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l=e[1]*t[1],d=e[0]*t[0],p=a?l:i,u=a?i:l,h=p/e[0],m=i/e[1];if(!((a&&h===4&&t[1]===4||!a&&(h===3||h===4))&&p%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 ${p} must be divisible by workgroupSize[0]${e[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${t[0]} must be 4.`);return` -var mm_Asub: array, ${p/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)"}; - ${n?`let batchIndices = ${n.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 * ${m}; - 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; - ${ll(a,n)} - } - - // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) { - let inputRow = tileRowB + innerRow; - let inputCol = tileCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${n?", batchIndices":""}); - } - kStart = kStart + tileInner; - workgroupBarrier(); - - // Compute acc values for a single thread. - for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { - let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; - let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; - let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; - ${h===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} - - ${ul(a,h)} - } - - workgroupBarrier(); - } - - for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { - mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); - } -}`},ei=(t,e)=>t?` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - kStart + inputRow, - globalRowStart + inputCol${e?", batchIndices":""}); - `:` - mm_Asub[inputRow][inputCol] = mm_readA(batch, - globalRowStart + inputRow, - kStart + inputCol${e?", batchIndices":""}); - `,dl=t=>t?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",ya=(t,e,r="f32",n,a=!1,i=32,s=!1,o=32,l=!1)=>{let d=t[1]*e[1],p=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 m=h/e[1],g=u/e[0],_=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) * ${p}; - - // 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]}) { - ${ei(a,n)} - } - } - // Load one tile of B into local memory. - for (var inputRow = localRow; inputRow < ${i}; inputRow = inputRow + ${e[1]}) { - for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${e[0]}) { - mm_Bsub[inputRow][inputCol] = mm_readB(batch, - kStart + inputRow, - globalColStart + inputCol${n?", batchIndices":""}); - } - } - kStart = kStart + tileInner; - workgroupBarrier(); - - // Compute acc values for a single thread. - var BCached : array<${r}, 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) * ${m}; -let tileColA = i32(localId.x) * ${g}; -let tileRowB = i32(localId.y) * ${_}; -// 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 < ${m}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < ${g}; innerCol = innerCol + 1) { - let inputRow = tileRowA + innerRow; - let inputCol = tileColA + innerCol; - ${ei(a,n)} - } - } - - // Load one tile of B into local memory. - for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) { - for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { - let inputRow = tileRowB + innerRow; - let inputCol = tileCol + innerCol; - mm_Bsub[inputRow][inputCol] = mm_readB(batch, - kStart + inputRow, - globalCol + innerCol${n?", batchIndices":""}); - } - } - kStart = kStart + tileInner; - workgroupBarrier(); - - // Compute acc values for a single thread. - var BCached : array<${r}, 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) { - ${dl(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)"}; - ${n?`let batchIndices = ${n.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} - } -`},cl=(t,e,r,n,a,i=!1)=>{let[s,o,l]=a,[d,p,u,h]=n,m=un(s,l),g=un(o,l),_=gt(n[0].type.tensor),b=()=>{let x=p.rank,C=d.rank,E=`var aIndices: ${p.type.indices};`;for(let A=x-2-1,k=C-1;A>=0;A--,k--)E+=` -aIndices[${A}] = ${C>1?`batchIndices[${k}]`:"batchIndices"};`;return m.forEach(A=>{E+=` -aIndices[${A}] = 0;`}),E+=` -aIndices[${x-2}] = u32(row); - aIndices[${x-1}] = u32(colIn);`,E},w=()=>{let x=u.rank,C=d.rank,E=`var bIndices: ${u.type.indices};`;for(let A=x-2-1,k=C-1;A>=0;A--,k--)E+=` -bIndices[${A}] = ${C>1?`batchIndices[${k}]`:"batchIndices"};`;return g.forEach(A=>{E+=` -bIndices[${A}] = 0;`}),E+=` -bIndices[${x-2}] = u32(row); - bIndices[${x-1}] = u32(colIn);`,E};return` - fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${rt(t,_)} { - var value = ${rt(t,_)}(0.0); - let col = colIn * ${t}; - if(row < uniforms.dimAOuter && col < uniforms.dimInner) - { - ${b()} - value = ${p.getByIndices("aIndices")}; - } - return value; - } - - fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${rt(t,_)} { - var value = ${rt(t,_)}(0.0); - let col = colIn * ${t}; - if(row < uniforms.dimInner && col < uniforms.dimBOuter) - { - ${w()} - value = ${u.getByIndices("bIndices")}; - } - return value; - } - - fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${rt(t,_)}) { - let col = colIn * ${t}; - if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { - var value = valueIn; - let coords = vec3(batch, row, colIn); - ${e?`value = value + ${i?"bias[colIn]":`${rt(t,_)}(bias[row])`};`:""} - ${r} - ${h.setByIndices("vec3(coords)","value")} - } - } - `},ms=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i.slice(0,-2),l=s.slice(0,-2),d=n?n.slice(0,-2):r.slice(0,-2),p=et(d.length),u=p?d.length:d,h=us("batchDims",t[0].dataType,u,1),m=Y.size(d),g=i[i.length-2],_=i[i.length-1],b=s[s.length-1],w=_%4===0&&b%4===0,x=g<=8?[4,1,1]:[4,4,1],C=[8,8,1],E=[Math.ceil(b/C[0]/x[0]),Math.ceil(g/C[1]/x[1]),Math.ceil(m/C[2]/x[2])],A=gt(t[0].dataType),k=w?4:1,P=[...o,g,_/k],N=et(P.length),L=N?P.length:P,j=[...l,_,b/k],O=et(j.length),K=O?j.length:j,se=[m,g,b/k],X=H("a",t[0].dataType,L,k),xe=H("b",t[1].dataType,K,k),te=we("result",t[0].dataType,se.length,k),re=[X,xe],Z=[{type:"int32",data:g},{type:"int32",data:b},{type:"int32",data:_}];p&&Z.push(...ee(d)),N&&Z.push(...ee(P)),O&&Z.push(...ee(j));let V=[];V.push(N?"rank":"dims"),V.push(O?"rank":"dims");let oe=t.length>2,{activationFunction:F,applyActivation:ae}=Ir(e,te.type.value),be=cl(k,oe,ae,[h,X,xe,te],[o,l,d],a);if(oe){let Re=a?k:1;re.push(H("bias",t[2].dataType,t[2].dims.length,Re)),Z.push(...ee(t[2].dims)),V.push("rank")}Z.push(...ee(se));let De=Re=>` - ${Re.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").registerInternalVariables(h).declareVariables(...re,te)} - ${F} - ${be} - ${w?ga(x,C,A,h):ya(x,C,A,h)} - `;return{name:"MatMul",shaderCache:{hint:e.activationCacheKey+`${x}${w}${a}`,inputDependencies:V},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:Z}),getShaderSource:De}}}),pl,dp,Zm=G(()=>{sr(),Ce(),or(),hs(),up(),Sa(),pl=(t,e,r,n,a=!1,i,s=4,o=4,l=4,d="f32")=>{let p=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); - `,m=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])",_=t?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",b=t?"row":"col",w=t?"col":"row",x=` - 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 = ${w} / (filterDims[1] * inChannels); - let WCol = ${w} / 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 = ${w} % inChannels; - var resData = ${rt(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 < ${_}) { - ${h} - let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); - ${p(s)} - } - return resData;`,C=t?e&&n?` - let col = colIn * ${s}; - ${x}`:` - let col = colIn * ${s}; - if (row < uniforms.dimAOuter && col < uniforms.dimInner) { - ${x} - } - return ${rt(s,d)}(0.0);`:n&&r?` - let col = colIn * ${s}; - ${x}`:` - let col = colIn * ${s}; - if (row < uniforms.dimInner && col < uniforms.dimBOuter) { - ${x} - } - return ${rt(s,d)}(0.0);`,E=`${u(o)}`,A=rt(l,d),k=rt(t?s:o,d),P=rt(t?o:s,d),{activationFunction:N,applyActivation:L}=Ir(i,A);return` - ${N} - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${k} { - ${t?C:E} - } - - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${P} { - ${t?E:C} - } - - fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${A}) { - 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])"}; - ${m} - ${ps(a)} - ${L} - setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); - } - }`},dp=(t,e,r,n,a,i,s,o)=>{let l=e.format==="NHWC",d=l?t[0].dims[3]:t[0].dims[1],p=r[0],u=l?r[2]:r[3],h=l?r[1]:r[2],m=l?r[3]:r[1],g=l&&(d%4===0||d%3===0)&&m%4===0,_=l?m:u*h,b=l?u*h:m,w=[8,8,1],x=n<=8?[4,1,1]:[4,4,1],C=[Math.ceil(_/w[0]/x[0]),Math.ceil(b/w[1]/x[1]),Math.ceil(p/w[2]/x[2])];Je("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${C}`);let E=g?l&&d%4!==0?3:4:1,A=w[1]*x[1],k=w[0]*x[0],P=Math.max(w[0]*E,w[1]),N=n%A===0,L=a%k===0,j=i%P===0,O=g?[E,4,4]:[1,1,1],K=gt(t[0].dataType),se=g?4:1,X=[{type:"int32",data:n},{type:"int32",data:a},{type:"int32",data:i}],xe=H("x",t[0].dataType,t[0].dims.length,E===3?1:E),te=H("w",t[1].dataType,t[1].dims.length,se),re=[xe,te];X.push(...ee(t[0].dims)),X.push(...ee(t[1].dims));let Z=` - fn setOutputAtIndex(flatIndex : i32, value : ${g?`vec4<${K}>`:K}) { - result[flatIndex] = ${g?`vec4<${K}>`:K}(value); - } - fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${g?`vec4<${K}>`:K}) { - let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); - setOutputAtIndex(flatIndex ${g?"/ 4":""}, value); - }`;if(s){let oe=H("bias",t[2].dataType,t[2].dims.length,se);re.push(oe),X.push(...ee(t[2].dims)),Z+=` - fn getBiasByOutputCoords(coords : vec4) -> ${g?`vec4<${K}>`:K} { - return bias[coords.${l?"w":"y"}${g?"/ 4":""}]; - }`}let V=we("result",t[0].dataType,r.length,se);return X.push(...ee(r)),{name:"Conv2DMatMul",shaderCache:{hint:e.cacheKey},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:X}),getShaderSource:oe=>` - ${fs("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 }; - ${oe.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...re,V)} - const filterDims : vec2 = vec2(${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]}); - ${Z} - ${pl(l,N,L,j,s,e,O[0],O[1],O[2],K)} - ${g?ga(x,w,K,void 0,!l,P):ya(x,w,K,void 0,!l,P,!1,void 0,o)}`}}}),Pi,Jm=G(()=>{Te(),Ce(),hp(),or(),Pi=(t,e,r)=>{let n=t.length>2,a=n?"value += b[output_channel];":"",i=t[0].dims,s=t[1].dims,o=s[0]/e.group,l=e.format==="NHWC",d=Ni(i,s,e.dilations,e.pads,e.strides,l),p=Y.size(d),u=we("output",t[0].dataType,d),{activationFunction:h,applyActivation:m}=Ir(e,u.type.value),g=H("x",t[0].dataType,i),_=H("w",t[1].dataType,s),b=[g,_];n&&b.push(H("b",t[2].dataType,t[2].dims));let w=x=>` - const strides: vec2 = vec2(${e.strides[0]}u, ${e.strides[1]}u); - const pads: vec2 = vec2(${e.pads[0]}u, ${e.pads[1]}u); - - ${x.declareVariables(...b,u)} - - ${h} - - ${x.mainStart()} - ${x.guardAgainstOutOfBoundsWorkgroupSizes(p)} - - 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 = ${_.get("output_channel","wInChannel","wHeight","wWidth")}; - value += xVal*wVal; - } - } - } - ${a} - ${m} - ${u.setByOffset("global_idx","value")} - }`;return{name:"GroupedConv",shaderCache:{hint:e.cacheKey},getRunData:()=>({outputs:[{dims:r?r(d):d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)}}),getShaderSource:w}}}),Di,hl,cp,pp=G(()=>{Te(),Sa(),Ce(),or(),Di=(t,e,r,n,a=!1)=>{let i=t[0].dims,s=t[1].dims,o=i[i.length-2],l=s[s.length-1],d=i[i.length-1],p=dt(l),u=dt(d),h=dt(o),m=Y.size(r)/p/h,g=t.length>2,_=n?n.slice(0,-2):r.slice(0,-2),b=[Y.size(_),o,l],w=[{type:"uint32",data:m},{type:"uint32",data:o},{type:"uint32",data:l},{type:"uint32",data:d},...ee(_),...ee(i),...ee(s)];g&&w.push(...ee(t[2].dims)),w.push(...ee(b));let x=C=>{let E=us("batch_dims",t[0].dataType,_.length),A=H("a",t[0].dataType,i.length,u),k=H("b",t[1].dataType,s.length,p),P=we("output",t[0].dataType,b.length,p),{activationFunction:N,applyActivation:L}=Ir(e,P.type.value),j=[A,k],O="";if(g){let Z=a?p:1;j.push(H("bias",t[2].dataType,t[2].dims.length,Z)),O=`${a?`value += bias[col / ${Z}];`:`value += ${P.type.value}(bias[row + i]);`}`}let K=i.slice(0,-2),se=s.slice(0,-2),X=un(K,_),xe=un(se,_),te=(Z,V)=>{let oe=Z.rank,F=Z.name;if(oe===2)return`var ${F}_indices = ${Z.type.indices}(0u, 0u);`;let ae=E.rank,be=`var ${F}_indices: ${Z.type.indices};`;for(let De=oe-2-1,Re=ae-1;De>=0;De--,Re--)be+=` -${F}_indices[${De}] = ${ae>1?`batch_indices[${Re}]`:"batch_indices"};`;return V.forEach(De=>{be+=` -${F}_indices[${De}] = 0;`}),be+=`${F}_indices[${oe-2}] = 0u; - ${F}_indices[${oe-1}] = 0u;`,be},re=()=>{let Z=`var a_data: ${A.type.value};`;for(let V=0;V; - for (var k: u32 = 0u; k < uniforms.K; k = k + ${u}) { - ${re()} - } - for (var i = 0u; i < ${h}u; i++) { - var value = values[i]; - ${O} - ${L} - let cur_indices = ${P.type.indices}(batch, row + i, col); - let offset = ${P.indicesToOffset("cur_indices")}; - ${P.setByOffset(`offset / ${p}`,"value")}; - } - } - `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activationCacheKey}_${p}_${u}_${h}_${a}`,inputDependencies:g?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:w}),getShaderSource:x}},hl=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.")