import math
import dgl
import torch
import torch.nn as nn
from dgl import function as fn
from dgl.nn.pytorch.linear import TypedLinear
from dgl.nn.pytorch.softmax import edge_softmax
from . import BaseModel, register_model
from ..utils import to_hetero_feat
[文档]@register_model('HGT')
class HGT(BaseModel):
r"""Heterogeneous graph transformer convolution from `Heterogeneous Graph Transformer
<https://arxiv.org/abs/2003.01332>`__
For more details, you may refer to `HGT<https://docs.dgl.ai/en/0.8.x/generated/dgl.nn.pytorch.conv.HGTConv.html>`__
Parameters
----------
in_dim: int
the input dimension
out_dim: int
the output dimension
num_heads: list
the list of the number of heads in each layer
num_etypes: int
the number of the edge type
num_ntypes: int
the number of the node type
num_layers: int
the number of layers we used in the computing
dropout: float
the feature drop rate
norm: boolean
if we need the norm operation
ntypes: list
the list of node type
"""
@classmethod
def build_model_from_args(cls, args, hg):
return cls(args.hidden_dim,
args.out_dim,
args.num_heads,
len(hg.etypes),
hg.ntypes,
args.num_layers,
args.dropout,
args.norm
)
def __init__(self, in_dim, out_dim, num_heads, num_etypes, ntypes,
num_layers, dropout=0.2, norm=False):
super(HGT, self).__init__()
self.num_layers = num_layers
self.ntypes = ntypes
self.hgt_layers = nn.ModuleList()
self.hgt_layers.append(
HGTConv(in_dim,
in_dim // num_heads,
num_heads,
len(ntypes),
num_etypes,
dropout,
norm)
)
for _ in range(1, num_layers - 1):
self.hgt_layers.append(
HGTConv(in_dim,
in_dim // num_heads,
num_heads,
len(ntypes),
num_etypes,
dropout,
norm)
)
self.hgt_layers.append(
HGTConv(in_dim,
out_dim,
1,
len(ntypes),
num_etypes,
dropout,
norm)
)
def forward(self, hg, h_dict):
"""
The forward part of the HGT.
Parameters
----------
hg : object
the dgl heterogeneous graph
h_dict: dict
the feature dict of different node types
Returns
-------
dict
The embeddings after the output projection.
"""
if hasattr(hg, 'ntypes'):
# full graph training,
with hg.local_scope():
hg.ndata['h'] = h_dict
g = dgl.to_homogeneous(hg, ndata='h')
h = g.ndata['h']
for l in range(self.num_layers):
h = self.hgt_layers[l](g, h, g.ndata['_TYPE'], g.edata['_TYPE'], presorted=True)
h_dict = to_hetero_feat(h, g.ndata['_TYPE'], self.ntypes)
else:
# for minibatch training, input h_dict is a tensor
h = h_dict
for layer, block in zip(self.hgt_layers, hg):
h = layer(block, h, block.ndata['_TYPE']['_N'], block.edata['_TYPE'], presorted=False)
h_dict = to_hetero_feat(h, block.ndata['_TYPE']['_N'][:block.num_dst_nodes()], self.ntypes)
return h_dict
@property
def to_homo_flag(self):
return True
class HGTConv(nn.Module):
r"""Heterogeneous graph transformer convolution from `Heterogeneous Graph Transformer
<https://arxiv.org/abs/2003.01332>`__
Given a graph :math:`G(V, E)` and input node features :math:`H^{(l-1)}`,
it computes the new node features as follows:
Compute a multi-head attention score for each edge :math:`(s, e, t)` in the graph:
.. math::
Attention(s, e, t) = \text{Softmax}\left(||_{i\in[1,h]}ATT-head^i(s, e, t)\right) \\
ATT-head^i(s, e, t) = \left(K^i(s)W^{ATT}_{\phi(e)}Q^i(t)^{\top}\right)\cdot
\frac{\mu_{(\tau(s),\phi(e),\tau(t)}}{\sqrt{d}} \\
K^i(s) = \text{K-Linear}^i_{\tau(s)}(H^{(l-1)}[s]) \\
Q^i(t) = \text{Q-Linear}^i_{\tau(t)}(H^{(l-1)}[t]) \\
Compute the message to send on each edge :math:`(s, e, t)`:
.. math::
Message(s, e, t) = ||_{i\in[1, h]} MSG-head^i(s, e, t) \\
MSG-head^i(s, e, t) = \text{M-Linear}^i_{\tau(s)}(H^{(l-1)}[s])W^{MSG}_{\phi(e)} \\
Send messages to target nodes :math:`t` and aggregate:
.. math::
\tilde{H}^{(l)}[t] = \sum_{\forall s\in \mathcal{N}(t)}\left( Attention(s,e,t)
\cdot Message(s,e,t)\right)
Compute new node features:
.. math::
H^{(l)}[t]=\text{A-Linear}_{\tau(t)}(\sigma(\tilde(H)^{(l)}[t])) + H^{(l-1)}[t]
Parameters
----------
in_size : int
Input node feature size.
head_size : int
Output head size. The output node feature size is ``head_size * num_heads``.
num_heads : int
Number of heads. The output node feature size is ``head_size * num_heads``.
num_ntypes : int
Number of node types.
num_etypes : int
Number of edge types.
dropout : optional, float
Dropout rate.
use_norm : optiona, bool
If true, apply a layer norm on the output node feature.
