From a58e76cb1677cdde4db4bff2e30fc9fddcbb64d9 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Fri, 17 Jul 2026 16:05:02 +0200 Subject: [PATCH 01/45] Add initial (dirty) version of gauge model and layers --- topobench/nn/backbones/graph/gauge.py | 561 ++++++++++++++++++++++++++ 1 file changed, 561 insertions(+) create mode 100644 topobench/nn/backbones/graph/gauge.py diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py new file mode 100644 index 000000000..817ec7c8d --- /dev/null +++ b/topobench/nn/backbones/graph/gauge.py @@ -0,0 +1,561 @@ +"""Riemannian Graph Foundation Model with neural vector bundles. + +This module implements the gauge-equivariant graph model described in +"Are Common Substructures Transferable: Riemannian Graph Foundation Model +with Neural Vector Bundles". + +The model learns, for every node, a local orthonormal frame (a gauge) that +spans an ``r``-dimensional subspace of the ``d``-dimensional embedding space, +smooths those frames across the graph, and uses them to update the node +features. The building blocks correspond directly to the equations of the +paper: + +- :class:`LocalCoordinatesLayer` -- equations (2)-(4). +- :class:`GatedFlatteningLayer` -- equations (5)-(8). +- :class:`NodeUpdateLayer` -- equations (9)-(10). +- :class:`GaugeLayer` -- one full message-passing step combining the above. +- :class:`GaugeModel` -- the full stack of gauge layers. +""" + +import torch +from torch import nn +from torch_geometric.typing import Tensor +from torch_scatter import scatter_add, scatter_mean, scatter_softmax + + +class FFBlock(nn.Module): + """Feed-forward block with GELU activations, dropout and a final LayerNorm. + + The block consists of ``n_hidden_layers`` hidden linear layers with GELU + activations followed by an output linear layer, with dropout applied after + every layer and layer normalization applied to the output. + + Parameters + ---------- + in_channels : int + Number of input features. + out_channels : int + Number of output features. + hidden_dim : int + Number of hidden units in the intermediate layers. + n_hidden_layers : int, optional + Number of hidden layers (must be at least 1) (default: 1). + drop : float, optional + Dropout probability (default: 0.3). + bias : bool, optional + Whether the linear layers use a bias term (default: True). + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + hidden_dim: int, + n_hidden_layers: int = 1, + drop: float = 0.3, + bias: bool = True, + ): + super().__init__() + + self.dropout = drop + self.in_channels = in_channels + self.out_channels = out_channels + self.hidden_dimension = hidden_dim + self.bias = bias + self.n_hidden_layers = n_hidden_layers + + assert self.n_hidden_layers >= 1 + + els = [] + + for layer_index in range(self.n_hidden_layers + 1): + if layer_index == 0: + els.append( + nn.Linear( + self.in_channels, self.hidden_dimension, bias=self.bias + ) + ) + els.append(nn.GELU()) + + elif layer_index < self.n_hidden_layers: + els.append( + nn.Linear( + self.hidden_dimension, + self.hidden_dimension, + bias=self.bias, + ) + ) + els.append(nn.GELU()) + + else: + els.append( + nn.Linear( + self.hidden_dimension, + self.out_channels, + bias=self.bias, + ) + ) + + els.append(nn.Dropout(self.dropout)) + + self.model = torch.nn.Sequential(*els) + self.norm = nn.LayerNorm(self.out_channels) + + def forward(self, x: Tensor) -> Tensor: + """Forward pass. + + Parameters + ---------- + x : Tensor + Input tensor of shape ``[..., in_channels]``. + + Returns + ------- + Tensor + Output tensor of shape ``[..., out_channels]``. + """ + + x = self.model(x) + x = self.norm(x) + + return x + + +class LocalCoordinatesLayer(torch.nn.Module): + """Local coordinate frame layer (equations (2)-(4)). + + For each node this layer projects the node embeddings into ``r`` different + subspaces, aggregates a smoothed reconstruction over the neighborhood using + attention weights, and applies a QR decomposition to obtain, per node, an + orthonormal basis (a local gauge) spanning an ``r``-dimensional subspace of + the embedding space. + + Parameters + ---------- + r_subspaces : int + Number of subspaces (frame vectors) ``r`` learned per node. + d_embedd : int + Dimension ``d`` of the node embeddings. + tau : float, optional + Temperature used to scale the attention logits (default: 1.0). + bias : bool, optional + Whether the linear layers use a bias term (default: True). + """ + + # eqns. 2-4 + def __init__( + self, + r_subspaces: int, + d_embedd: int, + tau: float = 1.0, + bias: bool = True, + ): + super().__init__() + + self.r = r_subspaces + self.tau = tau + self.d = d_embedd + self.bias = bias + + # combine the projectors into a single nn.Linear layer, reshape afterwards! + self.initial_projector = torch.nn.Linear( + self.d, self.d * self.r, bias=self.bias + ) + + # f_sim = f, computing similarity of node features + self.f_sim = torch.nn.Sequential( + torch.nn.Linear(2 * self.d, 1, bias=self.bias), + torch.nn.LeakyReLU(), + ) + + self.fflayer = FFBlock(self.d, self.d, self.d, bias=self.bias) + + def forward(self, edge_index: Tensor, Z: Tensor) -> Tensor: + """Forward pass computing per-node local orthonormal frames. + + Implements equations (2)-(4): the neighborhood-smoothed reconstruction + of the node embeddings (equations (2)-(3)) followed by a QR + decomposition yielding an orthonormal basis per node (equation (4)). + + Parameters + ---------- + edge_index : Tensor + Edge index tensor of shape ``[2, E]`` with source and destination + node indices. + Z : Tensor + Node embeddings of shape ``[N, d]``. + + Returns + ------- + Tensor + Per-node orthonormal frames of shape ``[N, r, d]``. + """ + + N = Z.size(0) # num_nodes + src, dst = edge_index[0], edge_index[1] + + # nr. 1: we project the input matrix x into r different subspaces + Zh = self.initial_projector(Z) # [N, r*d] + Zh = Zh.reshape(N, self.r, self.d) # [N, r, d] + + # EQUATION no. (3) + # f_vals has shape [N, r] + f_vals = ( + self.f_sim(torch.concat((Zh[src], Zh[dst]), dim=-1)) / self.tau + ) + f_vals = f_vals.squeeze(-1) # remove last singleton dimension + alphas = torch.softmax(f_vals, dim=-1).unsqueeze(-1) # [E, r, 1] + + # EQUATION no. (2) + out = scatter_add( + alphas * Zh[src, :, :], index=dst, dim=0, dim_size=N + ) # tensor of shape (E, r, d) + + # we need to clamp as nodes with degree 0 would have a scatter_add of 0 + # this should give us a tensor of shape [N, r] + norm = 1 / (scatter_add(alphas, dst, dim=0, dim_size=N).clamp(1e-6)) + + # norm*out should be [N, r, d] with norm broadcasted along the last dimension (d) + # norm*out is of shape [N,r,d] while Z is of shape [N,d], hence we insert a new axis at -2 + qhat = Z.unsqueeze(-2) - norm * out + + # there should be a feedforward module here and a norm module (combined into a single module for brevity) + qhat = self.fflayer(qhat) + + # EQUATION no. (4) + # xx has shape [N, r, d] so now we can do the QR decomposition to obtain an orthonormal basis + Q, _ = torch.linalg.qr(qhat.mT) + + return Q.mT + + +class GatedFlatteningLayer(nn.Module): + """Gated flattening layer that smooths local frames (equations (5)-(8)). + + This layer aligns each node's local frame with those of its neighbors. It + computes gating weights from the overlap between neighboring frames + (equation (6)), forms a gated aggregate that is blended with the original + frame (equation (7)), and re-orthonormalizes the result via a QR + decomposition (equation (8)). + + Parameters + ---------- + r : int + Number of subspaces (frame vectors) ``r`` per node. + gamma : float, optional + Blending coefficient between the original frame and the aggregated + neighbor frames (default: 0.01). + tau : float, optional + Temperature used to scale the gating logits (default: 1.0). + """ + + # eqns.5-8 + + def __init__(self, r: int, gamma: float = 0.01, tau: float = 1.0): + super().__init__() + + self.r = r + self.gamma = gamma + self.tau = tau + + def forward(self, edge_index: Tensor, Q: Tensor) -> Tensor: + """Forward pass smoothing the per-node frames over the graph. + + Implements equations (6)-(8): gating weights from neighboring frame + overlaps (equation (6)), a gated aggregate blended with the input frame + (equation (7)), and a final QR re-orthonormalization (equation (8)). + + Parameters + ---------- + edge_index : Tensor + Edge index tensor of shape ``[2, E]`` with source and destination + node indices. + Q : Tensor + Per-node orthonormal frames of shape ``[N, r, d]``. + + Returns + ------- + Tensor + Smoothed per-node orthonormal frames of shape ``[N, r, d]``. + """ + # eqns. (6-8) + + N = Q.size(0) # num_nodes + src, dst = edge_index[0], edge_index[1] + k = Q.size(-2) # with k fixed, the trace of eye(k) = k + + # EQUATION no. (6) + # the trace of the identity of size k = k + g_vec = scatter_softmax( + ((Q[src] * Q[dst]).sum((-2, -1)) - k) / self.tau, + index=dst, + dim_size=N, + ) + + # technical note: in theory wed need to compute gij + # for all pairs of nodes which becomes unnecessary only because we only + # sum over neighbors menaning that non-neighbor entries are irrelevant + + # EQUATION no. (7) + Qagg = scatter_add( + g_vec[:, None, None] * Q[src], dim=0, index=dst, dim_size=N + ) + Qhat = (1 - self.gamma) * Q + self.gamma * Qagg + + # EQUATION no. (8) + # lastly we do the QR decomposition again to obtain an orthonormal basis: + Qnew, _ = torch.linalg.qr(Qhat.mT) + + return Qnew.mT + + +class NodeUpdateLayer(torch.nn.Module): + """Node feature update layer using the local frames (equations (9)-(10)). + + Each node embedding is projected onto the subspace spanned by its local + frame and mapped through a learnable matrix (equation (9)). The projected + features are then aggregated over the neighborhood and combined with a + learnable residual transformation of the original embedding (equation (10)). + + Parameters + ---------- + in_channels : int + Number of input features. + out_channels : int + Number of output features. + """ + + # eqns. 9-10 + def __init__(self, in_channels: int, out_channels: int): + super().__init__() + + # this is the learnable function applied to z + self.phi = torch.nn.Linear(in_channels, out_channels) + + # this is the learnable matrix applied to tilde(z) + self.W = torch.nn.Linear(in_channels, out_channels, bias=False) + + def forward(self, edge_index: Tensor, Z: Tensor, Q: Tensor) -> Tensor: + """Forward pass updating the node features. + + Implements equations (9)-(10): the frame projection of the node + embeddings (equation (9)) followed by the neighborhood aggregation with + a learnable residual connection (equation (10)). + + Parameters + ---------- + edge_index : Tensor + Edge index tensor of shape ``[2, E]`` with source and destination + node indices. + Z : Tensor + Node embeddings of shape ``[N, d]``. + Q : Tensor + Per-node orthonormal frames of shape ``[N, r, d]``. + + Returns + ------- + Tensor + Updated node embeddings of shape ``[N, out_channels]``. + """ + + # step 0: bind commonly used values to variable names + src, _ = edge_index[0], edge_index[1] + N = Z.size(0) # num_nodes for scatter ops + + # step 1: calculate tilde(z) + + # EQUATION no. (9) + # Q has shape [N,r,d] and z has shape [N, d] + # we want to transform each vector in z via the matrix [r,d] batching over the first dimension + QtZ = torch.einsum("ijk,ik->ij", Q, Z) + Z_tilde = torch.einsum("ikj, ik->ij", Q, QtZ) + Z_tilde = self.W(Z_tilde) + + # EQUATION no. (10) + # DIVERGENCE FROM REFERENCE IMPLEMENTATION + # Contrary to the reference implementation we do not omit the "residual connection" + # realized via the self.phi function + # cf. equation (10) + Znew = scatter_mean( + Z_tilde[src], index=src, dim=0, dim_size=N + ) + self.phi(Z) + + return Znew + + +class GaugeLayer(torch.nn.Module): + """A single gauge message-passing layer. + + One layer computes per-node local frames with a + :class:`LocalCoordinatesLayer` (equations (2)-(4)), smooths them through a + stack of ``n_gated`` :class:`GatedFlatteningLayer` modules (equations + (5)-(8)), and updates the node features with a :class:`NodeUpdateLayer` + (equations (9)-(10)). + + Parameters + ---------- + d_embedd : int + Dimension ``d`` of the node embeddings. + r : int + Number of subspaces (frame vectors) ``r`` per node. + n_gated : int, optional + Number of gated flattening layers applied to the frames (default: 1). + gamma : float, optional + Blending coefficient used by the gated flattening layers (default: 0.01). + tau : float, optional + Temperature used to scale the attention and gating logits (default: 1.0). + bias : bool, optional + Whether the linear layers use a bias term (default: True). + """ + + def __init__( + self, + d_embedd: int, + r: int, + n_gated: int = 1, + gamma: float = 0.01, + tau: float = 1.0, + bias=True, + ): + super().__init__() + + self.r = r + self.bias = bias + self.tau = tau + self.n_gated = n_gated + self.gamma = gamma + self.d_embedd = d_embedd + + self.local_coords_layer = LocalCoordinatesLayer( + r_subspaces=r, d_embedd=d_embedd, tau=tau, bias=bias + ) + + self.gated_flattening_layers = nn.ModuleList( + [ + GatedFlatteningLayer(self.r, self.gamma, self.tau) + for _ in range(n_gated) + ] + ) + + self.node_update_layer = NodeUpdateLayer(self.d_embedd, self.d_embedd) + + def forward(self, edge_index: Tensor, x: Tensor) -> tuple[Tensor, Tensor]: + """Forward pass of a single gauge layer. + + Parameters + ---------- + edge_index : Tensor + Edge index tensor of shape ``[2, E]`` with source and destination + node indices. + x : Tensor + Node embeddings of shape ``[N, d]``. + + Returns + ------- + Znew : Tensor + Updated node embeddings of shape ``[N, d]``. + Q : Tensor + Per-node orthonormal frames of shape ``[N, r, d]``. + """ + + Q = self.local_coords_layer(edge_index, x) + + for layer in self.gated_flattening_layers: + Q = layer(edge_index, Q) + + Znew = self.node_update_layer(edge_index, x, Q) + + return Znew, Q + + +class GaugeModel(nn.Module): + """Riemannian graph foundation model with neural vector bundles. + + The model first projects the input features into a ``d_embedd``-dimensional + embedding space and then applies a stack of :class:`GaugeLayer` modules, + each of which learns per-node local frames and uses them to update the node + features. + + Parameters + ---------- + n_layers : int + Number of gauge layers. + in_channels : int + Number of input features. + r : int + Number of subspaces (frame vectors) ``r`` per node. + d_embedd : int + Dimension ``d`` of the node embeddings. + n_gated : int, optional + Number of gated flattening layers per gauge layer (default: 1). + gamma : float, optional + Blending coefficient used by the gated flattening layers (default: 0.01). + tau : float, optional + Temperature used to scale the attention and gating logits (default: 1.0). + bias : bool, optional + Whether the linear layers use a bias term (default: True). + """ + + def __init__( + self, + n_layers: int, + in_channels: int, + r: int, + d_embedd: int, + n_gated: int = 1, + gamma=0.01, + tau: float = 1.0, + bias=True, + ): + super().__init__() + + self.n_layers = n_layers + self.gamma = gamma + self.tau = tau + self.bias = bias + self.in_channels = in_channels + self.r = r + self.d_embedd = d_embedd + self.n_gated = n_gated + + self.input_projector = nn.Sequential( + nn.Linear(in_channels, d_embedd), nn.LayerNorm(d_embedd) + ) + + self.layers = nn.ModuleList( + [ + GaugeLayer( + d_embedd=self.d_embedd, + r=self.r, + gamma=self.gamma, + tau=self.tau, + n_gated=self.n_gated, + ) + ] + ) + + def forward(self, edge_index: Tensor, x: Tensor) -> tuple[Tensor, Tensor]: + """Forward pass of the full model. + + Parameters + ---------- + edge_index : Tensor + Edge index tensor of shape ``[2, E]`` with source and destination + node indices. + x : Tensor + Input node features of shape ``[N, in_channels]``. + + Returns + ------- + z : Tensor + Final node embeddings of shape ``[N, d_embedd]``. + Q : Tensor + Per-node orthonormal frames of shape ``[N, r, d_embedd]`` from the + last gauge layer. + """ + z = self.input_projector(x) + + for layer in self.layers: + z, Q = layer(edge_index, z) + + return z, Q From a2682261b194f223577a31d2a2a827a27f816d67 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 20 Jul 2026 09:45:52 +0200 Subject: [PATCH 02/45] fix: use correct index for scatter mean --- topobench/nn/backbones/graph/gauge.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 817ec7c8d..93c00059f 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -359,7 +359,7 @@ def forward(self, edge_index: Tensor, Z: Tensor, Q: Tensor) -> Tensor: """ # step 0: bind commonly used values to variable names - src, _ = edge_index[0], edge_index[1] + src, dst = edge_index[0], edge_index[1] N = Z.size(0) # num_nodes for scatter ops # step 1: calculate tilde(z) @@ -377,7 +377,7 @@ def forward(self, edge_index: Tensor, Z: Tensor, Q: Tensor) -> Tensor: # realized via the self.phi function # cf. equation (10) Znew = scatter_mean( - Z_tilde[src], index=src, dim=0, dim_size=N + Z_tilde[src], index=dst, dim=0, dim_size=N ) + self.phi(Z) return Znew From a97b26ba441b0ffaefd845d4adf9869f893b061d Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 20 Jul 2026 09:47:47 +0200 Subject: [PATCH 03/45] fix: actually create n_layers GaugeLayers --- topobench/nn/backbones/graph/gauge.py | 1 + 1 file changed, 1 insertion(+) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 93c00059f..dd84af0d6 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -531,6 +531,7 @@ def __init__( tau=self.tau, n_gated=self.n_gated, ) + for _ in range(self.n_layers) ] ) From a459bd9e3c7dc74e62aa04dd6d3621d8f93c800a Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 20 Jul 2026 09:53:40 +0200 Subject: [PATCH 04/45] fix: correctly pass on `bias` argument --- topobench/nn/backbones/graph/gauge.py | 1 + 1 file changed, 1 insertion(+) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index dd84af0d6..d35db95ec 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -530,6 +530,7 @@ def __init__( gamma=self.gamma, tau=self.tau, n_gated=self.n_gated, + bias=bias, ) for _ in range(self.n_layers) ] From a637dade3c919dfbf74a9ae44a258dfef6c76068 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 20 Jul 2026 10:00:45 +0200 Subject: [PATCH 05/45] fix: stick to reference repo convention (norm then feedforward) --- topobench/nn/backbones/graph/gauge.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index d35db95ec..eed94fb53 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -115,8 +115,8 @@ def forward(self, x: Tensor) -> Tensor: Output tensor of shape ``[..., out_channels]``. """ - x = self.model(x) x = self.norm(x) + x = self.model(x) return x From 5eb6e692d2774ff66dcaaf978e7a5ab417072a94 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 20 Jul 2026 11:39:44 +0200 Subject: [PATCH 06/45] improvement: make learnable residual phi more flexible --- topobench/nn/backbones/graph/gauge.py | 78 ++++++++++++++++++++++----- 1 file changed, 65 insertions(+), 13 deletions(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index eed94fb53..b2282313a 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -314,8 +314,10 @@ class NodeUpdateLayer(torch.nn.Module): Each node embedding is projected onto the subspace spanned by its local frame and mapped through a learnable matrix (equation (9)). The projected - features are then aggregated over the neighborhood and combined with a - learnable residual transformation of the original embedding (equation (10)). + features are then aggregated over the neighborhood and, when the residual is + enabled, combined with a learnable transformation ``phi`` of the original + embedding (equation (10)). Setting ``phi_hidden_layers`` to ``None`` disables + the residual and recovers the reference behavior. Parameters ---------- @@ -323,14 +325,36 @@ class NodeUpdateLayer(torch.nn.Module): Number of input features. out_channels : int Number of output features. + phi_hidden_layers : int or None, optional + Number of hidden layers of the MLP residual ``phi``. If ``None`` the + residual is disabled (matching the reference implementation) + (default: 1). + phi_hidden_dim : int or None, optional + Hidden width of the residual MLP ``phi``. Defaults to + ``max(in_channels, out_channels)`` when ``None`` (default: None). """ # eqns. 9-10 - def __init__(self, in_channels: int, out_channels: int): + def __init__( + self, + in_channels: int, + out_channels: int, + phi_hidden_layers: int | None = 1, + phi_hidden_dim: int | None = None, + ): super().