cd45b0c8d9
This is using Table-driven Declarative Rewrite Rules (DRR), the previous version of the tutorial only showed the C++ patterns. Closes tensorflow/mlir#187 PiperOrigin-RevId: 274852321
32 lines
1.8 KiB
Plaintext
32 lines
1.8 KiB
Plaintext
# RUN: toyc-ch3 %s -emit=mlir 2>&1 | FileCheck %s
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# User defined generic function that operates on unknown shaped arguments
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def multiply_transpose(a, b) {
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return a * transpose(b);
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}
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def main() {
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var a<2, 3> = [[1, 2, 3], [4, 5, 6]];
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var b<2, 3> = [1, 2, 3, 4, 5, 6];
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var c = multiply_transpose(a, b);
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var d = multiply_transpose(b, a);
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print(d);
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}
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# CHECK-LABEL: func @multiply_transpose(
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# CHECK-SAME: [[VAL_0:%.*]]: tensor<*xf64>, [[VAL_1:%.*]]: tensor<*xf64>)
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# CHECK-NEXT: attributes {toy.generic} {
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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.mul"([[VAL_0]], [[VAL_2]]) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*xf64>
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# CHECK-NEXT: "toy.return"([[VAL_3]]) : (tensor<*xf64>) -> ()
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# CHECK-LABEL: func @main() {
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# CHECK-NEXT: [[VAL_4:%.*]] = "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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# CHECK-NEXT: [[VAL_5:%.*]] = "toy.reshape"([[VAL_4]]) : (tensor<2x3xf64>) -> tensor<2x3xf64>
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# CHECK-NEXT: [[VAL_6:%.*]] = "toy.constant"() {value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64>} : () -> tensor<6xf64>
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# CHECK-NEXT: [[VAL_7:%.*]] = "toy.reshape"([[VAL_6]]) : (tensor<6xf64>) -> tensor<2x3xf64>
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# CHECK-NEXT: [[VAL_8:%.*]] = "toy.generic_call"([[VAL_5]], [[VAL_7]]) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
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# CHECK-NEXT: [[VAL_9:%.*]] = "toy.generic_call"([[VAL_7]], [[VAL_5]]) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
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# CHECK-NEXT: "toy.print"([[VAL_9]]) : (tensor<*xf64>) -> ()
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# CHECK-NEXT: "toy.return"() : () -> ()
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