1// RUN: mlir-opt --transform-interpreter %s | FileCheck %s 2 3// CHECK-LABEL: func.func @generalize_unary 4func.func @generalize_unary(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> { 5 6 // CHECK-NOT: linalg.elemwise_unary 7 // CHECK: linalg.generic 8 %0 = linalg.elemwise_unary ins(%arg0 : tensor<?x?xf32>) 9 outs(%arg1: tensor<?x?xf32>) -> tensor<?x?xf32> 10 return %0 : tensor<?x?xf32> 11} 12 13// CHECK-LABEL: func @map_no_inputs( 14func.func @map_no_inputs(%input: tensor<16x32x64xf32>, 15 %init: tensor<16x64xf32>) -> tensor<16x64xf32> { 16 // CHECK-NOT: linalg.map 17 // CHECK: linalg.generic 18 %reduce = linalg.reduce 19 ins(%input:tensor<16x32x64xf32>) 20 outs(%init:tensor<16x64xf32>) 21 dimensions = [1] 22 (%in: f32, %out: f32) { 23 %0 = arith.addf %out, %in: f32 24 linalg.yield %0: f32 25 } 26 func.return %reduce : tensor<16x64xf32> 27} 28module attributes {transform.with_named_sequence} { 29 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 30 %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op 31 %1 = transform.structured.generalize %0 : (!transform.any_op) -> !transform.any_op 32 transform.yield 33 } 34} 35