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jax.lax.associative_scan

jax.lax.associative_scan#

jax.lax.associative_scan(fn, elems, reverse=False, axis=0)[source]#

Performs a scan with an associative binary operation, in parallel.

For an introduction to associative scans, see [BLE1990].

Parameters:

  • fn (Callable) –

    A Python callable implementing an associative binary operation with signature r = fn(a, b). Function fn must be associative, i.e., it must satisfy the equation fn(a, fn(b, c)) == fn(fn(a, b), c).

    The inputs and result are (possibly nested Python tree structures of) array(s) matching elems. Each array has a dimension in place of the axis dimension. fn should be applied elementwise over the axis dimension (for example, by using jax.vmap() over the elementwise function.)

    The result r has the same shape (and structure) as the two inputs a and b.

  • elems – A (possibly nested Python tree structure of) array(s), each with an axis dimension of size num_elems.

  • reverse (bool) – A boolean stating if the scan should be reversed with respect to the axis dimension.

  • axis (int) – an integer identifying the axis over which the scan should occur.

Returns:

A (possibly nested Python tree structure of) array(s) of the same shape and structure as elems, in which the k’th element of axis is the result of recursively applying fn to combine the first k elements of elems along axis. For example, given elems = [a, b, c, ...], the result would be [a, fn(a, b), fn(fn(a, b), c), ...].

Example 1: partial sums of an array of numbers:

>>> lax.associative_scan(jnp.add, jnp.arange(0, 4))
Array([0, 1, 3, 6], dtype=int32)

Example 2: partial products of an array of matrices

>>> mats = jax.random.uniform(jax.random.key(0), (4, 2, 2))
>>> partial_prods = lax.associative_scan(jnp.matmul, mats)
>>> partial_prods.shape
(4, 2, 2)

Example 3: reversed partial sums of an array of numbers

>>> lax.associative_scan(jnp.add, jnp.arange(0, 4), reverse=True)
Array([6, 6, 5, 3], dtype=int32)
[BLE1990]

Blelloch, Guy E. 1990. “Prefix Sums and Their Applications.”, Technical Report CMU-CS-90-190, School of Computer Science, Carnegie Mellon University.

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