[fix] Embedding call didn't return a single worker call function when…

… hvd.size() is 0.

tensorflow_recommenders_addons/dynamic_embedding/python/keras/layers/embedding.py

@@ -34,13 +34,15 @@
 from tensorflow.python.framework import ops
 from tensorflow.python.eager import tape
 from tensorflow.python.ops.variables import VariableAggregation
+from tensorflow.python.platform import tf_logging
 try:  # The data_structures has been moved to the new package in tf 2.11
   from tensorflow.python.trackable import data_structures
 except:
   from tensorflow.python.training.tracking import data_structures
 
 from tensorflow_recommenders_addons.dynamic_embedding.python.ops.dynamic_embedding_ops import DistributedVariableWrapper, TrainableWrapperDistributedPolicy
 from tensorflow_recommenders_addons.dynamic_embedding.python.ops.dynamic_embedding_variable import make_partition
+from tensorflow_recommenders_addons.dynamic_embedding.python.ops.tf_save_restore_patch import de_fs_saveable_class_names
 
 
 def _choose_reduce_method(combiner, sparse=False, segmented=False):
@@ -245,6 +247,7 @@ def __init__(self,
           TrainableWrapperDistributedPolicy(VariableAggregation.NONE))
     else:
       self.shadow = self.shadow_impl.as_list()[0]
+    self.params._created_in_class = self  # To facilitate access to the primitive class through params
     super(Embedding, self).__init__(name=name,
                                     trainable=trainable,
                                     dtype=value_dtype)
@@ -550,7 +553,11 @@ def __init__(self,
     else:
       self._mpi_size = mpi_size
     super(HvdAllToAllEmbedding, self).__init__(*args, **kwargs)
-    self.params._created_in_class = self
+    if type(self.params.saveable).__name__ not in de_fs_saveable_class_names:
+      tf_logging.warning(
+          "Please use FileSystemSaver in KVCreator when use HvdAllToAllEmbedding. "
+          "It will allow TFRA save and restore KV files when Embedding tensor parallel in distributed training. "
+      )
 
   def __relocate_dense_feature__(self, ids, batch_size=None):
     """