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# 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: toy.func private @multiply_transpose(
# CHECK-SAME: [[VAL_0:%.*]]: !toy.struct<tensor<*xf64>, tensor<*xf64>>) -> tensor<*xf64>
# 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: toy.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: toy.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
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