o bi @s>dZddlZddlmZd ddZddZdd Zd d ZdS) z!!!DO NOT USE!!! Distribution related class for Tensorflow backend. This is just a prototype and we might want to unify it with other backends in the future. N)dtensorcCs~|r|nd}tjj|d}g}g}|D]}|jdkr$||q||q|dur8t|dkr6|n|}dd|DS)aReturn all the available devices based on the device type. Note that this should return the global devices in a distributed setting. Args: device_type: string of `"cpu"`, `"gpu"` or `"tpu"`. Default to `gpu` or `tpu` if available when device_type is not provided. Otherwise will return the `cpu` devices. Return: List of devices that are available for distribute computation. N) device_typecpurcSs,g|]}|jd|jddqS):)rlowernamesplit).0devicer a/home/ubuntu/.local/lib/python3.10/site-packages/keras/src/backend/tensorflow/distribution_lib.py *sz list_devices..)uppertfconfiglist_logical_devicesrrappendlen)r tf_devices cpu_devices other_devicesr r r r list_devices s   rcCsdSNr )value tensor_layoutr r r distribute_value0srcCs&tt|j|j}tj||jdS)zConvert the DeviceMesh to Tensorflow backend specific Mesh. Args: device_mesh: DeviceMesh instance to convert. Returns: A `tf.dtensor.Mesh` instance. ) mesh_dims local_devices)listzip axis_namesshapercreate_distributed_meshdevicesflatten) device_meshrr r r _to_backend_mesh5s  r'cCs8|jdur tddd|jD}|jj}tj||dS)zConvert the TensorLayout to Tensorflow backend specific Sharding. Args: tensor_layout: TensorLayout instance to convert. Returns: A `tf.dtensor.Layout` instance. NzDCannot create sharding when device mesh is not set for TensorLayout.cSsg|] }|r|ntjqSr )r UNSHARDED)r axisr r r rSsz&_to_backend_layout..)sharding_specsmesh)r& ValueErroraxes backend_meshrLayout)rr* dtensor_meshr r r _to_backend_layoutDs r1r) __doc__ tensorflowrtensorflow.experimentalrrrr'r1r r r r s  #