},cp=t=>{hl(t.inputs);let e=$r.calcShape(t.inputs[0].dims,t.inputs[1].dims,!0);if(!e)throw new Error("Can't use matmul on the given tensors");let r=e[e.length-1],n=t.inputs[0].dims[t.inputs[0].dims.length-1];r<8&&n<8?t.compute(Di(t.inputs,{activation:"",activationCacheKey:""},e)):t.compute(ms(t.inputs,{activation:"",activationCacheKey:""},e))}}),Ni,ti,fl,ri,Fi,ml,gl,Ui,hp=G(()=>{Te(),tt(),Zm(),Sa(),Jm(),or(),pp(),mn(),Ni=(t,e,r,n,a,i)=>{let s=t[0],o=t.slice(i?1:2,i?3:4),l=o.length,d=e[0],p=e.slice(2).map((h,m)=>h+(h-1)*(r[m]-1)),u=o.map((h,m)=>h+n[m]+n[m+l]).map((h,m)=>Math.floor((h-p[m]+a[m])/a[m]));return u.splice(0,0,s),u.splice(i?3:1,0,d),u},ti=[2,3,1,0],fl=(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 r=t[0].dims[e.format==="NHWC"?t[0].dims.length-1:1],n=t[1].dims[1]*e.group;if(r!==n)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")},ri=(t,e)=>{let r=t.kernelShape.slice();for(let i=2;i{let e=cs(t),r=t.format,n=["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 Ve({autoPad:n,format:r,dilations:a,group:i,kernelShape:s,pads:o,strides:l,wIsConst:d,...e})},ml=(t,e,r)=>{let n=ri(r,e),a=r.format==="NHWC";if(r.group!==1){t.compute(Pi(e,n));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],p=e[1].dims[3],u=Ni(e[0].dims,e[1].dims,r.dilations,n.pads,r.strides,a),h=u[a?1:2],m=u[a?2:3],g=u[a?3:1],_=a&&d===s&&p===o&&r.pads[0]===0&&r.pads[1]===0;if(_||d===1&&p===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 k=u[0],P,N,L,j=[];if(a){let se=t.kernelCustomData.wT??t.compute(Kt(e[1],ti),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=se),_){let X=s*o*l;P=e[0].reshape([1,k,X]),N=se.reshape([1,X,g]),L=[1,k,g]}else P=e[0].reshape([k,s*o,l]),N=se.reshape([1,l,g]),L=[k,h*m,g];j.push(P),j.push(N)}else P=e[0].reshape([k,l,s*o]),N=e[1].reshape([1,g,l]),L=[k,g,h*m],j.push(N),j.push(P);i&&j.push(e[2]);let O=L[2],K=j[0].dims[j[0].dims.length-1];O<8&&K<8?t.compute(Di(j,n,u,L,a),{inputs:j}):t.compute(ms(j,n,u,L,a),{inputs:j});return}let b=!0,w=t.kernelCustomData.wT??t.compute(Kt(e[1],ti),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!t.kernelCustomData.wT&&(t.kernelCustomData.wT=w);let x=[e[0],w];i&&x.push(e[2]);let C=a?h*m:g,E=a?g:h*m,A=d*p*l;t.compute(dp(x,n,u,C,E,A,i,b),{inputs:x})},gl=(t,e)=>{let r=e.format==="NHWC",n=[t.inputs[0].reshape(r?[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&&n.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=ri({...e,pads:a,strides:i,dilations:s,kernelShape:o},n);t.compute(Pi(n,l,d=>r?[d[0],d[2],d[3]]:[]))},Ui=(t,e)=>{fl(t.inputs,e),t.inputs[0].dims.length===3?gl(t,e):ml(t,t.inputs,e)}}),yl,fp,eg=G(()=>{sr(),Ce(),or(),hs(),up(),Sa(),yl=(t,e=!1,r,n=4)=>{let a=rt(n,"f32"),i=w=>{switch(w){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 ${w} 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]",p=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 = ${p} / outWidth; - let outCol = ${p} % 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; - ${s} - return x[getIndexFromCoords4D(coord, vec4(uniforms.x_shape))/${n}];`,m=t?` - let col = colIn * ${n}; - if (row < uniforms.dimAOuter && col < uniforms.dimInner) { - ${h} - } - return ${a}(0.0);`:` - let col = colIn * ${n}; - if (row < uniforms.dimInner && col < uniforms.dimBOuter) { - ${h} - } - return ${a}(0.0);`,g=` - let col = colIn * ${n}; - 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(n)} - } - return ${a}(0.0); - `,{activationFunction:_,applyActivation:b}=Ir(r,a);return` - ${_} - fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${a} { - ${t?m:g} - } - - fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${a} { - ${t?g:m} - } - - fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${a}) { - let col = colIn * ${n}; - if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { - var value = valueInput; - let outWidth = ${t?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; - ${o} - ${ps(e)} - ${b} - result[getIndexFromCoords4D(coords, vec4(uniforms.result_shape))/${n}] = value; - } - }`},fp=(t,e,r,n,a,i,s,o)=>{let l=e.format==="NHWC",d=l?t[0].dims[3]:t[0].dims[1],p=r[0],u=l?r[2]:r[3],h=l?r[1]:r[2],m=l?r[3]:r[1],g=l?d%4===0&&m%4===0:u%4===0&&m%4===0,_=l?m:u*h,b=l?u*h:m,w=g?[8,8,1]:[_<=4||b<=4?4:16,_>4&&b<=4?4:16,1],x=g?[4,4,1]:[_<=4?1:4,_>4&&b<=4?1:4,1],C=[Math.ceil(_/w[0]/x[0]),Math.ceil(b/w[1]/x[1]),Math.ceil(p/w[2]/x[2])];Je("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${C}`);let E=g?4:1,A=Math.max(w[0]*E,w[1]),k=g?4:1,P=[{type:"int32",data:n},{type:"int32",data:a},{type:"int32",data:i}],N=H("x",t[0].dataType,t[0].dims.length,k),L=H("w",t[1].dataType,t[1].dims.length,1),j=we("result",t[0].dataType,r.length,k),O=[N,L];P.push(...ee(t[0].dims)),P.push(...ee(t[1].dims));let K="";if(s){let se=H("bias",t[2].dataType,t[2].dims.length,k);O.push(se),P.push(...ee(t[2].dims)),K+=` - fn getBiasByOutputCoords(coords : vec4) -> ${g?"vec4":"f32"} { - return bias[coords.${l?"w":"y"}${g?"/ 4":""}]; - }`}return P.push(...ee(r)),{name:"Conv2DTransposeMatMul",shaderCache:{hint:e.cacheKey},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:C[0],y:C[1],z:C[2]},programUniforms:P}),getShaderSource:se=>` - ${fs("uniforms.result_strides")} - ${se.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...O,j)}; - const outBackprop : vec4 = vec4(${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 = ${n}; - const dimBOuter : i32 = ${a}; - const dimInner : i32 = ${i}; - ${K} - ${yl(l,s,e,E)} - ${g?ga(x,w,"f32",void 0,!l,A):ya(x,w,"f32",void 0,!l,A,!1,void 0,o)}`}}}),_l,Li,tg=G(()=>{sr(),Te(),Ce(),_l=(t,e,r,n,a,i,s=!1,o)=>{let l=r.format==="NHWC",d=l?1:2,p=l?2:3,u=l?3:1,h=Y.size(n),m=s?2:1,g=r.group,_=e[1].dims,b=_[0]/g,w=_[1],x=` - fn setOutputAtIndex(flatIndex : u32, value : ${s?`vec4<${o}>`:o}) { - result[flatIndex] = ${s?`vec4<${o}>`:o}(value); - }`;a&&(x+=` - fn getBiasByOutputCoords(coords : vec4) -> ${s?`vec4<${o}>`:o} { - return bias[coords.${l?"w":"y"}${s?"/ 4":""}]; - }`);let C=s?4:1,E=H("W",e[1].dataType,e[1].dims,C),A=H("Dy",e[0].dataType,e[0].dims,C),k=[A,E];a&&k.push(H("bias",e[2].dataType,[n[u]],C));let P=we("result",e[0].dataType,n,C),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"} * ${m}; - 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, ${m}>; - for (var i = 0; i < ${m}; i++) { - dotProd[i] = vec4<${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 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${A.get("batch","idyR","idyC","d2")}; - let tmpval = vec4<${o}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - dotProd[0] = dotProd[0] + tmpval; - - xValue = ${A.get("batch","idyR","idyC2","d2")}; - - dotProd[1] = dotProd[1] + vec4<${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 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${A.get("batch","idyR","idyC","d2")}; - let tmpval = vec4<${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 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1","d2")}; - let wValue1 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")}; - let wValue2 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")}; - let wValue3 = ${E.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")}; - - var xValue = ${A.get("batch","idyR","idyC2","d2")}; - let tmpval = vec4<${o}>(dot(xValue, wValue0), - dot(xValue, wValue1), - dot(xValue, wValue2), - dot(xValue, wValue3)); - dotProd[1] = dotProd[1] + tmpval; - } - } - } - } - - for (var i: u32 = 0; i < ${m}; i = i + 1) { - let value = dotProd[i] + ${a?"bias[c+i]":`vec4<${o}>(0.0)`}; - ${P.set("batch","r","c + i","d1","value")}; - } - }`,L=` - 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",p)}; - let dyCorner = vec2(i32(r), i32(c)) - pads; - let dyRCorner = dyCorner.x; - let dyCCorner = dyCorner.y; - let groupId = d1 / ${w}; - let wOutChannel = d1 - groupId * ${w}; - // 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 >= ${o}(outBackprop[${p}]) || - 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?A.get("batch","idyR","idyC","inputChannel"):A.get("batch","inputChannel","idyR","idyC")}; - let wValue = ${E.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")}; - dotProd = dotProd + xValue 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o&&p.push(...ee(e)),l&&p.push(...ee(n)),{name:"Expand",shaderCache:{hint:`${n.length}`,inputDependencies:[o?"rank":"dims"]},getShaderSource:d,getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:p})}},vp=t=>{Ol(t.inputs),t.compute(Rl(t.inputs),{inputs:[0]})}}),Bl,Pl,$p,xp,sg=G(()=>{qe(),Te(),tt(),Ce(),Bl=t=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.")},Pl=(t,e)=>{let r=t[0].dims,n=t[1].dims,a=r.length,i=Y.normalizeAxis(e.axis,a),s=r.slice(0);s.splice(i,1,...n);let o=r[i],l=t[0].dataType===9?4:1,d=Math.ceil(Y.size(s)/l),p=et(t[0].dims.length),u=p?t[0].dims.length:t[0].dims,h=et(t[1].dims.length),m=h?t[1].dims.length:t[1].dims,g=et(s.length),_=g?s.length:s,b=[{type:"uint32",data:d},{type:"int32",data:o},{type:"uint32",data:i}];p&&b.push(...ee(t[0].dims)),h&&b.push(...ee(t[1].dims)),g&&b.push(...ee(s));let w=[];w.push(p?"rank":"dims"),w.push(h?"rank":"dims");let x=C=>{let E=H("data",t[0].dataType,u,l),A=H("inputIndices",t[1].dataType,m),k=we("output",t[0].dataType,_,l),P=L=>{let j=n.length,O=`var indicesIndices${L} = ${A.type.indices}(0);`;for(let K=0;K1?`indicesIndices${L}[${K}]`:`indicesIndices${L}`} = ${s.length>1?`outputIndices${L}[uniforms.axis + ${K}]`:`outputIndices${L}`};`;O+=` - var idx${L} = ${A.getByIndices(`indicesIndices${L}`)}; - if (idx${L} < 0) { - idx${L} = idx${L} + uniforms.axisDimLimit; - } - var dataIndices${L} = ${E.type.indices}(0); - `;for(let K=0,se=0;K1?`dataIndices${L}[${K}]`:`dataIndices${L}`} = u32(idx${L});`,se+=j):(O+=`${a>1?`dataIndices${L}[${K}]`:`dataIndices${L}`} = ${s.length>1?`outputIndices${L}[${se}]`:`outputIndices${L}`};`,se++);return O},N;if(t[0].dataType===9){let L=(j,O,K="")=>` - let outputIndices${O} = ${k.offsetToIndices(`outputOffset + ${O}u`)}; - ${P(O)}; - let offset${O} = ${E.indicesToOffset(`dataIndices${O}`)}; - let index${O} = offset${O} / 4u; - let component${O} = offset${O} % 4u; - ${j}[${O}] = 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r=t.inputs;Bl(r),t.compute(Pl(t.inputs,e))}}),Dl,Nl,Sp,Ep,og=G(()=>{Te(),tt(),Ce(),Dl=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.`)},Nl=(t,e)=>{let r=t[0].dims,n=t[0].dataType,a=r.length,i=t[1].dims,s=t[1].dataType,o=Y.normalizeAxis(e.axis,a),l=r[o],d=i.slice(0),p=Y.size(d),u=H("input",n,a),h=H("indicesInput",s,i.length),m=we("output",n,d.length),g=[{type:"uint32",data:p},{type:"int32",data:l},{type:"uint32",data:o}];return g.push(...ee(r)),g.push(...ee(i)),g.push(...ee(d)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:g}),getShaderSource:_=>` - ${_.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(u,h,m)} - ${_.mainStart()} - ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - - let outputIndices = ${m.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")}; - - ${m.setByOffset("global_idx","value")}; - }`}},Sp=t=>Ve({axis:t.axis}),Ep=(t,e)=>{let r=t.inputs;Dl(r),t.compute(Nl(t.inputs,e))}}),Fl,Ul,Cp,Ip,lg=G(()=>{Te(),Ce(),Fl=t=>{if(!t)throw new Error("Input is missing");if(t.length<2||t.length>3)throw new Error("Invaid input number.");if(t.length===3&&t[2].dims.length>2)throw new Error("Invalid input shape of C");if(t[0].dataType!==t[1].dataType||t.length===3&&t[0].dataType!