\nvar mm_Bsub: array, ${p/e[0]}>, ${s}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${b};\nconst tileInner = ${s};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3,\n @builtin(global_invocation_id) globalId : vec3,\n @builtin(workgroup_id) workgroupId : vec3) {\n let localRow = i32(localId.y);\n let tileRow = localRow * rowPerThread;\n let tileCol = i32(localId.x);\n\n let globalRow =i32(globalId.y) * rowPerThread;\n let globalCol = i32(globalId.x);\n let batch = ${u?"0":"i32(globalId.z)"};\n ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n let globalRowStart = i32(workgroupId.y) * ${a};\n\n let numTiles = ${u?`${Math.ceil(l/s)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n var kStart = ${u?`i32(globalId.z) * ${l}`:"0"};\n\n var acc: array, rowPerThread>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${w};\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${il(n,o)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${o?", batchIndices":""});\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n ${b===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n ${sl(n,b)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n}`},Ni=(e,t)=>e?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart + inputCol${t?", batchIndices":""});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRowStart + inputRow,\n kStart + inputCol${t?", batchIndices":""});\n `,ul=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",fr=(e,t,r="f32",o,n=!1,s=32,u=!1,l=32,a=!1)=>{let p=e[1]*t[1],h=e[0]*t[0],g=n?p:s,b=n?s:p;if(!(b%t[1]===0&&g%t[0]===0&&s%t[1]===0))throw new Error(`tileAHight ${b} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${g} must be divisible by workgroupSize[0]${t[0]}, tileInner ${s} must be divisible by workgroupSize[1]${t[1]}`);let w=b/t[1],y=g/t[0],_=s/t[1],I=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${p};\n let globalColStart = i32(workgroupId.x) * ${h};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${b}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${g}; inputCol = inputCol + ${t[0]}) {\n ${Ni(n,o)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${t[1]}) {\n for (var inputCol = localCol; inputCol < ${h}; inputCol = inputCol + ${t[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalColStart + inputCol${o?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${r}, colPerThread>;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let ACached = ${n?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n ACached * BCached[innerCol];\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let gCol = globalColStart + localCol + innerCol * ${t[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${p};\n\nlet tileRowA = i32(localId.y) * ${w};\nlet tileColA = i32(localId.x) * ${y};\nlet tileRowB = i32(localId.y) * ${_};\n// Loop over shared dimension.\nfor (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < ${y}; innerCol = innerCol + 1) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${Ni(n,o)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalCol + innerCol${o?", batchIndices":""});\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array<${r}, colPerThread>;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n ${ul(n)}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n }\n }\n }\n\n workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n}\n`;return`\n var mm_Asub : array, ${b}>;\n var mm_Bsub : array, ${s}>;\n const rowPerThread = ${e[1]};\n const colPerThread = ${e[0]};\n const tileInner = ${s};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3,\n @builtin(global_invocation_id) globalId : vec3,\n @builtin(workgroup_id) workgroupId : vec3) {\n let batch = ${u?"0":"i32(globalId.z)"};\n ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n let numTiles = ${u?`${Math.ceil(l/s)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n var kStart = ${u?`i32(globalId.z) * ${l}`:"0"};\n\n var acc : array, rowPerThread>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${I}\n }\n`},dl=(e,t,r,o,n,s=!1)=>{let[u,l,a]=n,[p,h,g,b]=o,w=Ft(u,a),y=Ft(l,a),_=Le(o[0].type.tensor),I=()=>{let E=h.rank,A=p.rank,z=`var aIndices: ${h.type.indices};`;for(let R=E-2-1,V=A-1;R>=0;R--,V--)z+=`\naIndices[${R}] = ${A>1?`batchIndices[${V}]`:"batchIndices"};`;return w.forEach(R=>{z+=`\naIndices[${R}] = 0;`}),z+=`\naIndices[${E-2}] = u32(row);\n aIndices[${E-1}] = u32(colIn);`,z},$=()=>{let E=g.rank,A=p.rank,z=`var bIndices: ${g.type.indices};`;for(let R=E-2-1,V=A-1;R>=0;R--,V--)z+=`\nbIndices[${R}] = ${A>1?`batchIndices[${V}]`:"batchIndices"};`;return y.forEach(R=>{z+=`\nbIndices[${R}] = 0;`}),z+=`\nbIndices[${E-2}] = u32(row);\n bIndices[${E-1}] = u32(colIn);`,z};return`\n fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${Ke(e,_)} {\n var value = ${Ke(e,_)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dimAOuter && col < uniforms.dimInner)\n {\n ${I()}\n value = ${h.getByIndices("aIndices")};\n }\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${Ke(e,_)} {\n var value = ${Ke(e,_)}(0.0);\n let col = colIn * ${e};\n if(row < uniforms.dimInner && col < uniforms.dimBOuter)\n {\n ${$()}\n value = ${g.getByIndices("bIndices")};\n }\n return value;\n }\n\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ke(e,_)}) {\n let col = colIn * ${e};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n var value = valueIn;\n let coords = vec3(batch, row, colIn);\n ${t?`value = value + ${s?"bias[colIn]":`${Ke(e,_)}(bias[row])`};`:""}\n ${r}\n ${b.setByIndices("vec3(coords)","value")}\n }\n }\n `},tn=(e,t,r,o,n=!1)=>{let s=e[0].dims,u=e[1].dims,l=s.slice(0,-2),a=u.slice(0,-2),p=o?o.slice(0,-2):r.slice(0,-2),h=Re(p.length),g=h?p.length:p,b=Fr("batchDims",e[0].dataType,g,1),w=U.size(p),y=s[s.length-2],_=s[s.length-1],I=u[u.length-1],$=_%4===0&&I%4===0,x=y<=8?[4,1,1]:[4,4,1],E=[8,8,1],A=[Math.ceil(I/E[0]/x[0]),Math.ceil(y/E[1]/x[1]),Math.ceil(w/E[2]/x[2])],z=Le(e[0].dataType),R=$?4:1,V=[...l,y,_/R],T=Re(V.length),N=T?V.length:V,te=[...a,_,I/R],Y=Re(te.length),K=Y?te.length:te,Q=[w,y,I/R],Z=M("a",e[0].dataType,N,R),Ee=M("b",e[1].dataType,K,R),Pe=F("result",e[0].dataType,Q.length,R),fe=[Z,Ee],Ie=[{type:"int32",data:y},{type:"int32",data:I},{type:"int32",data:_}];h&&Ie.push(...L(p)),T&&Ie.push(...L(V)),Y&&Ie.push(...L(te));let he=[];he.push(T?"rank":"dims"),he.push(Y?"rank":"dims");let ye=e.length>2,{activationFunction:We,applyActivation:De}=gt(t,Pe.type.value),Ge=dl(R,ye,De,[b,Z,Ee,Pe],[l,a,p],n);if(ye){let ee=n?R:1;fe.push(M("bias",e[2].dataType,e[2].dims.length,ee)),Ie.push(...L(e[2].dims)),he.push("rank")}Ie.push(...L(Q));let G=ee=>`\n ${ee.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").registerInternalVariables(b).declareVariables(...fe,Pe)}\n ${We}\n ${Ge}\n ${$?mr(x,E,z,b):fr(x,E,z,b)}\n `;return{name:"MatMul",shaderCache:{hint:t.activationCacheKey+`${x}${$}${n}`,inputDependencies:he},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:Ie}),getShaderSource:G}}});var ll,Wi,Hi=j(()=>{"use strict";Ct();ve();It();Jr();Nn();hr();ll=(e,t,r,o,n=!1,s,u=4,l=4,a=4,p="f32")=>{let h=Y=>{switch(Y){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${p}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Y} is not supported.`)}},g=Y=>{switch(Y){case 1:return"return w[row * 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WCol - pad[1];\n let xCh = ${$} % inChannels;\n var resData = ${Ke(u,p)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the \'same\' padding type.\n if (xRow >= 0 && xRow < ${y} && xCol >= 0 && xCol < ${_}) {\n ${b}\n let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape));\n ${h(u)}\n }\n return resData;`,E=e?t&&o?`\n let col = colIn * ${u};\n ${x}`:`\n let col = colIn * ${u};\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${x}\n }\n return ${Ke(u,p)}(0.0);`:o&&r?`\n let col = colIn * ${u};\n ${x}`:`\n let col = colIn * ${u};\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${x}\n }\n return ${Ke(u,p)}(0.0);`,A=`${g(l)}`,z=Ke(a,p),R=e?Ke(u,p):Ke(l,p),V=e?Ke(l,p):Ke(u,p),{activationFunction:T,applyActivation:N}=gt(s,z);return`\n ${T}\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} {\n ${e?E:A}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${V} {\n ${e?A:E}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${z}) {\n let col = colIn * ${a};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n ${w}\n ${Xr(n)}\n ${N}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`},Wi=(e,t,r,o,n,s,u,l)=>{let a=t.format==="NHWC",p=a?e[0].dims[3]:e[0].dims[1],h=r[0],g=a?r[2]:r[3],b=a?r[1]:r[2],w=a?r[3]:r[1],y=a&&(p%4===0||p%3===0)&&w%4===0,_=a?w:g*b,I=a?g*b:w,$=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(_/$[0]/x[0]),Math.ceil(I/$[1]/x[1]),Math.ceil(h/$[2]/x[2])];Be("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let A=y?a&&p%4!==0?3:4:1,z=$[1]*x[1],R=$[0]*x[0],V=Math.max($[0]*A,$[1]),T=o%z===0,N=n%R===0,te=s%V===0,Y=y?[A,4,4]:[1,1,1],K=Le(e[0].dataType),Q=y?4:1,Z=[{type:"int32",data:o},{type:"int32",data:n},{type:"int32",data:s}],Ee=M("x",e[0].dataType,e[0].dims.length,A===3?1:A),Pe=M("w",e[1].dataType,e[1].dims.length,Q),fe=[Ee,Pe];Z.push(...L(e[0].dims)),Z.push(...L(e[1].dims));let Ie=`\n fn setOutputAtIndex(flatIndex : i32, value : ${y?`vec4<${K}>`:K}) {\n result[flatIndex] = ${y?`vec4<${K}>`:K}(value);\n }\n fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${y?`vec4<${K}>`:K}) {\n let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3));\n setOutputAtIndex(flatIndex ${y?"/ 4":""}, value);\n }`;if(u){let ye=M("bias",e[2].dataType,e[2].dims.length,Q);fe.push(ye),Z.push(...L(e[2].dims)),Ie+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${y?`vec4<${K}>`:K} {\n return bias[coords.${a?"w":"y"}${y?"/ 4":""}];\n }`}let he=F("result",e[0].dataType,r.length,Q);return Z.push(...L(r)),{name:"Conv2DMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:Z}),getShaderSource:ye=>`\n ${en("uniforms.result_strides")}\n //struct Uniforms { xShape : vec4, wShape : vec4, outShape : vec4,\n // outShapeStrides: vec3, filterDims : vec2, pad : vec2, stride : vec2,\n // dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n ${ye.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...fe,he)}\n const filterDims : vec2 = vec2(${t.kernelShape[0]}, ${t.kernelShape[1]});\n const pad : vec2 = vec2(${t.pads[0]}, ${t.pads[1]});\n const stride : vec2 = vec2(${t.strides[0]}, ${t.strides[1]});\n const dilation : vec2 = vec2(${t.dilations[0]}, ${t.dilations[1]});\n ${Ie}\n ${ll(a,T,N,te,u,t,Y[0],Y[1],Y[2],K)}\n ${y?mr(x,$,K,void 0,!a,V):fr(x,$,K,void 0,!a,V,!1,void 0,l)}`}}});var Wn,Gi=j(()=>{"use strict";$e();ve();Gn();It();Wn=(e,t,r)=>{let o=e.length>2,n=o?"value += b[output_channel];":"",s=e[0].dims,u=e[1].dims,l=u[0]/t.group,a=t.format==="NHWC",p=Hn(s,u,t.dilations,t.pads,t.strides,a),h=U.size(p),g=F("output",e[0].dataType,p),{activationFunction:b,applyActivation:w}=gt(t,g.type.value),y=M("x",e[0].dataType,s),_=M("w",e[1].dataType,u),I=[y,_];o&&I.push(M("b",e[2].dataType,e[2].dims));let $=x=>`\n const strides: vec2 = vec2(${t.strides[0]}u, ${t.strides[1]}u);\n const pads: vec2 = vec2(${t.pads[0]}u, ${t.pads[1]}u);\n\n ${x.declareVariables(...I,g)}\n\n ${b}\n\n ${x.mainStart()}\n ${x.guardAgainstOutOfBoundsWorkgroupSizes(h)}\n\n let outputIndices = ${g.offsetToIndices("global_idx")};\n let batch: u32 = outputIndices[0];\n let output_channel: u32 = outputIndices[${a?3:1}];\n let xRCCorner: vec2 = vec2(outputIndices[${a?1:2}], outputIndices[${a?2:3}]) * strides - pads;\n let group_id: u32 = output_channel / ${l}u;\n\n var value: ${g.type.value} = ${g.type.value}(0);\n for (var wInChannel: u32 = 0u; wInChannel < ${u[1]}u; wInChannel++) {\n let input_channel = group_id * ${u[1]}u + wInChannel;\n for (var wHeight: u32 = 0u; wHeight < ${u[2]}u; wHeight++) {\n let xHeight = xRCCorner.x + wHeight * ${t.dilations[0]}u;\n\n if (xHeight < 0u || xHeight >= ${s[a?1:2]}u) {\n continue;\n }\n\n for (var wWidth: u32 = 0u; wWidth < ${u[3]}u; wWidth++) {\n let xWidth = xRCCorner.y + wWidth * ${t.dilations[1]}u;\n if (xWidth < 0u || xWidth >= ${s[a?2:3]}u) {\n continue;\n }\n\n let xVal = ${a?y.get("batch","xHeight","xWidth","input_channel"):y.get("batch","input_channel","xHeight","xWidth")};\n let wVal = ${_.get("output_channel","wInChannel","wHeight","wWidth")};\n value += xVal*wVal;\n }\n }\n }\n ${n}\n ${w}\n ${g.setByOffset("global_idx","value")}\n }`;return{name:"GroupedConv",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r?r(p):p,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)}}),getShaderSource:$}}});var Ln,cl,Li,Fn=j(()=>{"use strict";$e();hr();ve();It();Ln=(e,t,r,o,n=!1)=>{let s=e[0].dims,u=e[1].dims,l=s[s.length-2],a=u[u.length-1],p=s[s.length-1],h=Fe(a),g=Fe(p),b=Fe(l),w=U.size(r)/h/b,y=e.length>2,_=o?o.slice(0,-2):r.slice(0,-2),$=[U.size(_),l,a],x=[{type:"uint32",data:w},{type:"uint32",data:l},{type:"uint32",data:a},{type:"uint32",data:p},...L(_),...L(s),...L(u)];y&&x.push(...L(e[2].dims)),x.push(...L($));let E=A=>{let z=Fr("batch_dims",e[0].dataType,_.length),R=M("a",e[0].dataType,s.length,g),V=M("b",e[1].dataType,u.length,h),T=F("output",e[0].dataType,$.length,h),{activationFunction:N,applyActivation:te}=gt(t,T.type.value),Y=[R,V],K="";if(y){let he=n?h:1;Y.push(M("bias",e[2].dataType,e[2].dims.length,he)),K=`${n?`value += bias[col / ${he}];`:`value += ${T.type.value}(bias[row + i]);`}`}let Q=s.slice(0,-2),Z=u.slice(0,-2),Ee=Ft(Q,_),Pe=Ft(Z,_),fe=(he,ye)=>{let We=he.rank,De=he.name;if(We===2)return`var ${De}_indices = ${he.type.indices}(0u, 0u);`;let Ge=z.rank,G=`var ${De}_indices: 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2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[1]*t.group;if(r!==o)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 n=e[0].dims.length-2;if(t.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(t.strides.length!==n)throw new Error(`strides should be ${n}D`);if(t.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},ji=(e,t)=>{let r=e.kernelShape.slice();for(let s=2;s{let t=Qr(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],n=e.dilations,s=e.group,u=e.kernel_shape,l=e.pads,a=e.strides,p=e.w_is_const();return ge({autoPad:o,format:r,dilations:n,group:s,kernelShape:u,pads:l,strides:a,wIsConst:p,...t})},ml=(e,t,r)=>{let o=ji(r,t),n=r.format==="NHWC";if(r.group!==1){e.compute(Wn(t,o));return}let s=t.length===3,u=t[0].dims[n?1:2],l=t[0].dims[n?2:3],a=t[0].dims[n?3:1],p=t[1].dims[2],h=t[1].dims[3],g=Hn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,n),b=g[n?1:2],w=g[n?2:3],y=g[n?3:1],_=n&&p===u&&h===l&&r.pads[0]===0&&r.pads[1]===0;if(_||p===1&&h===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let R=g[0],V,T,N,te=[];if(n){let Q=e.kernelCustomData.wT??e.compute(it(t[1],Fi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Q),_){let Z=u*l*a;V=t[0].reshape([1,R,Z]),T=Q.reshape([1,Z,y]),N=[1,R,y]}else V=t[0].reshape([R,u*l,a]),T=Q.reshape([1,a,y]),N=[R,b*w,y];te.push(V),te.push(T)}else V=t[0].reshape([R,a,u*l]),T=t[1].reshape([1,y,a]),N=[R,y,b*w],te.push(T),te.push(V);s&&te.push(t[2]);let Y=N[2],K=te[0].dims[te[0].dims.length-1];Y<8&&K<8?e.compute(Ln(te,o,g,N,n),{inputs:te}):e.compute(tn(te,o,g,N,n),{inputs:te});return}let I=!0,$=e.kernelCustomData.wT??e.compute(it(t[1],Fi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=$);let x=[t[0],$];s&&x.push(t[2]);let E=n?b*w:y,A=n?y:b*w,z=p*h*a;e.compute(Wi(x,o,g,E,A,z,s,I),{inputs:x})},fl=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let n=[0,t.pads[0],0,t.pads[1]],s=[1].concat(t.strides),u=[1].concat(t.dilations),l=[1].concat(t.kernelShape),a=ji({...t,pads:n,strides:s,dilations:u,kernelShape:l},o);e.compute(Wn(o,a,p=>r?[p[0],p[2],p[3]]:[]))},qn=(e,t)=>{pl(e.inputs,t),e.inputs[0].dims.length===3?fl(e,t):ml(e,e.inputs,t)}});var hl,qi,Ki=j(()=>{"use strict";Ct();ve();It();Jr();Nn();hr();hl=(e,t=!1,r,o=4)=>{let n=Ke(o,"f32"),s=x=>{switch(x){case 1:return"return w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = w[getIndexFromCoords4D(coord, vec4(uniforms.w_shape))];\n let v1 = w[getIndexFromCoords4D(coord1, vec4(uniforms.w_shape))];\n let v2 = w[getIndexFromCoords4D(coord2, vec4(uniforms.w_shape))];\n let v3 = w[getIndexFromCoords4D(coord3, vec4(uniforms.w_shape))];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${x} is not supported.`)}},u=e?`\n let coord = vec4(batch, iXR, iXC, xCh);\n `:`\n let coord = vec4(batch, xCh, iXR, iXC);\n `,l=e?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,a=e?"outBackprop[1]":"outBackprop[2]",p=e?"outBackprop[2]":"outBackprop[3]",h=e?"row":"col",g=e?"col":"row",b=`\n let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n let outRow = ${h} / outWidth;\n let outCol = ${h} % outWidth;\n\n let WRow = ${g} / (filterDims[1] * inChannels);\n let WCol = ${g} / inChannels % filterDims[1];\n let xR = f32(outRow - pads[0] + dilation[0] * WRow) / f32(strides[0]);\n let xC = f32(outCol - pads[1] + dilation[1] * WCol) / f32(strides[1]);\n if (xR < 0.0 || xR >= f32(${a}) || fract(xR) > 0.0) {\n return ${n}(0.0);\n }\n if (xC < 0.0 || xC >= f32(${p}) || fract(xC) > 0.0) {\n return ${n}(0.0);\n }\n let iXR = i32(xR);\n let iXC = i32(xC);\n let xCh = ${g} % inChannels;\n ${u}\n return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${o}];`,w=e?`\n let col = colIn * ${o};\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${n}(0.0);`:`\n let col = colIn * ${o};\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${n}(0.0);`,y=`\n let col = colIn * ${o};\n let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n let coordX = filterDims.x - 1 - row / (filterDims[1] * inChannels);\n let coordY = filterDims.y - 1 - (row / inChannels) % filterDims[1];\n if (${e?"row < uniforms.dimInner && col < uniforms.dimBOuter":"row < uniforms.dimInner && col < uniforms.dimAOuter"} && coordX >= 0 && coordY >= 0) {\n let rowInner = row % inChannels;\n let coord = vec4(coordX, coordY, col, rowInner);\n ${s(o)}\n }\n return ${n}(0.0);\n `,{activationFunction:_,applyActivation:I}=gt(r,n);return`\n ${_}\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} {\n ${e?w:y}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} {\n ${e?y:w}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) {\n let col = colIn * ${o};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n var value = valueInput;\n let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n ${l}\n ${Xr(t)}\n ${I}\n result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${o}] = value;\n }\n }`},qi=(e,t,r,o,n,s,u,l)=>{let a=t.format==="NHWC",p=a?e[0].dims[3]:e[0].dims[1],h=r[0],g=a?r[2]:r[3],b=a?r[1]:r[2],w=a?r[3]:r[1],y=a?p%4===0&&w%4===0:g%4===0&&w%4===0,_=a?w:g*b,I=a?g*b:w,$=y?[8,8,1]:[_<=4||I<=4?4:16,_>4&&I<=4?4:16,1],x=y?[4,4,1]:[_<=4?1:4,_>4&&I<=4?1:4,1],E=[Math.ceil(_/$[0]/x[0]),Math.ceil(I/$[1]/x[1]),Math.ceil(h/$[2]/x[2])];Be("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let A=y?4:1,z=Math.max($[0]*A,$[1]),R=y?4:1,V=[{type:"int32",data:o},{type:"int32",data:n},{type:"int32",data:s}],T=M("x",e[0].dataType,e[0].dims.length,R),N=M("w",e[1].dataType,e[1].dims.length,1),te=F("result",e[0].dataType,r.length,R),Y=[T,N];V.push(...L(e[0].dims)),V.push(...L(e[1].dims));let K="";if(u){let Q=M("bias",e[2].dataType,e[2].dims.length,R);Y.push(Q),V.push(...L(e[2].dims)),K+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${y?"vec4":"f32"} {\n return bias[coords.${a?"w":"y"}${y?"/ 4":""}];\n }`}return V.push(...L(r)),{name:"Conv2DTransposeMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:V}),getShaderSource:Q=>`\n ${en("uniforms.result_strides")}\n ${Q.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...Y,te)};\n const outBackprop : vec4 = vec4(${e[0].dims.join(",")});\n const filterDims : vec2 = vec2(${t.kernelShape[a?1:2]}, ${t.kernelShape[a?2:3]});\n const effectiveFilterDims : vec2 = filterDims + vec2(\n ${t.dilations[0]<=1?0:(t.kernelShape[a?1:2]-1)*(t.dilations[0]-1)},\n ${t.dilations[1]<=1?0:(t.kernelShape[a?2:3]-1)*(t.dilations[1]-1)});\n const pads : vec2 = vec2(i32(effectiveFilterDims[0]) - 1 - (${t.pads[0]+t.pads[2]})/2,\n i32(effectiveFilterDims[1]) - 1 - (${t.pads[1]+t.pads[3]})/2);\n const strides : vec2 = vec2(${t.strides[0]}, ${t.strides[1]});\n const dilation : vec2 = vec2(${t.dilations[0]}, ${t.dilations[1]});\n const dimAOuter : i32 = ${o};\n const dimBOuter : i32 = ${n};\n const dimInner : i32 = ${s};\n ${K}\n ${hl(a,u,t,A)}\n ${y?mr(x,$,"f32",void 0,!a,z):fr(x,$,"f32",void 0,!a,z,!1,void 0,l)}`}}});var gl,Kn,Yi=j(()=>{"use strict";Ct();$e();ve();gl=(e,t,r,o,n,s,u=!1,l)=>{let a=r.format==="NHWC",p=a?1:2,h=a?2:3,g=a?3:1,b=U.size(o),w=u?2:1,y=r.group,_=t[1].dims,I=_[0]/y,$=_[1],x=`\n fn setOutputAtIndex(flatIndex : u32, value : ${u?`vec4<${l}>`:l}) {\n result[flatIndex] = ${u?`vec4<${l}>`:l}(value);\n }`;n&&(x+=`\n fn getBiasByOutputCoords(coords : vec4) -> ${u?`vec4<${l}>`:l} {\n return bias[coords.${a?"w":"y"}${u?"/ 4":""}];\n }`);let E=u?4:1,A=M("W",t[1].dataType,t[1].dims,E),z=M("Dy",t[0].dataType,t[0].dims,E),R=[z,A];n&&R.push(M("bias",t[2].dataType,[o[g]],E));let V=F("result",t[0].dataType,o,E),T=`{\n let batch: u32 = ${s?"global_id.z":"workgroup_id.z"} / outShape[1];\n let r = ${s?"global_id.z":"workgroup_id.z"} % outShape[1];\n let c = ${s?"global_id.y":"workgroup_id.y"} * ${w};\n let d1: u32 = ${s?"global_id.x":"workgroup_id.x"} * 4;\n\n let dyCorner = vec2(i32(r), i32(c)) - vec2(pads);\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${w}>;\n for (var i = 0; i < ${w}; i++) {\n dotProd[i] = vec4<${l}>(0.0);\n }\n for (var wR: u32 = 0; wR < filterDims[0]; wR = wR + 1) {\n var dyR = (${l}(dyCorner.x) + ${l}(wR)) / ${l}(strides.x);\n let wRPerm = filterDims[0] - 1 - wR;\n if (dyR < 0.0 || dyR >= ${l}(outBackprop[1]) ||\n fract(dyR) > 0.0 || wRPerm < 0) {\n continue;\n }\n let idyR: u32 = u32(dyR);\n\n for (var wC: u32 = 0; wC < filterDims[1]; wC = wC + 1) {\n let dyC = (${l}(dyCorner.y) + ${l}(wC)) / ${l}(strides.y);\n let dyC2 = (${l}(dyCorner.y) + 1.0 + ${l}(wC)) / ${l}(strides.y);\n let wCPerm = filterDims[1] - 1 - wC;\n if (wCPerm < 0) {\n continue;\n }\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= ${l}(outBackprop[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= ${l}(outBackprop[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC: u32 = u32(dyC);\n let idyC2: u32 = u32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = outBackprop[3];\n for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${z.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${l}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n\n xValue = ${z.get("batch","idyR","idyC2","d2")};\n\n dotProd[1] = dotProd[1] + vec4<${l}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = outBackprop[${g}];\n for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${z.get("batch","idyR","idyC","d2")};\n let tmpval = vec4<${l}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = outBackprop[3];\n for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n var xValue = ${z.get("batch","idyR","idyC2","d2")};\n let tmpval = vec4<${l}>(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i: u32 = 0; i < ${w}; i = i + 1) {\n let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${l}>(0.0)`};\n ${V.set("batch","r","c + i","d1","value")};\n }\n }`,N=`\n let outputIndices = ${V.offsetToIndices("global_idx")};\n let batch = ${V.indicesGet("outputIndices",0)};\n let d1 = ${V.indicesGet("outputIndices",g)};\n let r = ${V.indicesGet("outputIndices",p)};\n let c = ${V.indicesGet("outputIndices",h)};\n let dyCorner = vec2(i32(r), i32(c)) - pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n let groupId = d1 / ${$};\n let wOutChannel = d1 - groupId * ${$};\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = ${l}(0.0);\n for (var wR: u32 = 0; wR < effectiveFilterDims.x; wR = wR + 1) {\n if (wR % dilations.x != 0) {\n continue;\n }\n let dyR = (${l}(dyRCorner) + ${l}(wR)) / ${l}(strides[0]);\n let wRPerm = filterDims.x - 1 - wR / dilations.x;\n if (dyR < 0.0 || dyR >= ${l}(outBackprop[${p}]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR: u32 = u32(dyR);\n\n for (var wC: u32 = 0; wC < effectiveFilterDims.y; wC = wC + 1) {\n if (wC % dilations.y != 0) {\n continue;\n }\n let dyC = (${l}(dyCCorner) + ${l}(wC)) / ${l}(strides.y);\n let wCPerm = filterDims.y - 1 - wC / dilations.y;\n if (dyC < 0.0 || dyC >= ${l}(outBackprop[${h}]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC: u32 = u32(dyC);\n var inputChannel = groupId * ${I};\n for (var d2: u32 = 0; d2 < ${I}; d2 = d2 + 1) {\n let xValue = ${a?z.get("batch","idyR","idyC","inputChannel"):z.get("batch","inputChannel","idyR","idyC")};\n let wValue = ${A.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n dotProd = dotProd + xValue * wValue;\n inputChannel = inputChannel + 1;\n }\n }\n }\n let value = dotProd + ${n?"bias[d1]":`${l}(0.0)`};\n ${V.setByOffset("global_idx","value")};\n `;return`\n ${e.declareVariables(...R,V)}\n ${x}\n const outShape : vec4 = vec4(${o.join(",")});\n const outBackprop : vec4 = vec4(${t[0].dims.join(",")});\n const strides : vec2 = vec2(${r.strides[0]}, ${r.strides[1]});\n const filterDims : vec2 = vec2(${r.kernelShape[a?1:2]}, ${r.kernelShape[a?2:3]});\n const dilations : vec2 = vec2(${r.dilations[0]}, ${r.dilations[1]});\n const effectiveFilterDims : vec2 = filterDims + vec2(\n ${r.dilations[0]<=1?0:(r.kernelShape[a?1:2]-1)*(r.dilations[0]-1)},\n ${r.dilations[1]<=1?0:(r.kernelShape[a?2:3]-1)*(r.dilations[1]-1)});\n const pads : vec2 = vec2(i32(effectiveFilterDims[0]) - 1 - (${r.pads[0]+r.pads[2]})/2,\n i32(effectiveFilterDims[1]) - 1 - (${r.pads[1]+r.pads[3]})/2);\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes(b)};\n ${u?T:N}}`},Kn=(e,t,r)=>{let o=e.length>2,n=t.outputShape,s=U.size(n),u=[Math.ceil(s/64),1,1];Be("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${u}`);let l=Le(e[0].dataType);return{name:"ConvTranspose2D",shaderCache:{hint:t.cacheKey},getRunData:()=>({dispatchGroup:{x:u[0],y:u[1],z:u[2]},outputs:[{dims:r?r(n):n,dataType:e[0].dataType}]}),getShaderSource:a=>gl(a,e,t,n,o,u[1]===1&&u[2]===1,!1,l)}}});var yl,bl,wl,Zi,Qi,vl,$l,Sl,xl,Xi,Ji=j(()=>{"use strict";je();Ki();Yi();It();jt();yl=(e,t,r,o,n,s)=>(e-1)*t+r+(o-1)*n+1-s,bl=(e,t,r,o,n)=>{let s=Math.floor(e/2);t==="SAME_UPPER"?(r[o]=s,r[n]=e-s):t==="SAME_LOWER"&&(r[o]=e-s,r[n]=s)},wl=(e,t,r,o,n,s,u,l,a,p)=>{let h=e.length-2,g=p.length===0;if(a.length===0)for(let y=0;y{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((b,w)=>b*w,1)===0){r.length=0;for(let b=2;bb+w,0)===0){let b=t[0].dims.length-2;a=new Array(b).fill(1)}let p=e.strides.slice();if(p.reduce((b,w)=>b+w,0)===0){let b=t[0].dims.length-2;p=new Array(b).fill(1)}wl(l,r,a,e.autoPad,e.group,n,p,o,u,s);let h=Object.assign({},e),g=e.cacheKey+[r.join("n,"),n.join(","),p.join(","),u.join(","),s.join(","),a.join(",")].join("_");return Object.assign(h,{kernelShape:r,pads:n,outputPadding:u,outputShape:s,dilations:a,strides:p,cacheKey:g}),h},Qi=e=>{let t=Qr(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof 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${s}D`);if(t.strides.reduce((h,g)=>h+g,0)>0&&t.strides.length!==s)throw new Error(`strides should be ${s}D`);if(t.pads.reduce((h,g)=>h+g,0)>0&&t.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(t.outputPadding.length!==s&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${s}D`);if(t.kernelShape.reduce((h,g)=>h+g,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},$l=[2,3,1,0],Sl=(e,t,r)=>{let o=Zi(r,t),n=r.format==="NHWC",s=o.outputShape,u=s[n?3:1],l=t[0].dims[n?3:1];if(o.group!==1||u===1&&l===1){e.compute(Kn(t,o));return}let a=s[n?1:2],p=s[n?2:3],h=t[1].dims[2],g=t[1].dims[3],b=n?a*p:u,w=n?u:a*p,y=h*g*l,_=!0,I=e.kernelCustomData.wT??e.compute(it(t[1],$l),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=I);let 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z=[{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`\n ${b.registerUniforms(z).declareVariables(...E)}\n\n ${b.mainStart()}\n ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let m = global_idx / uniforms.N;\n let n = global_idx % uniforms.N;\n\n var value = ${$}(0);\n for (var k: u32 = 0u; k < uniforms.K; k++) {\n ${w}\n }\n\n ${y}\n ${(()=>x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",A)}; value += ${$}(uniforms.beta) * ${x.getByOffset("cOffset")};`:"")()}\n output[global_idx] = value;\n }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:l,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:g}},ys=e=>{let t=e.transA,r=e.transB,o=e.alpha,n=e.beta;return{transA:t,transB:r,alpha:o,beta:n,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},bs=(e,t)=>{Dl(e.inputs),e.compute(Ml(e.inputs,t))}});var zl,Ul,Vl,vs,$s=j(()=>{"use strict";Ne();$e();ve();zl=(e,t)=>{let r=e[0].dims,o=r,n=2,s=U.sizeToDimension(r,n),u=U.sizeFromDimension(r,n),l=Fe(u),a=u/l,p=[r[0],r[1],a],h=["rank","type","type"],g=[{type:"uint32",data:u},{type:"uint32",data:a}];g.push(...L(p),...L(p));let b=w=>{let y=M("x",e[0].dataType,p.length,l),_=M("scale",e[1].dataType,e[1].dims),I=M("bias",e[2].dataType,e[2].dims),$=F("output",e[0].dataType,p.length,l),x=[y,_,I,$],E=y.type.value,A=l===1?"f32":`vec${l}`,z=64,R=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`\n var meanShared : f32;\n var squaredNormShared : f32;\n var workgroupShared : array<${A}, ${z}>;\n const workgroupSize = ${z}u;\n ${w.registerUniforms(R).declareVariables(...x)}\n ${w.mainStart(z)}\n let norm = global_idx / workgroupSize;\n let batch = norm / uniforms.x_shape[1];\n let channel = norm % uniforms.x_shape[1];\n let localIndex = local_id.x;\n\n // initialize workgroup memory\n var initial = ${A}(0);\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n initial = initial + ${A}(${y.get("batch","channel","h")});\n }\n workgroupShared[localIndex] = initial;\n workgroupBarrier();\n\n // Calculate the mean of current channel data.\n for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (localIndex < currSize) {\n workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n }\n workgroupBarrier();\n }\n if (localIndex == 0) {\n meanShared = ${Je("workgroupShared[0]",l)} / f32(uniforms.normSize);\n }\n workgroupBarrier();\n\n // reinitialize workgroup memory.\n initial = ${A}(0);\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let deviation = ${A}(${y.get("batch","channel","h")}) - ${A}(meanShared);\n initial = initial + deviation * deviation;\n }\n workgroupShared[localIndex] = initial;\n workgroupBarrier();\n\n // Calculate the sum of square of deviation of current channel data.\n for (var currSize = workgroupSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (localIndex < currSize) {\n workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n }\n workgroupBarrier();\n }\n if (localIndex == 0) {\n squaredNormShared = ${Je("workgroupShared[0]",l)};\n }\n workgroupBarrier();\n\n let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n let channelScale = invStdDev * f32(${_.getByOffset("channel")});\n let channelShift = f32(${I.getByOffset("channel")}) - meanShared * channelScale;\n for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n let value = ${y.get("batch","channel","h")} * ${E}(${A}(channelScale)) + ${E}(${A}(channelShift));\n ${$.set("batch","channel","h","value")};\n }\n }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${l}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:s},programUniforms:g}),getShaderSource:b}},Ul=(e,t,r,o,n,s,u,l)=>{let a=Fe(u),p=64,h=a===1?"vec2f":`mat2x${a}f`,g=a===1?"f32":`vec${a}f`,b=(R,V)=>`${h}(${R}, ${V})`,w=n*u/a,y=Math.ceil(s/p),_=["type"],I=[{type:"uint32",data:y},{type:"uint32",data:s},{type:"uint32",data:Math.floor(u/a)},{type:"uint32",data:Math.floor(s*u/a)}],$=R=>{let V=M("input",t.dataType,t.dims,a);return`\n ${R.declareVariables(V)}\n @group(0) @binding(1) var output : array<${h}>;\n struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n @group(0) @binding(2) var uniforms: Uniforms;\n\n ${R.mainStart(p)}\n let currentImageNumber = global_idx / ${p} / uniforms.C;\n let currentChannelNumber = (global_idx / ${p}) % uniforms.C;\n let wgId = global_idx % ${p};\n let wgOffset = wgId * uniforms.wg_size;\n if (wgOffset >= uniforms.H) {\n return;\n }\n let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);\n\n let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;\n var sum = ${Ze("f32",a)};\n var squaredSum = ${Ze("f32",a)};\n for (var i: u32 = wgOffset; i < wgMax; i++) {\n let value = ${g}(input[offset + i * uniforms.C]);\n sum += value;\n squaredSum += value * value;\n }\n output[global_idx] = ${b("sum","squaredSum")};\n }`},x=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${a}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:[n,u,p,2],dataType:1}],dispatchGroup:{x:n*u/a},programUniforms:I}),getShaderSource:$},{inputs:[t],outputs:[-1]})[0],E=[{type:"uint32",data:w},{type:"uint32",data:s},{type:"uint32",data:Math.floor(u/a)},{type:"uint32",data:Math.floor(p*u/a)}],A=["type","type","type"],z=R=>{let V=M("scale",r.dataType,r.dims,a),T=M("bias",o.dataType,o.dims,a);return`\n @group(0) @binding(0) var input : array<${h}>;\n @group(0) @binding(1) var scale : array<${V.type.storage}>;\n @group(0) @binding(2) var bias : array<${T.type.storage}>;\n @group(0) @binding(3) var output : array<${h}>;\n struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n @group(0) @binding(4) var uniforms: Uniforms;\n\n ${R.mainStart()}\n ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}\n let currentImageNumber = global_idx / uniforms.C;\n let currentChannelNumber = global_idx % uniforms.C;\n\n let offset = currentImageNumber * uniforms.image_size;\n var sum = ${Ze("f32",a)};\n var squaredSum = ${Ze("f32",a)};\n for (var i: u32 = 0; i < ${p}; i++) {\n let value = input[offset + i + currentChannelNumber * ${p}];\n sum += value[0];\n squaredSum += value[1];\n }\n sum = sum / f32(uniforms.H);\n squaredSum = squaredSum / f32(uniforms.H);\n let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${l}));\n let channelScale = invStdDev * ${g}(scale[currentChannelNumber]);\n let channelShift = ${g}(bias[currentChannelNumber]) - sum * channelScale;\n\n output[global_idx] = ${b("channelScale","channelShift")};\n }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${a};${l}`,inputDependencies:A},getRunData:()=>({outputs:[{dims:[n,u,2],dataType:1}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:E}),getShaderSource:z},{inputs:[x,r,o],outputs:[-1]})[0]},Vl=(e,t,r)=>{let o=t[0].dims,n=o,s=o[0],u=o[o.length-1],l=U.sizeFromDimension(o,1)/u,a=Fe(u),p=U.size(n)/a,h=[{type:"uint32",data:l},{type:"uint32",data:Math.floor(u/a)}],g=["type","type"],b=Ul(e,t[0],t[1],t[2],s,l,u,r.epsilon),w=y=>{let _=Le(t[0].dataType),I=a===1?"vec2f":`mat2x${a}f`,$=a===1?_:`vec${a}<${_}>`,x=M("input",t[0].dataType,t[0].dims,a),E=F("output",t[0].dataType,n,a);return`\n @group(0) @binding(0) var input : array<${x.type.storage}>;\n @group(0) @binding(1) var scaleInput : array<${I}>;\n @group(0) @binding(2) var output : array<${E.type.storage}>;\n struct Uniforms {H: u32, C : u32};\n @group(0) @binding(3) var uniforms: Uniforms;\n\n ${y.mainStart()}\n let currentImageNumber = global_idx / (uniforms.C * uniforms.H);\n let currentChannelNumber = global_idx % uniforms.C;\n\n let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;\n let scale = scaleInput[scaleOffset];\n output[global_idx] = fma(input[global_idx], ${$}(scale[0]), ${$}(scale[1]));\n }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${a}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:w},{inputs:[t[0],b]})},vs=(e,t)=>{t.format==="NHWC"?Vl(e,e.inputs,t):e.compute(zl(e.inputs,t))}});var Nl,Wl,Ss,xs=j(()=>{"use strict";Ne();$e();ve();Nl=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Wl=(e,t,r)=>{let o=e[0].dims,n=e[1],s=e[2],u=o,l=U.normalizeAxis(t.axis,o.length),a=U.sizeToDimension(o,l),p=U.sizeFromDimension(o,l),h=U.size(n.dims),g=s?U.size(s.dims):0;if(h!==p||s&&g!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.\n Size of scale and bias (if provided) must match this.\n Got scale size of ${h} and bias size of ${g}`);let b=[];for(let A=0;A1,$=r>2,x=A=>{let z=Le(e[0].dataType),R=[M("x",e[0].dataType,e[0].dims,w),M("scale",n.dataType,n.dims,w)];s&&R.push(M("bias",s.dataType,s.dims,w)),R.push(F("output",e[0].dataType,u,w)),I&&R.push(F("mean_data_output",1,b)),$&&R.push(F("inv_std_output",1,b));let V=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n ${A.registerUniforms(V).declareVariables(...R)}\n ${A.mainStart()}\n ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n let offset = global_idx * uniforms.norm_size_vectorized;\n var meanVector = ${Ze("f32",w)};\n var meanSquareVector = ${Ze("f32",w)};\n\n for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n let value = ${at(z,w,"x[h + offset]")};\n meanVector += value;\n meanSquareVector += value * value;\n }\n let mean = ${Je("meanVector",w)} / uniforms.norm_size;\n let invStdDev =\n inverseSqrt(${Je("meanSquareVector",w)} / uniforms.norm_size - mean * mean + uniforms.epsilon);\n\n for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n let f32input = ${at(z,w,"x[j + offset]")};\n let f32scale = ${at(z,w,"scale[j]")};\n output[j + offset] = ${R[0].type.value}((f32input - mean) * invStdDev * f32scale\n ${s?`+ ${at(z,w,"bias[j]")}`:""}\n );\n }\n\n ${I?"mean_data_output[global_idx] = mean":""};\n ${$?"inv_std_output[global_idx] = invStdDev":""};\n }`},E=[{dims:u,dataType:e[0].dataType}];return I&&E.push({dims:b,dataType:1}),$&&E.push({dims:b,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${w};${r}`,inputDependencies:y},getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(a/64)},programUniforms:_}),getShaderSource:x}},Ss=(e,t)=>{Nl(e.inputs),e.compute(Wl(e.inputs,t,e.outputCount))}});var Hl,Cs,_s,Gl,Xn,Is,As=j(()=>{"use strict";$e();je();Nr();Un();ve();jt();Hl=(e,t)=>{let r=e[0],o=e[1],n=e[2],s=e[3],u=e[4],l=e[5],a=e[6],p=e[7];if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let h=!1,g=r.dims[0],b=r.dims[1],w=r.dims.length===3?h?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],y=b,_=0,I=0,$=Math.floor(w/t.numHeads);if(a&&p){if(a.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(p.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');_=a.dims[2],I=a.dims[2]}else if(a||p)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let x;if(o){if(r.dims.length!==3)throw new Error(\'Input "query" is expected to have 3 dimensions when key is given\');if(o.dims.length<3||o.dims.length>5)throw new Error(\'Input "key" is expected to have 3, 4, or 5 dimensions\');if(r.dims[0]!==o.dims[0])throw new Error(\'Input "query" and "key" shall have same dim 0 (batch size)\');if(o.dims.length===3){if(o.dims[2]!==r.dims[2])throw new Error(\'Input "query" and "key" shall have same dim 2 (hidden_size)\');x=2,y=o.dims[1]}else if(o.dims.length===5){if(o.dims[2]!==t.numHeads||o.dims[3]!==2||o.dims[4]!==$)throw new Error(\'Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\');if(n)throw new Error(\'Expect "value" be none when "key" has packed kv format.\');x=5,y=o.dims[1]}else{if(o.dims[1]!==t.numHeads||o.dims[3]!==$)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');x=0,y=o.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error(\'Input "query" is expected to have 3 or 5 dimensions when key is empty\');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error(\'Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv\');x=3}if(s){if(s.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(n&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(u){E=8;let T=u.dims;throw T.length===1?T[0]===g?E=1:T[0]===3*g+2&&(E=3):T.length===2&&T[0]===g&&T[1]===y&&(E=5),E===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let A=!1,z=w;if(n){if(n.dims.length!==3&&n.dims.length!==4)throw new Error(\'Input "value" is expected to have 3 or 4 dimensions\');if(r.dims[0]!==n.dims[0])throw new Error(\'Input "query" and "value" shall have same dim 0 (batch_size)\');if(n.dims.length===3){if(y!==n.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');z=n.dims[2]}else{if(y!==n.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');z=n.dims[1]*n.dims[3],A=!0}}let R=_+y,V=!1;if(u)throw new Error("Key padding mask is not supported");if(l)throw new Error("extraAddQk is not supported");if(a)throw new Error("pastKey is not supported");if(p)throw new Error("pastValue is not supported");return{batchSize:g,sequenceLength:b,pastSequenceLength:_,kvSequenceLength:y,totalSequenceLength:R,maxSequenceLength:I,inputHiddenSize:0,hiddenSize:w,vHiddenSize:z,headSize:$,vHeadSize:Math.floor(z/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:E,scale:t.scale,broadcastResPosBias:V,passPastInKv:A,qkvFormat:x}},Cs=e=>ge({...e}),_s=ge({perm:[0,2,1,3]}),Gl=(e,t,r,o,n,s,u)=>{let l=[o,n,s],a=U.size(l),p=[{type:"uint32",data:a},{type:"uint32",data:u},{type:"uint32",data:s}],h=g=>{let b=F("qkv_with_bias",t.dataType,l),w=M("qkv",t.dataType,l),y=M("bias",r.dataType,l),_=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n ${g.registerUniforms(_).declareVariables(w,y,b)}\n ${g.mainStart()}\n ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:l,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:h},{inputs:[t,r],outputs:[-1]})[0]},Xn=(e,t,r,o,n,s,u,l)=>{let a=s;if(u){if(o===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return a=Gl(e,s,u,t,o,r*n,l),a=a.reshape([t,o,r,n]),e.compute(it(a,_s.perm),{inputs:[a],outputs:[-1]})[0]}else return s.dims.length===3&&(a=s.reshape([t,o,r,n])),e.compute(it(a,_s.perm),{inputs:[a],outputs:[-1]})[0]},Is=(e,t)=>{let r=Hl(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let o=e.inputs[1]&&e.inputs[2]&&e.inputs[1].dims.length===4&&e.inputs[2].dims.length===4,n=Xn(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,e.inputs[0],e.inputs[3],0);if(o)return Kr(e,n,e.inputs[1],e.inputs[2],e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t);let s=Xn(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,e.inputs[1],e.inputs[3],r.hiddenSize),u=Xn(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,e.inputs[2],e.inputs[3],2*r.hiddenSize);Kr(e,n,s,u,e.inputs[4],void 0,e.inputs[6],e.inputs[7],e.inputs[5],r,t)}});var Ll,Fl,jl,ql,Kl,Yl,Zl,Ql,Ts,Es=j(()=>{"use strict";Ne();$e();ve();Ll=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1)throw new Error("Input type must be float.");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].")