Source code for torch_geometric.nn.conv.message_passing

import inspect
from collections import OrderedDict

import torch
from torch_sparse import SparseTensor
from torch_scatter import gather_csr, scatter, segment_csr

msg_aggr_special_args = set([

msg_special_args = set([

aggr_special_args = set([

update_special_args = set([])

[docs]class MessagePassing(torch.nn.Module): r"""Base class for creating message passing layers of the form .. math:: \mathbf{x}_i^{\prime} = \gamma_{\mathbf{\Theta}} \left( \mathbf{x}_i, \square_{j \in \mathcal{N}(i)} \, \phi_{\mathbf{\Theta}} \left(\mathbf{x}_i, \mathbf{x}_j,\mathbf{e}_{j,i}\right) \right), where :math:`\square` denotes a differentiable, permutation invariant function, *e.g.*, sum, mean or max, and :math:`\gamma_{\mathbf{\Theta}}` and :math:`\phi_{\mathbf{\Theta}}` denote differentiable functions such as MLPs. See `here < create_gnn.html>`__ for the accompanying tutorial. Args: aggr (string, optional): The aggregation scheme to use (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"` or :obj:`None`). (default: :obj:`"add"`) flow (string, optional): The flow direction of message passing (:obj:`"source_to_target"` or :obj:`"target_to_source"`). (default: :obj:`"source_to_target"`) node_dim (int, optional): The axis along which to propagate. (default: :obj:`0`) """ def __init__(self, aggr="add", flow="source_to_target", node_dim=0): super(MessagePassing, self).__init__() self.aggr = aggr assert self.aggr in ['add', 'mean', 'max', None] self.flow = flow assert self.flow in ['source_to_target', 'target_to_source'] self.node_dim = node_dim assert self.node_dim >= 0 self.__msg_aggr_params__ = inspect.signature( self.message_and_aggregate).parameters self.__msg_aggr_params__ = OrderedDict(self.__msg_aggr_params__) self.__msg_params__ = inspect.signature(self.message).parameters self.__msg_params__ = OrderedDict(self.__msg_params__) self.__aggr_params__ = inspect.signature(self.aggregate).parameters self.__aggr_params__ = OrderedDict(self.__aggr_params__) self.__aggr_params__.popitem(last=False) self.__update_params__ = inspect.signature(self.update).parameters self.__update_params__ = OrderedDict(self.__update_params__) self.__update_params__.popitem(last=False) msg_aggr_args = set( self.__msg_aggr_params__.keys()) - msg_aggr_special_args msg_args = set(self.__msg_params__.keys()) - msg_special_args aggr_args = set(self.__aggr_params__.keys()) - aggr_special_args update_args = set(self.__update_params__.keys()) - update_special_args self.__user_args__ = set().union(msg_aggr_args, msg_args, aggr_args, update_args) self.__fuse__ = True # Support for GNNExplainer. self.__explain__ = False self.__edge_mask__ = None def __get_mp_type__(self, edge_index): if (torch.is_tensor(edge_index) and edge_index.dtype == torch.long and edge_index.dim() == 2 and edge_index.size(0)): return 'edge_index' elif isinstance(edge_index, SparseTensor): return 'adj_t' else: return ValueError( ('`MessagePassing.propagate` only supports `torch.LongTensor` ' 'of shape `[2, num_messages]` or `torch_sparse.SparseTensor` ' 'for argument :obj:`edge_index`.')) def __set_size__(self, size, idx, tensor): if not torch.is_tensor(tensor): pass elif size[idx] is None: size[idx] = tensor.size(self.node_dim) elif size[idx] != tensor.size(self.node_dim): raise ValueError( (f'Encountered node tensor with size ' f'{tensor.size(self.node_dim)} in dimension {self.node_dim}, ' f'but expected size {size[idx]}.')) def __collect__(self, edge_index, size, mp_type, kwargs): i, j = (0, 1) if self.flow == 'target_to_source' else (1, 0) ij = {'_i': i, '_j': j} out = {} for arg in self.__user_args__: if arg[-2:] not in ij.keys(): out[arg] = kwargs.get(arg, inspect.Parameter.empty) else: idx = ij[arg[-2:]] data = kwargs.get(arg[:-2], inspect.Parameter.empty) if data is inspect.Parameter.empty: out[arg] = data continue if isinstance(data, tuple) or isinstance(data, list): assert len(data) == 2 self.__set_size__(size, 1 - idx, data[1 - idx]) data = data[idx] if not torch.is_tensor(data): out[arg] = data continue self.__set_size__(size, idx, data) if mp_type == 'edge_index': out[arg] = data.index_select(self.node_dim, edge_index[idx]) elif mp_type == 'adj_t' and idx == 1: rowptr = for _ in range(self.node_dim): rowptr = rowptr.unsqueeze(0) out[arg] = gather_csr(data, rowptr) elif mp_type == 'adj_t' and idx == 0: col = out[arg] = data.index_select(self.node_dim, col) size[0] = size[1] if size[0] is None else size[0] size[1] = size[0] if size[1] is None else size[1] if mp_type == 'edge_index': out['edge_index_j'] = edge_index[j] out['edge_index_i'] = edge_index[i] out['index'] = out['edge_index_i'] elif mp_type == 'adj_t': out['adj_t'] = edge_index out['edge_index_i'] = out['edge_index_j'] = out['index'] = out['ptr'] = out['edge_attr'] = out['size_j'] = size[j] out['size_i'] = size[i] out['dim_size'] = out['size_i'] return out def __distribute__(self, params, kwargs): out = {} for key, param in params.items(): data = kwargs.get(key, inspect.Parameter.empty) if data is inspect.Parameter.empty: if param.default is inspect.Parameter.empty: raise TypeError(f'Required parameter {key} is empty.') data = param.default out[key] = data return out
