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"outputs": [], "source": [ "# Your model configuration (e.g., \"graph/gcn\", \"graph/gin\", \"graph/gat\")\n", - "MODEL_CONFIG = \"graph/gin\"" + "MODEL_CONFIG = \"graph/advdifformer\"" ] }, { diff --git a/configs/model/graph/advdifformer.yaml b/configs/model/graph/advdifformer.yaml new file mode 100644 index 000000000..39d2edbcf --- /dev/null +++ b/configs/model/graph/advdifformer.yaml @@ -0,0 +1,52 @@ +_target_: topobench.model.TBModel + +model_name: advdifformer +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.AdvDIFFormerEncoder + input_dim: ${model.feature_encoder.out_channels} + hidden_dim: ${model.feature_encoder.out_channels} + num_layers: 2 + heads: 1 + variant: series + propagation_steps: 2 + beta: 0.25 + theta: 0.0 + dropout: 0.1 + input_dropout: 0.0 + residual: true + layer_norm: true + head_aggregation: mean + make_undirected: true + +backbone_wrapper: + _target_: topobench.nn.wrappers.${model.backbone_wrapper.wrapper_name} + _partial_: true + wrapper_name: GNNWrapper + out_channels: ${model.feature_encoder.out_channels} + 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: ${model.feature_encoder.out_channels} + hidden_layers: [16] + out_channels: ${dataset.parameters.num_classes} + task_level: ${define_task_level:${dataset.parameters.task_level},${dataset.split_params.learning_setting}} + pooling_type: sum + dropout: 0.1 + act: "relu" + norm: null + final_act: null + +compile: false diff --git a/test/nn/backbones/graph/test_advdifformer.py b/test/nn/backbones/graph/test_advdifformer.py new file mode 100644 index 000000000..b95f694c2 --- /dev/null +++ b/test/nn/backbones/graph/test_advdifformer.py @@ -0,0 +1,275 @@ +"""Unit tests for AdvDIFFormer.""" + +from copy import deepcopy + +import pytest +import torch +from torch_geometric.data import Batch + +from topobench.nn.backbones.graph.advdifformer import ( + AdvDIFFormerEncoder, + AdvDIFFormerLayer, + _normalized_adjacency_matmul, +) + + +def _reference_series(layer, x, edge_index, batch, edge_weight=None): + """Direct implementation of the original series equations.""" + _, canonical = torch.unique(batch, sorted=True, return_inverse=True) + num_graphs = canonical.max().item() + 1 + outputs = [] + for query, key, output in zip( + layer.query, layer.key, layer.output, strict=True + ): + q = torch.nn.functional.normalize(query(x), dim=-1, eps=1e-12) + k = torch.nn.functional.normalize(key(x), dim=-1, eps=1e-12) + states = [x] + current = x + for _ in range(layer.propagation_steps): + attentive = torch.zeros_like(current) + for graph_id in range(num_graphs): + idx = torch.nonzero(canonical == graph_id, as_tuple=True)[0] + q_graph = q[idx] + k_graph = k[idx] + values = current[idx] + similarity = 1 + q_graph @ k_graph.T + attentive[idx] = ( + similarity @ values + / similarity.sum(dim=-1, keepdim=True).clamp_min(1e-12) + ) + current = attentive + layer.beta * _normalized_adjacency_matmul( + current, + edge_index, + edge_weight=edge_weight, + make_undirected=layer.make_undirected, + ) + states.append(current) + outputs.append(output(torch.cat(states, dim=-1))) + result = torch.stack(outputs).sum(dim=0) + return result / layer.heads if layer.head_aggregation == "mean" else result + + +class TestAdvDIFFormerEncoder: + """Test AdvDIFFormerEncoder.""" + + def setup_method(self): + """Set up test fixtures.""" + self.input_dim = 8 + self.hidden_dim = 16 + + def _features(self, num_nodes, feat_dim=None): + """Create random node features.""" + return torch.randn(num_nodes, feat_dim or self.input_dim) + + def test_initialization_default(self): + """Test default initialization.""" + model = AdvDIFFormerEncoder( + input_dim=self.input_dim, + hidden_dim=self.hidden_dim, + ) + + assert model.input_dim == self.input_dim + assert model.hidden_dim == self.hidden_dim + assert model.out_channels == self.hidden_dim + assert model.num_layers == 2 + assert model.variant == "series" + assert len(model.layers) == 2 + + def test_invalid_variant(self): + """Test that invalid variants fail clearly.""" + with