Remove model in anticipation of PR #18407
This commit is contained in:
Ankur Goyal
parent
02daaaf614
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1f7bede5e0
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@ -3,10 +3,6 @@
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"architectures": [
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"LayoutLMForQuestionAnswering"
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],
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"auto_map": {
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"AutoConfig": "configuration_layoutlm.LayoutLMConfig",
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"AutoModelForQuestionAnswering": "modeling_layoutlm.LayoutLMForQuestionAnswering"
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},
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"custom_pipelines": {
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"document-question-answering": {
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"impl": "pipeline_document_question_answering.DocumentQuestionAnsweringPipeline",
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@ -1,3 +0,0 @@
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# This model just uses the existing LayoutLMConfig which is just imported
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# as a thin wrapper
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from transformers.models.layoutlm.configuration_layoutlm import LayoutLMConfig
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@ -1,147 +0,0 @@
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# NOTE: This code is currently under review for inclusion in the main
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# huggingface/transformers repository:
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# https://github.com/huggingface/transformers/pull/18407
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""" PyTorch LayoutLM model."""
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import math
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from typing import Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from transformers.modeling_outputs import QuestionAnsweringModelOutput
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from transformers.models.layoutlm import LayoutLMModel, LayoutLMPreTrainedModel
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class LayoutLMForQuestionAnswering(LayoutLMPreTrainedModel):
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def __init__(self, config, has_visual_segment_embedding=True):
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super().__init__(config)
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self.num_labels = config.num_labels
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self.layoutlm = LayoutLMModel(config)
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self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.layoutlm.embeddings.word_embeddings
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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bbox: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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start_positions: Optional[torch.LongTensor] = None,
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end_positions: Optional[torch.LongTensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, QuestionAnsweringModelOutput]:
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r"""
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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 outside 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 outside of the sequence
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are not taken into account for computing the loss.
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Returns:
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Example:
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In this example below, we give the LayoutLMv2 model an image (of texts) and ask it a question. It will give us
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a prediction of what it thinks the answer is (the span of the answer within the texts parsed from the image).
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```python
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>>> from transformers import AutoTokenizer, LayoutLMForQuestionAnswering
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>>> from datasets import load_dataset
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>>> import torch
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>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased", add_prefix_space=True)
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>>> model = LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased")
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>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
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>>> example = dataset[0]
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>>> question = "what's his name?"
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>>> words = example["tokens"]
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>>> boxes = example["bboxes"]
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>>> encoding = tokenizer(
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... question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="pt"
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... )
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>>> bbox = []
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>>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
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... if s == 1:
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... bbox.append(boxes[w])
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... elif i == tokenizer.sep_token_id:
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... bbox.append([1000] * 4)
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... else:
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... bbox.append([0] * 4)
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>>> encoding["bbox"] = torch.tensor([bbox])
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>>> outputs = model(**encoding)
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>>> loss = outputs.loss
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>>> start_scores = outputs.start_logits
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>>> end_scores = outputs.end_logits
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```
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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outputs = self.layoutlm(
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input_ids=input_ids,
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bbox=bbox,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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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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return_dict=return_dict,
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)
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sequence_output = outputs[0]
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logits = self.qa_outputs(sequence_output)
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start_logits, end_logits = logits.split(1, dim=-1)
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start_logits = start_logits.squeeze(-1).contiguous()
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end_logits = end_logits.squeeze(-1).contiguous()
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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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# If we are on multi-GPU, split add a dimension
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if len(start_positions.size()) > 1:
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start_positions = start_positions.squeeze(-1)
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if len(end_positions.size()) > 1:
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end_positions = end_positions.squeeze(-1)
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# sometimes the start/end positions are outside our model inputs, we ignore these terms
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ignored_index = start_logits.size(1)
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start_positions = start_positions.clamp(0, ignored_index)
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end_positions = end_positions.clamp(0, ignored_index)
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loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
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start_loss = loss_fct(start_logits, start_positions)
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end_loss = loss_fct(end_logits, end_positions)
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total_loss = (start_loss + end_loss) / 2
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if not return_dict:
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output = (start_logits, end_logits) + outputs[2:]
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return ((total_loss,) + output) if total_loss is not None else output
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return QuestionAnsweringModelOutput(
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loss=total_loss,
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start_logits=start_logits,
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end_logits=end_logits,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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