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Do not cache _alpha in GATv2Conv #8753

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merged 1 commit into from
Jan 12, 2024
Merged

Do not cache _alpha in GATv2Conv #8753

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4 changes: 0 additions & 4 deletions test/nn/conv/test_gatv2_conv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,6 @@ def forward(
assert result[1][0].size() == (2, 7)
assert result[1][1].size() == (7, 2)
assert result[1][1].min() >= 0 and result[1][1].max() <= 1
assert conv._alpha is None

if torch_geometric.typing.WITH_PT113:
# PyTorch < 1.13 does not support multi-dimensional CSR values :(
Expand All @@ -66,7 +65,6 @@ def forward(
result = conv(x1, adj2.t(), return_attention_weights=True)
assert torch.allclose(result[0], out, atol=1e-6)
assert result[1].sizes() == [4, 4, 2] and result[1].nnz() == 7
assert conv._alpha is None

if is_full_test():

Expand All @@ -88,7 +86,6 @@ def forward(
assert result[1][0].size() == (2, 7)
assert result[1][1].size() == (7, 2)
assert result[1][1].min() >= 0 and result[1][1].max() <= 1
assert conv._alpha is None

if torch_geometric.typing.WITH_TORCH_SPARSE:

Expand All @@ -109,7 +106,6 @@ def forward(
result = jit(x1, adj2.t())
assert torch.allclose(result[0], out, atol=1e-6)
assert result[1].sizes() == [4, 4, 2] and result[1].nnz() == 7
assert conv._alpha is None

# Test bipartite message passing:
adj1 = to_torch_csc_tensor(edge_index, size=(4, 2))
Expand Down
24 changes: 11 additions & 13 deletions torch_geometric/nn/conv/gatv2_conv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,8 +129,6 @@ class GATv2Conv(MessagePassing):
or :math:`((|\mathcal{V_t}|, H * F_{out}), ((2, |\mathcal{E}|),
(|\mathcal{E}|, H)))` if bipartite
"""
_alpha: OptTensor

def __init__(
self,
in_channels: Union[int, Tuple[int, int]],
Expand Down Expand Up @@ -191,8 +189,6 @@ def __init__(
else:
self.register_parameter('bias', None)

self._alpha = None

self.reset_parameters()

def reset_parameters(self):
Expand Down Expand Up @@ -298,12 +294,12 @@ def forward( # noqa: F811
"simultaneously is currently not yet supported for "
"'edge_index' in a 'SparseTensor' form")

# propagate_type: (x: PairTensor, edge_attr: OptTensor)
out = self.propagate(edge_index, x=(x_l, x_r), edge_attr=edge_attr)
# edge_updater_type: (x: PairTensor, edge_attr: OptTensor)
alpha = self.edge_updater(edge_index, x=(x_l, x_r),
edge_attr=edge_attr)

alpha = self._alpha
assert alpha is not None
self._alpha = None
# propagate_type: (x: PairTensor, alpha: Tensor)
out = self.propagate(edge_index, x=(x_l, x_r), alpha=alpha)

if self.concat:
out = out.view(-1, self.heads * self.out_channels)
Expand All @@ -326,9 +322,9 @@ def forward( # noqa: F811
else:
return out

def message(self, x_j: Tensor, x_i: Tensor, edge_attr: OptTensor,
index: Tensor, ptr: OptTensor,
size_i: Optional[int]) -> Tensor:
def edge_update(self, x_j: Tensor, x_i: Tensor, edge_attr: OptTensor,
index: Tensor, ptr: OptTensor,
size_i: Optional[int]) -> Tensor:
x = x_i + x_j

if edge_attr is not None:
Expand All @@ -342,8 +338,10 @@ def message(self, x_j: Tensor, x_i: Tensor, edge_attr: OptTensor,
x = F.leaky_relu(x, self.negative_slope)
alpha = (x * self.att).sum(dim=-1)
alpha = softmax(alpha, index, ptr, size_i)
self._alpha = alpha
alpha = F.dropout(alpha, p=self.dropout, training=self.training)
return alpha

def message(self, x_j: Tensor, alpha: Tensor) -> Tensor:
return x_j * alpha.unsqueeze(-1)

def __repr__(self) -> str:
Expand Down
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