jax.ops.segment_prod#

jax.ops.segment_prod(data, segment_ids, num_segments=None, indices_are_sorted=False, unique_indices=False, bucket_size=None, mode=None)[source]#

Computes the product within segments of an array.

Similar to TensorFlow’s segment_prod

Parameters:

  • data (ArrayLike) – an array with the values to be reduced.

  • segment_ids (ArrayLike) – an array with integer dtype that indicates the segments of data (along its leading axis) to be reduced. Values can be repeated and need not be sorted.

  • num_segments (int | None | None) – optional, an int with nonnegative value indicating the number of segments. The default is set to be the minimum number of segments that would support all indices in segment_ids, calculated as max(segment_ids) + 1. Since num_segments determines the size of the output, a static value must be provided to use segment_prod in a JIT-compiled function.

  • indices_are_sorted (bool) – whether segment_ids is known to be sorted.

  • unique_indices (bool) – whether segment_ids is known to be free of duplicates.

  • bucket_size (int | None | None) – size of bucket to group indices into. segment_prod is performed on each bucket separately to improve numerical stability. Default None means no bucketing.

  • mode (lax.GatherScatterMode | str | None | None) – a jax.lax.GatherScatterMode value describing how out-of-bounds indices should be handled. By default, values outside of the range [0, num_segments) are dropped and do not contribute to the result.

Returns:

An array with shape (num_segments,) + data.shape[1:] representing the segment products.

Return type:

Array

Examples

Simple 1D segment product:

>>> data = jnp.arange(6)
>>> segment_ids = jnp.array([0, 0, 1, 1, 2, 2])
>>> segment_prod(data, segment_ids)
Array([ 0,  6, 20], dtype=int32)

Using JIT requires static num_segments:

>>> from jax import jit
>>> jit(segment_prod, static_argnums=2)(data, segment_ids, 3)
Array([ 0,  6, 20], dtype=int32)