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# SPDX-FileCopyrightText: Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Graph backend for creating DGL or PyG graphs."""
from types import NoneType
from typing import List, Optional, Tuple, TypeAlias, Union
import torch
from torch import Tensor, testing
try:
from dgl import DGLGraph
DGL_AVAILABLE = True
except ImportError:
DGL_AVAILABLE = False
DGLGraph: TypeAlias = NoneType
try:
import torch_geometric.utils as pyg_utils
from torch_geometric.data import Data as PyGData
from torch_geometric.data import HeteroData as PyGHeteroData
PYG_AVAILABLE = True
except ImportError:
PYG_AVAILABLE = False
PyGData: TypeAlias = NoneType
from physicsnemo.models.gnn_layers.utils import GraphType
from physicsnemo.utils.graphcast.graph_utils import (
azimuthal_angle,
geospatial_rotation,
polar_angle,
xyz2latlon,
)
class DglGraphBackend:
"""DGL graph backend."""
name: str = "dgl"
@staticmethod
def create_graph(
src: List,
dst: List,
to_bidirected: bool,
add_self_loop: bool,
dtype: torch.dtype,
) -> DGLGraph:
"""Create DGL graph."""
from physicsnemo.utils.graphcast.graph_utils_dgl import create_graph
return create_graph(src, dst, to_bidirected, add_self_loop, dtype)
@staticmethod
def create_heterograph(
src: List,
dst: List,
labels: str,
dtype: torch.dtype = torch.int32,
num_nodes_dict: Optional[dict] = None,
) -> DGLGraph:
"""Create heterogeneous graph using DGL."""
from physicsnemo.utils.graphcast.graph_utils_dgl import create_heterograph
return create_heterograph(src, dst, labels, dtype, num_nodes_dict)
@staticmethod
def add_edge_features(
graph: DGLGraph, pos: Tensor, normalize: bool = True
) -> DGLGraph:
"""Add edge features to DGL graph."""
from physicsnemo.utils.graphcast.graph_utils_dgl import add_edge_features
return add_edge_features(graph, pos, normalize)
@staticmethod
def add_node_features(graph: DGLGraph, pos: Tensor) -> DGLGraph:
"""Add node features to DGL graph."""
from physicsnemo.utils.graphcast.graph_utils_dgl import add_node_features
return add_node_features(graph, pos)
@staticmethod
def khop_adj_all_k(graph: DGLGraph, kmax: int):
"""Construct the union of k-hop adjacencies up to distance `kmax` for a graph."""
if not graph.is_homogeneous:
raise NotImplementedError("only homogeneous graph is supported")
min_degree = graph.in_degrees().min()
with torch.no_grad():
adj = graph.adj_external(transpose=True, scipy_fmt=None)
adj_k = adj
adj_all = adj.clone()
for _ in range(2, kmax + 1):
# scale with min-degree to avoid too large values
# but >= 1.0
adj_k = (adj @ adj_k) / min_degree
adj_all += adj_k
return adj_all.to_dense().bool()
class PyGGraphBackend:
"""PyG graph backend."""
name: str = "pyg"
@staticmethod
def create_graph(
src: List,
dst: List,
to_bidirected: bool,
add_self_loop: bool,
dtype: torch.dtype = torch.int64,
) -> PyGData:
"""Create PyG graph.
dtype is ignored for PyG graph backend since PyG only supports int64 dtype.
"""
edge_index = torch.stack([torch.tensor(src), torch.tensor(dst)], dim=0).long()
if to_bidirected:
edge_index = pyg_utils.to_undirected(edge_index)
if add_self_loop:
edge_index, _ = pyg_utils.add_self_loops(edge_index)
return PyGData(edge_index=edge_index)
@staticmethod
def create_heterograph(
src: List,
dst: List,
labels: str,
dtype: torch.dtype = torch.int64,
) -> GraphType:
"""Create heterogeneous graph using PyG.
Parameters
----------
src : List
List of source nodes
dst : List
List of destination nodes
labels : str
Label of the edge type
dtype : torch.dtype, optional
Graph index data type, ignored for PyG graph backend since PyG only supports int64 dtype.
