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Skip attention dropout in GATConv/GATv2Conv when dropout == 0 #709

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Skip attention dropout in GATConv/GATv2Conv when dropout == 0 #709

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2 changes: 2 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -37,6 +37,7 @@ Entries link to the pull request that introduced them.

**Fixed**
- Fixed `Enzyme.gradient` failing when differentiating `GCNConv`, `SGConv` and `TAGConv` on `:dense`/`:sparse` adjacency graphs: their adjacency-matrix fallbacks now convert via the Enzyme-differentiable `GNNGraphs._to_coo_graph` instead of the keyword `GNNGraph` constructor. Requires GNNGraphs ≥ 1.5.2 and, for Enzyme, Enzyme ≥ 0.13.197 ([#703]).
- `GATConv` and `GATv2Conv` skip attention dropout when `dropout == 0`, which also makes them differentiable with Mooncake on CUDA ([#XXX]).

## GraphNeuralNetworks.jl — Unreleased (towards 1.1.1)

Expand Down Expand Up @@ -238,4 +239,5 @@ Lux implementations of the graph convolutional, pooling, and temporal layers
[#704]: https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/pull/704
[#707]: https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/pull/707
[#706]: https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/pull/706
[#709]: https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/pull/709
[FluxML/Zygote.jl#1662]: https://github.com/FluxML/Zygote.jl/issues/1662
5 changes: 3 additions & 2 deletions GNNlib/src/layers/conv.jl
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Expand Up @@ -136,7 +136,8 @@ function gat_conv(l, g::AbstractGNNGraph, x, e::Union{Nothing, AbstractMatrix} =
message = Fix1(gat_message, l)
m = apply_edges(message, g, Wxi, Wxj, e)
α = softmax_edge_neighbors(g, m.logα)
α = dropout(α, l.dropout)
# Skip at p == 0: NNlib.dropout fetches the (CUDA) RNG before checking p, which Mooncake cannot trace.
iszero(l.dropout) || (α = dropout(α, l.dropout))
β = α .* m.Wxj
x = aggregate_neighbors(g, +, β)

Expand Down Expand Up @@ -188,7 +189,7 @@ function gatv2_conv(l, g::AbstractGNNGraph, x, e::Union{Nothing, AbstractMatrix}
message = Fix1(gatv2_message, l)
m = apply_edges(message, g, Wxi, Wxj, e)
α = softmax_edge_neighbors(g, m.logα)
α = dropout(α, l.dropout)
iszero(l.dropout) || (α = dropout(α, l.dropout))
β = α .* m.Wxj
x = aggregate_neighbors(g, +, β)

Expand Down