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"""
Modified HuggingFace transformer model classes
"""
from typing import Tuple
import numpy as np
import torch
from torch import nn
from torch.nn import BCELoss, BCEWithLogitsLoss, MSELoss, PoissonNLLLoss, KLDivLoss
from transformers import BertConfig, BertModel, RobertaConfig, RobertaModel
from transformers import BertPreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers import RobertaPreTrainedModel
class RobertaMeanPoolConfig(RobertaConfig):
model_type = "roberta"
def __init__(
self,
output_mode="regression",
freeze_base=True,
start_token_idx=0,
end_token_idx=1,
threshold=1,
alpha=0.5,
log_offset=1,
batch_norm=False,
**kwargs,
):
"""Constructs RobertaConfig."""
super().__init__(**kwargs)
self.output_mode = output_mode
self.freeze_base = freeze_base
self.start_token_idx = start_token_idx
self.end_token_idx = end_token_idx
self.threshold = threshold
self.alpha = alpha
self.log_offset = log_offset
self.batch_norm = batch_norm
class ClassificationHeadMeanPool(nn.Module):
"""Head for sentence-level classification tasks.
Modifications:
1. Using mean-pooling over tokens instead of CLS token
2. Multi-output regression
"""
def __init__(self, config: RobertaMeanPoolConfig):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dense2 = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
self.start_token_idx = config.start_token_idx
self.end_token_idx = config.end_token_idx
self.batch_norm = (
nn.BatchNorm1d(config.hidden_size) if config.batch_norm else None
)
if self.batch_norm is not None:
print("Using batch_norm")
def forward(self, features, attention_mask=None, input_ids=None, **kwargs):
x = self.embed(features, attention_mask, input_ids, **kwargs)
x = self.out_proj(x)
return x
def embed(self, features, attention_mask=None, input_ids=None, **kwargs):
attention_mask[input_ids == self.start_token_idx] = 0
attention_mask[input_ids == self.end_token_idx] = 0
x = torch.sum(features * attention_mask.unsqueeze(2), dim=1) / torch.sum(
attention_mask, dim=1, keepdim=True
) # Mean pooling over non-padding tokens
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
# Batchnorm
x = self.normalize(x)
# Second linear layer
x = self.dense2(x)
x = torch.tanh(x)
return x
def normalize(self, x: torch.Tensor) -> torch.Tensor:
if self.batch_norm is not None:
return self.batch_norm(x)
return x
class ClassificationHeadMeanPoolSparse(nn.Module):
"""Classification head that predicts binary outcome (expressed/not)
and real-valued gene expression values.
"""
def __init__(self, config):
super().__init__()
self.classification_head = ClassificationHeadMeanPool(config)
self.regression_head = ClassificationHeadMeanPool(config)
def forward(
self, features, attention_mask=None, input_ids=None, **kwargs
) -> Tuple[torch.Tensor]:
"""Compute binarized logits and real-valued gene expressions for each tissue.
Args:
features (torch.Tensor): outputs of RoBERTa
attention_mask (Optional[torch.Tensor]): attention mask for sentence
input_ids (Optional[torch.Tensor]): original sequence inputs
Returns:
(torch.Tensor): classification logits (whether gene is expressed/not for tissue)
(torch.Tensor): gene expression value predictions (real-valued)
"""
# Consider using .clone().detach()
attention_mask_copy = attention_mask.clone()
return (
self.classification_head(
features, attention_mask=attention_mask, input_ids=input_ids, **kwargs
),
self.regression_head(
features,
attention_mask=attention_mask_copy,
input_ids=input_ids,
**kwargs,
),
)
class SparseMSELoss(nn.Module):
"""Custom loss function that takes in two inputs:
1. Predicted logits for whether gene is expressed (1) or not (0)
2. Real-valued log-TPM values for gene expression predictions.
"""
def __init__(self, threshold: float = 1, alpha: float = 0.5):
"""
Args:
threshold (float): any value below this threshold (in natural
scale, NOT log-scale) is considered "not expressed"
alpha (float): parameter controlling importance of classification
in overall accuracy. alpha == 1 means this is identical to
classification. alpha == 0 means this is identical to regression.
