test/Dialect/Linalg/convert-elementwise-to-linalg.mlir

// RUN: mlir-opt -pass-pipeline="builtin.module(func.func(convert-elementwise-to-linalg))" -split-input-file %s | FileCheck %s

// In-depth checking of the linalg.generic op for a very trivial case.
// CHECK: #[[$MAP:.*]] = affine_map<() -> ()>
// CHECK-LABEL: func @addf_rank0
//  CHECK-SAME:   %[[ARG0:[0-9a-zA-Z]*]]: tensor<f32>
//  CHECK-SAME:   %[[ARG1:[0-9a-zA-Z]*]]: tensor<f32>
func.func @addf_rank0(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  //      CHECK: %{{.*}} = linalg.generic
  // CHECK-SAME: indexing_maps = [#[[$MAP]], #[[$MAP]], #[[$MAP]]]
  // CHECK-SAME: iterator_types = []
  // CHECK-SAME:  ins(%[[ARG0]], %[[ARG1]]
  // CHECK-SAME: outs(%[[ARG0]]
  //      CHECK: ^bb0(%[[LHS:.*]]: f32, %[[RHS:.*]]: f32, %{{.*}}: f32):
  //      CHECK:   %[[YIELD:.*]] = arith.addf %[[LHS]], %[[RHS]] : f32
  //      CHECK:   linalg.yield %[[YIELD]] : f32
  //      CHECK: } -> tensor<f32>
  %0 = arith.addf %arg0, %arg1 : tensor<f32>
  return %0 : tensor<f32>
}

// -----

// Check indexing maps and iterator types for the rank > 0 case.
// CHECK-LABEL: func @addf_rank1
//  CHECK-SAME:   %[[ARG0:[0-9a-zA-Z]*]]: tensor<?xf32>
//  CHECK-SAME:   %[[ARG1:[0-9a-zA-Z]*]]: tensor<?xf32>
func.func @addf_rank1(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<?xf32> {
  // CHECK: linalg.generic
  // CHECK-SAME: iterator_types = ["parallel"]
  // CHECK-SAME:  ins(%[[ARG0]], %[[ARG1]]
  // CHECK-SAME: outs(%[[ARG0]]
  %0 = arith.addf %arg0, %arg1 : tensor<?xf32>
  return %0 : tensor<?xf32>
}

// -----

// Check a unary op.
// CHECK-LABEL: func @exp
//  CHECK-SAME:   %[[ARG0:[0-9a-zA-Z]*]]: tensor<f32>
func.func @exp(%arg0: tensor<f32>) -> tensor<f32> {
  // CHECK: linalg.generic
  // CHECK-SAME:  ins(%[[ARG0]]
  // CHECK-SAME: outs(%[[ARG0]]
  // CHECK: ^bb0(%[[SCALAR:.*]]: f32, %{{.*}}: f32):
  // CHECK:   %[[YIELD:.*]] = math.exp %[[SCALAR]] : f32
  // CHECK:   linalg.yield %[[YIELD]] : f32
  %0 = math.exp %arg0 : tensor<f32>
  return %0 : tensor<f32>
}

// -----

// Check a case with varying operand types.
// CHECK-LABEL: func @select
//  CHECK-SAME:   %[[ARG0:[0-9a-zA-Z]*]]: tensor<i1>
//  CHECK-SAME:   %[[ARG1:[0-9a-zA-Z]*]]: tensor<i32>
//  CHECK-SAME:   %[[ARG2:[0-9a-zA-Z]*]]: tensor<i32>
func.func @select(%arg0: tensor<i1>, %arg1: tensor<i32>, %arg2: tensor<i32>) -> tensor<i32> {
  // CHECK: linalg.generic
  // CHECK-SAME:  ins(%[[ARG0]], %[[ARG1]], %[[ARG2]]
  // CHECK-SAME: outs(%[[ARG1]]
  // CHECK: ^bb0(%[[PRED:.*]]: i1, %[[TRUE_VAL:.*]]: i32, %[[FALSE_VAL:.*]]: i32, %{{.*}}: i32):
  // CHECK:   arith.select %[[PRED]], %[[TRUE_VAL]], %[[FALSE_VAL]] : i32
  %0 = arith.select %arg0, %arg1, %arg2 : tensor<i1>, tensor<i32>
  return %0 : tensor<i32>
}

// -----

// Spot-check an op that requires copying attributes properly to the created scalar op.
// Also checks proper init_tensor usage.
// CHECK-LABEL: func @cmpf(
//  CHECK-SAME:   %[[ARG0:[0-9a-zA-Z]*]]: tensor<f32>
//  CHECK-SAME:   %[[ARG1:[0-9a-zA-Z]*]]: tensor<f32>
func.func @cmpf(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<i1> {
  // CHECK: %[[INIT:.*]] = tensor.empty() : tensor<i1>
  // CHECK: linalg.generic
  // CHECK-SAME:  ins(%[[ARG0]], %[[ARG1]]
  // CHECK-SAME: outs(%[[INIT]]
  // CHECK: ^bb0(%{{.*}}: f32, %{{.*}}: f32, %{{.*}}: i1):
  // CHECK: arith.cmpf olt, %{{.*}}, %{{.*}} : f32
  %0 = arith.cmpf olt, %arg0, %arg1 : tensor<f32>
  return %0 : tensor<i1>
}

// -----

// Check proper init_tensor usage in a mixed case.
// CHECK-LABEL: func @cmpf(
//  CHECK-SAME:   %[[ARG0:[0-9a-zA-Z]*]]: tensor<4x?x?x8x2x?xf32>
//  CHECK-SAME:   %[[ARG1:[0-9a-zA-Z]*]]: tensor<4x?x?x8x2x?xf32>
func.func @cmpf(%arg0: tensor<4x?x?x8x2x?xf32>, %arg1: tensor<4x?x?x8x2x?xf32>) -> tensor<4x?x?x8x2x?xi1> {
  // CHECK: %[[C1:.*]] = arith.constant 1 : index
  // CHECK: %[[D1:.*]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<4x?x?x8x2x?xf32>
  // CHECK: %[[C2:.*]] = arith.constant 2 : index
  // CHECK: %[[D2:.*]] = tensor.dim %[[ARG0]], %[[C2]] : tensor<4x?x?x8x2x?xf32>
  // CHECK: %[[C5:.*]] = arith.constant 5 : index
  // CHECK: %[[D5:.*]] = tensor.dim %[[ARG0]], %[[C5]] : tensor<4x?x?x8x2x?xf32>
  // CHECK: %[[INIT:.*]] = tensor.empty(%[[D1]], %[[D2]], %[[D5]]) : tensor<4x?x?x8x2x?xi1>
  // CHECK: linalg.generic
  // CHECK-SAME:  ins(%[[ARG0]], %[[ARG1]]
  // CHECK-SAME: outs(%[[INIT]]
  // CHECK: ^bb0(%{{.*}}: f32, %{{.*}}: f32, %{{.*}}: i1):
  // CHECK: arith.cmpf olt, %{{.*}}, %{{.*}} : f32
  %0 = arith.cmpf olt, %arg0, %arg1 : tensor<4x?x?x8x2x?xf32>
  return %0 : tensor<4x?x?x8x2x?xi1>
}

// -----

// Check a mix of scalar and tensor input.
// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> ()> 
// CHECK: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0, d1)> 
// CHECK-LABEL: func @scalar_plus_tensor
func.func @scalar_plus_tensor(%arg0: f32, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
  // CHECK: %[[GEN:.*]] = linalg.generic
  // CHECK-SAME: iterator_types = ["parallel", "parallel"]
  // CHECK-SAME: ins(%[[S:.*]], %[[T:.*]] : f32, tensor<?x?xf32>)
  // CHECK-SAME: outs(%[[T]] : tensor<?x?xf32>)
  // CHECK: ^bb0(%[[SB:.*]]: f32, %[[TB:.*]]: f32, %[[OB:.*]]: f32):
  // CHECK:   "test.elementwise_mappable"(%[[SB]], %[[TB]]) : (f32, f32) -> f32
  // CHECK:   linalg.yield {{.*}} : f32
  // CHECK: } -> tensor<?x?xf32>
  %0 = "test.elementwise_mappable"(%arg0, %arg1)
       : (f32, tensor<?x?xf32>) -> tensor<?x?xf32>
  return %0 : tensor<?x?xf32>
}

// -----
// This test exercises the case where an elementwise op has two scalar-like
// operands and one ranked tensor operand. In this example, we chain two
// `test.elementwise_mappable` calls:
//   %0 = f(%s1, %t)
//   %1 = f(%s2, %0)
// CHECK-DAG: #[[$SC2:[A-Za-z0-9_]+]] = affine_map<(d0, d1) -> ()>
// CHECK-DAG: #[[$ID2:[A-Za-z0-9_]+]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-LABEL: func @scalar_tensor_scalar
func.func @scalar_tensor_scalar(%s1: f32, %t: tensor<?x?xf32>, %s2: f32) -> tensor<?x?xf32> {
  // First generic.
  // CHECK: %[[GEN0:.*]] = linalg.generic
  // CHECK-SAME: indexing_maps = [#[[$SC2]], #[[$ID2]], #[[$ID2]]]
  // CHECK-SAME: iterator_types = ["parallel", "parallel"]
  // CHECK-SAME: ins(%[[S1:[^,]+]], %[[T0:[^)]*]] : f32, tensor<?x?xf32>)
  // CHECK-SAME: outs(%[[T0]] : tensor<?x?xf32>)
  // CHECK: ^bb0(%[[S1E:.*]]: f32, %[[T0E:.*]]: f32, %[[O0E:.*]]: f32):
  // CHECK:   %[[APPLY0:.*]] = "test.elementwise_mappable"(%[[S1E]], %[[T0E]]) : (f32, f32) -> f32
  // CHECK:   linalg.yield %[[APPLY0]] : f32
  // CHECK: } -> tensor<?x?xf32>

  // Second generic.
  // CHECK: %[[GEN1:.*]] = linalg.generic
  // CHECK-SAME: indexing_maps = [#[[$SC2]], #[[$ID2]], #[[$ID2]]]
  // CHECK-SAME: iterator_types = ["parallel", "parallel"]
  // CHECK-SAME: ins(%[[S2:[^,]+]], %[[GEN0]] : f32, tensor<?x?xf32>)
  // CHECK-SAME: outs(%[[GEN0]] : tensor<?x?xf32>)
  // CHECK: ^bb0(%[[S2E:.*]]: f32, %[[G0E:.*]]: f32, %[[O1E:.*]]: f32):
  // CHECK:   %[[APPLY1:.*]] = "test.elementwise_mappable"(%[[S2E]], %[[G0E]]) : (f32, f32) -> f32
  // CHECK:   linalg.yield %[[APPLY1]] : f32
  // CHECK: } -> tensor<?x?xf32>
  // CHECK: return %[[GEN1]] : tensor<?x?xf32>
  %0 = "test.elementwise_mappable"(%s1, %t)
       : (f32, tensor<?x?xf32>) -> tensor<?x?xf32>
  %1 = "test.elementwise_mappable"(%s2, %0)
       : (f32, tensor<?x?xf32>) -> tensor<?x?xf32>
  return %1 : tensor<?x?xf32>
}

// ----
// CHECK-LABEL: func @negative_scalar_only_eltwise
// CHECK-NOT: linalg
func.func @negative_scalar_only_eltwise(%a: f32, %b: f32) -> f32 {
  %0 = arith.addf %a, %b : f32
  return %0 : f32
}