==t[2].dataType)throw new Error("Input types are mismatched")},Ul=(t,e)=>{let r=t[0].dims.slice(),n=t[1].dims.slice(),[a,i,s]=Wd.getShapeOfGemmResult(r,e.transA,n,e.transB,t.length===3?t[2].dims:void 0),o=[a,i];if(!o)throw new Error("Can't use gemm on the given tensors");let l=Y.size(o),d=[{type:"uint32",data:l},{type:"uint32",data:a},{type:"uint32",data:i},{type:"uint32",data:s},{type:"float32",data:e.alpha},{type:"float32",data:e.beta}],p=["type","type"];t.length===3&&(d.push(...ee(t[2].dims)),p.push("rank")),d.push(...ee(o));let u=h=>{let m="";e.transA&&e.transB?m="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?m="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?m="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(m="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let g=e.alpha===1?"":"value *= 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}`};return{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:u}},Cp=t=>{let e=t.transA,r=t.transB,n=t.alpha,a=t.beta;return{transA:e,transB:r,alpha:n,beta:a,cacheKey:`${t.transA};${t.transB};${t.alpha===1}`}},Ip=(t,e)=>{Fl(t.inputs),t.compute(Ul(t.inputs,e))}}),Ll,Wl,Vl,Tp,ug=G(()=>{qe(),Te(),Ce(),Ll=(t,e)=>{let r=t[0].dims,n=r,a=2,i=Y.sizeToDimension(r,a),s=Y.sizeFromDimension(r,a),o=dt(s),l=s/o,d=[r[0],r[1],l],p=["rank","type","type"],u=[{type:"uint32",data:s},{type:"uint32",data:l}];u.push(...ee(d),...ee(d));let h=m=>{let g=H("x",t[0].dataType,d.length,o),_=H("scale",t[1].dataType,t[1].dims),b=H("bias",t[2].dataType,t[2].dims),w=we("output",t[0].dataType,d.length,o),x=[g,_,b,w],C=g.type.value,E=o===1?"f32":`vec${o}`,A=64,k=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return` - var meanShared : f32; - var squaredNormShared : f32; - var workgroupShared : array<${E}, ${A}>; - const workgroupSize = ${A}u; - ${m.registerUniforms(k).declareVariables(...x)} - ${m.mainStart(A)} - 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 = ${E}(0); - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - initial = initial + ${E}(${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 = ${zt("workgroupShared[0]",o)} / f32(uniforms.normSize); - } - workgroupBarrier(); - - // reinitialize workgroup memory. - initial = ${E}(0); - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - let deviation = ${E}(${g.get("batch","channel","h")}) - ${E}(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 = ${zt("workgroupShared[0]",o)}; - } - workgroupBarrier(); - - let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${e.epsilon})); - let channelScale = invStdDev * f32(${_.getByOffset("channel")}); - let channelShift = f32(${b.getByOffset("channel")}) - meanShared * channelScale; - for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) { - let value = ${g.get("batch","channel","h")} * ${C}(${E}(channelScale)) + ${C}(${E}(channelShift)); - ${w.set("batch","channel","h","value")}; - } - }`};return{name:"InstanceNormalization",shaderCache:{hint:`${e.epsilon};${o}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:i},programUniforms:u}),getShaderSource:h}},Wl=(t,e,r,n,a,i,s,o)=>{let l=dt(s),d=64,p=l===1?"vec2f":`mat2x${l}f`,u=l===1?"f32":`vec${l}f`,h=(k,P)=>`${p}(${k}, ${P})`,m=a*s/l,g=Math.ceil(i/d),_=["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)}],w=k=>{let P=H("input",e.dataType,e.dims,l);return` - ${k.declareVariables(P)} - @group(0) @binding(1) var output : array<${p}>; - struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32}; - @group(0) @binding(2) var uniforms: Uniforms; - - ${k.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 = ${ut("f32",l)}; - var squaredSum = ${ut("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] = ${h("sum","squaredSum")}; - }`},x=t.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${l}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:[a,s,d,2],dataType:1}],dispatchGroup:{x:a*s/l},programUniforms:b}),getShaderSource:w},{inputs:[e],outputs:[-1]})[0],C=[{type:"uint32",data:m},{type:"uint32",data:i},{type:"uint32",data:Math.floor(s/l)},{type:"uint32",data:Math.floor(d*s/l)}],E=["type","type","type"],A=k=>{let P=H("scale",r.dataType,r.dims,l),N=H("bias",n.dataType,n.dims,l);return` - @group(0) @binding(0) var input : array<${p}>; - @group(0) @binding(1) var scale : array<${P.type.storage}>; - @group(0) @binding(2) var bias : array<${N.type.storage}>; - @group(0) @binding(3) var output : array<${p}>; - struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32}; - @group(0) @binding(4) var uniforms: Uniforms; - - ${k.mainStart()} - ${k.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 = ${ut("f32",l)}; - var squaredSum = ${ut("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};${o}`,inputDependencies:E},getRunData:()=>({outputs:[{dims:[a,s,2],dataType:1}],dispatchGroup:{x:Math.ceil(m/64)},programUniforms:C}),getShaderSource:A},{inputs:[x,r,n],outputs:[-1]})[0]},Vl=(t,e,r)=>{let n=e[0].dims,a=n,i=n[0],s=n[n.length-1],o=Y.sizeFromDimension(n,1)/s,l=dt(s),d=Y.size(a)/l,p=[{type:"uint32",data:o},{type:"uint32",data:Math.floor(s/l)}],u=["type","type"],h=Wl(t,e[0],e[1],e[2],i,o,s,r.epsilon),m=g=>{let _=gt(e[0].dataType),b=l===1?"vec2f":`mat2x${l}f`,w=l===1?_:`vec${l}<${_}>`,x=H("input",e[0].dataType,e[0].dims,l),C=we("output",e[0].dataType,a,l);return` - @group(0) @binding(0) var input : array<${x.type.storage}>; - @group(0) @binding(1) var scaleInput : array<${b}>; - @group(0) @binding(2) var output : array<${C.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], ${w}(scale[0]), ${w}(scale[1])); - }`};t.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:m},{inputs:[e[0],h]})},Tp=(t,e)=>{e.format==="NHWC"?Vl(t,t.inputs,e):t.compute(Ll(t.inputs,e))}}),Gl,Hl,Ap,dg=G(()=>{qe(),Te(),Ce(),Gl=t=>{if(!t||t.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Hl=(t,e,r)=>{let n=t[0].dims,a=t[1],i=t[2],s=n,o=Y.normalizeAxis(e.axis,n.length),l=Y.sizeToDimension(n,o),d=Y.sizeFromDimension(n,o),p=Y.size(a.dims),u=i?Y.size(i.dims):0;if(p!==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 ${p} and bias size of ${u}`);let h=[];for(let E=0;E1,w=r>2,x=E=>{let A=gt(t[0].dataType),k=[H("x",t[0].dataType,t[0].dims,m),H("scale",a.dataType,a.dims,m)];i&&k.push(H("bias",i.dataType,i.dims,m)),k.push(we("output",t[0].dataType,s,m)),b&&k.push(we("mean_data_output",1,h)),w&&k.push(we("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` - ${E.registerUniforms(P).declareVariables(...k)} - ${E.mainStart()} - ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} - let offset = global_idx * uniforms.norm_size_vectorized; - var meanVector = ${ut("f32",m)}; - var meanSquareVector = ${ut("f32",m)}; - - for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { - let value = ${Nt(A,m,"x[h + offset]")}; - meanVector += value; - meanSquareVector += value * value; - } - let mean = ${zt("meanVector",m)} / uniforms.norm_size; - let invStdDev = - inverseSqrt(${zt("meanSquareVector",m)} / uniforms.norm_size - mean * mean + uniforms.epsilon); - - for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { - let f32input = ${Nt(A,m,"x[j + offset]")}; - let f32scale = ${Nt(A,m,"scale[j]")}; - output[j + offset] = ${k[0].type.value}((f32input - mean) * invStdDev * f32scale - ${i?`+ ${Nt(A,m,"bias[j]")}`:""} - ); - } - - ${b?"mean_data_output[global_idx] = mean":""}; - ${w?"inv_std_output[global_idx] = invStdDev":""}; - }`},C=[{dims:s,dataType:t[0].dataType}];return b&&C.push({dims:h,dataType:1}),w&&C.push({dims:h,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${m};${r}`,inputDependencies:g},getRunData:()=>({outputs:C,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:_}),getShaderSource:x}},Ap=(t,e)=>{Gl(t.inputs),t.compute(Hl(t.inputs,e,t.outputCount))}}),ql,kp,oi,jl,Jn,Mp,cg=G(()=>{Te(),tt(),ss(),mc(),Ce(),mn(),ql=(t,e)=>{let r=t[0],n=t[1],a=t[2],i=t[3],s=t[4],o=t[5],l=t[6],d=t[7];if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let p=!1,u=r.dims[0],h=r.dims[1],m=r.dims.length===3?p?r.dims[2]/3:r.dims[2]:e.numHeads*r.dims[4],g=h,_=0,b=0,w=Math.floor(m/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');_=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 x;if(n){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');x=2,g=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==e.numHeads||n.dims[3]!==2||n.dims[4]!==w)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.');x=5,g=n.dims[1]}else{if(n.dims[1]!==e.numHeads||n.dims[3]!==w)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');x=0,g=n.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]!==e.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(i){if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(a&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let C=0;if(s){C=8;let N=s.dims;throw N.length===1?N[0]===u?C=1:N[0]===3*u+2&&(C=3):N.length===2&&N[0]===u&&N[1]===g&&(C=5),C===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let E=!1,A=m;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(r.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)');A=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)');A=a.dims[1]*a.dims[3],E=!0}}let k=_+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 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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:p},{inputs:[e,r],outputs:[-1]})[0]},Jn=(t,e,r,n,a,i,s,o)=>{let l=i;if(s){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=jl(t,i,s,e,n,r*a,o),l=l.reshape([e,n,r,a]),t.compute(Kt(l,oi.perm),{inputs:[l],outputs:[-1]})[0]}else return i.dims.length===3&&(l=i.reshape([e,n,r,a])),t.compute(Kt(l,oi.perm),{inputs:[l],outputs:[-1]})[0]},Mp=(t,e)=>{let r=ql(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 n=t.inputs[1]&&t.inputs[2]&&t.inputs[1].dims.length===4&&t.inputs[2].dims.length===4,a=Jn(t,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,t.inputs[0],t.inputs[3],0);if(n)return ma(t,a,t.inputs[1],t.inputs[2],t.inputs[4],void 0,void 0,void 0,t.inputs[5],r,e);let i=Jn(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,t.inputs[1],t.inputs[3],r.hiddenSize),s=Jn(t,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,t.inputs[2],t.inputs[3],2*r.hiddenSize);ma(t,a,i,s,t.inputs[4],void 0,t.inputs[6],t.inputs[7],t.inputs[5],r,e)}}),Kl,Yl,Xl,Ql,Zl,Jl,eu,tu,Op,pg=G(()=>{qe(),Te(),Ce(),Kl=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].")}},Yl=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` - k = i32(${t.indicesGet("indices",a)}) - ${ve("uniforms.pads",a,r)}; - if (k < 0) { - break; - } - if (k >= i32(${ve("uniforms.x_shape",a,e)})) { - break; - } - offset += k * i32(${ve("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; - ${n} - value = x[offset]; - } - `},Xl=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` - k = i32(${t.indicesGet("indices",a)}) - ${ve("uniforms.pads",a,r)}; - if (k < 0) { - k = -k; - } - { - let _2n_1 = 2 * (i32(${ve("uniforms.x_shape",a,e)}) - 1); - k = k % _2n_1; - if(k >= i32(${ve("uniforms.x_shape",a,e)})) { - k = _2n_1 - k; - } - } - offset += k * i32(${ve("uniforms.x_strides",a,e)}); - `;return` - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - `},Ql=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` - k = i32(${t.indicesGet("indices",a)}) - ${ve("uniforms.pads",a,r)}; - if (k < 0) { - k = 0; - } - if (k >= i32(${ve("uniforms.x_shape",a,e)})) { - k = i32(${ve("uniforms.x_shape",a,e)}) - 1; - } - offset += k * i32(${ve("uniforms.x_strides",a,e)}); - `;return` - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - `},Zl=(t,e,r)=>{let n="";for(let a=e-1;a>=0;--a)n+=` - k = i32(${t.indicesGet("indices",a)}) - ${ve("uniforms.pads",a,r)}; - if (k < 0) { - k += i32(${ve("uniforms.x_shape",a,e)}]); - } - if (k >= i32(${ve("uniforms.x_shape",a,e)})) { - k -= i32(${ve("uniforms.x_shape",a,e)}); - } - offset += k * i32(${ve("uniforms.x_strides",a,e)}); - `;return` - var offset = 0; - var k = 0; - ${n} - value = x[offset]; - `},Jl=(t,e,r)=>{switch(r.mode){case 0:return Yl(t,e,r.pads.length);case 1:return Xl(t,e,r.pads.length);case 2:return Ql(t,e,r.pads.length);case 3:return Zl(t,e,r.pads.length);default:throw new Error("Invalid mode")}},eu=(t,e)=>{let r=Y.padShape(t[0].dims.slice(),e.pads),n=t[0].dims,a=[{type:"uint32",data:Y.size(r)},{type:"uint32",data:e.pads}];if(e.mode===0){let o=Mt(t[0].dataType);a.push({type:o,data:e.value})}a.push(...ee(t[0].dims),...ee(r));let i=["rank"],s=o=>{let l=we("output",t[0].dataType,r.length),d=H("x",t[0].dataType,n.length),p=d.type.value,u=Jl(l,n.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:p}),` - ${o.registerUniforms(h).declareVariables(d,l)} - ${o.mainStart()} - ${o.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - - let indices = ${l.offsetToIndices("global_idx")}; - - var value = ${p}(0); - ${u} - output[global_idx] = value; - }`};return{name:"Pad",shaderCache:{hint:`${e.mode}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:r,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(Y.size(r)/64)},programUniforms:a}),getShaderSource:s}},tu=(t,e)=>{if(t.length>1){let r=t[1].getBigInt64Array(),n=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:n,pads:s}}else return e},Op=(t,e)=>{Kl(t.inputs);let r=tu(t.inputs,e);t.compute(eu(t.inputs,r),{inputs:[0]})}}),Qr,li,ui,di,ci,ru,nu,pi,hi,zp,Rp,fi,Bp,Pp,mi,Dp,Np,Fp,Up,hg=G(()=>{Ct(),Te(),Ce(),Qr=t=>{if(Pe.webgpu.validateInputContent&&(!t||t.length!==1))throw new Error("Pool ops requires 1 input.")},li=(t,e,r)=>{let n=e.format==="NHWC",a=t.dims.slice();n&&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();ha.adjustPoolAttributes(r,a,s,o,l,d);let p=ha.computePoolOutputShape(r,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=p.slice();return h.push(h.splice(1,1)[0]),[u,n?h:p]},ui=(t,e)=>{let r=e.format==="NHWC",n=Y.size(t),a=Y.size(e.kernelShape),i=[{type:"uint32",data:n},{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],p=e.pads[e.pads.length-1],u=!!(d+p);i.push({type:"uint32",data:o},{type:"uint32",data:l},{type:"uint32",data:d},{type:"uint32",data:p}),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 m=e.kernelShape[e.kernelShape.length-2],g=e.strides[e.strides.length-2],_=e.pads[e.pads.length/2-2],b=e.pads[e.pads.length-2];h=!!(_+b),i.push({type:"uint32",data:m},{type:"uint32",data:g},{type:"uint32",data:_},{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(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=Y.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,p)=>d+p);return[i,s,!!l,!1,!1]}},di=(t,e,r,n,a,i,s,o,l,d,p,u)=>{let h=a.format==="NHWC",m=e.type.value,g=we("output",e.type.tensor,n);if(a.kernelShape.length<=2){let _="",b="",w="",x=r-(h?2:1);if(p?