wehub-resource-sync
296 lines
11 KiB
Python
296 lines
11 KiB
Python
"""Torch Module for GraphSAGE layer"""
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# pylint: disable= no-member, arguments-differ, invalid-name
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import torch
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from torch import nn
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from torch.nn import functional as F
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from .... import function as fn
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from ....base import DGLError
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from ....utils import check_eq_shape, expand_as_pair
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class SAGEConv(nn.Module):
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r"""GraphSAGE layer from `Inductive Representation Learning on
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Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__
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.. math::
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h_{\mathcal{N}(i)}^{(l+1)} &= \mathrm{aggregate}
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\left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right)
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h_{i}^{(l+1)} &= \sigma \left(W \cdot \mathrm{concat}
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(h_{i}^{l}, h_{\mathcal{N}(i)}^{l+1}) \right)
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h_{i}^{(l+1)} &= \mathrm{norm}(h_{i}^{(l+1)})
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If a weight tensor on each edge is provided, the aggregation becomes:
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.. math::
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h_{\mathcal{N}(i)}^{(l+1)} = \mathrm{aggregate}
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\left(\{e_{ji} h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right)
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where :math:`e_{ji}` is the scalar weight on the edge from node :math:`j` to node :math:`i`.
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Please make sure that :math:`e_{ji}` is broadcastable with :math:`h_j^{l}`.
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Parameters
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----------
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in_feats : int, or pair of ints
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Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`.
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SAGEConv can be applied on homogeneous graph and unidirectional
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`bipartite graph <https://docs.dgl.ai/generated/dgl.bipartite.html?highlight=bipartite>`__.
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If the layer applies on a unidirectional bipartite graph, ``in_feats``
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specifies the input feature size on both the source and destination nodes. If
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a scalar is given, the source and destination node feature size would take the
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same value.
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If aggregator type is ``gcn``, the feature size of source and destination nodes
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are required to be the same.
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out_feats : int
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Output feature size; i.e, the number of dimensions of :math:`h_i^{(l+1)}`.
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aggregator_type : str
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Aggregator type to use (``mean``, ``gcn``, ``pool``, ``lstm``).
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feat_drop : float
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Dropout rate on features, default: ``0``.
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bias : bool
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If True, adds a learnable bias to the output. Default: ``True``.
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norm : callable activation function/layer or None, optional
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If not None, applies normalization to the updated node features.
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activation : callable activation function/layer or None, optional
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If not None, applies an activation function to the updated node features.
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Default: ``None``.
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Examples
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--------
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>>> import dgl
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>>> import numpy as np
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>>> import torch as th
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>>> from dgl.nn import SAGEConv
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>>> # Case 1: Homogeneous graph
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>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
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>>> g = dgl.add_self_loop(g)
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>>> feat = th.ones(6, 10)
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>>> conv = SAGEConv(10, 2, 'pool')
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>>> res = conv(g, feat)
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>>> res
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tensor([[-1.0888, -2.1099],
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[-1.0888, -2.1099],
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[-1.0888, -2.1099],
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[-1.0888, -2.1099],
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[-1.0888, -2.1099],
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[-1.0888, -2.1099]], grad_fn=<AddBackward0>)
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>>> # Case 2: Unidirectional bipartite graph
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>>> u = [0, 1, 0, 0, 1]
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>>> v = [0, 1, 2, 3, 2]
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>>> g = dgl.heterograph({('_N', '_E', '_N'):(u, v)})
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>>> u_fea = th.rand(2, 5)
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>>> v_fea = th.rand(4, 10)
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>>> conv = SAGEConv((5, 10), 2, 'mean')
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>>> res = conv(g, (u_fea, v_fea))
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>>> res
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tensor([[ 0.3163, 3.1166],
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[ 0.3866, 2.5398],
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[ 0.5873, 1.6597],
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[-0.2502, 2.8068]], grad_fn=<AddBackward0>)
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"""
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def __init__(
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self,
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in_feats,
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out_feats,
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aggregator_type,
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feat_drop=0.0,
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bias=True,
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norm=None,
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activation=None,
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):
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super(SAGEConv, self).__init__()
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valid_aggre_types = {"mean", "gcn", "pool", "lstm"}
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if aggregator_type not in valid_aggre_types:
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raise DGLError(
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"Invalid aggregator_type. Must be one of {}. "
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"But got {!r} instead.".format(
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valid_aggre_types, aggregator_type
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)
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)
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self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats)
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self._out_feats = out_feats
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self._aggre_type = aggregator_type
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self.norm = norm
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self.feat_drop = nn.Dropout(feat_drop)
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self.activation = activation
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# aggregator type: mean/pool/lstm/gcn
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if aggregator_type == "pool":
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self.fc_pool = nn.Linear(self._in_src_feats, self._in_src_feats)
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if aggregator_type == "lstm":
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self.lstm = nn.LSTM(
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self._in_src_feats, self._in_src_feats, batch_first=True
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)
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self.fc_neigh = nn.Linear(self._in_src_feats, out_feats, bias=False)
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if aggregator_type != "gcn":
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self.fc_self = nn.Linear(self._in_dst_feats, out_feats, bias=bias)
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elif bias:
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self.bias = nn.parameter.Parameter(torch.zeros(self._out_feats))
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else:
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self.register_buffer("bias", None)
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self.reset_parameters()
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def reset_parameters(self):
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r"""
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Description
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-----------
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Reinitialize learnable parameters.
