From 1b250ad8c3fd264e0f2f8bce4bbe83a6b76a1914 Mon Sep 17 00:00:00 2001 From: weeye Date: Sun, 2 Aug 2026 17:26:44 +0530 Subject: [PATCH 1/2] Add HyMN graph backbone --- 2026_tdl_challenge/run_evaluation.ipynb | 2 +- configs/model/graph/hymn.yaml | 52 +++ .../api/topobench.nn.backbones.graph.hymn.rst | 7 + docs/api/topobench.nn.backbones.graph.rst | 1 + test/nn/backbones/graph/test_hymn.py | 313 +++++++++++++ test/pipeline/test_pipeline.py | 8 +- topobench/nn/backbones/graph/hymn.py | 441 ++++++++++++++++++ 7 files changed, 818 insertions(+), 6 deletions(-) create mode 100644 configs/model/graph/hymn.yaml create mode 100644 docs/api/topobench.nn.backbones.graph.hymn.rst create mode 100644 test/nn/backbones/graph/test_hymn.py create mode 100644 topobench/nn/backbones/graph/hymn.py diff --git a/2026_tdl_challenge/run_evaluation.ipynb b/2026_tdl_challenge/run_evaluation.ipynb index 8542dbaab..130f37ec5 100644 --- a/2026_tdl_challenge/run_evaluation.ipynb +++ b/2026_tdl_challenge/run_evaluation.ipynb @@ -104,7 +104,7 @@ "outputs": [], "source": [ "# Your model configuration (e.g., \"graph/gcn\", \"graph/gin\", \"graph/gat\")\n", - "MODEL_CONFIG = \"graph/gin\"" + "MODEL_CONFIG = \"graph/hymn\"" ] }, { diff --git a/configs/model/graph/hymn.yaml b/configs/model/graph/hymn.yaml new file mode 100644 index 000000000..3efbf4950 --- /dev/null +++ b/configs/model/graph/hymn.yaml @@ -0,0 +1,52 @@ +_target_: topobench.model.TBModel + +model_name: hymn +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}} + # The reference model reserves 16 channels for the encoded CSE. + out_channels: 48 + proj_dropout: 0.0 + +backbone: + _target_: topobench.nn.backbones.HyMN + in_channels: ${model.feature_encoder.out_channels} + hidden_channels: 64 + # Published MolHIV HyMN (GIN, T=2) settings, translated to TopoBench. + num_layers: 2 + num_samples: 3 + cse_steps: 16 + cse_channels: 16 + dropout: 0.0 + train_eps: true + batch_norm: true + residual: true + use_centrality_encoding: true + sample_aggregation: mean + +backbone_wrapper: + _target_: topobench.nn.wrappers.${model.backbone_wrapper.wrapper_name} + _partial_: true + wrapper_name: GNNWrapper + out_channels: 64 + residual_connections: false + 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: MLPReadout + num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} + in_channels: 64 + hidden_layers: [64] + out_channels: ${dataset.parameters.num_classes} + task_level: ${define_task_level:${dataset.parameters.task_level},${dataset.split_params.learning_setting}} + pooling_type: sum + dropout: 0.0 + act: relu + norm: null + final_act: null + +compile: false diff --git a/docs/api/topobench.nn.backbones.graph.hymn.rst b/docs/api/topobench.nn.backbones.graph.hymn.rst new file mode 100644 index 000000000..23bf3ec99 --- /dev/null +++ b/docs/api/topobench.nn.backbones.graph.hymn.rst @@ -0,0 +1,7 @@ +topobench.nn.backbones.graph.hymn module +======================================== + +.. automodule:: topobench.nn.backbones.graph.hymn + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/topobench.nn.backbones.graph.rst b/docs/api/topobench.nn.backbones.graph.rst index 070ea3e1c..45b9e9077 100644 --- a/docs/api/topobench.nn.backbones.graph.rst +++ b/docs/api/topobench.nn.backbones.graph.rst @@ -22,5 +22,6 @@ Submodules topobench.nn.backbones.graph.gps topobench.nn.backbones.graph.graph_mlp + topobench.nn.backbones.graph.hymn topobench.nn.backbones.graph.identity_gnn topobench.nn.backbones.graph.nsd diff --git a/test/nn/backbones/graph/test_hymn.py b/test/nn/backbones/graph/test_hymn.py new file mode 100644 index 000000000..77287726f --- /dev/null +++ b/test/nn/backbones/graph/test_hymn.py @@ -0,0 +1,313 @@ +"""Tests for the paper-faithful HyMN graph backbone.""" + +import pytest +import torch +from torch_geometric.data import Batch, Data + +from topobench.nn.backbones.graph.hymn import HyMN + + +def _graph(edge_pairs, num_nodes, feature_dim=5): + """Create a simple undirected PyG graph.""" + directed_edges = [] + for source, target in edge_pairs: + directed_edges.extend([(source, target), (target, source)]) + edge_index = torch.tensor(directed_edges, dtype=torch.long) + if directed_edges: + edge_index = edge_index.t().contiguous() + else: + edge_index = torch.empty((2, 0), dtype=torch.long) + return Data(x=torch.randn(num_nodes, feature_dim), edge_index=edge_index) + + +@pytest.mark.parametrize( + ("kwargs", "message"), + [ + ({"in_channels": 0, "hidden_channels": 8}, "positive"), + ({"in_channels": 5, "hidden_channels": 0}, "positive"), + ( + {"in_channels": 5, "hidden_channels": 8, "num_layers": 0}, + "num_layers", + ), + ( + {"in_channels": 5, "hidden_channels": 8, "num_samples": 0}, + "num_samples", + ), + ( + {"in_channels": 5, "hidden_channels": 8, "cse_steps": 0}, + "cse_steps", + ), + ({"in_channels": 5, "hidden_channels": 8, "dropout": 1.1}, "dropout"), + ( + { + "in_channels": 5, + "hidden_channels": 8, + "sample_aggregation": "max", + }, + "sample_aggregation", + ), + ( + {"in_channels": 5, "hidden_channels": 8, "cache_size": -1}, + "cache_size", + ), + ( + {"in_channels": 5, "hidden_channels": 8, "cse_channels": 8}, + "cse_channels", + ), + ], +) +def test_hymn_rejects_invalid_configuration(kwargs, message): + """Every constructor constraint raises a useful error.""" + with