terminator.models.layers.term.matches.attn.TERMMatchTransformerLayer

class terminator.models.layers.term.matches.attn.TERMMatchTransformerLayer(hparams)

Bases: Module

TERM Match Transformer Layer

A TERM Match Transformer Layer that updates match embeddings via TERMMatchATtention

Variables:

• attention (TERMMatchAttention) – Transformer Attention mechanism

• dense (PositionWiseFeedForward) – Transformer position-wise FFN

__init__(hparams)

Parameters:

hparams (dict) – Dictionary of model hparams (see ~/scripts/models/train/default_hparams.json for more info)

Methods

__init__(hparams)	Parameters: hparams (dict) -- Dictionary of model hparams (see ~/scripts/models/train/default_hparams.json for more info)
forward(src, target[, src_mask, ...])	Apply one Transformer update to TERM matches

Attributes

T_destination	alias of TypeVar('T_destination', bound=Mapping[str, Tensor])
dump_patches	This allows better BC support for load_state_dict().

forward(src, target, src_mask=None, mask_attend=None, checkpoint=False)

Apply one Transformer update to TERM matches

Parameters:

• src (torch.Tensor) – TERM Match features Shape: n_batch x sum_term_len x n_matches x n_hidden

• target (torch.Tensor) – Embedded structural features per TERM residue of target structure Shape: n_batch x sum_term_len x n_matches x n_hidden

• src_mask (torch.ByteTensor or None) – Mask for attention regarding TERM residues Shape : n_batch x sum_term_len

• mask_attend (torch.ByteTensor or None) – Mask for attention regarding matches Shape: n_batch x sum_term_len # TODO: check shape

• checkpoint (bool, default=False) – Whether to use gradient checkpointing to reduce memory usage

Returns:

src – Updated match embeddings Shape: n_batch x sum_term_len x n_matches x n_hidden

Return type:

torch.Tensor