Source code for torch_geometric.loader.neighbor_loader

from collections.abc import Sequence
from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union

import torch
from torch import Tensor

from torch_geometric.data import Data, HeteroData
from torch_geometric.data.feature_store import FeatureStore, TensorAttr
from torch_geometric.data.graph_store import GraphStore
from torch_geometric.loader.base import DataLoaderIterator
from torch_geometric.loader.utils import (
    edge_type_to_str,
    filter_custom_store,
    filter_data,
    filter_hetero_data,
    to_csc,
    to_hetero_csc,
)
from torch_geometric.typing import InputNodes, NumNeighbors


class NeighborSampler:
    def __init__(
        self,
        data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]],
        num_neighbors: NumNeighbors,
        replace: bool = False,
        directed: bool = True,
        input_type: Optional[Any] = None,
        time_attr: Optional[str] = None,
        is_sorted: bool = False,
        share_memory: bool = False,
    ):
        self.data_cls = data.__class__ if isinstance(
            data, (Data, HeteroData)) else 'custom'
        self.num_neighbors = num_neighbors
        self.replace = replace
        self.directed = directed
        self.node_time = None

        # TODO Unify the following conditionals behind the `FeatureStore`
        # and `GraphStore` API

        # If we are working with a `Data` object, convert the edge_index to
        # CSC and store it:
        if isinstance(data, Data):
            if time_attr is not None:
                # TODO `time_attr` support for homogeneous graphs
                raise ValueError(
                    f"'time_attr' attribute not yet supported for "
                    f"'{data.__class__.__name__}' object")

            # Convert the graph data into a suitable format for sampling.
            out = to_csc(data, device='cpu', share_memory=share_memory,
                         is_sorted=is_sorted)
            self.colptr, self.row, self.perm = out
            assert isinstance(num_neighbors, (list, tuple))

        # If we are working with a `HeteroData` object, convert each edge
        # type's edge_index to CSC and store it:
        elif isinstance(data, HeteroData):
            if time_attr is not None:
                self.node_time_dict = data.collect(time_attr)
            else:
                self.node_time_dict = None

            # Convert the graph data into a suitable format for sampling.
            # NOTE: Since C++ cannot take dictionaries with tuples as key as
            # input, edge type triplets are converted into single strings.
            out = to_hetero_csc(data, device='cpu', share_memory=share_memory,
                                is_sorted=is_sorted)
            self.colptr_dict, self.row_dict, self.perm_dict = out

            self.node_types, self.edge_types = data.metadata()
            self._set_num_neighbors_and_num_hops(num_neighbors)

            assert input_type is not None
            self.input_type = input_type

        # If we are working with a `Tuple[FeatureStore, GraphStore]` object,
        # obtain edges from GraphStore and convert them to CSC if necessary,
        # storing the resulting representations:
        elif isinstance(data, tuple):
            # TODO support `FeatureStore` with no edge types (e.g. `Data`)
            feature_store, graph_store = data

            # TODO support `collect` on `FeatureStore`
            self.node_time_dict = None
            if time_attr is not None:
                # We need to obtain all features with 'attr_name=time_attr'
                # from the feature store and store them in node_time_dict. To
                # do so, we make an explicit feature store GET call here with
                # the relevant 'TensorAttr's
                time_attrs = [
                    attr for attr in feature_store.get_all_tensor_attrs()
                    if attr.attr_name == time_attr
                ]
                for attr in time_attrs:
                    attr.index = None
                time_tensors = feature_store.multi_get_tensor(time_attrs)
                self.node_time_dict = {
                    time_attr.group_name: time_tensor
                    for time_attr, time_tensor in zip(time_attrs, time_tensors)
                }

            # Obtain all node and edge metadata:
            node_attrs = feature_store.get_all_tensor_attrs()
            edge_attrs = graph_store.get_all_edge_attrs()

            self.node_types = list(
                set(node_attr.group_name for node_attr in node_attrs))
            self.edge_types = list(
                set(edge_attr.edge_type for edge_attr in edge_attrs))