pytest.raises(ValueError, match="variant"): + AdvDIFFormerEncoder( + input_dim=self.input_dim, + hidden_dim=self.hidden_dim, + variant="bad", + ) + + def test_variant_aliases(self): + """Test short variant aliases.""" + model = AdvDIFFormerEncoder( + input_dim=self.input_dim, + hidden_dim=self.hidden_dim, + variant="i", + ) + + assert model.variant == "inverse" + + def test_row_normalized_advection(self): + """Test D^{-1} A aggregation used by the official implementation.""" + edge_index = torch.tensor([[0, 1, 2], [1, 2, 2]]) + x = torch.tensor([[1.0], [2.0], [4.0]]) + + out = _normalized_adjacency_matmul( + x, + edge_index, + make_undirected=False, + ) + + expected = torch.tensor([[0.0], [1.0], [3.0]]) + assert torch.allclose(out, expected) + + def test_forward_scalable(self, simple_graph_0): + """Test AdvDIFFormer-S forward pass.""" + model = AdvDIFFormerEncoder( + input_dim=self.input_dim, + hidden_dim=self.hidden_dim, + num_layers=2, + heads=2, + variant="s", + propagation_steps=2, + ) + + x = self._features(simple_graph_0.num_nodes) + out = model(x=x, edge_index=simple_graph_0.edge_index) + + assert out.shape == (simple_graph_0.num_nodes, self.hidden_dim) + assert not torch.isnan(out).any() + assert not torch.isinf(out).any() + + def test_forward_inverse(self, simple_graph_0): + """Test AdvDIFFormer-I forward pass.""" + model = AdvDIFFormerEncoder( + input_dim=self.input_dim, + hidden_dim=self.hidden_dim, + num_layers=1, + heads=1, + variant="i", + theta=1.0, + ) + + x = self._features(simple_graph_0.num_nodes) + out = model(x=x, edge_index=simple_graph_0.edge_index) + + assert out.shape == (simple_graph_0.num_nodes, self.hidden_dim) + assert not torch.isnan(out).any() + assert not torch.isinf(out).any() + + def test_forward_batched_graphs(self, simple_graph_0, simple_graph_1): + """Test that batched graphs preserve graph boundaries.""" + batch_data = Batch.from_data_list([simple_graph_0, simple_graph_1]) + model = AdvDIFFormerEncoder( + input_dim=self.input_dim, + hidden_dim=self.hidden_dim, + num_layers=1, + heads=2, + variant="s", + ) + + x = self._features(batch_data.num_nodes) + out = model( + x=x, + edge_index=batch_data.edge_index, + batch=batch_data.batch, + ) + + assert out.shape == (batch_data.num_nodes, self.hidden_dim) + + def test_forward_with_edge_weight(self, simple_graph_0): + """Test optional edge weights.""" + model = AdvDIFFormerEncoder( + input_dim=self.input_dim, + hidden_dim=self.hidden_dim, + num_layers=1, + ) + edge_weight = torch.ones(simple_graph_0.edge_index.shape[1]) + + out = model( + x=self._features(simple_graph_0.num_nodes), + edge_index=simple_graph_0.edge_index, + edge_weight=edge_weight, + ) + + assert out.shape == (simple_graph_0.num_nodes, self.hidden_dim) + + def test_backward_pass(self, simple_graph_0): + """Test gradient flow.""" + model = AdvDIFFormerEncoder( + input_dim=self.input_dim, + hidden_dim=self.hidden_dim, + num_layers=1, + ) + x = self._features(simple_graph_0.num_nodes).requires_grad_(True) + + out = model(x=x, edge_index=simple_graph_0.edge_index) + loss = out.mean() + loss.backward() + + assert x.grad is not None + assert any( + param.grad is not None + for param in model.parameters() + if param.requires_grad + ) + + def test_series_matches_reference_outputs_and_gradients(self): + """Optimized series propagation must preserve values and gradients.""" + torch.manual_seed(7) + layer = AdvDIFFormerLayer( + hidden_dim=5, + heads=2, + propagation_steps=2, + beta=0.3, + dropout=0.0, + head_aggregation="mean", + make_undirected=True, + ) + reference_layer = deepcopy(layer) + edge_index = torch.tensor([[0, 1, 2, 3, 4], [1, 0, 3, 4, 2]]) + edge_weight = torch.tensor([0.5, 1.5, 2.0, 0.75, 1.25]) + batch = torch.tensor([10, 10, 3, 3, 3]) + x = torch.randn(5, 5, requires_grad=True) + x_reference = x.detach().clone().requires_grad_(True) + + actual = layer(x, edge_index, batch, edge_weight) + expected = _reference_series( + reference_layer, + x_reference, + edge_index, + batch, + edge_weight, + ) + actual.square().sum().backward() + expected.square().sum().backward() + + assert torch.allclose(actual, expected, atol=2e-6, rtol=2e-5) + assert