jax-ml/jax · error · NotImplementedError

out argument of {self.__name__}.accumulate()

Error message

out argument of {self.__name__}.accumulate()

What it means

ufunc.accumulate's out parameter is accepted for numpy compatibility but unsupported because JAX arrays are immutable. A non-None out raises NotImplementedError naming the ufunc.

Source

Thrown at jax/_src/numpy/ufunc_api.py:376

             [  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")
    axis = canonicalize_axis(axis, np.ndim(arr))

    if arr.size == 0:
      return lax.full(arr.shape, 0, dtype)

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Drop out= and use the returned array
2. Rely on jit + buffer donation for memory efficiency instead of out=

Example fix

// before
jnp.add.accumulate(x, out=cum)
// after
cum = jnp.add.accumulate(x)

assert out is None, 'accumulate does not support out='

When it happens

Trigger: jnp.add.accumulate(x, out=buf).

Common situations: Ported numpy code that used out= with cumsum-like operations for buffer reuse.

Related errors

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

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/098b658b3fb1a997. Report an issue: GitHub.