            # Set other required parameters:
            self._set_num_neighbors_and_num_hops(num_neighbors)

            assert input_type is not None
            self.input_type = input_type

            # Obtain CSC representations for in-memory sampling:
            row_dict, colptr_dict, perm_dict = graph_store.csc()
            self.row_dict = {
                edge_type_to_str(k): v
                for k, v in row_dict.items()
            }
            self.colptr_dict = {
                edge_type_to_str(k): v
                for k, v in colptr_dict.items()
            }
            self.perm_dict = {
                edge_type_to_str(k): v
                for k, v in perm_dict.items()
            }

        else:
            raise TypeError(f'NeighborLoader found invalid type: {type(data)}')

    def _set_num_neighbors_and_num_hops(self, num_neighbors):
        if isinstance(num_neighbors, (list, tuple)):
            num_neighbors = {key: num_neighbors for key in self.edge_types}
        assert isinstance(num_neighbors, dict)
        self.num_neighbors = {
            edge_type_to_str(key): value
            for key, value in num_neighbors.items()
        }
        # Add at least one element to the list to ensure `max` is well-defined
        self.num_hops = max([0] + [len(v) for v in num_neighbors.values()])

    def _sparse_neighbor_sample(self, index: Tensor):
        fn = torch.ops.torch_sparse.neighbor_sample
        node, row, col, edge = fn(
            self.colptr,
            self.row,
            index,
            self.num_neighbors,
            self.replace,
            self.directed,
        )
        return node, row, col, edge

    def _hetero_sparse_neighbor_sample(self, index_dict: Dict[str, Tensor],
                                       **kwargs):
        if self.node_time_dict is None:
            fn = torch.ops.torch_sparse.hetero_neighbor_sample
            node_dict, row_dict, col_dict, edge_dict = fn(
                self.node_types,
                self.edge_types,
                self.colptr_dict,
                self.row_dict,
                index_dict,
                self.num_neighbors,
                self.num_hops,
                self.replace,
                self.directed,
            )
        else:
            try:
                fn = torch.ops.torch_sparse.hetero_temporal_neighbor_sample
            except RuntimeError as e:
                raise RuntimeError(
                    "'torch_sparse' operator "
                    "'hetero_temporal_neighbor_sample' not "
                    "found. Currently requires building "
                    "'torch_sparse' from master.", e)

            node_dict, row_dict, col_dict, edge_dict = fn(
                self.node_types,
                self.edge_types,
                self.colptr_dict,
                self.row_dict,
                index_dict,
                self.num_neighbors,
                kwargs.get('node_time_dict', self.node_time_dict),
                self.num_hops,
                self.replace,
                self.directed,
            )
        return node_dict, row_dict, col_dict, edge_dict

    def __call__(self, index: Union[List[int], Tensor]):
        if not isinstance(index, torch.LongTensor):
            index = torch.LongTensor(index)

        if self.data_cls != 'custom' and issubclass(self.data_cls, Data):
            return self._sparse_neighbor_sample(index) + (index.numel(), )

        elif self.data_cls == 'custom' or issubclass(self.data_cls,
                                                     HeteroData):
            return self._hetero_sparse_neighbor_sample(
                {self.input_type: index}) + (index.numel(), )


