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jax.numpy.isscalar — JAX documentation

jax.numpy.isscalar(element)[source]#

Return True if the input is a scalar.

JAX implementation of numpy.isscalar(). JAX’s implementation differs from NumPy’s in that it considers zero-dimensional arrays to be scalars; see the Note below for more details.

Parameters:

element (Any) – input object to check; any type is valid input.

Returns:

True if element is a scalar value or an array-like object with zero dimensions, False otherwise.

Return type:

bool

Note

JAX and NumPy differ in their representation of scalar values. NumPy has special scalar objects (e.g. np.int32(0)) which are distinct from zero-dimensional arrays (e.g. np.array(0)), and numpy.isscalar() returns True for the former and False for the latter.

JAX does not define special scalar objects, but rather represents scalars as zero-dimensional arrays. As such, jax.numpy.isscalar() returns True for both scalar objects (e.g. 0.0 or np.float32(0.0)) and array-like objects with zero dimensions (e.g. jnp.array(0.0), np.array(0.0)).

One reason for the different conventions in isscalar is to maintain JIT-invariance: i.e. the property that the result of a function should not change when it is JIT-compiled. Because scalar inputs are cast to zero-dimensional JAX arrays at JIT boundaries, the semantics of numpy.isscalar() are such that the result changes under JIT:

>>> np.isscalar(1.0)
True
>>> jax.jit(np.isscalar)(1.0)
Array(False, dtype=bool)

By treating zero-dimensional arrays as scalars, jax.numpy.isscalar() avoids this issue:

>>> jnp.isscalar(1.0)
True
>>> jax.jit(jnp.isscalar)(1.0)
Array(True, dtype=bool)

Examples

In JAX, both scalars and zero-dimensional array-like objects are considered scalars:

>>> jnp.isscalar(1.0)
True
>>> jnp.isscalar(1 + 1j)
True
>>> jnp.isscalar(jnp.array(1))  # zero-dimensional JAX array
True
>>> jnp.isscalar(jnp.int32(1))  # JAX scalar constructor
True
>>> jnp.isscalar(np.array(1.0))  # zero-dimensional NumPy array
True
>>> jnp.isscalar(np.int32(1))  # NumPy scalar type
True

Arrays with one or more dimension are not considered scalars:

>>> jnp.isscalar(jnp.array([1]))
False
>>> jnp.isscalar(np.array([1]))
False

Compare this to numpy.isscalar(), which returns True for scalar-typed objects, and False for all arrays, even those with zero dimensions:

>>> np.isscalar(np.int32(1))  # scalar object
True
>>> np.isscalar(np.array(1))  # zero-dimensional array
False

In JAX, as in NumPy, objects which are not array-like are not considered scalars:

>>> jnp.isscalar(None)
False
>>> jnp.isscalar([1])
False
>>> jnp.isscalar(tuple())
False
>>> jnp.isscalar(slice(10))
False

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