import copy
from typing import Any, Dict, List, Optional, Tuple, Union, overload
import torch
from torch import Tensor
from torch_geometric.typing import (
Adj,
EdgeType,
MaybeHeteroAdjTensor,
MaybeHeteroEdgeTensor,
MaybeHeteroNodeTensor,
NodeType,
SparseStorage,
SparseTensor,
)
def filter_empty_entries(
input_dict: Dict[Union[Any], Tensor]) -> Dict[Any, Tensor]:
r"""Removes empty tensors from a dictionary. This avoids unnecessary
computation when some node/edge types are non-reachable after trimming.
"""
out_dict = copy.copy(input_dict)
for key, value in input_dict.items():
if value.numel() == 0:
del out_dict[key]
return out_dict
@overload
def trim_to_layer(
layer: int,
num_sampled_nodes_per_hop: List[int],
num_sampled_edges_per_hop: List[int],
x: Tensor,
edge_index: Adj,
edge_attr: Optional[Tensor] = None,
) -> Tuple[Tensor, Tensor, Optional[Tensor]]:
pass
@overload
def trim_to_layer(
layer: int,
num_sampled_nodes_per_hop: Dict[NodeType, List[int]],
num_sampled_edges_per_hop: Dict[EdgeType, List[int]],
x: Dict[NodeType, Tensor],
edge_index: Dict[EdgeType, Adj],
edge_attr: Optional[Dict[EdgeType, Tensor]] = None,
) -> Tuple[Dict[NodeType, Tensor], Dict[EdgeType, Adj], Optional[Dict[
EdgeType, Tensor]]]:
pass
[docs]def trim_to_layer(
layer: int,
num_sampled_nodes_per_hop: Union[List[int], Dict[NodeType, List[int]]],
num_sampled_edges_per_hop: Union[List[int], Dict[EdgeType, List[int]]],
x: MaybeHeteroNodeTensor,
edge_index: MaybeHeteroEdgeTensor,
edge_attr: Optional[MaybeHeteroEdgeTensor] = None,
) -> Tuple[MaybeHeteroNodeTensor, MaybeHeteroAdjTensor,
Optional[MaybeHeteroEdgeTensor]]:
r"""Trims the :obj:`edge_index` representation, node features :obj:`x` and
edge features :obj:`edge_attr` to a minimal-sized representation for the
current GNN layer :obj:`layer` in directed
:class:`~torch_geometric.loader.NeighborLoader` scenarios.
This ensures that no computation is performed for nodes and edges that are
not included in the current GNN layer, thus avoiding unnecessary
computation within the GNN when performing neighborhood sampling.
Args:
layer (int): The current GNN layer.
num_sampled_nodes_per_hop (List[int] or Dict[NodeType, List[int]]): The
number of sampled nodes per hop.
num_sampled_edges_per_hop (List[int] or Dict[EdgeType, List[int]]): The
number of sampled edges per hop.
x (torch.Tensor or Dict[NodeType, torch.Tensor]): The homogeneous or
heterogeneous (hidden) node features.
edge_index (torch.Tensor or Dict[EdgeType, torch.Tensor]): The
homogeneous or heterogeneous edge indices.
edge_attr (torch.Tensor or Dict[EdgeType, torch.Tensor], optional): The
homogeneous or heterogeneous (hidden) edge features.