[docs]class NeighborLoader(torch.utils.data.DataLoader): r"""A data loader that performs neighbor sampling as introduced in the `"Inductive Representation Learning on Large Graphs" <https://arxiv.org/abs/1706.02216>`_ paper. This loader allows for mini-batch training of GNNs on large-scale graphs where full-batch training is not feasible. More specifically, :obj:`num_neighbors` denotes how much neighbors are sampled for each node in each iteration. :class:`~torch_geometric.loader.NeighborLoader` takes in this list of :obj:`num_neighbors` and iteratively samples :obj:`num_neighbors[i]` for each node involved in iteration :obj:`i - 1`. Sampled nodes are sorted based on the order in which they were sampled. In particular, the first :obj:`batch_size` nodes represent the set of original mini-batch nodes. .. code-block:: python from torch_geometric.datasets import Planetoid from torch_geometric.loader import NeighborLoader data = Planetoid(path, name='Cora')[0] loader = NeighborLoader( data, # Sample 30 neighbors for each node for 2 iterations num_neighbors=[30] * 2, # Use a batch size of 128 for sampling training nodes batch_size=128, input_nodes=data.train_mask, ) sampled_data = next(iter(loader)) print(sampled_data.batch_size) >>> 128 By default, the data loader will only include the edges that were originally sampled (:obj:`directed = True`). This option should only be used in case the number of hops is equivalent to the number of GNN layers. In case the number of GNN layers is greater than the number of hops, consider setting :obj:`directed = False`, which will include all edges between all sampled nodes (but is slightly slower as a result). Furthermore, :class:`~torch_geometric.loader.NeighborLoader` works for both **homogeneous** graphs stored via :class:`~torch_geometric.data.Data` as well as **heterogeneous** graphs stored via :class:`~torch_geometric.data.HeteroData`. When operating in heterogeneous graphs, up to :obj:`num_neighbors` neighbors will be sampled for each :obj:`edge_type`. However, more fine-grained control over the amount of sampled neighbors of individual edge types is possible: .. code-block:: python from torch_geometric.datasets import OGB_MAG from torch_geometric.loader import NeighborLoader hetero_data = OGB_MAG(path)[0] loader = NeighborLoader( hetero_data, # Sample 30 neighbors for each node and edge type for 2 iterations num_neighbors={key: [30] * 2 for key in hetero_data.edge_types}, # Use a batch size of 128 for sampling training nodes of type paper batch_size=128, input_nodes=('paper', hetero_data['paper'].train_mask), ) sampled_hetero_data = next(iter(loader)) print(sampled_hetero_data['paper'].batch_size) >>> 128 .. note:: For an example of using :class:`~torch_geometric.loader.NeighborLoader`, see `examples/hetero/to_hetero_mag.py <https://github.com/pyg-team/ pytorch_geometric/blob/master/examples/hetero/to_hetero_mag.py>`_. The :class:`~torch_geometric.loader.NeighborLoader` will return subgraphs where global node indices are mapped to local indices corresponding to this specific subgraph. However, often times it is desired to map the nodes of the current subgraph back to the global node indices. A simple trick to achieve this is to include this mapping as part of the :obj:`data` object: .. code-block:: python # Assign each node its global node index: data.n_id = torch.arange(data.num_nodes) loader = NeighborLoader(data, ...) sampled_data = next(iter(loader)) print(sampled_data.n_id) Args: data (torch_geometric.data.Data or torch_geometric.data.HeteroData): The :class:`~torch_geometric.data.Data` or :class:`~torch_geometric.data.HeteroData` graph object. num_neighbors (List[int] or Dict[Tuple[str, str, str], List[int]]): The number of neighbors to sample for each node in each iteration. In heterogeneous graphs, may also take in a dictionary denoting the amount of neighbors to sample for each individual edge type. If an entry is set to :obj:`-1`, all neighbors will be included. input_nodes (torch.Tensor or str or Tuple[str, torch.Tensor]): The indices of nodes for which neighbors are sampled to create mini-batches. Needs to be either given as a :obj:`torch.LongTensor` or :obj:`torch.BoolTensor`. If set to :obj:`None`, all nodes will be considered. In heterogeneous graphs, needs to be passed as a tuple that holds the node type and node indices. (default: :obj:`None`) replace (bool, optional): If set to :obj:`True`, will sample with replacement. (default: :obj:`False`) directed (bool, optional): If set to :obj:`False`, will include all edges between all sampled nodes. (default: :obj:`True`) time_attr (str, optional): The name of the attribute that denotes timestamps for the nodes in the graph. If set, temporal sampling will be used such that neighbors are guaranteed to fulfill temporal constraints, *i.e.