Source code for torch_geometric.nn.conv.dna_conv

import math
from typing import Optional

import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Parameter

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.nn.inits import kaiming_uniform, uniform
from torch_geometric.typing import Adj, OptPairTensor, OptTensor, SparseTensor

class Linear(torch.nn.Module):
    def __init__(self, in_channels, out_channels, groups=1, bias=True):
        assert in_channels % groups == 0 and out_channels % groups == 0

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.groups = groups

        self.weight = Parameter(
            torch.empty(groups, in_channels // groups, out_channels // groups))

        if bias:
            self.bias = Parameter(torch.empty(out_channels))
            self.register_parameter('bias', None)


    def reset_parameters(self):
        kaiming_uniform(self.weight, fan=self.weight.size(1), a=math.sqrt(5))
        uniform(self.weight.size(1), self.bias)

    def forward(self, src):
        # Input: [*, in_channels]
        # Output: [*, out_channels]

        if self.groups > 1:
            size = src.size()[:-1]
            src = src.view(-1, self.groups, self.in_channels // self.groups)
            src = src.transpose(0, 1).contiguous()
            out = torch.matmul(src, self.weight)
            out = out.transpose(1, 0).contiguous()
            out = out.view(size + (self.out_channels, ))
            out = torch.matmul(src, self.weight.squeeze(0))

        if self.bias is not None:
            out = out + self.bias

        return out

    def __repr__(self) -> str:  # pragma: no cover
        return (f'{self.__class__.__name__}({self.in_channels}, '
                f'{self.out_channels}, groups={self.groups})')

def restricted_softmax(src, dim: int = -1, margin: float = 0.):
    src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.)
    out = (src - src_max).exp()
    out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).exp())
    return out

class Attention(torch.nn.Module):
    def __init__(self, dropout=0):
        self.dropout = dropout

    def forward(self, query, key, value):
        return self.compute_attention(query, key, value)

    def compute_attention(self, query, key, value):
        # query: [*, query_entries, dim_k]
        # key: [*, key_entries, dim_k]
        # value: [*, key_entries, dim_v]
        # Output: [*, query_entries, dim_v]

        assert query.dim() == key.dim() == value.dim() >= 2
        assert query.size(-1) == key.size(-1)
        assert key.size(-2) == value.size(-2)

        # Score: [*, query_entries, key_entries]
        score = torch.matmul(query, key.transpose(-2, -1))
        score = score / math.sqrt(key.size(-1))
        score = restricted_softmax(score, dim=-1)
        score = F.dropout(score, p=self.dropout,

        return torch.matmul(score, value)

    def __repr__(self) -> str:  # pragma: no cover
        return f'{self.__class__.__name__}(dropout={self.dropout})'

class MultiHead(Attention):
    def __init__(self, in_channels, out_channels, heads=1, groups=1, dropout=0,

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.heads = heads
        self.groups = groups
        self.bias = bias

        assert in_channels % heads == 0 and out_channels % heads == 0
        assert in_channels % groups == 0 and out_channels % groups == 0
        assert max(groups, self.heads) % min(groups, self.heads) == 0

        self.lin_q = Linear(in_channels, out_channels, groups, bias)
        self.lin_k = Linear(in_channels, out_channels, groups, bias)
        self.lin_v = Linear(in_channels, out_channels, groups, bias)


    def reset_parameters(self):

    def forward(self, query, key, value):
        # query: [*, query_entries, in_channels]
        # key: [*, key_entries, in_channels]
        # value: [*, key_entries, in_channels]
        # Output: [*, query_entries, out_channels]

        assert query.dim() == key.dim() == value.dim() >= 2
        assert query.size(-1) == key.size(-1) == value.size(-1)
        assert key.size(-2) == value.size(-2)

        query = self.lin_q(query)
        key = self.lin_k(key)
        value = self.lin_v(value)

        # query: [*, heads, query_entries, out_channels // heads]
        # key: [*, heads, key_entries, out_channels // heads]
        # value: [*, heads, key_entries, out_channels // heads]
        size = query.size()[:-2]
        out_channels_per_head = self.out_channels // self.heads

        query_size = size + (query.size(-2), self.heads, out_channels_per_head)
        query = query.view(query_size).transpose(-2, -3)

        key_size = size + (key.size(-2), self.heads, out_channels_per_head)
        key = key.view(key_size).transpose(-2, -3)

        value_size = size + (value.size(-2), self.heads, out_channels_per_head)
        value = value.view(value_size).transpose(-2, -3)

        # Output: [*, heads, query_entries, out_channels // heads]
        out = self.compute_attention(query, key, value)
        # Output: [*, query_entries, heads, out_channels // heads]
        out = out.transpose(-3, -2).contiguous()
        # Output: [*, query_entries, out_channels]
        out = out.view(size + (query.size(-2), self.out_channels))

        return out

    def __repr__(self) -> str:  # pragma: no cover
        return (f'{self.__class__.__name__}({self.in_channels}, '
                f'{self.out_channels}, heads={self.heads}, '
                f'groups={self.groups}, dropout={self.droput}, '

