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import logging
import typing
import torch
import torch.nn as nn
import torch.nn.functional as F
from .modeling_utils import ProteinConfig
from .modeling_utils import ProteinModel
from .modeling_utils import ValuePredictionHead
from .modeling_utils import SequenceClassificationHead
from .modeling_utils import SequenceToSequenceClassificationHead
from .modeling_utils import PairwiseContactPredictionHead
from ..registry import registry
logger = logging.getLogger(__name__)
URL_PREFIX = "https://s3.amazonaws.com/proteindata/pytorch-models/"
LSTM_PRETRAINED_CONFIG_ARCHIVE_MAP: typing.Dict[str, str] = {}
LSTM_PRETRAINED_MODEL_ARCHIVE_MAP: typing.Dict[str, str] = {}
class ProteinLSTMConfig(ProteinConfig):
pretrained_config_archive_map = LSTM_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self,
vocab_size: int = 30,
input_size: int = 128,
hidden_size: int = 1024,
num_hidden_layers: int = 3,
hidden_dropout_prob: float = 0.1,
initializer_range: float = 0.02,
temporal_pooling: str = 'attention',
freeze_embedding: bool = False,
**kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.input_size = input_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.hidden_dropout_prob = hidden_dropout_prob
self.initializer_range = initializer_range
self.temporal_pooling = temporal_pooling
self.freeze_embedding = freeze_embedding
class ProteinLSTMLayer(nn.Module):
def __init__(self, input_size: int, hidden_size: int, dropout: float = 0.):
super().__init__()
self.dropout = nn.Dropout(dropout)
self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
def forward(self, inputs):
inputs = self.dropout(inputs)
self.lstm.flatten_parameters()
return self.lstm(inputs)
class ProteinLSTMPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.scalar_reweighting = nn.Linear(2 * config.num_hidden_layers, 1)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
self.temporal_pooling = config.temporal_pooling
self._la_w1 = nn.Conv1d(config.hidden_size, int(config.hidden_size/2), 5, padding=2)
self._la_w2 = nn.Conv1d(config.hidden_size, int(config.hidden_size/2), 5, padding=2)
self._la_mlp = nn.Linear(config.hidden_size, config.hidden_size)
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
if self.temporal_pooling == 'mean':
return hidden_states.mean(dim=1)
if self.temporal_pooling == 'max':
return hidden_states.max(dim=1)
if self.temporal_pooling == 'concat':
_temp = hidden_states.reshape(hidden_states.shape[0], -1)
return torch.nn.functional.pad(_temp, (0, 2048 - _temp.shape[1]))
if self.temporal_pooling == 'topmax':
val, _ = torch.topk(hidden_states, k=5, dim=1)
return val.mean(dim=1)
if self.temporal_pooling == 'light_attention':
_temp = hidden_states.permute(0,2,1)
a = self._la_w1(_temp).softmax(dim=-1)
v = self._la_w2(_temp)
v_max = v.max(dim=-1).values
v_sum = (a * v).sum(dim=-1)
return self._la_mlp(torch.cat([v_max, v_sum], dim=1))
pooled_output = self.scalar_reweighting(hidden_states).squeeze(2)
pooled_output = self.dense(pooled_output)
pooled_output = self.activation(pooled_output)
return pooled_output
class ProteinLSTMEncoder(nn.Module):
def __init__(self, config: ProteinLSTMConfig):
super().__init__()
forward_lstm = [ProteinLSTMLayer(config.input_size, config.hidden_size)]
reverse_lstm = [ProteinLSTMLayer(config.input_size, config.hidden_size)]
for _ in range(config.num_hidden_layers - 1):
forward_lstm.append(ProteinLSTMLayer(
config.hidden_size, config.hidden_size, config.hidden_dropout_prob))
reverse_lstm.append(ProteinLSTMLayer(
config.hidden_size, config.hidden_size, config.hidden_dropout_prob))
self.forward_lstm = nn.ModuleList(forward_lstm)
self.reverse_lstm = nn.ModuleList(reverse_lstm)
self.output_hidden_states = config.output_hidden_states
def forward(self, inputs, input_mask=None):
all_forward_pooled = ()
all_reverse_pooled = ()
all_hidden_states = (inputs,)
forward_output = inputs
for layer in self.forward_lstm:
forward_output, forward_pooled = layer(forward_output)
all_forward_pooled = all_forward_pooled + (forward_pooled[0],)
all_hidden_states = all_hidden_states + (forward_output,)
reversed_sequence = self.reverse_sequence(inputs, input_mask)
reverse_output = reversed_sequence
for layer in self.reverse_lstm:
reverse_output, reverse_pooled = layer(reverse_output)
all_reverse_pooled = all_reverse_pooled + (reverse_pooled[0],)
all_hidden_states = all_hidden_states + (reverse_output,)
reverse_output = self.reverse_sequence(reverse_output, input_mask)
output = torch.cat((forward_output, reverse_output), dim=2)
pooled = all_forward_pooled + all_reverse_pooled
pooled = torch.stack(pooled, 3).squeeze(0)
outputs = (output, pooled)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
return outputs # sequence_embedding, pooled_embedding, (hidden_states)
def reverse_sequence(self, sequence, input_mask):
if input_mask is None:
idx = torch.arange(sequence.size(1) - 1, -1, -1)
reversed_sequence = sequence.index_select(1, idx, device=sequence.device)
else:
sequence_lengths = input_mask.sum(1)
reversed_sequence = []
for seq, seqlen in zip(sequence, sequence_lengths):
idx = torch.arange(seqlen - 1, -1, -1, device=seq.device)
seq = seq.index_select(0, idx)
seq = F.pad(seq, [0, 0, 0, sequence.size(1) - seqlen])
reversed_sequence.append(seq)
reversed_sequence = torch.stack(reversed_sequence, 0)
return reversed_sequence
class ProteinLSTMAbstractModel(ProteinModel):
