jax-ml/jax · error · ValueError

at least one array or dtype is required

Error message

at least one array or dtype is required

What it means

jax.numpy.result_type() requires at least one argument; calling it with no arguments raises this ValueError. Unlike np.result_type, JAX does not accept an empty argument list.

Source

Thrown at jax/_src/dtypes.py:1097

@overload
def result_type(*args: Any, return_weak_type_flag: Literal[False] = False) -> DType: ...

@overload
def result_type(*args: Any, return_weak_type_flag: bool = False) -> DType | tuple[DType, bool]: ...

@export
def result_type(*args: Any, return_weak_type_flag: bool = False) -> DType | tuple[DType, bool]:
  """Convenience function to apply JAX argument dtype promotion.

  Args:
    return_weak_type_flag : if True, then return a ``(dtype, weak_type)`` tuple.
      If False, just return `dtype`

  Returns:
    dtype or (dtype, weak_type) depending on the value of the ``return_weak_type`` argument.
  """
  if len(args) == 0:
    raise ValueError("at least one array or dtype is required")
  dtype: DType | ExtendedDType
  dtype, weak_type = lattice_result_type(*(default_float_dtype() if arg is None else arg for arg in args))
  if weak_type:
    dtype = default_types['f' if dtype in _custom_float_dtypes else dtype.kind]()
  return (dtype, weak_type) if return_weak_type_flag else dtype

def check_and_canonicalize_user_dtype(
    dtype, fun_name=None, *, allow_non_jax_dtypes: bool = False
) -> DType:
  """Checks validity of a user-provided dtype, and returns its canonical form.

  For Python scalar types this function returns the corresponding default dtype.
  """
  if dtype is None:
    raise ValueError("dtype must be specified.")
  if isinstance(dtype, Array):
    raise ValueError("Passing an array as a dtype argument is no longer "
                     "supported; instead of dtype=arr use dtype=arr.dtype.")

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Guard the call: if not args: use a sensible default such as jnp.float32
2. Require at least one argument in your wrapper and raise your own descriptive error
3. Pass jnp.result_type(*arrays) only after checking len(arrays) > 0

Example fix

# before
dt = jnp.result_type(*dtypes_list)  # crashes when empty

# after
dt = jnp.result_type(*dtypes_list) if dtypes_list else jnp.float32

if not args:
    args = [jnp.float32]  # or raise your own error
dt = jnp.result_type(*args)

def has_result_type_args(args) -> bool:
    return len(args) > 0

When it happens

Trigger: jnp.result_type() with zero args, typically from *args forwarding: jnp.result_type(*dtypes) where dtypes is an empty list.

Common situations: Generic wrapper functions that forward a variable-length list of arrays/dtypes which can be empty; refactors where a previously required argument became optional.

Understand the failure class

Background: Invalid argument type errors: "must be of type string", "expected X, got Y", and ERR_INVALID_ARG_TYPE explained — this error's family across 15 libraries.

Related errors

• `compute_on`'s compute_type argument must be a string.
• Argument '{x}' of type '{typ}' is not a valid JAX type
• Argument '{arg}' of type {type(arg)} is not a valid JAX type
• Expected kind to be a dtype, string, or tuple; got {kind=}
• dtype must be specified.

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/785dce8eacfd3ff4. Report an issue: GitHub.