[docs] def propagate(self, edge_index, size=None, **kwargs): r"""The initial call to start propagating messages. Args: adj (Tensor or SparseTensor): A :obj:`torch.LongTensor` or a :obj:`torch_sparse.SparseTensor` that defines the underlying message propagation. :obj:`edge_index` holds the indices of a general (sparse) assignment matrix of shape :obj:`[N, M]`. If :obj:`edge_index` is of type :obj:`torch.LongTensor`, its shape must be defined as :obj:`[2, num_messages]`, where messages from nodes in :obj:`edge_index[0]` are sent to nodes in :obj:`edge_index[1]` (in case :obj:`flow="source_to_target"`). If :obj:`edge_index` is of type :obj:`torch_sparse.SparseTensor`, its sparse indices :obj:`(row, col)` should relate to :obj:`row = edge_index[1]` and :obj:`col = edge_index[0]`. Hence, the only difference between those formats is that we need to input the *transposed* sparse adjacency matrix into :func:`propagate`. size (list or tuple, optional): The size :obj:`[N, M]` of the assignment matrix in case :obj:`edge_index` is a :obj:`LongTensor`. If set to :obj:`None`, the size will be automatically inferred and assumed to be quadratic. This argument is ignored in case :obj:`edge_index` is a :obj:`torch_sparse.SparseTensor`. (default: :obj:`None`) **kwargs: Any additional data which is needed to construct and aggregate messages, and to update node embeddings. """ # We need to distinguish between the old `edge_index` format and the # new `torch_sparse.SparseTensor` format. mp_type = self.__get_mp_type__(edge_index) if mp_type == 'adj_t' and self.flow == 'target_to_source': raise ValueError( ('Flow direction "target_to_source" is invalid for message ' 'propagation based on `torch_sparse.SparseTensor`. If you ' 'really want to make use of a reverse message passing flow, ' 'pass in the transposed sparse tensor to the message passing ' 'module, e.g., `adj.t()`.')) if mp_type == 'edge_index': if size is None: size = [None, None] elif isinstance(size, int): size = [size, size] elif torch.is_tensor(size): size = size.tolist() elif isinstance(size, tuple): size = list(size) elif mp_type == 'adj_t': size = list(edge_index.sparse_sizes())[::-1] assert isinstance(size, list) assert len(size) == 2 # We collect all arguments used for message passing in `kwargs`. kwargs = self.__collect__(edge_index, size, mp_type, kwargs) # Try to run `message_and_aggregate` first and see if it succeeds: if mp_type == 'adj_t' and self.__fuse__ and not self.__explain__: msg_aggr_kwargs = self.__distribute__(self.__msg_aggr_params__, kwargs) out = self.message_and_aggregate(**msg_aggr_kwargs) if out == NotImplemented: self.__fuse__ = False # Otherwise, run both functions in separation. if mp_type == 'edge_index' or not self.__fuse__ or self.__explain__: msg_kwargs = self.__distribute__(self.__msg_params__, kwargs) out = self.message(**msg_kwargs) if self.__explain__: edge_mask = self.__edge_mask__.sigmoid() if out.size(0) != edge_mask.size(0): loop = edge_mask.new_ones(size[0]) edge_mask =[edge_mask, loop], dim=0) assert out.size(0) == edge_mask.size(0) out = out * edge_mask.view(-1, 1) aggr_kwargs = self.__distribute__(self.__aggr_params__, kwargs) out = self.aggregate(out, **aggr_kwargs) update_kwargs = self.__distribute__(self.__update_params__, kwargs) out = self.update(out, **update_kwargs) return out
[docs] def message(self, x_j): r"""Constructs messages from node :math:`j` to node :math:`i` in analogy to :math:`\phi_{\mathbf{\Theta}}` for each edge in :obj:`edge_index`. This function can take any argument as input which was initially passed to :meth:`propagate`. Furthermore, tensors passed to :meth:`propagate` can be mapped to the respective nodes :math:`i` and :math:`j` by appending :obj:`_i` or :obj:`_j` to the variable name, *.e.g.* :obj:`x_i` and :obj:`x_j`. """ return x_j
[docs] def aggregate(self, inputs, index, ptr=None, dim_size=None): r"""Aggregates messages from neighbors as :math:`\square_{j \in \mathcal{N}(i)}`. Takes in the output of message computation as first argument and any argument which was initially passed to :meth:`propagate`. By default, this function will delegate its call to scatter functions that support "add", "mean" and "max" operations as specified in :meth:`__init__` by the :obj:`aggr` argument. """ if ptr is not None: for _ in range(self.node_dim): ptr = ptr.unsqueeze(0) return segment_csr(inputs, ptr, reduce=self.aggr) else: return scatter(inputs, index, dim=self.node_dim, dim_size=dim_size, reduce=self.aggr)
[docs] def message_and_aggregate(self, adj_t): r"""Fuses computations of :func:`message` and :func:`aggregate` into a single function. If applicable, this saves both time and memory since messages do not explicitly need to be materialized. This function will only gets called in case it is implemented and propagation takes place based on a :obj:`torch_sparse.SparseTensor`. """ return NotImplemented
[docs] def update(self, inputs): r"""Updates node embeddings in analogy to :math:`\gamma_{\mathbf{\Theta}}` for each node :math:`i \in \mathcal{V}`. Takes in the output of aggregation as first argument and any argument which was initially passed to :meth:`propagate`. """ return inputs