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import typing
import logging
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 get_activation_fn
from .modeling_utils import MLMHead
from .modeling_utils import LayerNorm
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__)
RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP: typing.Dict[str, str] = {}
RESNET_PRETRAINED_MODEL_ARCHIVE_MAP: typing.Dict[str, str] = {}
class ProteinAEConfig(ProteinConfig):
pretrained_config_archive_map = RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self,
vocab_size: int = 30,
hidden_size: int = 512,
num_hidden_layers: int = 30,
hidden_act: str = "gelu",
hidden_dropout_prob: float = 0.1,
initializer_range: float = 0.02,
layer_norm_eps: float = 1e-12,
temporal_pooling: str = 'attention',
freeze_embedding: bool = False,
max_size: int = 3000,
latent_size: int = 1024,
**kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.num_hidden_layers = num_hidden_layers
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.temporal_pooling = temporal_pooling
self.freeze_embedding = freeze_embedding
self.max_size = max_size
self.latent_size = latent_size
class MaskedConv1d(nn.Conv1d):
def forward(self, x, input_mask=None):
if input_mask is not None:
x = x * input_mask
return super().forward(x)
class ProteinResNetLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.norm = LayerNorm(config.hidden_size)
def forward(self, x):
return self.norm(x.transpose(1, 2)).transpose(1, 2)
class ProteinResNetBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.conv1 = MaskedConv1d(
config.hidden_size, config.hidden_size, 3, padding=1, bias=False)
# self.bn1 = nn.BatchNorm1d(config.hidden_size)
self.bn1 = ProteinResNetLayerNorm(config)
self.conv2 = MaskedConv1d(
config.hidden_size, config.hidden_size, 3, padding=1, bias=False)
# self.bn2 = nn.BatchNorm1d(config.hidden_size)
self.bn2 = ProteinResNetLayerNorm(config)
self.activation_fn = get_activation_fn(config.hidden_act)
def forward(self, x, input_mask=None):
identity = x
out = self.conv1(x, input_mask)
out = self.bn1(out)
out = self.activation_fn(out)
out = self.conv2(out, input_mask)
out = self.bn2(out)
out += identity
out = self.activation_fn(out)
return out
class ProteinResNetEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config):
super().__init__()
embed_dim = config.hidden_size
self.word_embeddings = nn.Embedding(config.vocab_size, embed_dim, padding_idx=0)
inverse_frequency = 1 / (10000 ** (torch.arange(0.0, embed_dim, 2.0) / embed_dim))
self.register_buffer('inverse_frequency', inverse_frequency)
self.layer_norm = LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, input_ids):
words_embeddings = self.word_embeddings(input_ids)
seq_length = input_ids.size(1)
position_ids = torch.arange(
seq_length - 1, -1, -1.0,
dtype=words_embeddings.dtype,
device=words_embeddings.device)
sinusoidal_input = torch.ger(position_ids, self.inverse_frequency)
position_embeddings = torch.cat([sinusoidal_input.sin(), sinusoidal_input.cos()], -1)
position_embeddings = position_embeddings.unsqueeze(0)
embeddings = words_embeddings + position_embeddings
embeddings = self.layer_norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class ResNetEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.output_hidden_states = config.output_hidden_states
self.encoder = nn.ModuleList(
[ProteinResNetBlock(config) for _ in range(config.num_hidden_layers)])
self.decoder = nn.ModuleList(
[ProteinResNetBlock(config) for _ in range(config.num_hidden_layers)])
self.bottleneck1 = nn.Linear(93*config.hidden_size, config.latent_size)
self.bottleneck2 = nn.Linear(config.latent_size, 94*config.hidden_size)
def forward(self, hidden_states, input_mask=None):
for i, layer_module in enumerate(self.encoder):
hidden_states = layer_module(hidden_states)
if i != 0 and i % 5 == 0:
hidden_states = nn.functional.avg_pool1d(hidden_states, 2, stride=2)
bs = hidden_states.shape[0]
latents = self.bottleneck1(hidden_states.reshape(bs, -1))
hidden_states = self.bottleneck2(latents).reshape(bs, -1, 94)