Examples
--------
"""
def __init__(self,
in_size,
head_size,
num_heads,
num_ntypes,
num_etypes,
dropout=0.2,
use_norm=False):
super().__init__()
self.in_size = in_size
self.head_size = head_size
self.num_heads = num_heads
self.sqrt_d = math.sqrt(head_size)
self.use_norm = use_norm
self.linear_k = TypedLinear(in_size, head_size * num_heads, num_ntypes)
self.linear_q = TypedLinear(in_size, head_size * num_heads, num_ntypes)
self.linear_v = TypedLinear(in_size, head_size * num_heads, num_ntypes)
self.linear_a = TypedLinear(head_size * num_heads, head_size * num_heads, num_ntypes)
self.relation_pri = nn.ParameterList([nn.Parameter(torch.ones(num_etypes))
for i in range(num_heads)])
self.relation_att = nn.ModuleList([TypedLinear(head_size, head_size, num_etypes)
for i in range(num_heads)])
self.relation_msg = nn.ModuleList([TypedLinear(head_size, head_size, num_etypes)
for i in range(num_heads)])
self.skip = nn.Parameter(torch.ones(num_ntypes))
self.drop = nn.Dropout(dropout)
if use_norm:
self.norm = nn.LayerNorm(head_size * num_heads)
if in_size != head_size * num_heads:
self.residual_w = nn.Parameter(torch.Tensor(in_size, head_size * num_heads))
nn.init.xavier_uniform_(self.residual_w)
def forward(self, g, x, ntype, etype, *, presorted=False):
"""Forward computation.
Parameters
----------
g : DGLGraph
The input graph.
x : torch.Tensor
A 2D tensor of node features. Shape: :math:`(|V|, D_{in})`.
ntype : torch.Tensor
An 1D integer tensor of node types. Shape: :math:`(|V|,)`.
etype : torch.Tensor
An 1D integer tensor of edge types. Shape: :math:`(|E|,)`.
presorted : bool, optional
Whether *both* the nodes and the edges of the input graph have been sorted by
their types. Forward on pre-sorted graph may be faster. Graphs created by
:func:`~dgl.to_homogeneous` automatically satisfy the condition.
Also see :func:`~dgl.reorder_graph` for manually reordering the nodes and edges.
Returns
-------
torch.Tensor
New node features. Shape: :math:`(|V|, D_{head} * N_{head})`.
"""
self.presorted = presorted
if g.is_block:
x_src = x
x_dst = x[:g.num_dst_nodes()]
srcntype = ntype
dstntype = ntype[:g.num_dst_nodes()]
else:
x_src = x
x_dst = x
srcntype = ntype
dstntype = ntype
with g.local_scope():
k = self.linear_k(x_src, srcntype, presorted).view(-1, self.num_heads, self.head_size)
q = self.linear_q(x_dst, dstntype, presorted).view(-1, self.num_heads, self.head_size)
v = self.linear_v(x_src, srcntype, presorted).view(-1, self.num_heads, self.head_size)
g.srcdata['k'] = k
g.dstdata['q'] = q
g.srcdata['v'] = v
g.edata['etype'] = etype
g.apply_edges(self.message)
g.edata['m'] = g.edata['m'] * edge_softmax(g, g.edata['a']).unsqueeze(-1)
g.update_all(fn.copy_e('m', 'm'), fn.sum('m', 'h'))
h = g.dstdata['h'].view(-1, self.num_heads * self.head_size)
# target-specific aggregation
h = self.drop(self.linear_a(h, dstntype, presorted))
alpha = torch.sigmoid(self.skip[dstntype]).unsqueeze(-1)
if x_dst.shape != h.shape:
h = h * alpha + (x_dst @ self.residual_w) * (1 - alpha)
else:
h = h * alpha + x_dst * (1 - alpha)
if self.use_norm:
h = self.norm(h)
return h
def message(self, edges):
"""Message function."""
a, m = [], []
etype = edges.data['etype']
k = torch.unbind(edges.src['k'], dim=1)
q = torch.unbind(edges.dst['q'], dim=1)
v = torch.unbind(edges.src['v'], dim=1)
for i in range(self.num_heads):
kw = self.relation_att[i](k[i], etype, self.presorted) # (E, O)
a.append((kw * q[i]).sum(-1) * self.relation_pri[i][etype] / self.sqrt_d) # (E,)
m.append(self.relation_msg[i](v[i], etype, self.presorted)) # (E, O)
return {'a' : torch.stack(a, dim=1), 'm' : torch.stack(m, dim=1)}