jax-ml/jax · error · ValueError

The input must be non-scalar to take a cumulative product, h

Error message

The input must be non-scalar to take a cumulative product, however a scalar value or scalar array was given.

What it means

jnp.cumulative_prod requires a non-scalar input; a 0-d array or scalar has no axis to accumulate over, so JAX raises ValueError before canonicalizing the axis.

Source

Thrown at jax/_src/numpy/reductions.py:2363

  See Also:
    - :func:`jax.numpy.cumprod`: alternative API for cumulative product.
    - :func:`jax.numpy.nancumprod`: cumulative product while ignoring NaN values.
    - :func:`jax.numpy.multiply.accumulate`: cumulative product via the ufunc API.

  Examples:
    >>> x = jnp.array([[1, 2, 3],
    ...                [4, 5, 6]])
    >>> jnp.cumulative_prod(x, axis=1)
    Array([[  1,   2,   6],
           [  4,  20, 120]], dtype=int32)
    >>> jnp.cumulative_prod(x, axis=1, include_initial=True)
    Array([[  1,   1,   2,   6],
           [  1,   4,  20, 120]], dtype=int32)
  """
  x = ensure_arraylike("cumulative_prod", x)
  if x.ndim == 0:
    raise ValueError(
      "The input must be non-scalar to take a cumulative product, however a "
      "scalar value or scalar array was given."
    )
  if axis is None:
    axis = 0
    if x.ndim > 1:
      raise ValueError(
        f"The input array has rank {x.ndim}, however axis was not set to an "
        "explicit value. The axis argument is only optional for one-dimensional "
        "arrays.")

  axis = canonicalize_axis(axis, x.ndim)
  if dtype is not None:
    dtype = dtypes.check_and_canonicalize_user_dtype(dtype)
  out = _cumulative_reduction("cumulative_prod", control_flow.cumprod, x, axis, dtype)
  if include_initial:
    zeros_shape = list(x.shape)
    zeros_shape[axis] = 1

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Reshape to at least 1-d: jnp.cumulative_prod(x[None])
2. Guard with an ndim check in generic accumulate helpers

Example fix

// before
jnp.cumulative_prod(jnp.prod(x))
// after
jnp.cumulative_prod(jnp.prod(x, keepdims=True))

import jax.numpy as jnp

def cumulative_prod_safe(x, **kw):
    if jnp.ndim(x) == 0:
        x = jnp.reshape(x, (1,))
    return jnp.cumulative_prod(x, **kw)

def is_nonscalar(x) -> bool:
    return jnp.asarray(x).ndim > 0

When it happens

Trigger: Calling jnp.cumulative_prod(jnp.asarray(5)) or with any x.ndim == 0 input.

Common situations: Chaining cumulative products after full reductions; per-element loops feeding Python scalars into jnp functions.

Related errors

• The input must be non-scalar to take a cumulative sum, howev
• len() of unsized object
• numpy masked arrays are not supported as direct inputs to JA
• Unsupported scalar attribute type: {type(val)}
• NumPy arrays with zero strides are not supported as MLIR att

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/b03f3806ba73f98e. Report an issue: GitHub.