for i, layer_module in enumerate(self.decoder):
if i != 0 and i % 5 == 0:
hidden_states = nn.functional.interpolate(hidden_states, scale_factor=2)
hidden_states = layer_module(hidden_states)
hidden_states = hidden_states[:,:,:self.config.max_size]
outputs = (hidden_states, latents)
return outputs
class ProteinAEAbstractModel(ProteinModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = ProteinAEConfig
base_model_prefix = "ae"
def __init__(self, config):
super().__init__(config)
def _init_weights(self, module):
""" Initialize the weights """
if isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Conv1d):
nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
if module.bias is not None:
module.bias.data.zero_()
@registry.register_task_model('embed', 'autoencoder')
class ProteinResNetModel(ProteinAEAbstractModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = ProteinResNetEmbeddings(config)
self.encoder = ResNetEncoder(config)
self.init_weights()
def forward(self,
input_ids,
input_mask=None):
pre_pad_shape = input_ids.shape[1]
if pre_pad_shape >= self.config.max_size:
input_ids = input_ids[:,:self.config.max_size]
if not input_mask is None:
input_mask = input_mask[:,:self.config.max_size]
else:
input_ids = F.pad(input_ids, (0, self.config.max_size - pre_pad_shape))
if not input_mask is None:
input_mask = F.pad(input_mask, (0, self.config.max_size - pre_pad_shape))
assert input_ids.shape[1] == self.config.max_size
if input_mask is not None and torch.any(input_mask != 1):
extended_input_mask = input_mask.unsqueeze(2)
# fp16 compatibility
extended_input_mask = extended_input_mask.to(
dtype=next(self.parameters()).dtype)
else:
extended_input_mask = None
embedding_output = self.embeddings(input_ids)
embedding_output = embedding_output.transpose(1, 2)
if extended_input_mask is not None:
extended_input_mask = extended_input_mask.transpose(1, 2)
sequence_output, pooled_output = self.encoder(embedding_output, extended_input_mask)
sequence_output = sequence_output.transpose(1, 2).contiguous()
return sequence_output, pooled_output
@registry.register_task_model('beta_lactamase', 'autoencoder')
@registry.register_task_model('language_modeling', 'autoencoder')
class ProteinResNetForMaskedLM(ProteinAEAbstractModel):
def __init__(self, config):
super().__init__(config)
self.resnet = ProteinResNetModel(config)
self.mlm = MLMHead(
config.hidden_size, config.vocab_size, config.hidden_act, config.layer_norm_eps,
ignore_index=-1)
self.init_weights()
self.tie_weights()
def tie_weights(self):
""" Make sure we are sharing the input and output embeddings.
Export to TorchScript can't handle parameter sharing so we are cloning them instead.
"""
self._tie_or_clone_weights(self.mlm.decoder,
self.resnet.embeddings.word_embeddings)
def forward(self,
input_ids,
input_mask=None,
targets=None):
pre_pad_shape = input_ids.shape[1]
if targets is not None:
targets = targets[:,:self.config.max_size]
outputs = self.resnet(input_ids, input_mask=input_mask)
outputs = self.mlm(outputs[0][:,:pre_pad_shape,:], targets) + (outputs[1],)
# (loss), prediction_scores, (hidden_states), (attentions)
return outputs
@registry.register_task_model('fluorescence', 'autoencoder')
@registry.register_task_model('stability', 'autoencoder')
class ProteinResNetForValuePrediction(ProteinAEAbstractModel):
def __init__(self, config):
super().__init__(config)
self.resnet = ProteinResNetModel(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.resnet.train(False)
outputs = self.resnet(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), (attentions)
return outputs
@registry.register_task_model('remote_homology', 'autoencoder')
class ProteinResNetForSequenceClassification(ProteinAEAbstractModel):
def __init__(self, config):
super().__init__(config)
self.resnet = ProteinResNetModel(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.resnet.train(False)
outputs = self.resnet(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), (attentions)
return outputs
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