__init__() - # this is the learnable function applied to z - self.phi = torch.nn.Linear(in_channels, out_channels) + self.phi = None + # this is the learnable function applied to z (if phi_hidden_dim isn't None) + if phi_hidden_layers is not None: + self.phi = FFBlock( + in_channels=in_channels, + out_channels=out_channels, + hidden_dim=phi_hidden_dim + if phi_hidden_dim is not None + else max(in_channels, out_channels), + n_hidden_layers=phi_hidden_layers, + ) # this is the learnable matrix applied to tilde(z) self.W = torch.nn.Linear(in_channels, out_channels, bias=False) @@ -339,8 +363,8 @@ def forward(self, edge_index: Tensor, Z: Tensor, Q: Tensor) -> Tensor: """Forward pass updating the node features. Implements equations (9)-(10): the frame projection of the node - embeddings (equation (9)) followed by the neighborhood aggregation with - a learnable residual connection (equation (10)). + embeddings (equation (9)) followed by the neighborhood aggregation and, + when enabled, a learnable residual connection (equation (10)). Parameters ---------- @@ -373,12 +397,15 @@ def forward(self, edge_index: Tensor, Z: Tensor, Q: Tensor) -> Tensor: # EQUATION no. (10) # DIVERGENCE FROM REFERENCE IMPLEMENTATION - # Contrary to the reference implementation we do not omit the "residual connection" - # realized via the self.phi function + # Contrary to the reference implementation we optionally add a "residual + # connection" realized via the self.phi function. It is enabled by + # default and can be disabled (recovering the reference behavior) by + # passing phi_hidden_layers=None, in which case self.phi is None. # cf. equation (10) - Znew = scatter_mean( - Z_tilde[src], index=dst, dim=0, dim_size=N - ) + self.phi(Z) + Znew = scatter_mean(Z_tilde[src], index=dst, dim=0, dim_size=N) + + if self.phi is not None: + Znew = Znew + self.phi(Z) return Znew @@ -406,6 +433,13 @@ class GaugeLayer(torch.nn.Module): Temperature used to scale the attention and gating logits (default: 1.0). bias : bool, optional Whether the linear layers use a bias term (default: True). + phi_hidden_layers : int or None, optional + Number of hidden layers of the MLP residual ``phi`` in the node update. + If ``None`` the residual is disabled (matching the reference + implementation) (default: 1). + phi_hidden_dim : int or None, optional + Hidden width of the residual MLP ``phi``. Defaults to ``d_embedd`` when + ``None`` (default: None). """ def __init__( @@ -416,6 +450,8 @@ def __init__( gamma: float = 0.01, tau: float = 1.0, bias=True, + phi_hidden_layers: int | None = 1, + phi_hidden_dim: int | None = None, ): super().__init__() @@ -437,7 +473,12 @@ def __init__( ] ) - self.node_update_layer = NodeUpdateLayer(self.d_embedd, self.d_embedd) + self.node_update_layer = NodeUpdateLayer( + self.d_embedd, + self.d_embedd, + phi_hidden_layers=phi_hidden_layers, + phi_hidden_dim=phi_hidden_dim, + ) def forward(self, edge_index: Tensor, x: Tensor) -> tuple[Tensor, Tensor]: """Forward pass of a single gauge layer. @@ -494,6 +535,13 @@ class GaugeModel(nn.Module): Temperature used to scale the attention and gating logits (default: 1.0). bias : bool, optional Whether the linear layers use a bias term (default: True). + phi_hidden_layers : int or None, optional + Number of hidden layers of the MLP residual ``phi`` in the node update. + If ``None`` the residual is disabled (matching the reference + implementation) (default: 1). + phi_hidden_dim : int or None, optional + Hidden width of the residual MLP ``phi``. Defaults to ``d_embedd`` when + ``None`` (default: None). """ def __init__( @@ -506,6 +554,8 @@ def __init__( gamma=0.01, tau: float = 1.0, bias=True, + phi_hidden_layers: int | None = 1, + phi_hidden_dim: int | None = None, ): super().__init__() @@ -531,6 +581,8 @@ def __init__( tau=self.tau, n_gated=self.n_gated, bias=bias, + phi_hidden_layers=phi_hidden_layers, + phi_hidden_dim=phi_hidden_dim, ) for _ in range(self.n_layers) ] From 2393564639b8637147572ce82708b5f59b3c5a9c Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 20 Jul 2026 11:55:24 +0200 Subject: [PATCH 07/45] fix: proper type import --- topobench/nn/backbones/graph/gauge.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index b2282313a..67dd9c0b5 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -18,8 +18,7 @@ """ import torch -from torch import nn -from torch_geometric.typing import Tensor +from torch import Tensor, nn from torch_scatter import scatter_add, scatter_mean, scatter_softmax From 70721afcb98e432898c92e09e5bae30c3c0061c9 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 20 Jul 2026 11:56:53 +0200 Subject: [PATCH 08/45] fix: stick to topobench argument order convention --- topobench/nn/backbones/graph/gauge.py | 56 +++++++++++++-------------- 1 file changed, 28 insertions(+), 28 deletions(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 67dd9c0b5..eac364fe5 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -169,7 +169,7 @@ def __init__( self.fflayer = FFBlock(self.d, self.d, self.d, bias=self.bias) - def forward(self, edge_index: Tensor, Z: Tensor) -> Tensor: + def forward(self, Z: Tensor, edge_index: Tensor) -> Tensor: """Forward pass computing per-node local orthonormal frames. Implements equations (2)-(4): the neighborhood-smoothed reconstruction @@ -178,11 +178,11 @@ def forward(self, edge_index: Tensor, Z: Tensor) -> Tensor: Parameters ---------- + Z : Tensor + Node embeddings of shape ``[N, d]``. edge_index : Tensor Edge index tensor of shape ``[2, E]`` with source and destination node indices. - Z : Tensor - Node embeddings of shape ``[N, d]``. Returns ------- @@ -257,7 +257,7 @@ def __init__(self, r: int, gamma: float = 0.01, tau: float = 1.0): self.gamma = gamma self.tau = tau - def forward(self, edge_index: Tensor, Q: Tensor) -> Tensor: + def forward(self, Q: Tensor, edge_index: Tensor) -> Tensor: """Forward pass smoothing the per-node frames over the graph. Implements equations (6)-(8): gating weights from neighboring frame @@ -266,11 +266,11 @@ def forward(self, edge_index: Tensor, Q: Tensor) -> Tensor: Parameters ---------- + Q : Tensor + Per-node orthonormal frames of shape ``[N, r, d]``. edge_index : Tensor Edge index tensor of shape ``[2, E]`` with source and destination node indices. - Q : Tensor - Per-node orthonormal frames of shape ``[N, r, d]``. Returns ------- @@ -325,9 +325,9 @@ class NodeUpdateLayer(torch.nn.Module): out_channels : int Number of output features. phi_hidden_layers : int or None, optional - Number of hidden layers of the MLP residual ``phi``. If ``None`` the - residual is disabled (matching the reference implementation) - (default: 1). + Number of hidden layers of the MLP residual ``phi``. Must be at least 1 + when not ``None``. If ``None`` the residual is disabled (matching the + reference implementation) (default: 1). phi_hidden_dim : int or None, optional Hidden width of the residual MLP ``phi``. Defaults to ``max(in_channels, out_channels)`` when ``None`` (default: None). @@ -344,7 +344,7 @@ def __init__( super().__init__() self.phi = None - # this is the learnable function applied to z (if phi_hidden_dim isn't None) + # this is the learnable function applied to z (if phi_hidden_layers isn't None) if phi_hidden_layers is not None: self.phi = FFBlock( in_channels=in_channels, @@ -358,7 +358,7 @@ def __init__( # this is the learnable matrix applied to tilde(z) self.W = torch.nn.Linear(in_channels, out_channels, bias=False) - def forward(self, edge_index: Tensor, Z: Tensor, Q: Tensor) -> Tensor: + def forward(self, Z: Tensor, Q: Tensor, edge_index: Tensor) -> Tensor: """Forward pass updating the node features. Implements equations (9)-(10): the frame projection of the node @@ -367,13 +367,13 @@ def forward(self, edge_index: Tensor, Z: Tensor, Q: Tensor) -> Tensor: Parameters ---------- - edge_index : Tensor - Edge index tensor of shape ``[2, E]`` with source and destination - node indices. Z : Tensor Node embeddings of shape ``[N, d]``. Q : Tensor Per-node orthonormal frames of shape ``[N, r, d]``. + edge_index : Tensor + Edge index tensor of shape ``[2, E]`` with source and destination + node indices. Returns ------- @@ -434,8 +434,8 @@ class GaugeLayer(torch.nn.Module): Whether the linear layers use a bias term (default: True). phi_hidden_layers : int or None, optional Number of hidden layers of the MLP residual ``phi`` in the node update. - If ``None`` the residual is disabled (matching the reference - implementation) (default: 1). + Must be at least 1 when not ``None``. If ``None`` the residual is + disabled (matching the reference implementation) (default: 1). phi_hidden_dim : int or None, optional Hidden width of the residual MLP ``phi``. Defaults to ``d_embedd`` when ``None`` (default: None). @@ -479,16 +479,16 @@ def __init__( phi_hidden_dim=phi_hidden_dim, ) - def forward(self, edge_index: Tensor, x: Tensor) -> tuple[Tensor, Tensor]: + def forward(self, x: Tensor, edge_index: Tensor) -> tuple[Tensor, Tensor]: """Forward pass of a single gauge layer. Parameters ---------- + x : Tensor + Node embeddings of shape ``[N, d]``. edge_index : Tensor Edge index tensor of shape ``[2, E]`` with source and destination node indices. - x : Tensor - Node embeddings of shape ``[N, d]``. Returns ------- @@ -498,12 +498,12 @@ def forward(self, edge_index: Tensor, x: Tensor) -> tuple[Tensor, Tensor]: Per-node orthonormal frames of shape ``[N, r, d]``. """ - Q = self.local_coords_layer(edge_index, x) + Q = self.local_coords_layer(x, edge_index) for layer in self.gated_flattening_layers: - Q = layer(edge_index, Q) + Q = layer(Q, edge_index) - Znew = self.node_update_layer(edge_index, x, Q) + Znew = self.node_update_layer(x, Q, edge_index) return Znew, Q @@ -536,8 +536,8 @@ class GaugeModel(nn.Module): Whether the linear layers use a bias term (default: True). phi_hidden_layers : int or None, optional Number of hidden layers of the MLP residual ``phi`` in the node update. - If ``None`` the residual is disabled (matching the reference - implementation) (default: 1). + Must be at least 1 when not ``None``. If ``None`` the residual is + disabled (matching the reference implementation) (default: 1). phi_hidden_dim : int or None, optional Hidden width of the residual MLP ``phi``. Defaults to ``d_embedd`` when ``None`` (default: None). @@ -587,16 +587,16 @@ def __init__( ] ) - def forward(self, edge_index: Tensor, x: Tensor) -> tuple[Tensor, Tensor]: + def forward(self, x: Tensor, edge_index: Tensor) -> tuple[Tensor, Tensor]: """Forward pass of the full model. Parameters ---------- + x : Tensor + Input node features of shape ``[N, in_channels]``. edge_index : Tensor Edge index tensor of shape ``[2, E]`` with source and destination node indices. - x : Tensor - Input node features of shape ``[N, in_channels]``. Returns ------- @@ -609,6 +609,6 @@ def forward(self, edge_index: Tensor, x: Tensor) -> tuple[Tensor, Tensor]: z = self.input_projector(x) for layer in self.layers: - z, Q = layer(edge_index, z) + z, Q = layer(z, edge_index) return z, Q From 11bb77a38d1e60a70b194a491720520e092319db Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 20 Jul 2026 14:40:32 +0200 Subject: [PATCH 09/45] fix: correct LayerNorm dim size --- topobench/nn/backbones/graph/gauge.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index eac364fe5..b856854e5 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -98,7 +98,7 @@ def __init__( els.append(nn.Dropout(self.dropout)) self.model = torch.nn.Sequential(*els) - self.norm = nn.LayerNorm(self.out_channels) + self.norm = nn.LayerNorm(self.in_channels) def forward(self, x: Tensor) -> Tensor: """Forward pass. From e823569333f7b2cc943f7cff8defb527693cda2f Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 20 Jul 2026 15:37:15 +0200 Subject: [PATCH 10/45] fix: add missing norm --- topobench/nn/backbones/graph/gauge.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index b856854e5..b5efc6855 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -168,6 +168,7 @@ def __init__( ) self.fflayer = FFBlock(self.d, self.d, self.d, bias=self.bias) + self.preqr_norm = nn.LayerNorm(self.d) def forward(self, Z: Tensor, edge_index: Tensor) -> Tensor: """Forward pass computing per-node local orthonormal frames. @@ -218,8 +219,9 @@ def forward(self, Z: Tensor, edge_index: Tensor) -> Tensor: # norm*out is of shape [N,r,d] while Z is of shape [N,d], hence we insert a new axis at -2 qhat = Z.unsqueeze(-2) - norm * out - # there should be a feedforward module here and a norm module (combined into a single module for brevity) + # feedforward followed by a LayerNorm, then QR (eq. 4) qhat = self.fflayer(qhat) + qhat = self.preqr_norm(qhat) # EQUATION no. (4) # xx has shape [N, r, d] so now we can do the QR decomposition to obtain an orthonormal basis From 4c804d0418cea1f09875c27e9b43c666912b750a Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Wed, 22 Jul 2026 14:06:55 +0200 Subject: [PATCH 11/45] implement a per-head MLP scoring fucntion --- topobench/nn/backbones/graph/gauge.py | 201 +++++++++++++++++++++++++- 1 file changed, 197 insertions(+), 4 deletions(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index b5efc6855..c73b4f5cb 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -17,10 +17,203 @@ - :class:`GaugeModel` -- the full stack of gauge layers. """ +import math +from collections.abc import Callable + import torch from torch import Tensor, nn from torch_scatter import scatter_add, scatter_mean, scatter_softmax +activation_dict: dict[str, Callable] = { + "relu": nn.ReLU, + "leaky_relu": nn.LeakyReLU, + "gelu": nn.GELU, + "sigmoid": nn.Sigmoid, +} + + +class MultiHeadLinear(nn.Module): + """Per-head (per-subspace) linear layer. + + Applies ``r_dim`` independent linear maps, one per head, so that head ``h`` + transforms its own slice of the input with its own weight matrix and bias. + The per-head weights are stored stacked in a single parameter and applied + with a batched ``einsum``. + + Parameters + ---------- + in_channels : int + Number of input features per head. + out_channels : int + Number of output features per head. + r_dim : int + Number of heads (subspaces) ``r``. + bias : bool, optional + Whether each head uses a bias term (default: True). + device : torch.device or str or None, optional + Device on which to allocate the parameters (default: None). + dtype : torch.dtype or None, optional + Data type of the parameters (default: None). + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + r_dim: int, + bias: bool = True, + device=None, + dtype=None, + ): + super().__init__() + + factory_kwargs = {"device": device, "dtype": dtype} + + self.in_channels = in_channels + self.out_channels = out_channels + self.bias = bias + self.r_dim = r_dim + + self.superW = nn.Parameter( + torch.empty( + self.r_dim, + self.out_channels, + self.in_channels, + **factory_kwargs, + ) + ) + + if self.bias: + self.superB = nn.Parameter( + torch.empty(self.r_dim, self.out_channels, **factory_kwargs), + ) + else: + self.register_parameter("superB", None) + + self.reset_parameters() + + def reset_parameters(self) -> None: + """Initialize the per-head weights and biases. + + Uses the same scheme as :class:`torch.nn.Linear` (a uniform + distribution bounded by ``1 / sqrt(in_channels)``), with the fan-in + taken per head rather than over the stacked parameter. + """ + bound = 1 / math.sqrt(self.in_channels) + + torch.nn.init.uniform_(self.superW, -bound, bound) + + if self.bias: + torch.nn.init.uniform_(self.superB, -bound, bound) + + def forward(self, Z: Tensor) -> Tensor: + """Apply the per-head linear maps. + + Parameters + ---------- + Z : Tensor + Input tensor of shape ``[N, r, in_channels]`` where ``r`` is the + number of heads. + + Returns + ------- + Tensor + Output tensor of shape ``[N, r, out_channels]``. + """ + if self.bias: + return torch.einsum("roi,Nri->Nro", self.superW, Z) + self.superB + + return torch.einsum("roi,Nri->Nro", self.superW, Z) + + +class MultiHeadFF(nn.Module): + """Per-head feed-forward network (a distinct MLP per subspace). + + Stacks :class:`MultiHeadLinear` layers interleaved with activations and + dropout so that each of the ``r`` heads is transformed by its own + multi-layer perceptron. Activations and dropout are applied only between + layers, never after the output layer. + + Parameters + ---------- + in_channels : int + Number of input features per head. + out_channels : int + Number of output features per head. + r : int + Number of heads (subspaces) ``r``. + hidden_dims : list of int or None, optional + Widths of the hidden layers. If ``None`` the network is a single + per-head linear map (default: None). + act : str, optional + Name of the activation applied between layers, resolved via + ``activation_dict`` (default: "leaky_relu"). + drop : float, optional + Dropout probability applied between layers (default: 0.0). + bias : bool, optional + Whether each per-head linear layer uses a bias term (default: True). + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + r: int, + hidden_dims: list[int] | None = None, + act: str = "leaky_relu", + drop: float = 0.0, + bias: bool = True, + ): + super().__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.act = act + self.dropout = drop + self.r = r + self.bias = bias + + self.layer_sizes = [self.in_channels] + + if hidden_dims is not None: + self.layer_sizes += [j for j in hidden_dims] + self.layer_sizes += [self.out_channels] + + els = [] + for j in range(len(self.layer_sizes) - 1): + els.append( + MultiHeadLinear( + self.layer_sizes[j], + self.layer_sizes[j + 1], + r_dim=self.r, + bias=self.bias, + ) + ) + + if j < len(self.layer_sizes) - 2: + els.append(activation_dict[self.act]()) + els.append(nn.Dropout(self.dropout)) + + self.model = nn.Sequential(*els) + + def forward(self, Z: Tensor) -> Tensor: + """Apply the per-head feed-forward network. + + Parameters + ---------- + Z : Tensor + Input tensor of shape ``[N, r, in_channels]`` where ``r`` is the + number of heads. + + Returns + ------- + Tensor + Output tensor of shape ``[N, r, out_channels]``. + """ + Zhat = self.model(Z) + + return Zhat + class FFBlock(nn.Module): """Feed-forward block with GELU activations, dropout and a final LayerNorm. @@ -162,9 +355,8 @@ def __init__( ) # f_sim = f, computing similarity of node features - self.f_sim = torch.nn.Sequential( - torch.nn.Linear(2 * self.d, 1, bias=self.bias), - torch.nn.LeakyReLU(), + self.f_sim = MultiHeadFF( + 2 * self.d, 1, r=self.r, hidden_dims=[2 * self.d] ) self.fflayer = FFBlock(self.d, self.d, self.d, bias=self.bias) @@ -199,10 +391,11 @@ def forward(self, Z: Tensor, edge_index: Tensor) -> Tensor: Zh = Zh.reshape(N, self.r, self.d) # [N, r, d] # EQUATION no. (3) - # f_vals has shape [N, r] + # f_vals has shape [N, r, 1] f_vals = ( self.f_sim(torch.concat((Zh[src], Zh[dst]), dim=-1)) / self.tau ) + f_vals = f_vals.squeeze(-1) # remove last singleton dimension alphas = torch.softmax(f_vals, dim=-1).unsqueeze(-1) # [E, r, 1] From 29d1240854eacfac7f171ae254b4875adde16537 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Wed, 22 Jul 2026 14:39:32 +0200 Subject: [PATCH 12/45] custom `GaugeWrapper` --- topobench/nn/wrappers/graph/gauge_wrapper.py | 32 ++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 topobench/nn/wrappers/graph/gauge_wrapper.py diff --git a/topobench/nn/wrappers/graph/gauge_wrapper.py b/topobench/nn/wrappers/graph/gauge_wrapper.py new file mode 100644 index 000000000..267b82296 --- /dev/null +++ b/topobench/nn/wrappers/graph/gauge_wrapper.py @@ -0,0 +1,32 @@ +"""Wrapper for the Gauge model.""" + +from topobench.nn.wrappers.base import AbstractWrapper + + +class GaugeWrapper(AbstractWrapper): + r"""Wrapper for the Gauge model. + + This wrapper defines the forward pass of the model. The Gauge backbone + returns the rank-0 cell embeddings together with the per-node local frames; + only the embeddings are propagated downstream. + """ + + def forward(self, batch): + r"""Forward pass for the Gauge wrapper. + + Parameters + ---------- + batch : torch_geometric.data.Data + Batch object containing the batched data. + + Returns + ------- + dict + Dictionary containing the updated model output. + """ + z, _Q = self.backbone(batch.x_0, batch.edge_index) + + model_out = {"labels": batch.y, "batch_0": batch.batch_0} + model_out["x_0"] = z + + return model_out From 94e79d8cf56e1593e7184f0091b7540e991a6e66 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Wed, 22 Jul 2026 15:42:37 +0200 Subject: [PATCH 13/45] first try of adding a config for the `GaugeModel` --- configs/model/graph/gauge.yaml | 43 ++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 configs/model/graph/gauge.yaml diff --git a/configs/model/graph/gauge.yaml b/configs/model/graph/gauge.yaml new file mode 100644 index 000000000..a267512d2 --- /dev/null +++ b/configs/model/graph/gauge.yaml @@ -0,0 +1,43 @@ +_target_: topobench.model.TBModel + +model_name: gauge +model_domain: graph + +feature_encoder: + _target_: topobench.nn.encoders.${model.feature_encoder.encoder_name} + encoder_name: AllCellFeatureEncoder + in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} + out_channels: 64 + proj_dropout: 0.0 + +backbone: + _target_: topobench.nn.backbones.GaugeModel + n_layers: 4 + in_channels: ${model.feature_encoder.out_channels} + d_embedd: ${model.feature_encoder.out_channels} # must match out_channels (residual + readout) + r: 4 # frame dim; must satisfy r <= d_embedd + n_gated: 2 + gamma: 0.01 + tau: 1.0 + bias: true + phi_hidden_layers: 1 # null -> disable residual (reference behavior) + phi_hidden_dim: null # null -> defaults to d_embedd + +backbone_wrapper: + _target_: topobench.nn.wrappers.GaugeWrapper + _partial_: true + wrapper_name: GaugeWrapper + out_channels: ${model.feature_encoder.out_channels} + num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} + +readout: + _target_: topobench.nn.readouts.${model.readout.readout_name} + readout_name: NoReadOut # Use in case readout is not needed Options: PropagateSignalDown + num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider + hidden_dim: ${model.feature_encoder.out_channels} + out_channels: ${dataset.parameters.num_classes} + task_level: ${define_task_level:${dataset.parameters.task_level},${dataset.split_params.learning_setting}} # Handles the edge case of node-inductive task + pooling_type: sum + +# compile model for faster training with pytorch 2.0 +compile: false From 1e7ed5c99ae629319f1ec9a480f583e8098ddb60 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Wed, 22 Jul 2026 16:34:23 +0200 Subject: [PATCH 14/45] fix: stick to paper defaults --- configs/model/graph/gauge.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/configs/model/graph/gauge.yaml b/configs/model/graph/gauge.yaml index a267512d2..1daf52982 100644 --- a/configs/model/graph/gauge.yaml +++ b/configs/model/graph/gauge.yaml @@ -7,15 +7,15 @@ feature_encoder: _target_: topobench.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} - out_channels: 64 + out_channels: 512 proj_dropout: 0.0 backbone: _target_: topobench.nn.backbones.GaugeModel - n_layers: 4 + n_layers: 2 in_channels: ${model.feature_encoder.out_channels} d_embedd: ${model.feature_encoder.out_channels} # must match out_channels (residual + readout) - r: 4 # frame dim; must satisfy r <= d_embedd + r: 16 # frame dim; must satisfy r <= d_embedd n_gated: 2 gamma: 0.01 tau: 1.0 From ec6f2de010bb16a86558eea97cf7d1dcd1ee4e16 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 12:21:44 +0200 Subject: [PATCH 15/45] test: add `gauge` to pipeline test --- test/pipeline/test_pipeline.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/pipeline/test_pipeline.py b/test/pipeline/test_pipeline.py index a61165ae9..d7179002e 100644 --- a/test/pipeline/test_pipeline.py +++ b/test/pipeline/test_pipeline.py @@ -7,7 +7,7 @@ DATASET = "graph/MUTAG" # ADD YOUR DATASET HERE -MODELS = ["graph/gcn", "cell/topotune", "simplicial/topotune"] # ADD ONE OR SEVERAL MODELS +MODELS = ["graph/gauge", "graph/gcn", "cell/topotune", "simplicial/topotune"] # ADD ONE OR SEVERAL MODELS class TestPipeline: From e9b7a7aa6d39d399552805937eb416bcaa8b2d4d Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 12:36:08 +0200 Subject: [PATCH 16/45] fix: use `activation_dict` in `FFBlock` --- topobench/nn/backbones/graph/gauge.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index c73b4f5cb..3372cd583 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -216,11 +216,12 @@ def forward(self, Z: Tensor) -> Tensor: class FFBlock(nn.Module): - """Feed-forward block with GELU activations, dropout and a final LayerNorm. + """Feed-forward block with activations, dropout and a final LayerNorm. - The block consists of ``n_hidden_layers`` hidden linear layers with GELU - activations followed by an output linear layer, with dropout applied after - every layer and layer normalization applied to the output. + The block consists of ``n_hidden_layers`` hidden linear layers with an + activation resolved via ``activation_dict`` followed by an output linear + layer, with dropout applied after every layer and layer normalization + applied to the output. Parameters ---------- @@ -236,6 +237,9 @@ class FFBlock(nn.Module): Dropout probability (default: 0.3). bias : bool, optional Whether the linear layers use a bias term (default: True). + act : str, optional + Name of the activation applied after each hidden layer, resolved via + ``activation_dict`` (default: "gelu"). """ def __init__( @@ -246,6 +250,7 @@ def __init__( n_hidden_layers: int = 1, drop: float = 0.3, bias: bool = True, + act: str = "gelu", ): super().__init__() @@ -255,6 +260,7 @@ def __init__( self.hidden_dimension = hidden_dim self.bias = bias self.n_hidden_layers = n_hidden_layers + self.act = act assert self.n_hidden_layers >= 1 @@ -267,7 +273,7 @@ def __init__( self.in_channels, self.hidden_dimension, bias=self.bias ) ) - els.append(nn.GELU()) + els.append(activation_dict[self.act]()) elif layer_index < self.n_hidden_layers: els.append( @@ -277,7 +283,7 @@ def __init__( bias=self.bias, ) ) - els.append(nn.GELU()) + els.append(activation_dict[self.act]()) else: els.append( From 847225d6c4f2174648d9f5393d3e67aee6e9cab8 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 12:36:28 +0200 Subject: [PATCH 17/45] test: add unit tests for new classes --- test/nn/backbones/graph/test_gauge.py | 544 ++++++++++++++++++++++++++ 1 file changed, 544 insertions(+) create mode 100644 test/nn/backbones/graph/test_gauge.py diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py new file mode 100644 index 000000000..e0e341c64 --- /dev/null +++ b/test/nn/backbones/graph/test_gauge.py @@ -0,0 +1,544 @@ +"""Unit tests for the gauge-equivariant graph backbone.""" + +import math + +import pytest +import torch +import torch_geometric +from torch import nn + +from topobench.nn.backbones.graph.gauge import ( + FFBlock, + GatedFlatteningLayer, + GaugeLayer, + GaugeModel, + LocalCoordinatesLayer, + MultiHeadFF, + MultiHeadLinear, + NodeUpdateLayer, + activation_dict, +) +from topobench.nn.wrappers.graph import GaugeWrapper + + +def _is_orthonormal(Q, atol=1e-5): + """Check that the frames in ``Q`` have orthonormal rows. + + Parameters + ---------- + Q : torch.Tensor + Per-node frames of shape ``[N, r, d]``. + atol : float, optional + Absolute tolerance for the identity comparison (default: 1e-5). + + Returns + ------- + bool + True if ``Q @ Q^T`` equals the ``r x r`` identity for every node. + """ + N, r, _ = Q.shape + gram = Q @ Q.transpose(-2, -1) + eye = torch.eye(r).expand(N, r, r) + return torch.allclose(gram, eye, atol=atol) + + +class TestMultiHeadLinear: + """Tests for the per-head linear layer.""" + + def test_forward_shape(self): + """Output has shape ``[N, r, out_channels]``.""" + layer = MultiHeadLinear(in_channels=4, out_channels=6, r_dim=3) + Z = torch.randn(10, 3, 4) + out = layer(Z) + assert out.shape == (10, 3, 6) + + def test_parameter_shapes(self): + """Stacked weight and bias have the expected per-head shapes.""" + layer = MultiHeadLinear(in_channels=4, out_channels=6, r_dim=3) + assert layer.superW.shape == (3, 6, 4) + assert layer.superB.shape == (3, 6) + + def test_no_bias(self): + """With ``bias=False`` no bias parameter is registered.""" + layer = MultiHeadLinear( + in_channels=4, out_channels=6, r_dim=3, bias=False + ) + assert layer.superB is None + Z = torch.randn(5, 3, 4) + assert layer(Z).shape == (5, 3, 6) + + def test_heads_are_independent(self): + """Each head applies its own weight matrix and bias. + + The batched ``einsum`` must agree with applying each head's linear map + one at a time. + """ + layer = MultiHeadLinear(in_channels=4, out_channels=6, r_dim=3) + Z = torch.randn(10, 3, 4) + out = layer(Z) + for h in range(3): + expected = Z[:, h, :] @ layer.superW[h].T + layer.superB[h] + assert torch.allclose(out[:, h, :], expected, atol=1e-5) + + def test_permuting_one_head_leaves_others_untouched(self): + """Changing a head's input only changes that head's output.""" + layer = MultiHeadLinear( + in_channels=4, out_channels=6, r_dim=3, bias=False + ) + Z = torch.randn(10, 3, 4) + out = layer(Z) + Z2 = Z.clone() + Z2[:, 1, :] = torch.randn(10, 4) + out2 = layer(Z2) + assert torch.allclose(out[:, 0, :], out2[:, 0, :]) + assert torch.allclose(out[:, 2, :], out2[:, 2, :]) + assert not torch.allclose(out[:, 1, :], out2[:, 1, :]) + + def test_reset_parameters_bound(self): + """Weights are initialized within ``1 / sqrt(in_channels)``.""" + in_channels = 9 + layer = MultiHeadLinear( + in_channels=in_channels, out_channels=6, r_dim=3 + ) + bound = 1 / math.sqrt(in_channels) + assert layer.superW.abs().max().item() <= bound + 1e-6 + assert layer.superB.abs().max().item() <= bound + 1e-6 + + def test_reset_parameters_changes_weights(self): + """Calling ``reset_parameters`` re-samples the weights.""" + layer = MultiHeadLinear(in_channels=4, out_channels=6, r_dim=3) + before = layer.superW.clone() + layer.reset_parameters() + assert not torch.allclose(before, layer.superW) + + +class TestMultiHeadFF: + """Tests for the per-head feed-forward network.""" + + def test_single_layer_shape(self): + """With ``hidden_dims=None`` the network is a single per-head map.""" + net = MultiHeadFF(in_channels=4, out_channels=2, r=3) + # exactly one MultiHeadLinear, no activation / dropout + assert len(net.model) == 1 + assert isinstance(net.model[0], MultiHeadLinear) + Z = torch.randn(7, 3, 4) + assert net(Z).shape == (7, 3, 2) + + def test_multi_layer_shape(self): + """Hidden dims add intermediate per-head layers.""" + net = MultiHeadFF( + in_channels=4, out_channels=2, r=3, hidden_dims=[8, 8] + ) + Z = torch.randn(7, 3, 4) + assert net(Z).shape == (7, 3, 2) + + def test_no_activation_after_output(self): + """Activation and dropout appear only between layers.""" + net = MultiHeadFF(in_channels=4, out_channels=2, r=3, hidden_dims=[8]) + # Sequence: Linear, Act, Dropout, Linear -> last module is Linear + assert isinstance(net.model[-1], MultiHeadLinear) + linears = [m for m in net.model if isinstance(m, MultiHeadLinear)] + assert len(linears) == 2 + + @pytest.mark.parametrize("act", list(activation_dict.keys())) + def test_activation_choice(self, act): + """Every activation in ``activation_dict`` is wired up correctly. + + Parameters + ---------- + act : str + Name of the activation function to test. + """ + net = MultiHeadFF( + in_channels=4, out_channels=2, r=3, hidden_dims=[8], act=act + ) + acts = [m for m in net.model if isinstance(m, activation_dict[act])] + assert len(acts) == 1 + Z = torch.randn(7, 3, 4) + assert net(Z).shape == (7, 3, 2) + + +class TestFFBlock: + """Tests for the feed-forward block.""" + + def test_forward_shape(self): + """Output has the requested number of channels.""" + block = FFBlock(in_channels=4, out_channels=8, hidden_dim=16) + x = torch.randn(5, 4) + assert block(x).shape == (5, 8) + + def test_norm_on_input_channels(self): + """The pre-norm LayerNorm normalizes over ``in_channels``.""" + block = FFBlock(in_channels=4, out_channels=8, hidden_dim=16) + assert block.norm.normalized_shape == (4,) + + def test_extra_leading_dims(self): + """The block broadcasts over arbitrary leading dimensions.""" + block = FFBlock(in_channels=4, out_channels=8, hidden_dim=16) + x = torch.randn(5, 3, 4) + assert block(x).shape == (5, 3, 8) + + def test_requires_at_least_one_hidden_layer(self): + """Fewer than one hidden layer is rejected.""" + with pytest.raises(AssertionError): + FFBlock( + in_channels=4, out_channels=8, hidden_dim=16, n_hidden_layers=0 + ) + + @pytest.mark.parametrize("n_hidden_layers", [1, 2, 3]) + def test_num_hidden_layers(self, n_hidden_layers): + """Multiple hidden layers still produce the right output shape. + + Parameters + ---------- + n_hidden_layers : int + Number of hidden layers to configure. + """ + block = FFBlock( + in_channels=4, + out_channels=8, + hidden_dim=16, + n_hidden_layers=n_hidden_layers, + ) + x = torch.randn(5, 4) + assert block(x).shape == (5, 8) + + def test_default_activation_is_gelu(self): + """The default activation stays GELU for backward compatibility.""" + block = FFBlock(in_channels=4, out_channels=8, hidden_dim=16) + acts = [m for m in block.model if isinstance(m, nn.GELU)] + assert len(acts) == 1 + + @pytest.mark.parametrize("act", list(activation_dict.keys())) + def test_activation_choice(self, act): + """Every activation in ``activation_dict`` is wired up correctly. + + Parameters + ---------- + act : str + Name of the activation function to test. + """ + block = FFBlock( + in_channels=4, + out_channels=8, + hidden_dim=16, + n_hidden_layers=2, + act=act, + ) + acts = [m for m in block.model if isinstance(m, activation_dict[act])] + # one activation per hidden layer + assert len(acts) == 2 + x = torch.randn(5, 4) + assert block(x).shape == (5, 8) + + +class TestLocalCoordinatesLayer: + """Tests for the local coordinate (frame) layer.""" + + def test_output_shape(self, simple_graph_0): + """The layer returns one ``[r, d]`` frame per node. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + d, r = 8, 3 + layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + Z = torch.randn(simple_graph_0.num_nodes, d) + Q = layer(Z, simple_graph_0.edge_index) + assert Q.shape == (simple_graph_0.num_nodes, r, d) + + def test_frames_orthonormal(self, simple_graph_0): + """The QR step yields orthonormal per-node frames. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + d, r = 8, 3 + layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + Z = torch.randn(simple_graph_0.num_nodes, d) + Q = layer(Z, simple_graph_0.edge_index) + assert _is_orthonormal(Q) + + def test_no_nan(self, simple_graph_0): + """Output frames are finite. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + d, r = 8, 4 + layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + Z = torch.randn(simple_graph_0.num_nodes, d) + Q = layer(Z, simple_graph_0.edge_index) + assert not torch.isnan(Q).any() + assert not torch.isinf(Q).any() + + +class TestGatedFlatteningLayer: + """Tests for the gated flattening (frame smoothing) layer.""" + + def test_output_shape_and_orthonormality(self, simple_graph_0): + """Smoothing preserves the shape and orthonormality of the frames. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + d, r = 8, 3 + N = simple_graph_0.num_nodes + # Build orthonormal input frames via the local-coords layer. + coords = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + Q = coords(torch.randn(N, d), simple_graph_0.edge_index) + + gate = GatedFlatteningLayer(r=r) + Qnew = gate(Q, simple_graph_0.edge_index) + assert Qnew.shape == (N, r, d) + assert _is_orthonormal(Qnew) + + def test_no_learnable_parameters(self): + """The gated flattening layer is parameter-free.""" + gate = GatedFlatteningLayer(r=3) + assert list(gate.parameters()) == [] + + +class TestNodeUpdateLayer: + """Tests for the node feature update layer.""" + + def test_output_shape(self, simple_graph_0): + """The update returns ``out_channels`` features per node. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + d, r = 8, 3 + N = simple_graph_0.num_nodes + layer = NodeUpdateLayer(in_channels=d, out_channels=d) + Z = torch.randn(N, d) + Q = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d)( + Z, simple_graph_0.edge_index + ) + out = layer(Z, Q, simple_graph_0.edge_index) + assert out.shape == (N, d) + + def test_residual_disabled(self, simple_graph_0): + """``phi_hidden_layers=None`` disables the residual MLP. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + d, r = 8, 3 + N = simple_graph_0.num_nodes + layer = NodeUpdateLayer( + in_channels=d, out_channels=d, phi_hidden_layers=None + ) + assert layer.phi is None + Z = torch.randn(N, d) + Q = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d)( + Z, simple_graph_0.edge_index + ) + assert layer(Z, Q, simple_graph_0.edge_index).shape == (N, d) + + def test_residual_enabled(self, simple_graph_0): + """The residual MLP is built when enabled. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + layer = NodeUpdateLayer( + in_channels=8, out_channels=8, phi_hidden_layers=1 + ) + assert isinstance(layer.phi, FFBlock) + + +class TestGaugeLayer: + """Tests for a single gauge message-passing layer.""" + + def test_output_shapes(self, simple_graph_0): + """The layer returns updated features and orthonormal frames. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + d, r = 8, 3 + N = simple_graph_0.num_nodes + layer = GaugeLayer(d_embedd=d, r=r, n_gated=2) + x = torch.randn(N, d) + Znew, Q = layer(x, simple_graph_0.edge_index) + assert Znew.shape == (N, d) + assert Q.shape == (N, r, d) + assert _is_orthonormal(Q) + + @pytest.mark.parametrize("n_gated", [0, 1, 3]) + def test_num_gated_layers(self, simple_graph_0, n_gated): + """The number of gated flattening sublayers is configurable. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + n_gated : int + Number of gated flattening layers to stack. + """ + d, r = 8, 3 + layer = GaugeLayer(d_embedd=d, r=r, n_gated=n_gated) + assert len(layer.gated_flattening_layers) == n_gated + x = torch.randn(simple_graph_0.num_nodes, d) + Znew, Q = layer(x, simple_graph_0.edge_index) + assert _is_orthonormal(Q) + + +class TestGaugeModel: + """Tests for the full gauge model.""" + + def test_forward_shapes(self, simple_graph_0): + """The model maps input features to embeddings and final frames. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + in_channels, d, r = 5, 8, 3 + N = simple_graph_0.num_nodes + model = GaugeModel( + n_layers=2, in_channels=in_channels, r=r, d_embedd=d + ) + x = torch.randn(N, in_channels) + z, Q = model(x, simple_graph_0.edge_index) + assert z.shape == (N, d) + assert Q.shape == (N, r, d) + assert _is_orthonormal(Q) + + @pytest.mark.parametrize("n_layers", [1, 2, 4]) + def test_num_layers(self, simple_graph_0, n_layers): + """The model stacks the requested number of gauge layers. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + n_layers : int + Number of gauge layers to stack. + """ + in_channels, d, r = 5, 8, 3 + model = GaugeModel( + n_layers=n_layers, in_channels=in_channels, r=r, d_embedd=d + ) + assert len(model.layers) == n_layers + x = torch.randn(simple_graph_0.num_nodes, in_channels) + z, _ = model(x, simple_graph_0.edge_index) + assert z.shape == (simple_graph_0.num_nodes, d) + + def test_no_nan(self, simple_graph_0): + """The final embeddings are finite. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + model = GaugeModel(n_layers=2, in_channels=5, r=3, d_embedd=8) + x = torch.randn(simple_graph_0.num_nodes, 5) + z, Q = model(x, simple_graph_0.edge_index) + assert not torch.isnan(z).any() and not torch.isinf(z).any() + assert not torch.isnan(Q).any() and not torch.isinf(Q).any() + + def test_backward_pass(self, simple_graph_0): + """Gradients flow back to the input and the parameters. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + model = GaugeModel(n_layers=2, in_channels=5, r=3, d_embedd=8) + x = torch.randn(simple_graph_0.num_nodes, 5, requires_grad=True) + z, _ = model(x, simple_graph_0.edge_index) + z.sum().backward() + assert x.grad is not None + has_grad = any( + p.grad is not None and p.grad.abs().sum() > 0 + for p in model.parameters() + if p.requires_grad + ) + assert has_grad + + def test_deterministic_in_eval(self, simple_graph_0): + """Two eval-mode passes on the same input agree. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + model = GaugeModel(n_layers=2, in_channels=5, r=3, d_embedd=8) + model.eval() + x = torch.randn(simple_graph_0.num_nodes, 5) + z1, _ = model(x, simple_graph_0.edge_index) + z2, _ = model(x, simple_graph_0.edge_index) + assert torch.allclose(z1, z2) + + def test_batched_graphs(self, simple_graph_0, simple_graph_1): + """The model handles a batch of disconnected graphs. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + First test graph fixture. + simple_graph_1 : torch_geometric.data.Data + Second test graph fixture. + """ + batch = torch_geometric.data.Batch.from_data_list( + [simple_graph_0, simple_graph_1] + ) + n_total = simple_graph_0.num_nodes + simple_graph_1.num_nodes + model = GaugeModel(n_layers=2, in_channels=5, r=3, d_embedd=8) + x = torch.randn(n_total, 5) + z, _ = model(x, batch.edge_index) + assert z.shape == (n_total, 8) + + +class TestGaugeWrapper: + """Tests for the topobench wrapper around the gauge model.""" + + def test_forward(self, simple_graph_0): + """The wrapper forwards node embeddings as ``x_0``. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + # The wrapper adds a residual (batch.x_0 + model output), so the input + # width must match the embedding width ``d``. + in_channels = d = 8 + r = 3 + N = simple_graph_0.num_nodes + model = GaugeModel( + n_layers=2, in_channels=in_channels, r=r, d_embedd=d + ) + wrapper = GaugeWrapper(model, out_channels=d, num_cell_dimensions=1) + batch = torch_geometric.data.Data( + x_0=torch.randn(N, in_channels), + edge_index=simple_graph_0.edge_index, + y=simple_graph_0.y, + batch_0=torch.zeros(N, dtype=torch.long), + ) + model_out = wrapper(batch) + assert model_out["x_0"].shape == (N, d) + assert "labels" in model_out + assert "batch_0" in model_out From 09936de0319878dddd16e917959babdc9c0d4f74 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 12:54:46 +0200 Subject: [PATCH 18/45] fix: make activation functions configurable --- configs/model/graph/gauge.yaml | 2 + test/nn/backbones/graph/test_gauge.py | 57 +++++++++++++++++++++++++++ topobench/nn/backbones/graph/gauge.py | 55 ++++++++++++++++++++++++-- 3 files changed, 111 insertions(+), 3 deletions(-) diff --git a/configs/model/graph/gauge.yaml b/configs/model/graph/gauge.yaml index 1daf52982..c00d33dc4 100644 --- a/configs/model/graph/gauge.yaml +++ b/configs/model/graph/gauge.yaml @@ -20,6 +20,8 @@ backbone: gamma: 0.01 tau: 1.0 bias: true + act: gelu # feed-forward activation: relu|leaky_relu|gelu|sigmoid + f_sim_act: leaky_relu # similarity-scorer (f_sim) activation: relu|leaky_relu|gelu|sigmoid phi_hidden_layers: 1 # null -> disable residual (reference behavior) phi_hidden_dim: null # null -> defaults to d_embedd diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index e0e341c64..b2ca1f937 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -511,6 +511,63 @@ def test_batched_graphs(self, simple_graph_0, simple_graph_1): z, _ = model(x, batch.edge_index) assert z.shape == (n_total, 8) + @pytest.mark.parametrize("act", list(activation_dict.keys())) + def test_activation_propagates_to_ffblocks(self, act): + """The ``act`` argument reaches every feed-forward block. + + The activation must flow from the model down to the ``fflayer`` of each + local-coordinates layer and the ``phi`` residual of each node update. + + Parameters + ---------- + act : str + Name of the activation function to test. + """ + model = GaugeModel(n_layers=2, in_channels=5, r=3, d_embedd=8, act=act) + expected = activation_dict[act] + for layer in model.layers: + fflayer = layer.local_coords_layer.fflayer + assert any(isinstance(m, expected) for m in fflayer.model) + phi = layer.node_update_layer.phi + assert any(isinstance(m, expected) for m in phi.model) + + def test_f_sim_act_defaults_to_leaky_relu(self): + """The similarity scorer defaults to LeakyReLU, independent of ``act``.""" + model = GaugeModel( + n_layers=2, in_channels=5, r=3, d_embedd=8, act="gelu" + ) + for layer in model.layers: + f_sim = layer.local_coords_layer.f_sim + assert any(isinstance(m, nn.LeakyReLU) for m in f_sim.model) + + @pytest.mark.parametrize("f_sim_act", list(activation_dict.keys())) + def test_f_sim_act_propagates(self, f_sim_act): + """The ``f_sim_act`` argument reaches every similarity network. + + The knob is independent of ``act``: it must only affect ``f_sim``, not + the ``fflayer`` feed-forward blocks. + + Parameters + ---------- + f_sim_act : str + Name of the similarity-scorer activation to test. + """ + model = GaugeModel( + n_layers=2, + in_channels=5, + r=3, + d_embedd=8, + act="gelu", + f_sim_act=f_sim_act, + ) + expected = activation_dict[f_sim_act] + for layer in model.layers: + f_sim = layer.local_coords_layer.f_sim + assert any(isinstance(m, expected) for m in f_sim.model) + # ``act`` still governs the feed-forward block independently. + fflayer = layer.local_coords_layer.fflayer + assert any(isinstance(m, nn.GELU) for m in fflayer.model) + class TestGaugeWrapper: """Tests for the topobench wrapper around the gauge model.""" diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 3372cd583..9a2e6cf09 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -338,6 +338,12 @@ class LocalCoordinatesLayer(torch.nn.Module): Temperature used to scale the attention logits (default: 1.0). bias : bool, optional Whether the linear layers use a bias term (default: True). + act : str, optional + Name of the activation used by the feed-forward block, resolved via + ``activation_dict`` (default: "gelu"). + f_sim_act : str, optional + Name of the activation used by the per-head similarity network + ``f_sim``, resolved via ``activation_dict`` (default: "leaky_relu"). """ # eqns. 2-4 @@ -347,6 +353,8 @@ def __init__( d_embedd: int, tau: float = 1.0, bias: bool = True, + act: str = "gelu", + f_sim_act: str = "leaky_relu", ): super().__init__() @@ -354,6 +362,8 @@ def __init__( self.tau = tau self.d = d_embedd self.bias = bias + self.act = act + self.f_sim_act = f_sim_act # combine the projectors into a single nn.Linear layer, reshape afterwards! self.initial_projector = torch.nn.Linear( @@ -362,10 +372,16 @@ def __init__( # f_sim = f, computing similarity of node features self.f_sim = MultiHeadFF( - 2 * self.d, 1, r=self.r, hidden_dims=[2 * self.d] + 2 * self.d, + 1, + r=self.r, + hidden_dims=[2 * self.d], + act=self.f_sim_act, ) - self.fflayer = FFBlock(self.d, self.d, self.d, bias=self.bias) + self.fflayer = FFBlock( + self.d, self.d, self.d, bias=self.bias, act=self.act + ) self.preqr_norm = nn.LayerNorm(self.d) def forward(self, Z: Tensor, edge_index: Tensor) -> Tensor: @@ -532,6 +548,9 @@ class NodeUpdateLayer(torch.nn.Module): phi_hidden_dim : int or None, optional Hidden width of the residual MLP ``phi``. Defaults to ``max(in_channels, out_channels)`` when ``None`` (default: None). + act : str, optional + Name of the activation used by the residual MLP ``phi``, resolved via + ``activation_dict`` (default: "gelu"). """ # eqns. 9-10 @@ -541,6 +560,7 @@ def __init__( out_channels: int, phi_hidden_layers: int | None = 1, phi_hidden_dim: int | None = None, + act: str = "gelu", ): super().__init__() @@ -554,6 +574,7 @@ def __init__( if phi_hidden_dim is not None else max(in_channels, out_channels), n_hidden_layers=phi_hidden_layers, + act=act, ) # this is the learnable matrix applied to tilde(z) @@ -640,6 +661,12 @@ class GaugeLayer(torch.nn.Module): phi_hidden_dim : int or None, optional Hidden width of the residual MLP ``phi``. Defaults to ``d_embedd`` when ``None`` (default: None). + act : str, optional + Name of the activation used by the feed-forward blocks, resolved via + ``activation_dict`` (default: "gelu"). + f_sim_act : str, optional + Name of the activation used by the per-head similarity network + ``f_sim``, resolved via ``activation_dict`` (default: "leaky_relu"). """ def __init__( @@ -652,6 +679,8 @@ def __init__( bias=True, phi_hidden_layers: int | None = 1, phi_hidden_dim: int | None = None, + act: str = "gelu", + f_sim_act: str = "leaky_relu", ): super().__init__() @@ -661,9 +690,16 @@ def __init__( self.n_gated = n_gated self.gamma = gamma self.d_embedd = d_embedd + self.act = act + self.f_sim_act = f_sim_act self.local_coords_layer = LocalCoordinatesLayer( - r_subspaces=r, d_embedd=d_embedd, tau=tau, bias=bias + r_subspaces=r, + d_embedd=d_embedd, + tau=tau, + bias=bias, + act=act, + f_sim_act=f_sim_act, ) self.gated_flattening_layers = nn.ModuleList( @@ -678,6 +714,7 @@ def __init__( self.d_embedd, phi_hidden_layers=phi_hidden_layers, phi_hidden_dim=phi_hidden_dim, + act=act, ) def forward(self, x: Tensor, edge_index: Tensor) -> tuple[Tensor, Tensor]: @@ -742,6 +779,12 @@ class GaugeModel(nn.Module): phi_hidden_dim : int or None, optional Hidden width of the residual MLP ``phi``. Defaults to ``d_embedd`` when ``None`` (default: None). + act : str, optional + Name of the activation used by the feed-forward blocks, resolved via + ``activation_dict`` (default: "gelu"). + f_sim_act : str, optional + Name of the activation used by the per-head similarity network + ``f_sim``, resolved via ``activation_dict`` (default: "leaky_relu"). """ def __init__( @@ -756,6 +799,8 @@ def __init__( bias=True, phi_hidden_layers: int | None = 1, phi_hidden_dim: int | None = None, + act: str = "gelu", + f_sim_act: str = "leaky_relu", ): super().__init__() @@ -767,6 +812,8 @@ def __init__( self.r = r self.d_embedd = d_embedd self.n_gated = n_gated + self.act = act + self.f_sim_act = f_sim_act self.input_projector = nn.Sequential( nn.Linear(in_channels, d_embedd), nn.LayerNorm(d_embedd) @@ -783,6 +830,8 @@ def __init__( bias=bias, phi_hidden_layers=phi_hidden_layers, phi_hidden_dim=phi_hidden_dim, + act=self.act, + f_sim_act=self.f_sim_act, ) for _ in range(self.n_layers) ] From 858691377691cb173cd40980e13138f5b4423a28 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 13:02:27 +0200 Subject: [PATCH 19/45] fix: better comments --- topobench/nn/backbones/graph/gauge.py | 87 +++++++++++++-------------- 1 file changed, 42 insertions(+), 45 deletions(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 9a2e6cf09..a7117835d 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -346,7 +346,7 @@ class LocalCoordinatesLayer(torch.nn.Module): ``f_sim``, resolved via ``activation_dict`` (default: "leaky_relu"). """ - # eqns. 2-4 + # Equations (2)-(4). def __init__( self, r_subspaces: int, @@ -365,12 +365,14 @@ def __init__( self.act = act self.f_sim_act = f_sim_act - # combine the projectors into a single nn.Linear layer, reshape afterwards! + # Combine the per-subspace projectors into a single nn.Linear layer; + # reshape into r separate projectors afterwards. self.initial_projector = torch.nn.Linear( self.d, self.d * self.r, bias=self.bias ) - # f_sim = f, computing similarity of node features + # f_sim is the learnable function f that scores the similarity of + # neighboring node features (one score per subspace). self.f_sim = MultiHeadFF( 2 * self.d, 1, @@ -408,38 +410,38 @@ def forward(self, Z: Tensor, edge_index: Tensor) -> Tensor: N = Z.size(0) # num_nodes src, dst = edge_index[0], edge_index[1] - # nr. 1: we project the input matrix x into r different subspaces + # Project the node embeddings into r different subspaces. Zh = self.initial_projector(Z) # [N, r*d] Zh = Zh.reshape(N, self.r, self.d) # [N, r, d] - # EQUATION no. (3) - # f_vals has shape [N, r, 1] + # Equation (3): score each edge per subspace; f_vals has shape [E, r, 1]. f_vals = ( self.f_sim(torch.concat((Zh[src], Zh[dst]), dim=-1)) / self.tau ) - f_vals = f_vals.squeeze(-1) # remove last singleton dimension + f_vals = f_vals.squeeze(-1) # drop the trailing singleton -> [E, r] alphas = torch.softmax(f_vals, dim=-1).unsqueeze(-1) # [E, r, 1] - # EQUATION no. (2) + # Equation (2): aggregate the weighted neighbor projections per node. out = scatter_add( alphas * Zh[src, :, :], index=dst, dim=0, dim_size=N - ) # tensor of shape (E, r, d) + ) # [N, r, d] - # we need to clamp as nodes with degree 0 would have a scatter_add of 0 - # this should give us a tensor of shape [N, r] + # Normalize by the aggregated weights; clamp so that degree-0 nodes + # (whose scatter_add is 0) do not produce a division by zero. [N, r, 1] norm = 1 / (scatter_add(alphas, dst, dim=0, dim_size=N).clamp(1e-6)) - # norm*out should be [N, r, d] with norm broadcasted along the last dimension (d) - # norm*out is of shape [N,r,d] while Z is of shape [N,d], hence we insert a new axis at -2 + # norm * out broadcasts norm along the last dimension (d) to give + # [N, r, d]. Z is [N, d], so we insert a subspace axis at -2 before + # subtracting. qhat = Z.unsqueeze(-2) - norm * out - # feedforward followed by a LayerNorm, then QR (eq. 4) + # Feed-forward followed by a LayerNorm, then QR (equation (4)). qhat = self.fflayer(qhat) qhat = self.preqr_norm(qhat) - # EQUATION no. (4) - # xx has shape [N, r, d] so now we can do the QR decomposition to obtain an orthonormal basis + # Equation (4): qhat is [N, r, d]; transpose to [N, d, r] and apply QR + # to obtain an orthonormal basis per node. Q, _ = torch.linalg.qr(qhat.mT) return Q.mT @@ -465,8 +467,7 @@ class GatedFlatteningLayer(nn.Module): Temperature used to scale the gating logits (default: 1.0). """ - # eqns.5-8 - + # Equations (5)-(8). def __init__(self, r: int, gamma: float = 0.01, tau: float = 1.0): super().__init__() @@ -494,32 +495,31 @@ def forward(self, Q: Tensor, edge_index: Tensor) -> Tensor: Tensor Smoothed per-node orthonormal frames of shape ``[N, r, d]``. """ - # eqns. (6-8) - + # Equations (6)-(8). N = Q.size(0) # num_nodes src, dst = edge_index[0], edge_index[1] k = Q.size(-2) # with k fixed, the trace of eye(k) = k - # EQUATION no. (6) - # the trace of the identity of size k = k + # Equation (6): gating weight from the overlap of neighboring frames. + # The trace of the k-by-k identity equals k. g_vec = scatter_softmax( ((Q[src] * Q[dst]).sum((-2, -1)) - k) / self.tau, index=dst, dim_size=N, ) - # technical note: in theory wed need to compute gij - # for all pairs of nodes which becomes unnecessary only because we only - # sum over neighbors menaning that non-neighbor entries are irrelevant + # Technical note: in principle g_ij is defined for all pairs of nodes, + # but because we only sum over neighbors, non-neighbor entries never + # contribute and need not be computed. - # EQUATION no. (7) + # Equation (7): blend the original frame with the gated neighbor + # aggregate. Qagg = scatter_add( g_vec[:, None, None] * Q[src], dim=0, index=dst, dim_size=N ) Qhat = (1 - self.gamma) * Q + self.gamma * Qagg - # EQUATION no. (8) - # lastly we do the QR decomposition again to obtain an orthonormal basis: + # Equation (8): re-orthonormalize the blended frame with another QR. Qnew, _ = torch.linalg.qr(Qhat.mT) return Qnew.mT @@ -553,7 +553,7 @@ class NodeUpdateLayer(torch.nn.Module): ``activation_dict`` (default: "gelu"). """ - # eqns. 9-10 + # Equations (9)-(10). def __init__( self, in_channels: int, @@ -565,7 +565,7 @@ def __init__( super().__init__() self.phi = None - # this is the learnable function applied to z (if phi_hidden_layers isn't None) + # Learnable function applied to z (only when phi_hidden_layers is not None). if phi_hidden_layers is not None: self.phi = FFBlock( in_channels=in_channels, @@ -577,7 +577,7 @@ def __init__( act=act, ) - # this is the learnable matrix applied to tilde(z) + # Learnable matrix applied to the frame-projected embedding tilde(z). self.W = torch.nn.Linear(in_channels, out_channels, bias=False) def forward(self, Z: Tensor, Q: Tensor, edge_index: Tensor) -> Tensor: @@ -603,26 +603,23 @@ def forward(self, Z: Tensor, Q: Tensor, edge_index: Tensor) -> Tensor: Updated node embeddings of shape ``[N, out_channels]``. """ - # step 0: bind commonly used values to variable names + # Step 0: bind commonly used values to local names. src, dst = edge_index[0], edge_index[1] - N = Z.size(0) # num_nodes for scatter ops - - # step 1: calculate tilde(z) + N = Z.size(0) # num_nodes, for the scatter ops - # EQUATION no. (9) - # Q has shape [N,r,d] and z has shape [N, d] - # we want to transform each vector in z via the matrix [r,d] batching over the first dimension + # Step 1: compute the frame-projected embedding tilde(z). + # Equation (9): Q is [N, r, d] and Z is [N, d]; project each node + # embedding onto its own frame, batching over the node dimension. QtZ = torch.einsum("ijk,ik->ij", Q, Z) Z_tilde = torch.einsum("ikj, ik->ij", Q, QtZ) Z_tilde = self.W(Z_tilde) - # EQUATION no. (10) - # DIVERGENCE FROM REFERENCE IMPLEMENTATION - # Contrary to the reference implementation we optionally add a "residual - # connection" realized via the self.phi function. It is enabled by - # default and can be disabled (recovering the reference behavior) by - # passing phi_hidden_layers=None, in which case self.phi is None. - # cf. equation (10) + # Equation (10): aggregate the projected embeddings over the neighborhood. + # + # Divergence from the reference implementation: we optionally add a + # residual connection through self.phi. It is enabled by default and can + # be disabled (recovering the reference behavior) by passing + # phi_hidden_layers=None, in which case self.phi is None. Znew = scatter_mean(Z_tilde[src], index=dst, dim=0, dim_size=N) if self.phi is not None: From d8ac0d883e717a5d734a8455e58ad7bdad1cd734 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 13:33:37 +0200 Subject: [PATCH 20/45] fix: make dropout configurable --- configs/model/graph/gauge.yaml | 11 ++++--- test/nn/backbones/graph/test_gauge.py | 44 +++++++++++++++++++++++++++ topobench/nn/backbones/graph/gauge.py | 44 ++++++++++++++++++++++++++- 3 files changed, 94 insertions(+), 5 deletions(-) diff --git a/configs/model/graph/gauge.yaml b/configs/model/graph/gauge.yaml index c00d33dc4..a2a737768 100644 --- a/configs/model/graph/gauge.yaml +++ b/configs/model/graph/gauge.yaml @@ -7,14 +7,14 @@ feature_encoder: _target_: topobench.