Source code for torchdrug.models.bert

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

from torchdrug import core, layers
from torchdrug.layers import functional
from torchdrug.core import Registry as R

[docs]@R.register("models.ProteinBERT") class ProteinBERT(nn.Module, core.Configurable): """ Protein BERT proposed in `Evaluating Protein Transfer Learning with TAPE`_. .. _Evaluating Protein Transfer Learning with TAPE: Parameters: input_dim (int): input dimension hidden_dim (int, optional): hidden dimension num_layers (int, optional): number of Transformer blocks num_heads (int, optional): number of attention heads intermediate_dim (int, optional): intermediate hidden dimension of Transformer block activation (str or function, optional): activation function hidden_dropout (float, optional): dropout ratio of hidden features attention_dropout (float, optional): dropout ratio of attention maps max_position (int, optional): maximum number of positions """ def __init__(self, input_dim, hidden_dim=768, num_layers=12, num_heads=12, intermediate_dim=3072, activation="gelu", hidden_dropout=0.1, attention_dropout=0.1, max_position=8192): super(ProteinBERT, self).__init__() self.input_dim = input_dim self.hidden_dim = hidden_dim self.output_dim = hidden_dim self.num_layers = num_layers self.num_heads = num_heads self.intermediate_dim = intermediate_dim self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.max_position = max_position self.num_residue_type = input_dim self.embedding = nn.Embedding(input_dim + 3, hidden_dim) self.position_embedding = nn.Embedding(max_position, hidden_dim) self.layer_norm = nn.LayerNorm(hidden_dim) self.dropout = nn.Dropout(hidden_dropout) self.layers = nn.ModuleList() for i in range(self.num_layers): self.layers.append(layers.ProteinBERTBlock(hidden_dim, intermediate_dim, num_heads, attention_dropout, hidden_dropout, activation)) self.linear = nn.Linear(hidden_dim, hidden_dim)
[docs] def forward(self, graph, input, all_loss=None, metric=None): """ Compute the residue representations and the graph representation(s). Parameters: graph (Protein): :math:`n` protein(s) input (Tensor): input node representations all_loss (Tensor, optional): if specified, add loss to this tensor metric (dict, optional): if specified, output metrics to this dict Returns: dict with ``residue_feature`` and ``graph_feature`` fields: residue representations of shape :math:`(|V_{res}|, d)`, graph representations of shape :math:`(n, d)` """ input = graph.residue_type size_ext = graph.num_residues # Prepend BOS bos = torch.ones(graph.batch_size, dtype=torch.long, device=self.device) * self.num_residue_type input, size_ext = functional._extend(bos, torch.ones_like(size_ext), input, size_ext) # Append EOS eos = torch.ones(graph.batch_size, dtype=torch.long, device=self.device) * (self.num_residue_type + 1) input, size_ext = functional._extend(input, size_ext, eos, torch.ones_like(size_ext)) # Padding input, mask = functional.variadic_to_padded(input, size_ext, value=self.num_residue_type + 2) mask = mask.long().unsqueeze(-1) input = self.embedding(input) position_indices = torch.arange(input.shape[1], device=input.device) input = input + self.position_embedding(position_indices).unsqueeze(0) input = self.layer_norm(input) input = self.dropout(input) for layer in self.layers: input = layer(input, mask) residue_feature = functional.padded_to_variadic(input, graph.num_residues) graph_feature = input[:, 0] graph_feature = self.linear(graph_feature) graph_feature = F.tanh(graph_feature) return { "graph_feature": graph_feature, "residue_feature": residue_feature }