jax-ml/jax · error · ValueError

dtype argument to `pareto` must be a float dtype, got {dtype

Error message

dtype argument to `pareto` must be a float dtype, got {dtype}

What it means

jax.random.pareto requires a floating-point output dtype; the sampler builds on jax.random.exponential (itself float-only) and then applies a power transform. Integer or complex dtypes raise ValueError. The dtype must also be safe to cast b to, checked separately by _check_all_safe_to_cast.

Source

Thrown at jax/_src/random/core.py:2572

      jax_enable_x64 is true, otherwise float32).
    out_sharding: Optional. Specifies how the output array should be sharded
      across devices in multi-device computation. Can be a
      :class:`~jax.sharding.NamedSharding`, a :class:`~jax.sharding.PartitionSpec`
      (``P``), or ``None`` (default). When specified, the output will be sharded
      according to the given sharding specification. Primarily used in explicit
      sharding mode.
      See the `explicit sharding tutorial <https://docs.jax.dev/en/latest/parallel.html>`_
      for more details.

  Returns:
    A random array with the specified dtype and with shape given by ``shape`` if
    ``shape`` is not None, or else by ``b.shape``.
  """
  key, _ = _check_prng_key("pareto", key)
  dtype = dtypes.check_and_canonicalize_user_dtype(
      float if dtype is None else dtype)
  if not dtypes.issubdtype(dtype, np.floating):
    raise ValueError(f"dtype argument to `pareto` must be a float "
                     f"dtype, got {dtype}")
  shape = _check_broadcast_shapes("pareto", shape, b)
  _check_all_safe_to_cast("pareto", dtype, b)
  out_sharding = canonicalize_sharding_for_samplers(out_sharding, "pareto", shape)
  return maybe_auto_axes(_pareto, out_sharding,
                         shape=shape, dtype=dtype)(key, b)

@jit(static_argnums=(2, 3))
def _pareto(key, b, shape, dtype) -> Array:
  b = lax.convert_element_type(b, dtype)
  e = exponential(key, shape, dtype)
  return lax.exp(e / b)


def t(key: ArrayLike,
      df: RealArray,
      shape: Shape | None = None,
      dtype: DTypeLikeFloat | None = None,

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Pass jnp.float32/jnp.float64 or omit dtype.
2. Also ensure the shape b broadcasts (shape is checked via _check_broadcast_shapes) and b can be safely cast to dtype.
3. Validate configurable dtypes against np.floating before calling.

Example fix

// before
x = jax.random.pareto(key, b, dtype=jnp.int32)

// after
x = jax.random.pareto(key, b, dtype=jnp.float32)

from jax._src import dtypes
assert dtypes.issubdtype(dtypes.check_and_canonicalize_user_dtype(dtype or float), np.floating)

def is_float_dtype(dtype) -> bool:
    from jax._src import dtypes
    import numpy as np
    return dtypes.issubdtype(dtypes.check_and_canonicalize_user_dtype(dtype or float), np.floating)

When it happens

Trigger: jax.random.pareto(key, b, dtype=jnp.int32) or any dtype where dtypes.issubdtype(dtype, np.floating) is False.

Common situations: Heavy-tail noise generation configs with a single shared dtype; porting NumPy pareto code that has no dtype parameter; combining with int-based samplers in the same module.

Related errors

• dtype argument to `exponential` must be a float dtype, got {
• dtype argument to `gamma` must be a float dtype, got {dtype}
• dtype argument to `gumbel` must be a float dtype, got {dtype
• dtype argument to `laplace` must be a float dtype, got {dtyp
• dtype argument to `logistic` must be a float dtype, got {dty

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/95dd7ab3b33ed5ee. Report an issue: GitHub.