jax-ml/jax · error · ValueError

Need at least one array to stack.

Error message

Need at least one array to stack.

What it means

jnp.stack requires a non-empty sequence of arrays; stacking zero arrays has no defined dtype/shape, so it raises ValueError immediately.

Source

Thrown at jax/_src/numpy/lax_numpy.py:4429

    >>> jnp.stack([x, y])
    Array([[1, 2, 3],
           [4, 5, 6]], dtype=int32)
    >>> jnp.stack([x, y], axis=1)
    Array([[1, 4],
           [2, 5],
           [3, 6]], dtype=int32)

    :func:`~jax.numpy.unstack` performs the inverse operation:

    >>> arr = jnp.stack([x, y], axis=1)
    >>> x, y = jnp.unstack(arr, axis=1)
    >>> x
    Array([1, 2, 3], dtype=int32)
    >>> y
    Array([4, 5, 6], dtype=int32)
  """
  if not len(arrays):
    raise ValueError("Need at least one array to stack.")
  if out is not None:
    raise NotImplementedError("The 'out' argument to jnp.stack is not supported.")
  if isinstance(arrays, (np.ndarray, Array)):
    axis = _canonicalize_axis(axis, arrays.ndim)
    return concatenate(expand_dims(arrays, axis + 1), axis=axis, dtype=dtype)
  else:
    arrays = util.ensure_arraylike_tuple("stack", arrays)
    if dtype is not None:
      arrays = [asarray(a, dtype=dtype) for a in arrays]
    else:
      arrays = util.promote_dtypes(*arrays)
    return lax.stack(arrays, axis=axis)


@export
@api.jit(static_argnames="axis", inline=True)
def unstack(x: ArrayLike, /, *, axis: int = 0) -> tuple[Array, ...]:
  """Unstack an array along an axis.

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Guard with `if not arrays: ...` and supply a default/zero-shaped result
2. Fix upstream logic so the list is never empty

Example fix

// before
jnp.stack(chunks, axis=0)
// after
if not chunks:
    raise ValueError('no chunks to stack')
jnp.stack(chunks, axis=0)

if not arrays:
    raise ValueError('nothing to stack')

def non_empty(seq) -> bool:
    return len(seq) > 0

When it happens

Trigger: jnp.stack([]) or jnp.stack([], axis=1); also jnp.stack of an empty generator/list built by a comprehension that filters everything out.

Common situations: Dynamic batch accumulation where a filter or conditions yield an empty list; empty minibatches; empty results in data pipelines.

Related errors

• Need at least one array to concatenate.
• repeated axis in lax.expand_dims: {dimensions}
• Invalid dimension range passed to collapse: {operand.shape}[
• Arguments to batch_matmul must be at least 2D, got {}, {}
• Arguments to batch_matmul must have same ndim, got {}, {}

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/ea917e2f2df281f0. Report an issue: GitHub.