GraphormerLayer(feat_size, hidden_size, num_heads, attn_bias_type='add', norm_first=False, dropout=0.1, attn_dropout=0.1, activation=ReLU())[source]¶
Graphormer Layer with Dense Multi-Head Attention, as introduced in Do Transformers Really Perform Bad for Graph Representation?
feat_size (int) – Feature size.
hidden_size (int) – Hidden size of feedforward layers.
num_heads (int) – Number of attention heads, by which
attn_bias_type (str, optional) –
The type of attention bias used for modifying attention. Selected from ‘add’ or ‘mul’. Default: ‘add’.
’add’ is for additive attention bias.
’mul’ is for multiplicative attention bias.
norm_first (bool, optional) – If True, it performs layer normalization before attention and feedforward operations. Otherwise, it applies layer normalization afterwards. Default: False.
dropout (float, optional) – Dropout probability. Default: 0.1.
attn_dropout (float, optional) – Attention dropout probability. Default: 0.1.
activation (callable activation layer, optional) – Activation function. Default: nn.ReLU().
>>> import torch as th >>> from dgl.nn import GraphormerLayer
>>> batch_size = 16 >>> num_nodes = 100 >>> feat_size = 512 >>> num_heads = 8 >>> nfeat = th.rand(batch_size, num_nodes, feat_size) >>> bias = th.rand(batch_size, num_nodes, num_nodes, num_heads) >>> net = GraphormerLayer( feat_size=feat_size, hidden_size=2048, num_heads=num_heads ) >>> out = net(nfeat, bias)
forward(nfeat, attn_bias=None, attn_mask=None)[source]¶
nfeat (torch.Tensor) – A 3D input tensor. Shape: (batch_size, N,
feat_size), where N is the maximum number of nodes.
attn_bias (torch.Tensor, optional) – The attention bias used for attention modification. Shape: (batch_size, N, N,
attn_mask (torch.Tensor, optional) – The attention mask used for avoiding computation on invalid positions, where invalid positions are indicated by True values. Shape: (batch_size, N, N). Note: For rows corresponding to unexisting nodes, make sure at least one entry is set to False to prevent obtaining NaNs with softmax.
y – The output tensor. Shape: (batch_size, N,
- Return type