"""
if layer <= 0:
return x, edge_index, edge_attr
if isinstance(num_sampled_edges_per_hop, dict):
assert isinstance(num_sampled_nodes_per_hop, dict)
assert isinstance(x, dict)
x = {
k: trim_feat(v, layer, num_sampled_nodes_per_hop[k])
for k, v in x.items()
}
x = filter_empty_entries(x)
assert isinstance(edge_index, dict)
edge_index = {
k:
trim_adj(
v,
layer,
num_sampled_nodes_per_hop[k[0]],
num_sampled_nodes_per_hop[k[-1]],
num_sampled_edges_per_hop[k],
)
for k, v in edge_index.items()
}
edge_index = filter_empty_entries(edge_index)
if edge_attr is not None:
assert isinstance(edge_attr, dict)
edge_attr = {
k: trim_feat(v, layer, num_sampled_edges_per_hop[k])
for k, v in edge_attr.items()
}
edge_attr = filter_empty_entries(edge_attr)
return x, edge_index, edge_attr
assert isinstance(num_sampled_nodes_per_hop, list)
assert isinstance(x, Tensor)
x = trim_feat(x, layer, num_sampled_nodes_per_hop)
assert isinstance(edge_index, (Tensor, SparseTensor))
edge_index = trim_adj(
edge_index,
layer,
num_sampled_nodes_per_hop,
num_sampled_nodes_per_hop,
num_sampled_edges_per_hop,
)
if edge_attr is not None:
assert isinstance(edge_attr, Tensor)
edge_attr = trim_feat(edge_attr, layer, num_sampled_edges_per_hop)
return x, edge_index, edge_attr
class TrimToLayer(torch.nn.Module):
@torch.jit.unused
def forward(
self,
layer: int,
num_sampled_nodes_per_hop: Optional[List[int]],
num_sampled_edges_per_hop: Optional[List[int]],
x: Tensor,
edge_index: Adj,
edge_attr: Optional[Tensor] = None,
) -> Tuple[Tensor, Adj, Optional[Tensor]]:
if (not isinstance(num_sampled_nodes_per_hop, list)
and isinstance(num_sampled_edges_per_hop, list)):
raise ValueError("'num_sampled_nodes_per_hop' needs to be given")
if (not isinstance(num_sampled_edges_per_hop, list)
and isinstance(num_sampled_nodes_per_hop, list)):
raise ValueError("'num_sampled_edges_per_hop' needs to be given")
if num_sampled_nodes_per_hop is None:
return x, edge_index, edge_attr
if num_sampled_edges_per_hop is None:
return x, edge_index, edge_attr
return trim_to_layer(
layer,
num_sampled_nodes_per_hop,
num_sampled_edges_per_hop,
x,
edge_index,
edge_attr,
)
# Helper functions ############################################################
def trim_feat(x: Tensor, layer: int, num_samples_per_hop: List[int]) -> Tensor:
if layer <= 0:
return x
return x.narrow(
dim=0,
start=0,
length=x.size(0) - num_samples_per_hop[-layer],
)
def trim_adj(
edge_index: Adj,
layer: int,
num_sampled_src_nodes_per_hop: List[int],
num_sampled_dst_nodes_per_hop: List[int],
num_sampled_edges_per_hop: List[int],
) -> Adj:
if layer <= 0:
return edge_index
if isinstance(edge_index, Tensor):
return edge_index.narrow(
dim=1,
start=0,
length=edge_index.size(1) - num_sampled_edges_per_hop[-layer],
)
elif isinstance(edge_index, SparseTensor):
size = (
edge_index.size(0) - num_sampled_dst_nodes_per_hop[-layer],
edge_index.size(1) - num_sampled_src_nodes_per_hop[-layer],
)
num_seed_nodes = size[0] - num_sampled_dst_nodes_per_hop[-(layer + 1)]
return trim_sparse_tensor(edge_index, size, num_seed_nodes)
raise ValueError(f"Unsupported 'edge_index' type '{type(edge_index)}'")
def trim_sparse_tensor(src: SparseTensor, size: Tuple[int, int],
num_seed_nodes: int) -> SparseTensor:
r"""Trims a :class:`SparseTensor` along both dimensions to only contain
the upper :obj:`num_nodes` in both dimensions.
It is assumed that :class:`SparseTensor` is obtained from BFS traversing,
starting from the nodes that have been initially selected.
Args:
src (SparseTensor): The sparse tensor.
size (Tuple[int, int]): The number of source and destination nodes to
keep.
num_seed_nodes (int): The number of seed nodes to compute
representations.
"""
rowptr, col, value = src.csr()
rowptr = torch.narrow(rowptr, 0, 0, size[0] + 1).clone()
rowptr[num_seed_nodes + 1:] = rowptr[num_seed_nodes]
col = torch.narrow(col, 0, 0, rowptr[-1])
if value is not None:
value = torch.narrow(value, 0, 0, rowptr[-1])
csr2csc = src.storage._csr2csc
if csr2csc is not None:
csr2csc = csr2csc[csr2csc < len(col)]
storage = SparseStorage(
row=None,
rowptr=rowptr,
col=col,
value=value,
sparse_sizes=size,
rowcount=None,
colptr=None,
colcount=None,
csr2csc=csr2csc,
csc2csr=None,
is_sorted=True,
trust_data=True,
)
return src.from_storage(storage)