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Note
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----
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The linear weights :math:`W^{(l)}` are initialized using Glorot uniform initialization.
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The LSTM module is using xavier initialization method for its weights.
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"""
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gain = nn.init.calculate_gain("relu")
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if self._aggre_type == "pool":
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nn.init.xavier_uniform_(self.fc_pool.weight, gain=gain)
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if self._aggre_type == "lstm":
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self.lstm.reset_parameters()
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if self._aggre_type != "gcn":
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nn.init.xavier_uniform_(self.fc_self.weight, gain=gain)
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nn.init.xavier_uniform_(self.fc_neigh.weight, gain=gain)
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def _lstm_reducer(self, nodes):
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"""LSTM reducer
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NOTE(zihao): lstm reducer with default schedule (degree bucketing)
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is slow, we could accelerate this with degree padding in the future.
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"""
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m = nodes.mailbox["m"] # (B, L, D)
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batch_size = m.shape[0]
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h = (
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m.new_zeros((1, batch_size, self._in_src_feats)),
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m.new_zeros((1, batch_size, self._in_src_feats)),
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)
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_, (rst, _) = self.lstm(m, h)
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return {"neigh": rst.squeeze(0)}
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def forward(self, graph, feat, edge_weight=None):
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r"""
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Description
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-----------
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Compute GraphSAGE layer.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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feat : torch.Tensor or pair of torch.Tensor
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If a torch.Tensor is given, it represents the input feature of shape
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:math:`(N, D_{in})`
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where :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes.
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If a pair of torch.Tensor is given, the pair must contain two tensors of shape
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:math:`(N_{in}, D_{in_{src}})` and :math:`(N_{out}, D_{in_{dst}})`.
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edge_weight : torch.Tensor, optional
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Optional tensor on the edge. If given, the convolution will weight
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with regard to the message.
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Returns
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-------
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torch.Tensor
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The output feature of shape :math:`(N_{dst}, D_{out})`
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where :math:`N_{dst}` is the number of destination nodes in the input graph,
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:math:`D_{out}` is the size of the output feature.
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"""
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with graph.local_scope():
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if isinstance(feat, tuple):
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feat_src = self.feat_drop(feat[0])
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feat_dst = self.feat_drop(feat[1])
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else:
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feat_src = feat_dst = self.feat_drop(feat)
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if graph.is_block:
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feat_dst = feat_src[: graph.number_of_dst_nodes()]
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msg_fn = fn.copy_u("h", "m")
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if edge_weight is not None:
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assert edge_weight.shape[0] == graph.num_edges()
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graph.edata["_edge_weight"] = edge_weight
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msg_fn = fn.u_mul_e("h", "_edge_weight", "m")
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h_self = feat_dst
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# Handle the case of graphs without edges
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if graph.num_edges() == 0:
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graph.dstdata["neigh"] = torch.zeros(
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feat_dst.shape[0], self._in_src_feats
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).to(feat_dst)
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# Determine whether to apply linear transformation before message passing A(XW)
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lin_before_mp = self._in_src_feats > self._out_feats
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# Message Passing
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if self._aggre_type == "mean":
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graph.srcdata["h"] = (
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self.fc_neigh(feat_src) if lin_before_mp else feat_src
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)
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graph.update_all(msg_fn, fn.mean("m", "neigh"))
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h_neigh = graph.dstdata["neigh"]
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if not lin_before_mp:
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h_neigh = self.fc_neigh(h_neigh)
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elif self._aggre_type == "gcn":
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check_eq_shape(feat)
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graph.srcdata["h"] = (
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self.fc_neigh(feat_src) if lin_before_mp else feat_src
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)
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if isinstance(feat, tuple): # heterogeneous
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graph.dstdata["h"] = (
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self.fc_neigh(feat_dst) if lin_before_mp else feat_dst
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)
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else:
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if graph.is_block:
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graph.dstdata["h"] = graph.srcdata["h"][
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: graph.num_dst_nodes()
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]
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else:
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graph.dstdata["h"] = graph.srcdata["h"]
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graph.update_all(msg_fn, fn.sum("m", "neigh"))
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# divide in_degrees
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degs = graph.in_degrees().to(feat_dst)
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h_neigh = (graph.dstdata["neigh"] + graph.dstdata["h"]) / (
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degs.unsqueeze(-1) + 1
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)
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if not lin_before_mp:
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h_neigh = self.fc_neigh(h_neigh)
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elif self._aggre_type == "pool":
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graph.srcdata["h"] = F.relu(self.fc_pool(feat_src))
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graph.update_all(msg_fn, fn.max("m", "neigh"))
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h_neigh = self.fc_neigh(graph.dstdata["neigh"])
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elif self._aggre_type == "lstm":
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graph.srcdata["h"] = feat_src
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graph.update_all(msg_fn, self._lstm_reducer)
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h_neigh = self.fc_neigh(graph.dstdata["neigh"])
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else:
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raise KeyError(
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"Aggregator type {} not recognized.".format(
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self._aggre_type
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)
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)
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# GraphSAGE GCN does not require fc_self.
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if self._aggre_type == "gcn":
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rst = h_neigh
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# add bias manually for GCN
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if self.bias is not None:
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rst = rst + self.bias
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else:
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rst = self.fc_self(h_self) + h_neigh
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# activation
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if self.activation is not None:
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rst = self.activation(rst)
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# normalization
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if self.norm is not None:
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rst = self.norm(rst)
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return rst
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