llvm/mlir/test/Examples/Toy/Ch7/codegen.toy

# RUN: toyc-ch7 %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: toy.func private @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: toy.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