more fixes.

python/dgl/graphbolt/feature_fetcher.py

@@ -169,12 +169,12 @@ def _cooperative_exchange(self, data):
             node_features = {key: {} for _, key in data.node_features.keys()}
             for (ntype, key), feature in data.node_features.items():
                 node_features[key][ntype] = feature
-            for key, feature in node_features.items():
+            for key, feature in sorted(node_features.items()):
                 new_feature = CooperativeConvFunction.apply(subgraph, feature)
                 for ntype, tensor in new_feature.items():
                     data.node_features[(ntype, key)] = tensor
         else:
-            for key in data.node_features:
+            for key in sorted(data.node_features):
                 feature = data.node_features[key]
                 new_feature = CooperativeConvFunction.apply(subgraph, feature)
                 data.node_features[key] = new_feature

python/dgl/graphbolt/impl/cooperative_conv.py

@@ -42,7 +42,7 @@ def forward(
             seed_sizes,
         )
         outs = {}
-        for ntype, typed_tensor in convert_to_hetero(tensor).items():
+        for ntype, typed_tensor in sorted(convert_to_hetero(tensor).items()):
             out = typed_tensor.new_empty(
                 (sum(counts_sent.get(ntype, [0])),) + typed_tensor.shape[1:],
                 requires_grad=typed_tensor.requires_grad,
@@ -70,7 +70,9 @@ def backward(
         ) = ctx.communication_variables
         delattr(ctx, "communication_variables")
         outs = {}
-        for ntype, typed_grad_output in convert_to_hetero(grad_output).items():
+        for ntype, typed_grad_output in sorted(
+            convert_to_hetero(grad_output).items()
+        ):
             out = typed_grad_output.new_empty(
                 (sum(counts_received[ntype]),) + typed_grad_output.shape[1:]
             )