Fix: properly filter custom feature/graph stores (#5088)

* init

* changelog

* typo

* typo

* update

Co-authored-by: rusty1s <matthias.fey@tu-dortmund.de>

CHANGELOG.md

@@ -29,7 +29,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `time_attr` argument to `LinkNeighborLoader` ([#4877](https://github.com/pyg-team/pytorch_geometric/pull/4877), [#4908](https://github.com/pyg-team/pytorch_geometric/pull/4908))
 - Added a `filter_per_worker` argument to data loaders to allow filtering of data within sub-processes ([#4873](https://github.com/pyg-team/pytorch_geometric/pull/4873))
 - Added a `NeighborLoader` benchmark script ([#4815](https://github.com/pyg-team/pytorch_geometric/pull/4815), [#4862](https://github.com/pyg-team/pytorch_geometric/pull/4862/files))
-- Added support for `FeatureStore` and `GraphStore` in `NeighborLoader` ([#4817](https://github.com/pyg-team/pytorch_geometric/pull/4817), [#4851](https://github.com/pyg-team/pytorch_geometric/pull/4851), [#4854](https://github.com/pyg-team/pytorch_geometric/pull/4854), [#4856](https://github.com/pyg-team/pytorch_geometric/pull/4856), [#4857](https://github.com/pyg-team/pytorch_geometric/pull/4857), [#4882](https://github.com/pyg-team/pytorch_geometric/pull/4882), [#4883](https://github.com/pyg-team/pytorch_geometric/pull/4883), [#4929](https://github.com/pyg-team/pytorch_geometric/pull/4929), [#4992](https://github.com/pyg-team/pytorch_geometric/pull/4922), [#4962](https://github.com/pyg-team/pytorch_geometric/pull/4962), [#4968](https://github.com/pyg-team/pytorch_geometric/pull/4968), [#5037](https://github.com/pyg-team/pytorch_geometric/pull/5037))
+- Added support for `FeatureStore` and `GraphStore` in `NeighborLoader` ([#4817](https://github.com/pyg-team/pytorch_geometric/pull/4817), [#4851](https://github.com/pyg-team/pytorch_geometric/pull/4851), [#4854](https://github.com/pyg-team/pytorch_geometric/pull/4854), [#4856](https://github.com/pyg-team/pytorch_geometric/pull/4856), [#4857](https://github.com/pyg-team/pytorch_geometric/pull/4857), [#4882](https://github.com/pyg-team/pytorch_geometric/pull/4882), [#4883](https://github.com/pyg-team/pytorch_geometric/pull/4883), [#4929](https://github.com/pyg-team/pytorch_geometric/pull/4929), [#4992](https://github.com/pyg-team/pytorch_geometric/pull/4922), [#4962](https://github.com/pyg-team/pytorch_geometric/pull/4962), [#4968](https://github.com/pyg-team/pytorch_geometric/pull/4968), [#5037](https://github.com/pyg-team/pytorch_geometric/pull/5037), [#5088](https://github.com/pyg-team/pytorch_geometric/pull/5088))
 - Added a `normalize` parameter to `dense_diff_pool` ([#4847](https://github.com/pyg-team/pytorch_geometric/pull/4847))
 - Added `size=None` explanation to jittable `MessagePassing` modules in the documentation ([#4850](https://github.com/pyg-team/pytorch_geometric/pull/4850))
 - Added documentation to the `DataLoaderIterator` class ([#4838](https://github.com/pyg-team/pytorch_geometric/pull/4838))

