1// RUN: mlir-opt --transform-interpreter --split-input-file --verify-diagnostics %s | FileCheck %s 2 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.generic 6 {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 : f32 10 linalg.yield %1 : f32 11 } -> tensor<?x?x?xf32> 12 return %0 : tensor<?x?x?xf32> 13} 14// CHECK-LABEL: specialize_exp 15// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?xf32>, %[[ARG1:.+]]: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> 16// CHECK-NOT: linalg.generic 17// 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_op 22 %1 = transform.structured.specialize %0 : (!transform.any_op) -> !transform.any_op 23 transform.yield 24 } 25} 26