tf.keras.dtensor.experimental.layout_map_scope

Apply the layout to all the tf.Variables created under the scope. (deprecated)

@contextlib.contextmanager
tf.keras.dtensor.experimental.layout_map_scope(
    layout_map
)

Create a scope that all the tf.Variable created under this scope will be lazily inited, and initialized later on with proper layout when the object path in the model is stable/finalized.

Note that the layout mapping will use the object/attribute names as the key to map the variable against the layout.

For subclassed models, the full object/attribute name is used as the key. For Functional/Sequential models, since the layers within the model do not get assigned to a meaningful attribute, we use layer.name as the key for the layer, followed by the attribute name. Keras ensures name uniqueness among the layers in all Functional/Sequential models.

See the following examples that show the variable object names for different Keras model types:

layout_map = layout_map_lib.LayoutMap(mesh=self.mesh)
layout_map['d1.kernel'] = layout_1
layout_map['d1.bias'] = layout_2
layout_map['d2.kernel'] = layout_3
layout_map['d2.bias'] = layout_4

## Subclassed model
class SubclassModel(tf.keras.Model):

  def __init__(self, name=None):
    super().__init__(name=name)
    self.d1 = tf.keras.layers.Dense(1000)
    self.d2 = tf.keras.layers.Dense(1000)

  def call(self, inputs):
    x = self.d1(inputs)
    return self.d2(x)

with layout_map_scope(layout_map):
  model = SubclassModel()
# Triggering the creation of weights within or outside of the scope works
inputs = tf.zeros((10, 10))
results = model(inputs)

model.d1.kernel.layout == layout_1
model.d1.bias.layout == layout_2
model.d2.kernel.layout == layout_3
model.d2.bias.layout == layout_4

## Functional model
with layout_map_scope(layout_map):
  inputs = tf.keras.Input((10,), batch_size=10)
  x = tf.keras.layers.Dense(20, name='d1')(inputs)
  output = tf.keras.layers.Dense(30, name='d2')(x)

  model = tf.keras.Model(inputs, output)

d1 = model.layers[1]
d2 = model.layers[2]

d1.kernel.layout == layout_1
d1.bias.layout == layout_2
d1.kernel.layout == layout_3
d1.bias.layout == layout_4

## Sequential model
with layout_map_scope(layout_map):
  model = tf.keras.Sequential([
      tf.keras.layers.Dense(20, name='d1', input_shape=(10,)),
      tf.keras.layers.Dense(30, name='d2')
  ])

d1 = model.layers[0]
d2 = model.layers[1]

d1.kernel.layout == layout_1
d1.bias.layout == layout_2
d1.kernel.layout == layout_3
d1.bias.layout == layout_4

layout_map a LayoutMap which contains the variable_object_path (string) -> Layout. When a layout is not found for the variable, a default all replicated layout will be created for the variable.

A context that will lazily initialize all tf.Variable objects within the model, with their attributed layouts.