jax-ml/jax · error · TypeError

x argument to bincount must have an integer type; got

Error message

x argument to bincount must have an integer type; got {x.dtype}

What it means

jnp.bincount counts occurrences of values, which requires the values to be integer bin numbers. The function fires when x's dtype (after bool promotion) is not an integer subtype, e.g. passing floats.

Solutions

Cast to integer: jnp.bincount(x.astype(jnp.int32))
If values are class probabilities, take argmax first: jnp.bincount(jnp.argmax(x, axis=-1))
Note bool input is handled automatically; no cast needed there

Example fix

// before
jnp.bincount(jnp.array([0.1, 1.7, 1.2, 0.0]))
// after
jnp.bincount(jnp.floor(x).astype(jnp.int32))

Defensive patterns

Strategy: type-guard

Validate before calling

if not jnp.issubdtype(x.dtype, jnp.integer) and x.dtype != jnp.bool_:
    x = x.astype(jnp.int32)

Type guard

def is_integer_like(a) -> bool:
    return jnp.issubdtype(a.dtype, jnp.integer) or a.dtype == jnp.bool_

Prevention

Cast indices to int32 right after argmax/squeeze ops
Assert dtype in pipeline tests

When it happens

Trigger: Calling jnp.bincount(jnp.array([0.5, 1.0, 1.5])) or bincount on float32/float64 logits, indices from argmax on float arrays, or complex arrays.

Common situations: Counting values after computing indices without casting; passing probabilities or normalized floats directly; feeding output of a float pipeline into a histogram-like count.

Related errors

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/9840c2271f5c6454. Report an issue: GitHub.

Appendix: source

Thrown at jax/_src/numpy/lax_numpy.py:2961

    Specifying a static ``length`` makes this jit-compatible:

    >>> jit_bincount = jax.jit(jnp.bincount, static_argnames=['length'])
    >>> jit_bincount(x, length=5)
    Array([0, 2, 1, 3, 0], dtype=int32)

    Any negative numbers are clipped to the first bin, and numbers beyond the
    specified ``length`` are dropped:

    >>> x = jnp.array([-1, -1, 1, 3, 10])
    >>> jnp.bincount(x, length=5)
    Array([2, 1, 0, 1, 0], dtype=int32)
  """
  x = util.ensure_arraylike("bincount", x)
  if x.dtype == bool:
    x = lax.convert_element_type(x, 'int32')
  if not issubdtype(x.dtype, np.integer):
    raise TypeError(f"x argument to bincount must have an integer type; got {x.dtype}")
  if np.ndim(x) != 1:
    raise ValueError("only 1-dimensional input supported.")
  minlength = core.concrete_or_error(
      operator.index, minlength,
      "The error occurred because of argument 'minlength' of jnp.bincount.")
  if length is None:
    x_arr = core.concrete_or_error(
        asarray, x,
        "The error occurred because of argument 'x' of jnp.bincount. "
        "To avoid this error, pass a static `length` argument.")
    length = max(minlength, x_arr.size and int(max(0, x_arr.max())) + 1)
  else:
    length = core.concrete_dim_or_error(
        length,
        "The error occurred because of argument 'length' of jnp.bincount.")

  if weights is None:
    weights = np.array(1, dtype=dtypes.int_)

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