| # RUN: toyc-ch5 %s -emit=mlir 2>&1 | FileCheck %s |
| |
| # User defined generic function that operates on unknown shaped arguments |
| def multiply_transpose(a, b) { |
| return transpose(a) * transpose(b); |
| } |
| |
| def main() { |
| var a<2, 3> = [[1, 2, 3], [4, 5, 6]]; |
| var b<2, 3> = [1, 2, 3, 4, 5, 6]; |
| var c = multiply_transpose(a, b); |
| var d = multiply_transpose(b, a); |
| print(d); |
| } |
| |
| # CHECK-LABEL: func @multiply_transpose( |
| # CHECK-SAME: [[VAL_0:%.*]]: tensor<*xf64>, [[VAL_1:%.*]]: tensor<*xf64>) -> tensor<*xf64> |
| # CHECK: [[VAL_2:%.*]] = toy.transpose([[VAL_0]] : tensor<*xf64>) to tensor<*xf64> |
| # CHECK-NEXT: [[VAL_3:%.*]] = toy.transpose([[VAL_1]] : tensor<*xf64>) to tensor<*xf64> |
| # CHECK-NEXT: [[VAL_4:%.*]] = toy.mul [[VAL_2]], [[VAL_3]] : tensor<*xf64> |
| # CHECK-NEXT: toy.return [[VAL_4]] : tensor<*xf64> |
| |
| # CHECK-LABEL: func @main() |
| # CHECK-NEXT: [[VAL_5:%.*]] = toy.constant dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64> |
| # CHECK-NEXT: [[VAL_6:%.*]] = toy.reshape([[VAL_5]] : tensor<2x3xf64>) to tensor<2x3xf64> |
| # CHECK-NEXT: [[VAL_7:%.*]] = toy.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64> |
| # CHECK-NEXT: [[VAL_8:%.*]] = toy.reshape([[VAL_7]] : tensor<6xf64>) to tensor<2x3xf64> |
| # CHECK-NEXT: [[VAL_9:%.*]] = toy.generic_call @multiply_transpose([[VAL_6]], [[VAL_8]]) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> |
| # CHECK-NEXT: [[VAL_10:%.*]] = toy.generic_call @multiply_transpose([[VAL_8]], [[VAL_6]]) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> |
| # CHECK-NEXT: toy.print [[VAL_10]] : tensor<*xf64> |
| # CHECK-NEXT: toy.return |
