► Keras 3 API documentation / Multi-device distribution / ModelParallel API

ModelParallel API

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ModelParallel class

keras.distribution.ModelParallel(device_mesh, layout_map, batch_dim_name=None)

Distribution that shards model variables.

Compare to DataParallel which replicates the variables across all devices, ModelParallel allows you to shard variables in addition to the input data.

To construct a ModelParallel distribution, you need to provide a DeviceMesh and a LayoutMap.

  1. DeviceMesh contains physical device information. The axis names in the mesh will be used to map the variable and data layout.
  2. LayoutMap contains the mapping between variable paths to their corresponding TensorLayout.

Example

devices = list_devices()    # Assume there are 8 devices.

# Create a mesh with 2 devices for data parallelism and 4 devices for
# model parallelism.
device_mesh = DeviceMesh(shape=(2, 4), axis_names=('batch', 'model'),
                         devices=devices)
# Create a layout map that shard the `Dense` layer and `Conv2D`
# layer variables on the last dimension.
# Based on the `device_mesh`, this means the variables
# will be split across 4 devices. Any other variable that doesn't
# match any key in the layout map will be fully replicated.
layout_map = LayoutMap(device_mesh)
layout_map['dense.*kernel'] = (None, 'model')
layout_map['dense.*bias'] = ('model',)
layout_map['conv2d.*kernel'] = (None, None, None, 'model')
layout_map['conv2d.*bias'] = ('model',)

distribution = ModelParallel(device_mesh=device_mesh,
                             layout_map=layout_map,
                             batch_dim_name='batch')
# Set the global distribution, or via `with distribution.scope():`
set_distribution(distribution)

model = model_creation()
model.compile()
model.fit(data)

You can quickly update the device mesh shape to change the sharding factor of the variables. E.g.

# With only the shape change for the device mesh, the variables will be
# sharded across 8 devices instead of 4, which further reduces the memory
# footprint of variables on each of the device.
device_mesh = DeviceMesh(shape=(1, 8), axis_names=('batch', 'model'),
                         devices=devices)

To figure out a proper layout mapping rule for all the model variables, you can first list out all the model variable paths, which will be used as the key to map the variables to TensorLayout.

e.g.

model = create_model()
for v in model.variables:
    print(v.path)

Arguments

  • device_mesh: DeviceMesh instance for physical device and its logical mapping.
  • layout_map: LayoutMap instance which map the variable path to the corresponding TensorLayout. The axis names of the TensorLayouts should match to the axis names in the device_mesh, or exception will be raised.
  • batch_dim_name: optional string, the axis name in the device_mesh that will be used to distribute data. If unspecified, the first axis from the device_mesh will be used.
ModelParallel API
ModelParallel class