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1// RUN: mlir-opt --transform-interpreter --split-input-file --verify-diagnostics %s | FileCheck %s2 3#umap = affine_map<(d0, d1, d2) -> (d0, d1, d2)>4func.func @specialize_exp(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {5 %0 = linalg.generic6 {indexing_maps = [#umap, #umap], iterator_types = ["parallel", "parallel","parallel"]}7 ins(%arg0 : tensor<?x?x?xf32>) outs(%arg1 : tensor<?x?x?xf32>) {8 ^bb0(%in: f32, %out: f32):9 %1 = math.exp %in : f3210 linalg.yield %1 : f3211 } -> tensor<?x?x?xf32>12 return %0 : tensor<?x?x?xf32>13}14// CHECK-LABEL: specialize_exp15// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?xf32>, %[[ARG1:.+]]: tensor<?x?x?xf32>) -> tensor<?x?x?xf32>16// CHECK-NOT: linalg.generic17// CHECK: linalg.exp ins(%[[ARG0]] : tensor<?x?x?xf32>) outs(%[[ARG1]] : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>18 19module attributes {transform.with_named_sequence} {20 transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {21 %0 = transform.structured.match interface{LinalgOp} in %arg0 : (!transform.any_op) -> !transform.any_op22 %1 = transform.structured.specialize %0 : (!transform.any_op) -> !transform.any_op23 transform.yield24 }25}26