"""
super().__init__()
self.threshold = np.log(threshold)
self.alpha = alpha
self.mse = MSELoss()
self.bce = BCEWithLogitsLoss()
def forward(self, logits: Tuple[torch.Tensor], labels: torch.Tensor):
classification_outputs, regression_outputs = logits
binarized_labels = (labels >= self.threshold).float()
mse_loss = self.mse(regression_outputs, labels)
bce_loss = self.bce(classification_outputs, binarized_labels)
# Weight the losses by the logits
# the mse loss should be weighted by the probability of being expressed
# the bce loss should be weighted by the probability of not being expressed
loss = self.alpha * bce_loss + (1 - self.alpha) * mse_loss
return loss
class ZeroInflatedNegativeBinomialNLL(nn.Module):
"""Custom loss function that calculates the negative log-likelihood
according to a zero-inflated negative binomial model.
"""
pass
# -------------------------------------- #
# #
# ---------- Modified RoBERTa ---------- #
# #
# -------------------------------------- #
class RobertaForSequenceClassificationMeanPool(RobertaPreTrainedModel):
"""RobertaForSequenceClassification using modified classification head
Args:
RobertaPreTrainedModel ([type]): [description]
Returns:
[type]: [description]
"""
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config: RobertaMeanPoolConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.output_mode = config.output_mode or "regression"
self.roberta = RobertaModel(config, add_pooling_layer=False)
self.threshold = config.threshold
self.alpha = config.alpha
self.log_offset = config.log_offset
if self.output_mode == "sparse":
self.classifier = ClassificationHeadMeanPoolSparse(config)
else:
self.classifier = ClassificationHeadMeanPool(config)
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(
sequence_output, attention_mask=attention_mask, input_ids=input_ids
)
loss = None
if labels is not None:
if self.output_mode == "regression":
loss_fct = MSELoss()
elif self.output_mode == "sparse":
loss_fct = SparseMSELoss(threshold=self.threshold, alpha=self.alpha)
elif self.output_mode == "classification":
loss_fct = BCEWithLogitsLoss()
elif self.output_mode == "poisson":
loss_fct = PoissonNLLLoss()
loss = loss_fct(
logits.view(-1, self.num_labels), labels.view(-1, self.num_labels)
)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def embed(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
"""Embed sequences by running the `forward` method up to the dense layer of the classifier"""
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
embeddings = self.classifier.embed(
sequence_output, attention_mask=attention_mask, input_ids=input_ids
)
return embeddings
def get_tissue_embeddings(self):
return self.classifier.out_proj.weight.detach()
def predict(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
logits = self.forward(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)[0]
if self.output_mode == "sparse":
binary_logits, pred_values = logits
# Convert logits to binary predictions
binary_preds = binary_logits < 0
# return binary_preds * pred_values
pred_values[binary_preds] = np.log(self.log_offset)
return pred_values
return logits
# -------------------------------------- #
# #
# ---------- Modified BERT ----------- #
# #
# -------------------------------------- #
class BertMeanPoolConfig(BertConfig):
model_type = "bert"
def __init__(
self, output_mode="regression", start_token_idx=2, end_token_idx=3, **kwargs
):
"""Constructs BertConfig."""
super().__init__(**kwargs)
self.output_mode = output_mode
self.start_token_idx = start_token_idx
self.end_token_idx = end_token_idx
class BertForSequenceClassificationMeanPool(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.output_mode = config.output_mode or "regression"
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = ClassificationHeadMeanPool(config)
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[0]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(
pooled_output, attention_mask=attention_mask, input_ids=input_ids
)
loss = None
if labels is not None:
if self.output_mode == "regression":
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = BCELoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
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