pytest.raises(ValueError, match=message): + HyMN(**kwargs) + + +def test_hymn_graph_key_is_edge_order_independent(): + """The cache key preserves graph structure but ignores COO ordering.""" + edge_index = torch.tensor([[0, 1, 1], [1, 0, 2]]) + reordered = edge_index[:, torch.tensor([2, 0, 1])] + + assert HyMN._graph_key(3, edge_index) == HyMN._graph_key(3, reordered) + assert HyMN._graph_key(4, edge_index) != HyMN._graph_key(3, edge_index) + assert HyMN._graph_key(0, torch.empty((2, 0), dtype=torch.long)) == ( + 0, + b"", + ) + + +def test_hymn_cse_and_selection_match_equation_four(): + """CSE columns equal diag(A^k)/k! and rank the top-SC node first.""" + graph = _graph( + [(0, 1), (1, 2), (2, 0), (0, 3), (3, 4), (4, 0)], + 5, + ) + roots, cse = HyMN._compute_graph_statistics( + graph.num_nodes, + graph.edge_index, + num_marked_views=2, + cse_steps=3, + ) + + assert roots[0] == 0 + torch.testing.assert_close(cse[:, 0], torch.zeros(5)) + torch.testing.assert_close( + cse[:, 1], torch.tensor([2.0, 1.0, 1.0, 1.0, 1.0]) + ) + assert cse[0, 2] == pytest.approx(2 / 3) + assert cse[1, 2] == pytest.approx(1 / 3) + + +def test_hymn_statistics_handle_empty_and_oversampled_graphs(): + """Algorithm 1 statistics cover empty graphs and the reference T>|V| rule.""" + empty_roots, empty_cse = HyMN._compute_graph_statistics( + 0, + torch.empty((2, 0), dtype=torch.long), + num_marked_views=2, + cse_steps=3, + ) + roots, cse = HyMN._compute_graph_statistics( + 1, + torch.empty((2, 0), dtype=torch.long), + num_marked_views=3, + cse_steps=2, + ) + + assert empty_roots == () + assert empty_cse.shape == (0, 3) + assert roots == (0, 0, 0) + torch.testing.assert_close(cse, torch.zeros((1, 2))) + with pytest.raises(ValueError, match="num_nodes"): + HyMN._compute_graph_statistics(-1, torch.empty((2, 0)), 1) + with pytest.raises(ValueError, match="num_marked_views"): + HyMN._compute_graph_statistics(1, torch.empty((2, 0)), -1) + + +def test_hymn_batch_statistics_mark_roots_and_use_lru_cache(): + """Batched preprocessing is graph-local and reuses cached CSEs.""" + first = _graph([(0, 1), (1, 2), (2, 0)], 3) + second = _graph([(0, 1)], 2) + batched = Batch.from_data_list([first, second]) + model = HyMN( + 5, 8, num_samples=2, cse_steps=3, cse_channels=2, cache_size=2 + ) + + markers, cse = model._statistics_for_batch( + batched.edge_index, + batched.batch, + batched.num_nodes, + ) + cached_markers, cached_cse = model._statistics_for_batch( + batched.edge_index, + batched.batch, + batched.num_nodes, + ) + + assert markers.shape == (5, 2) + assert markers[:, 0].sum() == 0 + assert markers[:3, 1].sum() == 1 + assert markers[3:, 1].sum() == 1 + assert cse.shape == (5, 3) + assert len(model._statistics_cache) == 2 + torch.testing.assert_close(markers, cached_markers) + torch.testing.assert_close(cse, cached_cse) + + +def test_hymn_batch_statistics_validate_assignments_and_skip_empty_ids(): + """Batched CSE validates assignments and tolerates empty graph IDs.""" + model = HyMN(5, 8, cse_steps=2, cse_channels=2, cache_size=0) + edge_index = torch.empty((2, 0), dtype=torch.long) + batch_with_gap = torch.tensor([0, 0, 2, 2]) + markers, cse = model._statistics_for_batch(edge_index, batch_with_gap, 4) + + assert markers.shape == (4, 3) + assert cse.shape == (4, 2) + assert not model._statistics_cache + with pytest.raises(ValueError, match="assign every node"): + model._statistics_for_batch(edge_index, torch.tensor([[0, 0]]), 2) + with pytest.raises(ValueError, match="negative"): + model._statistics_for_batch(edge_index, torch.tensor([-1, -1]), 2) + with pytest.raises(ValueError, match="grouped"): + model._statistics_for_batch( + edge_index, + torch.tensor([0, 1, 0, 1]), + 4, + ) + + +def test_hymn_expand_views_offsets_edges_and_markers(): + """The augmented bag is materialized as independent disjoint views.""" + model = HyMN(2, 4, num_samples=2, cse_steps=2, cse_channels=2) + x = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) + edge_index = torch.tensor([[0, 1], [1, 0]]) + markers = torch.tensor([[0.0, 1.0], [0.0, 0.0]]) + + expanded_x, expanded_edges, expanded_markers = model._expand_views( + x, + edge_index, + markers, + ) + + torch.testing.assert_close(expanded_x, x.repeat(2, 1)) + assert expanded_edges.tolist() == [[0, 1, 2, 3], [1, 0, 3, 2]] + assert expanded_markers.squeeze(-1).tolist() == [0.0, 0.0, 1.0, 0.0] + + +def test_hymn_forward_and_gradients(): + """HyMN returns node-aligned, finite, differentiable embeddings.""" + graph = _graph([(0, 1), (1, 2), (2, 0), (2, 3)], 4) + model = HyMN( + 5, + 8, + num_layers=2, + num_samples=3, + cse_steps=3, + cse_channels=2, + ) + output = model(graph.x, graph.edge_index) + + assert output.shape == (4, 8) + assert torch.isfinite(output).all() + output.sum().backward() + assert all( + parameter.grad is not None + for parameter in model.parameters() + if parameter.requires_grad + ) + + +def test_hymn_supports_no_cse_no_norm_no_residual_and_sum(): + """Paper-supported ablations and sum view pooling execute together.""" + graph = _graph([(0, 1), (1, 2)], 3) + model = HyMN( + 5, + 8, + num_layers=1, + num_samples=2, + cse_steps=2, + batch_norm=False, + residual=False, + use_centrality_encoding=False, + sample_aggregation="sum", + ) + + output = model(graph.x, graph.edge_index) + + assert model.cse_norm