}},Fl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n if (k < 0) {\n break;\n }\n if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n break;\n }\n offset += k * i32(${ce("uniforms.x_strides",n,t)});\n `;return`\n value = ${e.type.value}(uniforms.constant_value);\n for (var i = 0; i < 1; i++) {\n var offset = 0;\n var k = 0;\n ${o}\n value = x[offset];\n }\n `},jl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n if (k < 0) {\n k = -k;\n }\n {\n let _2n_1 = 2 * (i32(${ce("uniforms.x_shape",n,t)}) - 1);\n k = k % _2n_1;\n if(k >= i32(${ce("uniforms.x_shape",n,t)})) {\n k = _2n_1 - k;\n }\n }\n offset += k * i32(${ce("uniforms.x_strides",n,t)});\n `;return`\n var offset = 0;\n var k = 0;\n ${o}\n value = x[offset];\n `},ql=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n if (k < 0) {\n k = 0;\n }\n if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n k = i32(${ce("uniforms.x_shape",n,t)}) - 1;\n }\n offset += k * i32(${ce("uniforms.x_strides",n,t)});\n `;return`\n var offset = 0;\n var k = 0;\n ${o}\n value = x[offset];\n `},Kl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n if (k < 0) {\n k += i32(${ce("uniforms.x_shape",n,t)}]);\n }\n if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n k -= i32(${ce("uniforms.x_shape",n,t)});\n }\n offset += k * i32(${ce("uniforms.x_strides",n,t)});\n `;return`\n var offset = 0;\n var k = 0;\n ${o}\n value = x[offset];\n `},Yl=(e,t,r)=>{switch(r.mode){case 0:return Fl(e,t,r.pads.length);case 1:return jl(e,t,r.pads.length);case 2:return ql(e,t,r.pads.length);case 3:return Kl(e,t,r.pads.length);default:throw new Error("Invalid mode")}},Zl=(e,t)=>{let r=U.padShape(e[0].dims.slice(),t.pads),o=e[0].dims,s=[{type:"uint32",data:U.size(r)},{type:"uint32",data:t.pads}];if(t.mode===0){let a=Xe(e[0].dataType);s.push({type:a,data:t.value})}s.push(...L(e[0].dims),...L(r));let u=["rank"],l=a=>{let p=F("output",e[0].dataType,r.length),h=M("x",e[0].dataType,o.length),g=h.type.value,b=Yl(p,o.length,t),w=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&w.push({name:"constant_value",type:g}),`\n ${a.registerUniforms(w).declareVariables(h,p)}\n ${a.mainStart()}\n ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n let indices = ${p.offsetToIndices("global_idx")};\n\n var value = ${g}(0);\n ${b}\n output[global_idx] = value;\n }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(U.size(r)/64)},programUniforms:s}),getShaderSource:l}},Ql=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),o=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,n=e[0].dims.length,s=new Int32Array(2*n).fill(0);if(e.length>=4){let l=e[3].getBigInt64Array();for(let a=0;as[Number(a)]=Number(l));let u=[];return s.forEach(l=>u.push(l)),{mode:t.mode,value:o,pads:u}}else return t},Ts=(e,t)=>{Ll(e.inputs);let r=Ql(e.inputs,t);e.compute(Zl(e.inputs,r),{inputs:[0]})}});var nn,Os,Ps,ks,Rs,Xl,Jl,Bs,Ds,Ms,zs,Us,Vs,Ns,Ws,Hs,Gs,Ls,Fs,js=j(()=>{"use strict";Lt();$e();ve();nn=e=>{if(Gt.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Os=(e,t,r)=>{let o=t.format==="NHWC",n=e.dims.slice();o&&n.splice(1,0,n.pop());let s=Object.hasOwnProperty.call(t,"dilations"),u=t.kernelShape.slice(),l=t.strides.slice(),a=s?t.dilations.slice():[],p=t.pads.slice();Bt.adjustPoolAttributes(r,n,u,l,a,p);let h=Bt.computePoolOutputShape(r,n,l,a,u,p,t.autoPad),g=Object.assign({},t);s?Object.assign(g,{kernelShape:u,strides:l,pads:p,dilations:a,cacheKey:t.cacheKey}):Object.assign(g,{kernelShape:u,strides:l,pads:p,cacheKey:t.cacheKey});let b=h.slice();return b.push(b.splice(1,1)[0]),[g,o?b:h]},Ps=(e,t)=>{let r=t.format==="NHWC",o=U.size(e),n=U.size(t.kernelShape),s=[{type:"uint32",data:o},{type:"uint32",data:n}],u=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let l=t.kernelShape[t.kernelShape.length-1],a=t.strides[t.strides.length-1],p=t.pads[t.pads.length/2-1],h=t.pads[t.pads.length-1],g=!!(p+h);s.push({type:"uint32",data:l},{type:"uint32",data:a},{type:"uint32",data:p},{type:"uint32",data:h}),u.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let b=!1;if(t.kernelShape.length===2){let w=t.kernelShape[t.kernelShape.length-2],y=t.strides[t.strides.length-2],_=t.pads[t.pads.length/2-2],I=t.pads[t.pads.length-2];b=!!(_+I),s.push({type:"uint32",data:w},{type:"uint32",data:y},{type:"uint32",data:_},{type:"uint32",data:I}),u.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[s,u,!0,g,b]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let l=U.computeStrides(t.kernelShape);s.push({type:"uint32",data:l},{type:"uint32",data:t.pads},{type:"uint32",data:t.strides}),u.push({name:"kernelStrides",type:"u32",length:l.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let a=t.pads.reduce((p,h)=>p+h);return[s,u,!!a,!1,!1]}},ks=(e,t,r,o,n,s,u,l,a,p,h,g)=>{let b=n.format==="NHWC",w=t.type.value,y=F("output",t.type.tensor,o);if(n.kernelShape.length<=2){let _="",I="",$="",x=r-(b?2:1);if(h?_=`\n for (var i: u32 = 0u; i < uniforms.kw; i++) {\n xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n if (xIndices[${x}] < 0 || xIndices[${x}]\n >= uniforms.x_shape[${x}]) {\n pad++;\n continue;\n }\n let x_val = x[${t.indicesToOffset("xIndices")}];\n ${s}\n }`:_=`\n for (var i: u32 = 0u; i < uniforms.kw; i++) {\n xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n let x_val = x[${t.indicesToOffset("xIndices")}];\n ${s}\n }`,n.kernelShape.length===2){let A=r-(b?3:2);g?I=`\n for (var j: u32 = 0u; j < uniforms.kh; j++) {\n xIndices[${A}] = indices[${A}] * uniforms.sh - uniforms.phStart + j;\n if (xIndices[${A}] < 0 || xIndices[${A}] >= uniforms.x_shape[${A}]) {\n pad += i32(uniforms.kw);\n continue;\n }\n `:I=`\n for (var j: u32 = 0u; j < uniforms.kh; j++) {\n xIndices[${A}] = indices[${A}] * uniforms.sh - uniforms.phStart + j;\n `,$=`\n }\n `}return`\n ${e.registerUniforms(a).declareVariables(t,y)}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n let indices = ${y.offsetToIndices("global_idx")};\n var xIndices = ${y.offsetToIndices("global_idx")};\n\n var value = ${w}(${l});\n var pad = 0;\n ${I}\n ${_}\n ${$}\n ${u}\n\n output[global_idx] = value;\n }`}else{if(b)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let _=n.kernelShape.length,I=n.pads.length,$="";return p?$=`\n if (xIndices[j] >= uniforms.x_shape[j]) {\n pad++;\n isPad = true;\n break;\n }\n }\n if (!isPad) {\n let x_val = x[${t.indicesToOffset("xIndices")}];\n ${s}\n }`:$=`\n }\n let x_val = x[${t.indicesToOffset("xIndices")}];\n ${s}\n `,`\n ${e.registerUniforms(a).declareVariables(t,y)}\n\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n let indices = ${y.offsetToIndices("global_idx")};\n var xIndices = ${y.offsetToIndices("global_idx")};\n\n var offsets: array;\n\n var value = ${w}(${l});\n var pad = 0;\n var isPad = false;\n\n for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n var offset = i;\n for (var j = 0u; j < ${_-1}u; j++) {\n offsets[j] = offset / ${ce("uniforms.kernelStrides","j",_)};\n offset -= offsets[j] * ${ce("uniforms.kernelStrides","j",_)};\n }\n offsets[${_-1}] = offset;\n\n isPad = false;\n for (var j = ${r-_}u; j < ${r}u; j++) {\n xIndices[j] = indices[j] * ${ce("uniforms.strides",`j - ${r-_}u`,_)}\n + offsets[j - ${r-_}u] - ${ce("uniforms.pads","j - 2u",I)};\n ${$}\n }\n ${u}\n\n output[global_idx] = value;\n }`}},Rs=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Xl=e=>`${Rs(e)};${e.countIncludePad}`,Jl=e=>`${Rs(e)};${e.storageOrder};${e.dilations}`,Bs=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}),Ds=(e,t,r,o)=>{let[n,s]=Os(t,o,r),u=M("x",t.dataType,t.dims.length),l=u.type.value,a="value += x_val;",p="";n.countIncludePad?p+=`value /= ${l}(uniforms.kernelSize);`:p+=`value /= ${l}(i32(uniforms.kernelSize) - pad);`;let[h,g,b,w,y]=Ps(s,n);h.push(...L(t.dims),...L(s));let _=["rank"];return{name:e,shaderCache:{hint:`${o.cacheKey};${b};${w};${y}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(U.size(s)/64)},programUniforms:h}),getShaderSource:I=>ks(I,u,t.dims.length,s.length,n,a,p,0,g,b,w,y)}},Ms=e=>{let t=e.count_include_pad!==0,r=Bs(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let o={countIncludePad:t,...r,cacheKey:""};return{...o,cacheKey:Xl(o)}},zs=(e,t)=>{nn(e.inputs),e.compute(Ds("AveragePool",e.inputs[0],!1,t))},Us={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Vs=e=>{let t=e.format;return{format:t,...Us,cacheKey:t}},Ns=(e,t)=>{nn(e.inputs),e.compute(Ds("GlobalAveragePool",e.inputs[0],!0,t))},Ws=(e,t,r,o)=>{let[n,s]=Os(t,o,r),u=`\n value = max(x_val, value);\n `,l="",a=M("x",t.dataType,t.dims.length),p=["rank"],[h,g,b,w,y]=Ps(s,n);return h.push(...L(t.dims),...L(s)),{name:e,shaderCache:{hint:`${o.cacheKey};${b};${w};${y}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(U.size(s)/64)},programUniforms:h}),getShaderSource:_=>ks(_,a,t.dims.length,s.length,n,u,l,-1e5,g,b,w,y)}},Hs=(e,t)=>{nn(e.inputs),e.compute(Ws("MaxPool",e.inputs[0],!1,t))},Gs=e=>{let t=e.storage_order,r=e.dilations,o=Bs(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(o.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let n={storageOrder:t,dilations:r,...o,cacheKey:""};return{...n,cacheKey:Jl(n)}},Ls=e=>{let t=e.format;return{format:t,...Us,cacheKey:t}},Fs=(e,t)=>{nn(e.inputs),e.compute(Ws("GlobalMaxPool",e.inputs[0],!0,t))}});var tc,rc,qs,Ks=j(()=>{"use strict";Lt();Ne();ve();tc=(e,t,r)=>{let o=e===t,n=et&&r>0;if(o||n||s)throw new Error("Range these inputs\' contents are invalid.")},rc=(e,t,r,o)=>{let n=Math.abs(Math.ceil((t-e)/r)),s=[n],u=n,l=Xe(o),a=[{type:"uint32",data:u},{type:l,data:e},{type:l,data:r},...L(s)],p=h=>{let g=F("output",o,s.length),b=g.type.value,w=[{name:"outputSize",type:"u32"},{name:"start",type:b},{name:"delta",type:b}];return`\n ${h.registerUniforms(w).declareVariables(g)}\n ${h.mainStart()}\n ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n output[global_idx] = uniforms.start + ${b}(global_idx) * uniforms.delta;\n }`};return{name:"Range",shaderCache:{hint:`${o}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:s,dataType:o}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:a})}},qs=e=>{let t=0,r=0,o=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],o=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],o=e.inputs[2].getFloat32Array()[0]),Gt.webgpu.validateInputContent&&tc(t,r,o),e.compute(rc(t,r,o,e.inputs[0].dataType),{inputs:[]})}});var nc,oc,ac,ic,sc,uc,dc,lc,cc,pc,mc,Ys,fc,hc,gc,yc,bc,Zs,Qs,Xs=j(()=>{"use strict";$e();je();ve();nc=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n 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")}},oc=(e,t,r)=>{t.every(n=>n>=0&&n{throw new Error("Resize requires axes input values to be positive and less than rank")}));let o=new Array(r).fill(1);return t.forEach((n,s)=>o[n]=e[s]),o},ac=(e,t,r,o,n,s)=>{let[u,l,a]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],p=e[0].dims.length;if(u>0&&e.length>u&&e[u].dims.length>0)e[u].getFloat32Array().forEach(h=>s.push(h));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(l>0&&e.length>l&&e[l].dims.length>0){if(e[l].getFloat32Array().forEach(h=>o.push(h)),o.length!==0&&o.length!==p&&r>=18&&o.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");nc(o,t),t.axes.length>0&&oc(o,t.axes,p).forEach((h,g)=>o[g]=h)}if(a>0&&e.length>a&&(e[a].getBigInt64Array().forEach(h=>n.push(Number(h))),n.length!==p||r>=18&&n.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(o.length!==t.axes.length)throw new Error(\'Resize requires "scales" input size to be of axes rank when axes attributes is specified\');if(n.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 o<"u"&&typeof n<"u"&&o.length>0&&n.length>p)throw new Error("Resize requires only of scales or sizes to be specified")},ic=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;\n } else {\n return 0.0;\n }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n return 0.0;\n } else {\n // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n // offset-by-one error later in floor().\n let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n let fract =\n ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);\n return whole + fract;\n }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n return ${t}(roiStart) * ${t}(lengthOriginal - 1) +\n (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /\n ${t}(lengthResized - 1);\n } else {\n return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);\n }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);\n const adjustment = ${t}(lengthResized) / outputWidth;\n const center = ${t}(lengthOriginal) / 2;\n const offset = center * (1 - adjustment);\n return offset + ((${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`)}})()+"}",sc=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(t<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",uc=(e,t,r)=>{let o=new Array(r).fill(0).concat(new Array(r).fill(1)),n=e.length===0?o:e.slice();return t.length>0?(t.forEach((s,u)=>{o[s]=n[u],o[u+r]=n[t.length+u]}),o):n},dc=(e,t,r,o)=>{let n=[];if(r.length>0)if(o.length>0){if(e.forEach(s=>n.push(s)),Math.max(...o)>e.length)throw new Error("axes is out of bound");o.forEach((s,u)=>n[s]=r[u])}else r.forEach(s=>n.push(s));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");n=e.map((s,u)=>Math.round(s*t[u]))}return n},lc=(e,t,r)=>{let o=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(s=>t[s]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(s=>t[s]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let n=e.slice();return r.axes.length>0?(r.axes.forEach(s=>t[s]=o),r.axes.forEach(s=>n[s]=Math.round(e[s]*t[s]))):(t.fill(o,0,t.length),n.forEach((s,u)=>n[u]=Math.round(s*t[u]))),n},cc=(e,t,r,o,n)=>`\n fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {\n var original_indices: array<${e.type.value}, ${r.length}>;\n for (var i:u32 = 0; i < ${r.length}; i++) {\n var output_index = ${e.indicesGet("output_indices","i")};\n var scale = ${ce("uniforms.scales","i",o)};\n var roi_low = ${ce("uniforms.roi","i",n)};\n var roi_hi = ${ce("uniforms.roi",`i + ${t.length}`,n)};\n if (scale == 1.0) {\n original_indices[i] = ${e.type.value}(output_index);\n } else {\n var input_shape_i = ${ce("uniforms.input_shape","i",t.length)};\n var output_shape_i = ${ce("uniforms.output_shape","i",r.length)};\n original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n input_shape_i, roi_low, roi_hi);\n }\n }\n return original_indices;\n }`,pc=(e,t,r,o,n,s,u)=>`\n fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n var input_indices: ${e.type.indices};\n for (var i:u32 = 0; i < ${o.length}; i++) {\n var output_index = ${t.indicesGet("output_indices","i")};\n var input_index: u32;\n var scale = ${ce("uniforms.scales","i",n)};\n if (scale == 1.0) {\n input_index = output_index;\n } else {\n var roi_low = ${ce("uniforms.roi","i",s)};\n var roi_hi = ${ce("uniforms.roi",`i + ${r.length}`,s)};\n var input_shape_i = ${ce("uniforms.input_shape","i",r.length)};\n var output_shape_i = ${ce("uniforms.output_shape","i",o.length)};\n var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n input_shape_i, roi_low, roi_hi);\n if (!${u} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {\n if (original_idx < 0) {\n input_index = 0;\n } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {\n input_index = input_shape_i - 1;\n } else {\n input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n }\n } else {\n input_index = u32(original_idx);\n }\n }\n ${e.indicesSet("input_indices","i"," input_index")}\n }\n return input_indices;\n }`,mc=(e,t)=>`\n fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n for (var i:u32 = 0; i < ${t.length}; i++) {\n var input_index = ${e.indicesGet("input_indices","i")};\n if (input_index < 0 || input_index >= ${ce("uniforms.input_shape","i",t.length)}) {\n return false;\n }\n }\n return true;\n }`,Ys=(e,t,r,o)=>e.rank>o?`\n ${e.indicesSet("input_indices",t,"channel")};\n ${e.indicesSet("input_indices",r,"batch")};\n`:"",fc=(e,t,r,o,n)=>{let[u,l,a,p]=r.length===2?[-1,0,1,-1]:[0,2,3,1],h=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${h} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",l,`max(0, min(row, ${r[l]} - 1))`)};\n ${e.indicesSet("input_indices",a,`max(0, min(col, ${r[a]} - 1))`)};\n ${Ys(e,p,u,2)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${h} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var row:${h} = originalIndices[${l}];\n var col:${h} = originalIndices[${a}];\n ${o?`if (row < 0 || row > (${r[l]} - 1) || col < 0 || col > (${r[a]} - 1)) {\n return ${n};\n }`:""};\n row = max(0, min(row, ${r[l]} - 1));\n col = max(0, min(col, ${r[a]} - 1));\n var row1: u32 = u32(row);\n var col1: u32 = u32(col);\n var row2: u32 = u32(row + 1);\n var col2: u32 = u32(col + 1);\n var channel: u32 = ${r.length>2?`u32(originalIndices[${p}])`:"0"};\n var batch: u32 = ${r.length>2?`u32(originalIndices[${u}])`:"0"};\n var x11: ${h} = getInputValue(batch, channel, row1, col1);\n var x12: ${h} = getInputValue(batch, channel, row1, col2);\n var x21: ${h} = getInputValue(batch, channel, row2, col1);\n var x22: ${h} = getInputValue(batch, channel, row2, col2);\n var dx1: ${h} = abs(row - ${h}(row1));\n var dx2: ${h} = abs(${h}(row2) - row);\n var dy1: ${h} = abs(col - ${h}(col1));\n var dy2: ${h} = abs(${h}(col2) - col);\n if (row1 == row2) {\n dx1 = 0.5;\n dx2 = 0.5;\n }\n if (col1 == col2) {\n dy1 = 0.5;\n dy2 = 0.5;\n }\n return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n }`},hc=(e,t,r,o,n,s,u,l,a,p)=>{let h=r.length===2,g=!0,[b,w]=h?[0,1]:g?[2,3]:[1,2],y=e.type.value,_=I=>{let $=I===b?"row":"col";return`\n fn ${$}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${y} {\n var output_index = ${t.indicesGet("output_indices",I)};\n var originalIdx: ${y} = getOriginalCoordinateFromResizedCoordinate(output_index, ${n[I]},\n ${o[I]}, ${r[I]}, ${s[I]}, ${s[I]} + ${r.length});\n var fractOriginalIdx: ${y} = originalIdx - floor(originalIdx);\n var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n if (${l} && (originalIdx < 0 || originalIdx > (${r[I]} - 1))) {\n return ${a};\n }\n var data: array<${y}, 4> = array<${y}, 4>(0.0, 0.0, 0.0, 0.0);\n for (var i: i32 = -1; i < 3; i++) {\n var ${$}: ${y} = originalIdx + ${y}(i);\n if (${$} < 0 || ${$} >= ${r[I]}) {\n ${(()=>p?`coefs[i + 1] = 0.0;\n continue;`:l?`return ${a};`:`${$} = max(0, min(${$}, ${r[I]} - 1));`)()};\n }\n var input_indices_copy: ${e.type.indices} = input_indices;\n ${e.indicesSet("input_indices_copy",I,`u32(${$})`)};\n data[i + 1] = ${I===b?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n }\n return cubicInterpolation1D(data, coefs);\n }`};return`\n ${_(b)};\n ${_(w)};\n fn getCubicInterpolationCoefs(s: ${y}) -> array<${y}, 4> {\n var absS = abs(s);\n var coeffs: array<${y}, 4> = array<${y}, 4>(0.0, 0.0, 0.0, 0.0);\n var oneMinusAbsS: ${y} = 1.0 - absS;\n var twoMinusAbsS: ${y} = 2.0 - absS;\n var onePlusAbsS: ${y} = 1.0 + absS;\n coeffs[0] = ((${u} * onePlusAbsS - 5 * ${u}) * onePlusAbsS + 8 * ${u}) * onePlusAbsS - 4 * ${u};\n coeffs[1] = ((${u} + 2) * absS - (${u} + 3)) * absS * absS + 1;\n coeffs[2] = ((${u} + 2) * oneMinusAbsS - (${u} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n coeffs[3] = ((${u} * twoMinusAbsS - 5 * ${u}) * twoMinusAbsS + 8 * ${u}) * twoMinusAbsS - 4 * ${u};\n return coeffs;\n }\n\n fn cubicInterpolation1D(x: array<${y}, 4>, coefs: array<${y}, 4>) -> ${y} {\n var coefsSum: ${y} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n }\n\n fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${y} {\n var input_indices: ${e.type.indices} = output_indices;\n return colCubicInterpolation(input_indices, output_indices);\n }\n `},gc=(e,t,r,o,n)=>{let[u,l,a,p,h]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],g=e.type.value;return`\n fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${g} {\n var input_indices: ${e.type.indices};\n ${e.indicesSet("input_indices",l,`max(0, min(depth, ${r[l]} - 1))`)};\n ${e.indicesSet("input_indices",a,`max(0, min(height, ${r[a]} - 1))`)};\n ${e.indicesSet("input_indices",p,`max(0, min(width, ${r[p]} - 1))`)};\n ${Ys(e,h,u,3)}\n return ${e.getByIndices("input_indices")};\n }\n\n fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${g} {\n var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n var depth:${g} = originalIndices[${l}];\n var height:${g} = originalIndices[${a}];\n var width:${g} = originalIndices[${p}];\n ${o?`if (depth < 0 || depth > (${r[l]} - 1) || height < 0 || height > (${r[a]} - 1) || width < 0 || (width > ${r[p]} - 1)) {\n return ${n};\n }`:""};\n\n depth = max(0, min(depth, ${r[l]} - 1));\n height = max(0, min(height, ${r[a]} - 1));\n width = max(0, min(width, ${r[p]} - 1));\n var depth1: u32 = u32(depth);\n var height1: u32 = u32(height);\n var width1: u32 = u32(width);\n var depth2: u32 = u32(depth + 1);\n var height2: u32 = u32(height + 1);\n var width2: u32 = u32(width + 1);\n var channel: u32 = ${r.length>3?`u32(originalIndices[${h}])`:"0"};\n var batch: u32 = ${r.length>3?`u32(originalIndices[${u}])`:"0"};\n\n var x111: ${g} = getInputValue(batch, channel, depth1, height1, width1);\n var x112: ${g} = getInputValue(batch, channel, depth1, height1, width2);\n var x121: ${g} = getInputValue(batch, channel, depth1, height2, width1);\n var x122: ${g} = getInputValue(batch, channel, depth1, height2, width2);\n var x211: ${g} = getInputValue(batch, channel, depth2, height1, width1);\n var x212: ${g} = getInputValue(batch, channel, depth2, height1, width2);\n var x221: ${g} = getInputValue(batch, channel, depth2, height2, width1);\n var x222: ${g} = getInputValue(batch, channel, depth2, height2, width2);\n var dx1: ${g} = abs(depth - ${g}(depth1));\n var dx2: ${g} = abs(${g}(depth2) - depth);\n var dy1: ${g} = abs(height - ${g}(height1));\n var dy2: ${g} = abs(${g}(height2) - height);\n var dz1: ${g} = abs(width - ${g}(width1));\n var dz2: ${g} = abs(${g}(width2) - width);\n if (depth1 == depth2) {\n dx1 = 0.5;\n dx2 = 0.5;\n }\n if (height1 == height2) {\n dy1 = 0.5;\n dy2 = 0.5;\n }\n if (width1 == width2) {\n dz1 = 0.5;\n dz2 = 0.5;\n }\n return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n }`},yc=(e,t,r,o,n,s)=>{let u=e.dims,l=uc(s,t.axes,u.length),a=dc(u,o,n,t.axes),p=o.slice();o.length===0&&(p=u.map((x,E)=>x===0?1:a[E]/x),t.keepAspectRatioPolicy!