Reimplementation of Graph Neural Networks with Scatter/Gather layers (L…

…LNL#1933)

* Added lbann data generator

* Remove extraneous data generator file

* Updated GNN implementations to use 2D Scatter Gather
- Added new ChannelwiseGRU implementation

* Added new implementation of PROTEINS dataset for Sparse Scatter-Gather based GNNs
- Adds new implementation of the sparse graph trainer for new GNN modules with 2D scatter-gather based message passing
- PROTEINS dataset ready for unit testing of sparse GNNs (GCN, GIN, GatedGraph, and Graph conv modules)

* Updated graph trainer with new graph neural network calls
- Added sample graph data slice function

* - Add 2D-scatter-gather based implementation of graph kernel
- Add distconv enabled graph kernel for channelwise fully connected layer based kernel

* - Fixed typos and added documentation for Graph modules
- Updated integration test
- Integration tests in application/graph/GNN/test passing
- Minor change in Reshape.hpp to include layer name when throwing error
- Updated trainer code according to new Sparse data format

* Removed MNIST_Superpixel dataset for PyTorch dependency
 - Updated integration tests on applications/graph/GNN/test/ for stable testing of GNNs
 - Added complete distconv support to NNConv

* Updated README for test directions
- Added ChannelwiseGRU class documentation

* Updated NNConvModel to include distconv through command line interface

* Fix Channelwise-RNN implementation with correct Slice impl

- Add layer info to error message in Reshape
- Fix typos in Python documentation of implementation

Co-authored-by: Tim Moon <[email protected]>

* Added Channelwise GRUCell unit cell
- Fixed link to point to correct PyTorch doc for GRUCell

* Move lbann import in Bamboo test inside test function

Co-authored-by: Tim Moon <[email protected]>

applications/graph/GNN/Dense_Graph_Trainer.py

@@ -70,7 +70,6 @@ def DGraph_Layer(feature_matrix,adj_matrix, node_features):
 def make_model(num_vertices = None, 
                node_features = None, 
                num_classes = None,
-               dataset = None,
                kernel_type = 'GCN',
                callbacks = None,
                num_epochs = 1):
@@ -80,9 +79,6 @@ def make_model(num_vertices = None,
         num_vertices (int): Number of vertices of each graph (default: None) 
         node_features (int): Number of features per noded (default: None)
         num_classes (int): Number of classes as targets (default: None)
-        dataset (str): Preset data set to use. Either a datset parameter has to be 
-                       supplied or all of num_vertices, node_features, and 
-                       num_classes have to be supplied. (default: None) 
         kernel_type (str): Graph Kernel to use in model. Expected one of 
                             GCN, or Graph (deafult: GCN)
         callbacks (list): Callbacks for the model. If set to None the model description, 
@@ -94,22 +90,9 @@ def make_model(num_vertices = None,
                                presets, and graph kernels. 
     '''   
 
-    assert num_vertices != dataset #Ensure atleast one of the values is set 
-
-    if dataset is not None:
-        assert num_vertices is None
-
-        if dataset == 'MNIST':
-            num_vertices = 75
-            num_classes = 10
-            node_features = 1
-
-        elif dataset == 'PROTEINS':
-            num_vertices = 100
-            num_classes = 2
-            node_features = 3
-        else:
-            raise Exception("Unkown Dataset")
+    num_vertices = 100
+    num_classes = 2
+    node_features = 3
 
     assert num_vertices is not None
     assert num_classes is not None 
@@ -120,7 +103,7 @@ def make_model(num_vertices = None,
     # Reshape and Slice Input Tensor 
     #----------------------------------
 
-    input_ = lbann.Input(target_mode = 'classification')
+    input_ = lbann.Input(target_mode='N/A')
 
     # Input dimensions should be (num_vertices * node_features + num_vertices^2 + num_classes )
     # input should have atleast two children since the target is classification