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jax.lax.dynamic_update_slice_in_dim

jax.lax.dynamic_update_slice_in_dim#

jax.lax.dynamic_update_slice_in_dim(operand, update, start_index, axis)[source]#

Convenience wrapper around dynamic_update_slice() to update a slice in a single axis.

Parameters:

  • operand (Array | np.ndarray) – an array to slice.

  • update (ArrayLike) – an array containing the new values to write onto operand.

  • start_index (ArrayLike) – a single scalar index

  • axis (int) – the axis of the update.

Return type:

Array

Returns:

The updated array

Examples

>>> x = jnp.zeros(6)
>>> y = jnp.ones(3)
>>> dynamic_update_slice_in_dim(x, y, 2, axis=0)
Array([0., 0., 1., 1., 1., 0.], dtype=float32)

If the update slice is too large to fit in the array, the start index will be adjusted to make it fit:

>>> dynamic_update_slice_in_dim(x, y, 3, axis=0)
Array([0., 0., 0., 1., 1., 1.], dtype=float32)
>>> dynamic_update_slice_in_dim(x, y, 5, axis=0)
Array([0., 0., 0., 1., 1., 1.], dtype=float32)

Here is an example of a two-dimensional slice update:

>>> x = jnp.zeros((4, 4))
>>> y = jnp.ones((2, 4))
>>> dynamic_update_slice_in_dim(x, y, 1, axis=0)
Array([[0., 0., 0., 0.],
       [1., 1., 1., 1.],
       [1., 1., 1., 1.],
       [0., 0., 0., 0.]], dtype=float32)

Note that the shape of the additional axes in update need not match the associated dimensions of the operand:

>>> y = jnp.ones((2, 3))
>>> dynamic_update_slice_in_dim(x, y, 1, axis=0)
Array([[0., 0., 0., 0.],
       [1., 1., 1., 0.],
       [1., 1., 1., 0.],
       [0., 0., 0., 0.]], dtype=float32)

See also

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