_=` - for (var i: u32 = 0u; i < uniforms.kw; i++) { - xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i; - if (xIndices[${x}] < 0 || xIndices[${x}] - >= uniforms.x_shape[${x}]) { - pad++; - continue; - } - let x_val = x[${e.indicesToOffset("xIndices")}]; - ${i} - }`:_=` - for (var i: u32 = 0u; i < uniforms.kw; i++) { - xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i; - let x_val = x[${e.indicesToOffset("xIndices")}]; - ${i} - }`,a.kernelShape.length===2){let C=r-(h?3:2);u?b=` - for (var j: u32 = 0u; j < uniforms.kh; j++) { - xIndices[${C}] = indices[${C}] * uniforms.sh - uniforms.phStart + j; - if (xIndices[${C}] < 0 || xIndices[${C}] >= uniforms.x_shape[${C}]) { - pad += i32(uniforms.kw); - continue; - } - `:b=` - for (var j: u32 = 0u; j < uniforms.kh; j++) { - xIndices[${C}] = indices[${C}] * uniforms.sh - uniforms.phStart + j; - `,w=` - } - `}return` - ${t.registerUniforms(l).declareVariables(e,g)} - - ${t.mainStart()} - ${t.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - - let indices = ${g.offsetToIndices("global_idx")}; 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- offset -= offsets[j] * ${ve("uniforms.kernelStrides","j",_)}; - } - offsets[${_-1}] = offset; - - isPad = false; - for (var j = ${r-_}u; j < ${r}u; j++) { - xIndices[j] = indices[j] * ${ve("uniforms.strides",`j - ${r-_}u`,_)} - + offsets[j - ${r-_}u] - ${ve("uniforms.pads","j - 2u",b)}; - ${w} - } - ${s} - - output[global_idx] = value; - }`}},ci=t=>`${t.format};${t.ceilMode};${t.autoPad};${t.kernelShape.length}`,ru=t=>`${ci(t)};${t.countIncludePad}`,nu=t=>`${ci(t)};${t.storageOrder};${t.dilations}`,pi=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}),hi=(t,e,r,n)=>{let[a,i]=li(e,n,r),s=H("x",e.dataType,e.dims.length),o=s.type.value,l="value += x_val;",d="";a.countIncludePad?d+=`value /= ${o}(uniforms.kernelSize);`:d+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[p,u,h,m,g]=ui(i,a);p.push(...ee(e.dims),...ee(i));let _=["rank"];return{name:t,shaderCache:{hint:`${n.cacheKey};${h};${m};${g}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(Y.size(i)/64)},programUniforms:p}),getShaderSource:b=>di(b,s,e.dims.length,i.length,a,l,d,0,u,h,m,g)}},zp=t=>{let e=t.count_include_pad!==0,r=pi(t);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:e,...r,cacheKey:""};return{...n,cacheKey:ru(n)}},Rp=(t,e)=>{Qr(t.inputs),t.compute(hi("AveragePool",t.inputs[0],!1,e))},fi={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Bp=t=>{let e=t.format;return{format:e,...fi,cacheKey:e}},Pp=(t,e)=>{Qr(t.inputs),t.compute(hi("GlobalAveragePool",t.inputs[0],!0,e))},mi=(t,e,r,n)=>{let[a,i]=li(e,n,r),s=` - value = max(x_val, value); - `,o="",l=H("x",e.dataType,e.dims.length),d=["rank"],[p,u,h,m,g]=ui(i,a);return p.push(...ee(e.dims),...ee(i)),{name:t,shaderCache:{hint:`${n.cacheKey};${h};${m};${g}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(Y.size(i)/64)},programUniforms:p}),getShaderSource:_=>di(_,l,e.dims.length,i.length,a,s,o,-1e5,u,h,m,g)}},Dp=(t,e)=>{Qr(t.inputs),t.compute(mi("MaxPool",t.inputs[0],!1,e))},Np=t=>{let e=t.storage_order,r=t.dilations,n=pi(t);if(e!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let a={storageOrder:e,dilations:r,...n,cacheKey:""};return{...a,cacheKey:nu(a)}},Fp=t=>{let e=t.format;return{format:e,...fi,cacheKey:e}},Up=(t,e)=>{Qr(t.inputs),t.compute(mi("GlobalMaxPool",t.inputs[0],!0,e))}}),au,iu,Lp,fg=G(()=>{Ct(),qe(),Ce(),au=(t,e,r)=>{let n=t===e,a=te&&r>0;if(n||a||i)throw new Error("Range these inputs' contents are invalid.")},iu=(t,e,r,n)=>{let a=Math.abs(Math.ceil((e-t)/r)),i=[a],s=a,o=Mt(n),l=[{type:"uint32",data:s},{type:o,data:t},{type:o,data:r},...ee(i)],d=p=>{let u=we("output",n,i.length),h=u.type.value,m=[{name:"outputSize",type:"u32"},{name:"start",type:h},{name:"delta",type:h}];return` - ${p.registerUniforms(m).declareVariables(u)} - ${p.mainStart()} - ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} - output[global_idx] = uniforms.start + ${h}(global_idx) * uniforms.delta; - }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:d,getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:l})}},Lp=t=>{let e=0,r=0,n=0;t.inputs[0].dataType===6?(e=t.inputs[0].getInt32Array()[0],r=t.inputs[1].getInt32Array()[0],n=t.inputs[2].getInt32Array()[0]):t.inputs[0].dataType===1&&(e=t.inputs[0].getFloat32Array()[0],r=t.inputs[1].getFloat32Array()[0],n=t.inputs[2].getFloat32Array()[0]),Pe.webgpu.validateInputContent&&au(e,r,n),t.compute(iu(e,r,n,t.inputs[0].dataType),{inputs:[]})}}),su,ou,lu,uu,du,cu,pu,hu,fu,mu,gu,gi,yu,_u,wu,bu,vu,Wp,Vp,mg=G(()=>{Te(),tt(),Ce(),su=(t,e)=>{if(t.every(r=>r>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")}},ou=(t,e,r)=>{e.every(a=>a>=0&&a{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return e.forEach((a,i)=>n[a]=t[i]),n},lu=(t,e,r,n,a,i)=>{let[s,o,l]=r>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(p=>i.push(p));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(p=>n.push(p)),n.length!==0&&n.length!==d&&r>=18&&n.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");su(n,e),e.axes.length>0&&ou(n,e.axes,d).forEach((p,u)=>n[u]=p)}if(l>0&&t.length>l&&(t[l].getBigInt64Array().forEach(p=>a.push(Number(p))),a.length!==d||r>=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(n.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 n<"u"&&typeof a<"u"&&n.length>0&&a.length>d)throw new Error("Resize requires only of scales or sizes to be specified")},uu=(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`)}})()+"}",du=(t,e,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{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`)}})()+"}",cu=(t,e,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),a=t.length===0?n:t.slice();return e.length>0?(e.forEach((i,s)=>{n[i]=a[s],n[s+r]=a[e.length+s]}),n):a},pu=(t,e,r,n)=>{let a=[];if(r.length>0)if(n.length>0){if(t.forEach(i=>a.push(i)),Math.max(...n)>t.length)throw new Error("axes is out of bound");n.forEach((i,s)=>a[i]=r[s])}else r.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},hu=(t,e,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(i=>e[i]),Number.MAX_VALUE):Math.min(...e,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(i=>e[i]),Number.MIN_VALUE):Math.max(...e,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();e.fill(1,0,e.length);let a=t.slice();return r.axes.length>0?(r.axes.forEach(i=>e[i]=n),r.axes.forEach(i=>a[i]=Math.round(t[i]*e[i]))):(e.fill(n,0,e.length),a.forEach((i,s)=>a[s]=Math.round(i*e[s]))),a},fu=(t,e,r,n,a)=>` - fn calculateOriginalIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> array<${t.type.value}, ${r.length}> { - var original_indices: array<${t.type.value}, ${r.length}>; - for (var i:u32 = 0; i < ${r.length}; i++) { - var output_index = ${t.indicesGet("output_indices","i")}; - var scale = ${ve("uniforms.scales","i",n)}; - var roi_low = ${ve("uniforms.roi","i",a)}; - var roi_hi = ${ve("uniforms.roi",`i + ${e.length}`,a)}; - if (scale == 1.0) { - original_indices[i] = ${t.type.value}(output_index); - } else { - var input_shape_i = ${ve("uniforms.input_shape","i",e.length)}; - var output_shape_i = ${ve("uniforms.output_shape","i",r.length)}; - original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, - input_shape_i, roi_low, roi_hi); - } - } - return original_indices; - }`,mu=(t,e,r,n,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 < ${n.length}; i++) { - var output_index = ${e.indicesGet("output_indices","i")}; - var input_index: u32; - var scale = ${ve("uniforms.scales","i",a)}; - if (scale == 1.0) { - input_index = output_index; - } else { - var roi_low = ${ve("uniforms.roi","i",i)}; - var roi_hi = ${ve("uniforms.roi",`i + ${r.length}`,i)}; - var input_shape_i = ${ve("uniforms.input_shape","i",r.length)}; - var output_shape_i = ${ve("uniforms.output_shape","i",n.length)}; - var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, - input_shape_i, roi_low, roi_hi); - if (!${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; - }`,gu=(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 >= ${ve("uniforms.input_shape","i",e.length)}) { - return false; - } - } - return true; - }`,gi=(t,e,r,n)=>t.rank>n?` - ${t.indicesSet("input_indices",e,"channel")}; - ${t.indicesSet("input_indices",r,"batch")}; -`:"",yu=(t,e,r,n,a)=>{let[i,s,o,l]=r.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, ${r[s]} - 1))`)}; - ${t.indicesSet("input_indices",o,`max(0, min(col, ${r[o]} - 1))`)}; - ${gi(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}]; - ${n?`if (row < 0 || row > (${r[s]} - 1) || col < 0 || col > (${r[o]} - 1)) { - return ${a}; - }`:""}; - row = max(0, min(row, ${r[s]} - 1)); - col = max(0, min(col, ${r[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 = ${r.length>2?`u32(originalIndices[${l}])`:"0"}; - var batch: u32 = ${r.length>2?`u32(originalIndices[${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); - }`},_u=(t,e,r,n,a,i,s,o,l,d)=>{let p=r.length===2,[u,h]=p?[0,1]:[2,3],m=t.type.value,g=_=>{let b=_===u?"row":"col";return` - fn ${b}CubicInterpolation(input_indices: ${t.type.indices}, output_indices: ${e.type.indices}) -> ${m} { - var output_index = ${e.indicesGet("output_indices",_)}; - var originalIdx: ${m} = getOriginalCoordinateFromResizedCoordinate(output_index, ${a[_]}, - ${n[_]}, ${r[_]}, ${i[_]}, ${i[_]} + ${r.length}); - var fractOriginalIdx: ${m} = originalIdx - floor(originalIdx); - var coefs = getCubicInterpolationCoefs(fractOriginalIdx); - - if (${o} && (originalIdx < 0 || originalIdx > (${r[_]} - 1))) { - return ${l}; - } - var data: array<${m}, 4> = array<${m}, 4>(0.0, 0.0, 0.0, 0.0); - for (var i: i32 = -1; i < 3; i++) { - var ${b}: ${m} = originalIdx + ${m}(i); - if (${b} < 0 || ${b} >= ${r[_]}) { - ${d?`coefs[i + 1] = 0.0; - continue;`:o?`return ${l};`:`${b} = max(0, min(${b}, ${r[_]} - 1));`}; - } - var input_indices_copy: ${t.type.indices} = input_indices; - ${t.indicesSet("input_indices_copy",_,`u32(${b})`)}; - data[i + 1] = ${_===u?t.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; - } - return cubicInterpolation1D(data, coefs); - }`};return` - ${g(u)}; - ${g(h)}; - fn getCubicInterpolationCoefs(s: ${m}) -> array<${m}, 4> { - var absS = abs(s); - var coeffs: array<${m}, 4> = array<${m}, 4>(0.0, 0.0, 0.0, 0.0); - var oneMinusAbsS: ${m} = 1.0 - absS; - var twoMinusAbsS: ${m} = 2.0 - absS; - var onePlusAbsS: ${m} = 1.0 + absS; - coeffs[0] = ((${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 * ${s}; - return coeffs; - } - - fn cubicInterpolation1D(x: array<${m}, 4>, coefs: array<${m}, 4>) -> ${m} { - var coefsSum: ${m} = 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}) -> ${m} { - var input_indices: ${t.type.indices} = output_indices; - return colCubicInterpolation(input_indices, output_indices); - } - `},wu=(t,e,r,n,a)=>{let[i,s,o,l,d]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],p=t.type.value;return` - fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${p} { - var input_indices: ${t.type.indices}; - ${t.indicesSet("input_indices",s,`max(0, min(depth, ${r[s]} - 1))`)}; - ${t.indicesSet("input_indices",o,`max(0, min(height, ${r[o]} - 1))`)}; - ${t.indicesSet("input_indices",l,`max(0, min(width, ${r[l]} - 1))`)}; - ${gi(t,d,i,3)} - return ${t.getByIndices("input_indices")}; - } - - fn trilinearInterpolation(output_indices: ${e.type.indices}) -> ${p} { - var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); - var depth:${p} = originalIndices[${s}]; - var height:${p} = originalIndices[${o}]; - var width:${p} = originalIndices[${l}]; - ${n?`if (depth < 0 || depth > (${r[s]} - 1) || height < 0 || height > (${r[o]} - 1) || width < 0 || (width > ${r[l]} - 1)) { - return ${a}; - }`:""}; - - depth = max(0, min(depth, ${r[s]} - 1)); - height = max(0, min(height, ${r[o]} - 1)); - width = max(0, min(width, ${r[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 = ${r.length>3?`u32(originalIndices[${d}])`:"0"}; - var batch: u32 = ${r.length>3?`u32(originalIndices[${i}])`:"0"}; - - var x111: ${p} = getInputValue(batch, channel, depth1, height1, width1); - var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2); - var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1); - var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2); - var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1); - var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2); - var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1); - var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2); - var dx1: ${p} = abs(depth - ${p}(depth1)); - var dx2: ${p} = abs(${p}(depth2) - depth); - var dy1: ${p} = abs(height - ${p}(height1)); - var dy2: ${p} = abs(${p}(height2) - height); - var dz1: ${p} = abs(width - ${p}(width1)); - var dz2: ${p} = abs(${p}(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); - }`},bu=(t,e,r,n,a,i)=>{let s=t.dims,o=cu(i,e.axes,s.length),l=pu(s,n,a,e.axes),d=n.slice();n.length===0&&(d=s.map((x,C)=>x===0?1:l[C]/x),e.keepAspectRatioPolicy!