57540c96be
Summary: The dead function elimination pass in toy was a temporary stopgap until we had proper dead function elimination support in MLIR. Now that this functionality is available, this pass is no longer necessary. Differential Revision: https://reviews.llvm.org/D72483
46 lines
2.7 KiB
Plaintext
46 lines
2.7 KiB
Plaintext
# 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 @multiply_transpose(
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# CHECK-SAME: [[VAL_0:%.*]]: !toy.struct<tensor<*xf64>, tensor<*xf64>>) -> tensor<*xf64>
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# CHECK-SAME: attributes {sym_visibility = "private"}
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# CHECK-NEXT: [[VAL_1:%.*]] = "toy.struct_access"([[VAL_0]]) {index = 0 : i64} : (!toy.struct<tensor<*xf64>, tensor<*xf64>>) -> tensor<*xf64>
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# CHECK-NEXT: [[VAL_2:%.*]] = "toy.transpose"([[VAL_1]]) : (tensor<*xf64>) -> tensor<*xf64>
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# CHECK-NEXT: [[VAL_3:%.*]] = "toy.struct_access"([[VAL_0]]) {index = 1 : i64} : (!toy.struct<tensor<*xf64>, tensor<*xf64>>) -> tensor<*xf64>
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# CHECK-NEXT: [[VAL_4:%.*]] = "toy.transpose"([[VAL_3]]) : (tensor<*xf64>) -> tensor<*xf64>
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# CHECK-NEXT: [[VAL_5:%.*]] = "toy.mul"([[VAL_2]], [[VAL_4]]) : (tensor<*xf64>, tensor<*xf64>) -> 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"() {value = [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"([[VAL_6]]) {callee = @multiply_transpose} : (!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"() {value = dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>} : () -> tensor<2x3xf64>
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# OPT-NEXT: [[VAL_1:%.*]] = "toy.transpose"([[VAL_0]]) : (tensor<2x3xf64>) -> tensor<3x2xf64>
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# OPT-NEXT: [[VAL_2:%.*]] = "toy.mul"([[VAL_1]], [[VAL_1]]) : (tensor<3x2xf64>, tensor<3x2xf64>) -> 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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