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} - out_channels: 512 + out_channels: 128 proj_dropout: 0.0 backbone: _target_: topobench.nn.backbones.GaugeModel n_layers: 2 in_channels: ${model.feature_encoder.out_channels} - d_embedd: ${model.feature_encoder.out_channels} # must match out_channels (residual + readout) + d_embedd: 512 # must match out_channels (residual + readout) r: 16 # frame dim; must satisfy r <= d_embedd n_gated: 2 gamma: 0.01 @@ -22,6 +22,8 @@ backbone: bias: true act: gelu # feed-forward activation: relu|leaky_relu|gelu|sigmoid f_sim_act: leaky_relu # similarity-scorer (f_sim) activation: relu|leaky_relu|gelu|sigmoid + dropout: 0.3 # dropout in the feed-forward blocks (fflayer + phi) + f_sim_dropout: 0.0 # dropout in the similarity-scorer (f_sim) phi_hidden_layers: 1 # null -> disable residual (reference behavior) phi_hidden_dim: null # null -> defaults to d_embedd @@ -29,14 +31,15 @@ backbone_wrapper: _target_: topobench.nn.wrappers.GaugeWrapper _partial_: true wrapper_name: GaugeWrapper - out_channels: ${model.feature_encoder.out_channels} + out_channels: ${model.backbone.d_embedd} + residual_connections: false # not part of the paper; also lets d_embedd differ from the encoder width num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} readout: _target_: topobench.nn.readouts.${model.readout.readout_name} readout_name: NoReadOut # Use in case readout is not needed Options: PropagateSignalDown num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} # The highest order of cell dimensions to consider - hidden_dim: ${model.feature_encoder.out_channels} + hidden_dim: ${model.backbone.d_embedd} # readout consumes the backbone output width out_channels: ${dataset.parameters.num_classes} task_level: ${define_task_level:${dataset.parameters.task_level},${dataset.split_params.learning_setting}} # Handles the edge case of node-inductive task pooling_type: sum diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index b2ca1f937..b57ed8380 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -568,6 +568,50 @@ def test_f_sim_act_propagates(self, f_sim_act): fflayer = layer.local_coords_layer.fflayer assert any(isinstance(m, nn.GELU) for m in fflayer.model) + def test_dropout_propagates_to_ffblocks(self): + """The ``dropout`` argument reaches every feed-forward block. + + The probability must flow from the model down to the ``fflayer`` of + each local-coordinates layer and the ``phi`` residual of each node + update. + """ + model = GaugeModel( + n_layers=2, in_channels=5, r=3, d_embedd=8, dropout=0.42 + ) + for layer in model.layers: + fflayer = layer.local_coords_layer.fflayer + phi = layer.node_update_layer.phi + for block in (fflayer, phi): + dropouts = [ + m for m in block.model if isinstance(m, nn.Dropout) + ] + assert dropouts + assert all(m.p == 0.42 for m in dropouts) + + def test_f_sim_dropout_propagates(self): + """The ``f_sim_dropout`` knob reaches ``f_sim`` and is independent. + + It must only affect the similarity network, leaving the feed-forward + block dropout governed by ``dropout``. + """ + model = GaugeModel( + n_layers=2, + in_channels=5, + r=3, + d_embedd=8, + dropout=0.1, + f_sim_dropout=0.5, + ) + for layer in model.layers: + f_sim = layer.local_coords_layer.f_sim + f_sim_drops = [m for m in f_sim.model if isinstance(m, nn.Dropout)] + assert f_sim_drops + assert all(m.p == 0.5 for m in f_sim_drops) + # The feed-forward block keeps its own dropout probability. + fflayer = layer.local_coords_layer.fflayer + ff_drops = [m for m in fflayer.model if isinstance(m, nn.Dropout)] + assert all(m.p == 0.1 for m in ff_drops) + class TestGaugeWrapper: """Tests for the topobench wrapper around the gauge model.""" diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index a7117835d..8b929c744 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -344,6 +344,11 @@ class LocalCoordinatesLayer(torch.nn.Module): f_sim_act : str, optional Name of the activation used by the per-head similarity network ``f_sim``, resolved via ``activation_dict`` (default: "leaky_relu"). + dropout : float, optional + Dropout probability used by the feed-forward block (default: 0.3). + f_sim_dropout : float, optional + Dropout probability used by the per-head similarity network ``f_sim`` + (default: 0.0). """ # Equations (2)-(4). @@ -355,6 +360,8 @@ def __init__( bias: bool = True, act: str = "gelu", f_sim_act: str = "leaky_relu", + dropout: float = 0.3, + f_sim_dropout: float = 0.0, ): super().__init__() @@ -364,6 +371,8 @@ def __init__( self.bias = bias self.act = act self.f_sim_act = f_sim_act + self.dropout = dropout + self.f_sim_dropout = f_sim_dropout # Combine the per-subspace projectors into a single nn.Linear layer; # reshape into r separate projectors afterwards. @@ -379,10 +388,16 @@ def __init__( r=self.r, hidden_dims=[2 * self.d], act=self.f_sim_act, + drop=self.f_sim_dropout, ) self.fflayer = FFBlock( - self.d, self.d, self.d, bias=self.bias, act=self.act + self.d, + self.d, + self.d, + bias=self.bias, + act=self.act, + drop=self.dropout, ) self.preqr_norm = nn.LayerNorm(self.d) @@ -551,6 +566,8 @@ class NodeUpdateLayer(torch.nn.Module): act : str, optional Name of the activation used by the residual MLP ``phi``, resolved via ``activation_dict`` (default: "gelu"). + dropout : float, optional + Dropout probability used by the residual MLP ``phi`` (default: 0.3). """ # Equations (9)-(10). @@ -561,6 +578,7 @@ def __init__( phi_hidden_layers: int | None = 1, phi_hidden_dim: int | None = None, act: str = "gelu", + dropout: float = 0.3, ): super().__init__() @@ -575,6 +593,7 @@ def __init__( else max(in_channels, out_channels), n_hidden_layers=phi_hidden_layers, act=act, + drop=dropout, ) # Learnable matrix applied to the frame-projected embedding tilde(z). @@ -664,6 +683,11 @@ class GaugeLayer(torch.nn.Module): f_sim_act : str, optional Name of the activation used by the per-head similarity network ``f_sim``, resolved via ``activation_dict`` (default: "leaky_relu"). + dropout : float, optional + Dropout probability used by the feed-forward blocks (default: 0.3). + f_sim_dropout : float, optional + Dropout probability used by the per-head similarity network ``f_sim`` + (default: 0.0). """ def __init__( @@ -678,6 +702,8 @@ def __init__( phi_hidden_dim: int | None = None, act: str = "gelu", f_sim_act: str = "leaky_relu", + dropout: float = 0.3, + f_sim_dropout: float = 0.0, ): super().__init__() @@ -689,6 +715,8 @@ def __init__( self.d_embedd = d_embedd self.act = act self.f_sim_act = f_sim_act + self.dropout = dropout + self.f_sim_dropout = f_sim_dropout self.local_coords_layer = LocalCoordinatesLayer( r_subspaces=r, @@ -697,6 +725,8 @@ def __init__( bias=bias, act=act, f_sim_act=f_sim_act, + dropout=dropout, + f_sim_dropout=f_sim_dropout, ) self.gated_flattening_layers = nn.ModuleList( @@ -712,6 +742,7 @@ def __init__( phi_hidden_layers=phi_hidden_layers, phi_hidden_dim=phi_hidden_dim, act=act, + dropout=dropout, ) def forward(self, x: Tensor, edge_index: Tensor) -> tuple[Tensor, Tensor]: @@ -782,6 +813,11 @@ class GaugeModel(nn.Module): f_sim_act : str, optional Name of the activation used by the per-head similarity network ``f_sim``, resolved via ``activation_dict`` (default: "leaky_relu"). + dropout : float, optional + Dropout probability used by the feed-forward blocks (default: 0.3). + f_sim_dropout : float, optional + Dropout probability used by the per-head similarity network ``f_sim`` + (default: 0.0). """ def __init__( @@ -798,6 +834,8 @@ def __init__( phi_hidden_dim: int | None = None, act: str = "gelu", f_sim_act: str = "leaky_relu", + dropout: float = 0.3, + f_sim_dropout: float = 0.0, ): super().__init__() @@ -811,6 +849,8 @@ def __init__( self.n_gated = n_gated self.act = act self.f_sim_act = f_sim_act + self.dropout = dropout + self.f_sim_dropout = f_sim_dropout self.input_projector = nn.Sequential( nn.Linear(in_channels, d_embedd), nn.LayerNorm(d_embedd) @@ -829,6 +869,8 @@ def __init__( phi_hidden_dim=phi_hidden_dim, act=self.act, f_sim_act=self.f_sim_act, + dropout=self.dropout, + f_sim_dropout=self.f_sim_dropout, ) for _ in range(self.n_layers) ] From 5fdd7403cbcad370e96b5df93c9fdfbb70fa878a Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 14:30:13 +0200 Subject: [PATCH 21/45] adjust model name for `run_evaluation.ipynb` --- 2026_tdl_challenge/run_evaluation.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/2026_tdl_challenge/run_evaluation.ipynb b/2026_tdl_challenge/run_evaluation.ipynb index 8542dbaab..ad6c01ef6 100644 --- a/2026_tdl_challenge/run_evaluation.ipynb +++ b/2026_tdl_challenge/run_evaluation.ipynb @@ -98,13 +98,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "config_cell", "metadata": {}, "outputs": [], "source": [ "# Your model configuration (e.g., \"graph/gcn\", \"graph/gin\", \"graph/gat\")\n", - "MODEL_CONFIG = \"graph/gin\"" + "MODEL_CONFIG = \"graph/gauge\"" ] }, { From f1cc28928c13343a79d08224c21cea027662711e Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 15:21:26 +0200 Subject: [PATCH 22/45] small consistency fixes --- configs/model/graph/gauge.yaml | 2 +- test/nn/backbones/graph/test_gauge.py | 33 +++++++++++++++++++++++++++ 2 files changed, 34 insertions(+), 1 deletion(-) diff --git a/configs/model/graph/gauge.yaml b/configs/model/graph/gauge.yaml index a2a737768..ad38ccc55 100644 --- a/configs/model/graph/gauge.yaml +++ b/configs/model/graph/gauge.yaml @@ -14,7 +14,7 @@ backbone: _target_: topobench.nn.backbones.GaugeModel n_layers: 2 in_channels: ${model.feature_encoder.out_channels} - d_embedd: 512 # must match out_channels (residual + readout) + d_embedd: 512 # backbone embedding width; readout consumes this (see readout.hidden_dim). r <= d_embedd r: 16 # frame dim; must satisfy r <= d_embedd n_gated: 2 gamma: 0.01 diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index b57ed8380..126e852e6 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -643,3 +643,36 @@ def test_forward(self, simple_graph_0): assert model_out["x_0"].shape == (N, d) assert "labels" in model_out assert "batch_0" in model_out + + def test_forward_residual_off(self, simple_graph_0): + """With the residual disabled the backbone width may differ from the input. + + This mirrors the shipped config (``residual_connections: false``, + ``out_channels = d_embedd`` != encoder width), so the embedding width is + free to differ from ``in_channels``. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + in_channels, d, r = 5, 8, 3 + N = simple_graph_0.num_nodes + model = GaugeModel( + n_layers=2, in_channels=in_channels, r=r, d_embedd=d + ) + wrapper = GaugeWrapper( + model, + out_channels=d, + num_cell_dimensions=1, + residual_connections=False, + ) + assert wrapper.residual_connections is False + batch = torch_geometric.data.Data( + x_0=torch.randn(N, in_channels), + edge_index=simple_graph_0.edge_index, + y=simple_graph_0.y, + batch_0=torch.zeros(N, dtype=torch.long), + ) + model_out = wrapper(batch) + assert model_out["x_0"].shape == (N, d) From a8dab81afb3873d84dcefc8a9d93314f406d1ec9 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 15:35:33 +0200 Subject: [PATCH 23/45] doc: add comment about divergence from reference implementation --- topobench/nn/backbones/graph/gauge.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 8b929c744..a2f9057f0 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -380,8 +380,14 @@ def __init__( self.d, self.d * self.r, bias=self.bias ) - # f_sim is the learnable function f that scores the similarity of - # neighboring node features (one score per subspace). + # f_sim is the learnable function f of equation (3) that scores the + # similarity of neighboring node features (one score per subspace). + # + # Divergence from the reference implementation: the reference scores + # each edge with a single linear map on the raw embeddings + # (score_lin(cat([x[src], x[dst]])), a Linear(2*d -> r)). Here we use a + # more expressive per-subspace MLP applied to the projected multi-path + # embeddings Zh (shape [E, r, 2*d]), yielding the same [E, r] scores. self.f_sim = MultiHeadFF( 2 * self.d, 1, From 4f50048c1d6dfe1c867ea7d98b6d5e424906e501 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 15:55:41 +0200 Subject: [PATCH 24/45] fix: stick to reference dropout rate --- configs/model/graph/gauge.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configs/model/graph/gauge.yaml b/configs/model/graph/gauge.yaml index ad38ccc55..4b60cc1d1 100644 --- a/configs/model/graph/gauge.yaml +++ b/configs/model/graph/gauge.yaml @@ -22,7 +22,7 @@ backbone: bias: true act: gelu # feed-forward activation: relu|leaky_relu|gelu|sigmoid f_sim_act: leaky_relu # similarity-scorer (f_sim) activation: relu|leaky_relu|gelu|sigmoid - dropout: 0.3 # dropout in the feed-forward blocks (fflayer + phi) + dropout: 0.1 # dropout in the feed-forward blocks (fflayer + phi); reference default f_sim_dropout: 0.0 # dropout in the similarity-scorer (f_sim) phi_hidden_layers: 1 # null -> disable residual (reference behavior) phi_hidden_dim: null # null -> defaults to d_embedd From 299193467b17fb45442bb34b9ba4833de51326a6 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 23 Jul 2026 15:56:21 +0200 Subject: [PATCH 25/45] fix: apply activation function after final layer in `f_sim` --- test/nn/backbones/graph/test_gauge.py | 44 +++++++++++++++++++++++++++ topobench/nn/backbones/graph/gauge.py | 17 ++++++++++- 2 files changed, 60 insertions(+), 1 deletion(-) diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index 126e852e6..8269513c3 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -157,6 +157,24 @@ def test_activation_choice(self, act): Z = torch.randn(7, 3, 4) assert net(Z).shape == (7, 3, 2) + def test_final_activation(self): + """``final_activation`` appends the activation after the output layer.""" + net = MultiHeadFF( + in_channels=4, + out_channels=2, + r=3, + hidden_dims=[8], + act="leaky_relu", + final_activation=True, + ) + # Output layer is followed by the activation, not left bare. + assert isinstance(net.model[-1], nn.LeakyReLU) + # Two activations total: one between layers, one after the output. + acts = [m for m in net.model if isinstance(m, nn.LeakyReLU)] + assert len(acts) == 2 + Z = torch.randn(7, 3, 4) + assert net(Z).shape == (7, 3, 2) + class TestFFBlock: """Tests for the feed-forward block.""" @@ -540,6 +558,32 @@ def test_f_sim_act_defaults_to_leaky_relu(self): f_sim = layer.local_coords_layer.f_sim assert any(isinstance(m, nn.LeakyReLU) for m in f_sim.model) + @pytest.mark.parametrize("f_sim_act", list(activation_dict.keys())) + def test_f_sim_act_applied_to_final_score(self, f_sim_act): + """``f_sim`` ends on its activation, matching the reference score_lin. + + The reference ``score_lin`` (Linear -> activation) activates the score + before the softmax; here ``f_sim`` is built with ``final_activation``, + so the last module of its sequential is the activation. + + Parameters + ---------- + f_sim_act : str + Name of the similarity-scorer activation to test. + """ + model = GaugeModel( + n_layers=2, + in_channels=5, + r=3, + d_embedd=8, + f_sim_act=f_sim_act, + ) + expected = activation_dict[f_sim_act] + for layer in model.layers: + f_sim = layer.local_coords_layer.f_sim + assert f_sim.final_activation is True + assert isinstance(f_sim.model[-1], expected) + @pytest.mark.parametrize("f_sim_act", list(activation_dict.keys())) def test_f_sim_act_propagates(self, f_sim_act): """The ``f_sim_act`` argument reaches every similarity network. diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index a2f9057f0..5044121c0 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -152,6 +152,9 @@ class MultiHeadFF(nn.Module): Dropout probability applied between layers (default: 0.0). bias : bool, optional Whether each per-head linear layer uses a bias term (default: True). + final_activation : bool, optional + Whether to apply the activation after the output layer as well. When + ``False`` the activation is applied only between layers (default: False). """ def __init__( @@ -163,6 +166,7 @@ def __init__( act: str = "leaky_relu", drop: float = 0.0, bias: bool = True, + final_activation: bool = False, ): super().__init__() @@ -172,6 +176,7 @@ def __init__( self.dropout = drop self.r = r self.bias = bias + self.final_activation = final_activation self.layer_sizes = [self.in_channels] @@ -194,6 +199,11 @@ def __init__( els.append(activation_dict[self.act]()) els.append(nn.Dropout(self.dropout)) + # Optionally activate the output as well (e.g. the reference score_lin + # applies its activation to the final score before the softmax). + if self.final_activation: + els.append(activation_dict[self.act]()) + self.model = nn.Sequential(*els) def forward(self, Z: Tensor) -> Tensor: @@ -385,9 +395,11 @@ def __init__( # # Divergence from the reference implementation: the reference scores # each edge with a single linear map on the raw embeddings - # (score_lin(cat([x[src], x[dst]])), a Linear(2*d -> r)). Here we use a + # (score_lin = Sequential(Linear(2*d -> r), LeakyReLU())). Here we use a # more expressive per-subspace MLP applied to the projected multi-path # embeddings Zh (shape [E, r, 2*d]), yielding the same [E, r] scores. + # As in the reference, final_activation applies f_sim_act (LeakyReLU by + # default) to the score itself before the softmax. self.f_sim = MultiHeadFF( 2 * self.d, 1, @@ -395,6 +407,7 @@ def __init__( hidden_dims=[2 * self.d], act=self.f_sim_act, drop=self.f_sim_dropout, + final_activation=True, ) self.fflayer = FFBlock( @@ -436,6 +449,8 @@ def forward(self, Z: Tensor, edge_index: Tensor) -> Tensor: Zh = Zh.reshape(N, self.r, self.d) # [N, r, d] # Equation (3): score each edge per subspace; f_vals has shape [E, r, 1]. + # f_sim ends on its activation (final_activation=True), matching the + # reference score_lin (Linear -> LeakyReLU) before the softmax. f_vals = ( self.f_sim(torch.concat((Zh[src], Zh[dst]), dim=-1)) / self.tau ) From e0802b05fb1d58bab987d3d2158e9d8e56d68fba Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Fri, 24 Jul 2026 14:28:34 +0200 Subject: [PATCH 26/45] fix: switch `f_sim` to 0 hidden layers for reduced computational complexity --- topobench/nn/backbones/graph/gauge.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 5044121c0..966f7cdf7 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -404,7 +404,7 @@ def __init__( 2 * self.d, 1, r=self.r, - hidden_dims=[2 * self.d], + hidden_dims=None, act=self.f_sim_act, drop=self.f_sim_dropout, final_activation=True, From 100d935da4677c723823f6cae50f67ef1078ea56 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Fri, 24 Jul 2026 15:08:24 +0200 Subject: [PATCH 27/45] fix: make residual `phi` a `FFBlock` without hidden layers to reduce computational cost --- configs/model/graph/gauge.yaml | 4 +- test/nn/backbones/graph/test_gauge.py | 17 +++++-- topobench/nn/backbones/graph/gauge.py | 72 +++++++++++++-------------- 3 files changed, 50 insertions(+), 43 deletions(-) diff --git a/configs/model/graph/gauge.yaml b/configs/model/graph/gauge.yaml index 4b60cc1d1..f05844522 100644 --- a/configs/model/graph/gauge.yaml +++ b/configs/model/graph/gauge.yaml @@ -24,8 +24,8 @@ backbone: f_sim_act: leaky_relu # similarity-scorer (f_sim) activation: relu|leaky_relu|gelu|sigmoid dropout: 0.1 # dropout in the feed-forward blocks (fflayer + phi); reference default f_sim_dropout: 0.0 # dropout in the similarity-scorer (f_sim) - phi_hidden_layers: 1 # null -> disable residual (reference behavior) - phi_hidden_dim: null # null -> defaults to d_embedd + phi_hidden_layers: 0 # 0 -> single linear residual; null -> disable residual (reference behavior) + phi_hidden_dim: null # null -> defaults to d_embedd (unused when phi_hidden_layers == 0) backbone_wrapper: _target_: topobench.nn.wrappers.GaugeWrapper diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index 8269513c3..742a11299 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -196,13 +196,24 @@ def test_extra_leading_dims(self): x = torch.randn(5, 3, 4) assert block(x).shape == (5, 3, 8) - def test_requires_at_least_one_hidden_layer(self): - """Fewer than one hidden layer is rejected.""" + def test_rejects_negative_hidden_layers(self): + """A negative number of hidden layers is rejected.""" with pytest.raises(AssertionError): FFBlock( - in_channels=4, out_channels=8, hidden_dim=16, n_hidden_layers=0 + in_channels=4, out_channels=8, hidden_dim=16, n_hidden_layers=-1 ) + def test_zero_hidden_layers_is_single_linear(self): + """With no hidden layers the block is a single linear map.""" + block = FFBlock( + in_channels=4, out_channels=8, hidden_dim=16, n_hidden_layers=0 + ) + linears = [m for m in block.model if isinstance(m, nn.Linear)] + assert len(linears) == 1 + assert linears[0].in_features == 4 + assert linears[0].out_features == 8 + assert block(torch.randn(5, 4)).shape == (5, 8) + @pytest.mark.parametrize("n_hidden_layers", [1, 2, 3]) def test_num_hidden_layers(self, n_hidden_layers): """Multiple hidden layers still produce the right output shape. diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 966f7cdf7..0c3882168 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -242,7 +242,8 @@ class FFBlock(nn.Module): hidden_dim : int Number of hidden units in the intermediate layers. n_hidden_layers : int, optional - Number of hidden layers (must be at least 1) (default: 1). + Number of hidden layers (must be at least 0). With ``0`` the block + reduces to a single ``Linear(in_channels, out_channels)`` (default: 1). drop : float, optional Dropout probability (default: 0.3). bias : bool, optional @@ -272,40 +273,31 @@ def __init__( self.n_hidden_layers = n_hidden_layers self.act = act - assert self.n_hidden_layers >= 1 + assert self.n_hidden_layers >= 0 els = [] - for layer_index in range(self.n_hidden_layers + 1): - if layer_index == 0: - els.append( - nn.Linear( - self.in_channels, self.hidden_dimension, bias=self.bias - ) - ) - els.append(activation_dict[self.act]()) - - elif layer_index < self.n_hidden_layers: - els.append( - nn.Linear( - self.hidden_dimension, - self.hidden_dimension, - bias=self.bias, - ) - ) - els.append(activation_dict[self.act]()) - - else: - els.append( - nn.Linear( - self.hidden_dimension, - self.out_channels, - bias=self.bias, - ) - ) - + # Hidden layers (none when n_hidden_layers == 0). + for layer_index in range(self.n_hidden_layers): + in_dim = ( + self.in_channels if layer_index == 0 else self.hidden_dimension + ) + els.append( + nn.Linear(in_dim, self.hidden_dimension, bias=self.bias) + ) + els.append(activation_dict[self.act]()) els.append(nn.Dropout(self.dropout)) + # Output layer. With no hidden layers this is a single linear map from + # in_channels to out_channels. + out_in_dim = ( + self.in_channels + if self.n_hidden_layers == 0 + else self.hidden_dimension + ) + els.append(nn.Linear(out_in_dim, self.out_channels, bias=self.bias)) + els.append(nn.Dropout(self.dropout)) + self.model = torch.nn.Sequential(*els) self.norm = nn.LayerNorm(self.in_channels) @@ -578,11 +570,13 @@ class NodeUpdateLayer(torch.nn.Module): out_channels : int Number of output features. phi_hidden_layers : int or None, optional - Number of hidden layers of the MLP residual ``phi``. Must be at least 1 - when not ``None``. If ``None`` the residual is disabled (matching the - reference implementation) (default: 1). + Number of hidden layers of the MLP residual ``phi``. With ``0`` the + residual is a single linear map (no hidden layer). If ``None`` the + residual is disabled entirely (matching the reference implementation) + (default: 1). phi_hidden_dim : int or None, optional - Hidden width of the residual MLP ``phi``. Defaults to + Hidden width of the residual MLP ``phi`` (unused when + ``phi_hidden_layers`` is ``0``). Defaults to ``max(in_channels, out_channels)`` when ``None`` (default: None). act : str, optional Name of the activation used by the residual MLP ``phi``, resolved via @@ -693,8 +687,9 @@ class GaugeLayer(torch.nn.Module): Whether the linear layers use a bias term (default: True). phi_hidden_layers : int or None, optional Number of hidden layers of the MLP residual ``phi`` in the node update. - Must be at least 1 when not ``None``. If ``None`` the residual is - disabled (matching the reference implementation) (default: 1). + With ``0`` the residual is a single linear map (no hidden layer). If + ``None`` the residual is disabled entirely (matching the reference + implementation) (default: 1). phi_hidden_dim : int or None, optional Hidden width of the residual MLP ``phi``. Defaults to ``d_embedd`` when ``None`` (default: None). @@ -823,8 +818,9 @@ class GaugeModel(nn.Module): Whether the linear layers use a bias term (default: True). phi_hidden_layers : int or None, optional Number of hidden layers of the MLP residual ``phi`` in the node update. - Must be at least 1 when not ``None``. If ``None`` the residual is - disabled (matching the reference implementation) (default: 1). + With ``0`` the residual is a single linear map (no hidden layer). If + ``None`` the residual is disabled entirely (matching the reference + implementation) (default: 1). phi_hidden_dim : int or None, optional Hidden width of the residual MLP ``phi``. Defaults to ``d_embedd`` when ``None`` (default: None). From c80e6fbd6dccc65e431e28a57a61f25879890839 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Fri, 24 Jul 2026 15:14:35 +0200 Subject: [PATCH 28/45] fix: stale test failure due to 0-hidden layers in `f_sim` --- test/nn/backbones/graph/test_gauge.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index 742a11299..a0b17d64c 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -646,8 +646,11 @@ def test_dropout_propagates_to_ffblocks(self): def test_f_sim_dropout_propagates(self): """The ``f_sim_dropout`` knob reaches ``f_sim`` and is independent. - It must only affect the similarity network, leaving the feed-forward - block dropout governed by ``dropout``. + It must only configure the similarity network, leaving the + feed-forward block dropout governed by ``dropout``. Because ``f_sim`` + has no hidden layers, ``MultiHeadFF`` (which applies dropout only + between layers) instantiates no active dropout module, so the check is + on the propagated probability rather than on the module list. """ model = GaugeModel( n_layers=2, @@ -658,13 +661,13 @@ def test_f_sim_dropout_propagates(self): f_sim_dropout=0.5, ) for layer in model.layers: - f_sim = layer.local_coords_layer.f_sim - f_sim_drops = [m for m in f_sim.model if isinstance(m, nn.Dropout)] - assert f_sim_drops - assert all(m.p == 0.5 for m in f_sim_drops) + local = layer.local_coords_layer + assert local.f_sim_dropout == 0.5 + assert local.f_sim.dropout == 0.5 # The feed-forward block keeps its own dropout probability. - fflayer = layer.local_coords_layer.fflayer - ff_drops = [m for m in fflayer.model if isinstance(m, nn.Dropout)] + ff_drops = [ + m for m in local.fflayer.model if isinstance(m, nn.Dropout) + ] assert all(m.p == 0.1 for m in ff_drops) From db788c5f3dc702b299a8875224d79d07c58fe0a5 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Fri, 24 Jul 2026 15:41:44 +0200 Subject: [PATCH 29/45] fix: reduce model size --- configs/model/graph/gauge.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/configs/model/graph/gauge.yaml b/configs/model/graph/gauge.yaml index f05844522..fda770602 100644 --- a/configs/model/graph/gauge.yaml +++ b/configs/model/graph/gauge.yaml @@ -7,14 +7,14 @@ feature_encoder: _target_: topobench.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} - out_channels: 128 + out_channels: 64 proj_dropout: 0.0 backbone: _target_: topobench.nn.backbones.GaugeModel n_layers: 2 in_channels: ${model.feature_encoder.out_channels} - d_embedd: 512 # backbone embedding width; readout consumes this (see readout.hidden_dim). r <= d_embedd + d_embedd: 128 r: 16 # frame dim; must satisfy r <= d_embedd n_gated: 2 gamma: 0.01 From 8c65f41a275a9d83214042ae915ee56aa3e3c7c9 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 27 Jul 2026 10:46:30 +0200 Subject: [PATCH 30/45] adding benchmarking results as produced by challenge notebook --- .../outputs/2026-07-26_13-33-37/results.json | 5776 +++++++++++++++++ 1 file changed, 5776 insertions(+) create mode 100644 2026_tdl_challenge/outputs/2026-07-26_13-33-37/results.json diff --git a/2026_tdl_challenge/outputs/2026-07-26_13-33-37/results.json b/2026_tdl_challenge/outputs/2026-07-26_13-33-37/results.json new file mode 100644 index 000000000..e49016b40 --- /dev/null +++ b/2026_tdl_challenge/outputs/2026-07-26_13-33-37/results.json @@ -0,0 +1,5776 @@ +{ + "metadata": { + "study_id": "2026-07-26_13-33-37", + "model_config": "graph/gauge", + "generated_at_utc": "2026-07-27T03:56:22.420714+00:00", + "n_runs": 72, + "train_seeds": [ + 42, + 43, + 44 + ], + "heatmap_note": "Cells show mean \u00b1 std over train_seeds (in-distribution test)." + }, + "results": [ + { + "experiment": "community_detection", + "wandb_project": "challenge_community_detection", + "wandb_run_name": "gauge_hom_0-0.1__deg_1-2.5__gamma_1.5-2__s42", + "train_seed": 42, + "homophily": "h_lo", + "avg_degree": 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"/raven/u/klettl/Projects/topobench/logs/train/runs/notebook_gu_grid_2026-07-26_13-33-37__triangle_counting__11__h_hi__d_hi__pl_hi__s44", + "wandb_config": { + "AvgTime/train_epoch_mean": 27.106997072696686, + "AvgTime/train_epoch_std": 0.03179205461286115, + "model/params/total": 680014, + "model/params/trainable": 680014, + "model/params/non_trainable": 0 + } + } + ] +} From c822a37b504352b9122c42f588553b869c0122e2 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 27 Jul 2026 15:23:28 +0200 Subject: [PATCH 31/45] preparing for implementing custom loss by saving initial `Z` and `Q` --- topobench/nn/backbones/graph/gauge.py | 15 ++++++++++++++- topobench/nn/wrappers/graph/gauge_wrapper.py | 6 +++++- 2 files changed, 19 insertions(+), 2 deletions(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 0c3882168..75a6cfdf5 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -893,7 +893,9 @@ def __init__( ] ) - def forward(self, x: Tensor, edge_index: Tensor) -> tuple[Tensor, Tensor]: + def forward( + self, x: Tensor, edge_index: Tensor, return_initial: bool = True + ) -> tuple[Tensor, Tensor]: """Forward pass of the full model. Parameters @@ -903,6 +905,8 @@ def forward(self, x: Tensor, edge_index: Tensor) -> tuple[Tensor, Tensor]: edge_index : Tensor Edge index tensor of shape ``[2, E]`` with source and destination node indices. + return_initial : bool + Whether to return the initial projection of x (default: True). Returns ------- @@ -911,10 +915,19 @@ def forward(self, x: Tensor, edge_index: Tensor) -> tuple[Tensor, Tensor]: Q : Tensor Per-node orthonormal frames of shape ``[N, r, d_embedd]`` from the last gauge layer. + z0 : Tensor + The initial projection of ``x``, of shape ``[N, d_embedd]``. Only + returned when ``return_initial`` is True. """ z = self.input_projector(x) + if return_initial: + z0 = z.clone() + for layer in self.layers: z, Q = layer(z, edge_index) + if return_initial: + return z, Q, z0 + return z, Q diff --git a/topobench/nn/wrappers/graph/gauge_wrapper.py b/topobench/nn/wrappers/graph/gauge_wrapper.py index 267b82296..93f6788b7 100644 --- a/topobench/nn/wrappers/graph/gauge_wrapper.py +++ b/topobench/nn/wrappers/graph/gauge_wrapper.py @@ -24,9 +24,13 @@ def forward(self, batch): dict Dictionary containing the updated model output. """ - z, _Q = self.backbone(batch.x_0, batch.edge_index) + z, Q, z0 = self.backbone( + batch.x_0, batch.edge_index, return_initial=True + ) model_out = {"labels": batch.y, "batch_0": batch.batch_0} model_out["x_0"] = z + model_out["z_0"] = z0 + model_out["Q"] = Q return model_out From 04193552d99a5e6b5472ac117a449a8f00af5118 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 27 Jul 2026 16:58:10 +0200 Subject: [PATCH 32/45] fix: change default of `return_initial` to `False` --- topobench/nn/backbones/graph/gauge.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 75a6cfdf5..d08205ab4 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -894,7 +894,7 @@ def __init__( ) def forward( - self, x: Tensor, edge_index: Tensor, return_initial: bool = True + self, x: Tensor, edge_index: Tensor, return_initial: bool = False ) -> tuple[Tensor, Tensor]: """Forward pass of the full model. @@ -906,7 +906,7 @@ def forward( Edge index tensor of shape ``[2, E]`` with source and destination node indices. return_initial : bool - Whether to return the initial projection of x (default: True). + Whether to return the initial projection of x (default: False). Returns ------- From 527b821c9140a2a573eff8818be585e761c8bb31 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 27 Jul 2026 16:58:30 +0200 Subject: [PATCH 33/45] test: reflect changed behviour of `forward` in unit tests --- test/nn/backbones/graph/test_gauge.py | 62 ++++++++++++++++++++++++++- 1 file changed, 61 insertions(+), 1 deletion(-) diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index a0b17d64c..8a4f9711a 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -452,6 +452,61 @@ def test_forward_shapes(self, simple_graph_0): assert Q.shape == (N, r, d) assert _is_orthonormal(Q) + def test_return_initial_true_returns_initial_projection( + self, simple_graph_0 + ): + """With ``return_initial=True`` the initial projection is returned. + + The default forward pass yields the triple ``(z, Q, z0)`` where ``z0`` + is exactly the input projection of ``x`` (before any gauge layer). + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + in_channels, d, r = 5, 8, 3 + N = simple_graph_0.num_nodes + model = GaugeModel( + n_layers=2, in_channels=in_channels, r=r, d_embedd=d + ) + model.eval() + x = torch.randn(N, in_channels) + + out = model(x, simple_graph_0.edge_index, return_initial=True) + assert len(out) == 3 + z, Q, z0 = out + assert z.shape == (N, d) + assert Q.shape == (N, r, d) + assert z0.shape == (N, d) + # z0 is precisely the input projection, unaffected by the gauge layers. + assert torch.allclose(z0, model.input_projector(x)) + + def test_return_initial_default_returns_pair(self, simple_graph_0): + """Omitting ``return_initial`` returns the ``(z, Q)`` pair by default. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + model = GaugeModel(n_layers=2, in_channels=5, r=3, d_embedd=8) + x = torch.randn(simple_graph_0.num_nodes, 5) + assert len(model(x, simple_graph_0.edge_index)) == 2 + + def test_return_initial_false_returns_pair(self, simple_graph_0): + """With ``return_initial=False`` only ``(z, Q)`` is returned. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + model = GaugeModel(n_layers=2, in_channels=5, r=3, d_embedd=8) + x = torch.randn(simple_graph_0.num_nodes, 5) + out = model(x, simple_graph_0.edge_index) + assert len(out) == 2 + @pytest.mark.parametrize("n_layers", [1, 2, 4]) def test_num_layers(self, simple_graph_0, n_layers): """The model stacks the requested number of gauge layers. @@ -675,7 +730,10 @@ class TestGaugeWrapper: """Tests for the topobench wrapper around the gauge model.""" def test_forward(self, simple_graph_0): - """The wrapper forwards node embeddings as ``x_0``. + """The wrapper forwards node embeddings and the initial state. + + In addition to ``x_0`` it exposes the initial projection ``z_0`` and + the final frames ``Q`` (consumed by the custom loss). Parameters ---------- @@ -699,6 +757,8 @@ def test_forward(self, simple_graph_0): ) model_out = wrapper(batch) assert model_out["x_0"].shape == (N, d) + assert model_out["z_0"].shape == (N, d) + assert model_out["Q"].shape == (N, r, d) assert "labels" in model_out assert "batch_0" in model_out From 01d05537cb9fc7ff816900ea98e4129e622eaa64 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 27 Jul 2026 18:19:40 +0200 Subject: [PATCH 34/45] feat: implemented Dirichlet Loss --- topobench/loss/model/DirichletLoss.py | 82 +++++++++++++++++++++++++++ topobench/nn/backbones/graph/gauge.py | 3 + 2 files changed, 85 insertions(+) create mode 100644 topobench/loss/model/DirichletLoss.py diff --git a/topobench/loss/model/DirichletLoss.py b/topobench/loss/model/DirichletLoss.py new file mode 100644 index 000000000..c88815fc6 --- /dev/null +++ b/topobench/loss/model/DirichletLoss.py @@ -0,0 +1,82 @@ +"""Dirichlet-energy regularization loss for the Gauge model.""" + +import torch +from torch import Tensor +from torch.nn import functional as F +from torch_geometric.data import Data +from torch_scatter import scatter + +from topobench.loss.base import AbstractLoss + + +class DirichletLoss(AbstractLoss): + r"""Dirichlet-energy regularization loss for the Gauge model. + + Measures how smoothly the node embeddings vary across edges once projected + onto each node's local frame ``Q``. Each node embedding is projected onto + its ``r`` frame vectors and L2-normalized; the projection of the current + embedding is then averaged over neighbors and compared to the projection of + the (detached) initial embedding. The resulting term is scaled by ``lamb`` + and added to the task loss as a regularizer. + + Parameters + ---------- + lamb : float, optional + Weight (lambda) of the regularizer (default: 0.1). + reduction : str, optional + Neighbor aggregation reduction, either "mean" or "sum" (default: "mean"). + """ + + def __init__(self, lamb: float = 0.1, reduction: str = "mean"): + super().__init__() + + if reduction not in ["mean", "sum"]: + raise NotImplementedError( + f"reduction '{reduction}' not implemented. Valid choices are 'mean', 'sum'." + ) + + self.lamb = lamb + self.reduce = reduction + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(lamb={self.lamb}, reduction={self.reduce})" + + def forward(self, model_out: dict, batch: Data) -> Tensor: + r"""Compute the Dirichlet-energy regularization loss. + + Parameters + ---------- + model_out : dict + Dictionary containing the model output. Uses ``x_0`` (the final node + embeddings ``[N, d]``), ``z_0`` (the initial node embeddings + ``[N, d]``) and ``Q`` (the per-node frames ``[N, r, d]``). + batch : torch_geometric.data.Data + Batch object containing the batched domain data. Uses + ``edge_index`` for the neighbor aggregation. + + Returns + ------- + Tensor + Scalar regularization loss scaled by ``lamb``. + """ + + zL = model_out["x_0"] # [N, d] + Q = model_out["Q"] # Q has shape [N, r, d] + z0 = model_out["z_0"] # [N, d] + + N = z0.size(0) + src, dst = batch.edge_index[0], batch.edge_index[1] + + # this is essentially the zhat=StopGrad(z0) + zhat = z0.detach() + + src_term = F.normalize(torch.einsum("ijk,ik->ij", Q, zhat), dim=-1) + agg_term = F.normalize(torch.einsum("ijk,ik->ij", Q, zL), dim=-1) + + agg_term = scatter( + agg_term[src], index=dst, dim=0, dim_size=N, reduce=self.reduce + ) + + loss = ((src_term - agg_term) ** 2).sum(dim=-1).mean() + + return self.lamb * loss diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index d08205ab4..7edf9a9ac 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -835,6 +835,8 @@ class GaugeModel(nn.Module): f_sim_dropout : float, optional Dropout probability used by the per-head similarity network ``f_sim`` (default: 0.0). + **kwargs : dict + Additional arguments. """ def __init__( @@ -853,6 +855,7 @@ def __init__( f_sim_act: str = "leaky_relu", dropout: float = 0.3, f_sim_dropout: float = 0.0, + **kwargs, ): super().__init__() From 4ea0415814f7aedeb768b4229922abd742ab5a2d Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 27 Jul 2026 18:23:44 +0200 Subject: [PATCH 35/45] test: add unit tests for `DirichletLoss` --- test/loss/test_dirichlet_loss.py | 129 +++++++++++++++++++++++++++++++ 1 file changed, 129 insertions(+) create mode 100644 test/loss/test_dirichlet_loss.py diff --git a/test/loss/test_dirichlet_loss.py b/test/loss/test_dirichlet_loss.py new file mode 100644 index 000000000..97db78d8e --- /dev/null +++ b/test/loss/test_dirichlet_loss.py @@ -0,0 +1,129 @@ +"""Test the DirichletLoss class.""" + +import pytest +import torch +import torch_geometric + +from topobench.loss.model.DirichletLoss import DirichletLoss + + +def _make_inputs(N=3, r=2, d=4, edge_index=None, requires_grad=False): + """Build a ``(model_out, batch)`` pair for the Dirichlet loss. + + Parameters + ---------- + N : int, optional + Number of nodes (default: 3). + r : int, optional + Number of frame vectors (default: 2). + d : int, optional + Embedding dimension (default: 4). + edge_index : torch.Tensor or None, optional + Edge index of shape ``[2, E]``. Defaults to a directed cycle over the + ``N`` nodes so that every node has exactly one incoming edge. + requires_grad : bool, optional + Whether the embedding/frame tensors require gradients (default: False). + + Returns + ------- + tuple of (dict, torch_geometric.data.Data) + The mock model output and batch. + """ + if edge_index is None: + src = torch.arange(N) + dst = torch.roll(src, -1) + edge_index = torch.stack([src, dst], dim=0) + + model_out = { + "x_0": torch.randn(N, d, requires_grad=requires_grad), + "z_0": torch.randn(N, d, requires_grad=requires_grad), + "Q": torch.randn(N, r, d, requires_grad=requires_grad), + } + batch = torch_geometric.data.Data(edge_index=edge_index, num_nodes=N) + return model_out, batch + + +def test_dirichlet_loss_init(): + """Default hyperparameters are stored as given.""" + loss_fn = DirichletLoss() + assert loss_fn.lamb == 0.1 + assert loss_fn.reduce == "mean" + + +def test_dirichlet_loss_init_invalid_reduction(): + """An unsupported reduction raises ``NotImplementedError``.""" + with pytest.raises(NotImplementedError): + DirichletLoss(reduction="max") + + +def test_dirichlet_loss_repr(): + """The repr reports the configured hyperparameters.""" + assert repr(DirichletLoss()) == "DirichletLoss(lamb=0.1, reduction=mean)" + + +def test_dirichlet_loss_forward_is_nonnegative_scalar(): + """The forward pass returns a non-negative scalar tensor.""" + loss_fn = DirichletLoss() + model_out, batch = _make_inputs() + loss = loss_fn.forward(model_out, batch) + assert isinstance(loss, torch.Tensor) + assert loss.dim() == 0 + assert loss.item() >= 0.0 + + +@pytest.mark.parametrize("reduction", ["mean", "sum"]) +def test_dirichlet_loss_reductions_run(reduction): + """Both supported reductions produce a valid scalar. + + Parameters + ---------- + reduction : str + The neighbor aggregation reduction to test. + """ + loss_fn = DirichletLoss(reduction=reduction) + model_out, batch = _make_inputs() + loss = loss_fn.forward(model_out, batch) + assert loss.dim() == 0 + assert torch.isfinite(loss) + + +def test_dirichlet_loss_lambda_scales_linearly(): + """The output scales linearly with ``lamb``.""" + model_out, batch = _make_inputs() + base = DirichletLoss(lamb=0.1).forward(model_out, batch) + scaled = DirichletLoss(lamb=0.5).forward(model_out, batch) + assert torch.allclose(scaled, 5.0 * base) + + +def test_dirichlet_loss_zero_when_frames_and_embeddings_align(): + """Identical embeddings and frames over a cycle give zero loss. + + When every node shares the same embedding and the same frame, each + projection is identical, so the neighbor-averaged projection of the current + embedding equals the projection of the (equal) initial embedding, and the + loss vanishes. The directed cycle guarantees every node has one incoming + edge, so no node is left with an all-zero aggregate. + """ + N, r, d = 3, 2, 4 + shared_emb = torch.ones(N, d) + shared_frame = torch.randn(1, r, d).expand(N, r, d).contiguous() + model_out = {"x_0": shared_emb, "z_0": shared_emb.clone(), "Q": shared_frame} + + src = torch.arange(N) + edge_index = torch.stack([src, torch.roll(src, -1)], dim=0) + batch = torch_geometric.data.Data(edge_index=edge_index, num_nodes=N) + + loss = DirichletLoss().forward(model_out, batch) + assert torch.allclose(loss, torch.tensor(0.0), atol=1e-6) + + +def test_dirichlet_loss_detaches_initial_embedding(): + """Gradients flow to ``x_0`` and ``Q`` but not to the detached ``z_0``.""" + model_out, batch = _make_inputs(requires_grad=True) + loss = DirichletLoss().forward(model_out, batch) + loss.backward() + + assert model_out["x_0"].grad is not None + assert model_out["Q"].grad is not None + # z_0 is used only as a detached target, so no gradient reaches it. + assert model_out["z_0"].grad is None From b9e9b5abc0944ae18ce908ba45349bfc2d52d826 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 27 Jul 2026 18:28:29 +0200 Subject: [PATCH 36/45] config: add DirichletLoss to model config --- configs/model/graph/gauge.yaml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/configs/model/graph/gauge.yaml b/configs/model/graph/gauge.yaml index fda770602..8c267e301 100644 --- a/configs/model/graph/gauge.yaml +++ b/configs/model/graph/gauge.yaml @@ -26,6 +26,10 @@ backbone: f_sim_dropout: 0.0 # dropout in the similarity-scorer (f_sim) phi_hidden_layers: 0 # 0 -> single linear residual; null -> disable residual (reference behavior) phi_hidden_dim: null # null -> defaults to d_embedd (unused when phi_hidden_layers == 0) + loss: # Dirichlet-energy regularizer, added to the task loss (see configs/loss/default.yaml) + _target_: topobench.loss.model.DirichletLoss + lamb: 0.1 # lambda; weight of the Dirichlet-energy term + reduction: mean # neighbor aggregation: mean|sum backbone_wrapper: _target_: topobench.nn.wrappers.GaugeWrapper From 37e2f562c7c1f1e5473ae22f654e72b3b389b8e0 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Mon, 27 Jul 2026 19:02:26 +0200 Subject: [PATCH 37/45] fix: test error due to class shadowing --- test/loss/test_dirichlet_loss.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/test/loss/test_dirichlet_loss.py b/test/loss/test_dirichlet_loss.py index 97db78d8e..a32443d46 100644 --- a/test/loss/test_dirichlet_loss.py +++ b/test/loss/test_dirichlet_loss.py @@ -4,7 +4,11 @@ import torch import torch_geometric -from topobench.loss.model.DirichletLoss import DirichletLoss +# Import from the package (populated by the loss-discovery mechanism), not the +# submodule: `from ...model.DirichletLoss import DirichletLoss` would force-import +# the submodule and shadow the class registered on the package, breaking hydra's +# `_target_: topobench.loss.model.DirichletLoss` resolution in later tests. +from topobench.loss.model import DirichletLoss def _make_inputs(N=3, r=2, d=4, edge_index=None, requires_grad=False): From a9b9cba5e56676152efa6d50e019f7fc461fdc62 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Tue, 28 Jul 2026 11:48:57 +0200 Subject: [PATCH 38/45] doc: add reference to paper equation in `DirichletLoss` --- topobench/loss/model/DirichletLoss.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topobench/loss/model/DirichletLoss.py b/topobench/loss/model/DirichletLoss.py index c88815fc6..e658a704d 100644 --- a/topobench/loss/model/DirichletLoss.py +++ b/topobench/loss/model/DirichletLoss.py @@ -10,7 +10,7 @@ class DirichletLoss(AbstractLoss): - r"""Dirichlet-energy regularization loss for the Gauge model. + r"""Dirichlet-energy regularization loss for the Gauge model (cf. paper equation (13)). Measures how smoothly the node embeddings vary across edges once projected onto each node's local frame ``Q``. Each node embedding is projected onto @@ -42,7 +42,7 @@ def __repr__(self) -> str: return f"{self.__class__.__name__}(lamb={self.lamb}, reduction={self.reduce})" def forward(self, model_out: dict, batch: Data) -> Tensor: - r"""Compute the Dirichlet-energy regularization loss. + r"""Compute the Dirichlet-energy regularization loss according to the paper's eq. 13. Parameters ---------- From a328fa0eab4badaf480a09113b45d231664fc1fd Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Tue, 28 Jul 2026 12:04:57 +0200 Subject: [PATCH 39/45] added results.json for model run with DirichletLoss enabled (lambda=0.1) --- .../outputs/2026-07-27_18-50-27/results.json | 5776 +++++++++++++++++ 1 file changed, 5776 insertions(+) create mode 100644 2026_tdl_challenge/outputs/2026-07-27_18-50-27/results.json diff --git a/2026_tdl_challenge/outputs/2026-07-27_18-50-27/results.json b/2026_tdl_challenge/outputs/2026-07-27_18-50-27/results.json new file mode 100644 index 000000000..5a419b34c --- /dev/null +++ b/2026_tdl_challenge/outputs/2026-07-27_18-50-27/results.json @@ -0,0 +1,5776 @@ +{ + "metadata": { + "study_id": "2026-07-27_18-50-27", + "model_config": "graph/gauge", + "generated_at_utc": "2026-07-28T09:53:20.793950+00:00", + "n_runs": 72, + "train_seeds": [ + 42, + 43, + 44 + ], + "heatmap_note": "Cells show mean \u00b1 std over train_seeds (in-distribution test)." + }, + "results": [ + { + "experiment": "community_detection", + "wandb_project": "challenge_community_detection", + "wandb_run_name": "gauge_hom_0-0.1__deg_1-2.5__gamma_1.5-2__s42", + "train_seed": 42, + "homophily": "h_lo", + "avg_degree": "d_lo", + "power_law": "pl_lo", + "run_slug": "h_lo__d_lo__pl_lo", + "test_loss": 2.514934778213501, + "test_best_rerun_accuracy": 0.24031630158424377, + "test_best_rerun_mse": null, + "test_triangles_total_structural": null, + "test_mse_by_total_triangles": null, + "ood_test": { + "h_lo__d_lo__pl_hi": { + "test_best_rerun_accuracy": 0.23875010013580322, + "test_best_rerun_mse": null + }, + "h_lo__d_hi__pl_lo": { + "test_best_rerun_accuracy": 0.264840692281723, + "test_best_rerun_mse": null + }, + "h_lo__d_hi__pl_hi": { + "test_best_rerun_accuracy": 0.26407670974731445, + "test_best_rerun_mse": null + }, + "h_mid__d_lo__pl_lo": { + "test_best_rerun_accuracy": 0.25754451751708984, + "test_best_rerun_mse": null + }, + "h_mid__d_lo__pl_hi": { 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"test_mse_by_total_triangles": 1.8527279165892625 - }, - "h_mid__d_hi__pl_hi": { - "test_best_rerun_accuracy": null, - "test_best_rerun_mse": 93690.4296875, - "test_triangles_total_structural": 14262.0, - "test_mse_by_total_triangles": 6.569235008238676 - }, - "h_hi__d_lo__pl_lo": { - "test_best_rerun_accuracy": null, - "test_best_rerun_mse": 80259.9609375, - "test_triangles_total_structural": 19509.0, - "test_mse_by_total_triangles": 4.113996665000768 - }, - "h_hi__d_lo__pl_hi": { - "test_best_rerun_accuracy": null, - "test_best_rerun_mse": 117602.5078125, - "test_triangles_total_structural": 5625.0, - "test_mse_by_total_triangles": 20.9071125 - }, - "h_hi__d_hi__pl_lo": { - "test_best_rerun_accuracy": null, - "test_best_rerun_mse": 406829.0625, - "test_triangles_total_structural": 88403.0, - "test_mse_by_total_triangles": 4.601982540185288 - } - }, - "output_dir": "/raven/u/klettl/Projects/topobench/logs/train/runs/notebook_gu_grid_2026-07-26_13-33-37__triangle_counting__11__h_hi__d_hi__pl_hi__s44", - "wandb_config": { - "AvgTime/train_epoch_mean": 27.106997072696686, - "AvgTime/train_epoch_std": 0.03179205461286115, - "model/params/total": 680014, - "model/params/trainable": 680014, - "model/params/non_trainable": 0 - } - } - ] -} From b7df7597784609548b51ae4df44f7465bfd4fb45 Mon Sep 17 00:00:00 2001 From: GhazalMst <136063158+GhazalMst@users.noreply.github.com> Date: Wed, 29 Jul 2026 14:54:51 +0200 Subject: [PATCH 41/45] test: add edge-case coverage for GaugeModel backbone and DirichletLoss --- test/loss/test_dirichlet_loss.py | 99 +++++++++++++++++ test/nn/backbones/graph/test_gauge.py | 150 ++++++++++++++++++++++++++ 2 files changed, 249 insertions(+) diff --git a/test/loss/test_dirichlet_loss.py b/test/loss/test_dirichlet_loss.py index a32443d46..0e1b15fab 100644 --- a/test/loss/test_dirichlet_loss.py +++ b/test/loss/test_dirichlet_loss.py @@ -131,3 +131,102 @@ def test_dirichlet_loss_detaches_initial_embedding(): assert model_out["Q"].grad is not None # z_0 is used only as a detached target, so no gradient reaches it. assert model_out["z_0"].grad is None + + +def test_dirichlet_loss_one_isolated_node_finite(): + """A single isolated node among otherwise-connected nodes stays finite.""" + N = 3 + edge_index = torch.tensor([[0, 1], [1, 0]], dtype=torch.long) + model_out, batch = _make_inputs(N=N, edge_index=edge_index) + loss = DirichletLoss().forward(model_out, batch) + assert torch.isfinite(loss) + assert loss.item() >= 0.0 + + +def test_dirichlet_loss_all_nodes_isolated(): + """A zero-edge graph still produces a finite, non-negative loss.""" + N = 3 + edge_index = torch.tensor([[], []], dtype=torch.long) + model_out, batch = _make_inputs(N=N, edge_index=edge_index) + loss = DirichletLoss().forward(model_out, batch) + assert torch.isfinite(loss) + assert loss.item() >= 0.0 + + +def test_dirichlet_loss_near_zero_projection_grad_finite(): + """Gradients stay finite when a node's projection is exactly zero.""" + model_out, batch = _make_inputs(requires_grad=True) + with torch.no_grad(): + model_out["z_0"][0] = 0.0 + loss = DirichletLoss().forward(model_out, batch) + loss.backward() + assert torch.isfinite(model_out["Q"].grad).all() + assert torch.isfinite(model_out["x_0"].grad).all() + + +def _cycle_edge_index(N, offset=0): + """Build a directed cycle over ``N`` nodes, indices shifted by ``offset``. + + Parameters + ---------- + N : int + Number of nodes in the cycle. + offset : int, optional + Amount to shift node indices by, for packing into a larger batch + (default: 0). + + Returns + ------- + torch.Tensor + Edge index of shape ``[2, N]``. + """ + src = torch.arange(N) + offset + dst = torch.roll(src, -1) + return torch.stack([src, dst], dim=0) + + +def test_dirichlet_loss_no_cross_graph_leakage(): + """Perturbing one graph in a batch must not change another graph's gradient. + + Two independent cycle graphs are packed into a single batch, Since the final reduction + is a plain mean over all nodes, graph B's gradient should be identical + regardless of what graph A's embeddings are. + """ + N_a, N_b, r, d = 3, 3, 2, 4 + N = N_a + N_b + edge_index = torch.cat( + [_cycle_edge_index(N_a), _cycle_edge_index(N_b, offset=N_a)], dim=1 + ) + batch = torch_geometric.data.Data(edge_index=edge_index, num_nodes=N) + + x_0 = torch.randn(N, d, requires_grad=True) + z_0 = torch.randn(N, d) + Q = torch.randn(N, r, d, requires_grad=True) + model_out = {"x_0": x_0, "z_0": z_0, "Q": Q} + loss = DirichletLoss().forward(model_out, batch) + loss.backward() + grad_b_before = Q.grad[N_a:].clone() + + # Perturb graph A's embeddings only; graph B's data is untouched. + x_0b = x_0.detach().clone() + x_0b[:N_a] = torch.randn(N_a, d) + x_0b.requires_grad_(True) + Qb = Q.detach().clone().requires_grad_(True) + model_out_2 = {"x_0": x_0b, "z_0": z_0, "Q": Qb} + loss2 = DirichletLoss().forward(model_out_2, batch) + loss2.backward() + grad_b_after = Qb.grad[N_a:] + + assert torch.allclose(grad_b_before, grad_b_after) + + +def test_dirichlet_loss_reductions_actually_differ(): + """``sum`` and ``mean`` reductions must produce different losses.""" + N, r, d = 3, 2, 4 + edge_index = torch.tensor([[0, 1], [2, 2]], dtype=torch.long) + model_out, batch = _make_inputs(N=N, r=r, d=d, edge_index=edge_index) + + loss_mean = DirichletLoss(reduction="mean").forward(model_out, batch) + loss_sum = DirichletLoss(reduction="sum").forward(model_out, batch) + + assert not torch.allclose(loss_mean, loss_sum) diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index 8a4f9711a..645f822e1 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -307,6 +307,86 @@ def test_no_nan(self, simple_graph_0): assert not torch.isnan(Q).any() assert not torch.isinf(Q).any() + def test_all_nodes_isolated_no_nan(self): + """ + A graph with zero edges still produces finite, orthonormal frames. + + """ + d, r = 8, 3 + edge_index = torch.tensor([[], []], dtype=torch.long) + Z = torch.randn(3, d) + layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + Q = layer(Z, edge_index) + assert Q.shape == (3, r, d) + assert _is_orthonormal(Q) + assert not torch.isnan(Q).any() + + def test_isolated_nodes_independent(self): + """ + With no edges, changing one node's features must not affect others. + + """ + d, r = 8, 3 + edge_index = torch.tensor([[],[]], dtype =torch.long) + Z = torch.randn(3, d) + Z1 = Z.clone() + Z1[1] = torch.randn(d) + layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + Q = layer(Z, edge_index) + Q1 = layer(Z1, edge_index) + assert torch.allclose(Q[0], Q1[0]) + assert torch.allclose(Q[2], Q1[2]) + assert not torch.allclose(Q[1], Q1[1]) + + @pytest.mark.xfail( + reason="LocalCoordinatesLayer silently truncates r to d instead of " + "keeping r subspaces when r > d_embedd." + ) + def test_r_greater_than_d(self, simple_graph_0): + """The frame must keep ``r`` subspaces even when ``r > d_embedd``. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + d, r = 3, 8 + N = simple_graph_0.num_nodes + layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + Z = torch.randn(N, d) + Q = layer(Z, simple_graph_0.edge_index) + assert Q.shape == (N, r, d) + + def test_respects_edge_direction(self): + """A directed edge only influences its destination, never its source.""" + d, r = 8, 3 + edge_index = torch.tensor([[0], [1]], dtype=torch.long) + Z = torch.randn(2, d) + Z1 = Z.clone() + Z1[1] = torch.randn(d) + layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + Q = layer(Z, edge_index) + Q1 = layer(Z1, edge_index) + assert torch.allclose(Q[0], Q1[0]) + assert not torch.allclose(Q[1], Q1[1]) + + def test_star_topology_no_nan(self): + """A hub node with far more neighbors than the rest of the graph must not produce NaNs.""" + d, r = 8, 3 + n_leaves = 20 + N = n_leaves + 1 + leaves = torch.arange(1, N) + hub = torch.zeros(n_leaves, dtype=torch.long) + src = torch.cat([hub, leaves]) + dst = torch.cat([leaves, hub]) + edge_index = torch.stack([src, dst]) + layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + Z = torch.randn(N, d) + Q = layer(Z, edge_index) + assert Q.shape == (N, r, d) + assert _is_orthonormal(Q) + assert not torch.isnan(Q).any() and not torch.isinf(Q).any() + class TestGatedFlatteningLayer: """Tests for the gated flattening (frame smoothing) layer.""" @@ -335,6 +415,24 @@ def test_no_learnable_parameters(self): gate = GatedFlatteningLayer(r=3) assert list(gate.parameters()) == [] + @pytest.mark.xfail( + reason="QR's backward is undefined for rank-deficient input; " + "gamma=1.0 zeroes an isolated node's blend, causing NaN/Inf grads." + ) + def test_isolated_node_gamma_one_backward(self): + """Gradients must stay finite even when gamma=1.0 zeroes an isolated node's blend.""" + d, r = 8, 3 + N = 3 + edge_index = torch.tensor([[], []], dtype=torch.long) + Z = torch.randn(N, d, requires_grad=True) + Q = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d)(Z, edge_index) + gate = GatedFlatteningLayer(r=r, gamma=1.0) + Qnew = gate(Q, edge_index) + Qnew.sum().backward() + assert Z.grad is not None + assert not torch.isnan(Z.grad).any() + assert not torch.isinf(Z.grad).any() + class TestNodeUpdateLayer: """Tests for the node feature update layer.""" @@ -390,6 +488,26 @@ def test_residual_enabled(self, simple_graph_0): ) assert isinstance(layer.phi, FFBlock) + def test_supports_different_in_out_channels(self, simple_graph_0): + """The update maps to ``out_channels`` even when it differs from ``in_channels``. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + d, r = 8, 3 + out_channels = 12 + N = simple_graph_0.num_nodes + Z = torch.randn(N, d) + Q = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d)( + Z, simple_graph_0.edge_index + ) + layer = NodeUpdateLayer( + in_channels=d, out_channels=out_channels, phi_hidden_layers=1 + ) + assert layer(Z, Q, simple_graph_0.edge_index).shape == (N, out_channels) + class TestGaugeLayer: """Tests for a single gauge message-passing layer.""" @@ -429,6 +547,23 @@ def test_num_gated_layers(self, simple_graph_0, n_gated): Znew, Q = layer(x, simple_graph_0.edge_index) assert _is_orthonormal(Q) + def test_all_nodes_isolated_no_nan(self): + + """A zero-edge graph stays finite through the full gauge layer.""" + + d, r = 8, 3 + N = 3 + edge_index = torch.tensor([[], []], dtype=torch.long) + layer = GaugeLayer(d_embedd=d, r=r, n_gated=2) + x = torch.randn(N, d) + Znew, Q = layer(x, edge_index) + assert Znew.shape == (N, d) + assert Q.shape == (N, r, d) + assert _is_orthonormal(Q) + assert not torch.isnan(Znew).any() and not torch.isinf(Znew).any() + assert not torch.isnan(Q).any() and not torch.isinf(Q).any() + + class TestGaugeModel: """Tests for the full gauge model.""" @@ -725,6 +860,21 @@ def test_f_sim_dropout_propagates(self): ] assert all(m.p == 0.1 for m in ff_drops) + def test_all_nodes_isolated_no_nan(self): + """A zero-edge graph stays finite through the full stacked model.""" + in_channels, d, r, N = 5, 8, 3, 3 + edge_index = torch.tensor([[], []], dtype=torch.long) + model = GaugeModel( + n_layers=2, in_channels=in_channels, r=r, d_embedd=d + ) + x = torch.randn(N, in_channels) + z, Q = model(x, edge_index) + assert z.shape == (N, d) + assert Q.shape == (N, r, d) + assert _is_orthonormal(Q) + assert not torch.isnan(z).any() and not torch.isinf(z).any() + assert not torch.isnan(Q).any() and not torch.isinf(Q).any() + class TestGaugeWrapper: """Tests for the topobench wrapper around the gauge model.""" From 71c635eeb008fadf3a29bd9b2f7444550a2e5598 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Wed, 29 Jul 2026 16:06:58 +0200 Subject: [PATCH 42/45] fix: add `layer.eval()` call to disable non-deterministic behaviour in tests caused by `Dropout` modules --- test/nn/backbones/graph/test_gauge.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index 645f822e1..acc2d96d9 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -332,6 +332,7 @@ def test_isolated_nodes_independent(self): Z1 = Z.clone() Z1[1] = torch.randn(d) layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + layer.eval() Q = layer(Z, edge_index) Q1 = layer(Z1, edge_index) assert torch.allclose(Q[0], Q1[0]) @@ -365,6 +366,7 @@ def test_respects_edge_direction(self): Z1 = Z.clone() Z1[1] = torch.randn(d) layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) + layer.eval() Q = layer(Z, edge_index) Q1 = layer(Z1, edge_index) assert torch.allclose(Q[0], Q1[0]) From da3dda05251f4a64196ae90f5bfe4c69c1ccd535 Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Wed, 29 Jul 2026 16:26:36 +0200 Subject: [PATCH 43/45] fix: raise `ValueError` if `r>d_emb edd` --- test/nn/backbones/graph/test_gauge.py | 22 ++++++---------------- topobench/nn/backbones/graph/gauge.py | 10 ++++++++++ 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index acc2d96d9..aa5b25243 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -339,24 +339,14 @@ def test_isolated_nodes_independent(self): assert torch.allclose(Q[2], Q1[2]) assert not torch.allclose(Q[1], Q1[1]) - @pytest.mark.xfail( - reason="LocalCoordinatesLayer silently truncates r to d instead of " - "keeping r subspaces when r > d_embedd." - ) - def test_r_greater_than_d(self, simple_graph_0): - """The frame must keep ``r`` subspaces even when ``r > d_embedd``. + def test_r_greater_than_d_raises(self): + """``r > d_embedd`` is rejected instead of silently truncating to d. - Parameters - ---------- - simple_graph_0 : torch_geometric.data.Data - Test graph fixture. + A d-dimensional space admits at most d orthonormal frame vectors, so + the layer must refuse to be constructed rather than dropping subspaces. """ - d, r = 3, 8 - N = simple_graph_0.num_nodes - layer = LocalCoordinatesLayer(r_subspaces=r, d_embedd=d) - Z = torch.randn(N, d) - Q = layer(Z, simple_graph_0.edge_index) - assert Q.shape == (N, r, d) + with pytest.raises(ValueError): + LocalCoordinatesLayer(r_subspaces=8, d_embedd=3) def test_respects_edge_direction(self): """A directed edge only influences its destination, never its source.""" diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 7edf9a9ac..66ecd1825 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -367,6 +367,16 @@ def __init__( ): super().__init__() + # A d-dimensional space admits at most d orthonormal frame vectors, so + # r > d would be silently truncated to d by the reduced-mode QR in + # forward. Reject it explicitly instead of dropping subspaces. + if r_subspaces > d_embedd: + raise ValueError( + f"r_subspaces ({r_subspaces}) cannot exceed d_embedd " + f"({d_embedd}): a d-dimensional space admits at most d " + "orthonormal frame vectors." + ) + self.r = r_subspaces self.tau = tau self.d = d_embedd From cd435f20326b4c23fc130ce3c905843ae618b3dd Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Wed, 29 Jul 2026 17:15:22 +0200 Subject: [PATCH 44/45] fix: remove self-loops in `GaugeModel` and `DirichletLoss` --- test/loss/test_dirichlet_loss.py | 21 ++++++++++++++++++ test/nn/backbones/graph/test_gauge.py | 31 +++++++++++++++++++++++++++ topobench/loss/model/DirichletLoss.py | 6 +++++- topobench/nn/backbones/graph/gauge.py | 6 ++++++ 4 files changed, 63 insertions(+), 1 deletion(-) diff --git a/test/loss/test_dirichlet_loss.py b/test/loss/test_dirichlet_loss.py index 0e1b15fab..cd548e19f 100644 --- a/test/loss/test_dirichlet_loss.py +++ b/test/loss/test_dirichlet_loss.py @@ -121,6 +121,27 @@ def test_dirichlet_loss_zero_when_frames_and_embeddings_align(): assert torch.allclose(loss, torch.tensor(0.0), atol=1e-6) +def test_dirichlet_loss_ignores_self_loops(): + """Adding a self-loop on every node leaves the loss unchanged. + + The loss strips self-loops from ``edge_index`` before aggregating over + neighbors, so an otherwise identical batch augmented with self-loops must + produce the same value as the original. + """ + model_out, batch = _make_inputs() + looped, _ = torch_geometric.utils.add_self_loops( + batch.edge_index, num_nodes=batch.num_nodes + ) + batch_looped = torch_geometric.data.Data( + edge_index=looped, num_nodes=batch.num_nodes + ) + + loss_fn = DirichletLoss() + base = loss_fn.forward(model_out, batch) + looped_loss = loss_fn.forward(model_out, batch_looped) + assert torch.allclose(base, looped_loss, atol=1e-6) + + def test_dirichlet_loss_detaches_initial_embedding(): """Gradients flow to ``x_0`` and ``Q`` but not to the detached ``z_0``.""" model_out, batch = _make_inputs(requires_grad=True) diff --git a/test/nn/backbones/graph/test_gauge.py b/test/nn/backbones/graph/test_gauge.py index aa5b25243..d0c9f1006 100644 --- a/test/nn/backbones/graph/test_gauge.py +++ b/test/nn/backbones/graph/test_gauge.py @@ -579,6 +579,37 @@ def test_forward_shapes(self, simple_graph_0): assert Q.shape == (N, r, d) assert _is_orthonormal(Q) + def test_self_loops_do_not_affect_output(self, simple_graph_0): + """Adding self-loops leaves the model output unchanged. + + The model strips self-loops from ``edge_index`` before aggregating, so + feeding a graph augmented with a self-loop on every node must yield the + same embeddings and frames as the original graph. + + Parameters + ---------- + simple_graph_0 : torch_geometric.data.Data + Test graph fixture. + """ + in_channels, d, r = 5, 8, 3 + N = simple_graph_0.num_nodes + model = GaugeModel( + n_layers=2, in_channels=in_channels, r=r, d_embedd=d + ) + model.eval() # disable dropout so the comparison is deterministic + x = torch.randn(N, in_channels) + + edge_index = simple_graph_0.edge_index + looped, _ = torch_geometric.utils.add_self_loops( + edge_index, num_nodes=N + ) + + z, Q = model(x, edge_index) + z_looped, Q_looped = model(x, looped) + + assert torch.allclose(z, z_looped, atol=1e-6) + assert torch.allclose(Q, Q_looped, atol=1e-6) + def test_return_initial_true_returns_initial_projection( self, simple_graph_0 ): diff --git a/topobench/loss/model/DirichletLoss.py b/topobench/loss/model/DirichletLoss.py index e658a704d..0bc2874a5 100644 --- a/topobench/loss/model/DirichletLoss.py +++ b/topobench/loss/model/DirichletLoss.py @@ -4,6 +4,7 @@ from torch import Tensor from torch.nn import functional as F from torch_geometric.data import Data +from torch_geometric.utils import remove_self_loops from torch_scatter import scatter from topobench.loss.base import AbstractLoss @@ -65,7 +66,10 @@ def forward(self, model_out: dict, batch: Data) -> Tensor: z0 = model_out["z_0"] # [N, d] N = z0.size(0) - src, dst = batch.edge_index[0], batch.edge_index[1] + # Removed here (and, consistently, in the Gauge backbone) so the loss + # aggregates over the same self-loop-free neighborhoods as the model. + edge_index, _ = remove_self_loops(batch.edge_index) + src, dst = edge_index[0], edge_index[1] # this is essentially the zhat=StopGrad(z0) zhat = z0.detach() diff --git a/topobench/nn/backbones/graph/gauge.py b/topobench/nn/backbones/graph/gauge.py index 66ecd1825..a740ca7f6 100644 --- a/topobench/nn/backbones/graph/gauge.py +++ b/topobench/nn/backbones/graph/gauge.py @@ -22,6 +22,7 @@ import torch from torch import Tensor, nn +from torch_geometric.utils import remove_self_loops from torch_scatter import scatter_add, scatter_mean, scatter_softmax activation_dict: dict[str, Callable] = { @@ -932,6 +933,11 @@ def forward( The initial projection of ``x``, of shape ``[N, d_embedd]``. Only returned when ``return_initial`` is True. """ + # Self-loops would make each node its own neighbor in the per-layer + # aggregation, which is not intended. Removed here (and, consistently, + # in DirichletLoss) so both aggregate over the same neighborhoods. + edge_index, _ = remove_self_loops(edge_index) + z = self.input_projector(x) if return_initial: From 273c442c1802e390aacff753d030848f77dfe0cb Mon Sep 17 00:00:00 2001 From: Kolya Lettl Date: Thu, 30 Jul 2026 11:42:26 +0200 Subject: [PATCH 45/45] re-run benchmarks with self-loops removed --- .../results.json | 3126 ++++++++--------- 1 file changed, 1563 insertions(+), 1563 deletions(-) rename 2026_tdl_challenge/outputs/{2026-07-27_18-50-27 => 2026-07-29_18-09-01}/results.json (62%) diff --git a/2026_tdl_challenge/outputs/2026-07-27_18-50-27/results.json b/2026_tdl_challenge/outputs/2026-07-29_18-09-01/results.json similarity index 62% rename from 2026_tdl_challenge/outputs/2026-07-27_18-50-27/results.json rename to 2026_tdl_challenge/outputs/2026-07-29_18-09-01/results.json index 5a419b34c..a2ba5321f 100644 --- a/2026_tdl_challenge/outputs/2026-07-27_18-50-27/results.json +++ b/2026_tdl_challenge/outputs/2026-07-29_18-09-01/results.json @@ -1,8 +1,8 @@ { "metadata": { - "study_id": "2026-07-27_18-50-27", + "study_id": "2026-07-29_18-09-01", "model_config": "graph/gauge", - "generated_at_utc": "2026-07-28T09:53:20.793950+00:00", + "generated_at_utc": "2026-07-30T09:36:50.432550+00:00", "n_runs": 72, "train_seeds": [ 42, @@ -21,61 +21,61 @@ "avg_degree": "d_lo", "power_law": "pl_lo", "run_slug": "h_lo__d_lo__pl_lo", - "test_loss": 2.514934778213501, - "test_best_rerun_accuracy": 0.24031630158424377, + "test_loss": 2.6258316040039062, + "test_best_rerun_accuracy": 0.2415768951177597, "test_best_rerun_mse": null, "test_triangles_total_structural": null, "test_mse_by_total_triangles": null, "ood_test": { "h_lo__d_lo__pl_hi": { - "test_best_rerun_accuracy": 0.23875010013580322, + "test_best_rerun_accuracy": 0.23806250095367432, "test_best_rerun_mse": null }, "h_lo__d_hi__pl_lo": { - "test_best_rerun_accuracy": 0.264840692281723, + "test_best_rerun_accuracy": 0.27901291847229004, "test_best_rerun_mse": null }, "h_lo__d_hi__pl_hi": { - "test_best_rerun_accuracy": 0.26407670974731445, + "test_best_rerun_accuracy": 0.27859270572662354, "test_best_rerun_mse": null }, "h_mid__d_lo__pl_lo": { - "test_best_rerun_accuracy": 0.25754451751708984, + "test_best_rerun_accuracy": 0.2668271064758301, "test_best_rerun_mse": null }, "h_mid__d_lo__pl_hi": { - "test_best_rerun_accuracy": 0.2508212924003601, + "test_best_rerun_accuracy": 0.2562457025051117, "test_best_rerun_mse": null }, "h_mid__d_hi__pl_lo": { - "test_best_rerun_accuracy": 0.28176331520080566, + "test_best_rerun_accuracy": 0.3047597110271454, "test_best_rerun_mse": null }, "h_mid__d_hi__pl_hi": { - "test_best_rerun_accuracy": 0.2868057191371918, + "test_best_rerun_accuracy": 0.30827412009239197, "test_best_rerun_mse": null }, "h_hi__d_lo__pl_lo": { - "test_best_rerun_accuracy": 0.30754831433296204, + "test_best_rerun_accuracy": 0.3323401212692261, "test_best_rerun_mse": null }, "h_hi__d_lo__pl_hi": { - "test_best_rerun_accuracy": 0.2965849041938782, + "test_best_rerun_accuracy": 0.3183589279651642, "test_best_rerun_mse": null }, "h_hi__d_hi__pl_lo": { - "test_best_rerun_accuracy": 0.3616013526916504, + "test_best_rerun_accuracy": 0.40465277433395386, "test_best_rerun_mse": null }, "h_hi__d_hi__pl_hi": { - "test_best_rerun_accuracy": 0.38272595405578613, + "test_best_rerun_accuracy": 0.42669418454170227, "test_best_rerun_mse": null } }, - "output_dir": "/raven/u/klettl/Projects/topobench/logs/train/runs/notebook_gu_grid_2026-07-27_18-50-27__community_detection__00__h_lo__d_lo__pl_lo__s42", + "output_dir": "/raven/u/klettl/Projects/topobench/logs/train/runs/notebook_gu_grid_2026-07-29_18-09-01__community_detection__00__h_lo__d_lo__pl_lo__s42", "wandb_config": { - "AvgTime/train_epoch_mean": 27.791832673549653, - "AvgTime/train_epoch_std": 0.28815387715268825, + "AvgTime/train_epoch_mean": 27.257680437781595, + "AvgTime/train_epoch_std": 0.09545168304552551, "model/params/total": 682465, "model/params/trainable": 682465, "model/params/non_trainable": 0 @@ -90,61 +90,61 @@ "avg_degree": "d_lo", "power_law": "pl_lo", "run_slug": "h_lo__d_lo__pl_lo", - "test_loss": 2.506336212158203, - "test_best_rerun_accuracy": 0.24291390180587769, + "test_loss": 2.4898829460144043, + "test_best_rerun_accuracy": 0.24493849277496338, "test_best_rerun_mse": null, "test_triangles_total_structural": null, "test_mse_by_total_triangles": null, "ood_test": { "h_lo__d_lo__pl_hi": { - "test_best_rerun_accuracy": 0.24001069366931915, + "test_best_rerun_accuracy": 0.24146229028701782, "test_best_rerun_mse": null }, "h_lo__d_hi__pl_lo": { - "test_best_rerun_accuracy": 0.2631599009037018, + "test_best_rerun_accuracy": 0.26461151242256165, "test_best_rerun_mse": null }, "h_lo__d_hi__pl_hi": { - "test_best_rerun_accuracy": 0.2614409029483795, + "test_best_rerun_accuracy": 0.262930691242218, "test_best_rerun_mse": null }, "h_mid__d_lo__pl_lo": { - "test_best_rerun_accuracy": 0.2539154887199402, + "test_best_rerun_accuracy": 0.2525402903556824, "test_best_rerun_mse": null }, "h_mid__d_lo__pl_hi": { - "test_best_rerun_accuracy": 0.24390709400177002, + "test_best_rerun_accuracy": 0.24585530161857605, "test_best_rerun_mse": null }, "h_mid__d_hi__pl_lo": { - "test_best_rerun_accuracy": 0.27630069851875305, + "test_best_rerun_accuracy": 0.27439069747924805, "test_best_rerun_mse": null }, "h_mid__d_hi__pl_hi": { - "test_best_rerun_accuracy": 0.27614790201187134, + "test_best_rerun_accuracy": 0.27546030282974243, "test_best_rerun_mse": null }, "h_hi__d_lo__pl_lo": { - "test_best_rerun_accuracy": 0.2979601323604584, + "test_best_rerun_accuracy": 0.2955535054206848, "test_best_rerun_mse": null }, "h_hi__d_lo__pl_hi": { - "test_best_rerun_accuracy": 0.2908167243003845, + "test_best_rerun_accuracy": 0.28760790824890137, "test_best_rerun_mse": null }, "h_hi__d_hi__pl_lo": { - "test_best_rerun_accuracy": 0.33612194657325745, + "test_best_rerun_accuracy": 0.33596912026405334, "test_best_rerun_mse": null }, "h_hi__d_hi__pl_hi": { - "test_best_rerun_accuracy": 0.35067614912986755, + "test_best_rerun_accuracy": 0.34987393021583557, "test_best_rerun_mse": null } }, - "output_dir": "/raven/u/klettl/Projects/topobench/logs/train/runs/notebook_gu_grid_2026-07-27_18-50-27__community_detection__00__h_lo__d_lo__pl_lo__s43", + "output_dir": "/raven/u/klettl/Projects/topobench/logs/train/runs/notebook_gu_grid_2026-07-29_18-09-01__community_detection__00__h_lo__d_lo__pl_lo__s43", "wandb_config": { - "AvgTime/train_epoch_mean": 27.493898644166833, - "AvgTime/train_epoch_std": 0.1559904840179135, + "AvgTime/train_epoch_mean": 27.074480921030045, + "AvgTime/train_epoch_std": 0.022286526056669996, "model/params/total": 682465, "model/params/trainable": 682465, "model/params/non_trainable": 0 @@ -159,61 +159,61 @@ "avg_degree": "d_lo", "power_law": "pl_lo", "run_slug": "h_lo__d_lo__pl_lo", - "test_loss": 2.561490058898926, - "test_best_rerun_accuracy": 0.24902589619159698, + "test_loss": 2.609708309173584, + "test_best_rerun_accuracy": 0.24165329337120056, "test_best_rerun_mse": null, "test_triangles_total_structural": null, "test_mse_by_total_triangles": null, "ood_test": { "h_lo__d_lo__pl_hi": { - "test_best_rerun_accuracy": 0.24230270087718964, + "test_best_rerun_accuracy": 0.23726029694080353, "test_best_rerun_mse": null }, "h_lo__d_hi__pl_lo": { - "test_best_rerun_accuracy": 0.2821071147918701, + "test_best_rerun_accuracy": 0.28103750944137573, "test_best_rerun_mse": null }, "h_lo__d_hi__pl_hi": { - "test_best_rerun_accuracy": 0.2821834981441498, + "test_best_rerun_accuracy": 0.28420811891555786, "test_best_rerun_mse": null }, "h_mid__d_lo__pl_lo": { - "test_best_rerun_accuracy": 0.2753075063228607, + "test_best_rerun_accuracy": 0.27752310037612915, "test_best_rerun_mse": null }, "h_mid__d_lo__pl_hi": { - "test_best_rerun_accuracy": 0.26667430996894836, + "test_best_rerun_accuracy": 0.2612881064414978, "test_best_rerun_mse": null }, "h_mid__d_hi__pl_lo": { - 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