=="stretch"&&(l=hu(s,d,e)));let p=we("output",t.dataType,l.length),u=H("input",t.dataType,s.length),h=Y.size(l),m=s.length===l.length&&s.every((x,C)=>x===l[C]),g=e.coordinateTransformMode==="tf_crop_and_resize",_=e.extrapolationValue,b=u.type.value,w=x=>` - ${m?"":` - ${uu(e.coordinateTransformMode,b)}; - ${(()=>{switch(e.mode){case"nearest":return` - ${gu(u,s)}; - ${du(e.nearestMode,r,b)}; - ${mu(u,p,s,l,d.length,o.length,g)}; - `;case"linear":return` - ${fu(p,s,l,d.length,o.length)}; - ${(()=>{if(s.length===2||s.length===4)return`${yu(u,p,s,g,_)}`;if(s.length===3||s.length===5)return`${wu(u,p,s,g,_)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; - `;case"cubic":return` - ${(()=>{if(s.length===2||s.length===4)return`${_u(u,p,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")}})()}; - `} - ${x.registerUniform("output_size","u32").registerUniform("scales","f32",d.length).registerUniform("roi","f32",o.length).declareVariables(u,p)} - ${x.mainStart()} - ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} - ${m?"output[global_idx] = input[global_idx];":` - let output_indices = ${p.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}|${r}|${d.length>0?d:""}|${a.length>0?a:""}|${o.length>0?o:""}|${m}|${s}`,inputDependencies:["rank"]},getShaderSource:w,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},...ee(s),...ee(l)]})}},vu=t=>{let e=t.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},Wp=(t,e)=>{let r=[],n=[],a=[],i=vu(t);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");lu(t.inputs,e,i,r,n,a),t.compute(bu(t.inputs[0],e,i,r,n,a),{inputs:[0]})},Vp=t=>{let e=t.antialias,r=t.axes,n=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 Ve({antialias:e,axes:r,coordinateTransformMode:n,cubicCoeffA:a,excludeOutside:i,extrapolationValue:s,keepAspectRatioPolicy:o,mode:l,nearestMode:d})}}),$u,xu,Gp,Hp,gg=G(()=>{qe(),Te(),tt(),Ce(),$u=t=>{if(!t||t.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=t[0],r=t[1],n=t[2];if(e.dataType!==r.dataType||e.dataType!==n.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(r.dims.length!==3&&r.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(r.dims[r.dims.length-1]!==a)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==i)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==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")}},xu=(t,e,r,n)=>{let a=t[0].dims,i=Y.size(a),s=a,o=i,l=a.slice(-1)[0],d=n?a.slice(0,-1).concat(1):[],p=t.length>3,u=t.length>4,h=n&&r>1,m=n&&r>2,g=r>3,_=dt(l),b=[H("x",t[0].dataType,t[0].dims,_),H("skip",t[1].dataType,t[1].dims,_),H("gamma",t[2].dataType,t[2].dims,_)];p&&b.push(H("beta",t[3].dataType,t[3].dims,_)),u&&b.push(H("bias",t[4].dataType,t[4].dims,_)),b.push(we("output",t[0].dataType,s,_)),h&&b.push(we("meanOutput",1,d)),m&&b.push(we("invStdOutput",1,d)),g&&b.push(we("inputSkipBiasSum",t[0].dataType,s,_));let w=gt(t[0].dataType),x=E=>` - const hiddenSize: f32 = ${l}; - const hiddenSizeVectorized: u32 = ${l/_}; - const epsilon: f32 = ${e.epsilon}; - - ${E.declareVariables(...b)} - - ${E.mainStart()} - ${E.guardAgainstOutOfBoundsWorkgroupSizes(o/l)} - let offset = global_idx * hiddenSizeVectorized; - var sum = ${ut("f32",_)}; - var squareSum = ${ut("f32",_)}; - 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 = ${Nt(w,_,"value")}; - sum += f32Value; - squareSum += f32Value * f32Value; - } - let mean = ${zt("sum",_)} / hiddenSize; - let invStdDev = inverseSqrt(${zt("squareSum",_)} / hiddenSize - mean * mean + epsilon); - ${h?"meanOutput[global_idx] = mean;":""} - ${m?"invStdOutput[global_idx] = invStdDev;":""} - for (var i: u32 = 0; i < hiddenSizeVectorized; i++) { - output[offset + i] = (output[offset + i] - ${w}(mean)) * ${w}(invStdDev) * gamma[i] - + ${p?"beta[i]":"0.0"}; - } - }`,C=[{dims:s,dataType:t[0].dataType}];return r>1&&C.push({dims:d,dataType:1}),r>2&&C.push({dims:d,dataType:1}),r>3&&C.push({dims:a,dataType:t[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:e.cacheKey},getShaderSource:x,getRunData:()=>({outputs:C,dispatchGroup:{x:Math.ceil(o/l/64)}})}},Gp=(t,e)=>{$u(t.inputs);let r=[0];t.outputCount>1&&r.push(-3),t.outputCount>2&&r.push(-3),t.outputCount>3&&r.push(3),t.compute(xu(t.inputs,e,t.outputCount,!1),{outputs:r})},Hp=t=>{let e=t.epsilon;return Ve({epsilon:e})}}),Su,Zr,Eu,yi,Cu,Iu,qp,jp,yg=G(()=>{qe(),Te(),tt(),Ce(),Su=(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((r,n)=>{if(t[n+1].dataType!==6&&t[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},Zr=(t,e)=>{let r=[];if(t.length>e)if(t[e].dataType===7)t[e].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(t[e].dataType===6)t[e].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${e} must be an array of int32 or int64`);return r},Eu=(t,e)=>{if(t.length>1){let r=Zr(t,1),n=Zr(t,2),a=Zr(t,3);return a.length===0&&(a=[...Array(t[0].dims.length).keys()]),Ve({starts:r,ends:n,axes:a})}else return e},yi=(t,e,r,n,a)=>{let i=t;return t<0&&(i+=r[n[e]]),a[e]<0?Math.max(0,Math.min(i,r[n[e]]-1)):Math.max(0,Math.min(i,r[n[e]]))},Cu=(t,e,r)=>`fn calculateInputIndices(output_indices: ${e.type.indices}) -> ${t.type.indices} { - var input_indices: ${t.type.indices}; - var carry = 0u; - for (var i = ${r.length}; i >= 0; i--) { - let input_shape_i = ${ve("uniforms.input_shape","i",r.length)}; - let steps_i = ${ve("uniforms.steps","i",r.length)}; - let signs_i = ${ve("uniforms.signs","i",r.length)}; - let starts_i = ${ve("uniforms.starts","i",r.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; - }`,Iu=(t,e)=>{let r=t[0].dims,n=Y.size(r),a=e.axes.length>0?Y.normalizeAxes(e.axes,r.length):[...Array(r.length).keys()],i=Zr(t,4);i.forEach(w=>w!==0||(()=>{throw new Error("step cannot be 0")})),i.length===0&&(i=Array(a.length).fill(1));let s=e.starts.map((w,x)=>yi(w,x,r,a,i)),o=e.ends.map((w,x)=>yi(w,x,r,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!==r.length)for(let w=0;wMath.sign(w));i.forEach((w,x,C)=>{if(w<0){let E=(o[x]-s[x])/w,A=s[x],k=A+E*i[x];s[x]=k,o[x]=A,C[x]=-w}});let d=r.slice(0);a.forEach((w,x)=>{d[w]=Math.ceil((o[w]-s[w])/i[w])});let p={dims:d,dataType:t[0].dataType},u=we("output",t[0].dataType,d.length),h=H("input",t[0].dataType,t[0].dims.length),m=Y.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}],_=[{type:"uint32",data:m},{type:"uint32",data:s},{type:"int32",data:l},{type:"uint32",data:i},...ee(t[0].dims),...ee(d)],b=w=>` - ${w.registerUniforms(g).declareVariables(h,u)} - ${Cu(h,u,r)} - ${w.mainStart()} - ${w.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:[p],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:_})}},qp=(t,e)=>{Su(t.inputs,e);let r=Eu(t.inputs,e);t.compute(Iu(t.inputs,r),{inputs:[0]})},jp=t=>{let e=t.starts,r=t.ends,n=t.axes;return Ve({starts:e,ends:r,axes:n})}}),Tu,Au,Kp,Yp,_g=G(()=>{Te(),tt(),Ce(),Tu=t=>{if(!t||t.length!==1)throw new Error("Softmax op requires 1 input.")},Au=(t,e)=>{let r=t.dims,n=Y.size(r),a=64,i=e.axis;if(i<0&&(i=r.length+i),iw===4?`max(max(${b}.x, ${b}.y), max(${b}.z, ${b}.w))`:w===2?`max(${b}.x, ${b}.y)`:w===3?`max(max(${b}.x, ${b}.y), ${b}.z)`:b,u=H("x",t.dataType,t.dims,l),h=we("result",t.dataType,t.dims,l),m=u.type.value,g=gt(t.dataType)==="f32"?`var threadMax = ${m}(-3.402823e+38f);`:`var threadMax = ${m}(-65504.0h);`,_=b=>` - var rowMaxShared : ${m}; - var rowSumShared : ${m}; - var threadShared : array<${m}, ${a}>; - - fn getValue(row: i32, col: i32, row_stride: i32) -> ${m} { - let index = row * row_stride + col; - return x[index]; - } - - fn setValue(row: i32, col: i32, row_stride: i32, value: ${m}) { - let index = row * row_stride + col; - result[index] = value; - } - 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}`;return{name:"Softmax",shaderCache:{hint:`${l}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:t.dataType}],dispatchGroup:{x:o},programUniforms:[{type:"uint32",data:d}]}),getShaderSource:_}},Kp=(t,e)=>{Tu(t.inputs),t.compute(Au(t.inputs[0],e))},Yp=t=>Ve({axis:t.axis})}),ku,Mu,Ou,zu,Ru,Xp,Qp,wg=G(()=>{Te(),tt(),Ce(),ku=t=>{if(!t||t.length<1)throw new Error("too few inputs")},Mu=(t,e)=>{let r=[],n=e.numOutputs;return t[1].dims[0]>0&&(t[1].getBigInt64Array().forEach(a=>r.push(Number(a))),n=r.length),Ve({numOutputs:n,axis:e.axis,splitSizes:r})},Ou=t=>` -fn calculateOutputIndex(index: u32) -> u32 { - for (var i: u32 = 0u; i < ${t}u; i += 1u ) { - if (index < ${ve("uniforms.size_in_split_axis","i",t)}) { - return i; - } - } - return ${t}u; -}`,zu=t=>{let e=t.length,r=[];for(let n=0;n{let r=t[0].dims,n=Y.size(r),a=t[0].dataType,i=Y.normalizeAxis(e.axis,r.length),s=new Array(e.numOutputs),o=H("input",a,r),l=new Array(e.numOutputs),d=[],p=[],u=0,h=[{type:"uint32",data:n}];for(let g=0;gh.push(...ee(g)));let m=g=>` - ${g.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(o,...s)} - ${Ou(l.length)} - ${zu(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 -= ${ve("uniforms.size_in_split_axis","output_number - 1u",l.length)}; - ${o.indicesSet("indices",i,"index")}; - } - writeBufferData(output_number, indices, global_idx); - }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:m,getRunData:()=>({outputs:d,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:h})}},Xp=(t,e)=>{ku(t.inputs);let r=t.inputs.length===1?e:Mu(t.inputs,e);t.compute(Ru(t.inputs,r),{inputs:[0]})},Qp=t=>{let 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u=[n.batchSize,n.numHeads,n.sequenceLength,n.kvSequenceLength+n.pastSequenceLength],l=s.scale===0?1/Math.sqrt(n.headSize):s.scale,a=Fe(n.headSize),p=n.headSize/a,h=12,g={x:Math.ceil(n.totalSequenceLength/h),y:Math.ceil(n.sequenceLength/h),z:n.batchSize*n.numHeads},b=Xe(t.dataType),w=[{type:"uint32",data:n.sequenceLength},{type:"uint32",data:p},{type:"uint32",data:n.totalSequenceLength},{type:"uint32",data:n.kvSequenceLength},{type:b,data:l}],y=[t,r],_=$=>{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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0&&(o=e),o.format="RGBA",o.height=p,o.width=u,e!==void 0){const h=l();h.width=u,h.height=p;const m=d(h);if(m!=null)m.putImageData(t,0,0),s=m.getImageData(0,0,u,p).data;else throw new Error("Can not access image data")}else s=t.data}else if(a){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const p=l();p.width=t.width,p.height=t.height;const u=d(p);if(u!=null){const h=t.height,m=t.width;return u.drawImage(t,0,0,m,h),s=u.getImageData(0,0,m,h).data,o.height=h,o.width=m,$i(s,o)}else throw new Error("Can not access image data")}else{if(i)return new Promise((p,u)=>{const h=l(),m=d(h);if(!t||!m)return u();const g=new Image;g.crossOrigin="Anonymous",g.src=t,g.onload=()=>{h.width=g.width,h.height=g.height,m.drawImage(g,0,0,h.width,h.height);const _=m.getImageData(0,0,h.width,h.height);o.height=h.height,o.width=h.width,p($i(_.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return $i(s,o);throw new Error("Input data provided is not supported - aborted tensor creation")},Dg=(t,e)=>{const{width:r,height:n,download:a,dispose:i}=e,s=[1,n,r,4];return new Ot({location:"texture",type:"float32",texture:t,dims:s,download:a,dispose:i})},Ng=(t,e)=>{const{dataType:r,dims:n,download:a,dispose:i}=e;return new Ot({location:"gpu-buffer",type:r??"float32",gpuBuffer:t,dims:n,download:a,dispose:i})},Fg=(t,e,r)=>new Ot({location:"cpu-pinned",type:t,data:e,dims:r??[e.length]}),_a=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["float16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),Vi=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let Ku=!1;const Ug=()=>{if(!Ku){Ku=!0;const t=typeof BigInt64Array<"u"&&typeof 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this.dims=Qu(this.dims,e),this}flatten_(e=0,r=-1){r=(r+this.dims.length)%this.dims.length;let n=this.dims.slice(0,e),a=this.dims.slice(e,r+1),i=this.dims.slice(r+1);return this.dims=[...n,a.reduce((s,o)=>s*o,1),...i],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let n=0;ns!==r?a*i:a,1);e[r]=this.data.length/n}return new J(this.type,this.data,e)}neg_(){for(let e=0;ei*s);if(r!==n)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let a=t;for(let i=e.length-1;i>=0;i--)a=a.reduce((s,o)=>{let l=s[s.length-1];return l.lengthr!==1):typeof e=="number"?t[e]===1&&t.splice(e,1):Array.isArray(e)&&(t=t.filter((r,n)=>r!==1||!e.includes(n))),t}function Qu(t,e){return e=jt(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function jt(t,e,r=null){if(t<-e||t>=e)throw new Error(`IndexError: index ${t} is out of bounds for dimension${r===null?"":" "+r} with size ${e}`);return t<0&&(t=(t%e+e)%e),t}function Ea(t,e=0){e=jt(e,t[0].dims.length);const r=t[0].dims.slice();r[e]=t.reduce((s,o)=>s+o.dims[e],0);const n=r.reduce((s,o)=>s*o,1),a=new t[0].data.constructor(n),i=t[0].type;if(e===0){let s=0;for(let o of t)a.set(o.data,s),s+=o.data.length}else{let s=0;for(let o=0;o=0;--u){const g=l.dims[u];let _=h%g;u===e&&(_+=s),p+=_*m,m*=r[u],h=Math.floor(h/g)}a[p]=l.data[d]}s+=l.dims[e]}}return new J(i,a,r)}function Ca(t,e=0){return Ea(t.map(r=>r.unsqueeze(e)),e)}function Xg(t,e=null,r=1,n=!1){if(e===null){const d=t.data.reduce((m,g)=>m+g,0)/t.data.length,p=Math.sqrt(t.data.reduce((m,g)=>m+(g-d)**2,0)/(t.data.length-r)),u=new J(t.type,[d],[]);return[new J(t.type,[p],[]),u]}e=jt(e,t.dims.length);const a=Ih(t,e,n),i=t.dims.slice();i[e]=1;const s=new t.data.constructor(t.data.length/t.dims[e]);for(let l=0;l=0;--p){const m=t.dims[p];if(p!