| from dataclasses import dataclass
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| from typing import Optional, Tuple
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|
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| import torch
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| import torch.nn as nn
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| from transformers import ErnieModel, ErniePreTrainedModel, PretrainedConfig
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| from transformers.file_utils import ModelOutput
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|
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| from .decode_utils import UIEDecoder
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|
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| @dataclass
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| class UIEModelOutput(ModelOutput):
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| """
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| Output class for outputs of UIE.
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| losses (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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| Total spn extraction losses is the sum of a Cross-Entropy for the start and end positions.
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| start_prob (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
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| Span-start scores (after Sigmoid).
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| end_prob (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
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| Span-end scores (after Sigmoid).
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| hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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| Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layers, +
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| one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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| Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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| attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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| Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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| sequence_length)`.
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| Attention weights after the attention softmax, used to compute the weighted average in the self-attention
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| heads.
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| """
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| loss: Optional[torch.FloatTensor] = None
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| start_prob: torch.FloatTensor = None
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| end_prob: torch.FloatTensor = None
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| start_positions: torch.FloatTensor = None
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| end_positions: torch.FloatTensor = None
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| hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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| attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
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|
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| class UIEModel(ErniePreTrainedModel, UIEDecoder):
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| """
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| UIE model based on Bert model.
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| This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
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| library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
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| etc.)
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| This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
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| Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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| and behavior.
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| Parameters:
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| config ([`PretrainedConfig`]): Model configuration class with all the parameters of the model.
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| Initializing with a config file does not load the weights associated with the model, only the
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| configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
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| """
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|
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| def __init__(self, config: PretrainedConfig):
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| super(UIEModel, self).__init__(config)
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| self.encoder = ErnieModel(config)
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| self.config = config
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| hidden_size = self.config.hidden_size
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|
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| self.linear_start = nn.Linear(hidden_size, 1)
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| self.linear_end = nn.Linear(hidden_size, 1)
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| self.sigmoid = nn.Sigmoid()
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|
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| self.post_init()
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|
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| def forward(
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| self,
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| input_ids: Optional[torch.Tensor] = None,
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| token_type_ids: Optional[torch.Tensor] = None,
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| position_ids: Optional[torch.Tensor] = None,
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| attention_mask: Optional[torch.Tensor] = None,
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| head_mask: Optional[torch.Tensor] = None,
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| inputs_embeds: Optional[torch.Tensor] = None,
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| start_positions: Optional[torch.Tensor] = None,
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| end_positions: Optional[torch.Tensor] = None,
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| output_attentions: Optional[bool] = None,
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| output_hidden_states: Optional[bool] = None,
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| ) -> UIEModelOutput:
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| """
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| Args:
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| input_ids (`torch.LongTensor` of shape `({0})`):
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| Indices of input sequence tokens in the vocabulary.
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| Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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| [`PreTrainedTokenizer.__call__`] for details.
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| [What are input IDs?](../glossary#input-ids)
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| attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
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| Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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| - 1 for tokens that are **not masked**,
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| - 0 for tokens that are **masked**.
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| [What are attention masks?](../glossary#attention-mask)
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| token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
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| Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
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| 1]`:
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| - 0 corresponds to a *sentence A* token,
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| - 1 corresponds to a *sentence B* token.
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| [What are token type IDs?](../glossary#token-type-ids)
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| position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
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| Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
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| config.max_position_embeddings - 1]`.
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| [What are position IDs?](../glossary#position-ids)
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| head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
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| Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
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| - 1 indicates the head is **not masked**,
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| - 0 indicates the head is **masked**.
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| inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
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| Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
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| is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
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| model's internal embedding lookup matrix.
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| start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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| Labels for position (index) of the start of the labelled span for computing the token classification loss.
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| Positions are clamped to the length of the sequence (`sequence_length`). Position outsides of the sequence
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| are not taken into account for computing the loss.
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| end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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| Labels for position (index) of the end of the labelled span for computing the token classification loss.
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| Positions are clamped to the length of the sequence (`sequence_length`). Position outsides of the sequence
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| are not taken into account for computing the loss.
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| output_attentions (`bool`, *optional*):
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| Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
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| tensors for more detail.
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| output_hidden_states (`bool`, *optional*):
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| Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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| more detail.
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| return_dict (`bool`, *optional*):
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| Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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| """
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| outputs = self.encoder(
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| input_ids=input_ids,
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| token_type_ids=token_type_ids,
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| position_ids=position_ids,
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| attention_mask=attention_mask,
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| head_mask=head_mask,
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| inputs_embeds=inputs_embeds,
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| output_attentions=output_attentions,
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| output_hidden_states=output_hidden_states,
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| )
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| sequence_output = outputs[0]
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|
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| start_logits = self.linear_start(sequence_output)
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| start_logits = torch.squeeze(start_logits, -1)
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| start_prob = self.sigmoid(start_logits)
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|
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| end_logits = self.linear_end(sequence_output)
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| end_logits = torch.squeeze(end_logits, -1)
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| end_prob = self.sigmoid(end_logits)
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|
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| total_loss = None
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| if start_positions is not None and end_positions is not None:
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| loss_fct = nn.BCELoss()
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| start_loss = loss_fct(start_prob, start_positions)
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| end_loss = loss_fct(end_prob, end_positions)
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|
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| total_loss = (start_loss + end_loss) / 2.0
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|
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| return UIEModelOutput(
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| loss=total_loss,
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| start_prob=start_prob,
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| end_prob=end_prob,
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| hidden_states=outputs.hidden_states,
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| attentions=outputs.attentions,
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| )
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|
