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from abc import ABC
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch.nn.modules.loss import _Loss
from transformers import XLMRobertaXLPreTrainedModel, XLMRobertaXLModel, XLMRobertaXLConfig
from transformers import AutoModelForSequenceClassification, AutoConfig
from transformers.modeling_outputs import ModelOutput
from pytorch_metric_learning.losses import NTXentLoss
@dataclass
class HierarchicalSequenceEmbedderOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
embeddings: torch.FloatTensor = None
layer_embeddings: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class HierarchicalSequenceClassifierOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
embeddings: torch.FloatTensor = None
layer_embeddings: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
class HierarchicalXLMRobertaXLConfig(XLMRobertaXLConfig):
model_type = "hierarchical-xlm-roberta-xl"
def __init__(self, label_smoothing: Optional[float] = None, **kwargs):
super().__init__(**kwargs)
self.label_smoothing = label_smoothing
class XLMRobertaXLHierarchicalClassificationHead(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.dense = torch.nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = torch.nn.Dropout(classifier_dropout)
self.out_proj = torch.nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = self.dropout(features)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
def distance_to_probability(distance: torch.Tensor, margin: float) -> torch.Tensor:
margin = torch.full(size=distance.size(), fill_value=margin,
dtype=distance.dtype, device=distance.device, requires_grad=False)
p = (1.0 + torch.exp(-margin)) / (1.0 + torch.exp(distance - margin))
del margin
return p
class DistanceBasedLogisticLoss(_Loss):
__constants__ = ['margin', 'reduction']
margin: float
def __init__(self, margin: float = 1.0, size_average=None, reduce=None, reduction: str = 'mean'):
super(DistanceBasedLogisticLoss, self).__init__(size_average, reduce, reduction)
self.margin = margin
def forward(self, inputs, targets):
inputs = inputs.view(-1)
targets = targets.to(inputs.dtype).view(-1)
p = distance_to_probability(inputs, self.margin)
return torch.nn.functional.binary_cross_entropy(input=p, target=targets, reduction=self.reduction)
class LayerGatingNetwork(torch.nn.Module):
__constants__ = ['in_features']
in_features: int
weight: torch.Tensor
def __init__(self, in_features: int, device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.in_features = in_features
self.weight = torch.nn.Parameter(torch.empty((1, in_features), **factory_kwargs))
self.reset_parameters()
def reset_parameters(self) -> None:
initial_layer_weights = np.array(
[1.0 / (self.in_features - layer_idx) for layer_idx in range(self.in_features)],
dtype=np.float32
)
initial_layer_weights /= np.sum(initial_layer_weights)
initial_layer_weights_pt = torch.tensor(
initial_layer_weights.reshape((1, self.in_features)),
dtype=self.weight.dtype,
device=self.weight.device
)
del initial_layer_weights
self.weight = torch.nn.Parameter(initial_layer_weights_pt)
del initial_layer_weights_pt
def forward(self, input: torch.Tensor) -> torch.Tensor:
return torch.nn.functional.linear(input, torch.softmax(self.weight, dim=-1))
def extra_repr(self) -> str:
return 'in_features={}'.format(self.in_features)
class XLMRobertaXLForHierarchicalEmbedding(XLMRobertaXLPreTrainedModel, ABC):
config_class = HierarchicalXLMRobertaXLConfig
def __init__(self, config: HierarchicalXLMRobertaXLConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.temperature = config.temperature
self.config = config
self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
self.layer_weights = LayerGatingNetwork(in_features=config.num_hidden_layers)
self.init_weights()
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
right_input_ids: Optional[torch.LongTensor] = None,
right_attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, HierarchicalSequenceEmbedderOutput]:
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=True,
return_dict=False
)
cls_hidden_states = torch.stack(
tensors=outputs[2][-self.config.num_hidden_layers:],
dim=1
)[:, :, 0, :]
cls_emb = self.layer_weights(cls_hidden_states.permute(0, 2, 1))[:, :, 0]
loss = None
if labels is not None:
cls_emb_ = cls_emb.view(-1, self.config.hidden_size)
emb_norm = torch.linalg.norm(cls_emb_, dim=-1, keepdim=True) + 1e-9
if (right_input_ids is not None) or (right_attention_mask is not None):
if right_input_ids is None:
raise ValueError(f'right_input_ids is not specified!')
if right_attention_mask is None:
raise ValueError(f'right_attention_mask is not specified!')
right_outputs = self.roberta(
right_input_ids,
attention_mask=right_attention_mask,
output_hidden_states=True,
return_dict=False
)
right_cls_hidden_states = torch.stack(
tensors=right_outputs[2][-self.config.num_hidden_layers:],
dim=1
)[:, :, 0, :]
right_cls_emb = self.layer_weights(right_cls_hidden_states.permute(0, 2, 1))[:, :, 0]
right_cls_emb_ = right_cls_emb.view(-1, self.config.hidden_size)
right_emb_norm = torch.linalg.norm(right_cls_emb_, dim=-1, keepdim=True) + 1e-9
distances = torch.norm(cls_emb_ / emb_norm - right_cls_emb_ / right_emb_norm, 2, dim=-1)
loss_fct = DistanceBasedLogisticLoss(margin=1.0)
loss = loss_fct(distances, labels.view(-1))
else:
loss_fct = NTXentLoss(temperature=self.temperature)
loss = loss_fct(cls_emb_ / emb_norm, labels.view(-1))
if not return_dict:
output = (cls_emb, cls_hidden_states) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return HierarchicalSequenceEmbedderOutput(
loss=loss,
embeddings=cls_emb,
layer_embeddings=cls_hidden_states,
hidden_states=outputs[2],
attentions=outputs[3] if output_attentions else None,
)
@property
def layer_importances(self) -> List[Tuple[int, float]]:
with torch.no_grad():
importances = torch.softmax(self.layer_weights.weight, dim=-1).detach().cpu().numpy().flatten()
indices_and_importances = []
for layer_idx in range(importances.shape[0]):
indices_and_importances.append((layer_idx + 1, float(importances[layer_idx])))
indices_and_importances.sort(key=lambda it: (-it[1], it[0]))
return indices_and_importances
class XLMRobertaXLForHierarchicalSequenceClassification(XLMRobertaXLForHierarchicalEmbedding, ABC):
def __init__(self, config: HierarchicalXLMRobertaXLConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.label_smoothing = config.label_smoothing
self.config = config
self.classifier = XLMRobertaXLHierarchicalClassificationHead(config)
self.init_weights()
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
right_input_ids: Optional[torch.LongTensor] = None,
right_attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, HierarchicalSequenceClassifierOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = super().forward(
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,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = torch.nn.MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
if self.label_smoothing is None:
loss_fct = torch.nn.CrossEntropyLoss()
else:
loss_fct = torch.nn.CrossEntropyLoss(label_smoothing=self.label_smoothing)
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = torch.nn.BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs
return ((loss,) + output) if loss is not None else output
return HierarchicalSequenceClassifierOutput(
loss=loss,
logits=logits,
embeddings=outputs.embeddings,
layer_embeddings=outputs.layer_embeddings,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions
)
AutoConfig.register("hierarchical-xlm-roberta-xl", HierarchicalXLMRobertaXLConfig)
AutoModelForSequenceClassification.register(
HierarchicalXLMRobertaXLConfig,
XLMRobertaXLForHierarchicalSequenceClassification
)
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