Refactor SAGEConv to use LSTMAggregation

CHANGELOG.md

@@ -20,7 +20,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added the `bias` vector to the `GCN` model definition in the "Create Message Passing Networks" tutorial ([#4755](https://github.com/pyg-team/pytorch_geometric/pull/4755))
 - Added `transforms.RootedSubgraph` interface with two implementations: `RootedEgoNets` and `RootedRWSubgraph` ([#3926](https://github.com/pyg-team/pytorch_geometric/pull/3926))
 - Added `ptr` vectors for `follow_batch` attributes within `Batch.from_data_list` ([#4723](https://github.com/pyg-team/pytorch_geometric/pull/4723))
-- Added `torch_geometric.nn.aggr` package ([#4687](https://github.com/pyg-team/pytorch_geometric/pull/4687), [#4721](https://github.com/pyg-team/pytorch_geometric/pull/4721), [#4731](https://github.com/pyg-team/pytorch_geometric/pull/4731), [#4762](https://github.com/pyg-team/pytorch_geometric/pull/4762), [#4749](https://github.com/pyg-team/pytorch_geometric/pull/4749), [#4779](https://github.com/pyg-team/pytorch_geometric/pull/4779))
+- Added `torch_geometric.nn.aggr` package ([#4687](https://github.com/pyg-team/pytorch_geometric/pull/4687), [#4721](https://github.com/pyg-team/pytorch_geometric/pull/4721), [#4731](https://github.com/pyg-team/pytorch_geometric/pull/4731), [#4762](https://github.com/pyg-team/pytorch_geometric/pull/4762), [#4749](https://github.com/pyg-team/pytorch_geometric/pull/4749), [#4779](https://github.com/pyg-team/pytorch_geometric/pull/4779), [#4863](https://github.com/pyg-team/pytorch_geometric/pull/4863))
 - Added the `DimeNet++` model ([#4432](https://github.com/pyg-team/pytorch_geometric/pull/4432), [#4699](https://github.com/pyg-team/pytorch_geometric/pull/4699), [#4700](https://github.com/pyg-team/pytorch_geometric/pull/4700), [#4800](https://github.com/pyg-team/pytorch_geometric/pull/4800))
 - Added an example of using PyG with PyTorch Ignite ([#4487](https://github.com/pyg-team/pytorch_geometric/pull/4487))
 - Added `GroupAddRev` module with support for reducing training GPU memory ([#4671](https://github.com/pyg-team/pytorch_geometric/pull/4671), [#4701](https://github.com/pyg-team/pytorch_geometric/pull/4701), [#4715](https://github.com/pyg-team/pytorch_geometric/pull/4715), [#4730](https://github.com/pyg-team/pytorch_geometric/pull/4730))

test/nn/conv/test_sage_conv.py

@@ -69,5 +69,5 @@ def test_lstm_sage_conv():
     assert torch.allclose(conv(x, adj.t()), out)
 
     edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 1, 0]])
-    with pytest.raises(ValueError, match="is not sorted by columns"):
+    with pytest.raises(ValueError, match="'index' tensor is not sorted"):
         conv(x, edge_index)

torch_geometric/nn/conv/sage_conv.py

@@ -1,16 +1,13 @@
-from typing import Optional, Tuple, Union
+from typing import Tuple, Union
 
-import torch
 import torch.nn.functional as F
 from torch import Tensor
 from torch.nn import LSTM
-from torch_scatter import scatter
 from torch_sparse import SparseTensor, matmul
 
 from torch_geometric.nn.conv import MessagePassing
 from torch_geometric.nn.dense.linear import Linear
 from torch_geometric.typing import Adj, OptPairTensor, Size
-from torch_geometric.utils import to_dense_batch
 
 
 class SAGEConv(MessagePassing):
@@ -76,9 +73,6 @@ def __init__(
         bias: bool = True,
         **kwargs,
     ):
-        kwargs['aggr'] = aggr if aggr != 'lstm' else None
-        super().__init__(**kwargs)
-
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.normalize = normalize
@@ -88,6 +82,12 @@ def __init__(
         if isinstance(in_channels, int):
             in_channels = (in_channels, in_channels)
 
+        if aggr == 'lstm':
+            kwargs['aggr_kwargs'] = dict(in_channels=in_channels[0],
+                                         out_channels=in_channels[0])
+
+        super().__init__(aggr, **kwargs)
+
         if self.project:
             self.lin = Linear(in_channels[0], in_channels[0], bias=True)
 
@@ -140,25 +140,6 @@ def message_and_aggregate(self, adj_t: SparseTensor,
         adj_t = adj_t.set_value(None, layout=None)
         return matmul(adj_t, x[0], reduce=self.aggr)
 
-    def aggregate(self, x: Tensor, index: Tensor, ptr: Optional[Tensor] = None,
-                  dim_size: Optional[int] = None) -> Tensor:
-        if self.aggr is not None:
-            return scatter(x, index, dim=self.node_dim, dim_size=dim_size,
-                           reduce=self.aggr)
-
-        # LSTM aggregation:
-        if ptr is None and not torch.all(index[:-1] <= index[1:]):
-            raise ValueError(f"Can not utilize LSTM-style aggregation inside "
-                             f"'{self.__class__.__name__}' in case the "
-                             f"'edge_index' tensor is not sorted by columns. "
-                             f"Run 'sort_edge_index(..., sort_by_row=False)' "
-                             f"in a pre-processing step.")
-
-        x, mask = to_dense_batch(x, batch=index, batch_size=dim_size)
-        out, _ = self.lstm(x)
-        return out[:, -1]
-
     def __repr__(self) -> str:
-        aggr = self.aggr if self.aggr is not None else 'lstm'
         return (f'{self.__class__.__name__}({self.in_channels}, '
-                f'{self.out_channels}, aggr={aggr})')
+                f'{self.out_channels}, aggr={self.aggr})')