==e){const g=u%m;d+=g*h,h*=i[p]}u=Math.floor(u/m)}s[d]+=(t.data[l]-a.data[d])**2}for(let l=0;ls+o,0);return new J(t.type,[i/t.data.length],[])}e=jt(e,t.dims.length);const n=t.dims.slice();n[e]=1;const a=new t.data.constructor(t.data.length/t.dims[e]);for(let i=0;i=0;--o){const p=t.dims[o];if(o!==e){const u=l%p;s+=u*d,d*=n[o]}l=Math.floor(l/p)}a[s]+=t.data[i]}if(t.dims[e]!==1)for(let i=0;i0||o>0;)switch(l.push(s-1),d.push(o-1),i[s][o].item()){case 0:--s,--o;break;case 1:--s;break;case 2:--o;break;default:throw new Error(`Internal error in dynamic time warping. 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Se=Object.freeze({Text:"Text",NumericLiteral:"NumericLiteral",BooleanLiteral:"BooleanLiteral",StringLiteral:"StringLiteral",Identifier:"Identifier",Equals:"Equals",OpenParen:"OpenParen",CloseParen:"CloseParen",OpenStatement:"OpenStatement",CloseStatement:"CloseStatement",OpenExpression:"OpenExpression",CloseExpression:"CloseExpression",OpenSquareBracket:"OpenSquareBracket",CloseSquareBracket:"CloseSquareBracket",OpenCurlyBracket:"OpenCurlyBracket",CloseCurlyBracket:"CloseCurlyBracket",Comma:"Comma",Dot:"Dot",Colon:"Colon",Pipe:"Pipe",CallOperator:"CallOperator",AdditiveBinaryOperator:"AdditiveBinaryOperator",MultiplicativeBinaryOperator:"MultiplicativeBinaryOperator",ComparisonBinaryOperator:"ComparisonBinaryOperator",UnaryOperator:"UnaryOperator",Set:"Set",If:"If",For:"For",In:"In",Is:"Is",NotIn:"NotIn",Else:"Else",EndIf:"EndIf",ElseIf:"ElseIf",EndFor:"EndFor",And:"And",Or:"Or",Not:"UnaryOperator"});Object.freeze({set:Se.Set,for:Se.For,in:Se.In,is:Se.Is,if:Se.If,else:Se.Else,endif:Se.EndIf,elif:Se.ElseIf,endfor:Se.EndFor,and:Se.And,or:Se.Or,not:Se.Not,"not in":Se.NotIn,true:Se.BooleanLiteral,false:Se.BooleanLiteral});Se.OpenStatement,Se.CloseStatement,Se.OpenExpression,Se.CloseExpression,Se.OpenParen,Se.CloseParen,Se.OpenCurlyBracket,Se.CloseCurlyBracket,Se.OpenSquareBracket,Se.CloseSquareBracket,Se.Comma,Se.Dot,Se.Colon,Se.Pipe,Se.ComparisonBinaryOperator,Se.ComparisonBinaryOperator,Se.ComparisonBinaryOperator,Se.ComparisonBinaryOperator,Se.ComparisonBinaryOperator,Se.ComparisonBinaryOperator,Se.AdditiveBinaryOperator,Se.AdditiveBinaryOperator,Se.MultiplicativeBinaryOperator,Se.MultiplicativeBinaryOperator,Se.MultiplicativeBinaryOperator,Se.Equals;const t0=(()=>{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 r=0;for(let a=0;a<256;++a)t.includes(a)||(t.push(a),e.push(256+r),r+=1);const n=e.map(a=>String.fromCharCode(a));return Object.fromEntries(t.map((a,i)=>[a,n[i]]))})();Qf(t0);const r0=[["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"]];new Map(r0);async function n0(t,e){return await la(t,"config.json",!0,e)}class a0{constructor(e){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e)}static async from_pretrained(e,{progress_callback:r=null,config:n=null,cache_dir:a=null,local_files_only:i=!1,revision:s="main"}={}){let o=n??await n0(e,{progress_callback:r,config:n,cache_dir:a,local_files_only:i,revision:s});return new this(o)}}class wr{static async from_pretrained(...e){return a0.from_pretrained(...e)}}class Zu extends Sr{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,r){for(let n of r)this.processors.forEach(a=>a(e,n))}[Symbol.iterator](){return this.processors.values()}}class Rt extends Sr{_call(e,r){throw Error("`_call` should be implemented in a subclass")}}class i0 extends Rt{constructor(e){super(),this.force_token_map=Object.fromEntries(e??[])}_call(e,r){let n=this.force_token_map[e.length];return em(n)&&(r.data.fill(-1/0),r.data[n]=0),r}}class s0 extends Rt{constructor(e){super(),this.bos_token_id=e}_call(e,r){return e.length===1&&(r.data.fill(-1/0),r.data[this.bos_token_id]=0),r}}class o0 extends Rt{constructor(e,r){super(),this.max_length=e,this.forced_eos_token_id=r}_call(e,r){}}class l0 extends Rt{constructor(e,r){super(),this.begin_suppress_tokens=e,this.begin_index=r}_call(e,r){if(e.length===this.begin_index)for(let n of this.begin_suppress_tokens)r.data[n]=-1/0;return r}}class u0 extends Rt{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,r){const n=r.data;if(n[this.no_timestamps_token_id]=-1/0,e.length===this.begin_index-1)return n.fill(-1/0),n[this.timestamp_begin]=0,r;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?n.subarray(this.timestamp_begin).fill(-1/0):n.subarray(0,this.eos_token_id).fill(-1/0)),e.length===this.begin_index&&this.max_initial_timestamp_index!==null){const p=this.timestamp_begin+this.max_initial_timestamp_index;n.subarray(p+1).fill(-1/0)}const o=fm(n),l=Math.log(o.subarray(this.timestamp_begin).map(Math.exp).reduce((p,u)=>p+u)),d=Yt(o.subarray(0,this.timestamp_begin))[0];return l>d&&n.subarray(0,this.timestamp_begin).fill(-1/0),r}}class d0 extends Rt{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const r=e.length,n=[];for(let i=0;i0&&(a=a.map(i=>i/this.generation_config.temperature)),a}randomSelect(e){let r=e.reduce((a,i)=>a+i,0),n=Math.random()*r;for(let a=0;a1)return new _0(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new g0(e)}}class g0 extends Ia{sample(e,r=-1){let n=this.getLogits(e,r);return[[Yt(n)[1],0]]}}class y0 extends Ia{sample(e,r=-1){let n=e.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(e,r),i=od(a,n),s=hn(i.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},()=>{const o=this.randomSelect(s);return[i[o][0],Math.log(s[o])]})}}class _0 extends Ia{sample(e,r=-1){let n=e.dims.at(-1);this.generation_config.top_k>0&&(n=Math.min(this.generation_config.top_k,n));const a=this.getLogits(e,r),i=od(a,n),s=hn(i.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},(o,l)=>[i[l][0],Math.log(s[l])])}}const xi=Object.freeze({fp32:"fp32",fp16:"fp16",int8:"int8",uint8:"uint8",float32:"fp32",float16:"fp16"}),$e={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5},ba=new Map,Th=new Map,on=new Map;async function Ht(t,e,r){let n="";if(r.dtype){if(!xi.hasOwnProperty(r.dtype))throw new Error(`Invalid dtype: ${r.dtype}. Should be one of: ${Object.keys(xi).join(", ")}`);xi[r.dtype]!=="fp32"&&(n=`_${r.dtype}`)}else r.quantized&&(n="_quantized");const a=`onnx/${e}${n}.onnx`,i=await ki(t,a,!0,r),s=r.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: ${n.join(", ")}.`);const a=Object.keys(e).length,i=t.inputNames.length;if(a>i){let s=Object.keys(e).filter(o=>!t.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${a} > ${i}). The following inputs will be ignored: "${s.join(", ")}".`)}return r}async function xr(t,e){const r=w0(t,e);try{const n=Object.fromEntries(Object.entries(r).map(([i,s])=>[i,s.ort_tensor]));let a=await t.run(n);a=Ah(a);for(const[i,s]of Object.entries(r))i.startsWith("past_key_values")&&s.dispose();return a}catch(n){throw console.error(`An error occurred during model execution: "${n}".`),console.error("Inputs given to model:",r),n}}function Ah(t){for(let e in t)Eh(t[e])?t[e]=new J(t[e]):typeof t[e]=="object"&&Ah(t[e]);return t}function b0(t){if(t instanceof J)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 J("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new J("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function gs(t,e){let r=t.config.pad_token_id??null,n=t.config.eos_token_id??null;Jf(n)&&(n=[n]);let a=e.indexOf(r)!==-1,i=n===null||!n.includes(r);if(a&&i){let s=BigInt64Array.from(e.data.map(o=>o!=r));return new J("int64",s,e.dims)}else return e0(e)}function kh(t,e,r){if(!t.inputNames.includes("position_ids"))return;const n=new BigInt64Array(e.attention_mask.data.length);for(let a=0;a0&&a.push(new d0(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&a.push(new f0(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&a.push(new p0(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 h0(r,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&a.push(new s0(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&a.push(new o0(e.max_length,e.forced_eos_token_id)),e.begin_suppress_tokens!==null){let i=r>1||e.forced_bos_token_id===null?r:r+1;e.forced_decoder_ids!==null&&(i+=e.forced_decoder_ids[e.forced_decoder_ids.length-1][0]),a.push(new l0(e.begin_suppress_tokens,i))}return e.forced_decoder_ids!==null&&a.push(new i0(e.forced_decoder_ids)),n!==null&&a.extend(n),a}_get_generation_config(e){let r=new m0(this.config);return"generation_config"in this&&Object.assign(r,this.generation_config),e!==null&&Object.assign(r,e),r}async generate(e,r=null,n=null,{inputs_attention_mask:a=null}={}){if(!this.can_generate){let b=`The current model class (${on.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;const w=this.config.model_type,x=xs.get(w)??If.get(w)??Cf.get(w)??Tf.get(w);throw x&&(b+=` Please use the following class instead: '${x[0]}'`),Error(b)}if(!(e instanceof J)&&!Zf(e)&&!Array.isArray(e))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${e.constructor.name}".`);let i;if(this.config.is_encoder_decoder)i=0;else if(i=e instanceof J?e.dims.at(-1):e.length,i===0)throw Error("Must supply a non-empty array of input token ids.");r=this._get_generation_config(r),n=n??new Zu,n=this._get_logits_processor(r,i,n);let s=r.eos_token_id;s!==null&&!Array.isArray(s)&&(s=[s]);let o=1;const l=o+(r.max_new_tokens??1/0),d=Number.isInteger(r.max_length)&&(r.max_new_tokens??null)===null;let p=Ia.getSampler(r),u=this.getStartBeams(e,r,o,a);for(;u.some(_=>!_.done)&&o=r.max_length){b.done=!0,_.push(b);continue}let w=await this.runBeam(b);r.output_attentions&&this.addAttentionsToBeam(b,w),r.output_scores;let x=w.logits.slice(null,-1,null);n(b.output_token_ids,x);let C=p(x);for(let[E,A]of C){let k={...b};this.updateBeam(k,E),k.score+=A,s&&s.includes(E)&&(k.done=!0),_.push(k)}}++o,_=this.groupBeams(_).map(b=>b.sort((w,x)=>x.score-w.score).slice(0,r.num_beams)),u=_.flat(),r.callback_function&&r.callback_function(u)}const h=this.groupBeams(u),m=_=>h.map(b=>r.num_return_sequences>1?b.slice(0,r.num_return_sequences).map(w=>w[_]):[b[0][_]]).flat(),g=m("output_token_ids");if(r.return_dict_in_generate){const _=m("decoder_attentions"),b=m("cross_attentions");return{sequences:g,decoder_attentions:_,cross_attentions:b}}else return g}addAttentionsToBeam(e,r){if(this.config.is_encoder_decoder){if(!r.cross_attentions||r.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(r.cross_attentions)}if(!r.decoder_attentions||r.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(r.decoder_attentions)}groupBeams(e){const r=Object.create(null);for(const n of e)r[n.id]===void 0?r[n.id]=[n]:r[n.id].push(n);return Object.values(r)}getPastKeyValues(e,r){const n=Object.create(null);for(const a in e)if(a.startsWith("present")){let i=a.replace("present","past_key_values");r&&a.includes("encoder")?n[i]=r[i]:n[i]=e[a]}return n}getAttentions(e){const r=Object.create(null);for(const n of["cross_attentions","decoder_attentions"]){const a=[];for(const i in e)if(i.startsWith(n)){const s=i.split(".").pop();a[s]=e[i]}r[n]=a}return r}addPastKeyValues(e,r){if(r)Object.assign(e,r);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 p=Array.from({length:this.config.decoder_layers},(b,w)=>Ea(d.map(x=>x[w]),2)),u=Ca(r.map(([b,w])=>n?p[b].slice(null,w,null,[0,n]):p[b].slice(null,w)));u=u.transpose(1,0,2,3);let[h,m]=Xg(u,-2,0,!0),g=u.clone();for(let b=0;bu[w+1]-u[w]),g=tm([1],m).map(b=>!!b),_=[];for(let b=0;ba*i,1);e.input_labels=new J("int64",new BigInt64Array(n).fill(1n),r)}return await xr(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 jw(await super._call(e))}}class jw extends ct{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}}class gf extends q{}class Kw extends gf{}class Yw extends gf{constructor(e,r,n,a){super(e,r),this.decoder_merged_session=n,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 yf extends q{}class Xw extends yf{}class Qw extends yf{constructor(e,r,n,a){super(e,r),this.decoder_merged_session=n,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 lr extends q{}class Zw extends lr{}class Jw extends lr{async _call(e){return new Tr(await super._call(e))}}class eb extends lr{async _call(e){return new ke(await super._call(e))}}class tb extends lr{async _call(e){return new nt(await super._call(e))}}class bs extends q{}class rb extends bs{}class nb extends bs{async _call(e){return new Tr(await super._call(e))}}class ab extends bs{async _call(e){return new ke(await super._call(e))}}class Ra extends q{}class ib extends Ra{}class sb extends Ra{async _call(e){return new Tr(await super._call(e))}}class ob extends Ra{async _call(e){return new ke(await super._call(e))}}class lb extends Ra{async _call(e){return new nt(await super._call(e))}}class vs extends q{}class ub extends