config_class = ProteinLSTMConfig
pretrained_model_archive_map = LSTM_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "lstm"
def _init_weights(self, module):
""" Initialize the weights """
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
@registry.register_task_model('embed', 'lstm')
class ProteinLSTMModel(ProteinLSTMAbstractModel):
def __init__(self, config: ProteinLSTMConfig):
super().__init__(config)
self.embed_matrix = nn.Embedding(config.vocab_size, config.input_size)
self.encoder = ProteinLSTMEncoder(config)
self.pooler = ProteinLSTMPooler(config)
self.output_hidden_states = config.output_hidden_states
self.init_weights()
def forward(self, input_ids, input_mask=None):
if input_mask is None:
input_mask = torch.ones_like(input_ids)
# fp16 compatibility
embedding_output = self.embed_matrix(input_ids)
outputs = self.encoder(embedding_output, input_mask=input_mask)
sequence_output = outputs[0]
pooled_outputs = self.pooler(outputs[1])
outputs = (sequence_output, pooled_outputs) + outputs[2:]
return outputs # sequence_output, pooled_output, (hidden_states)
@registry.register_task_model('language_modeling', 'lstm')
class ProteinLSTMForLM(ProteinLSTMAbstractModel):
def __init__(self, config):
super().__init__(config)
self.lstm = ProteinLSTMModel(config)
self.feedforward = nn.Linear(config.hidden_size, config.vocab_size)
self.init_weights()
def forward(self,
input_ids,
input_mask=None,
targets=None):
outputs = self.lstm(input_ids, input_mask=input_mask)
sequence_output, pooled_output = outputs[:2]
forward_prediction, reverse_prediction = sequence_output.chunk(2, -1)
forward_prediction = F.pad(forward_prediction[:, :-1], [0, 0, 1, 0])
reverse_prediction = F.pad(reverse_prediction[:, 1:], [0, 0, 0, 1])
prediction_scores = \
self.feedforward(forward_prediction) + self.feedforward(reverse_prediction)
prediction_scores = prediction_scores.contiguous()
# add hidden states and if they are here
outputs = (prediction_scores,) + outputs[:2]
if targets is not None:
loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
lm_loss = loss_fct(
prediction_scores.view(-1, self.config.vocab_size), targets.view(-1))
outputs = (lm_loss,) + outputs
# (loss), prediction_scores, seq_relationship_score, (hidden_states)
return outputs
@registry.register_task_model('fluorescence', 'lstm')
@registry.register_task_model('stability', 'lstm')
class ProteinLSTMForValuePrediction(ProteinLSTMAbstractModel):
def __init__(self, config):
super().__init__(config)
self.lstm = ProteinLSTMModel(config)
self.predict = ValuePredictionHead(config.hidden_size)
self.freeze_embedding = config.freeze_embedding
self.init_weights()
def forward(self, input_ids, input_mask=None, targets=None):
if self.freeze_embedding:
self.lstm.train(False)
outputs = self.lstm(input_ids, input_mask=input_mask)
sequence_output, pooled_output = outputs[:2]
outputs = self.predict(pooled_output, targets) + outputs[2:]
# (loss), prediction_scores, (hidden_states)
return outputs
@registry.register_task_model('remote_homology', 'lstm')
class ProteinLSTMForSequenceClassification(ProteinLSTMAbstractModel):
def __init__(self, config):
super().__init__(config)
self.lstm = ProteinLSTMModel(config)
self.classify = SequenceClassificationHead(
config.hidden_size, config.num_labels)
self.freeze_embedding = config.freeze_embedding
self.init_weights()
def forward(self, input_ids, input_mask=None, targets=None):
if self.freeze_embedding:
self.lstm.train(False)
outputs = self.lstm(input_ids, input_mask=input_mask)
sequence_output, pooled_output = outputs[:2]
outputs = self.classify(pooled_output, targets) + outputs[2:]
# (loss), prediction_scores, (hidden_states)
return outputs
@registry.register_task_model('secondary_structure', 'lstm')
class ProteinLSTMForSequenceToSequenceClassification(ProteinLSTMAbstractModel):
def __init__(self, config):
super().__init__(config)
self.lstm = ProteinLSTMModel(config)
self.classify = SequenceToSequenceClassificationHead(
config.hidden_size * 2, config.num_labels, ignore_index=-1)
self.init_weights()
def forward(self, input_ids, input_mask=None, targets=None):
outputs = self.lstm(input_ids, input_mask=input_mask)
sequence_output, pooled_output = outputs[:2]
amino_acid_class_scores = self.classify(sequence_output.contiguous())
# add hidden states and if they are here
outputs = (amino_acid_class_scores,) + outputs[2:]
if targets is not None:
loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
classification_loss = loss_fct(
amino_acid_class_scores.view(-1, self.config.num_labels),
targets.view(-1))
outputs = (classification_loss,) + outputs
# (loss), prediction_scores, seq_relationship_score, (hidden_states)
return outputs
@registry.register_task_model('contact_prediction', 'lstm')
class ProteinLSTMForContactPrediction(ProteinLSTMAbstractModel):
def __init__(self, config):
super().__init__(config)
self.lstm = ProteinLSTMModel(config)
self.predict = PairwiseContactPredictionHead(config.hidden_size, ignore_index=-1)
self.init_weights()
def forward(self, input_ids, protein_length, input_mask=None, targets=None):
outputs = self.lstm(input_ids, input_mask=input_mask)
sequence_output, pooled_output = outputs[:2]
outputs = self.predict(sequence_output, protein_length, targets) + outputs[2:]
# (loss), prediction_scores, (hidden_states), (attentions)
return outputs
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