[docs]class DNAConv(MessagePassing): r"""The dynamic neighborhood aggregation operator from the `"Just Jump: Towards Dynamic Neighborhood Aggregation in Graph Neural Networks" <>`_ paper. .. math:: \mathbf{x}_v^{(t)} = h_{\mathbf{\Theta}}^{(t)} \left( \mathbf{x}_{v \leftarrow v}^{(t)}, \left\{ \mathbf{x}_{v \leftarrow w}^{(t)} : w \in \mathcal{N}(v) \right\} \right) based on (multi-head) dot-product attention .. math:: \mathbf{x}_{v \leftarrow w}^{(t)} = \textrm{Attention} \left( \mathbf{x}^{(t-1)}_v \, \mathbf{\Theta}_Q^{(t)}, [\mathbf{x}_w^{(1)}, \ldots, \mathbf{x}_w^{(t-1)}] \, \mathbf{\Theta}_K^{(t)}, \, [\mathbf{x}_w^{(1)}, \ldots, \mathbf{x}_w^{(t-1)}] \, \mathbf{\Theta}_V^{(t)} \right) with :math:`\mathbf{\Theta}_Q^{(t)}, \mathbf{\Theta}_K^{(t)}, \mathbf{\Theta}_V^{(t)}` denoting (grouped) projection matrices for query, key and value information, respectively. :math:`h^{(t)}_{\mathbf{\Theta}}` is implemented as a non-trainable version of :class:`torch_geometric.nn.conv.GCNConv`. .. note:: In contrast to other layers, this operator expects node features as shape :obj:`[num_nodes, num_layers, channels]`. Args: channels (int): Size of each input/output sample. heads (int, optional): Number of multi-head-attentions. (default: :obj:`1`) groups (int, optional): Number of groups to use for all linear projections. (default: :obj:`1`) dropout (float, optional): Dropout probability of attention coefficients. (default: :obj:`0.`) cached (bool, optional): If set to :obj:`True`, the layer will cache the computation of :math:`\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2}` on first execution, and will use the cached version for further executions. This parameter should only be set to :obj:`True` in transductive learning scenarios. (default: :obj:`False`) normalize (bool, optional): Whether to add self-loops and apply symmetric normalization. (default: :obj:`True`) add_self_loops (bool, optional): If set to :obj:`False`, will not add self-loops to the input graph. (default: :obj:`True`) bias (bool, optional): If set to :obj:`False`, the layer will not learn an additive bias. (default: :obj:`True`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. Shapes: - **input:** node features :math:`(|\mathcal{V}|, L, F)` where :math:`L` is the number of layers, edge indices :math:`(2, |\mathcal{E}|)` - **output:** node features :math:`(|\mathcal{V}|, F)` """ _cached_edge_index: Optional[OptPairTensor] _cached_adj_t: Optional[SparseTensor] def __init__(self, channels: int, heads: int = 1, groups: int = 1, dropout: float = 0., cached: bool = False, normalize: bool = True, add_self_loops: bool = True, bias: bool = True, **kwargs): kwargs.setdefault('aggr', 'add') super().__init__(node_dim=0, **kwargs) self.bias = bias self.cached = cached self.normalize = normalize self.add_self_loops = add_self_loops self._cached_edge_index = None self._cached_adj_t = None self.multi_head = MultiHead(channels, channels, heads, groups, dropout, bias) self.reset_parameters()
[docs] def reset_parameters(self): super().reset_parameters() self.multi_head.reset_parameters() self._cached_edge_index = None self._cached_adj_t = None
[docs] def forward( self, x: Tensor, edge_index: Adj, edge_weight: OptTensor = None, ) -> Tensor: r"""Runs the forward pass of the module. Args: x (torch.Tensor): The input node features of shape :obj:`[num_nodes, num_layers, channels]`. edge_index (torch.Tensor or SparseTensor): The edge indices. edge_weight (torch.Tensor, optional): The edge weights. (default: :obj:`None`) """ if x.dim() != 3: raise ValueError('Feature shape must be [num_nodes, num_layers, ' 'channels].') if self.normalize: if isinstance(edge_index, Tensor): cache = self._cached_edge_index if cache is None: edge_index, edge_weight = gcn_norm( # yapf: disable edge_index, edge_weight, x.size(self.node_dim), False, self.add_self_loops, self.flow, dtype=x.dtype) if self.cached: self._cached_edge_index = (edge_index, edge_weight) else: edge_index, edge_weight = cache[0], cache[1] elif isinstance(edge_index, SparseTensor): cache = self._cached_adj_t if cache is None: edge_index = gcn_norm( # yapf: disable edge_index, edge_weight, x.size(self.node_dim), False, self.add_self_loops, self.flow, dtype=x.dtype) if self.cached: self._cached_adj_t = edge_index else: edge_index = cache # propagate_type: (x: Tensor, edge_weight: OptTensor) return self.propagate(edge_index, x=x, edge_weight=edge_weight)
def message(self, x_i: Tensor, x_j: Tensor, edge_weight: Tensor) -> Tensor: x_i = x_i[:, -1:] # [num_edges, 1, channels] out = self.multi_head(x_i, x_j, x_j) # [num_edges, 1, channels] return edge_weight.view(-1, 1) * out.squeeze(1) def __repr__(self) -> str: return (f'{self.__class__.__name__}({self.multi_head.in_channels}, ' f'heads={self.multi_head.heads}, ' f'groups={self.multi_head.groups})')