is None + assert model.cse_encoder is None + assert output.shape == (3, 8) + + +def test_hymn_mean_and_sum_aggregation_are_consistent(): + """Sum aggregation equals mean aggregation times the bag size.""" + graph = _graph([(0, 1), (1, 2), (2, 0)], 3) + model = HyMN(5, 8, num_samples=3, cse_steps=3, cse_channels=2).eval() + + mean_output = model(graph.x, graph.edge_index) + model.sample_aggregation = "sum" + sum_output = model(graph.x, graph.edge_index) + + torch.testing.assert_close(sum_output, mean_output * model.num_samples) + + +def test_hymn_is_deterministic_and_batch_local_in_eval_mode(): + """Selection is deterministic and another graph cannot change outputs.""" + first = _graph([(0, 1), (1, 2), (2, 0), (2, 3)], 4) + second = _graph([(0, 1), (1, 2), (2, 3)], 4) + model = HyMN( + 5, + 8, + num_layers=2, + num_samples=3, + cse_steps=3, + cse_channels=2, + dropout=0.2, + ).eval() + + isolated = model(first.x, first.edge_index) + repeated = model(first.x, first.edge_index) + batched = Batch.from_data_list([first, second]) + combined = model(batched.x, batched.edge_index, batch=batched.batch) + + torch.testing.assert_close(isolated, repeated) + torch.testing.assert_close(isolated, combined[: first.num_nodes]) + + +def test_hymn_permutation_equivariance_with_unique_root(): + """Relabelling an asymmetric graph relabels node embeddings.""" + graph = _graph( + [(0, 1), (1, 2), (2, 0), (0, 3), (0, 4), (4, 5)], + 6, + ) + permutation = torch.tensor([3, 0, 5, 2, 1, 4]) + inverse = torch.empty_like(permutation) + inverse[permutation] = torch.arange(permutation.numel()) + permuted = Data( + x=graph.x[permutation], + edge_index=inverse[graph.edge_index], + ) + model = HyMN( + 5, + 8, + num_layers=2, + num_samples=2, + cse_steps=4, + cse_channels=2, + ).eval() + + original_output = model(graph.x, graph.edge_index) + permuted_output = model(permuted.x, permuted.edge_index) + torch.testing.assert_close( + original_output[permutation], + permuted_output, + rtol=1e-5, + atol=1e-6, + ) + + +def test_hymn_forward_validates_tensor_shapes(): + """Forward rejects malformed node and edge tensors.""" + model = HyMN(5, 8, cse_steps=2, cse_channels=2) + with pytest.raises(ValueError, match="two-dimensional"): + model(torch.randn(5), torch.empty((2, 0), dtype=torch.long)) + with pytest.raises(ValueError, match="edge_index"): + model(torch.randn(2, 5), torch.empty((3, 0), dtype=torch.long)) diff --git a/test/pipeline/test_pipeline.py b/test/pipeline/test_pipeline.py index a61165ae9..2b6b32b04 100644 --- a/test/pipeline/test_pipeline.py +++ b/test/pipeline/test_pipeline.py @@ -1,13 +1,11 @@ """Test pipeline for a particular dataset and model.""" import hydra -import pytest from test._utils.simplified_pipeline import run - -DATASET = "graph/MUTAG" # ADD YOUR DATASET HERE -MODELS = ["graph/gcn", "cell/topotune", "simplicial/topotune"] # ADD ONE OR SEVERAL MODELS +DATASET = "graph/MUTAG" +MODELS = ["graph/hymn"] class TestPipeline: @@ -34,6 +32,6 @@ def test_pipeline(self): "paths=test", "callbacks=model_checkpoint", ], - return_hydra_config=True + return_hydra_config=True, ) run(cfg) diff --git a/topobench/nn/backbones/graph/hymn.py b/topobench/nn/backbones/graph/hymn.py new file mode 100644 index 000000000..f9d4c4762 --- /dev/null +++ b/topobench/nn/backbones/graph/hymn.py @@ -0,0 +1,441 @@ +"""Hybrid Marking Network (HyMN) graph backbone. + +This module implements the centrality structural encodings, centrality-guided +node marking, and shared message passing proposed by Southern et al. [1]. The +implementation follows Algorithm 1 and Equations (4) and (45)--(48) of the +paper, and the authors' reference implementation [2]. + +References +---------- +[1] J. Southern et al., "Balancing Efficiency and Expressiveness: Subgraph + GNNs with Walk-Based Centrality," ICML 2025. +[2] https://github.com/jks17/HyMN (``graphgps/network/colour_gnn.py`` and + ``graphgps/transform/posenc_stats.py``). +""" + +from __future__ import annotations + +import math +from collections import OrderedDict + +import torch +from torch import nn +from torch_geometric.nn import GINConv + + +class HyMN(nn.Module): + r"""Centrality-guided Hybrid Marking Network. + + For every input graph, HyMN computes the order-:math:`K` Centrality + Structural Encoding (CSE) + + .. math:: + C^{\mathrm{CSE}}_{v,k} = (A^k)_{vv} / k!, + + selects the :math:`T` nodes with the largest truncated Subgraph Centrality, + and processes an unmarked view plus the :math:`T` marked views with shared + GIN layers. These are Algorithm 1, Equation (4), and Equations (45)--(48) + in [1]. As in the reference implementation, the CSE is batch-normalized, + linearly encoded, and concatenated with the input node representation. + + Views are represented as a disjoint expanded graph during message passing, + then reduced back to node-aligned embeddings before TopoBench's task + readout. This is algebraically the same node-wise view aggregation used by + ``MeanAveraging``/``SumAveraging`` in [2], while satisfying TopoBench's + backbone API for both node- and graph-level tasks. + + Parameters + ---------- + in_channels : int + Number of input node features after TopoBench's feature encoder. + hidden_channels : int + Width of the shared marked GIN and output node embeddings. + num_layers : int, optional + Number of shared GIN message-passing layers. + num_samples : int, optional + Total number of views. Following the augmented policy in Equation + (45), this includes one unmarked view and ``num_samples - 1`` marked + views. + cse_steps : int, optional + Maximum walk length :math:`K` in Equation (4). + cse_channels : int, optional + Width of the linearly encoded CSE. + dropout : float, optional + Dropout after each marked GIN layer. + train_eps : bool, optional + Whether GIN's epsilon parameters are trainable. + batch_norm : bool, optional + Whether the two-layer GIN MLPs use Batch Normalization, as in [2]. + residual : bool, optional + Whether to use the layer-wise residual connections reported in [1]. + use_centrality_encoding : bool, optional + Whether to concatenate CSEs to node features. Disabling this exposes + the paper's ``HyMN no CSE`` ablation without changing node selection. + sample_aggregation : str, optional + ``"mean"`` or ``"sum"`` aggregation over the augmented bag, both used + in the reference configurations. + cache_size : int, optional + Maximum number of per-graph CSE computations retained by a model. + **kwargs : dict, optional + Extra configuration values accepted for TopoBench compatibility. + + Notes + ----- + Node features are expected to be ordered graph-by-graph when ``batch`` is + supplied, as they are in :class:`torch_geometric.data.Batch`. + """ + + def __init__( + self, + in_channels: int, + hidden_channels: int, + num_layers: int = 2, + num_samples: int = 3, + cse_steps: int = 16, + cse_channels: int = 16, + dropout: float = 0.0, + train_eps: bool = True, + batch_norm: bool = True, + residual: bool = True, + use_centrality_encoding: bool = True, + sample_aggregation: str = "mean", + cache_size: int = 4096, + **kwargs, + ) -> None: + super().__init__() + if in_channels < 1 or hidden_channels < 1: + raise ValueError( + "in_channels and hidden_channels must be positive" + ) + if num_layers < 1: + raise ValueError("num_layers must be positive") + if num_samples < 1: + raise ValueError("num_samples must include the unmarked view") + if cse_steps < 1: + raise ValueError("cse_steps must be positive") + if not 0.0 <= dropout <= 1.0: + raise ValueError("dropout must be between zero and one") + if sample_aggregation not in {"mean", "sum"}: + raise ValueError("sample_aggregation must be mean or sum") + if cache_size < 0: + raise ValueError("cache_size cannot be negative") + if use_centrality_encoding and not 0 < cse_channels < hidden_channels: + raise ValueError( + "cse_channels must lie between zero and hidden_channels" + ) + + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = hidden_channels + self.num_layers = num_layers + self.num_samples = num_samples + self.cse_steps = cse_steps + self.cse_channels = cse_channels + self.dropout = dropout + self.train_eps = train_eps + self.batch_norm = batch_norm + self.residual = residual + self.use_centrality_encoding = use_centrality_encoding + self.sample_aggregation = sample_aggregation + self.cache_size = cache_size + + node_channels = ( + hidden_channels - cse_channels + if use_centrality_encoding + else hidden_channels + ) + self.node_encoder = ( + nn.Identity() + if in_channels == node_channels + else nn.Linear(in_channels, node_channels) + ) + if use_centrality_encoding: + self.cse_norm = nn.BatchNorm1d(cse_steps) + self.cse_encoder = nn.Linear(cse_steps, cse_channels) + else: + self.cse_norm = None + self.cse_encoder = None + + self.convs = nn.ModuleList() + for _ in range(num_layers): + mlp = nn.Sequential( + nn.Linear(hidden_channels + 1, hidden_channels), + nn.BatchNorm1d(hidden_channels) + if batch_norm + else nn.Identity(), + nn.ReLU(), + nn.Linear(hidden_channels, hidden_channels), + ) + self.convs.append(GINConv(mlp, train_eps=train_eps)) + self.dropout_layer = nn.Dropout(dropout) + self._statistics_cache: OrderedDict[ + tuple[int, bytes], tuple[tuple[int, ...], torch.Tensor] + ] = OrderedDict() + + @staticmethod + def _graph_key( + num_nodes: int, + local_edge_index: torch.Tensor, + ) -> tuple[int, bytes]: + """Return an exact, edge-order-independent key for the CSE cache. + + Parameters + ---------- + num_nodes : int + Number of nodes in the graph. + local_edge_index : torch.Tensor + Local COO edge indices of shape ``[2, num_edges]``. + + Returns + ------- + tuple[int, bytes] + Node count and sorted linearized edge list. + """ + if local_edge_index.numel() == 0: + return num_nodes, b"" + linear_edges = local_edge_index[0].to( + torch.int64 + ) * num_nodes + local_edge_index[1].to(torch.int64) + edge_bytes = torch.sort(linear_edges.cpu()).values.numpy().tobytes() + return num_nodes, edge_bytes + + @staticmethod + def _compute_graph_statistics( + num_nodes: int, + local_edge_index: torch.Tensor, + num_marked_views: int, + cse_steps: int = 16, + ) -> tuple[tuple[int, ...], torch.Tensor]: + r"""Compute Algorithm 1's CSE and top-centrality marked nodes. + + The returned columns are :math:`\operatorname{diag}(A^k)/k!