fix message passing (#95)

Author: chhsiao93

gns/graph_network.py

@@ -187,6 +187,13 @@ def forward(
         # Start propagating messages.
         # Takes in the edge indices and all additional data which is needed to
         # construct messages and to update node embeddings.
+        # Call PyG propagate() method:
+        # 1. Message phase - compute messages for each edge
+        # 2. Aggregate phase - aggregate messages for each node
+        # 3. Update phase - updates only the node features
+        # Update uses the message from step 1 and any original arguments passed to
+        # propagate() to update the node embeddings. This is why we need to store
+        # the updated edge features to return them from the update() method.
         x, edge_features = self.propagate(
             edge_index=edge_index, x=x, edge_features=edge_features
         )
@@ -212,8 +219,8 @@ def message(
         """
         # Concat edge features with a final shape of [nedges, latent_dim*3]
         edge_features = torch.cat([x_i, x_j, edge_features], dim=-1)
-        edge_features = self.edge_fn(edge_features)
-        return edge_features
+        self._edge_features = self.edge_fn(edge_features)  # Create and store
+        return self._edge_features  # This gets passed to aggregate()
 
     def update(
         self, x_updated: torch.tensor, x: torch.tensor, edge_features: torch.tensor
@@ -233,9 +240,13 @@ def update(
         """
         # Concat node features with a final shape of
         # [nparticles, latent_dim (or nnode_in) *2]
+        # This gets called later, after message() and aggregate()
+        # Update modified from MessagePassing takes the output of aggregation
+        # as first argument and any argument which was initially passed to
+        # propagate hence we need to return the stored value of edge_features
         x_updated = torch.cat([x_updated, x], dim=-1)
         x_updated = self.node_fn(x_updated)
-        return x_updated, edge_features
+        return x_updated, self._edge_features
 
 
 class Processor(MessagePassing):

test/test_message_edge_features.py

@@ -0,0 +1,78 @@
+from gns.graph_network import *
+import torch
+from torch_geometric.data import Data
+import pytest
+
+
+@pytest.fixture
+def interaction_network_data():
+    model = InteractionNetwork(
+        nnode_in=2,
+        nnode_out=2,
+        nedge_in=2,
+        nedge_out=2,
+        nmlp_layers=2,
+        mlp_hidden_dim=2,
+    )
+    edge_index = torch.tensor([[0, 1], [1, 0]], dtype=torch.long)
+    x = torch.tensor([[1, 2], [3, 4]], dtype=torch.float)  # node features
+    edge_attr = torch.tensor([[1, 1], [2, 2]], dtype=torch.float)  # edge features
+
+    return model, x, edge_index, edge_attr
+
+
+def test_edge_update(interaction_network_data):
+    """Test if edge features are updated and finite and are not simply doubled"""
+    model, x, edge_index, edge_attr = interaction_network_data
+    old_edge_attr = edge_attr.clone()  # Save the old edge features
+
+    # One message passing step
+    _, updated_edge_attr = model(x=x, edge_index=edge_index, edge_features=edge_attr)
+
+    # Check if edge features shape is correct
+    assert (
+        edge_attr.shape == old_edge_attr.shape
+    ), f"Edge features shape is not preserved, changed from {old_edge_attr.shape} to {edge_attr.shape}"
+    # Check if edge features are updated
+    assert not torch.equal(
+        updated_edge_attr, old_edge_attr * 2
+    ), "Edge features are simply doubled"
+    assert not torch.equal(
+        updated_edge_attr, old_edge_attr
+    ), "Edge features are not updated"
+    # Check if edge features are finite
+    assert torch.all(torch.isfinite(edge_attr)), "Edge features are not finite"
+
+
+def test_gradients_computed(interaction_network_data):
+    """Test if gradients are computed and finite"""
+    model, x, edge_index, edge_attr = interaction_network_data
+    x.requires_grad = True
+    edge_attr.requires_grad = True
+
+    # First pass
+    aggr, updated_edge_features = model(
+        x=x, edge_index=edge_index, edge_features=edge_attr
+    )
+    updated_node_features = x + aggr
+    # Second pass
+    aggr, updated_edge_features = model(
+        x=updated_node_features,
+        edge_index=edge_index,
+        edge_features=updated_edge_features,
+    )
+    updated_node_features = updated_node_features + aggr
+    # Compute loss
+    loss = (updated_edge_features).sum()
+    loss.backward()
+
+    # Check if gradients are computed
+    assert x.grad is not None, "Gradients for node features are not computed"
+    assert edge_attr.grad is not None, "Gradients for edge features are not computed"
+    # Check if gradients are finite
+    assert torch.all(
+        torch.isfinite(x.grad)
+    ), "Gradients for node features are not finite"
+    assert torch.all(
+        torch.isfinite(edge_attr.grad)
+    ), "Gradients for edge features are not finite"