mlir/test/Dialect/Linalg/transform-op-specialize-elemwise-binary.mlir

// RUN: mlir-opt --transform-interpreter --split-input-file --verify-diagnostics %s | FileCheck %s

#map = affine_map<(d0, d1) -> (d0, d1)>
func.func @specialize_add(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
  %0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) {
  ^bb0(%in: f32, %in_0: f32, %out: f32):
    %1 = arith.addf %in, %in_0 : f32
    linalg.yield %1 : f32
  } -> tensor<?x?xf32>
  return %0 : tensor<?x?xf32>
}
// CHECK-LABEL: specialize_add
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>,  %[[ARG2:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK-NOT: linalg.generic
// CHECK: linalg.add ins(%[[ARG0]], %[[ARG1]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) -> tensor<?x?xf32>

func.func @specialize_sub(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
  %0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) {
  ^bb0(%in: f32, %in_0: f32, %out: f32):
    %1 = arith.subf %in, %in_0 : f32
    linalg.yield %1 : f32
  } -> tensor<?x?xf32>
  return %0 : tensor<?x?xf32>
}
// CHECK-LABEL: specialize_sub
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>,  %[[ARG2:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK-NOT: linalg.generic
// CHECK: linalg.sub ins(%[[ARG0]], %[[ARG1]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) -> tensor<?x?xf32>

func.func @specialize_sub_swapped_operands(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
  %0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) {
  ^bb0(%in: f32, %in_0: f32, %out: f32):
    %1 = arith.subf %in_0, %in : f32
    linalg.yield %1 : f32
  } -> tensor<?x?xf32>
  return %0 : tensor<?x?xf32>
}
// CHECK-LABEL: specialize_sub
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>,  %[[ARG2:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK-NOT: linalg.generic
// CHECK: linalg.sub ins(%[[ARG1]], %[[ARG0]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) -> tensor<?x?xf32>

func.func @specialize_mul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
  %0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) {
  ^bb0(%in: f32, %in_0: f32, %out: f32):
    %1 = arith.mulf %in, %in_0 : f32
    linalg.yield %1 : f32
  } -> tensor<?x?xf32>
  return %0 : tensor<?x?xf32>
}
// CHECK-LABEL: specialize_mul
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>,  %[[ARG2:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK-NOT: linalg.generic
// CHECK: linalg.mul ins(%[[ARG0]], %[[ARG1]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) -> tensor<?x?xf32>

func.func @specialize_div(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
  %0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) {
  ^bb0(%in: f32, %in_0: f32, %out: f32):
    %1 = arith.divf %in, %in_0 : f32
    linalg.yield %1 : f32
  } -> tensor<?x?xf32>
  return %0 : tensor<?x?xf32>
}
// CHECK-LABEL: specialize_div
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>,  %[[ARG2:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK-NOT: linalg.generic
// CHECK: linalg.div ins(%[[ARG0]], %[[ARG1]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) -> tensor<?x?xf32>


module attributes {transform.with_named_sequence} {
  transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
    %0 = transform.structured.match interface{LinalgOp} in %arg0 : (!transform.any_op) -> !transform.any_op
    %1 = transform.structured.specialize %0 : (!transform.any_op) -> !transform.any_op
    transform.yield
  }
}