` for + :math:`k=1,\ldots,K`, matching ``centrality_posenc`` in [2]. Their sum + is the paper's truncated estimate of Subgraph Centrality. + + Parameters + ---------- + num_nodes : int + Number of nodes in the graph. + local_edge_index : torch.Tensor + Local COO edge indices of shape ``[2, num_edges]``. + num_marked_views : int + Number :math:`T` of centrality-ranked nodes to mark. + cse_steps : int, optional + Maximum walk length :math:`K`. + + Returns + ------- + tuple[tuple[int, ...], torch.Tensor] + Selected local node indices and the ``[num_nodes, cse_steps]`` CSE. + """ + if num_nodes < 0: + raise ValueError("num_nodes cannot be negative") + if num_marked_views < 0: + raise ValueError("num_marked_views cannot be negative") + + adjacency = torch.zeros((num_nodes, num_nodes), dtype=torch.float32) + if local_edge_index.numel(): + edges = local_edge_index.to(device="cpu", dtype=torch.long) + adjacency.index_put_( + (edges[0], edges[1]), + torch.ones(edges.size(1), dtype=adjacency.dtype), + accumulate=True, + ) + + encodings = [] + adjacency_power = adjacency + for walk_length in range(1, cse_steps + 1): + encodings.append( + torch.diagonal(adjacency_power) / math.factorial(walk_length) + ) + adjacency_power = adjacency_power @ adjacency + cse = torch.stack(encodings, dim=-1) + + if num_nodes == 0: + roots: tuple[int, ...] = () + else: + order = torch.argsort(-cse.sum(dim=-1), stable=True) + # The authors' implementation retains T views even when T > |V|, + # filling the remaining marks with node zero. + selected = torch.zeros(num_marked_views, dtype=torch.long) + available = min(num_nodes, num_marked_views) + selected[:available] = order[:available] + roots = tuple(int(index) for index in selected) + return roots, cse + + def _statistics_for_batch( + self, + edge_index: torch.Tensor, + batch: torch.Tensor, + num_nodes: int, + ) -> tuple[torch.Tensor, torch.Tensor]: + """Compute or retrieve Algorithm 1 statistics for a PyG batch. + + Parameters + ---------- + edge_index : torch.Tensor + Batched COO edge indices. + batch : torch.Tensor + Graph assignment for every node. + num_nodes : int + Total number of nodes. + + Returns + ------- + tuple[torch.Tensor, torch.Tensor] + Marker matrix ``[num_nodes, num_samples]`` and CSE matrix + ``[num_nodes, cse_steps]`` on ``edge_index.device``. + """ + if batch.ndim != 1 or batch.numel() != num_nodes: + raise ValueError("batch must assign every node to one graph") + if batch.numel() and int(batch.min()) < 0: + raise ValueError("batch graph indices cannot be negative") + + markers = torch.zeros( + (num_nodes, self.num_samples), + dtype=torch.float32, + device=edge_index.device, + ) + cse = torch.zeros( + (num_nodes, self.cse_steps), + dtype=torch.float32, + device=edge_index.device, + ) + num_graphs = int(batch.max()) + 1 if batch.numel() else 0 + graph_sizes = ( + torch.bincount(batch, minlength=num_graphs).cpu().tolist() + ) + + node_offset = 0 + for graph_index, graph_size in enumerate(graph_sizes): + next_offset = node_offset + graph_size + if graph_size == 0: + node_offset = next_offset + continue + if not torch.all(batch[node_offset:next_offset] == graph_index): + raise ValueError("batch nodes must be grouped by graph") + + edge_mask = batch[edge_index[0]] == graph_index + local_edges = (edge_index[:, edge_mask] - node_offset).cpu() + key = self._graph_key(graph_size, local_edges) + cached = self._statistics_cache.get(key) + if cached is None: + cached = self._compute_graph_statistics( + graph_size, + local_edges, + self.num_samples - 1, + self.cse_steps, + ) + if self.cache_size: + self._statistics_cache[key] = cached + if len(self._statistics_cache) > self.cache_size: + self._statistics_cache.popitem(last=False) + elif self.cache_size: + self._statistics_cache.move_to_end(key) + + roots, graph_cse = cached + for sample_index, root in enumerate(roots, start=1): + markers[node_offset + root, sample_index] = 1.0 + cse[node_offset:next_offset] = graph_cse.to(edge_index.device) + node_offset = next_offset + return markers, cse + + def _expand_views( + self, + x: torch.Tensor, + edge_index: torch.Tensor, + markers: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Materialize the augmented bag as one disjoint expanded graph. + + Parameters + ---------- + x : torch.Tensor + Node features shared by every view. + edge_index : torch.Tensor + Original batched COO edge indices. + markers : torch.Tensor + Marker matrix with one column per view. + + Returns + ------- + tuple[torch.Tensor, torch.Tensor, torch.Tensor] + Repeated features, view-offset edges, and flattened markers. + """ + num_nodes = x.size(0) + expanded_x = x.repeat(self.num_samples, 1) + offsets = ( + torch.arange(self.num_samples, device=edge_index.device) + * num_nodes + ) + expanded_edges = edge_index.unsqueeze(0) + offsets[:, None, None] + expanded_edges = expanded_edges.permute(1, 0, 2).reshape(2, -1) + expanded_markers = markers.transpose(0, 1).reshape(-1, 1) + return expanded_x, expanded_edges, expanded_markers + + def forward( + self, + x: torch.Tensor, + edge_index: torch.Tensor, + batch: torch.Tensor | None = None, + **kwargs, + ) -> torch.Tensor: + """Encode the unmarked graph and its top-centrality marked views. + + Parameters + ---------- + x : torch.Tensor + Node feature matrix of shape ``[num_nodes, in_channels]``. + edge_index : torch.Tensor + COO edge indices of shape ``[2, num_edges]``. + batch : torch.Tensor, optional + Graph assignment for each node. A single graph is assumed when + omitted. + **kwargs : dict, optional + Extra wrapper arguments, such as unused edge weights. + + Returns + ------- + torch.Tensor + Node-aligned view-aggregated embeddings of shape + ``[num_nodes, hidden_channels]``. + """ + if x.ndim != 2: + raise ValueError("x must be a two-dimensional node feature matrix") + if edge_index.ndim != 2 or edge_index.size(0) != 2: + raise ValueError("edge_index must have shape [2, num_edges]") + if batch is None: + batch = torch.zeros(x.size(0), dtype=torch.long, device=x.device) + + with torch.no_grad(): + markers, cse = self._statistics_for_batch( + edge_index, + batch, + x.size(0), + ) + + node_features = self.node_encoder(x) + if self.use_centrality_encoding: + cse = cse.to(dtype=x.dtype) + cse_features = self.cse_encoder(self.cse_norm(cse)) + node_features = torch.cat((node_features, cse_features), dim=-1) + + hidden, expanded_edges, expanded_markers = self._expand_views( + node_features, + edge_index, + markers.to(dtype=x.dtype), + ) + for conv in self.convs: + marked_hidden = torch.cat((hidden, expanded_markers), dim=-1) + update = torch.relu(conv(marked_hidden, expanded_edges)) + update = self.dropout_layer(update) + hidden = hidden + update if self.residual else update + + view_embeddings = hidden.reshape( + self.num_samples, + x.size(0), + self.hidden_channels, + ) + if self.sample_aggregation == "mean": + return view_embeddings.mean(dim=0) + return view_embeddings.sum(dim=0) From e7bdea06170f19db048640ce8ecec7937c4b8de9 Mon Sep 17 00:00:00 2001 From: weeye Date: Sun, 2 Aug 2026 20:08:21 +0530 Subject: [PATCH 2/2] updates in desc + missing files --- .../outputs/2026-08-02_13-03-24/results.json | 5272 +++++++++++++++++ configs/model/graph/hymn.yaml | 23 +- test/nn/backbones/graph/test_hymn.py | 113 +- topobench/nn/backbones/graph/hymn.py | 63 +- 4 files changed, 5455 insertions(+), 16 deletions(-) create mode 100644 2026_tdl_challenge/outputs/2026-08-02_13-03-24/results.json diff --git a/2026_tdl_challenge/outputs/2026-08-02_13-03-24/results.json b/2026_tdl_challenge/outputs/2026-08-02_13-03-24/results.json new file mode 100644 index 000000000..046f1bd99 --- /dev/null +++ b/2026_tdl_challenge/outputs/2026-08-02_13-03-24/results.json @@ -0,0 +1,5272 @@ +{ + "metadata": { + "study_id": "2026-08-02_13-03-24", + "model_config": "graph/hymn", + "generated_at_utc": "2026-08-02T13:50:21.507574+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": "hymn_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": 2.369328498840332, + "test_best_rerun_accuracy": 0.2861563265323639, + "test_best_rerun_mse": null, + 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"/root/topobench-hymn/logs/train/runs/notebook_gu_grid_2026-08-02_13-03-24__triangle_counting__11__h_hi__d_hi__pl_hi__s43" + }, + { + "experiment": "triangle_counting", + "wandb_project": "challenge_triangle_counting", + "wandb_run_name": "hymn_hom_0.9-1__deg_4-5__gamma_4-5__s44", + "train_seed": 44, + "homophily": "h_hi", + "avg_degree": "d_hi", + "power_law": "pl_hi", + "run_slug": "h_hi__d_hi__pl_hi", + "test_loss": 12017.615234375, + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 12594.6748046875, + "test_triangles_total_structural": 60093.0, + "test_mse_by_total_triangles": 0.20958638784363404, + "ood_test": { + "h_lo__d_lo__pl_lo": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 12124.26171875, + "test_triangles_total_structural": 1388.0, + "test_mse_by_total_triangles": 8.735058875180115 + }, + "h_lo__d_lo__pl_hi": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 7799.87353515625, + "test_triangles_total_structural": 190.0, + 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"test_triangles_total_structural": 43064.0, + "test_mse_by_total_triangles": 1.1719120995785344 + }, + "h_mid__d_hi__pl_hi": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 10879.400390625, + "test_triangles_total_structural": 14262.0, + "test_mse_by_total_triangles": 0.7628243157078249 + }, + "h_hi__d_lo__pl_lo": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 5888.3291015625, + "test_triangles_total_structural": 19509.0, + "test_mse_by_total_triangles": 0.30182629051014914 + }, + "h_hi__d_lo__pl_hi": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 5815.607421875, + "test_triangles_total_structural": 5625.0, + "test_mse_by_total_triangles": 1.033885763888889 + }, + "h_hi__d_hi__pl_lo": { + "test_best_rerun_accuracy": null, + "test_best_rerun_mse": 974160.1875, + "test_triangles_total_structural": 88403.0, + "test_mse_by_total_triangles": 11.019537657093085 + } + }, + "output_dir": "/root/topobench-hymn/logs/train/runs/notebook_gu_grid_2026-08-02_13-03-24__triangle_counting__11__h_hi__d_hi__pl_hi__s44" + } + ] +} diff --git a/configs/model/graph/hymn.yaml b/configs/model/graph/hymn.yaml index 3efbf4950..b6c45c02e 100644 --- a/configs/model/graph/hymn.yaml +++ b/configs/model/graph/hymn.yaml @@ -7,31 +7,37 @@ feature_encoder: _target_: topobench.nn.encoders.