torch.allclose(x.grad, x_reference.grad, atol=2e-6, rtol=2e-5) + for parameter, reference_parameter in zip( + layer.parameters(), reference_layer.parameters(), strict=True + ): + assert torch.allclose( + parameter.grad, + reference_parameter.grad, + atol=2e-6, + rtol=2e-5, + ) + + def test_interleaved_non_contiguous_batch_ids(self): + """Graph-local attention supports unusual batch IDs and node ordering.""" + torch.manual_seed(11) + layer = AdvDIFFormerLayer(hidden_dim=4, propagation_steps=1) + x = torch.randn(6, 4) + batch = torch.tensor([9, 2, 9, 2, 9, 2]) + edge_index = torch.empty((2, 0), dtype=torch.long) + + actual = layer(x, edge_index, batch) + expected = _reference_series(layer, x, edge_index, batch) + + assert torch.allclose(actual, expected, atol=1e-6, rtol=1e-5) + + @pytest.mark.parametrize("heads", [1, 2, 4]) + def test_different_heads(self, simple_graph_0, heads): + """Test different numbers of heads.""" + model = AdvDIFFormerEncoder( + input_dim=self.input_dim, + hidden_dim=self.hidden_dim, + num_layers=1, + heads=heads, + head_aggregation="mean", + ) + + out = model( + x=self._features(simple_graph_0.num_nodes), + edge_index=simple_graph_0.edge_index, + ) + + assert out.shape == (simple_graph_0.num_nodes, self.hidden_dim) diff --git a/test/pipeline/test_pipeline.py b/test/pipeline/test_pipeline.py index d41ebb9c6..f3c9c4f05 100644 --- a/test/pipeline/test_pipeline.py +++ b/test/pipeline/test_pipeline.py @@ -1,11 +1,11 @@ """Test pipeline for a particular dataset and model.""" import hydra -from test._utils.simplified_pipeline import run +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 OF YOUR CHOICE HERE +MODELS = ["graph/advdifformer"] # ADD ONE OR SEVERAL MODELS OF YOUR CHOICE HERE class TestPipeline: diff --git a/topobench/nn/backbones/graph/advdifformer.py b/topobench/nn/backbones/graph/advdifformer.py new file mode 100644 index 000000000..80d216af5 --- /dev/null +++ b/topobench/nn/backbones/graph/advdifformer.py @@ -0,0 +1,761 @@ +"""Advective Diffusion Transformer graph backbone. + +This module implements a TopoBench-compatible AdvDIFFormer encoder following +Wu et al., "Supercharging Graph Transformers with Advective Diffusion" +(ICML 2025) and the official ``qitianwu/AdvDIFFormer`` implementation. +The propagation layer corresponds to the model's global attentive diffusion +plus observed-graph advection operator. +""" + +from __future__ import annotations + +import torch +from torch import nn +from torch.nn import functional as F +from torch_geometric.utils import coalesce + + +class _PreparedGraph: + """Row-normalized graph data reused throughout one encoder forward.""" + + __slots__ = ("dst", "norm_weight", "num_nodes", "src") + + def __init__(self, src, dst, norm_weight, num_nodes) -> None: + self.src = src + self.dst = dst + self.norm_weight = norm_weight + self.num_nodes = num_nodes + + def matmul(self, x: torch.Tensor) -> torch.Tensor: + """Apply the prepared row-normalized adjacency to node features.""" + if self.src.numel() == 0: + return torch.zeros_like(x) + messages = x.index_select(0, self.src) * self.norm_weight.unsqueeze(-1) + out = torch.zeros_like(x) + out.index_add_(0, self.dst, messages) + return out + + +class _PreparedBatch: + """Canonical graph assignments and contiguous graph segments.""" + + __slots__ = ( + "batch", + "counts", + "counts_list", + "num_graphs", + "ptr", + "restore_indices", + "sorted_batch", + "sorted_indices", + ) + + def __init__( + self, + batch, + sorted_batch, + ptr, + counts, + counts_list, + sorted_indices, + restore_indices, + ) -> None: + self.batch = batch + self.sorted_batch = sorted_batch + self.ptr = ptr + self.counts = counts + self.counts_list = counts_list + self.num_graphs = counts.numel() + self.sorted_indices = sorted_indices + self.restore_indices = restore_indices + + +def _prepare_batch(batch: torch.Tensor | None, num_nodes: int, device) -> _PreparedBatch: + """Canonicalize graph IDs and build contiguous segments once.""" + identity = torch.arange(num_nodes, device=device) + if batch is None or num_nodes == 0: + canonical = torch.zeros(num_nodes, dtype=torch.long, device=device) + counts = torch.tensor([num_nodes], dtype=torch.long, device=device) + ptr = torch.tensor([0, num_nodes], dtype=torch.long, device=device) + return _PreparedBatch( + canonical, + canonical, + ptr, + counts, + [num_nodes], + identity, + identity, + ) + + _, canonical = torch.unique(batch, sorted=True, return_inverse=True) + counts = torch.bincount(canonical) + ptr = torch.cat([counts.new_zeros(1), counts.cumsum(0)]) + + is_contiguous = bool( + num_nodes < 2 or torch.all(canonical[1:] >= canonical[:-1]).item() + ) + if is_contiguous: + sorted_indices = identity + restore_indices = identity + sorted_batch = canonical + else: + sorted_indices = torch.argsort(canonical, stable=True) + restore_indices = torch.empty_like(sorted_indices) + restore_indices[sorted_indices] = identity + sorted_batch = canonical.index_select(0, sorted_indices) + + return _PreparedBatch( + canonical, + sorted_batch, + ptr, + counts, + counts.tolist(), + sorted_indices, + restore_indices, + ) + + +def _prepare_graph( + edge_index: torch.Tensor, + edge_weight: torch.Tensor | None, + num_nodes: int, + dtype: torch.dtype, + device: torch.device, + make_undirected: bool, +) -> _PreparedGraph: + """Prepare receiving-node row normalization once per encoder forward.""" + if edge_index.numel() == 0: + empty = edge_index.new_empty(0) + return _PreparedGraph(empty, empty, torch.empty(0, dtype=dtype, device=device), num_nodes) + + edge_index = edge_index.to(device=device) + if edge_weight is None: + edge_weight = torch.ones(edge_index.shape[1], dtype=dtype, device=device) + else: + edge_weight = edge_weight.to(dtype=dtype, device=device) + if make_undirected: + edge_index, edge_weight = _as_undirected(edge_index, edge_weight) + + src, dst = edge_index + degree = torch.zeros(num_nodes, dtype=dtype, device=device) + degree.index_add_(0, dst, edge_weight) + inverse_degree = torch.zeros_like(degree) + positive = degree > 0 + inverse_degree[positive] = degree[positive].reciprocal() + return _PreparedGraph(src, dst, inverse_degree[dst] * edge_weight, num_nodes) + + +def _as_undirected( + edge_index: torch.Tensor, + edge_weight: torch.Tensor | None, +) -> tuple[torch.Tensor, torch.Tensor | None]: + """Return an undirected version of an edge list.""" + if edge_index.numel() == 0: + return edge_index, edge_weight + + rev_edge_index = edge_index.flip(0) + edge_index = torch.cat([edge_index, rev_edge_index], dim=1) + + if edge_weight is not None: + edge_weight = torch.cat([edge_weight, edge_weight], dim=0) + + return coalesce(edge_index, edge_weight, reduce="mean") + + +def _normalized_adjacency_matmul( + x: torch.Tensor, + edge_index: torch.Tensor, + edge_weight: torch.Tensor | None = None, + make_undirected: bool = False, +) -> torch.Tensor: + """Compute D^{-1} A X with sparse edge indices. + + The official implementation constructs a transposed sparse adjacency and + left-normalizes it by the receiving-node degree before multiplying node + features, which is equivalent to row-normalized message aggregation. + """ + prepared = _prepare_graph( + edge_index, + edge_weight, + x.shape[0], + x.dtype, + x.device, + make_undirected, + ) + return prepared.matmul(x) + + +def _dense_normalized_adjacency( + num_nodes: int, + edge_index: torch.Tensor, + edge_weight: torch.Tensor | None, + device: torch.device, + dtype: torch.dtype, + make_undirected: bool = False, +) -> torch.Tensor: + """Build a dense row-normalized adjacency matrix.""" + if edge_index.numel() == 0: + return torch.zeros(num_nodes, num_nodes, device=device, dtype=dtype) + + if edge_weight is None: + edge_weight = torch.ones(edge_index.shape[1], device=device, dtype=dtype) + else: + edge_weight = edge_weight.to(dtype=dtype, device=device) + + if make_undirected: + edge_index, edge_weight = _as_undirected(edge_index, edge_weight) + + row, col = edge_index + deg = torch.zeros(num_nodes, device=device, dtype=dtype) + deg.index_add_(0, col, edge_weight) + deg_inv = torch.zeros_like(deg) + nonzero = deg > 0 + deg_inv[nonzero] = deg[nonzero].reciprocal() + norm = deg_inv[col] * edge_weight + + adj = torch.zeros(num_nodes, num_nodes, device=device, dtype=dtype) + adj[col, row] = norm + return adj + + +class AdvDIFFormerLayer(nn.Module): + """One AdvDIFFormer propagation layer. + + Parameters + ---------- + hidden_dim : int + Dimension of node embeddings. + heads : int + Number of propagation heads. + variant : str + Propagation variant. ``"series"`` uses the scalable polynomial + propagation, while ``"inverse"`` uses a dense linear solve. The + aliases ``"s"`` and ``"i"`` are accepted for compatibility. + propagation_steps : int + Number of propagation powers, called ``K_order`` in the official + implementation, for the scalable variant. + beta : float + Weight of observed-graph advection. + theta : float + Identity coefficient for the inverse variant. + dropout : float + Dropout rate applied inside the layer. + head_aggregation : str + Either ``"sum"`` or ``"mean"`` for combining heads. + make_undirected : bool + Whether to symmetrize the input edge list before local propagation. + """ + + def __init__( + self, + hidden_dim: int, + heads: int = 1, + variant: str = "series", + propagation_steps: int = 2, + beta: float = 0.5, + theta: float = 0.0, + dropout: float = 0.0, + head_aggregation: str = "sum", + make_undirected: bool = False, + ) -> None: + super().__init__() + variant_aliases = {"s": "series", "i": "inverse"} + variant = variant_aliases.get(variant, variant) + if variant not in {"series", "inverse"}: + raise ValueError("variant must be 'series'/'s' or 'inverse'/'i'.") + if propagation_steps < 1: + raise ValueError("propagation_steps must be at least 1.") + if head_aggregation not in {"sum", "mean"}: + raise ValueError("head_aggregation must be either 'sum' or 'mean'.") + + self.hidden_dim = hidden_dim + self.heads = heads + self.variant = variant + self.propagation_steps = propagation_steps + self.beta = beta + self.theta = theta + self.head_aggregation = head_aggregation + self.make_undirected = make_undirected + + self.query = nn.ModuleList( + nn.Linear(hidden_dim, hidden_dim, bias=False) + for _ in range(heads) + ) + self.key = nn.ModuleList( + nn.Linear(hidden_dim, hidden_dim, bias=False) + for _ in range(heads) + ) + + output_input_dim = ( + hidden_dim * (propagation_steps + 1) + if variant == "series" + else hidden_dim + ) + self.output = nn.ModuleList( + nn.Linear(output_input_dim, hidden_dim, bias=False) + for _ in range(heads) + ) + self.dropout = nn.Dropout(dropout) + + def forward( + self, + x: torch.Tensor, + edge_index: torch.Tensor, + batch: torch.Tensor | None = None, + edge_weight: torch.Tensor | None = None, + prepared_graph: _PreparedGraph | None = None, + prepared_batch: _PreparedBatch | None = None, + ) -> torch.Tensor: + """Forward pass.""" + if prepared_batch is None: + prepared_batch = _prepare_batch(batch, x.shape[0], x.device) + batch = prepared_batch.batch + if prepared_graph is None: + prepared_graph = _prepare_graph( + edge_index, + edge_weight, + x.shape[0], + x.dtype, + x.device, + self.make_undirected, + ) + + if self.variant == "series": + return self._forward_series_all_heads(x, prepared_graph, prepared_batch) + + head_outputs = [] + for head in range(self.heads): + q = F.normalize(self.query[head](x), p=2, dim=-1, eps=1e-12) + k = F.normalize(self.key[head](x), p=2, dim=-1, eps=1e-12) + + out = self._forward_inverse( + x, q, k, edge_index, batch, edge_weight + ) + projected = self.output[head](out) + head_outputs.append(projected) + + out = torch.stack(head_outputs, dim=0).sum(dim=0) + if self.head_aggregation == "mean": + out = out / self.heads + return self.dropout(out) + + def _forward_series_all_heads( + self, + x: torch.Tensor, + graph: _PreparedGraph, + batch: _PreparedBatch, + ) -> torch.Tensor: + """Apply AdvDIFFormer-S while batching heads in the hot path.""" + q = torch.stack( + [ + F.normalize(query(x), p=2, dim=-1, eps=1e-12) + for query in self.query + ], + dim=0, + ) + k = torch.stack( + [ + F.normalize(key(x), p=2, dim=-1, eps=1e-12) + for key in self.key + ], + dim=0, + ) + context = self._prepare_multihead_attention_context(q, k, batch) + weights = torch.stack([output.weight for output in self.output], dim=0) + weight_blocks = weights.split(self.hidden_dim, dim=2) + + current = x.unsqueeze(0).expand(self.heads, -1, -1) + projected = torch.einsum("hni,hoi->hno", current, weight_blocks[0]) + for step in range(self.propagation_steps): + attentive = self._linear_multihead_attention_prepared(current, context) + advective = self._multihead_graph_matmul(graph, current) + current = attentive + self.beta * advective + projected = projected + torch.einsum( + "hni,hoi->hno", + current, + weight_blocks[step + 1], + ) + + out = projected.sum(dim=0) + if self.head_aggregation == "mean": + out = out / self.heads + return self.dropout(out) + + def _multihead_graph_matmul( + self, + graph: _PreparedGraph, + x: torch.Tensor, + ) -> torch.Tensor: + """Apply local propagation to all heads with one graph aggregation.""" + flat = x.permute(1, 0, 2).reshape(x.shape[1], self.heads * self.hidden_dim) + out = graph.matmul(flat) + return out.view(x.shape[1], self.heads, self.hidden_dim).permute(1, 0, 2) + + def _forward_scalable_projected( + self, + x: torch.Tensor, + q: torch.Tensor, + k: torch.Tensor, + graph: _PreparedGraph, + batch: _PreparedBatch, + output: nn.Linear, + ) -> torch.Tensor: + """Propagate while applying output weight blocks incrementally.""" + context = self._prepare_attention_context(q, k, batch) + weights = output.weight.split(self.hidden_dim, dim=1) + projected = F.linear(x, weights[0]) + current = x + for step in range(self.propagation_steps): + attentive = self._linear_attention_prepared(q, k, current, context) + current = attentive + self.beta * graph.matmul(current) + projected = projected + F.linear(current, weights[step + 1]) + return projected + + def _forward_scalable( + self, + x: torch.Tensor, + q: torch.Tensor, + k: torch.Tensor, + edge_index: torch.Tensor, + batch: torch.Tensor, + edge_weight: torch.Tensor | None, + ) -> torch.Tensor: + """Apply the linear-complexity AdvDIFFormer-S propagation.""" + states = [x] + current = x + for _ in range(self.propagation_steps): + attentive = self._linear_attention_matmul(q, k, current, batch) + + advective = _normalized_adjacency_matmul( + current, + edge_index, + edge_weight=edge_weight, + make_undirected=self.make_undirected, + ) + current = attentive + self.beta * advective + states.append(current) + + return torch.cat(states, dim=-1) + + def _forward_inverse( + self, + x: torch.Tensor, + q: torch.Tensor, + k: torch.Tensor, + edge_index: torch.Tensor, + batch: torch.Tensor, + edge_weight: torch.Tensor | None, + ) -> torch.Tensor: + """Apply the dense AdvDIFFormer-I linear solve.""" + num_nodes = x.shape[0] + attention = torch.zeros( + num_nodes, num_nodes, device=x.device, dtype=x.dtype + ) + for graph_id in batch.unique(sorted=True): + mask = batch == graph_id + idx = mask.nonzero(as_tuple=True)[0] + attention[idx[:, None], idx[None, :]] = self._dense_attention( + q[mask], k[mask] + ) + + adj = _dense_normalized_adjacency( + num_nodes, + edge_index, + edge_weight, + device=x.device, + dtype=x.dtype, + make_undirected=self.make_undirected, + ) + identity = torch.eye(num_nodes, device=x.device, dtype=x.dtype) + operator = (1 + self.theta) * identity - attention - self.beta * adj + + jitter = 1e-6 * identity + return torch.linalg.solve(operator + jitter, x) + + @staticmethod + def _linear_attention_matmul( + q: torch.Tensor, + k: torch.Tensor, + values: torch.Tensor, + batch: torch.Tensor, + ) -> torch.Tensor: + """Compute C values for eta(q, k) = 1 + cosine(q, k). + + This vectorized form is algebraically the same as applying the + operation independently to each graph in a batch, but avoids an inner + Python loop over graphs for every layer, head, and propagation step. + """ + prepared = _prepare_batch(batch, values.shape[0], values.device) + context = AdvDIFFormerLayer._prepare_attention_context(q, k, prepared) + return AdvDIFFormerLayer._linear_attention_prepared(q, k, values, context) + + @staticmethod + def _prepare_attention_context( + q: torch.Tensor, + k: torch.Tensor, + batch: _PreparedBatch, + ) -> tuple[ + _PreparedBatch, + torch.Tensor, + torch.Tensor, + torch.Tensor, + ]: + """Compute graph-local denominators once for all propagation steps.""" + q_sorted = q.index_select(0, batch.sorted_indices) + k_sorted = k.index_select(0, batch.sorted_indices) + key_sums = k.new_zeros(batch.num_graphs, k.shape[-1]) + key_sums.index_add_(0, batch.sorted_batch, k_sorted) + + counts = batch.counts.to(dtype=q.dtype).unsqueeze(-1) + denominator = counts.index_select(0, batch.sorted_batch) + denominator = denominator + (q_sorted * key_sums.index_select(0, batch.sorted_batch)).sum(dim=-1, keepdim=True) + return batch, q_sorted, k_sorted, denominator.clamp_min(1e-12) + + @staticmethod + def _linear_attention_prepared( + q: torch.Tensor, + k: torch.Tensor, + values: torch.Tensor, + context: tuple[ + _PreparedBatch, + torch.Tensor, + torch.Tensor, + torch.Tensor, + ], + ) -> torch.Tensor: + """Graphwise linear attention without a node-wise outer-product tensor.""" + batch, q_sorted, k_sorted, denominator = context + values_sorted = values.index_select(0, batch.sorted_indices) + parts = [] + q_parts = q_sorted.split(batch.counts_list, dim=0) + k_parts = k_sorted.split(batch.counts_list, dim=0) + value_parts = values_sorted.split(batch.counts_list, dim=0) + denominator_parts = denominator.split(batch.counts_list, dim=0) + for q_graph, k_graph, value_graph, denominator_graph in zip( + q_parts, + k_parts, + value_parts, + denominator_parts, + strict=True, + ): + numerator = value_graph.sum(dim=0, keepdim=True) + q_graph @ ( + k_graph.T @ value_graph + ) + parts.append(numerator / denominator_graph) + + out_sorted = parts[0] if len(parts) == 1 else torch.cat(parts, dim=0) + return out_sorted.index_select(0, batch.restore_indices) + + @staticmethod + def _prepare_multihead_attention_context( + q: torch.Tensor, + k: torch.Tensor, + batch: _PreparedBatch, + ) -> tuple[_PreparedBatch, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute graph-local denominators for all heads at once.""" + q_sorted = q.index_select(1, batch.sorted_indices) + k_sorted = k.index_select(1, batch.sorted_indices) + heads, _, hidden_dim = k_sorted.shape + graph_ids = batch.sorted_batch.unsqueeze(0) + torch.arange(heads, device=k.device).unsqueeze(-1) * batch.num_graphs + + key_sums = k.new_zeros(heads * batch.num_graphs, hidden_dim) + key_sums.index_add_(0, graph_ids.reshape(-1), k_sorted.reshape(-1, hidden_dim)) + key_sums = key_sums.view(heads, batch.num_graphs, hidden_dim) + + counts = batch.counts.to(dtype=q.dtype).view(1, batch.num_graphs, 1) + denominator = counts.index_select(1, batch.sorted_batch) + denominator = denominator + (q_sorted * key_sums.index_select(1, batch.sorted_batch)).sum(dim=-1, keepdim=True) + return batch, q_sorted, k_sorted, denominator.clamp_min(1e-12) + + @staticmethod + def _linear_multihead_attention_prepared( + values: torch.Tensor, + context: tuple[_PreparedBatch, torch.Tensor, torch.Tensor, torch.Tensor], + ) -> torch.Tensor: + """Graphwise linear attention for values shaped [heads, nodes, dim].""" + batch, q_sorted, k_sorted, denominator = context + values_sorted = values.index_select(1, batch.sorted_indices) + parts = [] + q_parts = q_sorted.split(batch.counts_list, dim=1) + k_parts = k_sorted.split(batch.counts_list, dim=1) + value_parts = values_sorted.split(batch.counts_list, dim=1) + denominator_parts = denominator.split(batch.counts_list, dim=1) + for q_graph, k_graph, value_graph, denominator_graph in zip( + q_parts, + k_parts, + value_parts, + denominator_parts, + strict=True, + ): + key_value = k_graph.transpose(-1, -2) @ value_graph + numerator = value_graph.sum(dim=1, keepdim=True) + q_graph @ key_value + parts.append(numerator / denominator_graph) + + out_sorted = parts[0] if len(parts) == 1 else torch.cat(parts, dim=1) + return out_sorted.index_select(1, batch.restore_indices) + + @staticmethod + def _dense_attention(q: torch.Tensor, k: torch.Tensor) -> torch.Tensor: + """Build the dense row-normalized positive similarity matrix.""" + sim = 1 + q @ k.T + return sim / sim.sum(dim=-1, keepdim=True).clamp(min=1e-12) + + +class AdvDIFFormerEncoder(nn.Module): + """Advective Diffusion Transformer encoder for graph node embeddings. + + Parameters + ---------- + input_dim : int + Dimension of input node