=="stretch"&&(a=lc(u,p,t)));let h=F("output",e.dataType,a.length),g=M("input",e.dataType,u.length),b=U.size(a),w=u.length===a.length&&u.every((x,E)=>x===a[E]),y=t.coordinateTransformMode==="tf_crop_and_resize",_=t.extrapolationValue,I=g.type.value,$=x=>`\n ${w?"":`\n ${ic(t.coordinateTransformMode,I)};\n ${(()=>{switch(t.mode){case"nearest":return`\n ${mc(g,u)};\n ${sc(t.nearestMode,r,I)};\n ${pc(g,h,u,a,p.length,l.length,y)};\n `;case"linear":return`\n ${cc(h,u,a,p.length,l.length)};\n ${(()=>{if(u.length===2||u.length===4)return`${fc(g,h,u,y,_)}`;if(u.length===3||u.length===5)return`${gc(g,h,u,y,_)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n `;case"cubic":return`\n ${(()=>{if(u.length===2||u.length===4)return`${hc(g,h,u,a,p,l,t.cubicCoeffA,y,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n `;default:throw Error("Invalid resize mode")}})()};\n `}\n ${x.registerUniform("output_size","u32").registerUniform("scales","f32",p.length).registerUniform("roi","f32",l.length).declareVariables(g,h)}\n ${x.mainStart()}\n ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n ${w?"output[global_idx] = input[global_idx];":`\n let output_indices = ${h.offsetToIndices("global_idx")};\n var input_indices: ${g.type.indices};\n ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n if (checkInputIndices(input_indices)) {\n output[global_idx] = ${g.getByIndices("input_indices")};\n } else {\n output[global_idx] = ${t.extrapolationValue};\n }`;case"linear":return`output[global_idx] = ${u.length===2||u.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};\n`}\n }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${p.length>0?p:""}|${n.length>0?n:""}|${l.length>0?l:""}|${w}|${u}`,inputDependencies:["rank"]},getShaderSource:$,getRunData:()=>({outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(b/64)},programUniforms:[{type:"uint32",data:b},{type:"float32",data:p},{type:"float32",data:l},...L(u),...L(a)]})}},bc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Zs=(e,t)=>{let r=[],o=[],n=[],s=bc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");ac(e.inputs,t,s,r,o,n),e.compute(yc(e.inputs[0],t,s,r,o,n),{inputs:[0]})},Qs=e=>{let t=e.antialias,r=e.axes,o=e.coordinateTransformMode,n=e.cubicCoeffA,s=e.excludeOutside!==0,u=e.extrapolationValue,l=e.keepAspectRatioPolicy,a=e.mode,p=e.nearestMode===""?"simple":e.nearestMode;return ge({antialias:t,axes:r,coordinateTransformMode:o,cubicCoeffA:n,excludeOutside:s,extrapolationValue:u,keepAspectRatioPolicy:l,mode:a,nearestMode:p})}});var wc,vc,Js,eu,tu=j(()=>{"use strict";Ne();$e();je();ve();wc=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],o=e[2];if(t.dataType!==r.dataType||t.dataType!==o.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let n=t.dims[t.dims.length-1],s=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==n)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(o.dims.length!==1)throw new Error("Gamma must be 1D");if(o.dims[o.dims.length-1]!==n)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let u=e[3];if(u.dims.length!==1)throw new Error("Beta must be 1D");if(u.dims[u.dims.length-1]!==n)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let u=e[4];if(u.dims.length!==1)throw new Error("Bias must be 1D");if(u.dims[u.dims.length-1]!==n)throw new Error("Bias must have the same hidden size as input")}},vc=(e,t,r,o)=>{let n=e[0].dims,s=U.size(n),u=n,l=s,a=n.slice(-1)[0],p=o?n.slice(0,-1).concat(1):[],h=e.length>3,g=e.length>4,b=o&&r>1,w=o&&r>2,y=r>3,_=Fe(a),I=[M("x",e[0].dataType,e[0].dims,_),M("skip",e[1].dataType,e[1].dims,_),M("gamma",e[2].dataType,e[2].dims,_)];h&&I.push(M("beta",e[3].dataType,e[3].dims,_)),g&&I.push(M("bias",e[4].dataType,e[4].dims,_)),I.push(F("output",e[0].dataType,u,_)),b&&I.push(F("meanOutput",1,p)),w&&I.push(F("invStdOutput",1,p)),y&&I.push(F("inputSkipBiasSum",e[0].dataType,u,_));let $=Le(e[0].dataType),x=A=>`\n const hiddenSize: f32 = ${a};\n const hiddenSizeVectorized: u32 = ${a/_};\n const epsilon: f32 = ${t.epsilon};\n\n ${A.declareVariables(...I)}\n\n ${A.mainStart()}\n ${A.guardAgainstOutOfBoundsWorkgroupSizes(l/a)}\n let offset = global_idx * hiddenSizeVectorized;\n var sum = ${Ze("f32",_)};\n var squareSum = ${Ze("f32",_)};\n for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {\n let skipValue = skip[offset + i];\n let biasValue = ${g?"bias[i]":"0.0"};\n let inputValue = x[offset + i];\n let value = inputValue + skipValue + biasValue;\n ${y?"inputSkipBiasSum[offset + i] = value;":""}\n output[offset + i] = value;\n let f32Value = ${at($,_,"value")};\n sum += f32Value;\n squareSum += f32Value * f32Value;\n }\n let mean = ${Je("sum",_)} / hiddenSize;\n let invStdDev = inverseSqrt(${Je("squareSum",_)} / hiddenSize - mean * mean + epsilon);\n ${b?"meanOutput[global_idx] = mean;":""}\n ${w?"invStdOutput[global_idx] = invStdDev;":""}\n for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {\n output[offset + i] = (output[offset + i] - ${$}(mean)) * ${$}(invStdDev) * gamma[i]\n + ${h?"beta[i]":"0.0"};\n }\n }`,E=[{dims:u,dataType:e[0].dataType}];return r>1&&E.push({dims:p,dataType:1}),r>2&&E.push({dims:p,dataType:1}),r>3&&E.push({dims:n,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:t.cacheKey},getShaderSource:x,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/a/64)}})}},Js=(e,t)=>{wc(e.inputs);let o=[0];e.outputCount>1&&o.push(-3),e.outputCount>2&&o.push(-3),e.outputCount>3&&o.push(3),e.compute(vc(e.inputs,t,e.outputCount,!1),{outputs:o})},eu=e=>{let t=e.epsilon;return ge({epsilon:t})}});var $c,on,Sc,ru,xc,_c,nu,ou,au=j(()=>{"use strict";Ne();$e();je();ve();$c=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,o)=>{if(e[o+1].dataType!==6&&e[o+1].dataType!==7)throw new Error(`Input ${o} must be an array of int32 or int64`)})},on=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(o=>r.push(Number(o)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(o=>r.push(Number(o)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},Sc=(e,t)=>{if(e.length>1){let r=on(e,1),o=on(e,2),n=on(e,3);return n.length===0&&(n=[...Array(e[0].dims.length).keys()]),ge({starts:r,ends:o,axes:n})}else return t},ru=(e,t,r,o,n)=>{let s=e;return e<0&&(s+=r[o[t]]),n[t]<0?Math.max(0,Math.min(s,r[o[t]]-1)):Math.max(0,Math.min(s,r[o[t]]))},xc=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n var input_indices: ${e.type.indices};\n var carry = 0u;\n for (var i = ${r.length}; i >= 0; i--) {\n let input_shape_i = ${ce("uniforms.input_shape","i",r.length)};\n let steps_i = ${ce("uniforms.steps","i",r.length)};\n let signs_i = ${ce("uniforms.signs","i",r.length)};\n let starts_i = ${ce("uniforms.starts","i",r.length)};\n var output_index = ${t.indicesGet("output_indices","i")};\n var input_index = output_index * steps_i + starts_i + carry;\n carry = input_index / input_shape_i;\n input_index = input_index % input_shape_i;\n if (signs_i < 0) {\n input_index = input_shape_i - input_index - 1u + starts_i;\n }\n ${e.indicesSet("input_indices","i","input_index")};\n }\n return input_indices;\n }`,_c=(e,t)=>{let r=e[0].dims,o=U.size(r),n=t.axes.length>0?U.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],s=on(e,4);s.forEach($=>$!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(n.length).fill(1));let u=t.starts.map(($,x)=>ru($,x,r,n,s)),l=t.ends.map(($,x)=>ru($,x,r,n,s));if(n.length!==u.length||n.length!==l.length)throw new Error("start, ends and axes should have the same number of elements");if(n.length!==r.length)for(let $=0;$Math.sign($));s.forEach(($,x,E)=>{if($<0){let A=(l[x]-u[x])/$,z=u[x],R=z+A*s[x];u[x]=R,l[x]=z,E[x]=-$}});let p=r.slice(0);n.forEach(($,x)=>{p[$]=Math.ceil((l[$]-u[$])/s[$])});let h={dims:p,dataType:e[0].dataType},g=F("output",e[0].dataType,p.length),b=M("input",e[0].dataType,e[0].dims.length),w=U.size(p),y=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:u.length},{name:"signs",type:"i32",length:a.length},{name:"steps",type:"u32",length:s.length}],_=[{type:"uint32",data:w},{type:"uint32",data:u},{type:"int32",data:a},{type:"uint32",data:s},...L(e[0].dims),...L(p)],I=$=>`\n ${$.registerUniforms(y).declareVariables(b,g)}\n ${xc(b,g,r)}\n ${$.mainStart()}\n ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n let output_indices = ${g.offsetToIndices("global_idx")};\n let input_indices = calculateInputIndices(output_indices);\n ${g.setByOffset("global_idx",b.getByIndices("input_indices"))}\n }`;return{name:"Slice",shaderCache:{hint:`${a.length}_${u.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[h],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:_})}},nu=(e,t)=>{$c(e.inputs,t);let r=Sc(e.inputs,t);e.compute(_c(e.inputs,r),{inputs:[0]})},ou=e=>{let t=e.starts,r=e.ends,o=e.axes;return ge({starts:t,ends:r,axes:o})}});var Cc,Ic,iu,su,uu=j(()=>{"use strict";$e();je();ve();Cc=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Ic=(e,t)=>{let r=e.dims,o=U.size(r),n=64,s=t.axis;if(s<0&&(s=r.length+s),s$===4?`max(max(${I}.x, ${I}.y), max(${I}.z, ${I}.w))`:$===2?`max(${I}.x, ${I}.y)`:$===3?`max(max(${I}.x, ${I}.y), ${I}.z)`:I,g=M("x",e.dataType,e.dims,a),b=F("result",e.dataType,e.dims,a),w=g.type.value,y=Le(e.dataType)==="f32"?`var threadMax = ${w}(-3.402823e+38f);`:`var threadMax = ${w}(-65504.0h);`,_=I=>`\n var rowMaxShared : ${w};\n var rowSumShared : ${w};\n var threadShared : array<${w}, ${n}>;\n\n fn getValue(row: i32, col: i32, row_stride: i32) -> ${w} {\n let index = row * row_stride + col;\n return x[index];\n }\n\n fn setValue(row: i32, col: i32, row_stride: i32, value: ${w}) {\n let index = row * row_stride + col;\n result[index] = value;\n }\n ${I.registerUniform("packedCols","i32").declareVariables(g,b)}\n ${I.mainStart()}\n let gindex = i32(global_idx);\n let lindex = i32(local_idx);\n const wg = ${n};\n let row = gindex / wg;\n let cols = uniforms.packedCols;\n let row_stride : i32 = uniforms.packedCols;\n\n // find the rows max\n ${y}\n for (var col = lindex; col < cols; col += wg) {\n let value = getValue(row, col, row_stride);\n threadMax = max(threadMax, value);\n }\n if (lindex < cols) {\n threadShared[lindex] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, wg);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (lindex < currSize) {\n threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);\n }\n workgroupBarrier();\n }\n if (lindex == 0) {\n rowMaxShared = ${w}(${h("threadShared[0]",a)});\n }\n workgroupBarrier();\n\n // find the rows sum\n var threadSum = ${w}(0.0);\n for (var col = lindex; col < cols; col += wg) {\n let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);\n threadSum += subExp;\n }\n threadShared[lindex] = threadSum;\n workgroupBarrier();\n\n for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) {\n if (lindex < currSize) {\n threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];\n }\n workgroupBarrier();\n }\n if (lindex == 0) {\n rowSumShared = ${w}(${Je("threadShared[0]",a)});\n }\n workgroupBarrier();\n\n // calculate final value for each element in the row\n for (var col = lindex; col < cols; col += wg) {\n let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;\n setValue(row, col, row_stride, value);\n }\n }`;return{name:"Softmax",shaderCache:{hint:`${a}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:l},programUniforms:[{type:"uint32",data:p}]}),getShaderSource:_}},iu=(e,t)=>{Cc(e.inputs),e.compute(Ic(e.inputs[0],t))},su=e=>ge({axis:e.axis})});var Ac,Tc,Ec,Oc,Pc,du,lu,cu=j(()=>{"use strict";$e();je();ve();Ac=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Tc=(e,t)=>{let r=[],o=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),o=r.length),ge({numOutputs:o,axis:t.axis,splitSizes:r})},Ec=e=>`\nfn calculateOutputIndex(index: u32) -> u32 {\n for (var i: u32 = 0u; i < ${e}u; i += 1u ) {\n if (index < ${ce("uniforms.size_in_split_axis","i",e)}) {\n return i;\n }\n }\n return ${e}u;\n}`,Oc=e=>{let t=e.length,r=[];for(let o=0;o{let r=e[0].dims,o=U.size(r),n=e[0].dataType,s=U.normalizeAxis(t.axis,r.length),u=new Array(t.numOutputs),l=M("input",n,r),a=new Array(t.numOutputs),p=[],h=[],g=0,b=[{type:"uint32",data:o}];for(let y=0;yb.push(...L(y)));let w=y=>`\n ${y.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",a.length).declareVariables(l,...u)}\n ${Ec(a.length)}\n ${Oc(u)}\n\n ${y.mainStart()}\n ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}\n\n var indices = ${l.offsetToIndices("global_idx")};\n var index = ${l.indicesGet("indices",s)};\n let output_number = calculateOutputIndex(index);\n if (output_number != 0) {\n index -= ${ce("uniforms.size_in_split_axis","output_number - 1u",a.length)};\n ${l.indicesSet("indices",s,"index")};\n }\n writeBufferData(output_number, indices, global_idx);\n }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:w,getRunData:()=>({outputs:p,dispatchGroup:{x:Math.ceil(o/64)},programUniforms:b})}},du=(e,t)=>{Ac(e.inputs);let r=e.inputs.length===1?t:Tc(e.inputs,t);e.compute(Pc(e.inputs,r),{inputs:[0]})},lu=e=>{let t=e.axis,r=e.splitSizes,o=e.numOutputs<0?r.length:e.numOutputs;if(o!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return ge({axis:t,numOutputs:o,splitSizes:r})}});var pu,kc,Rc,Bc,mu,fu=j(()=>{"use strict";Ne();$e();ve();pu=e=>Array.from(e.getBigInt64Array(),Number),kc=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, 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(pu(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")},Rc=(e,t)=>{let r=[];for(let o=0;o{let t=e[0].dims,r=pu(e[1]),o=Rc(t,r),n=U.size(o),s=e[0].dataType,u=M("input",s,t.length),l=F("output",s,o.length),a=p=>`\n const inputShape = ${u.indices(...t)};\n ${p.registerUniform("output_size","u32").declareVariables(u,l)}\n ${p.mainStart()}\n ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n let output_indices = ${l.offsetToIndices("global_idx")};\n var input_indices: ${u.type.indices};\n for (var i = 0; i < ${t.length}; i++) {\n let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")};\n let input_dim_value = ${l.indicesGet("output_indices","i")} % input_dim_i;\n\n ${u.indicesSet("input_indices","i","input_dim_value")}\n }\n ${l.setByOffset("global_idx",u.getByIndices("input_indices"))}\n }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:[{type:"uint32",data:n},...L(e[0].dims),...L(o)]}),getShaderSource:a}},mu=e=>{kc(e.inputs),e.compute(Bc(e.inputs),{inputs:[0]})}});var Dc,Mc,hu,gu=j(()=>{"use strict";Ne();$e();ve();Dc=(e,t,r,o,n)=>{let s=F("output_data",n,r.length,4),u=M("a_data",t[1].dataType,t[1].dims.length,4),l=M("b_data",t[2].dataType,t[2].dims.length,4),a=M("c_data",t[0].dataType,t[0].dims.length,4),p,h=(g,b,w)=>`select(${b}, ${g}, ${w})`;if(!o)p=s.setByOffset("global_idx",h(u.getByOffset("global_idx"),l.getByOffset("global_idx"),a.getByOffset("global_idx")));else{let g=(b,w,y="")=>{let _=`a_data[index_a${w}][component_a${w}]`,I=`b_data[index_b${w}][component_b${w}]`,$=`bool(c_data[index_c${w}] & ${4278190080>>>(3-w)*8}u)`;return`\n let output_indices${w} = ${s.offsetToIndices(`global_idx * 4u + ${w}u`)};\n let offset_a${w} = ${u.broadcastedIndicesToOffset(`output_indices${w}`,s)};\n let offset_b${w} = ${l.broadcastedIndicesToOffset(`output_indices${w}`,s)};\n let offset_c${w} = ${a.broadcastedIndicesToOffset(`output_indices${w}`,s)};\n let index_a${w} = offset_a${w} / 4u;\n let index_b${w} = offset_b${w} / 4u;\n let index_c${w} = offset_c${w} / 4u;\n let component_a${w} = offset_a${w} % 4u;\n let component_b${w} = offset_b${w} % 4u;\n ${b}[${w}] = ${y}(${h(_,I,$)});\n `};n===9?p=`\n var data = vec4(0);\n ${g("data",0,"u32")}\n ${g("data",1,"u32")}\n ${g("data",2,"u32")}\n ${g("data",3,"u32")}\n output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:p=`\n ${g("output_data[global_idx]",0)}\n ${g("output_data[global_idx]",1)}\n ${g("output_data[global_idx]",2)}\n ${g("output_data[global_idx]",3)}\n `}return`\n ${e.registerUniform("vec_size","u32").declareVariables(a,u,l,s)}\n ${e.mainStart()}\n ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n ${p}\n }`},Mc=e=>{let t=e[1].dims,r=e[2].dims,o=e[0].dims,n=e[1].dataType,s=!(U.areEqual(t,r)&&U.areEqual(r,o)),u=t,l=U.size(t);if(s){let p=dt.calcShape(dt.calcShape(t,r,!1),o,!1);if(!p)throw new Error("Can\'t perform where op on the given tensors");u=p,l=U.size(u)}let a=Math.ceil(l/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:p=>Dc(p,e,u,s,n),getRunData:()=>({outputs:[{dims:u,dataType:n}],dispatchGroup:{x:Math.ceil(l/64/4)},programUniforms:[{type:"uint32",data:a},...L(o),...L(t),...L(r),...L(u)]})}},hu=e=>{e.compute(Mc(e.inputs))}});var yu,bu=j(()=>{"use strict";Wa();Un();La();ja();Ci();Mi();Vi();Gn();Ji();rs();ss();ls();ms();gs();ws();$s();xs();Fn();As();Es();js();Ks();jr();Xs();tu();au();uu();cu();fu();jt();Vn();gu();yu=new 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an,wu=j(()=>{"use strict";Lt();Ct();ve();an=class{constructor(t){this.backend=t;this.repo=new Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,r){this.repo.set(t,r)}run(t,r,o,n,s){kt(t.programInfo.name);let u=this.backend.device,l=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2),l.setPipeline(t.computePipeline);let a=[];for(let h of r)a.push({binding:a.length,resource:{buffer:h.buffer}});for(let h of o)a.push({binding:a.length,resource:{buffer:h.buffer}});s&&a.push({binding:a.length,resource:s});let p=u.createBindGroup({layout:t.computePipeline.getBindGroupLayout(0),entries:a,label:t.programInfo.name});l.setBindGroup(0,p),l.dispatchWorkgroups(...n),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),Rt(t.programInfo.name)}dispose(){}build(t,r){kt(t.name);let o=this.backend.device,n=[];o.features.has("shader-f16")&&n.push("enable f16;");let s=ma(r),u=t.getShaderSource(s),l=`${n.join(`\n`)}\n${s.additionalImplementations}\n${u}`,a=o.createShaderModule({code:l,label:t.name});Be("verbose",()=>`[WebGPU] ${t.name} shader code: ${l}`);let p=o.createComputePipeline({compute:{module:a,entryPoint:"main"},layout:"auto",label:t.name});return 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(should not happen)");let t=this.kernelCustomData.get(this.currentKernelId);return t||(t={},this.kernelCustomData.set(this.currentKernelId,t)),t}async initialize(t,r){this.env=t;let o=[],n={requiredLimits:{maxComputeWorkgroupStorageSize:r.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.limits.maxStorageBufferBindingSize,maxBufferSize:r.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:r.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:r.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:r.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:r.limits.maxComputeWorkgroupSizeZ},requiredFeatures:o};r.features.has("chromium-experimental-timestamp-query-inside-passes")?o.push("chromium-experimental-timestamp-query-inside-passes"):r.features.has("timestamp-query")&&o.push("timestamp-query"),r.features.has("shader-f16")&&o.push("shader-f16"),this.device=await r.requestDevice(n),this.gpuDataManager=ca(this),this.programManager=new an(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,ia(t.logLevel,!!t.debug),this.device.onuncapturederror=s=>{s.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${s.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder(),this.setQueryType(),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE}))),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let t={};this.queryType==="at-passes"&&(t.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=this.getCommandEncoder().beginComputePass(t)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;kt(),this.endComputePass();let t;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),t=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(t,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&t.mapAsync(GPUMapMode.READ).then(()=>{let r=new BigUint64Array(t.getMappedRange()),o=this.pendingQueries.get(t);for(let n=0;n"u"&&(this.queryTimeBase=w);let _=Number(w-this.queryTimeBase),I=Number(y-this.queryTimeBase);if(!Number.isSafeInteger(_)||!Number.isSafeInteger(I))throw new RangeError("incorrect timestamp 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. 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All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - *//** - * @license - * Copyright 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. - * ============================================================================= - */const f0=Object.freeze(Object.defineProperty({__proto__:null,get InferenceSession(){return Hs},get TRACE(){return Dr},get TRACE_FUNC_BEGIN(){return Ut},get TRACE_FUNC_END(){return Wt},get Tensor(){return gt},get TrainingSession(){return qs},default:h0,get env(){return Ne},get registerBackend(){return 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this.evaluateSliceExpression(n,t.property,e);r=this.evaluate(t.property,e)}else r=new Re(t.property.value);let a;if(n instanceof Kt){if(!(r instanceof Re))throw new Error(`Cannot access property with non-string: got ${r.type}`);a=n.value.get(r.value)??n.builtins.get(r.value)}else if(n instanceof ut||n instanceof Re)if(r instanceof Ge)a=n.value.at(r.value),n instanceof Re&&(a=new Re(n.value.at(r.value)));else if(r instanceof Re)a=n.builtins.get(r.value);else throw new Error(`Cannot access property with non-string/non-number: got ${r.type}`);else{if(!(r instanceof Re))throw new Error(`Cannot access property with non-string: got ${r.type}`);a=n.builtins.get(r.value)}return a instanceof an?a:new Ft}evaluateSet(t,e){const n=this.evaluate(t.value,e);if(t.assignee.type==="Identifier"){const r=t.assignee.value;e.setVariable(r,n)}else if(t.assignee.type==="MemberExpression"){const r=t.assignee,a=this.evaluate(r.object,e);if(!(a instanceof Kt))throw new Error("Cannot assign to member of non-object");if(r.property.type!