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from dataclasses import dataclass
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from typing import Optional, Tuple
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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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from .decode_utils import UIEDecoder
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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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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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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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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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self.post_init()
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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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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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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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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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total_loss = (start_loss + end_loss) / 2.0
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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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