Source code for torch_geometric.nn.conv.cugraph.gat_conv

from typing import Optional, Tuple

import torch
from torch import Tensor
from torch.nn import Linear, Parameter

from torch_geometric.nn.conv.cugraph import CuGraphModule
from torch_geometric.nn.conv.cugraph.base import LEGACY_MODE
from torch_geometric.nn.inits import zeros

        from pylibcugraphops.torch.autograd import mha_gat_n2n as GATConvAgg
        from pylibcugraphops.pytorch.operators import mha_gat_n2n as GATConvAgg
except ImportError:

[docs]class CuGraphGATConv(CuGraphModule): # pragma: no cover r"""The graph attentional operator from the `"Graph Attention Networks" <>`_ paper. :class:`CuGraphGATConv` is an optimized version of :class:`~torch_geometric.nn.conv.GATConv` based on the :obj:`cugraph-ops` package that fuses message passing computation for accelerated execution and lower memory footprint. """ def __init__( self, in_channels: int, out_channels: int, heads: int = 1, concat: bool = True, negative_slope: float = 0.2, bias: bool = True, ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.heads = heads self.concat = concat self.negative_slope = negative_slope self.lin = Linear(in_channels, heads * out_channels, bias=False) self.att = Parameter(torch.empty(2 * heads * out_channels)) if bias and concat: self.bias = Parameter(torch.empty(heads * out_channels)) elif bias and not concat: self.bias = Parameter(torch.empty(out_channels)) else: self.register_parameter('bias', None) self.reset_parameters()
[docs] def reset_parameters(self): self.lin.reset_parameters() gain = torch.nn.init.calculate_gain('relu') torch.nn.init.xavier_normal_( self.att.view(2, self.heads, self.out_channels), gain=gain) zeros(self.bias)
[docs] def forward( self, x: Tensor, csc: Tuple[Tensor, Tensor, int], max_num_neighbors: Optional[int] = None, ) -> Tensor: graph = self.get_cugraph(csc, max_num_neighbors) x = self.lin(x) if LEGACY_MODE: out = GATConvAgg(x, self.att, graph, self.heads, 'LeakyReLU', self.negative_slope, False, self.concat) else: out = GATConvAgg(x, self.att, graph, self.heads, 'LeakyReLU', self.negative_slope, self.concat) if self.bias is not None: out = out + self.bias return out
def __repr__(self) -> str: return (f'{self.__class__.__name__}({self.in_channels}, ' f'{self.out_channels}, heads={self.heads})')