Refactor creation of a_input into separate function

this function helps clarify the steps in creating a_input by breaking it down into steps and adding descriptive comments. Also, some of the '-1's inside the 'view' functions have been replaced by the exact number to help with readability

layers.py

@@ -8,7 +8,6 @@ class GraphAttentionLayer(nn.Module):
     """
     Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
     """
-
     def __init__(self, in_features, out_features, dropout, alpha, concat=True):
         super(GraphAttentionLayer, self).__init__()
         self.dropout = dropout
@@ -17,31 +16,70 @@ def __init__(self, in_features, out_features, dropout, alpha, concat=True):
         self.alpha = alpha
         self.concat = concat
 
-        self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
+        self.W = nn.Parameter(torch.empty(size=(in_features, out_features)))
         nn.init.xavier_uniform_(self.W.data, gain=1.414)
-        self.a = nn.Parameter(torch.zeros(size=(2*out_features, 1)))
+        self.a = nn.Parameter(torch.empty(size=(2*out_features, 1)))
         nn.init.xavier_uniform_(self.a.data, gain=1.414)
 
         self.leakyrelu = nn.LeakyReLU(self.alpha)
 
-    def forward(self, input, adj):
-        h = torch.mm(input, self.W)
-        N = h.size()[0]
-
-        a_input = torch.cat([h.repeat(1, N).view(N * N, -1), h.repeat(N, 1)], dim=1).view(N, -1, 2 * self.out_features)
+    def forward(self, h, adj):
+        Wh = torch.mm(h, self.W) # h.shape: (N, in_features), Wh.shape: (N, out_features)
+        a_input = self._prepare_attentional_mechanism_input(Wh)
         e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(2))
 
         zero_vec = -9e15*torch.ones_like(e)
         attention = torch.where(adj > 0, e, zero_vec)
         attention = F.softmax(attention, dim=1)
         attention = F.dropout(attention, self.dropout, training=self.training)
-        h_prime = torch.matmul(attention, h)
+        h_prime = torch.matmul(attention, Wh)
 
         if self.concat:
             return F.elu(h_prime)
         else:
             return h_prime
 
+    def _prepare_attentional_mechanism_input(self, Wh):
+        N = Wh.size()[0] # number of nodes
+
+        # Below, two matrices are created that contain embeddings in their rows in different orders.
+        # (e stands for embedding)
+        # These are the rows of the first matrix (Wh_repeated_in_chunks): 
+        # e1, e1, ..., e1,            e2, e2, ..., e2,            ..., eN, eN, ..., eN
+        # '-------------' -> N times  '-------------' -> N times       '-------------' -> N times
+        # 
+        # These are the rows of the second matrix (Wh_repeated_alternating): 
+        # e1, e2, ..., eN, e1, e2, ..., eN, ..., e1, e2, ..., eN 
+        # '----------------------------------------------------' -> N times
+        # 
+
+        Wh_repeated_in_chunks = Wh.repeat(1, N).view(N * N, self.out_features)
+        Wh_repeated_alternating = Wh.repeat(N, 1)
+        # Wh_repeated_in_chunks.shape == Wh_repeated_alternating.shape == (N * N, out_features)
+
+        # The all_combination_matrix, created below, will look like this (|| denotes concatenation):
+        # e1 || e1
+        # e1 || e2
+        # e1 || e3
+        # ...
+        # e1 || eN
+        # e2 || e1
+        # e2 || e2
+        # e2 || e3
+        # ...
+        # e2 || eN
+        # ...
+        # eN || e1
+        # eN || e2
+        # eN || e3
+        # ...
+        # eN || eN
+
+        all_combinations_matrix = torch.cat([Wh_repeated_in_chunks, Wh_repeated_alternating], dim=1)
+        # all_combinations_matrix.shape == (N * N, 2 * out_features)
+
+        return all_combinations_matrix.view(N, N, 2 * self.out_features)
+
     def __repr__(self):
         return self.__class__.__name__ + ' (' + str(self.in_features) + ' -> ' + str(self.out_features) + ')'