vs{}class db extends vs{async _call(e){return new Tr(await super._call(e))}}class cb extends vs{async _call(e){return new ke(await super._call(e))}}class pb extends lr{}class hb extends lr{async _call(e){return new Tr(await super._call(e))}}class fb extends lr{async _call(e){return new ke(await super._call(e))}}class Tn extends q{}class mb extends Tn{}class gb extends Tn{async _call(e){return new Tr(await super._call(e))}}class yb extends Tn{async _call(e){return new ke(await super._call(e))}}class _b extends Tn{async _call(e){return new u2(await super._call(e))}}class wb extends Tn{async _call(e){return new nt(await super._call(e))}}class _f extends q{}class bb extends _f{}class vb extends _f{constructor(e,r,n,a){super(e,r),this.decoder_merged_session=n,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,r,{threshold:n=.5,minlenratio:a=0,maxlenratio:i=20,vocoder:s=null}={}){const o={input_ids:e},{encoder_outputs:l,encoder_attention_mask:d}=await cn(this,o),p=l.dims[1]/this.config.reduction_factor,u=Math.floor(p*i),h=Math.floor(p*a),m=this.config.num_mel_bins;let g=[],_=null,b=null,w=0;for(;;){++w;const E=ys(!!b);let A;b?A=b.output_sequence_out:A=new J("float32",new Float32Array(m),[1,1,m]);let k={use_cache_branch:E,output_sequence:A,encoder_attention_mask:d,speaker_embeddings:r,encoder_hidden_states:l};this.addPastKeyValues(k,_),b=await xr(this.decoder_merged_session,k),_=this.getPastKeyValues(b,_);const{prob:P,spectrum:N}=b;if(g.push(N),w>=h&&(Array.from(P.data).filter(L=>L>=n).length>0||w>=u))break}const x=Ea(g),{waveform:C}=await xr(s.session,{spectrogram:x});return{spectrogram:x,waveform:C}}}class $b extends q{main_input_name="spectrogram"}class xb extends q{constructor(e,r,n){super(e,r),this.generation_config=n,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 Sb extends xb{}class wf extends q{constructor(e,r,n){super(e,r),this.generation_config=n,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 Eb extends wf{}class Cb extends wf{}class bf extends q{constructor(e,r,n){super(e,r),this.generation_config=n,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 Ib extends bf{}class Tb extends bf{}class vf extends q{constructor(e,r,n){super(e,r),this.generation_config=n,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 Ab extends vf{}class kb extends vf{}class $s extends q{}class Mb extends $s{}class Ob extends $s{static async from_pretrained(e,r={}){return r.model_file_name??="text_model",super.from_pretrained(e,r)}}class zb extends $s{static async from_pretrained(e,r={}){return r.model_file_name??="audio_model",super.from_pretrained(e,r)}}class Rb extends q{}class $f extends Rb{async _call(e){return new c2(await super._call(e))}}class xf extends q{}class Bb extends xf{}class Pb extends xf{}class Db extends q{constructor(e,r,n){super(e,r),this.generation_config=n,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class Nb extends Db{}class Fb{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{quantized:r=!0,progress_callback:n=null,config:a=null,cache_dir:i=null,local_files_only:s=!1,revision:o="main",model_file_name:l=null,device:d=null,dtype:p=null,session_options:u={}}={}){let h={quantized:r,progress_callback:n,config:a,cache_dir:i,local_files_only:s,revision:o,model_file_name:l,device:d,dtype:p,session_options:u};if(a=await wr.from_pretrained(e,h),h.config||(h.config=a),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let m of this.MODEL_CLASS_MAPPINGS){const g=m.get(a.model_type);if(g)return await g[1].from_pretrained(e,h)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${a.model_type}", attempting to construct from base class.`),await q.from_pretrained(e,h);throw Error(`Unsupported model type: ${a.model_type}`)}}const Sf=new Map([["bert",["BertModel",A0]],["nomic_bert",["NomicBertModel",B0]],["roformer",["RoFormerModel",P0]],["electra",["ElectraModel",q0]],["esm",["EsmModel",yy]],["convbert",["ConvBertModel",L0]],["camembert",["CamembertModel",Q0]],["deberta",["DebertaModel",ry]],["deberta-v2",["DebertaV2Model",oy]],["mpnet",["MPNetModel",Ey]],["albert",["AlbertModel",Ry]],["distilbert",["DistilBertModel",py]],["roberta",["RobertaModel",t_]],["xlm",["XLMModel",s_]],["xlm-roberta",["XLMRobertaModel",c_]],["clap",["ClapModel",Mb]],["clip",["CLIPModel",v_]],["clipseg",["CLIPSegModel",A_]],["chinese_clip",["ChineseCLIPModel",T_]],["siglip",["SiglipModel",S_]],["mobilebert",["MobileBertModel",vy]],["squeezebert",["SqueezeBertModel",ky]],["wav2vec2",["Wav2Vec2Model",Zw]],["wav2vec2-bert",["Wav2Vec2BertModel",ub]],["unispeech",["UniSpeechModel",rb]],["unispeech-sat",["UniSpeechSatModel",ib]],["hubert",["HubertModel",pb]],["wavlm",["WavLMModel",mb]],["audio-spectrogram-transformer",["ASTModel",g_]],["vits",["VitsModel",$f]],["detr",["DetrModel",hw]],["table-transformer",["TableTransformerModel",yw]],["vit",["ViTModel",tw]],["mobilevit",["MobileViTModel",iw]],["owlvit",["OwlViTModel",ow]],["owlv2",["Owlv2Model",uw]],["beit",["BeitModel",cw]],["deit",["DeiTModel",bw]],["convnext",["ConvNextModel",Pw]],["convnextv2",["ConvNextV2Model",Nw]],["dinov2",["Dinov2Model",Uw]],["resnet",["ResNetModel",$w]],["swin",["SwinModel",Sw]],["swin2sr",["Swin2SRModel",Cw]],["donut-swin",["DonutSwinModel",Bw]],["yolos",["YolosModel",Ww]],["dpt",["DPTModel",Tw]],["glpn",["GLPNModel",Ow]],["hifigan",["SpeechT5HifiGan",$b]]]),Ef=new Map([["t5",["T5Model",Ny]],["longt5",["LongT5Model",Uy]],["mt5",["MT5Model",Wy]],["bart",["BartModel",Gy]],["mbart",["MBartModel",jy]],["marian",["MarianModel",Kw]],["whisper",["WhisperModel",__]],["m2m_100",["M2M100Model",Xw]],["blenderbot",["BlenderbotModel",Qy]],["blenderbot-small",["BlenderbotSmallModel",Jy]]]),Ub=new Map([["bloom",["BloomModel",Y_]],["gpt2",["GPT2Model",M_]],["gptj",["GPTJModel",D_]],["gpt_bigcode",["GPTBigCodeModel",F_]],["gpt_neo",["GPTNeoModel",z_]],["gpt_neox",["GPTNeoXModel",B_]],["codegen",["CodeGenModel",L_]],["llama",["LlamaModel",V_]],["qwen2",["Qwen2Model",H_]],["phi",["PhiModel",j_]],["mpt",["MptModel",Q_]],["opt",["OPTModel",J_]],["mistral",["MistralModel",Eb]],["starcoder2",["Starcoder2Model",Ib]],["falcon",["FalconModel",Ab]]]),Cf=new Map([["speecht5",["SpeechT5ForSpeechToText",bb]],["whisper",["WhisperForConditionalGeneration",w_]]]),Lb=new Map([["speecht5",["SpeechT5ForTextToSpeech",vb]]]),Wb=new Map([["vits",["VitsModel",$f]]]),Vb=new Map([["bert",["BertForSequenceClassification",M0]],["roformer",["RoFormerForSequenceClassification",N0]],["electra",["ElectraForSequenceClassification",K0]],["esm",["EsmForSequenceClassification",wy]],["convbert",["ConvBertForSequenceClassification",V0]],["camembert",["CamembertForSequenceClassification",J0]],["deberta",["DebertaForSequenceClassification",ay]],["deberta-v2",["DebertaV2ForSequenceClassification",uy]],["mpnet",["MPNetForSequenceClassification",Iy]],["albert",["AlbertForSequenceClassification",By]],["distilbert",["DistilBertForSequenceClassification",hy]],["roberta",["RobertaForSequenceClassification",n_]],["xlm",["XLMForSequenceClassification",l_]],["xlm-roberta",["XLMRobertaForSequenceClassification",h_]],["bart",["BartForSequenceClassification",qy]],["mbart",["MBartForSequenceClassification",Yy]],["mobilebert",["MobileBertForSequenceClassification",xy]],["squeezebert",["SqueezeBertForSequenceClassification",Oy]]]),Gb=new Map([["bert",["BertForTokenClassification",O0]],["roformer",["RoFormerForTokenClassification",F0]],["electra",["ElectraForTokenClassification",Y0]],["esm",["EsmForTokenClassification",by]],["convbert",["ConvBertForTokenClassification",G0]],["camembert",["CamembertForTokenClassification",ey]],["deberta",["DebertaForTokenClassification",iy]],["deberta-v2",["DebertaV2ForTokenClassification",dy]],["mpnet",["MPNetForTokenClassification",Ty]],["distilbert",["DistilBertForTokenClassification",fy]],["roberta",["RobertaForTokenClassification",a_]],["xlm",["XLMForTokenClassification",u_]],["xlm-roberta",["XLMRobertaForTokenClassification",f_]]]),If=new Map([["t5",["T5ForConditionalGeneration",Fy]],["longt5",["LongT5ForConditionalGeneration",Ly]],["mt5",["MT5ForConditionalGeneration",Vy]],["bart",["BartForConditionalGeneration",Hy]],["mbart",["MBartForConditionalGeneration",Ky]],["marian",["MarianMTModel",Yw]],["m2m_100",["M2M100ForConditionalGeneration",Qw]],["blenderbot",["BlenderbotForConditionalGeneration",Zy]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",e_]]]),xs=new Map([["bloom",["BloomForCausalLM",X_]],["gpt2",["GPT2LMHeadModel",O_]],["gptj",["GPTJForCausalLM",N_]],["gpt_bigcode",["GPTBigCodeForCausalLM",U_]],["gpt_neo",["GPTNeoForCausalLM",R_]],["gpt_neox",["GPTNeoXForCausalLM",P_]],["codegen",["CodeGenForCausalLM",W_]],["llama",["LlamaForCausalLM",G_]],["qwen2",["Qwen2ForCausalLM",q_]],["phi",["PhiForCausalLM",K_]],["mpt",["MptForCausalLM",Z_]],["opt",["OPTForCausalLM",ew]],["mbart",["MBartForCausalLM",Xy]],["mistral",["MistralForCausalLM",Cb]],["starcoder2",["Starcoder2ForCausalLM",Tb]],["falcon",["FalconForCausalLM",kb]],["trocr",["TrOCRForCausalLM",Sb]],["stablelm",["StableLmForCausalLM",Nb]]]),Hb=new Map([["bert",["BertForMaskedLM",k0]],["roformer",["RoFormerForMaskedLM",D0]],["electra",["ElectraForMaskedLM",j0]],["esm",["EsmForMaskedLM",_y]],["convbert",["ConvBertForMaskedLM",W0]],["camembert",["CamembertForMaskedLM",Z0]],["deberta",["DebertaForMaskedLM",ny]],["deberta-v2",["DebertaV2ForMaskedLM",ly]],["mpnet",["MPNetForMaskedLM",Cy]],["albert",["AlbertForMaskedLM",Dy]],["distilbert",["DistilBertForMaskedLM",gy]],["roberta",["RobertaForMaskedLM",r_]],["xlm",["XLMWithLMHeadModel",o_]],["xlm-roberta",["XLMRobertaForMaskedLM",p_]],["mobilebert",["MobileBertForMaskedLM",$y]],["squeezebert",["SqueezeBertForMaskedLM",My]]]),qb=new Map([["bert",["BertForQuestionAnswering",z0]],["roformer",["RoFormerForQuestionAnswering",U0]],["electra",["ElectraForQuestionAnswering",X0]],["convbert",["ConvBertForQuestionAnswering",H0]],["camembert",["CamembertForQuestionAnswering",ty]],["deberta",["DebertaForQuestionAnswering",sy]],["deberta-v2",["DebertaV2ForQuestionAnswering",cy]],["mpnet",["MPNetForQuestionAnswering",Ay]],["albert",["AlbertForQuestionAnswering",Py]],["distilbert",["DistilBertForQuestionAnswering",my]],["roberta",["RobertaForQuestionAnswering",i_]],["xlm",["XLMForQuestionAnswering",d_]],["xlm-roberta",["XLMRobertaForQuestionAnswering",m_]],["mobilebert",["MobileBertForQuestionAnswering",Sy]],["squeezebert",["SqueezeBertForQuestionAnswering",zy]]]),Tf=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",b_]]]),jb=new Map([["vit",["ViTForImageClassification",rw]],["mobilevit",["MobileViTForImageClassification",sw]],["beit",["BeitForImageClassification",pw]],["deit",["DeiTForImageClassification",vw]],["convnext",["ConvNextForImageClassification",Dw]],["convnextv2",["ConvNextV2ForImageClassification",Fw]],["dinov2",["Dinov2ForImageClassification",Lw]],["resnet",["ResNetForImageClassification",xw]],["swin",["SwinForImageClassification",Ew]],["segformer",["SegformerForImageClassification",Bb]]]),Kb=new Map([["detr",["DetrForObjectDetection",fw]],["table-transformer",["TableTransformerForObjectDetection",_w]],["yolos",["YolosForObjectDetection",Vw]]]),Yb=new Map([["owlvit",["OwlViTForObjectDetection",lw]],["owlv2",["Owlv2ForObjectDetection",dw]]]),Xb=new Map([["detr",["DetrForSegmentation",mw]],["clipseg",["CLIPSegForImageSegmentation",k_]]]),Qb=new Map([["segformer",["SegformerForSemanticSegmentation",Pb]]]),Zb=new Map([["sam",["SamModel",qw]]]),Jb=new Map([["wav2vec2",["Wav2Vec2ForCTC",Jw]],["wav2vec2-bert",["Wav2Vec2BertForCTC",db]],["unispeech",["UniSpeechForCTC",nb]],["unispeech-sat",["UniSpeechSatForCTC",sb]],["wavlm",["WavLMForCTC",gb]],["hubert",["HubertForCTC",hb]]]),e2=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",eb]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",cb]],["unispeech",["UniSpeechForSequenceClassification",ab]],["unispeech-sat",["UniSpeechSatForSequenceClassification",ob]],["wavlm",["WavLMForSequenceClassification",yb]],["hubert",["HubertForSequenceClassification",fb]],["audio-spectrogram-transformer",["ASTForAudioClassification",y_]]]),t2=new Map([["wavlm",["WavLMForXVector",_b]]]),r2=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",lb]],["wavlm",["WavLMForAudioFrameClassification",wb]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",tb]]]),n2=new Map([["vitmatte",["VitMatteForImageMatting",aw]]]),a2=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Iw]]]),i2=new Map([["dpt",["DPTForDepthEstimation",Aw]],["depth_anything",["DepthAnythingForDepthEstimation",Mw]],["glpn",["GLPNForDepthEstimation",zw]]]),Af=[[Sf,$e.EncoderOnly],[Ef,$e.EncoderDecoder],[Ub,$e.DecoderOnly],[Vb,$e.EncoderOnly],[Gb,$e.EncoderOnly],[If,$e.Seq2Seq],[Cf,$e.Seq2Seq],[xs,$e.DecoderOnly],[Hb,$e.EncoderOnly],[qb,$e.EncoderOnly],[Tf,$e.Vision2Seq],[jb,$e.EncoderOnly],[Xb,$e.EncoderOnly],[Qb,$e.EncoderOnly],[n2,$e.EncoderOnly],[a2,$e.EncoderOnly],[i2,$e.EncoderOnly],[Kb,$e.EncoderOnly],[Yb,$e.EncoderOnly],[Zb,$e.MaskGeneration],[Jb,$e.EncoderOnly],[e2,$e.EncoderOnly],[Lb,$e.Seq2Seq],[Wb,$e.EncoderOnly],[t2,$e.EncoderOnly],[r2,$e.EncoderOnly]];for(const[t,e]of