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45 lines
2.4 KiB
45 lines
2.4 KiB
# RUN: toyc-ch7 %s -emit=mlir 2>&1 | FileCheck %s
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# RUN: toyc-ch7 %s -emit=mlir -opt 2>&1 | FileCheck %s --check-prefix=OPT
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struct Struct {
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var a;
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var b;
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}
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# User defined generic function may operate on struct types as well.
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def multiply_transpose(Struct value) {
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# We can access the elements of a struct via the '.' operator.
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return transpose(value.a) * transpose(value.b);
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}
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def main() {
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# We initialize struct values using a composite initializer.
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Struct value = {[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]};
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# We pass these arguments to functions like we do with variables.
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var c = multiply_transpose(value);
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print(c);
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}
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# CHECK-LABEL: func private @multiply_transpose(
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# CHECK-SAME: [[VAL_0:%.*]]: !toy.struct<tensor<*xf64>, tensor<*xf64>>) -> tensor<*xf64>
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# CHECK-NEXT: [[VAL_1:%.*]] = toy.struct_access [[VAL_0]][0] : !toy.struct<tensor<*xf64>, tensor<*xf64>> -> tensor<*xf64>
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# CHECK-NEXT: [[VAL_2:%.*]] = toy.transpose([[VAL_1]] : tensor<*xf64>) to tensor<*xf64>
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# CHECK-NEXT: [[VAL_3:%.*]] = toy.struct_access [[VAL_0]][1] : !toy.struct<tensor<*xf64>, tensor<*xf64>> -> tensor<*xf64>
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# CHECK-NEXT: [[VAL_4:%.*]] = toy.transpose([[VAL_3]] : tensor<*xf64>) to tensor<*xf64>
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# CHECK-NEXT: [[VAL_5:%.*]] = toy.mul [[VAL_2]], [[VAL_4]] : tensor<*xf64>
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# CHECK-NEXT: toy.return [[VAL_5]] : tensor<*xf64>
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# CHECK-LABEL: func @main()
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# 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>>
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# CHECK-NEXT: [[VAL_7:%.*]] = toy.generic_call @multiply_transpose([[VAL_6]]) : (!toy.struct<tensor<*xf64>, tensor<*xf64>>) -> tensor<*xf64>
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# CHECK-NEXT: toy.print [[VAL_7]] : tensor<*xf64>
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# CHECK-NEXT: toy.return
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# OPT-LABEL: func @main()
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# 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>
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# OPT-NEXT: [[VAL_1:%.*]] = toy.transpose([[VAL_0]] : tensor<2x3xf64>) to tensor<3x2xf64>
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# OPT-NEXT: [[VAL_2:%.*]] = toy.mul [[VAL_1]], [[VAL_1]] : tensor<3x2xf64>
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# OPT-NEXT: toy.print [[VAL_2]] : tensor<3x2xf64>
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# OPT-NEXT: toy.return
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