=="Identifier")throw new Error("Cannot assign to member with non-identifier property");a.value.set(r.property.value,n)}else throw new Error(`Invalid LHS inside assignment expression: ${JSON.stringify(t.assignee)}`);return new Br}evaluateIf(t,e){const n=this.evaluate(t.test,e);return this.evaluateBlock(n.__bool__().value?t.body:t.alternate,e)}evaluateFor(t,e){const n=new Rs(e),r=this.evaluate(t.iterable,n);if(!(r instanceof ut))throw new Error(`Expected iterable type in for loop: got ${r.type}`);let a="";for(let i=0;i0?r.value[i-1]:new Ft],["nextitem",ithis.evaluate(n,e)));case"ObjectLiteral":{const n=new Map;for(const[r,a]of t.value){const i=this.evaluate(r,e);if(!(i instanceof Re))throw new Error(`Object keys must be strings: got ${i.type}`);n.set(i.value,this.evaluate(a,e))}return new Kt(n)}case"Identifier":return this.evaluateIdentifier(t,e);case"CallExpression":return this.evaluateCallExpression(t,e);case"MemberExpression":return this.evaluateMemberExpression(t,e);case"UnaryExpression":return this.evaluateUnaryExpression(t,e);case"BinaryExpression":return this.evaluateBinaryExpression(t,e);case"FilterExpression":return this.evaluateFilterExpression(t,e);case"TestExpression":return this.evaluateTestExpression(t,e);default:throw new SyntaxError(`Unknown node type: ${t.type}`)}}};function Wa(t){switch(typeof t){case"number":return new Ge(t);case"string":return new Re(t);case"boolean":return new Qe(t);case"object":return t===null?new Br:Array.isArray(t)?new ut(t.map(Wa)):new Kt(new Map(Object.entries(t).map(([e,n])=>[e,Wa(n)])));case"function":return new gn((e,n)=>{const r=t(...e.map(a=>a.value))??null;return Wa(r)});default:throw new Error(`Cannot convert to runtime value: ${t}`)}}var sw=class{parsed;constructor(t){const e=V0(t,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=rw(e)}render(t){const e=new Rs;e.set("false",!1),e.set("true",!0),e.set("raise_exception",a=>{throw new Error(a)}),e.set("range",aw);for(const[a,i]of Object.entries(t))e.set(a,i);return new iw(e).run(this.parsed).value}};async function Ff(t,e){const n=await Promise.all([Yn(t,"tokenizer.json",!0,e),Yn(t,"tokenizer_config.json",!0,e)]);return e.legacy!==null&&(n[1].legacy=e.legacy),n}function ow(t,e){const n=[];let r=0;for(const a of t.matchAll(e)){const i=a[0];r0&&n.push(i),r=a.index+i.length}return rthis.tokens_to_ids.get(n)??this.unk_token_id)}convert_ids_to_tokens(e){return e.map(n=>this.vocab[n]??this.unk_token)}}class hw extends Kr{constructor(e){super(e),this.tokens_to_ids=lo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.max_input_chars_per_word=e.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[n,r]of this.tokens_to_ids)this.vocab[r]=n}encode(e){const n=[];for(const r of e){const a=[...r];if(a.length>this.max_input_chars_per_word){n.push(this.unk_token);continue}let i=!1,s=0;const o=[];for(;s0&&(c=this.config.continuing_subword_prefix+c),this.tokens_to_ids.has(c)){d=c;break}--l}if(d===null){i=!0;break}o.push(d),s=l}i?n.push(this.unk_token):n.push(...o)}return n}}class fw extends Kr{constructor(e,n){super(e);const r=e.vocab.length;this.vocab=new Array(r),this.scores=new Array(r);for(let a=0;a[a,i])),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=gc(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new N0,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(e){const n=e.sentence,r=n.length;let a=0;for(;a{const t=[...Array.from({length:94},(a,i)=>i+33),...Array.from({length:12},(a,i)=>i+161),...Array.from({length:82},(a,i)=>i+174)],e=t.slice();let n=0;for(let a=0;a<256;++a)t.includes(a)||(t.push(a),e.push(256+n),n+=1);const r=e.map(a=>String.fromCharCode(a));return Object.fromEntries(t.map((a,i)=>[a,r[i]]))})(),mw=Rg(Wf);class gw extends Kr{constructor(e){super(e),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=lo(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[n,r]of this.tokens_to_ids)this.vocab[r]=n;this.bpe_ranks=new Map(e.merges.map((n,r)=>[n,r])),this.merges=e.merges.map(n=>n.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=e.end_of_word_suffix,this.continuing_subword_suffix=e.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=new Map}bpe(e){if(e.length===0)return[];const n=this.cache.get(e);if(n!==void 0)return n;const r=Array.from(e);this.end_of_word_suffix&&(r[r.length-1]+=this.end_of_word_suffix);let a=[];if(r.length>1){const i=new D0((l,d)=>l.score`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):n.push(this.unk_token)}return n}}class _w extends Kr{constructor(e,n){super(e),this.tokens_to_ids=lo(n.target_lang?e.vocab[n.target_lang]:e.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[r,a]of this.tokens_to_ids)this.vocab[a]=r}encode(e){return e}}class xt extends Et{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"BertNormalizer":return new Cw(e);case"Precompiled":return new qw(e);case"Sequence":return new kw(e);case"Replace":return new ww(e);case"NFC":return new yw(e);case"NFKC":return new bw(e);case"NFKD":return new vw(e);case"Strip":return new $w(e);case"StripAccents":return new xw(e);case"Lowercase":return new Sw(e);case"Prepend":return new Ew(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class ww extends xt{normalize(e){const n=pi(this.config.pattern);return n===null?e:e.replaceAll(n,this.config.content)}}class yw extends xt{normalize(e){return e=e.normalize("NFC"),e}}class bw extends xt{normalize(e){return e=e.normalize("NFKC"),e}}class vw extends xt{normalize(e){return e=e.normalize("NFKD"),e}}class $w extends xt{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class xw extends xt{normalize(e){return e=Uf(e),e}}class Sw extends xt{normalize(e){return e=e.toLowerCase(),e}}class Ew extends xt{normalize(e){return e=this.config.prepend+e,e}}class kw extends xt{constructor(e){super(e),this.normalizers=e.normalizers.map(n=>xt.fromConfig(n))}normalize(e){return this.normalizers.reduce((n,r)=>r.normalize(n),e)}}class Cw extends xt{_tokenize_chinese_chars(e){const n=[];for(let r=0;r=19968&&e<=40959||e>=13312&&e<=19903||e>=131072&&e<=173791||e>=173824&&e<=177983||e>=177984&&e<=178207||e>=178208&&e<=183983||e>=63744&&e<=64255||e>=194560&&e<=195103}stripAccents(e){return e.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(e){switch(e){case" ":case` -`:case"\r":return!1;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/u.test(e)}}_clean_text(e){const n=[];for(const r of e){const a=r.charCodeAt(0);a===0||a===65533||this._is_control(r)||(/^\s$/.test(r)?n.push(" "):n.push(r))}return n.join("")}normalize(e){return this.config.clean_text&&(e=this._clean_text(e)),this.config.handle_chinese_chars&&(e=this._tokenize_chinese_chars(e)),this.config.lowercase?(e=e.toLowerCase(),this.config.strip_accents!==!1&&(e=this.stripAccents(e))):this.config.strip_accents&&(e=this.stripAccents(e)),e}}class Mt extends Et{static fromConfig(e){if(e===null)return null;switch(e.type){case"BertPreTokenizer":return new Tw(e);case"Sequence":return new jw(e);case"Whitespace":return new Kw(e);case"WhitespaceSplit":return new Yw(e);case"Metaspace":return new Hf(e);case"ByteLevel":return new Iw(e);case"Split":return new Aw(e);case"Punctuation":return new Mw(e);case"Digits":return new Ow(e);case"Replace":return new Xw(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,n){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,n){return(Array.isArray(e)?e.map(r=>this.pre_tokenize_text(r,n)):this.pre_tokenize_text(e,n)).flat()}_call(e,n){return this.pre_tokenize(e,n)}}class Tw extends Mt{constructor(e){super(),this.pattern=new RegExp(`[^\\s${Ur}]+|[${Ur}]`,"gu")}pre_tokenize_text(e,n){return e.trim().match(this.pattern)||[]}}class Iw extends Mt{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=Wf,this.text_encoder=new TextEncoder}pre_tokenize_text(e,n){return this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),(this.use_regex?e.match(this.pattern)||[]:[e]).map(a=>Array.from(this.text_encoder.encode(a),i=>this.byte_encoder[i]).join(""))}}class Aw extends Mt{constructor(e){super(),this.config=e,this.pattern=pi(this.config.pattern,this.config.invert)}pre_tokenize_text(e,n){return this.pattern===null?[]:this.config.invert?e.match(this.pattern)||[]:ow(e,this.pattern)}}class Mw extends Mt{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${Ur}]+|[${Ur}]+`,"gu")}pre_tokenize_text(e,n){return e.match(this.pattern)||[]}}class Ow extends Mt{constructor(e){super(),this.config=e;const n=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(n,"gu")}pre_tokenize_text(e,n){return e.match(this.pattern)||[]}}class hi extends Et{constructor(e){super(),this.config=e}static fromConfig(e){if(e===null)return null;switch(e.type){case"TemplateProcessing":return new zw(e);case"ByteLevel":return new Rw(e);case"RobertaProcessing":return new Gf(e);case"BertProcessing":return new Vf(e);default:throw new Error(`Unknown PostProcessor type: ${e.type}`)}}post_process(e,...n){throw Error("post_process should be implemented in subclass.")}_call(e,...n){return this.post_process(e,...n)}}class Vf extends hi{constructor(e){super(e),this.cls=e.cls[0],this.sep=e.sep[0]}post_process(e,n=null,{add_special_tokens:r=!0}={}){r&&(e=dt([this.cls],e,[this.sep]));let a=new Array(e.length).fill(0);if(n!==null){const i=r&&this instanceof Gf?[this.sep]:[],s=r?[this.sep]:[];e=dt(e,i,n,s),a=dt(a,new Array(n.length+i.length+s.length).fill(1))}return{tokens:e,token_type_ids:a}}}class Gf extends Vf{}class zw extends hi{constructor(e){super(e),this.single=e.single,this.pair=e.pair}post_process(e,n=null,{add_special_tokens:r=!0}={}){const a=n===null?this.single:this.pair;let i=[],s=[];for(const o of a)"SpecialToken"in o?r&&(i.push(o.SpecialToken.id),s.push(o.SpecialToken.type_id)):"Sequence"in o&&(o.Sequence.id==="A"?(i=dt(i,e),s=dt(s,new Array(e.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(i=dt(i,n),s=dt(s,new Array(n.length).fill(o.Sequence.type_id))));return{tokens:i,token_type_ids:s}}}class Rw extends hi{post_process(e,n=null){return n&&(e=dt(e,n)),{tokens:e}}}class St extends Et{constructor(e){super(),this.config=e,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=e.trim_offsets}static fromConfig(e){if(e===null)return null;switch(e.type){case"WordPiece":return new Fw(e);case"Metaspace":return new Hw(e);case"ByteLevel":return new Lw(e);case"Replace":return new Bw(e);case"ByteFallback":return new Pw(e);case"Fuse":return new Dw(e);case"Strip":return new Nw(e);case"Sequence":return new Ww(e);case"CTC":return new Uw(e);case"BPEDecoder":return new Vw(e);default:throw new Error(`Unknown Decoder type: ${e.type}`)}}_call(e){return this.decode(e)}decode(e){return this.decode_chain(e).join("")}decode_chain(e){throw Error("`decode_chain` should be implemented in subclass.")}}class Bw extends St{decode_chain(e){const n=pi(this.config.pattern);return n===null?e:e.map(r=>r.replaceAll(n,this.config.content))}}class Pw extends St{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const n=[];let r=[];for(const a of e){let i=null;if(a.length===6&&a.startsWith("<0x")&&a.endsWith(">")){const s=parseInt(a.slice(3,5),16);isNaN(s)||(i=s)}if(i!==null)r.push(i);else{if(r.length>0){const s=this.text_decoder.decode(Uint8Array.from(r));n.push(s),r=[]}n.push(a)}}if(r.length>0){const a=this.text_decoder.decode(Uint8Array.from(r));n.push(a),r=[]}return n}}class Dw extends St{decode_chain(e){return[e.join("")]}}class Nw extends St{constructor(e){super(e),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(e){return e.map(n=>{let r=0;for(let i=0;i(r!==0&&(n.startsWith(this.config.prefix)?n=n.replace(this.config.prefix,""):n=" "+n),this.cleanup&&(n=uo(n)),n))}}class Lw extends St{constructor(e){super(e),this.byte_decoder=mw,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(e){const n=e.join(""),r=new Uint8Array([...n].map(i=>this.byte_decoder[i]));return this.text_decoder.decode(r)}decode_chain(e){const n=[];let r=[];for(const a of e)this.added_tokens.find(i=>i.content===a)!==void 0?(r.length>0&&(n.push(this.convert_tokens_to_string(r)),r=[]),n.push(a)):r.push(a);return r.length>0&&n.push(this.convert_tokens_to_string(r)),n}}class Uw extends St{constructor(e){super(e),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(e){if(e.length===0)return"";const n=[e[0]];for(let i=1;ii!==this.pad_token).join("");return this.cleanup&&(a=uo(a).replaceAll(this.word_delimiter_token," ").trim()),a}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class Ww extends St{constructor(e){super(e),this.decoders=e.decoders.map(n=>St.fromConfig(n))}decode_chain(e){return this.decoders.reduce((n,r)=>r.decode_chain(n),e)}}class Vw extends St{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map((n,r)=>n.replaceAll(this.suffix,r===e.length-1?"":" "))}}class Gw extends St{decode_chain(e){let n="";for(let r=1;rr.normalize("NFKC")).join("～"):e=e.normalize("NFKC"),e}}class jw extends Mt{constructor(e){super(),this.tokenizers=e.pretokenizers.map(n=>Mt.fromConfig(n))}pre_tokenize_text(e,n){return this.tokenizers.reduce((r,a)=>a.pre_tokenize(r,n),[e])}}class Kw extends Mt{constructor(e){super()}pre_tokenize_text(e,n){return e.match(/\w+|[^\w\s]+/g)||[]}}class Yw extends Mt{constructor(e){super()}pre_tokenize_text(e,n){return dw(e)}}class Xw extends Mt{constructor(e){super(),this.config=e,this.pattern=pi(this.config.pattern),this.content=this.config.content}pre_tokenize_text(e,n){return this.pattern===null?[e]:[e.replaceAll(this.pattern,this.config.content)]}}const Qw=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Zw(t,e,n,r){for(const a of Object.keys(t)){const i=e-t[a].length,s=n(a),o=new Array(i).fill(s);t[a]=r==="right"?dt(t[a],o):dt(o,t[a])}}function Jw(t,e){for(const n of Object.keys(t))t[n].length=e}class Ee extends Et{return_token_type_ids=!1;_default_chat_template=`{% for message in messages %}{{'<|im_start|>' + message['role'] + ' -' + message['content'] + '<|im_end|>' + ' -'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant -' }}{% endif %}`;constructor(e,n){super(),this._tokenizer_config=n,this.normalizer=xt.fromConfig(e.normalizer),this.pre_tokenizer=Mt.fromConfig(e.pre_tokenizer),this.model=Kr.fromConfig(e.model,n),this.post_processor=hi.fromConfig(e.post_processor),this.decoder=St.fromConfig(e.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const r of e.added_tokens){const a=new pw(r);this.added_tokens.push(a),this.model.tokens_to_ids.set(a.content,a.id),this.model.vocab[a.id]=a.content,a.special&&(this.special_tokens.push(a.content),this.all_special_ids.push(a.id))}this.additional_special_tokens=n.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.map(r=>`${r.lstrip?"\\s*":""}(${pc(r.content)})${r.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=n.model_max_length,this.remove_space=n.remove_space,this.clean_up_tokenization_spaces=n.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=n.do_lowercase_and_remove_accent??!1,this.padding_side="right",this.legacy=!1,this.chat_template=n.chat_template??null,this._compiled_template_cache=new Map}getToken(...e){for(const n of e){const r=this._tokenizer_config[n];if(r)if(typeof r=="object"){if(r.__type==="AddedToken")return r.content;throw Error(`Unknown token: ${r}`)}else return r}return null}static async from_pretrained(e,{progress_callback:n=null,config:r=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main",legacy:o=null}={}){const l=await Ff(e,{progress_callback:n,config:r,cache_dir:a,local_files_only:i,revision:s,legacy:o});return new this(...l)}_call(e,{text_pair:n=null,add_special_tokens:r=!0,padding:a=!1,truncation:i=null,max_length:s=null,return_tensor:o=!0}={}){const l=Array.isArray(e);let d;if(l){if(e.length===0)throw Error("text array must be non-empty");if(n!==null){if(Array.isArray(n)){if(e.length!==n.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");d=e.map((u,h)=>this._encode_plus(u,n[h],{add_special_tokens:r}))}else d=e.map(u=>this._encode_plus(u,null,{add_special_tokens:r}))}else{if(e===null)throw Error("text may not be null");if(Array.isArray(n))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");d=[this._encode_plus(e,n,{add_special_tokens:r})]}if(s===null?a==="max_length"?s=this.model_max_length:s=Ot(d.map(u=>u.input_ids.length))[0]:i||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."),s=Math.min(s,this.model_max_length),a||i)for(let u=0;us?i&&Jw(d[u],s):a&&Zw(d[u],s,h=>h==="input_ids"?this.pad_token_id:0,this.padding_side));const c={};if(o){if(!(a&&i)&&d.some(h=>{for(const f of Object.keys(h))if(h[f].length!==d[0][f]?.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 u=[d.length,d[0].input_ids.length];for(const h of Object.keys(d[0]))c[h]=new ee("int64",BigInt64Array.from(d.flatMap(f=>f[h]).map(BigInt)),u)}else{for(const u of Object.keys(d[0]))c[u]=d.map(h=>h[u]);if(!l)for(const u of Object.keys(c))c[u]=c[u][0]}return c}_encode_text(e){return e===null?null:(this.added_tokens_regex?e.split(this.added_tokens_regex).filter(a=>a):[e]).map((a,i)=>{if(this.added_tokens.find(o=>o.content===a)!==void 0)return a;{if(this.remove_space===!0&&(a=a.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(a=lw(a)),this.normalizer!==null&&(a=this.normalizer(a)),a.length===0)return[];const o=this.pre_tokenizer!==null?this.pre_tokenizer(a,{section_index:i}):[a];return this.model(o)}}).flat()}_encode_plus(e,n=null,{add_special_tokens:r=!0}={}){const a=this._encode_text(e),i=this._encode_text(n),s=this.post_processor?this.post_processor(a,i,{add_special_tokens:r}):{tokens:dt(a??[],i??[])},o=this.model.convert_tokens_to_ids(s.tokens),l={input_ids:o,attention_mask:new Array(o.length).fill(1)};return this.return_token_type_ids&&s.token_type_ids&&(l.token_type_ids=s.token_type_ids),l}encode(e,n=null,{add_special_tokens:r=!0}={}){const{input_ids:a}=this._encode_plus(e,n,{add_special_tokens:r});return a}batch_decode(e,n={}){return e instanceof ee&&(e=e.tolist()),e.map(r=>this.decode(r,n))}decode(e,n={}){if(e instanceof ee&&(e=Lf(e)),!Array.isArray(e)||e.length===0||!hc(e[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(e,n)}decode_single(e,{skip_special_tokens:n=!1,clean_up_tokenization_spaces:r=null}){let a=this.model.convert_ids_to_tokens(e);n&&(a=a.filter(s=>!this.special_tokens.includes(s)));let i=this.decoder?this.decoder(a):a.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(i=i.replaceAll(this.decoder.end_of_word_suffix," "),n&&(i=i.trim())),(r??this.clean_up_tokenization_spaces)&&(i=uo(i)),i}get default_chat_template(){return this._warned_about_chat_template||(console.warn("No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format. If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information."),this._warned_about_chat_template=!0),this._default_chat_template}apply_chat_template(e,{chat_template:n=null,add_generation_prompt:r=!1,tokenize:a=!0,padding:i=!1,truncation:s=!1,max_length:o=null,return_tensor:l=!0}={}){n??=this.chat_template??this.default_chat_template;let d=this._compiled_template_cache.get(n);d===void 0&&(d=new sw(n),this._compiled_template_cache.set(n,d));const c=Object.create(null);for(const h of Qw){const f=this.getToken(h);f&&(c[h]=f)}const u=d.render({messages:e,add_generation_prompt:r,...c});return a?this._call(u,{add_special_tokens:!1,padding:i,truncation:s,max_length:o,return_tensor:l}).input_ids:u}}class ey extends Ee{return_token_type_ids=!0}class ty extends Ee{return_token_type_ids=!0}class ny extends Ee{return_token_type_ids=!0}class ry extends Ee{return_token_type_ids=!0}class ay extends Ee{return_token_type_ids=!0}class iy extends Ee{return_token_type_ids=!0}class sy extends Ee{return_token_type_ids=!0}class oy extends Ee{return_token_type_ids=!0}class ly extends Ee{return_token_type_ids=!0}class uy extends Ee{}class dy extends Ee{}class cy extends Ee{return_token_type_ids=!0;constructor(e,n){super(e,n),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class py extends Ee{return_token_type_ids=!0}class hy extends Ee{}class qf extends Ee{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}'}class fy extends Ee{}class jf extends Ee{constructor(e,n){super(e,n),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(r=>this.languageRegex.test(r)),this.lang_to_token=r=>r}_build_translation_inputs(e,n,r){return co(this,e,n,r)}}class my extends jf{}class gy extends Ee{}class _y extends qf{constructor(e,n){const r=".,!?…。，、।۔،",a=e.pre_tokenizer?.pretokenizers[0]?.pattern;a&&a.Regex===` ?[^(\\s|[${r}])]+`&&(a.Regex=` ?[^\\s${r}]+`),super(e,n)}}const Pa="▁";class Kf extends Ee{_default_chat_template=`{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<> -' + system_message + ' -<> - -' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<> -' + content.strip() + ' -<> - -' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}`;DEFAULT_SYSTEM_PROMPT=`You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. - -If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.`;constructor(e,n){super(e,n),this.use_default_system_prompt=n.use_default_system_prompt??!1,this.legacy=n.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new Hf({replacement:Pa,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(e){if(e===null)return null;if(this.legacy||e.length===0)return super._encode_text(e);let n=super._encode_text(Pa+e.replaceAll(Pa," "));return n.length>1&&n[0]===Pa&&this.special_tokens.includes(n[1])&&(n=n.slice(1)),n}get default_chat_template(){return super.default_chat_template.replaceAll("USE_DEFAULT_PROMPT",this.use_default_system_prompt?"true":"false").replaceAll("DEFAULT_SYSTEM_MESSAGE",this.DEFAULT_SYSTEM_PROMPT.replaceAll(` -`,"\\n").replaceAll("'","\\'"))}}class wy extends Kf{}class yy extends Ee{}class by extends Ee{}class vy extends Ee{}class $y extends Ee{}class xy extends Ee{}class Sy extends Ee{}class Ey extends Ee{_default_chat_template=`{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + ' -' + message['content'] | trim + ' -' }}{% endfor %}{% if add_generation_prompt %}{{'model -'}}{% endif %}`}function co(t,e,n,r){if(!