Af)for(const[r,n]of t.values())ba.set(r,e),on.set(n,r),Th.set(r,n);const s2=[["CLIPTextModelWithProjection",$_,$e.EncoderOnly],["CLIPVisionModelWithProjection",x_,$e.EncoderOnly],["SiglipTextModel",E_,$e.EncoderOnly],["SiglipVisionModel",C_,$e.EncoderOnly],["ClapTextModelWithProjection",Ob,$e.EncoderOnly],["ClapAudioModelWithProjection",zb,$e.EncoderOnly]];for(const[t,e,r]of s2)ba.set(t,r),on.set(e,t),Th.set(t,e);class o2 extends Fb{static MODEL_CLASS_MAPPINGS=Af.map(e=>e[0]);static BASE_IF_FAIL=!0}class l2 extends ct{constructor({logits:e,past_key_values:r,encoder_outputs:n,decoder_attentions:a=null,cross_attentions:i=null}){super(),this.logits=e,this.past_key_values=r,this.encoder_outputs=n,this.decoder_attentions=a,this.cross_attentions=i}}class ke extends ct{constructor({logits:e}){super(),this.logits=e}}class u2 extends ct{constructor({logits:e,embeddings:r}){super(),this.logits=e,this.embeddings=r}}class nt extends ct{constructor({logits:e}){super(),this.logits=e}}class at extends ct{constructor({logits:e}){super(),this.logits=e}}class st extends ct{constructor({start_logits:e,end_logits:r}){super(),this.start_logits=e,this.end_logits=r}}class Tr extends ct{constructor({logits:e}){super(),this.logits=e}}class d2 extends ct{constructor({alphas:e}){super(),this.alphas=e}}class c2 extends ct{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}}const ft=typeof self<"u",p2=ft&&self.constructor.name==="DedicatedWorkerGlobalScope";let nr,kf,qt;if(ft)nr=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},qt=self.createImageBitmap,kf=self.ImageData;else if(Ge)qt=async t=>{const r=(await t.metadata()).channels,{data:n,info:a}=await t.raw().toBuffer({resolveWithObject:!0}),i=new mt(new Uint8ClampedArray(n),a.width,a.height,a.channels);return r!==void 0&&r!==a.channels&&i.convert(r),i};else throw new Error("Unable to load image processing library.");const h2={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},f2=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class mt{constructor(e,r,n,a){this.data=e,this.width=r,this.height=n,this.channels=a}get size(){return[this.width,this.height]}static async read(e){if(e instanceof mt)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!ft)throw new Error("fromCanvas() is only supported in browser environments.");const n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new mt(n,e.width,e.height,4)}static async fromURL(e){const r=await Ai(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);const n=await r.blob();return this.fromBlob(n)}static async fromBlob(e){if(ft){const r=await qt(e),n=nr(r.width,r.height).getContext("2d");return n.drawImage(r,0,0),new 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i=this.toSharp();if(n>=0&&a>=0)i=i.extract({left:Math.floor(n),top:Math.floor(a),width:e,height:r});else if(n<=0&&a<=0){const s=Math.floor(-a),o=Math.floor(-n);i=i.extend({top:s,left:o,right:e-this.width-o,bottom:r-this.height-s})}else{let s=[0,0],o=0;a<0?(s[0]=Math.floor(-a),s[1]=r-this.height-s[0]):o=Math.floor(a);let l=[0,0],d=0;n<0?(l[0]=Math.floor(-n),l[1]=e-this.width-l[0]):d=Math.floor(n),i=i.extend({top:s[0],bottom:s[1],left:l[0],right:l[1]}).extract({left:d,top:o,width:e,height:r})}return await qt(i)}}async toBlob(e="image/png",r=1){if(!ft)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:r})}toCanvas(){if(!ft)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),r=nr(e.width,e.height),n=new kf(e.data,e.width,e.height);return r.getContext("2d").putImageData(n,0,0),r}_update(e,r,n,a=null){return this.data=e,this.width=r,this.height=n,a!==null&&(this.channels=a),this}clone(){return new mt(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: ${this.channels}`)}return this}async save(e){if(ft){if(p2)throw new Error("Unable to save an image from a Web Worker.");const r=e.split(".").pop().toLowerCase(),n=f2.get(r)??"image/png",a=await this.toBlob(n),i=URL.createObjectURL(a),s=document.createElement("a");s.href=i,s.download=e,s.click(),s.remove()}else{if(Ze.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(ft)throw new Error("toSharp() is only supported in server-side environments.");return 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Array(t.length));for(let s=0;st+n*i)}function pn(t,e,r,n,a,i=null,s="htk",o=!1){if(i!==null&&i!=="slaney")throw new Error('norm must be one of null or "slaney"');const l=Si(r,s),d=Si(n,s),p=ed(l,d,e+2);let u=y2(p,s),h;if(o){const g=a/(t*2);h=Si(Float64Array.from({length:t},(_,b)=>b*g),s),u=p}else h=ed(0,Math.floor(a/2),t);const m=_2(h,u);if(i!==null&&i==="slaney")for(let g=0;ga)throw Error(`frame_length (${r}) may not be larger than fft_length (${a})`);if(E!==r)throw new Error(`Length of the window (${E}) must equal frame_length (${r})`);if(n<=0)throw new Error("hop_length must be greater than zero");if(s){if(o!=="reflect")throw new Error(`pad_mode="${o}" not implemented yet.`);const te=Math.floor((a-1)/2)+1;t=w2(t,te,te)}const A=Math.floor(1+Math.floor((t.length-r)/n)),k=l?Math.floor(a/2)+1:a;let P=A,N=A;w!==null&&(w>A?x&&(N=w):N=P=w);const L=new ym(a),j=new Float64Array(a),O=new Float64Array(L.outputBufferSize),K=new Array(P);for(let te=0;te=1;--V)j[V]-=d*j[V-1];j[0]*=1-d}for(let V=0;VMath.pow(o,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(r&&(s=s.subarray(0,t)),n===null)return s;if(t>n)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${n})`);return s}function $2([t,e,r,n]){return[t-r/2,e-n/2,t+r/2,e+n/2]}function Ss(t,e=.5,r=null,n=!1){const a=t.logits,i=t.pred_boxes,[s,o,l]=a.dims;if(r!==null&&r.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 p=0;pe&&w.push(C)}else{let C=Yt(b.data)[1];if(C===l-1)continue;w.push(C),x=hn(b.data)}for(const C of w){let E=g[_].data;E=$2(E),u!==null&&(E=E.map((A,k)=>A*u[(k+1)%2])),h.boxes.push(E),h.classes.push(C),h.scores.push(x[C])}}d.push(h)}return d}function An(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${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 td(t,e,r=0,n=null){let a=Math.round(t/e)*e;return n!==null&&a>n&&(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:n}))}async crop_margin(e,r=200){const n=e.clone().grayscale(),a=mm(n.data)[0],s=Yt(n.data)[0]-a;if(s===0)return e;const o=r/255;let l=n.width,d=n.height,p=0,u=0;for(let h=0;hthis.preprocess(i)));return{pixel_values:Ca(n.map(i=>i.pixel_values),0),original_sizes:n.map(i=>i.original_size),reshaped_input_sizes:n.map(i=>i.reshaped_input_size)}}}class x2 extends He{post_process_semantic_segmentation(e,r=null){const n=e.logits,a=n.dims[0];if(r!==null&&r.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[x]&&(h[x]=w[x],u.data[x]=b)}const m=new Array(l.dims[0]),g=u.data;for(let b=0;bb!==void 0);i.push({segmentation:u,labels:_})}return i}}class S2 extends He{}class E2 extends He{}class C2 extends He{}class I2 extends He{}class T2 extends He{}class A2 extends He{}class k2 extends He{}class Of extends He{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){const r=this.size?.shortest_edge;if(r===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(r<384){const n=Math.floor(r/this.crop_pct),[a,i]=this.get_resize_output_image_size(e,{shortest_edge:n});e=await e.resize(a,i,{resample:this.resample}),e=await e.center_crop(r,r)}else e=await e.resize(r,r,{resample:this.resample});return e}}class M2 extends Of{}class O2 extends He{}class z2 extends He{}class R2 extends He{}class zf extends He{post_process_object_detection(...e){return Ss(...e)}}class B2 extends zf{}class P2 extends He{}class D2 extends He{}class Rf extends He{pad_image(e,r,n,a={}){const[i,s,o]=r;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 p=l.map((u,h)=>-u/this.image_std[h]);return super.pad_image(e,r,n,{center:!0,constant_values:p,...a})}}class N2 extends Rf{}class F2 extends He{async _call(e){const r=await super._call(e),n=[r.pixel_values.dims[0],64,64],a=new J("int64",new BigInt64Array(n.reduce((i,s)=>i*s)).fill(1n),n);return{...r,pixel_mask:a}}post_process_object_detection(...e){return Ss(...e)}remove_low_and_no_objects(e,r,n,a){let i=[],s=[],o=[];for(let l=0;ln&&(i.push(p),s.push(m),o.push(u))}return[i,s,o]}check_segment_validity(e,r,n,a=.5,i=.8){let s=[],o=0,l=0;for(let p=0;p=a&&++l;let d=o>0&&l>0;return d&&(d=o/l>i),[d,s]}compute_segments(e,r,n,a,i,s=null,o=null){let[l,d]=o??e[0].dims,p=new J("int32",new Int32Array(l*d),[l,d]),u=[];if(o!==null)for(let _=0;_m[w]&&(h[w]=_,m[w]=e[_].data[w])}let g=0;for(let _=0;_a!==r.dims[i]))throw Error(`The first ${n.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new J("int64",e.flat(1/0).map(BigInt),n)}async _call(e,r=null,n=null){const a=await super._call(e);if(r&&(a.input_points=this.reshape_input_points(r,a.original_sizes,a.reshaped_input_sizes)),n){if(!a.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");a.input_labels=this.add_input_labels(n,a.input_points)}return a}post_process_masks(e,r,n,{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&&(w[x]=1);b=new J("bool",w,b.dims)}m.push(b)}o.push(Ca(m))}return o}generate_crop_boxes(e,r,{crop_n_layers:n=0,overlap_ratio:a=512/1500,points_per_crop:i=32,crop_n_points_downscale_factor:s=1}={}){}}class W2 extends He{pad_image(e,r,n,a={}){const[i,s,o]=r;return super.pad_image(e,r,{width:i+(n-i%n)%n,height:s+(n-s%n)%n},{mode:"symmetric",center:!1,constant_values:-1,...a})}}class V2 extends He{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);const n=await Promise.all(e.map(s=>this.preprocess(s))),a=await Promise.all(r.map(s=>this.preprocess(s,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:Ca(n.map((s,o)=>Ea([s.pixel_values,a[o].pixel_values],0)),0),original_sizes:n.map(s=>s.original_size),reshaped_input_sizes:n.map(s=>s.reshaped_input_size)}}}class G2 extends ur{constructor(e){super(e),this.config.mel_filters??=pn(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=Pa(this.config.n_fft,"hann")}_extract_fbank_features(e){const{data:r,dims:n}=Ba(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=Yt(r)[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`."),r=e.slice(0,this.config.n_samples)):(r=new Float32Array(this.config.n_samples),r.set(e));const{data:n,dims:a}=this._extract_fbank_features(r);return{input_features:new J("float32",n,[1,...a])}}}class H2 extends ur{_zero_mean_unit_var_norm(e){const n=e.reduce((i,s)=>i+s,0)/e.length,a=e.reduce((i,s)=>i+(s-n)**2,0)/e.length;return e.map(i=>(i-n)/Math.sqrt(a+1e-7))}async _call(e){An(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=e;this.config.do_normalize&&(r=this._zero_mean_unit_var_norm(r));const n=[1,r.length];return{input_values:new J("float32",r,n),attention_mask:new J("int64",new BigInt64Array(r.length).fill(1n),n)}}}class q2 extends ur{constructor(e){super(e);const r=this.config.sampling_rate,n=pn(256,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);for(let a=0;an*32768),Ba(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:r,transpose:!0})}async _call(e,{padding:r=!0,pad_to_multiple_of:n=2,do_normalize_per_mel_bins:a=!0,return_attention_mask:i=!0}={}){An(e,"SeamlessM4TFeatureExtractor");let s=this._extract_fbank_features(e,this.config.max_length);if(a){const[g,_]=s.dims;for(let b=0;b<_;++b){let w=0;for(let A=0;A0){const w=new Float32Array(_*(g+b));w.set(s.data),w.fill(this.config.padding_value,s.data.length);const x=g+b;s={data:w,dims:[x,_]},i&&(o=new J("int64",new BigInt64Array(x),[1,x]),o.data.fill(1n,0,g))}}const[l,d]=s.dims,p=this.config.stride;if(l%p!==0)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${p}).`);const h=new J("float32",s.data,s.dims).view(1,Math.floor(l/p),d*p),m={input_features:h};if(i){const g=h.dims[1],_=new J("int64",new BigInt64Array(g),[1,g]);if(o)for(let b=1,w=0;b0)if(n==="rand_trunc"){s=!0;const l=Math.floor(Math.random()*(o+1));e=e.subarray(l,l+r),i=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),i.dims=[1,...i.dims]}else throw new Error(`Truncation strategy "${n}" not implemented`);else{if(o<0){let l=new Float64Array(r);if(l.set(e),a==="repeat")for(let d=e.length;d{sa=Number(qi.value),Es.feature_extractor.size={shortest_edge:sa},rv.textContent=sa});qi.disabled=!1;Da.textContent="Ready";let Ei=!1,Ci;const ad=va.getContext("2d",{willReadFrequently:!0}),nv=Hi.getContext("2d",{willReadFrequently:!0});function Df(){const{width:t,height:e}=va;Ei||(Ei=!0,async function(){ad.drawImage(nn,0,0,t,e);const r=ad.getImageData(0,0,t,e),n=new mt(r.data,t,e,4),a=await Es(n),{output:i}=await Pf({input:a.pixel_values}),s=await mt.fromTensor(i[0].mul(255).to("uint8")).resize(t,e),o=r;for(let l=0;l{nn.srcObject=t,nn.play();const e=t.getVideoTracks()[0],{width:r,height:n}=e.getSettings();nn.width=Hi.width=va.width=r,nn.height=Hi.height=va.height=n;const a=r/n,[i,s]=a>720/405?[720,720/a]:[405*a,405];nd.style.width=`${i}px`,nd.style.height=`${s}px`,setTimeout(Df,50)}).catch(t=>{alert(t)});