jax-ml/jax · error · ValueError

accumulate only supported for binary ufuncs

Error message

accumulate only supported for binary ufuncs

What it means

ufunc.accumulate (cumulative reduction, e.g. cumsum via jnp.add.accumulate) requires a binary ufunc (nin == 2). Calling .accumulate on a unary ufunc raises this ValueError.

Source

Thrown at jax/_src/numpy/ufunc_api.py:372

      :func:`jax.numpy.cumprod` along the specified axis:

      >>> jnp.multiply.accumulate(x, axis=1)
      Array([[  1,   2,   6],
             [  4,  20, 120]], dtype=int32)
      >>> jnp.cumprod(x, axis=1)
      Array([[  1,   2,   6],
             [  4,  20, 120]], dtype=int32)

      For other binary ufuncs, the accumulation is an operation not available
      via standard APIs. For example, :meth:`jax.numpy.bitwise_or.accumulate`
      is essentially a bitwise cumulative ``any``:

      >>> jnp.bitwise_or.accumulate(x, axis=1)
      Array([[1, 3, 3],
             [4, 5, 7]], dtype=int32)
    """
    if self.nin != 2:
      raise ValueError("accumulate only supported for binary ufuncs")
    if self.nout != 1:
      raise ValueError("accumulate only supported for functions returning a single value")
    if out is not None:
      raise NotImplementedError(f"out argument of {self.__name__}.accumulate()")
    accumulate = self.__static_props['accumulate'] or self._accumulate_via_scan
    return accumulate(a, axis=axis, dtype=dtype)

  def _accumulate_via_scan(self, arr: ArrayLike, axis: int = 0,
                           dtype: DTypeLike | None = None) -> Array:
    assert self.nin == 2 and self.nout == 1
    check_arraylike(f"{self.__name__}.accumulate", arr)
    arr = lax.asarray(arr)

    if dtype is None:
      dtype = api.eval_shape(self._func, lax._one(arr), lax._one(arr)).dtype

    if axis is None or isinstance(axis, tuple):
      raise ValueError("accumulate does not allow multiple axes")

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Use only binary ufuncs with .accumulate
2. For cumulative unary effects, express them with jax.lax.scan or apply the op before accumulating

Example fix

// before
jnp.negative.accumulate(x)
// after
jnp.add.accumulate(-x)

assert ufunc.nin == 2 before ufunc.accumulate(...)

def is_binary_ufunc(u): return u.nin == 2

When it happens

Trigger: jnp.negative.accumulate(x) or .accumulate on any ufunc with a single input.

Common situations: Generic code that reflects over ufuncs; mistaking accumulate for a general scan over unary ops.

Related errors

• accumulate only supported for functions returning a single v
• reduce only supported for binary ufuncs
• reduce only supported for functions returning a single value
• out argument of {self.__name__}.accumulate()
• accumulate does not allow multiple axes

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/0da2d633170f3c04. Report an issue: GitHub.