("language_codes"in t)||!Array.isArray(t.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in t)||!(t.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in t)||typeof t.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const a=r.src_lang,i=r.tgt_lang;if(!t.language_codes.includes(i))throw new Error(`Target language code "${i}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);if(a!==void 0){if(!t.language_codes.includes(a))throw new Error(`Source language code "${a}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);for(const s of t.post_processor.config.single)if("SpecialToken"in s&&t.languageRegex.test(s.SpecialToken.id)){s.SpecialToken.id=t.lang_to_token(a);break}}return r.forced_bos_token_id=t.model.convert_tokens_to_ids([t.lang_to_token(i)])[0],t._call(e,n)}class ky extends Ee{constructor(e,n){super(e,n),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(r=>this.languageRegex.test(r)),this.lang_to_token=r=>r}_build_translation_inputs(e,n,r){return co(this,e,n,r)}}class Cy extends Ee{constructor(e,n){super(e,n),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(r=>this.languageRegex.test(r)).map(r=>r.slice(2,-2)),this.lang_to_token=r=>`__${r}__`}_build_translation_inputs(e,n,r){return co(this,e,n,r)}}const Yf=[["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"]],Da=new Map(Yf),Ty=new Map([...Yf.map(([t,e])=>[e,t]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class Iy extends Ee{_default_chat_template='{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}';_decode_asr(e,{return_timestamps:n=!1,return_language:r=!1,time_precision:a=null,force_full_sequences:i=!0}={}){if(a===null)throw Error("Must specify time_precision");let s=null;const o=n==="word";function l(){return{language:s,timestamp:[null,null],text:""}}const d=[];let c=l(),u=0;const h=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let f=[],g=[],w=!1,b=null;const y=new Set(this.all_special_ids);for(const S of e){const T=S.tokens,A=o?S.token_timestamps:null;let P=null,N=h;if("stride"in S){const[M,G,O]=S.stride;if(u-=G,b=M-O,G&&(N=G/a+h),O)for(let R=T.length-1;R>=0;--R){const H=T[R];if(H>=h){if(P!==null&&(H-h)*a=h){const O=(G-h)*a+u,R=Ar(O,2);if(P!==null&&G>=P)w=!0;else if(w||f.length>0&&G0?(f.push(V),o&&g.push(j)):f.every(M=>M.length===0)&&(c=l(),f=[],V=[],g=[],j=[])}if(f.length>0){if(i&&n)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[S,T]=this.findLongestCommonSequence(f,g),A=this.decode(S);c.text=A,o&&(c.words=this.collateWordTimestamps(S,T,s)),d.push(c)}let $=Object.create(null);const k=d.map(S=>S.text).join("");if(n||r){for(let S=0;S0;let o=s?[]:null,l=s?n[0]:null;for(let d=1;dR===M[H]).length,O=G/S+T;G>1&&O>u&&(u=O,h=[A,P,V,j])}const[g,w,b,y]=h,$=Math.floor((w+g)/2),k=Math.floor((y+b)/2);i.push(...r.slice(0,$)),r=c.slice(k),a=r.length,s&&(o.push(...l.slice(0,$)),l=n[d].slice(k))}return i.push(...r),s?(o.push(...l),[i,o]):[i,[]]}collateWordTimestamps(e,n,r){const[a,i,s]=this.combineTokensIntoWords(e,r),o=[];for(let l=0;l=a){const o=Ar((s-a)*r,2);i.push(`<|${o}|>`),i.push([])}else i[i.length-1].push(s);return i=i.map(s=>typeof s=="string"?s:super.decode(s,n)),i.join("")}splitTokensOnUnicode(e){const n=this.decode(e,{decode_with_timestamps:!0}),r="�",a=[],i=[],s=[];let o=[],l=[],d=0;for(let c=0;c=this.model.tokens_to_ids.get("<|endoftext|>"),g=c.startsWith(" "),w=c.trim(),b=l.test(w);if(f||g||b||i.length===0)i.push(c),s.push(u),o.push(h);else{const y=i.length-1;i[y]+=c,s[y].push(...u),o[y].push(...h)}}return[i,s,o]}mergePunctuations(e,n,r,a,i){const s=structuredClone(e),o=structuredClone(n),l=structuredClone(r);let d=s.length-2,c=s.length-1;for(;d>=0;)s[d].startsWith(" ")&&a.includes(s[d].trim())?(s[c]=s[d]+s[c],o[c]=dt(o[d],o[c]),l[c]=dt(l[d],l[c]),s[d]="",o[d]=[],l[d]=[]):c=d,--d;for(d=0,c=1;cu),o.filter(u=>u.length>0),l.filter(u=>u.length>0)]}get_decoder_prompt_ids({language:e=null,task:n=null,no_timestamps:r=!0}={}){const a=[];if(e){e=e.toLowerCase();let i=Ty.get(e);if(i===void 0)if(Da.has(e))i=e;else{const l=e.length===2?Da.keys():Da.values();throw new Error(`Language "${e}" is not supported. Must be one of: ${JSON.stringify(l)}`)}const s=this.model.tokens_to_ids.get(`<|${i}|>`);if(s===void 0)throw new Error(`Unable to find language "${i}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);a.push(s)}else a.push(null);if(n){if(n=n.toLowerCase(),n!=="transcribe"&&n!=="translate")throw new Error(`Task "${n}" is not supported. Must be one of: ["transcribe", "translate"]`);const i=this.model.tokens_to_ids.get(`<|${n}|>`);if(i===void 0)throw new Error(`Unable to find task "${n}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);a.push(i)}else a.push(null);if(r){const i=this.model.tokens_to_ids.get("<|notimestamps|>");if(i===void 0)throw new Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');a.push(i)}return a.map((i,s)=>[s+1,i]).filter(i=>i[1]!==null)}}class Ay extends Ee{}class My extends Ee{}class Oy extends Ee{}class zy extends Ee{constructor(e,n){super(e,n),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(r=>this.languageRegex.test(r)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;const[n,...r]=e.trim().split(this.languageRegex);if(r.length===0)return super._encode_text(n);if(r.length===2){const[a,i]=r;return this.supported_language_codes.includes(a)||console.warn(`Unsupported language code "${a}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),dt([a],super._encode_text(i))}}}class Ry extends Ee{}class Xf extends Ee{_default_chat_template="{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ ' ' }}{% endif %}{% endfor %}{{ eos_token }}"}class By extends Xf{}class Py extends Ee{}class Dy extends Ee{}class Ny extends Ee{constructor(e,n){super(e,n),this.decoder=new Gw({})}}class lt{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:hy,DistilBertTokenizer:uy,CamembertTokenizer:dy,DebertaTokenizer:ay,DebertaV2Tokenizer:iy,BertTokenizer:ey,HerbertTokenizer:sy,ConvBertTokenizer:oy,RoFormerTokenizer:ly,XLMTokenizer:cy,ElectraTokenizer:py,MobileBertTokenizer:ny,SqueezeBertTokenizer:ry,AlbertTokenizer:ty,GPT2Tokenizer:qf,BartTokenizer:fy,MBartTokenizer:jf,MBart50Tokenizer:my,RobertaTokenizer:gy,WhisperTokenizer:Iy,CodeGenTokenizer:Ay,CLIPTokenizer:My,SiglipTokenizer:Oy,MarianTokenizer:zy,BloomTokenizer:_y,NllbTokenizer:ky,M2M100Tokenizer:Cy,LlamaTokenizer:Kf,CodeLlamaTokenizer:wy,XLMRobertaTokenizer:yy,MPNetTokenizer:by,FalconTokenizer:vy,GPTNeoXTokenizer:$y,EsmTokenizer:xy,Wav2Vec2CTCTokenizer:Ry,BlenderbotTokenizer:Xf,BlenderbotSmallTokenizer:By,SpeechT5Tokenizer:Py,NougatTokenizer:Dy,VitsTokenizer:Ny,Qwen2Tokenizer:Sy,GemmaTokenizer:Ey,PreTrainedTokenizer:Ee};static async from_pretrained(e,{quantized:n=!0,progress_callback:r=null,config:a=null,cache_dir:i=null,local_files_only:s=!1,revision:o="main",legacy:l=null}={}){const[d,c]=await Ff(e,{quantized:n,progress_callback:r,config:a,cache_dir:i,local_files_only:s,revision:o,legacy:l}),u=c.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let h=this.TOKENIZER_CLASS_MAPPING[u];return h||(console.warn(`Unknown tokenizer class "${u}", attempting to construct from base class.`),h=Ee),new h(d,c)}}async function Fy(t,e){return await Yn(t,"config.json",!0,e)}class Ly{constructor(e){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e)}static async from_pretrained(e,{progress_callback:n=null,config:r=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main"}={}){let o=r??await Fy(e,{progress_callback:n,config:r,cache_dir:a,local_files_only:i,revision:s});return new this(o)}}class Gn{static async from_pretrained(...e){return Ly.from_pretrained(...e)}}class nc extends Et{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,n){for(let r of n)this.processors.forEach(a=>a(e,r))}[Symbol.iterator](){return this.processors.values()}}class Jt extends Et{_call(e,n){throw Error("`_call` should be implemented in a subclass")}}class Uy extends Jt{constructor(e){super(),this.force_token_map=Object.fromEntries(e??[])}_call(e,n){let r=this.force_token_map[e.length];return Pg(r)&&(n.data.fill(-1/0),n.data[r]=0),n}}class Wy extends Jt{constructor(e){super(),this.bos_token_id=e}_call(e,n){return e.length===1&&(n.data.fill(-1/0),n.data[this.bos_token_id]=0),n}}class Vy extends Jt{constructor(e,n){super(),this.max_length=e,this.forced_eos_token_id=n}_call(e,n){}}class Gy extends Jt{constructor(e,n){super(),this.begin_suppress_tokens=e,this.begin_index=n}_call(e,n){if(e.length===this.begin_index)for(let r of this.begin_suppress_tokens)n.data[r]=-1/0;return n}}class Hy extends Jt{constructor(e){super(),this.eos_token_id=e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(e.forced_decoder_ids||[]).length+2,e.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,n){const r=n.data;if(r[this.no_timestamps_token_id]=-1/0,e.length===this.begin_index-1)return r.fill(-1/0),r[this.timestamp_begin]=0,n;const a=e.slice(this.begin_index),i=a.length>=1&&a[a.length-1]>=this.timestamp_begin,s=a.length<2||a[a.length-2]>=this.timestamp_begin;if(i&&(s?r.subarray(this.timestamp_begin).fill(-1/0):r.subarray(0,this.eos_token_id).fill(-1/0)),e.length===this.begin_index&&this.max_initial_timestamp_index!==null){const c=this.timestamp_begin+this.max_initial_timestamp_index;r.subarray(c+1).fill(-1/0)}const o=Xg(r),l=Math.log(o.subarray(this.timestamp_begin).map(Math.exp).reduce((c,u)=>c+u)),d=Ot(o.subarray(0,this.timestamp_begin))[0];return l>d&&r.subarray(0,this.timestamp_begin).fill(-1/0),n}}class qy extends Jt{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const n=e.length,r=[];for(let i=0;i0&&(a=a.map(i=>i/this.generation_config.temperature)),a}randomSelect(e){let n=e.reduce((a,i)=>a+i,0),r=Math.random()*n;for(let a=0;a1)return new eb(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new Zy(e)}}class Zy extends fi{sample(e,n=-1){let r=this.getLogits(e,n);return[[Ot(r)[1],0]]}}class Jy extends fi{sample(e,n=-1){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const a=this.getLogits(e,n),i=Zn(a,r),s=ht(i.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},()=>{const o=this.randomSelect(s);return[i[o][0],Math.log(s[o])]})}}class eb extends fi{sample(e,n=-1){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const a=this.getLogits(e,n),i=Zn(a,r),s=ht(i.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},(o,l)=>[i[l][0],Math.log(s[l])])}}const ps=Object.freeze({fp32:"fp32",fp16:"fp16",int8:"int8",uint8:"uint8",float32:"fp32",float16:"fp16"}),Se={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5},ai=new Map,Qf=new Map,Pr=new Map;async function hn(t,e,n){let r="";if(n.dtype){if(!ps.hasOwnProperty(n.dtype))throw new Error(`Invalid dtype: ${n.dtype}. Should be one of: ${Object.keys(ps).join(", ")}`);ps[n.dtype]!=="fp32"&&(r=`_${n.dtype}`)}else n.quantized&&(r="_quantized");const a=`onnx/${e}${r}.onnx`,i=await ys(t,a,!0,n),s=n.session_options??{};if(s.externalData!==void 0)for(let o=0;o0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${r.join(", ")}.`);const a=Object.keys(e).length,i=t.inputNames.length;if(a>i){let s=Object.keys(e).filter(o=>!t.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${a} > ${i}). The following inputs will be ignored: "${s.join(", ")}".`)}return n}async function Qn(t,e){const n=tb(t,e);try{const r=Object.fromEntries(Object.entries(n).map(([i,s])=>[i,s.ort_tensor]));let a=await t.run(r);a=Zf(a);for(const[i,s]of Object.entries(n))i.startsWith("past_key_values")&&s.dispose();return a}catch(r){throw console.error(`An error occurred during model execution: "${r}".`),console.error("Inputs given to model:",n),r}}function Zf(t){for(let e in t)Bf(t[e])?t[e]=new ee(t[e]):typeof t[e]=="object"&&Zf(t[e]);return t}function nb(t){if(t instanceof ee)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new ee("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new ee("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function po(t,e){let n=t.config.pad_token_id??null,r=t.config.eos_token_id??null;hc(r)&&(r=[r]);let a=e.indexOf(n)!==-1,i=r===null||!r.includes(n);if(a&&i){let s=BigInt64Array.from(e.data.map(o=>o!=n));return new ee("int64",s,e.dims)}else return P0(e)}function Jf(t,e,n){if(!t.inputNames.includes("position_ids"))return;const r=new BigInt64Array(e.attention_mask.data.length);for(let a=0;a0&&a.push(new qy(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&a.push(new Xy(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&a.push(new Ky(e.min_length,e.eos_token_id)),e.min_new_tokens!==null&&e.eos_token_id!==null&&e.min_new_tokens>0&&a.push(new Yy(n,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&a.push(new Wy(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&a.push(new Vy(e.max_length,e.forced_eos_token_id)),e.begin_suppress_tokens!==null){let i=n>1||e.forced_bos_token_id===null?n:n+1;e.forced_decoder_ids!==null&&(i+=e.forced_decoder_ids[e.forced_decoder_ids.length-1][0]),a.push(new Gy(e.begin_suppress_tokens,i))}return e.forced_decoder_ids!==null&&a.push(new Uy(e.forced_decoder_ids)),r!==null&&a.extend(r),a}_get_generation_config(e){let n=new Qy(this.config);return"generation_config"in this&&Object.assign(n,this.generation_config),e!==null&&Object.assign(n,e),n}async generate(e,n=null,r=null,{inputs_attention_mask:a=null}={}){if(!this.can_generate){let b=`The current model class (${Pr.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;const y=this.config.model_type,$=$i.get(y)??bo.get(y)??yo.get(y)??vo.get(y);throw $&&(b+=` Please use the following class instead: '${$[0]}'`),Error(b)}if(!(e instanceof ee)&&!Bg(e)&&!Array.isArray(e))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${e.constructor.name}".`);let i;if(this.config.is_encoder_decoder)i=0;else if(i=e instanceof ee?e.dims.at(-1):e.length,i===0)throw Error("Must supply a non-empty array of input token ids.");n=this._get_generation_config(n),r=r??new nc,r=this._get_logits_processor(n,i,r);let s=n.eos_token_id;s!==null&&!Array.isArray(s)&&(s=[s]);let o=1;const l=o+(n.max_new_tokens??1/0),d=Number.isInteger(n.max_length)&&(n.max_new_tokens??null)===null;let c=fi.getSampler(n),u=this.getStartBeams(e,n,o,a);for(;u.some(w=>!w.done)&&o=n.max_length){b.done=!0,w.push(b);continue}let y=await this.runBeam(b);n.output_attentions&&this.addAttentionsToBeam(b,y),n.output_scores;let $=y.logits.slice(null,-1,null);r(b.output_token_ids,$);let k=c($);for(let[S,T]of k){let A={...b};this.updateBeam(A,S),A.score+=T,s&&s.includes(S)&&(A.done=!0),w.push(A)}}++o,w=this.groupBeams(w).map(b=>b.sort((y,$)=>$.score-y.score).slice(0,n.num_beams)),u=w.flat(),n.callback_function&&n.callback_function(u)}const h=this.groupBeams(u),f=w=>h.map(b=>n.num_return_sequences>1?b.slice(0,n.num_return_sequences).map(y=>y[w]):[b[0][w]]).flat(),g=f("output_token_ids");if(n.return_dict_in_generate){const w=f("decoder_attentions"),b=f("cross_attentions");return{sequences:g,decoder_attentions:w,cross_attentions:b}}else return g}addAttentionsToBeam(e,n){if(this.config.is_encoder_decoder){if(!n.cross_attentions||n.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.cross_attentions||(e.cross_attentions=[]),e.cross_attentions.push(n.cross_attentions)}if(!n.decoder_attentions||n.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.decoder_attentions||(e.decoder_attentions=[]),e.decoder_attentions.push(n.decoder_attentions)}groupBeams(e){const n=Object.create(null);for(const r of e)n[r.id]===void 0?n[r.id]=[r]:n[r.id].push(r);return Object.values(n)}getPastKeyValues(e,n){const r=Object.create(null);for(const a in e)if(a.startsWith("present")){let i=a.replace("present","past_key_values");n&&a.includes("encoder")?r[i]=n[i]:r[i]=e[a]}return r}getAttentions(e){const n=Object.create(null);for(const r of["cross_attentions","decoder_attentions"]){const a=[];for(const i in e)if(i.startsWith(r)){const s=i.split(".").pop();a[s]=e[i]}n[r]=a}return n}addPastKeyValues(e,n){if(n)Object.assign(e,n);else{const a=this.config.precision||"float32",i=a==="float16"?new Uint16Array:[];if(this.config.is_encoder_decoder&&(this.add_encoder_pkv??!0)){let s=[1,this.num_encoder_heads,0,this.encoder_dim_kv],o=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let l=0;l{let c=Array.from({length:this.config.decoder_layers},(b,y)=>di(d.map($=>$[y]),2)),u=ci(n.map(([b,y])=>r?c[b].slice(null,y,null,[0,r]):c[b].slice(null,y)));u=u.transpose(1,0,2,3);let[h,f]=O0(u,-2,0,!0),g=u.clone();for(let b=0;bu[y+1]-u[y]),g=dt([1],f).map(b=>!!b),w=[];for(let b=0;ba*i,1);e.input_labels=new ee("int64",new BigInt64Array(r).fill(1n),n)}return await Qn(this.prompt_encoder_mask_decoder,{input_points:e.input_points,input_labels:e.input_labels,image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings})}async _call(e){return new T$(await super._call(e))}}class T$ extends kt{constructor({iou_scores:e,pred_masks:n}){super(),this.iou_scores=e,this.pred_masks=n}}class Fm extends Z{}class I$ extends Fm{}class A$ extends Fm{constructor(e,n,r,a){super(e,n),this.decoder_merged_session=r,this.generation_config=a,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class Lm extends Z{}class M$ extends Lm{}class O$ extends Lm{constructor(e,n,r,a){super(e,n),this.decoder_merged_session=r,this.generation_config=a,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class Mn extends Z{}class z$ extends Mn{}class R$ extends Mn{async _call(e){return new nr(await super._call(e))}}class B$ extends Mn{async _call(e){return new Me(await super._call(e))}}class P$ extends Mn{async _call(e){return new ft(await super._call(e))}}class go extends Z{}class D$ extends go{}class N$ extends go{async _call(e){return new nr(await super._call(e))}}class F$ extends go{async _call(e){return new Me(await super._call(e))}}class vi extends Z{}class L$ extends vi{}class U$ extends vi{async _call(e){return new nr(await super._call(e))}}class W$ extends vi{async _call(e){return new Me(await super._call(e))}}class V$ extends vi{async _call(e){return new ft(await super._call(e))}}class _o extends Z{}class G$ extends _o{}class H$ extends _o{async _call(e){return new nr(await super._call(e))}}class q$ extends _o{async _call(e){return new Me(await super._call(e))}}class j$ extends Mn{}class K$ extends Mn{async _call(e){return new nr(await super._call(e))}}class Y$ extends Mn{async _call(e){return new Me(await super._call(e))}}class oa extends Z{}class X$ extends oa{}class Q$ extends oa{async _call(e){return new nr(await super._call(e))}}class Z$ extends oa{async _call(e){return new Me(await super._call(e))}}class J$ extends oa{async _call(e){return new q1(await super._call(e))}}class e1 extends oa{async _call(e){return new ft(await super._call(e))}}class Um extends Z{}class t1 extends Um{}class n1 extends Um{constructor(e,n,r,a){super(e,n),this.decoder_merged_session=r,this.generation_config=a,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.hidden_size/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.hidden_size/this.num_encoder_heads}async generate_speech(e,n,{threshold:r=.5,minlenratio:a=0,maxlenratio:i=20,vocoder:s=null}={}){const o={input_ids:e},{encoder_outputs:l,encoder_attention_mask:d}=await Wr(this,o),c=l.dims[1]/this.config.reduction_factor,u=Math.floor(c*i),h=Math.floor(c*a),f=this.config.num_mel_bins;let g=[],w=null,b=null,y=0;for(;;){++y;const S=ho(!!b);let T;b?T=b.output_sequence_out:T=new ee("float32",new Float32Array(f),[1,1,f]);let A={use_cache_branch:S,output_sequence:T,encoder_attention_mask:d,speaker_embeddings:n,encoder_hidden_states:l};this.addPastKeyValues(A,w),b=await Qn(this.decoder_merged_session,A),w=this.getPastKeyValues(b,w);const{prob:P,spectrum:N}=b;if(g.push(N),y>=h&&(Array.from(P.data).filter(V=>V>=r).length>0||y>=u))break}const $=di(g),{waveform:k}=await Qn(s.session,{spectrogram:$});return{spectrogram:$,waveform:k}}}class r1 extends Z{main_input_name="spectrogram"}class a1 extends Z{constructor(e,n,r){super(e,n),this.generation_config=r,this.config.pad_token_id=this.config.eos_token_id,this.num_encoder_layers=this.num_decoder_layers=this.config.decoder_layers,this.num_encoder_heads=this.num_decoder_heads=this.config.decoder_attention_heads,this.encoder_dim_kv=this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads}}class i1 extends a1{}class Wm extends Z{constructor(e,n,r){super(e,n),this.generation_config=r,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class s1 extends Wm{}class o1 extends Wm{}class Vm extends Z{constructor(e,n,r){super(e,n),this.generation_config=r,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class l1 extends Vm{}class u1 extends Vm{}class Gm extends Z{constructor(e,n,r){super(e,n),this.generation_config=r,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class d1 extends Gm{}class c1 extends Gm{}class wo extends Z{}class p1 extends wo{}class h1 extends wo{static async from_pretrained(e,n={}){return n.model_file_name??="text_model",super.from_pretrained(e,n)}}class f1 extends wo{static async from_pretrained(e,n={}){return n.model_file_name??