jax.lax.scan(f, init, xs=None, length=None, reverse=False, unroll=1, _split_transpose=False)[source]#

Scan a function over leading array axes while carrying along state.

The Haskell-like type signature in brief is

scan :: (c -> a -> (c, b)) -> c -> [a] -> (c, [b])

where for any array type specifier t, [t] represents the type with an additional leading axis, and if t is a pytree (container) type with array leaves then [t] represents the type with the same pytree structure and corresponding leaves each with an additional leading axis.

When the type of xs (denoted a above) is an array type or None, and the type of ys (denoted b above) is an array type, the semantics of scan() are given roughly by this Python implementation:

def scan(f, init, xs, length=None):
  if xs is None:
    xs = [None] * length
  carry = init
  ys = []
  for x in xs:
    carry, y = f(carry, x)
  return carry, np.stack(ys)

Unlike that Python version, both xs and ys may be arbitrary pytree values, and so multiple arrays can be scanned over at once and produce multiple output arrays. None is actually a special case of this, as it represents an empty pytree.

Also unlike that Python version, scan() is a JAX primitive and is lowered to a single WhileOp. That makes it useful for reducing compilation times for JIT-compiled functions, since native Python loop constructs in an jit() function are unrolled, leading to large XLA computations.

Finally, the loop-carried value carry must hold a fixed shape and dtype across all iterations (and not just be consistent up to NumPy rank/shape broadcasting and dtype promotion rules, for example). In other words, the type c in the type signature above represents an array with a fixed shape and dtype (or a nested tuple/list/dict container data structure with a fixed structure and arrays with fixed shape and dtype at the leaves).


scan() compiles f, so while it can be combined with jit(), it’s usually unnecessary.

  • f (Callable[[Carry, X], tuple[Carry, Y]]) – a Python function to be scanned of type c -> a -> (c, b), meaning that f accepts two arguments where the first is a value of the loop carry and the second is a slice of xs along its leading axis, and that f returns a pair where the first element represents a new value for the loop carry and the second represents a slice of the output.

  • init (Carry) – an initial loop carry value of type c, which can be a scalar, array, or any pytree (nested Python tuple/list/dict) thereof, representing the initial loop carry value. This value must have the same structure as the first element of the pair returned by f.

  • xs (X | None) – the value of type [a] over which to scan along the leading axis, where [a] can be an array or any pytree (nested Python tuple/list/dict) thereof with consistent leading axis sizes.

  • length (int | None) – optional integer specifying the number of loop iterations, which must agree with the sizes of leading axes of the arrays in xs (but can be used to perform scans where no input xs are needed).

  • reverse (bool) – optional boolean specifying whether to run the scan iteration forward (the default) or in reverse, equivalent to reversing the leading axes of the arrays in both xs and in ys.

  • unroll (int | bool) – optional positive int or bool specifying, in the underlying operation of the scan primitive, how many scan iterations to unroll within a single iteration of a loop. If an integer is provided, it determines how many unrolled loop iterations to run within a single rolled iteration of the loop. If a boolean is provided, it will determine if the loop is competely unrolled (i.e. unroll=True) or left completely unrolled (i.e. unroll=False).

  • _split_transpose (bool) – experimental optional bool specifying whether to further split the transpose into a scan (computing activation gradients), and a map (computing gradients corresponding to the array arguments). Enabling this may increase memory requirements, and so is an experimental feature that may evolve or even be rolled back.


A pair of type (c, [b]) where the first element represents the final loop carry value and the second element represents the stacked outputs of the second output of f when scanned over the leading axis of the inputs.

Return type:

tuple[Carry, Y]