Refactor Dataloading and Dataset (#795)

*Issue #, if available:*
#755 
#756 

*Description of changes:*
Change the Dataloader API and Dataset API according to #755 and #756.
Update the unit tests accordingly
Update the training and inference codes accordingly.

TODO: Update examples
TODO: Update docs.


By submitting this pull request, I confirm that you can use, modify,
copy, and redistribute this contribution, under the terms of your
choice.

---------

Co-authored-by: Xiang Song <[email protected]>

docs/source/tutorials/own-data.rst

@@ -244,8 +244,8 @@ Required DGL graph format
 ```````````````````````````
 - a `dgl.heterograph <https://docs.dgl.ai/generated/dgl.heterograph.html#dgl.heterograph>`_.
 - All nodes/edges features are set in nodes/edges' data field, and remember the feature names, which will be used in the later steps.
-    - For nodes' features, the common way to set features is like ``g.nodes['nodetypename'].data['featurename']=nodefeaturetensor``, The formal explanation of DGL's node feature could be found in the `Using node features <https://docs.dgl.ai/generated/dgl.DGLGraph.nodes.html>`_.
-    - For edges' features, the common way to set features is like ``g.edges['edgetypename'].data['featurename']=edgefeaturetensor``, The formal explanation of DGL's edge feature could be found in the `Using edge features <https://docs.dgl.ai/generated/dgl.DGLGraph.edges.html>`_.
+    - For nodes' features, the common way to set features is like ``g.nodes['nodetypename'].data['featurename']=nodefeaturetensor``, The formal explanation of DGL's node feature could be found in the `Using node features <https://docs.dgl.ai/generated/dgl.DGLGraph.nodes.html>`_. Please make sure every node feature is a 2D tensor.
+    - For edges' features, the common way to set features is like ``g.edges['edgetypename'].data['featurename']=edgefeaturetensor``, The formal explanation of DGL's edge feature could be found in the `Using edge features <https://docs.dgl.ai/generated/dgl.DGLGraph.edges.html>`_. Please make sure every edge feature is a 2D tensor.
 - Save labels (for node/edge tasks) into the target nodes/edges as a feature, and remember the label feature names, which will be used in the later steps.
     - The common way to set node-related labels as a feature is like ``g.nodes['predictnodetypename'].data['labelname']=nodelabeltensor``.
     - The common way to set edge-related labels as a feature is like ``g.nodes['predictedgetypename'].data['labelname']=edgelabeltensor``.
@@ -435,7 +435,7 @@ Similar to the :ref:`Quick-Start <quick-start-standalone>` tutorial, users can l
               --node-feat-name paper:feat author:feat subject:feat \
               --restore-model-path /tmp/acm_nc/models/epoch-0 \
               --save-prediction-path  /tmp/acm_nc/predictions
-    
+
     # Link Prediction
     python -m graphstorm.run.gs_link_prediction \
               --inference \

python/graphstorm/config/argument.py

@@ -754,6 +754,14 @@ def input_activate(self):
                                    "and 'relu' for torch.nn.functional.relu")
         return None
 
+    @property
+    def edge_feat_name(self):
+        """ User defined edge feature name
+
+        Not used by GraphStorm, reserved for future usage.
+        """
+        return None
+
     @property
     def node_feat_name(self):
         """ User defined node feature name

python/graphstorm/dataloading/__init__.py

@@ -35,10 +35,7 @@
                           GSgnnLinkPredictionDataLoaderBase,
                           GSgnnNodeDataLoaderBase)
 
-from .dataset import GSgnnEdgeTrainData, GSgnnLPTrainData
-from .dataset import GSgnnEdgeInferData
-from .dataset import GSgnnNodeTrainData
-from .dataset import GSgnnNodeInferData
+from .dataset import GSgnnData
 
 from .dataloading import (BUILTIN_LP_UNIFORM_NEG_SAMPLER,
                           BUILTIN_LP_JOINT_NEG_SAMPLER,