Remove filtering of node/edge types in trim_to_layer (#9021)

This is not safe in most cases, since a filtering of an empty edge type
may lead to the unexpected drop of node features.

Fixes #9015

CHANGELOG.md

@@ -15,6 +15,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Remove filtering of node/edge types in `trim_to_layer` functionality ([#9021](https://github.com/pyg-team/pytorch_geometric/pull/9021))
 - Default to `scatter` operations in `MessagePassing` in case `torch.use_deterministic_algorithms` is not set ([#9009](https://github.com/pyg-team/pytorch_geometric/pull/9009))
 - Made `MessagePassing` interface thread-safe ([#9001](https://github.com/pyg-team/pytorch_geometric/pull/9001))
 - Breaking Change: Added support for `EdgeIndex` in `cugraph` GNN layers ([#8938](https://github.com/pyg-team/pytorch_geometric/pull/8937))

test/utils/test_trim_to_layer.py

@@ -197,39 +197,3 @@ def test_trim_to_layer_with_neighbor_loader():
     assert out2.size() == (2, 16)
 
     assert torch.allclose(out1, out2, atol=1e-6)
-
-
-def test_trim_to_layer_filtering():
-    x_dict = {
-        'paper': torch.rand((13, 128)),
-        'author': torch.rand((5, 128)),
-        'field_of_study': torch.rand((6, 128))
-    }
-    edge_index_dict = {
-        ('author', 'writes', 'paper'):
-        torch.tensor([[0, 1, 2, 3, 4], [0, 0, 1, 2, 2]]),
-        ('paper', 'has_topic', 'field_of_study'):
-        torch.tensor([[6, 7, 8, 9], [0, 0, 1, 1]])
-    }
-    num_sampled_nodes_dict = {
-        'paper': [1, 2, 10],
-        'author': [0, 2, 3],
-        'field_of_study': [0, 2, 4]
-    }
-    num_sampled_edges_dict = {
-        ('author', 'writes', 'paper'): [2, 3],
-        ('paper', 'has_topic', 'field_of_study'): [0, 4]
-    }
-    x_dict, edge_index_dict, _ = trim_to_layer(
-        layer=1,
-        num_sampled_nodes_per_hop=num_sampled_nodes_dict,
-        num_sampled_edges_per_hop=num_sampled_edges_dict,
-        x=x_dict,
-        edge_index=edge_index_dict,
-    )
-    assert list(edge_index_dict.keys()) == [('author', 'writes', 'paper')]
-    assert torch.equal(edge_index_dict[('author', 'writes', 'paper')],
-                       torch.tensor([[0, 1], [0, 0]]))
-    assert x_dict['paper'].size() == (3, 128)
-    assert x_dict['author'].size() == (2, 128)
-    assert x_dict['field_of_study'].size() == (2, 128)

torch_geometric/utils/_trim_to_layer.py

@@ -1,5 +1,4 @@
-import copy
-from typing import Any, Dict, List, Optional, Tuple, Union, overload
+from typing import Dict, List, Optional, Tuple, Union, overload
 
 import torch
 from torch import Tensor
@@ -17,18 +16,6 @@
 )
 
 
-def filter_empty_entries(
-        input_dict: Dict[Union[Any], Tensor]) -> Dict[Any, Tensor]:
-    r"""Removes empty tensors from a dictionary. This avoids unnecessary
-    computation when some node/edge types are non-reachable after trimming.
-    """
-    out_dict = copy.copy(input_dict)
-    for key, value in input_dict.items():
-        if value.numel() == 0:
-            del out_dict[key]
-    return out_dict
-
-
 @overload
 def trim_to_layer(
     layer: int,
@@ -96,7 +83,6 @@ def trim_to_layer(
             k: trim_feat(v, layer, num_sampled_nodes_per_hop[k])
             for k, v in x.items()
         }
-        x = filter_empty_entries(x)
 
         assert isinstance(edge_index, dict)
         edge_index = {
@@ -110,15 +96,13 @@ def trim_to_layer(
             )
             for k, v in edge_index.items()
         }
-        edge_index = filter_empty_entries(edge_index)
 
         if edge_attr is not None:
             assert isinstance(edge_attr, dict)
             edge_attr = {
                 k: trim_feat(v, layer, num_sampled_edges_per_hop[k])
                 for k, v in edge_attr.items()
             }
-            edge_attr = filter_empty_entries(edge_attr)
 
         return x, edge_index, edge_attr