update

test/nn/test_encoding.py

@@ -1,11 +1,22 @@
 import torch
 
-from torch_geometric.nn import PositionalEncoding
+from torch_geometric.nn import PositionalEncoding, TemporalEncoding
+from torch_geometric.testing import withCUDA
 
 
-def test_positional_encoding():
-    encoder = PositionalEncoding(64)
+@withCUDA
+def test_positional_encoding(device):
+    encoder = PositionalEncoding(64).to(device)
     assert str(encoder) == 'PositionalEncoding(64)'
 
-    x = torch.tensor([1.0, 2.0, 3.0])
+    x = torch.tensor([1.0, 2.0, 3.0], device=device)
+    assert encoder(x).size() == (3, 64)
+
+
+@withCUDA
+def test_temporal_encoding(device):
+    encoder = TemporalEncoding(64).to(device)
+    assert str(encoder) == 'TemporalEncoding(64)'
+
+    x = torch.tensor([1.0, 2.0, 3.0], device=device)
     assert encoder(x).size() == (3, 64)

torch_geometric/nn/__init__.py

@@ -4,7 +4,7 @@
 from .to_hetero_transformer import to_hetero
 from .to_hetero_with_bases_transformer import to_hetero_with_bases
 from .to_fixed_size_transformer import to_fixed_size
-from .encoding import PositionalEncoding
+from .encoding import PositionalEncoding, TemporalEncoding
 from .model_hub import PyGModelHubMixin
 from .summary import summary
 
@@ -27,6 +27,7 @@
     'to_hetero_with_bases',
     'to_fixed_size',
     'PositionalEncoding',
+    'TemporalEncoding',
     'PyGModelHubMixin',
     'summary',
 ]

torch_geometric/nn/encoding.py

@@ -1,9 +1,11 @@
+import math
+
 import torch
 from torch import Tensor
 
 
 class PositionalEncoding(torch.nn.Module):
-    r"""The positional encoding scheme from `"Attention Is All You Need"
+    r"""The positional encoding scheme from the `"Attention Is All You Need"
     <https://arxiv.org/pdf/1706.03762.pdf>`_ paper
 
     .. math::
@@ -54,3 +56,41 @@ def forward(self, x: Tensor) -> Tensor:
 
     def __repr__(self) -> str:
         return f'{self.__class__.__name__}({self.out_channels})'
+
+
+class TemporalEncoding(torch.nn.Module):
+    r"""The time-encoding function from the `"Do We Really Need Complicated
+    Model Architectures for Temporal Networks?"
+    <https://openreview.net/forum?id=ayPPc0SyLv1>`_ paper, which first maps
+    each entry to a vector with monotonically exponentially decreasing values,
+    and then uses the cosine function to project all values to range
+    :math:`[-1, 1]`:
+
+    .. math::
+        y_{i} = \cos \left( \sqrt{d}^{-(i - 1)/\sqrt{d}} \right)
+
+    where :math:`d` defines the output feature dimension, and
+    :math:`1 \leq i \leq d`.
+
+    Args:
+        out_channels (int): Size :math:`d` of each output sample.
+    """
+    def __init__(self, out_channels: int):
+        super().__init__()
+        self.out_channels = out_channels
+
+        sqrt = math.sqrt(out_channels)
+        weight = 1.0 / sqrt**torch.linspace(0, sqrt, out_channels).view(1, -1)
+        self.register_buffer('weight', weight)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        pass
+
+    def forward(self, x: Tensor) -> Tensor:
+        """"""
+        return torch.cos(x.view(-1, 1) @ self.weight)
+
+    def __repr__(self) -> str:
+        return f'{self.__class__.__name__}({self.out_channels})'