Add default value of has_edge_importance

[ryantd]

There is an issue that will be raised in distributed training, like

Traceback (most recent call last):
  File "/usr/local/bin/dglke_server", line 33, in <module>
    sys.exit(load_entry_point('dglke==0.1.0.dev0', 'console_scripts', 'dglke_server')())
  File "/usr/local/lib/python3.6/site-packages/dglke-0.1.0.dev0-py3.6.egg/dglke/kvserver.py", line 178, in main
  File "/usr/local/lib/python3.6/site-packages/dglke-0.1.0.dev0-py3.6.egg/dglke/kvserver.py", line 159, in start_server
  File "/usr/local/lib/python3.6/site-packages/dglke-0.1.0.dev0-py3.6.egg/dglke/kvserver.py", line 120, in get_server_data
  File "/usr/local/lib/python3.6/site-packages/dglke-0.1.0.dev0-py3.6.egg/dglke/train_pytorch.py", line 100, in load_model
  File "/usr/local/lib/python3.6/site-packages/dglke-0.1.0.dev0-py3.6.egg/dglke/models/general_models.py", line 212, in __init__
AttributeError: 'Namespace' object has no attribute 'has_edge_importance'

Because the has_edge_importance argument is required for KEModel, kvserver.py and kvclient.py should have a default value of this, and then pass to the KEModel in dglke/models/general_models.py

[ryantd]

Bump

[classicsong]

Can you add an assertion as assert args.has_edge_importance == False in kvclient and kvserver, as this is a workaround. You did not implement has_edge_importance for distributed training.

[ryantd]

Can you add an assertion as assert args.has_edge_importance == False in kvclient and kvserver

@classicsong May I have more explanations on this assertion? Why == False?

In my opinion, the distributed training example should be done as expected without any raised errors. And for KEModel class itself, it required the args.has_edge_importance passing from kvclient.py and kvserver.py. So I think the argument has_edge_importance in kvclient.py and kvserver.py, should be set explicitly by default.

dgl-ke/python/dglke/models/general_models.py

Lines 183 to 212 in 7b2975a

	class KEModel(object):
	""" DGL Knowledge Embedding Model.
	Parameters
	----------
	args:
	Global configs.
	model_name : str
	Which KG model to use, including 'TransE_l1', 'TransE_l2', 'TransR',
	'RESCAL', 'DistMult', 'ComplEx', 'RotatE', 'SimplE'
	n_entities : int
	Num of entities.
	n_relations : int
	Num of relations.
	hidden_dim : int
	Dimension size of embedding.
	gamma : float
	Gamma for score function.
	double_entity_emb : bool
	If True, entity embedding size will be 2 * hidden_dim.
	Default: False
	double_relation_emb : bool
	If True, relation embedding size will be 2 * hidden_dim.
	Default: False
	"""
	def __init__(self, args, model_name, n_entities, n_relations, hidden_dim, gamma,
	double_entity_emb=False, double_relation_emb=False):
	super(KEModel, self).__init__()
	self.args = args
	self.has_edge_importance = args.has_edge_importance