features. + hidden_dim : int + Dimension of hidden node embeddings. + num_layers : int, optional + Number of stacked AdvDIFFormer propagation layers. + heads : int, optional + Number of heads in each propagation layer. + variant : str, optional + ``"series"``/``"s"`` for AdvDIFFormer-S or ``"inverse"``/``"i"`` + for AdvDIFFormer-I. + propagation_steps : int, optional + Number of propagation powers for AdvDIFFormer-S. + beta : float, optional + Weight of observed-graph advection. + theta : float, optional + Identity coefficient for AdvDIFFormer-I. + dropout : float, optional + Dropout rate. + input_dropout : float, optional + Dropout applied after the input projection. + residual : bool, optional + Whether to use residual connections around propagation layers. + layer_norm : bool, optional + Whether to apply layer normalization after each layer. + head_aggregation : str, optional + Either ``"sum"`` or ``"mean"``. + make_undirected : bool, optional + Whether to symmetrize the input edge list before local propagation. + """ + + def __init__( + self, + input_dim: int, + hidden_dim: int, + num_layers: int = 2, + heads: int = 1, + variant: str = "series", + propagation_steps: int = 2, + beta: float = 0.5, + theta: float = 0.0, + dropout: float = 0.0, + input_dropout: float = 0.0, + residual: bool = True, + layer_norm: bool = True, + head_aggregation: str = "sum", + make_undirected: bool = False, + **kwargs, + ) -> None: + super().__init__() + if num_layers < 1: + raise ValueError("num_layers must be at least 1.") + + self.input_dim = input_dim + self.hidden_dim = hidden_dim + self.out_channels = hidden_dim + self.num_layers = num_layers + self.heads = heads + variant_aliases = {"s": "series", "i": "inverse"} + self.variant = variant_aliases.get(variant, variant) + self.propagation_steps = propagation_steps + self.beta = beta + self.theta = theta + self.residual = residual + self.layer_norm = layer_norm + self.make_undirected = make_undirected + + self.input_proj = ( + nn.Identity() + if input_dim == hidden_dim + else nn.Linear(input_dim, hidden_dim) + ) + self.input_dropout = nn.Dropout(input_dropout) + self.layers = nn.ModuleList( + AdvDIFFormerLayer( + hidden_dim=hidden_dim, + heads=heads, + variant=self.variant, + propagation_steps=propagation_steps, + beta=beta, + theta=theta, + dropout=dropout, + head_aggregation=head_aggregation, + make_undirected=make_undirected, + ) + for _ in range(num_layers) + ) + self.norms = nn.ModuleList( + nn.LayerNorm(hidden_dim) if layer_norm else nn.Identity() + for _ in range(num_layers) + ) + self.dropout = nn.Dropout(dropout) + + def forward( + self, + x: torch.Tensor, + edge_index: torch.Tensor, + batch: torch.Tensor | None = None, + edge_weight: torch.Tensor | None = None, + edge_attr: torch.Tensor | None = None, + **kwargs, + ) -> torch.Tensor: + """Forward pass. + + Parameters + ---------- + x : torch.Tensor + Node feature matrix of shape ``[num_nodes, input_dim]``. + edge_index : torch.Tensor + Edge indices of shape ``[2, num_edges]``. + batch : torch.Tensor, optional + Batch assignment for each node. + edge_weight : torch.Tensor, optional + Optional scalar edge weights. + edge_attr : torch.Tensor, optional + Ignored unless it is one-dimensional and ``edge_weight`` is unset. + **kwargs : dict + Additional arguments ignored for wrapper compatibility. + + Returns + ------- + torch.Tensor + Node embeddings of shape ``[num_nodes, hidden_dim]``. + """ + if edge_weight is None and edge_attr is not None and edge_attr.dim() == 1: + edge_weight = edge_attr + + x = self.input_dropout(self.input_proj(x)) + prepared_batch = _prepare_batch(batch, x.shape[0], x.device) + prepared_graph = _prepare_graph( + edge_index, + edge_weight, + x.shape[0], + x.dtype, + x.device, + self.make_undirected, + ) + for layer, norm in zip(self.layers, self.norms, strict=False): + propagated = layer( + x, + edge_index, + batch=prepared_batch.batch, + edge_weight=edge_weight, + prepared_graph=prepared_graph, + prepared_batch=prepared_batch, + ) + if self.residual: + x = x + self.dropout(propagated) + else: + x = self.dropout(propagated) + x = norm(x) + + return x