="audio_model",super.from_pretrained(e,n)}}class m1 extends Z{}class Hm extends m1{async _call(e){return new K1(await super._call(e))}}class qm extends Z{}class g1 extends qm{}class _1 extends qm{}class w1 extends Z{constructor(e,n,r){super(e,n),this.generation_config=r,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class y1 extends w1{}class Ze{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{quantized:n=!0,progress_callback:r=null,config:a=null,cache_dir:i=null,local_files_only:s=!1,revision:o="main",model_file_name:l=null,device:d=null,dtype:c=null,session_options:u={}}={}){let h={quantized:n,progress_callback:r,config:a,cache_dir:i,local_files_only:s,revision:o,model_file_name:l,device:d,dtype:c,session_options:u};if(a=await Gn.from_pretrained(e,h),h.config||(h.config=a),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let f of this.MODEL_CLASS_MAPPINGS){const g=f.get(a.model_type);if(g)return await g[1].from_pretrained(e,h)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${a.model_type}", attempting to construct from base class.`),await Z.from_pretrained(e,h);throw Error(`Unsupported model type: ${a.model_type}`)}}const jm=new Map([["bert",["BertModel",cb]],["nomic_bert",["NomicBertModel",_b]],["roformer",["RoFormerModel",wb]],["electra",["ElectraModel",Tb]],["esm",["EsmModel",Jb]],["convbert",["ConvBertModel",xb]],["camembert",["CamembertModel",zb]],["deberta",["DebertaModel",Nb]],["deberta-v2",["DebertaV2Model",Vb]],["mpnet",["MPNetModel",ov]],["albert",["AlbertModel",gv]],["distilbert",["DistilBertModel",Kb]],["roberta",["RobertaModel",Dv]],["xlm",["XLMModel",Wv]],["xlm-roberta",["XLMRobertaModel",jv]],["clap",["ClapModel",p1]],["clip",["CLIPModel",n2]],["clipseg",["CLIPSegModel",d2]],["chinese_clip",["ChineseCLIPModel",u2]],["siglip",["SiglipModel",i2]],["mobilebert",["MobileBertModel",rv]],["squeezebert",["SqueezeBertModel",pv]],["wav2vec2",["Wav2Vec2Model",z$]],["wav2vec2-bert",["Wav2Vec2BertModel",G$]],["unispeech",["UniSpeechModel",D$]],["unispeech-sat",["UniSpeechSatModel",L$]],["hubert",["HubertModel",j$]],["wavlm",["WavLMModel",X$]],["audio-spectrogram-transformer",["ASTModel",Zv]],["vits",["VitsModel",Hm]],["detr",["DetrModel",K2]],["table-transformer",["TableTransformerModel",Z2]],["vit",["ViTModel",P2]],["mobilevit",["MobileViTModel",L2]],["owlvit",["OwlViTModel",W2]],["owlv2",["Owlv2Model",G2]],["beit",["BeitModel",q2]],["deit",["DeiTModel",t$]],["convnext",["ConvNextModel",_$]],["convnextv2",["ConvNextV2Model",y$]],["dinov2",["Dinov2Model",v$]],["resnet",["ResNetModel",r$]],["swin",["SwinModel",i$]],["swin2sr",["Swin2SRModel",o$]],["donut-swin",["DonutSwinModel",g$]],["yolos",["YolosModel",x$]],["dpt",["DPTModel",u$]],["glpn",["GLPNModel",h$]],["hifigan",["SpeechT5HifiGan",r1]]]),Km=new Map([["t5",["T5Model",bv]],["longt5",["LongT5Model",$v]],["mt5",["MT5Model",Sv]],["bart",["BartModel",kv]],["mbart",["MBartModel",Iv]],["marian",["MarianModel",I$]],["whisper",["WhisperModel",e2]],["m2m_100",["M2M100Model",M$]],["blenderbot",["BlenderbotModel",zv]],["blenderbot-small",["BlenderbotSmallModel",Bv]]]),b1=new Map([["bloom",["BloomModel",A2]],["gpt2",["GPT2Model",p2]],["gptj",["GPTJModel",w2]],["gpt_bigcode",["GPTBigCodeModel",b2]],["gpt_neo",["GPTNeoModel",f2]],["gpt_neox",["GPTNeoXModel",g2]],["codegen",["CodeGenModel",$2]],["llama",["LlamaModel",S2]],["qwen2",["Qwen2Model",k2]],["phi",["PhiModel",T2]],["mpt",["MptModel",O2]],["opt",["OPTModel",R2]],["mistral",["MistralModel",s1]],["starcoder2",["Starcoder2Model",l1]],["falcon",["FalconModel",d1]]]),yo=new Map([["speecht5",["SpeechT5ForSpeechToText",t1]],["whisper",["WhisperForConditionalGeneration",t2]]]),Ym=new Map([["speecht5",["SpeechT5ForTextToSpeech",n1]]]),Xm=new Map([["vits",["VitsModel",Hm]]]),Qm=new Map([["bert",["BertForSequenceClassification",hb]],["roformer",["RoFormerForSequenceClassification",bb]],["electra",["ElectraForSequenceClassification",Ab]],["esm",["EsmForSequenceClassification",tv]],["convbert",["ConvBertForSequenceClassification",Eb]],["camembert",["CamembertForSequenceClassification",Bb]],["deberta",["DebertaForSequenceClassification",Lb]],["deberta-v2",["DebertaV2ForSequenceClassification",Hb]],["mpnet",["MPNetForSequenceClassification",uv]],["albert",["AlbertForSequenceClassification",_v]],["distilbert",["DistilBertForSequenceClassification",Yb]],["roberta",["RobertaForSequenceClassification",Fv]],["xlm",["XLMForSequenceClassification",Gv]],["xlm-roberta",["XLMRobertaForSequenceClassification",Yv]],["bart",["BartForSequenceClassification",Tv]],["mbart",["MBartForSequenceClassification",Mv]],["mobilebert",["MobileBertForSequenceClassification",iv]],["squeezebert",["SqueezeBertForSequenceClassification",fv]]]),Zm=new Map([["bert",["BertForTokenClassification",fb]],["roformer",["RoFormerForTokenClassification",vb]],["electra",["ElectraForTokenClassification",Mb]],["esm",["EsmForTokenClassification",nv]],["convbert",["ConvBertForTokenClassification",kb]],["camembert",["CamembertForTokenClassification",Pb]],["deberta",["DebertaForTokenClassification",Ub]],["deberta-v2",["DebertaV2ForTokenClassification",qb]],["mpnet",["MPNetForTokenClassification",dv]],["distilbert",["DistilBertForTokenClassification",Xb]],["roberta",["RobertaForTokenClassification",Lv]],["xlm",["XLMForTokenClassification",Hv]],["xlm-roberta",["XLMRobertaForTokenClassification",Xv]]]),bo=new Map([["t5",["T5ForConditionalGeneration",vv]],["longt5",["LongT5ForConditionalGeneration",xv]],["mt5",["MT5ForConditionalGeneration",Ev]],["bart",["BartForConditionalGeneration",Cv]],["mbart",["MBartForConditionalGeneration",Av]],["marian",["MarianMTModel",A$]],["m2m_100",["M2M100ForConditionalGeneration",O$]],["blenderbot",["BlenderbotForConditionalGeneration",Rv]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Pv]]]),$i=new Map([["bloom",["BloomForCausalLM",M2]],["gpt2",["GPT2LMHeadModel",h2]],["gptj",["GPTJForCausalLM",y2]],["gpt_bigcode",["GPTBigCodeForCausalLM",v2]],["gpt_neo",["GPTNeoForCausalLM",m2]],["gpt_neox",["GPTNeoXForCausalLM",_2]],["codegen",["CodeGenForCausalLM",x2]],["llama",["LlamaForCausalLM",E2]],["qwen2",["Qwen2ForCausalLM",C2]],["phi",["PhiForCausalLM",I2]],["mpt",["MptForCausalLM",z2]],["opt",["OPTForCausalLM",B2]],["mbart",["MBartForCausalLM",Ov]],["mistral",["MistralForCausalLM",o1]],["starcoder2",["Starcoder2ForCausalLM",u1]],["falcon",["FalconForCausalLM",c1]],["trocr",["TrOCRForCausalLM",i1]],["stablelm",["StableLmForCausalLM",y1]]]),Jm=new Map([["bert",["BertForMaskedLM",pb]],["roformer",["RoFormerForMaskedLM",yb]],["electra",["ElectraForMaskedLM",Ib]],["esm",["EsmForMaskedLM",ev]],["convbert",["ConvBertForMaskedLM",Sb]],["camembert",["CamembertForMaskedLM",Rb]],["deberta",["DebertaForMaskedLM",Fb]],["deberta-v2",["DebertaV2ForMaskedLM",Gb]],["mpnet",["MPNetForMaskedLM",lv]],["albert",["AlbertForMaskedLM",yv]],["distilbert",["DistilBertForMaskedLM",Zb]],["roberta",["RobertaForMaskedLM",Nv]],["xlm",["XLMWithLMHeadModel",Vv]],["xlm-roberta",["XLMRobertaForMaskedLM",Kv]],["mobilebert",["MobileBertForMaskedLM",av]],["squeezebert",["SqueezeBertForMaskedLM",hv]]]),eg=new Map([["bert",["BertForQuestionAnswering",mb]],["roformer",["RoFormerForQuestionAnswering",$b]],["electra",["ElectraForQuestionAnswering",Ob]],["convbert",["ConvBertForQuestionAnswering",Cb]],["camembert",["CamembertForQuestionAnswering",Db]],["deberta",["DebertaForQuestionAnswering",Wb]],["deberta-v2",["DebertaV2ForQuestionAnswering",jb]],["mpnet",["MPNetForQuestionAnswering",cv]],["albert",["AlbertForQuestionAnswering",wv]],["distilbert",["DistilBertForQuestionAnswering",Qb]],["roberta",["RobertaForQuestionAnswering",Uv]],["xlm",["XLMForQuestionAnswering",qv]],["xlm-roberta",["XLMRobertaForQuestionAnswering",Qv]],["mobilebert",["MobileBertForQuestionAnswering",sv]],["squeezebert",["SqueezeBertForQuestionAnswering",mv]]]),vo=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",om]]]),v1=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",om]]]),tg=new Map([["vit",["ViTForImageClassification",D2]],["mobilevit",["MobileViTForImageClassification",U2]],["beit",["BeitForImageClassification",j2]],["deit",["DeiTForImageClassification",n$]],["convnext",["ConvNextForImageClassification",w$]],["convnextv2",["ConvNextV2ForImageClassification",b$]],["dinov2",["Dinov2ForImageClassification",$$]],["resnet",["ResNetForImageClassification",a$]],["swin",["SwinForImageClassification",s$]],["segformer",["SegformerForImageClassification",g1]]]),ng=new Map([["detr",["DetrForObjectDetection",Y2]],["table-transformer",["TableTransformerForObjectDetection",J2]],["yolos",["YolosForObjectDetection",S$]]]),rg=new Map([["owlvit",["OwlViTForObjectDetection",V2]],["owlv2",["Owlv2ForObjectDetection",H2]]]),ag=new Map([["detr",["DetrForSegmentation",X2]],["clipseg",["CLIPSegForImageSegmentation",c2]]]),ig=new Map([["segformer",["SegformerForSemanticSegmentation",_1]]]),$1=new Map([["sam",["SamModel",C$]]]),sg=new Map([["wav2vec2",["Wav2Vec2ForCTC",R$]],["wav2vec2-bert",["Wav2Vec2BertForCTC",H$]],["unispeech",["UniSpeechForCTC",N$]],["unispeech-sat",["UniSpeechSatForCTC",U$]],["wavlm",["WavLMForCTC",Q$]],["hubert",["HubertForCTC",K$]]]),og=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",B$]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",q$]],["unispeech",["UniSpeechForSequenceClassification",F$]],["unispeech-sat",["UniSpeechSatForSequenceClassification",W$]],["wavlm",["WavLMForSequenceClassification",Z$]],["hubert",["HubertForSequenceClassification",Y$]],["audio-spectrogram-transformer",["ASTForAudioClassification",Jv]]]),x1=new Map([["wavlm",["WavLMForXVector",J$]]]),S1=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",V$]],["wavlm",["WavLMForAudioFrameClassification",e1]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",P$]]]),E1=new Map([["vitmatte",["VitMatteForImageMatting",F2]]]),lg=new Map([["swin2sr",["Swin2SRForImageSuperResolution",l$]]]),ug=new Map([["dpt",["DPTForDepthEstimation",d$]],["depth_anything",["DepthAnythingForDepthEstimation",p$]],["glpn",["GLPNForDepthEstimation",f$]]]),dg=[[jm,Se.EncoderOnly],[Km,Se.EncoderDecoder],[b1,Se.DecoderOnly],[Qm,Se.EncoderOnly],[Zm,Se.EncoderOnly],[bo,Se.Seq2Seq],[yo,Se.Seq2Seq],[$i,Se.DecoderOnly],[Jm,Se.EncoderOnly],[eg,Se.EncoderOnly],[vo,Se.Vision2Seq],[tg,Se.EncoderOnly],[ag,Se.EncoderOnly],[ig,Se.EncoderOnly],[E1,Se.EncoderOnly],[lg,Se.EncoderOnly],[ug,Se.EncoderOnly],[ng,Se.EncoderOnly],[rg,Se.EncoderOnly],[$1,Se.MaskGeneration],[sg,Se.EncoderOnly],[og,Se.EncoderOnly],[Ym,Se.Seq2Seq],[Xm,Se.EncoderOnly],[x1,Se.EncoderOnly],[S1,Se.EncoderOnly]];for(const[t,e]of dg)for(const[n,r]of t.values())ai.set(n,e),Pr.set(r,n),Qf.set(n,r);const k1=[["CLIPTextModelWithProjection",r2,Se.EncoderOnly],["CLIPVisionModelWithProjection",a2,Se.EncoderOnly],["SiglipTextModel",s2,Se.EncoderOnly],["SiglipVisionModel",o2,Se.EncoderOnly],["ClapTextModelWithProjection",h1,Se.EncoderOnly],["ClapAudioModelWithProjection",f1,Se.EncoderOnly]];for(const[t,e,n]of k1)ai.set(t,n),Pr.set(e,t),Qf.set(t,e);class Va extends Ze{static MODEL_CLASS_MAPPINGS=dg.map(e=>e[0]);static BASE_IF_FAIL=!0}class rc extends Ze{static MODEL_CLASS_MAPPINGS=[Qm]}class C1 extends Ze{static MODEL_CLASS_MAPPINGS=[Zm]}class hs extends Ze{static MODEL_CLASS_MAPPINGS=[bo]}class T1 extends Ze{static MODEL_CLASS_MAPPINGS=[yo]}class I1 extends Ze{static MODEL_CLASS_MAPPINGS=[Ym]}class A1 extends Ze{static MODEL_CLASS_MAPPINGS=[Xm]}class M1 extends Ze{static MODEL_CLASS_MAPPINGS=[$i]}class O1 extends Ze{static MODEL_CLASS_MAPPINGS=[Jm]}class z1 extends Ze{static MODEL_CLASS_MAPPINGS=[eg]}class R1 extends Ze{static MODEL_CLASS_MAPPINGS=[vo]}class B1 extends Ze{static MODEL_CLASS_MAPPINGS=[tg]}class P1 extends Ze{static MODEL_CLASS_MAPPINGS=[ag]}class D1 extends Ze{static MODEL_CLASS_MAPPINGS=[ig]}class N1 extends Ze{static MODEL_CLASS_MAPPINGS=[ng]}class F1 extends Ze{static MODEL_CLASS_MAPPINGS=[rg]}class L1 extends Ze{static MODEL_CLASS_MAPPINGS=[sg]}class U1 extends Ze{static MODEL_CLASS_MAPPINGS=[og]}class W1 extends Ze{static MODEL_CLASS_MAPPINGS=[v1]}class V1 extends Ze{static MODEL_CLASS_MAPPINGS=[lg]}class G1 extends Ze{static MODEL_CLASS_MAPPINGS=[ug]}class H1 extends kt{constructor({logits:e,past_key_values:n,encoder_outputs:r,decoder_attentions:a=null,cross_attentions:i=null}){super(),this.logits=e,this.past_key_values=n,this.encoder_outputs=r,this.decoder_attentions=a,this.cross_attentions=i}}class Me extends kt{constructor({logits:e}){super(),this.logits=e}}class q1 extends kt{constructor({logits:e,embeddings:n}){super(),this.logits=e,this.embeddings=n}}class ft extends kt{constructor({logits:e}){super(),this.logits=e}}class mt extends kt{constructor({logits:e}){super(),this.logits=e}}class _t extends kt{constructor({start_logits:e,end_logits:n}){super(),this.start_logits=e,this.end_logits=n}}class nr extends kt{constructor({logits:e}){super(),this.logits=e}}class j1 extends kt{constructor({alphas:e}){super(),this.alphas=e}}class K1 extends kt{constructor({waveform:e,spectrogram:n}){super(),this.waveform=e,this.spectrogram=n}}const It=typeof self<"u",Y1=It&&self.constructor.name==="DedicatedWorkerGlobalScope";let kn,cg,fn;if(It)kn=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},fn=self.createImageBitmap,cg=self.ImageData;else if(je)fn=async t=>{const n=(await t.metadata()).channels,{data:r,info:a}=await t.raw().toBuffer({resolveWithObject:!0}),i=new pt(new Uint8ClampedArray(r),a.width,a.height,a.channels);return n!==void 0&&n!==a.channels&&i.convert(n),i};else throw new Error("Unable to load image processing library.");const X1={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},Q1=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class pt{constructor(e,n,r,a){this.data=e,this.width=n,this.height=r,this.channels=a}get size(){return[this.width,this.height]}static async read(e){if(e instanceof pt)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!It)throw new Error("fromCanvas() is only supported in browser environments.");const r=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new pt(r,e.width,e.height,4)}static async fromURL(e){const n=await qa(e);if(n.status!==200)throw new Error(`Unable to read image from "${e}" (${n.status} ${n.statusText})`);const r=await n.blob();return this.fromBlob(r)}static async fromBlob(e){if(It){const n=await fn(e),r=kn(n.width,n.height).getContext("2d");return r.drawImage(n,0,0),new this(r.getImageData(0,0,n.width,n.height).data,n.width,n.height,4)}else{const n=je(await e.arrayBuffer());return await fn(n)}}static fromTensor(e,n="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(n==="CHW")e=e.transpose(1,2,0);else if(n!=="HWC")throw new Error(`Unsupported channel format: ${n}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new pt(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let n=0,r=0;n=0?l=r:c=-r,a>=0?d=a:u=-a,o.drawImage(s,l,d,e,n,c,u,e,n),new pt(o.getImageData(0,0,e,n).data,e,n,4).convert(i)}else{let i=this.toSharp();if(r>=0&&a>=0)i=i.extract({left:Math.floor(r),top:Math.floor(a),width:e,height:n});else if(r<=0&&a<=0){const s=Math.floor(-a),o=Math.floor(-r);i=i.extend({top:s,left:o,right:e-this.width-o,bottom:n-this.height-s})}else{let s=[0,0],o=0;a<0?(s[0]=Math.floor(-a),s[1]=n-this.height-s[0]):o=Math.floor(a);let l=[0,0],d=0;r<0?(l[0]=Math.floor(-r),l[1]=e-this.width-l[0]):d=Math.floor(r),i=i.extend({top:s[0],bottom:s[1],left:l[0],right:l[1]}).extract({left:d,top:o,width:e,height:n})}return await fn(i)}}async toBlob(e="image/png",n=1){if(!It)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:n})}toCanvas(){if(!It)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),n=kn(e.width,e.height),r=new cg(e.data,e.width,e.height);return n.getContext("2d").putImageData(r,0,0),n}_update(e,n,r,a=null){return this.data=e,this.width=n,this.height=r,a!==null&&(this.channels=a),this}clone(){return new pt(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(It){if(Y1)throw new Error("Unable to save an image from a Web Worker.");const n=e.split(".").pop().toLowerCase(),r=Q1.get(n)??"image/png",a=await this.toBlob(r),i=URL.createObjectURL(a),s=document.createElement("a");s.href=i,s.download=e,s.click(),s.remove()}else{if(at.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(It)throw new Error("toSharp() is only supported in server-side environments.");return je(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}async function Z1(t,e){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");const n=await(await qa(t)).arrayBuffer(),r=new AudioContext({sampleRate:e});typeof e>"u"&&console.warn(`No sampling rate provided, using default of ${r.sampleRate}Hz.`);const a=await r.decodeAudioData(n);let i;if(a.numberOfChannels===2){const s=Math.sqrt(2),o=a.getChannelData(0),l=a.getChannelData(1);i=new Float32Array(o.length);for(let d=0;d2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,n=15,r=27/Math.log(6.4))=>t>=e?n+Math.log(t/e)*r:3*t/200};function fs(t,e="htk"){const n=J1[e];if(!n)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?n(t):t.map(r=>n(r))}const ex={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,n=15,r=Math.log(6.4)/27)=>t>=n?e*Math.exp(r*(t-n)):200*t/3};function tx(t,e="htk"){const n=ex[e];if(!n)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?n(t):t.map(r=>n(r))}function nx(t,e){const n=Float64Array.from({length:e.length-1},(s,o)=>e[o+1]-e[o]),r=Array.from({length:t.length},()=>new Array(e.length));for(let s=0;snew Array(t.length));for(let s=0;st+r*i)}function Vr(t,e,n,r,a,i=null,s="htk",o=!1){if(i!==null&&i!=="slaney")throw new Error('norm must be one of null or "slaney"');const l=fs(n,s),d=fs(r,s),c=ic(l,d,e+2);let u=tx(c,s),h;if(o){const g=a/(t*2);h=fs(Float64Array.from({length:t},(w,b)=>b*g),s),u=c}else h=ic(0,Math.floor(a/2),t);const f=nx(h,u);if(i!==null&&i==="slaney")for(let g=0;ga)throw Error(`frame_length (${n}) may not be larger than fft_length (${a})`);if(S!==n)throw new Error(`Length of the window (${S}) must equal frame_length (${n})`);if(r<=0)throw new Error("hop_length must be greater than zero");if(s){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const te=Math.floor((a-1)/2)+1;t=rx(t,te,te)}const T=Math.floor(1+Math.floor((t.length-n)/r)),A=l?Math.floor(a/2)+1:a;let P=T,N=T;y!==null&&(y>T?$&&(N=y):N=P=y);const V=new Zg(a),j=new Float64Array(a),M=new Float64Array(V.outputBufferSize),G=new Array(P);for(let te=0;te=1;--Y)j[Y]-=d*j[Y-1];j[0]*=1-d}for(let Y=0;YMath.pow(o,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(n&&(s=s.subarray(0,t)),r===null)return s;if(t>r)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${r})`);return s}function sx([t,e,n,r]){return[t-n/2,e-r/2,t+n/2,e+r/2]}function $o(t,e=.5,n=null,r=!1){const a=t.logits,i=t.pred_boxes,[s,o,l]=a.dims;if(n!==null&&n.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let d=[];for(let c=0;ce&&y.push(k)}else{let k=Ot(b.data)[1];if(k===l-1||($=ht(b.data),$[k]T*u[(A+1)%2])),h.boxes.push(S),h.classes.push(k),h.scores.push($[k])}}d.push(h)}return d}function la(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${t?.constructor?.name??typeof t} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function sc(t,e,n=0,r=null){let a=Math.round(t/e)*e;return r!==null&&a>r&&(a=Math.floor(t/e)*e),ai?d=Math.floor(i*l/a):i>a&&(l=Math.floor(a*d/i)),await e.resize(d,l,{resample:r}))}async crop_margin(e,n=200){const r=e.clone().grayscale(),a=gc(r.data)[0],s=Ot(r.data)[0]-a;if(s===0)return e;const o=n/255;let l=r.width,d=r.height,c=0,u=0;for(let h=0;hthis.preprocess(i)));return{pixel_values:ci(r.map(i=>i.pixel_values),0),original_sizes:r.map(i=>i.original_size),reshaped_input_sizes:r.map(i=>i.reshaped_input_size)}}}class ox extends Ke{post_process_semantic_segmentation(e,n=null){const r=e.logits,a=r.dims[0];if(n!==null&&n.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const i=[];for(let s=0;sh[$]&&(h[$]=y[$],u.data[$]=b)}const f=new Array(l.dims[0]),g=u.data;for(let b=0;bb!==void 0);i.push({segmentation:u,labels:w})}return i}}class lx extends Ke{}class ux extends Ke{}class dx extends Ke{}class cx extends Ke{}class px extends Ke{}class hx extends Ke{}class fx extends Ke{}class hg extends Ke{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){const n=this.size?.shortest_edge;if(n===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(n<384){const r=Math.floor(n/this.crop_pct),[a,i]=this.get_resize_output_image_size(e,{shortest_edge:r});e=await e.resize(a,i,{resample:this.resample}),e=await e.center_crop(n,n)}else e=await e.resize(n,n,{resample:this.resample});return e}}class mx extends hg{}class gx extends Ke{}class _x extends Ke{}class wx extends Ke{}class fg extends Ke{post_process_object_detection(...e){return $o(...e)}}class yx extends fg{}class bx extends Ke{}class vx extends Ke{}class mg extends Ke{pad_image(e,n,r,a={}){const[i,s,o]=n;let l=this.image_mean;Array.isArray(this.image_mean)||(l=new Array(o).fill(l));let d=this.image_std;Array.isArray(d)||(d=new Array(o).fill(l));const c=l.map((u,h)=>-u/this.image_std[h]);return super.pad_image(e,n,r,{center:!0,constant_values:c,...a})}}class $x extends mg{}class xx extends Ke{async _call(e){const n=await super._call(e),r=[n.pixel_values.dims[0],64,64],a=new ee("int64",new BigInt64Array(r.reduce((i,s)=>i*s)).fill(1n),r);return{...n,pixel_mask:a}}post_process_object_detection(...e){return $o(...e)}remove_low_and_no_objects(e,n,r,a){let i=[],s=[],o=[];for(let l=0;lr&&(i.push(c),s.push(f),o.push(u))}return[i,s,o]}check_segment_validity(e,n,r,a=.5,i=.8){let s=[],o=0,l=0;for(let c=0;c=a&&++l;let d=o>0&&l>0;return d&&(d=o/l>i),[d,s]}compute_segments(e,n,r,a,i,s=null,o=null){let[l,d]=o??e[0].dims,c=new ee("int32",new Int32Array(l*d),[l,d]),u=[];if(o!==null)for(let w=0;wf[y]&&(h[y]=w,f[y]=e[w].data[y])}let g=0;for(let w=0;wa!==n.dims[i]))throw Error(`The first ${r.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new ee("int64",e.flat(1/0).map(BigInt),r)}async _call(e,n=null,r=null){const a=await super._call(e);if(n&&(a.input_points=this.reshape_input_points(n,a.original_sizes,a.reshaped_input_sizes)),r){if(!a.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");a.input_labels=this.add_input_labels(r,a.input_points)}return a}post_process_masks(e,n,r,{mask_threshold:a=0,binarize:i=!0,pad_size:s=null}={}){const o=[];s=s??this.pad_size;const l=[s.height,s.width];for(let d=0;da&&(y[$]=1);b=new ee("bool",y,b.dims)}f.push(b)}o.push(ci(f))}return o}generate_crop_boxes(e,n,{crop_n_layers:r=0,overlap_ratio:a=512/1500,points_per_crop:i=32,crop_n_points_downscale_factor:s=1}={}){}}class kx extends Ke{pad_image(e,n,r,a={}){const[i,s,o]=n;return super.pad_image(e,n,{width:i+(r-i%r)%r,height:s+(r-s%r)%r},{mode:"symmetric",center:!1,constant_values:-1,...a})}}class Cx extends Ke{async _call(e,n){Array.isArray(e)||(e=[e]),Array.isArray(n)||(n=[n]);const r=await Promise.all(e.map(s=>this.preprocess(s))),a=await Promise.all(n.map(s=>this.preprocess(s,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:ci(r.map((s,o)=>di([s.pixel_values,a[o].pixel_values],0)),0),original_sizes:r.map(s=>s.original_size),reshaped_input_sizes:r.map(s=>s.reshaped_input_size)}}}class Tx extends On{constructor(e){super(e),this.config.mel_filters??=Vr(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=Si(this.config.n_fft,"hann")}_extract_fbank_features(e){const{data:n,dims:r}=xi(e,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}),a=Ot(n)[0];for(let i=0;ithis.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`."),n=e.slice(0,this.config.n_samples)):(n=new Float32Array(this.config.n_samples),n.set(e));const{data:r,dims:a}=this._extract_fbank_features(n);return{input_features:new ee("float32",r,[1,...a])}}}class Ix extends On{_zero_mean_unit_var_norm(e){const r=e.reduce((i,s)=>i+s,0)/e.length,a=e.reduce((i,s)=>i+(s-r)**2,0)/e.length;return e.map(i=>(i-r)/Math.sqrt(a+1e-7))}async _call(e){la(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let n=e;this.config.do_normalize&&(n=this._zero_mean_unit_var_norm(n));const r=[1,n.length];return{input_values:new ee("float32",n,r),attention_mask:new ee("int64",new BigInt64Array(n.length).fill(1n),r)}}}class Ax extends On{constructor(e){super(e);const n=this.config.sampling_rate,r=Vr(256,this.config.num_mel_bins,20,Math.floor(n/2),n,null,"kaldi",!0);for(let a=0;ar*32768),xi(e,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:n,transpose:!0})}async _call(e,{padding:n=!0,pad_to_multiple_of:r=2,do_normalize_per_mel_bins:a=!0,return_attention_mask:i=!0}={}){la(e,"SeamlessM4TFeatureExtractor");let s=this._extract_fbank_features(e,this.config.max_length);if(a){const[g,w]=s.dims;for(let b=0;b0){const y=new Float32Array(w*(g+b));y.set(s.data),y.fill(this.config.padding_value,s.data.length);const $=g+b;s={data:y,dims:[$,w]},i&&(o=new ee("int64",new BigInt64Array($),[1,$]),o.data.fill(1n,0,g))}}const[l,d]=s.dims,c=this.config.stride;if(l%c!==0)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${c}).`);const h=new ee("float32",s.data,s.dims).view(1,Math.floor(l/c),d*c),f={input_features:h};if(i){const g=h.dims[1],w=new ee("int64",new BigInt64Array(g),[1,g]);if(o)for(let b=1,y=0;b0)if(r==="rand_trunc"){s=!0;const l=Math.floor(Math.random()*(o+1));e=e.subarray(l,l+n),i=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),i.dims=[1,...i.dims]}else throw new Error(`Truncation strategy "${r}" not implemented`);else{if(o<0){let l=new Float64Array(n);if(l.set(e),a==="repeat")for(let d=e.length;dpt.read(e)))}async function ii(t,e){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(n=>typeof n=="string"||n instanceof URL?Z1(n,e):n instanceof Float64Array?new Float32Array(n):n))}function gg(t,e){e&&(t=t.map(s=>s|0));const[n,r,a,i]=t;return{xmin:n,ymin:r,xmax:a,ymax:i}}class Je extends Et{constructor({task:e,model:n,tokenizer:r=null,processor:a=null}){super(),this.task=e,this.model=n,this.tokenizer=r,this.processor=a}async dispose(){await this.model.dispose()}}class Fx extends Je{constructor(e){super(e)}async _call(e,{topk:n=1}={}){const r=this.tokenizer(e,{padding:!0,truncation:!0}),a=await this.model(r),i=this.model.config.problem_type==="multi_label_classification"?l=>l.sigmoid().data:l=>ht(l.data),s=this.model.config.id2label,o=[];for(const l of a.logits){const d=i(l),u=Zn(d,n).map(h=>({label:s[h[0]],score:h[1]}));n===1?o.push(...u):o.push(u)}return Array.isArray(e)||n===1?o:o[0]}}class Lx extends Je{constructor(e){super(e)}async _call(e,{ignore_labels:n=["O"]}={}){const r=Array.isArray(e),a=this.tokenizer(r?e:[e],{padding:!0,truncation:!0}),s=(await this.model(a)).logits,o=this.model.config.id2label,l=[];for(let d=0;d[f,g]).filter(f=>f[1]>d),u=Array.from(ht(i.end_logits[o].data)).map((f,g)=>[f,g]).filter(f=>f[1]>d),h=Dg(c,u).filter(f=>f[0][1]<=f[1][1]).map(f=>[f[0][1],f[1][1],f[0][0]*f[1][0]]).sort((f,g)=>g[2]-f[2]);for(let f=0;f{const f=[...o];return f[l]=h[0],{score:h[1],token:h[0],token_str:this.tokenizer.model.vocab[h[0]],sequence:this.tokenizer.decode(f,{skip_special_tokens:!0})}}))}return Array.isArray(e)?i:i[0]}}class xo extends Je{_key="generated_text";constructor(e){super(e)}async _call(e,n={}){Array.isArray(e)||(e=[e]),this.model.config.prefix&&(e=e.map(l=>this.model.config.prefix+l));const r=this.model.config.task_specific_params;r&&r[this.task]&&r[this.task].prefix&&(e=e.map(l=>r[this.task].prefix+l));const a=this.tokenizer,i={padding:!0,truncation:!0};let s;this instanceof _g&&"_build_translation_inputs"in a?s=a._build_translation_inputs(e,i,n).input_ids:s=a(e,i).input_ids;const o=await this.model.generate(s,n);return a.batch_decode(o,{skip_special_tokens:!0}).map(l=>({[this._key]:l}))}}class Vx extends xo{_key="summary_text";constructor(e){super(e)}}class _g extends xo{_key="translation_text";constructor(e){super(e)}}class Gx extends Je{constructor(e){super(e)}async _call(e,n={}){const r=Array.isArray(e);r||(e=[e]);const a=n.add_special_tokens??!1;this.tokenizer.padding_side="left";const{input_ids:i,attention_mask:s}=this.tokenizer(e,{add_special_tokens:a,padding:!0,truncation:!0}),o=await this.model.generate(i,n,null,{inputs_attention_mask:s}),l=this.tokenizer.batch_decode(o,{skip_special_tokens:!0}),d=Array.from({length:e.length},c=>[]);for(let c=0;c[n.toLowerCase(),r])),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(e,n,{hypothesis_template:r="This example is {}.",multi_label:a=!1}={}){const i=Array.isArray(e);i||(e=[e]),Array.isArray(n)||(n=[n]);const s=n.map(d=>r.replace("{}",d)),o=a||n.length===1,l=[];for(const d of e){const c=[];for(const f of s){const g=this.tokenizer(d,{text_pair:f,padding:!0,truncation:!0}),w=await this.model(g);o?c.push([w.logits.data[this.contradiction_id],w.logits.data[this.entailment_id]]):c.push(w.logits.data[this.entailment_id])}const h=(o?c.map(f=>ht(f)[1]):ht(c)).map((f,g)=>[f,g]).sort((f,g)=>g[0]-f[0]);l.push({sequence:d,labels:h.map(f=>n[f[1]]),scores:h.map(f=>f[0])})}return i?l:l[0]}}class qx extends Je{constructor(e){super(e)}async _call(e,{pooling:n="none",normalize:r=!1}={}){const a=this.tokenizer(e,{padding:!0,truncation:!0}),i=await this.model(a);let s=i.last_hidden_state??i.logits;if(n!=="none")if(n==="mean")s=M0(s,a.attention_mask);else if(n==="cls")s=s.slice(null,0);else throw Error(`Pooling method '${n}' not supported.`);return r&&(s=s.normalize(2,-1)),s}}class jx extends Je{constructor(e){super(e)}async _call(e,{topk:n=null}={}){const r=!Array.isArray(e),a=this.processor.feature_extractor.config.sampling_rate,i=await ii(e,a),s=this.model.config.id2label,o=[];for(const l of i){const d=await this.processor(l),u=(await this.model(d)).logits[0],f=Zn(ht(u.data),n).map(g=>({label:s[g[0]],score:g[1]}));n===1?o.push(...f):o.push(f)}return!r||n===1?o:o[0]}}class Kx extends Je{constructor(e){super(e)}async _call(e,n,{hypothesis_template:r="This is a sound of {}."}={}){const a=!Array.isArray(e);a&&(e=[e]);const i=n.map(c=>r.replace("{}",c)),s=this.tokenizer(i,{padding:!0,truncation:!0}),o=this.processor.feature_extractor.config.sampling_rate,l=await ii(e,o),d=[];for(const c of l){const u=await this.processor(c),h=await this.model({...s,...u}),f=ht(h.logits_per_audio.data);d.push([...f].map((g,w)=>({score:g,label:n[w]})))}return a?d[0]:d}}class Yx extends Je{constructor(e){super(e)}async _call(e,n={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(e,n);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(e,n);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(e,n={}){n.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),n.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const r=!Array.isArray(e);r&&(e=[e]);const a=this.processor.feature_extractor.config.sampling_rate,i=await ii(e,a),s=[];for(const o of i){const l=await this.processor(o),c=(await this.model(l)).logits[0],u=[];for(const f of c)u.push(Ot(f.data)[1]);const h=this.tokenizer.decode(u);s.push({text:h})}return r?s[0]:s}async _call_whisper(e,n={}){const r=n.return_timestamps??!1,a=n.chunk_length_s??0,i=n.chunk_callback??null,s=n.force_full_sequences??!1;let o=n.stride_length_s??null;r==="word"&&(n.return_token_timestamps=!0);const l=qo(n,"language",null),d=qo(n,"task",null);if(l||d||r){if(n.forced_decoder_ids)throw new Error("Cannot specify `language`/`task`/`return_timestamps` and `forced_decoder_ids` at the same time.");const b=this.tokenizer.get_decoder_prompt_ids({language:l,task:d,no_timestamps:!r});b.length>0&&(n.forced_decoder_ids=b)}const c=!Array.isArray(e);c&&(e=[e]);const u=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,h=this.processor.feature_extractor.config.hop_length,f=this.processor.feature_extractor.config.sampling_rate,g=await ii(e,f),w=[];for(const b of g){let y=[];if(a>0){if(o===null)o=a/6;else if(a<=o)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const S=f*a,T=f*o,A=S-2*T;let P=0;for(;P=b.length;y.push({stride:[N.length,j?0:T,M?0:T],input_features:V.input_features,is_last:M}),P+=A}}else y=[{stride:[b.length,0,0],input_features:(await this.processor(b)).input_features,is_last:!0}];for(const S of y){n.num_frames=Math.floor(S.stride[0]/h);const T=await this.model.generate(S.input_features,n);r==="word"?(S.tokens=T.sequences[0],S.token_timestamps=T.token_timestamps.tolist()[0].map(A=>Ar(A,2))):S.tokens=T[0],S.stride=S.stride.map(A=>A/f),i!==null&&i(S)}const[$,k]=this.tokenizer._decode_asr(y,{time_precision:u,return_timestamps:r,force_full_sequences:s});w.push({text:$,...k})}return c?w[0]:w}}class Xx extends Je{constructor(e){super(e)}async _call(e,n={}){const r=Array.isArray(e),a=await sn(e),{pixel_values:i}=await this.processor(a),s=[];for(const o of i){o.dims=[1,...o.dims];const l=await this.model.generate(o,n),d=this.tokenizer.batch_decode(l,{skip_special_tokens:!0}).map(c=>({generated_text:c.trim()}));s.push(d)}return r?s:s[0]}}class Qx extends Je{constructor(e){super(e)}async _call(e,{topk:n=1}={}){const r=Array.isArray(e),a=await sn(e),{pixel_values:i}=await this.processor(a),s=await this.model({pixel_values:i}),o=this.model.config.id2label,l=[];for(const d of s.logits){const u=Zn(ht(d.data),n).map(h=>({label:o[h[0]],score:h[1]}));n===1?l.push(...u):l.push(u)}return r||n===1?l:l[0]}}class Zx extends Je{constructor(e){super(e),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(e,{threshold:n=.5,mask_threshold:r=.5,overlap_mask_area_threshold:a=.8,label_ids_to_fuse:i=null,target_sizes:s=null,subtask:o=null}={}){if(Array.isArray(e)&&e.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const d=await sn(e),c=d.map(y=>[y.height,y.width]),{pixel_values:u,pixel_mask:h}=await this.processor(d),f=await this.model({pixel_values:u,pixel_mask:h});let g=null;if(o!==null)g=this.subtasks_mapping[o];else for(let[y,$]of Object.entries(this.subtasks_mapping))if($ in this.processor.feature_extractor){g=this.processor.feature_extractor[$].bind(this.processor.feature_extractor),o=y;break}const w=this.model.config.id2label,b=[];if(o==="panoptic"||o==="instance"){const y=g(f,n,r,a,i,s??c)[0],$=y.segmentation;for(const k of y.segments_info){const S=new Uint8ClampedArray($.data.length);for(let A=0;A<$.data.length;++A)$.data[A]===k.id&&(S[A]=255);const T=new pt(S,$.dims[1],$.dims[0],1);b.push({score:k.score,label:w[k.label_id],mask:T})}}else if(o==="semantic"){const{segmentation:y,labels:$}=g(f,s??c)[0];for(const k of $){const S=new Uint8ClampedArray(y.data.length);for(let A=0;Ar.replace("{}",h)),o=this.tokenizer(s,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:l}=await this.processor(i),d=await this.model({...o,pixel_values:l}),c=this.model.config.model_type==="siglip"?h=>h.sigmoid().data:h=>ht(h.data),u=[];for(const h of d.logits_per_image){const g=[...c(h)].map((w,b)=>({score:w,label:n[b]}));g.sort((w,b)=>b.score-w.score),u.push(g)}return a?u:u[0]}}class e3 extends Je{constructor(e){super(e)}async _call(e,{threshold:n=.9,percentage:r=!1}={}){const a=Array.isArray(e);if(a&&e.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const i=await sn(e),s=r?null:i.map(f=>[f.height,f.width]),{pixel_values:o,pixel_mask:l}=await this.processor(i),d=await this.model({pixel_values:o,pixel_mask:l}),c=this.processor.feature_extractor.post_process_object_detection(d,n,s),u=this.model.config.id2label,h=c.map(f=>f.boxes.map((g,w)=>({score:f.scores[w],label:u[f.classes[w]],box:gg(g,!r)})));return a?h:h[0]}}class t3 extends Je{constructor(e){super(e)}async _call(e,n,{threshold:r=.1,topk:a=null,percentage:i=!1}={}){const s=Array.isArray(e),o=await sn(e),l=this.tokenizer(n,{padding:!0,truncation:!0}),d=await this.processor(o),c=[];for(let u=0;u({score:b.scores[k],label:n[b.classes[k]],box:gg($,!i)})).sort(($,k)=>k.score-$.score);a!==null&&(y=y.slice(0,a)),c.push(y)}return s?c:c[0]}}class n3 extends Je{constructor(e){super(e)}async _call(e,n,r={}){const a=(await sn(e))[0],{pixel_values:i}=await this.processor(a),s=`${n}`,o=this.tokenizer(s,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,l=await this.model.generate(i,{...r,decoder_input_ids:o,max_length:this.model.config.decoder.max_position_embeddings}),c=this.tokenizer.batch_decode(l)[0].match(/(.*?)<\/s_answer>/);let u=null;return c&&c.length>=2&&(u=c[1].trim()),[{answer:u}]}}class r3 extends Je{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(e){super(e),this.vocoder=e.vocoder??null}async _call(e,{speaker_embeddings:n=null}={}){return this.processor?this._call_text_to_spectrogram(e,{speaker_embeddings:n}):this._call_text_to_waveform(e)}async _call_text_to_waveform(e){const n=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:r}=await this.model(n),a=this.model.config.sampling_rate;return{audio:r.data,sampling_rate:a}}async _call_text_to_spectrogram(e,{speaker_embeddings:n}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await Va.from_pretrained(this.DEFAULT_VOCODER_ID,{quantized:!1})),(typeof n=="string"||n instanceof URL)&&(n=new Float32Array(await(await fetch(n)).arrayBuffer())),n instanceof Float32Array)n=new ee("float32",n,[1,n.length]);else if(!(n instanceof ee))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:r}=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:a}=await this.model.generate_speech(r,n,{vocoder:this.vocoder}),i=this.processor.feature_extractor.config.sampling_rate;return{audio:a.data,sampling_rate:i}}}class a3 extends Je{constructor(e){super(e)}async _call(e){const n=await sn(e),r=await this.processor(n),a=await this.model(r),i=[];for(const s of a.reconstruction){const o=s.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");i.push(pt.fromTensor(o))}return i.length>1?i:i[0]}}class i3 extends Je{constructor(e){super(e)}async _call(e){const n=await sn(e),r=await this.processor(n),{predicted_depth:a}=await this.model(r),i=[];for(let s=0;s1?i:i[0]}}const lc=Object.freeze({"text-classification":{tokenizer:lt,pipeline:Fx,model:rc,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:lt,pipeline:Lx,model:C1,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:lt,pipeline:Ux,model:z1,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:lt,pipeline:Wx,model:O1,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:lt,pipeline:Vx,model:hs,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:lt,pipeline:_g,model:hs,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:lt,pipeline:xo,model:hs,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:lt,pipeline:Gx,model:M1,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:lt,pipeline:Hx,model:rc,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:jx,model:U1,processor:bt,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:lt,pipeline:Kx,model:Va,processor:bt,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:lt,pipeline:Yx,model:[T1,L1],processor:bt,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:lt,pipeline:r3,model:[A1,I1],processor:[bt,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:lt,pipeline:Xx,model:R1,processor:bt,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Qx,model:B1,processor:bt,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:Zx,model:[P1,D1],processor:bt,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:lt,pipeline:Jx,model:Va,processor:bt,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:e3,model:N1,processor:bt,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:lt,pipeline:t3,model:F1,processor:bt,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:lt,pipeline:n3,model:W1,processor:bt,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:a3,model:V1,processor:bt,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:i3,model:G1,processor:bt,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:lt,pipeline:qx,model:Va,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"}}),s3=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function o3(t,e=null,{quantized:n=!0,progress_callback:r=null,config:a=null,cache_dir:i=null,local_files_only:s=!1,revision:o="main",device:l=null,dtype:d=null,session_options:c={}}={}){t=s3[t]??t;const u=lc[t.split("_",1)[0]];if(!u)throw Error(`Unsupported pipeline: ${t}. Must be one of [${Object.keys(lc)}]`);e||(e=u.default.model,console.log(`No model specified. Using default model: "${e}".`));const h={quantized:n,progress_callback:r,config:a,cache_dir:i,local_files_only:s,revision:o,device:l,dtype:d,session_options:c},f=new Map([["tokenizer",u.tokenizer],["model",u.model],["processor",u.processor]]),g=await l3(f,e,h);g.task=t,Vn(r,{status:"ready",task:t,model:e});const w=u.pipeline;return new w(g)}async function l3(t,e,n){const r=Object.create(null),a=[];for(let[i,s]of t.entries()){if(!s)continue;let o;Array.isArray(s)?o=new Promise(async(l,d)=>{let c;for(let u of s){if(u===null){l(null);return}try{l(await u.from_pretrained(e,n));return}catch(h){if(h.message?.includes("Unsupported model type"))c=h;else{d(h);return}}}d(c)}):o=s.from_pretrained(e,n),r[i]=o,a.push(o)}await Promise.all(a);for(let[i,s]of Object.entries(r))r[i]=await s;return r}at.backends.onnx.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.17.1/dist/";at.backends.onnx.wasm.numThreads=1;const Ei=document.getElementById("status"),uc=document.getElementById("container"),wg=document.getElementById("overlay"),si=document.getElementById("canvas"),Tn=document.getElementById("video"),Bs=document.getElementById("threshold"),u3=document.getElementById("threshold-value"),Ps=document.getElementById("size"),d3=document.getElementById("size-value"),Ds=document.getElementById("scale"),c3=document.getElementById("scale-value");function yg(t,e){Tn.width=si.width=Math.round(t),Tn.height=si.height=Math.round(e)}Ei.textContent="Loading model...";async function p3(){try{return(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{return!1}}const h3=await p3()?"fp16":"fp32";let ua;try{ua=await o3("object-detection","Xenova/yolos-tiny",{device:"webgpu",dtype:h3})}catch(t){throw Ei.textContent=t.message,alert(t.message),t}let Ns=.7;Bs.addEventListener("input",()=>{Ns=Number(Bs.value),u3.textContent=Ns.toFixed(2)});Bs.disabled=!1;let Ga=256;ua.processor.feature_extractor.size={longest_edge:Ga};Ps.addEventListener("input",()=>{Ga=Number(Ps.value),ua.processor.feature_extractor.size={longest_edge:Ga},d3.textContent=Ga});Ps.disabled=!1;let qn=.5;Ds.addEventListener("input",()=>{qn=Number(Ds.value),yg(Tn.videoWidth*qn,Tn.videoHeight*qn),c3.textContent=qn});Ds.disabled=!1;Ei.textContent="Ready";const dc=["#EF4444","#4299E1","#059669","#FBBF24","#4B52B1","#7B3AC2","#ED507A","#1DD1A1","#F3873A","#4B5563","#DC2626","#1852B4","#18A35D","#F59E0B","#4059BE","#6027A5","#D63D60","#00AC9B","#E64A19","#272A34"];function f3({box:t,label:e,score:n}){const{xmax:r,xmin:a,ymax:i,ymin:s}=t,o=dc[ua.model.config.label2id[e]%dc.length],l=document.createElement("div");l.className="bounding-box",Object.assign(l.style,{borderColor:o,left:100*a+"%",top:100*s+"%",width:100*(r-a)+"%",height:100*(i-s)+"%"});const d=document.createElement("span");d.textContent=e,d.className="bounding-box-label",d.style.backgroundColor=o,l.appendChild(d),wg.appendChild(l)}let ms=!1,gs;const cc=si.getContext("2d",{willReadFrequently:!0});function bg(){const{width:t,height:e}=si;cc.drawImage(Tn,0,0,t,e),ms||(ms=!0,async function(){const n=cc.getImageData(0,0,t,e).data,r=new pt(n,t,e,4),a=await ua(r,{threshold:Ns,percentage:!0});if(wg.innerHTML="",a.forEach(f3),gs!==void 0){const i=1e3/(performance.now()-gs);Ei.textContent=`FPS: ${i.toFixed(2)}`}gs=performance.now(),ms=!1}()),window.requestAnimationFrame(bg)}navigator.mediaDevices.getUserMedia({video:!0}).then(t=>{Tn.srcObject=t,Tn.play();const e=t.getVideoTracks()[0],{width:n,height:r}=e.getSettings();yg(n*qn,r*qn);const a=n/r,[i,s]=a>720/405?[720,720/a]:[405*a,405];uc.style.width=`${i}px`,uc.style.height=`${s}px`,window.requestAnimationFrame(bg)}).catch(t=>{alert(t)});