terminator.models.layers.term.graph.s2s.TERMNodeTransformerLayer

class terminator.models.layers.term.graph.s2s.TERMNodeTransformerLayer(num_hidden, num_in, num_heads=4, dropout=0.1)[source]

Bases: Module

TERM Node Transformer Layer

A TERM Node Transformer Layer that updates nodes via TERMNeighborAttention

Variables:
__init__(num_hidden, num_in, num_heads=4, dropout=0.1)[source]
Parameters:

  • num_hidden (int) – Hidden dimension, and dimensionality of querys in TERMNeighborAttention

  • num_in (int) – Dimensionality of keys and values

  • num_heads (int, default=4) – Number of heads to use in TERMNeighborAttention

  • dropout (float, default=0.1) – Dropout rate

Methods

__init__(num_hidden, num_in[, num_heads, ...])

Parameters:

  • num_hidden (int) -- Hidden dimension, and dimensionality of querys in TERMNeighborAttention

forward(h_V, h_VE[, mask_V, mask_attend])

Apply one Transformer update on nodes in a TERM graph

Attributes

T_destination

alias of TypeVar('T_destination', bound=Mapping[str, Tensor])

dump_patches

This allows better BC support for load_state_dict().
forward(h_V, h_VE, mask_V=None, mask_attend=None)[source]

Apply one Transformer update on nodes in a TERM graph

Parameters:

  • h_V (torch.Tensor) – Central node features Shape: n_batch x n_terms x n_nodes x n_hidden

  • h_VE (torch.Tensor) – Neighbor features, which includes the node vector concatenated onto the edge connecting the central node to the neighbor node Shape: n_batch x n_terms x n_nodes x n_neighbors x n_in

  • mask_V (torch.ByteTensor or None) – Mask for attention regarding TERM residues Shape : n_batch x n_terms x n_nodes

  • mask_attend (torch.ByteTensor or None) – Mask for attention regarding neighbors Shape: n_batch x n_terms x n_nodes x k

Returns:

h_V – Updated node embeddings Shape: n_batch x n_terms x n_nodes x n_hidden

Return type:

torch.Tensor