tf.experimental.dtensor.relayout_like

Changes the layout of tensor to the same as layout_tensor.

tf.experimental.dtensor.relayout_like(
    tensor: tf.Tensor,
    layout_tensor: tf.Tensor,
    name: Optional[str] = None
) -> tf.Tensor

relayout_like is often used inside a tf.function, to ensure a tensor is placed to the same mesh and with the same layout as another tensor.

The backward gradient of a relayout is a relayout_like operation, to ensure the backward tensor has the same layout as the forward input tensor:

@ops.RegisterGradient("Relayout")
def _relayout_gradient(op, grad):
  return relayout_like(grad, layout_input=op.inputs[0])

Here is another illustrative example:

@tf.function
def func(x):
  z = tf.ones(x.shape)
  z = dtensor.relayout_like(z, x)
  return x + z

with dtensor.default_mesh(cpu_mesh):
  x = tf.ones((4, 4))

with dtensor.default_mesh(gpu_mesh):
  y = func(x)

# y would be on the cpu mesh, following the mesh of x.

Args
`tensor`	A DTensor to specify a new layout for.
`layout_tensor`	A Tensor object whose layout will be used for the layout of result. The shape and type of layout_tensor are irrelevant.
`name`	name of the Op.

Returns
A DTensor output from the RelayoutLike op.

tf.experimental.dtensor.relayout_like

Args

Returns