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1func.func @depthwise_conv_1d_nwc_wcm(%input: tensor<1x12x8xf32>, %filter: tensor<3x8x8xf32>)2// <- function.builtin3// ^ function4// ^ variable.parameter5// ^ type.builtin6// ^ variable.parameter7// ^ type.builtin8 -> tensor<1x10x8x8xf32> {9// ^ operator10// ^ type.builtin11 %zero = arith.constant 0.000000e+00 : f3212// ^ variable13// ^ function.builtin14// ^ number15// ^ type.builtin16 %init = tensor.empty() : tensor<1x10x8x8xf32>17// ^ variable18// ^ function.builtin19// ^ type.builtin20 %fill = linalg.fill ins(%zero : f32) outs(%init : tensor<1x10x8x8xf32>) -> tensor<1x10x8x8xf32>21// ^ variable22// ^ function.builtin23// ^ keyword24// ^ variable25// ^ type.builtin26// ^ keyword27 %0 = linalg.depthwise_conv_1d_nwc_wcm {dilations = dense<1> : tensor<1xi64>,28// ^ variable29// ^ function.builtin30// ^ attribute31// ^ constant.builtin32 strides = dense<1> : tensor<1xi64>}33// ^ constant.builtin34 ins(%input, %filter : tensor<1x12x8xf32>, tensor<3x8x8xf32>)35// ^ variable.parameter36// ^ variable.parameter37 outs(%fill : tensor<1x10x8x8xf32>) -> tensor<1x10x8x8xf32>38// ^ variable39 return %0 : tensor<1x10x8x8xf32>40// ^ function.builtin41// ^ variable42}43 44func.func @fastmath(%arg0: f32, %arg1: f32) {45// <- function.builtin46// ^ function47// ^ variable.parameter48// ^ type.builtin49// ^ variable.parameter50// ^ type.builtin51 %5 = arith.negf %arg0 fastmath<fast> : f3252// ^ function.builtin53// ^ attribute54 %6 = arith.addf %arg0, %arg1 fastmath<none> : f3255// ^ function.builtin56// ^ attribute57 %8 = arith.mulf %arg0, %arg1 fastmath<reassoc,nnan,ninf,nsz,arcp,contract,afn> : f3258// ^ function.builtin59// ^ attribute60 return61// ^ function.builtin62}63 64#map0 = affine_map<(d0, d1) -> (d0, d1)>65// <- attribute66// ^ attribute67#map1 = affine_map<(d0, d1) -> (d0)>68// <- attribute69// ^ attribute70#map2 = affine_map<(d0) -> (d0)>71// <- attribute72// ^ attribute73 74func.func @add_broadcast_mul_fusion(%arg0: tensor<?xf32>, %arg1 : tensor<?xf32>,75 %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>76{77 %c0 = arith.constant 0 : index78 %c1 = arith.constant 1 : index79 %0 = tensor.dim %arg0, %c0 : tensor<?xf32>80 %1 = tensor.empty(%0) : tensor<?xf32>81 %2 = linalg.generic {indexing_maps = [#map2, #map2, #map2], iterator_types = ["parallel"]}82// ^ attribute83// ^ attribute84// ^ attribute85 ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)86// ^ keyword87 outs(%1 : tensor<?xf32>) {88// ^ keyword89 ^bb0(%arg3: f32, %arg4: f32, %arg5: f32):90 %3 = arith.addf %arg3, %arg4 : f3291 linalg.yield %3 : f3292 } -> tensor<?xf32>93 %3 = tensor.dim %arg2, %c1 : tensor<?x?xf32>94 %4 = tensor.empty(%0, %3) : tensor<?x?xf32>95 %5 = linalg.generic {indexing_maps = [#map1, #map0, #map0], iterator_types = ["parallel", "parallel"]}96// ^ function.builtin97 ins(%2, %arg2 : tensor<?xf32>, tensor<?x?xf32>)98 outs(%4 : tensor<?x?xf32>){99 ^bb0(%arg5: f32, %arg6: f32, %arg7: f32):100 %6 = arith.mulf %arg5, %arg6 : f32101 linalg.yield %6 : f32102 } -> tensor<?x?xf32>103 return %5 : tensor<?x?xf32>104}105 106func.func @broadcast(%input: tensor<8x32xf32>,107 %init: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {108 %bcast = linalg.broadcast109// ^ function.builtin110 ins(%input:tensor<8x32xf32>)111// ^ keyword112 outs(%init:tensor<8x16x32xf32>)113// ^ keyword114 dimensions = [1]115// ^ attribute116 func.return %bcast : tensor<8x16x32xf32>117}118