| // RUN: mlir-opt --transform-interpreter %s | FileCheck %s |
| |
| // CHECK-LABEL: func.func @generalize_unary |
| func.func @generalize_unary(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> { |
| |
| // CHECK-NOT: linalg.elemwise_unary |
| // CHECK: linalg.generic |
| %0 = linalg.elemwise_unary ins(%arg0 : tensor<?x?xf32>) |
| outs(%arg1: tensor<?x?xf32>) -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // CHECK-LABEL: func @map_no_inputs( |
| func.func @map_no_inputs(%input: tensor<16x32x64xf32>, |
| %init: tensor<16x64xf32>) -> tensor<16x64xf32> { |
| // CHECK-NOT: linalg.map |
| // CHECK: linalg.generic |
| %reduce = linalg.reduce |
| ins(%input:tensor<16x32x64xf32>) |
| outs(%init:tensor<16x64xf32>) |
| dimensions = [1] |
| (%in: f32, %out: f32) { |
| %0 = arith.addf %out, %in: f32 |
| linalg.yield %0: f32 |
| } |
| func.return %reduce : tensor<16x64xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %1 = transform.structured.generalize %0 : (!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |