test/Examples/Toy/Ch3/codegen.toy

# RUN: toyc-ch3 %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>) -> tensor<*xf64>
# CHECK-NEXT:    [[VAL_3:%.*]] = "toy.transpose"([[VAL_1]]) : (tensor<*xf64>) -> tensor<*xf64>
# CHECK-NEXT:    [[VAL_4:%.*]] = "toy.mul"([[VAL_2]], [[VAL_3]]) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*xf64>
# CHECK-NEXT:    "toy.return"([[VAL_4]]) : (tensor<*xf64>) -> ()

# CHECK-LABEL: func @main()
# CHECK-NEXT:    [[VAL_5:%.*]] = "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>
# CHECK-NEXT:    [[VAL_6:%.*]] = "toy.reshape"([[VAL_5]]) : (tensor<2x3xf64>) -> tensor<2x3xf64>
# CHECK-NEXT:    [[VAL_7:%.*]] = "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>
# CHECK-NEXT:    [[VAL_8:%.*]] = "toy.reshape"([[VAL_7]]) : (tensor<6xf64>) -> tensor<2x3xf64>
# CHECK-NEXT:    [[VAL_9:%.*]] = "toy.generic_call"([[VAL_6]], [[VAL_8]]) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
# CHECK-NEXT:    [[VAL_10:%.*]] = "toy.generic_call"([[VAL_8]], [[VAL_6]]) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
# CHECK-NEXT:    "toy.print"([[VAL_10]]) : (tensor<*xf64>) -> ()
# CHECK-NEXT:    "toy.return"() : () -> ()