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1# RUN: toyc-ch7 %s -emit=mlir 2>&1 | FileCheck %s2# RUN: toyc-ch7 %s -emit=mlir -opt 2>&1 | FileCheck %s --check-prefix=OPT3 4struct Struct {5 var a;6 var b;7}8 9# User defined generic function may operate on struct types as well.10def multiply_transpose(Struct value) {11 # We can access the elements of a struct via the '.' operator.12 return transpose(value.a) * transpose(value.b);13}14 15def main() {16 # We initialize struct values using a composite initializer.17 Struct value = {[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]};18 19 # We pass these arguments to functions like we do with variables.20 var c = multiply_transpose(value);21 print(c);22}23 24# CHECK-LABEL: toy.func private @multiply_transpose(25# CHECK-SAME: [[VAL_0:%.*]]: !toy.struct<tensor<*xf64>, tensor<*xf64>>) -> tensor<*xf64>26# CHECK-NEXT: [[VAL_1:%.*]] = toy.struct_access [[VAL_0]][0] : !toy.struct<tensor<*xf64>, tensor<*xf64>> -> tensor<*xf64>27# CHECK-NEXT: [[VAL_2:%.*]] = toy.transpose([[VAL_1]] : tensor<*xf64>) to tensor<*xf64>28# CHECK-NEXT: [[VAL_3:%.*]] = toy.struct_access [[VAL_0]][1] : !toy.struct<tensor<*xf64>, tensor<*xf64>> -> tensor<*xf64>29# CHECK-NEXT: [[VAL_4:%.*]] = toy.transpose([[VAL_3]] : tensor<*xf64>) to tensor<*xf64>30# CHECK-NEXT: [[VAL_5:%.*]] = toy.mul [[VAL_2]], [[VAL_4]] : tensor<*xf64>31# CHECK-NEXT: toy.return [[VAL_5]] : tensor<*xf64>32 33# CHECK-LABEL: toy.func @main()34# 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>>35# CHECK-NEXT: [[VAL_7:%.*]] = toy.generic_call @multiply_transpose([[VAL_6]]) : (!toy.struct<tensor<*xf64>, tensor<*xf64>>) -> tensor<*xf64>36# CHECK-NEXT: toy.print [[VAL_7]] : tensor<*xf64>37# CHECK-NEXT: toy.return38 39# OPT-LABEL: toy.func @main()40# 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>41# OPT-NEXT: [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<2x3xf64>) to tensor<3x2xf64>42# OPT-NEXT: [[VAL_2:%.*]] = toy.mul [[VAL_1]], [[VAL_1]] : tensor<3x2xf64>43# OPT-NEXT: toy.print [[VAL_2]] : tensor<3x2xf64>44# OPT-NEXT: toy.return45