torch_geometric/loader/link_neighbor_loader.py

@@ -9,8 +9,8 @@
 from torch_geometric.loader.base import DataLoaderIterator
 from torch_geometric.loader.neighbor_loader import NeighborSampler
 from torch_geometric.loader.utils import (
+    filter_custom_store,
     filter_data,
-    filter_feature_store,
     filter_hetero_data,
 )
 from torch_geometric.typing import InputEdges, NumNeighbors, OptTensor
@@ -339,9 +339,9 @@ def filter_fn(self, out: Any) -> Union[Data, HeteroData]:
         else:
             (node_dict, row_dict, col_dict, edge_dict, edge_label_index,
              edge_label) = out
-            feature_store, _ = self.data
-            data = filter_feature_store(feature_store, node_dict, row_dict,
-                                        col_dict, edge_dict)
+            feature_store, graph_store = self.data
+            data = filter_custom_store(feature_store, graph_store, node_dict,
+                                       row_dict, col_dict, edge_dict)
             edge_type = self.neighbor_sampler.input_type
             data[edge_type].edge_label_index = edge_label_index
             if edge_label is not None:

torch_geometric/loader/neighbor_loader.py

@@ -10,8 +10,8 @@
 from torch_geometric.loader.base import DataLoaderIterator
 from torch_geometric.loader.utils import (
     edge_type_to_str,
+    filter_custom_store,
     filter_data,
-    filter_feature_store,
     filter_hetero_data,
     to_csc,
     to_hetero_csc,
@@ -415,9 +415,9 @@ def filter_fn(self, out: Any) -> Union[Data, HeteroData]:
         else:  # Tuple[FeatureStore, GraphStore]
             # TODO support for feature stores with no edge types
             node_dict, row_dict, col_dict, edge_dict, batch_size = out
-            feature_store, _ = self.data
-            data = filter_feature_store(feature_store, node_dict, row_dict,
-                                        col_dict, edge_dict)
+            feature_store, graph_store = self.data
+            data = filter_custom_store(feature_store, graph_store, node_dict,
+                                       row_dict, col_dict, edge_dict)
             data[self.neighbor_sampler.input_type].batch_size = batch_size
 
         return data if self.transform is None else self.transform(data)

torch_geometric/loader/utils.py

@@ -8,6 +8,7 @@
 
 from torch_geometric.data import Data, HeteroData
 from torch_geometric.data.feature_store import FeatureStore
+from torch_geometric.data.graph_store import GraphStore
 from torch_geometric.data.storage import EdgeStorage, NodeStorage
 from torch_geometric.typing import EdgeType, OptTensor
 
@@ -31,10 +32,6 @@ def edge_type_to_str(edge_type: Union[EdgeType, str]) -> str:
     return edge_type if isinstance(edge_type, str) else '__'.join(edge_type)
 
 
-def str_to_edge_type(key: Union[EdgeType, str]) -> EdgeType:
-    return key if isinstance(key, tuple) else tuple(key.split('__'))
-
-
 # TODO deprecate when FeatureStore / GraphStore unification is complete
 def to_csc(
     data: Union[Data, EdgeStorage],
@@ -188,8 +185,9 @@ def filter_hetero_data(
     return out
 
 
-def filter_feature_store(
+def filter_custom_store(
     feature_store: FeatureStore,
+    graph_store: GraphStore,
     node_dict: Dict[str, Tensor],
     row_dict: Dict[str, Tensor],
     col_dict: Dict[str, Tensor],
@@ -204,14 +202,15 @@ def filter_feature_store(
 
     # Filter edge storage:
     # TODO support edge attributes
-    for key in edge_dict:
-        edge_index = torch.stack([row_dict[key], col_dict[key]], dim=0)
-        data[str_to_edge_type(key)].edge_index = edge_index
+    for attr in graph_store.get_all_edge_attrs():
+        key = edge_type_to_str(attr.edge_type)
+        if key in row_dict and key in col_dict:
+            edge_index = torch.stack([row_dict[key], col_dict[key]], dim=0)
+            data[attr.edge_type].edge_index = edge_index
 
     # Filter node storage:
-    attrs = feature_store.get_all_tensor_attrs()
     required_attrs = []
-    for attr in attrs:
+    for attr in feature_store.get_all_tensor_attrs():
         if attr.group_name in node_dict:
             attr.index = node_dict[attr.group_name]
             required_attrs.append(attr)