Returns
-------
GraphType
Heterogeneous graph object
"""
g = PyGHeteroData()
g[labels].edge_index = torch.stack(
[torch.tensor(src), torch.tensor(dst)], dim=0
).long()
return g
@staticmethod
def add_edge_features(
graph: PyGData,
pos: Union[Tensor, Tuple[Tensor, Tensor]],
normalize: bool = True,
) -> PyGData:
"""Add edge features to PyG graph."""
if isinstance(pos, tuple):
src_pos, dst_pos = pos
else:
src_pos = dst_pos = pos
if isinstance(graph, PyGData):
src, dst = graph.edge_index
elif isinstance(graph, PyGHeteroData):
src, dst = graph[graph.edge_types[0]].edge_index
else:
raise ValueError(f"Invalid graph type: {type(graph)}")
src_pos, dst_pos = src_pos[src.long()], dst_pos[dst.long()]
dst_latlon = xyz2latlon(dst_pos, unit="rad")
dst_lat, dst_lon = dst_latlon[:, 0], dst_latlon[:, 1]
# Azimuthal & polar rotation (same logic as DGL version)
theta_azimuthal = azimuthal_angle(dst_lon)
theta_polar = polar_angle(dst_lat)
src_pos = geospatial_rotation(
src_pos, theta=theta_azimuthal, axis="z", unit="rad"
)
dst_pos = geospatial_rotation(
dst_pos, theta=theta_azimuthal, axis="z", unit="rad"
)
# Validation checks
try:
testing.assert_close(dst_pos[:, 1], torch.zeros_like(dst_pos[:, 1]))
except ValueError:
raise ValueError(
"Invalid projection of edge nodes to local coordinate system"
)
src_pos = geospatial_rotation(src_pos, theta=theta_polar, axis="y", unit="rad")
dst_pos = geospatial_rotation(dst_pos, theta=theta_polar, axis="y", unit="rad")
# More validation checks
try:
testing.assert_close(dst_pos[:, 0], torch.ones_like(dst_pos[:, 0]))
testing.assert_close(dst_pos[:, 1], torch.zeros_like(dst_pos[:, 1]))
testing.assert_close(dst_pos[:, 2], torch.zeros_like(dst_pos[:, 2]))
except ValueError:
raise ValueError(
"Invalid projection of edge nodes to local coordinate system"
)
# Prepare edge features
disp = src_pos - dst_pos
disp_norm = torch.linalg.norm(disp, dim=-1, keepdim=True)
if normalize:
max_disp_norm = torch.max(disp_norm)
graph.edge_attr = torch.cat(
(disp / max_disp_norm, disp_norm / max_disp_norm), dim=-1
)
else:
graph.edge_attr = torch.cat((disp, disp_norm), dim=-1)
return graph
@staticmethod
def add_node_features(graph: PyGData, pos: Tensor) -> PyGData:
"""Add node features to PyG graph."""
latlon = xyz2latlon(pos)
lat, lon = latlon[:, 0], latlon[:, 1]
graph.x = torch.stack((torch.cos(lat), torch.sin(lon), torch.cos(lon)), dim=-1)
return graph
@staticmethod
def khop_adj_all_k(graph: PyGData, kmax: int):
"""Construct the union of k-hop adjacencies up to distance `kmax` for a graph."""
from torch_sparse import SparseTensor
if not isinstance(graph, PyGData):
raise ValueError(
f"Invalid graph type: {type(graph)}, only Data type is supported."
)
if graph.edge_index is None:
raise ValueError("Graph must have edge_index defined.")
n_nodes = graph.num_nodes
# Build SparseTensor adjacency: shape [n_nodes, n_nodes]
# row = source, col = target
adj = SparseTensor.from_edge_index(
graph.edge_index, sparse_sizes=(n_nodes, n_nodes)
)
adj_k = adj.clone()
adj_all = adj.clone()
for _ in range(2, kmax + 1):
adj_k = adj @ adj_k
adj_all = adj_all + adj_k
return adj_all.to_dense().bool()
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