${model.feature_encoder.encoder_name} encoder_name: AllCellFeatureEncoder in_channels: ${infer_in_channels:${dataset},${oc.select:transforms,null}} - # The reference model reserves 16 channels for the encoded CSE. - out_channels: 48 + # The MolHIV reference config uses dim_inner=300 and reserves 16 + # channels for the encoded CSE, leaving 284 node-feature channels. + out_channels: 284 proj_dropout: 0.0 backbone: _target_: topobench.nn.backbones.HyMN in_channels: ${model.feature_encoder.out_channels} - hidden_channels: 64 - # Published MolHIV HyMN (GIN, T=2) settings, translated to TopoBench. + hidden_channels: 300 + # Published MolHIV HyMN hyperparameters (Table 18 / with-CSE config), + # paired with the authors' edge-less marked-GIN path for unattributed graphs. num_layers: 2 + # Two centrality-marked views plus the augmented policy's unmarked view. num_samples: 3 cse_steps: 16 cse_channels: 16 dropout: 0.0 train_eps: true + # Internal shared-MLP BN; the reference's separate between-layer BN is off. batch_norm: true residual: true use_centrality_encoding: true sample_aggregation: mean + # Semantic-only reuse of reference-style preprocessing across model instances. + global_cache_size: 65536 backbone_wrapper: _target_: topobench.nn.wrappers.${model.backbone_wrapper.wrapper_name} _partial_: true wrapper_name: GNNWrapper - out_channels: 64 + out_channels: 300 residual_connections: false num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} @@ -39,11 +45,12 @@ readout: _target_: topobench.nn.readouts.${model.readout.readout_name} readout_name: MLPReadout num_cell_dimensions: ${infer_num_cell_dimensions:${oc.select:model.feature_encoder.selected_dimensions,null},${model.feature_encoder.in_channels}} - in_channels: 64 - hidden_layers: [64] + in_channels: 300 + # layers_post_mp=1 in the reference config is a single linear head. + hidden_layers: [] out_channels: ${dataset.parameters.num_classes} task_level: ${define_task_level:${dataset.parameters.task_level},${dataset.split_params.learning_setting}} - pooling_type: sum + pooling_type: mean dropout: 0.0 act: relu norm: null diff --git a/test/nn/backbones/graph/test_hymn.py b/test/nn/backbones/graph/test_hymn.py index 77287726f..ec33c3832 100644 --- a/test/nn/backbones/graph/test_hymn.py +++ b/test/nn/backbones/graph/test_hymn.py @@ -4,7 +4,10 @@ import torch from torch_geometric.data import Batch, Data -from topobench.nn.backbones.graph.hymn import HyMN +from topobench.nn.backbones.graph.hymn import ( + _GLOBAL_STATISTICS_CACHE, + HyMN, +) def _graph(edge_pairs, num_nodes, feature_dim=5): @@ -50,6 +53,14 @@ def _graph(edge_pairs, num_nodes, feature_dim=5): {"in_channels": 5, "hidden_channels": 8, "cache_size": -1}, "cache_size", ), + ( + { + "in_channels": 5, + "hidden_channels": 8, + "global_cache_size": -1, + }, + "global_cache_size", + ), ( {"in_channels": 5, "hidden_channels": 8, "cse_channels": 8}, "cse_channels", @@ -152,6 +163,57 @@ def test_hymn_batch_statistics_mark_roots_and_use_lru_cache(): torch.testing.assert_close(cse, cached_cse) +def test_hymn_global_cache_reuses_reference_preprocessing(monkeypatch): + """Independent models reuse the authors' deterministic preprocessing.""" + graph = _graph([(0, 1), (1, 2), (2, 0)], 3) + batch = torch.zeros(graph.num_nodes, dtype=torch.long) + _GLOBAL_STATISTICS_CACHE.clear() + try: + first = HyMN( + 5, + 8, + num_samples=2, + cse_steps=3, + cse_channels=2, + cache_size=0, + global_cache_size=2, + ) + expected = first._statistics_for_batch( + graph.edge_index, + batch, + graph.num_nodes, + ) + assert len(_GLOBAL_STATISTICS_CACHE) == 1 + + def fail_if_recomputed(*args, **kwargs): + raise AssertionError("global cache entry was recomputed") + + monkeypatch.setattr( + HyMN, + "_compute_graph_statistics", + staticmethod(fail_if_recomputed), + ) + second = HyMN( + 5, + 8, + num_samples=2, + cse_steps=3, + cse_channels=2, + cache_size=0, + global_cache_size=2, + ) + actual = second._statistics_for_batch( + graph.edge_index, + batch, + graph.num_nodes, + ) + + torch.testing.assert_close(actual[0], expected[0]) + torch.testing.assert_close(actual[1], expected[1]) + finally: + _GLOBAL_STATISTICS_CACHE.clear() + + def test_hymn_batch_statistics_validate_assignments_and_skip_empty_ids(): """Batched CSE validates assignments and tolerates empty graph IDs.""" model = HyMN(5, 8, cse_steps=2, cse_channels=2, cache_size=0) @@ -192,6 +254,55 @@ def test_hymn_expand_views_offsets_edges_and_markers(): assert expanded_markers.squeeze(-1).tolist() == [0.0, 0.0, 1.0, 0.0] +def test_hymn_disjoint_views_match_authors_wide_tensor_update(): + """Disjoint views exactly reproduce the authors' shared wide-tensor GIN.""" + graph = _graph([(0, 1), (1, 2), (2, 0)], 3, feature_dim=4) + model = HyMN( + 4, + 4, + num_layers=2, + num_samples=3, + cse_steps=2, + use_centrality_encoding=False, + dropout=0.0, + residual=True, + ).eval() + markers = torch.tensor([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]]) + + disjoint, expanded_edges, expanded_markers = model._expand_views( + graph.x, + graph.edge_index, + markers, + ) + reference = graph.x[:, None, :].expand(-1, model.num_samples, -1) + source, target = graph.edge_index + + for conv in model.convs: + disjoint_input = torch.cat((disjoint, expanded_markers), dim=-1) + disjoint_update = torch.relu(conv(disjoint_input, expanded_edges)) + disjoint = disjoint + disjoint_update + + # The reference implementation stores all views in one wide tensor, + # aggregates each view independently, then applies one shared MLP. + reference_input = torch.cat((reference, markers.unsqueeze(-1)), dim=-1) + wide_input = reference_input.reshape(graph.num_nodes, -1) + wide_messages = torch.zeros_like(wide_input) + wide_messages.index_add_(0, target, wide_input[source]) + wide_update = (1 + conv.eps) * wide_input + wide_messages + reference_update = torch.relu( + conv.nn(wide_update.reshape(-1, model.hidden_channels + 1)) + ).reshape(graph.num_nodes, model.num_samples, model.hidden_channels) + reference = reference + reference_update + + disjoint = disjoint.reshape( + model.num_samples, + graph.num_nodes, + model.hidden_channels, + ).permute(1, 0, 2) + torch.testing.assert_close(disjoint, reference) + torch.testing.assert_close(disjoint.mean(dim=1), reference.mean(dim=1)) + + def test_hymn_forward_and_gradients(): """HyMN returns node-aligned, finite, differentiable embeddings.""" graph = _graph([(0, 1), (1, 2), (2, 0), (2, 3)], 4) diff --git a/topobench/nn/backbones/graph/hymn.py b/topobench/nn/backbones/graph/hymn.py index f9d4c4762..6ff5dde49 100644 --- a/topobench/nn/backbones/graph/hymn.py +++ b/topobench/nn/backbones/graph/hymn.py @@ -8,9 +8,11 @@ References ---------- [1] J. Southern et al., "Balancing Efficiency and Expressiveness: Subgraph - GNNs with Walk-Based Centrality," ICML 2025. -[2] https://github.com/jks17/HyMN (``graphgps/network/colour_gnn.py`` and - ``graphgps/transform/posenc_stats.py``). + GNNs with Walk-Based Centrality," ICML 2025, + https://proceedings.mlr.press/v267/southern25a.html. +[2] J. Southern et al., HyMN reference implementation, revision + ``adde55268307ff69527375757ec31a146d59ccae``, + https://github.com/jks17/HyMN. """ from __future__ import annotations @@ -22,6 +24,13 @@ from torch import nn from torch_geometric.nn import GINConv +# The reference implementation computes CSEs once in the dataset transform. +# TopoBench constructs a fresh backbone for every benchmark run, so a bounded +# process cache preserves that preprocessing behavior across model instances. +_GLOBAL_STATISTICS_CACHE: OrderedDict[ + tuple[int, int, int, bytes], tuple[tuple[int, ...], torch.Tensor] +] = OrderedDict() + class HyMN(nn.Module): r"""Centrality-guided Hybrid Marking Network. @@ -76,6 +85,10 @@ class HyMN(nn.Module): in the reference configurations. cache_size : int, optional Maximum number of per-graph CSE computations retained by a model. + global_cache_size : int, optional + Maximum number of CSE computations shared across model instances in a + process. This mirrors the reference implementation's dataset-level + preprocessing when TopoBench creates a model per benchmark run. **kwargs : dict, optional Extra configuration values accepted for TopoBench compatibility. @@ -83,6 +96,22 @@ class HyMN(nn.Module): ----- Node features are expected to be ordered graph-by-graph when ``batch`` is supplied, as they are in :class:`torch_geometric.data.Batch`. + + The authors use marked GINE layers when molecular bond attributes are + available and marked GIN layers for unattributed counting graphs. The + standard TopoBench :class:`~topobench.nn.wrappers.GNNWrapper` does not + expose bond attributes, so this backbone follows the latter, edge-less + reference path (``counting_substructures/conv.py`` in [2]). It contains + no GraphUniverse-specific features, labels, or task logic. + + Exact centrality ties are resolved by stable node-index order. This is a + deterministic instance of the arbitrary tie-breaking policy explicitly + allowed by [1]; as with the authors' ``torch.argsort`` implementation, + tied selections need not be permutation equivariant. + + Both statistics caches store only the deterministic output of Algorithm + 1. They reproduce the authors' dataset-level preprocessing without + changing the mathematical forward pass. """ def __init__( @@ -100,6 +129,7 @@ def __init__( use_centrality_encoding: bool = True, sample_aggregation: str = "mean", cache_size: int = 4096, + global_cache_size: int = 65536, **kwargs, ) -> None: super().__init__() @@ -119,6 +149,8 @@ def __init__( raise ValueError("sample_aggregation must be mean or sum") if cache_size < 0: raise ValueError("cache_size cannot be negative") + if global_cache_size < 0: + raise ValueError("global_cache_size cannot be negative") if use_centrality_encoding and not 0 < cse_channels < hidden_channels: raise ValueError( "cse_channels must lie between zero and hidden_channels" @@ -138,6 +170,7 @@ def __init__( self.use_centrality_encoding = use_centrality_encoding self.sample_aggregation = sample_aggregation self.cache_size = cache_size + self.global_cache_size = global_cache_size node_channels = ( hidden_channels - cse_channels @@ -320,12 +353,28 @@ def _statistics_for_batch( key = self._graph_key(graph_size, local_edges) cached = self._statistics_cache.get(key) if cached is None: - cached = self._compute_graph_statistics( - graph_size, - local_edges, - self.num_samples - 1, + global_key = ( self.cse_steps, + self.num_samples - 1, + *key, ) + cached = _GLOBAL_STATISTICS_CACHE.get(global_key) + if cached is None: + cached = self._compute_graph_statistics( + graph_size, + local_edges, + self.num_samples - 1, + self.cse_steps, + ) + if self.global_cache_size: + _GLOBAL_STATISTICS_CACHE[global_key] = cached + if ( + len(_GLOBAL_STATISTICS_CACHE) + > self.global_cache_size + ): + _GLOBAL_STATISTICS_CACHE.popitem(last=False) + elif self.global_cache_size: + _GLOBAL_STATISTICS_CACHE.move_to_end(global_key) if self.cache_size: self._statistics_cache[key] = cached if len(self._statistics_cache) > self.cache_size: