mlir/test/Examples/Toy/Ch7/struct-codegen.toy

# RUN: toyc-ch7 %s -emit=mlir 2>&1 | FileCheck %s
# RUN: toyc-ch7 %s -emit=mlir -opt 2>&1 | FileCheck %s --check-prefix=OPT

struct Struct {
  var a;
  var b;
}

# User defined generic function may operate on struct types as well.
def multiply_transpose(Struct value) {
  # We can access the elements of a struct via the '.' operator.
  return transpose(value.a) * transpose(value.b);
}

def main() {
  # We initialize struct values using a composite initializer.
  Struct value = {[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]};

  # We pass these arguments to functions like we do with variables.
  var c = multiply_transpose(value);
  print(c);
}

# CHECK-LABEL:   func @multiply_transpose(
# CHECK-SAME:                             [[VAL_0:%.*]]: !toy.struct<tensor<*xf64>, tensor<*xf64>>) -> tensor<*xf64>
# CHECK-SAME:        attributes {sym_visibility = "private"}
# CHECK-NEXT:      [[VAL_1:%.*]] = toy.struct_access [[VAL_0]][0] : !toy.struct<tensor<*xf64>, tensor<*xf64>> -> tensor<*xf64>
# CHECK-NEXT:      [[VAL_2:%.*]] = toy.transpose([[VAL_1]] : tensor<*xf64>) to tensor<*xf64>
# CHECK-NEXT:      [[VAL_3:%.*]] = toy.struct_access [[VAL_0]][1] : !toy.struct<tensor<*xf64>, tensor<*xf64>> -> tensor<*xf64>
# CHECK-NEXT:      [[VAL_4:%.*]] = toy.transpose([[VAL_3]] : tensor<*xf64>) to tensor<*xf64>
# CHECK-NEXT:      [[VAL_5:%.*]] = toy.mul [[VAL_2]], [[VAL_4]] : tensor<*xf64>
# CHECK-NEXT:      toy.return [[VAL_5]] : tensor<*xf64>

# CHECK-LABEL:   func @main()
# CHECK-NEXT:      [[VAL_6:%.*]] = toy.struct_constant [dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>, dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>] : !toy.struct<tensor<*xf64>, tensor<*xf64>>
# CHECK-NEXT:      [[VAL_7:%.*]] = toy.generic_call @multiply_transpose([[VAL_6]]) : (!toy.struct<tensor<*xf64>, tensor<*xf64>>) -> tensor<*xf64>
# CHECK-NEXT:      toy.print [[VAL_7]] : tensor<*xf64>
# CHECK-NEXT:      toy.return

# OPT-LABEL:   func @main()
# OPT-NEXT:      [[VAL_0:%.*]] = toy.constant dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>
# OPT-NEXT:      [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<2x3xf64>) to tensor<3x2xf64>
# OPT-NEXT:      [[VAL_2:%.*]] = toy.mul [[VAL_1]], [[VAL_1]] : tensor<3x2xf64>
# OPT-NEXT:      toy.print [[VAL_2]] : tensor<3x2xf64>
# OPT-NEXT:      toy.return