* neighbors have an earlier timestamp than the center node. (default: :obj:`None`) transform (Callable, optional): A function/transform that takes in a sampled mini-batch and returns a transformed version. (default: :obj:`None`) is_sorted (bool, optional): If set to :obj:`True`, assumes that :obj:`edge_index` is sorted by column. This avoids internal re-sorting of the data and can improve runtime and memory efficiency. (default: :obj:`False`) filter_per_worker (bool, optional): If set to :obj:`True`, will filter the returning data in each worker's subprocess rather than in the main process. Setting this to :obj:`True` is generally not recommended: (1) it may result in too many open file handles, (2) it may slown down data loading, (3) it requires operating on CPU tensors. (default: :obj:`False`) **kwargs (optional): Additional arguments of :class:`torch.utils.data.DataLoader`, such as :obj:`batch_size`, :obj:`shuffle`, :obj:`drop_last` or :obj:`num_workers`. """ def __init__( self, data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], num_neighbors: NumNeighbors, input_nodes: InputNodes = None, replace: bool = False, directed: bool = True, time_attr: Optional[str] = None, transform: Callable = None, is_sorted: bool = False, filter_per_worker: bool = False, neighbor_sampler: Optional[NeighborSampler] = None, **kwargs, ): # Remove for PyTorch Lightning: if 'dataset' in kwargs: del kwargs['dataset'] if 'collate_fn' in kwargs: del kwargs['collate_fn'] self.data = data # Save for PyTorch Lightning < 1.6: self.num_neighbors = num_neighbors self.input_nodes = input_nodes self.replace = replace self.directed = directed self.transform = transform self.filter_per_worker = filter_per_worker self.neighbor_sampler = neighbor_sampler node_type, input_nodes = get_input_nodes(data, input_nodes) if neighbor_sampler is None: self.neighbor_sampler = NeighborSampler( data, num_neighbors, replace, directed, input_type=node_type, time_attr=time_attr, is_sorted=is_sorted, share_memory=kwargs.get('num_workers', 0) > 0, ) super().__init__(input_nodes, collate_fn=self.collate_fn, **kwargs) def filter_fn(self, out: Any) -> Union[Data, HeteroData]: if isinstance(self.data, Data): node, row, col, edge, batch_size = out data = filter_data(self.data, node, row, col, edge, self.neighbor_sampler.perm) data.batch_size = batch_size elif isinstance(self.data, HeteroData): node_dict, row_dict, col_dict, edge_dict, batch_size = out data = filter_hetero_data(self.data, node_dict, row_dict, col_dict, edge_dict, self.neighbor_sampler.perm_dict) data[self.neighbor_sampler.input_type].batch_size = batch_size else: # Tuple[FeatureStore, GraphStore] # TODO support for feature stores with no edge types node_dict, row_dict, col_dict, edge_dict, batch_size = out feature_store, graph_store = self.data data = filter_custom_store(feature_store, graph_store, node_dict, row_dict, col_dict, edge_dict) data[self.neighbor_sampler.input_type].batch_size = batch_size return data if self.transform is None else self.transform(data) def collate_fn(self, index: Union[List[int], Tensor]) -> Any: out = self.neighbor_sampler(index) if self.filter_per_worker: # We execute `filter_fn` in the worker process. out = self.filter_fn(out) return out def _get_iterator(self) -> Iterator: if self.filter_per_worker: return super()._get_iterator() # We execute `filter_fn` in the main process. return DataLoaderIterator(super()._get_iterator(), self.filter_fn) def __repr__(self) -> str: return f'{self.__class__.__name__}()'
############################################################################### def get_input_nodes( data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], input_nodes: Union[InputNodes, TensorAttr], ) -> Tuple[Optional[str], Sequence]: def to_index(tensor): if isinstance(tensor, Tensor) and tensor.dtype == torch.bool: return tensor.nonzero(as_tuple=False).view(-1) return tensor if isinstance(data, Data): if input_nodes is None: return None, range(data.num_nodes) return None, to_index(input_nodes) elif isinstance(data, HeteroData): assert input_nodes is not None if isinstance(input_nodes, str): return input_nodes, range(data[input_nodes].num_nodes) assert isinstance(input_nodes, (list, tuple)) assert len(input_nodes) == 2 assert isinstance(input_nodes[0], str) node_type, input_nodes = input_nodes if input_nodes is None: return node_type, range(data[node_type].num_nodes) return node_type, to_index(input_nodes) else: # Tuple[FeatureStore, GraphStore] # NOTE FeatureStore and GraphStore are treated as separate # entities, so we cannot leverage the custom structure in Data and # HeteroData to infer the number of nodes. As a result, here we expect # that the input nodes are either explicitly provided or can be # directly inferred from the feature store. feature_store, _ = data assert input_nodes is not None if isinstance(input_nodes, Tensor): return None, to_index(input_nodes) # Can't infer number of nodes from a group_name; need an attr_name if isinstance(input_nodes, str): raise NotImplementedError( f"Cannot infer the number of nodes from a single string " f"(got '{input_nodes}'). Please pass a more explicit " f"representation. ") if isinstance(input_nodes, (list, tuple)): assert len(input_nodes) == 2 assert isinstance(input_nodes[0], str) node_type, input_nodes = input_nodes if input_nodes is None: raise NotImplementedError( f"Cannot infer the number of nodes from a node type alone " f"(got '{input_nodes}'). Please pass a more explicit " f"representation. ") return node_type, to_index(input_nodes) assert isinstance(input_nodes, TensorAttr) assert input_nodes.is_set('attr_name') node_type = getattr(input_nodes, 'group_name', None) if not input_nodes.is_set('index') or input_nodes.index is None: num_nodes = feature_store.get_tensor_size(input_nodes)[0] return node_type, range(num_nodes) return node_type, input_nodes.index